In economic literature the effect of minimum wage on the labour market and its relevance as an anti-poverty, equality-enhancing policy tool, is a matter of vigorous debate. The focus of this policy brief is a hypothetical effect on poverty rates, particularly among women, following an increase in the minimum wage in Georgia. A simulation exercise (Babych et al., 2022) by the ISET-PI research team shows that, in Georgia, a potential increase in the minimum wage is likely to result in an overall positive albeit small reduction in poverty rates in general. At the same time, women are likely to gain more from such minimum wage policy than men. The findings are consistent with the literature claiming that a minimum wage increase alone may not result in meaningful poverty reduction. Any minimum wage increase should thus be enhanced by other policies such as training programs increasing labor force participation among women.
Many countries around the world have enacted minimum wage laws. According to the International Labour Organization (ILO) “Minimum wages can be one element of a policy to overcome poverty and reduce inequality, including those between men and women” (ILO, 2023). In economic literature, the minimum wage debate has been particularly acute, with pros and cons of the minimum wage increases, their effect on the labor market, and their relevance as an anti-poverty and equality-enhancing policy tool fiercely contested in empirical studies and simulation studies. In this policy brief, we focus on the effect of a minimum wage increase in Georgia on poverty rates, and in particular poverty rates among women.
Minimum Wage Effects
According to the European Commission (2020) a number of benefits is associated with the introduction of minimum wage. These benefits include a reduction in in-work poverty, wage inequality and the gender pay gap, among others.
International evidence, however, cautions against considering an increase in minimum wage as the silver bullet to end poverty. A 2019 report by the International Labour Organization (ILO, 2019) shows that the incidence of poverty among the working poor is comparable to the incidence of poverty among individuals outside of the labor market. Therefore, even if an increase in minimum wages would lift all working poor out of poverty, a substantial number of poor would remain.
Moreover, minimum wage can have a potential adverse effect on employment of the most vulnerable by deterring firms from hiring low-wage, low-skilled labor (Neumark, 2018). The adverse employment effect will be stronger if current wages correspond more closely to the real productivity of labor. In such scenario companies would lose by retaining low-productivity workers and, likely respond to the increase in minimum wage by laying off workers, resulting in the loss of wages, rather than in their increase. On the other hand, if salaries are lower than the real productivity of the less productive workers, companies might still be able to profit from employing them and will not be forced to lay them off, resulting in a wage increase for low-wage workers.
Whether – and to what extent – the introduction of a minimum wage reduces poverty and/or assists low-income households then depends on how many individuals are going to lose their jobs, how many workers will maintain their jobs and receive a higher wage, and where these winners and losers are positioned along the distribution of family incomes.
With regard to employment effects, the results are not perfectly homogeneous. On the one hand, a large body of evidence suggests that minimum wages do lower the number of jobs accessible to low-skill employees (Sabia, Burkhauser and Hansen, 2012; Sotomayor, 2021; Neumark, 2018) On the other hand, some scholars argue that once the study design is changed to take into account the non-random distribution of minimum wage policies in different parts of the country in question, the “disemployment effect” of minimum wage policies (considering the example of United States) largely disappear (Allegretto et al., 2013; Dube et al., 2010).
With regards to poverty, a number of studies look at minimum wage as an anti-poverty policy tool for developing countries and consider its effectiveness in reducing poverty and/or inequality. For example, a study by Sotomayor (2021) suggests that poverty and income inequality in Brazil decreased by 2.8 and 2.4 percent respectively within three months of a minimum wage increase. Effects diminished with time, particularly for bottom-sensitive distribution measures, a process that is consistent with resulting job losses being more frequent among poorer households. The fact that the subsequent yearly increase in the minimum wage in Brazil resulted in a renewed drop in poverty and inequality shows that possible unemployment costs might be outweighed by benefits in the form of higher pay among working persons and – potentially – by positive spillover effects such as increased overall consumption.
Minimum Wage and Female Poverty
As in the case of poverty in general, there is some discrepancy in the literature on whether a minimum wage increase would help reduce poverty among women. Single mothers have been the focus of research in this regard since they are typically the most vulnerable low-wage workers, likely to be hurt by the loss of employment following an increase/ introduction of a minimum wage. Burkhauser and Sabia (2007) argue that the minimum wage increases in the U.S. (1988-2003) did not have any effect on the overall poverty rates, on the poverty rates among the working poor, or on poverty among single mothers. They argue that an increase in Earned Income Tax Credit (EITC), which provides a wage subsidy to workers depending on income level, tax filing status, and the number of children, would have a higher impact on poverty, in particular among single mothers.
In the meantime, Neumark and Wascher (2011) find that EITC and minimum wage reinforce each other’s positive effect for single women with children (boosting both employment and earnings), but negatively affects childless single women and minority men. Another study on the U.S. (Sabia, 2008) looked at the effect of minimum wage increases on the welfare of single mothers, finding that most of them were unaffected as they earned above-minimum wage. Single mothers with low-education levels did not see an increase in net incomes due to the negative effect on employment and hours worked: for low-skilled individuals, a 10 percent increase in minimum wage resulted in an 8.8 percent decline in employment and an 11.8 percent reduction in hours worked.
Yet another study (DeFina, 2008) focus on child poverty rates and show that minimum wage increases have a positive (reducing) impact on child poverty in female-headed families. The effect is small but significant (a 10 percent increase in the minimum wage decreases child poverty rates by 1.8 percentage points), controlling for other factors.
Ultimately, the effect of minimum wage on poverty among women or female-headed households is somewhat ambiguous. It depends on the poverty threshold used, other policy instruments (such as the EITC), existing incentives to enter employment and how, in the specific country of interest, labor laws may affect the employer’s cost of hiring (e.g. for France, see Laroque and Salanie, 2002).
The discussion is however relevant for countries like Georgia, where the wage gap between men and women is quite large, and where more women than men tend to work in low-wage and vulnerable jobs. While the overall poverty gap between men and women in Georgia is insignificant (mainly because poverty is measured at the household level), the gap becomes apparent when comparing female-headed households to male-headed ones. The poverty rates in the former case are nearly 2 percentage points higher in Georgia (20 percent vs. 18.3 percent in 2021). The poverty rates are the highest among households with only adult women (39.3 percent for all-female households vs. 20.1 percent overall in 2018).
A Simulation of a Minimum Wage Raise in Georgia
The Georgian minimum wage legislation dates back to 1999. The presidential decree N 351 from June 4, 1999 states that the minimum (monthly) wage that is to be set in Georgia is equal to 20 GEL (with some specific exceptions in the public sector). This is a non-binding threshold. Therefore, one has to think carefully what consequences might arise from raising the minimum wage to a much higher level. In addition to previously discussed aspects, one issue to keep in mind is the different average wages across different regions in Georgia. For example, a national minimum wage increase might have more of an impact in poorer regions, where both wages and incomes are lower, while it may still be non-binding in Tbilisi.
The ISET-PI research team (Babych et al., 2022) use Georgian micro data from the Labor Force Survey (LFS) and the Household Integrated Expenditure Survey (HIES), to simulate the effect of instituting a nation-wide minimum wage on both employment and poverty rates in different regions of Georgia. One focus area of the study was to analyze the effects of a minimum wage increase on female poverty. As with any exercise using a simulation approach, this study is subject to limitations imposed by the assumptions used, e.g. how much labor demand would respond to changes in the minimum wage, etc. The study considered two hypothetical thresholds of the minimum wage; 250 and 350 GEL respectively.
Figure 1. Share of private sector employees earning below certain thresholds, by gender, 2021.
The expected household income after the minimum wage increase was calculated and then compared to the poverty threshold (for each household in a standard way, using the “adult equivalence” scale). According to this methodology, any person who lives in a household which falls below the poverty threshold is considered to be poor. A “working poor” household is defined as a household below the poverty threshold where at least one adult is working.
Figure 1 shows that there is a substantial share of both men and women whose monthly wage income falls below the hypothetical minimum wage thresholds. In addition, women are more than two times as likely to be earning below these thresholds. However, the possible impact from an increased minimum wage on female vs. male poverty is not clear-cut. Since many women are part of larger households which include adult males, their possible income losses/gains may be counterbalanced by income gains/losses of male family members, leaving the overall effect on household income ambiguous.
In addition, poverty rates are not likely to be much affected by a minimum wage increase if most poor households are “non-working poor” (where adult family members are either unemployed or outside of the labor force), a consideration particularly relevant for Georgia. The share of poor individuals who live in “working poor” households (with at least one household member employed) is just 41 percent nationally (and 35 percent in rural areas), meaning that close to 60 percent of poor individuals nationwide (and 65 percent in rural areas) are not likely to be directly affected by minimum wage increases.
Female vs. Male Poverty: Scenarios Following a Minimum Wage Increase
As one can see in Figure 2, increased minimum wages tend to reduce poverty, but the impact is not larger than one percentage point. Not surprisingly, females benefit more than males (0.3 and 0.8 percentage points vs. 0.2 and 0.9 percentage points poverty reduction for men and women respectively, under different threshold scenarios). The maximum positive impact on poverty reduction is observed under a higher minimum wage threshold.
Figure 2. Estimated impact on poverty rates, based on the national subsistence minimum.
The impact of an increased minimum wage on the expected median consumption of households doesn’t exceed a few percentage points either, as illustrated in Figure 3.
The impact is greatest in urban areas other than Tbilisi (between a 2.5 percent and a 4.2 percent increase in median consumption relative to the status quo). The lower impact in Tbilisi is most likely driven by relatively higher wages, while the low impact in rural areas is likely driven by lower participation in wage employment.
In the hypothetical case of Georgia, an impact of a minimum wage increase on poverty rates is expected to be limited, in line with the literature. In our study this finding is mostly driven by the fact that only a relatively small share of poor individuals live in “working poor” households (about 40 percent, nationally). The remaining 60 percent of poor individuals will be unaffected by the reform.
The quantitative impact on female and male poverty is estimated to be low, although the female poverty rate reduction is somewhat larger than among males.
It is important to note that the analysis doesn’t consider possible differential impacts on different groups of vulnerable families, such as families with small children and single mothers with small children. Some reasons to why groups of households may or may not be affected by the hypothetical minimum wage increase, based on their employment status and other factors, have been discussed above.
Another important point is that our exercise should not be seen as an argument against an increase of the minimum wage in Georgia. Instead, it suggests that such a reform would not have much of an impact if done in isolation. Indeed, the existing literature on minimum wage seems to be in consensus on the fact that minimum wage policies would be more impactful if supplemented by the following measures:
- Maintain and expand targeted social assistance to groups that do not benefit or that are losing jobs/incomes as a result of the minimum wage changes
- Have job re-training programs in place to help laid-off workers
- Have human capital investment programs in place to increase workers’ productivity, in particular for low-productivity sectors
- Consider other support instruments targeted toward the most affected groups of the population such as single working mothers etc.
These recommendations should be incorporated in the policy making regarding minimum wages in Georgia.
We are grateful to Expertise France for financially supporting the original report (Babych et al., 2022), which features some of the results and points raised in this policy brief.
- Allegretto, S., Dube, A., Reich, M., & Zipperer, B. (2017). Credible Research Designs for Minimum Wage Studies: A Response to Neumark, Salas, and Wascher. ILR Review, 70(3), 559–592. https://doi.org/10.1177/0019793917692788
- Babych, Y., Pignatti, N., Chapichadze, A., Lobzhanidze, G. and Shubitidze, E. (2022). Report on Minimum Wage in Georgia. ISET Policy Institute. Unpublished manuscript.
- Belman, D. and Wolfson, Paul J. (2014). What Does the Minimum Wage Do? Kalamazoo, MI: W.E. Upjohn Institute for Employment Research. https://doi.org/10.17848/9780880994583
- Burkhauser, R. V. and Sabia, J. J. (2007). The effectiveness of minimum‐wage increases in reducing poverty: Past, present, and future. Contemporary Economic Policy, 25(2), 262-281. https://doi.org/10.1111/j.1465-7287.2006.00045.x
- DeFina, R. H. (2008). The impact of state minimum wages on child poverty in female-headed families. Journal of Poverty, 12(2), 155-174. https://doi.org/10.1080/10875540801973542
- Dube, A., T.W. Lester, and M. Reich. 2010. Minimum Wage Effects Across State Borders: Estimates Using Contiguous Counties. The Review of Economics and Statistics, 92(4), 945–964. https://doi.org/10.1162/REST_a_00039
- European Commission. (2020). Proposal for a directive of the European parliament and of the council on adequate minimum wages in the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020PC0682GEOSTAT
- International Labour Organization (ILO). (2023). https://www.ilo.org/global/topics/wages/minimum-wages/definition/lang–en/index.htm
- International Labour Organization (ILO). (2019). The working poor or how a job is no guarantee of decent living conditions chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.ilo.org/wcmsp5/groups/public/—dgreports/—stat/documents/publication/wcms_696387.pdf
- Geostat. (2021). https://www.geostat.ge/en
- Laroque, G. & Salanié, B. (2002). Labour market institutions and employment in France. Journal of Applied Econometrics, 17(1), 25-48. https://doi.org/10.1002/jae.656
- Neumark, D. & Wascher, W. (2011). Does a higher minimum wage enhance the effectiveness of the Earned Income Tax Credit? ILR Review, 64(4), 712-746. https://doi.org/10.1177/001979391106400405
- Neumark, D. (2018). Employment effects of minimum wages. IZA World of Labor 2018: 6. https://wol.iza.org/articles/employment-effects-of-minimum-wages/long
- Sabia, J. J., Burkhauser, R. V. & Hansen, B. (2012). Are The Effects Of Minimum Wage Increases Always Small? New Evidence From A Case Study Of New York State. Sage Publications, 350-376. https://doi.org/10.1177/001979391206500207
- Sabia, J. J. (2008). Minimum wages and the economic wellbeing of single mothers. Journal of Policy Analysis and Management, 27(4), 848-866. https://doi.org/10.1002/pam.20379
- Sotomayor, O. J. (2021). Can the minimum wage reduce poverty and inequality in the developing world? Evidence from Brazil. World Development 138. https://doi.org/10.1016/j.worlddev.2020.105182.
Disclaimer: Opinions expressed during events and conferences are those of the authors; they do not necessarily reflect those of the FREE Network and its research institutes.
Since March 12, 2020, Poland has been under an increasing degree of quarantine due to the COVID-19 pandemic. The strict isolation-driven lockdown measures have implied significant restrictions to social interactions and economic activity. While the duration of this lockdown and the resulting overall scope of economic implications are highly uncertain at this point, in this brief we take a closer look at the possible extent of the first wave of economic consequences of the pandemic faced by Polish households. This is done by identifying sectors of the economy whose operation has been severely limited due to the lockdown, such as those involving travel, close interpersonal contact and public gatherings or those related to the retail trade. We find that about 17.2% of Polish households include members active in these sectors, and for 5.2% of households, the risk can be described as high due to the nature of the employment relationship. According to our estimates, 780K people (57% of whom are women) face a high risk of negative economic consequences as a result of the first direct wave of implications of the pandemic.
The full scale of the socio-economic impact of the COVID-19 outbreak is incalculable today, given the uncertainty of lockdown duration and the severity of the pandemic-driven slowdown in the international economy. Still, it is possible to analyze the direct implications of the lockdown, self-isolation and quarantine measures introduced over the last few weeks in an attempt to formulate a preliminary assessment of how the outbreak will affect households in economic terms. The priority challenge now is, of course, to contain the spread of the coronavirus, but as we identify the scale of potential economic consequences associated with the pandemic, we may help calibrate the safeguards that could protect households from the impact of the imminent economic slowdown.
In this commentary paper, based on the Household Budget Survey (HBS) data, the percentage of households (HHs) whose members are most at risk of losing their job or compromising their income due to the first wave of economic consequences of the pandemic is taken as a measure of the economic impact of the COVID-19 outbreak. The analysis looks into the population of people who are economically active (through employment or self-employment) in those sectors of the economy which are most exposed to the effects of the lockdown. We discuss the HHs with a particularly high risk of income deterioration in the breakdown according to the level of household income, the place of residence, and the family type. The first part of the paper presents a detailed description of the economic sectors which were considered to be particularly exposed to the risk associated with the first wave of economic consequences of the pandemic, together with risk level definitions. Analytical findings are presented in the second part of the paper.
Households at Risk of the Negative Impact of the First Wave of Economic Consequences of the COVID-19 Pandemic
The granularity of HBS data collected annually by Poland Statistics (GUS) is not sufficient for a very precise determination of the size of risk groups in terms of individual activity on the labor market, but the data can help identify the HHs whose members have been employed in the sectors of the national economy particularly affected by the pandemic, i.e. on the first line of exposure to its economic consequences. These are, in particular, economic sectors that involve frequent interpersonal contacts and large public gatherings: following the announcement of the state of epidemiological hazard in Poland on March 14th, 2020, serious restrictions have been imposed in those sectors in an effort to prevent the rapid spread of the coronavirus.
Pursuant to the Regulation of the Minister of Health of March 13th, 2020, on the announcement of the state of epidemiological hazard in the territory of the Republic of Poland, restrictions on doing business in the food industry, as well as in culture and entertainment, sport and recreation, hospitality and tourism have been imposed on a temporary basis (Ministry of Health 2020). The operation of large-size retail commerce facilities has also been restricted. In addition, self-isolation and social distancing result in significant decreases in the overall level of trade turnover. In view of the lockdown, we decided that the risk of economic slowdown also applies to the service sector and education (personal services included) for the purpose of this paper. The workforce from the above-mentioned sectors has been divided by type of employment contract, and those hired under a contract of employment (fixed-term or open-ended, regardless) have been ranked as less exposed to the risk of job loss or lower earnings, while all the others employed on civil law contracts (service contract, zero-hours contract, etc.) have been grouped under an elevated risk label. The elevated risk category includes all those who are self-employed in the above-mentioned sectors in Poland or abroad, regardless of whether they have employees onboard or not.
Exposure to Financial Risks in Families and Households
In accordance with the risk categories applicable to the economically active population, we can conclude that there are over 780 thousand members of the workforce (57 percent of them are women) who are particularly exposed to the negative economic consequences of the pandemic, as they work in the affected sectors of the economy on the basis of self-employment or contracts other than the contract of employment. In addition, 1.9 million people (70 percent of them are women) are employed in these sectors of the economy on contracts of employment. The status of the latter group is less precarious in the short term, but if the lockdown should continue in the long term, this population may also be affected.
The adverse impact of job loss or lower earnings will affect an entire household whose member works in a sector particularly affected by the crisis. Therefore, the risks below are presented in a breakdown by family type and by HH group aggregated according to the place of residence and income level. Moreover, the HHs were also grouped according to their members’ activity on the labor market, with analytical findings presented for all HHs and for the group of HHs with at least one economically active member in the HH.
The highest percentage of HHs whose members are particularly exposed to the negative consequences of the pandemic is reported in cities (Figure 1). For example, in cities with a population above 500,000, it is 6.6 percent of all HHs, and 9.1 percent of the HHs with at least one active member on the labor market. Additionally, in cities with a population count exceeding 500,000, 12.4 percent and 17.1 percent of the population, respectively, is employed in the affected sectors on the basis of an employment contract. In smaller cities/towns and in rural areas the percentage of HHs with the population most exposed to the crisis are slightly lower. In rural areas, it is 4.8 percent of all HHs and 6.4 percent of the HHs with at least one economically active member of the HH.
In terms of HH income levels, middle-income HHs demonstrate the highest percentage of those exposed to the negative consequences of the first wave of pandemic-driven impact on the economy (Figure 2). For example, in the 6th income decile group, in the population of HHs with at least one economically active member, 8.5 percent of HHs include a member who is economically active in an affected sector and working either on a self-employment basis or on a contract other than a contract of employment. Together with HH members who are economically active in those sectors on a contract of employment, the rate exceeds 30 percent.
Figure 1. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by place of residence
The percentage distribution of the HHs economically active in the affected sectors by family type is also uneven (Figure 3). In the group of families with at least one economically active member, the largest proportion of such HHs is reported in the group of single parents, with 31.5 percent working in the affected sectors and 6.6 percent in self-employment or on the basis of a contract other than the contract of employment. Similar percentages are reported for couples with children and at least one economically active HH member (24.2 percent and 7.8 percent, respectively.) Among working singles and couples with no dependent children, on average, one in five HHs has a HH member economically active in an affected sector. Of these HHs, 4.5 percent of the singles and 5.6 percent of the couples with no children are economically active in the affected sectors with contracts other than a contract of employment.
Figure 2. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by income decile
Figure 3. Financial risk in the households in connection with the first wave of COVID-19 impact on the economy, by family type
Although our estimates of the percentage of families and households potentially exposed to the negative effects of the first wave of economic consequences of the COVID-19 pandemic do not necessarily imply that such a high share will actually be affected, the mere fact that so many families face the prospect of a deteriorating financial condition should stimulate a wide array of public policy support mechanisms. The economic support package called the “anti-crisis shield”, announced by the Government of Poland on March 18th, is a reaction to this challenge, though specific details of the announced version of the program have not been disclosed to date (Government announcement 2020). Still, the main focus of the package is on support for enterprises and entrepreneurs to help them continue business operation by postponing the due dates of business taxes and levies, and partially subsidizing employment of the workforce already on board. There is no doubt, however, that if the general economic slowdown continues for more than a few months, enterprises will be forced to start the layoffs and the self-employed will have to deregister. Therefore, the public finance system must be prepared to provide direct financial support to the households and offer a comprehensive benefit package to those who are laid off and to their families.
It is to be hoped that the economic consequences of the pandemic will be short-lived, and business activity will recover quite quickly to the pre-existing levels. For this to happen, first of all, we must keep the enterprises afloat, especially the small and medium-sized enterprises. Secondly, a fast economic reboot will be easier if the existing employment relations are preserved, even if the workload or the wages are curtailed. To that end, one solution would be to provide periodic financial support to employees in the affected sectors, even without formal termination of the contract between the employee and the employer. If the lockdown continues for more than two or three months, the financial support provided for in the “anti-crisis shield” package, representing 40 percent of the wage, may turn out to be inadequate to keep current employment levels intact.
If the pandemic-driven economic slowdown is prolonged – and there is no way this option can be ruled out today – it should be remembered that, apart from the sectors included in the analysis, the remaining sectors of the Polish economy will also be affected by the negative consequences of the recession; and the prolonged slowdown will eventually lead to a significant increase in unemployment rates. If that happens, households will need support through social transfers, both in the form of the unemployment benefit and benefits not related to a beneficiary’s track record in social security contributions paid, i.e. the housing benefit and social welfare benefits. With the expected substantial increase in public spending, the current policy of the state, focused primarily on universal public benefits, would have to be refocused on the transfers targeted at the most vulnerable households.
Ministry of Health (2020). Regulation of the Minister of Health of the Republic of Poland of the 13th March 2020 on the announcement of the state of epidemiological hazard in the territory of the Republic of Poland.
Government announcement (2020). “Anti-crisis Shield” will protect companies and employees from the consequences of coronavirus epidemics.
This brief was originally published as a CenEA Commentary Paper of 28th March 2020 on www.cenea.org.pl. The analyses outlined in this brief make part of the microsimulation research program pursued by CenEA Foundation. The data used in the analyses is based on the 2018 Household Budget Survey, as provided by Poland Statistics (GUS). Poland Statistics (GUS) has no liability for the results presented in the brief or its conclusions. Conclusions presented in the brief are based on Authors’ calculations based on the SIMPL model.
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.
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
|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.
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.
Belarus proudly calls itself a social state. Indeed, Belarus boasts one of the lowest poverty and inequality levels in the region. Fiscal policy in Belarus is equalizing and pro-poor, effectively redistributing income from rich to poor. As in Russia and many other Post-Soviet states, the equalizing effect of the fiscal policy in Belarus is mostly attributable to the pension system. Some of the other social policies are highly inefficient, failing to redistribute income. The prominent examples are utility subsidies and student stipends, which mainly benefit the upper part of the income distribution. The lack of adequate unemployment benefits is an opportunity to improve the efficiency of the social support system in Belarus.
The Constitution of Belarus characterizes Belarus as a social state, and Belarus takes its social state status seriously. The economic growth in the beginning of the 2000’s was strongly pro-poor (Chubrik, 2007). Poverty according to the national definition (calorie-based poverty line, which in 2015 corresponded to $10.67 PPP per day) declined from 42% in 2000 to 5.7% in 2016, while the poverty according to the international threshold of $3.1 per day in PPP terms is fully eradicated. Belarus also has one of the lowest levels of income inequality in the region with a Gini coefficient of only 0.27 (UNDP, 2016).
How much of the pro-poor and equalizing effects could be attributed to the government policy? Probably it is impossible to give a complete answer to the question. Many non-formalized and not easily quantifiable government policies lead to the decrease in poverty and inequality. For example, the policy of support to state-owned enterprises might have redistributive effects through job creation. However, the absence of access to relevant data makes it impossible to estimate the effects of the policy.
Some of the government policies, on the other hand, are easily quantifiable with available data. Bornukova, Chubrik and Shymanovich (2017) analyze the redistributive effects of fiscal policies in Belarus using the Commitment to Equity methodology (Lustig, 2016). The authors find that the direct taxes and transfers in Belarus (taxes, transfers, and subsidies) are equalizing and pro-poor, lowering the national poverty headcount by 17 percentage points and the income Gini coefficient from 0.41 to 0.27. The high equalizing effect of the fiscal policies in Belarus surpasses those in other developing countries, including Russia where the direct taxes and subsidies reduced the income Gini coefficient by 0.13 (Lopez-Calva et al., 2017). The remaining discussion in this brief is based on the results from Bornukova, Chubrik and Shymanovich (2017), if not otherwise stated.
Fiscal policies and their redistributive effects
The two types of direct personal taxes – the personal income tax and the social contributions tax – are both almost flat in Belarus. To fight tax evasion, the Belarusian authorities introduced flat tax rates in 2009, following a successful experiment in Russia. The personal income tax has some small exemptions for families with children, while the social contributions tax has a lower rate for agriculture employees. However, the effect of these deductions is relatively small: the direct taxes decrease the Gini coefficient by only 0.015.
The indirect taxes – the value-added tax, the import duties, and the excises – are weakly regressive, putting the burden of taxation on the poor. This is particularly true for the alcohol and tobacco excises. Again, the main purpose of these taxes is to penalize unwelcome behavior, and not to redistribute income, hence the result is not unexpected, and common for many countries. Overall the indirect taxes in Belarus increase the Gini coefficient by 0.05.
Direct transfers are responsible for most of the equalizing effects of the fiscal policies. This is not surprising, given that the main purpose of the direct transfers is to fight poverty and provide support for those in need. However, most of the transfers are not need-based or targeted to the poor. Instead they are assigned to households based on their socio-economic characteristics aside income, such as age and maternity status.
Pensions are the main factor of reducing poverty and inequality. They reduced the Gini coefficient by 0.11 and decreased poverty (according to national definition) by 19 percentage points. The incredible effectiveness of the pensions is largely explained by the absence of other sources of income of the retirees. The majority of them does not work, and have no other pension savings or passive income. Pensions in Belarus are also redistributive in nature since they only weakly depend on one’s income during the working life.
Different benefits and privileges also decrease poverty and inequality, but at a much smaller scale. The childcare benefits (for families with children aged 0-3 years) contribute most to the effects, decreasing the Gini coefficient by 0.013 and poverty by 3 percentage points. The variety of privileges does not contribute much due to their relatively small size.
Utilities and transport subsidies are also important elements of the social support system, and their existence is usually justified by the necessity to support those in need. Since the utilities subsidies are incorporated into tariffs and available for everyone independent of need, they are in fact benefitting the rich (i.e. people with big apartments and houses).
Figure 1. Incidence of utilities subsidies by income deciles
As seen on Figure 1, upper deciles receive more support through utilities subsidies, and this support is quite substantial, often surpassing $1 per day in PPP. However, as a share of income the utilities subsidies are still progressive, and they in fact decrease the Gini coefficient by the tiny amount of 0.006, and decrease poverty (as any handout). The same is true for transport subsidies.
What could be improved?
Due to the flat nature of direct taxation and an absence of well-targeted needs-based transfers, some of the people in need still fall through the cracks. 1.9% of the population actually becomes poor after we account for the direct taxes and transfers. This headcount increases to 3.3% if we account for indirect taxes.
Another important issue is the efficiency of government transfers and subsidies in fighting poverty and inequality. It is not surprising that pensions have the largest equalizing contribution, as the government spends almost 11% of GDP on pensions. If we account for this fact and look at the efficiency (effect on poverty and inequality per dollar spent), pensions are not the leading program. It is in fact surpassed by different kinds of child support. Given that mothers in Belarus are allowed to take 3 years of unpaid maternity leave, which decreases household income, childcare benefits are relatively efficient.
The unexpected leader in efficiency is unemployment benefits, despite (or maybe due to) their negligible size. Shymanovich (2017) shows that unemployed face high risks of poverty, suggesting that an increase in the size of unemployment benefits and an easier access may bring huge benefits. The current minuscule size of the benefits (around $10-15 per month) is still enough to lift some people out of poverty, and has important equalizing effects, generating the biggest “bang for the buck” out of all benefits.
The student grants (stipends), the utilities subsidy and the transport subsidy have very low efficiency. These programs relocate a lot of funds to the upper deciles of the income distribution. Our calculations show that if all benefits, privileges and subsidies were not available to those in the top two income deciles, the Belarusian budget could save 1.4% of GDP.
Fiscal policies in Belarus are quite effective in redistributing income. Bornukova, Chubrik and Shymanovich (2017) show that the direct taxes and transfers in Belarus result in a decrease of poverty by 17 percentage points, and decrease the Gini coefficient of inequality from 0.41 to 0.27. The pension system has the most important contribution, decreasing poverty by 19 percentage points, and the Gini coefficient by 0.11.
However, the absence of a needs-based, well-targeted social support system leads to many inefficiencies. Direct and indirect taxes lead to impoverishment of 3.3% of population, which is not compensated by direct transfers.
The absence of targeting also leads to 1.4% of GDP redistributed towards the two upper income deciles through benefits, privileges and subsidies. This is, of course, highly inefficient. Better targeting could allow saving these funds or redirecting them to unemployment benefits – the most efficient but a very small benefits program so far.
- Bornukova, Kateryna, Alexander Chubrik and Gleb Shymanovich, 2017. “Fiscal Incidence in Belarus: a Commitment to Equity Analysis”, BEROC Working Paper Series, WP no. 42
- Chubrik, Alexander, 2007. “GDP Growth and Income Dynamics: Who Reaps the Benefits of Economic Growth in Belarus?” In Haiduk, K., Pelipas, I., Chubrik, A. (Eds.) Growth for All? Economy of Belarus: The Challenges Ahead; IPM research Center
- Lopez-Calva, L. F., Lustig, N., Matytsin, M., Popova, D., 2017. “Who Benefits from Fiscal Redistribution in Russia?”,in The Distributional Impact of Fiscal Policy: Experience from Developing Countries, edited by Gabriela Inchauste and Nora Lustig (Washington: World Bank, forthcoming).
- Gleb Shymanovich, 2017. “Poverty and Vulnerable Groups in Belarus: The Consequences of 2015-2016 Recession (in Russian)”, IPM Research Center Bulletin
- UNDP, 2016. “Regional Human Development Report 2016: Progress at Risk”, United Nations Development Programme, Istanbul Regional Hub, Regional Bureau for Europe and the CIS
This brief summarizes the results of a study by Lanchava and Abramishvili (2015), which investigates the impact on university enrollment of an unconditional cash transfer in Georgia, designed to help households living below the subsistence level. The program, introduced in 2005, selects recipients based on a quantitative poverty threshold, which gives us the opportunity to measure the influence on university enrollment with an econometric regression discontinuity design. We use data on program recipients from the Social Service Agency of Georgia (SSA) and university admissions from the National Examination Center (NAEC) to create a single dataset and compare the enrollment rates of applicants who are just above and below the threshold. We find that being a program recipient significantly increases a student’s likelihood of university enrollment by as much as 1.4 percentage points (while the sample mean of university enrollment is 12.7%). We also find that the impact is stronger for males and the firstborn children in a family. Our analysis also shows that the effect is equally strong across different locations in the country. Our straightforward policy recommendation is that if a government is trying to increase enrollment in tertiary education, need-based university scholarships may prove to be an appropriate instrument.