## wp1793

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### I. Introduction — context and main conclusion
- Djibouti: small East African state dependent on rents from ports and military bases; population approaching one million and a nominal GDP of about US$ 2 billion.
- Vision Djibouti 2035 targets:
  - raising medium-term growth to 7.5–10 percent,
  - making growth more inclusive,
  - tripling per capita income,
  - reducing unemployment.
- Data sources and period:
  - two household surveys (EDAM-IS) for 2002 and 2013 as primary sources; supplementary: EDESIC 2015-16 and a 2015 employment survey.
  - Period covered: 2002–13.
- Main conclusion: growth in Djibouti in the past decade has not been inclusive; the richest groups benefitted more than the poorest, inequality increased, and the negative distributional component largely offset benefits of economic growth for the poor. Public policies, in particular fiscal and structural, may play a significant role in making growth more inclusive.

### II. Growth, consumption, and poverty dynamics (2002–13)
- Growth and population:
  - Economic growth averaged 4 percent during the past decade, with fluctuations from 1.6 percent in 2009 to over 6 percent in recent years (investment program financed by China).
  - Average population growth: about 2.8 percent.
  - Under an optimistic scenario growth is projected to remain robust; under an adverse scenario growth may return to about 4 percent.
- Poverty lines (2013, annualized, consumption basis):
  - overall poverty line: DJF 147,936
  - extreme poverty line: DJF 98,709
- Poverty levels and changes:
  - overall poverty in 2013: 41 percent
  - extreme poverty in 2013: 23 percent
  - overall poverty rate declined from 47 percent (2002) to 41 percent (2013)
  - extreme poverty declined from 24 percent (2002) to 23 percent (2013)
- Regional disparities (2013):
  - Djibouti-ville overall poverty: 34 percent; extreme poverty: 17 percent
  - Rest of country overall poverty: about 63 percent; extreme poverty: 45 percent
  - Ali Sabieh and Obock regions have the highest poverty levels
- Poverty gaps:
  - overall poverty gap declined from 17 percent (2002) to 16 percent (2013)
  - extreme poverty gap increased from 7.4 percent (2002) to 7.9 percent (2013) (increase not statistically significant)

### III. Inequality and distributional indicators
- Gini index:
  - 2013: 0.44
  - 2002: 0.39
- Distribution-sensitive indicators (2002 → 2013):
  - squared poverty gap: 2.8 → 3.6
  - Watts index: 8.2 → 11.6
  - Gini coefficient (index as presented): 40 → 44
  - mean log deviation (MLD): 27.3 → 35.0
- Decile and ratio changes:
  - median vs poorest: 3.0 → 3.9
  - richest vs average: 4.5 → 5.1
  - richest vs poorest: 13.3 → 19.9
- Selected consumption statistics (preserve source presentation):
  - 2013: Mean 147.7; Poverty line (PPP$/day) 1.9; Headcount 22.5; Poverty gap 7.5; Squared poverty gap 3.8; Watts index 11.6; Gini 44.1; Median 106.7; MLD 35.0; Population (mil.) 0.9
  - 2002: Mean 134.2; Poverty line (PPP$/day) 1.9; Headcount 20.6; Poverty gap 6.0; Squared poverty gap 2.6; Watts index 8.2; Gini 40.0; Median 100.6; MLD 27.3; Population (mil.) 0.8

### IV. Demographics, employment, and structural constraints
- Demographics and labor force:
  - almost 75 percent of the population younger than 35 years
  - overall population a little less than 1 million
  - economically active population (15–64): 62 percent; over 82 percent of them live in cities
  - migration: 13 percent of the population consider themselves migrant (may be underestimated)
- Labor market (2015 survey):
  - unemployment rate: 39 percent (2015)
  - female unemployment: 49 percent
  - male unemployment: 34 percent
  - rural unemployment: 59 percent
  - urban unemployment (implied in figure): 36.5 percent
  - labor participation rate did not exceed 25 percent (overall)
- Employment composition:
  - public sector provides 60 percent of jobs: 46 percent in central government, 16 percent in public enterprises
  - private sector employs 20 percent
  - self-employed: 10 percent
  - services and construction account for about 13 percent of formal employment
- Structural factors limiting inclusiveness:
  - underdeveloped private sector, oversized public sector, capital-intensive investment in ports with limited trickle-down
  - many jobs taken by expatriates due to low domestic skills base
  - miniscule agriculture, high costs of doing business, weak contract enforcement, red tape, low access to finance

### V. Gender and inequality
- Household and expenditure patterns:
  - female-headed households: about 19 percent of total households
  - poverty in female-headed households: 21 percent (same as male-headed)
  - rural disparity: female-headed households poverty 29 percent vs male-headed 17 percent
  - average expenditure per person broadly equal across male- and female-headed households
  - average household expenditures: male-headed 105 percent of average vs female-headed 86 percent of average
- Labor market participation and education:
  - female labor market activity (age 15–64): 25 percent vs 54 percent for men
  - women with at least three years of university studies in labor market: 54 percent vs 76 percent for men
  - female literacy: 53 percent vs 67 percent for men
  - female literacy in rural areas: 20 percent
  - school enrollment for girls: over 50 percent overall; in rural areas: 40 percent of primary school–aged girls and 24 percent of secondary school–aged girls attend school
  - noted high maternal mortality ratio (Djibouti has the highest ratio in the region and one of the highest in the world, per DISED, 2016)

### VI. Data availability, decomposition of poverty change, and statistical caveats
- Data constraints:
  - analysis requires at least two comparable household surveys; most of the population employed in informal sector complicates data collection (2016 survey: informal sector employs about 20 percent of the economically active population, compared with 46 percent employed by the public administration and 14 percent by public enterprises).
  - outliers, changing definitions (household size, sociodemographic variables), and timing/coverage differences can affect comparability.
  - World Bank support to DISED for the 2017 EDAM aims to apply best practices for better disaggregated indicators.
- Decomposition framework:
  - poverty rate Pt = f(z, Lt, μt); change decomposed into growth effect G, distribution effect D, and residual R.
  - α parameter: α = 0 (headcount), α = 1 (poverty gap).
- Empirical decomposition (2002 → 2013) — key figures preserved from source:
  - Overall poverty rate dropped by 5.9 percentage points (ppts):
    - Growth effect: 8.8 ppts
    - Distribution effect: -2.9 ppts (worked in opposite direction)
  - Extreme poverty: improvement marginal, only 1 ppts, as gains from growth were offset by distribution effect.
  - Poverty gap: overall improvement marginal, about one percentage point, because growth effect was almost entirely offset by negative distribution effect.
  - Extreme poverty gap increased by about 0.5 percentage points as the negative distribution effect could not be offset by growth.
- Selected numerical decomposition excerpts (as presented):
  - For α = 0, poverty lines 94,636 (2002) → 147,936 (2013):
    - Distribution_1: 0.4670, St. error: 0.0213, Lower bound: 0.4251, Upper bound: 0.5089
    - Distribution_2: 0.4081, St. error: 0.0223, Lower bound: 0.3641, Upper bound: 0.4520
    - Difference (d2-d1) = -0.0589, St. error: 0.0454, Lower bound: -0.1483, Upper bound: 0.0304
    - Growth: -0.0908, St. error: 0.0542, Lower bound: -0.1976, Upper bound: 0.0159
    - Distribution: 0.0256, St. error: 0.0550, Lower bound: -0.0825, Upper bound: 0.1338
  - For α = 1, poverty lines 94,636 (2002) → 147,936 (2013):
    - Distribution_1: 0.1689, St. error: 0.0105, Lower bound: 0.1482, Upper bound: 0.1896
    - Distribution_2: 0.1586, St. error: 0.0104, Lower bound: 0.1383, Upper bound: 0.1790
    - Difference (d2-d1) = -0.0103, St. error: 0.0210, Lower bound: -0.0517, Upper bound: 0.0311
    - Growth: -0.0434, St. error: 0.0240, Lower bound: -0.0907, Upper bound: 0.0038
    - Distribution: 0.0326, St. error: 0.0150, Lower bound: 0.0031, Upper bound: 0.0620
  - For α = 0, poverty lines 63,145 (2002) → 98,709 (2013):
    - Distribution_1: 0.2411, St. error: 0.0172, Lower bound: 0.2073, Upper bound: 0.2750
    - Distribution_2: 0.2304, St. error: 0.0179, Lower bound: 0.1951, Upper bound: 0.2657
    - Difference (d2-d1) = -0.0107, St. error: 0.0363, Lower bound: -0.0821, Upper bound: 0.0606
    - Growth: -0.0635, St. error: 0.0360, Lower bound: -0.1344, Upper bound: 0.0073
    - Distribution: 0.0506, St. error: 0.0443, Lower bound: -0.0365, Upper bound: 0.1378
  - For α = 1, poverty lines 63,145 (2002) → 98,709 (2013):
    - Distribution_1: 0.0741, St. error: 0.0061, Lower bound: 0.0621, Upper bound: 0.0860
    - Distribution_2: 0.0785, St. error: 0.0065, Lower bound: 0.0658, Upper bound: 0.0913
    - Difference (d2-d1) = 0.0045, St. error: 0.0128, Lower bound: -0.0208, Upper bound: 0.0297
    - Growth: -0.0234, St. error: 0.0123, Lower bound: -0.0477, Upper bound: 0.0009
    - Distribution: 0.0311, St. error: 0.0107, Lower bound: 0.0101, Upper bound: 0.0522
- Statistical caveats:
  - Multiple decomposition approaches yield broadly similar results but many estimates have low statistical significance and wide confidence intervals; margins of error are high.

### VII. Growth incidence curves (GIC) findings
- GIC interpretation and formula (as presented):
  - gt(p) = Lt'(p) / Lt-1'(p) * (γt + 1) − 1
  - Horizontal GIC: distributionally neutral growth.
  - Negatively sloping GIC: inclusive growth (poorer deciles grow faster).
  - Positively sloping GIC: growth not inclusive.
- Empirical findings for Djibouti (2002–13):
  - Household consumption increased on average: mean of the GIC is above zero.
  - GIC is clearly positively sloped: growth benefited most people in the middle and high end of the income distribution (2nd to 10th deciles); high-income deciles (above the 8th decile) clearly increased consumption.
  - For the poorest 10 percent, the GIC is below the horizontal axis: these groups experienced negative growth rates of living standards.
  - Shifting the GIC to align its mean with the X-axis shows that up to the 8th decile the curve is below the X-axis: for 80 percent of the poorest, consumption grew slower relative to the wealthiest 20 percent.
- GIC data cautions:
  - Potential incomparability between 2002 and 2013 consumption aggregates due to more detailed questionnaires in 2013 and different methods to estimate housing expenditure.
  - Several estimated coefficients are not statistically significant; the 95 percent confidence interval around the GIC touches the X-axis multiple times (notably 5th to 8th percentiles), implying substantial margin of error.

### VIII. Key diagnostic conclusions (2002–13)
- (i) Overall poverty declined in 2002-13, although there has been no significant change in extreme poverty.
- (ii) Inequality remains high, especially between different income groups, urban and rural areas, and men and women; indications exist that inequality may have worsened.
- (iii) Growth in 2002-13 has not been inclusive: it benefited mainly people in the upper side of the income distribution while the poorest groups became poorer in relative terms.
- (iv) Based on comparator countries (Senegal and Mauritania), growth in rural areas most likely was less inclusive than in urban areas, and gender disparity may have increased; existing Djibouti statistics do not allow direct assessment of these two effects.
- (v) Underlying data are marginally sufficient and not entirely comparable between surveys; statistical significance of most estimates is low and margins of error are high — results should be treated as indicative pending better data.

### IX. Policy implications and policy options to increase growth inclusiveness
- Macroeconomic and growth prerequisites:
  - Sustained overall economic growth is a precondition for further poverty reduction.
  - Evidence cited: Kraay (2004): growth of average income explains 70 percent of the variation in poverty reduction across countries in the short run; Berg and Ostry (2011): longer growth spells associated with more equality; Lopez and Servén (2006): poorer countries rely more on growth for poverty reduction; Affandi and Peiris (2012): a 1 percent increase in real per capita income leads to about a 2 percent decline in the poverty headcount ratio.
  - Policy direction: successful pro-poor growth strategy should combine sustained and rapid economic growth with measures to create jobs (supplement debt-financed capital-intensive growth with job-creating growth).
- Fiscal, social protection, and revenue measures:
  - Fiscal reforms need to generate revenue to ensure affordability of a social safety net for the poorest.
  - Short-term protection: redirect resources from generalized subsidies to better-targeted measures such as school lunches, public works programs, and better-targeted tariffs for small quantities of electricity.
  - Medium-term systems: establish a well-targeted and conditional cash transfer system as the best option for assistance for the poorest.
  - Expand coverage: extend compulsory health insurance available to government employees to the poorest population; introduce health insurance and social housing.
  - Reform investment incentive system to broaden the tax base; study distributional impacts before introducing measures such as a poverty tax or a surcharge on large investors and FTZ.
- Public utilities, education, and labor market policies:
  - Ensure low-cost provision and improvement of basic utilities, prioritizing electricity and water supply.
  - Reform educational curricula and training programs to align labor force skills with job market needs so that Djibouti nationals—rather than expatriates—fill jobs from the investment boom.
  - Coherent labor market policies and rural job creation, especially in agriculture, are important to increase consumption growth among poorer households.
- Economic diversification, business climate, and financial access:
  - Diversify beyond services (ports) into sectors with high growth and employment potential, such as tourism and fishing.
  - Improve business climate: reduce red tape, step up anti-corruption efforts, promote transparency, improve public financial management, strengthen central bank governance.
  - Financial inclusion: operationalize the Partial Credit Guarantee Fund; accelerate national strategy for modernization of payments and credit reporting; set up a framework for mobile payments; simplify taxation to encourage formalization; simplify access to land and mortgage procedures.
- Gender inclusion and institutions:
  - Address legal and practical gender-based restrictions to increase women’s participation in economic activities.
  - Strengthen inclusive institutions that ensure rule of law, access to public services, and protection of property and contract rights.

*Source: wp1793 (IMF working paper excerpt).*

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

### wp1793 - References

### I. Introduction
- Djibouti is a small East African state dependent on rents from ports and military bases; population approaching one million and a nominal GDP of about US$ 2 billion.
- Authorities’ “Vision Djibouti 2035” development plan aims at making the country a middle-income economy within the next twenty years, targeting:
  - raising medium-term growth to 7.5–10 percent,
  - making growth more inclusive,
  - tripling per capita income,
  - reducing unemployment.
- The plan comprises five pillars: peace and national unity, good governance, economic diversification, human capital development, and regional integration.
- Purpose of the paper: analyze inclusiveness of growth in Djibouti and draw policy conclusions; inclusive growth is defined as growth that helps reduce inequality across all dimensions – between rich and poor, males and females, and urban and rural areas.
- Data basis: two household surveys (EDAM-IS) for 2002 and 2013 as primary sources; supplementary: EDESIC 2015-16 and a 2015 employment survey. Period covered: 2002–13.
- Main conclusion: growth in Djibouti in the past decade has not been inclusive; the richest groups benefitted more than the poorest, inequality increased, and the negative distributional component largely offset benefits of economic growth for the poor. Public policies, in particular fiscal and structural, may play a significant role in making growth more inclusive.

### II. Factors of Growth Inclusiveness

#### A. Growth and Consumption
- Economic growth averaged 4 percent during the past decade, with fluctuations from 1.6 percent in 2009 to over 6 percent in recent years (investment program financed by China).
- Average population growth: about 2.8 percent.
- Under an optimistic scenario growth is projected to remain robust; under an adverse scenario growth may return to about 4 percent.
- Poverty lines (2013, annualized, consumption basis):
  - overall poverty line: DJF 147,936
  - extreme poverty line: DJF 98,709
- Poverty levels and changes:
  - overall poverty in 2013: 41 percent
  - extreme poverty in 2013: 23 percent
  - overall poverty rate declined from 47 percent (2002) to 41 percent (2013)
  - extreme poverty declined from 24 percent (2002) to 23 percent (2013)
- Regional disparities (2013):
  - Djibouti-ville overall poverty: 34 percent; extreme poverty: 17 percent
  - Rest of country overall poverty: about 63 percent; extreme poverty: 45 percent
  - Ali Sabieh and Obock regions have the highest poverty levels
- Poverty gaps:
  - overall poverty gap declined from 17 percent (2002) to 16 percent (2013)
  - extreme poverty gap increased from 7.4 percent (2002) to 7.9 percent (2013) (increase not statistically significant)

#### B. Inequality and Distribution
- Gini index:
  - 2013: 0.44
  - 2002: 0.39
- International comparisons:
  - Djibouti’s inequality higher than Mauritania, Senegal, or lower-middle-income countries average (0.414 for 2005–12), but lower than Comoros, Cape Verde, Mozambique.
- Distribution-sensitive indicators (2002 → 2013):
  - squared poverty gap: 2.8 → 3.6
  - Watts index: 8.2 → 11.6
  - Gini coefficient (presented elsewhere): 40 → 44 (index as presented)
  - mean log deviation (MLD): 27.3 → 35.0
- Decile and ratio changes:
  - median vs poorest: 3.0 → 3.9
  - richest vs average: 4.5 → 5.1
  - richest vs poorest: 13.3 → 19.9
- Consumption share by deciles (selected values):
  - 2013 mean/poverty line/other stats row: Mean 147.7; Poverty line (PPP$/day) 1.9; Headcount 22.5; Poverty gap 7.5; Squared poverty gap 3.8; Watts index 11.6; Gini 44.1; Median 106.7; MLD 35.0; Population (mil.) 0.9
  - 2002 comparable row: Mean 134.2; Poverty line (PPP$/day) 1.9; Headcount 20.6; Poverty gap 6.0; Squared poverty gap 2.6; Watts index 8.2; Gini 40.0; Median 100.6; MLD 27.3; Population (mil.) 0.8

#### C. Demographics and Employment
- Demographics:
  - almost 75 percent of the population younger than 35 years
  - overall population a little less than 1 million
  - economically active population (15–64): 62 percent; over 82 percent of them live in cities
  - migration: 13 percent of the population consider themselves migrant (may be underestimated)
- Employment and labor market (2015 survey):
  - unemployment rate: 39 percent (2015)
  - female unemployment: 49 percent
  - male unemployment: 34 percent
  - rural unemployment: 59 percent
  - urban unemployment (implied in figure): 36.5 percent (presented in figure)
  - labor participation rate did not exceed 25 percent (overall)
  - public vs private employment:
    - public sector provides 60 percent of jobs: 46 percent in central government, 16 percent in public enterprises
    - private sector employs 20 percent
    - self-employed: 10 percent
  - sectoral employment: services and construction account for about 13 percent of formal employment
- Structural factors: underdeveloped private sector, oversized public sector, capital-intensive investment in ports with limited trickle-down, many jobs taken by expatriates due to low domestic skills base, miniscule agriculture, challenging business environment (high costs, weak contract enforcement, red tape, low access to finance)

#### D. Gender and Inequality
- Poverty by household head gender:
  - female-headed households: about 19 percent of total households
  - poverty in female-headed households: 21 percent (same as male-headed)
  - rural disparity: female-headed households poverty 29 percent vs male-headed 17 percent
- Expenditure:
  - average expenditure per person broadly equal across male- and female-headed households
  - average household expenditures: male-headed 105 percent of average vs female-headed 86 percent of average
- Labor force participation and education:
  - female labor market activity (age 15–64): one in four women active (25 percent) vs 54 percent of men
  - women with at least three years of university studies in labor market: 54 percent vs 76 percent for men
  - female literacy: 53 percent vs 67 percent for men
  - female literacy in rural areas: 20 percent
  - national primary and secondary school enrollment rate for girls: over 50 percent overall; in rural areas: 40 percent of primary school–aged girls and 24 percent of secondary school–aged girls attend school
  - high maternal mortality ratio in the region (Djibouti has the highest ratio in the region and one of the highest in the world, per DISED, 2016)

### III. Inclusive Growth Diagnostics

#### A. Measures of Growth Inclusiveness
- Operational definition used: growth is inclusive if it helps improve equality (focus on distributional characteristics).
- Inclusive growth dimensions highlighted:
  - high and sustained growth,
  - broad-based across sectors,
  - creation of productive employment opportunities,
  - benefits a large part of the population,
  - gender equality, regional diversification, empowerment of the poor,
  - inclusive institutions.
- Analytical focus of the paper: distributional characteristics of growth, including splitting the rate of poverty reduction into growth and distributional components and use of growth incidence curves.

### Policy-relevant implications (as presented in the source)
- Growth to date has not been inclusive; distributional outcomes have offset some poverty-reducing effects of growth.
- Public policies, particularly fiscal and structural reforms, may play a significant role in making growth more inclusive.
- Vision Djibouti 2035 priorities and implementation efficiency of large investments are critical to whether growth becomes more inclusive or returns to trend levels.

*Source: wp1793 - References (excerpt), IMF working paper content provided.*

### 20.      The quality of the analysis of growth inclusiveness depends on data availability

### 20.      The quality of the analysis of growth inclusiveness depends on data availability

### Data availability and quality constraints
- Analysis requires at least two household surveys based on a comparable methodology, and data on income and consumption by households.
- In Djibouti most of the population is employed in the informal sector, making income and consumption data difficult to collect.
  - The 2016 survey estimates that the informal sector employs about 20 percent of the economically active population, compared with 46 percent employed by the public administration and 14 percent by public enterprises (DISED, 2016).
- Survey data issues:
  - Outliers at both tails of the distribution may lead to negative growth rates of the incidence curve for both tails in some years; while outliers have been routinely corrected in Djibouti’s household surveys, they may still affect results.
  - Parameters such as size of households and other sociodemographic variables (household head, education level, marital status, employment sector, place of residence, regional distribution, etc.) can vary from survey to survey, affecting poverty measures.
  - Timing and definitions of key variables, including coverage of rural and urban areas, must be the same across surveys to achieve consistent poverty estimates.
- Recent efforts:
  - World Bank support to the Directorate of Statistics and Demographic Studies (DISED) for the 2017 household income and expenditure survey (EDAM) aims to apply best practices to better understand household consumption patterns and produce reliable indicators disaggregated by region, gender, vulnerability status, etc.

### Decomposition of changes in poverty: growth and distribution effects
- Framework:
  - Poverty rate Pt expressed as Pt = f(z, Lt, μt) where z is the poverty line, μ is mean income, and Lt is the Lorenz curve at time t.
  - Change in the poverty rate over time can be decomposed into a growth effect G, a distribution effect D, and a residual R:
    - G: change in poverty due to change in mean income assuming Lorenz curve constant.
    - D: change in poverty due to change in relative income inequality assuming mean income constant.
  - Poverty can be calculated with parameter α:
    - α = 0: headcount poverty index.
    - α = 1: poverty gap index.
- Empirical decomposition for Djibouti (2002 to 2013):
  - Overall poverty rate dropped by 5.9 percentage points (ppts).
    - Growth effect contributed 8.8 ppts.
    - Distribution effect subtracted 2.9 ppts (i.e., worked in the opposite direction).
  - Extreme poverty:
    - Improvement marginal, only 1 ppts, as most gains from growth were offset by losses from the distribution effect.
- Poverty gap:
  - Overall poverty gap improvement marginal, about one percentage point, because growth effect was almost entirely offset by negative distribution effect.
  - Extreme poverty gap increased by about 0.5 percentage points as the strongly negative distribution effect could not be offset by growth.
- Multiple approaches (Datt & Ravallion reference periods t1 and t2, and Shapley) give broadly similar results; exceptions in statistical significance:
  - Confidence intervals suggest statistical significance of these findings is relatively low.
- Selected numerical decomposition excerpts (preserve source values and labels as presented):
  - For parameter alpha : 0 with poverty line 94,636 (2002) and 147,936 (2013):
    - Distribution_1: 0.4670, St. error: 0.0213, Lower bound: 0.4251, Upper bound: 0.5089
    - Distribution_2: 0.4081, St. error: 0.0223, Lower bound: 0.3641, Upper bound: 0.4520
    - Difference: (d2-d1) = -0.0589, St. error: 0.0454, Lower bound: -0.1483, Upper bound: 0.0304
    - Growth: -0.0908, St. error: 0.0542, Lower bound: -0.1976, Upper bound: 0.0159
    - Distribution: 0.0256, St. error: 0.0550, Lower bound: -0.0825, Upper bound: 0.1338
  - For parameter alpha : 1 with poverty line 94,636 (2002) and 147,936 (2013):
    - Distribution_1: 0.1689, St. error: 0.0105, Lower bound: 0.1482, Upper bound: 0.1896
    - Distribution_2: 0.1586, St. error: 0.0104, Lower bound: 0.1383, Upper bound: 0.1790
    - Difference: (d2-d1) = -0.0103, St. error: 0.0210, Lower bound: -0.0517, Upper bound: 0.0311
    - Growth: -0.0434, St. error: 0.0240, Lower bound: -0.0907, Upper bound: 0.0038
    - Distribution: 0.0326, St. error: 0.0150, Lower bound: 0.0031, Upper bound: 0.0620
  - For parameter alpha : 0 with poverty line 63,145 (2002) and 98,709 (2013):
    - Distribution_1: 0.2411, St. error: 0.0172, Lower bound: 0.2073, Upper bound: 0.2750
    - Distribution_2: 0.2304, St. error: 0.0179, Lower bound: 0.1951, Upper bound: 0.2657
    - Difference: (d2-d1) = -0.0107, St. error: 0.0363, Lower bound: -0.0821, Upper bound: 0.0606
    - Growth: -0.0635, St. error: 0.0360, Lower bound: -0.1344, Upper bound: 0.0073
    - Distribution: 0.0506, St. error: 0.0443, Lower bound: -0.0365, Upper bound: 0.1378
  - For parameter alpha : 1 with poverty line 63,145 (2002) and 98,709 (2013):
    - Distribution_1: 0.0741, St. error: 0.0061, Lower bound: 0.0621, Upper bound: 0.0860
    - Distribution_2: 0.0785, St. error: 0.0065, Lower bound: 0.0658, Upper bound: 0.0913
    - Difference: (d2-d1) = 0.0045, St. error: 0.0128, Lower bound: -0.0208, Upper bound: 0.0297
    - Growth: -0.0234, St. error: 0.0123, Lower bound: -0.0477, Upper bound: 0.0009
    - Distribution: 0.0311, St. error: 0.0107, Lower bound: 0.0101, Upper bound: 0.0522
- Interpretation:
  - Distribution effect worked opposite to the growth effect; absent the negative distribution effect, poverty reduction would have been substantially higher.
  - Statistical significance of many estimates is low; margins of error are high.

### Growth incidence curves (GIC) and distributional characteristics of growth
- GIC concept:
  - GICs identify how each percentile/decile benefits from growth; vertical axis: growth rate of consumption expenditure; horizontal axis: consumption expenditure percentiles.
  - Growth reduces poverty if mean income of the poor rises; growth reduces inequality if it straightens the Lorenz curve.
  - GIC formula (from Ravallion and Chen, 2003):
    - gt(p) = Lt'(p) / Lt-1'(p) * (γt + 1) − 1  (as presented in source)
  - Interpretation of slope:
    - Horizontal GIC: growth is distributionally neutral.
    - Negatively sloping GIC: inclusive growth (poorer deciles grow faster).
    - Positively sloping GIC: growth not inclusive.
- Empirical findings for Djibouti, 2002–13:
  - Household consumption increased on average: mean of the GIC is above zero.
  - Growth benefited most people in the middle and high end of the income distribution (from the 2nd to the 10th deciles).
  - The GIC is clearly positively sloped, suggesting an increase in inequality during this period.
  - For low percentiles (at least the poorest 10 percent), the GIC is below the horizontal axis: those groups experienced negative growth rates of living standards.
  - Middle percentiles (roughly 2nd to 8th decile) show trends pointing at a worsening of growth inclusiveness, though results may not be statistically significant as the 95 percent confidence interval crosses the X-axis multiple times.
  - Consumption of the high-income deciles (above the 8th decile) clearly increased.
- Additional diagnostic:
  - Shifting the GIC down by aligning its mean with the X-axis shows that up to the 8th decile the curve is below the X-axis: for 80 percent of the poorest, consumption grew slower relative to the wealthiest 20 percent.
- Data cautions for GIC interpretation:
  - Possible incomparability of consumption aggregates between 2002 and 2013 surveys: both food and nonfood components were estimated with more detailed questionnaires in 2013; different methods were used to estimate housing expenditure.
  - Some estimated coefficients are not statistically significant and lie outside conventional confidence intervals.
  - The 95 percent confidence interval around the GIC touches the X-axis several times (notably 5th to 8th percentiles), implying substantial margin of error.

### Key conclusions on distributional effects and inclusiveness (2002–13)
- (i) Overall poverty declined in 2002-13, although there has been no significant change in extreme poverty.
- (ii) Inequality remains high, especially between different income groups, urban and rural areas, and men and women; indications exist that inequality may have worsened.
- (iii) Growth in 2002-13 has not been inclusive: it benefited mainly people in the upper side of the income distribution while the poorest groups became poorer in relative terms.
- (iv) Based on comparator countries (Senegal and Mauritania), growth in rural areas most likely was less inclusive than in urban areas, and gender disparity may have increased; existing Djibouti statistics do not allow direct assessment of these two effects.
- (v) Underlying data are marginally sufficient and not entirely comparable between surveys; statistical significance of most estimates is low and margins of error are high — results should be treated as indicative pending better data.

### Policy implications and options to increase growth inclusiveness
- Sustained overall economic growth is a precondition for further poverty reduction.
  - Supporting evidence cited:
    - Kraay (2004): growth of average income explains 70 percent of the variation in poverty reduction across countries in the short run.
    - Berg and Ostry (2011): longer growth spells associated with more equality in income distribution.
    - Lopez and Servén (2006): for a given inequality level, poorer countries rely more on growth for poverty reduction.
    - Affandi and Peiris (2012): a 1 percent increase in real per capita income leads to about a 2 percent decline in the poverty headcount ratio.
- For Djibouti policy direction:
  - Successful pro-poor growth strategy should have measures to achieve sustained and rapid economic growth.
  - Djibouti should supplement its debt-financed capital-intensive growth with job-creating growth.

*Source: wp1793 - 20.      The quality of the analysis of growth inclusiveness depends on data availability*

### 39.      Special attention could be given to the distributional dimensions of growth. An

### 39. Special attention could be given to the distributional dimensions of growth.

### Distributional dimensions and inclusiveness of growth
- About two-thirds of poverty reduction within a country comes from growth, and greater equality contributes the other third.
- A 1 percent increase in incomes in the most unequal countries produces a mere 0.6 percent reduction in poverty, while in the most equal countries, it yields a 4.3 percent cut (Ravallion, 2013).
- Increased inequality may offset and even exceed the beneficial impact on poverty reduction of the same increase in income (Affandi and Peiris, 2012).
- Inclusiveness of growth is associated with a number of macroeconomic outcomes and policies; growth and inclusiveness should be analyzed simultaneously.
- Trade-offs: increased inequality may dampen growth, but poorly designed measures to increase inclusiveness could undermine growth.
- Long-run benefit: attention to inclusiveness can bring significant benefits for growth.

### Economic diversification and business climate
- Diversification is essential for Djibouti to develop opportunities in sectors with high growth and employment potential, such as tourism and fishing, and reduce the risks associated with relying on a single sector (services) catering mainly to one client (Ethiopia).
- Improving the business climate is indispensable.
- Measures to cut costs of doing business include:
  - Reducing red tape.
  - Stepping up anti-corruption efforts.
  - Promoting transparency.
  - Improving public financial management.
  - Strengthening central bank governance.

### Well-designed public policies to promote inclusiveness
- Social protection and revenue measures:
  - Social policies could protect the poor and vulnerable from high costs of living.
  - Reforming the investment incentive system could broaden the tax base.
  - Additional revenue measures may include a poverty tax or a surcharge on large investors and FTZ, although the distributional impact of such taxes should be studied before introduction.
  - With current policies, the fiscal benefits of the investment boom are likely to be modest, with the share of tax revenues in GDP projected to decline over the medium term (IMF, 2014).
- Fiscal reforms and social safety net:
  - Fiscal reforms will need to generate the revenue needed to ensure affordability of a social safety net for the poorest population.
  - Government should expand coverage to the poorest population, extend the compulsory health insurance available to government employees to the poorest population, and introduce health insurance and social housing.
- Short-term protection and medium-term systems:
  - Protect poor households in the short term by redirecting resources from generalized subsidies to better-targeted measures such as school lunches, public works programs, and better-targeted tariffs for small quantities of electricity.
  - In the medium term, a well-targeted and conditional cash transfer system is the best option for assistance for the poorest.
- Basic utilities and investment constraints:
  - Ensure low-cost provision and improvement of basic utilities, prioritizing electricity and water supply as major constraints for the poor and obstacles to investing in Djibouti.
- Education and training:
  - Reform educational curricula and training programs to align labor force skills with job market needs and to ensure Djibouti nationals—rather than expatriates—fill jobs created during the investment boom.

### Gender inclusion
- Gender inequality can impede economic growth; gender inequalities, including legal gender-based restrictions, are negatively associated with per capita GDP growth (Hakura et al., 2016).
- The negative effect prevails mainly in low-income countries.
- Per capita income growth in sub-Saharan Africa could be higher by as much as 0.9 percentage points on average if inequality was reduced to the levels observed in the fast growing emerging Asian countries.
- Policies that influence opportunities for women to participate in economic activities, if well designed and targeted, could play a role in alleviating inequalities.

### Inclusive institutions
- Economic and political institutions that ensure the rule of law, provide adequate access to public services, and protect property and freedom to contract for the whole population without discrimination have been found to accelerate growth (Acemoglu and Robinson, 2012).
- The role of the state: impose law and order, enforce contracts, and prevent theft and fraud.
- When the state fails to provide such institutions, growth becomes extractive.

### Labor market policies and rural job creation
- Coherent labor market policies are needed to increase inclusiveness; creating productive employment opportunities throughout the economy is important to generate inclusive growth (IMF, 2013).
- In Djibouti, creation of employment opportunities and increasing productivity in rural areas, particularly in agriculture, would prompt higher consumption growth among poorer households.
- Cross-country examples:
  - Stronger per capita consumption growth among the poorest in Cameroon and Uganda seems related to high agricultural employment growth (IMF, 2011).
  - Rural agricultural employment fell in Mozambique and Zambia where the poorest experienced weaker or negative per capita consumption growth.

### Financial sector deepening and access for the poor
- Financial development generally increases incomes of the poorest households (Claessens, 2005); unequal access to financial markets can reduce incomes by impeding investments in human and physical capital.
- Barriers in Djibouti: most people lack access to the formal financial system.
- Policy options to expand access:
  - Operationalize the Partial Credit Guarantee Fund.
  - Accelerate implementation of the national strategy for modernization of payments and credit reporting systems.
  - Set up a framework for mobile payments to make financial services accessible to low-income groups.
  - Simplify the taxation system to encourage small enterprises to migrate to the formal sector.
  - Simplify access to land and improve mortgage procedures.

*Source: IMF working paper section on inclusiveness and growth (wp1793).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp1793.pdf_