Minimum Wage Impacts on Employment in Greece: Estimates for the Period 2016–2024

Abstract

This paper aims to provide evidence of the impact on the minimum wage to employment in Greece over the period 2016 to 2024. The main contribution of this paper is the examination of the effects of the minimum wage during a period characterized by many difficulties and research interest not only nationwide but also across regions with high heterogeneity. The case of Greece is particularly interesting to study during this period as it provides a unique context to explore the effects of minimum wage increases on employment. Greece constitutes a distinctly singular case within the European context due to the exceptional structural characteristics of its labor market. Following a protracted economic crisis, successive waves of labor market reforms, and the additional disruptions generated by the COVID-19 pandemic, Greece provides an illustrative, and in many respects unique, example of how extensive policy interventions interact with a gradually recovering economy and persistently elevated unemployment levels. Overall, the results strongly indicate that there is little to no impact of the minimum wage on employment and the findings vary considerably across the different regional contexts. Finally, the DiD methodology used supports the credibility of the findings and suggests that the lack of impact of the minimum wage is not due to model specification or timing bias.

1. Introduction

The impact of the minimum wage on employment has been a deeply controversial issue in the economic literature, both internationally and within the Greek context, and has led to a heated academic debate, as there is an essential discrepancy between theoretical predictions and empirical evidence. The neoclassical theory suggests that higher minimum wage should reduce employment. However, empirical evidence is far more heterogeneous, with many studies, including the present one, reporting little to no effect, or even positive employment effects (Roupakias, 2024).

The opponents of the minimum wage policies argue that they prompt employers to reduce other elements of the compensation packages, restrict employment opportunities for low-wage workers and may lead to unexpected employment effects (Wang & Gunderson, 2011; Roupakias, 2024). In contrast, proponents emphasize that the minimum wage has a safety-net function and a capacity to raise earnings among low-paid employees. Even when some job losses occur, evidence suggests that the overall gains in workers’ income often exceed the associated costs (Dube & Zipperer, 2024). Furthermore, theory indicates that the minimum wage may reduce employer-provided training, while also encouraging workers to invest more in their own skill development (Neumark & Wascher, 1998).

Several empirical studies have been conducted supporting that controversy occurs, since their findings indicate that the minimum wage policies may lead to negative, positive, or even mixed employment impacts (Roupakias, 2024). A key reason for this heterogeneity is that the empirical literature consists of two fundamentally different types of studies: causal and correlational.

Causal studies, typically using quasi-experimental identification strategies, attempt to isolate exogenous variation in minimum wage policies to infer credible causal effects. Prominent examples include natural-experiment approaches such as Card and Krueger (1994), who exploit policy differences across U.S. states, and Neumark et al. (2014), who leverage federal- and state-level minimum wage variations. Similar identification strategies have been employed in more recent European studies (e.g., Harasztosi & Lindner, 2019; Dustmann et al., 2022). These designs, by relying on exogenous or policy-driven shocks, attempt to minimize biases from reverse causality or omitted variables and therefore tend to produce more reliable causal estimates.

In contrast, correlational studies rely on simple time-series associations, cross-sectional differences, or reduced-form regressions without strong identification. Such approaches may suffer from endogeneity, as minimum wage changes may respond to labor-market conditions rather than vice versa, leading to ambiguous or misleading conclusions. Many of the early international studies, as well as several analyses concerning Greece, fall into this category.

Recognizing this methodological heterogeneity is crucial for interpreting the mixed findings in the literature. Differences in identification strategies, datasets, institutional settings, and underlying assumptions across studies may explain the variation in findings. More specifically, some studies report negative effects (e.g., Neumark & Wascher, 1992; Neumark et al., 2014), others find no systematic impact (e.g., Georgiadis et al., 2018; Andriopoulou & Karakitsios, 2021), while other studies document positive employment responses (e.g., Card & Krueger, 1994; Addison et al., 2009; Dolton et al., 2012).

To clarify this variation,

Table 1. Minimum wage studies Overview.

Study	Type-Identification Strategy	Strength of Causal Claim	Geographic Scope	Main Finding
Neumark and Wascher (1992)	Causal study: state-level panel variation DiD	Moderate to strong	US	Small negative employment effects
Card and Krueger (1994)	Causal study: fast-food restaurants, quasi-experiment, DiD	Strong	US (regional)	No significant negative employment effects, increase in MW increased employment
Neumark and Wascher (1998)	Cross-state variation, state-level panel DiD	Strong	US	Negative effect for on-the-job training
Addison et al. (2009)	Causal study: fixed-effects panel regression	Moderate to strong	US	Positive employment effects
Wang and Gunderson (2011)	Causal study: prespecified panel/quasi experimental design	Strong	Urban areas of China	Regionally heterogeneous employment effects
Dolton et al. (2012)	Causal study: DiD panel	Strong	UK	No significant employment effects/small positive effects in employment after 2003.
Neumark et al. (2014)	Causal study: panel-data reanalysis, DiD/FE controls with data-driven controls	Strong	US	Negative effects, especially for younger workers
Harasztosi and Lindner (2019)	Causal study: firm levelDiD	Strong	Hungary	Small effects on employment
Chorna (2021)	Correlational/panel regression (not fully DiD)	Moderate to strong	Poland	Increase in MW reduces productivity and increases the share of capital
Derenoncourt et al. (2021)	Causal study: DiD	Strong	Brazil	Increase in MW no significant disemployment effects
Drucker et al. (2021)	Causal study: DiD	Strong	Israel	Increase in MW reduces profits, limited redistribution
Maarek and Moiteaux (2021)	Causal study: fixed effects panel + IV	Moderate to strong	28 European countries	Mixed effects (in high minimum wage countries, negative employment effect, inlow minimum wage countries, neutral effect)
Paun et al. (2021)	Causal study: random andfixed effects panel, DiD	Strong	22 EU countries	High minimum wage reduce employment
Dustmann et al. (2022)	Causal study: individual-level DiD	Strong	Germany	MW increases has no employment losses
Dreepaul-Dabee and Tandrayen-Ragoobur (2022)	Causal study: DiD	Strong	Mauritius	Negative employment effect, women impacted compared to men
Gregory and Zierahn (2022)	Causal study: DiD + unconditional quantile regression	Strong	Germany	MW can reduce wage inequality, negative effects on employment of high-skilled workers
Lopez-Tamayo et al. (2022)	Causal study: spatial panel econometric models (FE/SAR, SEM, SDM and SAC)	Strong	Spanish provinces (46/52)	No significant negative elasticity of youth employment to minimum wage, (except those 16–19)
Van der Westhuizen (2022)	Causal study: DiD	Strong	New Zealand	Small positive effects on employment of teenagers
Nguyen (2023)	Causal study: DiD with FE	Strong	Vietnam	No significant employment effects
Kunaschk (2024)	Causal study: DiD	Strong	Germany	MW barely affected overall employment. Regular employment was positively, while marginal employment was affected negatively
Dube and Zipperer (2024)	Meta-analysis with updates (OWN—own wage elasticity estimates from literature)	Moderate	88 studies	Varying but generally modest employment effects of MW
Popp (2024)	Correlational and causal study combination: HHI + FE panel	Moderate to strong	Germany	High labor market concentration reduced wages and employment, indicating monopsonistic behavior by firms
Dütsch et al. (2025)	Review of causal studies (not primary causal)	Moderate	Germany	Overall employment stable, with reductions mainly in marginal positions
Greek Studies
Yannelis (2014)	Causal study: age-based reform (<25)—DiD	Strong	Greece	Cuts in statutory MW led to employment increases. Higher MW wage, very high minimum wage do have significant disemployment effects
Kanellopoulos (2015)	Correlational study: time-series correlations	Moderate	Greece	MW increase leads to employment reductions for both genders
Georgiadis et al. (2018)	Causal study: DiD, FE+ FE-IV	Strong	Greece	Wage gains, no employment effects
Karamanis et al. (2018)	Correlational study: time-series correlations	Moderate	Greece	No clear MW–unemployment correlation
Georgiadis et al. (2020)	Causal study: firm-level heterogeneity, DiD	Strong	Greece	Heterogeneous firm-responses to the change in the MW
Andriopoulou and Karakitsios (2021)	Causal study: micro-data transitions, Random Effects	Strong	Greece	Small or insignificant impact on transitions into and out of unemployment
Roupakias (2024)	Causal study: DiD	Strong	Greece	Wage compression, small/neutral effect on employment
Present study	Regional prespecified design+ DiD	Moderate	Greece (NUTS 1 and 2)	Heterogeneous regional effects

summarizes key international and Greek studies according to their identification strategy and the strength of their causal claims. This classification highlights the extent to which conclusions depend on methodological choices rather than purely on economic conditions. By exploiting exogenous policy variation, causal studies generate more robust and credible estimates, in contrast to correlational studies, which rely on observational data and are prone to endogeneity. As highlighted by Popp (2024), traditional competitive models may not universally apply, as they fail to account for the heterogeneity of market structures and varying degrees of competition across labor markets.

In Greece, the minimum wage system has evolved through a series of major reforms closely linked to the country’s shifting economic conditions. A major institutional change occurred in 2012 under the Second Adjustment Programme, agreed between Greece and the European Commission, ECB, and the IMF. The minimum wage moved from a collectively negotiated framework to a statutory one, accompanied by a sharp 22% reduction, highlighting how wage-setting policy responded to the prevailing economic and political pressures.

A substantial body of research has examined the impact of the minimum wage on the Greek economy, producing mixed and often conflicting results. To begin with, Yannelis (2014), exploiting the age-based minimum wage reform and Labour Force Survey data for 2009–2013, found that cuts in statutory minimum wage led to employment increases. This effect occurred through new hires, job-to-job transitions, and a combination of reduced labor demand and increased search incentives induced by the minimum wage. Overall, he concluded that very high minimum wages have significant disemployment effects.

Kanellopoulos (2015) reported that the minimum wage significantly reduced employment for both men and women over 2004–2013. In contrast, Georgiadis et al. (2018), analyzing data for 2009–2017, showed that the minimum wage substantially raises individual and firm-level wages with positive spillovers but found no systematic effects on employment. Karamanis et al. (2018) also detected no clear correlation between the minimum wage and unemployment for the period 2000–2017, suggesting that other factors shape labor market outcomes.

Georgiadis et al. (2020), studying the sharp 2012 minimum wage reduction, further documented heterogeneous firm responses that deviate from the predictions of the standard competitive labor market model. Furthermore, using micro-data from the Labour Force Survey (2004–2019), Andriopoulou and Karakitsios (2021) concluded that the real minimum wage has no statistically significant or only very small effects on transitions into and out of unemployment. More recently, Roupakias (2024) found that minimum wage policies in 2015–2020 generated wage compression and sizeable gains for low-paid workers without imposing short-run employment losses.

Finally, the present empirical study significantly differs from previous studies focusing on the case of Greece and, by extension, other countries with similar characteristics, such as high regional heterogeneity, in several ways.

Firstly, the Greek case offers unique insights, not available from international comparisons with countries such as Portugal, Italy, or Spain, which exhibit similar regional heterogeneity and institutional structures. The features of the Greek labor market can explain the resilience to minimum wage effects. For instance, Greece is characterized by a high rate of self-employment (around 33% of the workforce) and a significant informal sector (estimated at 25–30% of employment), which are not directly bound by statutory minimum wage limits.

Furthermore, the present empirical study complements previous Greek studies, such as Georgiadis et al. (2018) and Roupakias (2024), primarily with respect to the regional dimension. To the best of our knowledge, the effects of the minimum wage changes on employment in the Greek regional context have not been fully studied, and pronounced regional disparities, such as differences in the economic structure and employment opportunities between regions, for instance, between the region of Attica and the Aegean islands, could play a critical role.

Additionally, we use a difference-in-differences (DiD) approach in regional data. While earlier studies primarily examined periods marked by the substantial minimum wage reduction implemented in 2012, the present analysis focuses on a more recent period, offering new evidence in the context of multiple minimum wage increases and substantial macroeconomic shocks. For instance, while Georgiadis et al. (2018) focused on the national impact of the 2012 minimum wage reduction, the present study extends the analysis to the regional level during a period of increases (2019, 2022, 2023, and 2024), offering a more detailed picture of the heterogeneity of effects. More specifically, this period is characterized by significant macroeconomic shocks, such as the recovery from the economic crisis, the disruptions caused by the COVID-19 pandemic, and the inflationary pressures following the conflict in Ukraine.

Consequently, the present study provides a mechanism-based understanding of the Greek labor market, suggesting the presence of structural characteristics that make it relatively resilient to minimum wage effects. In this respect, the Greek case, marked by persistently high unemployment and a sizeable informal sector, may not respond to minimum wage increases in the same way as other economies.

More analytically, in the present study, the impact of the minimum wage on employment is estimated for the total of Greece, NUTS 1-major socio-economic regions, and NUTS 2-regions by using the methodology that Wang and Gunderson (2011) employed, which follows a prespecified research design proposed by Neumark (2001) and Campolieti et al. (2006). Furthermore, for each region (NUTS 2), the estimates are obtained by combining the difference-in-differences (DiD) approach (Ashenfelter, 1978; Card & Krueger, 1994; Caliendo et al., 2025). Furthermore, supplementary to the DiD approach, an event study was applied and additional robustness checks (such as a placebo test, wild cluster bootstrap and weighted estimates) were performed in order to check the validity of the model.

Our empirical findings indicate that, overall, there is no or only small impact of the minimum wage on employment. Furthermore, the results highlight that the minimum wage affects regions differently. For instance, in Attica and the North Aegean, the effect is negative, whereas in Mainland Greece and Eastern Macedonia and Thrace, it is positive. Such heterogeneity challenges the notion of a uniform impact of minimum wage across the economy, suggesting that regional heterogeneity should be interpreted as evidence that a uniform minimum wage policy may not be suitable in the case of Greece.

Additionally, the analysis revealed that GDP consistently influences employment, whereas the minimum wage does not. This finding is controversial as it questions the relevance of the minimum wage as a primary policy instrument for employment.

In summary, the present study lies in its regional analysis of minimum wage effects in Greece over the period 2016–2024. Unlike prior national-level studies, we highlight heterogeneity across regions, showing that structural features like high unemployment and a large informal sector shape labor market responses. This provides both theoretical insight and empirical evidence that a uniform minimum wage policy may not be suitable for all regions.

Finally, the remainder of the paper is structured as follows: Section 2 outlines the national minimum wage setting mechanisms that Greece adopted. Section 3 presents the methodology of the empirical study followed in the present paper. Section 4 refers to the data, Section 5 illustrates the empirical results, and finally, Section 6 summarizes the conclusions.

2. The Minimum Wage Determination in Greece: A Historical Perspective

Under Greek law, the legal minimum wage and daily wage represent the minimum amount of payment that employers are obliged to provide, as determined by the Minister of Labour and Social Security. The legal wage includes both the base minimum wage and any legally mandated allowances established through legislation, collective agreements, arbitration decisions, or ministerial acts (Hellenic Labour Inspectorate, 2025).

Greece’s minimum wage framework has undergone significant institutional changes. Between 1982 and 1990, it operated under an automatic inflation-indexation mechanism, followed by a semi-automatic adjustment system from 1991 to 2002. A major reform occurred in 2012, when minimum wage setting shifted to a statutory regime, with wage levels established directly by law (Law 4093/2012, Hellenic Republic, 2012).

More analytically, Greece’s national minimum wage evolution started in the mid-1930s with very little available information; consequently; the literature typically focuses on developments from 1975 onward. Additionally, based on the mechanisms used to determine the minimum wage, the period 1975–2025 can be divided into four distinct institutional phases.

The first period (1975–1990) was characterized by annual collective bargaining between trade unions and employers’ associations, conducted under government supervision. Negotiations involved the GSEE and three employer organizations (GSEVEE, SEV, and the Athens Confederation of Commerce), resulting in legally binding national general collective labor agreements of one-year duration. In several instances during this period, minimum wage levels were also determined through arbitration.

The second period (1991–2011) marked the withdrawal of government oversight, allowing the social partners to negotiate independently. Bargaining took place between the GSEE and three employer organizations—GSEVEE, SEV, and the Hellenic Confederation of Commerce, which, by then, held broader national representativeness. Agreements typically had a two-year duration (with the exception of 1993), and the minimum wage followed a generally upward path with limited volatility. As the economy entered recession in 2007, the final national general collective labor agreement on the minimum wage was concluded in 2010.

The third period (2012–2018) coincided with Greece’s severe economic crisis, which led to a statutory restructuring of the minimum wage. Law 4093/2012 (Government Gazette A, 222) set the nominal minimum wage at €586.08 for employees and €26.18 for manual workers, a 22% reduction from previous levels. It also introduced a sub-minimum wage for workers under 25 and froze seniority allowances until unemployment fell below 10%. Law 4172/2013 (Government Gazette A, 167, Article 103; Hellenic Republic, 2013) established a new government-led wage-setting mechanism, following consultation with social partners and expert assessments, that determines the statutory minimum wage considering economic conditions, productivity, employment, and competitiveness. Law 4254/2014 (Government Gazette A, 85; Article 1, subparagraph IA.6; Hellenic Republic, 2014) further defined the statutory minimum wage as a single reference value for all employees not covered by collective agreements. Between 2015 and 2022, disputes over the “three-year allowances” created legal uncertainty, which was resolved with Law 5053/2023 (Government Gazette A, 158; Hellenic Republic, 2023), reintroducing the allowances from January 2024, excluding work experience accumulated between 2012 and 2023.

The fourth period (2019–2025) saw the implementation of the statutory minimum wage mechanism established by Law 4172/2013 (Government Gazette A, 167; Hellenic Republic, 2013). Under this framework, the government determines the minimum wage in consultation with the central bank, social partners, research institutes, and an independent panel of experts.

The first application of the new statutory minimum wage mechanism occurred in 2019, (Ministry of Labour and Social Security, 2019) following Law 4564/2018 (Government Gazette, A, 170, Article 2; Hellenic Republic, 2018). After consultations with social partners and scientific bodies, the Ministry of Labour issued Decision No. 4241/127/30-1-2019, which increased the minimum wage by 11.1% and abolished the sub-minimum wage for workers under 25. The statutory minimum was set at €650 for full-time employees and €29.04 for manual workers, corresponding to a 12-month equivalent of €758.3.

The second application of the new mechanism for setting the statutory minimum wage took place in 2021, (Decision No. 107675/27-12-2021, Ministry of Labour and Social Security, 2021), effective from 1 January 2022. Following consultations with social partners and scientific bodies, the Ministry of Labour increased the minimum wage by 2%, setting it at €663 for full-time employees and €29.62 for manual workers, corresponding to a 12-month equivalent of €773.5.

The third application of the new mechanism took place in 2022, (Decision No. 38866/21-04-2022, Ministry of Labour and Social Security, 2022), effective from 1 May. Following consultations with social partners and scientific bodies, the Ministry of Labour increased the minimum wage by 7.5%, setting it at €713 for full-time employees and €31.85 for manual workers, equivalent to €831 per month on a 12-month basis.

Additionally, the fourth application of the new mechanism occurred in 2023, (Decision No. 31986/24-03-2023, (Ministry of Labour and Social Security, 2023) effective from 1 April. The Ministry of Labour increased the minimum wage by 9.4%, setting it at €780 for full-time employees and €34.84 for manual workers, equivalent to €910 per month on a 12-month basis.

It should be noted that this mechanism frequently resulted in decisions exceeding expert recommendations; in three of the four applications (2019, 2022, 2023), the Council of Ministers set the minimum wage above the upper limit recommended by the Committee of Experts or the Economic and Social Council (IOBE, 2024).

The fifth adjustment of the minimum wage and daily wage took place on 1 April 2024. The Ministry of Labour and Social Security issued Decision No. 25058/29.3.2024, (Ministry of Labour and Social Security, 2024), setting the statutory minimum wage for employees engaged in intellectual work at €830.00 and the statutory daily wage for primarily manual workers at €37.07, expressed on a monthly basis.

The most recent adjustment was implemented on 1 April 2025. The Ministry of National Economy and Finance, in collaboration with the Ministry of Labour and Social Security, issued Decision No. 8233/27.3.2025, (Ministry of National Economy and Finance & Ministry of Labour and Social Security, 2025) establishing the minimum wage at €880.00 for employees and the minimum daily wage at €39.30 for manual workers.

In a few words, since the 2012 memorandum and Law 4172/2013, Greece adopted a new statutory minimum wage system, which remained unchanged until Decision 4241/127/30.1.2019 (Ministry of Labour and Social Security, 2019) raised the minimum wage in February 2019, marking a new era in wage determination.

The historical review of minimum wage determination in Greece provides the necessary context for understanding institutional changes and mechanisms that shaped worker compensation over time, which is crucial for interpreting the effects of wage policies on employment.

The institutional changes, legal reforms, and shifts in bargaining mechanisms over the decades provide essential context for the statistical analysis, as they shape both the timing and magnitude of minimum wage adjustments. In brief, by integrating historical insight with robust empirical methods, the analysis ensures that the results are meaningful and contextually grounded.

Figure 1 illustrates the progress of the minimum wage from 2016 until early 2025. Additionally, the economic crisis and the implementation of memorandum-related policies led to the minimum wage being set at €586.08, alongside the suspension of seniority-based wage increments. The first post-memorandum adjustment occurred in 2019, increasing the minimum wage to €650. This was followed by successive increases to €713 in 2022, €780 in 2023, €830 in 2024, and €880 in 2025 (Ministry of Labour and Social Security, 2025). All reported figures refer to gross monthly earnings, which include statutory social security contributions and applicable tax deductions.

Expressed in hourly terms, and following the standard conversion applied in Greek labor law (monthly wage ÷ 25 × 6 ÷ 40), the minimum wage corresponds approximately to €3.52 in 2016–2018, €3.90 in 2019–2021, €4.28 in 2022, €4.68 in 2023, €4.98 in 2024, and €5.28 in 2025.

While the figure presents nominal developments, a more comprehensive assessment requires consideration of real wage dynamics and the relative position of the minimum wage within the wage distribution. In real terms, the purchasing power of the minimum wage has been influenced by inflationary pressures, particularly during the post-2021 period, partly offsetting nominal gains. Moreover, the relative level of the minimum wage can be assessed through its ratio to the average wage, which provides an indication of its binding effect in the labor market. In this context, the EU Directive on adequate minimum wages (European Union, 2022) introduces indicative reference values for adequacy, namely, 60% of the median wage and 50% of the average wage, which are widely used as benchmarks across member states. Evidence from OECD data (OECD, 2024) suggests that despite recent increases, the Greek minimum wage remains below or close to these thresholds. Therefore, considering both real and relative dimensions is essential for a more accurate interpretation of minimum wage developments.

Finally, Figure 2 presents the evolution of the share of employed individuals affected by the minimum wage over time. Specifically, the reported percentage is calculated as the ratio of employees earning the minimum wage to the total employed population. This measure captures the proportion of the workforce directly influenced by minimum wage policies and allows for an assessment of how the extent of this impact changes across years. The share of employed individuals affected by the minimum wage shows a clear upward trend over the period, increasing from 49.73% in 2016 to 61.22% in 2024. This suggests that minimum wage policies have progressively influenced a larger proportion of the workforce, with a noticeable acceleration around 2020 and relative stabilization thereafter.

3. Empirical Strategy and Estimation Methodology

The present paper estimates the effects of the minimum wage on employment in Greece. Regarding the methodology, the present paper follows the approach adopted by Wang and Gunderson (2011), who estimate the impact of the minimum wage impacts in China for three different regions of the country. More specifically, the empirical methodology applied in the present paper involves a prespecified research design proposed by Neumark (2001) and Campolieti et al. (2006).

The proposed estimation equation is

$$E_{it}=\alpha +{\beta }_{2}M{W}_{it}+{\beta }_{3}M{W}_{it-1}+{\beta }_4{{\rm X}}_{it}+{\beta }_{5}REGIO{N}_i+{\beta }_{6}REGION{4}_j+{\beta }_{7}YEA{R}_t+{\epsilon }_{it}$$

(1)

where $E_{it}$ is the employment/population ratio given by Equation (2).

$$E_{it}={\displaystyle \frac{employmen{t}_{it}}{populatio{n}_{it}}}$$

(2)

More analytically, $employmen{t}_{it}$ denotes the total persons employed in region $i$ at year $t$; $populatio{n}_{it}$ denotes the total population in year t in region $i$; $M{W}_{it}$ is the minimum wage index (defined as the ratio of the minimum wage to the average wage of workers in region $i$ at year $t$); $M{W}_{it-1}$ is the minimum wage index of the previous year ($t-1$); $X_{it}$ denotes the unemployment rate (of each region of interest in year $t$); $REGIO{N}_i$ represents NUTS2-Regions, $i=1,\dots ,13$; $REGION{4}_j$ denotes the NUTS1-Major socio-economic regions, $j=1,\dots ,4$; $YEA{R}_t$ is a variable indicating each year ($t=2016,\dots ,2024);$ and ${\epsilon }_{it}$ is the error term (Neumark, 2001; Campolieti et al., 2006).

The main independent variable is the minimum wage index, $M{W}_{it}$. This measure is preferred over the nominal minimum wage as it captures the relative position of the minimum wage within the wage distribution and more accurately reflects its potential binding effect in the labor market. In addition, using a relative measure enhances comparability across time and regions by accounting for differences in overall wage levels and broader economic conditions. This specification is consistent with the existing literature (e.g., Wang & Gunderson, 2011).

Furthermore, the impact of the minimum wage is estimated using Equation (1) and subsequently expressed in terms of elasticity estimates. The elasticity estimates are calculated following Wang and Gunderson (2011), based on the estimated minimum wage coefficients, in order to ensure comparability.

More analytically, Equation (3) illustrates how elasticity estimates are calculated using the mean values of each variable, as reported in

Table A1. Descriptive statistics.

	E: Employment/ Population Ratio	Population	Employed	Average Wage	X: % Unemp	GDP *	MW
Case 1
Greece
Mean	0.20	10,645,706	2,173,119.56	1072.06	16.27	187,848.58	0.62
Median	0.20	10,816,286	2,161,414	1022.42	16.30	182,595.12	0.64
Maximum	0.24	10,816,286	2,525,419	1272.36	23.60	225,196.89	0.66
Minimum	0.16	10,432,481	1,778,764	980.55	10.10	167,539.52	0.57
Std. Dev	0.03	202,283	249,338.86	100.93	4.65	19,149.49	0.03
Skewness	−0.03	−0.19	−0.11	0.83	0.15	0.85	−0.37
Kurtosis	−1.68	−2.17	−1.54	−0.92	−1.55	−0.84	−1.77
Observations	9	9	9	9	9	8	9
Case 2
North Greece
Mean	0.17	3,029,903	504,544	921.6	18.13	40,898	0.73
Median	0.16	3,110,835	504,535	889.2	18.20	39,805	0.75
Maximum	0.20	3,110,835	581,949	1032.4	24.80	48,078	0.82
Minimum	0.13	2,928,737	416,457	865.9	12.30	36,480	0.57
Std. Dev	0.02	95,974.07	55,643.36	68.03	4.16	3787	0.08
Skewness	−0.01	−0.19	−0.14	0.66	0.17	0.78	−0.70
Kurtosis	−1.77	−2.17	−1.55	−1.41	−1.46	0.93	−0.82
Observations	9	9	9	9	9	8	9
Central Greece
Mean	0.14	2,669,942	367,162	913.3	16.69	38,462	0.74
Median	0.135	2,745,706	371,075	835.7	17.30	37,300	0.76
Maximum	0.17	2,745,706	426,509	1191.2	24.50	45,642	0.82
Minimum	0.11	2,575,237	297,187	819.5	9.70	34,520	0.60
Std. Dev	0.02	89,845.05	43,283.72	134.68	4.71	3814	0.06
Skewness	−0.05	−0.19	−0.20	1.03	0.04	0.82	−0.83
Kurtosis	−1.76	−2.17	−1.54	−0.65	−1.27	−1.01	−0.31
Observations	9	9	9	9	9	8	9
Attica
Mean	0.29	3,812,449.56	1,094,605.33	1206.16	15.10	90,488.33	0.55
Median	0.29	3,828,434.00	1,096,881.00	1139.86	14.10	87,649.71	0.56
Maximum	0.34	3,828,434.00	1,275,658.00	1477.37	23.00	109,653.75	0.59
Minimum	0.23	3,792,469.00	896,771.00	1105.52	9.10	81,340.79	0.51
Std. Dev	0.03	18,955.22	126,434.77	135.57	5.44	9631.17	0.03
Skewness	−0.08	−0.19	−0.10	1.06	0.22	0.90	−0.39
Kurtosis	−1.55	−2.17	−1.51	−0.71	−1.81	−0.76	−1.58
Observations	9	9	9	9	9	8	9
Aegean Islands
Mean	0.18	1,133,412	206,808	964.6	14.58	18,000	0.75
Median	0.17	1,131,311	196,551	954.8	15.90	17,482	0.70
Maximum	0.22	1,136,038	248,576	1188.3	20.40	21,823	0.80
Minimum	0.15	1,131,311	168,349	854.9	8.70	15,198	0.55
Std. Dev	0.02	2491.35	26,814.66	102.59	3.97	2022	0.09
Skewness	0.27	0.19	0.27	0.94	−0.15	0.56	−0.32
Kurtosis	−1.47	−2.17	−1.49	−0.19	−1.58	−0.84	−1.77
Observations	9	9	9	9	9	8	9
Case 3
Eastern Macedonia and Thrace
Mean	0.13	587,687.33	77,210.89	922.71	16.51	7003.50	0.75
Median	0.13	608,182.00	77,962.00	830.56	16.20	6827.70	0.79
Maximum	0.15	608,182.00	86,726.00	1430.60	22.80	8134.69	0.84
Minimum	0.11	562,069.00	65,146.00	804.12	11.60	6267.59	0.41
Std. Dev	0.02	24,303.68	7176.18	199.90	3.54	599.79	0.13
Skewness	−0.02	−0.19	−0.24	1.74	0.18	0.73	−1.66
Kurtosis	−1.82	−2.17	−1.48	1.67	−1.13	−0.93	1.50
Observations	9	9	9	9	9	8	9
Central Macedonia
Mean	0.19	1,842,090.67	348,329.67	914.92	18.20	25,908.05	0.73
Median	0.18	1,882,108.00	347,200.00	883.74	18.40	25,144.08	0.75
Maximum	0.23	1,882,108.00	408,069.00	1064.75	24.50	31,000.69	0.79
Minimum	0.15	1,792,069.00	284,102.00	840.34	12.80	23,228.56	0.66
Std. Dev	0.03	47,454.72	41,942.76	75.04	4.07	2694.49	0.04
Skewness	0.01	−0.19	−0.09	0.77	0.15	0.84	−0.17
Kurtosis	−1.75	−2.17	−1.58	−0.89	−1.62	−1.00	−1.80
Observations	9	9	9	9	9	8	9
Western Macedonia
Mean	0.13	270,963.22	34,337.67	1019.86	22.04	4125.35	0.66
Median	0.12	283,689.00	34,641.00	1024.90	19.80	4268.04	0.67
Maximum	0.14	283,689.00	35,781.00	1091.83	31.30	4435.10	0.77
Minimum	0.11	255,056.00	31,584.00	951.59	12.50	3519.36	0.55
Std. Dev	0.01	15,090.92	1247.85	45.12	6.28	316.75	0.08
Skewness	−0.04	−0.19	−1.04	0.07	0.07	−0.80	−0.05
Kurtosis	−1.81	−2.17	−0.04	−1.31	−1.57	−1.02	−1.64
Observations	9	9	9	9	9	8	9
Epirus
Mean	0.14	329,161.33	44,666.22	828.99	17.39	3861.04	0.81
Median	0.13	336,856.00	44,822.00	801.05	16.40	3772.24	0.82
Maximum	0.16	336,856.00	51,373.00	963.03	24.80	4507.60	0.88
Minimum	0.11	319,543.00	35,625.00	783.73	10.70	3464.61	0.73
Std. Dev	0.02	9124.75	5485.63	60.21	4.90	322.21	0.05
Skewness	−0.13	−0.19	−0.29	1.18	0.33	0.83	−0.21
Kurtosis	−1.75	−2.17	−1.55	−0.08	−1.47	−0.59	−1.68
Observations	9	9	9	9	9	8	9
Thessaly
Mean	0.13	712,657.56	95,538.11	827.14	17.36	9564.92	0.81
Median	0.13	732,762.00	96,957.00	790.31	16.90	9306.81	0.82
Maximum	0.16	732,762.00	111,474.00	1016.24	25.50	11,180.53	0.87
Minimum	0.10	687,527.00	76,409.00	755.67	10.00	8712.32	0.74
Std. Dev	0.02	23,840.94	11,704.95	81.81	4.41	875.57	0.05
Skewness	−0.07	−0.19	−0.23	1.30	0.12	0.67	−0.36
Kurtosis	−1.74	−2.17	−1.52	0.33	−0.70	−1.19	−1.49
Observations	9	9	9	9	9	8	9
Ionian Islands
Mean	0.15	204,686.56	31,483.22	827.07	15.23	3153.43	0.81
Median	0.14	207,855.00	29,768.00	788.33	16.00	3128.96	0.82
Maximum	0.19	207,855.00	38,709.00	1016.50	19.70	3766.12	0.84
Minimum	0.12	200,726.00	25,704.00	732.51	12.40	2581.09	0.77
Std. Dev	0.02	3757.31	4484.51	98.95	2.25	345.16	0.02
Skewness	0.40	−0.19	0.43	0.87	0.46	0.13	−0.61
Kurtosis	−1.56	−2.17	−1.46	−0.97	−0.79	−0.76	−1.20
Observations	9	9	9	9	9	8	9
Western Greece
Mean	0.12	663,597.33	79,314.44	991.70	19.54	8286.80	0.74
Median	0.12	679,796.00	81,393.00	808.16	21.60	7999.63	0.78
Maximum	0.14	679,796.00	91,726.00	2235.02	29.80	9785.21	0.87
Minimum	0.09	643,349.00	64,395.00	787.83	9.80	7395.25	0.32
Std. Dev	0.02	19,209.26	9599.30	469.66	7.36	776.20	0.17
Skewness	−0.11	−0.19	−0.24	2.02	−0.15	0.84	−1.69
Kurtosis	−1.79	−2.17	−1.64	2.47	−1.74	−0.85	1.67
Observations	9	9	9	9	9	8	9
Mainland Greece
Mean	0.16	528,669.56	84,231.11	1044.92	16.76	8802.47	0.64
Median	0.16	547,390.00	86,105.00	971.09	17.20	8390.83	0.62
Maximum	0.19	547,390.00	96,605.00	1381.99	25.00	10,595.57	0.72
Minimum	0.12	505,269.00	68,179.00	950.29	8.60	7894.73	0.60
Std. Dev	0.02	22,199.72	9446.01	143.40	5.23	994.62	0.04
Skewness	−0.09	−0.19	−0.32	1.37	−0.18	0.80	0.63
Kurtosis	−1.76	−2.17	−1.45	0.50	−1.26	−1.20	−1.13
Observations	9	9	9	9	9	8	9
Attica
Mean	0.29	3,812,449.56	1,094,605.33	1206.16	15.10	90488.33	0.55
Median	0.29	3,828,434.00	1,096,881.00	1139.86	14.10	87649.71	0.56
Maximum	0.34	3,828,434.00	1,275,658.00	1477.37	23.00	109,653.75	0.59
Minimum	0.23	3,792,469.00	896,771.00	1105.52	9.10	81,340.79	0.51
Std. Dev	0.03	18,955.22	126,434.77	135.57	5.44	9631.17	0.03
Skewness	−0.08	−0.19	−0.10	1.06	0.22	0.90	−0.39
Kurtosis	−1.55	−2.17	−1.51	−0.71	−1.81	−0.76	−1.58
Observations	9	9	9	9	9	8	9
Peloponnese
Mean	0.14	560,331.00	76,594.67	875.57	12.92	8654.54	0.76
Median	0.14	577,903.00	78,230.00	859.96	12.60	8299.38	0.78
Maximum	0.17	577,903.00	89,028.00	1012.50	19.20	10,314.49	0.84
Minimum	0.11	538,366.00	62,500.00	823.01	7.60	7706.74	0.67
Std. Dev	0.02	20,837.83	8664.03	62.28	3.52	911.84	0.06
Skewness	−0.03	−0.19	−0.19	1.08	0.30	0.82	−0.33
Kurtosis	−1.73	−2.17	−1.47	−0.18	−1.04	−1.11	−1.52
Observations	9	9	9	9	9	8	9
North Aegean
Mean	0.11	196,966.56	21,794.78	826.17	15.53	2535.22	0.81
Median	0.11	199,231.00	22,586.00	811.07	16.50	2460.36	0.80
Maximum	0.13	199,231.00	24,381.00	965.88	22.50	2956.66	0.87
Minimum	0.10	194,136.00	19,199.00	752.98	7.10	2274.80	0.75
Std. Dev	0.01	2685.30	1787.53	67.90	5.34	212.21	0.04
Skewness	−0.22	−0.19	−0.30	0.79	−0.12	0.79	0.25
Kurtosis	−1.54	−2.17	−1.45	−0.63	−1.53	−0.68	−1.54
Observations	9	9	9	9	9	8	9
South Aegean
Mean	0.21	315,915.89	67,302.22	1052.92	14.14	6118.15	0.65
Median	0.21	309,015.00	63,707.00	1051.42	16.00	6030.11	0.71
Maximum	0.27	324,542.00	87,460.00	1300.57	18.00	7395.73	0.74
Minimum	0.18	309,015.00	55,248.00	893.66	7.10	4981.34	0.45
Std. Dev	0.03	8183.45	10,940.68	135.39	4.03	711.64	0.12
Skewness	0.60	0.19	0.55	0.35	−0.48	0.23	−0.63
Kurtosis	−1.02	−2.17	−1.20	−1.27	−1.47	−0.83	−1.63
Observations	9	9	9	9	9	8	9
Crete
Mean	0.19	620,529.44	117,711.22	1014.68	14.50	9346.78	0.69
Median	0.18	623,065.00	113,133.00	940.19	13.40	9017.69	0.76
Maximum	0.22	623,065.00	138,530.00	1846.57	22.60	11,470.76	0.79
Minimum	0.15	617,360.00	93,900.00	813.94	8.60	7942.23	0.35
Std. Dev	0.02	3006.80	14961.31	321.95	4.33	1116.98	0.14
Skewness	0.08	−0.19	0.07	1.84	0.41	0.69	−1.57
Kurtosis	−1.50	−2.17	−1.46	2.00	−1.12	−0.89	1.14
Observations	9	9	9	9	9	8	9

Note: * Million Euro.

, and the minimum wage estimates are presented in

Table A2. OLS estimation.

Case 1
Greece
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.356 ***	0.0639	5.57	0.00510
MWt	−0.0982	0.0607	−1.62	0.181
MWt−1	0.0197	0.0565	0.348	0.745
Unemployment	−0.00630 ***	0.000606	−10.4	0.000482
Mean of Depended	0.205	F-statistic	102.0	Residual SE	0.00359
R2	0.987	p-value F-statistic	0.000311	AIC	−62.9
Adj. R2	0.977	SSE	0.0000516	BIC	−62.5
CASE 2
North Greece
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.207 **	0.0603	3.43	0.0265
MWt	0.0219	0.0379	0.578	0.595
MWt−1	0.0409	0.0374	1.09	0.336
Unemployment	−0.00469 **	0.00113	−4.15	0.0142
Mean of Depended	0.167	F-statistic	38.3	Residual SE	0.00508
R2	0.966	p-value F-statistic	0.00210	AIC	−57.4
Adj. R2	0.941	SSE	0.000103	BIC	−57.0
Central Greece
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.191 **	0.0491	3.90	0.0176
MWt	−0.0122	0.0380	−0.322	0.764
MWt−1	0.0389	0.0371	1.05	0.354
Unemployment	−0.00438 ***	0.000621	−7.06	0.00212
Mean of Depended	0.138	F-statistic	18.7	Residual SE	0.00622
R2	0.933	p-value F-statistic	0.00811	AIC	−54.1
Adj. R2	0.884	SSE	0.000155	BIC	−53.7
Attica
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.760 ***	0.0842	9.03	0.000834
MWt	−0.329 **	0.0857	−3.84	0.0185
MWt−1	−0.293 **	0.0985	−2.97	0.0411
Unemployment	−0.00846 ***	0.000640	−13.2	0.000189
Mean of Depended	0.287	F-statistic	160.0	Residual SE	0.00361
R2	0.992	p-value F-statistic	0.000128	AIC	−62.8
Adj. R2	0.986	SSE	0.0000521	BIC	−62.4
Aegean Islands
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.377 **	0.108	3.13	0.0352
MWt	−0.0585	0.0882	−0.664	0.543
MWt−1	−0.0342	0.0630	−0.543	0.616
Unemployment	−0.00614 *	0.00241	−2.55	0.0631
Mean of Depended	0.182	F-statistic	3.22	Residual SE	0.0150
R2	0.707	p-value F-statistic	0.144	AIC	−40.0
Adj. R2	0.488	SSE	0.000899	BIC	−39.7
CASE 3
Eastern Macedonia and Thrace
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.108 **	0.0354	3.04	0.0384
MWt	0.0483 *	0.0225	2.15	0.0981
MWt−1	0.0431	0.0235	1.83	0.141
Unemployment	−0.00258 *	0.00119	−2.17	0.0960
Mean of Depended	0.132	F-statistic	6.96	Residual SE	0.00819
R2	0.839	p-value F-statistic	0.0458	AIC	−49.7
Adj. R2	0.719	SSE	0.000268	BIC	−49.3
Central Macedonia
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.313 ***	0.0555	5.65	0.00484
MWt	−0.0166	0.0403	−0.413	0.701
MWt−1	0.0185	0.0413	0.449	0.677
Unemployment	−0.00689 ***	0.000648	−10.6	0.000444
Mean of Depended	0.190	F-statistic	127.0	Residual SE	0.00332
R2	0.990	p-value F-statistic	0.000202	AIC	−64.1
Adj. R2	0.982	SSE	0.0000442	BIC	−63.8
Western Macedonia
	Estimate	Standard Error	Statistic	p-Value
Intercept	−0.124	0.232	−0.536	0.621
MWt	0.0902	0.0927	0.973	0.386
MWt−1	0.215	0.783	1.17	0.306
Unemployment	0.00259	0.00332	0.779	0.479
Mean of Depended	0.127	F-statistic	6.56	Residual SE	0.00509
R2	0.831	p-value F-statistic	0.0503	AIC	−57.3
Adj. R2	0.705	SSE	0.000104	BIC	−56.9
Epirus
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.0502	0.119	0.420	0.696
MWt	0.00757	0.120	0.0631	0.953
MWt−1	0.148	0.0892	1.66	0.172
Unemployment	−0.00210	0.00135	−1.56	0.195
Mean of Depended	0.136	F-statistic	16.6	Residual SE	0.00636
R2	0.926	p-value F-statistic	0.0101	AIC	−53.8
Adj. R2	0.870	SSE	0.000162	BIC	−53.4
Thessaly
	Estimate	Standard Error	Statistic	p-Value
Intercept	−0.0356	0.116	−0.308	0.774
MWt	0.0572	0.101	0.565	0.603
MWt−1	0.196	0.146	1.35	0.250
Unemployment	−0.00189	0.00170	−1.11	0.327
Mean of Depended	0.135	F-statistic	13.6	Residual SE	0.00718
R2	0.911	p-value F-statistic	0.0144	AIC	−51.8
Adj. R2	0.844	SSE	0.000206	BIC	−51.4
Ionian Islands
	Estimate	Standard Error	Statistic	p-Value
Intercept	−0.211	0.465	−0.454	0.673
MWt	0.134	0.469	0.286	0.789
MWt−1	0.361	0.387	0.933	0.404
Unemployment	−0.00211	0.00442	−0.477	0.658
Mean of Depended	0.154	F-statistic	0.686	Residual SE	0.0250
R2	0.340	p-value F-statistic	0.606	AIC	−31.9
Adj. R2	−0.156	SSE	0.00249	BIC	−31.5
Western Greece
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.146 ***	0.0120	12.2	0.000259
MWt	0.0105	0.0101	1.05	0.354
MWt−1	0.0177	0.0109	1.63	0.179
Unemployment	−0.00241 ***	0.000266	−9.04	0.000829
Mean of Depended	0.120	F-statistic	27.7	Residual SE	0.00448
R2	0.954	p-value F-statistic	0.00388	AIC	−59.4
Adj. R2	0.920	SSE	0.0000802	BIC	−59.0
Mainland Greece
	Estimate	Standard Error	Statistic	p-Value
Intercept	−0.0835	0.0661	−1.26	0.275
MWt	0.170 **	0.0495	3.43	0.0264
MWt−1	0.272 **	0.0613	4.44	0.0114
Unemployment	−0.00242 **	0.000561	−4.31	0.0126
Mean of Depended	0.160	F-statistic	37.6	Residual SE	0.00516
R2	0.966	p-value F-statistic	0.00388	AIC	−57.1
Adj. R2	0.940	SSE	0.000107	BIC	−56.7
Attica
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.760 ***	0.0842	9.03	0.000834
MWt	−0.329 **	0.0857	−3.84	0.0185
MWt−1	−0.293 **	0.0985	−2.97	0.0411
Unemployment	−0.00846 ***	0.000640	−13.2	0.000189
Mean of Depended	0.287	F-statistic	160.0	Residual SE	0.00361
R2	0.992	p-value F-statistic	0.000128	AIC	−62.8
Adj. R2	0.986	SSE	0.0000521	BIC	−62.4
Peloponnese
	Estimate	Standard Error	Statistic	p-Value
Intercept	−0.154 **	0.0516	−2.99	0.0402
MWt	0.0516	0.0846	0.610	0.575
MWt−1	0.317 **	0.0804	3.94	0.0170
Unemployment	0.00130	0.00102	1.28	0.271
Mean of Depended	0.137	F-statistic	59.4	Residual SE	0.00351
R2	0.978	p-value F-statistic	0.000898	AIC	−63.3
Adj. R2	0.962	SSE	0.0000492	BIC	−62.9
North Aegean
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.219 *	0.0830	2.64	0.0578
MWt	−0.146	0.0758	−1.93	0.126
MWt−1	0.0572	0.0655	0.874	0.431
Unemployment	−0.00215 **	0.000619	−3.48	0.0254
Mean of Depended	0.111	F-statistic	31.1	Residual SE	0.00250
R2	0.959	p-value F-statistic	0.00313	AIC	−68.7
Adj. R2	0.928	SSE	0.0000249	BIC	−68.3
South Aegean
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.294 **	0.0981	2.99	0.0401
MWt	−0.131	0.178	−0.738	0.501
MWt−1	0.126	0.137	0.920	0.409
Unemployment	−0.00504	0.00266	−1.90	0.131
Mean of Depended	0.213	F-statistic	1.77	Residual SE	0.0251
R2	0.571	p-value F-statistic	0.291	AIC	−31.8
Adj. R2	0.249	SSE	0.00252	BIC	−31.4
Crete
	Estimate	Standard Error	Statistic	p-Value
Intercept	0.304 **	0.0735	4.14	0.0144
MWt	−0.0261	0.0560	−0.467	0.665
MWt−1	−0.0420	0.0522	−0.804	0.466
Unemployment	−0.00464	0.00249	−1.86	0.136
Mean of Depended	0.190	F-statistic	1.54	Residual SE	0.0194
R2	0.536	p-value F-statistic	0.335	AIC	−39.5
Adj. R2	0.187	SSE	0.00151	BIC	−35.5

Note: Standard error in parenthesis; statistical significance is denoted by *** at 1% level, ** at 5% level, and * at 10% level.

.

$$e={\displaystyle \frac{\widehat{{\beta }_k}mean\left(indipendentvariable\right)}{mean\left(dependentvariable\right)}}$$

(3)

Additionally, the standard error is calculated simultaneously by using the following equation:

$$se={\displaystyle \frac{s{e}_{\widehat{{\beta }_k}}mean(indipendent variable)}{mean(dependent variable)}}$$

(4)

In the present analysis, Equation (1) is estimated for 3 different cases:

Case 1

The minimum wage impact in Greece is estimated by modifying Equation (1) as follows:

$$E_t=\alpha +{\beta }_{2}M{W}_t+{\beta }_{3}M{W}_{t-1}+{\beta }_4{{\rm X}}_t+{\beta }_{7}YEA{R}_t+{\epsilon }_t$$

(5)

Case 2

The minimum wage impact for NUTS1 major socio-economic regionsis estimated by modifying Equation (1) as follows:

$$E_{jt}=\alpha +{\beta }_{2}M{W}_{jt}+{\beta }_{3}M{W}_{jt-1}+{\beta }_4{{\rm X}}_{jt}+{\beta }_{6}REGIO{N4}_j+{\beta }_{7}YEA{R}_t+{\epsilon }_{jt}$$

(6)

and

Case 3

The minimum wage impact for NUTS2-Regions is estimated by modifying Equation (1) as follows:

$$E_{it}=\alpha +{\beta }_{2}M{W}_{it}+{\beta }_{3}M{W}_{it-1}+{\beta }_4{{\rm X}}_{it}+{\beta }_{5}REGIO{N}_i+{\beta }_{7}YEA{R}_t+{\epsilon }_{it}$$

(7)

Furthermore, the present paper aims to investigate the minimum wage impact on employment by using an additional methodology. More specifically, the present paper applies the newly developed difference-in-differences (DiD) approach, which, as Caliendo et al. (2025) noted, “Difference-in-Differences estimators are robust to heterogeneous treatment effects in a setting with a staggered treatment adoption”, in order to further investigate the impact of the minimum wage on employment in each region (Ashenfelter, 1978; Card & Krueger, 1994). DiD methodology allows the assessment of the impact of minimum wage changes on the employment over time and across regions.

DiD framework builds on the standard setup described in the literature (e.g., Card & Krueger, 1994; Angrist & Pischke, 2009), in which identification relies on comparing pre- and post-treatment outcomes between treated and control units under the parallel trends assumption. In canonical applications, treatment effects are identified by deviations from a common underlying trend between treated and untreated groups, while fixed effects account for time-invariant differences across units. In such settings, the counterfactual outcome for treated units is inferred from the evolution of the control group in the absence of treatment.

In the present study, however, the institutional setting differs, as the minimum wage is implemented nationally, so that all units are exposed to the policy. Consequently, there is no natural untreated control group to serve as a counterfactual. Identification is therefore achieved through variation in treatment intensity across regions and over time, rather than through a conventional binary treatment-control comparison. This continuous-intensity DiD framework can be viewed as an extension of the standard DiD design, in which differential exposure to the policy substitutes the control group in identifying causal effects.

The baseline DiD is given by the following equation:

$$E_{it}={\beta }_0+{\beta }_{1}pos{t}_{it}+{\beta }_{2}M{W}_{it}+{\beta }_3\left(pos{t}_{it}\times M{W}_{it}\right)+\gamma GD{P}_{it}+{{\alpha }_i+\epsilon }_{it}$$

(8)

where $E_{it}$ is given by Equation (2), ${\alpha }_i$ denotes the fixed effect for region i, capturing time-invariant characteristics of region $i\left(i=1,\dots ,13\right).\mathrm{T}\mathrm{h}\mathrm{e}\mathrm{v}\mathrm{a}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}pos{t}_{it}$ is an indicator equal to 1 in the post-treatment period (after the first minimum wage increase) and 0 otherwise. The variable $pos{t}_{it}$ represents the period after 2019, with 2018 serving as the reference year, as it was the last pre-treatment year before the first national minimum wage increase. All estimated coefficients thus represent deviations relative to this baseline. $M{W}_{it}$ denotes the minimum wage index; $pos{t}_{it}\times M{W}_{it}$ captures the differential effect of the policy after implementation. Additionally, in the DiD approach, the gross domestic product $GD{P}_{it}$ of region $i$ was included as a control variable, and ${\epsilon }_{it}$ denotes the error term.

While this baseline DiD provides a first estimate of the policy’s impact, it has important limitations. All regions are affected to some degree by the national minimum wage increase, so there is no “pure” control group. Pre-treatment trends, such as those observed in 2016, exhibit some deviations, suggesting that the parallel trends assumption may not fully hold in the baseline DiD. Furthermore, while the continuous-intensity DiD framework provides a suitable identification strategy in the absence of a natural control group, it is important to acknowledge potential methodological concerns related to the construction of the treatment variable. In particular, the use of $M{W}_{it}$, defined as the ratio between the minimum wage and the regional average wage, may introduce endogeneity, as the denominator itself can be affected by contemporaneous changes in the minimum wage. This mechanical relationship could bias the estimated effects if wage adjustments are correlated with the outcome variable. To address this issue, the average wage is fixed at a pre-treatment level, thereby ensuring that the denominator is not influenced by contemporaneous policy changes. Following this approach, the analysis constructs an alternative version of the $M{W}_{it}$ index using the average wage in 2018, mitigating concerns related to simultaneity and providing a more exogenous measure of policy exposure. This adjusted variable, denoted $M{W}_{it}^{2018},$ is defined as the ratio of the minimum wage in region $i$ at year $t$ to the average wage of in region $i$ at year 2018.

The baseline DiD given in Equation (8) is modified as follows:

$$E_{it}={\beta }_0+{\beta }_{1}pos{t}_{it}+{\beta }_{2}M{W}_{it}^{2018}+{\beta }_3\left(pos{t}_{it}\times M{W}_{it}^{2018}\right)+\gamma GD{P}_{it}+{{\alpha }_i+\epsilon }_{it}$$

(9)

Additionally, to further address concerns regarding the DiD specification, we implement an event study approximation that incorporates region and year fixed effects. To address multicollinearity concerns associated with the inclusion of year fixed effects in the event study specification, the minimum wage index is mean-centered prior to estimation. In this specification, year fixed effects control for common aggregate shocks, while the interaction terms capture the dynamic effects of the minimum wage across regions and time. Region fixed effects and region-specific linear trends are maintained, ensuring control for time-invariant heterogeneity and differential pre-trends. Specifically, the centered version of the $M{W}_{it}^{2018}$ index is defined as the deviation of $M{W}_{it}^{2018}$ from its sample mean and is denoted as $M{W}_{it}^C$.

The modified model is specified as

$$E_{it}={\beta }_0+\sum _{t\ne 2018}{\theta }_t(D_t\times M{W}_{it}^C)+\gamma {GDP}_{it}+{\alpha }_i+{\lambda }_t+{\epsilon }_{it}$$

(10)

In Equation (10), $D_t$ are the year dummies (excluding 2018 as the baseline), and ${\theta }_t$ captures how the effect of the minimum wage index on $E_{it}$ varies in each year, relative to the base year, thereby providing the dynamic event study affects. ${\alpha }_i$ denotes the region fixed effects, capturing time-invariant characteristics of each region, such as geography or demographic structure; ${\lambda }_t$ denotes the year fixed effects capturing common time shocks; and the variable $\gamma$ measures how employment responds to the region’s GDP. Standard errors are clustered at the region level.

To further evaluate the robustness of the identification strategy, we conduct a placebo event study analysis by assigning a fake policy date in 2017, two years before the actual minimum wage reform. We re-estimate the full event study approximation specification, including region fixed effects and year fixed effects, using placebo event-time indicators centered around 2017.

Since no policy change occurred in that year, all placebo coefficients should be statistically insignificant. If the placebo interactions are small in magnitude and not statistically different from zero, this confirms that the estimated dynamic effects in the main analysis are not driven by pre-existing differential trends or model artifacts, reinforcing the internal validity of the empirical strategy.

The equation estimated is given by modifying Equation (10) as follows:

$$E_{it}={\beta }_0+\sum _{t\ne 2017}{\theta }_t\left(D_t^{placebo}\times M{W}_{it}^C\right)+\gamma GD{P}_{it}+{\alpha }_i+{\lambda }_t+{\epsilon }_{it}$$

(11)

More analytically, a set of year indicator variables $D_t^{placebo}$ is created, where each dummy corresponds to a specific year relative to the placebo treatment year (2017). Additionally, the model in Equation (11) includes region fixed effects, year fixed effects, and standard errors clustered at the region level.

Furthermore, we estimate again our modified DiD model and the eventstudy approximation model, Equations (9) and (10) by using the lagged ${GDP}_{it}$, ${GDP}_{it-1}$ instead of the contemporaneous GDP. This serves as an additional robustness check to ensure that our results are not driven by short-term fluctuations in economic activity.

$$E_{it}={\beta }_0+{\beta }_{1}pos{t}_{it}+{\beta }_{2}M{W}_{it}^{2018}+{\beta }_3\left(pos{t}_{it}\times M{W}_{it}^{2018}\right)+\gamma GD{P}_{it-1}+{{\alpha }_i+\epsilon }_{it}$$

(12)

and

$$E_{it}={\beta }_0+\sum _{t\ne 2018}{\theta }_t(D_t\times M{W}_{it}^C)+\gamma {GDP}_{it-1}+{\alpha }_i+{\lambda }_t+{\epsilon }_{it}$$

(13)

Furthermore, given the relatively small number of regions in the sample, conventional cluster robust standard errors may be less reliable. To improve inference in this context, it would be advisable to consider techniques specifically designed for small-sample settings, such as the wild cluster bootstrap, which can provide more accurate estimates of statistical significance. Specifically, the modified DiD model given by Equation (9) is re-estimated using this technique, which provides more reliable standard errors and statistical significance in small-sample settings.

Finally, to account for the differences in population size and employment levels across regions, we re-estimate both the modified DiD and event study specifications using employment-weighted regressions. These robustness checks ensure that the results are not disproportionately influenced by small regions and that inference remains valid in a small-sample context.

4. Data

In the present paper, the impact of the minimum wage on employment is estimated for the period 2016–2024. More analytically, the analysis is conducted at 3 levels: for the country as a whole, for NUTS1 Major socio-economic regions (North Greece, Central Greece, Attica and Aegean Islands), and for NUTS2 Regions, comprising the 13 distinct Greek regions.

The data are drawn from a combination of administrative sources (for average wage levels and employment) and official statistical datasets (for population, GDP, and unemployment indicators), rather than survey microdata at the individual level. A limitation of the dataset relates to the presence of informal employment, which is not fully captured in official statistics. As a result, employment levels may understate actual labor market activity. However, since the analysis relies on consistently measured official series over time, the potential measurement error is unlikely to systematically bias the estimated relationships, unless informal employment responds differently to minimum wage changes.

The dataset includes six main variables. More specifically, the dataset contains information on the employment–population ratio, the minimum wage index, the unemployment rate, and the GDP. The remaining two variables are dummy variables indicating the region and the year.

The data, as already mentioned, come from multiple sources, more specifically, the data for employment and the average wages are, upon request, from the IME GSEVEE.1 The population data are drawn from the website of the Hellenic Statistical Authority, census 2021, and finally the data for the unemployment rate and the GDP are from published statistics of Eurostat (Eurostat, 2025b, 2025c).

Appendix A Table A1 presents the descriptive statistics for each variable and case. Although the main dataset covers the period 2016–2024, the inclusion of GDP as a control variable in the DiD specifications requires the availability of consistent regional GDP data. Since GDP data for 2024 were not available at the time of analysis, DiD regressions that include GDP are estimated over the subsample 2016–2023. This ensures consistency in the inclusion of control variables across observations and avoids potential biases arising from missing covariates. All other variables remain defined over the full sample.

Furthermore, Figure 3 illustrates the employment–population ratio for each region. It can be noticed that the region of Attica has the biggest ratio and the regions of South Aegean, Ionian Islands, and Crete after the pandemic crisis, and specifically in 2022, have a substantial increase compared to their value in 2020. These regions include Greece’s most popular islands, which are very popular touristic destinations; thus, we could consider that the employment increase in 2022 may be due to a rise in tourist arrivals. More specifically, the tourist arrivals in 2022 compared to 2021 increased by 89.3% (INSETE, 2025).

Finally, Figure 4 presents the time series of the unemployment rate for NUTS1 major socio-economic regions compared to the total unemployment rate in Greece. As it can be noticed, the unemployment rate of Greece was decreasing every year since 2016, and so do the unemployment rates of the Major socio-economic regions, except for the Aegean Islands, in which, during the pandemic crisis, the unemployment rate increased.

5. Empirical Results

In

Table 2. Elasticities.

MWt		MWt−1	Unemployment Rate
Case 1
Greece	−0.299 (0.185)	0.0596 (0.171)	−0.501 *** (0.0482)
Case 2
North Greece	0.0952 (0.165)	0.175 (0.161)	−0.508 *** (0.122)
Central Greece	−0.0653 (0.203)	0.206 (0.197)	−0.530 *** (0.0751)
Attica	−0.636 *** (0.166)	−0.565 *** (0.190)	−0.445 *** (0.0336)
Aegean Islands	−0.224 (0.338)	−0.129 (0.237)	−0.491 ** (0.192)
Case 3
Eastern Macedonia and Thrace	0.273 ** (0.127)	0.240 * (0.131)	−0.323 ** (0.149)
Central Macedonia	−0.0641 (0.155)	0.0708 (0.158)	−0.661 *** (0.0622)
Western Macedonia	0.466 (0.479)	1.09 (0.929)	0.449 (0.576)
Epirus	0.0448 (0.710)	0.869 * (0.523)	−0.268 (0.172)
Thessaly	0.343 (0.608)	1.17 (0.873)	−0.244 (0.219)
Ionian Islands	0.704 (2.46)	1.89 (2.03)	−0.208 (0.437)
Western Greece	0.0650 (0.0621)	0.107 (0.0656)	−0.392 *** (0.0434)
Mainland Greece	0.682 *** (0.199)	1.10 *** (0.248)	−0.253 *** (0.0587)
Attica	−0.636 *** (0.166)	−0.565 *** (0.190)	−0.445 *** (0.0336)
Peloponnese	0.287 (0.470)	1.74 *** (0.443)	0.122 (0.0959)
North Aegean	−1.07 * (0.554)	0.415 (0.474)	−0.302 *** (0.0868)
South Aegean	−0.399 (0.541)	0.377 (0.409)	−0.335 * (0.177)
Crete	−0.0957 (0.205)	−0.151 (0.188)	−0.354 * (0.190)

Note: Standard error in parenthesis; statistical significance is denoted by *** at 1% level, ** at 5% level, and * at 10% level. Source: Calculated by the estimates given in Appendix A Table A2.

are illustrated the elasticities of the impact of the minimum wage and the unemployment rate to employment for each case. The original estimates are given in Appendix A Table A2. The elasticities and the standard errors, presented in Table 2, are calculated by using Equations (3) and (4).

To begin with, taking into consideration the estimates of Appendix A Table A2 and consequently the elasticities in Table 2, it can be concluded that for Greece, the minimum wage does not have a statistical impact on employment.

Furthermore, in order to investigate if there is additional evidence regarding the impact of the minimum wage on employment, Equation (6) was estimated for NUTS1 major socio-economic regions. From the elasticities in Table 2, we notice that all socio-economic regions have statistically significant negative impact of unemployment on employment–population ratio, as expected, and only the region of Attica shows the greatest sensitivity to changes in the minimum wage, possibly due to different urban characteristics or behavioral patterns. For the rest of the socio-economic regions, the positive and statistically insignificant values likely indicate weak or negligible relationships of the minimum wage to employment.

The first evidence indicates that heterogeneity occurs within the regions; thus, the analysis went further and examined the impact for NUTS2 regions. Taking into account the elasticities obtained by the estimates of Equation (7) in Table 2, it can be noticed that statistically significant impact of the minimum wage on the employment–population ratio occurs only for the regions of Eastern Macedonia and Thrace, Mainland Greece, Attica, and North Aegean. Furthermore, it can be noticed that only in the regions of Attica and North Aegean is the impact negative; as expected, the other regions have a positive impact, a finding that strongly supports heterogeneity within regions. Additionally negative but statistically insignificant impact was identified for the regions of Central Macedonia, South Aegean, and Crete. For the rest of the regions, the impact is positive but statistically insignificant.

Additionally, taking into consideration the elasticities of the minimum wage of the previous year, it can be noticed that statistically negative impact exists only for the region of Attica, and statistically positive impact is indicated for the regions of Eastern Macedonia and Thrace, Epirus, Mainland Greece, and Peloponnese; the rest of the regions have no statistical significance. Additionally, the elasticities of each region regarding unemployment for most of the regions have, as expected, negative statistically significant impact on employment.

Overall, the results lead to the conclusion that only in a very few regions does the minimum wage impact employment. In general, there is no impact of the minimum wage on employment, a finding that is consistent with the literature where many studies have found that there is small or even no impact of the minimum wage on employment. Additionally, it can be concluded that heterogeneity exists within the regions, which may depend on structural factors of the labor market and the characteristics of the employees. As Petrakos and Saratsis (2000) supported, regional inequalities exist and follow a pro-cyclical trajectory—widening during economic booms and narrowing in times of recession. This dynamic behavior highlights the differentiated capacity of regions to respond to macroeconomic conditions. Moreover, regional growth is shaped by a variety of local structural characteristics, including the composition of industrial activity, the level of human capital, the degree of integration into broader economic frameworks such as the EU, and the availability of natural or cultural resources that support tourism. These factors underscore the heterogeneous nature of regional economies and further justify the argument that significant variations exist across regions.

To continue with, in

Table 3. Basic DiD, Modified DiD, and Event Study estimates.

Basic DiD Fixed Effects: Regions 13 Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Error	Statistic	p-Value
postit	0.01796943	0.01333826	1.347209	0.2028020
MWit	−0.00954312	0.01434574	−0.665223	0.5184893
${\mathit{post}}_{\mathit{it}}\times$ MWit	0.01142272	0.01795044	0.636348	0.5364992
GDPit	0.00000263 ***	0.00000081	3.249098	0.0069681
RMSE	0.011912
Adj. R2	0.932086
Within R2	0.632148
Modified DiD Fixed Effects: Regions 13 Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Errors	Statistic	p-Value
postit	0.02483266 **	0.010849017	2.28893	0.04101069
MW2018it	0.13894670 ***	0.021892507	6.34677	0.00003682
${\mathit{post}}_{\mathit{it}}\times$ MW2018it	−0.01804514	0.014143716	−1.27584	0.22615476
GDPit	0.00000175 ***	0.000000407	4.30539	0.00102213
RMSE	0.010779
Adj. R2	0.944393
Within R2	0.698805
Event Study Approximation Estimates Fixed Effects: Regions 13, Year 8 Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Errors	Statistic	p-Value
D2016 ×MWCit	0.01498446	0.015964316	0.938622	0.3664338
D2017 × MWCit	−0.01079139	0.013064293	−0.826022	0.4249104
D2019 × MWCit	0.00534681	0.006298676	0.848879	0.4125627
D2020 × MWCit	0.01295151	0.028950742	0.447364	0.6625807
D2021 × MWCit	0.00610155	0.019684337	0.309970	0.7619011
D2022 × MWCit	−0.01835913	0.039119800	−0.469305	0.6472637
D2023 × MWCit	0.00123612	0.013404474	0.092217	0.9280471
GDPit	0.00000121 ***	0.000000360	3.371532	0.0055544
RMSE	0.007431
Adj. R2	0.969743
Within R2	0.159949
		REGION Fixed Effects	YEAR Fixed Effect
Eastern Macedonia and Thrace		0.09635769	2016	0.000000000
Central Macedonia		0.12879275	2017	0.008367295
Western Macedonia		0.09604919	2018	0.014297013
Epirus		0.10361898	2019	0.019470507
Thessaly		0.09475669	2020	0.023849141
Ionian Islands		0.12136633	2021	0.039422486
Western Greece		0.08229010	2022	0.049039215
Mainland Greece		0.12097932	2023	0.044235723
Attica		0.14673336
Peloponnese		0.09855898
North Aegean		0.08102538
South Aegean		0.17652986
Crete		0.14976966

Note: Statistical significance is denoted by *** at 1% level and ** at 5% level.

are presented the baseline DiD approach estimates given by Equation (8) and the modified DiD estimates given by Equation (9). For the basic DiD, the GDP is the only factor that appears to significantly affect the employment–population ratio. On the other hand, DiD estimates using pre-treatment exposure (MW fixed at 2018 levels) indicate no statistically significant effect of the treatment on employment, but the coefficient of the $M{W}_{it}^{2018}$ term is positive and highly significant (0.13894670, p < 0.001), reflecting substantial baseline differences across regions rather than a causal effect. In addition, the post-treatment indicator is positive and statistically significant (0.02483266, p < 0.05), pointing to an overall increase in employment over time. The control variable GDP remains positive and significant across specifications. Overall, the results do not provide evidence of a causal impact of the treatment intensity on employment.

Compared to the baseline specification, the modified model (with pre-treatment $M{W}_{it}^{2018})$ shows a change in the sign of the interaction term (post_it$\times M{W}_{it}^{2018})$, from positive to negative, while the effect remains statistically insignificant in both models. This difference suggests that the initial specification may have been influenced by endogeneity or by changes in the treatment over time. Using pre-treatment exposure provides a potentially more credible identification strategy. However, the main conclusion remains the same, as there is no statistically significant evidence of a treatment effect.

In Table 3 are given the results of the Event study approximation, determined by Equation (10). All year-specific interaction coefficients are small and statistically insignificant, suggesting that there is no strong evidence of either anticipatory effects before the policy or delayed effects after its implementation. Furthermore, GDP is positive and statistically significant, indicating that higher regional economic activity is associated with higher values of the dependent variable. This highlights the importance of controlling for regional economic conditions when assessing the impact of the minimum wage policy.

Additionally, in Figure 5 are illustrated the event study coefficient estimates showing the impact of minimum wage intensity $M{W}_{it}^C$ on employment to population ratio across 13 Greek regions. The coefficients represent the difference relative to the baseline year 2018. Error bars correspond to 95% confidence intervals, and the dashed line at zero represents no effect. As illustrated, the results suggest that the pre-treatment trends are parallel and that minimum wage increase had no statistically significant impact on employment during the period 2019–2023.

Taking into account the fixed effects for regions, regions such as South Aegean (0.176) and Crete (0.150) exhibit the highest baseline levels, indicating systematically higher values of employment–population, whereas Western Greece (0.082) and North Aegean (0.081) show the lowest. These estimates reflect persistent regional heterogeneity and highlight the importance of accounting for region-specific factors when evaluating the effects of minimum wage policies. On the other hand, the year fixed effects indicate a general upward trend in the dependent variable over time, independent of regional differences or treatment status. Values rise steadily from 0 in 2016 to a peak in 2022, with a slight decline in 2023. This highlights the importance of controlling for common annual shocks when estimating the impact of the minimum wage policy.

Additionally, compared to the modified DiD, notable differences in model fit can be observed. The event study yields an RMSE of 0.0074, an adjusted R² of 0.97, and a Within R² of 0.16, indicating that while the fixed effects capture most of the total variation, the included regressors explain only a small portion of the within-region variation.

In contrast, the modified DiD model has a higher RMSE of 0.0108 and a slightly lower adjusted R² of 0.94 but a substantially higher Within R² of 0.698, suggesting that this specification better explains the variation within regions over time, despite slightly lower overall fit. This comparison highlights the trade-off between capturing total variation through fixed effects and explaining within-region dynamics with interaction terms and covariates. Overall, the results suggest that while economic conditions and regional trends matter, there is no statistically significant evidence of an effect of the minimum wage increase on the outcome during the observed period.

To continue, the results of the additional robustness checks that took place in order to verify the validity of the model, such as a placebo test, given by Equation (11), and additional robustness checks using lagged GDP, given by Equations (12) and (13), are illustrated in

Table 4. Placebo event study and lagged GDP.

Placebo Event Study—Fake Treatment Year: 2017 Fixed Effects: Regions 13, YEAR 8 Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Errors	Statistic	p-Value
Dplacebo2016  $\times$  MWCit	0.02577585	0.02082320	1.237843	0.2394466
Dplacebo2018  $\times$  MWCit	0.01079139	0.01306429	0.826022	0.4249104
Dplacebo2019  $\times$  MWCit	0.01507700	0.01670478	0.902556	0.3845205
Dplacebo2020  $\times$  MWCit	0.02268169	0.04036411	0.561927	0.5845089
Dplacebo2021  $\times$  MWCit	0.01583173	0.02966755	0.533638	0.6033366
Dplacebo2022  $\times$  MWCit	−0.00948869	0.03171740	−0.299164	0.7699331
Dplacebo2023  $\times$  MWCit	0.00934461	0.01758843	0.531293	0.6049109
GDPit	0.00000121 ***	0.00000036	3.371532	0.0055544
RMSE	0.007431
Adj. R2	0.969743
Within R2	0.159949
Modified DiD Model with Lagged GDP Estimates Fixed Effects: Regions 13 Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Errors	Statistic	p-Value
postit	0.03048834 **	0.013272591	2.29709	0.04041
MW2018it	0.17413619 ***	0.027195856	6.40304	0.000033872
${\mathit{post}}_{\mathit{it}}\times$  MW2018it	−0.02885947	0.017352242	−1.66316	0.12216
GDPit−1	0.00000113 **	0.000000501	2.24906	0.044072
RMSE	0.011418
Adj. R2	0.937601
Within R2	0.662018
Event Study Approximation with Lagged GDP Estimates Fixed Effects: Regions 13 Year 8 Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Errors	Statistic	p-Value
D2016 ×  MWCit	0.01538192	0.016137153	0.953199	0.35929597
D2017 ×  MWCit	−0.01218636	0.012400634	−0.982721	0.34514394
D2019 ×  MWCit	0.00539565	0.006534367	0.825734	0.42506771
D2020 ×  MWCit	0.02278177	0.025923631	0.878803	0.39676398
D2021 ×  MWCit	−0.00188576	0.024121279	−0.078178	0.93897458
D2022 × MWCit	−0.02243033	0.038738295	−0.579022	0.57328287
D2023 ×  MWCit	0.00291734	0.012149990	0.240111	0.81429704
GDPit−1	0.00000213 ***	0.000000378	5.638841	0.00010919
RMSE	0.007335
Adj. R2	0.970518
Within R2	0.181471
		REGION Fixed Effects		YEAR Fixed Effects
Eastern Macedonia and Thrace		0.09010338	2016	0.000000000
Central Macedonia		0.10676344	2017	0.008624739
Western Macedonia		0.09198299	2018	0.014505924
Epirus		0.10012531	2019	0.019576329
Thessaly		0.08641082	2020	0.021775662
Ionian Islands		0.11849437	2021	0.041436777
Western Greece		0.07497460	2022	0.050805804
Mainland Greece		0.11346735	2023	0.043409539
Attica		0.07061587
Peloponnese		0.09105244
North Aegean		0.07866745
South Aegean		0.17106688
Crete		0.14171161

Note: Statistical significance is denoted by *** at 1% level and ** at 5% level.

.

To begin with, the placebo event study, assigning 2017 as the hypothetical treatment year, includes region and year fixed effects and interaction terms for each year except 2017. All placebo coefficients are small and statistically insignificant, indicating no evidence of spurious pre-treatment effects. Meanwhile, the GDP remains highly significant, confirming that the model captures important economic variation. Overall, these results strengthen the internal validity of the main event study.

Additionally, the estimations of the DiD model and the event study approximation with substituting GDP by the lagged GDP are also illustrated in Table 4. More specifically, both the DiD-modified model with lagged GDP (Equation (12)) and the event study approximation with lagged GDP (Equation (13)) confirm the robustness of the main results. For the DiD specification, the interaction term between $pos{t}_{it}\times M{W}_{it}^{2018}$ is small and not statistically significant (−0.029, p = 0.122), indicating no strong differential effect of the minimum wage before the treatment period.

Similarly, in the event study approximation, the coefficients for the pre-treatment years (2016 and 2017) are close to zero and not significant, while the post-treatment years show no large unexpected deviations. The lagged GDP variable is significant in both specifications, as expected, and the region-specific trend in the event study is positive and significant, capturing underlying growth.

Furthermore, the estimated region fixed effects capture persistent differences in the outcome variable across regions. For instance, the South Aegean (0.171) and Crete (0.142) exhibit higher baseline levels, whereas regions such as the North Aegean (0.079) and Attica (0.071) show lower baseline outcomes. These fixed effects account for unobserved, time-invariant regional characteristics, ensuring that the estimated treatment effects are not confounded by systematic regional differences. Additionally, the year fixed effects are increasing over time, independent of regional differences or treatment status. Values rise steadily from 0 in 2016 to a peak in 2022, with a slight decline in 2023, exactly the same as in the case with no lagged GDP.

Additionally, in

Table 5. Wild cluster bootstrap.

Wild Cluster Bootstrap—p-Values and 95% Confidence Intervals Fixed Effects: Regions 13 Standard Errors: Clustered (REGIONS) 5000 REPLICATIONS n = 104, Period 2016–2023
	Estimate	p-Value_Boot	95% CI_Lower	95% CI_Upper
postit	0.02483266	0.7678	0.02071	0.03814
MW2018it	0.13894670 ***	0.0000	0.00482	0.02671
${\mathit{post}}_{\mathit{it}}\times$  MW2018it	−0.01804514	0.9520	−0.03967	−0.01731
GDPit	0.00000175195 ***	0.0000	0.00000056	0.00000110

Note: Statistical significance is denoted by *** at 1% level.

are illustrated the results of the wild cluster bootstrap approach that we applied in order to account for the small number of clusters (13 regions). After 5000 replications, we found that the coefficients of $M{W}_{it}^{2018}$ and GDP remain positive and statistically significant, indicating a robust positive effect on employment. The estimated effect of post, capturing the policy change after 2019, remains positive but is not statistically significant. Importantly, the interaction term $pos{t}_{it}\times M{W}_{it}^{2018}$, which measures whether the effect of $M{W}_{it}^{2018}$ changes after the policy, is also not statistically significant in the bootstrap analysis (p = 0.952), confirming that the policy did not significantly modify employment. These results are largely consistent with the estimates of Equation (9); however, the bootstrap provides more reliable inference given the small number of clusters, correcting for potential underestimation of p-values in the standard approach. Overall, the wild cluster bootstrap confirmed that there is no statistically significant effect of the treatment on employment.

In

Table 6. Modified DiD and event study estimates weighted by population.

Modified DiD (Weighted by Population) Fixed Effects: Regions 13 Weights: POPULATION Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Errors	Statistic	p-Value
postit	0.04445525 **	0.017222018	2.58130	0.02404
MW2018it	0.15786129 ***	0.024110239	6.54748	0.000027395
postit × MW2018it	−0.04004942	0.023644356	−1.69383	0.11607
GDPit	0.00000130 ***	0.000000169	7.69664	0.0000055728
RMSE	9.1191
Adj. R2	0.97505
Within R2	0.815588
Event Study Approximation Estimates (Weighted by Population) Fixed Effects: Regions 13 Year 8 Weights: POPULATION Standard Errors: Clustered (REGIONS) n = 104, Period 2016–2023
	Estimate	Standard Errors	Statistic	p-Value
D2016 × MWCit	0.03195301	0.022779069	1.402736	0.18603477
D2017 × MWCit	0.01064210	0.015757644	0.675361	0.51225044
D2019 × MWCit	−0.00579174	0.009065270	−0.638894	0.53489706
D2020 × MWCit	−0.02646973	0.029433503	−0.899306	0.38618018
D2021 × MWCit	−0.00978071	0.018503962	−0.528574	0.60673900
D2022 × MWCit	−0.00395051	0.021263992	−0.185784	0.85571757
D2023 × MWCit	−0.00133056	0.015721898	−0.084631	0.93395049
GDPit	0.00000105 ***	0.000000213	4.908972	0.00036044
RMSE	5.43738
Adj. R2	0.989846
Within R2	0.350308
		REGION Fixed Effects		YEAR Fixed Effects
Eastern Macedonia and Thrace		0.09444199	2016	0.000000000
Central Macedonia		0.13049543	2017	0.008765063
Western Macedonia		0.09433082	2018	0.015346831
Epirus		0.10194002	2019	0.021936986
Thessaly		0.09411891	2020	0.030467362
Ionian Islands		0.11956745	2021	0.045472357
Western Greece		0.08138593	2022	0.048957149
Mainland Greece		0.11970919	2023	0.050193921
Attica		0.15889569
Peloponnese		0.09763096
North Aegean		0.07930116
South Aegean		0.17466736
Crete		0.14863971

Note: Statistical significance is denoted by *** at 1% level and ** at 5% level.

are illustrated the estimates of the modified DiD and the event study estimates weighted by population. Taking into consideration the estimates illustrated below, it can be noticed that the population-weighted modified DiD estimates show that the interaction term $pos{t}_{it}\times M{W}_{it}^{2018}$ is negative but statistically insignificant, indicating no robust evidence of a causal policy effect. While the post-treatment indicator is positive and significant, suggesting a general increase in the outcome variable over time, this effect is common across all regions rather than driven by the intervention. Similarly, $M{W}_{it}^{2018}$ and GDPare positive and highly significant. Overall, even when accounting for population size, the results do not support a statistically significant effect of the policy.

Additionally, the weighted event study estimates confirm the absence of a statistically significant treatment effect. All interaction terms between the treatment variable and year dummies are insignificant, both before and after the intervention. Post-treatment estimates remain small and statistically insignificant, suggesting that the policy did not produce any measurable impact over time. Importantly, these results hold when weighting observations by population, implying that the findings are robust and not driven by the relative size of regions. Additionally, GDP enters positively and significantly across specifications, highlighting the role of macroeconomic conditions in shaping the outcome variable.

Finally, a comparison between weighted (Table 6) and unweighted (Table 3) specifications reveals a consistent absence of a statistically significant treatment effect across all models. In both cases, the interaction term $pos{t}_{it}\times M{W}_{it}^{2018}$ remains negative and insignificant, although its magnitude is larger in the population-weighted estimates, suggesting a slightly stronger but still non-robust, negative effect when greater emphasis is placed on more populous regions. The post-treatment indicator is positive and statistically significant in both specifications, indicating a common upward trend over time, while the treatment group coefficient remains positive and highly significant, reflecting persistent structural differences between treated and control regions. Similarly, the event study results show no significant coefficients in either specification, supporting the parallel trends assumption and confirming the lack of dynamic effects (although we can notice some differences at their sign and magnitude). Overall, the similarity of findings across weighted and unweighted models strengthens the conclusion that the policy did not have a measurable impact, and that results are not driven by the relative size of regions.

The results above indicate that the estimated effects of the minimum wage on employment are not driven by pre-existing trends or omitted variable bias, supporting the internal validity of the empirical strategy. Furthermore, our robustness checks, including the placebo event study and models incorporating lagged GDP, confirm the reliability of the main findings. Additionally DiD modifications and event study approximations, both weighted and unweighted, show that pre-treatment coefficients are small and statistically insignificant, suggesting that no anticipatory or spurious effects influence the results. Interaction terms prior to the intervention are consistently small and statistically insignificant, suggesting that differential pre-existing trends do not drive the results. Moreover, the post-treatment estimates remain stable across specifications. Taken together, these findings reinforce the internal validity of our empirical strategy and provide consistent evidence that the estimated effects reflect the true impact of the treatment rather than artifacts of the data or model design.

Summing up, it should be mentioned that while our analysis provides consistent evidence of no detectable impact of the minimum wage on employment, several limitations should be acknowledged. Firstly, the present study lacks a fully untreated control group, as all regions are exposed to the policy, which constrains identification and may bias estimates toward zero. Secondly, the relatively small number of regions (13) limits statistical power, meaning that modest but meaningful effects could remain undetected. Therefore, null findings should be interpreted cautiously, as they do not rule out small or heterogeneous policy effects. Future research with broader datasets or experimental/quasi-experimental designs could help address these identification and power limitations.

6. Conclusions

The present paper investigates the impact of the minimum wage on employment in Greece for the period of 2016–2024, a period marked by an alternative policy scenario regarding the minimum wage. During this time, Greece was dealing with the aftershocks of a severe economic crisis, the global pandemic that caused widespread economic issues, and a war in Ukraine, which put additional pressure on inflation.

The analysis is extended to NUTS 1 and NUTS 2 regions. Overall, for Greece as a whole, there is no evidence of a statistically significant impact of the minimum wage on employment. At the regional level, the results similarly indicate limited or statistically insignificant effects across most specifications.

Additional analyses using DiD approach, event study specifications, placebo event study, wild cluster bootstrap, and weighted models indicate that the effects are not driven by spurious correlations, pre-existing trends, or idiosyncratic yearly shocks.

The absence of statistically significant effects of the minimum wage index across time and regions, along with the consistent significance of GDP, supports the robustness and internal validity of the main findings.

While our results do not detect statistically significant effects of minimum wage changes on employment over the study period, the Greek labor market appears to exhibit limited responsiveness. This observation made under contrasting policy regimes (reductions versus increases) may reflect underlying structural features, such as persistently high unemployment, a substantial informal sector, and the concentration of industries in specific regions. These factors could influence the labor market’s response in ways that differ from prevailing international patterns. However, it is important to emphasize that these interpretations are speculative and not directly supported by the statistical results of this study.

Furthermore, our findings align with some previous studies that have reported varying results on the relationship between minimum wage and employment. For example, Giotis and Mylonas (2022, p. 1880) stated, “the literature does not provide a clear and definite sign of the relationship, but the trend seems to be driven towards a negative direction of the impact for the more sensitive groups”. Additionally, they mentioned that although the theoretical approaches and the results of the empirical studies have results that vary, the meta-analysis indicates that there is a lack of significant correlation between employment and the minimum wage.

More analytically, we found no statistically significant effect of the minimum wage on the outcome variable, the employment–population ratio, neither in the aggregate or across the majority of the individual regions. On the other hand, the GDP consistently shows a positive and significant effect, highlighting the influence of macroeconomic conditions.

Robustness checks—including a placebo event study, lagged variables, and additional methodologies such as wild cluster bootstraps and weighted by population DiD and event study—support the credibility of the findings and suggest that the lack of the minimum wage impact is not due to model specification or timing bias.

Although the nationwide results do not detect statistically significant effects of the minimum wage on employment, some variation is observed across regions. These regional differences suggest that the response of the Greek labor market may not be entirely uniform. Therefore, while the findings provide descriptive insights into regional heterogeneity, they do not allow for definitive conclusions regarding the validity of neoclassical labor market models in Greece.

Negative effects are observed only in a few regions (e.g., Attica, North Aegean), suggesting that a uniform policy may not be suitable for all regions. Consequently, these findings highlight the importance for policymakers of considering regional differences in labor market structures and dynamics when designing minimum wage policies.

Furthermore, as GDP appears to play a more influential role in employment than the minimum wage, the present study suggests that investment and growth-oriented strategies may play a particularly important role in shaping labor market outcomes. Finally, our findings suggest that, over the study period, the minimum wage has limited detectable effects on employment. Its primary role should be viewed as a tool to reduce income inequality and support earnings for low-paid workers, rather than as a direct lever to increase employment.

In conclusion, the present study provides a foundation for further investigation into the impact of the minimum wage on employment among specific population subgroups. Specifically, the forthcoming research will examine how the effects of the minimum wage vary by gender, age, employment type, and other relevant characteristics, both nationwide and across regions.