metadata: authors: 0: author: Rodriguez Castelan,Carlos
1: author: Araar,Abdelkrim
2: author: Malasquez Carbonel,Eduardo Alonso
3: author: Granguillhome Ochoa,Rogelio
count: Ethiopia
docna: 0: docna: Competition Reform and Household Welfare : A
            Microsimulation Analysis of the Telecommunication Sector in Ethiopia
docty: Policy Research Working Paper
subtopic: Inequality,ICT Economics,Telecommunications Infrastructure,Information Technology
teratopic: Information and Communication Technologies,Poverty Reduction
abstracts: cdata!: This paper presents a novel method for
            estimating the likely welfare effects of competition reforms
            for both current and new consumers. Using household budget
            survey data from 2015/16 for Ethiopia and assuming a reform
            scenario that dilutes the market share of the
            telecommunications state-owned monopoly to 45 percent, the
            model predicts a 25.3 percent reduction in the price of
            mobile services and an increase of 4.6 million new users of
            mobile phone services. This reform is expected to generate a
            welfare gain of 1.37 percent among all consumers. Poverty
            rates are expected to decline by 0.31 percentage point,
            driven by a reduction of 0.22 percentage point for current
            consumers and 0.09 percentage point among new users.
            Inequality would increase by 0.23 Gini point since
            better-off consumers are more likely to reap the benefits of
            greater competition. This method represents a powerful tool
            for supporting the analysis of competition reforms in
            developing countries, particularly in sectors known for
            excluding significant segments of the population due to high
            consumer prices.
geo_reg_key: 517191
repnme: repnme: Competition Reform and Household Welfare :
            A Microsimulation Analysis of the Telecommunication Sector
            in Ethiopia
display_title: Competition Reform and Household
            Welfare : A Microsimulation Analysis of the
            Telecommunication Sector in Ethiopia
listing_relative_url: /research/2021/01/32764930/competition-reform-household-welfare-microsimulation-analysis-telecommunication-sector-ethiopia
topicv3: Information and Communication Technologies
            m1326624 133,Poverty m1326687 695,ICT Industry and Services m1327158 281,Broadband Networks m1327576 291,Mobile Phone m1505675 3092
publishtoextweb_dt: 2021-01-20T00:00:00Z
historic_topic: Information and Communication Technologies,Poverty Reduction
origu: EFI-MNA-POV-Poverty and Equity (EMNPV)
no_of_pages: 22
author_keys: 0: author_key: 1180219
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2: author_key: 1389197
3: author_key: 1649264
model_extractions: Policy Research Working Paper 9516

# Competition Reform and Household Welfare

A Microsimulation Analysis of the Telecommunication Sector in Ethiopia _Carlos Rodríguez-Castelán Abdelkrim Araar Eduardo A. Malásquez Rogelio Granguillhome Ochoa_ Poverty and Equity Global Practice January 2021
Policy Research Working Paper 9516

# **Abstract**

This paper presents a novel method for estimating the likely welfare effects of competition reforms for both current and new consumers. Using household budget survey data from 2015/16 for Ethiopia and assuming a reform scenario that dilutes the market share of the telecommunications stateowned monopoly to 45 percent, the model predicts a 25.3 percent reduction in the price of mobile services and an increase of 4.6 million new users of mobile phone services. This reform is expected to generate a welfare gain of 1.37 percent among all consumers. Poverty rates are expected to decline by 0.31 percentage point, driven by a reduction of 0.22 percentage point for current consumers and 0.09 percentage point among new users. Inequality would increase by 0.23 Gini point since better-off consumers are more likely to reap the benefits of greater competition. This method represents a powerful tool for supporting the analysis of competition reforms in developing countries, particularly in sectors known for excluding significant segments of the population due to high consumer prices.

This paper is a product of the Poverty and Equity Global Practice. 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 crodriguezc@worldbank.org and emalasquez@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
# **Competition Reform and Household Welfare: A Microsimulation Analysis of the**

# **Telecommunication Sector in Ethiopia**

Carlos Rodríguez-Castelán<sup>a</sup> Abdelkrim Araar<sup>b</sup> Eduardo A. Malásquez<sup>c</sup> Rogelio Granguillhome Ochoa<sup>d</sup>

**Keywords:** Competition Reform, ICT, Welfare Effects, Simulations, Ethiopia **JEL codes:** C15, D40, D60, I32, L86, N77

## __________________________

The authors wish to thank Tania Begazo, Tom Bundervoet, Ambar Narayan, Nistha Sinha, Christina Wieser, and Robert Zimmermann for their comments and suggestions. The authors would also like to thank participants in the learning days of the 2020 Poverty and Equity Global Practice retreat and 2020 Summer University. The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.

a. Lead Economist, Poverty and Equity Global Practice, World Bank, Washington, DC (crodriguezc@worldbank.org). b. Research Fellow, Université Laval, Quebec City; World Bank, Washington, DC; and Partnership for Economic Policy, Nairobi

> (araar.abdelkrim@ecn.ulaval.ca).

c. Economist, Poverty and Equity Global Practice, World Bank, Washington, DC (emalasquez@worldbank.org) (corresponding author). d. Extended Term Consultant, Poverty and Equity Global Practice, World Bank, Washington, DC (rgranguillhomeoc@worldbank.org).
# **1. Introduction**

Broad access to information and communication technology (ICT) has great potential for poverty reduction and inclusive growth in developing countries, where ICT adoption is growing rapidly (World Bank 2016). Recent studies have shown that the expansion of broadband through fiber optics and mobile internet produces substantial benefits in household income and consumption, employment and productivity, and helps lift people out of poverty (see Bahia et al. 2020 on Nigeria, which accounts for the largest ICT market in Africa; Masaki, Granguillhome Ochoa, and Rodríguez-Castelán 2020 for Senegal; and Hjort and Poulsen 2019 on Africa). However, that ICT markets in most countries in Africa rank among the most concentrated in the world has limited the potential for equity and efficiency gains in digital technology adoption among households and firms across the region.<sup>1</sup> The considerable concentration in the ICT sector is linked to the fact that the region registers the highest relative economic rents per capita and the highest relative prices for ICT bundles among ICT firms in the world. Currently, 8 of the 10 most expensive countries for mobile communication services and 9 of the 10 most expensive countries in fixed broadband are in Africa.<sup>2</sup> The high prices of ICT services and assets translate into the allocation of large shares of household disposable income for these products, that could otherwise be used for alternative productive activities or consumption. The high prices of ICT services also reduce the potential for the adoption of digital technologies because poorer households may be priced out of these services, limiting their capacity to harness the benefits of ICT (Rodríguez-Castelán et al. 2020).

This paper introduces a novel method to estimate direct economy-wide welfare effects of expanding competition in the ICT sector, first, by estimating the welfare impacts on current consumers and, second, by identifying potential new consumers brought into the market by a reduction in market prices (due to greater competition) and then estimating the welfare impacts. This approach is applied to Ethiopia, where, until 2019, the ICT market was dominated by a monopoly.

The analysis relies on data from the 2015/16 Household Consumption Expenditure Survey (HCES) in Ethiopia (CSA 2018). The simulation follows a reform scenario whereby the mobile services market share of Ethio Telecom, the state-owned telecommunication enterprise, declines from 100 percent to 45 percent, virtually ending its monopoly. The paper then models and predicts the extent of the adoption of ICT services and the expected expenditures on mobile services among both current and new users and, thereafter, the total welfare effects among all consumers.<sup>3</sup> Overall, the introduction of competition in mobile services in Ethiopia would reduce poverty by 0.31 percentage point. This effect can be disaggregated into poverty reduction among current consumers and among new users. Competition in the mobile market would reduce poverty among current users by 0.22 percentage

> 1 According to data of the International Telecommunication Union (ITU 2020), the region hosts 21 of the 25 most expensive countries in mobile voice, 19 of the 25 most expensive in mobile data, and 22 of the 25 most expensive in fixed broadband.

> 2 See Inclusive Internet Index 2020 (database), Economist Intelligence Unit, London, https://theinclusiveinternet.eiu.com/. 3 This part of the analysis introduces WELCOM’s Market Competition and the Extensive Margin Analysis (mcema) module (RodríguezCastelán et al. 2021). For WELCOM, see WELCOM Stata Tool, Global Solutions Group on Markets and Institutions for Poverty Reduction and Shared Prosperity, Poverty and Equity Global Practice, World Bank, Washington, DC, <u>http://dasp.ecn.ulaval.ca/webwel/welcom.html.</u>

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point in the short run. In addition, the price reduction is also expected to have a positive impact on service uptake and welfare among new users. The analysis estimates that a decline in prices because of competition would also lead to a 5.2 percentage point increase in the number of users (approximately 4.6 million additional users) and a reduction in poverty among these new consumers equivalent to 0.09 percentage point. Because better-off households benefit more than poorer households from greater competition in the ICT industry, in both absolute and relative terms, inequality is expected to widen by 0.23 Gini point. The combined price drop and expansion in the number of users would generate an average relative welfare gain of 1.09 percent among current users and a 0.28 percent gain among new users, for a combined welfare gain of 1.37 percent among all consumers.

This paper contributes to the economic literature and to the development policy agenda in three ways. First, it provides a practical tool for simulating the likely welfare effects of policy reforms that promote competition in highly concentrated sectors of the economy. Estimates based on this method may be useful as inputs to motivate such reforms in developing countries. Second, this method and this empirical application demonstrate the importance of analyzing the welfare effects among groups that are completely excluded from certain markets, thereby complementing studies that have focused on those households and individuals already consuming certain goods and services (Rodríguez-Castelán et al. 2019). Third, the approach is particularly useful in analyzing industries that are known for excluding significant segments of the population because of high prices and that are also relevant in enhancing the income-generating capacity of households, such as ICT, energy, and transport.

The rest of this paper is organized as follows. Section 2 describes the underlying model and data requirements of the proposed approach.<sup>4</sup> Section 3 describes the data and presents stylized facts on the ICT industry in Ethiopia. Section 4 shows results, and section 5 concludes.

# **2. Simulation approach, modeling alternatives, and data requirements**

Greater competition in consumer markets typically benefits current consumers through lower prices, enhancements in the quality of products and services, and an overall boost in innovation. In most cases, competition also brings prices down, thereby allowing individuals and households that have previously been priced out to gain access to the more affordable goods and services. This paper proposes a new empirical approach to estimating the likely welfare effects of greater competition that involves two complementary effects: (1) direct welfare effects of the price reductions generated by the entry of new service providers on individuals already consuming certain goods or services and (2) the welfare effects on new consumers who are at the margin in the consumption of such goods or services, but had been excluded from the market by the higher prices.

The first set of effects is discussed by Rodríguez-Castelán et al. (2019), who present a simulation method to estimate the likely direct welfare effects of a price decline generated by greater market

> 4 For more details on the models available in the tool, see the WELCOM user manual at WELCOM Stata Tool, Global Solutions Group on Markets and Institutions for Poverty Reduction and Shared Prosperity, Poverty and Equity Global Practice, World Bank, Washington, DC, http://dasp.ecn.ulaval.ca/webwel/welcom.html.

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competition. Using money metric measures, their method simulates the change in welfare among households with positive consumption. The approach proposed in this paper complements Rodríguez-Castelán et al. (2019) by simulating the likely welfare effects of greater competition among new consumers. It accomplishes this by building on the basic setting discussed in Decoster et al. (2019), who estimate the potential uptake of competition reforms. The approach proposes to model consumer behavior, specifically the probability of adoption by new users based on observable sociodemographic characteristics and policy parameters. Subsequently, based on previous welfare gains calculated for the case of current users, the approach projects the increase in new consumers and the potential monetary gains of these consumers at the margin. The approach also allows to simulate the welfare effects if the number of new users is an exogenous parameter. Such a parameter might simply be based on the coverage targets of new service providers or might be the output of a computable general equilibrium model. The latter would be useful if the number of current users of the good or service is low and hinders the proper estimation of the extensive margin of consumption using statistical techniques that rely on current consumption. This second method might also serve as a robustness check for the results estimated through the probabilistic model.

The welfare effects on current consumers and on new consumers of any given good or service should be clearly distinguished. The welfare effects among current users or consumers is estimated as a function of the observed expenditures of these consumers on any given good (that is, the share of spending on the good relative to total expenditure) and the expected change in prices. The change in welfare among new consumers (that is, the extensive margin of consumption) is estimated in three steps. First, a probabilistic model of the likelihood of positive consumption in a good or service is estimated based on observable characteristics and policy variables. Second, the price change or an equivalent change in income because of the increase in competition is applied to estimate the change in the probability of using or consuming the good or service. Third, the change in welfare is estimated as the product of the estimated change in the probability of consumption and the expected consumption. This change in welfare can be used to estimate the equivalent impact on poverty and inequality among new users.<sup>5,6</sup> Two main pieces of data are required to estimate the extensive margin of changes in welfare. First, the model must identify the proportion of new users relative to current users. Second, the model needs to estimate the expected expenditures of the new users. This proposed approach shares similarities

> 5 In addition, the mcema module allows the expected expenditure of new consumers to be estimated using several alternative models. These approaches include simple linear estimators, such as average expenditures by population groups, random imputation techniques, nonlinear regression models, and the random imputation of residuals. See Rodríguez-Castelán et al. 2021.

> 6 One way to interpret the impact of changes in prices on poverty among current users is to assume that the savings due to the lower prices are allocated on the consumption of other goods and services, thus increasing the total consumption of the household (the implicit assumption being that any savings due to lower prices is fully consumed).  In the case of new users, who did not consume the good or service before the reduction in price, the intuition is not as direct. In this case we need to simulate what would had been the consumption of the good or service of interest for this cohort of individuals or households, then estimate the equivalent saving due to lower prices and lastly, estimate the potential equivalent increase in consumption.

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with two-part models in the literature (for instance, see Cragg 1971; Dow and Norton 2003; Heckman 1976; Nargis et al. 2013).<sup>7</sup>

# **a. The probability model and the proportion of entrants**

The proposed model exploits the variability of either prices or a welfare variable at the household level (such as consumption) to estimate a probit model of to estimate demand.<sup>8</sup> Assuming independent and normally distributed error terms, and because of a lack of accurate price data in the observations of individuals in household surveys, the probit model is written as follows:<sup>9</sup> P P h h), (1) h,ii �dd �= ff(log (ww ), XX where h denotes a welfare measure (such as income) for household _h_ , and corresponds to a binary variable taking the value of 1 if household _h_ reports positive consumption in good or service _i_ , h,ii ww dd or 0 otherwise. For the sake of simplicity, all explanatory variables are denoted with ; the h and indicators that identify households and goods or services are omitted; and welfare is referred to as XX ii income in the rest of this section, as follows: P P , (2) (dd= 1|XX) = Φ(XX′ββ)

or, alternatively, as follows: where Φ(. ) denotes the normal cumulative distribution function (CDF). Then, the probabilistic elasticity is given by the following: P where I���������� and P�������P refer to the average welfare and average probability of positive consumption at the population level (individual of reference), respectively. IIPPIIuu Taking derivatives and applying the chain rule on the probit model specification, it is possible to rewrite equation (4) as follows:<sup>10</sup>

> 7 The literature on two-part Heckman models focuses on estimating population statistics, such as the change in average quantity, the price elasticity, and so on. In contrast, the proposed framework focuses on the distributional impact of price changes on well-being. Using a Taylor approximation approach, the estimation of the impact on current consumers does not require additional estimates because the necessary information on the expenditures of this group and on price changes is available. However, to estimate the impact on the extensive margin, the consumption decision is modeled conditional on the positive quantities of the good consumed.

> 8 In Stata, the stepwise prefix can be used to perform the selection of explanatory variables automatically according to significance levels.

> 9 If information on the price of the item of interest is available, mcema enables the user to adjust the probability unit model to include price thereby causing the probability of use to be equal to a function of welfare, price, and household characteristics (Rodríguez-Castelán et al. 2021).

> 10 For more information on price elasticity and the two-part Heckman models, see, for instance, Saha, Capps, and Byrne (1997).

5
The estimation of the probabilistic income elasticity does not require the observation of final incomes (that is, the variability in income across households is exploited). The absolute change in probability is denoted by the following: Because the expenditure shares on a specified item might vary across the income distribution, it is important to control for consumer characteristics. The added value of this model is that it allows the calculation of income elasticities across different groups of consumers and, thus, a determination of the distributional effects of income variations considering heterogeneous consumption patterns.

After estimating the proportion of entrants in the total population, the analysis assumes that the change in the probability of a representative household or individual (assigned using the mean household or individual characteristics) represents the proportion of new entrants. As discussed above, one may evaluate the change in the probability of the reference individual as follows:<sup>11</sup>

> 11 Another method to estimate the proportion of entrants is to calculate the expected change in probabilities. On nonlinearity, equation 8 may be less precise. An alternative approach is to compute the difference between the averages of the predicted probabilities with initial and final welfare (which might lead to more accurate estimates), resulting in an estimated proportion of entrants. Formally, the change in the probability of use of the household h is as follows: = P h(u h) - P h(u h) The expected change in probability is equal to the expected predicted probability after the increase in welfare (AApp,h uu= 1|XX′ uu= 1|XX ), minus the expected probability under the initial values of welfare. The advantage of this second method is that the density for the reference individual does not need to be evaluated, thereby giving more accurate result because the two measurements capture the main part of the change in XX′ probability.

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# **b. Expenditure estimates**<sup>12</sup>

In addition to estimating the proportion of entrants, it is necessary to estimate expenditures on the relevant product among new consumers or users. The estimation of consumption expenditure among new entrants is conditioned on the probability or likelihood of consumption and is modeled as , where denotes expenditure. The unobserved expenditures of entrants are treated similarly to missing values, where these missing values are the unobserved expenditures of entrants. EE[uu |uu> 0] uu To address the non-randomness of the decision to consume, the analysis assumes that, at the margin, the new consumers of the product share observable characteristics with current consumers.<sup>13</sup>

The suggested approach relies on the first-order Taylor approximation to measure the welfare of current users.<sup>14</sup> The Taylor approximation allows an assessment of the impact of price changes on well-being under the assumption of a given basket of goods. Following a first-order Taylor approximation, the impact of price changes on well-being can be approximated as follows: , (9) ii ii ddww= −uu ∗ddpp , where denotes the change in well-being, the expenditures on the good , and the observed proportional change in the price of good i. For instance, if the price increases by 1 percent, well-being ii ii decreases by ddww 0.01 ∗e . uu ii ddpp ii In addition to measuring the impact of price changes on the welfare of current consumers, the analysis must also include the changes in the well-being of entrants to measure adequately the welfare impacts of competition. The average expected expenditures on good among new consumers in group (for example, decile 2) is denoted by ∂ , and the average expected expenditures of current consumers in the same group is denoted by gg,ii ww ∂ .15 If the estimated proportion of entrants in group ii gg is uu ∂∂cc∂ cc denoted by (estimated using a probabilistic model), then the following is true: gg,ii uu gg gg,ii ππ ∂ ∂ (10) ww ∂∂ccnn∂ nn gg,ii gg,ii gg,ii uu = ππ uu

> 12 See the mcema user manual for more information (Rodríguez-Castelán et al. 2021). A detailed discussion on the implications of the availability of price data on the items of interest is also presented in the manual.

> 13 The mcema module for Stata allows a selection among six methods to estimate the expenditure of new entrants: (a) imputation using average expenditures (unconditional mean imputation), (b) random imputation using the expenditures of current users, (c) imputation based on expenditure models and linear regressions (regression, conditional mean imputation), (d) imputation using an expenditure model and the random imputation of residuals (stochastic regression imputation), (e) imputation based on an expenditure model and quantile regressions, and (f) imputation using a quantile regression model and a random imputation of residuals.

> 14 Jackson (1984) introduces the foundations of the hierarchic demand system whereby the change in a bundle of goods can be modeled. In what follows, a selective demand model is introduced to link the selection of goods with real income. There are two hot deck approaches. The first is the distance function approach, and the second is the pattern matching approach. The first imputes the missing value based on the smallest squared distance statistic to the case with the missing value. The matching pattern method used here assumes that the sample can be stratified into separate homogenous groups and that imputation can be performed randomly within each group (Fox-Wasylyshyn and El-Masri 2005). See also Ahmad and Stern (1984, 1991); Araar (1998); Araar and Paolo (2016); Creedy (1998); Newbery (1995); Yitzhaki and Lewis (1996).

> 15 Current users are users with positive expenditure under a concentrated market (status quo). Nonusers would then be the remaining users who do not exhibit positive expenditures under a concentrated market (status quo).

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If the exogenous total number of entrants by region is available, then:

# **c. Data requirements**

The proposed approach may be applied to data on household consumption, typically data from household expenditure or consumption surveys. Three key variables must be drawn from the surveys: (1) a per capita welfare measure to estimate poverty (often calculated as the total consumption or income of a household, divided by the number of household members or adult equivalent members), (2) per capita consumption or expenditure on a good or service of interest, and (3) a relevant poverty line estimated according to household consumption or income.

In addition to microdata from household surveys, a minimum set of parameters defining baseline and reform scenarios of the underlying market structure or a given industry are required, including the number of new firms competing in a market and price elasticities of demand. The model for the extensive margin module that estimates new consumers also requires demographic and spatial variables derived from the household expenditure or consumption survey. The dependent variable for the probabilistic model is a binary dummy indicating whether the household or individual (denoted by h) has any positive or null consumption in the good or service of interest. The (proportional) price change h and the (proportional) welfare change, h, must be included, along with a set of household and individual characteristics and policy variables linked to the use or consumption of the ddpp ddI IIPPIIuu good or service of interest (age, sex, geographical location, literacy, educational attainment, employment status, household assets, and services).<sup>16</sup>

# **3. The mobile telecommunication industry in Ethiopia**

As of 2019, Ethiopia was one of the three countries in the world in which the mobile phone market was a monopoly. In 2018, among countries in Africa, Ethiopia ranked 4th lowest in mobile phone penetration (37.2) per 100 people and 12th lowest in fixed broadband subscriptions (0.05) (figure 1). The government of Ethiopia is undertaking a reform to modernize the economy, including a gradual privatization of key sectors, such as the mobile phone market, which has been dominated by a state-

> 16 The (proportional) welfare change is calculated directly using the WELCOM Stata Tool.

8
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Data from the 2015/16 HCES show that household expenditures on mobile services in 2016 corresponded to 1.4 percent of total consumption (CSA 2018). The share was lower among households at the bottom of the income distribution. Thus, the average expenditure of the lowest income decile on mobile phone services was about Br 83 a year (US$10 a year at purchasing power parity) (table 1). This represented 0.43 percent of the decile’s total consumption. Meanwhile, the top income decile spent Br 1,729 (about 2.31 percent of the corresponding decile consumption). In demand across the consumption distribution, only 14 percent of households in the poorest decile reported positive spending on telecommunication services, while the corresponding share among households in the top consumption decile was 76 percent. The low spending on mobile services may be partially explained by the lack of adequate network infrastructure. An estimated 15 million Ethiopians are out of reach of the cellular network, and 60 million are without access to the internet. This barrier is coupled with the high costs of handsets that are driven by import duties and a concentrated retail market.

**Table 1. Household Expenditures on ICT Services, by Consumption Decile, 2015/16, %**

|_Household expenditure_|_All_|_Poorest_<br>_1_|_2_|_Con_<br>_3_|_sumption_<br>_4_|_decile_<br>_5_|_6_|_7_|_8_|_9_|_Richest_<br>_10_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|ICT|1.456|0.427|0.663|0.882|1.020|0.947|0.928|1.185|1.331|1.628|2.314|
|Mobile services|1.439|0.423|0.657|0.874|1.014|0.937|0.917|1.181|1.319|1.615|2.274|
|Fixed line telephone services|0.016|0.004|0.006|0.007|0.006|0.010|0.011|0.004|0.012|0.012|0.036|
|Fixed internet services|0.001|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.001|0.004|
|Households with positive<br>expenditure on mobile services, %|42.05|13.99|24.97|33.46|36.30|37.79|37.41|44.60|52.74|63.18|76.06|

_Source:_ Elaboration based on data of the 2015/16 HCES; see CSA 2018.

The government has made substantial strides in poverty reduction since 2004. National estimates of the poverty headcount ratio declined from 39 percent in 2005 to 24 percent in 2015 (World Bank 2020). These gains have largely been focused in urban areas, leading to an increase in inequality, albeit the Gini index, at 35.0, is low relative to peer countries.

# **4. Results**

This section discusses the main results of the proposed approach. First, it shows the impacts of the expected reduced concentration on current users. Second, it examines the welfare effects generated among potential new users by the estimated price declines caused by greater competition. Third, it discusses limitations of the simulation approach proposed.

# **4.1. Welfare effects among current users**

For the simulation, the baseline scenario is a monopoly in the telecommunication industry.<sup>21</sup> The reform scenario simulates a shift to a competitive market through gradual steps. One way to interpret the results is to picture the perfectly competitive scenario as a long-run counterfactual and the different steps to reach that equilibrium as short- and medium-term counterfactuals.

> 21 See annex 1 for information on how the approach models the monopoly. The approach also allows the user to model oligopoly and partial collusive oligopoly.

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Diluting the market share of the monopoly in ICT from 100 percent to 45 percent is expected to reduce consumer prices by 25.3 percent (table 2). In addition, the results of implementing the simulation in multiple steps suggest that the welfare impact of reducing the monopoly’s market share would increase at a declining rate as new telecommunication providers enter the market (figure 2).

**Table 2. Price Changes after Diluting the Market Share of the ICT Monopoly**

|MONOPOLY MARKET<br><br>|STEP 1<br><br>|COMPETITIVE MARKET<br><br>|
|---|---|---|
|MMMsMM(%) = 11<br>η= −1. 5<br>|MPMuMuhMPu(%) = 45<br>η= −0.81<br>|MPMuMuhMPu(%) = 0<br>η<sup>∗</sup>= −0.25<br>|
|<br>p1= 3. 1<br>_Source:_Elaboration based on data derive<br>_Note:_The elasticity is interpreted not on|<br>p1 = 2.24<br>d from the WELCOM Stata Tool and t<br>ly as the sensitivity of current consum|p0 = 1.0<br>he 2015/16 HCES; see CSA 2018.<br>ers of the service, but also as the expected|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ The elasticity is interpreted not only as the sensitivity of current consumers of the service, but also as the expected response to the demand of both current and new users. The changes in the value of the elasticity occur because of the simulation of a large price drop for the item of interest, resulting in a larger magnitude in the quantity demanded and a decline in the absolute value of elasticity.

All current users of ICT services across the consumption distribution are expected to benefit from greater competition in the telecommunication sector, but the gains will disproportionately benefit households in the top consumption deciles because they adopt ICTs at higher rates and spend more on the services, both in absolute and relative terms. In contrast, the effects on poverty reduction among current consumers are expected to be modest because most households at the bottom of the consumption distribution and around the poverty line exhibit low ICT adoption and low ICT expenditures.

**Figure 2. The Market Share of the Telecommunication Monopoly and Consumer Prices**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Market power (market size in %)<br>3<br>2.5<br>2<br>Price (competitive market = 1) 1.5<br>1<br><!-- End of picture text --> _Source:_ Elaboration based on data derived from the WELCOM Stata Tool.

The results indicate that, based on simulating a decline in Ethio Telecom’s market share in mobile services from 100 percent to 45 percent and a price elasticity of demand of −1.50, a shift to a more competitive market would induce a 0.22 percentage point reduction in poverty among current users 11
in the short run with respect to the baseline headcount poverty rate of 24.21 percent and up to 0.80 in the long run because of the direct effect of lower prices (table 3).<sup>22</sup>

**Table 3. Poverty and Inequality Effects, Loss of Ethio Telecom Monopoly, Current Users**

|_Scenario_||_Poverty indic_<br>|_ators_<br>||_Inequality indicators_<br> <br>|
|---|---|---|---|---|---|
||_Headcount_|_Poverty gap_|_Squaredpoverty gap_|_Gini_|_Atkinson(epsilon = 0.5)_<br>_Entropy (theta = 0.0)_|
|Baseline|24.21|6.86|2.83|33.3|0.0934<br>0.1871|
|Reform scenario|23.99|6.78|2.80|33.5|0.0947<br>0.1898|
|Difference|−0.22|−0.08|−0.03|0.23|0.0013<br>0.0026|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ The reform scenario corresponds to a reduction in the market share of Ethio Telecom from 100 percent to 45 percent. The Gini index is based on consumption.

The results also show an average relative welfare gain of 1.09 percent among all current users of telecommunication services (table 4). The richest decile will gain Br 474.8 (in birr of 2015) on average, relative to Br 9.8 among the poorest decile. This corresponds to a relative gain of 1.7 percent among the top decile and 0.3 percent among the bottom decile. This suggests that households in the richest consumption deciles will benefit more in both absolute and relative terms from greater competition in the ICT sector compared with the poorest households, which is consistent with an expected rise in the Gini index of 0.23 point with respect to a baseline of 33.3 (see table 3).

**Table 4. Welfare Effects, Loss of Ethio Telecom Monopoly, Current Users**

|_Consumption values_|_All_|_Poorest_<br>_1_|_2_|_3_|_Co_<br>_4_|_nsumption d_<br>_5_|_ecile_<br>_6_|_7_|_8_|_9_|_Richest_<br>_10_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Annual mean per capita<br>consumption, birr|10,111|3,062|4,775|5,928|6,908|7,867|8,806|9,851|11,621|14,779|27,525|
|Absolute incidence, birr|110.36|9.8|23.8|39.2|53.1|55.9|61.3|88.3|116.3|181.1|474.8|
|Relative incidence|1.09|0.32|0.50|0.66|0.77|0.71|0.70|0.90|1.00|1.23|1.73|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ Consumption values are in birr of 2015.

The results correspond to the potential welfare effects only among current consumers (intensive margin effects). This likely underestimates the total welfare gains derived from breaking up the monopoly in the telecommunication sector because it does not include the potential benefits among new consumers. Thus, the expectation is that the total welfare effects of greater competition in the ICT industry will become more equalizing across the consumption distribution if one also considers the likely welfare effects among potential new consumers (extensive margin effects), given the lower uptake among households at lower income deciles.

# **4.2. Welfare effects among new users**

To test the above conclusions, the analysis next uses the change in prices deriving from the reduced market share of the dominant firm in the market to estimate the welfare effects among new users by

> 22 The price elasticity of demand used was −1.50 because there was no price elasticity of demand specific to Ethiopia’s mobile services market (Waverman, Meschi, and Fuss 2005). This preliminary estimate should be viewed with caution because it refers to 2005 and corresponds to values obtained for a pool of developing countries. However, this solution is adequate because of the scant empirical evidence on price elasticities of demand in mobile services in developing countries, particularly in Africa.

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simulating the estimated change in the uptake among users and the corresponding expected change in consumption, which represents a novelty of the approach.<sup>23</sup> For model selection, the analysis implements a backward stepwise probit regression on per capita consumption (in logarithmic scale) and observable household characteristics. Based on the results of this model, it applies the proportional income change to estimate the marginal effects of competition, resulting in the predicted probabilities of usage.<sup>24</sup> For the analysis, a variable selection filter with a 5 percent threshold of statistical significance was used. Covariates in the probit model include characteristics of household heads (employment status, literacy, sex, and age) and household characteristics (access to electricity, material of dwelling’s walls and floors, and household size). After estimating the predicted probabilities of demand, the analysis imputes the expected expenditures for those households that were previously priced out of the market.<sup>25</sup> Specifically, estimates are calculated by running a quantile regression model with a random imputation of residuals.<sup>26</sup> The results show that a 25.3 percent drop in the price of mobile services would increase the uptake of mobile services by 5.2 percentage points with respect to the baseline of 42.0 percent. The increase in demand is 1.6 percentage points among households in the lowest consumption decile, while it corresponds to 6.1 percentage points among households in the top decile. The rise in the share of users because of the greater competition is then associated with an additional reduction in poverty of 0.09 percentage point with respect to the baseline 24.2 poverty rate (table 5). New users do not have a significant effect on the Gini index (which is thus not reported in table 5).

**Table 5. Poverty Impacts, Loss of Ethio Telecom monopoly, New Users**

|_Scenario_|_Poverty indicators_<br>_Headcount_<br>_Poverty gap_|
|---|---|
|Baseline|24.21<br>6.864|
|Increase in new users by 5.2 percentage points|24.13<br>6.836|
|Difference|−0.09<br>−0.028|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ The Gini coefficient is based on consumption rather than income. Gini coefficient results were not significant for new entrants. Due to rounding the difference between the baseline and the increase in new users is not exact.

The increase in uptake is associated with an average relative impact on welfare of 0.28 percent (table 6).<sup>27</sup> New entrants who belong to the richest consumption decile are expected to gain an average of

> 23 These extensive margin estimates have been carried out using the WELCOM mcema module. Please refer to Rodríguez-Castelán et al. 2021.

> 24 See the WELCOM user manual at WELCOM Stata Tool, Global Solutions Group on Markets and Institutions for Poverty Reduction and Shared Prosperity, Poverty and Equity Global Practice, World Bank, Washington, DC, http://dasp.ecn.ulaval.ca/webwel/welcom.html.

> 25 The mcema module allows the user to run six alternative models to estimate expenditures. See the mcema user manual for more information on each methodology.

> 26 Alternatives considered but not used for this simulation involve taking the average at the level of the primary sampling unit, random imputation, linear regression imputation, stochastic regression imputation, and quantile regression imputation. For the purpose of this exercise, estimates of the random imputation and the mean of expenditures at the level of the primary sampling unit were also carried out. These serve as upper- and lower-bound measures of the relative impact on welfare.

> 27 mcema allows the user to employ six different models to estimate household expenditures of those households that were previously priced out by imperfect competition. Expenditure estimates are calculated by running a quantile regression model with random

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Br 68.8 (birr of 2015), while new entrants in the bottom consumption decile will gain an average of Br 2.9. This translates into a relative gain of 0.25 percent among the top decile and 0.09 percent among the bottom decile. New entrants in the top decile will also benefit more than new entrants in the bottom decile, but less than proportionately in the case of the relative welfare gains among current consumers.

**Table 6. Welfare Effects, Loss of Ethio Telecom Monopoly, New Users**

|_Consumption value_|_All_|_Poorest_<br>_1_|_2_|_3_|_Co_<br>_4_|_nsumption d_<br>_5_|_ecile_<br>_6_|_7_|_8_|_9_|_Richest_<br>_10_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Annual mean per capita<br>consumption, birr|10,111|3,062|4,775|5,928|6,908|7,867|8,806|9,851|11,621|14,779|27,525|
|Absolute incidence, birr|28.2|2.9|10.7|15.1|17.3|21.8|17.8|24.8|42.1|60.4|68.8|
|Relative incidence|0.28|0.09|0.22|0.25|0.25|0.28|0.20|0.25|0.36|0.41|0.25|

_Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ Consumption values are in birr of 2015.

Despite the positive but unequalizing welfare effects of greater competition on new users, an accounting of only the welfare effects on new users shows that the relative welfare gain among households in the bottom decile of the consumption distribution is 6.9 percent, compared with an average of 4.9 percent among households in the richest decile (figure 3).

**Figure 3. Relative Welfare Gains of Greater Competition in Telecommunication, New Users**

<!-- Start of picture text --> 8.0%<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>incidence (percent)<br>Relative welfare monetary<br><!-- End of picture text --> _Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ Bars show relative welfare in percentage. Relative welfare is the per capita welfare incidence divided by per capita expenditure, only for new consumers. Expenditure estimates were carried out by running a quantile regression model and random imputation of residuals.

# **4.3. Aggregate welfare effects of greater competition in the ICT industry**

Next, the results obtained above among both current and new users, except for the Gini index, can be added linearly to calculate the aggregate welfare effects of the breakup of the monopoly power of Ethio Telecom. Thus, the relative mean aggregate welfare gains among current and new users are equivalent to 1.37 percent. Richer households in higher consumption deciles would also experience

> imputation of residuals. using the average expenditures among primary sampling units, which give the most conservative estimates. Hot deck random imputation was also carried out to estimate expenditure; this could serve as an upper-bound estimate. Other expenditure imputation models are linear regression imputation, stochastic regression imputation, quantile regression imputation, and quantile regression with random imputation of residuals. See the mcema user manual for more information on each methodology (RodríguezCastelán et al. 2021). The relative impact on welfare is the average absolute impact on the average income, times 100.

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the largest gains in both absolute and relative terms in comparison with poorer households. Specifically, the combined monetary gains of the top consumption decile will be approximately 2.0 percent, compared with 0.4 percent among the lowest consumption decile (figure 4).<sup>28</sup> These welfare gains among current and new users will also translate into an overall reduction in the poverty rate of 0.31 percentage point and an expected increase in inequality of 0.23 Gini point.

**Figure 4. Relative Incidence of Greater Competition, Mobile Services**

<!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>New users relative welfare monetary incidence (percentage)<br>Current users relative welfare monetary incidence (percentage)<br>(percent)<br>Relative welfare monetary incidence<br><!-- End of picture text --> _Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ Relative welfare is the per capita welfare incidence, divided by per capita expenditure. Expenditure estimates were carried out by running a quantile regression model and random imputation of residuals. Expenditure estimates were also carried out using random imputation and calculating the average expenditure by primary sampling unit. These serve as lower- and upper-bound estimates. These have been omitted for simplicity.

# **5. Conclusions**

This paper proposes a practical microsimulation approach to assessing the distributional impact of enhancing market competition in key sectors of economic activity. The approach combines data from household budget surveys with the parameters of reform scenarios in selected markets to estimate the welfare gains among current and new consumers likely to benefit from enhancing competition. This novel approach is applied to the case of the mobile telecommunication market in Ethiopia, which has been dominated by a state-owned monopoly until recently. The results show that a reform scenario that reduces the market share of the monopoly to 45 percent would be associated with a 25.3 percent drop in the prices of mobile services and an increase in the number of new users by 4.6 million. The

> 28 To calculate the money metric welfare of a price decrease, the expected expenditures of new entrants are assumed to be equal to that of the current user group. See the WELCOM user manual for more information; see WELCOM Stata Tool, Global Solutions Group on Markets and Institutions for Poverty Reduction and Shared Prosperity, Poverty and Equity Global Practice, World Bank, Washington, DC, http://dasp.ecn.ulaval.ca/webwel/welcom.html. The change in well-being is equal to the negative value of the product of the estimated change in the proportion of users, the average expected expenditures among new users of the good of interest, and the observed change in price. Average expected expenditures among new users are estimated by running a quantile regression model and random imputation of the residuals for those households with positive expenditures. mcema also provides the option to run a linear regression to estimate these expenditures. Please refer to Rodríguez-Castelán et al. 2021 for a detailed discussion.

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predicted drop in prices and the increase in the number of users will generate an average relative welfare gain of 1.09 percent among current users and 0.28 percent among new users, for a total combined relative welfare gain of 1.37 percent. The combined effect on poverty is a reduction by 0.31 percentage point and a change in inequality of 0.23 Gini point.

Besides these potential direct welfare gains of expanding competition in the ICT market in Ethiopia, the potential marginal effects on adoption may have additional indirect welfare implications because mobile phones are transitioning from simple communication tools to stand-alone service delivery platforms and because digital technologies have the potential of significantly reducing the costs of economic activities (Goldfarb and Tucker 2019; Rennhoff and Routon 2016). Recent research on Africa, for example, has shown the importance of internet access in poverty reduction, increasing household consumption, and improving labor market outcomes (Bahia et al. 2020; Hjort and Poulsen 2019; Masaki, Granguillhome Ochoa, and Rodríguez-Castelán 2020; Masaki, Raja, and RodríguezCastelán 2020). Furthermore, if complementary reforms in other sectors are undertaken, such as the financial sector, mobile devices would have the potential to enable poor people to lower transaction costs, raise access to credit, and apply risk mitigation safeguards, leading to a reduction in vulnerability and poverty (Jack and Suri 2014; Wieser et al. 2019).

The results on Ethiopia are also a useful illustration for policy makers on the potential welfare gains of competition-enhancing policies. This case study shows that breaking up monopoly power in a key sector of the economy, such as mobile services, may have substantial direct positive effects on welfare, especially among lower-income households that are often priced out of sectors that are key to boosting household productive capacity. The analysis presented in this paper can thus be easily replicated in any country in the case of other industries affected by high market concentration.

As in most simulation methods, there are areas of improvement in this proposed approach. First, the simulation is based on partial equilibrium analysis. So, it focuses on the first-order effects of competition on consumer welfare through the price channel, potentially missing other impacts through alternative channels, such as labor markets, asset accumulation, or the diversity of alternatives available in purchasing. Second, by using household budget surveys, which typically do not include detailed information on the quality or brand of the goods and services consumed, the analysis may not capture the potential welfare effects brought about by changes in quality or the effects of alternative pricing schemes. This work may be the subject of future research to improve the simulation approach, which has the potential of becoming a powerful tool to motivate competition reforms around the world.

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