# Unemployment impact of network sectors and employment protection legislation reforms: Evidence from selected african countries

Kwamivi Mawuli Gomado, Isaac Amedanou

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Kwamivi Mawuli Gomado, Isaac Amedanou. Unemployment impact of network sectors and employment protection legislation reforms: Evidence from selected african countries. World Development, 2026, 200, pp.107304. 10.1016/j.worlddev.2025.107304. halshs-05429684

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Regular research article

# Unemployment impact of network sectors and employment protection legislation reforms: Evidence from selected african countries

Kwamivi Mawuli Gomado<sup>a, b,*</sup> ◐, Isaac Amedanou<sup>c, d</sup> ◐

## HIGHLIGHTS

* The impact of network sector and EPL reforms is analyzed using a quasi-experimental approach.

* Network sector and EPL reforms contribute to a reduction in the unemployment rate.

* The unemployment effects of network-sector reforms and EPL reforms are complementary.

* Additional analyses show positive effects of these reforms on other labor market indicators.

* Reforms reduce unemployment by boosting investment, total factor productivity, and reducing informality.

## ARTICLE INFO

**JEL classification:**

E24

E60

J48

J64

L51

**Keywords:**

Unemployment

Africa

Network sector

EPL

Local projections

Structural reforms

## ABSTRACT

This paper examines the dynamic effects of Employment Protection Legislation (EPL) reforms and Network Sector reforms on unemployment in selected African countries from 1990 to 2014. Reforms refer to changes in the EPL or Network Sector institutions index. Using local projections combined with augmented inverse probability weighting (LP-AIPW) and Entropy Balancing techniques to address endogeneity concerns, our findings show that EPL reforms reduce unemployment from the first year after their implementation, while Network Sector reforms also lower unemployment, with significant effects emerging from the second year onward. Robustness checks confirm that these results are consistent across alternative specifications, different definitions of reform episodes, and alternative labor-market outcomes such as employment and labor force participation. The heterogeneity analysis shows that both types of reforms reduce unemployment among men and young workers aged 15-24, while significant effects for women are concentrated among young female workers. Finally, we identify key transmission channels through which the reforms operate, including reductions in informality, increases in domestic investment and foreign direct investment, and short-run improvements in total factor productivity.

## 1. Introduction

Around 37.1 million people were unemployed in Africa in 2024. The total number of unemployed on the continent has gradually increased in recent years, compared to 2010, when it stood at 26.2 million (Horne, 2024). Recent global downturns have worsened unemployment in Africa, raising concerns about labor market challenges driven by debt distress, fiscal instability, and climate change. With 18 countries facing a debt-to-GDP ratio over 70 %, resources are being diverted from job-creating investments to debt servicing. This strain, compounded by inflation and weak tax revenues, limits funding for public services like

employment programs (UN DESA, 2024). Meanwhile, climate change is destabilizing agriculture, a vital job sector, worsening the crisis. The climate crisis further amplifies unemployment issues, highlighting the urgent need for solutions addressing both environmental and employment challenges (Kappel, 2021).

Africa is currently facing a major demographic challenge, with massive youth unemployment and widespread recourse to precarious employment. Not long ago, while the COVID-19 crisis posed challenges to labor absorption and economic development worldwide (Fox and Gandhi, 2021), its impact exacerbated the unemployespecially youth

<footnote><sup>a</sup> Normandie Universités, University of Rouen Normandie, LERN, 76000 Rouen, France</footnote>

<footnote><sup>b</sup> EDEHN - Equipe D'Économie Le Havre Normandie, Le Havre Normandy University, 25 Rue Philippe Lebon, 76063 Le Havre, France</footnote>

<footnote><sup>c</sup> Université Clermont Auvergne CNRS IRD CERDI, 26 Avenue Léon Blum, 63008 Clermont-Ferrand, France</footnote>

<footnote><sup>d</sup> Macroeconomics Analysis Section, Macroeconomics and Governance Division, United Nations Economic Commission for Africa, Addis Ababa, Ethiopia</footnote>

<footnote>* Corresponding author at: Normandie Universités, University of Rouen Normandie, LERN, 76000 Rouen, France.
Email address: kwamivi.gomado@gmail.com (K.M. Gomado).</footnote>
unemployment (Barford et al., 2021). The economic consequences were evident, with Africa having the highest unemployment rates in the world at the end of 2020 (Duval and Shibata, 2021). In South Africa, the number of employed people declined significantly throughout 2020, and the unemployment rate reached a record high of 30.8%. Similarly, the unemployment rate in Nigeria reached 27.1% in the same year. Notably, the number of unemployed in Nigeria stands at around 21 million, a figure that exceeds the population of any of the more than 30 countries on the continent (United Nations, 2021). Even more recently, in 2023, South Africa had the highest unemployment rate in the world at 28.4%. Overall, of the ten countries with the highest unemployment rates, nine were in sub-Saharan Africa.

Unemployment has long been a central concern of public policy. However, these efforts have not successfully stimulated economic growth to generate sufficient and high-quality employment. Fundamental changes are needed to address the increasingly complex employment challenges. Governments have a critical role in managing market institutions, which they often do through Employment Protection Legislation (EPL) reforms—such as minimum wage laws, job security provisions, and targeted labor market policies—and through network sector (de)regulations. Our focus is on the effectiveness of these reforms. Therefore, we are concerned with the causal impact of EPL and network reforms on unemployment in the context of Africa. Specifically, we address whether EPL and network reforms lead to a quantifiable impact on unemployment in Africa, where employment issues remain central to public policies. If so, are these reforms complementary, or can each one impact unemployment independently of the other? In other words, is the impact of the reforms on unemployment quantifiable individually or complementarily?

To address this question, we assembled a unique database that combines unemployment rates for African countries from World Development Indicators (WDI), indices of network sector and Employment Protection Legislation (EPL) institutions compiled by the Research Department of the International Monetary Fund (IMF) (Alesina et al., 2023), as well as a range of confounding factors from various complementary sources. In line with the empirical literature, we define a reform as a change—or a major change—in the indices of network sector or in the employment protection legislation (Alesina et al., 2023; Giuliano et al., 2013). Our sample comprises 18 African countries over the period 1990–2014, totaling 355 observations, which provide substantial heterogeneity in unemployment rates both within and between countries.

Several scholars examine the impact of product and labor market reforms on unemployment, growth and/or wages. For instance, Griffith et al. (2007) analyze the effect of product market competition on unemployment in Organization for Economic Co-operation and Development (OECD) countries, considering the role of labor market institutions, as do Nicoletti and Scarpetta (2005), who focus on employment rates. A key difference is that Nicoletti and Scarpetta (2005) uses a broader measure of product market reforms, covering both tradable and non-tradable sectors, and seven regulated ‘network’ indicators to assess labor market conditions, unlike the single regulation indices typically used in prior studies. Overall, it is widely accepted that product market reforms do not affect unemployment independently of labor market institutions. Most authors argue that product market reforms influence labor market outcomes primarily by shaping labor market institutions rather than through direct effects.

A strand of the literature highlights that labor and product market reforms have differentiated effects depending on the time horizon. In the short term, labor market deregulation, such as reducing dismissal costs, can increase unemployment, particularly during recessions, while cutting unemployment benefits tends to stimulate activity during economic slowdowns (Cacciatore et al., 2016). Similarly, deregulation measured by the employment protection legislation index has negative effects on unemployment in the short run, with positive impacts only emerging in the long term. Regarding product markets, deregulation of state controls

and government involvement in business tends to raise unemployment, whereas broader product market liberalization helps reduce the unemployment rate (Adascalitei and Pignatti Morano, 2016; Duval et al., 2022). In the medium and long term, reforms promote employment, competitiveness, and productivity. For instance, reducing protection against collective dismissals helps lower unemployment, and product market deregulation enhances productivity by intensifying competition. However, external constraints, such as limited access to international financing, can slow down these positive adjustments. Overall, while reforms have beneficial effects in the medium and long term, their short-term impact remains highly dependent on the economic and institutional context.

Two recent studies examine the impact of product and labor market reforms on unemployment in European countries (Piton and Rycx, 2018) and on growth in OECD countries (De Haan and Wiese, 2022). Our analysis aligns with these studies in both concept and methodology. Like the first, we explore the effects of both reforms on unemployment, extending the analysis to their potential complementary effects. Drawing on the second, we use a combination of local projections and the Augmented Inverse Probability Weighted (LP-AIPW) method to address endogeneity and identify causal effects. We also apply alternative methods, including dummy variables and entropy balancing, to ensure robust estimation and eliminate differences between treatment and control groups.

Studying the impact of reforms in the labor and product markets in African countries is crucial due to the distinct economic characteristics of the continent, which differentiate it from OECD countries and may lead to divergent results. The private sector in Africa, which accounts for 70% of production, 70% of investments and employs 90% of the workforce (AfDB, 2024), is a key driver of growth and development. However, structural challenges, such as one of the world's largest informal sectors, which represents 42.9% of economic activity in sub-Saharan Africa (Abid, 2016), restrict productivity, formal job creation and tax revenue mobilization. The expanding labor force and dynamic informal economy (Meagher, 2018) place additional pressure on the labor market, where the supply of formal jobs lags behind demand, exacerbating unemployment and underemployment, particularly among youth. The formal sector's dual structure, dominated by micro and small enterprises (MSEs) and a few large, often foreign-owned firms, with a scarcity of medium-sized enterprises, reflects capital constraints, high transaction costs, and entry barriers. Furthermore, the region faces significant challenges, including a high unemployment rate of 6.7% in 2022, compounded by a population growth rate of 2.5%, which threatens to worsen youth unemployment and undermine development outcomes and political stability. The region also suffers from a higher incidence of conflict-affected countries than any other, underscoring the urgent need for more and better private sector jobs to improve livelihoods and foster stability (World Bank, 2023). Given these dynamics, labor and product market reforms in Africa are likely to have distinct and immediate effects compared to OECD countries, owing to the continent's unique context of developing labor markets and institutions.

Our study contributes to the literature in several ways. First, it fills a significant gap in the empirical research on Africa, where evidence on the effects of Employment Protection Legislation (EPL) reforms and Network Sector reforms on unemployment remains scarce—contrasting with the more extensive work conducted for regions such as South Africa (Arora and Ricci, 2005) and the Middle East and North Africa (MENA) region (Agénor et al., 2007). By examining a broad set of African countries, we show that reforms in both areas play a central role in improving labor-market outcomes. Second, we highlight the distinct and complementary contributions of EPL reforms and Network Sector reforms, challenging the conventional view that reforms in network sectors affect labor outcomes only when labor-market institutions are simultaneously strengthened. Our results demonstrate that Network Sector reforms can directly reduce unemployment, even in the absence of immediate changes in labor regulations. Third, our study expands the scope of analysis by relying on comprehensive indicators of institutional
change, particularly in labor-market regulation, and by separately iden-
tifying the effects of liberalization in key infrastructure sectors such as
telecommunications and electricity. Finally, we shed light on the mecha-
nisms through which the reforms operate. We document that reductions
in informality, increases in domestic capital formation and foreign direct
investment, and improvements in total factor productivity constitute
key transmission channels linking EPL and Network Sector reforms to
improved labor-market performance.

The evidence shows that Employment Protection Legislation (EPL) reforms and Network Sector reforms significantly improve labor-market outcomes in African economies. Our results indicate that EPL reforms reduce unemployment from the first year following their implementation, while Network Sector reforms also lower unemployment, with significant effects emerging from the second year onward. These findings remain robust across alternative specifications, different definitions of reform episodes, and various labor-market indicators, including employment and labor force participation. The heterogeneity analysis further reveals that both types of reforms reduce unemployment among men and young workers aged 15 – 24, while significant effects for women are concentrated among young female workers. In addition, the reforms operate through several transmission channels—most notably reductions in informality, increased investment in fixed capital and foreign direct investment, and improvements in total factor productivity. Together, these results underscore the consistent impact of EPL and Network Sector reforms on labor-market performance across countries and worker groups. Together, these results underscore the consistent impact of EPL and Network Sector reforms on labor-market performance across countries and worker groups.

The remainder of the paper is organized as follows: Section 2 presents the theoretical background. Section 3 describes the data sources and provides some descriptive statistics. Section 4 outlines the empirical estimation procedure, while Section 5 presents the results. Section 6 offers some robustness checks. Section 7 provides a heterogeneity analysis, while Section 8 identifies the main transmission channels. The last Section concludes and provides some policy implications.

## 2. Theoretical background

Product and labor market reforms often aim to improve economic efficiency and stimulate growth, but their impact on the unemployment rate is not always direct. Understanding the underlying mechanisms of this relationship is essential, as many dynamics come into play in explaining the causal link between these reforms and unemployment. Although several studies have attempted to explore the consequences of structural reforms, the channels through which they affect unemployment or employment are not so obvious. This section therefore aims to clarify these mechanisms while reviewing the empirical studies that have highlighted them. As for our focus, we concentrate specifically on the mechanisms through which product and labor market reforms interact with unemployment.

Investment is the first channel through which market reforms could have an impact on unemployment or employment. Changes to the regulation of product and labor markets influence firms' investment decisions, thereby affecting their ability to hire and maintain jobs. However, (de)regulation of product markets significantly influences investment in various ways: by reducing entry barriers, which decrease the price margins over costs and encourage capital accumulation (Blanchard and Giavazzi, 2003). Moreover, adjustment costs imposed by regulation, such as administrative burdens, can hinder business expansion and limit their ability to respond quickly to market changes. Market regulation can also reduce investment. For example, Escribá-Pérez and Murgui-García (2017) considers that barriers to entrepreneurship and trade negatively affect investment by reducing the productivity of capital. Similarly, stricter labor market regulations can increase labor costs. However,

(de)regulations related to hiring and firing, as well as employment pro-
tection measures, can enhance capital productivity, thereby encouraging
investment. Also, Alesina et al. (2005) mentioned that regulatory reform
of product markets is associated with an increase in investment, with a
substantial role played by entry liberalization and privatization. Finally,
in sectors such as telecommunications, poorly designed access rules can
discourage investment due to excessively low prices or lead to overca-
pacity with excessively high prices. As such, reforms aimed at reducing
barriers to competition or increasing flexibility in product and labor
markets can encourage companies to boost their investments. A more
favorable investment environment can support long-term job creation,
thus reducing unemployment.

A second channel worth highlighting is productivity. Market re-
forms serve as an important lever for improving productivity, which
in turn influences unemployment and employment. Regarding labor
market reforms, Murtin and Robin (2018) show that employment in-
centives and deregulation of labor markets are essential policy tools
for reducing unemployment by increasing overall productivity. These
reforms stimulate competition and reduce barriers to entry, thereby en-
hancing the efficiency of firms. In terms of product market reforms,
Nicodème and Sauner-Leroy (2007) explain that these reforms affect
productivity through three mechanisms: allocative efficiency, which re-
allocates resources to the most productive sectors; productive efficiency,
which optimizes the use of production factors within firms; and dy-
namic efficiency, which encourages innovation and the adoption of
more advanced technologies. Additionally, Konte et al. (2022) show
that product market reforms, along with trade and financial sector re-
forms, have contributed to productivity growth by improving resource
allocation within firms. These reforms create an environment where
competition fosters innovation and efficiency. However, some reforms
can have negative effects. Jona-Lasinio and Vallanti (2013) highlight
that the increased flexibility of temporary contracts in the labor market
has led to a decline in productivity in sectors requiring high technologi-
cal flexibility and lower skill content. This flexibility has also resulted in
a shift of employment toward less productive industries, thus affecting
productivity reallocation. By documenting the main channels through
which structural reforms impact productivity growth, Gomado (2024b)
reveal that these reforms, including those in product markets, stimu-
late labor productivity by inducing dynamic, productive, and allocative
efficiency. Therefore, these reforms are essential for supporting produc-
tivity growth and, consequently, improving economic performance and
long-term employment.

A third and final channel we identify between market reforms and unemployment is informality. Regarding labor market reforms, the literature highlights ambiguous effects on the informal economy. On the one hand, reducing labor costs and dismissal costs, or increasing contract flexibility, can encourage firms to formalize more jobs. For instance, Saraçoğlu (2020) proposed labor market policy adjustments, such as reductions in the minimum wage and social charges, to decrease the share of informal employment. On the other hand, if reforms create short-term incentives to bypass regulation, they may reinforce informality, increasing the number of undeclared workers and complicating the accurate assessment of unemployment. In this respect, Lehmann and Muravyev (2012) argues that a more regulated labor market tends to expand the size of the informal economy. However, the deregulation of labor and product markets can reduce informality while intensifying competition in product markets. Several authors converge in showing that well-coordinated reforms can not only mitigate short-term negative effects but also promote better resource allocation between the formal and informal sectors. For example, Charlot et al. (2011) points out that easing market regulations reduces both informality and unemployment, while the slow reallocation of resources between sectors can create short-term negative effects, especially in economies where the informal sector is dominant (Anand and Khera, 2016).
## 2.1. How network sector and EPL reforms affect employment and productivity?

Building on the channels described above, it is important to clarify the focus of our empirical analysis on two specific network sectors, telecommunications and electricity, and why reforms in these sectors can plausibly influence unemployment and productivity dynamics, despite their relatively small share in GDP. These sectors are foundational for economic activity and exhibit strong forward and backward links across industries.

First, reforms in network sectors reduce production and transaction costs, improving the overall business environment and enabling firms to enter or expand operations. This, in turn, increases labor demand not only within the network sectors themselves but also in downstream industries that rely on reliable electricity and telecommunications services. Second, network sector liberalization attracts investment, both domestic and foreign, by providing the infrastructure needed for manufacturing, services, and digital enterprises. Such investment creates new jobs directly in the sectors and indirectly through supply chains. Third, productivity gains and labor reallocation arise from improved access to electricity and telecommunications, allowing firms to operate more efficiently, innovate, and hire additional workers. Finally, telecommunications liberalization in particular can foster employment in emerging activities, such as mobile financial services, digital platforms, and call centers, extending the labor market impact well beyond the direct sectoral jobs.

While reforms in network sectors can stimulate employment through investment, productivity, and firm entry, the effects of labor market reforms, particularly the flexibilization of Employment Protection Legislation (EPL), are more nuanced. Empirical studies, including Jonasinio and Vallanti (2013); Murtin and Robin (2018), indicate that deregulation of the labor market can reduce the activity of the informal sector, encourage investment, and improve total factor productivity (TFP) in the short and medium term. However, the longer-term effects on productivity are more ambiguous, as excessive flexibility may weaken firm-specific learning and internal knowledge accumulation.

Recent European research highlights that the productivity impact of labor market reforms depends on the prevailing technological and innovation regime (Cetrulo et al., 2018; Hoxha and Kleinknecht, 2024; Kleinknecht et al., 2014; Peneder, 2010; Wachsen and Blind, 2016). In highly cumulative innovation regimes—where innovation relies on the retention of skilled workers and the accumulation of tacit knowledge—labor market deregulation can hinder innovation and productivity growth. In contrast, in low-cumulative regimes, characterized by standardized technologies and limited R&D intensity, the negative impact of labor market flexibility on productivity is weaker, and employment gains may dominate. Given that most African economies are dominated by low-cumulative regimes and labor-intensive production structures, EPL reforms could initially enhance employment through cost competitiveness and informality reduction, even if the long-term productivity effects remain modest. These heterogeneous mechanisms help explain why the employment effects of labor market reforms may differ from those of network sector reforms in the African context.

Given that most African economies are dominated by low-cumulative regimes and labor intensive production structures (Mensah et al., 2023), EPL reforms could initially enhance employment through cost competitiveness and informality reduction (IMF, 2024), even if the long-term productivity effects remain modest. Unlike many European economies, where labor markets are generally more rigid and firms rely on long-term employment to accumulate knowledge (Cetrulo et al., 2018; Kleinknecht et al., 2014), African labor markets are comparatively more flexible, informal and labor intensive (Benjamin et al., 2012; Bizimana and Arzoumanian, 2023). This structural difference helps explain why EPL reforms may have more immediate employment effects in Africa, but less impact on long-term productivity, while in Europe, the same reforms could reduce productivity in high-innovation sectors. These

heterogeneous mechanisms help explain why the employment effects of labor market reforms may differ from those of network sector reforms in the African context.

# 3. Data and descriptive statistics

## 3.1. Data

We use the IMF's Structural Reform Database (SRD), compiled by Alesina et al. (2023), which provides a comprehensive set of indicators on structural policies. The data include indices for domestic financial markets, capital account, trade or tariff, product market, and labor market liberalization. These data have the advantage of covering a wide range of both developed and developing countries and being available over a long time span. For the purposes of our study, we specifically extract data on labor and product markets, given our focus on the implications of such reforms for unemployment. With data available for the selected sample of 18 African countries, our analysis focuses on the period from 1990 to 2014. We concentrate on two variables of interest including labor and product market institutions. We adapt the terminology used by Alesina et al. (2023) to more precisely describe the types of reforms analyzed. In their study, product market reforms cover only two sectors—electricity and telecommunications—which we collectively refer to as network sector reforms. Similarly, since labor market reforms are limited to Employment Protection Legislation, we refer to them directly as employment protection legislation reforms.<fnref n="1" />

* **Network sector institutions:** covers liberalization in the telecommunications and electricity sectors. Liberalization in the telecommunication is the simple average of four sub- indicators: access, competition, regulation, and ownership. For the electricity sector, it examines the consolidation or unbundling of generation, transmission, and distribution, state ownership, the presence of an independent regulator, and the extent of liberalization. Electricity sector liberalisation is the simple average of four sub- indicators: wholesale market, unbundling, regulation, and ownership. The range of scores for each sub-dimension can be found in Table 1. The aggregate index is constructed by summing the two sub- indicators (telecommunications and electricity) and normalizing it to range between 0 and 1, with higher values indicating a greater degree of liberalization in the sector.

* **Employment protection legislation institutions:** pertains to the liberalization of employment protection legislation (EPL) and the termination of full-time permanent contracts. Employment protection legislation encompasses procedural requirements (such as third-party approval), redundancy costs (including severance pay and notice requirements), and grounds for dismissal with or without recourse.<fnref n="2" /> The aggregate index is constructed by summing the sub- indicators and normalizing the total to range from 0 to 1, with higher values indicating a greater degree of labor market liberalization.

The Fig. 1 illustrates the average evolution of the composite indicator for the network sector (Panel A) and employment protection legislation (Panel B), along with their specific dimensions, over the period

<footnote n="1">¹ For more details on the construction of the SRD indicators, we encourage readers to consult the detailed documentation on the IMF website. Our study focuses on this sample of countries due to the limited number of empirical studies dedicated to this region. However, the lack of recent data beyond 2014, which would allow for the inclusion of the most recent developments in economic policies regarding structural reforms, represents a limitation of our analysis.</footnote>

<footnote n="2">² The dimensions covered by labor market institutions do not include aspects such as minimum wages, unemployment benefits, unions, or active labor market programs, which represents a limitation of the database in Alesina et al. (2023). However, other databases, although limited to OECD countries, include these dimensions (De Haan and Wiese, 2022; Wiese et al., 2024).</footnote>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 1. Liberalization indices and sub-indices for the network sector and employment protection legislation. Note: Panel (A) Reform indicator for the network sector incorporates liberalization in two network sectors: telecommunications and electricity. The aggregated index (reform in network sector market) is the sum of two sub-indicators (sub-indices for the network sector and electricity sector), normalized from zero to one. Reform in telecommunications market is the simple average of four sub-indicators: access, competition, regulation, and ownership. Reform in electricity market is the simple average of four sub-indicators: wholesale market, unbundling, regulation, and ownership. Panel (B) The aggregated employment protection legislation index is constructed as the sum of the following five sub-indices: Valid Grounds, Procedural Inconvenience, Firing Costs, Redress Measures, and Additional Requirements for Collective Dismissals. The aggregated index is normalized to range from zero to one. Each sub-index is calculated as the simple average of several more granular indicators and is also normalized, where 0 represents the highest level of regulation and 1 represents the highest level of liberalization. Source: Author's based on study's datasets.</figcaption></figure>

1990–2014. During these 24 years, restrictions in the network sector declined markedly, with the index increasing from 0.004—corresponding to an almost fully restricted market—in 1990 to approximately 0.66 in 2014. In contrast, unlike the institutions governing the network market, the composite indicator for employment protection legislation and its specific dimensions exhibited little change over the period. Nevertheless, it is worth noting that these indicators already reflected a relatively high level of flexibility at the beginning of the observed period, which partly explains their relative stability thereafter.

To identify reforms in the network sector and employment protection legislation, we follow the empirical literature (Alesina et al., 2023; De Haan and Wiese, 2022; Giuliano et al., 2013; Prati et al., 2013). Accordingly, reforms are defined as changes (or major policy shifts) in the institutional index of network sectors or employment protection legislation:

$$Reform_{i,t}^I = Index_{i,t}^I - Index_{i,t-1}^I \quad (1)$$

$$Reform\_episodes_{i,t}^I = \begin{cases} 1 & \text{if } \Delta Index_{i,t}^I = Index_{i,t}^I - Index_{i,t-1}^I > \sigma \\ 0 & \text{otherwise.} \end{cases} \quad (2)$$

where $I = \{\text{employment protection legislation index, network sector index}\}$, $\Delta Index_{i,t}^I = Reform_{i,t}^I = Index_{i,t}^I - Index_{i,t-1}^I$ denotes the change in the employment protection legislation index or the network sector institutions index. A positive value of $Reform_{i,t}^I$ indicates a liberalizing reform,

whereas a negative value reflects a tightening, i.e., a counter-reform. $\sigma$ represents the standard deviation for each category of institutions across the sample. We identify episodes of network sector and employment protection legislation reforms when, for a given country in a specific year, the annual change in the corresponding institutional index exceeds one standard deviation from the sample mean (DAVID et al., 2022; Gomado, 2024a; Tola and Waelti, 2018). Hence, reform episodes—unlike simple variations in the market liberalization index—capture major institutional transformations that have occurred in the network sector and employment protection legislation.

The dependent variable, unemployment, represents the official unemployment statistics for each country, with data sourced directly from the World Bank's WDI database. The unemployment rate is measured as the total number of unemployed individuals aged 15 or over, expressed as a percentage of the total labor force, typically comprising individuals aged 15 and above.

Following the empirical literature, we include several control variables. These consist of the human capital index, trade (as a percentage of GDP), annual GDP growth, and elections, represented by a binary variable indicating whether executive elections took place in a given year. We also consider the number of years remaining in the current political term, economic crises—captured by two binary variables, one indicating whether a country experienced a monetary or debt crisis and another indicating whether it faced a systemic banking crisis—IMF program participation (active for five months or more), democracy (measured
<table><caption>**Table 1**
Network sector and employment protection legislation dimensions description.</caption><thead><tr><th>Overall index</th><th>Dimension</th><th>Dimension description</th></tr></thead><tbody><tr><td rowspan="10">Network sector institutions</td><td>[1] Access (Telecommunication)</td><td>It captures the degree of government intervention in the access to electricity [0-2].</td></tr><tr><td>[2] Competition (Telecommunication)</td><td>It captures the degree of the market competition by the number of existing companies that is, one (monopoly), two (duopoly), or three or more (competitive) [0-2]</td></tr><tr><td>[3] Regulation (Telecommunication)</td><td>It examines whether there is an independent regulatory agency [0-2]</td></tr><tr><td>[4] Ownership (Telecommunication)</td><td>It quantifies the extent of state-owned firms in the market [0-2]</td></tr><tr><td>[1] Wholesale (Electricity)</td><td>It examines whether there is a liberalized wholesale market [0-2]</td></tr><tr><td>[2] Unbundling (Electricity)</td><td>It captures the degree of vertical integration in the market that is, whether generation, transmission, and distribution are unbundled [0-2]</td></tr><tr><td>[3] Regulation (Electricity)</td><td>It examines whether there is an independent regulatory agency [0-2].</td></tr><tr><td>[4] Ownership (Electricity)</td><td>It quantifies the extent of state-owned firms in the market [0-2].</td></tr><tr><td rowspan="5">Employment protection legislation institutions</td><td>[1] Valid grounds</td><td>It captures the freedom of the employer in deciding when to dismiss workers and which workers to dismiss.</td></tr><tr><td>[2] Procedural inconvenience</td><td>It includes provisions such as consultation with workers' representatives and third-party approval.</td></tr><tr><td>[3] Firing costs</td><td>It consists of minimum notice periods and severance payments.</td></tr><tr><td>[4] Redress measures</td><td>It concerns provisions such as the possibility for the worker of being reinstated in employment or to receive a compensation following an unfair dismissal.</td></tr><tr><td>[5] Additional requirements for collective dismissals</td><td>It accounts for additional restrictions imposed to the employer when dismissing a large number of workers for economic reasons.</td></tr></tbody></table>

Source: Author's illustration based on Alesina et al. (2023)'s dataset. The range of scores is in brackets. For employment protection legislation institutions, each sub-indicator is constructed as the average of several different variables. See the detailed documentation on the IMF website.

by the Polity2 index), income inequality (captured by the Gini index), the degree of liberalization in the employment protection legislation or network sector markets, and inflation (measured by average consumer prices). Definitions of all variables are provided in Appendix A.

The rationale for including these control variables is as follows. Regarding trade openness, Mitra and Ranjan (2010) argue that higher levels of trade openness enhance the international competitiveness of domestic firms, allowing them to expand locally, regionally, and internationally. This expansion can generate additional employment and contribute to a reduction in the unemployment rate. Conversely, Helpman and Itskhoki (2010) suggest that trade openness may increase unemployment, as large firms might source materials more cheaply abroad and establish production facilities in other countries through offshoring, thereby relocating jobs.

With respect to human capital development, Samiullah (2014) emphasize that it enhances individual productivity by providing the necessary professional skills. Similarly, Massingham and Tam (2015) highlight that human capital encompasses better education, improved health, enhanced skills, and increased professional knowledge, all of

<table><caption>**Table 2**
Summary statistics.</caption><thead><tr><th>Variable</th><th>Mean</th><th>Std. Dev.</th><th>Min.</th><th>Max.</th><th>N</th></tr></thead><tbody><tr><td>Employment Protection Legislation Reforms</td><td>-0.002</td><td>0.029</td><td>-0.192</td><td>0.323</td><td>450</td></tr><tr><td>Network Sector Reforms</td><td>0.021</td><td>0.056</td><td>-0.154</td><td>0.308</td><td>450</td></tr><tr><td>Unemployment, total (% of total labor force)</td><td>8.305</td><td>7.07</td><td>0.599</td><td>33.473</td><td>423</td></tr><tr><td>Human capital index (HCI)</td><td>1.683</td><td>0.375</td><td>1.03</td><td>2.635</td><td>422</td></tr><tr><td>Inflation rate, average consumer prices</td><td>9.621</td><td>15.654</td><td>-72.729</td><td>156.964</td><td>432</td></tr><tr><td>Elections</td><td>0.26</td><td>0.439</td><td>0</td><td>1</td><td>450</td></tr><tr><td>Years Left in Current Term crisis</td><td>2.185</td><td>1.609</td><td>0</td><td>6</td><td>389</td></tr><tr><td>IMF program active for 5 or more months</td><td>0.027</td><td>0.161</td><td>0</td><td>1</td><td>450</td></tr><tr><td>Democracy</td><td>0.416</td><td>0.494</td><td>0</td><td>1</td><td>411</td></tr><tr><td>Trade (% of GDP)</td><td>58.827</td><td>18.833</td><td>23.059</td><td>116.048</td><td>410</td></tr><tr><td>Income Inequality (gini index)</td><td>53.934</td><td>10.2</td><td>31.877</td><td>77.085</td><td>432</td></tr><tr><td>GDP growth (annual %)</td><td>4.182</td><td>4.491</td><td>-17.669</td><td>33.736</td><td>432</td></tr><tr><td>Employment Protection Legislation index - level</td><td>0.71</td><td>0.14</td><td>0.216</td><td>0.981</td><td>450</td></tr><tr><td>Network Sector index - level</td><td>0.287</td><td>0.236</td><td>0</td><td>0.769</td><td>450</td></tr></tbody></table>

which improve employability. Consequently, higher levels of human capital may contribute to a reduction in unemployment.

Economic growth, measured by GDP growth, is also expected to affect employment. According to Thirlwall (1989), a larger economy can employ more people, thus reducing unemployment. However, Abdul-Khaliq et al. (2014) note that higher economic growth does not always translate into greater employment.

The inverse relationship between unemployment and inflation, known as the Phillips curve, has been documented both theoretically and empirically. When labor demand exceeds supply, wages tend to rise, leading to higher inflation and lower unemployment. This relationship has been confirmed in numerous studies (Furuoka and Munir, 2014; Vermeulen et al., 2015; Yelwa et al., 2015).

Elections are included to capture potential economic and political cycles affecting unemployment Cahan (2019). The level of democracy is also considered to control for the quality of institutions, as different regimes exhibit varying tolerance for unemployment GonçalvesVeiga and Chappell Jr (2002). Participation in IMF programs has been shown to significantly increase unemployment in beneficiary countries Chletsos and Sintos (2023).

From a Keynesian perspective, high income inequality tends to reduce aggregate demand, as wealthier households have a lower marginal propensity to consume. Lower demand can decrease firms' incentives to hire, thereby increasing unemployment. Similarly, economic crises reduce household consumption and business investment; the resulting drop in demand lowers production, prompting firms to lay off workers and causing cyclical unemployment.

Finally, the number of years remaining in the current political term is relevant, as governments may increase public spending or cut taxes in the final years of their mandate to stimulate employment and reduce unemployment prior to elections.

## 3.2. Data description and stylized facts

Table 2 presents the descriptive statistics of all the variables included in our base model. The Fig. A1 in Appendix A shows the evolution of the quality of market institutions (both network sector and employment protection legislation) as well as the unemployment rate for the countries studied. In contrast, the Fig. 3 illustrates the evolution of reforms regarding the quality of product and labor market institutions, as well as the unemployment rate, in these countries.
<figure><img src="image_5.png" /><figcaption>Fig. 2. The average unemployment rate for each country over the sample period, 1990–2014.</figcaption></figure>

One can notice a heterogeneous trend in the unemployment rate across the different countries studied. For instance, in countries like Burkina Faso, Côte d'Ivoire, Mozambique, Egypt, Uganda, and Senegal, the unemployment rate has followed an upward trend in recent years. However, in other countries such as Ethiopia, Algeria, and Cameroon, a downward trend is observed. Fig. 2 illustrates the average unemployment rates between 1990 and 2014 for the countries studied. Sub-Saharan Africa generally displays relatively low unemployment rates, below 6%, with the notable exception of South Africa, which records an average rate of around 28%. These low rates are mainly explained by the absence of unemployment benefit systems in most countries. This situation forces unemployed individuals to quickly find means of subsistence, often in the informal economy or through various agricultural activities, whether as urban farmers, agricultural laborers, farm helpers, or family workers. In contrast, North African countries exhibit higher average unemployment rates, around 10%.

Regarding the indicators of the quality of network sector and employment protection legislation institutions, a relatively homogeneous evolution can be observed across all countries (see Fig. A1 in Appendix A). The evolution of reforms in these indices, presented in Fig. 3, shows that significant changes occurred within network sector market institutions, reflecting the liberalization programs initiated by the IMF, commonly referred to as “structural adjustment programs.” These reforms primarily took place during the 1990s, as observed, for instance, in Côte d'Ivoire and South Africa. In the telecommunications sector, for example, in 1997, three of Africa's major telecommunications networks underwent privatization reforms. Senegal privatized the National Telecommunications Company (Sonatel) by selling 33.3% of its capital (later increased to 42.3% in 1998) to France Câble Radio (France Telecom). In Côte d'Ivoire, the government sold 51% of CI-Telecom to the same French company. In South Africa, 30% of Telkom was sold to a consortium composed of SBC-USA (holding 60%) and Telekom Malaysia (holding 40%) (Plane, 2002). In the electricity sector, particularly in Ghana, reforms began in 1993 following a supply crisis caused by increasing demand and drought conditions. Support from the World Bank was made conditional on tariff increases, the opening of the sector to private participation, and the adoption of a comprehensive

reform plan (Williams and Ghanadan, 2006). However, only limited changes (reforms) are observed in the labor market institutions index (see Fig. 3). This observation is consistent with the findings of Adascalitei and Pignatti Morano (2016), who report that between 2008 and 2014, reforms aimed at reducing employment protection accounted for only 15% in the MENA region and 30% in Sub-Saharan Africa. For example in our study, Cameroon recorded just one negative change in this indicator in 1993, which corresponds to a counter-reform. This trend can be explained by the country's earlier efforts to regulate labor market institutions, notably through the establishment of tripartite social dialogue frameworks (Adascalitei and Pignatti Morano, 2016).

Reform episodes are presented in Fig. 4 (a distribution of the reform episodes can be found in Table A1 in the Appendix A). While there are a total of 62 episodes of product market reforms, labor market reforms show only 10 episodes. The identified reform episodes occurred in countries such as Algeria, Burkina Faso, Côte d'Ivoire, Madagascar, Senegal, South Africa, Tunisia, and Zimbabwe.

## 4. Empirical strategy

To study the impact of employment protection legislation and network sector reforms on the unemployment rate, we use the local projection method as proposed by Jordà (2005). This method, which allows for the estimation of impulse response functions, offers the advantage of not only evaluating the dynamic effects of market liberalization reforms on unemployment rates but also accounting for nonlinearities in the dynamic response thanks to its flexibility, which does not impose the dynamic restrictions inherent in vector autoregressive specifications. Indeed, the effects of reforms may depend on the initial conditions prevailing at the time of their implementation, such as the business cycle, the quality of political institutions, the level of financial development, etc. Thus, the local projection method has been used in recent macroeconomic studies to assess the macroeconomic effects of consolidation policies, structural reforms, etc. (Alesina et al., 2023; Gomado, 2023; Jordà and Taylor, 2016; Ramey and Zubairy, 2018). Our baseline specification follows the work of Alesina et al. (2023) according to the following equation:

$$ \Delta Y_{i,t+h} = \alpha_i^h + \gamma_t^h + \delta_k^h \sum_{k=1}^{5} \Delta Y_{i,t-k-1} + \beta^h R_{i,t} + \zeta^h X_{i,t} + \epsilon_{i,t+h} \quad (3) $$

where $\Delta Y_{i,t+h} = \log y_{i,t+h} - \log y_{i,t-1}$ represents the cumulative change in the logarithm of the unemployment rate between $t-1$ and $t+h$ for country $i$. The time horizon $h$ takes values in $\{0, 1, 2, 3, 4, 5\}$. The variable $R_{i,t}$ captures changes (reforms) in employment protection legislation and network sector market institutions between periods $t-1$ and $t$. To reduce potential selection bias, we adopt an alternative approach: instead of using $R_{i,t}$ directly, we rely on the reform episodes (Reform_episodes) as defined in Section 3. This latter definition forms the basis of our results in this paper. We assume that changes in the quality of pro-market institutions can contemporaneously affect the dependent variable, given our annual frequency. Thus, reform episodes observed at time $t$ begin to have an effect between year $t$ and year $t+1$. Consequently, when using reform episodes, we replace $R_{i,t}$ with $R_{i,t+1}$. This method provides a robust framework for assessing the impact of significant policy changes while mitigating risks associated with selection bias. The term $\sum_{k=1}^{5} \Delta Y_{i,t-k-1}$ represents an AR(5) component for the unemployment rate, capturing lagged changes between $t-k-1$ and $t-k$. $X_{i,t}$ is a set of control variables as previously described. We also control for time $(\alpha_i^h)$ and country $(\alpha_i^k)$ fixed effects to capture common shocks and time-invariant country characteristics, respectively. $\epsilon_{i,t+k}$ is a residual term following a normal distribution with a mean of zero and a standard deviation of 1.

### 4.1. Identification strategy

We estimate Eq. (3) using ordinary least squares (OLS) and generate impulse responses using the local projection method (LP-OLS).
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Reforms in Network Sector and Employment Protection Legislation Institutions and the Unemployment Rate, 1990–2014. Source: Author's based on study's datasets.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Network sector and EPL reforms episodes. Source: Author's based on study's datasets.</figcaption></figure>

However, estimating Eq. (3) using OLS poses an econometric identification challenge, which compromises a causal interpretation of the impact of reforms on the unemployment rate. Indeed, according to the work

of Alesina et al. (2023), the implementation of reforms is not random; therefore, governments may choose the ideal timing based on their political interests to implement or delay a reform. Moreover, reforms may
be implemented to respond to an economic crisis (Alesina and Drazen, 1991). Thus, failing to account for this aspect creates a selection bias, leading to a bias in estimating our coefficient of interest β. To correct the selection bias and accurately identify the causal effects of product and labor market reforms, we follow the work of Jordà and Taylor (2016) by conducting quasi-random experiments.<fnref n="3" />

## 4.2. Dealing with endogeneity: the LP-AIPW framework

We focus on reform episodes identified by significant changes in reform indices. Our analytical strategy centers on estimating a specific Average Treatment Effect (ATE). In the first step, we estimate a propensity score that captures the probability that a country experiences a major change in the product or labor market reform indicator, conditional on observable characteristics. This first step is implemented by estimating the following equation:

$$ \log \left( \frac{P[R_{i,t+1} = 1 | X_{i,t}]}{P[R_{i,t+1} = 0 | X_{i,t}]} \right) = \alpha_i + \theta X_{i,t} + \epsilon_{i,t} \quad (4) $$

where $X_{i,t}$ denotes the set of control variables previously described, augmented with the unemployment rate prevailing at the time of the reform. We include country fixed effects to account for cross-country differences in reform capacity. Once model Eq. (4) is estimated, we predict the propensity score denoted by $\hat{\rho}_{i,t}$.

In the second step, we reweight the sample using inverse propensity scores. The weights are defined as follows: $\omega_{i,t} = R_{i,t+1}/\hat{\rho}_{i,t} + (1 - R_{i,t+1})/(1 - \hat{\rho}_{i,t})$. In our baseline specification, we do not truncate the weights. As is common in the empirical literature, we report in Section 3 robustness checks in which the weights are truncated at 10.

Third, we employ a “doubly robust” estimator by combining the local projection method with the Augmented Inverse Probability Weighted (AIPW), following empirical work (De Haan and Wiese, 2022; Jordà and Taylor, 2016).<fnref n="4" /> By combining a treatment model and a regression model, the AIPW model simultaneously accounts for observed confounders that explain exposure to treatment (countries experiencing significant changes in the reform indicator).<fnref n="5" /> The AIPW estimator is said to be “doubly robust” because it withstands specification errors in the models (Imbens, 2004; Lunceford and Davidian, 2004; Wooldridge, 2007, 2010). This robustness means that it remains reliable even if one of the two models—treatment or outcome—is misspecified, provided the other is correctly specified. In regression analysis, the AIPW estimator simultaneously incorporates these models to adjust for observed confounding factors, reducing biases and improving the precision of estimates. By combining the strengths of both models, it serves as an effective method for estimating causal effects in observational studies (Wooldridge, 2010).

The AIPW method relies on two key assumptions. The first, known as the conditional independence assumption (or unconfoundedness), posits that, conditional on a rich set of observable covariates, treatment assignment can be considered as-if random.<fnref n="6" /> In our analysis, we consider this

condition to be reasonably satisfied, as suggested by the empirical results presented in Section 5.2. The second assumption requires that, for each observational unit and given the same covariates, the probability of receiving treatment — in this case, the occurrence of a reform episode — is strictly positive. To ensure this condition holds, the literature recommends excluding observations with estimated negative propensity scores after modeling treatment assignment. In our case, no such observations are present, which confirms the validity of this assumption.

# 5. Main results

## 5.1. Local projection results: some naive regressions

We adopt a parsimonious approach, first presenting the results without correcting for potential selection bias in reform implementation. Specifically, we estimate the baseline specification in Eq. (3) using two definitions of reform. In the first case, the reform indicator is expressed in its continuous form—that is, the change in the quality of network sector and EPL institutions as defined in Section 3. In the second case, the indicator is expressed in its binary form, corresponding to the reform episodes defined in the same section.

Fig. 5 illustrates the dynamic effects of network sector and EPL reforms on the unemployment rate at different horizons, estimated using the ordinary least squares (OLS) method. The corresponding regression results are presented in Table B1 in Appendix B. The x-axis represents the time horizon, the solid line depicts the average estimated coefficients, and the shaded areas indicate the 90 % confidence intervals. To account for cross-sectional dependence in the error terms, standard errors are computed following Driscoll and Kraay (1998).

In Panels A and C of Fig. 5, we use the continuous version of the reform indicator—that is, the change in the network sector or EPL market liberalization index. The results suggest that both types of reforms tend to reduce the unemployment rate. In the case of EPL reforms, the reduction in unemployment is significant in the first year following the reform, becomes insignificant during the next two years, and regains significance from the third year onward. Regarding network sector reforms, the reduction is not significant in the short run but becomes statistically significant from the fifth year onward.

Alternatively, Panels B and D report the results obtained using reform episodes. We find that both network sectors and EPL reform episodes are associated with a significant decline in the unemployment rate, particularly in the medium term.

Although these results are informative, they may still be affected by selection bias, as the decision to implement reforms depends on economic, political, and social conditions. Therefore, without further discussing the LP-OLS results, we next address this issue using a quasi-experimental framework based on reform episodes.

## 5.2. Quasi-experimental results using local projection with AIPW

This section presents the results based on a quasi-experimental approach by combining the Local Projections method with the Augmented Inverse-Probability Weighting (LP-AIPW). As discussed in Section 3, we begin by estimating the propensity score model, that is, the treatment model which describes the likelihood of implementing network sector or EPL reforms. We estimate the treatment model using the complementary log-log (cloglog) specification to correct for bias associated with rare events, due to the limited number of observed reform episodes (da Piedade et al., 2025; Gutting and Steinwand, 2017; Long and Freese, 2006; Nguyen et al., 2022; Scheubel et al., 2024). The control variables from the model in Eq. (3), along with the initial level of the unemployment rate, are used to estimate the propensity score. Table B2 in the appendix reports the estimation of the propensity score.

<footnote n="3">³ A common method to address endogeneity bias is the instrumental variable technique. In fact, identifying a valid instrument for product and labor market reforms that satisfies the exogeneity criterion remains a significant challenge. In the empirical literature on the effects of structural reforms, it is common to use the level of reform in neighboring countries or trade partners as an instrument. However, we believe that these instruments do not fully satisfy the exogeneity criterion, as the level of reforms in neighboring countries is itself endogenous, being influenced by internal or external factors specific to those countries.</footnote>

<footnote n="4">⁴ This identification strategy is relatively new and has proven effective in macroeconomic analysis in recent years (Jordà and Taylor, 2016).</footnote>

<footnote n="5">⁵ Indeed, the AIPW estimator introduces a bias-correction term to the IPW estimator. When the treatment model is correctly specified, this term equals zero, and the AIPW estimator coincides with the IPW estimator. Conversely, if the treatment model is misspecified but the outcome model is correctly specified, the correction term compensates for the resulting bias, thereby ensuring the robustness of the estimation.</footnote>

<footnote n="6">⁶ This assumption remains debated in the literature, as unobserved factors may still influence self-selection into treatment (Wooldridge, 2010).</footnote>
<figure><img src="image_8.png" /><figcaption>Fig. 5. LP-OLS: Impact of network sector and EPL reforms on unemployment rate, baseline results. Note: The charts display the impulse responses from local projection estimates for Employment Protection Legislation (EPL) reforms (upper pink panel) and Network Sector reforms (lower blue panel) on the cumulative unemployment rate. Year 0 corresponds to the year in which the reforms were implemented, and the position of the line at *t* = 6 reflects the cumulative impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 6. ROC curves and Overlap of propensity scores for different types of reforms. Source: Author's based on study's datasets.</figcaption></figure>

Before discussing the results, we examine some essential characteris-
tics of the AIPW model. The predictive capacity of the first-step equation
of the LP-AIPW model, which is the treatment model, is illustrated in
Fig. 6, panels A and B. The results show a strong predictive ability of the
treatment model, as the area under the ROC curve ranges between 0.60

and 0.86 for episodes of Network sector and employment protection leg-
islation reforms, and it is statistically different from 0.5. Furthermore,
panels C and D of Fig. 6 present smooth kernel density estimates for
the distribution of propensity scores of treated and control units to
check for overlap. The results indicate a significant overlap between the
<figure><img src="image_10.png" /><figcaption>Fig. 7. LP-AIPW: Impact of product and labor market reforms on unemployment rate. Note: The charts display the average treatment effect (ATE) responses from Augmented Inverse Probability Weighted (AIPW) local projection estimates, showing the impact of Employment Protection Legislation (EPL) reforms (left pink panel) and Network Sector reforms (right blue panel) on the cumulative unemployment rate. Year 0 corresponds to the year in which the reforms were implemented, and the position of the line at *t* = 6 reflects the cumulative impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. Alternative weighting using the entropy balancing method. Note: The charts show the impulse responses from local projection estimates, using an alternative method to generate weights for the observations based on entropy balancing of the binary treatment. Panel A presents the estimates for EPL reforms (pink solid line), and Panel B presents the estimates for network sector reforms (blue solid line) on the cumulative unemployment rate. Year 0 marks the year the reforms were implemented, and the position of the line at *t* = 6 reflects the accumulated impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author, based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Alternative specifications. Note: The charts display the average treatment effect (ATE) responses from Augmented Inverse Probability Weighted (AIPW) local projection estimates, using an alternative specification, showing the impact of EPL reforms (top pink line) and Network Sector reforms (bottom blue line) on the cumulative unemployment rate. Year 0 corresponds to the year in which the reforms were implemented, and the position of the line at *t* = 6 reflects the cumulative impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

distributions of treated and control units, suggesting that our treatment model is adequate for correctly identifying the average treatment effect (ATE) using our quasi-experimental estimation approach.<fnref n="7" /> Therefore, we randomly reassign the treatment based on observed variables after weighting the sample by the inverse of the estimated propensity scores.

### 5.2.1. *The main results*

Fig. 7 presents the main results obtained using the doubly robust quasi-experimental AIPW estimator. These estimates indicate that episodes of employment protection legislation reforms, as well as reforms in network sector reforms, lead to a significant reduction in the unemployment rate. More specifically, as early as the first year following their implementation, employment protection legislation reforms are associated with a statistically significant decrease in the unemployment rate of 9.7 %. In contrast, for network sector reforms, a significant effect appears from the second year onward, with a reduction of approximately 5.8 %. The corresponding regression coefficients are reported in Table B3 of Appendix B.<fnref n="8" />

Compared to previous results obtained using the LP-OLS method applied to the reform episodes, the estimates from the LP-AIPW method yield nearly identical coefficients for employment protection legislation reforms. Regarding network sector reforms, although their short-term effect on unemployment reduction is not significant in the LP-OLS model, the selection bias correction implemented by the AIPW estimator reveals a significant effect in both the short and medium term. This convergence of results strengthens the robustness of our conclusions regarding the correction of selection bias associated with the implementation of the reforms.

In short, these results suggest that the intensification of competition induced by employment protection legislation reforms, as well as by reforms in network sectors, leads to a significant reduction in the unemployment rate within our sample of African countries over the short and medium term. These findings are consistent with empirical evidence from other contexts using different econometric approaches (Amsble et al., 2010; DeSerres et al., 2012). Therefore, liberalizing the institutions governing network sectors and employment protection legislation appears to be a relevant policy lever for African governments seeking to achieve a sustained reduction in unemployment.

<footnote n="7">⁷ It should be emphasized that the estimated probabilities are relatively low and tend to approach zero. Consequently, the AIPW estimator applies an adjustment to the treatment effect whenever the estimated propensity scores are near 0 for treated units or near 1 for control units.</footnote>

<footnote n="8">⁸ All our main results are estimated using Eq. (18) in Jordà and Taylor (2016). Following Jordà and Taylor (2016), and given the rarity of reform episodes, we impose the restriction $\theta_1^h = \theta_0^h$. This constraint allows for a consistent comparison</footnote>

<footnote>between the results obtained using the LP-OLS method and those from the LP-AIPW approach. It corresponds to an implicit restriction commonly applied in the empirical macro-VAR literature. When we relax this restriction, that is, when $\theta_1^h \neq \theta_0^h$, the results—although not reported here but available upon request—remain qualitatively similar (see Jordà and Taylor (2016) for more details).</footnote>
<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Alternative specifications: simultaneous impact of EPL and Network sector reforms. Note: The charts display the average treatment effect (ATE) responses from Augmented Inverse Probability Weighted (AIPW) local projection estimates for joint reforms on the cumulative unemployment rate. Panel A shows the impulse response functions estimated without truncating the weights, while Panel B presents the results with truncated weights. Year 0 corresponds to the year in which the reforms were implemented, and the position of the line at *t* = 6 reflects the cumulative impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets.</figcaption></figure>

## 6. Robustness checks

Our main results show that employment protection legislation and network sector reforms contribute to reducing unemployment rates in the African countries studied over the short and medium term. In this section, we assess the robustness of these findings by addressing issues related to alternative weighting through the entropy balancing method, alternative model specifications (including the treatment of low probabilities, controlling for reforms occurring within the response horizon, managing counter-reforms, accounting for reform interaction effects, and addressing Nickell bias), conducting sensitivity analyses that account for cross-country heterogeneity, and examining alternative labor market outcomes.

### 6.1. Alternative weighting using the entropy balancing method

In this section, building on the work of Atsebi et al. (2025), we employ an alternative method for constructing observation weights based on entropy balancing. This approach uses a binary treatment variable (Hainmueller, 2012) and allows for the reweighting of treated and control observations. It ensures maximum comparability between the two groups by adjusting the weights so that the moments (means, variances, etc.) of the control variables are balanced, while remaining as close as possible to the original weights.

The Fig. 8 presents the results obtained using the binary treatment version of the entropy balancing method (The corresponding regression coefficients are reported in Table Table C1 of Appendix C). In this specification, reform episodes in employment protection legislation and the network sector are associated with cumulative reductions in the unemployment rate of 13.1 % and 10.1 %, respectively, by the sixth year. These results are qualitatively consistent with those of the baseline estimations, confirming that our findings are robust to alternative econometric approaches that address selection bias in reform implementation.

### 6.2. Alternative specifications

**Handling Low Probabilities:** Fig. 6 shows that some treated units have propensity scores very close to 0, which results in particularly large weights when applying inverse probability weighting. As noted by Jordà and Taylor (2016) and De Haan and Wiese (2022), the AIPW estimator incorporates an adjustment factor when estimated probabilities are very low for treated units (and very close to 1 for control units). To assess whether this adjustment sufficiently stabilizes the estimator, we nonetheless truncate the propensity scores for both reforms, following the recommendations of Imbens (2004) and Cole and Hernán (2008). This weight adjustment is illustrated in Fig. 9, in panels A and B for the employment protection legislation and network sector reforms, respectively. It appears that low probabilities do not materially affect our estimator, and the results remain consistent with the baseline estimates.

**Control for reforms occurring within the response horizon $t+h$:** We control for reforms that occur within the response horizon $t+h$ (for $h > 1$) but are not captured by $R_{i,t+1}$. As shown by Teulings and Zubanov (2014), omitting such future reforms can lead to model misspecification and biased estimates. This precaution is particularly relevant in our context, especially for network sector reforms, which exhibit a high frequency of reform episodes in the data. To address this issue, we follow Ciminelli et al. (2022) and include dummy variables for future reforms in the baseline equation (see Eq. (3)), expressed as $\sum_{k=2}^{h} R_{i,t+k}$. The results from this alternative specification are presented in Fig. 9, panels C and D, for employment protection legislation reforms and network sector reforms, respectively. We find that the results remain qualitatively and quantitatively similar to our baseline estimates. In other words, explicitly accounting for future reforms within the time horizon does not alter our main conclusion: reform episodes in the labor or product markets are associated with a significant reduction in the unemployment rate.

**Handling of Counter-Reforms:** Wiese et al. (2025) show that counter-reforms can affect employment. Although selection bias is less
<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Sensitivity analysis by successively excluding one country. Note: The charts display the average treatment effect (ATE) responses from Augmented Inverse Probability Weighted (AIPW) local projection estimates, excluding one country at a time, for Employment Protection Legislation (EPL) reforms (pink solid line) and Network Sector reforms (blue dashed line) on the cumulative unemployment rate. Year 0 corresponds to the year in which the reforms were implemented, and the position of the line at *t* = 6 reflects the cumulative impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

of a concern for counter-reforms in the context of our study, we be-
lieve that periods of counter-reforms could influence the sensitivity of
our results and should therefore be included as control variables in the
LP estimation and accounted for within the forecast horizon. Following
Alesina et al. (2023) and Gomado (2024a), we identify reversals or
counter-reforms, i.e., cases where network sector or employment pro-
tection legislation becomes more restrictive. We control for the effect
of these reversals in network sector and employment protection legisla-
tion on the unemployment rate. Over the study period, there was only
one reversal in network sector institutions and twenty reversals in em-
ployment protection legislation reforms. We therefore include dummy
variables capturing counter-reforms as controls in the baseline model
and re-estimate it. The results are presented in Fig. 9, panels E and F,
respectively for employment protection legislation reforms and network
reforms. Accounting for counter-reforms does not affect the sensitivity
of our results, which remain qualitatively similar to those of the baseline
estimations.

**Reform Interaction Effect:** We adopt an alternative specification to capture the idea that reforms may be complementary, such that the effect of each reform on unemployment depends on the implementation of the other—an argument supported by Nicoletti and Scarpetta (2005) and Griffith et al. (2007), among others. To empirically test this hypothesis, we follow an approach similar to that of De Haan and Wiese (2022), who define a joint reform variable equal to one when, within a given year, a country undertakes at least one reform episode in both areas.

However, given the limited number of employment protection legis-
lation (EPL) reform episodes, we broaden this definition. Specifically, we
construct a joint treatment indicator that equals one when both EPL and
network sector reforms occur during the same period. To capture short-
term complementarities arising from closely timed reforms, we further
extend this definition to a three-year window centered on the year of
joint exposure (t - 1, t, t + 1). This approach allows us to account not
only for strictly simultaneous reforms but also for those implemented
in close temporal proximity, which may jointly influence labor market
outcomes and broader economic dynamics.

The results are presented in Fig. 10 (with the associated coefficients reported in regression Table C2 in Appendix C). Panel A shows the impulse response functions without truncating the estimated weights. As discussed previously, although the AIPW estimator adds an adjustment factor to the treatment effect when the estimated propensity scores are close to 0 for treated observations (and close to 1 for control observations), this may not be sufficient to stabilize the estimator for joint reforms (De Haan and Wiese, 2022). Therefore, we truncate the propensity scores for joint reforms, and the results based on these truncated weights are presented in Panel B.

The results across both panels indicate that reforms do not under-
mine each other; on the contrary, they reinforce one another through
a synergy effect, in which improvements in one market—for example,
increased labor market flexibility—facilitate and amplify the positive
effects of reforms in the other market, such as product market flexibility.
<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Alternative dependent variable. Note: The charts display the average treatment effect (ATE) responses from Augmented Inverse Probability Weighted (AIPW) local projection estimates for Employment Protection Legislation (EPL) reforms (pink solid line) and Network Sector reforms (blue dashed line). The dependent variables include several alternatives, such as employment levels and labor force participation rate, both in total and by gender. Year 0 corresponds to the year in which the reforms were implemented, and the position of the line at *t* = 6 reflects the cumulative impact of the reform (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

**Dealing with Nickell Bias:** Finally, another potential concern regarding our baseline results is the possible presence of “Nickell bias”. Indeed, our baseline specification includes the lagged dependent variable among the explanatory variables. As shown by Nickell (1981), the combination of country fixed effects and lagged dependent variables in panel data estimations can induce bias, particularly when serial correlation is high and the time dimension is short. In our case, however, this risk is limited: the number of countries analyzed (N = 18) is relatively small, while the time dimension is larger (T = 24). As noted by De Haan and Wiese (2022), in a comparable setting (small N, large T), the “Nickell bias” can be considered negligible. Beyond these considerations, the empirical literature using the local projection method addresses this concern by excluding fixed effects from the analysis (Delgado-Téllez et al., 2022; Kpodar and Abdallah, 2017; Mei et al., 2023). In our case, results (not shown here but available upon request) indicate that excluding fixed effects does not significantly alter our findings. We therefore conclude that the “Nickell bias” is negligible in the context of our study.

### 6.3. Sensitivity analysis accounting for country-level disparities

We also examined the potential influence of cross-country disparities in unemployment rates. Fig. 2 highlights these variations, with some countries showing significantly higher unemployment rates compared to others. Such differences raise concerns about the impact of extreme values on the reliability of our results. To address this issue, we conducted a sensitivity analysis by systematically excluding one country from the baseline sample in each iteration, resulting in 18 separate estimations. The results, displayed in Fig. 11 for both categories of reforms, indicate that our findings remain consistent regardless of which country is excluded. This confirms that the conclusions are robust and not

disproportionately influenced by any single country's unemployment rate.

### 6.4. Alternative labor market outcomes

Finally, we examine the impact of reforms on additional labor market outcomes. Given that our baseline results indicate that network sector and employment protection legislation reforms reduce unemployment, it is reasonable to expect them to also raise employment and labor force participation rates. To investigate this, we use World Bank data on the Employment to Population Ratio, 15 +, total (%), modeled ILO, as well as data on the total labor force participation rate (percentage of the population aged 15–64)<fnref n="9" />, including a breakdown by gender. Fig. 12 presents the results (The corresponding regression coefficients are reported in Table C3 of Appendix C): Panels A and E show the effects of network sector and employment protection legislation reforms on employment; Panels B and F illustrate the impact on overall participation rates; Panels C and G focus on male participation; and Panels D and H on female participation. Overall, both network sector and employment protection reforms have a positive influence on number of employed individuals and labor force participation for both men and women. These findings suggest that reforms promote greater labor market inclusion and dynamism, encouraging more individuals to work or seek employment. In short, making labor markets more flexible and easing product market constraints fosters job creation, leading to a simultaneous rise in labor force participation and a reduction in unemployment.

<footnote n="9">⁹ The total labor force includes all persons aged 15 and older who are economically active, as defined by the ILO, whether employed or unemployed.</footnote>
<figure><img src="image_16.png" /><figcaption>**Fig. 13.** Impact of EPL and Network sector reforms on unemployment rates across worker categories. Note: The charts display the average treatment effect (ATE) responses from Augmented Inverse Probability Weighted (AIPW) local projection estimate for Employment Protection Legislation (EPL) reforms (pink solid line) and Network Sector reforms (blue dashed line) on the cumulative unemployment rate for different groups of workers. Panel A presents the effects of EPL reforms for females versus males, as well as for youth females versus youth males aged 14–25, while Panel B presents the effects of Network Sector reforms for the same groups. Year 0 represents the year the reforms were implemented, and the position of the line at *t* = 6 reflects the accumulated impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

## 7. Heterogeneity analysis

Employment protection legislation and network sector reforms contribute to reducing unemployment. However, their impact may differ across categories of workers. This section examines how these reforms affect various groups, taking into account differences related to gender and age.

Specifically, we distinguish workers along two dimensions, which are treated separately to avoid ambiguities arising from their interaction. First, the gender-based analysis focuses on the unemployment rates of women and men, defined respectively as the share of the female and male labor forces that are unemployed. Second, the age-based analysis concentrates exclusively on the 15–24 age group, as data for other age cohorts are unavailable.

Accordingly, our evaluation considers four distinct groups: women, men, young women aged 15–24, and young men aged 15–24. Fig. 13 presents the impulse response functions for employment protection legislation (Panel A) and network sector reforms (Panel B). The corresponding coefficients are reported in Table D1 in Appendix D. The results indicate that both employment protection legislation and network sector reforms contribute to lowering unemployment rates, particularly among men—both for the overall male working-age population (15–64 years) and for young men aged 15–24. In contrast, for women,

these reforms lead to a significant reduction in unemployment only among young female workers. The absence of a noticeable effect on the overall female unemployment rate may be related to data quality limitations. Indeed, disaggregating unemployment data by gender substantially reduces cross-country coverage, particularly for female workers.

Overall, these findings are consistent with those of the reference model and confirm that employment protection legislation and network sector reforms help reduce unemployment. These reforms generate broadly similar effects across worker categories, including men, young men, and young women.

## 8. Influence mechanism

When analyzing the impact of labor and product market reforms on the unemployment rate, our results show that these reforms contribute to a reduction in unemployment. In this section, we discuss and identify the channels through which the studied reforms contribute to this decrease in unemployment. As discussed in the Section 2, we rely on empirical literature, notably the works of DAVID et al. (2022) and Gomado (2024a). DAVID et al. (2022) examines the impact of reforms on real GDP, employment, and total factor productivity (TFP). They identify investment, foreign direct investment (FDI), informality, and
<figure><img src="image_17.png" /><figcaption>**Fig. 14.** Effect of product and labor market reforms on unemployment: channels. Note: The charts show the average treatment effect (ATE) responses from local projection estimates, identifying the main transmission channels, for Employment Protection Legislation (EPL) reforms (upper pink solid line) and Network Sector reforms (lower blue dashed line) on the cumulative unemployment rate. Year 0 marks the year the reforms were implemented, and the position of the line at t = 6 represents the accumulated impact of the reform on the unemployment rate (in percentage change) six years after implementation. The shaded areas indicate confidence intervals: dark shading for 90 % and light shading for 95 %. Source: Author's calculations based on the study's datasets. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

business confidence as the main transmission channels. The authors show that product market reforms significantly increase the level of FDI while also significantly reducing the informality rate. Additionally, Gomado (2024a) explores the role of reforms in the relationship between economic uncertainty (due to events such as the Gulf War, the U.S. recession, the September 11 attacks, the financial crisis, and national political shocks like coups and revolutions) and economic growth. He shows that reforms improve investment and consumption during periods of economic uncertainty, thereby helping to mitigate the negative effects of uncertainty on economic growth.

Lacking data on the informality rate and business confidence, as in the work of DAVID et al. (2022) within our sample, our study examines the effects of product and labor market reforms on informal output,<fnref n="10" /> gross fixed capital formation (GFCF), foreign direct investment (FDI), and growth of total factor productivity. Product market reforms can reduce the size of the informal economy by facilitating the integration of businesses into the formal sector. Similarly, labor market reforms can decrease informal work practices by making formal employment more attractive. Regarding GFCF, product market reforms can stimulate investment in infrastructure and technology, thereby increasing GFCF (Gomado, 2024a). Labor market reforms, by increasing flexibility and lowering costs, encourage physical capital investment and attract FDI by simplifying regulations and enhancing labor competitiveness.

Fig. 14 documents the transmission channels through which the re-
forms operate and reveals marked differences between EPL reforms and
Network Sector reforms. EPL reforms generate an immediate and persis-
tent decline in informal sector output, indicating a sustained movement
of firms toward the formal economy. This pattern is consistent with
the idea that greater labor-market flexibility reduces hiring rigidities
and lowers the relative cost of formality. The EPL reforms also pro-
duce strong investment responses. Both domestic investment and foreign
direct investment increase following implementation, with effects that
build over the short and medium term. These results suggest that
easing employment protection contributes to a more predictable and
competitive environment, thereby strengthening investor confidence. In
contrast, the productivity effects of EPL reforms are concentrated in the
short run. TFP growth improves during the first years after reform, but
the gains appear to level off thereafter. This dynamic may reflect the fact
that initial efficiency improvements depend on labor reallocation and
greater operational flexibility, whereas longer-term productivity out-
comes hinge on job quality, skill upgrading, and the capacity of firms to
internalize the benefits of reform.

In parallel, Network Sector reforms also contribute to reducing in-
formality, although their effects are smaller and less persistent than
those of EPL reforms. By improving regulatory quality and competition
in key infrastructure sectors, these reforms support a gradual shift to-
ward formality, but the adjustment appears more modest and tends to
dissipate over time. Their impact on investment is similarly differenti-
ated. Network Sector reforms tend to foster domestic capital formation
in the short term, yet do not generate a clear response from foreign direct

<footnote n="10">¹⁰ Informal Economy Database (Elgin et al., 2021).</footnote>
investment. This suggests that improvements within network industries, while beneficial for domestic production conditions, may be insufficient to alter the perceptions of foreign investors regarding broader market conditions. Finally, the effects of Network Sector reforms on productivity remain limited. Although estimates generally point in a positive direction, there is no strong evidence of significant TFP gains over the short or medium term. This finding indicates that reforms in network industries may require a longer horizon, or complementary policies, before yielding measurable aggregate productivity improvements.

## 9. Conclusion and implications

This study investigates the dynamic impact of Employment Protection Legislation (EPL) reforms and Network Sector reforms on unemployment in selected African countries between 1990 and 2014. Using local projections combined with the Augmented Inverse Probability Weighted estimator proposed by Jordà and Taylor (2016), along with entropy balancing for binary treatment variables (Hainmueller, 2012), we address key endogeneity concerns related to reform adoption. The consistency of the findings across alternative econometric specifications confirms the robustness and reliability of our results.

The analysis provides strong evidence that both EPL reforms and Network Sector reforms reduce unemployment, although their effects emerge at different horizons. EPL reforms generate improvements from the first year after implementation, whereas Network Sector reforms produce statistically significant reductions from the second year onward. These distinct adjustment paths reflect the structural differences between the two reform types: EPL reforms rapidly ease labor-market rigidities, while reforms in telecommunications and electricity require more time to affect employment conditions through improved infrastructure, competition, and service efficiency. Robustness checks using alternative dependent variables—such as employment and labor force participation—and different definitions of reform episodes further reinforce these conclusions.

Heterogeneity analysis reveals important differences across demographic groups. Both types of reforms reduce unemployment among men and among young workers aged 15–24, while significant effects for women are concentrated among young female workers. No robust evidence emerges for the overall female working-age population, partly due to data limitations in gender-disaggregated unemployment figures. Nevertheless, these results remain consistent with the aggregate findings and underscore the uneven distribution of reform benefits across labor-force segments.

The study identifies several transmission channels through which the reforms operate. EPL and Network Sector reforms both contribute to reducing informality by encouraging firms to transition to the formal sector. They also stimulate domestic investment, strengthen capital formation, and attract foreign direct investment. EPL reforms further generate short-run improvements in total factor productivity, whereas the productivity effects of Network Sector reforms appear more modest and less persistent. These mechanisms help explain why EPL reforms yield earlier labor-market gains, while Network Sector reforms exhibit delayed but meaningful effects.

From a policy perspective, the results stress the relevance of advancing both labor-market and network-sector reforms to promote employment creation in African economies. Policymakers should consider sequencing and complementarity when designing reform strategies: EPL reforms can deliver rapid labor-market improvements, while Network Sector reforms enhance the broader economic environment required to sustain job creation in the medium term. Targeted interventions can also help ensure that women—particularly outside the youngest age group—benefit more evenly from the reforms.

Finally, the findings have broader implications for Africa's regional integration agenda, notably within the framework of the African

Continental Free Trade Area (AfCFTA). Enhancing labor-market flexibility, improving the performance of strategic network sectors, and reducing structural unemployment can strengthen countries' capacity to benefit from deeper regional trade integration. Well-designed market reforms, supported by complementary investment and skills-development policies, can contribute to more resilient and inclusive labor markets across the continent.

## CRediT authorship contribution statement

**Kwamivi Mawuli Gomado:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. **Isaac Amedanou:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We highly thank the Editor-in-Chief Angelika Rettberg, as well as anonymous reviewers for their valuable suggestions and insightful comments, which have significantly improved the current version of this paper. Any errors or omissions remain the sole responsibility of the authors. This work was supported by the Agence Nationale de la Recherche of the French government through the program "Investissements d'avenir ANR-10-LABX-1401".

## Appendix A. Data and descriptive statistics

### Data description and sources

**Product and Labor market reforms Index:** See the description in the main text. **Source:** The IMF's Structural Reform Database (Alesina et al., 2023)

**Trade (% of GDP):** Trade is the sum of exports and imports of goods and services measured as a share of gross domestic product. **Source:** WDI

**Foreign direct investment, net inflows (% of GDP):** Foreign direct investment is the net inflow of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. **Source:** WDI

**Unemployment, total (% of total labor force):** Unemployment refers to the share of the labor force that is without work but is available for and seeking employment. Definitions of labor force and unemployment differ by country. **Source:** WDI

**Inflation rate, average consumer prices:** Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. **Source:** WDI

**GDP growth (annual %):** Annual percentage growth rate of GDP at market prices based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. **Source:** WDI

**IMF program:** 1 if IMF program active for 5 or more months. **Source:** Dreher (2006)

**Democracy:** Index ranges from 0 to 6. **Source:** V-DEM
<figure><img src="image_18.png" /><figcaption>Fig. A1. Evolution of network sector and employment protection legislation institutions and unemployment rate by country, 1990–2014. Source: Author's based on study's datasets.</figcaption></figure>

<caption>Table A1</caption>

Network sector and employment protection legislation reforms in 18 selected african countries, 1990–2014.

<table><thead><tr><th></th><th>Employment<br>protection legislation</th><th>Network sector</th></tr></thead><tbody><tr><td>Major changes (# of obs.)</td><td>30</td><td>63</td></tr><tr><td>of which: reforms</td><td>10</td><td>62</td></tr><tr><td>of which: reversals</td><td>20</td><td>1</td></tr><tr><td>Share in the sample</td><td>6.66 %</td><td>14.00 %</td></tr><tr><td>of which: reforms</td><td>2.22 %</td><td>13.77 %</td></tr><tr><td>of which: reversals</td><td>4.44 %</td><td>0.22 %</td></tr><tr><td>Total (# of obs.)</td><td>450</td><td>450</td></tr></tbody></table>

Source: Author's calculations based on Alesina et al. (2023).

**Legislative or executive election:** A legislative or executive election dummy is 1 when there is a legislative or executive election in that year. Source: Scartascini et al. (2021)

**Crisis dummy:** Dummy variable for year of a banking, currency, or debt crisis. Source: Laeven and Valencia (2020)

**Gini Coefficient:** Income inequality. Source: Gradín (2021)

**Human capital index (HCI):** The HCI calculates the contributions of health and education to worker productivity. The final index score

ranges from zero to one and measures the productivity of a future worker born today relative to the benchmark of full health and complete education. Source: PTW

**Employment to population ratio, 15+, total (%):** Employment to population ratio is the proportion of a country's population that is employed. Source: WDI

**Labor force participation rate, total (% of total population ages 15–64):** Labor force participation rate is the proportion of the population ages 15–64 that is economically active: all people who supply labor for the production of goods and services during a specified period. Source: WDI

**Labor force participation rate, male (% of male population ages 15–64):** The labor force participation rate represents the proportion of the male population ages 15–64 that is economically active; that is, all individuals who supply labor for the production of goods and services during a specified period. Source: WDI.

**Labor force participation rate, female (% of female population ages 15–64):** The labor force participation rate represents the proportion of the female population ages 15–64 that is economically active; that is, all individuals who supply labor for the production of goods and services during a specified period. Source: WDI.
# Appendix B. Main results

See Tables B1–B3.

<caption>Table B1</caption>

Impact of network sector and EPL reforms on unemployment rate (some naive regressions).

<table><thead><tr><th colspan="7">Panel A [LP-OLS]: Change in indicator</th></tr><tr><th>Dependent variable: Unemployment rate</th><th>Year 0</th><th>Year 1</th><th>Year 2</th><th>Year 3</th><th>Year 4</th><th>Year 5</th></tr></thead><tbody><tr><td>Employment Protection Legislation Reforms</td><td>-0.427**<br>(0.170)</td><td>-0.265<br>(0.241)</td><td>-0.394<br>(0.297)</td><td>-0.815**<br>(0.346)</td><td>-0.811***<br>(0.250)</td><td>-1.004***<br>(0.210)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>Within R-square</td><td>0.264</td><td>0.270</td><td>0.264</td><td>0.277</td><td>0.298</td><td>0.311</td></tr><tr><td>Network Sector Reforms</td><td>-0.017<br>(0.057)</td><td>-0.086<br>(0.090)</td><td>-0.021<br>(0.121)</td><td>-0.141<br>(0.172)</td><td>-0.309*<br>(0.157)</td><td>-0.171*<br>(0.098)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>Within R-square</td><td>0.242</td><td>0.268</td><td>0.260</td><td>0.267</td><td>0.293</td><td>0.298</td></tr><tr><th colspan="7">Panel B [LP-OLS]: Reform episodes</th></tr><tr><td>Employment Protection Legislation Reforms</td><td>-0.067**<br>(0.028)</td><td>-0.121**<br>(0.055)</td><td>-0.092**<br>(0.042)</td><td>-0.067*<br>(0.033)</td><td>-0.037<br>(0.042)</td><td>-0.017<br>(0.049)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>Within R-square</td><td>0.260</td><td>0.285</td><td>0.266</td><td>0.268</td><td>0.289</td><td>0.300</td></tr><tr><td>Network Sector Reforms</td><td>-0.007<br>(0.012)</td><td>-0.032<br>(0.022)</td><td>-0.055<br>(0.033)</td><td>-0.064*<br>(0.033)</td><td>-0.095**<br>(0.044)</td><td>-0.075**<br>(0.034)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>Within R-square</td><td>0.243</td><td>0.274</td><td>0.273</td><td>0.277</td><td>0.308</td><td>0.310</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes. Standard errors in parentheses. *** $p \le 0.01$, ** $p \le 0.05$, * $p \le 0.1$.

<caption>Table B2</caption>

Complementary log-log (cloglog) regressions predicting the probability of reform at $t + 1$.

<table><thead><tr><th></th><th>(1)<br>Employment Protection<br>Legislation Reforms</th><th>(2)<br>Network Sector Reforms</th></tr></thead><tbody><tr><td>Human capital index</td><td>-3.892*<br>(2.149)</td><td>0.519<br>(0.399)</td></tr><tr><td>Inflation rate, average consumer prices</td><td>-0.070<br>(0.046)</td><td>-0.016<br>(0.011)</td></tr><tr><td>Elections (dummy)</td><td>-1.857<br>(1.206)</td><td>0.599*<br>(0.345)</td></tr><tr><td>Years left in current term (dummy)</td><td>-0.097<br>(0.285)</td><td>0.318***<br>(0.100)</td></tr><tr><td>Financial Crisis (dummy)</td><td>0.000<br>(.)</td><td>0.551<br>(0.697)</td></tr><tr><td>IMF program active for 5 or more months</td><td>2.947*<br>(1.539)</td><td>0.086<br>(0.309)</td></tr><tr><td>Democracy</td><td>-0.155<br>(1.405)</td><td>0.342<br>(0.282)</td></tr><tr><td>Trade (% of GDP) [1 lag]</td><td>0.024<br>(0.031)</td><td>0.009<br>(0.007)</td></tr><tr><td>Income inequality (Gini index)</td><td>0.110*<br>(0.064)</td><td>-0.031**<br>(0.013)</td></tr><tr><td>GDP growth (annual %)</td><td>0.167<br>(0.128)</td><td>0.019<br>(0.034)</td></tr><tr><td>Labor market institutions index</td><td>-8.594<br>(6.684)</td><td>2.910***<br>(0.995)</td></tr><tr><td>Product market institutions index</td><td>-5.049<br>(3.876)</td><td>-2.932***<br>(0.769)</td></tr><tr><td>Unemployment, total (% of total labor force) [1 lag]</td><td>0.287***<br>(0.097)</td><td>-0.007<br>(0.018)</td></tr><tr><td>_cons</td><td>-3.417<br>(4.844)</td><td>-3.752***<br>(1.235)</td></tr><tr><td>Observations</td><td>307</td><td>315</td></tr><tr><td>Number of countries</td><td>17</td><td>17</td></tr></tbody></table>

Notes. Standard errors in parentheses. *** $p \le 0.01$, ** $p \le 0.05$, * $p \le 0.1$.
<table><caption>**Table B3**
Quasi-experimental method: LP-AIPW (main baseline results).</caption><thead><tr><th>Dependent variable: Unemployment rate</th><th>Year 0</th><th>Year 1</th><th>Year 2</th><th>Year 3</th><th>Year 4</th><th>Year 5</th></tr></thead><tbody><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.097***<br>(0.011)</td><td>-0.181***<br>(0.021)</td><td>-0.137***<br>(0.022)</td><td>-0.111***<br>(0.028)</td><td>-0.092**<br>(0.036)</td><td>-0.060<br>(0.044)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>-0.013<br>(0.013)</td><td>-0.058*<br>(0.031)</td><td>-0.085**<br>(0.034)</td><td>-0.088**<br>(0.039)</td><td>-0.124**<br>(0.049)</td><td>-0.111*<br>(0.057)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes. Standard errors in parentheses. *** p≤0.01, ** p≤0.05, * p≤0.1.

## Appendix C. Robustness checks

See Tables C1–C3.

<table><caption>**Table C1**
Impact of network sector and EPL reforms on unemployment rate: entropy balancing.</caption><thead><tr><th>Dependent variable: Unemployment rate</th><th>Year 0</th><th>Year 1</th><th>Year 2</th><th>Year 3</th><th>Year 4</th><th>Year 5</th></tr></thead><tbody><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.114***<br>(0.016)</td><td>-0.212***<br>(0.029)</td><td>-0.166***<br>(0.030)</td><td>-0.147***<br>(0.040)</td><td>-0.131**<br>(0.051)</td><td>-0.101*<br>(0.059)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>-0.016<br>(0.013)</td><td>-0.072**<br>(0.032)</td><td>-0.095***<br>(0.036)</td><td>-0.103**<br>(0.042)</td><td>-0.141***<br>(0.053)</td><td>-0.131**<br>(0.061)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes. Standard errors in parentheses. *** p≤0.01, ** p≤0.05, * p≤0.1.

<table><caption>**Table C2**
LP-AIPW: Impact of joint network sector and EPL reforms on unemployment.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>ATE untruncate wts: Joint reform'</td><td>-0.106***<br>(0.035)</td><td>-0.162**<br>(0.069)</td><td>-0.138***<br>(0.038)</td><td>-0.084**<br>(0.042)</td><td>-0.084*<br>(0.050)</td><td>-0.100**<br>(0.050)</td></tr><tr><td>Observations</td><td>313.000</td><td>312.000</td><td>313.000</td><td>300.000</td><td>286.000</td><td>271.000</td></tr><tr><td>ATE truncate wts: Joint reform'</td><td>-0.112***<br>(0.006)</td><td>-0.175***<br>(0.012)</td><td>-0.150***<br>(0.014)</td><td>-0.089***<br>(0.019)</td><td>-0.088***<br>(0.023)</td><td>-0.129***<br>(0.025)</td></tr><tr><td>Observations</td><td>315.000</td><td>314.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr></tbody></table>

Notes. Standard errors in parentheses. *** p≤0.01, ** p≤0.05, * p≤0.1.

<table><caption>**Table C3**
LP-AIPW: Alternative labor market outcomes.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7">Panel A: Employment to population ratio, 15+, total (%)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>0.322***<br>(0.019)</td><td>0.660***<br>(0.034)</td><td>0.579***<br>(0.033)</td><td>0.555***<br>(0.046)</td><td>0.506***<br>(0.072)</td><td>0.281**<br>(0.106)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td colspan="7">Panel B: Labor force participation rate, total (% of total pop. ages 15-64)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.003<br>(0.013)</td><td>-0.007<br>(0.022)</td><td>0.079***<br>(0.024)</td><td>0.258***<br>(0.029)</td><td>0.571***<br>(0.045)</td><td>0.690***<br>(0.070)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td colspan="7">Panel C: Labor force participation rate, male (% of male pop. ages 15-64)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>0.006<br>(0.014)</td><td>-0.040<br>(0.025)</td><td>0.003<br>(0.034)</td><td>0.206***<br>(0.042)</td><td>0.477***<br>(0.054)</td><td>0.551***<br>(0.063)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td colspan="7">Panel D: Labor force participation rate, female (% of female pop. ages 15-64)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.006<br>(0.016)</td><td>0.035<br>(0.025)</td><td>0.172***<br>(0.031)</td><td>0.334***<br>(0.040)</td><td>0.691***<br>(0.061)</td><td>0.856***<br>(0.094)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr></tbody></table>

(continued on next page)
<table><caption>Table C3 (continued)</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7">Panel E: Employment to population ratio, 15+, total (%)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>0.131*<br>(0.070)</td><td>0.350**<br>(0.128)</td><td>0.550***<br>(0.178)</td><td>0.792***<br>(0.207)</td><td>0.958***<br>(0.225)</td><td>0.942***<br>(0.242)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td colspan="7">Panel F: Labor force participation rate, total (% of total pop. ages 15-64) (ILO)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>0.071*<br>(0.034)</td><td>0.154*<br>(0.077)</td><td>0.253*<br>(0.121)</td><td>0.473***<br>(0.151)</td><td>0.496**<br>(0.186)</td><td>0.496**<br>(0.214)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td colspan="7">Panel G: Labor force participation rate, male (% of male pop. ages 15-64)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>0.094**<br>(0.037)</td><td>0.131*<br>(0.071)</td><td>0.220*<br>(0.119)</td><td>0.328*<br>(0.156)</td><td>0.316<br>(0.199)</td><td>0.286<br>(0.232)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td colspan="7">Panel H: Labor force participation rate, female (% of female pop. ages 15-64)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>0.046<br>(0.070)</td><td>0.169<br>(0.114)</td><td>0.275<br>(0.169)</td><td>0.596**<br>(0.216)</td><td>0.650**<br>(0.258)</td><td>0.677**<br>(0.279)</td></tr><tr><td>Observations</td><td>314.000</td><td>315.000</td><td>315.000</td><td>301.000</td><td>287.000</td><td>272.000</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes. Standard errors in parentheses. *** p≤0.01, ** p≤0.05, * p≤0.1.

## Appendix D. Heterogeneity analysis

See Tables D1 and D2.

<caption>Table D1</caption>

LP-AIPW: Impact of network sector and EPL reforms on unemployment rates across worker categories.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7">Panel A: Unemployment, female (% of female labor force)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.000<br>(0.032)</td><td>0.055<br>(0.069)</td><td>-0.000<br>(0.078)</td><td>0.000<br>(0.037)</td><td>-0.000<br>(0.064)</td><td>-0.000<br>(0.197)</td></tr><tr><td>Observations</td><td>70.000</td><td>74.000</td><td>77.000</td><td>71.000</td><td>70.000</td><td>62.000</td></tr><tr><td colspan="7">Panel B: Unemployment, male (% of male labor force)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.063***<br>(0.006)</td><td>-0.135***<br>(0.011)</td><td>-0.118***<br>(0.013)</td><td>-0.115***<br>(0.016)</td><td>-0.103***<br>(0.018)</td><td>-0.052**<br>(0.020)</td></tr><tr><td>Observations</td><td>352.000</td><td>351.000</td><td>352.000</td><td>336.000</td><td>320.000</td><td>303.000</td></tr><tr><td colspan="7">Panel C: Unemployment, youth female (% of female labor force 15-24)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.063***<br>(0.005)</td><td>-0.126***<br>(0.010)</td><td>-0.114***<br>(0.013)</td><td>-0.111***<br>(0.016)</td><td>-0.097***<br>(0.018)</td><td>-0.059***<br>(0.022)</td></tr><tr><td>Observations</td><td>352.000</td><td>351.000</td><td>352.000</td><td>336.000</td><td>320.000</td><td>303.000</td></tr><tr><td colspan="7">Panel D: Unemployment, youth male (% of male labor force 15-24)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.067***<br>(0.007)</td><td>-0.146***<br>(0.013)</td><td>-0.145***<br>(0.015)</td><td>-0.155***<br>(0.020)</td><td>-0.143***<br>(0.023)</td><td>-0.100***<br>(0.026)</td></tr><tr><td>Observations</td><td>352.000</td><td>351.000</td><td>352.000</td><td>336.000</td><td>320.000</td><td>303.000</td></tr><tr><td colspan="7">Panel E: Unemployment, female (% of female labor force)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>0.090<br>(0.161)</td><td>0.029<br>(0.086)</td><td>-0.109<br>(0.310)</td><td>0.186<br>(0.115)</td><td>0.176<br>(0.130)</td><td>0.448<br>(0.488)</td></tr><tr><td>Observations</td><td>70.000</td><td>74.000</td><td>77.000</td><td>71.000</td><td>70.000</td><td>62.000</td></tr><tr><td colspan="7">Panel F: Unemployment, male (% of male labor force)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>-0.009<br>(0.010)</td><td>-0.049*<br>(0.027)</td><td>-0.104***<br>(0.028)</td><td>-0.097***<br>(0.035)</td><td>-0.125***<br>(0.041)</td><td>-0.105**<br>(0.048)</td></tr><tr><td>Observations</td><td>352.000</td><td>351.000</td><td>352.000</td><td>336.000</td><td>320.000</td><td>303.000</td></tr><tr><td colspan="7">Panel G: Unemployment, youth female (% of female labor force 15-24)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>-0.011<br>(0.011)</td><td>-0.046*<br>(0.027)</td><td>-0.090***<br>(0.028)</td><td>-0.090***<br>(0.034)</td><td>-0.113***<br>(0.042)</td><td>-0.094*<br>(0.051)</td></tr><tr><td>Observations</td><td>352.000</td><td>351.000</td><td>352.000</td><td>336.000</td><td>320.000</td><td>303.000</td></tr><tr><td colspan="7">Panel H: Unemployment, youth male (% of male labor force 15-24)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>-0.009<br>(0.012)</td><td>-0.045<br>(0.028)</td><td>-0.085***<br>(0.027)</td><td>-0.091**<br>(0.037)</td><td>-0.117**<br>(0.048)</td><td>-0.093*<br>(0.051)</td></tr><tr><td>Observations</td><td>352.000</td><td>351.000</td><td>352.000</td><td>336.000</td><td>320.000</td><td>303.000</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes. Standard errors in parentheses. *** p≤0.01, ** p≤0.05, * p≤0.1.
<table><caption>Table D2
LP-AIPW: Channels.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7">Panel A: Informal output (% of official GDP)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>-0.018***<br>(0.002)</td><td>-0.038***<br>(0.003)</td><td>-0.042***<br>(0.003)</td><td>-0.040***<br>(0.003)</td><td>-0.070***<br>(0.005)</td><td>-0.048***<br>(0.005)</td></tr><tr><td>Observations</td><td>290.000</td><td>290.000</td><td>290.000</td><td>276.000</td><td>262.000</td><td>247.000</td></tr><tr><td colspan="7">Panel B: Foreign direct investment, net inflows (% of GDP)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>0.565***<br>(0.044)</td><td>0.134***<br>(0.041)</td><td>0.601***<br>(0.052)</td><td>0.710***<br>(0.069)</td><td>0.787***<br>(0.095)</td><td>0.607***<br>(0.100)</td></tr><tr><td>Observations</td><td>311.000</td><td>310.000</td><td>311.000</td><td>297.000</td><td>284.000</td><td>269.000</td></tr><tr><td colspan="7">Panel C: Gross fixed capital formation (GFCF)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>0.158***<br>(0.023)</td><td>0.193***<br>(0.026)</td><td>0.332***<br>(0.038)</td><td>0.330***<br>(0.053)</td><td>0.441***<br>(0.081)</td><td>0.604***<br>(0.103)</td></tr><tr><td>Observations</td><td>282.000</td><td>282.000</td><td>282.000</td><td>270.000</td><td>258.000</td><td>245.000</td></tr><tr><td colspan="7">Panel D: Total factor productivity (TFP)</td></tr><tr><td>ATE u wts: Employment Protection Legislation Reforms</td><td>0.009**<br>(0.004)</td><td>0.017**<br>(0.007)</td><td>0.017*<br>(0.009)</td><td>0.030**<br>(0.014)</td><td>0.043**<br>(0.017)</td><td>0.047**<br>(0.023)</td></tr><tr><td>Observations</td><td>239.000</td><td>239.000</td><td>239.000</td><td>229.000</td><td>218.000</td><td>206.000</td></tr><tr><td colspan="7">Panel E: Informal output (% of official GDP)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>-0.007**<br>(0.003)</td><td>-0.009**<br>(0.004)</td><td>-0.014**<br>(0.006)</td><td>-0.014*<br>(0.007)</td><td>-0.010<br>(0.010)</td><td>-0.012<br>(0.013)</td></tr><tr><td>Observations</td><td>290.000</td><td>290.000</td><td>290.000</td><td>276.000</td><td>262.000</td><td>247.000</td></tr><tr><td colspan="7">Panel F: Foreign direct investment, net inflows (% of GDP)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>-0.110<br>(0.071)</td><td>-0.094<br>(0.097)</td><td>-0.059<br>(0.114)</td><td>0.053<br>(0.088)</td><td>-0.033<br>(0.147)</td><td>0.012<br>(0.140)</td></tr><tr><td>Observations</td><td>311.000</td><td>310.000</td><td>311.000</td><td>297.000</td><td>284.000</td><td>269.000</td></tr><tr><td colspan="7">Panel G: Gross fixed capital formation (GFCF)</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>0.054*<br>(0.031)</td><td>0.125***<br>(0.045)</td><td>0.114<br>(0.075)</td><td>0.122<br>(0.106)</td><td>0.099<br>(0.150)</td><td>0.117<br>(0.204)</td></tr><tr><td>Observations</td><td>282.000</td><td>282.000</td><td>282.000</td><td>270.000</td><td>258.000</td><td>245.000</td></tr><tr><td colspan="7">Panel H: Total factor productivity</td></tr><tr><td>ATE u wts: Network Sector Reforms</td><td>0.003<br>(0.007)</td><td>0.005<br>(0.013)</td><td>0.014<br>(0.020)</td><td>0.019<br>(0.029)</td><td>0.019<br>(0.036)</td><td>0.025<br>(0.043)</td></tr><tr><td>Observations</td><td>239.000</td><td>239.000</td><td>239.000</td><td>229.000</td><td>218.000</td><td>206.000</td></tr><tr><td>Year fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Control variables</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Notes. Standard errors in parentheses. ***p≤0.01, **p≤0.05, *p≤0.1.

## Data availability

Data will be made available on request.

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