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REDUCING CHILD UNDERNUTRITION HOW FAR DOES INCOME GROWTH TAKE US

REDUCING CHILD UNDERNUTRITION HOW FAR DOES INCOME GROWTH TAKE US
REDUCING CHILD UNDERNUTRITION HOW FAR DOES INCOME GROWTH TAKE US

FCND DP No. 137

FCND DISCUSSION PAPER NO. 137

Food Consumption and Nutrition Division

International Food Policy Research Institute

2033 K Street, N.W.

Washington, D.C. 20006 U.S.A.

(202) 862–5600

Fax: (202) 467–4439

August 2002

FCND Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised.

FCND DP No. 137

FCND DISCUSSION PAPER NO. 137

Food Consumption and Nutrition Division

International Food Policy Research Institute

2033 K Street, N.W.

Washington, D.C. 20006 U.S.A.

(202) 862–5600

Fax: (202) 467–4439

August 2002

FCND Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised.

ABSTRACT

How rapidly will child undernutrition respond to income growth? This study explores that question using household survey data from 12 countries. In addition, data on the undernutrition rates since the 1970s available from a cross-section of countries are employed in this investigation. Both forms of analysis yield similar results. Income increases at household and national levels imply similar rates of reduction in undernutrition. Using these estimates and better-than-historical income growth rates, we find that the Millennium Development Goal (MDG) of halving the levels of child underweight by 2015 is unlikely to be met through income growth alone. What is needed is a balanced strategy of income growth and investment in more direct interventions to accelerate reductions in undernutrition.

iv

CONTENTS

Acknowledgments (vii)

1. Introduction (1)

2. Data Sets and Models (3)

The Household Surveys (3)

The Cross-Country Data for 61 Countries, 1970-1995 (9)

3. Results: What Is the Impact of Income on Undernutrition? (10)

The Household Survey Results: Per Capita Income and Child Undernutrition (10)

The Cross-Country Results: GDP per Capita and Child Undernutrition (17)

4. Conclusions (24)

Appendix Tables (27)

References (31)

TABLES

1 Summary of household survey data sets used (4)

2 Summary of estimates of the impact of per capita income on Z-score

weight-for-age (11)

3 Projected underweight rates with 2.5 percent annual growth in per capita

income from the 1990s to 2025 (13)

4 Coefficients on parental characteristics (16)

5 Mean prevalence of low weight-for-age (< - 2) in cross-country data (for

all countries and countries with observations in all decades) (18)

6 OLS and country fixed-effects regressions: Dependent variable is prevalence

of low weight-for-age (<-2) (20)

7 OLS and country fixed-effects regressions with Gini coefficient: Dependent variable

is prevalence of low weight-for-age (<-2) (23)

APPENDIX TABLES

8 Countries used in the cross-country tables (27)

9 Full results on per capita consumption: Dependent variable is Z-score

weight-for-age (28)

FIGURE

1 The fitted relationship between child underweight rates and GNP per

capita (PPP) by decade, developing countries (18)

ACKNOWLEDGMENTS

This paper has benefited from the comments of John Maluccio, three anonymous reviewers, and from comments received at a seminar at the International Food Policy Research Institute (IFPRI).

Lawrence Haddad and Yisehac Yohannes

International Food Policy Research Institute

Harold Alderman

World Bank

Simon Appleton and Lina Song

University of Nottingham

1

1. INTRODUCTION

Great strides have been made in reducing child undernutrition over the past few decades. The prevalence of underweight in children under 5 in the developing countries was 37.4 percent in 1980. By 2000 this had dropped to 26.7 percent (ACC/SCN 2000). Nevertheless, 150 million children in the developing world remain underweight and 182 million remain stunted (low height-for-age). Moreover, progress in reducing prevalence rates has slowed in the past two decades, and in Africa the total number of underweight children has increased. Even the prevalence of underweight has risen in this region. At current trends it is clear that the goal of halving the prevalence of underweight children between 1990 and 2015—one of the Millennium Development Goal (MDG) indicator targets for poverty and hunger—will not be met (ACC/SCN 2000).

What is needed to accelerate reductions in undernutrition to meet this target?1 It is well accepted that income growth should lead to a reduction in undernutrition (Strauss and Thomas 1998). Greater incomes at the household level means more can be invested in food consumption; access to clean water, good hygiene, and health care, and more effective childcare arrangements. At the community level, greater income leads to improved access to, and quality of, health care centers and water and sanitation systems. But is moderate-income growth alone enough to meet these targets? If the relationship between income growth and undernutrition reduction is not sufficiently strong, more direct investments will be required to accelerate declines in undernutrition. Candidates for such investment include nutrition programs such as community-based behavior

1 We note Maxwell’s (1999, 93) reminder that “international targets can over-simplify and over generalize complex problems…and distort public expenditure priorities.” But even if one questions the analytical basis of such targets, the general question of how to hasten improvements in nutrition remains a concern.

change initiatives and micronutrient supplementation and fortification (Allen and Gillespie 2001).

The imperfect correlation between nutritional status and either national income levels or national income distribution is often used to distinguish those countries that are atypical or to motivate research to account for this. In places such as Sri Lanka or the Indian state of Kerala, higher levels of health status have been achieved than might have been expected, given their aggregate level of income or rates of poverty, often as a result of the provision of public actions that directly affect health or nutrition (Anand and Ravallion 1993). Correspondingly, in countries where nutritional status has not improved as rapidly as might have been expected, given income growth, there may be a need to make specific investments in human resources (Alderman and Garcia 1994).

The majority of studies addressing the causal link between income growth and malnutrition have, however, focused on the response of nutrient consumption to changes in income (Strauss and Thomas 1995; Bouis and Haddad 1992). Surprisingly, there has been no systematic multicountry analysis of the causal relationship between income and undernutrition. This paper helps fill that gap. Our goal is to answer the following question: How far does moderately rapid income growth take us toward reducing the rate of child undernutrition in line with the MDG? We use an anthropometric measure—low weight-for-age—of child nutritional status as an outcome of household decisions in health and childcare as well as in food consumption. We study the extent to which increased resources at household and national levels explain differences in this crucial outcome.

Using household survey data from 12 countries as well as aggregate data on a set of 61 developing countries, we model the relationship between child underweight and per capita income (proxied by total household consumption per capita in the micro studies and by per capita gross domestic product (GDP), 1987 purchasing power parity (PPP) in the cross-country regressions). We then use the model to predict the declines in undernutrition that can be expected from a sustained 2.5 percent annual increase in per capita income from the date of the survey (in the 1990s) to 2015. Despite these moderately rapid growth rates, declines in undernutrition rates fall short of the MDG target set above in 9 out of 12 countries. We conclude that income growth can play an important role in undernutrition reduction, but that it is not enough. We suggest (but cannot prove) that increases in the number and effectiveness of direct nutrition interventions have a crucial role to play if nutrition goals are to be met.

2. DATA SETS AND MODELS

This section describes the two data sources used to derive estimates of the response of child undernutrition to per capita income growth and outlines the models used to generate the results reported in Section 3.

THE HOUSEHOLD SURVEYS

We investigate how household resources affect the nutritional status of preschool children using household surveys from 12 countries.2 The countries were selected from

2 The age range was usually 0-60 months. In Kenya, the age range was 6-60 months and in Nepal it was under

3 years.

those with nationally representative household data available for the 1990s to cover a

range of locations, spanning four continents. They differ appreciably in their economic

position, including GNP per capita and rates of undernutrition at the national level (Table

1).3 Nevertheless, there is a common thread in the available data, namely that for all of

the countries studied, there has been a integrated household survey undertaken in the

1990s using a multipurpose, modular, living standards survey following a format utilized in over 20 countries (Grosh and Glewwe 2000). These surveys collect data on child

heights and weights as well as information on total expenditures and other socioeconomic conditions of the household.

Table 1: Summary of Household Survey Datasets Used

Rates of malnutrition (%) Weight-for-age Country Number of preschool children used in regressions Year of sample survey Maternal heights coverage (yes/no) GNP per capita a (Dollars) 1998 Annual % change in per capita GDP (PPP)1975-1999b Annual % change in per capita GDP (PPP)1990-1999 Male

Female All

Egypt 1,213 1997 Yes 1,290 2.9 2.4 10.3 11.1 10.7 Jamaica 752 1995 No 1,680 0.1 -0.6 4.9 5.2 5.0 Kenya 7,626 1994 No 330 0.4 -0.3 20.9 18.4 19.7 Kyrgyz 1,679 1997 Yes 350 -5.3 -6.4 13.4 13.1 13.3 Morocco 1,979 1990-1 Yes 1,250 1.4 0.4 14.7 15.4 15.0 Mozambique 3,268 1997 No 210 1.3 3.8 23.8 21.7 22.8

Nepal 1,560 1996 No 210 1.8 2.3 50.4 45.6 48.1 Pakistan 3,076 1991 Yes 480 2.9 1.3 48.4 43.2 45.7 Peru 3,075 1997 No 2,460 -0.8 3.2 7.5 5.5 6.5

Romania 3,625 1994 No 1,390 -0.5 -0.5 7.9 4.8 6.4 South Africa 4,132 1993 No 2,880 -0.8 -0.2 18.2 17.7 18.0 Viet Nam

2,637 1993 Yes 330 4.8 6.2 39.8 41.5 40.7 a

Taken from World Development Report 1999/2000. b Annual percent change in real per capita GDP data for both time periods are from UNDP’s 2001 Human Development Report (UNDP 2001): (https://www.wendangku.net/doc/2313973291.html,/hdr2001/back.pdf).

3

For reasons of data availability, we were unable to cover the half of the developing world’s population that lives in China and India.

The measure of nutritional status (N) that we study is weight-for-age, which is considered a general indicator of nutritional status of populations (Alderman 2000; WHO 1995). It is converted into standardized units called Z-scores after comparison with the U.S. data chosen as an international reference by the World Health Organization (WHO). The Z-scores are derived after subtracting the age- and gender-specific means from the reference data and after dividing by the corresponding standard deviation. In common with most of the literature, we pay particular attention to the proportion of children below two standard deviations from the median for the reference population. We refer to children with a weight-for-age Z-score below –2 as “underweight.” In the reference population, 2.3 percent have Z-scores below -2, while 16.0 percent are below –1. These levels might be expected for a normal population, and provide a basis for comparison. However, as there is no sharp difference in risk of mortality or functional impairment at this or any other commonly used cutoff (Pelletier 1994), the regressions focus on nutritional status itself and not the probability of undernutrition as defined by a Z-score below –2.

It is apparent from Table 1 that countries with higher per capita income tend to have less undernutrition. However, there are exceptions—South Africa has the highest income in our sample, but its rates of undernutrition are the fifth lowest, little better than those of Kenya, whose per capita income is less than an eighth of South Africa’s. However, our focus with the household data is on relations between household resources and nutritional outcomes across households within given countries. As is generally the case, we presume that expenditures reflect a household’s long-run income potential.

Thus, we estimate regressions for nutritional outcomes as a function of the logarithm of household expenditures per capita (Y).

Additional regressors include the educational levels of the child’s parents (or, where parentage is unknown, a proxy).4 Over and above income earning ability, education captures—albeit imperfectly—the availability to each parent of information about appropriate caring practices and health services for the child. To account for different patterns of undernutrition by age, all the regressions contain six dummy variables for age brackets. In addition, to control for health- and sanitation-related correlates of income that may have an independent impact on nutrition, the regressions include indicators for the type of drinking water and toilet used.5 Moreover, in countries where there are significant ethnic differences that relate to access to infrastructure—for example, South Africa or Peru—the regressions also include dummy variables for ethnic background.6 The height of the mother—an indicator of genetic endowment and of growth and development in the womb—is included in the regressions when this information is available. Finally, all models include demographic variables such as household size and the percentage of household size in different age groups.

4 If the child’s father could not be identified, the education of the most educated adult male in the household was used. In Jamaica and Kenya, neither of a child’s parents was identified, so the education of the household head and their spouse were used instead. Typically, education was measured in years, although this was not available for Kenya, in which case, dummy variables for educational level were used instead.

5 Typically, the distinction was whether the household had piped drinking water available within the dwelling and whether it had a flush toilet (see Burger and Esrey 1995 for a discussion of the role of water and sanitation interventions in reducing undernutrition).

6 However, WHO (1995) advocates having a single international reference for child growth. That is, there are few, if any, ethnic differences in growth patterns of young children; children from privileged or middle-class families in developing countries generally have height and weight distributions that do not differ from international references.

We undertake two specifications of the model. Model 1 includes expenditures, but excludes health, water, and sanitation infrastructure both external and internal to the household.7 Model 2 controls for the infrastructure in the community that is external to the household (E) by including cluster-level fixed effects, i.e., the model includes a dummy variable for each sample cluster. The impact of common attitudes and resources in the community or special local circumstances are also picked up by this dummy variable. In addition, Model 2 includes the variables for infrastructure within the household (I) via access to piped water and sanitation. The two models can be labeled in the following way:

Model 1: N = N(Y),

Model 2: N = N(Y, E, I).

Model 2 can be considered as giving the short-term effect of increasing household income or consumption, holding external infrastructure and internal health infrastructure constant. Over a longer period, a household whose income increases may choose to invest in water and sanitation or may have such investments made on its behalf by the public sector. Model 1, for which the short-term interpretation of the coefficient on income is biased to the degree that health and sanitation effects that influence nutritional

7 For both the household survey and the cross-country regressions we log the per capita expenditure variable. We do this to minimize the influence of extreme values of per capita expenditure. It also has the effect of increasing the marginal effect of resources on nutrition at lower income levels, since the marginal impact is the estimated coefficient on the log of expenditures divided by the observed level of expenditures. We conduct nonnested tests (Davidson and MacKinnon’s J-test as outlined in Greene 2000) to determine the appropriateness of this specification versus a model linear in expenditures. In those cases where the test did prove conclusive, the log model was favored in seven and the linear in two cases. In 3 out of 12 cases the test proved inconclusive.

status are correlated with household income, may better represent the total effect of resources under a long-term scenario.8

It should be noted that there are several reasons to suspect the endogeneity of the income variable in both models. The most obvious reason is measurement error in income or in the expenditure variable used in this study in lieu of income. As is well known, if random measurement error is present in an explanatory variable, OLS estimates will be biased toward zero. Another potential cause of the endogeneity of income is time allocation decisions that affect both income generation via labor supply and child nutrition via childcare. Consequently we estimate the models using both ordinary least squares and instrumental variables, both with and without the community fixed effects. While there are differences in the nature and number of identifying variables in each data set, the basis of our approach is to use land and livestock holding as well as other assets and durable goods in per capita terms where available as identifying instruments. In all cases we test (1) the strength of our proposed identifying instruments in predicting expenditures per capita (an F-test), (2) whether it is valid to exclude the proposed identifying instruments from the undernutrition equation (a chi-squared test for overidentification), and (3) the significance of the difference between the consistent IV estimates on income and the efficient OLS estimates (a chi-squared Hausman test).9

8 However, Model 2 does not include changes in parental education that may also be driven by long-term income growth. In principle, the education coefficient can be used to derive that impact under any assumption of changes in education.

9 The list of instruments and the full set of results of these tests are available from the authors. Further details on the tests are found in Bound, Jaeger, and Baker (1995) and Davidson and MacKinnon (1993).

THE CROSS-COUNTRY DATA FOR 61 COUNTRIES, 1970-1995

The dependent variable used in the cross-country analysis is the prevalence of children under 5 who are underweight for their age, i.e., whose weight falls more than two standard deviations below the median. All of these data are survey-based aggregates. The large majority of the underweight data, 75 percent, are from the World Health Organization’s Global Database on Child Growth and Malnutrition (WHO 1997). These data have been subjected to strict quality control standards.10 Other sources are

ACC/SCN (1993) and the World Bank (1997), and we have subjected these data to similar quality checks. We match each weight-for-age survey year with the corresponding year’s value of per capita GDP expressed in purchasing power parity (PPP)—comparable 1987 U.S. dollars.The data are from the World Bank’s World Development Indicators (World Bank 1998).11

The data set covers 61 developing countries, accounting for over 80 percent of the developing world’s population. Each country has at least two observations and many have three or four observations. The total number of country-year observations is 175, spanning the period 1970-1995 (Smith and Haddad 2000).12

10 The inclusion criteria are (1) a clearly defined population-based sampling frame, permitting inferences to be drawn about an entire population; (2) a probabilistic sampling procedure involving at least 400 children;

(3) use of appropriate equipment and standard measurement techniques; and (4) presentation of data in the form of Z-scores in relation to the NCHS/WHO reference population (WHO 1997).

11 These data are only reported for 1980-present. To arrive at comparable PPP GDP per-capita figures for the 1970s data points, it was necessary to impute growth rates from the data series on GDP in constant local currency units and apply them to countries’ 1987 PPP GDPs.

12 Related work by Smith and Haddad (2001) indicates that instrumenting GDP per capita with the investment share of GDP and the foreign investment share of GDP does not allow us to reject the exogeneity of GDP per capita in the cross-country sample. Hence we do not instrument GDP per capita in the cross-country regressions.

3. RESULTS: WHAT IS THE IMPACT OF INCOME ON UNDERNUTRITION?

This section presents the regression results for the effects of income growth at household and national levels on child undernutrition. First, we describe the results from the 12 household surveys; then we describe the results from the 61 countries used in the cross-country analysis.

THE HOUSEHOLD SURVEY RESULTS: PER CAPITA INCOME AND CHILD UNDERNUTRITION

Table 2 presents estimates of the coefficient of the logarithm of per capita consumption (our proxy for per capita income) for Models 1 and 2.13 OLS and IV estimates are presented, with and without mother’s height when that variable is available. Several things are worth noting.

First, as expected, the logarithm of per capita household consumption has a positive relationship with the nutritional status of children as measured by weight-for-age in all of the countries studied. All the OLS estimates of Model 1 (i.e., without controls for infrastructure) are significantly different from zero, and most other estimates are too.

Second, the estimated coefficients on the log of per capita consumption are usually larger in Model 1 than in Model 2. The exceptions to this are Egypt and Romania. The general pattern is consistent with the interpretation that Model 1 captures the long-term impact of income on undernutrition.

Third, the IV estimates are, without exception, larger than the OLS estimates. The increases range from 500 percent in Peru to 29 percent in Romania. This is consistent

13 Appendix Table 8 presents these summary results in more detail and lists the instruments used. The full set of results for each country is available from the authors upon request.

Table 2: Summary of estimates of the impact of per capita income on Z-score

weight-for-age

Model 1: N = N(Y) Model 2: N = N(Y, E, I)

OLS IV OLS IV OLS IV OLS IV Results for log of per capita total expenditure (lnpcxp) with mother’s height without mother’s height with mother’s height without mother’s height

Egypt

Estimated coefficient, lnpcxp 0.14380.36000.17130.40070.1652 0.2977 0.1736 0.3176

t-statistic 2.09 2.00 2.47 2.21 1.98 1.30 2.07 1.38 Hausman Test, OLS vs. IV (chi-squared) p = 0.1948 p = 0.1698 p = 0.5360 p = 0.5029

Mozambique Estimated coefficient, lnpcxp 0.31270.4595 0.1860 0.3403

t-statistic 10.68 8.76 3.94 3.62 Hausman Test, OLS vs. IV (chi-squared) p = 0.000746 p = 0.05807

Morocco Estimated coefficient, lnpcxp 0.42740.71740.48570.78140.1879 0.6007 0.2333 0.6330

t-statistic 8.44 9.18 9.62 10.16 2.78 3.86 3.46 4.10

Hausman Test, OLS vs. IV (chi-squared) p = 1.12e-06 p = 3.55e-07 p = 0.0032 p = 0.0040

South Africa Estimated coefficient, lnpcxp 0.20890.2790

0.1780 0.0807t-statistic 5.39 1.48 3.45 0.28 Hausman Test, OLS vs. IV (chi-squared) p = 0.7048 p = 0.7327

Kyrgyz Estimated coefficient, lnpcxp 0.21570.2893

0.1619 0.3553t-statistic 3.48 1.68 2.19 1.81 Hausman Test, OLS vs. IV (chi-squared) p = 0.6469 p = 0.2882

Peru Estimated coefficient, lnpcxp 0.2504 1.2001 0.2056 0.8150

t-statistic 5.51 5.38 4.09 3.52 Hausman Test, OLS vs. IV (chi-squared) p = 0.0000139 p = 0.0.0069

Kenya Estimated coefficient, lnpcxp 0.137 0.499 0.142 0.417

t-statistic 8.02 7.38 6.36 4.64

Hausman Test, OLS vs. IV (chi-squared) p = 0.000 p = 0.01

Jamaica Estimated coefficient, lnpcxp 0.257 0.742 0.191 0.411

t-statistic 3.13 3.10 2.11 1.51

Hausman Test, OLS vs. IV (chi-squared) p = 0.027 p = 0.393

Nepal Estimated coefficient, lnpcxp 0.319 0.971 0.204 0.533

t-statistic 6.16 5.15 2.98 2.78

Hausman Test, OLS vs. IV (chi-squared) p = 0.00 p = 0.068

Pakistan Estimated coefficient, lnpcxp 0.231 0.471 0.240 0.478 0.075 0.400 0.085 0.405

t-statistic 4.77 3.29 4.96 3.36 1.34 2.25 1.52 2.28

Hausman Test, OLS vs. IV (chi-squared) p = 0.073 p = 0.073 p = 0.053

p = 0.056 Romania Estimated coefficient, lnpcxp 0.140 0.180

0.287 0.658 t-statistic 3.28 2.00 2.78 2.89

Hausman Test, OLS vs. IV (chi-squared) p = 0.279 p = 0.066

Viet Nam Estimated coefficient, lnpcxp 0.265 0.437 0.293 0.471 0.198 0.261 0.105 0.275

t-statistic 6.73 7.02 7.37 7.52 1.76 2.55 1.87 2.67

Hausman Test, OLS vs. IV (chi-squared) p = 0.000 p = 0.000 p = 0.057 p = 0.049

with a high degree of measurement error on the per capita consumption variable and may partially explain the differences between patterns in the literature by income using expenditures and those using wealth indices that have swept out measurement error.

Fourth, the IV estimates are significantly different from zero and significantly different at the 5-percent level from the OLS estimates in 8 of 12 countries. OLS estimates are preferred to the IV estimates in 3 of the 12 countries. In South Africa and the Kyrgyz Republic, we cannot generate significant IV estimates for either model. In Romania, IV estimates can be generated that are significantly different from zero; however, the Hausman test fails to reject the equality of OLS and IV estimates, even at the low threshold of 20 percent that we arbitrarily select to take into account the low power of the test. In the remaining country, Egypt, we selected the IV estimate (0.36) rather than the lower OLS estimate (0.1438) for the subsequent projections, despite the fact that the Hausman test only rejected the equality of OLS and IV estimates at the 19-percent level.

Fifth, the estimated coefficients on log of per capita consumption are larger in the absence of data on mother’s height. The increases (in our preferred specifications) range from 1 percent in Pakistan to 11 percent in Egypt. This is consistent with the hypothesis that failing to control for mother’s height will lead to an omitted variables bias (Alderman 2000). However, the bias appears modest in the four cases where we can test for this.

Sixth, if we focus on our preferred estimates of Model 1 (those in bold in Table 2), the mean coefficient is 0.54—implying that doubling household income will increase weight-for-age by half a standard deviation of the reference population. The median

coefficient is 0.47. There is, however, considerable variation in the size of coefficients across countries, ranging from 0.14 in Romania to 1.20 in Peru.

The results reported in Table 2 are based on regressions that have nutritional status as a dependent variable. While this approach utilizes more information in the data sets than one that focuses on the probability of crossing a threshold, it does not allow us to directly infer the impact of income growth on undernutrition rates. However, under the assumption of a neutral distribution of income growth, it is relatively straightforward to simulate expected change in the level of undernutrition between the year of the respective surveys and 2015, the reference point for the MDG, using the coefficients in Table 2.

Table 3 indicates the expected proportional reduction in undernutrition following

a sustained 2.5-percent per capita income growth rate using the bolded estimates in Table

2 (all from model 1, the long-term specification). Because we are forcing income growth to be the same across countries, any differences in the impact of this growth reflect the Table 3: Projected underweight rates with 2.5 percent annual growth in per capita income from the 1990s to 2015

Country

Estimated

coefficient on

lnpcxp from

Model 1

Low WAZ

prevalence (%)

in survey year

Projected low

WAZ

prevalence in

2015 (%)

Percent decline

in WAZ

prevalence Arc

elasticity

Egypt 0.3600(IV)

0.1080 0.0800 -25.95

-0.464 Jamaica 0.7415

(IV) 0.0505 0.0226 -55.26 -0.865 Kenya 0.4994

(IV)

0.1963 0.1138 -42.02 -0.618 Kyrgyz 0.2157

(OLS)

0.1328 0.1144 -13.90 -0.248 Morocco 0.7174

(IV) 0.1379 0.0611 -55.68 -0.670 Mozambique 0.4595

(IV) 0.2304 0.1643 -28.69 -0.513

Nepal 0.9710 (IV) 0.4808 0.2599 -45.94 -0.767

Pakistan 0.4705

(IV) 0.4573 0.3467 -24.18 -0.299 Peru 1.2001

(IV)

0.0732

0.0270

-63.11

-1.127 Romania 0.1396

(OLS)

0.0640 0.0554 -13.36 -0.197 South Africa 0.2089 (OLS) 0.1802 0.1554 -13.79 -0.191

Viet Nam 0.4372 (IV) 0.4065 0.2813 -30.78 -0.427

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