POVERTY, LIVELIHOODS, AND ACCESS TO BASIC SERVICES IN GHANA

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GHANA CEM: MEETING THE CHALLENGE OF

ACCELERATED AND SHARED GROWTH

Partial and preliminary draft for review Updated: June 11, 2007

POVERTY, LIVELIHOODS,

AND ACCESS TO BASIC SERVICES IN GHANA

By

Harold Coulombe and Quentin Wodon

This draft study was prepared as part of the Ghana CEM growth aiming to provide analytical contribution and assist the government of Ghana in operationalizing its accelerated and shared growth agenda. The study was discussed at the Ghana CEM technical review growth workshop in Accra, May 2-3, 2007.

The authors are grateful to the authorities of Ghana for the extensive data and discussions on dimensions of Ghana’s growth and poverty reduction without which the study would not be possible. We are particularly grateful, without implication, to Dr. Akoto Osei, Prof. George Gyan-Baffour, the Minister of Trade, Industries, Private Sector and Special Presidential Initiatives, Honorable Alan Kyerematen, Chairman of the National Development Planning Commission Dr. J.H. Mensah, and Director-General of the NDPC, Dr. Regina Adutwum, and their staff, and all government ministries and agencies involved.

The estimates of poverty presented in this study are based on joint work with the Ghana Statistical Service. In particular, Grace Bediako, the Government Statistician, and Nicholas Nsowah-Nuamah, the Deputy Government Statistician, have been very generous in providing support. Some of the estimates of other dimensions of well-being presented in this study and based on the GLSS 5 survey for 2005/2006 should be considered as preliminary given the fact that the survey has not yet been officially released.

The authors acknowledge support from the Belgian Poverty Reduction Partnership and the Norwegian Trust Fund for Private Sector and Infrastructure. Parts of this study are based on joint work conducted over the last few years or in progress with (by alphabetical order) Mathieu Audet, Prospère Backiny-Yetna, Amadou Bassirou Diallo, Andrew McKay, Estelle Sommeiller, and Clarence Tsimpo. The authors are grateful to Zeljko Bogetic, Carlos Cavalcanti, and staff from GTZ for many useful comments, as well as to the feedback and suggestions provided by the peer reviewers for this study (Kathleen Beegle, Cletus Dordonou, Michael Lokshin, and Rose Kakari-Annang). Apart from the May 2007 CEM workshop, results from this work were presented at seminars in the World Bank resident mission in Accra in September 2006 through an informal seminar.

The views expressed here are those of the authors and need not necessarily reflect those of the World Bank, its Executive Directors or the countries they represent.

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POVERTY, LIVELIHOODS,

AND ACCESS TO BASIC SERVICES IN GHANA

Harold Coulombe and Quentin Wodon

Abstract

Ghana has achieved substantial poverty reduction over the last 15 years and is on track to reduce its poverty rate by half versus the level of 1990 well before the target date of 2015 for the Millennium development Goals. The objective of this study is to summarize key results obtained from an analysis of household surveys in Ghana. The study consists of four parts. The first part provides estimates of the trend in poverty using a range of surveys covering various periods of time, as well as a discussion of the characteristics of the poor and the determinants of poverty. This also includes a discussion of the geography of poverty with the construction of a poverty map at the district level based on census data. The second part of the study deals with the income sources of the population, including discussions of labour income, cash crop income, remittances, and income inequality. The third part of the study discusses the level of access to basic infrastructure services, as well as the targeting performance of public subsidies.

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1. INTRODUCTION

Ghana has achieved substantial poverty reduction over the last 15 years and is on track to reduce its poverty rate by half versus the level of 1990 well before the target date of 2015 for the Millennium development Goals1_{. The objective of this study is to document}

this remarkable achievement, and more broadly to review the evidence on a range of issues related to poverty reduction using the most recent household survey data available. The structure of the study is as follows. After a brief introduction, we discuss a) the trend in poverty and inequality in Ghana (Chapter 2); b) the profile of the poor, including the geography of poverty, as well as the determinants or correlates of poverty (Chapter 3); c) employment and wage trends, including the issues of youth unemployment, time use and child labour (Chapter 4); d) the income sources of the poor, including sections on income inequality, cash crop income (cocoa) and remittances (Chapter 5); d) the access of the poor to basic services in the areas of education, health, and basic infrastructure as well as the benefit incidence analysis of public spending in a few areas including electricity subsidies (Chapter 6).

Ghana has long been considered a star performer in Sub-Saharan Africa. Beginning with the presidency of Rawlings and aided by external support, Ghana embarked on a series of economic reforms in 1983. The focus of the reform package was initially on macroeconomic stabilization through fiscal, monetary and foreign exchange liberalization in the initial phase of reforms (see among others Roe, 1992; Kraus, 1991; IMF, 1990; Ahiakpor, 1991). Following a successful macroeconomic stabilization, the focus of reforms shifted towards structural adjustment measures to accelerate growth with sustained poverty reduction. Ghana during much of the 1990s had one of the strongest growth rates amongst Sub-Saharan countries. While GDP growth rates receded slightly in the late 1990s, they rebounded after 2002, and have reached about 6 percent in recent years (see Bogetic et al., 2007, for an analysis of Ghana growth performance). Given these high rates of economic growth, one could expect poverty to have decreased in Ghana since the late 1980s. There is indeed some evidence that substantial poverty reduction took place at the national level in Ghana over the 1988-92 period. This evidence is based on the analysis of the first three rounds of the GLSS Surveys (Ghana Statistical Service, 1995; Coulombe and McKay, 1995; Appiah et al, 2000). However, these studies had to contend with some comparability difficulties due to substantial changes in the survey questionnaire between the second and third rounds of the surveys. Furthermore, results of a participatory poverty assessment conducted in poor communities in 1993 and 1994 (Norton et al., 1995) gave a less than enthusiastic message about the evolution of poverty in the early 1990s. Urban communities considered that the initially beneficial effects of economic reform in the 1980s had not been

1_{This statement is based on the poverty trend in Ghana as computed by the authors in collabouration with the Ghana}

Statistical Services. The first target under the MDGs is to reduce by half by 2015 the proportion of the population living with less than one US dollar per day. However, while the poverty line of US$1 per day is appropriate for measuring global trends in poverty, it is not appropriate for measuring poverty trends in any given country, because the US$1 poverty line does not properly take into account the specificity of different countries in terms of cost of living and data issues. At the country level it is better to measure the achievement of the poverty target under the MDGs using the country-specific poverty line which tend to reflect better costs of living in any specific country.

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sustained, and in rural communities vulnerability of livelihoods was widely identified as a key issue, with widespread concern expressed that vulnerability was increasing.

More faith has been placed on the results based on the comparison of the third and four rounds of the GLLSS surveys, for respectively the years 1991/92 and 1998/99. Coulombe and McKay (2007) found that the share of the population in poverty had dropped between the two surveys from 51.7 percent to 39.5 percent. This achievement was however not as widespread as one might have hoped. Indeed, the national pattern masked a sharp disparity in performance between geographic areas. Most of the poverty reduction was concentrated in Accra and the Rural Forest area, while poverty fell much more modestly or even rose elsewhere. In the Savannah area, the share of the population in poverty rose in urban areas and other measures of poverty which take into account the distance separating the poor from the poverty line rose as well in rural areas.

After this brief introduction, in the second chapter of this study, we analyze how poverty has changed in Ghana over time, with a focus on changes since the late 1990s, but also with older data darting back from the 1960s. The work on recent poverty trends is based on the 2005/2006 nationally representative GLSS (Ghana Living Standards Survey) household survey conducted by Ghana’s Statistical Services. This survey is comparable to previous rounds of the GLSS for 1991/92 and 1998/99. In addition, we also rely on comparable CWIQ (Core Welfare Indicators Surveys) for 1997 and 2003. Finally, we also comment on results obtained with older surveys for part of the country which cover the period 1967 to 1997 in order to have a view on trends in well-being since the independence.

The main message that emerges from the analysis is that Ghana’s record in terms of poverty reduction since the early 1990s has been very impressive. The estimates presented here, which are based on work done in collaboration with the Ghana Statistical services, suggest that the share of the population living in poverty was reduced from 51.7 percent in 1991/92 to 39.5 percent in 1998/99 and 28.5 percent in 2005/2006. An order of magnitude for the reduction in poverty similar to that observed between the last two GLSS surveys is observed with the CWIQ surveys for the period 1997 to 2003 using asset-based measures of well-being. However, when considering loner periods of time (from independence to today), the results are less positive. Also, concerns exist today about an increase in inequality and about the fact that in the northern regions of the country poverty remains very widespread, even if it has decreased as well in recent years. There is also a concern that poverty may be on the rise in Accra, due in part to migration inflows.

In the third chapter of the study, we provide a basic poverty profile using the GLSS data, and comment on a similar profile based on asset poverty using the data from the CWIQ surveys. In addition, results from a poverty map of Ghana based on the combination of census and survey data are presented. Finally, we also conduct an analysis of the correlated or determinants of poverty. The main results of the profile of poverty and of the poverty map confirm the large differences in the incidence of poverty between regions of the country. The analysis also suggests large differences in poverty incidence according to demographic characteristics, education levels, sector of activity (type of industry) and employment status, whether using simple statistical tables or regressions to look at the determinants or correlates of consumption.

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By running the same regressions for the determinants of household consumption using the three GLSS surveys, it is also feasible to decompose changes in the mean level of consumption per equivalent adult of households over time into changes due to differences in household characteristics and changes due to differences in the returns to these characteristics. It turns out that for the full period (1991/92 to 2005/2006), general economic conditions helped improve household consumption by 20.5 percent in urban areas and 38.9 percent in rural areas. Changes in household characteristics also helped for improving standards. First, there was a reduction in household sizes which yielded a gain of 7.9 percent in consumption in urban areas, and 1.4 percent in rural areas. Second, there was an increase in the education level of household heads and spouses, which generated a gain in consumption of 7.8 percent in urban areas, and 2.0 percent in rural areas. The gains from the demographic and education transitions were thus much larger in urban than in rural areas. Finally, households also benefited in some cases from higher returns associated to selected characteristics. In urban areas, the gains from changes in the returns associated with different types of employment yielded a 12.2 percent increase in consumption. In rural areas however, the reverse was observed, with a consumption loss of 8.1 percent. This suggests that more attractive jobs became available in urban areas, while this was not the case in rural areas.

Chapter four provides a brief and preliminary diagnostic of employment and wage trends in Ghana over the last 15 years. A first interesting question is to assess to what extent the growing economy has been accompanied by a similar growth in the number of jobs. Data from the GLSS surveys suggest that in absolute terms, there has been an increase in employment between 1991 and 2006 of about 2.7 million jobs. When looking at paid employment only, the increase is similar, at 2.2 million jobs, which represents a gain of about 50 percent versus the base year. In terms of areas of work (by industry), there has been a decrease in the share of the population involved in agriculture as well as in community and other services, with a growth in the share of workers in all the other sectors, and especially in manufacturing. The analysis of the labour force participation rates suggests that since the early 1990s, paid employment rates have been fairly stable for the country as a whole but rural areas have experienced a significant decline in labour force participation. While part of this decline may be due to better schooling, it also probably reflects a lack of good rural jobs. By contrast, male individuals from urban areas in general, and in Accra in particular, have seen their employment rates going up considerably. The economic growth experienced by Ghana in the last 15 years has also been accompanied by changes in the structure of the labour market, with an increase in private wage employment, especially in urban areas. Earnings trends and patterns tend to corroborate the findings from the poverty analysis presented earlier. There has been a large increase in earnings since the late 1990s. At the same time, although annual earnings used to be much higher in Accra than elsewhere in the past, results from the latest survey show that workers in other urban areas have now caught up with Accra. The stagnation of earnings in Accra in recent years (associated with an apparent increase in poverty and inequality) might be due to a recent surge in migration, but a more detailed analysis would be required to establish this hypothesis.

Chapter five present a preliminary analysis of the role played by different income sources in the livelihoods of households, and their contribution to income inequality over time. The chapter also includes a discussion of two important income sources that have rapidly increased in recent

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years: revenues from cocoa production, and remittances, both domestic and international. The impact of these income sources on poverty is analyzed using simple techniques. Key results include the fact that income inequality has increased substantially over time, that poverty among cocoa producers has decreased especially rapidly thanks to rapid progress in that sub-sector, and that the impact of international worker’s remittances on poverty may be lower than often expected.

Finally, Chapter six provides a basic analysis regarding the access to basic services for education, health, and infrastructure (water, electricity and sanitation) for various segments of the population, comparing poor to non-poor households. We also provide trends in access over time. In addition, we provide estimates of the incidence of public spending in various areas. The results suggest that while there has been substantial progress in usage of basic services for health, thanks in part to the extension of pharmacy and chemical stores, less progress has been achieved in education (although our assessment based on the 2005/2006 GLSS predates some important initiatives taken by the government since then). The results also suggest that there has been an increase in access to water, sanitation, and electricity, but that subsidies for utilities implicit in the tariffs structures for residential customers tend to be very poorly targeted.

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PART

I:

POVERTY

AND

ITS

DETERMINANTS

2. TRENDS IN POVERTY AND INEQUALITY

This chapter is devoted to an analysis of how poverty has changed in Ghana over time, with a focus on changes since the late 1990s, but also with older data darting back from the 1960s. The work on recent poverty trends is based on the 2005/2006 nationally representative GLSS (Ghana Living Standards Survey) household survey conducted by Ghana’s Statistical Services. This survey is comparable to previous rounds of the GLSS for 1991/92 and 1998/99. In addition, we also rely on comparable CWIQ (Core Welfare Indicators Surveys) for 1997 and 2003. Finally, we comment on results obtained with older surveys for part of the country which cover the period 1967 to 1997 in order to have a view on trends in well-being since the independence. The main message is that Ghana’s poverty reduction since the early 1990s has been very impressive. The estimates presented here, which are based on work done in collaboration with the Ghana Statistical services, suggest that the share of the population living in poverty was reduced from 51.7 percent in 1991/92 to 39.5 percent in 1998/99 and 28.5 percent in 2005/2006. An order of magnitude for the reduction in poverty similar to that observed between the last two GLSS surveys is observed with the CWIQ surveys for the period 1997 to 2003 using asset-based measures of well-being. However, when considering loner periods of time (from independence to today), the results are less positive. Also, concerns exist today about an increase in inequality and about the fact that in the northern regions of the country poverty remains very widespread, even if it has decreased as well in recent years. There is also a concern that poverty may be rising in Accra due to migration inflows.

2.1. Trend in Consumption-Based Poverty Measures since the Early 1990s

This section presents estimates of the trend in poverty in Ghana from 1991/92 to 2005/2006 using repeated rounds of the GLSS surveys. The estimates were obtained in collabouration with staff from the Ghana Statistics Service (see the poverty profile prepared by Ghana Statistics Service, 2006). The details on the methodology used for obtaining the poverty estimates are provided in Annexes 1 and 2. The indicator of well being on which the poverty measures are based is the household’s total consumption per equivalent adult.

The poverty lines were estimated using the cost of basic needs method in order to pay for a food basket providing 2900 kilocalories per adult equivalent2, while also covering the cost of basic

2_{The requirement of 2900 kcal per equivalent adult is somewhat higher than the norms adopted in other countries,}

both internationally and within West Africa, but we adopted this threshold given the fact that it had been used by the Ghana Statistical Services in the past, as well as for the analysis of the GLLS5. If a lower caloric threshold had been used, the poverty measures would have been lower (both in 2005/2006 and in previous years), but the main messages of the4 analysis would have remained the same.

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non-foods needs. With the 1998/99 GLSS, the poverty line was estimated at 900,000 Cedis per adult equivalent per year in constant prices of Accra in January 1999, with appropriate deflators for the other regions of the country. The poverty lines for 1991/92 and 2005/2006 have been obtained from the Accra poverty line for 1998/99 by using data on the Consumer Price Index (CPI, hereafter) which has been computed by the Ghana Statistics Services separately for Accra, other urban areas, and rural areas. That is, the poverty lines were computed using various CPI indices going backward in time for the 1991/92 poverty lines, and forward in time for the 2005/06 poverty lines, starting from the Accra line in 1998/99. It should be noted that the official CPI may not reflect as closely as one would like the differences in prices (both regional and over time) that the poor face, given that the CPI reflects the prices of the goods consumed by the population as a whole rather than by the subset of the population that is poor. It would have been better to use a CPI derived from the survey to estimate the price changes faced by the poor over time. Unfortunately, the price data collected in the community module of the survey proved to be problematic, and could not be used with confidence to estimate the poverty trend. For this reason, the official CPI was used instead.

Table 1 provides the shares of the population in poverty as well as higher order measures of poverty for the various strata (there are seven strata or “localities” in the GLSS 3 and GLSS4 surveys, which are the ones listed in table 1). The sample of the surveys does not permit further disaggregation of the data in terms of geographic areas, but we will discuss later the issue of the geography of poverty using a poverty mapped based on the 2001 census. The GLSS5 permits to estimate poverty measures for each of the ten regions, but in order to provide comparisons over time we restrict here the analysis of poverty to the seven areas from the GLSS4 and GLSS5. Standard errors for the poverty estimates provided in table 1 are given in Annex 2. The share of the population in poverty (headcount “P0” in table 1) has fallen between 1991/92 and 1998/99 from 51.7 percent to 39.5 percent, and it has fallen further to 28.5 percent in 2005/06. As shown in Annex 1, which provides standard errors for poverty measures presented in this study, this is a statistically significant decline in poverty. Poverty fell by about 16 points in urban areas, and by 23 points in rural areas. The national pattern though masks disparities in performance by geographic region.

One concern is the fact that poverty may be rising in Accra, perhaps due to migration inflows. The analysis of employment and earnings in Chapter 4 suggests that earnings have stagnated in real terms in Accra since the late 1990s, while they have increased elsewhere. At the same time, within urban areas, one should be careful in interpreting the results from the poverty estimations too literally. The sharp drop in 1998/99 in the capital was probably due in part to a sampling issue (note that Accra is defined throughout this study as the Greater Accra Metropolitan Area which also covers urban areas in Ga East, Ga West and Tema districts), and the sharp drop for the urban coastal and urban forest areas in 2005/06 is also surprising, and may be due to similar issue. To be more specific, in the case of the capital and the surprising shifts in poverty there, given that the sharp drop in poverty was observed in 1998/99, it was feasible to compare the group of households that were sampled in the GLSS4 to the full set of households living in the Accra area in the census files from 2001. This comparison revealed that the households that had been sampled in 1998/99 were better off on average than the households in the census, which may have explained the very low poverty measures for Accra in 1998/99. This is why we believe that it is best to consider the results for urban areas as a whole rather than by subgroup,

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and to not infer too much from the changes in poverty estimates between any two surveys which are presented in table 1 separately for Accra and the Coastal and Forest urban areas.

Table 1: Consumption-Based Poverty measures by locality and urban/rural, 1991-2006

Poverty indices Contribution to national poverty Population Share Average welfare (thousands) P0 P1 P2 C0 C1 C2 1991/92 Region Urban 33.2 1580 0.277 0.074 0.029 17.8 13.2 10.9 Rural 66.8 909 0.636 0.240 0.117 82.2 86.8 89.1 Locality Accra 8.2 1840 0.231 0.051 0.017 3.7 2.2 1.6 Urban Coastal 8.7 1430 0.283 0.070 0.024 4.7 3.3 2.3 Urban Forest 11.0 1620 0.258 0.064 0.022 5.5 3.8 2.8 Urban Savanah 5.3 1320 0.378 0.136 0.069 3.9 3.9 4.2 Rural Coastal 14.2 1090 0.525 0.161 0.067 14.4 12.3 10.8 Rural Forest 29.6 938 0.616 0.227 0.106 35.3 36.4 35.8 Rural Savanah 23.1 763 0.730 0.305 0.161 32.6 38.1 42.5 National 100.0 1276 0.517 0.185 0.088 100.0 100.0 100.0 1998/99 Region Urban 33.7 1950 0.194 0.053 0.021 16.6 12.9 10.5 Rural 66.3 1140 0.496 0.182 0.089 83.4 87.1 89.5 Locality Accra 11.2 2460 0.044 0.009 0.003 1.3 0.7 0.4 Urban Coastal 5.9 1510 0.310 0.092 0.037 4.6 3.9 3.3 Urban Forest 11.8 2010 0.182 0.051 0.020 5.4 4.3 3.6 Urban Savanah 4.8 1190 0.430 0.114 0.042 5.2 4.0 3.1 Rural Coastal 14.4 1230 0.456 0.142 0.061 16.7 14.8 13.3 Rural Forest 31.3 1300 0.380 0.108 0.044 30.1 24.3 20.7 Rural Savanah 20.6 827 0.700 0.323 0.178 36.6 48.0 55.5 National 100.0 1513 0.395 0.139 0.066 100.0 100.0 100.0 2004/06 Region Urban 37.6 2560 0.108 0.031 0.013 14.3 12.1 10.6 Rural 62.4 1430 0.392 0.135 0.066 85.7 87.9 89.4 Locality Accra 11.8 2720 0.106 0.029 0.011 4.4 3.5 2.8 Urban Coastal 5.8 3030 0.055 0.009 0.002 1.1 0.6 0.3 Urban Forest 14.6 2520 0.069 0.017 0.007 3.5 2.6 2.2 Urban Savanah 5.4 1820 0.276 0.095 0.045 5.2 5.4 5.3 Rural Coastal 11.0 1630 0.240 0.053 0.018 9.2 6.0 4.2 Rural Forest 28.0 1520 0.277 0.068 0.024 27.2 19.8 14.4 Rural Savanah 23.4 1220 0.601 0.254 0.139 49.3 62.1 70.7 National 100.0 2050 0.285 0.096 0.046 100.0 100.0 100.0

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Perhaps more important than the urban-rural divide, there is also concern that the northern part of the country is being left behind in the growth process. In the case of the urban savannah, it seems well documented that these areas remain very poor. In rural areas, while there was a large drop in poverty in the coastal and forest areas, the drop was again smaller in the rural savannah, so that the gaps between the northern part of the country and other natural regions increased.

The evidence shows that the northern savannah area, which is by far the poorest of the ecological zones, has been left behind in the national reduction in poverty, even though poverty was smaller in 2005/06 than in 1991/92. This has resulted in an increase in the share of the poor living in the rural savannah areas (see the variable “C0” in table 1, which stands for contribution to the headcount of poverty “P0”), from 32.6 percent in 1991/92 to 36.6 percent in 1998/99 to 49.3 percent in 2005/06. Hence today, while the rural savannah areas in 2005/2006 accounted for only one fourth of the population, they accounted for half of the poor. Note also that in table 1, the decrease in national poverty between 1991/92 and 2005/2006 (at about 24 points) is larger than that observed in both urban (17 points), but similar to that observed in rural areas (24 points). The fact that the national decrease in poverty is not equal to the weighted average of the decreases in urban and rural areas is due to the fact that the population shares in urban and rural areas do not remain constant over time. There has been an increase in the urban population share (which may be underestimated in the GLSS surveys), which has also contributed to a reduction in poverty. This will be discussed in more details in section 2.4.

Some caveats are in order about the poverty measures by region. Overall, the headcount in rural areas (39.2 percent in 2005/06) exceeds that of urban areas (10.8 percent), and this is not surprising. Yet there may be some issues with the more detailed estimates by region were given earlier in table 1. In that table, we observe a greater headcount index of poverty in urban Savanah (27.6 percent) than in rural coastal areas (24 percent), a fact that was not observed in the data for 1991/92 and 1998/99 and which underscores the fact that poverty reduction was much weaker in the Savannah than elsewhere. Also, the capital city of Accra was displaying the lowest poverty indicators until 2005, when it ranked second after the urban coastal areas. It is worth mentioning however that the very low poverty headcount for Accra in 1998/99 is likely to have been due to a sampling error, so that the increase in poverty between 1998/99 and 2005/06 in Accra may reflect this error rather than a true worsening of the living conditions of the population there. Also, the very low poverty measures observed in Urban Coastal and Forest areas is somewhat surprising. As was the case for Accra in 1989/99, it could be that the sampling frame was not fully representative of these areas, and that the decrease in poverty may have been overestimated. Therefore, we believe that the trend for urban areas as a whole rather than for each of the sub-areas is probably the most trust-worthy one. In rural areas by contrast, given that the sample size of the survey is larger, the likelihood of similar problems is likely to be less prevalent there.

As noted by Coudouel et al. (2002; see also Ravallion, 1994), apart from the poverty headcount, higher order measures of poverty provide important information on poverty trends (precise definitions of these poverty measures are given in an annex). The depth of poverty (poverty gap, denoted by “P1” in table 1) provides information regarding how far off households are from the poverty line. It thereby measures the consumption shortfall to eradicate poverty relative to the poverty line across the whole population (i.e., considering a shortfall of zero for non-poor

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households). Put differently, it gives as a proportion of the poverty line the total resources needed to bring all the poor to the level of the poverty line. In addition, the poverty severity (squared poverty gap, denoted by “P2” in table 1) takes into account not only the distance separating the poor from the poverty line (i.e., the poverty gap), but also the inequality among the poor. That is, a higher weight is placed on those households who are further away from the poverty line. The measures of depth and severity of poverty are important complements of the incidence or headcount of poverty. It might be the case that some groups or regions have a high poverty incidence but low poverty gap (when numerous members are just below the poverty line), while other groups have a low poverty incidence but a high poverty gap for those who are poor (when relatively few members are below the poverty line, but with extremely low levels of consumption or income). In table 1 however, the trends for the poverty gap and squared poverty gap very much mirror those for the headcount. However, when considering the poverty or squared poverty gap, the contribution of rural areas, and especially the rural savannah region, comes out even stronger. For example, the rural savannah region, with less than a quarter of the population concentrates more than 70 percent of the severity of poverty.

2.2. Testing the Reliability of the Trend in Poverty Using National Accounts Data

Between 1991/92 and 2005/06, the estimates suggest that the share of the population in poverty decreased by almost half, from 51.7 percent to 28.5 percent. If these estimates are correct, Ghana is on path to reducing poverty by half versus its level of the early 1990s well below the target date of 2015 from the Millennium Development Goals. In proportional terms, the decrease in poverty observed between 1998/99 and 2005/06 is slightly larger than that observed between 1991/92 and 1998/99. The sharp reduction in poverty observed since 1998 may surprise some observers. To assess whether their results make intuitive sense, we can test whether the changes in real consumption that they observe between the GLSS4 and GLSS5 is believable in light of data available from the National Accounts.

In theory, one could argue that neither growth in real consumption nor growth in GDP as measured from the National Accounts automatically leads to a decline in poverty. One can indeed observe an increase in poverty despite growing GDP per capita, and poverty can fall even if real consumption or GDP per capita is falling. In practice however, the experience in West and Central Africa as elsewhere in the world suggests that over time, economic growth is strongly correlated to poverty reduction. That is, growth in GDP and aggregate consumption per capita tends to be accompanied by a reduction in poverty measures computed using household surveys, especially when the measurement of poverty is conducted over long periods of time during which the economy experienced substantial changes, as is the case in Ghana.

Therefore, this section summarizes the results of comparisons between GDP and aggregate consumption trends on the one hand, and poverty trend on the other. As shown in Figure 1 and table 2, growth in GDP per capita (i.e., after discounting GDP growth for population growth) was positive throughout the 1990s, and increased since 2001 to reach 3.60 percent in 2006 according top preliminary estimates. The average per capita GDP growth between 1991 and 1999 was 2.04 percent, and it increased to 2.36 percent between 1999 and 2006.

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Figure 1: GDP growth per capita, 1990-2006 1.000 1.100 1.200 1.300 1.400 1.500 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 C u m u la ti v e Gr ow th Inde x 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 A n n u a l G ro w th R a te (% )

Annual GDP growth per capita (right axis)

Cumulative GDP growth (left axis)

Source: Authors, based on IMF data.

Table 2: GDP growth and GDP growth per capita in Ghana, 1991-2006

1991 to 1999 1999 to 2006 1991 to 2006

Number of years 8 7 15

Average growth rate of GDP (%) 4.65 4.98 4.81

Average growth rate of GDP per capita (%) 2.04 2.36 2.20

Cumulative growth in GDP per capita 16.5 18.4 37.9

Beyond GDP growth, a second key factor seems to have contributed to the reduction in poverty, at least between 1999 and 2006. Or said differently, one would have expected a smaller reduction in poverty than what was actually observed if there had been a simple one-to-one correlation between GDP growth and the consumption of households. To explain this puzzle, it is important to recall that GDP growth is computed in real terms by taking into account the GDP deflator, which is a measure of changes in the cost of producing the various components of GDP in the economy. By contrast, in Ghana, the poverty lines used to estimate poverty depend in part on the trend in the Consumer Price Index, which measures changes over time in the price of the consumption of the population. Thus, if there is a divergence between the GDP deflator and the CPI, this is one of the factors that could lead to a divergence between the rate of real GDP growth per capita, and the rate of growth in consumption per equivalent adult observed in the surveys. Such discrepancies can occur for example when there is high inflation in the country, whether one considers the inflation in production costs, of the inflation in consumer prices.

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In Ghana, the years between 1999 and 2006 were marked by high rates of inflation, especially from 2000 to 2003. The available data suggest that there was a divergence between 1999 and 2006 between the GDP deflator, which is used to compute real GDP from nominal values by factoring in changes in the prices of the goods produced in the country, and the Consumer Price Index (CPI), which tracks changes in the cost of a basket of goods consumed (rather than produced) in the country. As shown in Figure 2, since 1998/99, the annual increase in the GDP deflator has been higher than the annual increase in the CPI, except for the year 2005 where both are virtually equal. In cumulative terms, the GDP deflator has grown much faster than the CPI in recent years. If we assume that nominal consumption as a share of nominal GDP has remained roughly constant over time, the divergence between the GDP deflator and the CPI suggests that real growth in consumption has been higher than real growth in GDP since 1999.

Figure 2: GDP Deflator and Consumer Price Index (1999-2006)

0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 1999 2000 2001 2002 2003 2004 2005 2006 A n n u a l i n cr eas e i n GD P an d C P I (% ) 0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 4.500 5.000 C u m u la ti v e I n cr eas e i n GD P a n d C P I (I n d e x ) GDP CPI

A number of reasons may have led to the lower increase in the CPI as opposed to the GDP deflator since 1999. First, within the CPI, food prices have increased at a lower pace than other goods, as well as at a lower pace than the GDP deflator. This is due in part to good rainfall, which have led to an increase in the production of cereals, thereby leading to relatively lower prices, as compared to other goods. Second, there has been an improvement in the terms of trade faced by the country, as well as an appreciation of the real exchange rate for the Cedis. The improvement in the terms of trade, in part related to an increase in the world price for cocoa, has led to high values for the GDP deflator without having a similar impact on the CPI. As for the appreciation of the real exchange rate, it has led to a relative decrease in the prices of the goods imported in the country, a large share of which are used for consumption purposes.

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Coming back to poverty measurement, in general any change in poverty can be formally explained by changes in the mean consumption per equivalent adult of household on the one hand, and by changes in inequality or in the distribution of consumption between households on the other hand. In most countries, inequality measures tend to change relatively slowly, so that one would expect growth to play a major role in poverty reduction. Said differently, assuming for the moment that inequality could have remained stable in Ghana (we will come back to that issue below in section 2.5), and given the discrepancy highlighted above between the GDP deflator and the CPI, we would expect a higher decrease in poverty than what would have been suggested according solely to the record on per capita GDP growth. Table 3b provides estimates of the contribution to GDP growth and the divergence between the CPI and the GDP deflator on the growth in real consumption. The first line reproduces from table 3b the cumulative GDP per capita growth rate observed between 1999 and 2006, at 18.4 percent. Thereafter, an estimate of the cumulative differential between the GDP deflator and the CPI is provided. This estimate, at 18.7 percent, means that as compared to the year 1999 which is taken as the baseline for the computations, the GDP deflator was 18.7 percent higher than the CPI in 2006. Thus, if we assume that the share of consumption in nominal GDP has remained roughly constant over time, this suggests that household have benefited from an increase in per capita consumption of 37.0 percent. This is actually what we found in the survey data, since the increase in real consumption per equivalent adult between the 1998/99 and 2005/06 surveys was 35.5 percent. When inflation rates are high, it is difficult to track costs of living and other similar indicators well. Hence the increase in the GDP deflator may have been overestimated, which would imply that real GDP growth rates would be higher than suggested in table 3a. This would reduce the cumulative differential between the GDP deflator and the CPI in table 3b. But the sum of the real GDP growth and the cumulative differential between the GDP deflator and the CPI would remain the same, and this is what matters for our purpose in terms of poverty measurement. Said differently, the observed increase in consumption between the two surveys is quite close to the real growth in consumption from the national accounts (assuming a constant share of nominal consumption to GDP), and this suggests that we can have some confidence in the results on the improvement in welfare measure and the reduction in poverty. Furthermore, the ratio of total consumption in the GLSS surveys (using expansion factors) to the consumption in the National Accounts are relatively close to unity, and, what is more important, do not change over time. This again suggests that the growth in real consumption observed over time is legitimate. Note that in table 3, total consumption in the GLSS is based on 99 percent of households with 0.5 percent of the households deleted from the sample at the two extremes of the distribution to correct for outliers and data errors.

Table 3: Contribution to growth in real consumption between 1999 and 2006

Macroeconomic data

Cumulative growth in GDP per capita 18.4%

Cumulative differential between GDP deflator and CPI 18.7%

Real growth in consumption assuming stable consumption share (1) +(2) 37.0%

Comparison of macro and microeconomic data from the GLSS

Increase in real consumption per equivalent adult between 1998/99 and 2005/06 35.5% Ratio of total consumption in 1998/99 GLSS survey to National Accounts 111.8% Ratio of total consumption in 2005/06 GLSS survey to National Accounts 111.3%

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2.3. Testing the Reliability of the Trend in Poverty Using Data from CWIQ Surveys

Despite the coherence between the data from the GLSS and the National Accounts discussed in the previous section, the very large reduction in consumption-based poverty observed between 1998-99 and 2005-2006 may still surprise some observers. Another test of the reliability of the estimates for the poverty trend obtained from the GLSS4 and GLSS5 surveys can be constructed thanks to the availability of two other nationally representative surveys covering a similar period, namely the 1997 and 2003 CWIQs (Core Welfare Questionnaire Indicators). Using these surveys, it is feasible to check whether the trend in “asset-based poverty” between 1997 and 2003 is similar that for consumption-based poverty in the GLSSs. This is what is done by Diallo and Wodon (2007). The authors examine the trend in asset-based poverty in Ghana between 1997 and 2003 as well as the determinants of determinants of asset-based poverty. They estimate the incidence of poverty based on ownership of a wide range of assets and housing characteristics using factorial analysis and giving the same weights in 1997 and 2003 to each of the assets included in the analysis. This enables the authors to construct an aggregate wealth indicator that is comparable between in both years (subject to caveats discussed below). Separate urban and rural asset-based poverty lines are then chosen so that for the 1997 CWIQ survey, the estimates of asset-based poverty are of the same order of magnitude of those obtained for monetary poverty using the 1998/99 GLSS in urban and rural areas.

There are limits to the poverty measurement approach based on assets. The idea is that since the asset based poverty lines are defined in terms of relatively comparable asset indices over time, they can be kept constant for the analysis of the poverty trends. Nevertheless, comparisons based on assets could either underestimate or overestimate the actual gains in wealth obtained by households because the comparisons do not actually use data ion the price of the assets. For example, given that the prices of assets (such as consumer electronics, small appliances, etc) have been decreasing globally over the period under review, simply estimating wealth through the number of assets owned by households could lead to an overestimation of wealth, given that this price decrease is not measured properly. On the other hand, it could be that thanks to higher earnings, households have purchased over time better equipments (for example better and more expensive television sets), in which case the increase in wealth over time would be underestimated. There may also be interaction effects between the price of assets and the number of assets owned which are not captured in the analysis. For example, if the price of assets is decreasing over time, households may have been able to increase the number of assets that they own, which would lead to an increase in wealth according to our method of analysis which may not be warranted. In some cases, an increase in the number of assets could happen even if the household living standards have deteriorated. These caveats are mentioned simply to say that asset-based poverty measurement is often not as precise as consumption-based poverty measurement. At the same time, trends in asset wealth do provide additional information, and can be used to assess whether an observed trend in consumption-based poverty appears to be reasonable. This is especially valuable if the trends in asset based poverty are obtained from different surveys than those used for measuring consumption-based poverty (similarly, it is often useful to compare trends in consumption to trends in household income; this is done later). It should also be noted that when using an asset based poverty measure, it may not be feasible to replicate exactly the consumption-based poverty measures obtained from another survey (for

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example when there is a larger concentration of households with similar values for the asset index nearby the poverty line; this can happen because an asset index obtained through factorial analysis take a finite number of values). Furthermore, the survey used for measuring asset poverty dates from 1997, while the survey used to measure consumption poverty dates from 1998/1999, with most of the samples interviewed in 1999. In a period of substantial GDP growth, it is therefore normal to observe some differences in poverty estimates between the two surveys due to the passage of time. It turns out that with the 1997 CWIQ survey, Diallo and Wodon (2007) obtain poverty estimates for urban and rural areas at 55.2 percent and 25.0 percent respectively. These estimates are a bit higher than the 1998/99 estimates for urban and rural area at 49.6 percent and 19.4 percent, respectively, but this was considered acceptable given that the GDP per capita growth observed between 1997 and 1999. Finally, note that the national poverty measures may also diverge a bit due between surveys due to the fact that the share of the population in urban and rural areas may also differ between the surveys.

Table 4 provides the results in more details. The national asset-based headcount of poverty is found to have decreased from 45.7 percent in 1997 to 38.9 percent in 2003. This decrease of seven percentage points is roughly in line with the ten points decrease observed using the last two rounds of the GLSS surveys. Indeed, the gap between the two GLSS surveys is a total of seven years, as opposed to six years between the two CWIQ surveys. In addition, economic growth picked up significantly after 2003, so that it is legitimate to expect a larger decrease in poverty in recent years. In addition, as already mentioned, the trend in asset poverty is based on ownership variables that indicate ownership or a lack thereof, without taking into account the value of the assets owned. It is likely that in a period of high growth, households will buy better televisions or radios over time, and this increase in the quality (and price) of the assets owned by households is not captured in an analysis of asset-based poverty.

Given the above, we would thus argue that it is not too surprising that we find a larger decrease in consumption-based poverty between 1998/99 and 2005/06 than what is found by Diallo and Wodon (2007) using the CWIQ surveys between 1997 and 2003. The convergence of results actually gives confidence in the validity of the consumption-based poverty trend obtained with the last two GLSS surveys. Note that in table 4, the decrease in national poverty is larger than that observed in both urban and rural areas. This is because the share of the population in urban areas increased over time, thereby generating additional poverty reduction which we cam loosely relate to migration (we will come back to the role of urbanization in poverty reduction in the next section).

Table 4: Asset-based poverty, inequality and growth, Ghana 1997-2003 (percentages)

Rural Urban National

Headcount index

Poverty in 1997 55.207 25.002 45.711

Poverty in 2003 51.831 21.072 38.880

Change in poverty -3.376 -3.930 -6.831

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2.5. Sectoral contributions to poverty reduction

As noted in Annex 1, the poverty measures used here are additive. This means that the poverty measure for the population as a whole is equal to the weighted sum of the poverty measures for the population subgroups, with the weights defined by the population shares of the subgroups. This additive property makes it feasible to analyze the contribution of various population subgroups to changes in overall poverty over time. Assume that households or individuals can be classified according to various sectors in the economy. These may be industrial sectors, geographic sectors (urban versus rural), or any other sectors that the analyst may suggest. The overall change in poverty over time can be decomposed into: 1) changes in poverty within specific sectors, or intra-sectoral changes, 2) changes in poverty due to changes in the population shares of sectors, or inter-sectoral changes, and 3) changes due to the possible correlation between intra-sectoral and inter-sectoral changes, or interaction effect. Details for the decomposition are provided in appendix. Here, we apply the decomposition to the urban-rural issue, and implicitly to a rough measure of the potential impact of migration on poverty.

Table 5 provides the results. The first column gives the absolute decrease in poverty observed over time (in percentage points) that can be attributed to intra-sectoral effects (i.e., the decrease in poverty in urban and rural areas), population shift effects (the increase in population living in urban areas over time) and interaction effects (this is typically a small reminder in the decomposition). The results are provided only for the headcount index (the share of the population in poverty), but the findings are very similar for the poverty gap and the squared poverty gap. It appears that over the period as a whole, most of the reduction in poverty (specifically, 95 percent of the total gain) was due to a reduction in poverty within urban and rural areas, while the gain that can loosely be associated with migration from rural to urban areas accounted only for 7 percent of the total reduction in poverty. This is a somewhat surprising result that needs to be checked further, and it may be related to an underestimation of the rate of urbanization in the GLSS surveys. Indeed, the share of the population in urban areas according to the GLSS surveys increased only from 33.2 percent in 1991/92 to 37.6 percent in 2005/2006, which seems very low for a period of a total of 15 years.

Table 5: Sectoral urban-rural decomposition of change in poverty, 1991/92 to 2005/06 Absolute

Change Percentage Change

1991 to 1999 Total Intra-sectoral effect -12.12 98.77

Population-shift effect -0.18 1.47

Interaction effect 0.03 -0.23

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The above sectoral decomposition was also applied by Diallo and Wodon (1997) using asset-based poverty measures from the 1997 and 2003 CWIQ surveys. The results were very different. Intra-urban and rural effects generated a reduction in poverty of -3.55 percentage points, but the contribution of migration or urbanization was almost as large, at -3.22 points. The interaction term or residual was negligible, as is the case for consumption-based poverty inn table 5 (decrease in poverty by 0.06 points). The large impact of urbanization in the decomposition was due to the fact that asset-based urban poverty measures were about half those obtained in rural areas, and in addition the share of the population in rural areas had decreased from 68.56 percent in 1997 to 57.89 percent in 2003, which in this case may have been an overestimation of the urbanization rate. Still, even if the decline in the rural population share has been lower than suggested by the CWIQ surveys, it may have been substantial (in many poor countries, the urban share grows by about one point per year), which helps explain the large contribution of the population-shift effect on total asset-based poverty.

2.6. Trend in Consumption-Based Inequality Measures since the Early 1990s

Poverty measures are affected only by changes in consumption for those households below the poverty line (or crossing the line). By contrast, inequality measures take into account the whole distribution of consumption per equivalent adult. While many different inequality measures are available and used in the empirical literature, we focus here on basic statistics of the ratios of consumption levels at various percentiles of the distribution, as well as on the most commonly used measures of inequality (see Annex 3 for a definition of these measures). The results at the national level are presented in table 6. For example, the consumption level per equivalent adult at the 90th percentile of the distribution was in 1991/92 5.2 times higher than at the tenth percentile, but by 2005/2006, this ratio had increased to 6.4. Without exceptions, all of the inequality measures show an increase over time, which in some cases is quite large.

Table 6: Trends in consumption-based inequality in Ghana, 1991/92 to 2005/06

1991/92 1998/99 2005/06 p90/p10 5.2 6.2 6.4 p90/p50 2.4 2.5 2.5 p10/p50 0.5 0.4 0.4 p75/p25 2.4 2.7 2.6 p75/p50 1.5 1.7 1.6 p25/p50 0.7 0.6 0.6

Generalized Entropy indices

GE(-1) 0.2745 0.3248 0.4149 GE(0) 0.2312 0.2550 0.3153 GE(1) 0.2490 0.2589 0.3730 GE(2) 0.3579 0.3552 0.9928 Gini 0.3728 0.3879 0.4245 Atkinson indices A(0.5) 0.1128 0.1205 0.1543 A(1) 0.2064 0.2251 0.2704 A(2) 0.3544 0.3938 0.4535 Gini index Gini 0.3728 0.3879 0.4245

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Of the various measures presented in table 6, the most widely used is probably the Gini index. This is in part because the Gini index is related in a very simple way to the Lorenz curve and takes a value between zero and one. In order to assess the sensitivity of the estimates of the Gini index to outliers or extreme values, we recomputed in table 7 the index on 99 percent of the distribution, after deleting the 0.5 percent most extreme observations at both ends of the distribution. While the increase in inequality is lower after this correction, it remains substantial. The adjusted Gini index for consumption per equivalent adult increased substantially, from 0.353 in 1991/92 to 0.378 in 1998/99 and finally 0.394 in 2005/06. Thus, this confirms that inequality has increased in Ghana. At the same time, it must be mentioned that in comparison to other West African countries, Ghana’s level of inequality is in the middle range, even if within Ghana itself, this increase in inequality is a concern (we discuss to the impact of changes in inequality on poverty in section 2.6; data on the trend in income inequality are provided in chapter 5). Table 7: Gini index without extreme values, by locality and urban/rural, 1991-2006

1991/92 1998/99 2005/06 Urban/rural Urban 0.321 0.340 0.355 Rural 0.329 0.358 0.361 Locality Accra 0.324 0.283 0.368 Urban Coastal 0.296 0.336 0.362 Urban Forest 0.318 0.342 0.317 Urban Savannah 0.338 0.298 0.379 Rural Coastal 0.316 0.344 0.320 Rural Forest 0.325 0.327 0.327 Rural Savannah 0.326 0.373 0.384 National 0.353 0.378 0.394

Source: Authors using GLSS data.

As mentioned in Annex 3, it is feasible to decompose inequality measures by groups, so as to better understand whether the increase in inequality observed over time is related to an increase in inequality within groups (such as urban and rural areas, or the various natural regions in the country which serve as strata for the GLSS surveys), or to an increase in the inequality between groups. The results of this exercise as applied to urban areas and key regions are given for the three survey years in tables 8 to 10. It can be seen that while there was an increase in between group inequality, most of the increase for all the inequality measures was due to higher within group inequality. This suggests that although there are more disparities between various areas of the country (such as between the northern savannah and the rest of the country), there are also changes that are not geographically based which tend to magnify the differences between households in terms of consumption. As will be discussed in more details in chapter 4, some of these changes are related to underlying trends in the labour markets, including changes in the returns to education which have tended to favour better educated workers over time.

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Table 8: Decomposition by group of selected inequality measures, 1991/92

Generalized Entropy indices Atkinson indices

GE(-1) GE(0) GE(1) GE(2) Gini A(0.5) A(1) A(2)

Subgroup indices urban 0.2329 0.1995 0.2127 0.2885 0.3471 0.0977 0.1808 0.3178 rural 0.2218 0.1937 0.2116 0.3070 0.3417 0.0958 0.1761 0.3073 Within-group inequality 0.2398 0.1956 0.2121 0.3189 0.0967 0.1783 0.3122 Between-group inequality 0.0347 0.0355 0.0369 0.0389 0.0179 0.0342 0.0614 Subgroup indices Western 0.1857 0.1724 0.1903 0.2606 0.3259 0.0865 0.1583 0.2708 Central 0.2282 0.1916 0.2002 0.2591 0.3378 0.0931 0.1744 0.3134 Greater Accra 0.2328 0.2053 0.2233 0.3125 0.3533 0.1014 0.1856 0.3176 Volta 0.1893 0.1741 0.1923 0.2685 0.3272 0.0872 0.1598 0.2746 Eastern 0.2063 0.1853 0.1974 0.2552 0.3383 0.0913 0.1691 0.2921 Ashanti 0.2678 0.2328 0.2564 0.3756 0.3759 0.1149 0.2077 0.3488 Brong Ahafo 0.2153 0.1980 0.2236 0.3224 0.3492 0.0998 0.1797 0.3010 Northern 0.3558 0.2760 0.2854 0.4226 0.3993 0.1305 0.2412 0.4157 Upper East 0.1813 0.1747 0.2032 0.3020 0.3254 0.0896 0.1603 0.2661 Upper West 0.2393 0.1972 0.1950 0.2274 0.3457 0.0937 0.1790 0.3236 Within-group inequality 0.2461 0.2035 0.2212 0.3292 0.1006 0.1844 0.3164 Between-group inequality 0.0284 0.0277 0.0278 0.0286 0.0136 0.0270 0.0556

Subgroup indices Urban 0.2559 0.2064 0.2065 0.2675 0.3493 0.0979 0.1865 0.3386 rural 0.2785 0.2283 0.2383 0.3387 0.3686 0.1098 0.2041 0.3577 Within-group inequality 0.2915 0.2209 0.2235 0.3180 0.1043 0.1959 0.3488 Between-group inequality 0.0333 0.0341 0.0353 0.0371 0.0181 0.0363 0.0691 Subgroup indices Western 0.1908 0.1737 0.1976 0.3384 0.3241 0.0875 0.1594 0.2762 Central 0.1956 0.1770 0.1880 0.2399 0.3314 0.0873 0.1622 0.2812 Greater Accra 0.1681 0.1490 0.1575 0.2163 0.3002 0.0733 0.1384 0.2516 Volta 0.2462 0.2010 0.1985 0.2359 0.3462 0.0952 0.1821 0.3299 Eastern 0.1734 0.1521 0.1597 0.2050 0.3036 0.0747 0.1411 0.2575 Ashanti 0.3155 0.2448 0.2398 0.2918 0.3803 0.1144 0.2172 0.3869 Brong Ahafo 0.2136 0.1826 0.1904 0.2422 0.3318 0.0890 0.1669 0.2993 Northern 0.2695 0.2468 0.2913 0.4888 0.3884 0.1251 0.2187 0.3502 Upper East 0.1926 0.1646 0.1608 0.1797 0.3150 0.0784 0.1517 0.2781 Upper West 0.1717 0.1609 0.1758 0.2301 0.3154 0.0807 0.1486 0.2557 Within-group inequality 0.2525 0.1909 0.1979 0.2935 0.0919 0.1718 0.3044 Between-group inequality 0.0724 0.0641 0.0610 0.0616 0.0316 0.0644 0.1286

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Subgroup indices urban 0.3022 0.2403 0.2602 0.4148 0.3738 0.1165 0.2136 0.3767 rural 0.3599 0.2942 0.4069 1.7583 0.4073 0.1521 0.2549 0.4185 Within-group inequality 0.3738 0.2739 0.3308 0.9489 0.1337 0.2334 0.3968 Between-group inequality 0.0411 0.0414 0.0423 0.0438 0.0238 0.0483 0.0940 Subgroup indices Western 0.2326 0.2069 0.2275 0.3185 0.3552 0.1027 0.1869 0.3175 Central 0.2776 0.2525 0.2981 0.5202 0.3924 0.1277 0.2232 0.3570 Greater Accra 0.3348 0.2810 0.3215 0.5698 0.4092 0.1388 0.2450 0.4010 Volta 0.5591 0.5472 0.9666 4.6356 0.5565 0.3025 0.4214 0.5279 Eastern 0.1974 0.1707 0.1859 0.2750 0.3186 0.0845 0.1569 0.2830 Ashanti 0.2805 0.2359 0.2525 0.3643 0.3765 0.1147 0.2102 0.3594 Brong Ahafo 0.2572 0.2158 0.2252 0.2955 0.3614 0.1044 0.1941 0.3397 Northern 0.3342 0.2698 0.2765 0.3631 0.4022 0.1282 0.2365 0.4006 Upper East 0.3187 0.2654 0.2769 0.3666 0.4016 0.1274 0.2331 0.3893 Upper West 0.3425 0.2914 0.3599 0.7561 0.4125 0.1477 0.2528 0.4065 Within-group inequality 0.3450 0.2609 0.3272 0.9516 0.1338 0.2301 0.3712 Between-group inequality 0.0699 0.0544 0.0459 0.0411 0.0236 0.0524 0.1310

2.6. Contribution of growth and changes in inequality to poverty reduction

It was mentioned above that inequality has increased over time. The Gini index for consumption per equivalent adult increased from 0.353 in 1991/92 to 0.378 in 1998/99 and finally 0.394 in 2005/06. We provide below a simple decomposition of the contribution to poverty reduction of growth (in consumption per equivalent adult) and changes in inequality (Datt and Ravallion, 1992; the details of the decomposition are given in Annex 1). Note that the growth component in this decomposition refers to the growth in real consumption per equivalent adult as measured in the surveys, as opposed to growth in real GDP per capita as measured from the national Accounts. As discussed earlier, growth in real consumption per equivalent adult can be due in part to GDP per capita growth, but also to changes in relative prices to the extent that there was a divergence between the GDP deflator and the CPI used to estimate trends in real consumption (in addition, there can be demographic effects affecting growth in consumption related to changes in household sizes as measured through the equivalent adult concept).

Table 11 provides the results from the decomposition. Over the full period under review, from 1991 to 2006, the headcount index of poverty was reduced by 23.2 percentage points. If there had been no change in inequality, the reduction in poverty would have reached 27.5 points, so that Ghana would have achieved the MDG target of reducing poverty by half versus its level of 1990. This target has not yet been achieved because the increase in inequality led to an increase in poverty of 4.3 points. Overall, while the increase in inequality was significant, it was still small as compared to the reduction in poverty obtained thanks to growth in real consumption (which takes into account the divergence between the CPI and the GDP deflator alluded to

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before; note that the effect of the CPI-GDP deflator divergence is different from that analyzed by Grimm and Guenther, 2006, using data from Burkina Faso).

Table 11: Decomposition of change in poverty headcount, by urban/rural

Share of change due to:

Total Change Growth in real consumption per equivalent adult in the survey Redistribution (change in inequality in consumption in the survey) 1991/92 to 1998/99 National -12.3 -13.1 0.9 Urban -8.3 -10.7 2.4 Rural -14.0 -14.4 0.3 1998/99 to 2005/06 National -10.9 -13.5 2.6 Urban -8.6 -8.6 0.0 Rural -10.4 -13.8 3.4 1991/92 to 2005/06 National -23.2 -27.5 4.3 Urban -16.9 -20.0 3.1 Rural -24.4 -28.7 4.3

Source: Authors using GLSS data. See also Ghana Statistical Services (2007).

Another way to look at the relationship between growth and inequality is to rely on growth incidence curves (Ravallion and Chen, 2003). These curves graph the growth rates in consumption at various points of the distribution of consumption, starting from the poorest on the left of the horizontal axis to the richest on the right. The growth incidence curve shows the percentage increase in consumption obtain for various groups of the population according to their consumption level. Clearly, as shown in Figures 3 to 5, the growth rates in consumption have been significantly higher in the upper part of the population, especially in the 1990s. For the period 1999 to 2006, while the upper echelons of the population benefited from very large gains in consumption, and while the very poor had lower gains than the rest of the population (but positive gains nevertheless), the pattern of gains was equitable for a fairly large segment of the population since the growth incidence curve is flat from the second decile to the ninth decile. Summing up (or in technical terms integrating) the growth curve up to any given share of the population ranked by increasing level of consumption gives the total growth in consumption of that share of the population. The results are displayed in table 12. Note that because we are dealing with approximations in logarithm and different baselines each year as to whom constitutes the bottom, say, 10 percent of the population, the total growth rate between 1991/92 and 2005/06 need not exactly be the sum of the growth rates between the two sub-periods. At the national level, the growth rate in consumption for the period as a whole was 12.1 percent for the bottom decile (the poorest 10 percent of the population). This growth rate in consumption increases to 19.5 percent for the two bottom deciles taken together, and 34.1 percent for the bottom three deciles. The cumulative growth rate in consumption at the poverty line (which in the baseline dataset of 1991/92 is near the median of the distribution since about

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half the population was poor) is 43.3 percent, which is well below the average growth rate for the population as a whole, at 63.7 percent, but still very large. The same information is provided for comparisons of consumption levels within urban and rural areas. These statistics suggest that growth was smaller in rural than in urban areas, but that the order of magnitude of the differences in growth rates for, say, the bottom 20 percent of the population and the average for the population as a whole was similar in both urban and rural areas, at about 20 percentage points. Thus, while all groups of the population benefited from growth, growth was not strictly speaking pro-poor since better off households gained more.

Table 12: Rate of pro-poor growth, by urban/rural (in %)

1991/92 to 1998/99 1998/99 to 2005/06 1991/92 to 2005/06 Urban at 10 percentile 9.7 18.1 29.2 at 20 percentile 11.7 24.9 39.4 at 30 percentile 13.6 27.2 44.5 at poverty line 13.0 24.5 43.3 at mean 23.7 31.0 62.0 Rural at 10 percentile 5.7 6.3 12.4 at 20 percentile 7.0 13.3 21.3 at 30 percentile 9.6 15.4 26.6 at poverty line 16.4 16.8 36.7 at mean 25.3 25.3 56.9 National at 10 percentile 7.4 12.1 20.4 at 20 percentile 9.5 17.3 28.4 at 30 percentile 12.1 19.5 34.1 at poverty line 16.1 20.6 41.6 at mean 24.9 31.1 63.7

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Figure 3: Growth Incidence Curve, 1991/92 to 1998/99 0 10 20 30 40 M ed ia n s pl ine 0 20 40 60 80 100 Percentiles

Figure 4: Growth Incidence Curve, 1998/99 to 2005/06

0 20 40 60 M ed ia n s pl ine 0 20 40 60 80 100 Percentiles

Figure 5: Growth Incidence Curve, 1991/92 to 2005/06

0 20 40 60 80 M ed ia n s pl ine 0 20 40 60 80 100 Percentiles

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To summarize, growth since the early 1990s was not pro-poor and inequality increased quite substantially (on the basis of 99 percent of the sample, the Gini index for consumption rose from 0.353 to 0.395, and the increase was larger on the full sample; also, the increase in income inequality was even more pronounced, as will be discussed in chapter 5). Yet this should not detract from the country’s achievements in reducing poverty. Ghana’s dramatic reduction in poverty by almost half between 1991 and 2006s is probably the best record in the whole of sub-Saharan Africa over the last 15 years. The share of the population in poverty decreased from 51.7 percent in the early 1990s to 39.5 percent in the late 1990s and 28.5 percent in 2006. Every year on average, the share of the population was thus reduced by about 1.5 percentage point. Given Ghana’s population, some 5 million persons were lifted out of poverty thanks to growth. That is, if there had been no reduction in poverty over the last 15 years, the number of the poor would be 5 million persons higher than it is today, at more than 11 million. Instead, not only the share of the population in poverty, but also the absolute number of the poor decreased, from 7.9 million in 1991/92 to 7.2 million in 1998/99 and 6.2 million in 2005/06.

At the same time, the extent of the reduction in poverty was lower in the poorest areas of the country (rural Savannah). As a result, the gap between the northern part and the rest of the country has widened. One could argue that future gains in poverty reduction will be more difficult to achieve because these gains will have to take place in more remote and less well endowed areas in terms of physical and human capital as well as agricultural potential. But one could also argue that as the share of the poor in poor areas becomes higher, it is becoming easy to reach a large number of the poor through well targeted policies and programs.

2.7. Longer Term Poverty Trend Going Back to the late 1960s

The record of poverty reduction of Ghana over the last 15 years is remarkable. Yet it may be useful to put this record in perspective with Ghana’s longer term record. It is worth recalling that at the time of its independence in 1957, Ghana had a vibrant economy. The country was a world leader in cocoa which created, along with gold and timber receipts, an enviable external reserve and facilitated the implementation of policies promoting stable price inflation and sustained economic growth. However a deteriorating sectoral, monetary and fiscal policy environment combined with a series of weather and external shocks led to political and economic instability during much of the 1960s and 1970s. During that period, GDP per capita declined continuously (see Figure 6) and inflation reached double- and triple-digit figures year after year. This mixture of high inflation, negative external reserves, bad policies, declining cocoa production and price, severe droughts and political instability led to a major social and economic crisis by early 1980s.