The impact of climate change on agricultural productivity: Evidence from panel data of Bangladesh

This paper studies the impact of climate change on agricultural productivity in Bangladesh for the period 1975-2008 for 23 regions. First, the study relies on descriptive statistics to explore the long term changes at both country and local level in climatic variables such as temperature, rainfall, humidity and sunshine. Second, it uses regression models to estimate the impact of climate change on agricultural productivity. Unlike the existing literature, this study exploits within-region time series variations (regional fixed effect) to estimate the impact of long term changes in climatic variables on agricultural productivity in order to control for regional differences, both observed and unobserved. Descriptive statistics shows that overall average, maximum and minimum temperatures in the dry and wet seasons have increased in recent years. Fluctuations in the minimum and maximum temperatures have also increased. Average rainfall in the dry season has increased substantially with greater fluctuations. Regression results show that an increase in the average minimum temperature in the dry season by one unit increases per acre rice output by 3.7% to 11.6%. Average minimum temperature in the wet season is also found to have a negative and significant impact even after controlling for region and year fixed effects. Standard deviations of maximum temperature in both dry and wet seasons are found to have a negative impact on agricultural productivity, though the impact in the wet season loses its significance after controlling for the year fixed effect. In the case of Boro rice, the only variable that is significant after controlling region and year specific heterogeneity is the standard deviation of maximum temperature in the Boro season. The regression results indicate that long term changes in means and standard deviations of the climatic variables have differential impacts on the productivity of rice and thus the overall impact of climate change on agriculture is not unambiguous. We found that when regional variations are considered, it significantly changes the sign and size of the estimates. The impact differs significantly with the choice of weather variables – mean vs. standard deviation (fluctuation), minimum vs. maximum, dry vs. wet seasons. For example, the impact of an increase in one unit of minimum temperature and maximum temperature on rice productivity are different in the same wet season. The choice of weather variables should therefore be driven by scientific knowledge, which the current economics literature lacks.