1999 period, households in Uganda were found to be more vulnerable to poverty in the period 2005/062009/10. Among the most recent studies, Diwakar et al. (2019) used a different approach and in addition to poverty transitions estimated vulnerability from cross-section data using the approach from Chaudhuri et al. (2002). In particular, they estimated expected mean and variance in consumption using 2012/13 data and derived a vulnerability rate of 59 percent, which was almost three times higher than poverty rate at this time.

Despite being useful, these studies did not delve into the relative importance of idiosyncratic and covariate shocks for the vulnerable population in Uganda. Ex-post analysis of poverty transitions also suffered from being based only on observed level of welfare. Finally, these studies were based on old data. In order to fill the gap, this study will empirically assess the households’ vulnerability to poverty (measured by the probability of falling below the poverty line) using the most recent Uganda National Household Survey 2019/20 (UNHS). The study will use a two-level hierarchical regression model first proposed by Guenther and Harttgen (2009) and used to assess vulnerability in Madagascar, Angola (World Bank 2020), and Ethiopia (Skoufias, Vinha and Beyene 2021).

The prevalence of vulnerability will be estimated across geographic regions within Uganda. The vulnerability rate will be split into the fraction that is due to low physical and human capital assets (poverty-induced vulnerability) and the fraction of the vulnerability rate that is due to high consumption volatility (risk induced). In addition, the relative impact of idiosyncratic and covariate shocks on households’ vulnerability will be assessed. Given that vulnerability is very likely to be distributed unequally across geographic locations, knowing the extent to which risk-induced vulnerability is due to covariate and idiosyncratic shocks will provide useful information for policy makers (Guenther & Harttgen, 2009).

The proposed method is based on two critical assumptions that should be kept in mind. The first one assumes that present cross-sectional variance can be used to estimate future inter-temporal variance in consumption. This assumption might be too strong but cannot be tested due to the non-existence of lengthy panel data (at least 20-30 years). The second one assumes that unexplained variance in the consumption model is to a large extent ‘‘economic” variance and only to a lesser extent measurement error in consumption. The impact of the second assumption can be empirically tested (Guenther & Harttgen, 2009; Gao, Vinha & Skoufias, 2020).

The remainder of the paper is organized as follows. The concept of vulnerability and the estimation technique are explained in section 2 on methodology. Section 3 describes the data and section 4 presents and discussed empirical findings. Section 5 concludes.

# 2. Methodology

_The concept of vulnerability to poverty_ The main difference between the concept of poverty and vulnerability to poverty is related to risk. Poverty is an ex-post measure of household wellbeing reflected in the inability to satisfy some basic needs. In contrast, vulnerability measures likely future wellbeing, which depends on households’ exposure to shocks and ability to cope with them. Without the consideration of future risks, the vulnerability and expost poverty measures would be identical. Therefore, vulnerability is always a function of the expected mean and variance of households’ consumption. The mean of consumption depends on household and community characteristics, while variance depends on the shocks that a household faces and the coping mechanism they follow.

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