18 <mark>Di Giuseppe, and Zezza, 2017), and explore the prevalence, patterns and performance of rural nonfarm household enterprises (Nagler and Naudé, 2017). Together they provide key micro stylized facts to inform policy directions for the structural transformation of rural Africa.</mark> _The agricultural labor productivity gap is smaller than commonly assumed and mainly due to underemployment, not intrinsic lower productivity in agriculture._ One common view, especially popular among macro economists, is that labor is intrinsically far less productive in agriculture than elsewhere in the economy, and that a great deal is thus to be gained from transferring labor out of agriculture, i.e. from accelerating the structural transformation. This view finds support in the national accounts which show that, in Africa, value added per nonagricultural worker is six times larger than the value added per agricultural worker. In developing countries, the ratio is 3.5.

Yet, this does not account for differences in human capital and income diversification across sectors. Recent work by Gollin, Lagakos and Waugh (2014) shows that adjusting for those factors would bring the ratio down to 3.3 for Africa (and 2.2 for developing countries). But even this may be misleading. In addition to neglecting differences in capital use across sectors, these macro numbers do not account very well for production for own consumption, which in developing countries makes up a substantial share of agricultural output as shown by the agricultural commercialization figures above. Using output measures from micro data instead reduces the gap by another 20 percent in the ten countries for which Gollin, Lagakos and Waugh (2014) had data.

The paper by McCullough (2017) in the special issue takes the argument one step further still. Instead of comparing (micro based) output estimates _per worker_ , she also uses detailed micro data on hours worked in both primary and secondary occupations to measure and compare labor productivity _per hour worked_ . Doing so shrinks the labor productivity gap to 1.6 on average, across the four countries she studies, compared with 3.9 when using output per worker (Table 4).<sup>15</sup> This is because workers in agriculture work fewer hours (700 hours per worker per year on average in the four countries she studies) than those outside agriculture (1,850 hours). Not only is the agricultural labor productivity gap not as large as commonly portrayed, it follows mainly from underemployment and not from intrinsic lower productivity in agriculture.

Table 4: Underemployment explains most of the agricultural labor productivity gap

|nonagricultural /agricultural output|Ethiopia|Malawi|Uganda|Tanzania|Average|
|---|---|---|---|---|---|
|Per person productivity gap|2.25|4.76|4.48|4.20|3.92|
|Employment gaps|2.66|3.30|2.10|2.22|2.57|
|Per‐hour productivity gaps|0.85|1.44|2.13|1.90|1.58|

Source: McCullough, 2017.

This shifts the policy focus from getting people out of agriculture per se to making better use of labor in agriculture. In agriculture, work hours are constrained or rationed. This is possibly because of the seasonal nature of crop production, another dimension of seasonality which deserves further investigation. In such a case, smallholder labor productivity could be raised by making more use of their labor off season, either on or off the farm. In environments with favorable temperature, water availability, and product demand,

> 15 These gap estimates are plausibly still upper bounds. They do not correct for cross‐sectoral differences in human and physical capital.