Pulses for Food and Nutritional Security of India: Production, Markets and Trade (India Studies in Business and Economics) 9811931844, 9789811931840

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India Studies in Business and Economics

Poornima Varma

Pulses for Food and Nutritional Security of India Production, Markets and Trade

India Studies in Business and Economics CMA Publication No. 253

The Indian economy is one of the fastest growing economies of the world with India being an important G-20 member. Ever since the Indian economy made its presence felt on the global platform, the research community is now even more interested in studying and analyzing what India has to offer. This series aims to bring forth the latest studies and research about India from the areas of economics, business, and management science, with strong social science linkages. The titles featured in this series present rigorous empirical research, often accompanied by policy recommendations, evoke and evaluate various aspects of the economy and the business and management landscape in India, with a special focus on India’s relationship with the world in terms of business and trade. The series also tracks research on India’s position on social issues, on health, on politics, on agriculture, on rights, and many such topics which directly or indirectly affect sustainable growth of the country. Review Process The proposal for each volume undergoes at least two double blind peer review where a detailed concept note along with extended chapter abstracts and a sample chapter is peer reviewed by experienced academics. The reviews can be more detailed if recommended by reviewers. Ethical Compliance The series follows the Ethics Statement found in the Springer standard guidelines here. https://www.springer.com/us/authors-editors/journal-author/journal-aut hor-helpdesk/before-you-start/before-you-start/1330#c14214

Poornima Varma

Pulses for Food and Nutritional Security of India Production, Markets and Trade

Poornima Varma Centre for Management in Agriculture Indian Institute of Management Ahmedabad Ahmedabad, Gujarat, India

ISSN 2198-0012 ISSN 2198-0020 (electronic) India Studies in Business and Economics ISBN 978-981-19-3184-0 ISBN 978-981-19-3185-7 (eBook) https://doi.org/10.1007/978-981-19-3185-7 © Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

The Centre for Management in Agriculture (CMA), Indian Institute of Management, Ahmedabad, is actively engaged in applied and problem-solving research on agriculture, food and agribusiness management, towards achieving the major goals of agricultural and rural development in the developing world. As a result, over the years, CMA has developed considerable expertise in a large spectrum of areas of agriculture and agribusiness, including the management of agricultural inputs, agroprocessing, agri-food marketing, rural infrastructure, grass-roots innovations, appropriate technologies for arid and semiarid regions, international agricultural trade and WTO issues, global competitiveness, commodity markets, food safety and quality including organic food, food supermarkets, food value chains and farmer collectives like producer companies. CMA undertakes research of this kind, especially for the Ministry of Agriculture, Government of India, and on its own as well as at the request of various other international and national organisations. Pulses are rich in protein content and a major source of protein in Indian diet of all categories of people. The protein content in pulses is double the protein content of wheat and three times more than that of rice. Cereals have predominantly contributed to protein intake in India despite pulse protein having higher amounts of requisite amino acids such as lysine that are associated with enhanced protein quality. Pulses are the cheapest source of non-cereal plant protein, yet pulses consumption has been declining over the last two decades, and the growth in pulse production has straggled behind cereal production. Moreover, the production of pulses was not commensurate with the demand as net availability lagged behind population growth. The excess demand is primarily due to the stagnation in productivity which is further accelerated by the stagnant area under cultivation. As a result, the per capita net availability of pulses in the country declined sharply over the years until recently. Of late, the Government of India’s interventions to improve the area and production of pulses seemed to have produced some positive impact as we can see an improvement in area and production of pulses. However, the persistent deficit and the soaring domestic prices of pulses made it inevitable for the country to import pulses. Despite being the second largest producer of pulses, the dependency on imported pulses continues to grow in the country. The imports have slowly come down when the v

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country has been able to improve the domestic production. But, this production and price uncertainty coupled with poor crop productivity has always been a concern especially when pulses play an important role in contributing to food and nutritional security of India. The present research examines the factors affecting the production of pulses (chickpea and pigeon pea), the impact of government policies such as MSP and NFSM on pulses production, the factors influencing the farmers access and utilisation of MSP and the pricing behaviour of pulses importers, exchange rate pass-through and its implications. The study makes use of both the secondary as well as primary data. The primary data is collected through a comprehensive household survey of 572 pulse-producing households in three major pulse-producing states—Karnataka, Maharashtra and Madhya Pradesh. Subsequently, the district that has high production of pulses was identified, and they were Gulbarga from Karnataka, Wardha from Maharashtra and Narsinghpur from Madhya Pradesh. The results offer unique policyrelevant insights on the factors influencing the production of pulses, the implications of import dependency to meet excess demand and the impact of government policy interventions. I am sure that the study will be found useful by policymakers, researchers as well as others interested in agricultural policy, supply response analysis and the welfare of farmers. Errol D’Souza Director, Indian Institute of Management Ahmedabad Ahmedabad, India

Acknowledgements

This study would not have been possible without the generous help and support of several individuals and institutions. I would like to express my sincere gratitude to the Ministry of Agriculture, Government of India, for financial support in undertaking this study. My special thanks to my colleagues Prof. Vasant. P. Gandhi and Prof. Sukhpal Singh for their valuable suggestions and comments at various stages of this study. I would like to also thank Prof. Vijay Paul Sharma, Chairman, CACP, for some of his valuable suggestions and comments during the initial stages of this study. I would like to express my sincere gratitude to Prof. M. Gopinath, Distinguished Professor of Agricultural Marketing from University of Georgia, for painstakingly going through this study and giving his valuable comments and suggestions on this study. His comments have helped me in enriching this study. Prof. Vikas Rawal, Professor, JNU also provided several key inputs for this study, and I sincerely acknowledge his support. I also thank Dr. Reji K Joseph, Associate Professor, ISID, for his useful comments. My thanks to Prof. Ranjan Ghosh for his timely and valuable inputs and suggestions without which this study would not have been possible. I also thank Dr. C. S. C. Sekhar, Director IEG, for his valuable comments and suggestions that helped in enriching this study. My study benefited a lot from the support of PRADAN to conduct the field visit of Madhya Pradesh. I record my deepest gratitude to PRADAN, for their support in collecting the data from Narsinghpur district of Madhya Pradesh. My sincere gratitude also goes to Maruti Manapede, State Vice President, Karnataka, Pranth Raith Sangh, Gouramma and all the members of All India Kisan Sabha (AIKS) who helped me in organising the meetings and discussions with farmers in Gulbarga, Karnataka. My sincere gratitude also goes to the officers of the Department of Agriculture, Gulbarga and Nagpur districts. I would like to extend my sincere gratitude to Kamalnayan Jamnalal Bajaj Foundation of Wardha especially Raju Petkar in coordinating farmers meeting in Wardha and also in collection of the data. I would like to also thank his team members for their valuable contributions. I would like to extend my gratitude to Viral of IIMA library for being very proactive in sending the materials vii

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and arranging the data. Thanks are also due to CMA staff—Uma, Mini, Dipali, Viji and also Ashwin for their enthusiastic help in each and every stages of this study. I would like to also thank Ashutosh for helping me with some of the data collection and compilation. Akash also helped me in obtaining the monthly import data from DGCI&S and through IIMA library. I would like to extend my special acknowledgement to Nikita Pandey for her invaluable contribution to this study. This study would not have materialised without her enthusiastic efforts in collecting the data from the library and field and assistance in writing and data analysis. My special thanks also go to Raja Mohanty for his invaluable help and support in analysing the data. Sonali Kaur Bhatia deserves special acknowledgement for her invaluable contribution in the initial stages of study by contributing to the writing of research proposal, analysis of data as well as field visit to Wardha and Gulbarga for collecting the data. I would like to also thank Nicky Johnson for all his timely support. I owe a lot to Anar and Jannet for their valuable contributions to supply response analysis. Nikhil Singh helped me immensely with editing and formatting of the thesis. Needless to mention while I owe a lot to the numerous individuals who contributed significantly to conduct this study, the data analysis and the views expressed in this report and any omissions or errors that remain in the report are entirely mine. Poornima Varma

Executive Summary

Pulses play a pivotal role in a country like India for all categories of people due to its rich protein content. The protein content in pulses is double the protein content of wheat and three times more than that of rice. Pulses are mostly cultivated under rainfed conditions and do not require intensive irrigation facility, and this is the reason why pulses are grown in areas left after satisfying the demand for cereals/cash crops. Apart from its rich protein content, pluses possess several other qualities such as they improve soil fertility and physical structure, fit in mixed/intercropping system, crop rotations and dry farming and provide green pods for vegetable and nutritious fodder for cattle as well. Although being the largest pulse crop cultivating country in the world, pulses share to total food grain production is only 6–7% in the country. As a result, the production of pulses was not commensurate with the demand. The excess demand is primarily due to the stagnation in productivity which is further accelerated by the decline in area under cultivation. As a result, the per capita net availability of pulses in the country declined sharply over the years. The persistent deficit and the soaring pulses domestic prices made it inevitable for the country to import pulses. Despite of being the second largest producer of pulses, the dependency on imported pulses continues to grow in the country. Against this backdrop, the present research examines the factors affecting the production of pulses (chickpea and pigeon pea), the impact of government policies such as MSP and NFSM on pulses production, the factors influencing the farmers access and utilisation of MSP and the pricing behaviour of pulses importers, exchange rate pass-through and its implications. This study has been divided into 11 chapters including introduction and conclusion. Chapter 1 as an introduction provided the background, objectives, data and methodology along with chapter scheme. Chapter 2 gave an overview of pulses economy. Chapter 3 discussed the importance of pulses for nutritional and food security, the importance of sustainable production practices to improve the pulses productivity and food security with an emphasis on India. Chapter 4 discussed the salient features of Government of India’s National Food Security Mission (NFSM) and its objectives especially in the context of pulses production. Chapter 5 provided ix

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a detailed discussion of socio-economic profile of the sample households. Chapter 6 provided an overview of pulses production, trade and government policies with a special focus on the trends in trade and its implications. Chapter 7 analysed the import pricing behaviour and exchange rate pass-through into prices of imported pulses. Chapter 8 provided an overview of an evolution of minimum support price policies and MSP for major pulses. Chapter 9 analysed the factors influencing the access to information regarding MSP and utilisation of MSP in a joint framework. Chapter 10 made an analysis of factors influencing the supply response of chickpea and pigeon pea with a special emphasis on MSP and NFSM. Chapter 11 provided the conclusion and policy implications of the study. The detailed household-level survey was conducted for three major pulseproducing states. They are Karnataka, Maharashtra and Madhya Pradesh. From each state, one of the major pulse-producing districts was selected for further analysis. From Karnataka, Gulbarga was selected, from Maharashtra, Wardha was selected, and from Madhya Pradesh, Narsinghpur was selected. Primary data was collected through a comprehensive household survey in the above-mentioned three districts of three major pulse-producing Indian states during 2017–2018. The farmers were selected through a random sampling technique. The sample consisted of 482 pigeon pea farmers and 316 chickpea, out of which 227 farmers were cultivating both chickpea and pigeon pea. The survey was conducted through questionnaire, framed in such way as to draw out details covering household characteristics, wealth and farm characteristics, institutional and access-related variables, risk and economic factors. After discussing the background, objectives, data and methodology in the first chapter, the second chapter provided an overview of pulses economy with a special emphasis on the trends in area, production and yield in comparison with world. The analysis broadly showed that there had been a substantial decline in area and production of pulses in India. Indian yield was much below the world average, and the yield gap between the two got widened since 2001. It was the same year, the decline in production of pulses was more prominent. However, in the year 1991, the yield gap got narrowed and came very close to the world average. Interestingly, this was the same year when India marked a record production in pulses. The fifth chapter provided an overview of the socio-economic profile of the sample households. The total households interviewed were 572 drawn from three major pulse-producing states—Karnataka, Maharashtra and Madhya Pradesh. Majority of the households in the sample were either semi-, medium or medium farmers, and agriculture was the main livelihood option for majority of the sample households. Narsinghpur (Madhya Pradesh) had the highest share of large farmers in the sample, whereas Wardha (Maharashtra) had the highest share of marginal and small farmers. In our sample, 482 farmers were cultivating pigeon pea and 316 farmers were cultivating chickpea, out of which 227 farmers were cultivating both the pigeon pea and chickpea. Majority of the sample households did not have any awareness of government schemes to promote pulses production or new production techniques to reduce crop loss and improve productivity. The farm size-wise analysis showed that large farmers were more aware about new production practices as compared to other

Executive Summary

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farm categories. However, the access to training offered by government and extension services were the highest among the sample households from Wardha (Maharashtra). Interestingly, despite having higher access to training, extension services and knowledge about government schemes and new production techniques, the information of MSP received by households in Wardha (Maharashtra) was lower than that of Narsinghpur (Madhya Pradesh). This is due to the fact that Narsinghpur (Madhya Pradesh) had the highest share of large farmers in the sample. The size-wise percentage of farmers who received training showed that large farmers had received more training. The training was relatively higher for semi-, medium, medium and large farmers as compared to marginal and small. In addition to the fact that Narsinghpur (Madhya Pradesh) had relatively large farmers with greater access to training, the households from Narsinghpur (Madhya Pradesh) had greater access to information regarding MSP. The access to MSP information was increasing as size of the farm increases. Interestingly, though households in Narsinghpur (Madhya Pradesh) had the highest information about MSP, households availing MSP was much lower and lower than Wardha (Maharashtra). In Maharashtra, almost all farmers who had information about MSP availed MSP. The percentage share of households with information was 52% and utilisation was 50%. The percentage share of households in each farm size category who were availing MSP was the highest among semi-, medium, medium and large households. The percentage share of households who were not availing MSP was the lowest among marginal and small farmers. The analysis in Chap. 6 showed that there has been a substantial increase in the imports of most of the pulses in the last several years. Also, the share of India’s imports in world imports of pulses also showed a sharp increase. This points out the increasing import dependency and severe supply deficit that India is facing in terms of meeting the demand for protein-rich crop. The widening gap between supply and demand and the domestic uncertainties with respect to the production, etc. might continue to increase the import dependency unless effective policy measures are undertaken to improve the production and productivity and pulses. The implications of long-term dependency on import depend upon the nature of import pricing that is undertaken by the importers as we have already discussed that the import of each type of pulses is dominated by one or two single largest importers. This may increase the potential for monopoly pricing. Chapter 7 did an analysis of pricing behaviour of pulses importers in Indian market and the exchange rate pass-through into imported pulses prices. When the currency of importing country depreciates, the import is expected to become costlier. However, if the exporter is absorbing part of the increase in price to retain the market share in the importing country, then the exchange rate pass-through into import prices will be partial or incomplete. The elasticities of import prices with regard to changes in the exchange rate can range from 0% to 100%, depending on the pricing strategy of exporters. Additionally, it also shows whether an exporter is following a producer pricing strategy or local currency pricing. The former takes place in a perfectly competitive setting where the low of one price is expected to prevail due, and as a result, any change in exchange rate will get fully transmitted to import prices. The latter takes place under imperfect competition. Employing the econometric technique

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of Panel Corrected Standard Errors (PCSE) estimation technique in pricing to market (PTM) framework, the results from our analysis showed that the most of the importers were practising non-competitive pricing behaviour due to both the market-specific characteristics as well as exchange rate-induced effects. The significance of the exchange rate parameter βi and the country-specific effects parameter λi in most of the models indicates that the importers work with a fluctuating exchange rate and a varying mark up over marginal cost. The analysis of the asymmetric effects of exchange rates through an interaction dummy showed that for majority of the products, the appreciation of the Indian rupee against the partner country had greater impact than the depreciation. We tested the PTM model under three different exchange rates, i.e. the nominal, the real and the commodity-specific (import) trade-weighted exchange rates. For all the products under study, we observed PTM in at least one of the destination markets either through exchange rate changes and/or through country-specific effects. The analysis also showed that the commodity-specific exchange rate better predicts the PTM behaviour in the case of kidney beans and peas, whereas the nominal exchange rate better predicts the PTM behaviour of chickpea and pigeon pea. The analaysis of the exchange rate effect showed that local currency price stabilisation by the Indian importers was more prominent than the amplification of exchange rates. This is indicating competition among other importers. Chapter 8 is devoted to examine the role of country-specific market share on exchange rate pass-through and pricing behaviour of major pulses imported to India. The analysis in this chapter shows that the exchange rate pass-through is increasing in market share, and after reaching a maximum, it declines. The results provide new empirical insights into an inverted U-shaped relationship between exchange rate pass-through and market share. There have not been many analyses to see the influence of market shares on exchange rate pass-through in the food and agricultural sector. This chapter is making an attempt to analyse the impact of market share on exchange rate pass-through trade by analysing the asymmetric nature of exchange rate pass-through in market share. Our analysis in this chapter also showed that the exchange rate pass-through in market share is asymmetric. The analysis of long run exchange rate pass-through is also undertaken in this chapter, and the results provide empirical support for incomplete of partial exchange rate pass-through in the long run as well. The long run elasticity came out to be significant. There are now ample pieces of evidences in the literature that the exchange rate pass-through varies under different market shares. The ERPT varies due to the changes in perceived elasticity of demand under various market shares. Our study provides new empirical evidence for an inverted U shape for ERPT in market share. Our results for interaction between ERPT and market share show that ERPT is increasing in market share. However, the interaction between ERPT and quadratic term of market share shows that a further increase in market share is leading to a low ERPT, and hence, ERPT is decreasing. The existing studies generally provide evidence for a U-shaped ERPT in market share especially for exports (Garetto, 2016; Auer and Schoenle, 2016). However, Devereux, Tomlin and Dong (2017) findings show a U-shaped relationship between pass-through and exporter market share but

Executive Summary

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a negative relationship between importer market share and pass-through. However, our results provide unique empirical evidence that has an overlap with the findings in the existing studies. As far as the impact of exchange rate changes on prices is concerned, the results show that the exchange rate pass-through was incomplete or partial both in the short run as well as in the long run. As a result, the importers exercise a non-competitive pricing behaviour in general. The negative coefficient implies that the import prices tend to be adjusted downwards when there is a depreciation of Indian currency in relation to the importer’s currency. This shows that the residual demand is elastic, which is an indicator of competitive behaviour. As expected, greater trade openness and domestic demand had a positive and statistically significant impact on the import price. The variable to capture the cost of the importing country—PPI—came out to be positive and statistically significant in all models for peas and in one model for kidney beans. The pass-through was also asymmetric in nature, indicating pass-through was not the same under both appreciation and depreciation scenarios. One point that is worth mentioning here is the depreciation that the Indian currency was generally facing against its trading partners. When the import became costly, the importers might have absorbed the part of the price rise and, therefore, less ERPT. The ability of the importer to absorb the price rise would have been higher under very high market share, and perhaps, this could be the reason for an inverted U shape of ERPT in market share. Chapter 9 discussed the evolution of agricultural and food security policies in India along with the effectiveness of MSP and procurement. The data and studies at the national level broadly indicated that MSP is an important policy instrument in encouraging farmers and to stabilise market prices. However, the percentage of farmers who were aware of MSP was less especially for pulses. This was also reflected in the lack of knowledge about procurement agencies. Interestingly, the percentage of households who sold their products to procurement agencies was even lower than the percentage of households who had information about procurement agencies. In Chap. 5, our analysis of sample households from three states selected for analysis also showed poor awareness of MSP. The farmers who avail MSP even with a positive information about MSP was also lower. Therefore, in Chap. 10, we analysed the factors influencing the access to information regarding MSP and the decision to avail MSP. The regression equation was estimated using the conditional mixed-process (CMP) command which uses the mixed-process estimator. The results showed that Maharashtra farmers were more enthusiastic in availing MSP despite of the fact that the information regarding MSP was highest among the farmers from Madhya Pradesh. However, farmers who had more diversified crop cultivation were not very enthusiastic in availing MSP. The majority of the farmers in Madhya Pradesh in our sample were large farmers, and most probably, they are more diversified. Market access came out to be as an important factor in information and in availing MSP. The risk faced by farmers also increased the chances to avail MSP, and this points out how important MSP is in mitigating the negative effects of risk.

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Executive Summary

In Chap. 11, the supply response of two major pulses—chickpea and pigeon pea—cultivated in India is analysed based on Nerlove’s expectation framework and using a dynamic panel data estimation technique. The analysis is based on secondary data collected at the district level from major pulse-producing states of India. The results broadly showed that chickpea and pigeon pea farmers are more sensitive to price factors than non-price factors. However, they were even more sensitive to government’s minimum support prices than own market prices. The Government of India’s NFSM to boost pulses production through technological interventions and supply of improved varieties of seed showed to have produced positive results in most cases. The acreage response of both the crops as well as the yield of pigeon pea benefited positively by the introduction of NFSM. The high cost of cultivation, as expected, had a negative impact on the acreage allocation, and yield of chickpea indicating the higher cost is problematic mainly for chickpea producers. Whereas in the case of pigeon pea, the cost was negative but insignificant in impacting the area and positive in impacting the yield. The positive results indicate that the cost incurred by pigeon farmers to buy better-quality seeds and other inputs were instrumental in improving the yield. The results showed that pigeon pea farmers are more integrated to the market and are generally in a better position to take advantage of the government policies and market prices. This was reflected in relatively higher response of acreage allocation to market prices and minimum support prices as well as the less sensitivity of cost of cultivation and non-price factors in affecting the area and yield. The pigeon pea farmers were also in a better position to adjust and revise their acreage allocation in response to the prices and yield prevailed in the previous year.

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Key Issues: Production Uncertainty and High Import Dependency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 The Detailed Objectives of the Study Can Be Listed as Follows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Chapter Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1

2

An Overview of Pulses Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Crop-Wise Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Production of Pulses in Major States . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Shift in the Area Under Pulses Cultivation . . . . . . . . . . . . . . . . . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 9 11 13 19 22 25

3

Pulses Production and Food Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Consumption of Pulses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Import of Pulses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Production Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 The Need for Sustainable Practices . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Major Varieties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 Rabi Pulses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Kharif Pulses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 27 33 34 36 41 41 41 42 43

2 4 5 5 6 6 7

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4

National Food Security Mission and Pulses Production . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 NFSM-Pulses Districts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Major Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 NFSM in Maharashtra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 NFSM in Karnataka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 NFSM in Madhya Pradesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 46 46 47 51 51 53

5

Socio-Economic Profile of the Sample Households . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 55 64

6

Pulses Production, Trade and Government Policies . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Country-Wise Imports of Major Pulses . . . . . . . . . . . . . . . . . . . . . . 6.3 Tariff Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67 67 70 77 77 78

7

Pricing and Exchange Rate Pass-Through in Pulses Imports . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 The Concept of Pricing to Market Behaviour and Exchange Rate Pass-Through . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Producer Currency Pricing Versus Local Currency Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Market Share Model Versus Bottlenecks Model . . . . . . . . 7.4 Model Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.1 Commodity-Specific Exchange Rate Model for Kidney Beans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.2 Commodity-Specific Exchange Rate Model for Peas . . . . 7.6.3 Nominal Exchange Rate Model for Chickpea . . . . . . . . . . 7.6.4 Nominal Exchange Rate Model for Pigeon Pea . . . . . . . . . 7.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 79

8

80 81 82 82 83 84 85 86 87 87 88 89 90 96

Asymmetric Exchange Rate Pass-Through, Market Share and Import Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 8.2 Empirical Studies on ERPT and Market Share . . . . . . . . . . . . . . . . 98 8.2.1 Pass-Through and Market Share . . . . . . . . . . . . . . . . . . . . . 100 8.3 Model Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8.4 Pulses Imports, Data and Exchange Rate Variable . . . . . . . . . . . . . 105

Contents

xvii

8.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 9

Minimum Support and Price Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 An Overview of Government Interventions in Agriculture . . . . . . 9.2 Minimum Support Prices Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 The Policy Bias and Crowding Out of Pulses . . . . . . . . . . . . . . . . . 9.4 The Relationship Between MSP and Wholesale Prices . . . . . . . . . 9.5 Procurement Policy and Operations . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115 115 118 119 122 125 129 129 132

10 Information and Utilisation of MSP: Major Determinants . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Model Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Description of Dependent and Explanatory Variables . . . . . . . . . . 10.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

135 135 136 136 137 138 143 144

11 Supply Response of Major Pulses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Key Government Interventions- MSP and NFSM . . . . . . . . . . . . . 11.3 Theoretical and Empirical Literature . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Model Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Data and Variable Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.7 Conclusion and Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

145 145 147 149 152 154 158 164 165 166

12 Conclusion and Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

List of Figures

Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6 Fig. 2.7

Fig. 2.8 Fig. 2.9 Fig. 2.10 Fig. 2.11 Fig. 2.12 Fig. 2.13 Fig. 2.14 Fig. 2.15 Fig. 2.16

Area under pulses in India in million ha. Source CMIE . . . . . . . . Trends in production of pulses in India in million tonnes. Source CMIE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trends in pulses in yield kg per ha. Source CMIE . . . . . . . . . . . . Yield of pulses in hg/ha. Source FAOSTAT . . . . . . . . . . . . . . . . . . Percentage share of area under arhar (pigeon pea) and gram (chickpea) in total pulses area. Source CMIE states of India . . . . Area under gram (chickpea) and arhar (pigeon pea) in India in million ha. Source CMIE states of India . . . . . . . . . . . . . . . . . . Percentage share of arhar (pigeon pea) and gram (chickpea) production in total pulses production. Source CMIE states of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production of gram (chickpea) and arhar (pigeon pea) in India in million tonnes. Source CMIE states of India . . . . . . . . Pulses production in Indian states. Source Ministry of Agriculture and Farmers’ Welfare, Government of India . . . . Total area under pulses production in India, state-wise, in thousand hectares. Source CMIE States of India . . . . . . . . . . . Area under chickpea in India in major pulse-producing states, in thousand hectares. Source CMIE States of India . . . . . . Area under pigeon pea in India in major pulse-producing states, in thousand hectares. Source CMIE States of India . . . . . . Area under kidney beans in major pulse-producing states of India, in thousand hectares. Source CMIE States of India . . . . Area under peas in major pulse-producing states of India, in thousand hectares. Source CMIE States of India . . . . . . . . . . . Production of pulses in major pulse-producing states, in thousand tonnes. Source CMIE States of India . . . . . . . . . . . . . Production of pigeon pea in major pulse-producing states. Source CMIE States of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 10 10 11 12 12

13 13 14 16 17 17 18 19 20 21

xix

xx

Fig. 2.17 Fig. 2.18 Fig. 2.19 Fig. 2.20 Fig. 2.21 Fig. 2.22 Fig. 2.23 Fig. 2.24 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 3.6 Fig. 3.7 Fig. 3.8 Fig. 3.9 Fig. 3.10 Fig. 3.11 Fig. 3.12 Fig. 3.13 Fig. 3.14 Fig. 3.15

List of Figures

Production of chickpea in major pulse-producing states. Source CMIE States of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production of peas in major pulse-producing states. Source CMIE States of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production of kidney beans in major pulse-producing states. Source CMIE States of India . . . . . . . . . . . . . . . . . . . . . . . . Shifting cultivation. Source Reddy et al. (2013) . . . . . . . . . . . . . . Shift in area under cultivation—the case of pigeon pea. Source CMIE states of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shift in area under cultivation—the case of chickpea. Source CMIE states of India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production of paddy and pigeon pea in thousand tonnes. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production of wheat and chickpea in thousand tonnes. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prevalence of undernourishment in world. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prevalence of undernourishment in India (annual value). Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Per capita food production variability (constant 2004– 2006 thousand in $per capita). Source FAOSTAT . . . . . . . . . . . . . Per capita food supply variability (kcal/cap/day). Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Per capita net availability of pulses in India from 1961 to 2013 (in gm/capita/day). Source FAOSTAT . . . . . . . . . . . . . . . Per capita net availability of pulses in India from 2010 to 19 (in gm/capita/day). Source FAOSTAT . . . . . . . . . . . . . . . . . Consumption volume of pulses in India in million metric tonnes. Source statistica.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Per capita net availability of chickpea in India (in gm/capita/day) Source FAOstat.com . . . . . . . . . . . . . . . . . . . . Per capita net availability of pigeon pea in India (in gm/capita/day). Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . Value of pulses imported into India in billion Indian rupees. Source statistica.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Volume of pulse import to India in 2019 by leading origin country in thousand tonnes. Source statistica.com . . . . . . . . . . . . Imports of top five importers of chickpea (in tonnes). Source statistica.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Import of chickpea in India form 1993–2020 (in tonnes). Source statistica.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imports of top five importers of peas (in tonnes). Source statistica.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imports of chickpea in India (in tonnes). Source statistica.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 22 22 23 23 24 24 25 28 28 28 29 29 30 30 31 34 35 35 36 36 36 37

List of Figures

Fig. 3.16 Fig. 3.17 Fig. 4.1 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5 Fig. 5.6 Fig. 5.7 Fig. 5.8 Fig. 5.9 Fig. 5.10 Fig. 5.11 Fig. 5.12 Fig. 5.13 Fig. 5.14 Fig. 5.15 Fig. 5.16 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 6.5 Fig. 6.6

Imports of top five importers of beans (in tonnes). Source statistica.com . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imports of beans in India (in tonnes). Source statistica.com . . . . States covered by NFSM for pulses. Source NFSM . . . . . . . . . . . Percentage of households with farming as main occupation in percentage. Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . Households according to farm size (in per cent). Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage share of households with awareness in any government schemes. Source Survey data . . . . . . . . . . . . . . . . . . . Households with government scheme awareness according to farm size (in per cent). Source Survey data . . . . . . . . . . . . . . . . Crop diversification state-wise (in per cent). Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Crop diversification according to farm size (in per cent). Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage of farmers with knowledge about new production techniques. Source Survey data . . . . . . . . . . . . . . . . . . Farm size-wise knowledge about new production techniques (in per cent). Source Survey data . . . . . . . . . . . . . . . . . Percentage of households with contact with government extension services. Source Survey data . . . . . . . . . . . . . . . . . . . . . Farm size-wise contact with government extension services (in per cent). Source Survey data . . . . . . . . . . . . . . . . . . . Percentage of households with access to training. Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Training received by farm size-wise (in per cent). Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage share of households with information about MSP, state-wise. Source Survey data . . . . . . . . . . . . . . . . . . Farm size-wise information about MSP (in per cent). Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage share of households with utilisation of MSP state-wise. Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . Utilisation of MSP farm size-wise (in per cent). Source Survey data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . India’s import of pulses in tonnes. Source FAOSTAT . . . . . . . . . . India’s share in total world import of pulses. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trends in imports of peas (dry) in tonnes. Source FAOSTAT . . . India’s peas (dry) import as a percent of total world import. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trends in imports of chickpea in tonnes. Source FAOSTAT . . . . India’s chickpea import as a percent of total world import. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxi

37 37 47 56 57 57 58 59 59 60 60 61 61 62 62 63 64 64 65 68 68 69 70 70 71

xxii

Fig. 6.7 Fig. 6.8 Fig. 6.9 Fig. 6.10 Fig. 6.11 Fig. 6.12 Fig. 6.13 Fig. 6.14 Fig. 6.15

Fig. 6.16

Fig. 6.17

Fig. 6.18

Fig. 8.1 Fig. 8.2 Fig. 8.3 Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 9.4 Fig. 9.5

List of Figures

Trends in India’s imports of lentils in thousand tonnes. Source wits.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . India’s import of lentils as a percent of total world import. Source wits.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Import of chickpea from Australia in thousand tonnes. Source DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Import of chickpea from Canada in thousand tonnes. Source DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Import of chickpea from Ethiopia in thousand tonnes. Source DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imports of peas from major importers in thousand tonnes. Source DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imports of kidney beans from major importers in thousand tonnes. Source DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imports of pigeon pea (tur) from major importers in thousand tonnes. Source DGCI&S . . . . . . . . . . . . . . . . . . . . . . Yearly average prices (Rs. per kg) of chickpea imported by major importers. Source Calculated using the data from DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yearly average prices (Rs. per kg) of peas imported by major importers. Source Calculated using the data from DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yearly average prices (Rs. per kg) of kidney beans imported by major importers. Source Calculated using the data from DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yearly average prices (Rs. per kg) of pigeon pea (tur) imported by major importers. Source Calculated using the data from DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . India’s import of pulses in thousand tonnes. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Import share of kidney beans by major importers. Source DGCI&S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Import share of peas by major importers. Source DGCI&S . . . . . Distribution of farm gate prices of chick pea. Source Author’s own analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of farm gate prices of pigeon pea. Source Author’s own analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marketable surplus ratio—tur (arhar). Source Directorate of Economics and Statistics, DAC&FW . . . . . . . . . . . . . . . . . . . . Marketable surplus ratio—gram. Source Directorate of Economics and Statistics, DAC&FW . . . . . . . . . . . . . . . . . . . . e-NAM working model. Source http://sfacindia.com/ima ges/NAM-Working-Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71 72 72 73 73 74 74 75

75

76

76

77 105 106 106 122 123 127 127 128

List of Figures

Fig. 9.6

Fig. 9.7

Fig. 9.8

Fig. 9.9

Fig. 9.10

Fig. 9.11 Fig. 9.12 Fig. 11.1 Fig. 11.2 Fig. 11.3 Fig. 11.4 Fig. 11.5 Fig. 11.6 Fig. 11.7

Wholesale price vis-à-vis MSP—tur (arhar). Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics and Statistics, DAC&FW . . . . . . . Wholesale price vis-à-vis MSP—urad. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics & Statistics, DAC&FW . . . . . . . . Wholesale price vis-à-vis MSP—moong. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics & Statistics, DAC&FW . . . . . . . . Wholesale price vis-à-vis MSP—gram. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics and Statistics, DAC&FW . . . . . . . Wholesale price vis-à-vis MSP—masoor. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics &Statistics, DAC&FW . . . . . . . . . Wholesale prices all zones—tur (arhar). Source Department of Consumer Affairs (Price Monitoring Cell) . . . . . . Wholesale prices all zones—gram. Source Department of Consumer Affairs (Price Monitoring Cell) . . . . . . . . . . . . . . . . Production of paddy and pigeon pea in thousand tonnes. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production of wheat and chickpea in thousand tonnes. Source FAOSTAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . NFSM pulses states and districts. Source Ministry of Agriculture and Farmers Welfare, Government of India . . . . . Shift in area under cultivation—The case of pigeon pea. Source CMIE data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shift in area under cultivation—The case of chickpea. Source CMIE data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coefficient of variation of farm harvest prices. Source Based on author’s own calculation . . . . . . . . . . . . . . . . . . . . . . . . . Coefficient of variation of yield. Source Based on author’s own calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xxiii

130

130

130

131

131 131 132 146 146 150 156 156 166 166

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 3.1 Table 3.2 Table 3.3 Table 4.1 Table 4.2 Table 4.3 Table 4.4 Table 4.5 Table 4.6 Table 4.7 Table 4.8

Share of major pulse-producing states area in total area . . . . . . Share of major chickpea-producing states area in total area in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Share of major pigeon pea-producing states in total area under pigeon pea in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production share by major pulse-producing states . . . . . . . . . . . Share of major pigeon pea-producing states’ production in total production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Share of major chickpea-producing states’ production in total production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nutritional content of major pulses in India (per 100 g) . . . . . . Developing countries where pulses contribute more than 10% of per capita total protein intake . . . . . . . . . . . . . . . . . Promising intercropping for different pulse-producing states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Districts covered (identified) under National Food Security Mission (2017–18) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Districts covered under NFSM-Pulses in the study states . . . . . Area, production and yield of pulses in NFSM District—Yavatmal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Area, production and yield of pulses in non-NFSM District—Dhule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Area, production and yield of pulses in NFSM District—Chitradurga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Area, production and yield of pulses in non-NFSM District—Mandya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Area, production and yield of pulses in NFSM District—Dewas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Area, production and yield of pulses in NFSM District—Dindori . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 15 16 17 18 18 32 33 39 48 49 50 50 51 51 52 52

xxv

xxvi

Table 4.9 Table 5.1 Table 5.2 Table 7.1 Table 7.2 Table 7.3 Table 7.4 Table 7.5 Table 7.6 Table 7.7

Table 7.8 Table 7.9 Table 7.10 Table 7.11 Table 7.12 Table 7.13

Table 7.14 Table 7.15

Table 8.1 Table 8.2 Table 9.1 Table 9.2 Table 9.3

List of Tables

Action plan for implementation of NFSM-Pulses in all states during 2017–18 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Households according to type . . . . . . . . . . . . . . . . . . . . . . . . . . . Percentage of households according to farm size in different states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for kidney beans—commodity-specific exchange rate model . . . . . . . . . . . Results of the PTM model for peas—commodity-specific exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for chickpea—nominal exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for pigeon pea—nominal exchange rate model (old currency) . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for pigeon pea—nominal exchange rate model (new currency) . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for pigeon pea—real exchange rate model (new currency) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for pigeon pea—commodity-specific exchange rate model (new currency) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for kidney beans—nominal exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for kidney beans—real exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for peas—nominal exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for peas—real exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for chickpea—real exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for chickpea—commodity-specific exchange rate model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for pigeon pea—real exchange rate model (old currency) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the PTM model for pigeon pea—commodity-specific exchange rate model (old currency) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exchange rate pass-through and market share—the case of kidney beans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exchange rate pass-through and market share—the case of peas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minimum support prices of various pulses in rs per quintal . . . Awareness about minimum support price (MSP) . . . . . . . . . . . . Procurement of pulses under PDS by NAFED . . . . . . . . . . . . . .

52 56 57 86 87 88 89 90 91

91 92 92 93 93 94

94 95

95 109 111 120 124 125

List of Tables

Table 9.4 Table 10.1 Table 10.2 Table 10.3 Table 11.1 Table 11.2 Table 11.3 Table 11.4 Table 11.5 Table 11.6 Table 11.7 Table 11.8

xxvii

Procurement of kharif pulses during 2016–17 . . . . . . . . . . . . . . Variable description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics for variables used in the model . . . . . . . . . Results for MSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minimum support prices of various pulses in Rs per quintal . . . Variable description and definition . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics of the variables . . . . . . . . . . . . . . . . . . . . . Estimates of area response for chickpea and pigeon pea—system GMM results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimates of production response for chickpea and pigeon pea—system GMM results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimates of yield response for chickpea and pigeon pea—system GMM results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimates for chickpea—OLS, fixed effects and difference GMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimates for pigeon pea—OLS, fixed effects and difference GMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

126 139 140 141 148 158 159 160 161 163 166 166

Chapter 1

Introduction

1.1 Introduction Pulses are an essential part of Indian diet as they are a dominant source of protein. Pulses are ‘the poor man’s meat’ because the consumption of dairy and animal products is very low among the poorest segment of both rural and urban India. Pulse crops are used as green manure and contribute in improving soil health. Therefore, pulses contribute in improving human health as well as conserving soil through their nitrogen fixing properties. The vital role played by pulses in the agriculture system and in the diets of people makes it an ideal crop for achieving food and nutritional security, reducing poverty and hunger. Despite being the largest producer and consumer of pulses in the world, the production of pulses in India was stagnant until recently for nearly 40 years or more (Sekhar & Bhatt, 2012). India accounts for 25% of world production and 27% of world consumption (Srivastava et al., 2010). There are many reasons for this lethargic growth of pulses production in the country, and out of them, the most prominent reasons are the pulses are rain-fed crop and are mainly grown as a residual crop on marginal lands (Sekhar & Bhatt, 2012). Apart from these two reasons, farmers do not have much incentives to cultivate pulses as a result of high production and price uncertainty (Sekhar & Bhatt, 2012). Though the government procurement for rice and wheat is implemented relatively well, the procurement operations of pulses are not sufficient. So, lack of assured market through procurement is also a factor contributing to price uncertainty. As a consequence, to these reasons, the area under pulses witnessed a decline especially during the 1960–70s. It was during the same period, the input-intensive green revolution was implemented and the cereals were given the priority. The area which was earlier allotted for pulses production was also reallocated for cereals. The introduction of green revolution and its technological improvement favoured rice and wheat through improvements in yield. As a result, the cereal crops became relatively more remunerative and competitive (Akinbode et al., 2011). © Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_1

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2

1 Introduction

The latest available data shows that the production of pulses in India was 17.15 million tonnes in 2014–15 which declined to 16.35 million tonnes in 2015–16 and further increased to 22.14 million tonnes in 2016–17 (Department of Agriculture and Cooperation, 2017). There could be several factors that might have contributed to short term increase in pulses production including the government interventions such as National Food Security Mission (NFSM), favourable rainfall. However, some newspaper reports show that the area under pulses has gone down in the latest Kharif season. Pulses acreage has fallen to 130.68 lakh hectares, from the earlier 135.42 lakh hectares (The pioneer, 2017). Therefore, the current trends in area and production of pulses generally reveal the uncertain and fluctuating nature of the production of pulses which are vital for food and nutritional security of the country. The major pulses cultivated and consumed in India are chickpeas (gram) and pigeon peas (tur). This is followed by green gram (moong) and urad (black gram). Chickpea (gram) is the largest in terms of area under cultivation and constitutes around 46% in the total area under pulses cultivation. Madhya Pradesh, Maharashtra, Uttar Pradesh, Rajasthan and Andhra Pradesh are the major pulse-producing states in India (Singh, 2013).

1.2 Key Issues: Production Uncertainty and High Import Dependency Though the production picked up in the recent years, until recently the production was stagnant, and this widened the supply and demand gap and resulted in the unprecedented hike in pulse price. An upwards trend was observed in the price of pulses especially after 2005. In 2006, there was a sudden increase in imports of pulses which led to a high global price. The year 2009 was a poor agricultural year which led to an increase in price due to shortage in supply. Further in 2012, high minimum support prices (MSP), high world price and depreciation of Indian rupee led to an exorbitant increase in pulse price (Reddy, 2015). A double-digit trend in wholesale price index (WPI) inflation of pulses was observed in 2015, reaching 39% in September 2015–16 which is very high relative to that of cereals (Ministry of Commerce & Industry, 2016). Due to growing population, declining pulse production and rising pulse prices, the net per capita availability of pulses in India has witnessed a sharp decline. In the year 1951, the per capita net availability of pulses was 60.7 g per day, but it is declined to 41.9 g per day in the year 2013. The current figures show that the current annual production of around 17 million tonnes of pulses is not sufficient to attain the pulses self-sufficiency, and as per the projections, the country needs to fill the gap of 33 million tonnes to meet the demand in the year 2050. This indicates the annual growth rate of 4.2% in production (Indian Institute of Pulses Research, 2013). The MSP for major pulses in the recent years has also witnessed an increase to provide incentives to farmers. The compound growth rate for MSP in pulses such

1.2 Key Issues: Production Uncertainty and High Import Dependency

3

as pigeon pea, chickpea, green gram and black gram was much higher than that of wheat and rice. However, the lack of effective procurement under this MSP for pulses is acting as a major hurdle in encouraging farmers to allocate more area for pulses cultivation. The pulses procured by procurement agencies such as National Agricultural Cooperative Marketing Federation of India Ltd. (NAFED) were only 1% to 4% of total production of pulses as compared to 28% to 30% of procurement of wheat and rice. The distress selling of pulses by farmers was also a reason why farmers were not much interested in cultivating the crop. The decline in production of pulses resulted in the widening of gap between supply and demand, and excess demand droves up the prices. This prompted the Government of India to increase the imports of pulses from other major producing countries leading to an increase in import bill. Prior to 1990s, the Government of India followed an import substitution strategy to protect the domestic producers and insulate them from the uncertainties in the world market. As a result, the imports were restricted by imposing trade barriers such as tariffs and import quotas. The trade liberalisation was initiated as part of the overall structural adjustment and macroeconomic stabilisation policies, and as a result from 2007 to 12, the import tariffs against the pulses were removed, and in the year 2013, the tariffs were brought down to zero to facilitate greater imports (Negi & Roy, 2015). Since then, there had been a huge dependency on the imported pulses to meet the domestic demand, and imports went down in recent years when the domestic production gradually picked up due to a series of government initiatives to boost the production. Though the production has improved, the current production scenario shows that the production needs to be again improved to meet the growing demand in the country. One major challenge is the differences in the specific type of pulses in different parts of the country as preferences vary quite considerably across regions. In addition to this, the scope for substation is also limited due to this strong preference for a particular type of pulse (Joshi et al., 2017). The constraints in the genetic potential of yield are acting as a hindrance in achieving higher yield, and the crop is vulnerable to pests and diseases. Adoption of short-duration varieties of pulses and the varieties that are resistant to pests and diseases can be useful in improving the production by around 5–6 million tonnes (Joshi et al., 2017). Mechanical harvesting of the pulse crop and crop production and protection technologies have also been limited (Indian Institute of Pulses Research, 2013). On the marketing front, farmers face several challenges as they do not have an assured price and market even though MSP is in place. The procurement operations are weak, and prices are volatile. Due to this, famer finds other competing crops as more lucrative than pulses (Thomas et al., 2013). Even an exorbitant increase in pulses price did not result in the higher relative profitability for pulses producing farmers. In order to encourage farmers to face greater risk associated with pulses production, adequate insurance coverage can be adopted, and as a result, they will be in a better position to respond to higher market prices (Joshi et al., 2017). In a study by Srivastava et al., (2010), revenue terms of trade between pulses and cereals were evaluated, and it was inferred that farmer’s

4

1 Introduction

preference was inclined towards production of cereals rather than pulses, despite of a higher MSP for pulses. As mentioned already, pulses are grown in rain-fed regions, and around 83% area under pulses is rain fed. In 2007, the National Food Security Mission was launched to boost rice production by 10 million tonnes, wheat by 8 million tonnes and pulses by 2 million tonnes by the end of the 11th Five Year Plan, i.e. 2011–12. NFSM pulses were initially adopted in 171 districts across 14 states of the country. The Integrated Scheme for Oilseeds, Pulses, Oil Palm and Maize (ISOPOM) was initially launched to serve the pulses growers in the districts that are not covered by NFSM pulses scheme. However, later the pulses component of ISOPOM was merged with NFSM to avoid administrative difficulties and duplication of efforts. Subsequently, 433 districts in the 14 states were covered by the pulse component of NFSM (Thomas et al., 2013). One of the main objectives of NFSM intervention in NFSM pulses districts was to provide quality seeds of improved varieties to farmers, and this resulted in an increased yield in 2010–11. In addition to this, the programme provided technological inputs for plant protection to the pulse farmers in the NFSM districts. The integrated soil nutrient management (INM) and the integrated pest management (IPM) were also an integral component of the NFSM pulses scheme (IPM) (Thomas et al., 2013). Given the significance of government intervention in pulse production, one of the objectives of the study is to analyse the impact of government intervention on supply of pulses in the form of NFSM on area, production and yield of India’s major pulses and to identify the major constraints in raising the production and productivity of pulses. The other objectives of the study are to analyse the factors influencing farmers’ access to MSP and the exchange rate pass-through and nature of pricing behaviour of pulses importers to India. Using the household-level data, the present study will make an attempt to examine the factors affecting the pulses farmers’ (chickpea and pigeon pea) access to information regarding MSP and utilisation of MSP. The major pulse-producing states of the country are selected for a detailed household-level analysis. The other two objectives are analysed using the secondary data. Area, cost of production, prices and non-price factors are used in analysing the factors influencing the supply of pulses. The import data, exchange rate and consumer price index (CPI) are used for the analysis of pricing behaviour and exchange rate pass-through.

1.3 The Detailed Objectives of the Study Can Be Listed as Follows 1. 2. 3.

To analyse the factors affecting the production of major pulses. To understand the impact of minimum support price (MSP) policy and NFSM on the production of pulses. To analyse the implications of pulses trade and the import pricing behaviour and exchange rate pass-through into major pulses imported to India.

1.5 Data Collection

4.

5

To understand the sources of MSP information and factors affecting the access to MSP.

1.4 Study Area The present study focuses on two major pulses produced in India for analysing the factors influencing the production, area and yield of pulses. The two major pulses produced and consumed in India are chickpea and pigeon pea. The import pricing behaviour and the analysis of exchange rate impact on import prices of pulses will make use of four major pulses imported to India. They are chickpea, pigeon pea, dry peas and kidney beans. The analysis of factors influencing the production, area and yield of pulses is undertaken by selecting all the major districts from all major pulse-producing states of India. For the household-level data analysis, three major pulse-producing states from India are identified. They are Maharashtra, Madhya Pradesh and Karnataka. Maharashtra (APY declining), Madhya Pradesh (APY improving) and Karnataka (area is declining, but production and yield are improving) are selected for the purpose of analysis for chickpea. Madhya Pradesh (area is declining, but production and yield are improving), Karnataka (APY improving) and Maharashtra (APY declining) are identified for the purpose of pigeon pea. The states for each pulse are selected in such a manner that one state generally shows an increase in the production, while the other shows a decline in production over the past 36 years. (Please see the figures in appendix for more details). The district selected for the purpose of analysis within Maharashtra is Nagpur. Narsinghpur is selected for the purpose of analysis from Madhya Pradesh. Gulbarga district is selected for the purpose of analysis from Karnataka. For secondary data analysis, of supply response, all the major pulse-producing states are selected. They are Karnataka, Madhya Pradesh, Maharashtra and Uttar Pradesh.

1.5 Data Collection The analysis is based on both primary as well as secondary data. The primary data will be collected through a comprehensive household-level survey. Villages/regions that have the highest production of the selected pulses are identified for the purpose of analysis. From each state, one of the major pulseproducing districts was selected for further analysis. From Karnataka, Gulbarga was selected, from Maharashtra, Wardha was selected, and from Madhya Pradesh, Narsinghpur was selected. Subsequently, a random sample of pulse-producing households is selected and interviewed. The interviews will be based on structured survey

6

1 Introduction

questionnaire administered by well-trained and experienced enumerators who have knowledge of the local farming system and the local language. The sample consisted of 482 pigeon pea farmers and 316 chickpea, out of which 227 farmers were cultivating both chickpea and pigeon pea. The survey was conducted through questionnaire, framed in such way as to draw out details covering household characteristics, wealth and farm characteristics, institutional and access-related variables, risk and economic factors. The secondary data will be collected from various sources. Trade data will be collected from World Bank’s World Integrated Trade Solution database (WITS). The unit import price will be calculated using the import quantity and import value data obtained from directorate general of commercial intelligence and statistics (DGCI&S). The all India as well as state-level data on area and production of pulses is Centre for Monitoring of Indian Economy’s (CMIE) states of India data. The data on exchange rate is obtained from OANIDA, and the consumer price index is obtained from World Bank indicators. The data on cost and prices was obtained from directorate of economics and statistics.

1.6 Methodology The access to MSP information and utilisation of MSP is analysed using an equation based on a conditional mixed-process (CMP) estimator developed by Roodman (2011, 2020). The pricing to market and exchange rate pass-through are analysed using Panel Corrected Standard Error (PCSE) estimation technique. The supply response of pulses taking area, production and yield as dependent variables is analysed using a dynamic supply response equation developed based on the theoretical framework of Nerlove’s adaptive expectation model.

1.7 Chapter Scheme This study has been divided into 11 chapters including introduction and conclusion. This chapter as an introduction provided the background, objectives, data and methodology along with chapter scheme. Chapter 2 gave an overview of pulses economy. Chapter 3 discussed the importance of pulses for nutritional and food security, the importance of sustainable production practices to improve the pulses productivity and food security with an emphasis on India. Chapter 4 discussed the salient features of Government of India’s National Food Security Mission (NFSM) and its objectives especially in the context of pulses production. Chapter 5 provided a detailed discussion of socio-economic profile of the sample households. Chapter 6 provided an overview of pulses production, trade and government policies with a special focus on the trends in trade and its implications. Chapter 7 analysed the

References

7

import pricing behaviour and exchange rate pass-through into prices of imported pulses. Chapter 8 makes a further analysis of exchange rate pass-through incorporating market share and nonlinear and asymmetric nature of exchange rate passthrough. Chapter 9 provided an overview of an evolution of minimum support price policies and MSP for major pulses. Chapter 10 analysed the factors influencing the access to information regarding MSP and utilisation of MSP in a joint framework. Chapter 11 made an analysis of factors influencing the supply response of chickpea and pigeon pea with a special emphasis on MSP and NFSM. Chapter 12 provided the conclusion and policy implications of the study.

References Akinbode, S. O., Dipeolu, A. O., & Ayinde, I. A. (2011). An examination of technical, allocative and economic efficiencies in Ofada rice farming in Ogun State, Nigeria. African Journal of Agricultural Research, 6(28), 6027– 6035 Government of India, Ministry of Commerce & Industry, Office of the Economic Adviser (2016). Index Numbers of Wholesale Price in India. Available at http://www.eaindustry.nic.in/cmonthly. pdf accessed on 12 July 2022 IIPR (2013). Vision 2050. Indian Institute of Pulses Research, Kanpur Joshi, P. K., Kishore, A., & Roy, D. (2017). Making pulses affordable again. Economic & Political Weekly, 52(1), 37. Negi, A., & Roy, D. (2015). The cooling effect of pulse imports on price: The case of the pigeon pea in India. IFPRI Discussion Paper 01439. Reddy, A. A. (2015). Pulses production trends and strategies to become self sufficient. Indian Farming, 65(6), 2–10. Roodman, D. (2011). Fitting fully observed recursive mixed-process models with CMP. The Stata Journal, 11(2), 159–206. Roodman, D. (2020). CMP: Stata module to implement conditional (recursive) mixed process estimator. Sekhar, C. S. C., & Bhatt, Y. (2012). Possibilities and constraints in pulses production in India and impact of national food security mission (final report). Institute of Economic Growth, New Delhi. Singh, R. P. (2013). Status paper on pulses. Directorate of Pulse Development, Bhopal, 89–90. Srivastava, S. K., Sivaramane, N., & Mathur, V. C. (2010). Diagnosis of pulses performance of India. Agricultural Economics Research Review, 23(1), 137–148. The Pioneer. (2017). Sown area of pulses dips by 3.5% in Kharif season. Available at http://www. dailypioneer.com/nation/sown-area-of-pulses-dips--by-35-in-kharif-season.html Thomas, L., Sundaramoorthy, C., & Jha, G. K. (2013). The impact of national food security mission on pulse production scenario in India: An empirical analysis. International Journal of AgriculTural and Statistical Sciences, 9(1), 213–223.

Chapter 2

An Overview of Pulses Economy

2.1 Introduction India contributes around 38% of world’s area under pulses. India’s share in world area was around 56% in 1961 but gradually declined to less than 40% since 2000. In terms of production, India contributed around 23% in 2016, whereas India’s contribution was around 45% in 1961. Though India’s pulses production was always fluctuating, the decline in the share of production was more prominent since 2001. Similarly, Indian pulses yield was also much below the world average. Though the area under pulses increased from around 22 million ha in 1966–67 to around 37 million ha in 1990–91, it further declined sharply to around 20 million ha in 2002–03. However, the area again expanded since 2002–03 and reached around 27–28 million ha in 2019–20 (see Fig. 2.1). The production and yield also experienced similar trend. The production was around 8 million tonnes in 1966–67 and increased to 20 million tonnes in 1990– 91 and thereafter declined sharply to 11 million tonnes in 2002–03. But, again the production increased to 23 million tonnes in 2019–20 (see Fig. 2.2). But as compared to area expansion, the production was more or less stagnant. The production was commensurate with the expansion of area, indicating the poor performance of yield. The yield was 377 kg per ha in 1966–67 and increased to 822 kg per ha in 2019– 20 (see Fig. 2.3). The figure shows that there has been an improvement in the yield over the years, but still the productivity is relatively lower as compared to the world average. The production was the highest in 1991. Interestingly, during the same year, the world production was also relatively high. The faster rate of decline in production in India as compared to world production was also reflected in the declining share of India’s production in world production. India’s share in production declined from around 45% in 1961 to only 23% in 2016. The share was the lowest in 2003 marking only 18%. India being the major producer of pulses, the stagnant size of area and poor

© Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_2

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2 An Overview of Pulses Economy 40 35 30 25 20 15 10 5 0

1966-67 1969-70 1972-73 1975-76 1978-79 1981-82 1984-85 1987-88 1990-91 1993-94 1996-97 1999-00 2002-03 2005-06 2008-09 2011-12 2014-15 2017-18

10

Fig. 2.1 Area under pulses in India in million ha. Source CMIE Fig. 2.2 Trends in production of pulses in India in million tonnes. Source CMIE

30 25 20 15 10 0

1966-67 1969-70 1972-73 1975-76 1978-79 1981-82 1984-85 1987-88 1990-91 1993-94 1996-97 1999-00 2002-03 2005-06 2008-09 2011-12 2014-15 2017-18

5

Fig. 2.3 Trends in pulses in yield kg per ha. Source CMIE

2.2 Crop-Wise Analysis 10000

Yield, Hg/Ha

Fig. 2.4 Yield of pulses in hg/ha. Source FAOSTAT

11

8000 6000 4000 2000 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015

0

Year

India

World

yield of pulses are an alarming factor especially considering the population growth and protein intake for the poor segments of the country and nutritional implications. The above is especially true when the Indian pulses yield is much below the world average (see Fig. 2.4). Interestingly, the trends in the yield for both India and the world were somewhat similar. Another interesting observation is in the year 1991, India’s yield in pulses were almost similar to world yield. Indian yield was 7096 hg/ha, whereas world yield was 7252 (see Fig. 2.4). Note that this was the year India achieved the record production in pulses. Therefore, an understanding of the factors that contributed to higher production and productivity in pulses in the year 1991 is very crucial to understand and promote policies that would help in enhancing pulses production. Again in 2001, the Indian yield got closer to world yield. Indian yield was 7879, whereas the world yield was 8674. Unfortunately, since 2001, the yield gap between the world and India got widened and Indian yield went much below the world yield. As observed earlier, the declining share of India’s pulses production in world pulses production was also more prominent since 2001. The decline and widening gap in the yield could be the reason for the declining share in India’s pulses production.

2.2 Crop-Wise Analysis Gram (chickpea) is the major pulses produced in India. Around 35% of total area under pulses belong to chickpea cultivation. Chickpea is followed by arhar (pigeon pea) with a share of around 16%. The share of chickpea declined between 1966–67 and 1991–92. The share of chickpea area was 36% in 1966–67, whereas it declined to 16% in 1991–92. However, thereafter the share increased and reached around 35% in 2019–20. But what is striking is the share remains more or less the same, and the decline in chickpea area in the early 1990s could be the reason why there was an overall decline in the area under pulses as we noticed in the previous section. As compared to chickpea, the share of pigeon pea was more or less stagnant throughout the period (see Fig. 2.5).

12

2 An Overview of Pulses Economy

Fig. 2.5 Percentage share of area under arhar (pigeon pea) and gram (chickpea) in total pulses area. Source CMIE states of India

The area under chickpea and pigeon pea witnesses the same trend as we seen in the case of their share in total area. So, the decline in the area that happened to chickpea in early 1990s was due to the decline in the area itself rather than an increase in the area of other pulses. The area under pigeon pea was around 2 million ha in 1966–67 and showed a marginal increase over the period and reached around 4.5 million ha in 2019–20 (see Fig. 2.6). The share of arhar (pigeon pea) production in total pulses was more or less stagnant throughout the period, whereas the share of chickpea drastically declined between 1966–67 and 1987–88. In 1996–97, the share of chickpea in total production was around 49%, and in 1987–88, the share was only 23%. However, since then, the share began to increase, and the share is around 48% in 2018–19 (see Fig. 2.7). The production of chickpea increased from 3.7 million tonnes in 1966–67 to 11 million tonnes in 2019–20. However, the production remained stagnant from 1966– 67 to 200–01. It was only since 2000–01, there was a sharp increase in production from around 4 million tonnes. As far as the pigeon pea is concerned, there has not been much improvement in production. The production was around 1 million tonnes in 1966–67 and marginally increased to 3.8 million tonnes in 2019–20 (see Fig. 2.8). Fig. 2.6 Area under gram (chickpea) and arhar (pigeon pea) in India in million ha. Source CMIE states of India

2.3 Production of Pulses in Major States

13

Fig. 2.7 Percentage share of arhar (pigeon pea) and gram (chickpea) production in total pulses production. Source CMIE states of India

Fig. 2.8 Production of gram (chickpea) and arhar (pigeon pea) in India in million tonnes. Source CMIE states of India

2.3 Production of Pulses in Major States India grows and consumes several types of pulses primarily because of heterogeneity in preference across regions. Pulses are grown in all three seasons. The three crop seasons for the commodity are: i. ii. iii.

Kharif: arhar (tur), urd (black gram), moong (green gram), lobia (cowpea), kulthi (horse gram) and moth; Rabi: gram, lentil, pea, lathyrus and rajmash; Summer: green gram, black gram and cowpea.

The major pulse-producing states for the year 2017–18 are given in Fig. 2.9. As per the figure, the major pulse-producing state is Madhya Pradesh with a share of around 32% in total production. Madhya Pradesh is followed by Rajasthan and Maharashtra with an equal share of around 13% (see Fig. 2.3). This is followed by Uttar Pradesh with a share of around 9%. The states like Karnataka, Gujarat, Andhra Pradesh, Jharkhand, Tamil Nadu and Chhattisgarh have a share of 8%, 4%, 5%, 3% and 2%, respectively. As far as the area of the total area under pulses production was the highest in Madhya Pradesh, this was followed by Rajasthan, Maharashtra, Karnataka, Uttar Pradesh and Andhra Pradesh (see Fig. 2.6). Out of these states, Madhya Pradesh

14

2 An Overview of Pulses Economy

Fig. 2.9 Pulses production in Indian states. Source Ministry of Agriculture and Farmers’ Welfare, Government of India

exhibited an improvement in area between 2014–15 and 2016–17. Maharashtra and Karnataka also showed an improvement, whereas in Uttar Pradesh, the area remained more or less stagnant. However, the share of these states in total area remains to be more or less the same with an increased share of Rajasthan (see Table 2.1). The major pulses produced in India are pigeon pea and chickpea. However, in the subsequent sections, we analyse the area and production scenario of two more pulses—kidney beans and peas. These crops are selected as they are heavily imported by India consistently over the last couple of years. We have also made an analysis

2.3 Production of Pulses in Major States

15

Table 2.1 Share of major pulse-producing states area in total area Madhya Pradesh

Rajasthan

Maharashtra

Karnataka

Uttar Pradesh

Andhra Pradesh

2014–15

23

14

14

10

10

4

2015–16

24

16

14

11

10

6

2016–17

23

18

15

10

9

5

Source CMIE States of India

Table 2.2 Share of major chickpea-producing states area in total area in India Year

Madhya Pradesh

Rajasthan

Maharashtra

Karnataka

2014–15

32

19

18

10

2015–16

36

11

17

17

2016–17

33

16

20

10

Source CMIE States of India

of import pricing behaviour of importers of all the above-mentioned crops (refer to Chap. 7). The area under chickpea was also the highest in Madhya Pradesh, and this was followed by Rajasthan, Maharashtra and Karnataka. When area under chickpea remained more or less the same in Madhya Pradesh during the three years under analysis, the area under chickpea in other states showed slightly more fluctuations (see Fig. 2.7). When all these states experienced a mild decline in area under chickpea, the area increased in Karnataka in 2015–16. However, when all the states experienced an increase in area, Karnataka experienced a decline in 2016–17. This actually shows that farmers are adjusting their area under cultivation based on the surplus production and the subsequent market prices. The increase in one year and then decline in the next year in area allocation are a testimony to this fact (Table 2.2). As compared to the concentration of production of chickpea area in few states, there were more states producing pigeon pea. The highest area under pigeon pea was in Maharashtra, and this was followed by Karnataka, Madhya Pradesh and Uttar Pradesh. Telangana, Gujarat and Andhra Pradesh were the other major producers (see Fig. 2.8). All the states experienced an increase in area under cultivation in 2016– 17. The increased area allocation could be due to the high market prices prevailed in the previous year. Though area had increased in 2016–17, the share of some of these states’ in total area under pigeon pea in India had declined as compared to the previous year. For example the share in area had declined in Maharashtra, Madhya Pradesh and Uttar Pradesh, while the share had increased in Karnataka (see Table 2.3). The production of kidney beans was concentrated only in Rajasthan with a negligible share also from Gujarat. Rajasthan contributes almost all of the kidney beans produced in India. The lack of sufficient supply in kidney beans could be the reason

16

2 An Overview of Pulses Economy

Table 2.3 Share of major pigeon pea-producing states in total area under pigeon pea in India Year

Maharashtra

Karnataka

Madhya Pradesh

Uttar Pradesh

Telangana

Gujarat

Andhra Pradesh

2014–15

29

21

12

8

7

5

5

2015–16

31

17

15

7

6

6

6

2016–17

27

23

13

6

7

7

7

Source CMIE States of India

for substantial imports in kidney beans. The area under kidney beans experienced an increase over the three periods under study (see Fig. 2.9). The area under peas was the highest in Uttar Pradesh and Madhya Pradesh. Madhya Pradesh experienced a continuous increase in area under peas during the three-year period (see Fig. 2.10). The other two producers were Odisha and Jharkhand. However, they had only a negligible share in total area under peas. As in the case of area, the production of pulses is also the highest in Madhya Pradesh (see Fig. 2.11). This was followed by Maharashtra, Rajasthan, UP, Karnataka and Andhra Pradesh. Though Karnataka had more area under production, UP has more production as compared to Karnataka. This points out the productivity differences in production. Though the production has increased in Madhya Pradesh, the share of the state in total production in the country declined to 27.20% as compared to 32.43% in 2015–16 (see Table 2.4). In the case of Maharashtra, both the production and the share had increased considerably in 2016–17 as compared to 2015–16. The share of Maharashtra increased from 9.45% to 16.29% (see Table 2.4). Production of pigeon pea was the highest in Maharashtra, and this was followed by Karnataka and Madhya Pradesh. The production of pigeon pea increased considerably in Maharashtra in 2016–17 as compared to 2015–16 (see Fig. 2.12). The 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Madhya Pradesh

Maharashtra 2014-15

Karnataka 2015-16

Uar Pradesh Andhra Pradesh 2016-17

Fig. 2.10 Total area under pulses production in India, state-wise, in thousand hectares. Source CMIE States of India

2.3 Production of Pulses in Major States

17

3500 3000 2500 2000 1500 1000 500 0 Madhya Pradesh

Rajasthan 2014-15

Maharashtra 2015-16

Karnataka

2016-17

Fig. 2.11 Area under chickpea in India in major pulse-producing states, in thousand hectares. Source CMIE States of India Table 2.4 Production share by major pulse-producing states Madhya Pradesh

Maharashtra

Rajasthan

Uttar Pradesh

2014–15

28.15

11.97

11.38

8.39

8.10

5.54

2015–16

32.43

9.45

12.17

7.12

6.97

7.52

2016–17

27.20

16.29

13.75

9.44

7.51

4.02

Year

Karnataka

Andhra Pradesh

Source CMIE States of India

1600 1400 1200 1000 800 600 400 200 0

2014-15

2015-16

2016-17

Fig. 2.12 Area under pigeon pea in India in major pulse-producing states, in thousand hectares. Source CMIE States of India

18

2 An Overview of Pulses Economy

production had increased in Karnataka also. But in the case of Madhya Pradesh, Gujarat and Uttar Pradesh, the production increased continuously over the three-year period (see Fig. 2.12). Due to substantial increase in production in Maharashtra, the share also increased from 22 to 31% (see Table 2.5). The share of Karnataka also increased from 9 to 19%. Madhya Pradesh was the highest producer of chickpea. This was followed by Maharashtra and Rajasthan (see Fig. 2.13). Though the absolute amount of production increased in Madhya Pradesh, the share declined from 48 to 38% during the threeyear period under study (see Table 2.6), whereas the share increased in Maharashtra, Table 2.5 Share of major pigeon pea-producing states’ production in total production Year

Maharashtra

2014–15

26

17

18

8

6

2015–16

22

9

24

10

7

2016–17

31

19

16

8

7

Karnataka

Madhya Pradesh

Gujarat

Uttar Pradesh

Source CMIE States of India 1600 1400 1200 1000 800 600 400 200 0 2014-15

2015-16 Rajasthan

2016-17

Gujarat

India

Fig. 2.13 Area under kidney beans in major pulse-producing states of India, in thousand hectares. Source CMIE States of India

Table 2.6 Share of major chickpea-producing states’ production in total production Year

Madhya Pradesh

Maharashtra

Rajasthan

Karnataka

Andhra Pradesh

Uttar Pradesh

2014–15

40

15

12

9

5

5

2015–16

48

11

12

9

7

2

2016–17

38

18

15

6

4

7

Source CMIE States of India

2.4 Shift in the Area Under Pulses Cultivation

19

500 450 400 350 300 250 200 150 100 50 0 Uar Pradesh

Madhya Pradesh 2013/14

2014-15

Odisha

Jharkhand

2015-16

Fig. 2.14 Area under peas in major pulse-producing states of India, in thousand hectares. Source CMIE States of India

Rajasthan and Uttar Pradesh (see Table 2.6). The share of Maharashtra increased from 11 to 18%, Rajasthan from 12 to 15% and Uttar Pradesh from 2 to 7%. The production of peas was mainly by Madhya Pradesh and Uttar Pradesh. Though Orissa was the third in area under cultivation of peas, the productivity of peas was very less in Orissa. This was the reason why Orissa was not appearing among the top producers of peas. Though Jharkhand was not appearing among the top states in terms of area under cultivation, it has appeared among the top in terms of production indicating better productivity of peas in Jharkhand as compared to Rajasthan or Orissa (see Fig. 2.14). The total peas production had increased from 742 thousand tonnes in 2015–16 to 1011 thousand tonnes in 2016–17. Rajasthan contributed almost all of the kidney beans produced in the country along with a negligible share from Gujarat (see Fig. 2.15).

2.4 Shift in the Area Under Pulses Cultivation Another interesting aspect of pulses production in India is a shift in area under cultivation from northern and eastern states to central and southern states of India (Reddy et al., 2013) (see Fig. 2.18). The crop-wise analysis further extending the period also showed these interesting shifts in area under pulses. The area under pigeon pea in southern and western states increased over the period, whereas the area under pigeon pea in northern and eastern states declined gradually. Similar trend can be observed in the case of chickpea as well. But, the major difference is in case of chickpea even central regions like Madhya Pradesh experienced a huge expansion

20

2 An Overview of Pulses Economy

7000 6000 5000 4000 3000 2000 1000 0

Madhya Pradesh

Maharashtra

Rajasthan Uar Pradesh Karnataka

2014-15

2015-16

Andhra Pradesh

2016-17

Fig. 2.15 Production of pulses in major pulse-producing states, in thousand tonnes. Source CMIE States of India

in area. The area under chickpea in central, southern and western states experienced a sharp increase, whereas the northern and eastern states experienced a decline. As per the studies, the reason for this decline in area under pulses in northern and eastern states is the expansion in irrigation facilities. Pulses are rain fed, whereas rice and wheat are more water intensive. Therefore, the greater availability of irrigation facilities in northern and eastern states encouraged them to shift away from pulses to rice and wheat for which there is an assured MSP and procurement. Despite the fact that India is the largest producer and consumer of pulses in the world, the introduction of green revolution in the mid-1960s and the government policy bias towards cereal crops such as wheat and rice to meet the objectives of food security inadvertently resulted in the crowding out of nutritious-rich crops such as pulses (Akinbode et al., 2011; Nelson et al., 2019; Pingali et al., 2017). The food security objectives have a twin mechanism of remunerative and stable price to producers on the one side and affordable price to consumers on the other side. The interests of producers were protected through minimum support price (policy), whereas the consumers through distribution of food grains through public distribution system (PDS) at an issue price. Though the objective of the price policy was to induce incentives to produce those crops where the domestic supply is less than the demand, the implementation of food security policies favoured mainly staple food grains such as rice and wheat (Tripathi, 2017). The government not only announced the MSP for rice and wheat but actively procured these grains and distributed via PDS, whereas the procurement was meagre for pulses. As a result of the distorted incentives, the production of pulses became stagnant as compared to wheat and rice. Wheat and chickpea are mainly cultivated in rabi season, whereas pigeon pea and rice are mainly cultivated in kharif season. So, wheat acted as a competing crop for chickpea, and rice acted as a competing crop for pigeon pea. Figures 2.23 and 2.24 reveal how the relative incentives encouraged the production of wheat and rice through enhancing the productivity of these two staple crops (see Figs. 2.16 and 2.17).

2.4 Shift in the Area Under Pulses Cultivation

21

1600 1400 1200 1000 800 600 400 200 0 Maharashtra

Karnataka 2014-15

Madhya Pradesh 2015-16

Gujarat

Uar Pradesh

2016-17

Fig. 2.16 Production of pigeon pea in major pulse-producing states. Source CMIE States of India

4000 3500 3000 2500 2000 1500 1000 500 0 Madhya Pradesh

Maharashtra

Rajasthan

2014-15

Karnataka

2015-16

Andhra Pradesh

Uar Pradesh

2016-17

Fig. 2.17 Production of chickpea in major pulse-producing states. Source CMIE States of India

The figures 2.23 and 2.24 are the eloquent testimony to the fact that the government intervention and policy bias lead to the crowding out of more protein-rich crops such as pulses (Figs. 2.18, 2.19, 2.20, 2.21 and 2.22).

22

2 An Overview of Pulses Economy

450 400 350 300 250 200 150 100 50 0 Madhya Pradesh

Uar Pradesh 2014-15

2015-16

Jharkhand

Rajasthan

2016-17

Fig. 2.18 Production of peas in major pulse-producing states. Source CMIE States of India

500 450 400 350 300 250 200 150 100 50 0 India

Rajasthan 2014-15

2015-16

Gujarat 2016-17

Fig. 2.19 Production of kidney beans in major pulse-producing states. Source CMIE States of India

2.5 Conclusion An analysis of area, production and yield of pulses and major pulses produced in India showed that there was a substantial decline in area and production of pulses in India. Indian yield was much below the world average, and the yield gap between the two got widened since 2001. It was the same year, the decline in production of pulses was more prominent. However, in the year 1991, the yield gap got narrowed

2.5 Conclusion

23

Fig. 2.20 Shifting cultivation. Source Reddy et al. (2013)

Fig. 2.21 Shift in area under cultivation—the case of pigeon pea. Source CMIE states of India

and came very close to the world average. Interestingly, this was the same year when India marked a record production in pulses. The declining share in area and production and widening gap between the yields is very alarming in the context of an increased demand for pulses. Since it is a protein-rich crop and there is a decline in per capita availability of pulses, considerable efforts are required to boost the

24

2 An Overview of Pulses Economy

Fig. 2.23 Production of paddy and pigeon pea in thousand tonnes. Source FAOSTAT

200000 150000 100000 50000 0 2017 2013 2009 2005 2001 1997 1993 1989 1985 1981 1977 1973 1969 1965 1961

Production in 1000 tonnes

Fig. 2.22 Shift in area under cultivation—the case of chickpea. Source CMIE states of India

Year Paddy Production

Pigeon Pea Production

production. The year 2016–17 shows marginal increase in the production of pulses. Though the dominant producing states have either continued or marginally improved the production, an increase in production was observed by other states who were not major contributors of pulses. This could be due to the impact of government policies such as an increase in MSP or the efforts to boost production through National Food Security Mission (NFSM). The subsequent chapters will make an analysis of these factors in greater detail.

25 120000 100000 80000 60000 40000 20000 0

1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018

Production in 1000 tonnes

References

Year

Chickpea Production

Wheat Production

Fig. 2.24 Production of wheat and chickpea in thousand tonnes. Source FAOSTAT

References Akinbode, S. O., Dipeolu, A. O., & Ayinde, I. A. (2011). An examination of technical, allocative and economic efficiencies in Ofada rice farming in Ogun State, Nigeria. African Journal of Agricultural Research, 6(28), 6027–6035 Nelson, A. R. L. E., Ravichandran, K., & Antony, U. (2019). The impact of the Green Revolution on indigenous crops of India. Journal of Ethnic Foods, 6(1), 1–10. Pingali, P., Mittra, B., & Rahman, A. (2017). The bumpy road from food to nutrition security–Slow evolution of India’s food policy. Global food security, 15, 77–84. Reddy, A., Bantilan, M. C. S., & Mohan, G. (2013). Pulses production scenario: policy and technological options. International Crops Institute for Semi-Arid Tropics, Policy Brief , (26). Tripathi, A. K. (2017). Price and profitability analysis of major pulses in India. Asian Journal of Agriculture and Development, 14(1362-2017-3063), 83–102.

Chapter 3

Pulses Production and Food Security

3.1 Introduction Substantial progress has been made globally in addressing the extreme forms of hunger as a result of improvements in agricultural productivity and food production. The share of undernourished people declined from nearly one-quarter of the global population in 1970 to around 11% of the global population in 2014–16 (Enahoro et al., 2018). Figure 3.1 from FAOSTAT shows a decline in the prevalence of undernourishment that is taking place globally. In India, notwithstanding its domestic food grain surpluses, nutrition security continues to be a complex challenge. As per the recent report of the Food and Agricultural Organization (FAO), around 15% of the Indian population is undernourished. The percentage of people who are undernourished has showed a marginal decline when we consider a 10-year period (see Fig. 3.2). The percentage of undernourished people declined from around 18 to 15% between 2000–2002 and 2018–2020. The share of undernourished people had increased from 18 to 22% during 2003–05, and thereafter, it showed a tendency to decline slowly. On the production front, India’s total food grain production substantially increased from around 80 million tonnes in 1965 to around 250 million tonnes in 2015 (Bhattacharjya et al., 2017). Figure 3.3 shows the per capita food production variability in India between 2001 and 2017, and there is huge variation from year to year. The per capita food production variability is also reflected in the per capita food supply variability (see Fig. 3.4). Though the improvement in self-sufficiency had a positive impact on production, the per capita availability declined consistently and the difference becomes starker when one looks at the fact that an average family of five had 198 kg of food grain less to eat than in 1991 (Pal et al., 2019). The scenario becomes more dismal in the case of pulses though with a paramount importance in contribution to food and nutritional security remained outside the ambit of productivity benefits (Bhattacharjya et. al., 2017).

© Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_3

27

28

3 Pulses Production and Food Security

Fig. 3.1 Prevalence of undernourishment in world. Source FAOSTAT

Fig. 3.2 Prevalence of undernourishment in India (annual value). Source FAOSTAT Fig. 3.3 Per capita food production variability (constant 2004–2006 thousand in $ per capita). Source FAOSTAT

3.1 Introduction

29

Fig. 3.4 Per capita food supply variability (kcal/cap/day). Source FAOSTAT

The per capita net availability of pulses in the country was 62.19 g/capita/day in 1961, which is reduced sharply to 34.42 g/capita/day in 1974. Although the figure showed some tendency to improve in the next few years to 44.45 g/capita/day, it further declined to 39.45 g/capita/day in 2013 (see Fig. 3.5). However, the per capita net availability of pulses showed a tendency to consistently improve since 2010. The ten-year data shows that the per capita availability of pulses increased from 37.21 g/capita/day in 2010 to 42.02 g/capita/day in 2019 (see Fig. 3.6). The current per capita availability, though showed a steady improvement over the last decade, it is still much lower than the per capita availability of pulses in the year 1961. The consumption of pulses in India from 2014 to 2019 shows that the pulses have a steady consumption over the years with an increase in consumption in 2018. The consumption was 18.6 million metric tonnes in 2014 and increased to 22.5 million metric tonnes in in 2018 and then marginally declined to 20.7 million metric tonnes in 2020 (see Fig. 3.6). 70 60 50 40 30 20 10

1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

0

Fig. 3.5 Per capita net availability of pulses in India from 1961 to 2013 (in gm/capita/day). Source FAOSTAT

30

3 Pulses Production and Food Security

Fig. 3.6 Per capita net availability of pulses in India from 2010 to 19 (in gm/capita/day). Source FAOSTAT

The two major pulses, especially pigeon pea, produced and consumed in India, did not show much improvement in terms of per capita availability. The per capita net availability of pigeon pea was stagnant during the ten-year period. Though the availability increased from 5 g/capita/day in 2010 to 6 g/capita/day in 2011, the remaining years showed a stagnancy in per capita net availability (see Fig. 3.8). The performance of chickpea was relatively better, and the per capita availability increased from 14 g/capita/day to 16 g/capita/day and then declined to 15 g/capita/day during the same ten-year period (see Fig. 3.7). The available projection based on supply demand gap reveals huge excess demand (Jadhav et al., 2018). The projection shows that the demand for chickpea (gram)

Fig. 3.7 Consumption volume of pulses in India in million metric tonnes. Source statistica.com

3.1 Introduction

31

Fig. 3.8 Per capita net availability of chickpea in India (in gm/capita/day) Source FAOstat.com

and pigeon pea (tur) would reach 143.30 lakh tonnes by the end of 2030. The corresponding demand for these crops would be 171.10 tonnes and 391.70 tonnes, respectively (Jadhav et al., 2018). The projected demand for pigeon pea (tur) is greater than chickpea (gram) possibly due to the low productivity of pigeon pea (tur) due to lack of moisture availability in soil as it is grown mainly in dry lands (Jadhav et al., 2018). As a result, the projected shortfall in supply due to the excess demand for chickpea (gram) would be 47.5 lakh tonnes by 2025 and 114.5 lakh tonnes by 2030. Similarly, for pigeon pea (tur), the projected shortfall would be around 211.6 lakh tonnes by 2015, and this is expected to increase to 365.6 lakh tonnes by the end of 2030 (Jadhav et al., 2018). Pulses, in India, assume significant relevance in promotion food and nutritional security as it is a staple source of protein to a significant share of Indian population. The estimates show that the daily protein requirement of an average person is 56 g, and 100 g of pulses contain 25 g of protein (Rampal, 2017). This is two times higher than the protein available in wheat and three times higher than the protein available in rice (Bhattacharjya et. al., 2017). Additionally, as per some studies around 31% of Indians are vegetarian (Rampal, 2017) and pulses play an important role in meeting the protein requirement of this section of the population. The share of pulses in total food grain basket in India is around 10% and is a cheaper source of protein. Pulses are also a rich source of fiber, vitamins and minerals, such as iron, zinc, folate and magnesium. Just as pulses provide nutritional benefits to humans, they also produce a number of different compounds that feed soil microbes, thus benefiting soil health (Bhattacharjya et al., 2017). They are known to fix atmospheric N with 72–350 kg N per hectare per year meeting around 70 to 90% of crop demand and also contributes to physical, chemical and biological improvements of soil apart from low carbon footprint (Rajender et al., 2021). One of the well-acknowledged reasons for malnutrition in developing countries is poor dietary diversity. This stems from the over reliance of few staple crops for dietary requirements and coupled with poor quality. The food consumption is skewed with less nutrients as most people consume high amounts of cereals with less vegetables, fruits and pulses. Dietary diversification that is achieved through agricultural production diversification can be used as an important tool to eradicate hunger (FAO, 2018). The

32

3 Pulses Production and Food Security

risk of micronutrient deficiency increases with limited production diversity. Based on the national scoping studies and international interdisciplinary review undertaken by Food and Agricultural Organisation (FAO), south and South-east Asian countries have identified and prioritised up to six promising neglected and underutilised species (NUS) as candidates for Future Smart Food (FSF). Accordingly, pulses have been listed as one of the candidates for FSF and among the pulses the varieties selected are black gram, cow pea, faba bean, grass pea, horse gram, lentil, mung bean, rice bean and soybean. Table 3.1 summarises the nutritional content of some of the major pulses produced and consumed in India. The protein content is the highest in urad (black gram) and lentils and is about 25 per 100-g. Chickpea has 19.3, pigeon pea 21.7 green gram 23.86, cowpea 23.85, pea 24.55 and kidney beans 23.58. Vitamin A is the highest in Table 3.1 Nutritional content of major pulses in India (per 100 g) Chickpea

Pigeon pea

Lentil

Urad (black gram)

Green gram

Cowpea

Pea

Kidney beans

19.3

21.7

25.8

25.21

23.86

23.85

24.55

23.58

Total lipid (fat) 6.04 g

1.49

1.06

1.64

1.15

2.07

1.16

0.83

Carbohydrate, by diff (g)

60.7

62.8

60.1

59

62.6

59.6

60.4

60

Fibre, total dietary (g)

17.4

15

30.5

18.3

16.3

10.7

25.5

24.9

8

2.23

130

56

138

132

85

55

143

Protein (g)

Sugar (g)

10.7

Calcium (mg)

105

2.03

6.6

Iron (mg)

6.24

5.23

7.54

7.57

6.74

9.95

4.43

8.2

Magnesium (mg)

115

183

122

267

189

333

115

140

Phosphorous (mg)

366

367

451

379

367

438

366

407

Potassium (mg)

875

1392

955

983

1246

1375

981

1406

Sodium (mg)

24

17

6

38

15

58

15

24

2.76

4.78

3.35

Zinc (mg)

3.43

Vitamin C (mg)

4

Vitamin B-6 (mg)

0.535

0.283

0.54

Vitamin A (mg)

67

28

39

2.68

6.11

3.01

2.79

4.8

1.5

1.8

4.5

0.281

0.382

0.361

0.174

0.397

23

114

33

149

53

4.4

Source Bhattacharjya et al., (2017)

3.2 Consumption of Pulses

33

pea and followed by green gram. Calcium is the highest in kidney beans and followed by urad (black gram). Pulses cultivation can play significant role in promoting the sustainability of the agricultural production system. Pulses cultivation has the ability to improve the soil fertility by fixing atmospheric nitrogen. Pulses cultivation has an additional advantage of releasing different types of amino acids and also the plant residues left after the harvesting of crop is beneficial for improving certain qualities of soil such as biochemical composition (Bhattacharjya et al., 2017).

3.2 Consumption of Pulses In the coming decades, the producers globally will need to feed an additional 3 billion people and a large part of that population would be from the developing regions of the world. The global demand for pulses has been increasing. Table 3.2 summarises the protein intake in developing countries where pulses contribute more than 10% of per capita total protein intake. The data shows that India’s protein intake is 13%. As mentioned previously, in India, the rate of an increase in the production of pulse has been less than the increase in the population. The declining per capita production of pulses (14 kg in mid-1990s to 12 kg in 2008) has been compensated by the increasing imports of the commodity. With the declining production globally, Table 3.2 Developing countries where pulses contribute more than 10% of per capita total protein intake Countries

Percentage (%)

Countries

Percentage (%)

Burundi

55

Mauritania

13

Rwanda

38

Sierra Leone

13

Uganda

20

India

13

Kenya

20

Brazil

13

Comoros

18

Trinidad and Tobago

12

Haiti

18

Mozambique

12

Eritrea

18

Cameroon

12

Nicaragua

16

D.R. Korea

11

Cuba

16

Guatemala

11

Niger

15

Mexico

10

Ethiopia

15

Togo

10

Malawi

15

Belize

10

Angola

15

Paraguay

10

Tanzania

14

Botswana

10

Source FAO (2005–07)

34

3 Pulses Production and Food Security

and rising prices both in domestic as well as international markets, the per capita availability of pulses has continued to deteriorate. The government policy initiatives such enhancing production through National Food Security Mission (NFSM) and higher minimum support prices (MSP) were considered to have played a positive role in encouraging production (Bhattacharjya et al., 2017). However, a breakthrough in technological innovations are highly required to reduce the crop loss and to improve productivity. The below sections will discuss the major production technologies of pulses, thereby highlighting the growing importance for sustainable production practices in agriculture.

3.3 Import of Pulses The total value of pulses imported to India was high and was steadily increasing till 2017. Since 2017 onwards, there has been a decline in import due to an increase in domestic production. The value of imports increased from 75.12 billion rupees in 2011 to 285.2 billion rupees in 2017. However, it declined in 119.38 billion rupees in 2021 (see Fig. 3.9). Canada is the largest importer of pulses to India, and in the year 2019, the country imported 1200. 35 thousand metric tonnes of pulses from Canada. Canada was followed by Myanmar and Tanzania. The volume of imports from Myanmar was 516.34 thousand metric tonnes, and Tanzania was 219.71 thousand metric tonnes in 2019. The other major importing countries of pulses to India are Mozambique, Russia, China, Ukraine, Turkey, Australia and the USA (see Fig. 3.9. India is the largest importer of chickpea in the world. India is followed by Pakistan, Bangladesh, UAE and Spain (see Fig. 3.10). The import of chickpea to India was steadily increasing until 2017 and thereafter experienced a sharp decline (see Fig. 3.11). The major importers of dry peas in the world are India, China, Belgium, Spain and Bangladesh (see Fig. 3.12). In this variety also India is the largest importer and Fig. 3.9 Per capita net availability of pigeon pea in India (in gm/capita/day). Source FAOSTAT

3.3 Import of Pulses

35

Fig. 3.10 Value of pulses imported into India in billion Indian rupees. Source statistica.com

Fig. 3.11 Volume of pulse import to India in 2019 by leading origin country in thousand tonnes. Source statistica.com

36

3 Pulses Production and Food Security

Fig. 3.12 Imports of top five importers of chickpea (in tonnes). Source statistica.com

similar to chickpea the imports of dry peas increased till 2017 and declined thereafter (see Fig. 3.13). Similar to chickpea and dry peas, India is the largest importer of dry beans as well. This is followed by Brazil, USA, Japan and UK (see Fig. 3.14) (Figs. 3.12,

Fig. 3.13 Import of chickpea in India form 1993–2020 (in tonnes). Source statistica.com

Fig. 3.14 Imports of top five importers of peas (in tonnes). Source statistica.com

3.3 Import of Pulses

Fig. 3.15 Imports of chickpea in India (in tonnes). Source statistica.com

3.13, 3.14, 3.15, 3.16, 3.17).

Fig. 3.16 Imports of top five importers of beans (in tonnes). Source statistica.com

Fig. 3.17 Imports of beans in India (in tonnes). Source statistica.com

37

38

3 Pulses Production and Food Security

3.4 Production Technologies The pulses production in the country is constrained by several factors such as unfavourable weather conditions, poor soil quality, lack of improved seed varieties, lack of technological breakthrough to address the persistent problems of pests and diseases along with credit, infrastructure and marketing constraints (Singh et al., 2015). Improved Varieties or Hybrids The food security policy bias towards cereals also resulted in less focus on research in pulses. The research focused more on cereals such as wheat and rice. As a result, the pulses yield began to deteriorate and lower than several other pulse-producing countries such as Canada and Australia. A technological breakthrough is desperately needed to increase the crop yield. The breeding is also very limited in the case of pulses due to the narrow genetic base of the varieties and its less tolerance towards pest attacks (Subramanian, 2016). Though there is some amount of progress is taking place in this direction, there is a need for higher research and development expenditure for the improvement of better seed varieties (Subramanian, 2016). In addition to the above, there is a need to widen the genetic base by strengthening the prebreeding of pulses. Also it is important to develop hybrid potential through CMS (cytoplasmic nuclear male sterility)-based hybrids in pigeon pea (Ali and Gupta, 2012). Vertical Approach Sequential cropping and intercropping Vertical approach is one of the possible methods and techniques which can be used to enhance the production without necessarily expanding the area under crop cultivation (Singh et al., 2015). Sequential cropping and intercropping of pulses is one the approaches in vertical methods. Agricultural research stations have begun developing few intercropping systems for pulses. In rain-fed states like Gujarat, Madhya Pradesh, Maharashtra and Karnataka farmers are already familiar with such practices and they are adopting such practices in traditional ways. However, there is a need to enhance the availability of seeds that are suitable for intercropping for farmers who are keen to adopt such practices. Not only that extension services and training are required to increase the farmers’ awareness about the benefits of such practices. For this purposes, demonstration can be given to farmers to make them understand suitable seeding devices. The distribution of seed mini-kits of pulses can also be improved. Table 3.3 presents the details about the promising intercropping for different pulse-producing states.

3.4 Production Technologies

39

Table 3.3 Promising intercropping for different pulse-producing states Intercropping systems

States

Soybean + Pigeon pea

Madhya Pradesh, Maharashtra

Pearl Millet/Sorghum + Pigeon pea

Karnataka, Andhra Pradesh, Gujarat, Maharashtra

Groundnut + Pigeon pea

Gujarat

Groundnut/Sorghum/Pearl Millet + Urad bean

Bihar, Maharashtra, Madhya Pradesh, Karnataka

Mung bean/Cowpea

Gujarat, Uttar Pradesh, Rajasthan

Sugarcane + Cowpea/Mung bean/Urad bean

Uttar Pradesh, Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu

Cotton + Mung bean/Urad bean/Cowpea

Punjab, Haryana, Madhya Pradesh, Gujarat, Andhra Pradesh, Maharashtra

Source Singh et al. (2015)

Seed replacement or multiplication strategy Seed replacement or multiplication strategy to overcome the inadequacy of the availability of quality seeds is the second vertical approach strategy. The promotion of quality seeds among farmers is often hindered by the availability of better seeds in adequate quantity on the right time. The seed replacement rate (SRR) estimated for the year 2006–07 was a mere 10.41%. However, a number of government’s interventions in the form of integrated scheme of pulses, and national food security mission for pulses, seed village programme, etc. were instrumental in raising the seed replacement rate to 22.5% in 2010–11. Balanced nutrient management Balanced nutrient management is the third method in the vertical approach. The nutrient management in pulses can be effectively undertaken by applying 20–40 kg per hectare of sulphur at the time of sowing and zinc sulphate at the rate of 25– 50 kg per hectare once in every two years. This is useful in effectively addressing the problem of nutrient deficiency (Singh et al., 2015). Additionally, most of the nitrogen requirement was met through biological N-fixation. Mechanisation The next vertical approach is mechanisation. The productivity of crop can be enhanced through adoption of deep ploughing methods, ridge planting, line sowing and interculture operations. So if these can be combined with further mechanisation, the cost of cultivation can be improved and resource use efficiency can be achieved (Singh et al., 2014). Farm mechanisation among the small holding farmers can be enhanced through custom hiring of the machines. The programme initiated by the

40

3 Pulses Production and Food Security

Madhya Pradesh government named ‘Haldhar’ provides a subsidy of Rs. 2000 per hectare to farmers for doing deep ploughing of their land (Singh et al., 2013). Resource Conservation Technologies The expansion of irrigation services by the use of resource conservation technologies also comes under the vertical approach. The adoption of microirrigation technologies such as sprinkler and drip irrigation can help in improving water use efficiency. Fertigation method has also been proved effective for widely spaced crops such as pigeon pea. Horizontal Approach Under the horizontal approach, Singh et al. (2015) have discussed the efficient utilisation of rice fallow lands and replacement of low productivity crops with pulses. Around 11.65 million hectares of area is left uncultivated after the cultivation of rice during kharif season. Such large areas uncultivated exists in states such as Bihar, Madhya Pradesh, Chhattisgarh, Odisha and Eastern parts of Uttar Pradesh. Around 25% of such area has the potential to be cultivated based on the soil quality. These areas can be very well utilised for the cultivation of rabi pulses. This is in effect bringing nearly 3–4 million hectares of additional land under rabi cultivation. Even if you assume an average yield of 600 kg per hectare, this can lead to a production of around 1.8–2.4 million tonnes of pulses. In order to supplement this, technological support in terms of suitable short duration varieties needs to be developed (Journal of Agrisearch, Vol. 2, No. 2). In addition to this, crops like mustard, barley and wheat can also be replaced with rabi pulses such as lentils or chickpeas. The harvested rainwater can also be made useful (Singh et al., 2013). Rhizobium Inoculation Jeswani and Baldev (1990) have pointed out that the pulses have a unique property of association with Rhizobium which lives freely in soils. The Rhizobium enters the root hair of pulse crops and fixes atmospheric nitrogen. Artificial inoculation with an efficient Rhizobium culture and in this way ensures the availability of maximum quantity of symbiotic nitrogen to the crop. Rhizobium inoculation increases yields. Various studies have suggested that up to 100% of the nitrogen requirement of the pulse crops could be met by providing efficient strains of Rhizobium coupled with sound agro-economic services. After meeting their own requirements, pulses leave behind sufficient residual nitrogen for the succeeding crop. Keeping this in mind, many microbiological laboratories have started producing Rhizobium culture and substantial funds are being provided to build up such laboratories. Integrated Pest Management Pulse crops are attacked by more than one disease or pest at a time, which makes a need for multiple disease resistant varieties a must. Reddy (2009) discusses that Integrated Pest Management (IPM) is essentially a farmer activity of using one or more

3.6 Major Varieties

41

than one management options to reduce pest population below the economic injury level, while ensuring productivity and profitability of the entire farming system. Cropping systems involving crop rotation or intercropping of non-host and host crops, different agro-economic practices like the use of solar energy by summer ploughing preceding Kharif pulses are some of the cost-effective components of IPM. However, this process takes time to yield results and also needs a community approach which makes some of the farmers hesitant to use it. Post-Harvest Technology Post-harvest losses account for nearly 9.5% of the total production of pulses. And among the post-harvest operations, storage operations are responsible for the maximum loss (7.5%). Further losses are caused due to processing (1%), threshing (0.5%) and transportation (0.5%) (Deshpande & Singh, 2001). Processing efficiency in dal mills must be increased. Over the years, the net availability of end products in modern dal mills has been increased to 70–75% compared to 65–66% in traditional dal mills. Appropriate storage structures must be popularised. Propagation of minidal mills through the formation of pulses producers and processors associations is one of the components of NFSM, which will not only decrease post-harvest losses but also increase rural employment (Reddy, 2009).

3.5 The Need for Sustainable Practices Despite a considerable fall in the global percentage of people employed in agriculture over the years, the contribution of greenhouse gas emissions from agriculture was nearly 25% of the total greenhouse gas emissions in the year of 2014 (The Guardian, July 2014). Further, with the ever increasing population in the already overpopulated nation, it has become very important now more than ever that we look towards more sustainable agricultural options. Sustainable agriculture works in harmony with the nature and not against it. Sustainable agriculture is the need of the hour as it reduces the use of energy, contributes to the conservation of water and nourishes the soil among other things. By ensuring use of alternate or renewable sources of energy, using crop rotation or crop diversity, making use of natural pesticides and by better water management, sustainability in farming techniques could be achieved.

3.6 Major Varieties The cultivation of pulses is distributed between two major seasons, viz. Kharif and Rabi. There are at least ten important pulse crops grown in India.

42

3 Pulses Production and Food Security

3.6.1 Rabi Pulses (a)

Chickpeas Chickpeas or Chana Dal, also known by the names of garbanzo bean, ceci bean, sanagalu, hummus and Bengal gram, is the most important pulse crop grown in the country. Currently, it is grown in India, Middle East and various parts of Africa. The states with the highest area under cultivation and production of chickpea in India are Madhya Pradesh, Uttar Pradesh, Rajasthan, Haryana and Maharashtra.

(b)

Adequate amount of rainfall is required to provide sufficient amounts of residual moisture for chickpea especially in the months of October and March. This residual moisture is very essential to sustain the crop. However, in semiarid regions in states like Haryana and Rajasthan is finding it difficult to maintain the residual moisture as a result of insufficient rainfall (Jeswani & Baldev, 1990). Though inadequate rainfall is adversely affecting the sustainability of the crop, the farmer still takes the risk in cultivating chickpea in alternate years. This is done in a single crop rotation with crops such as millets. In addition to this, unirrigated wheat and chickpea are an option for intercropping. The chickpea is able to compensate for the wheat yield loss that happens as a result of dry weather, and thus, the combined cultivation of these two crops can act as an effective safeguard against the vagaries of weather. Peas

(c)

In the Indo-Gangetic plains, peas, also called Matar, are one of the most important pulse crops and about 90% of its area and production is confined to Uttar Pradesh. Lentils

(d)

Lentils or Masur Dal are mostly grown in Uttar Pradesh, Madhya Pradesh, Maharashtra and West Bengal. Lentils are also rain-fed crops and grown under same circumstances as that of chickpea. Lathyrus Another popular rabi pulse crop is Lathyrus or Khesari. Lathyrus is a significant crop of the Indo-Gangetic plains, and about 80% of it is grown in Bihar, Madhya Pradesh, Maharashtra and West Bengal. This crop needs very little field preparation and has the ability to withstand extreme moisture-stress conditions and hence, is highly preferred by the farmers. Farmers also prefer Lathyrus as its moisture requirement is much lesser than that of chickpeas and lentils.

3.6.2 Kharif Pulses (a)

Pigeon pea

References

43

(b)

Pigeon peas or Tur Dal are the second most important pulse crop grown in India. The six states, viz. Maharashtra, Uttar Pradesh, Madhya Pradesh, Karnataka, Gujarat and Andhra Pradesh, together contribute to nearly 85% of the total area and production of the crop. In spite of its long duration and the attainment of grain-development stage long after the rainy season is over, farmers prefer pigeon pea especially in the low-rainfall areas as the crop is drought-tolerant. The long duration of pigeon pea in north India also makes it admirably suitable for mixed cropping and intercropping with sorghum or pearl millet or maize (Jeswani & Baldev, 1990). Green gram

(c)

Green gram or Mung Dal is the third most important pulse crop in India. Yield of green gram is only half of that of pigeon pea and chickpea. It is mainly grown as a kharif crop in Maharashtra and Andhra Pradesh and in Orissa as a rabi crop. Other states growing green gram are Madhya Pradesh and Rajasthan. Black gram

(d)

Black gram or Urad Dal is mostly grown in Madhya Pradesh, Maharashtra, Tamil Nadu, Uttar Pradesh and Rajasthan during kharif season and in Andhra Pradesh and West Bengal in the rabi season. However, low yield of green gram has restricted its cultivation to fields which are relatively poor in fertility status or moisture to suit other crops (Jeswani & Baldev, 1990). Cowpea

(e)

Cowpea also called Lobiya in Hindi is a dual-purpose crop grown either for grain or for the fodder. Though mainly grown as a kharif crop, cowpea has become a very important summer crop. It is mainly grown in Kerala, West Bengal and Punjab. Horse gram

(f)

Horse gram or Chana Dal is usually grown in the dry and upland areas of the peninsular and eastern states of India such as Orissa, Karnataka, Andhra Pradesh, Maharashtra, Madhya Pradesh and Tamil Nadu. Moth bean Moth bean or Keet Sem is one of the most drought-tolerant crops and is primarily grown in the dry western and central India. Rajasthan is the major contributor of this crop.

References Ali, M., & Gupta, S. (2012). Carrying capacity of Indian agriculture: pulse crops. Current Science, 874–881.

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3 Pulses Production and Food Security

Bhattacharjya, S., Chaudhury, S., and Nanda, N. (2017). Import Dependence and Food and Nutrition Security Implications: The Case of Pulses in India. Review of Market Integration, 9(1–2), 83–110. https://doi.org/10.1177/0974929217721763. Deshpande, S. D., & Singh, G. (2001, April). Long term storage structures in pulses. In National Symposium on Pulses for Sustainable Agriculture and Nutritional Security. Indian Institute of Pulses Research, New Delhi (pp. 17–19). Enahoro, D., Lannerstad, M., Pfeifer, C., & Dominguez- Salas, P. (2018). Contributions of livestockderived foods to nutrient supply under changing demand in low-and middle-income countries. Global Food Security, 19, 1–10. FAO, F. (2018). The future of food and agriculture: alternative pathways to 2050. Food and Agriculture Organization of the United Nations Rome. Jadhav, V., Swamy, M. N., & Gracy, C. P. (2018). Supply-demand gap analysis and projection for major pulses in India. Economic Affairs, 63(1), 277–285. Jeswani, L., & Baldev, B. (1990). Advances in pulse production technology. In L. Jeswani & B. Baldev (Eds.) (Vol. 79) ICAR publication. Pal, P., Gupta, H., Gupta, R. K., & Raj, T. (2019). Dynamics of food security in India: Declining per capita availability despite increasing production. In M. Behnassi, O. Pollmann, & H. Gupta (Eds.), Climate change, food security and natural resource management. Springer. Rajender, B., Tiwari, A. K., & Chaturvedi, S. K. (2021). Pulses revolution in India through rice-fallows management. Scaling-up Solutions for Farmers: Technology, Partnerships and Convergence, 229. Rampal, P. (2017). Situational analysis of pulse production and consumption in India. Available at https://opendocs.ids.ac.uk/opendocs/handle/20.500.12413/13350. Reddy, A. A. (2009). Pulses production technology: Status and way forward. Economic and Political Weekly, 73–80. Singh, A. K., Manibhushan, Bhatt, B. P., Singh, K. M., and Upadhyaya, A. (2013). An Analysis of Oilseeds and Pulses Scenario in Eastern India during 2050-51. Journal of Agril. Sci. 5(1): 241– 249. Singh, D, Patel, A. K., Baghel, S. K., Singh, M. S., Singh, A. and Singh, A. K. (2014). Impact of Front Line Demonstration on the Yield and Economics of Chickpea (CicerarietinumL.) in Sidhi District of Madhya Pradesh. Journal of AgriSearch, 1(1): 22–5 Singh, A. K., Singh, S. S., Prakash, V., Kumar, S., & Dwivedi, S. K. (2015). Pulses production in India: Present status, bottleneck and way forward. Journal of AgriSearch, 2(2), 75–83. Subramanian, A. (2016). Incentivising pulses production through minimum support price and related policies. Ministry of Finance, Government of India.

Chapter 4

National Food Security Mission and Pulses Production

4.1 Introduction As part of the eleventh five-year plan, the Government of India launched the National Food Security Mission (NFSM) in 2007–2008 with a major objective of boosting the food production to deal with the concerns of food insecurity. The programme targeted an increase in production of rice, wheat and pulses by 10, 8 and 2 million tonnes respectively by the end of the 11th five-year plan. The scheme was initially launched in 482 districts of 19 states. Out of which 144 districts in 16 states were under the rice scheme and 142 districts in 9 states under the wheat scheme and 468 districts in 16 states under the pulses scheme. The new targets were set in the twelfth five-year plan with an objective of distribution of technologies and better farm management practices guidelines. The scheme continued during the twelfth five-year plan with a new set of targets. The focus of all these schemes was the improvement of yield by bridging the gap between actual yield and potential yield. The NFSM had a two-pronged strategy. First strategy was to expand the area, and the second strategy was to enhance the productivity by bridging the gap between the actual and potential yield. However, the area expansion was confined to mainly wheat and pulses (Manjunatha & Kumar, 2015). In order to augment productivity, the NFSM had adopted several measures including (1) quality seed production; (2) emphasising integrated nutrient management and integrated pest management; (3) promotion of new technologies; (4) restoring soil fertility; (5) improved farm implements, etc. As a result, a total amount of Rs. 4500 crores have been spent under NFSM during the 11th five-year plan (Manjunatha & Kumar, 2015). Other objectives of the scheme include restoring soil fertility and productivity at the individual farm level, creation of employment opportunities, enhancing This chapter draws from the author’s previous work titled Varma, P. (2017). Rice productivity and food security in India, A Study of the System of Rice Intensification, Springer, Singapore.

© Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_4

45

46

4 National Food Security Mission and Pulses Production

farm profits, promotion and extension of improved technologies such as Integrated Nutrient Management. During the eleventh five-year plan, NFSM was implemented in 482 districts of 19 states. NFSM-Rice was implemented in 144 districts of 16 states, NFSM-Wheat in 142 districts of 9 states and NFSM-Pulses in 468 districts of 16 states. The mission when launched had five components, which were—(i) NFSM-Rice, (ii) NFSMWheat, (iii) NFSM-Pulses, (iv) NFSM-Coarse cereals and (v) NFSM-Commercial crops. During the same period of NFSM implementation, the Government of India launched Rashtriya Krishi Vikas Yojana (RKVY) programme. Several states as well as centrally sponsored schemes were also implemented during this period to enhance the food production. Subsequently, the wheat production increased by 19.1 million tonnes, paddy by 12.1 million tonnes and pulses by 3 million tonnes by the end of the 11th five-year plan (Fig. 4.1).

4.2 NFSM-Pulses Districts Table 4.1 elucidates the districts covered under rice, wheat, pulses and coarse cereals under the National Food Security Mission in the year 2017–18. The table shows that currently, a total of 638 districts are covered under NFSM-Pulses and the mission covers 30, 51 and 33 districts in Karnataka, Madhya Pradesh and Maharashtra, respectively.

4.3 Major Interventions Some of the major interventions that were undertaken to improve the productivity of pulses under National Food Security Mission are detailed here. Improved package of pulses are being provided. Provision of high yielding varieties of pulses has been done, and seeds are provided at 25 rupees per kg or 50% of the cost, whichever is less. Further, farm machineries/tools such as manual sprayers, chisellers, seed drills, multicrop planters, power weeders, etc., are being provided at half the actual cost. Farmers have access to better and improved water application tools along with plant protection measures. Soil ameliorants are also being provided such as gypsum, bentonite sulphur, liming materials and some of the biofertilisers. Farmers are also being given cropping system-based training. Table 4.2 lists the districts covered under the National Food Security Mission— Pulses in the states of Karnataka, Madhya Pradesh and Maharashtra.

4.4 NFSM in Maharashtra

47

Fig. 4.1 States covered by NFSM for pulses. Source NFSM

4.4 NFSM in Maharashtra National Food Security Mission—Wheat, National Food Security Mission—Rice and National Food Security Mission—Pulses, all are being implemented in the state of Maharashtra currently. As of 2014–15, Maharashtra covered more than 14% of the total area (nearly 3.5 million hectares) and almost 12% of the total production of pulses in the country. The area production and yield of pulses in both NFSM and non-NFSM districts are given in Tables 4.3 and 4.4. In Maharashtra, Yavatmal is the district where NSFM has been implemented since the advent of this scheme, while Dhule has been selected

48

4 National Food Security Mission and Pulses Production

Table 4.1 Districts covered (identified) under National Food Security Mission (2017–18) S. No.

State

NFSM-Rice

NFSM-Wheat

NFSM-Pulses

NFSM-Coarse

1

Andhra Pradesh

5

–

13

6

2

Arunachal Pradesh

10

–

17

17

3

Assam

13

–

27

4

4

Bihar

15

10

38

11

5

Chhattisgarh

13

–

27

9

6

Goa

–

–

2

–

7

Gujarat

2

5

26

8

8

Haryana

–

7

21

5

9

Himachal Pradesh

2

11

12

12

10

Jammu & Kashmir

8

8

22

22

11

Jharkhand

4

–

24

11

12

Karnataka

7

–

30

11

13

Kerala

1

–

14

1

14

Madhya Pradesh

8

16

51

16

15

Maharashtra

8

3

33

8

16

Manipur

9

–

9

9

17

Meghalaya

7

–

11

11

18

Mizoram

6

–

8

8

19

Nagaland

11

–

11

11

20

Odisha

8

–

30

6

21

Punjab

–

12

22

3

22

Rajasthan

–

14

33

12

23

Sikkim

2

–

4

4

24

Tamil Nadu

8

–

30

10

25

Telangana

4

–

9

6

26

Tripura

8

–

8

8

27

Uttar Pradesh

23

31

75

20

28

Uttarakhand

5

9

13

13

29

West Bengal

7

–

18

3

Total

194

126

638

265

Source Ready Reckoner (NFSM Cell, Crops Division), DAC & FW

as the non-NFSM district for the period starting 2007–2009. Post 2009, Dhule was also covered under this scheme. The data shows that area under the cultivation of pulses was higher in Yavatmal in both the years. However, despite a decline in production in Yavatmal in 2008, the average yield was much higher in the district in comparison with Dhule.

4.4 NFSM in Maharashtra Table 4.2 Districts covered under NFSM-Pulses in the study states

49 States

Districts

Karnataka (30 Districts)

Bagalkot

Haveri

Bangalore (Rural)

Gadag

Bangalore (Urban)

Gurbarga

Belgaum

Koppal

Bellary

Kodagu (Coorg)

Bidar

Kolar

Bijapur

Mandya

Chamarajanagar

Mysore

Chikballapur

Raichur

Chikmagalur

Ramnagar

Chitradurga

Shimoga

Dakshin Kannada

Tumkur

Davangiri

Udupi

Dharwad

Uttar Kannada

Hassan

Yadgiri

Madhya Pradesh (51 Agar Districts) Alirajpur

Mandla

Anup Pur

Morena

Ashok Nagar

Narsinghpur

Balaghat

Neemach

Barwani

Panna

Betul

Raisen

Bhind

Rajgarh

Bhopal

Ratlam

Burhanpur

Rewa

Chhattarpur

Sagar

Chhindwara

Satna

Damoh

Sahdol

Datia

Sehore

Dewas

Seoni

Dhar

Shajapur

Dindori

Sheopurkalan

East Nimar (Khandwa)

Shivpuri

Gwalior

Sidhi

Guna

Singrauli

Harda

Tikamgarh

Mansaur

(continued)

50 Table 4.2 (continued)

4 National Food Security Mission and Pulses Production States

Districts Hoshangabad

Ujjain

Indore

Umaria

Jabalpur

Vidisha

Jhabua

West Nimar (Khargaon)

Katni Maharashtra (33 Districts)

Ahmednagar

Nanded

Akola

Nandurbar

Amravati

Nasik

Aurangabad

Osmanabad

Beed

Parbhani

Bhandara

Pune

Buldhana

Raigad

Chandrapur

Ratnagiri

Dhule

Sangli

Gadchiroli

Satara

Gondia

Sindhudurga

Hingoli

Sholapur

Jalgaon

Thane

Jalna

Wardha

Kolhapur

Washim

Latur

Yavatmal

Nagpur Source https://www.nfsm.gov.in Table 4.3 Area, production and yield of pulses in NFSM District—Yavatmal

Year

Area (000 Ha) Production (000 Yield (kg/Ha) Tonnes)

2007–2008 241.86

224.81

929

2008–2009 160.23

89.05

556

Source National Food Security Mission, Ministry of Agriculture and Farmers Welfare Table 4.4 Area, production and yield of pulses in non-NFSM District—Dhule

Year

Area (000 Ha) Production (000 Yield (kg/Ha) Tonnes)

2007–2008 79.39

59.70

752

2008–2009 45.93

19.31

420

Source National Food Security Mission, Ministry of Agriculture and Farmers Welfare

4.6 NFSM in Madhya Pradesh Table 4.5 Area, production and yield of pulses in NFSM District—Chitradurga

51 Year

Area (000 Ha) Production (000 Yield (kg/Ha) Tonnes)

2007–2008 37.44

26.38

705

2008–2009 37

19.17

518

Source National Food Security Mission, Ministry of Agriculture and Farmers Welfare

Table 4.6 Area, production and yield of pulses in non-NFSM District—Mandya

Year

Area (000 Ha) Production (000 Yield (kg/Ha) Tonnes)

2007–2008 35.18

21.58

610

2008–2009 32.16

13.74

427

Source National Food Security Mission, Ministry of Agriculture and Farmers Welfare

4.5 NFSM in Karnataka National Food Security Mission—Rice and National Food Security Mission—Pulses and National Food Security Mission—Coarse cereals are being implemented in the state of Karnataka currently. Karnataka is the fifth largest producer of pulses in India. In 2015–16, Karnataka’s share of production was nearly 1.14 million tonnes in the total pulse production in the country. The area production and yield of pulses in both NFSM and non-NFSM districts are given in Tables 4.5 and 4.6. In Karnataka, the NFSM district was Chitradurga and non-NFSM district was chosen as Mandya. From the data, it can be observed that in Chitradurga, the average yield is marginally higher than in Mandya. In the period of two years, the area under cultivation in both the districts has remained nearly unchanged. After 2009, Mandya also came under the ambit of the scheme.

4.6 NFSM in Madhya Pradesh National Food Security Mission—Rice, National Food Security Mission—Wheat and National Food Security Mission—Pulses are being implemented in the state of Madhya Pradesh. Madhya Pradesh is the largest producer of pulses in India. It accounts for nearly 5.3 million tonnes (2015–16) of total pulses produced in the nation. Major districts producing pulses in Madhya Pradesh are—Dewas, Chhindwara, Narsinghpur, Raisen, etc. The area production and yield of pulses in both NFSM and non-NFSM districts are given in Tables 4.7 and 4.8.

52

4 National Food Security Mission and Pulses Production

Table 4.7 Area, production and yield of pulses in NFSM District—Dewas

Year

Area (000 Ha) Production (000 Yield (kg/Ha) Tonnes)

2007–2008 110.27

113.01

1025

2008–2009 115.76

127.47

1101

Source National Food Security Mission, Ministry of Agriculture and Farmers Welfare

Table 4.8 Area, production and yield of pulses in NFSM District—Dindori

Year

Area (000 Ha) Production (000 Yield (kg/Ha) Tonnes)

2007–2008 56.71

13.58

239

2008–2009 52.28

16.07

307

Source National Food Security Mission, Ministry of Agriculture and Farmers Welfare

From the data, it can be seen that there is a significant difference in the average yield of both the districts. While area under the cultivation of pulses in Dewas increased slightly in the second year, it decreased marginally in Dindori. Hence, a huge difference in the average yields of both the districts was witnessed. Table 4.9 provides a brief summary of interventions and patterns of assistance provided to the farmers in the implementation of National Food Security Mission— Pulses during 2017–18. Table 4.9 Action plan for implementation of NFSM-Pulses in all states during 2017–18 S. No.

Interventions

1

Demonstrations on improved technologies

Approved assistance (in Rupees)

Arhar

7500 per ha

Moong

7500 per ha

Urad

7500 per ha

Gram

7500 per ha

Lentil

7500 per ha

Other

7500 per ha

2

Production and Distribution of HYV seeds

2500/quintal or 50% of cost (whichever less)

3

Integrated Nutrient Management Micro-Nutrients

500/ha or 50% of cost (whichever less)

Gypsum/80% WG Sulphur

750/ha or 50% of cost (whichever less)

Lime

1000/ha or 50% of cost (whichever less)

Bio-Fertilisers

300/ha or 50% of cost (whichever less) (continued)

Reference

53

Table 4.9 (continued) S. No.

Interventions

4

Integrated Pest Management

5

6

Approved assistance (in Rupees)

Distribution of PP Chemicals

500/ha or 50% of cost (whichever less)

Weedicides

500/ha or 50% of cost (whichever less)

Resource Conservation Technologies/Tools Power Knap Sack Sprayers

3000/unit or 50% of cost (whichever less)

Manual Sprayers

600/unit or 50% of cost (whichever less)

Zero Till Seed Drills

15,000/unit or 50% of cost (whichever less)

Multi-Crop Planters

15,000/unit or 50% of cost (whichever less)

Seed Drills

15,000/unit or 50% of cost (whichever less)

Zero Till Multi Crop Planters

15,000/unit or 50% of cost (whichever less)

Ridge Furrow Planters

15,000/unit or 50% of cost (whichever less)

Rotavators

35,000/unit or 50% of cost (whichever less)

Chilseller

8000/unit or 50% of cost (whichever less)

Laser Land Levellers

1.5 lakh or 50% of cost (whichever less)

Tractor Mounted Sprayers

10,000/unit or 50% of cost (whichever less)

Multi Crop Threshers

40,000/unit or 50% of cost (whichever less)

Efficient Water Application Tools Sprinkler Sets

10,000/ha or 50% of cost (whichever less)

Pump Sets

10,000/unit or 50% of cost (whichever less)

Pipe for carrying water from source to field

20/meter or 50% of cost (whichever less)

Mobile Rain Gun

15,000/unit or 50% of cost (whichever less)

Source https://www.nfsm.gov.in/notifications

Reference Manjunatha, A. V., & Kumar, P. (2015). Impact of National Food Security Mission (NFSM) on input use, production, yield and income in Karnataka. Agricultural Development and Rural Transformation Centre Institute for Social and Economic Change Bangalore-560, 72.

Chapter 5

Socio-Economic Profile of the Sample Households

5.1 Introduction The present chapter provides an overview of socio-economic profile of the sample households. Considering the heterogeneous nature of the country and the study region, there were considerable differences across the states in terms of the profile of the households. The sections below undertake a discussion of the socio-economic profile of the total sample size as well as a detailed socio-economic profile at the district level. A detailed socio-economic profile at the district level is undertaken to understand the disparities in terms of various social, economic and institutional factors across different states. Household survey was conducted in three districts drawn from three states— Karnataka, Madhya Pradesh and Maharashtra. The major districts producing chickpea and pigeon are identified from each state. Accordingly, Gulbarga is selected from Karnataka, Narsinghpur is selected from Madhya Pradesh, and Wardha is selected from Maharashtra. A random sample of chickpea- and pigeon pea-producing farmers is selected from each district. The total number of households surveyed was 572. Subsequently, 195 farmers are selected from Gulbarga, 198 farmers are selected from Wardha, and 179 farmers are selected from Narsinghpur. The total sample consisted of 482 pigeon pea farmers and 316 chickpea farmers, and out of which 228 farmers were cultivating both chickpea and pigeon pea. In our sample, pigeon pea farmers were 189 from Gulbarga (Karnataka), 145 from Wardha (Maharashtra), 149 from Narsinghpur (Madhya Pradesh). Chickpea farmers were 40 from Gulbarga (Karnataka), 102 from Wardha (Maharashtra) and 175 from Narsinghpur (Madhya Pradesh). Similarly, those who are cultivating both chickpea and pigeon pea were 34 from Gulbarga (Karnataka), 49 from Wardha (Maharashtra), 145 from Narsinghpur (Madhya Pradesh) (see Table 5.1). Agriculture was the main occupation and livelihood strategy for most of the farm households in the study districts. Farming was the main occupation for 540 households interviewed. This constitutes around 95% of the total households interviewed. © Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_5

55

56

5 Socio-Economic Profile of the Sample Households

Table 5.1 Households according to type Farmer type Chickpea farmers Pigeon pea farmers Chickpea and pigeon pea farmers

Gulbarga (Karnataka)

Wardha (Maharashtra)

Narsinghpur (Madhya Total Pradesh)

40

102

175

317

189

145

149

483

34

49

145

228

Source Survey data

100 100 98 96

94

94 92 90

89

88 86 84 82

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.1 Percentage of households with farming as main occupation in percentage. Source Survey data

Out of which farming was main occupation for around 89% of the households in Gulbarga (Karnataka), 100% of households in Wardha (Maharashtra), 94% of households in Narsinghpur (Madhya Pradesh) (see Fig. 5.1). Majority of the farm households interviewed were either semimedium farmers or medium farmers. Marginal farmers were around 45%, small farmers around 36%, semimedium farmers around 16%, medium farmers around 3% and large famers were less than 1% (see Fig. 5.2). Marginal and small farmers were the highest in Wardha (Maharashtra) and Gulbarga (Karnataka), whereas medium and large farmers were highest in Narsinghpur (Madhya Pradesh) (see Table 5.2). As far as government schemes to promote pulses production, only 86% of households didn’t have any awareness of any such schemes. Among the total number of households who had awareness, 84% of households were from Wardha (Maharashtra). The awareness was lowest in Gulbarga (Karnataka) and Narsinghpur (Madhya Pradesh) (see Fig. 5.3). This also means 33% of total households interviewed from Wardha (Maharashtra) had information about government schemes to promote the cultivation of pulses (see Fig. 5.3). The percentage of households who had such information was very negligible in other two states.

5.1 Introduction

57

0.40 33.39%

0.35

35.49%

0.30 0.25 17.48%

0.20 0.15

10.49%

0.10 3.15%

0.05 0.00

Marginal

Small

Semi medium

Medium

Large

Fig. 5.2 Households according to farm size (in per cent). Source Survey data Table 5.2 Percentage of households according to farm size in different states State

Marginal

Small

Semimedium

Medium

Karnataka

6

21

37

30

Large 7

Maharashtra

2

26

40

29

2

Madhya Pradesh

2

4

22

49

24

Total

3

17

33

35

10

Source Survey data 33.33 35 30 25 20 15 10

2.56

4.47

5 0

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.3 Percentage share of households with awareness in any government schemes. Source Survey data

58

5 Socio-Economic Profile of the Sample Households

18

16

17

16 14 12 9

10 8 6

8

6

4 2 0

Marginal

Small

Semi medium

Medium

Large

Fig. 5.4 Households with government scheme awareness according to farm size (in per cent). Source Survey data

Farm size-wise awareness of government schemes showed that the awareness was the highest among the medium and semimedium farmers. The percentage of farmers with such awareness among medium and semimedium farmers were around 17% and 16%, respectively. Whereas the awareness was the lowest among marginal farmers followed by large and small farmers (Fig. 5.4). Note that medium and large farmers were more diversified in terms of crop cultivation (see Fig. 5.6). The crop diversification by large farmers can also be a reason why they were not paying much attention to the government schemes to promote pulses production. Around 77% of the households in the total sample had diversified crop cultivation. The crop diversification was the highest among the sample households from Wardha (Maharashtra) and lowest among the sample households from Gulbarga (Karnataka) (see Fig. 5.5). The crop diversification was lowest among the marginal farmers in the sample households and highest among the medium and large farmers (see Fig. 5.6). The crop diversification by small and medium farmers was more or less similar. Not only the awareness, even the knowledge about new production techniques were highest among the sampled households from Wardha (Maharashtra) (65%). The percentage of households with awareness of new production technique among the sample was only 33%. The knowledge was lowest in Gulbarga (Karnataka). The knowledge in Narsinghpur (Madhya Pradesh) and Gulbarga (Karnataka) were 28% and 5%, respectively (see Fig. 5.7). Farm size-wise knowledge about new production techniques among the sample households were the highest among the medium and large farmers. The knowledge of production techniques was increasing as farm size increases (see Fig. 5.8). But

5.1 Introduction

59 100

94

100 90 80 70 60 39

50 40 30 20 10 0

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.5 Crop diversification state-wise (in per cent). Source Survey data

86

90 80

70

88

72

70 60 50

39

40 30 20 10 0

Marginal

Small

Semi medium

Medium

Large

Fig. 5.6 Crop diversification according to farm size (in per cent). Source Survey data

even then, only 35–36% of medium and large farmers had knowledge about new production techniques which was very less. The poor access to government extension services can be the reason for poor knowledge in government schemes or new production techniques. The households with access to extension services were only 43% in the total sample households. The state-wise percentage of access to extension services in the sample households showed that households in Wardha (Maharashtra) had greater access to extension services (78%). The access to extension services was lowest among the households

60

5 Socio-Economic Profile of the Sample Households 65 70 60 50 28

40 30 20

5

10 0

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.7 Percentage of farmers with knowledge about new production techniques. Source Survey data

40.00

34.03

35.00 30.00 25.00

35.47

36.67

26.00 22.22

20.00 15.00 10.00 5.00 0.00

Marginal

Small

Semi medium

Medium

Large

Fig. 5.8 Farm size-wise knowledge about new production techniques (in per cent). Source Survey data

interviewed in Gulbarga (Karnataka) (8%). The percentage of households with access to extension services in Narsinghpur (Madhya Pradesh) was 43% (see Fig. 5.9). Farm size-wise access to extension services among the sample households showed that the access to extension services were highest for semimedium farmers (82%), and this was followed by medium and large farmers, 50% and 48% respectively. The access to extension services was the lowest for marginal farmers (22%) (see Fig. 5.10). As far as the training received from government department or NGOs are concerned, only 19% of the sample households had received any kind of training. Training received from government departments or NGOs was also highest in Wardha

5.1 Introduction

61 78

80 70 60

43

50 40 30

8

20 10 0

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.9 Percentage of households with contact with government extension services. Source Survey data

90

82

80 70 60

50

50 40 30

48

32 22

20 10 0

Marginal

Small

Semi medium

Medium

Large

Fig. 5.10 Farm size-wise contact with government extension services (in per cent). Source Survey data

(Maharashtra) (35%) and lowest in Gulbarga (Karnataka) (5%). The training received was 16% in Narsinghpur (Madhya Pradesh) (see Fig. 5.11). Interestingly, the size-wise percentage of farmers who received training showed that large farmers had received more training. The training was relatively higher for semi, medium, medium and large farmers as compared to marginal and small. The training was the lowest for small farmers (see Fig. 5.12). The poor access to training, extension services information about government schemes and new production techniques, etc. were reflected in the information regarding MSP received by households. In our sample, only 51% of the sample

62

5 Socio-Economic Profile of the Sample Households

35

40 35 30 25

16

20 15 5

10 5 0

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.11 Percentage of households with access to training. Source Survey data 25 25 21

22

20 15

11

10

6

5 0

Marginal

Small

Semi medium

Medium

Large

Fig. 5.12 Training received by farm size-wise (in per cent). Source Survey data

households had information about the MSP. Information regarding the MSP was the highest in Madhya Pradesh possibly due to the highest share of medium and large farmers in the sample by Narsinghpur (Madhya Pradesh). The information was the lowest in Gulbarga (Karnataka). Contact with extension services, access to training, knowledge of government schemes or new production techniques, crop diversification were also the lowest among the sample households from Gulbarga (Karnataka). It shows that the disadvantage faced by all these had a direct link with the access to information regarding MSP.

5.1 Introduction

63

94

100 90 80 70

52

60 50 40 30 20

11

10 0

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.13 Percentage share of households with information about MSP, state-wise. Source Survey data

Despite having higher access to training, extension services and knowledge about government schemes and new production techniques, the information of MSP received by households in Wardha (Maharashtra) was lower than that of Narsinghpur (Madhya Pradesh) (see Fig. 5.13). In Wardha (Maharashtra), around 52% of the sample households had information about MSP, whereas in Narsinghpur (Madhya Pradesh) around 94% of sample household had information about MSP. Again, this could be partly due to the high share of medium and large farmers in the sample by Narsinghpur (Madhya Pradesh). The reason why Narsinghpur (Madhya Pradesh) had the highest share of sample households with information regarding MSP is also clear from Fig. 5.14. Medium and large farmers had greater access to information, and the size of medium and large farmers in the sample households was the highest from Narsinghpur (Madhya Pradesh) as compared to the other two states. The access to information was increasing as the farm size increases. The access to information, however, was the lowest among the small farmers in the sample (see Fig. 5.14). Interestingly, though households in Narsinghpur (Madhya Pradesh) had the highest information about MSP, households availing MSP were much lower and lower than Wardha (Maharashtra). In Maharashtra, almost all farmers who had information about MSP availed MSP. The percentage share of households with information was 52%, and utilisation was 50%. The poor access to information by households in Gulbarga (Karnataka) was also reflected in the poor utilisation of MSP by these households (see Fig. 5.15). The percentage share of households in each farm size category who were availing MSP was the highest among semi-medium, medium, and large households. The percentage share of households who were not availing MSP was the lowest among small farmers (see Fig. 5.16). Though 78% of large farmers had information about

64

5 Socio-Economic Profile of the Sample Households 78

80 67

70 60 45

50 40 30

28 20

20 10 0

Marginal

Small

Semi medium

Medium

Large

Fig. 5.14 Farm size-wise information about MSP (in per cent). Source Survey data

50 50 45 40 35

26

30 25 20 15

6

10 5 0

Karnataka

Maharashtra

Madhya Pradesh

Fig. 5.15 Percentage share of households with utilisation of MSP state-wise. Source Survey data

MSP, only 33% of large farmers availed MSP. Similarly, 67% of medium farmers had information about MSP but only 31% availed MSP.

5.2 Conclusion This chapter provided an overview of the socio-economic profile of the sample households. The total households interviewed were 572 drawn from three major pulse-producing states—Karnataka, Maharashtra and Madhya Pradesh. Majority of

5.2 Conclusion

65

35

31

31

33

30 25 20

17

15

11

10 5 0

Marginal

Small

Semi medium

Medium

Large

Fig. 5.16 Utilisation of MSP farm size-wise (in per cent). Source Survey data

the households in the sample were either semimedium or medium farmers, and agriculture was the main livelihood option for majority of the sample households. Narsinghpur (Madhya Pradesh) had the highest share of large farmers in the sample, whereas Wardha (Maharashtra) had the highest share of marginal and small farmers. In our sample, 482 farmers were cultivating pigeon pea and 316 farmers were cultivating chickpea. Out of which 227 farmers were cultivating both the pigeon pea and chickpea. Majority of the sample households didn’t have any awareness of government schemes to promote pulses production or new production techniques to reduce crop loss and improve productivity. The farm size-wise analysis showed that large farmers were more aware about new production practices as compared to other farm categories. However, the access to training offered by government and extension services was the highest among the sample households from Wardha (Maharashtra). Interestingly, despite having higher access to training, extension services and knowledge about government schemes and new production techniques, the information of MSP received by households in Wardha (Maharashtra) was lower than that of Narsinghpur (Madhya Pradesh). This is due to the fact that Narsinghpur (Madhya Pradesh) had the highest share of large farmers in the sample. The size-wise percentage of farmers who received training showed that large farmers had received more training. The training was relatively higher for semi, medium, medium and large farmers as compared to marginal and small. In addition to the fact that Narsinghpur (Madhya Pradesh) had relatively large farmers with greater access to training, the households from Narsinghpur (Madhya Pradesh) had greater access to information regarding MSP. The access to MSP information was increasing as size of the farm increases. Interestingly, though households in Narsinghpur (Madhya Pradesh) had the highest information about MSP, households availing MSP was much lower and lower than Wardha (Maharashtra). In Maharashtra, almost all farmers who had information about MSP availed MSP. The percentage share of households with information was

66

5 Socio-Economic Profile of the Sample Households

52%, and utilisation was 50%. The percentage share of households in each farm size category who were availing MSP was the highest among semi, medium, medium and large households. The percentage share of households who were not availing MSP was the lowest among marginal and small farmers.

Chapter 6

Pulses Production, Trade and Government Policies

6.1 Introduction The dependence of pulses on rainfed production leads to highly volatile domestic production from one year to the next. Due to this erratic production, domestic pulses production faces the challenge of meeting domestic demand. Also, the production of pulses lagged behind population growth, and as a result, the per capita net availability of pulses declined over the years (refer Chap. 3). The sluggish production and widening gap between the supply and demand and volatility in prices are the major challenges faced by Indian pulses sector in the recent years. The data shows that India is the world’s largest consumer of pulses (Singh et al., 2015), yet the domestic production is not commensurate with demand, thereby making India a net importer of pulses. The last couple of years witnessed huge increase in imports of pulses to match the consumption requirements. Note that India is the largest importer of pulses despite of being the second-largest producer of pulses. The persistent deficit and the soaring pulses prices made it inevitable for the country to import pulses. The excess demand is primarily due to the stagnation in productivity which is further accelerated by the decline in area under cultivation which we observed in Chap. 2. The data shows that India’s import doubled over the last 10 years and it accounts for around 15–16% of total domestic production (Bhattacharjya et al., 2017). Growing import dependency and rising prices forced government to adopt duty-free import policy. India’s import demand has affected negatively due to the depreciation of Indian currency with respect to USA, Australian and Canadian dollars (Bhattacharjya et al., 2017). This had caused inflation of pulse prices in Indian domestic markets. Depreciation of Indian currency implies higher import bill for Indian pulse importers, making import less viable. India’s total pulses imports sharply increased from around 352 thousand tonnes in 2000 to 6185 thousand tonnes in 2016 (see Fig. 6.1). As a result, the share of India’s imports of pulses in the total world pulses import increased from mere 5% to around 36% in 2016 (see Fig. 6.2). Canada, Australia, Myanmar and China were among © Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_6

67

68

6 Pulses Production, Trade and Government Policies 7000000 6000000 5000000 4000000 3000000 2000000 1000000

2015

2016

2015

2016

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2003

2004

2002

2001

2000

0

Fig. 6.1 India’s import of pulses in tonnes. Source FAOSTAT 40 35 30 25 20 15 10 5 2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

0

Fig. 6.2 India’s share in total world import of pulses. Source FAOSTAT

the top exporters of pulses in the world. India had been always a major importer of pulses and the imports began to increase during the period of 1998–2000. The major importers of pulses to India were Australia, Canada, Myanmar, Tanzania and USA. Over time, the volume of pulses imports increased, and India also started to import from additional countries. For example, India started to import pulses from Ethiopia, Mozambique, Russia, China, etc. Additionally, in 2016, in the wake of soaring pulse prices in the domestic market, India signed an MoU to double pulses imports—mostly pigeon pea—from the east African nation over a five-year period. Peas, kidney beans, chickpea and pigeon pea were the major pulses that were imported to India. One of the key issues with regard to import of pulses is the high degree of concentration from few exporting nations. For each type of pulses, there

6.1 Introduction

69

3500000 3000000 2500000 2000000 1500000 1000000 500000 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Fig. 6.3 Trends in imports of peas (dry) in tonnes. Source FAOSTAT

has usually been a single largest importer with significant market share, for example Canada for peas, Australia for chickpea, China for kidney beans and Myanmar for pigeon pea. Therefore, any shifts in domestic–trade policies or crop failure can have huge implications on the pulses imported by India. For example, there has been stagnancy in area under peas cultivation in Canada and weather fluctuations in Myanmar that affected the output significantly (Bhattacharjya et al., 2017). Also, the study note that Canada, Australia and Myanmar have high instability with respect to production and area. Imports of all types of pulses were increasing over the period except for pigeon pea. For example, total imports of dry peas increased from around 137 thousand tonnes in 2000 to around 3061 thousand tonnes in 2016 (see Fig. 6.3). This marked an increase in India’s share in total world peas (dry) imports from around 5 to 47% during 2000–2016 (see Fig. 6.4). Similarly, India’s chickpea imports also increased during the same period from around 64 thousand tonnes in 2000 to around 873 thousand tonnes in 2016 (see Fig. 6.5). As a result, India’s share in world imports of chickpea also increased from around 10% in 2000 to around 45% in 2016 (see Fig. 6.6). But the imports of chickpea experienced more fluctuations as compared to peas. Another major pulse imported by India is lentils. The imports of lentils were sharply increasing over the last couple of years, especially since 2012. The imports of lentils increased from around 206 thousand tonnes in 1988 to 1123 thousand tonnes in 2017. In 2012, it was 441 thousand tonnes. The highest import occurred in the year 2015, and the total quantity imported was around 1162 (see Fig. 6.7). As a result of an increase in imports of lentils, the share of the same in total world import also sharply increased from around 1% in 1997 to around 39% in 2017 (see Fig. 6.8).

70

6 Pulses Production, Trade and Government Policies 50.00 45.00 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 20002001200220032004200520062007200820092010201120122013201420152016

Fig. 6.4 India’s peas (dry) import as a percent of total world import. Source FAOSTAT 1000000 900000 800000 700000 600000 500000 400000 300000 200000 100000 0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Fig. 6.5 Trends in imports of chickpea in tonnes. Source FAOSTAT

6.2 Country-Wise Imports of Major Pulses The below sections will have closer look at the import scenario by analysing the major importers of each crop. As mentioned already, Australia was the major importer of chickpea to India. For example, the import of chickpea from Australia to India sharply increased from around 55 thousand tonnes in 2002 to around 941 thousand tonnes in 2017 (see Fig. 6.9). The other important suppliers were Canada and Ethiopia. Canada’s import was highly fluctuating during 2002–2017 (see Figs. 6.10 and 6.11). In 2002, India imported around 114 thousand tonnes of chickpea from Canada, and in the remaining years, except 2016, the import of chickpea from Canada was negligible. In 2016, India

6.2 Country-Wise Imports of Major Pulses

71

50.00 45.00 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Fig. 6.6 India’s chickpea import as a percent of total world import. Source FAOSTAT 1400 1200 1000 800 600 400 200

2016

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

0

Fig. 6.7 Trends in India’s imports of lentils in thousand tonnes. Source wits.org

imported around 606 thousand tonnes of chickpea from Canada. In 2017 (in the first three quarters), India imported only three thousand tonnes (see Fig. 6.10). Similarly, from Ethiopia, the import was always less than 10 thousand tonnes except in 2015. In 2015, India imported around 15 thousand tonnes of chickpea from Ethiopia (see Fig. 6.11). As far as peas are concerned, Canada was the major importer of peas to India. The other major importers were USA, Ukraine, Australia and Russia. Among these countries, Russia emerged as a major importer in recent years (see Fig. 6.12). However, the share of Canada in total imports of peas much higher than the other countries. For example, the imports of peas from Canada increased around 333 thousand tonnes in 2002 to 1605 thousand tonnes in 2016. But the imports of peas from Canada only in

72

6 Pulses Production, Trade and Government Policies 45 40 35 30 25 20 15 10 5 2017

2015

2016

2014

2013

2012

2010

2011

2008

2009

2007

2005

2006

2004

2003

2002

2001

1999

2000

1998

1997

1996

0

Fig. 6.8 India’s import of lentils as a percent of total world import. Source wits.org 1000 900 800 700 600 500 400 300 200 100 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Fig. 6.9 Import of chickpea from Australia in thousand tonnes. Source DGCI&S

the last three quarters of data show that import touched around 1152 thousand tonnes (see Fig. 6.12). Though there was a decline in the imports of peas from Australia, there was an increase in the imports of peas from USA, Ukraine and Russia. The imports of peas from USA to India increased from around 3 thousand tonnes in 2002 to around 212 thousand tonnes in 2016. The import in the first three quarters of 2017 was around 49 thousand tonnes. Similarly, the imports from Russia increased from around 20 thousand tonnes in 2002 to around 414 thousand tonnes in 2016 and 237 thousand tonnes in the first three quarters of 2017. The imports from Ukraine also increased from 28 to 171 thousand tonnes but declined to 25 thousand tonnes in 2017. Australia was the biggest importer of peas after Canada in the initial years but the amount sharply declined in the later years. The imports from Australia were

6.2 Country-Wise Imports of Major Pulses

73

700 600 500 400 300 200 100 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Fig. 6.10 Import of chickpea from Canada in thousand tonnes. Source DGCI&S 16 14 12 10 8 6 4 2 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Fig. 6.11 Import of chickpea from Ethiopia in thousand tonnes. Source DGCI&S

around 143 thousand tonnes in 2002 but declined to around 70 thousand tonnes in 2016 and marginally increased to 82 thousand tonnes in 2017 (see Fig. 6.12). As far as the imports of kidney beans is concerned, as mentioned earlier China was the major importer to India. The other two importers were Ethiopia and Myanmar. The imports from Myanmar were higher than the imports from Ethiopia until 2010. But since then, Ethiopian imports were higher than Myanmar imports (see Fig. 6.13). The imports of kidney beans from China increased from 104 to 744 thousand tonnes during 2002–2015. In the subsequent years, the imports marginally fell to 586 and 393 thousand tonnes. But the figure for 2017 is only for the first three quarters. Similarly, the imports from Ethiopia increased from around 11 to 269 thousand tonnes during 2002–2016. The imports from Myanmar increased from 77 thousand

6 Pulses Production, Trade and Government Policies

In thousand tonnes

74 1800 1600 1400 1200 1000 800 600 400 200 0

2017

2016

2015

2013

2014

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

Year

Australia

Canada

US

Ukraine

Russia

Fig. 6.12 Imports of peas from major importers in thousand tonnes. Source DGCI&S 900 800 700 600 500 400 300 200 100 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

China

Ethiopia

Myanmar

Fig. 6.13 Imports of kidney beans from major importers in thousand tonnes. Source DGCI&S

tonnes in 2002 to 128 thousand tonnes in 2016. The imports from Myanmar were the highest in the year 2008 with the imports of around 194 thousand tonnes (see Fig. 6.13). In the case of pigeon pea, the, major importer was Myanmar, though the import experienced sharp fluctuations during the period. These fluctuations could be attributed to the domestic fluctuations with respect to the production. The imports of pigeon pea from Myanmar were 258 thousand tonnes in 2002 and 220 thousand tonnes in 2017 (see Fig. 6.14). However, the imports from Mozambique and Tanzania sharply increased during the period. The imports from Tanzania increased from around 11 thousand tonnes in 2002 to 166 thousand tonnes in 2016, almost close to the imports from Myanmar. Considering the imports in the first three quarters of

6.2 Country-Wise Imports of Major Pulses

75

450 400 350 300 250 200 150 100 50 0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Tanzania

Mozambique

Myanmar

Fig. 6.14 Imports of pigeon pea (tur) from major importers in thousand tonnes. Source DGCI&S

2017, the imports from Tanzania fell to 38 thousand tonnes. In the case of Mozambique, the imports experienced an increase over the period. The imports increased from 2 thousand tonnes in 2002 to 125 thousand tonnes in 2016. Next, we will turn into the analysis of import prices. The unit value of import is taken as the proxy for import price. Though Australia was the major importer of chickpea, the prices were lower for Australian imports as compared to the other two countries. This can be one of the reasons for Australia to dominate the import. The import prices of both Canada and Ethiopia were increasing since 2014 (see Fig. 6.15). The yearly average unit import prices for peas was very much similar until 2011, but since 2011, the USA and Russian price started to increase more than the prices of Australia, Canada and Ukraine (see Fig. 6.16). 90 80 70 60 50 40 30 20 10 0

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Australia

Canada

Ethiopia

Fig. 6.15 Yearly average prices (Rs. per kg) of chickpea imported by major importers. Source Calculated using the data from DGCI&S

76

6 Pulses Production, Trade and Government Policies 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Australia

Canada

US

Ukraine

Russia

Fig. 6.16 Yearly average prices (Rs. per kg) of peas imported by major importers. Source Calculated using the data from DGCI&S

The yearly average import price of kidney beans from China was higher than the other major importers. The gap between Chinese price and other prices was the highest during 2011–2015 (see Fig. 6.17). As mentioned already, China is also the major importer of kidney beans to India. Among the major importers, Ethiopian price was the lowest. Similar to kidney beans, the yearly average unit import price for pigeon pea was also the highest for the major importer of pigeon pea—Myanmar. As in the case of kidney beans, the gap between Myanmar price and other two importer’s price widened during 2014–16 period. The period also coincides with the deficit in pigeon pea that the country had faced (see Fig. 6.18). 90 80 70 60 50 40 30 20 10 0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

China

Ethiopia

Myanmar

Fig. 6.17 Yearly average prices (Rs. per kg) of kidney beans imported by major importers. Source Calculated using the data from DGCI&S

6.4 Conclusion

77

90 80 70 60 50 40 30 20 10 0

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Tanzania

Mozambique

Myanmar

Fig. 6.18 Yearly average prices (Rs. per kg) of pigeon pea (tur) imported by major importers. Source Calculated using the data from DGCI&S

6.3 Tariff Scenario The most favoured nation (MFN) tariff for peas is 50; though it was reduced to 10 in 2008, again the rate was increased to 50 in the subsequent years, whereas for chickpea, it was reduced to 10 in 2008 but again increased to 30 in the subsequent years. In 2015 and 2016, the MFN rate was again 10. Similarly, in the case of kidney beans and lentils, the MFN rate was 30 except 2008. In the year 2015, the MFN rate was again reduced to 10. For pigeon pea, the MFN rate was 30 from 2012 to 13 but reduced to 10 in 2015.

6.4 Conclusion The analysis in the above sections showed that there has been a substantial increase in the imports of most of the pulses in the last several years. Also, the share of India’s imports in world imports of pulses also showed a sharp increase. This points out the increasing import dependency and severe supply deficit that India is facing in terms of meeting the demand for protein rich crop. The data published by National Sample Survey Office (NSSO) in 2014 shows that pulses and pulses products as a whole, the per capita consumption increased by 7778 g between 2004–05 and 2011–12 out of which 705 g per month to 783 g per month in the rural sector and 824–901 g in the urban sector. Interestingly, 69 and 57 g of increase in the rural and urban areas were contributed by the four items split gram, whole gram, pea and besan. The four pulses arhar, moong, masur and urd—which in 2011–12 together made up about 64% of consumption of pulses and pulse products in rural India and 68% in

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urban India—registered a total increase in monthly per capita consumption of only 14 g in the rural sector and 18 g in the urban sector over this 7-year period. The widening gap between supply and demand and the domestic uncertainties with respect to the production, etc., might continue to increase the import dependency unless effective policy measures are undertaken to improve the production and productivity and pulses. The implications of long-term dependency on import depends upon the nature of import pricing that is undertaken by the importers as we have already discussed the import of each type of pulses is dominated by one or two single largest importers. This may increase the potential for monopoly pricing. Therefore, the next chapter will make an analysis of import pricing and exchange rate pass-through into pulses imported to India by major importers.

References Bhattacharjya, S., Chaudhury, S., & Nanda, N. (2017). Import dependence and food and nutrition security implications: The case of pulses in India. Review of Market Integration, 9(1–2), 83–110. https://doi.org/10.1177/0974929217721763 Singh, A. K., Singh, S. S., Prakash, V. E. D., Kumar, S., & Dwivedi, S. K. (2015). Pulses production in india: Present status, sent status, bottleneck and way forward. Journal of AgriSearch, 2(2), 75–83.

Chapter 7

Pricing and Exchange Rate Pass-Through in Pulses Imports

7.1 Introduction Over the past two decades, the Government of India sought import of pulses as a key trade policy measure to boost domestic availability of pulses. This was inevitable to address the soaring of domestic prices which was hurting the poor consumers. The decline in the consumption of pulses as a result of soaring of prices will have adverse implications for food security as pulses is an important staple crop consumed by all types of households in India. However, the import pulses can also lead to world price transmission to domestic market and the manner in which prices are transmitted depends upon the type of importers—whether they have monopoly in trade or not— and the nature of domestic demand. Therefore, trade plays a crucial role in domestic price formation. Since we are no more in a position to isolate domestic markets from world markets and the markets are getting integrated, the nature and dimensions of trade have profound implications on domestic production, consumption, prices and the supply chain that includes processing and marketing (Chandra et al., 2017). Additionally, Chap. 6 showed that India has consistent imports of peas, kidney beans, chickpea and pigeon pea from foreign countries, and for each of the pulses, we have a major importer along with two or three other importers. The analysis of unit import price also showed that the prices were generally high during the period when Indian experienced a deficit and also the prices of some of the dominant importers were also remained to be higher than the other importers. So, it is imperative to analyse the import pricing behaviour and exchange rate pass-through into import prices to understand whether these importers have any monopoly power in pricing the products.

© Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_7

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7 Pricing and Exchange Rate Pass-Through in Pulses Imports

7.2 The Concept of Pricing to Market Behaviour and Exchange Rate Pass-Through The concept of pricing to market (PTM) behaviour to explain the non-competitive pricing behaviour of exporters is developed by Krugman (1987). When the markets are perfectly competitive, the price is determined through the intersection of market forces—demand and supply, and such a price is a competitive price. In other words, the sellers cannot influence the market prices and they are price takers. The sellers can decide how much they want to sell at the prevailing market prices, and any small increase in price will lead to a greater reduction in quantity demanded due to the elastic nature of demand. Therefore, price elasticity of demand and the slope of the demand curve will tell us the monopoly power of the seller. The horizontal demand curve typically characterises the perfectly competitive market, whereas the vertical line is the pure monopoly. In pure monopoly irrespective of the level of prices, the same quantity can be sold. However, these two are the extreme scenarios as most of the markets lie between these two extreme possibilities. The steeper the demand curve is, the higher is the monopoly power. The slope of the demand curve will tell us the amount of monopoly power. If an exporter is facing a downward demand curve in the import market, it is a clear indication of some amount of monopoly power exercised by the exporter. In such a scenario, the exchange rate changes may not get fully reflected in the prices. Briefly, there are two sources of price discrimination or non-competitive pricing behaviour. They are (a) exchange rate-induced market pricing and (b) market-specific price discrimination. Exchange rate-induced price discrimination can happen when the supplier is unwilling to pass the full exchange rate changes to prices. Therefore, exchange rate pass-through will be partial or incomplete. Exchange rate pass-through can be defined as the elasticity of prices to exchange rate changes, and this elasticity can be less than 1 (less elastic), equal to 1 (unitary elastic) an greater than (highly elastic). For example, assume that India’s exchange rate with Australia is changed and now Australian dollar is more expensive as compared to Indian rupee. In other words, Australian currency is appreciated and Indian currency is depreciated against the Australian dollar. The average exchange rate in the year 2015 was 1 Australian dollar equal to Rs. 48.1903. In the year 2021, the average exchange rate was 1 Australian dollar equal to Rs. 54.121. This implies that the rupee value went down over the six-year period due to the rupee depreciation against Australian dollar. Assume that Australia had fixed the price of chickpea as 1 dollar, and as a result of appreciation of their currency, the Indian price went up from Rs. 48.1903 to Rs. 54.121. So, this means the Indian imports of pulses have become costlier and India’s import bill is increased. This can lead to a reduction in chickpea import from Australia due to the decline in Indian demand as per the law of demand. The law of demand states that the quantity demanded will go down when prices increases and there is an inverse relationship between quantity demanded and price. This decline in demand will take place only if the Indian consumers are reducing the consumption of chickpea and moving to other close substitutes. It is also possible that the demand for chickpea by

7.2 The Concept of Pricing to Market Behaviour and Exchange Rate …

81

Indian consumers are very rigid so an increase in price will not lead to greater decline in demand. However, if there is any change in the demand due to an increase in price, the Australian importer will try to fix the price as for example 51.16 by absorbing around 50% of an increase in price that happened due to the increase in their currency rate. The Australian importer is doing this to retain the Indian market. However, the behaviour of all exporters may not be symmetric as some exporters will not be able to absorb part of the increase in price. So, this ability to adjust the prices due to the exchange rate changes happens based on the price fixing ability of the exporter. If an exporter is price taker (perfectly competitive), then exporter will try to pass the price as it is without trying to absorb or adjust the prices. However, if an exporter is a price maker (monopoly power), he/she will try to adjust the prices based on the nature of elasticity prevailing in the importing market. If an exporter is passing the entire changes in exchange rates into export prices, then we say exchange rate passthrough is complete, and when an exporter is absorbing part of the changes in prices, then we say exchange rate pass-through is partial or incomplete. The exchange rate pass-through can be defined as the elasticity of export (import) prices with respect to changes in exchange rates. When exchange rate pass-through is partial or incomplete, the marginal revenue will not be equated to marginal cost. In a perfectly competitive market, marginal revenue is equal to marginal cost and as a result zero profits for sellers. In an imperfectly competitive market, the price is greater than the marginal cost.

7.2.1 Producer Currency Pricing Versus Local Currency Pricing Prices are assumed to be sticky if they are expressed in the producing country’s currency, and when a firm is following producer currency pricing, the depreciation of the currency will make export cheaper and improves the export competitiveness (Antoniades, 2012). The opposite of the same will take place during the currency appreciation of the exporter. Therefore, exchange rate pass-through will be complete and law of one price takes place under producer currency pricing, whereas under local currency pricing, the prices in the importing market will be sticky, and as a result, the changes in exchange rates will not get fully reflected in the export prices. As a result, the price will deviate from the marginal cost and law of one price does not hold. Quite often, PTM takes place when the buyers preset the price of the commodity traded in importer’s currency (Byrne et al., 2013). Open economy macroeconomic models have been increasingly addressing the local currency pricing behaviour.

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7.3 Literature Review The pricing to market (PTM) framework to explain the non-competitive behaviour of traders was developed by Krugman (1987). The two major sources of noncompetitive pricing behaviour are exchange rate-induced price discrimination and market-specific price discrimination. The export price elasticity to exchange rate changes will help us in calculating the exchange rate pass-through (Mallick & Marques, 2012). Market-specific price discrimination happens when the exporter fixes different prices in different markets, and as a result, the mark-up over marginal cost varies in destination markets. This type of market-specific discrimination is possible only when the nature price elasticity of demand varies in these markets. But these differences in pricing may not be always due to the price discrimination, and sometimes, these differences can reflect the quality differences. The quality differences can also be due to the differences in consumer’s preference and awareness about the quality of products. Now since international trade is increasingly facing the quality standards, both public and private, the exporters may sell the produce at a high quality in market where the quality standards are very stringent.

7.3.1 Market Share Model Versus Bottlenecks Model The responses of prices to exchange rate changes need not be symmetric. The response during depreciation can be different from the response during appreciation and such an asymmetric response can take place due to a number of reasons (Knetter, 1992a, 1992b, 1992c). As per the bottlenecks model, supply side constraints or marketing bottlenecks can take place during the depreciation of currency; as a result, the export volume may not rise proportionate to the decline in price. As per the market share model, during appreciation, the exporter may try to retain the market share by absorbing the rise in prices, and this can be the reason for pricing to market behaviour. Knetter (1989) did the pioneering study by undertaking a comprehensive analysis of the PTM behaviour for USA and German exporters using a fixed effect model. The results showed that both the exporters are exhibiting a pricing to market behaviour. There have been plenty of empirical attempts to analyse the PTM behaviour of both exporters both from an importing country perspective and from an exporting country perspective. However, most of the empirical studies on PTM is in the context of manufactured products. There are few studies in the context of food and agricultural products. Pick and Park’s (1991) analysis was one of the early attempts in the area of food and agricultural products. Similarly, the analysis done by Yumkella et al. (1994) also provided evidence for non-competitive pricing behaviour by exporters. An analysis of pricing behaviour of wheat, pulses and tobacco exported from USA and Canada is analysed by Carew (2000). The results from the analysis provided evidence for market imperfection and price discrimination with wheat exports showing greater market

7.4 Model Specification

83

imperfection and price discrimination in the destination markets. Miljkovic et al. (2003) quantified the effects of exchange rate changes on US beef, pork and poultry export prices using the PTM model where the exchange rate were market-specific exchange rates. Using monthly data, the export pricing behaviour of Canadian wheat exporters were analysed by Lavoie (2005). As per the study findings, the Canadian Wheat Board (CWB) was practising price discrimination across various importing countries. Another study undertaken by Jin and Miljkovic (2008) analysed the case of US exports of wheat. Their study made use of the quarterly data. The study observed that exchange rate-induced price discrimination in the case of wheat exports to 9 out of 22 importing markets. Another study by Pall et al. (2013) also observed non-competitive pricing behaviour for Russian exporters of wheat in 25 importing countries. Some studies make use of both nominal and real exchange rates in the analysis (Issar & Varma, 2016; Pall et al., 2013; Varma & Issar, 2016), and some studies also use commodity-specific trade-weighted exchange rates (Issar & Varma, 2016; Miljkovic & Zhuang, 2011; Varma & Issar, 2016). Some of the earlier studies have used aggregate trade-weighted exchange rates as their studies dealt with aggregate studies (Goldberg, 2004; Pollard & Coughlin, 2006). Their analysis shows that the results are sensitive to the kind of exchange rates employed in the analysis. There have been few sector-specific empirical studies in the context of India as well. For example, Varma and Issar (2016) analysed the pricing to market behaviour of India’s high-value agri-food exporters using nominal, real and commodity-specific exchange rates. Similarly, an analysis for basmati and non-basmati rice was also undertaken to see the exchange rate-induced and market-specific price discrimination evidence among the exporters (Issar & Varma, 2016). Both the studies have provided evidence for non-competitive pricing behaviour. However, there have been no analysis for India from the import perspective. The present chapter is intended to fill up this gap by analysing the import pricing behaviour of pulses imported to India by major importers of pigeon pea, chickpea, kidney beans and peas.

7.4 Model Specification Following (Knetter, 1989) the basic empirical equation to analyse the exchange rate pass-through can be specified as follows: ln pit = θt + λi + βi (ln E it ) + u it

(7.1)

where ln(pit ) is the log of the import price by country i at period t, measured in Indian rupees per kg. θ t represents the time effects corresponding to the t periods. The term λi refers to the time-invariant market-specific effects. The β i coefficient measures the exchange rate pass-through for the individual i countries. The ln(eit ) is the log

84

7 Pricing and Exchange Rate Pass-Through in Pulses Imports

of importer-specific exchange rate expressed as the units of the importer’s currency per unit of Indian rupees. Finally, uit is the regression error term distributed. The exchange rate pass-through may not be symmetric implying the behaviour of the seller in terms of pricing may not be the same during the appreciation and depreciation. The pass-through may be high during depreciation than appreciation and vice versa. This kind of asymmetric behaviour occurs when the price elasticity of demand is different in appreciation scenario than depreciation scenario (Knetter, 1994). As per the previous studies, an interaction dummy is also added in our basic equation to capture the asymmetric behaviour of pricing in exchange rate changes (Knetter, 1992a, 1992b, 1992c; Vergil, 2011). E t = (β1 + β2 Dt )E t = β1 E t + β2 Dt × E t Dt in the equation refers to the dummy variable which will take the value of 1 during appreciation and 0 otherwise. By respecifying Eq. (7.1), we will get the following equation: ln pit = θt + λi + β1 (ln E 1t ) + β2 (ln E 2t ) + u it

(7.2)

ln pit = θt + λi + β1 (ln E 1t ) + β2 (ln E 1t × Dt ) + u it

(7.3)

7.5 Data Description The data for imports is obtained from the Directorate General of Commerce and Intelligence (DGCI&S). The analysis is based on the quarterly data from 2002 to 2017 to sufficiently capture the period in which India’s imports were high. Since the import price is not available, the study makes use of the unit value of import as a proxy for import price. Firstly, the major pulses imported by India are identified. They are kidney beans, peas, chickpea and pigeon pea. Subsequently, the top importing countries for each type of pulses are identified. Accordingly, China, Ethiopia and Myanmar are selected for kidney beans, Australia, Canada, USA and Ukraine are selected for peas, Tanzania, Mozambique and Myanmar are selected for pigeon pea, Australia, Canada and Ethiopia are selected for chickpea. Nominal exchange rates and the consumer price index (CPI) to compute real exchange rates for the importing countries are obtained from the Bloomberg and Thomson Reuters databases. In case of pigeon pea, two types of data set have been used. The currency of major importer of pigeon pea, Mozambique, redenominated the metical at a rate of 1000:1 on 1 July 2006 owing to inflation. Due to the lack of availability of data prior to 2006, the present study makes use of the exchange rates based on both the new and old currencies. The exchange rates were available for old

7.6 Results and Discussion

85

currency till 2007. The exchange rates based on new currency were available from 2008 onward. Also due to the lack of considerable import of pigeon pea from 2002 to 2004, the present study of pigeon pea is from 2004 to 2017 for pigeon pea based on old currency exchange rate and 2008–2017 based on new currency exchange rate of Mozambique. The data is unbalanced for most of the pulses import as the import of pulses were missing in some quarters from some countries. In order to calculate the real exchange rates, the nominal exchange rates were multiplied with the consumer price index (CPI) of India and divided it by CPI of the respective countries (Knetter, 1989; Pall et al., 2013; Pick & Park, 1991). The commodity-specific trade-weighted exchange rates were calculated using the following formula. p

XERt =



pi

pi

wt · RERit ,

i

X pi where wt =  t pi i Xt

7.6 Results and Discussion The results from the analysis showed that for peas and kidney beans, the commodityspecific exchange rate model better predicted the pricing to market behaviour, whereas for pigeon pea and chickpea nominal exchange rate model better predicted the pricing to market behaviour. The R square value was high in the commodityspecific exchange rate model in the case of peas and kidney beans and in the nominal exchange rate model for chickpea and pigeon pea. The analysis was undertaken using the panel corrected standard errors (PCSE). The model also accounted for panel-level heteroskedastic errors, and errors contemporaneously correlated across panels. The results from the analysis provided empirical evidence for non-competitive pricing behaviour of importers. The β coefficient came out to be statistically significant in the case of all the importing countries. The results therefore provided empirical evidence for partial or incomplete exchange rate pass-through. The β coefficient exhibited statistically significant relationship in the case of Ethiopia and Myanmar for kidney beans, Australia and USA for peas, Australia, Canada and Ethiopia for chickpea, Tanzania and Myanmar for pigeon pea (old currency) (see Tables 7.3, 7.6, 7.7 and 7.10). The analysis based on new currency of Mozambique also showed significant exchange rate effect in the case of Myanmar (see Table 7.3). First, we will discuss the results of commodity-specific exchange rate models for peas and kidney beans and nominal exchange rate model results for pigeon pea and chickpea.

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7.6.1 Commodity-Specific Exchange Rate Model for Kidney Beans As mentioned already commodity-specific exchange rate model was the best in predicting the pricing behaviour of the importers so our discussion of the results will focus on commodity-specific exchange rate model for kidney beans. The results from the analysis based on commodity-specific exchange rate model for peas showed that the exchange rate effect was significant in the case Ethiopia and Myanmar. In other words, exchange rate changes were only partially reflected in the import prices of kidney beans from Ethiopia and Myanmar indicating the non-competitive pricing behaviour of importers of kidney beans. The sign of the bet coefficient was negative for both countries, and this showed local currency price stabilisation. Therefore, change in import prices with respect to the changes in exchanges was inverse. In other words, a 1% appreciation in Indian currency was leading to 2% decline in the import price of Ethiopian and Myanmar prices of kidney beans imported to India. This shows the residual demand is elastic, which is an indicator of competitive behaviour (Varma & Issar, 2016). The country-specific effect was also positive in the case of Ethiopia and Myanmar as compared to China indicating the prices of kidney beans from these two countries were higher than Chinese price of kidney beans. The interaction of dummy variable with exchange rate changes to capture the asymmetric effect was not significant in this model (see Table 7.1). Table 7.1 Results of the PTM model for kidney beans—commodity-specific exchange rate model Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

China

−0.02

−1.37

(0.02) Ethiopia

−0.02

* −2.25**

(0.01) Myanmar

−0.02 (0.01)

*

−2.32**

*

0.01

−0.4

−5.13*** 0.01

0.07

0.02

−0.15 0.05

0.59

(0.02)

−3.18*** −0.01

0.71 −0.52

0.02

Observations 172 Wooldridge test

47.97 (0.0202)

R-squared

0.9824

Wald chi-sq.

2586.29 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, China is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

7.6 Results and Discussion

87

Table 7.2 Results of the PTM model for peas—commodity-specific exchange rate model Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

Australia

−0.04

−3.81*** −0.09

(0.01)

(0.10)

Canada

−0.02

−0.91

Ukraine

−0.93

0.04

−4.66*** −0.01

(0.01)

(0.08)

(0.01)

2.27**

(0.02)

−0.05 −0.01

1.46

(0.02)

(0.02) USA

0.03

−0.95

0.01 (0.07)

−0.18

−0.01

−0.7

(0.03) 0.08

0.02

0.81

(0.02)

Observations 232 Wooldridge test

13.2551 (0.0357)

R-squared

0.9831

Wald chi-sq.

3889.50 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Canada is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

7.6.2 Commodity-Specific Exchange Rate Model for Peas As mentioned already commodity-specific exchange rate model was the best in predicting the pricing behaviour of the importers so our discussion of the results will focus on commodity-specific exchange rate model for peas. The results from the analysis based on commodity-specific exchange rate model for peas showed that the exchange rate effect was significant in the case of imports from Australia and USA. The sign of the coefficient was negative indicating local currency stabilisation. In other words, a 1% appreciation in currency was leading to 4% decline in the import price for Australia and 5% decline in the import price for USA. The country-specific effect was not significant in this model indicating the prices charged by Australia, USA and Ukraine were not statistically different from Canadian price (see Table 7.2).

7.6.3 Nominal Exchange Rate Model for Chickpea As mentioned, the nominal exchange rate model better predicted the pricing behaviour of imported chickpea. The results from the analysis showed that exchange rate effect was significant in the import pricing of all the three major importers

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7 Pricing and Exchange Rate Pass-Through in Pulses Imports

Table 7.3 Results of the PTM model for chickpea—nominal exchange rate model Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

Australia

−0.47 (0.27)

Canada

−1.03 (0.33)

Ethiopia

0.84 (0.23)

−1.76*

1.95

3.04***

(0.64) −3.16*** *

(1.52)

2.28**

(0.03) *

* 3.58*** 4.92

0.07 0.03

0.94

(0.03) 3.23***

−0.02

−0.44

(0.04)

Observations 152 Wooldridge test

406.457 (0.0025)

R-squared

0.9740

Wald chi-sq.

1728.12 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Canada is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

of Chickpea, Australia, Canada and Ethiopia. The sign of the bet coefficient was negative in the case of Australia and Canada, whereas it was positive in the case of Ethiopia. This shows that Ethiopia was practising amplification of exchange rate, whereas the other two were practising local currency stabilisation (see Table 7.3). The country-specific effect was also significant for Australia and Ethiopia as compared to USA. This is indicating significant price differences across importers. Dummy variable to capture the asymmetric effect was significant and positive for Australia indicating an appreciation of exchange rate had a significant impact on the import prices from Australia.

7.6.4 Nominal Exchange Rate Model for Pigeon Pea The nominal exchange rate models better predicted the pricing behaviour of pigeon pea analysis based on both the currencies. The results from the analysis showed that the exchange rate effect was statistically significant and negative in the case of two out of three major importers of pigeon pea, Tanzania and Myanmar. The sign was negative indicating these countries are practising local currency price stabilisation. However, the market-specific effect was significant for Mozambique indicating that though exchange rate pass-through is complete the prices charged by Mozambique was lower than the other importers and the mark-up of price over cost remain to be

7.7 Conclusion

89

Table 7.4 Results of the PTM model for pigeon pea—nominal exchange rate model (old currency) Country Tanzania

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect −0.16

−1.75*

(0.09) Mozambique 0.01 (0.01) Myanmar

−0.55

−0.89

(0.62) 1.41

−1.53

−2.99**

0.00

*

0.01

(0.51)

−0.47

−3.11*** *

(0.15)

*

−0.01

−0.55

(0.03) −0.07

(0.02) 0.27

(0.02)

Observations 157 Wooldridge test

43.225 (0.0224)

R-squared

0.9844

Wald chi-sq.

4391.99 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Myanmar is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

constant. This can also be due to the quality difference in pigeon pea imported by Mozambique as compared to the other two countries. Dummy variable to capture the asymmetric effect was not significant in this model. When we analysed the PTM using the new currency of Mozambique only for the period 2008–2017 showed that exchange rate was significant in the case of import from Myanmar. As in the case of old currency model, the sign of the coefficient was negative indicating local currency stabilisation by Myanmar (see Table 7.4 and 7.5). However, the country-specific effect was significant and negative for Mozambique and Tanzania indicating differential prices for imported pigeon pea. Dummy variable to capture the asymmetric effect was not significant in this model.

7.7 Conclusion The analysis revealed non-competitive pricing behaviour of major pulses importers to India. The sources for non-competitive pricing behaviour were both the exchange rate-induced price discrimination and destination-specific mark-up over marginal cost. The exchange rate parameter β and the country-specific effects parameter λi came out to be statistically significant in most models. In addition to this, the results showed that there was asymmetric effect of exchange rate changes on the pricing behaviour implying the pricing behaviour was not the same in both the appreciation

90

7 Pricing and Exchange Rate Pass-Through in Pulses Imports

Table 7.5 Results of the PTM model for pigeon pea—nominal exchange rate model (new currency) Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

Tanzania

−0.08

−0.78

(0.10) Mozambique 0.01 −0.53 (0.20)

−2.59**

(0.71) 0.76

(0.01) Myanmar

−1.84 −1.70 * *

0.47

(0.02) −2.46**

(0.69) −2.61**

0.01 −0.01

−0.34

(0.03) *

0.03

1.18

(0.02)

Observations 111 Wooldridge test

29.004 (0.0328)

R-squared

0.9805

Wald chi-sq.

1237.35 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Myanmar is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

scenario and depreciation scenario. The results from the asymmetric dummy revealed that for majority of the products appreciation of the Indian rupee against the partner country had greater impact than the depreciation. The study made use of three types of exchange rates as in the case of some studies. They are the nominal, the real and the commodity-specific exchange rates. Our analysis revealed non-competitive pricing behaviour of importers of pulses to India through exchange rate-induced price discrimination. The analysis also showed that the commodity-specific exchange rate better predicts the PTM behaviour in the case of kidney beans and peas, whereas the nominal exchange rate better predicts the PTM behaviour of chickpea and pigeon pea. Another major observation from the analysis was the importers were following local currency stabilisation indicating tough competition among the importers.

Appendix See Tables 7.6, 7.7, 7.8, 7.9, 7.10, 7.11, 7.12, 7.13, 7.14 and 7.15.

Appendix

91

Table 7.6 Results of the PTM model for pigeon pea—real exchange rate model (new currency) Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

Tanzania

−0.03

−1.30

0.02 Mozambique 0.01 −0.05

−1.13

0.14 0.58

0.01 Myanmar

−0.16 −0.07

0.04

*

0.57

0.03 −0.46

0.15 −1.14

0.02 0.03

1.06

0.03 *

*

0.04

1.44

0.03

Observations 110 Wooldridge test

60.723 (0.0161)

R-squared

0.9612

Wald chi-sq.

1118.35 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Myanmar is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets) Table 7.7 Results of the PTM model for pigeon pea—commodity-specific exchange rate model (new currency) Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

Tanzania

0.01

1.52

0.01 Mozambique −0.02 0.01 0.01

−2.00

0.04 −1.11

0.01 Myanmar

−0.09 * −0.14 0.04

###

0.03 *

* 0.89

0.01 0.00

0.1

0.03 −3.66

0.03

1.2

0.03

Observations 109 Wooldridge test

286.701 (0.0035)

R-squared

0.9681

Wald chi-sq.

1173.97 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Mozambique is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

92

7 Pricing and Exchange Rate Pass-Through in Pulses Imports

Table 7.8 Results of the PTM model for kidney beans—nominal exchange rate model Country China

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect −0.09

−0.62

0.50

(0.13) Ethiopia

−0.08

−0.72

(0.12) Myanmar

−0.01

0.03

1.96*

(0.02) −0.30

0.05

(0.38) −0.46

(0.01)

0.04

2.75**

(0.02) 1.26

(0.27)

−0.04

−2.27**

(0.02)

Observations 182 Wooldridge test

100.3 (0.009)

R-squared

0.972

Wald chi-sq.

1882.25 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Ethiopia is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets) Table 7.9 Results of the PTM model for kidney beans—real exchange rate model Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

China

−0.02

−1.13

0.01

0.02 Ethiopia

−0.05

−2.53**

−0.37

−1.14

−0.14

0.02 Myanmar

−0.01 0.01

0.49

0.02 −5.8***

0.06 0.04

−0.01

−0.52

0.02 −3.43***

0.01

0.39

0.02

Observations 172 Wooldridge test

137.65 (0.0072)

R-squared

0.978

Wald chi-sq.

2102.59 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, China is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

Appendix

93

Table 7.10 Results of the PTM model for peas—nominal exchange rate model Country Australia

Exchange rate effect

Z-statistic

Country-specific effect

Z-statistic

Asymmetric effect

Z-statistic

−0.03

−0.49

−0.18

−0.42

−0.02

−0.6

0.07 Canada

−0.08

USA

−0.01

0.43 −1.12

−0.48

0.07

0.3 −1.17

−0.015

0.41

0.02

−0.08

−0.004

0.11 Ukraine

0 239

Wooldridge test

4.122 (0.1353)

R-squared

0.9450

Wald chi-sq.

5280.63 (0.0000)

−0.13

0.03 0.05

−0.16

0.03 Observations

−0.74

−0.34

−0.02

0.48

−0.78

0.02

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, USA is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

Table 7.11 Results of the PTM model for peas—real exchange rate model Country

Exchange rate effect

Australia

0.11

Z-statistic 2.59**

0.04 Canada

0.05 0.00

2.25**

0.00 0.01

Observations

232

Wooldridge test

12.15 (0.0399)

R-squared

0.9669

Wald chi-sq.

2907.71 (0.0000)

Asymmetric effect

Z-statistic

*

*

0

0.01

−0.26

0.02 −2.17**

0.12 −0.17

0.02 Ukraine

Z-statistic

*

0.02 USA

Country-specific effect

−0.25 −0.43 0.12

0.78

0.02 −1.97*

0.13 −0.25

0.02 0.05

1.59

0.03 −3.49***

0.01

0.57

0.02

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Australia is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

94

7 Pricing and Exchange Rate Pass-Through in Pulses Imports

Table 7.12 Results of the PTM model for chickpea—real exchange rate model Country Australia

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect −0.07

−0.86

0.08 Canada

0.01

0.12

0.06 Ethiopia

−0.09

*

*

* 0.40

0.04

0.07

−0.32

0.04 1.97

0.20 −2.31

−0.01 −0.06

−1.52

0.04 0.31

0.22

0.00

−0.07

0.04

Observations 153 Wooldridge test

120.815 (0.0082)

R-squared

0.9674

Wald chi-sq.

997.21 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Australia is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets) Table 7.13 Results of the PTM model for chickpea—commodity-specific exchange rate model Country Australia

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect −0.11

−4.12

0.03 Canada

−0.01

−1.09

0.01 Ethiopia

−0.03 0.01

−0.46

−3.39

0.14 *

*

* −2.32

−0.20 0.13

0.05

1.56

0.03 0.02

0.52

0.04 −1.52

−0.03

−0.67

0.05

Observations 153 Wooldridge test

21.399 (0.0437)

R-squared

0.9673

Wald chi-sq.

1628.53 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Canada is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

Appendix

95

Table 7.14 Results of the PTM model for pigeon pea—real exchange rate model (old currency) Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

Tanzania

−0.03

−1.50

0.02 Mozambique 0.01 −0.05

−0.05

0.15 1.63

0.01 Myanmar

−0.01 −0.09

0.04

*

1.33

0.02 −0.73

0.12 −1.32

0.03 0.00

0.13

0.02 *

*

0.01

0.45

0.02

Observations 155 Wooldridge test

179.074 (0.0055)

R-squared

0.9736

Wald chi-sq.

4366.21 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Myanmar is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets). Table 7.15 Results of the PTM model for pigeon pea—commodity-specific exchange rate model (old currency) Country

Exchange Z-statistic Country-specific Z-statistic Asymmetric Z-statistic rate effect effect effect

Tanzania

−0.01

−1.59

0.01 Mozambique 0.00 0.00 0.01

−2.00

−0.03

*

−0.05

0.03 0.66

0.01 Myanmar

−0.07 *

0.02

* −0.27

−0.09 0.03

−1.17 −2.40

0.02 −3.69

0.00

0.05

0.02

Observations 155 Wooldridge test

104.108 (0.0095)

R-squared

0.9821

Wald chi-sq.

4334.20 (0.0000)

Source Author’s own analysis Notes Standard errors are in brackets. The superscripts *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively. For the cross-sectional specification, Mozambique is the intercept. Wooldridge Autocorrelation Test null hypothesis is no first-order autocorrelation (p-values in brackets)

96

7 Pricing and Exchange Rate Pass-Through in Pulses Imports

References Antoniades, A. (2012). Local versus producer currency pricing: Evidence from disaggregated data. International Economic Review, 53(4), 1229–1241. Byrne, J. P., Kortava, E., & MacDonald, R. (2013). A new approach to tests of pricing-to-market. Journal of International Money and Finance, 32, 654–667. Carew, R. (2000). Pricing to market behavior: Evidence from selected Canadian and U.S. agri-food exports. Journal of Agricultural and Resource Economics, 25, 578–595. Chandra, R., Joshi, P. K., Negi, A., & Roy, D. (2017). Dynamics of pulses trade in India. In IFPRI book chapters (pp. 179–220). Goldberg, L. S. (2004). Industry-specific exchange rates for the United States. Federal Reserve Bank of New York Economic Policy Review, 10(1), 1–16. Retrieved July 2015, from http://www. nyfedeconomists.org/research/epr/04v10n1/0405gold.pdf Issar, A., & Varma, P. (2016). Are Indian rice exporters able to price discriminate? Empirical evidence for basmati and non-basmati rice. Applied Economics, 48(60), 5897–5908. Jin, H. J., & Miljkovic, D. (2008). Competitive structure of US grain exporters in the world market: A dynamic panel approach. East Asian Economic Review, 12(1), 33–62. Knetter, M. M. (1989). Price discrimination by US and German exporters. The American Economic Review, 79(1), 198–210. Knetter, M. M. (1992a). International comparisons of pricing-to-market behaviour. American Economic Review, 83, 473–486. Knetter, M. M. (1992b). International comparisons of pricing-to-market behavior (NBER Working Paper No. 4098). National Bureau of Economic Research. Retrieved July 2015, from http://www. nber.org/papers/w4098 Knetter, M. M. (1992c). Is price adjustment asymmetric?: Evaluating the market share and marketing bottlenecks hypothesis (NBER Working Paper No. 4170). National Bureau of Economic Research. Retrieved July 2015, from http://www.nber.org/papers/w4170 Knetter, M. M. (1994). Is export price adjustment asymmetric?: evaluating the market share and marketing bottlenecks hypotheses. Journal of International Money and Finance, 13(1), 55–70. Krugman, P. (1987). Pricing to market when the exchange rate changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-financial linkages among open economies (pp. 49–70). MIT Press. Lavoie, N. (2005). Price discrimination in the context of vertical differentiation: An application to Canadian wheat exports. American Journal of Agricultural Economics, 87(4), 835–854. Mallick, S., & Marques, H. (2012). Pricing to market with trade liberalization: The role of market heterogeneity and product differentiation in India’s exports. Journal of International Money and Finance, 31(2), 310–336. Miljkovic, D., Brester, G. W., & Marsh, J. M. (2003). Exchange rate pass-through, price discrimination, and US meat export prices. Applied Economics, 35(6), 641–650. Miljkovic, D., & Zhuang, R. (2011). The exchange rate pass-through into import prices: The case of Japanese meat imports. Applied Economics, 43(26), 3745–3754. Pall, Z., Perekhozhuk, O., Teuber, R., & Glauben, T. (2013). Are Russian wheat exporters able to price discriminate? Empirical evidence from the last decade. Journal of Agricultural Economics , 64(1), 177–196. Pick, D. H., & Park, T. A. (1991). The competitive structure of US agricultural exports. American Journal of Agricultural Economics, 73(1), 133–141. Pollard, P. S., & Coughlin, C. C. (2006). Pass-through estimates and the choice of an exchange rate index. Review of International Economics, 14(4), 535–553. Varma, P., & Issar, A. (2016). Pricing to market behaviour of India’s high value agri-food exporters: An empirical analysis of major destination markets. Agricultural Economics, 47(1), 129–137. Vergil, H. (2011). Does trade integration affect the asymmetric behavior of export prices? The case of manufacturing exports of Turkey. African Journal of Business Management, 5(23), 9808–9813. Yumkella, K. K., Unnevehr, L. J., & Garcia, P. (1994). Non-competitive pricing and exchange rate pass-through in selected U.S. and Thai rice markets. Journal of Agricultural and Applied Economics, 26(2), 406–416.

Chapter 8

Asymmetric Exchange Rate Pass-Through, Market Share and Import Pricing

8.1 Introduction The current chapter is devoted to examine the role of country-specific market share on exchange rate pass-through and pricing behaviour of major pulses imported to India. The analysis in this chapter shows that the exchange rate pass-through is increasing in market share and after reaching a maximum it declines. The results provide new empirical insights into an inverted U shape relationship between exchange rate passthrough and market share. There have not been many analyses to see the influence of market shares on exchange rate pass-through in the food and agricultural sector. This chapter is making an attempt to analyse the impact of market share on exchange rate pass-through trade by analysing the asymmetric nature of exchange rate pass-through in market share. Our analysis in this chapter also provided empirical evidence for asymmetric exchange rate pass-through in market share. The analysis of long-run exchange rate pass-through is also undertaken in this chapter, and the results provide empirical support for incomplete of partial exchange rate pass-through in the long run as well. The long-run elasticity came out to be significant. A plethora of studies in international trade has analysed the relevance of market share and market structure of exporting firms in influencing the market prices in the destination markets. When the focus of theoretical studies on models of imperfect competition, the empirical analysis focus on the econometric exercise analysing the price elasticity of exchange rate changes and exporter’s market-specific price discrimination or mark-up of price over marginal cost. Price elasticity to exchange rate changes is known as exchange rate pass-through (ERPT) (Mallick & Marques, 2012). The exchange rate pass-through (ERPT) can be partial, complete or more than complete. The non-competitive pricing behaviour is evident when the ERPT is partial or more than complete, and as a consequence of this, the prices will deviate from marginal cost. There are ample pieces of evidences of incomplete exchange rate passthrough arising from the market power of exporting firms (Amiti et al., 2014; Atkeson and Burstein, 2008; Auer and Schoenle, 2016; Berman et al., 2012; Devereux et al., © Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_8

97

98

8 Asymmetric Exchange Rate Pass-Through, Market Share …

2017; Feenstra et al., 1996; Garetto, 2016). Market share plays an important role in determining the market power of the exporters as perceived elasticity of demand varies with different market shares (Krugman, 1986). Therefore, an important factor that gives rise to market power, and non-competitive pricing behaviour is the market share. There are a couple of studies that explicitly incorporate the market share in the analysis of ERPT (e.g. Amiti et al., 2014; Auer and Schoenle, 2016; Devereux et al., 2017; Feenstra et al., 1996; Garetto, 2016). However, the studies analysing the impact of market share on ERPT is limited for agricultural and food commodity trade. The present chapter examines the ERPT into the prices of major pulses imported by India, under varying market shares of exporting countries. The study also analyses the nonlinear exchange rate pass-through and the long-run elasticity. The study assumes special significance for a country like India due to its increased dependency on imports of pulses for meeting domestic food demand, as well as the continuous depreciation of currency that the country is experiencing concerning its major trading partners. Furthermore, pulses trade is concentrated among a few exporting nations. For each type of pulses, there has usually been a single largest importer with a significant market share. Therefore, the present study examines the asymmetric exchange rate pass-through into the prices of pulses imported by India, under varying market shares of exporting countries. The study also analyses the nonlinear exchange rate pass-through and the long-run elasticity. This analysis is important for several reasons. First, there are hardly any studies examining the ERPT in food and agricultural products under varying market shares. Second, there are hardly any studies in the Indian context that examines the ERPT under varying market shares in general and in the context of agricultural products in particular. Third, we extend the existing empirical analysis by incorporating the asymmetric behaviour of ERPT in various market shares. This is based on the assumption that the impact of market shares in exchange rate pass-through may or may not be symmetric. This chapter proceeds as follows. Section 8.2 discusses the empirical studies on ERPT along with a specific discussion on ERPT and market share. Section 8.3 specifies the empirical model followed by a discussion of pulses trade and the data used for the analysis in Sect. 8.4. In Sect. 8.5, we discuss the results, and conclusions and policy implications are given in Sect. 8.6.

8.2 Empirical Studies on ERPT and Market Share While attracting considerable academic interest, most of the initial empirical studies of ERPT were in the context of manufactured products (e.g. Athukorala, 1991; Falk and Falk, 2000; Knetter, 1989, 1994; Menon, 1996) with a handful of studies in the context of food and agricultural products as well (e.g. Carew, 2000; Carew and Florkowski, 2003; Dawson et al., 2017; Pick & Park, 1991; Varma & Issar, 2016; Yumkella et al., 1994). Lately, there has been an increased interest in agri-good

8.2 Empirical Studies on ERPT and Market Share

99

export pricing among the policymakers (Dawson et al., 2017), and Pick and Park’s (1991) analysis was one of the early attempts in the area of food and agricultural products. However, the studies in the context of food and agricultural products were predominantly undertaken from the perspective of exporting countries using the pricing to market (PTM) framework developed by Knetter (1989, 1994). The reason for this bias is not deliberate as most of the earlier studies were originated from developed countries that are net agricultural exporting countries such as the USA or Canada (Miljkovic & Zhuang, 2011). The analysis of Pick and Park (1991) for major agricultural exports from the USA such as wheat, cotton, corn and soybeans showed the non-competitive pricing behaviour of exporters. After this, there has been plenty of analysis, mainly from the developed country’s perspective (Carew, 2000; Jin & Miljkovic, 2008; Lavoie, 2005; Miljkovic et al., 2003; Miljkovic & Zhuang, 2011; Yumkella et al., 1994). There are couple of studies from a developing country’s perspective as well. For example, an analysis by Pall et al., (2013) observed non-competitive pricing behaviour for Russian exporters of wheat in 25 importing countries. The study also noted that the exporters worked with varying mark-up over marginal costs across markets. The study observed that the exporters were able to price discriminate in a few destination markets. Similarly, Gafarova et al., (2015) analysed the non-competitive pricing behaviour of exporters in Kazakhstan, Russia and Ukraine in response to bilateral exchange rate fluctuations during 1996–2012. Their results showed that there is evidence of PTM behaviour. Similarly, Uhl et al., (2016) analysing for two time periods, i.e. 2002–2011 and 2006–2011, using a firm-level dataset, showed that there is a high degree of PTM with price discriminating behaviour by Russian firms in 25 of 61 destination markets. There have been few attempts in the Indian context as well. The PTM analysis undertaken by Varma and Issar (2016) for India’s exports of high-value agricultural products (groundnut, banana, onion and cereal preparations, dairy products) provided evidence for non-competitive pricing behaviour in the case of most of the products exported. Another study was undertaken on the exports of basmati and nonbasmati rice (Issar & Varma, 2016). The results from the analysis showed that Indian exporters were able to price discriminate and exchange rate pass-through which were incomplete at least in some of the major destination markets. As evident from the above review, much of the empirical literature is in the context of developed countries and even the available studies for developing countries are mainly from an export perspective. The available import perspective studies are at the aggregate level (e.g. Brun-Aguerre et al., 2012, 2017; Campa and Goldberg, 2005) with few aggregate studies in the context of developing countries (Barhoumi, 2006; Mallick and Marques, 2008). There are some in the context of food and agricultural products for developing countries (Miljkovic & Zhuang, 2011). The study by Miljkovic and Zhuang (2011) for Japanese meat imports showed that for beef and poultry import prices indicate partial exchange rate pass-through and for pork zero

100

8 Asymmetric Exchange Rate Pass-Through, Market Share …

exchange rate pass-through. However, there has been no analysis for India’s agrifood trade neither from an import perspective nor by examining the role of market share in pricing behaviour.

8.2.1 Pass-Through and Market Share The market power of the exporter plays a crucial role in deciding the export price elasticity to exchange rate changes since this will determine who will absorb movements in the exchange rate. Pricing to market behaviour depends on the shape of the demand curve that the exporter is facing in the import market. As mentioned earlier, there can be shifts in this perceived elasticity of demand when there are changes in market share (Krugman, 1986). A plethora of papers have analysed the theoretical and empirical relationship between exchange rate pass-through and market share of exporters (e.g. Amiti et al., 2014; Auer and Schoenle, 2016; Berman et al., 2012; Devereux et al., 2017; Feenstra et al., 1996; Froot and Klemperer, 1988; Garetto, 2016; Krugman, 1986). A couple of studies show that, under certain conditions, pass-through monotonically decreases in market share in an export market (Amiti et al., 2014; Berman et al., 2012). Using a Bertrand differentiated products model, Feenstra et al. (1996) derived a theoretical relationship between pass-through and market share. The results from the analysis showed that pass-through tends to be highest when a group of source country exporters has a very high market share. Garetto’s (2016) analysis also provides evidence for a U-shaped relationship between exporter market share and pass-through based on the analysis of car-price data sets. The analysis of Auer and Schoenle (2016) also shows that the response of import prices to exchange rate changes is U-shaped in exporter market share using microdata from the Bureau of Labor Statistics. By developing a model of monopolistic competition and trade, Devereux et al. (2017) findings show a U-shaped relationship between pass-through and exporter market share, but a negative relationship between importer market share and pass-through. Market share is defined as the exporting firm’s share of total sales in an importing country market. In our study, we calculate market share by taking each country’s share of imports in total world imports of the specific product to India. A very high market share helps in facing little or no competition from other importers who may not have experienced such cost changes, and as a result, the ability to fully pass-through the exchange rate changes for a given market schedule faced in the destination market increases (Feenstra et al., 1996). Pass-through may increase rapidly along with an increase in market share from low to high, or it may decrease with low market share and then increases when market share becomes too large. Let us assume that the marginal cost and elasticity of demand faced by the exporter in the importing markets remain to be constant. In such a case, there would not be any pricing to market unless the exporter face competition (Krugman, 1986). When an exporting firm faces constant elasticity of market demand, as per the assumption made in the Cournot model the perceived elasticity faced by an exporting firm will

8.3 Model Specification

101

be equal to E/ms, where E is the elasticity of demand and ms is the exporter’s market share. Let ms be the market share of the ith firm, and ms* = 1 − ms be the market share of the kth firm. Then, the pricing rules of the two firms will be: 

   mεi it peit   pit = MC εit peit − ms     εkt pekt   pkt = MC εkt pekt − ms

(8.1)

(8.2)

When the market share is high, the perceived elasticity of demand will be low, and as a result, the firm will charge a higher price for any given marginal cost. In other words, mark-up over marginal cost increases with market share. Now assume that the elasticity of demand rises due to the devaluation of the importing country’s currency. This will make the imports costly for the country, and as a result, the domestic demand of the importing country as well as the exporting country’s market share falls. Therefore, the only solution to retain the market share in the importing country is by reducing the mark-up of over marginal cost. As a result, the exporting firm’s price will shift down proportionately to this change. However, note that there would not be much reduction in the actual price of the product as the increase in market share will lead to a reduction in perceived elasticity of demand. In other words, inelastic demand faced by the exporter will help in counterbalancing a part of the fall in price that occurred by reducing the mark-up. Its actual price will, however, not fall by as much, because its market share will rise, and thus, its perceived elasticity of demand will fall.

8.3 Model Specification The econometric equation to test the ERPT can be specified as follows: pit = θt + λi + βi (E it ) + u it

(8.3)

By separating exchange rates from other effects, we can rewrite: pit = θt + λi + β1 (E it ) + β2 (Z it ) + u it

(8.4)

By first differencing to correct for non-stationarity in the variables and rewriting equation in natural logarithm form, we obtain:  ln pit = θt + λi + β1 ( ln E it ) + β2 ( ln Z it ) + u it

(8.5)

102

8 Asymmetric Exchange Rate Pass-Through, Market Share …

where ln(pit ) is the log of the import price by country i at period t, measured in Indian rupees per kg. The time effects of t time period are captured through θ t . The destination-specific mark-up over marginal cost pricing behaviour of exporters will be captured through the term λi . The β 1 coefficient captures the exchange rate pass-through, and if the coefficient is significant, then it shows the exchange rate pass-through is incomplete or partial. That is, changes in exchange rates are not fully reflected in the import prices. Z it are various control variables. Control variables include producer price index (PPI) to reflect foreign production cost,1 the value of output to reflect the domestic demand for imports,2 trade openness and yield differences between the countries. To capture trade openness, the study uses the import orientation ratio (IOR), and it is calculated as the ratio of import value to the value of output for respective disaggregated products. The regression error term distributed is given by uit . If β 1 coefficient is coming out to be statistically significant, it indicates the non-competitive pricing behaviour (Carew, 2000). Similarly, a statistically significant λi coefficient but statistically insignificant β 1 coefficient shows that that the exporting country works with constant mark-up over marginal cost across different countries, but it can vary over time and across different destination markets. But it is to be kept in mind that a significant λi does not always show price discrimination as the quality differences can also lead to differences in market prices across destination markets (Falk and Falk, 2000; Knetter, 1989; Pall et al., 2013). In other words, the price differences across different exporting countries could be also due to the quality differences in the product. So, we can say that if the parameters for both β 1 and λi are significant it indicates both exchange rate-induced price discrimination and market-specific price discrimination. Market-specific discrimination may or may not be due the quality differences. At times, the differences in market elasticity of demand can also be a source for market-specific price discrimination. The elasticity of demand can also vary along with changes in exchange rates. As a consequence, the coefficients β 1 and λi are significantly different from zero (β 1 = 0 and λi = 0). The coefficients estimated for the β i coefficient can be either positive or negative, and as per literature, an incomplete or partial exchange rate pass-through will take place if the coefficient is negative, whereas the exchange rate pass-through will be more than complete if the coefficient is positive (Knetter, 1994). When both the estimated coefficients are significantly different from zero (β 1 = 0 and λi = 0), this indicates both the incomplete or partial exchange rate pass-through along with destination-specific changes in mark-ups which will get reflected as price discrimination (Pall et al., 2013). However, the above specifications indicate only the static nature of the passthrough. The price elasticity to exchange rate changes may be low in the short run as an instantaneous adjustment of prices may not be always feasible. Therefore, it 1

Marazzi et al., (2005) and Aziz et al., (2013). Studies generally uses Gross Domestic Product (GDP) of the economy as a proxy to capture the demand for imports (Barhoumi, 2006; Campa and Goldberg, 2005; Gaulier et al., 2008). Since the present study is at a disaggregated level, we use value of output for these crops instead of GDP as a proxy for domestic demand.

2

8.3 Model Specification

103

is important to account for the potential inertia by the exporters in changing the prices immediately in the short run by estimating a dynamic equation (see, e.g. Bussière, 2013; Gopinath et al., 2010, among others). This is typically accomplished by including lagged import prices as an explanatory variable. This allows for the possibility of delayed adjustment of domestic currency import prices. Also, due to the lagged adjustment of import price, the long-run ERPT can be computed. In addition to these two, we also incorporate a nonlinear term for the exchange rate and market share into the equation. Thus, we modify our static pass-through Eq. (8.5) by incorporating a lagged dependant variable, the long-run ERPT as well as the nonlinear terms as follows:    ln pit = θt + λi + β1 ( ln pit−1 ) + β2 ( ln E it ) + β3  ln E it2   + β4 ( ln Z it ) + β5 (γit ) + β6 (M Sit ) + β7 M Sit2 + u it

(8.6)

where the β 5 coefficient will measure the long-run exchange rate pass-through and β1 . As a next step, our main specification the long-run ERPT can be computed as 1−β 5 is augmented with an interaction of market share and exchange rate movements:    ln pit = θt + λi + β1 ( ln pit−1 ) + β2 ( ln E it ) + β3  ln E it2   + β4 ( ln E it × M Sit ) + β5 E it × M Sit2 + β6 ( ln Z it ) + β7 (γit ) + u it

(8.7)

As mentioned in the previous chapter, the exchange rate pass-through can be symmetric or asymmetric due to the differences in the price elasticity of demand. Therefore, price elasticity of exchange rate changes can also be asymmetric during the appreciation and depreciation scenario. The response of export prices to exchange rate changes may be asymmetric due to a plenty of reasons (Knetter, 1992a, 1992b, 1992c). As per the marketing bottlenecks model, the supply restriction or the inability of supply to expand can be a reason for asymmetric response when the currency is depreciated. When the currency depreciates, the export will become cheaper, and thereby, volume of export is supposed to increase. However, such an export expansion may not be always possible to supply side constraints. The tendency of exporters to increase the market share can be the reason for asymmetric response during appreciation. The former is known as the ‘bottlenecks model’ (capacity constraints or supply side constraints), while the latter is known as the ‘market share model’. As per the capacity constraints theory, the exporting firms operating at full capacity cannot accommodate the surge in demand resulting from an appreciation of the importer’s currency (Brun-Aguerre et al., 2017). The market share theory posits that foreign firms who want to retain or gain market share may absorb part of the depreciation of the importer’s currency (e.g. Krugman, 1987; Marston, 1990). Naturally, the degree of competition is expected to play a role as exporters operating in weakly competitive markets may systematically pass-through depreciation to preserve their mark-ups (Bussière, 2013).

104

8 Asymmetric Exchange Rate Pass-Through, Market Share …

The ERPT behaviour can be greater during the exporter’s currency depreciation when there are export volume constraints (Knetter, 1994). The volume constraints can be either induced by firm-specific factors or government policies. When the exporting country’s currency depreciates, these constraints eliminate the possibility of increasing sales volume. Instead, exporters would increase its foreign currency prices to clear the market. Another argument is the implications of appreciation and depreciation of importer’s currency with respect to the exporter’s currency in pricing, and mark-up may not be symmetric, and the incentive to pass a depreciation is higher than that of appreciation (Delatte and López-Villavicencio, 2012). When the currency of an exporter depreciates, absorbing importing country’s appreciation (i.e. keeping the final prices constant) can have a positive impact on the seller’s mark-up. But if an exporter decides to keep the final price of the imported good constant at the time of deprecation of importer’s currency against exporter’s currency may reduce the seller’s mark-up. Therefore, in order to estimate the impact of appreciation and depreciation separately, an interaction of the dummy variable with the exchange rate is constructed. This dummy variable will capture the asymmetric effect of exchange rate changes. This is common in the literature (Knetter, 1992a, 1992b, 1992c; Vergil, 2011). The asymmetries in the response of export prices to exchange rate changes can be specified as: E t = (β| |1 + β2 Dt )E t

(8.8)

β1 E t + β2 Dt × E t

(8.9)

We give a value 1 to capture the periods of currency appreciation and value zero for capturing the periods of currency depreciation. The same asymmetric effect of exchange rate pass-through to import prices can be seen in varying market shares as well. By incorporating an asymmetric dummy into the equation, the final equation to be estimated can be written as follows:  ln pit = θt + λi + β1 ( ln pit−1 ) + β2 ( ln E it )     + β3  ln E it2 + β4 ( ln E it × M Sit ) + β5  ln E it × M Sit2 + β6 ( ln Z it ) + β7 (γit ) + β4 ( ln E it × app)   + β4 ( ln M Sit × app)β4  ln M Sit2 × app + u it

(8.10)

The interaction term of the exchange rate and appreciation dummy is incorporated to capture asymmetric nature of the exchange rate pass-through. If the result is positive and statistically significant, then we can say that the impact of the appreciation of India’s currency exchange rates on prices is greater than depreciation. Similarly, a significant and negative coefficient implies that the effect of the appreciation of exchange rates on prices is greater than depreciation (Byrne and Nagayasu, 2010). Along similar lines, if the interaction variable of market share and the asymmetric

8.4 Pulses Imports, Data and Exchange Rate Variable

105

dummy is positive and significant, the effect of the appreciation of Indian currency on export prices is greater than depreciation, in market share.

8.4 Pulses Imports, Data and Exchange Rate Variable The data shows that India is the world’s largest consumer of pulses (Reddy et al., 2012); yet, the domestic production is not commensurate with demand, thereby making India a net importer of pulses. The last couple of years, except 2018 and 2019, witnessed a huge increase in imports of pulses to match the consumption requirements. India is the largest importer of pulses despite being the second-largest producer of pulses. The persistent deficit and the soaring pulses prices made it inevitable for the country to import pulses. The imports began to increase from 1998 to 2000. India’s total pulses imports sharply increased from around 352 thousand tonnes in 2000 to 6185 thousand tonnes in 2016 (see Fig. 8.1). As a result, the share of India’s imports of pulses in the total world pulses import increased from a mere 5% to around 36% in 2016. However, there has been a decline in the import in the latest two years due to the increase in domestic production of pulses. The government policy initiatives such as enhancing production through National Food Security Mission (NFSM) and higher minimum support prices (MSP) were considered to have played a positive role in encouraging the production (Bhattacharjya et al., 2017). The major importers of pulses to India are Australia, Canada, Myanmar, Tanzania, and the USA. Over time, the volume of pulses, imports increased and India also started to import from additional countries. For example, India started to import pulses from Ethiopia, Mozambique, Russia and China. India’s import demand has been affected negatively due to the depreciation of Indian currency with respect to USA, Australian, and Canadian dollars (Bhattacharjya et al., 2017). This had caused inflation of pulse prices in Indian domestic

Fig. 8.1 India’s import of pulses in thousand tonnes. Source FAOSTAT

106

8 Asymmetric Exchange Rate Pass-Through, Market Share …

markets. Depreciation of Indian currency ideally implies a higher import bill for Indian pulse importers, making imports less viable. One of the key issues concerning the import of pulses is the high degree of concentration from a few exporting nations. For each type of pulses, there has usually been a single largest importer with a significant market share, for example Canada for peas, Australia for chickpea, China for kidney beans and Myanmar for pigeon pea. The present analysis is limited to two major pulses imported—peas and kidney beans. China, Ethiopia and Myanmar are the major importers of kidney beans to India (see Fig. 8.2). The share of China in the import increased from around 35% in 2002 to around 84% in 2005. Though the country’s share was fluctuating, it remained to be as the main importer throughout the year. Australia, Canada, the USA, and Ukraine are the major importers of peas (see Fig. 8.3). Canada’s share increased from 37 to 82% during the same period.

Fig. 8.2 Import share of kidney beans by major importers. Source DGCI&S

Fig. 8.3 Import share of peas by major importers. Source DGCI&S

8.5 Results and Discussion

107

Throughout the period, the share of Canada remained to be much greater than the other major importers.

8.5 Results and Discussion The short-run and long-run exchange rate pass-through is estimated using both the linear and nonlinear regression equations. The econometric model employed to analyse the data was panel corrected standard errors (PCSE). PCSE is estimated accounting for panel-level heteroskedastic errors and errors contemporaneously correlated across panels. We correct for AR (1) disturbances (i.e. first-order autocorrelation) within panels, and since we have no theoretical basis for assuming the process is panel-specific, we treat the AR (1) process as common to all panels. The analysis is undertaken under three exchange rate models: nominal, real and commodity-specific (import trade-weighted) exchange rates. The R-squared results for all three exchange rate models showed that the exchange rate pass-through for both the crops selected for analysis is better predicted under the nominal exchange rate model. Therefore, the discussions in the following section are based on the results of the best-fitted model. The results showed that the exchange rate changes did not get fully reflected in the prices, and as a result, the exchange rate impact on prices was statistically significant. This was true in the case of both the pulses crops analysed in this chapter and most of the importing countries selected for the analysis. This also means the exchange rate pass-through was incomplete or partial, and as a result, the importers exercise a non-competitive pricing behaviour in general. The exchange rate variable (β 2 coefficient) exhibited a statistically significant and negative relationship for all the countries except for Ukraine for peas import. The negative coefficient implies that the exchange rate pass-through is partial or incomplete and the exporter has a tendency to absorb part of the increase in price that happens due to appreciation of their currency against the importing country’s currency. In addition to this the estimates for long-run elasticity of demand showed that in the long-run exchange rate pass-through was significant and negative for all the models indicating incomplete pass-through in the long run as well. The linear and nonlinear variables showed that the exchange rate impact on prices was linear as the variable to capture nonlinearity came out to be insignificant in all the models. As expected, greater trade openness and domestic demand had a positive and statistically significant impact on the import price. The variable to capture the cost of the importing country (producer price index (PPI))—came out to be positive and statistically significant in all models for peas and one model for kidney beans. The lagged prices had a negative and statistically significant impact on the current year’s price. The results showed that the degree of exchange rate pass-through is increasing in market share. Our estimates of the interaction effect (E it ∗ M S) show that the degree of exchange rate pass-through is increasing in market share in the case of both

108

8 Asymmetric Exchange Rate Pass-Through, Market Share …

the products. In particular, our estimates for kidney beans imply that the rate of passthrough for a country with negligible market share is 10%, whereas for a monopolist, it is around 11% higher than a country with negligible market share. This implies that the firm with greater market share is experiencing higher pass-through. Our estimates for peas also imply that the rate of pass-through for a country with negligible market share is much lower than that of a monopolist. The pass-through for a monopolist is around 26% greater than that of a country with negligible market share. Our findings are in line with Feenstra et al. (1996). According to their study, pass-through is high when the market share is high. When we add an interaction of market share squared with the exchange rate to the above specification, we find that the pass-through is showing an inverted U shape in market share. This shows that a very high market share is leading to a low ERPT, or in other words, ERPT is declining in high market share. The coefficients of the linear and quadratic terms remain significant at 0.22 and −0.22 for kidney beans and 0.26 and −0.30 for peas. This implies that, for kidney beans, the degree of exchange rate pass-through reaches its maximum around a market share of 48% (= 0.217/2 * 0.224) and thereafter decreases in market share. Similarly, for peas, the degree of exchange rate pass-through reaches its maximum around a market share of 43% (= 0.259/2 * 0.301) and thereafter decreases in market share. The results are in line with few studies that showed that, under certain conditions, pass-through monotonically decreases in market share (Amiti et al., 2014; Berman et al., 2012). The existing studies generally provide evidence for a U-shaped ERPT in market share, especially for exports (Auer and Schoenle, 2016; Garetto, 2016). However, Devereux et al. (2017) findings show a U-shaped relationship between pass-through and exporter market share, but a negative relationship between importer market share and pass-through. Though our findings have some overlap with the existing studies, it offers unique empirical insights for agricultural trade. The asymmetric dummy to capture the effect of the appreciation of Indian currency exchange rates on import prices showed that the effect of the appreciation of India’s currency exchange rates on prices is greater than depreciation in the case of few countries, whereas for Myanmar in the case of kidney beans, the USA, and Canada in the case of peas, the results showed that the effect of the depreciation of Indian currency had a greater impact on import prices. The appreciation of importing country’s currency will make the import cheaper, whereas the depreciation will make the import costlier. The asymmetric effect was more pronounced in the case of kidney beans. The coefficient of an interaction term (asymmetric dummy and market share) to capture the asymmetric nature of exchange rate changes and market share on import prices showed that the impact was symmetric in the case of kidney beans. The results were statistically insignificant for kidney beans, whereas for peas, the appreciation had a greater impact when the market share is relatively less, whereas the depreciation had a greater impact when the market share is high. The results point out the asymmetric nature of the exchange rate pass-through in varying market shares (Tables 8.1 and 8.2).

8.5 Results and Discussion

109

Table 8.1 Exchange rate pass-through and market share—the case of kidney beans Variables

Model (1)

Model (2)

Model (3)

Model (4)

β 1 (log Pit−1 )

0.037

−0.007

−0.658***

−0.639***

0.068 β 2 (log E i ) China

−0.108

Ethiopia

−1.23

Myanmar

0.008

0.074

(0.162)

(0.173)

−0.012

−0.092***

−0.105***

0.019

(0.021)

(0.023)

(0.406)** 0.310*** 0.018 β 3 (log E i * MSi )

0.035

0.163***

0.217***

(0.016)**

(0.041)

(0.047)

−0.142***

−0.224***

β 4 (log E i * MSi 2 )

(0.042)

(0.056) 0.036

Asymmetry * MSi

(0.102) Asymmetry * MSi β5



β2 1−β1

−0.061

2

(0.044)



−0.016*** (0.004)

−0.015*** (0.004) −0.000

βE i 2

(0.000) β 6 (log PPIi )

0.096**

0.017

0.036

(0.047)

(0.054)

(0.054)

β 7 (log TOIi )

0.001

0.097***

0.101***

0.008

(0.025)

(0.024)

β 8 (log DDi)

0.047

0.104

0.134*

0.089

(0.075)

(0.075)

−0.100

−0.095

−0.061

0.082

(0.155)

(0.153)

β 9 (log Yielddiffik ) β 10 Ui China

0.029

0.043

−0.012

0.006

0.027

0.019**

(0.016)

(0.019)

−0.017

Ethiopia

(0.019) Myanmar

0.148***

0.109***

0.017 (continued)

110

8 Asymmetric Exchange Rate Pass-Through, Market Share …

Table 8.1 (continued) Variables

Model (1)

Model (2)

(0.024)

(0.023)

Model (3)

Model (4)

0.133

0.070

−0.022

−0.022

0.032***

0.023***

(0.023)

(0.057)

0.159

0.095

0.021

0.031

0.029***

0.023***

(0.020)

(0.025)

−0.082

−0.061

−0.029

−0.022

0.023***

0.025**

(0.025)

(0.028)

173

164

116

116

0.68

0.68

0.70

0.71

(0.019)

Asymmetric dummy China Ethiopia Myanmar Observations Wooldridge test R-squared

Source Author’s own analysis Note The superscripts ∗, ∗∗, and ∗∗∗ represent the 10%, 5%, and 1% levels of significance

8.6 Conclusion There are now ample pieces of evidences in the literature that the exchange rate passthrough varies under different market shares. The ERPT varies due to the changes in perceived elasticity of demand under various market shares. Our study provides new empirical evidence for an inverted U shape for ERPT in market share. Our results for interaction between ERPT and market share show that ERPT is increasing in market share. However, the interaction between ERPT and quadratic term of market share shows that a further increase in market share is leading to a low ERPT, and hence, ERPT is decreasing. The existing studies generally provide evidence for a U-shaped ERPT in market share, especially for exports (Auer and Schoenle, 2016; Garetto, 2016). However, Devereux et al. (2017) findings show a U-shaped relationship between pass-through and exporter market share, but a negative relationship between importer market share and pass-through. However, our results provide unique empirical evidence that has an overlap with the findings in the existing studies. As far as the impact of exchange rate changes on prices is concerned, the results show that the exchange rate pass-through was incomplete or partial both in the short run as well as in the long run. As a result, the importers exercise a non-competitive pricing behaviour in general. The negative β coefficient indicates that the exchange rate pass-through is partial or incomplete. In other words, when the Indian currency is depreciated in relation to the exporting country’s currency, the exporting country has a tendency to absorb part of the increase in export price that happened due to the currency appreciation so that the importing country will not reduce the consumption due to higher prices. As expected, greater trade openness and domestic demand had a positive and statistically significant impact on the import price. The variable to capture the cost

8.6 Conclusion

111

Table 8.2 Exchange rate pass-through and market share—the case of peas Variables

Model (1)

Model (2)

Model (3)

Model (4)

β 1 (log Pit−1 )

−0.119***

−0.096***

−0.855***

−0.816***

(0.039) β 2 (log E i ) Australia

−1.522***

Canada

−1.860***

USA

−1.536***

(0.029)

(0.060)

(0.061)

−0.769***

−0.066***

−0.021**

(0.056)

(0.015)

(0.011)

0.020

0.387***

0.259**

(0.117) (0.149) (0.172) Ukraine

0.583*** (0.081)

β 3 (log E i * MSi )

(0.027) β 4 (log E i * MSi 2 )

(0.117)

(0.117)

−0.394***

−0.301**

(0.105)

(0.113)

Asymmetry * MSi

1.606**

Asymmetry * MSi 2

−1.418**

(0.626)

β5



β2 1−β1

βE i

(0.574)



−0.827***

−0.771***

(0.059)

(0.060)

2

1.075 (1.648)

β 6 (log PPIi )

0.587*** (0.038)

(0.041)

(0.043)

β 7 (log TOIi )

0.244***

0.289***

0.227***

(0.049)

(0.068)

(0.071)

β 8 (log DDi) β 9 (log Yielddiffik )

0.638***

0.617***

0.197***

0.201***

0.211***

(0.032)

(0.034)

(0.034)

−0.005

0.036

−0.012

(0.022)

(0.023)

0.022

0.007

0.017

(0.029)

(0.028)

β 10 Ui Australia

−0.070

−0.036

(0.054)

(0.048)

Canada

−0.067

−0.006 (continued)

112

8 Asymmetric Exchange Rate Pass-Through, Market Share …

Table 8.2 (continued) Variables

Model (1)

Model (2)

(0.057) USA

Model (3)

Model (4)

(0.024) 0.059

−0.014

(0.040)

(0.025)

0.002 (0.023)

0.016

0.047

0.001

(0.068)

(0.043)

(0.023)

Australia

0.094

0.168**

(0.063)

(0.069)

(0.056)

(0.074)

Canada

0.036

0.033

−0.001

−0.443**

(0.065)

(0.042)

(0.039)

(0.190)

Ukraine Asymmetric dummy

0.049

−0.118

−0.081

−0.064

0.066***

−0.167**

(0.068)

(0.047)

(0.047)

(0.061)

Ukraine

−0.100

−0.034

0.152

0.024

(0.079)

(0.049)

(0.047)

(0.056)

Observations

233

182

164

164

0.76

0.91

0.92

0.92

USA

Wooldridge test R-squared

Source Author’s own analysis Note The superscripts ∗, ∗∗, and ∗∗∗ represent the 10%, 5%, and 1% levels of significance

of the importing country, PPI, came out to be positive and statistically significant in all models for peas and in one model for kidney beans.

References Amiti, M., Itskhoki, O., & Konings, J. (2014). Importers, exporters, and exchange rate disconnect. American Economic Review, 104(7), 1942–78. Athukorala, P. (1991). Exchange rate pass-through: The case of Korean exports of manufactures. Economics Letters, 35(1), 79–84. Atkeson, A., and Burstein, A. (2008). Pricing-to-market, trade costs, and international relative prices. American Economic Review, 98(5), 1998–2031. Auer, R. A., and Schoenle, R. S. (2016). Market structure and exchange rate pass-through. Journal of International Economics, 98, 60–77. Aziz, M. N., Rahman, M. S., Majumder, A., and Sen, S. (2013). Exchange rate pass-through to external and internal prices: A developing country perspective. Journal of Applied Business and Economics, 15(3), 128–143 Barhoumi, K. (2006). Differences in long-run exchange rate pass-through into import prices in developing countries: An empirical investigation. Economic Modelling, 23(6), 926–951. Berman, N., Martin, P., and Mayer, T. (2012). How do different exporters react to exchange rate changes?. The Quarterly Journal of Economics, 127(1), 437–492.

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Knetter, M. M. (1992c). Is price adjustment asymmetric?: Evaluating the market share and marketing bottlenecks hypothesis (NBER Working Paper No. 4170). National Bureau of Economic Research. Retrieved July 2015, from http://www.nber.org/papers/w4170 Knetter, M. M. (1992). International comparisons of pricing-to-market behavior (No. w4098). National Bureau of Economic Research. Knetter, M. M. (1994). Is export price adjustment asymmetric? evaluating the market share and marketing bottlenecks hypotheses. Journal of International Money and Finance, 13(1), 55–70. Krugman, P. (1986). Pricing to market when the exchange rate changes (No. w1926). National Bureau of Economic Research. Krugman, P. (1987). Pricing to market when the exchange rate changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-financial linkages among open economies (pp. 49–70). MIT Press. Lavoie, N. (2005). Price discrimination in the context of vertical differentiation: An application to Canadian wheat exports. American Journal of Agricultural Economics, 87(4), 835–854. Mallick, S., & Marques, H. (2012). Pricing to market with trade liberalization: The role of market heterogeneity and product differentiation in India’s exports. Journal of International Money and Finance, 31(2), 310–336. Mallick, S., & Marques, H. (2008). Pass-through of the exchange rate and tariffs into import prices of India: currency depreciation versus import liberalization. Review of International Economics, 16(4), 765–782. Marazzi, M., Sheets, N., Vigfusson, R., Faust, J., Gagnon, J., Marquez, J., and Rogers, J. (2005). Exchange rate pass-through to US import prices: some new evidence. International Finance Discussion Papers, 833. Marston, R. C. (1990). Pricing to market in Japanese manufacturing. Journal of International Economics, 29(3-4), 217–236. Menon, J. (1996). The degree and determinants of exchange rate pass-through: market structure, non-tariff barriers, and multinational corporations. The Economic Journal, 106(435), 434–444 Miljkovic, D., Brester, G. W., & Marsh, J. M. (2003). Exchange rate pass-through, price discrimination, and US meat export prices. Applied Economics, 35(6), 641–650. Miljkovic, D., & Zhuang, R. (2011). The exchange rate pass-through into import prices: The case of Japanese meat imports. Applied Economics, 43(26), 3745–3754. Pall, Z., Perekhozhuk, O., Teuber, R., & Glauben, T. (2013). Are Russian wheat exporters able to price discriminate? Empirical evidence from the last decade. Journal of Agricultural Economics, 64(1), 177–196. Pick, D. H., & Park, T. A. (1991). The competitive structure of US agricultural exports. American Journal of Agricultural Economics, 73(1), 133–141. Reddy, A. A., Bantilan, M. C. S., and Mohan, G. (2012). Enabling Pulses Revolution in India. Policy Brief. Open Access Repository of International Crops Research Institute for Semi-Arid Tropics. Uhl, K., Perekhozhuk, O., & Glauben, T. (2016). Price discrimination in Russian wheat exports: Evidence from firm-level data. Journal of Agricultural Economics, 67(3), 722–740. Varma, P., & Issar, A. (2016). Pricing to market behaviour of India’s high value agri-food exporters: An empirical analysis of major destination markets. Agricultural Economics, 47(1), 129–137. Vergil, H. (2011). Does trade integration affect the asymmetric behavior of export prices? The case of manufacturing exports of Turkey. African Journal of Business Management, 5(23), 9808–9813. Yumkella, K. K., Unnevehr, L. J., & Garcia, P. (1994). Non-competitive pricing and exchange rate pass-through in selected U.S. and Thai rice markets. Journal of Agricultural and Applied Economics, 26(2), 406–416.

Chapter 9

Minimum Support and Price Policies

9.1 An Overview of Government Interventions in Agriculture A defining feature of agricultural economic policy making in India until the nineties has been its inward orientation with high government intervention. In an agricultural economy like India which has highly inequitable socio-economic structure, an intervention by the government is very important. The high rate of growth of population on the one side and the sluggishness of the industrial sector on the other side gave rise to a very high demand for land. The unequal distribution of land resulted in a sharecropping system that accords monopoly power to the landlords reflected in their high share of production and monopoly power to evict the tenants from their land (Bhattacharyya et al., 1996). These inequalities and imperfections in the agricultural market prompted the government to intervene in the market with multiplicity of tools. Initially, the government intervention began in the agricultural market with the aim of the reduction of inequalities by removing the production bottlenecks and thereby promoting agricultural growth. Later, the economic policy framework for the agricultural sector indeed went a long way with the objective of achieving selfsufficiency in food production on the one hand and agrarian surplus for investment in industrial sector on the other hand. This approach is best illustrated in the context of direct interventions and indirect interventions in the agricultural market. Direct interventions include price policy by employing tariffs as well as more direct measurers of control on trade, such as bans, quotas, minimum export prices and intervention in the domestic market through state subsidies and government procurement. Import substitution strategies and the overvaluation of exchange rates and the delayed adjustment of nominal exchange rates come under the indirect interventions. In the wake of Bengal Famine of 1943, A Food grains Policy Committee was appointed under the chairmanship of George Theodore which called upon the attention of the government to the importance of rationing. Further, India struggled with a number of price controls on essential agricultural commodities post-independence. © Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_9

115

116

9 Minimum Support and Price Policies

In 1964, another committee, Food grains Prices Committee, was appointed by the government under the chairmanship of L. K. Jha, which led to the formation of Food Corporation in India (FCI) and the Agricultural Prices Commission (APC) in 1965. The objective of APC was to determine a balanced and well-integrated price policy that would be fair to both producers and consumers. Agricultural price policy plays an important role in the economic development of an agrarian economy. The Food Corporation of India (FCI) was also established in the same year with the objective of stabilising food prices. Until the establishment of both APC and FCI, the primary concern of the government’s food price policy was the stabilisation of the food prices for the consumer with little concern or recognition of the role of price incentives for encouraging production of the producers. To protect the farmers from the fluctuations in the agricultural prices and to incentivise them to continue farming, the Government of India started to announce procurement or support prices for major agricultural commodities. Further, the government also proposed a price policy in order to stabilise the general prices and promote an increase in production. The government introduced minimum support prices for rice and wheat. A positive agricultural price policy results in the stabilisation of prices, increase in the overall production and most importantly, an increase in the income of the farmers. A proper price policy also leads to an effective and judicial use of the resource endowments. Further, it leads to the formation of better price policies in the areas of marketing, extension services, growth in agricultural inputs, etc. Until the APC and FCI were formed, there were no public procurement of food grains for public distribution; rather, the distribution requirements were met mainly through imports from other countries (Sukhatme & Abler, 1997). The procurement took place only an ad hoc basis, and the amount of procurement was also negligible. It was after the formation of APMC, the procurement operations were formalised, and the prices were set more systematically. The procurement prices were fixed based on the cost of production and past trends in prices. The FCI became the nodal agency for procurement, storage, public distribution and foreign trade in food grains, especially wheat and rice. Over the years, several policies related to agricultural prices have been devised and all of them serve nearly the same purposes, of increasing the production, stabilising the prices and maintaining adequate stocks of food grains. The APC, later renamed as the Commission for Agricultural Costs and Prices (CACP), provided detailed suggestions to the government regarding interventions in the agricultural markets and price support policies. Procurement prices recommended by the CACP are usually acceptable to the Government of India. The commission recommends the procurement prices for wheat and rice ‘in the perspective of the overall needs of the economy and with due regard to the interests of the producers and consumers’. Apparently, when recommending prices, the CACP considers production costs, domestic prices, world prices, effects of price changes on living costs and industrial production costs and the desire to maintain some predetermined intercrop price parity (Sukhatme & Abler, 1997). However, there is no formula per se. The intervention broadly aimed stabilising the prices which are prone to short-term price fluctuations (Zant, 1998).

9.1 An Overview of Government Interventions in Agriculture

117

The decline in prices as well as the increase in prices had policy implications. For example, the unrestrained increase in the price of agricultural commodities would affect the standard of living of the masses and the other sectors of the economy because agriculture provides wage goods to the industrial sector. Moreover, the change in agricultural prices would have an adverse impact on the distribution of income between the agricultural and non-agricultural sectors of the economy. Ostensibly, the price policy in India resulted in artificially kept prices which were either below or above the world prices. As far as rice is concerned, throughout 1960s and 1970s the open-market prices and MSP were below the world prices and when the world prices fell sharply in 1980s the wedge between domestic rice prices and world prices went down (Sukhatme & Abler, 1997). On the contrary to this, prior to mid-1970s, the domestic wheat prices were higher than world prices, and since 1970s, the domestic prices went below the world prices. Since the domestic production was picking up rapidly with the onset of green revolution, the government was unwilling to pass the higher prices to domestic market. In the mid-1980s, the world price of wheat was about 40% above the openmarket producer price and about 55% above the procurement price. Government controls over trade and capital flows during the past decades accounted for these large differences between domestic and world prices. Foreign trade in agricultural commodities in most cases was guided by ‘residuary surplus factor’; i.e. the agricultural commodities were allowed to be exported when there existed surplus after meeting the domestic requirements. Similarly, the import was mainly done to meet the excess demand and thereby to prevent the upward movement of the domestic prices (Nayyar & Sen, 1994). As a result, the domestic prices were controlled primarily by the domestic demand and supply conditions and were isolated from the world prices. Therefore, an important feature of the trade regime was a restrictive trade policy with strict regulation of both imports and exports of agricultural commodities. Thus, the trade regime in most cases aimed to provide protection to the domestic producers and consumers by insulating the domestic economy from external shocks. However, the trade policy regime, especially the import substitution strategy, came in for severe criticism by the World Bank, IMF and academic proponents of structural adjustment, during the 1970s and 1980s (Bhalla, 1995). The critics argued that both the overall planning framework and sector-specific governmental policies have been discriminatory against agriculture. Discrimination had been inherent in the import substitution strategy of industrialisation adopted by the country for several reasons. It was argued that the high protection accorded to industry raised the relative prices of modern farm inputs for the agricultural sector and thereby implicitly taxed agriculture. The protectionist trade regime, which resulted in the non-alignment of internal prices with border prices, resulted in inefficiency of resource use, distorted the cropping pattern and also prevented the producers from deriving benefits of comparative advantage in agriculture (Bhalla, 1995; Gulati, 1998). India does not provide generally any product-specific support other than market price support which is implemented through a device of ‘minimum support prices’ (hereafter MSP). The commodities included in the minimum support price policies

118

9 Minimum Support and Price Policies

were paddy rice, wheat, coarse cereals, various pulses, various oilseeds, sugarcane, cotton and tobacco (Orden et al., 2007). The MSP is given directly to wheat farmers in the primary market when they sell the crop, whereas for rice farmers, half of the total procured quantity is from primary market in the form of paddy and the remaining is procured in the form of milled rice from selected states at a statutory, fixed price fixed for millers (Jha et al., 2007). As part of this scheme, rice millers have to provide a share of the rice they process to government agencies at a fixed, below market price. The levy shares vary from state to state (from a low of 10% to a high of 75%). The farmers in states with high levies receive farm price which is below the MSP. Grain procured by government is stored by the FCI. The FCI either makes the grain available to state governments for subsidised distribution or, when conditions permit, allocates surplus grain for export (Jha et. al., 2007).

9.2 Minimum Support Prices Scheme Surplus production in a year usually results in the sharp decline in price of an agricultural commodity. To cushion the farmers against the unexpected and inevitable losses, the Government of India came up with the concept of Minimum Support Price Scheme in 1966–67. Minimum support price or MSP continues to be as an integral part of the country’s price policy and is announced by the Government in order to protect the farmers from the shocks or volatility in the food market. The MSPs are decided based on the recommendations of Commission for Agricultural Costs and Prices (CACP). CACP puts forward recommendations separately for both the seasons—kharif and rabi. The calculation of MSP is largely based on the cost of production which takes into account the variable cost, land rental value, the imputed value of family labour and a 10% return to family labour (Jha et. al., 2007). The attempt to eliminate quantitative restrictions on cereal exports in the second half of the 1990s benefited producers as the world price for cereals at that time was quite high. However, a fall in world prices in the late nineties paved way for an additional pressure for increasing MSP in order to compensate producers’ losses due to low world price. The basic staples in India, therefore, continue to be subject to MSP to the farmers, even though the government interventions in the market to procure crops have weakened. The fundamental objective of MSP is to ensure certain market and stable prices for farmers so that they do not have to go for distress selling at the farm gate level at a throw away price. The procurement at MSP is also essential for the distribution of rice through PDS. Government through MSPs, incentivises the farmers in order to maintain an adequate amount of food grain production in the economy. At present, 24 crops are covered under this scheme, including seven cereals (paddy, wheat, barley, jowar, bajra, maize and ragi); five pulses (gram, arhar/tur, moong, urad and lentil); eight oilseeds (groundnut, rapeseed/mustard, toria, soybean, sunflower seed, sesame, safflower seed and niger seed); copra, raw cotton, raw jute and Virginia flu cured (VFC) tobacco. Procurement of the crops is done by the Food Corporation

9.3 The Policy Bias and Crowding Out of Pulses

119

of India (FCI) for release through the PDS. Other interventions such as Market Intervention Schemes (MIS), various Price Support Schemes (PSS), etc., are used for the procurement of crops that are not covered under the MSP scheme. The MSP announced for various pulses is given in Table 8.1. The MSP figures shows that there has been an increase in the MSP for almost all the crops. The MSP for pigeon pea (arhar) increased from Rs. 2300 per quintal in 2009–10 to Rs 5675 per quintal in 2018–19. Similarly, the MSP for gram increased from Rs. 1760 in 2009–10 to 4620 in 2018–19 (see Table 9.1).

9.3 The Policy Bias and Crowding Out of Pulses The emphasis of India’s food security policies to improve the productivity of rice and wheat, the introduction of green revolution in the 1960s through chemical-intensive farming for enhancing production, the procurement of rice and wheat at the MSP for public distribution resulted in the crowding out of traditional protein-rich crops such as pulses. The major lacuna in the food security policies has been the greater emphasis on food grain self-sufficiency and calorie availability rather than the nutrition availability or quality of diet, and this undermined the importance of nutrition security. In recent years, there is a gradual transition in food policies with a renewed understanding that food and nutritional security is not just the calorie availability, but having access to varied sources of food to improve the diet quality through improvements in micronutrients. The return of some of the nutritious crops such as millets to farm fields is a testimony to this fact. Termed as nutricereals, millets are not only rich in micronutrients but are also very climate-friendly as they are drought resistant, growing in areas with low rainfall and infertile soil. In a world that is increasingly being challenged with climate changes, rise in population, water scarcity and environmental degradation, these crops can provide an alternative to meet the food and nutrition challenges to a large extent. However, not just in India, even in other developing countries, the focus of food security policies had been more on food grain self-sufficiency and calorie availability rather than the nutrition availability or quality of diet, and this undermined the importance of nutrition security (Pingali et al., 2017). Some studies show that more than the quantity of food, the quality of diet appears to have a strong impact on malnutrition (Meenakshi, 2016). Until the establishment of both Agricultural Prices Commission (APC) and Food Corporation of India (FCI), the primary concern of the government’s food price policy was the stabilisation of the food prices distributed through the Public Distribution System (PDS) for the consumer to ensure food security. The procurement of crops at the government-administered minimum support price (MSP) was intended to protect farmers from price uncertainty and thereby providing an assured market for their produce. It helps farmers to mitigate risks in technology adoption and encourages higher investment and production (Hazrana et al., 2020). Though the scope of MSP has widened over the years, the focus is still largely on the two cereal crops, rice and

1870

Lentils (Masur)

Source CACP

1760

Gram

2250

2100

2900

2520

Urad

Rabi

3000

3170

2300

2760

2010–11

Moong

2009–10

Tur (arhar)

Kharif

Crops

2800

2800

3300

3500

3200

2011–12

2900

3000

4300

4400

3850

2012–13

Table 9.1 Minimum support prices of various pulses in rs per quintal

2950

3100

4300

4500

4300

2013–14

3075

3175

4350

4600

4350

2014–15

3325

3425

4625

4850

4625

2015–16

3950

4000

5000

5225

5050

2016–17

4250

4400

5400

5575

5450

2017–18

4475

4620

5600

6975

5675

2018–19

120 9 Minimum Support and Price Policies

9.3 The Policy Bias and Crowding Out of Pulses

121

wheat, and these were the two main crops procured for distribution through PDS at a subsidised price for consumers. The emphasis of India’s food security policies to improve the cereal crop productivity and the procurement of rice and wheat at the MSP for public distribution alleged to result in the crowding out of traditional micronutrient-rich crops such as pulses (Pingali et al., 2017). As far as the yield is concerned, there has been considerable improvements in the case of wheat and rice over the years whereas the yield for pulses remained almost stagnant. There has been a gradual shift in the food security policies to address the diet quality for nutrition security, and as a result, there are policy initiatives to promote both production and consumption of pulses and millets. The NFSM-2007 also incorporates the millets mission of the Government of India. NFSM-Coarse Cereals that include millets have two important components. One of them is the nutricereals scheme that is implemented in 202 districts of the 14 states. Millets were also being promoted under Nutritional Security through Intensive Millets Promotion (INSIMP) during 2011–12 to 2013–14. Although there is gradual shift in government policies to encourage the farmers to produce pulses, the production is facing several constraints including poor crop yield, the lack of remunerative prices, availability of seed and inefficient price support and procurement. Even though pulses are distributed through PDS, the NFSA is biased towards rice and wheat distribution and relies on outdated calorie norms without much focus on micronutrient consumption and diet diversity. Importance of Public Farm Investment There are plenty of studies that show crop diversification improves nutritional outcomes of households (Mango et al., 2018) (Islam et al., 2018) (Paul et al., 2016). However, public farm investment plays a crucial role in removing the production bottlenecks that the small and marginal farmers face. However, the public farm investment declined drastically since the introduction of macroeconomic stabilisation and structural adjustment policies at the beginning of nineties. The public investment during the early 1980s witnessed a decline from 2.43% to a mere 0.59% of GDP in mid-1990s. Though there was a marginal improvement in mid-2000 to 1.28%, it was still grossly inadequate. This decline in public investment hurts the agricultural sector and its productivity. The public investment not only plays a complementary relationship with private investment but is also capable of removing several constraints the small farmers are facing. The amount of public investment in agricultural sector also showed differential impact of COVID and subsequent lockdown in some of the states. For example, there were considerable differences in the manner in which the COVID lockdown affected the two states, Haryana and Odisha, owing to the structural differences in marketing infrastructure facilities in these states (Ceballos et al., 2020). In Odisha, the farmers experience distress selling due to the lack of proper marketing and procurement system for crops. In Odisha, the farm mechanisation is also relatively limited. On the contrary to this, the other state, Haryana, was least affected due to relatively better market infrastructure and as a result effective procurement of crops. Therefore, the

122

9 Minimum Support and Price Policies

small farming was less affected in Haryana as compared to Odisha. However, as consumers, farmers in Haryana faced more disruptions than those in Odisha. This was primarily due to the reduced availability of food in the markets. Consumers in Odisha was at a better position in terms of food consumption due to the crop diversification and greater availability of local supply of essential food commodities (Ceballos et al., 2020) At the same time, greater farm mechanisation and public procurement facilities enabled the government to undertake effective procurement operations in Haryana even during the COVID lockdown. In Odisha, the distress sale of the crops and income loss were higher due to the lack of such institutions combined with less farm mechanisation. At the same time, the higher prevalence of homestead gardens and shorter agricultural value chains benefited the farmers who are also the net buyers of food in Odisha.

9.4 The Relationship Between MSP and Wholesale Prices There are studies that argue that the MSP for pulses benefit pulses traders more than the farmers as MSP is acting as a focal point or Schelling point for traders to go for an implicit collusion (Sekar et al., 2017). Figures 9.1 and 9.2 show the clustering of farm gate prices of chickpea around the MSP. So, there is a possibility for a collusion by the traders by fixing a price close to MSP. The prices received by the pulses producing farmers, considering the supply and demand gap in the country, would have been higher than the actual prices if there was no MSP.

.0004 0

.0002

Density

.0006

.0008

Kernel density estimate

-5000

0

5000

10000

Wedge kernel = epanechnikov, bandwidth = 133.4582

Fig. 9.1 Distribution of farm gate prices of chick pea. Source Author’s own analysis

9.4 The Relationship Between MSP and Wholesale Prices

123

.0003 .0002 0

.0001

Density

.0004

.0005

Kernel density estimate

-10000

-5000

0

5000

Wedge2 kernel = epanechnikov, bandwidth = 217.1931

Fig. 9.2 Distribution of farm gate prices of pigeon pea. Source Author’s own analysis

Awareness of MSP and Procurement Agency Among farmers The lack of information or awareness of any scheme can act as a biggest hurdle in the effective implementation and the success of any programme. Failure to do so can become counterproductive. MSP is one of such programmes which have flaws at the implementation level. As far as MSP is concerned, farmers lack information pertaining to the scheme, the MSP fixed for various crops, the time and the process of procurement as well as the government facilities associated with MSP. A survey conducted by the National Sample Survey Organization (NSS Report 2012–13) collected information on the awareness of the agricultural households regarding the minimum support prices. The households considered were the ones which have reported the sale of their harvested crops. The data showed that the awareness of MSP and procurement agency was pretty low among the households. The data of percentage of farmers (number per 1000 households) who are aware of MSP for various crops is given in Table 8.2. It can be observed that the awareness of MSP was the highest for wheat, paddy and sugar cane the lowest for pulses. Among various pulses, the awareness was the lowest for pigeon pea (arhar), lentils (moong) and chickpea (gram) (see Table 9.2). The poor awareness of MSP was reflected in the poor awareness of procurement agency as well. But what was more striking was the percentage of people who sold their crop to procurement agencies were lower than the percentage of people who had awareness about procurement agencies. Similarly, percentage of sale at MSP was also lower. A study conducted by NITI Aayog in 2016 also highlighted state-wise differences in awareness levels and lacunas in MSP announcements.

124

9 Minimum Support and Price Policies

Table 9.2 Awareness about minimum support price (MSP) Number per 1000 of agricultural households reported sale of crops having awareness about MSP

Crop

Aware of MSP (%)

Number per 1000 of households reporting sale of crops

Of the households sold to procurement agency

Aware of procurement agency (%)

Sold to procurement agency (%)

% of sale at MSP to total sale

Average sale rate received at MSP (| per Kg.)

25.1

13.5

27

13.08

July 2012–December 2012 Paddy

32.2

Arhar(tur)

4.6

3.8

1.3

1

35.47

Urad

5.7

3.7

1.6

1

37.61

Moong

9.8

7.2

1.8

1

53.33

39.8

36.1

31

34

2.79

31.5

18.7

10

14

13.15

Sugarcane

January 2013–June 2013 Paddy Wheat

39.2

34.5

16.2

35

13.99

Gram

12.6

9.7

3.9

5

29.96

Arhar (tur)

14.2

13.1

4.7

1

47

Moong

9.1

3.7

1.9

2

58

Masur

18.1

15.5

2

0

36

Sugarcane

45.4

40.7

36.6

33

3.25

Source NSSO Some Aspects of Farming

Price Deficit Financing Scheme Price Deficit Financing Scheme or Bhavantar Bhugtan Yojana (BBY) is a pilot scheme launched by the government of Madhya Pradesh, in which it transfers the difference between the MSP and modal rate directly in the bank accounts of the farmers. Modal rate is the average price of a particular commodity in APMCs of MP and of two other neighbouring states. The scheme was launched in the aftermath of the massive farmers’ protest in Mandsaur district of the state, in which six farmers were killed in police firing. The objective is to avoid the harm done to the farmers due to the volatility in prices of agricultural commodities, mainly oilseeds and pulses. Under this scheme, the government does not procure food grains from the farmers as it would under the MSP scheme, hence minimising government intervention. The government pays for the difference between the MSP of a commodity and the modal rate if the commodity is auctioned at a price higher or equal to the latter. The scheme was initially launched for eight notified kharif crops—soybean, moong, urad, pigeon pea, groundnut, maize and oilseeds—and was further extended to four rabi crops—chickpea, mustard, lentils and onions.

9.5 Procurement Policy and Operations

125

Wholesale Prices and MSP for Major Pulses The wholesale prices of pigeon pea, lentils, black gram and chickpea were below were higher than the MSP during 2009 to 2018 (see Appendix Figures from 9.6 to 9.10). The wholesale prices of pigeon pea and chickpea were more or less converged across different zones (see appendix Figs. 9.11 and 9.12).

9.5 Procurement Policy and Operations In order to achieve the objective of national food security, the Government of India has established the Public Distribution System (PDS) through which food grains, kerosene, sugar, etc., are made easily accessible to the class which is at the bottom of the income pyramid, at subsidised rates. The central government procures, stores, allocates and transports the food grains, while the state government distributes the commodities among consumers through Fair Price Shops (FPS) ration shops. The scheme was launched in June 1947, and the agency responsible for the procurement of food grains is the Food Corporation of India (FCI). The pulses procurement undertaken by National Agricultural Cooperative Marketing Federation of India (NAFED) is given in Table 9.3. Due to the special emphasis on enhancing pulses production and to give incentives for the pulses producing farmers, the quantity procured also showed an increase. For example, in the year 2014–15, the total quantity of gram (chickpea) procured was 279,611, 125 million tonnes as compared Table 9.3 Procurement of pulses under PDS by NAFED Commodity

Year

Quantity procured (million tonnes)

Rupees (lakhs)

Major state of procurement

Gram

2013–14

34,306

10,736.57

Maharashtra, AP, Karnataka

2014–15

279,611.125

94,123.66

Maharashtra, Gujarat, MP, UP, Rajasthan, Karnataka

Urad

Arhar

Moong Source NAFED

2012–13

1.57

2013–14

77,050.806

2014–15

7453.262

2015–16

6.70

2012–13

16,004.835

0.63 34,543.75

3611.45 6.56 6328.15

Rajasthan Maharashtra, AP, UP, MP, Gujarat, WB, Rajasthan, Karnataka, Jharkhand Jharkhand, WB, AP, Maharashtra, UP Maharashtra Maharashtra, AP, MP

2013–14

42,693

18,755.12

Maharashtra, AP

2014–15

1079.648

1069.87

Maharashtra, AP

2016–17

8267.58

3968.43

Maharashtra and Karnataka

126

9 Minimum Support and Price Policies

to 34,306 million tonnes in the year 2013–14. Maharashtra, Andhra Pradesh and Karnataka were the major states for procurement, whereas in the year 2014–15, Gujarat, Madhya Pradesh, Uttar Pradesh and Rajasthan were also added to the list. The procurement of black gram (urd) was the highest in the year 2013–14, whereas in the case of pigeon pea (arhar), the procurement was the highest in 2012–13. Maharashtra, Andhra Pradesh and Madhya Pradesh were the major states for procuring pigeon pea (arhar). The procurement operations are undertaken by agencies such as National Agricultural Cooperative Marketing Federation of India (NAFED), National Cooperative Consumers Federation of India (NCCF), Small Farmers’ Agri-Business Consortium (SFAC) and Central Warehousing Corporation (CWC). The procurement of kharif pulses by various agencies in the year 2016–17 is given in Table 9.4. The procurement by the NAFED was the highest, and SFAC was the lowest. The marketable surplus ratio of pigeon pea (arhar) was slowly increasing in Karnataka, whereas the same was either stagnant of declining in states like Madhya Pradesh, Maharashtra, Odisha and Uttar Pradesh (see Fig. 9.3). The same was the case with chickpea (gram) except Bihar. In Bihar, though there was a decline in marketable surplus ration in the year 2010–11, the marketable surplus ratio showed an increase in the remaining years (see Fig. 9.4). E-NAM The e-NAM or National Agriculture Market Electronic Trading (e-NAM) platform was launched by the Ministry of Agriculture, Government of India on 14 April 2016. The e-NAM provides a single window platform, both at state-level and at nationallevel, for information regarding the prices, quality, variety, etc., to all the farmers, traders and other stakeholders. The e-NAM is a brainchild of R Ramaseshan, a former Indian Administrative Services officer from Karnataka. Ramaseshan after becoming the Chief Executive Officer of National Commodity and Derivatives Exchange (NCDEX) proposed the idea of electronic market which actually began at the Agricultural Produce Marketing Committee (APMC) in Kalaburagi, Karnataka, back in December 2011. The model initiated in Kalaburagi was called the Rashtriya electronic Market Scheme (ReMS). SFAC is the agency responsible for the implementation of e-NAM across the country. This shift from an actual physical market to that of an online one serves a Table 9.4 Procurement of kharif pulses during 2016–17 (Quantity in MTs) Pulses

FCI

NAFED

SFAC

Total

Moong

64,737

128,886

26,225

219,848

Urad

18,235

59,602

10,746

88,582

Tur

175,299

919,667

71,079

1,166,045

Total

258,271

1,108,155

108,049

1,474,475

Source FCI

9.5 Procurement Policy and Operations

127

120 100 MSR

80 60 40 20 0 Karnataka

Madhya Pradesh

Maharashtra

Odisha

Uttar Pradesh

All-India

2010-11

87.15

78.49

69.48

65.86

65.4

73.82

2011-12

89.16

89.34

72.72

61.37

95.1

81.45

2012-13

96.34

94.07

83.3

74.58

67.24

84.33

2013-14

97.81

93.43

89.13

73.85

53.54

86.99

2014-15

97.4

93.36

85.16

54.37

84.82

88.21

MSR

Fig. 9.3 Marketable surplus ratio—tur (arhar). Source Directorate of Economics and Statistics, DAC&FW 100 90 80 70 60 50 40 30 20 10 0

Bihar

Madhya Pradesh

Rajasthan

Uttar Pradesh

All-India

2010-11

77.27

92.92

86.46

56.83

86.68

2011-12

56.93

88.75

88.62

66.38

85.25

2012-13

68.06

89.04

80.23

61.91

83.67

2013-14

77.97

90.3

91.23

80.59

89.58

2014-15

80.42

93.31

94.14

67.42

91.1

Fig. 9.4 Marketable surplus ratio—gram. Source Directorate of Economics and Statistics, DAC&FW

number of benefits, such as real-time price discovery, transparent online trading, reduced transaction cost for buyers, better accessibility to the markets, efficient supply chain, etc. The provision of electronic market addresses a number of problems, some of which are—fragmentation of the state into multiple market areas, multiple levy of mandi fees, licencing barriers which lead to conditions of monopoly, poor quality of infrastructure and lower use of technology. The facility has enabled farmers to opt to trade either by themselves through their mobile phones or through commission agents. Currently, the e-NAM is linked with 585 APMC in 16 states and two union territories.

128

9 Minimum Support and Price Policies

The states will be eligible for support under the e-NAM scheme only if they have a single valid licence across the state, a single point levy of market fee and a provision for electronic auction as a mode for price discovery. The respective states are required to administer agriculture marketing as per their regulations. Each state is divided into several market areas, and each separate area is administered by a separate APMC. The government through e-NAM has found a middle ground for the stakeholders, i.e. the farmers, traders, buyers, exporters, etc. Due to e-NAM, the farmers are now not dependent on the middlemen for the selling of their produce, while traders have received a greater access to the national market. Electronic market would also benefit the buyers as the intermediation costs would be reduced. This would further eliminate information asymmetry and regulate traders and commission agents in a better way. The initial target for the coverage of e-NAM was 200 APMCs, and later on, the target was expanded to 585 APMCs by March 2018. The focus areas of the budget 2017–18 were farmers, rural employment and infrastructure. According to the budget, assistance of up to 75 lakh rupees would be provided to every e-NAM for the establishment of cleaning, grading and packaging facilities. Further, in budget 2018–19, the Ministry of Finance announced the creation of a 2,000 crore rupees agriculture market infrastructure fund and proposed the strengthening of electronic national agriculture market. The ministry also announced the development and upgradation of existing 22,000 rural haats into Gramin Agricultural Markets (GrAMs), which would benefit more than 86 percent small and marginal farmers. The GrAMs would be electronically linked to e-NAM and exempted from the regulations of APMCs. Currently, 90 commodities are traded on e-NAM (Fig. 9.5). NAM is not a parallel marketing structure but a mechanism to create a unified network of physical mandis, which could be accessed online. NAM builds on the

Fig. 9.5 e-NAM working model. Source http://sfacindia.com/images/NAM-Working-Model

Appendix

129

strength of local markets and allows them to offer their produce at a national level. Further, e-NAM increases the choice of the farmers for selling their produce. The scheme is pro-farmer in a number of ways, such as the farmers through e-NAM would get better prices as they would now have an option to sell it wherever he wishes, the farmers would get the whole payment on time, etc.

9.6 Conclusion The present chapter discussed the evolution of agricultural and food security policies in India along with the effectiveness of MSP and procurement. The data and studies at the national level broadly indicated that MSP is an important policy instrument in encouraging farmers and to stabilise market prices. However, the percentage of farmers who were aware of MSP was less, especially for pulses. This was also reflected in the lack of knowledge about procurement agencies. Interestingly, the percentage of households who sold their products to procurement agencies were even lower than the percentage of households who had information about procurement agencies. In Chap. 5, our analysis of sample households from three states selected for analysis also showed poor awareness of MSP. The farmers who avail MSP even with a positive information about MSP was also lower. The next chapter will therefore make an analysis of factors influencing the information access to MSP along with the factors influencing the utilisation of MSP.

Appendix See Figs. 9.6, 9.7, 9.8, 9.9, 9.10, 9.11 and 9.12.

130

9 Minimum Support and Price Policies

Rs./Quintal

Wholesale Price MSP

2018 Q1

2017 Q3

2017 Q1

2016 Q3

2016 Q1

2015 Q3

2015 Q1

2014 Q3

2014 Q1

2013 Q3

2013 Q1

2012 Q3

2012 Q1

2011 Q3

2011 Q1

2010 Q3

2010 Q1

2009 Q3

2009 Q1

16000 14000 12000 10000 8000 6000 4000 2000 0

Fig. 9.6 Wholesale price vis-à-vis MSP—tur (arhar). Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics and Statistics, DAC&FW

Rs./Quintal

16000 14000 12000 10000 8000 6000 4000 2000 0

Wholesale Price MSP

2018 Q1

2017 Q3

2017 Q1

2016 Q3

2016 Q1

2015 Q3

2015 Q1

2014 Q3

2014 Q1

2013 Q3

2013 Q1

2012 Q3

2012 Q1

2011 Q3

2011 Q1

2010 Q3

2010 Q1

2009 Q3

2009 Q1

Fig. 9.7 Wholesale price vis-à-vis MSP—urad. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics & Statistics, DAC&FW 12000

Rs./Quintal

10000 8000 6000

Wholesale Price

4000

MSP

2000 0 2018 Q1

2017 Q3

2017 Q1

2016 Q3

2016 Q1

2015 Q3

2015 Q1

2014 Q3

2014 Q1

2013 Q3

2013 Q1

2012 Q3

2012 Q1

2011 Q3

2011 Q1

2010 Q3

2010 Q1

2009 Q3

2009 Q1

Fig. 9.8 Wholesale price vis-à-vis MSP—moong. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics & Statistics, DAC&FW

Appendix

131

12000

Rs/Quintal

10000 8000 6000

Wholesale Price

4000

MSP

2000 2018 Q1

2017 Q3

2017 Q1

2016 Q3

2015 Q3

2016 Q1

2014 Q3

2015 Q1

2014 Q1

2013 Q3

2012 Q3

2013 Q1

2012 Q1

2011 Q3

2010 Q3

2011 Q1

2009 Q3

2010 Q1

2009 Q1

0

Fig. 9.9 Wholesale price vis-à-vis MSP—gram. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics and Statistics, DAC&FW 12000

Rs/Quintal

10000 8000 6000

Wholesale Price

4000

MSP

2000 2018 Q1

2017 Q3

2017 Q1

2016 Q3

2016 Q1

2015 Q3

2015 Q1

2014 Q3

2014 Q1

2013 Q3

2013 Q1

2012 Q3

2012 Q1

2011 Q3

2011 Q1

2010 Q3

2010 Q1

2009 Q3

2009 Q1

0

Fig. 9.10 Wholesale price vis-à-vis MSP—masoor. Source Department of Consumer Affairs (Price Monitoring Cell) and Directorate of Economics &Statistics, DAC&FW 18000 16000

Rs./Quintal

14000 12000

SOUTH ZONE

10000

WEST ZONE

8000

EAST ZONE

6000 4000

WEST ZONE

2000

NORTH ZONE

0 2018 Q1

2017 Q3

2017 Q1

2016 Q3

2015 Q3

2016 Q1

2015 Q1

2014 Q3

2014 Q1

2013 Q3

2013 Q1

2012 Q3

2012 Q1

2011 Q1

2011 Q3

2010 Q3

2010 Q1

2009 Q1

2009 Q3

Fig. 9.11 Wholesale prices all zones—tur (arhar). Source Department of Consumer Affairs (Price Monitoring Cell)

132

9 Minimum Support and Price Policies 14000

Rs./Quintal

12000 10000

NORTH ZONE

8000

WEST ZONE

6000

SOUTH ZONE

4000

NORTH-EAST ZONE

2000

EAST ZONE

0 2018 Q1 2017 Q3 2017 Q1 2016 Q3 2016 Q1 2015 Q3 2015 Q1 2014 Q3 2014 Q1 2013 Q3 2013 Q1 2012 Q3 2012 Q1 2011 Q3 2011 Q1 2010 Q3 2010 Q1 2009 Q3 2009 Q1

Fig. 9.12 Wholesale prices all zones—gram. Source Department of Consumer Affairs (Price Monitoring Cell)

References Bhattacharyya, A., Bhattacharyya, A., & Kumbhakar, S. C. (1996). Government interventions, market imperfections, and technical inefficiency in a mixed economy: A case study of Indian agriculture. Journal of Comparative Economics, 22(3), 219–241. Bhalla, G. S. (1995). Presidential Address: Globalisation and Agricultural Policy in India. Indian Journal of Agricultural Economics, 50(902-2018- 3344), 7–26. Ceballos, F., Kannan, S., & Kramer, B. (2020). Impacts of a national lockdown on smallholder farmers’ income and food security: Empirical evidence from two states in India. World Development, 136, 105069. Gulati, A. (1998). Indian agriculture in an open economy: will it prosper?: In India’s Economic Reforms and Development: Essays for Manmohan Singh, (pp. 122-46). Isher Judge Ahluwalia, I.M.D. Little (eds), Oxford University Press: Delhi, Newyork. Hazrana, J., Kishore, A., & Roy, D. (2020). Supply response of staple food crops in the presence of policy distortions: Some evidence from India. Islam, A. H. M. S., von Braun, J., Thorne-Lyman, A. L., et al. (2018). Farm diversification and food and nutrition security in Bangladesh: Empirical evidence from nationally representative household panel data. Food Security, 10, 701–720. https://doi.org/10.1007/s12571-018-0806-3 Jha, S., Srinivasan, P. V., & Landes, M. R. (2007). Indian wheat and rice sector policies and the implications of reform. USDA-ERS Economic Research Report, (41) Mango, N., Makate, C., Lawrence, M. & Mathinda, S. (2018). The role of crop diversification in improving household food security in central Malawi. Agriculture and Food Security, 7(7). https://doi.org/10.1186/s40066-018-0160-x. Meenakshi, J. (2016). Trends and patterns in the triple burden of malnutrition in India. Agricultural Economics., 47, 115–134. https://doi.org/10.1111/agec.12304 Nayyar, D., & Sen, A. (1994). International trade and the agricultural sector in India. Economic and Political Weekly, 1187–1203. Orden, D., Cheng, F., Nguyen, H., Grote, U., Thomas, M., Mullen, K., & Sun, D. (2007). Agricultural producer support estimates for developing countries: Measurement issues and evidence from India, Indonesia, China, and Vietnam (Vol. 152). Intl Food Policy Res Inst Paul, S. Laha, A. & Kuri, P. K. (2016). Crop diversification and its implications to food security: A study in India with special reference to West Bengal Agriculture. Asia Pacific Journal of Rural Development, XXVI(1), 61–84.

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Pingali, P., Mittra, B., & Andaleeb, R. A. (2017). The bumpy road from food to nutrition security— Slow evolution of India’s food policy. Global Food Security, 15, 77–84. https://doi.org/10.1016/ j.gfs.2017.05.002 Sekar, I., Roy, D., & Joshi, P. K. (2017). Temporal and spatial dynamics of pulse production in India. IFPRI book chapters, 63–108. Sukhatme, V. A., & Abler, D. G. (1997). Economists and food price policy distortions: The case of India. Economic Development and Cultural Change, 46(1), 79–96. Zant, W. (1998). Stockholding, price stabilization and futures trading: some empirical investigations of the Indian natural rubber market. International Books.

Chapter 10

Information and Utilisation of MSP: Major Determinants

10.1 Introduction The prices of agricultural commodities are inherently more volatile than nonagricultural commodity prices. The major reasons are the inelastic nature of supply to prices. Lack of market integration and information asymmetry also play a role. A very good harvest in one year will result in sharp fall in the prices of that commodity, and farmers will be discouraged from continuing production due to heavy loss. As a result of this, supply will go down in the next year and price will increase. Somewhat similar to this, we experience in the case of pulses. A severe deficit in supply led to soaring of prices in the year 2015–16. However, an increased price and other government interventions again encouraged the production of surplus. Even with an increase in production, the import dependency to meet the excess demand is growing. Additionally, an increase in production is still lagging behind the demand. To counter this, the government announces MSP for various crops including pulses in each year. MSP acts as an instrument in enabling government to guarantee minimum prices to farmers prior to the cropping season so that farmers are encouraged to allocate acreage under pulses cultivation. Thus, the provision of MSP provides an assured market for farmers. However, our analysis of the profile of sample households in Chap. 5 and the discussion in Chap. 9 showed that percentage of farmers who have information about pulses MSP and those who are availing MSP were very less. The farmers who sold crop to procurement agencies even when they had information about MSP and procurement agencies were also less. Therefore, the present chapter will make an analysis of factors influencing the information access to MSP and utilisation of MSP.

© Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_10

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10 Information and Utilisation of MSP: Major Determinants

10.2 Conceptual Framework As far as small holder farmers in developing countries are concerned, they face information constraints in availing the MSP. The decision to avail MSP is constrained by the information about MSP. The information, i, that is required for a farmer to make the decision can be given as:  Infoi =

1 if Infoi > 0 0 if Infoi ≤ 0

(10.1)

Now the farmer is aware of MSP, and the information is received. After passing the first hurdle of information access, farmers move to the second stage. In the second stage, farmers are potential users of MSP as they have the required information and now it’s up to them to decide whether they want to avail MSP or not amidst various other constraints. This is expected to help a farmer in evaluating the benefits of the MSP. Whether the MSP has been availed or not by the households can be given as:  A = Ai Ad =

1, if MSP is availed 0, if MSP is not availed

(10.2)

The decision to avail MSP depends on their household and farm-level characteristics along with other factors.

10.3 Model Specification The farmer’s demand for MSP can be written as below: yi∗ = αx i + u i

(10.3)

where x i is a vector of variables that influences the demand for MSP, α is a parameter vector, u is an error term with mean 0 and variance σ u . Similarly, the variables that can influence farmer’s access to information can be specified as follows: I n f oi∗ = β z i + i (Access to information)

(10.3)

The notation z i in the above equation captures various factors that can affect the access to information or availability of information. And β is the parameters to be estimated;  is the error term with mean 0 and variance 1. The demand for MSP is obtained through the interaction of models 2 and 3. The model is estimated using the joint probability of adoption method developed

10.4 Description of Dependent and Explanatory Variables

137

by Roodman (2009, 2011) using the conditional (recursive) mixed process estimator (CMP). CMP helps us in estimating multiple equations which are recursive. They are also called mixed process models as different equations can be estimated employing different dependent variables. They are called recursive models as CMP can fit only models that have clearly defined stages. In other words, A and B can be determinants of C, and C a determinant of D—but D cannot be a determinant of A, B or C (Roodman, 2011). Equations are estimated using probit models.

10.4 Description of Dependent and Explanatory Variables The dependent variables in our analysis are access to information and utilisation of MSP. In order to understand the utilisation of MSP, the farmers were asked whether they are availing MSP or not. Though there have not been any study analysing the factors influencing the farmers access to MSP information and decision to avail MSP, the studies in the context of farmer’s decision making in general are useful to understand the important determinants of farmer’s decision. Therefore, the present study makes use of such studies to draw the list of explanatory variables. The literature on household decision-making has incorporated several household and farm-level attributes in the decision-making process (Feder et al., 1985; Manda et al., 2015; Ogada et al., 2014; Teklewold et al., 2013; Uaiene, 2011). Several studies include age of the farmer as a major determinant and as per one set of studies age has a positive relationship with decision making (Meshram et. al., 2012; Kassie et. al., 2013) while as per another set of studies age has a negative relationship with the decision-making (Manda et. al., 2015). Those who find a positive relationship say that the age has a direct correlation with experience so that will have a positive influence on decision-making (Kassie et al., 2013). However, those who postulate a negative relationship argue that older farmers are less amenable to change and, therefore, unwilling to change (Adesina & Zinnah, 1993). The gender of the head of the household can also have an impact on the decisionmaking due to certain fixed social bias (Langyintuo & Mungoma, 2008). It is a commonly held view that women farmers face greater constraints in terms of access to resources and time and it can adversely affect their ability take a decision (Pender & Gebremedhin, 2008). As far as the importance of total farm size is concerned, it is quite possible that large farmers will have greater access to information and therefore higher possibility to avail MSP. Another important variable that plays an important role in accessing and availing MPS is the education of the household. In addition to this, access to off-farm employment and income can also have a positive impact on decision-making. Membership in farmer organisation is another important factor that can influence farmers’ accessibility to information. Greater market access is important determinant of transaction cost faced by the farmers and therefore can have an impact on farmer’s access to information and decision to avail MSP (Kassie et al., 2015). In our analysis, we treat the distance to main market as a proxy for market access. The farmers were

138

10 Information and Utilisation of MSP: Major Determinants

also asked whether they sell their product in APMC or not assuming it to have an implication to access information and avail MSP. Access to extension services also plays a significant role. The present study has included the variable ‘crop failure’ that the farmers experienced in the last five years as a proxy to capture the impact of production risk-related factors in receiving information and in availing MSP. The description of variables is given in Table 10.1. The descriptive statistics of the model is given in Table 10.2 and the results are presented in Table 10.3.

10.5 Results and Discussion The results from the conditional (recursive) mixed process equation model are given in Table 10.1. The results from the analysis showed that Madhya Pradesh had greater access to information about MSP as compared to other states. This was also seen in the analysis of socio-economic profile of the sample households in Chap. 5. However, the utilisation of MSP was more in Maharashtra. Education and training received by farmers from government departments of NGOs had a positive and significant impact in accessing the information regarding MSP. Whereas market access and distance to market reduced the access to information. The variable for pigeon pea farmer also came out to be significant and negative. This indicates that pigeon pea farmers had less probability to receive information. Information access was less in Karnataka (refer Chap. 5) and Karnataka had greater share of pigeon pea farmers in our sample. As far as the utilisation of MSP is concerned those who are cultivating other crops or more diversified farmers availed MSP less. This could be the reason for statistically significant and negative relationship between utilisation of MSP and cultivation of other crops. This can also be the reason why large farmers in Madhya Pradesh despite having greater access to information about MSP is not availing MSP. This can also be the reason why the dummy variable for only Maharashtra came out to be significant in the utilisation model. Age of the farmer had a significant and negative relationship with utilisation of MSP. Similarly, education of the farmer increased the chances of availing MSP as the relationship between education variable and utilisation of MSP was positive and statistically significant. Similarly, those farmers who sell their crop in APMC also had a greater probability to avail MSP. Those farmers who receiving training had greater probability in receiving information as well as in availing MSP. The risk factor increased the probability to avail MSP. This is an interesting result. The variable number of crop failure in last five years had a positive and statistically significant relationship with availing MSP. The market access came out to be as a significant factor in availing MSP. Those who have less market access had lower probability to avail MSP. The variable distance to market came out to be negative and statistically significant in our analysis. Interestingly, the probability of chickpea and pigeon pea farmers in availing MSP was also negative and statistically significant.

10.5 Results and Discussion

139

Table 10.1 Variable description Variable

Definition

Age of the HOHn

Age of the Head of the Household

Gender of HoH

Gender of the head of the household, dummy variable = 1 if the household has a male head of the household

Education

Number of years of education of the Head of the Household

Household size

Number of the family members in the household including children

Farm Size

Total size of owned and rented land holdings cultivated by household in hectares

Membership in the input supply organisations

Membership of any of the family member in the input supply organisations, dummy variable = 1 if any of the family member has membership in farmer organisations, = 0 otherwise

ICT (Component 1)

First principal component of three ICT dummy Radio (= 1 if any of the household member has a radio, = 0 otherwise), TV (= 1 if any of the household member has a TV, = 0 otherwise) and Mobile (= 1 if any of the household member has a mobile phone, = 0 otherwise)

Distance from main market

Distance to the nearest main market in kilometres

Access to off farm activities

= 1 if the household had access to off farm activities, = 0 otherwise

Awareness of KCC

= 1 if the household had awareness of KCC (Kisan Call Centre), = 0 otherwise

Contact with government extension agents

= 1 if the household had contact with government extension agents, = 0 otherwise

Training received for farming

= 1 if the household had received training for farming from government departments or NGO’s, = 0 otherwise

Awareness of government schemes promoting pulses production

= 1 if the household had awareness of government schemes promoting pulses production, = 0 otherwise

Chick pea cultivated

= 1 if the household cultivates chick pea, = 0 otherwise

Pigeon pea cultivated

= 1 if the household cultivates pigeon pea, = 0 otherwise

Other crops cultivated

Total size of land under cultivation for crops besides pulses (continued)

140

10 Information and Utilisation of MSP: Major Determinants

Table 10.1 (continued) Variable

Definition

Place of selling produce

= 1 if the household sold its produce in APMC, = 0 otherwise

Crop failure

= 1 if the household has experienced any type of crop failure in the last 5 years, = 0 otherwise

Maharashtra State dummy

= 1 for those households residing in Maharashtra, = 0 otherwise

Madhya Pradesh State dummy

= 1 for those households residing in Madhya Pradesh, = 0 otherwise

Source Survey data Table 10.2 Descriptive statistics for variables used in the model

Variables

Adopters

Age of the HOH

48.7 (13.13)

Gender of HoH

0.98 (0.15)

Education

6.83 (5.52)

Household size

5.64 (3.18)

Farm size

3.33 (0.98)

Membership in the input supply organisations

0.22 (0.42)

ICT (Component 1)

0 (1.48)

Distance from main market

14.21 (7.11)

Access to off farm activities

0.14 (0.35)

Awareness of KCC

0.29 (0.46)

Contact with government extension agents

0.43 (0.5)

Training received for farming

0.19 (0.39)

Awareness of government schemes promoting pulses production

0.14 (0.35)

Chick pea cultivated

0.55 (0.5)

Pigeon pea cultivated

0.84 (0.36)

Other crops cultivated

5.44 (7.52)

Place of selling produce

0.42 (0.49)

Crop failure

1.53 (0.97)

Maharashtra state dummy

0.35 (0.48)

Madhya Pradesh state dummy

0.31 (0.46)

Number of observations

572

Source Survey data Note Standard deviation is given in parentheses

10.5 Results and Discussion Table 10.3 Results for MSP

141 A: Information access to MSP Karnataka

0

Maharashtra

1.004

Madhya Pradesh

3.119a

(.) (1.085) (0.636) Scores for component 1

−0.106

Other crops acres

−0.00992

Age of HOH

−0.00831

(0.319) (0.0227) (0.00923) Gender of HOH (Male 1, Female 0)

0.694

Number of family members

−0.0157

Years of education of HOH

0.106a

(1.165) (0.0278) (0.0286) Access to off farm activity (Yes 1, No 0)

−0.00172

Annual family income from farm (in Lakhs)

−0.00216

Contact with government agents(yes 1, No 0)

−0.165

(0.311) (0.0166) (0.218) Member of input supply cooperation (Yes 1, No 0.207 0) (0.288) Walking distance to markets (Kms)

−0.0449a

KCC awareness (Yes 1, No 0)

0.180

Chickpea farmer

−0.365

(0.0116) (0.372) (0.301) Pigeon pea farmer

−0.756b

Farm size

0.236

Training received (Yes 1 No 0)

1.292a

(0.367) (0.148) (continued)

142

10 Information and Utilisation of MSP: Major Determinants

Table 10.3 (continued)

(0.229) Constant

−1.702 (1.429)

B. Utilisation of MSP Karnataka (.) Maharashtra

0.267a

Madhya Pradesh

0.0542

(0.0582) (0.0709) Scores for component 1

−0.0174

Other crops acres

−0.00557b

Age of HOH

−0.00274c

(0.0152) (0.00218) (0.00106) Gender of HOH

0.0439

Number of family members

0.00127

Years of education of HOH

0.0143a

(0.0335) (0.00453) (0.00362) Access to off farm activity

0.0261

Place where farmer sells produce = 0

0

Place where farmer sells produce = 1

0.0876c

(0.0460) (.) (0.0367) Annual Family Income from farm (in Lakhs)

0.00313 (0.00460)

Contact with government agents

−0.0883c

Member of input supply cooperation

0.0597

Walking distance to Markets (Kms)

−0.0129a

(0.0408) (0.0349) (0.00299) Awareness of government schemes promoting pulses

0.259a (0.0506) (continued)

10.6 Conclusion Table 10.3 (continued)

143 Number of crop failures in last 5 years

0.0315

Chickpea farmer

−0.0814b

(0.0232) (0.0331) Pigeon pea farmer

−0.132c

Training received

0.213a

Farm size

0.0617a

(0.0481) (0.0505) (0.0182) Constant

0.156 (0.108)

lnsig_2 Constant

−1.082a (0.0341)

atanhrho_12 Constant

0.498a

Observations

572

(0.111) Source Auhtor’s own analysis Notes a , b , c denote statistical significance at 10%, 1% and 5% respectively. Standard errors are given in the parenthesis

10.6 Conclusion The present chapter analysed the factors influencing the access to information regarding MSP and the decision to avail MSP. The regression equation was estimated using the cmp command which uses the mixed process estimator. The results showed that Maharashtra farmers were more enthusiastic in availing MSP despite of the fact that the information regarding MSP was highest among the farmers from Madhya Pradesh. However, farmers who had more diversified crop cultivation were not very enthusiastic in availing MSP. The majority of the farmers in Madhya Pradesh in our sample were large farmers, and most probably, they are more diversified. Market access came out to be as an important factor in information and in availing MSP. The risk faced by farmers also increased the chances to avail MSP, and this points out how important MSP is in in mitigating the negative effects of risk.

144

10 Information and Utilisation of MSP: Major Determinants

References Adesina, A. A., & Zinnah, M. M. (1993). Technology characteristics, farmers’ perceptions and adoption decisions: A Tobit model application in Sierra Leone. Agricultural Economics, 9(4), 297–311. https://doi.org/10.1016/0169-5150(93)90019-9 Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: A survey. Economic Development and Cultural Change, 255–298. Kassie, M., Jaleta, M., Shiferaw, B., Mmbando, F., & Mekuria, M. (2013). Adoption of interrelated sustainable agricultural practices in smallholder systems: Evidence from rural Tanzania. Technological Forecasting and Social Change, 80(3), 525–540. https://doi.org/10.1016/j.agsy.2009. 01.001 Kassie, M., Teklewold, H., Jaleta, M., Marenya, P., & Erenstein, O. (2015). Understanding the adoption of a portfolio of sustainable intensification practices in eastern and southern Africa. LandUsePolicy, 42, 400–411. http://dx.doi.org/10.1016/j.landusepol.2014.08.016. Langyintuo, A. S., & Mungoma, C. (2008). The effect of household wealth on the adoption of improved maize varieties in Zambia. Food Policy, 33(6), 550–559. https://doi.org/10.1016/j.foo dpol.2008.04.002 Manda, J., Alene, A. D., Gardebroek, C., Kassie, M., & Tembo, G. (2015). Adoption and impacts of sustainable agricultural practices on maize yields and incomes: Evidence from Rural Zambia. Journal of Agricultural Economics, 67(1), 130–153. https://doi.org/10.1111/1477-9552.12127 Meshram, V., Chobitkar, N., Paigwar, V., & Dhuware, S. R. (2012). Factors affecting on SRI system of paddy cultivation in Balaghat Ddistrict of Madhya Pradesh. Indian Research Journal of Extension Education, I (Special Issue), 202–204. Ogada, M. J., Mwabu, G., & Muchai, D. (2014). Farm technology adoption in Kenya: A simultaneous estimation of inorganic fertilizer and improved maize variety adoption decisions. Agricultural and Food Economics, 2(1), 1–18. https://doi.org/10.1186/s40100-014-0012-3 Pender, J., & Gebremedhin, B. (2008). Determinants of agricultural and land management practices and impacts on crop production and household income in the highlands of Tigray, Ethiopia. Journal of African Economies, 17(3), 395–450. https://doi.org/10.1093/jae/ejm028 Roodman, D. (2009). Estimating fully observed recursive mixed-process models with cmp. Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1392466. Roodman, D. (2011). Fitting fully observed recursive mixed-process models with cmp. The Stata Journal, 11(2), 159–206. Teklewold, H., Kassie, M., & Shiferaw, B. (2013). Adoption of multiple sustainable agricultural practices in rural Ethiopia. Journal of Agricultural Economics, 64(3), 597–623. https://doi.org/ 10.1111/1477-9552.12011 Uaiene, R. (2011). Determinants of agricultural technology adoption in Mozambique. In African Crop Science Conference Proceedings, 10, 375–380.

Chapter 11

Supply Response of Major Pulses

11.1 Introduction The present chapter analyses the supply response of chickpea and pigeon pea, using dynamic panel data estimation technique. The analysis is undertaken based on a district-level panel data collected from four major pulses producing states of India from 2005 to 2015.1 Despite the fact that India is the largest producer and consumer of pulses in the world, the production remained stagnant for several years and as a consequence to this the share of pulses in total food grain production declined from 16.6% in 1950– 51 to 7% in 2014–15 (John et al., 2021). The introduction of green revolution in the mid-1960s and the government policy bias towards cereal crops such as wheat and rice to meet the objectives of food security inadvertently resulted in the crowding out of nutritious rich crops such as pulses (Akinbode et al., 2011; Eliazer Nelson et al., 2019; Pingali et al., 2017). The food security objectives have a twin mechanism of remunerative and stable price to producers on the one side, and affordable price to consumers on the other side. The interests of producers were protected through Minimum Support Price (Policy), whereas the consumers through distribution of food grains through Public Distribution System (PDS) at an issue price. Though the objective of the price policy was to induce incentives to produce those crops where the domestic supply is less than the demand, the implementation of food security policies favored mainly staple food grains such as rice and wheat (Tripathi, 2017). The government not only announced the MSP for rice and wheat but actively procured these grains and distributed via PDS. Whereas the procurement was meager for pulses. As a result, there is a widening gap between the demand and supply of pulses, and this eventually led to a sharp decline in the per capita net availability of 1

This chapter is drawn from an early version of the work presented at Agricultural and Applied Economic Conference held at Atlanta, Georgia from July 21–23, 2019. I would like to thank Jannet John and Anar Bhatt for some very preliminary contribution to the earlier version of the paper.

© Centre for Management in Agriculture (CMA), Indian Institute of Management Ahmedabad (IIMA) 2022 P. Varma, Pulses for Food and Nutritional Security of India, India Studies in Business and Economics, https://doi.org/10.1007/978-981-19-3185-7_11

145

146

11 Supply Response of Major Pulses

Fig. 11.1 Production of paddy and pigeon pea in thousand tonnes. Source FAOSTAT

200000 150000 100000 50000 0 2018 2015 2012 2009 2006 2003 2000 1997 1994 1991 1988 1985 1982 1979 1976 1973 1970 1967 1964 1961

Production in 1000 tonnes

pulses over the last several years. The excess domestic demand for pulses and soaring domestic prices resulted in an increased import dependency by the country to meet the domestic consumption requirements and making India the largest importer of pulses in the world (Negi and Roy, 2015). Lack of substitutability in consumption due to strong region specific preferences for each type of pulses is also adding to the problem (Joshi and Rao, 2017). The genetic potential for high yields is also limited and as a result the gap between actual and potential yield remains to be very high, which is also negatively affected by pests and diseases. In the recent years, the government has taken some initiatives to enhance the domestic production and productivity of pulses through an array of interventions like an increase in MSP and National Food Security Mission (NFSM). The present article examines various factors influencing the acreage allocation and production of two major pulses produced and consumed in India-chickpea and pigeon pea, using a comprehensive district level panel data collected across major producing states from 2005 to 2017. The major competing crops selected for these two crops are wheat and rice. The wheat and chickpea are cultivated mainly in rabi season, whereas the pigeon pea and rice are cultivated mainly in kharif season. Figures. 11.1 and 11.2 reveal how the relative incentives encouraged the production of wheat and rice through enhancing the productivity of these two staple crops (see Figs. 10.1 and 10.2). This article contributes to the literature on agricultural supply response in several ways. First, it extends traditional supply response analysis by including several non-price factors such as farm power availability, agricultural credit, irrigation and fertiliser consumption which are relevant in the agricultural production of developing

Year Pigeon Pea Production

120000 100000 80000 60000 40000 20000 0 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018

Fig. 11.2 Production of wheat and chickpea in thousand tonnes. Source FAOSTAT

Production in 1000 tonnes

Paddy Production

Year Chickpea Production

Wheat Production

11.2 Key Government Interventions- MSP and NFSM

147

countries like India. Along with several non-price factors, key policy variables that have implications in altering the farm level incentives have also been included in the analysis. They are the MSP, NFSM and the government support for wheat and rice. As a result, the study offers unique policy-relevant insights on the effectiveness of government policy interventions, specifically the minimum support price, in augmenting production to bridge the gap between widening supply and demand of crops such as chickpea and pigeon pea which are very vital from the point of view of food and nutritional security. Second, the present study makes use of dynamic panel data estimation technique. The causality between the explanatory variables and the dependant variables is assumed to be as dynamic over time by incorporating the lagged values of the dependant variable in as an explanatory variable. This is based on the assumption that current year’s acreage allocation decisions made by a farmer is influenced by previous years’ acreages under cultivation. Using a systemgeneralised method of moments (GMM) approach, the present study tackles the issue of endogeneity.

11.2 Key Government Interventions- MSP and NFSM The major objective of government of India’s minimum support price policy is to ensure reasonable and a stable price of crops for farmers so that they have incentive to cultivate these crops which are also very important from the point of view of procurement and public distribution. To achieve this objective, based on the recommendations provided by the Commission for Agricultural Costs and Prices (CACP), the government fixes the MSP for 25 major agricultural commodities each year in both the kharif and rabi crop seasons.2 Subsequent to this, the procurement operations are undertaken through various public and cooperative agencies such as Food Corporation of India (FCI) and National Agricultural Cooperative Marketing Federation of India Ltd. (NAFED). Some amount of procurement operations is also undertaken by the state government using state-level agencies. The MSP is announced every year well before the cropping/sowing season so that the farmers can make an informed decision about the crop they are cultivating and its profitability. The calculation of MSP is largely based on the cost of production which takes into account the variable cost, land rental value, the imputed value of family labour and a 10 percent return to family labour (Jha et al., 2007). The MSP announced for various pulses is given in Table 11.1. The MSP figures show that there has been an increase in the MSP for almost all the crops. The MSP for pigeon pea increased from Rs 2300 per quintal in 2009–10 to Rs 5675 per quintal in 2018–19. Similarly,

2

Apart from Sugarcane for which FRP is declared by the Department of Food &Public Distribution, twenty two crops covered under MSP are Paddy, Jowar, Bajra, Maize, Ragi, Arhar, Moong, Urad, Groundnut-in-shell, Soyabean, Sunflower, Seasamum, Nigerseed, Cotton, Wheat, Barley, Gram, Masur (lentil), Rapeseed/Mustardseed, Safflower, Jute and Copra.

1870

Lentils (Masur)

Source CACP

1760

Chickpea (Gram)

2250

2100

2900

2520

Rabi season

Urad

3000

3170

2300

2760

2010–11

Pigeon pea (arhar)

2009–10

Moong

Kharif season

Type of pulse

2800

2800

3300

3500

3200

2011–12

2900

3000

4300

4400

3850

2012–13

Table 11.1 Minimum support prices of various pulses in Rs per quintal

2950

3100

4300

4500

4300

2013–14

3075

3175

4350

4600

4350

2014–15

3325

3425

4625

4850

4625

2015–16

3950

4000

5000

5225

5050

2016–17

4250

4400

5400

5575

5450

2017–18

4475

4620

5600

6975

5675

2018–19

148 11 Supply Response of Major Pulses

11.3 Theoretical and Empirical Literature

149

the MSP for chickpea increased from Rs. 1760 in 2009–10 to 4620 in 2018–19 (see Table 11.1). The data shows that the MSP for major pulses in the last five years was increasing continuously. Also, the growth rate of MSP for pulses was higher than that of the MSP for cereals. Although the MSP showed a steady increase, the procurement operations were very weak and as a result the procurement of pulses was very negligible. For example, during the period of 2012–13 to 2014–15, only 1–4% of total production of pulses was procured, whereas in the case of cereals the percentage of procurement was 28–30%. This forced farmer to sell their crops at a loss. There are studies that show that farmers are more interested in cultivating cereals than pulses despite having relatively higher MSP for pulses (Srivastava, 2010). One of the major objectives of the NFSM launched by the Government of India during 2007–08 at the beginning of the 11th five-year plan was to increase the production of pulses by 2 million tons by the end of Eleventh Five Year plan (2011–12). The NFSM had a two pronged strategy of enhancing both the area under cultivation as well as the productivity by bridging the gap between actual and potential yield. Improve the quality seed production, emphasis integrated nutrient management and integrated pest management, promotion of new technologies, restoration of soil fertility and improved farm equipment were among the several strategies adopted by NFSM to augment crop productivity (Manjunatha & Kumar, 2015; Thomas et al., 2013). The emphasis on high-quality seed delivery had a positive impact on the production of pulses in 2010–1. Around 171 districts from 14 major pulses producing states were initially selected to boost pulses production within the NFSM framework. The Integrated Scheme for Oilseeds, Pulses, Oil Palm and Maize (ISOPOM) served the same objective in non-NFSM districts. However, the pulses component of ISOPOM was merged with NFSM to avoid administrative difficulties and duplication of efforts. As a result of the merging of the schemes, pulses received an extensive coverage by bringing 433 districts from 14 states under the purview of NFSM (Thomas et al., 2013). At present, pulses are operational in 29 states covering 639 districts (see Fig. 11.3).

11.3 Theoretical and Empirical Literature There is a plethora of theoretical and empirical studies on supply response from both developed as well as developing countries (Abdulai & Rieder, 1995; Haile et al., 2016; Houck & Ryan, 1972; Lahiri & Roy, 1985; Lee, & Helmberger, 1985; Miao et al., 2016; Nerlove, 1956, Yu et al. 2012). There is a renewed interest in the analysis of factors influencing the supply of agricultural commodities in recent times due to the occurrence of high food inflation, and the discussion of policies to reduce the production and price risk to favor both producers as well as consumers (Haile et al., 2016). The present analysis builds on the extensive supply response literature and models that calculate the elasticities to measure the magnitude of desired supply

150

11 Supply Response of Major Pulses

Fig. 11.3 NFSM pulses states and districts. Source Ministry of Agriculture and Farmers Welfare, Government of India

response to expected returns, relevant non-price factors as well as government policy interventions. Since it is difficult to observe the expected returns while making the acreage allocation decisions, the empirical literature employs different kinds of proxies to hypothesise the supply response to returns. The econometric models specified based on the theoretical underpinnings of naive expectations, adaptive expectations and rational expectations are the popular choices by researchers. Under naïve expectation, the farmers are naïve and their decisions are based on the latest observed price, whereas under the adaptive expectations, farmers are assumed to revise their price

11.3 Theoretical and Empirical Literature

151

expectations based on the mistakes in predicting the price in the previous period. Contrary to these two, rational expectations are forward looking as it assumes that farmers are rational and make efficient choices by using all the information available to them (Haile et al., 2016). Under the rational expectation models, future prices are used to proxy expected future prices. This is especially true in developing countries where farmers are not capable of making any futures transactions mainly due to the lack of information from exchange markets. Also as cited by Haile et al., (2016), the empirical studies show that the farmers’ expectations are heterogeneous in nature. Few of the recent studies make use of Nerlovian expectation in supply response analysis (Haile et al., 2016; Vitale et al., 2009; Yu et al., 2012; Wani et al., 2015). Area under crop(s), production or yield can be used to specify the empirical equation. The present article makes use of Nerlovian expectations model in our analysis. The standard version of Nerlovian expectation model consists of the following functional form. At = α0 + α1 Pt ∗ +εt

(11.1)

where At is the acreage allocated for the crop cultivation in the current period t and Pt* is the expected price in current period t. By expressing the expected price in terms of directly observable variable, we can write, Pt ∗ = Pt−1 ∗ +α(Pt−1 −Pt−1 ∗)

(11.2)

  ∗ ∗ Pt∗ − Pt−1 = α Pt−1 − Pt−1 0≤α