Tag: Poverty Measure

Poverty Dynamics in Belarus from 2009 to 2016

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This brief is based on research that studies the incidence and determinants of poverty in Belarus using data from the yearly Household Budget Surveys for 2009-2016. Poverty is evaluated from a consumption perspective applying the cost of basic needs approach. According to the results, in 2015-2016, absolute poverty in Belarus increased twofold and reached 29% of the population. Large household size, high number of children, single mothers and unemployment negatively affect household welfare and increase poverty risk. Moreover, living in rural areas increases the likelihood of being poor and correlates negatively with welfare.


Sizeable and increasing poverty poses a threat to social stability and long-term sustainability for every country. Before 2009, Belarus registered over a decade of high and sustainable economic growth that enhanced the average standard of living and raised a substantial number of Belarusians out of poverty. According to the National Statistical Committee of the Republic of Belarus (Belstat), the poverty rate in Belarus (by official definition) has decreased from 41.9% of the population in 2000 down to 6.1% in 2008. The largest reported decline in poverty was in 2001 – from 41.9% to 28.9%.

Since then, Belarus experienced several episodes of economic crises – in 2009, 2011 and 2015-2016 (Kruk and Bornukova, 2014; Mazol, 2017a). Such economic downturns typically introduce considerable survival problems for many households. For example, according to the World Bank, in some countries the poverty rate may reach 50% (World Bank, 2000). In light of this, the small increase (0.3%) in the official poverty measure during these periods casts doubt on the official methodology used for poverty calculations. This brief describes an alternative measure of absolute poverty based on the widely recognized cost of basic needs approach; and summarizes the results of the study of how economic downturns in Belarus influenced welfare and poverty at the household level.

Data and methodology

The data used in this research are pooled cross-sections from 2009 to 2016 of the yearly Belarusian Household Budget Surveys with on average 5000 households in each sample obtained from Belstat. These surveys consist of household and individual questionnaires that contain important data about households including decomposition of expenditures and income by categories, detailed data on consumption of food items, the size, age and gender composition of households, living conditions, etc.

The analysis applies the cost of basic needs approach (Kakwani, 2003). It first estimates the cost of acquiring enough food for adequate nutrition (nutrition requirements for households of different size and demographic composition) per person (food poverty line) and then adds the cost of non-food essentials (absolute poverty line).  The calculated poverty lines for each sampled household are compared with the household consumption per person. All measures are preliminary deflated to take into account differences in purchasing power over time and regions of residence.

In contrast, the official poverty measurement compares per capita disposable income of a household with national (official) poverty line, which is the average per capita subsistence minimum budget of a family with two adults and two children (see Table 1).

Table 1. Consumer budgets and absolute poverty line by year in Belarus, in constant BYN

Year 2009 2010 2011 2012 2013 2014 2015 2016
Subsistence minimum budget1 247 258 293 317 332 362 369 373
Minimum consumer budget2 372 396 367 448 491 517 554 620
Absolute poverty line3 383 395 437 448 468 475 499 520

Source: 1 Belstat; 2 Ministry of Labour and Social Protection Republic of Belarus; 3 author’s own calculations.

The empirical strategy of the analysis assumes setting the food, non-food and absolute poverty lines using the cost of basic needs approach, estimating poverty measures at the level of entire Belarus and its regions based on Foster-Greer-Thorbecke’s poverty indices (Foster et al., 1984), and analyzing the determinants of welfare and poverty using OLS and probit regressions.

Poverty incidence

The timeline of poverty analysis for Belarus can be subdivided into three periods: crisis of 2009-2011, recovery of 2012-2014, and a crisis of 2015-2016 (see Figure 1).

The results show that during the first period (from 2009 to 2011), absolute poverty at the national level increased from 30.9% to 32.6%. Incidence of absolute poverty for rural and urban areas in 2011 reached 45% and 28% of the population, correspondingly.

Figure 1. Incidence of absolute poverty and GDP per capita growth in Belarus

Source: Author’s own calculations.
Note: Estimates reflect weighted household data.

The second period (from 2012 to 2014) was characterized by a sharp poverty reduction. For example, the absolute national poverty headcount ratio has plummeted from 32.6% in 2011 to 14.9% in 2014, rural poverty dropped by 22.1 percentage points or almost by half and urban poverty decreased by 16.2 percentage points.

In turn, the third period saw a sharp rise in the incidence of poverty. From 2015 to 2016, the headcount ratio for absolute poverty increased by 14.4 percentage points. As a result, in 2016 absolute poverty in Belarus reached 29.3% or almost the same as in 2009 and 2011 (Mazol, 2017b).

Causes and determinants of poverty

The significant increase in poverty in 2015-2016 was due to a combination of several factors, including the household income decline in comparison with its growth in previous years, the increasing need to spend more on food necessities and the growth in food and especially non-food price levels.

As the Figure 2 shows, starting from 2015 there has been a rapid increase in the real cost of non-food budget for Belarusian households, while the food budget has remained almost the same in real terms. Correspondingly, in 2016 the non-food poverty line increased by 14.6%, while the food poverty line went up only by 2.9%.

Figure 2. Real monthly average per capita household expenditure on food and non-food items and real monthly standardized food and non-food poverty lines, 2009-2016, in BYN

Source: Author’s own calculations.
Note: Estimates reflect weighted household data.

Furthermore, as income fell (by 7.2% in 2015-2016), the share of food items in total expenditure increased and real non-food expenditure decreased. This is because household income was not enough to cover both expenditures on food and non-food items. Due to the 2015-2016 economic crisis the cost of meeting the food essentials increased so fast that it has squeezed the non-food budget, leaving insufficient purchasing power for non-food items.

The study also shows that among factors that substantially influence household welfare and poverty at the household level in Belarus are family size, the number of children in a household, presence in the household of economically inactive members. Moreover, single mothers in Belarus appear to be noticeably more vulnerable to macroeconomic shocks than full families both from welfare and poverty perspectives.

Additionally, one of the most important determinants of welfare and poverty in Belarus is spatial location of a household. In particular, poverty highly discriminates against living in rural areas. The poverty incidence for rural areas over 2009-2016 is approximately 10.5 percentage points (or 44%) higher than the national average, while that of the urban areas is nearly 4 percentage points (or 16%) below national average. Moreover, in 2015-2016 urban and rural disparity for poverty widened even more and reached 25.3% for urban vs 40.6% for rural areas.

Finally, two more factors, savings and access to a plot of land, have on average a large positive influence on consumption expenditure and aa negative one on the chance of getting poor.


Poverty alleviation and development reflect economic and social progress in any country. While Belarus initially achieved noticeable progress in this dimension, the economic and social development in recent years seems to increase problem of poverty in Belarus. The estimates show that in 2015-2016, absolute poverty in Belarus increased almost twofold. Household size, large numbers of children in a household, the presence in the household of economically inactive members are all factors that decrease household welfare and increase poverty. Single mothers also appear to be substantially more vulnerable to macroeconomic shocks. Finally, one of the most important determinants of welfare and poverty in Belarus is if a household is rural. These findings are important warning signals for the design of pro-poor policies in Belarus.


  • Foster, J., J. Greer, and E. Thorbecke. (1984). A Class of Decomposable Poverty Measures. Econometrica, 52: 761-766.
  • Kakwani, N. (2003). Issues in Setting Absolute Poverty Lines, Poverty and Social Development Papers No. 3, June 2003. Asian Development Bank.
  • Kruk, D., Bornukova, K. (2014). Belarusian Economic Growth Decomposition, BEROC Working Paper Series, WP no. 24.
  • Mazol, A. 2017a. The Influence of Financial Stress on Economic Activity and Monetary Policy in Belarus, BEROC Working Paper Series, WP no. 40.
  • Mazol, A. 2017b. Determinants of Poverty With and Without Economic Growth. Explaining Belarus’s Poverty Dynamics during 2009-2016, BEROC Working Paper Series, WP no. 47.
  • World Bank (2000). Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia. Washington DC, The World Bank.

Disclaimer: Opinions expressed in policy briefs and other publications are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.

Old-Age Poverty and Health – How Much Does Income Matter?

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The question concerning the material situation of older people and its consequences for their wellbeing seems to be more important than ever. This is especially true given rapid demographic changes in the Western World and economic pressures on governments to reduce public spending.  We use data from the Survey of Health, Ageing and Retirement in Europe (SHARE) to examine different aspects of old-age poverty and its possible effects on deterioration in health. The data contains information on representative samples from 12 European countries including the Czech Republic and Poland. We use the longitudinal dimension of the data to go beyond cross sectional associations and analyze transitions in health status controlling for health in the initial period and material conditions. We find that poverty matters for health outcomes in later life. Wealth-defined and subjective poverty correlates much more strongly with health outcomes than income-defined measure. Importantly subjective poverty significantly increases mortality by 58.3% for those aged 50–64 (for details see Adena and Myck, 2013a and 2013b). 

Measuring Poverty

When measuring poverty, the standard approach is to define the poverty threshold at 60% of median equalized income. This standardized measure offers some advantages, such as simplicity and comparability with already existing studies. However, there are valid arguments against its use when analyzing old-age poverty. The permanent-income theory provides arguments against current income as a major determinant of quality of life of older people. Moreover, poverty defined with respect to current income while taking account of household size through equalization, ignores other important aspects of living costs such as disability or health expenditures. Additionally, most analysis using income-poverty measures ignore such aspects as housing ownership and housing costs.

Our analysis examines different aspects of poor material conditions of the elderly. The first poverty definition refers to respondents’ wealth as an alternative to income-defined poverty. Poor households, defined with reference to wealth (“wealth poverty” – WEALTH), are those that belong to the bottom third of the wealth distribution of the sample in each country. For this purpose, household wealth is the sum of household real assets (net of any debts) and household gross financial assets. Secondly, we compare the above poverty measures to a subjective measure of material well-being. This measure is based on subjective declarations by respondents, in which case (“subjective poverty” – SUB) individuals are identified as poor on the basis of a question of how easily they can make ends meet. If the answer is “with some” or “with great” difficulty, individuals in the household are classified as “poor”.

One reflection of potential problems with the standard income poverty measure becomes visible when it is compared with the subjective measure. The graph below shows the differences in country rankings when using one or the other poverty measure.  The country with the greatest disproportion is Czech Republic. While being ranked as second according to the income measure, it is ninth according to the subjective measure.

Figure 1. Country Ranks in Old-Age Poverty According to an Income versus a Subjective Measure


Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0).

Even more striking is the fact that the differences between ranks are not because of over or under classification of individuals as poor, but rather because of misclassification. Figure 2 shows that there is little overlap between different poverty measures. The share of individuals classified as poor according to all three measures is only 7.95%, whereas it is 60% according to at least one of the measures.

Figure 2. Poverty Measure Overlap


Notes: Data weighted using Wave 2 sample weights. Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0).

Measuring Well-Being

We examine three binary outcomes measuring the well-being of the respondents – two reflecting physical health, and one measuring individuals’ subjective health. The two measures of physical health are generated with reference to the list of twelve symptoms of bad health and the list of twenty-three limitations in activities of daily living (ADLs). In both cases, we define someone to be in a bad state if they have three or more symptoms or limitations. The two definitions are labelled as: “3+SMT” (three or more symptoms) and “3+ADL” (three or more limitations in ADLs). Subjective health “SUBJ” is defined to be bad if the subjective health assessment is “fair” or “poor”. Finally, we also analyze mortality as an “objective” health outcome.

Poverty and Transitions in Well-Being and Health

There is some established evidence in the literature that poverty negatively affects health and other outcomes at different stages of life.[1] At the same time, there is little evidence on how the choice of the poverty measure might result in under- or over-estimation of the effects of poverty. We address this question by examining different poverty measures as potential determinants of transitions from good to bad states of health.

The results confirm that living in poverty increases an individual’s probability of deterioration of health. In a compact form, Figure 3 presents our results from 12 separate regressions (4 outcomes, three poverty measures). Here we report the odds ratios related to the respective estimated poverty dummies. Individuals classified as poor according to the income measure are 37.7% more likely to report bad subjective health in a later wave of the survey than their richer counterparts; they are 4.5% more likely to suffer from 3 or more symptoms; 18.7% more likely to suffer from 3 or more limitations; and 5% more likely to die. The last three effects, however, are not statistically significant.

In contrast, the effects of wealth-defined poverty and subjectively assessed poverty are 2-8 times stronger than those of income poverty, and they are also significant for all outcomes but death. Overall, wealth-defined poverty and subjective assessment of material well-being strongly correlate with deterioration in physical health (exactly the same goes for improvements in health, see Adena and Myck 2013b).

Figure 3. Poverty and Transitions from Good to Bad States Overlap


Notes: Data weighted using Wave 2 sample weights. Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0, Wave 3, release 1, Wave 4, release 1).

Poverty and Mortality in the Age Group 50-64

Our analysis reveals differences between age groups and confirms the decreasing importance of income (and thus income defined poverty) with age. As compared to the average effects presented in Figure 3, for the younger age group 50–64 income poverty proves more important as a determinant of bad outcomes, with transition probabilities between 20 and 40% for all outcomes (see Figure 4). The magnitudes are closer to those of other poverty measures, but still lower in all cases. Importantly, we find that wealth-defined and subjective poverty is an important determinant of death in the age group 50–64.

Figure 4. Poverty and Transitions from Good to Bad States 50-64 Slide3
Notes: Data weighted using Wave 2 sample weights. Source: Authors’ calculations using SHARE data (Wave 2, release 2.5.0, Wave 3, release 1, Wave 4, release 1).


The role of financial conditions for the development of health of older people significantly depends on the measure of material well-being used. In this policy brief, we defined poverty with respect to income, subjective assessment, and relative wealth. Of these three, wealth-defined poverty and subjective assessment of material well-being strongly and consistently correlate with deterioration and improvements in physical and subjective health. We found little evidence that relative income poverty plays a role in changes in physical health of older people. This suggests that the traditional income measure of household material situation may not be appropriate as a proxy for the welfare of older populations, and may perform badly as a measure of improvements in their quality of life or as a target for old-age policies. To be valid, such measures should cover broader aspects of financial well-being than income poverty. They could incorporate aspects of wealth and the subjective assessment of material situations as well as indicators more specifically focused on the consumption baskets of the older population.


  • Adena, Maja and Michal Myck (2013a): “Poverty and transitions in key areas of quality of life”, in: Börsch-Supan, Axel,  Brandt, Martina , Litwin, Howard and Guglielmo Weber (eds.) “Active Ageing and Solidarity between Generations in Europe – First Results from SHARE after the Economic Crisis.”
  • Adena, Maja and Michal Myck (2013b) Poverty and Transitions in Health, IZA Discussion Paper 7532, IZA-Bonn.


[1] For a literature review, see our publications.