Haughton and Khandker
11
quantity bought). The results are summarized in table 11.8 (from table 4 in Suryahadi et al. 2000): In the upper panel, an iterative method was used to compute the poverty rate in February 1996. The three methods were used to compute poverty rates for February 1999. They range from 15.3 to 22.4 percent—all of them purporting to measure the same thing. If method 3 is excluded—and it tends to deflate the poverty line by almost as much as the change in food prices, which is excessive—then the measures of the headcount index of poverty varied from 15.3 to 17.9 percent in 1999, up about 70 percent from the level of 9.75 percent that was computed (using the iterative method) for February 1996. In the lower panel, an iterative method was used to compute the poverty rate in February 1999, and methods 1 and 2 were used to deflate to get the rates in February 1996. Although both approaches yield similar trends, they give strikingly different overall poverty rates, which is not particularly satisfactory. All of the discussion above is based on the use of Susenas data. Indonesia is unusual in that there is a second set of high-quality survey data that provide information on living standards. The Indonesia Family Life Survey (IFLS) is “an ongoing longitudinal survey of individuals, households, families, and communities in Indonesia” (Frankenberg, Thomas, and Beegle 1999, 1). Households and individuals were interviewed in 1993–94 for IFLS1, in late 1997 for IFLS2, and a 25 percent subset of the same individuals and households about a year later for IFLS2+. The survey covers half of the country’s provinces, including the most populous ones. The interviewers made a lot of effort to track down households for the IFLS2+, with the result that 98.5 percent of the households interviewed for IFLS2 were also interviewed for IFLS2+, a very low attrition rate. In common with other researchers, Frankenberg, Thomas, and Beegle (1999) wrestled with the problem of how to deflate expenditures (or, equivalently, poverty lines) between the two surveys. They first used the CPI figures; they then noted that the CPI numbers are based on urban prices, and rural prices appear to have risen more quickly than urban ones between 1997 and 1998; and finally they inflated all CPI numbers by about 15 percent, on the grounds that the official CPI showed lower inflation that the prices collected in the course of the survey. Not surprisingly, this led to different poverty rates, as table 11.9 shows. It is disturbing that the data are not accurate enough for us to determine whether the headcount poverty rate rose by about a quarter, or about four-fifths, between 1997 and 1998. To finish this section, we reproduce in table 11.10 the main features of a useful table from Suryahadi et al. (2000), which summarizes the poverty rates that have been estimated using different methods, and by different researchers. Despite the real differences in estimates, a clear picture emerges: Poverty continued to fall until 224