# Can past informality impede registered firms' access to credit?

Dorgyles C.M. Kouakou

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## To cite this version:

Dorgyles C.M. Kouakou. Can past informality impede registered firms' access to credit?. Journal of Comparative Economics, 2025, 53 (3), pp.786-815. 10.1016/j.jce.2025.05.002. hal-05305016

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# Can past informality impede registered firms’ access to credit?

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Dorgyles C.M. Kouakou ID

Université de Rennes, CNRS, CREM - UMR 6211, F-35000 Rennes, France

## ARTICLE INFO

JEL classification:

G20

O12

O16

O17

Keywords:

Past informality status

Credit constraints

Entropy balancing

## ABSTRACT

Using a large firm-level dataset from the World Bank Enterprise Surveys, which covers 134 countries and includes over 134,000 observations, we examine whether past informality affects the credit constraints of registered firms. Estimations, based on the entropy balancing method, indicate that registered firms that began operations informally are more likely to be credit-constrained than those that started in the formal sector. This finding is extremely robust to a variety of robustness tests, including instrumental variable, propensity score matching, alternative balancing methods that combine weighting and regression, potential omitted variables, restricted samples, alternative measures of credit constraints, different specifications such as Linear Probability, Logit, and Probit models, and clustering standard errors at the country level. Heterogeneity analysis reveals that the detrimental impact of past informality lessens with firm size, firm age, and better structural factors like regulatory quality, trade openness, entrepreneurial dynamism, and public spending. Productivity, competition from the informal sector, and the quality of financial statements are key channels through which past informality increases credit constraints for registered firms.

## 1. Introduction

What drives the credit constraints of firms? A large body of literature in both development economics and finance has sought to answer this question by analyzing the determinants of credit constraints in firms worldwide (see Asiedu et al., 2013; Banerjee and Duflo, 2014; Briggeman et al., 2009; Brown et al., 2014; Cheng and Yang, 2022; Distinguin et al., 2016; Kim, 2006; Rand, 2007, among others).<fnref n="1" /> Analyzing these determinants is crucial, as credit constraints directly hinder firm investment (García-Posada Gómez, 2019), which in turn limits innovation and overall performance. Indeed, when firms face credit constraints, they are less likely to invest in new technologies or processes (Hall, 2002; Hottenrott and Peters, 2012; Pellegrino and Savona, 2017), leading to decreased productivity and slower growth. This stagnation not only affects the firms themselves but also translates into fewer job opportunities, as firms are unable to expand or hire more staff. The reduction in innovation and firm performance can have widespread consequences, stalling economic progress and undermining social development within countries. By understanding the factors that contribute to these credit constraints, policymakers can devise targeted strategies to alleviate them. Effective policy interventions can, therefore, enhance firm investment and performance, spur innovation, and boost economic and social development. Consequently, tackling the roots of credit constraints is essential for fostering a dynamic and thriving economy.

A thorough review of the literature, however, reveals that the role of past informality status (i.e., the fact that a firm currently registered was not registered when it began operations) in explaining the credit constraints of registered firms has not been analyzed,

<footnote>☆ We are grateful to the Editor, Ruben Enikolopov, and the reviewers for insightful comments. We also thank Pierre Mohnen for his comments on an earlier draft. The usual caveats apply.</footnote>

<footnote>E-mail address: dorgyles.kouakou@univ-rennes.fr</footnote>

<footnote n="1">¹ For some stylized results on the financing constraints of firms, see Carreira and Silva (2010). The literature has also extensively focused on measuring financing constraints. For seminal contributions on this topic, see Fazzari et al. (1988, 2000) and Kaplan and Zingales (1997, 2000), among others.</footnote>
despite the existence of informality in all countries worldwide,<fnref n="2" /> and the potential lasting effects of informal origins – including low productivity and weak innovation capacity (Fu et al., 2018; Kouakou, 2023b) – on registered firms’ operations.³⁴ We believe that past informality can impact registered firms’ probability of being credit-constrained for at least three reasons.

First, past informality status may increase registered firms' probability of being credit-constrained by reducing their productivity. Indeed, past informality reduces a firm's innovativeness, as demonstrated recently by Mendi and Mudida (2018). Firms with a history of informality often lack openness to international markets, which is detrimental to their innovativeness. Increased competition from such exposure typically encourages firms to innovate (Balsmeier, 2017). Additionally, these firms face severe informational disadvantages compared to their formal counterparts due to weaker professional networks. Moreover, firms that transition to the formal sector may develop a negative perception of the need to innovate (Mendi and Mudida, 2018). In fact, informal firms usually produce goods similar to those produced in the formal sector but of lower quality (Banerji and Jain, 2007), and such imitation behavior may persist even years after formalization. Furthermore, starting as an informal firm can foster a long-term culture of risk aversion and limited strategic planning, which may further diminish the likelihood of innovation.

The decrease in innovation reduces firms' productivity (Crespi and Zuniga, 2012; Fu et al., 2018; Goedhuys et al., 2013; Geroski, 1989). Indeed, a decline in innovation causes processes to stagnate and technologies to become outdated, leading to reduced efficiency and increased production costs. This stagnation hampers a firm's ability to optimize resource use and limits opportunities for new sales, resulting in decreased output per unit of input. The decline in productivity, in turn, increases firms' probability of being credit-constrained (Distinguin et al., 2016). In fact, a drop in productivity typically results in reduced profitability and cash flow, weakening a firm's financial position and making it more challenging to meet debt obligations. Consequently, lenders may perceive the firm as a higher risk, leading to stricter credit terms or even denial of credit. This restriction on credit further hinders the firm's capacity to invest in initiatives that could enhance productivity, creating a vicious cycle of declining performance and financial strain.

Second, past informality may increase a firm's probability of being credit-constrained by intensifying competition from the informal sector. Indeed, informal firms often compete with formal firms (Heredia Pérez et al., 2018; Kouakou, 2023a; Mendi and Mudida, 2018; Tokman, 1978), typically by producing goods similar to those of their formal counterparts but of lower quality (Banerji and Jain, 2007), as previously discussed. These goods often succeed because they are cheaper and better align with the financial capacities of consumers, particularly in developing countries (Kouakou, 2023a). Informal firms generally compete with registered firms of similar size and scale, using comparable production methods. Firms with a history of informality are more likely to fit this profile. As a result, these firms are expected to face greater competition from the informal sector.

Competition from the informal sector, in turn, increases the probability of registered firms to be credit-constrained, as highlighted by Distinguin et al. (2016).<fnref n="5" /> This aligns with the entrepreneurial perspective and the “parasite” view of informality, which suggest that informal firms can compete with registered firms and diminish their profits (Farrell, 2004; Levy, 2008; Siqueira et al., 2016). Indeed, such competition can significantly reduce the profit margins of registered firms, primarily by eroding their market share (Kouakou, 2023a). This reduction in profits makes it more challenging for registered firms to present a strong financial position to lenders. Additionally, registered firms often face high operational costs due to the burden of regulations and taxes, which exacerbates the financial strain caused by competition from informal firms. Furthermore, the unregulated nature of informal firms contributes to market instability, increasing the risk for lenders and heightening the likelihood of credit constraints for registered firms.

Third, past informality can increase a firm's probability of being credit-constrained by reducing the quality of financial statements. Indeed, a firm's history of operating informally can impact its ability to produce high-quality financial statements once

<footnote n="2">² Indeed, informality exists in all countries worldwide, but it is more prevalent in developing countries, particularly in Africa. For recent estimates of the size of the informal economy worldwide, see Elgin et al. (2022).</footnote>

<footnote n="3">³ Informality may also have some benefits (e.g., informal employment can serve as a safety net; see Loayza and Rigolini (2011)). See Kouakou (2023a) and Ulyssea (2018) for a discussion of some of these benefits. However, the literature generally finds that negative effects prevail. For instance, Meghir et al. (2015) propose an equilibrium wage-posting model involving heterogeneous firms that can choose to operate in either the formal or informal sector, and workers who search randomly both on and off the job. The study explores the potential costs and benefits of informality and demonstrates that the negative effects of informality outweigh the positives, concluding that enhanced enforcement to reduce informality improves overall welfare in the economy.</footnote>

<footnote n="4">⁴ At the macroeconomic level, informality also has adverse effects on the economy, particularly through tax evasion, which reduces the tax base and revenue mobilization, thereby weakening the government's ability to support economic and social development.</footnote>

<footnote n="5">⁵ It can be imagined that firms recently transitioning to the formal sector may suffer more from competition with informal firms, which could incentivize them to innovate more, improve their performance, and potentially enhance their access to credit. However, Kouakou (2023a) shows that severe competition from the informal sector leads firms to significantly reduce their commitment to key innovation activities, especially investment in research and development (R&D), thereby weakening their innovation capacity. This reduction is due to a diminished self-financing capacity following losses in market share and profits (Kouakou, 2023a). For further discussions on the detrimental effects of competition from the informal sector on market share and profits, see also Amin (2023), Mendi and Costamagna (2017), and Perry et al. (2007), among others. Lower innovativeness and capacity to innovate negatively impact firms' performance, especially productivity (Fu et al., 2018). The author uses a dataset of Ivorian firms, primarily small and medium-sized enterprises (SMEs), which often emerge from the informal sector and fit the profile of firms recently transitioning to the formal sector. Similarly, Mendi and Costamagna (2017), using firm-level data from African and Latin American countries, find an inverted U-shaped relationship between competition from the informal sector and a firm's probability to innovate. This indicates that severe competition from the informal sector significantly reduces firms' probability to innovate, consistent with the findings of Kouakou (2023a). Furthermore, Distinguin et al. (2016), using data from SMEs across 86 countries – which are more likely to have started informally, as previously explained –, show that competition from the informal sector significantly increases the probability of registered firms to be credit-constrained. Additionally, firms recently transitioning to the formal sector are typically small, and the literature extensively demonstrates that small firms are more likely to be credit-constrained than medium-sized and large firms (see Asiedu et al., 2013; Beck et al., 2005; Distinguin et al., 2016, among others). Based on this discussion, it seems unlikely that firms recently transitioning to the formal sector would be less credit-constrained.</footnote>
it becomes registered. Informal operations often lack the structured financial practices and rigorous bookkeeping necessary for transparent and accurate record-keeping (La Porta and Shleifer, 2014). This absence of formal financial controls can pose challenges when the firm transitions to a formal status and faces the stringent requirements of external audits. Additionally, firms with informal origins might have developed a culture of minimal compliance, in contrast to those that started in the formal sector. Consequently, these firms may be less inclined to prioritize or invest in the external certification of their financial statements. Thus, registered firms with a history of informal operations may be less likely to produce high-quality financial statements compared to those that began as formally registered entities.

A firm's likelihood of facing credit constraints can increase if its financial statements are of poor quality. Indeed, these statements significantly impact financing decisions (Beck et al., 2005). Firms with subpar financial reports often have difficulty supplying the detailed information that lenders need to evaluate their creditworthiness. This inadequacy can result in increased credit constraints as inaccurate or incomplete financial records may lead creditors to perceive these firms as high-risk investments. Without reliable financial statements, firms struggle to showcase their financial stability and ability to repay loans, which complicates efforts to obtain financing or favorable credit terms. Moreover, the lack of transparency and accountability in financial reporting can dissuade potential investors and creditors, further aggravating the firm's credit constraints.

In summary, past informality may reduce firm productivity and the quality of financial statements, increase competition from the informal sector, and these factors, in turn, should elevate the probability of registered firms to be credit-constrained. Given this possibility, it is essential to conduct an econometric analysis to formally explore the relationship between past informality and the credit constraints faced by registered firms. A significant and positive impact of past informality would underscore the need for public policies that provide strong incentives for firms to start operations in the formal sector. The rationale would be that once a firm begins informally, its informal origins can have a lasting effect on its ability to secure external financing, even after transitioning to the formal sector. This, in turn, can harm the firm's growth, profitability, and capacity to hire, potentially impeding the country's economic and social development.

This paper addresses the gap in the literature by analyzing the effect of past informality on registered firms' probability of being credit-constrained, using firm-level data from the World Bank Enterprise Surveys spanning the 2006–2023 period. The dataset comprises repeated cross-sections, includes only registered firms, contains over 134,000 observations, and covers 134 countries. By employing the entropy balancing method to correct for the endogeneity of past informality status, we find that registered firms that began operations informally are more likely to be credit-constrained compared to those that started in the formal sector. This finding is extremely robust across various robustness tests, including instrumental variable, propensity score matching, alternative balancing methods that combine weighting and regression, potential omitted variables, restricted samples, alternative measures of credit constraints, different model specifications such as Linear Probability, Logit, and Probit models, and clustering standard errors at the country level. Additionally, heterogeneity analysis reveals that the detrimental impact of past informality diminishes with firm size, firm age, and improved structural factors like regulatory quality, trade openness, entrepreneurial dynamism, and public spending. Finally, we analyze three transmission channels through which past informality may affect the likelihood of registered firms to be credit-constrained. We demonstrate that reductions in the quality of financial statements and firm productivity, along with increases in competition from the informal sector, are key channels through which past informality heightens credit constraints for registered firms.

This paper contributes to the literature on multiple levels. First, it is the first to demonstrate, to the best of our knowledge, that past informality significantly increases the probability of registered firms to be credit-constrained, using a large global sample. This finding is crucial as it reveals an unexamined link between a firm's historical informality and its current credit constraints, highlighting the lasting impact of informal origins on access to finance. The use of a large global sample enhances the applicability and relevance of this research, deepening our understanding of how past operational practices affect financial opportunities across various economic contexts. Second, we provide evidence that firm productivity, the quality of financial statements, and competition from the informal sector are key transmission channels through which past informality impacts credit constraints. Identifying these specific mechanisms is significant as it helps policymakers and financial institutions design more targeted interventions to address the root causes of credit constraints and improve access to finance for firms with informal origins. Third, we find that the adverse impact of past informality diminishes with certain firm characteristics such as size and age, as well as improvements in structural factors like regulatory quality, trade openness, entrepreneurial dynamism, and public spending. This insight is valuable for policymakers, as it highlights ways to mitigate the long-term disadvantages associated with informal origins. Focusing on enhancing structural conditions can help reduce these persistent challenges and support firm growth and stability.

The rest of the paper is organized as follows. Section 2 presents the econometric methodology. Section 3 describes the data, variables, and descriptive statistics. Section 4 presents and discusses the baseline results. Section 5 provides a series of analyses to test the robustness of our findings. Section 6 presents the heterogeneity analysis. Section 7 analyzes the transmission channels. Section 8 concludes the paper and discusses policy implications and potential extensions.

## 2. Methodology

This paper aims to analyze how past informality affects the credit constraints of registered firms, a notable challenge due to the non-random nature of past informality status. Indeed, the probability of having past informality status can be correlated with various factors, including firm size, firm age, sector of activity, economic development, economic conditions, and financial development. This correlation introduces a self-selection issue. Additionally, these factors can significantly influence credit constraints, leading to omitted variable bias and making past informality status endogenous.
To address these issues, we employ the entropy balancing impact evaluation method developed by Hainmueller (2012), which has gained considerable traction in the literature. For instance, Apeti (2023) and Apeti and Edoh (2023) investigate the effects of mobile money adoption on consumption volatility and tax revenue, respectively. Balima (2020) analyzes the effect of a coup d'état on debt. Gasmi et al. (2024) study the effect of the provision of basic goods on citizen political participation in the Middle East and North Africa region. Gasmi et al. (2025) analyze the effect of firm informality on the introduction of sustainable and responsible innovation in registered firms in Nigeria. Neuenkirch and Neumeier (2016) investigate the effect of U.S. sanctions on poverty.

The entropy balancing method offers several benefits over conventional treatment effects techniques, such as Propensity Score Matching (PSM) and difference-in-differences. Among these benefits, first, entropy balancing ensures a higher degree of covariate balance. Second, since it does not rely on a treatment model, it mitigates model dependency, thereby reducing issues of multicollinearity and endogeneity. Third, entropy balancing uses a reweighting approach that minimizes information loss by maintaining weights close to their original values. Fourth, Hainmueller (2012) shows through Monte Carlo simulations and empirical analyses that entropy balancing surpasses many traditional methods, including propensity score matching, genetic matching, and Mahalanobis distance matching, in terms of bias and root mean squared error. Fifth, this method accounts for the data structure by incorporating fixed effects in the estimation of treatment effects.

The entropy balancing method also has advantages over traditional balancing methods that combine weighting and regression, specifically those that involve the estimation of propensity scores in the process of computing the balancing weights. These include methods such as “combining PSM with linear regression” (Dehejia and Wahba, 2002) and “combining Inverse Probability of Treatment Weighting (IPTW) with linear regression” (Chesnaye et al., 2022). Among its advantages, first, these traditional methods require estimating a propensity score model as part of the process of computing balancing weights. As a result, the treatment effects obtained with them are sensitive to misspecifications in the propensity score model, such as multicollinearity, endogeneity, and incorrect functional forms. As emphasized by Chesnaye et al. (2022), misspecifications in the propensity score model lead to biased treatment effects and complicate their interpretation as causal. A key advantage of the entropy balancing method is that, in contrast to these methods, it does not rely on a treatment econometric model when computing balancing weights. Consequently, it mitigates issues related to model misspecification, yielding consistent estimates of the treatment effect.

Second, as noted by Hainmueller (2012), entropy balancing eliminates the need for constant balance checking and the iterative search process over propensity score models, which may stochastically balance covariate moments. Third, and more specifically, as pointed out by Chesnaye et al. (2022), weights obtained using IPTW can fail to balance covariates, even when including (theoretically) relevant matching covariates. When this happens, the researcher is forced to modify the vector of covariates included in the propensity score model (Chesnaye et al., 2022), such as by removing or adding variables, incorporating interaction effects, splines, and transformations, without any guarantee of success. This introduces an element of randomness into the ability of IPTW to produce weights that ensure covariate balance. This is an important limitation of IPTW. In contrast, entropy balancing guarantees improved balance across all covariate moments included in the reweighting process (Hainmueller, 2012).

Fourth, in PSM combined with linear regression (Dehejia and Wahba, 2002), the weighted regression implemented in the second step is performed using the treated and matched control units, with the control units weighted by the number of times they are matched to a treated unit. As a result, unmatched control units are removed from the estimation, reducing the sample size and potentially leading to a significant loss of efficiency. In contrast, the entropy balancing reweighting scheme helps preserve information and maintain efficiency for subsequent analysis (Hainmueller, 2012). Indeed, by allowing the weights to vary smoothly across units, entropy balancing enhances efficiency by eliminating the weight constraints that mandate a unit to be either matched or discarded (Hainmueller, 2012).

In this paper, past informality status is considered as a “treatment” and is represented by a binary variable that takes the value 1 if the firm began operations as an informal firm (i.e., it was not registered at the start), and 0 otherwise. The “treated group” includes observations with past informality status, while the “control group” includes observations without past informality status. The outcome variable is credit constraints, a binary variable that takes the value 1 if the firm is credit-constrained, and 0 otherwise. Our focus is on measuring the average treatment effect on the treated (ATT), which indicates the impact of past informality on a registered firm's probability of being credit-constrained and is calculated as follows:

$$ \tau_{ATT} = E(\text{constrained}_{(1)}|T=1) - E(\text{constrained}_{(0)}|T=1) \quad (1) $$

where constrained and T represent the outcome and treatment variables, respectively. Here, T = 1 indicates that the firm has past informality status, while T = 0 indicates that it does not. The term $E(\text{constrained}_{(1)}|T=1)$ refers to the expected outcome given past informality status, and $E(\text{constrained}_{(0)}|T=1)$ represents the expected outcome if the firm had not experienced past informality status, also known as the counterfactual outcome of past informality status.

A significant challenge arises because $E(\text{constrained}_{(0)}|T=1)$ is unobservable. To address this, a common strategy is to match observations that have experienced the treatment with those that have not, ensuring that the matched non-treated observations are as similar as possible to the treated observations in terms of observable characteristics. These covariates are assumed to be related to both the outcome and the treatment variables. This matching process provides an appropriate proxy for $E(\text{constrained}_{(0)}|T=1)$. Within this framework, Eq. (1) is transformed as follows:

$$ \tau_{ATT} = E(\text{constrained}_{(1)}|T=1, X=x) - E(\text{constrained}_{(0)}|T=0, X=x) \quad (2) $$

where $X=x$ represents a vector of observable characteristics that are correlated with both credits constraints and past informality status. The terms $E(\text{constrained}_{(1)}|T=1, X=x)$ and $E(\text{constrained}_{(0)}|T=0, X=x)$ denote the expected outcomes with and without past informality status, respectively, for observations with characteristics $X=x$.
<figure><img src="image_7.png" /><figcaption>Fig. 1. Method for constructing the credit constraints variable, based on the World Bank Enterprise Surveys.</figcaption></figure>

Source: The author, adapted from Distinguin et al. (2016).

The estimation of the ATT using the entropy balancing method involves two steps. First, weights are calculated for the control group. These weights are designed to satisfy balancing constraints related to the sample moments of the observable characteristics. In the literature, the focus is on balancing the first moment (see Apeti, 2023; Apeti and Edoh, 2023; Balima, 2020; Gasmi et al., 2024, 2025; Neuenkirch and Neumeier, 2016, among others), which involves ensuring that the means of the observable characteristics are equal between the treated and control groups. The goal is to make the observations without past informality status, on average, as similar as possible to those with past informality status.

In the second step, these weights are incorporated into a regression analysis where the dependent variable is credit constraints. The main explanatory variable is past informality, and the covariates used in the first step are included as controls. We also include year fixed effects, country fixed effects, and (interactive) country-sector-year fixed effects in the second step of entropy balancing. This estimation allows us to obtain the ATT. More precisely, the second step involves estimating a Linear Probability model<fnref n="6" /> that includes the entropy balancing weights, using the Weighted Least Squares (WLS) method. The estimated coefficient, representing the average marginal effect, yields the ATT. Including the covariates in this second step enhances the efficiency of the estimation (Gasmi et al., 2024; Neuenkirch and Neumeier, 2016) and is analogous to including control variables in a randomized experiment (Neuenkirch and Neumeier, 2016). Country and year fixed effects allow us to control for country-specific factors (such as the population's financial culture) and macroeconomic shocks (such as global pandemics and financial crises) that may influence both the probabilities of being credit-constrained and having past informality status. Country-sector-year fixed effects allow us to control for time-varying factors, such as countries' business cycles and fiscal and monetary policies, which are likely to affect both the probability of being credit-constrained and the probability of having past informality status. This approach also accounts for the fact that the sector of activity (i.e., manufacturing vs. services) is expected to be correlated with both probabilities. By including these fixed effects, the model effectively controls for time-varying macroeconomic factors, mitigating concerns about omitted variable bias.

Besides, note that despite its previously discussed advantages, entropy balancing, like any impact evaluation method, may have some limitations. Indeed, it may fail to address endogeneity issues resulting from unobserved time-varying factors affecting both

<footnote n="6">⁶ As will be seen in the robustness checks section, using either a Logit model or a Probit model in the second step, instead of the Linear Probability model, yields similar results.</footnote>
the probabilities of past informality status and credit constraints, as well as potential reverse causality. To test the robustness of our results, we complement the entropy balancing method with alternative estimation methods, including the Instrumental Variable (IV) method, four variants of the PSM method (namely Nearest-Neighbor Matching, Radius Matching, Kernel Matching, and Local Linear Regression Matching), two alternative balancing methods that combine weighting and regression (namely “combining PSM with linear regression” and “combining IPTW with linear regression”), Linear Probability, Logit, and Probit regressions. To further address potential omitted variable issues, we incorporate a wide range of additional controls or potential omitted covariates in our entropy balancing specification (see Apeti and Edoh, 2023, for the same approach), in addition to including country, year, and country-sector-year fixed effects as previously explained. We also consider alternative specifications in the second step of the entropy balancing method. Additionally, we consider alternative measures of credit constraints. We also assess the sensitivity of our results to restricted samples. In the final robustness check, we cluster standard errors at the country level.

# 3. Data, variables, and descriptive statistics

## 3.1. Data sources

We utilize firm-level data from the World Bank Enterprise Surveys (WBES) spanning the 2006–2023 period. The dataset comprises repeated cross-sections, includes only registered firms, contains over 134,000 observations, and covers 134 countries.<fnref n="7" /> The WBES are comprehensive, nationally representative surveys that gather data from top managers and business owners in over 150 countries. These surveys provide valuable insights into various aspects of firms’ activities, including informality, access to finance, firm performance, ownership, exports, and other relevant areas. The extensive data collected facilitate comparisons across different economies and over time. The information and data from these surveys are publicly accessible at https://www.entertainmentsurveys.org/.

The WBES are our primary data source, which we complement with macroeconomic variables to account for country-level factors that may be correlated with both the probabilities of credit constraints and having past informality status. Macroeconomic data on regulatory quality and corruption, publicly accessible at https://www.govindicators.org/, are sourced from the Worldwide Governance Indicators (WGI) database developed by Kaufmann et al. (2010). The remaining macroeconomic variables are obtained from the World Development Indicators (WDI) database of the World Bank, which is also publicly accessible at https://datatopics.worldbank.org/world-development-indicators/.

## 3.2. Variables

### 3.2.1. Credit constraints

Our dependent variable is “Credit constraints”. This is a binary variable equal to 1 if the firm is credit-constrained, and 0 otherwise.<fnref n="8" /> Based on the WBES and the literature (Artola and Genre, 2011; Ferrando and Mulier, 2015; Ferrando et al., 2019; García-Posada Gómez, 2019), a firm is considered credit-constrained in any of the following four circumstances: (i) it applied for a loan and was denied; (ii) it applied and only received a part of the requested loan (i.e., quantity rationing)<fnref n="9" />; (iii) it applied and refused the lender’s proposal because the borrowing costs were too high (i.e., price rationing); (iv) it needed a loan but chose not to apply due to various constraints such as complex application procedures, high interest rates, substantial collateral requirements, or because it feared that the application would be rejected (i.e., discouraged borrowers). A firm is not regarded as credit-constrained if it received the entire requested loan or possesses sufficient capital.

Fig. 1 gives more details on how the “Credit constraints” variable is constructed.

Note that when a firm declares it has sufficient capital, it may either have received external funds, such as bank loans, from a recent period, or it may have sufficient internal funds. The WBES do not allow us to distinguish between these two cases. In the first case, where firms have sufficient capital because they received external funds recently, they are considered not credit-constrained and are not excluded from the sample, as their current financial sufficiency is often the result of a recent successful loan application. Their current situation is influenced by their recent access to credit. In the second case, where firms have sufficient internal funds, this situation is often the result of successful operations supported by external financing, including bank loans. In other words, the availability of sufficient internal funds is often, in some way, a result of a recent successful loan application. Hence, these firms are regarded as not facing credit constraints and are maintained in the sample.

Furthermore, from an econometric point of view, excluding firms with sufficient capital may introduce selection bias (as such an exclusion is likely not random) and bias the estimation of the effect of past informality. For instance, if registered firms with a history of informality tend to accumulate capital and rely more on self-financing, excluding them would lead to an underestimation of the actual impact of past informality on credit constraints. More precisely, registered firms with past informality status that

<footnote n="7">⁷ The list of countries is presented in Table A.1 of Appendix A.</footnote>

<footnote n="8">⁸ Measuring credit constraints through a binary variable is a rather standard approach in the literature. See Distinguin et al. (2016), Ferrando and Mulier (2015), Ferrando et al. (2019), and García-Posada Gómez (2019), among others.</footnote>

<footnote n="9">⁹ Regarding quantity rationing, note that a threshold of 75% is often set in the literature, i.e., a firm is considered to be facing quantity rationing if it received less than 75% of the requested loan. However, the WBES do not allow us to set any threshold, i.e., we consider a firm to be facing quantity rationing if it received less than the requested loan, without setting any threshold. Therefore, we adopt a more stringent measure of quantity rationing, as also done by Ferrando and Mulier (2015).</footnote>
have generated enough capital to finance themselves are likely to possess characteristics (such as greater profitability or stronger management capabilities) that set them apart from those still dealing with credit constraints. If these firms are omitted, the analysis would miss important differences in outcomes between firms, resulting in distorted conclusions. Relatedly, excluding these firms would create an issue of representativeness in the sample. In fact, such exclusion would leave out firms that have managed to overcome credit constraint issues by relying on internal funds, focusing only on firms that are either credit-constrained or significantly likely to exhibit credit constraints. The remaining firms may not be representative of the entire population, thereby biasing the estimate of the effect of past informality on credit constraints. Additionally, the substantial reduction in sample size resulting from excluding these firms may also reduce estimation efficiency.

From a conceptual perspective, even firms with ample internal funds may opt to access credit markets at certain times for strategic purposes. Including them provides a more comprehensive view of credit allocation.<fnref n="10" />

### 3.2.2. Past informality

Our independent variable is “Past informality”. This is a binary variable that takes the value of 1 if the firm began operations informally (i.e., it was not registered at the start), and 0 otherwise. To construct this variable, we use the following question asked in the WBES: “b6a: *Was this establishment formally registered when it began operations?*” A firm has past informality status if the answer to this question is “no”. In contrast, the firm does not have past informality status if the answer is “yes”.

### 3.2.3. Matching covariates or control variables

The matching covariates or control variables are selected from variables that are correlated with both the probabilities of being credit-constrained and having past informality status. This approach helps mitigate issues related to potential omitted variable bias and enhances estimation efficiency. Based on the literature on the determinants of credit constraints, informality, and the transition to the formal sector, we select the following covariates: firm size, firm age, sector of activity, affiliation with a large firm, private foreign ownership, real GDP per capita (as a proxy for economic development), inflation (as a proxy for economic conditions), and domestic credit to the private sector (as a proxy for financial development). Macroeconomic variables are included to control for country-level factors that may be correlated with both the probabilities of being credit-constrained and having past informality status.

Financial constraints tend to diminish as firms become larger (Asiedu et al., 2013; Beck et al., 2005; Distinguin et al., 2016). Larger firms often enjoy better access to capital markets, allowing them to obtain funding more easily and at lower costs compared to smaller firms. Additionally, these larger entities benefit from more diversified income streams and assets, as well as a stronger capacity for self-financing. This combination reduces the perceived risk for lenders and investors, thereby further alleviating credit constraints. The literature also suggests that larger firms are less likely to have started operations in the informal sector because the advantages of formalization become more significant as a firm expands (Distinguin et al., 2016; Perry et al., 2007). Indeed, while entrepreneurs can often manage implicit contracts with a few clients in small-scale operations, the need for formal contracts and legal protections becomes more pronounced as the business grows, leading them to formalize their operations (Distinguin et al., 2016). Additionally, informal firms are typically small (La Porta and Shleifer, 2014), and they rarely become large firms after formalization. Therefore, a large firm is more likely to have started operations in the formal sector rather than in the informal sector. Firm size is measured by the natural logarithm of the total number of full-time employees, adjusted to account for temporary workers. We anticipate that firm size will be negatively associated with both the probabilities of being credit-constrained and having past informality status.

Older and more established firms usually have stronger ties with financial institutions, a solid credit history, and a better reputation, which often results in more favorable credit terms and conditions, thereby decreasing the likelihood of facing credit constraints (Carreira and Silva, 2010; Distinguin et al., 2016). These firms often started as small-scale, informal operations and transitioned to a formal status as they grew and sought legitimacy and access to additional resources. Firm age is measured as the natural logarithm of the number of years since the firm commenced operations. We expect firm age to be negatively correlated with credit constraints and positively correlated with the likelihood of having started operations informally.

Firms in the WBES are categorized into the manufacturing and services sectors, and the literature highlights notable differences between these sectors, particularly in terms of financial constraints (Beck et al., 2005; Carreira and Silva, 2010). Recent research by Distinguin et al. (2016) using WBES data indicates that SMEs in the manufacturing sector are more likely to experience credit constraints compared to those in the services sector. This disparity may arise because manufacturing firms often face higher financial vulnerability and rely more heavily on external credit due to significant capital investments in machinery and inventory. Conversely, service firms generally have lower startup and capital requirements. The substantial startup costs in manufacturing may also lead new firms to operate informally initially to overcome financial barriers before transitioning to a formal status. The sector of activity is represented by a binary variable: 1 for manufacturing and 0 for services. We anticipate that the sector of activity will be positively correlated with both the probabilities of being credit-constrained and having a history of informality.

Affiliation with a large firm or a group is likely to decrease the probability of facing credit constraints, as it provides access to broader capital resources (Mendi and Costamagna, 2017). Furthermore, firms associated with a large firm or a group often exhibit higher levels of professionalism and are more inclined to operate formally (Gasmi et al., 2025). We measure this affiliation using

<footnote n="10">¹⁰ In a robustness check exercise, we excluded from the sample the firms that did not apply for any lines of credit or loans because they had sufficient capital. As will be seen in the robustness checks section, our findings do not change, meaning that our results are not driven by the presence of these firms.</footnote>
<table><caption>**Table 1**

Descriptive statistics before weighting.</caption><thead><tr><th></th><th>(1)<br>Past informality</th><th>(2)<br>No past informality</th><th>(3) = (2)-(1)<br>Difference</th></tr></thead><tbody><tr><td>Firm size</td><td>2.973</td><td>3.472</td><td>0.499***</td></tr><tr><td>Firm age</td><td>2.849</td><td>2.74</td><td>-0.109***</td></tr><tr><td>Sector of activity</td><td>0.624</td><td>0.557</td><td>-0.067***</td></tr><tr><td>Affiliation with a large firm</td><td>0.119</td><td>0.171</td><td>0.052***</td></tr><tr><td>Private foreign ownership</td><td>0.237</td><td>0.451</td><td>0.214***</td></tr><tr><td>Real GDP per capita</td><td>8.99</td><td>9.391</td><td>0.401***</td></tr><tr><td>Inflation</td><td>6.427</td><td>6.001</td><td>-0.426***</td></tr><tr><td>Domestic credit to the private sector</td><td>3.292</td><td>3.578</td><td>0.286***</td></tr><tr><td>Observations</td><td>13,920</td><td>124,369</td><td></td></tr></tbody></table>

a binary variable, where 1 indicates that the firm is part of a large firm and 0 otherwise. We anticipate that such affiliation will be negatively correlated with both the likelihood of being credit-constrained and the probability of having a history of operating informally.

Furthermore, financial barriers are typically lower for foreign-owned firms compared to domestic ones (Asiedu et al., 2013; Aterido et al., 2013; Beck et al., 2006; Blalock et al., 2008; Carreira and Silva, 2010). Indeed, private foreign ownership is associated with reduced credit constraints due to the additional financial support provided by foreign investors (Un and Cuervo-Cazurra, 2008). Additionally, foreign investors generally prefer businesses that are already formalized to ensure legal protection and regulatory compliance. They are drawn to stable and transparent environments and often engage with or acquire established firms. The involvement of foreign ownership usually entails stringent due diligence and regulatory scrutiny, which further diminishes the likelihood of a history of informal operations. Private foreign ownership is measured as the natural logarithm of the percentage of the firm owned by private foreign individuals, companies, or organizations. We expect private foreign ownership to be negatively correlated with both the probabilities of experiencing credit constraints and having a background in informal operations.

Besides, the literature suggests that both economic and financial development help mitigate financial constraints, reducing the probability of firms to be credit-constrained (Beck et al., 2006). Indeed, higher economic and financial development improves market conditions and infrastructure, making it easier for firms to access financing. Specifically, advancements in financial systems and services increase the availability of credit, thereby lowering the probability of credit constraints (Asiedu et al., 2013). Additionally, it is well-established that the size of the informal sector tends to shrink as a country's level of development increases (La Porta and Shleifer, 2014), which in turn reduces the likelihood that registered firms began their operations informally. Similarly, greater financial development enhances the opportunities for financing formal businesses, thus decreasing the chances that firms start out informally. We use real GDP per capita (in natural logarithm) as a proxy for economic development, and domestic credit to the private sector (also in natural logarithm) as a proxy for financial development.<fnref n="11" /> We anticipate that each variable will be negatively correlated with both the probabilities of being credit-constrained and having past informality status.

Inflation serves as an indicator of economic conditions, which can influence both the likelihood of credit constraints and the probability of having past informality status. Findings by Asiedu et al. (2013) and Distinguin et al. (2016) indicate that firms are more likely to experience credit constraints in countries with high inflation rates. This is because inflation reduces the real value of revenues and assets, increasing uncertainty and making it more difficult for firms to meet collateral requirements and secure financing. Additionally, rising prices and economic instability may drive firms to operate informally in an effort to avoid higher taxes and regulatory costs. Economic conditions are measured by the annual inflation rate. We anticipate that inflation will be positively correlated with both the probabilities of being credit-constrained and having past informality status.<fnref n="12" /> A summary of the definitions of the variables can be found in Appendix B.

### 3.3. Descriptive statistics and covariate balance

We begin this subsection by evaluating the performance of the entropy balancing method. This is done by presenting descriptive statistics obtained before and after applying the entropy balancing weighting used to estimate the treatment effect of past informality on registered firms' probability of being credit-constrained. Table 1 provides the sample mean of each covariate in the treated (observations with past informality status) and control (observations without past informality status) groups in columns (1) and (2), respectively, obtained before applying the balancing weighting. The difference between (2) and (1) is presented in column (3), along with the level of statistical significance.

<footnote>* p < 0.10, ** p < 0.05, *** p < 0.01.</footnote>

<footnote n="11">¹¹ For a similar approach to measuring financial development, see Asiedu et al. (2013) and Beck et al. (2005), among others.</footnote>

<footnote n="12">¹² Note that economic development (proxied by real GDP per capita), financial development (proxied by domestic credit to the private sector), and economic conditions (proxied by inflation) are structural factors. These factors often exhibit hysteresis, meaning their current levels are correlated with their past levels. Therefore, including these variables also helps control, at least in part, for recent changes in economic development, economic conditions, and financial development across countries.</footnote>
<table><caption>**Table 2**
Descriptive statistics after weighting.</caption><thead><tr><th></th><th>(1)<br>Past informality</th><th>(2)<br>No past informality</th><th>(3) = (2)-(1)<br>Difference</th></tr></thead><tbody><tr><td>Firm size</td><td>2.973</td><td>2.973</td><td>0</td></tr><tr><td>Firm age</td><td>2.849</td><td>2.849</td><td>0</td></tr><tr><td>Sector of activity</td><td>0.624</td><td>0.624</td><td>0</td></tr><tr><td>Affiliation with a large firm</td><td>0.119</td><td>0.119</td><td>0</td></tr><tr><td>Private foreign ownership</td><td>0.237</td><td>0.237</td><td>0</td></tr><tr><td>Real GDP per capita</td><td>8.99</td><td>8.99</td><td>0</td></tr><tr><td>Inflation</td><td>6.427</td><td>6.427</td><td>0</td></tr><tr><td>Domestic credit to the private sector</td><td>3.292</td><td>3.292</td><td>0</td></tr><tr><td>Observations</td><td>13,920</td><td>124,369</td><td></td></tr><tr><td>Total of weights</td><td>13,920</td><td>13,920</td><td></td></tr></tbody></table>

<table><caption>**Table 3**
Credit constraints among firms with and without past informality status.</caption><thead><tr><th></th><th>(1)<br>Past informality</th><th>(2)<br>No past informality</th><th>(3) = (2)-(1)<br>Difference</th></tr></thead><tbody><tr><td>Percentage of observations that are<br>subject to credit constraints</td><td>47%</td><td>33%</td><td>-14%***</td></tr></tbody></table>

From Table 1, we observe that observations with past informality status are characterized by significantly (at the 1% level) lower firm size, higher firm age, higher percentage of manufacturing firms, lower percentage of affiliation with a large firm, lower percentage of private foreign ownership, lower real GDP per capita (a proxy for economic development), higher inflation (a proxy for economic conditions), and lower domestic credit to the private sector (a proxy for financial development). These results confirm the expected relationships between the covariates and firms' probability of having past informality status. More importantly, they confirm that past informality status does not occur at random, indicating the necessity of constructing an appropriate synthetic control group to correctly estimate the treatment effect of past informality status on firms' probability of being credit-constrained.

In Table 2, we present the sample mean of each covariate in the treated and control groups in columns (1) and (2), respectively, obtained after applying the balancing weighting. Column (3) presents the difference between (2) and (1), along with the level of statistical significance. We see from Table 2 that the differences observed in the previous table have disappeared, indicating the effectiveness of the entropy balancing method. Thus, the entropy balancing method allows us to construct a perfect control group comprising observations that are closely similar to treated observations in terms of the means of the matching covariates. This enables a correct estimation of the treatment effect of past informality status on firms' probability of being credit-constrained.

To gain an initial insight into the relationship between past informality and credit constraints, we present in Table 3 statistics on credit constraints in the treated and control groups. Columns (1) and (2) report the percentage of observations subject to credit constraints in the treated and control groups, respectively. Column (3) reports the difference between (2) and (1), along with its statistical significance level. From Table 3, we observe that 47% of the observations with past informality status are subject to credit constraints, compared to 33% of those without past informality status, with a difference of -14% that is statistically significant at the 1% level. Although no causal relationship between past informality and credit constraints can be derived from these descriptive results, they provide an indication of both the nature (whether positive or negative) and the significance of the treatment effect of past informality.

Table A.2 in Appendix A presents the correlation matrix of the baseline variables. There are two key insights from this table. First, we observe that past informality is highly significantly and positively correlated (at the 1% level) with credit constraints. This result aligns with those presented in Table 3, suggesting that past informality status is likely positively related to a firm's probability of being credit-constrained. Second, columns (1) and (2) of Table A.2 show that our matching covariates are all highly significantly correlated (at the 1% level) with both past informality status and credit constraints. This supports the relevance of the matching covariates considered in our analysis. Additionally, the signs of (almost) all the covariates correspond to the expected relationships between them and both past informality and credit constraints, further reinforcing the relevance of our choice of matching covariates.

Furthermore, in Table A.3 in Appendix A, we present descriptive statistics of the baseline variables, which provide an overview of the percentage of observations that are subject to credit constraints and past informality status. We observe that around 34% of observations are subject to credit constraints, which is relatively significant. Meanwhile, 10% of observations are subject to past informality status.

## 4. Baseline results

Using the synthetic controls in Table 2, we estimate the effect of past informality on registered firms' probability of being credit-constrained (ATT) using the Weighted Least Squares (WLS) method. Table 4 reports the results of the estimations.

<footnote>* p < 0.10, ** p < 0.05, *** p < 0.01.</footnote>

<footnote>* p < 0.10, ** p < 0.05, *** p < 0.01.</footnote>
<table><caption>**Table 4**
The effect of past informality on the credit constraints of registered firms.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.079***<br>(0.005)</td><td>0.075***<br>(0.005)</td><td>0.052***<br>(0.005)</td><td>0.051***<br>(0.005)</td><td>0.051***<br>(0.005)</td><td>0.079***<br>(0.004)</td><td>0.074***<br>(0.004)</td><td>0.057***<br>(0.005)</td><td>0.056***<br>(0.005)</td><td>0.055***<br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Robust standard errors are in parentheses. * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$.

Columns (1) to (5) present the results without including the matching covariates used in the first step of the entropy balancing methodology to compute the synthetic controls group. Column (1) excludes fixed effects from the second step. Columns (2) and (3) include only year and country fixed effects, respectively. Column (4) includes both year and country fixed effects. Column (5) includes only country-sector-year fixed effects. Columns (6) to (10) repeat this exercise with the only difference being that the matching covariates, including firm size, firm age, sector of activity, affiliation with a large firm, private foreign ownership, real GDP per capita, inflation, and domestic credit to the private sector, are included in all five cases.<fnref n="13" /> Including year, country, and country-sector-year fixed effects in the second step of entropy balancing eliminates any year- or country-specific effects in the assessment of the effect of past informality on registered firms' probability of being credit-constrained, whereas including matching covariates improves estimation efficiency.

We see from Table 4 that, irrespective of the specification, past informality significantly increases (at the 1% level) registered firms' probability of being credit-constrained. The magnitude of the effects ranges between 0.051 (see columns (4) and (5)) and 0.079 (see columns (1) and (6)), with an average of 0.063. This means that the probability of being credit-constrained increases by around 6% in registered firms that began operations as informal compared to those firms that started as formal. The relative stability of the coefficients across all ten specifications is a first indication that this effect is robust. We further check the robustness of this finding in the next section.

## 5. Robustness checks

Our results show that past informality significantly increases registered firms' probability of being credit-constrained. This section aims to test the robustness of this finding.

### 5.1. Alternative specifications

This subsection tests the robustness of our results to alternative specifications in the second step of entropy balancing. Given the binary nature of the credit constraints dependent variable and the large set of fixed effects, the second step of the entropy balancing method involved estimating a Linear Probability model using the WLS method. One could also estimate either a Logit model or a Probit model instead of a Linear Probability model. We analyze the results obtained using these two alternative specifications. The results of the estimations are presented in Table 5. The upper part, labeled as “(A)”, of the table reports results for the Logit model, whereas the lower part, labeled as “(B)”, is about the Probit model. Regarding fixed effects, the Logit and Probit models include only country and year fixed effects, as these nonlinear models do not handle specifications with large sets of fixed effects well.

Irrespective of the specification, past informality significantly increases the probability of registered firms to be credit-constrained (at the 1% level) in all ten estimations. For both the Logit and Probit models, the magnitude of the effects ranges from 0.051 (see column (4)) to 0.078 (see column (1)). The effects are identical across specifications, except in column (5), where they are 0.076 in the Logit model and 0.077 in the Probit model. The average effects are also very similar: 0.064 in the Logit model and 0.065 in the Probit model. This is very close to the average effect of 0.063 obtained in the Linear Probability model (see Table 4), despite the latter including country-sector-year fixed effects in the second step of entropy balancing, whereas the Logit and Probit models do not. Overall, these results indicate that using a Logit or Probit model in the second step instead of the Linear Probability model does not alter the findings, demonstrating the robustness of our results to the specification used in the second step of the entropy balancing method.<fnref n="14" />

<footnote n="13">¹³ In column (10), the binary variable for the sector of activity is excluded to avoid redundancy. The same approach is applied throughout the paper in all regressions that include only country-sector-year fixed effects.</footnote>

<footnote n="14">¹⁴ More generally, all results presented in the paper have also been obtained using the Logit and Probit models in the second step of the entropy balancing method. These results are available upon request.</footnote>
<caption>**Table 5**</caption>

Robustness checks: Alternative specifications in the second step of entropy balancing.

<table><thead><tr><th></th><th colspan="8">(A) Logit model in the second step of entropy balancing</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.078<sup>***</sup><br>(0.005)</td><td>0.075<sup>***</sup><br>(0.005)</td><td>0.052<sup>***</sup><br>(0.005)</td><td>0.051<sup>***</sup><br>(0.005)</td><td>0.076<sup>***</sup><br>(0.004)</td><td>0.072<sup>***</sup><br>(0.004)</td><td>0.056<sup>***</sup><br>(0.005)</td><td>0.055<sup>***</sup><br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td></tr></tbody><tbody><tr><th></th><th colspan="8">(B) Probit model in the second step of entropy balancing</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr><tr><td>Past informality</td><td>0.078<sup>***</sup><br>(0.005)</td><td>0.075<sup>***</sup><br>(0.005)</td><td>0.052<sup>***</sup><br>(0.005)</td><td>0.051<sup>***</sup><br>(0.005)</td><td>0.077<sup>***</sup><br>(0.004)</td><td>0.072<sup>***</sup><br>(0.004)</td><td>0.056<sup>***</sup><br>(0.005)</td><td>0.055<sup>***</sup><br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Robust standard errors are in parentheses. Regarding fixed effects, the Logit and Probit models include only country and year fixed effects, as these nonlinear models do not handle specifications with large sets of fixed effects well. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

<caption>**Table 6**</caption>

Robustness checks: Additional controls in the second step only.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td></td><td>Manager's experience</td><td>Manager's gender</td><td>Total sales</td><td>Firm's export capacity</td><td>Criminality</td><td>Taxation</td><td>Real lending interest rate</td><td>Interest rate spread</td><td>Bank NPLs</td><td>Real GDP growth</td></tr><tr><td>Past informality</td><td>0.055<sup>***</sup><br>(0.005)</td><td>0.054<sup>***</sup><br>(0.005)</td><td>0.050<sup>***</sup><br>(0.005)</td><td>0.055<sup>***</sup><br>(0.005)</td><td>0.054<sup>***</sup><br>(0.005)</td><td>0.049<sup>***</sup><br>(0.005)</td><td>0.064<sup>***</sup><br>(0.005)</td><td>0.062<sup>***</sup><br>(0.005)</td><td>0.048<sup>***</sup><br>(0.006)</td><td>0.055<sup>***</sup><br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>131,533</td><td>117,631</td><td>119,346</td><td>133,111</td><td>133,218</td><td>116,661</td><td>106,806</td><td>95,849</td><td>100,308</td><td>134,050</td></tr><tr><td></td><td>(11)</td><td>(12)</td><td>(13)</td><td>(14)</td><td>(15)</td><td>(16)</td><td>(17)</td><td>(18)</td><td>(19)</td><td>(20)</td></tr><tr><td></td><td>Unemployment</td><td>FDI</td><td>Gross fixed capital formation</td><td>Banking accessibility</td><td>New business density</td><td>Remittances</td><td>Public spending</td><td>Urbanization</td><td>Debt</td><td>Corruption</td></tr><tr><td>Past informality</td><td>0.054<sup>***</sup><br>(0.005)</td><td>0.055<sup>***</sup><br>(0.005)</td><td>0.053<sup>***</sup><br>(0.005)</td><td>0.055<sup>***</sup><br>(0.005)</td><td>0.062<sup>***</sup><br>(0.005)</td><td>0.055<sup>***</sup><br>(0.005)</td><td>0.045<sup>***</sup><br>(0.006)</td><td>0.054<sup>***</sup><br>(0.005)</td><td>0.054<sup>***</sup><br>(0.005)</td><td>0.055<sup>***</sup><br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>132,750</td><td>133,988</td><td>126,658</td><td>131,638</td><td>109,396</td><td>134,050</td><td>103,197</td><td>133,386</td><td>113,237</td><td>134,050</td></tr><tr><td></td><td>(21)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td colspan="10">All covariates</td></tr><tr><td>Past informality</td><td>0.028<sup>***</sup><br>(0.012)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>22,563</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Constant terms are included but not reported for brevity. NPLs: nonperforming loans. Robust standard errors are in parentheses. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

## 5.2. Additional controls or potential omitted covariates

In this subsection, we test the robustness of our results to potential omitted covariates by introducing in the analysis a wide range of additional controls, including both firm and country characteristics, that may be correlated with both the probabilities of a firm being credit-constrained and having past informality status. This is achieved by incorporating these additional controls in the second step of our entropy balancing specification (see also Apeti and Edoh, 2023, for the same approach). These variables include: manager's experience, manager's gender, total sales, firm's export capacity, criminality in the business environment, taxation, real lending interest rate, interest rate spread, bank nonperforming loans, real GDP growth, unemployment, foreign direct investment (FDI), gross fixed capital formation, banking accessibility, new business density (a proxy for entrepreneurial dynamism), remittances, public spending, urbanization, debt, and corruption. Appendix B summarizes the definitions of the additional controls, while Table A.4 in Appendix A presents the descriptive statistics.

The results of the estimations are reported in Table 6. We start by including additional controls one at a time in the second step of the entropy balancing method. This allows us to analyze the sensitivity of our findings to the inclusion of each control. These results are reported in columns (1)-(20). Then, in a final estimation presented in column (21), we include all the additional controls simultaneously. The results indicate that including additional controls yields similar results to the initial findings.

Moreover, as in theory any factors that can predict a firm's likelihood of experiencing past informality can be a source of endogeneity provided they impact credit constraints, we include the additional controls in the first step of entropy balancing to
<caption>**Table 7**</caption>

Robustness checks: Additional controls in the first step only.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td></td><td>Manager's experience</td><td>Manager's gender</td><td>Total sales</td><td>Firm's export capacity</td><td>Criminality</td><td>Taxation</td><td>Real lending interest rate</td><td>Interest rate spread</td><td>Bank NPLs</td><td>Real GDP growth</td></tr><tr><td>Past informality</td><td>0.055***<br>(0.005)</td><td>0.054***<br>(0.005)</td><td>0.058***<br>(0.005)</td><td>0.055***<br>(0.005)</td><td>0.054***<br>(0.005)</td><td>0.049***<br>(0.005)</td><td>0.064***<br>(0.005)</td><td>0.063***<br>(0.005)</td><td>0.050***<br>(0.006)</td><td>0.056***<br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>131,533</td><td>117,631</td><td>119,346</td><td>133,111</td><td>133,218</td><td>116,661</td><td>106,806</td><td>95,849</td><td>100,308</td><td>134,050</td></tr><tr><td></td><td>(11)</td><td>(12)</td><td>(13)</td><td>(14)</td><td>(15)</td><td>(16)</td><td>(17)</td><td>(18)</td><td>(19)</td><td>(20)</td></tr><tr><td></td><td>Unemployment</td><td>FDI</td><td>Gross fixed capital formation</td><td>Banking accessibility</td><td>New business density</td><td>Remittances</td><td>Public spending</td><td>Urbanization</td><td>Debt</td><td>Corruption</td></tr><tr><td>Past informality</td><td>0.054***<br>(0.005)</td><td>0.055***<br>(0.005)</td><td>0.053***<br>(0.005)</td><td>0.056***<br>(0.005)</td><td>0.062***<br>(0.005)</td><td>0.055***<br>(0.005)</td><td>0.046***<br>(0.006)</td><td>0.054***<br>(0.005)</td><td>0.054***<br>(0.005)</td><td>0.056***<br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>132,750</td><td>133,988</td><td>126,658</td><td>131,638</td><td>109,396</td><td>134,050</td><td>103,197</td><td>133,386</td><td>113,237</td><td>134,050</td></tr><tr><td></td><td>(21)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>All covariates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Past informality</td><td>0.054***<br>(0.013)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>22,563</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Constant terms are included but not reported for brevity. NPLs: nonperforming loans. Robust standard errors are in parentheses. * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$.

<caption>**Table 8**</caption>

Robustness checks: Additional controls in both the second and first steps.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td></td><td>Manager's experience</td><td>Manager's gender</td><td>Total sales</td><td>Firm's export capacity</td><td>Criminality</td><td>Taxation</td><td>Real lending interest rate</td><td>Interest rate spread</td><td>Bank NPLs</td><td>Real GDP growth</td></tr><tr><td>Past informality</td><td>0.055***<br>(0.005)</td><td>0.054***<br>(0.005)</td><td>0.050***<br>(0.005)</td><td>0.055***<br>(0.005)</td><td>0.054***<br>(0.005)</td><td>0.049***<br>(0.005)</td><td>0.064***<br>(0.005)</td><td>0.063***<br>(0.005)</td><td>0.050***<br>(0.006)</td><td>0.056***<br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>131,533</td><td>117,631</td><td>119,346</td><td>133,111</td><td>133,218</td><td>116,661</td><td>106,806</td><td>95,849</td><td>100,308</td><td>134,050</td></tr><tr><td></td><td>(11)</td><td>(12)</td><td>(13)</td><td>(14)</td><td>(15)</td><td>(16)</td><td>(17)</td><td>(18)</td><td>(19)</td><td>(20)</td></tr><tr><td></td><td>Unemployment</td><td>FDI</td><td>Gross fixed capital formation</td><td>Banking accessibility</td><td>New business density</td><td>Remittances</td><td>Public spending</td><td>Urbanization</td><td>Debt</td><td>Corruption</td></tr><tr><td>Past informality</td><td>0.054***<br>(0.005)</td><td>0.055***<br>(0.005)</td><td>0.053***<br>(0.005)</td><td>0.056***<br>(0.005)</td><td>0.062***<br>(0.005)</td><td>0.055***<br>(0.005)</td><td>0.046***<br>(0.006)</td><td>0.054***<br>(0.005)</td><td>0.054***<br>(0.005)</td><td>0.056***<br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>132,750</td><td>133,988</td><td>126,658</td><td>131,638</td><td>109,396</td><td>134,050</td><td>103,197</td><td>133,386</td><td>113,237</td><td>134,050</td></tr><tr><td></td><td>(21)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>All covariates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Past informality</td><td>0.052***<br>(0.013)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>22,563</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Constant terms are included but not reported for brevity. NPLs: nonperforming loans. Robust standard errors are in parentheses. * $p < 0.10$, ** $p < 0.05$, *** $p < 0.01$.

construct the synthetic control group. The results are presented in Table 7, from column (1) to column (21), and are similar to the initial findings. Since including in the second step of entropy balancing the matching covariates used in the first step improves estimation efficiency, in a final analysis, we include the additional controls in both the second and first steps of entropy balancing. Table 8 presents the results of the estimations, which are similar to the initial findings.<fnref n="15" />

In summary, including additional controls in our entropy balancing specification does not alter our initial findings. This indicates that these findings are not driven by omitted variable bias.

<footnote n="15">¹⁵ In all the specifications considered, unreported results show that the balancing property is satisfied. These results are available upon request.</footnote>
### 5.3. Alternative estimation methods

This subsection aims to analyze the robustness of our results to alternative estimation methods. To do so, we conduct four different analyses.

First, we estimate the effect of past informality on credit constraints using standard discrete choice models. More precisely, we estimate Linear Probability, Logit, and Probit models. We start with the model containing our baseline covariates and then add additional control variables one by one cumulatively. The results are presented in columns (1) to (21) of Tables 9 and 10, and 11. From these tables, we observe that the results are similar to the initial results. Including additional controls cumulatively does not alter the significance of the effect of past informality.

Second, we use the Propensity Score Matching method (PSM) as an alternative matching method. PSM is an impact evaluation method introduced by Rosenbaum and Rubin (1983) that corrects for endogeneity issues, especially those stemming from selection bias. We employ four variants of PSM,<fnref n="16" /> namely Nearest-Neighbor Matching, Radius Matching, Kernel Matching, and Local Linear Regression Matching.<fnref n="17" /> Table 12 presents the results of the estimations of the effect of past informality (ATT). Irrespective of the matching method, the ATT is significant (at the 1% level) and positive, indicating that the effect of past informality is independent of the matching method used. This demonstrates the consistency of our findings. Table 12 reports Rubin's B and R statistics to assess the balancing property. For the covariates to be sufficiently balanced, the B statistic should be lower than 25%, and the R statistics should lie in the [0.5; 2] interval (Rubin, 2001). As can be seen, this is the case in all estimations, meaning that the covariates are balanced.

Third, we use the Instrumental Variable (IV) method to estimate the effect of past informality status on credit constraints. This method corrects for endogeneity issues, especially for possible reverse causality problems. Indeed, firms that initially operated informally often did so due to a lack of access to the credit necessary for entering the formal sector. As these firms grew, they transitioned to the formal sector while facing resource limitations, which hindered their performance and resulted in financial instability. This led to significant credit constraints, which persist today, further increasing the level of credit constraints that a typical registered firm faces. This suggests that a registered firm facing significant credit constraints today may be more likely to have begun operations informally. Additionally, a key factor that lenders consider when providing loans is a firm's credit history, i.e., a registered firm facing significant credit constraints today may be more likely to lack a solid credit history. However, firms that began informally typically have shorter credit histories compared to those that were formally registered from the start. Therefore, it is possible that a registered firm facing substantial credit constraints today has a greater chance of having originated from the informal sector.

Our IV approach utilizes the widely used “cell-average method” (see Amin and Soh, 2021; Brancati et al., 2024; Distinguin et al., 2016; Fisman and Svensson, 2007, among others). In this method, the endogenous variable for firm *i* is instrumented using the average value of that variable across all other firms within the same cell, excluding firm *i*. This technique is commonly applied at the country or sector level (see Amin and Soh, 2021; Brancati et al., 2024; Distinguin et al., 2016; Fisman and Svensson, 2007, among others). Specifically, we instrument the past informality variable for firm *i* using a country-level variable: the percentage of registered firms having past informality status in the country, excluding the observation for firm *i*. By excluding firm *i* from the calculation, we ensure that the instrument is correlated with past informality, while remaining independent of the specific past informality of firm *i*.

The percentage of registered firms with past informality status in a country reflects the broader dynamics of informality in the market. This metric is relevant because when a larger share of registered firms began operations informally, it normalizes the practice of starting a business without formal registration. As a result, more firms are likely to begin their operations informally before later transitioning to formal status. More precisely, in countries where informality is prevalent, the stigma associated with informal operations tends to be lower, motivating more firms to adopt similar practices. This creates a reinforcing cycle, as the behavior of existing firms influences newcomers to start informally and eventually formalize their businesses. This potential influence on the probability of having past informality status underscores the relevance of the IV.

Furthermore, this country-level metric can be considered exogenous to the financial conditions of individual registered firms, as it is influenced by broader economic, regulatory, and social factors, rather than the specific financial situation of any single firm. The exclusion restriction is likely valid, as the country-level percentage of firms with a past informality status does not directly impact the credit constraints faced by registered firms. Instead, it indirectly affects access to credit by increasing the likelihood that registered firms initially operate informally. It captures the overall informality in the market without directly influencing access to credit. Consequently, by fulfilling these criteria, our instrument effectively isolates the causal effect of past informality status on registered firms' credit constraints.

We estimate the effect of past informality status on registered firms' probability of being credit-constrained using the Two-Stage Least Squares (2SLS) method. The estimation results are presented in Table 13.

From Table 13, we observe that the effect of past informality is significant (at the 1% level) and positive, confirming our initial findings. The effect is close to the average effect obtained using the entropy balancing method, further reinforcing the robustness of our results.

<footnote n="16">¹⁶ These variants were also recently used by Apeti (2023) and Apeti and Edoh (2023) to test the robustness of the results obtained by entropy balancing.</footnote>

<footnote n="17">¹⁷ For theoretical details on PSM, see Abadie and Imbens (2016) and Dehejia and Wahba (2002). Caliendo and Kopeinig (2008) provide practical guidance on the implementation of PSM.</footnote>
<table><caption>Table 9
Robustness checks: Linear probability regression.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th><th>(13)</th><th>(14)</th><th>(15)</th><th>(16)</th><th>(17)</th><th>(18)</th><th>(19)</th><th>(20)</th><th>(21)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.063***<br>(0.004)</td><td>0.063***<br>(0.005)</td><td>0.063***<br>(0.005)</td><td>0.057***<br>(0.005)</td><td>0.057***<br>(0.005)</td><td>0.056***<br>(0.005)</td><td>0.048***<br>(0.006)</td><td>0.056***<br>(0.006)</td><td>0.055***<br>(0.007)</td><td>0.049***<br>(0.007)</td><td>0.049***<br>(0.007)</td><td>0.050***<br>(0.007)</td><td>0.050***<br>(0.007)</td><td>0.045***<br>(0.008)</td><td>0.045***<br>(0.010)</td><td>0.059***<br>(0.011)</td><td>0.059***<br>(0.011)</td><td>0.048***<br>(0.012)</td><td>0.048***<br>(0.012)</td></tr><tr><td>Firm size</td><td>-0.044***<br>(0.001)</td><td>-0.044***<br>(0.001)</td><td>-0.042***<br>(0.002)</td><td>-0.018***<br>(0.002)</td><td>-0.017***<br>(0.002)</td><td>-0.018***<br>(0.002)</td><td>-0.016***<br>(0.002)</td><td>-0.018***<br>(0.002)</td><td>-0.023***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.024***<br>(0.003)</td><td>-0.020***<br>(0.003)</td><td>-0.016***<br>(0.003)</td><td>-0.016***<br>(0.003)</td><td>-0.016***<br>(0.003)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td></tr><tr><td>Firm age</td><td>-0.013***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.014***<br>(0.002)</td><td>-0.014***<br>(0.002)</td><td>-0.014***<br>(0.002)</td><td>-0.013***<br>(0.003)</td><td>-0.015***<br>(0.003)</td><td>-0.012***<br>(0.004)</td><td>-0.013***<br>(0.004)</td><td>-0.013***<br>(0.004)</td><td>-0.013***<br>(0.004)</td><td>-0.013***<br>(0.004)</td><td>-0.014***<br>(0.004)</td><td>-0.018***<br>(0.004)</td><td>-0.018***<br>(0.005)</td><td>-0.015***<br>(0.005)</td><td>-0.015***<br>(0.005)</td><td>-0.014***<br>(0.005)</td></tr><tr><td>Affiliation with<br>a large firm</td><td>-0.024***<br>(0.004)</td><td>-0.024***<br>(0.004)</td><td>-0.023***<br>(0.004)</td><td>-0.015***<br>(0.004)</td><td>-0.015***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.015***<br>(0.005)</td><td>-0.023***<br>(0.005)</td><td>-0.023***<br>(0.006)</td><td>-0.023***<br>(0.006)</td><td>-0.023***<br>(0.006)</td><td>-0.023***<br>(0.006)</td><td>-0.021***<br>(0.007)</td><td>-0.021***<br>(0.007)</td><td>-0.021***<br>(0.007)</td><td>-0.011<br>(0.008)</td><td>-0.011<br>(0.008)</td><td>-0.016*<br>(0.009)</td><td>-0.016*<br>(0.009)</td></tr><tr><td>Private foreign<br>ownership</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.005***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.004***<br>(0.001)</td><td>-0.001<br>(0.002)</td><td>-0.001<br>(0.002)</td><td>-0.001<br>(0.002)</td><td>-0.001<br>(0.002)</td></tr><tr><td>Real GDP<br>per capita</td><td>0.190<br>(0.281)</td><td>0.146<br>(0.283)</td><td>0.124<br>(0.284)</td><td>0.023<br>(0.283)</td><td>-0.046<br>(0.283)</td><td>-0.056<br>(0.284)</td><td>0.226<br>(0.337)</td><td>-0.669***<br>(0.179)</td><td>-1.014<br>(0.203)</td><td>1.958***<br>(0.674)</td><td>-0.321*<br>(0.181)</td><td>0.377***<br>(0.105)</td><td>0.022<br>(0.047)</td><td>0.047<br>(0.043)</td><td>0.061<br>(525.830)</td><td>1.953***<br>(0.360)</td><td>5.952***<br>(1.009)</td><td>0.582***<br>(0.102)</td><td>0.721***<br>(0.223)</td><td>1.104***<br>(0.284)</td></tr><tr><td>Inflation</td><td>-0.031***<br>(0.008)</td><td>-0.030***<br>(0.009)</td><td>-0.030***<br>(0.009)</td><td>-0.026***<br>(0.009)</td><td>-0.027***<br>(0.009)</td><td>-0.027***<br>(0.021)</td><td>-0.027***<br>(0.021)</td><td>0.033<br>(0.104)</td><td>0.120<br>(0.106)</td><td>-0.011<br>(0.008)</td><td>0.032***<br>(0.005)</td><td>0.031***<br>(0.005)</td><td>0.014***<br>(0.005)</td><td>0.040***<br>(0.006)</td><td>0.042<br>(42.144)</td><td>-0.182***<br>(0.037)</td><td>-0.424***<br>(0.079)</td><td>0.112***<br>(0.030)</td><td>0.074**<br>(0.119)</td><td>-0.107<br>(0.007)</td></tr><tr><td>Domestic credit to<br>the private sector</td><td>0.188***<br>(0.027)</td><td>0.192***<br>(0.027)</td><td>0.183***<br>(0.027)</td><td>0.078*<br>(0.040)</td><td>0.035<br>(0.040)</td><td>0.035<br>(0.040)</td><td>-0.012<br>(0.044)</td><td>0.540<br>(0.385)</td><td>-0.108<br>(0.102)</td><td>-1.384***<br>(0.362)</td><td>0.137***<br>(0.028)</td><td>-0.041*<br>(0.023)</td><td>-0.222***<br>(0.058)</td><td>-0.005<br>(0.021)</td><td>0.092<br>(3191.099)</td><td>1.270***<br>(0.219)</td><td>-7.762***<br>(1.343)</td><td>-0.120***<br>(0.036)</td><td>0.310<br>(0.441)</td><td>-0.024<br>(0.035)</td></tr><tr><td>Manager's<br>experience</td><td>0.005*<br>(0.002)</td><td>0.005*<br>(0.002)</td><td>0.007**<br>(0.002)</td><td>0.006**<br>(0.002)</td><td>0.003<br>(0.003)</td><td>0.006**<br>(0.003)</td><td>0.003<br>(0.003)</td><td>0.003<br>(0.003)</td><td>0.003<br>(0.003)</td><td>0.007**<br>(0.004)</td><td>0.007**<br>(0.004)</td><td>0.007*<br>(0.004)</td><td>0.007*<br>(0.004)</td><td>0.008**<br>(0.004)</td><td>0.008**<br>(0.004)</td><td>0.011***<br>(0.004)</td><td>-0.011***<br>(0.004)</td><td>-0.011***<br>(0.004)</td><td>0.009<br>(0.005)</td><td>-0.017**<br>(0.005)</td></tr><tr><td>Manager's gender</td><td>-0.001<br>(0.004)</td><td>-0.004<br>(0.004)</td><td>-0.004<br>(0.004)</td><td>-0.004<br>(0.004)</td><td>-0.002<br>(0.004)</td><td>-0.004<br>(0.004)</td><td>-0.005<br>(0.005)</td><td>-0.005<br>(0.005)</td><td>-0.007<br>(0.007)</td><td>-0.005<br>(0.005)</td><td>-0.005<br>(0.005)</td><td>-0.005<br>(0.005)</td><td>-0.005<br>(0.006)</td><td>-0.007<br>(0.006)</td><td>-0.005<br>(0.006)</td><td>-0.007<br>(0.007)</td><td>-0.007<br>(0.007)</td><td>-0.006<br>(0.007)</td><td>-0.006<br>(0.007)</td><td>-0.010<br>(0.008)</td></tr><tr><td>Total sales</td><td>-0.021***<br>(0.001)</td><td>-0.021***<br>(0.001)</td><td>-0.021***<br>(0.001)</td><td>-0.021***<br>(0.001)</td><td>-0.022***<br>(0.001)</td><td>-0.020***<br>(0.001)</td><td>-0.019***<br>(0.002)</td><td>-0.019***<br>(0.002)</td><td>-0.019***<br>(0.002)</td><td>-0.019***<br>(0.002)</td><td>-0.019***<br>(0.002)</td><td>-0.019***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.022***<br>(0.002)</td><td>-0.022***<br>(0.002)</td><td>-0.022***<br>(0.002)</td><td>-0.022***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.024***<br>(0.002)</td></tr><tr><td>Firm's export<br>capacity</td><td>-0.006***<br>(0.001)</td><td>-0.005***<br>(0.001)</td><td>-0.006***<br>(0.001)</td><td>-0.008***<br>(0.001)</td><td>-0.006***<br>(0.001)</td><td>-0.006***<br>(0.001)</td><td>-0.007***<br>(0.002)</td><td>-0.007***<br>(0.002)</td><td>-0.007***<br>(0.002)</td><td>-0.007***<br>(0.002)</td><td>-0.007***<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.004***<br>(0.002)</td></tr><tr><td>Criminality</td><td>0.017**<br>(0.004)</td><td>0.017**<br>(0.004)</td><td>0.017**<br>(0.005)</td><td>0.018***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td><td>0.020***<br>(0.005)</td></tr><tr><td>Taxation</td><td>0.488**<br>(0.224)</td><td>0.801<br>(0.557)</td><td>0.573***<br>(0.138)</td><td>0.760***<br>(0.193)</td><td>0.211**<br>(0.087)</td><td>-0.457***<br>(0.065)</td><td>-0.434***<br>(0.060)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.007)</td><td>0.080<br>(0.007)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td><td>0.080<br>(0.006)</td></tr><tr><td>Real lending<br>interest rate</td><td>0.080<br>(0.031)</td><td>-0.026<br>(0.044)</td><td>-0.113**<br>(0.009)</td><td>0.464***<br>(0.009)</td><td>0.006<br>(0.007)</td><td>0.057***<br>(0.014)</td><td>0.057***<br>(0.012)</td><td>0.032<br>(545.701)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td><td>0.032<br>(0.012)</td></tr><tr><td>Interest rate<br>spread</td><td>-0.056**<br>(0.024)</td><td>-0.285***<br>(0.073)</td><td>-0.285***<br>(0.020)</td><td>0.012***<br>(0.016)</td><td>0.128***<br>(0.016)</td><td>-0.240**<br>(0.019)</td><td>-0.240**<br>(0.016)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td><td>0.112***<br>(0.070)</td></tr><tr><td>Bank NPLs</td><td>0.017**<br>(0.104)</td><td>-0.395**<br>(0.032)</td><td>-0.395**<br>(0.032)</td><td>0.128***<br>(0.097)</td><td>0.032<br>(0.097)</td><td>-0.240**<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(4121.011)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td><td>0.008<br>(0.027)</td></tr><tr><td>Real GDP growth</td><td>0.048***<br>(0.012)</td><td>0.045***<br>(0.012)</td><td>-0.004<br>(0.009)</td><td>0.045***<br>(0.009)</td><td>0.004<br>(0.009)</td><td>-0.004<br>(0.009)</td><td>0.016***<br>(0.006)</td><td>0.016<br>(3.061)</td><td>0.110***<br>(0.014)</td><td>0.158***<br>(0.037)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td><td>0.080***<br>(0.026)</td></tr><tr><td>Unemployment</td><td>-0.134***<br>(0.025)</td><td>-0.033<br>(0.044)</td><td>-0.135***<br>(0.026)</td><td>-0.315<br>(5789.235)</td><td>-0.315***<br>(0.026)</td><td>-0.060***<br>(0.026)</td><td>-0.046<br>(0.254)</td><td>0.391***<br>(0.043)</td><td>0.205***<br>(0.124)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td></tr><tr><td>FDI</td><td>0.045***<br>(0.015)</td><td>0.013**<br>(0.006)</td><td>-0.004<br>(0.006)</td><td>-0.345***<br>(572.373)</td><td>-0.345***<br>(0.056)</td><td>-0.004<br>(0.006)</td><td>-0.046<br>(0.254)</td><td>0.391***<br>(0.043)</td><td>0.205***<br>(0.124)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td><td>-0.041***<br>(0.017)</td></tr><tr><td>Gross fixed<br>capital formation</td><td>-0.398***<br>(0.107)</td><td>-0.398***<br>(0.114)</td><td>-0.398***<br>(0.114)</td><td>-0.398***<br>(0.114)</td><td>-0.398***<br>(0.114)</td><td>-0.398***<br>(0.114)</td><td>-0.398***<br>(0.114)</td><td>0.314***<br>(0.043)</td><td>0.323***<br>(0.061)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td><td>-0.304***<br>(0.025)</td></tr><tr><td>Banking<br>accessibility</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td><td>0.845.338<br>(0.107)</td></tr><tr><td>New business<br>density</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td><td>0.7201***<br>(1.244)</td></tr><tr><td>Remittances</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td><td>-0.097***<br>(0.016)</td></tr><tr><td>Public spending</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td><td>-0.429***<br>(0.106)</td></tr><tr><td>Urbanization</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td><td>-1.448<br>(1.446)</td></tr><tr><td>Debt</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td><td>-1.049*<br>(0.611)</td></tr><tr><td>Corruption</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td><td>-1.1049*<br>(0.611)</td></tr><tr><td>Main controls<br>Country-sem<br>fixed effects</td><td>Yes<br>(134,050)</td><td>Yes<br>(131,533)</td><td>Yes<br>(115,775)</td><td>Yes<br>(102,662)</td><td>Yes<br>(102,198)</td><td>Yes<br>(101,893)</td><td>Yes<br>(87,818)</td><td>Yes<br>(68,870)</td><td>Yes<br>(58,856)</td><td>Yes<br>(44,835)</td><td>Yes<br>(44,835)</td><td>Yes<br>(44,660)</td><td>Yes<br>(44,660)</td><td>Yes<br>(41,859)</td><td>Yes<br>(41,308)</td><td>Yes<br>(31,162)</td><td>Yes<br>(31,162)</td><td>Yes<br>(27,229)</td><td>Yes<br>(27,229)</td><td>Yes<br>(22,563)</td><td>Yes<br>(22,563)</td></tr><tr><td>Observations</td><td>Yes<br>(134,050)</td><td>Yes<br>(131,533)</td><td>Yes<br>(115,775)</td><td>Yes<br>(102,662)</td><td>Yes<br>(102,198)</td><td>Yes<br>(101,893)</td><td>Yes<br>(87,818)</td><td>Yes<br>(68,870)</td><td>Yes<br>(58,856)</td><td>Yes<br>(44,835)</td><td>Yes<br>(44,835)</td><td>Yes<br>(44,660)</td><td>Yes<br>(44,660)</td><td>Yes<br>(41,859)</td><td>Yes<br>(41,308)</td><td>Yes<br>(31,162)</td><td>Yes<br>(31,162)</td><td>Yes<br>(27,229)</td><td>Yes<br>(27,229)</td><td>Yes<br>(22,563)</td><td>Yes<br>(22,563)</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.129</td><td>0.129</td><td>0.122</td><td>0.131</td><td>0.131</td><td>0.131</td><td>0.136</td><td>0.125</td><td>0.130</td><td>0.121</td><td>0.121</td><td>0.121</td><td>0.121</td><td>0.118</td><td>0.121</td><td>0.121</td><td>0.126</td><td>0.126</td><td>0.116</td><td>0.116</td><td>0.116</td></tr></tbody></table>

Constant terms are included but not reported for brevity. NPLs: nonperforming loans. Robust standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
<caption>Table 10</caption>

Robustness checks: Logit regression, average marginal effects reported.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th><th>(13)</th><th>(14)</th><th>(15)</th><th>(16)</th><th>(17)</th><th>(18)</th><th>(19)</th><th>(20)</th><th>(21)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.056***<br>(0.004)</td><td>0.056***<br>(0.004)</td><td>0.055***<br>(0.004)</td><td>0.050***<br>(0.005)</td><td>0.050***<br>(0.005)</td><td>0.049***<br>(0.005)</td><td>0.042***<br>(0.005)</td><td>0.051***<br>(0.006)</td><td>0.048***<br>(0.006)</td><td>0.040***<br>(0.007)</td><td>0.040***<br>(0.007)</td><td>0.041***<br>(0.007)</td><td>0.041***<br>(0.007)</td><td>0.037***<br>(0.007)</td><td>0.032***<br>(0.009)</td><td>0.050***<br>(0.009)</td><td>0.050***<br>(0.010)</td><td>0.050***<br>(0.010)</td><td>0.041***<br>(0.011)</td><td>0.041***<br>(0.011)</td></tr><tr><td>Firm size</td><td>-0.047***<br>(0.001)</td><td>-0.047***<br>(0.001)</td><td>-0.045***<br>(0.001)</td><td>-0.026***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.023***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.030***<br>(0.002)</td><td>-0.030***<br>(0.002)</td><td>-0.030***<br>(0.002)</td><td>-0.029***<br>(0.002)</td><td>-0.030***<br>(0.002)</td><td>-0.027***<br>(0.003)</td><td>-0.023***<br>(0.003)</td><td>-0.020***<br>(0.003)</td><td>-0.019***<br>(0.003)</td><td>-0.019***<br>(0.003)</td><td>-0.019***<br>(0.004)</td><td>-0.019***<br>(0.004)</td></tr><tr><td>Firm age</td><td>-0.012***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.014***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.014***<br>(0.003)</td><td>-0.016***<br>(0.003)</td><td>-0.012***<br>(0.003)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.018***<br>(0.004)</td><td>-0.015***<br>(0.005)</td><td>-0.015***<br>(0.005)</td><td>-0.014***<br>(0.005)</td><td>-0.014***<br>(0.005)</td></tr><tr><td>Sector of activity</td><td>0.044***<br>(0.003)</td><td>0.044***<br>(0.003)</td><td>0.042***<br>(0.003)</td><td>0.035***<br>(0.003)</td><td>0.038***<br>(0.003)</td><td>0.039***<br>(0.003)</td><td>0.038***<br>(0.003)</td><td>0.040***<br>(0.004)</td><td>0.041***<br>(0.004)</td><td>0.041***<br>(0.005)</td><td>0.041***<br>(0.005)</td><td>0.041***<br>(0.005)</td><td>0.039***<br>(0.005)</td><td>0.037***<br>(0.005)</td><td>0.038***<br>(0.006)</td><td>0.037***<br>(0.006)</td><td>0.040***<br>(0.006)</td><td>0.040***<br>(0.006)</td><td>0.040***<br>(0.007)</td></tr><tr><td>Affiliation with a large firm</td><td>-0.030***<br>(0.004)</td><td>-0.030***<br>(0.004)</td><td>-0.029***<br>(0.004)</td><td>-0.024***<br>(0.004)</td><td>-0.024***<br>(0.004)</td><td>-0.023***<br>(0.004)</td><td>-0.023***<br>(0.005)</td><td>-0.010***<br>(0.006)</td><td>-0.028***<br>(0.007)</td><td>-0.026***<br>(0.007)</td><td>-0.026***<br>(0.007)</td><td>-0.025***<br>(0.007)</td><td>-0.025***<br>(0.007)</td><td>-0.025***<br>(0.008)</td><td>-0.024***<br>(0.008)</td><td>-0.015***<br>(0.009)</td><td>-0.015***<br>(0.009)</td><td>-0.020***<br>(0.010)</td><td>-0.020***<br>(0.010)</td></tr><tr><td>Private foreign ownership</td><td>-0.008***<br>(0.001)</td><td>-0.008***<br>(0.001)</td><td>-0.009***<br>(0.001)</td><td>-0.006***<br>(0.001)</td><td>-0.005***<br>(0.001)</td><td>-0.005***<br>(0.001)</td><td>-0.006***<br>(0.002)</td><td>-0.005***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.002***<br>(0.002)</td><td>-0.002***<br>(0.002)</td><td>-0.002***<br>(0.002)</td><td>-0.001***<br>(0.002)</td><td>-0.000***<br>(0.003)</td><td>-0.000***<br>(0.003)</td></tr><tr><td>Real GDP per capita</td><td>-0.088***<br>(0.008)</td><td>-0.106***<br>(0.008)</td><td>-0.068**<br>(0.008)</td><td>-0.028**<br>(0.008)</td><td>-0.028**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td><td>-0.035**<br>(0.008)</td></tr><tr><td>Inflation</td><td>0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>0.002**<br>(0.001)</td><td>0.002**<br>(0.001)</td><td>0.002**<br>(0.001)</td><td>0.002**<br>(0.001)</td><td>0.005***<br>(0.001)</td><td>0.006***<br>(0.002)</td><td>0.006***<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td><td>0.002**<br>(0.002)</td></tr><tr><td>Domestic credit to the private sector</td><td>0.000<br>(0.008)</td><td>-0.001<br>(0.008)</td><td>0.012<br>(0.010)</td><td>0.011<br>(0.010)</td><td>0.012<br>(0.010)</td><td>-0.005<br>(0.011)</td><td>-0.034**<br>(0.013)</td><td>-0.040***<br>(0.013)</td><td>-0.052***<br>(0.015)</td><td>-0.057***<br>(0.016)</td><td>-0.059***<br>(0.016)</td><td>-0.066***<br>(0.017)</td><td>-0.061***<br>(0.017)</td><td>0.025<br>(0.020)</td><td>-0.026<br>(0.051)</td><td>-0.218***<br>(0.060)</td><td>-0.089<br>(0.074)</td><td>-0.097<br>(0.077)</td><td>0.489<br>(0.301)</td><td>0.284**<br>(0.142)</td></tr><tr><td>Manager's experience</td><td></td><td>0.006***<br>(0.002)</td><td>0.006***<br>(0.002)</td><td>0.007***<br>(0.002)</td><td>0.008***<br>(0.002)</td><td>0.007***<br>(0.002)</td><td>0.004<br>(0.003)</td><td>0.004<br>(0.003)</td><td>0.010***<br>(0.004)</td><td>0.010***<br>(0.004)</td><td>0.010***<br>(0.004)</td><td>0.010***<br>(0.004)</td><td>0.009**<br>(0.004)</td><td>0.010***<br>(0.004)</td><td>0.013***<br>(0.004)</td><td>0.011**<br>(0.004)</td><td>0.011**<br>(0.005)</td><td>0.011**<br>(0.005)</td><td>0.018***<br>(0.005)</td><td>0.018***<br>(0.005)</td></tr><tr><td>Manager's gender</td><td></td><td></td><td>0.000<br>(0.004)</td><td>-0.002<br>(0.004)</td><td>-0.002<br>(0.004)</td><td>0.000<br>(0.004)</td><td>0.000<br>(0.004)</td><td>0.000<br>(0.005)</td><td>0.000<br>(0.005)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.006)</td><td>0.000<br>(0.007)</td><td>0.000<br>(0.007)</td><td>0.000<br>(0.008)</td><td>0.000<br>(0.008)</td></tr><tr><td>Total sales</td><td></td><td></td><td>-0.016***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.017***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.015***<br>(0.001)</td><td>-0.015***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.015***<br>(0.001)</td><td>-0.017***<br>(0.001)</td><td>-0.020***<br>(0.002)</td><td>-0.020***<br>(0.002)</td><td>-0.020***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.023***<br>(0.002)</td><td>-0.023***<br>(0.002)</td></tr><tr><td>Firm's export capacity</td><td></td><td></td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.009***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.008***<br>(0.002)</td><td>-0.008***<br>(0.002)</td><td>-0.007***<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.005***<br>(0.002)</td><td>-0.005***<br>(0.002)</td><td>-0.005***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.004***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td></tr><tr><td>Criminality</td><td></td><td></td><td>0.018***<br>(0.004)</td><td>0.018***<br>(0.004)</td><td>0.018***<br>(0.005)</td><td>0.021***<br>(0.005)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td></tr><tr><td>Taxation</td><td></td><td></td><td>-0.001<br>(0.012)</td><td>-0.031**<br>(0.014)</td><td>-0.032**<br>(0.014)</td><td>-0.196***<br>(0.025)</td><td>-0.201***<br>(0.026)</td><td>-0.190***<br>(0.026)</td><td>-0.197***<br>(0.027)</td><td>-0.204***<br>(0.029)</td><td>-0.176***<br>(0.030)</td><td>-0.173***<br>(0.063)</td><td>0.017<br>(0.072)</td><td>0.018<br>(0.072)</td><td>0.036***<br>(0.002)</td><td>-0.003<br>(0.013)</td><td>-0.006<br>(0.015)</td><td>-0.060**<br>(0.026)</td><td>-0.074***<br>(0.021)</td><td>-0.074***<br>(0.021)</td></tr><tr><td>Real lending interest rate</td><td></td><td></td><td>0.006***<br>(0.001)</td><td>0.008***<br>(0.001)</td><td>0.011***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.011***<br>(0.002)</td><td>-0.005<br>(0.003)</td><td>0.001<br>(0.006)</td><td>0.036***<br>(0.009)</td><td>-0.003<br>(0.009)</td><td>-0.003<br>(0.009)</td><td>-0.003<br>(0.009)</td><td>-0.003<br>(0.009)</td><td>-0.003<br>(0.009)</td><td>-0.003<br>(0.009)</td></tr><tr><td>Interest rate spread</td><td></td><td></td><td></td><td>-0.003<br>(0.002)</td><td>-0.007***<br>(0.003)</td><td>-0.007***<br>(0.003)</td><td>-0.006***<br>(0.003)</td><td>-0.006***<br>(0.003)</td><td>-0.007**<br>(0.003)</td><td>0.009**<br>(0.004)</td><td>0.005<br>(0.006)</td><td>-0.021***<br>(0.004)</td><td>-0.021***<br>(0.004)</td><td>0.021***<br>(0.004)</td><td>0.021***<br>(0.004)</td><td>0.021***<br>(0.004)</td><td>0.021***<br>(0.004)</td><td>0.021***<br>(0.004)</td><td>0.021***<br>(0.004)</td><td>0.021***<br>(0.004)</td></tr><tr><td>Bank NPLs</td><td></td><td></td><td></td><td></td><td>0.036***<br>(0.009)</td><td>0.033***<br>(0.010)</td><td>0.033***<br>(0.011)</td><td>0.023**<br>(0.012)</td><td>0.017<br>(0.012)</td><td>0.018<br>(0.012)</td><td>0.004<br>(0.012)</td><td>0.037<br>(0.037)</td><td>0.037<br>(0.037)</td><td>0.037<br>(0.037)</td><td>0.038<br>(0.052)</td><td>0.038<br>(0.052)</td><td>0.038<br>(0.052)</td><td>0.038<br>(0.052)</td><td>0.038<br>(0.052)</td><td>0.038<br>(0.052)</td></tr><tr><td>Real GDP growth</td><td></td><td></td><td></td><td></td><td></td><td>0.002<br>(0.002)</td><td>-0.001<br>(0.002)</td><td>-0.001<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.009<br>(0.007)</td><td>0.009<br>(0.007)</td><td>0.025**<br>(0.011)</td><td>0.025**<br>(0.011)</td><td>0.027**<br>(0.013)</td><td>0.027**<br>(0.013)</td><td>0.027**<br>(0.013)</td><td>0.027**<br>(0.013)</td><td>0.027**<br>(0.013)</td></tr><tr><td>Unemployment</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.037**<br>(0.019)</td><td>0.036**<br>(0.019)</td><td>0.038**<br>(0.019)</td><td>0.029<br>(0.019)</td><td>0.083<br>(0.057)</td><td>0.145**<br>(0.058)</td><td>0.083<br>(0.078)</td><td>0.124<br>(0.164)</td><td>0.172<br>(0.258)</td><td>0.172<br>(0.258)</td><td>0.172<br>(0.258)</td><td>0.172<br>(0.258)</td><td>0.172<br>(0.258)</td><td>0.172<br>(0.258)</td></tr><tr><td>FDI</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.001<br>(0.001)</td><td>-0.001<br>(0.001)</td><td>-0.000<br>(0.001)</td><td>0.001**<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.003***<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Gross fixed capital formation</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.037<br>(0.039)</td><td>-0.061<br>(0.126)</td><td>-0.064<br>(0.039)</td><td>-0.123<br>(0.128)</td><td>-0.123<br>(0.128)</td><td>-0.085<br>(0.128)</td><td>-0.463<br>(0.265)</td><td>-0.484<br>(0.273)</td><td>-0.359<br>(0.356)</td><td>-0.359<br>(0.356)</td><td>-0.359<br>(0.356)</td><td>-0.359<br>(0.356)</td><td>-0.359<br>(0.356)</td><td>-0.359<br>(0.356)</td></tr><tr><td>Banking accessibility</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>New business density</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Remittances</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Public spending</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urbanization</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Debt</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Corruption</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Main controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>134,050</td><td>131,533</td><td>115,775</td><td>102,662</td><td>102,198</td><td>101,893</td><td>87,818</td><td>68,870</td><td>58,856</td><td>44,835</td><td>44,835</td><td>44,660</td><td>44,660</td><td>41,859</td><td>41,308</td><td>31,162</td><td>31,162</td><td>27,229</td><td>27,229</td><td>22,563</td></tr></tbody></table>

Constant terms are included but not reported for brevity. NPIs: nonperforming loans. Regarding fixed effects, only country and year fixed effects are included, as the Logit model does not handle specifications with large sets of fixed effects well. Robust standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
<caption>Table 11</caption>

Robustness checks: Probit regression, average marginal effects reported.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th><th>(13)</th><th>(14)</th><th>(15)</th><th>(16)</th><th>(17)</th><th>(18)</th><th>(19)</th><th>(20)</th><th>(21)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.058***<br>(0.004)</td><td>0.057***<br>(0.004)</td><td>0.056***<br>(0.004)</td><td>0.051***<br>(0.005)</td><td>0.051***<br>(0.005)</td><td>0.050***<br>(0.005)</td><td>0.044***<br>(0.005)</td><td>0.051***<br>(0.006)</td><td>0.048***<br>(0.006)</td><td>0.041***<br>(0.007)</td><td>0.041***<br>(0.007)</td><td>0.041***<br>(0.007)</td><td>0.041***<br>(0.007)</td><td>0.032***<br>(0.007)</td><td>0.050***<br>(0.009)</td><td>0.050***<br>(0.009)</td><td>0.051***<br>(0.010)</td><td>0.051***<br>(0.010)</td><td>0.040***<br>(0.011)</td><td>0.040***<br>(0.011)</td></tr><tr><td>Firm size</td><td>-0.046***<br>(0.001)</td><td>-0.046***<br>(0.001)</td><td>-0.044***<br>(0.001)</td><td>-0.025***<br>(0.002)</td><td>-0.023***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.023***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.029***<br>(0.002)</td><td>-0.029***<br>(0.002)</td><td>-0.029***<br>(0.002)</td><td>-0.029***<br>(0.002)</td><td>-0.026***<br>(0.002)</td><td>-0.023***<br>(0.003)</td><td>-0.020***<br>(0.003)</td><td>-0.018***<br>(0.003)</td><td>-0.018***<br>(0.003)</td><td>-0.019***<br>(0.004)</td><td>-0.019***<br>(0.004)</td><td>-0.018***<br>(0.004)</td></tr><tr><td>Firm age</td><td>-0.013***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.015***<br>(0.002)</td><td>-0.014***<br>(0.003)</td><td>-0.016***<br>(0.003)</td><td>-0.013***<br>(0.003)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.016***<br>(0.004)</td><td>-0.018***<br>(0.004)</td><td>-0.015***<br>(0.005)</td><td>-0.015***<br>(0.005)</td><td>-0.014***<br>(0.005)</td><td>-0.014***<br>(0.005)</td><td>-0.014***<br>(0.005)</td></tr><tr><td>Sector of activity</td><td>0.043***<br>(0.003)</td><td>0.044***<br>(0.003)</td><td>0.042***<br>(0.003)</td><td>0.035***<br>(0.003)</td><td>0.038***<br>(0.003)</td><td>0.039***<br>(0.003)</td><td>0.038***<br>(0.003)</td><td>0.040***<br>(0.004)</td><td>0.041***<br>(0.004)</td><td>0.041***<br>(0.005)</td><td>0.041***<br>(0.005)</td><td>0.041***<br>(0.005)</td><td>0.041***<br>(0.005)</td><td>0.039***<br>(0.005)</td><td>0.037***<br>(0.005)</td><td>0.038***<br>(0.006)</td><td>0.040***<br>(0.006)</td><td>0.040***<br>(0.006)</td><td>0.041***<br>(0.007)</td><td>0.041***<br>(0.007)</td></tr><tr><td>Affiliation with a large firm</td><td>-0.030***<br>(0.004)</td><td>-0.029***<br>(0.004)</td><td>-0.028***<br>(0.004)</td><td>-0.024***<br>(0.004)</td><td>-0.023***<br>(0.004)</td><td>-0.025***<br>(0.004)</td><td>-0.023***<br>(0.004)</td><td>-0.010***<br>(0.005)</td><td>-0.027***<br>(0.006)</td><td>-0.024***<br>(0.006)</td><td>-0.024***<br>(0.006)</td><td>-0.023***<br>(0.006)</td><td>-0.023***<br>(0.007)</td><td>-0.022***<br>(0.007)</td><td>-0.023***<br>(0.008)</td><td>-0.022***<br>(0.008)</td><td>-0.013***<br>(0.009)</td><td>-0.013***<br>(0.009)</td><td>-0.018***<br>(0.010)</td><td>-0.018***<br>(0.010)</td></tr><tr><td>Private foreign ownership</td><td>-0.008***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.008***<br>(0.001)</td><td>-0.006***<br>(0.001)</td><td>-0.005***<br>(0.001)</td><td>-0.005***<br>(0.001)</td><td>-0.005***<br>(0.001)</td><td>-0.005***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.003***<br>(0.002)</td><td>-0.002***<br>(0.002)</td><td>-0.002***<br>(0.002)</td><td>-0.001***<br>(0.002)</td><td>-0.000***<br>(0.002)</td><td>-0.000***<br>(0.002)</td><td>-0.000***<br>(0.002)</td></tr><tr><td>Real GDP per capita</td><td>-0.099***<br>(0.028)</td><td>-0.118***<br>(0.029)</td><td>-0.079**<br>(0.032)</td><td>0.002**<br>(0.035)</td><td>0.002**<br>(0.035)</td><td>0.002**<br>(0.035)</td><td>0.002**<br>(0.036)</td><td>0.002**<br>(0.047)</td><td>0.005***<br>(0.048)</td><td>0.006***<br>(0.078)</td><td>0.007***<br>(0.078)</td><td>0.007***<br>(0.079)</td><td>0.007***<br>(0.094)</td><td>0.008***<br>(0.094)</td><td>0.008***<br>(0.094)</td><td>0.008***<br>(0.094)</td><td>0.008***<br>(0.094)</td><td>0.008***<br>(0.094)</td><td>0.008***<br>(0.094)</td><td>0.008***<br>(0.094)</td></tr><tr><td>Inflation</td><td>0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>0.002**<br>(0.001)</td><td>0.002**<br>(0.001)</td><td>0.002**<br>(0.001)</td><td>0.005***<br>(0.001)</td><td>0.005***<br>(0.002)</td><td>0.006***<br>(0.002)</td><td>0.003***<br>(0.002)</td><td>0.003***<br>(0.002)</td><td>0.004***<br>(0.002)</td><td>0.003***<br>(0.002)</td><td>0.003***<br>(0.002)</td><td>0.002***<br>(0.002)</td><td>0.002***<br>(0.002)</td><td>0.002***<br>(0.002)</td><td>0.002***<br>(0.002)</td><td>0.002***<br>(0.002)</td></tr><tr><td>Domestic credit to the private sector</td><td>0.001<br>(0.008)</td><td>0.000<br>(0.008)</td><td>0.014<br>(0.010)</td><td>0.013<br>(0.010)</td><td>0.014<br>(0.010)</td><td>-0.004<br>(0.011)</td><td>-0.035***<br>(0.013)</td><td>-0.042***<br>(0.013)</td><td>-0.052***<br>(0.015)</td><td>-0.057***<br>(0.016)</td><td>-0.059***<br>(0.016)</td><td>-0.065***<br>(0.017)</td><td>-0.061***<br>(0.020)</td><td>-0.018<br>(0.049)</td><td>-0.192***<br>(0.056)</td><td>-0.079<br>(0.069)</td><td>-0.087<br>(0.072)</td><td>0.478<br>(0.297)</td><td>0.275**<br>(0.139)</td><td>0.275**<br>(0.139)</td></tr><tr><td>Manager's experience</td><td></td><td>0.006**<br>(0.002)</td><td>0.006***<br>(0.002)</td><td>0.007***<br>(0.002)</td><td>0.007***<br>(0.002)</td><td>0.007***<br>(0.002)</td><td>0.007***<br>(0.003)</td><td>0.007***<br>(0.003)</td><td>0.010***<br>(0.004)</td><td>0.010***<br>(0.004)</td><td>0.009**<br>(0.004)</td><td>0.009**<br>(0.004)</td><td>0.010**<br>(0.004)</td><td>0.010**<br>(0.004)</td><td>0.010**<br>(0.004)</td><td>0.011**<br>(0.004)</td><td>0.011**<br>(0.005)</td><td>0.011**<br>(0.005)</td><td>0.011**<br>(0.005)</td><td>0.011**<br>(0.005)</td></tr><tr><td>Manager's gender</td><td></td><td></td><td>0.000<br>(0.004)</td><td>0.002<br>(0.004)</td><td>0.002<br>(0.004)</td><td>0.002<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.003<br>(0.004)</td></tr><tr><td>Total sales</td><td></td><td></td><td>-0.017***<br>(0.001)</td><td>-0.017***<br>(0.001)</td><td>-0.017***<br>(0.001)</td><td>-0.017***<br>(0.001)</td><td>-0.017***<br>(0.001)</td><td>-0.017***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.016***<br>(0.001)</td><td>-0.017***<br>(0.002)</td><td>-0.020***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.021***<br>(0.002)</td><td>-0.024***<br>(0.002)</td><td>-0.024***<br>(0.002)</td></tr><tr><td>Firm's export capacity</td><td></td><td></td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.009***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.007***<br>(0.001)</td><td>-0.006***<br>(0.001)</td><td>-0.006***<br>(0.001)</td><td>-0.005**<br>(0.001)</td><td>-0.005**<br>(0.001)</td><td>-0.004**<br>(0.001)</td><td>-0.004**<br>(0.001)</td><td>-0.003**<br>(0.001)</td><td>-0.003**<br>(0.001)</td><td>-0.003**<br>(0.001)</td><td>-0.003**<br>(0.001)</td></tr><tr><td>Criminality</td><td></td><td></td><td></td><td>0.018***<br>(0.004)</td><td>0.018***<br>(0.004)</td><td>0.019***<br>(0.005)</td><td>0.021***<br>(0.005)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.023***<br>(0.006)</td><td>0.023***<br>(0.006)</td><td>0.020***<br>(0.006)</td><td>0.021***<br>(0.007)</td><td>0.024***<br>(0.007)</td><td>0.024***<br>(0.007)</td><td>0.021***<br>(0.007)</td><td>0.021***<br>(0.007)</td><td>0.021***<br>(0.007)</td></tr><tr><td>Taxation</td><td></td><td></td><td></td><td></td><td>0.003<br>(0.012)</td><td>-0.034**<br>(0.014)</td><td>-0.035**<br>(0.014)</td><td>-0.195***<br>(0.025)</td><td>-0.199***<br>(0.025)</td><td>-0.189***<br>(0.026)</td><td>-0.196***<br>(0.026)</td><td>-0.202***<br>(0.028)</td><td>-0.178***<br>(0.030)</td><td>-0.180***<br>(0.063)</td><td>0.000<br>(0.070)</td><td>0.170<br>(0.104)</td><td>0.162<br>(0.106)</td><td>-0.221**<br>(0.091)</td><td>-0.149**<br>(0.062)</td><td>-0.221**<br>(0.091)</td><td>-0.149**<br>(0.062)</td></tr><tr><td>Real lending interest rate</td><td></td><td></td><td></td><td></td><td></td><td>0.006***<br>(0.001)</td><td>0.009***<br>(0.001)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>0.010***<br>(0.002)</td><td>-0.005<br>(0.003)</td><td>0.001<br>(0.006)</td><td>0.033***<br>(0.008)</td><td>-0.003<br>(0.012)</td><td>-0.005<br>(0.013)</td><td>-0.059**<br>(0.026)</td><td>-0.059**<br>(0.026)</td><td>-0.059**<br>(0.026)</td><td>-0.073***<br>(0.021)</td><td>-0.073***<br>(0.021)</td></tr><tr><td>Interest rate spread</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.003<br>(0.002)</td><td>-0.007***<br>(0.003)</td><td>-0.007***<br>(0.003)</td><td>-0.005**<br>(0.003)</td><td>-0.006**<br>(0.003)</td><td>-0.006**<br>(0.003)</td><td>0.009**<br>(0.004)</td><td>0.004<br>(0.006)</td><td>-0.021***<br>(0.008)</td><td>-0.006<br>(0.009)</td><td>-0.007<br>(0.009)</td><td>0.124***<br>(0.038)</td><td>0.121***<br>(0.037)</td><td>0.121***<br>(0.037)</td><td>0.121***<br>(0.037)</td></tr><tr><td>Bank NPLs</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.036***<br>(0.009)</td><td>0.034***<br>(0.009)</td><td>0.024**<br>(0.011)</td><td>0.017<br>(0.012)</td><td>0.019<br>(0.011)</td><td>0.055<br>(0.012)</td><td>0.055<br>(0.012)</td><td>0.055<br>(0.012)</td><td>0.055<br>(0.012)</td><td>0.023<br>(0.035)</td><td>0.025<br>(0.050)</td><td>-0.007<br>(0.086)</td><td>-0.065<br>(0.040)</td><td>-0.065<br>(0.040)</td></tr><tr><td>Real GDP growth</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.002<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.002<br>(0.002)</td><td>0.007<br>(0.007)</td><td>0.006<br>(0.007)</td><td>0.022**<br>(0.010)</td><td>0.025**<br>(0.012)</td><td>0.081**<br>(0.021)</td><td>0.086**<br>(0.221)</td><td>0.081**<br>(0.221)</td><td>0.081**<br>(0.221)</td></tr><tr><td>Unemployment</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.037**<br>(0.019)</td><td>0.036**<br>(0.019)</td><td>0.036**<br>(0.019)</td><td>0.028<br>(0.019)</td><td>0.071<br>(0.054)</td><td>0.143**<br>(0.055)</td><td>0.075<br>(0.075)</td><td>0.152<br>(0.152)</td><td>0.224<br>(0.254)</td><td>0.085<br>(0.085)</td><td>0.224<br>(0.254)</td><td>0.085<br>(0.085)</td></tr><tr><td>FDI</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.001<br>(0.001)</td><td>-0.000<br>(0.001)</td><td>0.001*<br>(0.001)</td><td>0.003***<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Gross fixed capital formation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.036**<br>(0.037)</td><td>-0.063**<br>(0.038)</td><td>-0.132<br>(0.123)</td><td>-0.110<br>(0.124)</td><td>-0.443<br>(0.255)</td><td>-0.462<br>(0.261)</td><td>-0.363<br>(0.345)</td><td>-0.148<br>(0.186)</td><td>-0.363<br>(0.345)</td></tr><tr><td>Banking accessibility</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>New business density</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Remittances</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Public spending</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urbanization</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Debt</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Corruption</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Main controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</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><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>134,050</td><td>131,533</td><td>115,775</td><td>102,662</td><td>102,198</td><td>101,893</td><td>87,818</td><td>68,870</td><td>58,856</td><td>44,835</td><td>44,835</td><td>44,660</td><td>44,660</td><td>41,859</td><td>41,308</td><td>31,162</td><td>31,162</td><td>27,229</td><td>27,229</td><td>22,563</td><td>22,563</td></tr></tbody></table>

Constant terms are included but not reported for brevity. NPLs: nonperforming loans. Regarding fixed effects, only country and year fixed effects are included, as the Probit model does not handle specifications with large sets of fixed effects well. Robust standard errors are in parentheses. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.
<table><caption>**Table 12**

Robustness checks: Propensity score matching.</caption><thead><tr><th rowspan="2"></th><th colspan="3">(1)<br>Nearest-neighbor matching</th><th colspan="3">(2)<br>Radius matching</th><th colspan="2">(3)<br>Kernel matching</th></tr><tr><th>N = 1</th><th>N = 2</th><th>N = 3</th><th>r = 0.005</th><th>r = 0.01</th><th>r = 0.05</th><th></th><th></th></tr></thead><tbody><tr><td>Past informality</td><td>0.070<sup>***</sup><br>(0.007)</td><td>0.070<sup>***</sup><br>(0.006)</td><td>0.074<sup>***</sup><br>(0.005)</td><td>0.080<sup>***</sup><br>(0.005)</td><td>0.080<sup>***</sup><br>(0.005)</td><td>0.091<sup>***</sup><br>(0.005)</td><td>0.089<sup>***</sup><br>(0.005)</td><td>0.082<sup>***</sup><br>(0.007)</td></tr><tr><td>Rubin's B statistic (%)</td><td>4.4</td><td>3.7</td><td>3.2</td><td>4.4</td><td>4.3</td><td>11</td><td>9.8</td><td>4.4</td></tr><tr><td>Rubin's R statistic</td><td>1.04</td><td>1.01</td><td>0.89</td><td>0.79</td><td>0.80</td><td>0.99</td><td>0.99</td><td>1.04</td></tr><tr><td>Observations</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td><td>134,050</td></tr></tbody></table>

N and r denote the number of nearest neighbors and the radius, respectively. Standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

<table><caption>**Table 13**

Robustness checks: 2SLS estimates.</caption><tbody><tr><td>Past informality</td><td>0.068<sup>***</sup><br>(0.006)</td></tr><tr><td>Firm size</td><td>-0.043<sup>***</sup><br>(0.001)</td></tr><tr><td>Firm age</td><td>-0.013<sup>***</sup><br>(0.002)</td></tr><tr><td>Affiliation with a large firm</td><td>-0.024<sup>***</sup><br>(0.004)</td></tr><tr><td>Private foreign ownership</td><td>-0.007<sup>***</sup><br>(0.001)</td></tr><tr><td>Real GDP per capita</td><td>0.190<br>(0.281)</td></tr><tr><td>Inflation</td><td>-0.031<sup>***</sup><br>(0.008)</td></tr><tr><td>Domestic credit to the private sector</td><td>0.189<sup>***</sup><br>(0.027)</td></tr><tr><td>Country-sector-year fixed effects</td><td>Yes</td></tr><tr><td>Kleibergen-Paap underidentification test (p-value)</td><td>0.0000</td></tr><tr><td>Kleibergen-Paap weak identification statistic</td><td>22,083.83</td></tr><tr><td>Stock-Yogo weak identification highest critical value</td><td>16.38</td></tr><tr><td colspan="2">First-stage regression</td></tr><tr><td>Instrument</td><td>8.626<sup>***</sup><br>(0.058)</td></tr><tr><td>Observations</td><td>134,050</td></tr></tbody></table>

The constant term is included but not reported for brevity. Robust standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.

The bottom part of Table 13 reports the estimated coefficient of the IV in the first-stage regression. The sign of the relationship between the IV and the endogenous regressor is as expected: the higher the percentage of registered firms with a history of informality in the country, the higher the probability that a given registered firm began operations informally. The estimated coefficient is both economically and statistically significant (at the 1% level), supporting the strength of the IV.

Additionally, Table 13 presents results from the Kleibergen-Paap weak identification statistic, a robust extension of the Cragg-Donald statistic. The Kleibergen-Paap statistic significantly exceeds the Stock-Yogo weak identification highest critical value, further confirming the strength of the IV. The p-value of the Kleibergen-Paap underidentification test is 0.0000, supporting the relevance of the IV.

Fourth, we estimate the effect of past informality status on credit constraints using two alternative balancing methods that combine weighting and regression, namely “combining PSM with linear regression” (Dehejia and Wahba, 2002) and “combining Inverse Probability of Treatment Weighting (IPTW) with linear regression” (Chesnaye et al., 2022). This allows us to further test the robustness of our main findings to the choice of balancing method by focusing on methods that, like entropy balancing, combine weighting and regression.

Estimating the effect of past informality on credit constraints by combining PSM with linear regression involves two steps (Dehejia and Wahba, 2002). In the first step, we implement PSM, specifically, single Nearest-Neighbor Matching with replacement,<fnref n="18" /> by running a Logit regression of past informality on the vector of matching covariates. In the second step, we run a weighted linear

<footnote n="18">¹⁸ The reason for selecting a matching algorithm with replacement is that, according to Dehejia and Wahba (2002), it reduces the potential bias of the estimates. The rationale is that matching with replacement minimizes the propensity-score distance between the matched comparison units and the treatment units because each treatment unit can be matched to the nearest comparison unit, given that a comparison unit may be matched more than once. Similarly, the reason for choosing single Nearest-Neighbor Matching is to reduce potential bias.</footnote>
<table><caption>**Table 14**

Robustness checks: Combining PSM with linear regression.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.070<sup>***</sup><br>(0.007)</td><td>0.068<sup>***</sup><br>(0.007)</td><td>0.049<sup>***</sup><br>(0.007)</td><td>0.048<sup>***</sup><br>(0.007)</td><td>0.048<sup>***</sup><br>(0.007)</td><td>0.073<sup>***</sup><br>(0.006)</td><td>0.068<sup>***</sup><br>(0.006)</td><td>0.055<sup>***</sup><br>(0.007)</td><td>0.054<sup>***</sup><br>(0.007)</td><td>0.054<sup>***</sup><br>(0.007)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>25,022</td><td>25,022</td><td>25,022</td><td>25,022</td><td>25,022</td><td>25,022</td><td>25,022</td><td>25,022</td><td>25,022</td><td>25,022</td></tr><tr><td colspan="11" style="text-align: center;">Rubin's B and R statistics</td></tr><tr><td>Rubin's B statistic (%)</td><td colspan="10">4.4</td></tr><tr><td>Rubin's R statistic</td><td colspan="10">1.04</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Robust standard errors are in parentheses. \* *p* < 0.10, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

regression using the treated and matched control observations, with the control observations weighted by the number of times they are matched to a treated observation.

The results of the estimations are reported in Table 14. Columns (1) to (5) present the results without including the matching covariates in the second step. Column (1) excludes fixed effects from the second step. Columns (2) and (3) include only year and country fixed effects, respectively. Column (4) includes both year and country fixed effects. Column (5) includes only country-sector-year fixed effects. Columns (6) to (10) repeat this exercise, with the only difference being that the matching covariates are included in all five cases. Column (10) excludes the sector of activity binary variable to avoid redundancy.

From Table 14, we see that, irrespective of the specification, past informality significantly (at the 1% level) increases credit constraints for registered firms, confirming our main findings. The effects are similar to those obtained with the main estimation, reinforcing the robustness of our findings.

Rubin's B and R statistics (Rubin, 2001) are reported at the bottom of Table 14 to assess the balancing quality in PSM. As can be seen, they meet the required standard.

Furthermore, to assess covariate balance in the weighting approach, we compute the Absolute Standardized Mean Difference (ASMD) for each covariate.<fnref n="19" /> The results are reported in Table C.1 in Appendix C. For the balancing property to be achieved, each ASMD should be less than 0.1 (i.e., less than 10%). As can be seen from Table C.1, this condition is met after weighting, indicating that covariate balance has been achieved.

Besides, the estimation of the effect of past informality on credit constraints by combining IPTW with linear regression also involves two steps (Chesnaye et al., 2022). In the first step, we balance the characteristics of treated and control units by weighting each unit by the inverse probability of receiving the actual treatment (Robins, 1986), which is estimated using the propensity score (also called the “probability of exposure”). Weights are calculated for each unit as 1/propensity score for the exposed group (i.e., treated group) and 1/(1 – propensity score) for the unexposed group (i.e., control group).<fnref n="20" /> In the second step, the weights are incorporated into a linear regression to obtain estimates adjusted for confounders.

As stressed by Chesnaye et al. (2022), a key methodological challenge when applying IPTW is the presence of extreme weight values, which inflate the confidence interval and variance of the treatment effect estimate. Extreme weights arise when individuals have very high or very low probabilities of being exposed to the treatment, i.e., a propensity score close to 1 for the unexposed and close to 0 for the exposed. Following Chesnaye et al. (2022), we address extreme weights by stabilizing and truncating weights. Weight stabilization is performed by replacing the unstabilized weight, “1”, with the so-called “crude probability of exposure”, which in this case corresponds to the proportion of observations subject to past informality status (the “proportion of exposed”). More precisely, stabilized weights are calculated as proportion exposed/propensity score for the exposed group and proportion unexposed/(1 – propensity score) for the unexposed group.<fnref n="21" /> Regarding truncation (i.e., trimming), weights are typically truncated at the 1st and 99th percentiles (Chesnaye et al., 2022).

The results of the estimations are reported in Table 15. Columns (1) to (5) present the results without including the matching covariates in the second step. Column (1) excludes fixed effects from the second step. Columns (2) and (3) include only year and country fixed effects, respectively. Column (4) includes both year and country fixed effects. Column (5) includes only country-sector-year fixed effects. Columns (6) to (10) repeat this exercise, with the only difference being that the matching covariates are included in all five cases. Column (10) excludes the sector of activity binary variable to avoid redundancy.

We see from Table 15 that, irrespective of the specification, the effect of past informality is significant (at the 1% level) and positive, confirming our initial findings.

<footnote n="19">¹⁹ Austin and Stuart (2015) and Chesnaye et al. (2022) adopt the same approach. Note that the ASMD can be used for both binary and continuous variables. See Austin (2009) and Austin and Stuart (2015) for more details.</footnote>

<footnote n="20">²⁰ By doing so, treatment units with a lower probability of exposure and control units with a higher probability of exposure receive larger weights, thereby increasing their relative influence on the comparison.</footnote>

<footnote n="21">²¹ proportion unexposed = 1 – proportion exposed.</footnote>
<table><caption>**Table 15**

Robustness checks: Combining IPTW with linear regression.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.134***<br>(0.005)</td><td>0.124***<br>(0.005)</td><td>0.075***<br>(0.005)</td><td>0.075***<br>(0.005)</td><td>0.073***<br>(0.005)</td><td>0.082***<br>(0.005)</td><td>0.081***<br>(0.005)</td><td>0.063***<br>(0.005)</td><td>0.061***<br>(0.005)</td><td>0.061***<br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>130,607</td><td>130,607</td><td>130,607</td><td>130,607</td><td>130,607</td><td>116,871</td><td>116,871</td><td>116,871</td><td>116,871</td><td>116,871</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Robust standard errors are in parentheses. \* *p* < 0.10, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

To assess covariate balance, the ASMD for each covariate is reported (Austin and Stuart, 2015; Chesnaye et al., 2022) in Table C.2 in Appendix C. As can be seen, after weighting, the ASMD is less than 0.1 (i.e., less than 10%) for each covariate, indicating that the balancing property is achieved.

Overall, the estimations show that our main findings are invariant to the estimation method, particularly to the selected balancing method. This supports the robustness of our results.

## 5.4. Alternative measures of credit constraints

In this subsection, we employ alternative measures of credit constraints. This approach allows us to test the robustness of our results to different levels of stringency in the definition of credit constraints. Following Asiedu et al. (2013) and Beck et al. (2008), we measure credit constraints based on responses to the following question from the WBES: “k30: To what degree is access to finance an obstacle to the current operations of this establishment?” The answers are: “no obstacle”, “minor obstacle”, “moderate obstacle”, “major obstacle”, or “very severe obstacle”. We construct three measures of credit constraints based on the rationale that firms identifying access to finance as an obstacle are considered credit-constrained.

First, we create a binary variable, labeled as the “First alternative measure of credit constraints”, which is equal to 1 if access to finance is reported as an obstacle (minor, moderate, major, or very severe), and 0 if it is not reported as an obstacle. Second, we develop another binary variable, called the “Second alternative measure of credit constraints”, which is equal to 1 if access to finance is reported as a moderate, major, or very severe obstacle, and 0 otherwise. Third, we construct a binary variable, referred to as the “Third alternative measure of credit constraints”, which is equal to 1 if access to finance is reported as a major or very severe obstacle, and 0 otherwise.

Using these alternative measures of credit constraints, we estimate the effect of past informality on credit constraints (ATT) employing the entropy balancing method. Note that as the proportion of credit-constrained firms is not the same from one type of credit constraint variable to the other, we need to compute semielasticities. Semielasticity is the ratio of each marginal effect to the unconditional probability, i.e., the proportion of observations in which the dependent variable under consideration equals 1.

The semielasticities are presented in Table 16. The upper part of the table, labeled as “(A)”, displays the results for the first alternative measure of credit constraints. The middle part, labeled as “(B)”, shows the results for the second alternative measure. The bottom part, labeled as “(C)”, presents the results for the third alternative measure.

From Table 16, we observe that the results of all these estimations confirm our initial findings, suggesting that they are not affected by the level of stringency in the definition of credit constraints. More importantly, we observe from Table 16 that the effect of past informality on the probability of being credit-constrained is weaker with the least stringent definition of credit constraints (Panel (A)) than with the moderate definition (Panel (B)), and the latter is weaker than the effect obtained with the most stringent definition (Panel (C)). These findings suggest that registered firms with past informality status are more likely to face major or severe forms of credit constraints rather than minor ones. This reaffirms the detrimental role that past informality plays in registered firms’ access to credit. It further demonstrates that it is in the interest of firms to start operations formally rather than informally, particularly in terms of the severity of the credit constraints they may face.

## 5.5. Excluding firms with sufficient capital

In this subsection, we exclude from the sample the firms that did not apply for bank credit because they possessed sufficient capital. This allows us to test the robustness of our findings to the presence of these firms in the sample. The results of the estimations are reported in Table 17.

We see from Table 17 that, in all ten specifications, the effect of past informality is significant (at the 1% level) and positive, confirming our initial findings. This supports the robustness of our results.
<caption>**Table 16**</caption>

Robustness checks: Alternative measures of credit constraints, semielasticities reported.

<table><thead><tr><th></th><th colspan="10">(A) First alternative measure of credit constraints</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.102<sup>***</sup><br>(0.006)</td><td>0.099<sup>***</sup><br>(0.006)</td><td>0.062<sup>***</sup><br>(0.006)</td><td>0.061<sup>***</sup><br>(0.006)</td><td>0.055<sup>***</sup><br>(0.006)</td><td>0.102<sup>***</sup><br>(0.006)</td><td>0.099<sup>***</sup><br>(0.006)</td><td>0.066<sup>***</sup><br>(0.006)</td><td>0.062<sup>***</sup><br>(0.006)</td><td>0.056<sup>***</sup><br>(0.006)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td></tr><tr><th></th><th colspan="10">(B) Second alternative measure of credit constraints</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr><tr><td>Past informality</td><td>0.126<sup>***</sup><br>(0.011)</td><td>0.121<sup>***</sup><br>(0.011)</td><td>0.070<sup>***</sup><br>(0.011)</td><td>0.070<sup>***</sup><br>(0.011)</td><td>0.066<sup>***</sup><br>(0.011)</td><td>0.126<sup>***</sup><br>(0.011)</td><td>0.119<sup>***</sup><br>(0.011)</td><td>0.079<sup>***</sup><br>(0.011)</td><td>0.077<sup>***</sup><br>(0.011)</td><td>0.070<sup>***</sup><br>(0.011)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td></tr><tr><th></th><th colspan="10">(C) Third alternative measure of credit constraints</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr><tr><td>Past informality</td><td>0.177<sup>***</sup><br>(0.017)</td><td>0.173<sup>***</sup><br>(0.017)</td><td>0.108<sup>***</sup><br>(0.017)</td><td>0.117<sup>***</sup><br>(0.017)</td><td>0.108<sup>***</sup><br>(0.017)</td><td>0.177<sup>***</sup><br>(0.017)</td><td>0.169<sup>***</sup><br>(0.017)</td><td>0.121<sup>***</sup><br>(0.017)</td><td>0.125<sup>***</sup><br>(0.017)</td><td>0.117<sup>***</sup><br>(0.017)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td><td>135,675</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Semielasticity refers to the ratio of each marginal effect to the unconditional probability, i.e., the proportion of observations in which the dependent variable under consideration equals 1. Robust standard errors are in parentheses. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

<caption>**Table 17**</caption>

Robustness checks: Excluding from the sample the firms that did not apply for bank credit because they possessed sufficient capital.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td>Past informality</td><td>0.045<sup>***</sup><br>(0.005)</td><td>0.046<sup>***</sup><br>(0.005)</td><td>0.024<sup>***</sup><br>(0.005)</td><td>0.024<sup>***</sup><br>(0.005)</td><td>0.023<sup>***</sup><br>(0.005)</td><td>0.043<sup>***</sup><br>(0.005)</td><td>0.042<sup>***</sup><br>(0.005)</td><td>0.028<sup>***</sup><br>(0.005)</td><td>0.029<sup>***</sup><br>(0.005)</td><td>0.028<sup>***</sup><br>(0.005)</td></tr><tr><td>Covariates in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects in the second step</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Country fixed effects in the second step</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>75,247</td><td>75,247</td><td>75,247</td><td>75,247</td><td>75,247</td><td>75,247</td><td>75,247</td><td>75,247</td><td>75,247</td><td>75,247</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Robust standard errors are in parentheses. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.

## 5.6. Additional robustness: Clustering standard errors at the country level

Since some of our control variables are macroeconomic, we cluster standard errors at the country level in our final robustness check. For brevity, the estimation results are presented in Table C.3 of Appendix C, covering columns (1) to (10).

Across all specifications, the effect of past informality on credit constraints remains positive and significant at the 1% level, confirming our initial findings. Notably, clustering standard errors at the country level does not affect the significance level of the effect of past informality. This outcome was expected since our treatment variable is measured at the firm level, while the macroeconomic variables in question serve only as controls. These results demonstrate the robustness of our findings.

## 6. Heterogeneity

Our results indicate that past informality significantly increases credit constraints for registered firms. In this section, we analyze the sensitivity of this finding across different sectors of activity, varying levels of firm size and age, subsamples of countries, and varying levels of structural factors.
<table><caption>**Table 18**
Heterogeneity: Different sectors of activity, semielasticities reported.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th></tr><tr><th></th><th>Manufacturing</th><th>Services</th></tr></thead><tbody><tr><td>Past informality</td><td>0.148<sup>***</sup><br>(0.017)</td><td>0.185<sup>***</sup><br>(0.022)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>75,642</td><td>58,408</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Semelasticity refers to the ratio of each marginal effect to the unconditional probability, i.e., the proportion of observations in which the dependent variable equals 1 in the sector under consideration. Robust standard errors are in parentheses. \* *p* < 0.10, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

## 6.1. Different sectors of activity and varying levels of firm size and age

In this subsection, we assess the sensitivity of our baseline results to different sectors of activity and varying levels of firm size and age. Indeed, to fully grasp the relationship between past informality and credit constraints, it is essential to evaluate how our baseline findings vary across several factors, including sector, firm size, and firm age. For instance, different industries may experience varying levels of credit constraints due to sector-specific elements, such as capital requirements or market demand, which can affect the influence of past informality status. Firm size is another critical factor, with smaller firms usually facing more severe credit constraints compared to their larger counterparts (Asiedu et al., 2013; Beck et al., 2005; Distinguin et al., 2016) due to disparities in self-financing capacity and market influence, among others. Additionally, smaller firms are more likely to have begun operations in the informal sector than larger firms, as informal firms are typically small (La Porta and Shleifer, 2014) and rarely become large firms after their transition to the formal sector, which could influence the effect of past informality. Furthermore, a firm's age can impact its creditworthiness (and thereby affect access to credit) as well as its interactions with formal financial institutions, potentially influencing how past informality affects current credit constraints. Our analysis is organized into three parts.

First, we estimate the effect of past informality for the different sectors of activity, including the manufacturing and services sectors. Note that comparing the marginal effects across different subsamples is not a correct method to assess for which type of firms the effect is stronger, as the proportion of credit-constrained firms in each subsample is not the same. The correct approach is to compute the semielasticity, which is the ratio of each marginal effect to the unconditional probability (i.e., the proportion of observations in which the dependent variable equals 1) in each subsample.

The semielasticities are reported in Table 18. Columns (1) and (2) present the results for the manufacturing and services sectors, respectively. Irrespective of the sector, the effect of past informality is significant (at the 1% level) and positive. The effect is higher in the services sector. This may be due to newly formalized service firms' ongoing reliance on informal networks and personal relationships, complicating the shift to the formal credit system.

Second, we examine the effect of past informality on credit constraints at varying levels of firm size. To do this, we include the interaction between past informality and firm size in the second step of entropy balancing. The results are presented in the first graph of Fig. 2, which illustrates the effect of past informality on registered firms' probability of being credit-constrained at varying levels of firm size, along with 95% confidence intervals. From Fig. 2, we observe that as firm size increases, the effect of past informality on credit constraints decreases. This confirms our previous intuition. Indeed, larger firms usually face weaker credit constraints (Asiedu et al., 2013; Beck et al., 2005; Distinguin et al., 2016) and are less likely to have started operations informally compared to their smaller counterparts, resulting in a lower magnitude of the effect of past informality.

Third, we analyze the effect of past informality on credit constraints at varying levels of firm age. To do this analysis, we include the interaction between past informality and firm age in the second step of entropy balancing. The results are presented in the second graph of Fig. 2, which illustrates the effect of past informality on registered firms' probability of being credit-constrained at varying levels of firm age, along with 95% confidence intervals. From Fig. 2, we observe that as firm age increases, the effect of past informality on credit constraints decreases. This also confirms our intuition. Indeed, older firms have been found in the literature to face lower financial obstacles (see Beck et al., 2006; Carreira and Silva, 2010, among others). As firms become older, they amass financial records, build credit histories, and establish strong relationships with financial institutions. This mitigates the detrimental influence of their informal origins on access to credit.

## 6.2. Subsamples of countries

This subsection aims to test the sensitivity of our findings to subsamples of countries. Conducting this investigation is worthwhile because countries or groups of countries possess diverse institutional frameworks, varying levels of financial market development, and different regulatory environments, all of which can affect the relationship between past informality and credit constraints. Analyzing sub-samples can reveal whether the observed effects are consistent across different economic contexts or if they are influenced by specific regional characteristics. We consider six different subsamples of countries, with the results of the semielasticity estimation presented in Table 19.
<figure><img src="image_8.png" /></figure>

The 95% confidence intervals are reported.

<caption>Fig. 2. Heterogeneity: Effects of past informality on registered firms’ probability of being credit-constrained at varying levels of firm size and age.</caption>

<table><caption>**Table 19**
Heterogeneity: Subsamples of countries, semielasticities reported.</caption><thead><tr><th></th><th>(1)<br>G20</th><th>(2)<br>Removing G20</th><th>(3)<br>SSA</th><th>(4)<br>Removing SSA</th><th>(5)<br>LAC</th><th>(6)<br>Removing LAC</th></tr></thead><tbody><tr><td>Past informality</td><td>0.201<sup>***</sup><br>(0.028)</td><td>0.156<sup>***</sup><br>(0.016)</td><td>0.110<sup>***</sup><br>(0.017)</td><td>0.172<sup>***</sup><br>(0.020)</td><td>0.114<sup>***</sup><br>(0.035)</td><td>0.174<sup>***</sup><br>(0.014)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>32,573</td><td>101,477</td><td>21,590</td><td>112,460</td><td>28,771</td><td>105,279</td></tr></tbody></table>

Constant terms are included but not reported for brevity. Semielasticity refers to the ratio of each marginal effect to the unconditional probability, i.e., the proportion of observations in which the dependent variable equals 1 in the subsample under consideration. SSA: sub-Saharan Africa. LAC: Latin America and the Caribbean. Robust standard errors are in parentheses. \* *p* < 0.10, \*\* *p* < 0.05, \*\*\* *p* < 0.01.

We start by restricting the sample to G20 countries, which include the most developed economies in the world. Firms in these countries face lower financial constraints due to higher financial development compared to firms in the rest of the world. Additionally, the level of informality is generally lower in these countries, as the scope of the informal economy decreases with higher development (La Porta and Shleifer, 2014). In this context, it is worthwhile to examine how past informality influences credit constraints for registered firms in these countries. The semielasticity is reported in column (1).

Next, we restrict the sample by removing G20 countries to assess whether the effect of past informality is stronger or weaker in the rest of the world compared to the most developed economies represented by the G20 countries. The semielasticity is presented in column (2).

Furthermore, we focus on two regions where informality is most pervasive: sub-Saharan Africa (SSA) and Latin America and the Caribbean (LAC). Firms from these regions also face significant credit constraints. Given the importance of informality in these regions, it is valuable to investigate whether the influence of past informality persists when focusing on firms from each region individually or when excluding them. To explore these issues, we consider SSA countries only in column (3) and exclude SSA in column (4). We repeat this exercise for LAC in columns (5) and (6), respectively. The semielasticities are reported in the respective columns.

From Table 19, we observe that, irrespective of the subsample considered, past informality has a significant (at the 1% level) and positive effect on credit constraints. The magnitude of the semielasticity is larger for G20 countries. This greater impact in G20 countries suggests that past informality is more detrimental in developed economies, potentially due to their more advanced financial systems and stringent regulatory frameworks. Indeed, the high standards for transparency and creditworthiness in most G20 countries make past informality a more significant problem.
The magnitudes of the semielasticity of past informality in SSA and LAC are similar, and lower than those obtained when these regions are excluded. Additionally, these semielasticities are about half of the value obtained with G20 countries. All of this confirms our intuition that past informality is less problematic in developing countries than in developed countries, supporting the discussion we had when analyzing results for G20 countries and reinforcing the possible explanation we put forward.

### 6.3. The role of structural factors

In this subsection, we analyze whether the effect of past informality depends on the level of certain structural factors. We focus on four different structural factors.

First, we analyze the effect of past informality on registered firms' credit constraints at varying levels of regulatory quality. Indeed, regulatory environments are pivotal in shaping how easily firms can obtain credit (Carreira and Silva, 2010). In countries with high-quality regulation, financial systems tend to be more transparent and efficient, which can lessen the unfavorable effects of past informality by providing clearer and fairer pathways for integrating informal firms into the formal economy. Conversely, in countries with weaker regulatory quality, financial systems may lack transparency and efficiency, potentially intensifying the challenges that firms with informal histories face when seeking credit. Examining the role of regulatory quality helps determine whether robust regulatory frameworks mitigate or exacerbate the impact of past informality on access to credit. To test this, we include both regulatory quality and its interaction with the past informality variable in the second step of entropy balancing. The results are presented in the first graph of Fig. 3, which shows the effect of past informality on registered firms' probability of being credit-constrained at varying levels of regulatory quality. We observe that the higher the regulatory quality, the lower the detrimental effect of past informality on credit constraints. This finding highlights that improving regulatory quality could boost financial inclusion and lower barriers for firms moving from the informal to the formal sector.

Second, we explore the effect of past informality at varying levels of trade openness. The degree of a country's openness to international trade can significantly influence its financial environment and development (Baltagi et al., 2009; Kim et al., 2010). Indeed, in countries with high trade openness, firms encounter greater global competition and are often compelled to adhere to stricter financial practices. This pressure can help firms address issues related to their informal past, as increased competition encourages greater transparency and formality, thereby easing credit constraints. In contrast, in countries with low trade openness, firms may not face the same international pressures, which might result in less emphasis on overcoming past informality. By examining the role of trade openness, we can determine whether participation in the global market facilitates better access to credit for firms with informal histories. To analyze this, we include both trade openness and its interaction with the past informality variable in the second step of entropy balancing. The results are presented in the second graph of Fig. 3, which shows the effect of past informality on registered firms' probability of being credit-constrained at varying levels of trade openness. It emerges that the higher the level of trade openness, the lower the effect of past informality. This confirms our intuition and indicates that fostering openness to international trade could help countries improve financial inclusion and mitigate the detrimental effects of informal origins.

Third, we analyze the effect of past informality on registered firms' credit constraints at varying levels of entrepreneurial dynamism, as proxied by new business density. A dynamic entrepreneurial environment can indeed influence access to credit. High new business density signals a vibrant entrepreneurial ecosystem where competition and innovation flourish, potentially leading to more robust financial systems and improved access to credit. In such environments, firms with an informal past may find it easier to transition into the formal financial sector and overcome financial constraints due to the supportive infrastructure and resources available for new and growing businesses. Conversely, in countries with low new business density, the entrepreneurial ecosystem might be less developed, offering fewer resources and support systems for firms moving from informality, thereby exacerbating credit constraints. This analysis helps determine whether a thriving entrepreneurial environment can mitigate the harmful effects of past informality on access to credit. To explore this role, we include both new business density and its interaction with the past informality variable in the second step of entropy balancing. The results are presented in the third graph of Fig. 3, which shows the effect of past informality on registered firms' probability of being credit-constrained at varying levels of new business density. We see from this graph that the higher the level of new business density, the lower the effect of past informality on registered firms' probability of being credit-constrained, confirming our intuition. This indicates that a thriving entrepreneurial environment can improve access to credit and mitigate the lasting effects of starting operations informally.

Last but not least, we examine the effect of past informality on registered firms' credit constraints at varying levels of public spending. Indeed, public spending significantly shapes the economic environment and the support available for businesses. High public spending often results in better financial infrastructure, business support programs, and regulatory enforcement, which can facilitate easier access to credit for firms with a history of informality. Investments in education, infrastructure, and technology also contribute to a favorable business climate, mitigating the detrimental impacts of past informality. In contrast, low public spending can lead to weaker financial systems and reduced support for businesses, making it more challenging for firms with informal backgrounds to secure credit. By examining the role of public spending, we can determine whether increased public spending aids firms in overcoming credit constraints related to their informal past. To study this role, we include both public spending and its interaction with the past informality variable in the second step of entropy balancing. The results are presented in the fourth graph of Fig. 3, which shows the effect of past informality on the probability of being credit-constrained at varying levels of public spending. We observe that the higher the level of public spending, the lower the detrimental effects of past informality, which confirms our intuition. Hence, public spending could help mitigate financial constraints for firms with an informal history.
<figure><img src="image_9.png" /></figure>

The 95% confidence intervals are reported.

<caption>Fig. 3. Heterogeneity: Effects of past informality on registered firms’ probability of being credit-constrained at varying levels of structural factors.</caption>

## 7. Channels

Our results indicate that past informality significantly increases the probability of registered firms to be credit-constrained. In this section, we investigate the transmission channels of this effect. As discussed in the introduction, we test three potential channels: firm productivity, competition from the informal sector, and the quality of financial statements. Productivity is measured as the natural logarithm of the ratio of turnover to the total number of full-time employees, adjusted for temporary workers. Competition from the informal sector is measured by a binary variable equal to 1 if the firm faces competitors from the informal sector, and 0 otherwise. The quality of financial statements is captured by a binary variable equal to 1 if the firm’s annual financial statements are audited and certified by an external auditor, and 0 otherwise. We define high-quality financial statements as those that are audited and certified by an external auditor.

We start by analyzing the relevance of our potential channels to credit constraints by estimating a Linear Probability model. Country-sector-year fixed effects are included to control for both time-varying and country-specific factors that can affect both credit constraints and the channels. This approach aims to determine if the three potential channels identified are correlated with credit constraints (see Apeti and Edoh, 2023, for a similar approach). The results of the estimations are presented in Table 20. We observe that firm productivity, competition from the informal sector, and the quality of financial statements are highly correlated (at the 1% level) with a firm’s probability of being credit-constrained. More precisely, productivity and the quality of financial statements are negatively correlated with credit constraints, whereas competition from the informal sector is positively correlated with credit constraints, as expected. This suggests that these factors represent potentially relevant channels through which past informality heightens registered firms’ probability of being credit-constrained.

Now that we have assessed the relevance of our three potential transmission channels, we use the entropy balancing method to analyze whether past informality is related to each channel, employing the same matching covariates as our baseline specification and including country-sector-year fixed effects (see Apeti and Edoh, 2023, for a similar approach). The results of the estimations are presented in Table 21. We observe that past informality is significantly associated (at the 1% level) with lower productivity, a lower probability of high-quality financial statements, and a higher probability of competition from the informal sector.<fnref n="22" />

In summary, taking into account the results presented in both Tables 20 and 21, past informality significantly heightens credit constraints for registered firms by reducing productivity and the quality of financial statements, and increasing competition from the informal sector.

<footnote n="22">²² As an alternative specification, we have estimated the effect of past informality on each channel using the 2SLS method, employing the same IV used previously in subsection 5.3. The results confirm those obtained by entropy balancing. To maintain brevity, these results are not reported in this paper. They are available upon request.</footnote>
<table><caption>**Table 20**
Correlation between the transmission channels and credit constraints.</caption><thead><tr><th></th><th>(1)<br>Credit constraints</th><th>(2)<br>Credit constraints</th><th>(3)<br>Credit constraints</th></tr></thead><tbody><tr><td>Productivity</td><td>-0.017<sup>***</sup><br>(0.001)</td><td></td><td></td></tr><tr><td>Competition from the informal sector</td><td></td><td>0.038<sup>***</sup><br>(0.002)</td><td></td></tr><tr><td>Quality of financial statements</td><td></td><td></td><td>-0.045<sup>***</sup><br>(0.003)</td></tr><tr><td>Main controls</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects</td><td>No<sup>+</sup></td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>156,950</td><td>155,058</td><td>170,617</td></tr></tbody></table>

<table><caption>**Table 21**
Transmission channels.</caption><thead><tr><th></th><th>(1)<br>Productivity</th><th>(2)<br>Competition from<br>the informal sector</th><th>(3)<br>Quality of financial<br>statements</th></tr></thead><tbody><tr><td>Past informality</td><td>-0.339<sup>***</sup><br>(0.015)</td><td>0.060<sup>***</sup><br>(0.005)</td><td>-0.096<sup>***</sup><br>(0.004)</td></tr><tr><td>Covariates in the second step</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Country-sector-year fixed effects in the<br>second step</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>122,393</td><td>124,240</td><td>136,005</td></tr></tbody></table>

## 8. Concluding remarks, policy implications, and extensions

This paper analyzes the effect of past informality on the credit constraints of registered firms using firm-level data from the World Bank Enterprise Surveys spanning the 2006–2023 period. The dataset comprises repeated cross-sections, includes only registered firms, contains over 134,000 observations, and covers 134 countries. By employing the entropy balancing method to address the endogeneity of past informality status, we find that past informality significantly increases a firm’s probability of being credit-constrained. This finding is extremely robust across various tests and analyses, with the magnitude of the effect of past informality varying by sector, firm size, firm age, country subsample, and structural factors. Specifically, the detrimental impact of past informality diminishes with firm size, firm age, and improved structural factors such as regulatory quality, trade openness, entrepreneurial dynamism, and public spending. We show that firm productivity, competition from the informal sector, and the quality of financial statements are the primary transmission channels through which past informality heightens the credit constraints faced by registered firms. Our results provide empirical support for the idea that past informal operations can have lasting harmful effects on firms’ activities. This insight is particularly relevant for policymakers and financial institutions working to improve access to credit for firms, especially SMEs, which often emerge from the informal sector.

Our findings suggest several key policy implications. First, our results suggest that public policies should prioritize providing firms with sufficient incentives to start operations in the formal sector to avoid the lasting detrimental effects of informality. By increasing the number of firms that begin operations formally, such policies could help reduce the credit constraints faced by registered firms. In practice, public authorities might consider offering exemptions from minimum wage requirements and corporate taxes for a certain period to firms that start in the formal sector. As noted by Kouakou (2023a), such measures should be provisional and limited to ensure they do not hinder progress toward fairer competition.

Second, as a complement to the previously discussed policy, public authorities should implement measures to provide sufficient incentives for informal firms to transition to the formal sector, even though newly formalized firms may still face some constraints compared to those that started as formal, especially regarding credit constraints. Indeed, newly formalized firms generally face lower credit constraints compared to their informal counterparts. This reflects the traditional development policy approach to formalization.<fnref n="23" /> A recent meta-analysis by Jessen and Kluve (2021) demonstrated that tax incentives combined with information campaigns are an effective policy approach to increasing the rate of formalization of informal firms. Such a policy approach should be adopted by countries to boost the rate of formalization.

<footnote><sup>+</sup>: Column (1) includes only country and year fixed effects (rather than only country-sector-year fixed effects) due to difficulties in estimating standard errors for certain coefficients. Constant terms are included but not reported for brevity. Robust standard errors are in parentheses. Estimations are based on OLS. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.</footnote>

<footnote>Constant terms are included but not reported for brevity. Robust standard errors are in parentheses. * *p* < 0.10, ** *p* < 0.05, *** *p* < 0.01.</footnote>

<footnote n="23">²³ For recent studies suggesting this policy, see Amin (2023), Distinguin et al. (2016), Gasmi et al. (2025), Heredia Pérez et al. (2018), Jessen and Kluve (2021), and Kouakou (2023a), among others.</footnote>
Third, our findings indicate that the detrimental effects of past informality diminish with improvements in regulatory quality, trade openness, entrepreneurial dynamism, and public spending. Hence, it is crucial to implement regulatory reforms that enhance transparency, efficiency, and fairness within the financial system. These reforms should also simplify the process for informal firms to transition to formal status, making the benefits of formalization clear and accessible. Governments should explore trade agreements and incentives designed to integrate newly formalized firms into the global market, thereby improving their access to credit. Building a supportive entrepreneurial ecosystem is essential; investments in infrastructure, education, and technology can stimulate business creation and growth. Support programs for startups and SMEs are particularly important, as they provide the resources and networks needed for informal firms to formalize. Similarly, increased public spending on initiatives that promote firm formalization and growth can alleviate credit constraints. Resources should be directed toward programs offering financial assistance, mentorship, and access to market for firms with a history of informality. This may include grants, subsidized loans, and tax incentives for newly formalized firms as previously discussed.

Fourth, based on the identified pathways through which past informality influences credit constraints, policymakers should focus on two main areas: managing competition from the informal sector and enhancing financial literacy and reporting standards. This can involve implementing stricter enforcement of business regulations and reducing the costs associated with formalization. Additionally, initiatives aimed at improving financial literacy among business owners and managers are essential. Providing training programs and support services to help firms adopt more rigorous accounting practices can enhance the quality of their financial statements. This, in turn, will improve their creditworthiness and facilitate better access to credit.

Fifth, by highlighting the lasting detrimental effects of operating informally on credit constraints, this research underscores the need to address informality to promote financial inclusion. Greater financial inclusion would help reduce disparities among firms and foster equitable economic growth.

This paper can be extended in various ways. First, while we believe that the three transmission channels we identified are the main pathways through which past informality heightens credit constraints for registered firms, there may be additional channels. Future studies should investigate more transmission channels. Similarly, future research could explore additional structural factors in the heterogeneity analysis. Second, since credit constraints can adversely affect firm performance, future research might analyze how past informality impacts firms’ performance, including growth and profitability. Third, to the best of our knowledge, the question of when the detrimental effects of past informality disappear after formalization (if they do at all) remains unexplored. Investigating this could be a fruitful avenue for future research. Fourth, in this paper, we used a binary measure of past informality to indicate whether a registered firm began operations in the informal sector. Data on the duration of time spent in the informal sector before transitioning to the formal sector could provide additional insights into our research.

Fifth, since our main finding indicates that registered firms starting operations in the formal sector are less credit-constrained than those that began in the informal sector, it is crucial to investigate the factors that might increase a firm’s likelihood of starting operations in the formal sector. A comparative analysis of potential firm-specific, sector-specific, and country-specific determinants could help in better understanding what drives a firm’s decision to begin operations in the formal sector.

## Declaration of competing interest

The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A

See Tables A.1–A.4.

## Appendix B. Definitions of the variables and data sources

**Credit constraints:** Binary variable equal to 1 if the firm is credit-constrained, and 0 otherwise. Source: World Bank Enterprise Surveys (WBES). Internet link to access the WBES: https://www.entertainmentsurveys.org/

**Past informality:** Binary variable that takes the value of 1 if the firm began operations as an informal firm (i.e., it was not registered at the start), and 0 otherwise. Source: WBES.

**Firm size:** Natural logarithm of the total number of full-time employees, adjusted to account for temporary workers. Source: WBES.

**Firm age:** Natural logarithm of the number of years since the firm commenced operations. Source: WBES.

**Sector of activity:** Binary variable equal to 1 for manufacturing and 0 for services. Source: WBES.

**Affiliation with a large firm:** Binary variable equal to 1 if the firm is part of a large firm, and 0 otherwise. Source: WBES.

**Private foreign ownership:** Natural logarithm of the percentage of the firm owned by private foreign individuals, companies, or organizations. Source: WBES.

**Real GDP per capita:** Natural logarithm of GDP per capita based on purchasing power parity (PPP) in constant 2021 international U.S. dollars. Source: World Development Indicators (WDI) database of the World Bank. Internet link to access the WDI: https://datatopics.worldbank.org/world-development-indicators/

**Inflation:** Annual growth rate of the GDP implicit deflator. Source: WDI.