Measuring the Informal Economy in Latin America and the Caribbean Guillermo Vuletin

Route Map Introduction - Definitions - Why does the informal economy (IE) arise? - Why is it important to measure the IE? Methods of measuring the IE - Several → MIMIC Empirical study - Data - Preliminary evidence - MIMIC estimation results - Informal economy estimations - Relative contribution of each cause variable Conclusions

Introduction Definitions - Illegal or black economy: Involve illegal dealings. - Unreported economy: Legal and illegal activities that evade tax code. - Informal Economy (IE): Activities that do not comply with government-imposed taxes and regulations, Soto (1989). Activities “…unregulated by the institutions of society, in a legal and social environment in which similar activities are regulated”, Portes et al. (1989).

Why does the Informal Economy arise? - Benefits of informal economy: Avoid “excessive” taxes and regulations. Increase entry cost (e.g. license fees, registration requirements) Increase cost of remain legal (e.g. taxes, red tape, labor and environmental regulations) - Costs of informal economy: Not fully enjoy public and private services (police, courts, capital markets, insurance, social security)

Why is it important to measure the IE? - VAT tax evasion - Social security system viability. - Could reduce effectiveness of policies. IE (early 90s)Tax evasion (early 90s) New Zealand12 %5% Sweden16 %6 % Argentina21 %30% Honduras47 %35 % Bolivia66 %44 % tax evasion = 1 – actual VAT revenue 100 potential VAT revenue

Methods of measuring the IE Direct methods - Sample surveys Pros Detail information Cons - Sensitive to the way the questionnaire is formulated - Willingness to cooperate - Unlike to capture all IE activities

Indirect methods - Electricity approach Kauffman and Kaliberda (1996) Idea (time series) : Assuming an electricity-GDP elasticity close to one, then ∆ IE % = ∆ electricity consumption% - ∆ official GDP % Pros Simple and appealing Cons - Not all IE require electricity (personal services) - Extra information to pin down value of IE - Technical progress in electricity use - Differences in elasticities across countries We DO NOT have data for ECCU

- Transactions approach Feige (1979) Idea (time series) : Changes in Money not explained by Velocity or official GDP correspond to changes in IE. Money ∙ Velocity = Price ∙ Transactions Price ∙ Transactions = constant ( official GDP + IE ) Money ∙ Velocity = constant ( official GDP + IE) Pros Theoretically attractive Cons - Extra information to pin down value of IE - constant invariant over time - What is “Money” in a dollarized economy? Money = M Local Currency + exch. rate ∙ M Foreign Currency (M LC + e ∙ M FC ) ∙ Velocity = constant (official GDP + IE) We DO have data for ECCU

- Currency Demand approach Tanzi (1980) Idea (time series) : Changes in not explained by the underlined below variables correspond to changes in IE. Pros Most commonly used method Cons - Extra information to pin down value of IE - Not all transactions in the IE are paid in cash - What are Money and Cash in a dollarized economy? We DO have data for ECCU interest rate

- The Multiple Indicators, Multiple Causes Model (MIMIC) Frey and Weck (1983) Idea (time series and cross section) : - All previous methods consider just one indicator (e.g. electricity consumption or money or cash/M2) of all the effects of the IE. - The MIMIC consider multiple causes and multiple consequences of the IE. - It is based on structural equation model to estimate latent variables.

How does it work? causes indicators x 1 y 1 x 2 Informal Economy y 2 … ( IE ) … x 3 y 3 - First step: estimate and. - Second step: estimate relative distribution of IE using. - Third step: pin down the level value of IE using extra information: time series → some year IE cross section → some country IE

Empirical Study Several real causes and indicators. Similar to Loayza (1996): He calculate the size of the IE for 14 Latin American countries for “early 90s”. Cross section of 32 countries for “early 2000s” - 6 ECCU - 23 western hemisphere Chile Argentina Nicaragua Paraguay - Cyprus, Fiji and Malta

- Variables used and expected signs causes indicators / effects Informal Economy Tax burden Importance of agriculture Inflation Strength of enforcement system - + Labor rigidity Workers contributing to social security (% of LF) Gross enrolment ratio for secondary school Degree of unionization +

Correlation between causes and indicators Expected sign (+) Expected sign (-) - “Preliminary” evidence: Workers contributing to social security Gross enrolment ratio for secondary school Degree of unionization Tax burden Labor rigidity Importance of agriculture Inflation Strength of enforcement system

Unions (% of LF) Workers contributing to social security IE (% of LF) Secondary gross enrollment ratio *** ** *** *** ¤ Share of variance explained by the IE 35.1 % 76.3 % 57.2 % Share of IE variance explained by the causes 78.6% Overall model fit : Discrepancy function (CMIN) (p-value) : > 0.05 Goodness to fit (GFI) : > 0.9 Root mean square error of approximation (RMSEA) : 0 < MIMIC estimation results (Benchmark) Labor rigidity Importance of agriculture Inflation *** *** Tax burden

- Informal economy estimations

- Relative contribution of each cause variable

Conclusions We confirm that: - “Excessive” taxes and labor regulations raise the IE. - “High” inflation and importance of agriculture sector raise IE. - Higher IE reduce worker “benefits” and school attendance. Sizes and factors underling the IE vary considerably among countries. Some policy implications: - “ Excessive” taxes: lower and homogenizing effective tax rate across sectors in the economy. - “Excessive” labor regulations: labor market reform and flexibility. - “High” inflation: tighten monetary and fiscal policies. - “High” importance of agriculture sector: improve strength and expertise of government officials.