Shadow Economy in Austria the Latest Developments up to 2016

Transcription

1 ShadEc_Austria.doc 28 July 2016 Prof. Dr. Friedrich Schneider Johannes Kepler University Linz Department of Economics Altenbergerstraße 69 A-4040 Linz Phone: , Fax: Shadow Economy in Austria the Latest Developments up to Introduction In Austria the size and development of the shadow economy (in Austria it s called the Pfusch ) have been intensively and controversially discussed. In 2016 it is especially difficult to forecast the magnitude of the shadow economy in Austria, as we observe countervailing tendencies, which have on one side an increasing and on the other side a decreasing effect on the shadow economy. On one hand a shadow economy will decrease due to a reduction in the direct tax rate and on the other hand we have an increase in unemployment by about 50,000 people, compared to the year 2014, to roughly 450,000. Moreover, we have roughly 50,000 refugees, some of whom might engage in shadow economy activities. Which of these effects will dominate in determining the size of the shadow economy will be shown in the following simulations. Part 2 gives a description of the development of the shadow economy in Austria and the Austrian states to A disaggregation of the shadow economy into various sectors is also shown, followed by an international comparison of the Austrian shadow economy with those of the 28 European Union member states. In part 3 the most important results are summarized, and in part 4 concrete policy measures are discussed which might reduce the size and development of the shadow economy. Finally, part 5 gives a résumé. 1 / 14

2 2. The Size of the Shadow Economy in Austria up to The latest figures on the size and development of the shadow economy are presented in table 2.1 for all nine federal states, as well as for Austria in total, from 1990 to For 2016 the figures are forecasts based on actual forecasts of the Austrian economy by the WIFO and IHS, like the GDP, which will increase by 1.4% in The shadow economy had been decreasing since the year 2005 and this negative trend continued up to 2008: in 2007 the shadow economy decreased from 21.2 billion euros (year 2006) to 20.8 billion euros. This is a decrease of 1.89%; compare also table 2.1 and figure 2.1. In the year 2008 the shadow economy in Austria reached a size of billion euros meaning it decreased for the fourth time; decreasing by 880 million euros or 4.23%. Due to the world economic crisis and the resulting unemployment, the shadow economy increased by 2.91% to 20.5 billion euros in In the year 2010 the shadow economy decreased due to the upswing of the official economy to billion euros, or 8.10% of the official GDP. In the year 2013 the shadow economy further declined, due to positive development in the official economy, to billion euros or 7.52% of official GDP, a decrease of 1.48% (compare table 2.1 and figure 2.1). In the year 2014 the shadow economy grew again to billion euros or 7.84% of official GDP, due to rising unemployment and a high tax burden, direct tax burden and social security burden. In the year 2015 the shadow economy further increased to billion euros or 8.14% of official GDP due to rising unemployment and the continuously high tax and social security burden on labor. The annual growth of the shadow economy in 2015 was 4.50%; apart from the increase of 5.75% in 2014 this was the steepest increase since A forecast about the development of the shadow economy for this year (2016) is especially difficult as we have countervailing trends which on the one hand cause a decrease in the shadow economy, and on the other hand an increase. These trends are presented in table 2.2. From table 2.1 one clearly sees that the decrease in the direct tax burden due to tax reform decreased the shadow economy by 1.8 billion euros (variant 1) and by 2.25 billion euros (variant 2). The different variants arise due to different assumptions about how much the additional income from the tax cut will reduce the incentive to work in the shadow economy. Additionally, if the tax deduction for household services continues in the year 2016, this will also 1 The calculation of the size and development of the shadow economy is done with the MIMIC (Multiple Indicators and Multiple Causes) estimation procedure. Using the MIMIC estimation procedure one gets only relative values, and other methods such as the Currency Demand Approach are required to calibrate the MIMIC values into absolute ones. For a further explanation see Appendix 1. For a detailed explanation of these calculation methods see Schneider (2005, 2011), Schneider and Buehn (2015), Schneider and Williams (2013) and Williams and Schneider (2016). 2 / 14

3 lead to a further decrease in the shadow economy because taxpayers only get part of their taxes back from the state if they have an official bill. Due to the implementation of this tax deduction, it will be assumed that a further diminution of the shadow economy would vary between 500 million and 1 billion euros, as the new continuation up to 2016 has just been put into effect. The increasing drivers include a rise in unemployment by about 50,000 persons compared to the year Also a first attempt is made to estimate how many refugees might work in the shadow economy. If I make an assumption that 50,000 refugees work in the shadow economy, the increase of the shadow economy will be 240 billion euros, and if I make the assumption that 100,000 refugees are engaged in shadow economy activities, the increase will be 480 million euros. In table 2.2 the effects of the factors influencing the shadow economy are calculated. Table 2.2 indicates that the size of the shadow economy will decrease by 720 million euros in 2016, and including the allowance to deduct household services from the tax bill the shadow economy will shrink by 1.17 billion euros. This leads to the magnitude of the shadow economy being billion euros or 7.76% of official GDP in This would be a decrease in the shadow economy of 3.33% compared to Additionally, the shadow economy has been calculated for each of the nine Austrian states and the results are shown in table 2.1. The most sizeable shadow economies in the country are Vienna with 5.88 billion euros, Upper Austria with 3.75 and Lower Austria with 3.46 billion euros in Burgenland has the smallest shadow economy with a value of 450 million euros, followed by Vorarlberg with 930 million euros and Carinthia with 1.27 billion euros. It should be mentioned, however, that the disaggregation of the size and development of the shadow economy to these nine Austrian federal states is a rough approximation. As there are no boundaries between these states, it might well be that a shadow economy worker of Upper Austria produces black value added in Lower Austria or Salzburg or vice versa. These estimations should be seen as a rough approximation, related to the size of the official economy and the size of those sectors in which shadow economy activities most often take place, such as the service and construction sectors. Additionally, a decomposition of the shadow economy of Austria into different sectors (service, construction and craftsmanship, etc.) is undertaken. The results are shown in table 2.3 for Austria and for the states Upper Austria, Lower Austria and Vienna. From table 2.3 one sees that construction and craftsmanship (including repairing) has by far the greatest share of the shadow economy with 39% of the overall shadow economy. In 2016 the size of the 3 / 14

4 shadow economy of the sector construction and craftsmanship (including repairing) is 8.05 billion euros in total Austria, 2.22 billion euros in Vienna, 1.35 billion euros in Upper Austria and 1.31 billion euros in Lower Austria. The sector household services is quite sizeable with 17%, which means that 3.51 billion euros of the overall shadow economy in Austria are produced in this sector. In Vienna this sector has a value of 966 million euros, in Upper Austria 587 million euros and in Lower Austria 570 million euros. The sectors mostly industrial services and services like hotels, restaurants, etc. follow with 16%, each of which has a value of 3.30 billion euros. In Vienna each of these sectors has a size of 909 million euros, in Upper Austria 553 million euros and in Lower Austria 536 million euros. The lowest share occurs in the entertainment sector with 12% or 2.48 billion euros for Austria overall, 682 million euros in Vienna, 415 million euros in Upper Austria and 402 million euros in Lower Austria. Finally, in order to make a comparison of the size and development of the shadow economy in Austria with the 28 European Union countries, the shadow economy figures of these 28 countries are shown in table 2.4. With the size of the shadow economy at 7.8% in the year 2016, Austria has one of the smallest shadow economies, followed by Luxembourg with 8.4% and the Netherlands with 8.8%. On average the shadow economy of the 28 EU countries decreased from 18.3% in 2015 to 17.9% in In the year 2003 the average size of the shadow economy (unweighted average) of the 28 European Union countries was 22.6%. In the year 2015 the average size of the shadow economy was 18.3%; quite a drastic decline of 4.3 percentage points compared to The country with the largest shadow economy is Bulgaria with a value of 30.6% in the year 2015 (compared to 35.9% in the year 2003), followed by Romania with 28.0% in 2015 (compared to 33.6% in 2003) and Croatia with 27.7% in 2015 (compared to 32.3% in 2003). Slovakia had a shadow economy of 18.4% in 2003, which decreased more or less continuously, with the exception of the year 2009, to 13.7% in The Czech Republic had a shadow economy size of 19.5% in 2003, which declined to 14.9% in In general, we observe a substantial decrease in the size and development of the shadow economy for most European Union countries over the period 2003 to Summary of Results In 2016 the Austrian shadow economy will further decrease due to tax reform (reduction in the direct tax rate), and the continuation of the tax allowance to deduct craftsmanship services. Most likely, the shadow economy will shrink to billion euros or 7.76% of official 4 / 14

5 GDP (assumption: growth of official GDP of 1.4%). A reduction would have been much more pronounced if unemployment had not increased by 50,000 persons compared to Policy Measures to Reduce the Shadow Economy It is necessary to provide policy advice to reduce the shadow economy as (i) the state has tax and social security contribution losses and (ii) ruinous competition between those firms which do and do not work in the shadow economy takes place. Only if it is attractive to do the job in the official economy, will shadow economy activities be reduced and transferred into the white economy. Hence, it s a major task for all state institutions from the federal to the commune level to install incentive-orientated policy measures to reduce the shadow economy. The following policy measures could be implemented: (i) A reduction in the value added tax for work-intensive services for a certain time. (ii) A permanent continuation of the allowance to deduct taxes for craftsmanship services. (iii) Expelling those firms from public contracting which do (or allow) shadow economy work for 3 to 5 years. (iv) Reduction of the social security and tax burden on wage costs. 5. A Résumé Finally, I would like to discuss the following question: Who wins and who loses with the shadow economy? (1) The biggest loser is the state, which loses social security contributions and tax income averaging 2.0 to 3.5 billion euros per year in Austria. Part of the tax loss is compensated as the black money earned is immediately spent in the official economy. (2) Further distinct losers are the health insurance companies, which have to carry the additional costs of accidents which happen when one works in the shadow economy. (3) 66% of the value added of the shadow economy is produced by Austrians or EU citizens living in Austria, who are either self-employed or employed in the official economy, and who carry the full taxation and social security burden but do not declare their black hours worked on weekends and in the evening. 16% of the value added is due to organized crime activities (prostitution, construction), 7% due to the unemployed and 8% due to early-pensioners. 5 / 14

6 (4) 40% of all activities in the shadow economy are complements, meaning they would not have been demanded in the official economy due to much higher prices. 35% are substitutive, meaning they would be demanded in the official economy and 25% of the people would produce the goods or services on a do-it-yourself basis. (5) The economy and all people who work in the shadow economy or let work be done in the shadow economy profit from shadow economy activities. A lot of houses would not be built without a shadow economy. 6 / 14

7 Table 2.1: Size and development of the shadow economy in Austria and in the nine Austrian states from 1990 to 2016 year B (billion ) Regional value added of the shadow economy at current prices. Estimation method for overall Austria MIMIC and for calibration the currency demand approach. NÖ OÖ S ST T V W (billion ) (billion ) (billion ) (billion ) (billion ) (billion ) (billion ) K (billion ) Overall Austria (billion ) in % of official GDP % % % % % % % % % % % % % % % % % % ) % 1) Preliminary calculations due to official forecasts of the Austrian economy. The forecasted growth rate of official GDP is 1.4% for B (Burgenland), K (Kärnten=Carinthia), NÖ (Niederösterreich=Lower Austria), OÖ (Oberösterreich=Upper Austria), S (Salzburg), ST (Steiermark=Styria), T (Tirol=Tyrol), V (Vorarlberg), W (Wien=Vienna). Source: Own calculations, May Prof. Dr. Friedrich Schneider. University of Linz. 7 / 14

8 in percent Figure 2.1: Development of the size of the Austrian shadow economy in % from 1998 to Source: Own calculations, May 2016, Prof. Dr. Friedrich Schneider, University of Linz. 8 / 14

9 Table 2.2: Scenarios about the size of the Austrian shadow economy in 2016 Variables (1) Decrease due to tax reform (lower tax rate) (2) Allowance to deduct craftsman services in a household from the tax bill (3) Rise of unemployment by 50,000 persons compared to 2014 (4) Shadow economy activities of 50,000 and 100,000 refugees (variant 1, variant 2) Effect on the shadow economy Variant 1 (million euros) Variant 2 (million euros) 1, , , , Sum (1)+(3)+(4) Sum (1)+(2)+(3)+(4) 1, ,710.0 Value of the shadow economy in 2015 Value of the shadow economy in 2016 (without considering measure 2 tax deduction of craftsman services) 21, , , ) Assumption: A refugee earns 5 euros per hour; he works 20 hours per week, in total 80 hours per month, which results in 400 euros per month or 4,800 euros per year. For 50,000 refugees this adds up to million euros and for 100,000 refugees to million euros. Source: Own calculations, May 2016, Prof. Dr. Friedrich Schneider, University of Linz. 9 / 14

10 Table 2.3: Decomposition of the shadow economy in different sectors in Austria, Lower Austria, Upper Austria and Vienna for 2016 Sector (1) Construction and craftsmanship (including repairing) (2) Mostly industrial services (car repairing, repairing of machines) (3) Service sector (hotels, catering, restaurants) Austria year 2016 Lower Austria year 2016 Upper Austria year 2016 Vienna year 2016 in % million in % million in % million in % million 39% 8,050 39% 1,307 39% 1,348 39% 2,217 16% 3,303 16% % % % 3,303 16% % % 909 (4) Entertainment branch/sector 12% 2,478 12% % % 682 (5) All household services (hairdressing, cleaning, babysitting, etc.) 17% 3,509 17% % % 966 Total shadow economy 100% 20, % 3, % 3, % 5,684 Source: Own calculations, May 2016, Prof. Dr. Friedrich Schneider, University of Linz. 10 / 14

11 Table 2.4: Size and development of the shadow economy of 28 EU countries as a percentage of official GDP Country / Year ) ) Austria Belgium Bulgaria Croatia Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia South-Cyprus Spain Sweden United Kingdom EU states / Average (unweighted) Source: Own calculations, May 2016, Prof. Dr. Friedrich Schneider, University of Linz. 1) Values for 2015 and 2016 are forecasts based on preliminary values. 11 / 14

12 6. Appendix 1: Econometric Methodology Estimating the size and trend of a shadow economy is a difficult and challenging task. Methods designed to estimate the size and trend of the shadow economy, such as the currency demand approach or the electricity approach, only consider one indicator that must capture all effects of the shadow economy. 2 However, it is obvious that shadow economy effects show up simultaneously in the production, labor, and financial markets. The empirical method used in this paper is based on the statistical theory of unobserved variables, which considers multiple causes and multiple indicators of the phenomenon to be measured; i.e. it explicitly considers multiple causes leading to the existence and growth of the shadow economy, as well as the multiple effects of the shadow economy over time. In particular, I use a Multiple Indicators Multiple Causes (MIMIC) model a Structural Equation Model (SEM) with one latent variable (the shadow economy) for the empirical analysis. The main idea behind a SEM is to examine the relationships between unobserved variables in terms of the relationships between a set of observed variables by using the covariance information of the latter. In particular, a SEM compares a sample covariance matrix, i.e. the covariance matrix of the observed variables, with the parametric structure imposed on it by a hypothesized model. The relationships between the observed variables are described in terms of their covariance and it is assumed that they are generated by (usually a smaller number of) unobserved variables. In the MIMIC model presented in this paper, the shadow economy is the unobserved variable and is analyzed with respect to its relationship to the observed variables using the covariance matrix of the latter. For this purpose, the unobserved variable is, in a first step, linked to the observed indicator variables in a factor analytical model, also called a measurement model. Second, the relationships between the unobserved variable and the observed explanatory (causal) variables are specified through a structural model. Thus, a MIMIC model is the simultaneous specification of a factor model and a structural model. In this sense, the MIMIC model tests the consistency of a structural theory through data and has two goals: (i) estimating the parameters (coefficients) and (ii) assessing the fit of the model. Applying this to the shadow economy research, these two goals mean (i) measuring the relationships of a set of observed causes and indicators to the shadow economy (latent variable), and (ii) testing if the researcher s theory or the derived hypotheses, as a whole, fit the data used. 2 In this paper no detailed discussion about the various measurement methods is undertaken. See Schneider and Buehn (2015), Schneider and Enste (2000), Feld and Schneider (2010) and Schneider (2015). 12 / 14

13 The estimated MIMIC coefficients only allow the determination of relatively estimated sizes of the shadow economy, which describe the pattern of the shadow economy in a particular country over time. In order to calculate the size and trend of a shadow economy, one must convert the MIMIC index into real world figures measured as a percentage of official GDP or in currency units. This final step requires an additional procedure, so-called benchmarking or calibration. Unfortunately, no consensus exists in the literature on which benchmarking procedure to use. In the first step, the MIMIC model index of the shadow economies is calculated using the structural equation, i.e. by multiplying the coefficients of the significant causal variables with the respective time series. Secondly, this index is converted into absolute values of the size of the shadow economies, taking base values in a particular base year. The base values necessary for this final step of the calibration procedure are mostly taken from other approaches, e.g. from the currency demand approach. With these starting values the size and development of a shadow economy can be calculated. The notion of a currency demand approach involves the following: For almost all shadow economy activities, cash is used as a method of payment. The idea is to estimate a currency demand function (i.e. demand for currency outside banks) and to include all factors which drive a shadow economy like tax pressure, regulations, etc. Then a simulation with and without these variables is undertaken, keeping the cause variables of a shadow economy (tax pressure, regulations, etc.) at the lowest level. From the difference one can calculate the amount of currency used for shadow economy activities. Multiplying this with the velocity of money one gets a value added figure and quite often this value added figure is used as a calibration value. 13 / 14

14 7. References Feld, L. & Schneider, F. (2010). Survey on the shadow economy and undeclared earnings in OECD countries. German Economic Review, 11(2), pp Schneider, F. (2005). Shadow economies around the world: What do we really know? European Journal of Political Economy, 21(3), pp Schneider, F. (ed.) (2011). Handbook on the Shadow Economy. Cheltenham (UK): Edward Elgar Publishing Company. Schneider, F. (2015). Schwarzarbeit, Steuerhinterziehung und Korruption: Was ökonomische und nicht-ökonomische Faktoren zur Erklärung beitragen. Perspektiven der Wirtschaftspolitik, 16(4), pp Schneider, F. & Buehn, A. (2015). Estimating the size of the shadow economy: Methods, problems and open questions. Discussion Paper, University of Linz, Linz, Austria. Schneider, F. & Enste, D. (2000). Shadow economies: Size, causes, and consequences. The Journal of Economic Literature, 38(1), pp Schneider, F. & Williams, C.C. (2013). The shadow economy. London: Institute of Economic Affairs. Williams, C.C. & Schneider, F. (2016). Measuring the Global Shadow Economy: The Prevalence of Informal Work and Labour. Cheltenham (UK): Edward Elgar Publishing Company. 14 / 14