How many children, men, and women are living in poverty globally?

An approach combining structural and machine-learning models

Dean Jolliffe, Christoph Lakner, Daniel Mahler, Samuel Tetteh-Baah

World Bank

2026-05-21

Introduction

Motivation

Standard per-capita measures of global poverty assume:

  1. Equal distribution of household resources
  2. All consumption is private
  3. Identical needs across individuals

Motivation

Standard per-capita measures of global poverty assume:

  1. Equal distribution of household resources
  2. All consumption is private
  3. Identical needs across individuals

But the evidence shows:

  1. Inequality within households (men vs. women vs. children)
  2. Sharing of resources (e.g., housing) thus economies of scale
  3. Different needs (adults vs. children)

This is the first global study to integrate all three facts.

  • Identifies poor individuals (not just poor households)
  • Improves poverty monitoring and policy targeting

Key findings

  • Of the 802 million extreme poor in 2024:
    • Women: 39%
    • Men: 30%
    • Children: 31%
    • Children in Sub-Saharan Africa: 26%
    • Women in Sub-Saharan Africa: 21%
  • Children and women especially in Sub-Saharan Africa are disproportionately poor.
  • A quarter of a million children are poor.
  • 74 million more women than men are poor.

Millions living in extreme poverty, 2024

Presentation outline

  • Why does the World Bank make strong assumptions when measuring global poverty?
  • How do we incorporate the three facts into poverty measurement?
    • Structural models, Machine learning, Derive global distribution (inequality)
    • Derive a scaled, adult-equivalized poverty line (sharing, different needs)
  • Global and regional poverty estimates
  • Conclusion

Why the Bank makes strong assumptions

1. Legacy

2. Data limitations

Source: Nigeria Living Standards Survey 2018–2019

Structural models of collective household decision-making

Structural model: core idea

Household resources are not equally shared.

Each individual type \(i \in \{m, f, c\}\) receives a share \(\eta_i\) of total private resources \(C\):

\[ x_i = \eta_i C \tag{1} \]

Individual consumption \(x_i\) is not observed.

Instead, use assignable goods (e.g., clothing).

The individual’s budget share \(w_g\) on an assignable good \(g\) is:

\[ w_g = a_g + b_g \ln x_i \tag{2} \]

  • Engel curve above links spending to resources
  • \(b_g\): sensitivity to income (i.e., preferences)

Combining (1) and (2), household budget share

\[ W_g = \eta_i \left[a_g + b_g \ln(\eta_i C)\right] \tag{3} \]

Differentiating (3) with respect to \(\ln C\) gives:

\[ \frac{\partial W_g}{\partial \ln C} = b_g \eta_i \tag{4} \]

Slope = preferences × resource shares

Under Similarity Across People (SAP), resource shares are proportional to Engel curve slopes:

\[ \eta_i = \frac{g_i}{g_m + g_f + g_c} \tag{5} \]

Higher spending response ⇒ larger share

Empirical strategy of this paper

Combine:

  • structural estimates of intra-household resource shares for 45 countries

with

  • machine-learning predictions for the remaining countries

to construct an individualized global consumption distribution and re-estimate poverty.

Country group Women /Men Children /Adult Obs Pop. (%)
East Asia & Pacific 0.70 0.14 2 0.2
Europe & Central Asia 0.90 0.24 5 3
Latin America & Caribbean 0.88 0.45 11 77
Middle East & North Africa 0.72 0.20 4 42
South Asia 0.86 0.38 2 97
Sub-Saharan Africa 0.79 0.26 21 70
Low-income countries 0.81 0.27 15 61
Lower-middle-income 0.83 0.33 15 75
Upper-middle-income 0.84 0.40 13 21
High-income countries 1.03 1.03 2 2
All 0.83 0.34 45 42

Source: Aminjonov et al. (2025)

Ratio of women-to-men resource shares from Aminjonov et al.

Machine-learning models

Outcome variables from Aminjonov et al.

Prediction variables from different data sources

Source Description Countries Period Variables
World Bank Gender Data Portal 217 1960–2025 1392
World Bank Poverty & Inequality Platform 218 1977–2026 190
Gethin & Saez (2025) Hours worked by gender/age 159 1900–2023 189
Gallup Polls Cultural values and beliefs 165 2006–2024 135
United Nations Gender Development Index 195 1990–2023 15
World Values Survey Cultural values and beliefs 65 2017–2023 13
Pew Research Center Population shares of religions 195 1990–2024 12
World Bank World Governance Indicators 206 1996–2023 6
World Economic Forum Global Gender Gap Index 157 2004–2021 5
World Inequality Lab Female labor income share 211 1990–2023 1
Jolliffe et al. (2025) Food share of consumption 167 2022–2022 1
All data 218 1900–2026 1959

XGBoost is the preferred ML model

  • Linear models: LASSO, Ridge, Elastic Net
  • Non-linear models: Random Forest, XGBoost

Why XGBoost?

  • Model builds trees sequentially
  • Each tree corrects previous errors

\[ \hat{y}_i^{(t)} = \hat{y}_i^{(t-1)} + f_t(x_i) \tag{6} \]

  • Captures interactions
  • Handles missing data
  • Controls risk of overfitting (p=1935 ≫ n=45)

Regularization addresses overfitting

The regularization term is given as: \[ \Omega(f_t) = \gamma T + \frac{\lambda}{2} \sum_{j=1}^{T} w_j^2 + \alpha \sum_{j=1}^{T} |w_j| \tag{7} \]

  • \(\gamma\) → limits number of leaves (simpler trees)
  • \(\lambda\) → shrinks predictions (stability)
  • \(\alpha\) → keeps only strong signals (sparsity)

Feature trimming, hyperparameter tuning, model selection

  • Grid search (128 specifications):
    • top_k ∈ {10,15,20,25}
    • learning rate, η ∈ {0.03, 0.05}
    • maximum tree depth ∈ {2,3}
    • λ ∈ {1,5}, α ∈ {0,0.5}, γ ∈ {0,0.5}
  • Model selection:
    • Leave-One-Out CV (LOOCV)
    • Optimize out-of-sample R²
  • Key results:
    • Best model: 15 predictors
    • Inequality by gender: R² ≈ 0.52
    • Inequality by age: R² ≈ 0.70
Top predictors of women-to-men resource share
Variable Gain
Female GNI per capita (2021 PPP$) 0.200
Women’s employment rate 0.106
Female vocational enrollment (% of secondary) 0.101
Share of men who believe religion is important 0.083
Top predictors of child-to-adult resource share
Variable Gain
GNI per capita, Atlas method (current US$) 0.323
Female road traffic injury mortality (per 100,000) 0.126
Life expectancy at birth, female (years) 0.109
Government effectiveness: estimate 0.033

Note: Gain reflects each variable’s contribution to reducing prediction error.

Ratio of women’s share to men’s share - structural model

Ratio of women’s share to men’s share - structural model + ML model

Ratio of children’s share to adults’ share - structural model + ML model

Individual-level global distribution and scaled poverty line

From Ratios to Resource Shares

  • Step 1: Define inequality ratios

\[ \frac{w_{shr}}{m_{shr}} = wm_{gap} \qquad\qquad \frac{c_{shr}}{0.5(m_{shr} + w_{shr})} = ca_{gap} \tag{8–9} \]

  • Step 2: Resource constraint, total private household expenditure/resources:

\[ T_{prv} = M \cdot m_{shr} + W \cdot w_{shr} + C \cdot c_{shr} \tag{10} \]

  • Step 3: Solve system (3 equations, 3 unknowns)

\[ m_{shr} = \frac{T_{prv}} {M + wm_{gap}\cdot W + 0.5\cdot ca_{gap}\cdot(1+wm_{gap})\cdot C} \tag{11} \]

\[ w_{shr} = wm_{gap}\cdot m_{shr} \qquad c_{shr} = 0.5\cdot ca_{gap}\cdot(1+wm_{gap})\cdot m_{shr} \tag{12-13} \]

Welfare Aggregation and Equivalent Poverty Line

Total household welfare is decomposed as:

\[ T = T_{pub} + T_{prv} \tag{13} \]

\[ T_{pub} = welf_{pc}\,(1 - pvt)\,N \tag{14} \]

\[ T_{prv} = Nwelf_{pc}\,pvt\,N/A \tag{15} \]

Note:
- Household size, \(N = W + M + C\)
- Adult-equivalent size, \(A = W + M + 0.6C\)
- \(welf_{pc}\): welfare per capita (2021 PPP$/day)
- \(pvt\): private share of household welfare

Allocation:
- Public consumption enjoyed by everyone
- Private consumption allocated via Eqs. (8-10)

Adjustments account for:
- Economies of scale
- Different needs of children and adults

Scaled, adult-equivalized poverty line \(z\) solves:

\[ F(z) = \int_0^z f(y(x))\,dx = P^0 \tag{16} \]

where:
- \(f(y(\cdot))\): transformed global distribution
- \(z\): equivalent poverty line
- \(P^0\): global per-capita poverty rate (11%)

Poverty lines used:
- International poverty line: $3.00 per capita
- Equivalent international poverty line: $7.50 per adult [60% for children (i.e. under 14)]

Global and regional poverty estimates

Extreme poverty rate by gender, age, region - per capita approach

  • Global poverty rates, 2024
    • Women: 9%
    • Men: 8%
    • Children: 21%
    • All: 11%
  • Men and women have similar poverty rates.
  • Children are much poorer than adults.

Extreme poverty rate by gender, age, region - alternative approach

  • Global poverty rates, 2024
    • Women: 12%
    • Men: 9%
    • Children: 15%
    • All: 11%
  • Child poverty falls by 6pp.
  • Still, children are poorer than adults (Africa effect).
  • Women face higher poverty risk than men.

Millions of extreme poor by gender, age, region - alternative approach

  • Of the 802 million extreme poor in 2024:
    • Women: 39%
    • Men: 30%
    • Children: 31%
    • Children in Sub-Saharan Africa: 26%
    • Women in Sub-Saharan Africa: 21%
  • Children and women especially in Sub-Saharan Africa are disproportionately poor.
  • A quarter of a million children are poor.
  • 74 million more women than men are poor.

Conclusion

Global poverty looks different once we move from poor households to poor individuals.

The paper’s central conclusion is:

  • women are substantially more likely than men to live in extreme poverty
  • child poverty is the highest, even after accounting for sharing, inequality, and needs
  • the demographic and regional composition of global poverty changes materially once we account for sharing, inequality, and needs

Thank you

Comments welcome

Samuel Tetteh-Baah
World Bank

stettehbaah@worldbank.org

Important limitations

  • structural estimates are available for only 45 countries
  • machine-learning prediction is informative, but imperfect
  • country-level inequality parameters are applied uniformly within countries
  • the current version focuses on 2024, not trends

Still

Even with these constraints, the paper provides a meaningful first global picture of poverty by age and gender under more realistic household assumptions.