Insurance and risk aversion interactions in shaping rural development
In a recent research paper Davide Pietrobon investigates the relationship between insurance and the use of agricultural inputs, focusing on the use of fertilizer in rural India. Insurance plays a vital role in developing rural regions where households encounter significant income fluctuations due to unpredictable factors like weather conditions. However, existing insurance systems are not perfect, often due to the challenge of monitoring effort and the choices made by farmers.
By Davide Pietrobon
The role of insurance for rural development
The paper discusses two main effects of insurance on fertilizer use. The first suggests that if fertilizer increases risk, insurance could lead to its intensified use. The second posits that due to the difficulty in observing farmers' effort choices, insurance might discourage farmers from working hard, leading to a decrease in the use of effort-related inputs, like fertilizer.
While the use of fertilizer can dramatically increase returns on many types of crops, fertilizer requires careful and timely application, and its use results in higher yields, demanding more labor for tasks like harvesting. More fertilizer also results in increased weed growth, which requires more labor to be allocated to weeding. In other words, there's a strong reason to believe that effort and fertilizer are complementary inputs. Consequently, if insurance leads to reduced effort, it may discourage the use of fertilizer. The balance between these effects remains theoretically ambiguous.
Through a detailed model and empirical examination using data from the Indian semi-arid tropics, I assess how various risk-sharing levels can influence farming decisions regarding effort and fertilizer use. The results suggest that median fertilizer use is significantly higher under no sharing than under full insurance, varying based on the farmers' risk aversion levels.
I analyzed recent data (from 2009 to 2014) collected by ICRISAT in India. These data tell us about how farmers in dry areas of India farm and how much they spend on things like fertilizers. By using information on the prices of fertilizer that these farmers pay, how much fertilizer they use, and how much labor they supply to their fields, the structural model allows me to learn different aspects of how the agricultural economy works in this region. For example, I obtained estimates for how much farmers value their leisure time, and how complementary effort and fertilizer are. I then asked: "If farmers had different ways of managing risks, would they farm differently?" By simulating different scenarios, I found that if farmers didn't share risks, they might use up to three times more fertilizer and put in up to ten times more effort than when they do share risks.
Risk aversion matters for the effect of insurance on agricultural production decisions
The main results are summarized by two diagrams: Figure 1 showing the median change in how hard farmers work (effort supply) and Figure 2 showing the median change in how much fertilizer they use when going from a situation in which they are fully insured to one in which there is no sharing.
Figure 1: Median change in effort supply when going from full insurance to no sharing under different levels of risk aversion. e(0) and e(1) denote effort under no sharing and effort under full insurance.
Notably, for many farmers who aren't very risk-averse, they work harder and use more fertilizer when there's no insurance. This observation suggests that when farmers don't have insurance, they might be trying to get the most out of their fields because they don't want to rely on others. But there's a twist. At a certain point, when farmers become very wary of risks (around a level we've measured as 0.07), they start using more fertilizer if they have insurance. In this scenario, the benefit of being protected from bad outcomes starts to outweigh the disincentive effect of insurance. In simple terms, the diagrams tell us that, depending on how tolerant a farmer is to risk, not having any insurance can mean they use up to three times more fertilizer and work almost ten times harder than if they were fully insured.
Figure 2: Median change in fertilizer use when going from full insurance to no sharing under different levels of risk aversion. f(0) and f(1) denote effort under no sharing and effort under full insurance.
Finally, I looked at what would happen if the government helped farmers by making fertilizer cheaper. If fertilizer prices were cut in half, the overall well-being or satisfaction of farmers could increase by about 37%. This means that they would feel almost 37% better off due to the reduced cost of fertilizer.