2. How Inclusive Has Africa’s Recent High-Growth Episode Been?

International Monetary Fund. African Dept.

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Author: International Monetary Fund. African Dept.

Language:: English

Keywords:: REO; Sub-Saharan Africa; growth performance; education-consumption profile; consumption share; consumption differential; Uganda's consumption growth performance; Household consumption; Income; Employment; Poverty reduction

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Abstract

This chapter focuses on the apparent disconnect between recent growth and poverty outcomes in sub-Saharan Africa:

INTRODUCTION AND SUMMARY

This chapter focuses on the apparent disconnect between recent growth and poverty outcomes in sub-Saharan Africa:

Since the mid-1990s, many sub-Saharan African (SSA) countries have experienced a marked acceleration in economic growth. Whereas region-wide real GDP growth averaged some 2¼ percent between 1980 and 1995, since 1995 growth has averaged more than 5½ percent and a higher still percent from 2000 to 2010 (Figure 2.1).
Progress on poverty reduction, however, looks to have been much more limited. Region-wide estimates, which are available only through 2005, show that the proportion of people living below the poverty line (US$1.25 a day purchasing-power parity [PPP] adjusted) declined only modestly from 59 percent in 1996 to 51 percent in 2005. And when one looks at the link between per capita growth and poverty reduction in a sample of SSA countries that includes more recent data points, the relationship is a weak one (Figure 2.2).

This weak relationship between per capita growth and poverty reduction has prompted concern that the region’s recent high growth has not been sufficiently inclusive. But the cross-country analysis on which this conclusion is based has significant limitations. For one, when the sample size is limited to those SSA countries that have actually sustained high growth for a longer duration, the elasticity of poverty reduction with respect to growth is higher. But beyond this, the elasticity depends greatly on the interaction between the distribution of income and the position of the poverty line in individual countries. A robust assessment of the inclusiveness of the region’s growth requires more of a case study approach through closer examination of household survey data. We set out to do this in this chapter as follows:

We start by delving into the stylized facts of the link between growth and poverty reduction in the region. In particular, given the diversity of growth outcomes, we consider whether similar variation is evident in poverty outcomes and other measures of well-being.
We then go beyond the correlations at the aggregate level between growth and poverty rates, which tend to characterize much of the debate on sub-Saharan Africa. To this end, we use case studies of six countries—Cameroon, Ghana, Mozambique, Tanzania, Uganda, and Zambia¹—to address the following questions:
- Did output growth translate into higher living standards for the majority of the population in these countries?
- Has the incidence of growth been evenly distributed among the population, or have some groups been left out?
- What was the impact of the high-growth episode on employment creation?
- Are we measuring the growth of real GDP per capita accurately, or are we underestimating true growth, as suggested by a recent study by Young (2010)?

Our main findings are as follows:

The pickup in growth since the mid-1990s has been accompanied by fairly modest reductions in poverty headcounts among the full set of SSA countries, although considerable progress has been made in terms of improving social and health indicators. That said, when one looks at the experience of countries in the region that have sustained growth at high levels, there is a closer link between income improvements and poverty reduction. Still, even for this group of high-growth countries, the elasticity of poverty reduction with respect to per capita GDP growth is lower than that observed in other regions.
Close examination of household survey data for the six countries, however, suggests that high per capita economic growth does have a strong bearing on the inclusiveness of growth. Specifically, we consider two measures of inclusiveness in this study. 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 experienced low (Cameroon) or even negative (Zambia) changes in consumption. The results for Mozambique depend on whether one uses the consumer price index (CPI) or regional price indices to deflate nominal household per capita consumption, with the former showing relatively high growth and the latter showing negative growth for the poorest quartile.
We also find (tentative) evidence of the importance of employment opportunities in rural areas, and in particular in agriculture, for higher consumption growth among poorer households. The stronger per capita consumption growth observed in Cameroon and Uganda at the poorest levels, for example, seems related to high agricultural employment growth. By contrast, rural agricultural employment fell between the surveys considered 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 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, the employment-to-working-age-population ratio in five of the countries increased between surveys, Ghana being the exception.
We also find some evidence that the growth in real incomes is being underestimated, most likely the result of biases in the measurement of the consumer price index. In particular, we considered the change in the share of consumption devoted to food between surveys 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 real income growth was significantly underestimated.²

It is inevitably difficult to draw sweeping conclusions on outcomes in a region as diverse as sub-Saharan Africa, but overall the evidence suggests that the recent high-growth episode has been fairly inclusive. In particular, we find reasonably strong evidence on the importance of growth for the fate of the poorest households. In all of the countries where per capita growth was high, the poorest quartiles of the consumption distribution have seen significant increases in real consumption. This is consistent with earlier findings that, as Kraay (2006) puts it, “underscore the importance of growth in average incomes for poverty reduction.” On a more cautionary note, the paucity of reliable data requires care in the interpretation of some results, and we flag this wherever possible. A good example is that of Mozambique. Whether one deflates nominal consumption per capita using regional price deflators from the survey or from the national consumer price index yields dramatically different results. And indeed in the last section of this chapter we provide evidence of significant biases that might have caused real income per capita to be underestimated in some countries.

THE GROWTH-POVERTY DISCONNECT IN SUB-SAHARAN AFRICA: MORE APPARENT THAN REAL?

For sub-Saharan Africa as a whole, the link between growth and poverty reduction is weak. The simple correlation coefficient between growth and changes in the headcount poverty rate in those countries for which poverty data are available is only −0.14 (Figure 2.3, upper left panel).³ But the picture changes markedly when the sample is split between the high-and low-growth countries in the region.⁴ In the high-growth group (Figure 2.3, upper right panel), higher growth is more clearly associated with poverty reduction, although the correlation remains modest. Indeed, the estimated elasticity of changes in the poverty level with respect to per capita GDP growth is about −1 for the high growth group, compared with −1.4 for the fast-growing Asian low-income countries and −2.3 for all fast-growing countries in Asia. But in the low-growth sub-Saharan Africa group, the correlation is close to zero (Figure 2.3, lower left panel).⁵ Moreover, even if we censor the low-growth sample to positive growth episodes, there is no indication that positive growth rates are associated with poverty reduction in this group (Figure 2.3, lower right panel).

What of the association between economic growth and nonincome measures of well-being? We consider two measures here: infant mortality and the United Nations Development Program’s Human Development Index (HDI). Both of these variables can be directly and indirectly influenced by income levels as well as being useful proxies for populations’ access to government services.

The link between improved social outcomes and growth is stronger for the high-growth countries than for the entire sub-Saharan Africa sample:

Declines in infant mortality and growth for the entire region are practically uncorrected. But the correlation is considerably higher for the high-growth countries (Figure 2.4, upper panel).
High-growth SSA countries have also achieved better human development outcomes than slower-growing SSA countries (Figure 2.4, lower panel). The HDI is a broad measure of socioeconomic well-being, with a weight of one-third assigned to per capita income, one-third to life expectancy at birth, and one-third to a basket of education indicators (literacy rate and combined school enrollment rates).

However, factors other than growth influenced the observed improvements in social outcomes. In particular, as Kenny (2011) argues, global factors such as the diffusion of technology in, for example, health services have likely played a major role. Thus, for example, between 1995 and 2010, infant mortality declined on average by 1.8 per thousand live births annually in SSA, nearly double the pace registered in other developing regions. But growth in SSA was actually slower during this period—2.1 percent per capita compared with 3.8 percent elsewhere. The most likely explanation for these developments is that initial infant mortality levels in sub-Saharan Africa were extremely high, and these countries benefited most from the diffusion of medical technology.⁶

Explaining these stylized facts

In the broadest of terms, there are two views on the relatively weak link between poverty and growth.

Perhaps the dominant view is that poverty reduction has not been rapid in sub-Saharan Africa (including in the high-growth countries) because of a highly unequal initial distribution of income and/or unpropitious patterns of growth. From a theoretical perspective, if the initial distribution of income is highly unequal, the impact of growth on poverty will be smaller (Bourguignon, 2003).⁷ And regarding the patterns of growth, Teal (2011), for example, argues that uncompetitive industrial sectors in SSA have failed to channel investment and labor into the highest-yielding activities that would support faster growth of employment and productivity.
The second perspective, however, is that there has actually been much more poverty reduction in the region. In this view, measurement difficulties mask the positive developments that have taken place⁸ Sala-i-Martin and Pinkovskiy (2010) for example, argue that between 1995 and 2006, poverty in the region fell by as much as 25.7 percent using the US$1 per day poverty line and 12.4 percent using the US$2 per day poverty line. But they arrive at this estimate by combining data from household surveys on the consumption shares accruing to different population groups with national income accounts data on real per capita GDP growth to draw inferences about the evolution of poverty. They do not justify their use of data from the national accounts to determine the shifts in the mean of the distribution rather than the changes implied by the household surveys, which show slower growth in consumption per capita levels. Using a different tack, Young (2010) also suggests that real income growth as estimated by national accounts data in SSA may have been underestimated by several percentage points. He arrives at this result by looking at the growth in ownership of assets, durable goods, and improvements in health outcomes from Demographic and Health Survey data.

Notwithstanding the usefulness of these cross-country regressions in describing the relationship between changes in poverty and those in real GDP, there is a limit as to what one can conclude from them. One of the limitations of this type of cross-country regression analysis is that the elasticities depend on the shape of the distribution of income or consumption per capita and on the position of the poverty line with respect to the distribution. In particular, the closer the poverty line is to the median of the distribution, the more sensitive the poverty headcount will be to changes in real GDP per capita. In what follows, we analyze the whole distribution of per capita consumption to avoid this limitation when using aggregate data.

In the rest of this chapter, we aim to improve our understanding of the impact of the region’s high-growth episode on the well-being of different segments of the population as follows. First, we use household consumption survey data—the “gold standard” to gauge the status of the poor—to consider the inclusiveness of growth in the region.⁹ Second, we turn to one of the oldest established empirical regularities in economics—Engel’s Law, which posits that the share of income allocated to food consumption decreases with the level of income—to gauge the veracity of newer claims that real income growth in the region may have been underestimated.

In sum, there is evidence of aggregate growth being positively associated with poverty reduction and other measures of improvements in well-being. And the link is somewhat stronger for the countries in the region that have been enjoying higher growth in recent years. This of course is different from inferring causality between these outcomes. Rather, our sense is that growth has been more of a facilitator—for example, by providing the fiscal resources needed to provide better health and education services. On the more marked progress that the region has made on measures of social development, such as declines in infant mortality and improvements in the HDI, the contribution of the better economic environment has been supported by other factors, such as improvements in technology, increasingly responsive political processes, and better diffusion of improvements in medical technologies (UNDP, 2010;Kenny, 2011).

INSIGHTS FROM CASE STUDIES

This section aims to enrich our understanding on the inclusiveness of growth in the region using six case studies—Cameroon, Ghana, Mozambique, Tanzania, Uganda, and Zambia (see Appendix I for survey details). The sample choice is driven by data availability and is not fully representative of SSA countries in general—there are no post-conflict or fragile states and no large oil exporters (Cameroon is a marginal net exporter), and only one francophone country is included.¹⁰ With the exception of Cameroon and Zambia, the countries enjoyed average per capita income growth of more than 2¼ percent during 1995–2010 (among the region’s faster-growing economies). In what follows, we start by reporting on the incidence of growth in these countries, consider the determinants of total household consumption based on household characteristics, and review the evidence on the evolution of employment outcomes. Finally, we also report the results of our work on estimating CPI bias using Engel curves to corroborate the growth rates of real GDP per capita reported in the system of national accounts.

The incidence of growth

The estimation of growth incidence curves (GICs) is a useful way to identify the extent to which both poorer and richer households have benefited from growth. If an estimated GIC is above zero everywhere, this satisfies the absolute measure of inclusiveness in the sense that per capita consumption is growing along all points of the distribution. If, in addition, the curve slopes downward, this points to consumption growth of poorer households being higher than that of richer households and satisfies the relative measure of inclusiveness.¹¹ Figure 2.5 shows the GICs of real household consumption per capita for the total populations of our six case study countries. The red line surrounded by the shaded area in the figure is the actual GIC, the green line is the average consumption growth rate for all deciles, and the orange line corresponds to the growth rate for households in the middle of the consumption per capita distribution (the representative household). Our main findings are as follows:

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 economic growth (Table 2.1), with the elasticity between per capita consumption growth of the poorest quartile and per capita growth at 0.87 and significant. In four of the six countries in the sample (Ghana, Mozambique, Tanzania, Uganda), per capita income expanded by 4¼ percent annually between the relevant surveys, and mirroring this, annual household consumption growth averaged a relatively high 3½ percent for the poorest quartile of the consumption distribution.¹² In the other two countries, where annual per capita consumption growth was 1 percent or lower between surveys (Cameroon, 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 consumption.
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 high-growth Uganda. In the other three high-growth countries (Ghana, Mozambique, Tanzania), the poorest quartile experienced lower growth in consumption rates relative to the highest quartile (Table 2.1 and Figure 2.5).
For the six case studies, per capita GDP growth and poverty reduction are not closely correlated, but the link between our two measures of inclusiveness and poverty reduction is strong. In particular, the elasticity of the change in the poverty headcount in relation to the growth in consumption of the poorest quartile is −0.7 and significant, while the relationship between the change in the poverty headcount and per capita growth among the country sample is insignificant. Thus, in sample countries in which consumption growth of the poorest quartile was positive (Ghana, 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 2.1). In Mozambique, too, headcount poverty fell. It was only in Zambia, where consumption growth was strongly negative for the poorest quartile, that poverty increased significantly.

Table 2.1.

Macroeconomic, Poverty, and Consumption Aggregates in Sample Countries

(annual percent change, except where noted)

Sources: IMF, World Economic Outlook database; IMF, Information Notice System; household surveys; Besley and Cord (2007); Klump and Bonschab (2005); and International Monetary Fund (2006).Note: NIPA = National Income and Product Account (Bureau of Economic Analysis, U.S. Department of Commerce).

^¹Estimate based on Bangladesh growth incidence curve.

^²For Cambodia and Vietnam, the poorest quintile replaces the poorest quartile.

Table 2.1.

Macroeconomic, Poverty, and Consumption Aggregates in Sample Countries

(annual percent change, except where noted)

	Period	Growth per Capita	Real Exchange Rate	Terms of Trade	Employment		Poverty Headcount		Gini Coefficient			Per Capita Consumption
											NIPA data	Survey data
			Percent change over the period	Percent change over the period		Employment- output elasticity	Latest estimate		Initial estimate	Latest estimate		All households	Poorest quartile	Ratio of poorest quartile to average
Cameroon	2001–07	0.57	6.9	56.2	2.7	0.8	9.6	−3.9	0.4	0.39	1.0	0.82	1.0	1.24
Ghana	1998–2005	2.33	−29.1	−33.6	3.4	0.7	30.0	−1.3	0.41	0.43	3.6	3.66	2.6	0.71
Mozambique	2003–09	5.54	6.4	32.8	4.4	0.6	60.0	−2.5	0.47	0.46	7.2	3.50	2.9	0.82
Tanzania	2000–07	4.38	−34.6	−47.2	3.3	0.5	67.9	−3.0	0.35	0.38	3.7	6.73	3.9	0.58
Uganda	2002–09	4.45	0.4	−5.0	7.5	1.0	28.7	−4.1	0.46	0.44	3.6	3.40	4.7	1.37
Zambia	1998–2004	1.16	9.8	20.9	1.9	0.6	64.3	1.5	0.53	0.51	0.5	−3.43	−1.9	0.55
Memo items:
Bangladesh¹	1992–2000	3.00	…	−4.8	…	…	57.8	−1.1	0.28	0.33	0.8	1.80	1.0	0.56
Cambodia²	1994–2004	5.70	−33.1	51.4	…	…	40.2	−0.8	0.35	0.42	5.8	2.80	0.80	0.29
Vietnam²	1993–2002	5.90	−9.0	6.8	…	0.3	40.1	−2.6	0.34	0.38	4.2	5.50	4.0	0.73

^¹Estimate based on Bangladesh growth incidence curve.

^²For Cambodia and Vietnam, the poorest quintile replaces the poorest quartile.

Table 2.1.

Macroeconomic, Poverty, and Consumption Aggregates in Sample Countries

(annual percent change, except where noted)

	Period	Growth per Capita	Real Exchange Rate	Terms of Trade	Employment		Poverty Headcount		Gini Coefficient			Per Capita Consumption
											NIPA data	Survey data
			Percent change over the period	Percent change over the period		Employment- output elasticity	Latest estimate		Initial estimate	Latest estimate		All households	Poorest quartile	Ratio of poorest quartile to average
Cameroon	2001–07	0.57	6.9	56.2	2.7	0.8	9.6	−3.9	0.4	0.39	1.0	0.82	1.0	1.24
Ghana	1998–2005	2.33	−29.1	−33.6	3.4	0.7	30.0	−1.3	0.41	0.43	3.6	3.66	2.6	0.71
Mozambique	2003–09	5.54	6.4	32.8	4.4	0.6	60.0	−2.5	0.47	0.46	7.2	3.50	2.9	0.82
Tanzania	2000–07	4.38	−34.6	−47.2	3.3	0.5	67.9	−3.0	0.35	0.38	3.7	6.73	3.9	0.58
Uganda	2002–09	4.45	0.4	−5.0	7.5	1.0	28.7	−4.1	0.46	0.44	3.6	3.40	4.7	1.37
Zambia	1998–2004	1.16	9.8	20.9	1.9	0.6	64.3	1.5	0.53	0.51	0.5	−3.43	−1.9	0.55
Memo items:
Bangladesh¹	1992–2000	3.00	…	−4.8	…	…	57.8	−1.1	0.28	0.33	0.8	1.80	1.0	0.56
Cambodia²	1994–2004	5.70	−33.1	51.4	…	…	40.2	−0.8	0.35	0.42	5.8	2.80	0.80	0.29
Vietnam²	1993–2002	5.90	−9.0	6.8	…	0.3	40.1	−2.6	0.34	0.38	4.2	5.50	4.0	0.73

^¹Estimate based on Bangladesh growth incidence curve.

^²For Cambodia and Vietnam, the poorest quintile replaces the poorest quartile.

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 Cambodia (between 1994 and 2004), consumption growth of the poorest quartile was only about 1 percent per year, whereas in Vietnam (between 1993 and 2002), the corresponding consumption growth was significant at 4 percent per year. The highest consumption quartiles saw significantly higher consumption increases than the poorest quartiles (see, for example, the estimated GIC for Vietnam in Figure 2.6). In Cambodia (between 1994 and 1999), the consumption growth rate was high among the urban population (3¹/2 percent per year) but not in rural areas. Consistent with higher growth at the upper end of the income distribution in all three countries, the Gini coefficients rose during the 1990s.

Determinants of household consumption

This section considers the factors that might help explain the incidence of growth in the six country case studies, with particular focus on the households in the lowest quartile of the consumption distribution. We first identify the main household characteristics that help explain the level of consumption for both the whole sample and for those in the lowest quartile. We then try to get a sense of whether changes in the value of attributes that characterize poor households might be related to the incidence of growth.

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.¹³ As can be seen in Table 2.2 and Figure 2.7, in general between 60 percent and 70 percent of the variation in household consumption can be explained by household size, sex and age, employment status, sector of employment, and education level of the head of the household, and 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 total household consumption, albeit at a declining rate; age (as a proxy for experience) is also associated with higher household consumption, whereas a consistent positive education-consumption profile is evident across countries. Specifically:

Table 2.2.

Log Household Consumption Determinants

(Most Recent Survey)¹

Sources: IMF staff estimates based on data from various household surveys (see Appendix I).Note: ***, **, * indicate statistical significance at the 99 percent, 95 percent, and 90 percent levels, respectively.

^¹Characteristics refer to head of household except for household size and urban dummy.

^²For Zambia, the manufacturing dummy refers to nonagriculture, nongovernment salaried employment.

Table 2.2.

Log Household Consumption Determinants

(Most Recent Survey)¹

	Ghana	Cameroon	Uganda	Mozambique	Tanzania	Zambia
	2005	2007	2009	2008/09	2007	2004
Household size (log)	0.37 ***	0.29 ***	0.24 ***	0.26 ***	0.31 ***	0.17 ***
Age (log)	0.13 ***	0.18 ***	0.20 ***	0.16 ***	0.02	0.05 ***
Male head of household	0.03 ***	0.01	0.08 ***	0.04 ***	0.06 **	0.02
Employment dummy	0.16 ***	0.04 **	0.02	0.07 ***	0.21 ***	0.07 ***
Agriculture sector dummy	−0.23 ***	−0.15 ***	−0.09 ***	−0.12 ***	−0.26 ***	−0.04 ***
Manufacturing sector dummy²	−0.08 ***	−0.03 **	−0.10 *	−0.11 ***		0.03 *
Government sector dummy	−0.12 ***	0.19 ***	0.16 ***	0.02	0.15 ***	0.02
Primary schooling	0.07 **	0.08 ***	−0.14 ***	0.12 ***	0.13 ***	0.04 *
Lower secondary schooling	0.16 ***	0.16 ***	−0.04	0.22 ***	0.44 ***	0.13 ***
Upper secondary schooling	0.38 ***	0.29 ***	0.01	0.56 ***	0.71 ***	0.47 ***
College/nursing/teacher training	0.69 ***	0.59 ***	0.87 ***	1.00 ***	1.23 ***	1.03 ***
Urban dummy	0.24 ***	0.21 ***	0.20 ***	0.12 ***	0.23 ***	0.12 ***
Diagnostic statistics
Number of observations	7280	10416	6117	9836	9332	17824
R -squared	0.68	0.69	0.63	0.66	0.66	0.59

^¹Characteristics refer to head of household except for household size and urban dummy.

^²For Zambia, the manufacturing dummy refers to nonagriculture, nongovernment salaried employment.

Table 2.2.

Log Household Consumption Determinants

(Most Recent Survey)¹

	Ghana	Cameroon	Uganda	Mozambique	Tanzania	Zambia
	2005	2007	2009	2008/09	2007	2004
Household size (log)	0.37 ***	0.29 ***	0.24 ***	0.26 ***	0.31 ***	0.17 ***
Age (log)	0.13 ***	0.18 ***	0.20 ***	0.16 ***	0.02	0.05 ***
Male head of household	0.03 ***	0.01	0.08 ***	0.04 ***	0.06 **	0.02
Employment dummy	0.16 ***	0.04 **	0.02	0.07 ***	0.21 ***	0.07 ***
Agriculture sector dummy	−0.23 ***	−0.15 ***	−0.09 ***	−0.12 ***	−0.26 ***	−0.04 ***
Manufacturing sector dummy²	−0.08 ***	−0.03 **	−0.10 *	−0.11 ***		0.03 *
Government sector dummy	−0.12 ***	0.19 ***	0.16 ***	0.02	0.15 ***	0.02
Primary schooling	0.07 **	0.08 ***	−0.14 ***	0.12 ***	0.13 ***	0.04 *
Lower secondary schooling	0.16 ***	0.16 ***	−0.04	0.22 ***	0.44 ***	0.13 ***
Upper secondary schooling	0.38 ***	0.29 ***	0.01	0.56 ***	0.71 ***	0.47 ***
College/nursing/teacher training	0.69 ***	0.59 ***	0.87 ***	1.00 ***	1.23 ***	1.03 ***
Urban dummy	0.24 ***	0.21 ***	0.20 ***	0.12 ***	0.23 ***	0.12 ***
Diagnostic statistics
Number of observations	7280	10416	6117	9836	9332	17824
R -squared	0.68	0.69	0.63	0.66	0.66	0.59

^¹Characteristics refer to head of household except for household size and urban dummy.

^²For Zambia, the manufacturing dummy refers to nonagriculture, nongovernment salaried employment.

Large urban-rural consumption differentials are evident in the six country cases, varying between 12 percent (Mozambique) and 24 percent (Ghana), and these have generally remained stable over time. These differentials have provided the incentive for a continued exodus from rural to urban areas over the past decade, consistent with the prediction of the Harris-Todaro model of migration. Between 2001 and 2009, the share of the population in rural areas fell by more than 6 percentage points (median) in the sample of countries to 62 percent.¹⁴
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, the premium for college education has risen over time in most of the sample countries.
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 and 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 regressions is nongovernment services).
However, the consumption differential between agricultural workers and those in other sectors has declined over time.¹⁵

Changes in the determinants of consumption among the poorest quartile between surveys do not shed much light on the incidence of growth, except for in Uganda. As the majority of the poor are engaged in agricultural activities in rural areas and primary education is generally the highest level attained, we focused on these characteristics to help understand the incidence of growth. Our tentative reading of the numbers is that Uganda’s consumption growth performance among the poorest quartile may be partly explained by the sharp improvement in agricultural incomes and in the education premium (primary level) of these households relative to the rest of the population (Figure 2.8). Between surveys, the consumption level of poor families in Uganda with household heads employed in agriculture rose by 10 percentage points more than in families whose head is employed in nongovernment services. In Cameroon, too, where the poorest quartile also experienced the highest consumption growth, there was a relative improvement in the education premium. However, changes in the determinants of consumption in other countries were not consistent with the incidence of growth. For example, the rise in the value of education in Mozambique and the decline in the urban premium in Zambia were not accompanied by propoor growth.

The results of the regressions, in which a high percentage of the variance of household consumption can be explained by a few characteristics, show that household surveys among the sample of countries can be used for targeting the poorest households to receive income transfers. Clearly differentiated location clusters have been identified with different levels of household consumption, whereas the health component of the surveys can also be used to identify health impediments, such as sickness or disability. Several developing countries have introduced direct cash transfer programs (conditional or unconditional) and other targeted safety net programs as a feasible and cost-effective way of protecting the poorest households against shocks and providing them with some of the growth dividend. Brazil and Mexico are prime examples, Their most successful programs (Bolsa Escola—Bolsa Familia and Progresa-Oportunidades, respectively) made use of the geographical information from their household surveys in targeting the poor population; this was supplemented with the selection of beneficiary households within the targeted communities based on socioeconomic data collected for all households. Although there are several challenges that need to be overcome before implementing targeted transfers or safety nets, these barriers are perhaps more political than technical in nature (for example, the claim that “everybody is poor”).

Employment

Household income and expenditure surveys can also be used to analyze the role of employment patterns in the inclusiveness of growth in the case studies. The frequency of data is limited to two or three data points, and changes in questionnaires between surveys make comparisons difficult (see Appendix II for a discussion of the methodology used to generate labor force data). Moreover, the meaning of employment for SSA households differs considerably from that used in developed 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 sector employment. Our findings for the six countries include the following:

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 countries analyzed, with a median estimate of about 3¼ percent growth per year (Table 2.3).¹⁶ Such 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 attributable to youth remaining in school for a longer period (Figure 2.9). 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.
Agricultural employment growth has been particularly strong in countries that have demonstrated propoor 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.
Formal sector employment is often used as a measure of the development process among LICs because formal sector 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).

Table 2.3.

Employment Indicators (annual percent change, except where noted)

Sources: Household surveys; Vietnam Ministry of Planning and Investment and UNDP (2010); World Bank (2008).

^¹Latest estimate in percent of working-age population.

^²Agricultural employment is for 2000-08.

Table 2.3.

Employment Indicators (annual percent change, except where noted)

	Period	Total Employment	Employment Output Elasticity	Urban Employment	Agricultural Employment	Rural Agricultural Employment	Formal Sector Employment¹
Cameroon	2001–07	2.7	0.8	5.6	5.9	4.2	9.5
Ghana	1999–2005	3.4	0.7	6.1	3.5	1.4	13.3
Mozambique	2003–09	4.4	0.6	7.4	3.4	-0.4	16.7
Tanzania	2000–09	3.3	0.5	8.8	2.3	2.1	9.5
Uganda	2002–09	7.5	1.0	9.8	6.0	6.4	13.9
Zambia	1998–2004	1.9	0.6	5.1	-0.2	-1.6	13.8
Memo items:
Cambodia	2004–07	4.2	0.4	4.5	3.9	4.7	25.0
Vietnam²	2000–07	2.9	0.4	6.1	-0.3	n.a.	44.0
Sub-Saharan Africa
(sample median)		3.3	0.6	6.8	3.5	1.8	13.6

Sources: Household surveys; Vietnam Ministry of Planning and Investment and UNDP (2010); World Bank (2008).

^¹Latest estimate in percent of working-age population.

^²Agricultural employment is for 2000-08.

Table 2.3.

Employment Indicators (annual percent change, except where noted)

	Period	Total Employment	Employment Output Elasticity	Urban Employment	Agricultural Employment	Rural Agricultural Employment	Formal Sector Employment¹
Cameroon	2001–07	2.7	0.8	5.6	5.9	4.2	9.5
Ghana	1999–2005	3.4	0.7	6.1	3.5	1.4	13.3
Mozambique	2003–09	4.4	0.6	7.4	3.4	-0.4	16.7
Tanzania	2000–09	3.3	0.5	8.8	2.3	2.1	9.5
Uganda	2002–09	7.5	1.0	9.8	6.0	6.4	13.9
Zambia	1998–2004	1.9	0.6	5.1	-0.2	-1.6	13.8
Memo items:
Cambodia	2004–07	4.2	0.4	4.5	3.9	4.7	25.0
Vietnam²	2000–07	2.9	0.4	6.1	-0.3	n.a.	44.0
Sub-Saharan Africa
(sample median)		3.3	0.6	6.8	3.5	1.8	13.6

Sources: Household surveys; Vietnam Ministry of Planning and Investment and UNDP (2010); World Bank (2008).

^¹Latest estimate in percent of working-age population.

^²Agricultural employment is for 2000-08.

In sum, employment growth over the past decade has been strong across the six countries, especially among the urban population. This increase has helped to raise the ratio of employment to the working-age population. Although formal employment has also increased in relation to the working-age population, it remains far below the levels in Cambodia and Vietnam. Large cross country differences in agricultural employment growth are the most likely candidate for explaining disparities in consumption growth. One of the features of the GICs is the contrast between propoor per capita consumption growth in Cameroon and Uganda and the relatively lower/ negative per capita consumption growth among the poor in Mozambique and Zambia. Employment developments provide some explanation as to why agricultural employment growth has been strong in Cameroon and Uganda whereas it has been much weaker in Mozambique and actually negative in Zambia. Moreover, these differences are even sharper when we consider the rural population.

NEW EVIDENCE ON THE EVOLUTION OF REAL INCOME IN SSA FROM ENGEL CURVES

In this section, we turn to one of the bestestablished empirical regularities in economics, Engel’s Law, to see if it can help explain the apparent dissonance between changes in income and poverty reduction in our case studies. As discussed in the first section of this chapter, there is an apparent disconnect between per capita growth and improvements in other welfare indicators. Several recent studies, including Kenny (2011), Sala-i-Martin and Pinkovskiy (2010), and Young (2010), suggest 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 and within countries (see Figures 2.10 and 2.11). 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 accurately, 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, 2011). Could the same factor be at work in SSA where there has arguably been even more rapid economic change?

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.¹⁷ 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. Figure 2.12 depicts the Engel curve for Ghana¹⁸ estimated using data for the period 1998–2005. In particular, it shows the fitted regression line (in red) and also shows the fitted regression line including the negative coefficient associated with a year dummy variable (in green), which shifts the original Engel curve toward the origin. Given that for every level of real total household consumption, the green line associates a lower share of total household consumption allocated to food than the red line—one conclusion we can draw is that real total household consumption may be underestimated.¹⁹

The reason for the underestimation of real income growth is generally acknowledged to be overestimation on 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 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 and establishments that often experience sharp initial declines in prices.

Turning to our results:

As shown in Table 2.4, which illustrates regression results for the case of Ghana (1991–2005), there is an upward bias in CPI inflation in the later period (1998–2005), because the coefficient associated with the time dummy for 2005 is negative and statistically significant.²⁰ In contrast, there was a downward bias in the first period (1991–98), because the first period dummy variable is positive.
The result of this regression formalizes the intuition shown in the figure for the case of Ghana (Figure 2.12), 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, suggesting that CPI inflation overestimated the true cost of living increases.²¹

Table 2.4.

Engel Curves for Food in Ghana over the Period 1991–2005