Deininger et al. 2012). In Uganda, for example, Deininger et al. (2012) find that crop production might be underestimated by half due to recall biases, suggesting that standard surveys might also be underestimating net benefit ratios from higher food prices.

Other critiques focus on behavioral and market responses to higher food prices. Even if many rural people are net food consumers, their engagement in farming provides them scope to adjust production in response to higher food prices (Headey and Fan 2008, 2010). Agricultural supply responses could stem from a range of inputs, including labor, land, variable farm inputs like improved seeds or fertilizers, or longer term capital inputs such as irrigation and increased R&D. Obviously, major capital investments in R&D, irrigation, and land conversion will only induce a supply response over the longer term, but labor and other variable inputs might adjust quickly (Ivanic and Martin 2014). Land is potentially an intermediate case, depending on the availability of fallow land requiring little new investment, only more variable inputs like labor, seeds, and chemicals.

Given what we know about the diversification of activities within households, it seems likely that labor reallocation is the major mode of adjustment and the input most likely to have broader general equilibrium impacts on the demand for unskilled labor and its associated wages. To explore this more systematically, Jacoby (2016) develops a simple general equilibrium model to show that, if agriculture is a large sector, then a given agricultural supply response will result in a relatively large increase in demand for unskilled labor, which will need to be accommodated by shifting labor out of the services and manufacturing sectors. However, if the services sector is large, laborintensive, and non-tradable (as it mostly is in poor countries), then the wage adjustment required to equilibrate labor demands across sectors will mostly need to come from the manufacturing sector and will need to be relatively large to extract sufficient labor out of that sector. Hence, the model predicts that a standard economic structure (large agricultural and services sectors) will result in an elasticity of wages with respect to food prices that is large and positive in the long run, typically close to unity (Jacoby 2013; Ivanic and Martin 2014). Much less clear, theoretically and empirically, is the speed of wage adjustments to higher food prices, though econometric estimates suggest that short-run wage elasticities vary between 0.25 and 0.69 (Ravallion 1990; Palmer‐Jones 1993; Rashid 2002; Lasco et al. 2008).

In summary, the effects of higher food prices on the incomes or expenditures of the poor are complex, dynamic, and ambiguous _a priori_ . Undoubtedly, higher food prices create both winners and losers. But given that the bulk of the world’s poor are still predominantly rural (Ravallion et al. 2007), any sizeable agricultural supply and wage responses to higher food prices could well result in net poverty reduction over the medium term.

# III. DATA AND METHODS

This section describes in detail the data and methods used in the analysis with particular attention to some of the limitations involved.

# _Poverty Indicators_

In this paper, we first explore these medium-term impact of higher prices on changes in national poverty rates in a sample of around 300 observations from the World Bank’s (2014) POVCAL 4