dropped several hyperinflation observations, such as Zimbabwe), and was available for a large range of developing countries.<sup>6</sup> However, since domestic price movements might be endogenous with respect to poverty (see below), we use an international price index to identify exogenous externally driven variation in domestic prices. To do so we construct an international price index with country-specific weights based on consumption (calorie) patterns for nine commodities for which international prices were available. This has the effect of creating an international price series that has cross-country as well as time series variation. For example, rice accounts for around 70 percent of caloric intake in Bangladesh, so international rice prices receive a very high weight in Bangladesh’s index, while wheat, maize, sorghum, and so on get very low weights. This approach helps minimize the weak instrument problem stemming from the fact that international price transmission may be highly variable across countries for a variety of reasons.

# _Agricultural Indicators_

In section V, we chiefly focus on agricultural production indicators to test for agricultural supply response and to circumvent some of the issues with the POVCAL data raised above. These indicators are mostly sourced from the FAO (2004). This set includes value-added measures of all agricultural production as well as food and crop production components. Growth in crop production can also be decomposed into increases in production per hectare and increases in land area harvested, often referred to as the intensive and extensive margins, respectively. We also examine another common indicator of the intensive margins: cereal yields.

# _Descriptive Statistics_

Table 1 shows some summary statistics for some of our key indicators, all of which are measured as annualized changes or percentage changes across consecutive surveys. The statistics are broken up by regions to illustrate one other serious limitation of POVCAL data, namely, that it is overpopulated with countries from Latin America (approximately half the sample), while there are only about 50 observations (i.e., first differences in poverty) each for sub-Saharan Africa and Asia (South and East Asia combined). Eastern Europe and Central Asia contain around 40 observations (and are also somewhat overrepresented relative to their population sizes), while the Middle East and Africa contains only a handful of observations for Tunisia and Morocco only. Average changes in $1.25/day poverty over these observations are highest for East Asia (-2.2 points), as expected, followed by Eastern Europe and Central Asia (-1.6), sub-Saharan Africa (-1.5) and South Asia (- 1.2). Mean changes in food prices are positive in all regions but substantially larger in Asia and sub-Saharan Africa.

Table 1. Summary statistics for key variables

> 6 .We investigated a number of different series, including the FAO series on producer prices and an index based on GDP deflators. However, results reported in appendix S1 suggest that these two series are measured with much more error than the consumer price index series. Both series are weakly correlated with international prices, for example. It also seems conceptually less suitable to focus on producer prices, since even producers also make substantial purchases of food.

6