In this section, we will describe how we construct the dataset used in this thesis. The data we use is delivered from several sources, and below we will describe in detail how we construct the final sample we utilize in our analysis, and from which sources the data are collected. In both of our hypotheses we use the same dataset; the only difference is what we use as dependent variables and the construction of these.
Definition of PE-backed companies
The term PE-backed companies refers in this thesis to companies that are currently owned by PE-firms, and which were initially acquired through a buyout investment. This means that we have excluded PE-backed portfolio companies that are considered to be in the
investment stages “seed” or “venture”. We choose to do so because seed and venture investments are fundamentally different from buyouts, and will have much smaller incentives to engage in tax planning activities as such investments will usually not face positive results for several years into the investment horizon (Fenn, Lang, & Prowse, 1995).
Choice of Peer Group
We limit our analysis to focus on Norwegian companies only, and by Norwegian companies we refer to companies that are tax domiciled in Norway. This is done because tax planning in Norwegian PE-backed portfolio companies is, to our knowledge, an unexplored research area. We use Norwegian private limited companies (AS) as our peer group to the PE-backed and PE-target companies. This is because we believe this group of companies will share a greater number of characteristics with PE-backed and PE-target companies than for instance public limited companies, partnerships or sole proprietorships. This limitation is in accordance with Badertscher Katz & Rego’s (2010) study, which resembles ours to a certain extent. We will refer to the peers as non-PE-backed companies or peer companies.
Base Set
Data we received from the Norwegian Tax Authorities (Skatteetaten) make up the basis of our dataset. This dataset consists of ”Næringsoppgave 2” for the years 2003-2014, an appendix to the Tax Returns Form, which is a statement of the main items on the income statement and balance sheet of a company. ”Næringsoppgave 2” is mandatory to report for every Norwegian private limited company (AS), and we thus have the income statements and balance sheets of every Norwegian private limited company for 12 consecutive years, which we will refer to as the base set further on. This dataset initially consists of 350,836 companies and 2,217,483 observations.
Data Quality
We are given access to complete and detailed income statements and balance sheets for all Norwegian private companies from the Norwegian Tax Authorities. Because of this, we believe that the quality of our data material is greater than the quality of the data material used in studies comparable to ours, such as Alahuhta (2013) and Badertscher, Katz, & Rego (2010). Alahuhta’s (2013) study is limited by the fact that all taxation related data
attributable to the companies is confidential in Finland, and only tax authorities have access
to the data. Of these reasons, Alahuhta (2013) is forced to create estimates of different taxation figures, while we have access to the exact figures from the information provided by the Norwegian Tax Authorities. Badertscher, Katz, & Rego’s (2010) data sample consists of private firms that have publicly-traded debt. In the US, private companies are in general not required to file their information with the Security and Exchange Commission (SEC).
However, because the debt in the companies Badertscher, Katz, & Rego (2010) are looking at is public, these firms must file financial statements with the SEC, even though their equity is privately held. In order to more precisely identify the specific means of tax planning used by portfolio companies, they have to hand-collect tax footnote information from SEC financial filings. Their sample of hand-collected data includes 76 PE-backed companies and 38 companies that are non-PE-backed. This hand-collected taxation
information will be less accurate and extensive than the information we have received from the Norwegian Tax Authorities. The thorough and reliable taxation data we are in
possession of will thus represent a strength of our analysis.
Identifying PE-Backed Companies
The next step in the construction of our dataset is to merge the base set with data that can identify the companies that are PE-backed and also the time horizon for which they are PE-backed. This information is collected from the Argentum Center of Private Equity (ACPE) database, which we were given access to by Carsten Bienz. By merging our base set with ACPE-data based on organization numbers, we are able to create dummy variables that indicate PE-ownership. The term PE-backed companies refers to companies that are currently owned by PE-firms, and which were initially acquired through a buyout
investment. As a result of this, we drop all portfolio companies that are classified as in the investment stages “seed” or “venture”. This leaves us with 161 PE-backed companies.
Inclusion of Holding Company Debt
As a lot of debt related to the PE-backed companies can be kept in holding companies, we want to identify each PE-backed company’s holding structure, and attach the holding company’s debt to the PE-backed company if possible. This is because we believe that the holding companies might have taken on substantial amounts of debt related to the buyouts of the PE-backed companies. From Carsten Bienz we were given access to a dataset where the holding structures of 134 PE-backed companies were already identified, and further we hand-collect the holding structures for an additional 42 PE-backed companies by looking
them up in The Brønnøysund Register Center (Brønnøysundregistrene). PE-backed companies whose holding company has ownership stakes in other companies as well are excluded, as we in these cases will be unable to identify the debt attached to the specific PE-backed company in question. By attaching the debt from the holding companies we have identified to the PE-backed companies, we believe to have created a more realistic debt structure of the PE-backed companies than without attaching this debt.
A problem that arises with regard to this, however, is the fact that we do not have
information on the holding structures of all the companies in our base set. Within the time frame of this thesis, it would not be possible to look up the holding structures of each company in our base set. This can potentially create artificially larger leverage ratios of our PE-backed companies versus the non-PE-backed, which represents a weakness of our analysis.
Industry Classification
When calculating the tax planning proxy Discretionary Permanent Book-Tax-Differences (as described later on in this paper), we find the residual of a regression, which is estimated by industry and year. Because of this, we need industry codes attached to each company in our sample. We also use the industry codes in the propensity score matching method as an observational firm characteristic. The industry codes are collected from the SNF database; a database owned by the Norwegian School of Economics (NHH) and the SNF foundation.
This database contains accounting and corporate information for all Norwegian public and private firms. From the SNF database, we use the industry classification system called
“Bransjekode 2”, which consists of 973 different codes, and assign one code to each company in our sample.
Omitted Observations
Many of the proxies for tax planning, and also the observable firm characteristics used in the propensity score matching, are lagged variables. Our dataset is therefore incomplete for the years 2003 and 2004, as many missing values are generated for these years. Because of this, we choose to leave these years out of our analysis. Observations from these years have although been useful when generating lagged values for later years. Omitting the years 2003 and 2004 from our dataset reduces the number of PE-backed companies to 129. We also
drop observations where variables used in the analysis are missing from our final dataset.
This further reduces the number of PE-backed companies to 74.
Some of the observations in our sample are observations of companies that have been exited by PE-firms. These would have been classified as non-PE-backed firms. However, since they might have been affected by their previous PE-backing, we choose to exclude observations of firms that have previously been PE-backed.
After merging our base set with data from ACPE and SNF, in addition to correcting for the PE-backed companies’ holding company debt, we have constructed the final datasets to be used in our analysis. In the section below, we will describe the observation and company distributions for both Hypothesis 1 and 2 in greater detail.
Table 1: Yearly Observation Distribution in the Final Dataset for Hypothesis 1
Table 1 reports the observation distribution of PE-Backed and Non-PE-backed companies in Hypothesis 1 for each year. The column “Non-PE-Backed” shows the number observations of companies that are not PE-Backed in our sample for each year. The column “PE-Backed” shows the number of observations of PE-backed companies for each year in our sample. “Total” displays the total number of company observations we have in our sample for each year.
Year Non-PE-Backed PE-backed Total
2005 41,536 8 41,544
2006 52,367 13 52,380
2007 59,520 20 59,540
2008 55,269 30 55,299
2009 57,828 24 57,852
2010 61,264 25 61,289
2011 63,211 31 63,242
2012 65,438 23 65,461
2013 64,197 22 64,219
2014 62,150 24 62,174
Total 582,780 220 583,000
For hypothesis 1, our final dataset consists of 160,846 companies, where 74 of these are marked as PE-Backed companies. Over the years 2005-2014, we have a total of 583,000 firm-year observations, where 220 of these are PE-Backed company observations.
Dataset for Hypothesis 2
For Hypothesis 2, we extend the final dataset using in Hypothesis 1 by creating a dummy variable indicating whether a company is going to get PE-backed at a later stage or not, called PE-Target. If the company is to get PE-backed at a later stage, it is assigned a dummy value of 1 for the years prior to the PE-backing. At the time when a company actually is PE-backed, this year and the subsequent firm years are deleted from the sample, as we want to compare the companies prior to8 getting PE-backed to valid peers, which we believe a PE-backed company will not be. The observation distribution for each firm year is displayed below.
Table 3: Yearly Observation Distribution in the Final Dataset for Hypothesis 2
Table 3 reports the observation distribution of Non-PE-Targets and PE-Target companies in Hypothesis 2 for each year. The column “Non-PE-Targets” shows the number of observations of companies that are not targets for PE-firms in our sample for each year. The column “PE-Targets” shows the number of
observations of PE-Target companies for each year in our sample. “Total” displays the total number of
Table 2: Total Observation Distribution in the Final Dataset for Hypothesis 1
Table 2 reports the number of companies and observations used in the analysis of hypothesis 1, specified by whether the company is PE-backed or not. The full sample consists of 160,846 unique firms and 583,000 observations. The column “PE-Backed” specifies the number of PE-backed companies and the number of observations of PE-backed companies in the sample. The column “Non-PE-Backed” specifies the number of non-PE-backed companies and observations of non-PE-backed companies in the sample. “Total”
specifies the total number of companies in the sample, consisting of both PE-backed and non-PE-backed companies. The last column is the sum of the first two columns. In this column, the numbers are larger than the “Total” column due to the fact that some of the companies in the sample are non-PE-backed in certain years and PE-backed in other years. One company can thus be classified as PE-backed in one year and non-PE-backed in another, but it will never have two different classifications in the same year.
PE-Backed Non-PE-Backed Total PE+Non-PE
Firms 74 160,800 160,846 160,874
Observations 220 582,780 583,000 583,000
observations of companies we have in our sample for each year.
Year Non-PE-Targets PE-Targets Total
2005 48,249 28 48,277
2006 59,615 20 59,635
2007 64,886 17 64,903
2008 58,956 9 58,965
2009 60,977 9 60,986
2010 64,277 8 64,285
2011 66,154 5 66,159
2012 68,364 5 68,369
2013 67,012 1 67,013
2014 64,910 0 64,910
Total 623,400 102 623,502
We see that there are zero observed PE-targets for year 2014 and only one observation for year 2013, and we hence omit these years from our analysis of Hypothesis 2.
Table 4: Total Observation Distribution in the Final Dataset for Hypothesis 2
Table 4 reports the number of companies and observations used in the analysis of Hypothesis 2, specified by whether the company is a PE-Target or not. The full sample consists of 149,181 unique firms and 491,579 observations. The column “PE-Target” specifies the number of PE-target companies and the number of observations of PE-Target companies in the sample. The column “Non-PE-Target” specifies the number of companies and observations that are not PE-Targets in the sample. “Total” specifies the total number of companies and observations in the sample, consisting of both target and non-target companies. PE-backed companies are not included among the Non PE-targets, as we believe such an inclusion could possibly bias our analysis.
PE-Targets Non PE-Targets Total
Firms 41 149,140 149,181
Observations 101 491,478 491,579
For Hypothesis 2, our final dataset consists of 149,181 companies, where 41 of these are marked as PE-targets. Over the years 2005-2012, we have a total of 491,579 firm-year observations, where 101 of these are PE-target observations.
Limitations to Our Dataset
When looking at individual years, we have a relatively small number of observations of PE-backed companies and PE-Targets, which might represent a small-sample bias. This applies particularly to the years 2013 and 2014 in Hypothesis 2. These years are therefore excluded from our analysis. Nevertheless, we note that the small-sample bias might still be present, as we continue to have a limited number of observations. We view our small samples as a limitation to our analysis. However, this is not an uncommon phenomenon in the area of private equity-related research. In addition to this, the Norwegian private equity market is small, so the population of PE-backed companies and PE-targets is inherently limited.
Proxies for Tax Planning
Definition of Tax Planning
The term tax planning will in this thesis refer to activities carried out in order to minimize a company’s tax bill, but we limit the analysis to only looking at legal activities that are utilized in order to maximize shareholder value. This means that we do not aim at detecting
underreporting of income or any other illegal tax related activities. We use five different proxies for tax planning in order to compare the tax planning activities of PE-backed and PE-target companies to comparable companies. These proxies are used in previous research, and we describe them in greater detail in Section II of this thesis.
Description of Our Proxies for Tax Planning
In our analysis, we rely on five proxies of tax planning. Each of the proxies reflects
different types of tax planning. We utilize four proxies that reflect book-tax nonconforming tax planning, which are measures that reduce the firm’s income tax liability but not its financial income. We also use Leverage Ratio of the firms as a proxy for tax planning, as larger leverage ratios will produce larger tax shields. Below we will describe further what these proxies measure, how we have constructed them and why we have chosen to use them.
Total Book Tax Differences
Like Badertscher, Katz, & Rego (2010), our first proxy for tax planning is an estimate of the difference between a firm’s pretax book income and taxable income, scaled with total assets, which we refer to as Total Book Tax Differences. This proxy is formally found by:
𝑇𝑜𝑡𝑎𝑙 𝐵𝑜𝑜𝑘 𝑇𝑎𝑥 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒𝑠 =(𝑁𝑒𝑡 𝑃𝑟𝑜𝑓𝑖𝑡𝑠 −𝑇𝑎𝑥𝑒𝑠 𝑃𝑎𝑦𝑎𝑏𝑙𝑒 + ∆𝐷𝑒𝑓𝑒𝑟𝑟𝑒𝑑 𝑇𝑎𝑥𝑒𝑠 𝑆𝑡𝑎𝑡𝑢𝑡𝑜𝑟𝑦 𝑇𝑎𝑥 𝑅𝑎𝑡𝑒 ) (𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)
The taxable income is estimated by dividing the total tax cost by the statutory tax rate. The statutory tax rate for Norwegian companies was 28 percent for all of the years in our sample up until 2013, but as the Norwegian tax rules changed in 2014, the tax rate changed to 27 percent for this year (Bjertnæs, 2015).
There are a number of studies that suggest that book tax differences can be used as a signal of tax planning activity (Badertscher, Katz, & Rego, 2010). Wilson (2009) finds that book-tax differences are positively associated with cases of book-tax sheltering, while Mills (1998) finds that proposed IRS audit adjustments are positively related to large positive book tax
differences. Despite the evidence of book tax differences being associated with tax planning activities, the book-tax difference measure has limitations (Badertscher, Katz, & Rego, 2010). For instance, Manzon & Plesko (2002) identify firm specific characteristics associated with book tax differences that are not necessarily reflective of corporate tax planning.
Discretionary Permanent Book Tax Differences
Our second proxy for tax planning is Frank, Lynch, & Rego’s (2009) measure called
Discretionary Permanent Book Tax Differences. This proxy captures tax planning activities that directly affect net income through a reduction in total tax expenses. However, the proxy does not capture tax planning activities that generate a deferral of cash taxes paid to the tax authorities (Badertscher, Katz, & Rego, 2010). Larger values of the proxy
Discretionary Permanent Tax Differences indicate larger levels of tax planning (McGuire,
Omer, & Wang, 2010). We calculate this proxy as the residual of the following regression, estimated by industry and year, in accordance with Frank, Lynch, & Rego’s (2009) research:
𝑃𝐸𝑅𝑀𝐷𝐼𝐹𝐹𝑖𝑡 = 𝛽0 + 𝛽𝐼𝑁𝑇𝐴𝑁𝐺𝑖𝑡 + 𝛽3Δ𝑁𝑂𝐿𝑖𝑡+ 𝛽4𝐿𝐴𝐺𝑃𝐸𝑅𝑀𝑖𝑡+ 𝜀𝑖𝑡
𝑃𝐸𝑅𝑀𝐷𝐼𝐹𝐹 = [{𝐵𝐼 − (𝐶𝑇𝐸
𝑆𝑇𝑅)} − (𝐷𝑇𝐸 𝑆𝑇𝑅)]
(𝑏𝑒𝑔𝑖𝑛𝑛𝑖𝑛𝑔 𝑜𝑓 𝑦𝑒𝑎𝑟 𝑎𝑠𝑠𝑒𝑡𝑠)
Table 5: Explanation of the variables in the proxy calculation
BI= Ordinary Result Before Taxes CTE=Current Year Payable Taxes STR= Statutory Tax Rate
DTE= Deferred Taxes
ΔNOL=Change Net Operating Loss Carry Forwards
LAGPERM=Lagged Permanent Differences)= PERMDIFF in year t-1
INTANG= Intangible Assets= 𝑅&𝐷+(𝐶𝑜𝑛𝑐𝑒𝑠𝑠𝑖𝑜𝑛𝑠+𝑃𝑎𝑡𝑒𝑛𝑡𝑠&𝐿𝑖𝑐𝑒𝑛𝑠𝑒𝑠)+𝐺𝑜𝑜𝑑𝑤𝑖𝑙𝑙+𝐷𝑒𝑓𝑒𝑟𝑟𝑒𝑑 𝑇𝑎𝑥 𝐴𝑠𝑠𝑒𝑡𝑠
𝑆𝑢𝑚 𝐴𝑠𝑠𝑒𝑡𝑠
The left hand side of the regression above reflects the permanent book tax differences. The right hand side controls for items that are unrelated to tax planning, but that lead to
permanent differences. The residual is thus intended to reflect the permanent differences caused by tax planning.
Like Frank, Lynch, & Rego (2009), we control for intangible assets since differences between financial accounting and tax accounting rules create differences between taxable and financial income that are unrelated to tax planning. As changes in deferred taxes are connected to amortizations, which in turn are not regarded as tax planning activities, we control for changes in net operating loss carry forwards (Miller & Skinner, 1998). We also control for permanent differences that are persistent through time, and therefore are less likely to reflect tax planning, by including lagged permanent differences in our regression.
However, as Frank, Lynch, & Rego (2009) point out, controlling for lagged permanent differences might exclude some tax planning activities if the firm is consistent in its tax planning through time.
In order to adapt the Discretionary Permanent Book-Tax Differences proxy to Norwegian tax rules, we make some modifications to Frank, Lynch, & Rego’s (2009) original proxy.
In order to adapt the Discretionary Permanent Book-Tax Differences proxy to Norwegian tax rules, we make some modifications to Frank, Lynch, & Rego’s (2009) original proxy.