Social and fiscal impacts of statutory minimum wages in EU countries: a microsimulation analysis with EUROMOD

One of the main motivations behind MWs is to support the earnings of low earners and protect them from unfairly low wages. MWs are thus expected to reduce wage inequality. This hypothesis is borne out by the literature focusing on longer-term developments in wage inequality both in the US and Europe. While there are differences between their quantitative results, both Lee (1999) and Autor et al. (2016) attribute a significant part of the increase in US wage inequality since the 1980s to the erosion of the federal MW. Part of the explanation for these results is that MWs have a positive effect on higher wage levels as well (these are called “spillover” or “ripple effects”). When MWs are not updated, this may result in stagnating wages for a broader spectrum of workers, and not only for workers earning around the MW. Similarly, for Europe, Pereira and Galego (2019) find that MW increases have been among the important factors driving differences in wage inequality in Europe since the early 2000s.

While the literature on the impacts of MWs on the gender pay gap is scarcer than the other strands of literature discussed here, it has been known that the majority of MW earners are women, and therefore the impacts of MWs have a gender aspect. For instance, Belman and Wolfson (2014, p. 16) find in their survey that “[a]lthough the magnitude of the effect remains in play, there is universal agreement that the MW reduces wage inequality, particularly among women.” Focusing on Europe, and in particular on the introduction of the MW in Ireland and the UK, Bargain et al. (2018) show a large reduction of the gender wage gap at the bottom of the distribution in Ireland but a low impact in the UK. The authors suggest that the contrasting results between the two countries may be due to the degree of non-compliance with the UK NMW legislation. In the case of Poland, Majchrowska and Strawiński (2018) find MW increases significantly lowered the gender wage gap among young workers, although the impact was not large for adult workers.

There is a significant academic literature on the impact of the MW on poverty outcomes that also considers the sociodemographic characteristics of MW earners. The poverty-alleviating impacts of MWs are mediated by demographic and other factors because many MW earners do not live in poor households (for instance because their partner earns a higher income), while many poor people are not MW earners (instead they are unemployed, inactive, self-employed, or in informal employment). For this reason, some papers have found that MWs have little impact on overall poverty rates (see, e.g., the survey of Belman and Wolfson, 2014). However, more recent research has found beneficial impacts of MWs on poverty outcomes. Based on individual-level data from the US for the period 1984–2013, Dube (2019a) finds that MWs significantly reduce the non-elderly poverty rate.

Relevant studies in Europe largely focus on countries that recently introduced a NMW, including the UK (1999), Ireland (2000), and Germany (2015). In most cases, these studies use microsimulation tools and generally find that the introduction of a MW had small but beneficial effects on poverty outcomes. In particular, Sutherland (2001b), studying the UK NMW, concludes that “the main contribution of the NMW is in underpinning the strategy of in-work benefits to supplement the family incomes of the low paid.” This conclusion is supported by Atkinson et al. (2017) who find, when assessing proposals by Atkinson (2015) to reduce inequality, that increasing the UK MW to a “living wage” level would reduce the poverty headcount slightly (by 0.4 percentage points), and it would also strengthen the impact of other hypothetical poverty-reducing tax-benefit reforms by about the same magnitude. Similarly, the literature on Ireland found that the MW is a relatively “blunt tool” to reduce poverty, but it is still effective in protecting the wages of low-skilled workers. In particular, Maitre et al. (2017) found that 17% of MW employees belong to a household that is at risk of poverty, compared to 3.3% of non–MW employees. In addition, Holton and O’Neill (2017) found that the Irish MW is an effective tool in protecting the income of low-skilled workers, particularly during recessions. Finally, in the case of Germany, microsimulation analyses by Müller and Steiner (2009, 2013) have concluded that the MW has only a small impact on overall poverty, both because it does not target poor households and because wage gains of poor households would be dampened by increased taxes and benefits withdrawn.

The other side of this coin is that, in countries with strong anti-poverty policies, MW may have a stronger impact on improving public budgets. See results on fiscal impacts in Section 5.8.

Recent EU-wide work supports the view that MWs have a small reducing effect on overall poverty, but it also finds a more significant impact on in-work poverty. Analyzing the labor market status of households in the EU at risk of poverty, Eurofound (2014) conclude that the impact of increased MWs “on relative poverty at the household level would be limited.” Simulating the impacts of a hypothetical increase (or introduction) of a SMW at 50% of the national average wage in all EU Member States, Matsaganis et al. (2015) find that the “at-risk-of-poverty rate would fall by at least 1 percentage point in 13 out of 28 Member States.” Simulations by the European Commission (2016) also find small but beneficial impacts of HMW increases on poverty rates in the EU. Finally, Detragiache et al. (2020) find that “[a] hypothetical European MW set at 60% of each country's median wage would reduce in-work poverty but have limited effects on overall poverty.”

In this context, this paper focuses on in-work poverty as a relevant poverty-related outcome of MW policies since MW policies have a more direct impact on the poverty rate of workers than that of the total population. While MWs are not the only policy measures to fight poverty, they are found to be an important element in an effective policy mix to reduce in-work poverty and to improve work incentives.

See, for example, Peña-Casas et al. (2019), and Eurofound (2017).

Some researchers have argued that the MW may fail to effectively protect low-wage earners because of its negative impacts on their employment. For instance, Neumark and Wascher (2008, p. 6) suggest that, “although MWs compress the wage distribution, because of employment and hours declines among those whose wages are most affected by MW increases, a higher MW tends to reduce rather than to increase the earnings of the lowest-skilled individuals.” Nevertheless, conclusions from the recent literature are in contrast to this warning. While negative employment impacts are possible, they tend to be small as compared to the wage increases for beneficiaries. For instance, in a survey of the most recent research, Dube (2019b) finds that “[o]verall the most up to date body of research from US, UK and other developed countries points to a very muted effect of MWs on employment, while significantly increasing the earnings of low paid workers. Importantly, this was found to be the case even for the most recent ambitious policies.” This paper relies on the survey of Dube (2019b) to calibrate the simulation of employment impacts.

There is a large literature on employment effects of MW. However, the effects of MW on both, the working hours that people would like to supply and the hours that people actually work, are less studied.

At the time of the early survey of Brown et al. (1982), the few studies on working hours produced ambiguous results. And neither has recent research found conclusive results on the effect of MWs on actual hours worked. After a MW increase for young workers in New Zealand, Hyslop and Stillman (2007) find increasing teenage working hours. In contrast, evidence from Germany of its MW introduction in 2015 suggests that it may have reduced working hours (Caliendo et al., 2019). Evidence from the introduction of the MW in the UK in 1999 was mixed instead (Dolton et al., 2010).

A priori, it is plausible that higher wage prospects should have a positive effect on labor supply. Nevertheless, the effect of MWs on hours offered by workers is still less studied than the effect on actual hours worked. This is related to the fact that hours offered are hard to observe. In a rare example of such studies, Dickens et al. (2014) study the discontinuity caused by youth MWs in the UK. They find evidence for an increase in job search intensity for 22-year-olds as compared to similar people a few months younger to whom lower minima applied. Whether positive effects on hours offered translate to an increase in actual hours worked likely depends on the economic context as well as on institutional factors.

To account for the interactions between HMW increases and the tax-benefit system, this analysis uses the EU-wide microsimulation tool EUROMOD, version I2.0+. The tax-benefit systems simulated in this version of the model refer to those in place as of June 30, 2019. For each individual in the data, tax liabilities and social benefit entitlements are simulated according to the laws of the respective country. Disposable income is calculated by adding benefits to and subtracting taxes from gross income of each individual. EUROMOD provides the same framework for all European countries and makes results comparable. Sutherland (2001a), and Sutherland and Figari (2013) provide a detailed description of the EUROMOD model.

The underlying data used in EUROMOD come from EU-SILC surveys from 2017. EUSILC surveys collect information on sociodemographic characteristics, income sources, employment status, and gross income for representative samples of the national populations. In some countries, data are enriched by country-specific data sources.

EUROMOD results are representative at the country level and validated against aggregate national statistics. A detailed description of the national models is published every year in the country reports, which can be downloaded from: https://euromod-web.jrc.ec.europa.eu/resources/country-reports

In order to align the data on earnings and other non-simulated income components to the actual situation in 2019, data on individual income sources are inflated using uprating factors. Uprating factors are collected from national tax authorities, national statistical offices, or Eurostat. The sociodemographic characteristics of the population recorded in EU-SILC 2017 are kept constant.

EUROMOD can be used to assess the effects of actual or hypothetical policy changes and alternative economic and demographic scenarios. In this study, EUROMOD is used to assess the impact of hypothetical policy scenarios of increases in SMWs. These scenarios involve raising the gross wage of individuals in the data in cases where it is below the level of a HMW. Hence, the model calculates the taxes due, social insurance contributions, and benefits for each individual and household, both in the baseline and in the hypothetical scenarios.

EUROMOD allows us to take full account of the interactions between HMW increases and the tax-benefit system. An increase in the gross wage of a MW earner generally results in an increase of the net income of the worker's family, although the impact on net income is dampened by increased income taxes and reduced benefits.

EUROMOD is a static microsimulation model that simulates first-round effects of policy changes.

These static first-round effects are sometimes called “overnight effects.”

To account for potential measurement errors in the calculation of hourly wages, we adopt a set of correction methods. The technical details of the calculation of hourly wages and the data correction method can be found in Appendix B. We then assign the new HMW to potential MW workers. This assignment is done by increasing hourly wages to the level of the new HMW when the observed hourly wage is lower than that level. This is consistent with increased compliance and a reduced use of variations and exemptions (see Appendix B for a more detailed discussion). At the same time, the simulations assume no impact of MW increases on wages slightly above the new HMW; in other words, the simulations do not account for possible “spillover” effects.

Other recent studies conducted in parallel to ours made somewhat different methodological choices. In particular, Detragiache et al. (2020, p. 12) assume that wages below the old MW are increased by the rate of the MW increase, which is more conservative than our assumption. In turn, they do not apply an outlier correction, and they assume spillover effects of MW up to 75% of the median wage, which are less conservative assumptions than those employed in our methodology.

Statutory MWs apply to employees. Therefore, in this analysis, we do not change the incomes of individuals earning other types of income, such as self-employment or pension income. More specifically, the potential sample of HMW earners is selected under the following conditions: (1) positive employment income, (2) no self-employment income, (3) no pension income, and (4) not younger than 18 years. The wages of the remainder of the population remain unaffected by the HMW. In a next step, we recalculate annual earnings by multiplying the new hourly wage rate by the reported yearly working time.

Following EU's Working Time Directive, the working time for which a HMW is assigned is capped at 48 h/week.

Once the earnings are recalculated based on a specific HMW scenario, we run EUROMOD to calculate taxes, social insurance contributions, and benefits at the new level of gross earnings. This is needed to calculate the fiscal effects of the hypothetical scenarios and also the disposable income of households, which in turn is needed to assess the policy impact on in-work poverty.

Besides its main results on the social and fiscal impacts of MWs, which are simulated as first-round effects, this paper includes two extensions to assess the possible labor market impacts of HMW increases. The first extension calculates possible negative employment effects based on elasticities estimated by the empirical literature. The second extension calculates labor supply adjustments at the intensive margin, i.e., modeling how increased wages may induce some workers to offer more working hours on the labor market.

HMWs at 60% of the median wage and 50% of the average wage are the two highest ones of the six reference values assessed. As can be seen in Figure 1 above, they are close to the highest SMWs currently observed in the EU. The MWs in Bulgaria, France, Portugal, and Slovenia are at or close to 60% of the median, while the countries approximating 50% of the average wage are France, Portugal, Slovenia, and Spain.

In contrast, the lowest reference values would imply a gap to be closed for about one-quarter to one-third of Member States. A reference value of 50% of the median wage would imply increases for nine Member States from their 2019 levels (Czechia, Croatia, Estonia, Germany, Greece, Ireland, Latvia, Malta, the Netherlands; the implied increase would be small in Croatia, Greece, and the Netherlands). Meanwhile, a reference value of 40% of the average wage would imply increases for six Member States: Czechia, Estonia, Hungary, Ireland, Latvia, and Malta.

Intermediate reference values would imply gaps to close for one-half to two-thirds of the Member States. In particular, an intermediate reference value of 55% of the median wage would imply increases for 15 Member States. These are (in addition to the ones below 50% in 2019): Belgium, Slovakia, Hungary, Lithuania, Luxembourg, and Poland. Meanwhile, an intermediate reference value of 45% of the average wage would imply increases for 17 Member States. These are (in addition to the ones below 40% in 2019): Belgium, Bulgaria, Croatia, Germany, Greece, Lithuania, Luxembourg, the Netherlands, Poland, Romania, and Slovakia (Figure 1 above).

The percentage increase in SMWs, implied by the various indicative reference values, are shown in Figure A1 in Appendix. (Table A1 in Appendix presents the implied increases in nominal terms.) The largest increases in MWs (i.e., reaching 30% in the case of the highest reference values) are implied in Member States such as Czechia, Estonia, Ireland, Latvia, and Malta, while the smallest increases (below 10% for the highest reference values) are implied for France, Portugal, Slovenia, and Spain.

While higher or lower reference values can be defined both in terms of the average and the median wage, the two indicators have somewhat different implications across Member States. In particular, reference values based on the average wage imply somewhat higher MWs for Member States such as Bulgaria, Hungary, Portugal, and Romania, while the reverse is true for Member States such as Belgium, Germany, Greece, Malta, and the Netherlands. The reason is that, while the average wage is higher than the median wage in all countries, the difference between both is not uniform across Member States.

Figure A2 in Appendix shows that the relative difference between the average and the median wage ranges from slightly above 10% in Scandinavian countries to about 40% in Bulgaria and Portugal.

Countries can be divided into three groups based on the share of workers affected by the highest reference values. In seven Member States, the share of workers earning the MW would exceed 20% if the MW were set at 60% of the median wage; these countries are Estonia, Greece, Ireland, Luxembourg, Poland, Spain, and Romania. If set at 50% of the average wage, the share of workers would reach 20% also in Bulgaria and Hungary (see Figure A4 in Appendix; Figure A3 shows the share of workers earning the MW in the baseline).

In contrast, the share of MW earners would remain below 10% in Belgium, France, Lithuania, the Netherlands, and Slovenia even if the MW were set at 60% of the median or 50% of the average wage. In the rest of the countries, the share of MW earners is estimated to be between 10% and 20% at the highest reference values for SMWs (Figure A4 in Appendix).

The increase in the wages of beneficiaries (i.e., those workers originally earning at or below the HMW) would reach 20% in a number of countries under all scenarios. The average wage increase for the workers affected depends mainly on the initial level of the SMW and the shape of the wage distribution close to the MW, that is, the number of workers around the SMW that are affected when the MW increases. For the scenario in which MWs are set at 60% of the gross median wage, the average wage increase would reach 30% in Estonia and 25% in Germany, Greece, and Ireland. In the scenario where MWs are set at 50% of the average wage, the wage increase for affected workers would reach 25% only in Estonia (Figure A5 in Appendix).

The simulated increase of the wage bill depends on two factors: the share of workers affected and the average increase in earnings triggered by the new MW.

In the scenario where MWs are set at 60% of the median wage, the largest increase in the wage bill would be recorded in Greece, exceeding 4%. Other EU countries with an increase in the wage bill above 2% would be Ireland, Estonia, and Poland. In the scenario where MWs are set at 50% of the average wage, the largest increase in the wage bill would be recorded in Romania (above 4%) followed by Bulgaria, Estonia, Greece, Ireland, and Poland (above 2%; see Figure A6 in Appendix). These countries exhibit both a high share of MW earners at such levels of the MW (especially Greece and Poland) and a large increase in the wages of affected workers (especially Estonia and Ireland).

A reduction of at least 10% in wage inequality would be observed in 12 Member States if their MWs were raised to 60% of the median wage. A reduction of at least 15% in wage inequality would be observed in seven Member States (Czechia, Germany, Estonia, Spain, Luxembourg, Poland, Slovakia; see Figure A7 in Appendix). These decreases occur from a high initial level of wage inequality in Spain but lower initial levels in other countries, such as Czechia, Poland, and Slovakia.

In the scenario where MWs are set at 50% of the average wage, the largest decreases in wage inequality are observed in Estonia and Romania (above 20%). The group of countries with a decrease exceeding 15% largely overlaps with the group of countries with a similar decrease in the scenario of 60% of the median wage, but it also includes Bulgaria and Greece, while excluding Germany.

Eight countries would record a reduction by >20% in in-work poverty should they increase their SMW to a reference value of 60% of the median gross wage or 50% of the average (Figure A8 in Appendix).

The indicator measures the share of persons aged 18 or over who are employed and have an equalized disposable income below the at-risk-of-poverty threshold, which is set at 60% of the national median equalized disposable income (after social transfers). For the purpose of this indicator, an individual is considered as being employed if he/she was employed for more than half of the reference year.

In some countries, such as Slovenia and the Netherlands, MW increases do not always reduce in-work poverty in the simulations. This is due to increased taxes (in the Netherlands) and reduced means-tested benefits (in Slovenia) for some beneficiary households. It is possible that the parameters of the tax-benefit systems would be adjusted by governments in the wake of MW adjustments to avoid such effects or to keep incentive effects unchanged. Such adjustments are not modeled (see related discussion in Section 1).

Such adjustments may also affect the fiscal impact of MW increases. Accordingly, in the current simulations, MW increases improve the budget balance in the Netherlands and Slovenia. Adjusting tax and benefit rules to keep social benefits of MW increases positive would likely reduce these positive fiscal impacts.

The gap between the average wages of men and women declines in all EU countries as the MW increases. This is because a majority of MW earners are women in all EU Member States.

Based on EU-SILC data, the share of women among those earning a wage around the NMW ranges between slightly above 50% and just below 80%, averaging at about 60%. See, for example, European Commission (2020b, p. 7 and p. 134).

In the scenario where MWs are set at 60% of the median wage, the gender pay gap declines by >20% in Greece and by >10% in Spain, Romania, and Slovakia (Figure A9 in Appendix). In the scenario in which MWs are set at 50% of the average wage, the gender pay gap declines by 25% in Romania and by >10% in Greece, Luxembourg, Poland, and Slovakia. In these hypothetical scenarios, the reduction in the gender pay gap is significant, exceeding 5% in a number of countries, including in some where the gap in average wages between men and women is high (e.g., Czechia, Latvia, Germany).

MWs affect public budgets in a number of ways. As a direct cost, higher MWs may increase the public-sector wage bill in the case where a share of public-sector employees earn the MW; the public-sector wage bill can also increase due to possible links of public-sector pay scales to the MW. Higher MWs may also raise the cost of some public procurements.

For an explanation of these effects in the case of the US, see CBO (2019).

An increase in the MW raises revenues from labor taxes and contributions and may also reduce benefits expenditure. This effect is indirect but larger than any negative effect on the public-sector wage bill because few public employees earn wages close to the MW. For instance, Zandvliet et al. (2019) estimate that, in the Netherlands, increased revenues from labor taxes and benefits exceed direct costs related to the public wage bill by a factor between 4 and 5. Similarly, for the US, Zipperer et al. (2021) estimate that an increase of the federal MW would significantly reduce expenditure on public assistance programs and increase social security-related revenue.

These impacts are confirmed by the Congressional Budget Office's simulations (CBO 2021) of the same proposal, although the CBO's assessment also includes significant increases in estimated healthcare-related expenditure.

According to the simulations, MWincreases have a small but positive effect on public budgets, driven by increases in tax revenues and reductions of benefit expenditure (Figure A10 in Appendix). The magnitude of these effects is small; the overall improvement of public budgets is smaller than or equal to 0.1% of gross domestic product (GDP) in the scenarios implying smaller changes (50% of the median or 40% of the average wage), reaching 0.4% of GDP only in a few cases where MWs are increased to 60% of the median wage (in Estonia, Germany, Greece, and the Netherlands) and 50% of the average (in the Netherlands, Poland, and Romania). In turn, the simulations imply a small negative impact on the public budget balance for Hungary and Spain. Negative fiscal effects are driven by lower tax revenues in Hungary and by lower revenues from social security contributions in Spain. Results may be sensitive to modeling assumptions, including those related to how other policies, which are not automatically linked to the MW (e.g., tax brackets, rules of tax credits), would change under the various scenarios.