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Adaptive Governance, Status Quo Bias, and Political Competition: Why the Sharing Economy Is Welcome in Some Cities but Not in Others


Abstract

Examining the variation in policies across 47 U.S. cities, we show that political competition has a significant effect on governments’ regulatory responses to the sharing economy. Specifically, a greater level of political competition is associated with a more favorable regulatory response for sharing companies such as Airbnb. This finding is explained using the concepts of governmental status quo bias and adaptive governance. When considering the interests of the public and market incumbents, governments generally favor the latter (i.e., the status quo). This is because single-industry economic interests can more easily be organized into interest groups that can influence policymaking through lobbying. However, a greater level of political competition was found to reduce the power of entrenched market incumbents. This finding provides broad support to the hypothesis that the promotion of political accountability through greater political competition is conducive to adaptive governance.



5. Results

We first present a number of scatterplots depicting the associations among our key variables.In Figure 2, we show the association between political competition and the measured level of the favorable policy environment for short-term rentals in 47 U.S. cities. As shown in Figure2, we found a strong positive association between the treatment and the dependent variable; governments tend to implement more favorable policies for short-term rentals, and thus for the operation of Airbnb, in cities with high levels of political competition. This evidence supports the hypothesis that political competition can lead government regulators to consider the interests of the wider public (i.e., consumers) more heavily than the narrowly concentrated interests of local business (i.e., the lodging industry). This results in lower barriers to entry for new services, such as the short-term accommodation sharing service of Airbnb.In Figure 3, we discuss the second hypothesis, and show the association between the lodging tax rate and the measured level of favorable policy environment for short-term rentals.

in 47 U.S. cities. As indicated, a strong negative association exists between the treatment and dependent variables; governments tend to implement more favorable policies for short-term rentals, and thus for the operation of Airbnb, in cities with low lodging tax rates. Once again, the scatterplot supports the main hypothesis of this study. That is, government regulators tend to maintain a high barrier for new market entrants such as Airbnb if government regulators themselves have a stake in the interests of local business (i.e., the lodging industry) through taxation. In Figure 4, we explore the third hypothesis by examining the association between the technological capacity of citizens and the measured level of favorable policy environment for short-term rentals in 47 U.S. cities. Specifically, we tested whether governments tended to adopt more favorable short-term rental regulations in cities where citizens exhibited greater technological capacity as measured by the city-level share of households with Internet access.As can be seen in Figure 4, the two variables were positively associated as we hypothesized, but the association was not statistically significant, as can be seen from the OLS results.In addition to the basic interpretations afforded by the graphical analyses, we also estimated a series of OLS regressions in which regressions were made for the measured level of favorable policy environment for short-term rentals on the three treatment variables (i.e.,political competition, lodging tax rate, and the technological capacity of citizens). Table 2summarizes the results of the regressions that test these hypotheses. In the first three columns of Table 2, we reported binary regressions between each of the three treatments and the dependent variable, and checked whether the effects of the treatments reported in Figures 2, 3, and 4 were statistically significant. Consistent with the results of the graphical inspections, we found that government regulators tended to adopt more favorable policies toward short-term rentals when political competition was high (in column 1) and the lodging tax rate was low (in column 2). However, we failed to find evidence that government regulators take the technological capacity of citizens into account when making their decisions; as hypothesized, the coefficient of our interest was positive, but did not meet the conventional threshold. Further, the variables of political competition and tax policy were found to affect the dependent variable independently (in column 4). These findings remain largely unaffected by the inclusion of control variables (in column 5).The results in column 5 suggest that a one-standard deviation increase in the levels of political competition and the lodging tax rate is associated with a 0.36 standard deviation increase and a 0.25 standard deviation decrease, respectively, in the measured level of the favorable policy environment for short-term rentals. Overall, the OLS results provide strong support for hypotheses 1 and 2; with greater political competition and lower fiscal dependence on local businesses, public interest is more heavily considered in governmental regulatory policy decisions than the interests of local businesses. This results in lower barriers to entrepreneurial entry. However, we found no clear support for hypothesis 3. We found a positive, but non-significant, association between the technological capacity of citizens and the measured level of the favorable policy environment for short-term rentals.Before we conclude, we should mention the three main limitations of our analyses. First, our research focused primarily on the role of city governments. It is possible, however, that state actors may also play a prominent role in shaping the regulations of the sharing economy, and the incentives faced by state and local actors may be significantly different. In this regard, future study may benefit from investigating the differences in regulatory responses between state and local governments in the context of multi-level governance. Second, we simplified our analysis by assuming that the accommodation of a sharing economy service benefits the public, but threatens market incumbents. Although this simplification has been widely accepted





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