Dynamic price monitoring, affordability, and economic equality in Lusaka, Zambia

Abstract

This study investigates the efficacy of Dynamic Price Monitoring (DPM) as a policy tool for improving household affordability of critical goods and fostering economic equity in Lusaka, Zambia. The research fills an important gap in the literature: while other studies have shown descriptive connections between price monitoring and welfare outcomes, there is a lack of empirical information on the distributional impacts of DPM across different income groups. An integrated theoretical framework that combines signalling theory and income inequality theory guided this research. The study utilised a quantitative research design, incorporating cross-sectional household survey data from 384 respondents situated in three income-diverse residential neighbourhoods (Woodlands, Chalala, and Zingalume), alongside thirty quarters of secondary price data spanning from Q3 2017 to Q4 2024. The analytical framework integrated treatment-effects estimation via Least Absolute Shrinkage and Selection Operator (LASSO) regression, Difference-in-Differences (DiD) analysis categorised by income cluster, and methodologies for inequality decomposition. We measured the dependent variable, Affordability of Essential Commodities (AEC), by looking at the percentage of household income spent on basic goods. We measured the independent variable, DPM exposure, by using a simulated treatment based on Dynamic Price Index (DPI) volatility thresholds. The Economic Equality Index (EEI), based on Gini coefficients of AEC distributions, was the main measure of inequality. The results show that exposure to DPM leads to statistically significant increases in affordability, with an average treatment effect of 0.1872 (p < 0.01). Distributional analysis shows that the effects were progressive: low-income households had a 3.8 percentage point drop in their essential expenditure burden (from 68.0% to 64.2%), while high-income households saw no meaningful changes. The DiD estimates reveal varied treatment effects, with low-income clusters showing the highest and most significant improvements (coefficient = -0.254, p < 0.001). A coping strategy study shows that low-income households have made big changes to their negative coping behaviours. For example, they skip meals less often (31% less often) and rely less on high-interest informal debt (22% less often). An inequality study shows that the EEI went up by 0.05, which means that spending-based inequality went down by 12.2%. The research finds that DPM is a policy tool that promotes equality and has clear progressive distributional impacts. Suggestions include making the Lusaka Dynamic Price Index a permanent part of the Zambia Statistics Agency; adding DPM triggers to social protection programmes that can change over time; making market regulation stronger by making prices more clear; starting public information campaigns on multiple channels; and setting up a DPM governance framework for ongoing policy learning. These findings enhance theoretical comprehension of information-driven welfare interventions and offer empirical assistance for urban policy formulation in environments marked by price instability and systemic inequality.