metadata: authors: 0: author: Chen,Xiaomeng
1: author: Mungai,Rose
2: author: Nakamura,Shohei
3: author: Pearson,Thomas Patrick
4: author: Wambile,Ayago Esmubancha
5: author: Yoshida,Nobuo
count: Ghana
docna: 0: docna: How Useful is CPI Price Data for Spatial
            Price Adjustment in Poverty Measurement? : A Case from Ghana
docty: Policy Research Working Paper
subtopic: Municipal Management and Reform,Urban Housing,Urban Governance and Management,Urban Housing and Land Settlements,Inequality,Poverty Diagnostics,Poverty Lines,Poverty Assessment,Poverty Impact Evaluation,Small Area Estimation Poverty Mapping,Poverty Monitoring & Analysis,Macroeconomic Management,Food Security,Nutrition
teratopic: Macroeconomics and Economic Growth,Communities and Human Settlements,Agriculture,Public Sector Development,Urban Development,Health, Nutrition and Population,Poverty Reduction
abstracts: cdata!: Measuring and comparing the levels of
            household welfare and poverty in a country require
            cost-of-living differences across regions to be properly
            adjusted. In measuring the spatial cost of living, the
            recent literature underscores the importance of detailed
            product quality information in the price data. Taking
            advantage of the price data availability in Ghana, this case
            study explores the Consumer Price Index price data as a
            source for spatial price measurement. It applies the country
            product dummy method to the Consumer Price Index price data
            and compares the results with other methods based on
            different price data. The empirical analysis indicates a
            potential bias in estimating spatial prices stemming from
            the lack of product quality information and, therefore,
            suggests the potential usefulness of the Consumer Price
            Index price data for spatial price adjustment in poverty
            analysis in low- and middle-income countries.
geo_reg_key: 517191,1927320
repnme: repnme: How Useful is CPI Price Data for Spatial
            Price Adjustment in Poverty Measurement? : A Case from Ghana
display_title: How Useful is CPI Price Data for
            Spatial Price Adjustment in Poverty Measurement : A Case
            from Ghana
listing_relative_url: /research/2020/09/32387123/useful-cpi-price-data-spatial-price-adjustment-poverty-measurement-case-ghana
topicv3: Food Price Analysis m1326440 200,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Land and Housing m1327648 817,Price Surveys m1504546 3124
publishtoextweb_dt: 2020-09-07T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Communities and Human Settlements,Agriculture,Public Sector Development,Urban Development,Health, Nutrition and Population,Poverty Reduction
origu: EFI-AFR1-POV-Poverty and Equity (EAEPV)
no_of_pages: 47
author_keys: 0: author_key: 1524077
1: author_key: 562403
2: author_key: 1389722
3: author_key: 1725877
4: author_key: 1108212
5: author_key: 642421
model_extractions: Policy Research Working Paper 9388 How Useful Is CPI Price Data for Spatial Price Adjustment in Poverty Measurement?

A Case from Ghana _Xiaomeng Chen Rose Mungai Shohei Nakamura Thomas Pearson Ayago Esmubancha Wambile Nobuo Yoshida_ Poverty and Equity Global Practice September 2020
Policy Research Working Paper 9388

# **Abstract**

Measuring and comparing the levels of household welfare and poverty in a country require cost-of-living differences across regions to be properly adjusted. In measuring the spatial cost of living, the recent literature underscores the importance of detailed product quality information in the price data. Taking advantage of the price data availability in Ghana, this case study explores the Consumer Price Index price data as a source for spatial price measurement.

It applies the country product dummy method to the Consumer Price Index price data and compares the results with other methods based on different price data. The empirical analysis indicates a potential bias in estimating spatial prices stemming from the lack of product quality information and, therefore, suggests the potential usefulness of the Consumer Price Index price data for spatial price adjustment in poverty analysis in low- and middle-income countries.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at snakamura2@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
# **How Useful Is CPI Price Data for Spatial Price Adjustment in Poverty Measurement? A Case from Ghana**

Xiaomeng Chen, World Bank Rose Mungai, World Bank Shohei Nakamura, World Bank<sup>1</sup> Thomas Pearson, Boston University Ayago Esmubancha Wambile, World Bank Nobuo Yoshida, World Bank

## **JEL Classification:** D12, E31, O15

**Keywords:** price indexes, household welfare, purchasing power parity, housing, Ghana

> 1 Corresponding author (snakamura2@worldbank.org). We would like to thank the Ghana Statistical Services (GSS) for allowing us to use the CPI price data for the analysis in this paper. We also thank Giovanni Vecchi and Nicola Amendola for their useful comments on the earlier version of the paper. 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 (IBRD) or the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **1. Introduction**

The basis for poverty and inequality analysis is household welfare, which is commonly measured by household consumption (Deaton and Zaidi 2002; Ravallion 2008).<sup>2</sup> A key component of welfare measurement is price adjustment. To compare the welfare level of households over time and across regions, household consumption aggregates need to be adjusted by accounting for inflation (that is, temporal price adjustment) and cost-of-living differences across regions (that is, spatial price adjustment).<sup>3</sup> Inappropriate adjustments of such price difference cause a bias in poverty estimation. Spatial price adjustment becomes particularly important when policy makers need to know not only who is poor but also where poverty is concentrated.<sup>4</sup> Despite the importance of spatial price adjustment on welfare measurement for poverty and inequality analysis, its underpinning theory is not necessarily clear to guide the practice. Many low- and middle-income countries apply different methods, without a rigorous methodological foundation, often facing the limited availability of price data suitable for spatial price adjustment as the biggest challenge.<sup>5</sup> The recently collected survey by the World Bank illustrates how the current practice of spatial price adjustment varies among Sub-Saharan African countries (Figure 1).<sup>6</sup> In its internal process of developing a regional poverty database, the World Bank collected from 47 Sub-Saharan African countries information about the data sources and methods used for price adjustment. In terms of data sources (Panel A), about 40 percent of African countries rely on survey unit values, which is a proxy measure of prices calculated by dividing households’ expenditures by the purchased quantity for each good based on household budget or welfare surveys. While unit values might be useful in cases where price data are lacking, the problems related to unit values have been reported in several studies (Gibson and Kim 2019; McKelvey 2011). Another 26 percent of the countries use market price surveys, which are often collected in parallel to the official household budget or welfare surveys. A small number of countries use their Consumer Price Index (CPI) data for spatial price adjustment. The methodology of spatial price adjustment is more divided with countries applying different approaches (Panel B), including the Paasche index (25 percent), Laspeyres index (15 percent), Fisher index (13 percent), and so on.

> 2 Some countries rely on household income, instead of consumption, for poverty measurement (Ferreira et al. 2016).

> 3 ‘Region’ refers to subnational regions in this paper unless otherwise noted.

> 4 For example, as the cost of living in African cities is high relative to their income levels (Nakamura et al. 2019), urban poverty can be underestimated without adequate spatial price adjustment.

> 5 Some approaches to circumventing the data limitation include the Engel curve method (discussed in Appendix B of this paper) and price data collection from local experts (Gibson and Le 2018).

> 6 Africa refers to Sub-Saharan Africa in this paper unless otherwise mentioned.

2
<!-- Start of picture text --> Hybrid: Unit values<br>and CPI or price<br>survey, 2, 4%<br>Others (regional PLs, no<br>spatial price adjustment, |<br>unknown, etc.), 9, 19%<br>—Y<br>—_ Unit values,38% 18,<br>CPI, 6, 13% ~~<br>'<br>Price survey, 12,<br>26%<br><!-- End of picture text -->

<!-- Start of picture text --> Other (other index, no spatial<br>deflation, unknown), 8, 17% -<br>Paasche index,<br>12, 25%<br>Regional CPI, 2, _~<br>4%<br>~~ Laspeyres index,<br>| 7, 15%<br>Regional PLs, 12,<br>26% \ Fisher index, 6,<br>13%<br><!-- End of picture text -->
This paper is structured as follows. Section 2 reviews the theory and practice of spatial price adjustment to set out key issues to be addressed. Section 3 describes our methodology by explaining the empirical approach and data. Section 4 presents the results. Section 5 discusses the key findings and conclusions.

# **2. Spatial price adjustment: Theory and practice**

In this section, we first review the theory and practice of standard price index approaches. Then, we discuss methodological issues on non-food items, particularly housing, in measuring spatial differentials in costs of living. Finally, we introduce the CPD approach, which is the main approach in our empirical analysis.

## **2.1. How can standard price index approaches be improved given the limited data availability?**

_Choice of price index_ Calculating a bilateral price index for spatial price adjustment is a common approach in practice. The most commonly used indexes in welfare measurement include the Paasche, Laspeyres, Fisher, and (to a lesser extent) Törnqvist indexes. The Paasche index ( _P_ ) and Laspeyres index ( _L_ ) are respectively expressed as follows: and where _k_ indicates the base region, _i_ indicates every other region, and _j_ indicates each item in the consumption basket, and _q_ and _p_ are quantities and prices, respectively. The Paasche price index is <u>p q</u> sometimes calculated at the household level as = ~~.~~<sup>7hh</sup> Deaton and Zaidi (2002) argue that to convert total expenditure into money metric utility, the price PP<sup>h</sup> pp<sup>0</sup> qq<sup>h</sup> index must be tailored to the household’s own demand pattern, which varies with the household’s income, demographic composition, location, and other characteristics. The Paasche index gives a utility-consistent measure (first-order approximation) as recommended by Deaton and Zaidi (2002).

While the standard fixed-weight price index approach is commonly used, it has many limitations when used for spatial price adjustment in poverty measurement. One of the problems is substitution bias, which is expected to matter more in the measurement of spatial price variation than in the measurement of temporal price changes. Alternatively, a variable-weight superlative price index, such as Fisher and Törnqvist, is known to be closer to the true cost-of-living index (Balk 2008; Diewert 0 7 This household-level Paasche index can be rewritten as h h ~~(~~ <u>kh))</u><sup>−1</sup> , which can be approximated by h h 0 pp from individual households) and ⁄ ), where h is the reference price vector (typically the median of the prices observed is the share of household PP = (∑ww _h_ ’s budget devoted to good kk ppkk _k_ . kk kk kk lnPP<sup>h</sup> ≈∑ww ln (pp pp pp<sup>0</sup> kk ww 4
1976). Mathematically, the Fischer index ( _F_ ) is a geometric average of the Laspeyres and Paasche indexes, (3), and the Törnqvist index ( _T_ ) is expressed as FF= (LL× PP)<sup>1/2</sup> where _wij_ is the average share that item _j_ has in the consumption basket in region _i_ .

The Laspeyres price index provides an upper limit on the true cost-of-living index (COLI) based on the standard of living in the initial price situation, while the Paasche price index provides a lower limit on the true COLI based on the standard of living in the given price situation. The Laspeyres index is relatively easy to calculate but sensitive to a substitution bias. The Fisher index is also utilityconsistent (second-order approximation), bounded by the Paasche and Laspeyres indexes. However, the Fisher index is susceptible to income bias in case of non-homothetic preference. For the practicality of poverty and inequality analysis, the Fisher price index may work preferably, as the gap between the Paasche and Laspeyres price indexes tends to be wide in poor areas. However, the degree of the Paasche-Laspeyres spread is an empirical question.

# _Reference area and transitivity_

Real household consumptions—that is, nominal consumptions deflated by a spatial price index—are compared to a poverty line to measure poverty. An important issue in a bilateral price index approach is the choice of reference area. Reference area in spatial price index needs to be the same as the location where the basket for poverty line is priced. For instance, if a poverty line is constructed based on the costs of basket items measured by the national prices (PLA), the consumption expenditure of household _i_ in region _r_ needs to be spatially deflated as E (5), AA where is a spatial price index that adjusts the consumption from the region PPii,rr × ππrr<sup>AA</sup> = DDE PPii,rr _r_ prices to the national E (5), where _r_ prices to the national prices ( _A_ ). The price index can be considered as the ratio of price levels between region _r_ and the rr<sup>AA</sup> national average ππ _A_ ( ⁄ ). Then, the poverty status of household _i_ can be identified by rr<sup>AA</sup> ππ AA rr pp pp (6). AA Similarly, if a poverty line is constructed based on the capital region prices (DDE PPii,rr ⋚PPLLAA _a_ ), household consumption needs to be deflated by a spatial price index , which indicates the price-level ratio between region _r_ and region _a_ . rr<sup>aa</sup> ππ Without transitivity satisfied in a spatial price index, the comparison of prices between two areas changes depending on the choice of the reference area. This is problematic in poverty measurement, as the choice of the reference area for the poverty line (and thus the reference area for the spatial price index) can alter poverty estimates. The Gini, Elteö, Köves, and Szulc (GEKS) procedure (Deaton and Heston 2010) is proposed to transform a bilateral index to a multilateral index. This approach is used 5
where _F_<sup>_1j_</sup> is the Fisher index for region _j_ with region 1 as the reference area. This procedure is equivalent to taking a geometric mean of every possible combination of two regions. As discussed in Section 2.3, a multilateral price index can also be calculated by the CPD method.<sup>8</sup>

# _Choice of price data_

While the choice of price index calculation methods matters, the quality and types of price data used for the calculation may be even more important. There are three types of data commonly available for spatial price adjustment. The first type of price data is the market price survey data that are typically collected in parallel with an official household budget survey. The second type of price data that can be potentially used for spatial price adjustment is the CPI price data. The third type of price data is unit values that can be calculated based on the consumption module of official household budget surveys. Gaddis (2016) nicely summarizes the general characteristics of these price data from a poverty analysis perspective as in Table 1. We encourage readers to refer to Gaddis (2016) for the discussion, and only highlight here some key factors particularly relevant to spatial price adjustment for poverty measurement below.<sup>9</sup>

> 8 The GEKS procedure can also be applied to the Törnqvist index (Caves, Christensen, and Diewert 1982; Balk 2008). Another common approach to calculate a multilateral index is the Geary-Khamis method, which is used by the Penn World Table (Feenstra, Inklaar, and Timmer 2015) and the regional price parities in the United States (Aten 2017).

> 9 Also see Chapter V of United Nations Statistics Division (2005). Aside from these commonly available price data, there are other types of price data, such as the price data collected for the PPP calculation under the ICP, scanner data collected by private companies, and price inventories of specific goods and services (agricultural products, housing, and so on). For example, recent research that takes advantage of scanner data demonstrates the impacts of quality (or item) variations on the costs of living across cities. Handbury and Weinstein (2015) use detailed barcode data to address heterogeneity bias (stemming from the comparison of different goods in different locations) and variety bias (stemming from not correcting for the fact that some goods are unavailable in some locations), finding that prices are lower (as opposed to higher) in larger cities in the United States. Feenstra, Xu, and Antoniades (2017) analyze China in a similar way and compare it to the United States. Other research with scanner data includes Handbury (2013), which constructs a spatial price index for different income levels (that is, non-homothetic) across the U.S. metropolitan areas. It finds that price levels for high-income households tend to be overestimated when a standard homothetic index is used.

6
**Table 1. Stylized comparison of different price data sources**

||Market price survey|CPI price data|Unit values|
|---|---|---|---|
|Nationally<br>representative|(+)<br>(but a potentially large<br>number of missing values)|(−)<br>(often only urban,<br>purposive sampling)|(+)<br>(but potentially biased toward<br>purchasing households)|
|Representative<br>for survey<br>strata|(+)<br>(but a potentially large<br>number of missing values)|(−)<br>(data collection delinked<br>from household survey)|(+)|
|Sample<br>size/number<br>of<br>observations|(−)<br>(only few price<br>observations per item and<br>cluster, missing values)|(−)<br>(limited number of price<br>observations per item)|(+)<br>(large number of price<br>observations, especially for<br>diary surveys)|
|Food and non-<br>food coverage|(+)<br>(often only few non-food<br>items)|(+)|(−)|
|Precisely<br>defined items|(+)<br>(if enumerators are well<br>instructed)|(+)<br>(typically well-trained<br>enumerators, every month)|(−)<br>(generally not, especially<br>recall surveys with aggregated<br>item list)|
|Direct<br>measurement|(+)|(+)|(−)<br>(computed as value divided by<br>quantity)|
|Collection<br>process|(−)<br>(especially with nonresident<br>enumerators)|(−)<br>(nonresident enumerators)|(+)<br>(based on actual transactions)|

_Source:_ Gaddis (2016, 10) Although not many Sub-Saharan African countries have conducted market price surveys (see the list of countries in Table A1 in Appendix A), those price data are potentially a good source for spatial price adjustment. The market price surveys are often conducted in a sample of communities or market centers close to the enumeration areas (EAs) in parallel with official nationally representative budget surveys. Typically, only one price is recorded for each item in each community. A major disadvantage of market price surveys is the lack of detailed quality information. Without detailed information about item descriptions, it is not possible to compare prices of the same products across regions. Gibson, Le, and Kim (2017) explain that Vietnam’s price survey was carefully designed to record detailed information for each product.

As stated earlier, CPI price data are another potential source for spatial price adjustment. Unlike market price surveys, CPI price data exist in almost all countries (Berry et al. 2019). Since the CPI is an indicator of inflation, it does not indicate spatial differentials in costs of living.<sup>10</sup> However, it is possible to calculate the spatial price index by using the CPI price data (not CPI values per se). The problem is that many countries collect price information only in urban areas (see Table A2 in Appendix A). Nevertheless, if the geographic coverage of the data is adequate, using the same price data for spatial and temporal price adjustment can be an efficient approach in terms of the data collection costs, and it could also enhance consistency between temporal and spatial price patterns.<sup>11</sup> In addition, compared to market price surveys, a wide variety of food and non-food price information is collected in the CPI data, and the data collection is frequent (for example, monthly). A well-known issue is that the basket used for the calculation of the CPI is often skewed toward higher-income households. For

> 10 Some countries report regional CPIs. Such regional CPIs still do not indicate price differences across regions as they only indicate inflation in each region and do not utilize the data for determining spatial variations in cost of living across the regions.

> of living across the regions. 11 Inconsistency in temporal and spatial price movements is not uncommon. Let us consider a spatial price index at year 0 ( ) and year 1 ( ). By applying the regional CPIs between _t_ 0 and _t_ 1 to , one can obtain another spatial price index ( ∗ ). This index ∗ often deviates from . tt0 tt1 tt0 SSPPSS SSPPSS SSPPSS tt1 tt1 tt1 SSPPSS SSPPSS SSPPSS

7
spatial price adjustment for poverty analysis, it may be useful to exclude items that are not relevant to low-income households.<sup>12</sup> A few but growing number of countries have either officially reported or published as experiments subnational PPPs calculated based on the CPI price data. Those examples include the United States (Aten 2017), the United Kingdom (Office for National Statistics 2018), Germany (Weinand and von Auer 2019), and Italy (Biggeri and Tiziana 2014; Laureti and Rao 2018). An example from a developing country is Dikhanov, Palanyandy, and Capilit (2011) for the Philippines.

Finally, when price data (based on price surveys) are not available, unit values are often used as a proxy for price. As shown in Figure 1, about 40 percent of African countries currently rely on unit values for spatial price adjustment for their official poverty measurement. Unit values have advantages in several aspects, including their availability and geographic coverage. Nevertheless, the limited information about product quality and non-food goods and services is clearly a serious drawback for spatial price adjustment. An example is a substitution bias due to unobserved quality differences in unit values. Since Deaton (1986) established the use of unit values several decades ago, various adjustment methods have been devised to address concerns about the reliability of unit values (Gibson, n.d.). Recent research, however, has emphasized limitations in the use of unit values as a proxy for price data (Gibson and Kim 2019; McKelvey 2011). Thus, it would be better to avoid resorting to unit values for spatial price adjustment unless other price data are not available. In this paper, we focus on market price surveys and the CPI price data.

# **2.2. How should non-food items (particularly housing) be treated in measuring spatial differentials in the cost of living?**

_Treatment of non-food prices_ As people become richer, they tend to spend a lesser portion of their budget on food expenditures. This empirical association between income and food budget share is known as Engel’s law. A similar pattern is observed even at a cross-country comparison, as the average food budget share is higher in low- and middle-income countries (Figure 2). In many African countries, people typically spend more than half of their budget on food expenditures. However, this does not justify the construction of a spatial deflator for poverty measurement solely on food prices. As economies develop, non-tradable non-food items tend to account for a large portion of price dispersion across regions (for example, the Balassa-Samuelson effect), highlighting the importance of measuring those prices.

> 12 For example, Rwanda uses CPI price data to calculate a COLI for poverty measurement (National Institute of Statistics of Rwanda 2016). In the calculation of the COLI, it uses only items that are included in the basket used for poverty line construction. Another interesting study is Dikhanov et al. (2017), which calculates the PPP by focusing on items relevant for low-income households in Africa.

8
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question is how to consider housing costs faced by households that pay no rent and the housing price level of a region where a large portion of residents are with such rent-free accommodations. In practice, either actually paid or imputed housing rents or (self-reported) housing values are used to incorporate housing costs in measuring costs of living across space. While housing rents are often imputed for owner-occupied housing units, rental housing markets need to be reasonably large for such imputation. Rent imputation can also be useful for housing units with nonmarket rate rents, such as public housing. More relevant in the context of the developing world is informal housing. Residents in informal settlements sometimes pay no rents or negligible rents. When constructing a consumption aggregate for welfare measurement, it may be better to use imputed rents for those residents. If the actual rent values do not reflect market values of their housing units, the welfare levels of the residents could be underestimated.<sup>13</sup> By contrast, in case of constructing a spatial price index, it may not be necessary to impute rents for informal settlements. When measuring the price level of a region in which a majority of residents are in rent-free accommodations, imputing their rents could result in an overestimation of prices.

The second question is whether to adjust for housing quality differences in spatial price adjustment for welfare and poverty measurement. There are several ways to calculate a housing price index across space. A simple way is to take the average (or median) of housing rents at some geographic level. For example, Moretti (2013) averages gross housing rents (that is, housing rents plus utilities) of two- or three-bedroom apartments in each U.S. metropolitan area. An alternative and common approach is to estimate a hedonic regression model: (9), where _y_ is housing rent (or the natural logarithm of housing rent) of housing unit yyiijj = αα+ ββEEiijj + γγjjDDjj + εεiijj _i_ in region _j_ , _X_ is a where _y i_ in region _j_ , _X_ is a vector of housing characteristics, and _D_ is a location dummy. The coefficients for the location dummy indicate price levels (relative to the reference location) after controlling for observed difference in housing quality. This type of hedonic approach is commonly used to calculate a housing price index (Moulton 1995). However, it may not be appropriate to control for housing quality in measuring spatial price differentials in the cost of living.

The following example illustrates such a case. Let us consider that one aims to compare housing price levels between region A and region B in a country (the United States). In both regions, there are only three types of housing units—one-bedroom, two-bedroom, and three-bedroom apartments—and each unit of the same type has exactly the same characteristics. Table 2 summarizes the number of housing units and rents in both regions. If you compare housing rents of each type of unit, region B’s rents are higher in all types. For instance, the rents of one-bedroom apartments are US$1,500 in region B, higher than US$1,000 in region A. By running a hedonic regression model in Equation 9, one can estimate region B’s housing price level is 31 percent higher than region A’s price level. Similar to other food and non-housing non-food items, these are the price comparisons of items after controlling for their quality differences.<sup>14</sup> However, as a spatial cost of living measurement, this conclusion may not be right, once you consider the availability of items. Unlike other food and non-housing non-food goods and services, the supply and stock of housing units is limited. Going back to the example in Table 2, notice that the majority of housing units in region B are one-bedroom apartments. By contrast, two-thirds of the housing units in

> 13 For housing rent imputation for welfare measurement (not for spatial price adjustment), see Balcazar et al. (2017).

> 14 Applying sampling weights to the hedonic regression does not affect the result as the type of housing units is anyway controlled for.

10
region A are expensive three-bedroom apartments. Both mean and median values of housing rents are indeed lower in region B than in region A. If one assumes housing stock is unlimited like other goods, then the hedonic regression approach may work. Otherwise, it would be better not to control for quality differences in measuring housing costs in spatial price adjustment. This statement sounds contradictory to our emphasis on the detailed product specification information in other goods and services. This is because housing is a unique case in which its supply and stock are limited in a given market.

**Table 2. A simple example of housing** **<u>price index</u>**

||Reg<br>|ion A<br>|Reg<br>|ion B<br>|
|---|---|---|---|---|
||Units|Rent ($)|Units|Rent ($)|
|One-bedroom|10|1,000|40|1,500|
|Two-bedroom|10|3,000|10|4,000|
|Three-bedroom|40|5,000|10|6,000|
|Price index based on hedonic regression|60|1,000|60|1,310|
|Mean rent|60|4,000|60|2,670|
|Median rent|60|5,000|60|1,500|

_Note:_ Number of housing units is expressed in thousands.

# **2.3. What are the alternatives to the standard price index approaches? How are they better?**

With the aforementioned methodological discussion in mind, we introduce here the CPD approach based on the CPI price data. Before getting there, however, we briefly touch upon another approach in spatial adjustment, which calculates a spatial price index based on multiple poverty lines.

## _Multiple poverty line approach_

An alternative and less common approach to measuring poverty is to construct regional poverty lines that reflect regional differences in the costs and needs to achieve the threshold utility. Poverty is measured by comparing each household’s nominal (that is, not spatially deflated) consumption aggregate to the region’s poverty line.<sup>15</sup> An advantage of this approach is that regional price levels of non-food items can be flexibly calculated. Since non-food allowance is typically determined based on the expenditure shares of households around food poverty lines (Ravallion and Bidani 1994), price data for non-food items is not required. When analyzing inequality or comparing household welfare across regions, one can construct a spatial price index by taking ratios of regional poverty lines. Due to the complexity of the methodology, however, regional poverty lines are used in only a few countries. The analysis of this approach is outside the scope of this paper.

## _CPD approach_

A challenge in spatial comparison of prices is that some items are not sold in some regions, and therefore, those prices are missing when calculating a price index. The CPD approach was originally developed to handle such missing values in price observations across regions (Rao 2004, 2005; Summers 1973).

15 Examples include Ravallion and Lokshin (2006) for the Russian Federation; Jolliffe, Datt, and Sharma (2004) for the Arab Republic of Egypt, and Marivoet and de Herdt (2015) for the Democratic Republic of Congo.

11
Gibson, Le, and Kim (2017) propose fixed-weights and variable-weights approaches.<sup>16</sup> When the CPD is applied to individual price quotes, additional controls, such as outlet types and urban/rural, can be included (Hill and Syed 2015).

The CPD method has several advantages. First, the CPD method can be applied to the price data with missing observations across regions. In calculation of a standard price index (for example, Fisher price index), one has to drop items whose prices are not recorded in some regions. The CPD can be still applied to such data without dropping those items. Second, subnational PPPs calculated from the CPD model � satisfy transitivity (Rao 2005). Therefore, one can directly calculate a multilateral price index without relying on the GEKS procedure. Finally, as the CPD is a regression model, it is jj jj ηη = exp�ββ̂ possible to obtain standard errors for the price index values. This stochastic approach is, however, not backed up by the economic theory.

The CPD approach has been used in the research and practices on spatial price measurement. The CPD approach was originally used by Summers (1973) to calculate the international PPPs. Recent applications for the calculations of subnational price measurement include Laureti and Rao (2018); Weinand and von Auer (2019); Gibson, Le, and Kim (2017); and Dikhanov, Palanyandy, and Capilit (2011). In practice, the CPD can be used to either aggregate the price data from the variety level to the item (or basic heading) level or directly calculate a price index, which satisfies transitivity. The ICP applies the CPD method for the aggregation to the basic heading level in its PPP calculation (World Bank 2015b).

> 16 The variable-weights approach is equivalent to the Törnqvist index as shown in Diewert 2005.

12
In the next section, we explain our methodology to examine the issues described above based on a case study of Ghana.

# **3. Methodology**

## **3.1. Ghana context and price data**

### _Ghana context_

The official poverty level in Ghana is measured based on the nationally representative household budget survey (Ghana Living Standard Survey [GLSS]). GLSS7 covered 14,009 households in 1,000 EAs and was conducted over a 12-month period between October 2016 and September 2017 (GSS 2018).<sup>17</sup> The sampling was designed so that the survey gives information representative of urban and rural areas and across the 10 regions. Household-level consumption aggregates include both food (including auto-consumption) and non-food (including imputed housing rents) consumption. Further, consumption aggregates are converted to per adult equivalent annual consumption for poverty measurement to reflect differences in household size and composition and differences in calorie requirements of household members.

Consumption aggregates are deflated both temporally and spatially. The temporal deflation adjusts the differences in the timing of data collection between October 2016 and September 2017. Monthly regional CPI indexes are used for the within-survey-months temporal deflation. The spatial deflation on the other hand adjusts for the differences in the cost of living across the 10 regions based on the official regional CPI values. The reference region of the spatial deflation is Greater Accra (January 2017).

Ghana measures poverty based on the single poverty line approach, that is, there is only one poverty line (GSS 2018). Spatial cost of living differentials are adjusted at the consumption side (as opposed to the multiple poverty line approach where poverty lines account for spatial price differences). The poverty line value is GH₵1,760.8 per adult equivalent per year, which was deflated by using a mixed deflator based on CPI raw price data and survey weights from the original poverty line of January 2013 (GH₵1,314). The official poverty headcount ratio in Ghana in 2016/17 is 23.4 percent, where urban and rural poverty is 7.8 percent and 39.5 percent, respectively (GSS 2018). Gini coefficients are 41.6 at the national level, 36.5 in urban areas, and 40.5 in rural areas.

### _Price data_

For this study, the main price data sets we use are the market price survey data from GLSS7 and CPI price data.<sup>18</sup> The market price survey was collected from 398 clusters (or EAs in the GLSS7 data collection), of which 209 clusters are located in urban areas and 189 clusters are located in rural areas (Table 3). Those clusters cover 174 districts in all the 10 regions of the country. Out of 109 food items found in the GLSS7 consumption module, the market price survey has price information for 96 of them. The CPI price data are regularly collected from 44 markets, covering all the 10 regions of the

> 17 The previous rounds of GLSS surveys were conducted in 1987/88, 1988/89, 1991/92, 1998/99, 2005/06, and 2012/13.

> 18 Survey unit values cannot be calculated based on the GLSS7 consumption module due to the prevalence of nonstandard units and lack of conversion factors.

13
country.<sup>19</sup> Table C1 and Table C2 in Appendix C show detailed information about the list of goods and services considered for our study and associated budget shares (calculated based on GLSS7).

**Table 3. Price data in Ghana**

||GLSS7 consumption module|Market price survey|CPIprice data|
|---|---|---|---|
|Geographic<br>coverage|• 1,000 clusters<br>• 214 districts<br>• 10 regions|• 398 clusters (urban 209;<br>rural 189)<br>• 174 districts<br>• 10 regions|• 44 markets<br>• 10 regions|
|Food items|109 items|96 items in GLSS7<br>consumption module<br>matched|67 items in GLSS7<br>consumption module<br>matched|
|Non-food items|Paid rents and imputed rents<br>available with quality<br>information|Limited housing<br>information|Limited housing<br>information|

We compare price distributions of major food items in the market price survey and CPI price data. Figure 3 shows price distributions of the following food items: smoked herring and salmon, bread, smoked fish, maize (ground or dough), imported rice, fresh tomatoes, water, yam and water yam, local rice, and large onions.<sup>20</sup> Median price values between the market price survey and the CPI database are overall similar with smoked fish and water as exceptions.

> 19 Prices of about 307 items are regularly collected using the 2018 Classification of Individual Consumption According to Purpose (COICOP).

> 20 For each item, observations are treated as outliers and removed if their log prices are above or below 2.5 standard deviations.

14
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# **3.2. Empirical approach**

The main question we attempt to address is whether, and how, the CPI price data is useful for spatial price adjustment in poverty measurement, given the importance of detailed product quality information. To empirically examine it in this case study of Ghana, we compare the results of various spatial price measurement methods. The main methods we test are (a) the bilateral/multilateral price index approach, (b) the CPD approach, and (c) the Engel curve approach. The main data sets include (a) the market price survey data collected in parallel to the household budget survey and (b) CPI raw price data. The Engel approach requires no price data.

## _Price index approach_

We first calculate several bilateral price indexes, such as the Paasche index (Equation 1), Laspeyres index (Equation 2), and Fisher index (Equation 3), as described in Section 2.1, and compare the spatial price patterns indicated based on each approach. We also compare these results with those based on a multilateral price index (GEKS-Fisher index in Equation 8) to assess how transitivity (or lack thereof) influences the results.

## _CPD approach_

We examine the two types of CPD methods. The first approach is to use the weighted CPD (Equation 12) to calculate the multilateral price index by applying it to the item-level price data for which budget share information is available as the weight. While we apply both variable weights (Equation 13) and fixed weights (Equation 14), following Gibson, Le, and Kim (2017), we report only the results based on variable weights, as no substantial difference is found in the results depending on the weights.<sup>21</sup> The result of this approach is compared with a multilateral price index calculated by a GEKS-Fisher index method. Of particular interest in this comparison is how handling missing values by the CPD approach affects the spatial price measures.

## _Non-food prices_

As discussed in Section 2.2, the treatment of non-food items, particularly housing, is a vital issue in the calculation of spatial price indexes. The combination of using the CPD method and the CPI price data allows us to include many non-food items without losing quality information in the price data. In the approach, we compare the spatial patterns of (a) the food price index and (b) the food plus nonfood price index. In addition, we test the sensitivity of price measures to the omission of quality information (for both food and non-food items) by comparing the results with or without variety-level quality information in price observations.

For housing, we examine different approaches in the following aspects. First, we explore the influence of controlling for quality differences in housing across regions. We compare the results of spatial price measures based on the median values (that is, no control of quality) or hedonic regression (that is, with control of quality). Second, we also compare the results based on a price index that uses actually paid rents (that is, only rental units) and imputed rents (that is, owner-occupied and/or rent-free units included). The combination of these two aspects results in the following four housing approaches:

- _Median paid rent_ approach takes a median value of paid rents in each region.

- _Median imputed rent_ approach takes a median value of paid and imputed rents in each region.

- • _Hedonic paid rent_ approach estimates a hedonic regression model on paid rents in which coefficients for the regional dummy indicate housing price levels.

> 21 Results based on fixed weights are available upon request.

16
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**Table 5. Poverty and inequality measures by spatial deflation methodologies**

||||Food||F+|NF||F+N|F+H||
|---|---|---|---|---|---|---|---|---|---|---|
||GLSS|price|CPI (item)|CPI(variety)|CPI(item)|CPI(variety)||CPI (variety)+|GLSS housi|ng|
||||||||i|i<br> <br>WCP|i<br> <br>D +|i<br>|
||GEKS-F<br>(1)|WCPD<br>(2)|WCPD<br>(3)|WCPD<br>(4)|WCPD<br>(5)|WCPD<br>(6)|Medan<br>paid rent<br>(7)|Medan<br>imputed rent<br>(8)|Hedonc<br>paid rent<br>(9)|Hedonc<br>imputed rent<br>(10)|
|**Poverty head**|**count ratio (FGT**|**0)**|||||||||
|Ghana|24.4|24.0|24.1|24.5|24.5|24.6|24.1|24.4|24.3|24.3|
|Urban|8.1|8.0|7.8|8.1|8.4|8.3|8.5|8.3|8.4|8.3|
|Rural|41.3|40.5|40.9|41.5|41.1|41.5|40.1|41.0|40.5|40.7|
|**Poverty gap (**|**FGT1)**||||||||||
|Ghana|8.7|8.4|8.5|8.7|8.7|8.9|8.5|8.8|8.6|8.7|
|Urban|1.9|1.9|1.9|1.9|1.9|2.0|2.0|2.0|2.0|2.0|
|Rural|15.8|15.1|15.4|15.7|15.7|16.0|15.1|15.8|15.5|15.6|
|**Poverty squar**|**ed gap (FGT2)**||||||||||
|Ghana|4.5|4.2|4.3|4.4|4.4|4.6|4.3|4.5|4.4|4.4|
|Urban|0.7|0.7|0.7|7.0|0.7|0.7|0.7|0.7|0.7|0.7|
|Rural|8.3|7.8|8.1|8.2|8.3|8.5|8.0|8.4|8.2|8.3|

_Source:_ Authors’ calculations based on the GLSS7 and the CPI price data. _Note:_ F = Food; H = Housing; NF = Non-food; Variable weights are applied to the WCPD regression.

**Table 6. Regional poverty rates by spatial deflation methodologies**

||||Food||<br>F+|<br>NF||F+N|F+H||
|---|---|---|---|---|---|---|---|---|---|---|
||GLSS|price|CPI (item)|CPI(variety)|CPI(item)|CPI(variety)||CPI (variety)+|GLSS hous|ing|
||||||||Mdi|Mdi<br>WC|Hdi<br>PD +|Hdi|
||GEKS-F|WCPD|WCPD|WCPD|WCPD|WCPD|ean<br>paid rent|ean<br>imputed rent|eonc<br>paid rent|eonc<br>imputed rent|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Western|19.1|19.2|19.1|20.8|23.9|21.1|21.3|21.0|21.4|21.3|
|Central|17.8|16.3|14.4|14.2|14.3|14.2|13.5|13.7|13.7|13.7|
|Greater Accra|2.7|3.2|3.2|2.7|3.1|2.7|3.2|3.2|3.2|3.0|
|Volta|37.8|37.2|40.7|41.4|32.5|36.6|34.8|36.4|34.8|35.5|
|Eastern|15.7|16.6|15.2|16.2|17.6|15.5|15.5|14.9|14.9|15.2|
|Ashanti|14.6|14.2|14.0|14.1|12.7|14.1|13.8|13.7|13.9|13.9|
|Brong Ahafo|25.7|27.5|28.6|28.9|31.4|29.6|28.9|28.8|29.0|29.0|
|Northern|55.6|53.0|52.4|53.6|54.6|57.3|55.0|57.7|56.7|56.5|
|Upper East|67.1|60.3|60.6|60.7|62.2|62.1|61.0|61.1|61.3|61.1|
|Upper West|71.6|70.9|75.4|75.6|78.7|78.5|76.1|77.4|76.8|77.2|
|Ghana|24.4|24.0|24.1|24.5|24.5|24.6|24.0|24.4|24.2|24.3|
|Urban|8.1|8.0|7.8|8.1|8.4|8.3|8.5|8.3|8.4|8.3|
|Rural|41.3|40.4|40.8|41.4|41.1|41.5|40.1|40.9|40.5|40.6|

_Source:_ Authors’ calculations based on the GLSS7 and the CPI price data. _Note:_ Variable weights are applied to the WCPD regression.

22
# **5. Discussion and conclusion**

In this paper, we examine the spatial price adjustment methodology in poverty measurement with a focus on the potential use of the CPI price data by applying the CPD method. Previous studies have highlighted the importance of the information about detailed product quality in the price data in the spatial comparison of the cost of living. Taking advantage of the data availability in Ghana, we estimate spatial prices in the country by applying the CPD method to the CPI raw price data. We also develop different approaches to measuring housing costs and combine them with the CPD method. The resulting spatial price indexes are compared to other indexes based on different items (food only, with and without housing), different index calculations (for example, Laspeyres, Paasche, and Fisher), and different price data (the market price survey).

Our analysis indeed underscores the importance of detailed product quality information in the price data. Expanding quality information in the price data from the market survey data to the item-level CPI price data to the variety-level CPI price data, we observe a convergence of regional price levels, due to the falling price level of Greater Accra relative to other regions (or conversely due to the increasing price levels of other regions relative to Greater Accra). This is because of the differences in the quality of goods and services available across the regions. Not controlling for these quality differences leads to a biased estimate of the price levels, which biases the price-level estimates of regions with higher-quality items (like Greater Accra) upward and those with lower-quality items downward.

Our Ghana case study suggests that CPI price data appear to be useful for constructing a spatial deflator poverty measurement, conditional on its availability and quality. The key conditions include the geographic coverage of price data collection and the detailed product specification list that guides data collection in a consistent manner across regions. For the primary purpose of the CPI—that is, the monitoring of temporal price changes—price data collection from rural areas is not necessary. However, using the CPI price data for spatial price adjustment requires rural areas to be well covered for poverty measurement and analysis.

Admittedly, most low- and middle-income countries do not have the CPI price data readily suitable for spatial price adjustment. Gibson rightly points out in the handbook published by the United Nations Statistics Division (2005, 184), The final choice, of relying on existing price collection efforts, is unlikely to work in many settings. The Consumer Price Index in many countries relies almost solely on urban prices, so these would not be applicable for calculating either poverty lines or spatial deflators and for imputing the value of consumption for rural households. Moreover, as explained above, the commodity weighting in a CPI is much more towards the consumption pattern of richer households, so the index values are unlikely to be relevant to poverty-related analysis.

and recommends as follows: Given the need for price data and the concerns about both unit values and relying on existing price collection efforts, it would be worthwhile for statistical agencies to invest more effort in gathering prices from local stores and markets and opinions about prices when their household surveys are fielded.

While this remains true even today, there are a few countries that have considerably invested in CPI data collection even in Africa, such as Ghana, Rwanda, Ethiopia, and Zimbabwe. Importantly, these countries also already use the CPI price data for spatial price adjustment for official poverty 23
measurement. South Africa is the most recent case that updated its CPI price database so that subnational PPPs can be calculated from it.

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27
# **Appendix A. Additional tables and figures**

**Table A1. List of price surveys in Sub-Saharan African countries**

|No.|Country|Survey|Year|
|---|---|---|---|
|1.|Burkina Faso|EMC2014|2014|
|2.|Central African Republic|ECASEB2008|2008|
|3.|Gabon|EGEPII-2017|2017|
|4.|Ghana|GLSS3, GLSS4, GLSS5, GLSS6, and|1991, 1998, and 2005,|
|||GLSS7|2012/13, and 2016/17|
|5.|Guinea|EIBEP2002, ELEP|2002 and 2012|
|6.|Gambia, The|IHS2015|2015|
|7.|Guinea-Bissau|ILAP2010|2010|
|8.|Kenya|KIHBS2015|2015|
|9.|Liberia|HIES2014 and HIES2016|2014 and 2016|
|10.|Mauritania|EPCV2004. EPCV2008, EPCV2014|2004, 2008, and 2014|
|11.|Niger|ECVMA2014|2014|
|12.|Nigeria|NLSS2003-04, NLSS2008-09|2003/04 and 2008/09|
|13.|Sierra Leone|SLIHS2003|2003|
|14.|Uganda|UNHS2005, UNHS2012|2005 and 2012|
|15.|Zambia|LCMS2010|2010|
|16.|Zimbabwe|PICES2017|2017|

_Source:_ Authors

**Table A2. List of CPI price data in Sub-Saharan African countries**

|No.|Country|Geographic coverage of data collection|
|---|---|---|
|1|Burkina Faso|Urban|
|2|Cameroon|Urban|
|3|Congo, Dem. Rep.|Urban|
|4|Côte d’Ivoire|Urban|
|5|Ethiopia|Urban and rural|
|6|Ghana|Urban and rural|
|7|Madagascar|Urban|
|8|Mauritius|Urban|
|9|Mozambique|Urban|
|10|Nigeria|Urban and rural|
|11|Rwanda|Urban and rural|
|12|Senegal|Urban|
|13|South Africa|Urban and rural|
|14|Tanzania|Urban|
|15|Togo|Urban|
|16|Uganda|Urban|
|17|Zimbabwe|Urban and rural|

_Source:_ Authors’ work building on “Consumer Price Indices,” Laborsta Internet, ILO, Geneva (Dabalen, Gaddis, and Nguyen 2019).

28
**Table A3. Official poverty rates and spatial price indexes in Ghana**

||||Officialprice i|ndex|
|---|---|---|---|---|
||Povertyrate|Total|Food|Non-food|
|National|23.41|0.96|0.94|1.01|
|Urban|7.76|0.97|0.96|1.02|
|Rural|39.52|0.95|0.93|1|
|Western|21.10|1.02|1|1.04|
|Central|13.77|0.98|0.94|1.03|
|Greater Accra|2.47|1.02|1.02|1.04|
|Volta|37.27|0.99|0.94|1.08|
|Eastern|12.56|0.95|0.94|0.96|
|Ashanti|11.60|0.95|0.9|1.03|
|Brong Ahafo|26.81|0.94|0.91|0.97|
|Northern|61.08|0.97|0.98|0.96|
|Upper East|54.83|0.86|0.8|0.93|
|Upper West|70.86|0.92|0.9|0.96|

_Source:_ Authors’ calculations based on GLSS7.

_Note:_ Price index is calculated by the population weighted average of monthly (from October 2016 to September 2017) index values. Because of this, Greater Accra’s value is not equal to 1.

**Table A4. Comparison of food price indexes: Laspeyres, Paasche, and Fisher**

||Laspeyres<br>(1)|Paasche<br>(2)|Fisher<br>(3)|
|---|---|---|---|
|Western Urban|104.9|101.4|103.2|
|Western Rural|96.5|92.4|94.4|
|Central Urban|110.3|105.4|107.8|
|Central Rural|107.4|103.9|105.6|
|Greater Accra Urban|109.9|100.2|105.0|
|Greater Accra Rural|114.6|104.0|109.1|
|Volta Urban|99.3|97.0|98.1|
|Volta Rural|102.5|98.4|100.5|
|Eastern Urban|102.8|102.1|102.4|
|Eastern Rural|104.6|100.9|102.7|
|Ashanti Urban|115.1|112.4|113.7|
|Ashanti Rural|105.9|102.3|104.1|
|Brong Ahafo Urban|92.2|90.5|91.4|
|Brong Ahafo Rural|93.5|89.1|91.3|
|Northern Urban|105.4|70.2|86.0|
|Northern Rural|105.5|70.6|86.3|
|Upper East Urban|130.0|100.3|114.2|
|Upper East Rural|116.4|93.8|104.5|
|Upper West Urban|104.7|87.5|95.7|
|Upper West Rural|108.2|78.6|92.2|
|Ghana|106.5|95.1|100.0|

_Source:_ Authors’ calculations based on GLSS7 market price survey.

29
**Table A5. Comparison of bilateral and multilateral food price index**

||GL<br>|SS7 market price sur<br>|vey<br>|CPI (item)<br>|CPI (variety)<br>|
|---|---|---|---|---|---|
||Fisher|GEKS-Fisher|WCPD|WCPD|WCPD|
||(1)|(2)|(3)|(4)|(5)|
|Western|98.4|99.5|100.1|98.7|100.6|
|Central|106.3|105.5|104.1|99.3|97.9|
|Greater Accra|106.6|107.9|116.9|114.4|109.6|
|Volta|98.9|98.9|99.4|104.9|105.6|
|Eastern|102.1|99.9|103.6|99.0|100.0|
|Ashanti|108.5|106.8|106.9|104.0|104.4|
|Brong Ahafo|91.0|91.1|96.6|97.9|98.0|
|Northern|85.8|86.4|84.2|83.3|85.1|
|Upper East|108.9|111.5|95.9|96.0|96.1|
|Upper West|93.6|92.6|92.4|102.5|102.8|
|Ghana|100.0|100.0|100.0|100.0|100.0|

_Source:_ Authors’ calculations based on the GLSS7 market price survey data and the CPI price data.

**Table A6. Summary of food and non-food price indexes**

||Food+ <br>|non-food<br>||Food+non-foo<br>|d+housing<br>||
|---|---|---|---|---|---|---|
||CPI (item)|CPI (variety)|CP<br>|I (variety) and GL<br>|SS7 housing<br>|data<br>|
||WCPD|WCPD|WCPD +<br>median<br>paid rent|WCPD +<br>median<br>imputed rent|WCPD +<br>hedonic<br>paid rent|WCPD +<br>hedonic<br>imputed rent|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Western|105.7|100.9|101.1|100.3|101.4|100.8|
|Central|97.5|96.7|95.9|96.5|96.3|96.7|
|Greater Accra|111.1|106.3|113.8|112.1|111.5|110.7|
|Volta|88.2|94.8|92.7|94.9|93.0|94.1|
|Eastern|103.9|98.9|98.2|97.8|97.9|98.1|
|Ashanti|97.7|103.9|103.3|102.6|103.4|103.6|
|Brong Ahafo|101.9|98.6|97.2|96.9|97.3|97.4|
|Northern|84.7|89.3|85.9|90.3|88.6|88.2|
|Upper East|98.0|97.8|94.0|96.3|95.3|95.9|
|Upper West|110.6|109.5|100.6|105.6|102.6|104.7|
|Ghana|100.0|100.0|100.0|100.0|100.0|100.0|

_Source:_ Authors’ calculations based on the GLSS7 data, the GLSS7 market price survey data, and the CPI price data.

**Table A7. Housing** **<u>price indexes</u>**

||Median<br>paid rent<br>(1)|Median<br>imputed rent<br>(2)|Hedonic<br>paid rent<br>(3)|Hedonic<br>imputed rent<br>(4)|
|---|---|---|---|---|
|Western|44.5|45.4|57.0|54.8|
|Central|36.6|47.9|48.3|56.5|
|Greater Accra|100.0|100.0|100.0|100.0|
|Volta|31.4|50.8|39.1|50.1|
|Eastern|39.3|40.2|43.0|48.3|
|Ashanti|40.1|39.9|50.0|55.6|
|Brong Ahafo|34.0|36.2|41.3|46.0|
|Northern|25.0|50.5|43.8|45.3|
|Upper East|30.5|44.3|41.9|46.7|
|Upper West|25.2|44.0|37.2|46.1|

_Source:_ Authors’ calculations based on the GLSS7 data.

30
**Table A8. Estimation results of hedonic regression models**

||Paid rent|Imputed rent|
|---|---|---|
||(1)|(2)|
|Number of bed rooms|<br>0.355<sup>***</sup><br>|<br>0.387<sup>***</sup><br>|
||(16.30)|(163.1)|
|Dwelling type: separate house|0.464<sup>***</sup>|0.261<sup>***</sup>|
||(3.63)<br>|(9.22)<br>|
|Dwelling type: semidetached house|0.448<sup>**</sup>|0.298<sup>***</sup>|
||(3.29)|(9.58)|
|Dwelling type: flat|0.809<sup>***</sup><br>|0.470<sup>***</sup><br>|
||(5.81)|(14.17)|
|Dwelling type: compound house|0.324<sup>**</sup>|0.118<sup>***</sup>|
||(2.60)|(4.23)|
|Dwelling type: huts|0.095<br>|0.109<sup>***</sup><br>|
||(0.48)|(3.50)|
|Dwelling type: tent and others (reference)|||
|<br>Wall materials: high quality|0.306<sup>***</sup>|0.076<sup>***</sup>|
||(6.98)|(8.55)|
|Wall materials: wood|<br>−0.042|<br>−0.131<sup>***</sup>|
||(−0.48)|(−5.25)|
|Wall materials: mud and others (reference)|||
|<br>Floor materials: high quality|0.349<sup>**</sup>|0.459<sup>***</sup>|
||(3.26)|(24.60)|
|Floor materials: tiles|−0.152|−0.039<sup>**</sup>|
||(−1.60)|(−2.99)|
|Floor materials: mud (reference)<br>|||
|Roof materials: wood|−0.348|−0.084|
||(−1.24)|(−1.63)|
|Roof materials: metal sheet|−0.035|−0.029<sup>*</sup>|
||(−0.23)|(−1.94)|
|Roof materials: asbestos|0.014|0.007|
||(0.09)|(0.38)|
|Roof materials: concrete|0.059|0.084<sup>**</sup>|
||(0.36)|(3.29)|
|Roof materials: mud bricks and others (reference)|||
|<br>Toilet type: pit or bucket|0.203<sup>***</sup>|0.027<sup>**</sup>|
||(3.41)|(2.45)|
|Toilet type: public toilet|0.060|0.003|
||(1.09)|(0.26)|
|Toilet type: WC|0.444<sup>***</sup>|0.278<sup>***</sup>|
||(7.90)|(23.97)|
|Toilet type: no facility (reference)|||
|Water type: pipe inside|0.111|0.156<sup>***</sup>|
||(1.92)|(9.92)|
|Water type: pipe outside|−0.054|−0.035<sup>**</sup>|
||(−1.85)|(−4.33)|
|Water type: Borehole and others (reference)|||
|National grid connection (1 = connected; 0 = none)|0.145<sup>**</sup>|0.166<sup>***</sup>|
||(2.93)|(18.64)|
|Region: Western (urban)|<br>−0.356<sup>***</sup>|<br>−0.367<sup>***</sup>|
||(−6.33)<br><sup>***</sup>|(−20.71)<br><sup>***</sup>|
|Region: Western (rural)|−0.765|−0.895|
||(−8.77)|(−53.50)|
|Region: Central (urban)|<br>−0.511<sup>***</sup>|<br>−0.340<sup>***</sup>|
||(−8.61)|(−19.03)|
|Region: Central (rural)|<br>−0.981<sup>***</sup><br>|<br>−0.910<sup>***</sup><br>|
||(−13.71)|(−51.15)|
|Region: Greater Accra (urban) (reference)|||
|Region: Greater Accra (rural)|−0.094|−0.459<sup>***</sup>|
||(−1.14)|(−17.26)|

31
**Table A8. Estimation results of hedonic regression models (continued)**

||Paid rent<br>(1)|Imputed rent<br>(2)|
|---|---|---|
|Region: Volta (urban)|−0.664<sup>***</sup>|−0.542<sup>***</sup>|
||(−9.37)|(−26.00)|
|Region: Volta (rural)|−1.135<sup>***</sup>|−0.835<sup>***</sup>|
||(−10.03)|(−46.89)|
|Region: Eastern (urban)|−0.591<sup>***</sup>|−0.584<sup>***</sup>|
||(−10.11)|(−33.58)|
|Region: Eastern (rural)|−1.109<sup>***</sup>|−0.923<sup>***</sup>|
||(−15.36)|(−54.83)|
|Region: Ashanti (urban)|−0.545<sup>***</sup>|−0.472<sup>***</sup>|
||(−13.17)|(−35.92)|
|Region: Ashanti (rural)|−0.997<sup>***</sup>|−0.907<sup>***</sup>|
||(−15.23)|(−59.85)|
|Region: Brong Ahafo (urban)|−0.848<sup>***</sup>|−0.757<sup>***</sup>|
||(−13.81)|(−41.52)|
|Region: Brong Ahafo (rural)|−0.931<sup>***</sup>|−0.856<sup>***</sup>|
||(−11.47)|(−48.39)|
|Region: Northern (urban)|−1.092<sup>***</sup>|−0.902<sup>***</sup>|
||(−10.19)|(−41.42)|
|Region: Northern (rural)|−0.739<sup>***</sup>|−0.795<sup>***</sup>|
||(−3.69)|(−41.74)|
|Region: Upper East (urban)|−0.565<sup>**</sup>|−0.825<sup>***</sup>|
||(−3.26)|(−23.16)|
|Region: Upper East (rural)|−0.981<sup>***</sup>|−0.790<sup>***</sup>|
||(−3.60)|(−34.09)|
|Region: Upper West (urban)|−0.666<sup>***</sup>|−0.840<sup>***</sup>|
||(−3.84)|(−17.64)|
|Region: Upper West (rural)|−1.075<sup>**</sup>|−0.804<sup>***</sup>|
||(−2.99)|(−32.90)|
|Constant|5.476<sup>***</sup>|5.730<sup>***</sup>|
||(25.37)|(165.7)|
|Observations<br>|2860|14008|
|Adjusted_R_<sup>2</sup>|0.502|0.802|

_Note: t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001. Dependent variables are the natural logarithm of paid housing rents (columns 1 and 3) or imputed rents (columns 2 and 4).

32
# **Appendix B. Is the Engel curve approach a promising alternative?**

Some studies developed and applied several Engel curve approaches to construct a spatial price index. The most common use of the Engel curve approach is motivated by temporal focus (rather than spatial), estimating and correcting a CPI bias.<sup>24</sup> For example, Dabalen, Gaddis, and Nguyen (2019) use this method and find a substantial impact of CPI bias on poverty estimates among several African countries. Other examples of the use of Engel curve methods for temporal analysis include Nakamura, Steinsson, and Liu (2016), who argue that China’s economic growth is overestimated due to CPI bias (that is, underestimation of inflation).

More relevant uses of Engel curve approaches for our study are those from spatial (or spatiotemporal) perspectives. Almas (2012) uses an Engel curve method to correct PPP bias across countries, finding that the income of poorer countries tends to be overestimated. Almas and Kjelsrud (2017) incorporate the expenditure-specific cost of living in Engel curve model to measure income inequality in India. They find that the relative price changes were pro-poor during the study period and thus growing income inequality was overestimated further. Almas and Johnsen (2018) estimate an Engel curve model to measure poverty comparable across time and space in China. They find that inflation was mild in urban/rich areas and thus poverty reduction has been more moderate than previously assumed. Most importantly, Gibson, Le, and Kim (2017) compare the performance of Engel curve methods as a tool for spatial price adjustment with other methods. Their analysis based on Vietnam’s detailed price data concludes that the Engel curve approach is not suitable for spatial adjustment.<sup>25</sup> This line of approaches assumes that households with the same expenditure share for food have the same level of utilities across space. In other words, the difference in nominal income (or expenditure) among households with the same expenditure share for food reflects spatial cost-of-living differentials. However, it is possible that many other unobserved factors affect food budget shares. As Gibson, Le, and Kim (2017) argue, however, this assumption may not hold across space.

_Engel curve method with no price data._ A potential advantage of the Engel curve method for spatial price adjustment is that price data are not necessary in some cases. Almas, Kjelsrud, and Somanathan (2019) applied such a method to estimate poverty in India. In a standard Engel curve model, the relative (aggregated) prices of food and non-food items are required. However, they argue that a restricted version of the Engel curve model below can be used by assuming that the budget share for food is not influenced by relative prices. (15), where indicates the budget share for food of household wwiijjtt = aa+ bb�lnyyiijjtt −lnPPjjtt�+ θθEEiijjtt _i_ in region + εεiijjtt _j_ (or urban/rural) at time _t_ , iijjtt � is the real income for household _i_ (nominal expenditure divided by the overall ww price level of region _j_ , ), is a vector of household-specific control variables, and is an error iijjtt jjtt iijjtt �lnyy −lnPP yy term. Given that varies only for region/time, it can be identified through region- and time-specific jjtt iijjtt iijjtt PP EE εε dummies ( ): jjtt PP DDjjtt . (16) iijjtt hsstt iijjtt jj1 jj1 jj2 jj2 iijjtt ww = aa+ bblnyy + θθEE + ∑dd DD + ∑dd DD + εε

> 24 It is noted that even in the case of temporal analysis as the primary purpose, the Engel curve method requires a regional price index to identify CPI bias (discussed later).

> 25 Other studies that construct a spatial price index and estimate poverty/inequality using an Almost Ideal Demand System (AIDS) type demand system include Chakrabarty, Majumder, and Ray (2015); Majumder, Ray, and Sinha (2015); Navamuel, Morollon, and Vazquez (2018); and Ravallion and van de Walle (1991).

33
It is common to group items (for example, rice instead of white rice and brown rice) when estimating an AIDS-type demand system (Deaton and Muellbauer 1980), including the Engel curve method above. Such aggregation can be done by taking either the mean or median of items under the same commodity groups, calculating a standard price index (for example, Laspeyres), or estimating a CPD regression (for example, Almas, Kjelsrud, and Somanathan 2019). Since prices in the same commodity groups tend to vary widely across space (Gibson and Kim 2015), it is important to ensure the variety of goods included in the groups.<sup>29</sup>

# _Results_

Figure B1 shows Engel curves for three different regions (Greater Accra, Upper West, and Eastern). It is worth noting that the shifts in the Engel curves across regions are not parallel—a violation of the assumptions implicit in our Engel curve specifications. This is also one of the two facts documented in India in Atkin et al. (2018) which motivate their alternative approach. These authors also find the

> 26 Almas, Kjelsrud, and Somanathan (2019) justify this assumption by stating “the evidence on the effect of relative prices on food shares is mixed but most studies find insignificant or small effects.” See also a study of the Russian Federation by Gibson, Stillman, and Le (2008), which reports similar CPI bias in Engel curve models with and without relative food prices.

> 27 If we simply distinguish price levels between urban and rural sectors (instead of regional levels), then price relatives need to be calculated at the regional level (instead of district levels). In this case, the regional CPI may be used if available.

28 Allowing the budget share for food to depend on relative prices in the demand model above means that the cost of living is income-specific (that is, non-homothetic). In other words, the estimated cost of living represents one reference household income level, which is not necessarily the same for the reference in the CPI. It is well known that as a plutocratic price index, the CPI measures changes in the cost of living for households in the upper end of the income distribution (Gaddis 2016). Some recent studies attempt to estimate the cost of living for different points of the income distribution. For example, Almas, Beatty, and Crossley (2018) propose a new method, the translated Engel curve (TEC) method, to estimate expenditure-specific CPI bias.

29 This is called ‘Hicksian separability’—relative prices of elementary goods within a commodity group are constant, allowing the price of a single representative good to proxy for the group price level (Gibson and Kim 2015).

34
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# **Appendix C: Budget shares**

**Table C1. Food items and budget shares in GLSS7**

|Consumption Survey Group|Budget Share|Price Survey Item|
|---|---|---|
|Rice - local|0.032|Rice - local|
|Rice - imported|0.048|Rice - imported|
|Guinea corn|0.004|Guinea corn/sorghum red grains|
|||Guinea corn/sorghum white grains|
|Maize grains (white/yellow)|0.025|Maize (dried, white grains)|
|||Maize (dried, yellow grains)|
|||Maize, new dried grains|
|Millet|0.003|Millet|
|Millet flour|0.002|Millet flour|
|Wheat flour|0.000|Takoradi flour mill|
|||GMG|
|||Wheat semolina, suji|
|Maize, ground/dough|0.048|Maize, ground (flour)|
|||Corn dough|
|Cerelac (baby food)|0.003|Cerelac made from maize|
|||Cerelac made from millet|
|||Cerelac made from rice|
|||Cerelac made from wheat|
|Oats|0.001|White oats|
|Cassava - kokonte|0.007|Cassava - kokonte|
|Cassava - dough|0.009|Cassava - dough|
|Gari (yellow/white)|0.008|Cassava - gari|
|Bread|0.064|Sugar bread|
|||Whole wheat bread|
|||Butter bread<br>Tea bread|
|Biscuits|0.007|Picadilly biscuit|
|||Kings crackers|
|||Malt and milk biscuit<br>Cream crackers|
|||Digestive biscuit|
|||Jack and Jill|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Other cereals|0.002|Cornflakes (Kellogg's)|
|Meat|0.028|Beef with bones|
|||Beef without bones|
|||Cow leg, local|
|||Cow leg, imported|
|Pork|0.003|Pork meat|
|||Pork - ribs|
|||Pork - fillet|
|||Pork - feet|

37
**Table C1. Food items and budget shares in GLSS7 (continued)**

|Consumption Survey Group|Budget Share|Price Survey Item|
|---|---|---|
|Mutton|0.001|Mutton chop|
|||Mutton mixed cut|
|Goat|0.005|Sheep - live|
|||Goat - live|
|||Goat meat|
|Chicken|0.005|Live chicken (local)|
|||Live chicken (broiler)|
|||Live chicken (layers)|
|Frozen chicken|0.018|Chicken parts (thighs) - frozen|
|||Chicken parts (wings)|
|Guinea fowl|0.001|Guinea fowl - live|
|Corned beef|0.001|Exeter corned beef|
|||Corned beef (other brands)|
|Sausage (all types)|0.000|Sausage|
|Grasscutter|0.002|Bushmeat (grasscutter)|
|Game bird|0.000|(a)|
|Other meat|0.001|(b)|
|Fresh/frozen fish|0.015|Kpala (starvids) frozen|
|||Fresh river fish|
|||Tilapia - fresh /frozen|
|Shrimps|0.000|Shrimps|
|Snails|0.001|Snails|
|Crab|0.001|Crab|
|Smoked fish|0.058|Smoked river fish|
|Smoked herring/salmon|0.091|Herrings - smoked|
|Fried fish|0.013|(c)|
|Dried fish|0.004|Dried fish - koobi|
|Other fish|0.009|Fish (salted)|
|Sardines/tuna in vegetable oil|0.004|Titus|
|||Princess|
|||Obaapa|
|||Gino|
|||Starkist|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Mackerel in tomato sauce|0.004|Geisha|
|||African queen|
|||Ena pa|
|||Obaapa|
|||Delay|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Fresh milk|0.001|Fresh milk|
|||Baby milk (lactogen)|
|||Fan milk|

38
**Table C1. Food items and budget shares in GLSS7 (continued)**

|Consumption Survey Group|Budget Share|Price Survey Item|
|---|---|---|
|Powdered milk|0.004|Nido milk (sachet)|
|||Peak milk (sachet)<br>Cowbell (sachet)<br>Ideal milk (sachet)<br>Nunu milk (sachet)<br>Loya milk (sachet)<br>Miksi milk (sachet)<br>Vega milk (sachet)|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Evaporated milk|0.007|Evaporated milk - ideal|
|||Evaporated milk - peak|
|||Evaporated milk - nunu|
|||Evaporated milk - vega|
|||Evaporated milk - carnation|
|Condensed milk|0.000|Condensed milk, tin - peak|
|Other milk products|0.005|Other (specify) _ _ _ _ _ _ _ _ _ _|
|Chicken eggs|0.009|Chicken eggs (fresh, single)|
|Margarine|0.000|Margarine|
|Coconut oil|0.000|Coconut oil|
|Groundnut oil|0.002|Groundnut oil|
|Palm oil (red oil)|0.008|Palm oil (red oil)|
|Shea butter|0.005|Shea butter|
|Palm kernel oil|0.000|Palm kernel oil|
|Other oils|0.021|Vegetable oil - frytol|
|||Vegetable oil - gino|
|||Vegetable oil - obaapa|
|||Vegetable oil (other, specify)|
|Coconut (fresh/dried)|0.002|Coconut (fresh)|
|Banana|0.005|Banana|
|Oranges|0.003|Oranges|
|Pineapple|0.001|Pineapple|
|Mango|0.001|Mango (local)|
|||Mango (grafted)|
|Watermelon|0.003|Watermelon|
|Avocado pear|0.001|Avocado pear|
|Apples/sweet apple|0.001|Apples (foreign)|
|||Sweet apple|
|Grapes|0.000|Grapes|
|Lime|0.000|Lime|
|Cashew|0.000|(d)|
|Pawpaw|0.001|Pawpaw|
|Other fruits|0.000|(e)|
|Canned fruit|0.000|Canned (processed) fruits|

39
**Table C1. Food items and budget shares in GLSS7 (continued)**

|Consumption Survey Group|Budget Share|Price Survey Item|
|---|---|---|
|||Cocoyam leaves (kontomire) or|
|Cocoyam leaves (kontomire)|0.007|<br>Alefu|
|Sweet pepper|0.000|Sweet pepper|
|Carrot|0.001|Local carrot|
|||Carrot|
|Garden eggs|0.009|Garden eggs|
|Okro (fresh)|0.017|Okro (fresh)|
|Pepper (fresh)|0.017|Pepper (fresh)|
|Powder pepper|0.002|Powder pepper|
|Onions (large)|0.028|Onions (large)|
|Tomatoes (fresh)|0.040|Tomatoes (fresh)|
|Other vegetables|0.005|(f)|
|Tomato paste|0.020|Gino|
|||Obaapa|
|||Pomo|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|White beans (cowpea)|0.007|White beans (cowpea)|
|Palm nut (fruits)|0.003|Palm fruits|
|Groundnuts (shelled)|0.005|Groundnuts (shelled)|
|Groundnut (paste)|0.006|Groundnuts (roasted)|
|Agushie seeds (or milled)|0.001|(g)|
|Plantain|0.025|Plantain (green)|
|Cassava (fresh)|0.026|Cassava (fresh)|
|Cocoyam|0.004|Cocoyam|
|Yam/water yam|0.038|Yam - puna|
|||Water yam|
|Taro|0.000|(h)|
|Potatoes|0.000|(i)|
|Other tubers|0.000|(j)|
|Sugar|0.013|Cube sugar (Saint Louis)|
|||Granulated sugar|
|Honey|0.000|Honey (bottle)|
|Chocolate bar|0.000|Kings bite|
|||Golden tree chocolate|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Chewing gum|0.001|Chewing gum|
|||Trident|
|||PK|
|||Mentos|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Garlic|0.001|Garlic|
|Ginger|0.002|Ginger|
|Vinegar|0.000|Vinegar|

40
**Table C1. Food items and budget shares in GLSS7 (continued)**

|Consumption Survey Group|Budget Share|Price Survey Item|
|---|---|---|
|Dawadawa|0.007|(k)|
|Curry powder|0.000|(l)|
|Black/dried red pepper|0.007|Pepper (dried)|
|||Chili powder (black pepper)|
|Other spices|0.000|(m)|
|Other condiments|0.013|Royco|
|||Onga cube|
|||Benny|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Salt|0.010|Salt (iodized - local)|
|||Anapuna iodated salt|
|||U2 iodated salt|
|Coffee (Nescafe)|0.000|Nescafe - tin|
|||Pure cocoa powder (for example,|
|Pure cocoa powder|0.000|Brown Gold)|
|Tea|0.002|Tea bags (for example, Lipton)|
|Cocoa with milk powder|0.012|Cocoa with milk powder|
|||Bournvita|
|||Cow bell (coffee, choco, and so on)<br>This way chocolate drink|
|||Country milk - chocolate|
|Water|0.038|Sachet water|
|||Coca Cola/Fanta (bottle)/Sprite|
|Soft drinks|0.006|(bottle)|
|Malt|0.012|Malta Guinness|
|||Magic malt|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|
|Fruit juice|0.005|Don Simon|
|||Frutelli|
|||Blue skies|
|||Pure Heaven|
|||Kalipo|
|||Healthy live|
|||Ceres|
|||Other (specify) _ _ _ _ _ _ _ _ _ _|

_Note:_ Average budget shares is each item’s household expenditure share on average. For items with multiple prices per consumption survey group, the price relativities are averaged before mapping to the budget shares. Items with () use prices of similar items as follows: (a) guinea fowl - live; (b) average of the meat group (excluding sausage and corned beef); (c) dried fish; (d) groundnuts (shelled) and groundnuts (roasted); (e) average of the fruit group; (f) average of the vegetable group; (g) half of the price of groundnuts (shelled); (h) yam - puna and water yam; (i) cassava (fresh); (j) average of the tuber group; (k) white beans (cowpea); (l) pepper (dried) and chili powder (black pepper); and (m) royco, onga cube, benny, and other condiments.

41
**Table C 2. Food items and budget shares in CPI price database**

|Consumption Survey Group|Budget Share|CPI Item|
|---|---|---|
|Rice - local|0.032|Rice - local|
|Rice - imported|0.048|Rice - imported|
|Guinea corn|0.004|Sorghum white grains|
|||Sorghum red grains|
|Maize grains (white/yellow)|0.025|Maize grains|
|Millet|0.003|Millet|
|Millet flour|0.002|Millet flour|
|Wheat flour|0.000|Wheat flour|
|Maize, ground/dough|0.048|Corn dough|
|||Corn flour (Akpele)|
|Cerelac (baby food)|0.003|Cerelac (baby food)|
|||Cerelac (tin)|
|||Yumvita|
|Oats|0.001|White oats (other cereals)|
|||Quaker|
|Cassava - kokonte|0.007|Cassava - kokonte|
|Cassava dough|0.009|Cassava - processed|
|Gari (yellow/white)|0.008|Yellow gari|
|||Gari|
|Bread|0.064|Bread|
|||Other bread|
|Biscuits|0.007|Biscuit (simple cookie)|
|||Jack and Jill|
|||Ginger biscuits|
|Other cereals|0.002|Indomie or instant noodles|
|||Spaghetti|
|||Other cereal products|
|||Other spaghetti|
|Meat|0.028|Cow meat|
|||Beef without bones|
|||Cow leg, local|
|||Cow leg, imported|
|Pork|0.003|Pork meat|
|||Pork, ribs|
|||Pork, shoulder|
|Mutton|0.001|Mutton|
|Goat|0.005|Goat meat|
|Chicken|0.005|Live chicken|
|||Live chicken, local|
|Frozen chicken|0.018|Frozen chicken|
|Guinea fowl|0.001|Guinea fowl - live|
|Corned beef|0.001|Corned beef|
|||Exeter corned beef|

42
**Table C2. Food items and budget shares in CPI price database (continued)**

|Consumption Survey Group|Budget Share|CPI Item|
|---|---|---|
|Sausage (all types)|0.000|Sausage|
|Grasscutter|0.002|Bushmeat|
|Game bird|0.000|(a)|
|Other meat|0.001|Dog meat|
|Fresh/frozen fish|0.015|Frozen fish|
|Shrimps|0.000|Shrimps|
|Snails|0.001|Snails|
|Crab|0.001|Crab|
|Smoked fish|0.058|Smoked river fish|
|Smoked herring/salmon|0.091|Fish and seafood|
|Fried fish|0.013|Fish fried|
|Dried fish|0.004|Dried fish - koobi|
|Other fish|0.009|Fish (salted)|
|Sardines/tuna in vegetable oil|0.004|Tuna in vegetable oil|
|||Tuna, canned, starkist|
|||Tuna, canned, geisha|
|||Sardines in vegetable oil|
|Mackerel in tomato sauce|0.004|Mackerel in tomato sauce|
|Fresh milk|0.001|Milk (fresh)|
|Powdered milk|0.004|Powdered milk|
|||Peak (sachet)|
|||Powdered milk (sachet)|
|Evaporated milk|0.007|Evaporated milk|
|||Cowbell (sachet)|
|Condensed milk|0.000|(b)|
|Other milk products|0.005|Fan milk products|
|Chicken eggs|0.009|Chicken eggs (fresh, single)|
|Margarine|0.000|Margarine|
|Coconut oil|0.000|Coconut oil|
|Groundnut oil|0.002|Oils and fats (ND)|
|Palm oil (red oil)|0.008|Palm oil (red oil)|
|Shea butter|0.005|Oils and fats (ND)|
|Palm kernel oil|0.000|Cooking oil|
|Other oils|0.021|Oils and fats (ND)|
|Coconut (fresh/dried)|0.002|Coconut (fresh)|
|Banana|0.005|Banana|
|Oranges|0.003|Oranges|
|Pineapple|0.001|Pineapple|
|Mango|0.001|Mango|
|Watermelon|0.003|Watermelon|
|Avocado pear|0.001|Avocado pear|
|Apples/sweet apple|0.001|Apples (foreign)|
|||Sweet apple|

43
**Table C2. Food items and budget shares in CPI price database (continued)**

|Consumption Survey Group|Budget Share|CPI Item|
|---|---|---|
|Grapes|0.000|Grapes|
|Lime|0.000|Lemon/lime|
|Cashew|0.000|(c)|
|Pawpaw|0.001|Pawpaw|
|Other fruits|0.000|(d)|
|Canned fruit|0.000|(e)|
|Cocoyam leaves (kontomire)|0.007|Cocoyam leaves (kontomire)|
|Sweet pepper|0.000|Fresh pepper|
|Carrot|0.001|Local carrot|
|||Foreign carrot|
|Garden eggs|0.009|Garden eggs|
|Okro (fresh)|0.017|Okro|
|Pepper (fresh)|0.017|Fresh pepper|
|||Pepper|
|Powder pepper|0.002|Powder pepper|
|Onions (large)|0.028|Onions|
|Tomatoes (fresh)|0.040|Tomatoes|
|Other vegetables|0.005|Fresh vegetables (ND)|
|Tomato paste|0.020|Gino|
|||Lele|
|||Tomato paste|
|White beans (cowpea)|0.007|White beans (cowpea)|
|Palm nut (fruits)|0.003|Palm nut|
|Groundnuts (shelled)|0.005|Groundnuts|
|Groundnut (paste)|0.006|Oils and fats (ND)|
|Agushie seeds (or milled)|0.001|(f)|
|Plantain|0.025|Plantain (green)|
|Cassava (fresh)|0.026|Cassava|
|Cocoyam|0.004|Cocoyam|
|Yam/water yam|0.038|Yam|
|Taro|0.000|(g)|
|Potatoes|0.000|(h)|
|Other tubers|0.000|(i)|
|Sugar|0.013|Sugar/honey|
|Honey|0.000|Sugar/honey|
|Chocolate bar|0.000|Chocolate bar|
|Chewing gum|0.001|Chewing gum|
|Garlic|0.001|Garlic|
|Ginger|0.002|Ginger|
|Vinegar|0.000|Vinegar|
|Dawadawa|0.007|(j)|
|Curry powder|0.000|(k)|
|Black/dried red pepper|0.007|(l)|

44
**Table C2. Food items and budget shares in CPI price database (continued)**

|Consumption Survey Group|Budget Share|CPI Item|
|---|---|---|
|Other spices|0.000|Cube spices|
|||Benny|
|||Maggi cube (crevette)|
|Other condiments|0.013|(m)|
|Salt|0.010|Salt - iodized|
|Coffee (Nescafe)|0.000|Coffee<br>Pure cocoa powder (for example,|
|Pure cocoa powder|0.000|Brown Gold)|
|Tea|0.002|Tea bag|
|Cocoa with milk powder|0.012|Cocoa with milk powder|
|Water|0.038|Mineral water (bottled)<br>Bottle water|
|||Sachet water|
|Soft drinks|0.006|Soft drink|
|||Fanta (can)|
|||Sprite (bottle)|
|||Coca Cola (can)|
|Malt|0.012|Malt drinks (bottle)|
|||Malt drinks (can)|
|Fruit juice|0.005|Fruit juice|

_Note:_ Average budget shares is each item’s household expenditure share on average. For items with multiple prices per consumption survey group, the price relativities are averaged before mapping to the budget shares. Items with () use prices of similar items as follows: (a) guinea fowl - live; (b) evaporated milk; (c) groundnuts; (d) average of the fruit group; (e) half of the average price of the fruit group; (f) half of the price of groundnuts (shelled); (g) yam; (h) cassava; (i) average of the tuber group; (j) white beans (cowpea); (k) powder pepper; (l) powder pepper; and (m) cube spices, benny, and maggi cubes.

45