Commodity Price Volatility and Inclusive Growth in Low-Income Countries
Chapter

Chapter 13. Inclusive Growth in Sub-Saharan Africa: Evidence from Six Countries during the Recent High-Growth Period

Author(s):
Rabah Arezki, Catherine Pattillo, Marc Quintyn, and Min Zhu
Published Date:
October 2012
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Author(s)
Rodrigo Garcia-Verdu, Abebe Aemro Selassie and Alun Thomas 

Introduction

Most countries in sub-Saharan Africa experienced a period of high economic growth beginning in the mid-1990s, leading to renewed optimism about the region’s development prospects.1 Furthermore, most countries in the region weathered the global economic crisis of 2008–09 remarkably well, contrasting with previous episodes when growth collapsed as a result of external shocks.

Despite this acceleration of growth, how certain are we of the magnitude of the acceleration? The statistical base on which real GDP per capita is measured is extremely weak in most countries in sub-Saharan Africa, so there is a high degree of uncertainty associated with these growth estimates.2

Even if one is willing to take data on the growth of real GDP at face value, the perception exists among policymakers and citizens in the region that growth in sub-Saharan Africa has not been shared evenly among the population or accompanied by an increase in employment opportunities in many countries (i.e., jobless growth), especially where growth has been concentrated on the extraction of natural resources. What is the evidence that higher levels of output are being translated into greater job creation, improved access to key services, and higher living standards for the majority of the population?

In this chapter, we present a diagnostic of whether the population at large has benefited from the recent high-growth episode in sub-Saharan Africa and focus on well-being indicators measured by access to basic services, ownership of durable goods, and household consumption using household survey data from Cameroon, Ghana, Mozambique, Tanzania, Uganda, and Zambia.3 We then look at the distribution of changes over time in real consumption per capita among the population through the estimation of the growth incidence curve (GIC) for each country and analyze the determinants of household consumption using Mincer-type regressions and the impact of growth on employment opportunities through the estimation of standard measures of labor market performance. We then apply a methodology to estimate the bias in the consumer price index using Engel curves as a way to corroborate the growth rates of real GDP per capita from the System of National Accounts. The basic conclusions of the chapter are as follows:

  • Close examination of household survey data suggests that high per capita economic growth does have a strong bearing on the inclusiveness of growth. Ownership of consumer durables has increased extremely rapidly over the sample period in all case study countries except for Zambia, which experienced weak per capita GDP growth. In terms of consumption growth, we consider two measures of inclusiveness. Our first (absolute measure) is whether the poorest quartile of the consumption distribution registered positive real per capita consumption growth. The second measure, which is more of a relative concept of inclusiveness, compares the ratio of consumption growth between the lowest and highest quartiles of the consumption distribution. Under the absolute measure, the poorest quartile experienced substantial annual household per capita consumption growth in three of the four high-growth countries (Ghana, Tanzania, Uganda). By contrast, the poorest quartile of the consumption distribution in the low-growth countries saw low (Cameroon) or even negative (Zambia) changes in real consumption per capita. The results for Mozambique depend on whether one uses the CPI or regional price indices to deflate nominal household consumption per capita, with the former showing relatively high growth and the latter showing negative growth for the poorest quartile.
  • We also found evidence of the importance of employment opportunities in rural areas, particularly in agriculture, for higher consumption growth among the poorer households. The stronger per capita consumption growth observed in Cameroon and Uganda at the poorest levels is correlated with high agricultural employment growth. By contrast, rural agricultural employment between the surveys considered fell in both Mozambique and Zambia, where the poorest experienced weaker or negative per capita consumption growth. The importance of rural employment outcomes is intuitive given the fact that on average about 70 percent of the population in the six countries resided in rural areas in the early 2000s.
  • There is also evidence of significant employment growth in the case study countries. Surveys include questionnaires about the level of formal employment as well as involvement in other income-generating activities (which would also capture subsistence agriculture). When the two numbers are considered together, with the exception of Ghana, the employment-to-population ratio in the countries increased between surveys.
  • Regarding the evolution of real incomes in the region, we provide evidence that the growth in real consumption is being underestimated, most likely due to biases in the measurement of the CPI. In particular, we considered the change between surveys in the share of consumption devoted to food in each country. According to Engel’s law, this share varies negatively with the level of income. The estimated shifts over time in the Engel curves for three (Cameroon, Ghana, Zambia) of the four countries considered suggest that in our sample, real income growth was significantly underestimated.4

Measures of Welfare

The issue of whether GDP is an appropriate measure of economic performance and welfare has been debated ever since the introduction of the System of National Accounts in the late 1940s, and it remains a hotly contested issue. Sen, Stiglitz, and Fitoussi (2010), in a recent report written for the French government on “the measurement of economic performance and social progress,” argue that GDP is neither a measure of income nor a measure of well-being, but rather an indicator of market activity constructed by adding up the market value of goods and services produced in the economy.

One of the contested issues is whether national income and product accounts (NIPA) data or household survey data provide a more accurate reflection of household welfare. Sala-i-Martin and Pinkovskiy (2010) have argued that a very sharp decline in poverty rates has been registered in sub-Saharan African countries based on combining the growth rates from NIPA data and data on the distribution of consumption from household budget surveys, although these authors do not justify why NIPA data are better than survey data for measuring changes in means. In contrast, Deaton (2010) has argued that there is no reason to believe that NIPA data are better than survey data for measuring consumption and concludes that the true poverty level lies between the estimates using NIPA and survey consumption growth rates (see Box 13.1).

Although there is general agreement that well-being is multidimensional and covers material living standards, health, education, political voice, social connections, environment, and security (Sen, Stiglitz, and Fitoussi, 2010), this chapter limits its analysis to material living standards and employment opportunities. We first consider welfare measured through ownership of assets and access to public services and then turn to household consumption and employment opportunities.

Box 13.1Differences between Survey and National Accounts Estimates of Consumption

Many argue that living standards or welfare are more closely associated with household consumption estimated from surveys rather than with the alternative measure based on national income and product accounts (NIPA) estimates of private consumption expenditure. This is because household surveys provide detailed information on household market and nonmarket consumption and imputed housing services, whereas private consumption expenditure in many developing countries is derived as a residual. It is calculated by taking the difference between nominal GDP calculated from the production approach and those components of aggregate demand that are calculated directly. On the other hand, surveys often fail to capture households at the top end of the income distribution and exclude nonprofit establishment expenditures on services that are provided to households.

To assess differences between the two estimates, the value of private consumption expenditure from the NIPA is compared to aggregate consumption estimates from the household surveys used in this chapter (see Table 13.1). The decline over time in the ratio of the survey estimate to the NIPA estimate of consumption is consistent with the experience of other countries and likely reflects an increase in the number of people at the top end of the income distribution that are not sampled in the survey. The speed at which the ratio has declined in Cameroon, Ghana, and Zambia is comparable to that of China, although it is faster than in India and the United States. In Mozambique, Tanzania, and Uganda, the ratio has remained fairly constant.

Comparison between Survey and NIPA Expenditure Change
Ratio of survey to NIPA private expenditure totals
Cameroon0.9 (2001)0.77 (2007)
Ghana0.92 (1998)0.79 (2005)
Mozambique0.83 (2002)0.86 (2009)
Tanzania1.00 (2001)1.02 (2007)
Uganda1.3 (2002)1.2 (2009)
Zambia0.88 (1998)0.75 (2004)
China10.95 (1990)0.8 (2000)
India10.68 (1983)0.56 (1999)
United States10.8 (1984)0.64 (2001)
Sources: Household data surveys unless otherwise indicated.Note: NIPA: national income and product accounts.

Source is Deaton (2005).

Sources: Household data surveys unless otherwise indicated.Note: NIPA: national income and product accounts.

Source is Deaton (2005).

Table 13.1Macroeconomic, Poverty, and Consumption Aggregates in Sample of Countries
PeriodGrowth per capitaReal

exchange

rate
Terms of

trade
Poverty headcountGini coefficientNIPAPer capita consumption
Survey data
Percent change over the periodPercent change over the periodLatest estimateAnnual percent changeInitial estimateLatest estimateAll householdsPoorest quartileRatio of poorest quartile to average
Cameroon2001–070.576.956.29.6−3.90.450.431.00.821.01.24
Ghana1998–052.33−29.1−33.630.0−1.30.410.433.63.662.60.71
Mozambique2003–095.546.432.860.0−2.50.470.467.23.502.90.82
Tanzania2000–074.38−34.6−47.267.9−3.00.350.383.76.733.90.58
Uganda2002–094.450.4−5.028.7−4.10.460.443.63.404.71.37
Zambia1998–041.169.820.964.31.50.530.510.5−3.43−1.90.55
Memo items:
Bangladesh1992–20003.00n.a.−4.857.8−1.10.280.330.81.80
Cambodia11994–20045.70−33.151.440.2−0.80.350.425.82.800.800.29
Vietnam11993–20025.90−9.06.840.1−2.60.340.384.25.504.00.73
Sources: Household surveys; Klump and Bonschab (2004); World Bank (2007); and IMF, World Economic Outlook database and Information Notice System. Note: Percent change per year, except where stated. NIPA: National income and product accounts.

For Cambodia and Vietnam, the poorest quintile replaces the poorest quartile.

Sources: Household surveys; Klump and Bonschab (2004); World Bank (2007); and IMF, World Economic Outlook database and Information Notice System. Note: Percent change per year, except where stated. NIPA: National income and product accounts.

For Cambodia and Vietnam, the poorest quintile replaces the poorest quartile.

Welfare Measured through Asset Holdings and Access to Services

A number of analysts have used indices based on ownership of consumer durable goods and assets and access to public services as an alternative measure of economic well-being to household consumption (Booysen and others, 2008; Filmer and Scott, 2008; Sahn and Stifel, 2000; Sahn and Younger, 2010; Young, 2010). Items such as a radio, television, refrigerator, bicycle, motorcycle, and car are normally chosen as the consumer durables, while dwelling characteristics such as building materials, the quality of flooring and roofing, main source of drinking water, and type of toilet facilities are used to measure access to services. The methodology of principal components is a common method for providing the weights used to aggregate these indicators into a single asset index.

The rank correlation between per capita expenditures and these types of asset indices is typically greater than 0.50, with the correlation higher for countries outside sub-Saharan Africa. For example, Filmer and Scott (2008) found that the correlation for Brazil is 0.64, while the correlation for Ghana and Zambia is about 0.40. The lower correlation among sub-Saharan African countries is likely related to the fact that a large subset of low-income households do not own the consumer durables used in the index, while access to piped water and sanitation is very low, especially in rural areas. Booysen and others (2008) emphasized that the limited discrimination ability at the lower end of the income scale makes asset indices a poor tool for analyzing the extremely poor.

We aim to enrich the current understanding on the inclusiveness of growth in the region using six case studies—from Cameroon, Ghana, Mozambique, Tanzania, Uganda, and Zambia. The sample choice is driven by data availability, and the sample is not fully representative of sub-Saharan African countries in general—there are no postconflict or fragile states and no large oil exporters (Cameroon is a marginal net exporter), and only one francophone country is included (Appendix 13.1). With the exception of Cameroon and Zambia, the countries all enjoyed average per capita income growth of more than 2¼ percent during 1995–2010 (among the region’s faster-growing economies).

For the sample of countries, data on access to publicly provided services and consumer durables is obtained through the Afrobarometer surveys supplemented by the household budget surveys (Figures 13.1 and 13.2).5 Both types of surveys indicate that ownership of consumer durables has increased extremely rapidly over the past decade in all case study countries except for Zambia, which registered negative growth in the fraction of the population that reported owing a given asset. If we weigh ownership of radios, televisions, and cars equally, the annual change in consumer durables varies between no change in Zambia to an increase of 2.2 percent per year in Ghana. Except for Ghana, the changes are broadly inversely related to initial ownership shares. Cameroon had the highest television and motor vehicle ownership share, with a 0.4 percent annual increase and Mozambique had the lowest share in 2002 and the highest annual increase (1.5 percent).

Figure 13.1Access to Resources

Sources: Afrobarometer and household surveys.

Note: Probability of 0 signifies no access and 1 signifies full access. CMR: Cameroon; GHA: Ghana; MOZ: Mozambique; TZA: Tanzania; UGA: Uganda; ZMB: Zambia.

1Data for Cameroon in 2007 include hospitals and health clinics. For Mozambique, the data reflect the probability of reaching a health unit within 30 minutes.

Figure 13.2Consumer Durables

(Percent of sample)

Sources: Afrobarometer and household budget surveys.

Access to publicly provided services has also become much more widespread over time across most countries.6 Ghana and Cameroon have the highest levels of access to the electricity grid, piped water, and sewage system, which is consistent with their higher levels of GDP per capita. Moreover, Ghana has also demonstrated the fastest increase in coverage over this period, with Mozambique a close second. The increase in access to publicly provided goods is buttressed by the proportion of respondents who indicated that they seldom go without food, water, medical care, and cooking fuel. Except for in regard to access to cooking fuel in Uganda, all countries show a rising share over time of households that report seldom going without these basic needs, with Ghana remaining above the other countries in terms of levels.

Based on the demand for durables, various housing characteristics, children’s health status, and family conditions, Young (2010) has argued that the growth rate of per capita consumption among sub-Saharan African countries was about 3½ percent per year over the 15-year period ending in 2005–06, which is three times the average estimate from NIPA data. His analysis is based on the relationship between these factors and educational attainment, under the assumption that educational attainment is a good proxy for family income (as supported by the Mincer regressions below). He shows that the elasticity of education with respect to owning a car is positive and significant and is much higher than the elasticity with respect to owning a radio. Using these relationships between educational attainment and the identified characteristics, combined with an assumption about the rate of return to education, he derives consumption growth estimates.7

In contrast to Young’s findings, Harttgen, Klasen, and Vollmer (2012) have argued that the relationship between asset growth and per capita income growth is very weak, especially among non-African countries where concerns about NIPA statistics are less serious. They conclude that inferring income growth from changes in asset indices is not very robust.

Welfare Measured through Household Consumption

An alternative benchmark of household welfare is aggregate consumption using household survey data on home production for self-consumption, consumption of purchased goods (i.e., expenditure), and consumption of imputed housing services. Consumption is preferred over income as the measure of welfare or living standards for a variety of reasons.

First, because surveys can only hope to measure financial flows over a short period, consumption is a better measure of living standards since it is less volatile than income. Indeed, many people in low-income countries do not receive any income during their lifetime because they are paid in kind or are unremunerated employees in unincorporated family enterprises. Therefore, measuring inequality based on data on the previous month’s income will overstate inequality. Second, the concept of consumption is clearer to survey participants than the concept of income, especially in countries where income from self-employment is the norm and salaried employment is the exception. Third, respondents are generally more reluctant to share information about their income than about their consumption. Because income is usually taxable, it may be hard for respondents to be persuaded that their income information will not be passed to the tax authorities.

Evidence on the Incidence of Growth in Sub-Saharan Africa

One common concern among policymakers and citizens alike in the sub-Saharan Africa region is whether the recent growth has been evenly distributed among the population. Estimating the GIC proposed by Ravallion and Chen (2003) can identify the incidence of growth in real consumption per capita. The GIC depicts the annual growth rate of real consumption per capita between two periods (vertical axis) with comparable surveys for each group of households ordered according to their position in the distribution of consumption per capita (horizontal axis). If the GIC lies above zero all along the entire distribution of real consumption per capita, then all households experienced positive growth and growth is said to be inclusive according to the absolute definition of inclusive growth. If, in addition to lying above the zero-growth line, the GIC has a negative slope throughout (i.e., it decreases monotonically), then growth is said to be inclusive according to the relative definition of inclusive growth. In practice, the GIC tends to have more complex forms, often crossing the horizontal axis (negative growth) at one or more points, so one cannot categorically say that growth was inclusive or not inclusive for the entire distribution.

Figure 13.3 shows the GIC of real household consumption per capita for the total populations of our six case study countries. Our main findings are as follows:

Figure 13.3Growth Incidence Curves of Real Household Consumption Per Capita

Source: IMF staff estimates based on data from various household surveys (see Appendix 13.1).

Note: The black line surrounded by the shaded area is the actual growth incidence curve, the lighter gray line is the average consumption level for all deciles, and the darker gray line corresponds to the growth rate for households in the middle of the consumption per capita distribution (the representative household).

  • In absolute terms, the poorest quartile fares best where economic growth is higher. In particular, in the six country case studies, the pattern of household consumption growth for the poorest quartile is closely linked to the evolution of overall per capita GDP growth (Table 13.1). Indeed, the correlation between the two variables is 0.7. In four of the six countries in the sample (Ghana, Mozambique, Tanzania, Uganda), per capita GDP expanded by 4.25 percent annually between the relevant surveys, and mirroring this annual household consumption, growth averaged a relatively high 3.5 percent for the poorest quartile of the consumption distribution.8 In the other two sample countries, where annual per capita GDP growth was 1 percent or lower between surveys (Cameroon and Zambia), the poorest quartile did rather badly. In Cameroon, annual household consumption per capita growth was 1 percent for the poorest quartile, and in the case of Zambia, this group actually experienced an annual decline of 1.9 percent.
  • In relative terms, however, the extent to which growth is inclusive is not related to the level of economic growth. The poorest quartile did better in relative terms than richer households in low-growth Cameroon and Zambia as well as in high-growth Uganda. In the other three high-growth countries (Ghana, Mozambique, Tanzania), the poorest quartile experienced lower growth in consumption relative to the highest quartile (see Table 13.1 and Figure 13.3).
  • In terms of national poverty estimates, both the relative and absolute measures of the inclusiveness of consumption seem to matter. Thus, in five of the six countries in which overall consumption growth was positive (Ghana, Mozambique, Tanzania, Uganda) or relatively inclusive (Cameroon, where the poorest quartile fared much better than the richest quartile, even though overall growth was low), estimates show a decline in poverty headcount (Table 13.1). It was only in Zambia, where per capita GDP growth was low and consumption growth was strongly negative for the poorest quartile, that poverty increased significantly.

The diverse pattern of inclusive growth observed in sub-Saharan Africa is broadly similar to the experience of a number of comparable Asian countries. In Bangladesh (between 1991 and 2000) and Vietnam (between 1993 and 2002), overall consumption growth was positive (5.5 percent per year in Vietnam and 2 percent in Bangladesh). The highest consumption quartiles also saw significantly higher consumption increases than the poorest quartiles (Table 13.1). In Cambodia (between 1994 and 1999), the consumption growth rate was high among the urban population (3.5 percent per year), but not in rural areas. Consistent with higher growth at the upper end of the income distribution in all three countries, their Gini coefficients rose during the 1990s.

Determinants of Household Consumption

Having identified large differences in the incidence of growth across countries, we now consider the factors that might help explain these differences, with particular focus on the households in the lowest quartile of the consumption distribution.

The coefficients associated with the determinants of consumption are similar among the sample of countries and can explain a large fraction of the variation in household consumption. As can be seen in Table 13.2, on average, between 60 and 70 percent of the variation in household consumption can be explained by household size, age, sex, employment status, sector of employment, and education level of the household head as well as whether the household is located in an urban or rural area. Household size has the highest explanatory power in all six countries, with each additional household member raising household consumption but at a declining rate. This may reflect more children that consume less than household adults and/or more family members with less earnings potential than the household head. The log of the age of the household head is also positive and reflects a rising consumption/income profile for more experienced adults, whereas a consistent positive education-consumption profile is evident across countries. Specifically,

Table 13.2Log Household Consumption Determinants(Most Recent Survey)
GhanaCameroonUgandaMozambiqueTanzaniaZambia
19982005200120072005200920022008/092001200719982004
Household size (log)0.31***0.37***0.29***0.29***0.28***0.24***0.23***0.26***0.24***0.31***0.28***0.17***
Age (log)0.10***0.13***0.19***0.18***0.20***0.20***0.17***0.16***0.06**0.020.030.05***
Male head of household0.05***0.03***0.020.010.03**0.08***0.03**0.04***0.13***0.06**0.04**0.02
Employment dummy0.16***0.16***0.05***0.04**0.020.020.10***0.07***0.07***0.21***0.150.07***
Agriculture sector dummy−0.26***−0.23***−0.20***−0.15***−0.31***−0.09***−0.17***−0.12***−0.20***−0.26***−0.15***−0.04***
Manufacturing sector dummy0.01−0.08***0.05***−0.03**−0.07**−0.10*−0.09***−0.11***−0.04**0.03*
Government sector dummy0.03−0.12***0.16***0.19***0.16***0.16***0.02−0.13***0.15***0.010.02
Primary schooling0.030.07**0.06***0.08***−0.05**−0.14***0.13***0.12***0.16***0.13***−0.050.04*
Lower secondary schooling0.10***0.16***0.15***0.16***0.15**−0.040.31***0.22***0.37***0.44***0.09***0.13***
Upper secondary schooling0.28***0.38***0.31***0.29***0.21***0.010.81***0.56***0.49***0.71***0.43***0.47***
College/nursing/teacher training0.31***0.69***0.62***0.59***0.71***0.87***1.00***0.78***1.23***1.10***1.03***
Urban dummy0.25***0.24***0.10***0.21***0.39***0.20***0.15***0.12***0.10***0.23***0.27***0.12***
Coefficients of lowest quartile
Employment dummy−0.020.050.04−0.010.030.060.010.29***0.000.000.05−0.09*
Agriculture sector dummy0.11**0.13***0.11***0.040.16***0.020.15***0.07*0.17***0.16**0.11**−0.01
Manufacturing sector dummy0.00−0.03−0.01−0.010.010.030.060.060.13**0.02
Government sector dummy0.030.38***−0.24***−0.21***0.080.00−0.150.14**−0.070.040.05
Primary schooling0.07*0.06−0.030.08***0.14***0.21***−0.11***0.03−0.10**0.10*0.15***0.07**
Lower secondary schooling0.020.04−0.010.11***0.010.13**−0.26***−0.04−0.22**−0.19*0.10**0.10***
Upper secondary schooling−0.15−0.39***−0.050.020.010.18**−0.98***−0.29**−0.43***−0.64***−0.16**−0.20***
College/nursing/teacher training−0.22*−0.76***−0.39***−0.16**−0.49*−1.01***−0.62***−1.50***−0.81−1.08***
Urban dummy−0.15***−0.13***−0.03−0.13***−0.19**−0.21***−0.10***−0.13***−0.05−0.17***−0.040.01
Diagnostic statistics
Number of observations5,1517,28010,02110,4166,7296,1177,8579,83620,2559,33215,28317,824
R-squared0.680.680.660.690.650.630.640.660.530.660.660.59
Source: IMF staff estimates based on data from various household surveys (see Appendix 13.1).

Characteristics refer to head of household except for household size and urban dummy.

For Zambia, the manufacturing dummy refers to nonagriculture, nongovernment salaried employment.

*** Statistical significance at the 99 percent level; **at the 95 percent level; *at the 90 percent level.
Source: IMF staff estimates based on data from various household surveys (see Appendix 13.1).

Characteristics refer to head of household except for household size and urban dummy.

For Zambia, the manufacturing dummy refers to nonagriculture, nongovernment salaried employment.

*** Statistical significance at the 99 percent level; **at the 95 percent level; *at the 90 percent level.
  • Large urban-rural consumption differentials are evident in the six case study countries, varying between 12 percent (Mozambique) and 24 percent (Ghana), and these have generally remained stable over time. These differentials have provided the incentives for workers to move from rural to urban areas over the past decade, consistent with the Harris-Todaro model of migration. Between 2001 and 2009, the share of the population in rural areas fell more than 6 percentage points (median) in the sample of countries to 62 percent. Moreover, Nsowah-Nuamah, Teal, and Awoonor-Williams (2010) have shown for Ghana that in urban areas, the likelihood of being employed rises in line with the level of education, so it is likely that the more educated have made the rural-to-urban move.
  • Regional consumption differentials have hardly changed in any sample country over recent surveys, remaining stable in Ghana and Mozambique and actually diverging in Cameroon (not shown). The differential between Cameroon’s richest regions (Yaounde and Douala) and other regions doubled between 2001 and 2007 to 30 percent, while in Mozambique, Central Maputo has maintained a 50 percent positive consumption differential over other regions, and in Ghana, Accra has maintained a 40 percent consumption differential over the poorest regions (the Upper East and West). These nominal consumption differentials are likely to be partially compensated for by differences in regional price indices. Indeed, deflating nominal consumption in Ghana by the regional price indices reduces the differential between the richest and poorest regions by 10 percentage points to 30 percent, and the ranking of the richer regions is changed, with Accra losing the top spot.
  • Household heads with primary school education earn between 0 and 13 percent (Tanzania) more than those without education, whereas college-educated household heads earn between 60 percent (Cameroon) and more than 100 percent (Mozambique, Tanzania, Zambia) more than uneducated household heads. Moreover, in contrast to the stability of education differentials at lower levels of education, the college premium has increased substantially over time, consistent with the findings of Fox and Gaal (2008).9
  • Large consumption differentials also exist for household heads employed in government relative to the primary sector. In most countries, government workers are among the highest paid (for example, Cameroon, Tanzania, Uganda), whereas agricultural workers earn the least, and manufacturing workers are only slightly higher up the consumption scale than agriculture workers in half of the countries in the sample (the reference group omitted from the sectoral coefficients in nongovernment services). Over the past decade, the consumption differential between agricultural workers and those in other sectors has declined over time.

Very limited differences exist in regard to characteristics for the poorest quartile of the consumption distribution:

  • Across time within a single country and across countries, the distribution of consumption between those living in urban and rural areas is very similar, suggesting limited incentives to migrate to urban areas for those at the bottom end of the consumption distribution. With other characteristics (work experience, household size, and employment sector) controlled for, an urban premium for the poor is identified only in Cameroon and Ghana. This is supported by Kakwani, Soares, and Son (2005), who found that a cash transfer system that targets the poor in rural areas is able to reduce the poverty gap considerably more in Cameroon and Ghana than in the other case study countries.
  • Agriculture and nongovernment service work and various education categories exhibit little variation in consumption. Although higher-educated households are likely to be positioned at the upper end of the consumption distribution of the poorest households, the modal estimate is the same as for household heads with no education. A possible explanation is that more highly educated household heads have unobservable characteristics that make them stay in the poorest segment of the population. With other factors controlled for, the regression estimates reveal a positive consumption differential for primary and lower secondary education for the poorest individuals, suggesting that education incentives exist for the poorest in these countries.

Employment Developments

Against the backdrop of strong growth in sub-Saharan Africa in recent years, the perception exists that this growth experience was not accompanied by increased employment opportunities, especially in countries concentrated on the extraction of natural resources. This is an important issue because household consumption is clearly dependent on employment income, as shown in the coefficient estimates from the regressions in the previous section. One difficulty in making this assessment is the general absence of employment data among sub-Saharan Africa countries (only Botswana, Mauritius, and South Africa provide annual data).

Household income and expenditure surveys can be used to overcome this problem because almost all surveys have a labor market component and can provide periodic snapshots of employment developments. However, the frequency of data is limited to two or three data points, and changes in questionnaires between surveys make comparisons difficult (see Appendix 13.1 for a discussion of the methodology used to generate the labor force data). Moreover, the meaning of employment for households in sub-Saharan Africa differs considerably from that used in developing countries because subsistence living represents a large share of household activity and formal employment represents a low share of total employment. For these reasons, we prefer to view employment as all income-generating activities rather than just formal employment.

The increase in the number of people engaged in income-earning activities (a proxy for employment) has been strong over the past decade among the sample of countries analyzed, with a median estimate of 3.25 percent per year (Table 13.3). This outcome compares favorably with Cambodia and Vietnam, two other fast-growing LICs. The high employment growth rates have helped raise the ratio of employment to the working-age population in all sample countries except Ghana, where there has been a sharp increase in the number of people out of the labor force, which is attributable to youth remaining in school for a longer peri-od.10 In addition, economic growth in these countries has been characterized by high employment intensity, with the median employment-output growth elasticity at 0.6 compared with 0.4 for Cambodia and Vietnam.

Table 13.3Employment Indicators(Annual percent change, unless otherwise indicated)
PeriodTotal

employment
Employment

output

elasticity
Urban

employment
Agricultural

employment
Rural

agricultural

employment
Formal sector

employment 1
Cameroon2001–072.70.85.65.94.29.5
Ghana1999–20053.40.76.13.51.413.3
Mozambique2003–094.40.67.43.4−0.416.7
Tanzania2000–093.30.58.82.32.19.5
Uganda2002–097.51.09.86.06.413.9
Zambia1998–20041.90.65.1−0.2−1.613.8
Memo items:
Cambodia2004–074.20.44.53.94.725.0
Vietnam22000–072.90.46.1−0.3n.a.44.0
Sub-Saharan3.30.66.83.51.813.6
Africa (sample median)
Source: Household surveys; Coxhead and others (2010); and Economic Institute of Cambodia (2008).

Latest estimate in percent of working-age population.

Agricultural employment is for 2000–08.

Source: Household surveys; Coxhead and others (2010); and Economic Institute of Cambodia (2008).

Latest estimate in percent of working-age population.

Agricultural employment is for 2000–08.

Agricultural employment growth has been particularly strong in sample countries that have demonstrated inclusive growth over the past decade. Agricultural employment has grown at 6 percent per year in both Cameroon and Uganda, whereas the growth rate has been much weaker in the other sample countries, and even negative in Zambia. The correlation between consumption growth of the poorest quartile and agricultural employment growth is even stronger for the rural population at 0.62, slightly below the correlation between growth of real GDP per capita and consumption growth of the poor.

The growth in urban employment has been extremely rapid, with a median estimate of almost 7 percent per year, over twice the employment growth rate among the whole population. However, given the rapid migration from rural to urban areas, the increase in the ratio of employment to the working-age population has been more modest, at almost 1 percentage point (Figure 13.4). The increase in the ratio of employment to the working-age population among sub-Saharan African countries is comparable to the experience in Cambodia and Vietnam in recent years.

Figure 13.4Ratio of Total Employment to Working-Age Population

Source: Household surveys.

1For Cameroon, the employment-to-population ratio for 2007 refers to those who worked at least 25 hours per week.

Formal sector employment is often used as a measure of the development process among LICs because formal jobs generally provide social security benefits and more stable incomes. Formal employment is proxied by salaried employment (government and other salaried workers) in this chapter given the unavailability of information on social benefits from most surveys. Based on this definition, formal employment in relation to the working-age population for the whole economy has risen in all sample countries except for Cameroon, and in regard to urban areas, it has risen in all sample countries except for Cameroon and Tanzania. However, at 13.6 percent of the working-age population (median estimate for the six sample countries), it remains considerably below the levels registered in Cambodia (25 percent in 2007) and Vietnam (44 percent in 2007).

On the other hand, the fact that salaried employment has grown less rapidly than total employment among sub-Saharan African countries is not necessarily synonymous with adverse welfare developments. First, formal employment may not reflect jobs with health and social security benefits because of lack of data. Second, Fox and Gaal (2008) show that informal sector earnings grew more rapidly than formal sector earnings during the 1990s in Cameroon and Mozambique. Third, Perry and others (2008) argue that many labor force participants in Latin America prefer the flexibility afforded by working for themselves in a nonfarm business rather than being an employee. This is because of low economy-wide productivity levels and the fact that informal sector workers may have access to mechanisms that substitute for formal social protection programs financed by payroll taxes.

Engel Curves

We now turn to one of the best-established empirical regularities in economics, Engel’s law, to help explain the apparent dissonance between changes in income and poverty reduction in our case studies. Several recent studies, including Kenny (2011), Sala-i-Martin and Pinkovskiy (2010), and Young (2010), have suggested that well-being in the African region might actually be higher than is generally believed. Engel’s law, which states that the share of total household resources allocated to food consumption decreases with the level of total household resources, has been found to hold across countries and across households in a given country (see Figures 13.5 and 13.6). Our aim here is to exploit this empirical regularity for insights on the evolution of real incomes. Perhaps real incomes in the region are not being measured well, giving rise to the dissonance between growth and progress in poverty reduction. In other countries, including Brazil, Mexico, and the United States, among others, there is evidence that real income growth has been underestimated on account of the overestimation of true cost-of-living increases by CPI inflation (see Costa, 2001; Hamilton, 2001; and de Carvalho and Chamon, 2012). Could the same factor be at work in sub-Saharan Africa, where there has arguably been even more rapid economic change?

Figure 13.5Food Expenditure Share and Household Consumption Expenditure per Capita in a Sample of 84 Countries, 2010

Source: U.S. Department of Agriculture Economic Research Service, based on data from Euromonitor.

Figure 13.6Ghana: Food Expenditures as a Share of Total Household Consumption by Deciles of the Total Household Consumption Distribution

Source: IMF staff estimates based on data from the Ghana Living Standards Surveys for 1991, 1998, and 2005, and the Ghana Statistical Service.

The basic intuition for the approach used in this section is as follows. Assuming household preferences are stable over time and given a well-specified model, we should be able to infer the evolution of real incomes from shifts in the estimated Engel curve.11 For example, if the estimated Engel curve shifts over time to the left (right), it implies that a lower (higher) level of total household consumption corresponds to each food share.12Figure 13.7 depicts the Engel curve for Ghana estimated using data for the period 1998–2005. In particular, it shows the fitted regression line (in darker gray) and the fitted regression line including the negative coefficient associated with a year dummy variable (in lighter gray), which shifts the original Engel curve toward the origin. Given that for every level of real total household consumption, the lighter gray line associates a lower share of total household consumption allocated to food than the darker gray line, one conclusion we can draw is that real total household consumption may be underestimated.13

Figure 13.7Engel Curves for Ghana Estimated Using Data for the Period 1998–2005

Source: IMF staff estimates based on data from the Ghana Living Standards Surveys for 1998 and 2005 and the Ghana Statistical Service.

The reason for the underestimation of real income growth is generally acknowledged to be overestimation of inflation. There are various upward biases associated with measuring cost of living with a Laspeyres-type CPI index. First, the use of a fixed basket of products in most CPI indexes overestimates changes in the cost of living because consumers change their consumption bundles in response to relative price changes (substitution bias). Second, most statistical agencies ignore changes in the quality of products, so that any increase in the price of a product will be accounted for as inflation, even if it corresponds to a product of higher quality. Third, statistical agencies are also slow in changing their sampling schemes to incorporate new products (which often experience sharp initial declines in prices) and establishments.

As shown in Table 13.4, which illustrates regression results for the case of Ghana (1991–2005), our results show that there is an upward bias in CPI inflation in the later period (1998–2005) because the coefficient associated with the time dummy for 2005 (d2005) is negative and statistically significant.14 In contrast, there was a downward bias in the first period (1991–98) because the first period dummy variable (d1998) is positive.

Table 13.4Engel Curves for Food in Ghana for the Period 1991–2005
(1)(2)(3)(4)(5)(6)
Constant1.547***1.528***1.607***1.524***1.535***1.521***
Total real household−0.064***−0.062***−0.069***−0.066***−0.066***−0.067***
consumption (log)
2005 dummy−0.016***−0.013***−0.014***−0.014***−0.014***
1998 dummy0.013***0.015***0.014***0.014***0.013***
Household size0.005***0.004***0.004***0.003***
Age of household0.001***0.001***0.001***
head
Male head of household−0.009−0.006***
Employed0.032***
Number of19,03619,03619,03619,03619,03618,444
observations
R-squared0.09990.10700.11410.12520.12610.1341
Adjusted R-squared0.09980.10690.11390.12500.12580.1338
Source: IMF staff estimates based on data from the Ghana Living Standards Surveys for 1991, 1998, and 2005 and the Ghana Statistical Service.Note: Dependent variable: Food consumption as a share of total household consumption.***Statistical significance at the 99 percent level; **at the 95 percent level; *at the 90 percent level.
Source: IMF staff estimates based on data from the Ghana Living Standards Surveys for 1991, 1998, and 2005 and the Ghana Statistical Service.Note: Dependent variable: Food consumption as a share of total household consumption.***Statistical significance at the 99 percent level; **at the 95 percent level; *at the 90 percent level.

The result of this regression formalizes the intuition shown for the case of Ghana (Figure 13.7), which suggests that the rapid decline over the period 1998–2005 in the share allocated to food consumption from the household survey is too large to be accounted for by the increase in real GDP per capita or in real consumption expenditure per capita from national accounts, thus suggesting that CPI inflation overestimated the true cost of living increases.15

The specification in column (6) is used for contrasting the four countries for which comparable data on consumption by category are available for at least two years, namely, Cameroon, Ghana, Uganda, and Zambia. The magnitude of the CPI bias implied by the parameter estimates in each of the regressions was obtained by combining the parameter estimates for the coefficient of real income and the dummy variable with an estimate of the food price elasticity and the corresponding relative inflations of the food and nonfood components of the CPI in each country. Because no estimate of the food price elasticity was available for any of the countries in our sample, the estimate by Hamilton (2001) of 0.0369 for the United States was used.

The results for three out of the four countries for which the Engel curves are estimated—Cameroon, Ghana, and Zambia—show a drift to the left over time of the Engel curve, thus suggesting that CPI inflation has overestimated the increase in the true cost of living and that real income growth has been underestimated (Table 13.5). In the case of Uganda, the opposite has been the case because the Engel curve has drifted to the right over time, suggesting that CPI inflation has underestimated the increase in the true cost of living and that real income growth has been overestimated. The estimates of the annual CPI bias are a 10 percent underestimation (annual) in the case of Zambia, 8.6 percent in the case of Cameroon and 2 percent in the case of Ghana, and a 9 percent overestimation in the case of Uganda. Although the magnitude of these estimates is larger than that found for estimates for developed countries (which generally are in the range of 1 percent to 3 percent annually), they are comparable with those obtained for some developing countries, including those of de Carvalho and Chamon (2012) for Brazil during the period 1987–96, which found an overestimation of close to 9.5 percent using a similar specification and estimator, and those of Gibson, Stillman, and Le (2008) for Russia during the period 1994–2001, which found an overestimation of 1 percent per month.

Table 13.5Engel Curves for Food in Cameroon, Ghana, Uganda, and Zambia
Cameroon

2001–07
Ghana

1998–2005
Uganda

2002–10
Zambia

1998–2004
Constant1.546***1.515***1.970***1.283***
Total real household−0.089***−0.065***−0.108***−0.061***
consumption (log)
Second-year dummy−0.065***−0.027***0.049***−0.063***
Household size0.013***0.002***0.011***0.001***
Age of household head0.001***0.001***0.001***0.001***
Male head of household−0.006**−0.006**0.016***0.031***
Employed0.065***0.032***0.006*-0.008***
Number of observations22,14013,95016,72729,246
R-squared0.21060.13180.25100.1403
Adjusted R-squared0.21040.13140.25070.1402
Source: IMF staff estimates based on data from the various household surveys (see Appendix 13.1).Note: Dependent variable: Food consumption as a share of total household consumption.***Statistical significance at the 99 percent level; **at the 95 percent level; *at the 90 percent level.
Source: IMF staff estimates based on data from the various household surveys (see Appendix 13.1).Note: Dependent variable: Food consumption as a share of total household consumption.***Statistical significance at the 99 percent level; **at the 95 percent level; *at the 90 percent level.

The apparent underestimation of the growth rate in true real income in Cameroon, Ghana, and Zambia, particularly during the period when growth accelerated in the region, has important implications. First, it supports the conclusions of Young (2010), who argued that real consumption per capita growth has been underestimated in national accounts using a completely different methodology. The evidence of an underestimation of real income growth in three of the four countries for which data are available suggests that real income growth may be underestimated in other countries in the region, although given the data limitations (in terms of coverage of the region’s population with comparable household surveys), this is a conjecture that requires further research to be confirmed or rejected.

Conclusion

Broadly, then, our main findings are as follows:

  • There is evidence of growth having been fairly inclusive in the region’s high-growth countries. We find, for example, that the lowest quartile in three out of the four case study countries (Ghana, Tanzania, Uganda) enjoyed fairly high increases in consumption. But there are signs that in many of these countries, higher-income households enjoyed still higher growth in consumption. This implies some increase in inequality, broadly in line with patterns observed in a number of high-growing Asian countries.
  • We find evidence of real income growth having been underestimated in some countries—fairly significantly in some cases. In these cases, real consumption gains have accordingly been underestimated (and thus poverty rates likely overstated). The main reason for this appears to be biases in the way that CPI is measured. This is consistent with the views of Young (2010) that income growth has been much higher than is registered in NIPA statistics.

Some of the policy implications that we can infer from our findings are as follows:

  • The focus of many sub-Saharan African policymakers on policies that promote broad and sustainable growth are likely the means by which the poor can be helped the most.
  • Still, this does not imply that high average growth is a sufficient condition to ensure inclusiveness. Once it has been established that growth has not indeed been inclusive, temporary and well-targeted transfer programs could be considered to help those being left out by the growth process. In terms of targeting, as shown above, even a few observable household characteristics—such as education levels, region of residence, sector of employment, employment status, and so on—go a long way toward explaining, in a statistical sense, the difference in consumption levels across households.
  • Perhaps more importantly, as shown in the case of the six case studies, those countries that experienced higher growth in agricultural employment also experienced higher poverty reduction. Some public policies could, if properly implemented, lead to short-term increases in agricultural output and productivity, including diffusion of fertilizers and improved seeds, whereas others, such as investments in electrification, irrigation, rural roads, and agricultural extension services, will require time to be implemented properly and will thus have medium-term effects. At any rate, with about two-thirds of the region’s population living in rural areas and with most of them deriving their income from agricultural activities, increasing agricultural productivity is necessary for accelerating poverty reduction.
Appendix 13.1: Survey Characteristics
Table 13.A1Survey Characteristics
CountrySurveyYearsAcronymData collection

agency or agencies
Start date of

data collection
End date of data

collection
Sampling frameSampling scheme
CameroonEnquête Camerounaise Aupres des Ménages III2007ECAM 3Institut National de la StatistiqueSeptember-07December-073eme Recensement Général de la Population et de I’Habitat de novem-bre-décembre 2005Two-stage stratified random sampling
Enquête Camerounaise Auprès des Ménages II2001ECAM 2Institut National de la StatistiqueSeptember-01December-012eme Recensement Général de la Population et de I’Habitat de 1987Two- and three-stage stratified random sampling
GhanaGhana Living Standards Survey 52005GLSS5Ghana Statistical ServiceSeptember-05August-06Complete list of the 2000 Population and Housing Census Enumeration AreasTwo-stage stratified random sampling
Ghana Living Standards Survey 41998GLSS4Ghana Statistical ServiceApril-98March-99Complete list of the 1984 Population and Housing Census Enumeration AreasTwo-stage stratified random sampling
MozambiqueInquérito sobre Oçamento Familiar2008–09IOF 2008–09Intituto Nacional de EstadísticaAugust-08September-09Master Sample (amostra mãe) from the 2007 Population Census (Censo Populacional)Three-stage stratified random sampling
Inquérito aos Agregados Familiares2002–03IAF 2002–03Intituto Nacional de EstadísticaJuly-02June-03Master Sample (amostra mãe) from the 1997 Population Census (II Recenseamento Geral da Popuçáo e Habftção 1997)Three-stage stratified random sampling
TanzaniaNational Household Budget Survey2007HBS 2007National Bureau of StatisticsJanuary-07December-07National Master Sample developed from the 2002 Population and Housing CensusTwo-stage stratified random sampling
National Household Budget Survey2000–01HBS 2000/01National Bureau of StatisticsMay-00June-01National Master Sample [NMS) based on the 1978 Population Census and later updated with information from the 1988 Population CensusTwo-stage stratified random sampling
UgandaUganda National Household Survey IV2009–10UNHS 2009/10Uganda Bureau of StatisticsMay-09April-102002 Population and Housing Census FrameTwo-stage stratified random sampling
Uganda National Household Survey II2002–03UNHS 2002/03Uganda Bureau of StatisticsMay-02April-03List of enumeration areas with number of households based on cartographic work for the 2002 Population and Housing CensusTwo-stage stratified random sampling
ZambiaLiving Conditions Monitoring Survey IV2004LCMS IVCentral Statistical OfficeNovember-04December-042000 Census of Population and HousingTwo-stage stratified cluster sampling
Living Conditions Monitoring Survey II1998LCMS IICentral Statistical OfficeNovember-98December-98Updated master frame based on the 1990 Census of Population and HousingTwo-stage stratified cluster sampling
CameroonEnumeration areas or zones de dénombrement (742), households (ménages)18,659,93812,60911,39151,83790.34360National, urban, and rural, for 10 administrative regions (provinces), and for the metropolitan regions of Yaounde and Douala
Enumeration areas or zones de dénombrement (612), households (ménages)16,242,47811,55310,99256,44395.14288National, urban, and rural, for 10 administrative regions (provinces), and for the metropolitan regions of Yaounde and Douala
GhanaEnumeration areas (550), households (15)22,279,8468,7008,68737,12899.85600.1National, urban, and rural, for 10 administrative regions, with a minimum sample size of 400 households, for three ecological zones (coastal, forest, and northern), and for the Greater Accra metropolitan region
Enumeration areas (300), households (20)18,724,2756,0005,99825,69499.97728.7National, urban, and rural
MozambiquePrimary sampling units (Unidades Primárias de Amostragem), enumeration areas (Áreas de Enumeção), households (Agregados Familiares)22,638,41411,00010,83251,17798.47442.4National, urban, and rural, for three regions (north, center, and south), and 10 provinces (Cabo Delgado, Niassa, Nampula, Tete, Zambézia, Manica, Sofala, Inhambane, Gaza, Maputo Provincia) and the capital city (Maputo Capital)
Primary sampling units (Unidades Primárias de Amostragem), enumeration areas (Áreas de Enumeração), households (Agregados Familiares)19,521,5468,7278,70044,10099.69442.7National, urban, and rural, and for three regions (north, center, and south)
TanzaniaClusters (447), households (24)41,276,20910,75210,46637,89697.341,089.2Mainland Tanzania, Dares Salaam region (urban), other urban and rural areas
Clusters (1,158), households (24)34,514,83522,58422,178108,08498.20319.3Mainland Tanzania, Dares Salaam region (urban), other urban, and rural areas, and mainland Tanzania’s 20 regions
UgandaEnumeration areas (712), households (10)30,700,0006,8006,77536,43299.63842.7National, urban, and rural, and for these regions (central, eastern, northern, and western)
Enumeration areas (1,000), households (10)25,000,00010,0009,71150,51397.11494.9National, urban, and rural, and for these regions (central, eastern, northern, and western)
ZambiaStandard enumeration areas (1048), households (around 20)11,583,17620,00019,350103,29596.75112.1National, urban, and rural, for nine provinces, and for the 72 districts
Standard enumeration areas (820), households (around 20)10,039,84616,74016,71593,47199.85107.4National, urban, and rural, for nine provinces, and for the 72 districts
Source: Household surveys.
Source: Household surveys.
Appendix 13.2: A Methodology for Calculating Labor Force Components

The labor force definition used in this chapter comprises individuals between 16 and 65 years old who are employed or are actively seeking work; this definition is comparable to the UN definition used for most countries. In all countries, employment status corresponds with the main job, so that students working part time are not counted in the labor force because they are not working as their primary activity.

For Cameroon and Ghana, the employed are defined as those who have worked during the preceding 12 months, and this amount is divided by the total working-age population to derive the employment ratio. This figure is compared with the number of people who indicate their sector of employment and the minimum of these two figures is used. For Zambia, the employed are defined as those who had an active economic status in terms of working for wages, running a business, working in agriculture, and unpaid family workers, while for Tanzania, those who indicate an industry affiliation are assumed employed. For Mozambique and Uganda, only status during the preceding seven days is used for employment, with the employment total defined as the sum of those who worked during the preceding seven days and those who did not work during this period but normally have a job.

In Ghana and Cameroon, the split between the unemployed and those out of the labor force is obtained using the question, “Did you search for work during the past seven days?” Those who searched for work are defined as the unemployed, and the unemployment rate is derived using this figure divided by the working-age population. Those out of the labor force are defined as working-age population minus employed minus unemployed. If the number of unemployed derived in this way looks as if it is miscoded, the figure for those out of the labor force is used based on the question, “Why have you not worked or looked for work?” with the unemployment rate derived as a residual. If there is disparity between the employment totals based on questions about activities during the past 12 months and the unemployment and out-of-the-labor force totals based on questions about activities during the preceding week, the ratios of the latter two variables are applied to the difference between the working-age population and the employment total.16

To identify salaried employees, government workers are first separated out in all countries based on the assumption that all of these workers receive wage income. Nongovernment salaried workers are defined as follows: In Ghana, a worker potentially receiving payment is asked, “How are you paid in your main job?” All categories except “payment in kind” and “not remunerated” are summed. In Mozambique, salaried workers are identified in response to the question, “Are you a salaried worker?” In Cameroon, salaried workers are defined as senior executives, middle management, and qualified and semiqualified workers. In Tanzania, nongovernment salaried workers are defined as those working for NGOs, religious workers, parastatal employees, and other employees, while in Zambia, nongovernment salaried workers are defined as parastatal, private sector, and NGO employees. In Uganda, salaried workers are derived from the question on employment status.

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    World Bank2007World Development Indicators 2007 (Washington).

    YoungAlwyn2010“The African Growth Miracle” (unpublished; London: London School of Economics, Department of Economics).

This chapter is based on Chapter 2 of the IMF’s Regional Economic Outlook: Sub-Saharan Africa—Sustaining the Expansion (October 2011). Yemisrach Amare and Cleary Haines assisted in the preparation of the chapter. At the time of writing, the authors were associated with the IMF’s African Department.
1Contrast, for example, the papers of Collier and Gunning (1999a, 1999b) and Artadi and Sala-i-Martin (2003), on the one hand, with those of Sala-i-Martin and Pinkovskiy (2010) and Young (2010) or the books by Kenny (2011), Miguel (2009), and Radelet (2010), on the other.
2For examples of the uncertainty surrounding GDP estimates in sub-Saharan Africa, see Jerven (2009, 2010a, 2010b, 2011a, 2011b).
3The choice of countries analyzed in this chapter was driven by data availability and, in particular, by the need to have at least two household surveys collected using the same methodology, so that changes in measured total household consumption and changes in household characteristics were not the result of changes in sampling scheme, questionnaires, definitions, data collection procedures, and so on.
4In the other case (Uganda), we find real income growth to have been overestimated.
5Afrobarometer is an independent, nonpartisan research group funded by the United Kingdom’s Department for International Development and the U.S. Agency for International Development that surveys nationally representative, random, stratified probability samples for 20 sub-Saharan African countries every three years.
6Although access to health clinics in Cameroon and Uganda seems to have stalled, the question asked in Cameroon is not comparable across surveys because in 2007, access to hospitals was included together with access to health clinics.
7Luminosity data from satellites and anthropometric measures such as height for age and weight for age provide additional measures of welfare, but these give a mixed picture of living standards in sub-Saharan Africa. See Chen and Nordhaus (2011), Deaton (2010), and Henderson, Storeygard, and Weil (2011) for a discussion.
8As mentioned previously, the Mozambique household survey data provide their own set of regional price indices that can be use to deflate total household consumption per capita in 2008–09 and compare it with the same variable in 2002–03. If one does so, instead of using the CPI to deflate nominal household consumption per capita, one obtains a growth incidence curve that is shifted downward, with the lowest three deciles in fact experiencing negative consumption growth. The use of regional price indices is in general preferable to the use of the CPI to deflate nominal consumption because it is well known that there are significant differences in prices across regions. The reason this chapter uses the CPI to deflate nominal is for uniformity, because regional price deflators are not available for the other countries.
9The stability of the coefficients over time provides support for the estimation of the GIC based on repeated cross-sectional household survey data because it requires the assumption that the same groups of households occupy the same position in the distribution of consumption over time.
10The proportion of those out of the labor force and remaining in school has risen from 7 to 50 percent in Ghana between the two surveys, compared to a jump of 38 to 80 percent in Cameroon and flat at 60 percent in Mozambique.
11Nakamura (1997) was the first to suggest that Engel’s law could be used to measure changes in real income. His motivation was the possibility that the measured productivity slowdown that began in the early 1970s in the United States and in other developed countries was actually a result of the overestimation of inflation, which resulted in a decrease in the growth rate of real income. Both Costa (2001) and Hamilton (2001) formalized Nakamura’s intuition using regression analysis, with which they analyzed the relation between food expenditure and real total household expenditure after controlling for household characteristics. In particular, they employ Deaton and Muellbauer’s (1980) Almost Ideal Demand System (AIDS) specification, reaching similar conclusions, because they both find that inflation measured through the CPI in the United States has overestimated true cost-of-living increases.
12Engel curves, by definition, require that all other variables be held constant. In particular, Engel curves generally take the form w = f(p,y,z), where w is the share of total household resources (income, expenditure, or consumption) allocated to food consumption, p is a vector of prices (including the food price index), y is a measure of total household resources, and z is a vector of household characteristics. Although it can be argued that prices are held constant when data from a cross-sectional household survey (as long as the law of one price holds) are used, several household characteristics change over time, and thus regression analysis is used to control for these changing characteristics.
13If, on the contrary, the coefficient of the year dummy variable were positive, then for every level of real total household consumption, the darker gray line would be associated with a higher share of total household consumption allocated to food, and one would have to conclude that inflation measured through the CPI was downward biased and that the growth of real total household consumption was overestimated.
14All the regressions were estimated using the ordinary least squares (OLS) estimator, and the sample was restricted to households whose food consumption as a share of total household consumption was greater than 5 percent and smaller than 90 percent. In all cases, this restriction reduced the sample by less than 2 percent of the original sample size, and the sign and magnitude of the estimated biases are not sensitive to this sample selection rule.
15The results of the regression are shown only for the whole sample in the case of each country. Nevertheless, all deciles of the consumption per capita distribution show similar changes over time in the food shares as the mean (see Figure 13.6 for evidence from Ghana), which suggests the bias is not driven by changes in the consumption patterns of any particularly group, but is a common phenomenon. Thus, in principle there is no reason to believe that the poorest quartile is experiencing more or less underestimation of real income than the average.
16This is the case for Ghana.

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