Which Income Group Benefits from Commuting Subsidies? Lessons from Germany

Most countries in the Organization for Economic Cooperation and Development (OECD) offer one form or another of tax breaks for commuting expenses. In countries like Germany and Denmark, the cost of commuting to and from one’s place of work is exempted from taxable income. In the United States, parking expenses are exempt from taxes. These tax breaks are usually provided with the dual goals of ensuring equity so the cost of commuting isn’t a barrier to seeking employment, and labor market efficiency so that workers are incentivized to seek higher-paying jobs. However, given the substantial costs incurred by governments, it is necessary for researchers and policymakers to evaluate how well these tax breaks deliver on their stated goals.

In their new paper, Heuermann et al. evaluate the effect of commuting subsidies in Germany by examining the case of a partial reduction in such subsidies. Germany had allowed employees to deduct a lump-sum of 920 euros from their taxable income towards “income expenses,” or expenses incurred as part of their employment. Before 2007, if annual “income expenses” exceeded 920 euros, workers could alternatively claim 0.3 euros for each kilometer of their commute to and from work. On January 1, 2007, this policy was modified to subsidize only those employees who traveled more than 20 kilometers during their daily commute. Under the new policy, Germany provided a subsidy of 0.3 euros per kilometer commuted only for the 21^st kilometer onward.

The authors study the impact of this policy change on wages and tax savings among employees in Germany in order to answer two main questions. First, how much would workers have to be compensated by their company in the absence of the commuting subsidy? Second, do commuting subsidies benefit high-income workers more than they benefit low-income workers?

To answer the first question, the authors use an instrumental variable approach to study the relationship between wages and reductions in tax savings due to the change in the subsidy policy. This approach allows the authors to infer causal relationships—i.e., how changes in tax savings impact the wages earned by workers. The authors also control for several factors that might impact the relationship between tax savings lost and wages, like the coverage of collective wage agreements and compensation by way of non-wage benefits. The results show that where individual employees have the flexibility to negotiate their own wages, employers compensate employees by 38 cents for every euro of tax saving lost.

To address their second question—whether certain income groups benefit more than others from commuting subsidies—the authors examine the differences in savings among different income brackets before and after the partial reduction in commuting subsidies. They find that without the subsidy, high-income workers lose more in tax savings than low-income workers, implying that commuting subsidies are regressive when they lack a lower bound on distance. The authors additionally find that tax savings are higher for people working in suburban and urban labor markets.

These findings suggest that applying a lower bound on the exemptible commuting distance helps to mitigate disparities in tax savings among workers of different income brackets, while also reducing losses due to foregone tax revenue. In addition, the potentially negative impact on labor market efficiency is partially ameliorated as some firms step in to compensate the tax savings lost by workers. These results likely vary from country to country, depending on the ways in which labor movements occur and wage agreements are negotiated. However, the findings and methodologies of this research provide important guidance on the factors to be considered when implementing commuting subsidies in developed nations.

Article source: Heuermann, Daniel, Franziska Assmann, Philipp vom Berge, and Florian Freund. “The distributional effects of commuting subsidies – evidence from geo-referenced data and a large scale policy reform.” Regional Science and Urban Economics 67 (2017): 11-24.

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