metadata: authors: 0: author: Grootaert, Christiaan
1: author: Kanbur, Ravi
count: Cote d'Ivoire
docna: 0: docna: A new regional price index for Cote
            d'Ivoire using data from the international comparisons project
docty: Policy Research Working Paper
subtopic: Economics of Education,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Hydrology,Education Finance,Inequality,Poverty Assessment,Poverty Diagnostics,Poverty Lines,Small Area Estimation Poverty Mapping,Transport Services
teratopic: Poverty Reduction
abstracts: cdata!: The authors report on an exercise in
            economic statistics. They develop a regional price index for
            Cote d'Ivoire building on the strengths of two
            independent data sources: the Cote d'Ivoire Living
            Standards Survey (CILSS) and the International Comparisons
            Project (ICP). The CILSS collected detailed information on
            household incomes, spending, employment, and so on, but its
            coverage of prices left much to be desired. The ICP
            collected a wealth of information on prices across the
            country, but collected no information on household spending
            patterns or other socioeconomic data. The authors bring
            together these two sources to produce a regional price index
            that they argue is superior to previous estimates based
            solely on the Living Standards Survey. The procedures they
            follow should be of interest to practitioners faced with
            similar data shortcomings, particularly when working on
            Africa. They show this to be no mere statistical exercise.
            Using the new price index can have a significant effect on
            earlier evaluations of poverty in Cote d'Ivoire. They
            also use the new price information to construct
            disaggregated indices by commodity category and by poverty group.
geo_reg_key: 1927320,517191
repnme: repnme: A new regional price index for Cote
            d'Ivoire using data from the international comparisons project
display_title: A new regional price index for Cote
            d'Ivoire using data from the international comparisons project
listing_relative_url: /research/1993/01/699170/new-regional-price-index-cote-divoire-using-data-international-comparisons-project
topicv3: Water m1326522 137,Transport m1326661 135,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,User Fees in Education m1327515 1493
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Development Economics and Chief Economist
no_of_pages: 26
model_extractions: .4 <!-- Start of picture text --> Policy Research<br>Poverty Analysis and Policy<br><!-- End of picture text -->

**WPS 1080**

This paper-a product of the Pover;y and Social Policy Division, Africa Technical Department-is part of the output of the research project "Poverty and the Social Dimensions of Structural Adjustment in C6te d'Ivoire, 1985-88: A Policy-Oriented Analysis" (RPO 675-26). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Elena Vitanov, room J2-24 1, extension 38400 (January 1993, 21 pages).

Grootaert and Kanbur report on an exercise in economic statistics. They develop a regional price index for CBte d'Ivoire building on the strengths of two independent data sources: the C6te d'Ivoire Living Standards Survey (CILSS) and the International Comparisons Project (ICP).

Te CILSS collected detailed information on household incomes, spending, employment, and so on, but its coverage of prices left much to be desired. The ICP collected a wealth of information on prices across the country, but collected no information on household spending patterns **or** other socioeconomic data.

Grootaert and Kanbur bring together these two sources to produce a regional price index that they argue is superior te previous estimates based solely on the Living Standards Survey. The procedures they follow should be of interest to practitioners faced with similar data shortcomings, particularly when working on Africa.

They show this to be no mere statistical exercise. Using the new price index can have a significant effect on earlier evaluations of poverty in CBte d'lvoire. They also use the new price information to construct disaggregated indices by commodity category and by poverty group.

ThePolicy ResearchWorking PaperSeriesdisseminates thefmdings of work **under** way **in tieBank.** Anobjectiveof the series is to **get these** findings out quickly, even if presentations are less than **fully** polished. The **fmdings,** interpretations, and **conclusions** in these papers do not necessarily represent official Bank policy.

Produced by **the** PoUcy Research Dissamiation Center
Poverty and Social Policy Division Technical Department Africa Region The World Bank

**A NEW REGIONAL PRICE INDEX FOR COTE D'IVOIRE USING DATA FROM THE INTERNATIONAL COMPARISONS PROJECT**

Christiaan Grootaert Ravi Kanbur This paper is an output of the research project "Poverty and Social Dimensions of Structural Adjustment in Cote d'Ivoire, 1985-88 - A Policy-Oriented Analysis" RPO 675-26). The authors would like to thank Philippe Callier and Jean-Luc Dubois for helpful comments on an earlier draft, and Meera Venkataraman and Gi-Taik Oh for excellent computer programming and general analytic assistance.
# 1. **Introduction**

The theory of cost of living indices is now well established. Text books such as that by Deaton and Muellbauer (1980) give an account of the concepts and principles involved in the theoretically ideal price index, and how these link to the standard usage of the Laspeyres and Paasche indices. In practice, application of this theory requires information on prices and on expenditures for the households whose standard of living is to be evaluated, and this is where the problems begin. The information base in many developing countries, especially in Africa, is such that we do not have a sufficiently broad based coverage of prices and expenditures for households in the country. The Consumer Price Index (CPI) for many countries is based on prices and expenditure patterns of the capital city only, or of urban areas. Rural areas are not covere4 in any systematic way. While inadequate at the best of times, these indices are particularly inappropriate at a time of structural adjustment policies, whose whole raison d'etre is to alter price relativities, most of which have a significant effect on rural households.

This paper reports on an exercise in economic statistics -- an attempt to estimate a regional price index for C8te d'Ivoire using the strengths of two indepen tent data sources. The two sources are the CMte d'Ivoire Living Standards Survey (henceforth CILSS) and the Intemational Comparisons Project (henceforth ICP). The CILSS has revolutionized the analysis of household behavior in CMte d'Ivoire because of the detailed information it provides on household incomes, expenditures, employment, and other socio-economic characteristics, but, as we shall see, the price data it provides leaves much to be desired. The ICP provides a more detailed account of regional variation in prices than any other source for CMte d'Ivoire, but does not have any data on expenditure patterns or other socioeconomic variables.

The story of this paper is the story of how we set about combining these two sources to produce a regionai price index which we would argue to be superior to earlier estimates for Cote d'Ivoire. The account will be of interest to those who are struggling to combine 1
different sources of information on prices and expenditures, particularly for Africa. It will also highlight the value of ICP data for this purpose. However, we show that this is no mere statistical exercise -- previous evaluations of poverty in Cote d'Ivoire are affected significantly by the use of our new regional price index.

# 2. The **CPI and Previous Regional Price Indices in C6te d'Ivoire**

CMte d'Ivoire has traditionally compiled two different CPI series: one for African households and one for European households. Since 1985, a further disaggregation has become available for the former, based on the occupation of the head of househoid - the series is now available for "workers and traditional craftsmen" and for "professional and managerial occupations. " However, while even this disaggregation is not available in most - - African countries, this CPI like many others in Africa is inadequate because its coverage excludes rural households. Even for urban households, the data cover only Abidjan and four main cities. In particular, therefore, this information base cannot be used to construct a regional price index.

rhe construction of a regional cost of living index requires information on prices and on expenditure patterns across the country. With this information, a common operational procedure is to construct a Paasche cost of living index: where X is the region under consideration, A is the reference region, p is the price vector and q is the quantity vector. As is well known, dividing nominal expenditures in a region by this index will give the consumption basket of that region evaluated at the prices of the reference region.

2
The Cote d'Ivoire Living Standards Survey was designed to collect information on expenditure patterns, and a host of other socio-economic variables, for a nationally representative sample of households (for a detailed description of the survey, see Grootaert, 1986). It therefore satisfies one of the informational requirements for the construction of a regional price index. However, the price data that it collects, while national in coverage, do not have adequate commodity coyerage. Glewwe (1990) gives an excellent account of the problems to which this gives rise: "The price data were recorded on a separate price questionnaire which was administered in the local markets of the sampling areas from which CILSS households were drawn. Prices were obtained for 18 food items and 4 nonfood items... The prices of the four non-food items (domestic cloth, plastic sandals, enamel bowl and menthol) varied widely, and further investigation led to the conclusion that [they were] unusable for the construction of a price index... Since variation in the price of non-food items is largely due to transportation costs, it seemed advisable to use some of the food items which were relatively non-perishable and found throughout the country. The only food item to meet these criteria was canned tomato paste. Thus the price of cans of tomato paste are used as a proxy for the prices of non-food items." The Glewwe index was used by Glewwe (1987, 1990), and by Kanbur (1990) to analyze poverty and welfare in Cote d'Ivoire. The problems with the index should be obvious. A more recent attempt to derive a regional price index (McKay, 1992) addresses the oroblem by simply excluding, non-food items and restricting attention to only food items, which is hardly a satisfactory solution.

The basic problem is thus the inadequate commodity coverage of prices in the CILSS. The only way to resolve this problem is to have access to a data base with adequate geographical and commodity coverage of prices. The ICP data base makes this possible and we turn to that now.

3
# 3. **ICP Data and an Alternative Regional Price Index**

Data collection for the International Comparisons Project was carried out in CMte d'Ivoire in 1985 (Eurostat, 1989). The localities for the price collection were selected on the basis of the same sampling frame as the CILSS (although of course the localities selected in the final sample were not necessarily the same). Price collection covered both urban aid rural areas in all major regions of the country (East Forest, West Forest, Savannah). In total well over 20,000 prices were collected covering 912 product codes. The first challenge in using these data was to establish a correspondence chart between those product codes and the expenditure categories distinguished in the CILSS. This correspondence chart is shown in Annex I.

A characteristic feature of ICP data collection is the very specific and well defined product descriptions. This is necessary in order to ensure the international comparability needed for the project. The drawback for local use of the data is that not all items will be found in all parts of the country. For the purposes of establishing a within-country regional price index it is clearly necessary that the same product is found in all regions which need to be distinguished. This reduced the number of ICP prices which were available for our purposes. Nevertheless a total of 260 product categories were used to calculate the regional price index and these were matched with 27 food categories and 25 non-food categories derived from the CILSS. The detailed expenditure shares derived from the CILSS over these categories are shown in Annex II.

Two restrictions slightly hampered our use of the ICP data. First, very few non-food items were available for rural Savannah. We made the assumption that in view of the low population density in the Savannah, many non-food items would only be available in the urban centers of the region, and therefore we used the urban Savannah prices to impute values for rural Savannah for these products. Second, there were a few products which we felt were important representatives for a given expenditure category but for which prices were not available in all regions. In this case some imputations were made: for example, 4
if rural West Forest prices were not available, we imputed them on the basis of rural East Forest prices. This type of imputation affected only a very small number of prices.

After having estahlished the correspondence chart and the price ratios between the regions and Abiajan, a "-sual inspection of these ratios was conducted and a small number of individual prices were omitted because they appeared to be outlying or erroneously recorded values. In most cases this could be traced to the use of units which differed from the reference product description.

The new resulting regional price index (N)is shown in Table 1 with Abidjan set equal to 100. For purposes of comparison we have reproduced the Glewwe (G) and McKay (M) indices discussed in the previous section.

Table 1: Regional Price Indices. 1985

||N|G|M|
|---|---|---|---|
|Abidjan|100.0|100.0|100.0|
|Other Cities|92.8|86.7|70.2|
|East Forest|87.0|84.0|_65.5_|
|West Forest|78.2|81.8|61.2|
|Savannah|76.0|85.1|54.0|

Note, first of all, that the differences between the Glewwe and the McKay indexes are strildng. Glewwe finds no more than an 18 percent price difference with Abidjan in any region, while McKay finds price differences of 30 percent or more in all regions, reaching almost 50 percent in the case of the Savannah region. It seems reasonable to assume that the McKay index overestimates regional price differences by relying only on food items. Indeed, in C6te d'Ivoire many food items are locally produced in the rural areas and are henceforth more likely to be more expensive in the cities (because of transport costs.) On _5_
the other hand, many non-food items are either imported or produced in Abidjan and are thus more likely to be more expensive in rural areas. An index relying only on food prices would therefore overestimate price lifferences and hence underestimate regional welfare differences.

With the new index, N, tjie figures show that prices in other urban areas are on the average about 8 percent below those in Abidjan. In rural areas, price differences are greater - with the biggest difference being recorded for the Savannah about 25 percent below Abidjan. The figures of the N index thus confirm, by and large, our suspicion that the G index would tend to underestimate urban-rural price differences, while the M index would tend to overestimate them. In any event, the comprehensive commodity coverage of the N index give us greater confidence in its validity.

But does all this make a difference? A major use to which the regional price index has been put is in the calculation of real household expenditures and, thence, in the calculation of poverty. The next section investigates alternative estimates of poverty with the N, G and M regional prices indices.

# 4. **Implications for Poverty Estimation**

This is not the appropriate place to go into the details of how poverty is measured at a conceptual level, or the problems with operationalizing measures of the "standard of living" from household income and expenditure surveys (see Kanbur, 1987). Suffice it to say that a common procedure for measuring the standard of living for an individual is to take the real per capita expenditure (with suitable imputation for items such as home produced consumption) of the household in which he or she lives. When a single snapshot is being considered, the term "real" refers to adjustments of nominal expenditure by a regional price index. There is also the vexed question of what poverty line to choose. All these questions are discussed further in the literature and, in the context of Cote d'Ivoire, in Grootaert (1992). The poverty line chosen there is 128,600 CFAF per person per annum in 1985 6
- Abidjan prices. The question we wish to tackle is this how is the estimated pattern of poverty affected by the use of different regional price indices.

Table 2 shows average nominal household expenditure per capita for each of the regions in C6te d'Ivoire and these values deflated with the N, G and M indices. For the country as a whole, average real expenditure per capita is very close when using the N and G indices, but the M index leads to a 23 percent overestimate. However, larger differences a. revealed in the estimates by region and by socio-economic group. i'he Glewwe index leads to underestimates for West Forest and Savannah and for food-crop farmers (who are concentrated in Savannah). Real expenditures for other groups and regions are slightly overestimated (by 1 % to 7%). In contrast, the McKay index leads to a significant overestimation of expenditure in all regions, but mostly so in Savannah (41 %). Among the socio-economic groups, the overestimation is most pronounced for food-crop farmers (34%) and export-crop farmers (31%). Among the mostly urban-based groups, the overestimate is in the order of 10-20 percent (except for the unemployed). The omission of non-food items in the M price index thus leads to a major bias in estimated real household expenditure per capita at the national level and to a distortion of regional and socio-economic patterns.

It stands to reason that any poverty index based on household expenditure will similarly be affected by the choice of cost-of-living index. In Table 3, we show three poverty indices derived from the P-a class of measures (Foster, Greer and Thorbecke, 1984), given by where n is the population size, q is the number of people below the poverty line (Yp) and Y, (i = 1,..., q) are the expenditures of the .poor. a is a parameter reflecting sensitivity to 7
**Table** 2: **Household Expenditure Per Capita deflated with Alternative Regional Price Indices**

||Nominal|<br>N|G|G/N|M|M/N|
|---|---|---|---|---|---|---|
|Abidjan|376,108|376,108|376,108|1.00|376,108|1.00|
|Other Cities|252,387|271,864|291,104|1.07|359,526|1.32|
|East Forest|143,104|164,472|170,362|1.04|218,479|1.33|
|West Forest|187,120|239,134|228,753|0.96|305,751|1.28|
|Savannah|115,910|152,573|136,204|0.89|214,648|1.41|
|Export Crop Farmers|162,761|194,418|194,371|1.00|254,312|1.31|
|Food Crop Farmers|139,209|171,802|165,692|0.96|230,656|1.34|
|Public Sector Employees|412,795|431,171|445,083|1.03|503,620|1.17|
|Formal Private Sector Employees|354,478|364,835|371,294|1.02|403,111|1.10|
|Informal Private Sector Employees|220,088|230,593|236,341|1.02|265,560|_1.15_|
|Self-Employed|219,258|234,910|239,728|1.02|279,657|1.19|
|Inactive|242,515|253,049|257,824|1.02|287,152|1.13|
|Unemployed|303,553|306,624|309,658|1.01|320,447|1.04|
|C6te d'Ivoire|t13,634|237,853|238,389|1.00|291,799|1.23|

poverty. For cx = 0, the index is simply the head-count ratio (H = q/n). With a = 1, the index becomes HI where I is the expenditure gap ratio: 8
With the N index, the incidence of poverty in Cote d'Ivoire is estimated at 30 percent in 1985. Even though the G index led to slightly higher average real household expenditure per capita, it affected the distribution so that estimated poverty was higher (this is true for PO, PI and P2). While at the national level, the difference is marginal, the regional and socio-economic pattern of poverty is affected more. For example, the G index underestimates poverty (PO) in urban areas other than. Abidjan by 14 percent and overestimates it in Savannah by 13 percent. This bias is not necessarily constant over poverty measures. For example, P2 in Savannah is overestimated by 26 percent. In general, the use of the G index leads to an underestimation of urban poverty and an overestimation of rual poverty.

Not surprisingly, the M index displays much larger biases in estimated poverty than the G index. The incidence of poverty in CMte d'Ivoire is estimated at only 17.6<sup>percent -</sup> 41 percent less than the incidence estimated with the N index. The difference even<sup>widens</sup> when P1 and P2 are considered. Clearly, the M index yields a falsely opftimistic picture<sup>of</sup> poverty in Cote d'Ivoire. Regional and socio-economic patterns also differ widely.<sup>For</sup> example, the M index estimates poverty in West Forest at only 6.2 percent, while in<sup>reality</sup> it is 17.8 percent. It yields PO of 14.7 percent for export crop farmers, while according<sup>to</sup> the N index it is 36.6 percent.

Such differences in poverty estimates can obviously have a profound impact on policy decisions regarding the need for poverty alleviation interventions, where they should<sup>be</sup> targeted, and what amount of resources is required. The main lesson from this exercise<sup>is</sup> thus that what may appear as a relatively simple academic choice<sup>-</sup> how to treat prices of - non-food items in a price index can have a major impact on estimates of poverty which could be the basis for important policy decisions.

9
Table 3

# Poverty Mleasures based on Alternative Price Indices

||N|PO<br>G|M|N|PI<br>G|M|N|P2<br>G|M|
|---|---|---|---|---|---|---|---|---|---|
|Abidjan|.034|.034|.034|.009|.009|.009|.004|.004|.004|
|Other Cities|.236|.204|.119|.075|.064|.042|.037|.033|.022|
|East Forest|.479|.463|.285|_.155_|.144|.076|.069|.063|.032|
|West Forest|.178|.214|.062|.036|.043|.015|.013|.015|.006|
|Savannah|.502|.591|.322|.183|.221|.093|.088|.111|.037|
|Export Crop Farmers|.366|.366|.147|.094|.095|.042|.038|.039|.015|
|Food Crop Farmers|.434|.468|.263|.144|.156|.071|.065|.072|.028|
|Public Sector Employees|.049|.038|.003|.007|.004|.001|.001|.001|.000|
|Formal Private Sector Employees|.071|.070|.023|.014|.012|.005|.005|.004|.002|
|Informal Private Sector Employees|.262|.225|.225|.075|.069|.037|.028|.024|.008|
|Self-Employed|.262|.260|.185|.104|.103|.067|.058|.058|.038|
|Inactive|.183|.191|.152|.075|.072|.053|.043|.040|.030|
|Unemployed|.041|.041|.041|.005|.005|.005|.001|.001|.001|
|C6te d'Ivoire|.300|.314|.176|.098|.102|.050|.045|.048|.022|

# **_5._** Disafgregated Price **Indices**

For certain policy purposes it may be useful to have a price index available which only covers a sub-set of the consumption basket (for example, food or non-food only) or which covers the entire basket but pertains only to certain groups of households (for example, farmers or the poor). The detailed price information in the ICP data in 10
combination with the detailed expenditure shares available from the CILSS made it possible to construct such disaggregated price indices. In this section we show some examples.

Perhaps the most useful breakdown of the consumption basket is between food and non-food. This is relevant for poverty analysis, in view of Engel's law. Also, in developing countries many food items are subsidized - and those subsidies often need to be eliminated under structurd adjustment programmes. To address nutritional concerns a further breakdown according to types of food is also useful. Table 4 shows iegional price indices for 3 food categories and 5 non-food categories. It needs to be pointed out that conceptually such disaggregation requires the assumption that consumer preferences are separable, i.e. that prices of items outside the sub-index do not affect the consumption of items included in the sub-index (see Deaton and Muellbauer, 1980). Clearly, this assumption becomes less tenable the finer the disaggregation.

Table 4 shows that in general food items display a wider regional price variation than non-food items. Food is significantly cheaper in rural areas than in Abidjan, and more so than is the case for non-food. Several non-food categories are in fact more expensive in some rural areas than in Abidjan. This directly confirms our hypothesis stated in section 3 that the omission of non-food prices in a price index (as was the case for the McKay index) wil result in over-estimation of regional price differences.

In the context of structural adjustment, the distinction between tradeable and nontradeable goods and services is important since adjustment programs aim to change relative prices in favor of tradeables (World Bank, 1990). Table 4 also shows the regional price index for food and non-food items, broken down according to their tradeability. For food items, there is no systematic pattern: in some regions the price differential of tradeable food exceeds that of non-tradeable food, but in other regions it is the reverse. Tradeable non-food items however are systematically priced lower in rural areas than in Abidjan, with the biggest differential in East Forest and Savannah. Non-tradeable non-food items are priced 11
the same or higher in rural areas than in Abidjan (except in Savannah). This' may reflect higher production costs for these items in rural areas.

Table 4: Regional Price Index by Expenditure Categorv

||Abidjan|Other<br>Cities|East<br>Forest|West<br>Forest|Savamnah|
|---|---|---|---|---|---|
|Food|100.0|86.7|85.5|66.7|72.6|
|- grains&bread|100.0|96.6|111.1|74.4|64.8|
|<br>- roots & vegetables|100.0|75.4|82.7|43.1|82.1|
|- other food|100.0|88.6|80.9|87.2|71.6|
|Non-Food|100.0|99.8|89.2|101.9|87.6|
|- clothing|100.0|105.8|80.2|106.2|66.0|
|- home & furniture|100.0|93.9|106.4|92.9|79.1|
|- transport & communications|<br>100.0|108.3|*76.2|114.4|109.6|
|<br>- education & health|<br>100.0|89.4|116.2|99.6|79.1|
|- other non-food|100.0|95.4|100.2|90.4|90.4|
|Tradeable Food|100.0|86.2|104.8|76.1|62.6|
|Non-Tradeable Food|100.0|87.0|80.8|62.5|79.6|
|Tradeable Non-Food|100.0|99.8|79.5|97.8|84.8|
|Non-Tradeable Non-Food|100.0|99.9|115.5|110.4|94.7|

As we mentioned earlier, the Government of Cote d'Ivoire produces two series for the CPI, depending upon the type of occupation of the head of household. This reflects the existence of differences in expenditure patterns across socio-economic categories in the population. With the CILSS, it is possible to distinguish more than two groups. Table _5_ shows the cost-of-living index recalculated for eight socio-economic groups, taking into account the expenditure pattern of each group. In general, the differences are fairly minor (at least, if one ignores the cells with a small number of observations). The main differences are that the regional cost-of-living differential appears somewhat larger for farmers. The other groups, which are primarily urban based, display smaller regional variations.

12
Especially employees in the formal sector, both private and public, have smaller than average cost-of-living differences between Abidjan and other cities.

The data also permit to calculate a cost-of-living index by poverty status, and here the differences are more pronounced (Table 5). Between Abidjan and other cities, the costof-living differential narrows with rising total expenditure: for the very poor it is 14%, while for the non-poor it is only 7%. In contrast, in the rural areas, the differential widens with rising expenditure: for example, in West Forest the price differential for the very poor - is 7%, but for the non-poor it is 12%. This implies that the very poor who are mostly located in rural areas - fail to benefit from the lower cost of living in rural areas as much as other groups. This also means that the deflation of expenditures (or income) with a single index will underestimate real expenditure levels for urban poor and overestimate those of rural poor. By implication, if real expenditure are used as a welfare measure to estimate poverty (as we have done earlier in this paper), the simple index will overestimate urban poverty and underestimate rural poverty.

The cost-of-living differences reflected in Table 5 are of course only those due to different expenditure patterns across groups. The ICP data permit to estimate regional price differences only on an average basis, since the prices are collected from selected points of sale without taking into account the characteristics of actual purchasers. There could be further cost-of-living differences if unit prices differ systematically across socio-economic or poverty groups. In particular, it has been argued that the poor pay higher prices than the non-poor for many food items because they have to buy in small quantities. Also, price differences may occur if the poor and non-poor buy at different types of outlets or markets, and if there is systematic price variation across these. Such differences cannot readily be captured by conventional price data collection, but are best relegated to household surveys. Questions on actually paid prices can then be related to the household's socio-economic characteristics captures in the survey.

13
# **Table** 5: **Cost-of-Livine Index differentiated by Socio-Economic Group and Poverty Status**

||Abidjan|Other<br>Cities|East<br>Forest|West<br>Forest|Sarah|
|---|---|---|---|---|---|
|Export Crop Farmers|(100.0)|88.0|87.4|78.2|70.2|
|Food Crop Farmers|(100.0)|88.9|87.8|76.7|75.6|
|Public Sector Employees|100.0|93.7|83.7|(96.5)|(80.1)|
|Formal Private Sector Employees|100.0|94.6|(88.8)|(84.0)|(77.5)|
|Informal Private Sector Employees|100.0|92.7|(81.3)|(75.0)|(118.1)|
|Self-Employed|100.0|93.1|83.2|85.4|80.6|
|Unemployed|100.0|(97.7)|-|-|-|
|Inactive|100.0|92.3|88.2|(83.5)|(79.5)|
|Very Poor|100.0|86.3|99.7|93.1|77.0|
|NMid-Poor|100.0|90.9|89.7|78.3|76.1|
|Non-Poor|100.0|93.1|85.9|78.1|75.8|
|C6te d'lvoire|100.0|92.8|87.0|78.2|76.0|

Note: The very poor are the bottom decile of individuals ranked by per capita expenditure, and the mid-poor are the 2nd and 3rd decile. Numbers in parentheses indicate cells with less than 10 observations.

# **6. Conclusion**

Regional price variations can make a big difference to estimates of poverty. It is important, therefore, that analysts use the best and most comprehensive combinations of available information sets in constructing these indices. We have shown how the CILSS and 14
the ICP can be combined to produce a regional price index that is superior in terms of coverage than previous indices. And we have shown that this is no mere statistical exercise - estimates of the levels and patterns of poverty are altered significantly when the improved index is used.

15