WPS8150 Policy Research Working Paper 8150 Poverty-Specific Purchasing Power Parities in Africa Yuri Dikhanov Nada Hamadeh William Vigil-Oliver Tefera B. Degefu Inyoung Song Development Economics Development Data Group July 2017 Policy Research Working Paper 8150 Abstract The paper revisits the issue of poverty-specific purchasing Second, the basket of goods and services used for collect- power parities (PPPs), using the most recent (2011) Inter- ing prices for the ICP is not poverty specific. On the first national Comparison Program (ICP) results. The World issue, using data from 28 African countries, the paper Bank’s global poverty count uses a common international concludes that the poverty-specific PPPs estimated with poverty line—currently $1.90 at 2011 international household expenditure survey weights are very similar to prices—based on the ICP PPPs for consumption. The the ICP PPPs. On the second issue, poverty-specific PPPs use of these PPPs is often criticized for two reasons. First, were estimated after removing items deemed to be irrel- the ICP PPPs are based on patterns of aggregate house- evant for the poor. The overall effect of removing these hold consumption, not the consumption of the poor. items from consumption PPPs is shown to be negligible. This paper is a product of the he Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ydikhanov@worldbank.org, nhamadeh@worldbank.org, wvigiloliver@worldbank.org, tdegefu@worldbank. org, or isong@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Poverty-Specific Purchasing Power Parities in Africa‡ YURI DIKHANOV*, NADA HAMADEH, WILLIAM VIGIL-OLIVER, TEFERA B. DEGEFU AND INYOUNG SONG THE WORLD BANK JEL codes: C43, F31, I32 Keywords: PPPs, Poverty Measurement, Price Indexes ‡ All authors are with the World Bank’s Development Economics Data Group (DECDG). Funding from DFID under the World Bank’s Strategic Research Program “Research on poverty-specific PPPs” (P151176) is gratefully acknowledged. * Corresponding author: Yuri Dikhanov (ydikhanov@worldbank.org). The World Bank, Washington, D.C. POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA 1. Introduction F or its $1.90-a-day global poverty estimates, the World Bank uses an International Poverty Line (IPL) anchored to the 2011 purchasing power parities (PPPs) for household consumption expenditure from the International Comparison Program (ICP). The World Bank’s practice of using ICP PPPs for household consumption expenditure to set its IPL, and convert it to local currencies, dates back nearly two decades and has not been without its critics.1 The question often asked is how the IPL would change if these ICP PPPs were more poverty specific. Critiques of using PPPs for household consumption expenditure (“consumption PPPs”) to set the IPL often note that these PPPs are constructed using patterns of aggregate household consumption, not the consumption of the poor. In addition, it is often stressed that the basket of consumer goods and services used for ICP price surveys—and thus, underlying ICP consumption PPPs—is not poverty specific. At the heart of such critiques lies the presumption that consumption PPPs would generally be more appropriate for poverty analysis if the ICP accounted for these two aspects. In this paper, we examine the relevance of this presumption by constructing new consumption PPPs for Africa that take account of the above-mentioned critiques. We refer to these PPPs as poverty-specific PPPs and produce two varieties, both based on the 2011 ICP PPPs. The first are consumption PPPs recomputed with poverty-specific weights, drawing from Deaton and Dupriez (2011). The second are consumption PPPs recomputed without the items deemed outside the consumption patterns of the poor— for example, extra virgin olive oil, which the ICP priced in Africa. We report two main findings, both pertaining to Africa but easily extended to other regions. First, we find that consumption PPPs estimated with poverty-specific weights are similar to 2011 ICP consumption PPPs. This corroborates the findings by Deaton and Dupriez (2011) who reached the same conclusion using 2005 ICP consumption PPPs from Africa and other regions. Second, we find that the overall effect of removing non-poverty items from consumption PPPs was negligible. 2. Concepts and Preliminaries 2.1 ICP and PPPs The ICP is a worldwide statistical initiative to collect comparative price data and compile detailed expenditure data of the world’s economies. The program’s main outputs are PPPs of countries’ gross domestic product (GDP) and its main expenditure components.2 ICP price data are collected via specially designed price surveys, while expenditure values in local currency are compiled from countries’ national accounts. The latest ICP comparison to date is the 2011 ICP, which took place six years after the 2005 ICP. In this sense, ICP comparisons have historically occurred at infrequent time intervals, though this will change starting with the forthcoming 2017 ICP and onwards.3 ICP comparisons are made from the expenditure side of the national accounts. PPPs are therefore calculated for different expenditure levels of aggregation, starting with basic headings and up to GDP. To maintain consistency with expenditures on GDP, ICP items underlying PPPs at each expenditure level were selected with the idea of approximating the full range of goods and services making up each expenditure level. The different expenditure levels for which PPPs are calculated are illustrated by Figure 1. The second row from the bottom refers to the basic heading level, which is the building block for the ICP exercise. It 1 Before 2001 the World Bank set its two previous IPLs using ICP PPPs for gross domestic product. 2 Here and throughout the rest of the paper we use the term “economy” interchangeably with “country” to refer to territories for which authorities report separate statistics. 3 At its 47th Session (March 2016), the United Nations Statistical Commission (UNSC) agreed that the ICP should be conducted more frequently, with shorter intervals between successive rounds. For more details, see United Nations Statistical Commission (2016). 2 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA is at this level that the expenditure shares used are defined and estimated, items are selected for pricing, prices are collected and validated, and PPPs are first estimated and averaged. FIGURE 1. HIERARCHICAL APPROACH TO THE 2011 ICP Against this background, it is worth expanding on the topic of how PPPs are estimated. The process, at least broadly speaking, involves two stages. At the first stage, price relatives for individual items are averaged to obtain basic heading level PPPs using the Country Product Dummy (CPD), first formulated by Summers (1973).4 The Country Product Dummy (CPD) method is carried out within each basic heading by regressing the logarithm of observed country item prices on item and country dummies. In actual computations, the CPD formulation with weights (CPD-W) is used.5 However, the version of the CPD-W used in the ICP incorporates information on the relative importance of items in a country’s consumption rather than actual weights: weights of 3 and 1 are assigned to important and less important items, respectively.6 At the second stage, basic heading PPPs are aggregated using the GEKS-Fisher method to produce above-basic heading PPPs. The method uses the Fisher ideal index to construct bilateral PPPs for each pair of countries, using basic heading expenditure weights from each country in turn. The bilateral PPPs are then averaged using the Gini-Éltető-Köves-Szulc (GEKS) approach to arrive at a final vector of above- basic headings PPPs, containing one PPP for each country relative to the numeraire country. For more details on the PPP estimation process and other ICP concepts and methods, see World Bank (2015). 2.2 Poverty and PPPs Because of the need to adjust for price differences between countries, ICP PPPs have largely been of interest to researchers working on global poverty, including those at the World Bank. The World Bank’s interest in ICP PPPs gained notoriety in 1990 when it used the 1985 ICP PPPs for GDP to set its dollar- a-day IPL. Ever since, the use of ICP PPPs in revisions to the World Bank IPL has generated significant 4 For a more detailed description of the method see, for example, World Bank (2015), Chapter 23. 5 The Eurostat-OECD and CIS regions used the Jevons Gini-Éltető-Köves-Szulc* (Jevons-GEKS*) method rather than the CPD to estimate basic heading PPPs. 6 The decision on whether an item is important or less important is taken at the country level once the ICP price collection is complete. The procedure is not entirely precise so some subjective judgement is involved. 3 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA commentary, which in turn has prompted research on the role of PPPs in methods to measure global poverty. Deaton and Dupriez (2011) were one of the first to study the effect of estimating global poverty using poverty-specific PPPs. Using 2005 ICP data, they found that consumption PPPs reweighted to a poverty basis are quite similar to the regular 2005 ICP consumption PPPs that use weights from national accounts. They note that weighting differences are probably not of great importance for estimating global poverty counts. To reach this conclusion, they used household expenditure surveys from 62 poor countries around the world to reweight the 2005 ICP consumption PPPs and produce a set of poverty-weighted PPPs. These PPPs were then used to calculate new IPLs and global poverty counts according to various definitions. Similar research at the regional level was conducted by the Asian Development Bank (ADB). The Asian Development Bank (2008) used price data for 16 Asian countries to compile a set of poverty- specific PPPs. To that end, a separate price collection, using modified items from the 2005 ICP, was organized in 2006. The study examined whether data collected on prices for the items that were considered typical of the consumption patterns of the poor would produce significantly different poverty PPPs. This research concluded that indeed PPPs could change more substantially when using items consumed by the poor and poverty-specific weights; however, it is difficult to compare its results directly to the 2005 ICP because of differences in methodology, timing and geographical scope (number of countries). In particular, because of inclusion of more items with loose specifications, comparability of items across countries could be more problematic in the ADB’s study than in the 2005 ICP. 3. Source Data 3.1 ICP and CPI data Throughout, we make extensive use of 2011 ICP data, classifications and concepts. Our interest though is on PPPs for consumption, so we only work with basic headings belonging to the consumption component of GDP. In section 4 we focus on the reweighted consumption PPPs. The reweighting process was done at the basic heading level and we worked with 108 of the 110 basic headings for household consumption. Our calculations exclude two basic headings that are outside the scope of most household expenditure surveys, namely those corresponding to expenditures in the domestic market by non-resident households and expenditures of resident households when traveling abroad. In addition to ICP data, computations in section 4 also required data on consumer price indexes (CPIs) of individual countries.7 CPIs for general consumption were used to deflate the local currency value of the IPL to prices prevalent in the year each household expenditure survey was conducted. This was necessary when calculating poverty-specific PPPs that required identifying households below or around the IPL. All CPI series used were sourced from ICP regional implementing agencies, the IMF Statistics Department, and, to a lesser extent, the World Bank World Development Indicators (WDI). In Section 5 we turn to the item level. Computations in this section relied on item-level price data and related metadata from the 2011 ICP Africa exercise, which included 50 countries. Unlike in section 4, we used information from all 110 basic headings for household consumption when possible, even if the two basic headings excluded in section 4 contain no item-level information. 3.2 Standardized household expenditure survey data Our reweighting of consumption PPPs required switching regular ICP national accounts sourced expenditures with those from household expenditure surveys. In particular, we used expenditures from a set of standardized data sets derived from existing household expenditure survey data files. Table 1 lists the survey title and year of the household expenditure surveys underlying each of the 28 standardized data 7 CPIs measure the average change over time in the prices of consumer goods and services purchased for consumption by a reference population. 4 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA sets used. In addition, it provides information on the number of survey items and ICP basic headings in each standardized file. While we aimed to cover and reweight PPPs for all countries in Sub-Saharan Africa, we were limited to the 28 countries with available standardized data sets at the time of our study.8 TABLE 1 — HOUSEHOLD EXPENDITURE SURVEYS BY YEAR, ITEMS AND ICP BASIC HEADINGS, 28 COUNTRIES ICP basic Country Survey title Year Items headings Burkina Faso Enquête Burkinabé sur les Conditions de vie des Ménages 2013 237 83 Burundi Questionnaire des Indicateurs de Base du Bien-Etre 2006 85 41 Cameroon Enquête Camerounaise auprès des Ménages III 2007 942 105 Cabo Verde Questionário Unificado de Indicadores Básicos de Bem-Estar 2007 265 92 Congo, Dem. Rep. Enquête 1-2-3 sur l'emploi,le secteur informel et les conditions de vie 2005 710 101 Congo, Rep. Questionnaire des Indicateurs de Base du Bien-être 2005 768 106 Côte d'Ivoire Enquête Niveau de vie des Ménages 2008 195 62 Ethiopia Household Consumption and Expenditure Survey 2010 1,153 98 Gabon Enquête Gabonaise pour l’Evaluation et le suivi de la Pauvreté 2005 317 94 Ghana Ghana Living Standards Survey VI 2012 527 98 Guinea Enquête Légère pour l'Evaluation de la Pauvreté 2007 418 79 Kenya Kenya Integrated Household Budget Survey 2005 508 91 Liberia Household Income and Expenditure Survey 2014 228 79 Madagascar Enquête Permanente Auprès des Ménages 2005 261 67 Malawi Third Integrated Household Survey 2010 220 69 Mali Enquête Légère Intégrée auprès des Ménages 2006 228 90 Mozambique Inquérito aos Agregados Familiares 2008 918 103 Namibia Household Income Expenditure Survey 2009 30 4 Niger Enquête Nationale sur le Budget et la Consommation des Ménages 2007 386 87 Nigeria General Household Survey, Panel, Wave 2 2012 227 70 Rwanda Enquete Intégrale sur les Conditions de Vie de Menage 2010 388 90 Senegal Enquete de Suivi de la Pauvrete 2005 69 38 South Africa Income and Expenditure Survey 2010 775 102 Swaziland Household Income and Expenditure Survey 2009 482 86 Tanzania Household Budget Survey 2011 547 89 Togo Questionnaire des Indicateurs de Base du Bien-être 2006 256 87 Uganda National Household Survey 2010 168 69 Zambia Living Conditions Monitoring Survey V 2010 265 79 We must underline that the feasibility of much of our results in section 4 is reliant on these standardized data sets. In this sense, it is useful to have some understanding of the standardization process and its output. The standardization process provides household expenditure survey data in a more accessible and manageable format—a considerable feat considering the lack of harmonization of household expenditure surveys across countries. The process utilizes a common data dictionary (i.e., common variable names, formats, and data structures) to extract consumption expenditure data from household expenditure surveys. The process involves three main steps that we summarize as follows: (i) mapping survey items to ICP basic headings, (ii) annualizing consumption values; and (iii) identifying and fixing outliers. The first of these steps involves mapping survey items from each household expenditure survey to one of the 110 ICP 2011 consumption basic headings. This ensures some correspondence between the survey data and the ICP.9 The second step is required because household expenditure surveys collect data using 8 Standardized household expenditure surveys were originally prepared for Deaton and Dupriez (2011) and are part of the World Bank’s Global Consumption Database (GCD). For more information on the GCD, visit: http://datatopics.worldbank.org/consumption/ 9 Mapping survey items to ICP basic headings would be more straightforward if survey questionnaires grouped items according to the Classification of Individual Consumption According to Purpose (COICOP) classification, which corresponds largely to the ICP classification of final expenditures on GDP. This is rarely the case, so the mapping is generally a time-consuming manual process. 5 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA recall periods that vary depending on the type of goods and services. Finally, the third step focuses on detecting and fixing top outliers in consumption values to further ensure the reliability of the survey data. For more details on the standardization process, see Dupriez (2007). 4. Poverty-specific PPPs: Reweighting of consumption patterns 4.1 Method and approach The usage of national accounts instead of poverty relevant weights in calculating consumption PPPs is a typical critique of the IPL used for global poverty measurements. However, as mentioned earlier, this was not a problem as found by Deaton and Dupriez (2011) using 2005 ICP data. In revisiting this topic, we follow up on Deaton and Dupriez (2011) and calculate poverty-reweighted PPPs using 2011 ICP data. The reweighting procedure involves substituting the conventional national accounts-based basic heading expenditure weights with poverty-relevant weights from household expenditure surveys. Implicit in this process is the requirement to identify poor households consistently across countries, which in turn leads to a well-recognized circularity issue: PPPs determine the IPL below which the poor live, whose expenditure weights in turn affect the PPPs. We solve this issue using an iterative procedure to arrive at a final set of poverty-weighted PPPs. The procedure can be described as follows: the nth iteration would involve estimating a new set of PPPs— PPPn—with some previous set of poverty weights wn-1 and then use PPPn to estimate a new set of poverty weights wn to be used in the next iteration n+1. In this context, we estimated a variety of poverty-weighted PPPs using different methods to identify poor housing across household expenditures surveys. In particular, we used the uniform and bi-weight kernels to obtain the consumption patterns from households around the IPL. We then derived average basic heading level expenditure weights using a democratic method.10 As a control, we also ran the computation for poverty-weighted PPPs using expenditures from households below the IPL and averaged the basic heading weights using a plutocratic method. In discussing the poverty-weighted PPPs based on iterative procedures, the issue of uniqueness of the solution deserves special attention. Deaton and Dupriez (2011) note that there is no guarantee that a unique solution exists in the general case. They report that uniqueness is guaranteed though for log-linear budget shares and the Törnqvist index. However, it seems that uniqueness is also guaranteed in the general monotonic case for budget shares, not only log-linear, and even for a non-monotonic relationship without sharp oscillations. In practice, given actual data, there seems to be no problem with convergence to a unique solution, or, at least, cases of multiple solutions have not been discovered. 4.2 Convergence speed of iterations and kernels In general, the convergence to a unique solution was found to be extremely fast, but it did depend on the type of filter (i.e., kernel) and the bandwidth (bw) employed. On the latter, we explore three different bandwidths for each of the two kernel shapes.11 Understandably, the uniform kernel is less stable as it is affected by weight irregularities and distribution lumpiness at both ends of the band, whereas for the bi-weight kernel the discrepancies at the ends have almost no effect on the result. As the uniform kernel is actually a band with no re-weighting, 10 Plutocratic weights are the kind obtained from the national accounts, whereby all households are treated as one unit. Weights using this method are derived from the total expenditures of all households on a given basic heading, so richer households exert a greater influence on the computation. Democratic weights represent all households equally and are derived by taking the average of the expenditure shares of each household on a given basic heading, so all households exert an equal influence on the computation. 11 Kernel bandwidths used are from widest to most narrow: 1.0, 0.5, and 0.25. A wider bandwidth increases the width of the shape around the IPL so that more households are included in the sample when extracting expenditures from a household expenditure survey. 6 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA using this kernel as a comparator would show the effect of re-weighting in a kernel. The convergence speed for all poverty-weighted PPPs are presented in Figures 2 and 3. 10.00000% 1 2 3 4 5 6 7 8 (number of iterations) 1.00000% 0.10000% SD of PPP Ratios 0.01000% 0.00100% Below IPL Uniform Kernel (bw 1.0) Uniform Kernel (bw 0.5) 0.00010% Uniform Kernel (bw 0.25) Bi-weight Kernel (bw 1.0) Bi-weight Kernel (bw 0.5) Bi-weight Kernel (bw 0.25) 0.00001% FIGURE 2. ITERATION CONVERGENCE OF POVERTY-WEIGHTED PPPS, 28 COUNTRIES In the case of 28 countries, the fastest convergence, as expected, is produced by the bi-weight kernel based PPPs with bandwidths of 1.0 and 0.5. The same kernel with a bandwidth of 0.25 converges significantly slower. Poverty-weighted PPPs based on plutocratic weights below the IPL (“below IPL PPPs”) converge relatively fast in the beginning but then start oscillating. All indexes based on uniform kernels exhibit oscillations as well. It could be noticed that the only country that oscillated with the below IPL PPPs was Guinea. Removal of the Guinea data from the data set produces somewhat more consistent results for all indexes, with the below IPL PPPs discontinuing the oscillations altogether. 10.00000% 1 2 3 4 5 6 7 8 (number of iterations) 1.00000% 0.10000% SD of PPP Ratios 0.01000% 0.00100% Below IPL Uniform Kernel (bw 1.0) Uniform Kernel (bw 0.5) 0.00010% Uniform Kernel (bw 0.25) Bi-weight Kernel (bw 1.0) Bi-weight Kernel (bw 0.5) Bi-weight Kernel (bw 0.25) 0.00001% FIGURE 3. ITERATION CONVERGENCE OF POVERTY-WEIGHTED PPPS WITHOUT GUINEA, 27 COUNTRIES The results without Guinea price data are presented in Figure 2. In general, the convergence picture is quite similar to the 28 country case. As before, the fastest convergence is produced by the bi-weight 7 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA kernels with bandwidths of 1.0 and 0.5. The below IPL PPPs converge relatively fast as well. In this case, the uniform kernel with bandwidth 1.0 converges fully. The only two indexes that produce oscillations are based on the uniform kernels with bandwidths 0.5 and 0.25. The reason why one country could have such an effect lies in the lumpiness (i.e., discontinuity) of actual household survey expenditure data. For example, if a certain number of households in some country around the poverty line exhibited a rather unusual weight structure combined with an uneven density of the probability density function, it would result in oscillations for the below-IPL index. This has nothing to do with the methodology employed, but rather reflects imperfections of the original household expenditure data. Smoothing the input data would solve the problem and at the same time would make the household expenditure data more realistic, without affecting much the resulting PPPs. How critical are the oscillations in these PPP calculations? It turns out they are quite insignificant. From Figures 2 and 3 we can see that in the worst case scenario—uniform kernel with bandwidth 0.25— the oscillations are around 0.01% (!) on average, which is several orders of magnitude better than expected precision of the PPP computation. It also turns out that all bandwidth of kernel based PPPs produce very similar results, with most countries having results with a standard deviation (SD) of around 0.1–0.2%.12 Only the Gabon poverty weighted-PPPs have an SD of 0.4% (see Figure 4), with the bi-weight poverty-weighted PPPs systematically higher than their uniform kernel based counterparts (see Table A1 (annex)). This is probably related to some peculiarities of Gabon’s probability distribution function. 1.100 Uniform Kernel (bw 1.0) Uniform Kernel (bw 0.5) Uniform Kernel (bw 0.25) Bi-weight Kernel (bw 1.0) Bi-weight Kernel (bw 0.5) Bi-weight Kernel (bw 0.25) 1.050 PPP Ratios 1.000 0.950 0.900 Congo, Rep. Côte d'Ivoire Ethiopia Gabon Ghana Guinea Kenya Mozambique Liberia Tanzania Madagascar Uganda Zambia Congo, Dem. Rep. Malawi Mali Niger Senegal Cape Verde Burkina Faso Cameroon Namibia Rwanda South Africa Togo Nigeria Swaziland Burundi FIGURE 4. EFFECT OF KERNEL TYPE AND BANDWIDTH ON POVERTY-WEIGHTED PPPS, 28 COUNTRIES 4.3 Poverty-reweighted PPPs according to various methods 4.3.1 Effect of removing one country We have seen the effect of removing Guinea on the convergence of poverty-weighted PPPs, now let us look at this effect on the regular consumption PPPs based on expenditures from the national accounts (“SNA-based PPPs”). The results for SNA-based PPPs are shown in Figure 5. The scale is intentionally 12 SD is estimated with respect to the regional unweighted geometric mean in order to remove the base country effect. 8 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA kept the same as in Figure 4 for easy comparisons. The average effect of removing one country on the rest of countries is around 0.11% in this case. 1.100 1.050 PPP Ratios 1.000 0.950 0.900 Congo, Rep. Côte d'Ivoire Ethiopia Gabon Ghana Liberia Madagascar Tanzania Uganda Niger Cape Verde Congo, Dem. Rep. Malawi Senegal Guinea Kenya Mozambique South Africa Togo Burkina Faso Cameroon Namibia Nigeria Rwanda Swaziland Zambia Burundi Mali FIGURE 5. EFFECT OF REMOVAL OF GUINEA ON SNA-BASED PPPS, 27 COUNTRIES Next, we examine the effect of removing Guinea on the below IPL and kernel based poverty-weighted PPPs. Figure 6 shows that the below IPL and kernel based poverty-weighted PPPs change insignificantly in this case, with the below IPL PPPs being more stable. All kernel based PPPs display very similar magnitudes of the effect, with the biggest effect being for Rwanda (for the 27 country case, the SD of the effect was 0.19–0.23%). Interestingly, Rwanda’s PPP was quite stable under the below-IPL PPP computation. 1.100 Below IPL Uniform Kernel (bw 1.0) Uniform Kernel (bw 0.5) Uniform Kernel (bw 0.25) Bi-weight Kernel (bw 1.0) Bi-weight Kernel (bw 0.5) 1.050 PPP Ratios 1.000 0.950 0.900 Congo, Dem. Rep. Niger Senegal Cape Verde Côte d'Ivoire Mozambique Cameroon Congo, Rep. Ethiopia Gabon Ghana Guinea Kenya Liberia Namibia Rwanda South Africa Tanzania Togo Burkina Faso Madagascar Nigeria Swaziland Uganda Zambia Burundi Malawi Mali FIGURE 6. EFFECT OF REMOVAL OF GUINEA ON POVERTY-WEIGHTED PPPS, 27 COUNTRIES 9 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA 4.3.2 Effect of moving from below IPL PPPs to kernel based PPPs Once we established that all kernel based PPPs were quite similar, and the country-removal effect was of a quite limited importance, the next step would be to study the effect of moving from the below IPL PPPs to the kernel based PPPs. This effect is presented in Figure 7, with the biggest outlier being Rwanda with a 3.2–3.8% difference. We can also see that the SD of the differences in country poverty-weighted PPPs is 1.0–1.1% depending on the kernel. Those numbers again exceed the expected error in the ICP PPP computation. 1.100 Uniform Kernel (bw 1.0) Uniform Kernel (bw 0.5) Uniform Kernel (bw 0.25) Bi-weight Kernel (bw 1.0) Bi-weight Kernel (bw 0.5) Bi-weight Kernel (bw 0.25) 1.050 PPP Ratios 1.000 0.950 0.900 Côte d'Ivoire Gabon Ghana Liberia Madagascar Uganda Niger Senegal Cape Verde Congo, Rep. Congo, Dem. Rep. Mozambique Burkina Faso Cameroon Ethiopia Guinea Kenya Namibia Nigeria Rwanda South Africa Tanzania Togo Swaziland Zambia Burundi Malawi Mali FIGURE 7. EFFECT OF MOVING FROM BELOW IPL PPPS TO KERNEL BASED PPPS, 28 COUNTRIES 4.3.3 Effect of poverty-weighted PPPs on poverty rates With the poverty-weighted PPPs according to various definitions being that close to one another, the country poverty rates they generate are quite close as well. These poverty rates are presented in Table A3 (annex). We can see that all kernel based poverty weighted PPPs produce virtually identical poverty rates. Those poverty rates are also quite close to those originating from the below-IPL PPPs. 4.3.4 Effect of moving from SNA-based PPPs to poverty-weighted PPPs Finally, we are going to look into the effect of going from SNA-based PPPs to poverty-weighted PPPs. This effect is presented in Figure 8. One thing to note is that the effects in Figure 8 are significantly larger than those presented earlier. The resulting SDs for the various types of poverty-weighted PPPs estimated are 2.7–3.1%. These numbers are still significantly better than the expected precision of ICP PPPs (a 5– 10% range). Again, all the kernel based PPPs and the below IPL PPPs are quite close to each other. 10 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA 1.100 Below IPL Uniform Kernel (bw 1.0) Uniform Kernel (bw 0.5) Uniform Kernel (bw 0.25) Bi-weight Kernel (bw 1.0) Bi-weight Kernel (bw 0.5) Bi-weight Kernel (bw 0.25) 1.050 PPP Ratios 1.000 0.950 0.900 Côte d'Ivoire Congo, Rep. Ethiopia Gabon Ghana Guinea Liberia Madagascar Tanzania Uganda Malawi Niger Senegal Cape Verde Congo, Dem. Rep. Kenya Mozambique Burkina Faso Cameroon Namibia Rwanda South Africa Nigeria Togo Swaziland Zambia Burundi Mali FIGURE 8. EFFECT OF MOVING FROM SNA-BASED PPPS TO VARIOUS POVERTY-WEIGHTED PPPS, 28 COUNTRIES 5. Poverty-specific PPPs: Removing irrelevant items 5.1 Method and approach We now turn our focus away from the basic heading level and toward the item level. The poverty- specific PPPs in the previous section attenuated the influence of basic headings irrelevant to the poor through reweighting. However, if we want to produce PPPs untainted by any possible influence of items irrelevant to the poor, then the reweighting process is by and large effective, but by no means sufficient. Items seldom consumed by the poor, such as extra virgin olive oil, would still be present within basic headings like “other edible oils and fats”; which, overall, have an important role in the consumption basket of both poor and non-poor. As mentioned before, the inclusion of items that are arguably irrelevant to the poor in ICP consumption PPPs has often raised doubts on their applicability for poverty analysis. However, the effect (if any) of constructing consumption PPPs that exclude the price of, say, extra virgin olive oil or Kellogg's Cornflakes is not immediately evident. In light of this, we produce new consumption PPPs with items deemed irrelevant to the poor removed from the calculation. We refer to these poverty-specific PPPs as reduced-list PPPs and produce three scenarios: (1) after removing items priced only in supermarkets, (2) after removing clothing and footwear items belonging to a medium or high brand stratum (for brevity, we name them “branded garments & footwear”); and (3) after removing food and nonalcoholic beverage items that we categorized as premium beforehand. Within each reduced-list scenario, we computed two sets of PPPs for each of the 50 African countries. First, a full-list set, based on the full basket of items used for collecting prices for the 2011 ICP in Africa, and, second, a counterfactual reduced-list set after removing items deemed irrelevant according to each scenario.13 Basic heading PPPs for the two sets, in each of the three scenarios, were estimated using the 13 Full-list PPPs were only estimated for expenditure categories for which counterfactual PPPs were also produced. Otherwise, published 2011 ICP PPPs were used. Hence, the full-list PPPs for the ‘supermarket only’ and ‘premium food and nonalcoholic’ items are equivalent, but differ slightly from the full-list PPPs for the ‘excluding branded clothing & footwear’ scenario. 11 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA CPD method in its unweighted form. For comparison purposes all basic heading PPPs were then aggregated to the level of household consumption using the GEKS-Fisher procedure. Depending on the reduced-list scenario, the removal of items was contained to either the “food and non-alcoholic beverages” or the “clothing and footwear” ICP expenditure categories. These two categories were chosen because of their importance as basic necessities as well as for practical reasons. It turns out that the product dataset for the 2011 ICP Africa comparison contains quite detailed and harmonized metadata for items belonging to both categories. Determining what items to remove in each reduced-list scenario was not without its problems. At first, it would seem that the main difficulty lies in the circularity of the task at hand: it is necessary to consistently identify the poor in each country before assessing what items they consume. Yet, while the circularity is indeed an obstacle, it can be solved using the iterative procedure employed in Section 4. Instead, the lack of item-level detail in most of the household expenditure surveys proved to be the more intractable problem is. On this front, we mined the micro data from household expenditure surveys in 28 Sub-Saharan Africa countries and concluded that their item-level detail is generally insufficient to properly distinguish item-varieties and establish a one-to-one mapping with ICP items. Given this constraint, we chose the practical alternative of using information from the ICP product data set for Africa mentioned above to identify items that one would expect to be outside the consumption basket of the poor. To identify items priced only in supermarkets we used information from the “required outlet type” field provided for each item. In the case of garments and footwear, we stratified items within the “garments” and “shoes and other footwear” basic headings as branded or unbranded by exploiting the available item-level brand stratum information (high, medium, or low). As stated earlier, we grouped and labeled all high and medium garments and footwear as branded for brevity even if some low items also include some brand specification. Lastly, for the premium food and nonalcoholic beverages scenario we removed items with a relatively high (per-unit) price across all countries within each basic heading, and assume a linear relationship between quantity and prices for each item. We must underline that all three approaches are not without their drawbacks, but we cannot do any better given the current set of data. Nevertheless, market consumer reports by Nielsen (2014) and information from the Food and Agriculture Organization (2015) indicate that the poor in many African countries rarely shop in supermarkets. Likewise, it is not unreasonable to assume that high brand stratum garments and clothing are outside the scope of what the poor consume, at least on average. 5.2 Poverty-specific PPPs by reduced-list scenario To measure the effect of moving from PPPs based on a full-list to PPPs based on a reduced-list, we compute the SD across (normalized) relative differences in country PPPs due to the shift.14 These relative difference in PPPs are captured by the ratio between a country’s reduced-list PPP and its full-list PPP (“PPP ratio”).15 The SDs of the normalized PPP ratios in each scenario are presented in Table 2, while country PPP ratios by reduced-list scenario are available in Table A5 (annex). TABLE 2 — EFFECT OF REMOVING ITEMS (BY REDUCED-LIST SCENARIO), 50 COUNTRIES SD of normalized Reduced-list (poverty-specific) scenario PPP ratios (1) Removing supermarket only items 0.88 % (2) Removing branded garments & footwear items 0.29 % (3) Removing premium food & nonalcoholic beverages items 0.73% Source: Authors’ calculations 14 All PPP ratios presented are based on normalized country PPPs with respect to the regional geometric mean. This normalization procedure removes the base-country effect that would occur otherwise. 15 A PPP ratio greater (less) than 1.00 denotes an increase (decrease) in country PPP, relative to the region, due to the shift from a full- list to a reduced-list. 12 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA Table 2 reveals that the reduced-list PPPs in each scenario are not much different from their full-list analogues, as evidenced by the SDs. The effect of moving from a full-list set of consumption PPPs to each reduced-list scenario is displayed in Figure 9. 1.100 Excluding Supermarket-only items Excluding Branded Clothing and Footwear items Excluding Premium Food & Nonalcoholic beverages items 1.050 PPP Ratios 1.000 0.950 0.900 Seychelles Cape Verde Congo, Rep. Côte d'Ivoire Kenya Mozambique South Africa Tanzania Eq. Guinea Zimbabwe Ethiopia Gabon Ghana Guinea Liberia Madagascar Uganda Algeria Angola Central Afr. Rep. Morocco Sierra Leone Tunisia Malawi Niger Zambia Mali Senegal Djibouti Congo, D.R. Comoros Botswana Egypt, Arab Rep. Gambia, The São Tomé & P. Cameroon Namibia Rwanda Togo Benin Chad Guinea-Bissau Lesotho Mauritius Burkina Faso Nigeria Swaziland Sudan Burundi Mauritania FIGURE 9. EFFECT OF MOVING FROM FULL-LIST PPPS TO REDUCED-LIST PPPS, 50 COUNTRIES These results imply that the impact of removing items from the consumption PPPs is negligible for the three scenarios studied. In fact, the SDs of the PPP ratios for each scenario are below the ± 5–10% precision band accepted as target for the ICP PPPs. With this in mind, we proceed to examine each reduced-list scenario in more detail. 5.2.1 Effect of moving from full-list PPPs to PPPs excluding supermarket only items Figure 9 illustrates the (negligible) effect of moving from a full-list set of consumption PPPs to one excluding supermarket only items. In total, 41 items out of the 367 food and nonalcoholic beverage items were removed. The SD of the relative differences in country PPPs due to removing supermarket only items was 0.88%, as indicated earlier. Swaziland and Senegal had the most changes in their consumption PPPs, but these were still only [+]1.72% and [-]1.62%, respectively. Regression results in Table A4 (annex) indicate a positive statistically significant relationship between country income and the resulting change in country’s PPP after excluding supermarket items. However, the size of the effect is extremely small—it amounts to a 1% price increase over a 12-fold difference in country income levels. The effect indicates that non-supermarket prices in richer African countries may be relatively higher than those in poorer African countries. A possible explanation is that non-supermarket items are specified looser, and poorer countries may be pricing lower quality varieties. In any case, given that the magnitude of the effect is trivial, the impact of any association between income and changes in PPP is close to null. 13 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA 5.2.2 Effect of moving from full-list PPPs to PPPs excluding branded clothing and footwear items For this scenario, poverty irrelevant items under the “garments” and “shoes and other footwear” basic headings were removed to calculate the reduced list PPPs. The “garments” basic heading contains 65 items among which 23 are low stratum, while the “shoes and other footwear” basic heading contains 20 items among which 7 are low stratum. Only low stratum items were retained in our calculation of poverty- specific PPPs for this scenario. The change from a full-list to a reduced-list with only low brand stratum garment items resulted in a SD at the garment basic heading level of 6.33% across all countries, with no specific pattern. At the household consumption level, the SD of the PPP changes across all countries was 0.29%. The extent of the changes in country consumption PPPs for this scenario can be observed in Figure 9. Botswana showed the largest increase in its PPP at 0.66%, whereas Togo had the largest relative decrease at 0.67%. No significant relationship was found between country income and the resulting change in country’s PPPs after excluding branded clothing and footwear items (see Table A4, annex). 5.2.3 Effect of moving from full-list PPPs to PPPs excluding premium food & nonalcoholic beverage items By assuming that the poor are less likely to consume those items that are relatively expensive across all countries (within each basic heading), we removed 61 out of the 367 food and nonalcoholic beverage items priced in Africa. The effect of removing these premium food and nonalcoholic beverage items on the country PPP ratios is shown in Figure 9. It is remarkable that the effect of moving from a full-list set of consumption PPPs to one without premium food and nonalcoholic beverages was even smaller in this scenario than in the supermarket only scenario, despite removing 20 more items in the former. As with the other reduced-list scenarios, the effect of this shift was small and practically random in its outcome across countries. This is again evident by the regression results in Table A4 (annex). As a final and general observation for the item-level section, we must add that there could be many factors affecting the price level of the poor and they could even work in opposite directions. For instance, without further analysis, it is impossible to quantify to which degree the poor benefit from economies of scale versus the rich. Similarly, we do not know how item availability in rural areas, where many of the poor live and where many items are not even available, affect the effective price level faced by the poor. 6. Conclusions With the new $1.90 IPL and new 2011 ICP PPPs, it was important to see if the Deaton and Dupriez (2011) conclusion on poverty-weighted PPPs being close to ICP PPPs still holds, especially given the changes in ICP methodology that occurred since 2005. This paper found that the conclusion does indeed hold: the deviation between the two is around 2.7–3.1% on average, which is below the expected precision of ICP consumption PPPs of ±5–10%. In addition, the uniform kernel was employed alongside the bi-weight kernel, to study the effects of kernel shape. Those were contrasted with the below IPL plutocratic consumption PPPs. It was found that all the kernels with various bandwidths produced virtually identical results, and those results were very similar to the PPPs obtained with the below IPL plutocratic index. All indexes based on kernels and the below IPL PPPs, converged fast in the practical sense, meaning that even though they sometimes oscillated, the degree of oscillation was immaterial. All the indexes employed exhibited a high degree of stability to the selection of countries. At the same time, the overall effect of removing items from consumption PPPs has been shown to be negligible. Yet, it is important to acknowledge that by using the same set of prices in each of the calculations, it is implicitly assumed that the poor face the same prices as the non-poor. In addition, some critics have pointed out that poverty-specific PPPs should be constructed on the basis of prices paid by the poor. We do not address this issue, since unfortunately studying it fully would require a separate price 14 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA collection, parallel to the ICP. Instead, we attempted to examine feasible aspects related to the construction of poverty-specific PPPs. Future work at the item-level will explore whether the results from this section can be generalized to other item groupings or groups of countries. 15 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA 7. Annex TABLE A1 — KERNEL STABILITY, 28 COUNTRIES Poverty-weighted PPPs: Kernel based vs. Below IPL SNA- Uniform; Uniform; Uniform; Bi-weight; Bi-weight; Bi-weight; based vs. Bandwidth Bandwidth Bandwidth Bandwidth Bandwidth Bandwidth SD for kernels Below =1.0 =0.5 =0.25 =1.0 =0.5 =0.25 IPL PPPs Burkina Faso 1.0398 1.0046 1.0041 1.0048 1.0043 1.0041 1.0040 0.031% Burundi 1.0204 1.0146 1.0160 1.0165 1.0154 1.0159 1.0172 0.081% Cameroon 0.9671 0.9923 0.9931 0.9948 0.9927 0.9944 0.9951 0.109% Cabo Verde 1.0332 1.0028 1.0014 1.0017 1.0018 1.0007 0.9968 0.192% Congo, Rep. 0.9848 0.9960 0.9978 0.9983 0.9973 0.9985 0.9979 0.081% Congo, D.R. 0.9835 0.9865 0.9870 0.9878 0.9868 0.9870 0.9823 0.180% Côte d'Ivoire 0.9980 0.9992 0.9996 0.9998 0.9994 0.9994 0.9992 0.021% Ethiopia 0.9632 0.9968 0.9968 0.9951 0.9966 0.9958 0.9951 0.073% Gabon 0.9540 0.9867 0.9868 0.9947 0.9889 0.9920 0.9977 0.409% Ghana 0.9418 0.9872 0.9870 0.9895 0.9872 0.9887 0.9910 0.148% Guinea 0.9570 0.9938 0.9950 0.9962 0.9944 0.9955 0.9965 0.094% Kenya 1.0144 1.0019 1.0014 1.0021 1.0018 1.0018 1.0030 0.051% Liberia 0.9791 0.9829 0.9818 0.9794 0.9821 0.9800 0.9776 0.183% Madagascar 0.9767 0.9956 0.9949 0.9954 0.9952 0.9952 0.9968 0.062% Malawi 1.0382 0.9914 0.9920 0.9901 0.9916 0.9905 0.9898 0.083% Mali 1.0296 1.0077 1.0059 1.0042 1.0055 1.0044 1.0044 0.122% Mozambique 1.0300 0.9988 0.9975 0.9967 0.9980 0.9969 0.9964 0.083% Namibia 0.9820 0.9878 0.9888 0.9882 0.9887 0.9889 0.9896 0.055% Niger 0.9978 1.0019 1.0025 1.0026 1.0027 1.0030 1.0025 0.033% Nigeria 0.9589 1.0031 1.0043 1.0007 1.0027 1.0026 0.9997 0.153% Rwanda 1.0270 1.0317 1.0347 1.0360 1.0340 1.0359 1.0380 0.194% Senegal 1.0102 1.0027 1.0028 1.0026 1.0028 1.0027 1.0023 0.016% South Africa 1.0759 1.0079 1.0063 1.0061 1.0068 1.0061 1.0053 0.079% Swaziland 0.9946 1.0010 0.9988 0.9972 0.9994 0.9984 1.0006 0.129% Tanzania 1.0084 0.9995 0.9995 0.9965 0.9993 0.9979 0.9970 0.121% Togo 0.9998 1.0069 1.0068 1.0057 1.0069 1.0062 1.0058 0.050% Uganda 1.0330 1.0023 1.0014 1.0009 1.0017 1.0012 1.0008 0.051% Zambia 1.0155 1.0180 1.0177 1.0176 1.0176 1.0177 1.0193 0.061% Notes: All ratios are based on country PPPs normalized with respect to the regional geometric mean. Source: Authors’ calculations. 16 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA TABLE A2 — CONSUMPTION PPPs (BY VARIOUS METHODS), 28 COUNTRIES Poverty-weighted PPPs Kernel based SNA Below Uniform; Uniform; Uniform; Bi-weight; Bi-weight; Bi-weight; -based IPL Bandwidth Bandwidth Bandwidth Bandwidth Bandwidth Bandwidth = 1.0 = 0.5 = 0.25 = 1.0 = 0.5 = 0.25 Burkina Faso 43.9130 45.4372 45.2897 45.3369 45.3795 45.3248 45.3454 45.3782 Burundi 96.0123 101.2418 101.9139 102.2206 102.2853 102.1098 102.2239 102.4390 Cameroon 45.6249 50.7609 49.9746 50.0972 50.1918 50.0507 50.1688 50.2484 Cabo Verde 9.4389 9.8289 9.7787 9.7811 9.7861 9.7801 9.7754 9.7458 Congo, Rep. 58.6076 64.0289 63.2751 63.4865 63.5320 63.4258 63.5418 63.5559 Congo, D.R. 106.5974 116.6190 114.1441 114.3872 114.5000 114.3074 114.4036 113.9557 Côte d'Ivoire 46.7349 50.3859 49.9498 50.0506 50.0682 50.0132 50.0503 50.0823 Ethiopia 1.0883 1.2157 1.2023 1.2043 1.2025 1.2034 1.2033 1.2034 Gabon 72.0245 81.2330 79.5258 79.6582 80.3077 79.7871 80.0902 80.6216 Ghana 0.1572 0.1796 0.1759 0.1761 0.1766 0.1761 0.1765 0.1770 Guinea 511.2631 574.7884 566.7677 568.3278 569.1281 567.6834 568.7267 569.7393 Kenya 7.1100 7.5411 7.4966 7.5045 7.5113 7.5033 7.5087 7.5242 Liberia 0.1141 0.1254 0.1223 0.1223 0.1220 0.1223 0.1221 0.1219 Madagascar 138.7776 152.8748 151.0046 151.1476 151.2398 151.1086 151.2124 151.5877 Malawi 15.6651 16.2343 15.9678 16.0045 15.9754 15.9888 15.9816 15.9838 Mali 44.1795 46.1674 46.1589 46.1505 46.0811 46.1090 46.0871 46.1282 Mozambique 3.0803 3.2177 3.1887 3.1897 3.1875 3.1894 3.1882 3.1891 Namibia 1.0222 1.1200 1.0977 1.1005 1.1001 1.0999 1.1008 1.1025 Niger 45.2824 48.8294 48.5378 48.6466 48.6568 48.6300 48.6753 48.6954 Nigeria 15.8033 17.7322 17.6479 17.6964 17.6360 17.6606 17.6702 17.6342 Rwanda 48.9167 51.2475 52.4573 52.6931 52.7712 52.6311 52.7614 52.9129 Senegal 48.6598 51.8278 51.5589 51.6465 51.6459 51.6205 51.6495 51.6736 South Africa 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 Swaziland 0.8073 0.8732 0.8673 0.8667 0.8655 0.8668 0.8665 0.8692 Tanzania 116.3640 124.1585 123.1184 123.3150 122.9697 123.2283 123.1397 123.1324 Togo 46.1151 49.6279 49.5807 49.6512 49.6089 49.6310 49.6328 49.6519 Uganda 188.0210 195.8339 194.7488 194.8732 194.8216 194.8488 194.8771 194.9641 Zambia 494.2969 523.7306 528.9606 529.6466 529.6952 529.3684 529.7731 531.0393 Notes: South Africa is the numeraire country (South African rand=1) Source: Authors’ calculations. 17 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA TABLE A3 — EFFECT OF VARIOUS POVERTY-WEIGHTED PPPs ON POVERTY RATES, 28 COUNTRIES Kernel based Below Uniform; Uniform; Uniform; Bi-weight; Bi-weight; Bi-weight; IPL Bandwidth Bandwidth Bandwidth Bandwidth Bandwidth Bandwidth = 1.0 = 0.5 = 0.25 = 1.0 = 0.5 = 0.25 Burkina Faso 59.4 59.2 59.2 59.3 59.2 59.2 59.2 Burundi 84.9 85.1 85.2 85.2 85.2 85.2 85.3 Cameroon 31.5 30.7 30.8 30.8 30.7 30.8 30.9 Cabo Verde 5.6 5.5 5.5 5.5 5.5 5.5 5.4 Congo, Rep. 39.5 38.8 38.9 39.0 38.8 39.0 39.0 Congo, D.R. 95.7 95.4 95.4 95.5 95.4 95.4 95.4 Côte d'Ivoire 26.4 26.0 26.1 26.1 26.1 26.1 26.1 Ethiopia 43.5 42.8 42.9 42.8 42.8 42.8 42.8 Gabon 9.4 9.1 9.1 9.2 9.1 9.2 9.3 Ghana 12.6 12.0 12.0 12.1 12.0 12.0 12.1 Guinea 62.4 61.8 61.9 61.9 61.8 61.9 61.9 Kenya 31.2 30.9 31.0 31.0 31.0 31.0 31.1 Liberia 58.0 55.5 55.5 55.5 55.5 55.5 55.5 Madagascar 85.1 84.8 84.8 84.8 84.8 84.8 84.8 Malawi 72.7 71.9 72.1 71.9 72.0 72.0 72.0 Mali 56.8 56.8 56.8 56.8 56.8 56.8 56.8 Mozambique 73.8 73.4 73.4 73.4 73.4 73.4 73.4 Namibia 26.4 25.5 25.6 25.6 25.6 25.6 25.7 Niger 74.8 74.6 74.6 74.6 74.6 74.6 74.6 Nigeria 54.2 54.1 54.1 54.0 54.1 54.1 54.0 Rwanda 67.4 68.4 68.5 68.6 68.5 68.6 68.6 Senegal 60.2 60.0 60.1 60.1 60.1 60.1 60.1 South Africa 13.4 13.4 13.4 13.4 13.4 13.4 13.4 Swaziland 55.5 55.4 55.4 55.4 55.4 55.4 55.5 Tanzania 48.3 47.8 47.9 47.7 47.9 47.8 47.8 Togo 68.5 68.5 68.6 68.5 68.6 68.6 68.6 Uganda 45.7 45.5 45.5 45.5 45.5 45.5 45.5 Zambia 66.5 66.8 66.8 66.8 66.8 66.8 67.0 Source: Authors’ calculations. 18 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA TABLE A4 — PPP RATIOS REGRESSED ON GDP PER CAPITA AND REGIONAL BLOCS, 50 COUNTRIES Excluding supermarket Excluding branded Excluding premium food only items garments & footwear items nonalcoholic beverages items (1) (2) (3) (4) (5) (6) ln ln ln ln ln ln (Normalized (Normalized (Normalized (Normalized (Normalized (Normalized PPP Ratio) PPP Ratio) PPP Ratio) PPP Ratio) PPP Ratio) PPP Ratio) ln (GDP per 0.00398*** 0.00287* -0.0000484 -0.000130 0.00280** 0.00280* capita, 2011 PPP) (0.00112) (0.00116) (0.000399) (0.000407) (0.000991) (0.00122) Economic 0.00104 -0.000210 0.000513 Community Of (0.00262) (0.00131) (0.00272) West African States (ECOWAS) Economic -0.00345 -0.00118 -0.00305 Community of (0.00311) (0.00140) (0.00326) Central African States (ECCAS) Southern African 0.00798** 0.00137 0.00469 Development (0.00258) (0.00147) (0.00241) Community (SADC) Arab Maghreb 0.00535 -0.00113 -0.00569* Union (UMA) (0.00563) (0.00221) (0.00264) Constant 4.573*** 4.580*** 0.000387 0.00103 -0.0224** -0.0228* (0.00880) (0.00874) (0.00310) (0.00291) (0.00781) (0.00953) N 50 50 50 50 50 50 R2 0.207 0.397 0.000 0.099 0.150 0.334 Adjusted R2 0.191 0.328 -0.021 -0.003 0.133 0.258 Root MSE 0.00800 0.00728 0.00294 0.00291 0.00683 0.00632 Base dummy group: Common Market for Eastern and Southern Africa (COMESA) Robust standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 Source: Authors’ calculations. GDP per capita (2011 PPP adjusted) from the World Bank World Development Indicators (WDI). 19 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA TABLE A5—FULL-LIST AND REDUCED-LIST PPPs, 50 COUNTRIES (2) Branded garments (3) Premium food & (1) Supermarket only items & footwear items non-alcoholic beverages items Full- Reduced- Normalized Full-list Reduced- Normalized Full-list Reduced- Normalized list PPP list PPP* PPP ratio PPP list PPP* PPP ratio PPP list PPP* PPP ratio Burkina Faso 43.508 43.012 1.003 43.885 43.917 0.999 43.508 43.128 0.997 Burundi 97.962 95.856 0.993 96.286 96.467 1.000 97.962 96.691 0.993 Cameroon 45.188 44.642 1.002 45.358 45.277 0.997 45.188 44.890 0.999 Cabo Verde 9.325 9.223 1.004 9.383 9.416 1.002 9.325 9.324 1.006 Congo, Rep. 57.886 56.485 0.990 58.480 58.637 1.001 57.886 57.067 0.991 Congo, D.R. 105.720 103.188 0.990 106.079 106.166 0.999 105.720 105.187 1.001 Côte d'Ivoire 46.036 44.884 0.989 46.492 46.575 1.000 46.036 45.258 0.989 Ethiopia 1.053 1.032 0.995 1.074 1.077 1.002 1.053 1.043 0.996 Gabon 70.162 68.527 0.991 70.817 70.755 0.998 70.162 69.166 0.991 Ghana 0.155 0.152 1.000 0.155 0.155 0.999 0.155 0.155 1.005 Guinea 500.519 490.046 0.993 507.403 509.728 1.003 500.519 495.516 0.996 Kenya 6.995 6.901 1.001 6.988 7.011 1.002 6.995 6.961 1.001 Liberia 0.111 0.110 1.004 0.112 0.112 1.000 0.111 0.111 1.005 Madagascar 138.833 135.463 0.990 139.062 139.564 1.002 138.833 137.230 0.994 Malawi 15.274 14.981 0.995 15.391 15.415 1.000 15.274 15.154 0.998 Mali 43.251 42.399 0.995 43.742 43.701 0.998 43.251 42.693 0.993 Mozambique 3.022 2.998 1.007 3.063 3.073 1.002 3.022 3.000 0.998 Namibia 1.019 1.017 1.012 1.012 1.015 1.001 1.019 1.019 1.005 Niger 44.825 44.270 1.002 45.132 45.250 1.001 44.825 44.294 0.994 Nigeria 15.492 15.430 1.011 15.681 15.743 1.002 15.492 15.656 1.016 Rwanda 47.761 47.078 1.000 48.716 48.792 1.000 47.761 47.319 0.996 Senegal 47.845 46.390 0.984 48.507 48.644 1.001 47.845 46.930 0.986 South Africa 1.000 1.000 1.015 1.000 1.000 0.998 1.000 1.000 1.006 Swaziland 0.794 0.796 1.017 0.798 0.800 1.001 0.794 0.797 1.009 Tanzania 115.241 114.336 1.007 115.461 115.698 1.001 115.241 115.807 1.011 Togo 45.189 44.152 0.991 45.802 45.564 0.993 45.189 44.617 0.993 Uganda 186.133 181.845 0.991 186.807 186.469 0.997 186.133 183.914 0.994 Zambia 496.642 489.901 1.001 494.529 495.113 1.000 496.642 498.556 1.009 Algeria 6.372 6.351 1.011 6.272 6.283 1.000 6.372 6.301 0.994 Angola 14.369 14.282 1.008 14.565 14.671 1.006 14.369 14.442 1.011 Benin 44.596 44.246 1.007 44.415 44.290 0.996 44.596 44.598 1.006 Botswana 0.879 0.878 1.014 0.875 0.883 1.007 0.879 0.880 1.008 Central Afr. Rep. 52.385 50.861 0.985 52.821 53.008 1.002 52.385 51.295 0.985 Chad 49.226 48.600 1.002 49.578 49.606 0.999 49.226 48.738 0.996 Comoros 43.168 42.211 0.992 43.500 43.602 1.001 43.168 43.412 1.011 Djibouti 20.149 19.674 0.991 20.019 19.934 0.994 20.149 19.908 0.994 Egypt, Arab Rep. 0.365 0.362 1.006 0.360 0.359 0.996 0.365 0.363 1.001 Eq. Guinea 63.060 62.735 1.009 63.393 63.269 0.997 63.060 63.610 1.014 Gambia, The 2.115 2.059 0.988 2.135 2.141 1.001 2.115 2.101 0.999 Guinea-Bissau 48.428 47.665 0.999 48.910 49.048 1.001 48.428 48.168 1.000 Lesotho 0.763 0.759 1.010 0.762 0.761 0.998 0.763 0.761 1.003 Mauritania 22.067 21.903 1.007 22.271 22.213 0.996 22.067 21.837 0.995 Mauritius 3.616 3.557 0.998 3.607 3.604 0.998 3.616 3.591 0.999 Morocco 0.824 0.804 0.989 0.827 0.824 0.996 0.824 0.818 0.998 São Tomé & Prin. 2000.69 1949.384 0.989 2011.39 2010.139 0.998 2000.69 1989.587 1.000 Seychelles 1.571 1.550 1.001 1.561 1.566 1.002 1.571 1.582 1.013 Sierra Leone 343.579 335.768 0.992 348.584 349.054 1.000 343.579 340.770 0.997 Sudan 0.290 0.288 1.007 0.291 0.293 1.006 0.290 0.290 1.006 Tunisia 0.138 0.138 1.015 0.137 0.138 1.003 0.138 0.137 0.999 Zimbabwe 0.104 0.103 1.008 0.106 0.106 1.004 0.104 0.104 1.004 */ Indicates poverty-specific PPPs, i.e. reduced list poverty-specific PPPs. Notes: Full- and reduced-list PPPs are reported with South Africa as the numeraire country (South African rand=1). PPP ratios are based on normalized country PPPs with respect to the regional geometric mean. For an explanation of normalized PPP ratios see footnote 15. Source: Authors’ calculations. 20 POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA 8. References Asian Development Bank. 2008. “Research Study on 2005 International Comparison Program in Asia and the Pacific for Selected Countries in Asia and the Pacific - Poverty-specific Purchasing Power Parities”. Deaton, Angus and Olivier Dupriez. 2011. "Purchasing Power Parity Exchange Rates for the Global Poor." American Economic Journal: Applied Economics, 3(2): 137-66. Dupriez, Olivier. 2007. “Building a household consumption database for the calculation of poverty PPPs: Technical notes” Food and Agriculture Organization of the United Nations. 2015. “Agricultural Growth in West Africa, Market and policy drivers.” Nielsen. 2014. “Getting to Know the Diverse African Consumer.” Available at: http://www.nielsen.com/ssa/en/insights/news/2014/getting-to-know-the-diverse-african- consumer.html Summers, Robert. 1973, “International Price Comparisons Based on Incomplete Data”, Review of Income and Wealth, 19(1), 1–16. United Nations Statistical Commission. 2016. “Report on the forty-seventh session (8-11 March 2016).” Available at: https://unstats.un.org/unsd/statcom/47th-session/documents/Draft-report-on-the- 47th-session-of-the-statistical-commission-Rev1-E.pdf World Bank. 2015. “Operational Guidelines and Procedures for Measuring the Real Size of the World Economy: 2011 International Comparison Program”. Washington, DC: World Bank. Available at: http://www.worldbank.org/en/programs/icp/brief/2011-operational-guidelines 21