metadata: authors: 0: author: Atamanov,Aziz
1: author: Mukiza,Chris Ndatira
2: author: Ssennono,Vincent Fred
count: Uganda
docna: 0: docna: Quantifying Vulnerability to Poverty in Uganda
docty: Policy Research Working Paper
abstracts: cdata!: Robust poverty reduction in Uganda was
            disrupted by episodes of shocks during recent years. This
            paper estimates vulnerability to poverty in Uganda and
            explores the sources and main correlates of vulnerability
            using the most recent Uganda National Household Survey
            2019/20. The analysis reveals that about 50 percent of
            population in Uganda is vulnerable to poverty. Vulnerability
            rates are much higher than poverty in rural areas. Urban
            vulnerability is predominantly risk induced (high volatility
            of consumption) and mostly associated with idiosyncratic
            rather than covariate shocks. Rural vulnerability is equally
            split between risk-induced and poverty-induced vulnerability
            (permanently low consumption). Although in absolute terms
            vulnerability due to covariate shocks is still lower than
            vulnerability due to idiosyncratic shocks, in relative terms
            covariate shocks are more important in rural areas.
            Education is found to be one of the key variables related to
            lower vulnerability to poverty.
display_title: Quantifying Vulnerability to Poverty
            in Uganda
listing_relative_url: /research/2022/04/33788867/quantifying-vulnerability-poverty-uganda
topicv3: Poverty m1326687 695,Shocks and Vulnerability to Poverty
            m1326691 1160
publishtoextweb_dt: 2022-04-18T00:00:00Z
dois: 10.1596/1813-9450-10007
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1182029
model_extractions: Policy Research Working Paper 10007 Quantifying Vulnerability to Poverty in Uganda _Aziz Atamanov Chris Ndatira Mukiza Vincent Fred Ssennono_ Poverty and Equity Global Practice April 2022
Policy Research Working Paper 10007

# **Abstract**

Robust poverty reduction in Uganda was disrupted by episodes of shocks during recent years. This paper estimates vulnerability to poverty in Uganda and explores the sources and main correlates of vulnerability using the most recent Uganda National Household Survey 2019/20. The analysis reveals that about 50 percent of population in Uganda is vulnerable to poverty. Vulnerability rates are much higher than poverty in rural areas. Urban vulnerability is predominantly risk induced (high volatility of consumption) and mostly associated with idiosyncratic rather than covariate shocks. Rural vulnerability is equally split between risk-induced and poverty-induced vulnerability (permanently low consumption). Although in absolute terms vulnerability due to covariate shocks is still lower than vulnerability due to idiosyncratic shocks, in relative terms covariate shocks are more important in rural areas. Education is found to be one of the key variables related to lower vulnerability to poverty.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aatamanov@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Quantifying Vulnerability to Poverty in Uganda*

JEL Classification: Q54, C63, R11, R5, I3 Key words: Uganda, vulnerability, poverty, risk Authors: Aziz Atamanov*, Chris Ndatira Mukiza** and Vincent Fred Ssennono**. Corresponding author is Aziz Atamanov (aatamanov@worldbank.org).

Acknowledgments: The authors are thankful to Emmanuel Skoufias for peer-reviewing the paper. We would like to thank Jia Gao, Moritz Meyer and Cara Ann Myers for useful comments and suggestions how to improve the paper. We are thankful for overall guidance to Mukami Kariuki, Pierella Paci and Franklin Mutahakana.

* World Bank, ** Uganda Bureau of Statistics.
# Introduction

Frequent exposure to different shocks is common for households in developing countries. The shocks introduce substantial volatility in households’ income or consumption<sup>1</sup> and can lead to changes in poverty status. This makes the poverty indicator less accurate in measuring risk to poverty since it is based on the observed level of wellbeing. In contrast, assessing the likelihood of households falling into poverty or their vulnerability to poverty can be a more useful measure to assess the dynamic nature of poverty. Understanding the nature of vulnerability, which may be related to high consumption fluctuations, permanently low consumption, household or common shocks is also important for the design of effective poverty alleviation policies (Chaudhuri, 2003; Naudé, Santos-Paulino & McGillivray 2009).

In recent years, a significant share of the population of Uganda has experienced different idiosyncratic (household specific) and covariate (common for households in the community) shocks. According to the Uganda National Panel Survey 2019/20, 32 percent of the population experienced at least one shock during the last 12 months. The rural population was more likely to experience shocks than their urban counterparts (37 percent versus 19 percent, respectively). The population from the bottom quintile of consumption per adult equivalent was most likely to experience a shock at 42 percent.

Uganda is one of the most vulnerable and least adapted countries to climate change based on the Notre Dame Global Adaptation Initiative (ND-GAIN). Most of the land used for crops is rainfed and the agricultural sector consists of small subsistence farms averaging less than two hectares. This makes households in rural areas particularly vulnerable to weather changes. The increase in poverty observed in 2016/17 was to a largest extent due to severe drought and pests (World Bank, 2018). According to the UNPS, the most frequent shock in 2019/20 was also a drought, which affected 13 percent of the population at the national level and 21 percent in the Northern region. The inflow of refugees and the COVID-19 pandemic also stand out as prominent covariant shocks to have recently hit Uganda. One way in which experiencing shocks manifests itself is through a high volatility of economic growth. This is reflected in Uganda, whereby gross domestic product (GDP) per capita has dropped to less than one percent multiple times during last 20 years.

Addressing the challenges associated with high vulnerability to poverty requires a developed and adaptive social protection system which effectively targets the vulnerable population and provides adequate benefits to insure against the shock. The national development plans in Uganda (NDP) prioritize social protection as one of the key strategies for transforming Uganda from an agrarian to a modern society. However, the current levels of expenditure on social protection and the coverage are low. For example, according to the Atlas of Social Protection Indicators of Resilience and Equity (ASPIRE), programs providing cash transfers accounted for 0.16 percent of GDP in 2016. This was lower than what was spent in Tanzania (0.24 percent of GDP) and Rwanda (1.16 percent of GDP). The coverage of social protection programs is quite low, with the two largest direct income support programs in Uganda covering only three percent of the population (World Bank, 2020). This was lower than the average of nine percent in East Africa. Maintaining adequacy of the direct income programs is also an issue. Thus, the nominal value of the Senior Citizens Grant has not been adjusted since 2010.

To date there have been several empirical studies measuring vulnerability of population in Uganda to poverty. Lawson, Mckay and Okidi (2006) used panel data for the years 1992–1999 to show poverty transitions and analyzed their characteristics by using quantitative and qualitative analysis. Similar work was done by Ssewanyana and Kasirye (2012) who used panel data to show that compared to the 1992-

> 1 Given that poverty is measured predominantly by consumption, we use the term consumption throughout the text.

2
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.

3
<!-- Start of picture text --> MB actual consumption<br>@ mean expected consumption<br>A: Poor and vulnerable — variance of expected consumption<br>(chronic poor) B: Poor, but likely non-<br>—ii—ouo vulnerable<br>z3£w C: Non-poorvulnerableand not<br>§33 ——_@_H-<br>=<br>D: Non-poor, but vulnerable<br>(risk induced)<br>E: Non-poor, but vulnerable<br>(poverty induced)<br>Poverty line<br><!-- End of picture text -->
# _The estimation strategy_

In most household level studies that measure the vulnerability to poverty, vulnerability is related to the propensity or probability of a household experiencing an episode of poverty or a shock that brings its welfare below a socially accepted level (often established as a poverty line). As a result, assessing vulnerability requires information on the mean and variance of the household welfare collected ideally from many repeated observations in different periods of time. In reality, there are very few countries, in particular in the developing world, where panel data tracks the same households for more than 15-20 years, and this prevents the construction of a reliable estimate of the mean and variance. Therefore, the most feasible approach is to estimate vulnerability using cross-sectional data by making some strong assumptions (Naudé, Santos-Paulino & McGillivray 2009; Skoufias, Vinha and Beyene 2021).

In this study, the method used is based on Gunther and Hartgen (2009) who integrated a two-level hierarchical model (Goldstein, 1999) into Chaudhuri’s (2002) method. This allows one to differentiate unexplained variance at the household level (related to idiosyncratic household specific shocks) from unexplained variance at the community level (covariate community level shocks). This method also corrects for inefficient estimators that might occur if Chaudhuri’s (2002) method is applied to hierarchical data with household and community level variables used simultaneously in consumption modelling (Gunther and Hartgen, 2009).

The main hypothesis the method makes is that unexplained variance in the consumption of otherwise equal households captures the impact of both household-specific and community-specific shocks on households’ consumption. This variance is assumed to be correlated and can be explained by observable household and community characteristics.

Following Gunther and Hartgen (2009) and Skoufias, Vinha and Beyene (2021), let i =(1,... , n) denote households at level one and j =(1,... , j ) denote communities at level two, with households being nested within communities. Consumption, c, of household i in community j is specified as The coefficients (both constant terms and slopes) of each community are assumed to be affected by community observed characteristics (Z) and community unobserved factors denoted by u0j and u1j Thus, there are three error terms to be estimated u0j , u1j and eij where eij captures the idiosyncratic shocks and u0j and u1jXij capture the community covariate shocks. In particular, u0j captures the direct effect of covariate shocks affecting the intercept of each community and all households in the same community in the same manner, while u1jXij captures the indirect effect of covariate shocks. Equation (4a) may be rewritten as equation (4b) 5
During the first stage, the equation (4b) is estimated using Stata’s command for mixed-effects maximum likelihood regression. In the second stage, the squared residuals and their squares sum from equation (4a) may be regressed on Xij and Zj : The estimated coefficients of equations (4a), (5a), (5b) and (5c) may then be used to estimate the expected 2 2 2 mean and the expected idiosyncratic σeij , covariate σuoj , and the total σuoj+eij of a household’s consumption based on the observed characteristics of both the household and the community.

Assuming that consumption is log-normally distributed, one can estimate the probability of consumption falling below the poverty line z and using a threshold for this probability (for example 50 percent) to define who is vulnerable to poverty.

The probability of consumption falling below the poverty line z is estimated by assuming that consumption is log-normally distributed, where X and Z are household and community level characteristics i.e.

Where ∅ denotes the cumulative density of the standard normal distribution function; lnz, the log of poverty line; lncij⌃ the expected mean of per capita (log) consumption.

Expression (6) can be used to derive an estimate of the vulnerability to poverty from covariate or 2 2 community level shocks by replacing (σu0j+ eij) by σu0j , while an estimate of the vulnerability to poverty 2 2 from idiosyncratic shocks by using σeij in place of (σu0j+ eij) .

The last step to identify a vulnerable household involves a selection of a threshold for the probability of being poor. Following previous empirical literature (Gunther and Hartgen, 2009; Skoufias, Vinha & Beyene, 2021) households are considered vulnerable if they have a probability of 50 percent or higher to fall below the poverty line at least once in the next two years. This is equivalent to a 29 percent or higher probability in any given year. This threshold is chosen in the current study.

# 3. Data

## _Uganda National Household Survey 2019/20_

The data used in this paper is based on the Uganda National Household Survey (UNHS) conducted in 2019/20 by Uganda Bureau of Statistics (UBOS). This survey is the seventh in the series of household surveys conducted by UBOS. Previous Uganda National Household Surveys were conducted in 1999/2000, 2002/2003, 2005/2006, 2009/2010, 2012/2013 and 2016/2017. UNHS is the only survey used to monitor national poverty numbers in the country. The main objective of the survey is to collect high quality and timely data on demographic, social and economic characteristics of the household population; to inform as well as monitor international and national development frameworks.

6
A two-stage stratified approach for sampling was used. At the first stage, Enumeration Areas (EAs) were drawn from each of the sub-groups with Probability Proportional to Size (PPS) while at the second stage, households, being the ultimate sampling units, were drawn using Systematic Random Sampling (SRS). A total of 1,651 EAs were covered, with a total of 16,510 households expected to be covered. Of these households, 13,732 were interviewed giving a national response rate of 83 percent (UBOS, 2021).

UNHS 2019/20 is representative at the national level, for rural and urban areas, at the regional level, including Central, Eastern, Western and Northern regions, and at the sub-regional area, including the 15 sub-regions of Acholi, Ankole, Bukedi, Bugisu, Bunyoro, Busoga, Kampala, Karamoja, Kigezi, Lango, North Buganda, South Buganda, Tooro, Teso, and West Nile.

UNHS collects detailed expenditure data on an item-by-item basis, according to a 30-day recall period. Expenditures are then aggregated at the household level to produce the welfare aggregate. Food produced and consumed at home is valued using market prices and added to this aggregate. The welfare aggregate is further adjusted to account for inter-temporal and spatial price variation. Finally, the aggregate is adjusted to take into account household composition in terms of sex and age. A detailed explanation of the poverty methodology can be found in Appleton & Ssewanyana (2003). In this study, the updated poverty line, revised in 2021, is used.

In addition to the household questionnaire, UNHS administers the Community module, which collects information about the general characteristics of the community, availability and access to community facilities and services, client satisfaction with education and health services; water and sanitation in the community, economic activities, agricultural extension services; and changes in the community and community groups.

# _Descriptive statistics_

A number of household and community characteristics are included as explanatory variables in the twolevel consumption estimation. The household variables include household size and dependency ratio, as well as a range of different assets owned by households such as fridge, television, radio, motorcycle and bicycle. Household housing conditions are captured by the number of rooms per capita, and the main source of energy used for cooking. Head of household’s age, education, and employment status are included, as well as the ownership of nonfarm business, possession of at least two sets of clothes and a pair of shoes among each member of the household.

Community level variables were presented by four dummy variables. The first measure indicates that agriculture is the main economic activity in the community. The second one indicates whether or not the community is connected to the national grid. The third variable indicates during the last three years, if there was migration in the community (people were either coming or leaving the community). The last community variable measures if, during last three years, the community benefited from a new road.

Table 1 shows the mean values for each household and community level variable at the national, urban and rural levels and across consumption per adult equivalent quintiles. As one would expect, households in rural areas are characterized by a larger household size, lower employment rates and education levels of the heads of households, and lower ownership of assets. The same variables also have a strong income gradient varying across consumption quintiles.

7
|||Residence|||Adult eq|uivalent q|uintiles||
|---|---|---|---|---|---|---|---|---|
|Variable definition|National|Rural|Urban|Poorest|2Q|3Q|4Q|Richest|
|household owns fridge|0.05|0.01|0.14|0.00|0.00|0.01|0.03|0.19|
|householdownstelevision|0.19|0.11|0.44|0.02|0.06|0.13|0.24|0.53|
|<br>household owns radio|0.34|0.36|0.31|0.20|0.30|0.40|0.41|0.40|
|household owns motorcycle|0.10|0.10|0.09|0.02|0.05|0.07|0.15|0.21|
|household owns bicycle|0.22|0.25|0.14|0.19|0.23|0.26|0.25|0.18|
|household size|5.95|6.13|5.48|6.68|6.24|5.97|5.72|5.17|
|roomspercapita|0.45|0.44|0.47|0.35|0.39|0.43|0.47|0.59|
|<br>headofhouseholdage|45|46|44|45|46|46|45|44|
|<br>share of children 0-13 and elderly 60 plus in total household size|0.49|0.51|0.45|0.54|0.53|0.50|0.49|0.41|
|head is employed|0.59|0.55|0.71|0.43|0.53|0.57|0.66|0.76|
|head is notworking|0.19|0.20|0.18|0.27|0.19|0.19|0.16|0.15|
|head is in subsistence agriculture|0.22|0.26|0.12|0.31|0.28|0.24|0.18|0.10|
|head is without education|0.16|0.17|0.11|0.29|0.17|0.14|0.11|0.07|
|head with primary incomplete education|0.38|0.42|0.25|0.44|0.46|0.41|0.35|0.22|
|head with primary complete|0.18|0.18|0.17|0.15|0.17|0.19|0.20|0.18|
|head with some secondary education|0.22|0.19|0.32|0.11|0.18|0.23|0.26|0.33|
|head with post-secondary education|0.07|0.04|0.14|0.01|0.02|0.04|0.07|0.20|
|everymemberofthehouseholdhaveatleastonepairofshoes|0.64|0.57|0.82|0.31|0.51|0.66|0.79|0.92|
|<br>every member ofthe household have at leasttwo sets ofclothes|0.93|0.92|0.96|0.85|0.93|0.95|0.96|0.97|
|household owns at leastone hh enterprise|0.33|0.30|0.43|0.23|0.28|0.33|0.39|0.44|
|main source ofenergy for cooking: other than firewood and charcoal|0.03|0.01|0.07|0.01|0.02|0.02|0.02|0.07|
|main source ofenergy for cooking: charcoal|0.19|0.08|0.49|0.05|0.06|0.16|0.24|0.43|
|main source ofenergy for cooking: firewood|0.78|0.91|0.45|0.95|0.92|0.82|0.74|0.49|
|agriculture is the main activity in community|0.83|0.97|0.45|0.96|0.94|0.87|0.79|0.60|
|community is connected to the national grid or mini-grid|0.37|0.23|0.74|0.19|0.27|0.35|0.41|0.61|
|in the lastthree years more people were either movingin or outofthecc|0.61|0.61|0.62|0.70|0.64|0.61|0.59|0.54|
|in the lastthree years community benefited from a new road|0.21|0.20|0.25|0.19|0.20|0.23|0.22|0.23|
|Table 2. Regression results: estimates of consumption equation(logcon|sumptionper capita)|
|---|---|
|Variable|Log Per Capita Consumption|
|agriculture is the main activity in the community|-0.190***|
||(0.031)|
|the community is connected to the national grid|0.051***|
||(0.017)|
|community with migration (people were coming or leaving community)|-0.297***|
||(0.045)|
|community benefited from new road during last 3 years|0.059***|
||(0.016)|
|household owns fridge|0.388***|
||(0.029)|
|household owns television|0.297***|
||(0.016)|
|household owns radio|0.049**|
||(0.025)|
|household owns motorcycle|0.136***|
||(0.041)|
|household owns bicycle|0.067***|
||(0.013)|
|household size|-0.102***|
||(0.005)|
|age of household head|0.004***|
||(0.000)|
|share of children 0-13 and elderly 60 plus in total household size|<br>-0.059***<br>|
||(0.019)|
|each member has a pair of shoes|0.202***|
||(0.011)|
|each member had two pairs of clothes|0.120***|
||(0.018)|
|at least one household enterprise|0.049***|
||(0.011)|
|head of household is not working|-0.097***|
||(0.013)|
|head of household is in subsistence farming|-0.048***|
||(0.013)|
|household head has incomplete primary education|0.104***<br>|
||(0.014)|
|household head has complete primary education|0.158***|
||(0.017)|
|household head has some secondary education|0.207***|
||(0.017)|
|household head has post-secondary education|0.417***|
||(0.025)|
|energy used for cooking is charcoal|-0.280***|
||(0.039)|
|energy used for cooking is firewood|-0.412***|
||(0.038)|
|community with migration*charcoal|0.197***|
||(0.046)|
|community with migration*firewood|<br>0.224***|
||(0.048)|
|agricultural community*household size|0.027***<br>|
||(0.005)|
|agricultural community*motorcycle|<br>0.196***|

9
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# 5. Conclusions

Half of the population in Uganda was estimated to be vulnerable to poverty in 2019/20, which is much higher than the poverty rate of about 30 percent recorded in the same period. The vulnerability rate was much higher in rural areas and in the Eastern and Northern regions of the country. In selected sub-regions, the vulnerability rate was even more than twice as high as the poverty rate (e.g. West Nile, Ankole, Bunyora, Toora and Elgon). These findings signal that using only the poverty rate may not be enough to understand the dynamics of poverty in Uganda.

The types of vulnerability differ substantially across rural and urban areas. Vulnerability in urban areas and the Central and Western regions is predominantly due to high volatility of consumption or riskinduced. Vulnerability in rural areas can be equally attributed to high volatility (risk-induced) and povertyinduced (due to low human/physical capital and assets), while in the Northern and Eastern regions vulnerability to a larger extent is associated with low consumption or poverty-induced. Higher riskinduced vulnerability among urban households in relative terms calls for social security type policy measures. The almost equal importance of risk- and poverty-induced vulnerability in rural areas calls for policy measures that can tackle both structural poverty and high consumption volatility.

Idiosyncratic or household specific shocks have a higher impact on the population of Uganda than covariate or common shocks across rural/urban areas and in each region. Similar results were obtained in Madagascar and Ethiopia. One potential explanation for this higher impact of idiosyncratic shocks can be related to the better ability to anticipate and prepare for covariate shocks, but this requires further analysis. In relative terms, the role of idiosyncratic shocks is much more prevalent in urban areas and the Central and Western regions. However, the relative role of covariate shocks increases in rural areas and the Northern and Eastern regions. This signals that covariate shocks such as weather, locust, or price shocks play a more important role for rural residents and will require specific social insurance policies.

Several household characteristics are important predictors of vulnerability status, with the head of household’s education being the strongest correlate. Households with heads having higher than secondary education have a 57 percent lower probability of being vulnerable compared to households with head without any education. Ownership of assets, marital status and residence were also found to be important predictors of vulnerability. Interestingly, almost no difference was found between having heads out of the labor force head and heads employed in subsistence farming on the probability of being vulnerable. Both categories increased the probability of being vulnerable by about 6-8 percent compared to having household heads in paid employment.

In sum, high vulnerability to poverty and the inability to cope with negative shocks in Uganda may stall the progress of poverty reduction and reverse the trends. The obtained empirical findings on the nature of vulnerability point to the importance of having a targeted social policy that is adapted to the prevalent type of vulnerability and the types of shocks driving it.

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