Multilateral Price Indexes Research Articles

Price Measurement Using Scanner Data: Time‐Product Dummy Versus Time Dummy Hedonic Indexes

This paper compares two model‐based multilateral price indexes: the time‐product dummy (TPD) index and the time dummy hedonic (TDH) index, both estimated by expenditure‐share weighted least squares regression. The TPD model can be viewed as the saturated version of the underlying TDH model, and we argue that the regression residuals are “distorted toward zero” due to overfitting. We decompose the ratio of the two indexes in terms of average regression residuals of the new and disappearing items. The decomposition aims to explain the conditions under which the TPD index suffers from quality‐change bias or, more generally, lack‐of‐matching bias. An example using scanner data on packaged men's T‐shirts illustrates our framework.

Decomposing Multilateral Price Indexes into the Contributions of Individual Commodities

Abstract This article describes methods for decomposing price indexes into contributions from individual commodities, to help understand the influence of each commodity on aggregate price index movements. Previous authors have addressed the decomposition of bilateral price indexes, which aggregate changes in commodity prices from one time period to another. Our focus is the decomposition of multilateral price indexes, which aggregate commodity prices across more than two time periods or countries at once. Multilateral indexes have historically been used for spatial comparisons, and have recently received attention from statistical agencies looking to produce temporal price indexes from large and high frequency price data sets, such as scanner data. Methods for decomposing these indexes are of practical relevance. We present decompositions of three multilateral price indexes. We also review methods proposed by other researchers for extending multilateral indexes without revising previously published index levels, and show how to decompose the extended indexes they produce. Finally, we use a data set of seasonal prices and quantities to illustrate how these decomposition methods can be used to understand the influence of individual commodities on multilateral price index movements, and to shed light on the relationships between various multilateral and extension methods.

Sensitivity of global and regional poverty rates to alternative purchasing power parities

PurposeThe purpose of this study is to examine the sensitivity of regional and world poverty rates to the purchasing power parities (PPP) used in the calculations. The PPPs are required to convert the “international poverty line” typically denominated in US dollar to its local currency equivalent in the various countries. While recent studies on world poverty differ with respect to the specification of the international poverty line (IPL), they universally use the PPP available from the international comparison program (ICP). This study provides a departure and calculates PPPs using the Gini–Elteto–Koves–Szulc (GEKS) price index and country product dummy (CPD) model as alternatives to the ICP PPPs. The GEKS and CPD PPPs are compared with the ICP PPPs. The paper then compares the global and regional poverty rates based on the three sets of PPPs and presents evidence of significant revision to the poverty rates if we depart from the use of the ICP PPPs. The study tests for the presence of serial correlation between price movements in different countries and investigates its impact on the PPPs. The methodological contribution of this paper is to establish the close nexus between price indices and poverty rates via the PPPs used in obtaining the local currency unit (LCU) denominated IPL.Design/methodology/approachThe PPP calculations in this paper relate to the ICP round, 2011. Along with the ICP PPPs from published reports (with India as the numeraire country), we report the following indices, namely, the GEKS, weighted CPD and its two spatially correlated generalisations. The ICP PPPs are used as benchmark. The ICP group in the World Bank made the price and expenditure information for 2011 available. Corresponding poverty rates are calculated at the country, regional and global levels.FindingsThe empirical evidence points to the fact that while at the country level the alternative calculations have high impact on the implied poverty rates, at the regional and global level the rates are reasonably quite robust.Research limitations/implicationsThree points are worth noting, namely, as opposed to the PPP for “Individual consumption expenditure by households” (ICEH), which is the PPP used for international poverty monitoring by the World Bank and others, we have used the ICP PPPs for “Actual individual consumption” (AIC); although ICP uses the GEKS procedure above the BH level, we independently calculated these PPPs using the price information provided, and the base country has been moved from the USA to India.Practical implicationsOne can come up with independently estimated PPPs that do not require the elaborate and expensive procedure set up by the ICP and can arrive at robust poverty rates at the regional and global level.Social implicationsThe change in base has been made as India shares many of the features of a developing country including high poverty rates, but at the same time provides a market and an economy size that places it in the top tier of nations. In addition, poverty comparisons amongst developing countries can be made using these PPPs directly, without reference to the USA. The poverty calculations are based on the PovcalNet program.Originality/valueThere is no clear answer to the question “how robust are the global poverty numbers to departures from the ICP PPPs?” in the literature nor is there any evidence on the robustness of the ICP PPPs themselves to changes in the ICP methodology. Given that the ICP uses the Gini–Elteto–Koves–Szulc (GEKS) multilateral price index in aggregation of ICP PPP basic heading data, in an attempt to partially answer this question this study examines the sensitivity of measures of relative prices (and poverty) to using CPD (and various spatial versions) and GEKS methods, using price data provided by the World Bank. It also verifies how these PPPs track the published 2011 ICP PPPs, which are used as benchmark.

A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean

In this paper, a stochastic frontier analysis approach is applied to estimate energy demand functions in the transport sector. This approach allows us to obtain energy efficiency measures at country level that are a robust alternative to the energy intensity indicators commonly used for international comparisons. A transitive multilateral price index is constructed for aggregating the diverse energy components employed in the sector. Due to the likely unobserved heterogeneity among countries, the use of a random parameters model is proposed to accommodate these differences and to obtain different income and price elasticities per country. The estimated model is compared with alternative approaches of addressing this issue such as latent class, true fixed effects or true random effects models. This study is the first to use a random parameters stochastic frontier approach in the estimation of energy demand functions. The proposed procedure is applied to Latin America and the Caribbean, where the transport sector represents a large share of total energy consumption.

Prices, Engel Curves, and Time-Space Deflation: Impacts on Poverty and Inequality in Vietnam

Many developing countries lack spatially disaggregated price data. Some analysts use “no-price” methods by using a food Engel curve to derive the deflator as that needed for nominally similar households to have equal food shares in all regions and time periods. This method cannot be tested in countries where it is used as a spatial deflator since they lack suitable price data. In this paper, data from Vietnam are used to test this method against benchmarks provided by multilateral price indexes calculated from repeated spatial price surveys. Deflators from a food Engel curve appear to be a poor proxy for deflators obtained from multilateral price indexes. To the extent that such price indexes reliably compare real living standards over time and space, these results suggest that estimates of the level, location, and change in poverty and inequality would be distorted if the Engel method deflator was used in their stead.

Benchmark averaging and the measurement of changes in international income inequality

Using data for 113 countries covering the period 1980–2005 we show how international comparisons of income inequality and the way it changes over time are inherently sensitive to (i) the choice of multilateral price index formula used to convert per capita incomes into units of the same currency, (ii) the approach (if any) used for reconciling spatial benchmarks with national growth rates, and (iii) the way inequality is measured. We then consider how best to deal with these issues and highlight some distortions that can arise in such comparisons. Based on our preferred methods we observe convergence when countries are population weighted and divergence when they are not.

ESTIMATING SPATIAL CONSUMER PRICE INDICES THROUGH ENGEL CURVE ANALYSIS

In this paper we propose a method of estimating spatial multilateral price index numbers from cross‐section consumer expenditure data on different items using Engel curve analysis. The novelty of the procedure is that it overcomes the problem of data inadequacy, a problem that is shared by most of the developing countries. The procedure does not require item‐specific price/unit value data and price index numbers can be calculated from consumer expenditure data grouped by per capita income/total consumer expenditure class in a situation where unit level data are not available. To illustrate the method, we use published state‐specific data of the 50th round (1993–94) and 55th round (1999–2000) consumer expenditure surveys of India's National Sample Survey Organization (NSSO) and calculate the spatial consumer price index numbers for 15 major states of India, with All‐India taken as base, separately for the rural and the urban sector for each round.

Standard Errors as Weights in Multilateral Price Indexes

Various multilateral methods for computing price indexes use bilateral comparisons as their basic building blocks. Some give greater weight to those bilateral comparisons deemed more reliable. However, none of the existing reliability measures adjusts for gaps in the data. We show how the standard errors on bilateral price indexes derived using weighted least squares provide reliability measures that solve this problem. We then apply our methodology to a dataset on agricultural production in 103 countries. Our results demonstrate the appeal of weighted methods and the importance of using weights based on a comprehensive measure of reliability.

Purchasing Power Parity Exchange Rates from Household Survey Data: India and Indonesia

Purchasing power parity (PPP) exchange rates are extensively used by researchers and by policymakers. This paper proposes and implements a new methodology for calculating PPPs using information on unit values from household surveys. Although unit values are not identical to prices, they have compensating advantages. Large household surveys contain several million unit values, they are tied to actual transactions, and they are naturally linked to household characteristics such as income. In consequence, it is possible to calculate PPPs for different social groups, including PPPs for the poor. The paper calculates multilateral price indexes for the states and sectors of India, as well as PPPs for rural and urban Indonesia together with rural and urban India. PPPs for the poor are distinguished from general PPPs. The internal PPPs for India are not very different from previous estimates based on bilateral comparisons, but the estimated PPP between India and Indonesia is very different from the numbers calculated by either the Penn World Table or the World Bank. It implies that either India is much better-off, or Indonesia much poorer (or both) than is generally supposed.

Comparing Price Levels across Countries Using Minimum-Spanning Trees

It is shown how a comparison of price levels across a group of countries can be made by chaining bilateral price indexes across a spanning tree. It is argued that we should use the spanning tree whose resulting multilateral price indexes are least sensitive to the choice of bilateral formula. This minimum-spanning tree can be easily computed using Kruskal's algorithm. Results obtained by chaining Fisher indexes across a minimum-spanning tree are compared with the Penn World Table.