1 Introduction

The appropriate regulation of gambling markets is not an uncontroversial topic and it is still being intensively discussed in the literature (Coryn et al., 2007; Carran, 2018; Bühringer, 2018). While parts of the gambling market have long been organized in a legal, regulated framework, other parts of gambling take place in black markets. The reasons for regulation and also partial prohibition include not only various market failures triggered by information asymmetries and external effects, but also by boundedly rational or even the pathological behavior of some gamblers (Coryn et al., 2007). Partly, gambling also has a negative reputation (see, e.g., Yani-de Soriano et al. 2012).

From a regional economic perspective, casinos have positive and also negative effects (Coryn et al., 2007; Walker & Barnett, 1999). Some of the positive effects a casino has on the region include the jobs associated with a casino, the local income generated, possibly also tax revenues, and tourist attraction. On the negative side are possible external effects such as noise, the negative image already mentioned, an increase of gambling addiction, property crimes, and money laundering (Coryn et al., 2007). As Dümmler et al. (2001) explain, the macroeconomic benefit of casinos has a “mercantilistic background”: As little as possible should be imported and as much as possible should be exported, so that the largest part of the value creation remains in the domestic market. The bigger the share of labor, investors, and suppliers originating from the region of the casino location, the higher the benefit of a casino for the region. In contrast, the negative effect of the gaming business is ideally exported (i.e., consumed by persons from outside the region in which the casino is located) (Dümmler et al., 2001).

Accordingly, casinos have a certain similarity to so-called NIMBY (Not In My Backyard) goods. In the case of NIMBY goods, significant social costs of production are incurred locally (such as aircraft noise), while the benefits (from an airport) also arise elsewhere, so that, while many welcome the existence (of airports) in principle, they do not welcome it in their neighborhood (Frey et al., 1996; van der Horst, 2007). For casinos, the effects may be somewhat different, but related. From the point of view of a casino location, the social costs (e.g., from gambling addiction) should be exported as much as possible, while the benefits (e.g., from jobs and tax revenues) should remain as local as possible. This can also be viewed as a type of beggar-thy-neighbor policy. In this context, it is surprising that the locations of casinos has not yet been systematically analyzed from the beggar-thy-neighbor policy perspective. A very good case study for this purpose is Germany since it has the most neighboring states in Europe. In addition, Germany has a federal state system and the individual federal states bear the advantages and disadvantages of a casino location more or less independently. Hence, both state and federal borders should be relevant for the location of casinos. Especially since the responsibility for the location decision rests with the federal state and is, therefore, a public policy decision-making process.

In this paper, we want to analyze whether German casinos are located at borders and in tourist areas to export any potentially negative effects of consumption. The analysis of the location of casinos is important in order to analyze whether current locations are in line with the regulatory objectives of the State Treaty on Gaming (GlüStV) or whether they are more motivated by fiscal objectives. The GlüStV is Germany’s regulatory framework with an objective to provide legal gambling opportunities for the German population so as to steer the "natural gambling instinct" of the population into orderly and supervised channels by offering a limited range of games of chance as a suitable alternative to unauthorized gambling, and to counteract the development and spread of unauthorized gambling in black markets.

We use cross-sectional county-level data to analyze whether current casino locations are consistent with official policy objectives.Footnote 1 Due to the mercantilistic background of casinos, we conjecture that casinos are more likely to be found at borders and in tourist areas in order to raise revenues from gambling taxes and create jobs, though working all the while to “externalize” the negative effects of gambling. The State Treaty on Gambling, in contrast, has the main objective to provide legal gambling opportunities so that the population abstains from illegal offerings. For that purpose, legal gambling locations should be close to the respective population.

Our paper is structured as follows: First, we present an overview of the gambling regulation in Germany and a literature review. After the data and model section, we report the results. Finally, we discuss model limitations and summarize the findings in the conclusion.

2 Gambling regulation in Germany

Gambling is only allowed in regulated forms in Germany. The GlüStV is intended to limit the negative effects of gambling. Section 1 Number 1 GlüStV contains the objective of preventing gaming and betting addiction and creating the preconditions for an effective addiction control. Section 1 Number 2 GlüStV is about limiting and channeling the supply of gambling. Dietlein et al. (2012) consider the channeling objective as the most important instrument against gambling and betting addiction. In particular, the second objective aims to combat illegal gambling by channeling the existing gambling demand toward legal gambling activities (Dietlein et al., 2012; Haucap et al., 2017).Footnote 2

The regulation of casinos is at the level of the federal states. Regulation in the respective federal states is composed of the Spielbankgesetz and Spielordnung. The respective laws regulate who may operate a casino. A distinction is made between a concession model and a state monopoly. In the case of a state monopoly, the location is determined directly by the federal states. In the case of the concession model, the location is determined indirectly by regulation. Even if an operator chooses a location within the regulatory landscape and also meets all other legal criteria for opening a casino, the operator has no legal claim to the granting of a concession (Sect. 4, Number 2, Sentence 3, GlüStV). Since the operator model represents either a state monopoly or a concession model, the location decision is made by the federal states and should therefore be in line with the objectives of the GlüStV. Hence, the responsibility for the location decision rests with the federal states. For an overview of the operator models and the operators of casinos in the individual federal states, see Table 1.

Table 1 Overview of the Federal states–Operator model and operator
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Other regulations may apply in the respective federal states that affect casinos. However, standardization is achieved with the GlüStV, which was agreed between all 16 federal states. This treaty sets out the framework and includes certain policies relating to casinos (Sandhaus & Shirvani, 2019).Footnote 3

State laws limit the number of casinos and/or the municipalities in which a casino may be located. For example, the number of casinos is limited to a maximum of five in Saxony. A municipality restriction applies in Baden-Württemberg. Only in Baden-Baden, Konstanz, and Stuttgart may casino be operated. In Thuringia, both the number and the municipality are restricted. Only one casino is permitted and this may only be located in Erfurt. For an overview of the municipality restriction in the federal states, see the following Table 2.

The gambling marketFootnote 4 has several different forms of games. Casinos are one of the legal outlets of the gambling market in Germany. In contrast to arcades which only contain machine-based gaming, casinos also contain table games. There are 69 casinos in Germany. These are spread over 15 of the 16 federal states. Only in Thuringia are there no casinos. Of the 69 casinos, 35 are privately operated (BupriS, 2021) and 34 are state-owned (DSbV, 2021).

Table 2 Overview of the Federal states–Municipal restriction
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3 Literature review

The location of casinos in Germany has not been analyzed so far. However, there is various literature on the distribution and location of casinos in the US, but the literature is mostly based on the performance (Lambert et al., 2010; Navin & Sullivan, 2007; Wenz, 2008). Lambert et al. (2010) analyze whether the location of casinos has an impact on their success and efficiency. Cookson (2010) analyzed the distribution of Native American casinos with respect to Indian reservations. He concluded that “multiple-state tribes have more than twice the probability [...] of having a casino as do [a] single-state tribe.” With regard to non-Native American casinos, Wenz (2008) concludes that they tend to be located beyond state borders.

Regarding the effects of a casino on the local labor market, Humphreys and Marchand (2013) found that employment in Canada increased in both the gaming industry and the hospitality industry. Indeed, increased revenues result from the local gaming industry. In addition, Ishizaka et al. (2013) analyzed a suitable location to construct a new casino in the region of Greater London. In contrast to our work, Ishizaka et al. (2013) consider new locations for casinos in their analysis. They question the Casino Advisory Panel ’s (2007) recommendation that casinos should be located in Newham. In their analysis, they state that if profits are to be maximized, Westminster would be a more appropriate location. Westminster is known for generating the highest revenue in the tourism sector. However, if one considers not only profitability but also social criteria, they come to the same conclusion as the Casino Advisory Panel. Based on the work of Ishizaka et al. (2013) and Humphreys and Marchand (2013), we include tourism indicators in our regression. Spas and casinos have a historical connection in Germany. Until the early 1970 s, all 12 German casinos were located next to spas. It was not until the mid-1970 s that casinos were opened in large German cities or in their immediate neighborhood. This can be explained by the trend toward city tourism, which is especially reflected in the average capacity utilization.

The national border is considered to play a major role in the location of casinos. Assaf et al. (2013, p. 153) study the performance of Slovenian casinos. Their analysis shows that national borders lead to an increased performance of casinos. The idea is that international customers spend more, on average, than domestic customers (Roehl, 1996). Thereby, international customers contribute to mitigating negative consequences for the domestic population (Lee et al., 2010). Based on this literature, we include location indicators in our regression to reflect the border effect and the resulting fiscal benefits.Footnote 5 Lambert et al. (2010) also analyze the location of casinos and include a border effect in their regression. Their paper studies the success and efficiency of casinos in the US and finds that border effects are not very important. The authors explain this finding with a small variation of the variable in their sample. Still, they conclude that the most successful locations are in large urban areas near state borders (Lambert et al., 2010). In contrast, we believe that Germany is a better case study for analyzing the location of casinos, especially with respect to border effects, since 86 of 401 counties are located on state borders, which leads to a higher variation. In addition, Germany has the most neighboring countries and the longest border in Europe. Moreover, Germany has a federal system with additional domestic state borders within Germany.

There are social costs associated with casinos. In the literature on the legalization of casinos, among the issues discussed are the associated economic benefits and social costs. Social costs are not directly measurable (Eadington, 1998, p. 55). Strict prohibition or a severely limited supply of casinos leads to an increase in the demand of illegal gambling (Eadington, 1999, p. 183 f.). If, however, one considers the social costs associated with the presence of a casino, for example, Grinols and Mustard (2004) discuss the extent to which crime rates are influenced by casinos. On the one hand, it is argued that crime is directly reduced because casinos have a direct effect on the labor market and the economy. This is confirmed by the study of Humphreys and Marchand (2013). On the other hand, Grinols and Mustard (2004) suggest a link between crime rates and pathological gambling behavior. As also shown in the study by Strohäker and Becker (2017), the concentration of gamblers with pathological behaviors increases with the presence of a casino. The literature further includes many criteria associated with the social costs of casinos that can be attributed to pathological gambling behavior. Among the criteria, increased insolvency and suicide rates, neighborhood crime, health care costs, and family problems are suspected (Kearney, 2005, p. 285 f.; Eadington, 1999, p. 187; Mallach, 2010, p. 19). As can be seen, the social costs of casinos cover various externalities. Grinols & Mustard (2004, p. 24 f.) estimate, that crime-related social costs in US areas with casinos were about $75 per adult in 2003.

In addition, there is a large body of literature in the area of player barring. Pursuant to §8 of the GlüStV, operators are obliged to block players who either report themselves or are reported by third parties. There are studies on the effectiveness of the player barring system in Germany. Furthermore, player barring systems are also widespread regulatory instruments internationally. In Canada, the barring system started as early as 1989 (Nowatzki & Williams, 2002), in the USA in 1996 (O’Hare, 2004; Blaszczynski et al., 2007) and in New Zealand in 2003 (Townshend, 2007). In addition, there are barring systems in several countries in Europe and Asia (Strohäker & Becker, 2017, p. 8). The empirical study by Meyer and Hayer (2010) is considered the basis for the evaluation of the barring system in German casinos. Meyer and Hayer (2010) investigated the effectiveness and benefits of player bans over a longer period of time. They used questionnaires from consistent banned players—over a period of time—to evaluate the effectiveness of player barring with regard to pathological gambling behavior. The results of their analysis suggest a positive benefit from player bans. Another analysis is conducted by Strohäker and Becker (2017). They examined the decisive factors for self-exclusion, concluding that the proximity of a casino to the place of residence was a decisive criterion for excluded gamblers. They found that with an increased distance of a casino to the residence, the share of bans decreases. The location of casinos thus directly influences the concentration of pathological gambling behavior. For these reasons, a different regulatory tool was used in the past to prevent addiction. This was the Residenzverbot. Citizens who lived within five kilometers of a casino were not allowed to enter it (Strohäker & Becker, 2017). Fiedler (2015) also evaluated the blocking system in German casinos. Overall, a ban effect is considered positive, as a decrease in gambling participation is observed among banned gamblers. In both the USA and Canada, a barring system was found to reduce the share of pathological gamblers (Ladouceur et al., 2007, p. 91; Nelson et al., 2010, p. 143).

Based on the previous mentioned literature, the location of gambling supply seems to have an impact on pathological gambling behavior. Therefore, it is even more important to analyze current casino locations (Fig. 1). Due to the mercantilistic background of casinos, we assume that casinos are more likely to be found at borders and tourist areas. Considering the literature, this does not seem to be in line with the objective of the GlüStV, which is to provide legal gambling opportunities for the population so as to control harmful gambling.

4 Data and model

For our analysis, we created our own data set at the county level. We have included all counties and county-level cities in our data set. Most of the data were taken from the Federal Statistical Office or the statistical offices of the federal states. The definition for each variable can be found in Table 3. The individual sources for each variable can be found in Table 9 in the appendix.

The data set contains 401 observations with 26 missing values related to the variable average capacity utilization. The 401 observations are equal to the number of counties in Germany. We included 11 variables in the regression. To counteract the variation in the different regulations of the individual federal states, as can be seen in Table 2, dummy variables were included for each of the federal states (federal state FE). The dependent variable is casino. It represents a dummy variable. The independent variables of the regression are:

  • Location indicators: state border, federal state border, state border \(\times\) federal state border

  • Tourism indicators: spa, average capacity utilization

  • Control variables:

    • Socio-economic indicators: average age, disposable income, migration background

    • Other indicators: population, county-level city, federal state FE

Table 3 Variable definition
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Fig. 1
figure 1

Graphical illustration of counties with casinos

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Tables 4 and 5 show the descriptive data and Table 6 reflects the pairwise correlation and the variance inflation factor (VIF) of the variables.

Table 4 Descriptive data of the dummy variables
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Table 5 Descriptive data of the variables
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Table 6 Correlation matrix
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Only variables with a correlation of less than 0.6 were included in the regression. We also calculated the VIFs of our model. Some of the literature concludes that multicollinearity is a problem with a VIF above 10. Although Wooldridge (2019) only sees a limited use of VIF, he recommends looking at the VIF out of curiosity. Nevertheless, our model does not include VIFs above 10.

For our analysis we use a logit model,.Footnote 6 which takes the following form:

$$\begin{aligned} \begin{aligned}&\hat{P}(casino = 1|state\,border, ..., federal\,state\,FE ) \\&= \wedge (\hat{\beta }_0 + \hat{\beta }_1 state\,border + ... + \hat{\beta }_{11-25} federal\,state\,FE), \end{aligned} \end{aligned}$$
(1)

where \(\wedge (z) = exp(z)/[1 + exp(z)]\) is the logit function.

5 Results

As shown above, the explanatory variables were divided into location and tourism indicators. The control variables were subdivided into socioeconomic and other indicators. The indicator groups are successively included in the calculation of the regression. Based on this, the explanatory power of each indicator group is to be identified. The results are presented in Table 7. We estimated the models with robust standard errors.

Table 7 Results—Logit model
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As can be seen from models 1 and 2, both location and tourism indicators contribute most of the explanatory power to the variation of the variable casino. Accordingly, these indicators should be important for the location of casinos. In models 3 and 4, additional indicator groups were included as control variables.

As logit models are nonlinear, interpretations of the magnitude of the effects are not directly available (Wooldridge, 2019). However, with the help of the Average Partial Effects (APE), an interpretation of the average effects can be made. Here, the mean of the marginal effects is calculated across all observations. The results are presented in Table 8. We estimated the Average Partial Effects of the logit model with robust standard errors.

Table 8 Average partial effects of the model 4
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The variables state border, federal state border, and state border \(\times\) federal state border are all significantly different from zero. The null hypotheses can be rejected with a one percent probability of error. Compared to counties without any borders, the likelihood of having a casino increases, on average, by 26.98 percentage points for a county with a state border, but without a federal state border. Likewise, the probability increases by 13.75 percentage points in a county with a federal state border, but without a state border. If a county is located on both a state and federal state border, the likelihood increases by 28.36 percentage points, on average, compared to a county without any borders.

Looking at the tourism indicators, the null hypothesis can be rejected for spa with a 10 percent likelihood of error and for average capacity utilization with a 5 percent error likelihood. The probability of a casino location increases by an average of 1.82 percentage points when the number of spas increases by one unit and by an average of 0.78 percentage points when the average utilization rate increases by one unit. Our results confirm our hypothesis that casinos are more likely to be found at borders and in tourist areas.

6 Discussion

German casinos are significantly located in counties with state borders. One explanation is the mercantilistic background of casinos and the resulting fiscal reasons described earlier. Since it is rather unlikely at the border to steer the home population’s natural gambling desire into legal channels, this is inconsistent with official policy objectives.

Considering the results for both indicator groups, our results are consistent with the existing literature. The analysis by Roehl (1996) shows that international customers of casinos spend more on average. Thus, on the one hand, there is an increased demand at state borders by international customers. On the other hand, it is also possible to share the social costs of gambling with the neighboring countries at state borders (Lee et al., 2010). From an economic view, location on the state border is correspondingly advantageous. As long as fiscal interests do not harm the objectives of the GlüStV, economic benefits can be achieved (Fiedler, 2015). However, we assume that it is not possible to achieve the objectives of the GlüStV optimally at the state border. The gambling demand has to be channeled nationwide through an urbanized area to prevent the development and spreading of prohibited games on the black market. For that purpose, the distribution of the casinos should be closer to the local population. Assuming that channeling at borders is not optimal, our results are not consistent with the objective of the GlüStV at either the state or federal state border, since the official objective is to channel the own population’s gambling demand, not that of foreigners.

The significance of the interaction variable also underlines the relevance of the state and federal state border. A stronger overall effect is observed in counties with both a state and federal state border. Although state variation was included with the federal state FE variables, the location indicators are still strongly significant. This illustrates the relevance of the indicator group.

The tourism indicators can be used as a proxy for the number of tourists. The higher the tourism indicators, the higher the potential demand from foreign players. Ishizaka et al. (2013) even use tourism spending as criteria for the profitability of casinos. From an economic point of view, both variables should have a positive effect, since the number of potential customers increases with rising tourism and international customers tend to spend more (Roehl, 1996). In addition, the variable spa provides a historical context. Until the early 1970 s, all 12 German casinos were located in spas. Casinos were not opened in German cities or in their immediate vicinity until the mid-1970 s. The shift to urban areas can be explained by the trend of city tourism (Deutscher Tourismusverband e. V., 2006, p. 6). The low significance of the variable spa can be explained with the federal state FE. Table 2 shows that in some federal states only locations in spas are allowed. If we omit the federal state FE variables, the significance of the variable spa increases. However, this leads to biased results because the federal states regulate the location decision differently (omitted variable bias). Considering the literature and our results, casinos are more likely to be found in tourist areas. However, based on the objectives of the GlüStV, casinos should address local population instead of tourists.

Moreover, we like the idea of analyzing whether the influence of our explanatory variables differ in explaining the location decisions of early- vs. late-arriving casinos. Unfortunately, only four new casinos have opened in the last 10 years. If we consider the last 15 years, there were still only nine casino openings. Apart from that, old casinos should also be in line with the current policy. In each federal state, the concession of a casino can be either revoked and/or is limited to 10 or 15 years. Since the main objectives of the GlüStV that we consider in our analysis have remained essentially unchanged, locations that do not comply with current policy may already have had their concession revoked or may not be allowed to renew. With this paper, we want to show whether the current locations are still compatible with the current regulation. Our analysis shows that many casino locations should be reviewed for their compatibility with the objectives of the GlüStV.

As shown above, the location decision for both private and state-operated casinos is under the responsibility of the federal states and should thus be in line with the objectives of the GlüStV. Based on our results and the discussion, the implementation of regulation seems to fail here. The significance of the location and tourism indicators rather suggest that fiscal interests dominate in the location of casinos. Fiedler (2015) makes clear that fiscal interest should not be considered as the main reason for gambling supply, but only as a positive side effect. Eadington (1999, p. 184) shows that in the US many regulations intended to protect consumers are usually symbolic in nature. This phenomenon can be transferred to the German gambling market.

7 Limitations

The underlying data set covers all counties in Germany and thus 401 observations. Counties, like federal states, administrative districts, and municipalities, represent one of the administrative levels in Germany. However, the municipality level has a smaller subdivision and comprises 10,799 municipalities. Consequently, a data set at the municipality level would entail a significantly higher number of observations. The challenge of the different counties is that they have different sizes in terms of area. This can lead to bias in the variables. For most variables, density or ratio can be included in the data set, which minimizes inaccuracy. However, for dummy variables, the differences in the sizes of counties cannot be taken into account. This can be seen in Fig. 2 as an example of the dummy variable federal state border. The figure contrasts the two federal states Mecklenburg-Western Pomerania and Rhineland-Palatinate. Based on the figure, it is clear that the counties in Mecklenburg-Western Pomerania are significantly larger in area than, for example, the counties in Rhineland-Palatinate. The county of Mecklenburgische Seenplatte (yellow) has a federal state border, and so do the cities in the north of the county. In contrast, the county-level city of Neustadt an der Weinstraße (green), for example, has no federal state border. Consequently, there is a certain inaccuracy in counties with large surface areas for dummy variables. Using a data set at the municipality level, there is less inaccuracy. Nevertheless, we deliberately chose the county level because of the coverage of casinos. A casino covers the gambling supply across multiple municipalities, which makes an observation at the county level more meaningful.

Fig. 2
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Model limitation due to differences in size of counties. Note: yellow = Mecklenburgische Seenplatte, green = Neustadt an der Weinstraße (Color figure online)

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8 Conclusion

Due to various market failures, triggered by information asymmetries and externalities, but also by boundedly rational or even pathological behavior of some players, regulation is widespread in gambling markets. As assumed above, location decisions for both private and state-operated casinos are the responsibility of the federal states and should thus be in line with their official regulatory objectives. Due to the mercantilistic background of casinos, we conjecture that casinos are more likely to be found at borders and in tourist areas, even though this may contradict regulatory objectives. Considering the literature and our results, the regulation seems to fail here. We can observe casino locations on borders and in tourist areas, disregarding the regulators’ objectives to provide legal gambling opportunities for the home population so as to channel their gambling desires into legal and regulated spheres. The significance of the location and tourism indicators rather suggest that fiscal interests dominate in the location of casinos. This finding is also consistent with Calcagno et al. (2010) who find that casino legalization in the USA is motivated by keeping gambling revenues and gambling taxes within the state and to attract tourism or “export” taxes.

For a better implementation of the objectives of the GlüStV, a different distribution of casinos is recommended, away from borders and locations with strong tourism to locations close to the local population. Thus, channeling local demand comes to the fore and fiscal interests are merely a positive side effect. Above all, placement at the state border entails both increased demand and the sharing of social costs with neighboring countries. The phenomenon of locations of casinos at state borders is not an isolated case, which is why it is difficult to imagine implementation at the national level alone. Looking at the European environment, casinos are also observed at national borders. This is the case in countries such as Austria, Switzerland, the Czech Republic, and Italy. If Germany chose to distance itself from national borders, it bear the social costs of its own casinos as well as those of neighboring countries. Consequently, a Europe-wide approach may be desirable.