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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