## _wp15102

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### Introduction and background
- Macroeconomic policy models in developing countries historically emphasized aggregate growth and stability; incorporation of incomplete labor markets and unequal household opportunities into macro models became widespread in the last twenty years.
- Heterogeneous agent business cycle models (Rios-Rull, 1995) show distributions of income, consumption, and wealth matter because wealth and age affect risk preferences and coping strategies (Prasad, 2013).
- Recessions can cause persistent labor-market scarring while asset prices recover more quickly; monetary and fiscal policy choices during downturns have long-lasting distributional consequences, especially when expenditure reductions hit public services consumed disproportionately by the poor.
- Growth models evolved from hypotheses that inequality might benefit growth to recognizing channels through which inequality can lower growth in developing countries (Berg and Ostry, 2011): amplifying financial crisis risk, discouraging investment, and reducing human capital investment by the poor.

### Data constraints and modeling implications
- Application of heterogeneous agent models requires cross-sectional or panel data on demographics, labor market status, income sources by sector, assets (human, physical and financial) and returns, and government payments/receipts for calibration.
- Data availability:
  - Emerging market middle income countries (Latin America, Turkey, transition economies of Eastern Europe, larger economies in Asia) increasingly have such data (World Bank, 2014).
  - For African countries, microdata is much more limited: household consumption surveys are infrequent; employment data by sector often missing; agricultural income data commonly collected only in specialized rural surveys once or twice a decade (Fox and Pimhidzai, 2013; Guarcello et al., 2010).
  - Panel data for African countries is beginning to appear; projects such as LSMS-ISA are testing lower cost methods to collect rural household data.
- Interim modeling approaches when local data are scarce:
  - Use national accounts plus simple behavioral assumptions to classify households (as in Berg et al., 2010 for Uganda).
  - Calibrate models with stylized facts or data from data-rich countries, provided stylized facts identify key distributional differences relevant for endogenous household choice modeling.
- Modeling implication: sensitivity analysis is important given noisy and incomplete measurement of non-wage incomes.

### Stylized facts on household incomes, employment, and consumption
- Inequality and consumption patterns:
  - Inequality is high and persistent across African countries; it is not closely related to income level or growth and tends to be higher in mineral-exporting economies.
  - Both richer and poorer households spend a high share of income on food, implying a relatively weak Engel curve and reduced consumption-pattern heterogeneity with respect to price shocks.
- Rural-urban differences:
  - Rural households: majority of food is self-produced → insulation from external price fluctuations but vulnerability to domestic weather shocks.
  - Urban richer households: home-produced food less common and imported food more common → vulnerability to both weather shocks and external price shocks; richer urban households receive disproportionate remittances and have better access to banking and credit.
  - Informal savings and borrowing are widespread; funds held outside formal banks are insulated from interest rate movements but exposed to inflation.
- Livelihoods and income sources:
  - Mixed livelihoods are common; household production dominates income sources.
  - Almost all households produce some farm and/or non-farm goods or services for sale; for most rural households this production provides the earned income.
  - Agricultural wage income is neither common nor important, in contrast to Asia or Latin America.
  - Urban households often combine wage and self-employment income, supplemented by subsistence gardening.
  - Household enterprises mostly sell to other households, so they depend on incomes in farm and wage sectors to support demand; urban wage job growth is critical for household enterprise survival.

### Employment, earnings, and opportunity constraints
- Human capital and wage opportunities:
  - Greater human capital expands opportunities, particularly in nonfarm sectors where earnings are higher.
  - Labor markets, to the extent demand exists, often work fairly well; private sector wage job creation has grown rapidly during recent growth periods but from a low base, so structural employment composition has changed slowly.
  - Aggregate real wage levels are flexible and respond to macro policy (e.g., exchange rate devaluation).
  - Wages in private, mostly formal manufacturing employment are high in dollar terms, contributing to scarcity of such jobs.
- Education and job access:
  - Adults with complete secondary education and above obtain better jobs and higher earnings, especially in the public sector.
  - Adults without some secondary education cannot access the nonfarm wage labor market; they rely on casual, unskilled wage employment or self-employment, with casual wages often lower than self-employment.
  - Mobility between non-wage employment and regular wage employment is limited.
- Labor market dynamics and cycles:
  - Unemployment is low (difficult to finance), implying business cycles do not affect employment in the same way as in developed economies.
  - Tentative interpretation: poorest rural and urban households are fairly resilient to short-term macroeconomic cycles; longer-term structural factors affecting farm and firm productivity and demand for labor in medium and large enterprises are more important for living standards.

### Political economy and policy implications
- Urban middle and upper class households:
  - More vulnerable to short-run macroeconomic events (consume imported food and fuel, use formal banking, hold public sector jobs that could be threatened by fiscal consolidation).
  - Although better able to cope financially, they are more politically active and may resist policies (e.g., allowing depreciation) that help exporters but hurt their consumption and asset values.
- Policy trade-offs:
  - Choice of monetary policy instruments (interest rates vs. exchange rate policies) can have distributional consequences; appreciation harms exporters and can concentrate welfare gains among wealthier households.
  - Protection of social services and human-capital-building public expenditures during fiscal contractions is important to avoid long-term harm to poor households and growth, though implementation is challenging.
- Distributional effects of shocks:
  - External shocks and crisis management measures (currency crises, bank bailouts) have historically hurt poorer households more because labor earnings fell as a share of GDP and poorer households could not sustain consumption when faced with inflation or income shocks (World Bank, 2005).

### Key statistics and quantitative findings (preserved verbatim)
- Sub-Saharan Africa GDP growth:
  - Average 5 percent per year over 2000-2012, twice the rate achieved in 1990-2000.
- Household consumption per capita growth:
  - 2 percent per annum over 2000-2012.
- Inequality indicators:
  - About two thirds of African countries have a Gini index of inequality above 0.40.
  - China Gini: 0.42; India Gini: 0.33; Indonesia Gini: 0.34; Viet Nam Gini: 0.36.
- Section 4.1 and 4.9 — inequality and food expenditure patterns:
  - Population growth was 3 percent over the same period.
  - Country Gini comparisons: Zambia, Swaziland, Nigeria, Mauritania, Mozambique, Guinea-Bissau, and South Africa have Gini levels similar to Malaysia (0.46) and Papua New Guinea (0.50), but higher than Indonesia (0.34) or Mongolia (0.37).
  - Kenya (0.48) and Rwanda (0.51) flagged as high-inequality non-mineral exporters.
  - The poorest quintiles spend proportionally more on food than the richest, but the difference is only about 15 percentage points, and this ratio is relatively constant once countries reach an income level of about "$400/per capita in current prices."
  - A simple linear regression for Viet Nam found an average marginal propensity to spend on food of 0.25.
- Food price shock coping and savings:
  - Only "10 -13 percent" of adults in African countries reported saving any amount in a bank account.
  - An additional "15-30 percent" of adults who saved either kept cash at home or used informal savings mechanisms.
  - Example credit access in Uganda in 2010: just under "9 percent" of urban households and about "3 percent" of rural households reported they had sought credit from formal banking sources.
- Demographics:
  - The median person in Sub-Saharan Africa is 18 years old—7 years younger than the median age in South Asia.
  - In 2010, only "1/3" of the population in low and lower middle income countries was located in areas classified as urban.
  - Burkina Faso (2010) example: poorest quintile average household size "8.8"; richest quintile average household size "4.5".
  - Dependency ratios: "60 percent" of members of poor households were younger or older than working age, versus "43 percent" in the richest households.
- Mixed livelihoods and sectoral patterns:
  - Even in urban areas, "20-50 percent" of households report growing crops or livestock for sale, with an additional "5-10 percent" producing agricultural products for home consumption only.
  - Over "80 percent" of households in the poorest quintile in the countries shown have agricultural income; in poorer countries the share tends to be over "90 percent".
  - In richer and more urbanized countries such as Kenya and Zambia, about "80 percent" report agricultural income.
  - In Zambia and Cote d’Ivoire, only about "25-30 percent" of households in the highest quintile reported agricultural income; in poorer countries such as Rwanda and Malawi the percentage is much higher—up to "80 percent".
  - Even "80-90 percent" of households in poor countries living in areas classified as urban report some agricultural production.
  - In Uganda in 2010, "5 percent" of urban households reported subsistence farming as their main source of income.
- Mozambique (2005 rural survey) income shares (average rural household):
  - 11 percent of income from crop sales.
  - 2.5 percent of income from livestock sales.
  - 25 percent of income from nonfarm income sources (including nonfarm wage labor and household enterprise income).
  - By consumption quintile: bottom quintile 60 percent of income from retained food crops; 4th quintile 50 percent; top quintile 38 percent retained food crops, 50 percent nonfarm sources, 12 percent sale of crops or livestock.
- Remittances and transfers:
  - The average annual remittance from OECD countries is over $1200 per year per sender.
  - Receiving households in Senegal and Kenya reported average remittances of over $3000 per year from abroad.
  - In Mozambique rural data (2005), 23 percent of rural households received any kind of transfer, and transfers accounted for only 5 percent of total income.
- Employment structure and labor force participation:
  - Fox et al. (2013) estimates: 89 percent LFPR in low income countries; 86 percent LFPR in middle income countries; 65 percent LFPR in upper middle income countries (using a 12 month measurement window).
  - In 2010, 86 percent of employment was in household farms and firms (“the informal sector”).
  - Time-related underemployment: about 15 percent of the employed involuntarily working less than 35 hours a week.
  - Using ILO definition, Roubaud and Torelli (2013) report an average unemployment rate of 11 percent in West African capital cities.
- Employment totals and breakdowns (Figure 7 labels preserved): 183 m 40 m 150 m 21 m 395 m (country type categories and employment categories presented in source figures; percent axis labels 0%–100%).

### Measurement challenges for income and survey coverage (Section 5)
- Income is difficult to measure in Africa; household expenditure surveys typically collect wages and food produced for home consumption but often omit self-employment income or net agricultural income consumed or sold.
- Labor force surveys collect more reliable nonfarm income data but usually do not cover agricultural income because LFSs are typically short-duration.
- Specialized agricultural and panel surveys (e.g., MSU/USAID rural cross-sectional and panel surveys in Mali, Mozambique, Zambia) follow households over agricultural cycles and include both farm and non-farm income.
- Surveying agriculture is hard because farmers grow multiple crops in non-standard units, have multiple dispersed plots, rely on rainfall, keep livestock in open pastures, and operate in partially monetized economies with low literacy.
- Even full-income surveys may publish limited income breakdowns, contributing to divergence between household survey aggregates and National Accounts.
- World Bank attempts to create complete income aggregates found about half of households with primarily wage earnings and an even larger share with primarily agricultural or household enterprise earnings had negative savings — reported income (including transfers) did not cover consumption.
- Micro data suggest 30-40 percent of adults in Africa put aside cash over the year (bank, savings club, or hidden at home).
- More progress has been made collecting plausible consumption data than collecting income.

### Matching and mobility between sectors (Section 7)
- Overview:
  - Education is a primary determinant of access to good paying stable wage jobs; without education it is very difficult to obtain such jobs even after long searches.
  - Workers who obtain formal wage jobs tend to stay there; workers who start full time in non-wage jobs (household farm or firm) also tend to remain in those activities.
  - Limited movement between wage and nonwage sectors in response to business cycles; public sector downsizing often leads to withdrawal from the labor force rather than long unemployment spells.
- Stylized cross-section facts:
  - Most Africans in the labor force today did not complete primary education; for the next decade the majority of new entrants will not have more than primary education.
  - Those without education or with little primary education tend to be in agriculture; those with secondary education are overwhelmingly in formal wage jobs.
  - Studies report high premiums for wage jobs with a contract (up to 50 percent) and convex returns to education (very high returns to post-secondary education and no or very low returns to primary education).
  - Family/clan/ethnic networks play an important role in job finding; medium and large firms commonly rely on employee networks for hiring.
- Longitudinal evidence:
  - Urban Tanzania: youth may take about five years to transition from school to work; after settling into a sector they mostly stay there.
  - Ethiopia panel (urban residents, 1994-2004): of those in self-employment in 1994 and still in the labor force in 2004, 2/3 remained in self-employment; only 10 percent found a formal wage job. Seventy percent of those in formal private sector wage employment in 1994 stayed in this segment by 2004; another 14 percent had found public sector jobs.
  - SOE restructuring in Ethiopia (1997–2000): only 1/3 of those working in an SOE in 1997 were still in the sector in 2000; of those who left SOEs: 40 percent found a job in general government, 9 percent went to the private formal sector, 12 percent to the household enterprise sector, and 25 percent left the labor force; only 7 percent reported being unemployed after leaving SOEs.
  - Rural Mozambique drought: rural farmers seeking alternative employment mostly ended up in casual farm or nonfarm wage employment; household enterprise employment did not grow during the drought, but households tended to start household enterprises when the agricultural economy recovered.
- Interpretation and implications:
  - Mobility between broad employment categories is limited due to small size of wage sector relative to entrants, low skills of agricultural sector workers, and rapid transitions forced by lack of safety nets.
  - Education correlates with earnings, but explanatory power declines when controlling for unobservable individual and family characteristics.
  - Using cross-section Mincerian formulations plus sector/contract/location variables, researchers can explain about 50 percent of total variance in real wages (Fox and Oviedo, 2008).
  - Policy implications: expand targeted cash transfers, structural improvements to farm productivity and farmgate prices, invest in education (especially for females to mitigate high fertility effects), and recognize urban middle-class vulnerability when designing macro policies.

*Source: IMF working paper content unit _wp15102 (PDF chapter/section).*

### References

### _wp15102 - References

### Introduction and background
- Macroeconomic policy models in developing countries historically focused primarily on aggregate growth and stability; incorporation of incomplete labor markets and unequal household opportunities into macro models became widespread in the last twenty years.
- Heterogeneous agent business cycle models in developed countries incorporate distributions of income, consumption, and wealth (Rios-Rull, 1995); these models show that variables such as wealth and age affect risk preferences and coping strategies (Prasad, 2013).
- Recessions can have persistent labor-market scarring while asset prices may recover more quickly; policy choices (monetary or fiscal) during downturns have long-lasting distributional consequences, especially when expenditure reductions hit public services consumed disproportionately by the poor.
- Growth models evolved from hypotheses (Kaldor) that inequality might benefit growth to recognizing channels through which inequality can lower growth in developing countries (Berg and Ostry, 2011), for example by amplifying financial crisis risk, discouraging investment, and reducing human capital investment by the poor.

### Data constraints and modeling implications
- Application of heterogeneous agent models requires cross-sectional or panel data on demographics, labor market status, income sources by sector, assets (human, physical and financial) and returns, and government payments/receipts for calibration.
- Data availability:
  - Emerging market middle income countries (Latin America, Turkey, transition economies of Eastern Europe, larger economies in Asia) increasingly have such data (World Bank, 2014).
  - For African countries, microdata is much more limited: household consumption surveys are infrequent; employment data by sector often missing; agricultural income data commonly collected only in specialized rural surveys once or twice a decade (Fox and Pimhidzai, 2013; Guarcello et al., 2010).
  - Panel data for African countries is beginning to appear; projects such as LSMS-ISA are testing lower cost methods to collect rural household data.
- Interim modeling approaches when local data are scarce:
  - Use national accounts plus simple behavioral assumptions to classify households (as in Berg et al., 2010 for Uganda).
  - Calibrate models with stylized facts or data from data-rich countries, provided stylized facts identify key distributional differences relevant for endogenous household choice modeling.

### Stylized facts on household incomes, employment, and consumption in low and middle income African countries
- Main focus: sources of income, employment and earnings, and their connection to aggregate trends; stylized facts on wealth were not developed due to lack of data.
- Inequality and consumption patterns:
  - Inequality is high and persistent across African countries; it is not closely related to income level or growth and tends to be higher in mineral-exporting economies.
  - Both richer and poorer households spend a high share of income on food, implying a relatively weak Engel curve; this weak Engel curve reduces consumption-pattern heterogeneity with respect to price shocks.
- Rural-urban differences:
  - Rural households: majority of food is self-produced → insulation from external price fluctuations but vulnerability to domestic weather shocks.
  - Urban richer households: home-produced food less common and imported food more common → vulnerability to both weather shocks and external price shocks; richer urban households receive disproportionate remittances from abroad and have better access to banking and credit.
  - Informal savings and borrowing are widespread; funds held outside formal banks are insulated from interest rate movements but exposed to inflation.
- Livelihoods and income sources:
  - Mixed livelihoods are common; household production dominates income sources.
  - Almost all households produce some farm and/or non-farm goods or services for sale; for most rural households this production provides the earned income.
  - Agricultural wage income is neither common nor important, in contrast to Asia or Latin America.
  - Urban households often combine wage and self-employment income, supplemented by subsistence gardening.
  - Households with more non-farm income tend to be richer.
  - Household enterprises mostly sell to other households, so they depend on incomes in farm and wage sectors to support demand; urban wage job growth is critical for household enterprise survival.

### Employment, earnings, and opportunity constraints
- Human capital and wage opportunities:
  - Greater human capital expands opportunities, particularly in nonfarm sectors where earnings are higher.
  - Labor markets, to the extent that demand exists, often work fairly well; private sector wage job creation has grown rapidly during recent growth periods but from a low base, so structural employment composition has changed slowly.
  - Evidence suggests aggregate real wage levels are flexible and respond to macro policy (e.g., exchange rate devaluation).
  - Wages in private, mostly formal manufacturing employment are high in dollar terms, which may help explain scarcity of such jobs.
- Education and job access:
  - Adults with complete secondary education and above obtain better jobs and higher earnings, especially in the public sector.
  - Adults without some secondary education cannot access the nonfarm wage labor market; they rely on casual, unskilled wage employment or self-employment, with casual wages often lower than self-employment.
  - Mobility between non-wage employment and regular wage employment is limited.
- Labor market dynamics and cycles:
  - Unemployment is low (difficult to finance), implying business cycles do not affect employment in the same way as in developed economies.
  - Tentative interpretation: poorest rural and urban households are fairly resilient to short-term macroeconomic cycles; longer-term structural factors affecting farm and firm productivity and demand for labor in medium and large enterprises are more important for living standards.

### Political economy and policy implications
- Urban middle and upper class households:
  - More vulnerable to short-run macroeconomic events (consume imported food and fuel, use formal banking, hold public sector jobs that could be threatened by fiscal consolidation).
  - Although better able to cope financially, they are also more politically active and may resist policies (e.g., allowing depreciation) that would help exporters but hurt their consumption and asset values.
- Policy trade-offs:
  - Choice of monetary policy instruments (interest rates vs. exchange rate policies) can have distributional consequences; appreciation harms exporters and can concentrate welfare gains among wealthier households.
  - Protection of social services and human-capital-building public expenditures during fiscal contractions is important to avoid long-term harm to poor households and growth, though implementation is challenging.
- Distributional effects of shocks:
  - External shocks and crisis management measures (currency crises, bank bailouts) have historically hurt poorer households more because labor earnings fell as a share of GDP and poorer households could not sustain consumption when faced with inflation or income shocks (World Bank, 2005).

### Key statistics and quantitative findings preserved from source
- Sub-Saharan Africa GDP growth:
  - Average 5 percent per year over 2000-2012, twice the rate achieved in 1990-2000.
- Household consumption per capita growth:
  - 2 percent per annum over 2000-2012.
- Inequality indicators:
  - About two thirds of African countries have a Gini index of inequality above 0.40.
  - China Gini: 0.42; India Gini: 0.33; Indonesia Gini: 0.34; Viet Nam Gini: 0.36.
- Empirical sources and references mentioned in text:
  - Rios-Rull, 1995
  - Prasad, 2013
  - Berg and Ostry, 2011
  - World Bank, 2005; World Bank, 2013; World Bank, 2014
  - Marjit, 2003
  - Berg et al., 2010
  - Krueger, et al. (2009)
  - Olinto and Saavendra, 2011
  - Fox and Pimhidzai, 2013; Guarcello et al., 2010
  - Morton Jerven, 2013
  - Review of Economic Dynamics, Vol. 13(1)

*Content derived from the PDF chapter/section titled _wp15102 - References.*

### 4.1 and 4.9.

### _wp15102 - 4.1 and 4.9.

### Inequality and food expenditure patterns
- Population growth was 3 percent over the same period.
- Inequality in Africa is typically measured by expenditure per capita or adult equivalent; the Gini is the most widely available and accepted measure and is used here.
- Country Gini comparisons reported:
  - Zambia, Swaziland, Nigeria, Mauritania, Mozambique, Guinea-Bissau, and South Africa have Gini levels similar to Malaysia (0.46) and Papua New Guinea (0.50), but higher than Indonesia (0.34) or Mongolia (0.37).
  - Other African countries with high inequality but not mineral exporters include Kenya (0.48) and Rwanda (0.51).
- Food spending across households:
  - Both rich and poor households spend a lot on food once lumpy consumer durable expenditures are purged.
  - The poorest quintiles spend proportionally more on food than the richest, but the difference is only about 15 percentage points, and this ratio is relatively constant once countries reach an income level of about "$400/per capita in current prices."
  - A referenced simple linear regression for Viet Nam found an average marginal propensity to spend on food of 0.25.
  - Explanations for the relatively weak Engel curve effect in Africa include urban concentration of the richest 20 percent (where food is more expensive), higher PPP price of purchased food due to imports and high port and transport costs (especially for land-locked countries).

### Vulnerability to food price shocks and coping mechanisms
- Rural populations and the poorest urban populations (who also grow their own food) are actually less vulnerable to externally generated food price shocks than richer urban middle and upper class populations, provided rainfall allows farm production and storage.
- Evidence from the 2006—2008 externally generated food price shock: rural households reported increases in food security, urban households reported decreases (Verpoorten, 2013).
- However, high food expenditures across the distribution suggest all households may experience food price shocks from domestic weather events similarly; richer households likely have better coping mechanisms while poorer households would need food aid.
- Household coping mechanisms:
  - Liquid savings and borrowing are important.
  - FINDEX data (after controlling for country income level) shows African adults save as much or more than counterparts in other countries.
  - Only "10 -13 percent" of adults in African countries reported saving any amount in a bank account.
  - An additional "15-30 percent" of adults who saved either kept cash at home or used informal savings mechanisms (community savings and credit union, rotating savings scheme).
  - Non-bank savings schemes receive no interest and have no inflation protection.
  - Few have access to formal credit; example: in Uganda in 2010, just under "9 percent" of urban households and about "3 percent" of rural households reported they had sought credit from formal banking sources, and even fewer sought credit from semiformal microfinance organizations.

### Household demographics and implications
- Owing to high fertility, African households are young and large in both rural and urban areas; this trend is expected to continue for several decades.
- Demographic facts:
  - The median person in Sub-Saharan Africa is 18 years old—7 years younger than the median age in South Asia.
  - In 2010, only "1/3" of the population in low and lower middle income countries was located in areas classified as urban.
  - Poor households (mainly rural) tend to have much larger households: example from Burkina Faso in 2010:
    - Average household size in the poorest quintile was "8.8" people.
    - Average household size in the richest quintile was "4.5" people.
  - Dependency ratios: "60 percent" of members of poor households were younger or older than working age, versus "43 percent" in the richest households.
- Fertility and social drivers:
  - Fertility rates are substantially higher in rural areas than urban areas; urban fertility rates have been falling slowly, so urban population growth is mostly from natural growth.
  - High fertility has been partly explained by the tendency toward early marriage for females.
  - High fertility is associated with lower private savings, contributing to lower ability to cope with shocks and increased poverty in the next generation.
  - Possible mitigants include increased educational opportunities for females or changing marriage norms.

### Mixed livelihoods and household income portfolios
- Households commonly have multiple income sources; rural households almost universally have agriculture income in cash or kind, while nonfarm income sources are increasingly common as off-season activities or primary activities for one member.
- Three basic employment/income categories used:
  - Agricultural employment — smallholders (including income in kind and wage work in agriculture, fishing, and primary forestry). Regular wage employment in agriculture is rare.
  - Household enterprise employment — unincorporated, nonfarm family-owned businesses; income mostly cash, recorded as gross profits.
  - Wage employment (non-agricultural) — work outside agriculture paid by an unrelated individual, includes public and private sectors (public sector mostly in services).
- Patterns across countries and income distribution:
  - Shares of households with income from agriculture, household enterprises, and wages add to over 100 percent in many cases, indicating multiple income sources.
  - Even in urban areas, "20-50 percent" of households report growing crops or livestock for sale, with an additional "5-10 percent" producing agricultural products for home consumption only.
  - The number of households with multiple income types is growing in most countries shown; Ghana is an exception where the number of sources is decreasing.
- Drivers of diversification:
  - Non-farm incomes are generally higher than farm incomes, incentivizing rural households to add nonfarm income.
  - Agriculture underemployment due to lack of water management and seasonality pushes household members to seek off-season wage work or run off-season businesses.
  - Agriculture faces weather and price risk, so households may diversify income; only those able to manage risk would specialize in agriculture.
- Country examples and sectoral notes:
  - Ghana had agriculture accounting for "30 percent of GDP in 2010", compared with "17 percent" in Senegal, though both countries had similar shares of households with agricultural income—possibly explaining Ghana’s declining number of incomes per household due to increased ability to specialize.
  - Richer and urban households are less likely to have agricultural income and more likely to have wage income.
    - Over "80 percent" of households in the poorest quintile in the countries shown have agricultural income; in poorer countries the share tends to be over "90 percent".
    - In richer and more urbanized countries such as Kenya and Zambia, about "80 percent" report agricultural income.
    - In Zambia and Cote d’Ivoire, only about "25-30 percent" of households in the highest quintile reported agricultural income; in poorer countries such as Rwanda and Malawi the percentage is much higher—up to "80 percent".
    - Even "80-90 percent" of households in poor countries living in areas classified as urban report some agricultural production (sometimes only for home consumption).
    - In Uganda in 2010, "5 percent" of urban households reported subsistence farming as their main source of income.
  - Household enterprise income is more common in middle and upper quintiles; the highest quintile is less likely to have a household enterprise than middle quintiles in urban areas (except in very poorest countries).
  - Non-farm wage income shows a distinctly urban and upper income pattern; even in the poorest countries about half of urban households have wage income.
  - By age 30, about half of those reporting wage income as their primary income source also reported having a contract.
- Implications for modeling and policy:
  - Households are best modeled as having a household income portfolio (mixed livelihood), with portfolio composition varying by country level of development and household income.
  - As households better manage risk, they begin to reduce the number of income sources (specialize).
  - In remote areas, lack of local demand may prevent diversification into household enterprises because a crop failure eliminates both agricultural income and local market demand; conversely, in good years rural households often invest proceeds from agricultural sales into household enterprises.

*Source: _wp15102 - 4.1 and 4.9.*

### 5. What do we know about the importance of types of income in the

### 5. What do we know about the importance of types of income in the household portfolio?

### Measurement challenges and survey coverage
- Income is difficult to measure in Africa; major data improvements quantifying the household budget constraint are not likely soon.
- Household expenditure surveys typically collect data on wages and food produced for home consumption, but often do not collect self-employment income or net agricultural income consumed or sold.
- Labor force surveys (LFSs) collect more detailed and reliable data on nonfarm income but generally do not cover agricultural income because LFSs are usually conducted over a short period.
- Specialized agricultural and panel surveys exist (examples: agricultural census-type surveys; MSU/USAID rural cross-sectional and panel surveys in Mali, Mozambique, Zambia) that follow households over the agricultural cycle and include both farm and non-farm income.
- Reasons most African agriculture is “hard to survey” (Reardon and Glewwe):
  - Farmers grow multiple crops and report production in non-standard units.
  - Households have multiple spatially dispersed plots.
  - Farmers rely on rainfall for irrigation.
  - Livestock is kept in open pastures.
  - The farm economy is partially monetized and literacy is low.
- Collecting agricultural and livestock income data is expensive, time consuming, and requires well-trained enumerators; household survey estimates can conflict with National Accounts (differences in level and trend).
- Collection of agricultural income is often done separately from other income types to align with planting and harvest times.
- Even when all income types are collected, published statistical abstracts often omit tables showing the structure of household income (example: Uganda and Tanzania national panel surveys collected full income data but abstracts published only limited income breakdowns).

### Empirical findings on income composition (selected country evidence)
- Mozambique rural household survey (2005) findings:
  - For the average rural household:
    - 11 percent of income comes from crop sales.
    - 2.5 percent of income comes from livestock sales.
    - 25 percent of income comes from nonfarm income sources (including nonfarm wage labor and household enterprise income).
  - By consumption quintile:
    - Bottom quintile: 60 percent of income came from retained food crops.
    - 4th quintile: 50 percent of income came from retained food crops.
    - Top quintile: 38 percent of income came from retained food crops; 50 percent of income in the top quintile came from nonfarm sources; 12 percent from sale of crops or livestock.
- Comparative note: In Ghana (more developed), only 40 percent of the highest income group had any nonfarm income.
- Remittances and transfers:
  - The average annual remittance from OECD countries is over $1200 per year per sender.
  - If sent to one household, such a transfer could increase average income by about $3.50 per day.
  - Receiving households in Senegal and Kenya reported average remittances of over $3000 per year from abroad.
  - In Mozambique rural data (2005), 23 percent of rural households received any kind of transfer (public or private), but transfers accounted for only 5 percent of total income, with the income share fairly constant over the distribution.
  - Domestic transfers tend to be more frequent but much smaller as a share of income; remittances from abroad tend to be sizable and received by upper income households.
- Public transfers and social protection:
  - Public pension systems have very low coverage and mostly reach ex-civil servants in comparatively wealthy urban households.
  - Cash transfers for poor households are scaling up but:
    - Transfer size is about 25 percent of the monthly poverty level.
    - Governments tend to spend much less than 1 percent of GDP on these programs.
    - Partial donor financing is common.
  - In-kind transfers from NGOs (e.g., during planting season or weather shocks) are common for poor rural residents but are difficult to track in household surveys and may be recorded as gifts without identified source.
- Services in kind from governments:
  - If valued, services in kind would be an important part of income but analysis suggests more value is captured by the rich than the poor because services are more available in urban areas and richer households consume higher-value services (example: private primary school then public secondary/tertiary where costs per pupil are 4-10 times higher).
- Savings and income-consumption comparison:
  - World Bank researchers attempting to create complete income aggregates from LSMS-ISA and other household surveys found about half of households with primarily wage earnings and an even larger share of households with primarily agricultural or household enterprise earnings had negative savings — reported income (including transfers) did not cover consumption.
  - Microeconomic data suggest that 30-40 percent of adults in Africa do put aside cash over the year somewhere (bank, savings club, or hidden at home).
  - Household consumption is difficult to measure and often diverges from National Accounts; there is more progress collecting plausible consumption data than collecting income.

### Labor market and employment structure
- Primary activity reporting overstates agriculture’s role because 40-50 percent of those employed report a second activity in a different sector.
- Using a 12 month measurement window, labor force participation and seasonality:
  - Fox et al. (2013) estimates:
    - 89 percent LFPR in low income countries.
    - 86 percent LFPR in middle income countries.
    - 65 percent LFPR in upper middle income countries.
  - These participation rates are higher than World Development Indicators short-recall estimates.
- Employment composition (2010):
  - About the majority of individuals report agriculture as primary economic activity, but secondary activities often diversify household portfolios.
  - Household enterprises (primarily self-employment) are the next largest employment category.
  - Taken together, 86 percent of employment in 2010 was in household farms and firms (“the informal sector”).
  - Wage employment was a minority; the majority of wage employment was in the services sector and includes both formal and informal wage work.
  - In non-resource rich countries, the private sector created most wage jobs by 2010 after public sector shedding in the 1990s; resource-rich countries continued to have larger public sector wage employment.
- Secondary activities:
  - In low and lower middle income countries, 40-50 percent of labor force participants report a secondary economic activity.
  - For those engaged in agriculture, the secondary activity is usually running a household enterprise; for household enterprise owners, the secondary activity is usually agriculture.
- Unemployment and underemployment:
  - Urban unemployment is higher and concentrated among younger, educated people.
  - Using ILO definition, Roubaud and Torelli (2013) report an average unemployment rate of 11 percent in West African capital cities.
  - Time-related underemployment (involuntarily working less than 35 hours a week): about 15 percent of the employed fell into this category.

*Source: IMF working paper content unit _wp15102 - 5. What do we know about the importance of types of income in the household portfolio?*

### 7. Matching and mobility between sectors

### 7. Matching and mobility between sectors

### Overview
- The labor market in Africa is structured: education is a primary determinant of access to good paying stable wage jobs; without education it is very difficult to obtain such jobs even after long searches.
- Workers who obtain formal wage jobs tend to stay there; workers who start full time in non-wage jobs (household farm or firm) also tend to remain in those activities.
- There is limited movement between wage and nonwage sectors in response to business cycles; public sector downsizing often leads to withdrawal from the labor force rather than long unemployment spells.
- Agricultural shocks can induce short-term shifts (e.g., smallholder farmers seeking low-wage temporary employment in bad seasons, starting household enterprises in good seasons).

### Stylized facts from cross-section studies
- Education:
  - Education is the best predictor of labor market outcome.
  - Most Africans in the labor force today did not complete primary education; for the next decade at least the majority of new entrants will not have more than primary education.
  - Those without education or with little primary education tend to be found in the agricultural sector; those with secondary education are overwhelmingly in formal wage jobs.
  - HE sector and informal wage jobs fall between these extremes, with significant overlap across categories.
  - Gunther and Launov (2012): about half of those currently working in the HE sector in West Africa had similar observable characteristics as those with wage jobs.
- Returns and segmentation:
  - Analyses often find higher returns to education and experience in wage employment vs nonwage employment, leading to claims of segmentation (Teal, 2012).
  - Some studies find a high premium (up to 50 percent) for wage jobs with a contract (formal wage jobs) (Rouband Torelli, 2013).
  - Differences may reflect unmeasured/unobserved personal characteristics rather than structural segmentation (Bridges et al., 2014).
- Intergenerational persistence:
  - A study covering five West African economies: farmer’s sons born in 1960-69 had a 60-70 percent chance of being farmers themselves 40 years later; similar probabilities for children of nonfarmers.
  - Urban West Africa: more than 60 percent of those who were self-employed had fathers who were also self-employed (Pasquier-Dumer, 2011).
- Networks:
  - Family/clan/ethnic networks are important in finding jobs (Filmer and Fox, 2014).
  - Most medium and large firms rely on employees and their networks for hiring; common even in the public sector, contributing to ethnic homogeneity in firms/departments.
- Caveat:
  - Cross-section studies cannot control for unobserved heterogeneity and personal choice; conclusions on segmentation are thus subject to criticism (Teal, 2012).

### Evidence from longitudinal and panel data
- Transition to work and persistence:
  - Youth may take relatively longer (e.g., about five years in urban Tanzania) to transition from school to work, but once settled into a sector they mostly stay there (Filmer and Fox, 2014).
  - In urban Tanzania, almost no one moved from the HE sector to the wage sector after age 25 (Bridges et al. 2014).
- Ethiopia panel (urban residents, 1994-2004):
  - Of those in self-employment in 1994 and still in the labor force in 2004, 2/3 were still in self-employment, while only 10 percent had found a formal wage job (Bigsten, Mengistae, Shimeles).
  - Seventy percent of those who reported formal private sector wage employment in 1994 and were still in the labor force had stayed in this segment; another 14 percent had found jobs in the public sector (including SOEs).
  - During this period the share of private sector wage employment in total employment doubled.
- SOE restructuring in Ethiopia (1997–2000) (Bigsten et al. 2008):
  - Only 1/3 of those who reported working in an SOE in 1997 were still in the sector in 2000 (compared with 93 percent in 1994–1997).
  - Of those who left SOEs: 40 percent found a job in the general government sector, 9 percent went to the private formal sector, 12 percent went to the HE sector, and 25 percent left the labor force entirely (many taking early retirement under the SOE restructuring program).
  - Only 7 percent reported being unemployed after leaving SOEs.
  - The formal private wage sector accounted for 15 percent of urban employment in 2000.
- Rural evidence:
  - Rural Mozambique (mid-2000s drought): rural smallholder farmers seeking alternative employment mostly ended up in casual farm or nonfarm wage employment; household enterprise (HE) employment did not grow during the drought, but when the agricultural economy recovered people tended to start household enterprises (Cungara et al. 2011).
  - Urban Kenya (1990s, small area panels): in bad times the number of HEs grew rapidly when wage employment was scarce; in good times HE growth slowed (Mead and Lindholm, 1998).

### Interpretation of mobility patterns
- For labor force participants, mobility between broad employment categories appears limited.
- Contributing factors include:
  - Small size of the wage employment sector relative to new entrants.
  - Low skill levels of those in the agricultural sector, especially in rural areas.
  - Individual preferences and evolving information about economic opportunities (potential earnings and job characteristics).
- In the absence of safety nets, prolonged job search is often not an option for prime age workers, so transitions tend to be rapid into available alternatives (including leaving labor force).

### Key implications for earnings and households (linked findings)
- Education and earnings:
  - Education correlates with earnings, but explanatory power declines once unobservable individual and family characteristics are controlled for.
  - Participants with less education do poorly in the wage sector and often move to household enterprise (HE) activities.
  - Public sector employment typically requires relatively high education and often earns a wage and security premium; it is concentrated among the richest households.
- Estimating earnings variance:
  - Using cross-section data and standard Mincerian formulations plus sector/contract/location variables, researchers can explain about 50 percent of total variance in real wages.
  - Fox and Oviedo (2008): about 50 percent of observed variance explained using education, age, experience, tenure, and firm characteristics across 10 countries.
- Specific empirical findings on returns and premiums:
  - Returns to education are convex, with very high returns to post-secondary education and no or very low returns to primary education.
  - Instrumenting for education still produces high and convex returns (Kuepie, Nordman, and Roubaud, 2009).
  - Firm characteristics matter; larger firms pay a premium.
  - Having a contract and/or being in the public sector yields a premium; Fox and Pimhidzai (2013) found the public sector premium for teachers in Uganda to be over 50 percent after controlling for personal characteristics.
- Household consumption effects (Fox and Sohnesen, 2012):
  - Controlling for household education and characteristics, consumption is 11 - 27 percent higher in urban areas, and 11-32 percent higher in rural areas for households engaged in nonfarm self-employment.
  - Microenterprises show even greater effects (over 60 percent higher in Mozambique).
  - In urban areas, the marginal effect of HE earnings is higher than the marginal effect of non-farm private sector wage employment in many contexts; the marginal effect of public sector wage earnings is almost always higher than private sector nonfarm wage or HE earnings, but sometimes lower than microenterprise earnings.
  - These results suggest HEs can be a good income choice for many excluded from wage opportunities.
- Macro-sectoral wage comparisons:
  - Gelb et al. (2013): estimated labor cost per worker between US$ 950-1000 in Nigeria, Tanzania, and Uganda—about the same level as Indonesia despite higher education and productivity in Indonesia.
  - UNCTAD (2005 data): ratio of manufacturing wage per worker to GDP per capita was about 2 in Viet Nam versus nearly 54 in Ghana and Kenya.
  - High cost of living in African urban areas (land, transportation, telecommunications, and food) effectively puts a floor under wages.
  - Wage levels show flexibility in response to external shocks (e.g., Rama (2000) found downward adjustment of real wages after CFA devaluation in late 1990s; Uganda private sector real wages did not rise substantially over 2000–2010).

### Policy and modeling implications
- Data and modeling:
  - Mobility is understudied due to lack of longitudinal data; panel data are beginning to clarify persistence and transitions.
  - Macro modelers should focus on qualitative household livelihood differences (e.g., rural/urban, farm/nonfarm income sources) and the implications for household responses to shocks.
  - Sensitivity analysis is important during model calibration and policy analysis given noisy and incomplete measurement of non-wage incomes.
- Social protection and resilience:
  - Poor rural households have very limited cash income and depend on safety nets during lean seasons; structural improvements to farm productivity and farmgate prices are key.
  - Expansion of targeted cash transfers may help poorer rural households cope with covariate and idiosyncratic shocks.
- Political economy:
  - Middle and urban households can be vulnerable to external shocks affecting food or fuel prices due to high urban living costs; policy-makers should recognize urban middle-class vulnerability as these households can block policies perceived to hurt them.

*Source: _wp15102 - 7. Matching and mobility between sectors (IMF PDF chapter content provided).*

### References

### References

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### Figures and accompanying numeric labels (as presented in source)
- Figure 1. Sub-Saharan Africa: Gini index by Income Level  
  - Axis numeric labels shown: 0 10 20 30 40 50 60 70 80 (Gini Index) and 0 500 1000 1500 2000 2500 3000 3500 4000 (GDP per capita (US$ 2005))
- Figure 2. Food as a Share of Total Consumption Expenditures, by Country Income Level  
  - Sources note: Consumption data - most recent household surveys, selected countries; Country income - GDP per capita in USD, PPP adjusted, from World Development Indicators on line
- Figure 3. Total Fertility Rate, Rural vs. Urban, Low and Lower Middle Income African Countries  
  - Source: DHS Statcompiler  
  - Axis numeric labels shown: 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 and 0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00 14000.00  
  - Legend numeric labels shown: 0 1 2 3 4 5 6 7 8 9 (Total fertility rate Total fertility rate)
- Figure 4. Household Sources of Income, Selected Countries and Years  
  - Source: Household surveys  
  - Country-years shown: 2001 2007 2002 2008 1991 1998 2005 2005 2007 2000 2005 2010 2001 2005 2003 2011 2002 2005 2010  
  - Countries listed: Cameroon Côte d'Iviore Ghana Niger Rwanda Senegal Sierra Leone Uganda  
  - Income source categories shown: Wage H'hold enterprise Agriculture  
  - Axis numeric labels (percent-like scale): 0 20 40 60 80 100 120 140 160 180 200
- Figure 5. Household Sources of Income, Top and Bottom Quintiles  
  - Source: Household surveys  
  - Quintile labels repeated: Bottom 20% Top 20% (repeated across series)  
  - Countries listed (sequence): Liberia Niger Malawi Mozambique Rwanda Uganda Sierra Leone Tanzania Kenya Senegal Ghana Cameroon Côte d'Iviore Zambia Nigeria Angola Botswana  
  - Income source categories shown: Agriculture H'hold enterprise Wage  
  - Axis numeric labels (percent-like scale): 0 20 40 60 80 100 120 140 160 180 200
- Figure 6. Structure of Rural Household Income, Mozambique, 2005  
  - Source: computed from Mather et al. (2008)  
  - Income components listed: Retained food crops Food crop sales Other crop sales Livestock sales farm labor Non farm income Transfers
- Figure 7. Estimate Structure of Employment in Africa by Country Type, 2010  
  - Source: Fox et al. (2013)  
  - Employment totals and breakdown presented with numeric labels: 183 m 40 m 150 m 21 m 395 m  
  - Country type categories shown: Low Income Lower-Middle Income Resource Rich Upper-Middle Income Total  
  - Employment categories shown (percent of total): Agriculture HE Wage Services Wage Industry Unemployed  
  - Percent axis labels shown: 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
- Figure 8. Education by Job Type, Africa  
  - Source: Filmer and Fox (2014)  
  - Job types listed: Agriculture Non-farm household enterprise Wage without contract Wage with contract All  
  - Education attainment categories shown: No Education Primary incomp. Primary comp. Secondary +  
  - Axis numeric labels (percent-like scale): 0 20 40 60 80 100

*References and figures extracted verbatim from source content.*

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