Essays on risk, nutrition and poverty dynamics

This collection of essays explores questions on risk, poverty dynamics and early child development. The first essay analyses issues of cognitive development in early childhood. An extensive literature documents linkages between nutrition and cognition, but few studies show correlations to be causal. Building on Glewwe et al (2001) and Alderman et al (2006), we re-examine this relationship and find a negative cognitive effect of early nutritional disinvestments among a sample of Peruvian pre-schooling siblings. The second essay examines nutritional catch-up growth among poor Ethiopian children and the role household wealth in this process. We find that nutritional catch-up patterns vary across socioeconomic groups: average catch-up growth in height-for-age is near perfect among children in better-off households. However, for children above 5 years of age, household wealth no longer affects height velocity. Our findings suggest that nutritional remediation is effective early on in life, but that this window of opportunity might already be closed by the age of five. The third essay contributes to the literature on poverty traps and thresholds. In spite of their popularity, little empirical evidence supports their existence. Studies testing poverty traps often are restrictive in their methodology (e.g. Lokshin and Ravallion, 2004) or fail to provide causal estimates (Lybbert et al, 2004). This essay tests and finds evidence of multiple equilibria in income dynamics in rural India using a 30-year long panel. Simulations suggest the presence of two equilibria: a stable high-income equilibrium and a low-level unstable saddle point. To the best of our knowledge, the paper provides first evidence of multiple equilibria in income dynamics

Siblings, schooling, work and drought

In this paper we explore the effect that a recent drought in Andhra Pradesh, India, has had on the school and work patterns of children aged 11 to 12 years. Previous empirical studies have investigated the effect of shocks on outcomes for children but few have allowed for heterogeneous treatment effects across children. Ignoring such heterogeneity might lead to biases in the estimated impact of the shocks. The aim of this paper is to address this lacuna. Using data from Young Lives, a longitudinal cohort study of children, we estimate the average impact of the drought on participation in schooling. We then expand our empirical model to allow for heterogeneous effects across children of different demographic categories – namely gender and birth order. Our analysis shows that ignoring child heterogeneity would underestimate the severity of the effect of the drought on children’s welfare and human capital accumulation. In particular, we find that the drought significantly reduced the time spent on schooling by most demographic groups. The exception is the group most likely to have been involved in agricultural work when there is no drought; the schooling participation of eldest sons appears to increase because of the drought. Furthermore, we trace the impact of the drought on child labour and cognitive development, while we rule out the possibility that the uncovered heterogeneous patterns might be driven by social norms or cultural biases in favour of eldest sons.© Young Lives 2011. All rights reserved. Reproduction, copy, transmission, or translation of any part of this publication may be made only under the following conditions: • with the prior permission of the publisher; or • with a licence from the Copyright Licensing Agency Ltd., 90 Tottenham Court Road, London W1P 9HE, UK, or from another national licensing agency; or • under the terms set out below. This publication is copyright, but may be reproduced by any method without fee for teaching or non-profit purposes, but not for resale. Formal permission is required for all such uses, but normally will be granted immediately. For copying in any other circumstances, or for re-use in other publications, or for translation or adaptation, prior written permission must be obtained from the publisher and a fee may be payable. Available from: Young Lives Oxford Department of International Development (ODID) University of Oxford Queen Elizabeth House 3 Mansfield Road Oxford OX1 3TB, UK Tel: +44 (0)1865 281751 E-mail: [email protected] Web: www.younglives.org.u

Siblings, schooling, work and drought

In this paper we explore the effect that a recent drought in Andhra Pradesh, India, has had on the school and work patterns of children aged 11 to 12 years. Previous empirical studies have investigated the effect of shocks on outcomes for children but few have allowed for heterogeneous treatment effects across children. Ignoring such heterogeneity might lead to biases in the estimated impact of the shocks. The aim of this paper is to address this lacuna. Using data from Young Lives, a longitudinal cohort study of children, we estimate the average impact of the drought on participation in schooling. We then expand our empirical model to allow for heterogeneous effects across children of different demographic categories – namely gender and birth order. Our analysis shows that ignoring child heterogeneity would underestimate the severity of the effect of the drought on children’s welfare and human capital accumulation. In particular, we find that the drought significantly reduced the time spent on schooling by most demographic groups. The exception is the group most likely to have been involved in agricultural work when there is no drought; the schooling participation of eldest sons appears to increase because of the drought. Furthermore, we trace the impact of the drought on child labour and cognitive development, while we rule out the possibility that the uncovered heterogeneous patterns might be driven by social norms or cultural biases in favour of eldest sons.</p

Survey attrition and attrition bias in Young Lives

Longitudinal studies, such as the Young Lives study of childhood poverty, help us to analyse welfare dynamics in ways that are not possible using time-series or cross-sectional samples. However, analysis based on panel datasets can be heavily compromised by sample attrition. On the one hand, the number of respondents who do not participate in each round of data collection (wave non-response) will inevitably cumulate over time, resulting in falling sample sizes, which will undermine the precision of any research undertaken using such samples. On the other hand, unless it is random, attrition might lead to biased inferences. Analysts often presuppose that attrition is correlated with observable characteristics such as household education, health or economic well-being, resulting in samples that include only a selected group of households. However, even if that is the case, non-random attrition does not necessarily lead to attrition bias. Attrition bias is model-specific and, as previous studies have shown, biases might be absent even if attrition rates are high. We investigate the incidence and potential bias arising from attrition in Young Lives following the completion of the second round of data collection. Young Lives is a study concerned with analysing childhood poverty in four countries, Ethiopia, India, Vietnam and Peru. The study, which measures a range of child, household, and household-member characteristics , is following two cohorts of children in each country over 15 years – a younger cohort of 2,000 children who were born in 2001 to 2002 (i.e. aged 6 to 18 months when first surveyed) and 1,000 older children born in 1994-95 (i.e. aged 7.5 to 8.5 at the start of the survey). Sample attrition is particularly concerning in the context of a longitudinal study such as Young Lives where cohort sample sizes are modest and individuals are tracked over a relatively long period of time. This paper seeks to: • document the rates of attrition of the Young Lives study following completion of the second round of data collection; • investigate the extent to which sample attrition is non-random; • analyse whether non-random attrition in the Young Lives sample might lead to attrition bias

An assessment of the Young Lives sampling approach in Ethiopia

The sampling methodology adopted by Young Lives is known as a sentinel site surveillance system. In Ethiopia, the Young Lives team used multi-stage, purposive and random sampling to select the two cohorts of children. This methodology randomised households within a study site while the sites themselves were chosen on the basis of predetermined criteria, informed by the Young Lives objectives. To ensure the sustainability of the study, and for resurveying purposes, a number of well-defined sites was chosen. The sites were selected with a pro-poor bias and to ensure a balanced representation of the Ethiopian regional diversity as well as rural/urban differences. This paper assesses the sampling methodology by comparing the Young Lives sample with larger, nationally representative samples. In doing this, the Ethiopia team sought to: (i) analyse how the Young Lives children and households compare with other children in Ethiopia in terms of their living standards and other characteristics; (ii) examine whether this may affect inferences between the data; (iii) establish to what extent the Young Lives sample is a relatively poorer or richer subpopulation in Ethiopia; (iv) determine whether different levels of living standards are represented within the dataset. We found that households in the Young Lives sample were slightly wealthier than households in the DHS sample. Further analysis revealed that households in rural areas and in urban areas, except Young Lives households in Addis Ababa, were wealthier. Young Lives households in Addis Ababa were poorer than households in the DHS sample. A similar picture emerged when we use t-tests to compare the means for a range of living standard indicators between the Young Lives and the DHS samples. Young Lives households in rural areas had better access to public services such as drinking water and electricity supply, while households in Addis Ababa had less access to basic services. These findings were supported by the comparison of common variables in Young Lives and the WMS. However, Young Lives households were less likely to own land or a house, and had smaller livestock holdings than WMS households. To assess trends over time we compared the Young Lives sample with the DHS 2005 sample. Some of the differences, which we observed in the comparison of Young Lives with the DHS were reduced which indicates some improvements in living standards between 2000 and 2005. The analyses show that households in the Young Lives sample were slightly better-off and had better access to basic services than the average household in Ethiopia, as measured by the nationally representative DHS and the WMS. However, our detailed analysis reveals that, while Young Lives households are located at sites with better access to services and utilities, they hold less land, less livestock. And are less likely to own their own house than the average Ethiopia household. This evidence is consistent with the sampling methodology applied with the Young Lives samples in Ethiopia. Despite these biases, it is shown that the Ethiopian Young Lives sample covers the diversity of children in the country. Therefore, while not suited for simple monitoring of child outcome indicators, the Young Lives sample will be an appropriate and valuable instrument for analysing causal relations, modelling child welfare, and its longitudinal dynamics in Ethiopia

Survey attrition and attrition bias in Young Lives: Young Lives Technical Note 5

Longitudinal studies, such as the Young Lives study of childhood poverty, help us to analyse welfare dynamics in ways that are not possible using time-series or cross-sectional samples. However, analysis based on panel datasets can be heavily compromised by sample attrition. On the one hand, the number of respondents who do not participate in each round of data collection (wave non-response) will inevitably cumulate over time, resulting in falling sample sizes, which will undermine the precision of any research undertaken using such samples. On the other hand, unless it is random, attrition might lead to biased inferences. Analysts often presuppose that attrition is correlated with observable characteristics such as household education, health or economic well-being, resulting in samples that include only a selected group of households. However, even if that is the case, non-random attrition does not necessarily lead to attrition bias. Attrition bias is model-specific and, as previous studies have shown, biases might be absent even if attrition rates are high. We investigate the incidence and potential bias arising from attrition in Young Lives following the completion of the second round of data collection. Young Lives is a study concerned with analysing childhood poverty in four countries, Ethiopia, India, Vietnam and Peru. The study, which measures a range of child, household, and household-member characteristics , is following two cohorts of children in each country over 15 years – a younger cohort of 2,000 children who were born in 2001 to 2002 (i.e. aged 6 to 18 months when first surveyed) and 1,000 older children born in 1994-95 (i.e. aged 7.5 to 8.5 at the start of the survey). Sample attrition is particularly concerning in the context of a longitudinal study such as Young Lives where cohort sample sizes are modest and individuals are tracked over a relatively long period of time. This paper seeks to: • document the rates of attrition of the Young Lives study following completion of the second round of data collection; • investigate the extent to which sample attrition is non-random; • analyse whether non-random attrition in the Young Lives sample might lead to attrition bias

An assessment of the Young Lives sampling approach in Ethiopia: Young Lives Technical Note No. 1

The sampling methodology adopted by Young Lives is known as a sentinel site surveillance system. In Ethiopia, the Young Lives team used multi-stage, purposive and random sampling to select the two cohorts of children. This methodology randomised households within a study site while the sites themselves were chosen on the basis of predetermined criteria, informed by the Young Lives objectives. To ensure the sustainability of the study, and for resurveying purposes, a number of well-defined sites was chosen. The sites were selected with a pro-poor bias and to ensure a balanced representation of the Ethiopian regional diversity as well as rural/urban differences. This paper assesses the sampling methodology by comparing the Young Lives sample with larger, nationally representative samples. In doing this, the Ethiopia team sought to: (i) analyse how the Young Lives children and households compare with other children in Ethiopia in terms of their living standards and other characteristics; (ii) examine whether this may affect inferences between the data; (iii) establish to what extent the Young Lives sample is a relatively poorer or richer subpopulation in Ethiopia; (iv) determine whether different levels of living standards are represented within the dataset. We found that households in the Young Lives sample were slightly wealthier than households in the DHS sample. Further analysis revealed that households in rural areas and in urban areas, except Young Lives households in Addis Ababa, were wealthier. Young Lives households in Addis Ababa were poorer than households in the DHS sample. A similar picture emerged when we use t-tests to compare the means for a range of living standard indicators between the Young Lives and the DHS samples. Young Lives households in rural areas had better access to public services such as drinking water and electricity supply, while households in Addis Ababa had less access to basic services. These findings were supported by the comparison of common variables in Young Lives and the WMS. However, Young Lives households were less likely to own land or a house, and had smaller livestock holdings than WMS households. To assess trends over time we compared the Young Lives sample with the DHS 2005 sample. Some of the differences, which we observed in the comparison of Young Lives with the DHS were reduced which indicates some improvements in living standards between 2000 and 2005. The analyses show that households in the Young Lives sample were slightly better-off and had better access to basic services than the average household in Ethiopia, as measured by the nationally representative DHS and the WMS. However, our detailed analysis reveals that, while Young Lives households are located at sites with better access to services and utilities, they hold less land, less livestock. And are less likely to own their own house than the average Ethiopia household. This evidence is consistent with the sampling methodology applied with the Young Lives samples in Ethiopia. Despite these biases, it is shown that the Ethiopian Young Lives sample covers the diversity of children in the country. Therefore, while not suited for simple monitoring of child outcome indicators, the Young Lives sample will be an appropriate and valuable instrument for analysing causal relations, modelling child welfare, and its longitudinal dynamics in Ethiopia

Early Nutrition and Cognition in Peru: A Within-Sibling Investigation

This paper examines the causal link between early childhood nutrition and cognition, applying instrumental variables to sibling-differences for a sample of preschool aged Peruvian children. Child-specific shocks in the form of food price changes and household shocks during the critical developmental period of a child are used as instruments. The analysis shows significant and positive returns to early childhood nutritional investments. An increase in the Height-for-Age z-score of one standard deviation -keeping other factors constant- translates into increases in the Peabody Picture Vocabulary Test (PPVT) score of 17-21 percent of a standard deviation. The period of analysis includes the recent global food price crisis that also affected Peru between 2006 and 2008. This therefore is also a quantification of the nutritional and subsequent cognitive costs of food prices on the sample, which could be magnified in later years.

Early Nutrition and Cognition in Peru:A Within‐Sibling Investigation

This paper examines the causal link between early childhood nutrition and cognition, applying instrumental variables to sibling-differences for a sample of pre-school aged Peruvian children. Child-specific shocks in the form of food price changes and household shocks during the critical developmental period of a child are used as instruments. The analysis shows significant and positive returns to early childhood nutritional investments. An increase in the Height-for-Age z-score of one standard deviation—keeping other factors constant—translates into increases in the Peabody Picture Vocabulary Test (PPVT) score of 17-21 percent of a standard deviation. The period of analysis includes the recent global food price crisis that also affected Peru between 2006 and 2008. This therefore is also a quantification of the nutritional and subsequent cognitive costs of food prices on the sample, which could be magnified in later years