metadata: authors: 0: author: Arayavechkit,Tanida
1: author: Jooste,Charl
2: author: Urrutia Arrieta,Ana Francisca
count: Peru
docna: 0: docna: How Regulation and Enforcement of
            Competition Affects ICT Productivity : Evidence from Matched
            Regulatory-Production Surveys in Peru’s ICT Sector
docty: Policy Research Working Paper
abstracts: cdata!: How the enforcement of competition
            regulation of information and communications technology
            affects growth depends on how well firms adapt to
            competitive pressure. This paper tests this empirically
            using Peruvian firm-level data matched to a compilation of
            information and communications technology regulations and
            competition enforcement cases over 10 years. Based on the
            theoretical dispersion in markups, the paper shows that by
            increasing productivity, leaders in a market can avoid the
            effects of competition while maintaining market share.
            However, much depends on the regulatory structure, which
            affects productive firms differently depending on how long
            they have been in business. Highly productive older firms
            translate regulations that make processes more complex (such
            as raising quality standards) into more productivity;
            productive younger firms benefit more from simplifying rules
            that facilitate competition through lower entry barriers and
            improved operating conditions. This feature is consistent
            across different segments of the information and
            communications technology sector.
display_title: How Regulation and Enforcement of
            Competition Affects ICT Productivity : Evidence from Matched
            Regulatory-Production Surveys in Peru’s ICT Sector
listing_relative_url: /research/2022/08/33886747/regulation-enforcement-competition-affects-ict-productivity-evidence-matched-regulatory-production-surveys-peru’s-ict-sector
topicv3: Economic Growth m1326241 471,Competition Policy and Growth m1326469 1080,Information and Communication Technologies
            m1326624 133,Innovation and Growth m1582429 460
publishtoextweb_dt: 2022-08-23T00:00:00Z
dois: 10.1596/1813-9450-10151
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1501627
1: author_key: 1368476
2: author_key: 1855491
model_extractions: Policy Research Working Paper 10151 How Regulation and Enforcement of Competition Affects ICT Productivity Evidence from Matched Regulatory-Production Surveys in Peru’s ICT Sector _Tanida Arayavechkit Charl Jooste Ana Urrutia Arrieta_ Latin America and the Caribbean Region & Macroeconomics, Trade and Investment Global Practice August 2022
Policy Research Working Paper 10151

# **Abstract**

How the enforcement of competition regulation of information and communications technology affects growth depends on how well firms adapt to competitive pressure. This paper tests this empirically using Peruvian firm-level data matched to a compilation of information and communications technology regulations and competition enforcement cases over 10 years. Based on the theoretical dispersion in markups, the paper shows that by increasing productivity, leaders in a market can avoid the effects of competition while maintaining market share. However, much depends on the regulatory structure, which affects productive firms differently depending on how long they have been in business. Highly productive older firms translate regulations that make processes more complex (such as raising quality standards) into more productivity; productive younger firms benefit more from simplifying rules that facilitate competition through lower entry barriers and improved operating conditions. This feature is consistent across different segments of the information and communications technology sector.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region and the Macroeconomics, Trade and Investment 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 cjooste@worldbank.org, tarayavechkit@worldbank.org, aurrutiaarrieta@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
# How Regulation and Enforcement of Competition Affects ICT Productivity: Evidence from Matched Regulatory-Production Surveys in Peru’s ICT Sector<sup>*</sup>

Tanida Arayavechkit<sup>1</sup> , Charl Jooste<sup>1</sup> , and Ana Urrutia Arrieta<sup>1</sup> 1 World Bank

**JEL classification** : C31, D24 **Keywords** : ICT, competition policy, productivity, markups.

> *We are grateful to James Sampi, Ekaterina Vostroknutova, Chad Syverson, Doerte Doemeland, Jorge Araujo, William Maloney, Ana Paula Cusolito, Martin Rama, Elena Ianchovichina and Tania Begazo, for helpful and constructive comments. Jose David Prieto Campo provided excellent research assistance . Corresponding authors can be reached at: tarayavechkit@worldbank.org, aurrutiaarrieta@worldbank.org, cjooste@worldbank.org
# **1 Introduction**

This paper studies the role of competition regulation and enforcement in the ICT ecosystem and the effect on economic growth. We analyze how regulations that make processes more complex or simpler interact with enforcement to produce productivity outcomes.

Our economic framework characterizes the dispersion in markups as the channel of distortions. Markup dispersion is used to identify large market power and weak competition, which together reduce incentives to improve performance and raise productivity. For ICT firms, reducing the markup dispersion when misallocation is serious generates both productivity gains for ICT and productivity spillovers to other sectors.

Matching firm-level data to a compiled data set of regulations and enforcement sheds light on the underlying theory. Enforcing ICT competition, controlling for the regulatory framework, not always reduces the dispersion in markups but may improve productivity: the ability of a firm to adjust to competitive pressures can increase productivity without necessarily causing market share to shrink, but firms that cannot adjust to competition (laggards) lose profits and may eventually exit the market.

Adding to previous research on the importance of ICT to growth, we provide empirical evidence that through regulation more competition and higher growth create competitive pressure.

How ICT contributes to growth is well-established (for a literature review see Dedrick et al. (2003) and Draca et al. (2006)); for one thing, it lowers the cost of exchanging information. ICT impacts productivity in the direct production of ICT goods and services through spill-over or network effects in other industries that use ICT goods and services (Alam et al. (2008)). It also contributes to the stock of capital (e.g., network towers, fiberoptic cables) and supports worker upskilling, enhancing labor productivity through learning opportunities opened up by online platforms.

Spill-over or embedded channels make it more complicated to identify the role of ICT in productivity because they are not directly measured, and often cannot be seen. It is therefore not surprising that the ICT contribution to growth has historically been underestimated (Van Reenen (2010); Hempell (2005); Biagi (2013); Byrne & Corrado (2017)). ICT has been found to matter significantly to the rest of the economy . Not only does it make workers more productive (Brynjolfsson & Hit (2010)), it also gives small firms access to external markets, which improves competition and makes it possible to import skills (Dutz et al. (2018)). It enhances consumer welfare by reducing inefficiencies in the delivery of goods and services (Biagi (2013)) and improves the quality of goods and services produced. ICT enables countries to leapfrog the stages of deployment and hence growth by adopting frontier technologies without incurring the massive costs of first deploying outdated technologies (see examples provide by Hilbert & Katz (2003) - such as adapting 5G 1
technologies without first having to adopt 3G).

However, ICT can also disrupt traditional markets. First-mover firms that adopt ICT early may increase their market share (De Ridder (2019)), which happens when firms are able to substitute away from labor (reduce marginal costs) and produce outputs using ICT technologies (one-time fixed costs). Though this generates aggregate short-term growth benefits, however, in the long run it may reduce growth if new firms are unable to compete with incumbents due to major upfront fixed costs (Aghion et al. (2019)).

The length and efficacy of the ICT contribution to growth depends critically on the diffusion process such as uptake and learning (Hilbert & Katz (2003)). Fast diffusion may imply a quick growth spurt but not necessarily a prolonged one. Slow diffusion might imply a smaller but longer growth process. In present-value terms fast and slow diffusion can be equal unless continual innovations are part of the process. It is important to note, however, that growth depends on adequate infrastructure, hardware, and software. Weak regulations, whether misguided or intentional, can cause problems on both the demand side (diffusion and adoption) and the supply side (development and deployment) of infrastructure, hardware,and software. This paper focuses on the supply side effects of competition policy.

Historically, in most countries in Latin America and the Caribbean (LAC) the public sector provided broadband and ICT goods and services, on the theory that (1) the fixed cost was simply too large for private sector participation and (2) these were in any case considered public goods (Hilbert & Katz (2003)). Today economies of scale and in in some countries greater technological achievements have moved from the public provision model to one where some aspects of ICT are being privatized. Private initiatives can fill in gaps in public provision and address inequalities in access to knowledge and connectivity; however, although in theory regulation might try to promote private initiatives, in practice it may end up creating inefficiencies because of the mix of both inadequate institutional capacity (e.g., a lack of technical skills and inefficient procedural processes), and political will (e.g., inertia from historical views regarding the role of big companies and the state, and often myopic political preferences and corruption).

While the relationship between the use of ICT and firm performance is positive, competitive pressures complicate it (Iacovone et al. (2016)). ICT network development and the resultant productivity gains could be accelerated by removing regulatory bottlenecks. Examples of regulatory constraints include online services that countries tax differently. International players may gain an unfair competitive advantage in terms of pricing, and hence demand, relative to local producers of similar products and services (ITU (2017)). Some regulations can suppress growth by deterring important investments (Dutz et al. (2018); Alam et al. (2008); Cette et al. (2017)). Highly regulated ICT services may also lead to suboptimal allocation of resources in the upward value chain (Paterson et al. (2013)). As 2
ICT services linked to manufacturing become stronger, for example, so does the impact of regulation. Van Reenen (2010) shows that countries whose labor and product markets are highly regulated tend to be less productive, mainly because the regulatory hurdles discourage investment in ICT; as a result, the ICT potential is far below that of peer countries that impose fewer, or more supportive, regulations.

Privacy laws are also very contentious. Laws governing the use of data, how to target advertisements, and user consent are continuously changing. Campbell et al. (2015) show that stricter privacy laws are one reason why the EU has fewer internet advertising firms than the US: there is less incentive to enter a market when the expected revenues are lower. Developing infrastructure often entails large fixed costs. Land rights, access rights (via licenses or permits), sharing of infrastructure, and bidding for spectrum are important to competition both within markets but across users of infrastructure in other markets. Companies that win tenders, as in cases when there are natural monopolies, publicly funded monopolies, or when little product substitutes exist, might be tempted to abuse their dominance (despite concessional limiting abuse in awarded contracts) by charging high prices for use of the infrastructure which limits competition at a cost to consumers. Unregulated companies may also price-discriminate, which also creates uncompetitive outcomes. Restrictions on foreign ownership of infrastructure are another distortion that limits firm entry and technology spillovers (Lee & Shy (1992))and suppresses ICT penetration (Katz & Jung (2021)). Strong patent laws can restrict imitation of technologies, which heightens the market power of firms that innovated early; some regulations limit the number of distributors for certain software or hardware (e.g., chip devices) using quotas, licenses, or permits, which is another way regulation can deter firm entry; some regulations may be a burden for producers requiring important input components with high tariffs. In terms of software, some regulations may prohibit distribution rights (think of operating systems or sales of commercial software); poorly drawn regulations might not protect user data on digital platforms (think of directed advertising); or economies of scale where the first mover has an advantage in data collection and processing, which discourages new entrants from competing. Finally, certain regulations might not break up network externalities, such as tie-in sales where a company might create frontier hardware technology but allows only its own less effective software to operate on it. These are just a few examples of competition limiting cases that apply in different markets for ICT products.

The contribution of this paper is to study the impact on productivity from regulatory and enforcement changes that affect competition in the product market. We present evidence that pro-competition regulation can reduce distortions, especially if it is efficiently enforced. We present a stylized model for understanding some of mechanisms that drive the impact of competition policy on productivity within the ICT sector and the spill-over to other sectors.

3
In what follows, the paper describes the methodology for this study and the empirical results. We then review the deployment and uptake of ICT in LAC and compute measures of relative distortions. Section 3 describes economic framework that allows us to gauge how removing distortions can lead to competition and productivity improvements in the ICT sector. Section 4 looks at the empirical links of _de jure_ and _de facto_ policies on productivity outcomes in Peru. It maps firm-level data to regulations and enforcement data at the four-digit level where markets are delineated as segments of the ICT sector. Collection of regulation and enforcement data is important for studying how regulations may improve growth and reduce market distortions.

# **2 ICT Distortions in LAC**

The uptake of computers and the internet is evidence of the importance of ICT for both work and personal activities. Computer and internet penetration rates have increased across the globe. According to the International Telecommunication Union (ITU), in 2020 the share of LAC households that own a computer ranged from a high of 68% in Uruguay to a low of 17% in Honduras (ITU (2020)). The rates are slightly higher for access to the internet (households that do not own a computer may own a cellphone with internet capabilities; see Figures 1 and 2). ICT adoption varies widely in LAC: Uruguay, Argentina, and Brazil have the highest adoption rates; Honduras, Nicaragua, and Guyana the lowest (Figure 3).

Unfortunately, the benefits of ICT are not shared between and within countries in LAC. For instance download speeds in some countries are slow relative to peers and developing countries generally (Figure 4).

Slow uptake of ICT could be the result of several factors, including the current development context and the regulatory environment of each country. The ITU’s ICT Regulatory Tracker provides comparable scores for both regulatory and competition frameworks (ITU (2020)). The Tracker traces private vs. public ownership, the degree of competition for broadband, basic and leased line services, and whether the law recognizes the concept of significant market power. These are classified as G1, which cannot be considered a competition model, to G5, which features independent regulators, a pro-competition environment, and working together across agencies and firms to improve the deployment of ICT. The ITU scores each country according to these criteria. Table 1 summarizes the set of scores for Peru as an example of the different measurements.

LAC seems representative of a competitive environment. Figure 5 ranks the competitiveness of ICT from a monopoly (score = 4) to one that is fully competitive (score = 1). Because these scores are based on self-reporting, they do not necessarily relate to outcomes. Despite seeming to be pro-competition, in terms of costs LAC is more discouraging. The 4
ITU collects data on connection fees, broadband wireless charges, broadband tax rates, business telephone installation fees, broadband caps, monthly subscription charges and broadband speeds. From these measures we compute a cost index using simple principal components. An increase in the index implies that costs are rising over time. Note that data are standardized across countries and hence an increase should reflect idiosyncratic increases in costs that imperfectly controls for inflation, quality changes or upgrades. To construct this index, all the input variables should have the same sign - so that the interpretation of an increase implies a higher cost relative to a global mean (see Appendix A, Table 41).

The estimated indices are summarized in Figure 6. Costs have been rising over time for all countries in the sample. These costs, associated with higher fees, and lower comparative speeds indicate entry barriers and hence market concentration. Although rising total costs could also indicate quality improvements, the measures here are mainly associated with regulatory costs.

A second measure to compute gaps in the input market uses the statistic from Bartelme & Gorodnichenko (2015) and computes the deviations from a frontier market using inputoutput tables. This measure tracks distortions in input markets by sector, on the assumption that an undistorted economy functions like a market leader. A further assumption is that all countries use the same intermediate goods technology. The distortion is then computed as a ratio of inputs in LAC countries relative to a frontier country in the provision of ICT goods (here the Republic of Korea). A higher ratio implies that the input distortion has risen relative to the frontier economy.

The undistorted share of intermediates of country _i_ in sector _j_ in period _t_ ( _bi,j,t_ ) is related to the distorted share ( _γi,j,t_ ) via a scalar that captures distortions ( _τi,j,t_ ):<sup>1</sup> Given that input-output tables are based on observed variables (i.e., they do not distinguish between distorted and undistorted states); the size of the distortion is then expressed relative to a frontier country (e.g., the United States or Korea). We extract the relative distortions using IO data:

> 1The firm profit maximizing problem reads as max���� _Pj,tYj,t−wNj,t−rKj,t−_<sup>�</sup><sup>_n_</sup> _i_ =1<sup>(1+</sup><sup>_τi,j,t_)</sup><sup>_PiMi,j,t_,</sup> _Kj,t,Nj,t,Mi,j,t_

where the Cobb-Douglas production function is _Yj,t_ = � _Kj,t_<sup>_α_((</sup><sup>_Aj,tNj,t_)</sup><sup>_α_)</sup> �1 _−γj_ � _ni_ =1<sup>_M γ_</sup> _i,j,t_<sup>_i,j_,where</sup><sup>_N_is labor,</sup> _K_ capital stock, _M_ intermediate inputs, _P, w, r_ the price of output _Y_ , labor and capital respectively. The first order condition for the intermediate input is _bi,j,t_ =<sup>_Pi_</sup> _P_<sup>_M_</sup> _j Y_<sup>_i,j,_</sup> _j_<sup>_t_</sup> =<sup>_<u>γ</u>_</sup> _τi,j_<sup>_i,j_.Theundistortedeconomy(i.e.,without</sup> the tax wedge) is equal to _γi,j,t_ . The wedge in this setup reduces the input share coefficient.

5
If a country’s distortion is larger than that of the frontier country, then _τ >_ 0, since by design it produces less. Figure 7 summarizes the estimated distortions by industry in Peru, Colombia, Chile, Costa Rica, Mexico and Brazil. Except for Mexico, all the countries have significant distortions in ICT relative to Korea - in the case of Costa Rica up to 40%.

Two aspects should be noted from these estimates: (1) Output distortions have been rising over time - our hypothesis is that they limit entry, make markets more concentrated and have slowed productivity growth. (2) Input distortions in the ICT sector are high relative to frontier economies, but remained constant over the sample period.

These higher distortions are correlated with low spending by ICT firms on research and development (R&D) relative to middle-and high-income countries (Figure 8) and with low patent registrations (Figure 9).

# **3 ICT Misallocation and Productivity**

This section presents a framework to examine the role of ICT production and competition in aggregate productivity. It builds on Edmond et al. (2015) who analyzed the impact of competitive gains from international trade with varying markups and that where there is extensive misallocation, greater competition reduces markup distortions by creating competition and hence reduces productivity losses. In an extension, Edmond et al. (2018) decompose markup costs into three distinct channels: (1) Aggregate markups act as a tax on output. Competition within a market is low when markup dispersion is higher among firms in that market. (2) The result is a misallocation of factors of production. This is often associated with reallocation of production from firms with high labor shares to ones with low shares and (3) it leads to inefficient entry.

An interesting result of the Edmond et al. (2018) framework calibrated using U.S. census data is that while competition reduces the markup of incumbents, it also reallocates market share toward the largest incumbent firms, leaving the net effect of markups unchanged.

We recast Edmond et al. (2015) in the domestic market as two sectors, ICT and nonICT and three types of firms: final good producers, traditional intermediate goods firms and ICT intermediate goods firms.

## **3.1 Final Goods Producers**

A single consumption good is produced in a perfectly competitive industry using inputs _y_ ( _s_ ) from a continuum of sectors: 6
i> > ~~(—) (-) (—) (-)~~ fo ~~)~~ ~~7~~
Intermediate goods firms engage in Cournot competition within a sector. Taking the wage _w_ as given, a traditional intermediate good firm chooses a quantity to maximize its profit: and, likewise, an ICT intermediate good firm chooses a quantity to maximize its profit: where the demand for _xi_ ( _s_ ) and _zi_ ( _s_ ) is given by Equations (8) - (11). The maximization problems imply that intermediate good firms charge a markup over marginal cost where _ε_<sup>_x_</sup> _i_<sup>(</sup><sup>_s_)</sup><sup>_>_1 and</sup><sup>_εz_</sup> _i_<sup>(</sup><sup>_s_)</sup><sup>_>_1 can be written as:</sup> � _<u>ppxi</u>_ (<sup><u>(</u></sup> _s_<sup>_s_</sup> )<sup><u>)</u></sup> �1 _−γ_ and � _<u>ppzi</u>_ (<sup><u>(</u></sup> _s_<sup>_s_</sup> )<sup><u>)</u></sup> �1 _−γ_ are the firms’ share of sectoral revenue, and _εxi_<sup>(</sup><sup>_s_)and</sup><sup>_εz_</sup> _i_<sup>(</sup><sup>_s_)are</sup> the demand elasticity faced by firms. As _γ > θ_ , firms with larger shares of sectoral revenue face lower demand elasticity and can charge higher markup. The markup a firm charges is an increasing convex function of its market share. At one extreme, when there is no competition, a pure monopolist charges a markup of _θ/_ ( _θ −_ 1). On the other extreme, in a perfect competition environment, an infinitesimal firm charges a markup of _γ/_ ( _γ −_ 1). The more firms enter the market, the lower the markup is.

# **3.3 Free Entry Condition and Equilibrium**

An intermediate good firm pays a sunk cost _f_<sup>_x_</sup> or _f_<sup>_z_</sup> that allows it to draw a sector _s_ (together with sector productivity) and idiosyncratic productivity. Firm productivity _a_<sup>_x_</sup> _i_<sup>(</sup><sup>_s_) and</sup> _a_<sup>_z_</sup> _i_<sup>(</sup><sup>_s_)areaproductofasector-specificcomponent(</sup><sup>_ψx_(</sup><sup>_s_)and</sup><sup>_ψz_(</sup><sup>_s_))andanidiosyncratic</sup> component ( _ν_<sup>_x_</sup> ( _s_ ) and _ν_<sup>_z_</sup> ( _s_ )): 8
The sector-specific component is drawn independently and identically distributed from a Pareto distribution with shape parameter _ξ >_ 0. Within a sector _s_ , the idiosyncratic component _νi_<sup>_j_(</sup><sup>_s_)</sup><sup>_≥_0isdrawnfromadiscretedistribution</sup><sup>_{_0</sup><sup>_,_1</sup><sup>_,_ ̄</sup><sup>_νj_(</sup><sup>_s_)</sup><sup>_}_withprobability</sup> _{_ 1 _− p_<sup>_j_</sup> _l_<sup>(</sup><sup>_s_)</sup><sup>_−pj_</sup> _h_<sup>(</sup><sup>_s_)</sup><sup>_, pj_</sup> _l_<sup>(</sup><sup>_s_)</sup><sup>_, pj_</sup> _h_<sup>(</sup><sup>_s_)</sup><sup>_}_.An entrant draws a random variable</sup><sup>_ν_</sup> _i_<sup>_j_(</sup><sup>_s_) that determines</sup> if it is successful. With probability _p_<sup>_j_</sup> _l_<sup>(</sup><sup>_s_)+</sup><sup>_pj_</sup> _h_<sup>(</sup><sup>_s_), the entrant is successful and begins operat-</sup> ing. With probability _p_<sup>_j_</sup> _h_<sup>(</sup><sup>_s_), the entrant is successful and becomes a high-productivity firm.</sup> With the discrete distribution, in equilibrium each sector is characterized by where _n_<sup>_j_</sup> _l_<sup>and</sup><sup>_nj_</sup> _h_<sup>_j∈{x, z}_are the number of low-productivity and high-productivity firms,</sup> respectively.

The expected profit of an entrant is where _λ_<sup>_′_</sup> _l_<sup>is equal to</sup><sup>_λ_except that</sup><sup>_nj_</sup> _l_<sup>is replaced by</sup><sup>_nj_</sup> _l_<sup>+ 1, and</sup><sup>_λ′_</sup> _h_<sup>is equal to</sup><sup>_λ_except that</sup> _n_<sup>_j_</sup> _h_<sup>isreplacedby</sup><sup>_nj_</sup> _h_<sup>+ 1.</sup><sup>_π_</sup> _l_<sup>_j_and</sup><sup>_π_</sup> _h_<sup>_j_areanoperatingprofitforlowproductivityfirmsand</sup> high productivity firms as described in Equation (12) and (13), respectively. The number of traditional and ICT entrants _N_<sup>_x_</sup> and _N_<sup>_z_</sup> are pinned down by the free entry conditions: Lastly, there is a representative consumer that inelastically supplies one unit of labor and consumes the final good. The labor market clearing condition is then where _n_<sup>_j_</sup> _l_<sup>(</sup><sup>_s_) and</sup><sup>_nj_</sup> _h_<sup>(</sup><sup>_s_)</sup><sup>_j∈{x, z}_are the number of low-productivity and high-productivity</sup> firms, respectively. The market clearing condition for the final good is simply _C_ = _Y_ .

# **3.4 Markup and Productivity Loss**

Aggregate productivity of the economy is defined as: 9
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we assume that the probability of becoming a high or a low productivity firm is the same for traditional and ICT firms and across sector types. Values of the following parameters are chosen to minimize the distance between model moments and counterparts in the Peruvian data: _ξ,_ ̄ _ν_<sup>_x_</sup> _,_ ̄ _ν_<sup>_z_</sup> _, pl, ph, fx, fz, {_ Ω _k}_<sup>5</sup> _k_ =1<sup>_, γ, θ_.</sup> Parameters _ξ,_ ̄ _ν_<sup>_x_</sup> _, pl, ph, fx_ govern the productivity distribution within and between sectors. _ν_ ̄<sup>_z_</sup> and _fz_ determine the market share of ICT firms and aggregate fraction firms that undertake ICT activities. In the data, a firm is identified as an ICT firm if it operates in the ICT sector defined in the ISIC Rev.4.<sup>2</sup> _{_ Ω _k}_<sup>5</sup> _k_ =1<sup>are estimated to match the distribution of firms</sup> across 5 sectors: primary, manufacturing, wholesale trade, retail trade and services.

The relationship between the elasticities _θ_ and _γ_ is determined by equations (14)-(17), from which we can rewrite a firm’s inverse markup as where _ωi_ ( _s_ ) is the firm’s market share, and the inverse markup is simply the firm’s labor share. Letting _α_ 0 and _α_ 1 be the regression results of equation (34), we can estimate: We then calibrate _θ_ and _γ_ using the estimated aggregate markup of 10% for Peru and equation (35). Table (2) and (3) report calibration results.

# **3.6 Quantitative Exercise**

Figure 10a shows the distribution of markups _μi_ ( _s_ ) in our baseline model for traditional and ICT intermediate goods firms, pooling over all sectors. The baseline model has an aggregate markup of 11.15%, an average markup of 11.07% and a standard deviation of log markup of 0.0008. Markups are more dispersed among ICT firms. Firms with more a larger market shares also exhibit higher markups (Figure 10b). With competition among ICT producers , ICT firms with small market shares can charge higher markups than firms that produce traditional intermediate goods.

Reducing entry barriers (via a reduction in fixed costs, such as permit fees, licenses and competitive bidding) reduces market concentration. By lowering the dispersion in markups, sector-level productivity rises. Aggregation implies a net productivity gain, though only a small one.

> 2The activities in the ICT sector can be grouped into ICT manufacturing industries (2610, 2620, 2630, 2680), ICT trade industries (4651, 4652, 4741) and ICT services industries (5820, 6110, 6120, 6130, 6190, 6201, 6202, 6209, 6311, 6312, 9511, 9512).

11
The model provides insights into the link between entry barriers, markups, and productivity. It mainly implies that reducing in entry barriers (e.g., through pro-competitive policies) reduces the dispersion in markups, which then generate less concentrated ICT markets. The link to sectoral and aggregate productivity depends on the degree of price dispersion and the elasticities of substitution.

# **4 Empirical Results**

The previous section highlighted the causal link between the regulatory environment, distortions and productivity. This section focuses on microeconomic evidence by mapping exogenous variations in competition regulations (the sectoral laws) and competition enforcement data to firm level data. We estimate the impact of regulations and enforcing regulations on productivity and markups for different ICT sectors (hardware, infrastructure, telecommunications and software).

The results show that competition enforcement by either Indecopi (the National Institute for the Defense of Free Competition and the Protection of Intellectual Property) or Osiptel (the Supervisory Body of Private Investment in Telecommunications) has a positive impact on revenue total factor productivity (TFPR). Assuming that we account for variations in markups correctly, this indicates that there is a real productivity improvement. Interestingly, and as Edmond et al. (2018) would have expected, this result holds mainly for the most productive firms, the leaders of the productivity distribution. It appears that middle-aged, but more productive firms benefit most from enforcement of competition regulations, however, inconsistent validation by robustness checks raises doubts about the significance of firm age. Markups seem to not be affected by competition enforcement cases.

When isolating each ICT branch, we observe sector-specific patterns.

## **4.1 Data Description**

Firm-level data from the Annual Economic Survey (EEA) is collected by the National Institute of Statistics (INE) for 2007-17. The EEA draws its sample from a directory of formal firms with annual sales above 150 Tax Units,<sup>3</sup> based on administrative tax records. The survey stratifies the sampling frame by economic activity and firm size proxied by annual sales to ensure that it is nationally representative of Peruvian firms at the four-digit ISIC code level. For each year, given their sectoral importance, firms with annual sales above a certain threshold are selected using forced inclusion sampling (inclusion forzosa) to form

> 3150 Tax Units amounted to 517,500 in 2007 and 607,500 in 2017. The tax is called Unidad Impositiva Tributaria.

12
a panel. Note, however, that market entrance and exit can only be approximated; a firm may no longer be observed if its sales have fallen below the selection threshold or if it has actually left the market. Similarly, market size can only be approximated because the panel is drawn from a selection from the universe of firms, rather than from the whole market. We look specifically at the ICT sector, which we separate into hardware, software, telecommunications, and infrastructure subsectors.<sup>4</sup> The survey collected 2,955 observations from ICT firms over the 11-year period. Of these firms, about 34% were active in markets where competition was enforced. In other words, of 940 distinct firms from all ICT sectors, 57% were subject to competition enforcement in their sub-sector. The largest number of firms produced hardware, which accounted for 48% of total ICT sales in the 2007–17 period; telecommunications accounted for 22% of sales, software for 17%, and infrastructure for 14%. Apart from Hardware, the variations in markup were fairly constant over the sample period (Figure 11).

Most firms surveyed were established in the 2000s because of the inclusion criterion of large firms. Except for telecommunications, ICT employs a large share of labor in valueadded. Telecommunications and infrastructure have the highest shares in concentration (proxied by the four-firm ratio) and markups, calculated from variable profits. 5 This is to be expected given the large entry costs, economies of scale and scope.

ICT regulation and enforcement data are drawn from the Peruvian regulatory authorities: the Ministry of Transport and Communications (MTC), Osiptel for telecommunications and infrastructure, and Indecopi for other regulations pertaining to software and hardware.

The MTC is the technical authority for defining and enforcing policy; one of its goals is to connect economic agents nationwide. It has multiple functions, but we focus on its role in the design, coordination, and evaluation of regulations for infrastructure and telecommunications; the evaluation and granting of licenses; and the enforcement and supervision of spectrum assignment.

Osiptel is a specialized, autonomous, and decentralized public body tasked with promoting market competition. It regulates tariffs, regulates access, supervises, and enforces competition and technical norms for telecommunications. It therefore evaluates markets and carries out investigations both on its own initiative or in response to third-party complaints, resolving disputes as well as addressing anti-competitive behavior by imposing corrective measures or sanctions.

Indecopi is charged with promoting and defending competition in all other sectors. It can evaluate and approve corporate mergers in any sector and adjudicate cases that might

> 4For infrastructure we use ISIC 4321 and 4220.

> 5 <u>Π</u> _<u>t</u>_ Markups ( _μ_ ) can be calculated using profits (Π), sales ( _PQ_ ) and costs ( _CQ_ ): _PtQ_<sup>=</sup><sup><u>(</u></sup><sup>_Pt_</sup> _P_<sup>_−_</sup> _t_<sup>_C_</sup> _Q_<sup>_t_</sup> _t_<sup><u>)</u></sup><sup>_<u>Qt</u>_</sup> = 1 _− μ_<sup><u>1</u>.</sup>

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infringe competition. It is also charged with removing unnecessary regulatory barriers and simplifying administrative procedures through ex post assessment of norms.

The applicable regulations are laws and decrees issued between 2007 and 2017 that specifically target the ICT sector and either facilitate or complicate competition. Facilitating regulations improve the administrative efficiency of entering or operating in an ICT market, improving competition by facilitating both market entrance and resource allocation, and potentially contributing to productivity growth. Conversely, regulations that impose additional administrative requirements or make current ones more complex are likely to generate the opposite effect, hindering market entrance, limiting resource movement, and thus deterring productivity growth. However, it could also be argued that new regulations can also address market failures because an explicit purpose of some is to improve service quality. In this sense, they could raise quality or productivity standards for entering a market or for doing business and remaining in the market, which would translate into higher average market productivity. Possibly, each new regulation could boost or depress productivity—the complexity it adds makes it hard to determine whether it promotes or restricts competition.

To disentangle the layers of complexity, we look at how enforcement of any type of regulation affects productivity and markups: First, we study the behavior of firms that were affected by either an increase or a reduction in regulatory requirements. We then do the analysis separately by isolating observations that were affected only by one type of regulation.

We match changes in regulations and their enforcement over time to the firm-level panel. Regulatory cases are matched to firm data by looking at the market affected, defined by the 2-digit ISIC sector and by the year of implementation. We compare the behavior of these firms to that of firms in the same ICT branch that were not subjected to any new regulation, controlling for firm, sector, and year characteristics through fixed effects—if other sector-specific regulations had been introduced, their effect will not be confounded because it is controlled by the fixed effects. The regulatory variables are coded as dummies where 1s imply a change in regulation as defined above. Appendix A.2 presents a compilation table and a summary of important ICT regulatory changes with reference to dates and justifications of the legal changes. For interpretation purposes, the regulations identified are matched to the OECD competition checklist to assess whether they affect the number of suppliers, the ability of suppliers to compete, the incentives of suppliers to compete, or the choices and information available to customers.

Osiptel and Indecopi enforcement data capture the number of investigations of anticompetitive behavior that resulted in a fine or a corrective measure in each year and sector. Additionally, for telecommunications, the data set contains the fines by company name, which are then mapped to a corresponding ISIC code. Each resolution that found infringe14
ments to competition law defines the market affected at the 4-digit ISIC level and the year for which the correction of competition was enforced. Most cases did not specify a particular region, which suggests that the anti-competitive behavior was not limited to a narrow location, so each case is assumed to have national impact. Moreover, the limited number of observations per region in the firm database reduced the possibility of studying the few cases that had a region-specific scope. When a resolution applied to a sub-region rather than the whole national market, our coefficients would be picking up a diluted, underestimated effect that would represent a lower bound of what the actual effect is.

We assume that competition enforcement is exogenous to firm performance. Osiptel and Indecopi are independent and autonomous bodies that receive cases initiated externally by complaint from any physical or moral persons and internally based on monitoring and control organs. This ensures that competition enforcement is applied nationwide regardless of firm or regional idiosyncrasies. Violation of competition law needs to be legally confirmed, following pre-defined requirements, for a case to be accepted. Rulings can be appealed to independent courts. All these elements support the assumption that interventions are exogenous.

The information about _de jure_ and _de facto_ regulations was collected from the digital legislative archive from OSIPTEL and INDECOPI, following the criteria that the degree or resolution had to be relevant (i.e. pertain to at least one of the ICT branches of hardware, software, telecom or ICT infrastructure), timely (i.e. had to be published and implemented in the 2007-2017 period) and had to be related to competition (e.g., encouraging firm entry, reducing misallocation and administrative barriers that hinder innovation).

# **4.2 Empirical Approach**

The methodology described above requires that productivity and markups be identified consistently if the economic impacts of regulation and enforcement are to be accurately characterized. A widely used methodology to estimate productivity is Ackerberg et al. (2015) and a popular extension for estimating markups is De Loecker et al. (2020). Recent critiques of both methodologies require a change in assumptions or a very specific data set. As an example, Bond et al. (2020) show that it is theoretically impossible to recover markups if the firm-level data do not contain separate entries for quantities and prices. Another possible problem arises in the presence of demand shifters, which may render estimates biased (Doraszelski & Jaumandreu (2019)).

On these terms, the data sets used here would be criticized; we do not observe price and quantity data separately (only price times quantity) and we need to account for several demand shifters. Sampi et al. (2021) recently addressed some of these criticisms and produced unbiased policy estimates in the presence of demand shifters as well as missing price data. The methodology, which entails several deviations from Ackerberg et al. (2015) 15
is agnostic about the production function; it controls for demand shifters, is a workaround when firm-level price data are missing and controls for marginal costs, which would render previous regression equations mis-specified.

The demeaned (controlling for fixed effects) system of equations is summarized as: In addition to demeaning the data we include time fixed effects ( _τt, φt_ ) for each period _t_ , and interact them with sectors and states to control for sectoral and provincial shifts. We include two demand shifters: a dummy for the competition regulation laws ( _z_ 1 _jt_ ) that affect each firm _j_ and one for competition enforcement ( _z_ 2 _jt_ ). The dummies are coded as a 1 for when there is an enforcement case or a pro competition law is enacted, and for the years thereafter.

The first equation is the markup equation, but written in terms of the revenue share ( _SXjt_ ). The output elasticity is approximated by log( _γ_ 0(log _Kjt −_ log _Njt_ ))<sup>_γ_</sup> where _Kjt_ is the stock of capital and _Njt_ is the number of employees. The curvature of the elasticity relies on the estimated _γ_ parameters.

The second equation describes output, which is equal to the unknown functional form for the production function _Fjt_ , which is approximated by the fitted values from a Taylor series expansion, _F_ ( _Kjt_ ; _Ljt_ ; _Mjt_ ), where _M_ is intermediate inputs.

The system is jointly estimated using quasi-maximum likelihood (QLME) where the correlation (endogeneity) between error terms is directly estimated and hence controlled for ( _E_ ( _ε_<sup>_μ_</sup> _jt_<sup>(</sup><sup>_εω_</sup> _jt_<sup>+</sup><sup>_εjt_))=</sup><sup>_σ_=0).This controls for various other problems that might arise in</sup> the data. For instance, when price data are not observed, quality improvement will affect both prices and productivity.

Demeaning the system prevents us from recovering the value of _γo_ , thus making it impossible to back out the value of the output elasticity (therefore markups) when the DGP process is a CES production function with elasticity of substitution significantly different from zero, or any other type of unknown production function. However, the approximation in Equation 36 will provide enough information on the curvature of the output elasticity regardless of its value in levels, thus the system in Equation 36 should produce consistent estimates for _τ_<sup>_μ_</sup> and _φ_<sup>_ω_</sup> , independent of the form of the production function. Given that the system is demeaned, the impact of the regulation and enforcement variables is related to the dispersion in the markup around the mean markup of the ICT sectors. This relates to the previous section where aggregate productivity is a function of the markup dispersion of ICT firms.

Note that the same demand shifters ( _z_ ) enter both the markup and the output equation with different parameters. The regulatory variable is a signal to markets to change behavior 16
but also to indicate that a more competitive environment is desirable. A weak signal should not affect competition and productivity. The enforcement coefficient summarizes the direct and indirect implications of infringing. Here, too, if enforcement is weak then significant impacts should not necessarily be expected.

We add additional fixed effects regressions to aid the narrative. Specifically, we estimate the impact of our competition variables on real sales, the real profit rate (real value less variable cost divided by real sales) and marginal costs (proxied by real unit labor costs). Even if our estimates of productivity and markups are insignificant, we may still see changes in costs or sales. Sales, for example, may go up even if productivity does not. If market demand for ICT services increase due to quality changes, for example, it may lead to an increase in the retail prices, which also push up sales, regardless of whether more competition is generated (e.g., lower markup dispersion) or productivity has increased. It is therefore important that the estimator used is robust to changes in market demand. If productivity and markups do not change but sales increase, that can only be due to a change in consumer demand, which we proxy using the sectoral price elasticity of demand, or a reduction in marginal costs, which we approximate using unit labor costs. To approximate quality we require a measure of aggregate demand. Our quality measure follows Khandelwal et al. (2013) and is equal to sales ( _PY_ ) less the price elasticity of demand _β_ multiplied by price. Although we do not observe prices in our data, we can approximate it with our demeaned output elasticity, demeaned wage share and demeaned marginal cost (i.e., log( _P_ ) = log( _ε_<sup>_Y_</sup> _−_<sup>_<u>WN</u>_</sup> _PY_<sup>)</sup><sup>_−W_).Theoutputelasticityissimplyequalto</sup> _<u>K</u> ε_<sup>_Y_</sup> = _γ_ log �log � _N_ ��. Once we construct our price proxy, we estimate the price elasticity of demand as log( _Y_ ) = _β_ log( _P_ ) + _FE_ controlling for several fixed effects (FE). The quality measure derived here controls for markups (both firm and retail level) and changes in marginal cost. This is relevant for some ICT markets such as telecommunications. While competition regulation might not induce more entrants into the telecommunications sector (e.g., due to high fixed entry costs), it may improve the quality of outputs and thus increase sales.

# **4.3 Results**

We apply equation 36 to separate ICT subsectors and to the ICT sector as a whole. The results are summarized in several tables. Our results compare the QLME estimates to Ackerberg et al. (2015) (ACF) for productivity and De Loecker et al. (2020) (DLW) for markups. Because ACF and DLW are standard methodologies for estimating productivity and markups, they function as comparison cases.

Each column of results is indexed. The tables of ICT and its associated sectors summarize the impacts of regulations ( _de jure_ ) and enforcement ( _de facto_ ). The first two columns summarize the impacts on productivity using the QLME and ACF methodologies. The third 17
and fourth columns summarize the impact of competition policy on markups using QLME and DLW. The remaining columns summarize the impact of competition policy on profits (the profit ratio), deflated sales, employment, real marginal costs, real intermediate costs per worker, product quality and TFP covariance (or between effects).

Several robustness checks were performed on the aggregate impacts. We produce tables that differentiate the impacts of enforcements and regulations on the productivity distribution of firms in a given market as well as accounting for firm age.

Our sample consists of firms in the same market that were affected by an intervention (treatment group) and those that were not (control group). We cannot fully control for multi-product firms; however, by segmenting ICT into subsectors we get closer to homogeneous product groupings.

We repeat the exercise for both enforcement outcomes and regulatory changes. The direct estimates of _φ_ measure the impact of regulation and enforcement on TFPR but not necessarily quantity total factor productivity (TFPQ). However, since we are also estimating impacts on the dispersion in markups, when all else is held constant the difference between TFRP and the markup approximates the impact on TFPQ; this requires that marginal costs remain fixed.

# **Aggregate ICT**

The first estimates are for aggregate ICT sector. The impact of enforcement reduces the markup dispersion (note that the result is not statistically significant) (Index 3 and 4 of Table 6). TFPR is positive at the aggregate ICT level, but once again not statistically significant. The net impact on TFPQ, holding all else constant, is positive ( _φ_<sup>_w_</sup> 1<sup>_<τ μ_</sup> 1<sup>).This</sup> result seems robust regardless of methodology.

The impact of enforcement on productivity varies by productivity decile. Both the QLME and ACF methodologies show that TFPR significantly increases for the top 25% of productive firms (which we refer to as the leaders). TFRP for the lower 75% of firms is not statistically significant (Index 1 and 2 of Table 7). This result suggests that strong escape competition effects are at play, where leaders in the market upgrade to escape competition while less productive firms are unable to adjust to competitive forces. The effects on the dispersion in markups tend to not be significant except for firms in the bottom 25 percentile—competitive pressure leads to churning at the lower end of the productivity distribution. This supports the escape-competition channel in the sense that leaders are able to maintain market share in spite of competition and competitive forces create churning mainly for productivity laggards.

When the data are further disentangled into firm age and productivity percentiles it appears that leaders again adjust better than laggards to competitive forces (Index 1 and 8 of Table 8). In fact, the results are significant using both QLME and ACF methodologies for all age profiles; the adjustment is largest for middle-aged firms in business for 6–15 years.

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The impact on the dispersion in markups is generally not significant, except that firms aged 25 and above have higher markups using DLW (Index 8 of Table 9).

Regulation policies seem to generate results similar to those of enforcement (Index 1 and 2 of Table 10). They increase productivity regardless of the methodology applied. Interestingly, those that we coded as complexifying generate significant productivity improvements. In line with our earlier hypothesis, complexifying regulations are related to quality upgrading. Unlike enforcement, regulations that make processes more complex also increase dispersion in markups, although this is not a robust finding across methodologies. This result can mainly be explained by improvements in quality upgrading leading to higher profits, without any remedial action in terms of marginal costs.

As we disentangle these aggregate effects by looking at the firm productivity profiles, we find that leaders in the productivity distribution are more responsive to any regulation in terms of TFPR (Index 1 and 2 of Table 11). In other words, the more productive a firm is compared to its market peers, the more it benefits from regulation. The introduction of any regulation is associated with an increase in TFPR 26%-42% for firms in the 85th percentile compared to that of firms in markets unaffected by any regulation. These results appear to be driven by middle-aged firms, above 5 and below 15 years (Index 1 to 8 of Table 14).

The results hold equally well, though they are slightly smaller, for firms whose regulatory requirements have been simplified. Here a new layer of information is introduced: Laggards in productivity decrease product quality. Apparently low-productivity firms are unable to escape competition when it becomes easier for new firms to enter the market (Index 1 and 2 of Table 12). Disaggregated by age, the most productive middle-aged firms see a robust rise in TFPR while the oldest firms (more than 25 years) instead incur TFPR losses (Index 1 to 8 of Table 16). It is not always more experienced firms that innovate to escape competition. Competitive pressure on older firms seems to lead to productivity losses until ultimately they exit the market; meanwhile, productive middle-aged firms retain or increase market share.

The results also hold if we look only at firms whose regulatory requirements became more complex, and they seem to be larger (Index 1 and 2 of Table 13). This type of regulation stimulates higher product quality, which enables firms to earn larger profits—evidenced by higher markups (though this has not been confirmed by a DLW robustness check) and more profit measured as the log of price cost margins. It is also possible that in meeting the higher quality standards imposed by the new regulations, firms finance those efforts by charging more, which enables them to raise product quality.

Disaggregation by age suggests that older firms are better able to translate more complex regulatory requirements into TFPR gains (Index 1 to 8 of Table 18). Markups seem to rise significantly for older market leaders (Table 19). Only experienced and productive firms increase markups, which suggests that they acquire more market power when con19
fronted by competitive pressure, unlike firms that are subject to simplifying regulations. Because older firms may have access to more funds and knowledge to conform to new industry standards, they can adapt more easily to complexity. However, when regulation is simplified, the source of competitive pressure changes: new firms can enter a market and compete because barriers to entry are lower. Complexifying and simplifying regulations thus impact productivity differently according to firm age.

We also interact the enforcement and regulatory dummies, which allows us to study how much enforcement of competition by Indecopi or Osiptel is enhanced or eroded when a new regulation is introduced. This tells us about how the regulatory environment is improved on paper as well as by actively enforcing competition policy in the market. It also indicates the perverse effects of doing one but not the other, and the extent to which they could cancel each other out: enforcing competitive regulations significantly increases aggregate productivity, but the impact on the dispersion of markups is generally not significant (Index 1 and 2 of Table 20).

From the results for aggregate ICT we can infer that competitive pressure supports productivity, especially for middle-aged firms, across the entire productivity spectrum for competitive policies that are enforced. Leaders that can adapt can escape competitive pressures by boosting productivity without necessarily losing market share (as proxied by markup dispersion). The type of regulation also matters: while both complexifying and simplifying types can boost productivity, they are conditional on firm experience, as measured by age. Simplifying regulations support quality upgrading for young productive firms; complexifying regulations boost the productivity of more experienced firms.

# **ICT Sectors**

**The Infrastructure Subsector:** The few regulations affecting ICT infrastructure mainly apply to bids for the backbone network (see the appendix). Perhaps regulators do not prioritize having more players in infrastructure purely to heighten competition, given the high costs of entry into the subsector - i.e., the focus is on competition for the market, and bids are the instrument to promote competition in downstream markets. Instead, they attempt to regulate the natural oligopoly of infrastructure expansion. Thus, to support competition, regulators have focused on promoting access of new firms to existing infrastructure so that they can provide services, e.g., through infrastructure-sharing agreements with network neutrality.

A notable example of infrastructure regulation enacted in 2015 reduces administrative barriers to entry: It allows automatic approval of permits so that low-infrastructure projects do not have to wait for construction permits to be approved before starting operations; permits are verified a posteriori through a random selection of projects to be reviewed. While there have been several complaints of anti-competitive behavior, such as abuse of dominance and price-fixing, most decisions have been for the defendant.

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The regression results for markups are not statistically meaningful, although the point estimates suggest that TFPR increases with competition policy and enforcement (Index 1 and 2 of Table 21). The only meaningful impact is on quality upgrading due to enforcement across deciles in the productivity distribution. The youngest and most productive firms significantly raised both TFPR and markups as a result of regulations simplifying requirements and enforcement outcomes (Tables 22 and 24). Interestingly, infrastructure is the only sector where innovation upgraded the quality of the service (as measured by our indicator defined above) because of enforced competition, as indicated by an increase in intangible investments and service quality. The more productive a firm, the larger the increase in service quality. The quality measure represents consumer preferences, so improvements in competition regulation translate into welfare gains for consumers because innovation leads to higher-quality infrastructure.

**The Telecommunications Subsector:** Of the many regulations that apply to telecommunications. most relate to transparency and promotion of neutrality and non-discrimination principles. Although fixed costs were already high, several regulatory changes raised fees for compliance, which would make it harder for new companies to enter the market. Some enforcement actions resulted in firms being admonished but other firms incurred monetary fines because of unfair competition and misleading information. Similar to the effect observed for the whole ICT sector, competition enforcement is associated with a significant increase in TFPR for the most productive, middle-aged firms (Tables 26 and 27). Unlike general ICT trends, telecommunication firms whose TFPR rose apparently also raised markups, particularly firms aged 16 to 25 years (Table 28). Introduction of a new regulation is associated with a rise in TFPR and a decrease in markups for market leaders (Table 29).While statistically significant, these results are only confirmed by standard methodologies when the effects are broken up by age. Robust results for TFPR rises appear only for young firms and for reduced markups only for middle-aged firms (Table 30). However, there are not many observations, which calls for caution in interpreting these results, which should be taken simply as indicative.

**The Hardware Subsector:** Among the most significant policy changes affecting ICT hardware was making available for public procurement competitive private sector offerings in an electronic catalog and reducing the costs of switching mobile telephone operators while keeping the same mobile device. Most enforcement findings relate to misleading advertising. Competition enforcement has had hardly any effect on hardware firms (Tables 31-35). except that the least productive firms lowered their product quality, or that consumers opt out from these laggards. This situation poses a threat mainly to middle-aged low-productivity firms, which respond by lowering their markups, perhaps in an attempt to offset decreases in quality. Perhaps middle-aged firms that are not close to the hardware technology frontier struggle to compete when pressure builds. However, the limited effects

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of enforcing hardware competition may also be linked to the reduced size and dynamism of the hardware sector in Peru.<sup>6</sup>

**The Software Subsector:** Most enforcement rulings related to software were based on abuse of dominance and price-fixing—typical rulings to discourage monopoly power. Most of the important regulatory changes relate to tax deductions for spending on R&D; better protection of data; and protecting rights to freely choose an internet service provider; thus, the focus is less on competition and more on consumer welfare.

Regulation and enforcement have negligible impact on both markup dispersion and productivity in the software market (Tables 36-40). The only significant impact is on wages. A hypothesis not tested here is that the software sector, which is typically made up of small firms at the household or individual level; the sector also has more informal workers than other ICT sectors, and leads to increased demand for laborers without sacrificing profits. Interestingly, the impact of enforcement reduces both markup dispersions and TFPR, with TFPQ being positive when all else is held constant. Enforcement also has little impact on sales and marginal costs but considerably more impact on profits.

The reduction in profits correlates with the reduction in markup dispersion. In other sectors we saw markups rise as consumer demand for quality increases, perhaps signaling low elasticity in that market. Here instead we have that consumer preferences decrease as markups rise, pointing to higher elasticity or product substitutability in software products. Unlike in the other sectors, for software we can study the interaction between _de facto_ and _de jure_ regulation that raise the complexity of product market regulations. As a result, we observe an increase in markups and in the profit ratios of market leaders. It may be that leaders just adjust to new regulatory standards by decreasing the number of employees to reduce input costs, or that they simply charge higher prices.

The findings suggest that competition policy and enforcement have mainly improved the productivity of the firms that were already most productive (leaders) and those able to escape competition. It does not appear that heightening competition was a major concern of competition policy. However, if the results are productivity enhancement and quality upgrading, that implies a net welfare benefit. The insignificant markup result, however, suggests strong escape-competition effects; Leaders escape competitive pressures by increasing productivity or upgrading quality. They retain their market share—especially older, more productive firms. The impacts of competition regulation and enforcement on productivity also hold by sector, though regulation has more nuanced effects.

> 6The ICT hardware sector in Peru is very limited in comparison to its South American peers. In 2020, among South American countries, Peru accounted for only 10 percent of computer exports `https://oec. world/en/profile/hs/computers`

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# **5 Conclusion**

Though ICT is firmly integrated into business operations and personal use, in LAC it is not yet as optimized as in high-income countries. When competitive forces materialize, ICT can stimulate growth spurts by improving productivity. Competitive forces create strong incentives for leaders to escape the effects of competition, mainly by heightening productivity and upgrading quality. The direct and spillover effects of ICT productivity increases have meaningful implications for aggregate productivity. Poorly designed regulations and poorly enforced competition policy may not create a truly competitive environment or make a sector more productive.

This paper demonstrates that while countries in LAC in have been targeting pro-competitive regulations, distortions still exist. Because the business environment has not yet noticeably reduced these distortions, there is room for more productivity gains.

To explore the channels and consequences of competition regulation and enforcement we used a heterogeneous firm model that distinguishes between ICT and non-ICT firms, and within those groups productive from less productive firms. The model accounts for a dispersion in markups across ICT firms. Competitive forces (e.g., lower barriers to entry) reduce the dispersion of markups within the ICT sector and ICT sub-sectors and lead to a general increase in the market share of ICT firms. Ultimately, an increase in competition raises productivity within both ICT and non-ICT groups, thus improving aggregate productivity.

The theoretical findings are supported by firm-level empirical analysis. Data for firms are combined with data on ICT competition regulations and enforcement. The data are split further into ICT subsectors—infrastructure, hardware, software, and telecommunications. Ultimately, de jure and de facto competition does improve productivity, but primarily in firms that were already the most productive in support of the escape-competition hypothesis. This result is consistent across firm age groupings. Interestingly, the impact on markup dispersion is not significant across age and productivity groups. Older productive firms have both higher productivity and higher markups when regulations are made more complex; younger productive firms benefit from regulations that simplify processes. In several cases, low-productivity firms confronted by competitive pressures have even lower productivity and markups. This suggests that competition occurs leads to churning at the lower end of the productivity distribution, while market leaders, defined as the top 25 percent in that distribution, are able to escape competition and maintain or increase their market shares.

The subsector results reveal that the types of regulation and enforcement data collected relate to the regulator’s implicit objectives. The objections for infrastructure and telecommunications, as defined by the laws and enforcement rulings, are not necessarily 23
to generate more market entrants, but to mimic competition for the market by stipulating concessions that generate benefits for downstream industries such as connecting industries using the internet backbone. As a result, these sectors see significant quality upgrading and productivity improvements. Where the laws are clear and linked to generating competitive outcomes, as are several in the software sector, markups in those industries fall. The results are more ambiguous for the other subsectors because the intensity of enforcement and design of the laws focus on transparency, procedures, and misleading advertising; the result is a complex set of outcomes.

Our analysis suggests that the efforts of competition policy to generate competitive forces result in productivity gains—which raises the question: would economic growth be even greater if agencies are adequately staffed, roles more clearly delineated, and enforcement broadened? Future work should entail a study on the effects of specific regulatory interventions to distill more specific policy recommendations than the generalized concept of promoting competition, such as those initiatives undertaken by the government to enable ex ante regulatory assessments.<sup>7</sup>

> 7 see `https://www.gob.pe/22241-analisis-de-impacto-regulatorio-ex-ante-air`

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- Hilbert, M. R. & Katz, J. (2003), ‘Building an information society: A Latin American and Caribbean perspective’.

- Iacovone, L., Perreira-Lopez, M. & Schiffbauer, M. (2016), Competition makes IT better: Evidence on when firms use IT more effectively, Policy Research Working Paper 7638, World Bank.

- ITU (2017), ‘The challenge on managing digital content’. Paper for the ITU-TRAI regulatory roundtable.

- ITU (2020), ‘Regulatory tracker’. data retrieved from ITU, `https://www.itu.int/net4/ itu-d/irt/` .

- Katz, R. & Jung, J. (2021), ‘The impact of policies, regulation, and institutions on ICT sector performance’, _International Telecommunications Union_ .

- Khandelwal, A. K., Schott, P. K. & Wei, S.-J. (2013), ‘Trade liberalization and embedded institutional reform: Evidence from chinese exporters’, _American Economic Review_ **103** (6), 2169–95.

**URL:** _https://www.aeaweb.org/articles?id=10.1257/aer.103.6.2169_

- Lee, F. C. & Shy, O. (1992), ‘A welfare evaluation of technology transfer to joint ventures in the developing countries’, _The International Trade Journal_ **7** (2), 205–220.

26
- Paterson, I., Rinc ́on-Aznar, A., Sellner, R., Brandl, M.-B. & Robinson, C. (2013), ‘Regulation of services industries and ICT diffusion: Accounting for upstream and downstream linkages’, _SERVICEGAP Discussion Papers_ (DP23).

- Sampi, J., Jooste, C. & Vostroknutova, E. (2021), Identification properties for estimating the impact of regulation on markups and productivity, Policy Research Working Paper Series 9523, The World Bank.

**URL:** _https://EconPapers.repec.org/RePEc:wbk:wbrwps:9523_

- Van Reenen, J. (2010), The economic impact of ICT, Iterim Report 2007-0020, Smart.

27
# **Figure 1:** Households with Access to Computers

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>%<br>KSACHENDL DEUKAZCANPOLUSAMLT AUSMYSFRAHRVJPN AZEROURUSKORBELEGY URYMARARGCHLTUN TURCRIBRAMEXECUDZA JAMPANCOLBOLPER VNMDOMBWAPRYPHLZAF THAIDNSLVHNDGHA PAKINDKENBGDETHRWA<br><!-- End of picture text --> Source: ITU data, author calculations.

28
# **Figure 2:** Households with Access to the Internet

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>%<br>KSACANKORSWEBELNDLUSAJPNDEUAUSMYSCHLMLTURYKAZARGRUSFRAHUNAZEMARPOLBRACRIROUHRVTHATURDOMPRYMEXEGYTUNCHNVNMZAFCOLPERECUPANBWADZABOLGHASLVIDNPHLHNDINDHNDKENRWAPAKBGDETH<br><!-- End of picture text --> Source: ITU data, author calculations.

29
<!-- Start of picture text --> 0.80<br>0.70 m2014 m2016<br>0.60<br>*) 0.50<br>oO<br>=)<br>7 0.40<br>><br>a<br>S 0.30<br>0.20<br>0.10<br>0.00<br>PR ®adNHN ENWw SEN2CAIN.2@© ENHK© DINAh© DEIR «IC@ © IER.2®© ENO.92 SDN@ EN.AES OES A ENG,.@ O Y W e<br>SP NS s & & SP PF PP PO BL SE & Ro se<br>ys& oOSSFOo PK>’ PKx KF MeQW oS8 SrRY LH WDxe<br>oe vs<br><!-- End of picture text -->
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<!-- Start of picture text --> 4<br>3<br>2<br>4 a<br><= 4 3 = Mobile Services<br>& 2 = Leased Lines<br>1 | ] = Internet Services<br>4 PERT 12005 2008 2010 2012 2018005 2008 2010 2012 2016 aie<br>3<br>2<br>1<br>2005 2008 2010 2012 2015<br><!-- End of picture text -->

Competition Rank (4=Monopoly; 1=Fully Competitive)
# **Figure 6:** Distortions Index

<!-- Start of picture text --> ARG BRA CHL COL CRI MEX PER<br>3<br>2<br>1<br>0<br>−1<br>−2<br>2005 2008 2010 2012 2015<br>Distortion Index<br><!-- End of picture text --> Source: ITU data, author calculations.

Note:This figure is based on data from ITU. The index is generated using standard principal components on data standardized across countries. The variables included are connection fees, broadband wireless charges, broadband tax rates, business telephone installation fees, broadband caps, monthly subscription charges, and broadband speeds.

33
# **Figure 7:** Estimated Magnitude of Distortions, 2005-15

<!-- Start of picture text --> BR A CH L CO L CRI ME X PER 0.3<br>0.4<br>0.2<br>0.2<br>0.0<br>−0.2<br>0.1<br>−0.4<br>2005 2008 2010 2012 2015 2005 2008 2010 2012 2015<br>0.4<br>0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1<br>0.1<br>0.0<br>0.0<br>2005 2008 2010 2012 2015 2005 2008 2010 2012 2015<br>Distortion (IND) relative to KOR<br>Distortion (AGR) relative to KOR<br>Distortion (SRV) relative to KOR Distortion (ICT) relative to KOR<br><!-- End of picture text --> Source: OECD data, author calculations. Note: Values range from 1 to -1. A value larger than 0 implies that the country has relatively more input distortions than the frontier country chosen. A value less than 0 implies that a country has fewer distortions than the frontier country. Unlike in the previous figure, input distortions have been falling in ICT while output distortions have been rising.

34
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<!-- Start of picture text --> Infrastructure Software<br>1.6 15<br>Q Q<br>Z14 3 1.4<br>O q a. o 1-3 oo ae<br>2007 2010 2013 2016 2007 2010 2013 2016<br>Hardware Telecommunications<br>— 3175<br>2 1.25 2<br>1.204 _ eer X 1.50<br>S145 Tee eset, s (eee nee,<br>1.10 ws 1.25 p98 Ses55cc085s9F" “8a. 505¢<br>1.05<br>2007 2010 2013 2016 2007 2010 2013 2016<br><!-- End of picture text -->
# **Table 1:** The ITU’s ICT Regulatory Tracker

||**Regulatory framework**|**Regulatory mandate**|**Regulatory regime**|**Competition framework**|
|---|---|---|---|---|
|**G1**|Consolidated withpolicymaker/regulator|Business as usual|Doingas we have always done|State-owned monopoly|
|G2|Separate agency|First wave of regulatoryreform|Doingmore|Liberalization|
|**G3**|Separate agency, autonomous in decision making|Advanced liberalization of ICT sector|Doingthe right things|Partial competition|
|**G4 (85/100)**|Separate agencywith enforcementpower (18/20)|Adjacent issues become core mandate (11/22)|Doingthe things right (28/30)|Full competition (28/28)|
|**G5**|Separate agencyaspart of a network ofpartner regulators|Active collaboration across the board|Doingthings together|Intra-modal competition|

Note: Number in parenthesis are scores for Peru.
**Table 2:** Parameter Values

|Parameter|Description|Values|
|---|---|---|
|_ζ_|Sectoral productivity|0.51|
| ̄_ν_<sup>_x_</sup>|High productivity (intermediate)|1.16|
| ̄_ν_<sup>_z_</sup>|High productivity (ICT)|1.29|
|_pl_|Probability of low productivity|0.37|
|_ph_|Probability of high productivity|0.18|
|_fx_|Fixed cost of entry (traditional)|0.06|
|_fz_|Fixed cost of entry (ICT)|0.12|
|_γ_|Substitution within sector|10.1|
|_θ_|Substitution between sector|1.34|

40
**Table 3:** Model and Data Moments

|Moments|Data|Model|
|---|---|---|
|Mean market share of top 5% (within sector)|65 %|66%|
|Market share of top 10%|69%|54%|
|Market share of top 25%|84%|85%|
|Market share of top 50%|94%|93%|
|Market share of ICT frms|7.3%|7.7%|
|Fraction of ICT frms|4.1%|4.0%|
|Aggregate markup|10%|11%|
|Median number of frms|1077|1080|

41
# **Table 4:** Scenarios

|Scenario|Productivity Gain<br>(Relative to Baseline)|Number<br>of Firms|Share of<br>ICT Firms|ICT<br>Market Share|
|---|---|---|---|---|
|Baseline|0%|4,896|4.0%|7.7%|
|5% reduction in ICT entry cost|0.1%|4,922|4.6%|8.7%|
|50% reduction in ICT entry cost|0.9%|5,262|5.3%|10.2%|
|5% reduction in entry cost|1.1%|5,379|4.3%|8.5%|
|50% reduction in entry cost|7.0%|9,197|3.1%|6.2%|
|5% reduction in ICT price (subsidy)|0.5%|4,970|8.7%|24.2%|
|5% reduction in ICTprice (control)|-1%|4,438|6.1%|17.7%|

42
**Table 5:** Summary Statistics of ICT Firms

||**Hardware**|**Software**|**Infrastructure**|**Telecommunications**|
|---|---|---|---|---|
|**Number of frms (2017)**|126|77|30|34|
|**Median year of establishment**|1998|2000|1999|2000|
|**Wage bill/VA (mean)**|64.6%|64.4%|67.5%|66.0%|
|**Four-frm ratio (2017)**|32.7%|34.7%|50.1%|51.1%|
|**Sales/Total ICT (2007-17)**|48%|17%|14%|22%|
|**Median markup (using proft shares)**|1.29|1.20|1.19|1.24|

43
**Table 6:** Aggregate ICT: Enforcement impacts

||TF|PR|Mar|kup|Profts|Sales|Employees|Wages|Intermediate Cost|Product quality|Intangibles|
|---|---|---|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||||||||
||1|2|3|4|5|6|7|8|9|10|11|
|Any intervention|0.0601|0.0628|-0.0015|0.0286|-0.0238|-0.0332|-0.068|0.0807|0.0468|-0.0756***|-0.0426|
|Obs. total|2014|2953|2014|2903|2953|2014|2953|2951|2953|2951|1734|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

Note: Each column is numbered and corresponds to the index references in text. FE stands for Fixed Effects.
**Table 7:** Aggregate ICT: Enforcement impacts on Productivity Distribution

||TF|PR|Mar|kup|Quality|
|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||
||1|2|3|4|5|
|15 percentiles|-0.173**|0.0113|-0.117|0.0583|-0.377***|
|25 percentiles|-0.0691|0.0849|-0.140*|0.0194|-0.180**|
|35 percentiles|-0.0632|0.045|-0.130*|-0.275*|-0.114|
|45 percentiles|0.014|-0.0425|-0.0723|-0.167|-0.0712|
|55 percentiles|0.0972|0.0842|-0.0371|0.134|-0.0126|
|65 percentiles|0.0757|0.244**|-0.0499|0.175|-0.00569|
|75 percentiles|0.184**|0.241**|0.0463|0.0122|-0.0512|
|85 percentiles|0.248***|0.413***|0.075|0.162|-0.0746|
|95percentiles|0.186**|0.337**|-0.0385|0.112|-0.0473|
|Obs. total|2013|2953|2013|2903|2951|
|Firm FE|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

45
**Table 8:** Aggregate ICT: Enforcement Impacts on Productivity by Age

||5ye|ars|15y|ears|25y|ears|above 2|5years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.232|0.735|-0.0571|-0.241|-0.225|0.31|-0.633**|0.168|
|25 percentiles|-0.0282|0.255|-0.00992|-0.00412|-0.163|0.115|-0.288|0.00403|
|35 percentiles|0.02|0.26|-0.0506|-0.0465|-0.0457|0.0166|-0.461|0.175|
|45 percentiles|-0.0603|0.182|0.072|-0.0696|-0.0856|-0.0441|0.184|-0.22|
|55 percentiles|-0.12|-0.203|0.137|0.145|0.0562|0.024|0.206|0.146|
|65 percentiles|0.0598|0.243|0.0755|0.272**|0.0822|0.0499|0.0298|0.253|
|75 percentiles|0.442|0.449|0.138|0.254**|0.154|0.0698|0.321|0.0563|
|85 percentiles|0.557**|0.236|0.197*|0.437***|0.239|0.14|0.215|1.190**|
|95percentiles|0.568*|0.74|0.164|0.261|0.201|0.0423|-0.236|1.484**|
|Obs. total|2013|2953|2013|2953|2013|2953|2013|2953|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 9:** Aggregate ICT: Enforcement Impacts on Markups by Age

||5y|ears|15y|ears|25y|ears|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.0787|0.24|-0.0915|0.0338|-0.147|0.0774|-0.306|-0.0943|
|25 percentiles|0.0132|0.0321|-0.13|0.068|-0.173|-0.0821|-0.441|0.358|
|35 percentiles|-0.0006|-0.348|-0.147|-0.149|-0.104|-0.372|-0.341|0.534|
|45 percentiles|-0.0495|-0.311|-0.0589|0.00217|-0.0868|-0.241|-0.164|-0.728|
|55 percentiles|-0.0881|-0.436|-0.0453|0.298**|0.0104|-0.0467|-0.116|0.172|
|65 percentiles|-0.0081|0.141|-0.0761|0.22|0.00343|0.0189|-0.154|-0.101|
|75 percentiles|0.277|-0.0333|-0.00897|0.0384|0.0785|-0.0883|0.0728|0.513|
|85 percentiles|0.195|-0.156|0.0468|0.203|0.114|-0.108|-0.0889|1.289**|
|95percentiles|0.411|-0.0811|-0.0381|0.168|-0.0498|-0.238|-0.595*|1.716**|
|Obs. total|2013|2903|2013|2903|2013|2903|2013|2903|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

46
# **Table 10:** Aggregate ICT: Regulation Impacts

||TF|PR|Mar|kup|Profts|Sales|Employees|Wages|Intermediate Cost|Product quality|Intangibles|
|---|---|---|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||||||||
||1|2|3|4|5|6|7|8|9|10|11|
|Anyregulation|0.0622*|0.0713*|0.0237|0.117*|0.144|-0.0787|-0.0626|-0.0306|-0.0592|-0.0718***|0.341*|
|Simplifying|0.0163|0.0765|0.0161|0.240***|0.06|-0.191|-0.232*|-0.0778|0.0121|0.0852***|0.157|
|Complexifying|0.173***|0.159**|0.183***|0.253|0.326*|-0.114|-0.194|-0.14|-0.0724|0.195***|0.189|
|Obs any|2014|2953|2014|2902|2953|2953|2953|2953|2953|2951|1734|
|Obs Simplifying|995|1786|995|1773|1786|1786|1786|1785|1786|1785|924|
|Obs Complexifying|662|1352|662|1347|1352|1352|1352|1351|1352|1351|711|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.
**Table 11:** Aggregate ICT: Any Regulation Impacts on Productivity Distribution

||TF|PR|Mar|kup|Quality|
|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||
||1|2|3|4|5|
|15 percentiles|-0.0807|-0.174*|-0.0869|-0.268*|-0.0992|
|25 percentiles|0.057|-0.213**|0.00128|-0.131|-0.0273|
|35 percentiles|0.0668|-0.099|-0.0416|-0.234*|-0.0244|
|45 percentiles|0.0913|-0.0265|-0.0329|-0.192|-0.00744|
|55 percentiles|0.0832|0.14|-0.0786|-0.0654|-0.0216|
|65 percentiles|0.154**|0.0332|-0.0318|-0.128|-0.00247|
|75 percentiles|0.166**|0.118|0.00728|0.0953|0.0289|
|85 percentiles|0.232***|0.350***|0.0826|0.218|-0.0288|
|95percentiles|0.284***|0.176|0.0811|0.247|0.000794|
|Obs. total|2013|2953|2013|2902|2951|
|Firm FE|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

48
**Table 12:** Aggregate ICT: Simplifying Regulation Impacts on Productivity Distribution

||TFP|R|Mar|kup|Quality|
|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||
||1|2|3|4|5|
|15 percentiles|0.0031|-0.0627|-0.0135|-0.106|-0.210**|
|25 percentiles|0.0886|-0.184|0.0319|-0.0862|-0.191**|
|35 percentiles|0.104|0.0471|-0.00855|-0.0005|-0.171*|
|45 percentiles|0.170**|-0.109|0.0487|0.0845|-0.142*|
|55 percentiles|0.129|0.153|0.00983|0.148|-0.131|
|65 percentiles|0.183**|0.0423|0.0674|0.0697|-0.144*|
|75 percentiles|0.186**|0.0314|0.101|0.124|-0.0786|
|85 percentiles|0.214***|0.281**|0.109|0.235|-0.0984|
|95percentiles|0.275***|-0.0736|0.137*|0.0645|-0.0711|
|Obs. total|995|1786|995|1773|1785|
|Firm FE|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

49
**Table 13:** Aggregate ICT: Complexifying Regulation Impacts on Productivity Distribution

||TFP|R|Mar|kup|Quality|
|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||
||1|2|3|4|5|
|15 percentiles|-0.13|0.501|-0.158|1.033***|-0.144|
|25 percentiles|0.366|-0.0344|0.202|-0.209|0.316|
|35 percentiles|0.333|-0.181|0.244|-1.080*|0.211|
|45 percentiles|0.0881|-0.174|0.0311|-0.936|0.0174|
|55 percentiles|0.211|0.0695|0.0865|-1.381|0.112|
|65 percentiles|0.31|0.318|0.104|-0.106|0.229|
|75 percentiles|0.360**|0.171|0.123|0.552|0.223|
|85 percentiles|0.521***|0.205|0.415***|0.217|-0.11|
|95percentiles|0.545***|0.511*|0.268|2.075*|0.0262|
|Obs. total|662|1352|662|1347|1351|
|Firm FE|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 14:** Aggregate ICT: Any Regulation Impacts on Productivity by Age

||5ye|ars|15y|ears|25|years|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.0392|-0.476*|-0.205**|-0.0388|0.0246|-0.188|0.419|-0.167|
|25 percentiles|0.105|-0.278|-0.0374|-0.0886|0.0938|-0.0257|0.26|-0.994***|
|35 percentiles|0.122|-0.105|-0.0261|0.181|0.0691|-0.142|0.428*|-0.663**|
|45 percentiles|0.218|0.122|0.0108|0.0995|0.0952|-0.00661|0.28|-0.498*|
|55 percentiles|0.256|0.14|0.000925|0.19|0.0889|0.0695|0.318|-0.0525|
|65 percentiles|0.273|0.19|0.0995|0.0381|0.127|0.0502|0.358|-0.606|
|75 percentiles|0.645***|0.384|0.0333|0.0591|0.199|0.266**|0.246|-0.171|
|85 percentiles|0.639***|0.132|0.200**|0.392***|0.174|0.351***|0.082|-0.104|
|95percentiles|0.623***|0.143|0.255**|0.370**|0.164|0.395*|0.325|-0.892|
|Obs. total|2013|2953|2013|2953|2013|2953|2013|2953|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

50
**Table 15:** Aggregate ICT: Any Regulation Impacts on Markups by Age

||5ye|ars|15y|ears|25y|ears|above|25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.0721|-0.435|-0.185**|-0.391**|0.0787|0.102|-0.125|-0.266|
|25 percentiles|0.085|-0.88|-0.108|0.0582|0.161|0.0549|-0.0366|-0.681|
|35 percentiles|0.101|-0.25|-0.151|-0.182|0.0687|-0.186|0.056|-0.251|
|45 percentiles|-0.084|-0.0479|-0.0934|-0.137|0.102|-0.0789|-0.0205|-0.439*|
|55 percentiles|-0.0852|-0.277|-0.0818|0.096|-0.0746|-0.167|-0.0545|-0.193|
|65 percentiles|-0.0717|-0.373|-0.0182|0.0615|-0.05|-0.229|-0.014|-0.375|
|75 percentiles|0.121|-0.145|-0.047|0.0241|0.0355|0.0278|0.0965|0.588|
|85 percentiles|0.142|0.278|0.0825|0.0181|0.0482|0.376|0.0852|0.323|
|95percentiles|0.158|-0.237|0.0856|0.173|0.0867|0.698*|-0.102|-0.00226|
|Obs. total|2013|2902|2013|2902|2013|2902|2013|2902|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 16:** Aggregate ICT: Simplifying Regulation Impacts on Productivity by Age

||5y|ears|15|years|25|years|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.108|-0.936**|-0.0549|0.489*|0.134|-0.113|-0.232|0.0896|
|25 percentiles|0.0977|-0.47|0.0892|0.00467|0.138|-0.137|-0.126|-0.177|
|35 percentiles|0|-0.0845|0.0852|0.112|0.0964|-0.00231|0.123|0.0252|
|45 percentiles|0.133|-0.179|0.149|-0.135|0.179|0.128|0.436|-0.344|
|55 percentiles|0.15|-0.373|0.066|0.271**|0.233|0.282*|0.303|-0.322|
|65 percentiles|0.144|-0.337*|0.147|0.298**|0.261|0.155|0.337|-0.426*|
|75 percentiles|0.393**|-0.491***|0.109|0.0862|0.208|0.266**|0.182|0.0772|
|85 percentiles|0.426**|-0.249|0.196*|0.414***|0.294*|0.288*|-0.496*|-0.372*|
|95percentiles|0.403**|-0.588**|0.240**|0.232*|0.277|0.649***|0.0996|-1.547*|
|Obs. total|995|1786|995|1786|995|1786|995|1786|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

51
**Table 17:** Aggregate ICT: Simplifying Regulation Impacts on Markups by Age

||5y|ears|15|years|25y|ears|More than|25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.0194|-0.0654|0.0352|0.0334|-0.071|-0.186|-0.361|0.0493|
|25 percentiles|-0.0123|0.689**|0.0793|0.0333|0.0201|-0.320*|-0.181|-0.371|
|35 percentiles|0|0.457|0.0221|-0.0316|-0.0623|-0.164|-0.132|0.487**|
|45 percentiles|-0.0718|0.259|0.0686|-0.172|0.0981|0.179|0.13|0.0884|
|55 percentiles|-0.083|-0.0411|0.0102|0.301**|0.0733|-0.0342|0.0962|-0.318|
|65 percentiles|-0.0861|-0.0823|0.115|0.28|0.0603|-0.131|0.119|-0.306|
|75 percentiles|0.084|-0.339|0.111|0.254*|0.0779|0.106|0.12|0.0405|
|85 percentiles|0.143|0.502|0.175*|0.137|0.138|0.114|-0.548**|-0.235|
|95percentiles|0.073|-0.385|0.170*|0.137|0.134|0.424|-0.0748|-0.386|
|Obs. total|995|1773|995|1773|995|1773|995|1773|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 18:** Aggregate ICT: Complexifying Regulation Impacts on Productivity by Age

||5y|ears|15|years|25|years|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.495|-|-0.366|-0.0374|-0.246|0.235|2.521***|1.368***|
|25 percentiles|0|-0.396***|0.0294|0.299|0.963|0.283**|0.692*|-0.969***|
|35 percentiles|0|0.308|0.102|-0.117|0.271|-0.253**|0.630*|-0.206|
|45 percentiles|0|0.712|0.0474|-0.169|0.0432|0.0455|0.22|-0.265|
|55 percentiles|0|0.712|0.226|0.0255|0.215|-0.449|0.215|0.841|
|65 percentiles|0|0.712|0.208|0.287|0.381|0.0771|1.316|0.841|
|75 percentiles|1.025**|-|-0.0254|0.0829|0.708**|0.206|1.913|0.227|
|85 percentiles|1.084**|0.711***|0.405**|0.156|0.329|-0.00604|0.634**|0.734|
|95percentiles|0.451|0.711***|0.426*|0.974***|0.451|-0.514***|1.259**|0.734|
|Obs. total|995|1773|995|1773|995|1773|995|1773|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

52
**Table 19:** Aggregate ICT: Complexifying Regulation Impacts on Markups by Age

||5|years|15ye|ars|25|years|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.493|-|-0.322|0.166|-0.058|1.133***|0.894|1.442***|
|25 percentiles|0|-3.582***|-0.0362|0.928|1.079|0.868**|0.379|-1.830***|
|35 percentiles|0|1.191***|-0.193|-1.03|1.210**|-2.222***|0.453|-0.867|
|45 percentiles|0|-2.864***|-0.105|-0.727|0.134|-1.536**|0.195|-0.37|
|55 percentiles|0|-2.864***|0.117|-1.214|-0.0987|-1.05|0.325|4.982***|
|65 percentiles|0|-2.864***|0.216|-0.0485|-0.162|-0.258|2.042|4.982***|
|75 percentiles|-0.164|-|0.236|-0.193|-0.108|-0.124|2.312**|2.176**|
|85 percentiles|-0.347|0.673*|0.488***|-0.183|-0.074|-0.751**|0.559**|5.190***|
|95percentiles|0.0333|0.673*|0.387*|1.888|-0.16|0.0864|0.495|5.190***|
|Obs. total|662|1347|662|1347|662|1347|662|1347|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 20:** Aggregate ICT: Interacting Impacts on Productivity Distribution

||TF|PR|Mar|kup|Quality|
|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||
||1|2|3|4|5|
|15 percentiles|-0.09|-0.282*|-0.0652|0.0389|-0.234**|
|25 percentiles|-0.0272|-0.0181|-0.0864|-0.0268|-0.172*|
|35 percentiles|0.0323|-0.0136|0.118|-0.105|-0.067|
|45 percentiles|0.0685|0.0385|-0.0157|0.00691|0.000937|
|55 percentiles|0.0489|0.282**|0.0205|-0.091|-0.12|
|65 percentiles|0.0957|0.267**|0.0318|-0.0804|0.0353|
|75 percentiles|0.176**|0.291**|0.137*|0.244*|0.0917|
|85 percentiles|0.314***|0.472***|0.147**|0.29|0.139**|
|95percentiles|0.325***|0.236|0.116|0.166|0.148*|
|Obs. total|2013|2953|2013|2901|2951|
|Firm FE|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

53
**Table 21:** Infrastructure: Enforcement Impacts

||TF|PR|Ma|rkup|Profts|Sales|Employees|Wages|Intermediate Cost|Product quality|Intangibles|
|---|---|---|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||||||||
||1|2|3|4|5|6|7|8|9|10|11|
|Anyintervention|0.441*|-0.362|0.0257|0.524***|0.217|0.161|0.139|-0.825***|0.174|-0.0863***|0.232***|
|Obs. total|172|352|172|349|352|352|352|352|352|352|192|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.
**Table 22:** Infrastructure ICT: Enforcement Impacts on Productivity by Age

||5y|ears|15|years|25y|ears|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.947|0.00514|0.103|-1.314|-0.325|0.267|-1.112|0.267|
|25 percentiles|1.435|-14.07***|0.249|-1.302|-0.365|0.937|-1.112|1.93|
|35 percentiles|1.435|-2.181|0.195|-1.027|-0.397|0.679|0.329|1.93|
|45 percentiles|1.367|-1.518**|0.232|-1.714*|-0.381|-0.352|0.567|1.949|
|55 percentiles|2.99|-1.028|0.402|-1.805|-1.052|-0.824|0.567|1.949|
|65 percentiles|2.446|-1.147|0.222|0.307|-0.828|-0.947|0.0808|1.949|
|75 percentiles|2.368**|0.191|0.308|0.176|0.00465|0.0501|-1.096|1.009|
|85 percentiles|2.389**|0.729|0.514|-0.191|0.267|0.0501|-1.514|1.009|
|95percentiles|2.448**|0.729|0.47|-0.191|0.264|-0.265|-1.514|1.009|
|Obs. total|172|352|172|352|172|352|172|352|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

55
**Table 23:** Infrastructure ICT: Enforcement Impacts on Markups by Age

||5|years|15ye|ars|25y|ears|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|2.270*|5.116|0.123|2.437|-0.525|-0.0379|-0.94|1.462|
|25 percentiles|1.696*|16.92***|0.156|0.244|-0.503|1.632*|-0.94|8.209***|
|35 percentiles|1.696*|23.16|0.116|-0.78|-0.67|2.862|-0.327|8.209***|
|45 percentiles|0.396|2.355**|0.251|0.886|-0.548|-0.128|-0.0549|5.700**|
|55 percentiles|0.211|0.618|0.313|3.153*|-1.096*|-1.158|-0.0549|5.700**|
|65 percentiles|0.306|0.419|0.212|0.875|-0.852|-1.359|-0.325|5.700**|
|75 percentiles|1.515|1.188|0.00627|0.591|-0.699|-1.219|-1.122|1.08|
|85 percentiles|1.639*|1.22|0.136|1.302|-0.642|-1.219|-1.246|1.08|
|95percentiles|1.644*|1.22|0.237|1.302|-0.64|-0.741|-1.246|1.08|
|Obs. total|172|349|172|349|172|349|172|349|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 24:** Infrastructure ICT: Regulation Impacts on Productivity by Age

||5y|ears|15y|ears|25|years|More than|25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.131|-0.739|0.0633|-0.798|0.243|-0.207|-0.983|-0.366|
|25 percentiles|-0.0712|-2.670**|0.134|-0.527|0.21|0.0763|-0.0517|-0.324|
|35 percentiles|-0.0882|1.41|0.199|0.578|0.264|-0.0851|0.45|-0.0764|
|45 percentiles|0.233|-1.231*|0.116|0.715|0.306|-0.349|0.451|-0.076|
|55 percentiles|0.488|-1.241*|0.133|0.266|0.309|-1.016|-0.00514|-0.121|
|65 percentiles|0.433|-1.054*|-0.118|-0.101|0.339|-1.177*|-1.087|-0.354|
|75 percentiles|2.112**|0.836|-0.121|-0.529|0.393|-0.616|0.343|1.347**|
|85 percentiles|2.146**|-0.855|0.331|-0.0285|0.351|-0.232|0.343|1.347**|
|95percentiles|2.146**|-0.855|0.331|-0.242|0.355|-0.347|0.457|1.347**|
|Obs. total|172|352|172|352|172|352|172|352|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

56
**Table 25:** Infrastructure ICT: Regulation Impacts on Markups by Age

||5|years|15|years|25y|ears|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.562|1.324*|0.082|1.533**|0.186|0.361|-0.624|1.143|
|25 percentiles|0.677|2.251*|0.0815|0.782|0.253|0.94|-0.28|1.954*|
|35 percentiles|0.677|9.504***|0.0472|-0.34|0.349|0.545|0.0939|0.849|
|45 percentiles|0.0623|2.058|0.171|0.0559|0.355|0.465|0.0938|0.851|
|55 percentiles|0.256|2.181|0.145|0.748|0.395|0.247|-0.202|0.921|
|65 percentiles|0.245|1.546|-0.192|0.616|0.442|1.189|-0.854|1.808*|
|75 percentiles|1.769*|2.9|-0.223|1.151|0.351|1.384|0.0134|1.304***|
|85 percentiles|1.903*|-1.611|0.0509|0.987|0.135|0.743|0.0134|1.304***|
|95percentiles|1.903*|-1.611|0.0509|1.729**|0.239|1.084|0.055|1.304***|
|Obs. total|172|349|172|349|172|349|172|349|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

57
**Table 26:** Telecommunications: Enforcement Impacts

||TF|PR|Mar|kup|Profts|Sales|Employees|Wages|Intermediate Cost|Product quality|Intangibles|
|---|---|---|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||||||||
||1|2|3|4|5|6|7|8|9|10|11|
|Anyintervention|0.287**|0.355***|0.181|0.234|0.308|-0.0307|-0.253|0.293|0.212|-0.0463|-0.0649|
|Obs. total|250|391|250|390|390|390|390|390|390|390|241|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.
**Table 27:** Telecommunications ICT: Enforcement Impacts on Productivity by Age

||5|years|15y|ears|25y|ears|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.484|2.037***|0.0675|-0.939|0.0888|1.127|0|0.963*|
|25 percentiles|-0.683|1.948***|0.323|-0.235|0.417|0.719|0|-4.817***|
|35 percentiles|-0.486|0.385|0.247|-0.353|0.402|0.0364|-0.442|7.841***|
|45 percentiles|-1.125|1.161|-0.105|-0.505|0.416|-0.164|0.00493|-4.817***|
|55 percentiles|-1.046|1.188|-0.329|-0.241|0.543|-0.0524|0.00493|-|
|65 percentiles|-0.592|1.184|0.00414|-0.148|0.488|-0.502|0.155|-|
|75 percentiles|0.087|0.448|0.318|-0.0635|0.606*|-0.156|0.155|-|
|85 percentiles|-0.67|-0.241|1.088***|0.861**|0.606*|0.276|0.155|-|
|95percentiles|-1.125|1.157|0.914***|0.745**|0.543|0.363|0.155|-|
|Obs. total|250|391|250|391|250|391|250|391|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

59
**Table 28:** Telecommunications ICT: Enforcement Impacts on Markups by Age

||5y|ears|15|years|25y|ears|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.328|0.157|0.338|2.612**|-0.25|0.195|0|2.051|
|25 percentiles|0.178|0.0254|0.479|0.0135|-0.246|0.9|0|1.918***|
|35 percentiles|0.265|-0.461|-0.256|-0.369|-0.236|-0.453|-0.985|-0.201|
|45 percentiles|0.244|0.217|-0.702|-0.769**|-0.213|-0.666|0.236|1.918***|
|55 percentiles|0.166|0.194|-0.71|0.311|0.221|0.337|0.236|-|
|65 percentiles|0.522|0.134|-0.679|0.945|0.221|0.28|0.432|-|
|75 percentiles|0.208|0.119|-0.396|0.268|0.882**|0.444|0.432|-|
|85 percentiles|0.435|-2.848|0.425|0.896|0.882**|0.680**|0.432|-|
|95percentiles|0.244|0.157|0.554|0.489**|0.221|0.177|0.432|-|
|Obs. total|250|347|250|347|250|347|250|347|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 29:** Telecommunications ICT: Regulation Impacts on Productivity by Age

||5y|ears|15ye|ars|25|years|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|2.941***|4.938***|-0.132|0.113|0.57|-2.144*|0|2.623*|
|25 percentiles|2.561***|4.377***|-0.423|-0.578|1.272|-1.968**|1.136|2.623*|
|35 percentiles|2.709***|2.421|-0.274|-1.332|0.846|-1.343*|1.136|-25.29|
|45 percentiles|2.648***|2.217|-0.484|-1.814|0.201|-0.685|-0.541|-25.29|
|55 percentiles|2.534***|4.027***|-0.627|-1.632|0.0478|-0.399|0.463|-|
|65 percentiles|2.011**|4.024***|-0.359|-0.232|0.0956|-1.13|0.463|-|
|75 percentiles|2.842***|4.021***|-0.308|-1.102|-0.721|-1.951**|-1.509|-2.961***|
|85 percentiles|2.814***|0.833|0.797**|-0.64|-0.663|0.016|-1.509|-2.961***|
|95percentiles|2.777***|0.0602|0.941**|-1.55|0.445|0.016|-1.509|-|
|Obs. total|250|391|250|391|250|391|250|391|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

60
**Table 30:** Telecommunications ICT: Regulation Impacts on Markups by Age

||5y|ears|15y|ears|25ye|ars|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.334|12.45***|-1.052**|-4.852**|1.097|0.105|0|-0.0967|
|25 percentiles|0.432|3.993|-0.951*|-0.446|0.73|0.831|0.557|-0.0967|
|35 percentiles|0.185|-4.046**|-0.953*|-0.544|0.533|3.384**|0.557|8.389|
|45 percentiles|0.00861|-1.791*|-1.110*|0.977|-0.1|0.154|2.081|8.389|
|55 percentiles|-0.109|-0.0125|-1.170*|2.748**|-0.541|-0.947|1.363|-|
|65 percentiles|0.0635|-0.1|-0.622|0.482|-0.208|0.111|1.363|-|
|75 percentiles|0.15|3.009|-0.990**|0.307|0.000575|0.168|-2.522|2.044*|
|85 percentiles|0.0907|2.063|-1.456***|0.637|0.0563|0.547|-2.522|2.044*|
|95percentiles|0.0396|-0.939|-1.560***|0.321|0.0451|0.547|-2.522|-|
|Obs. total|250|346|250|346|250|346|250|346|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

61
**Table 31:** Hardware: Enforcement Impacts

||TF|PR|Mar|kup|Profts|Sales|Employees|Wages|Intermediate Cost|Product quality|Intangibles|
|---|---|---|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||||||||
||1|2|3|4|5|6|7|8|9|10|11|
|Anyintervention|0.0244|0.0164|-0.0402|-0.0357|-0.0913|0.0127|-0.0383|0.0739|0.0944|-0.0718***|-0.0346|
|Obs. total|1174|1626|1174|1624|1626|1626|1626|1626|1626|1626|908|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.
**Table 32:** Hardware ICT: Enforcement Impacts on Productivity by Age

||5y|ears|15|years|25|years|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.0291|0.134|-0.101|-0.273|-0.0772|-0.0388|-0.720**|0.733*|
|25 percentiles|0.0665|-0.25|-0.0101|0.0273|-0.0521|-0.0496|-0.336|1.483|
|35 percentiles|0.134|0.166|0.0446|-0.0668|0.0728|0.0116|-0.417|1.605*|
|45 percentiles|-0.0488|0.0635|0.022|-0.175|0.0605|-0.0606|-0.372|0.239|
|55 percentiles|0.00773|-2.247***|0.0108|0.0771|0.118|0.184|-0.0407|0.675|
|65 percentiles|0.135|0.079|-0.0403|0.204|0.13|0.164|0.00139|0.739|
|75 percentiles|-0.0966|0.337|0.059|0.213|0.119|0.0277|0.778|1.39|
|85 percentiles|-0.0553|-0.15|0.0252|0.384***|0.0197|-0.231*|0.939|1.181**|
|95percentiles|0.294|0.663|-0.0975|0.176|0.283|-0.338**|-0.709|3.036***|
|Obs. total|1174|1626|1174|1626|1174|1626|1174|1626|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

63
**Table 33:** Hardware ICT: Enforcement Impacts on Markups by Age

||5y|ears|15y|ears|25y|ears|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.19|-0.196|-0.237**|-0.504*|0.00776|0.236|-0.252|0.177|
|25 percentiles|-0.0441|-0.14|-0.134|0.0567|-0.133|-0.357|-0.396|1.052|
|35 percentiles|-0.113|0.288|-0.13|-0.309|0.0213|-0.518*|-0.316|1.331**|
|45 percentiles|-0.111|0.427|-0.103|-0.0776|0.0924|0.168|-0.308|0.78|
|55 percentiles|-0.181|-1.464***|-0.115|0.092|0.093|0.331|-0.184|0.698|
|65 percentiles|-0.0884|0.454|-0.165|0.173|0.191|0.335|-0.0143|0.289|
|75 percentiles|-0.233|0.792|-0.0781|-0.14|0.117|0.279|0.824|1.558|
|85 percentiles|-0.213|-0.0176|-0.124|0.0935|0.0428|-0.521*|0.787|1.727|
|95percentiles|-0.0455|0.362|-0.254**|-0.106|0.264|-0.624**|-0.939**|5.798**|
|Obs. total|1174|1626|1174|1626|1174|1626|1174|1626|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 34:** Hardware ICT: Regulation Impacts on Productivity by Age

||5y|ears|15y|ears|25y|ears|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0.239|-0.343|-0.0713|0.0906|0.214|-0.271|0.219|0.0559|
|25 percentiles|-0.0161|0.115|0.145|0.598|0.199|-0.155|-0.00552|-0.456|
|35 percentiles|0.13|-0.837|0.0273|0.628*|0.0897|-0.288|0.388|-0.402|
|45 percentiles|0.144|-0.343|0.0476|0.367|0.0852|0.484|0.06|-0.371*|
|55 percentiles|0.326|-0.629|-0.244|0.233|0.154|0.148|0.432|0.399|
|65 percentiles|0.259|-1.169*|0.241|-0.278*|0.21|0.115|0.0925|0.494|
|75 percentiles|0.778*|-1.799***|0.13|-0.33|0.111|0.317|-0.0479|0.0734|
|85 percentiles|0.846*|-0.804*|0.18|0.512|0.177|0.337|-0.417|-0.363|
|95percentiles|0.662|-2.052***|0.159|0.522|0.249|0.312|-0.146|-2.012***|
|Obs. total|1174|1626|1174|1626|1174|1626|1174|1626|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

64
**Table 35:** Hardware ICT: Regulation Impacts on Markups by Age

||5|years|15|years|25|years|More tha|n 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.502|-0.436|-0.239|0.0407|0.131|0.187|-0.121|-0.478**|
|25 percentiles|-0.592|0.889|-0.154|0.656**|0.21|-0.0468|-0.0568|-0.0147|
|35 percentiles|-0.486|0.157|-0.0919|0.576*|-0.11|-0.332|0.00717|0.721**|
|45 percentiles|-0.313|0.062|-0.113|-0.0972|0.0981|0.619**|-0.119|-0.0688|
|55 percentiles|-0.138|-0.488|-0.293*|0.141|0.095|0.304|0.152|0.0589|
|65 percentiles|-0.174|-1.089**|0.0367|-0.419**|0.0619|0.365|0.339|-0.089|
|75 percentiles|-0.059|-1.756***|0.0567|-0.247|0.126|0.378|0.31|-0.0336|
|85 percentiles|0.0536|-0.542|0.137|0.244|0.0871|0.580**|-0.329|-0.248|
|95percentiles|-0.0904|-1.971***|0.0477|-0.111|0.218|0.598**|-0.441|-1.574***|
|Obs. total|1174|1626|1174|1626|1174|1626|1174|1626|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

65
**Table 36:** Software: Enforcement Impacts

||T|FPR|Mar|kup|Profts|Sales|Employees|Wages|Intermediate Cost|Product quality|Intangibles|
|---|---|---|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|DLW||||||||
||1|2|3|4|5|6|7|8|9|10|11|
|Anyintervention|-0.119|-0.00053|-0.140*|0.0983|-0.0599|-0.279*|-0.0497|-0.0607|-0.223|-0.0723***|-0.165|
|Obs. total|386|584|386|583|584|584|584|584|584|584|393|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.
**Table 37:** Software ICT: Enforcement Impacts on Productivity by Age

||5|years|15y|ears|25|years|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0|1.199|-1.076|0.23|0.155|-0.0348|-0.174|-|
|25 percentiles|1.354|-|-0.241|0.109|1.812|0.136|0|-0.442***|
|35 percentiles|0|-0.906***|-0.990***|0.0993|0.248|-0.193|0|-0.442***|
|45 percentiles|0|-0.906***|-0.197|-0.149|0.318|0.109|0|0.463|
|55 percentiles|0|-|-0.172|-0.303**|0.377|-0.365|0|-|
|65 percentiles|0|-|-0.161|-0.479|-0.931|0.0109|5.25|-0.389|
|75 percentiles|0|-9.074*|0.468|-1.135**|0.534|0.119|-0.132|-|
|85 percentiles|1.779*|-5.181***|0.214|0.243|0.562|0.416***|0|2.112***|
|95percentiles|0|-6.511***|0.4|0.915**|0.424|0.397***|0|-|
|Obs. total|385|584|385|584|385|584|385|584|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

67
**Table 38:** Software ICT: Enforcement Impacts on Markups by Age

||5|years|15y|ears|25y|ears|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|0|0.544|0.000312|0.434|-0.242|0.397|0.411|-|
|25 percentiles|0.474|-|-0.384|-0.385|-0.914|-0.563|0|1.026***|
|35 percentiles|0|-7.820***|-0.861***|1.708|-0.0826|-1.161*|0|1.026***|
|45 percentiles|0|-7.820***|-0.339|1.899|0.581|-0.651|0|-1.428|
|55 percentiles|0|-|-0.156|1.400***|0.315|-1.351*|0|-|
|65 percentiles|0|-|-0.02|0.31|-0.616|0.126|2.514|-4.569***|
|75 percentiles|0|-38.06***|0.710**|-2.639***|1.104|1.416|-0.126|-|
|85 percentiles|-0.936|-5.070*|0.726***|-0.497|0.488|0.977|0|3.157***|
|95percentiles|0|-2.497|0.428|0.202|0.349|0.963|0|-|
|Obs. total|385|583|385|583|385|583|385|583|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

**Table 39:** Software ICT: Regulation Impacts on Productivity by Age

||5y|ears|15|years|25|years|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-1.485|-0.0403|-0.312|-0.295|-0.19|0.173|0.484|0.27|
|25 percentiles|-1.554*|-0.0142|0.203|-0.0233|0.612|0.405|1.058*|3.098***|
|35 percentiles|-1.514|1.417*|0.166|-0.480*|0.101|0.405|0.902*|0.781**|
|45 percentiles|-0.721|0.0589|0.042|-0.909***|-0.083|0.226|0.736|-0.0843|
|55 percentiles|-1.674|-0.744|0.0501|-0.147|0.175|0.478|0.0284|-|
|65 percentiles|-1.699|0.024|0.123|0.237|0.178|0.033|2.223|-0.311|
|75 percentiles|0.408|0.508***|-0.329|-0.223|0.131|0.271|0.987|4.905|
|85 percentiles|0.0182|-0.499|0.0306|-0.167|0.299|0.843**|0.0252|0.703|
|95percentiles|-1.442|3.712***|0.216|-1.588*|0.286|0.800**|-0.692|13.93***|
|Obs. total|385||385||385||385|584|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

68
**Table 40:** Software ICT: Regulation Impacts on Markups by Age

||5|years|15y|ears|25|years|More th|an 25years|
|---|---|---|---|---|---|---|---|---|
||QMLE|ACF|QMLE|ACF|QMLE|ACF|QMLE|ACF|
||1|2|3|4|5|6|7|8|
|15 percentiles|-0.987|-0.449|-0.117|-0.831**|-0.444|0.29|0.588|-1.268|
|25 percentiles|-1.037|-2.546***|0.00157|1.012|0.53|0.219|0.387|5.060***|
|35 percentiles|-0.985|-0.0171|-0.207|-0.189|0.441|-0.139|0.482|0.498|
|45 percentiles|-0.537|-0.559|-0.16|-0.511|-0.0533|-0.25|0.306|0.479|
|55 percentiles|-0.877|-3.717|-0.0562|-0.371|0.0253|0.985|0.213|-|
|65 percentiles|-0.886|-0.779|-0.00061|0.235|0.182|0.385|1.801|-1.033**|
|75 percentiles|-0.426|0.888***|0.0274|-0.339|-0.169|-0.37|1.274|5.621*|
|85 percentiles|-0.398|1.645|0.367**|-0.433|-0.0799|1.214|0.188|1.647**|
|95percentiles|-0.792|3.945***|0.0892|-1.75|-0.146|2.480***|-0.294|13.17***|
|Obs. total|385|583|385|584|385|584|385|583|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

*** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

69