# Regulation in the Network Sectors: Impact on the Innovation Process and the Employment Rate

Océane Vernerey, Jimmy Lopez

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Océane Vernerey, Jimmy Lopez. Regulation in the Network Sectors: Impact on the Innovation Process and the Employment Rate. Comparative Economic Studies, 2026, 68 (1), pp.4. 10.1057/s41294-025-00273-1. hal-05536453

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# Regulation in the Network Sectors: Impact on the Innovation Process and the Employment Rate

Océane Vernerey<fnref n="1" /> · Jimmy Lopez<fnref n="1" />

Received: 16 April 2025 / Accepted: 28 September 2025

© The Author(s) 2026

## Abstract

We investigate both the innovation and labor market effects of network sector regulation in a consistent framework. The estimated impact of regulation on the innovation process is based on the Community Innovation Survey and a system of equations modelling the firm’s choice of R&D expenditure, propensity to innovate, and performance. We then examine the regulation and innovation impact on the labor market using the European Union Labor Force Survey. From a sample of 330,604 firms and 8,594,055 individuals over the period 1998–2016 and five countries that have undergone important reforms (the Czech Republic, Hungary, Portugal, Slovakia and Spain), we find a strong negative effect of network regulation on firms’ performance and individuals’ employment probability. According to our estimates, the overall impact of the reforms implemented would be an average increase in the employment probability of 12.8%, almost entirely explained by an increase in firms’ performance.

**Keywords** Regulation · Innovation · Employment

**JEL codes** L51 · O30 · J23

## Introduction

The role of competition in the economy and of the policies that affect competition have been widely debated (see Campos et al. 2025; for a literature review of the impact of structural reforms). Among these policies, there is a rich literature on the effects of regulation that limits competition through entry barriers or direct government intervention in the network sectors, i.e., energy, transport and communication

<figure><img src="image_3.png" /></figure>

<footnote>Jimmy Lopez
jimmy.lopez@u-bourgogne.fr</footnote>

<footnote n="1">¹ Université Bourgogne Europe, 4 Boulevard Gabriel, 21000 Dijon, France</footnote>
services (see, for example, Barone and Cingano 2011 or Bourlès et al. 2013). Most of
this literature uses OECD indicators measuring the extent to which regulation is anti-
competitive and then finds, using cross-country sector panel data, a negative impact
of those regulation on productivity, with a strong effect on unregulated sectors via the
use of inputs produced by the network sectors.

There are also papers that investigate the impact of anti-competitive regulation on
innovation, again using cross-country sector panel data, but with ambiguous results:
Cette et al. (2017) report a negative effect on R&D investment, while Amable et al.
(2016) find mixed results on the number of patents, with sometimes positive effects
for countries that are leaders in a particular sector. At the same time, only a few
papers investigate the employment effects of these regulations and find a detrimental
impact (see, for example, Nicoletti and Scarpetta 2005).

The purpose of our paper is twofold. First, to further the understanding of the impact of network regulation on innovation by estimating a system of equations modelling the innovation process using firm-level data on R&D investment, but also on product innovation and sales growth. We then analyze the impact on employment using individual-level data. In particular, we examine the extent to which these employment effects can be explained by the effects of regulation on the innovation process. These two original contributions in a consistent framework allow us to provide policy recommendations that take into account both the effects on firms’ performance and on individuals’ employment.

Our investigation of the effects on the innovation process is based on the rich literature following the seminal paper by Crépon, Duguet, and Mairesse (1998), the so-called CDM approach. The CDM approach is at one and the same time a system of equations modelling the firm’s choice of R&D expenditure, its propensity to innovate, and the impact of innovation on firms’ performance, plus a set of estimation methods to deal with various endogeneity issues. The original CDM paper uses data from the 1990 French Community Innovation Survey (CIS), which provides numerous variables on the innovation process at the firm level. Our paper uses the CIS waves from 1998 to 2016 for five countries: the Czech Republic, Hungary, Portugal, Slovakia, and Spain.

Our study of the impact of regulation on employment is based on Vernerey and Lopez (2024). As workers are mobile across sectors, we use the European Labor Force Survey (LFS) individual database to examine the regional effects of regulation (at the NUTS 2 level). The LFS database provides harmonized information for people aged 15 and over in all European countries. Our identification assumption is that the impact of a sectoral regulation on a regional labor market increases with the size of the sector in the region. In this paper, we introduce into the estimated employment equation the impact of network regulation on firm innovation and performance as predicted by the CDM approach.

Our main estimation sample covers the period from 1998 to 2016 and includes five countries: the Czech Republic, Hungary, Portugal, Slovakia, and Spain. These five countries are the only ones available in both the CIS and LFS databases provided by Eurostat, primarily because many countries do not allow Eurostat to include their CIS data in the database made available to researchers. The sample includes 330,604 firm observations from nine market sectors and 8,594,055 individual observations from

<figure><img src="image_4.png" /></figure>
44 regions. The five countries included in our estimation sample all implemented significant network regulation reforms during the estimation period. They also have a GDP per capita below the European Union average, and three of them (the Czech Republic, Hungary, and Slovakia) were in transition during the estimation period. Consequently, firm innovation and productivity growth may depend more on imitation than on R&D expenses, unlike firms at the technological frontier.

We find significant negative effects of anti-competitive network regulation: (i) on R&D expenses, propensity to innovate and firm performance, measured by sales growth, within the regulated sectors; (ii) on firm performance of sectors that intensively use intermediate inputs from the network sectors; (iii) on people's employment probability, directly and through the effects on firm innovation and performance; and (iv) the employment effect is higher for the most vulnerable sub-samples, i.e., the low-skilled, the youngest, and the oldest. According to our estimates, the effects of the reforms implemented in the five countries over the period 1998–2016 are substantial. The overall impact on the employment rate appears to be an increase of 12.8% points, almost entirely explained by the impact of regulation on innovation and firm performance. This effect needs to be put into perspective, given the scale of the reforms implemented.

The paper is structured as follows. Section “Data” presents our data. Section “Model and Estimation Method” explains the estimated model and our estimation methodology. Section “Main Estimates and Simulations” presents our main estimation results and the simulated effects, highlighting the economic significance of these results. Section “Conclusions” concludes the paper.

## Data

Our investigation uses three main data sources with different statistical units: (i) the OECD Regulatory database and Input-Output tables for sectors, (ii) the Community Innovation Survey database for firms, and (iii) the Labor Force Survey database for individuals. By merging these data sources, our cleaned estimation sample covers the period 1998–2016 and only five countries: the Czech Republic, Hungary, Portugal, Slovakia, and Spain. These countries are quite different, but have in common that they implemented important reforms of network regulation during our estimation period. Our first step focuses exclusively on the innovation process and the estimation sample includes 330,604 firm observations from nine market sectors.<fnref n="1" /> Then, in a second step, our sample to investigate the employment effects includes 8,594,055 individual observations from the 44 administrative regions of the five countries. This section presents these data sources along with our computations and descriptive analyses.

<footnote n="1">¹ The nine sectors in the sample are: “Mining and quarrying”, “Manufacturing”, “Energy”, “Construction”, “Wholesale and retail trade”, “Transport, storage and communication”, “Accommodation and food services”, “Financial and insurance activities”, and “Professional services activity”.</footnote>
OECD Indicators of Anti-competitive Regulation

The OECD Regulatory Database provides indicators of anti-competitive regulation for all OECD countries, based on detailed information on laws, rules, and market settings, to measure the extent to which competition and firms’ choices are restricted when there is no a priori reason for government intervention, or when regulatory objectives could plausibly be achieved by less coercive means. The regulatory constraints on competition taken into account could be direct government intervention as well as barriers to entry (see Vitale et al., 2020, for details). The values of these indicators range from 0 to 6, with 0 representing the most pro-competitive regulation.

The OECD regulation indicators cover several sectors: (i) the network sectors, including energy (gas and electricity), transport (rail, road, and air), and communications (post, fixed and telecommunications); (ii) retail distribution; and (iii) professional services (accountants, lawyers, notaries, architects, engineers, real estate agents, etc.). This paper focuses on the regulation of the first three network sectors, as many pro-competitive reforms have been implemented in the countries in our sample. In contrast, there have been very few reforms in retail distribution and professional services in these countries, which makes it impossible to study the effects of regulations in these sectors.

Figure 1 shows the impact of these reforms on the values of the OECD indicators. At the beginning of our sample estimation period, the regulation of the network sectors was high according to these indicators, but with considerable heterogeneity across countries. There followed a rapid convergence towards very pro-competitive regulation for all countries in the communications sector in the first half of the period. We also observe a significant decline in the indicators for the energy and transport

<figure><img src="image_5.png" /><figcaption>Fig. 1 OECD indicators of network sectors regulation. Scale 0–6, 0 for the most pro-competitive regulation</figcaption></figure>

<figure><img src="image_6.png" /></figure>
sectors, but this phenomenon was more heterogeneous across countries and some restrictions on competition remained at the end of the period.

## Community Innovation Survey

Our firm-level variables come from the European Community Innovation Survey. This is a harmonized survey covering firms in each Member State of the European Union as well as Norway and Iceland. However, the corresponding data made available by Eurostat covers only nine countries. Of these, two are not members of the OECD and two others have joined recently, so we only have CIS data and OECD regulation indicators for a significant period for just five countries: the Czech Republic, Hungary, Portugal, Slovakia, and Spain. Each wave of the survey corresponds to three years, with some variables measured for the whole period or only for the last year, and some also for the first year. We use eight waves covering the period from 1998 to 2016. Firms from all market sectors are surveyed except “Agriculture, forestry and fishing”.

The CIS database provides many variables related to the innovation process.<fnref n="2" /> In this paper, our main measures of this process are: (i) innovation input, calculated as the ratio of R&D expenditure to sales for firms that report internal continuous R&D expenditure;<fnref n="3" /> (ii) innovation output (hereafter referred to as innovation intensity), calculated as the share of sales of new products in sales over the three-year period, where a new product is a product that is new to the market, new to the firm, or substantially improved; and (iii) firm performance, calculated as the growth of the firm’s sales over the wave period. We would like to be able to use labor productivity to measure firm performance, but we do not have a measure of value added for the five countries in our sample, nor do we have the number of employees as a continuous variable. This is a serious flaw with our database, but we hope that sales growth is still meaningful.

Table 1 shows for each country: (i) the share of firms that report internal continuous R&D expenditure; (ii) the share of firms that have engaged in product innovation, for R&D firms and non-R&D firms; and (iii) the average growth of firms’ sales, for firms that have engaged in product innovation or not. The proportion of R&D firms is relatively small compared to the EU average. However, we observe significant heterogeneity across countries, as well as for the other two variables. In particular, Hungary and Slovakia have the lowest shares of R&D firms and product innovators. However, if we focus exclusively on R&D firms, the share of product innovators in these two countries is comparable to the other countries. In fact, we observe strong links between the three variables: (i) most R&D firms engage in product innovation, while non-R&D firms hardly do so; and (ii) the sales growth of firms that are product

<footnote n="2">² The CIS database for research provided by Eurostat is anonymized. As a result: (i) we are unable to follow firms over time; (ii) some variables are excluded; and (iii) the values of others are aggregated at a higher level. In particular, the number of employees is not reported with a continuous variable but in three classes.</footnote>

<footnote n="3">³ R&D expenditure is measured in the last year of the wave, while sales is measured in the first and last years. To compute the R&D ratio, we use the first year’s turnover to avoid endogeneity due to simultaneity.</footnote>
<table><caption>**Table 1** Descriptive analysis of the innovation process</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Share of<br>R&D firms<br>(%)</th><th colspan="3">Share of product innovators</th><th colspan="3">Sales growth</th></tr><tr><th>All firms (%)</th><th>R&D<br>firms<br>(%)</th><th>Non-<br>R&D<br>firms (%)</th><th>All<br>firms<br>(%)</th><th>Product<br>innovators<br>(%)</th><th>Non-<br>Product<br>innova-<br>tors (%)</th></tr></thead><tbody><tr><td>Czech Rep.</td><td>17</td><td>31</td><td>87</td><td>20</td><td>9</td><td>10</td><td>9</td></tr><tr><td>Spain</td><td>17</td><td>22</td><td>69</td><td>12</td><td>5</td><td>7</td><td>5</td></tr><tr><td>Hungary</td><td>7</td><td>17</td><td>82</td><td>12</td><td>7</td><td>9</td><td>6</td></tr><tr><td>Portugal</td><td>13</td><td>35</td><td>85</td><td>27</td><td>5</td><td>7</td><td>4</td></tr><tr><td>Slovakia</td><td>7</td><td>16</td><td>80</td><td>11</td><td>9</td><td>11</td><td>9</td></tr><tr><td>Average</td><td>12</td><td>24</td><td>81</td><td>16</td><td>7</td><td>9</td><td>7</td></tr></tbody></table>

innovators is significantly higher than that of non-product innovators, regardless of the country.

Our estimated equations also use other firm-level variables: the number of employees (in three classes), a market share measure,<fnref n="4" /> and dummies for group membership, sales market (local, national, or international), innovation cooperation between firms, and public financial support for innovation from central governments or the European Union. See Appendix A for a more detailed descriptive analysis of the CIS variables.

## European Union Labor Force Survey

The European Union Labor Force Survey (LFS) database provides harmonized information for people aged 15 and over in all European countries up to 2017.<fnref n="5" /> Our analysis focuses on five countries, which are also included in the CIS and OECD regulatory databases. Our estimated sample therefore covers 44 regions and 8,594,055 people. We use the employment status of the person and the following individual characteristics: age, education, gender, sector, and region. We distinguish 10 age groups, and education is divided into three levels: (i) high, with at least some tertiary education; (ii) medium, with upper secondary education; and (iii) low. These three educational levels correspond to 15.4%, 45.5%, and 39.1% of the estimation sample respectively.

The five countries in our sample are characterized by low employment rates at the beginning of the sample period: 68% in the Czech Republic, 51% in Hungary, 65% in Portugal, 58% in Slovakia, and 50% in Spain. On average, the employment rate increased strongly over the period, by 5% points, despite the financial crisis, but there is strong heterogeneity in the dynamics of the employment rate across countries, especially after the 2008 financial crisis.

<figure><img src="image_7.png" /></figure>

<footnote n="4">⁴ Unfortunately, our database does not contain exhaustive data on firm turnover for our five countries. Therefore, we calculate our market share as follows: we divide the firm turnover from the CIS database (we use the data from the first year of the wave to avoid simultaneity) by the sectoral nominal value of production from the EUKLEMS database. Consequently, this control variable is poorly approximated.</footnote>

<footnote n="5">⁵ As with the CIS, the EU-LFS research database provided by Eurostat is anonymized. As a result: (i) we are unable to follow individuals over time; (ii) some variables are excluded; and (iii) the values of others are aggregated at a higher level. In particular, localization is only at NUTS2 level, which corresponds to administrative regions.</footnote>
# Model and Estimation Method

In this section, we present the model we use to estimate the impact of network regulation on the innovation process, and then the specification that allows us to estimate its impact on employment. In each case, we first present how we compute the regulatory “burden” indicators according to our identification strategy. Finally, we present how we deal with several endogeneity issues.

## Impact of Network Regulation on the Innovation Process

### Measure of the Network Regulatory Burden on Downstream Sectors

Anti-competitive sectoral regulation can impact the activity of the regulated sector, as well as other sectors that use intermediate inputs produced by these regulated sectors (referred to as “downstream” and “upstream” sectors, respectively). Firstly, strong market power among upstream firms can reduce the incentive for downstream firms to innovate, since they will have to share the expected profits from such improvements with upstream firms. If firms in downstream industries have to negotiate the terms and conditions of their contracts with suppliers, some of the expected benefits of adopting best-practice techniques will accrue to intermediate input providers. This, in turn, reduces innovation incentives and curbs productivity in downstream industries, even if there is competition there. Furthermore, a lack of competition in upstream industries can generate barriers to entry, reducing competition and pressure to innovate in downstream industries. The empirical literature confirms the strong effect of anti-competitive regulation on downstream sectors (i.e., sectors that use intermediate inputs produced by regulated sectors; see, for instance, Barone and Cingano 2011). A lack of competition enables producers of intermediate inputs to capture part of the rent created in sectors using these inputs, thereby reducing incentives to innovate. This could also reduce competition in these latter sectors (see Cette et al. 2016 and 2019).

The network sectors are important producers of intermediate inputs, so the impact on the other sectors of regulations in these sectors could be strong. To account for the network regulation effects on the downstream sectors, we calculate the following variable:

$$NR_{scw}^{up} = \sum_{j} int_{sj} \times NR_{cw}^{j}$$

Where $NR_{scw}^{up}$ is our measure of the importance of the burden of network sector regulation on sector $s$ in country $c$ and wave $w$. This burden depends on $NR^j$ the regulation in the network sector $j$ (energy, transport, or communication) and on $int_{sj}$ the intensity of use by sector $s$ of the intermediate goods produced by sector $j$. To calculate the intensity of use, we use the ratio of intermediate input consumption from sector $j$ to the output of sector $s$, using the 1997 US Input-Output Table. We use this table rather than country*wave specific values because regulations may also have an effect on the intensity of use of intermediate goods. Therefore, the use of

<figure><img src="image_8.png" /></figure>
the US I-O table helps to avoid endogeneity issues and facilitates the interpretation of $NR^{up}$ changes or differences across countries. As there is a strong persistence in the input-output tables over time and across countries, the USA 1997 table is a good proxy for the country*year specific tables (however, we have checked the robustness of our results with regard to the use of different input-output tables).

## The Innovation Process

To estimate the effects of network regulation on the innovation process, we use the model of Vernerey (2026), inspired by Crépon, Duguet, and Mairesse (1998) but adapted to our data. This model first explains firms' decisions on R&D expenses, then the innovation intensity and finally firms' performance in terms of sales growth (due to a lack of data on labor productivity). We introduce into this model the OECD indicators of anti-competitive network regulation and our measure of the network regulatory burden on downstream sectors at all stages of the innovation process.

Therefore, we estimate the following equations:

$$ \ln \left( \frac{RD_f}{Turn_f} \right) = d_f^{net} \times \bar{N}_R \times b^{NR,1} + N R_{scw}^{up} \times b^{NR^{up},1} + \sum_k \left[ x_f^{1,k} b^{1,k} \right] + \varphi_{cw}^1 + \varphi_{sw}^1 + \varphi_{cs}^1 + u_f^1 \quad (1) $$

$$ \ln (I_f) = d_f^{net} \times \bar{N}_R \times b^{NR,2} + N R_{scw}^{up} \times b^{NR^{up},2} + \ln \left( \frac{RD_f}{Turn_f} \right) \times b^{RD} + \sum_k \left[ x_f^{2,k} b^{2,k} \right] + \varphi_{cw}^2 + \varphi_{sw}^2 + \varphi_{cs}^2 + u_f^2 \quad (2) $$

$$ \begin{aligned} Turn_f^g = & d_f^{net} \times \bar{N}_R \times b^{NR,3} + N R_{scw}^{up} \times b^{NR^{up},3} + \ln (I_f) \\ & \times b^I + \sum_k \left[ x_f^{3,k} b^{3,k} \right] + \varphi_{cw}^3 + \varphi_{sw}^3 + \varphi_{cs}^3 + u_f^3 \end{aligned} \quad (3) $$

Where the dependent variables are: $RD_f$ the amount of R&D expenditure of firm f; $Turn$ the sales of the firm; $I$ the intensity of product innovation (the share of new products in sales); and $Turn^g$ the sales growth over the wave, calculated as the log difference between sales in the last and first years of the wave.<fnref n="6" /> For equation i, $x^{i,k}$ is the set of covariates, $b^{i,k}$ the estimated parameters, $\varphi_{cw}^i$, $\varphi_{sw}^i$, and $\varphi_{cs}^i$ the country*wave, sector*wave and country*sector fixed effects, and $u^i$ the residuals. In each equation the set of covariates includes the number of employees (in three classes) and a dummy for group membership. The R&D equation also includes the

<figure><img src="image_9.png" /></figure>

<footnote n="6">⁶ R&D spending may affect innovation and the impact of innovation on sales with a lag. However, we are unable to incorporate lags in R&D or innovation intensity into the innovation model due to the absence of lagged values in our database.</footnote>
firm's market share (in log) and dummies for the public financial support for innovation from central governments or the European Union.

$\bar{N}R_{cw}$ is the country*year average value of the three network sector regulation indicators, $d_f^{net}$ is a dummy equal to one if the firm is in the network sector, and thus $b^{NR}$ is the impact of the network regulation in this sector.<fnref n="7" /> $\bar{N}R_{scw}^{up}$ is our measure of the regulatory burden on the downstream sectors and $b^{NRup}$ the estimated impact of this burden. Regulation indicators are available annually, but CIS data is not. Since regulation reforms may take several years to have an impact, we use the value of the regulation indicator from the first year of each wave, while R&D expenses, innovation intensity and sales are measured in the final year. As our estimated specifications include country*wave and country*sector fixed effects, they allow us to test whether the impact of the network regulations on the other sectors increases with the intensity of use of the intermediate inputs from the regulated sector, in the sense of the difference-in-differences approach. In other words, we examine the differences in impact between sectors to identify the effects of regulation on R&D expenditure, innovation intensity, and sales growth.

# Impact of Network Regulation on Employment

## Measure of the Network Regulatory Burden on the Regional Labor Market

Since anti-competitive regulations are implemented at the sectoral level, most studies investigating their effects use cross-country-sector panel data. However, workers are mobile across sectors, so we may miss the full effect of regulation by using sectoral data. Vernerey and Lopez (2024) argue that the preferred level of analysis for employment effects would be local labor markets, i.e., the geographical areas where most workers live and work, and where establishments can find most of the labor force needed to fill the jobs on offer.

The anonymized EU-LFS database provided by Eurostat is only available at the NUTS2 level, which is more aggregated than local labor markets. This reduces the variability of the data, but this level of localization is still useful because our identification strategy requires only the lack of worker mobility between the geographical units and a very small share of the workers in our sample were in another region in the previous year: 0.6% in the Czech Republic and Hungary, 0.3% in Estonia, 1.2% in Portugal (this information is not available for Slovakia).

Our identification assumption is that the impact of a sectoral regulation on a regional labor market increases with the size of that sector in the region, measured by the share of workers in that sector in the total number of workers in that region. Therefore, in order to estimate the impact of network regulation on employment, we calculate the following variable:

<figure><img src="image_10.png" /></figure>

<footnote n="7">⁷ We have chosen to use the average over the three network sectors for consistency with the analysis of the impact on employment, as these three sectors are not separated in the LFS database.</footnote>
$$NR_{rct}^{emp} = \sum_{j} \omega_{rj} \times NR_{ct}^{j}$$

Where $NR_{rct}^{emp}$ is our measure of the network regulatory burden for employment in region r, country c, and year t. This burden depends on $NR^j$ the regulation in the network sector j (energy, transport, or communications) and on $\omega_{rj}$, the share of workers in region r engaged in the regulated sector j.<fnref n="8" /> As for the intensity of use of regulated intermediate inputs, in this formula we use regional shares of workers that are fixed over time so that changes in $NR^{emp}$ can only be interpreted as changes from network regulation. We use shares calculated over the first five years available in the LFS database for each country. These shares are good approximations to the annual in-sample shares because of the persistence of regional specialization in regulated industries. Indeed, using annual in-sample shares we find that regional fixed effects explain 70% of the variability in these shares.

As mentioned above, we can expect network regulation to have an impact on firm performance, which may affect employment. To estimate this effect, we use the same formula as above but applied to firm sales growth:

$$Turn_{rct}^{g,emp} = \sum_{s} \omega_{rs} \times Turn_{ct}^{g,s}$$

Where $Turn_{ct}^{g,s}$ is for sector s, country c, and year t, the average of the sales growth predicted using the system of Eqs. (1) to (3). Therefore, this measure enables both the direct and indirect effects of regulations to be taken into account. The CIS data are available for three-year waves. In order to obtain annual values, we first calculate the average sales growth for the waves and then we use the interpolation method.

## Estimated Equation for Employment

Finally, the estimated equation for the effects of network regulation on employment is as follows:

$$EMP_i = NR_{rct}^{emp} \times b^{NR^{emp}} + Turn_{rct}^{g,emp} \times b^{Turn^{g,emp}} + \sum_{k} [x_{f}^{4,k} b^{4,k}] + \varphi_{ct} + \varphi_{r} + u_{i}^{4} \quad (4)$$

Where $EMP_i$ is a dummy variable equal to one if individual i works; $\varphi_{ct}$ and $\varphi_{r}$ are country*year and regional fixed effects respectively. Using this set of fixed effects and the formulas for the network regulatory burden and the turnover effect on employ-

<figure><img src="image_11.png" /></figure>

<footnote n="8">⁸ This formula does not explicitly take into account the input/output linkages. Ideally, we would use regional input/output tables for this purpose, but unfortunately these are not available. However, we can expect the dependence of firms on regulated inputs to grow with the size of the regulated sector in the region. Therefore, our estimates also take input/output relationships into account. To take this further, we also use the following alternative measure of network regulation burden on employment:</footnote>

<footnote>$NR_{rct}^{emp, alt} = \sum_{j} [NR_{ct}^{j} (\omega_{rj} + \sum_{i \neq j} [\omega_{ri} \times int_{ij}])]$. We find estimated results very close to our main results.</footnote>
ment, we can identify the direct and indirect effects of regulation (through turnover) by examining the differences in impact between regions.

In order to study the relationship between employment and network regulation in depth, we also investigate the effects on people's activity and unemployment status. In addition, we estimate the effects of the network regulation and firm sales for the full sample and then the specific effects for the most vulnerable sub-samples: the low-skilled, the youngest and the oldest workers.

## Endogeneity Issues and Estimation Methods

There are several potential sources of endogeneity that we need to take into account when estimating Eqs. (1) to (4). One is the selection bias in Eqs. (1) and (2). Indeed, the logarithm of R&D expenditure is measured only for firms that invest in R&D and the innovation intensity is measured only for firms that innovate in products. To deal with these issues, we use the Tobit type II estimation method (Appendix B shows the corresponding selection equations).

There are still two other potential sources of endogeneity in the innovation process: (i) a possible simultaneity between R&D expenditure, innovation intensity and firm performance; and (ii) the difficulty of measuring innovation input and output. To deal with these issues, we use the Full Information Maximum Likelihood (FIML) estimator to simultaneously estimate the system of Eqs. (1) to (3), thus maximizing the likelihood function of the system.<fnref n="9" />

There may also be simultaneity between employment and sales growth in Eq. (4). To deal with this issue, we calculate the predicted values of sales growth according to the estimates from Eqs. (1) to (3), then we use these predicted values to calculate the variable *Turn*<sup>*g, emp*</sup> from formula (c) and the result is included in Eq. (4).<fnref n="10" /> As for the observed sales growth, these predictions are calculated for CIS waves and then annual values are imputed according to the interpolation method.

There is one final issue to consider. Equations (1) to (3) are estimated on firm data, but our main explanatory variables on regulation are calculated at the sectoral level. Similarly, Eq. (4) is estimated on individual data, but our main explanatory variables on regulatory burden are calculated at the regional level. Therefore, our identification of the parameters relies respectively on 45 country-sector units and 44 regions. Strong clustering issues can be expected, leading to an overestimation of the precision of the estimates. Therefore, we present the estimation results using “clustered” standard errors, i.e., standard errors corrected according to the “Moulton factor” taking into account the within-sector/region correlations of the residuals and explanatory variables.

<footnote n="9">⁹ In other words, the likelihood function of the R&D step is introduced in the function of the innovation step and the result is introduced in the function of the performance step. It results in a single likelihood function that we maximize. For more information on the estimation methods of the innovation process and various robustness tests, see Vernerey (2026).</footnote>

<footnote n="10">¹⁰ The innovation intensity and turnover growth predictions are calculated for the CIS waves. We use the interpolation method to calculate year values.</footnote>
# Main Estimates and Simulations

This section first presents the estimated effects of the main explanatory variables in Eqs. (1) to (4). The values of the estimated coefficients of the network regulation indicators are difficult to interpret directly, so we only interpret the sign and statistical significance of the results when commenting on the regulation coefficients in the estimation tables, and then provide a simulation that gives some economic meaning to these results.

## Main Estimates

Table 2 presents the estimated marginal effects calculated from the FIML estimation results of the system of Eqs. (1) to (3). We find a positive and significant effect on R&D intensity of public financial support for innovation from central governments as well as from the European Union. On the contrary, firms with a large market share in the first year of a wave have a lower R&D intensity. This suggests that enjoying a large market share reduces the incentives to innovate. Regarding our dependent variables, we find that a 100% increase in R&D expenditure would lead to an 18% increase in innovation intensity (i.e., the share of new products in sales) and then to 0.36% higher sales growth. This effect on innovation intensity is much smaller than in the original CDM, maybe because the five countries in our sample are quite far

<table><caption>**Table 2** Estimated marginal effects of the innovation process</caption><thead><tr><th></th><th>R&D intensity (log)</th><th>Innov. intensity (log)</th><th>Sales growth</th></tr></thead><tbody><tr><td>Market share (log)</td><td>-0.359***<br>[0.011]</td><td></td><td></td></tr><tr><td>Public financial support - Central Government</td><td>0.434***<br>[0.017]</td><td></td><td></td></tr><tr><td>Public financial support - European union</td><td>0.335***<br>[0.033]</td><td></td><td></td></tr><tr><td>R&D intensity (log)</td><td></td><td>0.183***<br>[0.017]</td><td></td></tr><tr><td>Innov. intensity (log)</td><td></td><td></td><td>0.020***<br>[0.002]</td></tr><tr><td>Network regulatory indicator</td><td>-0.309*<br>[0.164]</td><td>-0.381**<br>[0.183]</td><td>-0.104**<br>[0.042]</td></tr><tr><td>Network regulatory burden indicator</td><td>-3.297<br>[2.871]</td><td>-0.772<br>[3.018]</td><td>-3.423***<br>[0.807]</td></tr><tr><td>Observations</td><td>330,604</td><td></td><td></td></tr></tbody></table>

Estimation method: FIML estimation of the system of equations, using the Tobit type II model for the R&D and innovation equations

*** *p*<0.01, ** *p*<0.05, * *p*<0.1; Clustered standard errors in brackets; Cluster: Country*sector*wave

Included fixed effects: country*wave, sector*wave and country*sector

Other control variables included: number of employees in three classes and group membership for each equation

<figure><img src="image_12.png" /></figure>
from the technological frontier.<fnref n="11" /> There is a negative effect on firm's R&D, innovation, and sales growth of network regulation within the sector ($\overline{NR}$) and on the other sectors ($NR^{up}$). The within-sector effect is also always statistically significant and the effect on the other sectors is statistically significant for firm sales growth, but our estimates of the effect on R&D and innovation intensity in the other sectors lack precision.<fnref n="12" />

Table 3 shows the estimated marginal effects of network regulation and firm performance on employment, activity and unemployment. Firm performance is predicted according to the estimation results of Table 1. It can be used to calculate the effect of network regulation that involves firm performance according to Eqs. (1) to (4). The results are shown when estimating on the whole sample or on the most vulnerable sub-sample: the low-skilled or the youngest or oldest.

The estimated effects of the control variables are presented in Appendix C.<fnref n="13" /> We find that well-educated middle-aged men have the highest probability of activity and employment. Highly educated men are also less likely to be unemployed than women

<table><caption>**Table 3** Estimated marginal effect of network regulation</caption><thead><tr><th rowspan="2">Dep. var.</th><th colspan="2">Employment</th><th colspan="2">Activity</th><th colspan="2">Unemployment</th></tr><tr><th>Network regulatory burden</th><th>Firm performance burden</th><th>Network regulatory burden</th><th>Firm performance burden</th><th>Network regulatory burden</th><th>Firm performance burden</th></tr></thead><tbody><tr><td>Whole sample</td><td>-0.094*<br>(0.052)</td><td>0.398***<br>(0.083)</td><td>-0.146***<br>(0.038)</td><td>0.094<br>(0.058)</td><td>-0.058<br>(0.056)</td><td>-0.405***<br>(0.094)</td></tr><tr><td>Observations</td><td>8,594,055<br>(4)</td><td></td><td>8,594,055<br>(5)</td><td></td><td>5,664,706<br>(6)</td><td></td></tr><tr><td>Low education</td><td>-0.441***<br>(0.060)</td><td>0.404***<br>(0.095)</td><td>-0.437***<br>(0.039)</td><td>0.145**<br>(0.058)</td><td>0.093*<br>(0.053)</td><td>-0.545***<br>(0.110)</td></tr><tr><td>Observations</td><td>8,594,055<br>(7)</td><td></td><td>8,594,055<br>(8)</td><td></td><td>5,664,706<br>(9)</td><td></td></tr><tr><td>Age 17 to 26</td><td>0.082<br>(0.058)</td><td>0.670***<br>(0.093)</td><td>0.026<br>(0.046)</td><td>0.291***<br>(0.070)</td><td>-0.064<br>(0.056)</td><td>-0.538***<br>(0.095)</td></tr><tr><td>Age 57 to 66</td><td>-0.745***<br>(0.059)</td><td>0.283***<br>(0.087)</td><td>-0.727***<br>(0.047)</td><td>0.065<br>(0.064)</td><td>-0.068<br>(0.058)</td><td>-0.341***<br>(0.094)</td></tr><tr><td>Observations</td><td>8,594,055</td><td></td><td>8,594,055</td><td></td><td>5,664,706</td><td></td></tr></tbody></table>

For the unemployment equation the sample is reduced to the active population, while it is the working age population for employment and activity equations

Fixed effects: Country, region, year, and country*year; Control variables: age, education, and gender
Clustered standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

<figure><img src="image_13.png" /></figure>

<footnote n="11">¹¹ In the original CDM on French data, a 100% increase in R&D expenditure would lead to an increase in innovation intensity between 30% and 43%.</footnote>

<footnote n="12">¹² These estimation results are robust when different input-output tables are used (results available on request from the authors).</footnote>

<footnote n="13">¹³ Appendix C also shows estimation results taking account of innovation intensity. When introduced simultaneously with firm performance, the innovation intensity effect is sometimes statistically significant but very small.</footnote>
and little or moderately educated workers. The probability of unemployment is the lowest for older workers, who are likely to leave the labor market if they lose their job.

For the whole sample, we find a significant negative impact of the regulatory burden on the probability of activity, but not on the probability of unemployment, leading to a negative impact on the probability of employment. On the contrary, firm performance has a significant negative impact on the probability of unemployment, but no significant impact on activity, leading to a positive impact on the probability of employment. These results suggest that network regulation affects the probability of unemployment through firm performance but affects the probability of activity via a different channel.

The effects of network regulation on the probability of employment are much higher for the low-skilled and the oldest workers, through a large negative effect on activity and even a positive, slightly significant effect on unemployment for the low-skilled. For these two sub-samples, the effects from firm performance are much like the effects on the full sample. For the youngest, we find no direct effect of network regulation, but the effects from firm performance are stronger, with even a positive significant effect on the probability of activity, implying that all the effects of regulation pass through firm performance and that this effect may be large. We need simulations to give more economic meaning to these results.

## Simulations

In order to give economic significance to the impact of anti-competitive network regulation, we calculate for each country the long-term impact of the reforms implemented over the period 1998–2016, using the changes observed in the OECD network sector regulation indicators. As shown in Fig. 1, these reforms were substantial for all the countries in our sample. We first calculate the effects on sales growth and then the effects on employment.<fnref n="14" />

The sales growth effect is calculated according to Table 2 estimates. This gives us sectoral effects, which we weight by the number of firms per sector to calculate average effects for each country.<fnref n="15" /> Figure 2A shows these simulated effects of reforms on yearly sales growth in percentage points. The differences between countries are due to the size of the reform in each country, but also, to a lesser extent, to the relative importance of the different sectors in the sample. In the long run, the overall impact of the reforms would be an increase in sales growth of 2.7% points. This should be considered alongside the average yearly growth rate of 7% observed over the 1998–2016 period. The input/output channel dominates. Indeed, the within-network sector effect accounts for only 16% of the total effect (i.e., an increase of 0.4% points).<fnref n="16" /> The within-network sector effect shown in Fig. 2A includes the regulation effects

<figure><img src="image_14.png" /></figure>

<footnote n="14">¹⁴ The simulated effect on R&D expenditure is shown in Appendix D.</footnote>

<footnote n="15">¹⁵ These average effects may differ from country effects as our sample excludes the agricultural and non-market sectors.</footnote>

<footnote n="16">¹⁶ It is interesting to note that the network sectors account for 19% of the firms in our sample.</footnote>
<figure><img src="image_15.png" /><figcaption>**Fig. 2** Average impact of the 1998–2016 network regulation reforms</figcaption></figure>

through R&D and then innovation intensity, but this channel accounts for only 7% of the within-network sector effect on sales growth.

The employment effect of reforms is calculated according to Table 3 estimates. We obtain results at the regional level, which we aggregate to the national level using the number of people of working age in each region. Fig. 2B shows the simulated effects of the network reforms on employment through the different channels. The overall impact of the reforms would be 12.8% in the long-run. This is a huge effect, that should be seen in the context of major reforms implemented in countries with relatively low employment rates. Most of this total impact is due to the effect of regulation on firm performance. The effect which is not explained by firm performance, referred to as the “direct” effect in Fig. 2B, would lead to a 2.2% increase in the employment rate in the long run. Consistent with the results in Fig. 2A, the employment impact of the within-network sector effect on firm performance is relatively small, at 1%. The largest employment impact is explained by the effect of network regulation on the sectors using the network intermediate inputs: it should lead to a 10.6% increase in the long run.

Finally, we perform the same calculation of the employment effects of implemented reforms, but for the most vulnerable sub-samples. The average effect on employment probability is always higher than for the whole sample. The largest effect is for the oldest, with an increase in the probability of employment of 25%, followed by the low-educated and the youngest, with increases of 21% and 16% respectively.

## Conclusions

To the best of our knowledge, this paper is the first to investigate the impact of network regulations on both the innovation process and the labor market in a consistent framework. We find that network regulations have a significant negative impact on employment, which is almost entirely explained by their effects on firm performance. This was not self-evident: the creative destruction effects induced by an increase in firms’ innovation and performance could also have led to job creation and destruction and thus to more congestion in the labor market.
According to our simulations, the reforms implemented over the period 1998–2016 in the Czech Republic, Hungary, Portugal, Slovakia, and Spain could have had sizeable effects on firm performance and individual employment. However, these effects should be put into perspective: they correspond to the effects in countries with low employment rates at the beginning of the sample period and that have undergone impressive reforms in the network sectors during this period.

These results should be supplemented in at least two ways. First, by using data from other countries. This would make it possible to check whether the results are comparable across countries, but also to investigate the effects of the regulation of retail distribution and professional services. The data variability of the OECD indicators for these two sectors was too low in our sample to be able to identify their effects. The small number of countries in our sample is primarily a result of the limited availability of data in the CIS database of Eurostat. While the CIS is implemented in all European Union Member States, only a small proportion of the national statistical institutes responsible for this survey agree to make their data available through the Eurostat database. We hope that it will be possible to include more countries in the database in the near future.

Second, in this paper we use the OECD sector-level regulation indicators, which are based on the average of hundreds of questions on the various possible regulations. This aggregation limits our policy recommendations on regulatory design. Econometric tools are not useful for identifying the effects of these sub-indicators separately, while machine learning methods could provide interesting insights into the relationships between these sub-indicators, firm performance, and employment probability.

## Appendix A: CIS Data Descriptive Analyses

This section further elaborates on the descriptive analysis of our data from the Community Innovation Survey (CIS). Table 4 shows the average values of all variables of the estimated model from the CIS database for four subsamples of our estimation sample: product innovators/non-product innovators, R&D firms/non-R&D firms. It shows marked sample differences for the exogenous variables: the shares of group members, sellers on international markets, and large firms as well as the market share are much higher for product innovators and for R&D firms. This is also the case for the shares of innovation cooperation and financial support, but obviously non-innovative firms cannot cooperate or obtain financial support. We also see that there is a close relationship between investment in R&D and product innovation. Finally, we also observe that sales growth is higher for product innovators and R&D firms.

<table><caption>**Table 4** CIS database variable sub-sample averages</caption><thead><tr><th></th><th>R&D<br>firms</th><th>Non-R&D<br>firms</th><th>Product<br>innovators</th><th>Non-<br>product<br>innovators</th></tr></thead><tbody><tr><td><em>Exogeneous dummy variables</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Group membership</td><td>47.90%</td><td>27.01%</td><td>42.85%</td><td>26.05%</td></tr></tbody></table>

<figure><img src="image_16.png" /></figure>
<table><caption>**Table 4** CIS database variable sub-sample averages</caption><thead><tr><th></th><th>R&D firms</th><th>Non-R&D firms</th><th>Product innovators</th><th>Non-product innovators</th></tr></thead><tbody><tr><td>Sales market: only local</td><td>3.88%</td><td>22.86%</td><td>6.97%</td><td>24.21%</td></tr><tr><td>Sales market: only local or national</td><td>18.54%</td><td>32.45%</td><td>22.32%</td><td>32.96%</td></tr><tr><td>Sales market: local, national and international</td><td>77.57%</td><td>44.56%</td><td>70.69%</td><td>42.69%</td></tr><tr><td>Innovation cooperation</td><td>50.31%</td><td>7.44%</td><td>40.38%</td><td>5.32%</td></tr><tr><td>Government financial support for innovation</td><td>36.15%</td><td>3.20%</td><td>23.76%</td><td>3.09%</td></tr><tr><td>European Union financial support for innovation</td><td>13.02%</td><td>1.63%</td><td>9.83%</td><td>1.24%</td></tr><tr><td>&lt;50 employees</td><td>41.94%</td><td>61.66%</td><td>46.82%</td><td>62.53%</td></tr><tr><td>[50; 250] employees</td><td>35.57%</td><td>27.82%</td><td>33.84%</td><td>27.41%</td></tr><tr><td>&gt;250 employees</td><td>22.23%</td><td>9.74%</td><td>19.11%</td><td>9.20%</td></tr><tr><td colspan="5"><em>Exogeneous continuous variables</em></td></tr><tr><td>Market share<sup>(a)</sup></td><td>48.23%</td><td>14.74%</td><td>42.32%</td><td>12.50%</td></tr><tr><td colspan="5"><em>Selection variables</em></td></tr><tr><td>R&D firms</td><td>100.0%</td><td>0.00%</td><td>45.91%</td><td>4.91%</td></tr><tr><td>Product innovators</td><td>74.86%</td><td>15.35%</td><td>100.0%</td><td>0.00%</td></tr><tr><td colspan="5"><em>Endogenous variables</em></td></tr><tr><td>R&D intensity</td><td>8.13</td><td>0.00</td><td>3.71</td><td>0.41</td></tr><tr><td>Innovation intensity</td><td>27.68</td><td>5.06</td><td>34.82</td><td>0.00</td></tr><tr><td>Sales growth</td><td>115.31</td><td>110.96</td><td>114.89</td><td>110.56</td></tr></tbody></table>

## Appendix B: Selection Bias

There are two strong sources of selection bias in estimating the system of equations for the innovation process: (i) the log of R&D expenditure is measured only for firms that invest in R&D, and (ii) innovation intensity is measured only for firms that innovate in products. Appendix A shows marked differences between firms that invest in R&D and those that do not, and between firms that innovate in products and those that do not. It suggests that there are also marked differences on the unobservable variable, meaning that the endogeneity bias due to selection could be significant when using the OLS estimator.

To address these issues, we use the Tobit type II estimation method to simultaneously estimate Eqs. (1) and (2) with the following corresponding selection equations:

$$d_f^{RD} = \sum_k \left[ x_f^{1',k} b^{1',k} \right] + \varphi_{cw}^{1'} + \varphi_{sw}^{1'} + \varphi_{cs}^{1'} + u_f^{1'} \quad (5)$$

$$d_f^I = d_f^{RD} b^{dRD} + \sum_k \left[ x_f^{2',k} b^{2',k} \right] + \varphi_{cw}^{2'} + \varphi_{sw}^{2'} + \varphi_{cs}^{2'} + u_f^{2'} \quad (6)$$

Where $d_f^{RD}$ is a dummy equal to 1 if firm *f* reports R&D expenditures and $d_f^I$ is a dummy equal to one if the firm introduced a new product during the period of the wave. As for Eqs. (1) to (3), the set of covariates in these equations includes the num-

<figure><img src="image_17.png" /></figure>

<footnote>(a) The market share variable is calculated using CIS data only and is therefore a poor proxy for the market power of firms</footnote>
ber of employees (in three classes) and a dummy for group membership. Dummies for the sales market (local, national, and international) are introduced specifically in the R&D selection Eq. (5), as in the seminal CDM approach, and a dummy for innovation cooperation between firms is introduced in the product innovation selection Eq. (6).<fnref n="17" />

## Appendix C: Estimated Effects of Control Variables

While Table 3 in Section “Main Estimates and Simulations” shows the estimated impact of the regulation, this appendix shows the marginal effects of the control variables estimated from the same equation. These control variables are taken from the European Union Labor Force Survey database. As already mentioned in section “Main Estimates and Simulations”, we find that well-educated middle-aged men have the highest activity and employment probability. Highly educated men are also less likely to be unemployed than women and little or moderately educated workers. The probability of unemployment is lowest for older workers, who are likely to leave the labor market if they lose their job.

See Fig. 3.

<figure><img src="image_18.png" /><figcaption>Fig. 3 Marginal estimated effects of the LFS explanatory variables</figcaption></figure>

<figure><img src="image_19.png" /></figure>

<footnote n="17">¹⁷ The Tobit type II estimator could be consistent even if there is no variable specific to the selection equation, but the identification of parameters would then be based only on the assumption of normality of the residuals.</footnote>
# Appendix D: Simulated Impact of Network Regulation Reforms on R&D

Figure 2 in Section “Main Estimates and Simulations” illustrates the effect of the anti-competitive network regulation reforms implemented between 1998 and 2016 on firm sales growth and worker employment probability. This is based on the changes observed in the OECD network sector regulation indicators and the estimates in Table 2. In this Appendix, we present the impact of these reforms on R&D expenditure, again using the estimates in Table 2. The average long-term effect of these major reforms is substantial, with an increase of around 16%—rising from 12% in Spain to nearly 20% in Slovakia. However, these simulated effects should be treated with caution, given that the estimated impact of the regulatory and regulatory burden indicators on R&D expenditure are insignificant.

See Fig. 4.

<figure><img src="image_20.png" /><figcaption>Fig. 4 The average impact of the 1998–2016 network regulation reforms on R&D expenditures</figcaption></figure>

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