1. GISSELE GAJATE-GARRIDO IFPRI APRIL 2011 Excluding the poor: the impact of public expenditure on child malnutrition in Peru

2. Research question To answer this question I need to analyze the causal impact of total regional public expenditure on early childhood nutritional outcomes. Why is the urban-rural gap in child malnutrition increasing in Peru despite government efforts to improve the provision of public services? Introduction: 1/ 4

3. Summary of findings Public expenditure has no effect on child nutrition in rural areas. This statement is true regardless of the type of expenditure analyzed. These areas are shown to have lower levels and worst quality of public services. In urban areas, there is a positive and significant impact of public spending on children’s nutritional outcomes. Over 70% of the increase in malnutrition disparities between rural and urban areas can be explained by the impact of public expenditure. Introduction: 2/4

4. Summary of findings Spending in health and sanitation is the most important channel through which public expenditure impacts child health, followed by transportation and education. Spending in nutritional interventions do not impact child well-being. Yet, in cities, despite the availability of effective public goods and services, the worse off children do not benefit from public good provision. These are the children belonging to poor indigenous households and are the ones with the lowest birth weights. Introduction: 3/4

5. Contributions to the literature I use a panel of children  I control for unobserved heterogeneity. I provide exogenous variation to correct for the potential endogeneity of regional public expenditure. The variation comes from the level of natural resource royalties assigned to each region = natural resource endowments in each region × world prices. In contrast to health input prices this instrument is proven not to alter individual regional migration. Introduction: 4/4

6. Outline Introduction Child Nutrition and Public spending Data Identification Strategy Specification Results Robustness Checks Discussion

7. Motivation Thirty percent of children less than 5 years old endure chronic malnutrition in Peru. Malnourishment has a negative impact on children’s:  Cognitive development (Glewwe et. al. 2001, Walker et. al. 2005)  Human capital formation (Glewwe et. al., 1995, Maluccio et. al. 2006).  It may hamper a country's economic development (Thomas and Strauss 1998). Child Nutrition : 1/2

8. Motivation Between 2002 and 2006, per capita government expenditure in Peru increased by 34%. Almost all the most important spending categories increased even more: education by 35%, poverty alleviation programs by 16%, health and sanitary infrastructure by 38% and transport by 40%. Yet total the urban rural gap in child malnutrition increased from 26% to 32% during this time. Child Nutrition : 2/2

9. The impact of public spending Malnutrition in developing countries is caused by food deprivation and infectious diseases which are related to poverty, lack of access to markets, deficient transportation, inadequate health and sanitary infrastructure and low levels of education (Wolfe et al. 1982, Alderman et al. 2001, Escobal et al. 2005, Mwabu 2008, Aguiar et al. 2007). Public goods and services could directly affect a child’s nutritional status by providing access to health facilities, better sanitary infrastructure or nutritional programs (Valdivia 2004, Thomas et al. 1996, Frankenberg et al. 2005). Public Spending: 1/4

10. What determines the efficacy of public spending? 1. Quantity and quality of public goods and services 2. Possession of key assets that could affect demand and efficient use of public services Supply constraints Demand constraints Public Spending: 2/4

11. Supply Constraints There exist differences in the quality and availability of public services between urban and rural areas (Valdivia 2004). Van de Poel et al. (2007) found that Peru had the largest urban/rural gap in nutritional levels of 47 developing countries. In rural areas delivery of public goods is poorly monitored. Lack of accountability  high rates of absenteeism, irregular working hours. Public Spending: 3/4

12. Demand Constraints The ability of a household to take advantage of public goods and services is not only determined by the availability (the actual supply) and the quality of these. It is also related to the capability and motivation of this household to take full advantage of these goods and services. E.g.: language barriers, income barriers, child resilience, child gender. Public Spending: 4/4

13. Data Individual Longitudinal Data  The Young Lives Study (YLS - An International Study of Childhood Poverty) is a recently generated data set that tracks the lives of 2052 children growing up in Peru over 15 years (1 st round: June 2002, 2 nd round: Oct. 2006-Aug. 2007).  Information is available from birth until around the children’s 6th birthday. Macro Level Data  National Household Surveys: representative at regional level.  Public expenditure data from the Ministry of Economics and Finance. Data: 1/3

14. Main variables Dependent variable: nutritional status: height-for-age z- scores  long run indicator of the nutritional status of children. Independent variable: total public expenditure = investment + current expenditure. Most important spending categories: education (35%), poverty alleviation programs (27%), health and sanitary infrastructure (14%) and transport (9%). Data: 2/3 Percentage of children malnourished in the sample: 1 st round 17% (Urban areas 11%, Rural 29%) 2 nd round 32% (Urban areas 20%, Rural 58%)

15. Descriptive statistics for urban and rural Source: Young Lives Study Round 1 and 2

16. Identification Strategy I use the level of Mining Royalties assigned to each region each year (not spent) as a source of exogenous variation. Royalties assigned to each region had vary greatly geographically and have increased dramatically across years, due to the increase in the price of minerals. Thus providing sufficient variation for identification.pricevariation Royalties are arguably unrelated to children’s nutritional outcomes, provided their expansion is uncorrelated with changes in net per capita income, migration patterns and health status. Identification Strategy: 1/2

17. Exogeneity tests I verify that increases in royalties do not expand local economic activity by running a pooled OLS regression (with data from 2002 and 2006) of net per capita income on the level of royalties assigned to the region.income I test that the level of royalties is not related to the general population health level by running a Probit regression of health status on the amount of royalties assigned to the region.health Finally, I run a Probit regression of migration status on the royalty level assigned regionally.migration In all cases royalties do not have a statistically significant impact on the dependent variable. Identification Strategy: 2/2

18. Specification 1 st stage: P jt : level of public expenditure in period t and region j Z jt : level of mining royalties assigned in period t to region j 2 nd stage: N c ijt : height-per-age z-score level  i,  j and  t : individual, regional and year fixed effects, respectively X c t, X M t, X H t and X C t : child, parental, household and community characteristics, respectively X R jt : other regional characteristics that vary over time I use a first difference approach and cluster the standard errors at the region-year level to account for any variation within region and year. I also include regional and year fixed effects. Specification: 1/1

19. Pooled and Panel Data Estimation Pooled and Panel Data Estimation Public spending has no effect on child nutrition in rural areas. In urban areas, there is a positive and significant impact of public spending on children’s nutritional outcomes. Over 70% of the increase in malnutrition disparities between rural and urban areas can be explained by the impact of total public expenditure. Dependent Variable: Z-score height for age Panel OLS Pooled IV Panel IV Panel Rural IV Panel Urban IV Total Public Spending-0.0620.1490.086-0.0520.294 (0.048)(0.110)(0.098)(0.051)(0.093)*** Other ControlsYES Number of observations1,8773,8361,8776761,201 R-squared0.3530.328 Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. They include regional, individual and time fixed effects, as well as child’s age and health status, maternal education, # of children under 5 years of age in the household, drinking water availability, household size, wealth score, public programs in the community, population size, belonging to the highlands, being an urban area, a poverty index and the level of regional debt payments.. Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006. Impact of public expenditure on a child’s nutritional level for the whole sample, rural and urban areas

20. The most important channel through which public expenditure impacts child health is health and sanitation efforts, followed by transport and education. Spending in nutritional interventions do not impact child well- being. Note: each coefficient represents an independent regression. Impact of public expenditure by government sector on a child’s nutritional level Panel Data Estimation Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006. Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. They include regional fixed effects, individual fixed effects and time fixed effects and all the previous controls. Spending measured in 100 million soles. The instrument for all the IV estimations is the amount of natural resources royalties assigned to the regions in each fiscal year. Dependent Variable: Z-score height for age Whole Sample Rural Sample Urban Sample Total Public Spending in Health and0.89-0.4763.802 Sanitary(1.091)(0.480)(1.509)** Total Public Spending in Education0.192-0.1340.513 (0.198)(0.142)(0.133)*** Total Public Spending in Poverty0.413-0.1874.618 Alleviation(0.579)(0.183)(6.909) Total Public Spending in Transportation1.284-6.8791.813 (1.260)(40.510)(0.460)*** Other ControlsYES Number of observations1,8776761,201

21. Probability of accessing a health professional, program or facility in your locality when public spending increases Note: Each coefficient represents an independent regression. Dependent Variable: Availability of health inputs in the locality Panel IV RuralUrban General Physician-0.0920.438 (0.063)(0.082)*** Pediatrician/Gynecologist-0.060.325 (0.027)**(0.115)*** Midwife-0.1430.205 (0.120)(0.096)** Disease prevention-0.0390.166 (0.072)(0.098)* Child growth controls-0.0290.166 (0.079)(0.098)* Public Health Center0.1710.02 (0.098)*(0.094) Robust S.E. clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Controls include regional and time fixed effects, household wealth score, number of people living in the locality, average educational level in the region, highlands, poverty index and debt level. Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006.

22. Dependent Variable: Child health outcomes and inputs Panel IV RuralUrban Prenatal care intensity-0.0200.118 (0.064)(0.053)** Tuberculosis vaccine0.0010.014 (0.002)(0.007)* Low birth weight0.092-0.025 (0.017)***(0.016) Serious illness0.051-0.112 (0.029)*(0.049)** Impact of public expenditure on a child health outcomes and inputs by area Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. They include regional fixed effects, individual fixed effects and time fixed effects and all the previous controls. Increases in public spending generate worse health outcomes in rural areas, while improvement in urban ones. Moreover, public expenditure is increasing the level of child health inputs used only in urban areas. Probably poor quality services are not generating interest in the rural population decreasing the potential demand. Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006. IV Estimation

23. Public spending impact on child nutrition by personal characteristics in urban areas 0.262 *** 0.267 *** 0.294 *** 0.314 *** 0.334 *** 0.348 *** In cities, despite the availability of effective public goods, the worse off children (poor, indigenous and with the lowest birth weights) do not benefit from public good provision. Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance (MEF) Budgetary Expenditure for 2000 – 2006.

24. Robustness checks Tests for selection bias due to attrition (4.39%) reveal almost no statistically significant differences. The only significant differences of means are for mother’s height and probability of having an immunization program in your community. According to the first stage regression parameters the instrument is significant at a 1% level for all specifications and it displays a positive and non immaterial effect on the level of total public expenditure.first stage For all the estimations the F statistic (at a 5% significance level) ensured that the maximal bias of the IV estimator relative to OLS was no bigger than 5%. This fits the definition of a strong instrument according to Stock, Wright and Yogo (2002). Robustness checks: 1/1

25. Discussion This paper offers new evidence of the differential impact of public expenditure on child health outcomes in a developing country. In contrast to previous work:  It uses longitudinal micro data  it controls for unobserved heterogeneity  It exploits variation in natural resource royalties not related to child nutrition except through public spending  it corrects for the endogeneity of regional public expenditure. This study concludes that policy makers need to account for both supply and demand constraints to public service provision and access. This will improve the effectiveness of government expenditure and will help break the cycle of poverty and malnutrition. Discussion: 1/1

26. Evolution of World Mineral Prices: 2002-2006 Note: Copper, zinc, lead and tin are measured in US$/lb, gold and silver are measured in US$/Oz.tr. Oil is measured in US$/bbl. and natural gas in US$ per 1000 feet3. Silver and natural gas are measured on the right vertical axis. Source: London Metal Exchange and London Bullion Market Association and NGA, annual reports, Oil Patch Research Group Back

28. Impact of royalties on net income per capita Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors are clustered by region and year. Geographical Regions: 25. Observations: 171,903 Note: Royalties are measured in 10 million soles and net per capita income in soles per year. Source: ENAHO 2002, 2006 and Ministry of Economic and Finance Expenditure Statistics for 2002 - 2008. Dependent Variable: Net income per capitaModel 1Model 2Model 3Model 4 Natural Resources Royalties-2.99 (30.42) Next Year's Natural Resources Royalties18.28 (14.53) Average future Natural Resources Royalties14.65 (10.84) Two years from now Nat. Res. Royalties11.43 (8.24) Community poverty line26.2526.1626.1726.19 (3.03)***(3.04)*** (3.03)*** Dummy for year=2002-2,382-2,254-2,234-2,230 (194.14)***(178.09)***(185.81)***(189.22)*** Total Public Spending5.334.934.904.91 (0.42)*** (0.41)***(0.39)*** R-squared0.23 Back

29. Impact of royalties on the probability of being ill in the last 4 weeks Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors are clustered by region and year. Geographical Regions: 25. Observations: 171,903. Marginal effects calculated at the means. Controls: Community poverty line, total public spending, year fixed effects and regional fixed effects. Note: Royalties are measured in 10 million soles and net per capita income in soles per year. Source: ENAHO 2002, 2006 and Ministry of Economic and Finance Expenditure Statistics for 2000 - 2008. Back Dependent Variable: Health status Model 1 Model 2 Model 3 Model 4 Model 5 Average future Natural Resources Royalties0.001 (0.001) Next Year's Natural Resources Royalties0.001 (0.001) Natural Resources Royalties Current Year0.003 (0.002) Lagged Natural Resources Royalties0.004 (0.005) Double Lagged Nat. Res. Royalties0.003 (0.008) Other ControlsYES

30. Impact of royalties on the probability of migrating to another region Back Robust standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. Standard errors are clustered by region. Geographical Regions: 23. Observations: 1,962. Marginal effects calculated at the means Note: Public Spending and Royalties are measured in 100 million soles. Source: Young Lives Study Round 1 and 2, Ministry of Economic and Finance Expenditure Statistics for 2002 and 2006. Dependent Var. Probability of migratingModel 1Model 2Model 3Model 4Model 5 Average future Natural Resources Royalties0.001 (0.010) Next Year's Natural Resources Royalties0.004 (0.012) Natural Resources Royalties0.026 (0.021) Lagged Natural Resources Royalties0.041 (0.026) Double Lagged Nat. Res. Royalties0.034 (0.041) Poverty Index-0.126-0.127-0.140-0.139-0.138 (0.085) (0.084)*(0.085)*(0.085) Total Public Spending0.000 (0.000)

31. First stage estimation: Impact of royalties on public expenditure for the whole sample, rural and urban areas Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%. They include regional fixed effects and time fixed effects. Spending measured in 100 million soles. The independent variable is the amount of natural resources royalties assigned to the regions in each fiscal year. Source: Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and MEF Budgetary Expenditure for 2000 - 2006. Dependent Variable: Public SpendingPooled IVPanel IV Panel Rural IV Panel Urban IV Natural Resources Royalties2.0672.1252.8831.347 (0.047)***(0.069)***(0.175)***(0.053)*** Other ControlsYES Number of observations3,8361,8776761,201 Partial R-squared of excluded instrument0.3320.3400.2980.353 F-test for weak identification1929946271635 Back