As the focus of the chapter is to analyze the relationship between the implementation of the sales restriction of soft drugs to local residents only and whether this policy had a significant effect on the profitability of firms located within treated coffee shop municipalities. The empirical analysis within this chapter comes from two data sources.
The first data source is the Niet Financiele Ondernemingen (Non-Financial Firm, NFO) dataset which contains the annual financial data of non-financial firms located within municipalities which were obtained from the MicroData database maintained by the Center Bureau voor Statistiek (Dutch Statistics Bureau, CBS). The NFO is composed out of two alternative data sets: the Statistiek Niet Financiele Ondernemining / Statistics Small Non-Financial Firms (SFKO) and the Statistiek Niet-Financiele Groot Ondernemining / Statistics Large non-Financial Firms (SFGO).13 The SFKO contains firms with a total balance of less than 23 million euros while the SFGO contains firms with a total balance of more than 23 million euros. To measure the impact on firm profitability, the outcome variables of interest contained by the NFO data set are the firm’s liquid assets which are assets that are easily , operating result which is revenues minus operating costs such as the cost of sales, wages and social premium paid to employees, net profit which is the profit before taxation minus wages and deductions, and the profit-and-loss statement on their annual statement which is the net profit minus taxation and dividends.
In the case of analyzing the impact of the 2012 drug policy on firm profitability, two categories of firms are drawn from the SFKO. First are those that are classified as ”hospitality” firms meaning firms that deal in the following activities according to the Standard Bedrijven Index / Standard Business Index (SBI) including but not limited to such as hotels, restaurants, cafe’s, rental homes, camps, and cafes. Second are those companies classified as ”cultural” which contains firms with the following activities such as theater, events, cultural centers, attraction parks, museums, art galleries and so forth.
The second data source contained the socio-economic characteristics of coffee shop munici-palities collected from the Statline database maintained by CBS.14 In Section 4.3, an important procedure of the PSM method is the matching procedure between the counterfactual and treat-ment group. The outcome variable used for the matching procedure is the number of registered soft drug crimes prior the implementation of the local sales restriction with a set of explanatory variables which were gathered from the Statline database. The set of explanatory variables in-cludes: number of residents within a municipality that have employment, the number of residents that are on unemployment assistance from the government, the number of registered students in university, value of real estate within a municipality, density of restaurant establishments, and the number of coffee shops registered within a municipality.
Employment is the number of residents within a municipality that have either a part-time or a full-time job where they are paid for services provided. The CBS gathers employment statis-tics from the Belastingdienst (Internal Revenue Service, IRS) and Uitvoeringsinstituut Werkne-mersverzekeringen (Employee Insurance Agency, UWV) administration. The number of residents that receive unemployment is collected from the UWV which gathers data on unemployment through the Werkeloosheid Wet (Unemployment Insurance Act, WW) which states that previous
13The definitions used by the CBS can be found in Table B.2 and B.3 in Appendix B.1.2.
14Definition of the variables are presented in Table B.1 located in Appendix B.1.2.
employees who lost their part-time or full-time employment through no fault of their own are entitled to receive unemployment benefits for a minimum of three months.
The number of students registered that are following a university education is tracked by the Centraal Register Inschrijvingen Hoger Onderwijs (Central Registry of Registrations in Higher Education, CRIHO) where students are required to register when they start higher education within the Netherlands. The number of firms per municipality are collected by the CBS through combining the Algemeen Bedrijven Register (General Firm Registry, ABR) which contains the spatial information on firms located within the Netherlands and from Locatus which collects data on the number of firms from the retail-, hospitality, and recreational industry.
The real estate value within a municipality is dependent on the Valuation of Immovable Prop-erty Act requiring municipalities to report the value of real estate within a municipality domain.
The number of coffee shops within a municipality come from the annual report published by Biele-man et al. on the coffee shop industry within the Netherlands. In this annual report, the authors detail the number of coffee shop that are located within each municipality.
I constructed a balanced panel data set with annual financial and socio-economic data on municipalities located within the Netherlands from 2009 to 2014. I first merged the financial data from the NFO with data from the ABR which contained the spatial data of firms within the Netherlands. Afterward I aggregated firm level data to municipality based on the municipal code due to privacy requirement of at least ten firms per data cell and merged it with the socio-economic data from the Statline dataset on municipality code. Afterwards, I used research by Bieleman et al.
(2013) to identify which Dutch municipalities had given a permit to an establishment within their municipal domain to pursue commercial activities within the marijuana industry (coffee shop).
Out of 380 municipalities within the Netherlands, 83 were chosen to be included in the sample as these were categorized as coffee shop municipalities by having an establishment within the municipal domain that owned a permit to sell soft drugs. Out of the 83 coffee shop municipalities, 18 coffee shop municipalities are located within the provinces of Limburg, Noord-Brabant, and Zeeland where municipalities had to implement the sales restriction of soft drugs in the municipal drug policy and were therefore categorized as treated coffee shop municipalities. The remaining 65 coffee shop municipalities were located in other provinces and were therefore designated as control coffee shop municipalities. The final data set is composed out of 83 coffee shops over a period of 7 years from 2009 to 2015 leading to a total of 581 observations.
Table 4.1: Descriptive statistics of hospitality and cultural firms within coffee shop municipalities before the implementation of the 2012 drug policy
Note: ∗p <0.1,∗ ∗p <0.05,∗ ∗ ∗p <0.01.Firm characteristic variables are reported in thousands of EUROS.
Table 4.1 presents the descriptive statistics (mean, mean differences, and standard deviation) of hospitality- and cultural firms in all, treated, and control coffee shop municipalities prior to the implementation of the local sales restriction in the pre-treatment year of 2011. Table 4.1 reports there is no significance difference between firms located within the control- and treated coffee shop municipalities except for their operating result.
The results presented in Table 4.1 are puzzling considering the fact that the control coffee shop municipalities would have on average better financial performance due to being closer to traditional tourism spots. However, there is the possibility that due to treated coffee shop municipalities being located in close proximity to Belgium and Germany, the firms located within these regions significantly benefit.
Table 4.2: Descriptive statistics of hospitality and cultural firms within coffee shop municipalities after the implementation of the 2012 drug policy
Note: ∗p <0.1,∗ ∗p <0.05,∗ ∗ ∗p <0.01.Firm characteristic variables are reported in thousands of EUROS.
Table 4.2 presents the descriptive statistics (mean, mean differences, and standard deviation) of hospitality- and cultural firms in all-, treated-, and control coffee shop municipalities after the implementation of the local sales restriction in the post-treatment year of 2012. Though the discrepancy of the operating result between treated and control coffee shop municipalities has reduced, the issue is that the financial performance in other areas has decreased in 2012 as the net profit, the operating result, and the profit-and-loss account is reported as being statistically significant.
Prior qualitative studies have shown that the profits of firms located within control- coffee shop municipalities should increase in 2012 as a response due drug tourists being unable to purchase marijuana in Southern coffee shop municipalities. Additionally, qualitative studies on this theme have shown that the number of crimes associated with drugs increased after the initial introduction of the local sales restriction to only municipal residents. The descriptive statistics presented pro-vides a possible indication that the drug policy might have not had a significant negative impact on the profitability of firms within treated coffee shop municipalities.