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

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.

Standard models of hierarchy assume that agents and middle managers are better informed than principals. We estimate the value of the informational advantage held by supervisors—middle managers—when ministerial leadership—the principal—introduced a new monitoring technology aimed at improving the performance of agricultural extension agents (AEAs) in rural Paraguay. Our approach employs a novel experimental design that elicited treatment‐priority rankings from supervisors before randomization of treatment. We find that supervisors have valuable information—they prioritize AEAs who would be more responsive to the monitoring treatment. We develop a model of monitoring under different scales of treatment roll‐out and different treatment allocation rules. We semiparametrically estimate marginal treatment effects (MTEs) to demonstrate that the value of information and the benefits to decentralizing treatment decisions depend crucially on the sophistication of the principal and on the scale of roll‐out.

Standard models of hierarchy assume that agents and middle managers are better informed than principals about how to implement a particular task. We estimate the value of the informational advantage held by supervisors -middle managers -when ministerial leadership -the principalintroduced a new monitoring technology aimed at improving the performance of agricultural extension agents (AEAs) in rural Paraguay. Our approach employs a novel experimental design that elicited treatment-priority rankings from supervisors before randomization of treatment. We find that supervisors did have valuable information-they prioritized AEAs who would be more responsive to the monitoring treatment. We develop a model of monitoring under different allocation rules and roll-out scales (i.e., the share of AEAs to receive treatment). We semiparametrically estimate marginal treatment effects (MTEs) to demonstrate that the value of information and the benefits to decentralizing treatment decisions depend crucially on the sophistication of the principal and on the scale of roll-out.