Corruption as a Key Driver of Informality: Cross-Country Evidence on Bribery and Institutional Weakness

Abstract

This study investigated the impact of corruption on the persistence of informality across countries, offering new insights into the institutional dynamics that sustain informal economic activities. Drawing on firm-level data from World Bank Enterprise Surveys covering 159 countries, the analysis employed quantitative methods in Stata to assess four indicators of informality against five exogenous variables. These variables captured key institutional constraints, including corruption (with a focus on bribery), bureaucratic inefficiencies, and infrastructure deficits. Results revealed both linear and nonlinear effects of corruption on informality, suggesting that firms embedded in corrupt environments are more likely to remain informal over time. The role of political networks as facilitators of corruption is particularly significant in developing economies, where informal firms benefit from weak enforcement and institutional loopholes. The findings underscore the structural nature of informality and highlight corruption as a critical barrier to sustainable economic development. By exposing how informal payments and institutional weakness interact, this study contributes to global efforts to promote inclusive growth and effective governance under the Sustainable Development Goals (SDGs), particularly SDG 8 and SDG 16.

1. Introduction

Informality persists as a major challenge to sustainable economic development, particularly in regions where weak institutional frameworks and corruption are prevalent. Corruption, especially in the form of bribery and informal payments, has been identified as a structural barrier to formalization, discouraging businesses from registering and complying with regulations. In regions such as South Asia and sub-Saharan Africa, bribery incidence reaches 19.9% and 18.4%, respectively, reflecting environments where firms face substantial informal costs when interacting with public institutions. This often incentivizes businesses to remain informal to avoid the financial and bureaucratic burdens associated with corruption.

The relationship between corruption and informality is also evident in Latin America and the Caribbean, where 40.1% of firms identify corruption as a major constraint to doing business. These regions consistently display high levels of informal competition, with over 60% of firms reporting that they face unregistered competitors. Moreover, firms in these areas operate without formal registration for extended periods—up to 1.1 years on average in Latin America and the Caribbean—indicating structural disincentives to formalization. In contrast, regions like East Asia and the Pacific or Europe and Central Asia exhibit lower bribery incidence and shorter durations of informal operation, suggesting that more robust governance systems and regulatory efficiency support formal sector growth. These patterns are summarized in

Table 1. Key indicators of firm informality by region.

Indicator	All Countries	East Asia and Pacific	Europe and Central Asia	Latin America and Caribbean	Middle East and North Africa	South Asia	Sub-Saharan Africa
Firms competing against unregistered or informal firms (%)	44.1	36.5	28.8	60.4	44.4	34.5	60.6
Average number of years firm operated without formal registration	0.9	0.7	0.6	1.1	1.1	1.3	1.0
Firms identifying informal competitor practices as a major or very severe constraint (%)	23.8	10.7	16.9	32.6	32.2	17.3	33.7

Notes: 1. Values are percentages (%) or years as indicated. 2. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025). 3. Regional values are averages of included countries.

reinforcing the notion that corruption

These dynamics are supported by firm-level data from the World Bank Enterprise Surveys (WBES), which illustrate how corruption correlates with informal practices globally. Bribery depth—the proportion of public transactions involving informal payments—is highest in South Asia (16.9%) and the Middle East and North Africa (12.9%), further illustrating how corruption imposes hidden costs that deter formalization. Additionally, over one-third of firms in sub-Saharan Africa and Latin America identify informal competition as a severe constraint, reinforcing the notion that corruption is not only a governance issue but also a market distortion that undermines fair competition. Key corruption indicators are presented in

Table 2. Key indicators of firm corruption by region.

Indicator	All Countries	East Asia and Pacific	Europe and Central Asia	Latin America and Caribbean	Middle East and North Africa	South Asia	Sub-Saharan Africa
Bribery incidence (firms experiencing at least one bribe payment request) (%)	12.7	17.5	6.6	7.5	14.4	19.9	18.4
Bribery depth (public transactions where a gift or informal payment was requested) (%)	10.0	13.8	5.2	5.6	12.9	16.9	13.8
Firms identifying corruption as a major or very severe constraint (%)	26.2	14.5	15.6	40.1	41.1	23.4	35.2

Notes: 1. Values are percentages (%) as indicated. 2. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025). 3. Regional values are averages of included countries.

.

This study was guided by several key research questions: How does the incidence of bribery affect the persistence of informality? To what extent do corrupt practices drive firms to avoid formal registration? How do differences in institutional and regulatory frameworks influence firms’ decisions to formalize? What are the most effective policy interventions to reduce corruption and promote the transition from informality to formality?

The objective of this research was to empirically examine the global relationship between corruption and informality, with a particular focus on how bribery and governance quality influence firms’ decisions to remain informal. Using cross-regional data and statistical modeling, the study assessed how long firms operate informally in relation to corruption severity and evaluated policy responses that could mitigate both issues.

By offering a comprehensive cross-country analysis of the corruption–informality nexus, this study fills an important gap in the literature. While prior research has often addressed corruption and informality separately, this work highlights how institutional weaknesses—particularly in regulatory enforcement and anti-corruption mechanisms—act as systemic drivers of informality. The findings contribute to the broader sustainability agenda by identifying actionable, context-specific interventions that promote transparency, reduce corruption, and encourage formal sector participation. Ultimately, this research aligns with the Sustainable Development Goals (SDG 8 and SDG 16) by advocating for more inclusive, transparent, and resilient economic systems.

1.1. Theoretical Framework

Institutional theory, as articulated by North (1990), defines institutions as the “rules of the game”—both formal (laws, regulations, enforcement) and informal (norms, conventions, social practices)—that shape human interaction and economic performance. North argues that when formal institutions are weak, inconsistent, or poorly enforced, they fail to reduce uncertainty in economic exchanges. In such settings, corruption emerges as a symptom of institutional failure, distorting incentives and weakening the enforcement of rules. Rose-Ackerman (1978, 1999) extends this view by highlighting how corruption becomes embedded in the institutional fabric, allowing public officials and private actors to subvert the formal system for personal gain. This erosion of institutional integrity reduces trust in legal frameworks and incentivizes firms to operate informally to avoid arbitrary or exploitative state behavior.

Building on this, transaction cost economics—developed by Coase (1937) and Williamson (1985)—provides a microeconomic rationale for informality in corrupt environments. Coase emphasized that firms exist in part to reduce the costs of market transactions. Williamson later elaborated that high transaction costs—such as those caused by corruption, bureaucratic inefficiency, and regulatory opacity—discourage formal economic participation. In corrupt institutional environments, the costs of complying with regulation often include not only legal obligations but also unofficial payments, delays, and legal uncertainty. These hidden costs act as a disincentive to formalization, prompting firms to operate informally to avoid excessive burdens and maintain flexibility.

The shadow economy theory complements this perspective by focusing on the macroeconomic and fiscal implications of informality. The literature suggests that firms enter the informal sector to evade taxes, regulations, or other formal requirements that are perceived as excessive or unjust—especially in environments plagued by corruption (Schneider & Enste, 2000; Loayza, 1996). In such contexts, the informal sector becomes a rational refuge, enabling firms to avoid state-imposed costs and operate under more predictable, albeit extralegal, conditions. This reinforces a cycle in which corruption and informality mutually reinforce each other: corruption increases the cost of formality, and informality provides a shield from corrupt enforcement.

Finally, rent-seeking theory explains how actors exploit institutional loopholes and corrupt systems to extract unearned benefits. Originally proposed by Tullock (1967) and further developed by Krueger (1974), rent-seeking behavior thrives in environments where political connections and bribes determine access to markets, licenses, or favorable regulation. In such settings, productive economic activity is crowded out by unproductive competition for rents. For firms excluded from these networks—or unwilling to engage in bribery—the informal sector becomes an alternative mode of survival. As Rose-Ackerman notes, corruption distorts market incentives and deters investment in formal channels, thereby entrenching informal practices as a rational response to a flawed system.

1.2. Literature Review and Hypothesis

This comprehensive body of literature demonstrates a deep relationship between corruption and informality mediated by institutional strength, socio-economic contexts, globalization, and digital technologies. Informality can act as both a stopgap and a systemic obstacle. Addressing it sustainably requires nuanced, multidimensional policies: from strengthening institutions to deploying targeted digital tools; from recognizing sector-specific dynamics to mitigating the unintended consequences of globalization.

1.2.1. Economic Development

A. 

Developed Economies

Chen et al. (2024) and Kubbe et al. (2025) emphasize that corruption—rooted in cultural–historical norms—undermines business competitiveness. They stress that institutional development is vital for reducing bribery. Bodjongo and Kamdem (2024) quantify that while economic variables explain much of the informality gap between developed and developing nations, over 30% remains attributable to corruption control deficits.

Aluko et al. (2024) note a paradox: corruption can attract FDI in resource-rich, but weakly governed countries, whereas developed countries benefit from low corruption to attract investment. Nguyen et al. (2024) argue that in low-capacity contexts, corruption may stimulate domestic investment by bypassing bureaucracy, but Osuma et al. (2024) caution that corruption raises transaction costs and limits market access, with institutional strengthening the remedy.

B. 

Developing Economies

• India

Mishra and Ray (2013) explore the relationship between firm size and informality, finding that larger firms are more likely to engage in informal sales, particularly in contexts where corruption enables such practices. Their econometric model shows that while smaller firms avoid regulation through informality, larger firms exploit informal markets more effectively, facilitated by corruption that lowers the risk of enforcement.

In parallel, rapidly urbanizing cities display deep entanglement of informality and corruption, particularly within livelihoods and housing. Practices include manipulating informal workers’ organizations and embedding rent-seeking within urban services. Though formalization is a common governance remedy, its impact remains limited. Smart-city technologies offer mixed anti-corruption outcomes: some innovations reduce corruption, while others fall short (Zinnbauer, 2020; Alfano et al., 2024). Targeted, transparent, inclusive reforms show more promise than sweeping structural overhauls in fostering urban integrity.

• West Africa

Lavallée and Roubaud (2018) study bribery patterns in the informal sector across West African cities using original surveys. They reveal that corruption mechanisms in the informal sector closely mirror those in the formal sector, and that “constrained gazelles”—informal firms with the flexibility to exploit corrupt pathways—experience improved business performance in these environments. Lavallée and Roubaud (2009) further suggest that weak law enforcement, not necessarily overt corruption, is the primary driver of informality in seven West African cities. Even when only a minority of informal producers pay bribes, perceptions of corruption discourage efforts toward formalization.

• Sub-Saharan Africa

Baez-Camargo et al. (2021) highlight in Tanzania and Uganda how corruption and informality are structurally embedded within informal social networks. These networks, spanning public and private actors, facilitate bribery and procurement fraud—indicating that anti-corruption interventions must account for this structural embeddedness. Esaku (2021), focused on Uganda, finds a bidirectional relationship between corruption and the shadow economy: weak institutions push entrepreneurs into informality to avoid bribes and regulation. Addressing this cycle requires strengthening institutions and enforcement mechanisms.

Nguimkeu and Okou (2019) observe that informality functions as a social safety net, but simultaneously hampers development by reducing revenues and blocking formalization. They also propose that while digital technologies can boost productivity and financial access for informal firms, they may also expose regulatory vulnerabilities in the absence of supportive institutions.

Lah (2024) examines the interplay between formal, informal, and criminal sectors in developing economies: foreign capital can both spur and hinder growth. Corruption allows criminal elements to extract rents from productive and informal sectors, undermining progress. Policy responses must strike a balance—regulating to curb criminal capture without smothering entrepreneurship.

• Latin America and the Caribbean

In Brazil, Magalhães (2023) traces how social networks and societal tolerance of informality support informal firms. Reducing these tolerances constrains informal sector capital and labor. Improvements in financial access for formal entrepreneurs alongside stronger anti-corruption measures bolster GDP, capital accumulation, and wages. Singha (2025) extends this regionally, showing that corruption-related tax evasion and bribery distort markets and stifle innovation, thus promoting informality.

Bermúdez et al. (2024) conclude that both corruption and informality harm economic growth, though informality exerts the stronger negative impact. Education and human capital development can help mitigate these effects. However, Eng and Lim (2025) offer a contrarian view, suggesting that in underdeveloped nations, informality may temporarily support real GDP growth by reducing operational costs and providing employment, even if the outcomes are suboptimal. These apparently conflicting findings likely reflect differences in regional context, sectoral focus, and institutional capacity. Recognizing these nuances provides a more balanced understanding of how informality interacts with economic outcomes in Latin America.

• Russia and Eastern Europe

Smith and Thomas (2015) use electricity consumption as a proxy for informality in Russia (1995–2012), finding that higher corruption correlates with larger informal economies, whereas stronger regulation and multinational presence reduce informality. Local firm prevalence and unemployment also fuel informal growth. In Russia’s construction industry, Orlova and Boichev (2017) document how informal norms—including bribery and kickbacks—are essential for survival in a corrupt bureaucracy. Formal anti-corruption laws are bypassed by entrenched informal networks.

Sharafutdinova (2018) reveals that political instability and elite turnover increase perceived corruption, expanding reliance on informal economic networks. Frey (2024) investigates Serbia and Croatia, applying a non-essentialist lens that integrates historical and socio-political context. The study identifies informal practices in the private sector and offers regional, evidence-based anti-corruption recommendations. Gyurko (2021)’s comparison between Budapest and Glasgow shows that everyday corruption is normalized through rationalization, learning, and routine—challenging simplistic cultural explanations.

• Vietnam

Vu et al. (2024) find that formal firms in Vietnam are more likely to engage in rent-seeking bribery when local business conditions are poor. Improvements in the local business environment significantly reduce bribery. Pham (2024) adds that intellectual capital helps reduce informality—and thus potentially corruption—but only when institutions are strong.

C. 

Emerging Economies

Concerning sectoral distortions in the manufacturing and logistics sectors in the Philippines, Ravago et al. (2025) show that corruption-driven distortions raise costs and reduce investment in logistics and manufacturing, harming economic growth. These inefficiencies not only undermine competitiveness but also discourage both domestic and foreign investment, perpetuating structural underdevelopment.

Regarding general insights in developing contexts, Benjamin et al. (2014) identify burdensome regulation and corruption as dual drivers pushing firms into informality: corruption increases regulatory burdens and enforcement risks. Kelmanson et al. (2019) argue high corruption and bureaucratic complexity correlate with robust informal economies, though they struggle to clarify causality between informality and corruption—whether they are symptoms or reciprocal causes.

1.2.2. Thematic Sections

A. 

Globalization

Ajide and Dada (2023) find that economic and social globalization can reduce informality in developing countries through increased trade and cultural diffusion. Simatele and Bolarinwa (2024) warn that globalization may also amplify corruption in weak institutional environments, necessitating institutional reform alongside integration. Aluko et al. (2024) observe that corruption may encourage FDI in resource-rich contexts, but developed nations gain from low corruption. Nguyen et al. (2024) highlight how corruption can temporarily foster domestic investment by circumventing red tape, though Osuma et al. (2024) emphasize the long-term costs of corruption.

The construction and logistics sectors are particularly vulnerable to corruption-induced distortions, which raise operational costs, deter investment, and hinder overall economic development (Orlova & Boichev, 2017; Ravago et al., 2025). Addressing these challenges requires more than sector-specific solutions: across key themes such as globalization, the effectiveness of interventions depends fundamentally on the strength of institutions and the efficiency of regulatory frameworks. Thus, comprehensive institutional reform is essential to sustainably combat corruption, reduce informality, and promote inclusive economic growth.

B. 

Institutional Strength vs. Weakness

Yao (2024) asserts that governance quality—especially rule of law and regulatory efficiency—is a critical determinant of informality, as corruption undermines these institutions and promotes informal activity. Ohnsorge and Yu (2022) frame this relationship as a vicious cycle: corruption drives informality, which weakens state capacity and tax revenue, further enabling corruption.

Quiros-Romero et al. (2021) highlight informality as a structural and adaptive response to corrupted enforcement and regulatory overreach, while Francis (2019) emphasizes the microlevel costs of corruption for informal operators manifested through harassment and extortion, often without broader institutional connection. Schneider et al. (2010) identify tax burdens, overregulation, and public service quality as drivers of informality, treating corruption as one among several factors. OECD (2017, 2015, 1999) reports call for stronger detection and enforcement, but note that technocratic fixes may overlook vested interests benefiting from informality.

Nwosu and Folarin (2025) stress that education quality, taxation, finance, trade, and institutional strength are key determinants, with anti-corruption measures central. Tovar Jalles et al. (2025) show how corruption and informality undermine fiscal consolidation by reducing revenue and weakening fund management, while Bilan and Apostoaie (2025) note that high tax rates, bureaucratic complexity, and corruption deter entrepreneurship, reinforcing informality through eroded trust.

Corruption facilitates informality by allowing firms to evade regulations and exploit institutional gaps (Mishra & Ray, 2013; Lavallée & Roubaud, 2018; Magalhães, 2023), and informality can arise as an adaptive or strategic response to governance failures (Quiros-Romero et al., 2021), occasionally supporting economic growth by reducing costs and creating jobs in weak institutional settings (Eng & Lim, 2025). Thus, corruption and informality are deeply intertwined, reflecting both governance weaknesses and strategic responses by informal actors.

C. 

Digital Technologies

Zinnbauer (2020) emphasizes that smart-city technologies display mixed outcomes: while some forms of tech (e.g., ride-sharing platforms) can reduce corruption, many innovations fall short without concurrent institutional reform. Tactical, inclusive interventions may yield integrity more effectively than broad reforms. This aligns with findings from smart-city governance reviews: technology alone cannot transform governance—it must be paired with socio-economic, legal, regulatory, human capital, and inclusive reforms.

Gwaindepi (2024) finds that digital tools can improve tax collection efficiency, but only when backed by institutional reforms. Thuong and My-Linh (2025) show that combining good governance, FDI, streamlined procedures, anti-corruption measures, and tech support helps reduce informality. Castro and Guccio (2024) illustrate that strong institutions and anti-corruption policies significantly enhance firms’ technical efficiency, especially in low-governance environments.

As a hypothesis, corruption, particularly bribery, increases informality by elevating transaction costs and creating incentives for businesses to remain informal to avoid regulatory burdens and fixed costs. In high-corruption environments, firms are more likely to sustain informality due to the prohibitive costs of compliance within corrupt systems. Conversely, stronger regulatory environments with lower corruption are expected to reduce informality. Therefore, targeted anti-corruption interventions that enhance transparency and law enforcement can encourage formalization.

2. Method

This study employed a quantitative approach using data from the World Bank Enterprise Surveys, which provide comprehensive and comparable economic data from over 220,000 firms across 159 countries. These surveys capture key firm-level indicators relevant to informality, corruption, regulatory environment, and infrastructure constraints, offering a robust basis for analyzing the interplay among these factors in the context of sustainable economic development. Due to high multicollinearity between bribery incidence and depth, one variable was omitted to stabilize the models. Based on examination of the literature on corruption’s multidimensionality (Barasa, 2025; Guritno et al., 2025; Linhartova, 2025), this limitation is noted.

A notable limitation is that the data are cross-sectional and collected in different years across countries, which may affect temporal comparability. To support this methodological choice, previous studies using panel data (Gindling et al., 2025; Mara, 2025; Rdhaounia & Elweriemmi, 2025; Caucheteux et al., 2025; Cervantes Gil, 2025) have observed similar results, which helps contextualize and underpin my findings despite the cross-sectional nature of the data.

The dataset was imported from Excel into Stata 16 for numerical analysis. I used a limited set of informality indicators: competition with informal firms, initial registration, years without registration, and self-perceived constraints. To address missing dimensions, I refer to studies with broader indicators (Cruz et al., 2025; Guo & Lagos Mondragon, 2025). While this approach captures firm-level conditions, a limitation is the omission of broader socio-institutional variables (e.g., political commitment, human capital, or social trust) that have been considered in other studies (Noula & Petnga, 2025; Efthimiou, 2025). Their exclusion reflects data availability and comparability constraints, but future research should integrate such dimensions to enrich explanatory power.

Four indicators of informality serve as the endogenous variables, each reflecting different dimensions of informal economic activity. These are modeled against five exogenous variables, grouped into three key dimensions relevant for understanding barriers to formalization and sustainable business practices:

1.

Corruption Dimension

Focusing on bribery as a critical corruption proxy, two variables are considered:

• Bribery incidence—the percentage of firms experiencing at least one request for an informal payment.
• Bribery depth—the share of public transactions where a bribe or gift was requested.

2.

Regulatory Efficiency Dimension

Capturing the administrative burden through:

• Days required to obtain an operating license (two separate indicators).
• Senior management time spent dealing with the requirements of government regulation (%).

3.

Infrastructure Constraint Dimension

Represented by the share of firms identifying transportation as a major or very severe constraint, reflecting how physical infrastructure deficiencies increase operational costs and inhibit formalization.

Endogenous Variables of Informality

• Percentage of firms competing against unregistered/informal firms.
• Percentage of firms formally registered at inception.
• Number of years firms operated without formal registration.
• Percentage of firms reporting informal competitors as a major constraint

The econometric framework is based on ordinary least squares (OLS) regression models, designed to examine the relationships between informality and corruption while controlling for regulatory inefficiency and infrastructure constraints. While robust, OLS may not fully capture heterogeneity across 159 countries. More advanced methods were not feasible due to data and operational constraints.

Some nonlinear results may be difficult to interpret, as the observed effects of high corruption on formal registration could be influenced by statistical noise rather than causal relationships. While more advanced robustness checks or additional controls could clarify these dynamics, data and operational constraints limit these options. To ensure robustness, the variance inflation factor (VIF) test was applied after each model to rule out multicollinearity among explanatory variables, confirming the adequacy of the model specifications.

The general linear specification is:

$$Informality={\alpha }_0+{\alpha }_1\ast {Corruption}_i+{\alpha }_2\ast {Regulatoryefficiency}_i+{InfraestructureConstraint}_i+{\epsilon }_i$$

Given the complex, potentially nonlinear nature of corruption’s impact on informality, the analysis was extended to include quadratic terms of corruption indicators, allowing for the detection of threshold effects or diminishing returns of corruption on informality levels. The nonlinear model is specified as:

$$\begin{array}{c}Informality={\alpha }_0+{\alpha }_1\ast {Corruption}_i+{\alpha }_2\ast {Corruptionsquare}_i+{\alpha }_2\ast {Regulatoryefficiency}_i\hfill \\ \hspace{1em}\hspace{1em}\hspace{1em} \hspace{1em}\hspace{1em}+{InfraestructureConstraint}_i+{\epsilon }_i\hfill \end{array}$$

The inclusion of squared corruption terms significantly improves model fit, highlighting the nonlinear dynamics between corruption and informal economic activity. The robustness of these findings is supported by consistent results across different informality measures and the absence of multicollinearity confirmed by VIF statistics. This study acknowledges the limitation of potential overgeneralization, as findings may not fully capture contextual differences across countries and institutional settings.

This methodological approach contributes to the sustainability literature by emphasizing the importance of governance factors such as corruption and regulation in shaping informal economies, which have direct implications for sustainable development goals. Addressing corruption and improving regulatory and infrastructural conditions are pivotal to facilitating formalization, enhancing economic transparency, and ultimately fostering sustainable and inclusive economic growth.

3. Results

Econometric analysis of World Bank Enterprise Survey data shows that corruption, particularly bribery incidence, significantly impacts business informality. Firms facing higher corruption levels are more likely to compete with informal businesses, while infrastructure constraints also increase informality. Regulatory efficiency generally has no significant effect. Models including nonlinear corruption effects reveal that corruption’s influence on informality intensifies at higher corruption levels. These findings remain consistent after addressing multicollinearity, suggesting that policies targeting corruption reduction and infrastructure improvement are key to addressing informality.

Figure 1 illustrates a clear positive relationship between corruption and informality. As corruption levels increase, firms are more likely to remain informal, avoiding regulatory burdens and informal costs. This pattern is consistent across the data, suggesting that corruption acts as a structural barrier to formalization. The figure underscores the role of weak institutional environments in sustaining informal economic activities. Overall, higher corruption correlates with higher persistence of informality.

The first model in

Table 3. OLS econometric model with four indicators of informality.

Variable	Informality 1	Informality 2	Informality 3	Informality 4
Corruption 1	1.38 ** (0.63)	0.61 * (0.34)	−0.03 (0.03)	0.01 (0.40)
Corruption 2	−1.35 * (0.73)	−1.05 (0.40)	0.03 (0.04)	0.17 (0.46)
Regulatory efficiency 1	−0.20 (0.25)	0.22 (0.13)	−0.01 (0.01)	0.02 (0.16)
Regulatory efficiency 2	−0.03 (0.05)	0.03 (0.03)	0.00 (0.00)	−0.04 (0.03)
Infrastructural constraints	0.56 *** (0.14)	−0.27 *** (0.07)	0.01 (0.01)	0.68 *** (0.09)
_cons	33.25 *** (3.61)	91.57 *** (1.93)	0.83 (0.18)	10.27 *** (2.27)

Notes: 1. Values in parentheses are standard errors. 2. Significance levels: * p < 0.1; ** p < 0.05; *** p < 0.01. 3. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025).

analyzes the percentage of firms competing against unregistered or informal firms (Informality 1). The results indicate that corruption significantly impacts informality. Specifically, bribery incidence (Corruption 1) shows a positive association with informality, with a coefficient of 1.38. This means that higher levels of bribery incidence correspond to a greater presence of firms facing informal competitors. Essentially, as bribery increases, formal firms tend to compete with a larger number of informal enterprises. Additionally, infrastructure constraints strongly increase informality, with a coefficient of 0.56, implying that transportation difficulties worsen informal competition. Regulatory efficiency, however, does not have a significant effect on this type of informality.

The second model focuses on the percentage of firms that were formally registered at the start of their operations (Informality 2). Here, the relationship with corruption is mixed. Bribery incidence has a positive, but weaker effect than in the first model (coefficient 0.63), suggesting a slight increase in the likelihood of formal registration in more corrupt environments. On the other hand, bribery depth (Corruption 2) shows no significant impact. Regulatory efficiency, measured by the time senior management spends on government regulations, has a small positive effect (coefficient 0.22), indicating that regulatory involvement might encourage formal registration. Other variables in this model do not show significant relationships.

The third model examines the number of years firms operate without formal registration (Informality 3). Corruption plays a negligible role here, with coefficients near zero for both bribery incidence and depth, indicating no strong influence on the duration of informal operations. Similarly, all regulatory efficiency and infrastructure constraints have minimal effects.

The fourth model looks at the percentage of firms that view competitors’ informal practices as a major or severe constraint (Informality 4). Infrastructure constraints emerge as a key factor, with a significant coefficient of 0.68. Firms facing transportation problems are more likely to perceive informal competition as a major business constraint. Again, neither corruption measures nor regulatory efficiency show significant effects on firms’ perceptions in this area.

Table 4. Identification of multicollinearity through VIF values.

Variable	Informality 1	Informality 2	Informality 3	Informality 4
Corruption 1	30.64	31.04	31.04	30.64
Corruption 2	30.06	30.47	30.47	30.06
Regulatory efficiency 1	1.16	1.16	1.16	1.16
Regulatory efficiency 2	1.14	1.14	1.14	1.14
Infrastructure constraints	1.12	1.12	1.12	1.12
Mean VIF	12.82	12.99	12.99	12.82

Notes: 1. VIF = variance inflation factor. 2. Values indicate multicollinearity among independent variables. 3. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025).

reveals significant multicollinearity in the models, particularly between the two corruption variables and among regulatory efficiency and infrastructure constraints, as indicated by high VIF values (above 10). This multicollinearity can bias coefficient estimates and complicate the assessment of individual variable effects. To address this, the variable Corruption 2 was removed, which improved model reliability.

After removing Corruption 2,

Table 5. OLS econometric models with four indicators of informality without multicollinearity.

Variable	Informality 1	Informality 2	Informality 3	Informality 4
Corruption 1	0.250 ** (0.125)	−0.277 *** (0.068)	−0.003 (0.006)	0.151 * (0.078)
Regulatory efficiency 1	−0.157 (0.249)	0.256 * (0.135)	−0.014 (0.012)	0.010 (0.155)
Regulatory efficiency 2	−0.036 (0.047)	0.022 (0.026)	0.003 (0.002)	−0.036 (0.029)
Infrastructure constraints	0.588 *** (0.139)	−0.251 *** (0.075)	0.010 (0.007)	0.672 *** (0.086)
_cons	33.454 *** (3.638)	91.729 *** (1.973)	0.823 *** (0.182)	10.248 *** (2.263)

Notes: 1. Standard errors are in parentheses. 2. Significance levels: * p < 0.1; ** p < 0.05; *** p < 0.01. 3. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025).

shows updated models with clearer results. Corruption 1 maintains a positive and significant effect on informal competition (Informality 1), reinforcing that bribery increases exposure to informal firms. Infrastructure constraints remain strongly associated with informality, while regulatory efficiency continues to lack significance in most cases. For Informality 2, Corruption 1 has a significant negative effect, suggesting that lower corruption promotes formal registration when firms start. Regulatory efficiency has a modest positive impact here, and infrastructure constraints negatively affect formal registration. The effects on Informality 3 and Informality 4 are weaker, but bribery still influences firms’ perceptions of informal competition.

Table 6. OLS econometric models for different quantiles without multicollinearity.

Variable	Informality 1 (0.05 Quantile)	Informality 1 (0.1 Quantile)	Informality 1 (0.2 Quantile)	Informality 1 (0.75 Quantile)
Corruption 1	0.472 *** (0.149)	0.371 ** (0.163)	0.267 * (0.160)	0.357 * (0.210)
Regulatory efficiency 1	−0.388 (0.297)	−0.465 (0.326)	−0.377 (0.318)	0.218 (0.420)
Regulatory efficiency 2	0.032 (0.057)	0.0001 (0.062)	−0.024 (0.061)	−0.011 (0.080)
Infrastructure constraints	−0.029 (0.166)	0.167 (0.182)	0.344 * (0.177)	0.629 *** (0.234)
_cons	14.123 *** (4.346)	17.312 *** (4.767)	20.185 *** (4.657)	41.885 *** (6.138)

Notes: 1. Standard errors are in parentheses. 2. Significance levels: * p < 0.1; ** p < 0.05; *** p < 0.01. 3. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025).

presents quantile regressions, showing that corruption’s impact on informality varies across countries with different informality levels. In countries with low informality (lower quantiles), bribery incidence strongly increases informal competition. In higher-informality countries (upper quantiles), corruption remains relevant, but less influential, while infrastructure constraints become more important. This indicates corruption has a more pronounced effect where informality is lower, such as in developed economies.

The models also reiterate that the relationship between regulatory efficiency and informality varies by quantile, though insignificant coefficients emerge in all informality quantiles. Infrastructure constraint are also insignificant in the lower quantiles (0.05 and 0.1), but show a significant positive relationship in the higher quantiles (0.75), with a coefficient of 0.629, indicating that infrastructure challenges become a more critical factor in countries with higher informality levels. The findings suggest that while corruption consistently influences informality across all quantiles, its effect is most pronounced in lower-informality countries. As informality increases in a country, the impact of corruption becomes somewhat weaker, with other factors like infrastructure constraints gaining more importance.

Figure 2 illustrates the nonlinear relationship between corruption and informality. Including the squared term for corruption improves the association compared to a simple linear specification, revealing that the correlation is stronger and more pronounced at higher levels of corruption. This highlights that nonlinear effects of corruption amplify informality, emphasizing the structural influence of extreme corruption on firms’ decisions to remain informal.

Table 7. OLS econometric model with four indicators of informality, including the nonlinear effect of corruption.

Variable	Informality 1	Informality 2	Informality 3	Informality 4
Corruption 1	0.033 (0.030)	−0.249 *** (0.067)	−0.004 (0.006)	0.066 (0.062)
Corruption 1 squared	0.010 *** (0.000)	−0.001 *** (0.000)	0.000 (0.000)	0.004 *** (0.000)
Regulatory efficiency 1	−0.098 * (0.059)	0.248 * (0.132)	−0.014 (0.012)	0.033 (0.122)
Regulatory efficiency 2	−0.003 (0.011)	0.017 (0.025)	0.003 (0.002)	−0.023 (0.023)
Infrastructure constraints	0.008 (0.035)	0.175 ** (0.078)	0.007 (0.007)	0.443 *** (0.072)
_cons	20.460 *** (0.896)	93.419 *** (2.012)	0.745 *** (0.190)	5.103 *** (1.864)

Notes: 1. Standard errors are in parentheses. 2. Significance levels: * p < 0.1; ** p < 0.05; *** p < 0.01. 3. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025).

explores nonlinear effects of corruption. The linear term for bribery incidence is often insignificant, but the squared term is significant and positive for Informality 1 and Informality 4, indicating that the effect of corruption on informality intensifies at higher corruption levels. In contrast, Informality 2 shows a negative nonlinear effect, suggesting that very high corruption might encourage formal registration as firms seek legitimacy in highly corrupt environments. Informality 3 remains unaffected by corruption’s nonlinear effects, implying firms that start informally tend to remain so regardless of corruption level.

Regarding the Informality 2 variable, the linear term for Corruption 1 shows a strongly significant effect, with a coefficient of −0.249 and a significance level of 1%. Likewise, the square of Corruption 1 remains significant with a small, but notable negative coefficient of −0.001 at 1% significance, indicating that at very low levels of corruption, the effect on formal registration of firms starting their operations increases. This also implies that while bribery could initially encourage informal practices, extremely high levels of corruption might encourage firms to register formally, possibly due to a perception that the formal system is an option of distinction in the market.

Regarding the Informality 3 variable, the linear effect of Corruption 1 remains insignificant, with a coefficient of 0.004. Similarly, the squared term of Corruption 1 is also insignificant, with a minimal coefficient approaching zero. This reinforces the idea that the influence of corruption is more complex. The insignificant effects of the squared term suggest that regardless of whether corruption increases or decreases, firms that begin with informal practices continue to operate informally over time. As corruption deepens, its effect neither diminishes nor reverses, with firms persisting with various strategies within prolonged informality.

In the Informality 4 model, the linear effect of Corruption 1 is again insignificant, with a coefficient of 0.066. However, the square of Corruption 1 shows a strong significant positive effect of 0.004 at 1% significance, suggesting that higher levels of corruption may increase the perception of informal competition as a significant constraint. This result highlights that at elevated levels of corruption, businesses may increasingly view the informal sector as a more formidable challenge. Indeed, the nonlinear effects of corruption across these models reveal that its impact on informality is not linear, but grows more complex and significant as corruption levels increase, influencing various aspects of firm behavior differently depending on the level of corruption present.

Table 8. Identification of multicollinearity with nonlinear effect of corruption (VIF values).

Variable	Informality 1	Informality 2	Informality 3	Informality 4
Corruption 1	1.29	1.29	1.29	1.29
Corruption 1 squared	1.22	1.22	1.22	1.22
Regulatory efficiency 1	1.21	1.21	1.21	1.21
Regulatory efficiency 2	1.14	1.14	1.14	1.14
Infrastructure constraints	1.11	1.11	1.11	1.11
Mean VIF	1.19	1.19	1.19	1.19

Notes: 1. Variance inflation factor (VIF) values indicate absence of multicollinearity. 2. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025).

shows the VIF test results, demonstrating that there is no multicollinearity in the models after incorporating the nonlinear effect of corruption. The VIF values for all variables, including Corruption 1 and its squared term (Corruption 1 square), remain well below the commonly accepted threshold of 10, with the highest value being 1.29. The mean VIF values across all four models are consistent at 1.19, indicating that the inclusion of the nonlinear term does not introduce significant collinearity. Additionally, the VIF values for other explanatory variables such as Regulatory efficiency 2 and infrastructure constraints also show low values (ranging from 1.11 to 1.21), further confirming the absence of multicollinearity. These results suggest that the nonlinear effect of corruption can be added to the model without causing any multicollinearity concerns, ensuring that the coefficient estimates are reliable and not distorted by highly correlated predictors.

The results from the OLS econometric models in

Table 9. OLS econometric models for different quantiles without multicollinearity, including the nonlinear effect of corruption.

Variable	Informality 1 (0.15 Quantile)	Informality 1 (0.25 Quantile)	Informality 1 (0.3 Quantile)	Informality 1 (0.32 Quantile)
Corruption 1	0.107 * (0.056)	0.104 * (0.055)	0.103 ** (0.051)	0.087 * (0.052)
Corruption 1 squared	0.011 *** (0.000)	0.010 *** (0.000)	0.010 *** (0.000)	0.010 *** (0.000)
Regulatory efficiency 1	−0.184 * (0.110)	−0.240 ** (0.109)	−0.214 ** (0.101)	−0.217 ** (0.102)
Regulatory efficiency 2	0.002 (0.021)	−0.021 (0.021)	−0.016 (0.019)	−0.015 (0.019)
Infrastructure constraints	−0.083 (0.065)	−0.034 (0.064)	−0.054 (0.060)	−0.051 (0.060)
_cons	15.527 *** (1.678)	18.928 *** (1.661)	19.351 *** (1.542)	19.737 *** (1.556)

Notes: 1. Values are coefficients with standard errors in parentheses. 2. Significance levels: * p < 0.1; ** p < 0.05; *** p < 0.01. 3. Source: World Bank Enterprise Surveys (https://www.enterprisesurveys.org/en/data, accessed on 2 August 2025) (World Bank, 2025).

with both linear and nonlinear effects of corruption also show that the influence of corruption on informality is more significant for certain quantiles. For the Informality 1 model for the 0.15, 0.3, and 0.32 quantiles, both the linear and nonlinear effects of Corruption 1 (bribery incidence) are statistically significant. The linear term for Corruption 1 is positive, with coefficients of 0.107, 0.104, 0.103, and 0.087, respectively, indicating that an increase in bribery incidence is associated with more informal competition. The square of Corruption 1 shows more significant positive coefficients across these quantiles, suggesting a nonlinear relationship where higher levels of corruption intensify the effect on informality, reinforcing the idea that the impact of corruption on informality increases at higher levels of corruption.

For example, at the 0.25 quantile, the linear effect of Corruption 1 is significant (0.104) at the 10% significance level, indicating that bribery incidence does have a direct impact on informality within this range. However, the nonlinear effect remains significant, with a coefficient of 0.010 at the 1% significance level, confirming that even when the linear effect is weak, the higher-order effect of corruption continues to influence informality. The consistent significance of the nonlinear term across various quantiles suggests that corruption exhibits a more complex, nonlinear relationship with informality, where its effect becomes more pronounced as corruption intensifies, particularly at lower quantiles of the global sample. This analysis underscores the importance of considering both linear and nonlinear effects to fully understand the dynamics between corruption and informality in different contexts.

4. Discussion

4.1. Informality

My results differ notably from those of Mishra and Ray (2013), who found that larger formal firms adopt informal practices as they grow, using corruption to gain new advantages. In contrast, my findings show that firms in highly corrupt environments tend to operate informally from the outset, treating informality as a necessity rather than a strategic post-growth choice. This supports La Porta and Shleifer (2014), who suggest that in contexts where formal institutions are weak or predatory, the formal framework is seen more as an obstacle than a support. This preemptive use of informality aligns with the broader literature that views informal economies not just as a result of regulatory avoidance, but as adaptive mechanisms in hostile institutional environments (Lavallée & Roubaud, 2009; Nguimkeu & Okou, 2019). In this view, informality functions as a means of survival and resilience rather than mere evasion.

Similar findings emerge in sub-Saharan Africa, where Baez-Camargo et al. (2021) reveal that informal firms rely on corruption to operate in the absence of strong institutions. My results similarly show that bribery often replaces formal regulatory engagement, reinforcing the view that informal activity is both structurally and socially embedded. Lavallée and Roubaud (2009) also emphasize that weak enforcement—not just overt corruption—is the main driver of persistent informality in West Africa, a dynamic mirrored in my data. Smith and Thomas (2015), using Russian regional data, confirm that corruption correlates with the size of the informal sector, with better regulation and institutional quality serving to reduce informality. This resonates with my findings, which highlight the need for comprehensive governance and regulatory reforms as central to reducing informal activity.

In Brazil, Magalhães (2023) identifies how informal networks, rooted in social norms and relationships, help perpetuate informality and corruption. My findings reflect similar dynamics, where informal practices are not isolated acts, but sustained through broader social networks, reinforcing the embeddedness of informality in everyday business conduct. These patterns align with Meagher (2013), who argues that informality and corruption function as intertwined systems rather than isolated responses, with informal firms often relying on both to circumvent exclusion from formal institutions. This dual dependency is confirmed in my findings, where informality and corruption operate as mutually reinforcing responses to institutional weakness.

Indeed, informality in corrupt environments emerges not merely as a choice, but as a structural adaptation shaped by governance deficits, weak enforcement, and social norms. As shown by Lavallée and Roubaud (2009) and Baez-Camargo et al. (2021), when institutions are weak or predatory, informality becomes a survival strategy. My results affirm that reducing informality requires first addressing these institutional and social roots: through improved governance, streamlined regulation, and efforts to rebuild trust in public systems. Without such reforms, formalization policies risk reinforcing the very dynamics they aim to resolve.

Beyond these general dynamics, my results also reveal marked regional disparities, with East Asia showing lower levels of informality compared to Latin America. Although a full causal assessment was beyond the scope of this study, comparative studies suggest that stronger state capacity, higher bureaucratic quality, and more effective regulatory enforcement contribute to East Asia’s relative success in containing informality. By contrast, persistent weaknesses in these governance dimensions continue to challenge Latin America. I highlight this as an important direction for future research, which could further disentangle the institutional mechanisms behind such regional variations.

4.2. Rent-Seeking Behavior and Corrupt Practices

Rent-seeking emerged prominently in the findings, especially in urban settings where corrupt practices are used to extract or maintain economic advantage. This aligns with Zinnbauer (2020), who notes that in urban areas of the Global South, particularly those adopting “smart city” approaches, informal markets persist due to systemic rent-seeking and entrenched corruption. Both studies stress that formalization and anti-corruption initiatives must be targeted and inclusive, as sweeping reforms without addressing local power dynamics are often ineffective.

Lavallée and Roubaud (2018) observe that in West Africa, corruption permeates both formal and informal sectors, operating through bureaucratic interfaces. While the findings share this view of corruption as a pervasive tool for navigating economic and administrative systems, I place greater emphasis on how corruption supports survival in the informal economy. Firms often use bribery as a defensive mechanism to avoid bureaucratic roadblocks or enforcement risks, as also seen in Baez-Camargo et al.’s (2021) case studies in Tanzania and Uganda.

Additionally, the findings support the argument by Magalhães (2023) that social networks function as enablers of corruption, linking firms and public officials in mutually beneficial, but institutionally damaging relationships. These networks institutionalize rent-seeking behaviors, making corruption harder to eradicate without disrupting the underlying social and political relationships that sustain them. This reinforces insights from Putnam (1993) and Granovetter (1985), who underscore the role of social capital and embedded ties in shaping economic behavior.

The findings also resonate with the empirical work of Smith and Thomas (2015), who argue that regulation and governance quality are critical for curbing rent-seeking. The findings of this study add to this by showing that without functioning state capacity, anti-corruption measures alone are unlikely to break entrenched practices. The complex, often contradictory role of corruption—as both a barrier to formalization and a means of economic participation—was also reflected in Meagher (2013). In my research, this ambivalence appears clearly: firms rely on corruption to function in rigid or exclusionary systems, but this same reliance entrenches their informality and weakens institutional development.

Certainly, my findings demonstrate that rent-seeking and corruption are not aberrations, but systemic features in high-informality environments. They are sustained by weak institutions, embedded social relationships, and strategic firm behavior. Tackling them requires more than formal rules: it demands political will, enforcement capacity, and a deep understanding of the socio-political context in which informal and corrupt practices are rooted.

Beyond the general dynamics discussed, comparative evidence suggests that stronger state capacity, higher bureaucratic quality, and more effective regulatory enforcement contribute to lower informality in some regions. In terms of policy implications, while the recommendations herein align with SDGs 8 and 16, I recognize that more context-specific, actionable steps are needed. Future research should explore tailored strategies for different regional and institutional settings to enhance the practical relevance of policy interventions.

5. Conclusions

This study provides compelling evidence of a strong and complex relationship between corruption and informality, particularly within highly corrupt environments where firms tend to adopt informal practices from the inception of their operations. These findings challenge the traditional view that informality emerges primarily as a consequence of firm growth or regulatory evasion, instead emphasizing that corruption actively incentivizes firms to operate informally as a deliberate survival strategy. This reinforces the notion that corruption is not only a byproduct, but a functional tool that facilitates informal economic activity, especially in contexts where formal institutional frameworks are weak or inaccessible.

Governance reforms emerge as a crucial recommendation to disrupt this cycle, underscoring the need for comprehensive institutional strengthening that addresses both the supply and demand sides of corruption. Improved regulatory environments coupled with targeted anti-corruption measures have the potential to reduce the size of the informal sector by making formalization more viable and attractive to firms. The analysis also highlights the role of social networks within political systems, particularly in developing countries, where these networks often serve as channels for corrupt practices and consequently reinforce the persistence of informality. This social embeddedness of corruption adds an additional layer of complexity to efforts aimed at formalizing informal sectors.

Moreover, the nonlinear nature of the corruption–informality nexus, supported by these data and aligned with previous research, reveals that firms can derive simultaneous benefits from both corruption and informality. This dynamic poses significant challenges for policymakers, suggesting that simplistic or one-dimensional approaches may be insufficient. Instead, a nuanced understanding of how corruption interacts with different segments of the informal economy is necessary to design effective interventions.

It is important to acknowledge certain limitations of this research. The analysis relies on cross-sectional data from varying years across countries, which may limit temporal comparability and the ability to capture dynamic changes over time, a limitation that is consistent with previous studies employing panel data. One variable was omitted due to multicollinearity, and the limitation and corruption’s multidimensionality are noted. This study relies on a limited set of informality indicators. To address unmeasured dimensions, I reference studies with broader indicators.

These findings are based on OLS models, which may not fully capture cross-country heterogeneity. This methodological limitation should be considered when interpreting the results. Nonlinear effects of corruption should be interpreted cautiously, as data limitations prevent full robustness checks. Additionally, the multifaceted nature of informal economies and the diversity of corruption types were not fully explored, potentially constraining the depth of the analysis. The nonlinear effects observed indicate that more sophisticated modeling approaches could further illuminate the intricacies of this relationship.

Future research should seek to expand geographic scope and temporal coverage, with a particular focus on regions and countries characterized by distinct corruption and informality profiles. In-depth country-specific case studies, especially in developing economies with entrenched informality, could yield richer insights and inform more context-sensitive policy designs. A limitation of this study is the omission of broader socio-institutional variables, which future research should integrate to strengthen explanatory depth.

Irrevocably, these findings contribute to a growing body of literature that positions corruption as a pivotal factor shaping the informal sector. By addressing corruption through comprehensive governance reforms and enhancing regulatory quality, policymakers can make significant strides toward reducing informality, fostering economic formalization, and promoting sustainable development. Future research should undertake comparative or subgroup analyses to generate more context-specific policy insights.

While the results point to possible nonlinear effects of corruption, the present study does not allow us the identification of the specific thresholds at which these effects intensify. This constitutes a limitation of this approach, and future research could employ nonlinear or threshold models to better capture these dynamics.