food prices (through a large exchange rate devaluation), but also increased unemployment and reduced investment, and, therefore, likely affected poverty by other routes. In this example, an omitted variable (a financial crisis) induces a potentially spurious positive relationship between food prices and poverty, but one can also articulate biases in the other direction (e.g., expansionary monetary or fiscal policies), such that it is difficult to say in which direction OLS coefficients might be biased.

To address this concern we use a relatively simple instrumental variables strategy implemented with the two-step instrumental variables general method of moments (IV-GMM), using an optimal weighting matrix. The identification strategy rests upon the exogeneity of the international food price index (described above), which is assumed to only influence national poverty rates through its effect on domestic food prices. Hence, the IV-GMM approach results in an equation similar in structure to (1), but in which we use domestic price changes predicted by international price changes.

In addition to these basic least squares and IV-GMM approaches, we engage in several robustness tests. First, IV approaches can sometimes be sensitive to the choice of estimator. While results from standard IV and LIML estimators are also very similar (available on request), in our appendix we also report results for the Arellano and Bond (1991) dynamic panel estimator, in which lagged poverty rates are introduced.

Second, we conduct robustness tests to gauge whether the introduction of potentially confounding factors to the model has any influence on our core results. For example, increases in food prices might be strongly associated with increases in non-food commodities, so we control for changes in the overall terms of trade but also overall inflation, monetary expansion, exchange rate movements, changes in trade and foreign investment ratios, and non-agricultural growth rates.

Third, we test robustness of results to splitting up the sample by region and by time periods. The former is implemented to test whether any particular region might be driving the result, while the latter is done to test whether the effects of food prices were systematically different in the 2000s, a period of heightened international price volatility. We also extensively tested for interactions between changes in food prices and other structural indicators of interest (such as indicators of the size of agriculture in total GDP or total employment and the magnitude of initial poverty rates), to see whether there is observable heterogeneity in the effects of food price changes on national poverty (results available upon request).

Lastly, we also investigate whether changes in food prices influence agricultural production and input use and unskilled wages. For the agricultural production regressions, we adopt the same form as equation (1) and its IV-GMM analog, but we replace poverty rates with various measures of agricultural output or input use. We also exploit the availability of monthly data on rural and urban wages in Bangladesh to test whether rice prices differentially influence rural and urban labor markets, using both auto-distributed lag (ADL) models and vector error correction models (VECM). These results are reported in section V.

IV. ESTIMATING THE NET POVERTY IMPACTS OF INCREASES IN FOOD PRICES This section presents some core results on the associations between changes in poverty headcounts and changes in real food prices, followed by a description of their robustness to different methodological variations.

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