The impact of taxes on self-employment during business cycles role of taxation” “Business cycles and self -employment: The

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“Business cycles and self-employment: The

role of taxation”

The impact of taxes on self-employment during

business cycles

Master Thesis

MSc Business Administration – Small Business and Entrepreneurship

By

Marios Ioannis Pintzos

Supervisor: Dr. Florian Noseleit

Co-assessor: Dr. Clemens Lutz

Word count: 6.731

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Abstract

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1. Introduction

The state of the economy may be of importance for occupational choice and thus for rates of entry into entrepreneurship. In a growing economy various factors can affect the decision to become self-employed, for instance nascent entrepreneurs may positively react to new business opportunities and set up new ventures. In downturns on the other hand, self-employment may be a way to avoid unemployment and thus, the business cycle may act to pull and push individuals into employment, which makes it unclear whether self-employment rate is likely to be higher or lower in good times (Svaleryd, 2014). Evidence in the literature on national business cycles also show conflicts; i.e. Blanchflower (2000) and Koelling and Thurik (2012) find that the correlation between the national business cycle and self-employment differs across countries. Therefore, we still lack a clear answer whether business cycles are correlated with self-employment, and if they lag or lead entrepreneurship.

The reason for the inconsistent findings may reflect to the not significantly researched phenomenon of institutional differences and the way the labor market works across countries (Svaleryd, 2014). For instance, the fact that tax systems can both enhance and deter entrepreneurial behavior, meaning that high taxes can be a deterrent since they reduce the returns from the entrepreneurial effort, but also an incentive, if tax evasion opportunities are high as well. Indeed, taxation is one institutional factor that has received a lot of attention as potential determinant of the self-employment rate (Torrini, 2005). However, previous studies focused on the direct effect of taxation; whilst for this paper a different approach will be followed by concentrating on the indirect effect of taxation and its role in the relationship between business cycle and self-employment, which to the best of my knowledge its application to the topic is novel.

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and Development (OECD). The period of analysis is restricted to the years 2006-2016 and all the countries are members of the European Economic Area (EEA).

The findings indicate that the relationship between economic growth and self-employment rate is a-cyclical. Interestingly, when taxation is added in the econometric model, business cycles become significant and influence both positively and negatively the self-employment rates based on the level of implicit tax rates. This suggests that venture formation rates and individual transitions into entrepreneurship are depended on taxation during different phases in the business cycle. Finally, these findings are of importance for academics in order to further examine this complex phenomenon and policy makers for the adaptation of the right fiscal policy to spur entrepreneurship.

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2. Literature Review

2.1 Business cycle and self-employment

The determinants stimulating or hampering entrepreneurship can be investigated at many levels. Despite analyzing factors at the micro-level that influence entrepreneurial transitions, macro-economic conditions like the general business climate and the number of job opportunities may have an impact as well (Fritsch et al., 2015). In addition, institutional research can also benefit from complementary attention to the micro and the macro level (Powell & Colyvas, 2008), meaning that incentives or deterrents from the wider ecosystem can affect occupational decision making of individuals.

One of the factors influencing the rate of entrepreneurship at the societal level are the opportunities provided by the environment, which in turn is influenced by many aspects, such as the level of economic development, institutional factors and the demography of a society (Hofstede et al., 2004). First, according to Acs et al., (1994) self-employment is depended on changes of the per capita gross national product. A low level of prosperity usually coincides with a low wage level, implying little pressure to increase efficiency or the average scale of enterprise, meaning that small firms in crafts and the retail trade are dominant in such an economy; hence, a major route for ambitious wage earners to increase their income is to set up shops and become entrepreneurs (Hofstede et al., 2004).

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In principle, entrepreneurship could evolve pro-cyclically, counter-cyclically or a-cyclically, however the majority of available evidence suggests that venture formation rates and individual transitions into entrepreneurship are higher on average in good economic times and lower in bad ones. Hence, the first hypothesis of the paper is formulated:

H1: An increase in GDP per capita will cause an increase in self employment rate and the reverse.

2.2 The role of taxation

Entrepreneurship however does not concern only about sources of opportunities but also about the processes of discovery, evaluation and exploitation of profitable opportunities (Shane & Venkataraman, 2000). Some previous studies find contradicting results and different patterns across countries for the relationship between business cycles and the level of self-employment. A possible reason may be due to institutional differences and the way the labor markets work (Svaleryd, 2015), which in turn can increase or decrease the probability of opportunity exploitation. Moreover, taxes can encourage self-employment by treating self-self-employment income more favorably than wage income, given that the first is inherently risky (Cullen & Gordon, 2007). Taxation is thus a factor likely to determine distortions in the allocation of labour between paid employment and self-employment (Torrini, 2005), by making the decision to pursue opportunities easier.

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the role of taxation and its moderating effect in the relationship of business cycle and self-employment.

The rationale behind how taxation affects the relationship between business cycle and the level of self-employment is based on entrepreneurial opportunities, and in which way taxes can provide an incentive or a deterrent for individuals to exploit these opportunities and become self-employed. In a high tax regime the returns of income are reduced, especially for small firms and personal services (Robson & Wren, 1999) and consequently opportunities become less attractive. In addition, assuming that the income of the self-employed is indeed more sensitive to individual effort than wage income, high taxes will hurt self employed workers more than employees (Torrini, 2005). Therefore, no matter the business phase of the economy, a high tax regime suggests that less nascent entrepreneurs will exploit opportunities; thereby a high level of taxes is a deterrent to self-employment. Hence, the second hypothesis of the study is:

H2: A high level of taxes will weaken the (positive) relationship between economic growth and the level of self-employment.

On the other hand, while wage-and-salary workers have both income and payroll taxes withheld by their employers, the self-employed assume this responsibility individually (Bruce, 2000); meaning that the self-employed have higher opportunities to hide their income from the tax authorities. Hence, in a high tax regime with a high level of opportunities for tax evasion as well, the decision to enter self-employment becomes more attractive since individuals take advantage of these opportunities, and the degree of income underreporting increases (Torrini, 2005). In such an environment, besides the fact that taxes make the decision to exploit opportunities rather convenient, they also provide a means for new opportunities to tax evade and maximize the returns of the entrepreneurial effort. Therefore, high taxes can also encourage entrepreneurial activities and make the choice of being self-employed favorable. Bearing that in mind, the third hypothesis of the study is also composed:

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Summarizing this brief review, this paper investigates how the environment provides incentives or deterrents for people to entry or exit entrepreneurship, and thus affects self-employment rates. Hence, this study first contributes to literature of entrepreneurship and self-employment by examining aspects that encourage or discourage entrepreneurial behaviour, while also provides explanations for changes in self-employment rates. Secondly, the study also adds to literature of institutional theory by showing that the cognitive pillar is crucial for entrepreneurship and represents how the behaviors of individuals are based on constructed rules that limit appropriate beliefs and actions (Bruton et al., 2010).

2.3 Conceptual model

Below the conceptual model of this study is presented, providing an overview of the three hypotheses studied in this paper.

H1: +

H2

:

-

H3: +

GDP per capita

Self employment

_rates

Moderator:

Implicit tax rates

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3. Methodology

3.1 Data Collection

For this research a unique dataset is created, by combining different existing databases, in order to collect the desired data. It should be noted that the study is focused on countries-members of the Organization for Economic Co-operation and Development (OECD). The sample for testing the hypotheses consists of 18 countries, which have been selected due to available and credible data for a specific period of time, from 2006 to 2016. The first source of data is Eurostat, the statistical office of the European Union which mission is to provide high quality statistics for Europe, plus since this is a cross-sectional study, Eurostat is also a key source due to the fact that it enables comparisons between countries. It is used for the extraction of self-employment rates and also for data of macroeconomic factors. Furthermore, the website of OECD is used since it is a reliable source for data on taxation, in this case the rate of implicit taxes.

This particular setting for this study is appropriate for several reasons. First, the selected countries are all members of the European Union (EU) and use the same currency, except Norway which instead is only a member of the European Economic Area (EEA). Second, during the specific time period, the European debt crisis occurred which made it interesting to see how the countries encountered. Third, since the main objective of this study is the examination of the role of taxation on self-employment rates in business cycles, for all of these countries credible data exist.

3.2 Measurements

In this section the measurements of each variable will be explained in detail. All measures are extracted from existing literature.

Dependent Variable

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unpaid family workers. The latter are unpaid in the sense that they lack a formal contract to receive a fixed amount of income on regular intervals, but they share in the income generated by the enterprise. Thus, self-employed is everyone mentioned above as a percentage of the total employed population.

Independent Variable

GDP per capita or purchasing power parity is one of the indicators for the state of the economy, as it is a measure of prosperity. It provides a reflection of what people are actually able to buy with their money. The levels of GDP per capita are measured in million of Euros at current prices and they are internationally comparable across countries for any single year according to OECD.

Moderator

Taxation is the research’s key variable. Many previous studies used marginal tax rates (Blau, 1987; Parker 1996), while Robson and Wren (1998) provided a theoretical model that incorporated both marginal and average tax rates. For this study the European Commission’s implicit tax rate will be used, which captures both direct and indirect taxes, given the fact that the latter have increased the last decades for many European countries.

Control Variables

Population density is the ratio between (total) population and surface (land) area. This ratio can be calculated for any territorial unit for any point in time, depending on the source of the population data (OECD).

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Only secondary data will be used for this research, and once they were collected, the first step of the analysis was to generate descriptive statistics and correlations between the variables. Thereafter, the hypotheses were tested with panel data analysis, considering the conceptual model and dataset of this research. Panel data refer to data containing time observations of a number of actors; thus panel data analysis which is a cross-sectional time-series analysis matches this research paper’s examination of 18 countries for a decade. Moreover, this method allows controlling for variables that cannot be observed or measured, like international agreements. It also accounts for heterogeneity across the cross-sectional units, countries in this case, and finally it is suitable for hierarchical regression meaning that variables can be included at different levels of the analysis.

The objective of the paper is to examine the impact of variables (i.e. GDP per capita) that vary over time, thus the model of fixed effects is used. This model explores the relationship between predictors and the outcome variable within a country. However, each country has its own characteristics that may or may not influence the predictor variables, for example the political system of a country could have some effect on trade or GDP. By using this model I assume that something within the country may impact or bias the predictor or outcome variables; therefore control is needed. The utility of this model is that it removes the effect of time-invariant characteristics so we can assess the result of the predictors, in this case GDP per capita level, implicit tax rate, population density and unemployment rate on self-employment rate, which is the outcome variable.

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4. Findings

In this section the results of the quantitative analysis are presented, containing: descriptive statistics and correlations test, panel data analysis and predictive margins and a Marginsplot.

4.1 Descriptive statistics and correlations

Table 1 reports the means, standard deviations, minimum and maximum values for the variables. In general, and due to the fact that for every variable the raw value has been transformed to its natural logarithm, the data are normally spread. Standard deviation is relatively small for every variable, with the smallest being for taxes and the highest for GDP per capita. Another thing worth mentioning is that the average level of self-employment for this group of countries is 15% of the total employed population.

Table 1. Descriptive statistics (full sample: N=198)

Variable Obs. Mean Std. Dev. Minimum Maximum

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Self-employment GDP per capita Implicit taxes Population Density Unemployment Self-employment 1.00 GDP per capita -0.09 1.00 Implicit taxes -0.19*** 0.15** 1.00 Population Density 0.31*** 0.27*** -0.03 1.00 Unemployment 0.58*** 0.04 -0.19*** 0.07 1.00 *** p<0.01, ** p<0.05, * p<0.1

The correlation matrix (Table 2) provides the number of observations that were used in the correlation; in this dataset there are no missing values, so all correlations are based on all 198 observations. The results indicate that little to no correlation has been found between most variables with an exception of unemployment and self-employment where there is a relatively strong one, demonstrating the positive relationship between them and verifying that self-employment is a solution to unself-employment. The correlations of population density with self-employment rates and GDP per capita can also be stated as weak positive ones.

4.2 Panel data analysis with fixed-effects model

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indicators and for all the models it is close to zero, so all four are statistically significant.

The first model includes only the two control variables of the study. The p-values for the predictors are significant at the 5% level so both population density and unemployment rates have significant influence on self-employment. The coefficient of population density indicates that for each new person entering employment there is 55% possibility to become self-employed. Finally, the reported R-square shows that these predictors explain 15% of the changes in self-employment rates.

Moving to the second model, only the main effect variable of GDP per capita level will be included in order to test the first hypothesis: whether changes in self-employment behave pro-cyclical. The findings show that economic growth and self-employment rates are not significantly correlated, meaning that the relationship between them is actually a-cyclical; hence, the first hypothesis (H1) is not supported.

In the third model the last main effect variable is added: implicit tax rates. Comparing with the previous models, here only taxation and unemployment rates are significant, the first at the 1% level and the second at the 10% level. The coefficience of implicit tax rates is also interesting here, indicating a negative correlation with self employment, meaning that with one unit increase in tax rates, there is an about 30% decrease in self-employment rate. Finally, R-square is slightly better, and the model now explains 18% of the phenomenon.

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Model 1 Model 2 Model 3 Model 4

Population 0.55** (0.27) 0.63* (0.38) 0.50 (0.37) 0.24 (0.37) Unemployment 0.05*** (0.18) 0.04 (0.03) 0.05* (0.03) 0.07** (0.03) GDP per cap -0.03 (0.10) -0.04 (0.10) -1.75*** (0.51) Taxes -0.30*** (0.11) -13.23*** (3.77)

Taxes*GDP per cap 0.50***

(0.14) Constant 0.16 (1.25) 0.56 (1.94) 2.35 (2.01) 48.11*** (13.47) Observations 198 198 198 198 F test 0.007 0.010 0.002 0.000 R-square 0.147 0.147 0.183 0.238

Standard error in parentheses; *** p<0.01, ** p<0.05, * p<0.1

4.3 Predictive margins and Marginsplot

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capita exists, self-employment rates are: (i) low (10%) for a low level of taxes and (ii) high (21%) for a high one.

Table 4. Predictive margins

Predicted Value Std. Err. z 1. GDP per capita Taxes 24 3.2 3.12 (23%) 0.25 12.29 2. GDP per capita Taxes 24 3.8 2.34 (10%) 0.27 8.67 3. GDP per capita Taxes 29 3.2 2.32 (10%) 0.26 8.88 4. GDP per capita Taxes 29 3.8 3.03 (21%) 0.27 11.38

Predicted percentage of self-employment in parentheses

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5. Discussion

The empirical results of the study help to put the previous theoretical literature on the topic into perspective. In the first examination part of the study, no correlation between economic growth and self-employment has been found, meaning that the relationship is a-cyclical. This is not in line with several previous empirical findings that found a pro-cyclical, positive correlation between economic growth and self-employment. Yet, the results after the addition of taxes in the econometric model suggest that both a positive and a negative correlation between GDP per capita and self-employment rates exist, depending on the level of taxation. Moreover, the fact that both of these correlations exist provides a reason why no correlation is found between business cycles and self-employment rates in the first place. Finally, the importance of taxation is highlighted, and it has become clear that the relationship between the overall state of the economy of a country and its self-employment rates is different for countries that follow different taxation policies.

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opportunities of self-employed workers are sufficiently high, high taxes will encourage individuals to enter self-employment (Torrini, 2005). An excellent example is the study of Engstorm and Homlmund (2007) about tax evasion in Sweden, the country with the highest tax-to-GDP ratio in the world. The results unveil that for a household with at least one self-employed member underreporting is around 30%. The latter indicates that tax evasion is part of the story and future research is warranted. Finally, the higher the taxes and the social contribution wedge, the greater is the incentive for firms to replace employees with self-employed contractors to reduce the cost of labour (Hofstede et al., 2004).

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6. Conclusion

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7. Implications

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8. Limitations and future research

In this part, certain limitations of the research will be discussed. The first one I encountered is that time lag is not considered. National data suggest that employment creation and venture growth, can take a decade or more to play out (Fritsch & Mueller, 2008). However, the latter is about how changes in self-employment rates affect the level of prosperity and not the other way around. At this point is should be emphasized that the relationship between business cycle and self-employment is bicausal, meaning that variations in self-employment rate both cause and are caused by business cycle (Congregado et al, 2009). Nevertheless, the state of the environment can have rather fast effect changes in self-employment since opportunities exploitation can be time-sensitive, for instance in an emerging economy there can be a lack of constructing firms to undertake the creation of new roads. A second limitation is that the countries examined are all members of the OECD and are in general well developed countries, so the results cannot be generalized. Moreover, the sample data is rather homogenous; the countries are all alike and this is a limitation for the conducted panel data analysis, which expects each country to be heterogeneous. Finally, growing numbers of self-employed persons may be determined by various reasons, in this case the legal regulations are of interest. Very frequently occupational choices are not results of independent individual decisions, but are caused by pressure of former employers, who forced employees to establish their own businesses if they want to continue their jobs (Lechman & Dominiak, 2016). This may be the case in many countries during the time-period examined in the study, since it includes the Eurozone crisis.

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Acknowledgements

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The impact of taxes on self-employment during business cycles role of taxation” “Business cycles and self -employment: The

“Business cycles and self-employment: The

role of taxation”