In 1996, the World Bank and a network of non-governmental organizations (SAPRIN) started cooperating on an evaluation of 15 years of structural adjustment programs (SAPs). The background of our paper is the criticism raised with respect to the scientific quality of the summary report produced by the SAPRIN network. We make two types of fairly simple "quality checks" of their conclusions. Firstly, are the findings and conclusions presented in the SAPRIN Report with respect to trends and changes in poverty indicators corroborated when we compare them with poverty indicators in the underlying country studies, standard reference literature and public statistics? We find a tendency to depict trends in poverty as being more severe than what generally is supported by available statistics. This may be attributed to the fact that participatory methods have generated the bulk of the empirical data collected in the SAPRI process. This is of course valid information, but its source and its representativity ought to have been better documented in the reports. Secondly, are the inferences about the causality and attributed effects of SAP plausible given other major factors that have impacted on the countries' economies in the period concerned? Neither the country studies nor alternative data sources support the conclusions in the SAPRIN Report that SAP caused increased poverty and unemployment in the countries concerned. Existing data for Ghana and Bangladesh indicate that poverty was reduced and social indicators improved during the period of study, despite a history of severe crisis both before and during the SAP. The major causes of the poor performance of Ecuador, especially in recent times, are probably related to the large external shocks. The scientific weaknesses of the SAPRIN Report notwithstanding, its contribution to the international development policy discourse must be assessed in a wider perspective. The work has given important information on how vulnerable groups.
We evaluate the impact of disasters on income mobility by drawing on "natural experiments". While the poor have a much higher probability of remaining poor when entering a crisis compared to normal times, there is also a negative effect in the year after. Richer households seem to be unaffected. A simple bootstrap method is proposed to facilitate statistical inference for mobility matrices. Also, we simulate measurement error to illustrate its magnitude on these matrices. Small errors induce a substantial downward bias of the probability of remaining poor, while comp arisons across states seem more robust, which is promising for impact analysis.
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