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<p>This study examines the resilience of Nairobi’s low-income households to food price and income shocks using high-resolution expenditure data from 302 households across nine informal settlements. It quantifies how households modify food consumption, simulates welfare impacts under correlated shocks, and identifies vulnerability hotspots. To overcome the limitations of conventional models, the research integrates the Quadratic Almost Ideal Demand System (QUAIDS) to capture non-linear consumption adjustments, Monte Carlo simulations to assess systemic risks, and Causal Forests to detect heterogeneous vulnerabilities. Results show that a 10 percent rise in food prices increases food insecurity by 12.3 percentage points and reduces real purchasing power by 11 percent, forcing households to substitute away from proteins (− 23 percent in meat/fish) toward cereals (+ 18 percent). A 5 percent decline in income similarly raises food insecurity by 9.1 percentage points, with 63 percent of households cutting essential non-food spending—mainly education (− 23 percent) and health (− 21 percent). Vulnerability is most severe among casual laborers (2.5 × greater welfare loss), female-headed households (fivefold poverty risk), and those without formal schooling (inelastic food demand). The findings highlight the limitations of universal subsidies and provide empirical evidence to inform targeted social protection—particularly dynamic cash transfers and staple subsidies—as viable strategies for enhancing food security in Africa’s rapidly urbanizing informal economies.</p>

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Beyond eating cessation: Monte Carlo analysis of food inflation impacts in Nairobi's informal settlements

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月15日

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  • Gabriel Mwenjeri,
  • Kariuki Josphat Muiruri,
  • Florence Miima Abuyeka

摘要

This study examines the resilience of Nairobi’s low-income households to food price and income shocks using high-resolution expenditure data from 302 households across nine informal settlements. It quantifies how households modify food consumption, simulates welfare impacts under correlated shocks, and identifies vulnerability hotspots. To overcome the limitations of conventional models, the research integrates the Quadratic Almost Ideal Demand System (QUAIDS) to capture non-linear consumption adjustments, Monte Carlo simulations to assess systemic risks, and Causal Forests to detect heterogeneous vulnerabilities. Results show that a 10 percent rise in food prices increases food insecurity by 12.3 percentage points and reduces real purchasing power by 11 percent, forcing households to substitute away from proteins (− 23 percent in meat/fish) toward cereals (+ 18 percent). A 5 percent decline in income similarly raises food insecurity by 9.1 percentage points, with 63 percent of households cutting essential non-food spending—mainly education (− 23 percent) and health (− 21 percent). Vulnerability is most severe among casual laborers (2.5 × greater welfare loss), female-headed households (fivefold poverty risk), and those without formal schooling (inelastic food demand). The findings highlight the limitations of universal subsidies and provide empirical evidence to inform targeted social protection—particularly dynamic cash transfers and staple subsidies—as viable strategies for enhancing food security in Africa’s rapidly urbanizing informal economies.