The shift out of agriculture and into other more “modern” sectors has long been viewed as central to economic development. This structural transformation was a focus of influential early scholarship with the issue even stretching back to Soviet debates over whether to “squeeze” farmer surplus to hasten industrialization . A more recent macroeconomic empirical literature has revived interest in these issues, often using data from national accounts . This body of work has documented several important patterns that help shed light on the sources of income differences across countries. First, it shows that the share of labor in the agricultural sector correlates strongly with levels of per capita income: most workers in the poorest countries work in agriculture while only a small share do in wealthy countries. Importantly, while income per worker is only moderately larger for non-agricultural workers in wealthy countries relative to poor countries, agricultural workers are many times more productive in rich countries. This creates a double disadvantage for poor countries: agricultural work tends to be far less productive in low-income countries, yet the workforce is concentrated in this sector.Studies that explore the closely related gap between the urban and rural sectors reach similar conclusions. Several recent studies have examined the extent to which these productivity gaps across sectors can reasonably be viewed as causal impacts rather than mainly reflecting worker selection. By a causal impact of sector,wholesale grow bags we mean that a given worker employed in the non-agricultural sector is more productive than the same worker employed in the agricultural sector. In contrast, worker selection would reflect differences driven by the fact that workers of varying ability and skill levels are concentrated in particular sectors.
This paper seeks to disentangle these two competing explanations by estimating sectoral wage gaps using unusually long-run individual-level panel data from two low-income countries, Indonesia and Kenya. If there are causal impacts of sector, the large share of the workforce employed in the agricultural sector in low-income countries could be viewed as a form of input misallocation along the lines of Hsieh and Klenow and Restuccia and Rogerson . The resolution of this econometric identification issue, namely, distinguishing causal effects from selection, is not solely of scholarly interest: the existence of causal sectoral productivity gaps would imply that the movement of population out of rural agricultural jobs and into other sectors could durably raise living standards in low-income countries, narrowing cross-country differences. The existence of large causal sectoral productivity gaps also raises questions about the nature of the frictions that limit individual movement into more productive employment, and the public policies that might promote such moves or hinder them . Gollin, Lagakos, and Waugh and Young are two important recent studies that explore this identification issue. GLW examine labor productivity gaps in nonagricultural employment versus agriculture using a combination of national accounts and repeated cross-sectional data from micro-surveys, and document a roughly three-fold average productivity gap across sectors. In their main contribution, GLW show that accounting for differences in hours worked and average worker schooling attainment across sectors—thus partially addressing worker selection— reduces the average estimated agricultural productivity gap by a third, from roughly 3 to 2. They also find that agricultural productivity gaps and per capita consumption gaps based on household data remain large but tend to be somewhat smaller than those estimated using national labor surveys, possibly in part due to differences in how each source measures economic activity. GLW remain agnostic regarding the causal interpretation of the large agricultural productivity gaps that they estimate. If individual schooling captures the most important dimensions of worker skill and thus largely addresses selection, GLW’s estimates would imply that the causal impact of moving workers from agriculture to the non-agricultural sector in low-income countries would be to roughly double productivity, a large effect.
Of course, to the extent that educational attainment alone fails to capture all aspects of individual human capital, controlling for it would not fully account for selection. Young examines the related question of urban-rural differences in consumption , rather than productivity, and similarly finds large cross-sectional gaps.Using Demographic and Health Surveys that have retrospective information on individual birth district, Young shows that rural-born individuals with more years of schooling than average in their sector are more likely to move to urban areas, while urban-born individuals with less schooling tend to move to rural areas. Young makes sense of this pattern through a model which assumes that there is more demand for skilled labor in urban areas, shows that this could generate two-way flows of the kind he documents, and argues that he can fully explain urban-rural consumption gaps once he accounts for sorting by education.3 The current study directly examines the issue of whether measured productivity gaps are causal or mainly driven by selection using long-term individual-level longitudinal data on worker productivity. Use of this data allows us to account for individual fixed effects, capturing all time invariant dimensions of worker heterogeneity, not just educational attainment . We focus on two country cases – Indonesia and Kenya – that have long-term panel micro data sets with relatively large sample sizes, rich measures of earnings in both the formal and informal sector, and high rates of respondent tracking over time. The datasets, the Indonesia Family Life Survey and Kenya Life Panel Survey , are described in greater detail below.4 For both countries, we start by characterizing the nature of selective migration between non-agricultural versus agricultural economic sectors, and between urban versus rural residence. Like Young , we show that individuals born in rural areas who attain more schooling are significantly more likely to migrate to urban areas and are also more likely to hold non-agricultural employment, while those born in urban areas with less schooling are more likely to move to rural areas and into agriculture.
We exploit the unusual richness of our data, in particular, the existence of measures of cognitive ability , to show that those of higher ability in both Indonesia and Kenya are far more likely to move into urban and non-agricultural sectors, even conditional on educational attainment. This is a strong indication that conditioning on completed schooling is insufficient to fully capture differences in average worker skill levels across sectors. We next estimate sectoral productivity differences, and show that treating the data as a repeated cross-section generates large estimated sectoral productivity gaps, echoing the results in existing work. In our main finding, we show that the inclusion of individual fixed effects reduces estimated sectoral productivity gaps by over 80 percent. This pattern is consistent with the bulk of the measured productivity gaps between sectors being driven by worker selection rather than causal impacts. Specifically, we first reproduce the differences documented by GLW for Indonesia and Kenya, presenting both the unconditional gaps as well as adjusted gaps that account for worker labor hours and education . These are large for both countries,grow bags for gardening with raw gaps of around 130 log points, implying roughly a doubling of productivity in the non-agricultural sector. When we treat our data as a series of repeated cross-sections, the gaps remain large, at 60 to 80 log points. These are somewhat smaller than GLW’s main estimates, though recall that GLW’s estimates using household survey data also tend to be smaller. Conditioning on individual demographic characteristics as well as hours worked and educational attainment narrows the gap, but it remains large at between 30 and 60 log points. Finally, including individual fixed effects reduces the agricultural productivity gap in wages to 4.7 log points in Indonesia and to 13.4 log points in Kenya, and neither effect is statistically significant. Analogous estimates show that productivity gaps between urban and rural areas are also reduced substantially, to zero in Indonesia and 13.2 log points in Kenya. We obtain similar results for the gap in per capita consumption levels across sectors where this is available for Indonesia. This is useful since consumption measures may better capture living standards in less developed economies than earnings measures, given widespread informal economic activity. Furthermore, we show that the productivity gap is not simply a short-run effect by demonstrating that gaps do not emerge even up to five years after an individual moves to an urban area. We also find that productivity gaps are no larger even when considering only moves to the largest cities in Indonesia and Kenya .
Our methodological approach is related to Hendricks and Schoellman , who use panel data on the earnings of international migrants to the United States, including on their home country earnings. Mirroring our main results, the inclusion of individual fixed effects in their case greatly reduces the return to international migration . Similarly, McKenzie et al. show that cross-sectional estimates of the returns to international immigration exceed those using individual panel data or those derived from a randomized lottery. Bryan et al. estimate positive gains in consumption in the sending households of individuals randomly induced to migrate within Bangladesh, although no significant gains in total earnings. Bazzi et al. argue that cross-sectional estimates of productivity differences across rural areas within Indonesia are likely to overstate estimates derived from panel data using movers. Other related studies on the nature of selective migration include Chiquiar and Hanson , Yang , Beegle et al. , Kleemans , and Rubalcava et al , among others. A limitation of the current study is that we focus on two countries, in contrast to the scores of countries in GLW and Young . This is due to the relative scarcity of long-run individual panel data sets in low-income countries that contain the rich measures necessary for our analysis. That said, the finding of broadly similar patterns in both countries, each with large populations in two different world regions, suggests some generalizability. Another important issue relates to the local nature of our estimates, namely, the fact that the fixed effects estimates are derived from movers, those with productivity observations in both the non-agricultural and agricultural sectors. It is possible that productivity gains could be different among non-movers, an issue we discuss in Section 2 below. There we argue that, to the extent that typical Roy model conditions hold and those with the largest net benefits are more likely to move, selection will most likely produce an upward bias, leading our estimates to be upper bounds on the true causal impact of moving between sectors. However, absent additional knowledge about the correlation between individual preferences, credit constraints, and unobserved productivity shocks, it is in principle possible that selection could bias our estimates downward instead. Similarly, it is possible that very long-run and even inter-generational “exposure” to a sector could persistently change individual productivity due to skill acquisition, and this opens up the possibility that selection and causal impacts are both important. We return to these important issues of interpretation in the conclusion, including ways to reconcile our estimates with existing empirical findings. The paper is organized as follows. Section 2 presents a conceptual framework for estimating sectoral productivity gaps, and relates it to the core econometric issue of disentangling causal impacts from worker selection. Section 3 describes the two datasets ; characterizes the distinctions between the non-agricultural and agricultural sectors, and urban vs. rural areas; and presents evidence on individual selection between sectors. Section 4 contains the main empirical results on productivity gaps, as well as the dispersion of labor productivity across individuals by sector, consumption gaps, dynamic effects up to five years after migration, and effects in big cities versus other urban areas. The final section presents alternative interpretations of the results, and concludes. We present a development accounting framework to disentangle explanations for the aggregate productivity gap across sectors. We consider both observable and unobservable components of human capital, and whether intrinsic worker preferences for sector may bias direct measurement of the productivity gap. A standard model suggests that worker selection is most likely to bias sectoral productivity gaps upward when estimated among those moving into non-agriculture but lead to a downward bias when estimated among those moving into agriculture.