Recent research has pointed to large gaps in labor productivity between the agricultural and non-agricultural sectors in low-income countries, as well as between workers in rural and urban areas. Most estimates are based on national accounts or repeated cross-sections of micro-survey data, and as a result typically struggle to account for individual selection between sectors. This paper contributes to this literature using long-run individual-level panel data from two low-income countries (Indonesia, Kenya). Accounting for individual fixed effects leads to much smaller estimated productivity gains from moving into the non-agricultural sector (or urban areas), reducing estimated gaps by over 80 percent. Per capita consumption gaps between non-agricultural and agricultural sectors, as well as between urban and rural areas, are also close to zero once individual fixed effects are included. Estimated productivity gaps do not emerge up to five years after a move between sectors, nor are they larger in big cities. We evaluate whether these findings imply a re-assessment of the current conventional wisdom regarding sectoral gaps, discuss how to reconcile them with existing cross-sectional estimates, and consider implications for the desirability of sectoral reallocation of labor.. (Co-authors J. Hicks, M. Kleemans, N. Li)
Demand is growing for evidence-based policy making, but there is growing recognition in the social science community that limited transparency and openness in research have contributed to widespread problems. Explore transparency issues in social science research – and how to solve them. In this free online course, we discuss the major transparency and reproducibility issues across the social sciences today, including the problems of fraud, publication bias and data mining. We will also discuss many of the emerging solutions to these problems, including: pre-registering studies and writing pre-analysis plans; performing replications; conducting meta-analyses; making data open and available; visualizing data in ways that are honest and effective. The course has been developed by the Berkeley Initiative for Transparency in the Social Sciences (BITSS, bitss.org).