metadata: authors: 0: author: Rodriguez Castelan,Carlos
1: author: Araar,Abdelkrim
2: author: Malasquez Carbonel,Eduardo Alonso
3: author: Olivieri,Sergio Daniel
4: author: Vishwanath,Tara
count: Mexico
docna: 0: docna: Distributional Effects of Competition : A
            Simulation Approach
docty: Policy Research Working Paper
subtopic: Inequality,Information Technology,Telecommunications Infrastructure,Employment and Unemployment
teratopic: Information and Communication Technologies,Social Protections and Labor,Poverty Reduction
abstracts: cdata!: Understanding the economic and social
            effects of the recent global trends of rising market
            concentration and market power has become a policy priority,
            particularly in developing countries where markets are often
            more concentrated. In this context, since the poor are
            typically the most affected by lack of competition, new
            analytical tools to assess the distributional effects of
            variations in market concentration in a rapid and
            cost-efficient manner are required. To fill this knowledge
            gap, this paper introduces a simple simulation method, the
            Welfare and Competition tool (WELCOM), to estimate with
            minimum data requirements the direct distributional effects
            of market concentration through the price channel. Using
            this simple yet novel tool, this paper also illustrates the
            simulated distributional effects of reducing concentration
            in two markets in Mexico that are known for their high level
            of concentration: mobile telecommunications and corn
            products. The results show that increasing competition from
            four to 12 firms in the mobile telecommunications industry
            and reducing the market share of the oligopoly in corn
            products from 31.2 percent to 7.8 percent would achieve a
            combined reduction of 0.8 percentage points in the poverty
            headcount as well as a decline of 0.32 points in the Gini coefficient.
geo_reg_key: 119228,517191
repnme: repnme: Distributional Effects of Competition : A
            Simulation Approach
display_title: Distributional Effects of
            Competition : A Simulation Approach
listing_relative_url: /research/2019/05/31025713/distributional-effects-competition-simulation-approach
topicv3: Poverty m1326687 695,Telecommunications and Broadband Access
            m1327340 286,Broadband Networks m1327576 291,Unemployment m1504051 2747,Broadband m1627085 4739
publishtoextweb_dt: 2019-05-02T00:00:00Z
historic_topic: Information and Communication Technologies,Social Protections and Labor,Poverty Reduction
no_of_pages: 32
author_keys: 0: author_key: 1180219
1: author_key: 517267
2: author_key: 1389197
3: author_key: 926510
4: author_key: 124046
model_extractions: Policy Research Working Paper 8838 Distributional Effects of Competition A Simulation Approach _Carlos Rodríguez-Castelán Abdelkrim Araar Eduardo A. Malásquez Sergio Olivieri Tara Vishwanath_ Poverty and Equity Global Practice May 2019
Policy Research Working Paper 8838

# **Abstract**

Understanding the economic and social effects of the recent global trends of rising market concentration and market power has become a policy priority, particularly in developing countries where markets are often more concentrated. In this context, since the poor are typically the most affected by lack of competition, new analytical tools to assess the distributional effects of variations in market concentration in a rapid and cost-efficient manner are required. To fill this knowledge gap, this paper introduces a simple simulation method, the Welfare and Competition tool (WELCOM), to estimate with minimum data requirements the direct distributional effects of market concentration through the price channel. Using this simple yet novel tool, this paper also illustrates the simulated distributional effects of reducing concentration in two markets in Mexico that are known for their high level of concentration: mobile telecommunications and corn products. The results show that increasing competition from four to 12 firms in the mobile telecommunications industry and reducing the market share of the oligopoly in corn products from 31.2 percent to 7.8 percent would achieve a combined reduction of 0.8 percentage points in the poverty headcount as well as a decline of 0.32 points in the Gini coefficient.

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.

_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 Competition: A Simulation Approach**<sup>**1**</sup>

Carlos Rodríguez-Castelán<sup>2</sup> Abdelkrim Araar<sup>3</sup> Eduardo A. Malásquez<sup>4</sup> Sergio Olivieri<sup>5</sup> Tara Vishwanath<sup>6</sup>

**Keywords:** Poverty, Inequality, Market concentration, Distributional Effects, Simulation, Mexico **JEL codes:** C15, D31, D42, D43, E37

> 1 The authors would like to thank Rogelio Granguillhome for his outstanding research assistance. The authors would also like to thank two peer reviewers of a World Bank Quality Enhancement Review (QER) meeting: Samuel Freije-Rodriguez and Jasmin Chakeri. The paper and the simulation method have also benefited from comments by Paloma Anos-Casero, Najy Benhassine, Carolina SanchezParamo, Ambar Narayan, Oscar Calvo-Gonzalez, Mark Dutz, Mariana Vijil, Carlos Urzúa, Siddharth Sharma, Francesca de Nicola, Carlos Silva-Jauregui, Luis Felipe Lopez-Calva, Lourdes Rodriguez-Chamussy, Dennis Medvedev, Alan Fuchs, Moritz Muller, Martha Martinez Licetti, Tanja Goodwin, Mariana Iootty, Christine Qiang, Peter Kusek, Bob Rijkers, Leonardo Iacovone, Camilo Mondragon, Jan Imhof, Gabriel Lara, Rose Mungai, Arden Finn, and participants at the World Bank Poverty and Equity Global Practice (GP) BBL series (launch event of WELCOM), the World Bank Macroeconomics, Trade and Investment GP Week, the Poverty and Equity GP Management Team meetings, the Poverty and Equity GP 2018 Learning Week, and staff meetings of regional teams of the Poverty and Equity GP for South Asia and the Middle East and North Africa, as well as the Investment and Competition team of the Finance, Competitiveness, and Innovation GP and IFC. This paper was partially funded by the U.K. Prosperity Fund “Improving Business Environment for Prosperity” (IBEP) TF. 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.

> 2 Lead Economist, Poverty and Equity GP, World Bank (crodriguezc@worldbank.org) (corresponding author)

> 3 Research Fellow, University of Laval, World Bank and PEP (aabd@ecn.ulaval.ca)

> 4 Economist, Poverty and Equity GP, World Bank (emalasquez@worldbank.org)

> 5 Senior Economist, Poverty and Equity GP, World Bank (solivieri@worldbank.org)

> 6 Lead Economist, Poverty and Equity GP, World Bank (tvishwanath@worldbank.org)
# **1. Introduction**

Understanding the effects of market concentration on economic productivity and household welfare, poverty, and inequality has become a policy priority, especially in developing countries, which often have more concentrated markets.<sup>7</sup> In addition, a better understanding of the distributional effects of competition is central to policy making since poor households are typically the most affected by the lack of competition, given that they consume more homogeneous goods, have less opportunities to substitute consumption, and have limited access to markets (Creedy and Dixon 1998; Urzúa 2013). Moreover, policies fostering competition have the potential to provide a cost-effective and sustainable mechanism for poverty reduction, particularly compared with welfare interventions, such as social protection or fiscal policies.<sup>8</sup> Although most of the evidence in developed countries is typically centered on the effects of competition on economic productivity and prices, the rise of marker power and the capital share of income have received more attention in developing countries and have ignited the impetus of academics to conduct rigorous analysis on the causal relationship of market concentration and household welfare.<sup>9</sup> Atkin et al. (2018) analyze a regulatory reform in Mexico that allowed the entry of foreign retailers (Walmart supermarkets) during the 2000s by combining microdata sources and bar-coded information on prices. They find that an external source of market competition in local retail markets led to significant welfare gains (6.2 percent) for the average household. However, the positive welfare effects benefited richer households the most. The gains of these households were around 50 percent more than the overall welfare gains of the poorest households because the poor transfer less of their retail consumption to foreign stores. Similarly, Busso and Galiani (2019) take advantage of a random supply shock in the local retail sector in the Dominican Republic through the expansion of small grocery stores generated by the rollout of a social protection program and find significant welfare gains driven by a reduction in prices of about 6 percent. In addition, Urzúa (2013) estimates the distributive and regional effects of firms with market power in Mexico and finds evidence that welfare losses due to monopoly power are significant and larger, in relative terms, among lowerincome households and households in rural areas.

Such rigorous studies on the welfare effects of competition are intensive in data requirements—for example, price data, including barcode data; locally disaggregated data; data on the rollout of supply expansion, and so on—and resources, and usually depend on identifying natural experiments to measure impacts, though this may not be feasible in most developing countries. Moreover, undertaking reforms that improve competition through either the retail trade or regulatory reform are often fraught with political resistance because of concerns about likely adverse impacts on employment and incomes, especially among small local retailers. In such contexts, illustrative ex ante

> 7 Industries in developing countries tend to have high numbers of intermediaries and middlemen because of the lack of strong vertical links.

> 8 Evidence from Mexico points to the fact that higher local market concentration in the retail sector raises the extreme poverty headcount. An increase in one standard deviation in the Herfindahl-Hirschman index is linked to an increase of 1.13 to 1.59 percentage points in the Foster–Greer–Thorbecke headcount ratio (FGT0) for the extreme poverty line. Furthermore, in 2010, the program now known as Prospera reduced the poverty line by 2 percentage points, with 57 percent to 79 percent attributed to the effect of market concentration (Rodriguez-Castelan and Rodriguez-Chamussy 2018).

> 9 For instance, on the effects of mergers and acquisitions, see Connor (2014); Griffith and Harmgart (2012); Hausman and Leibtag (2007); Notaro (2014).

2
assessments through simulations to predict the direct distributional effects of changes in market concentration could serve as a useful starting point for inputs into a proposed reform program.

The simulation tool presented in this paper, the Welfare and Competition tool (WELCOM), has minimum data requirements: a representative household survey with detailed expenditure data, along with explicit parameters that define the market structure of the industry of interest. This simulation approach builds a set of flexible theoretical models (monopoly, oligopoly with a homogeneous good, and partial collusive oligopoly) into a user-friendly interface that can be used to assess the impact of market concentration on household welfare, poverty, and inequality. WELCOM is a Stata-based package conceived as part of a larger effort by the World Bank to improve the understanding of the links among competition, growth, and shared prosperity as well as to promote policies that foster competition, especially in developing countries.<sup>10</sup> This simulation approach contributes to development policy in three ways. First, it provides a simple tool for practitioners and scholars to implement a first-stage analysis of the distributional effects of changes in market concentration. Second, the simulation approach intends to attract the interest of and use by practitioners to motivate discussions on procompetition policy reforms, as well as to improve the understanding of the likely distributional impacts by identifying potential winners and losers of policy changes. Third, the technique contributes to the emerging literature on the effects of competition on household welfare described above, as well as to the literature on simulation methods, which, to the best of our knowledge, does not include other tools to measure the distributional effects of market concentration.

As with any microsimulation tool, WELCOM is not without limitations. First, the tool follows a partial equilibrium approach that relies on the expenditure of households on the products expected to be affected by the policy reform. It thus only captures the first-order effects of competition on welfare. Second, the current version of WELCOM only analyzes the distributional effects of changes in market concentration through the price channel, potentially missing other impacts from alternative channels, such as labor markets or the value of assets, the diversity of alternatives available for purchase, the reallocation of resources, and new consumers who had previously been priced out under imperfect competition. Notably, competition can improve the quality of the products and services provided without noticeable changes in prices. However, because WELCOM relies on household surveys (which typically do not include detailed information on the quality or brand of the goods and services consumed), the tool is not able to capture the potential welfare effects through quality. Third, the tool does not take into consideration firms that may behave outside the conventional norms established in the microeconomic literature, such as public monopolies that provide subsidized services.

This paper also illustrates an application of the tool by simulating the distributional effects of a rise in competition in two markets in Mexico known because of their high levels of concentration: mobile telecommunication and corn products. The application of WELCOM to Mexico relies on nationally representative household income and expenditure data from the 2014 round of the Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH, National Household Income and Expenditure Survey). This data set includes expenditure categories that could be matched with the two markets of interest.

> 10 WELCOM was developed by Poverty and Equity Global Practice in the World Bank. More information about this tool, including installation and documentation, can be found at http://dasp.ecn.ulaval.ca/webwel/welcom.html.

3
The analysis is also based on information from the Mexican Federal Competition Commission and assumes that the mobile telecommunication market behaves as an oligopoly (OLI-NASH) and that corn markets mimic a partial collusive oligopoly (PCO). Then, using WELCOM, data from ENIGH, and assumed parameters of these two markets from recent literature, the analysis shows that expanding competition from 4 to 12 firms in the mobile telecommunications industry and reducing the market share of the dominant oligopoly in corn products from 31.2 percent to 7.8 percent would produce a combined reduction of 0.8 percentage points in the poverty rate and of 0.31 points in the Gini coefficient. The contribution of each market to poverty reduction would be: 0.11 percentage points with mobile telecommunication and 0.66 percentage points with corn products.<sup>11</sup> The paper is organized as follows. Section 2 briefly describes the theoretical models adapted and included in the simulation approach. It also describes the options available for estimating the effects on household welfare. Section 3 introduces the simulation technique and describes the data requirements. Section 4 presents the rationale of selecting Mexico as a relevant case study, as well as some stylized facts on the degree of concentration of the industries selected, that is, mobile telecommunication and corn products, and some descriptive statistics, including data on household expenditures on categories linked to the selected markets. Section 5 discusses the parameters used in the analysis, the main results, and the limitations of the analysis. Section 6 concludes and lays out potential extensions for the simulation approach.

# **2. Conceptual framework**

This section presents an overview of some of the early theoretical approaches to studying the distributional effects of market concentration. Then, it discusses three main theoretical models used in WELCOM to simulate alternative market conditions: (a) the monopolistic structure, (b) the oligopolistic structure (Nash equilibrium), and (c) the PCO structure. These models are the theoretical foundations of the simulation method presented and implemented in the following sections.

# **2.1. Theoretical model**

Stigler (1964) and Dixit and Stern (1982) are among the authors of the first theoretical papers studying the distributional effects of market concentration. The seminal paper by Stigler (1964) proposes a model that focuses on the factors that influence oligopolistic behavior in determining prices above the market threshold. To test empirically his theories on the positive relationship between market concentration and prices, Stigler (1964) started the trend of using the Herfindahl-Hirschman index in econometric analysis. He found that small buyers are more likely to pay higher prices under market concentration than bigger buyers and that buyer diversity ultimately fosters competition. Dixit and Stern (1982) developed an oligopolistic market framework that supported the empirical assessment of welfare under imperfect market conditions. They explored welfare losses in a specific industry with a homogenous good and a small number of firms and noted several distributional issues associated with this framework. For example, if a dollar gain to consumers is perceived as more valuable than a dollar

> 11 The impacts on welfare are not linear. The aggregate effect is thus not necessarily equal to the sum of the individual impacts.

4
of profit, this could be addressed by giving a weight of less than one to the surplus among producers in the summation of overall welfare gains.

Farrell and Shapiro (1990) examined the effects of horizontal mergers on rival firms and consumers and provided a framework for the conditions under which welfare could be improved. Usually, the negative effects of the market power effect dominate, but there may be some conditions under which the efficiency effect dominates, that is, a fall in prices. These results were due to a low industry elasticity of demand because the market share of merged firms increased after the merger, and output shifted to the more efficient firm postmerger. On determining the net effect of prices, welfare implications can be drawn based on the conventional wisdom, that is, a higher price implies less welfare.

Recent papers by Blanchard and Giavazzi (2001), Rodriguez-Castelan (2015), and Spector (2002) have studied the links between the welfare distribution and concentration, but also discussed alternative mechanisms beyond prices, such as wages, employment, and profit shares. Blanchard and Giavazzi (2001) offer useful insights in the design and sequencing of deregulation policy tools, specifically, how product market deregulation raises real wages by lowering the price of goods. In addition, product market deregulation may also decrease unemployment if the barriers to entry are reduced. Moreover, by decreasing total rents, product market deregulation cuts into worker rent appropriation, easing labor market deregulation. However, even if overall unemployment falls, the displacement of workers in incumbent firms may occur, leading to unemployment among currently employed workers as firms shrink. Deregulation must be widespread to avoid rent imbalances between workers in deregulated and workers in regulated sectors. Spector (2002) studies the distributional effects of changes in the magnitude of product market competition. The model predicts that a rise in product market competition raises aggregate employment and the labor share in output in both the short and long run. Specifically, Spector examines how product and labor market regulation were political complements.

Rodriguez-Castelan (2015) examines the direct relationship between market concentration and poverty, more precisely, the policy options needed to achieve poverty reduction under a second-best scenario (of increasing market concentration). The model relies on equivalent income functions and poverty measures and involves studying these links within partial and general equilibrium frameworks. In the general equilibrium approach, the firm ownership and human capital mismatch are possible sources of inequality. If rich individuals are firm owners, high market concentration will increase poverty through the price effect, enabling rich firm owners to receive oligopolistic rents through the income effect. Poverty will increase overall in the model because the price effects outweigh the income effect. In this approach, where consumers have heterogenous skills, there is a threshold of market concentration under which poverty decreases, while concentration increases, but only if the profit shares of the unskilled workers are large enough and there is a significant productivity gap between low- and high-skilled workers. This paper finds that, while it is theoretically possible for high product market concentration to lower poverty, an increase in poverty is more likely.

On the basis of this review of the economic literature, three models have been selected for the simulation: (a) monopoly, (b) oligopoly, and (c) partial collusive monopoly. Each model relies on different assumptions about the underlying structure of the relevant market and the behaviors of the firms. A brief description of these models is presented below. This is useful in determining the parameters that are relevant in the simulation model.

5
MoNo = O p(q) c(q) qz0 p(q) -q — c(q) _= ~~————_~~ dp ~~MONO — Pcomp~~ COMP MONO PMONo MONO = ~~MONO~~ COMP _4 _ ~~—-1~~ COMP =(7 ~~~mc~~ 1+7 ). ~~—-~~ 1 Mono = ~~7,~~ —~1= ~~—7_—~~ 1+)n 1+)n x(p) x(p) > 0 pC) =x 1¢)
p(Q) Q=ait-tdn = PC) aC i n) qiz0 '(Q) = qi: p(Q — C(qi) azo '(q1 i n) = qi PCG i n) — Ci(qi) OLI — NASH dp = ~~POLI-NASH—~PCOMP~~ PcoMP OLI-NASH ~~=~~ OLI-NASH = ~14+7-N
<!-- Start of picture text --> fe)<br><!-- End of picture text --> O p2=Mc 0 = P(Q=Q0+ Qs) - Qo — C(Qo) O pco= | ~~———~~ ] ‘MC ~~<mark>(= 7)</mark>~~ (Pcm= MC) (Peco ~~=“)~~ PCO ~ <u>1-gon</u> dp = ~~pco~ Pcm~~ _ _ ~~0/~~ _ ~~O~~ cM 1+ 0/ n+bo

Ss a)
<!-- Start of picture text --> Inputs Simulation Impact<br>* Prices: 3 ¢ Welfare:3 * Concentration<br>alternatives alternatives —<br>Competition<br><!-- End of picture text -->
is an oligopoly market (OLI-NASH) and that the corn market behaves like a PCO. An additional feature of WELCOM is that it enables users to study multiple sectors simultaneously under different market structures.

WELCOM also allows the user to choose the type of welfare function applied to assess the impacts of the competition reforms. There are three alternative money metric measures available to assess changes in welfare: (a) Laspeyres, (b) equivalent variation, or (c) compensated variation. The latter two options also require the selection of the functional forms to define the utility function of a household. The options available for the simulation method are (a) Cobb-Douglas and (b) constant elasticity of substitution (CES).<sup>18</sup> The user then needs to define the original and final conditions or the characteristics of the market under analysis. The default scenario assumes that the policy reform enhances competition in the market (that is, the movement from concentration to competition); however, the tool also allows an analysis of the opposite scenario (from a competitive market to a market with higher concentration). WELCOM also enables the user to carry out the simulations in gradual steps, thereby providing users with the option to see the gradual liberalization or concentration of sectors of interest, while also focusing on providing the extreme case of perfect (imperfect) competition to determine upper-bound estimates of such an outcome.

In sum, the empirical analysis produced by the WELCOM tool will be based on microeconomic models in which the household is the unit of reference. The data requirements of the module include a representative sample of the population, information on total household expenditures, and household expenditures on the product of interest where the market concentration is observed, or the competition policy reform is expected.

# **4. The Mexico case study**

Mexico is well known for its history of monopolies, including TELMEX on fixed lines (privatized in 1990) and PEMEX in fuel (a state-owned enterprise opened to competition only after 2013), as well as an oligopoly in corn products, an important staple for households in the country.<sup>19</sup> These monopolies and oligopolies emerged because of government intervention and weak regulations and did not deliver the intended results they were originally established to meet (Del Villar 2009). Plagued by low productivity and limited innovation, resulting in high prices for consumers, these monopolies have become an integral part of Mexico’s paradoxical growth performance, especially because recent estimates have concluded that there exists an average 98.2 percent markup in goods across households (Aradillas 2018). This paper shows some stylized facts on the degree of concentration in the two industries selected (mobile telecommunication and corn products) to illustrate the simulation approach, as well as some descriptive statistics, including household expenditures on categories linked to these two industries.

The key data source necessary to use WELCOM is a representative household expenditure survey. In the case of the two sectors studied, the per capita household share of expenditures on mobile

> 18 See Abdelkrimm et al. (2018).

> 19 For TELMEX, see See Acemoglu and Robinson (2013). The category of corn products includes grain corn, maize flour, mass of corn, corn tortilla, toast, and other maize products.

10
telecommunication and corn products are calculated using the ENIGH from 2014. Furthermore, the per capita income (welfare variable) measured using the monthly income is also calculated from the ENIGH for each household. WELCOM requires the poverty line as a variable in the data set to provide insights on impacts on poverty levels. The poverty line used in this case is based on the same survey, following the calculations of the Consejo Nacional de Evaluación de la Política de Desarrollo Social (National Council for the Evaluation of Social Development Policy).<sup>20</sup> For the market structure parameters, the market share of the largest firms in each sector is calculated based on the 2014 economic census produced by the Instituto Nacional de Estadística y Geografía (INEGI, National Institute of Statistics and Geography). The demand price elasticities are derived from existing research papers and from the forthcoming Stata module for WELCOM that will enable users to estimate price and income elasticities.<sup>21</sup>

# **4.1. The mobile telecommunications industry**

The telecommunication sector became a key element of Mexico’s economy during the process of liberalization in 2013. According to the OECD (2017), since the reform started, there has been a 75 percent price reduction in mobile broadband packages alone, adding 50 million subscriptions, more than the entire population of all (seven) Central American countries. Furthermore, Mexico now has the eighth lowest fixed broadband price basket in the OECD for low end users. However, despite the advances in the reform, Mexico still has the highest fixed broadband price basket in the OECD for high end users, demonstrating that higher quality and speed of service remain out of the reach among lower-income groups.<sup>22</sup> Since 2012, mobile cellular basket prices have decreased by 61 percent, well below any of the aforementioned income groups as its subscriptions per 100 population increased by 6 percent (figure 2). However, the market remains highly concentrated. Mexico has the second most highly concentrated market in the world measured through the Herfindahl-Hirschman index (HHI), after the Philippines (Ayala et al. 2017), resulting in the highest HHI among OECD countries (Figure 3). Moreover, per the _OECD Review of Telecommunication Policy and Regulation in Mexico_ (2012), the average loss in consumer surplus due to overpricing and unrealized subscriptions in this sector was 1.8 percent of Mexico’s GDP (2005–09), highlighting the important implications of market concentration for consumer welfare.

> 20 For the purpose of this paper, the definition of poverty is based on household per capita income (instead of adult equivalent). It is thus not exactly the same as the official poverty headcount published by the government of Mexico. The indicator used is the percentage of the population with an income that is lower than the well-being line (linea de bienestar) as defined by the National Council for the Evaluation of Social Development Policy.

> 21 See http://dasp.ecn.ulaval.ca/webwel/welcom.html for more information.

> 22 OECD Prices and Termination rates June 2017. Includes 200 gigabyte/month and 25.0 megabytes per second and above.

11
**Figure 2. Mobile cellular and fixed broadband price baskets and coverage in Mexico relative to similar income regions, 2012–17**

# **a. Mobile cellular price basket b. Mobile cellular price basket vs. subscriptions**

<!-- Start of picture text --> 40 40 100<br>35 35 90<br>80<br>30 30<br>70<br>25 25 60<br>20 20 50<br>15 15 40<br>30<br>10 10<br>20<br>5 5 10<br>0 0 0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Mexico LDC Developed<br>Mobile Cellular Subscriptions Mexico<br>Developing World<br>Source: International Telecommunication Union.<br>Note: LDC = least developed countries. PPP = purchasing power parity. Mobile-cellular basket is composed of the price of standard mobile monthly<br>usage for 30 outgoing calls per month (on-net/off-net to a fixed line and for peak and off-peak times) in predetermined ratios, plus 100 SMS messages.<br>For more information, refer to https://www.itu.int/en/ITU-D/Statistics/Pages/definitions/pricemethodology.aspx.<br>In 2014, 99.9 percent of the market was divided among four companies: Telcel (66.9 percent),<br>Telefónica (21 percent), Iusacell-Unefon (9.1 percent), and Nextel (2.9 percent). The rest of the market<br>was divided between a few virtual mobile operators that had just entered the Mexican market by<br>2014. 23 The simulations in this study use data from 2014; hence, they do not consider the full effects<br>of the reforms, but they highlight the potential benefits these reforms could have in the future.<br>Figure 3. The Herfindahl-Hirschman index in mobile telecommunication, OECD, 2017<br>5000<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>US$ PPP US$ PPP<br>population)<br>Mobile Cellular Subscriptions (per 100<br>Herfindahl-Hirschman index Mexico Switzerland Australia Greece Korea, South Slovenia Hungary Japan Norway Luxembourg Ireland New Zealand Turkey Belgium Estonia Austria Czech Republic Portugal Germany Netherlands OECD Iceland Finland Slovakia Italy Chile Spain France United States of... Canada Poland Sweden Denmark<br>United Kingdom<br><!-- End of picture text --> In 2014, 99.9 percent of the market was divided among four companies: Telcel (66.9 percent), Telefónica (21 percent), Iusacell-Unefon (9.1 percent), and Nextel (2.9 percent). The rest of the market was divided between a few virtual mobile operators that had just entered the Mexican market by 2014.<sup>23</sup> The simulations in this study use data from 2014; hence, they do not consider the full effects of the reforms, but they highlight the potential benefits these reforms could have in the future.

_Source:_ GSMA Intelligence 2018.

23 ITF (2015). Informe Estadístico 4to Trimestre de 2014. Instituto Federal de Telecomunicaciones, July 15, 2015. http://www.ift.org.mx/sites/default/files/comunicacion-y-medios/informes/4ite14-vf-02.pdf 12
The communication products with highest expenditure shares by households in 2014 were cell phones monthly plans, Internet, cards for the cell phone, and local private lines (Table 1). Threequarters of the total expenditures in telecommunication services are related to mobile communication. In this paper, the focus will only be on aggregate mobile telecommunication items, composed of cell phones (monthly plan), cards for cell phones, and cell phones and equipment, making up a combined share of 2.17 percent of total expenditures.

**Table 1. Expenditure shares on communication services**

||**All**|Poor|est||Househ|old inco|me deci|les||R|ichest|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Category**||1|2|3|4|5|6|7|8|9|10|
|**Total communication**|**2.91**|**2.35**|**2.33**|**2.42**|**2.68**|**2.76**|**3.09**|**3.11**|**3.33**|**3.36**|**2.70**|
|**Mobile communication**|**2.17**|**1.74**|**1.75**|**1.67**|**1.87**|**1.93**|**2.24**|**2.22**|**2.44**|**2.47**|**2.13**|
|_Cell phones (monthly plan)_|_1.09_|_0.28_|_0.27_|_0.40_|_0.49_|_0.54_|_0.72_|_0.81_|_1.05_|_1.27_|_1.45_|
|_Card for cell phone_|_0.88_|_1.26_|_1.34_|_1.17_|_1.20_|_1.26_|_1.22_|_1.22_|_1.17_|_1.02_|_0.46_|
|_Cell phones and equipment_|_0.20_|_0.20_|_0.14_|_0.11_|_0.18_|_0.14_|_0.31_|_0.19_|_0.22_|_0.18_|_0.22_|
|**Remainder items**|**0.73**|**0.61**|**0.58**|**0.75**|**0.81**|**0.82**|**0.84**|**0.90**|**0.89**|**0.89**|**0.57**|
|_Local calls (private line)_|_0.39_|_0.20_|_0.28_|_0.41_|_0.46_|_0.46_|_0.45_|_0.51_|_0.49_|_0.48_|_0.30_|
|_Internet_|_0.24_|_0.07_|_0.10_|_0.13_|_0.17_|_0.22_|_0.23_|_0.25_|_0.29_|_0.31_|_0.23_|
|_Long distances (private line)_|_0.04_|_0.04_|_0.04_|_0.04_|_0.05_|_0.06_|_0.07_|_0.06_|_0.04_|_0.04_|_0.03_|
|_Public internet, public phone_|_0.04_|_0.21_|_0.13_|_0.11_|_0.11_|_0.07_|_0.08_|_0.05_|_0.03_|_0.02_|_0.01_|
|_Install. of private line phone_|_0.01_|_0.03_|_0.00_|_0.03_|_0.01_|_0.01_|_0.01_|_0.01_|_0.01_|_0.02_|_0.01_|
|_Publicphone_|_0.01_|_0.05_|_0.03_|_0.03_|_0.01_|_0.02_|_0.01_|_0.01_|_0.03_|_0.01_|_0.00_|

_Source:_ World Bank estimates using ENIGH 2014. _Note_ : Deciles based on household expenditure per capita.

# **4.2. The market of corn products**

Competition in the food and agriculture markets is a key component in determining food security in developing economies. Market concentration in the food production chain has been growing, driven by retail chains, raising concerns of higher commodity prices for consumers (McCorriston 2016). Mexico is no exception to this trend, with high market concentration among key food items such as corn grains. In 2014, the four largest firms amassed 31.2 percent of total gross production per INEGI’s economic census. A closer look at corn flour, an important Mexican dietary staple used to make tortillas, reveals that the firms Gruma and Minsa produced more than 90 percent of corn flour (Zahniser 2004). This concentration resulted in a 26 percent price margin increase due to high market concentration in the tortilla market (Aradillas 2018). Figure 4 shows that Mexico’s maize and maize tortilla consumer price index has been consistently greater than the total consumer price index. Since 2010, both have increased significantly in Mexico, suggesting an increase in the consumer food basket.

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Corn products are the core of basic food consumption for a large portion of the population (especially among the poor) above other cereals such as wheat or rice (see Table 2). In developing countries, the production of corn products typically escapes monopolistic controls in the first chain of the production since several small farms produce maize. However, the products of these small farms tend to converge into a small number of specialized firms for the stages of storage, processing, and commercialization. At this level, corn product manufacturers can exert market power.

**Figure 4. Consumer price index of selected corn products**

<!-- Start of picture text --> 150<br>140<br>130<br>120 Total<br>Maize<br>110<br>Maize Tortilla<br>100<br>90<br>Jan-10 May-10 Sep-10 Jan-11 May-11 Sep-11 Jan-12 May-12 Sep-12 Jan-13 May-13 Sep-13 Jan-14 May-14 Sep-14 Jan-15 May-15 Sep-15 Jan-16 May-16 Sep-16 Jan-17 May-17 Sep-17 Jan-18 May-18<br><!-- End of picture text --> <!-- Start of picture text --> Source: INEGI 2018.<br><!-- End of picture text --> Table 2 shows the expenditure shares on different cereal products in Mexico. The expenditure shares on corn products are relatively high among the poorest households, where they range from about 13.4 percent for the poorest to about 1.0 percent for the richest decile. Among cereal products, corn products are the most highly consumed, followed by wheat products.

**Table 2. Expenditure shares on cereal products**

||**All**|Poo|rest||Househ|old inc|ome dec|iles|||Richest|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Category**||1|2|3|4|5|6|7|8|9|10|
|**TOTAL**|**3.37**|**13.37**|**10.05**|**8.38**|**7.09**|**5.89**|**5.13**|**4.28**|**3.48**|**2.52**|**1.04**|
|**CORN**|**1.72**|**7.51**|**5.84**|**4.95**|**4.08**|**3.23**|**2.76**|**2.31**|**1.70**|**1.13**|**0.33**|
|_Grain corn_|_0.11_|_1.93_|_0.61_|_0.48_|_0.27_|_0.15_|_0.10_|_0.06_|_0.03_|_0.02_|_0.00_|
|_Maize flour_|_0.03_|_0.33_|_0.12_|_0.07_|_0.07_|_0.05_|_0.03_|_0.03_|_0.01_|_0.01_|_0.00_|
|_Mass of corn_|_0.05_|_0.29_|_0.21_|_0.19_|_0.13_|_0.11_|_0.08_|_0.06_|_0.05_|_0.03_|_0.01_|
|_Corn tortilla_|_1.46_|_4.82_|_4.78_|_4.06_|_3.49_|_2.81_|_2.42_|_2.06_|_1.51_|_1.00_|_0.28_|
|_Toast_|_0.06_|_0.10_|_0.08_|_0.11_|_0.09_|_0.08_|_0.10_|_0.09_|_0.08_|_0.06_|_0.03_|
|_Other maize products_|_0.02_|_0.03_|_0.04_|_0.02_|_0.03_|_0.02_|_0.03_|_0.02_|_0.02_|_0.02_|_0.01_|
|**WHEAT**|**1.32**|**4.58**|**3.27**|**2.74**|**2.41**|**2.14**|**1.93**|**1.6**|**1.46**|**1.11**|**0.55**|
|**RICE**|**0.15**|**0.93**|**0.61**|**0.44**|**0.32**|**0.25**|**0.21**|**0.17**|**0.14**|**0.09**|**0.04**|
|**OTHER**|**0.18**|**0.36**|**0.34**|**0.26**|**0.28**|**0.26**|**0.23**|**0.2**|**0.18**|**0.18**|**0.13**|

_Source:_ World Bank estimates using ENIGH 2014. _Note_ : Deciles based on household expenditure per capita.

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# **5. Results**

This section discusses the main results and limitations of the proposed approach to simulate the effects on poverty and inequality of reducing concentration in the markets of mobile telecommunication and corn products in Mexico. The section is organized in four subsections. The first two show the main results of simulating the effects of reducing concentration in the markets of mobile telecommunication and corn products individually (that is, reforming one market at a time). The third subsection shows the aggregate effects of implementing policies to reduce concentration in these two markets simultaneously. In this latter case, since the welfare function is not necessarily linear, the aggregate impact of reforming both markets simultaneously will not necessarily be equal to the sum of the individual impacts. The last subsection discusses some of the limitations of the proposed simulation approach.

# **5.1. Distributional impacts of market concentration in mobile telecommunication**<sup>**24**</sup>

The simulation assumes that, in 2014, the mobile telecommunication market in Mexico was organized as an oligopoly consisting of four firms. In addition, the results of simulating the movement from a concentrated market to a perfect competitive market in multiple steps are shown. One way to interpret these latter results is that, while the perfectly competitive scenario is a long-run counterfactual, the simulations of the different steps represent the likely scenarios in the short and medium term.<sup>25</sup> First, Table 3 shows the simulated impacts of moving from the initial scenario (4 incumbent firms) to a market with 6 firms (2 new entrants) in the short run. Then, the impact of moving to 8 firms (4 new entrants) is simulated, and then from 8 to 12 firms (8 new entrants). The final step assumes the impact of moving to perfect competition in the long run (although, in the mobile telecommunication market, the number of firms tends to be rather small). For the price elasticity of demand, the simulation relies on the empirical estimations reported in Hausman and Ros (2013) for mobile telecommunication services (η ��0.476).

**Table 3. Estimated price change and related parameters**

|**Oligopoly (Nash)**<br>**market**|**Step 1**|**Step 2**|**Step 3**|**Competitive market**|
|---|---|---|---|---|
|Firms�4|Firms� 6|Firms�8|Firms�12|Firms� ∞|
|η��0. 476|η��0.377|η��0.328|η��0.279|η<sup>∗</sup>� �0.1808|
|p1� 1. 344|p�� 1.306|p�� 1.275|p�� 1.229|p�� 1.0000|

_Source:_ World Bank estimates using ENIGH 2014; Hausman and Ros 2013.

_Note:_ Changes in elasticity occur due to the simulation of a large price drop in the item of interest, resulting in a quantity demanded of larger magnitude and decreasing the absolute value of the elasticity.

Table 3 shows illustrates the WELCOM prediction that market concentration increases prices in the mobile communication market in Mexico by about 34.4 percent with respect to a scenario of perfect

> 24 In this section the term telecommunications and communications will be used to refer to mobile communications services.

> 25 The rule that WELCOM follows is to multiply the original number of firms by 1.5 in the first step and then, for the step _n_ , multiply the initial number of firms by _n_ , when _n_ <u>> 2.</u>

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<!-- Start of picture text --> st Mobile Communication<br>9<br>(ia<br>o<br>=<br>o<br>=<br>@<br>ZN<br>ao<br>Q<br>€<br>fe)<br>2<br>@<br>O'S;<br>a -<br>0 100 200 300 400<br>Number of firms<br><!-- End of picture text -->
are based on the consumption of users already participating in the market (that is, households with positive consumption levels), so they might be underestimating potential welfare gains due to the entrance of new consumers, which is more likely to occur among households in the bottom of the income distribution.

**Table 4. Expenditure on mobile telecommunication and potential distributive impact of higher competition**

||**All**|Poor|est||Hou|sehold in|come dec|iles||Ric|hest|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||1|2|3|4|5|6|7|8|9|10|
|Average pc income<sup>a</sup>|3,499|558|1,012|1,326|1,634|1,987|2,395|2,933|3,772|5,338|14,041|
|Absolute impact<sup>b</sup>|6.43|1.00|1.77|2.09|2.80|3.55|4.71|5.54|7.47|10.71|24.72|
|Relative impact<sup>c</sup>|0.18|0.18|0.18|0.16|0.17|0.18|0.20|0.19|0.20|0.20|0.18|

_Source:_ World Bank estimates using ENIGH 2014. a. According to current income (MX pesos of August of 2014). b. The average impact on well-being (moving from concentrated to competitive market) c. The average absolute impact over the average income times 100.

Concerning the impact on poverty and inequality, Table 5 suggests that introducing reforms to enhance competition in the mobile telecommunication market could help reduce different measures of poverty such as the poverty headcount. The results show the upper-bound expected effects, equivalent to 0.39 percentage points of poverty reduction of simulating a movement from a concentrated market with four firms to a perfectively competitive market. The scenario of moving towards 12 firms yields a poverty reduction of 0.11 percentage point. However, the expected impact of entrance of new firms in the mobile communication market on inequality is negligible, even under the upper-bound scenario. This occurs partially due to a small share of expenditure on mobile telecommunication services and since the share of expenditure in mobile communication across the income distribution is relatively stable. In contrast, when the relative shares of expenditure in a specific good differ across the distribution, the relative impacts on inequality tend to be larger.

**Table 5. Market concentration in mobile telecommunication, poverty, and inequality**

||<br>Headcount|Poverty indicat<br>Poverty gap|ors<br>Squared<br>poverty gap|Gini|Inequality indic<br>Atkinson<br>(epsilon=0.5)|ators<br>Entropy<br>(theta=0.0)|
|---|---|---|---|---|---|---|
|Baseline|53.35|21.09|11.01|0.497|0.2096|0.4328|
|Moving into competition|53.24|21.03|10.97|0.497|0.2096|0.4328|
|Difference|-0.11|-0.06|-0.03|0.0000|0.0000|0.0000|

_Source:_ World Bank estimates using ENIGH 2014.

As suggested in Figure 6, the simulated reduction in the poverty headcount reduction as 2 new firms enter the market is just 0.062 percentage points, followed by a reduction of 0.065 percent when there are 8 firms instead of the original 4, reaching a reduction of 0.11 percent when there is a total of 12 17
<!-- Start of picture text --> Mobile Communication<br>: 534!<br>a<br>2531<br>é<br>Number offirms<br><!-- End of picture text --> 0 = <!-- Start of picture text --> n = —0.876<br>~ = 31.2 “9 = 15.60 “9 = 10.40 “9 = 7.80 Firms = 0<br>n = —0.876 | n = —0.65 n = —0.57 n = —0.54 | = —0.429<br>= 1.55 1= 1.31 = i2il 1= 1.16 9 = 1.0000<br><!-- End of picture text -->
<!-- Start of picture text --> o Corn<br>o-<br>~<br>a<br>"<br>2sot—oO<br>€<br>oO<br>2<br>s=o<br>a<br>&<br>8N<br>—<<br>ao<br>2[at=<br>=TTT<br>010 market size ;in % 20 30<br><!-- End of picture text -->
concentration would benefit those households at the bottom of the income distribution more (in relative terms), since households in the lower income deciles allocate a larger share of their consumption to corn products. For instance, improvement among households in the four lowest deciles of moving from a concentrated market to perfect competition would be equivalent to 2.89, 2.25, 1.90, and 1.57 percent of their consumption, respectively. In contrast, the average relative impact among households in the three highest decile is only about 0.4 percent (though the absolute impacts increase as one moves toward higher income deciles), since corn is a staple in the Mexican diet especially relevant among low-income groups.

**Table 7. Expenditures on corn products and potential distributive impact of higher competition**

||**All**|Poo|rest||Hous|ehold in|come d|eciles||Rich|est|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||1|2|3|4|5|6|7|8|9|10|
|Average pc income<sup>a</sup>|3,499|558|1,012|1,326|1,634|1,987|2,395|2,933|3,772|5,338|14,041|
|Absolute impact<sup>b</sup>|23.1|16.1|22.7|25.2|25.6|24.6|25.4|26.0|24.6|23.1|17.6|
|Relative impact<sup>c</sup>|0.66|2.89|2.25|1.90|1.57|1.24|1.06|0.89|0.65|0.43|0.13|

_Source:_ World Bank estimates using ENIGH 2014. a. According to current income (MX pesos of August of 2014). b. The average impact on well-being (moving from concentrated to competitive market) c. The average absolute impact over the average income (in %).

Regarding the impact of reducing concentration on poverty and inequality, Table 8 shows that moving from a concentrated to a more competitive market (and the price changes derived from this change) reduces the poverty headcount by 0.66 percentage points and the poverty gap by 0.53 percentage points. As shown in Figure 8, short-run estimates—corresponding to the first step of the stepwise simulation—suggest that the poverty headcount falls by 0.45 percentage points as the market share of incumbent firms decreases by 50 percent. The extreme case of perfect competition estimates an upper bound reduction of 0.88 percentage points. Inequality will also decline since households at the lower end of the income distribution have larger expenditure shares in corn products than those at the higher end of the distribution. Finally, the improvements in inequality are modest relative to the gains in poverty reduction.

**Table 8. Estimated effects on poverty and inequality of changes in competition in corn products**

||**Pov**|**erty indic**|**ators**|**Ine**|**quality indicators**||
|---|---|---|---|---|---|---|
||Headcount|Poverty<br>gap|Squared<br>poverty gap|Gini|Atkinson<br>(epsilon=0.5)|Entropy<br>(theta=0.0)|
|Baseline|53.35|21.09|11.01|0.4970|0.2096|0.4328|
|Moving into competition|52.69|20.55|10.61|0.4938|0.2071|0.4263|
|Difference|-0.66|-0.53|-0.39|-0.0032|-0.0025|-0.0065|

_Source:_ World Bank estimates using ENIGH 2014.

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<!-- Start of picture text --> Corn<br>534<br>532<br>E<br>bofo)|<br>oO<br>Tt<br>Oo 53-<br>o<br>—<br>x)<br>><br>we<br>> 5284<br>o<br>><br>oO<br>a<br>5264<br>524$s<br>0 10 market sizeanin % 20 30<br><!-- End of picture text -->
Although reducing concentration in the markets of mobile telecommunication and corn products would lead to a significant reduction in poverty, the impact of lower concentration on inequality reduction would be more moderate. These results are due to the relative shares of consumption across the income distribution. In particular, the share of expenditure allocated to mobile communication services is relatively consistent across income deciles.

# **5.4. Limitations**

Although the WELCOM tool is a useful application of simulations for ex ante distributional analysis and policy discussions, the methodology is not without limitations. First, the tool follows a partial equilibrium approach that relies on the expenditure of households on the products expected to be affected by the policy reform. In other words, it only captures the first-order effects of competition on welfare. This approach might be adequate for final goods that are not used intensively as intermediate products or inputs in the production of other goods. In contrast, this assumption could pose limitations when analyzing goods that are used as inputs in the elaboration of other products, in which case, alternative approaches that consider the different interactions of the goods with the rest of the economy (such as an input-output matrix) might be more appropriate.

In its current version, WELCOM focuses on the welfare effects of the policies promoting competition through the role of households as consumers. However, the members of a typical household play more than one role in the economy: they may be employees, employers, or firm owners, as well as consumers. Moreover, the current version of WELCOM only analyzes the impacts through the price channel, potentially missing other impacts in alternative channels (for example, labor markets or the value of assets) that could either reinforce or offset the welfare impacts of the policies under study in certain industries.

It is critical to understand that the consequences of product market concentration are not always confined to the product market and may impact other, related markets. Ultimately, an analysis of the ways reforms to promote competition affect household welfare would need to consider the direct effects through price and other channels, such as wages, employment, and profit shares, to determine the distributional effects of competition through its impact on three overlapping groups of economic agents, that is, consumers, workers, and capital owners. The competition conditions among firms affect the welfare of households in their role as consumers through changes in the prices, quality, and diversity of alternatives available for purchase (Atkin et al. 2016; Begazo and Nyman 2016). Although desirable, changes in the quality and diversity of products, as well as the effects of new consumers that were previously priced out, are not within the scope of analysis of WELCOM. The first is due to the use of household surveys, which do not account for the quality of the products consumed. An appropriate method is currently in development in an updated version of the tool and will be incorporating potential welfare gains in the extensive margin of the products of interest.

It is also important to highlight other channels, such as wages, employment, profit, and capital income. For instance, competition can affect household welfare through labor market adjustments induced by competition and the resulting effect on nominal wages, the number and composition of firms demanding labor (due to the potential entry or exit of firms), and labor’s bargaining power or the presence of oligopsonies in factor markets such as labor (CEA 2016b). Moreover, competition affects 22
household welfare through capital gains due to changes in firm profits (Outreville 2015). The specialization of productive activities implies that, unlike the price channel that tends to affect all consumers, the other channels have localized impacts on a specific group of firms and workers belonging to the affected industry. Finally, competition reforms also might have dynamic effects by reallocating resources to more productive activities and higher growth industries (World Bank 2016). All these additional mechanisms have not yet been incorporated into the simulation approach.

The application of the tool to Mexican data is not aimed at any specific discussion of regulatory reform, nor does it imply that the firms in the markets discussed in this paper have or are abusing their market power (that is, that they are involved in anticompetitive practices). Instead, this paper focuses on the potential distributional impacts of abstract policy interventions or changes in market conditions affecting competition in specific markets, taking as a given the policies or behaviors under analysis. Similarly, this paper does not deal with the question of what type of behavior constitutes an abuse of market power or what the best policies are to remedy it (see Motta 2004; Buccirossi 2008 for discussions on these topics), nor with firms that may behave outside the conventional norm established in the microeconomic literature, such as public monopolies that provide subsidized services. Users of WELCOM should be aware of public sector participation in certain industries, such as energy, as this can have significant implications in measuring welfare effects. Users should therefore focus on products and services that are not provided by public entities, specifically because redistributive policies that are channeled through public entities are currently outside the scope of study of WELCOM.

# **6. Conclusion**

The labor share of income has declined consistently across the world over recent decades. In advanced economies, this share reached its lowest point between 2007 and 2008 (49.9 percent), while, among emerging markets, it reached only 34.7 percent in 2006 (IMF WEO 2017). Data from the U.S. Bureau of Labor Statistics also show a decline in the labor share of income (in the nonfarm business sector), which implies that the share of income going to capital has risen (Furman and Orzag 2015). The empirical evidence suggests that the profit rate (measured as markups) has also risen globally (De Loecker and Eekhout 2017). Such an increase in the profit rate and capital share of incomes has been associated with rising inequality (Crane 2016; Khan and Vaheesan 2017; Stiglitz 2012). These recent trends indicating a global rise in market power and market concentration demand analytical tools that allow to assess in a rapid and cost-efficient manner the distributional effects of changes in market concentration. However, current empirical studies that rigorously evaluate the distributional effects of variations in competition are time consuming, costly, and intensive in data requirements.

To fill this gap, this paper introduces a simple, yet novel microsimulation approach to assess the distributional impact of competition that combines expenditure data from household surveys with parameters on market structure in certain industries. This simulation approach adapts a set of flexible theoretical models (monopoly, oligopoly with a homogeneous good, and partial collusive oligopoly) into a user-friendly interface, WELCOM, which can be used to assess the impact of variations in market concentration on household welfare, poverty, and inequality. WELCOM provides a sound, rapid, and efficient option for predicting ex ante likely distributional impacts of competition in a user23
friendly manner. While WELCOM is not meant to be a substitute for more detailed empirical analyses, it is useful for informing policy dialogue. WELCOM aims to simplify the estimation of reform scenarios and become the first building block of a broader project by focusing on the price channel and using data sources that are readily available in developing countries.

The results in the Mexican case study included in this paper could also be a useful illustration to inform policy makers and academics on the potential benefits of reducing concentration in key markets through competition-enhancing policies. The hypothetical case study shows that market concentration in key sectors of the Mexican economy has adverse effects on welfare, especially among poor and vulnerable households. Moving toward competitive markets is among the main objectives of the Mexican government, and this requires removing market imperfections and economic distortions to enhance economic performance. WELCOM illustrates the likely poverty and inequality impacts of reducing concentration in the corn and mobile telecommunication markets. The combined effect of expanding competition from 4 to 12 firms in the mobile telecommunications industry and reducing the market share of the dominant oligopoly in corn products from 31.2 percent to 7.8 percent would achieve an effect on poverty reduction of 0.8 percentage points and a drop in the Gini of 0.32 points.

Although this novel simulation method is not free of limitations, including that it does not incorporate into the analysis other relevant welfare-enhancing channels that directly affect consumers, such as the quality and diversity of products, and new consumers that were previously priced out, WELCOM yields reasonable results using readably available household survey data and minimum information on market structure parameters. The value added of WELCOM is thus its ability to estimate, in a timeand cost-efficient manner, the potential distributional effects of variations in product market competition, linking these changes directly to poverty and inequality. Forthcoming extensions of this simulation approach include modules to estimate price and income elasticities empirically, based on household survey data, and exploring additional extensions to include labor market impacts.

24