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SEBA – IE CASS – IEHAS Economics of Crisis, Education and Labour Chinese - Hungarian International Conference 30th June -1st July 2011, Budapest. An attempt to identify grey employment Estimation of wage under-reporting and tests of the predictions.
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SEBA – IE CASS – IEHAS Economics of Crisis, Education and Labour Chinese - Hungarian International Conference 30th June -1st July 2011, Budapest An attempt to identify grey employmentEstimation of wage under-reporting and tests of the predictions Péter Elek - János Köllő – Balázs Reizer - Péter A. Szabó Eötvös Loránd University,Institute of Economics, Central European University, Reformed Presbyterian Church of Central and Eastern Europe
We try to identify cases, when total remuneration consists of a reported MW and an unreported ‘envelope wage’. We do so in 3 steps: (i) Estimate a double hurdle (DH) model of the wage distribution, which takes into account: (i) the crowding of low-productivity workers at the MW (truncation) (ii) reporting of MW instead of the full wage (tax evasion) (ii) Relying on the DH results: (i) we estimate the probability that a MW earner is paid an ‘envelope wage’ (ii) simulate the ‘genuine’ wages of MW earners (iii) classify MW earners and their firms as ‘cheaters’ and ‘non-cheaters’ (iii) Test if our DH estimates have predictive power We look at strong exogeneuous shocks (Hungary 2001-2, 2007) to which cheaters and non-cheaters were expected to respond differently
Test 1: Doubling of the MW (2001-2002) The expectation is that under-reporting contained the growth of labor costs so the MW shock had weaker effect on cheating firms Test 2: Introduction of a minimum contribution base = 2MW (2007) Main rule: firms had to pay 2MW contribution even for wages lower than 2MW Firms were allowed to pay w<2MW but they faced a high risk of audit. Firms continuing to pay MW faced particularly high risk. Cheating firms had incentive to raise the reported wages of their ‘disguised’ MW earners (up to paying them an official wage of 2MW) Furthermore, we expect that cheating firms were adversely affected by the reform so their output and employment fell
Motivation (1): Scarce results on „fake” MWs Ample anecdotal evidence of „fake” MWs but scarce results on their magnitude and distribution across sectors, occupations, firm size, etc. Inspection of aggregate, country-level data Share of MW earners versus the Kaitz-index Size of the spike at the MW is correlated with estimated size of the informal economy (Tonin 2006) Survey-based evidence Turkey (Erdogdu 2008), Baltic states (Masso & Krillo 2009, Meriküll & Staehr 2008, Kris et al. 2007), EU (Eurobarometer 2007) Indirect evidence from gap between reported income and consumption Benedek at al. (2006): consumption fell in high-income households with MW earners during the large hikes in Hungary Tonin (2007): food consumption fell in household affected by the hikes compared to unaffected households of similar income
Motivation (2): policy relevance • Mostly in CEEs, MW policies are strongly influenced by the belief that ‘nearly all’ MW earners are paid envelope wages • Governments are tempted to whiten the grey economy by raising the MW and/or increasing the tax burden on low wages (Bulgaria 2003, Croatia 2003, Hungary 2001-2002, 2007) Motivation (3): Hungary’s unusual MW policies • Hungary’s unusual MW policies provide a unique opportunity to study wage under-reporting. Furthermore, the data background is better than in most countries
MW in Hungary – Doubling the MW in 2001-2002 MW/average wage, MW/median wage Fraction paid near the MW (5%) Decision to raise the MW from Ft 25,500 to Ft 40,000 (2001) and Ft 50,000 (2002). Over 70% inrease in real terms, given anticipated inflation. Primarily motivated by ‘making work pay’ Followed by a huge increase in the share of MW earners. The data clearly suggest that it was partly explained by the spread of envelope wages
After the large hikes: many MW earners in high-wage occupations By 2003, the share of MW earners reached high levels among • Small firm managers (27.4%) • Managers of larger firms (11.9%) • Lawyers, business and tax advisors etc. (14.9%) • Professionals in construction (17.9%) In businesses engaged in cash transactions with customers • Blue collars in house building (20.6%) versus civil engineering (4.3%) • Personal services (22%) versus other branches of services (<7%)
MW in Hungary: minimum contribution base, 2007 MW/average wage, MW/median wage Fraction paid near the MW (5%) Introduction of a minimum contribution base (2MW). MW viewed as a signal of wage under-reporting MW earners suddenly disappeared in all categories of firms. We believe it was partly explained by cheaters’ reaction to the increased risk of audit
Data • All data come from the Wage Survey: linked employer-employee data covering over 150,000 workers in more than 15,000 firms, annually. • The WS covers all large firms (>20 employees) and a random sample of smaller firms (5-20) • SMEs (5-50) report data on all workers. Larger firms report data on a sample of workers • The surveys are cross-section but firms can be linked across years directly and workers can be linked indirectly *** • DH model: cross-sections 2003, 2006 • Test 1: panel of small firms observed in 2000 and 2003 • Test 2: panels of workers and firms observed in 2006 and 2007
A worker’s genuine wage is observed if • her productivity is above the MW (jumps the first hurdle) • and her wage is fully reported (jumps the second hurdle) • The genuine log wage: • The reported log wage (where m=log(MW)): • where (u,v) is normally distributed with variance matrix:
The DH model – More insight • Tobit is a special case if the second hurdle is not effective • DH model first proposed by Cragg (1971) and widely used then in environmental economics, models of consumer choice, banking etc. • But only by Shelkova (2007) to analyse wage distributions
Assumptions behind the DH model • Unlike equilibrium models (e.g. Tonin 2006), we assume that many workers with productivity below the MW stayed in their jobs during/just after the episodes under investigation. Both hurdles were effective • When the plan of raising the MW to Ft 50,000 was announced, 32.7% of the employees earned less than that • When the double contribution base was announced, 58% earned less than 2MW • Generally, taxes can be evaded by reporting any wage below the genuine wage. Reporting the MW is the cost-minimising choice only if it does not increase the risk of audit. We assume that was true in Hungary prior to 2007* *) Elek and Szabó (2009) model a case of under-reporting, when the observed wage is not necessarily equal to the MW
Log wages are not truncated normal because of the crowding of wages just above the MW Preliminary transformation is needed Martinez-Espineira (2006) Moffatt (2005) use Box-Cox. Yen and Jones (1997) use inverse hyperbolic sine We apply: Preliminary transformation of wages
Transformed log wages are normal • r is estimated by two methods: • maximum likelihood on a cross section • from a quasi panel • Transformed log wages are approximately truncated normal
The DH model – Estimation • Likelihood function is given as: • Maximum likelihood estimate is consistent and asymptotically normal if the distributional assumptions are correct
Calculation of under-reporting probabilities and simulation of genuine wages • Under-reporting probabilities for MW-earners: • The „genuine” wage of each MW earner can be simulated: • Simulate (u,v) bivariate normal variables with covariance matrix S until Xβ+u<m or Zγ+v<0 holds • Then the genuine log wage: y=max(Xβ+u, m)
Classification of workers and firms • Workers: different criteria applied: cheater if MW is reported and: (1) P>0.5, (2) w>MW (3) w>1.5 MW. Further thresholds were tested (1.1MW, 2MW) without the qualitative conclusions being affected • Firms: cheater if at least one employee is caught cheating/victim of cheating
Results - DH DH estimates (for 2003) • 5-8% of all employees and 35-55% of the MW earners estimated to receive envelope wages • Mean simulated (‘genuine’) earnings of ‘cheating’ MW earners exceeded 220% of the MW • But we still have a huge spike at the MW (true MW earners) • Similar results for 2006
Test 1 – Empirical specification The MW substantially increased the costs of employing low-wage workers For firms starting or continuing under-reporting, the implied cost increase was smaller (for identical workers) The difference between cheaters and non-cheaters in terms of total cost increase varied with exposure Therefore we test if 1= 2 for • wage growth • residual wage growth • employment growth • share of unskilled workers between 2000 and 2003 ln(.) = 1(exposurecheater)+ 2(exposurenon-cheater)+Z + u
Test 1 - Measurement We have 4x3x2x2=48 equations/dependent var Alternative measures of exposure (4x) • Fraction affected = earning less than Ft 40,000 in May 2000 • Fraction affected = earning less than Ft 50,000 in May 2000 • MW shock = average wage increase implied by the first MW hike under full compliance, constant employment and no spillover (Machin-Manning-Rahman 2003) • MW shock = average wage increase implied by the first and second MW hikes under full compliance, constant employment and no spillover (Machin-Manning-Rahman 2003) Alternative measures of fraudulent behavior (3x) • Cheater if for at least one worker w>MW or w>1.5MW or P>0.5 Controls - base-period values (yes, no) • Firm size • Average wage • Capital/labor ratio • Profit/worker • Dummy for value subtractors • Skill shares, average age, share of men • Local unemployment rate • Industry dummies Alternative samples (2x) • All firms versus only low-wage firms
Test 1 - sample • 263 small firms (5-20 workers) observed in 2000 and 2003 in the WS • We choose small firms because they report data on all of their employees exposure and skill composition are precisely measured • Disadvantage: small firms are randomly sampled, year by year, so the panel is rather small • Selection to the estimation sample from the base-period population of small firms is examined with probit. The results hint at random selection
Test 1 – Results (example) Residual wage: firm-level mean residuals from benchmark Mincer equations estimated using WS 2000 and WS 2003
Test 2 – Models and samples • Wage change regressions using the data of MW earners (as of 2006) also observed in 2007 w* | (w0*=MW0) = f(X, cheater dummy) • Probits. Same worker panel Pr(w1*=2MW1 | w0*=MW)=(X, cheater dummy) • Firm-level regressions for wages, employment and sales. Sample: firm panel 2006-2007 lnL=h(Z, cheater dummy)
Test 2 – Resultsfirm-level regressions Effect of cheating behavior on changes of wages, employment and sales
Conclusions • The DH model seems to locate ‘fake’ MWs with some precision • The model might be used for statistical profiling but, more importantly,it points to the limits of tax enforcement • True MW earners exist. Substantially raising the MW (+taxes) in order to ‘whiten the grey economy’ may adversely affect non-cheating firms and workers • Research: merging cheaters and non-cheaters leads to strongly biased estimates of MW effects
