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We use newly-available Indian panel data to estimate how the returns to planting-stage investments vary by rainfall realizations. We show that the forecasts significantly affect farmer investment decisions and that these responses account for a substantial fraction of the inter-annual variability in planting-stage investments, that the skill of the forecasts varies across areas of India, and that farmers respond more strongly to the forecast where there is more forecast skill and not at all when there is no skill. We show, using an IV strategy in which the Indian government forecast of monsoon rainfall serves as the main instrument, that the return to agricultural investment depends substantially on the conditions under which it is estimated. Using the full rainfall distribution and our profit function estimates, we find that Indian farmers on average under-invest, by a factor of three, when we compare actual levels of investments to the optimal investment level that maximizes expected profits. Farmers who use skilled forecasts have increased average profit levels but also have more variable profits compared with farmers without access to forecasts. Even modest improvements in forecast skill would substantially increase average profits.

Zonder onderwerpscode: economie

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The livelihoods of the majority of the world's poor depend on agriculture. They face substantial risk from fluctuations in weather conditions. Better risk, credit and savings markets can improve productivity and welfare in rural areas but entail high administrative costs. We consider a classic public good with benefits that theoretically exceed those of perfect insurance contracts - improving the skill of long-run weather forecasts. We use an equilibrium model of agricultural production and labor migration, and a variety of Indian panel datasets to assess quantitatively the effects of improvements in seasonal forecasts of monsoon weather. We find that in areas where the forecast is accurate (has "skill") that investment, migration and rural wages respond to forecasts. We calculate that if such skill were pervasive across India, the total value of an accurate forecast for farmers and wage workers is in the tens of billions of rupees.

Zonder onderwerpscode: economie

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We use newly-available Indian panel data to estimate how the returns to planting-stage investments vary by rainfall realizations. We show that the forecasts significantly affect farmer investment decisions and that these responses account for a substantial fraction of the inter-annual variability in planting-stage investments, that the skill of the forecasts varies across areas of India, and that farmers respond more strongly to the forecast where there is more forecast skill and not at all when there is no skill. We show, using an IV strategy in which the Indian government forecast of monsoon rainfall serves as the main instrument, that the return to agricultural investment depends substantially on the conditions under which it is estimated. Using the full rainfall distribution and our profit function estimates, we find that Indian farmers on average under-invest, by a factor of three, when we compare actual levels of investments to the optimal investment level that maximizes expected profits. Farmers who use skilled forecasts have increased average profit levels but also have more variable profits compared with farmers without access to forecasts. Even modest improvements in forecast skill would substantially increase average profits.

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In an experiment providing fertilizer grants to women rice farmers in Mali, we found that women who received fertilizer increased both the quantity of fertilizer they used on their plots and complementary inputs such as herbicides and hired labor. This highlights that farmers respond to an increase in availability of one input by re-optimizing other inputs, making it challenging to isolate the returns to any one input. We also found that while the increase in inputs led to a significantly higher level of output, we find no evidence that profits increased. Our results suggest that fertilizer's impact on profits is small compared to other sources of variation. This may make it difficult for farmers to observe the impact of fertilizer on their plots, and accordingly this affects their ability to learn about the returns to fertilizer and could affect their decision to adopt even in the absence of credit constraints.

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Do non-traditional digital trace data and traditional survey data yield similar estimates of the impact of a cash transfer program? In a randomized controlled trial of Togo's COVID-19 Novissi program, endline survey data indicate positive treatment effects on beneficiary food security, mental health, and self-perceived economic status. However, impact estimates based on mobile phone data - processed with machine learning to predict beneficiary welfare - do not yield similar results, even though related data and methods do accurately predict wealth and consumption in prior cross-sectional analysis in Togo. This limitation likely arises from the underlying difficulty of using mobile phone data to predict short-term changes in wellbeing within a rural population with fairly homogeneous baseline levels of poverty. We discuss the implications of these results for using new digital data sources in impact evaluation.