This paper estimates the role of agronomic inputs in cereal yield improvements and the consequences for countries' processes of structural change. The results suggest a clear role for fertilizer, modern seeds and water in boosting yields. We then test for respective empirical links between agricultural yields and economic growth, labor share in agriculture and non-agricultural value added per worker. The identification strategy includes a novel instrumental variable that exploits the unique economic geography of fertilizer production and transport costs to countries' agricultural heartlands. We estimate that a half ton increase in staple yields generates a 14 to 19 percent higher GDP per capita and a 4.6 to 5.6 percentage point lower labor share in agriculture five years later. The results suggest a strong role for agricultural productivity as a driver of structural change.

What is the contribution of the 'Green Revolution' to improvements in child health during the 20th century? We provide global scale estimates of this relationship by constructing a novel, spatially-precise indicator of modern crop variety (MV) diffusion and leveraging child-level data from over 600,000 children across 21,604 sampling locations in 37 developing countries between 1961-2000. Results indicate that the diffusion of MVs reduced infant mortality by 2.4-5.3 percentage points (from a baseline of 18%), with stronger effects for male infants and among poor households. The sizable contribution of agricultural technology to improved welfare should inform global food and development policy.

The El Niño Southern Oscillation (ENSO) is a principal component of global climate variability known to influence a host of social and economic outcomes, but its systematic effects on human health remain poorly understood. We estimate ENSO’s association with child nutrition at global scale by combining variation in ENSO intensity from 1986-2018 with children’s height and weight from 186 surveys conducted in 51 teleconnected countries, containing 48% of the world’s under-5 population. Warmer El Niño conditions predict worse child undernutrition in most of the developing world, but better outcomes in the small number of areas where precipitation is positively affected by warmer ENSO. ENSO’s contemporaneous effects on child weight loss are detectable years later as decreases in height. This relationship looks similar at both global and regional scale, and has not appreciably weakened over the last four decades. Results imply that almost 6 million additional children were underweight during the 2015 El Niño compared to a counterfactual of neutral ENSO conditions in 2015. This demonstrates a pathway through which human well-being remains subject to predictable climatic processes.

Nighttime lights are routinely used as a proxy for economic activity when official statistics are unavailable and are increasingly applied to study the effects of shocks or policy interventions at small geographic scales. The implicit assumption is that the ability of nighttime lights to pick up changes in GDP does not depend on local characteristics of the region under investigation or the scale of aggregation. This study uses panel data on regional GDP growth from six countries, and nighttime lights from the Defense Meteorological Satellite Program (DMSP) to investigate potential nonlinearities and measurement errors in the light production function. Our results for high statistical capacity countries (the United States and Germany) show that nightlights are significantly less responsive to changes in GDP at higher baseline level of GDP, higher population densities, and for agricultural GDP. We provide evidence that these nonlinearities are too large to be caused by differences in measurement errors across regions. We find similar but noisier relationships in other high-income countries (Italy and Spain) and emerging economies (Brazil and China). We also present results for different aggregation schemes and find that the overall relationship, including the nonlinearity, is stable across regions of different shapes and sizes but becomes noisier when regions become few and large. These findings have important implications for studies using nighttime lights to evaluate the economic effects of shocks or policy interventions. On average, nighttime lights pick up changes in GDP across many different levels of aggregation, down to relatively small geographies. However, the nonlinearity we document in this paper implies that some studies may fail to detect policy-relevant effects in places where lights react little to changes in economic activity or they may mistakenly attribute this heterogeneity to the treatment effect of their independent variable of interest.

Subnational measures of economic activity are crucial for analyzing inequalities that persist across subnational regions and for tracking progress towards sustainable development within a country. Eighteen of the Sustainable Development Goals (SDG) indicators require having estimates of Gross Domestic Product (GDP), making subnational GDP estimates crucial for local SDG monitoring. However, many countries do not produce official subnational GDP estimates. Using Paraguay as an example, we show how nightlights imagery from the Visible Infrared Imaging Radiometer Suite’s Day-Night Band (VIIRS-DNB) and data from neighboring countries can be used to produce subnational GDP estimates. We first estimate the relationship between VIIRS and economic activity in South American countries at the first subnational administrative level, employing various econometric models. Results suggest that nightlights are strongly predictive of subnational GDP variation in South American countries with available data. We assess various models’ goodness-of-fit using both cross-validation against other countries’ subnational GDP data and comparing predictions against an input–output accounting of Paraguay’s subnational GDP. Finally, we use the preferred model to produce a time series of department-level GDP in Paraguay.

To test the impact of a nationwide Long-Lasting Insecticidal Nets [LLINs] distribution program in the Democratic Republic of Congo [DRC] on all-cause under-five child mortality exploiting subnational variation in malaria endemicity and the timing in the scale-up of the program across provinces. Geospatial Impact Evaluation using a difference-in-differences approach. Democratic Republic of the Congo. 52,656 children sampled in the 2007 and 2013/2014 DRC Demographic and Health Surveys. The analysis provides plausibly causal estimates of both average treatment effects of the LLIN distribution campaign and geospatial heterogeneity in these effects based on malaria endemicity. It compares the under-five, all-cause mortality for children pre- and post-LLIN campaign relative to children in those areas that had not yet been exposed to the campaign using a difference-in-differences model and controlling for year- and province-fixed effects, and province-level trends in mortality. We find that the campaign led to a 41% decline [3.7 percentage points, 95% CI 1.3 to 6.0] in under-5 mortality risk among children living in rural areas with malaria ecology above the sample median. Results were robust to controlling for household assets and the presence of other health aid programs. No effect was detected in children living in areas with malaria ecology below the median. The findings of this paper make important contributions to the evidence base for the effectiveness of large scale-national LLIN campaigns against malaria. We found that the program was effective in areas of the DRC with the highest underlying risk of malaria. Targeting bednets to areas with greatest underlying risk for malaria may help to increase the efficiency of increasingly limited malaria resources but should be balanced against other malaria control concerns.