Analysis of the shadow economy in the Spanish regions

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Analysis of the shadow economy in the Spanish regions Marcos González-Fernández 1 , Carmen González-Velasco ∗ Department of Business Economics and Management, Faculty of Economics and Business, University of León (Spain), Campus de Vegazana, 24071 León, Spain Received 19 March 2015; received in revised form 13 August 2015; accepted 26 September 2015

Abstract The aim of this paper is to analyze the shadow economy in the Spanish Autonomous Communities. In so doing, we employ the Currency Demand Approach to analyze the 1987–2010 period. The results show that the size of the shadow economy ranges from 18% to 30% of regional GDP and an approximate mean value of 25% for the entire territory. The Personal Income Tax has the greatest impact on the shadow economy. By region, Andalucía and the Islas Canarias have the highest values for the shadow economy, whereas Madrid presents the lowest value. We extract some implications for the public authorities. © 2015 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved. JEL classification: G18; O17; H11; H26 Keywords: Shadow economy; Currency Demand Approach; Black economy; Hidden economy; Black market

1. Introduction Researchers, politicians and economists have studied the shadow economy for decades because of its implications for the official economy of a nation-state. Its correct quantification may be crucial for the economic authorities of a nation or region to develop economic policy. Additionally, this analysis is important in the context of the current financial crisis because the shadow economy reduces public resources to face the crisis. In this sense, the objectives of this paper are twofold.

∗ 1

Corresponding author. Tel.: +34 987 291738. E-mail address: [email protected] (C. González-Velasco). Tel.: +34 987 293498.

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On the one hand, it attempts to analyze the current state of the shadow economy in the Spanish Autonomous Communities. On the other hand, it aims to examine the most appropriate economic policies to limit the impact of the shadow economy in these regions. The importance of the shadow economy is more than just the legal issues it raises because it also has a number of economic consequences that have significant effects on the economic and monetary policies of a country (Dell’Anno, Gómez-Antonio, & Ala˜nón-Pardo, 2007; Schneider & Enste, 2000). A large shadow economy may cause a country’s leaders to make decisions based on indicators that are unrealistic. When this occurs, the “positive” indicators (GDP, IPI, etc.) are undervalued and the “negative” indicators (unemployment, inflation, etc.) are overrated. A correct estimation of the shadow economy lets us adjust these measures to reflect the actual needs of a country. This analysis thus has budgetary implications and influences the taxation and distributive policy in a country (Giles, 1997). Overall, there are many international and national studies of the shadow economy based on different perspectives, different methodological approaches and different objectives. The main motivation of this paper is to extend the limited literature addressing this matter at the regional level in Spain and to analyze the implications that this phenomenon has on the economy of a country or territory. Because of the importance of the Spanish Autonomous Communities in the development of the Spanish economy, it is necessary to know the regional implications of the sizes of the regional shadow economies because they are relevant for the implementation of regional policy measures and the allocation of public resources. For this purpose, we use the Currency Demand Approach proposed by Tanzi (1980) to estimate the size of the underground economy in the Autonomous Communities. Before analyzing the shadow economy, it is necessary to determine the activities that are included in it. Schneider and Enste (2000) indicate that the shadow economy contains those economic activities that contribute to official GDP but do not appear in official records. According to the OECD, it comprises all economic activities whose final product is legal but that are deliberately hidden to avoid taxation or to avoid labor standards, such as minimum wages, social security contributions, etc. We assume that the shadow economy includes all goods and services produced legally and that are deliberately hidden from public authorities for any of following reasons2 : (i) to avoid paying taxes; (ii) to avoid payment of social security contributions; (iii) to avoid compliance with minimum legal requirements regarding wages, safety, quality, etc.; or (iv) to avoid complying with certain administrative procedures, such as completing statistical questionnaires or other forms. It is clear that there is a common denominator in all the activities included in the shadow economy: they consist of hidden activities that are not listed in official records (Choi & Thum, 2005). Throughout this paper, we refer to the shadow economy and irregular activities without distinction. This paper is structured as follows. The next section presents a brief review of the literature and present some of the major papers related to this issue. In the third section, we describe the data and methodology used in the analysis. The hypotheses formulation is presented in the fourth section. In the fifth section, we analyze and describe the results and determine whether the hypotheses are supported. The final section summarizes the main conclusions of the study.

2 This definition has been commonly used in the literature; Schneider (2005, 2008), Schneider and Savasan (2007) and Feld and Schneider (2010) use this definition, among others.

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2. Literature review The analysis and study of the shadow economy has grown over the past decade, see, e.g., Schneider (1997, 2002, 2005, 2008), Dell’Anno (2003), Dell’Anno et al. (2007), Feld and Schneider (2010), among others. However, there are no previous studies available on the estimation of the shadow economy for all the Spanish Autonomous Communities. This is an important contribution of this work because the values obtained can be used as a proxy for this variable in the Spanish regions, whether for future regional studies on the determinants of the shadow economy or as a variable to be included in other research. 2.1. Studies at the international level The shadow economy phenomenon has been widely analyzed in the international literature in studies such as those of Friedman, Johnson, Kaufmann, and Zoido-Lobaton (2000) and Schneider and Enste (2000). They analyze the main causes of the shadow economy and quantify its impact on a country’s official economy. The results vary according to the methodology used and depend on the category of countries analyzed. However, the general conclusion indicates that the shadow economy has grown in recent decades, and its value ranges from 10% to 25% of the official economy in developed countries (Feld & Schneider, 2010). There have been many studies on the international shadow economy. Schneider (2002) uses the Currency Demand Approach and DYMIMIC3 method to analyze the shadow economy in 22 transition countries4 and 21 OECD countries. Their results show that the level of shadow economy in the former doubles the value of the latter (30% versus 15%). They indicate that the tax burden and excessive regulation are the main triggers for irregular activity. Later, Schneider (2005) performs an analysis for 110 countries between 1990 and 2000. He uses the Currency Demand Approach and DYMIMIC model again. The countries are broken up according to their level of development, and he finds different results. The transition economies, in addition to the African and South American countries, have larger shadow economies than the OECD countries. The results show that the tax burden and excessive regulation are the main causes of irregular activity. Orviská, ˇ Caplánová, Medved, and Hudson (2006) analyze the size of the shadow economy in Slovakia and the Czech Republic through a cross section model based on the demand for money. They find that these phenomena represent 23.2% and 21.8% of GDP, respectively. Karlinger (2009) analyzes 45 countries with different levels of development (OECD countries, development countries and transition countries) between 1990 and 2005. He uses the results obtained by Schneider (2005) and his own results obtained from the consumption of electricity to analyze the determinants of the shadow economy in these states. He concludes that taxes, labor market regulation and tax compliance influence the level of the shadow economy. Friedman et al. (2000) study the determinants of the shadow economy in 69 countries. They use a proxy for the shadow economy size estimated in different previous studies, and they find that high marginal tax rates do not lead to larger shadow economies. However, an excess of government regulation and corruption have a direct effect on irregular activity. Feld and Schneider (2010) analyze 21 OECD countries between 1990 and 2005 with a MIMIC model. Their results show that taxes, over-regulation and the unemployment rate have a direct relationship with the shadow economy. By contrast, the

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The DYMIMIC estimation method is a variation of the MIMIC model (Multiple Indicators, Multiple Causes). See the work on transition economies of Alexeev and Pyle (2003).

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quality of institutions and the morality of individuals regarding tax payments support a negative relationship. According to these results, there is a relationship between tax burden, excessive regulation and the shadow economy, which is stronger in less-developed countries or in those that are in a process of transition to market economy. 2.2. The shadow economy in Spain At national level, we highlight some of the most important works about the Spanish shadow economy. The results place the Spanish economy in a range between 10% and 25%, depending on the methodology used, which is consistent with those obtained at international level. With respect to Spain, most of the studies have focused on the national level, and only a few have tried to reach the regional level. Gadea and Serrano-Sanz (2002) analyze the shadow economy in Spain from 1964 until 1998 using the Currency Demand Model. They consider different fiscal variables and a model of long-run equilibrium. They determine that direct taxes are those that provide the most consistent results and estimate that the size of the shadow economy ranges between 11% and 24%, depending on the velocity of monetary circulation. Prado-Domínguez (2004) uses the Currency Demand Approach over the 1964–2001 period. The results show that the size of the shadow economy in Spain ranges from a minimum of 12% to a maximum of 27% of GDP in the period analyzed. Arrazola, de Hevia, Mauleón, and Sánchez (2011) use different methodologies (Currency Demand Approach, energy demand approach, and MIMIC model) to estimate the size of the shadow economy in Spain between 1980 and 2008. The estimates are that the shadow economy accounts for approximately 17% of GDP in the considered period. Dell’Anno et al. (2007) use the MIMIC model to study the behavior of the shadow economy in France, Spain and Greece. They find that the tax burden and unemployment are significant to explain the size of the shadow economy. Spanish data provide that the magnitude of the shadow economy is approximately 25% of GDP. At the regional level, the studies are scarce. In general, these works are undertaken by regional governments or universities.5 In the academic field, González-Fernández and González-Velasco (2014) analyze the effects of the shadow economy and corruption on public debt levels for Spanish regions. They find that higher levels of underground economic activities positively impact public debt levels. Tafenau, Herwartz, and Schneider (2010) conducted an analysis of European regions using MIMIC methodology. The results for the Spanish regions indicate an average size of 16.5% of the GDP for the black economy. Our study analyzes possible differences at the regional level to provide useful information about the size of the shadow economy in the Autonomous Communities. Likewise, the results may help establish conclusions and recommendations about the measures to take in regulatory and budgetary areas to reduce the size of the shadow economy. 3. Methodology and data We employ the Currency Demand Approach because it is one of the most commonly used methods in the literature. The methodology used is panel data, which is a suitable technique for

5 See Ferraro et al. (2002), Serrano, Bandrés, Gadea, and Sanau (1998) and Cantarero and Blázquez (2013) for studies of Andalucía, Aragón and Cantabria, respectively.

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time and cross-sectional data, and it allows us to control for unobservable heterogeneity in the Spanish regions. We apply a fixed-effects model after comparing its suitability with appropriate statistical tests for this purpose. 3.1. Methodology There are multiple models that estimate the shadow economy. Each has advantages and disadvantages and none are not exempt from criticism or limitations. For the purposes of this paper, we use the Currency Demand Approach that is included in the monetary indirect estimation methods. This is one of the most commonly used approaches in the literature, along with the causal model or MIMIC. It was first presented by Cagan (1958) and Gutmann (1977). Tanzi (1980, 1983) proposed an econometric model employing the Currency Demand Approach to estimate the shadow economy. This approach begins from the assumption that irregular activities use cash as payment because it is fiscally opaque. Therefore, an increase in cash held by the public is associated with an increase in the size of the shadow economy. We must isolate excess cash demand over time through an econometric model. It takes into account the variables traditionally considered to influence the shadow economy (taxes, tax system complexity, over-regulation, etc.). Furthermore, it incorporates other control variables that may affect the demand for cash. In this way, we obtain the regression model proposed by Tanzi (1983):       C WS Y ln = β0 + β1 ln (1 + TW)t + β2 ln + β3 ln Rt + β4 ln + μt (1) M2 t Y t N t where C/M2 is the ratio between currency and the monetary aggregate M2 ,6 TW is the average tax rate, WS/Y is the share of wages and salaries relative to national income, R is a measure of the interest rate and used as a proxy of the opportunity cost of holding money, and Y/N is GDP per capita. Thus, excess cash demand is attributed to a set of variables in which taxes are included. The size and evolution of the shadow economy is calculated by comparing currency demand when tax rates are zero with respect to the current actual tax level. Subsequently, and assuming the same velocity of circulation of money in both the formal and informal sectors, we estimate the size of the shadow economy as a ratio of GDP. Although it is a method that attempts to solve the limitations of other techniques and allows us to specify and check certain assumptions statistically, it also has certain weaknesses and requirements (Thomas, 1999). Nevertheless, the Currency Demand Approach has been widely used in the literature and is frequently employed in many studies, both on international (Pickhardt & Sardà, 2006) and national (Gadea & Serrano-Sanz, 2002) scales. In the analysis, we estimate the size of the shadow economy utilizing the Currency Demand Approach. As with all methodologies, it is not exempt from criticism, but it is a commonly used method in the literature because other models do not solve the problems involved in estimating the size of the shadow economy (Ahumada, Alvaredo, & Canavese, 2008). For this reason, we estimate the model presented and developed by Tanzi (1980) shown in Eq. (1). Given the characteristics of the sample examined, which contains multiple temporal units and individuals, we apply panel data methodology to estimate the Currency Demand Approach equation.

6 According to the Bank of Spain, the monetary aggregate M includes currency, demand deposits and deposits with a 2 maturity of up to two years and deposits redeemable at a period of notice of up to three months.

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3.2. Data We describe the variables used in the analysis that are the most used in the literature to estimate the shadow economy by the Currency Demand Approach. Annual time series are considered for 17 regions. The autonomous cities of Ceuta and Melilla are not taken into account. The time horizon extends from 1987 to 2010, inclusive. This method provides an unbalanced data-panel structure with 408 observations. The variables studied are as follows: • The dependent variable is the amount of cash held by the public (currency in circulation) based on data obtained from the Bank of Spain. The actual variable is not disaggregated at a regional level, so we must distribute it; we employ the population variable for this purpose. Thus, from the national total currency level, we proceeded to regionalize the variable based on the regional population for each year. • The explanatory variables are fiscal variables (direct and indirect taxes collected by each of the Autonomous Communities). The data are taken from the tax office for regions of the general regime. For País Vasco and Navarra, we use their respective budgets. Data are in relative terms with respect to regional GDP and population. For direct taxes, we differentiate between the Personal Income Tax (PIT) and Corporate Income Tax (CIT) to determine which has a greater impact on the size of the shadow economy. We select the Value Added Tax (VAT) as the indirect tax. The other variables used include real GDP, obtained from the National Statistics Institute (INE), the legal interest rate as a proxy of the cost of opportunity of holding money (available at the Bank of Spain), and regional inflation obtained from the INE. Thus, the model equation of Tanzi (1980) is adapted to our analysis as follows: ln(C)t = β0 + β1 Tt + β2 ln GDPt + β3 Rt + β4 ln Inflationt + μt

(2)

where C is the amount of cash held by the public in real terms, T is a measure of the tax burden (expressed as a ratio to GDP or population), GDP is the real regional GDP, R is the type of legal interest and Inflation is inflation homogenized using 2005 as base year. To continue the analysis, it is useful to analyze the evolution of the dependent variable to determine the existence of structural changes. According to the graphs7 of the currency held by the public in each Autonomous Community, there are two significant drops in the cash volume. The first occurs between 1999 and 2001 and coincides with the implementation of the euro.8 The sharp drop in currency is related to the uncertainty before the arrival of the single currency. Citizens’ reaction was to transfer cash into other monetary instruments (savings deposits, demand deposits, etc.). This idea is confirmed if we analyze the graph of the monetary aggregate M2 , which shows an increase during those years (something similar occurs to the aggregate M1 and M3 ). The second drop is less pronounced and occurs at the onset of the financial crisis in 2007. We introduce two dummy variables to control for such changes and to avoid biased results. The first reflects the effect of the Euro, and takes the value 0 before 1999 and 1 after that year. The second serves to include the period of crisis. Its value is equal to 1 from 2007 and 0 otherwise. The logic behind this inclusion is that the crisis has affected the cash held by the

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To save space, some graphs are not included; the full document is available upon request from the authors. The introduction of the Euro has been considered in previous studies. Pickhardt and Sardà (2011) apply an exponential interpolation to calculate the cash held by the public in the 1995–2006 period to eliminate the bias introduced by the Euro. 8

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public by altering the monetary and financial capacity of the population, so we expect a negative sign. After introducing these variables in the model Eq. (2), the estimate obtained is as follows: ln (C)t = β0 + β1 Tt + β2 ln GDPt + β3 Rt + β4 ln Inflationt + β5 Euro + β6 Crisis + μt (3)

This Eq. (3) is estimated by panel data methodology to obtain the values of the shadow economy in each region. Specifically, we employ a fixed-effects panel data model, after checking if it was the appropriate method based on statistical tests. 4. Hypotheses formulation This section presents the hypothesis we want to contrast in our analysis. First, we show that certain arguments from the literature lend support for the assumptions that later establish our hypothesis. 4.1. Fiscal pressure and the shadow economy According to Laffer’s Curve Theory, an increase in the tax burden means that public fiscal revenues reach a peak from which they begin to descend. Therefore, some individuals decide that the burden of paying taxes is greater than the benefit of the payments obtained through public goods and services. Thus, the Autonomous Communities with higher taxes have larger shadow economies because the incentives to move to the informal sector are greater. This fact is shown in several of the studies mentioned (Dell’Anno, 2003; Giles, 1999; Schneider, 2005, among others). Thus, a country or region with a higher tax burden or higher marginal rate (Hill & Kabir, 1996) generates greater incentives to move to the shadow economy. According to these arguments, we propose the following hypothesis: Hypothesis 1 (H1 ). The tax burden has a positive influence on the amount of currency held by the public, which indicates a larger shadow economy. 4.2. What type of taxes influences the most on the shadow economy? According to Gadea and Serrano-Sanz (2002), direct taxes are the most significant factors in the estimates. Direct taxation, unlike indirect taxation, burdens income. As discussed above, some of the main causes of the shadow economy come from the performance and regulation of the labor market. Individuals generate rents taxed by direct taxes at this market. These taxes generate greater incentives to move to the shadow economy. Under this approach, we establish the second hypothesis: Hypothesis 2 (H2 ). Direct taxes are those that most directly affect the currency held by the public and thus have greater impact on the size of the shadow economy. Thus, in regions in which the direct tax burden is greater, the shadow economy is larger. Please cite this article in press as: González-Fernández, M., & González-Velasco, C. Analysis of the shadow economy in the Spanish regions. Journal of Policy Modeling (2015), http://dx.doi.org/10.1016/j.jpolmod.2015.09.006

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Table 1 Direct tax burden by region. Region

1987–1994 (%)

1995–2000 (%)

2001–2005 (%)

2006–2010 (%)

1987–2010 (%)

Andalucía Aragón Asturias Baleares Canarias Cantabria Castilla y León Castilla La Mancha Catalu˜na Com. Valenciana Extremadura Galicia Madrid Murcia Navarra País Vasco La Rioja

4.83 7.71 7.54 6.48 5.36 12.72 5.44 4.05 10.06 6.27 4.07 5.68 19.19 4.26 – – 5.85

4.83 8.54 7.87 6.93 5.15 11.20 5.28 4.25 10.06 6.71 4.29 6.53 16.89 5.04 8.01 8.86 6.34

5.33 7.63 6.94 7.21 4.85 12.60 5.39 4.78 10.38 7.75 4.34 6.80 17.20 6.07 8.05 8.51 7.07

5.53 7.65 6.55 6.92 4.51 14.17 5.30 5.72 10.39 7.70 4.68 6.84 18.52 6.48 8.44 8.62 6.80

5.08 7.89 7.29 6.83 5.02 12.62 5.36 4.60 10.20 6.99 4.31 6.37 18.06 5.29 5.44 5.79 6.42

Source: Own elaboration from Tax Office data. Notes: Data on taxes are not available before 1995 for País Vasco y Navarra (Comunidades Forales). The table shows the direct tax burden (income tax and corporate tax) on average by region with respect to the regional GDP for different periods.

5. Empirical results In this section, we discuss our results. First, we show the values of the tax burdens of different Autonomous Communities9 because they are necessary to know the structure of the tax burden to contrast the hypotheses (Table 1) and to know the direct tax burden for the entire time horizon (Fig. 1). The data show that Madrid supports the highest tax burden, followed by Cantabria and Catalu˜na. The results of the Communities of Madrid and Catalu˜na show that, despite taxes relativized by regional GDP, they continue to have higher levels of taxation because these regions have a larger business sector that pays the Corporate Income Tax (CIT). Simultaneously, this business sector results in greater industrial activity, increased competitiveness and higher labor rents in other areas and with consequent PIT, which encourages increased VAT. Cantabria’s case is atypical. The high tax burden results mainly from the presence of Banco Santander. Tax payments made by this multinational greatly increases the average tax burden supported by the region. Conversely, Extremadura and Castilla la Mancha have a smaller tax burden that does not reach 5% of the regional GDP. Following these two regions, the Islas Canarias present an average tax burden of 5.02% in the period analyzed, which results from the lack of industrial sector in those regions that negatively impacts tax collection. According to this context and the hypotheses proposed, it is expected that larger volumes of the shadow economy appear in those Autonomous Communities with greater fiscal pressure.10 9 The tax burden is computed as a ratio to regional GDP. We also calculate the per capita tax burden as the ratio between tax revenues and population. The results for the population, and for indirect and total taxes, are available upon request from the authors. 10 The main drawback of the Currency Demand Approach is that it only considers taxes as triggering factors in the shadow economy. Therefore, we must consider the results as estimates that are possibly lower than the actual size.

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Fig. 1. Tax burden by region. Source: Own elaboration from Tax Office data.

The estimates of Eq. (3) for fiscal variables expressed as a ratio to the regional GDP are presented in Table 2. We note that all fiscal variables are significant, except for indirect taxes (model 4), which indicates that indirect taxes have no effect on currency held by the public and, therefore, do not affect the size of shadow economy estimated by the model. The other variables are highly significant in all cases and with the expected signs. GDP has a positive relationship with the dependent variable, and it is expected that GDP growth improve all levels of monetary aggregates. By contrast, the proxy of the opportunity cost of holding money, which in this case is legal interest, shows the expected negative sign. Similarly, the dummy variables show a negative Table 2 Results of estimated models (I). Fiscal variable

Model 1 Direct

Constant T GDP R Dummy Euro Dummy crisis R2

−8.8493 3.1711 1.2412 −1.5281 −0.2708 −0.1402 0.9906

Model 2 PIT (−6.66) (4.89) (15.87) (−2.52) (−10.73) (−6.45)

−9.8801 5.1726 1.2096 −2.0253 −0.2545 −0.1537 0.991

Model 3 CIT (−7.32) (5.53) (15.45) (−3.29) (−10.17) (−7.24)

−7.7627 0.8499 1.2946 −1.0881 −0.2730 −0.1427 0.9903

Model 4 Indirect (−5.81) (3.30) (16.51) (−1.76) (−10.50) (−6.38)

−8.0357 0.1321 1.3428 −1.1882 −0.2651 −0.1527 0.9904

Model 5 Total (−5.58) (0.37) (16.92) (−1.91) (−10.10) (−7.03)

−9.6361 2.6026 1.3110 −1.3434 −0.2744 −0.1408 0.9909

(−6,82) (3.69) (16.98) (−2.19) (−10.67) (−6.33)

Source: Own elaboration. Notes: The table shows the estimates of Eq. (3) for fiscal variables. The dependent variable is the cash held by public in real terms. The fiscal variables are expressed as the ratio of the actual volume of taxes collected and regional GDP. Direct taxes include Personal Income Tax (PIT) and Corporate Income Tax (CIT). The indirect taxes include Value Added Tax (VAT) and the total column includes the sum of the previous three variables. T is a measure of the tax burden, GDP is the real regional GDP and R is the legal interest rate. We estimate the models without the inflation variable since the results are more significant and robust. T statistics are show in brackets

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Table 3 Results of estimated models (II). Fiscal variable

Model 6 Direct

Constant T GDP R Dummy Euro Dummy crisis R2

−7.6574 2.0176 1.2086 −1.2733 −0.2699 −0.1451 0.9906

Model 7 PIT (−5.79) (−3.89) (14.54) (−2.09) (−10.58) (−6.63)

−7.847 3.124 1.159 −1.529 −0.258 −0.156 0.9909

Model 8 CIT (−5.97) (4.16) (13.33) (−2.50) (−10.15) (−7.23)

−7.518 0.624 1.285 −1.053 −0.272 −0.145 0.9903

Model 10 Total

Model 9 Indirect (−5.60) (2.94) (16.17) (−1.70) (−10.43) (−6.47)

−7.8336 −0.0092 1.3377 −1.1969 −0.2636 −0.1585 0.9903

(−5.61) (−0.03) (17.03) (−1.92) (−10.04) (−7.09)

−8.3247 1.4602 1.2776 −1.166 −0.2713 −0.1468 0.9907

(−6,17) (2.62) (15.82) (−1.89) (−10.46) (−6.58)

Source: Own elaboration. Notes: The table shows the estimates of Eq. (3) for fiscal variables. The dependent variable is the cash held by public in real terms. The fiscal variables are expressed as the actual volume of taxes collected by regional population. Direct taxes include Personal Income Tax (PIT) and Corporate Income Tax (CIT). The indirect taxes include Value Added Tax (VAT) and the total column includes the sum of the previous three variables. T is a measure of the tax burden, GDP is the real regional GDP and R is the legal interest rate. We estimate the models without the inflation variable since the results are more significant and robust. T statistics are show in brackets

sign, as expected. The estimates are made without the inflation variable as a non-fundamental variable of the equation because it worsens the results of the analysis. The results that take into account fiscal variables with respect to the regional population are shown in Table 3. The results are consistent with those obtained in Table 2. We also note that, within the fiscal variables, indirect taxes are not significant (model 9). The other variables have the expected signs and appropriate significance, as shown in Table 2. The findings shown in Tables 2 and 3 provide a direct relationship between direct taxes-mainly PIT and total tax revenues-and the currency held by the public. By contrast, indirect taxes, by themselves, do not affect the dependent variable. These results confirm the Hypothesis 2 (H2 ) that direct taxes are those that generate greater incentives to move to the shadow economy. Specifically, personal direct taxes are those that have the greater impact. Subsequently, we assess the size of the shadow economy based on the estimates. We estimate the Currency Demand Approach according to the obtained parameters (Tables 2 and 3). This methodology is conducted for all the variables, and then we eliminate the tax variable. The aim is to quantify the size of currency demanded that does not depend on the fiscal variable. This estimation is the official level of currency demanded (Cof).11 Once the model is estimated with and without the tax variable, we obtain the currency demanded by the informal sector as the difference between total and official cash demand (Cof). Next, we apply the velocity of monetary circulation12 using the corresponding monetary aggregate and Fisher’s equation.13 The size of the shadow economy is eventually given by the following expression: GDPshadowt = Vt · Cshadowt

(4)

11 This statement requires considering that the level of shadow economy in the base year was either zero or was at its lowest level (Gadea & Serrano-Sanz, 2002). 12 The velocity of monetary circulation allows us to know the number of times a unit of currency changes hands during a period. 13 We obtain that the velocity of monetary circulation is equal to national income or GDP from the amount of money in circulation (M2 or M3 ) by solving Fisher’s equation.

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and is relativized with respect to official regional GDP to obtain a percentage measure of the size of the shadow economy. An essential task in this process is to select the monetary aggregate for the estimation. According to Gadea and Serrano-Sanz (2002), the use of smaller monetary aggregates suggests a higher velocity in the shadow economy because of the type of activities in the irregular sector that are associated with the provision of services, which require a smaller number of transactions. By contrast, the use of broader monetary aggregates implies considering that the velocity of circulation is reduced, as a result of which individuals increase savings, which are derived from official and regular incomes, and employ currency only in irregular activities. Because of these discrepancies in selecting the appropriate monetary aggregate, we employ the monetary aggregates M2 and M3 , and the mean value between them, to compare different possibilities and discuss the results with each. Therefore, the use of M3 reduces the velocity of circulation, and the estimates will be lower than with M2 . It is worth noting that this approach is correct when the elasticity of income (GDP) is equal to 1 (β = 1). If not, it is inappropriate to consider the same velocity of circulation in both the official and informal sectors and we should thus make an adjustment. This condition is not satisfied in the models presented because the values of the coefficient of GDP are greater than unity (see Tables 2 and 3). Ahumada, Alvaredo, and Canavese (2007) suggest a possible solution to solve this problem. They believe that in those cases in which β = / 1, we can make an adjustment in the ratio as follows: Yinformal = Yofficial



Yˆ informal Yˆ official

 β1 (5)

This adjustment corrects the bias in the estimates of the underground economy when the elasticity is different than one. Following this adjustment, Ahumada et al. (2007) corrected, upward or downward, the estimates of studies that did not take into account this appreciation. Subsequently, this adjustment has also been applied in other studies (Brambila Macias & Cazzavillan, 2009; Pickhardt & Sardà, 2011). To estimate the size of the shadow economy, we select the models that include direct taxes (models 1 and 6 estimated from Eq. (3) and listed in Tables 2 and 3). The results are shown in Tables 4 and 5 and in a summary table with estimates for each of the fiscal variables (Table 6). The values of the shadow economy range from 18% to 33%, depending on the monetary aggregate employed (Table 4). The M2 monetary aggregate, which assumes a higher velocity of circulation, yields superior results. M3 , however, seems to show more optimistic results about the size of the shadow economy. Thus, the average value of both aggregates (M2 and M3 ) may be the most representative to compensate overestimations and underestimations of the size of the shadow economy. According to the mean value of both aggregates, the average size of the shadow economy is approximately 23% of GDP. The estimations do not differ greatly from those obtained for Spain by other authors (Arrazola et al., 2011; Dell’Anno et al., 2007; Gadea & Serrano-Sanz, 2002; Prado-Domínguez, 2004). Regarding the values for each of the regions, we discover that those Autonomous Communities in which there is a greater tax burden do not present higher volumes for the shadow economy. According to the data presented in Table 4, the regions with greater shadow economy are Andalucía and Islas Canarias. Conversely, the Communities of Madrid, Catalu˜na, País Vasco, Navarra and La Rioja show lower volumes of the shadow economy. Please cite this article in press as: González-Fernández, M., & González-Velasco, C. Analysis of the shadow economy in the Spanish regions. Journal of Policy Modeling (2015), http://dx.doi.org/10.1016/j.jpolmod.2015.09.006

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Table 4 Estimates of the shadow economy (I). Region

M2 (%)

M3 (%)

M2 M3 (%)

Average (%)

Andalucía Aragón Asturias Baleares Canarias Cantabria Castilla y León Castilla La Mancha Catalu˜na Com. Valenciana Extremadura Galicia Madrid Murcia Navarra País Vasco La Rioja Total

43.21 30.24 34.97 31.93 42.77 33.57 31.35 35.24 28.97 35.22 39.17 40.33 21.10 40.24 26.80 25.10 28.10 33.43

26.37 15.60 19.26 17.72 27.45 19.11 15.98 19.96 14.10 19.57 23.39 20.86 10.48 22.52 13.27 12.47 15.47 18.45

32.49 20.30 24.56 22.54 33.22 24.07 20.88 25.22 18.66 24.87 29.05 27.12 13.77 28.56 17.48 16.42 19.72 23.47

34.02 22.05 26.26 24.06 34.48 25.58 22.74 26.81 20.58 26.55 30.54 29.44 15.12 30.44 19.18 18.00 21.10 25.11

Source: Own elaboration. Notes: The table shows the estimates for the shadow economy in the Autonomous Communities for the 1987–2010 period. We use total direct taxes (PIT + CIT) over regional GDP as fiscal variable. Column 5 shows the average values of the different monetary aggregates by region. The last row shows the average of each monetary aggregate for all the regions. Table 5 Estimates of the shadow economy (II). Region

M2 (%)

M3 (%)

M2 M3 (%)

Average (%)

Andalucía Aragón Asturias Baleares Canarias Cantabria Castilla y León Castilla La Mancha Catalu˜na Com. Valenciana Extremadura Galicia Madrid Murcia Navarra País Vasco La Rioja Total

38.58 27.31 31.43 29.03 38.48 30.45 28.10 31.56 26.19 31.70 34.89 36.14 19.07 36.22 24.37 22.68 25.47 30.10

23.48 13.94 17.19 15.95 24.62 17.19 14.19 17.76 12.59 17.49 20.75 18.59 9.34 20.15 11.90 11.13 13.88 16.48

28.99 18.23 22.00 20.38 29.86 21.75 18.62 22.51 16.75 22.31 25.83 24.25 12.35 25.64 15.77 14.73 17.77 21.04

30.35 19.82 23.54 21.78 30.98 23.13 20.30 23.94 18.51 23.83 27.15 26.32 13.58 27.33 17.34 16.18 19.03 22.54

Source: Own elaboration. Notes: The table shows the estimates for the shadow economy in the Autonomous Communities for the 1987–2010 period. We use total direct taxes (PIT + CIT) over regional population as fiscal variable. Column 5 shows the average values for each of the regions from the different monetary aggregates. The last row shows the average value of each monetary aggregate for all the regions.

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Table 6 Estimates of the shadow economy for all fiscal variables. Region

Direct (%)

PIT (%)

CIT (%)

Total (%)

Direct (%)

PIT (%)

CIT (%)

Total (%)

Andalucía Aragón Asturias Baleares Canarias Cantabria Castilla y León Castilla La Mancha Catalu˜na Com. Valenciana Extremadura Galicia Madrid Murcia Navarra País Vasco La Rioja Average

32.49 20.30 24.56 22.54 33.22 24.07 20.88 25.22 18.66 24.87 29.05 27.12 13.77 28.56 17.48 16.42 19.72 23.47

33.72 20.62 25.25 23.02 34.60 24.51 21.32 25.98 18.83 25.46 30.18 27.91 13.71 29.56 17.70 16.59 20.11 24.06

18.40 12.37 14.38 13.38 18.22 13.15 12.45 14.53 11.68 14.88 16.07 15.84 9.00 16.38 10.77 10.23 11.83 13.74

33.24 21.38 25.50 23.53 33.66 25.09 21.94 26.18 19.80 25.87 29.81 28.06 14.90 29.41 18.60 17.52 20.79 24.43

28.99 18.23 22.00 20.38 29.86 21.75 18.62 22.51 16.75 22.31 25.83 24.25 12.35 25.64 15.77 14.73 17.77 21.04

31.00 18.71 22.97 21.08 31.95 22.45 19.29 23.68 17.09 23.30 27.49 25.50 12.33 27.15 16.00 14.93 18.24 21.95

16.34 11.26 12.98 12.28 16.43 11.99 11.20 13.03 10.57 13.38 14.33 14.19 8.18 14.77 9.95 9.35 10.91 12.42

27.84 18.19 21.60 20.12 28.33 21.46 18.49 22.03 16.88 21.91 24.88 23.64 12.76 24.82 15.98 14.97 17.78 20.69

Source: Own elaboration. Notes: The table shows the average estimates for the shadow economy. We use the monetary aggregate M2 M3 for the estimation. Columns 2–5 show the results for the fiscal variables over regional GDP. Columns 6–9 show the data for taxes over regional population. Indirect taxes are not shown because they have no significance for the estimation (see Tables 2 and 3).

The values of the shadow economy estimated from direct taxes on the regional population are shown in Table 5. The results are robust and consistent with those shown in Table 4. The average level of the shadow economy ranges from 16% to 30%, depending on the monetary aggregate (M2 and M3 ). The average estimate of the two aggregates shows an economy value of 21%. The analysis by regions shows that Andalucía and the Islas Canarias are the regions with higher levels of irregular activity. Madrid, Catalu˜na, Comunidades Forales, La Rioja, Aragón and Castilla y Leon show lower values for the shadow economy, all of them below 20%. These results contradict the hypothesis that the higher the tax burden, the higher the shadow economy (H1 ). In fact, the findings confirm the arguments presented by Johnson, Kaufmann, and Zoido-Lobaton (1998) and Friedman et al. (2000), who claim that higher taxes do not lead to increased irregular activity and that higher taxation may even lead to higher tax revenues and a more stable legal and economic environment. In this sense, Catalu˜na and Madrid, despite having the highest average tax level (see Table 1), show lower levels of shadow economy, along with Comunidades Forales and La Rioja. Following the arguments of Friedman et al. (2000), these regions achieve a better balance between public goods and services, on the one hand, and revenues from taxes, on the other. Increasing tax revenues allows greater resources to be obtained to finance higher-quality public services. Thus, citizens of these regions are willing to pay more taxes without encouraging them to move to the shadow economy. Therefore, in the Spanish regions, there are other factors besides taxes that influence the size of the shadow economies. These factors may be related to distinctive features of the Autonomous Communities. Some of these elements are the performance of the tax system itself, the penalty system, the morality of economic agents, etc. For a more detailed analysis of these factors, it is Please cite this article in press as: González-Fernández, M., & González-Velasco, C. Analysis of the shadow economy in the Spanish regions. Journal of Policy Modeling (2015), http://dx.doi.org/10.1016/j.jpolmod.2015.09.006

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necessary to employ a different methodology or analyze the determinants of the shadow economy. In this sense, our analysis serves as a starting point for potential further research in this direction. The estimates of the shadow economy for each of the fiscal variables analyzed are summarized in Table 6. These results show the values for the M2 M3 monetary aggregate. The PIT is the fiscal variable that yields the best results. This variable has the greatest impact on currency demand and, by extension, on the shadow economy. By contrast, the estimates undertaken with the CIT reflect lower values than those obtained with the PIT. The interpretation of these results is of great interest to the fight against tax fraud in the shadow economy. In view of these data, policymakers should emphasize measures to combat fraud in PIT because they have the greatest impact on the size of the shadow economy. The results for the fiscal variables in terms of regional GDP do not differ markedly from those obtained based on the regional population, regardless of the tax variable used. To summarize, we can affirm that direct taxes, namely personal taxes (PIT), present the greatest impact on the shadow economy. By contrast, indirect taxes are not significant for the Currency Demand Approach. Regional analyses suggest that regions with high tax burdens (Madrid and Catalu˜na) do not support larger shadow economies, whereas lower tax territories show higher levels of irregular activity (Islas Canarias and Andalucía). These results are consistent with Friedman et al. (2000) and Johnson et al. (1998), who argue that the performance of the tax system as a whole-and not simply the tax burden-contributes to the increase of the size of the shadow economy. 6. Conclusions and policy implications In this paper, we estimate the size of the shadow economies in the Spanish Autonomous Communities between 1987 and 2010 using the Currency Demand Approach proposed by Tanzi (1980) and employed extensively in the international and national literature (Gadea & SerranoSanz, 2002; Pickhardt & Sardà, 2006; Pickhardt & Sardà, 2011; Prado-Domínguez, 2004). The results provide a good starting point for subsequent studies to gain a better understanding of the irregular activity in the Spanish regions. According to the estimates obtained, direct taxes are those that generate the greatest incentives to move to the shadow economy. Specifically, the PIT is the one that most affects the shadow economy. By contrast, indirect taxes have no influence on currency demand and thus appear to be irrelevant for irregular activities, according to the methodology applied. The analysis by regions shows that Andalucía and the Islas Canarias, with shadow economies of approximately 30% of regional GDP, are the Communities with the greatest levels of irregular activity. Economic and fiscal authorities must undertake further intervention in these regions. The lowest levels of irregular activities are in Madrid, Catalu˜na, Comunidades Forales and La Rioja; although it remains necessary to make further progress in the fight against illegal activities in these regions, they present smaller shadow economies than the rest of the regions. These Communities have higher tax burdens but show smaller shadow economies. It is clear that the assumption that higher taxation does not lead to larger shadow economies, according to our estimates. The results show that there is no direct relationship between the tax burden and the shadow economy. This confirms the claims of certain authors (Friedman et al., 2000; Johnson et al., 1998) that the level of taxation does not determine the volume of the shadow economy. There are other factors, such as the performance of the tax system, or morality of economic agents to fraud, that influence the level of the shadow economy. Based on these results, we recommend that the tax authorities apply deterrence measures for tax evasion that relate to direct taxes in order to reduce the size of the shadow economy. However, Please cite this article in press as: González-Fernández, M., & González-Velasco, C. Analysis of the shadow economy in the Spanish regions. Journal of Policy Modeling (2015), http://dx.doi.org/10.1016/j.jpolmod.2015.09.006

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these policy instruments on their own are not enough to reduce the shadow economy in Spanish regions, as the tax burden does not determine the shadow economy. Nevertheless, in those regions with higher levels of underground economy, we recommend increasing tax audits and fines, as they are significant deterrents for tax evasion (Park & Hyun, 2003). Nevertheless, as Orviská et al. (2006) note, tax audits can be more efficient if they are targeted to those individuals more prone to commit tax fraud, despite conducting them randomly. In this sense, and following those authors, we recommend targeting these tax audits following criteria of individual tax returns and taking into account the past behavior of these individuals regarding law abidance. These measures should be accompanied by tax policy modifications and deregulation (Feld & Schneider, 2010). However, the results indicate that the regions with higher tax burdens do not have a larger shadow economy. Therefore, other factors should exist that incentivize individuals to commit tax evasion. In this sense, Friedman et al. (2000) note the possibility that a higher level of taxes, accompanied by adequate public goods and services, may lead to smaller shadow economies and also to a more stable economic environment. If individuals perceive that the taxes they are paying are well used by the authorities to provide goods and services, then they will be less prone to commit tax fraud. Hence, economic authorities must encourage providing high quality public goods and services, which can reduce the incentives to move to the shadow economy. Additionally, there are other factors not related to tax burden that might influence the calculated size of the shadow economy. There are personal considerations that influence individual behavior (Orviská et al., 2006). Citizens’ honesty and tax morale, as well as the social tolerance toward this phenomenon, are questions that must not be forgotten regarding the shadow economy. In this sense, corruption appears as a trigger for the shadow economy. If corruption levels are high, then individuals will tolerate a greater amount of fraud, as they can transfer the responsibility of their acts to the government. Therefore, measures must be implemented to increase the levels of transparency of institutions and to intensify control over politicians and public authorities, as they have to act as behavioral models for taxpayers. Furthermore, the existence of a fair and proportional tax system might increase law abidance because otherwise, the society’s sense of morality becomes relaxed (González-Fernández & González-Velasco, 2014) and there is a higher tolerance for the shadow economy. References Ahumada, H., Alvaredo, F., & Canavese, A. (2007). The monetary method and the size of the shadow economy: A critical assessment. Review of Income and Wealth, 27(2), 363–371. Ahumada, H., Alvaredo, F., & Canavese, A. (2008). The monetary method to measure the shadow economy: The forgotten problem of the initial conditions. Economics Letters, 101(2), 97–99. Alexeev, M., & Pyle, W. (2003). A note on measuring the unofficial economy in the former Soviet Republics. Economics of Transition, 11(1), 153–175. Arrazola, M., de Hevia, J., Mauleón, I., & Sánchez, R. (2011). Estimación del volumen de economía sumergida en Espa˜na. Cuadernos de Información Económica, 220, 81–88. Brambila Macias, J., & Cazzavillan, G. (2009). The dynamics of parallel economies. Measuring the informal sector in Mexico. Research in Economics, 63(3), 189–199. Cagan, P. (1958). The demand for currency relative to the total money supply. Journal of Political Economy, 66(4), 303–328. Cantarero, D., & Blázquez, C. (2013). Una aproximación a la magnitud de la economía sumergida en Cantabria (2009–2012). Universidad de Cantabria, Grupo de Investigación de Economía Pública. Choi, J. P., & Thum, M. (2005). Corruption and the shadow economy. International Economic Review, 46(3), 817–836. Dell’Anno, R. (2003). Estimating the shadow economy in Italy: A structural equation approach. Economics Working Papers, (2003-7). School of Economics and Management, University of Aarhus.

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Dell’Anno, R., Gómez-Antonio, M., & Ala˜nón-Pardo, A. (2007). The shadow economy in three Mediterranean countries: France Spain and Greece. A MIMIC approach. Empirical Economics, 33(1), 51–84. Feld, L. P., & Schneider, F. (2010). Survey on the shadow economy and undeclared earnings in OECD countries. German Economic Review, 11(2), 109–149. Ferraro, F. J., Campayo, C., Rubio, C. M., & Millán, C. M. (2002). La economía sumergida en Andalucía. Colección Estudios. Consejo Económico y Social de Andalucía. Friedman, E., Johnson, S., Kaufmann, D., & Zoido-Lobaton, P. (2000). Dodging the grabbing hand: The determinants of unofficial activity in 69 countries. Journal of Public Economics, 76(3), 459–493. Gadea, M. D., & Serrano-Sanz, J. M. (2002). The hidden economy in Spain – A monetary estimation, 1964–1998. Empirical Economics, 27, 499–527. Giles, D. E. A. (1997). Causality between the measured and underground economies in New Zealand. Applied Economics Letters, 4(1), 63–67. Giles, D. E. A. (1999). Measuring the hidden economy: Implications for econometric modeling. Economic Journal, 109(456), 370–380. González-Fernández, M., & González-Velasco, C. (2014). Shadow economy, corruption and public debt in Spain. Journal of Policy Modeling, 36(6), 1101–1117. Gutmann, P. M. (1977). The subterranean economy. Financial Analysts Journal, 33(6), 26–34. Hill, R., & Kabir, M. (1996). Tax rates, the tax mix, and the growth of the underground economy in Canada: What can we infer? Canadian Tax Journal/Revue Fiscale Canadienne, 44(6), 1552–1583. Johnson, S., Kaufmann, D., & Zoido-Lobaton, P. (1998). Regulatory discretion and the unofficial economy. American Economic Review, 88(2), 387–392. Karlinger, L. (2009). The underground economy in the late 1990s: Evading taxes, or evading competition? World Development, 37(10), 1600–1611. ˇ Orviská, M., Caplánová, A., Medved, J., & Hudson, J. (2006). A cross-section approach to measuring the shadow economy. Journal of Policy Modeling, 28(7), 713–724. Park, C., & Hyun, J. K. (2003). Examining the determinants of tax compliance by experimental data: A case of Korea. Journal of Policy Modeling, 25(8), 673–684. Pickhardt, M., & Sardà, J. (2006). Size and scope of the underground economy in Germany. Applied Economics, 38(14), 1707–1713. Pickhardt, M., & Sardà, J. (2011). Size and causes of the underground economy in Spain: A correction of the record and new evidence from the MCDR approach. Institute of Spatial and Housing Economics Working Paper, (201280). Munster Universitary. Prado-Domínguez, J. (2004). Una estimación de la economía informal en Espa˜na, según un enfoque monetario. El Trimestre Económico, 71(282), 417–452. Schneider, F. (1997). El tama˜no de la economía sumergida en los países de Europa Occidental. Ekonomiaz, 39, 136–151. Schneider, F. (2002). The size and development of the shadow economies of 22 transition and 21 OECD countries. In IZA Discussion Papers, vol. 514. Schneider, F. (2005). Shadow economies around the world: What do we really know? European Journal of Political Economy, 21(3), 598–642. Schneider, F. (2008). The shadow economy in Germany: A blessing or a curse for the official economy? Economic Analysis & Policy, 38(1), 89–111. Schneider, F., & Enste, D. H. (2000). Shadow economies: Size, causes, and consequences. Journal of Economic Literature, 38(1), 77–114. Schneider, F., & Savasan, F. (2007). Dymimic estimates of the size of shadow economies of Turkey and of her neighbouring countries. International Research Journal of Finance and Economics, 9, 126–143. Serrano, J. M., Bandrés, E., Gadea, M. D., & Sanau, J. (1998). Desigualdades territoriales en la Economía Sumergida. Zaragoza: Confederación Regional de Empresarios de Aragón (CREA). Tafenau, E., Herwartz, H., & Schneider, F. (2010). Regional estimates for the shadow economy in Europe. International Economic Journal, 24(4), 629–636. Tanzi, V. (1980). The underground economy in the United States: Estimates and implications. Banca Nazionale del Lavoro Quarterly Review, 135(4), 427–453. Tanzi, V. (1983). The underground economy in the United States Annual estimates, 1930–1980. IMF Staff Papers, 30(2), 283–305. Thomas, J. (1999). Quantifying the black economy: ‘Measurement without theory’ yet again? Economic Journal, 109(456), 381–389.

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