From Income and Food Price Shocks to Food [In]security in Kenya: A Simulated Scenario Analysis

  • From
    CGIAR Initiative on National Policies and Strategies
  • Published on

Share this to :

On July 19, 2023, the CGIAR Research Initiative on National Policies and Strategies initiated a blog competition, inviting all attendees of the Food Security Simulator Kenya demonstration to participate. The competition winner would be eligible for a short-term industrial attachment/mentorship at IFPRI’s Kenya office and the top three blogs would be published on the CGIAR Research Initiative on National Policies and Strategies site. After receiving numerous submissions, our panel of judges composed of international and country experts has chosen the top three blogs below:

*Any opinions stated in the blog are those of the author(s) and are not necessarily representative of or endorsed by the CGIAR.

By Kefa Simiyu


The interplay between the COVID-19 pandemic, below average harvests, and the Russia-Ukraine war jeopardized households’ resilience to negative shocks while simultaneously heightening food insecurity. Negative disruptions of global food supply chains, amidst general declines in agricultural output in Kenya, have contributed to food inflation. As average incomes declined during the pandemic, purchasing power among poor and moderately-rich households declined. Yet, the extent to which these food price and income shocks affected Kenya’s food poverty is under-researched. It is against this backdrop that this blog analyzes how rural and urban food poverty are joint affected by income and price shocks using the new Food Security Simulator Kenya (hereafter, the FSSK) developed by the International Food Policy Research Institute (IFPRI) and partners.


The FSSK requires inputs of income shocks and/or price shocks. For the income changes, I leverage three datasets from nationwide household-level surveys that shed light on recent income trajectories. These datasets are the 2019 and 2021 Fin-Access Households Survey (Fin-Access) and the 2022 Kenya Demographic and Health Survey (KDHS). These datasets allow for household disaggregation based on income and residence. Similarly, households’ income trajectories can be analyzed over time using cross-sectional data. This is because I adopt a measure of annualized growth that does not necessarily require panel data (see Ray & Genicot (2023) for discussion).   Price shocks are proxied by year-on-year inflation using data for selected food commodities retrieved from the Consumer Price Index (CPI).

Households are disaggregated into poor and nonpoor based on the upper-middle-income poverty line (see World Bank’s Poverty & Equity Brief). This is important since food poverty and income dynamics vary across social classes. Average incomes and annualized growth are reported in Table 1. Despite a general post-COVID (2021–2022) recovery, average incomes among the rural poor did not fully recover, as suggested by negative 2019–2022 annualized growth. In this analysis, annualized growth offers a benchmark for income shocks, thereby ensuring that simulation statistics are data-backed.

Table 1: Historical average incomes and annualized income growth

Household group Average income (Kenya shilling) Annualized income growth
2019 2021 2022 2019–2021 2019–2022 2021–2022
Rural poor 2,888.31 2,627.00 2,681.50 -3.02% -1.79% 2.07%
Rural nonpoor 20,149.42 16,931.77 23,359.78 -5.32% 3.98% 37.96%
Urban poor 2,608.81 2,746.47 34,28.29 1.76% 7.85% 24.83%
Urban nonpoor 30,744.36 21,885.35 35,290.78 -9.61% 3.70% 61.25%

Note: A household in group m experiences annualized income growth equal to from year z to z+l.

Annualized growth and inflation data are subsequently utilized in the FSSK. The FSSK enables one to analyze how people’s diets and food consumption are affected by shocks to household incomes and food prices in a partial equilibrium model. Baseline poverty rates are calculated using the 2015/16 Kenya Integrated Households Budget Survey (KIHBS) and show 23.2 percent of households were food poor, although the food poverty rate among rural households was higher (26.0 percent) than urban (19.3 percent).


I investigate two scenarios informed by the historical data: (1) a reduction in average income alongside price increases for potatoes, sugar, kale, and beef, as was observed in the data, and (2) an increase in average income alongside price inflation of fortified maize flour, salad oil, fresh packaged cow milk, kale, and beef with bones, to explore the counterfactual of income growth.

In the first scenario (in the Table 2 below), an additional 3.0 percent and 4.6 percent of rural and urban households, respectively, fall below the food poverty line in the microsimulation. In the second scenario, income and price shocks have a negligible impact on rural and urban food poverty. In particular, an additional 0.3 percent of rural households become food nonpoor whereas 0.2 percent of urban households become food poor.

Table 2: Simulation results

Food poverty rate
  Baseline (%) Scenario 1 Scenario 2
Simulated Impact (%) Change from Baseline (% points) Simulated Impact (%) Change from Baseline (% points)
National 23.2% 26.9% 3.7% 23.1% -0.1%
Rural 26.0% 29.0% 3.0% 25.7% -0.3%
Urban 19.3% 24.0% 4.6% 19.5% 0.2%

These findings suggest that food price inflation and negative income shocks reinforce each other in raising food poverty. Urban food poverty rises more than rural food poverty. These findings further indicate that positive income shocks counterbalance food price inflation, such that rising incomes compensate households for inflation-induced changes in their purchasing power. This blogpost supports Mutea et al. (2022) and Onyango et al. (2021). Utilizing KIHBS, the logit estimates in Mutea et al. (2022) suggested that negative income shocks raised Kenya’s food insecurity. Relying on the 2017 Hungry Cities Partnership Survey, and employing the generalized linear mixed model, Onyango et al. (2021) revealed that Nairobi’s food security significantly declined in food price increments.


By analyzing the effects of food prices and income shocks on urban and rural food poverty in Kenya, I conclude that while falling incomes and rising food prices raise both rural and urban food poverty, rising food prices are counterbalanced by positive income growth. To address food insecurity in Kenya, I recommend the following:

  1. Expansion of the Hunger Safety Net Program and increases of cash transfer initiatives that target vulnerable groups by the national government.
  2. The roll-out of farm subsidies within Kenya’s arable rural lands by the Ministry of Agriculture in order to increase agricultural production and reduce rural food poverty.
  3. Economic diversification within the Bottom-Up Economic Transformation Agenda to raise household incomes and buffer households against food price inflation.

About the author

Kefa Simiyu (

This work is part of the CGIAR Research Initiative on National Policies and Strategies (NPS). CGIAR launched NPS with national and international partners to build policy coherence, respond to policy demands and crises, and integrate policy tools at national and subnational levels in countries in Africa, Asia, and Latin America. CGIAR centers participating in NPS are The Alliance of Bioversity International and the International Center for Tropical Agriculture (Alliance Bioversity-CIAT), International Food Policy Research Institute (IFPRI), International Livestock Research Institute (ILRI), International Water Management Institute (IWMI), International Potato Center (CIP), International Institute of Tropical Agriculture (IITA), and WorldFish. We would like to thank all funders who supported this research through their contributions to the CGIAR Trust Fund

Share this to :