Economic Policies and Issues on a Global Scale [1 ed.] 9781620817650, 9781611229370

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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

ECONOMIC ISSUES, PROBLEMS AND PERSPECTIVES

ECONOMIC POLICIES AND ISSUES ON A GLOBAL SCALE

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

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Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

ECONOMIC ISSUES, PROBLEMS AND PERSPECTIVES

ECONOMIC POLICIES AND ISSUES ON A GLOBAL SCALE

HENRY J. GROVER AND Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

NANCY C. REGMOND EDITORS

Nova Science Publishers, Inc. New York

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS.

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LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Economic policies and issues on a global scale / editors, Henry J. Grover and Nancy C. Regmond. p. cm. Includes index. ISBN 978-1-62081-765-0 (e Book) 1. Economic policy. 2. International economic relations. I. Grover, Henry J. II. Regmond, Nancy C. HD82.E2877 2010 338.9--dc22 2010047011ISBN 978-1-61122-937-0

Published by Nova Science Publishers, Inc. + New York

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

CONTENTS

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Preface

vii

Chapter 1

The Identification and Measurement of Poverty Thanasis Stengos and Brennan S. Thompson

Chapter 2

Working Under Time Pressure: An Increasing Risk for Women’s Health? Norma Barbini, Rosa Squadroni and Francesco Sera

1

27

Chapter 3

Entrepreneurship and Economic Policy Objectives Miguel-Ángel Galindo Martin and María Teresa Mendez Picazo

39

Chapter 4

Capital Flight and Economic Performance of the Philippines Edsel L. Beja

51

Chapter 5

Unanticipated Money Growth and GDP: Evidence from Korea Vladimir Hlasny

67

Chapter 6

Child Poverty in Rich Countries: An Overview Maryke Dessing

83

Chapter 7

Monetary Policy and Economic Growth Miguel-Ángel Galindo

Chapter 8

The Relationship between Restrictive Abortion Laws and the Number of Infants Relinquished for Adoption Marshall H. Medoff

133

Economic Implications of the Switch from Exhaustible to Inexhaustible Natural Resource Use in Electricity Production István Bessenyei and Tibor Kiss

147

Chapter 9

Index

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123

175

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PREFACE This book discusses economic policies and issues which affect various countries throughout the world. Topics discussed include the identification and measurement of poverty; entrepreneurship and economic policy objectives; unanticipated money growth and GDP; child poverty in rich countries; economic performance of the Philippines and monetary policy and economic growth. Chapter 1 - Measures of poverty are typically based on individual income shortfalls from a given poverty line. However, it is clear that the notion of poverty embodies much more than this; individuals may be considered “poor” whenever they are deprived of certain basic human needs (e.g., nutrition, longevity, literacy, self-respect, etc.). Determining poverty levels therefore requires careful consideration of exactly what criteria is used to identify those who are poor, and how this information can be used to develop useful poverty measures. In this paper the authors offer a basic overview of these two interrelated problems – the identification and measurement of poverty. Identification involves the selection of some criterion or criteria which, being met, characterizes someone as being poor. The second problem, measurement, involves aggregating the level of poverty in a community into a single statistic. Such statistics are useful for comparing the level of poverty between different populations, or, alternatively, between different subgroups of the same population (e.g., males vs. females). Furthermore, such statistics allow one to assess the efficacy of various antipoverty policies. Chapter 2 - Gender work segregation may be evidenced also in the different exposure of the two sexes to those working constraints which are considered more difficult with age, as the possibility to move from adverse work conditions to less demanding work plays un important role in the health related selection. Several studies carried out at European or national level, found a declining trend of physically demanding work in men, suggesting that men had more possibility to moving to less physically demanding jobs and that favourable differences between older and younger workers were more remarkable for older men than for older women as regards poor work postures and repetitive work. As to working under time pressure, this constraint had increased for both sexes, but the increase had been greatest among women. The high working rhythms are commonly associated with musculoskeletal pain, stress and poor perceived health. This study was mainly aimed at analysing gender differences in work-related health problems, focusing on relationships between the difficult in coping with work under time

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viii

Henry J. Grover and Nancy C. Regmond

pressure with advancing age and some health complaints, such as musculoskeletal symptoms and self-reported health. A population of 1195 Italian workers employed in different productive sectors and divided into 5 age cohorts were interviewed regarding the difficulty, with age, of coping with high working rhythms. The relationships between working under time pressure and the presence of musculoskeletal complaints (back pain and multiple complaints) and poor health self-assessment were then explored. Female workers were more exposed to repetitive work with tight deadlines and to time pressure. Analyzing the occupational exposure by cohorts, a decreasing exposure frequency may be observed for men in the oldest cohorts, while the opposite was observed for women, who complained about these constraints as particularly difficult with ageing. Working under time pressure appeared to be the least tolerated constraint for women, who had a significantly higher Odds Ratio than men in all cohorts of age, with a greatest risk in the 52 years cohort. The high working rhythms were associated with poor health, both for musculoskeletal pain and perceived health, especially when the exposure resulted particularly difficult to bear with ageing, but in different ways for the two sexes. In women the interaction between repetitive work with high deadlines and musculoskeletal complaints, showed a statistically significantly association both for upper limbs and for multiple musculoskeletal symptoms. The multivariate analysis showed an increasing risk with age for women, while in men repetitive work with tight deadlines was associated with a poorly perceived health. When analyzing interactions between repetitive work with tight deadlines and poor health-assessment, a progressive increased risk was observed from the 42 to 52 year cohorts for men, and in the 47 year cohort for women. The possibility for men in avoiding the more demanding or difficult work can be hypothesized, such as more autonomy and control over their work situation, while for women it seems that the possibility of avoiding those working constraints which are especially poorly tolerated with ageing is less probable. Chapter 3 - The economic growth and the progress of the nations have been two economic policy targets that have interested to the economists during centuries. Different approaches have been developed to determine which variables promote economic progress and growth so the policy maker could use them to design the adequate economic policy to achieve these objectives. During the last decades, “entrepreneurship” factor has been considered as a relevant element in the economic policy area, because it has an important role in the employment creation and improvement social welfare processes, achieving in this sense some of the most relevant economic policy objectives. In this paper, the authors analyze the relationship between entrepreneurship initiative and several economic policy targets, namely, employment and economic growth and progress. Also, the authors will consider the role of monetary and fiscal policies to promote the entrepreneurial activity, developing an empirical study for developed and developing countries to estimate the relationship between both policies and entrepreneurship activity. Chapter 4 - One serious implication of capital flight is that it aggravates the capital scarcity problem in a developing country; in turn, it restricts economic growth. The failing economic performance in turn induces more capital flight, thus producing a spiral of economic decline and more capital flight. Recent studies of Beja (2005), Beja (2006), and Beja (2007) confirm the earlier studies of Boyce and Zarsky (1988), Boyce (1992), Vos

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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Preface

ix

(1992), Boyce (1993), Vos and Yap (1996) that the Philippines indeed endured large capital flight. This paper extends these studies to determine the cost of capital flight to the Philippines. The results of the counterfactual calculations indicate that capital flight translates into large losses in economic growth and thus, in part, explain why the Philippines could not realize economic takeoff. Chapter 5 - Nominal and real macroeconomic variables are traditionally linked by the expectational Phillips curve. There is evidence that changes in employment and output result from unanticipated changes in money stock and inflation. Unfortunately, expectations about money stock and prices are not observed. Previous literature estimates them using static models with a limited number of lagged variables. The possibility of omitting important factors or lags is substantial. This study uses a state-space model where expectations about money stock depend on the long history of macroeconomic variables and their distributions. Priors about the growth rate are updated using a Kalman filter method. Quarterly data on the Korean economy is used to infer the one-period-ahead expected money stock, price level and price of oil, and to estimate the relationship between the unanticipated money stock and growth of national output. The study controls for the determinants of aggregate supply and demand, money stock and inflation. The results show that a one-percent shock to Korea’s money stock increases national demand by 0.02%. The study confirms validity of the expectational Phillips curve between shocks to unemployment and the unexpected inflation rate. Chapter 6 - In the 1990s, there had been a rising incidence of child poverty in many OECD countries, which is closely associated with the poverty of women in particular. Several macro-comparative studies of the OECD and UNICEF have examined various dimensions of child poverty across diverse institutional settings. The great diversity of outcomes suggests that child poverty can be eradicated (or at least become negligible) and points to possible solutions which need to be multifaceted. An institutional and gender perspective are added in this survey to facilitate the comparison of the studies under review, but also to gain further insights into the role of public family policies. Results suggest that overall the dual earner model of family support, prevalent in Nordic countries, is most successful in keeping children out of poverty, in line with their general philosophy of redistribution while helping mothers in particular reconcile employment and childrearing. Chapter 7 - Traditionally, different factors and variables have been considered in the economic growth models. Following Solow’s model, economists considered physical capital and technology during the 1950s–1980s as the main forces that promote economic growth. With the introduction of endogenous growth models, new forms of capital were introduced in the production function—human capital, public capital income distribution and more recently social capital. The improvement of statistical information has favored the introduction of different kinds of variables in the economic growth models. However, monetary policy has not been sufficiently analyzed in this literature. Except in the case of a few approaches, it is considered that monetary policy affects economic growth indirectly through other variables that directly have an influence on growth, for instnace, inflation. The main goal of this paper is to show the way monetary policy could have effects on the economic growth process, considering some approaches, such as Tobin, Levhari and Patinkin, Schumpeter and Kaleckian models. Chapter 8 - Do state laws that restrict women’s access to an abortion increase the number of infants relinquished for adoption? One of the arguments for the passage of restrictive

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Henry J. Grover and Nancy C. Regmond

abortion laws by states is the belief that restrictive abortion laws will increase the number of infants available for adoption. This study, using statewide data from the years 2002, 1992 and 1982, empirically estimates an adoption supply model based on the rational choice theory of fertility behavior. The empirical results find that increases in the price of an abortion and the enforcement of a parental consent law reduces the number of infants relinquished for adoption by women of childbearing age and by pregnant women who choose to terminate their unwanted pregnancies and unmarried pregnant women who give birth. States which deny funding for Medicaid abortions, mandate a waiting period, require mandatory counseling or enforce a parental notification law do not have greater infant relinquishment rates than states without such laws. The empirical results, which are contrary to conventional wisdom, suggest that for women with unwanted pregnancies, having an abortion or raising an infant is a more preferred alternative than relinquishing an infant for adoption. Chapter 9 - The authors formulate the general problem of optimal natural resource use in production and discuss some of the large variety of fundamental difficulties related to the concept of social welfare. The authors examine the surviving possibilities of industrial production in the framework of a simple, Cobb-Douglas technology based neoclassical model. The authors determine the criterion to be satisfied by the average proportion of saving and the partial elasticity of production of the exhaustible natural resources in order to avoid the collapse. The authors’ examination takes in consideration the depreciation, the changes in the elasticity of substitution, the growth of the population and the exogenous technical progress. The authors demonstrate that the sustainability of the industrial production can be ensured either by the exogenous technical progress or by the use of the inexhaustible natural resources. Turning to the subject of the inexhaustible natural resources the authors present a model in which the marginal cost of production based on inexhaustible natural resource exceeds the marginal cost of production using the exhaustible substitute. We show that – under certain assumptions – it is worth to start utilizing the more expensive inexhaustible natural resource strictly before the depletion of exhaustible substitute, even if the decision-maker company seeks to maximize its market value. The authors demonstrate this finding on the example of a Hungarian power station company in the city of Pécs in 2005. As the coal reserves in the surrounding hills were depleted, it had to be investigated whether the use of renewable energy resources in addition to natural gas in electricity production, is profitable, and in case it is, when should it be started? The analysis, presented in this case-study reveals that the use of renewable energy resources should have been started before the depletion of coal reserves. The power station of Pécs switched one of its unit's fuel to wood, and another unit's fuel will be switched to biomass in the near future. Hence, its resource exploitation path approaches to the optimal trajectory. This technological change will not only raise the power station's enterprise value, but also the income-generating ability of the region. Versions of these chapters were also published in Journal of Current Issues in Finance, Business and Economics, Volume 2, Numbers 1-4, published by Nova Science Publishers, Inc. They were submitted for appropriate modifications in an effort to encourage wider dissemination of research.

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In: Economic Policies and Issues on a Global Scale ISBN: 978-1-61122-937-0 c 2011 Nova Science Publishers, Inc. Editors: H. J. Grover and N. C. Regmond

Chapter 1

T HE I DENTIFICATION AND M EASUREMENT OF P OVERTY∗ Thanasis Stengos and Brennan S. Thompson ∗ University of Guelph, Guelph, Ontario, CA

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Abstract Measures of poverty are typically based on individual income shortfalls from a given poverty line. However, it is clear that the notion of poverty embodies much more than this; individuals may be considered “poor” whenever they are deprived of certain basic human needs (e.g., nutrition, longevity, literacy, self-respect, etc.). Determining poverty levels therefore requires careful consideration of exactly what criteria is used to identify those who are poor, and how this information can be used to develop useful poverty measures. In this paper we offer a basic overview of these two interrelated problems – the identification and measurement of poverty. Identification involves the selection of some criterion or criteria which, being met, characterizes someone as being poor. The second problem, measurement, involves aggregating the level of poverty in a community into a single statistic. Such statistics are useful for comparing the level of poverty between different populations, or, alternatively, between different subgroups of the same population (e.g., males vs. females). Furthermore, such statistics allow one to asses the efficacy of various anti-poverty policies.

1.

Introduction

While poverty has been an issue of great social and political concern for quite some time, there is still much controversy over what exactly constitutes poverty, and how poverty should be measured. What follows is an attempt to present a basic overview of these two interrelated problems, viz., the identification and measurement of poverty. The first problem, identification, involves the selection of some criterion or criteria which, being met, characterizes someone as being poor. For example, poverty may be defined as the state of having an income below a certain level (or poverty line). Alternatively, ∗A

version of this chapter also appears in Journal of Current Issues in Finance, Business and Economics, Volume 2, Number 1 published by Nova Science Publishers, Inc. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

2

Thanasis Stengos and Brennan S. Thompson

poverty may be defined as the state of being unable to acquire some minimum level of health and/or nourishment. Typically, economists assume the former definition and proceed directly to the next stage – measurement (see below). However, this stage should not be overlooked. Section 2. discusses some important issues that arise in the identification of the poor. The second problem, measurement, involves aggregating the level of poverty in a community into a single statistic. For example, it may be of interest to know what proportion of the population is poor. Such statistics are useful for comparing the level of poverty between different populations, or, alternatively, between different subgroups of the same population (e.g., males vs. females). Furthermore, such statistics allow one to asses the efficacy of various anti-poverty policies. This problem is discussed further in Section 3..

2.

Identification

Fundamentally, poverty can be viewed as a deprivation of some minimum needs. At the most basic level, these needs are merely physiological (e.g., food, shelter, etc.). However, these needs may be more complex psychological or sociological needs (e.g., such as selfrespect, education, political rights, etc.). 1 Of course, deciding exactly what needs one has to be deprived of in order to be considered poor is obviously a value judgment. Nevertheless, this decision can be better understood if the process of identification is first characterized a little more fully.

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2.1.

The Direct Method vs. The Income Method

The above definition logically leads to the so-called direct method of poverty identification (see, e.g. Sen, 1979), which involves simply assessing whether or not an individual has met the minimum needs decided upon. For example, someone may be classified as being poor if their caloric intake falls below a certain threshold and/or they are homeless. In this sense, poverty can be seen as inherently multi-dimensional in nature. In economic terms, the direct method can be seen to be based on information about actual consumption (e.g., how many calories did an individual consume, did an individual acquire housing or not, etc.). As it is typically quite difficult to obtain such information, one may have to resort to an alternative approach, known as the income method. The income method would classify someone as being poor if they do not have the income necessary to meet the decided upon needs. For example, if it is desired that someone be classified as being poor if their caloric intake falls below a certain threshold or they are homeless, then anyone who does not have the minimum level of income required to purchase both this desired number of calories as well as suitable housing would be classified as being poor.2 Typically, this minimum level of income is referred to as the poverty line. 1 One can think of Maslow’s hierarchy of needs, which places physiological needs below psychological and sociological needs. 2 This characterization implies that an individual would be considered poor if they are unable meet any of the needs under consideration. An alternative approach is to characterize an individual as being poor only if they are unable to meet all of the needs under consideration. This issue is discussed more in Section 2.4..

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The Identification and Measurement of Poverty

3

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Generally speaking, the income method can be seen to be based on the broader notion of identifying an individual as being poor if they lack the ability to meet some minimum needs. Sen (1979, 1983) characterizes this as the capabilities approach to poverty identification. It should be clear that this approach is more general than the income method: information on incomes alone may not be enough to determine an individual’s capabilities. For example, someone who has the income necessary to satisfy the minimum needs of the typical person, may not actually be able to satisfy those needs if they are disabled. At the same time, ability does not imply willingness. As Sen (1979, p. 292) points out, someone who, for example, chooses to fast may be classified as being poor if the direct method is used, but not if the capabilities approach is used. According to Sen, poverty therefore ought to be defined as being deprived of the capability to achieve some minimum needs. It should also be pointed out that an individual’s capabilities are not entirely determined by their income. Clearly, certain liberties are granted by the state. If, for example, women in some particular population are not allowed the right to vote, then even a woman with a relatively high level of income will still be deprived in some sense. Moreover, certain goods and services (e.g., education, health care, access to clean drinking water, etc.) may be provided by the state. The public provision of these goods and services is of extreme importance to those individuals who lack the ability to purchase them. Several studies, including Smeeding et al. (1993), attempt to include the value of access to public services in analyzing poverty differences between different nations. As the preceding discussion should make clear, a practical approach to identifying poverty might involve some elements of both the direct and income approaches. That is, information about the satisfaction of some specific needs may be used to supplement information about incomes. For example, Wu (2006) analyzes U.S. poverty levels using data on household levels of income and educational attainment.

2.2.

Absolute vs. Relative Poverty

At first, the above discussion may seem to imply an absolute definition of poverty. That is, given some absolute need to be satisfied, an individual is classified as being poor if they fail to satisfy that need, without any reference to the rest of the population. For example, if an individual is to be classified as being poor if they are deprived of the most basic physiological needs such as achieving a certain caloric intake, then each individual in a community can be classified independently of one another. In this example, it is certainly possible for a very large majority of the population of a country to be classified as poor if, say, this country is facing a famine (see, e.g., Sen, 1981). In other words, there is no relative aspect to poverty: everyone can be poor. Of course, some (non-physiological) needs themselves may have relative aspects. As Sen (1983, p. 159) suggests, “the absolute satisfaction of some ... needs might depend on a person’s relative position vis-a-vis others”. For example, a psychological/sociological need may be attaining self-respect. Such needs are best illustrated by Adam Smith in his Wealth of Nations (1776, Vol. II, pp. 351-352): By [needs] I understand not only the commodities which are indispensably nec-

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4

Thanasis Stengos and Brennan S. Thompson essary for the support of life, but what ever the custom of the country renders it indecent for creditable people, even the lowest order, to be without.... Custom ... has rendered leather shoes a necessary of life in [eighteenth-century] England. The poorest creditable person ... would be ashamed to appear in public without them.

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Here, meeting the absolute need of self-respect (i.e., avoiding shame in public), depends on one’s relative position. If, in Smith’s England, most individuals were not in possession of leather shoes, it certainly would not be shameful to appear in public without them. Of course, as Sen (1983, p. 159) makes clear, this argument can be taken to an extreme, noting that “it would be absurd to call someone poor just because he had the means to buy only one Cadillac a day when others in [his] community could buy two...each day”. However, within reason, this argument seems quite acceptable. Unfortunately, gathering information on the satisfaction of these higher needs is even more difficult than is the case for more basic physiological needs. Thus, one is led back to the income method. For example, as Barry (1973) argues, basic liberties, such as those emphasized by Rawls (1971), cannot be enjoyed by an individual unless they have first attained some minimum level of income. Therefore, those individuals who do not have this minimum level of income ought to be considered poor. More generally, since higher-level needs will often depend on an individual’s relative position vis-a-vis others, it is natural to suggest that the income required to meet such needs will depend on the relative incomes of others in the community. In fact, this approach is quite popular in practice, particularly in developed countries where basic physiological needs can be met by even those with relative incomes which are extremely low. For example, Rawls (1971, p. 98) suggests that an individual may be classified as being poor if they earn less than half of the median income. 3

2.3.

Poverty vs. Inequality

When considering poverty from a relative standpoint, it is important to make a distinction between poverty and inequality. This distinction is made most clearly in the so-called focus axiom popularized in the poverty measurement literature (see, e.g., Sen, 1981, p. 186). According to the focus axiom, the measurement of poverty should not be concerned with the welfare of those not considered poor. For example, if the income method of poverty identification is being used, any measure of poverty should be independent of the incomes of the non-poor. On the other hand, any reasonable measure of inequality would have to be dependent on the welfare of all individuals in a population, poor and non-poor. To see this distinction more clearly, consider the following example. Suppose that any individual in a certain population with income below half the median income level is considered poor. If, all else equal, the income of the richest individual in this population is doubled, there will be no impact on either the number of poor, or the magnitude of their deprivation (i.e., their distance from the poverty line). On the other hand, such a change 3 While there may be some justification for the use of the median level of income as a benchmark, choosing to set the poverty line at 50% of this level is obviously completely arbitrary. Thus, when analyzing relative poverty, one may want to consider what the level of poverty would be for a range of poverty lines (e.g., from 40% to 60% of the median). This issue is discussed in Section 2.7..

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The Identification and Measurement of Poverty

5

in the distribution of incomes would increase what would reasonably be considered as the notion of inequality. 4 Of course, as in the above example, the poverty line itself is often dependent on the welfare of the non-poor. As Foster (1984, p. 217) points out, the focus axiom specifies only that the welfare of the non-poor should be disregarded only “ once the poverty line is set” (italics his). If, in the above example, the poverty line were, say, half the mean income level, the effect of doubling the richest individual’s income would be quite different. In this case, the poverty line would be increased, which would not only increase the magnitude of the deprivation of all poor individuals, but could also lead to an increase in the absolute number of poor individuals. More generally, disregarding the welfare of non-poor individuals may not always be desirable in analyzing the level of poverty. If, for instance, one is interested in assessing what Sen (1979, p. 300) calls the “relative burden” of poverty (i.e., the percentage of a population’s total income required to wipe out poverty), information on the incomes of the non-poor will be required. 5 However, as Sen goes on to note, assessing this relative burden is “really a different exercise from the description of poverty” (p. 300). Even when ignoring the welfare of non-poor individuals, it is often of interest to consider the level of inequality between poor individuals. That is, it may be asked if some poor individuals are “more poor” than others. Of course, this requires the ability to characterize the amount of deprivation that a poor individual faces. When poverty is measured on an income basis, this characterization can be made in a fairly straightforward manner, viz., the difference between an individual’s income and the poverty line. Thus, it would seem natural to say that an individual with income further from the poverty line is more poor than an individual closer to it. 6 On the other hand, many needs are binary in nature. That is, it may be the case that a certain need is either met or not met. For example, suppose the right to vote is included in the list of basic liberties, in the sense of Rawls (1971), that an individual needs to be able to meet in order to not be considered poor. If, say, all women in a certain population are denied the right to vote, then every woman in this population is equally poor (as far as having the right to vote is concerned). However, it may be of interest to know if there are some women who are more able to meet other needs. That is, while it may be the case that no woman is allowed to vote in a certain population, some women in this population may be undernourished while others are not. In this case, those who are both unable to vote and are undernourished may be considered to be more poor than those who are just unable to vote. Moreover, if undernourishment is characterized as obtaining less than some specific number of calories, it is possible that some undernourished women may be “more undernourished” than others.

2.4.

Intensive vs. Extensive Poverty

The right-to-vote example in the previous section highlights an issue in identifying poverty in more than one dimension. Clearly, any individual who is able to meet all of the needs 4 Sen

(1997) provides a very readable introduction to the measurement of inequality. Anand (1977) for further discussion and an application. 6 How much poorer is another question. See Section 3.. 5 See

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6

Thanasis Stengos and Brennan S. Thompson

under consideration would not be identified as being poor, while any individual who is unable to meet all of these needs would be considered poor. However, it is unclear whether an individual who is able to meet some needs, but unable to meet other needs, should be identified as being poor. Two approaches are possible in this context. The first, which Wu (2006) refers to as the intensive approach, would only identify an individual as being poor if they can meet none of the needs under consideration. The second, which Wu refers to as the extensive approach, would identify an individual as being poor if there is at least one of the needs under consideration which they are unable meet. 7 In the right-to-vote example introduced in the previous section, all women would be considered poor under the extensive approach, while only women who are undernourished (in addition to being unable to vote) would be considered poor under the intensive approach. Clearly, this first group will always be larger than the second. It is not completely clear which of these notions of poverty is preferable. In support of the extensive notion, Bourguignon and Chakravarty (2003) give the example of an “old beggar”. While such an individual is “rich” in terms of longevity, they would clearly be unable to meet other needs, and, as result, should be probably be considered poor. However, as discussed in Tsui (2002), it could be questioned whether someone who is unable to meet a particular need could be, in some manner, compensated by being particularly well-off in terms of some other characteristic of well-being (see Hoy and Zheng, 2007). While this distinction may only seem to be an issue when the direct method of poverty identification (which is based on the fulfillment of various needs) is used, it also important when the income method is used. Although the income of an individual is often thought to be one-dimensional, it is important to keep in mind that incomes are not constant over time. Thus, an individual’s income may be below the poverty line at one point in time, but be above the poverty line at another point (see, e.g., Hoy and Zheng, 2007). If this is the case, it is not clear whether or not such an individual should be classified as being poor (on a lifetime basis). The intensive approach would only identify an individual as being poor if their income is always below the poverty line, while the extensive approach would identify an individual as being poor if their income is ever below the poverty line. The issue of lifetime poverty is discussed in the next section.

2.5.

Lifetime Poverty

In any given period, an individual may be characterized as being poor or not being poor. Such characterizations are made on a “snapshot” basis. The question is how these “snapshots” can be aggregated to determine an individual’s level of lifetime poverty. Clearly, an individual who spends each period of their life in a state of poverty would be considered poor on a lifetime basis (using either the intensive or extensive notion of poverty discussed above). On the other hand, it is unclear how to assess the lifetime of an individual who is poor during some periods of their lifetime but not poor during other periods. Hoy and Zheng (2007) suggest that a certain amount of affluence during periods spent out of poverty may be able to compensate for those periods spent in poverty. Specifically, they 7 These

two approaches are also known as the union and intersection approaches, respectively. See Atkinson

(2003). Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

The Identification and Measurement of Poverty

7

point out that periods of poverty early in an individual’s life may have negative long-term consequences (e.g., reduced stocks of health and/or education), which would require compensation during later stages in life (even if such an individual is not technically poor within those later periods). Of course, this sort of characterization would, in some sense, involve a violation of the focus axiom (see Section 2.3.). Concerns over lifetime poverty relate more generally to opportunities for social mobility (see, e.g., Dardanoni, 1993). It may be asked, for example, what the probability of being poor in one period is conditional upon being poor in the previous period. Statistical estimation of transition probabilities of this sort has recently been considered by Cappellari and Jenkins (2002, 2004) among others.

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2.6.

Household Poverty

Inherently, poverty is an individual characteristic. An individual is characterized as being poor if they are unable to satisfy a specified set of needs. Thus, when adopting the income method of poverty identification, an individual is characterized as being poor if they lack the income necessary to satisfy this set of needs. The problem with this approach is that the income of many individuals is determined on a household, rather than individual, basis. For example, while children typically have no income of their own, they do have some manner of access to the income of their parents. The same may be said of an adult who relies on the income of a spouse. Thus, when adopting the income method of poverty identification, it is often necessary to characterize poverty on a household basis. This presents several challenges. First, it is not clear how income is distributed within a family. It is often assumed that income is shared in some “fair” manner, with income being directed towards members of a household on a needs-basis. However, as shown by Haddad and Kanbur (1990), among others, neglecting the intra-household distribution of income can have serious consequences for the measurement of poverty. In some societies, for example, there may be a gender-bias in the amount of income directed towards children (see, e.g., Davies and Zhang, 1995). As a result, the realization and/or extent of poverty may not be uniform across all members of a household. A second challenge involves the manner in which the composition of a household determines its income requirements. It could be argued, for example, that a two-person household does not require twice the income that a single-person household would require to avoid poverty. In other words, there may be economies of scale within the household. This issue is typically dealt with through the use of equivalence scales. A fairly simple class of equivalence scales is considered by Buhmann et al. (1998). Using this scale, the equivalent income of a particular household is given by Y ∗ = Y /Se , where Y is the actual income of the household, S is the size of the household (i.e., the number of people living in the household), and e ∈ [0, 1] is a parameter which indicates the magnitude of economies of scale assumed by the scale. 8 8 Slightly more

complicated equivalence scales might treat adults and children differently. For example, the so-called “OECD-modified” equivalence scale, which assigns a weight of one to the first adult in a household, a weight of 0.5 to each additional adult, and a weight of 0.3 to each child. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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Thanasis Stengos and Brennan S. Thompson

Equivalence scales of this type can be seen to convert the income of a household to that of an equivalent individual. Thus, if the poverty line represents the income that an individual requires to meet a specified set of needs, then, using the Buhmann et al. equivalence scale, an S-person household would require an income that is Se times the poverty line in order to meet these needs.9 A typical rule-of-thumb is to set e = 0.5, so that equivalent income is simply calculated as actual income divided by the square-root of household size. However, as shown by Coulter et al. (1992), the choice of e may have a significant impact on estimates of the level of poverty (for some specific poverty measure; see Section 3.). This finding suggests that any analysis of poverty which relies on equivalence scales should include some type of sensitivity analysis. If, for example, it is found that, over time, poverty in a certain population has increased when using one choice of equivalence scale, but has decreased when using another choice of equivalence scale, then no clear conclusion should be drawn. In other words, there is no clear ordering of poverty levels between the two periods. 10

2.7.

Selection of the Poverty Line

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Just as the choice of equivalence scale may lead to ambiguous poverty orderings, so too can the choice of poverty line. For example, it may be found that, in a certain population, the level of poverty has increased over time when using one poverty line, but that the reverse is true when using another poverty line. As a result, it has been suggested by Atkinson (1987) and Foster and Shorrocks (1998), that any analysis of poverty should consider a range of poverty lines. For example, if a relative notion of poverty is used, it might be reasonable to think that the poverty line for a certain population should be between 40% and 60% of the median level of income in that population (see Section 2.2.). Thus, in comparing the level of poverty between, say, two different time periods, one period can only be said to have an unambiguously lower level of poverty if it has a lower level of poverty for every poverty line in this range. Otherwise, no clear poverty ordering can be made. An alternative approach involves abandoning the notion of a clearly defined poverty line altogether. Rather than having a clear poverty line which splits a population into two “crisp” sets (i.e., those who are poor and those are not poor), a “fuzzy” set approach can be used. Such an approach would typically split the population into two overlapping sets. Those individuals caught in the intersection of these two sets might be considered “quasipoor” (i.e., neither clearly poor nor clearly non-poor). Some recent work in this area has been compiled by Lemmi and Betti (2006).

9 Looked at in this way, it is clear that some adjustment for other household characteristics may be desirable. For example, if a member of a particular household has a serious disability, then such a household might require some higher amount of income in order to meet a certain set of needs (see the discussion of Sen’s capabilities approach in Section 2.1.). 10 See Foster and Shorrocks (1998) and Zheng (2000) for general discussions of poverty orderings.

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The Identification and Measurement of Poverty

3.

9

Measurement

Having established some characteristic(s) by which to identify the poor, the next problem is to aggregate the level of poverty in a population into a single statistic, referred to as a poverty measure. As mentioned above, poverty measures can be used to compare the poverty level between different populations, or between different subgroups of the same population.

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3.1.

One-dimensional Poverty Measures

This section introduces some well-known one-dimensional poverty measures, which are based only on a single characteristic of individuals. 11 For ease of exposition, this characteristic will be assumed to be income. However, the measures discussed here would be suitable for use with any other cardinally-measured characteristic, such as caloric intake. In what follows, the following notation will be employed. At a certain point in time, a population consists of n individuals (or equivalized households; see Section 2.6.). Let yi be the income (assumed to be positive) of the ith individual, for i = 1, . . ., n. Without any loss of generality, it is assumed that these incomes are sorted in non-decreasing order, i.e., 0 < y1 ≤ y2 ≤ . . . ≤ yn . For a given poverty line, z > 0, Donaldson and Weymark (1986) have pointed out that poverty can be defined in two possible ways. Using the weak definition of poverty, individual i will be said to be poor if yi < z; using the strong definition of poverty, individual i will be said to be poor if yi ≤ z. Empirically, the choice of poverty definition is not likely to be of any considerable importance. Theoretically, however, the definition may have important consequences for the properties of a particular poverty measure. As Donaldson and Weymark (1986) show, several popular poverty measures violate some fairly widely accepted conditions if the strong definition is used. Accordingly, in what follows, it will be assumed that the weak definition is used. Thus, a population will be said to contain q ≥ 0 poor individuals if yi < z, i = 1, 2, . . ., q and yi ≥ z, i = q + 1, . . ., n. 3.1.1. Primitive Measures Up to the mid-seventies, most empirical studies of poverty employed one of two relatively primitive measures (or slight variations thereon). The first, known as the head count ratio, H, simply expresses the number of poor as a proportion of the total population, i.e., q H= . n Of course, this measure is not at all sensitive to “how poor” (i.e., how far from the poverty line) the poor are. The second measure, known as the income gap ratio, is slightly more informative, being based on the what are called the relative income gaps of the poor. The relative income gap 11 Comprehensive

surveys of one-dimensional poverty measures are to be found in Foster (1984), Seidl (1988), and Zheng (1997). Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

10 of the ith individual is

Thanasis Stengos and Brennan S. Thompson 

 yi gi = 1 − I(y < z), z

i = 1, . . ., n,

where I(·) is an indicator function taking the value of one if its argument is true, and zero otherwise. The income gap ratio, I, is equal to the mean income gap among the poor, i.e., I=

1 q ∑ gi . q i=1

While I does give some picture of the extent of poverty, it is not sensitive to the numbers involved. This can be corrected by considering the product of H and I, i.e., HI =

1 q ∑ gi , n i=1

which is known as the normalized income gap ratio. This measure is simply the mean income gap among the entire population. 3.1.2. Sen’s Axiomatic Approach

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While the headcount and income-gap ratios may have some role in the measurement of poverty, even together they will be completely insensitive to the distribution of income among the poor. Dissatisfied by this shortcoming, Sen (1976) developed a more sophisticated approach to poverty measurement motivated by two axioms. As will be seen in what follows, these axioms underlie virtually all subsequent literature in the area of aggregate poverty measurement. Sen’s first axiom is as follows: D OWNWARD M ONOTONICITY A XIOM: Given other things, a reduction in income of a person below the poverty line must increase the poverty measure. While Sen (1976) simply refers to this as the “monotonicity axiom”, Donaldson and Weymark (1986) make it clear that this type of monotonicity (to which they add the downward qualification), is quite different from what they refer to as “upward monotonicity”, as seen in the following axiom: U PWARD M ONOTONICITY A XIOM: Given other things, an increase in income of a person below the poverty line must decrease the poverty measure. It should be clear that H violates both of these axioms, while I satisfies the first but not the second. To see that I would violate the upward monotonicity axiom, consider the following example. Suppose that (y1 , y2 , y3 , y4 ) = (5, 7, 9, 11) and that z = 10. Now suppose that the third individual’s income increases by one unit. Under the original distribution I = 0.3, while under the new distribution I = 0.4. This suggests that an increase in the income of a poor individual has actually increased poverty! Sen’s second axiom is as follows:

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The Identification and Measurement of Poverty

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S TRONG U PWARD T RANSFER A XIOM: Given other things, a pure transfer of income from a person below the poverty line to anyone who is richer must increase the poverty measure. Sen (1976) actually refers to this as simply the “transfer axiom”. The strong upward qualification, introduced by Donaldson and Weymark (1986), is added here, since, as originally stated by Sen, such a transfer does not rule out the possibility of the richer person crossing the poverty line. In fact, it turns out that Sen’s own poverty measure (discussed in the next section) actually violates this axiom. In a footnote to Sen (1976, p.77), he restates his original axiom as follows: W EAK U PWARD T RANSFER A XIOM: Given other things, a pure transfer of income from a person below the poverty line to anyone who is richer must increase the poverty measure unless the number of people below the poverty line is strictly reduced by the transfer. It is not completely clear that the strong version of this axiom is more acceptable than the weak version. As Sen (1979, p.302) points out, a reduction in the prevalence of poverty (i.e., the number of poor individuals) may be able to “compensate a rise in the extent of penury of those who remain below the poverty line”. That is, it may be a desirable trade-off to take one person out of poverty at the cost of making another person “more poor”. For completeness, consider also the following axiom:

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D OWNWARD T RANSFER A XIOM: Given other things, a pure transfer of income from a person below the poverty line to anyone who is poorer (and remains poorer after the transfer) must decrease the poverty measure. The weak/strong distinction is not required for this axiom as such a transfer could not possibly change the number of poor. In fact, as Donaldson and Weymark (1986) show, the weak upward transfer axiom and the downward transfer axiom are equivalent (i.e., one implies and is implied by the other). Hence, in what follows, these two axioms will be jointly referred to as the “weak transfer axiom”. Moreover, as it is clear that the strong upward transfer axiom implies the weak transfer axiom, it will be referred to simply as the “strong transfer axiom”. Of course, being insensitive to the distribution of income among the poor, both H and I violate all of these transfer axioms. In fact, an upward transfer would actually decrease H if it caused the richer person to cross the poverty line (a response Sen, 1976, p. 219, calls “perverse”). 3.1.3. The Sen Measure As an alternative to the headcount and income gap ratios, Sen (1976) proposes a general class of poverty measures, q

PS = A ∑ gi vi , i=1

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(1)

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Thanasis Stengos and Brennan S. Thompson

where A is a normalizing constant and vi is the weight put on gi , the relative income gap of the ith individual. 12 In order for PS to satisfy the weak upward transfer axiom, it must be the case that, vi > v j for yi < y j < z. Specifically, Sen suggests that vi = r(i), the rank order of individual i among the poor. Here, rank order should be interpreted as a ranking from highest to lowest income among the poor. That is, among the poor, the least poor individual would have rank 1, while the poorest individual would have rank q. This choice of weights can be seen to be motivated by the notion of relative deprivation. As Sen (1979, p. 297) notes, “the greater the rank value the more is the person deprived in terms of relative deprivation with respect to others in the same category”. For the purpose of normalization, Sen suggests that, if all the poor have the same income, then it should be the case that PS = HI, the normalized income gap ratio. The reasoning behind this is as follows. If all the poor have the same income, then the income distribution among the poor is not an issue, and therefore some function of H and I is an adequate poverty measure. Their product is simply a convenient choice for this function. Given these specifications, the poverty measure in (1) can be written as PS∗ =

q 2 ∑ gir(i). n(q + 1) i=1

(2)

It is interesting to consider the relationship between this poverty measure and the broader notion of inequality (see Section 2.3.). As Sen (1976) points out, for large q, the above measure can be written as

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PS∗ = H[I + (1 − I)G p ], where G p is the Gini measure of income inequality (see e.g., Sen, 1997, Ch. 2) among the poor. Sen’s measure satisfies the upward and downward monotonicity axioms, as well as the weak transfer axiom. However, as noted above, it does not satisfy the strong transfer axiom. This springs from the fact that, when the recipient of a transfer from a poor individual is pushed out of poverty, the rank order of all those individuals remaining poor is decreased. 3.1.4. Sen-like Measures Shorrocks (1995) suggests a slight modification to Sen’s normalization which allows it to satisfy the strong transfer axiom. Instead of setting PS = HI when all the poor have the same income, Shorrocks suggests setting PS = I when all the poor have the same income and everyone is poor (i.e., when G p = 0 and H = 1). Thus, whenever H = 1, the asymptotic (large q) version of Sen’s measure will take on the value I + (1 − I)G p =

1 n ∑ gi(2n − 2i + 1). n2 i=1

12 Sen

(1976) actually forms his measure as a function of the absolute income gaps of poor individuals (i.e., gi = z − yi for i = 1, . . . , q). However, by multiplying his choice of the normalizing constant, A, by z, his poverty measure can be written as in equation (2) below. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

The Identification and Measurement of Poverty

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Note that, even if some individuals in the population are non-poor, the value of the righthand side of the above expression is unchanged, since gi = 0, for i > q. Shorrocks thus suggests the measure 1 q PS∗∗ = 2 ∑ gi (2n − 2i + 1). n i=1 A very similar variation on Sen’s measure is proposed by Thon (1979), who lets vi = R(i), where R(i) is the income rank order of i among the entire population. 13 This leads to the poverty measure q 2 gi R(i). PT = ∑ n(n + 1) i=1 Like the Shorrocks (1995) measure, PT can be seen to satisfy the strong upward transfer axiom.14 An alternative line approach is suggested by Sen (1979), who notes that vi , the weight assigned to individual i in (1), can be made to be a general function of the income rank order of i among the poor. That is, vi = f (r(i)), where f (·) is some increasing function. One such function is that introduced by Kakwani (1980), who lets vi = r(i)k, where k is a “sensitivity” parameter. Using this and Sen’s (1976) normalization, the poverty measure in (1) can be shown to equal PS (k) =

q q gi r(i)k, ∑ nφq (k) i=1

where φq (k) = ∑i=1 ik . Note that PS (1) = PS∗, Sen’s measure. Alternatively, PS (0) = HI, the normalized income gap ratio. The motivation behind this specification is based on the notion of what Kakwani (1980) calls transfer sensitivity. Simply put, transfer sensitivity implies that the poorer is the transferrer, the greater should be the increase in the poverty measure. Formally, this notion can be embodied in one (or both) of the following axioms:

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q

W EAK U PWARD T RANSFER S ENSITIVITY A XIOM I: For any positive integer ρ and any poor pair of individuals i and j, if j > i, then (∆P)i,i+ρ > (∆P) j, j+ρ where (∆P)i,i+ρ is the increase in the poverty measure, P, due to a transfer of income for the ith poor to the (i + ρ)th poor. W EAK U PWARD T RANSFER S ENSITIVITY A XIOM II: If a transfer of income takes place for the ith poor with income y i to a poor with income (yi + h), then for a given h > 0, the magnitude of increase in poverty measure decreases as i increases. The weak upward qualifications are added here for completeness. However, as with the weak upward axiom and downward transfer axiom, if the number of poor is not allowed to 13 Note

that R(i) = n − 1 + 1, while r(i) = q + 1 − i. Shorrocks (1995) measure also satisfies the somewhat technical condition of “replication invariance” (see, e.g., Zheng, 1997), which requires the value of a poverty measure to be preserved when two or more identical populations are merged. Neither the Sen (1976) measure, nor the Thon (1979) measure, satisfy this condition. 14 The

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Thanasis Stengos and Brennan S. Thompson

be changed, the direction of the transfer does not matter (i.e. these axioms could be seen to have equivalent downward counterparts). Thus, in what follows they will be referred to as “weak transfer sensitivity axiom I” and “weak transfer sensitivity axiom II”, respectively. The first of these axioms requires the sensitivity of the poverty measure to depend on the position of the transferrer in the ordering of the poor when the number of positions between the transferrer and the recipient is fixed. The second requires the sensitivity of the poverty measure to depend on the income level of the transferrer when the income difference between the transferrer and the recipient is fixed. 15 As Kakwani (1980, p. 442443) points out, the first will be satisfied only for k > 1, while the second will be satisfied only if k is “sufficiently larger than unity”. Clark et al. (1981) note that PS (k) may actually attach greater weight to fixed equidistant transfers occurring closer to the mode (typically above the top of the income distribution of the poor), since more individuals will be by-passed. This possibility is permitted whenever the first, but not the second, of the above axioms is satisfied (i.e., when k is greater than unity, but not “sufficiently” so). As Clark et al. (1981, p. 518) note, “this is exactly the same problem that arises in using the Gini index to measure inequality in the distribution of incomes in society as a whole”. In general, Kakwani (1980, p. 442) suggests that k “be chosen according to society’s preference for the sensitivity of the measure to an income transfer at different income positions”. However, as Clark et al. (1981, p. 518) note, “such preferences should be independent of the particular distribution being considered,” and “ k does not have this property”.

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3.1.5. Social Welfare-based Measures Blackorby and Donaldson (1980) show that Sen’s (1976) measure can be seen to correspond to a specific social welfare function. Making this connection between a specific social welfare function and a poverty measure, Blackorby and Donaldson argue for an ethical interpretation of such measures. 16 To see this, begin by defining the equally distributed equivalent level of income as that level of income, which, if obtained by every individual in the population, would generate the same level of social welfare as the under the actual income distribution (see Atkinson, 1970). Letting y∗G be the equally distributed equivalent level of income when the Gini social welfare function (see Blackorby and Donaldson (1978) is used, Sen’s measure can be written as   y∗G ∗ . PS = H 1 − z A general class of poverty measures based on the specification of a social welfare function is introduced by Chakravarty (1983). This class of measures is defined as PC = 1 −

y∗ , z

(3)

15 Seidl (1988, p. 97) calls these the “rank transfer sensitivity” and “distance transfer sensitivity” axioms, respectively. 16 As Sen (1979, p. 301) points out, such an ethical interpretation “permits a descriptive interpretation as well”. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

The Identification and Measurement of Poverty

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where y∗ be the equally distributed equivalent level of income for a given social welfare function when the income distribution is censored at the poverty line. Members of this class of measures could potentially satisfy all of the axioms discussed above, but, this will, of course, depend on the specific social welfare function used. One member of this class is the poverty measure introduced by Clark et al. (1981). This measure is based on the assumption that social welfare is just the sum of each individual’s utility, i.e.,17 n

w(y) = ∑ u(yi ), i=1

where each individual has identical utility function 1 β u(yi) = yi , β

i = 1, . . ., n,

with the restriction that β ≤ 1 for concavity in income. Letting yc denote the n-vector of income censored at the at the poverty line, social welfare for the censored distribution is given by w(yc ) =

1 n ∑ min(yi, z)β β i=1

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Therefore, the equally distributed equivalent level of income, y∗, defined implicitly as nu(y∗) = w(y), is given by "

1 n y∗ = ∑ min(yi , z)β nβ i=1

#1/β

.

Substituting this into (3) yields the Clark et al. (1981) poverty measure, " #1/β 1 1 n PCHU (β) = 1 − ∑ min(yi , z)β . z nβ i=1 This measure satisfies the upward and downward monotonicity axioms, and, for β < 1, the strong transfer axiom, and both of Kakwani’s (1980) weak transfer sensitivity axioms. 18 As with the Sen (1976) measure, it is interesting to note this connection between this poverty measure and an inequality measure. Letting A p denote the level of Atkinson (1970) measure of income inequality among the poor, the Clark et al. (1980) poverty measure can be written as PCHU (β) = {1 − [(1 − A p )(1 − I)]β + (1 − H)}1/β. 17 Unlike

the utilitarian social welfare function used here, the Gini social welfare function which underlies Sen’s (1976) measure (see above) is not additively separable. 18 This measure is also replication invariant (see note 14). Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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Thanasis Stengos and Brennan S. Thompson

3.1.6. The Foster et al. Poverty Measure Foster et al. (1984) introduce the poverty measure PFGT (γ) =

1 q γ ∑ gi , n i=1

(4)

where γ ≥ 0. Note that PF GT (0) = H, the headcount ratio, while PFGT (1) = HI, the normalized income gap ratio. Foster et al. (1984, p. 763) view the parameter γ as a measure of ”poverty aversion”. As γ → ∞, this measure approaches Rawls’ (1971) maxi-min criterion. For γ > 1, this measure satisfies all of the axioms mentioned above, except Kakwani’s (1980) second weak transfer sensitivity axiom (this axiom is satisfied for γ > 2). A particularly attractive property of this measure (and the motivation behind its development) is that it satisfies the following: S UBGROUP C ONSISTENCY A XIOM: Let yˆ be a vector of income obtained from y by changing the incomes in subgroup j from y ( j) to yˆ( j) , where the number of individuals in subgroup j, n j , is unchanged. If yˆ( j) has more poverty than y ( j) , then yˆ must also have a higher level of poverty than y. 19

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In practice, the population may be split into subgroups based on characteristics such as age, race, family type, geographic location, etc. While this seems to be a very reasonable condition for a poverty measure to satisfy, interestingly, of the poverty measures considered in the previous sections, it is only satisfied by H, I, and the measure of Clark et al. (1981), PCHU (β). Moreover, of all the measures considered above, only H and PFGT (γ) satisfy: S UBGROUP D ECOMPOSABILITY A XIOM: A poverty measure for the entire population is the weighted sum of that same poverty measure applied to each subgroup of the population. 20 For example, if the population can be divided into J subgroups, PFGT (γ) can be expressed as J n j ( j) PFGT (γ) = ∑ PFGT (γ), j=1 n ( j)

where PFGT (γ) is the Foster et al. (1984) poverty measure in (4) for the jth subgroup, j = 1, . . ., J. This is an attractive property for a poverty measure, as it allows one to view the contribution to overall poverty from each subgroup. For this reason, the Foster et al. (1984) measure has gained wide popularity, particularly in designing and analyzing anti-poverty policies (which are typically tailored to the needs of different subgroups). 19 Foster

et al. (1984, p. 763) call this the “subgroup monotonicity axiom”. property is originally classified as a “proposition” by Foster et al. (1984, p. 764). However, in the subsequent literature, it has usually been labeled an axiom (see, e.g., Zheng, 1997, p. 136). 20 This

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The Identification and Measurement of Poverty

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3.1.7. Dalton-type Measures Hagenaars (1987) develops a class of poverty measures around the Dalton (1920) measure of income inequality. Dalton’s measure is D = 1−

w(y) , w(y) ˜

where, as above, w(·) is a social welfare function, and y˜ is the vector of incomes which maximizes w(·). To develop her class of poverty measures, Hagenaars assumes the utilitarian social welfare function used by Dalton, but bases it on the censored income distribution, i.e., 21 w(y) =

1 n ∑ u[min(yi, z)]. n i=1

(5)

If it is assumed that z is less then the mean level of income, then each element of y˜ is equal to z, so that w(y) ˜ = z. Thus Hagenaars’ Dalton-type class of poverty measures is PH =

1 q u(z) − u(yi) ∑ u(z) . n i=1

(6)

Members of this class of measures could potentially satisfy all of the axioms discussed above, but this will, again, depend on the specific social welfare function chosen. For example, letting γ

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u(yi ) = zγ (1 − gi ),

i = 1, . . ., n,

implies that PH = PFGT (γ), the Foster et al. (1984) measure. A slight variation on this class of measures is found by using a social welfare function of the form n

w(y) = ∑ wi min(yi , z), i=1

where wi is some weight placed on the income of the ith individual. If it is again assumed that z is less then the mean level of income, Hagenaars’ modified class of poverty measures is given by ∑n wi min(yi , z), . PH∗ = 1 − i=1 n z ∑i=1 wi As with PH , members of this class could potentially satisfy all of the axioms discussed above. It should be clear that, with the appropriate choices of wi this class of measures would include H, I, Sen’s measure, and all Sen-like measures (see Section 3.1.4.). These choices of wi are shown in Hagenaars (1987, Table 2). 21 Note

that this social welfare function is based on the average level of utility, while the social welfare function used by Clark et al. (1981) was based on the total level of utility. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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Thanasis Stengos and Brennan S. Thompson

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3.1.8. The Stochastic Dominance Approach An alternative way to view the poverty measures considered above is from a restricted stochastic dominance perspective (see, e.g., Atkinson, 1987, and Foster and Shorrocks, 1988). This approach allows one to compare two different income distributions with the intent of determining a clear poverty ordering. To understand this approach, some definitions are in order. First, income is treated as a non-negative continuous random variable, denoted Y , with distribution function F(y). In general, F(y) is said to α-order stochastically dominate G(y), some other distribution function, if Fα (y) ≤ Gα (y) for all y, and Fα (y) < Gα (y) for at least some y, where F1 (y) ≡ Ry F(y) and Fα (y) ≡ 0 Fα−1 (t)dt for α ≥ 2 (Gα (y) is defined similarly). For example, if F(y) is somewhere below, but nowhere above G(y), then F(y) first-order stochastically dominates G(y). On the other hand, F(y) is said to α-order stochastically dominate G(y) on a restricted basis if Fα (y) ≤ Gα(y) for all y in some range, say [y, y], ¯ and Fα (y) < Gα (y) for at least some y in this range. In using the restricted dominance approach to measuring poverty, it is useful draw a connection to the headcount ratio, H, discussed above. Note that, under income distribution F(y), H = F(z). That is, the headcount ratio is simply the probability that an individual’s income is less than or equal to the poverty line. 22 With this interpretation in mind, poverty under one distribution, F(y), is said to be lower, on a headcount basis, than under another distribution, G(y), if F(z) < G(z). The attraction of the restricted stochastic dominance approach to poverty measurement is that it allows one to easily consider a range of poverty lines (see Section 2.7.). Using the above definition, F(y) first-order stochastically dominate G(y) on a restricted basis if F(z) ≤ G(z) for all z ∈ [z, z¯] and F(z) < G(z) for at least some z ∈ [z, z¯]. This is equivalent to saying that, for all poverty lines z ∈ [z, z¯], the headcount ratio under distribution F(y) is nowhere higher, and somewhere lower than it is under distribution G(y). As a result, a clear poverty ordering can be made. That is, poverty, as measured by the headcount ratio, is unambiguously lower under F(y) than under G(y). While the headcount ratio suffers from many obvious drawbacks, higher-order forms of restricted stochastic dominance are compatible with more sophisticated poverty measures. For example, Foster and Shorrocks (1988, Proposition 1) show that the Foster et al. (1984) measure, PFGT (γ), under distribution F(y) will be nowhere higher, and at least somewhere lower than under distribution G(y) for z ∈ [z, z¯], if, and only if, F(y) (γ + 1)-degree stochastically dominates G(y) over this range.

3.2.

Multi-dimensional Poverty Measures

Having considered some important issues in measuring poverty in a single dimension, it should now be somewhat easier to understand multi-dimensional poverty measures. First, some notation. As above, a population consists of n individuals (or equivalized households). For each individual in this population, there is a set of m observable characteristics. 23 Let 22 This, of course, assumes the strong definition of poverty (see Section 3.1.). However, if incomes are assumed be continuously distributed, there is no difference between these definitions. 23 These characteristics may in fact be a set of the single characteristic measured at different points in time. See Section 2.5..

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The Identification and Measurement of Poverty

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xi, j be the value of the jth characteristic for individual i with i = 1, . . ., n and j = 1, . . ., m. Individual i is considered to be deprived in terms on characteristic j if xi, j < z j .24 3.2.1. The Aggregation Approach A very simple approach to measuring poverty in more than one dimension is to construct a poverty measure which is an aggregation of m one-dimensional poverty measures (of the type considered in Section 3.1.). For example, Anand and Sen (1997) recommend a poverty measure which is a function of three one-dimensional poverty measures: One which measures poverty in terms of life expectancy, one which measures poverty in terms of literacy, and one which measures poverty in terms of economic status. Denoting these three measures by P1 , P2 , and P3 , Anand and Sen’s poverty measure is η

η

η

PAS = (w1 P1 + w2 P2 + w3 P3 )1/η, where w1 , w2 , and w3 are weights which sum to unity, and η is a parameter. Besides the obvious difficulty of selecting appropriate weights, the problem with this approach is that it ignores the different dimensions of poverty on an individual basis. 25 It would be of interest to know if the same individual faces deprivation in more than one of these dimension. This information could be used to determine if there is any dependence between these different dimensions. Unfortunately, such information is lost with this approach.

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3.2.2. Individual-level Measures An alternative approach involves constructing a poverty measure which a function of each individual’s deprivation in terms of each of the characteristics under consideration. Of course, such an approach requires careful consideration of the intensive/extensive notion of poverty discussed in Section 2.4.. To see this, consider a simple measure of poverty such as the headcount ratio. Using the intensive notion of poverty, the headcount ratio would be equal to the fraction of individuals within a population who are deprived in terms of all of the characteristics under consideration. Using the extensive notion of poverty, the headcount ratio would equal to the fraction of individuals within a population who are deprived in terms of any of the characteristics under consideration. A slightly different type of measure, involving a compromise between these two notions of poverty, would be based on a “counting” of the number of characteristics for which each individual faces some deprivation (see Atkinson, 2003). 26 An individual who is deprived of all of the characteristics being considered would receive a weight of m, while an individual 24 This

corresponds to the weak definition of poverty (see Section 3.1.). fact, as Ravallion (1996) argues, there is no convincing reason why different measures should even be aggregated it the first place. In comparing poverty levels between say, different time periods, it would be of interest to know specifically how the different dimensions of poverty have changed. 26 A somewhat similar approach involves forming a ranking of welfare levels by summing the relative rankings of individuals in different dimensions (see Ravallion, 1996). However, it is not clear how such rankings could be aggregated to form a poverty measure. 25 In

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Thanasis Stengos and Brennan S. Thompson

who is not deprived of any of these characteristics would receive a weight of zero. Of course, this type of measure would not be useful in determining the dependence between the different forms of deprivation. For example, there would be no difference in the treatment of two individuals who are each deprived in one dimension, and a single individual who is deprived in two dimensions. A more sophisticated type of measure is recommended by Atkinson (2003) and Bourguignon and Chakravarty (2003). This type of measure can be seen to be similar to the aggregate measure considered by Anand and Sen, but focused on the individual level. Let gi, j = max(0, 1 − xi, j /z j ), be the relative deprivation gap of individual i in terms of characteristic j. The “overall deprivation” of individual i is then defined as m

Gi =

∑ w j gηi, j

!1/η

,

j=1

where, as above, ∑mj=1 w j = 1, and η is some parameter. These quantities, Gi , can then be treated as the relative income gaps (see Section 3.1.1.) in the one-dimensional poverty measures considered above. For example, in this context, the Foster et al. (1984) poverty measure would simply be PFGT (γ) =

1 q γ ∑ Gi . n i=1

Of course, there still remains the difficulty of choosing the appropriate weights for the different characteristics.

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3.2.3. Multi-dimensional Stochastic Dominance Multi-dimensional poverty orderings can also be made by using a stochastic dominance approach. Conceptually, the multi-dimensional case is a very simple extension of the onedimensional case. While specific definitions will not be given here (they can be found in, e.g., McCaig and Yatchew, 2007), it is useful to point out the implications of using such an approach in measuring multi-dimensional poverty. Suppose that an individual’s m-vector of characteristics, x = (x1 , . . ., xm ), is the realization of a continuous random vector X. If X has distribution function F(x), then F(z1 , . . ., zm) represents the probability that an individual is deprived in terms of all of the characteristics under consideration. Considering an individual in such a predicament to be “poor”, as in, e.g., McCaig and Yatcew, 2007), obviously implies an intensive notion of multi-dimensional poverty (see Section 2.4.). Thus, using stochastic dominance for the measurement of multidimensional poverty can be seen to be much more limited than the approaches considered above.

3.3.

Statistical Inference for Poverty Measures

Empirical studies of poverty are typically based on individual or household survey data. Because these surveys tend to be quite large, it is often assumed that estimates of poverty measures are quite precise, and, as a result, statistical inference procedures can safely be Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

The Identification and Measurement of Poverty

21

ignored. However, this conclusion has been contracted by several studies (see, e.g., Maasoumi, 1997). As a result, there has been an increasing amount of attention devoted to developing methods of inference for poverty measures in recent years. This section offers a quick overview of some of those methods. Most of the work in this area has focused on deriving the asymptotic distributions of some of the one-dimensional measures considered in Section 3.1.. For example, Bishop et al. (1997), develop some asymptotic results for the Sen (1976) measure by borrowing on the literature on U-statistics (see, e.g., Hoeffding, 1948). Kakwani (1993) considers methods of inference for several other measures, including those of Clark et al. 91981) and Foster et al. 91984). Since each of these measures turn out to correspond to the sample mean of various functions of an individual’s income, Kakwani is able to apply a simple central limit theorem to derive their asymptotic distributions. More recently, Zheng (2001) has considered the distributional implications of using relative poverty lines, which are themselves statistical estimates, when estimating various poverty measures. As an alternative to methods of inference based on these asymptotic distributions, several authors have recently considered nonparametric methods such as the bootstrap (see Biewen, 2002, and Davidson and Flachaire, 2005) and empirical likelihood (see Thompson, 2007).

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In addition to the work on one-dimensional poverty measures, statistical testing of restricted stochastic dominance has also received some attention. While most of this work has focused on univariate distributions (see Anderson, 1996, and Davidson and Duclos, 2000, 2006), recent studies by McCaig and Yatchew (2007) and Anderson (2007) have considered multivariate distributions. However, methods of statistical inference for multi-dimensional poverty measures (see Section 3.2.2.) have not yet been developed. Such methods would be very useful to understand the correlation between different characteristics of poverty at the individual level.

4.

Conclusion

The identification and measurement of poverty is far from being a straight-forward exercise. While plenty of attention has been devoted towards developing suitable measures of (onedimensional) poverty, other areas of poverty analysis have been somewhat neglected. Specifically, many of the issues around identifying and measuring poverty in more than one dimension require further attention from researchers. It is neither clear who should be identified as being poor, nor how the deprivation of such individuals should be measured and aggregated into informative statistics. An entirely different problem (not discussed here), is the modelling of poverty (see, e.g., Ravallion, 1996). Having determined who is poor (and how poor such individuals are), it is illuminating to consider why such poverty exists. 27 By identifying the factors, both micro and macro, which contribute to poverty, policies can be better tailored to reduce poverty. 27 Some

recent work in this area includes the models of poverty dynamics by Cappellari and Jenkins (2002, 2004) mentioned in Section 2.5.. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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References [1] Anand, S. (1977). Aspects of poverty in Malaysia. Review of Income and Wealth, 25 (4): 429-439. [2] Anand, S and Sen, A. (1997). Concepts of human development: A multidimensional perspective. Human Development Papers, United Nations Development Programme [3] Anderson, G. (1996). Nonparametric tests of stochastic dominance in income distributions. Econometrica, 64(5): 1183-1193. [4] Anderson, G. (2007). The empirical assessment of multidimensional welfare, inequality and poverty: sample weighted multivariate generalizations of the KolmogorovSmirnov two sample tests for stochastic dominance. Journal of Economic Inequality . In press. [5] Atkinson, A.B. (1970). On the measurement of inequality. Journal of Economic Theory, 2(3):244-263. [6] Atkinson, A.B. (1987).On the measurement of poverty. Econometrica, 55(4):749-764. [7] Atkinson, A.B. (2003). Multidimensional deprivation: Contrasting social welfare and counting approaches. Journal of Economic Inequality , 1(1):51-65. [8] Barry, B. (1973). The Liberal Theory of Justice. Clarendon Press, Oxford.

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[9] Biewen, M. 92002). Bootstrap inference for inequality, mobility and poverty measurement. Journal of Econometrics, 108(2):317-342. [10] Bishop, J.A., Formby, J.P., and Zheng,B. (1997). Statistical inference and the Sen index of poverty. International Economic Review, 38(2):381-387. [11] Blackorby, C. and Donaldson, D. (1978). Measures of relative equality and their meaning in terms of social welfare. Journal of Economic Theory, 18(1):59-80. [12] Blackorby, C. and Donaldson, D. (1980). Ethical indices for the measurement of poverty. Econometrica, 48(4):1053-1060. [13] Bourguignon, F. and Chakravarty, S.R. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality , 1(1):25-49. [14] Buhmann, B., Rainwater, L., Schmaus, G., and Smeeding, T.M. (1988). Equivalence scales, well-being, inequality and poverty: Sensitivity estimates across ten countries using the Luxembourg Income study (LIS) database. Review of Income and Wealth, 34(2):115-142. [15] Cappellari, L. and Jenkins, S.P. (2002). Who stays poor? Who becomes poor? Evidence from the British Household Panel Survey. Economic Journal, 112(478):C60C67. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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[16] Cappellari, L. and Jenkins, S.P. (2004). Modelling low income transitions. Journal of Applied Econometrics, 19(5):593-610. [17] Chakravarty, S.R. (1983). Ethically flexible measures of poverty. Canadian Journal of Economics, 16(1):78-85. [18] Clark, S., Hemming, R., and Ulph, D. (1981). On indices for the measurement of poverty. Economic Journal, 91(362):515-526. [19] Coulter, F.A.E., Cowell, F.A., and Jenkins, S.P. (1992). Equivalence scale relativities and the extent of inequality and poverty. Economic Journal, 102(414):1067-1082. [20] Dalton, H. (1920). The measurement of the inequality of incomes. Economic Journal, 30(119):348-361. [21] Dardanoni, V. (1993) Measuring social mobility. Journal of Economic Theory, 61(2):372-394. [22] Davidson, R. and Duclos, J.-Y. (2000). Statistical inference for stochastic dominance and the measurement of poverty and inequality. Econometrica, 66(6):1435-1464. [23] Davidson, R. and Duclos, J.-Y. (2006). Testing for restricted stochastic dominance. Unpublished manuscript. [24] Davidson, R. and Flachaire, E. (2005). Asymptotic and bootstrap inference for inequality and poverty measures. Unpublished manuscript.

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[25] Davies, J.B. and Zhang, J. (1995). Gender bias, investments in children, and bequests. International Economic Review, 36(3):795-818. [26] Donaldson, D. and Weymark, J.A. (1986). Properties of fixed-population poverty indices. International Economic Review, 27(3):667-688. [27] Foster, J., Greer, J., and Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3):761-766. [28] Foster, J. (1984). On economic poverty: A survey of aggregate measures. In Basman, R. and Rhodes, G., editors, Advances in Econometrics, volume III, JAI Press, Greenwhich, CT. [29] Foster, J., and Shorrocks, A.F. (1988). Poverty orderings. Econometrica, 56(1):173177. [30] Haddad, L. and Kanbur, R. (1990). How serious is the neglect of intra-household inequality? Economic Journal, 100(402):866-881. [31] Hagenaars, A. (1987). A class of poverty indices. International Economic Review, 28(3):583-607. [32] Hoeffding, W. (1948). A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics , 19(3):293-325. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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[33] Hoy, M. and Zheng, B. (2007). Measuring lifetime poverty. Unpublished manuscript. [34] Kakwani, N. (1980) On a class of poverty measures. Econometrica, 48(2):437-446. [35] Kakwani, N. (1993). Statistical inference in the measurement of poverty. Review of Economics and Statistics , 75(4):632-639. [36] Lemmi, A. and Betti, G. editors (2006) Fuzzy Set Approach to Multidimensional Poverty Measurement. Springer-Verlag, New York. [37] Maasoumi, E. (1997). Empirical analysis of inequality and welafre. In Pesaran, M.H. and Schmidt, P. editors, Handbook of Applied Econometrics , volume II, Blackwell, Oxford. [38] McCaig, B. and Yatchew, A. (2007). International welfare comparisons and nonparametric testing of multivariate stochastic dominance. Journal of Applied Econometrics, 22(5):951-969. [39] Ravallion, M. (1996). Issues in measuring and modelling poverty. Economic Journal, 106(438):1328-1343. [40] Rawls, J. (1971). A Theory of Justice. Harvard University Press, Cambridge, MA. [41] Seidl, C. (1988). Poverty measurement: A survey. In Bos, D., Rose, M., and Seidl, C., editors, Welfare and Efficiency in Public Economics . Springer-Verlag, Heidelberg. [42] Sen, A. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44(2):219-231. Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

[43] Sen, A. (1977). Social choice theory: A re-examination. Econometrica, 45(1):53-89. [44] Sen, A. (1979). Issues in the measurement of poverty. Scandinavian Journal of Economics, 81(2):285-307. [45] Sen, A. (1981). Poverty and Famines: An Essay on Entitlement and Deprivation. Clarendon Press, Oxford. [46] Sen, A. (1983). Poor, relatively speaking. Oxford Economic Papers, 35(2):153-169. [47] Sen, A., editor (1997). On Economic Inequality. Clarendon Press, Oxford, expanded edition. [48] Shorrocks, A.K. (1995). Revisiting the Sen poverty index. Econometrica, 63(5):12251230. [49] Smeeding, T., Saunders, P., Coder, J., Jenkins, S., Fritzell, J., Hagenaars, A., Hauser, R., and Wolfson, M. (1993). Poverty, inequality and family living standards impacts across seven nations: The effect of non-cash subsidies for health, education and housing. Review of Income and Wealth, 39(3):229-256. [50] Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations, Dent&Sons, London. Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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[51] Thompson, B.S. (2007). Statistical inference for poverty measures: An empirical likelihood approach. Unpublished manuscript. [52] Thon, D. (1979). On measuring poverty. Review of Income and Wealth, 25(4):429-439. [53] Tsui, K. (2002). Multidimensional poverty indices. Social Choice and Welfare, 19(1):69-93. [54] Wu, X. (2006). Intensive and extensive poverty: A multidimensional formulation. Unpublished manuscript. [55] Zheng, B. (1997). Aggregate poverty measures. Journal of Economic Surveys, 11(2): 123-162. [56] Zheng, B. (2000). Poverty orderings. Journal of Economic Surveys, 14(4):427-466.

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[57] Zheng, B. (2001). Statistical inference for poverty measures with relative poverty lines. Journal of Econometrics, 101(2): 337-356.

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In: Economic Policies and Issues on a Global Scale Editors: Henry J. Grover and Nancy C. Regmond

ISBN 978-1-61122-937-0 © 2011 Nova Science Publishers, Inc.

Chapter 2

WORKING UNDER TIME PRESSURE: AN INCREASING RISK FOR WOMEN’S HEALTH? Norma Barbini 1, Rosa Squadroni2 and Francesco Sera3 1 2

Epidemiological Center, INRCA, Ancona, Italy Polytechnic University of Marche, Ancona, Italy 3 CSPO, Firenze, Italy

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ABSTRACT Gender work segregation may be evidenced also in the different exposure of the two sexes to those working constraints which are considered more difficult with age, as the possibility to move from adverse work conditions to less demanding work plays un important role in the health related selection. Several studies carried out at European or national level, found a declining trend of physically demanding work in men, suggesting that men had more possibility to moving to less physically demanding jobs and that favourable differences between older and younger workers were more remarkable for older men than for older women as regards poor work postures and repetitive work. As to working under time pressure, this constraint had increased for both sexes, but the increase had been greatest among women. The high working rhythms are commonly associated with musculoskeletal pain, stress and poor perceived health. This study was mainly aimed at analysing gender differences in work-related health problems, focusing on relationships between the difficult in coping with work under time pressure with advancing age and some health complaints, such as musculoskeletal symptoms and self-reported health. A population of 1195 Italian workers employed in different productive sectors and divided into 5 age cohorts were interviewed regarding the difficulty, with age, of coping with high working rhythms. The relationships between working under time pressure and the presence of musculoskeletal complaints (back pain and multiple complaints) and poor health self-assessment were then explored. Female workers were more exposed to repetitive work with tight deadlines and to time pressure. Analyzing the occupational exposure by cohorts, a decreasing exposure frequency may be observed for men in the oldest cohorts, while the opposite was observed for women, who complained about these constraints as particularly difficult with ageing. Working under time pressure appeared to be the least tolerated constraint for women, who had a significantly higher Odds Ratio than men in all cohorts of age, with a greatest risk in the 52 years cohort. The high working rhythms were associated with poor health, both for

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28

Norma Barbini, Rosa Squadroni and Francesco Sera musculoskeletal pain and perceived health, especially when the exposure resulted particularly difficult to bear with ageing, but in different ways for the two sexes. In women the interaction between repetitive work with high deadlines and musculoskeletal complaints, showed a statistically significantly association both for upper limbs and for multiple musculoskeletal symptoms. The multivariate analysis showed an increasing risk with age for women, while in men repetitive work with tight deadlines was associated with a poorly perceived health. When analyzing interactions between repetitive work with tight deadlines and poor health-assessment, a progressive increased risk was observed from the 42 to 52 year cohorts for men, and in the 47 year cohort for women. The possibility for men in avoiding the more demanding or difficult work can be hypothesized, such as more autonomy and control over their work situation, while for women it seems that the possibility of avoiding those working constraints which are especially poorly tolerated with ageing is less probable.

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1. INTRODUCTION Despite increases in women’s labour force participation, gender differences in work-related health problems have received little research attention. Women are still employed in less important and qualifying jobs, with less responsibility and actually a more subordinate than flexible work situation, which has determined a “natural” invisibility of some aspects of women’s work. Among these, the perceived difficulty, with advancing age, in carrying out tasks objectively similar to those of men. In relation to horizontal gender work segregation, the main point is that men and women have different jobs and different exposure to risk factors and, even when men and women have the same job-title, there seems to be a gender difference in exposure due to differences in task assignment [15]. An equal exposure to work-related risks could have different effects on male and female workers due to an inadequate concept of ergonomic layout which doesn’t take into account differences of height, weight, body mass index and muscle force between the two sexes. In order to analyse gender differences in coping with one’s work with aging, relationships between age difficulties, including health problems, and work exposure including physical, cognitive and psychosocial aspects, must be investigated. Furthermore it is important to verify if the perceived difficulties in coping with certain job strains with age are related to the same work conditions for both men and women. A growing body of surveys and studies carried out by the European Foundation for the Improvement of Living and Working Conditions (2000, 2006) highlighted that time constraints not only continue to be common in the European working population, but they are even increasing. When analysing working exposure by sex, women are more concerned with the difficulty in facing time pressure with age and it seems that they have less chances, compared to older men, in avoiding less tolerated working conditions [1;3;11]. In fact, several studies found a declining trend of physically demanding work in men, suggesting that men had more possibility to moving to less physically demanding jobs with advancing age. Likewise, the Second European Survey on Working Conditions (1996) showed that favourable differences between older and younger workers were more remarkable for older men than for older women as regards poor work postures and repetitive work. As to working under time pressure, this constraint had increased for both sexes, but the increase had been greatest among women. Consequently, a greater number of older women

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Working Under Time Pressure: An Increasing Risk for Women’s Health?

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will be forced to work under time pressure, despite their reduced tolerance to high working rhythms with age [24]. The gender segregation may be also highlighted in the different exposure of the two sexes to those working constraints which are considered more difficult with age, as the possibility to move from adverse work conditions to less demanding work plays an important role in the health related selection. Regarding the consequences of time constraints on health, interactions between work pace, especially when associated with low job discretion, and health complaints were found for musculoskeletal disorders [4;10;17], cardiovascular apparatus [16;19], general health and, for aging women, sleeping tablet consumption [6]. It is well known that musculoskeletal pain is a major medical and economic problem in developed countries due to high prevalence rates and considerable consequences. There are indications that consequences of musculoskeletal pain differ for men and women. European data show similar levels of musculoskeletal disorders between women and men at work, however women report more upper limb pain, probably due to poor ergonomic work conditions, awkward postures, monotonous and repetitive tasks and increased rhythm of work. These factors are present in many jobs typically performed by women. Previous studies carried out in occupational health have also shown a greater frequency of multiple musculoskeletal complaints in women than men, at all ages [22]. Little is known about gender behavioural differences in the approach to treatment of pain. It seems that women tend to report more symptoms, probably because they are better at recognising, articulating and communicating their musculoskeletal disorders symptoms than men. There is also some evidence that women consult a doctor quicker than men do when health problems arise. Biological differences between men and women may explain some sex differences in pain perception and pain tolerance. Sex differences in pain perception have been extensively studied in the laboratory, and ratings of experimentally induced pain also show some sex disparity, with women generally reporting lower pain thresholds and tolerance than men [20]. The gender difference in reporting musculoskeletal symptoms may be also explained by work-related risk factors (repetitive work, poor ergonomic equipment), and domestic work, which allows less opportunity to relax and exercise outside to women [23]. Findings on associations between work-related risk factors and cardiovascular diseases are controversial. Some studies suggested that psychosocial stress at work, in particular job demands and lack of control over job may be related to the development of cardiovascular diseases [13;14], while in other studies this association was questioned [9;12]. However, it is quite clear that an equal exposure to the same stress factors could have different effects on male and female workers [18]. Job strain was found significantly associated with an increase in blood pressure in middle-aged men, but not in women [19]. Women generally had lower job control than men and low decision latitude resulted as positively related to cardiovascular disease. Besides, women may also find less social support in the workplace than their male counterparts. Workplace social support is a job feature, which has been suggested by some authors to mediate the effects of job strain. It seems that psychosocial factors and those ascribable to bad work organization are the main worsening factors for ageing workers [25]. In addition, management of cardiovascular risk is usually based on male characteristics and is poorly managed in women, especially during the

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menopausal transition, when the cumulated effect of working exposure and hormonal changes increase the susceptibility to cardiovascular events. Perceived health, as reported in several studies, may be a predictor of mortality and it may be a useful indicator also to highlight socioeconomic inequalities in health among the working population [21]. A poor self-rated health may be influenced both by psychological and material work conditions as well as lifestyle factors. When exploring relationships between working conditions and perceived health, the multidimensional engagement (task) of women, in order to match family demand and work, both the paid and non-paid, must be carefully taken into account [2;5]. Considering the repercussion of early exposure to risk factors on future health, what is currently lacking in terms of studies is what the impact of a “forced” exposure until old age to time pressure – considered as an unavoidable constraint - would have on women’s and men’s health. The aims of this study were: a) first, to analyse the trend of time constraints in women and men with ageing b) second, to investigate the relationships between high working rhythms, age and some health-illnesses, such as musculoskeletal complaints and self-reported health.

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2. METHODS Analyses were performed on data from the cross-sectional phase of the longitudinal Italian Survey on Health Work and Aging. Data concerned a population of 1195 Italian workers (297 women and 898 men) aged 32, 37, 42, 47 and 52 at the time of the first data collection (2000) and included work history, working conditions, medical history, perceived health and socio-demographic variables. In the present study ill health status was measured by two indicators, one concerned musculoskeletal complaints and the other was based on self-reported health. Musculoskeletal indicator was defined as reported pain, for over 6 months, in one of nine local sites of musculoskeletal apparatus: 3 for upper limbs, 3 for the spine, 3 for lower limbs. The presence of multiple pains was also considered, by using the cut point of at least two musculoskeletal complaints. SRH was assessed by the question “How do you rate your current health status?” on a Lickert scale ranging from 0 (very poor) to 10 (very good). Self-reported poor health was classified as belonging to ≤5. Work pace was investigated by the variables “repetitive work with frequent deadlines” and “working under time pressure”. The age difficulty in coping with time pressure was investigated in exposed workers by questions “Is it particularly difficult?” and, if affirmative, “Is it particularly difficult with age?”. Besides working constraints, housework, measured with number of hours per week (11, was also considered. Bivariate analysis was used to examine the distribution of health complaints, both musculoskeletal and perceived health, by gender and among cohorts. Pearson’s χ2 test was applied to determine whether differences were significant.An α of 0.05 was used to determine statistical significance.

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Several logistic regression models were fitted to assess associations between health complaints and selected working conditions. Age was considered in the models as a categorical variable, with each category corresponding to five years increment in age. In the first model, age and gender were included as covariates, while the second model was stratified for gender. First, interactions of health complaints and age were directly explored in order to understand the role of age; then time pressure, repetitive work under deadline and perceived difficulty, due to age, in working under time pressure, were also included in the model in order to investigate the relationships between health complaints and working variables. Finally, the difficulty, with age, in working under time pressure was also analysed as a dependent variable of health and job characteristics. Associations were expressed in terms of odds ratios with 95% confidence intervals. All the statistical analyses were performed using STATA 7 software.

3. RESULTS 3.1. Job Characteristics Working under time pressure concerned 61.3% of workers. Women reported significantly higher exposure to this constraint (70.8%; p 2bx , and the transversality condition implies y ( T ) > 0 . Consequently, unlike the case of social welfare maximization,

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the optimal exhaustion path is not continuous at t = T . Considering conditions (25)-(28), we can see that T takes a finite value. We show, that before the date of depletion, on the optimal exhaustion path the output decreases, and

λ ⋅ e ρt = a − μ − 2b( x + y)

(38)

Since T is defined as the date of depletion of the exhaustible resource, for each t < T moment: y > 0 , which implies by condition (35) that v = 0 and rearranging condition (32) ρt

we get Eq. (38). By reformulating Eq. (38) we can write q = (a − μ − λ ⋅ e ) / 2b , which implies decreasing resource use and output. This result shows that the ‘Property of decreasing extraction’ holds even if the Hotelling rule is inapplicable in this case. If x( 0) = 0 then α ( 0) = 0 in accordance with condition (34) and since t = 0 Eq. (31)





takes the form: a − η − 2by (0) + α = 0 and hence a − η ≤ 2by ( 0) because α ≥ 0 , see condition (33). On the other hand Eq. (38) yields λ = a − μ − 2by ( 0) and substituting the former inequality we get λ ≤ η − μ .



If 0 < x ( 0) < x then both α ( 0) and α = 0 according to conditions (33) and (34) and since t = 0 Eq. (10) implies a − η = 2b[ x ( 0) + y ( 0)] . Substituting in Eq. (38) we get

λ =η−μ.  If x ( 0) = x then α (0) = 0 in accordance with condition (33) and condition (34) implies that α ( 0) ≥ 0 , and by the same token we get λ ≥ η − μ .

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Therefore, the constant value of costate variable λ depends on the initial renewable resource use as listed in table 1. Table 1. The relation between the constant value of the costate variable and the initial renewable resource use on the optimal exhaustion path

λ ≤η−μ λ =η−μ λ ≥η−μ

x(0) = 0 0 < x (0) < x x (0) = x

The exact value of λ depends on x(0) and y(0) , as it is stated by (38). The extraction T

path y must satisfy the restriction

∫ ydt = Y

0

similar to (4), taking into account the ‘Property

0

of rational extraction’ mentioned in Subsection 3.1. Let y be the optimal exhaustion path and t a the maximal t satisfying Eq. (38) subject to the constraint x = 0 . Thus

λ ⋅ e ρt = a − μ − 2by ⇒ t ≤ t 0

(39)

If t a < 0 then let us set t a = 0 . This is the case when the initial stock of the exhaustible resource is not sufficiently high, hence profit maximization necessitates the use of the inexhaustible substitute at t = 0 . From Table 1 we can see that in this situation λ ≥ η − μ .

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t a can be taken as the endpoint of the time interval over which the production uses only the exhaustible resource on the optimal exhaustion path. Further, let t b be the minimal

t , which

satisfies Eg. (38) subject to the constraint x = x . Thus

λ ⋅ e ρt = a − μ − 2b( x + y) ⇒ t ≥ t 0

(40)

If t b < 0 then let t b = 0 . (This is the case when Y0 is not sufficiently high.) t b can be viewed as the starting point of the time interval on which all the production capacity of the inexhaustible resource is used according to the optimal exhaustion program. x ( t a ) = 0 condition (34) implies that α (t a ) = 0 . Therefore Since

a − η − 2bq (t a ) ≤ 0 , according to Eq. (31). On the other hand x (t b ) = x and thus  α (t b ) = 0 , see condition (33), and this way Eq. (31) implies a − η − 2bq (t b ) ≥ 0 . As q (t b ) ≤ (a − η) / 2b ≤ q (t a ) , and since on the optimal exhaustion path the output decreases, we have that t a comes before t b , or to be more precise: t a ≤ t b . Further on, from the assumption that the inexhaustible resource is scarce follows that t b ≤ T .



Conditions (33) and (34) imply that if t ∈[ t a , t b ] then both α ( 0) and α = 0 and Eq.

(31) implies a − η − 2bq ( t ) = 0 . Thus the output takes a constant amount on this time Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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interval: q ( t ) = ( a − η ) / 2b . On the other hand, we have seen that the output decreases before the depletion of the exhaustible resource. Hence t a = t b . This result implies that when the production begins to utilize the inexhaustible substitute, it starts with full capacity. We will denote this moment by t . Now t = t a = t b and

q ( t ) = ( a − η ) / 2b . Consequently, the x path is not continuous at t = t , similarly to the y path at t = T . t < T means that the use of the more expensive inexhaustible resource begins definitely before the depletion of the less expensive resource. Consequently, the optimal path exhibits the ‘Property of overlapping resource use’ if and only if the model parameters satisfy the inequality:

x
2bx , and we have seen that this is the consequence of the assumption that the inexhaustible resource is scarce. Thus, the scarcity of the inexhaustible resource implies the ‘Property of overlapping resource use’ on the optimal exhaustion path. The next section will demonstrate the practical implications of this result.

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5. TECHNOLOGICAL SWITCH AT THE POWER STATION OF PÉCS – CASE STUDY 2 The necessity to reconstruct the Pécs, the most important city from South-East Hungary, power station emerged already in the early 1970’s. Besides the growing demand for electricity, the district-heating of the new residential area also necessitated a capacity extension. The electricity demand of the residential district built in southern part of the city, and the extensively growing electricity needs of the industrial facilities in Pécs required a constant enlargement and reconstruction of the power plant. The largest renewal work started in 1983, when initially the two 50 MW powered turbo generators were replaced by two 60MW generators. The functioning of the power station guaranteed the output market for the bad quality coal, which would have been difficult to sell elsewhere, and consequently secured appropriate job and regular income for hundreds of miners from Pécs. However, the power plant was totally environment-pollutant, causing discomfort to the population of the surrounding region. The new macroeconomic condition, which came into existence after the change of the political and economical system in 1990, was unfavorable for coal production. Industrial orders of corporate enterprises gradually disappeared, like orders of the Hungarian steel industry, which obtained good quality coal from abroad, for the half of the price of the coal 2

The industrial-hystorical overview in this section is based on the work of Kaposi (2006), additional information can be found in the study of Huszár (2004).

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from Mecsek. The most important companies from Pécs more and more switched to the significantly cheaper gas fuelling. The same happened to the dwellings not part of the residential-heating system. On the other hand, out-of-date technology and resources located deep in the underground significantly increased extraction costs. Furthermore, governmental subvention was terminated, which caused a heavy increase in the marginal cost of production. According to subsection 3.1, from this moment ‘Property of rational extraction’ became invalid. Additionally, continuing coal exploitation was not worth also because of the penetration of cheap, imported coal, as a result of coal market liberalization. All these factors led to the recognition, that if not in geological way, but in economic meaning coal reserves of Pécs were fully depleted. As a result, coal mining in Pécs was given up at the beginning of the 1990’s, only stripmining remained as a production possibility. In the first pit - where DSBA started exploitation in the 1850’s – production was stopped in 1986, and it was filled with rubble in 1991. Underground mining was ultimately shutdown in 2000, while strip-mining was stopped in 2004. The shutdown of the coal mine had serious social and economic consequences. At least 1500 workers from Pécs lost their jobs in the first part of the 1990’s, and the same was available for the workers coming form the suburbia. The loss of incomes caused a dramatic drop in consumption goods demand, which negatively influenced the retail network of the city as well. Pécs power station is operating as a public company since 1 January 1992, joint with the two remaining strip-mines from the surroundings of Pécs, and with the underground mine of the neighboring Komló. By joining these companies, they wanted to cover the demand for raw materials, and in the same time, they tried to moderate employment related- and other social, economical problems of the region, resulting from the liquidation of coal mines. To secure district-heating for the city of Pécs, maintaining joint thermal- and electricity supply was required, even after the close-down of coal mining. Although the management of the power station struggled for a long time to keep coal-heating technology, they finally switched to natural gas and biomass at the end of 2004. The final decision was made based on the efficiency calculations completed by our department, under my coordination. The calculations were based on the isoperimetric optimal control problem, detailed in the previous section. In the present case study, I confine myself to the presentation of the most interesting results of the calculations requested by the power station. Furthermore, I will introduce the most important regional effects of the technological switch. Accordingly, one type of renewable and several types of non-renewable energy resources are given. Coal and natural gas are non-renewable resources, while biomass is a renewable one. In the previous section was shown, that – even in the case of positive discount factor – the introduction of the more costly, but renewable resource utilization before the total depletion of non-renewable natural resources, could be rational. This scenario can occur if the production utilizing renewable resources cannot exceed an upper capacity limit. This is the exact situation in the case of biomass. The model analyzed by our department does not take into consideration the social planner, since the decision regarding the optimal sequence of resource utilization has to be made by the power plant so, that they maximize the net present value of expected future profit-streams, in concordance with the expectations of the company’s shareholders.

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Let q stand for the amount of electricity to be produced, x for biomass used during the production, y1 the amount of coal, and y2 the amount of natural gas. Assuming constant returns and by choosing appropriate unit measures:

q = x + y1 + y2 Similarly to the previous section, we assume constant marginal cost, hence the total cost of production:

c = ηx + μ1 y1 + μ 2 y2 where the marginal costs satisfy:

η > μ 2 > μ1 , meaning that the marginal cost of energy

production by utilizing biomass is greater than the marginal cost of natural gas fueling, and this exceeds the marginal cost of coal fueling. Assuming a linear demand function the inverse demand function is: p = a − bq , and the profit of the company at any point of time is:

π ( x, y1, y2 ) = aq − bq 2 − c Let Y1 denote the amount of coal stocks, that still can be exploited, and Y2 the amount of disposable natural gas stock. The process of production decreases these resource stocks in the following way:

Yi = − yi , i = 1,2 0

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In this case, we have two transition equations. If Yi denotes the amount of the starting stocks of non-renewable resources, then: 0

Yi (0) = Yi and Yi (T ) = 0 i = 1,2 The last two equations express, that by T moment of time the all coal reserves and all natural gas reserves should be entirely consumed. The fact, that these are non-renewable resources is expressed by the following inequalities:

yi ≥ 0 i = 1,2 Taking into account the assumptions mentioned above, now we have to find the maximum of the following functional: T

(a − η ) x − bx 2

0

ρ

max ∫ e− ρtπ ( x, y1, y2 )dt + e− ρT T , x, y , y 1

2

,

where ρ > 0 is the discount rate of the objective statement, while x represents the maximum amount of biomass, that can be utilized. Beside x , y1 and y 2 , T is also a control Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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variable. The second term of the sum denotes the net present value of profit streams, generated after the depletion of non-renewable resources. The solution to this problem is an optimal resource-utilization path, which - independently from the exact amount of starting resources and marginal costs – has the following properties3: 1. If during the production process coal or natural gas is used, the ‘Property of decreasing extraction’ will be valid, hence the quantity of electricity produced has to show a decreasing tendency over the time. 2. Coal and natural gas cannot be used simultaneously in electricity production. Therefore, the optimal resource utilization path does not exhibit the ‘Property of overlapping coal and natural gas use’. 3. If in the production biomass is utilized, the power station should use the maximum possible quantity of it. 4. According to the inequality (41), the utilization of biomass has to be started, when the electricity production diminishes to the following level:

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q0 =

a −η 2b

If during the production this lower limit is reached already in the period of coal-, or only in the period of natural gas fueling, is also dependent on the starting stocks of non-renewable resources. As an observation, if the profit function is substituted by the social welfare function, properties 2. and 3. will not be satisfied: the switch from one energy source to another cannot happen overnight, it should be executed in a continuity 4. The consumer price and trading of electricity are standard, therefore, calculations are refer to the whole country. In the following the quantity will be determined where utilization of biomass has to be started, according to the electricity production. The following demand function is applied between the quantity and price of electricity:

p = a − bq , where a and b are positive parameters of the inverse demand function. p and q are known: q is the quantity of electricity sold in a given year in Gj, p is the deflated price in HUF/Gj (Statistical Data of the Hungarian Power System, 2006). Parameters a and b can be estimated by means of linear regression. b coefficient shows the effect of the increment of quantity to the price of electricity. If the quantity increases, the price decreases according to the general features of the demand function. Parameters are estimated from data between 1989 and 2006 (18 years). The estimated regression function is the following: Y=426.4+0,00004379X The sign of the b parameter is conflicting that shows the artificial pricing in Hungary during this period. However, between 1998 and 2003 prices were less distorted by direct

3 4

Proof of the optimal resourse-utilization path properties in Bessenyei (2005) See Amigues et al. (1998)

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governmental intervention, therefore this time interval was applied for estimation purposes. Results are the following: Y=5944.6-0,000007639X These parameter estimations will be used during the calculation process: parameter a= 5944.6 and parameter b= 0,000007639. As we have seen, η > μ 2 > μ1 , where η is the production cost of renewable energy for electricity. A research experiment was made in Hungary, Baranya County to set out the production cost (NKFP5, 2006). On the basis of this research the used value for η is: η=1614 HUF/Gj The quantity where utilization of biomass has to be started:

q0 =

a −η 5944.6 − 1614 = = 283453332 2b 2 * 0,000007639

This quantity is 283 billion Gj in the case of 1614 HUF/Gj of production cost. Currently (2006 the last available data) the quantity of electricity sold is 126 billion GJ, consequently, this point had occurred much earlier, and the utilization of biomass had had to start a long time ago. η value can be calculated to this date (2006) as well, where the current 126 billion GJ could be the quantity the biomass utilization started.

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η = −2bq0 − a = −2 * 0,000007639 *126 802 800 - 5944.6 = 4007,3 Accordingly, at η=4007.3 HUF/Gj (hypothetical) production cost 2006 is the year, where the biomass utilization should start. The economical explanation to the fact, that biomass utilization should not be started in the earliest moment of time, is the following: at the beginning of coal exploitation, when all non-renewable resources can be found in rich quantities, a higher level of production would increase electricity supply, lowering its price, which would lead to lower profits. Considering again the problem of Pécs power station, it had to be investigated, in what degree the company followed the optimal resource-utilization path. Related to this we can make the following statements: 1. The electricity production had not always exhibited a decreasing tendency. This was caused by the purchase of imported coal, which compensated the decreasing coal production in the region. 2. The company has never used coal and natural gas simultaneously as input for electricity production. 3. It is worth to increase the amount of utilized biomass to the highest possible level.

5

NKFP – National Research and Development Program in Hungary.

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4. Biomass should have been introduced in the production before the total depletion of coal resources in Mecsek. On the grounds of these results, after 2004, when coal fueling has been stopped, and a first step towards natural gas fueling has been made, a 50 MW powered biomass block has also been introduced.6 This used shavings, lopping and other wooden byproducts from the territories to the south-west from Danube. Raw materials were transported to the power plant partly by rail and a smaller part by road transportation, with strict timing, because of the limited inventory capacities. The developed just-in-time logistic system represented a great challenge for the foresters of the region, but in the same time, it promoted the naturalization of a new production culture. Beside these, an intensive program has been started to create energetical plantations in Baranya and in the neighboring county, Somogy. The transformation of agricultural production into energetical plant cultivation and forestry takes land and other production resources away mainly from the grain-farming industry (which usually faces inventory and selling difficulties). The power station of Pécs can realize this by offering favorable prices and contract conditions. The involvement in production of out of crop areas, recultivated mining and slurry territories also takes place. The cultivation of energy plants, not needing quality soil, can also help in solving the problems of the region called Ormánság, which faces heavy poverty and unemployment. In this area, the quality of the soil makes the cultivation of other agricultural plants impossible. Energy plant cultivation and forestry also helps in getting ground erosion under control. These plantations need a much lower quantity of pesticides especially during the ripening phase. Moreover, these plants have only 0.1% sulphur content, compared to the coal’s 0.5 4.0%. Consequently, emission of environment damaging substances is lower, both during cultivation and during burning. First of all, the social effects of the technological switch were significant in the field of employment and in increasing the income generating ability of the region. We have seen that the above presented model assumes that using biomass in production is more expensive, than coal- or natural gas fueling. The majority of the emerging costs, in contrast with natural gas fueling, represent an income for the companies and businesses in the region, decreasing in the same time welfare payments, and increasing local tax incomes.

6. CONCLUSIONS This paper has analyzed the optimal natural resource use with one of the most widely spread isoperimetric problem, with two control variables at Eqs. (1)-(4). However, prior to that, we pointed out the difficulties with the social welfare function in Eq.(1), which tends to be neglected by the mainstream literature. In Section 3, we demonstrated that the fulfillment of the conditions, needed to avoid the collapse of industrial production, is quite unrealistic. Therefore, the rest of this paper focuses on the inexhaustible natural resource use, replacing the intertemporal social welfare maximizing objective with the objective of the decision maker company to maximize its market value. We have shown, that these changes do not 6

For additional details and recent developments see http://www.pannonpower.hu/en/index.php

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fundamentally affect the most important properties of the optimal resource utilization path: ‘Property of decreasing extraction’ and ‘Property of overlapping resource use’. In this paper, we have outlined the theoretical idea of optimal natural resource use as the choice of the society or the decision maker company between available exhaustion paths. In the case of market value maximization we assumed a positive discount rate. This is usually used to ensure the convergence of the objective functional on an infinite planning horizon (e.g. Favard, 2002).But the models discussed in sections 4. and 5. use finite horizons. The principal reason for using positive discount rate is to have the ‘Property of decreasing exploitation’. The main result of the model depends on this property. A zero discount rate would result in constant production, and this would alter the optimal order of natural resource use. However, the assumption of positive discount rate in the utility function raises serious ethical and political problems, mentioned in Section 2. Since these problems seem to be unable to be solved, it is suitable to assume that the decision maker wishes to maximize the market value of the monopolist company. On the other hand, given the concern about the costs of economic growth and the limits of growth it may seem surprising that a paper entitled 'More renewable Resource Use – Higher Corporate Profit – Higher Regional Income' has been deliberately written to eschew any discussion of whether income or profit growth, in itself, is desirable. In fact, these matters are widely discussed in the economic literature and beyond, but it should be noted that the ideas encountered in this paper do not totally exclude the arguments of those who oppose growth, per se, and those who argue that nonnegative growth rate cannot be sustained. The latter problem is explained in Section 3. In principle, the framework of thought presented in this paper is neutral. It may be that higher profit or income has so many undesirable consequences that no growth would be preferable. Of course, this could be reflected in the form of the criterion function discussed in Section 2. Thus, for example, the same framework would apply if the objective was to choose a path for the economy that minimizes the stock of pollution over time. Similarly, if it is really believed that the global economic system is heading for collapse then this could be reflected in the form of the transition equation or the constraints of the optimal growth problem, see e.g. Martinet and Doyen (2007). However, the main conclusion of this paper is the similarity of the optimal exploitation path in case of social welfare maximization and in case of market value maximization. The possibility that the most desirable exhaustion path might exhibit the ‘Property of overlapping resource use’, even if the objective of the decision maker company is to maximize its market value, reflects that it is inappropriate to set social welfare against the companies’ profit maximizing ambition, or analogously corporate against regional interests, without any further consideration. These, mostly opposingly depicted goals and interests, are not unconditionally conflicting, in place of automatic confronting it would be more appropriate to examine, in which area and how much these interests overlap.

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Arrow, K. (1963). Social Choice and Individual Values (2nd edition). Yale University Press, New Haven. Atkeson, A. and Kehoe, P. J. (1999) Models of energy use: putty-putty versus putty-clay. The American Economic Review 89, pp. 1029-1034. Bessenyei, I. (2005) Does market value maximization affect the order of resource exploitation? Economic Modelling 22, pp. 1090-14. Bohi, D. R. (1981) Analyzing Demand Behavior: A Study of Energy Elasticities. John Hopkins Univ. Press, Baltimore. Chiang, A. C. (1992) Elements of dynamic optimization. McGraw-Hill. Chichilinsky, G., Heal, G. and Beltratti, A. (1995). The green golden rule. Economics Letters 49, pp. 175-179. Cigno, A. (1981). Growth with exhaustible resources and endogenous population. Review of Economic Studies 48, pp. 281-287. Cigno, A. and Zhang, W. B. (1988). Long waves in economic activity. Paper to the European Economic Association Congress, Bologna, 27-29 August 1988. Favard, P., 2002 Does productive capital affect the order of resource exploitation? Journal of Economic Dynamics and Control 26, pp. 911-918. Hotelling, H. (1931). The economics of exhaustible resources. Journal of Political Economy 39, pp. 137-175. Keynes, J. M. (1936). The General Theory of Employment, Invest and Money. Macmillan, London. Klump, R. and De La Grandville, O. (2000) Economic Growth and the Elasticity of Substitution: Two Theorems and Some Suggestions. The American Economic Review 90, pp. 282-291. Martinet, V. and Doyen, L. (2007) Sustainability of an economy with an exhaustible resource: A viable control approach. Resource and Energy Economics 29, pp. 17-39. Martinet, V. and Rotillon, G. (2007) Invariance in growth theory and sustainable development. Journal of Economic Dynamic & Control 31, pp. 2827-2846. Michl, T. R. and Foley, D. K. (2007) Crossing Hubbert's peak: Portfolio effects in a growth model with exhaustible resources. Structural Change and Economic Dynamics 18, pp. 212-230. NKFP 3A/061 - 8.3.3.2, 2006, Második T Bt Ramsey, F. P. (1928). A Mathematical Theory of Saving, The Economic Journal, 38, pp. 543559. Rawls, 1971 A theory of Justice. Clarendon, Oxford. Schleich, J., (1999). Environmental quality with endogenous domestic and trade policies. European Journal of Political Economy 15, pp.53-71. Shaw, W. D. and Woodward, R. T. (2007), Why environmental and resource economists should care about non-expected utility models, Resource and Energy Economics, doi: 10.1016/j.resenceco.2007.05.001 Solow, R. M. (1974). Intergenerational equity and exhaustible resources. Review of Economic Studies 41. In: Proceedings of the Symposium on the Economics of Exhaustible Resources. pp. 29-45. Solow, R. M. and Wan, F. Y. (1976). Extractions costs in the theory of exhaustible resources. Bell Journal of Economics 7, pp. 359-370. Statistical Data of the Hungarian Power System, 2006, Hungarian Energy Office

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Stiglitz, J. E. (1974). Growth with exhaustible resources. Review of Economic Studies, Symposium, Edinburgh, pp. 123-137. Varian, H. R., 1992. Microeconomic Analysis. W. W. Norton Company, New-York, London. von Weizsäcker, C. C., (1965). Existence of Optimal Programmes of Accumulation for an Infinite Time Horizon, The Review of Economic Studies, pp. 85-104

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INDEX

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A  access, ix, 3, 7, 53, 85, 133, 135, 144 accounting, 119 actual growth rate, 71, 76 adjustment, 8, 57, 70 adults, 7, 36, 83, 99, 106, 118 affluence, 6 Africa, 48, 65, 128 age, vii, viii, x, 16, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 45, 92, 95, 96, 97, 98, 99, 101, 102, 103, 108, 118, 133, 134, 136, 137, 138, 139, 144 agencies, 54 aggregate demand, 68, 69, 70, 71, 80, 81, 125 aggregate supply, ix, 67, 69, 80 aggregation, 19, 140 AIDS, 139 alertness, 49 alters, 126, 129 angina, 37 annual rate, 75 appointments, 55 Argentina, 48, 128 Asia, 52, 58, 60, 64, 65 Asian countries, 55 assassination, 53 assessment, viii, 22, 27, 28, 89, 91 assets, 52, 53, 56, 57, 61, 65, 127 assimilation, 44 Austria, 86, 89, 99, 100, 101, 106, 117 autonomy, viii, 28, 35, 63 average earnings, 105 aversion, 16, 69 avoidance behavior, 137, 141, 144

B  balance of payments, 51

banking, 70 banking sector, 70 banks, 61 barriers, 115, 117 basic services, 54, 62 Belgium, 48, 86, 89, 93, 95, 96, 99, 106, 108, 110, 114, 128 beneficiaries, 115, 118 benefits, 47, 85, 89, 91, 92, 98, 99, 100, 103, 105, 106, 108, 110, 111, 112, 114, 115, 116, 118, 119, 134, 137 biomass, x, 147, 161, 167, 168, 169, 170, 171 birth control, 134, 137, 142 births, 134, 137, 138, 142, 143, 144 bonds, 75 Brazil, 48, 128 Britain, 90, 105 bureaucracy, 42, 47, 55, 61, 63 businesses, 171

C  calculus, 137 caloric intake, 2, 3, 9 capital accumulation, 55, 125, 126, 127, 128, 130, 149, 152 capital controls, 62 capital flight, viii, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 capital flows, 61, 62 capital goods, 153, 154 capital inflow, 53, 56 capital markets, 53 cardiovascular disease, 29, 36 cardiovascular risk, 29, 37 career prospects, 100, 116, 119 case study, 36, 52, 64, 149, 167 cash, 24, 85, 91, 103, 114, 115, 116 causality, 127, 129

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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176

Index

CBS, 139 Census, 93, 96, 134, 138, 145 central bank, 70, 71, 76, 124, 126 central planning, 74 challenges, 7, 62, 84 Chicago, 49, 81 child benefit, 89, 91, 98, 110 child poverty, vii, ix, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 96, 98, 99, 100, 103, 104, 105, 106, 107, 111, 112, 117, 118, 119 child rearing, 117, 119 childcare, 84, 89, 91, 92, 98, 100, 112, 114, 115, 116, 117, 118, 119, 136 childhood, 83, 121 childrearing, ix, 83, 100 children, ix, 7, 23, 49, 83, 85, 86, 87, 88, 90, 92, 93, 95, 96, 98, 99, 100, 102, 103, 104, 105, 106, 111, 112, 113, 114, 115, 117, 118, 134, 145 China, 74 Christians, 139 classes, 46 classical economics, 39, 45 climate, 43, 46, 47, 70 clusters, 85, 91 coal, x, 74, 147, 149, 166, 167, 168, 169, 170, 171 cognitive abilities, 83 cognitive process, 71, 76 Cold War, 74 colonization, 75 competition, 74, 75, 153, 154, 155 competitiveness, 41, 44, 45 composition, 7, 86, 89, 91, 109, 110 computation, 73, 110 conflict, 35, 84, 100, 118 Congress, 173 consent, 142, 143 constant rate, 162 Constitution, 55 construction, 54, 149, 160 consumption, 2, 29, 45, 47, 61, 69, 125, 126, 127, 148, 149, 150, 151, 152, 153, 154, 167 Continental, 84, 85, 89, 93, 98, 99, 101, 103, 104, 105, 106, 110, 111, 112, 115, 116, 117, 118, 119 continuous random variable, 18 contraceptives, 137 control group, 138 controversial, 29 convergence, 124, 150, 157, 172 corporate restructuring, 74 corporation tax, 47 correlation, 21, 92, 98, 100 corruption, 53, 55, 61, 124

cost, ix, x, 11, 47, 51, 52, 56, 57, 58, 60, 61, 62, 63, 64, 69, 84, 89, 100, 112, 117, 119, 127, 134, 135, 136, 137, 138, 140, 141, 142, 147, 153, 161, 162, 167, 168, 170 counseling, x, 133, 136, 139, 141, 143, 144 covering, 52, 89 CPI, 75 crises, 52, 53, 56, 60, 61, 62, 63, 64, 130 criticism, 68 cronyism, 55 crowds, 115 cultivation, 171 culture, 43, 44, 45, 171 currency, 75 Czech Republic, 93, 103, 105

D  data collection, 30 data set, 85, 138 database, 22 debt service, 53, 54 debt servicing, 56 debts, 53, 54, 65 decomposition, 93 deduction, 153, 154 democracy, 131 demographic change, 93 demographic factors, 84, 89, 93, 98, 103, 104 Denmark, 48, 89, 100, 101, 108, 110, 117, 128 dependent variable, 31, 34, 138, 140, 141, 142, 143 deposits, 148 depreciation, x, 57, 126, 147, 151, 152, 153, 154, 155, 157, 158, 159 deprivation, 2, 4, 5, 12, 19, 20, 21, 22 depth, 84, 89 deregulation, 52, 53, 55, 57, 61, 74 derived demand, 145 destruction, 41, 54 developed countries, 4, 29, 40, 42, 144 developing countries, viii, 39, 40, 51, 130 deviation, 69, 70 diffusion, 70 diminishing returns, 56 direct taxes, 112, 119 disability, 8, 102, 106 discrimination, 36 diseases, 29 displacement, 69 disposable income, 47, 89, 103, 104, 106, 107, 108, 109, 118, 127

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

Index distribution, ix, 5, 7, 10, 11, 12, 14, 15, 18, 20, 30, 46, 68, 85, 90, 103, 111, 118, 119, 123, 126, 129, 130, 131, 154 distribution function, 18, 20 distribution of income, 5, 10, 11, 103, 111, 118, 119 diversity, ix, 83, 117 division of labor, 85, 92, 100 domestic economy, 44, 53, 56 domestic investment, 57, 61 dominance, 18, 20, 21, 22, 23, 24, 69, 70

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E  earnings, 44, 57, 85, 91, 92, 100, 103, 104, 105, 114, 116, 117, 137 East Asia, 65 economic activity, 44, 125, 173 economic change, 144 economic consequences, 167 economic crisis, 53, 105 economic development, 61, 132 economic efficiency, 42 economic growth, vii, viii, ix, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 59, 60, 61, 64, 75, 105, 123, 124, 125, 126, 127, 128, 129, 130, 131, 151, 152, 172 economic growth model, ix, 40, 46, 123, 124, 125, 126 economic growth rate, 40, 59, 124 economic performance, vii, viii, 51, 52, 55, 56, 57, 58, 60, 61, 63, 64 economic policy, vii, viii, 39, 40, 42, 46, 48, 124, 131 economic problem, 29, 46 economic progress, viii, 39, 40, 41, 45, 64 economic resources, 110, 111, 119 economic status, 19 economic transformation, 60 economics, 39, 45, 46, 51, 123, 148, 173 economies of scale, 7 education, 2, 3, 7, 24, 44, 46, 47, 97, 135, 137, 139, 140, 141, 143 educational attainment, 3, 83, 134 elaboration, 125 elasticity of demand, 126 electricity, x, 54, 147, 149, 150, 166, 167, 168, 169, 170 emission, 171 empirical studies, 9, 128, 134 employees, 36, 140 employment, viii, ix, 39, 40, 42, 67, 68, 83, 84, 85, 92, 100, 104, 105, 106, 111, 112, 114, 115, 116, 117, 118, 119, 123, 124, 125, 137, 167, 171

177

employment levels, 117 employment status, 111, 119 endowments, 57 energy, x, 147, 149, 150, 152, 167, 168, 169, 170, 171, 173 enforcement, x, 133, 137, 138, 142, 143, 144 England, 4 enlargement, 166 entrepreneurs, 40, 41, 43, 44, 128, 130 entropy, 150 environment, 37, 41, 43, 44, 46, 53, 54, 62, 144, 166, 171 epidemiology, 37 equality, 22, 23, 110, 111 equilibrium, 41, 43, 126 equity, 53, 151, 173 erosion, 171 ethics, 49 Europe, 45, 52, 85, 89, 93, 98, 99, 101, 103, 104, 105, 106, 110, 111, 112, 115, 116, 117, 118, 119, 122 European Union (EU), 36, 37, 87, 88, 95, 96, 98, 99, 106, 107,110, 120, 121 evidence, ix, 29, 36, 67, 80, 130, 132 evolution, 43, 47, 68, 70, 71 exchange rate, 126 exclusion, 83, 89 exercise, 5, 21, 29 expenditures, 69, 89, 97, 98, 99, 106, 117, 118 exploitation, x, 147, 148, 149, 153, 157, 167, 170, 172, 173 exports, 126 exposure, vii, viii, 27, 28, 29, 30, 31, 32, 33, 37 external environment, 61 extraction, 149, 153, 154, 164, 165, 167, 169, 172

F  factor cost, 161 family conflict, 35 family income, 99, 115 family life, 115 family support, ix, 83, 84, 85, 91, 92, 99, 106, 111, 115, 117 famine, 3 fears, 54 federal funds, 70, 136 federal government, 136 Ferdinand Marcos, 53 fertility, x, 84, 93, 100, 133, 134, 136, 137, 138, 141, 144 fertility rate, 93

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

178

Index

financial, 47, 52, 53, 55, 56, 57, 61, 74, 76, 100, 103, 106, 116, 118, 119, 135, 137 financial crisis, 74 financial institutions, 47, 76 financial resources, 57 financial support, 100, 106, 118 Finland, 48, 89, 92, 95, 96, 101, 105, 106, 108, 110, 111, 128 fiscal policy, 44, 47, 129 flexibility, 35 flight, viii, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 food, 2 force, 28, 68, 75, 83, 84, 92, 98, 100, 105, 115, 118, 141, 149 forecasting, 71, 76, 80 foreign exchange, 52 foundations, 55, 63 France, 27, 37, 48, 86, 89, 99, 101, 111, 114, 117, 128 free trade, 44, 47 free world, 75 full capacity, 166 funding, x, 133, 136, 139, 141, 143, 144 funds, 42, 52, 53, 56, 61, 70, 75, 136

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G  gambling, 56 GDP, vii, 48, 67, 75, 79, 98, 99, 105, 118, 128 gender differences, vii, 27, 28 gender role, 118 gender segregation, 29 Germany, 48, 86, 87, 88, 90, 91, 93, 95, 96, 99, 100, 101, 105, 108, 115, 120, 128 global economy, 61 globalization, 52, 63 GNP, 90, 118 goods and services, 3, 45 governance, 40, 61, 74, 124 government intervention, 62, 63 government spending, 107 governments, 150 grants, 74, 91, 100 graph, 104 Greece, 86, 89, 100, 101, 115, 116, 117 Greeks, 133 gross domestic product, 54, 68 growth, vii, viii, ix, x, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 59, 60, 61, 63, 64, 67, 68, 69, 70, 71, 73, 74, 75, 76, 80, 93, 105, 117, 123, 124, 125, 126, 127, 128, 129, 130, 131,

132, 147, 148, 151, 152, 154, 155, 156, 157, 158, 159, 160, 161, 172, 173 growth rate, ix, 40, 59, 60, 61, 64, 67, 68, 71, 75, 76, 80, 124, 152, 154, 155, 156, 158, 159, 160, 172 growth theory, 173

H  Hamiltonian, 163, 164 health, vii, viii, 2, 3, 7, 24, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 99, 115, 136, 139 health care, 3, 99 health insurance, 136 health problems, vii, 27, 28, 29 health promotion, 35 health risks, 136 health status, 30, 36 heart disease, 37 heterogeneity, 138 hierarchy of needs, 2 higher education, 44, 98 hiring, 44 history, ix, 30, 61, 67, 74, 80 HIV, 139 homogeneity, 159 household composition, 86, 89, 109, 110 household income, 129 housing, 2, 75, 97, 98 human, vii, ix, 1, 22, 45, 62, 117, 123, 124, 126 human capital, ix, 45, 117, 123, 124, 126 human development, 22 human rights, 62 human welfare, 46 Hungary, 86, 87, 88, 89, 90, 91, 93, 95, 96, 98, 103, 105, 115, 116, 166, 169, 170 hyperinflation, 85 hypothesis, 40, 68, 124, 129, 135, 141, 142

I  ideology, 139, 140, 143 imagination, 150 IMF, 132 import substitution, 75 imports, 126 improvements, 45, 61, 89 in transition, 84, 89, 90, 93, 99, 103, 118 incidence, ix, 37, 83, 84, 89, 91, 92, 98, 104, 118 income, vii, ix, x, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 46, 47, 48, 61, 75, 83, 84, 85, 86, 87, 88, 89, 90, 91, 93, 95, 96, 99, 100, 103, 104, 106, 107, 108, 109, 110,

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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Index 111, 112, 114, 115, 116, 118, 119, 123, 124, 125, 126, 127, 128, 129, 130, 131, 136, 139, 140, 143, 148, 149, 154, 166, 171, 172 income distribution, ix, 14, 15, 17, 18, 46, 90, 111, 123, 126, 129, 130, 131, 154 income inequality, 12, 15, 17, 129, 131 income method, 2, 3, 4, 6, 7 income tax, 103 increasing returns, 131 independent variable, 92, 140, 143 indirect effect, 43, 128 individual character, 7 individuals, vii, 1, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 18, 19, 20, 21, 40, 44, 45, 47, 48, 68, 70, 71, 73, 74, 76, 80, 123, 124, 128, 149, 150, 151 Indonesia, 60, 64 industrialization, 62, 75 industrialized countries, 75 industries, 41, 44, 55, 74 industry, 74, 161, 166, 171 ineffectiveness, 124 inefficiency, 42 inequality, 4, 5, 12, 14, 15, 22, 23, 24, 46, 52, 109, 110, 129, 131, 132, 156, 158, 164, 166, 169 inertia, 69, 76 inevitability, 150 infants, ix, 117, 133, 134, 135, 137, 138, 139, 140, 141, 142, 143, 144 infarction, 37 infertility, 134 inflation, ix, 44, 67, 68, 69, 70, 71, 73, 75, 76, 80, 123, 124, 125, 126, 127, 129, 130, 132 infrastructure, 54, 57 insecurity, 63 institutional infrastructure, 55 institutions, 40, 41, 47, 62, 68, 70, 84, 124 integration, 155, 156, 157 interest rates, 69, 75, 80 interference, 75 international relations, 62 intervention, 62, 63, 170 inventions, 41 inventors, 41 investment, 46, 56, 57, 125, 130, 152, 154, 155 investments, 23, 53, 55, 56, 57, 59, 61, 63, 117 investors, 61 Ireland, 48, 85, 90, 92, 98, 99, 100, 102, 105, 106, 111, 112, 115, 116, 117, 128 Israel, 85, 89, 92, 93 issues, vii, 2, 18, 21, 52, 54, 61, 68, 135, 162 Italy, 27, 48, 89, 92, 95, 96, 98, 102, 104, 105, 128

179

J  Japan, 48, 74, 75, 98, 102, 112, 115, 117, 128 jobless, 111

K  Keynes, 45, 46, 49, 161, 173 Keynesian, 130 Korea, ix, 67, 68, 71, 73, 74, 75, 76, 80, 81, 85, 115, 116, 117

L  labor force, 28, 68, 75, 84, 92, 100, 115, 118, 134, 135, 139, 140, 143 labor force participation, 84, 92, 100, 115, 118, 134, 135, 139, 140, 143 labor market, 36, 75, 83, 84, 89, 92, 97, 99, 100, 103, 104, 105, 106, 112, 115, 118 labor markets, 105 lack of control, 29 landscape, 76 Latin America, 52, 65 laws, x, 133, 135, 136, 137, 138, 139, 141, 142, 143, 144 LDCs, 84, 130 lead, 5, 8, 54, 56, 84, 126, 170 leadership, 54, 61, 74 learning, 71 liberalization, 52, 53, 55, 57, 61, 130, 167 liberty, 43 life expectancy, 19 lifetime, 6, 7, 24 liquidity, 70 literacy, vii, 1, 19 localization, 40 longevity, vii, 1, 6 Luxemburg, 99

M  macroeconomics, 131 magnitude, 4, 5, 7, 13, 59, 64, 80 majority, 3, 171 Malaysia, 22, 60, 64 management, 29, 41, 62, 167 manufacturing, 36, 74 marginal costs, 168, 169 marginal product, 154, 155, 159, 160 marital status, 135, 137, 139, 140, 141, 143

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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180

Index

market position, 83 marriage, 91, 93, 134 married women, 118, 134 Marx, 120 Maryland, 144 mass, 28 materials, 69, 75, 126, 136, 167, 171 mathematical programming, 73 matter, 14, 99 measurement, vii, 1, 2, 4, 5, 7, 10, 18, 20, 21, 22, 23, 24, 70, 72, 161 median, 4, 86, 87, 88, 89, 90, 91, 103, 104, 112 Medicaid, x, 133, 136, 139, 140, 141, 143, 144 medical, 29, 30, 135, 136, 137, 140 methodology, 110 Mexico, 65, 85, 86, 95, 96, 98, 104, 105, 112 military, 54, 74, 75 military government, 54 minimum wage, 112, 113, 119 minimum wages, 119 minors, 136, 139, 142 model specification, 73 modelling, 21, 24 models, ix, 21, 31, 40, 46, 49, 67, 68, 84, 89, 91, 100, 111, 114, 115, 117, 123, 124, 125, 126, 127, 136, 149, 150, 158, 172, 173 modifications, x, 1, 44 momentum, 56 monetary expansion, 70 monetary policy, vii, ix, 44, 47, 48, 70, 71, 123, 124, 125, 126, 127, 128, 129, 130 monetary transmission mechanism, 126 money supply, 48, 69, 70, 76, 80, 126, 127, 128, 129 monopoly, 162 mortality, 30, 93 mortality rate, 93 motivation, 13, 16 multidimensional, 22, 25, 30 multivariate analysis, viii, 28, 99 multivariate distribution, 21 myocardial infarction, 37

N  national income, 75, 127 National Survey, 134 natural gas, x, 147, 149, 167, 168, 169, 170, 171 natural rate of unemployment, 68 natural resources, x, 147, 148, 151, 161, 167 negative effects, 45, 129 negative relation, 127, 129, 141 neglect, 23 net social benefit, 108

Netherlands, 86, 90, 93, 99, 100, 101, 106, 110, 111, 128 neutral, 160, 172 new growth theories, 130 New Zealand, 48, 86, 100, 102, 114, 117, 128 non-renewable resources, 167, 168, 169, 170 normal distribution, 23 Norway, 48, 89, 95, 96, 99, 100, 101, 104, 105, 108, 110, 128 nursery school, 114 nurses, 140 nursing, 37 nutrition, vii, 1

O  obstacles, 40, 41, 112 occupational health, 29 officials, 55 oil, ix, 53, 67, 69, 70, 73, 75, 76, 80 old age, 30 omission, 99, 118 one dimension, 5, 19, 20, 21 operations, 70, 126 opportunities, 7, 40, 41, 42, 43, 44, 45, 47, 52, 56, 60, 61, 137 optimism, 55 optimization, 152, 155, 173 overlap, 172 oversight, 74

P  pain, vii, viii, 27, 28, 29, 30, 32, 33, 34, 37 parameter estimation, 170 parental consent, x, 133, 142, 143, 144 parental involvement, 136, 139, 140, 142, 143 parenthood, 98, 115, 118 parents, 7, 83, 92, 95, 96, 98, 100, 102, 103, 104, 105, 106, 111, 112, 113, 114, 115, 116, 117, 119, 133 peace, 42 per capita income, 85 perfect competition, 153, 154, 155 performers, 89, 93, 118 Philippines, vii, ix, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 Phillips curve, ix, 67, 68, 81 physicians, 140 physiological factors, 35 plants, 171 platform, 73

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

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Index Poland, 86, 92, 103 policy, vii, viii, ix, 39, 40, 42, 44, 46, 47, 48, 57, 60, 61, 63, 70, 71, 84, 89, 91, 93, 99, 103, 104, 110, 111, 117, 123, 124, 125, 126, 127, 128, 129, 130, 131, 137, 140, 143, 148 political crisis, 55 political instability, 129, 130 political problems, 172 population, vii, viii, x, 1, 2, 3, 4, 5, 8, 9, 10, 13, 14, 16, 18, 19, 23, 27, 28, 30, 37, 45, 57, 60, 64, 84, 87, 89, 90, 92, 97, 98, 139, 147, 151, 158, 160, 166, 173 population growth, 57, 60, 64 portfolio, 126 Portugal, 102, 111, 115, 117 positive correlation, 98, 100 positive externalities, 116 potential output, 57, 59, 63 poverty, vii, ix, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 52, 53, 63, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 103, 104, 105, 106, 107, 111, 112, 117, 118, 119, 171 poverty alleviation, 84 poverty line, vii, 1, 2, 4, 5, 6, 8, 9, 10, 11, 15, 18, 21, 86, 95, 112, 119 poverty reduction, 117 poverty trap, 119 pregnancy, 102, 134, 135, 136, 137, 141, 142, 143, 144 present value, 161, 167, 169 president, 54, 55 prestige, 41 price elasticity, 141 price index, 75 principles, 76, 151, 155, 157 privatization, 74 probability, 7, 18, 20, 35, 41 producers, 161 production costs, 126, 161, 162 production function, ix, 40, 46, 123, 128, 151, 152, 153, 155, 157, 158, 159, 160, 161 production technology, 153 profit, 41, 43, 162, 165, 167, 168, 169, 172 programming, 73 proposition, 16, 135 prosperity, 123 protection, 43, 47, 90, 111 protectorate, 74 prudential regulation, 55 psychosocial factors, 29, 37 psychosocial stress, 29 public capital, ix, 47, 123, 129

181

public policy, 144 public service, 3, 47, 100 public support, 61 public welfare, 61

Q  qualifications, 13

R  race, 16 rate of return, 57, 126 rational expectations, 68, 69, 70, 71, 74, 76, 80 rationality, 68, 153 raw materials, 69, 75, 126, 167 real rate of interest, 75 real terms, 54 reality, 52, 161 recession, 54, 105 recognition, 167 reconstruction, 166 recovery, 53, 54, 56 redistribution, ix, 83, 110, 112 reform, 63, 74, 105 reforms, 52, 54, 55, 61, 63, 85, 105 regression, 31, 33, 34, 91, 134, 138, 140, 141, 143, 169 regression analysis, 91, 134 regression model, 31, 140, 143 regulations, 52, 135 regulatory changes, 74 relevance, 46, 123, 130 religion, 42, 123 religiosity, 135, 139, 140, 141, 143 renewable energy, x, 147, 149, 167, 170 replication, 13, 15 repression, 62 requirements, 7, 35, 37, 135, 150 researchers, 21, 53 reserves, x, 147, 149, 167, 168 residuals, 80 resolution, 54, 137 resource utilization, 153, 161, 167, 169, 172 resources, x, 40, 42, 46, 47, 52, 56, 57, 60, 61, 74, 75, 99, 110, 111, 119, 128, 129, 147, 148, 149, 151, 152, 153, 157, 160, 161, 162, 167, 168, 169, 170, 171, 173, 174 response, 11, 39, 61, 149 responsiveness, 141 restrictions, 73, 76, 130, 135, 137, 142, 143, 144 restructuring, 74, 105

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

182

Index

retail, 167 revenue, 155 rewards, 116, 119 rhythm, 29 rights, 2, 133, 135 risk, viii, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 41, 42, 44, 47, 69, 92, 112, 118, 119, 134, 137, 141, 149 risk aversion, 69 risk factors, 28, 29, 30, 35, 36, 37 rule of law, 43 Russia, 85, 86, 87, 88, 89, 90, 93, 103

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S  sample mean, 21, 141 savings, 129, 130, 131, 155, 156 scarcity, viii, 51, 57, 166 schooling, 139 science, 50, 137 scope, 99 security, 54, 62, 99 segregation, vii, 27, 28, 29 self-assessment, viii, 27 sensitivity, 8, 13, 14, 15, 16, 131 services, 42, 45, 47, 54, 62, 99, 100, 115, 118, 123, 136, 140 sex differences, 29 sexual activities, 139 sexual activity, 137, 139, 141 sexual behavior, 136, 137, 139, 140 shame, 4, 135 shape, 54, 61 shareholders, 167 shelter, 2, 52, 63 shock, ix, 53, 55, 67, 68, 69, 80, 81 shortage, 134 showing, 76 signs, 48, 70, 80, 129, 135 simulation, 89, 106 Singapore, 48, 64, 128 skilled workers, 105 Slovakia, 92, 103, 116, 117 small firms, 44, 45 social behavior, 136 social benefits, 91, 98, 99, 103, 105, 108, 110, 111, 116, 118, 119 social capital, ix, 40, 57, 123, 126 social change, 45 social control, 52 social environment, 46 social exclusion, 83, 89 social expenditure, 89, 98, 99, 118

social policy, 100 social roles, 36 Social Security, 122 social stress, 46 social structure, 85 social support, 29, 36, 37 social transfers, 99, 103, 105, 116, 119 social welfare, viii, x, 14, 15, 17, 22, 39, 42, 56, 147, 149, 150, 151, 156, 164, 169, 171, 172 society, 14, 42, 45, 46, 47, 55, 63, 74, 129, 149, 150, 151, 172 solar system, 150 solidarity, 89 solution, 140, 149, 169 South Africa, 48, 128 South Korea, 68, 74, 75 Southeast Asia, 58, 60, 64 Spain, 39, 48, 86, 87, 88, 89, 92, 102, 123, 128 specialization, 131 specifications, 12, 73, 154 spending, 112, 118 spillovers, 124 stability, 54, 55, 62, 144 stagflation, 124 stakeholders, 63 standard error, 80 standard of living, 90 state, ix, 1, 2, 3, 6, 42, 61, 62, 67, 68, 70, 71, 73, 74, 80, 84, 89, 90, 92, 126, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 148, 149, 152, 162 state laws, ix, 133 states, x, 43, 84, 85, 89, 93, 102, 110, 129, 130, 133, 135, 136, 138, 139, 144, 149, 154 statistical inference, 20, 21 statistics, vii, 1, 2, 21, 23, 73, 138, 140, 143 steel industry, 166 stimulus, 80 store of value, 126 stress, vii, 27, 29, 46 stress factors, 29 stressful life events, 37 stressors, 36 structural changes, 41 structure, 41, 46, 54, 74, 85 subgroups, vii, 1, 2, 9, 16 subjective experience, 35 sub-Saharan Africa, 65 substitutes, 153 substitution, x, 75, 147, 151, 153, 155, 156, 157, 158, 161, 162 sulphur, 171 supply curve, 68, 76

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

Index supply shock, 68, 69, 70 Supreme Court, 135, 136 surplus, 154 survival, 153, 157, 158, 159 sustainability, x, 147, 158 sustainable development, 173 sustainable economic growth, 42 Sweden, 48, 84, 86, 89, 92, 95, 96, 99, 100, 101, 105, 108, 110, 111, 114, 128 Switzerland, 83, 86, 89, 93, 112, 114, 117 symptoms, viii, 27, 28, 29, 35, 37

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T  Taiwan, 84, 89, 90, 92, 93, 103 tax rates, 112 tax system, 91, 100, 116 taxation, 46, 52, 116, 129 taxes, 47, 48, 103, 106, 107, 108, 110, 111, 112, 114, 116, 119, 128 technical change, 149 techniques, 40, 52 technological advances, 41, 44, 47 technological change, x, 124, 148 technological progress, 46, 128 technologies, 42, 49, 128 technology, ix, x, 40, 44, 45, 46, 47, 51, 74, 123, 124, 128, 147, 151, 153, 161, 167 technology flows, 51 Thailand, 57, 58, 59, 60, 64 Third World, 64, 65 threats, 53 time constraints, 28, 29, 30, 33, 34, 35, 36, 37 time periods, 8, 19, 70, 138 time preferences, 150 time pressure, vii, viii, 27, 28, 30, 31, 32, 33, 34, 35, 36 time series, 75 tracks, 70 trade, 11, 43, 44, 47, 51, 55, 74, 76, 173 trade agreement, 43, 47 trade-off, 76 traditional gender role, 118 training, 44 trajectory, x, 60, 61, 71, 147 transactions, 53 transformation, 60, 159, 160, 171 transition economies, 85, 89, 90, 93, 98, 99, 103, 111, 112, 115, 117, 118 transmission, 126, 129 transportation, 47, 74, 171 turbulence, 90, 118 two-sided test, 78

183

U  underlying rate of inflation, 68 unemployment insurance, 102, 106 unemployment rate, 40, 68, 70, 73, 75, 76, 80, 92 unions, 46 United Kingdom (UK), 48, 81, 84, 86, 87, 88, 89, 90, 93, 95, 96, 98, 99, 100, 102, 105, 108, 111, 112, 114, 115, 116, 117, 118, 121, 128 United Nations (UN), 22, 130 United Nations Development Programme, 22 United States (USA), 45, 48, 84, 85, 89, 90, 91, 92, 95, 96, 98, 100, 103, 105, 108, 111, 114, 115, 116, 117, 118, 120, 121,128, 133, 134, 135, 138, 144, 145 universities, 44

V  variables, viii, ix, 30, 31, 39, 40, 45, 46, 48, 58, 67, 68, 70, 71, 74, 75, 76, 80, 84, 92, 98, 99, 123, 124, 126, 128, 129, 138, 139, 140, 141, 142, 143, 148, 153, 154, 162, 171 variations, 9, 37, 89, 98, 125, 134, 138, 139 vector, 15, 16, 17, 20 vessels, 74 vision, 41 volatility, 129, 130 vote, 3, 5, 6, 129, 150 vulnerability, 61

W  wage payments, 162 wages, 44, 47, 67, 69, 112, 119 Washington, 65, 145 wealth, 41, 52, 53, 63, 85, 117, 125, 126 welfare, viii, x, 4, 5, 14, 15, 17, 19, 22, 24, 39, 42, 46, 56, 61, 64, 84, 85, 89, 92, 93, 105, 110, 117, 139, 140, 143, 147, 149, 150, 151, 156, 161, 164, 169, 171, 172 welfare reform, 85, 105 welfare reforms, 85 welfare state, 84, 85, 89, 92, 93, 110 withdrawal, 61, 62, 112, 116, 119 work disincentives, 116 work environment, 37 workers, vii, viii, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 44, 47, 75, 92, 105, 116, 167 working conditions, 28, 30, 31, 33, 34, 35, 36, 37 working families, 105, 112

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.

184

Y  Yale University, 173 yield, 85, 93, 142, 143, 144 young adults, 36

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

working hours, 35 working population, 28, 30, 37 workload, 35, 36 workplace, 29, 35 World Bank, 48, 65, 128 World War I, 54

Index

Economic Policies and Issues on a Global Scale, edited by Henry J. Grover, and Nancy C. Regmond, Nova Science Publishers, Incorporated, 2011.