International Comparison Program

Abstract: I discuss the measurement of world poverty and inequality, with particular attention to the role of purchasing power parity (PPP) price indexes from the International Comparison Project. Global inequality increased with the latest revision of the ICP, and this reduced the global poverty line relative to the US dollar. The recent large increase of nearly half a billion poor people came from an inappropriate updating of the global poverty line, not from the ICP revisions. Even so, PPP comparisons between widely different countries rest on weak theoretical and empirical foundations. I argue for wider use of self-reports from international monitoring surveys, and for a global poverty line that is truly denominated in US dollars.

Abstract: We provide an overview of the theory and practice of constructing PPPs. We focus on four practical areas: how to handle international differences in quality; the treatment of urban and rural areas of large countries; how to estimate prices for government services, health, and education; and the effects of the regional structure of the latest International Comparison Program for 2005. We discuss revisions of the Penn World Table, and their effects on econometric analysis, and include health warnings. Some international comparisons are close to impossible, even in theory and in others, the practical difficulties make comparison exceedingly hazardous. (JEL C43, E01, E31, O57).

Abstract: In a 2003 report, International Evidence on Food Consumption Patterns, ERS economists estimated income and price elasticities of demand for broad consumption categories and food categories across 114 countries using 1996 International Comparison Program (ICP) data. This report updates that analysis with an estimated two-stage demand system across 144 countries using 2005 ICP data. Advances in ICP data collection since 1996 led to better results and more accurate income and price elasticity estimates. Low-income countries spend a greater portion of their budget on necessities, such as food, while richer countries spend a greater proportion of their income on luxuries, such as recreation. Low-value staples, such as cereals, account for a larger share of the food budget in poorer countries, while high-value food items are a larger share of the food budget in richer countries. Overall, low-income countries are more responsive to changes in income and food prices and, therefore, make larger adjustments to their food consumption pattern when incomes and prices change. However, adjustments to price and income changes are not uniform across all food categories. Staple food consumption changes the least, while consumption of higher-value food items changes the most.

Abstract: The new growth literature, following Paul M. Romer (1986) and Robert E. Lucas, Jr. (1988) has formulated models in which growth rates can differ systematically across countries. A large empirical literature has used cross-sectional techniques to examine the cause of international differences in growth rates.' This paper examines whether this work has used data which have been constructed in such a way as to introduce inadvertently a spurious correlation between growth and income levels. This effect occurs because relative prices evolve because of different rates of technological progress in different industries. Many of the new growth models assume single goods or goods produced by the same technology, so this phenomenon has so far escaped analysis.. If technological progress is more rapid in some sectors than others, relative prices will change during the process of development. The change of relative prices has two effects on measured growth rates. First, the choice of base prices affects the growth rate; this phenomenon is sometimes called the "Gerschenkron effect." Second, using any base prices or weights introduces a spurious correlation between income and growth. Less developed countries would tend to have lower growth rates, not because they grew more slowly, but because of data construction techniques. For the sake of simplicity, these phenomena will be called the Gerschenkron (or the level) effect and the spurious-correlation effect.3 This paper examines the International Comparison Program (ICP) and the Penn World Tables (PWT), which are based on the ICP. The Penn World Tables are the data most widely used in empirical studies of international differences in economic growth. Both the ICP and PWT use "international prices" to adjust for differences in the purchasing power of currencies. These prices most closely resemble Hungarian prices. Using such prices tends to produce time series with lower growth rates than those in the national accounts for countries less developed than Hungary; conversely, the growth rates would be higher for countries more developed than Hungary. However, the growth rates of poorer countries are not systematically lower in the Penn World Tables. The paper examines why the growth rates are not distorted and makes some specific recommendations for data construction and use. * Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0316. Part of this work was supported by a grant from the Institute of International Studies at Brown University and by a summer internship at the Division of International Finance at the Federal Reserve Board. I am grateful to Peter Garber, David Weil, Oded Galor, and anonymous referees for helpful comments. Alan Heston and Robert Summers have also helped me understand ICP and PWT methodology. Of course, I alone am responsible for any mistakes in this paper. IExamples include Steve Dowrick and Duc-Tho Nguyen (1989), Robert J. Barro (1991), J. Bradford De Long and Lawrence H. Summers (1991), and N. Gregory Mankiw et al. (1992). 2A series of papers by Christina D. Romer (1986a,b, 1989) has already emphasized the importance of data construction techniques in another context. 3There is a third effect, a real acceleration effect caused by shifting consumption patterns and the reallocation of resources between sectors. Growth accountants such as Angus Maddison (1987) adjust estimates of total factor productivity for this sort of shift. William J. Baumol et al. (1989) have also referred to this phenomenon. For an analysis in terms of a simple multisector Solow model, see Nuxoll (1992a).