(World Bank) - Successful economic development has typically been accompanied by structural transformation
in which manufacturing and industry’s share of output and employment rises at the expense of agriculture. At present, however, the agriculture and services sectors account for the vast majority of employment in Sudan, with manufacturing providing an almost negligible number of jobs.
A more competitive real exchange rate could support export and output growth and hence help attract much-needed FDI. Sudan’s real exchange rate is overvalued, which is similar to other African oil exporting countries, most of which experience Dutch disease symptoms. Empirical evidence presented here suggests that a 10 percent lower real exchange rate (RER) could raise economic growth by 0.9 percentage points in Sudan. Given that Sudan’s exports of non-natural resource and agriculture products comprise mainly low-value, raw, and unprocessed products, which compete primarily on prices, the historic and current RER overvaluation was and is a major inhibiting factor for export development in the country.
A major determinant of the RER is the level of inflation. And since 1999 inflation in Sudan has a history of high rates and increased volatility. But inflation became also a key symptom of the post-secession economy with rates above 40 percent in 2012 and 2013. A major driver of the upsurge was the approach to monetize the budget deficit by the Central Bank of Sudan through granting direct loans to the government.
Structural change shifts economic activities and employment from low to high productivity activities.
In the traditional definition of structural change manufacturing and industry’s share of output and employment rises at the expense of agriculture. This is the type of development model often associated with East Asia where there was a strong rise in manufacturing sector shares in the economies of China, Korea and Vietnam, to name a few. The non-traditional view of structural change increasingly also acknowledges the role of the services sector for structural transformation and productivity increases within sectors and within firms. Given Sudan’s early days in structural transformation, and the illustrative character of the traditional definition of structural change, the focus in this chapter is primarily on the (lack of) shift from agriculture to manufacturing activities in the economy.
But Sudan still stands at the beginning of structural transformation, while others advanced. Looking at the sector decomposition of GDP, with the exception of a rise in industry that is related to the oil economy and extractives, the sector shares are rather constant in the period covered. This is in stark contrast to Vietnam, where the importance of agriculture has decreased from 40 to 18 percent both on account of rising shares for services and industry (incl. manufacturing). Kenya’s structural transformation over the same period was less pronounced, and driven primarily by the services sector. The remainder of this section will provide further evidence of the lack of structural transformation in Sudan from the perspective of the labor market.
Demographics and education
Sudan has a very young, relatively rural population, with a low level of education attainment.
The total population of 29.2 million is largely rural (64.3 percent) and approximately gender-balanced (49.3 percent female). Rural areas are considerably younger on average than urban areas: 49 percent of the urban population is younger than 20 compared to 55.7 percent of the rural population. The gender ratio also differs across cohorts: women constitute 48.4 percent of the population younger than 20
and 50.4 percent of the population aged 20 or older. This partly reflects the legacy of the civil war, which led to disproportionately high male mortality, and may also be a reflection of male migrant workers to the Gulf States.
The age distribution of the population leads to a dependency ratio that is very high by global standards but comparable to some other African countries. The dependency ratio is the ratio of young and elderly to working-age people. The exact value depends on the definition of “working age” and shows dependency ratios for several possible definitions. Using a standard definition of working age, there are approximately six people aged 15–64 for every five people aged younger than 15 or older than 64.
An alternative definition includes youths aged 10–14 in the potentially working-age population (see following paragraphs), in which case there are approximately two working age people for each young or elderly person. The dependency ratio is consistently considerably higher in rural than urban areas, reflecting the relatively young rural population.
Half of the population in Sudan has never attended a formal school and only a tiny portion has some post-secondary education. Only 15.8 percent of the population has at most secondary school education, and only 3.8 percent have some post-secondary education shows the distribution of education attainment for the Sudanese population. Education levels are substantially lower in rural than urban areas and substantially lower for women than men. The gender gap in schooling is slightly smaller in urban than rural areas.
Older parts of the population have lower education than younger parts and the gender gap in education is smaller in younger cohorts than in older ones. Older cohorts have substantially lower education attainment: fewer than 30 percent of individuals aged 60 or older have any formal schooling. Individuals aged 20–29 have substantially higher levels of education: one in ten has some post-secondary education and a further one in three has some secondary education.
The gender gap in education is smaller in young cohorts than older cohorts but this is driven almost entirely by urban areas. In rural areas, the gender gap in education is approximately stable over cohorts and may in fact be widening slightly. Secondary school access appears to have increased more for younger men than younger women (relative to older men and older women respectively).
Employment and labor force participation
Sudan’s labor force participation rate is relatively high and formal unemployment is moderate. The labor force status for all individuals aged 10 or older, the youngest age at which the NBHS work module is administered. Across the entire population, 36 percent are employed and 6 percent are unemployed. 22 Of the remaining 58 percent of the population, approximately half report attending school and the other half are classified as neither studying nor participating in the labor force. Both unemployment and labor force non-participation are higher in rural than urban areas. Non-participation is far higher for women than men, a pattern discussed in more detail below.
While both wage work and self-employment are common, labor force non-participation is far higher for women than men. Many women are engaged in home production and there appears to be some variance in whether they self-report this as work. Non-participation for females rises sharply across cohorts. This pattern may reflect a life-cycle explanation in which many women complete education and do not transition into the formal labor force.
It may also reflect a cohort explanation in which younger women are obtaining more education than their predecessors and will go on to enter the labor force. These explanations cannot be separately tested until additional waves of household survey data become available. The same pattern is not visible for men, most of whom are either in schooling or in the labor force. Younger men are slightly more likely than older men to be neither studying nor in the labor force Child labor is not uncommon in Sudan but most working children are concurrently enrolled so this does not represent completely foregone educational opportunities. One in ten youths aged 10–16 is employed and another one in thirty is available to work but not currently working.
The rate of child employment is considerably higher in rural than urban areas (13.5 and 4.3 percent, respectively) and slightly higher for men than women (12.9 and 7.7 percent, respectively). The gender gap may, however, be considerably smaller if female youths are engaged in time-consuming home production but do not report this as work. Most working children are concurrently enrolled (67.8 percent), so much of this child labor does not represent completely foregone educational opportunities.
The overwhelming majority of women that are labor force non-participating report that they are full-time homemakers or housewives, which is not an uncommon reporting.
Reporting by women reflects a common phenomenon in which formal labor force participation rates substantially understate female economic activity because they do not measure home production. But there is a high number of discouraged job seekers that show labor force non-participation. This pool of untapped potential workers represents both a challenge and opportunity for Sudan. Almost 750,000 working-age respondents are not employed, not studying, and report that they perceive job search as useless. This accounts for a large majority of non-participation by men and by women who are not homemakers. The pattern is visible for both men and women and in both rural and urban areas. Discouragement is concentrated amongst respondents below median age, in line with a global phenomenon of falling youth labor market engagement.
The majority of men in both rural and urban areas are paid employees or own account workers, while this is not the case for women. A relatively small fraction are employers and most of these are in older cohorts. Unpaid employment within the household is relatively common in rural areas and concentrated in the agricultural sector. The pattern is very different for women. Unpaid work within
the family is the most common type of employment in rural areas, particularly for younger women.
Own account workers are slightly more common than paid employees and very few women are employers. The regularity of employment differs across gender and location. 12.4 percent of workers
report working less than five days in the past week, but this is higher for women than men (11.6 and 16.7 percent, respectively) and in rural than urban areas (15.3 and 8.2 percent, respectively). This is a crude measure of underemployment but suggests that the extensive margin employment data discussed above may understate the total female male and rural-urban employment differences.
In sum, and perhaps not surprisingly,
employment and labor force participation differs by education level. Respondents with no education are least likely to participate in the labor force and, conditional on participation, are more likely to be unemployed. Respondents
with post-secondary education are at the opposite extreme, with the highest rates of participation and lowest rates of employment.
People with primary and secondary education fall in between these two extremes but the differences between these two groups are not themselves large or statistically significant. The education gap in labor force participation is considerably larger than the education gap in unemployment. These patterns are visible for men and women and in rural and urban areas.
Economic sectors Employment
Employment is dominated by agriculture in rural areas and by services in urban areas. Agriculture and services almost completely dominate employment, accounting for 34.3 and 51.7 percent of employment, respectively.
Manufacturing provides only 1.8 percent of employment and other industries account for the remaining 12.2 percent.
Employment patterns differ significantly between rural and urban areas. In particular,
agriculture accounts for 50 percent of rural employment and 5.5 percent of urban employment. Services account for most urban jobs (74.6 percent) but are also important in rural areas (39.1 percent). Both manufacturing and industry are more common in urban areas (3.1 and 16.8 percent, respectively) but still present in rural areas (1.1 and 9.8 percent, respectively). Overall, however, manufacturing and
mining account for extremely small shares of employment in both young and old cohorts.
Prime-age cohorts are more likely to work in services and industry than younger and older cohorts. There is limited evidence that employment sectors vary by cohort. The only clear pattern is that prime-age cohorts are more likely to work in services and, to a much lesser extent, industry than younger and older cohorts. There is no trend toward greater employment in manufacturing or industry amongst younger cohorts, which is sometimes an indication of an economy’s movement toward structural transformation. But there is no evidence for such a demographic structural change happening..
The sector that employs most people in the economy—agriculture—is also the sector that employs most people without education. The distribution of education levels across sectors. Agriculture is a clear low-education outlier. Almost two in three workers in this sector have no education and less than one in fifty has post-secondary education. Workers in the remaining three sectors—manufacturing, non-manufacturing industry, and services—have relatively similar levels of education..
There is a substantial skill differential and gap across sectors, which are slightly decreasing in the younger population. The ratio of workers with secondary or post-secondary education to workers with no or primary education is highest in manufacturing (0.94), followed by services (0.96) and non-manufacturing industry (0.73), with agriculture scoring far lower (0.12).
This has clear implications for economic transformation. Raising employment in the manufacturing and non-manufacturing industry sectors is likely to require substantial increases in aggregate education levels. While education levels are indeed higher in younger than older cohorts but this is not (yet) reflected in higher youth employment in industry and manufacturing.
Labor productivity and wages across sectors
Both productivity and wages differ sharply across sectors: Workers in the agricultural sector have low productivity and earn low wages; workers in manufacturing and industry have high productivity and earn high wages; workers in services have low productivity and earn high wages. Two patterns are clear. First, labor productivity is highest in manufacturing ($16,000). This is consistent with nearly universal international trends and highlights the importance of the manufacturing sector in driving the upgrade of skills.
Second, labor productivity in non-manufacturing industry (which includes mining, electricity, water, construction, transport, and communications) is somewhat lower than manufacturing but still considerably higher than either agriculture or services.
Hence, labor productivity is inversely proportional to employment, with the most productive sectors employing the fewest people. This is partly a natural
consequence of higher capital-labor ratios in these sectors but also highlights the importance of growing these sectors.