CROP VARIETY IMPROVEMENT AND INFANT MORTALITY IN THE DEVELOPING WORLD

Transcription

1 CROP VARIETY IMPROVEMENT AND INFANT MORTALITY IN THE DEVELOPING WORLD Prabhat Barnwal, Aaditya Dar, Jan von der Goltz, Ram Fishman, Gordon C. McCord, Nathan Mueller

2 Motivation The health and welfare impacts of agricultural gains, particularly MV: Country level impacts on agricultural TFP (Evenson and Gollin, 2003; Walker and Alwang, 2015, Fuglie and Marder, 2015) Limited micro evidence of agricultural technology improvements on: Health and nutrition (Stewart, 2015) Income, poverty and food security: cross sectional correlations in survey data in SSA (Zeng et al., 2015; Larochelle et al., 2015) Debate about the role of nutritional/income gains in driving the decline in mortality (Cutler, Deaton and Lleras-Muney, 2006)

3 Estimating the causal impact of MV diffusion is challenging: Large scale, gradual transformation Severe data limitations (Development and) Adoption of MVs is endogenous, creating potentially spurious correlations with economic outcomes Country level relationships are inconclusive and highly prone to bias by various policy confounders

4 This Paper: Global Scale Sub-National Analysis Construct time-varying and spatially precise indicators of: Infant Mortality: Use DHS data to construct a panel of births and infant mortality at the village level 18,000 villages spread across 400 administrative regions in 36 countries. MV Diffusion: Exploit sub-national variation in crop distribution to construct a localized MV indicator (akin to a Bartik-instrument). Estimate a sub-national child level difference-in-difference model with flexible controls.

5 Summary of Findings Results suggest that a 1 SD higher proportion of area planted to MVs (12%) is associated with a decline of percentage points in infant mortality (in 1980, IM was 12.4 percent) Robust to usage of three independent global crop maps and flexible controls Evidence that the impact is stronger for relatively poorer households, less educated mothers, and more irrigated areas Impact of MVs are different by child sex (IM declines faster for boys)

6 Data: Infant Mortality Aggregate Demographic and Health Survey (DHS) data from 36 countries. Reconstruct village-birth year panel has over 600,000 records ranging from

7 Data: MV Adoption Evenson and Gollin (2003) report fraction of area planted with modern varieties for 11 major crops (barley, beans, cassava, groundnut, lentils, maize, pearl millet, potato, rice, sorghum and wheat) Time coverage: 1960 to 2000, quinquennially

8 Data: Global Crop Maps Monfreda et al. (2008) reports areas for 175 crops circa 2000 [henceforth Earthstat (2000)] Resolution: 5 arc-minute (10 x 10 km at equator)

9 Alternative Crop Map Datasets Spatial Production Allocation Model (SPAM) reports areas for 20 crops circa 2000 Similar to Earthstat but disaggregation to grid cell is based on a modeling approach that includes information on cropland areas, biophysical crop suitability assessments, population density, and crop prices Ray et al (2012) reports areas for 4 cereals (maize, rice, wheat and soybeans) annually from We use this to calculate cropped areas before MV diffusion ( )

10 Construction of Local MV Diffusion Indicator (MVDI) For village v in country c at time t, define: j = 11 (barley, beans, cassava, groundnut, lentil, maize, millet, potato, rice, sorghum, wheat) in Earthstat 2000 j = 10 (barley, beans, cassava, groundnut, maize, millet, potato, rice, sorghum, wheat) in SPAM 2000 j = 3 (maize, rice, wheat) in Earthstat This methodology is similar to Bartik ( shift-share ) instruments

11 Construction of MVDI (Earthstat 2000): Example for Nigeria

12 Validity of MVDI as a proxy for local MV diffusion MVDI is a valid proxy if Crop distributions are sticky MV shares for a crop are not systematically higher where the crop is little cultivated Direct tests of validity are difficult because of the paucity of observed localized MV diffusion rates. Using ICRISAT district-level data from India we find strong correlations:

13 Estimating Equation y ivct is a binary indicator of infant mortality (in the first year of life) of child i in village v in country c in birth year t. u v are village fixed effects and Z ct are country-by-year FE X ivct includes quadratic in mother s age and sex of child e ivct clustered at subnational (admin) level Sample restricted to rural villages and mothers who report to have never migrated

14 Identification Strategy Examines whether parts of the country where MVs were faster adopted display faster than average reductions in infant mortality Village FE absorb all time-invariant village characteristics that could confound inference, such as climate or soils or distance to the capital city Controlling for flexible time control (country x birth year FE) absorbs all economic and policy changes at the country level Clustering at admin level provides conservative estimates (as it allows for arbitrary correlation for any two observations located in the same admin region of a given country)

15 Interpretation of Effects We exploit variation in the geographical heterogeneity in the crop mix (and will not be able to pick up effects in countries where cropland is dominated by one crop) Only identifies effects occurring due to: Increases in consumption for subsistence farmers Income increases for farmers Localized increases in food availability (decreased prices) resulting from limited market connectivity Cannot identify country-wide improvements in nutrition if food prices decrease everywhere

16 Caveats Effects on health should not only be considered a response to the MVs themselves, but to the wholesale adoption of more intensive and productive cropping practices stimulated by the use of MVs. Our indicator tracks replacement of traditional crop varieties with modern varieties and so our approach measures effects on the extensive margin (average across all types of modern varieties that were adopted)

17 Results: Impact on IM of MV (exposure at birth year) Standard deviation of MVDI is 12%, which is associated with around pp decrease in IM MVDI in our sample goes from 0% in 1960 to 21% in 2000, associated with pp decrease in IM IM in our sample goes from 16% in the early 1960s to 8% in 2000.

18 Impacts on Malnutrition

19 Potential Confounders Non-MV related crop effects (such as prices): Controlling for Crop Specific Areas Year Region does not alter the results. Public Health Interventions: Most MNCH interventions are not strongly correlated with MVDI Controlling for available health variables (breastfeeding, ANC, immunization) does not alter the results. Including subnational administrative region x Year FEs does not alter results. Robustness tests: Controlling for Mother FE, birth order does not alter the results. Non-Ag Economic Growth: Controlling for geography (distance to coast and cities) flexible regional time trends does not alter the results. Cannot completely rule out other drivers of development leading to improved health and increased demand for MVs. General weakness of Bartik instrument.

20 Robustness Checks

21 Discussion We use rich subnational data to study the differential impact of MVs on infant mortality in 36 developing countries Preliminary results suggest that a 1 SD increase in area planted to MVs (12 percentage point) is associated with a decline of percentage points in infant mortality (in 1980, IM was 12.4 percent) Robust to usage of three independent global crop maps and flexible controls Impact pronounced in irrigated areas, and among poorer households Why is impact limited to boys? No strong evidence of an effect on the sex ratio Gender biased resource allocation within households? But we are seeing this effect in Latin America, the Middle East, and sub-saharan Africa, with very different gender norms. Biological effect (male fetus less robust in womb, more susceptible to malnutrition-induced mortality, and thus most helped by the caloric boost).

22 Acknowledgements We are very grateful to SPIA for funding this work, and to SPIA members for useful feedback throughout this research.