metadata: authors: 0: author: Bornukova,Kateryna
1: author: Shymanovich,Gleb
2: author: Chubrik,Alexander
count: Belarus
docna: 0: docna: Fiscal incidence in Belarus : a commitment
            to equity analysis
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Social Protections & Assistance,Educational Sciences,Social Development & Poverty,Economic Growth,Inequality
teratopic: Poverty Reduction
abstracts: cdata!: The paper employs the Commitment to
            Equity framework to present a first attempt at a
            comprehensive fiscal incidence analysis for Belarus,
            encompassing the revenue and expenditures components of the
            fiscal system, including direct and indirect taxes, as well
            as direct, indirect, and in-kind transfers. The analysis
            reveals that fiscal policies in Belarus effectively
            redistribute income from the top to the bottom of the income
            distribution. Direct transfers, especially pensions, are the
            most equalizing and pro-poor of the fiscal
            interventions—direct transfers and direct taxes lower the
            national poverty headcount by 17 percentage points and lower
            the Gini index of inequality from 0.407 to 0.267. Some of
            the indirect taxes, by contrast, are regressive and indirect
            transfers -- poorly targeted, such that the effect of these
            components of the fiscal system is not equalizing. Finally,
            the cost-efficiency of different parts of the fiscal system
            in Belarus varies considerably. Unemployment benefits,
            pensions, and child benefits are found to be cost --
            efficient, while indirect subsidies are highly
            cost-inefficient. The analysis points toward possible
            reforms that would allow reducing poverty and inequality
            more efficiently.
geo_reg_key: 517191,82689,517208
repnme: repnme: Fiscal incidence in Belarus : a commitment
            to equity analysis
display_title: Fiscal incidence in Belarus : a
            commitment to equity analysis
listing_relative_url: /research/2017/10/28072063/fiscal-incidence-belarus-commitment-equity-analysis
topicv3: Economic Growth m1326241 471,Poverty m1326687 695,Social Insurance m1326950 751,Education m1327048 470,Social Protection and Labor m1327194 697
publishtoextweb_dt: 2017-10-12T00:00:00Z
historic_topic: Poverty Reduction
no_of_pages: 43
author_keys: 0: author_key: 1502334
1: author_key: 1313181
model_extractions: WPS8216 Policy Research Working Paper 8216 Fiscal Incidence in Belarus A Commitment to Equity Analysis _Kateryna Bornukova Gleb Shymanovich Alexander Chubrik_ Poverty and Equity Global Practice Group October 2017
Policy Research Working Paper 8216

# **Abstract**

The paper employs the Commitment to Equity framework to present a first attempt at a comprehensive fiscal incidence analysis for Belarus, encompassing the revenue and expenditures components of the fiscal system, including direct and indirect taxes, as well as direct, indirect, and in-kind transfers. The analysis reveals that fis-cal policies in Belarus effectively redistribute income from the top to the bottom of the income distribution. Direct transfers, especially pensions, are the most equalizing and pro-poor of the fiscal interventions—direct transfers and direct taxes lower the national poverty headcount by 17 percentage points and lower the Gini index of inequality from 0.407 to 0.267. Some of the indirect taxes, by contrast, are regressive and indirect transfers—poorly targeted, such that the effect of these components of the fiscal system is not equalizing. Fi-nally, the cost-efficiency of different parts of the fiscal system in Belarus varies considerably. Unemployment benefits, pensions, and child benefits are found to be cost-efficient, while indirect subsidies are highly cost-inefficient. The analysis points toward possible reforms that would allow reducing poverty and inequality more efficiently.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at at bornukova@beroc.by, shymanovich@research.by, and chubrik@research.by.

_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
# **Fiscal Incidence in Belarus: A Commitment to Equity Analysis**

Kateryna Bornukova, Gleb Shymanovich, Alexander Chubrik<sup>*,**</sup> _JEL Codes_ : H22, H5, D31, I3 _Keywords_ : Fiscal incidence, social spending, inequality, poverty, Belarus

> * Kateryna Bornukova is academic director of BEROC Center for Economic Research, e-mail: bornukova@beroc.by; Gleb Shymanovich is economist at the IPM Research Center, e-mail: shymanovich@research.by; Alexander Chubrik is director of the IPM Research Center and fellow at CASE – Center for Social and Economic Research, e-mail: chubrik@research.by.

> ** We thank Alexandru Cojocaru, Mikhail Matytsin, participants of the World Bank CEQ workshop and referees for the excellent inputs, useful comments and suggestions. All errors are ours. We acknowledge the financial support from the World Bank.
# **Contents**

|List of the t|ables .................................................................................................................................................................................. 3|
|---|---|
|List of the fi|gures ................................................................................................................................................................................ 3|
|1.<br>Introd|uction ............................................................................................................................................................................... 4|
|2.<br>Belaru|sian “welfare state”: Overall principles ...................................................................................................................... 5|
|2.1.|Poverty, growth incidence, and trends in inequality .................................................................................... 5|
|2.2.|Revenues and expenditures of the general government .............................................................................. 6|
||Government revenues ....................................................................................................................................... 6|
||Government expenditures ................................................................................................................................ 8|
|2.3.|State social insurance system design ............................................................................................................... 9|
|3.<br>Metho|dology of the analysis ................................................................................................................................................. 10|
|3.1.|Data available and CEQ approach to income concepts construction ................................................... 10|
|3.2.|Direct taxes ...................................................................................................................................................... 12|
||Personal income tax and taxes on entrepreneurial income ...................................................................... 12|
||Social Protection Fund contributions.......................................................................................................... 13|
|3.3.|Indirect taxes ................................................................................................................................................... 14|
||VAT .................................................................................................................................................................. 14|
||Excises .............................................................................................................................................................. 16|
||Import duties ................................................................................................................................................... 17|
|3.4.|Direct transfers ................................................................................................................................................ 19|
|3.5.|Indirect Subsidies ............................................................................................................................................ 20|
|3.6.|In-kind transfers (health care and education) ............................................................................................. 22|
|4.<br>Results|and Discussion .......................................................................................................................................................... 23|
|4.1.|Main results ...................................................................................................................................................... 23|
|4.2.|Distributional impact and marginal contributions of fiscal interventions ............................................. 29|
|4.3.|Efficiency ......................................................................................................................................................... 31|
|4.4.|Targeting and vulnerable groups .................................................................................................................. 33|
|4.5.|Cross-country differences.............................................................................................................................. 34|
|5.<br>Conclu|sions .............................................................................................................................................................................. 34|
|References ..|........................................................................................................................................................................................ 35|
|Appendix ....|........................................................................................................................................................................................ 37|
|A.|Estimation of personal income tax .............................................................................................................. 37|
|B.|Concentration curves ..................................................................................................................................... 38|
|C.|Distribution of gains and losses at the level of final income ................................................................... 41|

2
# **List of tables**

|Table 1. General government revenue ........................................................................................................................................... 7|
|---|
|Table 2. General government expenditure and balance .............................................................................................................. 8|
|Table 3. Share of VAT in consumer prices by expenditure line .............................................................................................. 15|
|Table 4. Assumptions and inputs for estimating household payments of alcohol excises ................................................. 16|
|Table 5. Estimated parameters of alcohol consumption underreporting and share of excises in household<br>expenditures by quintile ................................................................................................................................................................... 17|
|Table 6. Assumptions and inputs for estimating import duties ............................................................................................... 17|
|Table 7. Main poverty and inequality indicators by income concepts .................................................................................... 24|
|Table 8. Decompositions of inequality changes into vertical and horizontal equity components .................................... 25|
|Table 9. Incidence of net effects from fiscal interventions in relation to market income by deciles ............................... 25|
|Table 10. Fiscal gains to the poor and fiscal impoverishment in relation to market income ............................................. 28|
|Table 11. Fiscal impoverishment in relation to market income by social vulnerable groups, headcount, % of group 29|
|Table 12. Progressivity of taxes and transfers in relation to income concepts ..................................................................... 30|
|Table 13. Marginal contributions to inequality and poverty ..................................................................................................... 31|
|Table 14. Efficiency measures for social fiscal interventions ................................................................................................... 32|
|Table 15. Sizes of transfers/taxes by vulnerable groups, relative to the rest of the population ........................................ 33|

# **List of figures**

|Figure 1. Poverty and economic growth, 2000–2015 .................................................................................................................. 5|
|---|
|Figure 2. Growth incidence curves in Belarus, 2000–2015 ........................................................................................................ 6|
|Figure 3. Redistribution and inequality in Belarus, 2000–2015 .................................................................................................. 6|
|Figure 4. General government balance, 2000–2015 .................................................................................................................... 7|
|Figure 5. Ratio of HBS data and data of national accounts, % ............................................................................................... 10|
|Figure 6. Construction of income concepts ................................................................................................................................ 12|
|Figure 7. Incidence of direct taxes by deciles .............................................................................................................................. 14|
|Figure 8. Incidence of indirect taxes by deciles ........................................................................................................................... 15|
|Figure 9. Incidence of import duties by deciles .......................................................................................................................... 18|
|Figure 10. Incidence of cash and in-kind benefits by deciles ................................................................................................... 20|
|Figure 11. Indirect subsidies by disposable income deciles ...................................................................................................... 21|
|Figure 12. Health and education expenditure by disposable income deciles ........................................................................ 22|
|Figure 13. Lorenz curves for basic income concepts ................................................................................................................ 24|
|Figure 14. Distribution of individuals by market and disposable income ............................................................................. 26|
|Figure 15. Distribution of gains and losses at the level of disposable income with respect to market income by<br>income bins set in USD PPP ......................................................................................................................................................... 27|
|Figure 16. Structure of net beneficiaries and payers of fiscal system by socio-economic status ....................................... 29|
|Figure 17. Cross-country comparisons of redistribution and poverty reduction effect of direct transfers and taxes 34|
|Figure 18. Concentration curves for direct taxes ........................................................................................................................ 38|
|Figure 19. Concentration curves for indirect taxes .................................................................................................................... 38|
|Figure 20. Concentration curves for direct transfers ................................................................................................................. 39|
|Figure 21. Concentration curves for indirect subsidies ............................................................................................................. 39|
|Figure 22. Concentration curves for in-kind expenditure ......................................................................................................... 40|
|Figure 23. Distribution of gains and losses at the level of final income with respect to market income by income bins<br>set in USD PPP ................................................................................................................................................................................. 41|

3
# **1. Introduction**

Belarus is often positioned as a country that has a “socially oriented economy,” as stated by the authorities. This statement is supported by low income inequality (one of the lowest in the region) and low incidence of poverty (the poverty headcount based on the international poverty line of USD 4 PPP is effectively zero). Different studies (e.g. Chubrik, 2007; Chubrik and Shymanovich, 2016) reveal the pro-poor nature of Belarusian economic growth, but there is no clear evidence whether low inequality and poverty have resulted from tax and subsidies systems design or are due to other factors.

This paper seeks to fill this knowledge gap by analyzing the impact of fiscal policy on poverty and inequality in Belarus. Methodologically, the analysis follows the _Commitment to Equity_ (CEQ) analysis, which has already been applied in more than 30 low- and middle-income countries (Lustig, 2016). This fiscal incidence analysis reveals who are the beneficiaries of public social expenditures and the contributors to the public finances, who bear the major tax burden.

The assessment of the impact of fiscal policies is timely for Belarus. Currently, the country is struggling with a prolonged recession and has to optimize its budget expenses. So far, the reform debate has been centered around the pension reform (Lisenkova and Bornukova, 2017, Shymanovich, 2016), and the elimination of utility subsidies (IMF, 2016; Chubrik, Shymanovich, 2016; Zhang and Hankinson, 2015). This paper seeks to inform the current debate with information on the poverty and inequality impacts of social programs and their cost efficiency. To the best of our knowledge, this is the first attempt at a comprehensive fiscal incidence analysis for Belarus.

The fiscal incidence approach captures only the effects of government policies in the form of taxes, subsidies and benefits collected from and provided to households. However, part of the social support is provided implicitly through the subsidization of state-owned enterprises (SOEs). It is partially reflected in budget expenditures (in 2015, 4.3% of GDP was spent on subsidies to the SOEs), and partially comes through quasi-fiscal operations not captured by fiscal data. On the one hand, this support helps SOEs to preserve excessive employment, on the other – it leads to inefficient resource allocation, thus reducing overall welfare. Thus, in addition to the general problems with imputation of the effects of SOEs subsidization at the household level, there is an open issue of the overall “sign” of its impact on poverty and inequality: it could be that lower subsidies to SOEs would result in faster job creation in the private sector and better job opportunities for the poor. That is why in this study we do not consider the social roles of SOEs, focusing only on the taxes paid by households and subsidies provided to them directly from the budget. Other limitations of the CEQ approach are that it does not evaluate the quality of government services, does not take into account the behavioral/rational responses to changes in the fiscal policy, and assumes an equal distribution of income and consumption within the household.

Our results suggest that fiscal policy in Belarus is very effective in lowering both poverty and inequality. Direct transfers (including pensions) and direct taxes lower the national poverty measure by 17 percentage points. They also decrease the Gini index from 0.407 to 0.267. The impressive magnitude of positive fiscal effects puts Belarus among the equalization leaders in the group of developing countries. Most of the effect could be attributed to pensions. When we adopt the pensions-as-deferred-income (PDI) approach, the poverty reduction amounts only to 2.5 percentage points, and the Gini coefficient decreases only by 0.02.

The results also point in the direction of possible reforms. As the government seeks to minimize expenditure, it is important to focus on the most efficient interventions. Indirect subsidies are highly cost-inefficient. A 1% of GDP spent on the utility subsidies delivers 3 times less poverty and inequality reduction compared to the same 1% spent on pensions. Indirect utility subsidies and transport tariffs are not targeted, available to everybody and regressive. Replacing indirect subsidies with well-targeted benefits programs would allow reducing poverty and inequality more efficiently. Unemployment benefits (currently at very low levels) are the most cost-efficient benefit program, suggesting that the plans to increase benefits will have significant impacts in reducing poverty and inequality.

The paper proceeds as follows. We describe the welfare state in Belarus in Section 2, also discussing the role of quasi-fiscal policies for social welfare. Section 3 describes the CEQ methodology and peculiarities of its application to the Belarusian data. In Section 4 we present and discuss the results of the CEQ assessment and the fiscal impact on poverty and inequality in Belarus. Section 5 concludes.

4
# **2. Belarusian “welfare state”: Overall principles**

# **2.1. Poverty, growth incidence, and trends in inequality**

In its recent history, Belarus demonstrated an impressive reduction in poverty. The poverty headcount based on the official poverty line<sup>1</sup> fell from 46% in 1999 to 4.8% in 2014; it has stayed well below 10% of population since 2007 (see Figure 1). Poverty measured with the international poverty line of USD 4 PPP was below 1% since 2011, and since 2013 it is close to 0. Economic growth (quite impressive between 2000–2010, when Belarus was among top-25 fastest growing countries in the world) was the key factor behind poverty reduction – the correlation between real GDP and the national poverty headcount is -0.91. The outliers are explained by extremely fast growth of housing and utility tariffs (2002) and a hyperinflation episode (2011).

However, Belarusian economic growth was not sustainable. It was driven by fast capital accumulation financed initially from the budget and later via directed lending at preferential interest rates. As a result, returns on investment fell, as well as total factor productivity (Kruk and Bornukova, 2014; 2015). On the demand side, GDP growth was driven by domestic demand; fast growth of investment and household consumption led to growing external imbalances that were financed via growing government borrowing. Altogether, the unsustainability of these factors caused the economic recession, which started in Belarus at the end of 2014, and growth prospects look gloomy: recent IMF and World Bank outlooks forecast very modest growth,<sup>2</sup> while statistical filters give real GDP long term trend growth rate below zero (Chubrik and Shymanovich, 2016).

The recent GDP decline was associated with a slight increase in poverty. The poverty rate according to the national definition grew from 4.8% in 2014 to 5.7% in 2016. Moreover, the national definition of poverty does not properly take into account the significant increases in utility tariffs, which have been taking place since 2014. Regional inequality has also been increasing, with the population outside the capital, and in particular in small cities and in rural areas lagging behind the large urban centers in terms of wages and other types of income (Chubrik, 2016b; Mazol, 2016).

**Figure 1. Poverty and economic growth, 2000–2015**

<!-- Start of picture text --> 95 44<br>90 40<br>85 36<br>80 32<br>75 28<br>70 24<br>65 20<br>60 16<br>55 12<br>50 8<br>45 4<br>40 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Real GDP, BYN bn (2015=100)<br>Poverty headcount, national poverty line, % of population (RHS)<br>Poverty headcount, $4 PPP, % of population (RHS)<br><!-- End of picture text --> _Source_ : Belstat, World Bank POVCAL (USD 4 PPP headcount).

Such a strong correlation between poverty and economic growth should mean that Belarusian economic growth on average had a pro-poor nature. Indeed, in general, the higher the initial income, the lower the rate at which it grew between 2000 and 2015. Income of the poorest decile grew by 0.65 percentage points a year faster than income of the richest decile (see Figure 2a). However, over time the real income growth rate was falling, following the real GDP growth rate, and profiles of the incidence curves changed as well. The rich benefited the most between 2005 and 2010, while the poorest – between 2010 and 2015, and the lower middle class – between 2000 and 2005 (Figure 2b). In the end, Belarus succeeded in delivering benefits of economic growth to all household groups, including

> 1 Absolute poverty line (“minimum subsistence basket”, or “subsistence minimum”) is calories-based poverty line; before 3Q2014, it included administratively defined set of food and non-food goods and basic services, since 3Q2014 it is calculated as the value of administratively defined food basket times 1.77.

> 2 See IMF WEO database, April 2017 (https://www.imf.org/external/pubs/ft/weo/2017/01/weodata/index.aspx), and World Bank’s - - - Belarus Economic Update, May 2017 (http://pubdocs.worldbank.org/en/819391494832531504/Eng <u>EcUpdate May14 17.pdf).</u>

5
(and especially) the poor, and the existing system of income redistribution could be one of the important reasons behind this achievement.

**Figure 2. Growth incidence curves in Belarus, 2000–2015**

<!-- Start of picture text --> 10.4 16<br>15<br>10.3<br>14<br>10.2 13<br>12<br>10.1 11<br>10<br>10.0<br>9<br>9.9 8<br>7<br>9.8<br>6<br>9.7 5<br>4<br>9.6 dec01 dec02 dec03 dec04 dec05 dec06 dec07 dec08 dec09 dec10<br>dec01dec02dec03dec04dec05dec06dec07dec08dec09dec10<br>2000-2005 2005-2010<br>2000-2015 mean median 2010-2015 mean, 2000-2005<br>(a) Real disposable resources growth rates, (b) Real disposable resources growth rates,<br>15-year annual average, % 5-year annual averages, %<br><!-- End of picture text --> _Source_ : own estimates based on HBS data (disposable resources) and Belstat data (inflation).

Historically, redistribution played an important role in Belarus. The share of general government expenditures in GDP stayed at the level of European welfare states (47.2% on average between 2000 and 2010). Even after impressive fiscal consolidation, when the average government expenditures dropped by 6.7% of GDP (see Figure 3), that share remained higher than in the upper-middle-income countries from Central and Eastern Europe and CIS. The fiscal consolidation of the last five years resulted only in a very moderate increase of the Gini index: from 0.266 between 2000 and 2010 to 0.281 between 2011 and 2015.

**Figure 3. Redistribution and inequality in Belarus, 2000–2015**

<!-- Start of picture text --> 51 0.290<br>49 0.285<br>47 0.280<br>45 0.275<br>43 0.270<br>41 0.265<br>39 0.260<br>37 0.255<br>35 0.250<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>General government expenditure, % of GDP Average, 2000-2010 Average, 2011-2015 Gini (RHS)<br><!-- End of picture text --> _Source_ : GFS, Belstat.

# **2.2. Revenues and expenditures of the general government**

# _Government revenues_

The need for fiscal consolidation was driven by a drastic reduction of general government revenues during the currency crisis of 2011 that had not been restored completely. As fiscal policy was quite conservative (the budget had a surplus of 0.6% of GDP on average between 2000 and 2010 and 1.4% of GDP between 2011 and 2015, see Figure 4a), general government expenditures followed revenues. That “conservative” policy was imposed by the size of operations “below the line”: deep interventions of the state into the economy required regular recapitalization of the largest state-owned banks and other types of support to the state-owned companies. For instance, 6
during the severe currency crisis of 2011 the government spent about 12% of GDP on net acquisitions of financial assets – far above the fiscal surplus of 3% of GDP, which required a substantial debt increase and assets sales. After the crisis of 2011, the size of these operations became smaller (see Figure 4b), but because of the debt accumulated between 2007 and 2011, the government needed to keep the fiscal surplus in order to make payments on the principal, which limited its capacity to redistribute.

**Figure 4. General government balance, 2000–2015**

<!-- Start of picture text --> 53 4.00 12<br>51 3.33<br>10<br>49 2.67<br>47 2.00 8<br>45 1.33<br>6<br>43 0.67<br>41 0.00 4<br>39 -0.67<br>2<br>37 -1.33<br>0<br>35 -2.00<br>-2<br>Balance (RHS)<br>Revenues<br>Expenditures Net acquisition of financial assets<br>Average, 2000-2010 (RHS) Net incurrence of liabilities<br>Average, 2011-2015 (RHS) Balance<br>(a) General government revenues, expenditures, and balance, (b) General government balance* and its financing,<br>2000–2015, % of GDP 2000–2015, % of GDP<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

* Net lending (+)/net borrowing (-). _Source_ : GFS.

The most stable sources of government revenue are contributions to social insurance and personal income tax (see Table 1), as their tax base is mainly wage income, whose share in GDP is quite stable. VAT and excise taxes are also quite stable, relying mainly on household consumption, which is even less volatile than household incomes. In 2015, these four sources together generated 60.1% of all general government revenues, while social insurance contributions and VAT – 45.1%.

**Table 1. General government revenue**

||**BYR mln***|**% of GDP**|
|---|---|---|
|**Total Revenue & Grants**|**37 666.540**|**43.3**|
|Tax Revenue|31 991.929|36.8|
|Direct taxes_of which_|7 319.485|8.4|
|Personal Income Tax|3 700.907|4.3|
|Corporate Income Tax|2 384.990|2.7|
|Taxes on Property|1 233.588|1.4|
|Contributions to Social Insurance|9 715.236|11.2|
|Indirect Taxes_of which_|14 853.340|17.1|
|VAT|7 267.080|8.4|
|Turnover & other general taxes on goods and services|567.897|0.7|
|Excise Taxes|1 944.165|2.2|
|Customs Duties|864.359|1.0|
|Taxes on Exports|2 992.432|3.4|
|Other indirect taxes|1 217.408|1.4|
|Other taxes|103.867|0.1|
|Nontax Revenue|5 599.073|6.4|
|Grants|75.539|0.1|

* Taking into account denomination in 10 000 times of 2016. _Source_ : GFS.

The corporate income tax, taxes on exports, and non-tax revenue together brought 29.1% of the general government revenues, but they are far more volatile for a variety of reasons. The corporate income tax and a large portion of non-tax revenue depend on the financial status of the SOEs. Thus, on the one hand, the government subsidizes them, on the other – withdraws profit and collects corporate income tax. _Ceteris paribus_ , the lower the subsidies, the lower the SOEs’ profit and the related government revenues. Fiscal challenges force the government to reduce subsidies, and, hence, the tax/revenue base. The size of the revenue from taxes on exports depends on the current 7
design of the agreements between Belarus and the Russian Federation concerning crude oil and oil products trade. Currently, Belarus gets all export duties on oil products produced by Belarusian refineries from Russian oil (which was not the case between 2007 and 2014), but Russia may cut the oil supply to Belarus, reducing its exports and related budget revenues.

# _Government expenditures_

A quick look at the structure of general government expenditures supports the “social orientation” of the fiscal policy: social spending amounts to 64.9% of the general government expenditures (see Table 2). The biggest portion (10.4% of GDP) is spent on old age pensions (from the Social Protection Fund and directly from the budget). Another 2.6% of GDP is spent on social allowances on temporary disability, childbirth allowance, family allowances, maternity pay, and disability. The IMF (2016) estimated government expenditures on housing and utilities subsidies to households of 1% of GDP.

**Table 2. General government expenditure and balance**

||**BYR mln***|**% of GDP**|
|---|---|---|
|**Total Expenditure & Grants**|**35 629.917**|**41.0**|
|Social Spending|23 131.264|26.6|
|Social Protection|12 991.387|14.9|
|Social Assistance_of which_|2 172.121|2.5|
|Noncontributory Pensions (Expenditure on social|684.931|0.8|
|protection, line “expenditure on old age”)|||
|Expenditure on family & children|168.322|0.2|
|Expenditure on housing|646.521|0.7|
|Other|672.346|0.8|
|Social Insurance (Social Protection Fund)**_of which_|10 819.266|12.4|
|Old-Age Pensions**|8 359.478|9.6|
|old age carer’s allowance, and funeral assis-|4 649.904|5.3|
|tance (financed by the SPF). Public expendi-|||
|tures on health care and education in Belarus|||
|are at the level of advanced economies (as a|||
|percentage of GDP). Indirect social spending|||
|also quite substantialEducation<sup>1</sup> _of which_|||
|Pre-primary and primary|977.513|1.1|
|Secondary|1 918.113|2.2|
|Post-secondary non-tertiary|476.392|0.5|
|Tertiary|793.725|0.9|
|Health<sup>2</sup>|3 872.301|4.5|
|Expenditure on housing & community amenities_of which_|1 617.671|1.9|
|Expenditure on community development|1 044.949|1.2|
|Spending on Defense, Public Order and Safety|2 622.543|3.0|
|Expenditure on public debt transactions|1 504.896|1.7|
|Grants|71.551|0.1|
|Other Government Expenditure|8 299.667|9.5|
|**Fiscal Balance**|||
|Primary net lending (+) / borrowing (–)|3 541.516|4.1|
|Net lending (+) /borrowing (–)|2 036.620|2.3|

* Taking into account denomination of 2016. 1, 2 Ministry of Finance of Belarus provides different figures for education and health care – BYR 4 186.4 mln and BYR 3 497.7 mln (denominated) respectively. These figures do not include investment financed within government investment programs. National classification by functions of government puts all financing of government investment programs under the line “Expenditure on general public services”, while GFS distributes these expenditures between the respective functional lines. Further imputation of health care and education subsidies is based on the Ministry of Finance data. _Source_ : GFS, except ** – Social Protection Fund of Belarus.

Although a large portion of the government expenditures was directed to subsidies to SOEs (4.7% of GDP in 2015), public investment (2.7% of GDP), and debt service (1.7% of GDP), the overall design of the redistribution system allows for inequality to remain relatively low. Not only government’s social programs, but also subsidies to the SOEs contribute to income redistribution. First, at least a part of SOEs have excessive employment (see Favaro et al., 2012; World Bank, 2012), such that government subsidies enable them to pay salaries to potentially unemployed people. Second, most of the SOEs bear the costs of cross-subsidization of utility tariffs, and, once again, government subsidies help them to pay higher tariffs for electricity, gas and utilities, while households pay below the level of cost recovery. Finally, SOEs apply the so called “wage grid” that sets different markups to some basic wages for different types of employees and puts limits on the difference between maximum and minimum wages 8
at any particular enterprise. Wage setting at private companies is not regulated through this mechanism, i.e. wage regulation at SOEs is inequality reducing.

However, one should not overestimate the role of SOEs in the overall system of social support. First, between 1995 and 2016, their share in total employment fell from 60% to 40%, and excessive employment fell accordingly (see Chubrik, 2016a). Second, comparing to the pre-crisis level, the government cut its direct and indirect support to SOEs: subsidies to SOEs fell from 9% of GDP in 2008–2009 to 4.3% of GDP in 2015; and the directed lending portfolio – from 25.2% of GDP in 2010 to 21.4% of GDP in 2015 (IMF, 2016). These cuts leave fewer resources for the “social” functions of SOEs.

# **2.3. State social insurance system design**

The Belarusian pension system preserved the main features of the PAYG system formed in the Soviet Union. There is no mandatory funded pillar with defined contribution design, and there is a rudimentary third pillar (mainly in the form of life insurance). It is organized in the form of the state social insurance system (hereafter – SSI), which is operated by the Social Protection Fund (hereafter – SPF) and funded mainly by a payroll tax (called “insurance contribution”). The tax rate is 35% of the total wage fund, of which 34% is funded by employers, and an additional 1% is paid from employees’ wages. In 2015, revenues collected via the employer contributions amounted to 91.8% of the SPF revenues. The second biggest source of the SPF revenues (5%) is subsidies from the central budget, i.e. the current design of the SSI system cannot ensure complete funding of its obligations.

Out of the 35% rate, 29 percentage points are directed to paying pensions, while the remaining 6 percentage points go to social allowances on temporary disability, childbirth allowance, family allowances, maternity pay, disability/old age care (attendance) allowance, and funeral assistance.<sup>3</sup> Within the pension system, the majority of employees are subject to a 29% tax rate, of which 28% is paid by the employer and 1% is paid from employees’ wages (although it is also accounted as employer contribution). Physical persons (e.g. individual entrepreneurs) usually pay 29% of the minimum wage for the accounting period. Employers – agricultural producers pay 25% (24+1). A very small group or employers (e.g. public associations of people with disabilities, pensioners, etc.) are subject to a 6% tax rate (5+1). Individuals employed in the High-Tech Park have a celling for this tax base amounting to one average wage in the economy (others have a celling of five average wages). The self-employed and those who get paid according to civil law contracts with foreign organizations do not pay contributions to the SPF (but they can do so at will). Several categories of employees are not subject to the state social insurance – military servants and the command and private personnel of the interior, and several state controlling, investigation, and emergency agencies. They do not pay contributions and receive pensions directly from the central budget. In addition, retired government officials with a state service record in excess of 20 years receive an additional pension also paid from the central budget.<sup>4</sup> Taking into account the high share of formal employment, the coverage is quite high: of the employed population of 4.5 million, about 3.4 million employees, 0.3 million individual entrepreneurs, and 0.3 million in other categories paid contributions in 2016.<sup>5</sup> However, the working age population is shrinking, while the number of pensioners is growing, such that the dependency ratio increased from 44.1% in 2000 to 48.2% in 2016.

Between 1956 and 2016, the pension age in Belarus remained constant: 55 years for women and 60 years for men. Starting with 2017, the pension age will increase by 6 months a year until it reaches 58 and 63 years, respectively. In addition, to be eligible for an old-age pension, in 2015 a person should have an “insurance record” (i.e. the period of paying contributions to the SPF) of no less than 15 years. In was increased from 5 years to 10 years (since

> 3 In addition, the SPF finances targeted social assistance and employment promotion, professional pensions, sanatorium-resort rehabilitation, etc.

> 4 The pension system of Belarus is regulated by laws (“On Basic Provision for State Social Insurance”, “On Pension Provision”, “On Civil Service in the Republic of Belarus”, “On Pension Provision for Military Servants, Command and Private Personnel of the Interior, Investigation Committee of the Republic of Belarus...”, etc.), Presidential edicts (“On the issues of Social Assistance”, “On the Social Protection Fund of the Ministry of Labor and Social Protection”, etc.), and other legislative acts.

> 5 High formal employment and its coverage with social security contributions are partially inherited from the times when state-owned enterprises dominated as employers. However, although their share in total employment fell from 60% to 40% between 1995 and 2016, the coverage of the employed population with social security contributions remained stable. Thus, SOEs are not the main “donor” of the Belarusian social protection system anymore, while private companies have similar payment discipline. In addition, despite the subsidies cut, the discipline of payments to the SPF remained very high: as of the beginning of 2010, overdue arrears for taxes, duties, and social contributions amounted to 0.22% of GDP, in 2016 – to 0.18% of GDP. In other words, SOEs’ payments to the SPF are their own burden, not those of the state budget, at least not anymore.

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2014) and to 15 years (since 2015). Starting with 2016, this record is increasing by 6 months a year until it reaches 20 years.

The design of the pension system ensures substantial income redistribution. First, an old age pension is equal to 55% of the “wage base”, but not less than the minimum old age pension.<sup>6</sup> . Second, one year of work record above 25 years for men and 20 years for women adds 1 percentage point to the 55%, but no more than 20% of wage base (or minimum old age pension). And the most serious redistribution comes from the method of the wage base calculation: where _w_ is average wage of an individual accounted for pension calculation and _w_ is the average wage in the economy in the same period. The redistributive effect is provided in the table below.

|Actual wage,% of average wage in the economy|50|100|130|400|500|
|---|---|---|---|---|---|
|Wage base, % of average wage in the economy|29.65|52.15|65.65|92.65|92.65|
|Wage base,% of actual wage|59.30|52.15|50.50|23.16|18.53|

As a result, the Gini index for old-age pensioners<sup>7</sup> in 2015 was 0.126, while for employees<sup>8</sup> – 0.269.

# **3. Methodology of the analysis**

# **3.1. Data available and CEQ approach to income concepts construction**

The analysis is based on the Household Budget Survey (HBS) data. This survey is conducted each year starting from 1995. It covers all oblasts and Minsk city, and includes observations from around 50 towns and rural councils. The sample of the survey is expected to be 6,000 households (0.2% of general population). In 2015 the actual sample included 6,269 households, including 313 households with zero sampling weights, as they did not provide basic information about their income and expenditures. The remaining households represent 9.1 million people or 96.3% of total population. The sample does not cover collective households, i.e. care homes, students’ dormitories, specialized institutions, etc. As any other survey of this kind, it does not properly represent the richest households and the most marginalized households, which refuse to participate in the survey. The sample is structured to be representative at the country level for key population groups and for total population at the oblast level. Still it inevitably has some distortions. For instance, the sample overestimates rural population by 10.1% and underestimates urban population by 7.8%.

**Figure 5. Ratio of HBS data and data of national accounts, %**

<!-- Start of picture text --> Consumption<br>Disposable<br>Income<br>Rural population<br>Urban population<br>Total population<br>0 20 40 60 80 100 120<br><!-- End of picture text --> 6 The minimum old age pension is equal to 25% of the subsistence minimum plus 20% of the average wage. As of 2017, it is 5.5% below the subsistence minimum.

> 7 HBS data: women 55+ and men 60+ who have pension income (total – 2,448,272 individuals).

> 8 HBS data: for those who received wages during 12 months, sum of all wage-related incomes (total – 3,186,139 individuals).

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# _Source_ : Belstat.

HBS data are used by the Statistical Committee while computing national accounts. However, HBS data permanently underestimate household consumption if compared to retail statistics. In 2015 the scale of underestimation was extraordinarily high (37.4%, see Figure 5). First, it is related to the increased volume of consumer lending. A purchase of goods on terms of consumer loans is reflected in the survey as loan servicing expenditures by households instead of consumption expenditures, which creates differences between HBS and retail statistics. Therefore, disposable income of households calculated based on total expenditures within HBS is substantially lower (22.3%) than estimated within national accounts. Second, households traditionally underreport expenditures on alcohol consumption, which is a common problem of the surveys. Third, households tend not to report purchases of tobacco and fuel (and alcohol as well) for the purpose of further resale abroad, which is a widespread coping strategy in western regions of Belarus. Hence, these expenditures, accounted in retail statistics, are not actually household consumption, but rather costs associated with their entrepreneurial activity, so they should not be taken into account while calculating households’ consumption or disposable income.

HBS data can be used to estimate household welfare and overall macroeconomic effects without additional adjustments as it represents almost the whole population and covers household expenditures in full, with the exception of alcohol consmption. Belstat uses these data for poverty and living standard analysis. Poverty analysis is based on comparison of disposable household income with the absolute poverty line. Disposable income is officially calculated as a sum of total household expenditures, net in-kind income and privileges (in-kind benefits). Hence, it is calculated based on reported expenditures rather than reported income, as it is believed to be underestimated (total reported cash income was 3.5% less than total reported expenditures in HBS 2015). The absolute poverty line is set at the level of subsistence minimum for a member of a household containing two adults and two children.

Following the official approach and assuming underreporting of households’ income, we also conduct the CEQ analysis based on disposable income data, assessed through the expenditure side. We also apply the same national poverty line for analysis of fiscal effects on poverty. However, we are not able to match the official estimate of poverty for 2015, as Belstat uses quarterly data for its estimation, while we work with the annual file. In addition to the national poverty line, we also calculate moderate poverty based on the annual average minimum consumer budget set for a member of a household containing two adults and two children. Nowadays, this budget is not widely used for the purposes of social policy. Still it is believed to serve a threshold for determining households with a risk of vulnerability. For instance, it is used as an eligibility criterion for privileged loans.

The core element of the CEQ analysis is the calculation of income concepts. Based on available data, we take disposable income as a starting point (see Figure 6). Subtracting reported direct transfers from disposable income and adding estimated direct taxes, we calculate market income. There are two approaches of assigning direct taxes and transfers based on the pension system of a country. Pensions can be viewed either as a government transfer (PGT) or a deferred income (PDI). In the first case, it implies that social security contributions are accounted as direct taxes while pensions are added to direct transfers. In the second case, pensions and related contributions are not taken into account while estimating market income – pensions are considered as a part of both market and disposable income concepts.

The pension system in Belarus is purely pay-as-you-go. The link between contributions and actual pension payments in Belarus is quite weak (see Section 2.3). From this point of view, is it more natural to consider pensions as transfers similarly to other contributory programs (like unemployment benefits).

The pension system in Belarus is redistributive, effectively weakening the link between the market income and pension income after retirement. Hence, even if we agree that the effects of the pension system on poverty are debatable, the redistribution effect is the direct consequence of the government fiscal policy and should be taken into account when the fiscal effects are analyzed.

Finally, two important benchmark cases for the Belarus CEQ study – Russia (Lopez-Calva et al., 2017) and EU (based on EUROMOD) consider pensions as public transfers in the main scenario (in case of Russia) or as the only scenario (EU). Similar treatment of pensions in Belarus will allow for proper comparison with these countries.

For the above reasons, we chose to model pensions as government transfers (PGT) in our primary scenario. We also consider the alternative approach. Pensions are often viewed not as a handout from the state, but rather as something earned in the working age. Hence it might make sense to view pensions as deferred income (PDI). Methodologically it means that now we include pensions into the definition of market income, or, since we go from consumption, we do not subtract pensions when going from disposable to the market income. Direct taxes 11
now also do not include social contributions tax, and only the personal income tax is added to the disposable income to get market income.

We calculate the consumable income as disposable income plus imputed indirect subsidies minus estimated indirect taxes. Further adding imputed in-kind transfers leads to final income. Detailed principles of estimation and imputation of related transfers and taxes are discussed in the next section.

# **Figure 6. Construction of income concepts**

<!-- Start of picture text --> Final income<br>(education and healthcare + in kind transfers + indirect subsidies – direct transfers (cash and in kind benefits,<br>public expenditures) (utility subsidy and transport subsidy)<br>pensions* )<br>Disposable income<br>Consumable income (total cash expenditures + net in Market income<br>kind income + in kind benefits)<br>– indirect taxes + direct taxes (PIT and<br>(VAT, excises, and import duties) SPF contributions* )<br><!-- End of picture text --> _Note_ . * Pensions and SPF contributions are included into direct transfers and taxes respectively only within PGT approach. _Source_ : own elaboration.

# **3.2. Direct taxes**

Taxed paid directly by people are personal income tax (PIT), property taxes, and taxes paid by entrepreneurs. The HBS data do not contain explicit information on these taxes. Household expenditures on _property taxes_ are included in the line “taxes and insurances” that feature expenditures on property tax payments, as well as on medical, life, auto insurance, stamp duties, fines, membership fees, and other. There is no feasible way to separate property taxes from other payments. Moreover, the role of these expenditures in the households’ welfare is marginal. On average, “taxes and insurances” constituted only 0.8% of the disposable income of households in 2015. The role of these expenditures increases with income. The richest decile spent 0.9% of their disposable income on “taxes and insurances” in 2015, the poorest decile - 0.6%. Hence, it may signal something about the progressivity of property taxes if one assumes that they are distributed the same way as total expenditures on “taxes and insurances”. However, the situation may have changed in 2016, as some privileges on property tax for old-age people were abolished. Still, these taxes play a limited role both in fiscal policy and households’ welfare, and ignoring them would not affect conclusions about overall impact of fiscal policy on poverty and inequality in Belarus.

# _Personal income tax and taxes on entrepreneurial income_

_Personal income tax_ (PIT) is paid from employment and related income at the flat rate of 13%. The HBS data present information on net income, implying that gross income and PIT payments should be estimated. We assumed that PIT is paid only from employment income. The HBS contains also information on income from sales of agricultural products, receipts from personal property and real estate sale, on dividends and rental income. However, they are fully (income from sales of agricultural products) or partly exempt from PIT. In order to simplify estimation, and due to the absence of information needed to make reliable assumptions on which part of income was taxed, we considered that all these lines of income are not subject to PIT.

Employment related income presented in HBS files at the household level contains information on self-employment income. This income was subtracted as entrepreneurs enjoy a special tax regime (see below).

PIT legislation provides various deductions from the tax base aimed at reducing the tax burden on vulnerable groups – low income deduction, deduction for parents of children below the age of 18, on spouse in maternal/paternal leave, on children above the age of 18 who are continuing education. We took into account deductions on children below 18, equaled to BYR 210,000 per month for one child and 410,000 for two or more children, and the low income deduction of 730,000 for persons with incomes less than 4,420,000 per month. As there is no information on relations between household members, we assigned deductions on children to all employed members of the household with children.

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According to our estimates (see Appendix A) the total volume of PIT should have amounted to BYR 36.2 trillion in 2015, which fits actual data. PIT revenues in the consolidated budget in 2015 were equal to BYR 37.0 trillion.<sup>9</sup> The ratio of estimated PIT paid by households and gross income from employment of household members is 11.3%, which can be viewed as the effective PIT rate in Belarus. The modelled PIT payments are distributed rather progressively both in absolute and relative terms (see Figure 7a). Lower deciles pay less in personal income taxes than upper deciles if measured as a share of their disposable income before taxation due to deductions provided to households with children and low-paid employees. Another factor is the lower share of employment income in disposable resources of lower deciles. Among the bottom five deciles, only the first decile relies on employment income to the same extent as the population on average (around 50% of disposable income of the first decile is generated by employment income). This stratum is comprised more of households with children and less so of households with pensioners compared to other low-income deciles<sup>10</sup> , which explains the higher role of employment income in their disposable resources. Consequently, the first decile has a slightly higher PIT tax burden than other low deciles.

_Taxation of entrepreneurs_ is not uniform. It depends on the type of entrepreneurial activity, the place of residence, and its scale. In general, entrepreneurs may pay taxes on a general basis, may apply a simplified tax regime for entrepreneurs, or pay a lump-sum tax set by local authorities. Therefore, the modeling of entrepreneurial taxes was based on macroeconomic data. The total amount of taxes paid by entrepreneurs in 2015 amounted to BYR 4,212.8 billion, while total number of entrepreneurs as of the end of 2015 was 240,781<sup>11</sup> . This means that on average an entrepreneur paid BYR 1,458,000 of taxes monthly. This volume of payments was assigned to every individual who had entrepreneurial income.

Information on related income is provided in HBS files together with income from side (day-to-day) jobs as a part of employment income. We assumed that entrepreneurial income was only that exceeding 2 minimal wages in annual terms. This threshold guaranteed that the number of entrepreneurs in HBS files corresponded to their total amount<sup>12</sup> .

# _Social Protection Fund contributions_

The Social Protection Fund (SPF) finances public expenditures on pensions and social benefits. Contributions to SPF are payroll taxes at the rate of 35%. Employers pay the main part of the tax (34%), while employees are charged 1% of their gross wage. According to legislation, lower rates (31%, i.e. 30 and 1%) are levied on those employed in the agricultural sector (at enterprises with agricultural production exceeding 50% of total production). As most of rural population in Belarus is employed in the agricultural sector, we assumed that all rural population pays the payroll tax at the rate of 31%.

Furthermore, there are upper and lower bounds for the payroll tax base. It should not be lower than the minimum wage and should not exceed 5 average wages in the case of full employment. In our sample there were only 3 observations, where gross wages exceeded 5 average wages. For these persons, SPF contributions were calculated as 35% of the upper bound.

In practice, the lower bound is not applied for the employed as the minimum wage regulation applies in Belarus. It is more relevant for entrepreneurs. They are obliged to pay contributions to the SPF at the rate not less than 35% from minimal wage. Entrepreneurs can choose to pay contributions from higher wage base but they are reluctant to do it, as it does not guarantee a feasible increase in future pensions. Hence, we assumed that entrepreneurs paid contributions to the SPF at the volume of 35% of minimum wage<sup>13</sup> .

> 9 In BYR before denomination of 2016 by 10,000 times. For revenues in BYR after denomination; see Table 1.

> 10 81% of households in the 1st decile ranked by disposable income before personal income taxation are households with children, while the average share is 47.5% (48.5% for the 4<sup>th</sup> decile). The share of households with at least one member above 60 years old is 37.3% in the first decile and 57% on average in the sample (72.1% in the 4<sup>th</sup> decile).

> 11 - Ministry on Taxes and Duties of Belarus, www.nalog.gov.by/uploads/folderFor

> <u>Links/Ежемесячно%20на%20сайт%20по%20СМП%20и%20ССП%20на%2001.01.2017.xlsx.</u>

> 12 According to this approach, the number of entrepreneurs in HBS file corresponds to the total number of 218,000.

> 13 SPF contributions by entrepreneurs are paid once a year in February. It means that in 2015 entrepreneurs paid contributions for 2014. So we estimated payment based on annul average minimum wage of 2014.

13
**Figure 7. Incidence of direct taxes by deciles**

<!-- Start of picture text --> 7 USD PPP % 10 16 USD PPP % 25<br>9<br>6 14<br>8 20<br>12<br>5 7<br>10<br>6 15<br>4<br>5 8<br>3<br>4 10<br>6<br>2 3<br>4<br>2 5<br>1 2<br>1<br>0 0 0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>% of disposable income net of intervention (right axis) % of disposable income net of intervention (right axis)<br>USD PPP per day USD PPP per day<br>(a) personal income tax (b) social security contribution<br><!-- End of picture text --> <!-- Start of picture text --> (a) personal income tax<br><!-- End of picture text --> _Note_ . Deciles are ranked by disposable income before personal income tax (a) and social security contribution (b). _Source_ : own estimates based on HBS data.

This approach generated relevant estimates of payroll taxes. The total amount of estimated SPF contributions equaled to BYR 94.3 trillion, which is close to the actual amount of contributions in 2015 (BYR 95.3 trillion). Estimated contributions are distributed progressively in absolute and relative terms (see Figure 7b). A fall in the share of SPF contributions in disposable income before SPF taxation in the third decile is related to the significant share of pensioners in this stratum<sup>14</sup> .

# **3.3. Indirect taxes**

# _VAT_

VAT generates the largest volume of general government revenues. Most of the goods and services either domestically produced or imported are taxed at the rate of 20%. Hence, the share of VAT in consumer prices of these goods and services is 16.7%. A lower rate of 10% (corresponding to the share of 9.1% in consumer prices) is applied to agricultural and most of food products, as well as children goods. Exported goods are taxed at the rate of 0%. Furthermore, some services are exempt from VAT. The list of these services reduced substantially in the last decade, as the government strives to keep the tax base stable despite the economic recession. As of 2015, health care, education, and utilities services were exempted from VAT. VAT was similarly not applied to purchases and rent of real estate by households. The VAT exemption implies that providers of corresponding services have no VAT refunds, so they report VAT on inputs as costs, including them into basic prices. Hence, the effect of VAT taxation on consumer prices for these services depends on the share of intermediates in total production. Related estimates were accomplished within input-output tables after matching household expenditure lines with the industries from national accounts. Results of these estimations are presented in Table 3.

According to these estimates, the share of VAT in household consumption is equal to 11.7%, which corresponds to the ratio of general government VAT revenue to national final consumption (11.9%).<sup>15</sup> It reinforces the good approximation of obtained estimates of the VAT burden on households to actual VAT payments. Still, due to the discrepancy between consumption data of HBS and national accounts we may underestimate total volume of VAT payments by households. The estimated volume is BYR 37.7 trillion, which constitutes only 51.8% of total general government revenues from VAT. For instance, the share of household final consumption in total final consumption is 77.1%.

Our estimates show that this burden is evenly distributed among the population if measured as a share of disposable income (see Figure 8a). It can be attributed to the fact that the structure of household expenditures does not

> 14 77.1% of households in the third decile ranked by disposable income before payment of SPF contributions are households with at least one member aged above 60 years. Average share is 57%.

> 15 The ratio of estimated VAT payments of households to their disposable income is 8.8%. According to administrative data, collected VAT revenue is equal to 8.4% of national disposable income.

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differ much across the population (with the exception of the 1<sup>st</sup> and 10<sup>th</sup> deciles), and most of the goods and services are taxed at the same VAT rate.

**Table 3. Share of VAT in consumer prices by expenditure line**

|**Expenditure line(COICOP)**|**Industry (ISIC)**|**VAT rate**|
|---|---|---|
|Food*|Food products|9.1|
|Alcohol and tobacco*||16.7|
|Clothing|Textiles, and textile products|16.7|
|Footwear|Leather and footwear|16.7|
|Fabrics|Textiles, and textile products|16.7|
|Housing, fuel for heating dwellings**|Electricity, gas and water supply and forestry|7.6|
|Housing, utilities***|Electricity, gas and water supply and other community, social and<br>personal services|7.3|
|Housing, other|Real estate activities and renting|0.0|
|Household appliances|Computer, electronic and optical equipment,|16.7|
|Furniture|Wood and products of wood and cork|16.7|
|Health care|Health and social work|3.9|
|Public transportation|Transport|16.7|
|Maintenance of private vehicles|Coke, refined petroleum products and nuclear fuel|16.7|
|Purchase of cars and other vehicles|Motor vehicles, trailers and semi-trailers|16.7|
|Communication services|Post and telecommunications|16.7|
|Culture, recreation and sports|Hotels and restaurants|16.7|
|Secondary and higher education|Education|3.0|
|Preschool education|Education|3.0|
|Eating out and restorans|Hotels and restaurants|16.7|
|Personal care|Chemicals and chemical products|16.7|
|Other goods and services|Manufacturing nec|16.7|
|Food purchased for animals and for cultivation of|Agriculture|9.1|
|land plot|||
|Construction andpurchase of real estate|Construction|8.1|

_Notes_ .

* VAT rate for alcohol and tobacco is 20%, while for majority of food products it is set at 10%.

** Structure of fuel used for heating in houses with autonomic heating is following: 40% wood fuel, 60% gas<sup>16</sup> . Based on these weights we estimated VAT rate for expenditures on fuel for heating as weighted average VAT rate for “electricity, gas and water supply” and “forestry”. *** VAT rate is estimated as average for “electricity, gas and water supply” and “other community, social and personal services” weighted on sectors’ total output.

_Source_ : own estimates based on the Tax Code of Belarus and Input-Output Tables for 2014.

**Figure 8. Incidence of indirect taxes by deciles**

<!-- Start of picture text --> 6 USD PPP % 10 1.0 USD PPP % 6<br>9 0.9<br>5 5<br>8 0.8<br>7 0.7<br>4 4<br>6 0.6<br>3 5 0.5 3<br>4 0.4<br>2 2<br>3 0.3<br>2 0.2<br>1 1<br>1 0.1<br>0 0 0.0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>% of disposable income (right axis) % of disposable income (right axis)<br>USD PPP per day USD PPP per day<br>(a) VAT (b) excises<br><!-- End of picture text --> _Note_ . Deciles are ranked by disposable income. _Source_ : own estimates based on HBS data.

> 16 <u>http://www.belstat.gov.by/upload-belstat/upload-belstat-pdf/oficial_statistika/Potreblenie_energii_v_dom_hoz.pdf.</u>

15
# _Excises_

Excises are levied on alcohol, tobacco, and fuel for motor vehicles. Excise rates are set to physical units and the government tends to review them regularly due to high inflation rates. Thus, we calculated excise payments of households based on quarterly files of HBS.

HBS files contain information on household expenditures on excise goods. However, households tend to underreport related consumption. The gap between expenditures on _alcohol_ reported by households and its actual consumption based on the retail trade turnover is especially high: alcohol expenditures reported in the HBS are only about 25% of retail sales of alcohol. It implies that HBS data needs an adjustment to retail trade statistics in order to receive reliable volume of excises paid by households.

We have HBS data on the total amount of alcohol expenditures on the one hand, and sales/average prices/excise rates by types on the other. Based on data on alcohol retail sales, its average prices, and respective excise rates, we estimated shares of excise taxes in retail prices by alcohol type. Next, we made an assumption about the structure of alcohol consumption by different types of households. We assumed that the cheapest alcoholic beverages are consumed by the poorer households, and shares of more expensive alcohol are growing with household income, distinguishing between quintiles of households by their expenditure (see Table 4). Having the retail trade data on sales of different types of alcohol and given assumptions about the structure of alcohol consumption by quintiles, we estimated total alcohol expenditures by household quintiles. Dividing imputed alcohol expenditures by HBS alcohol expenditures for every quintile, we got quintile-specific “alcohol expenditure underreporting coefficients” (see Table 5). Assuming that every household in a particular quintile that reported alcohol expenditures has the same “bias”, we imputed alcohol expenditures as _EXPialc_  _miq_ , where _EXPialc_ is initially reported expenditures on alcohol of household _i_ from quintile _q_ , and _miq_ is quintile-specific alcohol expenditure underreporting coefficient from the Table 5. Consequently, the amount of alcohol excises paid by household _i_ was calculated as _EXialc_  _EXPialc_  _miq_  _dEX q_ , where _dEX q_ is the share of excises in expenditures on alcohol by quintile from the Table 4.

**Table 4. Assumptions and inputs for estimating household payments of alcohol excises**

||**Vodka**|**Liquers**|**Wine**|**Fruit wine**|**Cognac**|**Sparkling**<br>**wine**|**Low alcohol**<br>**beverages**|**Beer**|
|---|---|---|---|---|---|---|---|---|
|**Share of exci**<br>|**se tax in reta**<br>|**il prices of alco**<br>|**holic bevera**<br>|**ges, %**<br>|||||
|1<sup>st</sup>quarter|43.1|32.6|7.6|36.9|20.5|9.6|28.0|17.5|
|2<sup>nd</sup>quarter|44.1|32.9|7.3|36.9|19.8|8.4|27.5|16.6|
|3<sup>rd</sup>quarter|44.2|29.2|7.8|39.0|18.2|8.8|27.2|16.1|
|4<sup>th</sup> quarter|43.9|30.0|7.8|39.0|17.9|8.5|27.2|15.6|
|**Structure of**<br>|**alcoholic bev**<br>|**erages consum**<br>|**ption by qui**<br>|**ntiles of populati**<br>|**on, %**<br>||||
|1<sup>st</sup>quintile|0|0|0|100|0|0|0|0|
|2<sup>nd</sup>quintile|30|0|0|0|0|0|0|30|
|3<sup>rd</sup>quintile|30|0|25|0|0|50|0|25|
|4<sup>th</sup>quintile|25|40|25|0|0|25|100|30|
|5<sup>th</sup>quintile|15|60|50|0|100|25|0|15|
|**Total**|**100**|**100**|**100**|**100**|**100**|**100**|**100**|**100**|

_Source_ : own estimates based on Belstat (retail prices for alcoholic beverages) and Ministry of Taxes and Duties (excise tax rates for 2015); own assumptions.

Expenditures on _tobacco_ and _fuel for motor vehicles_ reported in HBS files are also below levels in retail sales data. This discrepancy can be attributed to the widespread cross border trade in cigarettes and gasoline/diesel with EU countries. Hence, unlike in the case of alcohol products, there is no need to adjust consumption expenditures of households on tobacco and fuel for motor vehicles to the retail statistics.

In order to estimate tobacco excise payments, we calculated the average weighted share of excises in consumer prices of tobacco products. The weights for tobacco products (cigarettes with filter, cigarettes without filter, imported cigarettes with filter) were taken proportionally to their share in the consumer price index. According to our estimates, the share of excises in expenditures on tobacco equaled to 31.9% in 2015.

Estimates of fuel excises were based on the structure of fuel expenditures reported in HBS files and shares of excises in gasoline and diesel prices (13.4 and 7.6%).

16
**Table 5. Estimated parameters of alcohol consumption underreporting and share of excises in household expenditures by quintile**

||**1**<sup>**st**</sup> **quintile**|**2**<sup>**nd**</sup> **quintile**|**3**<sup>**rd**</sup> **quintile**|**4**<sup>**th**</sup> **quintile**|**5**<sup>**th**</sup> **quintile**|
|---|---|---|---|---|---|
|Scale of underreporting, times|7.9|6.5|5.6|4.5|2.8|
|Share of excises in expenditures for<br>alcohol,%|38.0|32.4|28.7|27.7|23.3|

_Source_ : own estimates based on Belstat (retail prices for alcoholic beverages) and Ministry of Taxes and Duties (excise tax rates for 2015); own assumptions.

According to our estimates, the total volume of excises paid by households in 2015 amounted to BYR 11.4 trillion. The total volume of excises collected by the general government was BYR 19.4 trillion. The difference is constituted by fuel excises paid by legal entities, as well purchases of tobacco and fuel by households for further sale abroad rather than for individual consumption. According to our estimates, excises are regressive in relative term (see Figure 8b), as lower deciles of population tend to spend a bigger share of their disposable resources on alcohol and tobacco products.

# _Import duties_

The impact of import duties on welfare of socially vulnerable groups was partly analyzed in research related to the consequences of Russian WTO accession to Belarus social policy (Shymanovich, 2013). These results showed that the reduction of import tariffs due to Russian WTO accession should have had a minor impact on population welfare, with a uniform distribution across the population. One of the reasons behind this was the small scale of the reduction of tariffs that is scheduled in the Russian WTO accession agreement.

In this analysis, we will apply the same methodology as in Shymanovich (2013) in order to see whether conclusions on the neutral influence of import duties on inequality hold within a full-fledged abolishment of import tariffs. The volume of import duties paid by households was estimate based on data on household consumption, the share of import products in retail sales and import tariffs according to the following formula: where _Ci_ is the consumption of the product _i_ from HBS data, _n_ is equal to 37 product groups (see table), _Sim_ is the share of imports in the consumption of the product _i_ , and _ti_ is a level of import tariff for the product _i_ .<sup>17</sup> Table 6 presents data required for these estimations. Import tariffs for expenditure lines were taken for corresponding product groups within the HS classification and weighted by import volumes. The share of import products in household consumption was supposed to be equal to the share of related goods in retail sales.

**Table 6. Assumptions and inputs for estimating import duties**

|**Expenditure line (COICOP)**|**Corresponding**<br>**HS code**|<br>**Weighted**<br>**average import**<br>**tariffs**|**Import share**<br>**in retail****|**Import price**<br>**elacticity*****|
|---|---|---|---|---|
|Expenditures for bread|1905|13.2|6.1|-0.8|
|Expenditures for pastry|1905|12.5|6.1|-0.8|
|Expenditures for flour|11|11.2|4.0|-1.2|
|Expenditures for cereals and beans|10|5.5|43.3|-0.9|
|Expenditures for macaroni food|1902|14.0|35.4|-0.7|
|Expenditures for milk|4|15.8|5.7|-1.4|
|Expenditures for sour cream and cream|4|15.8|5.7|-1.1|
|Expenditures for butter|405|18.2|1.3|-0.8|
|Expenditures for cheese|406|18.5|12.6|-0.6|
|Expenditures for other dairy products|4|15.8|5.7|-1.1|
|Expenditures for beef and veal|201|23.8|0.1|-1.0|
|Expenditures for pork|203|32.5|0.3|-2.9|
|Expenditures for sausages and smoked meat|16|14.8|0.6|-1.1|
|Expenditures for poultry|207|52.5|0.8|-2.9|

17 This formula allows estimating first order welfare effect of import duties abolishment on welfare of population, if one assumes that reduction of duties results in proportional reduction of prices. For estimating second order effect one should take into account import price elasticities _εi_ , estimated in Shymanovich (2013) based on data from Kee, Nicita, Olarreaga (2009). Related formula is:  _W_   _n Ci_  _ti_  _Sim_  (1  _ti_  _i_ ). _i_  1 1  _ti_ 1  _ti_ 17
|**Expenditure line (COICOP)**|**Corresponding**<br>**HS code**|<br>**Weighted**<br>**average import**<br>**tariffs**|**Import share**<br>**in retail****|**Import price**<br>**elacticity*****|
|---|---|---|---|---|
|Expenditures for fat|209|15.0|0.5|-1.8|
|Expenditures for other meats|2|34.2|0.7|-1.4|
|Expenditures for fish and seafood|3|8.0|42.1|-1.3|
|Expenditures for vegetable oil, margarine and other grease<br>|15|14.3|74.2|-1.0|
|Expenditures for eggs|407|0.2|0.2|-1.0|
|Expenditures for potatoes|701|13.8|6.8|-1.2|
|Expenditures for vegetables and melons|7|13.8|32.1|-0.8|
|Expenditures for fruits and berries|8|3.4|89.8|-1.0|
|Expenditures for sugar and confectionery|17|6.1|22.0|-1.0|
|Expenditures for tea, coffee, cocoa|9|12.9|76.6|-1.0|
|Expenditures for non-alcoholic drinks|2201, 2202*|10.9|17.2|-0.9|
|Expenditures for other food|food average|15.9|26.3|-1.2|
|Expenditures for alcohol and tobacco|22, 24*|7.7|11.7|-1.5|
|Expenditures for clothing|61, 62*|10.1|41.5|-1.9|
|Expenditures for footwear|64|2.3|52.4|-1.0|
|Expenditures for fabrics|59, 60*|5.8|30.9|-1.0|
|Expenditures for household appliances|85|5.3|74.7|-1.2|
|Expenditures for furniture|94|12.4|10.4|-0.9|
|Expenditures for health care|30|7.7|64.6|-0.9|
|Expenditures for maintenance of private vehicles|2710, 8708*<br>25% for 19.2%|1.2|18.5|-1.4|
|Expenditures for purchase of cars and other vehicles|of import|4.8|98.9|-1.2|
|Expenditures for personal care|34|10.6|77.7|-0.8|
|Expenditures for for food purchased for animals and for cultivation|||||
|of landplot|23|4.2|47.9|-1.0|

_Notes_ .

* Average import tariffs were weighted by import volume (from world in 2015). Data of tariff rates is taken from TRAINS database (as of 2014, partner – world).

** Data on share of import goods in retail sales is obtained from Belstat yearbook on retail trade<sup>18</sup> and monthly bulletins on retail trade.

*** Elasticities are from Shymanovich G. (2013). Absent elasticities were assumed as following: for “bread”, “other dairy products”, and “poultry” equaled to “pastry”, “sour cream and cream”, and “pork” respectively; for “eggs” equaled to -1; for “other food” as average of elasticities for food products; for “alcohol and tobacco” as average elasticity of alcohol and tobacco weighted by import; for “maintenance of private vehicles” as average elasticity for HS2710 and HS8708 weighted by import volume.

_Sources:_ TRAINS database, Shymanovich G. (2013), Belstat.

# **Figure 9. Incidence of import duties by deciles**

<!-- Start of picture text --> 0.8 USD PPP % 1.4<br>0.7 1.2<br>0.6<br>1.0<br>0.5<br>0.8<br>0.4<br>0.6<br>0.3<br>0.4<br>0.2<br>0.1 0.2<br>0.0 0.0<br>1 2 3 4 5 6 7 8 9 10<br>% of disposable income (right axis)<br>USD PPP per day<br><!-- End of picture text --> _Note_ . Deciles are ranked by disposable income. _Source_ : own estimates based on HBS data.

> 18 <u>http://www.belstat.gov.by/ofitsialnaya-statistika/realny-sector-ekonomiki/vnytrennia-torgovlya/roznichnaya-torgovlya/publikatsii_6/index_702/.</u>

18
According to the estimates, the total volume of import duties paid by households amounted to BYR 5.0 trillion in 2015. The Ministry of Finance reported BYR 8.6 trillion revenue from import duties. These numbers look reasonable as import of investment goods is largely exempted from import duties, while intermediate goods are mainly imported within the customs union with Russia and other CIS countries. According to our estimates import duties are progressive in absolute terms, but neutral in relative terms (see Figure 9). The payment of import duties accounts for similar shares of disposable income in lower and upper deciles. The only exception is the 10<sup>th</sup> decile, in which people tend to save more and spend more on real estate and services.

# **3.4. Direct transfers**

Data on the majority of direct transfers received by households is available in HBS files. They feature revenues from the following benefits and privileges:

- Benefits `o` pregnancy registration and child birth benefit `o` maternity benefit `o` children allowances for children aged below 3, `o` children allowances for children aged above 3, `o` attendance allowance `o` funeral benefit `o` pension for death of a breadwinner `o` pension for disabled children `o` unemployment benefit `o` severance pay `o` student grants `o` social assistance and other

- Privileges `o` Food privilege `o` Passenger transportation privilege `o` Hosing and utilities privileges `o` Fuel privileges `o` Electricity privileges `o` Communication service privileges `o` Health resort privileges `o` Privileges for pharmaceuticals `o` Privileges for social rehabilitation appliances `o` Preschool education privileges `o` Other privileges

The total amount of benefits received by households corresponds to public expenditures of BYR 17.6 trillion. It matches the official figure of BYR 17.5 trillion that SPF spent on financing social benefits in 2015. The volume of privileges, according to HBS data, equaled to public expenditures of BYR 3.2 trillion. The distribution of cash and in-kind direct transfers differs significantly. Benefits are progressive in absolute and relative terms (see Figure 10a). The first decile ranked by disposable income net of transfer receives on average almost 2 times more benefits in absolute terms than the second decile. Benefits increase the disposable income of the first decile by 47.6%, while the average effect over the whole sample is 8.6%. Privileges are less targeted. The absolute volume of privileges received by lower and upper deciles is almost the same (see Figure 10b). Still, the disposable income of lower deciles is more vulnerable to the revision of privileges than the income of upper deciles.

Belarus has a PAYG pension system, which implies that pensions have the nature of transfer rather than deferred income. Pensions reported by households are equal to total public expenditures of 96.2 trillion. Related expenditures of SPF were equal to 83.4 trillion. In addition, non-contributory pensions (provided to retired civil servants and persons retired from national security, defense and law enforcement agencies) were financed from the central government budget at the level of BYR 6.8 trillion. Still, HBS data overshoots actual public expenditures on pensions by 7.3%. This overshooting is related to the structure of the survey sample, which overestimates the actual number of population above working age by 9.5%.

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**Figure 10. Incidence of cash and in-kind benefits by deciles**

<!-- Start of picture text --> 3.5 USD PPP % 50 0.30 USD PPP % 3.0<br>45<br>3.0<br>0.25 2.5<br>40<br>2.5 35<br>0.20 2.0<br>30<br>2.0<br>25 0.15 1.5<br>1.5<br>20<br>0.10 1.0<br>1.0 15<br>10<br>0.05 0.5<br>0.5<br>5<br>0.0 0 0.00 0.0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>% of disposable income net of intervention (right axis) % of disposable income net of intervention (right axis)<br>USD PPP per day USD PPP per day<br>(a) benefits (b) privileges<br>20 USD PPP % 250<br>18<br>16 200<br>14<br>12 150<br>10<br>8 100<br>6<br>4 50<br>2<br>0 0<br>1 2 3 4 5 6 7 8 9 10<br>% of disposable income net of intervention (right axis)<br>USD PPP per day<br><!-- End of picture text --> <!-- Start of picture text --> (c) pensions<br><!-- End of picture text --> _Note._ The ratio of pensions to the disposable income net of pensions for the first decile is not calculated as related net income for this decile is close to 0.

_Source_ : own estimates based on HBS data.

# **3.5. Indirect subsidies**

Subsidies for utilities and urban public transportation are two major types of indirect subsidies in Belarus. Utilities subsidies amount to 2% of GDP, with around 1% covered by the cross-subsidization by the enterprises, and the other 1% coming directly from the budget (IMF, 2016). As of 2015, subsidies on utilities were available to everyone automatically in the form of subsidized tariffs in the utility bill (since September 2016 direct subsidies to the utilities are also available). Some households and apartments, however, are not eligible for the subsidy. These are the households where the household head owns more than one apartment or house, or if no one is registered in the apartment (usually the case when the apartment is rented out). According to IMF estimates, households with access to the subsidy covered 48.5% of the actual costs.

Expenditures on utilities are reported in the HBS, but the households do not report if they get the subsidy or not. To identify the households without access to subsidized tariffs we establish a cut-off in utilities cost per square meter. If the household is paying above the cut-off of BYR 15,000 per m<sup>2</sup> (two times higher than the average), or above BYR 1,000,000 in total per month (three time higher than the average), we assume that the household does not obtain the subsidy. The rest of the households are assigned a 51.2% subsidy on top of their actual utilities expenditure.

To check if we have allocated the utilities subsidy correctly, we gross up the allocated utilities, and find that they sum up to 1.96% of GDP, which coincides with the IMF estimate of 2% of GDP.

20
**Figure 11. Indirect subsidies by disposable income deciles**

<!-- Start of picture text -->
1.8 daily USD PPP  disposable Share of  7% 1.4 daily USD PPP disposable Share of  2.2%<br>1.6 income income<br>6% 1.2<br>2.1%<br>1.4<br>5% 1.0<br>1.2 2.0%<br>1.0 4% 0.8<br>1.9%<br>0.8 3% 0.6<br>0.6 1.8%<br>2% 0.4<br>0.4<br>1% 0.2 1.7%<br>0.2<br>0.0 0% 0.0 1.6%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>utility subsidies in $ PPP % of income transport subsidies in $ PPP % of income<br>(a) Incidence of indirect utility subsidy,   (b) Incidence of indirect transport subsidy,<br>by disposable income decile, USD PPP and % by disposable income decile, USD PPP and %<br><!-- End of picture text -->

_Source_ : own estimates As we see from Figure 11a, utility subsidies are regressive in absolute value: the top decile obtains twice the amount the bottom decile obtains through the subsidy. This result is not unexpected: the subsidies are equally available to the rich and the poor, but since higher income usually implies more spacious housing, higher income households face higher utility costs and receive a higher subsidy. However, in relative value the utility subsidy is progressive: lower deciles obtain a higher proportion of their income in the form of subsidy.

Many types of public transportation are indirectly subsidized in Belarus by budget support to the state-owned transport companies. However, we focus on the urban public transportation as the major source of transport subsidies, and also the only one on which the data on cost coverage is available. As with utility subsidies, the transport subsidies are built-in into the tariffs, but unlike utility subsidies, transport subsidies are available to every user without exemptions.

Transport expenditures are reported in HBS as a total for all kinds of expenditure, including but not limited to the urban public transport. To impute urban public transport expenditure, we follow the next steps:

1. We assume that all household transport expenditure below BYR 100,000 (a sum close to the average cost of round trip ticket between the regional centers) are expenditure on urban public transportation. A monthly pass cost above BYR 200,000 in 2015 in Minsk, hence this cut-off is not too high.

2. We build a truncated regression model for transport expenditure below the threshold of BYR 100,000. The explanatory variables are the number of working-age and retirement-age adults in the household, region, residence type (large cities or small cities) and car ownership. Income level was excluded as it turned out to be insignificant.

3. Using the estimated model, we imputed urban public transport expenditure for the rest of the households. If the imputed level was higher than the actual reported expenditure on transportation, we replaced it with the reported value.

4. We applied the subsidy of 62%<sup>19</sup> to the imputed urban transportation costs.

According to our imputation, transport subsidies amount to almost 1% of GDP. Unfortunately, we do not have the aggregate data on the value of transport subsidies, so we cannot check the validity of our imputation of transport subsidies by grossing up. Hence, all the results concerning those subsidies should be interpreted with caution.

Transport subsidies are regressive in both absolute and relative value: the top decile obtains twice the amount the bottom decile obtains through the subsidy (see Figure 11b). The result is mainly driven by the fact that only the urban population (with higher incomes) has access to the transport subsidy. Moreover, employed working-age individuals are more likely to use public transportation and enjoy the subsidy, and they also happen to be the ones with higher incomes.

19 See https://news.tut.by/society/505674.html.

21
# **3.6. In-kind transfers (health care and education)**

Health care in Belarus is dominated by the government, which remains the main provider of health services. The public health system is a Soviet-style centralized _Semashko_ system, with an extensive network of state-owned polyclinics and hospitals providing comprehensive health care. Health care in the public system is free to every citizen of Belarus, independent of income, employment or any other socioeconomic characteristics. No contributions are necessary to gain access to health care.

According to the official data (Ministry of Finance, 2016), total health expenditure in the government budget amounted to BYR 34,977 billion in 2015 (4.0% of GDP). Around 40% of the total public health expenditure is spent on primary and secondary care through polyclinics, and the rest covers tertiary care through hospitals (World Bank, 2013).

While health care expenditure is universal, and it would be tempting to distribute health expenditure (and benefits) equally, we prefer to assign health expenditure to the actual beneficiaries. In this case we would attribute expenditure to actual receivers of services. This approach would allow us to capture differences in needs, for example, by gender and age. Moreover, despite the universal system, de-facto access to health services is very different for rural and urban residents.

HBS stopped reporting doctor’s visits and hospital stays since 2008. We use the 2008 data set to model the number of doctor’s visits and the probability of a hospital stay, and then use the model to predict those variables in the 2015 data. We use the Poisson model for the number of doctor visits, and a probit to model the probability of a hospital stay. In both cases the explanatory variables are age, age squared, gender, a child dummy, smoker status, self-reported health evaluation, region, residence type, body mass index, and level of education.

**Figure 12. Health and education expenditure by disposable income deciles**

<!-- Start of picture text -->
1.8 USD PPP Share of  14% 3.5 USD PPP Share of  35%<br>disposable  disposable<br>1.6 income 12% 3.0 income 30%<br>1.4<br>10% 2.5 25%<br>1.2<br>1.0 8% 2.0 20%<br>0.8 6% 1.5 15%<br>0.6<br>4% 1.0 10%<br>0.4<br>2% 0.5 5%<br>0.2<br>0.0 0% 0.0 0%<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Primary and secondary healthcare Terciary Healthcare Preschool, primary school Secondary school Higher school<br>(a) Incidence of health expenditure,   (b) Incidence of education expenditure,<br>by disposable income decile, USD PPP (bars) and % (lines) by disposable income decile, USD PPP (bars) and % (lines)<br>Source : own estimates.<br><!-- End of picture text -->

After using the estimated models to predict the number of doctor’s visits and the probability of a hospital stay in 2015, we allocate primary and tertiary care expenditure on health care proportionately to them. In absolute values health expenditures are allocated rather flatly across different disposable income deciles, although there is a slight upward slope. _Ceteris paribus_ one might expect a negative relationship between health expenditure and income (with poorer people usually having lower health). However, in Belarus the lower income deciles are largely represented by rural non-retiree households, which have lower access to health care. Hence, the expenditure schedule across deciles looks flatter than expected. In relative terms, however, health expenditures are clearly progressive, reflecting the free universal access to health care (see Figure 12a).

Public education expenditure amounted to 4.8% of GDP in 2015: 1.1% of GDP was spent on pre-school and primary school education; 2.24% of GDP on general secondary (school) education; 0.56% on continued secondary education (vocational and specialized non-college education) and 0.90% of GDP on higher (college) education (Ministry of Finance, 2016).

22
Public school education is free, although households pay for textbooks and food out of pocket; private schools and colleges exist, but they are few and negligible in their coverage. At the tertiary level (vocational, specialized and college education) fees are widespread, and access to free education is conditional on performance. Usually the fees are below the total education cost.

As the primary and secondary school enrollment rates are 100% or higher in Belarus (World Bank, 2013), we allocate the preschool and primary and general secondary education expenditures to all children of ages 3-10 and 11-16 correspondingly. At 16 children graduate from the obligatory school, and are free to either continue in high school to enter college, go to the labor market or enroll in the vocational or specialized educational institutions. Since 2015 HBS data lacks information on the socioeconomic status, and we do not observe whether a young person is a student or not. To impute the probability of being a student, we use 2014 data which still had the socioeconomic status variable. We build a probit probability model for people aged 16-40, with age, gender, region, residence type, household size and education level being the explanatory variables. After imputing the probability of being a student, we assign expenditure on continued secondary and college education according to age. We do not scale down health and education expenditure. The main reasoning behind scaling down is that normally all the taxes and transfers in the CEQ analysis are not forced to be equal to their counterparts in the national accounts (see Higgins, Lustig, 2016). However, in our exercise the allocation of most of the taxes and transfers are quite close to their counterparts in the national accounts.

Major education expenditure categories, primary and secondary education, are highly progressive both in relative and absolute terms, as seen in Figure 12b. Households with children, the main recipients of educational expenditure, are on usually poorer (in per capita terms) than households without children; poorer households also tend to have more children. The expenditure on college (higher school) education, in contrast, is regressive in absolute terms, as individuals from higher-income households are more likely to enroll in colleges.

# **4. Results and discussion**

# **4.1. Main results**

The results of the CEQ analysis show that fiscal interventions contribute much to the reduction of inequality in Belarus if the pension system is modeled as a part of fiscal policy. Most of the effect on inequality comes from direct transfers and privileges in the case when pensions are treated as a government transfer (PGT) (see Figure 13). The decrease in inequality indicators calculated for market and disposable income, when pensions are viewed as a government transfer, is massive, from 0.407 to 0.267 for the Gini index. Particularly large improvements are observed for the ratio of the average income of the richest 10% to the poorest 10% - from 14.82 to 3.25 (see Table 7). A significant number of people without market income relying on direct transfers, on pensions in particular, explains this result<sup>20</sup> . When pensions are alternatively modelled as deferred income (PDI), the reduction of inequality related to fiscal interventions determining the difference between market and disposable income is only marginal. Furthermore, moving from disposable to consumable income does not influence the overall level of inequality, implying that the burden of indirect taxes and gains from indirect transfers is distributed among population proportionally to income. In-kind transfers are clearly more progressive, as inequality indicators fall substantially from consumable to final income.

Fiscal interventions also determine the level of absolute poverty. According to the international poverty lines of 2.5 and 5 USD PPP per day the risk of poverty is eliminated in Belarus at the level of disposable income. Moreover, the risk of absolute poverty at these lines is also negligible according to the market income concept when pensions are treated as deferred income (PDI). Only when excluding pensions from market income (PGT approach), poverty lines of 2.5 and 5 USD PPP per day can reveal some vulnerable population, as some households rely heavily on pensions. The risk of poverty is much higher if one considers the line of 10 USD PPP per day (which is often used for defining the middle class in international studies) or the national poverty line. In fact, the subsistence minimum in Belarus – national absolute poverty line – exceeds 10 USD PPP line, implying relatively high overall level of income in the country if measured in USD PPP terms. Hence, we will use national absolute poverty line as a main benchmark for analyzing the influence of fiscal policy on poverty in Belarus. For a more detailed analysis of vulnerable groups of population one can also apply the minimum consumer budget as a national moderate poverty line.

> 20 3.5% of the population lives in households without market income (modeled by the PGT approach).

23
**Figure 13. Lorenz curves for basic income concepts**

<!-- Start of picture text -->
1.0 market income (PGT)<br>market income (PDI)<br>0.9<br>final income<br>consumable income<br>0.8<br>disposable income<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>0.0 0.2 0.4 0.6 0.8 1.0<br><!-- End of picture text -->

_Source_ : own estimates based on CEQ methodology.

According to the national poverty line, 20.2% of the population would be poor if there were only market income without any fiscal interventions including the pension system<sup>21</sup> . In the case when pensions are treated as deferred income, the level of poverty is much lower (5.9%), stressing the significant role of the pension system in poverty reduction. On the one hand, it stems from the size of pensions that exceed the national poverty line. On the other hand, it stresses that pensions are a sole or dominant income for many households in Belarus. Fiscal redistribution related to other direct transfers and taxes offsets the risk of poverty down to 3.4%<sup>22</sup> . Moreover, the poverty gap, based on the disposable income concept, is also rather low, highlighting the absence of extreme poverty within the disposable income concept. On the contrary, the system of indirect taxes and subsidies increases the risk of poverty up to 5.2%, not affecting much its depth (the poverty gap remains low).

**Table 7. Main poverty and inequality indicators by income concepts**

|||**Market**<br>**PGT**|**Income**<br>**PDI**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|**Final**<br>**Income**|
|---|---|---|---|---|---|---|
|Gini||0.407|0.292|0.267|0.270|0.227|
|Theil Index||0.259|0.151|0.129|0.131|0.094|
|90/10||14.82|3.70|3.25|3.32|2.61|
|USD 2.5 PPP|Headcount Index|8.3|0.2|0.0|0.0|0.0|
||PovertyGap|5.9|0.1|0.0|0.0|0.0|
|USD 5 PPP|Headcount Index|11.6|0.7|0.0|0.1|0.0|
||PovertyGap|8.0|0.2|0.0|0.0|0.0|
|USD 10 PPP|Headcount Index|19.0|5.0|2.5|3.9|0.7|
||PovertyGap|11.6|1.3|0.4|0.7|0.1|
|National poverty line (USD|Headcount Index|20.2|5.9|3.4|5.2|0.8|
|10.62 PPP)|PovertyGap|12.1|1.6|0.5|0.9|0.1|
|National moderate poverty|Headcount Index|32.7|21.6|19.8|24.8|7.5|
|line(USD 16.69 PPP)|PovertyGap|17.2|5.7|4.2|5.6|1.3|

_Note_ . Minimum consumer budget represents national moderate poverty line. _Source_ : own estimates based on CEQ methodology.

The scale of redistribution caused by fiscal interventions is higher than the change in inequality indicators may suggest. The decomposition of the change in the Gini indicator reveals that vertical equity generated by fiscal policy is accompanied by significant horizontal effects (see Table 8). In particular, the reduction in Gini related to the influence of direct taxes and transfers, including pensions, would be 0.20 pp if there were no re-ranking effects between market (PGT) and disposable income. However, changes in the relative welfare of the population across these income concepts is natural as it is attributed to the pension system, which stands for a major part of direct

> 21 Application of national absolute poverty line to other income concepts than disposable income is for illustration purposes only, as it is constructed based on actual retail, i.e. post-fisc prices.

> 22 Poverty estimates based on disposable income and national poverty line should correspond to the official share of low-income people in Belarus. In practice there is significant difference, as official estimates are done based on quarterly data.

24
taxes and transfers. Modeling the pension system as a deferred income results in minor horizontal equity effects of direct taxes and transfers.

Less desirable is horizontal equity (reranking) caused by indirect taxation and subsidies, as well as in-kind transfers. The reranking effect of consumable income is higher than of disposable income, while vertical equity remains unchanged, irrespectively of the market income concept applied. Hence, indirect taxes and transfers do not reduce inequality, but lead to households switching places in the distribution by income. On the contrary, in-kind transfers are associated with a significant vertical equity effect. In the case when market income is modeled according to the assumption of pensions as deferred income, in-kind transfers generate most of the vertical equity effect of the fiscal policy. However, they also cause some horizontal equity effects if households are initially ranked by market income that includes pensions (PDI). On the contrary, the comparison of the household distribution by final income and market income modeled according to PGT approach reveals that in-kind transfers reduce the scale of reranking. This contradiction is rooted in the nature of modeled in-kind transfers. Namely, education transfers are inevitably lower for households with elderly members. Therefore, they make pensioners less wealthy relative to youth. When we compare the final income distribution to the market income distribution prior to pension system effects (PGT), we see that in-kind transfers limit the reranking effect associated with the pension system. Vice versa, the initial ranking of households by market income including pensions (PDI) results in deteriorated relative welfare of pensioners and increasing horizontal equity effects after accounting for in-kind transfers.

**Table 8. Decompositions of inequality changes into vertical and horizontal equity components**

||**Change t**<br>|**o market incom**<br>|**e(PGT)**<br>|**Change t**<br>|**o market incom**<br>|**e(PDI)**<br>|
|---|---|---|---|---|---|---|
||**Disposable**<br>**income**|**Consumable**<br>**income**|**Final**<br>**income**|**Disposable**<br>**income**|**Consumable**<br>**income**|**Final**<br>**income**|
|Gini change with respect to market income|0.139|0.137|0.180|0.025|0.022|0.065|
|Vertical equity (Reynolds-Smolensky Index)|0.200|0.202|0.236|0.031|0.030|0.087|
|Reranking (Atkinson-Plotnick Index of<br>horizontal equity)|0.060|0.065|0.056|0.006|0.008|0.022|

_Note_ . Vertical equity implies reduction of gap in welfare between rich and poor due to fiscal intervention. Horizontal equity implies that fiscal intervention does not influence ranking position of an individual, see Kakwani (1984). _Source_ : own estimates based on CEQ methodology.

The incidence analysis of income changes caused by fiscal interventions shows that benefits are concentrated within the lowest deciles. The bottom two deciles, ranked by market income that does not include pensions (PGT approach), enjoy a substantial average increase of income, which is partly related to the low base of market income (see Figure 14). The positive effect disappears by the 4–5 deciles, while 7–10 deciles suffer income reduction (see Table 9). If market income is modeled within the PDI approach, the positive effect of fiscal interventions holds only for the first deciles (with the exception of in-kind transfers) and its scale is much lower. Consequently, the scale of losses by upper deciles is also lower. Moreover, losses are distributed evenly among relatively wealthy deciles, implying a similar tax burden for upper deciles. The incidence of net effects changes substantially after accounting for in-kind transfers. At the level of final income, the effect from fiscal interventions steadily diminishes from lower deciles to the upper deciles (both compared to the market income modeled by PDI and PGT approach), implying progressivity of in-kind transfers. Moreover, the effect at the level of final income becomes negative only for the wealthiest deciles.

**Table 9. Incidence of net effects from fiscal interventions in relation to market income by deciles**

||**Market**<br>|**income by PGT app**<br>|**roach**<br>|**Market**<br>|**income by PDI app**<br>|**roach**<br>|
|---|---|---|---|---|---|---|
||**Disposable**<br>**Income**|**Consumable**<br>**Income**|**Final**<br>**Income**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|**Final**<br>**Income**|
|1|890.6|805.1|1214.1|18.2|8.0|56.5|
|2|111.4|95.2|169.7|4.9|-3.1|33.8|
|3|37.5|27.7|68.1|1.3|-6.0|23.6|
|4|13.8|5.9|36.2|-1.6|-8.4|18.0|
|5|1.2|-4.9|18.6|-2.4|-8.2|14.0|
|6|-7.1|-13.1|6.8|-3.6|-9.8|10.7|
|7|-12.7|-18.2|-0.8|-4.8|-10.8|8.4|
|8|-14.9|-20.1|-6.1|-5.5|-11.4|4.1|
|9|-17.6|-23.7|-11.4|-6.7|-13.7|-0.1|
|10|-19.5|-23.9|-17.3|-6.9|-12.1|-4.0|
|Total|-3.2|-9.5|9.3|-3.5|-9.8|9.0|

_Source_ : own estimates based on CEQ methodology.

25
Differences in the incidence of effects generated by moving from market income to disposable, consumable and final income are related not only to the progressivity of fiscal interventions, but also to the scale of redistribution, caused by them and the modeling of separate income concepts. The large size of the pension system compared to other social expenses determines the higher scale of effects if post-fiscal income is compared to the base of market income by the PGT approach, rather than by the PDI approach. A more positive effect at the level of final income compared to consumable income is rooted in the modeling, as total modeled final income is 9.3% higher than total market income by the PGT approach (9.0% by the PDI approach, see Table 9), while consumable income is 9.5% lower (9.8% lower by PDI approach).

**Figure 14. Distribution of individuals by market and disposable income**

<!-- Start of picture text --> 0 50 100 150 0 50 100 150<br>market income (PGT), USD PPP market income (PDI), USD PPP<br>150<br>100<br>50<br>0<br>150<br>100<br>50<br>0<br><!-- End of picture text --> _Source_ : own estimates based on CEQ methodology.

Overall, the progressivity of fiscal interventions and the average positive effect observed for lower deciles, does not necessary imply that everybody from low income groups automatically benefits from fiscal interventions (see dots below 45-degree line). Differences in the incidence of effects generated by moving from market income to disposable, consumable and final income are related not only to the progressivity of fiscal interventions, but also to the scale of redistribution, caused by them and the modeling of separate income concepts. The large size of the pension system compared to other social expenses determines the higher scale of effects if post-fiscal income is compared to the base of market income by the PGT approach, rather than by the PDI approach. A more positive effect at the level of final income compared to consumable income is rooted in the modeling, as total modeled final income is 9.3% higher than total market income by the PGT approach (9.0% by the PDI approach, see Table 9), while consumable income is 9.5% lower (9.8% lower by PDI approach).

# Figure 14).

On the one hand, distribution plots of net recipients and donors overlap to a rather small extent if they are ranked by market income modeled according to the PGT approach (see Figure 15a). Around 45% of all net beneficiaries within the system of direct taxes and transfers (that includes the pension system) have market incomes below 10 USD PPP (which is a proxy for the national poverty line). Moreover, 97.5% of population with market incomes below 10 USD PPP benefit from this system. The donors, in turn, are largely people with market incomes above 20 USDD PPP, which may be interpreted as a good targeting of the system. On the other hand, ranking people according to disposable (post-fiscal) income changes the situation dramatically. Distribution plots of net recipients and donors within the system of direct taxes and transfers largely coincide (see Figure 15b). As the result, 60% of population with disposable incomes below 10 USD PPP per day faces a reduction of income due to the system of direct taxes and transfers that includes the pension system. Hence, direct transfers and taxes, and the pension system in particular, substantially reduce poverty and inequality, but generate significant reranking effect as well.

If the pension system is excluded from the analysis, the number of net beneficiaries becomes much lower. Their distribution by income bins coincides to a significant degree with the distribution of net donors even if people are ranked by market income (see Figure 15c). The share of people benefiting from the system of direct transfers and taxes among the population with pre-fisc market income (PDI approach) below 10 USD PPP is 82.3%. However, they constitute only 14.6% of all net beneficiaries from the system of direct taxes and transfers. Furthermore, 26
15.8% of population with market income (PDI approach) below 10 USD PPP suffer a fall in their welfare due to direct taxes. The tax system limits the size of their losses, providing deductions for low income households (see PIT tax). The tax burden, if measured by net losses from the system of direct taxes and benefits (excluding pension system) is the same for all income bins starting from 10 USD PPP per day. Hence, the system of direct taxes and transfers (excluding the pension system) plays an important role in mitigating poverty (especially extreme poverty, as net benefits from direct transfers constitute 64% of disposable income of households with market income below 5 USD PPP), but at the same time a significant part of fiscal support is targeted to relatively wealthy households.

The distribution of net benefits and losses by income bins related to the difference between consumable income and disposable income has a similar profile. It implies that indirect taxes and subsidies do not have significant redistributive effects. The influence of fiscal interventions determining the difference between market and final income is more pronounced (see Appendix C). It increases the number of net beneficiaries within the relatively low-income population and shifts the distribution of net donors towards high-income bins. This is due to accounting for in-kind transfers that make the total final income of the population significantly higher than market income. However, there are still net fiscal donors among the population with disposable incomes below 10 USD PPP. The tax burden at the level of final income is more progressive than at the level of disposable income, but the difference between high- and low-income bins is not large (especially if the bins are formed based on disposable income).

**Figure 15. Distribution of gains and losses at the level of disposable income with respect to market income by income bins set in USD PPP**

<!-- Start of picture text --> Gains and losses compared to the level of market income (PGT approach) distributed by bins,<br>a) ranked by market income b) ranked by disposable income<br>500 100 300 90<br>thsd. pers. % thsd. pers. %<br>250 75<br>400 80<br>200 60<br>300 60<br>150 45<br>200 40<br>100 30<br>100 20<br>50 15<br>0 0 0 0<br>0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70<br>recipients, thsd recipients, thsd<br>donors, thsd donors, thsd<br>average reciept, % of disposable income (right axis) average reciept, % of disposable income (right axis)<br>average donation, % of disposable income (right axis) average donation, % of disposable income (right axis)<br>c) Gains and losses compared to the level of market income by<br>PDI approach distributed by bins, ranked by market income<br><!-- End of picture text --> <!-- Start of picture text --> 500 100<br>thsd. pers. %<br>400 80<br>300 60<br>200 40<br>100 20<br>0 0<br>0 5 10 15 20 25 30 35 40 45 50 55 60 65 70<br>recipients, thsd<br>donors, thsd<br>average reciept, % of disposable income (right axis)<br>average donation, % of disposable income (right axis)<br><!-- End of picture text --> _Source_ : own estimates based on CEQ methodology.

27
Reshuffling of the population by income ranking caused by the fiscal intervention leads to impoverishments for some households. According to the disposable income concept, 1.9% of population became poor or fall into deeper poverty due to the system of direct taxes and transfers (irrespective of the approach to pension system modelling, see Table 10). It means that more than half of the poverty (56.3%) is caused by fiscal redistribution. At the level of consumable income, the scale of fiscal impoverishment is even higher: 3.3% of population turned poor due to direct and indirect taxes and subsidies, including the pension system, which constituted 67% of the total poverty headcount (if fiscal interventions do not include the pension system, the scale of impoverishment is even larger).

Nevertheless, fiscal gains to the poor are much higher than fiscal impoverishment. Around 20% of the population, poor at the level of market income by the PGT approach (i.e. 97.3% of pre-fiscal poverty headcount) enjoy an increase in their welfare after receiving direct transfers and pensions, and paying direct taxes. Broader concepts of fiscal interventions create benefits for similar shares of pre-fiscal poor population. The scale of these benefits is around 60% of the national absolute poverty line, which is also higher than the impoverishment effect. If the pension system is not treated as a fiscal intervention, the scale of poverty reduction generated by direct transfers, as well as indirect subsidies, is much lower (around 4% of population). Nevertheless, around three-quarters of prefisc poor population benefits from fiscal interventions, and the size of the net benefit is around 25% of the poverty line.

The fact that fiscal gains to the poor surpass fiscal impoverishment in absolute terms is also illustrated by poverty gap dynamics, which is actually the difference between fiscal gains to the poor per capita and fiscal impoverishment per capita. Estimates show, that the poverty gap reduces from 12.1% at the level of market income by PGT approach to less than 1% at the level of disposable and consumable income. If the pension system is excluded from the analysis, the scale of the poverty gap reduction is much lower, as initially depth of poverty is rather low.

**Table 10. Fiscal gains to the poor and fiscal impoverishment in relation to market income**

||Pen<br>|sions asgove<br>|rnment trans<br>|fer<br>|Pe<br>|nsions as de<br>|ferred incom<br>|e<br>|
|---|---|---|---|---|---|---|---|---|
||National<br>povert|extreme<br>yline|National<br>povert|moderate<br>yline|National<br>povert|extreme<br>yline|National<br>pover|moderate<br>tyline|
||Disposable<br>Income<br>|Consumable<br>Income|Disposable<br>Income<br>|Consumable<br>Income|Disposable<br>Income<br>|Consumable<br>Income|<br>Disposable<br>Income|<br>Consumable<br>Income|
|||Fisca|l impoverish|ment|||||
|headcount, % of population|1.89|3.25|10.52|15.39|1.89|3.56|11.00|16.96|
|headcount, % of post-fiscal poor|56.30|62.96|53.03|62.01|56.12|68.98|55.47|68.33|
|fiscal impoverishment per capita, %|||||||||
|of market income|0.24|0.45|1.46|2.32|0.12|0.36|0.63|1.64|
|fiscal impoverishment per capita<br>among fiscally impoverished, % of<br>povertyline|12.49|13.73|13.85|15.06|6.59|10.00|5.76|9.66|
|||Fisca<br>|l gains to the<br>|poor<br>|||||
|headcount, % of population|19.67|19.29|28.38|26.89|4.44|4.13|12.22|10.86|
|headcount, % of pre-fiscal poor|97.30|95.42|86.88|82.30|75.26|70.04|56.60|50.33|
|fiscal gains to the poor per capita, %|||||||||
|of market income|11.77|11.65|14.51|13.92|1.14|1.04|2.14|1.69|
|fiscal gains to the poor per capita<br>among fiscal gainers, % of poverty<br>line|59.87|60.39|51.14|51.77|25.71|25.07|17.49|15.53|
|Poverty gap reduction, pp (fiscal<br>gains minus fiscal impoverishment<br>per capita)|11.54|11.20|13.06|11.60|1.02|0.68|1.50|0.05|

_Note_ . Fiscal impoverishment is a considered a situation when i) somebody who is non-poor according to the pre-fiscal income (market income) appears to be poor according to the post-fiscal income (disposable or consumable), ii) somebody poor according to the pre-fiscal income suffers further income reduction due to fiscal interventions. Fiscal gains to the poor take place when somebody poor according to the pre-fiscal income enjoys income increase due to fiscal interventions. _Source_ : own estimates based on CEQ methodology and Higgins, Lustig (2016).

According to the socioeconomic status, the pension age population enjoys most of the gains from fiscal interventions if the pension system is treated as a fiscal intervention, while the employed population of working age bear most of the costs (see Figure 16). If the pension system is excluded from the analysis, beneficiaries of the system of direct taxes and transfers are children, the non-employed population of working and above working age, while the employed population (both working and above working age) are net payers within the system. Indirect taxes and subsidies contribute to further increases of welfare of the unemployed population of working age and of 28
children, while the positive effect for non-employed people of working age reduces. In addition, in-kind transfers result in increasing redistribution within the employed population of working age.

**Figure 16. Structure of net beneficiaries and payers of fiscal system by socio-economic status**

<!-- Start of picture text --> payers payers<br>beneficiaries beneficiaries<br>payers payers<br>beneficiaries beneficiaries<br>payers payers<br>beneficiaries beneficiaries<br>Population Population<br>0% 50% 100% 0% 50% 100%<br>children children<br>working age, employed working age, employed<br>working age, partly employed working age, partly employed<br>working age, non-employed working age, non-employed<br>above working age, employed above working age, employed<br>above working age, non-employed above working age, non-employed<br>Effect with relation to the market income according to the PGT Effect with relation to the market income according to the PDI<br>approach approach<br>Final Final<br>income income<br>income income<br>Consumable Consumable<br>income income<br>Disposable Disposable<br><!-- End of picture text --> _Note_ . HBS does not contain information on social economic status of respondents. Employed where considered those receiving income employment for more than 6 months. Those receiving employment related income for less than 6 months and those reported income from self-employment where considered as partly employed. Those who did not report any employment related income were considered onoemployed. This group comprises unemployed and economically inactive, including housewives, students, disabled persons. _Source_ : own estimates based on CEQ methodology.

Despite generally benefiting from fiscal policy, children still have a higher risk of fiscal impoverishment than the population on average (see Table 11). An especially high risk is observed for large families with three or more children. The existing tax burden appears to be too high for households with several dependents and it is not fully mitigated by the system of child allowances. Other social vulnerable groups also face a high risk of fiscal impoverishment. In particular, fiscal policy may affect the welfare of partly employed or self-employed people. Being low paid and bearing tax obligations related to labor market participation determine the high risk of poverty for this group. The same applies to those living in rural areas, where employment opportunities are limited to low paid jobs in agriculture.

**Table 11. Fiscal impoverishment in relation to market income by social vulnerable groups, headcount, % of group**

||**Pensions asgov**<br>|**ernment transfer**<br>|**Pensions as d**<br>|**eferred income**<br>|
|---|---|---|---|---|
||**disposable income**|**consumable income**|**disposable income**|**consumable income**|
|children|3.96|6.41|3.40|5.85|
|working age partly employed|3.98|6.56|4.28|7.37|
|working age non-employed|2.71|3.74|2.88|4.58|
|rural area household member|3.52|5.89|2.70|6.53|
|lone parent household member|3.54|5.37|2.81|4.59|
|large family (3+ children)member|10.68|12.89|6.43|10.65|
|average|1.89|3.25|1.89|3.56|

_Source_ : own estimates based on CEQ methodology.

# **4.2. Distributional impact and marginal contributions of fiscal interventions**

Concentration curves of fiscal interventions help understand whether a particular intervention is equalizing or not. If the curve of the intervention is above the 45-degree line (i.e. concentration coefficient is negative), it is progressive and equalizing. However, the intervention can be equalizing even if it is regressive and below the 45degree line. To be equalizing, the concentration curve of a transfer only needs to be above the selected income 29
concept (the concentration coefficient of a transfer exceeds the Gini coefficient for that income). Taxes are equalizing when their concentration curves are below the Lorenz curve of related income concept (the concentration coefficient of a tax is lower than the Gini coefficient). To preserve space, the concentration curves are presented in Appendix B, while concertation coefficients, as well as Kakwani index, representing differences between the Gini coefficient of income and the concentration coefficient of an intervention, are presented in Table 12.

**Table 12. Progressivity of taxes and transfers in relation to income concepts**

||**Market inco**<br>|**me(PGT)**<br>|**Market inco**<br>|**me(PDI)**<br>|**Disposable**<br>|**income**<br>|
|---|---|---|---|---|---|---|
||**concentration**<br>**coefficient**|**Kakwani**<br>**index**|**concentration**<br>**coefficient**|**Kakwani**<br>**index**|**concentration**<br>**coefficient**|**Kakwani**<br>**index**|
|All direct transfers incl contributory pensions|**-0.285**|**0.691**|**-0.265**|**0.557**|0.098|0.169|
|benefits|**-0.121**|**0.527**|**-0.304**|**0.596**|-0.091|0.358|
|privileges|**-0.194**|**0.601**|**-0.051**|**0.343**|0.065|0.202|
|pensions|**-0.350**|**0.757**|**--**|**--**|0.169|0.098|
|All direct taxes|**0.425**|**0.018**|**0.337**|**0.045**|0.292|0.024|
|Personal income tax|**0.434**|**0.027**|**0.337**|**0.045**|0.296|0.029|
|Social Contributions|**0.422**|**0.015**|**--**|**--**|0.290|0.022|
|All indirect subsidies|0.127|0.280|0.188|0.104|**0.186**|**0.081**|
|Utilities subsidy|0.059|0.348|0.133|0.159|**0.135**|**0.133**|
|Public transport subsidy|0.268|0.139|0.300|-0.008|**0.293**|**-0.026**|
|All indirect taxes|0.186|-0.221|0.220|-0.072|**0.224**|**-0.044**|
|VAT|0.225|-0.182|0.260|-0.032|**0.265**|**-0.002**|
|Tobacco excise|0.145|-0.262|0.073|-0.219|**0.058**|**-0.209**|
|Alcohol excise|0.010|-0.397|0.049|-0.244|**0.048**|**-0.219**|
|Fuel excise|0.349|-0.058|0.326|0.033|**0.327**|**0.060**|
|Import tariffs|0.186|-0.221|0.240|-0.052|**0.249**|**-0.018**|
|All gross in-kind transfers|0.006|0.401|-0.070|0.362|**-0.075**|**0.342**|
|Gross health transfers|-0.052|0.459|0.025|0.267|**0.038**|**0.230**|
|Primary and secondary health expenditure|-0.055|0.462|0.034|0.258|**0.049**|**0.219**|
|Tertiary (hospital) health expenditure|-0.050|0.456|0.019|0.273|**0.030**|**0.237**|
|Gross education transfers|0.055|0.352|-0.150|0.442|**-0.170**|**0.437**|
|Preschool and primary school education|0.018|0.389|-0.238|0.530|**-0.249**|**0.516**|
|Secondary education|-0.032|0.439|-0.248|0.540|**-0.268**|**0.535**|
|Continued secondary education|0.139|0.268|-0.035|0.327|**-0.064**|**0.332**|
|Higher(college)education|0.253|0.154|0.113|0.179|**0.089**|**0.178**|
|All taxes|0.355|-0.052|0.267|-0.025|0.272|0.004|
|All net transfers and subsidies excl|||||||
|contributory pensions|-0.143|0.550|-0.079|0.371|0.049|0.218|
|Gini|0.407||0.292||0.267||

_Note._ Concentration coefficient of a tax exceeding Gini coefficient of related income implies positive Kakwani index and progressivity of tax. Concentration coefficient of a transfer exceeding Gini coefficient of related income implies negative Kakwani index and regressivity of a transfer. Bold characters reflect income concept influenced by the intervention. _Source_ : own estimates based on CEQ methodology.

Direct taxes levied on market income are minimally equalizing. The concentration curves of both the personal income tax and the social contributions tax are slightly below the income schedules (the Kakwaini index is positive, but close to zero). It is not surprising, given the flat schedule of taxes with some exemptions for low-income individuals.

Indirect taxes, paid from disposable income, in turn, are disequalizing. This is particularly true for the alcohol and tobacco excises, which put most of the burden on poor households. However, since the purpose of these taxes is not to be equalizers, and, ideally, not even to deliver revenues to the budget, but to penalize unhealthy behaviors, we cannot judge these excises on their redistribution properties. Import tariffs and VAT taxes have virtually no redistributive effects with respect to disposable income. A higher share of consumption within lower deciles results in a higher burden of VAT and import duties for them, but lower VAT rates for food products<sup>23</sup> mitigate this effect. The fuel excise, however, is equalizing as the fuel excise applies mainly to car owners, such that the burden falls mainly on the upper deciles.

The concentration curves for direct transfers show that most of transfer interventions are equalizing and progressive when ranked by market income (see Appendix B, Figure 20). Pensions play a major role in redistribution from market to disposable income. Benefits and privileges interventions are also equalizing. The scale of their progressivity depends on the analyzed income concept. While privileges are more progressive if households are ranked by market income before the pension system intervention, benefits play a more important redistributive role when

> 23 Difference in expenditure structure of the 1st and 10th deciles is mainly related to food purchase and savings (see Shymanovich, 2013).

30
pensions are considered as market income (see Table 12). Moreover, benefits are progressive with respect to disposable income. It implies that benefits, contrary to privileges, target groups of population that are not covered by the pension system. Furthermore, benefits do not fully employ their equalizing potential. On the contrary, pensions are not progressive and only slightly equalizing with respect to disposable income. This reinforces their significant role in redistribution and their notable contribution to the disposable income of population.

Indirect subsidies received at the level of disposable income are regressive, but equalizing. However, it is achieved only on account of utilities subsidy, while the transport subsidy is disequalizing with respect to disposable income. The transport subsidy has an equalizing effect only in the absence of the pension system (see Appendix B, Figure 21).

Health expenditures are distributed quite equally, and we can see at the Figure 22 (Appendix B), that health transfers are equalizing with respect to disposable income and all the curves lie very close to the 45-degree line. Consequently, concentration coefficients are close to zero. Education transfers are also equalizing. Furthermore, all types of education transfers except for the college education are progressive. Primary and basic secondary school have the most pronounced equalizing effect.

The concentration curves and related indices allow us to see the direction of the redistribution impact of fiscal interventions, but they do not allow us to estimate the size of this impact, or to see the impact on poverty. Marginal contributions in Table 13 show the change to inequality and local poverty measures after the application of the intervention.

**Table 13. Marginal contributions to inequality and poverty**

||**Gini**|**National Poverty**|**Moderate Poverty**|
|---|---|---|---|
|Benefits|2.0|5.1|3.9|
|Privileges|0.2|0.7|0.3|
|Pensions|11.1|23.5|19.2|
|Personal income tax|0.4|4.1|0.9|
|Social Contributions|1.1|8.9|2.1|
|VAT|0.0|5.2|1.2|
|Excises on tobacco|-0.1|0.4|0.0|
|Excises on alcohol|-0.4|2.1|0.3|
|Excises on fuel|0.0|0.1|0.0|
|Import duties|0.0|0.9|0.1|
|Utilities subsidy|0.6|3.6|1.2|
|Transport subsidy|-0.1|1.2|0.3|
|Primary health care|0.9|3.9|1.5|
|Tertiary health care|1.4|5.7|2.5|
|Primary education|1.4|3.2|2.8|
|Secondary education|3.6|5.8|6.6|
|Cont. secondary education|0.6|1.6|1.2|
|Higher education|0.7|2.2|1.3|

_Note_ . Changes to inequality and poverty for the direct benefits and taxes are measured in comparison to the disposable income prior to the related intervention (i.e. market income (PGT) plus direct taxes and transfers net of analysed intervention). For indirect subsidies and in-kind transfers changes to inequality and poverty are measured in comparison to the disposable income. Negative values for Gini mean increase of inequality due to fiscal intervention. Positive values for poverty mean increase of poverty headcount in case of abolishment of direct transfers, need to cover in full costs of utilities and public transportation, as well as pay for education and health care services, and increase of poverty headcount caused by presence of taxes. _Source_ : own estimates.

Pensions are the most effective fiscal intervention, lowering the Gini index by 11 points, and extreme poverty by over 23 percentage points. Benefits also have positive effects, but they are much smaller. The most effective of all benefits is the childcare benefit (for children aged 0-3), contributing 1.3 points to the Gini decrease and 3 points to poverty decrease. Excises on tobacco and alcohol increase inequality, although only modestly. Same is true of the transport subsidy. Utilities subsidy decreases inequality and poverty. Primary and secondary education and tertiary health care have sizable equalizing effects and gains for the poor.

# **4.3. Efficiency**

The marginal contributions of fiscal programs described in the previous section are especially useful when evaluating social programs, fiscal interventions designed primarily to combat poverty and inequality. In the case of Belarus, marginal contributions to poverty and inequality are the major indicators for the effectiveness of benefits, privileges, pensions and indirect subsidies. But the marginal contributions miss another dimension of the fiscal 31
interventions – their cost. While pensions have the biggest impact on poverty and inequality, they are also the costliest program of all.

Table 14 lists several efficiency measures which reflect both the impact on poverty/inequality, and the cost of the intervention. The size column lists the size of the intervention as a percentage of GDP according to our allocation in micro data, not according to aggregate data. Efficiency measures are derived as the ratio of marginal contributions to the relative size of intervention . Hence, efficiency measures represent the effect (on the reduction of the Gini index or poverty) of 1% of GDP spent on the particular intervention. The composite measure reflects the effect of 1% of GDP spent on the composite impact measure, consisting of poverty and inequality effects both weighted by 0.5.

**Table 14. Efficiency measures for social fiscal interventions**

||**Size (relative**<br>**to GDP)**|**Inequality**<br>**Efficiency**|**Poverty**<br>**Efficiency**|**Composite**<br>**Efficiency**|**Share spent on**<br>**top 5 deciles**|**Share spent on**<br>**top 2 deciles**|
|---|---|---|---|---|---|---|
|Benefits:|1.957%|1.04|2.02|1.53|42%|12%|
|pregnancy registration|0.110%|0.82|1.28|1.05|48%|15%|
|pregnancy and childbirth|0.069%|0.72|1.21|0.97|45%|17%|
|child 0-3|1.039%|1.21|2.28|1.74|34%|11%|
|child 3+|0.171%|1.28|2.00|1.64|31%|7%|
|Attendance|0.104%|1.06|1.19|1.12|40%|8%|
|Funeral|0.095%|-0.53|0.10|-0.21|88%|24%|
|child support after breadwinners death|0.126%|1.03|2.26|1.65|42%|9%|
|children-disabled|0.048%|0.83|1.30|1.07|45%|8%|
|assistance and other|0.048%|0.63|1.67|1.15|52%|5%|
|unemployment benefit|0.004%|0.00|5.50|2.75|16%|8%|
|severance pay|0.076%|-0.53|0.00|-0.26|88%|18%|
|student grants|0.069%|0.43|0.24|0.33|60%|23%|
|Privileges|0.360%|0.53|0.96|0.74|56%|15%|
|Pensions|10.708%|1.02|1.79|1.41|62%|11%|
|Utilities subsidy|1.956%|0.31|0.61|0.46|59%|26%|
|Transport subsidy|0.945%|-0.02|0.36|0.17|70%|37%|

_Note._ Deciles are identified based on market income. _Source_ : own estimates.

Pensions are not as efficient as total benefits (1.41 versus 1.53 composite efficiency), but nevertheless remain among the efficiency champions even after accounting for the cost. Among benefits, unemployment benefits are the most efficient program. Severance pay, funeral benefit and student stipends are among the most inefficient benefits – a direct result of the absence of means-testing. Unemployment benefits are notoriously low in Belarus (around 10 USD per month). Currently the government is considering increasing benefits at least to the subsistence minimum. Our results suggest that these plans will have significant (and efficient) impacts in reducing poverty and inequality.

Most of the child-related benefits are also efficient. Interestingly, the childcare benefit which is paid for mothers of children below 3 years (child 0-3) is more efficient than the pregnancy and childbirth benefit (it is paid from the end of the pregnancy and after the childbirth, for the total of 126 days). One possible explanation is that the childcare benefit is paid in full only if one of the parents stays at home to take care of the child. Parents with higher wages are less likely to take all three years of maternity leave.

On the other hand, privileges and indirect subsidies are highly inefficient. The result is not unexpected for the indirect subsidies: they are not targeted, available to everybody and usually regressive. Privileges, on the other hand, are targeted: households and individuals have to meet certain criteria to get access to privileges. The low efficiency of privileges suggests the low quality of targeting or the misuse of the privileges programs.

As an additional measure of fiscal efficiency, we also compute the proportion of the program expenditure going to the upper income deciles (by market income): to the top 5 deciles (everyone above the median); and top 2 deciles. More than half of the pensions go to the individuals above the market income median, reflecting the fact that the pensions program is not means-tested. According to this measure privileges and indirect subsidies are again highly inefficient. If all benefits, privileges and indirect subsidies were not available to the top 2 deciles of market income, savings would amount to 1.4% of GDP.

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The CEQ effectiveness measures provide a similar picture<sup>24</sup> . The highest effectiveness is assigned to pensions and direct benefits, while indirect subsidies are not effective.

# **4.4. Targeting and vulnerable groups**

To understand how the expenditures and taxes are focusing (or not) on vulnerable groups, we compare the size of the transfer/tax allocated to vulnerable groups to the average transfer/tax size for the rest of the population. The results are in Table 15. The first part (a) of the Table looks at vulnerable groups by type of individual, namely by age and employment status. HBS does not have the employment or socio-economic status variables. We assign the status of the employed to individuals who report some wage income and worked during more than 6 months over the year. Those who worked 6 months or less, and had wage or self-employment income, are marked as partially employed. The rest are classified as non-employed, which includes the traditional definition of unemployed, inactive (out of labor force), retired and those on childcare leave. Adults (18 and older) are divided in three age groups: youth (18-24), working age (25-55 for women and 25-60 for men) and elderly (above retirement age; over 60 for men and 55 for women).

**Table 15. Sizes of transfers/taxes by vulnerable groups, relative to the rest of the population**

**(a) By individual type (age/employment)**

||**Youth**<br>||**Working age**<br>|**Elderly**<br>|<br>|
|---|---|---|---|---|---|
|**Employed**|**Partly em-**<br>**ployed**|**Non-em-**<br>**ployed**|**non-em-**<br>**ployed**|**Employed**<br>**Partly em-**<br>**ployed**|<br>**Non-em-**<br>**ployed**|
|Benefits (without pensions)<br>69|127|139|225|40<br>61|52|
|Pensions<br>31|36|36|90|253<br>261|488|
|Direct taxes<br>143|126|109|69|131<br>74|26|
|Indirect subsidies<br>114|116|108|91|158<br>133|105|
|Indirect taxes<br>108|107|96|90|131<br>115|89|
|Health<br>82|98|93|116|106<br>109|141|
|Education<br>93|139|213|81|23<br>29|20|
|**(b) By household type**||||||
|**Rural H**|**H**<br>**HH**|**of elderly**|**HH with child**|**ren**<br>**HH with**<br>**loneparent**|**Large HH**|
|Benefits (without pensions)<br>74||55|306|189|328|
|<br>Pensions<br>102||414|15|13|9|
|Direct taxes<br>63||34|99|78|47|
|Indirect subsidies<br>48||154|60|104|40|
|Indirect taxes<br>78||101|79|88|57|
|Health<br>104||150|74|91|77|
|Education<br>75||7|298|297|261|

_Note._ The number of 69% in panel a) line “Benefits”, column “Youth employed” means that the young employed receive on average only 69% of benefits the rest of the population (not young and employed) receive. Description of household types and socio-economic economic status see in Table 11, Figure 16.

_Source_ : own estimates.

Despite the modest size of unemployment benefits, working age non-employed enjoy higher support through benefits programs than the population on average. Childcare benefits, which normally go to households with nonemployed mothers can explain this trend. In turn, elderly people and employed youth do not enjoy benefit support. For the elderly, this lack of social support is compensated by pensions. Employed (and partially employed) carry most of the burden of direct and indirect taxes, but they also enjoy more indirect subsidies. Unsurprisingly, young people receive more in-kind transfers from education, while the elderly receive higher than average support from health care.

In the panel (b) of Table 15 vulnerable groups are classified by household type. The major vulnerable types are rural households, households of elderly people (were the only adults are above the retirement age), households with children, especially households with a lone parent (household with at least one child and only one adult) and multi-child households (with three or more children).

Households with children are the major target group of benefits, with the childcare benefits being the most substantial program. Pensions are naturally skewed towards the elderly households. Most of the vulnerable groups pay

> 24 However, the CEQ package function _ceqef_ is bugged and the CEQ effectiveness measures are hence incomplete and are not provided in this paper.

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fewer taxes than average. Health and education are again following their corresponding demographic patterns, with health expenditure more substantial for the elderly, and education – for households with children.

While the vulnerable groups in general get more than average out of fiscal transfers and pay fewer direct taxes, there is space for improvement. Currently the benefits are focused on families with children, and pensions – on the elderly, while other vulnerable groups, like people in the rural areas and youth, remain untargeted by social programs. Indirect subsidies are particularly badly targeted, often offering less than average support to the vulnerable groups.

# **4.5. Cross-country differences**

Figure 17 shows the results of CEQ analysis for several countries including Belarus. Countries are ranked by the Gini reduction effect with pensions modeled as government transfers. All the effects are going from market to disposable income. On average fiscal interventions decrease the Gini index by 0.036, and poverty by 6.05 percentage points. Belarus is doing better than average on both counts. However, it is lagging behind the EU28 group of countries in redistribution.

**Figure 17. Cross-country comparisons of redistribution and poverty reduction effect of direct transfers and taxes**

<!-- Start of picture text --> 0.25 45<br>Gini reduction Poverty reduction, % 40<br>0.20 35<br>30<br>0.15<br>25<br>20<br>0.10<br>15<br>0.05 10<br>5<br>0.00 0<br>Gini PGT Gini PDI Poverty ($4 PPP)<br>Georgia (2013) South Africa (2010) Sri Lanka (2010) Honduras (2011) Colombia (2010) Indonesia (2012) Bolivia (2009) Peru (2009) Dominican Republic... Guatemala (2011) El Salvador (2011) Mexico (2010) Jordan (2010) Costa Rica (2010) Ecuador (2011) Brazil (2009) Chilie (2013) Average Uruguay (2009) United States (2011) Russia (2010) Belarus (2015) Argentina (2012) EU-28 (2011)<br><!-- End of picture text --> _Source_ : Lustig (2016), own estimation based on CEQ methodology The fiscal impact in Belarus is similar to that of Russia in terms of the Gini index reduction. As in Russia, in Belarus the PGT scenario delivers more equalization than the PDI scenario (Lopez-Calva et al., 2017). Poverty reduction in Belarus is lower. However, it does not mean that the fiscal interventions reduce poverty less in Belarus than in Russia, it is merely the reflection of the fact that the 4 USD PPP poverty in Belarus is zero in disposable income, so the reduction in poverty is at its maximum possible level.

The Belarusian taxation has a very different redistribution impact when compared to the EU. In most of the EU countries, the PIT tax is equalizing due to its progressive nature. The flat PIT structure in Belarus, however, delivers very little redistribution, while the redistribution task is completely delegated to the expenditure side of the fiscal policy. Given the high level of possible tax evasion in Belarus, this design of the fiscal policy is optimal. The VAT tax, in contrast, is not regressive in Belarus, unlike in many EU countries. This is achieved through multiple VAT exemptions.

# **5. Conclusions**

This paper presents the assessment of fiscal incidence in Belarus using the Commitment to Equity (CEQ) methodology developed in Lustig & Higgins (2016). Using the household budget survey and aggregate data we have allocated fiscal interventions across households. The allocation allows us to measure the effect of fiscal policies on redistribution and poverty.

Fiscal policies in Belarus effectively redistribute income from the top to the bottom of income distribution - 97.5% of population with market income below 10 USD PPP benefit from these policies. The direct transfers (including pensions) and direct taxes lower the national poverty measure by 17 percentage points. They also decrease the Gini 34
index from 0.407 to 0.267. The impressive magnitude of positive fiscal effects puts Belarus among the equalization leaders in the group of developing countries. However, most of the effect is attributed to the pension system. If we treat pensions as a deferred market income rather than government transfer, direct taxes and transfers result in a minor Gini index reduction of 0.025 pp. Indirect taxes and subsidies do not contribute to inequality reduction. Furthermore, fiscal interventions may be a cause of poverty, as 1.9% of population becomes poor due to direct taxes and transfers. The fiscal impoverishment headcount goes up to 3.3% when we account for indirect taxes and subsidies as well. Vulnerable groups like large and lone parent households, people living in rural areas and those not being fully employed have an especially high risk of becoming poor due to fiscal interventions. These groups are in general benefitting from social policy, but high impoverishment rates suggest that related social security measures need improvement and better targeting.

Direct transfers, in particular pensions, are the most equalizing and pro-poor of the fiscal interventions. Pensions, for example, are often assigned to households with zero market income, effectively pulling them out of poverty. Taxes in Belarus are not equalizing. Direct taxes are neutral in their influence over inequality. Indirect taxes are regressive. Indirect transfers and taxes increase poverty by 1.8 percentage points, mostly due to the regressive nature of the indirect taxes and poor targeting of subsidies.

Our results also point towards possible reforms. Unemployment benefits (currently at very low level) are the most cost-efficient benefits program, suggesting that the plans to increase benefits will have a significant impact on reducing poverty and inequality. Pensions and child-care benefits are also cost-efficient.

Indirect subsidies are highly cost-inefficient: 1% of GDP spent on utility subsidies delivers 3 times less reduction in poverty and inequality compared to the same 1% spent on pensions. The indirect subsidies to utility and transport tariffs are not targeted, but they are available to everybody and regressive. They are also offering lessthan-average support to vulnerable groups. Replacing indirect subsidies with a well-targeted benefits program will reduce poverty and inequality more efficiently. Restricting access to benefits (like student grants and childcare benefits) and subsidies for households at the top of the income distribution also might be a possibility worth exploring: even the most conservative estimate suggests possible savings of 1% of GDP from the better targeting of transfers and subsidies.

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