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Overview

Nutrition and Growth in Rural Ethiopia BREAD Summer School 2008 Ingo W. Outes-Leon, Oxford University. Overview. Introduction Data and Empirical Model Results Conclusions. A.1 Introduction – Aim and Motivation.

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Overview

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  1. Nutrition and Growth in Rural EthiopiaBREAD Summer School 2008Ingo W. Outes-Leon, Oxford University

  2. Overview Introduction Data and Empirical Model Results Conclusions

  3. A.1 Introduction – Aim and Motivation • Test whether poor nutrition and health has a negative effect on a HHs ability to generate future consumption • We estimate effect of adult low BMI on HH consumption growth, after controlling for other HH assets. • Necessary, but not sufficient, condition for nutritional poverty traps to exist. • Combine • Nutritional Poverty Trap (Dasgupta and Ray) - Productivity Effect • Morbidity theories (Deaton (2005) and Fogel (1992)) - Health Effect • With Micro-Growth models(Jalan and Ravallion (2003), Antman and McKenzie (2005) and Dercon and Shapiro (2007)) • And literatureon Non-linearity in BMI (Dasgupta (1993) and Strauss and Thomas (1998))

  4. A.2 Introduction – Aim and Motivation • Take inspiration from Mankiw, Romer and Weil (1992), estimate: • HH consumption growth (1995 to 2004) on • Lagged dependent variable • And HH Assets and Other characteristics on baseline (1995) • Adult Low BMI enters equation as further component of HH human capital • That is, Conditional convergence type of model. • So no actual test of poverty traps • But able to indicate if low BMI has a drag-down effect on HH growth

  5. A.3 Introduction – Contribution and Challenges Findings and Contribution: • IV methods provide evidence of negative Growth Effect of low BMI • Evidence of Persistence of the 1984 Drought on 1995 adult BMI • Application of ‘weak’ IV estimation and inference methods. • Estimation: IV Fuller Estimator • Inferences: Conditional Likelihood Ratio (CLR) Moreira p-values Challenges: • Validity of Instrumentation Methodology • Doubtful, but IV estimates still interesting • Disentangle village-specific nutritional effects from individual HH effects • Unobserved Village Effects vs local nutritional poverty traps (e.g. shocks and HH infrastructure) • Quartile regressions might provide further insight. • Treatment of Other Assets, especially Livestock

  6. B.1 ERHS Data – Is low BMI bad for growth? • ERHS: 1470 HHs in 15 villages in rural Ethiopia; 1994 to 2004 period; • High poverty: 35% poor HHs in 1995 (Dercon and Krishnan (2003)) • Incidence of Adult Underweight: 21% of HH Heads (<18.5) • 1995-2004 Growth pa: 4.3% (all HHs) ; 6.0% (Low BMI HH Heads). • Kernel smoothing: No apparent Poverty Trap • Quartile Kernel smoothing: ‘low BMI’ effect for poor HHS ?

  7. B.2 Empirical Model (1) LHS: Food Consumption Growth (1995 to 2004), per annum RHS: 1995 Baseline HH characteristics: • Lagged Dependent Var: Log Consumption 1995 • Life-cycle controls – cohort dummies • HH Assets: low BMI, Livestock, Land, Education, HH structure • Shocks: Rainfall, price shocks • Village Characteristics/ Village Fixed Effects Definition of Variables of Interest: • ‘low BMI (Head)’ – dummy for BMI<18.5 of HH head; • ‘low BMI (HH Share)’ – share of adults with BMI<18.5; Endogeneity Concerns: low BMI, livestock and lagged dependent variable

  8. B.3 Identification Strategies Method A: Lagged Endogenous Variables – 1994 • Strong but invalid instruments. • LATE interpretation of estimates. Method B: 1984 Drought Shock • IV: self-reported 1984 Drought Shock. • Valid if self-reported shock is unrelated to growth, after controlling for assets. • Some validity tests are passed – But validity remains questionable Comparison OLS vs IV estimates still interesting: • Analogous to LATE: BMI growth effect of long-term vulnerability Growth (t to t-p) 2004 1984 1994 1995 t t-p

  9. C.1 Naïve and Lagged Endog. IVs Naïve: • No BMI effect • Livestock non-linearities Lagged IV: • ‘Low BMI (Head)’ reduces growth by 4% (Robustness checks omitted): • BMI effect - robust to changing length of Lag. • But not robust to introduction of Village FE Note: T-statistics reported. Bold coefficients indicate significance at the 10% level. First-Stage estimates not reported. Periods defined as [t=2004], [t-1=1995] and [t-2=1994]. Livestock categories indicate single units of scaled livestock, except for categories 4 to 6 that correspond with ‘4 to 6’, ‘6 to 10’ and ‘more than 10’ scaled units respectively; livestock default category is ‘less than 1’. Cohort and demographic controls include: ‘hh size’, ‘nr male members’, ‘share of female members’, ‘Δ hh size’, ‘Δ nr children members’, ‘Δ nr female members’ and dummies for each decade of age of HH head.

  10. C.2 Quartile IV Regressions – Lagged Endog IVs • BMI effect most likely to exist only among poorest • Use village-specific quartiles • Effect is largest among the poor. • Robust to Village FE and Village-Specific Quartiles Note: T-statistics reported. Coefficients highlighted in bold indicate significance at the 5% level. F-statistics reported in bold indicate the presence of ‘strong’ IVs. All regressions reported include same controls as in slide C.1

  11. C.3 Drought IVs – IV GMM Note: T-statistics reported. Coefficients highlighted in bold indicate significance at the 10% level. Controls: HH Assets, Cohort dummies and HH demographic variables

  12. C.4 Drought IVs – Weak IVs IV GMM (First-Stage) F-Stats: • Stock and Yogo (2005) critical values • F-Stats from [5.31] to [6.26] suggest: IV estimates include 30% to 20% of the OLS Bias Weak IV GMM estimates are unreliable – under finite-samples, (Murray (2006)): • IV GMM point estimates can be substantially bias; • Standard Errors tend to be invalid and smaller (!!) Estimation: IV Fuller and IV LIML Methods are more robust to ‘weak’ IVs • In the literature: IV Fuller method is preferred estimator. • See Stock, Wright and Yogo (2002) and Murray (2006). Inferences: CLR Moreira and Anderson-Rubin tests more robust • CLR Moreira methods shown to dominate alternative methods • See Andrews, Moreira and Stock (2005) and Murray (2006).

  13. C.5 Estimates and Inferences – Robust to ‘Weak’ IVs Note: T-statistics reported. Coefficients highlighted in bold indicate significance at the 10% level. Wald, Moreira Conditional Likelihood Ratio (CLR) and Anderson-Rubin p-values and corresponding confidence intervals reported in square brackets.

  14. C.6 Further on Drought IVs Food Aid - Validity • Better connected HHs – obtain more Food Aid and grow faster. • No bias appreciable when comparing with ‘IV Set 2: 1984 Drought Only’ Self-Reported Drought likely endogenous • E.g. Better insured HHs in 1984 drought villages – not affected by Drought. • No available HH information prior to 1984 drought. • Use answer ‘Did “food sharing” increase during famine?’ as extra control. Livestock Endogeneity and Low BMI Bias • Current Implicit Ass: 1984 Drought affects exclusively HH health. • Persistence in Livestock Assets might be behind ‘low BMI’ effects • Over-id Drought IV estimates with Endog: Low BMI and Low Livestock • Model is unreliable due to ‘very weak’ IVs. • But ‘Low BMI’ effect remains large [-0.10]. • While ‘Low Livestock’ effect is increased substantially;

  15. C.8 Quartile IV Regressions – Drought IVs • BMI effect most likely to exist only among poorest • With Village FE – ‘low BMI’ effect disappears • Although it is robust to Village-Specific Quartiles Note: T-statistics reported. Coefficients highlighted in bold indicate significance at the 10% level. Moreira Conditional Likelihood Ratio (CLR) p-values reported in square brackets. ‘Undefined’ CLR p-value indicates that confidence intervals were unbounded. F-statistics reported in bold indicate the presence of ‘strong’ IVs. All regressions reported include same controls as in Slide C.4

  16. D.1 Conclusions • Uncover substantial persistence of 1984 drought on 1995 livestock and BMI • Low BMI has significant negative effect on subsequent HH growth. • Lagged Endog IVs and Drought IVs provide results consistent with each other. • Growth effect of Low BMI might not be causal • but can be interpreted as growth effect of low BMI persistence (Lagged IV) or HH vulnerability (Drought IV). • This Low BMI burden is overwhelmingly borne by the poor. • Growth effect is large in magnitude • For lowest quartile (with village FE), persistence of low BMI reduces growth by 7% percentage points per annum, for a period of nine years. • Drought IV estimates suggest a very large effect – up to 15% percentage points per annum, for nine-year period. • But partly include village and ‘livestock’ effects.

  17. D.2 Going Forward • Alternative IVs; Alternative tests of validity; • Arellano-Bond Panel Estimation • Can tackle Unobserved HH Heterogeneity • Unpack HH FE – seek for 1984 Drought effect • Testing for Mechanism of low BMI: • Income and productivity • Higher morbidity

  18. Many Thanks

  19. C.3 Lagged Endogenous IVs – Robustness Note: T-statistics reported. Bold coefficients indicate significance at the 10% level. First-Stage estimates not reported. Periods defined as [t=2004], [t-1=1995] and [t-2=1994]. Controls: HH Assets, Cohort dummies and HH demographic variables

  20. Note: (*) Indicates dummy variables. (**) Index between (0) and (-1), negative indicates a more serious shock. ‘High’ and ‘Low’ consumption HHs correspond with the top and bottom halves of the 1995 food consumption distribution. Variables used in the regressions models not reported here include: village asset means and decade-age dummies. Consumption growth variable is defined as the average growth between t and t-1, where p takes a value of nine years

  21. Note: Change in number of households from 713 to 707 is due to missing food aid information for 6 households. Pairs of columns (3)-(4) and (5)-(6) add to unity by village

  22. Note: Highlighted area indicates information used in the construction of the different sets of instruments. (*) Food aid totals reported include double-counting of households. That is, 201 households received food aid in 1984, but only a total of 371 different households received food aid at least once during the 1984 to 1994 period.

  23. Note: T-statistics reported. Coefficients highlighted in bold indicate significance at the 10% level. Moreira Conditional Likelihood Ratio (CLR) p-values reported in square brackets. ‘Undefined’ CLR p-value indicates that confidence intervals were unbounded. F-statistics reported in bold indicate the presence of ‘strong’ IVs. All regressions reported include same controls as in Slide D.2

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