the per-person agricultural labor productivity measure, net livestock revenue, taken from the RIGA dataset, is included. The workers who participate in livestock rearing are counted as agricultural labor force participants.

# **3. Corroborating Macro and Micro Evidence**

This section focuses on micro-based labor productivity measurements and what they imply about productivity gaps between sectors. I do not attempt to replicate national accounts based measures of value added or sector participation, but, rather, to carefully measure sector participation and labor supply using the LSMS-ISA datasets, and then to explore structural change concepts with these measures in mind.

# _3.1 Sector Labor Shares_

Often in the macro measures of sector productivity, individuals are constrained to one sector of participation, and it is assumed that individuals across sectors work the same number of hours and do not spend time working in secondary sectors. Each sector’s labor inputs are also usually assumed to be of the same skill and not adjusted for different levels of human capital. Initial examination of these assumptions using LSMS-ISA data suggests that they are indeed problematic and lead one to overestimate labor supplied to agriculture relative to other sectors, thereby artificially inflating estimates of the labor productivity gap between agriculture and other sectors (Gollin, Lagakos, and Waugh 2014b).

Figure 2 depicts different measures of sector labor shares using LSMS-ISA data along with two other commonly used measures – from national accounts and from Demographic and Health Surveys (DHS).<sup>5</sup> The first column in Figure 2 is based on the time use of all adult individuals in the LSMS-ISA dataset.<sup>6</sup> The second is based on the primary time use of each adult individual in the household, i.e., the sector to which each individual contributes the most hours.<sup>7</sup> The third is based on the primary time use of the household head. This sub-sample includes individuals who reported positive hours worked in any sector.

Figure 2 has several interesting features. First, agriculture is the dominant sector of participation across all data sources and aggregation methods, and participation in services is generally more common than participation in industry. Second, agricultural labor share estimates are higher when the sectors of all adult individuals in a household are considered rather than just the household head’s sector. This suggests that household non-heads are more likely to work in agriculture than household heads. Third, the individual-based estimates of agricultural labor

> 5 The national account measure comes from the sector employment dataset published by the International Labor Organization (ILO) and sector value added measures from the United Nations (UN) National Accounts Statistics, accessed through the World Bank World Development Indicators database. Sector employment statistics are generated from population censuses or labor force surveys, with methodologies varying across countries. The Malawi agricultural labor share estimate is comes from Gollin et al. (2014b). The DHS measure of sector labor shares are based on the self-reported primary occupations of adult respondents who work and do not attend school. These are taken from McMillan and Harttgen (2014).

> 6 Following McMillan and Harttgen (2014), adulthood is assumed to begin at age 25 to avoid confounding labor shares and educational attainment. Labor shares are robust to the adulthood threshold used.

> 7 In most LSMS-ISA surveys, respondents are not asked to name their primary occupations explicitly.

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