Population, Sanitation and Health: A Geographical Study Towards Sustainability 3031401271, 9783031401275

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Asraful Alam · Rukhsana · Nazrul Islam · Bappa Sarkar · Ranjan Roy   Editors

Population, Sanitation and Health A Geographical Study Towards Sustainability

Population, Sanitation and Health

Asraful Alam •  Rukhsana Nazrul Islam • Bappa Sarkar • Ranjan Roy Editors

Population, Sanitation and Health A Geographical Study Towards Sustainability

Editors Asraful Alam Department of Geography Serampore Girls’ College Serampore, West Bengal, India

Rukhsana Department of Geography Aliah University Kolkata, West Bengal, India

Nazrul Islam Department of Geography Cooch Behar Panchanan Barma University Cooch Behar, West Bengal, India

Bappa Sarkar Department of Geography Dinhata College Dinhata, West Bengal, India

Ranjan Roy Department of Geography and Applied Geography University of North Bengal Darjeeling, West Bengal, India

ISBN 978-3-031-40127-5    ISBN 978-3-031-40128-2 (eBook) https://doi.org/10.1007/978-3-031-40128-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

Population dynamics is part of ecology that deals with differences in time and space. Population dynamics is vital for health policy and planning. The Millennium Development Goals (MDGs 7C) call for halving the proportion of the world’s population without sustainability to finally access to safe drinking water and primary sanitation. According to the World Health Organization (WHO), the drinking water target was met in 2010. However, there are still more than 700 million people around the world who do not have access to improved drinking water. In terms of sanitation, open defecation continues to be the norm in most developing countries. About 2.5 billion people in developing countries lack access to enhanced sanitation facilities. WASH’s (Water, Sanitation, and Health) poor condition is associated with 6.6% of diseases and disabilities worldwide, and diarrhoea, followed by malnutrition and its aftermath, kills 2.4 million people a year. This book, entitled Population Dynamics, Health and Sanitation: A Geographical Study Towards Sustainability, is a collection of selected papers on population dynamics and health, including insights into impact on economy, society, agriculture and community and also modelling for sanitation, health, population dynamics and demographic variables from different perspectives, including data science, statistics, modelling, transformation and economics, as well as the natural sciences. This book discusses a broad range of vital issues about population dynamics, water, sanitation, hygiene and sustainability in term of past, present and future. The book arranged in three broad sections: Part I: Population Dynamics, Environment and Society, Part II: Health, Livelihood and Policy Response; and Part III: Water, Sanitation, and Hygiene (WASH) and Sustainability. Considering all these points, this book has been prepared to discuss and provide insights to generate awareness of socio-economics, demography, agriculture, health, water and livelihood future forecasting regarding a stable society. It will supposedly attract the attention of students, researchers, academicians, policymakers and other inquisitive readers interested in different aspects of the current hot cake of research “WASH poverty” and its impact on economy, culture and society. The book is organized into three sub-themes; each part includes a set of articles dealing with a particular issue of related topics. The volume is prepared to include number of chapters under three major sections preceded by an introductory note. v

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The book presents an in-depth read that includes a more methodological and innovative approach focused on overall population dynamics, water, sanitation, hygiene and sustainability, economy, employment, education, food supply, rural livelihoods, society, agriculture and health consequences that will escort sustainability at the local, regional and global level. The book has arranged into three broad sections, and each part will cover a set of articles dealing with a particular issue of the climate change, agriculture and society: approach towards sustainability. This volume supposedly attracts attention to the students, researchers, academicians and policymakers. Chapter 1 is the study of “Population Dynamics and Its Impact: A Historical Perspective” and was carried out by Asraful Alam, Sourav Biswas and Lakshminarayan Satpati (Kolkata and Mumbai, India), and this chapter is focus on to explore demographic changes from a historical perspective, considering the current economic, environmental and demographic developments and sustainable development. Asraful Alam, Sourav Biswas and Lakshminarayan Satpati (Kolkata and Mumbai, India) looked into the “Population Dynamics and Economic Growth in South-East Asia” (Chap. 2). In this chapter, they try to focus primarily on the influence of demographic changes in Southeast Asian countries on economic growth and also examine the projected future demographic changes that will further drive rapid developments in the region. Chapter 3 entitled “Impact of Rural Male Outmigration on Women Work Participation in Rarh Region of West Bengal, India” by Manoj Debnath (Siliguri, India) is another important contribution of this book. The present study intended to examine the impact of male outmigration on work participation and decision making autonomy of left-behind women in Rarh Region of West Bengal, India. For that purpose, a sample of 185 migrant households are selected randomly with a well-­ structured schedule, prepared to visit migrant households and collected all the information in a face-to-face interview process. The study also unfolds that women of landless households and scheduled castes and tribes engaged more in agricultural labour compared to marginal and small landholder migrant households due to their less social status. The work of Bhupen Barman and Ranjan Roy (Cooch Behar and Siliguri, India), “Socio-Economic Determinants of Rural Out-Migration in Koch Bihar District of West Bengal, India” (Chap. 4), addresses empirical evidence of socio-economic determinants of rural out-migration in the Koch Bihar district in India. Data were collected from primary sources, from the 12 community development (CD) blocks. They found around 68.3% (N = 272) were overall migrants, 31.7% (N = 126) were non-migrant respondents, and 60.3% (N = 164) of the respondents out-migrated due to work or unemployment. This out-migration job has been termed a survival strategy for rural people. Mst Tania Parveen and Suraj Tamang (Siliguri, India) authored “Fertility Transition and Differences Between the Hindu and Muslims: A Case of North 24 Pargana District, West Bengal” (Chap. 5). The study attempts to pinpoint the causes of reproductive disparities and comprehend the variables affecting fertility based on religion distinction. With a focus on Hindu and Muslim populations, this study

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intends to investigate the relationship between religious affiliation and fertility in India’s North 24 Parganas area. “Educational Impact on Tribal Fertility: A Case Study of Dinhata-II Block, Koch Bihar, West Bengal” (Chap. 6). The study by Bishnu Barman, Tamal Basu Roy, Abdul Halim Miah and Ranjan Roy (Raiganj and Siliguri, India), they work on the role of educational impact on tribal fertility through the contraceptive used among the tribal groups. They found that there was a negative correlation within tribal educations and tribal fertility through contraceptive used of scheduled tribes in field area. The field survey report showing the number of children ever alive, the total number of pregnancies, gender preference and desire for a son in the tribal families of study area depends on the education status of married women and husbands. Part II of the book combines the description and analysis of nine chapters (Chaps. 7, 8, 9, 10, 11, 12, 13, 14 and 15) relating to health, livelihood and policy response. Chapter 7, “Livelihood Sustainability and Knowledge on Climate Change Among Marginal Farmer Households in Indian Sundarbans: Findings from a Cross-­ Sectional Study”, contributed by Debojyoti Majumder, Sanghamitra Kanjilal-­ Bhaduri and M. N. Roy (Delhi, India), attempts to study vulnerabilities faced by marginal farmers in the coastal rural stretches of Indian Sundarbans delta region, their knowledge of climate change and status of livelihood within the Sustainable Livelihoods Framework, which is based on the combination of types of capitals that farmer households are able to build up over time. Field work for this cross-sectional study was carried out in the month of December 2022. Self-reported data was collected from the marginal farmer households across Gram Panchayats of Patharpratima and Namkhana blocks of Sundarbans. “Morbidity Prevalence and Treatment Seeking Behaviour of Women Age 15–49 in India” (Chap. 8) by Abhisek Bera and Manas Ranjan Pradhan (Mumbai, India) is the study on the effects of selected socio-economic, demographic and cultural factors on the prevalence and treatment-seeking behaviour for reproductive morbidity in India on the basis of self-reported reproductive health problems elicited from a sample size of 52,538 currently married women aged 15–49 in the second Indian Human Development Survey, 2011–2012 (IHDS-2). Chapter 9 is the study of “The Impacts on Environment and Health of Residents: A Case Study of Gazipur Dumpsite (Landfill), East Delhi”, and this chapter is carried out by Shilpi Yadav and Mahabir S. Negi (Srinagar, India). This chapter focuses on the waste disposal on landfills affects the environment and people living nearby. Questionnaires/schedules, vital site observation, field visits and immersive interview sessions with people living within 200 m to 4 km of the landfill site were used to gather data. Results of the study depict that due to the release of toxic gases from landfill sites, 84% of people are facing breathing problems and 72% of people are suffering from eye irritation. Diseases like dengue, typhoid and malaria cases (29%) are most prominent in the study area. People have also encountered water contamination problems. As a result, participants who lived near the landfill site were less comfortable with their residing locations in comparison to those who lived further away.

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Manjil Gupta, Sumana Saha, Kishore Kumar Thapa and Bappa Sarkar (Cooch Behar, India) study (Chap. 10) of “Musculoskeletal Symptoms Among Sital Pati Weavers of Koch Bihar District, West Bengal, India” explores the musculoskeletal disorders among Sital Pati workers. These discomforts are among the most frequent health issues that cause pain and an unpleasant feeling over a long period of work in the same sitting posture among the mat weavers. Musculoskeletal discomforts and symptoms were identified based on the self-reported expressions. “Health Care Utilization and Treatment Expenditure Among Ailing Aged in India” (Chap. 11) has been carried out by Renuka and Ashwani Kumar (Haryana, India). This study attempts to examine the treatment-seeking behaviour of ailing aged from various diseases and utilization of government and private health care facilities in India by using NSS 60th round survey data (January–June 2004). It also studies the health-care expenditure of ailing aged people. It shows that the percentage of treated illnesses was a little lower among aged as compared to India as a whole. As expected, the proportion of treated illnesses was higher in urban areas than rural areas. Aged people in urban areas utilized more private health-care facilities, while aged people in rural areas show more reliance in government health-care facilities. It clearly shows that medical expenditure and total expenditure were more in respect to aged people. It reveals that the cost of medical expenditure is more in urban areas than in rural areas, which may be because in urban areas, the cost of medical facilities increases. “Social Capital and Its Impact on the Declining Cognitive Abilities of the Elderly in India: Evidence from the Latest Longitudinal Ageing Study in India” (Chap. 12), authored by Akash Mallick and Arpita Santra (Odisha, India), is another important contribution of this book. This study focused on cognitive frailty (CF), the co-­ occurrence of physical frailty and cognitive impairment, which adds a heavy toll to the course of population ageing and cognitive dysfunction by threatening the health and living standards of the elderly. This study used data from the latest Longitudinal Ageing Study Wave-1 to evaluate CF among older adults. The study recommends for early detection of CF to reduce the risk of further complications and to expand social networks and provide social support as an effective strategy to deal with CF. The study by Subham Roy, Maitreyee Roy, Abdul Halim Miah and Ranjan Roy (Siliguri, India) on “The Status of Primary Health Care Services in Koch Bihar District, West Bengal: A Selected Block-Level Analytical Study” (Chap. 13) study focuses on the condition of primary health care centres of selected CD blocks of Koch Bihar district, block-level variation of health infrastructure and treatment satisfaction level of patients. For the analysis part, different infrastructural indices have been prepared; a rating scale evaluation has been incorporated for the quality assessment of services. The primary data revealed significant findings regarding health infrastructure and the present status in the selected CD blocks. The work of Asif Ali, Susanta Sen, Amit Banerjee, Soumitra Saha and Namita Chakma (Bardhaman, India) on “Prevalence and Changing Pattern of Under Nutrition Among Women in West Bengal: A Comparative Study Between NFHS-4 and 5” (Chap. 14) attempts to fill this gap by identifying the spatial pattern of under nutrition among women in West Bengal. The present study uses a cross-sectional

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sample of women in reproductive age group from the latest two rounds of the National Family Health Survey data, i.e. NFHS-4 (2015–2016) and NFHS-5 (2019–2021). The results revealed that the prevalence of under nutrition has declined in general at district levels, but a few districts bear a disproportionately higher prevalence of under nutrition. Chapter 15, entitled “Health, Healing and Democracy: A Comprehensive Study of Darjeeling Himalaya in West Bengal”, is authored by Sapan Tamang (Cooch Behar, India). This study is an attempt to examine and analyse the nature and scope of the age-old traditional faith healing practices of the Gorkha Community of Darjeeling Hills, an informal extra-legal healthcare practice and the modern representative democratic state of India. Part III of this book is focused on the Water, Sanitation, and Hygiene (WASH) and Sustainability, and it include Chaps. 16, 17, 18, 19, 20, 21, 22 and 23. Mohammad Afsar Alam and Saidur Rahman (Fiji and India) authored “Health Determinants in the Era of Sustainable Development Goals (SDGs): An Overview” (Chap. 16). They work on the health-related goal: “Ensure healthful lives and encourage well-being for all ages” is one of the 17 goals that has been given special attention and is written in intentionally universal words. There are 13 corresponding goals for this health objective, and sub-goal 3a through 3d detail the methods by which this objective is to be achieved. The SDGs have 169 targets in total. Prakriti Das (Kolkata, India) authored “Burdens of Household Water Collection from Gender Perspective: A NFHS-5 Study” (Chap. 17). This chapter work on to compare the level of participation of males and females (adult and children) in household water collection in the urban and rural areas across the states and union territories of India and also tries to find the location of source of water in urban and rural areas across the states and union territories of India. Household level data was taken from National Family Health Survey (NFHS-5) conducted in the year 2019–2021. Chapter 18 is the study of “Water, Sanitation, and Hygiene Condition in India: A State-Level Analysis” and was carried out by Arpita Trivedy and Moududa Khatun (Kolkata, India) based on secondary sources of data. This chapter provides an important method to evaluate the selected indices of WASH, including menstrual hygiene, which have been considered to prepare a composite index, and the state-­ level inequality of WASH conditions has been presented with the Lorenz curve. The state-wise spatial distribution of different variables has been mapped with Quantum GIS software. Besides, the work also tries to correlate the WASH condition with important underlying factors, i.e. education, exposure to the internet, reading newspapers and magazines, and poverty. The study identified a state-level WASH condition where Chandigarh records the best and Bihar the worst WASH condition among all the states/UTs of India. Most of the variables under study were found with high inequality across the country. Bikash Barman and Pradip Chouhan (Malda, India) work on “Utilization of MHC Services in Empowered Action Group (EAG) States of India: Evidence from National Family Health Survey (NFHS)-4” (Chap. 19) and try to discuss the level of utilization of MHC services and find out different determining factors in EAG states

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of India. The entire study depends on secondary data collected from the National Family Health Survey (NFHS)-4 in years 2015 and 2016 in India, which was conducted on 189,898 last born women in the age group of 15–49 years. For the proper depiction of the result, Binary Logistic Regression has been introduced. Chapter 20 is the study of “Finding Sustainable Livelihood Strategies Among the Rajbanshi Community of Dooars Region, North Bengal” and was carried out by Tejashi Roy (New Delhi, India). This study uses primary data to identify factors and patterns of transitions in livelihood options over time. It assesses the reliability of current livelihood strategies from a sustainable livelihood perspective. The study employs a mixed approach of qualitative (measuring countable parameters) and quantitative (interviews, participant observation) methods to examine multidisciplinary factors and livelihood behaviours. The study finds that most traditional livelihood practices have changed and mingled with new processes. Some traditional practices become less relevant or irrelevant. “Understanding the Menstrual Hygiene Practices Among Women: An Indian Perspective” (Chap. 21) authored by Punama Sen, Sangita Karmakar, Tanusree Sikdar and Ranjan Roy (Siliguri, India). This research chapter explores the factors affecting women’s (age 15–24) use of different menstrual hygiene methods and a whole study based on the data collected from the National Family Health Survey (NFHS-5) conducted in 2019–2020. The survey interviewed 2,41,180 women from the age group of 15–24 across 28 states and 8 union territories of India. For this study, 2,41,112 women have taken into consideration as they have experienced menstruation cycle. Binary Logistic Regression analysis has been used to find out the factors associated with the use of menstruation hygiene methods. Sanjoy Barman, Arjun Saha, Sangita Karmakar, Ranjan Roy, Nazrul Islam and Bipul Chandra Sarkar (Siliguri, India) work on the “Impacts of Socio-Economic Factors and Child Health Care Practices on Child Morbidity of Indo-Bangladesh Border Districts of India: A Spatio-Statistical Analysis from NFHS-5 Data” (Chap. 22). This chapter focuses on the child morbidity conditions and its determinants. Results show that the majority of districts of Meghalaya are in poor condition in terms of child morbidity cases, along with some districts in Assam. Highest child morbidity rate that is 3.06, found in Karimganj district of Assam followed by West Jaintia Hills with 3.01 of Meghalaya. Another results of relations implies that maximum parameters are negatively correlated with child morbidity which justifies child morbidity cases will be reduce with enhances the facilities. Facts from this work would be helpful for the policy makers to mitigate the child morbidity cases in these areas and to achieve the highest goal. Chapter 23 is entitled “Population Dynamics and Sustainable Educational Development in Kolkata Municipal Corporation, West Bengal: A Macro-Micro Level Study” authored by Mili Basak and Rukhsana (Kolkata, India). Work on the population change and how it relates to educational advancement, with a particular focus on Kolkata Metropolitan City as the state capital of West Bengal. The study also showed that Kolkata failed to meet the Sustainable Development Goals for equitable educational opportunity in a few categories, such as zero dropout, 100% retention, gender parity, and equal access to schools.

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Chapter 24 is the study of “Prevalence of Self-Reported Tuberculosis and Its Determinants in West Bengal: An Analysis of the NFHS-5 Data” and was carried out by Diksha Chettri, Astapati Hemram and Dipika Subba (Cooch Behar, India) based on secondary sources of data from the National Family Health Survey Round 5 (2019–2021). Parameters associated with the TB prevalence and participants’ knowledge about TB transmission are identified using logistic regression analysis. The study findings indicate a higher prevalence of self-reported TB in West Bengal. The analysis of the study also indicates the probability of self-reported TB is higher among scheduled castes and tribes. Serampore, West Bengal, India Kolkata, West Bengal, India Cooch Behar, West Bengal, India Dinhata, West Bengal, India Darjeeling, West Bengal, India

Asraful Alam Rukhsana Nazrul Islam Bappa Sarkar Ranjan Roy

Acknowledgements

As in our prefaces to the earlier edited book, we would like to acknowledge our debt to those colleagues and teachers who have assisted in a varied career. We would particularly like to thank Dr. Debkumar Mukhopadhyay, Vice-Chancellor, Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India; Prof. L.  N. Satpati, Professor of Geography and Director, UGC-HRDC, University of Calcutta, Kolkata; and Dr. Soma Roy, Principal, Serampore Girls’ College, Serampore, Hooghly, West Bengal, India, for his or her help and encouragement, as well as our parents, teachers and students. We would like to thank our colleagues at Serampore Girls’ College, Aliah University, Dinhata College, University of North Bengal and Cooch Behar Panchanan Barma University and who have worked with us, shared their experience and taught us all that we know about research, writing and teaching behaviours. We are sincerely indebted to the Indian Council of Social Science Research (ICSSR), Ministry of Education, New Delhi, for their sponsorship of collaborative short term major project on “Pradhan Mantri Ujjwala Yojana: An Impact Assessment in Relation to the Life of Women in Assam and West Bengal” which enables us to learn more in the Population and Health geography. We must also thank all the contributors who have contributed their valuable contributions to the success of this project. Our heartfelt thanks to all the team members of Springer who have done a great job producing this volume. West Bengal, India

Dr. Asraful Alam Dr. Rukhsana Dr. Nazrul Islam Dr. Bappa Sarkar Prof. Ranjan Roy

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Contents

Part I Population Dynamics, Environment, and Society  1 Population  Dynamics and Its Impact: A Historical Perspective����������    3 Asraful Alam, Sourav Biswas, and Lakshminarayan Satpati  2 Population  Dynamics and Economic Growth in Southeast Asia ��������   17 Asraful Alam, Sourav Biswas, and Lakshminarayan Satpati  3 Impact  of Rural Male Outmigration on Women Work Participation in Rarh Region of West Bengal, India����������������������������   29 Manoj Debnath  4 Socio-economic  Determinants of Rural Out-migration in Koch Bihar District of West Bengal, India����������������������������������������   47 Bhupen Barman and Ranjan Roy  5 Fertility  Transition and Differences Between the Hindu and Muslims: A Case of North 24 Pargana District, West Bengal������   69 Mst Tania Parveen and Suraj Tamang  6 Educational  Impact on Tribal Fertility: A Case Study of Dinhata-II Block, Koch Bihar, West Bengal��������������������������������������   83 Bishnu Barman, Tamal Basu Roy, Abdul Halim Miah, and Ranjan Roy Part II Health, Livelihood and Policy Response  7 Livelihood  Sustainability and Knowledge on Climate Change Among Marginal Farmer Households in Indian Sundarbans: Findings from a Cross-Sectional Study��������������������������������������������������   97 Debojyoti Majumder, Sanghamitra Kanjilal-Bhaduri, and M. N. Roy  8 Morbidity  Rates and Treatment-Seeking Behaviour of Women Aged 15–49 in India ��������������������������������������������������������������������������������  113 Abhisek Bera and Manas Ranjan Pradhan xv

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Contents

 9 The  Impacts on Environment and Health of Residents: A Case Study of Ghazipur Dumpsite (Landfill), East Delhi����������������  125 Shilpi Yadav and Mahabir S. Negi 10 Musculoskeletal  Symptoms Among Sital Pati Weavers of Koch Bihar District, West Bengal, India ������������������������������������������  137 Manjil Gupta, Sumana Saha, and Kishore Kumar Thapa 11 Health-Care  Utilization and Treatment Expenditure Among the Ailing Aged in India��������������������������������������������������������������������������  153 Renuka and Ashwani Kumar 12 Social  Capital and Its Impact on Cognitive Frailty Among the Elderly in India: Findings from the LASI Wave 1, 2017–2018������������  161 Akash Mallick and Arpita Santra 13 The  Status of Primary Healthcare Services in Koch Bihar District, West Bengal: A Selected Block-Level Analytical Study��������  183 Subham Roy, Maitreyee Roy, Abdul Halim Miah, and Ranjan Roy 14 Prevalence  and Changing Pattern of Undernutrition Among Women in West Bengal: A Comparative Study Between NFHS-4 and NFHS-5����������������������������������������������������������������������������������������������  203 Asif Ali, Susanta Sen, Amit Banerjee, Soumitra Saha, and Namita Chakma 15 Health,  Healing and Democracy: A Comprehensive Study of Darjeeling Himalaya in West Bengal ������������������������������������������������  215 Sapan Tamang Part III Water, Sanitation, and Hygiene (WASH) and Sustainability 16 Health  Determinants in the Era of Sustainable Development Goals (SDGs): An Overview��������������������������������������������������������������������  231 Mohammad Afsar Alam and Saidur Rahman 17 Burdens  of Household Water Collection from Gender Perspective: A NFHS-5 Study ����������������������������������������������������������������  259 Prakriti Das 18 Water,  Sanitation, and Hygiene Condition in India: A State-Level Analysis ����������������������������������������������������������������������������������������������������  275 Arpita Trivedy and Moududa Khatun 19 Utilization  of MHC Services in Empowered Action Group (EAG) States of India: Evidence from National Family Health Survey (NFHS)-4��������������������������������������������������������������������������������������  297 Bikash Barman and Pradip Chouhan

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20 Finding  Sustainable Livelihood Strategies Among the Rajbanshi Community of Dooars Region, North Bengal����������������������������������������  321 Tejashi Roy 21 Understanding  the Menstrual Hygiene Practices Among Women: An Indian Perspective��������������������������������������������������������������  343 Punama Sen, Sangita Karmakar, Tanusree Sikdar, and Ranjan Roy 22 Impacts  of Socioeconomic Factors and Child Healthcare Practices on Child Morbidity of Indo-Bangladesh Border Districts of India: A Spatio-Statistical Analysis from NFHS-5 Data ����������������������������������������������������������������������������������������������������������  359 Sanjoy Barman, Arjun Saha, Sangita Karmakar, Ranjan Roy, Nazrul Islam, and Bipul Chandra Sarkar 23 Population  Dynamics and Sustainable Educational Development in Kolkata Municipal Corporation, West Bengal: A Macro-/Micro-Level Study������������������������������������������������������������������  375 Mili Basak and Rukhsana 24 Prevalence  of Self-Reported Tuberculosis and Its Determinants in West Bengal: An Analysis of the NFHS-5 Data��������������������������������  395 Diksha Chettri, Astapati Hemram, and Dipika Subba Index������������������������������������������������������������������������������������������������������������������  409

Contributors

Asraful Alam  Department of Geography, Serampore Girls’ College, Serampore, Hooghly, West Bengal, India Mohammad Afsar Alam  Department of Social Sciences, Fiji National University, Suva, Fiji Asif Ali  Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, India Amit  Banerjee  Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, India Bhupen Barman  Department of Geography, Tufanganj Mahavidyalaya, Tufanganj, Cooch Behar, West Bengal, India Bikash Barman  Department of Geography, Malda Women’s College, Malda, West Bengal, India Bishnu  Barman  Department of Geography, Raiganj University, Raiganj, West Bengal, India Sanjoy Barman  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Mili  Basak  Department of Geography, Aliah University, Kolkata, West Bengal, India Abhisek  Bera  International Maharashtra, India

Institute

of

Population

Science,

Mumbai,

Sourav Biswas  Department of Population & Development, International Institute for Population Sciences, Mumbai, Maharashtra, India Namita  Chakma  Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, India

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Contributors

Diksha  Chettri  Department of Geography, Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India Pradip  Chouhan  Department of Geography, University of Gour Banga, Malda, West Bengal, India Prakriti  Das  Department of Geography, Victoria Institution (College), Kolkata, West Bengal, India Manoj Debnath  Department of Geography, School of Human and Environmental Sciences, North Eastern Hill University, Shillong, Meghalaya, India Manjil  Gupta  Department of Zoology, Dinhata College, Cooch Behar, West Bengal, India Astapati  Hemram  Department of Geography, Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India Nazrul  Islam  Department of Geography, Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India Sanghamitra Kanjilal-Bhaduri  SIGMA Foundation, Kolkata, West Bengal, India Sangita Karmakar  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Moududa  Khatun  Department of Geography, Aliah University, Kolkata, West Bengal, India Ashwani Kumar  Department of Geography, C.H.L. Government College, Chhara, Jhajjar, Haryana, India Debojyoti Majumder  SIGMA Foundation, Kolkata, West Bengal, India Akash Mallick  Department of Anthropology, Fakir Mohan University, Balasore, Odisha, India Abdul Halim Miah  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Mahabir  S.  Negi  Department of Geography, School of Earth Science HNB Garhwal University, Srinagar, Jammu and Kashmir, India Mst Tania Parveen  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Manas Ranjan Pradhan  International Institute of Population Science, Mumbai, Maharashtra, India Saidur  Rahman  Department of Sociology, Women’s College, Aligarh Muslim University, Aligarh, Uttar Pradesh, India Renuka  Department Haryana, India

of

Geography,

Secondary

Education,

Panchkula,

Contributors

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M. N. Roy  SIGMA Foundation, Kolkata, West Bengal, India Maitreyee Roy  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Ranjan  Roy  Department of Geography and Applied Geography, North Bengal University, Siliguri, Darjeeling, West Bengal, India Subham  Roy  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Tamal  Basu  Roy  Department of Geography, Raiganj University, Raiganj, West Bengal, India Tejashi Roy  CSRD/SSS, Jawaharlal Nehru University, New Delhi, India Rukhsana  Department of Geography, Aliah University, Kolkata, West Bengal, India Arjun  Saha  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Soumitra  Saha  Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, India Sumana  Saha  Department of Geography, Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India Arpita Santra  Biological Anthropology Unit, Indian Statistical Institute, Kolkata, West Bengal, India Bipul  Chandra  Sarkar  Department of Geography, Ananda Chandra College, Jalpaiguri, West Bengal, India Lakshminarayan  Satpati  UGC-HRDC, University of Calcutta, Kolkata, West Bengal, India Punama  Sen  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Susanta  Sen  Department of Geography, The University of Burdwan, Purba Bardhaman, West Bengal, India Tanusree Sikdar  Department of Geography and Applied Geography, University of North Bengal, Siliguri, Darjeeling, West Bengal, India Dipika  Subba  Department of Geography, Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India Sapan Tamang  Department of Political Science, Dinhata College, Cooch Behar, West Bengal, India Suraj Tamang  Department of Geography, Kalipada Ghosh Tarai Mahavidyalaya, University of North Bengal, Bagdogra, Darjeeling, West Bengal, India

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Contributors

Kishore  Kumar  Thapa  Department of Botany, Dinhata College, Cooch Behar, West Bengal, India Arpita  Trivedy  Department of Geography, Aliah University, Kolkata, West Bengal, India Shilpi Yadav  Department of Geography, School of Earth Science HNB Garhwal University, Srinagar, Jammu and Kashmir, India

About the Editors

Asraful Alam  is an assistant professor and HoD at the Department of Geography, Serampore Girls’ College, University of Calcutta, West Bengal, India. He received his MA and PhD degrees in Geography from Aligarh Muslim University, Aligarh, and Aliah University, Kolkata, India, respectively and also completed PG Diploma in Remote Sensing and GIS.  He completed his post-­ doctorate (PDF) from Department of Geography, University of Calcutta, Kolkata, India. Earlier, he was an assistant coordinator in PG Department of Geography, Calcutta Women’s College, University of Calcutta, Kolkata, India. His research interests include population geography, agricultural geography, climatology, health geography, remote sensing, GIS and developmental studies. He has contributed various research papers in reputed national and international journals and has edited book volumes. He has authored jointly edited books entitled “Habitat, Ecology and Ekistics: Case Studies of Human-Environment Interactions in India”, “Agriculture, Food and Nutrition Security: Case Study of Availability and Sustainability in India”, “Agriculture, Environment and Sustainable Development: Experiences and Case Studies”, “Life and Living Through Newer Spectrum of Geography, Self-Reliance (Atmanirbhar) and Sustainable Resource Management in India” and “Climate Change, Agriculture and Society – Approaches toward Sustainability”. He was a convener in the National Seminar on Self-Reliance (Atmanirbhar), Sustainable Development and Environment on the 25th and 26th of March 2022, which was sponsored by Indian Council of Social Science Research (ICSSR) and organized by the Department of Geography, Serampore Girls’ College. He has served as an editorial board member in peer-­reviewed international journal such as PLOS One, Earth Science, Scientific Journal of Health Science Research and Frontiers in Geochemistry. Rukhsana  is an assistant professor and former head of the Department of Geography at Aliah University, Kolkata. She has 12 years of teaching as well as research experience. She obtained her MA and PhD degrees in Geography from Aligarh Muslim University. She was awarded nine academic awards and fellowships such as International Young Geographer Award 2009, the AMU-JRF Award 2006–2007 and UGC-RF 2007–2009 Award. She has published more than 40 papers xxiii

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About the Editors

at national and international levels in reputed journals and 23 chapters in edited books. She has published six books and presented several research papers at national and international levels. She has attended XXV FIG International Congress 2014, Malaysia, and ICGGS 2018, Thailand Bangkok. She has successfully completed one major research project sponsored by ICSSR, Ministry of Education, New Delhi, in the agriculture field. Four PhDs have been successfully awarded under her supervision. She has served as a reviewer for many reputed international journals. Her specialization in research is agriculture development and planning, urban expansion and planning, environment, rural development, remote sensing and geographic information system. She has completed various trainings, workshops from different organizations and several training courses done by IIRS (Indian Institute of Remote Sensing), ISRO, Department of Space, Government of India. She is engaged in various professional activities and served the university in various posts. Presently, she is working as project director on a major research project sponsored by ICSSR New Delhi, India. Nazrul Islam  has been serving as an associate professor of Geography at Cooch Behar Panchanan Barma University, Cooch Behar, West Bengal, India. Presently, he is the head of the Department at Cooch Behar Panchanan Barma University. He did his BSc and MSc at Calcutta University in Geography. He was awarded a National Scholarship for his academic excellence at the undergraduate level. He did his PhD at the University of North Bengal. He has participated in many international and national level seminars/conference(s) and has published research articles in reputed journals and chapters in edited volumes. He has co-authored four edited books. He has participated in various trainings on RS and GIS from IIRS, Dehradun, DST, Kolkata, and different universities in India. He has visited three foreign countries – Bangladesh, Thailand, and Vietnam – to attend international conferences. His areas of interest include cartography, environmental and industrial geography. He has ample knowledge of GIS and remote sensing and the application of MCDM and machine learning in Geography. He taught his students Google Earth Engine, Python, SPSS, and R. Bappa Sarkar  is an assistant professor of Geography at Dinhata College, Cooch Behar; has completed his graduation from Cooch Behar in 2007; and secured first class in Geography. He completed his master’s degree (2009) from the Department of Geography and Applied Geography, NBU, with first class. He was awarded a National Scholarship for his academic excellence. He completed his PhD from University of North Bengal in 2018. He has participated in various remote sensing and geographic information system professional trainings from NRSC, Hyderabad, and IIRS, Dehradun. He has participated in many national and international seminars and published five articles in national and six international journals (till March 2023). He edited one book entitled “Trends in Bio-Geography-Eastern Himalaya and North Bengal”. His area of interest includes cartography, RS, GIS and agricultural geography.

About the Editors

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Ranjan Roy  is a well-known academician in Geography in West Bengal as well as in India and is a Professor and former HoD at the Department of Geography and Applied Geography of University of North Bengal, Siliguri, India. He received his MSc and PhD degree in Geography and Applied Geography from the University of North Bengal and specialized in Cartography. His research interests are in applied cartographic techniques, agricultural geography, urban geography, population problems, rural development, regional planning, remote sensing and GIS. He has published more than 90 articles in different reputed national and international journals and edited books. He has organized different national seminars and workshops. He has supervised a total of 10 scholars for PhD degree and 6 scholars for MPhil degree. He was co-investigator in “Preparation of Contour Map for Drainage Management in English Bazar Municipality, Malda”, which was sponsored by Malda municipality under Government of West Bengal and also co-investigated UGC SAP DRS-I Programme on “Geo-hazards in Sub-Himalayan West Bengal” from 2009 to 2014. He was a project investigator of North Bengal University assistance project on “An Appraisal of Urban Basic Services and Amenities in Newly Emerged Census Towns: A Case Study of Siliguri Subdivision of Darjeeling District, West Bengal” in 2017–2018. Recently, he has completed as a principal investigator of Winter School Programme on geospatial technology that funded and supported by Department of Space and Technology (DST) of Government of India. He has jointly edited a book that titled “Application of Geospatial Technology in Geomorphology and Environment”.

Part I

Population Dynamics, Environment, and Society

Chapter 1

Population Dynamics and Its Impact: A Historical Perspective Asraful Alam

, Sourav Biswas, and Lakshminarayan Satpati

1.1 Introduction Population refers to a group of individuals of the same species who occupy a specific area over a certain period of time (Population Dynamics | E-Cology, 2016). The traditional concept of population can be defined as “all coexisting individuals of the same species living in the same area at the same time (Van Dyke, 2008).” In relation to this, population dynamics refers to how populations of a species change over time (Population Dynamics | E-Cology, 2016). To be precise, it is the portion of ecology that deals with the variation in time and space of population size and density for one or more species. As a branch of history, population dynamics has a long history and is an inherently quantitative sub-discipline of ecology (Juliano, 2007). Population size and dynamics are influenced by several processes, including the per capita population growth rate, which is determined by birth, death, emigration, and migration rates (Population Dynamics | E-Cology, 2016). These factors describe population density changes over time, which are known as population dynamics (Encyclopedia.com, 2018). Both population trends and dynamics have a significant impact on development (UNFPA, 2014). Over the last 12,000  years, the human population has experienced a remarkable increase, estimated at 1860-fold (Henderson & Loreau, 2019). From 1900 to 1950, the global population grew by 50%, and it increased by 200% over the next 50  years (Hirschman, 2005). The A. Alam (*) Department of Geography, Serampore Girls’ College, Serampore, Hooghly, India S. Biswas Department of Population & Development, International Institute for Population Sciences, Mumbai, Maharashtra, India L. Satpati UGC-HRDC, University of Calcutta, Kolkata, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Alam et al. (eds.), Population, Sanitation and Health, https://doi.org/10.1007/978-3-031-40128-2_1

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world’s population crossed the 7 billion mark in 2011 and is expected to reach at least 9 billion by 2050 (Karunaratne et al., 2012). However, for the first 165,000 years of human existence, the global population remained low, with a few hundred thousand individuals (Henderson & Loreau, 2019). The earth’s surface has been undergoing significant changes over the past few centuries (Goldewijk, 2005). Historically, economies were marked by Malthusian stagnation, and technological progress and population growth were minuscule by modern standards (Ashraf & Galor, 2008). Industrialization, which began in eighteenth-­century England and spread globally, marked a turning point in world demographic history (Hirschman, 2005). Over the last 12,000 years, there has been exponential growth in technological and scientific advancements, coupled with significant changes in social norms and lifestyles. Therefore, it is difficult to compare demographic trends over time and establish driving factors of population change that hold true through multiple demographic transitions or time periods (Henderson & Loreau, 2019). Population dynamics, including changes in growth rates, age structures, and distributions of people, are closely linked to national and global developmental challenges and their solutions (UNFPA, 2014). It is expected that much of the projected growth in population in the coming decades will take place in developing and least developed countries, while developed countries will experience a balanced population growth rate (United Nations, 2015). As population dynamics vary widely from one country to another, policies dealing with population issues need to be adapted to their specific needs (UNFPA, 2014). Population dynamics have major environmental implications (Hunter, 2000). This study focuses primarily on demographic changes and their influence on the environment and resource availability over time (Henderson & Loreau, 2019). The influence of demography on technological advancements suggests a long-run positive feedback between population density and technological progress, as Boserup has suggested (Lee, 1987). Though technological advancements are considered favored solutions to maintaining human well-­ being and food production as the population grows, the trajectory of human development is uncertain. Therefore, this paper aimed to provide a possible mechanism for population change and illuminate practices that are beneficial to the sustainability of the global human–environment system (Henderson & Loreau, 2019).

1.2 Historical Evidence Population dynamics refers to the patterns of change in populations over time (Lee, 1987). The Malthusian theory suggests that throughout most of human history, there has been a persistent struggle for survival. Technological progress during those times was relatively slow compared to modern standards. The resources resulting from technological advancements and land expansion were primarily used to accommodate population growth, with minimal long-term impact on income per person. The positive relationship between living standards and population growth,

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combined with declining labor productivity, kept income per person close to subsistence levels. Stable population sizes and consistent income per person were observed during periods without changes in technology or land availability. Conversely, periods of improved technology or expanded land availability only provided temporary gains in income per person, eventually leading to a larger but not necessarily wealthier population. Notably, technologically advanced economies had higher population densities, but their standard of living did not match the extent of their technological progress (Ashraf & Galor, 2008).

1.3 Drivers of Demographic Change: Fertility and Mortality In the present era, the world’s population is growing each year due to a higher global birth rate compared to the death rate. Between 2000 and 2005, the population size increased by an average of 1.17% annually, resulting from a birth rate of 2.03% and a death rate of 0.86% (Bongaarts, 2009). Over the past few decades, there has been a noticeable increase in life expectancy worldwide. Most developing countries have experienced a decline in fertility rates, although some exceptions exist, particularly among the least developed nations. Nevertheless, even if fertility were to immediately decrease to replacement levels, populations would still continue to grow for some time (UNFPA Technical Division, 2012). To gain a comprehensive understanding, the key factors influencing demographic changes are discussed below.

1.3.1 Fertility Fertility is a crucial determinant and indicator of demographic change, typically measured by the total fertility rate (TFR). The total fertility rate represents the projected number of births a typical woman in a specific society can be expected to have during her reproductive years (Bloom, n.d.). Although fertility rates have generally decreased worldwide in recent years, population growth continues to be fueled by high fertility levels, particularly in developing regions such as Asia and Africa (Hunter, 2000). Notably, women who work outside the home tend to have fewer children compared to those who stay at home, and rural families tend to have more children than urban dwellers. To gain a better understanding of global fertility rates, a graph (Fig. 1.1) provided by the United Nations in 2004 is presented below. Fertility patterns exhibit substantial variation across countries. In 2006, the average number of births per 1000 people worldwide was approximately 21, with notable extremes ranging from as low as 8 or 9 (primarily in northern and western Europe and certain former Soviet republics) to 50 or more in select West African nations (Bloom & Rosenberg, 2011).

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Fig. 1.1  Total fertility rate. (Source: World Population Prospects, United Nations, 2004)

1.3.2 Mortality Mortality, the second significant factor influencing population trends, is a variable that shapes demographic patterns (Bloom & Rosenberg, 2011). Life expectancy (LE) at birth is commonly used to measure mortality, representing the average number of years a newborn would live based on age-specific mortality rates observed in a specific year (Bongaarts, 2009). Death rates tend to be highest among infants, young children, and the elderly. Consequently, societies with a higher proportion of elderly individuals are likely to have a higher number of deaths per 1000 people compared to societies where the majority are young adults. It is evident that developed countries with quality medical services tend to have larger elderly populations compared to developing countries. Therefore, demographers use life expectancy as an indicator to assess the longevity of a society. Life expectancy serves as a measure of the overall health of a population, which is influenced by factors such as adequate nutrition, clean water and sanitation, and access to medical services including vaccinations. A graph (Fig. 1.2) on life expectancy provided by the United Nations in 2004 is presented below for reference. The graph indicates that the UN’s projections assume ongoing improvements in life expectancies across all regions. Developed countries are expected to experience greater improvements in life expectancy by the year 2040. Additionally, developing countries in regions such as Asia and Latin America are also projected to narrow the gap with developed countries in terms of life expectancy (Bongaarts, 2009).

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90

Years

80

70

60

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40 1950

1960

1970 World

1980 1990 2000 2010 Five-year period beginning More developed regions

2020

2030

2040

Less developed regions

Fig. 1.2  Life expectancy. (Source: World Population Prospects, United Nations, 2004)

1.4 World Population Growth Through History Understanding the life history of human beings poses a unique challenge for scientists (Baldini, 2015). Current estimates suggest that modern humans, also known as Homo sapiens, evolved approximately 130,000–160,000  years ago (Bloom & Rosenberg, 2011). Throughout this extended period, humans lived in small populations as nomadic hunters and gatherers, resulting in very slow population growth (Coale, 1974; Kapitza, 1996; Lancaster, 1997). Studies indicate that various threats, ranging from diseases to climate fluctuations, led to short life expectancies and high death rates in pre-industrial societies. Consequently, it took until 1804 for the human population to reach one billion individuals (Bloom & Rosenberg, 2011). To provide a better understanding, Fig. 1.3 illustrates how the human population began to grow 100 thousand years ago. However, starting from the year 1804 (Table 1.1), population growth underwent a rapid acceleration (Bloom & Rosenberg, 2011). Approximately ten thousand years ago, during the Neolithic period, also known as the agricultural revolution, the population began to steadily increase (Kumar, 2020). This steady growth was driven by a growing dependence on a stable food supply. Scholars suggest that these changes led to lower death rates and higher birth rates (Fig. 1.3), resulting in a faster rate of population growth compared to earlier periods (Coale, 1974). However, it was with the advent of the industrial revolution in the eighteenth century that population growth truly surged (Kumar, 2020). To aid in comprehension, a table has been provided below, illustrating the steady growth of the world population over time (Bloom & Rosenberg, 2011).

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Fig. 1.3  Growth of human population. (Source: Brainard, J. and Henderson, R. (2018). History of Human Population Growth. Human Biology) Table 1.1  World Population Milestones

World population reached 1 billion 2 billion 3 billion 4 billion 5 billion 6 billion

Year 1804 1927 1960 1974 1987 1999

Time to add 1 billion 123 years 33 years 14 years 13 years 12 years

Source: World Population Prospects, United Nations, 2004

During the period of industrial revolution, countries were seen to be moving from a regime of high mortality and high fertility to a regime of low mortality and low fertility. This process of change in population is termed as demographic transition (Fig. 1.4) by researchers (Ranganathan et al., 2015). By the mid-twentieth century, it has been observed that most nations passed through phases of demographic transition and according to current population projections, demographers even believe that earth’s population will reach just over nine billion by the year 2050 (Bloom & Rosenberg, 2011).

1.5 Human–Nature Interactions Throughout history, humans have sought to comprehend the relationship between population dynamics and the environment. However, it was Sir Thomas Malthus in 1798 who proposed in his book, “Essay on the Principle of Population,” that

Births and deaths (per thousand per year)

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40 Birthrate 30

Phase 1: Preindustrial

Phase 2: Transitional

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Phase 3: Transitional

Phase 4: Industrial

Death rate Total population

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Time

Fig. 1.4 Major demographic characteristics in India. (Source: Major Demographic Characteristics—Indian Demographics (weebly.com))

population numbers tend to grow exponentially, while food production increases linearly, leading to natural checks on further growth (de Sherbinin et al., 2007). As we entered the twenty-first century, we witnessed a growing global population alongside increasing levels of per capita consumption, resulting in the depletion of natural resources and environmental degradation (Jorgenson, 2003). In this section, a literature review on human–nature interaction primarily focuses on land use and global climate change, which will be discussed in the following sections.

1.6 Land Use The transformation of natural landscapes into croplands, pastures, urban areas, reservoirs, and other human-made environments has become the most widespread form of human impact on the environment. In the present-day world, approximately 40% of the earth’s land surface is dedicated to agriculture, while around 85% has experienced some level of anthropogenic influence (de Sherbinin et  al., 2007). Changes in land use over time have had significant ecological consequences. For instance, the conversion of land for agricultural purposes has resulted in soil erosion, while the use of chemical fertilizers has contributed to soil degradation. Deforestation, too, has been linked to soil erosion, reducing the soil’s capacity to retain water and increasing the frequency and severity of floods. Numerous studies have shown that human-induced changes in land use often lead to habitat fragmentation and loss. If the current rates of deforestation persist, it is estimated that a quarter of all species on earth could be lost within the next 50 years (Hunter, 2000). Consequently, it is essential to employ a diverse range of research methods to improve our understanding of these location-specific connections. These methods

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include remote sensing, geographic information systems, ecosystem processes and multilevel modeling, surveys and interviews, participant observation, and stakeholder analyses (de Sherbinin et al., 2007).

1.7 Global Climate Change In recent years, we have experienced some of the warmest temperatures on record. Research indicates that the increase in greenhouse gas concentrations, which trap solar radiation and warm the atmosphere, has played a significant role in these temperature changes. Furthermore, it is believed that human activities have contributed substantially to alterations in atmospheric gas composition. The demographic impact can be observed in three primary areas. Firstly, industrial production and energy consumption have led to carbon dioxide emissions from the use of fossil fuels. Secondly, land-use changes, such as deforestation, affect the exchange of carbon dioxide between the earth and the atmosphere. Lastly, certain agricultural practices, like paddy–rice cultivation and livestock production, have been identified as significant sources of greenhouse gas emissions, particularly methane (Hunter, 2000). Researchers using national-level data have discovered a positive correlation between population size and CO2 emissions, demonstrating the influence of population on carbon dioxide production (de Sherbinin et  al., 2007). According to our scholars, population growth is projected to contribute to 35% of the global increase in the future. Therefore, addressing demographic issues and promoting sustainable production and consumption practices are crucial responses to the processes driving global warming (Hunter, 2000). The relationship between population growth and environmental degradation has been a subject of debate for decades. This relationship is complex, as population size and growth tend to amplify human impacts on the environment (Jorgenson, 2003). In the human–environment system, the impacts are not one-sided but rather reciprocal (de Sherbinin et al., 2007). Consequently, it is imperative for humanity to take action and restore environmental quality for the betterment of our planet (Bloom, n.d.).

1.8 Mediating Factors Impacting Population Dynamics: Technological Developments and Medical Advancements, Social Norms, and Education There are several factors that have impacted demographic changes with time. Some of the factors are discussed as follows.

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1.8.1 Technological Developments and Medical Advancements Technological advancements have emerged as a major catalyst for demographic changes. It is undeniable that population growth has been significantly influenced by the advent of new technologies and tools. Even in the Paleolithic Era, the introduction of advanced hunting tools contributed to an increase in population growth toward the end of that era (Henderson & Loreau, 2019). Technological advancements, particularly in the realm of energy use, have had a profound impact on environmental conditions. The consumption of oil, natural gas, and coal experienced a dramatic upsurge during the twentieth century (Hunter, 2000). Scholars argue that without advancements in agricultural technologies, the population boom of the 1900s would not have been possible, as agricultural production increased sixfold during the same period (Henderson & Loreau, 2019). In addition to technological developments, medical advancements have played a significant role in shaping contemporary society and its population growth. A notable surge in population growth has been observed since the early nineteenth century, concurrent with societal development (Henderson & Loreau, 2019). Over the past 50 years, medical expansions have garnered increasing attention in discussions on health, illness, and various human issues (Zheng & George, 2018). The progress in medical science has contributed to a decline in death rates through improvements in hygiene, sanitation, access to clean water, enhanced nutrition, and timely medical care during times of need (Henderson & Loreau, 2019).

1.8.2 Social Norms The relationship between population growth and societal development holds great significance (Clausen, 1985). Societal perspectives shed light on behavioral changes influenced by demographic factors. Over time, it has been observed that individuals from economically privileged backgrounds have shifted from having larger families to having slightly smaller ones compared to those with lower social status. Generally, researchers suggest that in regions characterized by income insecurity and uncertain living conditions, individuals tend to have more children as a means of ensuring future family support. Conversely, wealthier families aim to enhance the economic prospects of their children by providing more resources to a smaller number of individuals (Henderson & Loreau, 2019).

1.8.3 Education Education plays a significant role in promoting healthy lifestyles by empowering individuals with the necessary knowledge and skills to make informed decisions about their well-being. Highly educated individuals are adept at utilizing their

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knowledge, information, and past experiences to avoid health risks and adopt behaviors that enhance their overall health. Furthermore, education provides socio-­ psychological resources that contribute to better health and longer life expectancy through emotional and instrumental support (Luy et al., 2019). Additionally, higher levels of education have been found to be associated with reduced fertility rates, and studies consistently demonstrate a correlation between higher education levels and lower mortality rates (Henderson & Loreau, 2019). Therefore, it can be concluded that social norms, technological advancements, medical progress, and education are interconnected factors that shape the dynamics of a population. These factors exhibit strong correlations and should be taken into consideration to ensure that demographic changes over time have a positive impact on our natural environment, which serves as the habitat for all living beings (Henderson & Loreau, 2019).

1.9 Sustainable Development, a Major Aspect of Population Dynamics Understanding population dynamics is crucial as societies worldwide strive to achieve ambitious and comprehensive Sustainable Development Goals (UNECE, 2018). Despite population growth slowing down in most countries, the global population continues to grow at a significant rate (Herrmann, 2012). For instance, in 2011, the world population surpassed 7 billion, and according to population projections from the United Nations Population Division’s medium variant, it is expected to reach over 9 billion by the middle of the century (UNFPA Technical Division, 2012). In recent years, population growth, coupled with increased consumption, has intensified the challenges in reducing poverty, creating employment opportunities, and ensuring food, water, and energy security, while also safeguarding the environment. These issues were recognized nearly two decades ago, as the 1994 International Conference on Population and Development Programme of Action, following the 1992 Rio Declaration, outlined an approach to promote sustainable development. This approach emphasized the need for a shift toward sustainable production and consumption, along with appropriate policies to address demographic changes (Herrmann, 2012). Over the past few decades, global life expectancy has been on the rise, with developing and least developed countries experiencing population growth. Out of the current global population of 7 billion, over 1 billion people live in extreme poverty, and millions struggle to find productive and well-paying employment. The poor, who heavily rely on natural resources, are particularly vulnerable to the impacts of demographic changes and face difficulties in adapting to these changes (UNFPA Technical Division, 2012). Thus, lifting people out of poverty and ensuring a decent quality of life for future generations are a crucial societal need. Achieving this requires not only a more equitable distribution of economic resources,

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which is becoming increasingly challenging in an unequal world but also a higher economic output (Herrmann, 2012). Demographic changes can present opportunities for sustainable development. Countries can address population dynamics through effective policies based on human rights and sound planning. Proper planning enables countries to tackle the various challenges associated with rapid urbanization (Herrmann, 2012). Proactive planning for urban growth, particularly addressing the employment, land, and housing needs of the growing urban poor, serves as an appropriate policy response to rapid urbanization and a critical component of integrating the three pillars of sustainable development: economic, environmental, and social (UNFPA Technical Division, 2012). Rural–urban migration is expected to alleviate pressures on natural resources and help individuals adapt to changes in economic and environmental conditions. Consequently, urban population growth, driven by rapid migration in many impoverished countries, can contribute positively to sustainable development. Additionally, the decline in fertility levels is anticipated to temporarily reduce dependency ratios and provide households and countries with an opportunity to increase their investments in productive resources (Herrmann, 2012).

1.10 Conclusion Population dynamics are subject to fluctuations in response to external forces and the internal structure of the demographic renewal process (Lee, 1987). In contemporary societies, a portion of the world finds itself in a post-boom state, where later generations continue to experience exponential growth. Meanwhile, another portion of the population had a delayed onset of innovation and is currently experiencing peak recruitment rates, leading to a prolonged period of high growth and expansion. Consequently, we can observe two distinct demographic transitions occurring in our present society (Henderson & Loreau, 2019). While population growth varies across countries, it is widely acknowledged that global population growth will persist for several decades, reaching approximately 9.2 billion by 2050 and continuing to rise in the later part of the century (Henderson & Loreau, 2019). While the challenges posed by population growth are prominent globally, particularly in developing and least developed countries, it is crucial to implement concrete policies to ensure the sustainable utilization of resources for present and future generations (Herrmann, 2012). Population dynamics not only impact overarching development objectives such as poverty reduction, human well-being, and living standards but also have significant implications for the social, economic, and environmental dimensions of sustainable development (UNFPA Technical Division, 2012). To promote sustainable development, countries need to address key aspects, including universal access to sexual and reproductive healthcare, effective family planning, investments in education with a focus on gender parity, empowerment of women, and systematic integration of population projections into development strategies and policies (Herrmann, 2012). Furthermore, it is crucial for countries to appropriately

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incorporate population dynamics into regional and sectoral development strategies. In conclusion, promoting human well-being and improving living standards are not only the objectives of development but also integral elements in addressing population dynamics and fostering sustainable development pathways (UNFPA Technical Division, 2012).

References Ashraf, Q., & Galor, O. (2008). Malthusian population dynamics: Theory and evidence. Working Paper. Baldini, R. (2015). The importance of population growth and regulation in human life history evolution. PLoS One, 10(4), e0119789. Bloom, D. E. (n.d.). Unit 5: Human population dynamics. Bloom, D. E., & Rosenberg, L. (2011). The future of South Asia: Population dynamics, economic prospects, and regional coherence. Bongaarts, J. (2009). Human population growth and the demographic transition. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1532), 2985–2990. Clausen, A. W. (1985). Population growth and economic and social development. The Journal of Economic Education, 16(3), 165–176. Coale, A. J. (1974). The history of the human population. Scientific American, 231(3), 40–51. de Sherbinin, A., Carr, D., Cassels, S., & Jiang, L. (2007). Population and environment. Annual Review of Environment and Resources, 32, 345–373. Encyclopedia.com. (2018, May 11). Population dynamics. Encyclopedia.Com. https://www.encyclopedia.com/earth-­and-­environment/ecology-­and-­environmentalism/environmental-­studies/ population-­dynamics Goldewijk, K. K. (2005). Three centuries of global population growth: A spatial referenced population (density) database for 1700-2000. Population and Environment, 343–367. Henderson, K., & Loreau, M. (2019). An ecological theory of changing human population dynamics. People and Nature, 1(1), 31–43. Herrmann, M. (2012). Factoring population dynamics into sustainable development. Co-Operation Report, 71. Hirschman, C. (2005). Population and society: Historical trends and future prospects. In The Sage handbook of sociology (pp. 381–402). SAGE Publications Ltd. Hunter, L. M. (2000). Population and environment: A complex relationship. RAND Corporation. Jorgenson, A. K. (2003). Consumption and environmental degradation: A cross-national analysis of the ecological footprint. Social Problems, 50(3), 374–394. Juliano, S. A. (2007). Population dynamics. Journal of the American Mosquito Control Association, 23(2 Suppl), 265. Kapitza, S. P. (1996). The phenomenological theory of world population growth. Physics-Uspekhi, 39(1), 57. Karunaratne, V., Kottegoda, N., & De Alwis, A. (2012). Nanotechnology in a world out of balance. Journal of the National Science Foundation of Sri Lanka, 40(1), 3–8. Kumar, K. (2020, October 29). Modernization | Nature, Features, Examples, & Facts. Britannica. https://www.britannica.com/topic/modernization Lancaster, J. B. (1997). The evolutionary history of human parental investment in relation to population growth and social stratification. In Feminism and evolutionary biology: Boundaries, intersections and Frontiers (pp. 466–488). Lee, R. D. (1987). Population dynamics of humans and other animals. Demography, 24, 443–465.

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Luy, M., Zannella, M., Wegner-Siegmundt, C., Minagawa, Y., Lutz, W., & Caselli, G. (2019). The impact of increasing education levels on rising life expectancy: A decomposition analysis for Italy, Denmark, and the USA. Genus, 75, 1–21. Population Dynamics | e-cology. (2016). Duke Nicholas School of Environment. https://sites. nicholas.duke.edu/ecologyapp/modules/population-­dynamics/ Ranganathan, S., Swain, R. B., & Sumpter, D. J. T. (2015). The demographic transition and economic growth: Implications for development policy. Palgrave Communications, 1(1), 1–8. UNECE. (2018). United Nations Economic Commission for Europe Guidelines to resolution No. 40 ‘International Certificate for Operators of Pleasure Craft’. https://unece.org/DAM/trans/ doc/2018/sc3/Guidelines_e_rev._publication-­final.pdf UNFPA. (2014). Population dynamics and policies. United Nations Population Fund. https://www. unfpa.org/resources/population-­dynamics-­and-­policies UNFPA Technical Division. (2012, January 1). Population matters for sustainable development. UNFPA. https://www.unfpa.org/publications/population-­matters-­sustainable-­development United Nations. (2015). A world of 8 billion: Towards a resilient future for all  - Harnessing opportunities and ensuring rights and choices for all. United Nations. https://www.un.org/en/ observances/world-­population-­day Van Dyke, F. (2008). The conservation of populations: Concept, theory, and analysis. In Conservation biology: Foundations, concepts, applications (pp. 213–242). Springer, Dordrecht. Zheng, H., & George, L. K. (2018). Does medical expansion improve population health? Journal of Health and Social Behavior, 59(1), 113–132.

Chapter 2

Population Dynamics and Economic Growth in Southeast Asia Asraful Alam

, Sourav Biswas, and Lakshminarayan Satpati

2.1 Introduction The rate of population growth in Asia has experienced significant changes over the years. In the second half of the 1960s, the annual growth rate stood at 2.5%, but it has been steadily declining since then, reaching 1.0% in the early 2010s. However, the pace of this decline varies across different regions within Asia. Notably, Southeast Asia, a vast region situated to the east of the Indian subcontinent and south of China, has experienced a comparatively slower decline in population growth, with rates decreasing from 2.7% to 1.2% during the 1960s (Frederick & Leinbach, 2020). Southeast Asia consists of 11 countries, including Burma, Thailand, Laos, Cambodia, Vietnam, Malaysia, Singapore, Indonesia, the Philippines, Brunei, and East Timor (Andaya, 2019). The changes in population growth rates are influenced by various factors, such as fertility, mortality, and migration, which collectively contribute to the demographic transition process (Ogawa, 2015). In recent times, Southeast Asia’s total population reached 593 million in 2010, indicating a doubling of the population over 38 years since 1972 and a 48% increase over the quarter-century since 1985 (Jones, 2013). Historically, Southeast Asia had a low population density due to its peripheral position and the presence of relatively weak states with large frontiers occupied by shifting cultivators. However, the region has now emerged as a significant demographic, economic, and political entity (Hirschman & Bonaparte, 2012). A. Alam (*) Department of Geography, Serampore Girls’ College, Serampore, Hooghly, India S. Biswas Department of Population & Development, International Institute for Population Sciences, Mumbai, Maharashtra, India L. Satpati UGC-HRDC, University of Calcutta, Kolkata, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Alam et al. (eds.), Population, Sanitation and Health, https://doi.org/10.1007/978-3-031-40128-2_2

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Southeast Asia exhibits some variation in population growth rates among its countries. The Philippines, Laos, Malaysia, Vietnam, and Brunei experience higher growth rates, while Singapore, Thailand, and Indonesia have lower rates primarily due to effective family planning programs (Frederick & Leinbach, 2020). The region’s economies have transitioned from subsistence to dynamic engines of growth (Hirschman & Bonaparte, 2012). Southeast Asia’s strategic importance is evident in its participation in the annual meetings of the Association of Southeast Asian Nations (ASEAN), drawing representatives from major industrial blocs worldwide (Hirschman & Bonaparte, 2012). Understanding the relationship between population growth and economic output is crucial. Scholars have extensively studied how population growth influences various phenomena such as age structure, international migration, economic inequality, and workforce size, all of which are interconnected with economic growth (Brown, 2013). Economic growth, measured by changes in per capita GDP, is closely tied to population growth. Southeast Asia’s 11 countries collectively have a GDP of $1.9 trillion, with a population of nearly 600 million and an average per capita income comparable to China’s (Brown, 2013). Southeast Asia’s diverse and vibrant economies contribute significantly to global GDP.  From Singapore’s highly developed society to Vietnam’s emerging middle class, the region represents 3.4% of global GDP (Brown, 2013). ASEAN leaders remain committed to strengthening partnerships and integration to enhance the region’s economy (Kawai, 2005). Southeast Asia’s engagement with overseas markets has stimulated its economic development, driven by high demand for its products in Asia and Europe (Shimada, 2019). With its large, young population and rapidly growing economies, Southeast Asia’s strategic importance on the global stage is expected to continue rising (Kawai, 2005).

2.2 The Demographic Situation in Southeast Asia Southeast Asia was previously recognized as a thinly populated area in the Asian continent, with low population density prior to the twentieth century (Hirschman & Bonaparte, 2012; Jones, 2013). However, a significant population surge has been observed since 1900, with densely settled regions emerging in areas where wet rice cultivation was prevalent, such as the Red River delta, parts of Luzon and Visayas, and the Chao Phraya (Jones, 2013). Despite this rapid population growth in recent decades, Southeast Asia still maintains lower overall population densities compared to countries like Japan, Korea, Bangladesh, and India (Jones, 2013). The demographic dynamics of Southeast Asian countries are examined from multiple perspectives, including population size, stages of demographic transition, population policies, and the availability of demographic information (Gunasekaran, 1987). By investigating these factors, a comprehensive understanding of the population trends in Southeast Asia can be obtained.

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Table 2.1  Population size, population growth rate, and population density in South-East Asia

Population (in thousands)

Population growth rates (annual average) 1980– 1990– 2000– Country 1980 1990 2000 2010 85 95 05 Brunei 189 252 327 399 2.92 2.77 2.09 Cambodia 6506 9532 12,447 14,138 3.93 3.17 1.41 Indonesia 150,820 184,346 213,395 239,871 2.17 1.57 1.26 Lao PDR 3235 4192 5317 6201 2.40 2.69 1.58 Malaysia 13,833 18,209 23,415 28,401 2.61 2.59 2.17 Myanmar 32,865 39,268 44,958 47,963 1.89 1.41 0.60 Philippines 47,064 61,629 77,310 93,261 2.77 2.33 2.03 Singapore 2415 3017 3919 5086 2.30 2.87 1.70 Thailand 47,483 57,072 63,155 69,122 1.94 0.88 1.09 Timor-­ 581 743 830 1124 2.56 2.75 3.93 Leste Vietnam 54,023 67,102 78,758 87,848 2.20 1.96 1.09 SE ASIA 359,012 445,361 523,831 593,415 2.26 1.74 1.33

Population density (persons per sq. km) 2010 69 78 126 26 86 71 311 7447 135 76 265 132

Source: United Nations Population Division, 2010

To have a proper demographic proper understanding, the below table shows the basic facts of population size and growth rates in the Southeast Asian region as per reports of the United Nations Population Division, 2010 (Jones, 2013). In the late twentieth century, Indonesia emerged as the country with the highest population in Southeast Asia (Table 2.1), whereas Brunei had a considerably smaller population (Lindblad, 1995). Although the overall population density of Southeast Asian countries is not considered high on a global scale, specific regions within these countries exhibit high population densities. Notably, densely populated areas include Java-Bali in Indonesia, the Red River Delta in Vietnam, and the Visayan region of the Philippines. Conversely, low population density is observed in the region of Lao PDR (Jones, 2013). The variation in population density across different regions within Southeast Asia highlights the diverse demographic patterns within the region (Jones, 2013; Lindblad, 1995).

2.3 The Recent Population Trend The availability and quality of population data for early Southeast Asia are subject to uncertainty due to the indirect nature of early censuses, particularly those conducted in the nineteenth century. However, over the course of the twentieth century, the quality of demographic data in the region significantly improved. Following their political independence, each country in Southeast Asia established a national

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Table 2.2  Fertility Rate by Country from 1950–55 to 1980–85

World Asia Southeast Asia Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Vietnam

Total fertility rate 1950–55 1960–65 4.95 4.91 5.82 5.59 6.05 6.25 7.00 6.56 6.29 6.29 5.49 5.62 5.94 5.97 6.23 6.23 6.00 6.10 7.42 6.98 6.61 5.12 6.14 6.13 6.44 6.37 6.20 7.33

1970–75 4.45 5.00 5.62 5.87 5.54 5.30 5.99 4.58 5.90 5.98 2.82 5.05 5.54 7.15

1980–85 3.59 3.69 4.22 3.92 7.00 4.11 6.36 3.73 4.30 4.92 1.59 2.95 5.39 4.93

Source: United Nations, World Population Prospects, the 2010 Revision

statistics office, prioritizing the collection of census data (Hirschman, 1994). Population reports for Southeast Asia indicate that population growth rates reflect the transition from high levels of fertility and mortality to lower levels, leading to a new equilibrium of slower population growth. However, it is important to note that population growth rates vary significantly between countries within the region (Jones, 2013). Additionally, while most countries have experienced a continuous increase in life expectancy since the 1950s, political regimes and conflicts have adversely affected population growth in countries such as Myanmar and Cambodia (Chongsuvivatwong et al., 2011). The pace and timing of fertility reduction have varied across different regions. Table 2.2 presented below provides estimates of fertility rates for Southeast Asian countries from 1950–55 to 1980–85, highlighting the variations in fertility trends (Hirschman & Bonaparte, 2012). The table serves as a useful tool to better understand the dynamics of fertility in the region during the specified period. Singapore witnessed the earliest and most significant decline in fertility rates, with the total fertility rate decreasing from over six children per woman in 1957 to two in the mid-1970s. Since 2003, Singapore has consistently maintained an ultra-­ low fertility rate, ranking among countries with very low birth rates. Vietnam, Brunei, and Indonesia have fertility rates close to replacement level. On the other hand, Laos, Cambodia, and the Philippines continue to have total fertility rates exceeding three children per woman, which can be attributed to lower educational levels, as indicated by their lower secondary school enrollment rates compared to other countries in the region (Chongsuvivatwong et al., 2011). Population change in Southeast Asia is also influenced by internal and external migration. Rural-to-urban migration remains a significant factor driving

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Table 2.3  Population estimates and average annual growth rates by country: 1980 to 2010, with projections to 2050

World Asia Southeast Asia Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Vietnam

UN population estimate (millions) 1980 1990 2000 4453 5306 6123 2638 3199 4164 359 445 524 0.2 0.3 0.3 6.5 9.5 12.4 150.8 184.3 213.4 3.2 4.2 5.3 13.8 18.2 23.4 32.9 39.3 45.0 47.1 61.6 77.3 2.4 3.0 3.9 47.5 57.1 63.2 0.6 0.7 0.8 54.0 67.1 78.8

2010 6896 4164 593 0.4 14.1 239.9 6.2 28.4 48.0 93.3 5.1 69.1 1.1 87.8

Projected 2025 8003 4730 683 0.5 16.7 271.9 7.4 35.2 53.2 118.1 5.8 72.9 1.7 99.3

2050 9306 5142 759 0.6 19.0 293.5 8.4 43.5 55.3 154.9 6.1 71.0 3.0 104.0

Source: United Nations, World Population Prospects

demographic changes in almost all Southeast Asian nations (Frederick & Leinbach, 2020). The quality of demographic data in Southeast Asia has notably improved in recent decades (Hirschman, 1994). To gain a comprehensive understanding, a table provided by the United Nations presents the average annual growth rate in Southeast Asian countries and projected population figures for the years 2025 and 2050 (Hirschman & Bonaparte, 2012). This table serves as a valuable resource for obtaining a clear perspective on population growth trends in the region (Table 2.3).

2.4 Historical and Current Economic Situation Southeast Asia has a rich history of trade and economic development, with a significant role in the world trading system even before European influence. The region’s focus was primarily on shipping connections and the trade of spices such as pepper, ginger, cloves, and nutmeg. While Indian and Arab merchants initially dominated the spice trade, European powers, particularly the British and French, eventually became involved, leading to the expansion of their control and annexation of territories. This integration of Southeast Asia into the global economy had far-reaching effects on the region’s economic development, resulting in uneven patterns of population growth and economic activities (Frederick & Leinbach, 2020). The economic growth in Southeast Asia is believed to be rooted in private enterprises and unrestricted market forces (Table 2.4). A more detached view of European presence in the region, along with increasing quantification, prompted a reassessment of economic growth during the late colonial period. Since the 1950s, the

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Table 2.4  Per capita gross domestic product based on Purchasing Power Parity (PPP) and economic growth of South-East Asian countries Country Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Vietnam

1980 n/a n/a 730 342 2350 n/a 1334 6758 1090 n/a 299

1990 36,242 562 1543 685 4839 n/a 1873 17,394 2910 n/a 658

2000 43,320 908 2429 1180 9174 459 2442 32,262 5007 2330 1424

2010 48,621 2065 4353 2449 14,744 1255 3920 56,708 9222 7889 3143

Average Annual Growth Rate, PPP 1980–1990 1990–2000 2000–2010 n/a 2 1 n/a 5 9 8 5 6 7 6 8 7 7 5 n/a n/a 11 3 3 5 10 6 6 10 6 6 n/a n/a 13 8 8 8

1980–2010 n/a n/a 6 7 6 n/a 4 7 7 n/a 8

Source: International Monetary Fund, World Economic Outlook Database, April 2012

economic development strategies of capitalist Southeast Asian states have predominantly focused on urban industrialization, considering agricultural development as subsidiary to industrial growth (Frederick & Leinbach, 2020). To enhance our understanding, a table presenting the per capita Gross Domestic Product (GDP) based on purchasing power parity (PPP) and the economic growth of Southeast Asian countries is provided below (Jones, 2013). This table serves as a valuable resource for analyzing the economic performance and disparities among these nations in Southeast Asia. The table above reveals the economic performance of Southeast Asia, indicating a positive trend in recent decades. Singapore and Brunei emerge as the wealthiest countries, while Malaysia and Thailand are categorized as upper-middle-income nations (Jones, 2013). The robust economic growth experienced by Southeast Asian economies can be attributed to the expansion of foreign trade and investment, which has laid the foundation for their present-day prosperity (Parker, 1993). In particular, the region’s involvement in international trade has been instrumental in driving economic growth since the latter part of the twentieth century (Shimada, 2019). Singapore stands out with one of the highest per capita incomes globally, and countries such as Indonesia, Malaysia, Singapore, and Thailand have demonstrated remarkable growth over the past three decades (Hill & Arndt, 2002). Intra-Asian trade has also played a significant role in driving economic changes in Southeast Asia, contributing to the region’s socioeconomic development (Shimada, 2019). To gain further insight into this progress, a chart illustrating the advancements in international trade, and its impact on the socioeconomic landscape of Southeast Asia, is provided below (Shimada, 2019). This chart serves as a valuable resource for understanding the dynamics and trends of international trade that have shaped the region’s economic growth.

2  Population Dynamics and Economic Growth in Southeast Asia I. Pre Columbian Exchange Period: Before 1500

II. Early Modern Period: 1500–1870 Trade with Europe

Trady with India & China

Trade with Persia, India, China & Japan

Trade within Southeast Asia

Trade within Southeast Asia

- Indianization - Envoys by Zheng He - Emergence of port polity

- Expansion of overseas marker - New pattern of production - Chinese immigration and multiethnic society

III. Modern Period: 1800–1975 Trade with Europe & US

Trady with India, China & Japan

Trade within Southeast Asia - Opening of Suez Canal (1869) and emergence of steamed vessels - New institutional frameworks - Increase in Chinese immigration - Decolonization

23 IV. Contemporary Period: After 1975 Trade with Europe & US

Trade with China, Japan, Korea & Taiwan

Trade within Southeast Asia - Developmental dictatorship and democratization - Asian NIEs - Industrialization

Fig. 2.1  International trade and socioeconomic changes in Southeast Asia (Shimada, 2019)

Hence, it can be said that as Southeast Asian countries continue to prosper (Fig. 2.1), a global interconnection of trade, as well as investment flows, is being observed largely (Parker, 1993).

2.5 Urbanization Level in Southeast Asia While the level of urbanization in Southeast Asia may be relatively low when compared to global standards, it has been steadily increasing over time (Jones, 2013). According to a report published by the United Nations in July 2014, Southeast Asian cities account for 47% of the region’s population, with varying urbanization rates ranging from 20% in Cambodia to 53% in Indonesia and 100% in Singapore. Moreover, Southeast Asia has experienced a higher annual growth rate in urban population compared to the Asia-Pacific average, with a rate of 3.6% during the 1990s (Jones, 2013). The cities in this region are undergoing constant growth and transformation. From 1950 to 2014, Southeast Asia’s urban population increased by a staggering factor of 11, from 26 to 294 million (Dahiya, 2014). To provide a comprehensive overview of urbanization levels in Southeast Asia, the table below presents data from the United Nations Population Division in 2009 (Jones, 2013). Table 2.5 serves as a valuable resource for understanding the level of urbanization in different Southeast Asian countries, as well as the trends associated with urban population growth. The analysis of the table reveals that Singapore, Brunei, and Malaysia have high levels of urbanization, while approximately half of the populations in Indonesia and the Philippines reside in urban areas. However, most countries in Southeast Asia have relatively low levels of urbanization, particularly Cambodia (Jones, 2013). Nevertheless, the conditions for rural populations are undergoing significant

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Table 2.5  Level of Urbanization in Southeast Asia, 1950–2030 Country Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Vietnam SE Asia

1950 26.8 10.2 12.4 7.2 20.4 16.2 27.1 100 16.5 11.6 15.5

1975 62.0 4.4 19.3 11.1 37.7 23.9 35.6 100 23.8 18.8 23.3

2000 71.1 16.9 42.0 22.0 62.0 27.8 48.0 100 31.1 24.5 38.2

2010 75.7 20.1 44.3 33.2 72.2 33.6 48.9 100 34.0 30.4 41.8

2020 (Projected) 79.3 23.8 48.1 44.2 78.5 40.7 52.6 100 38.9 37.0 46.7

2030 (Projected) 82.3 29.2 53.7 53.1 82.2 48.1 58.3 100 45.8 44.2 52.9

Source: United Nations Population Division, 2009

changes, with the emergence of secondary and tertiary industries providing a growing number of job opportunities in rural areas (Jones, 2013). It is noteworthy that Southeast Asia is home to two megacities, namely Manila with a population of 11.8  million and Jakarta with a population of 10  million. Projections indicate that the proportion of urban populations residing in megacities will reach 13% by 2030, as Bangkok and Ho Chi Minh City are expected to attain megacity status (Dahiya, 2014). Consequently, it becomes crucial to adopt urbanization policies and strategies that promote balanced subnational development, considering the ongoing progress in urbanization trends (Dahiya, 2014). This would contribute to effectively managing the challenges and opportunities associated with urban growth in the region.

2.6 Changing Age Structure and Economic Growth Southeast Asian countries have demonstrated high productivity, contributing to their economic growth (Dahiya, 2014). The changing age structure of the population has also played a significant role in this growth. The decline in fertility rates has resulted in a larger proportion of the population belonging to productive age groups. This demographic shift has implications for educational planning and manpower management, particularly in relation to the younger age groups (Jones, 2013). The increase in the workforce has had a positive impact on the region’s economy. Between 1990 and 2012, the Gross Domestic Product (GDP) of the region tripled from $444 million to $1.3 billion. Moreover, since 2011, the average annual economic growth in the region has been around 5.2%, indicating its sustained dynamism (Jones, 2013). Alongside economic growth, there has been a shift in employment from the primary sector (including agriculture) to the secondary and

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Table 2.6  Gross Domestic Product

Country Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Vietnam

Gross Domestic Product Per Capita (Constant 2000 US $) 2006 2007p 18,304 19,085 445 439 983 1065 439 459 4535 4794 – – 1154 1195 27,125 28,764 2601 2783 576 595

Gross Domestic Product Per Capita, PPP (Constant 2005 International $) 2006 2007p 48,357 50,419 1569 1548 3348 3627 1919 2005 12,149 12,841 – – 3055 3162 43,328 45,946 7364 7879 2290 2367

Source: International Labour Organization, 2008 sourced from World Bank, World Development Indicators, 2008 Note: Here, “p” denotes projection, and estimates for 2007 are calculated on the basis of 2006 GDP figures together with GDP and population growth rates for 2007

tertiary sectors. From 1991 to 2012, primary sector employment decreased from 58% to 41%, while secondary sector employment increased from 14% to 19%, and tertiary sector employment rose from 28% to 40%. Additionally, the share of vulnerable employment within the total employment in the region declined from 68% to 61% during this period (Dahiya, 2014). To provide a comprehensive understanding, a table on the Gross Domestic Product per capita in the Southeast Asian region has been presented below (Martinez-­ Fernandez & Powell, 2010). These data (Table 2.6) further illustrate the economic progress of the region and highlight the increasing prosperity among its nations. From the above table, it can be interpreted that the highest Gross Domestic Product per capita in Southeast Asian nations is found in Singapore and Brunei Darussalam with over USD 18000 (2006). Malaysia comes next with over USD 4500 (2006) and Thailand above USD 2000. However, Viet Nam, Cambodia, Lao PDR, and Myanmar fall below USD 1000 (Martinez-Fernandez & Powell, 2010).

2.7 Linking Demographic Changes with Economic Growth The relationship between population growth and economic output has been extensively studied by analysts, as it influences various aspects such as age structure, international migration, economic inequality, and the size of the workforce, ultimately impacting a country’s overall economic growth (Peterson, 2017). Economic growth is typically measured by changes in Gross Domestic Product (GDP), which

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represents the total output net of capital depreciation and includes net income from external sources (Peterson, 2017). To comprehend the significance of demographic changes in Southeast Asia, it is crucial to examine the region’s economic history (Bloom & Rosenberg, 2011). As previously discussed, Southeast Asia has undergone significant demographic shifts, transitioning from high fertility and mortality rates to low levels and achieving a new balance of slower population growth. This decline in fertility rates has resulted in a larger proportion of the population belonging to productive age groups (Jones, 2013). It is generally observed that an increase in the ratio of working-age to non-­ working-­age population correlates with higher per capita economic growth rates (Bloom & Rosenberg, 2011). The growth in the workforce in Southeast Asia between 1990 and 2012 has contributed to a significant increase in GDP, tripling from $444 million to $1.3 billion. Additionally, there has been a steady shift from the primary sector to the tertiary sector, indicating a transition from agriculture-based industries to service-oriented sectors (Dahiya, 2014). The economy of Southeast Asia has been steadily improving, accompanied by an increase in the share of the working-age population in the region (Bloom et  al., 2011). Furthermore, satisfactory progress has been observed in terms of urbanization (Dahiya, 2014). Based on studies, it is anticipated that Southeast Asia will continue to experience a larger share of the working population and a rise in per capita incomes in the coming decades. Hence, positive demographic changes have played a significant role in strengthening the region’s economy (Bloom & Rosenberg, 2011).

2.8 Conclusion Southeast Asia stands out as one of the most diverse regions globally, characterized by its rich cultural and ethnic variety (Frederick & Leinbach, 2020). Historically, this region was sparsely populated; however, in the past century, a remarkable reversal in demographic patterns has occurred (Hirschman & Bonaparte, 2012). Southeast Asia has undergone a demographic transition, shifting from high levels of fertility and mortality to lower levels, resulting in a new equilibrium of slower population growth, and a significant increase in the population of working-age groups (Jones, 2013). The rise in the population of productive age groups has played a crucial role in the region’s economic growth, accompanied by a transition in employment from the primary sector to the tertiary sector. This shift has been observed not only in urban areas but also in rural regions, contributing to the overall national development of the region (Dahiya, 2014). The forthcoming demographic changes in Southeast Asia are expected to further benefit the region’s economic prospects. As the economies of the region continue to improve, a positive relationship between demographic changes and economic

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growth is anticipated to strengthen (Bloom & Rosenberg, 2011). Southeast Asia currently holds a strong position relative to other major Asian regions in terms of its economy (Jones, 2013). Over the past decade, the region has witnessed significant economic growth and finds itself at a crucial juncture. Sustaining and furthering South-East Asia’s growth necessitates deeper regional cooperation and integration from a policy perspective. Therefore, long-term policies and strategic changes are crucial for achieving balanced and robust national development in the region (Dahiya, 2014).

References Andaya, B.  W. (2019). Introduction to Southeast Asia. Asia Society. https://asiasociety.org/ education/introduction-­southeast-­asia Bloom, D. E., & Rosenberg, L. (2011). The future of South Asia: population dynamics, economic prospects, and regional coherence. Brown, J. (2013). Southeast Asia: Region on the Rise. Inbound Logistics. https://dev.inboundlogistics.com/cms/article/southeast-­asia-­region-­on-­the-­rise/ Chongsuvivatwong, V., Phua, K. H., Yap, M. T., Pocock, N. S., Hashim, J. H., Chhem, R., Wilopo, S. A., & Lopez, A. D. (2011). Health and health-care systems in southeast Asia: diversity and transitions. The Lancet, 377(9763), 429–437. Dahiya, B. (2014). Southeast Asia and sustainable urbanization. Global Asia, 9(3), 84–91. Frederick, W. H., & Leinbach, T. R. (2020). Southeast Asia. Encyclopedia Britannica. Gunasekaran, S. (1987). Demographic problems of Southeast Asia. Southeast Asian Affairs, 45–62. Hill, H., & Arndt, H.  W. (2002). The economic development of Southeast Asia. Edward Elgar. https://www.e-­elgar.com/shop/gbp/the-­economic-­development-­of-­southeast-­ asia-­9781858988009.html Hirschman, C. (1994). Population and society in twentieth-century Southeast Asia. Journal of Southeast Asian Studies, 25(2), 381–416. Hirschman, C., & Bonaparte, S. (2012). Population and society in Southeast Asia: ahistorical perspective. Demographic Change in Southeast Asia: Recent Histories and Future Directions. Cornell Southeast Asia Program Publications. Jones, G. W. (2013). The population of Southeast Asia (p. 196). Asia Research Institute, National University of Singapore. www.nus.ari.edu.sg/pub/wps.htm Kawai, M. (2005). East Asian economic regionalism: progress and challenges. Journal of Asian Economics, 16(1), 29–55. Lindblad, J.  T. (1995). Current trends in the economic history of Southeast Asia. Journal of Southeast Asian Studies, 26(1), 159–168. Martinez-Fernandez, C., & Powell, M. (2010). Employment and skills strategies in Southeast Asia: Setting the scene. Ogawa, N. (2015). Demographic change in Southeast Asia: Recent histories and future directions edited by Lindy Williams and Michael Philip Guest. Southeast Asia Program Publications, Southeast Asia Program, Cornell University, 2012, 221 pp. Wiley Online Library. Parker, S. (1993). Trade and investment in Southeast Asian development. Journal of Northeast Asian Studies, 12(3), 49–65. Peterson, E.  W. F. (2017). The role of population in economic growth. SAGE Open, 7(4), 2158244017736094. Shimada, R. (2019). Southeast Asia and international trade: continuity and change in historical perspective. In Paths to the emerging state in Asia and Africa (pp. 55–71). Emerging-Economy State and International Policy Studies.

Chapter 3

Impact of Rural Male Outmigration on Women Work Participation in Rarh Region of West Bengal, India Manoj Debnath

Abbreviations NREGA PDS S.E. SHGs

The National Rural Employment Guarantee Act Public Distribution System Standard error Self-help groups

3.1 Introduction Of late outmigration is acknowledged as a significant generator for socio-economic development in developing countries like India. More importantly, migration influences the social, political and economic life of the people. Several studies focus on the different processes of migration especially on female migration (Chin, 1997; Yeoh et al., 1999; Morokvasic, 2004; Shah, 2004), while little attention is given on the process of left-behind women (Hugo, 2000; Desai & Banerji, 2008). When the male folk migrate for work, they left their female partner along with children and the elderly at the source region (Démurger, 2015), which increased women workload and responsibility within the household (Jetly, 1987; Mascarenhas-Keyes, 1990; Desai & Banerji, 2008; Rodgers & Rodgers, 2011). Plenty of research on women empowerment and decision-making in India revealed confined autonomy over the family as well as on society (Mason & Smith, 2000; Jejeebhoy & Sathar, 2001; Bloom et al., 2001; Démurger, 2015; Ray & Debnath, 2019). On the other hand, it was found that in the absence of a male partner, female may need to fill all M. Debnath (*) Department of Geography, School of Human and Environmental Sciences, North Eastern Hill University, Shillong, Meghalaya, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Alam et al. (eds.), Population, Sanitation and Health, https://doi.org/10.1007/978-3-031-40128-2_3

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M. Debnath

the responsibilities such as animal care, work on the family farm, business, etc. (Jetly, 1987; Paris et al., 2005; Rodgers & Rodgers, 2011; Démurger, 2015). Female work participation improves their status of empowerment in rural West Bengal (Ray & Debnath, 2019). Rural male outmigration and its impact on socio-economic development have significant importance on village-level as well as national-level development and planning. According to the neoclassical economic theory, the decision of migration is taken by the individual, and government policies can influence on expected earnings at source and destination region (Massey et al., 1993; Taylor & Martin, 2001; Constant & Massey, 2002; Mendola, 2012). The new economics of labour migration (NELM) theory has a different point compared to neoclassical economic theory. The NELM theory explained that the decision of migration is not made by individual; rather it is the result of the collective strategy of family or household to maximize the income but minimize the risks (Stark & Levhari, 1982; Lauby & Stark, 1988; Taylor, 1986; Constant & Massey, 2002). In a developing country like India, a common characteristic of male outmigration is that they left their wife and children at the source place (Kanaiaupuni, 2000; Desai & Banerji, 2008). Due to male outmigration, women workload and responsibility within the household have increased comprehensively (Jetly, 1987; Mascarenhas-Keyes, 1990; Desai & Banerji, 2008; Rodgers & Rodgers, 2011). This present study focuses on the impact of male outmigration on the rural left-behind women of rural villages in eastern India. The rural Rarh region has been taken into consideration for this present research. This region is a traditionally active temporary male outmigration region in eastern India. A significant proportion of male outmigrated every year (Rogaly et  al., 2001, 2002; Rogaly & Rafique, 2003; Sengupta & Ghosal, 2011), and a common characteristic of male outmigration is that they leave their wife and children at the source place (Kanaiaupuni, 2000; Desai & Banerji, 2008). The left-behind women are doing cumbersome work from dawn to dusk and have to combat with the unprivileged ambience to commit their daily livelihood facile. In this context, the present study aims to investigate the impact of male outmigration on the rural left-behind women and to determine the probability of decision-making power by the left-behind women. Here, two hypotheses are established before the study: first, male outmigration increased the workload of left-­ behind women and, second, women work participation outside the household increased decision-making autonomy of left-behind women. This study mainly focuses on three important dimensions of left-behind women, i.e. the nature of work performed by rural women within the household, the work undertaken by women outside the household and the relation between decision-making by women in the study area and those who work outside.

3.2 Methods The present study is based on both quantitative and qualitative research. Both the quantitative and qualitative data were collected through a primary survey of the sample households with the help of well-structured schedules. Impact of male

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outmigration on rural women analysed as work participation at household, types of work outside the house, savings and decision-making autonomy. The present study asked the following some important questions: What kinds of work women are doing which she did not do earlier? Does she work outside the households? Do the women operate a bank account? Has there been any change in decision-making by women in the absence of male? Bankura district is selected for the present study because it is the highest outmigration district in West Bengal (Debnath, 2017; Debnath et al., 2019). For the selection of sample villages, two distinct criteria have been chosen. Three different landholding classes (landless, marginal and small landholding households) and mixed caste (scheduled, backward and non-scheduled) composition villages are selected for the present study. To assess the rural outmigration at the household level, the landholding sizes of the households have been divided into three categories, i.e. landless households, marginal landholding (less than 1 hectare) and small landholding (1.0–2.0 ha) as per the scheme of the Department of Agriculture, India. Landholding size and caste composition both have a direct impact on women empowerment in the absence of male outmigrants. A pilot survey has been done before selecting villages to understand the nature of outmigration and the impact of male outmigration on the left-behind women. Six villages are randomly selected based on selected criteria. Kurul Pahari, Dhabani, Haridihi, Dhula Danga, Patan and Kalagram villages in Bankura district of West Bengal in India are selected for the present study (Fig. 3.1). Out of the 756 households at the villages, 185 male migrant households were selected randomly. The interview was carried out in the local language. A well-structured schedule was prepared to visit migrant households and collected all the information in a face-to-face interview process. The present study focuses on two important dimensions of left-behind women, that is, women workload and decision-making power. In this study, the impact of male outmigration on rural women analyses the nature of work done by the rural female within the household chore and outside the household. The study also focuses on the determinants of decision-making autonomy of left-behind women. The field survey was carried out during March–October 2019. The decision-making power of rural women can be determined by the various socio-economic variables (Desai & Banerji, 2008; Démurger, 2015; Ray & Debnath, 2019). The logistic regression model has been fitted to analyse the relationship between the decision-making autonomy of left-behind women and socio-economic variables in the study area. Different socio-economic variables like level of education, social groups, bank operation, savings, size of landholding households, types of family and women workload outside the households have been taken into consideration. The probabilities of decision-making autonomy of rural left-behind women were coded in a binary form, the dependent variable considered as value “1” if women can make a decision and “0” if not able to make a decision. The binary logistic regression model has been fitted to analyse the probabilities of decision-making autonomy of rural left-behind women. The model is explained as Eq. (3.1):

Yi   o  1 Xi1   2 Xi 2  ..  k X ik   i

(3.1)

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M. Debnath

Fig. 3.1  Location of the study area, Bankura District, West Bengal, India

where Yi are dependent variables, βο is constant, β1 is coefficient of variables X1, X1 are independent variables and ε is an error term. The p-value can be derived as Eq. (3.2):



P

1 1 e

  0   1. X 1  2. X 2  .  k . Xk



(3.2)

The total sample has taken 185 migrant households. Among these sample households, 96 migrant household’s women were able to take decision. The independent variables, which have been taken into consideration, all are categorical form. Here, the level of education is classified into three categories, i.e. illiterate, up to 5 years

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33

of schooling and above 5 years of schooling (reference). Social group/caste category is classified as scheduled caste and tribes, other backward castes and forward castes (reference). Bank operation, savings, women who work outside the households and types of household categories as Yes/No (reference). The landholding size is the primary economic factor of outmigration in rural areas (Connell et al., 1976; Roy, 1991; Kumar et al., 1998; Parganiha et al., 2009), which directly affects the decision-making autonomy of left-behind women. Size of landholding has been classified into landless, marginal and small landholding households (reference). The descriptive statistics of all the independent variables used in the model are shown in Table 3.1. The lower level of education is noticed among the left-behind women in the study area. The caste composition clearly showed that decision-making power is higher among scheduled caste and tribe (56.22%). These selected variables directly or indirectly influence the decision-making autonomy of left-behind women in the study area.

Table 3.1 Descriptive statistics of selected variables used to determine the decision-making power

Variable Education:  Illiterate  Up to 5 years of schooling  Above 5 years of schooling Social group:  Scheduled castes and tribes  Other backward castes  Forward castes Bank operation:  Yes  No Savings:  Yes  No Work outside the households:  Yes  No Landholding:  Landless  Marginal (below 1 ha)  Small (1–2 ha) Family types:  Nuclear  Joint

Number Percentage 70 80 35

37.84 43.24 18.92

104 12 69

56.22  6.49 37.30

100 85

54.05 45.95

57 128

30.81 69.19

96 89

51.89 48.11

82 77 26

44.32 41.62 14.05

115 70

62.16 37.84

Source: Household Survey, 2018–2019

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M. Debnath

3.3 Results and Discussion 3.3.1 Household Peculiarity Bankura is a rain-fed area in West Bengal, which is well known as a drought-prone area. The north-western part of this district is under drought-prone areas (DDMP 2016). The western part of this district is covered by hilly terrain and undulating tract. Due to hilly terrain and undulating surface, the canal irrigation networks are not developed surrounding the dam region. Agriculture practices primarily depended on rainfall due to lack of irrigation facilities. There is no farming activity in the village for the rest of the year except monsoon paddy cultivation. Landless and tiny handholding households are unable to sustain their livelihood by working only as agricultural labourer and wage labourer. That is why a significant proportion of rural male folk from landless and marginal households migrated from this region. Due to the absence of the male agricultural worker in the villages, female agricultural labour increases substantially. Various demographic, economic and socio-­ cultural aspects affect the nature and intensity of outmigration in the rural areas. Moreover, sex, caste, literacy and socio-economic background also affect the intensity as well as the choice and decision of outmigration. It is essential to study the different demographic and socio-economic characteristics of migrant households in the study area. Table  3.2 provides a summary of the characteristics of migrant households. The sex ratio in the study area (933) is below national (940) as well as state (947) average (Census of India, 2011). The study area shows that each migrant household has three dependent members on an average. On the other hand, the majority of the rural households (84.86%) used firewood and dry fire leaf for the daily cooking. Indebtedness among migrant households is quite a typical incident in the study area. A little over half of the sample households availed loan for the current year due to a variety of reasons. The lower level of education is noticed among the left-behind women in the study area. The caste composition clearly showed that decision-making power is higher among scheduled caste and tribe (56.22%). The study shows that about 44.32% migrant households are landless among the total migrant households. Approximately 41.62% marginal and 14.05% small landholding households experienced outmigration among the total migrant households. The study area is also deficient in physical capital. About 70% households still have Kacha and semi-Pucca house. Around 36% houses are still Kacha (houses built by locally available materials) made by thatch, asbestos, tin, mud, etc. About 30% houses are semi-Pucca, made by natural elements as well as Pucca materials like bricks, stone cement, etc. The Pucca houses constitute only 30.27% of the households. The study area experienced the highest male outmigration in the state due to chronic backwardness. More than half of all the males (51.26%) from the migrant households had migrated out. The rate of male outmigration is even more in the age group of 14–65 years old. A significant proportion of rural male folk are migrated from this region. Due to the absence of the male agricultural worker in the villages, female agricultural labour increases substantially. The left-behind women are

3  Impact of Rural Male Outmigration on Women Work Participation in Rarh Region… Table 3.2 Household characteristics of the study area

35

Variable Number Percentage Sex ratio 933 Number of dependents 2.77 Firewood used for cooking 84.86 Loan is taken 50.81 Education level:  Illiterate 70 37.84  Up to 5 years of schooling 80 43.24  Above 5 years of schooling 35 18.92 Social groups:  Scheduled castes and tribes 104 56.22  Other backward castes 12 6.49  Forward castes 69 37.30 Landholding size:  Landless 82 44.32  Marginal (below 1 ha) 77 41.62  Small (1–2 ha) 26 14.05 Family types:  Nuclear 115 62.16  Joint 70 37.84 Housing characteristics:  Kacha house 68 36.76  Semi-Pucca 61 32.97  Pucca house 56 30.27 Propensity of migration:  Migration rate among the total male 51.26  Migration rate among the 58.28 14–65 years old Number of households 185 100 Source: Household Survey, 2018–2019

working hard from dawn to dusk and have to combat to make their daily livelihood facile. The study depicts that household responsibility of women increased due to the absence of a male.

3.3.2 Nature of Work: Within the Household In the absence of male, women need to take greater responsibilities in family decision-­making day by day, and in many cases, they engaged in different farm and off-farm activities in the countryside. In this study, the impact of male outmigration on rural women is divided into three parts, i.e. nature of work performed by rural women within the household, work undertaken by women outside the household

36

M. Debnath

Table 3.3  Nature of work done by the rural female at household after male migration Types of work Women participated in different HH work (%) Types of work participated by women (%)  Marketing  Participation in agriculture  Collecting ration from PDS  Bringing children from the school  Depositing electric bill  Banking activity  Livestock  Others

All HHs 84.32

Landless 85.37

Marginal 84.42

Small 80.77

27.03 33.51 28.65 14.05 3.24 31.35 40.00 24.86

30.49 0.00 42.68 17.07 3.66 29.27 43.90 31.71

28.57 59.74 20.78 11.69 3.90 32.47 40.26 15.58

11.54 61.54 7.69 11.54 0.00 34.62 26.92 34.62

Source: Household Survey, 2018–2019

and the relation between decision-making by women in the study area and those who work outside. Table 3.3 shows different types of household work done by the rural women which she did not do earlier. Rural women did different kinds of household work such as marketing and participating in own farm activity, PDS collection, child care and bringing children from school, depositing electric bill, banking activity, livestock rearing and so many other works. A very high proportion of households (80.85%) had women who complained an increase of household responsibility due to the absence of a male. The study reveals that women from landless (85.37%) and marginal (84.42) landholding households had to share higher workload compared to small (75.77%) landholding households. Majority of the landless (68.29%) and marginal (59.74%) households are nuclear families, while small landholding households (65.38%) have a joint family. Due to the large family size, small landholder women have comparatively less workload. About 30% women of the landless and marginal household go to the local market in the absence of adult male persons in the household. On the contrary, only 11.54% women of small landholding households go to the local market. The study reveals that a large percentage of women from marginal (59.74%) and small (61.54%) landholding households engaged in their own farm activity during the agricultural seasons. Collection of ration from the public distribution centre (PDS) is a common weekly or bi-monthly work for women. One-third of the women reported that they regularly visited PDS. Child care and child education is a major responsibility which women do due to the absence of a male. About 14% women reported that they are regularly bringing their children from school. Majority of the rural women of migrant households experienced tremendous workload due to the absence of an adult male. The landless and marginal landholder women have higher workload compared to women of small migrant households. Fewer women reported that they deposited electricity bills in the nearby block electric office. Banking activity of the households increased gradually after male outmigration. One-third of the women reported that in recent past, banking activity slowly increased which the women had to do. About 34.62% small landholder women reported that they access banking followed by marginal

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37

(32.47%) and landless (29.27%) households, respectively. A significant proportion of migrants sent remittances by a bank (43.91%). The study also noticed that migration remittances and banking activity of rural women have positive relationships. About 34.69% landless males sent migration remittances by the bank, and 29.27% landless women access banking activity. Half of the marginal landholder male sent migration remittances by bank, and 32.47% women access banking activity. Similarly, about 53.13% small landholder males sent migration remittances by bank, and 34.62% women regularly access banking activity. The study found that landless women have less banking activity compared to women of landed households. Livestock rearing is another essential daily work for rural women, especially during the absence of male persons in the household. The study reveals that women of landless (43.90%) and marginal (40.26%) households engaged more on their livestock rearing compared to women from small (26.92%) households.

3.3.3 Nature of Work: Outside the Household The study found that the rural women of left-behind households also work outside the households after male outmigration. A significant proportion of women reported that the remittances sent by the male outmigrant do not meet all the daily basic needs necessitating women of these households to work outside the households for more earning. Table 3.4 shows that about half of the women (51.89%) of migrant households worked outside to supplement household income. The proportion of such women, i.e. three-fourths of landless migrant households (76.61%), work for a wage. The size of remittances depicts that about 47% total landless migrant households received very low (below Rs. 3150 per month average) remittances compared to marginal and small landholder migrant households (Table 3.4). Most women of landless households worked outside the households as a maid, wage labourer, NREGA, agricultural labourer, etc. On the other hand, women from marginal and small landholding households work less outside the household. Majority of them were engaged in their agricultural field. About 38.96% of women from marginal landholder households worked outside the households. On the contrary, only Table 3.4  Nature of work done by the rural female outside the household after male migration Types of work Female worked outside (%) Types of work (%)  Maid  Wage labourer  Work for NREGA  Agricultural labourer

All HHs 51.89

Landless 75.61

Marginal 38.96

Small 15.38

3.24 11.89 22.70 44.32

4.88 23.17 30.49 63.41

2.60 3.90 20.78 33.77

0.00 0.00 3.85 15.38

Source: Household Survey, 2018–2019

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M. Debnath

15.38% women from small landholder households worked outside the household. The study found that upper caste and forward caste women have much more social barrier compared to scheduled caste/tribe women. The study depicts that 23.17% women from the landless migrant households worked as wage labourers compared to only 3.9% from marginal landholding migrant households. Similarly, about one-third women of landless households (30.49%) worked for NREGA followed by marginal (20.78%) and small (3.85%) landholding migrant households. Due to the absence of the male agricultural worker in the villages, female agricultural labour increases substantially. Table 3.4 reveals that about 63.41% women of the landless household worked as agricultural labourers followed by marginal (33.77%) and small (15.38%) landholding migrant households. It is also observed that the women of landless households engaged more on agricultural labour compared to marginal and small migrant households due to less social barriers to undertake extra-mural wage work. In contrast, women of forward caste households occupy themselves only in mural household work. The study shows that about 73.34% scheduled caste women work outside the households as wage earners, while a small proportion of forward caste women work outside the households.

3.3.4 Decision-Making This section examines the years of bank account operation, saving and decision-­ making by rural women of migrant households. The study reveals that about half of the women have a bank account. It is also found that banking activity of households increased after male outmigration. A significant proportion of male outmigrants sent their migration remittances by the bank. With this, the women who worked for NREGA and as members of self-help group have a bank account. About half of the women reported that in recent past, banking activity has increased. Year of banking also indicates that the majority of women (73%) operated bank for more than 1 year due to received migration remittances. Table  3.5 shows that about one-third of women from migrant households have saving. Banking activity of women has increased after the male outmigration in the study area. Banking activity and saving have increased women self-efficiency and economic status, which accelerate women to making different household decisions. Decision taken by the women during the absence of male members in the family has a significant increase among rural women in the study area. The study reveals that about half of the left-behind women take independent decisions due to the absence of a male. Table  3.5 shows that women of landless migrant households (56.10%) have more autonomy to take decisions in the prolonged absence of the males compared to women of marginal (40.26%) and small (42.31%) landholding migrant households where the frequency of visits by the migrant male members is comparatively more. In case of household expenditure, women take frequent decisions. On the other hand, children’s

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39

Table 3.5  Bank account operation, savings and decision-making by women Women operated bank account (%) Year of bank account  1 year  1–5 years  More than 5 years Women have saving (%) The decision was taken by women in the absence of male (%)

All HHs 54.05

Landless 53.66

Marginal 53.25

Small 57.69

11.00 73.00 16.00 31.35 47.57

6.82 86.36 6.82 29.27 56.10

14.63 63.85 19.51 32.47 40.26

13.33 53.33 33.33 34.62 42.31

Source: Household Survey, 2018–2019

education and household properties are issues which are taken jointly with menfolk. The study found that significant relation between decision-making by women and caste category of them. It is scheduled caste women (61.36%) who have more freedom to make decisions in the absence of male compared to forward caste women (34.09%) in the study area. Table 3.5 reveals significant relation between decision-­ making by women who worked outside and by women in the study area. This is indicative of the fact that if women work participation increases, then the decision made by the women also increases. Women participation in economic work is also determined by socio-cultural taboos, society’s rigidness, social prejudices, etc. Women who belong to lower strata of the region participated more in the economic works in the absence of her male counterpart that resulted in severe workload to her. Although outmigration of male folk increases the workload to women, they are getting an opportunity to earn some money to meet their household needs and have more decision-making power rather than the groups of women who engaged themselves inside the household chore. Duration of male outmigration is another important parameter for determining the decision-making power of left-behind women. It was found that the absence of male counterparts gives women more decision-making opportunity. In contemporary Indian society, measurement of decision-making autonomy of women is not an easy task. Various literatures on women empowerment in India revealed very limited decision-making autonomy (Mason & Smith, 2000; Jejeebhoy & Sathar, 2001; Bloom et al., 2001), while it was also noticed that left-behind women have a greater role within and outside the households during the absence of male counterparts (Desai & Banerji, 2008; Ray & Debnath, 2019). The present study explored a similar result. The left-behind women have greater responsibility to do work hard from dawn to dusk and have to combat to make their daily livelihood facile. It was noticed that rural to urban male outmigration brought new ideas, bits of knowledge, education, wisdom, feeling, realization, etc. (Desai & Banerji, 2008), which directly and indirectly affect women position within the households. These also affect gender role within the households.

40

M. Debnath

3.3.5 Determinants of Decision-Making The model reveals that the level of education, social groups, bank operation, women working outside the households, size of landholding household and types of the family have a statistically significant relationship with the decision-making autonomy of left-behind women in the study area. The probabilities of decision-making autonomy of rural left-behind women were coded in a binary form, the dependent variable considered as value “1” if women are able to make a decision and “0” if not able to make a decision. The total sample has taken 185 migrant households; among these, 96 migrant household’s women were able to take decision, and the rest were not able to make a decision. The chi-square value (114.451) indicates that the model is significant at 1% level of confidence. The selected variables explained 83.2% variation among the selected variables in rural areas of West Bengal (Table 3.6). Table 3.6  Logistic regression estimates of the probability of decision-making of left-behind women of Rarh region, West Bengal Variable Education:  Above 5 years of schoolinga  Illiterate  Up to 5 years of schooling Social group:  Forward castesa  Scheduled castes and tribes  Other backward castes Bank operation:  Noa Savings:  Noa Work outside the households:  Noa Landholding:  Small (1–2 ha)a  Landless  Marginal (below 1 ha) Family types:  Nucleara Statistics:  N  Constant  Chi-square  Nagelkerke R square

Coefficient

S.E.

Sig.

Odds ratio

−3.595 −1.549

0.752 0.724

0.000*** 0.000*** 0.032**

0.027 0.213

1.183 1.599

0.587 0.972

0.084* 0.044** 0.100

3.266 4.95

−1.413

0.528

0.008***

0.243

−0.946

0.577

0.101

0.388

−1.792

0.542

0.001***

0.167

1.329 −0.4

0.801 0.697

0.159 0.097* 0.566

0.265 0.67

0.863

0.478

0.071*

2.37

185 3.748*** 114.451 0.615

***, ** and * represent statistical significance at 1%, 5% and 10% level of confidence a Represents the reference category

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Level of education is another important factor to determine the decision-making autonomy of left-behind women (Ray & Debnath, 2019). Here, education level has been classified into three categories because of the lower level of schooling of rural women in the study area. Above 5 years of schooling is considered a reference category. The model reveals that all the education level has been statistically significant with decision-making autonomy of left-behind women. The regression coefficient value is negative for illiterate and up to 5  years of schooling of women. Above 5 years of schooling and the probability of decision-making have been found to be statistically significant at 1% level of confidence. It means that if the education level of rural women is increased, then their decision-making autonomy may enhance. On the other hand, it can be interpreted that a higher level of educated women has more autonomy to make a decision during the absence of male counterparts in households. Illiterate rural women have a negative impact on the probability of decision-making autonomy. It represents that illiterate women were 97% less likely to have the autonomy to make any decision than the group of women who have crossed above 5 years of schooling. It means that women with a lower level of education have lower autonomy to take any decision. Similarly, the odds ratio showed that up to 5 years of schooling is significant at 5% level of confidence. The value reveals that women up to 5  years of schooling were 79% less likely to have the autonomy to make a decision than those women who have above 5 years of schooling. The next variable is the social group of the left-behind women. Rural male outmigration is still affected by the deeply rooted rural caste prejudice in India (Haberfeld et al., 1999; Mosse et al., 2005), which directly affected the decision-­ making autonomy. Forward caste category has been taken into consideration as the reference category. The relation between decision-making autonomy of left-behind women and forward caste reveals to be statistically significant at 10% level of confidence, while scheduled castes and tribes are found significant at 5% level of confidence. The left-behind women who belong to scheduled castes and tribes are 3.3 times more likely to make a decision than the group of forward caste women. It implies that the left-behind women with lower social status have more autonomy to take any decision during the absence of male than the higher caste women. The study found that a large proportion of women from scheduled caste and tribe worked outside the village during the absence of a male. The majority of them have higher autonomy to take a decision. The study found that forward caste women have been tied with much more social barrier compared to scheduled group’s women. The available literature revealed a similar result (Ghosh, 2001; Sinha, 2005; Sengupta, 2012; Anupama et al., 2014; Ray, 2018). The study found that the decision-making autonomy of left-behind women with their banking operation has a significant relationship at 1% level of confidence. The odds ratio shows that those left-behind women who do not have banking operation were about 76% less likely to take any decision than those who have a bank account. It can be stated that the banking operation capacity of left-behind women helps them to increase their decision-­ making capacity. Women work participation for scheduled and backward caste households is quite normal and socially acceptable (Ghosh, 2001; Sinha, 2005; Hugo, 2000; Desai & Banerji, 2008; Sengupta, 2012; Ray, 2018). Similarly, this

42

M. Debnath

study explores that large proportion of scheduled caste and tribal women worked outside the households as a maid, agricultural labourer, wage labourer, etc. Majority of them mentioned that they have the freedom to take any household decision in absence of male migrants. The model reveals a similar result. The study found that those women who work outside the household have a significant relationship with decision-making autonomy at 1% level of confidence. The odds ratio shows that women who do not work outside the households have 83% less freedom to take any decision than the working women who worked outside the households. This result stated that if the percentage of women working in the economic activity outside the household increases, then the decision-making autonomy also enhances. The size of landholding household depicts that landless women have more freedom to take any decision compared to landholding households. Types of family and decision-­making autonomy of left-behind women have a significant relationship at 10% level of confidence. The odds ratio shows that women of joint family have lower freedom to take any decision than the nuclear migrant family. The above-mentioned analysis stated that the level of education, bank account operation, working women outside the household and family types have direct influence to take any household decision during the absence of male migrants.

3.4 Conclusion The present study ascertains the impact of male outmigration on the pattern of work participation and decision-making power of left-behind women. The left-behind women are working hard from dawn to dusk and have to combat to make their daily livelihood facile. The study depicts that household responsibility increased due to the absence of a male. Work participation of rural women in the primary sector also increased dramatically especially as agricultural labour. The study revealed that forward caste women have cramped into much more social taboos compared to scheduled caste women. That is why the present study disclosed that scheduled caste and tribal women are engaged more in agricultural labour compared to non-scheduled counterparts. The increasing rate of women work participation and a significant share of monthly household income improved their status within households and society. Dual activity of women increases the workload and they do not get sufficient leisure time. Although the participation of women outside the household increases the workload, they become economically stronger and capable to cope with livelihood vulnerability. Work participation has driven rural women to become self-empowered especially during the long absence of male counterpart in the households. The study found that those women who engaged in different economic activities have higher freedom to make any household decision in absence of a male. This study stated that if the rate of participation of women in economic activity increases, then their decision-making autonomy also enhances. The study also reveals that the level of education and social prejudices have a direct impact on decision-making autonomy of left-behind women in the study area. Policymakers

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need to trace the circumstances of rural male outmigration and their left-behind counterparts. The government must provide a work guarantee for landless and small landholder households. The government should take special attention to the health, education and empowerment of left-behind women. Acknowledgements  We would like to thank the participants who took the time to give their information regarding this research paper with us. Funding  Not applicable Data Availability  The metadata sets for the current study are not publicly available because the authors never ask the respondents to publish their data in a public repository for the analysis of the current study. Compliance with Ethical Standards Conflicts of Interest  The authors declare that they have no conflict of interest.

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Chapter 4

Socio-economic Determinants of Rural Out-migration in Koch Bihar District of West Bengal, India Bhupen Barman

and Ranjan Roy

4.1 Introduction Rural out-migration is the active constituent of population change (Mendola, 2012) and is a significant characteristic of the developing and developed countries of the world (Joshi, 1997). It is generally considered a human adjustment to socio-­ economic and environmental problems (Ratha et al., 2011), which is termed a “key component of human population movement” (Qin, 2010). It is a form of geographical mobility from one geographical unit to another, involving a change of residence from one place of origin to the other place of destination. Migration is a complex phenomenon identified by various factors that connect with the population size, structure, and distribution across geographical boundaries (Breuer, 1986; Thaware, 2013; Patra & Agasty, 2013). According to Roy (1991), migration study is very closely related to economic development. In India, it has been focused in two ways: international and intra-national. We observed various internal migration streams in West Bengal viz., rural-rural, rural-urban, urban-urban, and urban-rural. Todaro (1977) gives four aspects of migration and reasons, which are “relative benefits and costs primarily financial, but also psychological; except wage differential; the probability of jobs; urban-rural expected income differentials.” Lee (1966) mentioned migration is managed by four elements: connected with the place of origin, the area of destination or target, intervening elements, and personal factors. Migration is the outcome of an individual actively involved in decision-making (Tegegne & Penker, 2016).

B. Barman (*) Department of Geography, Tufanganj Mahavidyalaya, Cooch Behar, West Bengal, India e-mail: [email protected] R. Roy Department of Geography & Applied Geography, North Bengal University, Siliguri, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. Alam et al. (eds.), Population, Sanitation and Health, https://doi.org/10.1007/978-3-031-40128-2_4

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NSSO (2007–2008) identified that about 79.9% of males migrated for unemployment, 7.8% for studies, 7.6% for earning members, and the remaining 3% for other reasons. Delhi and Chandigarh mark 100% of male out-migration caused due to unemployment. This report also focused on male out-migration causes for unemployment of more than 80% in the states of Assam, Bihar, Goa, Jammu and Kashmir, Jharkhand, Odisha, Punjab, Rajasthan, Tamil Nadu, Tripura, Uttarakhand, Uttar Pradesh, and West Bengal. The Census of India (2001) mentioned that the place of last residence (POLR) was 9 years duration; the work/employment for the male and marriage were the main factors for out-migration. So, the cause of migration due to marriage is associated with females in India (Premi, 1980; Bhattacharya, 2000), while work or employment is dominated by males (Tumbe, 2015; Malhotra & Devi, 2016). Female migration patterns may be affected due to social norms of mating, resulting in more females moving into Indian society (Lingam, 1998; Skeldon, 1986). The phenomenon of out-migration is caused by the economic condition, landholding, and educational status of out-migrants (Keshri & Bhagat, 2012).

4.2 Literature Review There are several factors like poverty, unavailability of employment, marriage, personal reasons, political reasons, etc. that affect out-migration in an area. Sali and Astige (2015) highlighted that it is a natural process that measures various social, economic, cultural, and environmental factors. Roy et  al. (1992) argued that the decision to out-migration was linked with poverty and economic insecurities among South Asian countries. With several “push factors” like high work demand by middle-­class families, unavailability of jobs, and landlessness, people moved to the nearest suburbs. Pull factors of migration such as better jobs, education facilities, high salaries, healthy work situations, etc. attracted a large number of people from one place to another places. Sinha and Zacharia (1984) mentioned population pressure, social customs like marriage, lack of jobs, children’s education, and unsuitable geography revoke people to move from one space to another space. Kainth (2010) highlighted out-migration is mainly impacted by economic causes such as unavailability, low farming income, agricultural work, fewer industrial activities, etc. In a single word, it may be termed as a “depressed economic condition” in an area that generates rural out-migration. Kaur et al. (2011) studied in Punjab where 94.3% of people migrated due to less income from farming in the rural area. Similarly, FAO also mentioned unable income in cultivation pushed out-migration. Fields (1975) found less agricultural return and population pressure affected the out-migration of young people. Chandna (2008) studied the out-migration rate gradually increased among poor people in developing their basic needs and shows the majority of rural poor needs employment for poverty reduction by migration. Arthur (2005) highlighted family size and socio-economic condition and literacy having a great impact on out-migration. The study also identifies that smaller families have lower chances of out-migration than larger families. Zhang (2007) found marriage is the basic

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49

reason for the rural migration of females in India, whereas the males migrated for work opportunities. The study of Stark (1988) and Fulford (2015) found that females have migrated for marriage. The Census of India (2001 and 2011) revealed 43.8% and 56.07% of migration happened for marriage-related factors, respectively. According to Islam et al. (2013), places of origin in rural areas are more viable to a higher risk of out-migration comparatively than urban areas in developing countries. They have found that the place of origin, age, religion, educational status, occupation types, monthly income, and family size are the significant determinants of rural out-migration in Bangladesh. Caldwell (1968) discussed factors affecting rural out-migration in Ghana. The primary factor is divided into two ways, viz., village or household characteristics and individual characteristics of migrants. The higher propensity of out-migration is observed near towns. Poor economic conditions, low literacy, increasing family size, and non-agricultural occupation were the factors of rural-urban migration in Ghana. Tegegne and Penker (2016) found that rural out-migration is an individual utility-maximizing decision. Out-migration is determined by higher education, food insufficiency, age of the guardian of the family, family size, and economic activity in Ethiopia. Haberfeld et al. (1999) studied Indian cases that rural India is influenced by seasonal out-migration. Migrants leave their villages during October–November after harvesting rain crops, and they return home in the following summer season. More than 90% of the respondents are men. The logistic regression model results show that a more significant number of household members of the family and lower region development increase the probability of out-migration. A higher level of education, more livestock, and higher income indicate a lower probability of migration. Narayan and Singh (2016) have discussed the determinants of out-migration from remote and semi-urban villages in eastern Uttar Pradesh, India. They identified that overall, 67.5% of respondents out-migrated due to push factors from the studied area. Precisely, about 71.8% and 58.2% of respondents migrated due to unemployment, poverty, family crisis, education, and health-related problems from remote and semi-urban villages, respectively. The logistic model of male out-migration reveals that migrants from semi-urban villages were less likely (OR = 0.41, p 65 Gender Male Female Marital status Married Unmarried Widowed BPL status Yes No Social groups SC ST OBC Others POB Present place Another place Types of family Joint Nuclear Literacy Primary Upper primary Secondary Higher secondary Graduation and above Illiterate Agricultural land Yes No Amount of agricultural land (in bigha#) 6 Landlessness HH

Migrant (%)

Non-migrant (%)

0.8 67.1 0.5

0.0 30.2 1.5

59.8 8.5

26.6 5

54.3 13.6 0.5

25.6 5 1

17.1 51.3

20.6 11.1

44.7 2 19.1 2.5

19.1 1.5 7 4

59.8 8.5

31.2 0.5

10.1 58.3

18.1 13.6

19.1 12.1 11.6 2 5 18.6

8.5 8.5 3.5 4.5 3.5 3

49.2 19.1

16.1 15.6

36.2 7.5 5.5 19.1

Chi-square 8.411**

Cramer’s V 0.152

7.918**

0.145

4.158

0.102

58.901***

0.385

12.973***

0.181

12.465***

0.177

76.688***

0.439

36.442***

0.303

17.191***

0.208

19.334***

0.22

11.1 2 3 15.6 (continued)

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59

Table 4.3 (continued) Sl. no. 11

12

13

14

15

16

17

Total

Variables Main crops Rice Tobacco Others No cultivation Livestock Yes No Types of house Kutcha Semi-Pucca Pucca Separate kitchen room Yes No Monthly income (Rs.) 10,000 Income from MGNREGS (Rs.) 10,000 Monthly expenditure (Rs.) 9000 N = 398

Migrant (%)

Non-migrant (%)

37.2 10.1 2 19.1

11.1 5 0 15.6

29.1 39.2

21.1 10.6

36.7 22.1 9.5

9.5 11.1 11.1

56.3 12.1

28.6 3

53.8 12.6 4.5

7.5 22.6 1.5

34.7 29.1 4.5

10.6 19.6 1.5

6.5 29.1 23.6 6.5 2.5 68.30%

0 10.6 13.1 6.5 1.5 31.70%

Chi-square 21.795***

Cramer’s V 0.234

19.873***

0.223

28.817***

0.269

4.438**

0.106

111.979*** 0.53

12.809**

0.1179

23.321***

0.242

HH household, POB place of birth, BPL below poverty level **p 10,000a 10,000a 9000a 3000–5000 5000–7000 7000–9000 Constant

B

SE

4.316 1.487 −0.554 1.323 0.146 1.192 −1.706 1.170 0.711 3.106 0.703 1.702

1.154 1.198 1.155 2.826

Wald 45.718 8.428 0.175 12.545 0.015 2.126 13.826 0.379 6.721 0.371 0.363

Sig. (ρ) 0.000 0.004 0.675 0.002 0.902 0.145 0.008 0.538 0.010 0.543 0.547

95% C.I. for Odds ratio EXP(B) (OR) Lower Upper 74.852** 0.574

4.063 0.043

1378.928 7.685

1.157 0.182

0.112 0.018

11.963 1.799

2.035 22.324** 2.020 5.486

0.212 2.134 0.210

19.532 233.580 19.430

Source: Data have been calculated by the researcher based on a field survey S.E. standard error, HH household, POB place of birth, BPL below poverty level *p