How Do Firms Respond To Minimum Wage Increases In Macedonia?

Purpose We examine the effects of the 2017 minimum wage reform in North Macedonia on firm-level outcomes, with a focus on employment, productivity, and profitability. Contributing to the broader debate on the consequences of minimum wage increases (see, for example, Petreski et al., 2019; Jovanovikj et al., 2021; Jovanovikj and Naumovski, 2021; Trenovski et al., 2021; Petreski and Pehkonen, 2024), we apply the established concept of the minimum wage bite—defined as the share of workers in a firm earning the minimum wage prior to the reform—as a measure of treatment intensity. Our main contribution lies in applying this framework to the full population of registered firms using administrative panel data, allowing for a detailed and representative analysis of policy effects across the entire private sector. Design/methodology/approach We use firm-level panel data covering more than 46,000 registered firms across all sectors, observed in two waves (2016 pre-reform and 2018 post-reform), thus providing a comprehensive two-period firm-level panel. We apply a difference-in-differences (DiD) methodology (Draca et al., 2011; Skedinger, 2014; Yagan, 2015; Saez et al., 2017; Mayneris et al., 2018; Poncet and Zhang, 2018; Gerogiadis et al., 2020), and operationalize treatment intensity through the minimum wage bite, defined as the share of minimum-wage workers in each firm prior to the reform. Firms with a higher bite serve as the treatment group, while firms with little or no bite act as the control group. Mathematically, the bite_i of firm i is: bite_i=(# of employees in 2016 with minimum wage)/(# of employees in 2016). Using this variable, we construct the following model: y_it=b_0+b_1 bite_it+b_2 D_t bite_it+b_3 x_it+μ_t+η_i+e_it where y_it represents the outcome of interest for the firm i in year t, such as total employment, average wages (excluding minimum wage earners), productivity, profitability, or other expenses. D_t is a post-reform indicator (equal to 1 for 2018), x_it is a vector of control variables (the log of assets, revenues, expenditures, and number of employees, if they are not the dependent variable), μ_t denotes year fixed effects, η_i captures firm fixed effects, and e_it is the error term. The coefficient of primary interest is β_2, which captures the differential change in outcomes for firms with higher exposure to the minimum wage reform (i.e., those with a larger bite). This interaction term provides a strict measure of the policy’s effect, isolating the impact of the reform by comparing firms more and less affected by the increase in the minimum wage, while controlling for time-invariant firm characteristics and broader economic trends. Findings We find that the 2017 minimum wage reform did not result in employment losses among firms more exposed to the policy. The estimated coefficient on the number of employees is negative (-0.421), but statistically insignificant at the 0.01 level, suggesting that firms with a higher minimum wage bite did not reduce their workforce in response to the wage increase. Moreover, we observe statistically significant improvements in labor productivity among more affected firms. The coefficient on productivity is positive (0.0136), although not statistically significant at conventional levels, the direction of the effect is consistent with the hypothesis that the reform may have incentivized firms to reorganize production, enhance efficiency, or motivate workers more effectively. Importantly, no adverse effects are detected on profitability. The estimated coefficient for profitability is negative but very small (-0.003) and statistically insignificant, indicating that firms were largely able to absorb the cost of the wage increase without significant financial strain. We also find a statistically significant reduction in non-wage expenses among affected firms, with an estimated effect of -312.7 (p