Suchergebnisse

Although weather shocks are a major source of income fluctuation, most of the world's poor lack insurance coverage against them. Absence of formal insurance contributes to poverty traps, as investment decisions are conflicted with risk management ones: risk-averse farmers tend to underinvest and produce lower yielding yet safer crops. In the past few years, weather index insurance has gained increasing attention as an effective tool to provide small-scale farmers coverage against aggregate shocks. However, there is little empirical evidence about its effectiveness. This paper studies the effect of the recently introduced rainfall-indexed insurance on farmers' productivity, risk management strategies, as well as per capita income and expenditure in Mexico. The identification strategy takes advantage of the variation across counties and across time in which the insurance was rolled-out. The analysis finds that the presence of insurance in treated counties has significant and positive effects on maize productivity. Similarly, there is a positive association between the presence of insurance in the municipality and rural households' per capita expenditure and income, although no significant relation is found between the presence of insurance and the number of hectares destined for maize production.

Recently, Weather Index Insurance (WII) has received considerable attention as a tool to insure farmers against weather related risks, particularly in developing countries. Donor organizations, local governments, insurance companies, development economists as well as agricultural economists are discussing the costs and benefits of WII. While the literature on WII has mainly focused on many cases in Africa and Asia, in this article we analyze the WII program in Mexico, which is one of the largest WII programs worldwide. In this context we discuss potentially important spill-over effects on related markets which so far have not been considered in the academic literature. First, we argue that WII creates disincentives to invest in other non-insured crops leading to potential overspecialization and monoculture. Secondly, WII generates disincentives to invest in irrigation systems because farmers are insured only as long as production takes place on non-irrigated land. Third, in case of catastrophic events food prices can potentially inflate with indemnity payments at the expense of the uninsured poor. We also suggest that in Mexico the thresholds of the weather index be (continuously) re-calibrated in order to adjust for the development of drought resistant seeds. Finally, the index could relatively easily be extended to account for precipitation variances. We argue that these factors and spillover effects should be accounted for in cost benefit analysis of WII.

This paper examines the consequences of data error in data series used to construct aggregate indicators. Using the most popular indicator of country level economic development, the Human Development Index (HDI), we identify three separate sources of data error. We propose a simple statistical framework to investigate how data error may bias rank assignments and identify two striking consequences for the HDI. First, using the cutoff values used by the United Nations to assign a country as 'low', 'medium', or 'high' developed, we find that currently up to 45% of developing countries are misclassified. Moreover, by replicating prior development/macroeconomic studies, we find that key estimated parameters such as Gini coefficients and speed of convergence measures vary by up to 100% due to data error. -- Measurement error ; international comparative statistics