Reevaluating Agricultural Productivity Gaps with Longitudinal Microdata

Abstract

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 uses long-run individual-level panel data from two low-income countries (Indonesia and Kenya) to explore these gaps. 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 roughly 67 to 92%. Furthermore, gaps do not emerge up to five years after a move between sectors. We evaluate whether these findings imply a re-assessment of the 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.

Cooperation as an integral part of the social and solidarity economy (SSE)

“…According to research conducted in Indonesia and Kenya, the preservation of self-governing and self-sufficient rural locations depends on improving their infrastructure and the comfort of rural residents ' living space provided by consumer associations. [25] An analysis of global experience has revealed how cooperatives contribute to poverty reduction:…”

Long-Term and Intergenerational Effects of Education: Evidence from School Construction in Indonesia

“…Panel A of Appendix Table A.2 provides further details on living standards and shows no significant effects on educational expenditure. The last row in Panel A combines the expenditure 37 Empirical evidence for Indonesia (Hamory et al, 2020) and for developing countries in general (Young, 2013) shows positive selection from rural to urban areas and negative selection from urban to rural. 38 SUSENAS 2016 does not include information on income, unlike the 1995 Intercensal survey that Duflo (2001) used to measure the returns to education.…”

When Distance Drives Destination, Towns Can Stimulate Development

“…Rather the results indicate that there exists a type of respondent for whom the trade-off between distance and wealth no longer favours towns as a destination choice. Such differential frictions would lead to the kind of sorting documented by Hamory et al (2020) and Young (2013). This may result from education itself as well as other characteristics of the household where the more educated person grew up, such as wealth levels, access to networks, as well as mindset which may all help overcome migration costs.…”

“…Much of the migration literature over the past couple of decades has further emphasized the importance of selection, with the younger, better educated and richer found more likely to move. They are often better equipped to overcome migration costs and could stand to benefit more from the employment opportunities for the skilled that urban areas offer (Young, 2013;Lucas 2015;Hamory, 2020). As those categories are also smaller in number, this could help explain the smaller number eventually making it to the city, compared to the towns.…”