metadata: authors: 0: author: Timilsina,Govinda R.
1: author: Sebsibie,Samuel
count: Ethiopia,World
docna: 0: docna: Distributional Effects of Carbon Tax in
            Ethiopia : A Computable General Equilibrium Analysis
docty: Policy Research Working Paper
abstracts: cdata!: Developing countries are increasingly
            giving attention to carbon pricing to reduce their
            emissions, particularly in meeting their nationally
            determined contribution under the Paris Climate Agreement.
            However, they would like to understand the potential
            economic, distributional, and environmental impacts of
            carbon pricing policies before they consider implementation.
            Using a computable general equilibrium model of Ethiopia,
            this study examines the effects of a hypothetical carbon tax
            (US$20/total carbon dioxide) under several alternative
            schemes to recycle carbon tax revenue to the economy. The
            study finds that a carbon tax would be regressive in all
            schemes considered except those when the tax revenue is
            recycled, as a cash transfer, to household income groups
            either equally or inversely proportional to their incomes.
            The schemes that make the carbon tax progressive also cause
            a higher reduction of carbon dioxide emissions, thereby
            ensuring the alignment of equity and environmental outcomes
            of the carbon tax. However, these schemes are not
            necessarily economically efficient because they cause higher
            reductions of gross domestic product compared to other
            options considered.
display_title: Distributional Effects of Carbon Tax
            in Ethiopia : A Computable General Equilibrium Analysis
listing_relative_url: /research/2023/06/34076717/distributional-effects-carbon-tax-ethiopia-computable-general-equilibrium-analysis
topicv3: Carbon Tax m1326372 1845,Climate Change m1326829 567,Climate Change Mitigation m1327079 579
publishtoextweb_dt: 2023-06-08T00:00:00Z
dois: 10.1596/1813-9450-10476
author_keys: 0: author_key: 940446
1: author_key: NA
model_extractions: Policy Research Working Paper 10476 Distributional Effects of Carbon Tax in Ethiopia A Computable General Equilibrium Analysis _Govinda R. Timilsina Samuel Sebsibie_ Development Economics Development Research Group June 2023
Policy Research Working Paper 10476

# **Abstract**

Developing countries are increasingly giving attention to carbon pricing to reduce their emissions, particularly in meeting their nationally determined contribution under the Paris Climate Agreement. However, they would like to understand the potential economic, distributional, and environmental impacts of carbon pricing policies before they consider implementation. Using a computable general equilibrium model of Ethiopia, this study examines the effects of a hypothetical carbon tax (US$20/total carbon dioxide) under several alternative schemes to recycle carbon tax revenue to the economy. The study finds that a carbon tax would be regressive in all schemes considered except those when the tax revenue is recycled, as a cash transfer, to household income groups either equally or inversely proportional to their incomes. The schemes that make the carbon tax progressive also cause a higher reduction of carbon dioxide emissions, thereby ensuring the alignment of equity and environmental outcomes of the carbon tax. However, these schemes are not necessarily economically efficient because they cause higher reductions of gross domestic product compared to other options considered.

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 gtimilsina@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
# **Distributional Effects of Carbon Tax in Ethiopia: A Computable General Equilibrium Analysis**<sup>1</sup>

Govinda R. Timilsina and Samuel Sebsibie<sup>2</sup>

**Key Worlds** : Climate change, Carbon tax, General equilibrium model, Distributional impacts, Ethiopia

## **JEL Classification** : C68, Q43

1 The authors would like to thank Yazid Dissou, Salony Rajbhandari, Carolyn Fischer and World Bank Ethiopia Country Office for their valuable comments and suggestions. The views and interpretations are of authors and should not be attributed to the World Bank Group and the organizations they are affiliated with. We acknowledge World Bank’s Research Support Grant (RSB) for financial support.

2 Govinda Timilsina (gtimilsina@worldbank.org) is a Senior Economist at the Development Research Group, World Bank. Samuel Kebede (ssebsibie@gmail.com) was a Short-term Consultant to the World Bank during the implementation this study.
# **Distributional Effects of Carbon Tax in Ethiopia: A Computable General Equilibrium Analysis**

# **1. Introduction**

Ethiopia is a landlocked country located in Sub-Saharan Africa. While the country is classified as a low-income economy by the World Bank, with a per capita income of less than US$1,085 in 2021,<sup>3</sup> it was one of the fastest-growing economies in the last decade (2010-2020). It achieved the highest economic growth in the world, on average, 9.3% per year during the 20102020 period (World Bank, 2022). The economic growth was also accompanied by the rapid increase in CO2 emissions from fuel consumption, 12% per year, on average (World Bank, 2022). Considering the rapid growth of CO2 emissions along with its economy, Ethiopia has committed its Nationally Determined Contribution under the Paris Climate Agreement to limit its greenhouse gas (GHG) emissions by 68.8% below the business-as-usual situation in 2030 (FDRE, 2021).

Carbon pricing instruments, such as carbon tax, could be economically efficient for reducing GHG emissions (see e.g., Aldy et al. 2010; Timilsina, 2022; Timilsina et al. 2022). More than 120 countries, including developing countries, have considered carbon pricing as one of the policy options to achieve their NDC targets (World Bank, 2023). However, most countries do not know how these policies will impact their economy and the population. As low-income households’ expenditure share on energy commodities is likely to be higher than that of highincome households, the burden of energy price increases resulting from a carbon tax might fall on them disproportionally (see, e.g., Ohlendorf et al. 2021; Williams et al. 2015; Rausch et al. 2011). In the case of the United States, Rausch et al. (2011) find that a carbon pricing policy is regressive. Through a meta-analysis, Ohlendorf et al. (2021) report that the distributional impacts of carbon pricing are heterogeneous across countries depending on their economic structures and the energy supply mixes.

The regressive economic impacts of a carbon tax are politically sensitive. Developing countries might be interested in a carbon tax if it can be designed in such a way that it is progressive and welfare-improving. Some existing studies investigate situations when a carbon tax could be made progressive. For example, Garaffa et al. (2021) find that a carbon tax with tax revenue 3 https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups
transferred to low-income households would increase their income by 4.5% in Brazil. Wu et al. (2021) find that a standard ETS (emission trading scheme) would be regressive if the carbon revenues were used for government expenditures or to cut existing income taxes. The ETS would be slightly progressive if revenues are used to lower consumption taxes and most progressive with revenues recycled to households through equal lump-sum transfers. They also find that the impacts are different between rural and urban households. While analyzing the distributional effects of a carbon tax in the US, Goulder et al. (2019) find that the use-side impacts<sup>4</sup> of the carbon tax would be negative and also regressive under all four schemes they considered for carbon tax revenue recycling (lump-sum transfer to households, payroll tax cut, personal income tax cut, and corporate income tax cut). On the other hand, the type of revenue recycling scheme determines whether a carbon tax is progressive or regressive. It would be progressive if tax revenue is recycled to households as a lump-sum transfer. Under other schemes for revenue recycling, it is not necessarily progressive unless the value of leisure is also accounted for. Dissou and Siddiqui (2014) investigate the distributional impacts of a carbon tax by decomposing the impacts channeled through commodity and factor markets. They find that the impacts of carbon taxes passed on to the households through these two markets have opposite effects on income inequality. While a carbon tax reduces inequality through the factor price channel, it increases income inequality through the commodity price channel. Thus, the relationship between carbon taxes and inequality is U-shaped (non-monotonic). Carbon taxes are unpopular and yet, they could be the most effective tool to mitigate climate change.

Steckel et al. (2021) find heterogeneous distributional impacts across countries when analyzing the effects of four carbon pricing schemes in eight Asian developing countries (Bangladesh, India, Indonesia, Pakistan, Philippines, Thailand, Türkiye, and Vietnam).<sup>5</sup> They find that all four carbon pricing schemes are progressive in Pakistan and the Philippines. In Bangladesh and Indonesia, all schemes, except the carbon pricing on liquid fuels, would be progressive. Most carbon pricing schemes would be regressive for Thailand. They also find the vertical distributional

> 4 The use-side impact refers to the impacts on households due to changes in the prices of the goods and services, whereas source-side impact is the impacts due to changes in a household income (i.e., labor, capital, and transfer incomes).

> 5 The carbon prices are: (i) an internationally harmonized carbon price, (ii) a national carbon price, (iii) a carbon price that focuses on the electricity sector and (iv) a carbon price on liquid fuels, covering mainly the transport sector.
effects (i.e., within income groups) more pronounced than the horizontal distributional effects (i.e., across income groups). Alonso and Kilpatrick (2022) also analyze the distributional impact of a carbon tax in 14 economies in Asia and the Pacific region (Australia; China; Hong Kong SAR, China; India; Indonesia; Japan; Kiribati; the Republic of Korea; Mongolia; Myanmar; New Zealand; the Philippines; Singapore; and Taiwan, China). They show that the distributional impacts of a carbon tax vary across the country and the revenue recycling schemes. The costs on households (in terms of consumption loss) of a US$50/tCO2 carbon tax range from less than 2% (Kiribati and Myanmar) to 10% (Mongolia). They find the carbon tax regressive in China, Indonesia, and Mongolia, and progressive in India, Kiribati, Myanmar, and the Philippines.

Studies investigating this issue for Sub-Saharan African economies do not exist. Yet, some countries in the region are showing interest in carbon taxes to help achieve their NDCs under the Paris Agreement or their net zero goals in the long run. The objective of this study is to contribute to filling the research gap and provide new knowledge that could be helpful to policy makers in Ethiopia and similar economies in the region and elsewhere.

Ethiopia also subsidizes fossil fuels. While subsidies are gradually reduced, there was a significant subsidy to petroleum products in 2016, the base year used for this analysis. It would be relevant to understand the economic and distributional impacts of removing fossil fuel subsidies before analyzing the impacts of a carbon tax. It would be also interesting to compare the economic impacts removal of fossil fuel subsidies and the introduction of a carbon tax. It would be further important to analyze the economic impacts when both policies are implemented simultaneously. However, the lack of necessary data in the social accounting matrix (SAM), the main database for the analysis, we could not include the impacts of fossil fuel subsidy removals in this study.

The paper is organized as follows. Section 2 highlights the key features of the CGE model, and the database used for the study, followed by descriptions of scenarios considered in Section 3. The main results and their interpretations are presented in Section 4. This section also presents sensitivity analysis on selected model parameters. Finally, key conclusions and policy implications are derived in Section 5.
# **2. Model and Data**

## **2.1 The CGE model**

The study uses a static (single period) CGE model. The economy has four economic agents: households, government, enterprises and the rest of the world (ROW). The modeling of each economic agent is briefly described below.

## **2.1.1 Modeling the production sectors**

Production sectors are classified into 19 sectors (Table 1). The behavior of the representative firm in each sector is modeled through a nested functional form which uses either constant elasticity of substitution (CES) or Leontief functional forms in different nests (Figure 1) The top tier of the nested structure presents a CES combination of the aggregated production factors (value added) and the aggregate intermediate input. It can be expressed as: QAi = αiA ⋅[δiA ⋅QVAiρiA + (1 −δiA) ⋅QINTAiρiA]1/ρiA `(` 1 `)` where QAi is the total production of sector i, QVAi and QINTAi are the input of value-added and the aggregate intermediate input in sector i, respectively. δiA and αiA are the share parameters and A σ A efficiency parameters. ρi is related to the elasticity of substitution (σ) as follows: ρ= σ−1 . σi, is the substitution elasticity between the value-added and the aggregate intermediate input. Maximizing the Equation (1) subject to the budget constraint given in Equation (2) will yield the relationship in Equation (3).

where PAi is the producer price of sector i, PVAi and PINTAi are the prices of value-added and the aggregate intermediate input in sector i.
<!-- Start of picture text --> pf<br>po<br>po<br>pf<br>po<br>po<br>po<br><!-- End of picture text --> <!-- Start of picture text --> Domestic supply<br>5ectora ; QDA<br>Output (QA) CET<br>Exports (QXE)<br>CES<br>Value Added VA) Aggregate intermediate<br>input (QINT)<br>CES CES<br>Capital Labor Non-energy aggregate Energy aggregate<br>(QK) (QL) (QNE) (QE)<br>Leontief CES<br>ONEAI bene eeees Electricity Petroleum<br>(QEL) (QPP)<br><!-- End of picture text -->
In the left-hand side of the middle tier in Figure 1, labor and capital are combined through a CES functional form as follows: where QLi and QKi are, respectively, labor and capital inputs in sector i, δiva and αiva are the share va parameters and efficiency parameters. ρi is the substitution elasticity parameter between labor va 1 va and capital with σi = va where σi is the substitution elasticity between labor and capital. (1−ρi ) Like in Equations (2) and (3) above, the demand and price are defined as follows: PVAi ⋅QVAi = PLi ⋅QLi + PKi ⋅QKi (6) where PL and PK are, respectively, labor and capital prices in sector i. In the right-hand side of the middle tier in Figure 1, the aggregated energy good and the aggregated non-energy good are combined through a CES functional form as follows: QINTAi = αine ⋅[δine ⋅QNEiρinee + (1 −δine) ⋅QEiρinee]1/ρinee (7) where QNEi and QEi are the input of non-energy aggregate and energy composite in sector i, respectively, δine and αine are the share parameters and efficiency parameters; ρinee is the substitution elasticity parameter between the input of non-energy intermediates and energy, and nee 1 nee σi = (1−ρinee) ~~,~~ σi is the substitution elasticity between the non-energy intermediate inputs and energy. The demands and prices are derived as follows: where PNE and PE are the price of the non-energy aggregate and energy composite, respectively. The relationship of the price of the input of non-energy aggregate and energy component is given as: PINTAi ⋅QINTAi = PNEi ⋅QNEi + PEi ⋅QEi (9) In the right-hand side of the bottom tier, the aggregate energy is obtained by combining electricity and petroleum products through a CES function as follows:
where QPPi and QELi are the input of petroleum products and electricity in sector i, respectively, δiec and αiec are the share parameters and efficiency parameters; ρiec is the substitution elasticity ec 1 ec parameter between the input of fossil fuels and electric power, and σi = (1−ρiec), σi is the substitution elasticity between the input of fossil fuel aggregate and electric power. The optimal prices and quantities are derived as follows: where PPPi and PELi are, respectively, prices of petroleum products and electricity used in sector i. ‘indt’ is indirect tax rate. Subscripts ‘pp’ and ‘el’ refer to petroleum and electricity. In the lefthand side of the bottom tier in Figure 1, the quantities of individual non-energy intermediate inputs are derived using a Leontief functional form as follows: QNEAj,i = icaj,i ⋅QNEi j∈individual nonenergy commodity (13) The price of an individual intermediate input is given by where QNEAj,i and PNEAi are the quantity and prices of non-energy input j in sector i, icaj,i is the fixed proportion of non-energy commodity j in the total non-energy intermediate aggregate input of sector i.

# **2.1.2 Modeling international trade**

As shown in Figure 1, the output from a sector is allocated for the domestic market and foreign market (exports) using a Constant Elasticity of Transformation (CET) functional form as follows:
where QDAi and QEXi are the supply of commodities produced in sector i to the domestic market and exports, respectively, δit and αit are the share parameters and efficiency parameters; ρit is the t 1 transformation elasticity parameter between domestic market supply and export, and σi = (ρit−1) ~~,~~ t σi is the transformation elasticity between the domestic and the export markets. Maximizing Equation (15) subject to Equation (16), we will get the following relationships for demand and prices: where PDAi and PEXi are the domestic price and export price of the commodity produced by sector i. PEXi is related to global price (PWEi) through the exchange rate (EXR):

# PEXi = PWi ⋅EXR

Total domestic demand for a good is a CES composite of domestically produced and imported components and given by: where QQi , QDCi and QMi are the demand for composite commodity i, domestic commodity i and import commodity i, respectively, and δiq and αiq are the share parameters and efficiency q parameters; ρi is the substitution elasticity parameter between domestic and import commodities, and σiq = (1−ρ1 iq) ~~,~~ σiq is the substitution elasticity between domestic and import commodity. The demand and prices are derived through an optimization process and given as: where PQi is the price of composite commodity i, PDCi is the price of domestic commodity i, PMi is the price of import commodity i. Price of imported commodity (PMi) is linked to its world price
through exchange rate as follows: where tmi is the import tariff rate of commodity i.

# **2.1.3 Modeling household behavior**

Households are disaggregated into five categories (quintiles) based on their income. For each quantile, the households’ income (YHh) is composed of wage income, capital rents, and transfer payments from domestic institutions and foreign countries. It is given as: where TGHh and TWHh are the transfers of payments to household income group h from the government and foreign countries (or rest of the world), respectively, and shhk refers to the share of household h in ownership of capital. The consumption function of households is assumed to be derived from Stone-Geary utility function (Boer 2009; Geary 1951; Stone 1954). This functional form allows households to consume a minimum level of each good and service irrespective of its price or the consumer's income. Households’ choice for the consumption of a good or service above the minimum threshold level is based on the income and price of the good or service. Selection of this functional form to represent households’ behavior can be rationalized from the facts a level of basic goods such as food, and drinking water is essential for survival, and it is not a question of choice. In the case of basic goods, the choice is relevant above the minimum threshold level whereas it is relevant for the entire level of consumption for other goods and services. The demand function derived from the Stone-Geary utility function is a linear expenditure system (LES) with a fixed level of expenditure allotted for the minimum consumption of subsistence goods and services. Thus, the final consumption of households is given as follows: PQi . QHi,h = PQi . γi,h + βi,h (mpch. YHh −∑PQj . γj,h) (24) where QHi,h is the consumption of commodity i of household income group h, mpch is the marginal propensity to consume of household group h, γi,h is the minimum consumption of commodity i in household group h, and βi, h is the share of commodity i in the household group h’ consumption expenditure. Adding direct tax expenditures and transfers of payments of households to the rest of the world (THW), enterprises (THE) and the government (THG) on
consumption expenditure will give the total household expenditure (EH) as follows: Household saving is disposable income (DI) minus total household expenditures: HSAVh = DIh −EHh (26)

# **2.1.4 Modeling the government**

The total government revenues (YG) are generated through personal and corporate income taxes, excise taxes, import duties, and transfers from other economic agents (i.e., households, rest of the world).

YG= ∑[tmi i ⋅QMi ⋅PMWi ⋅EXR + txi ⋅QQi ⋅PQi] + ∑tki i. QKi ⋅PKi + tli⋅∑tli i. PLi ⋅ QLi + sek. ∑tei iPK⋅QKi+∑THGh h+TWG+CTREV + TEG (27) where tmi, txi, tki and tli are rates of import duty, excise tax, capital tax and labor tax respectively. sek is share of capital owned by enterprises and tei is corporate income tax rate. THG, TWG, and TEG are transfers from households, rest of world and enterprises to the government. CTREV is carbon tax revenue. The total government revenue is allocated for public expenditures, transfers to other agents, and public savings. The total government expenditure is fixed at the baseline level and allocated to various services (e.g., public administration) at the same proportion as in the base case. This means public expenditures, which mostly refer to expenditure on public administration, is kept at the level in the baseline.

where EG is government expenditure, QGi is consumption of good i by the government. TGH, TGW and TGE are the government’s transfer payments to households, foreign countries and enterprises. The government saving (GSAV) is given by: GSAV= YG−EG (29)

# **2.1.5. Modeling enterprises**

Enterprises get their income from capital rents and transfers as follows, YE= sek. ∑PK⋅QKi i + TGE+ ∑THEh h + TRE (30)
Where YE is enterprise income, sek is share of capital owned by enterprises, TGE, THE and TRE are transfers from government, households and foreign countries to enterprises. Enterprise’s expenditure which includes taxes and transfer payments, is computed as follows EExp= sek. ∑tei iPK⋅QKi + TEG+ ∑TEHh h + TEW (31) Where EExp is enterprise expenditure, te is the direct tax rate paid by enterprises and TEG, TEH and TEW are transfer payments from enterprises to the government, households and foreign countries. Finally, enterprise saving is the difference between enterprise income and expenditure, given as ESAV= YE−EExp (32)

# **2.1.6 Market clearing and macroeconomic closure**

A CGE model assumes an economy remains in equilibrium in terms of goods and services markets, factor markets and macroeconomic fronts. The closing rules for the commodity market is as given as follows: where QINVi is the demand for commodities i used as investment good. The factor market assumes that total demand for labor and capital is equal to their supplies.

QLS= ∑QLi i where QKS and QLS are total labor and capital supplies in the economy. The factor market assumes that total labor and capital demand equals their supplies. Labor and capital are mobile across sectors, but their total supply or endowment for a given year is fixed. Prices of labor (wage rate) and capital (user costs of capital) are different across the sectors. The macroeconomic balance suggests that the total value flow out of the country (due to imports) and into the country (due to exports and foreign borrowings) should be balanced. This condition determines the current account balance or surplus through the following relationship: ∑PWMi i ⋅QMi+ ∑PLi i ⋅QLi + TGW= ∑PWEi i ⋅QEi + ∑TWHh h + FSAV+ REMI (36 `)` where REMI is remittance earning which is linked to total labor income through a fixed ratio. FSAV is foreign savings, which is equal to the current account surplus if it is positive and the
current account balance if it is negative. The total investment is financed by the sum of all savings: TOTINV= ∑HSAVh h + GSAV+ ESAV+ EXR⋅FSAV+ WALRAS (37 `)` PQi ⋅QINVi = shareinvi ⋅TOTINV (38) where TOTINV is the total investment, WALRAS is the dummy variable, and shareinvi is the share of commodity i used as an investment in the total investment. The real GDP is calculated using the expenditure approach as follows: GDP= ∑(∑QHi h i,h + QGi+QINVi) + ∑QEi i −∑QMi i (39)

# **2.1.7 CO2 emissions and carbon tax**

CO2 emissions are calculated by multiplying fuel inputs in each production sector and final demand sectors by emission coefficients. CO2 emissions from sector i is calculated as: TCO2 = (∑QHh pp,h + QGpp+QINVpp + ∑QQi pp,i). coefpp (40) Where coefpp is CO2 emissions coefficient of petroleum products expressed in terms of ton of CO2 per unit monetary value of its consumption, carbon tax (CTAX) which is exogenous to the model, is converted to equivalent indirect tax ‘ctr’ as follows: ctrpp = CTAX. coefpp (41) This equivalent indirect tax rate is added to the existing indirect tax rate (indt). **2.2. Data** The main data source used for this study is the social accounting matrix (SAM) of Ethiopia. The SAM was built by the Policy Studies Institute of Ethiopia (PSI) using 2016 input-output tables and national accounts. It is available on request.<sup>6</sup> Other data needed are values for various parameters, such as elasticity of substitution used in CES functional forms used to model the 6 It can be argued that the analysis based on six-year-old data might not represent the economic reality of today in Ethiopia. However, SAMs are developed occasionally (such as at 5-year intervals) even in developed economies. Building a SAM more frequently in developing countries is challenging. Use of a SAM that is 5-10 years old is common practice in CGE modeling for two reasons. First, a SAM represents the structure of an economy, which is unlikely to change in a short period. Second, a CGE approach basically models the behavior of economic agents (consumers, producers). Behavioral change also requires a long period. The key findings of the model are unlikely to change even if a new SAM is built and used. Building a SAM is itself a data and time-intensive exercise.
production sector, and parameters used in the LES functional form to model the households. The values for elasticity of substitution are not available for Ethiopia. The only sources of these parameters are existing studies for developing countries which provide values for the elasticities of substitution. We use the values from Chen et al. (2013) and Mosa (2018). The parameters used in the LES function are taken from Mosa (2018) and Diao et al (2012). Since the values of elasticity of substitution and other parameters are taken from the literature, sensitivity analyses on these values should be conducted to validate the finding of the study. Other parameters such as household saving rates, rates for transfers between the agents, and distribution of total government consumption to individual categories are calibrated from the SAM.

# **3. Design of Scenarios**

The study considers a hypothetical carbon tax with a rate US$20/tCO2. We have developed one baseline scenario and four main scenarios depending on how the carbon tax revenue is recycled into the economy. These scenarios are defined in Table 2. A scenario comes up with multiple subscenarios or cases depending upon the criteria used to recycle the revenue under a given scenario. The selection of a single tax rate is guided by the objective of the study. The objective is to compare the distributional impacts of a carbon tax policy under the alternative schemes for revenue recycling. The rate may not be relevant for this purpose because the comparison is expected to hold unless the tax rates are altered by its multiples (e.g., two times, three times). In practice, tax rates are increased gradually overtime so that the economy can adjust to it. Moreover, if we use multiple rates, we will have too many cases because of the large number of alternative revenue recycling schemes considered in the study. It will introduce complications in the interpretation of the results and articulation of the main message.

**Table 2: Definition of Scenarios**

|**Scenario**|**Sub-Scenario**|**Definition**|
|---|---|---|
|**1. Government**<br>**use**|**(a) Like in base**<br>**case**|Carbon tax with tax revenues allocated in the same way the government<br>allocates its total revenues in the baseline|
||**(a) Investment**|Carbon tax with tax revenues allocated for investment|
|**2. Cash**<br>**transfer to**<br>**households**|**(a) Income**|Carbon tax with revenue rebated to different household income groups in<br>proportion to their income. A household income group with the highest<br>income gets thehighestrebate|
||**(b) Equal**|Carbon tax with revenue rebated to all household incomegroups equally|
||**(c) Reverse**<br>**income**|Carbon tax with revenue rebated to different household income groups in<br>inversely proportion to their income. A household income group with the<br>lowest incomegets the highest rebate|
|---|---|---|
|**3. Personal i**|**ncome tax cut**|Carbon tax with tax revenues recycled to cut personal income taxes of<br>employers in sectors excluding fossil fuels and thermal electricity<br>generation sectors|
|**4. Corporate**<br>**income tax**<br>**cut**|**(a) CO2**<br>**Intensity**|Carbon tax with tax revenues recycled to cut corporate income taxes<br>in sectors except for fossil fuels and thermal electricity generation<br>sectors; tax revenues are reallocated to a sector inversely proportional<br>to its CO2intensity (a sector with a higher emission-intensity gets<br>lower carbon tax revenues as the rebate)|
||**(b) Capital**<br>**account share**|Carbon tax with tax revenues recycled to cut corporate income as in<br>the previous case but tax revenues are reallocated to a sector in<br>proportion to its share of capital account to the total capital account (a<br>sector with a higher capital accountgets a higher rebate)|
||**(c) Export**<br>**intensity**|Similar to above two corporate income tax cut cases, but tax revenues<br>are reallocated to a sector based on its export intensity (a sector that<br>exports a higher volume of outputsgets a higher carbon tax rebate)|

Scenario 1 considers that the government uses the carbon tax revenue. It has two cases. In the first case, the government uses the revenue as it does with the rest of the revenues (“Like in the base case”). It means the government reallocates the carbon tax revenues for government expenditures, savings and transfers to other agents in the same proportions it does in the base case. In the second case, the carbon tax revenue is specifically used for public investment (“Investment”). Simulating scenario 1a does not need any change in the model described above. For the remaining scenarios, first, the increased revenue due to the carbon tax (YGAP) is calculated as follows: where YG0 is the government revenue in the base case. The intuition here is that government revenue is kept neutral to the carbon tax, this is because the government is not supposed to lose its revenue because of the carbon tax. The government might want to keep the incremental revenue and allocate it like in the baseline (Scenario 1), but there would be more economic loss doing so. Therefore, alternative schemes are considered for revenue recycling. But only revenue in excess of the baseline is recycled. Under the Scenario 1b (‘Investment’), the incremental revenue is added to the capital supply and Equation (35) is revised as follows: QKS+ YGAP= ∑QKi i
One could also implement this scenario by adding the YGAP in total investment in Equation (37). Doing so, however, can be interpreted as paying the foreign debt (or lowering foreign savings) instead of injecting the incremental revenue into the production process because foreign saving is endogenous in the model. If foreign savings is exogenous to the model, the investment scenario can be incorporated by adding incremental revenue to Equation (37).

In the second scenario (“Cash Transfers to Households”), the government gives the carbon tax revenues to households as a cash transfer. This is the most important scenario for analyzing the distributional effects of a carbon tax. There could be several ways how the cash transfers are actually implemented. We have considered three cases here. In the first case (Scenario 2a), the carbon tax revenue is rebated to different household income groups in proportion to their income (“Income” case). It means a household income group with the highest income gets the highest rebate. This case is more favorable to rich households and the results would be regressive, yet we consider it to examine the economy-wide impacts even though it may not be a desirable scenario from a distributional perspective. It is implemented by revising Equation (23) as follows: Under Scenario 2b, carbon tax revenue is transferred to all households equally (“Equal” case). This case is likely to be progressive (which we will confirm in the model simulation to be presented in the result section later) as the poorer households get relatively higher rebates than the richer households. This is because the recycled revenue accounts for a higher share of the total household income for lower-income households as compared to that in higher-income households. To incorporate this scenario, Equation (23) is revised as follows: Under Scenario 2c, the carbon tax revenues will be recycled to different income groups of households inversely proportional to their income (“Reverse Income” case). This means poor households receive more revenue (in absolute term) than rich households. This scenario is incorporated by revising Equation (23) as follows: In Scenario 3 (“Personal Income Tax Cut” scenario), carbon tax revenues are used to cut
personal income taxes paid by the labor force employed in productive sectors except fossil fuel and thermal electricity sectors. To incorporate this scenario, we first calculate a rate that will be used to adjust the wage rate and user cost of capital. The rate is calculated as follows: Labor price, PL and, capital price, PK are then adjusted by multiplying by (1 −pitcr) throughout the model. In the final set of scenarios, the carbon tax revenue is used to cut corporate income taxes of the firm (“Corporate Income Tax Cut” case). We have three cases under this scenario based on how the carbon tax revenues are reallocated to firms to cut their corporate income taxes. Under Scenario 4a (“CO2 Intensity” case), the carbon tax revenue is recycled to non-fossil fuel sectors inversely proportional to their CO2 intensities. This means a sector with a higher CO2 emission intensity gets a lower carbon tax revenue as a rebate. The rebate rate used to adjust corporate income tax etri adjusted for sectoral carbon intensity weights as YGAP etri = carbii. (48) (sek.QKiPKi) Where carbii is a weight computed from the SAM and used to adjust the rebate for all non-energy sectors according to their carbon intensity such that sectors with higher carbon intensity have a lower rebate then etri is used to adjust corporate tax expenditure in Equation 31 as follows: EExp= sek. ∑(te−etri i). PK⋅QKi + TEG+ ∑TEHh h + TEW (49) In Scenario 4b (“Capital account share” case), carbon tax revenue is recycled to non-fossil fuel sectors in proportion to their capital accounts. A sector with a higher capital account gets a higher rebate in this scenario, the tax rebate for corporates is computed as: QKiPKi YGAP etri = . (50) ∑QKi iPKi (sek.QKiPKi) In the last case (“Export Intensity” case), the carbon tax revenue is recycled to non-fossil fuel sectors in proportion to exports of their outputs. In this case, more export-oriented sectors get higher rebates from carbon tax revenues. Altogether, we have 9 cases. In all cases, we follow government budget neutrality, meaning that only the incremental government revenue due to the carbon tax is recycled.

Then, etr is used to modify equation 31 in the same way as 4a and 4b.
# **4. Results and Discussion**

## **4.1 The distributional impacts**

The normal indicator to measure the distributional impacts of a policy by the householdincome group is the change in their income due to the policy. Table 3 presents the percentage change in household income by quantile due to the carbon tax. There are several important observations discussed below.

**Table 3. Impacts of the carbon tax on household income by quantile**

(% change from the baseline)

|Scenario|Case|HH1<br>(Lowest<br>income)|HH2|HH3|HH4|HH5<br>(Highest<br>income)|
|---|---|---|---|---|---|---|
||(a) Like in the base|-0.06|-0.11|-0.04|-0.06|0.00|
|(1) Government|case||||||
|<br>use|(b) Investment|0.29|0.28|0.30|0.33|0.39|
||(a) Income|0.04|0.01|0.11|0.05|0.06|
|(2) Cash transfer to|(b) Equal|0.21|0.00|-0.25|-0.28|-0.59|
|<br>households|(c) Reverse income|1.22|0.94|0.43|0.49|0.05|
|(3) Personal income|tax cut|0.25|0.04|0.02|0.07|0.08|
||(a) Inversely<br>proportional to<br>CO2intensity|1.55|1.57|1.45|1.61|1.69|
||(b) Capital account|-0.10|-0.12|-0.06|-0.07|-0.02|
|(4) Corporate|share||||||
|<br>income tax cuts|(c) Export intensity|-0.09|-0.10|-0.04|-0.05|0.00|

The households gain from the carbon tax under some scenarios, whereas they lose under other scenarios. All groups of households gain when (a) carbon tax revenue is used for investment, (b) when it is transferred to households as a lump-sum rebate (i.e., cash transfer) based on their income or reverse income, (c) when the carbon tax revenue is used to cut personal income taxes and (d) when more carbon tax revenue is recycled to less emission-intensive industries (or inversely proportionally to their CO2-intensity) to cut their corporate income taxes. On the other hand, all groups of households will experience income loss due to the carbon tax (a) when the government uses carbon tax revenue for public consumption and saving as in the base case, (b) when carbon tax revenue is recycled to industries to cut their corporate income taxes based on their export-intensity and (c) when the carbon tax revenue is recycled to industries to reduce their
corporate income tax and the recycling is based on the amount of corporate income tax they pay. In the rest of the cases, some groups of households gain, whereas others lose.

While the impacts of carbon taxes on different groups of households under some scenarios (e.g., Scenarios 1a, 1b 2a, 2c) are intuitive, the results in other cases need explanation. Figures 2 and 3 help to explain these results. Figures 2a, 2b and 2c present, respectively, CO2 intensity of various goods and services in Ethiopia, corporate income tax paid by various sectors and export intensity of goods and services. Figures 3(a) – 3(e) present the consumption structure of households by income group. We have considered 20 goods/services in the model. Of which, 10 goods and services account for 99% of the total household consumption in the case of the lowest income group or household income group 1 (Figure 3a). Of the total household expenditure of the low-income group (HH1), almost 70% goes to food (56% for primary agriculture products, 8% for processed food or food products, beverages and tobacco and 5% for livestock and fisheries). On the other hand, food accounts for only 40% of the total household expenditures of the high-income group (HH5). This group spends 17% on financial services and real estate (FSRE), whereas the lowest-income group spends only 3% on FSRE. Of 20 commodities, 13 accounts for 99% of the total household consumption.

The carbon tax causes income to rise in all groups of households under Scenario 4a because low-emission intensive goods and services (Figure 2a) account for more than 95% of the total household consumption in all household groups (Figures 3a-3e). Since the production of these goods and services become relatively cheaper due to the carbon tax revenue recycling, households’ real income increases. On the other hand, only half of the total carbon tax rebate goes to goods and services that account for more than 90% of the total household expenditure of each household group under Scenario 4b and 4c. The other half of the total carbon tax rebate goes to goods and services that account for less than 10% of the total household expenditure by each group of households under these scenarios. Therefore, the household income impacts of the carbon tax are negative in these scenarios for all household groups.

The carbon tax is regressive under most scenarios. However, it is found progressive when the carbon tax revenue is transferred to households either equally or inversely proportional to their income. A carbon tax is considered regressive when it favors high-income households relative to low-income ones. It occurs under three conditions. First, if the carbon tax causes an increase in income for all household groups, the percentage of income increase of richer households is higher
than that of poorer households. Second, if the carbon tax causes all income groups of households to experience income loss, the percentage of income loss of the poorer households is greater than that of, the richer groups. Third, when the carbon tax increases the income of high-income households and reduces the income of low-income households. Results presented in Table 3 illustrate these conditions of the regressivity of the carbon tax. A carbon tax is considered progressive if each of these conditions are reversed. There are some exceptions or irregularities in some of the cases. These might be caused due to data problems. This finding implies that carbon tax could remain regressive unless the carbon tax revenue is recycled targeting low-income households.

# **Figure 2. Criteria used to recycle carbon tax revenue to cut corporate income tax under Scenario 4**

# **(a) CO2 intensity (tones CO2/million local currency) used in Scenario 4a**

<!-- Start of picture text --> 47.08<br>18.55 18.02<br>9.89<br>4.85 4.16 2.91 1.92 1.52 1.51 1.23 0.98 0.71 0.16 0.08 0.04 0.04 0.02 0.00<br><!-- End of picture text -->
# **(b) Capital account share (%) used in Scenario 4b**

<!-- Start of picture text --> 36.9<br>20.3<br>10.3<br>9.0<br>7.6<br>3.3 2.9 2.0 1.2 1.0 0.9 0.8 0.8 0.7 0.7 0.7 0.4 0.3 0.2<br>(c) Export intensity (%) used in Scenario 4c<br>40.7<br>23.9<br>14.6 13.6<br>10.2<br>6.5 5.4<br>3.6<br>1.6 1.4 1.1 0.7 0.3 0.3 0.3 0.1 0.0 0.0 0.0<br><!-- End of picture text -->
**Figure 3. Household consumption structures in the base year (%)**

# **(a) Lowest income group (HH1) (b) Lower-middle income group (HH2)**

<!-- Start of picture text --> 0.60 0.60<br>0.50 0.50<br>0.40 0.40<br>0.30 0.30<br>0.20 0.20<br>0.10 0.10<br>0.00<br>0.00<br>(c) Middle-income group (HH3) (d) Upper-middle income group (HH4)<br>0.60<br>0.50 0.60<br>0.40 0.50<br>0.30 0.40<br>0.30<br>0.20<br>0.20<br>0.10<br>0.10<br>0.00 0.00<br>(e) High-income group (HH5)<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Water<br>fishery Financial...<br>Agriculture products Livestock & & paper equipment Agriculture Chemicals,...<br>Machinery & Livestock &...<br>Forest & wood Food & beverage Other services Financial services & real estate Chemicals, pulp Textile & leather Welfare services Forest & wood... Other services Food & beverage Textile & leather Machinery &... Transport service Welfare services<br>Water Trade Water<br>Financial... Financial...<br>Agriculture Forest & wood... Other services Livestock &... Food & beverage Chemicals, pulp... Textile & leather Machinery &... Transport service Welfare services Agriculture Other services Forest & wood... Livestock &... Food & beverage Chemicals,... Textile & leather Transport service Welfare services Machinery &...<br>Trade Water<br>Agriculture Forest &... Food &... Financial... Chemicals,... Transport...<br>Livestock &... Machinery &...<br>Other services Textile & leather Welfare services<br><!-- End of picture text -->
The impacts of carbon tax differ across income groups based on how the carbon tax revenue is recycled to the economy. For example, under Scenario 2b where carbon tax revenues are distributed equally to each group of households, the poorest households experience income gain, whereas middle-income and high-income households experience income loss. Even though the carbon tax revenue received by each household group is equal, it would be relatively higher compared to the poorest households' household income. In contrast, it would not be that high as compared to the household income of other groups of households. Under the scenarios 4b and 4c, the household income loss of wealthier households would be lower than those of poorer households because the goods and services with higher household expenditure share in wealthier households gets a higher amount of carbon tax revenues than that of poor households. Under Scenario 4b, goods and services that receive 76% of the carbon tax revenues allocated for them (or 31.2% of the total carbon tax revenue recycled to the economy) account for 61% of the household expenditure of the richest households. On the other hand, the poorest households receive only 48% of the carbon tax revenues allocated for them (or 4.8% of the total carbon tax revenue recycled to the economy) for goods and services, accounting for 99% of their total household expenditure.

The detailed results on the impacts of the carbon tax on household consumption of each good and service by each income group of households under all scenarios are provided in Appendix A. Here we briefly discuss the key findings. The household expenditure structures of each income group presented in Figure 3 help to understand the impacts on the household consumption of individual goods and services. The impact of the carbon tax on the household consumption of a given commodity would be different (a) across the household income groups for the same revenue recycling scheme and (b) across the recycling schemes for the same household group. The difference is not limited to the magnitude of the impacts but also on its direction. The structure of household consumption (Figure 3) has important implications on the impacts of the carbon tax on the household consumption of goods and services under the alternative revenue recycling schemes. For example, the consumption of primary agricultural products increases under all three cases of cash transfers (Scenario 2) for household income groups 1 to 4. But high-income households’ consumption of this commodity decreases when the cash transfer follows the reverse income rule or Scenario 2c (see Table A5 in Appendix A). This is because the higher the household income of a group the lower the cash transfers it gets under this scenario. Similarly, the high-
income group’s consumption of welfare services that includes education, healthcare etc., decreases in all tax revenue recycling cases except when the tax revenue is recycled to households as a cash transfer in proportion to their income (Scenario 2a) or used for cutting personal income taxes (Scenario 3). In these two revenue recycling schemes, high-income group of households receives relatively higher transfers of carbon tax revenue either directly (Scenario 2a) or through their personal income tax cuts (Scenario 3). Thus, their real income increases, which in turn increases their consumption of welfare services. When tax revenue is recycled to households equally, the carbon tax causes an increase in the household consumption of textiles and leather for household income groups 1 and 2, whereas it decreases for the rest of the group (Appendix A).<sup>7</sup>

# **4.2 Distributional impacts versus economic impacts**

One of the key concerns regarding the impacts of the carbon tax is the trade-off between the equity (i.e., distribution by income) and efficiency effects of a carbon tax. This issue is prominent in developing economies like Ethiopia because they would like to achieve rapid and, at the same time, equitable economic growth. Therefore, it is important to investigate how a carbon tax's efficiency and distributional effects interact. Figure 4 presents the impacts of the carbon tax on GDP to illustrate the efficiency effects of the carbon tax. While the carbon tax with revenue transferred to households either equally or inversely proportional to their incomes is progressive (Table 3), the cash transfer scheme is found inferior to other schemes in terms of their economic impacts (Figure 4). The cash transfer options cause the highest reduction of GDP compared to other options. Moreover, the ‘Reverse income’ cash transfer scheme, where carbon tax revenue is transferred to households inversely proportional to their incomes and which is progressive from the distributional perspective, is the worst scheme in terms of its impact to the economy (i.e., highest reduction of GDP). The contradicting findings between the distributional and economic impacts are also observed under other revenue recycling schemes. For example, the carbon tax with revenues recycled to investment is regressive in terms of distributional effect (Table 3), but it is more efficient in terms of economic impacts compared to several revenue recycling options considered. The same is true when carbon tax revenue is recycled to cut existing personal income

> 7 Interested readers could have a close look at Tables A1 to A5 for more findings on the impacts of carbon tax on household consumption of goods and services.
tax rates. Under this case, there is a slight rise of GDP, but this revenue recycling option is also favorable to richer households if we ignore its anomalous impact on the poorest households.

**Figure 4. Impacts of carbon tax on GDP (% change from the base case)**

<!-- Start of picture text --> 0.002<br>-0.078 -0.076 -0.076<br>-0.085 -0.087 -0.085 -0.083<br>-0.091<br>Like in base Investment Income Equal Reverse Inversely Capital Export<br>case income proportional account share intensity<br>to CO2<br>intensity<br>Government use Cash transfer to households Personal Corporate income tax cuts<br>income tax<br>cut<br><!-- End of picture text --> Which revenue recycling scheme is the most appropriate for a given economy depends upon the specific economic conditions of that economy. For example, Timilsina et al. (2022) find that recycling carbon tax revenue to cut the corporate income tax rate of non-fossil fuel industries would be best for China when the carbon tax revenue is recycled based on export intensities of the industries. Since the Chinese economy is a highly export-based economy, the finding seems intuitive. However, the Ethiopian economy is not like the Chinese economy, therefore, the best revenue recycling scheme is different for Ethiopia from that for China. Figure 3 illustrates that recycling the carbon tax revenue to cut personal income taxes would be the best (or most efficient) revenue recycling scheme for Ethiopia from an economic perspective as it can increase the GDP. Similar findings are reported in many existing studies (McKibbin et al. 2015; Jorgenson et al. 2015).<sup>8</sup> It would also be relevant to examine how would the results of carbon tax change (a) between the sectors and (b) between the revenue recycling schemes (or scenarios considered). The detailed impacts of the carbon tax on sectoral outputs are presented in Appendix B. Sectoral

> 8 Timilsina (2022) discusses several studies reporting similar results.
impacts for a given revenue recycling scheme would differ due to differences in their fossil fuel inputs. Emission-intensive sectors, such as transportation, chemicals (including pulps and paper), metallic products & machinery, would experience more than 4% output loss under all scenarios. Less emission sector, such as agriculture, livestock, and welfare services, would experience relatively lower losses in their outputs. The sectoral output of a given sector differs across the scenarios because of different interactions of recycled revenues in the production process in that sector. For example, the output from the agricultural sector increases under Scenarios 2 and 3, but it decreases under Scenarios 1 and 4. This is because households receive cash transfers under Scenario 2 and a cut in their income tax under Scenario 3. It leads to an increase in their real income, which could result in increased consumption of agricultural products, which ultimately causes an increase in agriculture production. This phenomenon does not occur under the rest of the two scenarios. Another example is the chemical/pulp/paper sector. This sector receives the lowest carbon tax rebate because of its high CO2 emission intensity when carbon tax revenue is recycled to non-energy production sectors inversely proportional to their emission intensity (Scenario 4a). The negative impacts on its output under Scenario 4b would be lower than that under Scenario 4a because carbon tax revenue is recycled in proportion to capital account shares in the total capital account under former case, and the capital account share of chemical/pulp/paper is relatively high. It causes the sector to receive a higher carbon tax revenue to cut its corporate income taxes.

# **4.3 Distributional impacts versus environmental impacts**

Since one of the main objectives of a carbon tax is reducing CO2 emissions, it is therefore important to examine how the distributional impacts of carbon tax interact with CO2 reductions. Figure 5 presents percentage reductions of CO2 emissions due to a carbon tax of US$20/tCO2. For the same carbon tax, the CO2 reductions are significantly varying because of different interactions of revenue recycling schemes to the economy. Note that fossil fuel sectors are excluded from receiving benefits from carbon tax revenues, otherwise, the reductions of CO2 emissions would have been smaller further, as reported by Timilsina et al. (2022).

One of the most important findings is that the carbon tax scheme, which is progressive from the distributional perspective (when the tax revenue is recycled to households as a cash
transfer under Scenarios 2b and 2c) causes higher reductions of CO2 emissions. It implies that the distributional (equity) and environmental objectives are aligned in Ethiopia. Emission reductions are relatively smaller when the carbon tax revenue is used for investment or cutting corporate income taxes. Note that these schemes are more efficient economically (increased GDP or lower GDP loss). Recycling carbon tax revenues to cut personal income tax, which causes GDP increase, also causes relatively higher reductions of CO2 emissions.

**Figure 5. Impacts of carbon tax on CO2 emissions (% change from the base case)**

<!-- Start of picture text -->
-6.86<br>-7.08<br>-7.15 -7.15<br>-7.42<br>-7.58 -7.58 -7.56<br>-7.64<br>Like in base Investment Income Equal Reverse Inversely Capital Export<br>case income proportional account share intensity<br>to CO2<br>intensity<br>Government use Cash transfer to households Personal Corporate income tax cuts<br>income tax<br>cut<br><!-- End of picture text -->

# **4.4 Sensitivity analysis**

Several parameters used in the model, particularly, elasticities substitution used in the CES production functions to model production behavior, are used from the literature as discussed earlier. We conduct sensitivity analysis to confirm the key findings of the analysis hold. There could be a large number of sensitivity analyses by changing the values of elasticity of substitution in different sectors at different level. However, presenting too many sensitivity analysis results might distract the flow of paper. Therefore, we limit our sensitivity analysis in seleted scenarios/cases for demonstration purpose. We increase the values of all elasticities of substitution in the CES functional forms used in the production sectors and import-domestic products trade off by 20%. The results of model simulations are presented in Tables 4a and 4b.
**Table 4. Sensitivity analysis results vs. main analysis results**

(a) Percentage change in GDP from the baseline

|Scenario/Case|Main analysis|Sensitivity analysis|
|---|---|---|
|Government use forpublic investment(Scenario 1,Case b)|-0.078|-0.096|
|Equal lump-sum transfer to all households (Scenario 2,<br>Case b)|-0.085|-0.098|
|Lower corporate income taxes of firms with CO2 intensity<br>(Scenario 4, Case a)|-0.083|-0.097|

## (b) Percentage change in household income from the baseline

|Scenario/Case|Lowest incom<br>Main<br>analysis|e households<br>Sensitivity<br>analysis|Highest incom<br>Main analysis|e households<br>Sensitivity<br>analysis|
|---|---|---|---|---|
|Government use for public investment (Scenario<br>1, Case b)|0.29|0.30|0.39|0.39|
|Equal lump-sum transfer to all households<br>(Scenario 2, Case b)|0.21|0.22|-0.59|-0.56|
|Lower corporate income taxes of firms with<br>CO2 intensity (Scenario 4, Case a)|1.55|2.07|1.69|2.21|

As indicated by the sensitivity analysis results, there is slight change in GDP impacts and household income impacts when values of elasticities of substitution are increased by 20%. The key findings, however, hold. First, the direction of the impacts does not change, and secondly, the order or ranking of alternative revenue recycling schemes does not change. The GDP impacts presented in Table 4(a) indicate that the ranking or ordering of the revenue recycling schemes are the same both in the main analysis and the sensitivity analysis. The same is true in the case of household income (Table 4b). Based on the results from sensitivity analysis we gain the confidence that the findings of the study are robust.

# **5. Conclusions and Policy Implications**

This study analyzes the distributional impacts of a hypothetical carbon tax in Ethiopia using a static CGE model. It is one of the very few studies examining the distributional impacts of a carbon tax in the context of developing economies. It also examines the interactions of the distributional impacts with the economic efficiency and environmental performance of the carbon tax. It derives some interesting policy implications which are not available in the existing literature.

First, the distributional impacts of a carbon tax are highly sensitive to its design architectures, particularly the schemes to recycle the revenue to the economy. Our study finds that
all household groups gain income from the carbon tax under some schemes for revenue recycling (e.g., recycling the tax revenues for investment), whereas the income impacts are uneven not only in magnitude but also in direction under other schemes of revenue recycling. The carbon tax would be regressive unless it is made progressive by recycling the tax revenue to households either equally (relatively higher transfers to low-income households) or inversely proportional to their income (transferring higher revenues in absolute term to low-income households).

The distributional impacts (i.e., equity implications) of the carbon tax are aligned with its environmental performance in Ethiopia. When the tax revenue is recycled to households either equally or inversely proportional to their incomes, the carbon tax benefits low-income households more than high-income households. The same schemes also cause higher reductions of CO2 emissions ensuring better environmental performance. Our study also finds that the efficiency objective of a carbon tax contradicts its equity and environmental objectives. The carbon tax design architecture, which is economically efficient by either increasing GDP (when tax revenue is recycled to cut personal income tax rate), is regressive as well as environmentally inferior (i.e., low reduction of CO2 emissions).

While our finding – carbon tax could result in a GDP increase when the carbon tax revenue is recycled to cut the personal income tax rate – is consistent with the findings from existing studies, such as McKitrick (1997), van Heerden et al. (2006), the gain is very small. Moreover, one could argue that in many countries with the predominant existence of an informal economy, recycling tax revenues to cut indirect tax may not necessarily help a large segment of the population. On the other hand, a carbon tax with revenue used to further incentivize the formal economy could contribute to reducing the economic informality as demonstrated in Timilsina et al. (2021). Further investigations and debates are welcome on these issues.

# **References**

Alonso, Cristian and Joey Kilpatrick (2022). The Distributional Impact of a Carbon Tax in Asia and the Pacific. Working Paper # WP/22/116, International Monetary Fund, Washington, DC.

- Aldy, Joseph E., Alan J. Krupnick, Richard G., Newell, Ian W. H Parry, and William A. Pizer (2010). Designing Climate Mitigation Policy. Journal of Economic Literature, Vol. 48 (4), pp. 903-34.
Böhringer, Christoph, Sonja Peterson, Thomas F. Rutherford, Jan Schneider, Malte Winkler (2021). Climate policies after Paris: Pledge, Trade and Recycle: Insights from the 36th Energy Modeling Forum Study (EMF36). Energy Economics, Vol. 103: 105471.

Chen, Y.-H., Govinda Timilsina, Florin Landis (2013). Economic Implications of Reducing Carbon Emissions from Energy Use and Industrial Processes in Brazil, Journal of Environmental Management, Vol. 130, pp. 436-446.

Diao, Xinshen, James Thurlow, Samuel Benin, Shenggen Fan (2012). (eds.) Strategies and Priorities for African Agriculture: Economywide Perspectives from Country Studies. International Food Policy Research Institute (IFPRI), Washington, DC.

Dissou, Yazid and Muhammad Shahid Siddiqui (2014). Can Carbon Taxes be Progressive? Energy Economics, Vol. 42(c): 88-100.

Federal Democratic Republic of Ethiopia (FDRC) (2021). Updated National Communication. Climate Change Commission of the FDRC, Addis Ababa.

Garaffa, Rafael, Bruno S.L. Cunha, Talita Cruz, Paula Bezerra, Andr ́e F.P. Lucena and Angelo C. Gurgel (2021). Distributional effects of carbon pricing in Brazil under the Paris Agreement. Energy Economics, Vol. 101: 105396.

Goulder, Lawrence H., Marc A.C. Hafstead, GyuRim Kim and Xianling Long (2019). Impacts of a carbon tax across US household income groups: What are the equity-efficiency tradeoffs? Journal of Public Economics, Vol. 175, pp. 44–64.

Jorgenson, Dale W., Richard Goettle, Mun S. Ho and Peter J. Wilcoxen (2015). Carbon Taxes and Fiscal Reform in the United States. National Tax Journal, Vol. 68 (1), pp. 121-37.

McKibbin, Warwick J., Adele C. Morris, Peter J. Wilcoxen and Cai, Yiyong (2015). Carbon Taxes and U.S. Fiscal Reform. National Tax Journal, Vol. 68 (1), pp. 139-55.

- McKitrick, Ross (1997). Double Dividend Environmental Taxation and Canadian Carbon Emissions Control. Canadian Public Policy, Vol. 23 (4), pp. 417-434.

Mosa, Abdulaziz A. (2018). Non-agricultural Activities and Household Time Use in Ethiopia: A Computable General Equilibrium Model Analysis, Ph.D Dissertation, University of Hohenheim, Stuttgart, Germany.

Ohlendorf, Nils, Michael Jakob, Jan Christoph Minx, Carsten Schröder and Jan Christoph Steckel. (2021). Distributional Impacts of Carbon Pricing: A Meta-Analysis. Environmental and Resource Economics 78: 1–42.
Rausch, Sebastian, Gilbert E. Metcalf and John M. Reilly (2011). Distributional impacts of carbon pricing: A general equilibrium approach with micro-data for households. Energy Economics 33 (Supplement 1): S20-S33.

Steckel, Jan C., Ira I. Dorband, Lorenzo Montrone, Hauke Ward, Leonard Missbach, Fabian Hafner, Michael Jakob and Sebastian Renner (2021). Distributional impacts of carbon pricing in developing Asia. Nature Sustainability 4:1005–1014.

Telaye, Andualem, Pablo Benitez, Seneshaw Tamru, Haileselassie Medhin, Michael Toman (2019). Exploring Carbon Pricing in Developing Countries: A Macroeconomic Analysis in Ethiopia.

World Bank Policy Research Working Paper, WPS 8860. World Bank Washington, DC. Timilsina, Govinda R. (2022). Carbon Taxes. Journal of Economic Literature, Vol. 60 (4), pp. 1456–1502.

Timilsina, Govinda R., Jin Cao and Mun Ho (2018). Carbon Tax for Achieving China's NDC: Simulations of Some Design Features using a CGE Model. Climate Change Economics. Vol.9, No. 3, pp. 1-17.

Timilsina, Govinda R., Jun Pang and Yuanhua Chang (2022). Economic Impacts of Meeting China’s NDC through Carbon Taxes with Alternative Schemes for Recycling Tax Revenues. World Bank Policy Research Working Paper #10087, World Bank, Washington, DC.

Timilsina, Govinda R., Yazid Dissou, Mike Toman and Dirk Heine (2021). Carbon Tax in an Economy with Informality: A Computable General Equilibrium Analysis for Côte d’Ivoire”. World Bank Policy Research Working Paper, WPS 9710. World Bank Washington, DC.

van Heerden, J., R. Gerlagh, J. Blignaut, M. Horridge, S. Hess, R. Mabugu and M. Mabugu (2006). Searching for Triple Dividends in South Africa: Fighting CO2 Pollution and Poverty while Promoting Growth. The Energy Journal, Vol. 27 (2), pp. 113-141.

Williams, Roberton C., III, Hal Gordon, Dallas Burtraw, Jared C. Carbone, and Richard D. Morgenstern (2015). The Initial Incidence of a Carbon Tax across Income Groups. National Tax Journal 68 (1):195-213.

World Bank (2022a). World Development Indicator Database. <u>https://databank.worldbank.org/home Retrieved on August 25, 2022.</u> World Bank (2023). States and Trends of Carbon Pricing 2023. World Bank, Washington, DC.
Wu, Libo, Shuaishuai Zhang and Haoqi Qian (2021). Distributional effects of China's National Emissions Trading Scheme with an emphasis on sectoral coverage and revenue recycling. _Energy Economics_ , Vol. 101: 105770.
# **Appendix A: Household Consumption (% change from the baseline)**

# **Table A1. Lowest-Income Households (Household Income Group 1)**

||Govern|ment use|Cash tra|nsfer to ho|useholds<br>|Personal<br>income|Corpor<br>|ate income<br>|tax cut<br>|
|---|---|---|---|---|---|---|---|---|---|
||Base|Invest|Income|Equal|Reverse<br>income|<br>tax cut|CO2<br>intensity|Tax<br>volume|Export<br>intensity|
|Agriculture|-0.22|-0.30|0.36|0.90|1.05|0.35|-0.27|-0.27|-0.30|
|Livestock & Fishery|-0.35|-0.41|0.24|0.78|0.95|0.24|-0.39|-0.39|-0.41|
|Forestry & wood|-0.58|-0.68|0.01|0.56|0.68|-0.02|-0.64|-0.65|-0.68|
|Food & beverage|-0.96|-1.04|-0.37|0.37|0.76|-0.30|-1.06|-1.06|-1.04|
|Textile & leather|-0.83|-0.89|-0.36|0.34|0.76|-0.26|-0.94|-0.93|-0.89|
|Petroleum products|-0.11|-0.11|0.07|0.51|0.88|0.19|-0.18|-0.18|-0.12|
|Chemical, Pulp &<br>paper|-0.65|-0.68|-0.25|0.45|0.97|-0.11|-0.76|-0.75|-0.68|
|Minerals|-2.32|-2.40|-1.77|-1.05|-0.67|-1.69|-2.42|-2.42|-2.40|
|Metals|-0.22|-0.24|0.12|0.81|1.38|0.29|-0.33|-0.33|-0.24|
|Machinery|-0.38|-0.40|-0.03|0.67|1.22|0.14|-0.49|-0.48|-0.40|
|Electricity|-1.23|-1.27|-0.86|-0.38|-0.11|-0.80|-1.29|-1.29|-1.27|
|Water|-1.30|-1.35|-0.90|-0.42|-0.18|-0.86|-1.36|-1.36|-1.35|
|Construction|-1.04|-1.12|-0.31|0.52|0.92|-0.25|-1.13|-1.13|-1.12|
|Trade|-0.66|-0.90|0.28|1.06|1.07|0.15|-0.78|-0.79|-0.90|
|Transport|-2.34|-2.37|-2.01|-1.58|-1.32|-1.95|-2.39|-2.39|-2.37|
|Government|-0.85|-0.94|-0.06|0.72|1.02|-0.04|-0.92|-0.92|-0.94|
|Financial services &<br>real estate|-0.37|-0.46|0.22|0.86|1.11|0.24|-0.45|-0.45|-0.46|
|Welfare services|-0.56|-0.63|0.20|0.97|1.31|0.24|-0.63|-0.63|-0.63|
|Other services|-0.41|-0.54|0.32|1.11|1.40|0.34|-0.52|-0.52|-0.54|
|**Total**|-0.39|-0.48|0.19|0.78|0.98|0.19|-0.46|-0.46|-0.48|
# **Appendix A (Continue): Household Consumption (% change from the baseline)**

# **Table A2. Lower-Middle Income Households (Household Income Group 2)**

||Govern|ment use|Cash tra|nsfer to ho|useholds<br>|Personal<br>income|Corpora<br>|te income t<br>|ax cut<br>|
|---|---|---|---|---|---|---|---|---|---|
||Base|Invest|Income|Equal|Reverse<br>income|<br>tax cut|CO2<br>intensity|Tax<br>volume|Export<br>intensity|
|Agriculture|-0.29|-0.35|0.34|0.73|0.84|0.34|-0.33|-0.33|-0.35|
|Livestock & Fishery|-0.44|-0.47|0.21|0.60|0.73|0.23|-0.45|-0.45|-0.47|
|Forestry & wood|-0.68|-0.76|-0.04|0.36|0.44|-0.05|-0.73|-0.73|-0.76|
|Food & beverage|-1.11|-1.15|-0.47|0.06|0.39|-0.37|-1.18|-1.18|-1.16|
|Textile &leather|-0.91|-0.93|-0.42|0.03|0.39|-0.30|-0.98|-0.98|-0.93|
|Petroleum products|-0.19|-0.16|0.04|0.35|0.73|0.19|-0.24|-0.23|-0.16|
|Chemical, Pulp &paper|-0.58|-0.57|-0.25|0.11|0.46|-0.12|-0.63|-0.63|-0.57|
|Minerals|-1.89|-1.92|-1.44|-1.07|-0.83|-1.37|-1.94|-1.94|-1.92|
|Metals|-0.24|-0.22|0.04|0.39|0.78|0.19|-0.30|-0.29|-0.22|
|Machinery|-0.36|-0.34|-0.08|0.27|0.66|0.07|-0.42|-0.41|-0.35|
|Electricity|-1.37|-1.39|-0.96|-0.63|-0.39|-0.89|-1.41|-1.41|-1.39|
|Water|-1.44|-1.47|-1.01|-0.67|-0.46|-0.95|-1.48|-1.48|-1.47|
|Construction|-0.87|-0.89|-0.30|0.12|0.37|-0.23|-0.91|-0.91|-0.89|
|Trade|-0.66|-0.84|0.20|0.68|0.63|0.09|-0.75|-0.75|-0.84|
|Transport|-2.71|-2.71|-2.33|-2.01|-1.76|-2.25|-2.74|-2.74|-2.71|
|Government|-0.66|-0.69|-0.09|0.28|0.44|-0.06|-0.69|-0.69|-0.69|
|Financial services &<br>real estate|-0.32|-0.36|0.12|0.43|0.58|0.15|-0.36|-0.36|-0.36|
|Welfare services|-0.46|-0.48|0.09|0.46|0.65|0.14|-0.48|-0.48|-0.48|
|Other services|-0.35|-0.41|0.18|0.55|0.71|0.21|-0.40|-0.40|-0.41|
|**Total**|-0.48|-0.54|0.11|0.50|0.66|0.13|-0.52|-0.52|-0.54|
# **Appendix A (Continue): Household Consumption (% change from the baseline)**

# **Table A3. Middle Income Households (Household Income Group 3)**

||Govern|ment use|Cash t|ransfer to h|ouseholds|Personal|Corpor|ate income|tax cut|
|---|---|---|---|---|---|---|---|---|---|
|||||||income||||
||Base|Invest|Income|Equal|Reverse<br>income|<br>tax cut|CO2<br>intensity|Tax<br>volume|Export<br>intensity|
|Agriculture|-0.24|-0.31|0.45|0.44|0.38|0.37|-0.27|-0.27|-0.30|
|Livestock & Fishery|-0.39|-0.43|0.32|0.31|0.27|0.26|-0.39|-0.40|-0.43|
|Forestry & wood|-0.64|-0.73|0.07|0.07|-0.03|-0.03|-0.68|-0.68|-0.73|
|Food & beverage|-1.10|-1.15|-0.34|-0.39|-0.30|-0.34|-1.16|-1.16|-1.15|
|Textile &leather|-0.72|-0.75|-0.25|-0.30|-0.18|-0.23|-0.77|-0.77|-0.75|
|Petroleum products|-0.17|-0.14|0.18|0.10|0.38|0.21|-0.23|-0.23|-0.14|
|Chemical, Pulp &<br>paper|-0.52|-0.52|-0.15|-0.20|-0.03|-0.09|-0.57|-0.57|-0.52|
|Minerals|-1.85|-1.89|-1.35|-1.39|-1.32|-1.36|-1.90|-1.90|-1.89|
|Metals|-0.18|-0.17|0.15|0.09|0.29|0.22|-0.23|-0.23|-0.17|
|Machinery|-0.30|-0.29|0.03|-0.03|0.17|0.10|-0.35|-0.35|-0.29|
|Electricity|-1.14|-1.16|-0.75|-0.78|-0.72|-0.75|-1.17|-1.17|-1.16|
|Water|-1.20|-1.23|-0.79|-0.82|-0.77|-0.80|-1.23|-1.23|-1.23|
|Construction|-0.52|-0.54|-0.12|-0.14|-0.11|-0.13|-0.54|-0.54|-0.54|
|Trade|-0.39|-0.51|0.20|0.23|0.06|0.08|-0.44|-0.44|-0.51|
|Transport|-3.54|-3.55|-2.99|-3.03|-2.92|-2.20|-3.58|-3.58|-3.56|
|Government|-0.59|-0.63|0.01|-0.01|-0.02|-0.04|-0.61|-0.61|-0.63|
|Financial services &<br>real estate|-0.15|-0.17|0.11|0.10|0.10|0.15|-0.16|-0.16|-0.17|
|Welfare services|-0.39|-0.42|0.19|0.17|0.18|0.16|-0.41|-0.41|-0.42|
|Other services|-0.29|-0.35|0.28|0.26|0.24|0.23|-0.33|-0.33|-0.35|
|**Total**|-0.43|-0.48|0.21|0.20|0.17|0.16|-0.46|-0.46|-0.48|
# **Appendix A (Continue): Household Consumption (% change from the baseline) Table A4. Upper-Middle Income Households (Household Income Group 4)**

||Governm|ent use|Cash t|ransfer to h|ouseholds|Personal|Corpor|ate income|tax cut|
|---|---|---|---|---|---|---|---|---|---|
|||||||income||||
||Base|Invest|Income|Equal|Reverse<br>income|<br>tax cut|CO2<br>intensity|Tax<br>volume|Export<br>intensity|
|Agriculture|-0.27|-0.33|0.38|0.44|0.44|0.26|-0.31|-0.31|-0.33|
|Livestock & fishery|-0.43|-0.46|0.25|0.30|0.32|0.21|-0.44|-0.44|-0.46|
|Forestry & wood|-0.69|-0.78|-0.02|0.04|0.01|0.08|-0.74|-0.74|-0.78|
|Food & beverage|-0.94|-0.97|-0.39|-0.36|-0.21|-0.03|-1.00|-1.00|-0.97|
|Textile &leather|-0.73|-0.75|-0.34|-0.32|-0.14|-0.01|-0.79|-0.79|-0.75|
|Petroleum products|-0.16|-0.13|0.06|0.05|0.34|0.16|-0.22|-0.21|-0.13|
|Chemical, Pulp &<br>paper|-0.53|-0.52|-0.23|-0.22|0.01|0.05|-0.58|-0.58|-0.52|
|Minerals|-1.83|-1.85|-1.40|-1.38|-1.25|-0.60|-1.88|-1.87|-1.85|
|Metals|-0.20|-0.17|0.06|0.06|0.32|0.22|-0.25|-0.25|-0.17|
|Machinery|-0.32|-0.30|-0.05|-0.06|0.20|0.16|-0.37|-0.37|-0.30|
|Electricity|-1.21|-1.22|-0.85|-0.84|-0.72|-0.29|-1.24|-1.24|-1.22|
|Water|-1.27|-1.29|-0.90|-0.88|-0.78|-0.31|-1.31|-1.31|-1.29|
|Construction|-0.27|-0.28|-0.09|-0.08|-0.04|0.00|-0.28|-0.28|-0.28|
|Trade|-0.20|-0.26|0.07|0.10|0.04|0.07|-0.23|-0.23|-0.26|
|Transport|-2.92|-2.92|-2.53|-2.51|-2.35|-1.11|-2.96|-2.95|-2.92|
|Government|-0.52|-0.54|-0.06|-0.03|0.01|0.08|-0.54|-0.54|-0.54|
|Financial services &<br>real estate|-0.36|-0.40|0.17|0.21|0.27|0.16|-0.40|-0.40|-0.40|
|Welfare services|-0.35|-0.36|0.09|0.12|0.18|0.18|-0.37|-0.37|-0.36|
|Other services|-0.26|-0.31|0.17|0.20|0.23|0.22|-0.30|-0.30|-0.31|
|**Total**|-0.45|-0.49|0.13|0.17|0.21|0.16|-0.48|-0.49|-0.49|
# **Appendix A (Continue): Household Consumption (% change from the baseline)**

# **Table A5. High Income Households (Household Income Group 5)**

||Gove<br>u|rnment<br>se|Cash tra|nsfer to ho|useholds<br>|Personal<br>income tax|Corporat<br>|e income ta<br>|x cut<br>|
|---|---|---|---|---|---|---|---|---|---|
||Base|Invest|Income|Equal|Reverse<br>income|cut|CO2<br>intensity|Tax<br>volume|Export<br>intensity|
|Agriculture|-0.21|-0.26|0.28|0.04|-0.05|0.36|-0.24|-0.24|-0.26|
|Livestock & fishery|-0.34|-0.36|0.17|-0.07|-0.14|0.26|-0.35|-0.35|-0.36|
|Forestry & wood|-0.56|-0.63|-0.05|-0.28|-0.40|0.00|-0.60|-0.60|-0.63|
|Food & beverage|-0.36|-0.38|-0.17|-0.32|-0.31|-0.11|-0.39|-0.39|-0.38|
|Textile & leather|-0.71|-0.72|-0.37|-0.70|-0.63|-0.20|-0.77|-0.76|-0.72|
|Petroleum products|-0.09|-0.07|0.01|-0.20|-0.08|0.17|-0.13|-0.13|-0.07|
|Chemical, Pulp &<br>paper|-0.54|-0.52|-0.28|-0.62|-0.49|-0.07|-0.60|-0.59|-0.52|
|Minerals|-1.90|-1.92|-1.51|-1.82|-1.80|-1.41|-1.95|-1.95|-1.92|
|Metals|-0.19|-0.16|0.02|-0.33|-0.16|0.26|-0.25|-0.25|-0.16|
|Machinery|-0.32|-0.29|-0.10|-0.45|-0.28|0.14|-0.38|-0.37|-0.29|
|Electricity|-0.99|-0.99|-0.73|-0.94|-0.91|-0.65|-1.02|-1.02|-0.99|
|Water|-1.04|-1.05|-0.77|-0.97|-0.96|-0.70|-1.07|-1.07|-1.05|
|Construction|-1.02|-1.04|-0.40|-0.82|-0.82|-0.10|-1.07|-1.07|-1.04|
|Trade|-0.76|-0.98|0.22|-0.12|-0.51|0.09|-0.87|-0.88|-0.98|
|Transport|-1.86|-1.86|-1.64|-1.84|-1.80|-1.59|-1.89|-1.89|-1.86|
|Government|-0.83|-0.87|-0.15|-0.53|-0.60|-0.02|-0.87|-0.87|-0.87|
|Financial services &<br>real estate|-0.40|-0.45|0.15|-0.19|-0.23|0.27|-0.45|-0.45|-0.45|
|Welfare services|-0.55|-0.57|0.10|-0.29|-0.32|0.27|-0.59|-0.59|-0.57|
|Other services|-0.41|-0.48|0.22|-0.16|-0.23|0.36|-0.48|-0.48|-0.48|
|**Total**|-0.41|-0.46|0.09|-0.20|-0.26|0.19|-0.46|-0.46|-0.46|
# **Appendix B: Gross output (% change from the baseline)**

||Governm|ent use|Cash tr|ansfer to h|ouseholds<br>R|Personal<br>income tax|Corpora<br>CO2|te income t<br>T|ax cut<br>Et|
|---|---|---|---|---|---|---|---|---|---|
||Base|Invest|Income|Equal|everse<br>income|<br>cut|<br>intensity|ax<br>volume|xpor<br>intensity|
|Agriculture|-0.47|-0.59|0.13|0.25|0.23|0.08|-0.87|-0.51|-0.51|
|Livestock &<br>fishery|-0.62|-0.53|-0.15|-0.22|-0.19|-0.16|-0.55|-0.52|-0.52|
|Forestry & wood|-0.92|-1.12|-0.28|-0.09|-0.25|-0.34|-1.64|-0.96|-0.96|
|Mining|-2.88|-1.42|-3.24|-3.32|-3.38|-3.13|-1.05|-1.55|-1.54|
|Food & beverage|-1.80|-2.02|-1.22|-1.10|-1.21|-1.27|-2.42|-1.89|-1.89|
|Textile & leather|-3.07|-3.60|-2.15|-1.80|-2.41|-2.24|-4.95|-3.18|-3.20|
|Chemicals, pulp &<br>paper|-4.30|-5.18|-3.86|-3.40|-4.26|-3.94|-6.87|-4.64|-4.67|
|Minerals|-3.33|-2.22|-3.49|-3.51|-3.67|-3.41|-2.15|-2.25|-2.25|
|Metals|-2.56|-1.60|-2.48|-2.11|-3.04|-2.43|-2.87|-1.21|-1.23|
|Machinery|-5.45|-5.10|-4.95|-4.26|-5.79|-4.96|-7.54|-4.33|-4.37|
|Electricity|-1.27|-1.26|-1.32|-1.39|-1.38|-1.30|-1.19|-1.28|-1.28|
|Water|-2.31|-2.35|-2.40|-2.40|-2.43|-2.39|-2.39|-2.34|-2.34|
|Construction|-1.22|0.29|-1.58|-1.65|-1.73|-1.47|0.65|0.18|0.18|
|Trade|-1.51|-1.07|-1.64|-1.89|-1.34|-1.60|-0.05|-1.39|-1.38|
|Transport|-4.21|-3.88|-4.06|-4.06|-4.08|-4.06|-3.86|-3.88|-3.88|
|Government|2.22|-1.07|-0.55|-0.28|-0.83|-0.58|-2.07|-0.76|-0.78|
|Financial services<br>&realestate|-1.47|-1.36|-1.36|-1.46|-1.50|-1.34|-1.35|-1.36|-1.36|
|Welfare services|2.58|-0.73|0.04|0.28|-0.22|0.00|-1.68|-0.43|-0.44|
|Other services|-0.79|-0.87|-0.41|-0.51|-0.53|-0.42|-0.96|-0.85|-0.85|
|Total|-0.47|-0.59|0.13|0.25|0.23|0.08|-0.87|-0.51|-0.51|