## _wp13201

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---

### I. Introduction — framing and high-level findings
- Employment outcomes link the size and structure of economic growth to household welfare; growth in output alone is not enough to improve welfare.
- Development involves transformation from an agrarian, subsistence economy to an urbanized, enterprise-dominated one (Lewis, 1954).
- Recent decade in Sub-Saharan Africa (SSA):
  - agriculture declining as a share of GDP across the continent (despite increases in agricultural commodity prices which pushed up the share in current prices);
  - the share of higher productivity sectors increasing as a share of GDP.
- Data limitations:
  - almost half of low income SSA countries in the ILO KILM database have not published any data on structure of employment between 2000 and 2010;
  - much published data is over five years old;
  - data collection is irregular or not publicly available in many countries;
  - methodological problems often make data not comparable over time or between countries.
- Modeling approach summarized:
  - baseline employment distribution for 2005 produced using country data and a special methodology for comparable employment and unemployment estimates by country;
  - elasticity-type model estimated changes between 2005–10 based on economic growth, demographic outcomes, and available employment data;
  - model used with country forecasts for 2010–20 to forecast expected number of new jobs and regional employment structure by country profile under baseline, moderately optimistic, and very optimistic assumptions.
- Key high-level findings:
  - agriculture still employs the majority of the labor force despite accounting for about 13 percent of GDP in 2010;
  - employment transformation lags output transformation (Timmer, 1988);
  - demographic transition is lagging; labor force still large and growing rapidly—much faster than in low and lower-middle-income countries in Asia;
  - in resource rich countries, private enterprise did not create much new employment; resource rent income created public sector wage jobs instead;
  - projected rapid labor force growth and a low baseline private sector wage employment imply that even with another decade of growth as good as the past one, employment transformation will proceed slowly;
  - over 2010–20 the absolute number of people employed in agriculture is expected to continue to grow, not shrink, even as the share employed outside agriculture rises;
  - even under an optimistic scenario, the share of labor force employed in private firms is not expected to rise substantially because private wage employment is small relative to labor force size.
- Policy emphasis:
  - importance of a balanced employment policy;
  - attract private enterprises that provide wage employment for many people;
  - improve productivity in traditional sectors that will continue to absorb the majority of the labor force;
  - do not discourage the household enterprise sector, despite its informality.

### II. Output and demographic transformation — patterns and implications
- Growth performance since the mid 1990s:
  - SSA had the longest continuous expansion in over 50 years.
  - Economic growth averaged about 4 percent per annum among upper-middle income countries and about 6 percent per annum among low income sub-Saharan African countries.
  - These growth rates surpass those of middle and low income Asia over this period.
  - During the recent financial crisis, growth among lower income countries (LICs) in SSA was broadly unaffected, unlike the sharp downturn among LICs in Asia.
- Changes in output structure:
  - reduction in agriculture output share by about 8 percentage points, with a corresponding rise in services;
  - share of non-manufacturing industry displaced some manufacturing, aggregate industry share remained fairly flat;
  - manufacturing represented about 7 percent of output.
- Manufacturing exports:
  - share of manufacturing goods in the export basket of low and low middle income SSA countries averaged between 10–20 percent over the past two decades (based on Comtrade 2-digit industry export data).
- Demographics:
  - the median person in SSA is 18 years old—7 years younger than the median age in South Asia.
  - between 2005 and 2020, the working age population is projected to increase by over 200 million people—nearly 3 percent per annum.
  - Africa’s fertility rate is falling much more slowly than historical declines in Asia and Latin America.
- Implication:
  - the large, rapidly growing labor force complicates employment transformation; even rapid, labor-intensive private sector growth would struggle to absorb entrants.

### III. Baseline projection: methodology, data coverage, and classification
- Data coverage and standardization:
  - survey data collected from 28 of 47 countries in sub-Saharan Africa, covering 75 percent of the estimated 2010 labor force.
  - United Nations population estimates of the working age population (age 15–64) used to scale standardized labor force participation rates.
  - assumed group participation rates (LFPR) applied and held constant in projections: LICs 89 percent, LMICs 86 percent and UMICs 65 percent.
  - when questionnaires lacked 12-month recall or screening for non-marketed activities, individuals declared non-participants only if there was no evidence at all of any possible participation over the last twelve months.
  - analysis focuses on primary employment when individuals report multiple activities.
- Employment classification:
  - Agricultural employment: predominantly smallholder farmers; includes wage work in agriculture, fishing, primary forestry.
  - Household enterprise employment: unincorporated, nonfarm household businesses; includes self-employed and family workers.
  - Wage employment (industry or services): non-agricultural paid work from unrelated employers; includes public and private sectors.
  - Unemployed: labor force participants not in employment according to the broad definition.
- Country grouping criteria:
  - Resource rich: ratio of resource exports to total exports above 80 percent between 2008–12 (Botswana treated as UMIC exception).
  - Income thresholds (2012 per capita income): upper-middle income $4,036–$12,475; lower-middle income $1,026–$4,035; low-income $1,025 and below.
- Projection model:
  - E_it / E_i,t-1 = (GDP_it / GDP_i,t-1)^α where α is sectoral employment elasticity.
  - Model closure: for low, lower-middle and resource-rich countries agricultural employment closes the model; labor force participation held constant; unemployment held constant for low and lower-middle income groups and allowed to vary among upper income countries.
- Elasticity parameters (employment-weighted by country group, selected):
  - Low income: Wage industry 0.9; Nonwage industry 0.7; Wage services 0.8; Nonwage services 0.8; Agriculture: -1.0.
  - Lower-middle income: Wage industry 0.8; Nonwage industry 0.6; Wage services 0.8; Nonwage services 0.9; Agriculture: -1.0.
  - Resource rich: Wage industry 0.6; Nonwage industry 0.7; Wage services 0.8; Nonwage services 0.7; Agriculture: -1.0.
  - Upper middle income (excl. South Africa): Wage industry 0.6; Nonwage industry 0.3; Wage services 0.7; Nonwage services 0.6; Agriculture -0.8.
  - South Africa: Wage industry 0.5; Nonwage industry 0.3; Wage services 0.5; Nonwage services 0.5; Agriculture -0.8.

### IV. 2005 baseline, 2010 estimates, and 2010–20 projections — results
- 2005 baseline employment shares (Sub-Saharan Africa):
  - Agriculture 65.2 percent; Household enterprises 17.1 percent; Wage industry 2.7 percent; Wage services 11.2 percent; Total employment 96.2 percent; Unemployed 3.8 percent; Labor force 100.0 percent.
  - 86 percent of employment in 2005 was in household farms and firms (informal sector).
- Labor force totals reported:
  - 2005 baseline totals: 158 million (low income), 36 million (lower-middle), 131 million (resource rich), 20 million (upper-middle), total 344 million.
  - 2010 estimated totals: 183 million (low income), 40 million (lower-middle), 150 million (resource rich), 21 million (upper-middle), total 395 million.
  - 2020 projected totals: 248 million (low income), 52 million (lower-middle), 200 million (resource rich), 23 million (upper-middle), total 523 million.
- 2005–10 net employment change:
  - employed population increased by about 50 million (62 million entered, 13 million left).
  - of projected 62 million entrants (2005–10): Agriculture 30 percent; Household enterprise 47 percent; Wage services 20 percent; Wage industry 3 percent.
- 2010–20 baseline projections:
  - IMF sectoral projections used through 2018 and assumed constant in 2019–20.
  - Selected average annual growth by sector, 2010–15 and 2015–20 (selected excerpts):
    - Resource Rich Average 2010–15: Real GDP 6.8; Agriculture 6.5; Industry 4.6; Services 9.5. Average 2015–20: Real GDP 6.4; Agriculture 6.4; Industry 3.7; Services 8.5.
    - Low income Average 2010–15: Real GDP 6.2; Agriculture 5.0; Industry 7.9; Services 6.2. Average 2015–20: Real GDP 6.4; Agriculture 5.8; Industry 7.3; Services 6.2.
    - Upper-middle income (excl. South Africa) Average 2010–15: Real GDP 4.6; Agriculture 3.2; Industry 4.7; Services 5.1. Average 2015–20: Real GDP 4.5; Agriculture 4.7; Industry 4.1; Services 4.9.
    - South Africa Average 2010–15: Real GDP 3.1; Agriculture 2.1; Industry 2.2; Services 3.5. Average 2015–20: Real GDP 3.2; Agriculture 3.2; Industry 1.3; Services 3.8.
  - By 2020:
    - Agriculture remains dominant employer in low income and resource rich countries: agriculture ~60 percent of total labor force in low income countries and ~46 percent in resource rich countries.
    - Household enterprises share projected to rise by 4 percentage points in LICs to 22 percent of employment.
    - Upper-middle income countries: wage services share projected to grow by 5 percentage points to about 50 percent by 2020.
  - Job creation 2010–20:
    - 45 million will leave the labor force due to aging beyond 64.
    - net new jobs projected 125 million over 2010–20.
    - total of 170 million new entrants find jobs.
    - distribution of entrants (2010–20 baseline): Agriculture 37 percent; Household enterprise 38 percent; Wage services 21 percent; Wage industry 4 percent.
  - Almost half of net new jobs will be in the household enterprise sector; remainder divided between wage and agriculture jobs.

### V. Alternative "game changer" / industrial-push scenario — assumptions and outcomes
- Simulation assumptions:
  - raise wage employment elasticity to 1.2 (matching Bangladesh, Cambodia, Vietnam historical wage employment elasticity);
  - revise industrial growth projection for low income and lower-middle income countries to 10.0 percent per annum over 2015–20 (baseline vs alternative: Low income baseline 7.3 vs alternative 10.0; Lower-middle income baseline 6.9 vs alternative 10.0).
- Outcomes:
  - additional net new wage industry jobs by 2020: additional 7 million wage jobs (5 million among low income countries and 2 million among lower-middle income countries).
  - total wage industry jobs remain below some other studies: even with the scenario, total wage industry jobs ~40 million versus McKinsey estimate of 54 million.
  - employment distribution remains dominated by agriculture and household enterprises; limited shift by 2020.
- Explanation and caveats:
  - demographic and starting-condition constraints: with average labor force growth 2.8 percent per annum (2010–20) and starting shares of <5 percent in industry, even 10 percent annual industry growth can at best double the share of industry employment over 10 years.
  - general equilibrium possibility: higher industrial wage employment could stimulate household enterprise demand and agricultural investments, potentially amplifying transformation and poverty reduction beyond direct employment numbers.

### VI. Policy implications and recommendations
- Realistic expectation: employment transformation toward wage employment will be slow given current demographics, starting employment shares, and high labor force growth.
- Policy priorities recommended:
  - develop strategies to attract investment in modern labor-intensive firms (industry and services) to absorb more entrants;
  - place much greater policy attention on household enterprise sector because it is large and “here to stay”; support mechanisms to help entrants establish and increase productivity in nonwage jobs in agriculture and household enterprises;
  - improve agricultural productivity and earnings to sustain rural livelihoods; without improvements, rural opportunities and household enterprise prospects will shrink;
  - implement context-specific reforms to business environment and human capital to pursue a “game changer” industrialization path, recognizing constraints (urban cost of living, wage floors, demographic factors).

### Key statistics (preserved)
- Survey coverage: 28 of 47 countries; covers 75 percent of the estimated 2010 labor force.
- Assumed LFPRs used in projections: LICs 89 percent; LMICs 86 percent; UMICs 65 percent.
- Baseline Sub-Saharan Africa employment shares 2005: Agriculture 65.2 percent; Household enterprises 17.1 percent; Wage industry 2.7 percent; Wage services 11.2 percent; Total employment 96.2 percent; Unemployed 3.8 percent.
- Labor force sizes and totals reported:
  - 2005 baseline totals: 158 million (low income), 36 million (lower-middle), 131 million (resource rich), 20 million (upper-middle), total 344 million.
  - 2010 estimated totals: 183 million (low income), 40 million (lower-middle), 150 million (resource rich), 21 million (upper-middle), total 395 million.
  - 2020 projected totals: 248 million (low income), 52 million (lower-middle), 200 million (resource rich), 23 million (upper-middle), total 523 million.
- Gross job flows 2005–10 (projected 62 million entrants): Agriculture 30 percent; Household enterprise 47 percent; Wage services 20 percent; Wage industry 3 percent.
- Projected entrants and job creation 2010–20: 45 million leave due to aging; net new jobs 125 million; total 170 million entrants find jobs. Distribution of entrants: Agriculture 37 percent; Household enterprise 38 percent; Wage services 21 percent; Wage industry 4 percent.
- Alternative scenario industrial growth assumption for 2015–20: Low income 10.0 percent; Lower-middle income 10.0 percent.
- Alternative scenario outcome: additional 7 million wage industry jobs by 2020 (5 million LICs; 2 million lower-middle income).

*Source: _wp13201 - References .............................................................................................................*

### References .............................................................................................................

### _wp13201 - References .............................................................................................................

### I. Introduction
- Employment outcomes link the size and structure of economic growth to household welfare; growth in output alone is not enough to improve welfare.
- Economic development is characterized by transformation from an agrarian, subsistence economy to an urbanized, enterprise-dominated one (Lewis, 1954).
- Sub-Saharan Africa (SSA) has completed one of its best decades of growth, with:
  - agriculture declining as a share of GDP across the continent (despite increases in agricultural commodity prices which pushed up the share in current prices);
  - the share of higher productivity sectors increasing as a share of GDP.
- Despite output transformation, poverty rates have remained stubbornly high (Afrobarometer, 2013).
- Employment data by country and sector for SSA is scarce:
  - almost half of low income SSA countries in the ILO KILM database have not published any data on structure of employment between 2000 and 2010;
  - much of the published data is over five years old;
  - data collection is irregular or not publicly available in many countries;
  - methodological problems often make data not comparable over time or between countries (see Fox and Pimhidzai (2013) for extended discussion).
- As background for IMF and World Bank regional reports, baseline employment distribution for 2005 was produced using as much country data as possible and a special methodology to create comparable employment and unemployment estimates by country.
- An elasticity-type model estimated changes between 2005–10 based on economic growth, demographic outcomes, and available employment data, and was used with country forecasts for 2010–20 to forecast expected number of new jobs and regional employment structure by country profile and employment type under:
  - baseline,
  - moderately optimistic,
  - very optimistic assumptions.
- Key high-level findings:
  - agriculture still employs the majority of the labor force despite accounting for about 13 percent of GDP in 2010;
  - employment transformation lags output transformation (Timmer, 1988);
  - the demographic transition is lagging, so the labor force is still large and growing rapidly—much faster than in low and lower-middle-income countries in Asia;
  - in resource rich countries, private enterprise did not create much new employment; resource rent income created public sector wage jobs instead;
  - projected rapid labor force growth and a low baseline private sector wage employment imply that even with another decade of growth as good as the past one, employment transformation will proceed slowly;
  - over 2010–20 the absolute number of people employed in agriculture is expected to continue to grow, not shrink, even as the share employed outside agriculture rises;
  - even under an optimistic scenario, the share of labor force employed in private firms is not expected to rise substantially because private wage employment is small relative to labor force size.
- Policy implications emphasized:
  - the importance of a balanced employment policy;
  - governments should undertake measures to attract private enterprises that provide wage employment for many people;
  - governments should also focus on improving productivity in traditional sectors that will continue to absorb the majority of the labor force;
  - governments should not discourage the household enterprise sector, despite its informality.

### II. Output and Demographic Transformation in Sub-Saharan Africa
- Growth performance since the mid 1990s:
  - SSA had the longest continuous expansion in over 50 years.
  - Economic growth averaged about 4 percent per annum among upper-middle income countries and about 6 percent per annum among low income sub-Saharan African countries.
  - These growth rates surpass those of middle and low income Asia over this period.
  - During the recent financial crisis, growth among lower income countries (LICs) in SSA was broadly unaffected, unlike the sharp downturn among LICs in Asia.
- Changes in output structure (sectoral output shares):
  - main sectoral output movements: reduction in agriculture output share by about 8 percentage points, with a corresponding rise in services;
  - share of non-manufacturing industry displaced some manufacturing, aggregate industry share remained fairly flat;
  - manufacturing represented about 7 percent of output.
- Comparison with East and South Asia:
  - In LICs in East Asia agricultural output ratio fell by 15 percentage points of GDP.
  - In East Asian low-middle income (LMI) countries, manufacturing output share rose by 4 percentage points and by 2010 manufacturing output accounted for about twice the corresponding output of SSA.
  - Evolution of output shares in South Asia was closer to SSA, especially in LMI countries (where India dominates).
- Manufacturing exports:
  - share of manufacturing goods in the export basket of low and low middle income SSA countries averaged very low, between 10–20 percent over the past two decades (based on Comtrade 2-digit industry export data).
- Demographic trends:
  - the median person in SSA is 18 years old—7 years younger than the median age in South Asia.
  - between 2005 and 2020, the working age population is projected to increase by over 200 million people—nearly 3 percent per annum.
  - Africa’s fertility rate is falling much more slowly than historical declines in Asia and Latin America; a rapid, systematic reduction in fertility rates has yet to occur.
- Implication of demographic trends:
  - the lack of demographic transition complicates employment transformation because even with non-agricultural private sector enterprise growth as rapid and labor intensive as in East Asia, a similar employment transition could not occur—the enterprises would not be able to absorb the same share of the labor force because the labor force would be too big.
- Summary:
  - growth has been strong, but structural change concentrated in services;
  - little or no change in industry output share or manufacturing export share;
  - population is getting younger and labor force is growing rapidly, posing challenges for employment transition and reflected in subsequent projections.

### III. The Baseline Projection: Methodology, Data, and Results
- Data collection and inclusion criteria:
  - unit record data from as many nationally representative household surveys conducted between 2000 and 2010 as possible in SSA were collected;
  - to be used, a survey needed to be nationally representative and collect household-level data on:
    - individual labor force participation;
    - employment status (employed or unemployed);
    - if employed, sector of activity (1-digit ISIC) and type of employment;
  - in a few cases where unit record data was not available, special tabulations were made according to predetermined definitions of the variables.
- Baseline year and methodology:
  - baseline employment distribution produced for 2005;
  - a methodology designed to produce comparable employment and unemployment estimates by country was applied.
- Projection methodology:
  - an elasticity-type model estimated how the baseline might have changed between 2005–10 based on economic growth, demographic outcomes, and any available employment data for SSA countries;
  - country-level economic and demographic forecasts for 2010–20 were used with the model to forecast expected number of new jobs and regional employment structure by country profile and employment type;
  - sensitivity analysis included moderately optimistic and very optimistic scenarios to show potential structural changes by 2020.

*Source: _wp13201 - References .............................................................................................................*

### Appendix table 1. Ultimately, survey data was collected from 28 of 47 countries in sub-Saharan

### _wp13201 - Appendix table 1. Ultimately, survey data was collected from 28 of 47 countries in sub-Saharan Africa, covering 75 percent of the estimated 2010 labor force.

### Data coverage and standardization methodology
- Survey data collected from 28 of 47 countries in sub-Saharan Africa, covering 75 percent of the estimated 2010 labor force.
- United Nations population estimates of the working age population (age 15–64) by country were used to scale standardized labor force participation rates.
- Assumed group participation rates (LFPR) applied and held constant in projections: LICs 89 percent, LMICs 86 percent and UMICs 65 percent.
- When questionnaires lacked 12-month recall or screening questions for non-marketed activities, individuals were declared non-participants only if there was no evidence at all of any possible participation over the last twelve months (Fox and Pimhidzai, 2013 procedure).
- Analysis focuses on primary employment when individuals report multiple activities.

### Employment classification used
- Agricultural employment: predominantly smallholder farmers, includes wage work in agriculture, fishing, primary forestry.
- Household enterprise employment: unincorporated, nonfarm household businesses; includes self-employed and family workers.
- Wage employment (industry or services): non-agricultural paid work from unrelated employers; includes public and private sectors; wage employment disaggregated into industry and services.
- Unemployed: labor force participants not in employment according to the broad definition.

### Country grouping criteria
- Resource rich: ratio of resource exports to total exports above 80 percent between 2008–12 (Botswana treated as UMIC exception due to high unemployment).
- Income thresholds (2012 per capita income): upper-middle income $4,036–$12,475; lower-middle income $1,026–$4,035; low-income $1,025 and below.

### Baseline distribution (2005) — summary findings
- Sub-Saharan Africa (2005): Agriculture 65.2 percent; Household enterprises 17.1 percent; Wage industry 2.7 percent; Wage services 11.2 percent; Total employment 96.2 percent; Unemployed 3.8 percent; Labor force 100.0 percent.
- Majority of labor force still in agriculture in 2005; 86 percent of employment in 2005 was in household farms and firms (household farms and firms = informal sector).
- Real unemployment in low-income Africa estimated at 3 percent; higher unemployment concentrated among university graduates and upper-middle income countries (South Africa contributes over 80 percent of the labor force in the upper middle income group and pushes high unemployment in that group).

### Elasticity model and projection methodology (to 2010 and 2020)
- Employment projection model: E_it / E_i,t-1 = (GDP_it / GDP_i,t-1)^α where α is sectoral employment elasticity.
- Sectoral five-year average growth rates 2005–10 by country group (Table 3):
  - Resource Rich: Real GDP 6.8; Agriculture 7.5; Industry 3.1; Services 11.9.
  - Upper-middle income (excl. South Africa): Real GDP 3.0; Agriculture 1.5; Industry 1.5; Services 4.8.
  - Lower-middle income: Real GDP 4.4; Agriculture 4.1; Industry 4.9; Services 4.5.
  - Low income: Real GDP 6.5; Agriculture 4.8; Industry 6.6; Services 7.7.
  - South Africa: Real GDP 3.6; Agriculture 2.5; Industry 2.3; Services 4.1.
- Elasticity parameters (selected, employment-weighted by country group; Table 4 excerpts):
  - Low income: Wage industry 0.9; Nonwage industry 0.7; Wage services 0.8; Nonwage services 0.8; Agriculture: -1.0 (model closure).
  - Lower-middle income: Wage industry 0.8; Nonwage industry 0.6; Wage services 0.8; Nonwage services 0.9; Agriculture: -1.0.
  - Resource rich: Wage industry 0.6; Nonwage industry 0.7; Wage services 0.8; Nonwage services 0.7; Agriculture: -1.0.
  - Upper middle income (excluding South Africa): Wage industry 0.6; Nonwage industry 0.3; Wage services 0.7; Nonwage services 0.6; Agriculture -0.8.
  - South Africa: Wage industry 0.5; Nonwage industry 0.3; Wage services 0.5; Nonwage services 0.5; Agriculture -0.8.
- Model closure rules:
  - For low, lower-middle and resource-rich countries, agricultural employment closes the model (no elasticity required for agricultural employment).
  - Labor force participation assumed constant.
  - Unemployment held constant for low and lower-middle income groups; unemployment allowed to vary and close model among upper income countries.

### 2010 projection results (high-level)
- Estimated distribution of employment by country type and sector, 2010 (Figure 6 totals):
  - Labor force sizes: Low Income 183 million; Lower-Middle Income 40 million; Resource Rich 150 million; Upper-Middle Income 21 million; Total 395 million.
- Net employment change 2005–10: employed population increased by about 50 million (62 million entered, 13 million left).
- Gross flows of new entrants (2005–10): of projected 62 million entrants, 70 percent found nonfarm sector jobs; 30 percent in agriculture, 47 percent household enterprise, 20 percent wage services, 3 percent wage industry (Figure 7 breakdown: Agriculture 30%; Household enterprise 47%; Wage services 20%; Wage industry 3%).
- Robustness check (Table 5): average errors across Ghana, Nigeria, Rwanda, Uganda small; largest error for Nigeria where model over-predicted agricultural sector by 28 percent and under-predicted wage industry by 64 percent.

### Projections to 2020 — baseline results
- IMF sectoral projections used through 2018 and assumed constant in 2019–20.
- Average annual growth by sector, 2010–15 and 2015–20 (Table 6 excerpts):
  - Resource Rich Average 2010–15: Real GDP 6.8; Agriculture 6.5; Industry 4.6; Services 9.5. Average 2015–20: Real GDP 6.4; Agriculture 6.4; Industry 3.7; Services 8.5.
  - Low income Average 2010–15: Real GDP 6.2; Agriculture 5.0; Industry 7.9; Services 6.2. Average 2015–20: Real GDP 6.4; Agriculture 5.8; Industry 7.3; Services 6.2.
  - Upper-middle income (excl. South Africa) Average 2010–15: Real GDP 4.6; Agriculture 3.2; Industry 4.7; Services 5.1. Average 2015–20: Real GDP 4.5; Agriculture 4.7; Industry 4.1; Services 4.9.
  - South Africa Average 2010–15: Real GDP 3.1; Agriculture 2.1; Industry 2.2; Services 3.5. Average 2015–20: Real GDP 3.2; Agriculture 3.2; Industry 1.3; Services 3.8.
- Projected distribution of employment by 2020 (Figure 8 totals):
  - Labor force sizes: Low Income 248 million; Lower-Middle Income 52 million; Resource Rich 200 million; Upper-Middle Income 23 million; Total 523 million.
- By 2020:
  - Agriculture remains dominant employer in low income and resource rich countries; agriculture ~60 percent of total labor force in low income countries and ~46 percent in resource rich countries.
  - Household enterprises share projected to rise by 4 percentage points in LICs to 22 percent of employment.
  - Upper-middle income countries: wage services share projected to grow by 5 percentage points to about 50 percent by 2020.
- Job creation 2010–20:
  - 45 million will leave the labor force due to aging beyond 64; net new jobs projected 125 million over 2010–20; total of 170 million new entrants find jobs (Figure 9: Net new jobs by sector; Figure 10: Gross job flows show 37% agriculture, 38% household enterprise, 21% wage services, 4% wage industry among entrants).
  - Almost half of net new jobs will be in household enterprise sector; remainder divided between wage and agriculture jobs.

### Alternative "game changer" scenario (industrial push inspired by Asian experience)
- Simulation assumptions:
  - Raise wage employment elasticity to 1.2 (matching Bangladesh, Cambodia, Vietnam historical wage employment elasticity).
  - Revise industrial growth projection for low income and lower-middle income countries to 10.0 percent per annum over 2015–20 (Table 8 shows baseline vs alternative: Low income baseline 7.3 vs alternative 10.0; Lower-middle income baseline 6.9 vs alternative 10.0).
- Results of alternative scenario:
  - Additional net new wage industry jobs by 2020: additional 7 million wage jobs (5 million among low income countries and 2 million among lower-middle income countries) (Figure 11).
  - Total wage industry jobs remain below the levels suggested by other studies: even with the scenario, total wage industry jobs ~40 million versus McKinsey estimate of 54 million.
  - Employment distribution remains dominated by agriculture and household enterprises; limited shift by 2020 (Figure 12).
  - Explanation: demographic and starting-condition constraints — with average labor force growth 2.8 percent per annum (2010–20) and starting shares of <5 percent in industry, even 10 percent annual industry growth can at best double the share of industry employment over 10 years (Figure 13 illustrates path by different starting points).
- General equilibrium caveat: higher industrial wage employment could stimulate household enterprise demand and agricultural investments, potentially amplifying transformation and poverty reduction beyond direct employment numbers.

### Policy implications and recommendations highlighted in the analysis
- Realistic expectation: employment transformation toward wage employment will be slow given current demographics, starting employment shares, and the high labor force growth in sub-Saharan Africa.
- Policy focus recommended:
  - Develop strategies to attract investment in modern labor-intensive firms (industry and services) to absorb more entrants.
  - Place much greater policy attention on household enterprise sector because it is large and “here to stay”; support mechanisms to help entrants establish and increase productivity in nonwage jobs in agriculture and household enterprises.
  - Improve agricultural productivity and earnings to sustain rural livelihoods; without improvements, rural opportunities and household enterprise prospects will shrink.
  - Context-specific reforms to business environment and human capital are necessary to pursue a “game changer” industrialization path, but even optimistic scenarios face constraints (urban cost of living, wage floors, demographic factors).

### Key statistics (preserved as reported)
- Survey coverage: 28 of 47 countries; covers 75 percent of the estimated 2010 labor force.
- Assumed LFPRs used in projections: LICs 89 percent; LMICs 86 percent; UMICs 65 percent.
- Baseline Sub-Saharan Africa employment shares 2005: Agriculture 65.2 percent; Household enterprises 17.1 percent; Wage industry 2.7 percent; Wage services 11.2 percent; Total employment 96.2 percent; Unemployed 3.8 percent.
- Labor force sizes and totals reported:
  - 2005 baseline totals in Figure 4: 158 million (low income), 36 million (lower-middle), 131 million (resource rich), 20 million (upper-middle), total 344 million.
  - 2010 estimated totals in Figure 6: 183 million (low income), 40 million (lower-middle), 150 million (resource rich), 21 million (upper-middle), total 395 million.
  - 2020 projected totals in Figure 8: 248 million (low income), 52 million (lower-middle), 200 million (resource rich), 23 million (upper-middle), total 523 million.
- Gross job flows 2005–10 (projected 62 million entrants): Agriculture 30 percent; Household enterprise 47 percent; Wage services 20 percent; Wage industry 3 percent.
- Projected entrants and job creation 2010–20: 45 million leave due to aging; net new jobs 125 million; total 170 million entrants find jobs. Distribution of entrants: Agriculture 37 percent; Household enterprise 38 percent; Wage services 21 percent; Wage industry 4 percent (Figure 10).
- Alternative scenario industrial growth assumption for 2015–20: Low income 10.0 percent; Lower-middle income 10.0 percent (Table 8).
- Alternative scenario outcome: additional 7 million wage industry jobs by 2020 (5 million LICs; 2 million lower-middle income).

*Source: _wp13201 - Appendix table 1. Ultimately, survey data was collected from 28 of 47 countries in sub-Saharan Africa, covering 75 percent of the estimated 2010 labor force.*

### References

### _wp13201 - References

### Data sources and surveys
- Afrobarometer, (2013). Data available at www. Afrobarometer.org.
- International Labour Organization (ILO), 1982, Resolution Concerning Statistics of the Economically Active Population, Employment, Unemployment and Underemployment, Thirteenth International Conference of Labour Statisticians (Geneva: International Labour Organization).

### IMF and regional outlooks
- International Monetary Fund, 2012, “Structural Transformation in Sub-Saharan Africa,” Regional Economic Outlook, October 2012.
- International Monetary Fund, 2013, Regional Economic Outlook: Sub-Saharan Africa, Appendix tables, (Washington: May).

### World Bank and related policy research
- Dinh, Hinh T., Vincent Palmade, Vandana Chandra and Frances Cossar, 2012, “ Light Manufacturing in Africa” (Washington: World Bank Washington).
- Filmer, Deon, Fox, Louise, and others, forthcoming, “Africa Regional Report on Youth Employment,” (Washington: The World Bank).
- Fox, Louise, and Melissa S. Gaal, 2008, “Working out of Poverty: Job Creation and the Quality of Growth in Africa,” (Washington: The World Bank).
- Fox, Louise, and Obert Pimhidzai, 2011, “Is Informality Welfare-enhancing Structural Transformation? Evidence from Uganda,” Policy Research Working Paper No. WPS 5866 (Washington: The World Bank).
- Fox, Louise, and Obert Pimhidzai, 2013, “Different Dreams, Same Bed: Collecting, Using, and Interpreting Employment Statistics in Sub-Saharan Africa—The Case of Uganda,” Policy Research Working Paper No. 6436 (Washington: The World Bank).
- Fox, Louise, and Thomas Pave Sohnesen, 2012, “Household Enterprises in Sub-Saharan Africa: Why They Matter for Growth, Jobs, and Livelihoods,” Policy Research Working Paper No. 6184 (Washington: The World Bank).

### IMF working papers and cross-country analysis
- Crivelli, Ernesto, Davide Furceri, and Joël Toujas-Bernaté, 2012, “Can Policies Affect Employment Intensity of Growth? A Cross-Country Analysis,” IMF Working Paper 12/218 (Washington: International Monetary Fund).

### Academic and development research papers
- Bossuroy, Thomas, and Denis Cogneau, 2008, “Social Mobility and Colonial Legacy in Five African Countries,” Working Paper No. 2008–10 (Paris : Développement, Institutions & Analyses de Long terme (DIAL)).
- Gelb, Alan, 2010, “Economic Diversification in Resource Rich Countries,” Paper for the Conference Natural Resources, Finance, and Development: Confronting Old and New Challenges,” (Algiers: The Central Bank of Algeria and International Monetary Fund Institute, 4–5 November).
- Gelb, Alan, Christian Meyer, and Vijaya Ramachandran, 2013, “Does Poor Mean Cheap? A Comparative Look at Africa’s Industrial Labor Costs,” Center for Global Development Working Paper No. 325 (Washington: Center for Global Development).
- Kapsos, Steven, 2005, “The employment intensity of growth: trends and macroeconomic determinants,” Employment Strategy Paper 2005/12 (Geneva: International Labour Organization).
- Lewis, William Arthur, 1954, “Economic Development with Unlimited Supplies of Labour,” No. 28 (2) pp: 139–191 (Manchester: The Manchester School).
- Lin, Justin Yifu, and Célestin Monga, 2011, “Growth Identification and Facilitation: The Role of the State in the Dynamics of Structural Change,” Development Policy Review 29(3), pp: 259–310.
- Timmer, C. Peter, 1988, “The Agricultural Transformation.” In H. Chenery and T.N. Srinivasan,eds., Handbook of Development Economics. Vol. 1, pp. 276–331 (Amsterdam: Elsevier).

### Industry and policy reports
- Fine, David, Arend van Wamelen, Susan Lund, Armando Cabral, Mourad Taoufiki, Norbert Dörr, Acha Leke, Charles Roxburgh, Jörg Schubert, and Paul Cook, 2012, “Africa at Work: Job Creation and Inclusive Growth,” (Washington: McKinsey Global Institute).

*Source: _wp13201 - References*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13201.pdf_