Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata

Abstract

Recent research has pointed to large gaps in labor productivity between the agricultural and nonagricultural 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 and 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 crosssectional estimates, and consider implications for the desirability of sectoral reallocation of labor.* We would like to thank

Migration costs and observational returns to migration in the developing world

“…Using consumption measures as our main outcome of interest, we find observational returns to migration that are larger on average than those found in previous studies. In our preferred specification, returns to migration in Indonesia are similar to those of Hicks et al (2017), but larger on average in our other five countries -China, Ghana, Malawi, South Africa and Tanzania -with an overall average return of 23 percent in our meta-analysis. Returns measured based on income are similar, for the subset of our countries for which income data are available.…”

“…Yet other recent findings cast doubt on rural-urban migration as a viable growth strategy for developing nations. Using panel tracking studies from Kenya and Indonesia and an individual fixed effects specification, Hicks, Kleemans, Li, and Miguel (2017) find that observational returns to migration -meaning the returns for those observed to migrate in the data -are near zero on average. Alvarez (2020) finds a similar result in Brazil using a large panel survey.…”

Spatial Wage Gaps in Frictional Labor Markets

“…Figure 1 plots the average daily wage from the BHP, adjusted for cost-of-living differences from the BBSR survey, for each county in Germany. 9 The large wage gap is not driven by a few outlier counties: Figure 16 in Appendix E shows that close 7 Without the scope to survey the literature, some references where each hypothesis is central are as follows: i) Combes, Duranton, and Gobillon (2008); Gollin, Lagakos, and Waugh (2014) ;Hicks, Kleemans, Li, and Miguel (2017); ii) Brueckner, Thisse, and Zenou (1999); Diamond (2016); Lagakos, Mobarak, and Waugh (2018); iii) Kennan and Walker (2011); Bryan and Morten (2019) ;Diamond, McQuade, and Qian (2019).…”

“…First, we provide a new framework to interpret and refine the results of a recent literature that has studied spatial wage gaps with panel data. This literature has pointed out that spatial sorting plays an important role in accounting for spatial wage gaps and that it is necessary to use panel data to properly quantify the degree of sorting (see Combes, Duranton, and Gobillon (2008), Hicks, Kleemans, Li, and Miguel (2017) and Alvarez (2018)). 3 In our context, we confirm these results, but, importantly, we highlight that interpreting the data without a frictional theoretical framework, as done in the cited literature, could lead to misleading results.…”

Migration and household finances: How a different framing can improve thinking about migration

“…We know very little about the factors that make a difference, and even less about whether those factors affect outcomes. Hicks, Kleemans, Li, and Miguel (2017) examine the much discussed productivity gap between urban and rural areas in Kenya and Indonesia, and find that those who migrate to cities tend to be more productive ex-ante, accounting for up to 80% of the productivity gap. Imbert and Papp (2018) find there are large non-monetary costs to migration from village to city in India, leading to differences in who seasonally migrates.…”