Estimating the size of Shadow Economy with Electricity Consumption Method

Transcription

1 Estimating the size of Shadow Economy with Electricity Consumption Method OLYMPIA MISSIOU SID: SCHOOL OF SCIENCE & TECHNOLOGY A thesis submitted for the degree of Master of Science (MSc) in Energy Management NOVEMBER 2016 THESSALONIKI GREECE i

2 Estimating the size of Shadow Economy with Electricity Consumption Method OLYMPIA MISSIOU SID: Supervisor: Supervising Members: Committee Prof. Dimitris Psychoyios Assoc. Prof. Name Surname Assist. Prof. Name Surname SCHOOL OF SCIENCE & TECHNOLOGY A thesis submitted for the degree of Master of Science (MSc) in Energy Management NOVEMBER 2016 THESSALONIKI GREECE ii

3 Abstract This dissertation was written as a part of the MSc in Energy Management at the International Hellenic University. Shadow Economy (SE) is an undesirable attribute not only for developing countries but also for developed ones and it is caused by a range of factors such as tax burden, tax morality, quality of institutions and corruption. (Schneider and Enste,2000; Schneider and Williams, 2013) Although the size and structure of the informal sector differ substantially among countries, significant distortions such as tax revenue losses, ineffective macroeconomic policies and lower quality and quantity of public goods are caused in real economy in all cases. (Markellos et al, 2016) As a result, the measurement of SE is a crucial procedure. In this framework, a range of methods is suggested by bibliography. Direct methods, indirect methods or macroeconomic approaches and model approach are the main categories. In present analysis, the size of SE of 19 European Union (EU) countries is estimated over the period using a macroeconomic approach called Electricity Consumption Method (ECM) or physical input method due to its accuracy and reliability of energy data. The model improves simple ECM approach taking into account other factors affecting the growth rate of electricity consumption. The results indicate that SE is decreased on unweighted average by 3% over the period In general, both Southern and Eastern Europe countries present a greater size of the informal sector (%of official GDP) in comparison with Western Europe countries such as France and Austria. I would like to thank my thesis supervisor Assistant Professor Dimitrios Psychoyios of the Department of Industrial Management at University of Piraeus for his invaluable advice and constant support. In addition to this, I would like to thank my family and in particular my sister Aristea for her constant support during my studies. Olympia Missiou 20 November 2016 iii

4 Contents ABSTRACT... III CONTENTS...IV 1 INTRODUCTION LITERATURE REVIEW DEFINITION THE IMPORTANCE OF STUDYING THE SHADOW ECONOMY DETERMINANTS OF SHADOW ECONOMY Taxes and social security contributions Regulations Quality of institutions Corruption Sovereign Debt Markets Social Transfers Deterrence Other factors ESTIMATING THE SIZE OF SHADOW ECONOMY Direct methods Indirect methods Model Approach (MIMIC) EFFECTS OF SHADOW ECONOMY METHODOLOGY AND DATA METHODOLOGY Kaliberda-Kaufmann Approach A modified ECM (MECM) Final Energy Consumption Method (FECM) DATA DESCRIPTIVE STATISTICS EMPIRICAL RESULTS RESULTS OF SIMPLE ECM iv

5 4.2 MODIFIED ECM (MECM) Estimation output Size of SE based on MECM approach FINAL ENERGY CONSUMPTION METHOD (FECM) Estimation output Size of SE based on FECM approach COMPARISONS Comparison between MIMIC and MECM model Comparison between FECM and MECM model ROBUSTNESS TEST CONCLUSION REFERENCES APPENDIX v

6 List of Tables Table 1: Taxonomy of Shadow Economy activities... 6 Table 2: Studies applying or suggesting ECM Table 3: Unit Root Tests (Regarding variables of MECM and FECM) Table 4: Descriptive statistics of electricity consumption Table 5: Descriptive statistics of electricity prices Table 6: Descriptive statistics of energy use Table 7: Descriptive statistics of industry added value in GDP Table 8: Descriptive statistics of HDD index Table 9: Descriptive statistics of real oil price Table 10: Descriptive statistics of final energy consumption Table 11: The size of Shadow Economy (% official GDP) Table 12: Shadow Economy (% official GDP) of the UK using different elasticity scenarios Table 13: Relative size of SE (%of official GDP) based on MECM Table 14: Relative size of SE (%official GDP) based on FECM Table 15: Comparisons between MIMIC and MECM Table 16: Comparisons between FECM and MECM Table 17: Unit Root Tests (Regarding Causal Variables) Table 18: Results of robustness test regarding MECM Table 19: Redundant FE test (MECM) Table 20:Results of robustness test regarding FECM Table 21: Redundant FE test (FECM) Table 22: Countries Table 23: SE (% of official GDP) based on simple ECM (Analytical Results). 66 Table 24: SE (% of official GDP) based on MECM (Analytical Results) Table 25: SE (% of official GDP) based on FECM (Analytical Results) Table 26: Causal variables of SE Table 27: Simple ECM vs MECM

7 List of Abbreviations CDA ECM EU FE FECM HDD MECM MIMIC SE Currency Demand Approach Electricity Consumption Method European Union Fixed Effects Final Energy Consumption Method Heating Degree Days Modified Electricity Consumption Method Multiple Indicators for Multiple Causes Shadow Economy 2

8 1 Introduction Shadow Economy is a common parasitic attribute of all economies around the world. Its size and structure differ substantially among countries. Even developed countries present a non-negligible informal sector. In all cases, significant distortions are caused in economy and society. The size of SE affects mainly the efficacy of macroeconomic policies, tax revenues, quantity and quality of public goods and services, international competitiveness, sovereign debt markets, monetary policies, official unemployment rate and social interactions. As an indicative example, tax base is decreased due to the development of SE, implying (potentially) higher tax rates and lower quality and quantity of public goods and services (e.g. roads, health) and giving an incentive for a further shift from official sector to Shadow Market. This procedure is considered to be a vicious cycle amplifying the development of SE. (Schneider and Enste, 2000; Markellos et al, 2016; Eilat and Zinnes, 2002; Schneider and Williams, 2013) All the above distortions underline the necessity of measurement of SE. Before the estimation of SE, a definition is required. Bibliography suggests a variety of different definitions. According to the definition commonly used, SE consists of all undeclared economic activities without the inclusion of do-it-yourself (DIY) activities. (Buehn and Schneider, 2007) The main objective of these definitions is the exclusion or not of illegal activities. The study of structure and size of SE requires the examination of factors affecting its development. Among these are tax rates, tax morality, tax enforcement, tax complexity, social security contributions, corruption, rule of law, social transfers, intensity of regulation, quality of institutions and the degree of deterrence measures. Empirical surveys state that the most important parameters are taxes, social security contributions, tax morality and quality of institutions. According to the results of twelve surveys, taxes and social security contributions if considered as a single factor can explain 35%-38% of SE, tax morale 22%-25%, quality of institutions 10-12%. (Schneider and Williams, 2013) Among a range of methods divided into direct, indirect and MIMIC approach (see for example OECD, 2002; Schneider, 2005; Schneider and Williams, 2013), a physical input method is followed due to its vantage points: Firstly, the reliability of electricity consumption data and secondly, the absence of estimator errors in 3

9 comparison with other methods such as model approach which is based on strong assumptions and the use of complex econometric models. Initially, a simple Electricity Consumption Method (ECM) is used. According to this approach the electricity consumption is the best single proxy for the growth rate of total economic activity. However, this method has been criticized since it assumes that other factors such as electricity prices and energy efficiency that affect electricity consumption are cancelled out. In other words, it states that electricity/output elasticity remains constant across years. Moreover, Schneider and Enste (2000) state that there are alternative forms of energy. Therefore, a modified model is constructed that overcomes all of the above limitations motivated by vantage points of ECM. The modified model consists of a regression that isolates the effect of other factors related to the growth of electrical energy. The model includes adequate independent variables capturing substitution effect, structural-output (electricityintensity) effect, energy efficiency and weather effect. In addition to this, as a next step, the use of total final energy consumption overcomes the last limitation. SE among several EU countries is estimated for the first time using a modified ECM model over the recession period ( ). According to the results, simple ECM indicates that SE varies significantly across years in the majority of countries. Moreover, UK presents negative values for the years 2011, 2012 and Both extreme variations and negative values confirm the necessity of a modified ECM. The results of the modified ECM model point out that Eastern and Southern Europe countries present a greater unofficial sector as a percentage of official GDP in comparison with Western Europe countries such as Germany and Austria. On unweighted average, in Western Europe countries SE is reduced by 10% from the years to Southern Europe countries appear to follow the same pattern as a group. In other words, the informal sector of Spain, Greece and Portugal on average is decreased from 24.1(% official GDP) in to 21.5(% official GDP) in In contrast, Eastern Europe countries surge their Shadow Market during the recession period. Actually, Shadow Market is increased on average from 21.7(% official GDP) in to 24.5(% official GDP) in In an overall evaluation, SE is decreased on unweighted average by 3%. 4

10 The results of final energy consumption method are similar to those of the modified ECM model. As a final step, the robustness of the results is tested. The results appear to be strongly correlated with main driving forces of Underground Economy. The contribution to existing literature consists of the followings: Firstly, this is the first analysis which uses ECM regarding the estimation of SE in several EU countries during the recession period. Secondly, up to our knowledge, only few studies in literature provide estimations of SE in a cross-country level for the recent years, such as Schneider s estimations based on model approach (see for example Schneider, 2009; Schneider et al, 2010; Schneider et al, 2015). Thirdly, the model overcomes main limitations of simple ECM amplifying the attractiveness of method and finally, a Final Energy Consumption Method is applied since the composition of energy consumption has substantially changed in recent years. The present dissertation is organized as follows: Section 2 includes literature review, in Section 3 are presented methodology, data and descriptive statistics, section 4 includes results, comparisons with other methods and a robustness test while section 5 presents concluding remarks. 5

11 2 Literature Review Basic causes and consequences of the development of Shadow Economy are included in this section. Moreover, a range of methods that estimate the informal sector are presented. 2.1 Definition The bibliography provides a range of definitions pertaining to Shadow Economy (SE). Shadow activities are divided into legal and illegal. Many authors define Shadow Economy as the sum of all activities taking place outside of the formal sector, legal or not, such as drugs, undeclared work and do-it yourself activities. Furthermore, another distinction of Shadow activities is related to market-based and household activities. (Smith, 1994) Therefore, it is crucial for every study to clearly define Shadow Economy. More specifically, the definition that includes legal activities, the income of which remains undeclared is followed in the present analysis. (Schneider and Enste, 2000) Table 1 provides insightful information regarding the taxonomy of Shadow Economy activities. The majority of surveys attempt to measure legal economic activities of the informal sector such as barter of legal services and goods and undeclared work related to legal activities. Table 1: Taxonomy of Shadow Economy activities illegal Monetary Transactions Trade in drugs and other illegal goods Non-monetary Transactions Barter of illegal goods or production for own use Tax avoidance Tax evasion Tax avoidance Tax evasion legal employee Unregistered Do-it yourself Barter of legal discounts and income of self- activities and services and fringe benefits employed friend or goods regarding legal neighbor help activities 6

12 Source: Schneider and Enste (2000), Mirus and Smith (1997) 2.2 The importance of studying the Shadow Economy Policy makers impose policies based on official statistical data and ignore overall GDP which consists of official and unofficial GDP. Therefore, the size and growth rate of the informal market affect the efficiency of macroeconomic policies. In other words, the development of SE causes distortions in macroeconomic multipliers. (see for instance Adam and Ginsburgh, 1985). At the degree that statistical data ignore the real size of SE, a macroeconomic policy cannot be the most efficient one. Actually, the development of the informal sector prevents not only the economic growth but also the improvement of living standards since Underground Economy affects the quality and quantity of public goods and services due to reduced government revenues. Only by estimating Shadow Economy, could policies be efficient contributing to the increase of economic growth and the improvement of social welfare at a country level. The study of size, growth and structure of SE can reveal real causes of SE. In this framework, both estimation of SE and examination of causality mechanisms are crucial procedures. The role of estimation is twofold: Firstly, the size and structure of SE can be taken into account during the macroeconomic policy design. Secondly, proper measures are able to be taken in order to provide individuals with strong incentives for a shift to official sector. It is worth noting that a simple reduction of SE should not be the main objective. In other words, there is a variety of deterrence measures which either enhance the transition in the official economy or just lead to closures. However, closures imply a reduction of total economic activity. (Markellos et al, 2016; Schneider and Williams, 2013) 2.3 Determinants of Shadow Economy In this section, the most important driving forces of Underground Economy are presented. Although Shadow Economy is a common attribute of all countries, the main causes may vary in each country. (Dell Anno et al, 2006) 7

13 2.3.1 Taxes and social security contributions An increase of tax rates contributes to the development of Shadow Economy since people shift to the informal economy to avoid tax burden. Although tax evasion and SE are not identical, tax evasion is associated with legal activities of SE. (Schneider and Enste, 2000) Additionally, tax evasion and SE are often induced by same factors. (Schneider and Williams, 2013) The increase of Shadow Economy leads to the reduction of the expected tax revenues. As a result, government increases further tax rates in an attempt to collect revenues to fund public expenses. A fundamental consequence of the reduction of the tax revenues is the lower quality and quantity of public goods and services and this gives companies an incentive to move in the unofficial sector. (Schneider and Enste, 2000) This procedure resorts to a vicious cycle. However, Laffer curve could facilitate policy makers to examine if an increase of tax rates will lead to greater tax revenues. (Schneider and Williams, 2013; Johnson et al, 1997) Apart from tax rates, tax enforcement is another significant factor that affects SE. To put it in other words, the existence of efficient mechanisms regarding taxation system plays a significant role in the collection of taxes and consequently in the development of unofficial sector. (Johnson et al, 1998b) Tax evasion, tax compliance and SE are related to tax morale. Tax morale or the willingness of paying taxes is a link between the efficacy of public sector and Shadow Economy. To put it differently, a less efficient public sector contributes to the development of SE through the decrease of tax morality. Tax morale is considered to be a psychological factor and is related to the fairness and the efficacy of a tax system. This is based on the opinion of people about the taxation system. For instance, the existence of a progressive taxation and the connection of tax revenues with the provision of public goods and services indicate a high tax morality. Another factor surging tax morale is the friendly behavior that is shown to tax payers by the administration of tax system. It is remarkable that countries with high tax morale and small public sector present a small SE as a percentage of official GDP. Although, tax morale is not considered to be a new field for research, there are no adequate empirical studies regarding tax morality and SE. Torgler and Schneider (2009) confirmed a negative correlation between tax morality and SE. They also found the direction of causality. According to their results, tax morality causes changes in the size of SE. That is to say, if tax morale decreases, informal sector increases. 8

14 High social security contributions are a possible cause as well. Through the Black Economy, people can avoid both public security contributions and tax payments. In many developed countries non-wage costs are greater than the net amount of wage. (Schneider and Williams, 2013) Both tax burden and social security contributions are substantial driving forces of SE and if considered as a group, explain a 35%-38% of SE variation. This result stems from the average value of twelve independent surveys. The complexity of tax system affects the size of SE. Complexity allows people to avoid part of tax payments. (Schneider and Enste, 2000) Schneider and Neck (1993) studying the SE in Austria observed a negative relation between tax complexity and the development of SE. In many cases, a decrease of tax rates may not lead to a proportional decrease of the informal sector. This occurs due to other factors such as the complexity of tax system that cancel out the effect of tax rates. Actually, an increase of tax rates in conjunction with a less complex tax system and a high intensity of regulations can lead to a neutral result. The complexity of tax system is negatively related to tax evasion and supply of labor in SE. (Schneider and Enste, 2000) A high tax complexity system implies a small SE due to low incentive for a shift to the informal sector. In this framework, individuals avoid part of taxes without their participation in Shadow Employment, that is, without the risk of detection and penalization Regulations The increase of regulations in the formal economy such as licenses and permits gives the firms an incentive to shift to the informal sector since they require additional expenses. On many occasions, these costs burden employees through reduced wages enhancing Shadow Employment. The existence of over-regulation is correlated with a greater Underground Economy. (See for instance Friedman et al, 2000) Johnson et al (1998a, 1998b, 1997) stated that an increase of regulations leads to a greater SE. According to empirical results of Johnson et al, (1998b), if index for regulations is increased by one-point, implying that regulations are not in favor of businesses, the share of SE grows by 14.7%. However, taking into account the level of income, the share is decreased to 8.1% but with the presence of statistical significance. Johnson et al (1998b) concluded that a low intensity of regulations is correlated with a low share of SE although high-income countries present a lower share of SE. As a 9

15 result, they used a controlling variable for income to isolate the effect of regulation on the size of SE. Apart from this, according to the results of 12 studies, the intensity of regulation as a single variable is able to explain 7%-9% of SE variation. (Schneider and Williams, 2013) Some regulations regarding labor force affect positively the development of Shadow Employment. However, the reduction of these regulations does not guarantee a shift to the formal economy. (Kucera and Roncolato, 2008) Regulations that require the reduction of hours that people work in the official sector, lead to an increase of SE under the condition that this reduction is not in accordance with individuals preferences. A reduction of hours implies a decrease of wage and forces individuals to work in both sectors. (Schneider and Enste, 2000) Although the main objective of these regulations is the reduction of official unemployment rate, they cause distortions through the enhancement of SE. Johnson et al (1998a) observed that countries with many regulations presented a higher SE as a percentage of official GDP. In many cases, politicians increase regulations in an attempt to reduce and control the development of SE. However, the underlying objective of this policy may be often the increase of employment in public sector based on their preferences. Apart from the number of regulations, law enforcement is related to SE as well. For a highly regulated country, the degree of law enforcement is a crucial factor. To put it in other words, a highly regulated country in conjunction with a weak law enforcement implies a non-negligible SE. Moreover, rule of law is also associated with SE. Johnson et al (1998b) studied the relationship between rule of law and SE and concluded to a statistically significant negative correlation. The existence of regulatory discretion is considered to be another parameter affecting SE. Johnson et al (1998b) pointed out that the main factor causing SE is regulatory discretion, that is, the opportunity of politicians to impose the same regulation in a different way among individuals. Actually, regulatory discretion contributes to the reduction of tax revenues and leads to the decrease of public goods. As a consequence, SE is enhanced. It is noticeable that Johnson et al (1998b) used several different indices such as rule of law, bureaucratic quality and corruption in order to study the regulatory discretion. Regarding regulation variables, two out of four (bureaucratic quality, regulations and way of enforcement) indices remained 10

16 statistically significant after controlling of income. This reveals that the regulatory discretion influences SE Quality of institutions Countries with strong government institutions present a smaller percentage of SE. A country with high regulated economy and weak governance presents a high informal economy. That is to say, regulations and governance reinforce each other. In numbers, quality of institutions explains 10-12% of SE variation. (Schneider and Williams, 2013) Corruption The relationship of Shadow Economy with corruption is an ambiguous issue in bibliography due to the absence of strong evidence concerning the direction of causality. The examination and measurement of both corruption and SE are challenging procedures since they are considered to be unobserved variables. Johnson et al (1998a), Friedman et al, (2000) and Markellos et al (2016) stated that two variables are (partially) complementary based on their empirical results. In other words, lower corruption implies a less informal sector. The existence of a common factor accounting for both variables may be a possible explanation for this relation. Schneider and Dreher (2009) using cross-sectional data stated that the relationship between SE and corruption depends on the income of the country. In other words, SE and corruption are complementary in low-income countries while they are substitutes in high-income countries. In numbers, an increase of SE by 10% contributes to the reduction of corruption index by 0.7 points in high-income countries. Choi and Thum (2005) and Dreher et al (2008) stated that SE and corruption are substitutes for highincome countries. In developed countries, phenomena of corruption may be often punished through the strong regulatory framework. Moreover, in high-income countries, incidents of corruption such as bribes are related to formal sector. As a result, corruption is mainly associated with official activities. For instance, individuals bribe officials in order to overcome obstacles such as bureaucracy during the procedure of entrance in the formal sector. Consequently, the above considerations may explain the existence of substitution characteristics. 11

17 In low income countries, there are big companies operating in unofficial sector. Obviously, these companies can be easily detected. Thus, they are forced to bribe officials in order to remain in the shadow. Corruption and SE appear to be complementary variables since shadow economic activities require bribes. (Hindriks et al, 1999) Consequently, SE and corruption are positively related Sovereign Debt Markets The cost of sovereign debt is related to the size of Shadow Economy. More specifically, the existence of SE decreases the tax revenues shrinking the basic source of public revenues. A government is able to cover its expenses through an increase of tax rates only in the case that there is a high tax enforcement. Otherwise, the reduction of public expenses is an alternative. However, the latter option implies a contractionary fiscal policy which will decrease the incentive of firms to remain in the official sector due to lower quality and quantity of public goods. As a result, public lending is the only possible way for complementary funding of public expenses. A continuous public lending leads to a surge of credit rating risk that increases funding costs of the formal sector and enhances the development of SE. (Markellos et al, 2016) Social Transfers High amounts of social transfers give individuals an incentive to work in the SE. (Schneider and Enste, 2000) Individuals can receive social transfers while they are working in SE maximizing their total net income since they can avoid tax payments. The higher the amount of social transfer is, the greater the incentive for a shift would be. An increase of social transfers in conjunction with high marginal tax rates facilitate the development of SE. Individuals prefer to work in SE receiving social transfers rather than pay a great amount of money for taxes Deterrence Deterrence should affect the size of informal sector. This factor includes fines, punishments, detection and audits. Empirically, deterrence and SE do not present a clear relation. The direction of causality is considered to be a controversial issue. Most of surveys do not reveal the existence of a strongly negative effect of deterrence on the 12

18 informal sector. According to empirical results, fines in conjunction with other deterrence measures contributed to a decrease of SE in Germany. (Feld and Larsen, 2005) The decision for a shift to the informal sector is based on a cost-benefit analysis. To put it in other words, individuals attempt to evaluate potential costs and benefits derived from a transition between two sectors. (Eilat and Zinnes, 2002) Actually, benefits are related to the avoidance of tax payments and social security contributions while costs are derived from the possibility of detection, the existence of penalties, bribes, potential payments for social services (from private sector) and other factors. Only if benefits exceed costs, individuals will shift to the SE. In many countries, people ignore the possibility of detection, since they consider that the risk of detection is very low. This consideration is an indirect cause of SE and is related to the law enforcement and the quality of legal framework. As a result, both weak law enforcement and legal framework enhance the SE. (Schneider, 2013) Other factors In many countries, the majority of transactions takes place in the form of cash. Through the use of cash, transactions are practically non-observable. In this framework, the operation of Shadow Economy is mainly based on cash transactions. As a result, an increase of allowed use of cash in an economy contributes to the development of informal activities. Therefore, electronic payments and the promotion of other observable forms of money are considered to be a solution. (Schneider, 2013) Concerning developing countries, driving forces of SE are somewhat different in comparison with those of developed countries. World Bank Doing Business report (2016) 1 examined if companies can easily start their operation in the formal sector by country. Its study is based on variables such as regulatory framework and the degree of bureaucracy. The high score of Africa in contrast with that of high income countries reveals the existence of severe obstacles preventing the development of official sector. As a consequence, in developing countries, companies operate in the informal sector not for tax evasion purposes but due to entry obstacles of the formal sector. 1 Source: 13

19 Additionally, in these countries, the majority of labor is undeclared. This is sensible since the majority of firms operates in the informal sector and thus they cannot officially hire individuals. By virtue of different nature of informal activities between developed and developing countries, some authors use the term informal or unofficial sector which is considered to be more soft than the term Shadow Economy. In summary, the main determinants of Shadow Economy are tax burden and social security contributions, tax morality as a severe psychological factor, quality of institutions and the intensity of regulations. More analytically, tax burden and social security contributions explain 35%-38% of SE, tax morale 22%-25%, quality of institutions 10-12% while intensity of regulations 8% on average. It is worth to be remarked that there are plenty interactions among the above variables and they also might reinforce each other. (Schneider and Williams, 2013) 2.4 Estimating the size of Shadow Economy There are three main categories regarding methods for SE estimations. These are i) direct methods, ii) indirect methods and iii) model approach. The latter is considered to be more complex. On many occasions, different methods lead to different estimations of the SE for the same country. For example, SE of Canada ranged from 1.4% to 47.1% as a percentage of GDP. (Tanzi, 1999) This observation reveals that the estimation of the exact size of SE is a challenging procedure since all methods attempt to capture an unobserved variable. However, they contribute to the study of the SE trend among countries. In addition to this, data used for the estimation of SE are not always reliable since politicians often publish biased statistical data regarding national accounts for own interest (such as elections) Direct methods Direct methods such as surveys and tax audits are based on a microanalysis framework. The collection of information regarding structure and other characteristics of Shadow Market is considered to be the main advantage of microeconomic approaches. However, in most cases, direct methods lead to the underestimation of SE. Additionally, 14

20 their static nature prevents the examination of SE over the years. That is to say, they estimate the size of Shadow Economy only at a specific point in time and they cannot provide us with information for the growth of Shadow Economy. Furthermore, the direct measurement of shadow activities through survey method is a difficult procedure since people working in the informal sector desire to avoid the detection. Thus, they are reluctant to provide reliable information. Actually, researchers should carefully create specific questions in order to receive reliable answers. As a solution, face to face interviews are preferable but their cost is extremely high. (Schneider and Enste, 2000) As an indicative example, survey method has been used for the estimation of Shadow Employment in Denmark. Data for the time span between 2008 and 2010 were used for the examination of size and structure of Denmark Shadow Employment. According to the results, undeclared work represents approximately 3% of national income. (Hvidtfeldt et al, 2011) However, the results imply a relatively small Shadow Employment. This result confirms that direct methods underestimate SE. Additionally, in the specific case, the exclusion of several unofficial activities is another possible cause of underestimation. Regarding the structure of SE in Denmark, the proportion of men working in the informal sector is considerably higher compared to that of women. (Schneider and Williams, 2013) However, the unavailability of the comparisons in a cross-country level creates problems and reveals the limitation of direct approaches. For instance, the structure and content of questionnaires differ dramatically among countries due to culture or other domestic characteristics Indirect methods Indirect methods use macroeconomic variables as an indicator of the size of Shadow Economy. Their dynamic nature is considered to be an attractive attribute. On some occasions, they use econometric models which are complex. Indirect methods are divided into five categories: i) National income versus national expenditure, ii) official and real labor force, iii) the transaction approach, iv) currency demand approach and v) the physical input method. 15

21 National income versus national expenditure This approach compares national income with national expenditure and assumes that their discrepancy is caused by the existence of SE. To put it in other words, in the absence of informal economic activities national income should be equal to national expenditure. The above method is based on published statistical data, which may be unreliable, since statisticians often change real data in order for the above accounts to present a small discrepancy (Tanzi, 1999). Consequently, unreliable data imply a downward bias of SE. In addition to this, statistical errors contribute further to misleading results. Official and Real Labor Force The approach attempts to estimate SE through comparisons between official and real labor force. It states that official labor force is negatively related to shadow labor force. An increase of official labor force implies a decrease of unofficial employment. The method ignores other factors affecting the size of official labor force. An indicative example is a reduction of official labor force by virtue of an economic crisis. Furthermore, it is possible that individuals participate simultaneously in both sectors. (see for instance Schneider and Enste, 2000) The transactions approach The model developed by Edgar Feige and is based on Fischer s quantitative theory of money. In mathematical terms: M V 1 P T, where M is the quantity of money, V is the velocity of money circulation, P represents the level of prices while T is the volume of transactions. This model compares PT with the official Gross National Product (GNP). If the product of prices and volume of transactions exceeds the official GNP, a Shadow Economy exists. However, this method is characterized by weaknesses such as its strong assumptions. Moreover, it requires that the value of SE is zero in the base year. (Schneider and Enste, 2000) 16

22 The Currency Demand Approach (CDA) The method is based on Cagan s work which has been used almost twenty years later by Gutmann (1977) who estimated the SE of the U.S. (Schneider and Enste, 2000) More specifically, Cagan (1958) studied which factors affect currency demand and pointed out that the main factors are: i) tax rate, ii) interest rates and iii) income per capita. In 1977, Gutmann estimated SE considering that tax rate is the most important factor. He assumed that if tax rate increases, the demand of cash will be increased since people dealing with Shadow Economy will have a greater benefit from the avoidance of tax payments. As a result, currency ratio ( C / M ) will be increased. It is worth noting that CDA was extended further by Tanzi who constructed a regression in order to control other factors affecting currency demand. The basic concept of this method is that Shadow activities are associated with cash transactions. As a result, the measurement of SE can be derived from the excess of currency demand which is not explained by the tax burden. Although CDA is considered to be a non-complex method, it presents weaknesses. (Alm and Embaye, 2013) For instance, its basic assumption is totally unrealistic since the method considers that there is no SE in the base year. Furthermore, the method assumes that the velocity of money is the same in both sectors. However, this assumption is held only in the case that the value of income elasticity is equal to one. (Ahumada et al, 2009) Another drawback is the increased currency demand from other parameters, which may not be taken into account. For instance, as a result of the economic recession interest rates in Eurozone were dramatically reduced giving individuals the incentive to increase currency demand. (Takala and Viren,2010) This phenomenon was enhanced by the fear of a potential bankruptcy. In addition to this, the CDA approach states that only cash is used in the Shadow Market. Empirically, this statement is not confirmed. Concerning transition countries, the model is inappropriate since currency demand is extremely increased in the transition phase regardless of the existence of SE. Moreover, in this phase, people use foreign currency concerning the transactions in SE. In many cases, the barter is an alternative for the shadow transactions. (Hanousek and Palda, 2006) 17

23 The physical input method (Electricity Consumption Method) This part provides analytical information in relevance with Electricity Consumption Method (ECM), since this method is used in the empirical part. There are two approaches regarding ECM: i) Kaufmann-Kaliberda and ii) Lackó approach. Kaufmann and Kaliberda approach Kaufmann and Kaliberda (1996) used ECM according to which the electricity consumption is the best single proxy for the estimation of overall economic activity. They examined the size of Shadow Economy in FSU (Former Soviet Union) and CEE (Central and Eastern Europe) countries. The estimation of the overall GDP growth rate requires an assumption for the value of electricity per output elasticity. Assuming that electricity/output elasticity is constant, the growth rate of total economic activity can be derived from the growth rate of electricity consumption. However, the assumption of constant elasticity over the time may be invalid in particular in the case of transition countries due to mix-output changes. (Eilat and Zinnes, 2002) As a result, Kaufmann and Kaliberda assumed three different elasticity scenarios based on the efficacy of electricity use and then separated countries in groups. They assumed that the elasticity of less efficient countries was equal to 1.15 implying that the increase of production by 1% requires a 1.15% surge of the electric power consumption. In most cases, for simplicity purposes, a unitary elasticity scenario is applied. After this conversion, the difference between estimated growth rate of overall GDP and official GDP represents the relative size of Shadow Economy. Johnson et al (1997) used different elasticities for different countries in order for cross-country comparisons to be feasible. The method requires an initial value of SE for the base year. This value has to be derived from other studies which may apply different methods for the estimation of SE. It is remarkable that the initial value affects the results. Thus, estimations of initial value should be reliable. Apart from this, Kaufmann and Kaliberda recognized some limitations and weaknesses of their methodology. A basic limitation is that the electricity consumption can change due to factors which are irrelevant with the total economic activity. The improvement of efficiency, the movements in price of electricity and a change in electricity-intensity are basic parameters that affect electricity consumption and cause 18

24 either underestimation or overestimation of Shadow Economy. For instance, an increase of electricity prices leads to a downward bias of SE by virtue of the reduction of electric power consumption, since industry and households will shift to less expensive forms of energy. Respectively, a reduction of electricity prices will cause an overestimation of SE. Under the consideration that factors causing underestimation are cancelled out from factors that cause overestimation, a unitary elasticity scenario can be applied. Additionally, this method has been criticized since it cannot capture some unofficial economic activities that do not require energy consumption (e.g. personal services). In other words, not all informal activities are electricity-intensive. Another limitation is the existence of negative values (Feige and Urban, 2003). Feige and Urban estimated the value of SE for FSU and CEE countries using different initial values in an attempt to examine the robustness of ECM. Their initial values are based on calculations of Alexeev and Pyle (2003) which are considered to be more recent in comparison with those used by Kaufmann and Kaliberda. According to their results, although the use of high initial values improved the results regarding FSU countries, they obtained negative values for both cases (high initial values and low initial values) in some countries. However, negative values of CEE countries reveal unreliable initial values. As a solution of above limitations, Feige and Urban used a modified version of original ECM. This method was suggested by Eilat and Zinnes (2002). They estimated the SE using a regression for electricity consumption controlling the effect of other variables which are irrelevant with the total economic activity. Eilat and Zinnes constructed the below regression: EC a a P a SGDP a PSGDP it 0 1 it 2 it 3 it (2) Where ECit is the percentage change of electricity consumption, EPit represents the percentage change of electricity price and SGDPit measures the share of industry in GDP controlling for changes in mix-output. That is to say, the latter independent variable aims to detect changes of electricity intensity. Moreover, PSGDPit is the share of private sector in GDP and attempts to measure energy efficiency. Moreover, a 0 is a constant term while a1, a 2 and a 3 represent coefficients. Eilat and Zinnes (2002) obtained the residuals of the above regression which represent the part of electricity 19

25 consumption related to Shadow Economy ( ). After this procedure, they residuals C t obtained results for the percentage change of overall economic activity. If an independent variable is statistically insignificant, then it should be removed. For example, Feige and Urban resorted to a regression with one independent variable since according to their sample, a 2 and a 3 were insignificant implying that electricity consumption was not affected by these factors. Although the above modified model of ECM attempts to overcome weaknesses of the original one, Feige and Urban obtained negative values for some countries in specific years revealing that a modified method is not able to provide a solution for transition countries. A possible solution could be the relaxation of the assumption that all countries present the same value of the constant term a 0. (Feige and Urban, 2003) Lackó Approach Lackó approach attempts to estimate SE which is related to households based on residential electricity consumption. One of the main assumptions is that energy efficiency is constant over the years. In other words, changes of energy efficiency are not taken into account. Lackó (1996, 1998, 2000) stated that electricity consumption of households is correlated with Shadow Economy and pointed out that if household SE is high, overall SE should be great as well. The model includes two regressions. In the first regression, SE is one of the explanatory variables while in the second one, Shadow Economy is presented as the dependent variable. In a mathematical form, the model is given by the below equations: i 1 i 2 i 3 i 4 i 5 i i ln E a ln C a ln PR a G a Q a H u 3 With a1, a3, a 5 0 and a2, a 4 0. Where E i measures household electricity consumption per capita, consumption per capita in US dollars excluding electricity consumption, real residential price of 1 KWh in US $, C i represents real PR i expresses G i is the relative frequency of months which require heating, Q i is the ratio of energy sources excluding electricity to the total energy sources which are associated with household energy consumption and H is i 20

26 Shadow Economy per capita while u i is the disturbance term. Although Lackó suggests the above regression, real electricity price is not included in her final model due to lack of data. With b1, b 3 0 and b 2 0 i 1 i 2 i i 3 i H bt b ( s T ) b D 4 Where T i is the ratio of net personal income, corporate profit and taxes to GDP, S i is the ratio of public social welfare expenditures to GDP while decrease. D i measures output Although, all macroeconomic approaches consider only one cause for the phenomenon of SE, they can contribute to the estimation of the size and growth of SE. Obviously, macroeconomic approaches may be considered to be unsuitable only for transition countries, since their major assumption that these macroeconomic variables are either stable or change with a specific way is not held in the case of transition phase. For instance, in this phase, currency demand is extremely increased regardless of SE Model Approach (MIMIC) The model combines multiple indicators with multiple causes (MIMIC) for the estimation of a latent variable. A sub-category of this method is the SIMIC model that includes one indicator for multiple causes. This model was used by Frey and Weck- Hannemann (1984) who considered that Shadow Economy is a latent variable. The error of this method is approximately +/- 15%. The robustness of results depends on the proper selection of causes and indicators. Therefore, the detection of main causes of the informal economy is a necessary procedure. MIMIC approach requires an initial value for the SE. This is a common attribute of both ECM and MIMIC method. (Feige and Urban, 2003; Schneider and Enste, 2000) The MIMIC model consists of a structural model and a measurement model. The structural model includes the latent variable as a dependent variable and possible independent variables that contribute to the development of SE. The final structural 21

27 model is formed on the basis of statistical significance. A mathematical form of the structural model is the following: n a0 a1c 1 a2c2... akck u 5 Where n is the latent variable which is Shadow Economy in this case, C1, C2,..., C K are statistical significant causes, k is the number of causes, a 0 is the constant term while a1, a2,..., a k are multiple coefficients. A test for stationarity of variables is required. Therefore, in many cases the equation (5) is converted to first differences implying that the original variables include a unit root. As far as the measurement model is concerned, it includes indicators as a dependent variable while Shadow Economy is the explanatory variable. For p indicators there are p equations. C n, C n, C n,..., C n Expected value of disturbances u and is equal to zero. p p p In an overall evaluation of methods, they present advantages and disadvantages. To put it in other words, there is no best approach. Obviously, modified electricity consumption method combines accurate data, since electrical energy measurements cannot be unreliable, with isolation of other factors that affect electricity consumption leading to a precise estimation of Shadow Economy. 22

28 2.5 Effects of Shadow Economy There are two contradictory opinions regarding the relationship between SE and economic growth. The first is the positive effect of Shadow Economy in economic growth since the production of the informal economy is considered to be more efficient. Furthermore, empirical results of Schneider confirm the existence of a positive relation between SE and consumption expenditure. On the other hand, the second opinion states that SE leads to a reduction of GDP growth rate. Loyaza (1996) who studied Latin America countries stated that SE is a suspending factor for economic growth. As far as the relationship between the growth of official GDP and unofficial GDP is concerned, empirical results are ambiguous. (Schneider and Enste, 2000) La Porta and Schleifer (2008) pointed out three possible scenarios: i) romantic view, ii) parasite view and iii) dual economy. Dual economy approach implies that SE and formal sector operate independently. It is noticeable that according to this approach, informal sector can contribute to income improvement in the case of low-income groups. Loyaza (1996) studied SE in Latin America and confirmed a negative relationship between two variables under some specific conditions. According to his empirical analysis, an increase of SE by 1% as a percentage of GDP leads to a reduction of official GDP growth by 1.2%. However, Loyaza s model has been criticized due to its assumptions. From a theoretical perspective, this is based on a negative correlation between informal sector and infrastructure which is a fundamental factor for economic growth. A major part of funding for public goods and infrastructure stems from tax revenues which are reduced due to SE. However, participants of SE, which avoid tax payments, pay penalties in the case of the detection, that is, a different form of public revenues. At the degree that these public revenues are not related to the provision of public goods, a negative relationship between formal and informal sector is expected. The development of Shadow Economy is related to short-term investments since this market is labor-intensive. In this framework, long-term funding is not possible without an access in capital markets. (Kaufmann and Kaliberda, 1996) This situation worsens the international competitiveness of a country and the degree of innovation since capital goods and foreign investments are not enhanced by the existence of the informal sector. Actually, it is beneficial for a country to export goods since this procedure enhances the total demand especially in the case of inadequate 23