level cross-sector productivity differences, and how they relate to sector allocation decisions, is crucial for understanding the forces that power agricultural labor exits.

I use a new micro-level dataset to measure key structural change parameters – sector participation, time use, and labor productivity – from a micro perspective. This paper draws on the Integrated Surveys on Agriculture from the Living Standards Measurement Study group at the World Bank (LSMS-ISA datasets), which explicitly collect information about respondents’ time use across sectors. Particular attention is paid to farm labor, which is often neglected in large scale, multi-topic surveys because of the challenges involved in collecting detailed agricultural data. The analysis currently includes surveys from Ethiopia, Malawi, Tanzania and Uganda. The countries that comprise the LSMS-ISA dataset exhibit considerable heterogeneity with respect to GDP per capita, share of agriculture in the labor force, share of agriculture in the economy, and productivity gaps (Figure 1).

The micro perspective is quite relevant for this study for several reasons. First, it reflects the perspective of individuals and firm owners making labor allocation decisions in developing countries. Second, micro datasets contain the variables required to examine the assumptions on which macro statistics are generated. Third, micro datasets allow for productivity measures to be paired with relevant covariates of labor allocation decisions at the household and individual levels. This kind of micro perspective is largely absent from the literature about structural change in African economies. Demographic and Health Survey (DHS) datasets, also micro datasets, are sometimes used to calculate sector labor shares, as an alternative to measures based on population censuses or national accounts (e.g., McMillan and Harttgen 2014; McMillan and Rodrik 2011). While DHS surveys have very extensive coverage, they cannot be used to generate measures of time use or returns to sector participation.

I generate micro measures of sector participation shares that are fairly close to those in national accounts datasets. Microeconomic analogs of sector productivity and cross-sector productivity gaps based on output per sector participant per year are smaller than those generated from national accounts data but fairly close to the corrected gaps generated by Gollin et al. (2014b). However, micro measures of agricultural labor shares based on time use are much smaller than participation measures because participants tend to work fewer hours in agriculture than in other sectors. Consequently, measures of cross-sector productivity gaps disappear almost entirely when they are based on time inputs by sector workers. Cross-sector productivity gaps observed in national accounts data, therefore, may reflect gaps in employment levels rather than gaps in the returns to hours worked between sectors. These results suggest that the forces pulling labor into non-agriculture sectors may be weaker than many believe them to be. Because time inputs in agriculture are generally low, possibly due to biophysical constraints, there still exists an opportunity to achieve growth in annual output per worker by increasing labor supply to the industry and services sectors.

# **2. Data and Variable Construction**

To examine labor productivity gaps from a micro-economic perspective, I generate labor productivity measures and other key variables from the Living Standards Measurement Survey – Integrated Surveys in Agriculture (LSMS-ISA) dataset. I draw on a cross-section of the most recent LSMS-ISA datasets available, comprised of the Ethiopia _Rural Socioeconomic Survey_ 4