employment, especially indicators of own-use production work).<sup>34</sup> A continued narrow focus on employment and labor force participation would risk underestimating the importance of agriculture for rural livelihoods and undercount women’s work in agriculture.

The new standards also raise difficult issues for the macro measurement of labor productivity. Aggregate labor productivity is typically estimated as the ratio of GDP to total employment, while sectoral labor productivity is estimated as the ratio of a sector’s GDP to the sector’s employment. Under the 19<sup>th</sup> ICLS standards, agriculture for own consumption counts towards production in national accounts (and is hence included in GDP), but such a farmer is no longer considered employed. As a result, estimates of aggregate and sectoral labor productivity computed as ratios of GDP to employment could be severely biased, with an overestimation of labor productivity overall and in agriculture (where the employment estimate, i.e. the denominator, excludes some workers engaged in the production of output in the numerator) and an underestimation of labor productivity in non-agricultural sectors (where the employment estimate includes some workers who are disproportionately engaged in the production of agricultural output). This inconsistency between different parts of the statistical system is a serious dilemma, which (at least in the short term) could only be redressed by re-constructing and disseminating a parallel labor statistics series, which is aligned with the national accounts production boundary. Such a series could also be used to track changes in employment and labor force participation over time, at least in an interim period.

The analysis in this paper highlights the need for national statistical offices to tread carefully in the implementation of the revised standards. First, special attention has to be paid to the timing of data collection. As this paper shows, farmers’ labor allocation across crop and livestock products, and hence their self-reported intended use of output, can vary significantly over the course of the agricultural production cycle, and this poses challenges for cross-country and cross-survey comparability of rural employment statistics, especially if the data are collected at different points in time during the agricultural year. Our results suggest that end-of-season surveys may lead to higher employment ratios and labor force participation rates than surveys conducted at the beginning of the season, but these patterns are not necessarily generalizable to all parts of Sub-Saharan Africa. Moreover, there may be important nonlinearities over the course of the season, with farmers potentially allocating most of their labor to seasonal crop production during harvest time, followed by a sharp drop-off and reallocation of labor from crops to livestock thereafter. Since the end-of-season data used in this paper were collected, for the most part, after the main harvest they are not well suited to reveal such non-linearities. It would be useful for future

> 34 How this can be achieved is an open question, especially in the context of academic studies which are used to having a single indicator (e.g. employment or labor force participation) of the extensive margin of labor supply.

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