Theoretical approach

The dissertation largely builds on theory of the firm that aims to explain principal-agent relationships and agency costs (or more broadly contracting costs) (Jensen and Meckling 1976) and positive accounting theory (Watts and Zimmerman 1978; Watts and Zimmerman 1979) that aims to predict accounting behavior by understanding the incentives of an agent. The theories assume that information asymmetries exist between the principal and the agent and that individuals seek to maximize their own expected utilities and are innovative and creative in doing so.

Both theories view a firm as a nexus of contracts, both formal and informal. When a firm enters into a contract it imposes contracting costs including agency costs (e.g. monitoring costs, bonding costs, and the residual loss from dysfunctional decisions), information costs (e.g. an outsider’s cost of being informed), and renegotiation costs (the costs of rewriting existing contracts because the extant contract is made obsolete by some unforeseen event). Contracting costs can occur between a manager and firm owners (i.e. due to the separation of ownership and control), between a firm and its lenders, or even between a firm and its suppliers, customers, employees, or the tax authorities.

Contracting costs arise due to information asymmetries, and financial reporting is one remedy to resolve such asymmetries. On the one hand, contracts between the firm and an external stakeholder are not efficient when the firm has complete discretion over reported accounting numbers. On the other hand, the firm manager presumably has superior insider information about her firm that she can disclose through financial reporting. Therefore, managers are typically constrained by GAAP reporting requirements, however are still allowed discretion in the preparation of financial reports. The firm manager can then use such discretion to either increase the total wealth of all stakeholders (for example, by the dissemination of private information and thus resolving information asymmetries and decrease contracting costs), or to extract rents from firm stakeholders and thereby making the manager better off at the expense of for example owners or lenders (for example, by managing earnings and fool

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stakeholders). Firm managers then balance off the expected benefits and costs associated with earnings management in their determination of the optimal level of earnings management.

The three papers in this dissertation all draw on such theory. In the first paper, I empirically test for firms’ discretionary reporting choices when they are financially distressed – that is, a setting in which both incentives to manage earnings and costs of managing earnings are high. In the second paper, I use positive accounting theory to predict instances in which owner-managers engage in earnings management behavior (debt driven incentives and benchmark driven incentives), and further draw on earnings management theory and empirical insights from prior research to form hypotheses about the consequences of such behavior (for example the costs of being informed mitigating lenders’ propensity to collect private information). In the third paper, I use positive accounting theory to predict a setting in which the firm has an incentive to increase earnings (when the firm issues new debt) and complement with theory on criminology and corporate culture, to form hypotheses about the influence of rank-and-file employees and executives on earnings management.

3.3 Data

The three chapters are all based on large sample data on private firms’ financial statements. The data are obtained through the ORBIS database and the EXPERIAN database. These datasets are complemented with additional firm-level data, such as industry membership, data about bankruptcies, financial reporting dates, the number of employees, and proprietary data on revenue from tax filings. Additionally, in the second and the third chapter those databases are further complemented with person-level data on the individuals connected to those firms, it being executive managers, rank-and-file employees, owners, and individuals serving company boards. The latter data are rich and include income data and income sources, prior criminal convictions, personal wealth measures, gender, family data (marital status, number of children), residential information, as well as other personal information.

As is further elaborated in each of the three chapters the data allow me to shed light on certain issues of financial reporting that prior research has not examined, likely because these data are difficult to get access to. The dissertation has benefited greatly from access to such granular, very interesting, and rather unique data. For example, the data allow me to track managers’ salary over time and provide empirical evidence on income-shifting, and to measure the traits of rank-and-file employees directly, instead of relying on indirect proxies such as

39 geographical averages (see e.g. Call et al. 2017; McGuire et al. 2012). Further, with the data I can disentangle the effects of the manager (e.g. the wealth of the manager and other personal characteristics) from the effects of firm financial reporting when investigating the influence of SDEM on the cost of debt. The following table summarizes the dataset used in each of the three chapters.

Table 1: Overview of data sources

Data provider Dataset and description Used in chapter(s)

1 2 3

ORBIS (Bureau van Dijk)

Financial statement information of Danish limited liability firms, and data on number of full-time equivalent employees

X X X

Experian Detailed line-items on current assets and current liabilities of Danish limited liability firms. Data on financial reporting filing dates.

X X X

Statstidende.dk (The Danish Official Gazette)

Accessed through konkurs.dk: Data on bankruptcy filings. X

Statistics Denmark IDAN dataset: Annual individual-level data on employer- employee links, salary, employment start date, and employment end date.

X X

Statistics Denmark IND dataset: Annual individual level information on income and wealth.

X X

Statistics Denmark FIRM dataset: Data on proprietary revenue from tax and VAT filings, and complementary data on number of full-time equivalent number of employees

X X X

Statistics Denmark KRAF dataset: Data on criminal records of all sample firm employees and executives

X X

Statistics Denmark BEF dataset: Data on residential municipality and address, gender, marital status, birth date, ancestry country, and other family related information.

X X

The Danish Business Authority

Ownership dataset: Data on owner(individual)-firm and owner(firm)-firm links. Data on starting and termination dates of the ownership, along with ownership percentage.

X X

The Danish Business Authority

Executive data: Data on executive-firm links. Data on starting and termination dates of the executive employment.

X X

The Danish Business Authority

Board data: Data on board member-firm links. Data on position held, along with starting and termination dates of the board position.

X