Data sources and data description

I gather data from several sources. Throughout the process I use unique personal identifiers (CPR numbers13) and unique firm identifiers (CVR numbers14) to link employees and managers to the firms in which they work.

3.1.1Firm financials

From the ORBIS database, I obtain annual report data of all firms incorporated in Denmark for the period 1998-2016. From EXPERIAN, I obtain enriched line item accounting data on current assets and current liabilities enabling me to compute accruals. Non-current line items required for the estimation are available in the ORBIS dataset.

3.1.2Executive, ownership, employee, and criminal record data

Firm executives and ownership data are obtained through filings with the Danish Business Authority. I define an executive as an individual filed as “executive” with the Danish Business Authority15. I access the data through Statistic Denmark’s “Researcher Service” which enables me to link the data with other proprietary datasets held by Statistics Denmark16. I identify

13

All persons born or residing in Denmark are assigned a unique individual national identification number. CPR numbers are private information. In Denmark, CPR-numbers are used by banks, employers when paying salary, governmental bodies, etc., enabling me to merge information on individuals from a wide variety of sources.

14 _{All legal business entities in Denmark are assigned a unique CVR-number. CVR numbers are publicly disclosed.} 15

The Danish Business Authority requires all companies to file firm executives. Failing to do so may result in rejection of the firm establishment in case of a start-up or compulsory dissolution in the case of established firms. https://erhvervsstyrelsen.dk/sites/default/files/vejl_om_ledelses_revisor_vedtaegtsaendring.pdf.pdf.

Further, I have been in contact with the Danish Business Authority about the enforcement and accuracy of the executive data. From these interviews I have learned that firms benefit from filing firm executives in the way that executive status is a requirement for the individual to make significant decisions on behalf of the firm (for example apply for debt).

16 _{When accessing the executive and ownership data through Statistics Denmark data are delivered with proprietary}

155 employees through the Integrated Database for Labor Market Research (IDAN database) developed and maintained by Statistics Denmark. The database contains annual information on employer-employee links, employment starting and termination dates, and individual level data on salary received from the company. It is not costly for employers to report employee data to Statistics Denmark, because firms have salary software that automatically report each individual’s income to the Danish Tax Authorities, which is then collected by Statistics Denmark. I define a person as an employee of a firm if he/she (1) receives salary from the firm, (2) is registered as an employee at year-end, and (3) is not identified as an executive.

I acquire access to comprehensive criminal registers through Statistics Denmark’s Researcher Service. The registers cover all crimes convicted by a Danish court dating back to 1980 of all Danish citizens and foreigners registered with a Danish address, along with a classification code of the crime committed17 and the year of the conviction.

3.1.3Sample selection

I merge these datasets and impose several screens. I exclude (1) financial reports not covering 12 months, (2) hobby firms with total assets below DKK 1m (~EUR 134t), (3) companies that do not meet the European Commission’s SME thresholds18

, (4) extreme observations, potentially due to mergers or acquisitions that I cannot observe, (5) certain industries (financial, utilities, and state-owned) consistent with prior research, (6) subsidiaries, to avoid double counting of firms, (7) listed firms, (8) firm-year observations with less than 15 full-time equivalent employees to allow variation in employee traits, and (9) observations with missing explanatory variables. The screening procedure is displayed in Table C.1. The final dataset covers the years 2001-201419, 9,002 unique firms, 50,398 firm-years, 968,483 individual persons, 3,205,113 person-year observations, and 3,287,002 person-firm-year observations20.

are anonymized and coded by Statistics Denmark with a similar key across all datasets, I am able to link data on individuals across several datasets (including their criminal background and financial information) and to the firms in which they work, and unable to observe a person’s CPR number and name.

17

Description of criminal classification codes are available at

https://www.dst.dk/da/Statistik/dokumentation/Times/kriminalstatistik/afg-ger7 (in Danish)

18 _{The SME definition is based on total assets, revenue, and the number of employees. To extend the availability of}

revenue data (because the majority of firms are subject to exemption rules allowing them to not report revenue) needed to compute the SME category I obtain access to proprietary data on revenue from tax filings through Statistics Denmark. If revenue data are still unavailable I use only total assets and the number of employees to define SMEs.

19 _{I note that accounting information for the years preceding and following this period is included in the financial}

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Table C.1: Sample selection procedure

Note Screen applied Observations

dropped

Sample size Decrease in

sample size, %

Firm-years with employer-employee link 1,013,945

Keep financial reports with 12 months 308,515 705,430 30% 1 Keep firm-years with ta>1m 161,429 544,001 23% 2 Keep SMEs 46,761 497,240 9% 3 Remove extreme variables 51,926 445,314 10% 4 Remove certain industries 51,469 393,845 12% 5 Remove subsidiaries 5,323 388,522 1% Remove listed firms 545 387,977 0% 6 Remove firm-years with less than 15 employees 291,617 96,360 75% Keep observations with variables available for

estimation

45,962 50,398 48%

This table shows the sample selection procedure. Notes: (1): I exclude observations with less than DKK 1m in total assets to remove small hobby companies. (2): I follow the SME definition of the European Commission available at https://ec.europa.eu/growth/smes/business-friendly- environment/sme-definition_da. To extend the availability of revenue data (because the majority of firms are subject to exemption rules allowing them to not report revenue) needed to compute SME category I obtain access to proprietary data on revenue from tax filings through Statistics Denmark. If revenue data are still unavailable I use only total assets and the number of employees to define SMEs (3): In addition to winsorizing procedures, I apply several screens to avoid regressions being influenced by extreme outliers. I remove the following: ROA>1, ROA<1, firms with negative equity, growth in GP scaled by assets >1, growth in GP scaled by assets <1. (4): Consistent with prior accounting and finance research I exclude certain regulated industries (financials and utilities), and further exclude state-owned companies. (5) To avoid double counting I exclude subsidiaries. (6): For any given firm-year, I require at least 15 employees (measured as full-time equivalents) to allow variation in employee traits.