metadata: authors: 0: author: Ambel,Alemayehu A.
1: author: Tesfaye,Wondimagegn Mesfin
2: author: Yonis,Manex Bule
count: Ethiopia
docna: 0: docna: A Gendered Fiscal Incidence Analysis for
            Ethiopia : Evidence from Individual-Level Data
docty: Policy Research Working Paper
abstracts: cdata!: Using the Commitment to Equity
            methodology, this study investigates differences in the
            welfare impact of taxes and government spending on men and
            women in Ethiopia. It analyzes the incidence, progressivity,
            and pro-poorness of various taxes and transfers and their
            effects on income mobility, poverty, and inequality using
            individual-level data from the 2018/19 Ethiopia
            Socioeconomic Survey. The results show that the fiscal
            system as a whole is progressive, equalizing, and
            poverty-reducing. It moved about one in five individuals
            from one income group to another, and more women than men
            transitioned to a higher income group, making them
            relatively better off. However, some of its elements have
            differential effects on gender equality. Direct and indirect
            taxes have differential inequality-reducing and
            poverty-increasing effects for men and women. The
            inequality-reducing effects are stronger for men, whereas
            the poverty-increasing effects of some of them, including
            informal taxes and value-added taxes, are higher for women.
            On the transfer side, direct social protection transfers and
            indirect transfers, mainly spending on primary education and
            health services, promote gender equality better than other
            types of government spending.
display_title: A Gendered Fiscal Incidence Analysis
            for Ethiopia : Evidence from Individual-Level Data
listing_relative_url: /research/2022/07/33873315/gendered-fiscal-incidence-analysis-ethiopia-evidence-individual-level-data
topicv3: Fiscal Incidence Analysis m1326940 711,Gender m1327621 615,Equity Finance m1327686 334
publishtoextweb_dt: 2022-07-26T00:00:00Z
dois: 10.1596/1813-9450-10130
author_keys: 0: author_key: 1042144
1: author_key: 1762589
2: author_key: 1420927
model_extractions: Policy Research Working Paper 10130

# A Gendered Fiscal Incidence Analysis for Ethiopia

Evidence from Individual-Level Data _Alemayehu A. Ambel Wondimagegn M. Tesfaye Manex B. Yonis_ Development Economics Development Research Group July 2022
Policy Research Working Paper 10130

# **Abstract**

Using the Commitment to Equity methodology, this study investigates differences in the welfare impact of taxes and government spending on men and women in Ethiopia. It analyzes the incidence, progressivity, and pro-poorness of various taxes and transfers and their effects on income mobility, poverty, and inequality using individual-level data from the 2018/19 Ethiopia Socioeconomic Survey. The results show that the fiscal system as a whole is progressive, equalizing, and poverty-reducing. It moved about one in five individuals from one income group to another, and more women than men transitioned to a higher income group, making them relatively better off. However, some of its elements have differential effects on gender equality. Direct and indirect taxes have differential inequality-reducing and poverty-increasing effects for men and women. The inequality-reducing effects are stronger for men, whereas the poverty-increasing effects of some of them, including informal taxes and value-added taxes, are higher for women. On the transfer side, direct social protection transfers and indirect transfers, mainly spending on primary education and health services, promote gender equality better than other types of government spending.

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

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

Alemayehu A. Ambel, Wondimagegn M. Tesfaye & Manex B. Yonis<sup>1</sup> JEL Codes: H22; H50; J16 Keywords: CEQ, Commitment to Equity; Fiscal Incidence Analysis; Gender Equality; Ethiopia

> <sup>1</sup> Alemayehu A. Ambel, World Bank <u>aambel@worldbank.org; Wondimagegn Mesfin Tesfaye, World Bank wtesfaye@worldbank.org;  and  Manex Bule Yonis, World Bank myonis@worldbank.org.</u> The study is funded by the Global Tax Program. The authors thank Alan Fuchs, Caren Grown, Hitomi Komatsu, Jon Jellema, Marion Sosa, Matthew Wai-Poi, and Stephen Younger for their valuable comments on earlier versions of this paper. Any error is our responsibility alone.
# **1. Introduction**

Taxes and transfers—the fiscal system—may have differential impacts on the welfare of different groups of individuals within a society. The system could, e.g., affect the welfare of men and women differently due to implicit as well as explicit gender biases (Stotsky1996). Explicit provisions in the tax and transfer system that are outright discriminatory and tend to be rooted in patriarchal traditions could treat men and women inequitably. Though explicit bias against women in the tax code is not common, implicit bias can occur when taxes have different effects on men and women due to differences in their social or economic responsibilities (Stotsky 1996; Barnett and Grown 2004).

Implicit gender biases can be found in labor market participation, consumption patterns, asset ownership, and access to social services (Grown and Valodia 2010). For example, gender differences in labor market participation could affect how women pay personal income taxes (PIT) and their access to the rights and benefits often tied to formal employment (Doorley and Keane 2020; Grown and Valodia 2010). Women’s participation in the labor market tends to be discontinuous: they are more likely to have seasonal and part-time jobs, do most of the unpaid work in a family business, and are disproportionately represented in the informal sector (Grown and Valodia 2010; Pimkina and De La Flor 2020; Verick 2018). Consumption taxes like value added taxes (VAT) and excises could disproportionately affect women if they add to the cost of goods and services used by women, or if exemptions are not applied to products usually purchased by women (Stewart 2018; <mark>Lahey 2018).</mark> Gender differences in access to social services could have differential effects on how men and women benefit. For example, where parents do not enroll girls in school, for whatever reason, societal roles would lower the impact of education benefits for women. Girls receive less education if household dynamics make them drop out of school earlier than boys; spending more on education would not necessarily correct this inequity. Government spending on health and education could also have different effects on men and women due to the gender division of labor in production and social reproduction (Johannes and Noula 2011; Enríquez and Elson 2012 **;** Stewart 2018). Difficulty in accessing social services (due to income as well as mobility, norms, information or other barriers), user fees for public services (e.g., for hospital visits), and informal taxes on accessing public goods could have more impact on girls and women than on boys and men <mark>(Enríquez and Elson 2012).</mark> Different fiscal policies are proposed to address gender inequality through opportunities and the advancement of women in areas such as education, health, and economic empowerment (Kolovich 2018; Stewart 2018). Governments can integrate gender analysis into fiscal systems by ensuring that the systems contain neither explicit nor implicit negative bias; are responsive in meeting the needs and priorities of both men and women and males and females; or are transformative by contributing to shifts in and transformation of gender roles and power dynamics (Aziz, Norman, and Athene 2016). However, evidence to support this policy position is sparse. A comprehensive review of several gendered fiscal incidence studies found that most focus on measuring gender equity in government spending on education and health but neglect to assess the combined effect of both taxes and transfers (Greenspun and Lustig 2013). Although the few studies to date provide useful insights into the degree, if any, of gender equity implied by specific taxes and transfers or 2
combinations thereof, there appears to have been little research into the combined incidence and impacts of taxes and public spending focusing on gender (Greenspun and Lustig 2013).

Adding a gender dimension to fiscal incidence analysis can shed light on how the fiscal system affects gender equity. Gendered fiscal incidence analysis could be a useful diagnostic tool to inform decision-makers of areas of fiscal policy that might need reform to improve gender equity (Greenspun and Lustig 2013). However, the lack of gender-disaggregated data makes it difficult to study the differentiated impacts of the fiscal system as a whole. Studies of genderfocused fiscal incidence analysis in low- and middle-income countries are concentrated on only a few components of fiscal policy. The evidence available mostly relates to the PIT and payroll taxes (Grown and Valodia 2010; Joshi, Kankave, and <mark>van den Boogaard</mark> 2020; Lahey 2018; and Stotsky 1997), though a few studies have examined other elements. For example, Grown and Valodia (2010) explored the gender-differentiated impacts of direct and indirect taxes using data from countries in Africa, Asia, and Latin America. A study from two states in Nigeria highlights implicit biases related to presumptive taxation in the informal sector (Akpan and Sempere 2019). Another recent study on the gender-differentiated impact of rural land use fees and agricultural income taxes in Ethiopia finds that the disparate tax burden on women is due more to social norms that limit their role in agriculture and to a gendered agricultural productivity gap (Komatsu et al. 2021). From the benefit side of the fiscal system, an incidence analysis of agricultural extension and food security programs in Ethiopia finds, among other things, that men benefited more from extension services than women (Mogues 2013).

However, to evaluate the full effect of a fiscal system on both men and women, all the available instruments need to be analyzed together because, e.g., a fiscal system could have a regressive tax and still be equalizing if it is implemented along with other progressive taxes and transfers. Similarly, a poverty-increasing fiscal instrument could still be equalizing because poverty depends on absolute incomes and equality on relative incomes (Higgins and Lustig 2016). Recent fiscal incidence studies in Ethiopia document the distributional effects of various components of the fiscal system (Hill et al. 2017; Tesfaye and Gao 2020) but do not explore the gender dimension. This study aims to expand the evidence on the gendered impacts of the total fiscal system by addressing the following questions:

1. Are the burdens of taxation and the benefits from government spending different for men and women?

2. How equitable between genders are spending on and access to government services in education and health, in the aggregate and by income category?

3. What does each tax and government transfer contribute to the reduction of poverty and inequality among and between men and women?

The choice of Ethiopia for this study is motivated by a host of factors: (1) There have been recent reforms of its fiscal policy, such as the change in the PIT thresholds as of July 2016; gender-equityfocused interventions include a large government transfer program (PSNP: the Productive Safety Net Program); and education and health services have recently been expanded. (2) There is a need to capture the equity impacts of informal contributions that are important sources of local financing of public goods. (3) New microdata has disaggregated information at the individual level on earnings, taxes, contributions, and transfers. In addition, in this study, the components of the fiscal system are expanded by including informal taxes. The study also minimizes the limitations of 3
individual level fiscal incidence analysis using a combination of intrahousehold allocation mechanisms.

We combine data from the 2018/19 Ethiopian Socioeconomic Survey (ESS) with administrative data to conduct a gendered fiscal incidence analysis using the Commitment to Equity (CEQ) methodology. We analyze the gender-differentiated distributional impacts of various taxes and transfers focusing on progressivity, inequality, poverty, and pro-poorness.

The results show that, while the fiscal system is progressive, equalizing, and poverty-reducing, some of its elements promote gender equality better than others. On _progressivity_ , we find that PIT, PSNP transfers, kerosene subsidies, primary and secondary education, and health spending are progressive and have no gender differences. Agricultural income tax, informal taxes, wheat subsidies, and tertiary education are regressive but also have no considerable gender differences. In terms of the _pro-poorness of transfers and subsidies_ , only PSNP transfers and primary education are found to be pro-poor. While PSNP is pro-poor for both genders, primary education is pro-poor only for boys. All other transfers, including wheat and kerosene subsidies, secondary and tertiary education, and health spending are not pro-poor.

The impact of fiscal actions on _inequality_ is mixed. For instance, direct taxes are more equalizing for men and direct transfers more equalizing for women. However, agricultural income tax and informal taxes are not equalizing for either men or women; nor are VAT and excise taxes and wheat subsidies equalizing. For both genders, primary education is equalizing, and secondary and tertiary education are not. Health spending is more equalizing for women.

There are gender differences in the _poverty_ effect of some taxes and transfers. Both direct and indirect taxes increase poverty at different rates. For example, the poverty increasing effect of informal taxes and VAT is higher for women than men. Excise tax increases poverty more for men. The poverty reduction effect due to subsidies and transfers is mixed. Spending on PSNP, primary and secondary education, and health reduced poverty more for women than men. There are no gender differences in the poverty-reducing effect of tertiary education.

In what follows, Section 2 discusses the methods focusing on intrahousehold allocation, commitment-to-equity (CEQ) framework and poverty and inequality measures. Section 3 describes the data and assumptions. Section 4 reports the results and discusses gendered-impact differentials. Section 5 draws conclusions.

# **2. Methods**

The analytical approach begins with presenting the intrahousehold allocation mechanism used to identify individual level income. It then summarizes the commitment-to-equity (CEQ) framework used to analyze the incidence, progressivity, and pro-poorness of the fiscal actions as well as their impacts on poverty and inequality at different income concepts. The fiscal actions in this study cover most of the taxes and government spending items (see Annex 1).

## **2.1. Intrahousehold Allocation**

Availability of sex-disaggregated data on income and expenditures is key to examine genderdifferentiated welfare impacts of fiscal policy. When there are inequalities in intrahousehold 4
allocations, individual-level data allow investigation of the potential welfare loss and gains by household members as a result of taxes and transfers (see e.g., Thomas 1990; Browning, Chiappori, and Lewbel 2013). The challenge, however, is allocating expenditures to household members. Some studies that applied the per capita method allocated equal shares of resources to each household member (Glick, Saha, and Younger 2004; Greenspun and Lustig 2013); others used adult equivalence scales <u>(Siddiqui 2009; Browning, Chiappori, and Lewbel 2013; Aziz et al.</u> 2016); and others applied a household structural model (Calvi 2020; Calvi et al. 2020). These approaches do not consider differences in consumption patterns.

This study applies a two-tier framework to assign expenditure items to individuals within the household. The first tier is based on direct identification of the item from the survey data. For these items, either there is gender-disaggregated data or the data can be assigned to either men or women based on consumption patterns, such as alcoholic drinks and stimulants like tobacco and _khat_ . The second tier is allocating non-assignable goods<sup>2</sup> using adult equivalent scale weight. The details of the allocation mechanism are presented in Annex 2.

# **2.2. Income Concepts**

The core of the CEQ approach is the application of different income concepts (Figure 1). This study investigates the redistribution effect by comparing six individual-level income concepts, computed as −∑ + ∑ ,where is post-fiscal income (income after taxes and transfers) for individual _i_ ; depending on the fiscal intervention being analyzed. It denotes gross nn nn ii ii jj=1 jj mm=1 mm ii income, net market income, disposable income, consumable income or: YY = II TT BB YY final income. _Ii_ is the market or pre-fiscal income for individual _I_ ; _j_ =1,2,3...n is the type of tax paid, direct or indirect, by individual _i_ ; and m=1,2,3...n is the type of transfers, including subsidies received by individual _i_ . Our starting point is disposable income, which is proxied in the 2018/19 Ethiopia Socioeconomic Survey (ESS) data by total consumption spending. In addition, we construct two intermediate income concepts following market income, namely gross income and net market income. _Gross income_ is the cash available when the government has distributed direct transfers. _Net market income_ is cash available after direct taxes. _Consumable income_ is calculated by subtracting direct and indirect taxes from the sum of market income, subsidies, and direct transfers received; for this study, it is derived from disposable income by adding subsidies and deducting indirect taxes, which are simulated using data on purchased consumable items. Similarly, indirect subsidies are estimated using ESS wheat and kerosene consumption data. _Final income_ is equal to consumable income plus the monetized value of in-kind health and education services, less any co-payments, user fees, and participation costs for those services. Moving from consumable to final income highlights the distributional effects of public spending on health and education.

> 2 Spending on non-assignable goods consumption includes domestic food consumption (food grown by the household, purchases, and gifts); consumption of food away from home; and some non-food consumption and utilities expenditures. Based on ESS data, spending on non-assignable food consumption averages about 80 percent of total non-assignable goods.

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<!-- Start of picture text --> eS<br><!-- End of picture text --> <!-- Start of picture text --> a<br><!-- End of picture text --> <!-- Start of picture text --> ee<br><!-- End of picture text -->
Inequality indices are calculated for each income category to assess the redistributive effect of taxes and transfers. Inequality is measured using the Theil index, since the index provides the advantage of additive decomposability, i.e., aggregate inequality can be decomposed into 1 <u>i i</u> inequality within- and between- sample subgroups. Theil index, = ∑ l , where � � NN QQ QQ is the Theil index of the income _Q_ , is the income of individual QQ _i_ , is the average income ii=1 _Q_ QQ, TT NN QQ<sup>�</sup> QQ<sup>�</sup> TT and _N_ is the sample size. The index varies from 0, perfect equality, to ln( _N_ ), perfect inequality. ii h Total inequality is the sum of within-gender inequality: QQ = ∑ QQ<sup>�</sup> , and between-gender ∑ 2 <u>g</u> = <u>j</u> ww gg=1 gg gg inequality, = ∑ , where NNgg is gender g’s income share of total income, TT SS TT �l �SS ~~�~~ � ∑ j=1 QQjj <u>g</u> is the share of the gender g’s population of the total population (Andrei et al. 2017). TTbb gg=1 SSgg PPgg SSgg NNii=1 QQii NN gg= PP **3.** NN **Data and Assumptions**

# **3. Data and Assumptions**

## **3.1. Data**

The primary source of data for the study is the 2018/19 Ethiopia Socioeconomic Survey (ESS). Basic demographic characteristics, health care utilization, school enrollment status, and labor market outcomes are available at the individual level. Others, such as consumption expenditure, other income, property taxes, business taxes, land use fee and agricultural income tax, were captured for households.

The detailed consumption module allows us to use consumption spending as a proxy for disposable income, from which market income is computed through backward calculation by adding taxes and deducting transfers. Consumption data are also used to estimate indirect (VAT and excise) taxes. To estimate the indirect effects of indirect taxes, we use the 2015/16 social accounting matrix (SAM) input-output table (Mengistu et al. 2019). For this purpose, consumption item data from the ESS is combined with tax schedules from the Ethiopian Revenue and Customs Authority with sectors in the input-output matrix (Annex 4).

In addition to survey data, we use the following administrative information: (1) national public revenue and expenditure data for the 2018/19 fiscal year, and regional education and health spending from the national income and public finance accounts of the Ministry of Finance; (2) enrollment information from the Ministry of Education; and (3) government subsidies for kerosene from the Ethiopian Petroleum Supply Enterprise and wheat from the Ethiopian Trading Businesses Corporation.

## **3.2. Assumptions**

One of the assumptions in this study is about direct taxes. Though direct taxes are borne entirely by the income earner, we assume that these taxes have an effect on the welfare of all household members, who share the burden of these taxes. Employment income tax is computed from the estimated monthly chargeable wage using official tax rates for earnings over birr 600 per month, the threshold for paying taxes (Annex 4). This simulation assumes that all eligible taxpayers do pay taxes. Though firms also pay indirect taxes, we assume that these taxes are borne 100 percent by consumers regardless of the market structure. We also assume that all eligible taxpayers in fact pay the taxes, which may not necessarily be the case.

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Purchased consumption items are identified from the survey. The first- and second-round effects of VAT and excise taxes applied to those items are simulated using the SAM. We first calculated the price burden of all goods and services by using their effective tax rate. We then estimated the price burden on consumers resulting from indirect taxes paid for inputs of production to estimate how taxes on petroleum and coal affect the prices of final goods and services. For VAT, the second-round effects are estimated for exempted items.

The value of in-kind education and health services to individuals is estimated based on total government spending on these services. Copayments are deducted when the beneficiary paid any fee or contribution to use them. We used 2016/17 regional and federal administrative spending data to estimate the cost of providing education by level (primary, secondary, higher) and health services. To fill the data gap for 2018/19, we deflated the 2016/17 spending using the average annual growth rate of spending for each region.<sup>3</sup> We assume that each student enrolled in a public school in each region receives the education benefit. We calculate per-pupil education cost for each region by dividing total spending by the number of primary and secondary students enrolled. For tertiary education, total federal spending is divided by the number of students enrolled in post-secondary education. Most spending on tertiary education is capital spending, notably investments in infrastructure for the recent expansion in higher education; because the benefit is expected to accrue over several years, the analysis considers only a portion of the spending.<sup>4</sup> The per-beneficiary health benefit is obtained by dividing total health spending by the number of public health service users.<sup>5</sup> Indirect subsidies applied to wheat in urban areas and kerosene in all parts of the country<sup>6</sup> are estimated based on what the household spends on these items. The total value of the subsidy is calculated based on subsidy rates per kilogram for wheat and per liter for kerosene, as derived from government import and sales data.<sup>7</sup> This study does not include such categories as corporate income, international trade, and infrastructure investments, although these directly influence income distribution and poverty. It also does not consider the operations of state-owned enterprises. Moreover, this study does not consider behavioral, life cycle, or general equilibrium effects, and it assumes that both consumer demand and labor supply are perfectly inelastic. In estimating the monetized values of in-kind transfers, our calculations do not consider possible differences in the quality of services delivered to different income groups in each region. Nor do we capture whether individuals fail to visit health centers because of expected fees, which more heavily burden poorer individuals. Furthermore, we treat the social security contributions of employees as saving. The study does not evaluate whether specific taxes and spending are desirable.

> 3 The average annual growth rate of education and health spending is estimated based on nine years of spending data. 4 Because these expenditures would also serve future generations, we took into account only 10 percent of capital spending in tertiary education as the benefit current students are receiving.

> 5 We estimate public health service beneficiaries by region and nationally using ESS data.

> 6 As it is difficult to identify which household in which area is benefitting from the wheat subsidy, we assume that it targets the entire urban population. This assumption is based on evidence that indicates subsidized wheat is available in most urban centers (see World Bank 2018).

> 7 One data gap in wheat subsidy allocation is the lack of disaggregated consumption items for wheat products. We therefore calculate the subsidy based on wheat consumption in any form.

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# **4. Results**

## **4.1. Incidence of Taxes and Transfers**

The incidence of taxes and spending by gender is determined using the distribution of the share of taxes paid and transfers received as a proportion of market income. Figures 2a and 2b show a relatively equalizing distribution of income for both men and women. The poorest 10 percent gained more than they lost from the fiscal system, and women gained more than men relative to their market income. This result is mainly driven by in-kind transfers in the form of education and health, followed by direct transfers.

**Figure 2: Incidence of Taxes and Transfers by Income Decile**

<!-- Start of picture text --> a. Men<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>1 2 3 4 5 6 7 8 9 10<br>Market income decile<br>Direct transfers Direct taxes (broad)<br>Indirect taxes Indirect subsidies<br>In-kind transfer Co-payment<br>Total net benefit Direct transfers net of direct taxes<br>b. Women<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>1 2 3 4 5 6 7 8 9 10<br>Market income decile<br>Direct transfers Direct taxes (broad)<br>Indirect taxes Indirect subsidies<br>In-kind transfer Co-payment<br>Total net benefit Direct transfers net of direct taxes<br>Share of market income, percent<br>Share of market income, percent<br><!-- End of picture text --> _Source_ : Authors’ calculations; ESS 2018/19 data.

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For both women and men in the poorest decile, in-kind education and PSNP transfers account for a large share of market income (Figures 2a and 2b). The first and the second top 10 percent (top two deciles) pay more direct taxes relative to their market income. This holds for both genders, although women in the top income group lost a larger percentage of their market income than men (Figures 2a, 2b, and 2c). The net impact of the fiscal system is progressive for both groups: all but the top two deciles received more benefits relative to their market incomes than they paid out (see figure 2d). For the bottom 10 percent the impact appears to be stronger for women than men.

**Figure 2c: Direct Transfers Net of Direct Figure 2d: Net Benefits by Income Decile and Taxes by Income Decile and Gender Gender**

<!-- Start of picture text --> 10 50<br>7.7<br>42<br>6.7 40<br>30<br>40.3<br>0 20<br>-2.4<br>-4.5 10<br>-3.5<br>-2.6<br>-4.8 0<br>-4.3<br>-10 -10<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Market income decile Market income decile<br>Women Men Women Men<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Share of market income, percent Share of market income, percent<br><!-- End of picture text -->

# **4.2. Progressivity of Taxes and Transfers**

We assess the progressivity and pro-poorness of interventions by comparing the cumulative distribution of a tax burden or transfer benefit with the cumulative distribution of market income and the cumulative share of total population ranked by incom <mark>e (Duclos and Araar 2006).</mark> Concentration curves are used to show the impact of tax and transfer policies by mapping the cumulative share of taxes paid or benefits received from a particular category of taxes or transfers on the vertical axis against the cumulative share of the population, ordered by pre-fiscal income, on the horizontal axis <mark>(Duclos and Araar 2006)</mark> . The progressivity of fiscal interventions is also examined using the Kakwani coefficient (Annex 3). Figure 3 illustrates the progressivity of different taxes and transfers measured using the Kakwani index.<sup>8</sup> A positive Kakwani coefficient

> 8 The marginal contribution analysis for poverty reduction is based on the absolute poverty line, and the redistribution effect is measured using the Gini index.

10
indicates that the tax or transfer is progressive, and a negative coefficient indicates that it is regressive.

**Figure 3: Progressivity of taxes and transfers by gender (Kakwani Index)**

<!-- Start of picture text --> Women Men<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>Direct taxes Direct tax (including informal... Personal income tax Non-farm enterprise income tax Land use fee and agricultural... Urban housing tax Other income tax Informal tax Direct transfer PSNP Other transfers Indirect tax VAT Excise Indirect subsidies Kerosene subsidy Wheat subsidy In-kind transfers Education Primary school Secondary school Tertiary Health<br><!-- End of picture text --> _Source_ : Authors’ calculations; ESS 2018/19 data.

# _Direct Taxes_

In terms of progressivity, direct taxes, including informal taxes, are progressive for both women and men. They are more progressive for men than women. As discussed below, different direct taxes have different progressivity by gender.

PIT is the most progressive tax for both genders. Results from the Kakwani coefficients (Figure 3) and concentration curves show that PIT is more progressive for men (Figure 4a) than for women (Figure 4b). Income taxes from businesses (household-owned non-farm enterprises) and PIT are similar in their progressivity. However, other direct taxes, including agricultural income tax and rural land use fees, and informal taxes are regressive for both men and women (Figure 3). This is consistent with the evidence that, in developing countries, informal taxes have a more regressive impact than other types of direct taxes (Evans, Harkness, and Salomon 2020).

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<!-- Start of picture text --> Figure 4a: Concentration of Direct Taxes by Figure 4b: Concentration of Direct Taxes by<br>Income Groups, Men Income Groups, Women<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Pers. Income tax Market income Pers. Income tax<br>Firm income tax Agri. Income tax Firm income tax Agri. Income tax<br>Housing tax Other income tax Housing tax Other income tax<br>Direct tax Population share Direct tax Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of direct taxes Cumulative proportion of direct taxes<br><!-- End of picture text -->

# _Direct Transfers_

Direct transfers can be classified as PSNP-based and non–PSNP. Both types of transfers are progressive. While both PSNP and non-PSNP transfers are pro-poor (progressive in absolute terms) for women (Figure 5b), non-PSNP transfers appear to be regressive for men (Figure 5a).

The poorest women, those in the bottom income decile, capture about 1.9 percent of market income but 16.2 percent of the PSNP transfer; men in the bottom decile receive about 1.8 percent of market income but 14.8 percent of the PSNP transfer. Similarly, for women the bottom 40 percent account for 14.6 percent of market income and 57.5 percent of the PSNP benefit. For men, about 52.4 percent of the PSNP benefit goes to the bottom 40 percent, who also capture 14.3 percent of market income. Women in the top decile (the wealthiest 10 percent) capture 33 percent of market income but only 7.5 percent of PSNP transfers; men in that decile receive 34.9 percent of market income but only 4.8 percent of PSNP transfers.

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<!-- Start of picture text --> Figure 5a: Concentration of Direct Transfers Figure 5b: Concentration of Direct Transfers<br>by Income Groups, Men by Income Groups, Women<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Non PSNP transfer Market income Non PSNP transfer<br>PSNP transfer Direct transfer PSNP transfer Direct transfer<br>Population share Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of direct transfers Cumulative proportion of direct transfers<br><!-- End of picture text -->

# _Indirect Taxes and Subsidies_

Most of the assignable goods that are identified in this study are subject to VAT and excise taxes. There is a substantial difference in the total value of annual consumption of these items by gender. The total value of annual consumption by men is birr 1,595 and by women birr 917 (Table 1). However, the effective tax rate, 19.4 percent, is the same for both genders (Table A1.2). In addition, the richest individuals (top 20 percent) spent more on assignable goods than the poorest (bottom 20 percent); the value of assignable goods, on average, is 35 percent of the non-assignable goods for the richest but only 7 percent for the poorest (Table 1).

**Table 1: Distribution of Assignable and Non-assignable Goods by Gender and Income**

||**Assignable**|**Non-assignable**|**Share**<sup>*****</sup>|
|---|---|---|---|
|National|1,247.5|10,811.8|14.9|
|Female|917.5|10,088.6|12.1|
|Male|1,595.2|11,573.7|17.9|
|Poorest|185.3|2,964.3|7.2|
|Poor|371.2|5,486.7|8.3|
|Middle|631.1|8,106.3|10|
|Rich|1,165.1|11,891.6|14.2|
|Richest|3,885.7|25,616.9|34.8|

_Source_ : Authors’ calculations; ESS 2018/19 data. _Note:_<sup>*</sup> Share refers to the average of the ratio of assignable to nonassignable goods.

13
VAT and excise taxes are slightly progressive for women, mainly because of VAT. The VAT concentration curve for women (Figures 6b) is ambiguously progressive because it crosses the market income Lorenz curve; excise taxes are mildly regressive. Indirect subsidies are regressive for both genders, mainly because the wheat subsidy is so regressive (Figures 6a and 6b). This suggests that, although the wheat subsidy is meant to improve the welfare of the poor through price controls, richer consumers capture more of the total subsidy. The kerosene subsidy is also relatively progressive.

**Figure 6a: Concentration Curves for Indirect Figure 6b: Concentration Curves for Indirect Taxes and Subsidies, Men Taxes and Subsidies, Women**

<!-- Start of picture text --> 100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Kerosene Market income Kerosene<br>Wheat VAT Wheat VAT<br>Excise Population share Excise Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of indirect subsidies Cumulative proportion of indirect subsidies<br><!-- End of picture text -->

# _Education_

Public spending on education is progressive in relative terms. There is no difference by gender on progressivity.

Disaggregating the results by level of education, regardless of gender, spending on primary education has been found to be progressive (Figures 7a and 7b). Importantly, poor boys are benefiting relatively more than other boys, and primary education has dramatically reduced poverty for girls. The share of public spending on primary education for the poorest boys is 7.5 percent and for the richest 3.7 percent. Girls in the poorest decile capture 8.7 percent of primary education spending and those in the richest 5.8 percent.

Spending on secondary education is relatively progressive but public spending on tertiary education is regressive for both genders (Figure 7a and 7b). The poorest decile benefit from less than 1 percent of spending on tertiary education, but the top decile captures a remarkable 50 14
percent for girls and 64 percent for boys. The regressivity of tertiary education spending may be due to high primary and secondary dropout rates and low completion rates that depress tertiary enrollment. This could be more important for girls and children from poor households, for whom progression beyond primary education has long been difficult.

<!-- Start of picture text --> Figure 7a: Concentration of In-kind Figure 7b: Concentration of In-kind<br>Transfers, Men Transfers, Women<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Cumulative proportion of the population Cumulative proportion of the population<br>Market income Primary Market income Primary<br>Secondary Tertiary Secondary Tertiary<br>In-kind education In-kind health In-kind education In-kind health<br>Population share Population share<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Cumulative proportion of in-kind transfers Cumulative proportion of in-kind transfers<br><!-- End of picture text -->

# _Health_

Health spending is for both genders progressive (Figure 3), though more so for women than for men. The health benefits for the poorest women and men are relatively high as a share of their market incomes (Figure 7a and 7b). However, for both men and women health spending is not propoor: about 9 percent is concentrated in the poorest decile, but the top decile takes 13 percent. The relative progressivity of health spending could be due to the recent increases in almost all neighborhoods of health extension agents, who help ensure that basic health services are available to individuals, especially women.

Figures 8a and 8b summarize the progressivity and pro-poorness of government transfers (direct and in-kind transfers plus subsidies), which are assessed using concentration coefficients. For both men and women, PSNP is the most progressive and the only pro-poor spending. Direct transfers have variable progressivity and pro-poor impact across the genders (Figure 3 and Figure 8a&b), mainly driven by the PSNP. However, only PSNP transfers are pro-poor for men (Figure 8a); for them, transfers other than PSNP appear to be regressive. Primary education is progressive and pro-poor for men, and all women benefit from this transfer equally. Spending on tertiary education, the wheat subsidy, and indirect subsidies in the aggregate are all found to be regressive 15
for both men and women. In addition, there is room to improve the progressivity by making social spending on health more pro–poor.

**Figure 8a: Progressivity and Pro-poorness of Public Spending, Men**

**Figure 8b: Progressivity and Pro-poorness of Public Spending, Women**

<!-- Start of picture text --> Health Health<br>Tertiary Tertiary<br>Secondary school Secondary school<br>Primary school Primary school<br>Education Education<br>In-kind transfers In-kind transfers<br>Wheat subsidy Wheat subsidy<br>Kerosene subsidy Kerosene subsidy<br>Indirect subsidies Indirect subsidies<br>Other transfers Other transfers<br>PSNP PSNP<br>Direct transfer Direct transfer<br>-0.2 0 0.2 0.4 0.6 0.8 1 -0.2 0 0.2 0.4 0.6 0.8<br>Progressive but not pro-poor Progressive but not pro-poor<br>Regressive and not pro-poor Regressive and not pro-poor<br>Progressive and pro-poor Progressive and pro-poor<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br><!-- End of picture text -->

# **4.3. Welfare Effects of Fiscal Policy**

# _Effect on Income Distribution_

Figure 9 presents income distributions by gender before and after fiscal actions. Income is concentrated in the lower end of the distribution; only 18 percent of Ethiopian households earn more than US$2.5 at purchasing power parity per day.

Figure 9 depicts no shift in income distribution when there is a move from market to final income for women and men. Government intervention through taxes and transfers raises the median income for women and men, except that for men the change in mean is negative. This implies that the intervention contributes more to upward income mobility for women than men. Such a differential income distribution shift illustrates that taxes and spending are affecting the welfare of individuals differently.

16
<!-- Start of picture text --> 3 3<br>2/3 of of median 2/3 of of median,<br>Bir 6.426 Bin §.772<br>Median, . . Median,<br>Bir 9.639 Bin 10,157<br>BirMean,13,804Mean,13,80413,804 Bir 12,445<br>7r)r) mer)r)<br>Cc Cc<br>22<br>| 1. Mean,<br>EEE<br>oo<br>aaa<br>oa oa<br>aa<br>111<br>among the national5.3 the national5.3 national5.35.3 miion10menpercent10menpercentmenpercentpercent among the5.3 national miiontopmen105.3 national miiontopmen10 national miiontopmen10 miiontopmen10topmen10men1010 percent<br>i] 0<br>O 4000 8000 12000 16000 20000 24000 28000 32000 326000 40000 44000 48000 0 4000 8000 12000 16000 20000 24000 28000 32000 36000 40000 44000 45000<br>Income 2018/19 (BirrYear), Birr 500 bands Income 2018/19 (BirrYear), Birr 500 bands<br>3 3 2/3 of median,<br>BirrMedian,8,238Median,8,2388,238 BirMedian,8,723Median,8,7238,723<br>Mean, A A Mean,<br>Bir 11,478 lin 11,473<br>r) r)<br>Cc Cc<br>22<br>m2 =? |<br>EEE<br>oo<br>aaa<br>oa oa<br>aa<br>111<br>3.7 milion women women<br>3.7 mihon among the nationaltop the nationaltop nationaltoptop 10 percent<br>“pee<br>i] 0<br>O 4000 8000 12000 16000 20000 24000 28000 32000 326000 40000 44000 48000 0 4000 8000 12000 16000 20000 24000 28000 32000 36000 40000 44000 45000<br>Income 2018/19 (BirrYear), Birr 500 bands Income 2018/19 (BirrYear), Birr 500 bands<br><!-- End of picture text -->

<!-- Start of picture text --> 3 3<br>2/3 of of median 2/3 of of median,<br>Bir 6.426 Bin §.772<br>Median, . . Median,<br>Bir 9.639 Bin 10,157<br>BirMean,13,804Mean,13,80413,804 Bir 12,445<br>7r)r) mer)r)<br>Cc Cc<br>22<br>| 1. Mean,<br>EEE<br>oo<br>aaa<br>oa oa<br>aa<br>111<br>among the national5.3 the national5.3 national5.35.3 miion10menpercent10menpercentmenpercentpercent among the5.3 national miiontopmen105.3 national miiontopmen10 national miiontopmen10 miiontopmen10topmen10men1010 percent<br>i] 0<br>O 4000 8000 12000 16000 20000 24000 28000 32000 326000 40000 44000 48000 0 4000 8000 12000 16000 20000 24000 28000 32000 36000 40000 44000 45000<br>Income 2018/19 (BirrYear), Birr 500 bands Income 2018/19 (BirrYear), Birr 500 bands<br>3 3 2/3 of median,<br>BirrMedian,8,238Median,8,2388,238 BirMedian,8,723Median,8,7238,723<br>Mean, A A Mean,<br>Bir 11,478 lin 11,473<br>r) r)<br>Cc Cc<br>22<br>m2 =? |<br>EEE<br>oo<br>aaa<br>oa oa<br>aa<br>111<br>3.7 milion women women<br>3.7 mihon among the nationaltop the nationaltop nationaltoptop 10 percent<br>“pee<br>i] 0<br>O 4000 8000 12000 16000 20000 24000 28000 32000 326000 40000 44000 48000 0 4000 8000 12000 16000 20000 24000 28000 32000 36000 40000 44000 45000<br>Income 2018/19 (BirrYear), Birr 500 bands Income 2018/19 (BirrYear), Birr 500 bands<br><!-- End of picture text -->
**Table 2: Poverty Transitions from Pre- to Post-fiscal Policy, Percent A: Men**

|||**Post-fiscal**|**APL**|**Post-fiscal**|**RPL**|
|---|---|---|---|---|---|
||**Poverty Status**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|
|Pre-fiscal|Poor|15.92|4.84|20.84|5.84|
||Nonpoor|1.14|78.1|1.37|71.95|
|**B: Women **||||||
|||**Post-fiscal**|**APL**|**Post-fiscal**|**RPL**|
||**Poverty Status**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|
|Pre-fiscal|Poor|19.93|6.37|26.57|6.28|
||Nonpoor|1.56|72.14|1.86|65.29|

_Source_ : Authors’ calculations; ESS 2018/19 data. _Note_ : APL = absolute poverty line; RPL = relative poverty line.

As Figure 9 showed, the fiscal system shifts income distribution to the right. The mobility matrix in Table 3 also shows the share of individuals who transited from one income group to another as they moved from (1) market income to (2) disposable income to (3) final income.

**Table** **<u>3: Mobility Matrices from Market to Disposable and Final Income, Percent</u>**

|**A: Men**||||||||||
|---|---|---|---|---|---|---|---|---|---|
|Y<sup>d</sup><5050|96.3|3.3|0.2|0|76.5|17.9|4.5|0.9|20.8|
|B<sup>f</sup>|3.6|91.4|4.8|0.1|5.3|63.4|27.7|3.6|15.4|
|C<sup>g</sup>|0.2|2.8|95.5|1.5|0.6|5.7|74.7|19|21|
|Y>11053|0.2|0.3|2.1|97.4|0.5|0.3|5.3|93.9|42.9|
|**B: Women**||||||||||
|||**Di**|**sposab**|**le Income**<sup>**b**</sup>|||**Fin**|**al Income**<sup>**c**</sup>|**Percent of**|
|**Market Income**<sup>**a**</sup>|**Y<5050**|**B**|**C**|**Y>11053**|**Y<5050**|**B**|**C**|**Y>11053**|**Population**|
|Y<sup>d</sup><5050<sup>e</sup>|96.9|2.9|0.2|0.1|75.8|20.2|2.9|1.2|26.3|
|B<sup>f</sup>|3.7|92|4.2|0.1|6.6|64.6|26|2.8|17.4|
|C<sup>g</sup>|0.2|3.9|94.4|1.5|1.1|7|73.6|18.2|21.6|
|Y>11053<sup>h</sup>|0.2|0.1|3.2|96.5|0.5|0.5|6.4|92.5|34.7|

_Source_ : Authors’ calculations; ESS 2018/19 data. _Note_ : a. Market income refers to pretax income and private transfers.

b. Disposable income = Market income – direct tax + direct transfers.

c. Final income = Consumable income + in-kind transfers for education and health.

d. Y = Individual income for the corresponding income concept.

e. Income group below birr 5,050: a calipered absolute poverty line that could produce the same number of poor people as the Household Consumption Expenditure Survey using “disposable income.” f. Income group between the absolute poverty line and 7,287, the 40<sup>th</sup> percentile of market income.

g. Income group between 7,290 and the official poverty line at June 2019 prices.

h. Income group above the official poverty line.

There appears to be less transition or movement across income groups from market to disposable income—the diagonal values are above 92 percent for both women and men. In movement from market to final income, however, there is some change in income groups. Although it appears that the majority do not change income groups along the transition from market income to disposable and then to final income, net fiscal policy allows a higher share of both women and men to make 18
a transition from one income group to another—the share of individuals found in the diagonal is lower than for disposable income.

The net effect of direct transfers and taxes is to push more individuals down to a lower income group than are raised to a higher income group. Direct interventions push more women (3 percent) than men (2 percent) down to a lower income group, and many more women then fall below the absolute poverty line (birr 5,050).

Overall, fiscal policy moves about one in five individuals (20.8 percent for men, 18.4 percent of women) from one income group to another, and more women than men transition to a higher income group, making them relatively better-off. Moreover, the income mobility matrices show that the intervention resulted in more transitions to both lower (downward mobility) and higher (upward mobility) income groups among poor or near-poor women and men; among women, about 60 percent of those for whom there was a transition had market income less than the 40<sup>th</sup> percentile.

# _Effect on Poverty and Inequality_

The distributional impacts of fiscal policy are assessed using changes in poverty levels and income inequality. This analysis uses two poverty lines: (1) a relative line set at two-thirds of the median market income; and (2) an absolute line obtained by calibrating a threshold that could produce the official national poverty rate. Table 4 presents results of fiscal policy effects on poverty incidence, gap, and severity by gender at different CEQ income concepts. The poverty indices for both absolute and relative poverty lines tend to be higher for women at all income levels. Moving from market income to net market income shows the change in poverty due to direct taxes, which increased absolute as well as relative poverty for both men and women.

**Table 4: Poverty** **<u>by CEQ Income Concepts and Gender</u>**

||**Mark**<br>|**et Income**<br>|**Gros**<br>|**s Income**<br>|**Net**<br>**In**<br>|**Market**<br>**come**<br>|**Dis**<br>**In**<br>|**posable**<br>**come**<br>|**Con**<br>**In**<br>|**sumable**<br>**come**<br>|**Fina**<br>|**l Income**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Men**|**Women**|**Men**|**Women**|**Men**|**Women**|**Men**|**Women**|**Men**|**Women**|**Men**|**Women**|
|**Relative poverty line**|||||||||||||
|Poverty incidence|26.9|33.2|26|32|27.9|34|26.9|32.9|29.6|35.4|22.5|28.8|
|Poverty gap|9.5|11.7|8.9|11.1|9.9|12.1|9.3|11.5|10.4|12.8|8|9.6|
|Poverty severity|4.6|5.7|4.3|5.2|4.9|5.9|4.5|5.5|5.1|6.2|8.9|6.8|
|**Absolute poverty line**|||||||||||||
|Poverty incidence|20.8|26.3|20|25.4|21.6|27.1|20.7|26.2|22.6|28.7|17.1|21.5|
|Poverty gap|6.9|8.5|6.5|7.9|7.2|8.9|6.8|8.3|7.6|9.4|5.9|6.9|
|Poverty severity|3.3|3.9|3|3.6|3.4|4.1|3.1|3.7|3.6|4.3|9.6|6.3|

Source: Authors’ calculations; ESS 2018/19data.

In general, looking at the final income, we see that the fiscal actions reduced poverty and inequality for both men and women. Moving along the income concepts, we see the changes in poverty and inequality due to different taxes and transfers. For example, from market to gross income, there is evidence that direct transfers reduce both absolute and relative poverty; absolute poverty falls by 0.8 pp for both women and men (Table 3). These positive distributional effects could be due to PSNP, the large transfer program. Apparently, direct transfers are sufficient to offset the povertyinducing impact of direct taxes for both relative and absolute poverty lines. Indirect taxes worsen 19
poverty considerably; absolute poverty increases by 2.4 pp for women and 2 pp for men—more women than men became poorer due to consumption taxes (Table 4). And both women and men close to defined poverty lines consume both food and non-food items that are subject to indirect taxes. Moreover, they pay more in indirect than direct taxes.

In-kind indirect transfers constitute the largest part of government spending (Annex 1). Moving from consumable income to final income, we see that these transfers substantially reduce poverty (Table 4). Using the absolute line, the poverty reduction effect of the transfers is higher for women than men. However, the transfers allow more men than women to escape relative poverty. Though the headcount and poverty gap go down, poverty severity increases when there is a move from consumable to final income, and poverty appears to be more severe among men.

In-kind transfers on education and health services reduced absolute poverty by 4.7 pp for women and 3.6 pp for men, though they increased the severity of poverty mainly among men. At the relative poverty line, the poverty-reducing effects are 4.4 pp for women and 4.5 pp for men. This suggests that the poverty impact of the fiscal system is weaker for women in terms of their position relative to the absolute poverty line.

Table 5 presents income inequality by gender at different CEQ income concepts. Within group (within all men or within all women) inequality is higher for men than for women at all income concepts. Almost all (99%) of this reduction in inequality is the within group inequality. The between group inequality (between men and women) is so small that it is not affected by the fiscal system.

# **Table** **<u>5: Inequality by CEQ Income Concepts and Gender</u>**

||**Market**<br>**Income**|**Gross**<br>**Income**|**Net**<br>**Market**<br>**Income**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|**Final**<br>**Income**|
|---|---|---|---|---|---|---|
|Men|0.386|0.381|0.376|0.370|0.372|0.358|
|Women|0.344|0.337|0.338|0.331|0.332|0.321|
|Within-group component|0.366|0.360|0.358|0.352|0.354|0.341|
|Between-group component|0.004|0.004|0.004|0.004|0.004|0.004|
|Total|0.371|0.364|0.362|0.356|0.357|0.344|

_Source_ : Authors’ calculations; ESS 2018/19 data.

Indirect taxes and subsidies have little effect on inequality for both men and women; the Theil index remains the same for both disposable and consumable incomes (Table 5). However, direct taxes and transfers reduce within group inequality for both groups; when we move from market to disposable income, the Theil index declines from 0.385 to 0.370 for men, and from 0.344 to 0.331 for women (Table 4).

However, in-kind indirect transfers help reduce inequality. The movement from consumable to final income is equalizing for women and men. Monetized in-kind transfers for education and health services reduce the Theil index from 0.332 to 0.321 for women and from 0.372 to 0.358 for men. Overall, comparing within-group inequalities for market and final incomes, the fiscal system 20
had an equalizing effect: it reduced inequality for both men (from 0.385 to 0.358) and women (from 0.344 to 0.321) The higher reduction in income inequality is driven by in-kind transfers.

# **4.4. Marginal Contributions of Taxes and Transfers**

Table 6 presents the marginal contributions to inequality and poverty of the different taxes and transfers.

## _Direct Taxes_

In terms of redistributive effects of specific taxes, we find that direct taxes, including informal taxes, are equalizing (inequality reducing) but worsen poverty for both women and men. They are more equalizing for men than women. The poverty-inducing effect of direct taxes appears to be higher for women. However, as discussed below, different direct taxes have different redistributive effects by gender.

After the business profit tax, PIT is the second most important source of Ethiopia’s direct tax revenue. PIT, the most progressive tax for both genders, is responsible for most of the inequalityreducing effect of direct taxes. The gender gap in the redistributive effect of direct taxes is also a result of PIT. While this may not be attributable to any gender-based discrimination in the fiscal system that could cause differential gender impacts of direct taxes, it still indicates important differences. However, PIT does exacerbate poverty among both men and women, but more so for men.

Income taxes from household-owned non-farm enterprises are like the PIT in terms of their redistributive effect. However, as presented earlier, other direct taxes, including agricultural income tax and rural land use fees, and informal taxes increase inequality and poverty for both genders (Tables 4 and 5). This is consistent with the evidence that in developing countries informal taxes have a more regressive impact than other types of direct taxes (Evans, Harkness, and Salomon 2020). For agricultural income taxes, this could be partly because rural residents are likely to be poorer than urban residents; the median market incomes are birr 7,530 for rural and birr 14,682 for urban residents. There appears to be little difference by gender in the distributional impact of agricultural income taxes. However, the poverty-inducing effect of the tax is worse for women.

## _Direct Transfers_

Direct transfers, both PSNP-based and non–PSNP transfers, are equalizing and povertyreducing. They are redistributive, reducing inequality by about 0.43 pp for women and 0.33 pp for men, and poverty-reducing, cutting the numbers of the poor by about 1 pp for women and 0.8 pp for men. However, for men non-PSNP transfers appear to be an unequalizing (Table 6).

21
# **Table 6: Marginal Contributions of Taxes and Transfers to Inequality and Poverty** **<u>Reduction</u>**

|||**Redistribu**<br>**Effect**<br>|**tion**<br> <br>|**Poverty R**<br>**Effe**<br>|**eduction**<br>**ct**<br>|
|---|---|---|---|---|---|
|||**Men**|**Women**|**Men**|**Women**|
|Direct t|axes|0.5241|0.4314|-0.5791|-0.5911|
|Direct t|axes (including informal tax)|0.4645|0.3758|-0.7886|-0.8641|
|•|Personal income tax|0.4793|0.4083|-0.2579|-0.2366|
|•|Non-farm enterprise income tax|0.1118|0.0839|-0.0166|-0.0125|
|•|Land use fee and agricultural income<br>tax|-0.0761|-0.0825|-0.2804|-0.2876|
|•|Urban housing tax|0.0003|0.0067|-0.0063|-0.0031|
|•|Other income tax|0.0085|0.0129|0|-0.0174|
|•|Informal tax|-0.0587|-0.0546|-0.3095|-0.3549|
|Direct t|ransfers (all)|0.3257|0.4251|0.7959|1.0425|
|•|PSNP|0.3075|0.3724|0.6444|0.8914|
|•|Other transfers|-0.0289|0.0574|0.1094|0.1065|
|Indirec|t tax (all)|-0.1345|-0.0735|-1.7757|-2.3759|
|•|VAT|-0.0887|-0.0098|-1.4454|-1.8335|
|•|Excise|-0.0555|-0.0734|-0.6108|-0.5086|
|Indirec|t subsidies (all)|-0.0185|-0.0371|0.0215|0.0807|
|•|Kerosene subsidy|0.0004|0.0005|0|0|
|•|Wheat subsidy|-0.0189|-0.0376|0.0215|0.0807|
|In-kind|transfers|1.2335|1.1798|7.2199|9.2815|
|_Educat_|_ion (all)_|-0.252|-0.4331|3.7104|5.2462|
|•|Primary education|1.7633|1.624|3.5244|4.898|
|•|Secondary education|-0.2567|-0.3547|0.1519|0.3175|
|•|Tertiary education|-1.6217|-1.5366|0.0341|0.0307|
|_Health_||1.3802|1.5309|2.7075|3.6565|

_Source: Authors’ calculations; ESS2018/19 data._ _Note:_ The marginal contributions—redistributive and poverty reduction effects—are in percentage points. A positive marginal contribution means it reduces inequality and poverty; a negative means that it increases inequality and poverty.

PSNP reduces income inequality by 0.37 pp for women and 0.31 pp for men. It also reduces the poverty headcount by 0.9 pp for women and 0.6 pp for men. The effectiveness of PSNP transfers in improving welfare—reducing inequality and poverty—is in line with the findings of PSNP impact evaluation studies (Gilligan, Hoddinott, and Taffesse 2009; Hill et al. 2017). The positive distribution impacts of PSNP might also be attributable to the program’s targeting (Coll-Black et al. 2013).

22
## _Indirect Taxes and Subsidies_

Both VAT and excise taxes increase inequality and poverty regardless of gender. Although VAT and excise taxes are unequalizing for both men and women, VAT appears to be more unequalizing for men, suggesting that the burden of VAT falls more on men, possibly because men consume more alcohol and tobacco, which have higher VAT (EPHI 2016). The poverty-increasing effect of indirect taxes is higher than that of direct taxes for both men and women. However, indirect taxes are poverty-increasing mainly for women. The poverty-inducing effect of VAT is stronger for women and the effect of excise tax is stronger for men (Table 6). The findings suggest that people near the poverty threshold consume both food and non–food items that are subject to indirect taxes.

Indirect subsidies are unequalizing mainly because of the effect of wheat subsidy. Although unequalizing, the wheat subsidy appears to be poverty-reducing for both women and men. However, kerosene subsidy has little effect on inequality- and poverty-reduction.

## _Education_ and Health Spending

Public spending on education is unequalizing and poverty-reducing. The unequalizing effect is mainly the result of how much is spent on secondary and tertiary education (Table 6). The povertyreducing impact on girl students is powerful. Disaggregating the results by level of education, regardless of gender, spending on primary education has been found to be equalizing and poverty-reducing. Importantly, primary education has dramatically reduced poverty for girls. Both secondary and tertiary spending, though unequalizing, reduce poverty. Health spending is for both genders equalizing and poverty-reducing (Tables 6), though more so for women than for men.

# **5. Conclusion**

Using the Commitment to Equity methodology, this study investigates differences in the welfare impact of taxes and government spending on men and women in Ethiopia. It analyzes the incidence, progressivity, and pro-poorness of various taxes and transfers and their effect on income mobility, poverty and inequality. The study uses data from the 2018/19 Ethiopia Socioeconomic Survey, which collected individual level information on such variables as labor market outcomes, land ownership, transfers, and formal and informal taxes. In addition, the study generated individual-level income using a combination of intrahousehold allocation approaches. The gendered distributional impacts of fiscal policy are then examined at different income concepts.

The net fiscal system is progressive, equalizing, and poverty-reducing. It moved about one in five individuals from one income group to another, and more women than men transitioned to a higher income group, making them relatively better-off. However, different elements of taxes and transfers are found to have different effects on men and women. Direct taxes are progressive and equalizing, but their progressivity and inequality-reducing effects are stronger for men. Informal taxes and agricultural income taxes appear to be regressive, unequalizing, and poverty-inducing. However, informal taxes do have more poverty effects on women. Agricultural income taxes and land user fees, levied mostly on rural households, are regressive and exacerbate poverty. In part, this reflects the fact that agricultural households tend to be poorer than non-agricultural 23
households. Although consumption taxes (mainly VAT) are slightly progressive, they are unequalizing and increase poverty; the poverty-inducing effect of VAT is more pronounced for women than for men. This could be due to differences in consumption patterns.

On the spending side, the country’s flagship Productive Safety Net Program is progressive, propoor, and helps to reduce poverty for both women and men. The effectiveness of PSNP could be associated with its targeting—the poor capture a large proportion of the benefits. However, for men, other direct transfers appear to be regressive and unequalizing. Among subsidies, those on wheat appear to be regressive and unequalizing, doing little to reduce poverty regardless of gender. The kerosene subsidy is relatively progressive, but regardless of gender its inequality and povertyreducing effect is negligible.

For both women and men, in-kind transfers that include public spending on education and health are progressive, equalizing, and poverty-reducing. There is no difference in progressivity by gender, but education transfers have different effects on girls and boys. Education spending is unequalizing for both, but more poverty-reducing for girls. The unequalizing effect is driven primarily by spending on tertiary education, which is also regressive, mainly because few who are poor enroll. Spending on primary education is the most progressive, equalizing, and povertyreducing type of education for both genders but it is pro-poor only for boys. Public spending on health is also progressive, unequalizing, and poverty-reducing but has more effect for women.

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