metadata: authors: 0: author: Jones, Christine
1: author: Xiao Ye
count: Cote d'Ivoire
docna: 0: docna: Issues in comparing poverty trends over time
            in Cote de'Ivoire
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Labor & Employment Law,Crops and Crop Management Systems,International Trade and Trade Rules,Inflation,Inequality
teratopic: Science and Technology Development,Agriculture,Poverty Reduction,Environment,Macroeconomics and Economic Growth
abstracts: cdata!: Cote d'Ivoire conducted household
            surveys between 1985 and 1995, making it possible to trace
            changes in urban and rural poverty and changes in poverty
            among different socioeconomic groups.  During the decade
            surveyed, the country experienced a major recession.
            Poverty increased substantially and continued unabated in
            the year after devaluation, in part because much of the
            increase in export crop prices, especially for cocoa, was
            taxed away.  Between 1988 and 1993, urban poverty increased
            faster than rural poverty did, though mean expenditures in
            urban areas remained substantially above mean expenditures
            in rural areas.  Food crop farmers apparently suffered more
            than export crop farmers.  All in all these trends persisted
            after devaluation.  However, the authors found that
            exploring poverty trends across regions and socioeconomic
            groups raised questions about the patterns of change and the
            reliability of the data and the methods used to derive
            poverty estimates.  Secondary data cast some doubt on
            certain survey findings, suggesting that they may not have
            truly reflected real economic trends. The authors emphasized
            the need to draw reliable conclusions from time-series data
            in order to understand how policy reform affects poverty.
            They urged that far more attention be paid to comparability
            issues in designing and analyzing data and to developing
            better regional and temporal price indices, if reliable
            time-series data are to be generated for Sub-Saharan Africa.
geo_reg_key: 517191,1927320
repnme: repnme: Issues in comparing poverty trends over
            time in Cote de'Ivoire
display_title: Issues in comparing poverty trends
            over time in Cote de'Ivoire
listing_relative_url: /research/1997/01/695025/issues-comparing-poverty-trends-over-time-cote-deivoire
topicv3: Crop Production m1326627 174,Poverty m1326687 695,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Household Surveys m1504071 2761
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Science and Technology Development,Agriculture,Poverty Reduction,Environment,Macroeconomics and Economic Growth
origu: Macroeconomics and Growth Division
no_of_pages: 84
model_extractions: POLICY RESEARCH WORKING PAPER 1711

# Summary findings

C6te d'Ivoire is one of the few cotntries in Sub-Saharan Africa that conducted household surveys between I _985_ and 1995. These surveys make it possible to trace changes in urban and rural poverty and changes in poverty arnong different socioeconomic groups.

During the decade surveyed, the country experienced a major recession. Economic recovery began only with the devaluation of the CFA franc in January 1994. Poverty increased substantially during the recession and did not diminish in the year after devaluation, in part because rnuch of the increase in export crop prices, especially for cocoa, was taxed away.

Moreover, the effects of the recession and devaluation were not uniform. Between 1988 and 1993, urban poverty increased faster than rural poverty did, though mean expenditures in urban areas remained substantially ,ibove mean expenditures in rural areas. And food crop farmers apparently suffered more than export crop farmers. These trends persisted after devaluation.

But exploring poverty trends across regions and socioeconomic groups raises questions about the patterns of change and the reliability of the data and the methods used to derive poverty estimates. Secondary data cast some doubt on certain survey findings, suggesting that they may be more an artifact of the survey data than a reflection of real economic trends.

These problems are not unique to C6te d'Ivoire. Results from surveys in Uganda and to a lesser extent Ghana have also raised issues of comparability.

The ability to draw reliable conclusions from time series data is crucial for our understanding of how policy reform affects poverty. Far more attention must be paid to comparison issues in designing surveys and analyzing data. Attention must also be paid to developing better regional and temporal price indices, if reliable time series data are to be generated for Sub-Saharan Africa.

- - This paper a product of the Macroeconomics and Growth Division, Policy Research Department is part of a larger effort in the department to understand the link between economic policy change and poverty. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rebecca Martin, room N1i-059, telephone 202-473-1320, fax 202-522-3518, Internet address rmartinl@worldbank.org. January 1997. (77 pages)

_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 paperare entirely those of the author. They do notnecessarily represent the view ofthe WorldBank, itsExecutive Directors, orthecountries [they represent._ Produced by the Policy Research Dissemination Center
Issues in Comparing Poverty Trends Over Time in Cote d'Ivoire _Christine Jones Xiao Ye_ The authors are very grateful to Lionel Demery, Christiaan Grootaert, Lawrence Hinkle, and Roger Key for their useful comments on this paper.
I
C6te d'Ivoire is one of the few countries in Sub-Saharan Africa to have conducted household surveys over a ten year period between 1985 and 1995 that allow one to trace the evolution of poverty in urban and rural areas and among different socioeconomic groups. This ten year period spans an economic recession of major proportion and ends with the beginning of an economic recovery following the devaluation of the CFA franc in January 1994. Poverty increased substantially over the course of the recession, and did not diminish in the year following the devaluation in part because much of increase in export crop prices, especially cocoa, was taxed away.

Moreover, the impact of the recession and devaluation was not uniform. Between 1988 and 1993, urban poverty increased more rapidly than rural poverty, even though mean expenditure in the urban areas remained substantially above mean expenditure in rural areas throughout the period. And food crop farmers appear to have suffered more than export crop farmers. These trends persist after the devaluation.

However, an exploration of the poverty trends over time across regions and socioeconomic groups raises a number of questions about the pattern of change and about the reliability of the data or the methodology used to derive the poverty estimates. Secondary data cast some doubt on certain of the survey findings, suggesting that they may be more an artifact of the survey data than a reflection of real economic trends. These problems are not unique to C6te d'Ivoire; results from surveys in Uganda and to a lesser extent Ghana have also raised issues of comparability.' The ability to draw reliable conclusions from time series data is crucial for our understanding of the impact of policy reforms on poverty. Far more attention needs to be paid to the issues of comparability in designing surveys and analyzing data and also to the development of better regional and temporal price indices if reliable time series data on poverty is to be generated for sub-Saharan Africa.

# **_Economic Crisis and the Increase in Poverty_**

C6te d'lvoire's economic history is marked by a long economic boom period that ended in the early 1980s with the onset of a brutal economic recession. In the two decades following independence, C6te d'Ivoire achieved an impressive economic growth record. Real GDP per capita grew at 5.7 percent per year between 1960 and 1979, buoyed by strong growth in cocoa and coffee exports, favorable terms of trade, and growth in the manufacturing sector.

A decline in the coffee harvest in Brazil led to a dramatic increase in coffee and cocoa prices in 1976. The terms of trade increased by 58 percent between 1975 and 1977. The cocoa and coffee boom generated large surpluses for the Agricultural Price Stabilization Fund (CSSPA) of more than 10 percent of GDP. The government undertook a major investment program which was financed in part by the export crop revenues and a large increase in external borrowing. Investment rose to 26 percent of GDP on average between 1975 and 1979 (table 1).

See, for example, Appleton (1994) and Jones and Ye (1996).

1
Table **I Selected Macroeconomic Indicators, 1975-1984**

||_1975_|_1976_|_1977_|_1978_|_1979_|_1980_|_1981_|_1982_|_1983_|_1984_||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Real GDP growth rates**(%**p.a.)||||||||||||
|GDP per capita<br>|4.4%<br>|8.1%<br>|3.6%<br>|7.8%|-1.0%|-15.4%|-0.2%|-3.8%<br>|*-6.6%|-6.4%||
|GDP|8.7%|12.4%|7.6%|12.1%|2.9%|-12.0%|3.8%|-0.1%|-3.0%|-2.7%||
|Consumption growth rate (% p.a.)<br>|<sup>0</sup>|||||||||||
|Private<br>|11.0/<br>|12.7%<br>|9.9%<br>|11.8%<br>|3.0%|-19.4%|2.3%|-3.7%|0.7%|3.7%||
|Public|11.1%|16.6%|7.7%|19.4%|8.1%|-24.0%|7.3%|-4.4%|0.0%|-8.4%||
|Gross domestic investment (% of GDP)|22.4%|23.0%|27.3%|29.8%|28.0%|26.5%|25.9%|23.2%|18.4%|11.2%||
|Central Govemment (% of GDP)<br>||||||||||||
|Primary Balance||||||-6.4%|8.6%|-6.1%|3.2%|4.1%||
|Overall Balance||||||-8.5%|-12.1%|-11.5%|-9.0%|-3.0%||
|Prices<br>||||||||||||
|Annual Change inCPI(%)|11.44%|12.05%|27.46%|12.93%|16.67%|14.63%|8.86%|7.31%|5.91%|4.25%||
|REERlndex(1985=100)|93.7|87.3|97.3|99.4|101.9|133.9|112.8|111.5|104.6|99.1||
|Exchangerate(CFAfi-ancsperUSSannualaverage)<br> <br>|214.3|239.0|245.7|225.6|212.7|211.3|271.7|328.6|381.1|437.0||
|TermsofTrade<br>(1985=100)<br>**Table I Selected Macroeconomic Indicators, 1975-1984**|88.5<br>**(continued)**|97.1|139.6|130.7|122.9|117.3|105.1|95.9|91.2|101.3||
||_1985_|_1986_|_1987_|_1988_|_1989_|<br>_1990_|_1991_|_1992_|_1993_|_1994_|_1995_|
|Real GDP growth rates (% p.a.)<br>||||||||||||
|GDP per capita|0.6%|-0.4%|-5.2%|-5.6%|4.6%|-5.7%|4.2%|-3.4%|4.6%|1.9%|3.1%|
|GDP|4.5%|3.4%|-1.6%|-2.0%|-1.0%|-2.1%|-0.8%|0.0%|-1.1%|1.8%|7.0%|
|Consumption growth rate (% p.a.)||||||||||||
|Private<br>|4.2%<br>|4.8%<br>|5.2%<br>|4.9%<br>|-1.7%|-3.4%|4.3%|-0.5%|-0.7%|||
|Public|0.0%|12.5%|1.4%|0.3%|-3.2%|-12.5%|-1.1%|4.3%|-0.1%|||
|Grossdomesticinvestment<br>(%ofGDP)|12.6%|11.4%|12.6%|14.4%|8.1%|8.5%|9.0%|8.1%|7.7%|11.8%|13.1%|
|Central Goverunent (% of GDP)<br>||||||||||||
|Primary Balance|9.9%|4.2%|-1.3%|-6.1%|-7.2%|-2.2%|-2.1%|-1.3%|-3.2%|1.4%|3.2%|
|Overall Balance|3.2%|-2.7%|-8.5%|-14.6%|-16.6%|-12.0%|-13.0%|-11.7%|-12.0%|-7.0%|-3.6%|
|Prices||||||||||||
|AnnualChangeinCPI(%)|1.86%|6.86%|6.95%|7.00%|1.03%|-0.83%|1.68%|3.58%|2.60%|25.80%|14.30%|
|REERlndex(1985=100)|100.0|116.4|122.3|117.0|117.8|115.4|110.9|122.0|123.5|75.8|83.2|
|Exchange rate (CFA francs per USS annual average)<br>|449.3|346.3|300.5|297.8|319.0|272.3|282.1|264.7|283.2|555.2|499.3|
|Tenns of Trade (1985=100)|100.0|102.2|91.5|87.4|78.2|75.2|75.5|72.6|70.8|76.9|85.4|
|Source: World Bank data.||||||||||||

2
The boom in prices did not last long, however, and by 1980 about half the gain in the terms of trade had been eroded. While the boom did not persist, the problems created by the large increase in spending and lack of monetary discipline did. Real GDP per capita declined in 1979 for the first time since 1960, and fell sharply in 1980. The macroeconomic situation deteriorated, as inflation persisted and the budget deficit grew due to difficulties in cutting back the large public investment program.

Faced with growing macroeconomic instability and a slow down in growth, in 1981 the government embarked upon an adjustment program.<sup>2</sup> While some progress was made in reducing the budget deficit and inflation, the continued slump in cocoa and coffee prices in the early 1980s, the impact of the tight monetary and fiscal policies, and a severe drought in 1983 contributed to decline in real GDP growth between 1982 and 1984. This was followed by a brief turnaround in growth in 1985, due to a good harvest in 1984/85, improved cocoa and coffee prices, and the depreciation of CFA relative to the US dollar. The brief turnaround was short-lived, however, as coffee and cocoa prices resumed their downward slide. The terms of trade fell by 13 percent between 1985 and 1988 while at the same time external competitiveness was eroded as the real effective exchange rate appreciated by 17 percent. In 1987 real GDP declined by 1.6 percent. In 1988 the recession deepened as real GDP fell by 3.6 percent. The government's fiscal position worsened as the government tried to protect farmers by maintaining cocoa and coffee producer prices at the level set in 1985. The deficit of the CSSPA reached 5.9 percent of GDP in 1989, contributing to worsening of the primary deficit, equal to 7.2 percent of GDP.

To restore growth and macroeconomic stability, in 1989 the government undertook a new wave of adjustment measures, the primary objectives of which were to reduce the primary fiscal deficit and to restore competitiveness through structural reforms and tight monetary and fiscal policy. In the absence of an nominal exchange rate adjustment,<sup>3</sup> the government attempted to achieve a depreciation of the real effective exchange rate by keeping CMte d'Ivoire's inflation rate below that of its main trading partners and increasing wage and price flexibility through labor market and other policy reforms. However, it did not address the issue of high civil service salaries. The government made massive cuts in public investment and also cut non-recurrent wage expenditures but the government wage bill continued to increase. Public investment expenditure fell from 9 percent of GDP in 1981 to 3 percent of GDP in 1993. This represented a drastic cut, as real GDP per capita fell by almost 40 percent over the same period.

However, the measures undertaken by the government were not sufficient to restore external competitiveness and growth. Between 1985 and 1993, the real effective exchange rate appreciated by 33 percent, due to the appreciation of the French franc against the US dollar, while the terms of trade deteriorated by almost the same amount. Real GDP per capita declined by 4.8 percent per year between 1987 and 1993. The decline in economic activity hit the tertiary sector hardest during the first part of the recession, while growth in the agricultural sector remained positive for the first several years of the period. Employment in the modern sector fell by six percent between 1985 and 1992, despite the growth in public sector employment. The overall decline in economic activity, combined with rapid population growth and continuing rural-urban migration contributed to a 61 percent rise in informal 2 For a discussion of the policies measures undertaken as part of C6te d'Ivoire's adjustment program and their economic impact, see Demery (1994).

3 As a member of the CFA franc zone, CMte d'Ivoire's currency is pegged to the French franc. A nominal devaluation was long opposed by members of the zone.

3
sector employment over the same period (World Bank, 1995). Private consumption per capita fell by 31 percent between 1987 and 1995, consistent with the 35 percent fall in household expenditure per capita over the same period as measured by the household surveys.

Due to four years of C6te d'Ivoire Living Standards Survey (CILSS) data between 1985 and 1988 and two Priority Household Surveys (PHS) conducted in 1993 and 1995, it is possible to trace the evolution of poverty during this period of severe economic recession in C6te d'lvoire, although the differences in the two types of surveys raise questions about comparability of the poverty estimates across surveys.<sup>4</sup> In 1985, the headcount index of poverty in C6te d'Ivoire was 11 percent based on the extreme poverty line of 75,000 CFAF per capita per year adopted by Grootaert (1996) shown in table 2. The 75,000 CFAF poverty line is close to the one dollar per day poverty line used in the 1990 World Development Report.<sup>5</sup> Between 1985 and 1993, poverty almost tripled, with almost three-fourths of the 26 percentage point increase occurring in the five year period between 1988 and 1993. Looking at the change in poverty by region and socioeconomic groups raises some questions about the validity of the data and about why certain groups fared better than others.

# Declining per **capita** expenditures **and** increasing poverty, 1985-1988

Between 1985 and 1988 the decline in household expenditure per capita was greater in the urban areas than in the rural areas, setting aside for the moment the West Forest region (table 3). Mean per capita expenditures fell by almost 30 percent in Abidjan and 40 percent in Other Cities, compared to the 15 percent decline in East Forest and the 21 percent cent decline in the Savannah. Despite the large fall in household expenditure in Abidjan, there was no increase in poverty, since even in 1988 mean household expenditure per capita was still three times greater than the poverty line of 75,000 CFAF per year. The decline in expenditures in rural areas was accompanied by a large increases in poverty in West Forest and in the Savannah. West Forest registered the largest fall in expenditure, almost 50 percent between 1985-1988.

Between 1985 and 1988 per capita expenditure fell by 33 percent, a much larger fall than the decline in real private consumption per capita of 6 percent. The large difference in expenditure trends between the household surveys and national accounts estimates has not been accounted for. The large decline in household expenditure per capita and the increase in poverty in the West Forest region-- a cocoa and coffee growing region like the East Forest-- is especially puzzling. Household expenditure per capita in the West Forest was 50 percent higher than East Forest in 1985, but fell about 20 percent year, bringing it to a level below that of the East Forest in 1988. In 1985 and 1986, West Forest had a 4 The 1985-1988 household survey data come from the four COte d'Ivoire Living Standards Surveys (CILSS) and are based on the expenditure aggregate described in Oh and Venkataraman (1992). The 1993 and 1995 data are taken from the Household Priority Survey (HPS). Changes to the cleaned 1993 and 1995 INS data files (as of March 1996) are described in Annex 1. The comparability of the methods used to estimate food expenditure in the CILLS and LPS surveys are discussed in Appendix B.

5 Grootaert chose the 75,000 CFAF per day poverty line as the index of extreme poverty because it corresponded to a headcount index of 10 percent in 1985, based on the set of deflators he used. This paper uses the CPI series published by the INS as the basis for its deflators. For a discussion of the construction of the deflator series for the household surveys, see Appendix B. The 75,000 CFAF/day poverty line translates into $PP 410 (purchasing power parity), slightly exceeding the poverty line of $PP 370 (1985$PP) used in the 1990 World Development Report. This calculation is based on the purchasing power parity exchange rate for 1985 for private consumption of 182.85 CFAF/$PP from the Summers and Heston data which was used in Chen, Datt, and Ravallion's (1994) estimations of the incidence of poverty in developing countries.

4
poverty rate that was close to Abidjan, but then rose abruptly between 1986 and 1987. Was the West Forest region so much better off than the East Forest region in 1985 and 1986? If so, what would account for the dramatic decline in per capita expenditures over the four years?

**Table 2 Headcount Index of Poverty by Region**

_(Poverty Line = 75000 CFAFper year in 1985 CFAF)_

|_Region_|_1985_|_1986_|_1987_|_1988_|_1993_|_1995_|
|---|---|---|---|---|---|---|
|Abidjan|0.007|0.014|0.018|0.004|0.051|0.202|
|Other Cities|0.080|0.064|0.059|0.125|0.312|0.286|
|East Forest|0.144|0.095|0.105|0.176|0.389|0.410|
|West Forest|0.016|0.019|0.105|0.193|0.382|0.501|
|Savannah|0.259|0.132|0.248|0.354|0.494|0.494|
|C6te d'Ivoire|0.111|0.069|0.115|0.178|0.323|0.368|

**Table 3 Mean Household Expenditure Per Capita by Region**

|_(in_<br>_1985 CFAFpe_|_r year)_||||||
|---|---|---|---|---|---|---|
|_Region_|_1985_|_1986_|_1987_|_1988_|_1993_|_1995_|
|_Mean expenditure_|_per capita_||||||
|Abidjan|376108|308092|342923|267570|225274|186251|
|Other Cities|261867|258792|218578|158534|125445|118605|
|East Forest|164035|169269|156271|140286|101667|<br>98269|
|West Forest|252047|202475|159314|130142|105986|<br>96247|
|Savannah|142588|150127|126188|112673|86040|91240|
|C6te<br>d'Ivoire|234867|218366|197680|158410|129306|121486|
|_Index ofmean exp_|_enditure per c_|_apita_|||||
|Abidjan|100.0|81.9|91.2|71.1|59.9|49.5|
|Other Cities|100.0|98.8|83.5|60.5|47.9|45.3|
|East Forest|100.0|103.2|95.3|85.5|62.0|59.9|
|WestForest|100.0|80.3|63.2|51.6|42.1|38.2|
|Savannah|100.0|105.3|88.5|79.0|60.3|64.0|
|Cote d'Ivoire|100.0|93.0|84.2|67.4|55.1|51.7|

Grootaert (1995) explains the decline in the West Forest as largely a result of the decline in farm income. His analysis shows that West Forest export crop farmers suffered a decline of about 34 percent in farm income, due in part to the decline in the cocoa and coffee sales between 1985-1988 (table 4). **6** The quantity of cocoa sold declined by 51 percent per farmer, while coffee sales declined by 65 percent. In contrast, farm income of East Forest export crop farmers declined only by 18 percent, compared to a decline in cocoa sales of 11 percent and coffee sales of 33 percent.<sup>7</sup> The relatively better

> 6 The farm income figures reported by Grootaert (1995) in his Table 9 are redeflated using the INS deflators and shown in Table 4.

7 Our calculations of cocoa and coffee sales show a smaller decline in cocoa and coffee than Grootaert's. We omitted several large outliers where we suspected that the unit had been incorrectly coded as tons instead of kilograms, and discarded several observations where the unit of sale was nonstandard. The means are weighted and based on reported sales of all cocoa-growing households in the region. Based on our calculations, the survey data show that sales of cocoa by the cocoa-growing population declined by 3 percent in the East Forest between 5
performance of East Forest farm households is mirrored in the outcomes for food crop farmer households. Food crop farmers in the East Forest had a 19 percent decrease in farm income compared to the 52 percent drop in the West Forest. While the household expenditure data are more or less in line with the farm income data, they nonetheless beg the question of why West Forest farmers fared so much worse than East Forest farmers during this period.

Table 4 Farm Income of Farmers Across Rural Regions _(in 1985 CFAFperyear)_

||_Percentagze charge, 1985-1988_|
|---|---|
|_Region_|_Export crop farmer_<br>_Food crop farmer_|
|East Forest|-17.9<br>-3.5|
|West Forest|-33.6<br>-51.6|
|Savannah|-42.1<br>-18.6|

Source: Grootaert (1995). Redeflated with the INS deflators.

Cocoa production data from other sources are at some variance with the household survey results for West Forest.8 The cocoa production data, put together by Agricultural Price Stabilization Fund (CSSPA), are broken down by administrative department and reflect the estimation of cocoa production based on cocoa sales (table 5). The survey data show that sales increased by roughly 20 percent in the East Forest if we compare 1985 with 1987 or 1988 and decreased by roughly 40 percent in the West Forest. Trends in the survey data are roughly consistent with the CSSPA data for East Forest in that both show an increase in the 1986/87, 1987/88 and 1988/89 campaigns compared to 1984/85. The strong increase in the sales per household evident in the 1988 East Forest survey data is 1985 and 1988, and by 28 percent in the West Forest. Coffee sales declined by 7 percent in the East Forest and by 47 percent in the West Forest over the same period.

The CILSS surveys were fielded over the following periods: 1985: February 1985 to Januaiy 1986 1986: February 1986 to January 1987

- 1987: March 1987 to February 1988

1988: May 1988 to April 1989.

Most of the cocoa and coffee crop is purchased between November and March. Thus it is likely that the survey years of 1985 and 1986 will correspond to the cocoa and coffee marketing campaign years of 1984/85 and 1985/86. For the 1987 survey, some farmers will be reporting on the 1986/87 harvest, while others will be reporting on the 1987/88 harvest. And for the 1988 survey, some households will be reporting on the 1987/88 campaign year, while others will be reporting on the 1988/89 campaign year.

6
Table 5 C6te d'Ivoire: Cocoa Production **By** Region

||||_CSSPA_|||||||||_S_|_urvey es_|_timates_||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_Coc_|_oa product_<br>_(1000 t)_|_ion_|_Pro_<br>_(19_|_duction i_<br>_84/85=1_|_ndex_<br>_00)_||_Producti_<br>_(1985_|_on index_<br>_=100)_||_Produ_<br>_(regio_|_ction per_<br>_nal popu_<br>_(kg)_|_capita_<br>_lation)_|_Prod_<br>_(c_<br>_po_|_uction per_<br>_ocoa-grow_<br>_pulation)_|_capita_<br>_ing_<br>_(kg)_|_Cocoa-g_<br>_as sh_<br>|_rowing p_<br>_are of re_<br>_populatio_|_opulation_<br>_gional_<br>_n_|
|_Campaign_<br>_year_|_East_<br>_Forest_|_West_<br>_Forest_|_Total_|_East_<br>_Forest_|_West_<br>_Forest_|_Total_|_Survey_<br>_year_|<br>_East_<br>_Forest_|_West_<br>_Forest_|_C6te d'_<br>_Ivoire_|_East_<br>_Forest_|_West_<br>_Forest_|_CBte d'_<br>_Ivoire_|_East_<br>_Forest_|_West_<br>_Forest_|_Cote d'_<br>_Ivoire_|_East_<br>_Forest_|_West_<br>_Forest_|_C6ted'_<br>_Ivoire_|
|1982-83|138650|107358|260301|51|39|46||||||||||||||
|1983-84|244833|187703|446640|90|69|79||||||||||||||
|1984-85|271870|272551|563901|100|100|100|1985|100|100|100|109|72|39|198|146|171|0.55|0.49|0.23|
|1985-86|326592|285193|630955|120|105|112|1986|111|100|117|122|72|46|197|151|184|0.62|0.47|0.25|
|1986-87|299754|294852|610992|110|108|108|1987|117|67|102|127|48|40|174|101|154|0.73|0.48|0.26|
|1987-88|306516|337497|659145|113|124|117|1988|95|75|93|103|54|37|195|106|153|0.53|0.51|0.24|
|1988-89|396337|444784|867770|146|163|154||||||||||||||
|1989-90|323736|389543|727686|119|143|129||||||||||||||
|1990-91|318921|465390|799396|117|171|142||||||||||||||
|1992-93|247633|431493|697030|91|158|124|1993|76|140|106|84|101|41|143|159|150|0.58|<br>0.63|<br>0.28|
|1993-94|305533|600414|917203|112|220|163||||||||||||||
|1994-95|312369|559067|883329|115|205|157|1995|76|104|95|83|74|37|129|147|137|0.65|<br>0.51|0.27|

7
also evident in the CSSPA data for 1988/89, but the decline in the percentage of households growing cocoa in the 1988 in East Forest causes a decline in the survey regional sales index in 1988. In contrast, the sharp decline in the West Forest survey data in sales per capita among cocoa-growing households and also in the regional sales index between 1986 and 1987 is not at all evident in the CSSPA production data.

Another inconsistency between the survey and the CSSPA data concerns the distribution of production between the East and West Forest Regions. The survey data indicate that sales in East Forest far outstripped that in the West Forest, while the CSSPA data indicate that production in the West Forest was roughly equal to the East Forest in 1985 and then outstripped production in the East Forest. According to the survey data, cocoa sales per capita was roughly 36 percent higher in East Forest than in the West Forest in 1985. Since the population of East Forest was 66 percent higher than West Forest (based on the survey weights), overall cocoa production should have been 126 percent higher in East Forest than West Forest, extrapolating from the survey data. According to CSSPA data, however, cocoa production in East Forest equaled cocoa production in West Forest in 1984/85. The disparity between the survey and CSSPA data widens in 1988: according to the survey data, sales per capita in the East Forest were 91 percent higher than in the West Forest, with the East Forest having 53 percent more population. Thus, East Forest in principle would have produced 192 percent more cocoa than West Forest. The CSSPA regional production estimates, however, show that in 198 8/89 West Forest produced about 10 percent more cocoa than East Forest.

A sharp break in coffee sales in West Forest between 1986 and 1987 is also clearly apparent in the survey data (table 6). The CSSPA data confirm that there was a fall in coffee production in the West Forest between 1985/85 and 1988/89, though the decline is not as dramatic as the fall in the survey data.

One problem with trying to match up the survey data with the CSSPA data is that urban areas within the forest region are considered either as belonging to Abidjan or Other Cities regions for the purposes of the household surveys. The CSSPA production estimates do not distinguish between the rural and urban areas within the Forest and Savannah regions. Even if we assume however, that in 1985, all the cocoa produced in Other Cities is attributable to the West Forest region, sales per capita in the West Forest would still far slightly below that of the East Forest. The disparity between the two regions in terms of total cocoa production would still be large, due to the difference in population size. And in 1988, even with the assumption that all cocoa sold by the households classified as living in Other Cities is attributable to the West Forest, per capita production in the West Forest would still be substantially below that of the East Forest. However, some of the disparity between the survey and the CSSPA production figures could be due to the possibility that households living in the East Forest reporting sales of cocoa may have actually grown cocoa in the West Forest. However, even if this were the case, it would not explain why sales declined so dramatically in the West Forest between 1986 and 1987, and not in East Forest.

Thus, the sharp decline in cocoa and coffee production in the West Forest survey data between 1986 and 1987 do not seem to be completely consistent with the CSSPA production data. The decline in per capita expenditures and thus the increase in poverty in West Forest may therefore be overstated to some degree. Indeed, there does seem to have been some sampling bias in the survey data. Demery and Grootaert (1993) hypothesize that sampling bias accounts for the decline in household size observed 8
Table 6 Cote d'Ivoire: Coffee Production By Region

|||_CSSPA_|_roduct_|_ion estim_|_ates_|||||||_Survey_|_estimates_||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_Coff_|_ee productio_<br>_(I OOOt)_|_n_|_Pro_<br>_(1_|_duction in_<br>_984/85=10_|_dex_<br>_0)_|_Pro_<br>|_duction i_<br>_(1985=10_|_ndex_<br>_0)_|_Produ_<br>_(regio_|_ction per_<br>_nal popul_<br>_(kg)_|_capita_<br>_ation)_|_Produ_<br>_(coffee-g_|_ction per_<br>_rowing po_<br>_(kg)_|_capita_<br>_pulation)_|_Cof_<br>_popula_<br>_regio_|_fee-growi_<br>_tion as sh_<br>_nal popul_|_ng_<br>_are of_<br>_ation_|
|_Campaign_|_East_|_West_|_All_|_East_|_West_|_Total_|_East_|_West_|_C6te d'_|_East_|_West_|_C6te d'_|_East_|_West_|_Cote d'_|<br>_East_|<br>_West_|<br>_Cote d'_|
|_Year_|_Forest_<br>_(Ch_|_Forest_<br>_erries)_|_Green_|_Forest_|_Forest_||_Forest_|_Forest_|_Ivoire_|_Forest_|_Forest_|_Ivoire_|_Forest_|_Forest_|_Ivoire_|_Forest_|_Forest_|_Ivoire_|
|1984-85|189967|270066|277|100|100|100|100|100|100|75|187|50|146|236|169|0.5|0.7|0.3|
|1985-86|207761|245111|265|109|91|96|121|93|118|91|174|59|154|213|180|0.5|0.8|0.3|
|1986-87|239095|226013|270|126|84|97|156|35|89|117|65|44|222|93|161|0.5|0.7|0.2|
|1987-88|134502|183586|187|71|68|68|64|43|59|48|80|29|136|125|126|0.3|0.6|0.2|
|1988-89|173132|182278|250|91|67|90|||||||||||||
|1989-90|||290|||105|||||||||||||
|1990-91|||239|||86|||||||||||||
|1991-92|||257|||93|||||||||||||
|1992-93|||146|||53|43|34|47|32|63|23|89|125|112|0.3|0.5|0.2|
|1993-94|||148|||53|||||||||||||
|1994-95|||196|||71|38|17|33|28|33|16|71|93|84|0.4|0.3|0.1|

9
across the surveys, with a particularly strong decline between 1986 and 1987. They reweighted the sample to correct this bias.<sup>9</sup> However, it is entirely possible that the reweighting scheme based on household size did not correct for other sampling biases which could have resulted in non-poor households being oversampled in 1985 and 1986 in West Forest.

An indication that there may have been other sampling problems in West Forest emerges from the comparison of the incidence of poverty in 1987 in the West Forest clusters that were retained from the 1986 sample and those that were added **_in_** 1987 to replace the ones that dropped out. The new clusters added in 1987 had three times the incidence of poverty as the old clusters. This was not just the case of high poverty clusters in 1987 replacing high poverty clusters in 1986: the clusters that were dropped from the 1986 sample did not exhibit a higher incidence of poverty in 1986 relative to the ones that were retained. While not conclusive, the difference in the incidence of poverty between the two sets of clusters suggests that there may have been some bias in the sampling design at some point.

The survey results for the Savannah are also somewhat surprising. While export crop farmers at least in the East Forest seemed to have been protected from the worst of the recession in 1987 and 1988, Savannah farmers did not fare so well. Mean expenditures per capita declined around 30 percent between 1985 and 1988 among both export and food crop farmers, although export crop farmers suffered a larger drop in farm income than food crop farmers. The production of cotton, the main export crop of the region, increased substantially over this period (table 7), so even with the real decline in cotton prices, the large decrease in mean household expenditures and farm income among export crop farmers in the Savannah is somewhat surprising.

Table 7 **Cotton Production: National Estimates and Household Survey Results**

||_Nation_|_al Estimates_|||_Survey Resul_|_ts_||
|---|---|---|---|---|---|---|---|
|_Campaign_<br>_year_|_Production_<br>_(1000_**_t)_**|_Production_<br>_index_<br>_(1984/85=100)_|_Survey_<br>_year_|_Production per_<br>_capita (cotton-_<br>_growing_<br>_households)_|_Production_<br>_per capita_<br>_(all_<br>_households)_|_Cotton_<br>_growing_<br>_population as_<br>_share of_<br>_survey_<br>_population_|_Production_<br>_Index_|
|1984-85|212.0|100|1985|222|19.1|8.6|100|
|1985-86|189.3|89|1986|338|24.3|7.2|127|
|1986-87|213.5|101|1987|287|34.2|11.9|179|
|1987-88|255.8|121|1988|254|27.7|10.9|145|
|1988-89|290.4|137||||||
|1989-90|241.7|114||||||
|1990-91|261.1|123||||||
|1991-92|193.8|91||||||
|1992-93|238.8|113|1993|310|16.7|5.4|87|
|1993-94|258.3|122||||||
|1994-95|209.0|99|1995|374|16.1|4.3|84|

# Accelerating increase in poverty, 1988-1993

As the economic recession deepened between 1988 and 1993, poverty in CMte d'Ivoire more than doubled. The increase was particularly strong in Abidjan, where poverty increased tenfold. With the large increase in poverty in Abidjan and to a lesser extent in Other Cities, the contribution of urban areas to the headcount index of poverty increased by more than 60 percent between 1988 and 1993 9 The poverty results for 1985-1988 presented in this paper take account of the reweighting scheme proposed by Demery and Grootaert (1993).

10
(table 8). The percentage decline in mean urban incomes was slightly less than the decline in rural incomes, however. One might have expected the agriculture sector (and therefore the rural sector), as the most important producer of tradables, to have fared much worse than the urban sector, but that does not appear to have been the case.

Table 8 Urban and Rural Contribution to Poverty

|_(Poverty li_|_ne = 75, 000_|_CFAFper_|_year in 19_|_85 CFAF)_|||
|---|---|---|---|---|---|---|
||_Contrib_<br>_ind_|_ution to hea_<br>_ex of pove_|_dcount_<br>_rty_|_Shar_|_e ofpopula_|_tion_|
||_1985_|_1988_|_1993_|_1985_|_1988_|_1993_|
|Urban|16.7|15.7|25.7|41.4|38.7|42.9|
|Rural|63.3|84.3|74.3|58.6|61.3|57.1|

It is also curious that export crop farmers fared somewhat better than food crop farmers, who are largely producers of nontradables (except for rice, the price of which was controlled by the government and allowed to decline in real terms prior to the devaluation). Within the rural areas, poverty increased much faster among food crop farmers than export crop farmers between 1988 and 1993. In 1988, export and food crop farmers had roughly the same incidence of poverty (Grootaert, 1996).'° By 1993 the incidence of poverty was some 10 percentage points higher among food crop farmers than among export crop farmers (table 9). This is somewhat surprising because there was a large decline in real export crop prices, particularly cocoa, which declined over 50 percent between 1988 and 1993 (table 10). In contrast, real staple food prices declined between 20 percent to 30 percent between 1988 and 1993 (table 11), not quite as much as real export crop producer prices. So all other things being equal, we would have expected food crop farmers to have been relatively more insulated from the recession than export crop farmers.

A partial explanation for the finding may be that the decline in producer prices had already been absorbed in the 1988 survey results. There is some evidence that farmgate cocoa and coffee producer prices were lower than official prices before the prices were officially lowered in 1989 (Azam, 1994). Food crop farmers may have also suffered disproportionately to the extent that they provided wage labor for export crop farmers, and wages or employment fell. There is, however, little information about the behavior of the rural labor market available.

The decline in real staple food prices merits some comment. Several factors may have been responsible. One factor is that as the real returns to cocoa, coffee and cotton production fell in the second half of the 1980s, farmers may have shifted some resources out of export crop production into the production of staple food crops, thus increasing the supply of staples and putting downward pressure on staple food prices. As the recession intensified and incomes fell, demand for staple foods fell as well (except perhaps for staples such as maize and cassava that are inferior goods). Finally, the decline in the real price of rice--due in part to government's pricing policy--also likely placed downward pressure on the real price of other staple food crops.

10 Unfortunately, the aggregate data file we obtained for the 1985-88 LSMS data does not include the socioeconomic group variable so we cannot reproduce Grootaert's results using the INS deflators. In 1988, the poverty incidence, using his deflators, was .210 for export crop farmers and .197 for food crop farmers, based on the 75,000 CFAF poverty line.

11
**Table 9 Headcount Index of Poverty among Farm Households, 1985-1993**

|_(poverty line = 75,000 CFAF per year in 1985 CFAF)_||
|---|---|
|Socioeconomic Group<br>1985<br>1988|1993|
|Export crop farmers<br>.086<br>.210|.394|
|Food crop farmer<br>.150<br>.197|.492|
|Source: 1985 and 1988: Grootaert (n.d.).||

1993: Household Poverty Survey.

Note: Poverty indices for 1985 and 1988 are calculated using Grootaert (1995) deflators, rather than the INS deflators. See Appendix B for a discussion of the difference in the two sets of deflators.

**Table 10 Official Export Crop Producer Prices**

|_Campaign_||||_Consumer_<br>_Price_|_Real_|_Producer Price_<br>_(1984/85 = 10_|_Index_<br>_0)_|
|---|---|---|---|---|---|---|---|
|_Year_|_Cocoa_|_Coffee_|_Cotton_|_Index_|_Cocoa_|_Coffee_|_Cotton_|
|1984/85|375|385|115|104.7|100.0|100.0|100.0|
|1985/86|400|200|115|111.1|100.5|48.9|94.2|
|1986/87|400|200|115|118.5|94.3|45.9|88.4|
|1987/88|400|200|115|126.6|88.2|42.9|82.7|
|1988/89|400|200|115|128.0|87.2|42.5|81.8|
|Jul-89|250|||||||
|1989/90|200|200|115|126.7|44.1|42.9|82.6|
|1990/91|200|100|100|128.9|43.3|21.1|70.6|
|1991/92|200|100|90|134.5|41.5|20.2|60.9|
|1992/93|200|170|90|138.3|40.4|33.4|59.2|
|1993/94|200|170|90|||||
|Jan-94|240|220|105|||||
|Mar-94|290|275|105|173.7|46.6|43.0|55.0|
|Sep-94|315|530||||||
|1994/95||||||||
|Feb-95|315|650|160|198.1|44.4|89.2|73.5|
|Sep-95|315|695|170|||||

Note: Campaign year is from October to September. CPI series is on a calendar year basis.

**Table 11 Staple Food Prices, Deflated by CPI** _(1993=100)_

||||_Yams_||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|_Year_|_Assawa_|_Bete_|_Flori_|_Kingle_|_Kponan_|_Plantain_|_Manioc_|_Maize_|_Millet_|_Rice_|
|1987|151.5|117.5|128.6|127.0|153.8|120.3|143.3|128.1|111.5|138.1|
|1988|134.8|102.4|119.5|130.2|132.9|119.8|120.0|130.2|135.3|121.2|
|1989|125.9|103.2|110.5|118.2|121.2|105.6|105.4|116.8|122.9|119.4|
|1990|129.2|106.2|114.6|108.7|131.3|119.7|109.8|116.2|114.1|116.2|
|1991|113.3|92.9|97.5|106.2|107.6|97.0|108.1|118.2|119.9|111.6|
|1992|98.4|85.7|88.9|84.2|101.5|97.7|103.5|96.4|108.0|104.7|
|1993|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|1994|86.1|78.5|81.5|81.2|87.2|87.8|84.2|76.1|82.3|84.6|
|1995|85.4|74.6|78.6|78.8|90.0|100.5|79.0|85.0|68.6|96.7|

Note: Price data obtained the office d'Aide A la Commercialization des Produits Vivriers. Price data covers 10 markets in C6te d'Ivoire and is collected monthly. Mean price for each commodity was obtained by regressing logarithm of the price on dummies for years, markets, and month of observation. Nominal prices were deflated by the CPI series shown in Table 10.

12
# _The Devaluation and Macroeconomic Performance_

The CFA franc was devalued in January 1994 by 100 percent in domestic currency terms. The devaluation was expected to reverse the eight years of negative real GDP per capita growth. Although real GDP growth per capita declined by 1.9 percent in 1994, that was an improvement over the 3.9 percent decline in 1993. The fiscal position of the government was improved due to the substantial export tax revenues it was able to collect by not passing through the increase in export earnings (in local currency terms) due to both the devaluation and the surge in the international price of cocoa, coffee and cotton. Prior to the devaluation, cocoa and coffee farmers were receiving approximately two-thirds of the c.i.f. export price. Immediately after the devaluation, they received about one third of the c.i.f. price. These ratios were adjusted upwards in the following campaign years. The indicative cocoa producer price relative to the average export price rose to 43 percent in the 1995/96 campaign, while for coffee the ratio more than doubled, rising to 78 percent in 1995/96. Despite the fact that the ratio of the producer price to the export price declined for cocoa, the real cocoa producer price did increase, albeit not dramatically. Coffee prices, on the other hand, more than doubled in real terms.

The turnaround in the economy was more pronounced in 1995.11 Real GDP growth increased by 7 percent in 1995, implying a real growth rate per capita of 3.1 percent. Export growth increased by 9 percent, in part reflecting the more than 14 percent increase in the terms of trade in 1995. There was a major turnaround in domestic demand, which grew at 13.7 percent in 1995 compared to the 2.7 percent decline in 1994. Total investment rebounded strongly, increasing in 1995 by about 39 percent in volume, up from the 16 percent increase in 1994. Private consumption grew at 7.2 percent in 1995, in contrast to the decline of 6.7 percent observed in 1994, largely due to the decline in purchasing power following the devaluation. Growth in the primary sector was 6 percent in 1995 compared to 4 percent m 1994, due in part to the strong growth in agricultural exports, especially coffee, but especially to the expansion of the petroleum sector. Manufacturing increased by more than 10 percent in 1994, with the strong increase due in part to the coming on stream of new oil and gas fields. Capacity utilization increased from 76 percent in 1993 to an average of about 85 percent in the first three quarters of 1995. All branches of the tertiary sector registered positive growth in 1995, in contrast to 1994. The tertiary sector also registered strong growth of 10 percent, with the growth in export crops and import substitution having a positive effect on domestic transport and commerce.

# _The Impact of the Devaluation on Poverty_

This section examines the expected effects of the impact of devaluation on household real expenditures. Essentially, the two channels through which a devaluation operates on real household expenditure is through its effect on consumer prices and its effect on incomes. While a common approach to tracing the expected impact of the devaluation on real household expenditures to use a two good, tradable/nontradable model, in the case of C6te d'Ivoire, it makes more sense to consider a three good model where the tradable sector is broken down in exportables and importables.'<sup>2</sup> The distinction

## The data for 1995 are from CMte d'Ivoire (1996).

12 We follow the lead of Hinkle and Nsengiyumva (1995b) in their discussion of the three good model and its relation to various measures of the intemal exchange rate by referring to the three sectors as importables, exportables, and nontradables. In practice, as they point out, when intemal exchange rate measures are calculated, data limitations often force one to use indices for import, export, and domestic production in calculating the relevant price indices.

13
between importables and exportables is important, because of the changes terms of trade that occurred at the same time as the devaluation, and also because of differences in domestic taxes on importables and exportables.

As we discuss below, on the price side, the evidence shows that there was relatively little difference in the increase in the prices of tradable and nontradable consumer goods. Thus, one would not expect that the impact of the devaluation would vary significantly among socioeconomic groups. The relatively similar composition of their consumption baskets would also tend to reduce differences in the impact of the devaluation. The impact of the devaluation on real incomes earned from production of exportables and nontradable goods (or wages in these sectors) is likely to be a far more important determinant of how different households fared. This section looks at some of the theory and evidence for what we would expect to have happened on the consumer price and income side, and then compares the expected outcomes with the household survey results based on the 1993 and 1995 surveys.

# Devaluation, inflation, **and the** implications **for poverty**

CMte d'Ivoire was among the more successful of the CFA franc zone countries in containing inflation following the devaluation of the CFA franc by 100 percent in local currency terms. In 1994, inflation (based on the blue collar consumer price index) at the end of the first year following the devaluation was 32 percent. At end 1995, prices had risen a total of 44 percent since the devaluation. The relatively low rate of overall inflation was due to imported prices rising less than would be expected assuming a simple doubling of prices in local currency, to the small rise in wages, and also to the subsidies on rice consumption which accounts for 5 percent of the consumption basket.

As Leenhardt and Massuyeau (1995) point out, there was not, however, a large difference in the behavior of prices of consumer tradable and nontradable goods. 1<sup>3</sup> They found a difference of ten percentage points in the tradable (largely imported consumed goods) and nontradable consumer price index between the period December 1993 to December 1994. **14** Based on a similar classification of the CPI, we found that the price index for tradable goods, which account for 46.5 percent of the CPI (table 12), rose 155 percent between December 1993 and April 1995, while the price index for nontradable goods rose 140 percent (table 13).'5 This difference would be even smaller if one were to use the expenditure shares generated by the survey results.'<sup>6</sup> 13 This should not be interpreted to mean that the real exchange rate did not depreciate signi.ficantly. The tradable component of the CPI is comprised of goods that are primarily imported or goods that are domestically produced close substitutes for imported goods. The CPI-based price index for tradable goods in all likelihood does not accurately the index of prices of all imports, including capital and intermediate goods, that would be used in computing the internal real exchange rate for imports. The closest comparison to the calculation based on CPI tradable and nontradable price indices would be the estimate of the internal real exchange rate for imports, valued at domestic prices relative to the price of nontraded goods. This is not the same as the change in the internal real exchange rate based on an index of traded good prices, where traded goods comprise both imports and exports. The change in the real effectiv **e** xchange rate between 1993 and 1995 was 33 percent. For a discussion of the interpretation and calculation of these different measures of the real exchange rate, see Hinkle and Nsengiyumva (1995a and 1995b).

14 Locally grown cereals and starchy crops were classified as nontradables.

> 15 Household expenditures were divided into domestic and traded goods following the classification of Grootaert (1996). Most food products (except meat, rice, wheat products, milk products, sugar, salt, tomato paste, bouillon cubes, refined oils and drinks, vehicles and gasoline) and all services were classified as domestic goods, while purchased household goods, drugs, and clothing were classified as tradables.

14
**Table 12 Share of Tradable and Non-Tradable in the Consum 3tion Basket** _(percent)_

|||CPI|||1993 Survey||
|---|---|---|---|---|---|---|
|_Expenditure Categories_|_Expenditure_<br>_share_|_Tradable_<br>_share_|_Non-_<br>_tradable_<br>_share_|_Expenditure_<br>_share_|<br>_Tradable_<br>_share_|_Non-_<br>_tradable_<br>_share_|
|Food|48.0|47.8|52.8|47.4|35.6|64.4|
|Lodging|7.8||100.0|12.6||100.0|
|Utilities|8.5||100.0|5.8||100.0|
|Household furnishing|3.4|100.0||1.4|100.0||
|Clothinga)|10.1|100.0||6.7|84.6|15.4|
|Transport|6.8|100.0|100.0|7.0|23.2|76.8|
|Vehicles|5.4|81.8|18.2||||
|Household maintenance|0.6|100.0||0.2||100.0|
|Hygiene|1.0|100.0||2.1|100.0||
|Health|0.7|96.6|3.4|4.6|72.8|27.2|
|Education|1.0|29.5|80.5|3.3|41.5|58.5|
|Miscellaneous|6.7|45.7|54.3|1.0||100.0|
|Non-food in kind||||0.2||100.0|
|Remittances||||7.6||100.0|
|Total|100.00|_46.5_|53.5|100.0|32.5|67.6|

a) The clothing category in the 1993 priority survey includes expenses for hairdressers and tailors, which were classified as non-tradable.

**Table 13** Evolution **of the Tradable and Non-tradable Components of the CPIa)**

||_Apr_|_il 1995_|_Janua_|_rv 1996_|
|---|---|---|---|---|
|_Components_|||||
|_of the CPI_|_Tradable_|_Non- tradable_|_Tradable_|_Non- tradable_|
|Food|146.3|150.3|155.5|133.9|
|Total|155.3|139.7|159.3|133.4|

a CPI index is the INS index for "ouvrier, employd qualifid on artisan traditionnel." Base period is October 1992 to November 1993. For the breakdown of posts into tradable and non-tradables, see text.

What are the implications for poverty? To the extent that there is relatively little differentiation in the rise of nontradable and tradable goods prices in the CPI consumption basket, differences in the share of importables in the consumption basket across socioeconomic groups or between the poor and nonpoor will have relatively little influence on poverty outcomes. Moreover, in 1993 there was relatively little difference between the poor and nonpoor population in the share of tradables in total household expenditure (table 14). The nonpoor consume slightly less tradables than the poor, given that they have a higher share of expenditure on lodging. The two factors taken together suggest that in terms 16 Based on the expenditure shares generated by the survey, we found that 35.2 percent of household consumption in 1993 was on tradables, compared to 46.5 percent **in** the CPI (table 12). Remittances, which account for 7 percent of the survey basket, but do not figure into the CPI basket, were classified in the same proportion as the remainder of the consumption basket. The difference in the share of tradables between the CPI basket and the survey basket is due primarily to the fact the share of nontradables in total food expenditure is higher in the survey--75 percent compared with 53 percent in the CPI basket. The reason for the smaller percentage point difference in the price indices for tradables and nontradables in the survey basket is due primarily to the larger share of nontradable food in the survey basket. Because of the large seasonal price increases for starchy food, the index for nontradable food prices was actually greater than that of traded food products in April 1995 though by January 1996 the situation had reversed itself (table 13).

15
of the impact of price changes on consumption, the devaluation had relatively little differential impact on the consumption basket of the poor relative to the nonpoor. The devaluation increases the share of tradables among both the poor and the nonpoor. The poor show a slightly larger increase bringing their share of consumption of tradables closer to the nonpoor. Thus, far more important to outcomes for the poor is what happened to their real income. That of course depends on what the particular source of income was, as the devaluation was accompanied by changes in tax policy which varied by crop and also by changes in the terms of trade.

**Table 14 Mean Share of Tradable Household Expenditure and Food Expenditure**

||19<br>|93<br>|19<br>|95<br>|
|---|---|---|---|---|
|_Region_<br>|_Share of_<br>_tradable_<br>_expenditure_|_Share of tradable_<br>_food_|_Share of_<br>_tradable_<br>_expenditure_|_Share of_<br>_tradable food_|
|Abidjan<br>|||||
|Poor<br>|0.354<br>|0.469|0.384|0.491|
|Non-poor|0.332|0.441|0.352|0.486|
|Other Cities<br>|||||
|Poor|0.355|0.418|0.408|0.446|
|Non-poor<br>|0.361|0.426|0.399|0.474|
|East Forest|||||
|Poor<br>|0.273|0.238|0.332|0.307|
|Non-poor|0.304|0.292|0.336|0.312|
|West Forest|||||
|Poor|0.334|0.368|0.403|0.408|
|Non-poor<br>|0.337|0.333|0.399|0.424|
|Savannah|||||
|Poor|0.255|0.244|0.301|0.301|
|Non-poor|0.259|0.244|0.287|0.279|
|Cote d'Ivoire<br>|||||
|Poor<br>|0.303<br>|0.311|0.358|0.367|
|Non-poor|0.328|0.365|0.355|0.404|

# **Sources of income and the impact on poverty**

In theory, one would expect that real product wage of tradables to be bid up relative to nontradables to bring about a reallocation of labor from nontradables to tradables. **1**<sup>**7**</sup> Depending on the share of tradables goods in the consumption basket, and the size of the nominal wage increase, wage laborers who consume relatively few tradables could still benefit from the fall in the product wage. While in theory wages should equalize across sectors, in practice there may be some segmentation between the urban and rural labor market, or even within the rural labor market. In the short run real consumption wages in the tradable sector are likely to be bid up to attract labor. Thus, wage earners in the tradable sectors would have been the most likely to have benefited from the devaluation. Whether they benefited, however, depends on the size of the wage increase and the share of tradables in their consumption basket.

> 17 See World Bank (1990) for a discussion of the impact of devaluation on wage earners and farm households.

16
For households that are producers of both tradables and nontradables-- largely farm households that produce primarily exportables or nontradable food crops--the impact of the devaluation on their real expenditures would depend on whether they are net sellers or buyers of goods and labor.<sup>1</sup> s Abstracting for the moment from the fact that changes in the prices of exports and imports may not be uniform, whether net sellers of tradables who are net buyers of labor would benefit depends on whether the gain in the revenue from the rise in the price of tradables (relative to the price of nontradables) exceeds the additional cost associated any increase in the wage rate. The lower the elasticity of the wage rate to the price of traded goods, the more likely they are to benefit. The reverse would be true for net buyers of traded goods who are net sellers of labor; the higher the elasticity of the wage rate to the price of traded goods, the more likely they would be to benefit from the price increase of traded goods.

Since consumption of domestic goods, particularly food, is relatively high, most export crop producers could be assumed to be net sellers of traded goods. Smaller farmers are also more likely to be net sellers of labor. Therefore, there is a high probability that in theory poor export-crop farming households would benefit from the rise in the price on exportables.

The above discussion assumes that the price increase in exportables and importables is the same. However, to the extent that export prices rise less than import prices due to govemment tax and pricing policy, even households that were net sellers of tradables prior to the devaluation might lose out if the increase in the cost of the imports they consume exceeds the additional revenue they earn from the production of exports. In CMte d'Ivoire, the improvement in the terms of trade that occurred at the same time as the devaluation meant that export prices increased more than import prices (at the border). However, government taxed away much of the benefit of the export price increase, although the rate varied by export crop. Thus, one cannot not assume that the impact of the devaluation and associated policies was identical across households classified as export crop farmer households. The more the gain in export prices was taxed away, the less likely the reduction in poverty for that group of export crop farmers.

Farmers who specialize in the production of nontradables, such as most staple food crops, would lose out from the rise in the relative price of tradable to nontradables if they were net sellers of nontradables as well as being net buyers of labor. If they are net sellers of labor, which is more likely, they could benefit even if they are net sellers of domestic goods if their additional earnings from hiring out their labor exceeded the additional cost associated with purchasing tradables. This would imply a relatively high elasticity of the wage rate with respect to the price of nontradables, particularly imports and close import substitutes. If they were net sellers of labor as well as being net buyers of nontradables, in principle they would benefit from the devaluation. But it would seem far more likely that they would be net sellers of nontradables, since virtually all of their farm income is likely to come from production of nontradable food crops.

In assessing the impact of the devaluation and associated policies on different categories or rural households, one needs to look carefully at what happened to the prices of tradable and nontradable agricultural goods. With respect to export crop prices, there was considerable variation in the real price increase depending on the extent to which the government taxed away the benefits of the devaluation and international price rises. The real producer price of cocoa increased by 10 percent and cotton 24 percent, while the real price of coffee jumped by 167 percent (table 10). Farmers specializing more in coffee would obviously have benefited far more than those cultivating primarily cocoa. With respect to cotton, in the campaigns following the devaluation, the policy was changed to require farmers to finance

> 18 See Ravallion (1990) for a discussion of the conditions under which farm households will benefit from price changes.

17
the inputs upfront. The removal of what was essentially an interest-free loan on the cost of the inputs had some financial costs for farmers. Due to the relatively small real price increase in cocoa and cotton, one cannot necessarily conclude that all export crop farmers were better off as a result of the devaluation. Cocoa farmers whose incomes also depended heavily on domestically produced staple food crops or who consumed a higher than average share of imports could well have suffered a decline in real expenditures.

On the food crop side, the real price of locally produced staple food crop prices declined. If one looks at the staple food component of the blue collar CPI, between December 1993 and December 1994, the real price of staple foods actually decreased (table 15). Between 1994 and 1995, the staple food index had essentially caught up to the overall price index. Breaking it down into the importable and nontradable foodstuffs, however, shows that most of the price increase was in the price of importables rather than nontradable foodstuffs. Between December 1993 and December 1995, the price of imported staple foods (essentially rice and wheat products) increased by 67 percent, compared to a 29 percent increase in locally produced staple foods. Over the same period, the CPI increased by 44 percent, so there was actually a decrease in the real price of locally produced foodstuffs of about 11 percent (table 15). Our analysis of the staple food price data collected across ten markets in CMte d'Ivoire shows that real staple food prices declined anywhere from 0 to 20 percent depending on the particular product between 1993 and 1995 (table 11). Only the real price of plantains remained stable, according to the market price data. In view of the decline in staple food crop prices, one would expect an increase in poverty among farners specializing in staple food crop production.

Table 15 Index of Prices of Staple Foods, **1993-1995a)**

|_Expenditure item_||_CPI price index_||
|---|---|---|---|
||_December 1993_|_December 1994_|_December 1995_|
|Tradable staple foods|100|129.2|166.7|
|Rice|100|126.2|178.3|
|Macaroni|100|189.5|194.0|
|Bread|100|127.9|127.9|
|Non-tradable staple foods|100|114.0|128.8|
|Maize|100|117.1|123.5|
|Millet|100|117.7|121.0|
|Manioc|100|94.1|111.8|
|Attidke|100|133.0|172.9|
|Yams|100|144.8|137.1|
|Potatoes|100|164.0|120.7|
|Plantain|100|101.4|121.7|
|Staple foods|100|120.4|144.9|
|CPI|100|132.2|142.4|

> a) Indice des prix (ouvrier, employe qualifee ou artisan traditionnel.)

These observations suggest that the impact of the 1994 devaluation of the CFA franc is likely to be less favorable than suggested by Goreux (1995). He argues that in the presence of good rainfall in the year following the devaluation, it is reasonable to think that consumption of home production did not diminish in the rural areas. Export crop producers would have benefited, particularly those producing coffee. Furthermore, it is likely that food crop producers benefited as well, since the CPI indicates that staple food prices went up at the same rate as the devaluation, and transport costs did not. All in all, he argues that it is likely that the monetary revenue of the rural population increased after the devaluation.

18
However, the price of nontradable staple food prices did decline. Moreover, the fact that the imputed value of home consumption likely fell in real terms must be factored into the analysis. When it is, and when account is taken of the fact that real cocoa prices did not increase substantially, there is reason to think that in the short run following the devaluation, poverty would have increased, or at least failed to decrease, among a substantial segment of the rural population.

# Household survey results

Two household expenditure surveys are available that allow us to trace the short-run impact of the devaluation on poverty. The pre-devaluation survey was carried out between June and November, 1993, except Abidjan, which was carried out in March-April 1992. A total of 9600 households were surveyed. The post-devaluation survey, with a much smaller sample of 1000 households, was carried out in April-May 1995. With several exceptions, we maintained the INS data as it was processed, cleaned, and aggregated. The data take into account the change that was made to the consumption of home production data to correct an error made in computer data entry, as described in Appendix C. We also re-estimated the housing expenditure variable and changed the definition of agricultural workers to correct some inconsistencies, described in Appendix A.

In view of the fact that GDP growth per capita was negative in 1994, one would expect that the devaluation would not have had a strong impact on reducing poverty by early 1995, when survey results are available. Overall, the survey results, based on the cleaned INS data set (table 2), show that the incidence of poverty increased from 32.3 percent in 1993 to 38.6 percent in 1995. Given the various shortcomings of the surveys-- which are discussed below--the increase in poverty is probably overstated.

While overall there was little change in mean household expenditure and poverty, there were big differences in the changes in mean expenditure and poverty across regions and socioeconomic groups. Within urban and rural areas there was no uniform trend: poverty increased most dramatically in Abidjan, but declined in Other Cities, while within the rural areas, poverty in the East Forest and the Savannah remained essentially unchanged, while poverty worsened significantly in the West Forest. However, first order dominance testing showed that the 1993 and 1995 "poverty incidence curves" (plot of headcount index of poverty on the vertical axis and the poverty line on the horizontal axis) for Other Cities, East Forest, and the Savannah intersect each other repeatedly within the range of 50,000 to 100,000. This CFAF (in 1985 CFAF) indicates that the poverty ranking of the two years is sensitive to the choice of poverty line.

Looking at households classified by the socioeconomic group of the household head --which does not necessarily indicate what the major source of income of the household is'<sup>9</sup> --we find, not too surprisingly, that poverty increased among food crop farmers (table 16). However, poverty also increased slightly among export crop farmers, contrary to our expectations, given the increase in real producer prices. This is primarily due to the large increase in poverty among export crop farmers in the 19 The definition of socioeconomic groups was based first of all on the declared occupation and sector of activity of the household head. Households whose heads, for example, declared themselves to be farmers where then classified into export or food crop households on the basis of whether or not their export crop revenue was at least 50 percent greater than the revenue from marketed sales of food crops. Public sector employee households were classified on the basis of the principal activity of the household head. The definitions used in 1993 and 1995 are not strictly comparable to those used in 1985-1988, which did take into account sources of income.

19
West Forest, which more than offset the decline in poverty among export crop farmers in the Savannah (table 17).20 The large increase in poverty among export crop farmers in West Forest is puzzling, and is considered below in more detail. The reduction in poverty was greater among export crop farmers whose major source of export crop revenue is coffee than among those who depend most heavily on cocoa, as expected.

**Table 16: Headcount Index of Poverty by Socioeconomic Group**

_(Poverty Line= 75, 000 CFAF per year in 1995 CFAF)_

||_Headc_|_ount Index_|_Weig_<br>_popu_|_hted_<br>_lation_|
|---|---|---|---|---|
|_Socioeconomic Group_|_1993_|_1995_|_1993_|_1995_|
|Export crop farm|0.394|0.432|16576|1582|
|Food crop farmer|0.492|0.584|13507|1149|
|Agriculture worker|0.531|0.364|843|237|
|Public sector employee|0.076|0.140|6062|446|
|Private formal sector employee|0.071|0.075|4137|431|
|Private informal employee|0.254|0.310|2464|976|
|Self-employed formal sector|0.078||125||
|Self-employed informal sector|0.248|0.293|8229|267|
|Unemployed|0.202|0.528|661|60|
|Inactive|0.271|0.191|4096|312|

**Table 17 Household Expendit re Per Capita and Headcount Index of Poverty Among Farm Households**

||_East_|_Forest_|_West_|_Forest_|_Sava_|_nnah_|
|---|---|---|---|---|---|---|
||___<br>_1993_|_1995_|_1993_|_1995_|_1993_|_1995_|
|_Expenditure per capita_|||||||
|_(in 1985 CFAFperyear)_<br>Export crop farmer|96768|99169|110082|94840|82372|89929|
|Food crop farmer|98157|83564|92117|90038|.81088|87115|
|_Headcount index_|||||||
|Export crop farmer|0.407|0.406|0.340|0.515|0.524|0.406|
|Food crop farmer|0.414|0.567|0.503|0.594|0.532|0.577|

Poverty decreased among agricultural workers, though in view of the small sample size the difference may not be judged significant. However, there are some reports that labor has become more difficult to mobilize in rural areas, and that after the devaluation there was a sharp increase in labor demand primarily for weeding coffee. One publication, based on evidence from a few villages, reported that nominal wages had increased by 60 percent which would imply a significant real wage increase on the order of about 20 percent in early 1995, greater than the real increase in cocoa prices, though less than the real coffee price increase.21 If indeed agricultural workers did benefit from wage increases of 20 Poverty also declined among export crop farmers in Other Cities, but they accounted for only 5 percent of the export farmer population in 1995.

21 Wages increased by 60 percent in the villages of Gobarza and Kremouye post devaluation (Pallix, 1995). The precise date was not specified.

20
this magnitude, the dramatic reduction in poverty would be understandable. Given the increase in poverty in urban areas, and return migration from the urban areas to the countryside, the labor constraints may slacken and real wages fall over the next few years. However, one should be careful in interpreting the results of the socioeconomic breakdown in the survey data, as we do not know what percentage of household income is made up of wage labor, and whether wage incomes increased in the households classified as wage laborers. Moreover, given the various categories of hired labor that exist, it is not clear that the surveys do an adequate job of identifying all the various forms of hired labor and coding them correctly. So it is entirely possible that the observed decline in poverty in this group of households may have little or nothing to do with labor earnings per se.

There was a substantial increase in poverty among public sector employees, as expected given that real public sector wage bill declined in real terms. Following the devaluation in January 1994, the government granted a 10 percent increase in wages and salaries across the board, although fringe benefits remained unchanged. Thus, the overall increase in personnel remuneration amounted to about 8 percent. The number of employees remained essentially unchanged. The rate of inflation between the two surveys was 48 percent for Abidjan (the pre-devaluation survey was conducted April-May 1992) and 43 percent for the rest of the country (the pre-devaluation survey was conducted between JuneNovember 1993). Thus remuneration of Abidjan civil servants fell by 27 percent in real terms, while remuneration of civil servants outside Abidjan fell by 24 percent. The survey data show that household expenditure per capita of Abidjan public employee households fell by 15 percent, while expenditure per capita of public sector employee households in Other Cities fell by 24 percent. Thus, it seems that public sector employee households in Abidjan were better placed to draw on other sources of income or savings to mitigate the decline in their public sector salaries than public sector employees outside Abidjan.

Tlhe outcome for the informal sector seems to be strongly influenced by the outcome of other segments of the local economy. The big difference between Abidjan and Other Cities is that poverty in the informal sector (i.e. employees in the informal sector or self-employed) in Abidjan showed a big increase, while in Other Cities it declined (table 18). Informal sector households in Other Cities may have benefited from higher derived demand from the rural areas and have more agricultural income. It is hard to draw firm conclusions about self-employed in rural areas, since the sample size of the rural self-employed in 1995 amounts to only 5 percent of the population; poverty declined among these households in West Forest and the Savannah, but increased in East Forest.

**Table 18 Urban Headcount Index of Poverty by Major Socioeconomic Group** _(Poverty Line = 75,000 CFAF per year in 1985 CFAF)_

||_Abi_|_djan_|_Othe_|_r Cities_|
|---|---|---|---|---|
|_Socioeconomic Group_|_1993_|_1995_|_1993_|_1995_|
|Public sector employee|0.002|0.065|0.140|0.274|
|Private sector formal employee|0.049|0.055|0.128|0.188|
|Informal sector|0.058|0.291|0.338|0.278|

21
# _Factors Affecting the Credibility of the 1993-1995 Results_

There are a number of problems associated with the 1993 and 1995 survey data, including sampling problems in 1993, the small sample size in 1995, the estimation of housing expenditure, the limited time period of the year during which the survey was conducted, the problem in correctly coding the consumption of home production data in 1993, the method used to estimate purchased food expenditure, and the non-representativeness of the consumer price index. In many cases the biases appear to be in the direction of overestimating the incidence of poverty in 1995.

## Survey design **and implementation**

A major difference between the 1993 and 1995 priority surveys is the size of the sample. The 1993 survey is based on a sample of 9,600 households, while the 1995 sample of 1,000 households is a little more than a tenth the size. The size of the some of the cells is very small in 1995, thus restricting the analysis one can do. Second, the 1993 sampling procedure was based on a household listing done in 1991. Surveying of the households began in March 1992, almost exclusively in Abidjan, but it was interrupted a few months later due to lack of money. Surveying resumed in June 1993 and was completed in November 1993. On average 22 months passed between the household listing and survey. A problem arose that many of the households that were listed in 1991 were not relocated in 1993. In urban areas, about 50 percent of the households selected were not surveyed, mostly because they could not be located, while in rural areas about a third of the households were not surveyed. Thus, the more stable households are over-represented in the sample, while the least stable-- and perhaps the poorest-are underrepresented. However, because of the high replacement rate in Abidjan, INS decided to keep the 1680 households in Abidjan that had been surveyed in 1992 as part of the sample, and to reject those that had been surveyed in 1993. Presumably, therefore, the Abidjan sample suffers less from this bias.

Another worrisome finding is the relatively large number of households in the 1993 survey that reported zero value for the sum of purchased expenditure and imputed value of consumption of home production (Appendix C). About one percent of households fall in this category. Some of these households did report receiving transfers of food from people outside the household, but is unlikely that transfers from outside the household would be the sole source of food. The relatively large number of households with zero food expenditure suggests that there may been problems with implementation of the survey or with data entry.

## **Decline in household size**

A troubling finding is the decline in household size between 1993 and 1995 (table 19). This would tend to raise per capita expenditure, all other things being equal, assuming that per capita household expenditure and household size are inversely correlated as is usually the case. Mean household size fell from 5.9 persons in 1993 to 5.5 persons in 1995, with the fall particularly acute in the West Forest. For Cote d'Ivoire as the whole, the 1993 estimate is not too dissimilar to the 1988 estimate of 6.2 persons. For export crop farmers, the 1988 estimate was 6.8, compared to the 1993 estimate of 6.7 and the 1995 estimate of 5.9 persons (table 20). The fall in household size is particularly large among export crop farmers who grow cocoa: household size declined from 7.1 to 5.9 persons between 1993 and 1995 (table 21). In contrast, food crop farmer household size did not decrease: mean household size rose slightly from 5.5 to 5.7 persons. Both estimates are somewhat lower than the 1988 estimate of 6.4 persons. Unfortunately without another source of information it is impossible to determine whether the fall in household size is a real phenomenon, unlikely as that is. Since the 1993 and 1988 household size estimates are reasonably close, it is more likely that decline in 22
household size reflects problems with the 1995 sampling design or administration of the questions regarding household membership.

**Table 19 Mean Household Size by Region**

|||_Hous_|_ehold size_||
|---|---|---|---|---|
|_Region_|_1985_|_1988_|_1993_|_1995_|
|Abidjan|6.18|5.88|5.98|5.82|
|Other Cities|6.41|6.33|6.19|5.27|
|East Forest|6.60|6.46|6.14|5.86|
|West Forest|6.03|5.60|5.68|4.63|
|Savannah|7.24|6.35|5.47|5.57|
|C6te d'Ivoire|6.51|6.16|5.91|5.46|

Source: 1985 and 1988: Demery and Grootaert (1993); 1993 and 1995: Priority Surveys.

**Table 20 Mean Size of Farm Households**

|||_House_<br>|_hold size_<br>||
|---|---|---|---|---|
|_Region_|_1985_|_1988_|_1993_|_1995_|
|East Forest|||||
|Export crop farmer|||7.38|6.23|
|Food crop farmer|||5.51|6.09|
|West Forest|||||
|Export crop farmer|||6.34|5.30|
|Food crop farmer|||4.77|4.15|
|Savannah|||||
|Export crop farm|||6.06|5.75|
|Food crop farmer|||5.50|6.10|
|C6te d'Ivoire|||||
|Export crop farm|6.92|6.70|6.70|5.86|
|Food crop farmer|7.06|6.38|5.47|5.66|

Source: 1985 and 1988: Demery and Grootaert (1993); 1993 and 1995: Priority Surveys.

**Table 21 Mean Size of Cocoa- Growing Export Farmer Households**

||_House_|_hold size_|
|---|---|---|
|_Region_|_1993_|_1995_|
|East Forest|7.49|6.10|
|West Forest|6.65|5.38|
|Savannah|7.16|6.25|
|C6te d'Ivoire|7.09|5.92|

23
# **Incorrect coding of the consumption of home production data**

In the process of working with the 1993 data, we discovered that the data, as it was originally processed, showed a dramatic decline in consumption of home production. The reason was that in 98 percent of the 25,570 cases, the number of months of consumption of home production were coded as one month, in contrast to 2 percent of the cases in the 1995 survey (table 22). In examining a few questionnaires it was evident that the data for the number of months of consumption of home-produced items had been incorrectly entered on the computer file. Unfortunately, INS was not able to reenter the data for 1993.

**Table 22 CMte d'Ivoire: Number of Months of Consumption of Home Production 1993 and 1995**

|_Reported Months_|_Unrevised 1_|_993 survey_|_1993 correc_|_ted survev_|_19_|_95_|
|---|---|---|---|---|---|---|
|_of Consumption_|_Frequency_|_Percent_|_Frequency_|_Percent_|_Frequency_|_Percent_|
|1|25107|98.3|320|1.3|57|1.9|
|2|1|0.0|774|3.0|169|5.6|
|3|||1110|4.3|184|6.1|
|4|||1076|4.2|185|6.1|
|5|4|0.0|752|2.9|123|4.1|
|6|2|0.0|1696|6.6|285|9.5|
|7|||532|2.1|82|2.7|
|8|||886|3.5|128|4.2|
|9|||322|1.3|61|2.0|
|10|124|_0.5_|3442|13.5|117|3.9|
|11|288|1.1|175|0.7|11|0.4|
|12|3|0.0|14444|56.6|1612|53.5|

Fortunately, however, a mistake was made in filling out the questionnaires that allowed us to make a first-order correction. Even in cases where households had purchased none of a particular food item that they produced at home, they still filled out the number of months of consumption question under the entry for the number of months a purchased good was consumed. In the small number of original questionnaires that we were able to examine, the number of months of purchased consumption corresponded in most cases to the number of months of consumption of home production. Hence we used the number of months of purchased consumption (in the case where no purchased value was declared) to correct what was assumed to be the incorrectly coded number months of consumption of home production (almost always one month). Other procedures were used to correct the cases where purchased food value was nonzero. These are described in more detail in Appendix C.

Once the corrections were made, the frequency of months of consumption between 1993 and 1995 are fairly similar, though there is a higher frequency of 10 months of home consumption in 1993 data compared to the 1995 data (table 22). The mean number of months in the corrected 1993 sample is 9.7, compared to 8.9 in 1995. Whether this difference is a result of the cleaning procedure or reflects actual reported consumption patterns cannot be ascertained. However, it does suggest that there may be some upward bias in the 1993 consumption of home production and thus that it would be desirable to reenter the incorrectly entered 1993 data, rather than rely on the imputed number of months.

24
# **Housing expenditure**

_Housing expenditures._ In the CMte d'Ivoire surveys, as in other surveys, statistical techniques are used to impute housing expenditures for the households that do not pay rent. Using the sample of households that do pay rent, regression analysis was used to estimate a hedonic rental equations relating the amount of rent paid to different household characteristics, with regional dummy variables added to capture spatial variation in rental prices. The small sample size was a problem in 1995; only 9 households in West Forest and 12 households in the Savannah rented lodging in **1995.22** In the initial processing of the data, housing expenditures were estimated separated for the 1993 and 1995 using different specifications of the hedonic rental equation. Real housing expenditures increased substantially in rural areas. The increase in the Savannah of 47 percent was particularly suspect. There is little reason to think the imputed value of housing would have increased significantly, especially since the housing costs rose less rapidly than other goods after the devaluation.

Another approach to deriving imputed housing costs is to pool the 1993 and 1995 data and use the same equation to estimate housing costs, with dummy variables for each region allowed to vary between 1993 and 1995. This improves comparability but does not eliminate entirely the problem caused by the small sample size in 1995. With the pooled sample, real housing expenditures fall in all regions but the Savannah, where they remain unchanged. The two methods yield different estimations of the headcount index of poverty in 1995 (table 23). While recognizing that housing expenditures in the Savannah may be overestimated even with the pooled estimate, it is the better of the two estimates.

Table 23 Headcount Index of Poverty Using Different Estimates of Housing **Expenditure**

|_(poverty line_|_= 75,000 C_|_FAF in 1985_|_CFAF)_||
|---|---|---|---|---|
||_Separate E_<br>_1993 a_|_quations for_<br>_nd 1995_|_Pooled E_<br>_1993 a_|_quation for_<br>_nd 1995_|
||_1993_|_1995_|_1993_|_1995_|
|Abidjan|.048|.183|.051|.202|
|Other Cities|.313|.285|.312|.286|
|East Forest|.373|.396|.389|.410|
|West Forest|.356|.460|.382|.501|
|Savannah|.485|.417|.494|.494|
|All|.313|.339|.323|.386|

**22** See Appendix A for a discussion of the estimation of housing expenditure.

25
# Seasonality

The 1993 and 1995 surveys were conducted only over a period of several months: June to November 1993 and April and May 1995. Households were interviewed once during that period, and were asked about expenditures over the past week, month, three months or year depending on the category of expenditure. If either of these surveys were conducted during periods of exceptionally low or high expenditure, then the estimates of mean expenditure and the poverty incidence calculations could be biased. To assess whether there is a bias, we need some way of determining how expenditure varies over the year and correcting for seasonal fluctuations. Fortunately the 1985-1988 CILSS surveys can be used for this purpose.

Households in the CILSS surveys were interviewed once about their expenditures, with the interviews conducted randomly throughout the year. Since the CILSS surveys had a two week recall period for both purchased food and many nonfood items, a high percentage of expenditure would potentially be subject to seasonal variation. This variation in expenditure could arise from seasonal fluctuations prices, particularly food prices, that are not offset by variations in quantity, or to seasonal variation in income, to the extent that households do not smooth their consumption over the course of a year. There could also be seasonal variation present in the estimate of expenditure on items for which the recall period was a year if the period of the year during which the interview takes place influences the size of the recall error. For example, households interviewed during a period in which they typically purchase more durable goods, send more remittances, or consume more home production might remember more of their expenditures than households interviewed during other periods of the year. Or the value households attach to the consumption of home production may vary with the market price of the home-consumed product. Or the estimate of the quantity consumed each day or the number of days per month a home-produced good is consumed may reflect the pattern during the month that household is interviewed rather than the average over the year.<sup>23</sup> Thus any seasonal variation evident in the surveys could reflect genuine seasonal variation in expenditure as well as systematic seasonal recall biases.

To assess the extent of seasonal variation, we regressed the log of household expenditure on dummy variables for the season during which the household was interviewed as well as on a set of variables included to control for possible non-randomness in the distribution of households across months of the year, including household size, the square of household size, and dummy variables for education levels. To determine whether there were region-specific seasonal interactions, a set of regional dummies was then interacted with dummy variables for three time periods during year: April/May, correspondingly to the 1995 survey; June-November, corresponding to the 1993 survey, and December-March, the remaining months. Imposing the restriction that the sum of the three time period 23 To examine whether there was any seasonality present in the annual estimate of consumption of home production, we regressed the annual estimate of consumption of home production (for those households reporting positive home consumption) on the region and crop dummy variables interacted with the time period variables. The June-November time period was split into two sub-periods: June-August, and September -November, corresponding more closely to the pre and post-harvest periods. In the regression over the sample of households with expenditures less than or equal to 100,000 CFAF per capita, only the East Forest*June-August dummy is significant at the 5 percent level, indicating that expenditures among East Forest non export crop farmers are significantly lower than the December-March period. Over the much larger sample of households whose expenditur **e** xceeds 100,000 CFAF per capita, the Other Cities, East Forest, West Forest and Savannah JuneAugust dummies are significant and negative. This suggests that the time of the year during which the interview biases the estimate of consumption of home production.

26
dummies for each region add up to zero allows a unique set of regression coefficients to be estimated. The coefficients of the regional dummy variables interacted with the time period dummies (referred to as the regional*time period dummies) can then be interpreted as the deviation from average expenditure in that region over the year (Suits, 1984) over the average of all other variables.<sup>24</sup> Since we suspected that cash crop income might also have an effect on seasonal spending patterns, we included a dummy for whether a household was a cotton farmer and a dummy for whether a household farmed cocoa and/or coffee. These crop dummies were interacted with the time period dummies. Since households with a high ratio of home consumption might also show different patterns of seasonality, we also added a dummy variable for households whose home consumption was greater or equal to 0.6 of purchased consumption, and interacted the home consumption dummy with the time period dummies.

We also suspected that households at different levels of expenditure might exhibit different seasonal patterns. To allow for this possibility, we stratified the 1985-1988 sample into two subsamples: households with expenditures of less than 100,000 CFAF per capita and those with expenditure greater than 100,000 CFA. Stratifying the sample at levels less than 100,000 CFAF per capita yielded too few observations for the months of March and April. And since our interest for the poverty profile is the seasonal correction for expenditures in the range of 75,000 CFAF, we wanted to pick a relatively low level of household expenditure as the cut-off point.

The regression analysis did reveal some seasonality (table 24). For nonexport-crop-growing households with a low ratio of consumption of home production to purchased food (the defaults), households interviewed in the June-November period in Other Cities had significantly lower expenditures, as did households interviewed in the December-March period in West Forest. In general, however, seasonality was not pronounced in the nonexport crop-growing households in the rural regions with low ratio of home-produced to purchased food expenditure. As might be expected, cocoa/coffeeand cotton-growing households interviewed in the December-March period when the crop is marketed (with low ratios of home produced to purchased food expenditure) had significantly higher levels of total expenditure. And among non-export crop-producing households with a high share of home produced to purchased food expenditure, expenditures were higher in households interviewed in the April-May period and lower in those inteniewed in the December-March period.

The major difference between the two income groups in the pattern of seasonality, aside from Abidjan, which is discussed below, is the case of cotton farmers. The expenditure of poor cotton farmers shows more pronounced seasonality than better off cotton farmers. Expenditure is highest among poor cotton farmers interviewed in December-March, and then falls off precipitously in the April-May period. For better off cotton farmers, expenditure is lower among households interviewed in the June-November period. These results suggest that better off cotton farmers may either have

**24** Suits' (1984) procedure imposes the restriction that the unweighted sum of the coefficients of each set of dumnmy variable **s** ums to zero. Rather than using the unweighted sum, however, we restricted the weighted sum to equal zero. In the case of the regional*time period dummy coefficients, the weights are the mean value of regional*time dummy variables, divided by the mean value of the regional dummy variable. The weights sum to one.

27
**Table 24 Regression of the Logarithm of Household Expenditure per Capita** _(t-statistics in parentheses)_

|_Dummy_|_Household ex_|_penditure per capita_|_<100, 000 CFAF_|_Household ex_|_penditure per capita_|_2100,000 CFAF_|
|---|---|---|---|---|---|---|
|_variablesa)_|_April-May_|_June-November_|_December-March_|_April-May_|_June-November_|_December-March_|
|Abidjan|0.144<br>(1.377)|-0.022<br>(-0.730)|-0.044<br>(-0.433)|-0.043<br>(-1.867)|-0.005<br>_(-0.455)_|0.041<br>(2.191)|
|Other Cities|-0.042<br>(-0.618)|-0.020<br>(-1.333)|0.073<br>(1.876)|0.072<br>(2.466)|-0.020<br>(-2.289)|0.013<br>(0.578)|
|East Forest|-0.072<br>(-1.433)|0.024<br>(1.128)|-0.001<br>(-0.036)|-0.034<br>(-0.797)|0.025<br>(1.536)|-0.026<br>(-1.017)|
|West Forest|0.009<br>(0.106)|0.048<br>(1.493)|-0.080<br>(1.686)|-0.021<br>(-0.474)|0.000<br>(0.019)|0.015<br>(0.470)|
|Savannah|-0.001<br>(-0.024)|0.025<br>(0.928)|-0.029<br>(-0.906)|-0.019<br>(-0.450)|-0.003<br>(-0.148)|0.010<br>(0.444)|
|Cotton|-0.169<br>(-2.975)|-0.014<br>(-0.436)|0.119<br>(3.143)|0.022<br>(0.586)|-0.042<br>(-2.610)|0.057<br>(2.323)|
|Coffee/Cocoa|-0.024<br>(-0.440)|-0.034<br>(-1.610)|0.071<br>(2.135)|0.022<br>(0.586)|-0.042<br>(-2.610)|0.057<br>(2.323)|
|High consumption of<br>h dtib|0.071|0.006|-0.046|0.043|-0.006|-0.009|
|ome proucon)|(1.693)|(0.313)|(1.674)|(1.170)|(-0.399)|(-0.411)|
|R'<br>N||0.7420<br>1160|||0.6660<br>5173||

a) Dummy variables shown in this table are interacted with the dunmny for the specified time period in the table.

b) Defined as the ratio of consumption of home production to production to purchased food 2 0.6.

28
more capacity to smooth expenditure or that cotton income is a smaller share of total income and therefore does not distort expenditure patterns as much as it does for poor cotton farmers. In contrast there is very little difference in the seasonality pattern for coffee/cocoa farmers, somewhat surprisingly.

To de-seasonalize the 1993 and 1995 expenditure estimates, we subtracted the relevant dummy variables from the log of each household expenditure for each household in the 1993 and 1995 surveys. For the 1993 survey, we used the June-November time period dummies interacted with the region, crop, and home consumption dummies, whereas for the 1995 survey we used the April -May dummies interacted with the region, crop, and home consumption dummies. No seasonal correction was made to the Abidjan observations, since only 8 households with expenditures of 100,000 CFAF or less were interviewed in March-April in the 1985-1988 surveys.<sup>25</sup> The poverty profile, corrected for the seasonal bias, is presented in table 25. The only region in which there is a reversal of trend is in East Forest, where poverty falls instead of rises. However, neither the magnitude of the difference between the 1993 and 1995 original estimates nor the difference between the seasonally corrected estimates is large for East Forest, as well as for Savannah and Other Cities. The big increase in poverty in West Forest remnains even with the seasonal correction, and is investigated below. The reason that the poverty estimates change so little is that the relatively large changes for the cash-cropping households tend to be offset in part by the regional and home consumption dummies. Among the major socioeconomic groups in each region, the seasonally adjusted poverty figures show that poverty falls slightly among export crop farmers in East Forest, and drops more significantly among export crop farmers (primarily cotton farmers) in the Savannah (table 26).

**Table 25 Headcount Index of Poverty Corrected for Seasonal Variation** _(Poverty line = 75, 000 CFAF per year in 1985 CFAF)_

||_Seasonallv_|_unadjusted_|_Seasonal_|_ly adjusted_|
|---|---|---|---|---|
|_Region_|_1993_|_1995_|_1993_|_1995_|
|Abidjan|.051|.202|.051|.202|
|Other Cities|.312|.286|.293|.275|
|East Forest|.389|.410|.392|.383|
|West Forest|.382|.501|.393|.532|
|Savannah|.494|.494|.510|.509|
|Coted'Ivoire|.323|.368|.323|.367|

25 The Abidjan*April-May dummy regression coefficient indicates that expenditures are 14 percent higher for households interviewe **d** uring this period, but the coefficient is not significant at even the 10 percent level. For the sample of households of expenditur **e** xceeding 100,000 CFA per capita, the coefficient for Abidjan*April-May dummy showed that expenditures of households interviewed in this period are about 4 percent lower than average (significant at the ten percent level). In any event, since the Abidjan households were interviewed in the same period for both the 1993 and 1995 surveys, any seasonal correction would alter the poverty estimates in the same direction.

29
**Table 26 Headcount Index of Poverty Among Farmers, Corrected for Seasonal Variation** _(Poverty line = 75, 000 CFAF per year in 1985 CFAF)_

||_Seasonally_|_unadjusted_|_Seasonall_|_y adiusted_|
|---|---|---|---|---|
|_Region_|_1993_|_1995_|_1993_|_1995_|
|Other Cities|||||
|Export crop farmer|0.334|0.071|0.296|0.071|
|Food crop farmer|0.540|0.645|0.523|0.645|
|East Forest|||||
|Export crop farmer|0.407|0.406|0.400|0.363|
|Food crop farmer|0.414|0.567|0.429|0.557|
|West Forest|||||
|Export crop farmer|0.340|0.515|0.352|0.561|
|Food crop|0.503|0.594|0.516|0.604|
|Savannah|||||
|Export crop farmer|0.524|0.406|0.531|0.406|
|Food crop farmer|0.532|0.577|0.552|0.603|

One problem in using the coefficients derived from the 1985 -1988 LSMS data to correct the 1993 and 1995 surveys is that the total expenditure aggregate for many food and some nonfood items is based on the average of the two week (annualized by multiplying by 26 weeks) and the normative estimate of monthly food expenditure multiplied by typical number of months of purchase. Unless there is seasonal recall bias, one would expect that there would be no seasonal variation in the normative estimate. Thus, the average of the two estimates would exhibit less seasonal variation and could result in a downward bias in the seasonal correction parameters.

One way of assessing the possible extent of this bias is to compare the difference in the seasonal variation in the two different estimates of food expenditure. One estimate is based on the two week recall, while the other is average of the two week and the normative estimates. The JuneNovember time period was split into two sub-periods: June-August, and September-November, corresponding more closely to the pre and post-harvest periods, which may have some bearing on the seasonality in food purchase. With a few exceptions, the coefficients are generally of the same magnitude. The averaged estimate does not appear exhibit less pronounced seasonal variation than the two week estimate alone (table 27). This suggests that there may also be some seasonal bias in the annual recall estimate of food expenditure in the same direction as the two week estimate. We opted to use the average of the two week-annualized estimate and the normative estimate as the dependent variable in the regressions used to produce the seasonal adjustment variable, as the expenditure aggregate was the only cleaned expenditure aggregate variable available to us in the 1985-1988 data file.

30
|**Table 27 Regression of the L**|**ogarithm of**|**Purchased F**|**ood****_(t_**-_stati_|_stics in paren_|_theses)_||
|---|---|---|---|---|---|---|
||_April -_|_Mav_|_June -_|_August_|_September_|_- November_|
|_Dummy variables_|_<100,000_|_2100,000_|_<100,000_|_2100,000_|_<100,000_|_>100,000_|
|_Two week/normative average_|||||||
|Abidjan|0.100<br>(0.282)|0.056<br>(-1.108)|-0.029<br>(-1.113)|-0.002<br>(-0.049)|0.030<br>(0.199)|-0.032<br>(-0.662)|
|Other Cities|-0.111<br>(-.500)|-0.024<br>(-0.394)|0.030<br>(0.199)|-0.034<br>(-0.692)|-0.046<br>(-0.298)|-0.047<br>(-0.945)|
|East Forest|-0.303<br>(-1.803)|-0.164<br>(-2.038)|0.216<br>(1.381)|0.072<br>(1.062)|-0.017<br>(-0.016)|0.235<br>(3.639)|
|WestForest|-0.088<br>(-0.314)|-0.078<br>(0.854)|0.639<br>(2.883)|0.044<br>(0.563)|0.151<br>(0.664)|0.008<br>(0.094)|
|Savannah|0.152|0.149|0.383|0.227|-0.230|-0.299|
||(0.916)|(1.890)|(2.513)|(3.518)|(-1.535)|(4.369)|
|Coffee/cocoa farner|0.142<br>(0.763)|0.088<br>(1.145)|-0.343<br>(-2.196)|-0.211<br>(-3.318)|-0.371<br>(-2.294)|-0.348<br>(-5.491)|
|Cotton farmer|-0.495<br>(-2.306)|-0.483<br>(-3.581)|-0.909<br>(4.748)|-0.295<br>(-3.080)|-0.013<br>(-0.061)|0.000<br>(0.002)|
|_Two week estimate of purchased_|_food_||||||
|Abidjan|0.245<br>(0.608)|-0.052<br>(-0.871)|.0210<br>(0.709)|0.014<br>(0.264)|-0.077<br>(-0.306)|-0.034<br>(-0.587)|
|Other Cities|0.077<br>(0.306|-0.028<br>(-0.387)|-0.011<br>(-0.007)|-0.072<br>(-1.260)|-0.064<br>(-0.359)|-0.028<br>(0.488)|
|East Forest|-0.252<br>(-1.321)|-0.133<br>(-1.404)|0.057<br>(0.319)|0.073<br>(0.916)|0.094<br>(-0.547)|0.229<br>(3.012)|
|West Forest|-0.340|-0.083|0.393|0.062|-0.036|-0.072|
||(-1.063)|(-0.772)|(1.556)|(0.679)|(-0.139)|(-0.707)|
|Savannah|0.299|-0.262|0.330|0.263|-0.214|-0.378|
||(1.530)|(2.837)|(1.893)|(3.463)|(-1.240)|(4.709)|
|Coffee/cocoa farmer|0.101|0.060|-0.249|-0.215|-0.320|-0.358|
||(0.477)|(0.672)|(-1.399)|(2.871)|(-1.717)|(4.815)|
|Cotton farmer|-0.817|-0.451|-0.919|-.0387|-0.001|-0.010|
||(3.340)|(-2.850)|(-4.208)|(-3.435)|(-0.003)|(-0.063)|

31
# **Comparability** in **the method of** estimating **purchased food expenditure**

Both the 1993 and 1995 surveys use the same method of estimating food expenditure, which essentially involves multiplying the estimate of purchases made over the last month for each month by the number of months the commodity is declared to be purchased. The drawback of this method is that it underestimates annual food purchases for the sample as a whole, since it makes no correction for the households that did not purchase the commodity in the previous month, but do purchase it during other months of the year. Typically surveys deal with this problem by multiplying purchases made during the previous month by 12 months, rather than by the declared number of months. Multiplying by 12 months yields a correct sample mean under various assumptions (see Appendix B). When the survey is not conducted over the course of the year, and there is a variation across the months of the year in the number of households reporting zero purchases the last month, the procedure of multiplying by 12 will not necessarily yield the mean-preserving result. However, it may still be a better estimate than the one produced by multiplying by the declared number of months.

We re-estimated purchased food expenditures by multiplying by 12 months instead of multiplying by the declared number of months, recomputed total household expenditure, and applied the seasonal correction parameters as described in the section above to the total expenditure aggregate. Because in 1995 food expenditures are much higher using the annualized rather than declared months method, poverty estimates are correspondingly lower in 1995 and thus the increase in poverty between 1993 and 1995 smaller (table 28). The difference between the annualized and the declared months method is particularly large in the case of East Forest and the Savannah, because of the large number of observations from households that do not purchase food during the entire year.

**Table 28 Comparison of the Seasonally Adjusted Headcount Index of Poverty Based on the Annualized Monthly and the Declared Months * Monthly Value Estimates of Purchased Food Expenditure for 1993 and 1995**

||_Annualize_<br>_purchas_<br>_expenditu_|_Headcount I_<br>_d monthly_<br>_edfood_<br>_re estimate_|_ndex of Poverty_<br>_Declared months * monthly_<br>_value purchasedfood_<br>_expenditure estimate_|
|---|---|---|---|
|_Region_|_1993_|_1995_|_1993_<br>_1995_|
|Abidjan|.040|.192|.051<br>.202|
|Other Cities|.291|.249|.293<br>.275|
|East Forest|.376|.307|.392<br>.383|
|West Forest|.390|.503|.393<br>.532|
|Savannah|.506|.432|.510<br>.509|
|Cote d'Ivoire|.316|.323|.323<br>.367|

The decision about which aggregate to use for estimating purchased food expenditures in 1993 and 1995 is not clear-cut, however. If, for example, for seasonal or other reasons, the percentage of households with zero purchases the previous month but positive purchases at some point during the year is less that what would be expected given the average number of months of reported expenditure (assuming no recall bias), then the annualized method would overestimate expenditure. Appendix B describes some of the drawbacks of the two methods.

32
This paper maintains the purchased food expenditure aggregate as calculated in the INS file, with the proviso that it may lead to a downward bias in the expenditure data for 1995. It should be noted, however, that some of the puzzling trends noted in the next section of the paper (e.g. the large increase in poverty among West Forest export crop farmers, the larger growth in household expenditure among Savannah farmers relative to East Forest farmers) persist even if the 12 month estimate of purchased food expenditure is used in calculating total household expenditure.

# Shortcomings of the **CPI**

Published CPI information is available for three categories of households (blue-collar workers, white collar workers managers, and European-type consumption) in Abidjan. The expenditure shares are based on a 1976 consumption survey, while base prices currently in use are the mean November 1992-October 1993 prices. Prior to 1993, the CPI was based on the August 1984 to July 1985 prices. In addition in 1993, the INS shifted to actually purchasing goods at the market. So there may be some problem of comparability between the pre- and post-1993 consumer price indices. The major shortcomings of the deflators used to calculate real household expenditure and thus the poverty indicators are (1) the difference between the expenditure shares used to construct the CPI and the survey shares and (2) the absence of regional price indices. The large differences in expenditure patterns across the country illustrate the need for region-specific CPIs based on actual expenditure patterns and price increases.

There is a large difference in the expenditure shares on which the CPI is based and those derived from the surveys. Table 29 compares the consumption weights in the CPI basket for blue-collar Abidjan workers with the expenditure shares derived for Abidjan poor and non-poor households. The major difference is poor households spend a much greater proportion of their expenditure on housing and utilities than is indicated by the CPI basket. Even m non-poor Abidjan households the share of expenditure on housing and utilities as revealed by the survey exceeds the share in the CPI basket. Moreover, the difference is actually understated, since remittances are part of the survey consumption basket, but not part of the CPI. If remittances were eliminated from the survey consumption basket, the survey share of expenditure on housing would be even greater.

Since lodging and utilities had the lowest rates of inflation of any category in the CPI between December 1993 and March 1995 (table 30), a higher share of expenditure allocated to housing and utilities would result in a lower inflation rate, and thus in higher real per capita expenditures on 1995. As a rough indication of how the inflation rate would change if one were to use the survey consumption weights base, mean expenditure per capita on average would be about 2 percent higher using the survey expenditure shares. However, this calculation abstracts from the fact that the composition within the **26** various groups of expenditure may differ significantly between the CPI and the household surveys, as the discussion of the composition of the food subcomponent, below, illustrates.

A second shortcoming of the INS CPI series is that there is no CPI series for the rural areas. To deflate expenditures in the household surveys, INS constructed a deflator for each region for 1985 based on price differentials calculated from the International Comparison of Prices survey data (Grootaert and Kanbur, 1994). The price deflator for Abidjan was used to construct the price series

**26** We assume that the inflation rates for the categories in the remittances, non-food in kind and education in the survey is equal to the weighted inflation rate of the other categories. This calculation is based on the December 1993 and March 1994 CPI inflation rates.

33
Table 29 Composition of Expenditure _(percent)_

|||||_1993 Priority_|_survey results_|||
|---|---|---|---|---|---|---|---|
|_Expenditure Categories_|_CPI basket_<br>_Abidjan blue_<br>_collar worker_<br>_households_|_Abidjan_<br>_poor_|_Abidjan_<br>_non- poor_|_Abidjan_|_Savannah_|_C6te_<br>_d 'Ivoire_<br>_poor_|_C6te_<br>_d 'Ivoire_<br>_non- poor_|
|Food|48.0|44.7|34.7|34.8|6.1|56.8|46.0|
|Lodging and utilities|16.3|27.8|20.0|20.1|16.2|21.4|17.9|
|Clothing|10.1|5.4|6.3|6.3|6.1|6.8|6.7|
|Transport|12.2|5.6|12.5|12.4|3.3|2.8|7.7|
|Household maintenance|4.0|0.9|2.0|1.6|1.2|1.1|1.6|
|Hygiene|1.0|2.5|2.0|2.0|2.1|2.4|2.1|
|Health|0.7|5.4|6.7|6.7|1.3|2.3|5.0|
|Education|1.0|4.0|5.0|5.0|0.9|1.9|3.5|
|Miscellaneous|6.7|0.6|1.1|1.1|1.0|0.7|1.1|
|Remittances sent||3.0|9.7|9.6|5.2|3.6|8.3|
|Non-food in kind receipts||0.1|0.1|0.3|0.4|0.3|0.2|

Table 30 Consumer Price Index Blue - Collar Worker

|_(November 1992 - October_|_1993 = 100)_|||
|---|---|---|---|
|_Expenditure Categories_|_December 1993_|_March 1995_|_December 1995_|
|Food|100.3|144.8|142.3|
|Lodging|100.0|114.7|114.7|
|Utilities|104.7|119.4|129.2|
|Furnishing|103.0|166.4|161.6|
|Clothing|100.0|168.3|167.9|
|Transport|100.0|138.4|138.4|
|Vehicles|108.0|141.9|141.9|
|Household Maintenance|100.3|131.7|131.2|
|Hygiene|100.1|149.2|156.3|
|Health|101.4|147.3|154.9|
|Miscellaneous|104.6|172.9|171.9|
|Total|101.4|145.0|144.4|

over time for each region. Other countries, such as Ghana, have a rural price index available. The urban and rural deflators for the Ghana poverty analysis are based on rural and urban price indices. Given the difference in expenditure shares between the survey and CPI baskets in Ghana, the food and nonfood price indices were weighted by the survey shares to derive the deflators. Such a procedure might be envisioned for CMte d'Ivoire, though in the absence of regional price indices over time and given the differences even in the composition of food expenditure between the survey and the CPI basket, it is not clear that the result would necessarily be more accurate.

The use of the Abidjan price deflator as the price deflator for other regions could potentially be a problem, since there is a big difference between Abidjan and the Savannah in the share of expenditure on food. The food share is 62 percent in the Savannah compared with 35 percent in Abidjan, according to the 1993 survey (table 31). The higher share on food in the Savannah was offset by a somewhat lower share of expenditure on lodging and utilities, health and education, transport and remittances. Since the price increase in lodging and utilities was far less than the overall increase in prices, this would have contributed to the Savannah having a slightly higher rate of inflation than Abidjan, if the price index were recalculated on the basis of the Savannah consumption basket.

34
|**Table 31 Share of Stapl**<br>_Expenditure Categories_|**e Foods in House**<br>_Share of_<br>_maize, millet,_<br>_manioc and_<br>_yams in food_<br>_expenditure_|**hold Expenditu**<br>_Share of_<br>_rice and_<br>_plantain in_<br>_food_<br>_expenditure_|**re Based on th**<br>_Food share_<br>_in total_<br>_expenditure_|**e 1993 Survey**<br>_Share of_<br>_maize, millet,_<br>_manioc and_<br>_yams in total_<br>_expenditure_|**Results**<br>_Share of rice_<br>_and plantain_<br>_in total_<br>_expenditure_|
|---|---|---|---|---|---|
|_Region_||||||
|Abidjan|12.0|20.1|34.8|4.2|7.0|
|Other Cities|18.4|33.6|43.9|8.1|2.7|
|East Forest|29.8|23.4|56.8|16.9|13.3|
|West Forest|28.7|33.5|59.9|17.2|10.3|
|Savannah|43.5|17.2|61.5|26.8|4.6|
|CPI||||||
|Blue collar worker CPI|10.3|7.2|48.0|4.9|8.3|
|White collar worker CPI|6.8|15.5|38.8|2.6|6.0|

Offsetting this difference, however, is the difference in the composition of food expenditure between Abidjan and the Savannah. Twenty-seven percent of total expenditure in the Savannah was on the four main staples whose prices went down in real terms between 1993 and 1995, compared to 4 percent in Abidjan, and 5 percent in the CPI blue collar basket (table 31). Thus, most of the 27 percentage point difference in the food shares between Savannah and Abidjan is for the four major staples foods whose real price fell between 1993 and 1995. This suggests that, in the Savannah, the price increase in food basket may not have kept up with the overall rate of inflation, and may have more than offset any other differences in the consumption basket, such as the lower expenditure share of lodging, that would tend to raise the Savannah's inflation rate above that of other regions. In any event, information on differences in expenditure shares would need to be complemented by information on region-specific changes in prices, which does not exist.

Another shortcoming is that the same price index is used for both poor and non-poor households, even though expenditure shares differ between the two group. Overall the poor spend 10.8 percentage points more on food, 3.5 percentage points more on lodging and utilities, and substantially less on education, health, transport, and remittances (table 29). About half of the difference on food spending is for the four staple food items. Assuming that the rate of inflation of education, health, transport, and remittances is roughly the same as the overall rate of inflation, then the inflation rate for the poor, although they spend a higher proportion of their expenditure on food, would actually be lower than for the non-poor, taking into account the larger share of expenditure on staple food and lodging by the poor. Thus, the average inflation rate is likely to overstate the actual inflation rate for the poor, and understate it for the nonpoor. The result would be an apparent adverse redistribution effect, as the expenditure of the nonpoor would grow at a faster rate than mean per capita expenditure, while the expenditure of the poor would grow at a slower rate.<sup>27</sup> 27 This abstracts from changes in expenditure shares that arise from substitution effects. If, for example, preferences of the non-poor were such that they substituted goods with the least price inflation for those with the highest, then their true cost of living index would show less inflation that the poor, all other things being equal. For a discussion of true cost of living indices for households of different standards of living, see Deaton (1980). For an empirical application to the problem of defining spatial cost-of-living differences, see Ravallion and van den Walle (1989).

35
# **_A Closer Look at the Impact on the Devaluation on Farm Households_**

This section of the paper discusses the trends in rural poverty evidenced by the household data. On the one hand, the data raise questions about the reliability of the survey results. On the other hand, they also suggest that many rural farm households may not have benefited -- at least in the short term from the devaluation -- to the extent commonly supposed (Goreux, 1995) because of export taxes imposed at the time of the devaluation.

The large increase in poverty in West Forest seems to be driven by the large decrease in home consumption. Consumption of home-produced food fell by 13 percent in East Forest and by 20 percent in the Savannah, compared to the 40 percent fall in West Forest (table 32). Breaking it down further, the fall was about 21 percent for food crop farmers in the West Forest, compared to 33 percent in the East Forest and 17 percent in the Savannah. For export crop farmers, the fall in consumption of home production was 43 percent in the West Forest, compared to only 6 percent in East Forest, and 13 percent in the Savannah (table 33). Thus, the huge decline in consumption of home production, particularly among West Forest export crop farmers, raises questions. The estimation of Engel curves shows that food shares drop significantly in the West Forest between 1993 and 1995, and not in the other rural areas (table 34).8 However, for West Forest, the dummy for 1995 becomes insignificant if a dummy is added for 1995 export crop farmers. The export crop-1995 dummy is significant and negative, indicating that in 1995, West Forest export crop farmers had significantly lower food shares than other West Forest residents.<sup>29</sup> Table **32 Mean Household Expenditure Per Capita** _(in 1985 CFAF per year)_

|||_199_|_3_|||_1995_|||
|---|---|---|---|---|---|---|---|---|
||_Consumption_||||_Consumption_||||
||_of home_<br>_production_|_Purchased_<br>_food_|_Non -_<br>_food_|_Household_<br>_expenditure_|_ofhome_<br>_production_|_Purchased_<br>_food_|_Non -_<br>_food_|_Household_<br>_expenditure_|
|Abidjan|142|78275|146857|<br>225274|364|64081|121805|<br>186251|
|Other Cities|5235|49835|70375|125445|3416|52931|62259|<br>118605|
|East Forest|29102|28657|43908|101667|25328|31885|41055|<br>98269|
|West Forest|43008|20480|42497|105986|25643|27008|43596|<br>96247|
|Savannah|35814|17109|33117|86040|28680|27066|35494|<br>91240|
|Cote d'lvoire|21544|39773|67989|129306|16104|41709|63673|<br>121486|

28 There is a significant, though much smaller drop, in food shares in Abidjan between 1993 and 1995.

29 The decline in consumption of home production does not appear to be related to the procedure used to correc **t** he months of home consumption in 1993. There is a decline in the home consumption of the major staple foods not only in the months of consumption, but also in the number of days per month of consumption and especially in the value per day.

36
**Table 33 Household Expenditure Per Capita, 1993 and 1995** _(in 1985 CFAF per year)_

||_Consumption_<br>_of home_<br>_products_|_1993_<br>_Purchased_<br>_food_|_Total_<br>_expenditure_|_Consumption_<br>_of home_<br>_products_|_1995_<br>_Purchased_<br>_food_|_Total_<br>_expenditure_|
|---|---|---|---|---|---|---|
|East Forest|||||||
|Export crop farmers|32397|21420|96768|31066|27186|99169|
|Food crop farmers|32837|27998|98157|21905|30278|83564|
|West Forest|||||||
|Export crop farmers|48114|18864|110082|26419|21959|94840|
|Food crop farmers|40418|16233|92117|33089|22790|90038|
|Savannah|||||||
|Export crop farmers|39654|12096|82372|33986|19605|89929|
|Foodcropfarmers|38645|13305|81088|31354|23793|87115|

**Table 34 Regional Estimates for Food Expenditure Budget** _(t-values in parentheses)_

|_Independent_<br>_Variables_|_Abidjan_|_Other Cities_|_East Forest_|_West Forest_|_Savannah_|_West Forest_|
|---|---|---|---|---|---|---|
|LOGHI-HEXP|-0.112|-0.110|0.020|0.0314|0.019|0.031|
||(-36.368)|(34.899)|(-4.037)|(5.686)|(3.623)|(3.553)|
|LOGHHSZ|0.061|0.051|0.037|0.097|0.083|0.098|
||(5.030)|(4.925)|(2.456)|(4.843)|(4.894)|(4.900)|
|LOGHHSZ2|-0.007|-.006|0.016|-0.037|-0.034|-0.037|
||(-1.990)|(-2.158)|(-4.044)|(-6.527)|(-7.608)|(-6.554)|
|YR85|0.126|0.058|0.035|-0.009|0.013|-0.004|
||(20.161)|(-.326)|(4.131)|(-0.789)|(1.328)|(-0.307)|
|YR86|0.073|0.031|0.027|-0.039|-0.015|-0.034|
||(11.804)|(5.277)|(3.153)|(-3.174)|(-1.710)|(-2.683)|
|YR87|0.047|0.045|0.047|-0.020|-0.073|-0.015|
||(7.316)|(7.651)|(5.548)|(-1.642)|(7.330)|(-1.117)|
|YR88|0.030|0.042|0.051|0.013|0.016|-0.008|
||(4.625)|(7.098)|(6.132)|(-1.237)|(1.808)|(-0.674)|
|YR95|-0.022|0.009|0.007|-0.050|-0.012|0.011|
||(-2.995)|(1.046)|(0.648)|(-3.809)|(-0.998)|(0.542)|
|1993 Export farrmer||||||-0.0069<br>(0.873)|
|1995 Export farmer||||||-0.0918<br>(-3.604)|
|Adj. R-Square|-0.3255|0.2171|0.0499|0.0387|0.0587|0.0403|
|Source: Estimated fro|m CILSS 1985-|1988, Priority Surv|ey 1993 and 1995.||||

37
One possible explanation is that during the period in which households were interviewed in 1995 (April-May), food expenditures were at a seasonal low and nonfood expenditures were at a seasonal high for West Forest export crop farmers. However, the 1985-1988 data do not support this hypothesis. Carrying out regressions similar to those presented in table 24, the regression coefficient for West Forest*cocoa and coffee farners*April-May dummy is positive but not significant with respect to food, and negative but not significant for nonfood expenditures. Using these coefficients to produce a seasonally corrected estimate of food expenditure would reduce food expenditures and increase nonfood expenditures, thus further reducing the food shares of West Forest export crop households in 1995.

A second finding that casts some doubt on the large decline in poverty in the West Forest is the apparent undersampling of cocoa-growing households in 1995 compared to 1993. We suspect that there may have been undersampling because the percentage of cocoa-growing households in the West Forest fell from 63.3 percent to 50.6 percent of the total regional population, a drop of 20 percent.<sup>30</sup> As a result, in the West Forest, mean cocoa revenues per capita showed no increase -- from 16200 to 16032 CFAF -- between 1993 and 1995, in contrast to the East Forest, they increased by 30 percent from 11942 CFAF to 15489 CFAF (table 35). For the subset of households that declared earning cocoa revenues, mean real cocoa revenues went up 17 percent in the East Forest and 24 percent in the West Forest (table 36). Thus the stagnation in cocoa revenues in the West Forest is due to a decline in the percentage of households declaring cocoa revenues, rather than to a decline in the revenues of the households that do grow cocoa.

**Table 35 Mean Export Crop Revenues Per Capita by Region** _(in 1985 CFAFper year)_

|_Region_|_Cocoa_|_Coffee_|_Cotton_|_Total_|
|---|---|---|---|---|
|East Forest|||||
|1993|11943|2197|23|14162|
|1995|15489|8381|0|23870|
|West Forest|||||
|1993|16200|5990|444|22634|
|1995|16032|12646|4069|32747|
|Savannah|||||
|1993|1556|1244|7315|10165|
|1995|852|1247|5387|7486|

**30** lWhile there was also a decline in the mean household size among cocoa growers, this does not account for the drop in the cocoa-growing population. Cocoa-growing households as a percentage of all households in West Forest fell from 55 percent **in** 1993 to 43 percent in 1995.

38
|**Table 36**|**Mean Expo**<br>|**rt Crop Re**<br>_Cocoa Farm_<br>|**venue of H**<br>_ers_<br>|**ouseholds G**<br> <br>|**rowing Ex**<br>_Coffee Farm_<br>|**port Crops**<br>_ers_<br>|_(in 1985C _<br>_C_|_FAFper ye_<br>_otton Farme_|_ar)_<br>_r_<br>|
|---|---|---|---|---|---|---|---|---|---|
|_Region_|_Mean cocoa_<br>_revenue per_<br>_capita_|<br>_Weighted_<br>_population_|_Share in_<br>_regional_<br>_population_|_Mean coffee_<br>_revenue per_<br> <br>_capita_|<br>_Weighted_<br>_population_|_Share in_<br>_regional_<br>_population_|_Mean cotton_<br>_revenue per_<br>_capita_|<br>_Weighted_<br>_population_|_Share_**_in_**<br>_regional_<br>_population_|
|East Forest||||||||||
|1993|20444|7148|58.4|6097|4408|36.0|9292|30|0.2|
|1995|23968|853|64.6|21134|524|39.7||||
|West Fores|t|||||||||
|1993|25590|6391|63.3|11882|5090|50.4|21771|206|2.0|
|1995|31693|440|50.6|35894|306|35.2|64075|55|6.3|
|Savannah||||||||||
|1993|15257|1026|10.2|8728|1434|14.3|26965|2729|27.1|
|1995|7599|111|11.2|9758|127|12.8|32460|164|16.5|

It is highly unlikely that there would be no growth in cocoa revenues in the region of West Forest as a whole, in view of the fact that there was about a 10 percent increase in the real producer price of cocoa, and a 30 percent increase in cocoa production West Forest according to the CSSPA data. Thus, we conclude that cocoa-growing households are likely to have been undersampled in the West Forest.<sup>3</sup> ' If in fact cocoa-growing households were undersampled, it is likely that the households producing as much or more cocoa than the mean that were undersampled, since the increase in mean cocoa revenues among cocoa-growing households seems if anything understated, given the reported increase in production and producer prices.

Even if cocoa growing households were undersampled in the West Forest, however, it would not explain the drop in food shares, controlling for total expenditure, within the population of cocoagrowing households (and other export crop -growing households) that were sampled. Thus, it is an unresolved question as to why food shares dropped so significantly among this group. The drop in food shares seems to result from an underestimation of consumption of home production, leading to a downward bias in total expenditure. If so, then the incidence of poverty as indicated by the survey results would be biased upwards. However, we do not have a clear hypothesis about why home consumption fell precipitously among West Forest export crop farmers.

# **Poverty reduction among export crop farmers in the Savannah and East Forest**

Mean household expenditure per capita of export crop farm households increased by 2.5 percent in East Forest, and by 9.2 percent in the Savannah (table 17). In view of the relatively large increase in household expenditure in the Savannah, it is surprising to find that export crop revenues from cocoa, coffee and cotton earned by export crop farm households in the Savannah actually declined by 7 percent, while export crop revenues increased by 55 percent in East Forest (table 37). What accounts, then, for the increase in mean household expenditure?

**31** Coffee-growing households show a similar drop, not surprisingly, since there is a great deal of overlap between the two.

39
|**Table 37 Real E**<br>**Export Crop Far**|**xport Crop Rev**<br>**mers**_(in 1985 C_|**enue of House**<br>_FAF peryear)_|**holds Classi**|**fied As**|
|---|---|---|---|---|
|_Region_|_Cocoa_|_Coffee_|_Cotton_|_Total_|
|East Forest|||||
|1993|21492|3801|18|25311|
|1995|25702|13414|0|39116|
|West Forest|||||
|1993|22905|8305|506|31716|
|1995|24738|19493|6638|50869|
|Savannah|||||
|1993|5156|3484|21592|302322|
|1995|3173|4297|20615|208085|

Disaggregating households according to their primary source of export crop revenue reveals some interesting differences within regions that help to explain the apparent inconsistency between the increase in export crop earnings and the increase in household expenditures. Looking first at the East Forest, we find that household expenditures per capita increased by 10 percent among the group of households whose primary source of export revenue is cocoa (table 3 8). Cocoa revenues increased 27 percent among these households, and mean cocoa revenues in 1993 were 31 percent of household expenditure per capita. So the magnitude of the increase in per capita household expenditure is roughly what one expect.

The story for coffee farmers, however, is less consistent. Among the group of households whose primary source of revenue is from coffee, export crop revenues increased by 151 percent, primarily from coffee, and accounted for 19 percent of household expenditure in 1993. However, household expenditure per capita declined by 3 percent. Poverty declined among this group, however, suggesting that the decline in expenditure was more pronounced at the upper end of the expenditure distribution. The group of coffee farmers raises the mean export crop earnings while bringing down the mean expenditure increase for the region as a whole. So the puzzling finding is why expenditure went down among the group of coffee farmers, even though coffee revenues and total export crop earnings more than doubled.

Inconsistencies also exist among cotton farmers in the Savannah. In cotton-growing households, there is a 19 percent increase in cotton revenue, and a 24 percent increase in household expenditure, a much bigger increase than would be expected given that cotton revenue is equal to 44 percent of household expenditure. Hence the question is what accounts for the large rise in household expenditure among Savannah cotton farmers.

While the slight decline in export crop earnings among export crop farmers in the Savannah is due partly to the decline in the export earnings of the non cotton-growing export crop farmers, it also reflects the fact that the number of export farmers for whom cotton is the primary source of export crop revenue declines as a share of the total export crop household population from 61.4 percent in 1993 to 49.2 percent in 1995 (table 38). These results are mirrored if we look at all Savannah households growing cotton, not just export crop households. Mean cotton revenue per capita among the households growing cotton actually increased, but the cotton growing population declined from 27.1 to 16.5 percent (table 36). Thus, mean cotton revenue in the region declined. This decline in mean cotton revenue in the region is unexpected, as since real cotton prices went up by about 23 percent, whereas cotton production declined by about 12 percent between the 1992/93 and the 1994/95 campaigns. This would imply about an 8 percent increase in total cotton revenues. The decline in mean regional revenues suggests that cotton farmers were undersampled. However, some decline in the cotton-growing 40
**Table 38 Export Crop Farmers Classified by Primary Source of Export Crop Revenue**

|_Region_|_Population_|_Percent of_<br>**_total_**_export_<br>_growing_<br>_population_|_Headcount_<br>_index of_<br>_poverty_|_Mean_<br>_cocoa_<br>_revenuea)_|_Mean_<br>_cotton_<br>_revenuea)_|_Mean_<br>_coffee_<br>_revenuea)_|_Mean export_<br>_crop revenue_<br>_per capitaa)_|_Household_<br>_expenditure_<br>_per capita)_|
|---|---|---|---|---|---|---|---|---|
|_East Forest_|||||||||
|Cocoa fanners|||||||||
|1993<br>|4979<br>|81.3<br>|0.412<br>|25577<br>|0<br>|2701<br>|29226<br>|93035<br>|
|1995|463|61.7|0.414|33593|0|3373|37023|102712|
|Coffee fanners|||||||||
|1993|704|11.5|0.458|3520|0|13187|17401|92983|
|1995|275|36.4|0.412|12934|0|30737|43684|90401|
|Cotton farmers|||||||||
|1993|11|0.2|0.000|0|9544|0|9544|107088|
|1995|0|0.0|0.000|0|0|0|0|0|
|Other export|||||||||
|1993|426|7.0|0.285|4017|0|1232|27980|146419|
|1995|13|17.5|0.000|14663|0|4189|37571|159864|
|_West Forest_|||||||||
|Cocoa farmers|||||||||
|1993|4457|65.6|0.293|32693|47|5764|38814|118092|
|1995|276|51.8|0.539|41251|858|5120|47483|102629|
|Coffee farmers|||||||||
|1993|1960|28.8|0.459|4439|0|15626|20170|90464|
|1995|199|37.3|0.448|9005|0|44865|53870|91389|
|Cotton farners|||||||||
|1993|101|1.5|0.160|266|31977|698|32941|126533|
|1995|52|9.8|0.592|0|63885|860|64745|72823|
|Other export|||||||||
|1993|276|4.1|0.329|4282|0|133|38208|114046|
|1995|6|1.1|1.000|0|0|0|5441|41005|
|_Savannah_|||||||||
|Cocoa farrners|||||||||
|1993|613|22.4|0.342|21248|0|2686|24841|94775|
|1995|63|25.6|0.455|9374|0|1692|11066|77242|
|Coffee farmers|||||||||
|1993|403|14.8|0.721|2310|540|19288|22695|71205|
|1995|62|25.2|0.572|3025|0|15361|18386|83969|
|Cotton fanners|||||||||
|1993|1677|61.4|0.541|55|35037|63|35206|80387|
|1995|121|49.2|0.295|0|41970|0|41970|99628|
|Other export|||||||||
|<br>1993|39|1.4|0.574|1149|0|0|6091|88156|
|1995|0|0.0|0.000|0|0|0|0|0|

**a)** In 1985 CFAF per year.

**41**
population might be expected, due to the fact that some households may have dropped out of cotton production because they were unable to finance the costs of the insecticides which they had to pay for upfront in the 1994/95 campaign. If so, revenues of the households that did continue to farm cotton should have increased enough to result in the overall projected increase in regional cotton revenues. Thus, poverty among export crop farmers in the Savannah may be biased upwards if indeed cotton farmers were undersampled in 1995.

Another difference between the East Forest and Savannah is revealed by the decomposition of the change in the poverty into the growth and redistribution effects index (table 39). The growth effect essentially indicates how much the poverty index would have changed had the change in each person in the population's expenditure been equal to the mean expenditure change, while the redistribution effect measures how much the poverty index would have changed due to the change in the distribution of expenditure, holding mean expenditure constant.

**Table 39 Decomposition of the Change in Headcount Index of Poverty** _(percentage points)_

|_Region_|_Change in_<br>_poverty_|_Growth_<br>_component_|_Distribution_<br>_component_|_Residual_|
|---|---|---|---|---|
|_East Forest_|||||
|Export crop farmer|-0.15|-4.44|3.85|0.45|
|Food crop farmer|15.33|9.78|6.33|-0.77|
|_West Forest_|||||
|Export crop farmer|17.49|8.58|10.44|-1.53|
|Food crop farmer|9.11|0.90|5.89|2.32|
|_Savannah_|||||
|Export crop farmer|-11.78|-7.11|-5.25|0.58|
|Food crop farmer|4.49|-4.57|9.14|-0.08|
|_C6te d'Ivoire_|||||
|Export crop farmer|4.55|0.75|5.09|-1.30|
|Food crop farmer|8.74|0.89|8.31|-0.46|

The redistribution effect was favorable in the Savannah, but sufficiently adverse in the East Forest that it more than offset the growth effect. One hypothesis is that the increase in coffee and cocoa prices tended to benefit nonpoor households more than poor households, under the assumption that the nonpoor households were in a better position to benefit from the increase in prices (e.g., ability to command more hired labor), but cotton revenues were more equally distributed. However, if anything, cotton revenues became more unequally distributed than cocoa or coffee revenues. In 1993, 28 percent of export revenue from cocoa went to the bottom 40 percent of the East Forest export crop farmer population, ranked by household expenditure per capita (table 40). In 1995, only 20 percent of the revenue went to the bottom 40 percent of the expenditure distribution.32 32 Of course, one cannot automatically assume that a worsening of the distribution of export crop income reflects an inability of households with less export crop revenue to increase their export crop earnings at the same rate as households with more export crop revenue. For example, it is possible that all cocoa-growing households earned the same percentage increase in cocoa revenue, but that the distribution of other sources of income shifted, changing the apparent distribution of cocoa revenue. For example, households in the upper 40 percent of the expenditure distribution with relatively little cocoa revenue could have dropped into the bottom 40 percent as their other sources of income declined, while households in the lower 40 percent with large cocoa revenues may have received a boost in other sources of income that put them in the upper 60 percent. This would result in a 42
**Table 40 Share of Export Crop Revenue earned by the Bottom 40% of the Rural Export Farmer Populationa)**

|_Region_|__Cocoa_|_Coffee_|_Cotton_|_Total export_<br>_crop revenue_|
|---|---|---|---|---|
|_East Forest_|||||
|1993<br>|28.3<br>|25.8||27.0|
|1995|19.|22.ov7||20.3|
|_West Forest_|||||
|1993|17.8|24.1|6.7|19.6|
|1995|19.1|23.7|12.8|19.8|
|_Savannah_|||||
|1993|16.0|40.6|25.5|_25.5_|
|1995|40.7|41.5|7.9|14.1|

a) Population is ranked by household expenditure per capita.

In 1993 the Savannah, a similar percentage of cotton revenue -- 26 percent -- went to the bottom 40 percent of the export crop farm household population. In 1995, however, only 8 percent of cotton revenue went to the bottom 40 percent. One partial explanation for this finding might be due to the policy change that eliminated refinancing of inputs by the cotton marketing authority in the 1994/95 season. Farmers who were better able to afford the inputs might be in a better position to expand their production of cotton, with the result that cotton revenues became more unequally distributed.

The combination of the positive redistribution effect in the poverty decomposition and the apparent negative redistribution of cotton (and export crop) revenue in the Savannah is curious. The hypothesis that poor cotton growing households were undersampled in 1995 could account for the increase in per capita household expenditure as well as the favorable redistribution effect between 1993 and 1995, but it is unlikely to account for the increasing inequality in the distribution of cotton revenues. It is possible that cotton revenues may have become more unequally distributed at the same time that cotton farmers were undersampled. Given the deficiencies of the data, this remains a speculation.

# The increase in poverty among **food** crop farmers

There was an increase in poverty between 1993 and 1995 among households classified as food crop farmers in all three rural regions (table 17). One reason may be the large decline in real staple food crop prices between 1993 and 1995. Yam, manioc, maize and millet prices declined between 10 and 25 percent, depending on the variety of yam. Only the real price of plantain and local rice remained relatively constant. The decline in real prices would benefit net purchasers of these commodities and work to the disadvantage of net sellers. Unfortunately, most households were not net purchasers. In deterioration in the distribution of cocoa revenue which would not have arisen from households being differentially placed to benefit from the increase in cocoa prices. Since the level of cocoa (and total export crop) revenues are positively and significantly associated with the level of household expenditure among export farm households, however, and since mean export crop revenue is about 30 percent of mean household expenditure, it is likely that there was some adverse redistribution of cocoa revenue among East Forest farmers. The same is true for cotton revenue among Savannah export crop farmers.

43
1993 only about a quarter of the farm population in East Forest and the Savannah were net purchasers, rising to 43 percent in West Forest (table 41). Even fewer food crop farm households were net purchases. Thus, few food crop farm households would have benefited from the decline in real staple food prices.

**Table 41 Percentage of Farm Population by Net Sales Position of Major Food Staples a)**

|||1993|||1995||
|---|---|---|---|---|---|---|
|_Region_|_Net_<br>_sellers_|_Neutral_|_Net_<br>_purchases_|_Net sellers_|_Neutral_|_Net_<br>_purchases_|
|_Export crop farmers_|||||||
|EastForest|36|41|23|41|49|11|
|West Forest|24|29|47|41|30|29|
|Savannah|35|28|37|26|42|32|
|_Food crop farmers_|||||||
|East Forest|45|36|18|35|53|12|
|West Forest|43|28|29|28|31|41|
|Savannah|66|14|20|52|41|7|
|_Export andfood_|||||||
|_crop farmers_|||||||
|EastForest|40|39|21|39|50|11|
|West Forest|28|29|43|37|31|32|
|Savannah|56|18|26|44|41|15|

a Staple foods are manioc, yams, plantain, millet/sorghum, and maize. Net sellers are households in which the sales revenue from these crops exceeds purchases by 500 CFAF; which net consumers are households in which purchases exceed sales by 500 CFAF; Nuteral households comprise the remainder.

Nonetheless, among food crop farmers in the Savannah, mean per capita expenditures increased by 7.4 percent, in contrast to the other rural regions, where mean expenditures declined. However, poverty increased in the Savannah because there was an adverse redistribution effect. The redistribution effect was also adverse in the Forest regions. This raises two questions: what accounts for the increase in mean expenditure among primarily the better off food crop farm households in the Savannah, and what accounts for the adverse redistribution effect among food crop farmers in rural CMte d'lvoire.

It is not clear why per capita expenditure of food crop farmers should have increased in the Savannah, compared to a 14.9 percent decline in East Forest and a 2.3 percent decline in West Forest.. Note that the better performance of the Savannah food crop farm household subgroup relative to East Forest food crop farmers parallels the differential observed in per capita household expenditure growth among cotton farmers in the Savannah and cocoa farmers in East Forest. One partial hypothesis stems from the observation that the Savannah is the only region in which mean per capita housing expenditures did not decline. While this might explain part of the relative difference in the change in mean per capita expenditure between regions, it certainly does not account for all of the disparity between East Forest and the Savannah.

One hypothesis for the adverse redistribution effect among rural food crop farmers is that the ratio of net sales of the four main staple food crops whose real price declined to total expenditure is larger for poor households than for non poor. Thus a decline in staple food crop revenues would cause a larger percentage decline in household expenditure in poor households. (Here we are assuming 44
implicitly that household expenditure is roughly equivalent to household income, so that a decline in net sales income would be reflected in the same absolute decline in household expenditure, all other things equal). What we find, however, is that among food crop households the ratios are quite close between the bottom 40 percent and top 60 percent of the population, and they are relatively small (table 42). So while this explanation does not seem promising to account for the adverse redistribution effect, it may also be that the surveys do not do a very good job of estimating marketed sales of food crops.

**Table 42 Net Revenue From Staple Food Crops of Rural Food Crop Farmers')**

||_Ne_<br>_19_<br>|_t staple food crop reve_<br>_93_<br>|_nue/household expendit_<br>_19_<br>|_ure_<br>_95_<br>|
|---|---|---|---|---|
|_Region_|_Bottom 40% of_<br>_population_|_Top 60% of_<br>_population_|_Bottom 40% of_<br>_population_|_Top 60% of_<br>_population_|
|East Forest|2.5|2.3|0.9|1.0|
|West Forest|7.7|7.0|-2.3|-1.1|
|Savannah|9.1|8.6|6.0|1.7|

**a)** Net revenue is crop revenue less purchases. The staple crops are maize, manioc, millet and yam.

b) Population is ranked by household expenditure per capita.

Furthermore, as explained above, the used of a single inflation rate to deflate the expenditures of poor and non-poor households may also bias the results slightly towards an adverse redistribution pattern. Another partial explanation may relate to the housing variable. The estimating equation for housing cost yields large negative dummies for the 1995 regional dummies. Since housing as a share of expenditure declines as expenditure increases, the use of a single dummy would result in a larger decrease, relative to total expenditure, of total per capita expenditure among poorer households. All other things being equal, this would tend to yield an adverse redistribution effect.

# _Conclusion_

In the short run, the devaluation accompanied by the increased in export taxation did not appear to alter the pattern of poverty that took shape prior to the devaluation, assuming that the results of the 1993 and 1995 surveys are roughly accurate, about which there is considerable cause for doubt. Abidjan experienced the largest increase in poverty between 1993 and 1995. Food crop farmers in rural areas got absolutely poorer. And depending on whether or not one accepts the 1995 West Forest results, export crop farmers got slightly worse off or remained about the same. The increase in poverty among food crop farmers is likely linked to the downward trend in real food crop prices evident in the pre-devaluation period 1987-1993 , which continued after the devaluation. As producers of nontradables, it would be expected that food crop farmer would not have benefited from the devaluation in the short term unless a substantial part of their income comes from wage labor or off-farm sources. Unfortunately we know very little about the sources of income of food crop farmers. The failure of export crop farmers to show a more important decline in poverty is due to several things: first the survey took place relatively soon after the devaluation, so that coffee and cocoa farmers had little time to respond to higher crop prices. A second reason, particularly relevant for cocoa, is that the government taxed away much of the benefit of the devaluation and the price increase. Only for coffee was there a real price increase of any appreciable magnitude between the devaluation and the time of the 1995 survey. And curiously, among the households in East Forest and West Forest whose major source of income is coffee, there was little if any growth in household expenditure per capita.

45
It is important to remember that the 1995 survey was carried out in the early part of 1995, before the economic recovery had taken hold. The 1995 survey thus provides a picture only of the short-run impact of the devaluation and accompanying policy measures. In aggregate, one would expect that poverty would have begun to fall in 1996, given the strong economic turnaround observed in 1995 and 1996. The strong growth in the secondary and tertiary sector of the economy is likely to have a positive impact on reducing urban poverty. While agricultural growth is positive, it would appear to be somewhat selective. To the extent that it bypasses many of the food crop and only partially touches cocoa farmers, its impact will be limited. Since food crop farmers account for roughly a quarter of CMte d'Ivoire's population, and are the poorest socioeconomic group, whether or not they benefit from growth in aggregate demand will have a large bearing on what happens to overall rates of poverty. The rate at which poverty declines among this group of farmers may depend more on the fortunes of the export crop sector and what happens in the rural labor market, assuming that real producer price of the main staple food crops continues to decline. Heavy taxation of export crop farmers has repercussions not only on export crop farm households but also on food crop farm households.

While taxation of export crop farmers (as a share of the world price) has decreased since the devaluation, export crop farmers, especially cocoa farmers, are still being taxed. The tax is often justified on the grounds that Cote d'Ivoire has an important share of the cocoa market, and that restricting supply by taxing cocoa producers raises world prices and increases the gains to Cote d'Ivoire. Analysis suggests that in the past the actual level of taxation exceeded the optimal level (World Bank, 1994; Panagariya and Schiff, 1990) although the gap between the optimal and actual export tax may be narrowing at present. Unfortunately, the burden of taxation falls heavily on the poor as over a third of the export crop farmers who derive the majority of their revenue from cocoa are poor. And of course, there are also poor farmers who derive the majority of their income from coffee who also grow cocoa, as well as some poor food crop farmers who earn some revenue from cocoa. Even if the level of actual taxation were justified from the standpoint of maximizing CMte d'Ivoire's earnings from cocoa, it is not at all clear that the use to which the tax revenue from cocoa is put has a significant impact on poverty reduction. As Demery et al. (1995) point out, social spending is not highly progressive. And the benefits that would come from an increase in public investment spending on infrastructure may not accrue to the rural poor in the short term, to the extent that spending takes place largely in urban areas. Clearly, a reexamination of cocoa taxation policies is an essential element of a poverty reduction strategy.

Finally, the analysis of the data revealed a number of suspected biases which lead us to conclude that overall that in the short run, the results of the 1993 and 1995 surveys are probably biased in the direction of overestimating the incidence of poverty in 1995 relative to 1993. The principal sources of suspected bias are the following. First, the sampling bias in 1993 which resulted in the oversampling of less mobile households is suspected to have resulted in an underestimation of poverty in 1993 in the non-Abidjan part of the sample. Second, the procedure used to correct the consumption of home production aggregate in 1993 may have resulted in an overestimate of home production in 1993, and thus an underestimate of poverty. Third, the procedure used to calculate purchased food expenditure (declared months multiplied by monthly expenditure) may have resulted in a downward bias in food expenditures in 1995 relative to 1993. Fourth, the fact that the expenditure shares used in the CPI assume much higher shares for luxury items (and in particular non-staple foods) may result in an overestimate of the inflation rate, particularly for poor households, and thus to an underestimate of household expenditure in rural areas in 1995 relative to 1993. Fifth, there are good reasons to question the validity of the sharp increase in poverty in West Forest between 1993 and 1995. On the other hand, it is difficult to know whether the relatively stronger performance of the Savannah farmers between 1993 and 1995 is simply a reflection of the relative absence of the type of suspected biases described above, or whether it too reflects a bias, but in the opposite direction. Thus, while the direction of the 46
biases generally point in the direction of an overestimate of poverty in 1995, there are still a number of unknowns--particularly the small sample size and lack of region-specific price data--that make it impossible to draw firm conclusions about the direction of the change in poverty between 1993 and 1995.

The deficiencies of the 1993 and 1995 household surveys lead to a number of recommendations to improve the reliability of the conclusions. First, more attention needs to be paid to assuring the comparability of the surveys as well as to developing region-specific consumer price indicators if one of the purposes of the household surveys is to monitor the change in poverty over time. A particularly important issue for surveys of the priority-type format is the reliability and comparability over time of the method used to estimate food expenditures. A second lesson is that the ability to explain the trends observed in the poverty data depends to a large part on being able to understand the trends in the various components of household income. This is a weak point of many household surveys, especially household priority surveys. A third recommendation would be to pursue some of the qualitative analysis once the results of the quantitative surveys are available to explore whether the trends reported in the data make sense on the ground. A fourth recommendation would be to give some thought to sample size so that region-specific cell sizes for the major socioeconomic groups are sufficiently large to make meaningful comparisons possible. For example, given the difference in pricing policies applied to cocoa and coffee, it would be interesting in future studies to examine the evolution of poverty between these two groups of farmers. A fifth recommendation would be to examine how well the socioeconomic classification used in the 1993 and 1995 surveys correlates with the primary source of income of the household.33 A sixth recommendation would be to consider in greater detail whether the limited time frame of the priority survey will yield accurate estimates of expenditure given seasonality in expenditures. If a limited time frame is adopted, including questions on normative monthly food purchases would be a useful supplement to the monthly recall estimate for those households that do not purchase an item during all months of the year.

Three puzzles emerge from the household survey data that merit special consideration in future household surveys in CMte d'Ivoire. First, what accounts for the huge increase in poverty at various points in time in the West Forest ? Are there special sampling problems in the West Forest? Or special difficulties in estimating the value of consumption of home production? Second, why did the Savannah do relatively much better than the Forest region after the devaluation? Here one suspects that the problem may be misspecification of the regional deflators. But perhaps off-farm sources of income may vary significantly from the rest of the country. And third, did the situation of agricultural workers really improve between 1993 and 1995? If so, will it continue to improve? What will happen to labor earnings as the land frontier vanishes? Labor will become a less scarce commodity, and the forms of labor hire, which have depended heavily on sharecropping-type contracts which give the sharecropper eventual rights to the land, will likely shift. It would be useful in future surveys to think about ways of distinguishing among different types of agricultural workers. At this point in time, the household surveys in C6te d'Ivoire raise as many questions as they answer about the changes in poverty over time.

33 Unfortunately the analyses that have been carried out of poverty rates among different socioeconomic groups with respec **t** o the 1985-1988 do not provide information about how the classification of the socioeconomic groups correlates with sources of income.

47