Tax Progressivity and Self-Employment Dynamics

MODELS of choices facing wage earners typically neglect the fact that taxpayers may exit or enter self-employment because of differences in tax schedules. Since the interplay between the occupational choices is typically not considered in models of labor supply, these models are silent on how tax differences across occupational choice affect decisions.^1,2 However, in contrast, models of choice of the self-employed are dominated by perspectives where decisions are based on implicit or explicit comparisons to the wage sectors. One obvious reason for this asymmetry is the relative sizes of the sectors. For example, the self-employment rate (as a percentage of total employment) in Norway is $7%$⁠, whereas the European Union average is approximately $15%$ (OECD, 2018).

The relationship to the wage sector is not the only factor that complicates the assessment of the effects of taxation on self-employment. From a theoretical perspective, the tax effects are ambiguous. On the one hand, an increase in the tax rate may diminish the self-employment rate as it reduces expected returns. On the other hand, high taxes may encourage self-employment if loss offsetting is allowed because the government provides an implicit insurance by sharing the risk associated with self-employment (Domar & Musgrave, 1944).³

A large majority of empirical studies on the effect of taxes on the level of self-employment activity focuses on the United States. These studies examine the extensive margin in occupational choice models (see Bruce, 2000, 2002; Gentry & Hubbard, 2000, 2004; Schuetze, 2000; Schuetze & Bruce, 2004; Cullen & Gordon, 2007; and Moore, 2004).⁴ Studies for other countries include Hansson (2012) for Sweden; Fossen (2007, 2009) and Fossen and Steiner (2009) for Germany, and Wen and Gordon (2014) for Canada. Results from these studies are mixed. Results for the United States, for example, do not provide an unambiguous answer about the relationship between tax progressivity and self-employment. However, in other countries, tax progressivity is generally found to discourage self-employment.⁵

The representation of the tax schedule is important in any analysis of tax effects on self-employment. Some studies include measures of marginal and/or average taxes in a quasi-experimental or reduced-form analysis to investigate the effect of nonlinearities in taxes on entrepreneurship.⁶ In other studies, authors have used measures of expected net-income differences and/or tax progressivity to capture the tax effects. For example, Gentry and Hubbard (2000, 2004) use the spread in the marginal (or average) tax rates faced by a self-employed individual at various levels of “success,” where success is defined as the observed distribution of the three-year real wage growth for entrants into self-employment.

In two recent studies (Fossen, 2009; Wen & Gordon, 2014), authors derive the tax variables within a structural framework where the decision making is based on the difference in expected utilities. Yet the two papers differ in many aspects and draw different conclusions. The use of different utility functions and assumptions regarding the pretax income distribution of the individual result in different variables that capture the effects of nonlinearities in the tax schedule. They also use different statistical models (logit versus probit).

Fossen (2009) models the transitions between wage and self-employment using data from the German Socio-Economic Panel (GSOEP) over the period 2002 to 2006 and a logit model in which agents are assumed to trade-off risks and returns. He uses a constant relative risk-aversion utility and assumes normally distributed pretax income. The two relevant model-generated variables are (i) the difference in net-of-tax incomes in the two occupations and (ii) the variances of the individual's posttax income distributions in the transition equation.

In contrast, Wen and Gordon (2014) use a pooled cross-sectional sample from the Canadian Survey of Labour and Income Dynamics over the years 1999 to 2005 to estimate the probability of self-employment in a probit model.⁷ They assume risk neutrality and a log-normal distribution for the pretax income. The relevant “tax variables” are (i) the difference in log net-of-tax incomes in the occupations (netincdiff) and (ii) a variable that they call convexity. The variable convexity has an intuitive interpretation as the “increase in tax-liability taken on by the self-employed due to the volatility of their business income, expressed as a proportion of their disposable income.”

Both studies use selectivity-corrected income equations to predict individual pretax incomes and then use a tax-transfer microsimulation model to generate the relevant expected value and variance of after-tax incomes in wage employment and self-employment. The estimated models are subsequently used to simulate the effects of hypothetical tax policy scenarios that reduced progressivity. Fossen finds the “flatter-tax” reforms considered discourage individuals from choosing self-employment;⁸ Wen and Gordon find a “small” positive effect on the probability of finding someone in self-employment.⁹

Here we use the two variables netincdiff and convexity used by Wen and Gordon (2014). Although some of the tax effects in both studies are captured via net-income differences, the additional variable convexity in Wen and Gordon (2014) is an individual-specific measure that intuitively captures the interaction between the progressivity of the tax schedule and the volatility of self-employment income relative to wage income.

Our work complements the existing empirical literature in various ways. First, our definitions of wage employment and self-employment are based on reported incomes from tax records and not on survey responses. We use data drawn from various Norwegian population registers over the period 1993 to 2011. The data include rich sociodemographic information together with highly accurate income measures from the annual tax returns. Second, we model the evolution of employment spells using a two-state multispell duration model that controls for observed and unobserved heterogeneity correlated across spells and accounts for left and right censoring in the observed spells. This contrasts with several previous contributions, which mainly focus on self-employment entries or exits using survey data with self-reported employment status and short panels of individuals.

We generally find significant effects of both netincdiff and convexity on the probability of exit from both types of employment spells, conforming to theoretical predictions as discussed in section VA. The increase in convexity is found to increase the probability of exiting self-employment and to decrease the probability of entry into self-employment; that is, convexity has a discouraging effect on self-employment, ceteris paribus. On the other hand, an opposite effect is found for netincdiff: negative (positive) in the self-employment (wage-employment) equation. Additionally, in our base model, we find a larger effect of convexity relative to that of netincdiff, implying that small increases in convexity will require large increases in netincdiff to discourage the self-employed from quitting and to encourage wage earners to enter self-employment.

Given the way the tax variables are constructed, a change in the progressivity of the tax schedule will have an impact on the convexity and on the netincdiff by changing the expected net income difference in self-employment and wage employment. From this, the total effect on the rate of self-employment of a decrease in the progressivity of the tax schedule is hard to predict. Hence, to better understand the net effect, we simulate a tax experiment that replaces the personal income tax structure in the year 2000 with a less progressive, revenue-neutral tax schedule, as explained in section VB. The overall estimated effect of this policy change is positive on the share of self-employment. The average exit rate from self-employment is estimated to go down by $0.018$ (s.e. $0.184$⁠) percentage points, and the estimated exit rate from wage-employment is estimated to increase by $0.119$ (s.e. $0.010$⁠) percentage points. This change results in a net increase of predicted inflow into self-employment changing from about $2.8%$ to $5.3%$⁠.

The rest of the paper is organized as follows. Section II describes the taxation of self-employment income and wages during our sample period. Section III sets out our econometric model. In section IV we provide details of the data and the sample selected for our analyses. We also present the procedure used for estimating the tax variables. The estimation results are discussed in section V along with the results from our policy simulation and some sensitivity checks. Finally, section VI concludes the paper.

Tax reforms undertaken in 1992 introduced a dual-income tax system in Norway. Under this regime, all types of capital income are taxed at a flat rate, but a progressive schedule applies to labor and pension income. Individuals pay income tax on two different tax bases: (i) ordinary income and (ii) personal income.

Income from wages, self-employment, capital, transfers, and pensions are first grouped as ordinary income. After deductions, individuals pay tax at a flat rate (28$%$ during most of the sample period) on ordinary income.¹⁰ The other tax base—personal income—includes wage income, transfers, and pension income and self-employment income due to active efforts, but not capital income. Individuals pay a surtax and social security contributions levied on the personal income.

Taxation is more complicated for the self-employed because income represents the reward to the labor of the individual as well as the returns to the capital invested in the firm. Given the lower tax rate on capital income, the decision about how to declare the income was not left to the discretion of the self-employed; rules were established to split the profits into labor and capital income.¹² The dashed line in figure 1 represents the marginal tax rates that apply to self-employment income in the case where no capital is invested in the firm. The main differences to the wage income case are the lack of basic allowance and the higher social security contribution (10.7$%$ in 2005).

Drawing heavily on the framework of Ham et al. (2016), we model employment transitions using a two-state multispell discrete duration model accounting for unobserved individual heterogeneity.¹⁴ The two employment states are self-employment and wage employment. The duration variable is measured in terms of the Norwegian financial year, which is the calendar year (January–December). Approximately 70$%$ of individuals in our sample have a first spell that is left censored. Without dropping these individuals from the analysis sample, we include them and specify a different model of exit rates for them (Ham et al., 2016). We check for sensitivity of our estimates to excluding the left-censored spells, which is equivalent to using an inflow sample.

With regard to the unobserved heterogeneity, we follow the literature and assume this to be distributed independently across individuals and of the covariates included but fixed over the same type of spell, but correlated across the two employment states and the type of spell (fresh versus left-censored). A discrete distribution is assumed for the unobserved heterogeneity.

where $hj$ is the duration dependence function, $xi,j(t)$ contains time-fixed and time-varying observed individual characteristics, taxation contains the tax variable(s), and $ωi,j$ is the unobserved heterogeneity. $F$ is specified as the complementary log-log distribution function.¹⁵ To achieve convergence with stable parameter estimates, we restrict the duration dependence function to a log linear form and model the unobserved heterogeneity to be discrete with two points of support.¹⁶ We keep the hazard-specific intercepts, set $ωsf=ωef=ωsc=ωec=0$ as a normalization, and estimate the associated probability, $p$⁠.

The present study benefits from rich longitudinal Norwegian administrative data for the period 1993 to 2011. The main data source is the Income and Wealth Statistics for Persons and Families (Statistics Norway, 2005). The data are drawn from the annual tax returns and the education registers (years of education and fields of studies). The data also contain individual and family sociodemographic characteristics. Our focus is on wage earners and the self-employed who have strong labor market attachment, and so we restrict our analysis to Norwegian citizens aged 25 to 61 and exclude those who have reported any income from agricultural, forestry, or fishing activities.¹⁷

We use an income-based definition to identify periods or spells of self-employment and wage employment. In our main analysis, we classify an individual observation as “self-employed” if the major source of income is self-employment income, i.e., if the reported self-employment income (net of expenses) is larger in absolute value than the wage income and is also larger than government transfers (which include disability insurance, unemployment benefits, and other types of pensions).¹⁸ Additionally, we restrict our sample to those who have been classified as either being in wage employment or self-employment during the observation period 1993 to 2011.¹⁹

Our analysis is based on the theoretical exposition of an expected utility maximization approach discussed by Wen and Gordon (2014), who in turn base their model on the one developed by Rees and Shah (1986). Assuming risk neutrality, a convex tax schedule, and log-normally distributed pretax income, they show how the probability of self-employment can be written as a function of the tax schedule using two representations of the effects of taxation.²⁰ These are (1) netincdiff, which is the difference in log of expected net incomes in self-employment and wage employment, and (2) convexity, which is a measure of how the expected tax liability changes due to the volatility of their self-employment income relative to the net income in wage employment (see online appendix A.1 for further details).

The construction of the two tax variables requires net-income distributions for each individual. We use a tax simulator to generate these (see online appendix A.2). The simulator considers the yearly rules for taxing self-employment income net of expenses, wages, and other sources of income. Other sources of income are taken to be exogenous; these are added to the predicted self-employment or wage income. The simulator also accounts for the main deductions and allowances, as well as for the system for taxation of the labor and capital parts of net self-employment income; see section II.

where $τ$ is a tax parameter from the tax function (see footnote 21). For each individual, we first estimate the selectivity-corrected expected pretax income $(y¯j)$ for each occupation in each period.²¹ We then use the tax simulator to generate the individual specific net incomes in both occupations: $netincomes$ and $netincomee.$

Next, we define the second individual specific tax variable representation: convexity. This variable is defined as the difference between the expected tax liability $E[T(ys)]$ and the tax liability at the expected income $T(y¯s)$⁠, relative to the expected net income $x¯s=y¯s-T(y¯s)$⁠.²² Wage employment is generally less riskier than self-employment. Hence, following Wen and Gordon (2014), we derive our convexity variable by setting the coefficient of variation for wage income equal to 0, so that convexity is associated with uncertainties in self-employment income only.

Summary statistics for the main estimation sample are provided in table 1. On average, in the weighted sample, the proportion of individuals exiting out of a period of work and into a period of self-employment is less than $1%$⁠, whereas the average share of exits out of a period of self-employment is $11%$⁠. We next turn to our tax variables.

In addition to the two tax variables, the models also include time-varying and time-invariant control variables. The time-invariant variables are sex, age at the start of the spell, indicator variables for highest education level achieved, and regional dummies to account for local labor market conditions. Calendar time dummies control for macroeffects. The data are an unbalanced panel; see descriptive information in table 1. Self-employed individuals are on average older and less educated than individuals who are paid wages, and a lower proportion of females is found among the self-employed. Self-employment is also highly concentrated in the more densely populated areas of eastern Norway (the Oslo region) and western Norway (the Bergen region).

Our base model estimates are presented in table 2.²⁸ All four hazard functions are estimated simultaneously. Except for the left-censored SE hazard, the other three hazards show negative duration dependence, ceteris paribus. Insignificant duration dependence estimated for the left-censored SE spells is consistent with the observation that the probability of exiting is almost zero for high duration spells, and the sample of left-censored spells has a higher probability of containing large-duration spells.

We focus our discussions on the interpretation of the estimated effects of the tax variables. The theory predicts a positive (negative) effect of the netincdiff variable on the probability of exit from WE (SE). For example, the higher the proportionate increase in the net-income differential with respect to the net income from WE, the higher the exit rate from WE (Wen & Gordon, 2014; Taylor, 1996; Fossen, 2009). On the other hand, the theoretical prediction of the effect of convexity is negative on exit rate from WE since higher “convexity” would be expected to discourage SE. The estimated effects of the two tax variables conform to these theoretical predictions.

These estimated coefficients are also found to be higher in absolute value for WE exit probabilities (columns 2 and 4). These results suggest that, compared to exits from SE, the probability of an exit from WE is more sensitive to changes in both expected net-income differences and tax progressivity. This is consistent with the fact that the SE tend to continue their business activities even if they experience lower earnings growth (Hamilton, 2000).

These estimates also indicate that a one percentage point increase in convexity requires an increase of approximately nine to fourteen percentage points in netincdiff to keep these hazards unchanged. Note that increases in convexity in this calculation are assumed to take place via changes to the volatility of SE income (online appendix A.1 equation [A.4]) because we assume no uncertainty in WE income in the calculation of this variable. Similarly, the increase in netincdiff is assumed to work either via a reduction in the pretax income in WE or via an increase in the expected pretax SE income (not altering the variance of the SE income distribution). To further explore these effects accounting for the relationship between the two tax variables, we simulated a policy experiment. The results are presented below.

Our policy experiment is to replace two of the surtaxes applied to personal income with one surtax, to create a flatter tax schedule (solid line in figure 10). The surtax value of $11%$ on gross income above $200,000$ NOK is chosen to ensure revenue neutrality, given a “no behavioral reaction” assumption. Other features of the taxation are held constant. New values of netincdiff and convexity were generated under the hypothetical scenario using our tax simulator and the transition rates predicted from the estimated models.

The average values of the netincdiff and convexity variables in our weighted sample are $-0.374$ and $0.0071$ under the new policy regime, compared to the original figures for the year 2000 of $-0.382$ and $0.0087$⁠, respectively. As expected, the less progressive tax schedule leads to a decrease of $0.16$ percentage points in convexity. The hypothetical policy also leads to a small increase in the mean netincdiff, so that average ratio of net income in SE to net income in WE changes from $68.2%$ to $68.8%$⁠.

The predicted transition probabilities and the corresponding standard errors, under the old and the new tax regimes, are reported in table 3.³⁰ In the benchmark year 2000, the model predicts that around $9.33%$ of self-employed individuals will transit out of SE to WE (case A).³¹ However, the reform reduces this figure to $9.32%$ (case B). Under the new regime, the predicted transitions from WE to SE are higher at $0.68%$ compared to $0.56%$ in the base model. Since a very large proportion of individuals are in WE compared to SE, even with this small increase in the exit rates out of WE can generate a substantial net inflow into SE. The change in the exit rates induced by the policy reform is not significant for the self-employed.

To further explore how the model predicts responses to separate changes in the two tax variables, we look at these effects separately. In case C we hold the convexity variable fixed at a value that is the same as in the base case scenario and let the netincdiff variable change. Conversely, in case D we see a change in the convexity variable only. Table 3 shows that the partial effect of a change in netincdiff is an increase in transitions out of both SE and WE. This result is consistent with the fact that mean netincdiff experiences a decrease in the reform scenario for the self-employed, whereas it increases for wage earners. A possible explanation for this effect is that the reduced progressivity of the tax system would encourage a larger share of wage earners who expect to be successful in self-employment to transit into SE. On the other hand, because a majority of self-employed individuals have been predicted to have a higher posttax income in regular employment, a flatter tax scenario would increase the proportion of them leaving SE for WE. In contrast, the decrease in convexity, common to both WE and SE observations, reduces the transitions from SE and increases the exit from WE. In summary, the hypothetical tax scenario is found to encourage the net inflow into SE. Translating these estimates to numbers, we find that such a policy would have resulted in an increase from 2.76$%$ to 5.34$%$ in the net inflow into SE.³²

Finally, we briefly compare our results to the findings of Wen and Gordon (2014), given that the same variables are used to capture the effects of taxes and uncertainty. Wen and Gordon (2014) also simulated the effect of a flatter tax schedule in the year 2000 using Canadian data. Their policy reform implied decreases in the average values of (i) netincdiff and convexity from $-22.5%$ to $-23.3%$ (a decrease of $4%$⁠) and (ii) from $1.2%$ to $0.8%$ (a reduction of $33%$⁠). The policy reform we considered increased the average values of netincdiff by around $2%$⁠, and reduced the average values of convexity by $18%$⁠. From the simulated policy reform, Wen and Gordon (2014) estimate an increase in the number of self-employed individuals of $0.78%$ (⁠$5.76%$ to $5.80%$⁠), which is substantially below our estimate of $2.6%$ (our experiment implies an increase of the self-employment share in 2001 from $4.56%$ to $4.68%$⁠). One should however note that Wen and Gordon (2014) do not model transitions.

In this subsection we present results of some of our investigations into key assumptions of our empirical approach. We consider the following: (i) redefinition of a self-employment spell; (ii) estimation based only on the inflow sample; (iii) trimming the netincdiff with respect to extreme values; (iv) controlling for local unemployment rates; (v) including a dummy variable for individuals receiving some unemployment insurance during the year; and (vi) allowing for the share of capital in SE income to be nonzero. Table 4 reports the results of these investigations. The estimated effects of the tax variables are qualitatively unchanged. The full set of results is available in the online appendix A.3.

Our first investigation examines the influence of the definition of an SE spell. In our base model we included individuals in the sample if they had at least three years of labor market attachment, that is, if the net SE income or WE is larger in absolute value than the basic amount for at least three years over the years the individual is observed in data. We now redefine the sample requiring only one year of labor market attachment. The results using this new definition are presented in panel B of table 4. Individuals with less attachment to the labor market would be expected to be more sensitive to changes in the tax variables, and this is what we find when we include these individuals in the estimation sample. The results are qualitatively similar to the results from our base case (panel A). However, the coefficient for convexity in the SE fresh spells hazard decreased substantially. Individuals with less attachment to the labor market with low predicted SE income might be expected to be less sensitive to the progressivity of the tax system.

The base model was estimated using both the left-censored and fresh spells. We reestimate our model using only the inflow sample. This reduces the total number of unweighted observations to $229,036$⁠. The definition of an SE spell is the same as the one used in our base model. The results are presented in panel C of table 4. The results are broadly similar to our base model results. As expected, dropping those spells for which we have no information about the length of time they had spent in a particular state prior to the sample start slightly increases the estimates.

The third investigation involves omitting observations with extreme predicted values for the variable netincdiff. As shown in figure 5, the distribution of netincdiff exhibits some lumpiness in the tails. To assess the effect of extreme values of netincdiff, we drop those individuals who have at least one occupation-specific netincdiff above the top 1$%$ or below the 1$%$ cut-off values.³³ Since individuals with very high or low netincdiff would be expected to be less sensitive than the others, we would expect the estimated effects of netincdiff to be higher in absolute values. This is what we see with the results reported in panel D. In the base model (panel A), we found the WE exits to be more sensitive than the SE exits, and now we see that the effect of netincdiff goes up for the WE exits without much change for in SE exits.

The next investigation examines the influence of local labor market conditions. In the main specification we use regional dummies to partially control for labor market conditions. Perhaps a better control for local labor market conditions would be the use of local unemployment rates. Unfortunately such information is available only from 1996, so we report two sets of results. In panel E we substitute the regional dummies with regional unemployment rates. In panel F we reestimate our base model using the restricted sample of 1996 to 2011. The results are very similar to each other and qualitatively similar to the baseline results.³⁴

As described in section IV, in our base model we drop individuals who received more in social security benefits than their self-employment income or wages in any year. However, it can be the case that individuals are unemployed for a short period and the unemployment insurance is small enough so that the individual is still defined as a self-employed or a wage earner. Individuals with an interruption to their work might behave differently from individuals transiting directly from WE to SE. We therefore include a dummy variable for those individuals who received unemployment insurance during the year. As panel G shows, the results are similar to those from the base model.

In Norway self-employed individuals have the option of having a share of the self-employed income declared as capital income, which is taxed at a lower rate than labor income, as explained in section 2. Tax variables used in our main model are generated under the assumption that the share of capital income in total income is zero (see online appendix A.2). We believe our assumption is reasonable for the following reasons. First, it is not clear what is an appropriate assumption regarding the proportion of capital income used in the generation of counterfactual SE income distributions for the wage earners, which are also exogenous. Second, during our sample period, the share declared as capital income is either 0 or very small (median value is 0.037). However, we check for sensitivity by regenerating our tax variables allowing for $3.7%$ of the predicted SE income to be reported as capital income instead of 0. The results are given in panel H. The effect of convexity is slightly stronger on the SE exit rates, and the rest of the estimated effects remain similar to the base model estimates.

We look at the effect of taxation on self-employment and wage employment durations. Our work complements the existing literature on many dimensions. First, in contrast to many existing studies, our definitions of self-employment and wage employment are based on income reported in Norwegian tax returns. The rest of the variables used come from various other registry data. Norwegian registry data are considered to be exceptional in terms of coverage and reliability (Blundell, Graber & Mogstad, 2015). Second, we look at the evolution of self-employment and wage employment spells over a very long period, from 1993 to 2011. We model these transitions using a two-state multispell duration model allowing for correlated unobserved heterogeneity and controlling for a rich set of sociodemographic characteristics.

We focus on the effects of two tax variables: netincdiff and convexity, obtained from Wen and Gordon (2014). netincdiff is defined as the difference in log net-of-tax income in the two occupations, and convexity is an individual-specific measure that captures the interaction between the progressivity of the tax schedule and the volatility of self-employment income relative to wage income. We use the model to predict the transitions under a simulated tax regime that reduced the progressivity of the tax schedule in the year 2000. We also provide some sensitivity checks with respect to the definition of self-employment, the selection of the estimation sample, and other factors. The estimated effects of our two tax variables of interest are qualitatively unchanged, and the quantitative differences are as expected.

The main finding is that, as predicted by theory, higher expected net earnings in self-employment relative to wage employment reduces the probability of exiting out of a self-employment spell. The entry into self-employment—or equivalently the exit out of wage employment—is found to be more sensitive to changes in the two variables than exit from self-employment. In our base model, the estimated effect of changes to netincdiff that are required when convexity changes by a percentage point, to encourage self-employment, is about nine to fourteen times larger in percentage point terms. To shed further light on this, we carried out a policy experiment by implementing a flatter tax schedule in the year 2000 that resulted in reduced tax progressivity. The hypothetical scenario was found to encourage entry into self-employment but not significantly the exit from self-employment, with the estimated inflow into self-employment increasing to $5.34%$ from the base model prediction of $2.76%$⁠.