Sharing economy and government

Hong, Sounman; Lee, Sanghyun Article Sharing economy and government Journal of Open Innovation: Technology, Market, and Complexity Provided in Cooperation with: Society of Open Innovation: Technology, Market, and Complexity (SOItmC) Suggested Citation: Hong, Sounman; Lee, Sanghyun (2020) : Sharing economy and government, Journal of Open Innovation: Technology, Market, and Complexity, ISSN 2199-8531, MDPI, Basel, Vol. 6, Iss. 4, pp. 1-17, https://doi.org/10.3390/joitmc6040177 This Version is available at: https://hdl.handle.net/10419/241563 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Open Innovation: Technology, Market, and Complexity Article Sharing Economy and Government Sounman Hong 1,* and Sanghyun Lee 2 1Department of Public Administration, Yonsei University, Seoul 03722, Korea 2Graduate School of Information, Yonsei University, Seoul 03722, Korea; [email protected]r *Correspondence: [email protected] Received: 5 October 2020; Accepted: 27 November 2020; Published: 3 December 2020   Abstract: We compared sharing economy development in 90 countries to demonstrate that higher qualities of government are associated with greater sharing economy growth. To explain this finding, we assumed that sharing economy benefits are enjoyed by the public, whereas its costs are chiefly borne by market incumbents. In considering these competing interests, policymakers tend to favor the latter as single-industry interests that can be more easily organized to influence policymaking. We then hypothesized that an electorally competitive, depoliticized, and effective government may tilt the balance against the entrenched market incumbents, leading to the growth of sharing economy industries. Overall, we found some support for this hypothesis. We especially found that electoral competitiveness strongly impacted sharing economy development and that this impact was significantly greater in a country with a depoliticized bureaucracy and effective government. Keywords: sharing economy; quality of government; electoral competitiveness; politicization; effective government 1. Introduction Digital and information technologies have enabled the rise of the so-called “sharing economy”, in which, through peer-to-peer activities, people grant each other temporary access to their underutilized assets for financial compensation [ 1 – 15 ]. This new industry’s rapid growth and disruptive nature has initiated a fierce debate over the proper role of governments [ 8 , 16 – 18 ]. Proponents argue that governments should remove regulatory barriers for sharing economy firms, such as Airbnb or Uber, to improve economic efficiency, whereas critics call for stricter regulations to protect the rights of consumers, workers, and incumbent industries [19]. Governments around the globe have also adopted substantially different solutions in response to the rise of sharing economy. For example, the United States Conference of Mayors issued a resolution recommending “support for making cities more shareable.” Despite such rhetoric, city governments have responded in a variety of ways to this new economy, with many cities announcing their intentions to ban popular sharing services such as Uber or Airbnb [ 12 ]. This heterogeneity in government responses is observed internationally as well. Unlike countries that welcome a sharing economy, such as Finland, Sweden, and Singapore, the South Korean government completely banned Uber, while Japan adopted strict regulations against short-term home-sharing services in 2018. In this study, we asked why some governments around the world welcome the rise of the sharing economy, while others strongly oppose it. In order to contribute to this query, we hypothesized that higher quality government institutions would be associated with more favorable government policies for a sharing economy. We primarily focus on three aspects of institutional qualities: (1) electoral competitiveness, (2) depoliticized civil service, and (3) overall government effectiveness. Framing the rise of sharing economy as a battle between new entrants (e.g., Airbnb or Uber) and incumbent industries (e.g., taxi or hotels), we first explain why politicians have incentives to support J. Open Innov. Technol. Mark. Complex. 2020,6, 177; doi:10.3390/joitmc6040177 www.mdpi.com/journal/joitmc J. Open Innov. Technol. Mark. Complex. 2020,6, 177 2 of 17 the interests of market incumbents if their decisions are not held accountable. We then argue that higher quality governments, as measured by electoral competitiveness, depoliticized bureaucracy, and overall effectiveness, may weaken the established market incumbents’ political power, leading to growth in sharing economy industries. We examine these arguments by demonstrating positive associations between the three institutional measures and the size of the sharing economy in a given country. Our theoretical rationale is based on three different strands of literature. First, we regarded sharing services as typical examples of disruptive innovations, creating competition between sharing economy firms and market incumbents. Disruptive innovations generally incorporate new and unprecedented features, deviating so radically from existing alternatives that they destroy existing rules of competition [ 20 , 21 ]. Second, following Stigler’s [ 22 ], we assume that the two economic interests (i.e., sharing economy firms and market incumbents) compete for political favor. Market incumbents seek to raise regulatory barriers to entry, while new entrants try to lower them. Third, we use the concept of “agile governance” or “adaptive governance” [ 12 , 13 , 23 ] to explain why governments decide to raise or lower regulatory barriers to entry. We argue that governments suffer from “status quo bias,” in which single-industry economic interests “capture” policymakers. However, if the political system possesses higher qualities, governments become more flexible, agile, and adaptive to environment changes and technological innovations, as they are held accountable for their decisions. 2. Sharing Economy and the Roles of Government Previous literature on sharing economy and government may be grouped into several broad categories. The first set of literature examines the question of “who” benefits from the growth of sharing economy firms [ 8 , 16 , 24 – 28 ]. Overall, this group of studies focuses on sharing economy’s “disruptive” nature [ 29 ]. These new business models repackage old technology to create new markets, disrupting the incumbent firms. Other studies have examined the cost–benefit distribution of sharing economy regulations [ 30 ]. For instance, some studies have explained that the benefits of sharing economy services are widely dispersed across the public, whereas their costs are concentrated on a relatively small number of incumbent firms [ 12 , 13 ]. Identifying the “winners” and “losers” from sharing economy growth provides the theoretical underpinnings for regulating this new innovation. The second set of previous literature explores the question of “how” governments should regulate sharing economy firms [ 7 , 31 – 33 ]. In other words, this group of works explores regulatory issues related to the rapid growth of sharing economy industries. For instance, Edelman and Geradin [ 31 ] identified potential market failures associated with sharing economy businesses and proposed a regulatory framework that creates an efficient delivery of services while protecting service providers, consumers, and third parties. Similarly, Miller [ 33 ] offered a number of principles for regulating the sharing economy and proposed future options for governments to improve existing approaches to regulating the industry. Lastly, a relatively small set of literature explains “why” some governments choose to heavily regulate sharing economy services, while others do not [ 12 , 13 , 34 ]. Some studies have applied the political economic perspective such as the capture theory to explain the varied response to this new industry, while others applied the adaptive governance framework. This latter group assumes that government responsiveness to the sharing economy may be analyzed from a more general framework of adaptive and agile governance [ 23 ]. This literature explores the political, economic, and social factors that may make governments flexible, agile, and adaptive to environment changes and technological innovations. This concept was applied to various government responses to the rise of sharing economy [12,13]. This study contributes to this last strand of sharing economy research by making several theoretical and empirical advancements. Previous studies have clearly advanced our understanding of the sharing economy and governments’ roles. To our knowledge, however, none of them have attempted to explain, from an international perspective, why sharing service industries have grown rapidly in some countries but not in others. Further, previous research has overlooked the possibility that, holding other economic J. Open Innov. Technol. Mark. Complex. 2020,6, 177 3 of 17 factors constant, the institutional qualities of governments may have substantial impacts on digital innovation growth. This is an important omission in the literature, which limits our understanding of sharing economy growth and, more generally, crowd-based digital innovation. This current research addresses this paucity. In sum, this study contributes to the literature by presenting, at a multinational level, novel empirical evidence of the association between sharing economy growth and various institutional qualities of governments. The remainder of the article proceeds as follows. In the next section, we explain our hypotheses. Then, we describe our methodology and explain how we constructed the dataset. In Section 5, we present the test of our proposed hypotheses. Based on the findings, we present the conclusion and discuss our findings in Section 6. 3. Hypotheses 3.1. Politics of Sharing Economy Regulation Previous research on the politics of regulation attempted to explain who stands to gain or lose from government regulations, and what forms of regulation will be instituted, along with their welfare implications [ 22 , 35 , 36 ]. Wilson [ 30 ] presented a framework to understand the political significance of these economic stakes. In this framework, a regulation (or, more generally, a policy) is classified in terms of the perceived distribution of its benefits and costs. Specifically, both benefits and costs are either widely distributed across the public at large or narrowly concentrated on a definable segment of society (such as occupations, industries, or localities). This classification produces a two-by-two matrix, as shown in Figure 1. Figure 1. Cost–benefit distribution and four different types of policy situations. The first quadrant of the matrix is where both benefits and costs are widely distributed across the public at large, a situation known as majoritarian politics. The opposite case is the so-called interest group politics, in which a policy’s benefits and costs are all chiefly borne by a definable segment of citizens. In majoritarian politics, the public has little incentive to organize around common interests. On the other hand, in interest group politics, both groups have strong incentives to organize around common interests to influence policy decisions. We will not focus on these two situations in this study as they do not fit with the benefit–cost distribution of sharing economy regulations. Specifically, it would be incorrect to regard the sharing economy case as an interest group politics situation, as those individuals who stand to lose from entry barriers are those who would run sharing economy businesses if these barriers did not exist. In other words, the interest group politics case J. Open Innov. Technol. Mark. Complex. 2020,6, 177 4 of 17 cannot adequately describe the benefit–cost distribution of an entry regulation for a new industry, as the group chiefly bearing the costs has not yet entered into the market and is thus not properly defined. In analyzing the benefit–cost distribution of entry regulations for disruptive innovations, the other two situations are more useful. Client politics refers to a situation in which the costs of a policy or regulation are widely distributed across the public, while the benefits are enjoyed by a defined segment of society. Entrepreneurial politics presents an opposing situation; the benefits are dispersed, while the costs are concentrated. As Hong and Lee [ 12 , 13 ] demonstrated, the regulation of a sharing economy industry can be best described by either client politics or entrepreneurial politics. In fact, these two cases are like the two sides of a coin. Instituting entry regulation is a client politics situation, whereas removing it is an entrepreneurial politics situation. From an efficiency point of view, any regulations that slow down the growth of disruptive innovations will benefit the market incumbents, but reduce competition in the market, resulting in less competitive markets and higher prices for consumers. The market incumbents are the winners, whereas the consumers (and the early innovators such as Airbnb or Uber) are the losers. Most importantly, in the long run, such policies may even stifle the rate of market innovation and entrepreneurship. In fact, previous scholars have paid special attention to entry regulations for disruptive innovations. Stigler [ 22 ] argued that regulation is acquired by the industry (in order to promote its own benefits), and the most typical form of “acquired regulation” is entry control. For instance, as Stigler [ 22 ] (p. 5) stated, “every industry or occupation that has enough political power to utilize the state will seek to control entry.” As disruptive innovations emerge, the benefit–cost distribution of entry control follows the client politics situation. The benefits are enjoyed by the entrenched industries (e.g., taxi or hotels), while the costs are widely dispersed across potential consumers. In such cases, political science research has predicted that policymakers will serve the “client,” with industrial economic interests carrying more weight in policy decisions than the public interest [ 37 , 38 ]. This is because “the [political] system is calculated to implement [ . . . ] many strongly felt preferences of minorities but to disregard the lesser preferences of majorities” [22] (p. 12). 3.2. Testable Hypotheses We thus far explained that, when disruptive innovations emerge, policymakers generally suffer from “status quo bias” (i.e., they design regulatory policies that slow the growth of new entrants). This phenomenon is described as “capture” in economics [ 39 – 41 ] and as “policy monopoly” in political science [ 38 ]. In this study, we propose that adaptive governance achieved by higher institutional qualities of governments may mitigate this bias. In the context of our case, we defined adaptive governance as a form of governance that advances public interests by overcoming “status quo bias” and accommodating disruptive innovations in the economy. Previous research has shown that decentralization makes governments more flexible, agile, and adaptive to environment changes [ 23 , 42 ] (but see [ 13 ]). As Janssen and Van der Voort [ 23 ] (p. 3) stated, the “core characteristics of adaptive governance [include] decentralized decision-making.” In general, decentralization would make governments adaptive as it brings the government “closer to the people” in Montesquieu’s sense. However, it is not clear whether this logic can apply to the case of sharing economy, because the benefits of the disruptive innovation are widely dispersed beyond local boundaries (e.g., see [13]). Instead, we propose three institutional qualities of governments that may lead to adaptive governance: (1) electoral competitiveness, (2) depoliticized civil service, and (3) overall government effectiveness. From the standpoint of the public at large (who bears the costs of the regulations), elections are arguably the most effective institutional arrangements available for influencing public officials, as policymakers’ fears of losing office in coming elections lead them to respond to citizen demands [43,44] . This institutional impact may be greater when politicians face competitive elections [12,45] . Therefore, it follows that more electoral competition will increase policymakers’ responsiveness to citizen demands. This weakens the market incumbents’ political power, which can lead to sharing economy growth. We thus present the following hypothesis: J. Open Innov. Technol. Mark. Complex. 2020,6, 177 5 of 17 Hypothesis 1: A greater level of electoral competitiveness may be associated with greater sharing economy development. Policymakers use regulations to favor organized interests, and thereby obtain campaign contributions and votes. This political incentive defies the rational process of cost–benefit analysis, and thus it would have a greater impact on policymaking when the civil service system is highly politicized. The notion that the neutral competence of bureaucracy would enhance public service performance can be traced back to earlier works, including Max Weber [ 46 ] and Woodrow Wilson [ 47 ]. Although a complete division between politics and administration is unrealistic, most scholars tend to agree that the politicization of civil service has negative impacts on public administration performance [ 48 – 51 ]. It therefore follows that a depoliticized civil service system that insulates itself from the political influence of organized interests will be less swayed by market incumbent voices, leading to sharing economy growth. We thus present the following hypothesis: Hypothesis 2: Depoliticized civil service (i.e., a lower level of politicization in civil service) may be associated with greater sharing economy development. Lastly, we also consider overall government effectiveness as a quality by which to measure governments. In this study, we follow Kaufmann et al. [ 52 ] to define overall government effectiveness as including the quality of the bureaucracy, the quality of public service provision, the competence of civil servants, the insulation of civil service from political influence, and the credibility of the government’s commitment. Effective governments may also be more flexible, agile, and adaptive to environmental changes and technological innovations. It therefore follows that an effective government will be conducive to adaptive governance that considers public interest more heavily than the interests of market incumbents. We thus present the following hypothesis: Hypothesis 3: A greater level of government effectiveness may be associated with greater sharing economy development. As we will report in the results section, we found a robustly positive association between electoral competitiveness and the dependent variable, but failed to find comparably strong impacts from depoliticized civil service and overall government effectiveness. However, we argue that the impacts of these three institutional qualities complement one another. A greater level of electoral competitiveness makes policymakers more responsive to citizen demands as they fear losing office in the coming election. However, such impact would be smaller in a highly politicized civil service system, in which the organized interests of market incumbents have strong political influences. Similarly, the impact of greater electoral competitiveness would be smaller if the government is highly ineffective overall. In other words, even if the main effects of depoliticized civil service and overall government effectiveness were limited, these two institutional qualities could still play significant moderating roles as they greatly enhance the impact of electoral competitiveness on sharing economy development. We thus present the following hypotheses: Hypothesis 4: The association between electoral competitiveness and sharing economy development may be greater in a country with a depoliticized civil service system. Hypothesis 5: The association between electoral competitiveness and sharing economy development may be greater in a country with an effective government system. J. Open Innov. Technol. Mark. Complex. 2020,6, 177 6 of 17 4. Methods and Data 4.1. Method To evaluate the proposed hypotheses, we estimated the effects of three institutional qualities of governments on the development of sharing economy services. Among the many institutional characteristics, we chose (1) electoral competitiveness, (2) depoliticized civil service, and (3) overall government effectiveness as the treatment variables. The dependent variable was the nation-level development of sharing economy services, as measured by the global sharing economy index published by Timbro, a think tank based in Sweden. Specifically, the models used in this study are as follows: Yi=µ+%ElecCompi+σDepoliti+τGovEf fecti+Xiγ+ηi(1) where Yi is this study’s dependent variable, the sharing economy development in country i. This variable is constructed by compiling traffic volume and scraped data to estimate the overall development of the peer-to-peer economy [ 53 ]. The three treatment variables are (1) ElecCompi , the level of electoral competitiveness; (2) Depoliti , the level of depoliticized bureaucracy; and (3) GovEf fecti , the level of overall government effectiveness of government in country i. The coefficients of interest are σ , and τ , with ηi as the error term. We used ordinary least squares (OLS) to estimate the impacts of the treatment variables. We also included several control variables, X i . First, we accounted for the potential differences in political systems by controlling for an indicator of a presidential system. Second, we controlled for the overall economy size, as measured by GDP per capita. We also included the country’s degree of economic freedom, as measured by the economic freedom index from the Fraser Institute [ 54 ]; the regulatory environment conducive to business operation, as measured by the ease of doing business score from the World Bank; and the level of globalization, as measured by the globalization index [ 55 ]. Finally, the share of population using the Internet was also controlled for, as previous studies have cited infrastructure for information and communication technology as the most important determinant of sharing economy development [56]. 4.2. Data The primary challenge to the empirical investigation was the limited observations on the development of sharing economy services at country levels. We thus took advantage of the global sharing economy index recently published by Timbro, which measures the level of sharing economy usage in a country [ 53 ]. This variable combines online traffic and the number of active suppliers associated with the online peer-to-peer industry, producing a normalized per capita usage of a sharing economy. To our knowledge, this variable is the only measure that can compare sharing economy development across countries. We thus used this as our dependent variable. The variables to the right were all drawn from several public sources. Electoral competitiveness measures, margin of minority, and the indicator of a presidential political system were all collected from the Database of Political Institutions 2017 (DPI 2017) compiled by the Development Research Group of the World Bank [ 57 ]. The level of depoliticized bureaucracy was collected from the QoG Expert Survey Data [ 58 ]. This variable was constructed by averaging the answers to survey items A, B, D, E, F, G, I, and J of Question 2, which we considered most relevant to a lower level of politicization. The level of government effectiveness was drawn from the Worldwide Governance Indicators compiled by the World Bank Group [ 52 ]. The size of the economy, as measured by GDP per capita, was taken from the Maddison Project Database [ 59 ]. The economic freedom index, ease of doing business score, and index of globalization were drawn, respectively, from the Economic Freedom of the World Database published by the Fraser Institute [ 54 ], ease of doing business report published by the World Bank Group [ 60 ], and the KOF index of globalization [ 61 ]. Finally, the share of individuals with Internet access as a percentage of the population was taken from the World Development Indicators compiled J. Open Innov. Technol. Mark. Complex. 2020,6, 177 7 of 17 by the World Bank Group [ 62 ]. In Table 1, we present the summary statistics of all the variables included in this study. Table 1. Summary statistics. Variable Obs. Mean Std. Dev. Min Max The development of sharing economy 90 8.879 16.019 0 100 Legislative electoral competitiveness 90 6.667 1.049 1 7 Executive electoral competitiveness 90 6.522 1.265 1 7 Margin of minority 90 0.398 0.141 0 0.679 Depoliticized bureaucracy 90 4.539 0.443 3.017 5.427 Government effectiveness 90 0.287 0.925 − 1.298 2.237 Presidential system 90 0.456 0.501 0 1 GDP per capita (in log) 90 9.380 1.073 6.867 11.242 Economic freedom 90 63.724 9.357 37.6 89.4 Ease of doing business 90 65.113 11.575 43.22 91.71 Globalization 90 67.576 12.802 45.012 90.474 Internet access 90 53.672 26.278 4.174 98.2 We note three issues regarding our data. First, the dependent variable of this study—the measured level of sharing economy usage in a country—was provided by a think tank that supports free markets. Some may be worried that the ideological position of this think tank may have introduced bias in the data. We strongly believe, however, that this potential measurement error was not critical. First, this think tank measured the level of sharing economy quantitatively, rather than qualitatively, by combining online traffic and the number of online peer-to-peer industry suppliers. Furthermore, the main goal of our analyses was to estimate not the level of sharing economy usage but the association between sharing economy usage and institutional qualities of the government. For the measurement error to bias our coefficients of interests, one should systematically overor underestimate the level of sharing economy usage for countries with certain government qualities. Such a concern is not very likely to be realized, as there is no reason to believe that any researcher would introduce such systematic bias in the data, given the way this variable is constructed (i.e., quantitatively combining online traffic and the number of suppliers). Second, we note that our sample size was 90; although the global sharing economy index was produced for 213 countries, we limited our analyses to the countries that have reliable data on the institutional qualities of government. As explained, the right-hand side variables, including the institutional qualities, were collected from various sources; the full set of variables were available only for the 90 countries, which constituted our final sample. We thus had no discretion over the inclusion of a particular country into the sample. Table 2lists the 90 countries included for our analyses. Third, the dependent variable, the global sharing economy, was constructed in 2018 on the basis of monthly-only traffic data and the number of active suppliers using automated “web scraping” techniques. Conversely, we used the most recently available data for the right-hand side variables. As these variables are generally not measured annually, the year each institutional characteristic was constructed differs across data sources. Such an issue is common in comparative analyses of government institutions. Furthermore, because of right-hand side variables not being measured annually, one may regard them as lagging by a year or two. J. Open Innov. Technol. Mark. Complex. 2020,6, 177 8 of 17 Table 2. List of countries included in the analyses. Country Index Country Index Country Index Iceland 100 Singapore 5.6 Senegal 0.7 Malta 58.2 South Africa 4.7 Kazakhstan 0.7 New Zealand 52.8 Fiji 4 Egypt 0.6 Croatia 52.2 Brazil 4 Kyrgyzstan 0.6 Denmark 45.9 Armenia 3.7 El Salvador 0.6 Ireland 41 Germany 3.4 Ukraine 0.6 Barbados 29.4 Sri Lanka 3.4 Indonesia 0.6 Norway 29 Albania 3.3 Nepal 0.5 Australia 26.2 Dominican Republic 3 Guyana 0.5 Portugal 25.6 Mexico 3 Slovenia 0.5 Spain 22.7 Argentina 2.9 Peru 0.4 Greece 22.5 Morocco 2.5 Colombia 0.3 Italy 21.2 Thailand 2.4 China 0.3 Georgia 20.3 Romania 2.4 Zimbabwe 0.3 Canada 16.6 Japan 1.9 Ghana 0.3 Switzerland 16 Nicaragua 1.8 Rwanda 0.2 Netherlands 14.6 Poland 1.8 Algeria 0.2 Estonia 14 Turkey 1.8 Uganda 0.2 Sweden 13.4 Ecuador 1.8 Mozambique 0.1 Israel 13.1 Lebanon 1.4 Togo 0.1 Finland 12.5 Philippines 1.3 Madagascar 0.1 Uruguay 11.7 Lithuania 1.2 India 0.1 Mauritius 11.3 Kenya 1.2 Cameroon 0.1 Chile 9.8 Guatemala 1.1 Benin 0.1 Belgium 9.4 Botswana 1 Tajikistan 0.1 Jamaica 6.9 Austria 0.9 Slovakia 0.1 Latvia 6.9 Cambodia 0.9 Malawi 0.1 Hungary 6.5 Vietnam 0.8 Nigeria 0 Bulgaria 6.1 Azerbaijan 0.7 Guinea 0 Bosnia 5.7 Costa Rica 0.7 Bangladesh 0 5. Results 5.1. Main Impacts of Government Qualities Before we present the results of our regression analysis, we first present descriptive figures that visually depict the associations between the dependent and treatment variables. In Figure 2, we show the association between sharing economy development in a country and electoral competitiveness. The key variable of our interest, electoral competitiveness, measures how much competition political actors who occupy the legislative and executive branches face in elections. This variable is measured separately for each branch. In Figure 2a, we show the association between sharing economy development and legislative electoral competitiveness, whereas in Figure 2b we have executive electoral competitiveness. As observed, we found a positive association between the two treatments and dependent variable. Overall, a greater level of electoral competitiveness in either the legislative or the executive branch was associated with greater sharing economy development. In Figure 2c,d we discuss the second and third hypotheses. In Figure 2c, we show the association between sharing economy development in a country and depoliticized civil service (i.e., a lower level of politicization). Here, the figure fails to show a clear positive or a clear negative association between the two variables; thus, the second hypothesis is not strongly supported in the figure. On the other hand, in Figure 2d we explore the third hypothesis by examining the association between sharing economy development and government effectiveness. Overall, the figure shows a clear association between the two variables, supporting the third hypothesis: a greater level of government effectiveness is associated with greater sharing economy development. J. Open Innov. Technol. Mark. Complex. 2020,6, 177 15 of 17 15. 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