## 7. Determinants of Informality: Institutions

## Source details

**Canonical URL:** [7. Determinants of Informality: Institutions](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05160.pdf)

## Other formats

- [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05160.pdf.md)
- [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05160.pdf.json)

---

### Introduction
- Informal activity definition used: all economic activity that contributes to GDP but is currently unregistered and untaxed; focus primarily on unregulated production by firms that engage in tax evasion.
- Size of informal sector (summary from literature):
  - between 10 and 20 percent of aggregate output in developed countries;
  - more than a third of aggregate output in developing countries, reaching in some countries more than 50 percent (Schneider and Enste, 2002).
- Empirical literature links informality to:
  - tax burden (e.g., Cebula, 1997; Giles and Tedds, 2002),
  - entry costs (Auriol and Warlters, 2005),
  - institutional quality and regulatory burden, in particular labor regulation (Friedman and others, 2000; Johnson and others, 1997, 1998, 2000; Botero and others, 2004),
  - financial development (Straub, 2005).
- Recent trends: more micro-based, firm-level survey analysis; some work finds tax burden less important (Friedman and others, 2000).

### Analytical framework
- Model setup and key equations:
  - Firms produce y_i = a_i f(L), f’>0, f”<0; a_i uniformly distributed on [0,1].
  - Formal sector profits: P_i^F = a_i f(L) – wL – C, where C is cost of complying with regulatory requirements and bribes.
  - Informal sector expected profits: P_i^I = [a_i f(L) – wL](1-p), where p is probability of being caught.
  - Labor demand: a f'(L(a)) – w = 0, L(a) = G(w/a), G' < 0.
  - Worker vs informal-firm cutoff: [a f(L(a)) – wL(a)](1-p) = w.
  - Formal vs informal-firm cutoff: [a* f(L(a*)) – wL(a*)]p – C = 0; a* = a*(w; C/p).
- Theoretical propositions:
  - Proposition 1: At equilibrium, lowest productivity individuals become workers; intermediate productivities are informal entrepreneurs; high productivity are formal entrepreneurs.
  - Proposition 2: Informality is negatively associated with firms’ productivity and their size.
  - Proposition 3: A higher regulatory burden and a weaker legal enforcement give rise to a larger informal sector. Stronger legal enforcement also implies higher workers’ wage rate.
  - Proposition 4: The stronger the legal system, the less responsive the size of the informal sector to regulatory costs (d2 a*/dCdp < 0).
- Interpretation of parameters:
  - p captures strength of legal enforcement; higher p reduces expected gains from hiding output.
  - Simplifying assumption: when caught, firm is fined by the full amount of its profits.
  - Production technology assumed identical across sectors; model extensible to hiding fractions of activity.

### Empirical strategy and data
- Primary dataset: World Business Environment Survey (WBES) — firm-level survey with over 4,000 firms in 41 countries (sample reduced from original 10,000 firms in 80 countries due to missing variables).
  - Survey covers firms with a minimum of five employees; firms in WBES are registered.
  - Dependent variable: reported category of share of sales kept informal (SI), with responses:
    - j=1 if none at all,
    - j=2 if 1–10 percent,
    - j=3 if 11–20 percent,
    - j=4 if 21–30 percent,
    - j=5 if 31–40 percent,
    - j=6 if 41–50 percent,
    - j=7 if more than 50 percent.
- Firm-level variables: perceptions (rated 1–4) of obstacles: legal, financial, regulatory, corruption; detailed courts questions (rated 1–6).
- Firm size classification:
  - Small: 5 to 50 employees,
  - Medium: 51 to 500 employees,
  - Large: more than 500 employees.
- Country-level complementary data:
  - Rule of Law index (Kaufmann et al., 1999).
  - Cost of regulation and taxes (Djankov et al., 2002) measured as share of 1999 per capita GDP.
  - Financial development: private credit by deposit money banks and other financial institutions as a share of GDP (Beck et al., 2000).
  - Entrepreneurial talent proxied by average years of schooling in population over 25 and firm age.
  - GDP per capita used to control for development level.
- Correlations (Table 2 summary):
  - Informality is negatively correlated with real GDP per capita, educational levels, efficiency of government service delivery, rule of law, and availability of private credit (the latter correlation is very small).
  - Informality is positively correlated with regulatory burden and survey-based obstacles including financing, corruption, tax and regulation constraints, and enforceability of court decisions.

### Empirical model and identification
- Estimation method: ordered probit model for the 7-category hidden sales dependent variable.
- Reduced-form structure: SI_i = f(Z_i; p, C, a_i, S) where Z includes firm productivity proxies, firm size, regulation costs, and legal system quality.
- Expected empirical implications:
  - Smaller, less experienced, or less able firms more likely to hide sales (Propositions 1 and 2).
  - Higher regulation costs associated with greater informality (Proposition 3).
  - The effect of regulation costs on informality is moderated by legal system strength; stronger legal systems reduce responsiveness (Proposition 4).

### Results — Firm-level obstacles and informality
- Main empirical findings:
  - All firm level obstacles (financial, legal, corruption, and tax and regulatory) have a positive and significant effect on the incidence of informality, including when all obstacles are considered simultaneously (final specification: column 6).
  - Propensity for firms to hide sales is decreasing in the effectiveness of government service delivery.
  - Small firms tend to have a higher incidence of informality relative to large firms.
  - Firm age is negatively associated with informality, but this effect is not significant when fixed effects are included.
  - Educational attainment is negatively and significantly associated with informality.
- Marginal predicted probabilities (ordered probit fitted values, Table 4 / Table 3 column 6):
  - A firm that sees financing as a major obstacle has a 16 percent probability of having over 50 percent of its sales off the books; if financing is a minor obstacle the probability is 7.6 percent.
  - A firm that sees corruption as a major obstacle has a 17 percent probability of having over 50 percent of its sales off the books; if corruption is a minor obstacle the probability is 8 percent.
  - A firm that sees regulatory constraints as a major obstacle has a 16 percent probability of having over 50 percent of its sales off the books; if regulatory constraints are a minor obstacle the probability is 9 percent.

### Firm size interactions with obstacles
- Interaction specification captures country fixed effects, firm size level, and firm-specific idiosyncratic exposures.
- Key interaction results:
  - Financing obstacles increase informality for small firms relative to medium firms; no statistically significant differing effect between medium and large firms (Table 5, column 1).
  - Corruption and taxes and regulatory obstacles have a larger effect on the extent of informality in both small and large firms (Table 5, columns 2 and 3).
  - Legal obstacles have a larger effect on informality for large firms relative to small and medium firms (Table 5).
  - When all obstacles and interactions are included, financing and tax regulation constraints appear to have a larger effect on the incidence of informality for small firms; legal and corruption interactions become no longer significant (possible high collinearity).
- Selected coefficient magnitudes from Table 5 (dependent variable: percent of total sales kept off the books):
  - Small (baseline effect): positive coefficients in columns (1)-(6) range from 0.125 to 0.148 with significance *** (exact column coefficients: 0.148, 0.123, 0.128, 0.141, 0.129, 0.125).
  - Large (baseline effect): negative coefficients in columns (1)-(6) range from -0.212 to -0.806 in different tables.

### Disaggregated constraints (Table 6)
- Financing subcomponents that significantly increase informality:
  - Need for special connections with banks (finance constraint-special connections): coefficient 0.059 (0.021)***.
  - Access to non-bank equity: coefficient 0.048 (0.021)**.
  - Inadequate credit information on customers: coefficient 0.067 (0.021)***; interaction Inadequate credit * Large: 0.113 (0.051)**.
- Regulatory subcomponents that significantly increase informality:
  - Customs/foreign trade regulations: coefficient 0.061 (0.019)***; interaction Customs regulations * Large: 0.095 (0.047)**.
  - Labor regulations: coefficient 0.045 (0.022)**.
  - Fire and safety regulations: coefficient 0.053 (0.022)**.
- Corruption measures:
  - "It is common for firms to make irregular payments": coefficient -0.095 (0.017)***, with interaction Common to make additional payments * Small: -0.089 (0.026)***.
  - Percent of sales in bribes: coefficient 0.06 (0.019)***; Percent of sales in bribes * Large: 0.085 (0.049)*.
- Legal constraint components:
  - Courts-honest: coefficient 0.033 (0.016)**; Courts honest * Small: 0.048 (0.022)**.
  - Courts-enforceability: coefficient 0.024 (0.015); Courts enforceability * Large: 0.097 (0.039)**.
  - Courts-quick and Courts-affordable are not significant in the reported breakdown.

### Country-specific institutions and interactions
- Country-wide institutional variables: rule of law, regulatory burden, availability of financing (private credit / GDP).
- Key findings:
  - Rule of law is negatively associated with informality: e.g., rule of law coefficient -0.565 (0.070)*** in column (1).
  - Regulatory burden generally has a positive effect on informality, but is not statistically significant in all specifications (example: 0.091 (0.031)*** in column (1)).
  - Private credit / GDP is significant but often of the unexpected sign in many specifications (example: private sector credit coefficient 1.587 (0.159)*** in some columns). Instrumenting private credit using dummies for origin of legal system yields coefficients that are either insignificant or continue to be of the wrong sign in several specifications.
- Interaction tests:
  - Rule of law * Regulatory burden: interaction coefficient -0.162 (0.075)** (column 5) — consistent with the model prediction that a stronger legal system reduces the impact of regulatory burden on informality.
  - Private sector credit (IV) * Rule of law: interaction coefficient -0.270 (0.142)* (column 6) — indicates financial development reduces informality in countries with a better rule of law, even if financial development alone does not.

### Theoretical reinforcement (Appendix I)
- Extended model: firms may hide a fraction n_i of activity; regulatory cost C depends on formal share and detection probability p depends on informal share.
- Example functional forms: C(1-n)=β(1-n) and p(n)=γn with 0 < β, γ < 1.
- Comparative statics under example:
  - dn/dC > 0 (extent of informality increases with regulatory burden),
  - dn/dp < 0 (extent of informality decreases with detection/enforcement).
- These comparative statics reinforce empirical results: informality rises with regulatory burden and falls with the quality of enforcement.

### Summary conclusions (V. Concluding remarks)
- Informality is widespread; the informal sector can constitute half of economic activity in some developing and transition economies.
- Using the WBES firm-level survey combined with country institutional indicators, the analysis finds:
  - Quality of the legal system is an important predictor of informality.
  - Controlling for rule of law, there is no significant evidence that lower regulatory burden or more limited access to bank credit leads to lower informality as measured by the share of hidden sales.
  - Results are consistent with the general-equilibrium model: strength of the legal system determines expected punishment for being informal, shaping firms’ informality decisions.

### Key summary statistics (from Table 1 and related tables)
- Number of observations in sample: 4,477.
- Percent of sales not reported to tax authorities (SI variable): Mean 2.93, Standard Deviation 2.16, Minimum 1.00, Maximum 7.00.
- Small firms: Mean 0.37; Large firms: Mean 0.18.
- Efficiency of service delivery: Mean 3.11, Standard Deviation 1.17, range 1.00–6.00.
- General constraint-financing: Mean 2.81, Standard Deviation 1.14, range 1.00–4.00.
- General constraint-corruption: Mean 2.43, Standard Deviation 1.17, range 1.00–4.00.
- General constraint-taxes and regulations: Mean 3.07, Standard Deviation 0.98, range 1.00–4.00.
- Rule of law (country-wide): Mean 4.19, Standard Deviation 0.86, range 3.10–6.01.
- Entry costs (regulatory burden): Mean 0.58, Standard Deviation 0.75, range 0.02–4.95.
- Private credit / GDP: Mean 0.54, Standard Deviation 0.41, range 0.07–1.41.
- Real GDP per capita (mean): 6,474.63; Standard Deviation 8,466.95; Minimum 449.93; Maximum 31,772.66.
- Educational attainment (mean): 7.77, Standard Deviation 2.23, range 2.45–12.25.

*Source: _wp05160 - 7. Determinants of Informality: Institutions; 2. Firm level obstacles including financial, legal, corruption, and tax and regulatory obstacles.*

### References..............................................................................................................

### References

### Tables
- 1.  Summary Statistics..............................................................................................................17
- 2.  Correlation Matrix ..............................................................................................................18
- 3.  Determinants of Informality: Basic Specification ..............................................................19
- 4.  Predicted Level of Informality Rate from Ordered Probit Results in 
     Table 3, Column (6) ............................................................................................................20
- 5.  Determinants of Informality: Firm Size and Obstacles ......................................................21
- 6.  Determinants of Informality: Firm Size and Breakdown of Obstacles...............................22

*Source: _wp05160 - References.*

### 7.  Determinants of Informality: Institutions ..........................................................................

### 7.  Determinants of Informality: Institutions

### Introduction
- Informal activity definition used: all economic activity that contributes to GDP but is currently unregistered and untaxed; focus primarily on unregulated production by firms that engage in tax evasion.
- Size of informal sector (summary from literature):
  - between 10 and 20 percent of aggregate output in developed countries;
  - more than a third of aggregate output in developing countries, reaching in some countries more than 50 percent (Schneider and Enste, 2002).
- Concerns: effect of informality on economic growth and erosion of the tax base, with detrimental effects on publicly provided goods and services.
- Empirical literature links informality to:
  - tax burden (e.g., Cebula, 1997; Giles and Tedds, 2002),
  - entry costs (Auriol and Warlters, 2005),
  - institutional quality and regulatory burden, in particular labor regulation (Friedman and others, 2000; Johnson and others, 1997, 1998, 2000; Botero and others, 2004),
  - financial development (Straub, 2005).
- Recent trends: more micro-based, firm-level survey analysis; some work finds tax burden less important (Friedman and others, 2000).

### Analytical framework
- Model highlights:
  - Firms produce y_i = a_i f(L), f’>0, f”<0 (equation (1)); a_i interpreted as firm-specific productivity, uniformly distributed on [0,1].
  - Formal sector profits: P_i^F = a_i f(L) – wL – C (equation (2)), where C is cost of complying with regulatory requirements and bribes in the formal sector.
  - Informal sector expected profits: P_i^I = [a_i f(L) – wL](1-p) (equation (4)), where p is the probability of being caught (interpreted as dependent on quality of the legal system).
  - Labor demand determined by a f'(L(a)) – w = 0 (equation (5)); L(a) = G(w/a), G' < 0 (equation (6)).
  - Worker vs informal-firm cutoff determined by [a f(L(a)) – wL(a)](1-p) = w (equation (7)).
  - Formal vs informal-firm cutoff determined by [a* f(L(a*)) – wL(a*)]p – C = 0 (equation (8)); a* = a*(w; C/p).
- Key theoretical propositions:
  - Proposition 1: At equilibrium, lowest productivity individuals become workers; those with intermediate productivities are informal sector entrepreneurs; and high productivity people are formal sector entrepreneurs.
  - Proposition 2: Informality is negatively associated with firms’ productivity and their size.
  - Proposition 3: A higher regulatory burden and a weaker legal enforcement give rise to a larger informal sector. Stronger legal enforcement also implies higher workers’ wage rate.
  - Proposition 4: The stronger the legal system, the less responsive the size of the informal sector to regulatory costs (d2 a*/dCdp < 0).
- Model assumptions and interpretations:
  - p interpreted as capturing strength of legal enforcement; higher p reduces expected gains from hiding output.
  - When caught, firm is fined by the full amount of its profits (assumption for simplicity).
  - Production technology assumed identical in both sectors for tractability; model can be extended to hiding fractions of activity (appendix).

### Empirical strategy and data
- Primary dataset: World Business Environment Survey (WBES) — firm-level survey with over 4,000 firms in 41 countries (sample reduced from original 10,000 firms in 80 countries due to missing variables).
  - Survey covers firms with a minimum of five employees.
  - Firms in WBES are registered; variable on hidden sales measures percent of total sales typical firm in area keeps “off the books”.
- Dependent variable: reported category of share of sales kept informal (SI), with responses:
  - j=1 if none at all,
  - j=2 if 1–10 percent,
  - j=3 if 11–20 percent,
  - j=4 if 21–30 percent,
  - j=5 if 31–40 percent,
  - j=6 if 41–50 percent,
  - j=7 if more than 50 percent.
- Firm-level survey variables include perceptions (rated 1–4) of obstacles: legal, financial, regulatory, corruption; additional detailed questions on courts (rated 1–6) and quality/efficiency of court system (1 to 6).
- Firm size classification:
  - Small: 5 to 50 employees,
  - Medium: 51 to 500 employees,
  - Large: more than 500 employees.
- Sample summary statistics (Table 1 highlights):
  - Small firms: 37 percent of sample;
  - Large firms: 18 percent of sample (more than 500 employees);
  - Ownership: 90 percent private;
  - Nationally owned: slightly over 80 percent;
  - Sector distribution: services 44 percent, manufacturing 37 percent;
  - Firm age: average 20 years, some firms over 400 years old.
  - Firms report financing and corruption obstacles as minor to moderate on average; tax regulations/administration pose a moderate to major obstacle on average.
  - A larger share of small and medium sized firms operate in informal settings (Panel B of Table 1).
  - Firms with higher degrees of informality report moderate to major obstacles across constraint categories.
- Country-level complementary data:
  - Rule of Law index from Kaufmann et al. (1999) as proxy for quality of legal institutions/enforcement.
  - Cost of regulation and taxes from Djankov et al. (2002) — regulatory burden measured as direct monetary costs and opportunity cost of time related to establishing a business, as a share of 1999 per capita GDP.
  - Financial development proxied by private credit by deposit money banks and other financial institutions as a share of GDP from Beck et al. (2000).
  - Entrepreneurial talent proxied by average years of schooling in population over 25 and firm age.
  - GDP per capita used to control for development level.
- Correlations (Table 2 summary):
  - Greater informality is negatively correlated with real GDP per capita, educational levels, efficiency of government service delivery, rule of law, and availability of private credit (the latter correlation is very small).
  - Informality is positively correlated with regulatory burden and survey-based obstacles including financing, corruption, tax and regulation constraints, and enforceability of court decisions.

### Empirical model and identification
- Estimation method: ordered probit model for the 7-category hidden sales dependent variable.
- Reduced-form structure: SI_i = f(Z_i; p, C, a_i, S) where Z includes firm productivity proxies, firm size, regulation costs, and legal system quality.
- Expected empirical implications:
  - Smaller, less experienced, or less able firms more likely to hide sales (Propositions 1 and 2).
  - Higher regulation costs associated with greater informality (Proposition 3).
  - The effect of regulation costs on informality is moderated by legal system strength; stronger legal systems reduce responsiveness (Proposition 4).

### Results (overview up to presented content)
- Basic specification reported in Table 3 uses firm characteristics, educational attainment, and country fixed effects.
- The effectiveness of government service delivery is included as a proxy for benefits of operating formally beginning in column 1 (description of model setup and objectives included; detailed coefficient estimates are in Table 3 which follows).

*Source: _wp05160 - 7.  Determinants of Informality: Institutions (IMF working paper PDF content unit).*

### 2. Firm level obstacles including financial, legal, corruption, and tax and regulatory obstacles

### 2. Firm level obstacles including financial, legal, corruption, and tax and regulatory obstacles

### Main empirical findings on firm-level obstacles and informality
- All firm level obstacles (financial, legal, corruption, and tax and regulatory) have a positive and significant effect on the incidence of informality, including when all obstacles are considered simultaneously (final specification: column 6).
- Propensity for firms to hide sales is decreasing in the effectiveness of government service delivery (efficiency of service delivery).
- Small firms tend to have a higher incidence of informality relative to large firms.
- Firm age is negatively associated with informality, but this effect is not significant when fixed effects are included, suggesting unobserved heterogeneity across firms and countries correlated with firm age.
- Educational attainment is negatively and significantly associated with informality.

- Marginal predicted probabilities (from ordered probit fitted values, Table 4 / Table 3 column 6):
  - A firm that sees financing as a major obstacle has a 16 percent probability of having over 50 percent of its sales off the books; if financing is a minor obstacle the probability is 7.6 percent.
  - A firm that sees corruption as a major obstacle has a 17 percent probability of having over 50 percent of its sales off the books; if corruption is a minor obstacle the probability is 8 percent.
  - A firm that sees regulatory constraints as a major obstacle has a 16 percent probability of having over 50 percent of its sales off the books; if regulatory constraints are a minor obstacle the probability is 9 percent.

### Firm size interactions with obstacles (Table 5 and discussion)
- Specification includes interactions between size dummies (small, large) and individual obstacles to capture:
  - (i) country-level effects (fixed effects),
  - (ii) firm size level,
  - (iii) firm-specific idiosyncratic exposures (interaction terms).
- Key interaction results:
  - Financing obstacles increase the incidence of informality for small firms relative to medium firms; no statistically significant differing effect between medium and large firms (Table 5, column 1).
  - Corruption and taxes and regulatory obstacles have a larger effect on the extent of informality in both small and large firms (Table 5, columns 2 and 3).
  - Legal obstacles have a larger effect on informality for large firms relative to small and medium firms (Table 5).
  - When all obstacles and interactions are included, financing and tax regulation constraints appear to have a larger effect on the incidence of informality for small firms; legal and corruption interactions become no longer significant (possible high collinearity).

- Selected coefficient magnitudes from Table 5 (dependent variable: percent of total sales kept off the books):
  - Small (baseline effect): positive coefficients in columns (1)-(6) range from 0.125 to 0.148 with significance *** (exact column coefficients: 0.148, 0.123, 0.128, 0.141, 0.129, 0.125).
  - Large (baseline effect): negative coefficients in columns (1)-(6) range from -0.212 to -0.806 in different tables; see respective table entries for exact values and significance.

### Disaggregated elements of constraints (Table 6)
- Financing subcomponents that significantly increase informality:
  - Need for special connections with banks (finance constraint-special connections): coefficient 0.059 (0.021)***.
  - Access to non-bank equity: coefficient 0.048 (0.021)**.
  - Inadequate credit information on customers: coefficient 0.067 (0.021)***; interaction Inadequate credit * Large: 0.113 (0.051)** (inadequate credit particularly problematic for large firms).
- Regulatory subcomponents that significantly increase informality:
  - Customs/foreign trade regulations: coefficient 0.061 (0.019)***; interaction Customs regulations * Large: 0.095 (0.047)** (particularly problematic for large firms).
  - Labor regulations: coefficient 0.045 (0.022)**.
  - Fire and safety regulations: coefficient 0.053 (0.022)**.
- Corruption measures:
  - "It is common for firms to make irregular payments" reported coefficient -0.095 (0.017)*** (see Table 6, column 5) but interactions show:
    - Common to make additional payments * Small: -0.089 (0.026)*** (small firms in industries where irregular payments are common are more likely to increase informality).
  - Percent of sales in bribes: coefficient 0.06 (0.019)***; Percent of sales in bribes * Large: 0.085 (0.049)* (a larger share of sales in bribes particularly increases informality for large firms).
- Legal constraint components:
  - Courts-honest: coefficient 0.033 (0.016)**; Courts honest * Small: 0.048 (0.022)** (dishonesty of courts is a greater problem for small firms).
  - Courts-enforceability: coefficient 0.024 (0.015); Courts enforceability * Large: 0.097 (0.039)** (lack of enforceability of court decisions is more problematic for large firms).
  - Courts-quick and Courts-affordable are not significant in the reported breakdown.

### Country-specific institutions and interactions (Table 7 and discussion)
- Three country-wide institutional variables included: rule of law, regulatory burden, availability of financing (private credit / GDP).
- Key findings:
  - Rule of law is negatively associated with informality: stronger legal institutions reduce the incidence of informality (Table 7, rule of law coefficients: e.g., -0.565 (0.070)*** in column (1); other columns show negative coefficients with varying significance).
  - Regulatory burden generally has a positive effect on informality, but is not statistically significant in all specifications (reported coefficient examples: 0.091 (0.031)*** in column (1); other columns show mixed significance).
  - Private credit / GDP (financial development indicator) is significant but often of the wrong sign in many specifications (e.g., private sector credit coefficient 1.587 (0.159)*** in some columns). Instrumenting private credit using dummies for origin of legal system (French/English) yields coefficients that are either insignificant or continue to be of the wrong sign in several specifications.
- Interaction tests:
  - Rule of law * Regulatory burden: interaction coefficient -0.162 (0.075)** (column 5) — consistent with model prediction that a stronger legal system reduces the impact of regulatory burden on informality.
  - Private sector credit (IV) * Rule of law: interaction coefficient -0.270 (0.142)* (in column 6) — indicates financial development reduces informality in countries with a better rule of law, even if financial development alone does not.

### Theoretical reinforcement (Appendix I)
- Extended model allows firms to hide a fraction n_i of activity; regulatory cost C depends on formal share and detection probability p depends on informal share.
- Under example functional forms C(1-n)=β(1-n) and p(n)=γn with 0 < β, γ < 1, differentiation yields:
  - dn/dC > 0 (extent of informality increases with regulatory burden),
  - dn/dp < 0 (extent of informality decreases with detection/enforcement).
- These comparative statics reinforce main empirical results: informality rises with regulatory burden and falls with the quality of enforcement.

### Summary conclusions (V. Concluding remarks)
- Informality is widespread; the informal sector can constitute half of economic activity in some developing and transition economies.
- Using the WBES firm-level survey combined with country institutional indicators, the analysis finds:
  - Quality of the legal system is an important predictor of informality.
  - Controlling for rule of law, there is no significant evidence that lower regulatory burden or more limited access to bank credit leads to lower informality as measured by the share of hidden sales.
  - Results are consistent with the general-equilibrium model: strength of the legal system determines expected punishment for being informal, shaping firms’ informality decisions.

### Key summary statistics (from Table 1 and related tables)
- Number of observations in sample: 4,477 (Total number of firms in the sample is 4,477).
- Percent of sales not reported to tax authorities: Mean 2.93, Standard Deviation 2.16, Minimum 1.00, Maximum 7.00 (WBES Variables).
- Small firms: Mean 0.37, Large firms: Mean 0.18.
- Efficiency of service delivery: Mean 3.11, Standard Deviation 1.17, range 1.00–6.00.
- General constraint-financing: Mean 2.81, Standard Deviation 1.14, range 1.00–4.00.
- General constraint-corruption: Mean 2.43, Standard Deviation 1.17, range 1.00–4.00.
- General constraint-taxes and regulations: Mean 3.07, Standard Deviation 0.98, range 1.00–4.00.
- Rule of law (country-wide): Mean 4.19, Standard Deviation 0.86, range 3.10–6.01.
- Entry costs (regulatory burden): Mean 0.58, Standard Deviation 0.75, range 0.02–4.95.
- Private credit / GDP: Mean 0.54, Standard Deviation 0.41, range 0.07–1.41.
- Real GDP per capita (mean): 6,474.63; Standard Deviation 8,466.95; Minimum 449.93; Maximum 31,772.66.
- Educational attainment (mean): 7.77, Standard Deviation 2.23, range 2.45–12.25.

*Source: _wp05160 - 2. Firm level obstacles including financial, legal, corruption, and tax and regulatory obstacles (IMF PDF content unit).*

### REFERENCES

### REFERENCES

### Bibliographic entries
- Allingham, M., and A. Sandmo, 1972, “Income Tax Evasion: A Theoretical Aanalysis,” Journal of Public Economics, Vol. 1, pp. 323–38.
- Arrow, K., 1962, “Economic Welfare and the Allocation of Resources for Invention,” in The Rate and Direction of Inventive Activity: Economic and Social Factors, ed. by National Bureau of Economic Research (Princeton, New Jersey: Princeton University Press).
- Auriol, E., and M. Warlters, 2005, “Taxation Base in Developing Countries,” Journal of Public Economics, Vol. 89, pp. 625–46.
- Azuma, Y., and H. Grossman, 2002, “A Theory of the Informal Sector,” NBER Working Paper No. 8823 (Cambridge, Massachusetts: National Bureau of Economic Research).
- Beck, T., A. Demirgüç-Kunt, and R. Levine, 2000, “A New Database on the Structure and Development of the Financial Sector,” World Bank Economic Review, Vol. 14, pp. 597–605.
- Botero, J., S. Djankov, R. La Porta, F. Lopez-de-Silanes, and A. Shleifer, 2004, “The Regulation of Labor,” Quarterly Journal of Economics, Vol. 119, No. 4, pp. 1339–82.
- Cebula, R., 1997, “An Empirical Analysis of the Impact of Government Tax and Auditing Policies on the Size of the Underground Economy: The Case of the United States, 1973–94,” American Journal of Economics and Sociology, Vol. 56, pp. 73–186.
- Dabla-Norris, E., and A. Feltenstein, 2005, “The Underground Economy and Its Macroeconomic Consequences,” Journal of Policy Reform, Vol. 8, pp. 153–74.
- De Soto, H., 1989, The Other Path (New York: Harper and Row).
- Djankov, S., R. La Porta, F. Lopez-de-Silanes, and A. Shleifer, 2002, “The Regulation of Entry,” Quarterly Journal of Economics, Vol. 117, pp. 1–37.
- Erickson, L., 2004, “Bureaucratic Rent-Seeking and Firm Size: An Examination of Formality and Informality” (unpublished: Washington: International Monetary Fund).
- Friedman, E., S. Johnson, D. Kaufman, and P. Zoido-Lobatón, 2000, “Dodging the Grabbing Hand: The Determinants of Unofficial Activity in 69 Countries,” Journal of Public Economics, Vol. 76, pp. 459–92.
- Giles, D., and L. Tedds, 2002, “Taxes and the Canadian Underground Economy,” Canadian Tax Paper No. 106 (Toronto: Canadian Tax Foundation).

- 31 -

- Johnson, S., D. Kaufmann, and A. Shleifer, 1997, “The Unofficial Economy in Transition,” Brookings Papers on Economic Activity: 2, Brookings Institution, pp. 159–239.
- Johnson, S., D. Kaufmann, J. McMillan and C. Woodruff, 2000, “Why Do Firms Hide? Bribes and Unofficial Activity after Communism,” Journal of Public Economics, Vol. 76, pp. 495–520.
- Johnson, S, D. Kaufmann, and P. Zoido-Lobaton, 1998, “Regulatory Discretion and the Unofficial Economy,” American Economic Review, Papers and Proceedings, Vol. 88 (May) pp. 387–92.
- Johnson, S., J. McMillan and C. Woodruff, 2002, “Property Rights and Finance,” American Economic Review, Vol. 92, No. 5, pp. 1335–56.
- Kaufmann, D., A. Kraay, and P. Zoido-Lobato, 1999, Available on the Web at http://www.worldbank.org /wbi/governance/datasets.htm#dataset.
- La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny, 1997, “Legal Determinants of External Finance,” Journal of Finance, Vol. 52, pp. 1131–50.
- ———, 1998, “Law and Finance,” Journal of Political Economy, Vol. 106, pp. 1113–55.
- Loayza, N., 1996, “The Economics of the Informal Sector: A Simple Model and some Empirical Evidence from Latin America,” Carnegie-Rochester Conference Series on Public Policy, Vol. 45, pp. 129–62.
- Lucas, R., 1978, “On the Size Distribution of Business Firms,” Bell Journal of Economics, Vol. 9, pp. 508–23.
- Maddala, G., 1983, Limited Dependent and Qualitative Models in Econometrics (Cambridge, England: Cambridge University Press).
- Marcouiller, D., and L. Young, 1995, “The Black Hole of Graft: The Predatory State and the Informal Economy,” American Economic Review, Vol. 85, pp. 630–46.
- Rauch, J.E., 1991, “Modeling the Informal Sector Formally,” Journal of Development Economics, Vol. 35, pp. 33–47.
- Sarte, P., 2000, “Informality and Rent-Seeking Bureaucracies in a Model of Long-Run Growth,” Journal of Monetary Economics, Vol. 46, pp. 173–97.
- Schneider, F., and D. Enste, 2002, “Shadow Economies: Size, Causes and Consequences,” Journal of Economic Literature, Vol. 38, pp. 77–114.
- Schneider, F., and R. Klingmair, 2003, “Shadow Economies Around the World: What Do We Know?” (upublished manuscript; Austria: University of Linz).

- 32 -

- Shleifer, A., 1997, “Schumpeter Lecture: Government in Transition,” European Economic Review, Vol. 41, pp. 385–410.
- Straub, S., 2005, “Informal Sector: The Credit Market Channel,” Journal of Development Economics (forthcoming).

*Source: _wp05160 - REFERENCES*

---


_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05160.pdf_