Loss or Lost? Economic Consequences of

Internal Capital Markets in Business Groups⇤

Marcel Olbertolbert@uni-mannheim.de

University of Mannheim - Business School

December 4, 2019

Abstract

This paper examines when business groups support distressed member firms and how suchgroup support impacts firm survival and productivity. I exploit plausibly exogenous vari-ation in the value of loss-related tax shields that affect the incentives to support distressedmember firms. Evidence from a cross-sectional difference-in-differences design based ona large international sample, as well as a single-country regression discontinuity design,suggests that business groups avoid member firms’ defaults if loss-related tax shields aremore valuable. This tax-motivated group support keeps low-quality member firms alive,resulting in lower productivity at the firm and the market level. The findings indicate thatmanagers exploit tax-related cash incentives through internal capital markets at the costof less efficient resource allocation.

Keywords: Business groups; Internal capital markets; Resource allocation; Financial distress;Losses; Corporate taxation; Zombie firms; ProductivityJEL classifications: G32, G33, H25, M41, M48, D22, D24

The Online Appendix is available here.⇤I greatly appreciate the guidance and support of my dissertation committee: Jannis Bischof, Lisa De

Simone, and Christoph Spengel (chair). I gratefully acknowledge valuable comments and suggestions fromJonathan Cohn, Holger Daske, Philipp Doerrenberg, John Gallemore, Joachim Gassen, Stephen Glaeser, NathanGoldman, Shane Heitzman, Jeffrey Hoopes, Jon Kerr (discussant), Rebecca Lester, Christian Leuz, PetroLisowsky, Christopher Ludwig, Ernst Maug, Ed Maydew, Valentin Quinkler, Jeri Seidman, Sebastian Siegloch,Johannes Voget, Malcolm Wardlaw, and Ann-Catherin Werner. I thank participants of the Corporate FinanceArea Seminar and the Accounting & Taxation Brownbag Seminar at the University of Mannheim, workshopparticipants at North Carolina State University, as well as participants at the 2019 Emerging ResearchersConsortium in Accounting (Bolzano), the 9th EIASM Conference on Current Research in Taxation (Barcelona),the 2019 AAA Annual Meeting, and the TRR 266 Accounting for Transparency Annual Conference for helpfulcomments and suggestions. I also thank Kathleen Andries, Martin Jacob, and the members of the WHU andTexas A&M Tax Readings Groups for feedback. I highly appreciate the collaboration of Yannik Schneiderand Peter Severin during the data collection process. This paper benefited from feedback received during myresearch visit at the University of North Carolina at Chapel Hill. I thank the UNC accounting group for theirhospitality and gratefully acknowledge financial support from the Julius-Paul-Stiegler Gedaechtnis foundationand the University of Mannheim Business School. This paper received the TRR 266 Accounting for TransparencyAnnual Conference Best Paper Award.

1 Introduction

Internal capital markets account for the largest share of resource allocation in the economy

(Matvos & Seru 2014), and business groups with access to internal capital markets make up

a substantial part of corporate activity around the world.1 While the literature has shown

that business groups reallocate resources to mitigate the negative effects of financial crises and

external market frictions, large-sample evidence on the incentives and economic outcomes of

within-group support is scarce (e.g., Gopalan et al. 2007, Buchuk et al. 2014, Almeida et al.

2015). This paper uses variation in tax incentives to examine business group managers’ decision

to support financially distressed member firms and investigates whether loss-related tax shields

affect the efficiency of internal resource allocation.

Internal capital markets provide diversified businesses with the opportunity to transfer cap-

ital or off-balance sheet assets such as debt assurances across entities. Such internal resource

sharing is a fundamental managerial choice (Stein 1997), circumvents the need to access ex-

ternal capital markets, and is sometimes called cross-subsidization (e.g., Berger & Ofek 1995

and Almeida et al. 2015). I study the internal capital markets of 0.5 million business groups

using Bureau van Dijk’s Orbis database. I construct a panel by tracking all member firms’

direct ownership links over the period 2005 to 2017, representing 4.1 million member firm-year

observations in Europe. In contrast to the segments of diversified conglomerates, these member

firms are separate legal entities filing unconsolidated financial statements. Consequently, man-

agers can decide to either support distressed member firms or let them default because they

can individually file for bankruptcy (Khanna & Yafeh 2005, Beaver et al. 2019).2

To identify group support, I relate member firms’ defaults to their accumulated accounting

losses over time. This proxy for member firm-specific net operating losses (NOLs) measures

both the size of a member firm’s loss-related tax shield and the level of financial distress.3 I

compare group member firms’ default rates in response to higher levels of distress with those

of standalone firms. A weaker association between NOLs and defaults of group member firms1See, e.g., La Porta et al. (1999), Khanna & Palepu (2000), Gopalan et al. (2014), Beaver et al. (2019).2The notion of business groups in this paper is in line with the definition of Granovetter (1995), considering

business groups as a collection of cooperating firms with personal, legal (ownership), and operational ties.3As firms in my sample are mostly non-listed member firms of larger business groups, I cannot rely on

measures of financial distress commonly used for public firms in other settings. I show that NOLs are a validmeasure of financial distress as they strongly correlate with the predicted default likelihood. The predicteddefault likelihood is based on a standard default prediction model (Beaver et al. 2019). It is thus similar to aZ -score for private firms (Altman et al. 2017).

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relative to standalone firms would indicate group support through internal capital markets.

To show that business groups make strategic use of internal capital markets, I exploit that

more valuable tax shields inherent to the NOLs decrease the cost of supporting distressed

member firms. I use loss carryforward (LCF) regulations that determine the value of these tax

shields. Lenient LCF provisions allow a member firm to use past accumulated losses to shield

future profits from taxation and thus generate larger cash flows upon return to profitability,

whereas stricter rules limit this opportunity.4 These rules provide a rich testing ground to ex-

amine internal capital market decisions because i) losses can be particularly valuable tax shields

to diversified business groups relative to standalone firms (Berger & Ofek 1995), ii) greater op-

portunities to offset losses against future profits stimulate corporate risk-taking (Ljungqvist

et al. 2017, Langenmayr & Lester 2018), and iii) lenient loss-offset tax policies can alleviate

financing constraints (Bethmann et al. 2018). However, once a group member firm defaults

or is liquidated, loss-related tax shields typically expire. The value of loss-related tax shields

might thus alter managers’ decisions to support a member firm and prevent its default.

Supporting distressed member firms avoids operational disruptions and credit-risk spillovers

within groups due to intra-group lending, loan guarantees, and external borrowers rationing

credit to the group (Gopalan et al. 2007, Beaver et al. 2019). However, such propping is costly

if it subsidizes weaker business units with relatively less rewarding investment projects (Scharf-

stein & Stein 2000). If a member firm has accumulated losses, more lenient LCF provisions

make group support financially more attractive in net present value terms. As managers con-

sider cash flow effects when allocating resources (Stein 1997), they might thus view the member

firm’s NOLs as a valuable loss that generates future cash flows. Holding other incentives for

group support constant, one would then expect fewer defaults of member firms. Under stricter

LCF rules that limit the deductibility of NOLs from future profits, managers might instead

consider the position lost and let the member firm default.

If more valuable tax shields incentivize group support, they might lead managers to engage in

inefficient subsidization of some business units. Specifically, managers might support member

firms with more valuable tax shields but fewer investment opportunities compared to other

member firms. All else equal, resources would then not be channeled towards the business4Figure 1 and Table A.14 in the Online Appendix illustrates a simplified cash flow effect of lenient vs.

strict LCF regulation. When approximating the NOL position, I consider country-specific legislation and firms’pre-tax profits over time net of potential loss carrybacks.

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units that can make the best use of them (Stein 1997, Scharfstein & Stein 2000). Thus, tax

incentives could explain why headquarters deviate from (pre-tax) optimal resource reallocation.

Despite the significant economic benefits of loss-related tax factors, it is not obvious that

managers would consider such tax shields when making internal capital market decisions.

Decision-makers often consult nominal or backward-looking effective tax rates reported in fi-

nancial statements that do not reflect the value of future loss-related tax shields (Graham et al.

2017). Recent evidence further suggests that firms forgo tax benefits inherent to losses due to

the complexity of the tax system (Zwick 2018).

I use two identification strategies to examine the extent and outcomes of group support

contingent on the loss-related tax incentives. In the first approach, I use large-sample panel

data and exploit variation in the opportunities to offset losses against future profits across 31

European countries. I measure the effect of within-firm changes in NOLs on the default proba-

bility of firms in the same country-year and in the same industry-year. I hold constant common

determinants of default and compare the effect of a change in NOL of firms in countries with

strict versus more lenient LCF rules. As I focus on within-country-year variation, I circumvent

known issues with approaches using cross-time changes in regulation as policy shocks (Breuer

2018, Bethmann et al. 2018). Nonparametric nearest-neighbor matching of group firm-years

to standalone firm-years further mitigates concerns that results are attributable to mechanical

cash flow effects or endogenous tax policy. I use the same within-firm, within-country-year,

and within-industry-year variation when studying the real effects of resource reallocation. I

compare how changes in NOLs of similarly distressed firms differentially affect productivity

depending on the leniency of LCF provisions, again holding other financial firm characteristics

constant. I rely on the identifying assumption that losses are plausibly idiosyncratic as they

cannot be explained by member firm and business group time-invariant characteristics such as

the complexity and seasonality of the business model or country- and industry-specific trends.

The panel data analysis cannot rule out that unobserved time-varying firm characteristics

simultaneously drive the recognition of losses, tax considerations, and the workings of internal

capital markets. In my third strategy, I therefore exploit a single-country setting using a

regression discontinuity design (RDD) to affirm the internal validity of my results. In 2011,

Spain restricted the opportunity to offset NOL against future profits for the first time, but only

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for firms exceeding EUR 20 million in sales in the previous year. The regulators determined the

size-based threshold retroactively and with no systematic considerations, leading to a plausibly

exogenous discontinuity in the value of loss-related tax shields of firms just below and just

above the threshold that can be exploited to estimate causal treatment effects (Lee & Lemieux

2010, Leuz & Wysocki 2016, Langenmayr & Lester 2018).

I find that group support avoids bankruptcies of individual member firms, consistent with

such defaults causing negative credit-risk spillovers (Gopalan et al. 2007). However, I document

significantly less group support when the future tax shields of distressed member firms are

less valuable. Evaluated at the sample mean, a one standard deviation increase in NOLs is

associated with an 18 percent higher likelihood of default if NOLs can be netted against 60

percent of future pre-tax profits (as in Germany since 2004). If firms can fully net NOLs against

future pre-tax profits (as in the United Kingdom until 2016), the default likelihood decreases

by 10 percent. While accumulated losses are strongly associated with defaults of standalone

firms, I document no influence of NOL-related tax incentives in these firms. The single-country

RDD lends compelling support to the large-sample evidence.

These estimates suggest an economically significant impact of tax incentives on group sup-

port to avoid corporate defaults as the most extreme realization of distress risk. They also

support the conclusion that tax shields do not mechanically help firms survive, but that cash

tax incentives influence risky internal capital markets decisions. Governments in countries with

generous LCF rules share more of the downside risk of supporting a distressed firm by pro-

viding larger and earlier tax rebates for incurred losses compared to governments in countries

with stricter rules. Consistent with more lenient loss-offset provisions stimulating corporate

risk-taking (Domar & Musgrave 1944, Ljungqvist et al. 2017, Langenmayr & Lester 2018, Arm-

strong et al. 2019), managers are thus more likely to engage in group support with uncertain

outcomes when governments share more of the associated risk.

Several cross-sectional tests reinforce the inference that groups strategically exploit loss-

related tax shields to avoid member firm defaults. Results are particularly strong when a

business group has fewer cash proceeds to reallocate. This finding is consistent with theory and

evidence on more active internal capital markets under financial constraints (Giroud & Mueller

2019) and suggests that groups prevent defaults through non-financial support. Further, tax

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shields particularly influence group support if groups are smaller and member firms are more

tightly integrated within the group. This finding is consistent with managers having a harder

time identifying the (tax) benefits of internal capital markets once groups become more complex

(Stein 1997). Also, groups might already have access to other tax shields if they consist of more

member firms. Finally, results are more pronounced for more highly leveraged and smaller

member firms. These findings suggest that groups exploit tax shields to overcome external

financing constraints, mitigate bankruptcy risk under high leverage, and avoid relatively high

bankruptcy costs (Leland & Toft 1996, Hovakimian et al. 2012).

The analysis then turns to the real effects of group support. I test whether more tax-

motivated group support leads to different productivity outcomes relative to less tax-motivated

support. I define zombie firms as entities which are artificially kept alive and thereby likely

cause inefficient resource allocation both within groups and within entire industries (Caballero

et al. 2008, Acharya et al. 2019).5 I also look at total factor productivity as the standard

measure of the efficient use of capital. Using standalone firms as counterfactuals, I document

that member firms are less likely to convert into zombies and become more productive after

accumulating losses, hinting at efficient capital markets in times of distress.

However, I find that group support increases the likelihood of firms becoming zombies and

reduces total factor productivity if managers respond to tax incentives.6 As I compare member

firms in similarly weak economic conditions but with more or less valuable tax shields, this result

suggests that groups prevent otherwise unhealthy firms from exiting the market to exploit tax

benefits. The effects are stronger when loss-related tax shields are most valuable (i.e., under

lenient LCF provisions and high tax rates). On the aggregate industry level, I find that more

extensive group support is associated with better financial health of other market participants,

consistent with bankruptcies having negative spillover effects not only within local markets

(Bernstein et al. 2019) but also within domestic industries. Yet, the tax-motivated retention of

capital at loss-making, less productive member firms comes at the cost of lower industry-wide

productivity. This result is in line with theory that business groups’ internal capital markets

can have negative spillovers (Almeida & Wolfenzon 2006).5As my analysis is based on the Orbis data, I follow Storz et al. (2017) and classify a firm as a zombie if it

has a negative return on assets, negative net investments, and a debt servicing capacity (EBITDA over totalfinancial debt) of less than 0.05 for at least two consecutive years.

6The results are insensitive to using a variety of different measures of firm-level total productivity.

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Supplementary tests identify intra-group trade and the reallocation of assets as channels of

group support in the absence of valuable tax shields. Groups can artifically increase member

firms’ revenues and/or reduce their input costs if they manipulate transfer prices of internal

transactions (Huizinga & Laeven 2008). I find that member firms report higher sales, assets,

and profitability relative to the group after they accumulate losses, consistent with the group

reallocating resources through internal trade. More lenient LCF provisions attenuate these

effects, consistent with larger cash tax benefits decreasing the need for reallocating financial

assets. Again, the RDD supports the large-sample results. This evidence indicates that group

support happens primarily through non-financial resource allocation, such as extended guaran-

tees, if a distressed firm can benefit from lower future cash tax burdens. Taken together with the

evidence that tax-motivated group support hampers productivity, it also indicates that groups

allocate resources to the more productive units if tax policy does not distort decision-making.

Several tests validate the results.7 Domestic tax rules could respond to industry-specific

shocks that also affect NOLs, inducing my results if there is a systematic composition of group

member versus standalone firms in these country-industry segments. However, results are

insensitive to using industry-country-year fixed effects. Further, I confirm that sample firms are

almost identical along a host of characteristics across countries with and without limitations in

the deductibility of NOLs. These statistics mitigate concerns that firm characteristics correlate

with the leniency of LCF provisions and outcomes of resource allocation or that business groups

endogenously avoid NOLs in countries with strict provisions. I also account for the opportunity

to offset profits and losses across member firms of the same group (tax consolidation).

This study contributes to the finance, accounting, and tax literatures. First, I provide novel

evidence on the incentives and the outcomes of business groups’ internal capital markets using

a representative, international setting.8 Almeida et al. (2015) document that the direction of

capital flows across Korean chaebol member firms is consistent with efficient resource allocation

during the Asian financial crisis. I extend their findings by showing that group support under7I refer the reader to the Robustness Section and the Online Appendix for the complete set of tests.8Business groups’ internal capital markets have predominantly been studied in single-country settings in

developing economies (e.g., Gopalan et al. 2007, Buchuk et al. 2014, Almeida et al. 2015). The economicoutcomes of internal capital markets are largely unexplored and ex ante ambiguous. Studying publicly listedconglomerates in the U.S., Giroud & Mueller (2015) show that reallocation of capital across domestic plantsleads to higher firm-level productivity, but only if firms are financially constrained. Outside the setting offinancial distress, Cho (2015) finds that U.S. conglomerates reallocate capital to segments with more investmentopportunities after the degree of external monitoring increases due to improved segment disclosures.

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distress increases productivity but that tax incentives induce group support with adverse out-

comes, both at the firm and at the market level. This finding is consistent with less efficient

resource allocation when managers do not evaluate the best set of investment opportunities (Ra-

jan et al. 2000, Cho 2015). Further, my evidence adds to existing evidence on intra-group loans

(Desai et al. 2004, Gopalan et al. 2007, Buchuk et al. 2014) and equity investments (Gopalan

et al. 2014, Almeida et al. 2015) as channels of internal capital markets. My analyses identify

internal trade and asset transfers as instruments to alleviate financial distress and suggest that

tax benefits substitute for these types of internal funds.

Second, my results add to the nascent stream of accounting research on the determinants of

corporate defaults within business groups (Beaver et al. 2016, 2019) and on the relevance and

information content of accumulated losses (Heitzman & Lester 2019). The evidence that tax

shields decrease within-group default rates and allow financial turnarounds with less internal

cash is of primary interest to financial statement users such as creditors, rating agencies, and

researchers because group support impacts the creditworthiness of individual member firms and

the group as a whole (e.g., Beaver 2010, Standard & Poors 2013).

Third, this study sheds new light on the real effects of taxes. Building on recent findings

that lower tax rates and lenient loss-offset provisions increase risky investment (Ljungqvist

et al. 2017, Langenmayr & Lester 2018), I show that loss-related tax shields affect the arguably

risky forward-looking decision of group support. Other research documents that firms engage

in tax planning through timing their loss recognition (Maydew 1997, Erickson et al. 2013)

or cross-border profit shifting to loss-making affiliates (De Simone et al. 2017, Hopland et al.

2018). While consistent with De Simone et al. (2017), my results speak to the productivity

outcomes of profit-to-loss shifting. Balakrishnan et al. (2019) show that tax planning strategies

in conjunction with organizational complexity come at the cost of lower financial transparency.

My results complement Balakrishnan et al. (2019) by showing that exploiting tax benefits across

the organizational hierarchy has the real cost of a less productive use of the group’s capital.

This study has implications for the longstanding debate among policymakers considering

more generous tax loss-offset provisions as tools to alleviate financial frictions and stimulate

investments (Altshuler & Auerbach 1990, Bethmann et al. 2018). My findings suggest that

lenient tax loss rules might have unintended effects if firms have access to internal capital

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markets. More stringent rules as mandated by the 2017 U.S. tax reform and recent policy in

Europe appear to increase corporate default rates in business groups but mitigate inefficient

subsidization of unproductive entities.

2 Institutional Background and Hypotheses

2.1 Taxes and Corporate Decisions under Financial Distress

Financial distress is a firm’s intermediate status between solvency and bankruptcy (Titman

1984, Purnanandam 2008). The setting of financial distress is particularly suited to study the

mechanisms of internal capital markets since group support is a key instrument to avoid adverse

outcomes of financial distress (Gopalan & Xie 2011). Business groups may seek to cushion

financial distress of member firms since it is costly and typically associated with negative

performance both for the individual firm and the whole business group due to operational

disruptions (Opler & Titman 1994, Gopalan et al. 2007). In particular, parent firms might

become liable for subsidiaries’ debt (veil piercing), and defaults of separate group firms entail

credit-risk spillovers for other group members (Standard & Poors 2013, Beaver et al. 2019).

Managers need to weigh the costs and benefits of supporting member firms, including tax-

related factors. I exploit the tax treatment of losses that affects the benefits of group support

versus bankruptcy and, thus, the trade-off between making use of a distressed member firm’s

tax assets and the uncertainty about its future performance. I focus on loss carryforward rules,

which are one of the most relevant tax factors for distressed firms. The tax treatment of profits

and losses is typically asymmetric. While profits are always taxed, losses generally do not result

in immediate tax refunds (Altshuler & Auerbach 1990, Bethmann et al. 2018). To deduct these

losses from taxable profits in the future, firms have to create an accumulated loss position (see

Heitzman & Lester 2019 for a detailed discussion).

Figure 1 illustrates how tax loss carryforward rules and tax rates might influence corporate

decision-making under financial distress. The net present value (NPV) of supporting a group

firm depends on the non-tax cost of group support, the expected future profitability, and the

tax environment. Given the firm has been loss-making for at least one year, a forward-looking

manager should consider the value of the NOLs, which depends on the rules that can limit the

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offset of past losses with future profits.9 The most critical limit is the share of pre-tax profits

that can be netted against the NOLs (hereafter LCF Deductibility). The extent to which the

tax rate decreases future cash flows decreases in LCF Deductibility. Figure 1 illustrates this

relationship, and Table A.14 in the Online Appendix presents a simplified example. If the tax

rate increases, the NPV decreases. This relationship is modest when there is no limit on the

deductibility of LCF (i.e., when LCF Deductibility is 100 percent). The relationship becomes

very steep for lower values of LCF Deductibility because pre-tax profits are less shielded by

deductible losses. Overall, there is a strong negative relationship between LCF Deductibility

and the NPV for any given tax rate greater than zero. Thus, if decision-makers take into

account that taxes affect the net present value (NPV) of group support, the LCF provisions

should affect the extent to which business groups support distressed member firms.

2.2 Hypotheses

The opportunity to offset operating profits of some projects with losses of other projects is

a valuable tax benefit available to diversified firms but not to standalone firms (Berger &

Ofek 1995). As member firms are individual corporate taxpayers, loss-related tax shields can

only be exploited if a group prevents the default of the member firm, which would otherwise

lead to an expiration of the tax shield. Accordingly, Khanna & Yafeh (2005) note that ‘tax

considerations may also make within-group risk-sharing worthwhile’. Specialized practitioners

focus on maximizing tax benefits in bankruptcy proceedings or situations when bankruptcies

are avoided (OECD 2011, KPMG 2016). Specifically, the use of NOLs is a central concern of

corporate taxpayers. If expected future cash flows are higher because NOL can offset profits

earlier or to a greater extent, supporting member firms is less costly and requires fewer internal

funds to be reallocated compared to a scenario of restrictive LCF rules. Further, theory predicts

that more lenient LCF rules introduce a co-insurance effect alleviating the tax discrimination

of risky projects (Domar & Musgrave 1944). In the setting of this paper, the government shares

a greater part of the downside risk of supporting a distressed firm, which should lead to more

risk-taking and motivates the following prediction.9In this example, I assume that the group incurs some fixed costs of group support and that the distressed firm

has generated losses in the past that have been accumulated in the NOL position for tax purposes. Associatedopportunity costs, such as foregone profits at other group firms, are assumed to be captured in the FixCost.The notes to Figure 1 lay out the exemplary NPV calculation.

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H1: Business groups are more likely to avoid member firms’ default if the future tax shield of

accumulated losses is more valuable.

Theory predicts that corporate headquarters can add value through internal capital markets

but only if resources are channeled to business units that can make the best use of them (Stein

1997, Scharfstein & Stein 2000). If managers consider the cash tax benefits when deciding about

supporting a distressed member firm, they might deviate from optimal resource allocation. As

a distressed member firm facing lenient LCF provisions expects lower tax burdens in the future,

its pre-tax hurdle rate to make support worthwhile decreases. All else equal, managers might

then decide to save member firms with ex-ante lower investment opportunities relative to other

business units, leading to the following hypothesis.

H2: Conditional on group support, distressed member firms are less productive if the future

tax shield of accumulated losses is more valuable.

There are at least three reasons why group support might not respond to tax incentives. First,

non-tax factors such as managers’ primary duty to serve debt holders when under financial

distress might dominate resource sharing for tax purposes. Second, survey evidence suggests

that managers mostly rely on statutory or backward-looking GAAP effective tax rates when

making financial decisions (Graham et al. 2017). Such heuristics neglect the tax shield of

a distressed firm’s NOL position because it depends on future tax legislation and the firm’s

financial status. Third, recent evidence shows that corporations in the U.S. often do not use

tax losses to claim potential refunds, probably due to complexity in the tax code (Zwick 2018).

3 Research Design and Data

3.1 Identification Strategy

Cross-sectional Differences in Differences

The main analysis is based on the following model estimated via ordinary least squares (OLS).

Outcomei,t = ↵1NOLi,t + ↵2NOLi,t ⇤ LCF Deductibilityc,t+1 +X�+ �c,t + �j,t + �i + ✏i,t (1)

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I begin by examining corporate defaults as the outcome variable, where Default(t+ 1)i,t is an

indicator variable taking on the value of 1 if member firm i goes bankrupt in year t+1 and zero

otherwise.10NOLi,t is the variable of interest and represents the accumulated net operating

loss position of member firm i in year t. The measure results from accumulating unconsolidated

accounting losses since 2000, net of pre-tax profits and net of potential loss carrybacks, scaled

by total assets. As opposed to the consolidated measure for publicly listed firms hand-collected

by Heitzman & Lester (2019), my NOL variable captures losses at the separate legal entity level

located in a specific country at a specific point in time. It is thus a proxy for financial distress

of individual member firms. LCF Deductibilityc,t+1 measures the share of future pre-tax profits

that can be netted against NOL according to a country c’s tax provisions in year t+1. I use

tax regulation as of t+1 because, by the end of year t, managers are typically aware of the

provisions in the following year and can estimate the value of current NOLs. The interaction

term NOL * LCF Deductibility captures the value of a distressed member firm’s tax shield. If

LCF Deductibility is less than 100 percent, only a fraction of the accumulated NOL position can

be used to offset a member firm’s future profits to generate cash tax savings. I include measures

for profitability, leverage, size, and debt coverage as time-varying member firm characteristics

in the vector X. These variables are common predictors of default (e.g., Altman et al. 2017,

Beaver et al. 2019). Following Bethmann et al. (2018), I additionally include a firm’s cash

holdings, compensation expenses, capital intensity, and age as firm characteristics potentially

correlated both with the propensity to incur losses and the default probability.

�c,t denotes country-year fixed effects and �j,t denotes industry-year fixed effects, where the

four-digit NACE classification defines industry j. This fixed-effects structure accounts for any

time-varying factors at the country level such as GDP levels and growth, regulatory changes

(i.e., also tax policy) as well as at the industry level such as technological developments or

seasonal patterns. The combination of country-year and industry-year fixed effects isolates

variation in firms’ level of NOL within the same country and year (across different industries)

and within the same industry and year (across countries). I also include firm fixed effects (�i)

that control for any time-invariant firm characteristics, such as the complexity of the business10I use the firm’s legal status, i.e., default vs. active, in t+1 since financial data used for the control variables

relate to fiscal year t. Thus, default is captured as the legal status after the firm files financial accounts for thelast time as an active firm (see also Beaver et al. 2019). I multiply the variable by 100 to facilitate the economicinterpretation of regression coefficients. All variables are defined in Appendix A. The Dataset Section in theOnline Appendix provides for a detailed discussion of all variables used in the analysis.

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model, management style and corporate culture, or risk aversion, potentially correlating with

loss recognition and internal capital market outcomes. The inclusion of firm fixed effects leads

to an identification based on a cross-sectional triple differences design similar to the approach

in Acharya et al. (2019). The effect of group support is identified from within-firm variation

(first difference) in accumulated losses that increase in the level of financial distress and serve

as a tax shield in the future. Firms in the same industry at the same point of time and in the

same country at the same point of time serve as counterfactuals (second difference). The third

difference stems from the interaction term NOL * LCF Deductibility that effectively compares

the response of NOL-varying firms and their counterfactuals in countries with stricter versus

laxer LCF rules.11

As a result, the coefficient ↵1 measures by how much an increase in NOL affects the likelihood

of default of a group member firm compared to a member firm in the same industry-year and

country-year, holding other common, firm-level determinants of defaults constant. The second

coefficient of interest is ↵2. It captures the differential effect of such a change in NOL on the

default probability if the firm is located in a country with more lenient versus stricter forward-

looking loss-offset provisions. If managers consider the value of tax shields when supporting

distressed group firms, this group support will manifest in a negative coefficient ↵2.

The empirical design laid out in equation 1 treats the firm-year observations (i.e., the panel

data) as a repeated cross-section, using variation within a given year across countries and

industries rather than over time (see also Breuer 2018). To account for the repeated cross-

section in the estimation of standard errors, I cluster standard errors at the firm and the country-

year level. These levels of clustering account for arbitrary dependence within a given country

in a given year and within firms across the sample period (Petersen 2009). The within-country-

year design addresses concerns about the endogeneity of tax policy in countries. It controls

for cross-country differences and within-country changes over time, thereby overcoming known

shortcomings of usual (time-series) difference-in-differences designs that use tax law changes in

single countries as potentially exogenous shocks (Bethmann et al. 2018, Breuer 2018).12

11While my specifications include country-year and industry-year fixed effects as in Breuer (2018), Acharyaet al. (2019) and Bethmann et al. (2018) allow for country-industry-year specific time trends. My results arenot sensitive to using this fixed effects structure (see Table A.7 in the Online Appendix).

12Accordingly, I do not include the tax factors as separate control variables since they vary at the country level.The fixed-effects structure particularly controls for the potential confounding effect of group tax consolidationopportunities and other tax policy factors and changes at the country level.

12

A remaining threat to identifying group support via a causal relationship between the value

of loss-related tax shields and member firm defaults is that firms’ NOLs and LCF provisions

are correlated with other unobserved factors that determine internal capital market outcomes.

I argue that my measure of NOL captures plausibly idiosyncratic losses since they are not

explained by industry- or country-specific shocks. Firm fixed effects mitigate concerns that

groups anticipating losses and altering their behavior will bias results. Such factors are likely

captured in time-invariant firm characteristics, e.g., overall firm risk or seasonal fluctuations in

performance (Bethmann et al. 2018). To the extent that manager turnover is limited or at least

the authority of management boards is relatively time-invariant, this strategy accounts for man-

agerial power and social ties that affect resource allocation within U.S. conglomerates (Glaser

et al. 2013, Duchin & Sosyura 2013). Also, the firm fixed effects absorb any time-invariant

business group characteristics as well as correlations between member firm and overall group

performance that might affect the recognition of losses and internal capital market outcomes.

Thus, the effect of group support can be attributed to a change in NOLs, and any incremental

effect under different tax rules should be evidence for tax incentives influencing group support.13

Another concern is that a negative coefficient ↵2 captures a mechanical relationship between

higher future cash flows and survival that would also apply to standalone firms that cannot be

supported by other group firms. I address this issue through estimating equation 1 based on a

carefully matched sample of standalone firms. Specifically, I search for the nearest neighbor for

each group member firm-year within the same NACE industry section and country as follows. I

restrict the potential control sample to standalone firm-years that have also either a positive or

negative return on assets (Neg. ROA) and that have an absolute distance of less than one stan-

dard deviation in return on assets, total assets, and leverage, three arguably important default

predictors. I then calculate the Euclidean distance based on these three continuous variables

and keep the standalone firm-year with the lowest distance. If these firms are sufficiently simi-

lar along several dimensions of observable characteristics, any differential estimates on ↵1 and

↵2 should be attributable to the essential difference between group and standalone firms: the

existence of internal capital markets in business groups. Effectively, this strategy aims at a13To the extent that member firms of the same business group share many of these characteristics (which is

likely in my sample of only directly and majority-owned affiliated firms), these confounding firm-level factorsare differenced out. In this extended specification, the effect of tax-motivated group support (↵1 and ↵2) isidentified by comparing two firms belonging to the same business group but being located in a country withstrict versus more lenient LCF provisions.

13

treatment effect of business groups conditional on observable firm characteristics (Abadie &

Imbens 2006).14 This approach also alleviates concerns of endogenous tax policy driving my

results. If tax policy is related to potential outcomes because it responds to economic develop-

ments in prior periods, any bias should not differ between standalone and group member firms,

both directionally and economically.

The analysis then turns to the real effects of group support for member firms that are under

financial distress but do not default in the year following the increase in financial distress. I focus

on two proxies for the economic consequences of group support as outcome variables. First,

I adopt the measure Zombie from studies on the misallocation of bank debt (Caballero et al.

2008, Acharya et al. 2019). I use this measure because group support might artificially keep

firms alive, although they should exit the market from an economic welfare point of view. Based

on a set of cumulative financial factors indicating productivity and solvency (Storz et al. 2017),

zombie firms are defined as unproductive low-quality firms that should not receive additional

funding since this leads to inefficient resource allocation. Second, I calculate member firm-

level total factor productivity as the residuals from a within country-industry-year regression

based on a Cobb Douglas production function (TFP (Cobb Douglas)).15 I am interested in

the outcome of group support given the firm was supported and did not default in year t. All

else equal, a significant coefficient on the interaction term NOL * LCF Deductibility indicates

different productivity outcomes of member firm i if its loss-related tax shield is more valuable.14In this specification, I do not include firm fixed effects as I search for matched standalone firms for each

business group member firm-year observation. Thus, the matched sample of standalone firms is imbalanced anddoes not allow to exploit a large number of within-firm changes.

15I discuss these measures in more detail in the Online Appendix and define them in Appendix A. Resultsare insensitive to using alternative measures of total factor productivity with different assumptions on the inputelasticities of output or labor added. For example, inferences do not change when following the simplified indexapproach assuming the same typical labor and capital elasticities for all countries and industries (e.g., Syverson2011 and Breuer 2018), when calculating the residuals based on the full sample and including industry-year fixedeffects (Barrios et al. 2019) or when accounting for intermediary inputs to proxy for unobserved productivity(Gopinath et al. 2017) using a generalized method of moments framework proposed by Wooldridge (2009).Results when using these alternative measures are reported in Table A.9 in the Online Appendix. I slightlymodify equation 1 and study contemporaneous effects in these productivity measure (i.e., in year t) in responseto changes in firms’ net operating loss position in the previous year (t-1 ), depending on the contemporaneousshare of future pre-tax profits that can be netted against NOL according to a country’s tax provisions (LCF

Deductibilityc,t). I use tax regulation as of t because, by the end of fiscal year t-1, managers are typically awareof the rules in the following year. In vector Z, I include the same time-varying firm-level control variables as inequation 1 and lag them by one year. To control for the incentive to shift losses to low or high tax countries, Iadditionally include the interaction term of NOL and Tax Rate (De Simone et al. 2017, Andries et al. 2017).

14

Single-country evidence from a regression discontinuity design

To affirm the internal validity of the large-sample evidence, I exploit a single-country setting

in a regression discontinuity design (RDD). In 2011, a LCF limitation was enacted in Spain.

This limit in the deductibility of NOL from future profits only applies to firms above a size

threshold of EUR 20 million in annual sales. Spain introduced the rule retroactively limiting

the NOL deductibility to 75 percent of future profits for firms exceeding this threshold in the

prior year (2010), providing a suitable setting for a sharp RDD (Langenmayr & Lester 2018).

I follow the recommendations in the literature (Cattaneo et al. 2018, Lee & Lemieux 2010)

and employ a non-parametric RDD around the EUR 20 million sales threshold in 2010 for

the limitation of LCF in the years 2011 to 2013. This method implicitly assumes an equal

distribution of characteristics of the treated and control firms just above and below the threshold

and does not rely on assumptions of the conditional mean functions concerning the running

variable (sales). Under the assumption that firms did not exercise control over the running

variable to influence the treatment assignment, this estimation strategy provides for a quasi-

random treatment of firms with respect to the limitation in the loss-offset provisions.

I test two critical assumptions of RDDs. First, I examine whether firms manipulated their

sales to circumvent the new rule, a crucial threat to the validity of assuming randomness in

the treatment. Although the threshold and the rule changes were not mentioned before 2011

(Langenmayr & Lester 2018), firms might still have had some insider knowledge and potentially

managed their sales downwards. Figure 3 plots the density of observations around the cutoff of

EUR 20 million sales. The strongly overlapping 95 percent confidence intervals at both sides

of the cutoff indicate no discontinuity in these densities that would hint at strategic sorting

(McCrary 2008).16 Second, non-parametric RDDs rely on the comparability of treated and

control firms. I check pre-treatment covariate balance around the cutoff and plot the formal

tests in Figure A.2 in the Online Appendix. The tests suggest no discontinuities in NOL or

other control variables around the threshold. I then estimate variants of the following model.

DVi,t = �Treatedi+JX

j=1

�jRVji +

JX

j=1

jRVji ⇤Treatedi+Pre. Controls �+FixedEffects (2)

16Importantly, this density test is unlikely to be biased towards showing a null result by firms manipulatingsales at both sides of the threshold because firms had no incentive to manipulate revenues upwards.

15

I estimate treatment effects on the dependent variable (DVi,t) Default(t+1) for group mem-

ber firms and standalone firms separately to investigate the internal capital markets effect.17

Treated indicates whether a firm reported sales of more than EUR 20 million in 2010. RV is

the running variable, coded as sales in 2010 minus EUR 20 million. The vector Pre. Controls

refers to the same set of control variables as used in the main analysis as of 2010, respectively.

Fixed Effects denote industry and/or year fixed effects. To provide a sense for the sensitivity

of results, I run both non-parametric local linear regressions (J=1) and regressions including

third order polynomials (J=3) with interactions. Mean-squared error optimal bandwidths are

derived according to Imbens & Kalyanaraman (2012).18 As the RDD plausibly captures causal

treatment effects around the threshold, I always begin the analysis by graphing potential dis-

continuities in the outcome variables which also helps to assess issues when assuming functional

forms in the data distribution (Lee & Lemieux 2010). I also run a parametric test to increase

precision in the estimates (at the cost of a potentially larger bias) using all firms in Spain with

sales below EUR 60 million in 2010 and interacting the treatment indicator with a binary vari-

able distinguishing between group member and standalone firms. This approach also formally

tests the difference in the treatment effects between group and standalone firms.

3.2 Data

Sample selection and business group construction

To create a large-panel dataset on financial, status, and ownership information of business

groups, I use the Orbis database provided by Bureau van Dijk (BvD).19 The extensive coverage

of private firms is particularly important for the setting of this study since it allows tracing the

internal legal structures of large business groups and does not restrict the analysis to listed firms

that represent only a small fraction of the economic activity in many relevant countries (Kalemli-

Ozcan et al. 2015, Beaver et al. 2019). I download ownership and legal status information from17In supplementary tests looking at the channels of group support, I also examine Sales to Group-Sales, Assets

to Group-Assets, and Abnormal ROA as dependent variables for group member firms.18A triangular kernel function is used to construct the local estimators, which places greater weight on

observations closer to the threshold (Lee & Lemieux 2010). If selected bandwidths are sufficiently small andstill provide enough power, my approach does not rely on correctly specifying the functional form of testedoutcomes as a function of the running variable (Angrist & Pischke 2009). If selected bandwidths are toowide to justify this assumption, estimating both linear and polynomial models addresses the concern that ofmisclassifying a non-linearity at the threshold as a discontinuity.

19Please refer to the Online Appendix for a more detailed discussion.

16

each annual release (Orbis Historics) from 2005 to 2017. I rely on the Orbis Generics Update

as of July 2018 to download firm-level financial information for the years 2000 through 2016.

To construct groups, I start with identifying a corporate global ultimate owner of a group in

line with other studies (Shroff et al. 2014, Beuselinck et al. 2019, Beaver et al. 2019). However,

I do not use the BvD’s definition of a global ultimate owner being an "independent" entity,

i.e., not owned by a single shareholder by more than 25 percent. Instead, I first test whether a

firm owning other subsidiaries is itself listed as a subsidiary of another firm. If not, I assign it

the status of a corporate global ultimate owner. This step avoids dropping large and important

business groups with large ownership stakes of families (e.g., The Porsche-Piëch family being

the majority owner of Volkswagen AG in Germany) or institutional investors (e.g., Blackrock’s

private equity segment or Qatar Foundation). As financial data on these global ultimate owners

are typically not available, the empirical analysis would neglect these firms. Also, the BvD’s

ultimate owner variable is often missing. I then follow the recent literature and employ a top-

down approach to create business groups. I iteratively connect member firms only when the

direct ownership stake is larger than 50 percent. This procedure ensures that subsidiaries do

not appear multiple times in the group structure. It is further consistent with the setting of

this paper, assuming corporate decisions within groups and across subsidiaries require effective

control throughout the ownership chains. I identify firms as subsidiaries of the corporate global

ultimate owner (tier 1 subsidiaries) and complement this information with the characteristics

of their corporate global ultimate owner (see also De Simone & Olbert 2019). Next, I identify

firms as subsidiaries of all tier 1 subsidiaries (i.e., tier 2 subsidiaries) and complement their

information with the characteristics of the corporate global ultimate owner and the direct

parent firm. I iterate this process for twelve levels. Figure A.1 in the Online Appendix depicts

snapshots of the ownership structures of Adidas AG (Inc.) as of 2006 and 2015 in my sample.

Table 1 outlines the sample construction process. When restricting the sample to mem-

ber firms in European Union countries (EU 28) and Switzerland, Iceland, and Norway as well

as merging ownership, legal status, and unconsolidated financial information, the sample con-

sists of more than 8 million member firm-year observations. I employ several sample selection

criteria to construct the final dataset. I disregard the industry classifications museums and

education, private households, membership organizations, and public services. Due to differ-

17

ent incentives and regulatory environments, I also exclude financial institutions and non-profit

organizations. I further exclude firms whose total assets or sales do not exceed EUR 10.000 at

least once in the sample period and drop observations with missing total assets, total liabilities,

equity, return on assets, with negative long-term liabilities, and without industry classifica-

tion. The regression analysis starts with a sample size of 6,782,896 member firm-years with

ownership, financial, and legal status information for the period 2005-2017, belonging to more

than 640,000 business groups with affiliated member firms and corporate ultimate parent firms

across the globe. 4,100,343 firm-year observations have information on all control variables.

Tables A.1, A.2, and A.3 in the Online Appendix report the distribution of the observations

across countries, years, and subsidiary levels. For 2,016,944 group member firm-years, I find

a nearest-neighbor-matched standalone firm. When requiring further financial information on

non-defaulting member firms to analyze the channels and economic consequences of group

support, the sample consists of 3,042,019 and 2,574,335 observations, respectively.

Tax regulation data

This study relies on detailed annual information on the country’s domestic and international cor-

porate tax law from the European Center for Economic Research (ZEW) database (Bräutigam

et al. 2017). I hand-collect further information from the IBFD European Tax Handbooks. The

extent to which firms can offset losses against past and future profits (LCF Deductibility) varies

across countries. While only Germany, France, the United Kingdom, Ireland, and the Nether-

lands allow for an immediate offset of losses against profits of the preceding period (resulting

in an immediate tax refund), loss carryforward provisions exist in all sample countries. These

provisions vary in their attractiveness. For instance, in Belgium losses can be fully netted with

profits in future periods, and unused NOLs do not expire. France, in contrast, introduced

stricter LCF provisions in 2010, limiting the deductibility of losses incurred after to 60 percent

of future profits (50 percent as of 2013). For additional tests, I also collect information on the

availability of domestic and cross-border group tax consolidation regimes. Table A.15 in the

Online Appendix shows the variation in the tax factors across countries and over time.

18

4 Empirical Results

4.1 Descriptive Statistics

Table 2 reports summary statistics for the main regression sample of group member firms. The

sample is comparable to those used in recent studies that also use Orbis data. However, the

data collection procedure yields substantially more observations as in these earlier studies and I

note that the sample firms are larger in total assets and have higher leverage ratios compared to

the sample firms in Beaver et al. (2019). All variables are defined in Appendix A, and financial

variables are winsorized at the 1 and 99 percent level. The average default rate is 0.69 percent.20

6.82 percent of firm-year observations are zombie firm-years. The ratio of mean logged total

factor productivity to the productivity dispersion between the 25th and 75th percentile is 3.2

percent. The average member firm has NOL that amount to 21 percent of total assets while

the largest NOL are concentrated among a smaller fraction of (financially distressed) sample

firms. The average profitability (ROA) ranges between -1 and 8 percent within the 25th and

75th percentiles of firms.21 Member firms on average report EUR 3.34 million in total assets

and have leverage ratios of 72 percent and capital intensities of 20 percent.

Table 2 also provides information on the characteristics of the member firms’ business

groups. The average sample member firm’s business group consists of 59 firms, operates in

more than five countries, and has 12 affiliated firms in the same country. 36 percent of member

firm-years belong to multinational groups. The sample member firms’ average hierarchical po-

sition within its group’s ownership chain is at level 2.18. In 22 percent of member firm-years,

the corporate ultimate owner is located in a foreign country and in 40 percent, member firms

are named after their corporate ultimate owner, suggesting a tighter operational integration.

Table 3 compares summary statistics on group member and matched standalone firm-years.

Drawing from a sample of more than 30 million standalone firm observations, I am able to match

2,016,944 million firm-years within the same country and industry that are also either profitable

or unprofitable and that have an absolute distance of less than one standard deviation in

ROA, Leverage, and Total Assets. After nonparametric nearest-neighbor matching, standalone20The average rate is 1.01 when not requiring information on all control variables, consistent with the statistics

in Beaver et al. (2019).21Around 30 percent of firm-year observations are loss-making which stresses the relevance of (more or less)

financially distressed firms in the economy. See also Heitzman & Lester (2019).

19

and group member firms are largely comparable along the covariates used in the regression.

However, standalone firms have lower NOLs and higher debt coverage ratios. The standardized

difference in means for all other variables is well below 0.25, suggesting satisfactory covariate

balance to estimate treatment (e.g., group support) effects (Imbens & Wooldridge 2009).22

4.2 Corporate Defaults in Business Groups

Main results

Figure 2 presents graphical evidence on tax-motivated group support. The bin scatterplots

relate the average value in NOL to average default probabilities for group member firms in

Subfigure (1) and for matched standalone firms in Subfigure (2). The figures show these rela-

tionships separately for firms located in countries with restrictions in the deductibility of NOLs

from future profits (i.e., LCF Deductibility less than 100 percent) and in countries without such

restrictions (i.e., LCF Deductibility equal to 100 percent).

In general, there is a positive association between firms’ NOL position and average default

rates, consistent with NOLs increasing in the level of distress. This relationship is notably

stronger for group member firms if the deductibility of NOLs from future pre-tax profits is

restricted, as both the red dots (scattered averages) and the steeper red fitted linear regression

line indicate. The lack of such a difference for standalone firms suggests that business groups

respond to more valuable tax shields of distressed member firms by greater support, resulting

in fewer defaults.

Table 4 reports the results of estimating the linear probability model based on equation

1. Columns (1) and (2) present the baseline results without considering the tax environment

including firm-level controls as well as country-year and industry-year fixed effects for group

member and standalone firms, respectively. In columns (3) and (4) the variable of interest,

NOL, is interacted with LCF Deductibility to test for a differential effect of an increased level

of distress on corporate defaults depending on the extent to which future profits can be netted

against NOL for tax purposes. The specification in column (5) is based on the sample of all

group member firms only and additionally includes firm fixed effects.22In Table A.4 in the Online Appendix, I compare the group firms to the full sample of standalone firms and

particularly document differences in profitability.

20

The regression results confirm the visual impression. Column (1) suggests NOL are pos-

itively associated with the likelihood of default of group member firms. The coefficient is

statistically insignificant and economically weak, on average. For matched standalone firms

(column (2)), NOLs are strongly associated with higher likelihoods of default. The difference

in coefficients is 0.888 and highly statistically significant, suggesting that member firms benefit

from group support that alleviates financial distress (Gopalan et al. 2007, Beaver et al. 2016).

In line with the first hypothesis, the result in column (3) suggests that the association between

a member firm’s NOLs and the default likelihood is decreasing in the deductibility of NOLs.

The point estimate on NOL is 0.482, and the interaction term NOL*LCF Deductibility is -0.563,

both statistically significant at the 5 percent level. For the matched standalone firms, the co-

efficient on the interaction term is close to zero and statistically insignificant. The statistically

significant difference of 0.604 suggests that business groups support member firms if greater

future tax benefits make resource reallocation financially more rewarding and that firms do not

mechanically survive by saving themselves due to future cash tax benefits. Given an sample

mean default probability of 0.69 percent, a one standard deviation increase NOL is associated

with a 17.6 percent higher likelihood of default if NOLs can be netted against 60 percent of

future pre-tax profits (as in Germany since 2004). If NOLs can offset profits by 80 percent (as

in Italy since 2011), the default likelihood increases by only 3.9 percent. If firms can fully net

NOL against pre-tax profits (as in several other countries), the default likelihood decreases by

9.9 percent. Results are robust to including firm fixed effects. When using the full sample of

group firms (column (5)), the coefficients are economically stronger. A one standard deviation

increase in NOL is associated with a relative increase in the default likelihood by 28 percent

if no LCF limitation is in place and a 71 percent increase if NOLs can be netted against only

60 percent of future pre-tax profits. In absolute terms, an increase of 10 percentage points in

NOLs scaled by total assets results in relative increases in the default likelihood by 2.4 percent

under no restrictions in LCF Deductibility and by 8.5 percent under a restriction of 60 percent.

Evidence from single-country RDD

Graphical and regression results based on the RDD confirm the finding that business groups

are more likely to avoid member firms’ default if the loss-related tax shield is more valuable.

21

Figure 4 plots average default ratios in the period 2011-2013 of firms within evenly spaced bins

around the threshold of EUR 20 million. Based on both, linear and third order polynomial fits,

the discontinuities at the threshold suggest that default ratios increase after the limitation of

the LCF provisions in Spain, particularly for business group member firms (Subfigure (1)).

Table 5 presents the regression results based on equation 2. Columns (1) and (2) ((3) and

(4)) present results of local linear and third order polynomial regressions for group member

(standalone) firms based on the full sample and including pre-treatment control variables.

Columns (5) and (6) present results based on the sample of group member firms with non-zero

NOL in 2011. Column (6) also includes industry fixed effects (NACE section). Column (7)

depicts results of a parametric test based on the full sample of firms reporting EUR 0 to EUR 60

million in sales in 2010 and including an interaction of the treatment indicator with a dummy

variable splitting the sample into group versus standalone firms.23

Across all specifications, I document statistically significant treatment effects.24 Results

suggest that group firms reporting just above EUR 20 million in sales exhibit a 2.7 to 10

percentage points higher likelihood of default after the introduction of the LCF limitation,

relative to control firms which can still make use of a full loss-offset against future profits.

Consistent with loss-making firms being financially distressed, the effect is largest when member

firms have non-zero NOL (columns (5) and (7)). In some specifications, I also find statistically

significant treatment effects for standalone firms suggesting that the LCF limitation increased

the overall likelihood of corporate defaults in Spain. However, estimated treatment effects are

statistically and economically much weaker. Also, the formal non-parametric test (column (7))

suggests that the effect is only present for group member firms. Overall, the RDD confirms the

presence of tax-motivated group support and suggests that the stricter rules in Spain led to an

around 3 to 4 percentage points higher default rate for treated group member firms.23I exclude firms reporting more than EUR 60 million in sales as a tighter restriction of 50 percent applies

above this threshold.24When excluding pre-treatment controls, treatment estimates are slightly smaller but still statistically highly

significant. Summary statistics for the RDD sample are reported in Table A.6 in the Online Appendix. Todecrease noise and increase the power of the tests, I use observations from years 2011 to 2013 to construct theoutcome variable. Default(t+1) [2011-2013], equals one if the firm defaults within the period 2011-2013.

22

Cross-sectional tests

I draw on the literature on common drivers of internal capital market reallocations to further

investigate how groups respond to tax shields. I create sample splits to study different incentives

for strategic group support to exploit tax benefits in Table 6. In Panel A, I partition the

sample at the median values of the business group’s growth in total sales (Growth Group-Sales,

columns (1) and (2)) to assess the importance of tax shields when the business group has more

or less cash proceeds available to reallocate, which captures the level of financing constraints

(Giroud & Mueller 2019). I split at the median value of the number of member firms within

each business group (Groupsize, columns (3) and (4)) to gauge the relative importance of an

individual member firm, and based on two binary variables indicating i) whether a member firm

is named after its corporate ultimate owner (Same Parent Name, columns (5) and (6)) and ii)

whether a member firm as a foreign corporate ultimate owner (Foreign Parent, columns (7) and

(8)) to proxy for the degree of operational integration. In Panel B, I split at the median debt-

to-equity ratio to study group managers’ trade-off considerations in capital structure decisions

(Leverage, columns (1) and (2)), member firm size to proxy for bankruptcy costs and financial

strength (Total Assets, columns (3) and (4)), and hierarchical level to proxy for the individual

firm’s importance in the ownership chain (Subsidiary Level, columns (5) and (6)).

Results indicate that business groups strategically support member firms when the incentives

to use future loss-related tax benefits are particularly strong. Estimates are only statistically

significant and economically stronger for the subsample of business groups with below-median

growth in total group sales (Panel A, column (1)). This finding is consistent with more active

internal capital markets when groups face cash constraints (Giroud & Mueller 2019). It indicates

that groups avoid member firms’ default through non-financial support such as extending debt

assurances or deliberately delaying bankruptcy filings and that future cash benefits from tax

shields substitute for financial transfers in cash-constrained groups. Further results suggest that

group support is more responsive to tax shields if the member firm is relatively more important

as the statistically stronger effects for the subsamples of higher-tier member firms and smaller

groups suggest. As theory predicts (Stein 1997), headquarters seem to be more effective in

discerning the benefits of resource reallocation when the number of projects (here member

firms) is smaller. At the same time, the group might be less responsive to member firm-specific

23

tax shields as more tax shields within the group already exist. Also, member firms that share

the name with their parent and that are ultimately owned by another domestic corporation

are less likely to default when LCF provisions are more lenient. These results indicate that tax

shields matter more for relatively more important member firms, both in terms of organizational

hierarchy and operational integration. Business groups seem to use internal capital markets

more strategically when the risk of credit-risk spillovers and operational disruptions is high.

I document that the negative relationship between the value of loss-related tax shields and

member firm default probability is concentrated in highly leveraged member firms (Panel B,

column (2)). This finding is consistent with higher leverage increasing bankruptcy risk (Leland

& Toft 1996, Altman 1984), which makes tax-motivated group support to avoid bankruptcy

more valuable. It further provides an explanation why firms with low marginal tax rates (due to

their loss positions) still favor substantial debt financing (Hovakimian et al. 2012). Under more

lenient LCF provisions, corresponding interest payments can be carried forward and deducted

from future profits earlier (or to a greater extent). Tax-motivated internal capital market

considerations thus seem to allow member firms to benefit from external or internal debt even

when the associated default risk is high. Consistent with higher bankruptcy costs of smaller

firms (Hovakimian et al. 2012), the main effects seem to be concentrated among member firms

with relatively lower values in total assets, albeit the difference in coefficients is insignificant.

4.3 Economic Consequences of Group Support

Firm-level outcomes

To examine whether the tax incentives of group support impact the efficiency of such support,

I study productivity outcomes and report results in Table 7. In Panel A, the outcome variable

Zombie is binary and indicates whether a firm is unproductive and insolvent. In Panel B, the

outcome variable is total factor productivity (TFP (Cobb Douglas)). I compare the effects for

group firms (columns (1) and (3)) with those for the full sample of standalone firms (columns

(2) and (4)).25

Firms are on average less likely to turn into zombies and become more productive after25Instead of nearest neighbor matching, I use the universe of standalone firms as control firms as this specifi-

cation includes firm fixed effects to account for time series dependence and unobserved confounding factors.

24

increases in NOLs (columns (1) and (2)). I document particularly strong negative (positive)

associations between NOL(t-1) and Zombie (TFP (Cobb Douglas)) for group member firms and

weaker (for Zombie) or insignificant (for TFP (Cobb Douglas)) results for standalone firms.

These differences are highly statistically significant, suggesting that business groups employ

internal capital markets and facilitate financial turnarounds. This finding is consistent with

internal resource allocation being more prevalent and more efficient when firms are financially

distressed and potentially constrained by external credit markets (Gopalan et al. 2007, Giroud

& Mueller 2015). However, this effect diminishes significantly in the availability of future tax

benefits for group member firms due to generous LCF provisions at the distressed member firms’

level. For member firms, a one standard deviation increase in NOL is associated with a 6.6 lower

likelihood of becoming a zombie firm under 60 percent LCF deductibility while this likelihood is

only 4.9 percent lower if losses can be fully offset. Similarly, a one standard deviation increase

in NOL is associated with an 12 percent increase in (logged) total factor productivity if the LCF

deductibility is limited to 60 percent while this increase is only 7 percent under full future loss

offset. For standalone firms, the association between NOL-related tax shields and productivity

is economically insignificant. As I compare outcomes of distressed member firms in similar

financial conditions but facing different LCF rules, these results suggests that tax-motivated

group support leads to the allocation of resources away from their best use.26

To gauge the effect of the monetary value of firm-specific tax shields on productivity out-

comes, Figure 5 plots the marginal effects of NOL on Zombie and TFP (Cobb Douglas) de-

pending on LCF Deductibility and the Tax Rate. The visual evidence for group member firms

(Subfigures (1) and (2)) shows that the more generous LCF provisions are, the less productive

the distressed but non-defaulting member firms become. These associations are steeper for

higher tax rates, suggesting that more valuable tax shields have a stronger impact on corpo-

rate decision-making. For standalone firms (Subfigures (3) and (4)), the relationship between

NOL and productivity is not significantly driven by the tax environment as the flat lines and

overlapping confidence intervals suggest. Reconciling this result with the finding that business

groups delay distressed firms’ exit if tax benefits await (Table 4), I conclude that internal capital26In untabulated tests, I document consistent results when including business group-year fixed effects that

absorb any shocks to financial performance at the group level, further strengthening the conjecture that capitalis misallocated at the distressed member firm level.

25

markets become less efficient if resource allocation is tax-motivated.27

Aggregate market-level outcomes

I also look at real effects on an aggregate level as bankruptcies and the inefficient allocation

of capital might spill over to other market participants (Almeida & Wolfenzon 2006, Bernstein

et al. 2019, Acharya et al. 2019). I focus on the domestic industry level as a market where

producers often emulate the use of inputs by peer firms and competition can shape average

industry productivity (Syverson 2011, Breuer 2018). I aggregate the data based on the full

sample of group member and standalone firms at the industry-country-year level. I regress

industry-averages for Zombie and TFP (Cobb Douglas), Output (the sum of sales of all firms in

a given market), and Market Concentration (based on the Herfindahl-Hirschman Index) on the

aggregated NOL positions of group member firms scaled by total assets of all firms in a given

industry. I report results in Table 8. All models include control variables based on aggregated

firm-level variables and country-year as well as industry fixed effects.28

Column (1) shows that higher market-wide NOLs of group member firms are associated with

more zombie firms in an industry. This relationship reverses under more lenient LCF rules,

suggesting that, while firms receiving tax-motivated group support are more likely to become

zombies, this kind of group support leads to a higher share of other solvent, productive firms

at the market level. It complements the recent evidence that restructurings, as opposed to

bankruptcies, prevent negative spillover effects at the local market level (Bernstein et al. 2019).

In line with the firm-level analysis, however, I find that increasing group member firm NOLs are

associated with higher productivity but only under strict LCF provisions (column (2)). This

result suggests that the tax-motivated group support leads to adverse productivity outcomes

on the market level, consistent with theory predicting that business groups’ internal capital

markets can have negative externalities (Almeida & Wolfenzon 2006). Notably, the coefficient is

substantially larger than in the firm-level analysis, suggesting economically significant spillover

effects. I document no effects on other potential market-level outcomes.27When studying the productivity outcomes in the RDD framework in the Spanish setting, I find no significant

treatment effects. This might be due to lack of power. Other explanations are that only regulatory changestowards more lenient LCF provisions alter the efficiency of resource reallocation or that the rule change did notimpact the outcomes of the specific sample of firms in Spain closely around the size threshold at the time.

28Summary statistics for the market-level analysis are presented in Table A.5 in the Online Appendix.

26

4.4 Supplementary Analysis: Channels of Group Support

I additionally examine potential channels of resource allocation to distressed member firms.

Related-party transactions within business groups are an important channel of internal capital

markets (Buchuk et al. 2014). I focus on intra-group trade and the reallocation of assets,

building on evidence that multinational business groups can shift reported pre-tax profits to

subsidiaries in low-tax jurisdictions (see Dharmapala 2014 and Heckemeyer & Overesch (2017)

for reviews). Profit shifting occurs via intra-group trade and the pricing of these internal

transactions (Huizinga & Laeven 2008). Recent studies show that profits are also shifted to

loss-making affiliates in order to benefit from a zero marginal tax rate (De Simone et al. 2017,

Hopland et al. 2018). While not explicitly stated in this strand of literature, shifting profits

across group firms to exploit tax rate differentials is one way of sharing resources within internal

capital markets.

I do not directly observe (reported) transactions, but I build on prior literature that has

used abnormal unconsolidated profits at the member firm level as a proxy for via tax-motivated

within-group trade (e.g., Huizinga & Laeven 2008). Manipulated trade and internal pricing

results in abnormally high revenues and pre-tax profits of supported member firms (Olbert &

Werner 2019). If business groups engage in intra-group trade to assist member firms in need

of additional resources to avoid default, one would expect distressed member firms to report

higher output and profitability after an increase in losses. I estimate the effect of changes in

NOLs on Sales to Group-Sales, Assets to Group-Assets, and Abnormal ROA as the dependent

variables. Sales to Group-Sales (Assets to Assets-Sales) is the ratio of member firm i ’s sales

(total assets) to the total sum of sales (total assets) of all member firms that belong to member

firm i ’s business group in year t. An increase in these ratios indicates that a member firm

reports a greater share of overall group sales (total assets). Abnormal ROA is a member firm’s

return on assets less the average return on assets of all member firms that belong to member

firm i ’s business group in year t.

Table 9 reports the results on the channels of group support received by member firms if

they do not default. I separately estimate the effects on average (columns (1), (3), (5)) and

depending on the LCF rules ((2), (4), (6)). I find evidence of within-group transfers of financial

resources to distressed member firms. While, on average, increases in NOLs are economically

27

weakly associated with a lower share in group sales (column (1)), a member firm’s share in group

assets (column (2)) and, particularly, its profitability relative to the average group profitability

(column (3)) increases in response to a higher level of financial distress. Across all three

outcome variables, I document that future cash tax benefits inherent to more valuable tax

shields substitute for transfers of internal funds. After conditioning on LCF Deductibility, the

reallocation of financial resources to distressed firms is particularly prevalent if LCF provisions

are strict. Under 50 percent LCF deductibility (as in France since 2013), a one standard

deviation increase in NOL is associated with a 0.34 (0.42) percentage points higher share in

group sales (assets) while the effect is only 0.08 (0.25) if a full loss-offset is available (columns

(2) and (4)). Results are economically more significant for Abnormal ROA. A one standard

deviation increase in NOL is associated with a 4.75 percentage point increase in profitability

relative to the average group profitability if the firm faces a restriction in LCF deductibility

of 50 percent while such increase is only 3.6 percentage points if no limits in the opportunity

to offset losses exist. Overall, the results show that business groups reallocate resources via

intra-group trade but only if the value of loss related tax shields is limited. Business groups

instead seem to use non-financial resources or debt capital (Desai et al. 2004, Buchuk et al.

2014) to provide group support when tax benefits are available as additional internal funds.

Again, I use the introduction of the LCF limitation in Spain as of 2011 as a negative shock

to the value in loss-related tax shields and examine discontinuities around the sales threshold

in the outcome variables Sales to Group-Sales, Assets to Group-Assets, and Abnormal ROA.

Consistent with the large-sample results, all graphs in Figure A.5 in the Online Appendix hint

at a positive discontinuity at the threshold. Table 10 presents the regression results based on

equation 2 when examining the three different channels of group support. Except for a negative

but close-to-zero effect in column (1) of Panel A when the sample includes non-distressed firms,

the regression results largely confirm the panel analysis when Sales to Group-Sales and Assets

to Group-Assets are the dependent variables. Overall, results indicate that group support of

Spanish firms after the LCF limitation in 2011 occurred primarily via the internal transfer of

assets and sharing of revenues. The estimates suggest that the share of treated group member

firms’ assets in total group assets (sales) was 5 to 18 (around 13) percentage points higher than

that of untreated control firms.

28

5 Robustness Tests

I conduct several tests to corroborate the robustness of the main results and rule out alternative

explanations and briefly discuss them here. I provide more detailed discussions in the Online

Appendix, where I also report results of additional analyses and robustness tests.

Alternative fixed effects structure: Results could be driven by domestic tax rule changes

addressing industry-specific shocks if these shocks also affect firms’ losses and if there is a

systematic composition of group member versus standalone firms in these country-industry

segments. In such a case, my strategy of identifying group support by comparing member firms

to standalone firms would be problematic. To mitigate this concern, I replicate the main results

and replace the industry-year and country-year effects by industry-country-year fixed effects in

Table A.7 in the Online Appendix. This strategy is generally more conservative as it controls

away any time-varying factors that might be different in each industry and in each country and

correlate with firms’ losses and the outcomes of interest. All inferences remain unchanged.

Potential endogeneity in tax policy: While I do not exploit cross-time changes in tax loss-

offset provisions, my large-sample evidence might still be biased if policymakers introduce more

lenient tax loss-offset provisions to loosen financing frictions in times of economic downturns

and my sample firms systematically differ across countries with more or less lenient provisions.29

One obvious candidate for a correlation between LCF Deductibility and systematic differences

affecting loss provisioning, financial performance, and internal capital market outcomes would

be business groups avoiding the accumulation of losses in countries with strict LCF provisions.

To mitigate these concerns, I check for covariate balance around the existence of restrictions in

the deductibility of NOLs. If the empirical analysis relies on a balanced sample, it is more likely

that the observed change in treated firms (facing more lenient LCF restrictions) reflects the

impact of these rules, rather than other differences between the treated and control firms (Im-

bens & Wooldridge 2009, Atanasov & Black 2016). Figure 6 shows that firm-year observations

in countries where LCF Deductibility is less than 100 percent are slightly larger and older while

the standardized differences are still below 0.2. All other control variables exhibit standardized

differences within the range of -0.1 to 0.1. In particular, the standardized differences in NOL

is smaller than 0.05. Another threat to capturing causal effects is that tax policy could be29In particular, the third difference (equation 1) in the fixed effects regressions stem from firms being located

in countries with strict versus more lenient LCF regulation.

29

related to firm-level potential outcomes. For instance, regulators might introduce more lenient

LCF provisions in response to financial crises when firms typically accumulate losses and are

more likely to default. To address this concern, I run several tests in Table 11 that relate tax

policy to the sample firms’ NOLs aggregated at the country level. Results collectively suggest

that LCF provisions do not change in response to NOLs.

Validation of NOL as a measure of distress: To confirm that my NOL variable not only

captures tax shields but also proxies for financial distress, I correlate it with predicted default

likelihoods obtained from the non-linear default prediction model in Beaver et al. (2019), which

are similar to a Z -score of private firms (Altman et al. 2017). In Table 12, I regress the firm-

year-specific predicted default likelihood on NOL in a given year (default likelihood expressed

in the Odds Ratio based on Model (1) in Table A.13 in the Online Appendix). I employ several

fixed effects structures and consistently document a statistically very significant and positive

relationship between NOL and the predicted default likelihood.

Additional tests: In Table A.8, I address the concern that loss-related tax incentives based on

financial accounting data are misclassified if groups can make use of tax consolidation to offset

losses across legal entities. I split the sample based on whether a member firm has access to

such group tax consolidation. I find that the relationship between corporate defaults of group

firms and NOL depending on LCF Deductibility is somewhat weaker when a group has the

opportunity to offset the member firm’s losses with profits of other domestic member firms or

its foreign parent. However, this difference is not statistically significant, suggesting that the

main results are not confounded by groups making use of loss offsetting across member firms.

Group support of weaker, unproductive firms by financially healthy firms through internal

capital transfers might have negative externalities on other group firms because of inefficient

transfers from high- to low-productivity firms (Rajan et al. 2000). I therefore test for an effect

of the overall NOL at the group level conditional on the tax incentives for group support

on outcomes at the level of non-distressed group firms. I report results in Table A.10 along

with a more detailed explanation. Overall, I find no evidence for negative spillovers of group

support, which also suggests that the stable unit treatment assumption (SUTVA) in the main

tests likely holds. However, consistent with the evidence that distressed member firms receive

cash transfers through intra-group trade if their tax shields are less valuable, I document a

30

statistically significant negative coefficient on Group NOL(t-1) when non-distressed member

firms’ sales is the outcome variable.

To validate the RDD results, I test the sensitivity with respect to bandwidth selection (Table

A.11 in the Online Appendix). Inferences remain unchanged. I also conduct three placebo tests.

First, I repeat the analysis based on Spanish firms with a falsified cutoff of EUR 40 million.

Second, I falsify the reform year using sales as of 2007 as the running variable. Third, I replicate

the main analysis on a sample of firms located in Italy that should have experienced similar

economic conditions in the period 2010 to 2013 but did not face changes in the LCF regulation.

Results in Table A.12 do not suggest any systematic (placebo) effects.

Last, I validate my dataset and asses whether taxes matter for corporate default prediction.

I estimate a commonly used discrete hazard model (e.g., Beaver et al. 2019, Beaver 2010). I

confirm the main findings of this paper and show that default prediction slightly improves after

accounting for the tax environment (Table A.13).

6 Conclusions

I study business groups’ support of distressed member firms and its impact on member firm

survival and productivity. I find that group support is more prevalent if loss-related tax shields

are more valuable as they generate higher cash flows upon return to profitability. If tax shields

are less valuable, managers seem to consider them lost and are more likely to let member firms

default. The findings also suggest that tax incentives induce distortions in internal capital

markets decisions, leading to lower productivity of distressed member firms.

This paper contributes to three strands of literature. First, it adds to the finance litera-

ture on internal capital markets. Based on a large, representative sample, I document that

financial incentives stemming from the tax environment affect the boundary, the capacity, and

the efficiency of internal capital markets. Second, the results contribute to the accounting lit-

erature on the value and relevance of losses as determinants of bankruptcy and within-group

resource allocation. Third, I complement studies on the effect of taxes on corporate decisions

such as profit shifting and corporate risk-taking by showing that loss-offset provisions affect the

incentives and economic outcomes of internal capital markets.

The results are of interest to financial statement users and policymakers. Business groups

31

and unprofitable firms represent a large part of the corporate universe. Their distress risk

and trade activity affects the overall economy, specifically in times of economic crises. My

results suggest reforms like the 2017 U.S. tax reform or the amendments in French tax law

since 2011 might lead to higher corporate default rates but stimulate more extensive internal

capital markets with more productive outcomes once a group supports its member firms.

32

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37

Appendix A: Variable Definitions

Variables Definition & Source

Dependent Variables

Default(t+1) Indicator variable set equal to 1 if firm classifies as a corporatedefault in the following year. (Source: Orbis)

Zombie Indicator variable set equal to 1 if firm classifies as a Zombie firmfollowing the definition in Koetter et al. (2017): negative returnon assets, negative net investments, and a debt servicing capacity(EBITDA over total financial debt) of less than 0.05 for at leasttwo consecutive years. (Source: Orbis)

TFP (Cobb Douglas) Firm-level total factor productivity computed as the residuals froma regression based on a Cobb-Douglas production function; esti-mated separately for each one-digit NACE industry section - coun-try - year segment if at least ten observations per segment areavailable. (Source: Orbis)

Sales to Group-Sales Firm’s share of overall group sales calculated as firm’s sales overthe sum of the same business group’s other affiliated firms’ uncon-solidated sales (as identified in the sample). (Source: Orbis, owncoding)

Assets to Group-Assets Firm’s share of overall group assets calculated as firm’s total assetsover the sum of the same business group’s other affiliated firms’unconsolidated total assets (as identified in the sample). (Source:Orbis, own coding)

Abnormal ROA Firm’s abnormal profitability: firm’s EBIT over total assets minusthe overall group’s average EBIT over total assets (over the samebusiness group’s other affiliated firms as identified in the sample).(Source: Orbis, own coding)

Variable of Interest

NOL Firm’s accumulated losses since 2000 (or a value of 0) in a givenyear calculated as the sum of losses in any number of consecutiveloss years netted with profits and accounting for the use of losscarrybacks. This sum is multiplied by -1, scaled by a firm’s totalassets, and set equal to 0 if cumulative losses are negative. (Source:Orbis)

Control Variables

ROA Firm’s return on assets: net income over total assets. (Source:Orbis)

Leverage Firm’s leverage ratio: total liabilities over total assets. (Source:Orbis)

Debt Coverage Firm’s interest coverage rate: ratio of earnings before interest andtaxes to total liabilities. (Source: Orbis)

ln(1+Total Assets) Natural logarithm of firm’s total assets. (Source: Orbis)ln(1+Cash) Natural logarithm of firm’s cash holdings. (Source: Orbis)ln(1+Comp. Expense) Natural logarithm of firm’s compensation expenses. (Source: Or-

bis)Capital Intensity Firm’s capital intensity: the ratio of fixed assets to total assets in

year t. (Source: Orbis)Age Age of firm: the period between t and the date of incorporation.

(Source: Orbis)

38

Variables Definition & Source

Group Variables

Groupsize Number of affiliated member firms within a business group.(Source: Orbis, own coding)

Vertical Integration Maximum number of subsidiary levels within a member firm’s busi-ness group. See definition of Subsidiary Level. (Source: Orbis, owncoding)

Internationalization Number of different countries in which of least one member firm ofthe business group is located. (Source: Orbis, own coding)

Intl. Group Indicator variable set equal to 1 if a firm’s business group has mem-ber firms in at least two different countries. (Source: Orbis, owncoding)

Firms in Country Number of affiliated member firms of the same business group lo-cated in the same country. (Source: Orbis, own coding)

Growth Group-Sales Annual logarithmic change in the sum of the same business group’smember firms’ unconsolidated sales (as identified in the sample).(Source: Orbis, own coding)

Subsidiary level Hierarchical level of firm within its business group’s ownershipstructure; ranging from 1 (firm is corporate global ultimate owner)to 12 (vertically most deeply integrated subsidiary). (Source: Or-bis, own coding)

Foreign Parent Indicator variable set equal to 1 if the firm and its corporate globalultimate owner are located in different countries. (Source: Orbis,own coding)

Same Parent Name Indicator variable set equal to 1 if the firm and its corporate globalultimate owner share the same name (first five letters). (Source:Orbis, own coding)

Tax Regulation Variables

LCF Deductibility Maximum relative amount of NOL that might be deducted fromcurrent year pre-tax profits measured as the share of current yearpre-tax profits. (Source: IBFD)

Tax Rate Statutory corporate income tax rate (including trade and businesstaxes and surcharges regularly applicable to ordinary corporateprofits). (Source: OECD, ZEW, KPMG, European Commission)

Notes: This table defines the variables used in the main analysis of this paper. The definition ofthe variables and the source of the raw data are presented. "Orbis (own coding)" means that theunderlying raw data was obtained from Bureau van Dijk’s Orbis database and variables were codedbased on the ownership structures of business groups which were constructed following the procedurelaid out in Section 3.2.

39

Figure 1: Net Present Value of Group Support Depending on Tax Incentives

Notes: This graph visualizes the financial incentive to support a member firm under financial distressdepending on the corporate income tax rate and the opportunity to offset net operating losses fromfuture profits. The financial incentive is modeled in a simplified net-present-value (NPV) calculationas follows.NPV = �FixCost+

PTt=1

PBTt � (PBTt �min(LCF Deductibilityt ⇤ PBTt;NOLt�1)) ⇤ (TaxRatet)

(1 + r)t.

For illustrative purposes, I make the following assumptions. A business group’s member firm is underfinancial distress after several loss-making years. It has accumulated net operating losses of 1000. Thefixed cost of group support is 100 plus 50% of the member firm’s accumulated losses, i.e., FixCost is600 in this case. If the member firm is supported, it is expected to return to profitability and generateprofits before taxes (PBT ) of 100 in t = 1 that grow by 2.5% per year. The decision-makers at thegroup level now calculate the NPV of group support based on a 10-year horizon (T = 10) and a costof capital (r) of 10%. Each period’s net cash flows depend on LCF Deductibility (share of deductibleNOL to pre-tax profits depicted on the right horizontal (y-) axis) and the applicable corporate incomeTax Rate (left horizontal (x-) axis).

40

Table 1: Sample Construction

Samples & Sample Selection Steps Obs.

European business group member firm-years 30,285,115with available ownership information in 2005-2017

- requiring annual legal status and financial statement informationBusiness group member firm-years with ownership,legal status, and unconsolidated financial information 6,782,896

- requiring non-missing annual financial information on all variables

Default regression sample 4,100,343

- requiring nearest-neighbor-matched standalone firm-years

Matched default regression sample 2,016,944

- non-defaulting (t+1) business group member firm-years,requiring financial information on >1 member firms in group

Resource shifting regression sample 3,042,019

- requiring non-missing information to construct productivity measures

Productivity regression sample 2,574,335

- Spanish firms in 2011-2013 below EUR 60 million in sales

RDD (Spain) sample standalone firm-years 1,650,475

RDD (Spain) sample business group member firm-years 194,889

- aggregating full sample on industry-country-year level

Market-level regression sample 14,257

Notes: This table documents the sample construction procedure and reports the resulting numbersof observations (firm-years or industry-country-years) for the different samples used in the regressionanalysis. Aggregated industry-level refers to two-digit NACE Rev. 2 classifications.

41

Table 2: Summary Statistics

Obs. Mean SD P1 P25 Median P75 P99

Dependent Variables

Default(t+1) 4,100,343 0.69 8.29 0.00 0.00 0.00 0.00 0.00Zombie 3,795,055 6.82 25.20 0.00 0.00 0.00 0.00 100.00TFP (Cobb Douglas) 3,501,688 0.03 1.26 -4.42 -0.35 0.11 0.58 2.83Sales to Group-Sales 4,089,061 0.57 0.42 0.00 0.09 0.68 1.00 1.00Assets to Group-Assets 4,100,343 0.55 0.41 0.00 0.11 0.57 1.00 1.00Abnormal ROA 4,100,343 0.00 0.20 -0.67 -0.02 0.00 0.03 0.67Variable of Interest

NOL 4,100,343 0.21 0.84 0.00 0.00 0.00 0.06 4.41Control Variables

ROA 4,100,343 0.01 0.22 -1.05 -0.01 0.02 0.08 0.54Leverage 4,100,343 0.72 0.67 0.02 0.42 0.67 0.87 3.76Debt Coverage 4,100,343 0.13 0.60 -1.61 -0.01 0.05 0.19 2.58ln(1+Total Assets) 4,100,343 15.02 2.03 10.09 13.68 15.00 16.32 20.34ln(1+Cash) 4,100,343 11.61 2.59 4.74 10.00 11.79 13.38 17.28ln(1+Comp. Expense) 4,100,343 13.13 2.16 6.62 11.89 13.27 14.57 17.83Capital Intensity 4,100,343 0.20 0.25 0.00 0.02 0.09 0.30 0.95Age 4,100,343 19.20 13.84 1.00 9.00 16.00 25.00 67.00Group Variables

Groupsize 4,100,343 59.19 248.91 1.00 2.00 4.00 14.00 1180.00Vertical Integration 4,100,343 3.11 1.98 1.00 2.00 2.00 3.00 11.00Internationalization 4,100,343 5.59 12.84 1.00 1.00 1.00 3.00 71.00Intl. Group 4,100,343 0.36 0.48 0.00 0.00 0.00 1.00 1.00Firms in Country 4,100,343 12.37 68.36 1.00 1.00 2.00 4.00 239.00Growth Group-Sales 3,342,239 0.01 0.81 -3.24 -0.11 0.02 0.15 3.32Subsidiary level 4,100,343 2.18 1.19 1.00 1.00 2.00 2.00 7.00Foreign GUO 4,100,343 0.22 0.41 0.00 0.00 0.00 0.00 1.00Same GUO Name 4,100,343 0.40 0.49 0.00 0.00 0.00 1.00 1.00Tax Regulation Variables

LCF Deductibility 4,100,343 0.84 0.20 0.50 0.60 1.00 1.00 1.00Tax Rate 4,100,343 29.07 6.47 10.00 26.00 30.00 34.43 38.00

Notes: This table presents summary statistics for all variables included in the analysis for the samplefirms with available ownership information (group firms). All variables are defined in Appendix A. Thenumber of observations, mean, standard deviation, 1st, 25th, 50th (median), 75th, 99th percentile areshown for each of these variables. Default(t+1) and Tax Rate are stated in percentage terms. Variablesare categorized into dependent variables, the variable of interest (NOL), firm-level control variables,group characteristics, and tax regulation variables. Firm-level dependent and control variables arewinsorized at the 1 and 99 percent level. The non-financial group variables are calculated beforefinancial data from Orbis is matched to the panel of firms with ownership information.

42

Tabl

e3:

Nea

rest

Nei

ghbo

rM

atch

ing

Stat

istic

s(G

roup

Mem

ber

Firm

svs

.St

anda

lone

Firm

s)

Gro

upM

embe

rFi

rm-Y

ears

Stan

dalo

neFi

rm-Y

ears

Mat

chin

gSt

atist

ics

Varia

bles

Obs

.M

ean

SDM

edia

nM

ean

SDM

edia

nR

el.

Diff

.St

d.D

iff.

Varia

ble

ofInterest

NO

L2,

016,

944

0.17

0.69

0.00

0.12

0.41

0.00

32.6

0%0.

27ControlVaria

ble

s

RO

A2,

016,

944

0.02

0.19

0.03

0.03

0.16

0.03

12.2

8%0.

08N

eg.

RO

A2,

016,

944

0.27

0.44

0.00

0.27

0.44

0.00

0.63

%0.

00Le

vera

ge2,

016,

944

0.68

0.51

0.65

0.66

0.39

0.66

2.10

%0.

01D

ebtC

over

age

2,01

6,94

40.

140.

570.

060.

200.

910.

0649

.82%

0.28

ln(1

+To

talA

sset

s)2,

016,

944

14.5

61.

6114

.63

13.8

11.

9813

.86

5.16

%0.

04ln

(1+

Cas

h)2,

016,

944

11.3

02.

3911

.55

10.9

62.

3911

.05

3.03

%0.

02ln

(1+

Com

p.Exp

ense

)2,

016,

944

12.8

71.

9113

.06

12.0

22.

0812

.13

6.55

%0.

05C

apita

lInt

ensi

ty2,

016,

944

0.20

0.24

0.09

0.25

0.27

0.15

29.6

9%0.

18Age

2,01

6,94

419

.31

12.8

217

.00

15.5

412

.72

13.0

019

.49%

0.15

Note

s:

Thi

stab

lepr

esen

tsm

atch

ing

stat

isti

csfo

rthe

sam

ple

ofgr

oup

mem

bera

ndst

anda

lone

firm

suse

din

the

anal

ysis

ofgr

oup

(gro

upfir

mde

faul

tsun

der

LCF

tax

ince

ntiv

es,T

able

4).

All

vari

able

sare

defin

edin

App

endi

xA

.The

tabl

ede

pict

ssum

mar

yst

atis

tics

fort

heva

riab

leof

inte

rest

and

cont

rolv

aria

bles

used

inth

ere

gres

sion

sba

sed

onth

esa

mpl

esof

grou

pm

embe

ran

dst

anda

lone

firm

-yea

rsaf

ter

cond

ucti

nga

nonp

aram

etri

cne

ares

tne

ighb

orm

atch

ing

onco

ntin

ous

mat

chin

gva

riab

les

RO

A,L

everage,a

ndTota

lA

ssets

for

firm

sw

ithi

nth

esa

me

year

,cou

ntry

,and

indu

stry

sect

ion

acco

rdin

gto

the

NA

CE

Rev

.2

clas

sific

atio

n,an

dfir

ms

that

also

have

eith

era

posi

tive

orne

gati

vere

turn

onas

sets

(Neg.

RO

A).

The

last

two

colu

mns

show

the

rela

tive

diffe

renc

ein

mea

nsan

dth

est

anda

rdiz

eddi

ffere

nce

inm

eans

betw

een

grou

pm

embe

ran

dst

anda

lone

firm

s.V

alue

sof

belo

w0.

25fo

rth

est

anda

rdiz

eddi

ffere

nce,

also

know

nas

the

Imbe

ns-W

oold

ridg

est

atis

tic,

indi

cate

that

the

two

subs

ampl

es,i

.e.

grou

pm

embe

rfir

ms

and

stan

dalo

nefir

ms,

are

fair

lyco

mpa

rabl

e(I

mbe

ns&

Woo

ldri

dge

2009

),ex

cept

for

stan

dalo

nefir

ms

havi

ngle

ssac

cum

ulat

edlo

sses

(NO

L)

and

ahi

gher

debt

cove

rage

rati

o(D

ebtCoverage).

43

Figure 2: Group Support and Loss Tax Shields - Relationship between NOL and Default(t+1)Depending on LCF Deductibility

(1) Group Firms (2) Standalone Firms

Notes: These graphs depict bin scatterplots showing the relationship between a firm’s net operatingloss position (NOL) and the default probability (Default(t+1)) for business group member firms andtheir matched standalone firms in subfigures (1) and (2), respectively. Within these subsamples, firmsare sorted into percentile bins based on their value of NOL. NOL is a firm’s accumulated net operatingloss position (accumulated negative EBIT since 2000 multiplied by -1 and set to zero if negative, i.e.,if absorbed by positive EBIT) scaled by total assets. The dots depict the average values within eachbin for NOL and Default(t+1). The lines represent the linear regression fit. Averages and linear fitsare estimated separately for firm-years in countries without and with restrictions in the deductibilityof NOLs from future profits (i.e., in which LCF Deductibility is equal to 1 (blue) and smaller than 1(red)).

44

Table 4: Group Support and Loss Tax Shields

Dep. Var.: Default(t+1) (1) (2) (3) (4) (5)

Matched Sample Full Sample

Groups Standalones Groups Standalones Groups

NOL 0.019 0.906*** 0.482** 0.873*** 1.108***(0.037) (0.083) (0.214) (0.234) (0.226)

NOL*LCF Deductibility -0.563** 0.040 -0.875***(0.241) (0.279) (0.238)

ROA -3.916*** -3.077*** -3.920*** -3.077*** -1.699***(0.553) (0.298) (0.553) (0.298) (0.232)

Leverage 0.356*** 0.250*** 0.372*** 0.250*** 0.605***(0.074) (0.049) (0.075) (0.049) (0.112)

Debt Coverage 0.079 -0.212*** 0.080 -0.212*** -0.008(0.059) (0.021) (0.059) (0.021) (0.025)

ln(1+Total Assets) 0.011 0.053*** 0.011 0.053*** -0.295***(0.017) (0.015) (0.017) (0.015) (0.040)

ln(1+Comp. Expense) -0.086*** -0.024 -0.087*** -0.024 -0.362***(0.017) (0.016) (0.017) (0.017) (0.053)

ln(1+Cash) -0.032*** -0.119*** -0.031*** -0.119*** 0.006(0.011) (0.009) (0.011) (0.009) (0.012)

Capital Intensity -0.256*** -0.272*** -0.261*** -0.272*** -0.025(0.063) (0.053) (0.064) (0.053) (0.139)

Age -0.001 -0.016*** -0.001 -0.016*** 0.033***(0.001) (0.002) (0.001) (0.002) (0.007)

� NOL 0.888*** 0.392� NOL*LCF Deductibility 0.604*

Obs. 2,016,944 2,016,944 2,016,944 2,016,944 4,100,343Adj. R-squared 0.022 0.021 0.022 0.021 0.387Firm FE No No No No YesCtry-Year FE Yes Yes Yes Yes YesInd-Year FE Yes Yes Yes Yes Yes

Notes: This table presents estimation results from regressions of a firm’s default status in t + 1 ona firm’s NOL position and several firm-level control variables. Default(t + 1) is a binary variableindicating whether a firm goes bankrupt or is liquidated in t+ 1 and is multiplied by 100 to facilitateinterpreting regression coefficients. NOL is a firm’s accumulated net operating loss position (accumu-lated negative EBIT since 2000 multiplied by -1 and set to zero if negative, i.e., if absorbed by positiveEBIT) scaled by total assets. LCF Deductibility is the share of pre-tax profits that can be nettedagainst accumulated losses (NOL) in a given year in a given country (i.e., the immediate tax shield ofNOL ranging from 0 to 1). Regressions in columns (1) and (3) are based on the subsample of groupmember firms for which nearest-neighbor-matched standalone firm-years were obtained. Regressionsin columns (2) and (4) are based on the subsample of nearest neighbor-matched standalone firms. Thedifference between the coefficients (�) on NOL and NOL*LCF Deductibility in columns (1) and (2) ((3)and (4)) are obtained from an auxiliary regression based on the combined sample. Standalone firmshave been matched to group firms based on a non-parametric, nearest neighbor matching using Neg.

ROA, country, year, and industry section as discrete and ROA, Leverage, and Total Assets as continu-ous matching variables. The regressions in columns (1) to (4) include country-year and industry-yearfixed effects (where the industries are defined using four-digit NACE classifications). The regressionreported in column (5) is based on the full sample of group firms and also includes member firm fixedeffects. Standard errors are reported in parentheses and are clustered at the firm level and the country-year level. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels (two-tailed),respectively.

45

Figure 3: Evidence from RDD - Manipulation Test around Sales-Threshold

Notes: This graph plots point estimates and the 95 percent confidence intervals of local polynomialdensities of firm observations according to the rating variable in the RDD framework (Sales) around thethreshold of EUR 20 million in Spain in 2010. The test is based on McCrary (2008) and implementedby the procedure put forward in Cattaneo et al. (2018).

Figure 4: Evidence from RDD - Group Support (Discontinuities in Default(t+1) (2011-2013))

(1) Group Firms (2) Standalone Firms

Notes: These graphs plot average values of defaults of firms in Spain in the period 2011 to 2013 forevenly spaced bins and their 95 percent confidence intervals. The number of bins is selected by the data-driven procedure that balances squared-bias and variance to minimize the integrated mean squarederror. The graphs also show fitted linear trends if the polynomial fit is of order one (linear) or thirdorder polynomials of a smoothed data distribution. The figure is divided into different subsamples:(1) plots results for the sample of Spanish group member firms and (2) for the sample of Spanishstandalone firms.

46

Tabl

e5:

Evi

denc

efr

omR

DD

-Gro

upSu

ppor

tan

dLC

FLi

mita

tion

inSp

ain

afte

r20

10

Dep

.Va

r.:

Def

ault(

t+1)

[201

1-20

13]

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Gro

ups

Stan

dalo

nes

Gro

ups

Full

Sam

ple

Trea

tmen

t2.

696*

*4.

312*

*2.

580*

2.89

410

.079

**8.

604*

0.02

6(1

.252

)(1

.851

)(1

.472

)(1

.871

)(4

.608

)(4

.941

)(1

.460

)Tr

eatm

ent*

Gro

upFi

rm3.

858*

(1.9

75)

Sam

ple

Full

Full

Full

Full

NO

LN

OL

NO

LO

rig.

Obs

.48

,686

48,6

8646

0,17

346

0,17

324

,407

24,4

0726

9,26

6B

andw

idth

5.96

10.0

62.

606.

399.

029.

02[-2

0;40

]O

bs.

inB

andw

idth

3,70

37,

023

1,59

24,

236

2,26

52,

265

269,

266

Poly

nom

ials

Line

ar3

Line

ar3

33

3Fi

rmC

ontr

ols

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Ind

FEN

oN

oN

oN

oN

oYe

sYe

s

Note

s:

Thi

sta

ble

pres

ents

the

resu

lts

ofes

tim

atin

gse

vera

lsp

ecifi

cati

ons

ofth

eR

DD

base

don

the

sam

ple

ofSp

anis

hgr

oup

mem

ber

and

stan

dalo

nefir

ms.

The

outc

ome

vari

able

isan

indi

cato

req

ualt

oon

eif

Defa

ult(t+

1)

iseq

ualt

oon

ein

any

year

ofth

epe

riod

2011

-201

3.A

llsp

ecifi

cati

ons

incl

ude

the

sam

eco

ntro

lvar

iabl

esas

inth

em

ain

test

sre

port

edin

Tabl

e4

base

don

fisca

lyea

ren

d20

10(i

.e.,

pre-

trea

tmen

t).

The

sam

ple

isei

ther

the

full

sam

ple

ofSp

anis

hfir

ms

wit

hin

the

band

wid

thor

cons

ists

only

ofSp

anis

hfir

ms

wit

hno

n-ze

rova

lues

for

NO

Lin

agi

ven

year

.T

hesa

mpl

eis

furt

her

split

betw

een

grou

pm

embe

rfir

ms

and

stan

dalo

nefir

ms

exce

ptfo

rth

esp

ecifi

cati

onin

colu

mn

(7).

The

mod

els

inco

lum

ns(1

)an

d(3

)ar

eba

sed

onno

npar

amet

ric

loca

llin

ear

poly

nom

ial

regr

essi

ons.

Col

umns

(2)

and

(4)

to(6

)ar

eba

sed

onno

npar

amet

ric

thir

dor

der

poly

nom

ial

regr

essi

ons.

Inco

lum

ns(1

)to

(5),

mea

n-sq

uare

der

ror

opti

mal

band

wid

ths

are

calc

ulat

edan

dse

lect

edba

sed

onC

alon

ico

etal

.(20

14).

The

spec

ifica

tion

sre

port

edin

colu

mn

(6)

uses

the

opti

mal

band

wid

ths

from

colu

mn

(5)

and

incl

ude

indu

stry

(NA

CE

sect

ion)

.T

hesp

ecifi

cati

onin

colu

mn

(7)

isba

sed

ona

para

met

ric

appr

oach

usin

gal

lob

serv

atio

nsof

Span

ish

firm

sre

port

ing

sale

sbe

twee

n0

and

EU

R60

mill

ion

in20

10.

The

spec

ifica

tion

incl

udes

thir

dor

der

poly

nom

ials

ofth

eru

nnin

gva

riab

lean

dth

eiri

nter

acti

ons

wit

hth

etr

eatm

enti

ndic

ator

.T

hetr

eatm

enti

ndic

ator

asw

ella

sth

eru

nnin

gva

riab

lean

dit

sin

tera

ctio

nsw

ith

the

trea

tmen

tin

dica

tore

are

inte

ract

edw

ith

adu

mm

yin

dica

ting

whe

ther

afir

mbe

long

sto

abu

sine

ssgr

oup

(Group

Fir

m).

The

coeffi

cien

ton

the

inte

ract

ion

Term

Treatm

ent*

Group

Fir

min

dica

tes

the

diffe

rent

ialr

espo

nse

ofgr

oup

firm

sto

the

thre

shol

d-ba

sed

impl

emen

tati

onof

the

LCF

limit

atio

nsi

nce

2011

.R

obus

tst

anda

rder

rors

are

repo

rted

inpa

rent

hese

s.In

Col

umns

(1)

to(6

),st

anda

rder

rors

are

bias

-cor

rect

edfo

llow

ing

Cal

onic

oet

al.

(201

4).

***,

**,

and

*de

note

stat

isti

cals

igni

fican

ceat

the

1%,5

%,a

nd10

%le

vels

(tw

o-ta

iled)

,res

pect

ivel

y.

47

Tabl

e6:

Gro

upSu

ppor

tan

dLo

ssTa

xSh

ield

s-C

ross

-sec

tiona

lAna

lysis

Dep

.Va

r.:

Def

ault(

t+1)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Pan

elA

:C

ross

-sec

tion

alsp

litby

:G

rowt

hG

roup

-Sal

esG

roup

size

Sam

ePar

entN

ame

Fore

ign

Par

ent

Smal

lLa

rge

Smal

lLa

rge

No

Yes

No

Yes

NO

L0.

578*

*0.

261*

1.11

7***

0.23

60.

429*

*0.

802*

*0.

715*

*0.

134

(0.2

59)

(0.1

37)

(0.3

55)

(0.1

80)

(0.2

12)

(0.3

10)

(0.2

81)

(0.1

41)

NO

L*LC

FD

educ

tibili

ty-0

.631

**-0

.196

-1.2

76**

*-0

.224

-0.4

76**

-0.9

04**

*-0

.824

***

-0.0

70(0

.291

)(0

.157

)(0

.372

)(0

.208

)(0

.229

)(0

.332

)(0

.303

)(0

.156

)

�N

OL

-0.3

17**

-0.8

81**

*0.

374*

*-0

.581

***

�N

OL*

LCF

Ded

uctib

ility

0.43

5**

1.05

2***

-0.4

28**

0.75

4***

Obs

.1,

641,

501

1,70

0,11

02,

243,

039

1,85

6,63

02,

459,

726

1,63

9,99

03,

218,

469

881,

080

Adj

.R

-squ

ared

0.02

80.

017

0.02

60.

019

0.02

20.

024

0.02

40.

021

Firm

Con

trol

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

sFi

rmFE

No

No

No

No

No

No

No

No

Ctr

y-Ye

arFE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Ind-

Year

FEYe

sYe

sYe

sYe

sYe

sYe

sYe

sYe

s

48

Tabl

e6:

Gro

upSu

ppor

tan

dLo

ssTa

xSh

ield

s-C

ross

-sec

tiona

lAna

lysis

(con

tinue

d)

Dep

.Va

r.:

Def

ault(

t+1)

(1)

(2)

(3)

(4)

(5)

(6)

Pan

elB

:C

ross

-sec

tion

alsp

litby

:Le

vera

geTo

talA

sset

sSu

bsid

iary

Leve

l

Low

Hig

hSm

all

Larg

eH

igh

Low

NO

L0.

238

0.38

60.

522*

*0.

525

0.81

6***

0.20

8(0

.161

)(0

.248

)(0

.222

)(0

.335

)(0

.287

)(0

.165

)N

OL*

LCF

Ded

uctib

ility

0.05

4-0

.639

**-0

.548

**-0

.480

-0.9

39**

*-0

.165

(0.1

92)

(0.2

69)

(0.2

32)

(0.3

94)

(0.3

04)

(0.1

90)

�N

OL

0.14

80.

003

-0.6

08**

*�

NO

L*LC

FD

educ

tibili

ty-0

.693

***

0.06

80.

774*

**

Obs

.2,

081,

709

2,01

8,00

11,

769,

927

2,32

9,67

73,

123,

767

975,

843

Adj

.R

-squ

ared

0.01

60.

026

0.02

80.

021

0.02

40.

020

Firm

Con

trol

sYe

sYe

sYe

sYe

sYe

sYe

sFi

rmFE

No

No

No

No

No

No

Ctr

y-Ye

arFE

Yes

Yes

Yes

Yes

Yes

Yes

Ind-

Year

FEYe

sYe

sYe

sYe

sYe

sYe

s

Note

s:

Thi

sta

ble

pres

ents

esti

mat

ion

resu

lts

from

regr

essi

ons

ofa

mem

ber

firm

’sde

faul

tst

atus

int+

1on

afir

m’s

NO

Lpo

siti

onan

dse

vera

lfirm

-leve

lco

ntro

lva

riab

les

for

diffe

rent

sam

ple

split

s.D

efa

ult(t+

1)

isa

bina

ryva

riab

lein

dica

ting

whe

ther

afir

mgo

esba

nkru

ptor

isliq

uida

ted

int+

1an

dis

mul

tipl

ied

by10

0to

faci

litat

ein

terp

reti

ngre

gres

sion

coeffi

cien

ts.

NO

Lis

afir

m’s

accu

mul

ated

net

oper

atin

glo

sspo

siti

on(a

ccum

ulat

edE

BIT

sinc

e20

00m

ulti

plie

dby

-1an

dse

tto

zero

ifne

gati

ve)

scal

edby

tota

lass

ets.

LC

FD

eductibility

isth

esh

are

ofpr

e-ta

xpr

ofits

that

can

bene

tted

agai

nst

accu

mul

ated

loss

es(N

OL)

ina

give

nye

arin

agi

ven

coun

try

(i.e

.,th

eim

med

iate

tax

shie

ldof

NO

Lra

ngin

gfr

om0

to1)

.T

here

gres

sion

sar

eba

sed

onth

esa

mpl

eof

grou

pm

embe

rfir

ms

only

.T

hesa

mpl

eis

split

base

don

cros

s-se

ctio

nalc

hara

cter

isti

cs.

Spec

ifica

lly,i

nPa

nelA

,the

sam

ple

issp

litbe

twee

nfir

ms

belo

ngin

gto

grou

psw

ith

valu

esbe

low

oreq

ualt

oth

em

edia

nof

Growth

Group-Sale

s(c

olum

ns(1

)an

d(2

)),a

ndG

roupsize

(col

umns

(3)

and

(4))

,and

betw

een

firm

sba

sed

onth

ein

dica

tor

vari

able

sSam

eParent

Nam

e,

indi

cati

ngw

heth

erth

egr

oup

firm

isna

med

afte

rit

sco

rpor

ate

ulti

mat

eow

ner

(bas

edon

the

first

five

stri

ngs

ofth

efir

ms’

nam

e)(c

olum

ns(5

)an

d(6

))an

dForeig

nParent,

indi

cati

ngw

heth

erth

egr

oup

firm

isul

tim

atel

yow

ned

bya

fore

ign

corp

orat

esh

areh

olde

r(c

olum

ns(7

)an

d(8

)).

InPa

nelB

,the

sam

ple

issp

litbe

twee

nfir

ms

wit

hva

lues

belo

wor

equa

lto

the

med

ian

ofLeverage

(col

umns

(1)

and

(2))

,Tota

lA

ssets

(col

umns

(3)

and

(4))

,an

dSubsid

iary

Level

(col

umns

(5)

and

(6))

.T

hees

tim

ates

onth

edi

ffere

nce

inth

eco

effici

ents

ofin

tere

st(�

NO

Lan

d�

NO

L*LC

FD

ecuctibility)

are

obta

ined

from

auxi

liary

regr

essi

ons

that

exte

ndth

em

odel

spr

esen

ted

inco

lum

n(3

)of

Tabl

e4

byin

tera

ctin

gth

ecr

oss-

sect

iona

lind

icat

orva

riab

lew

ith

NO

Lan

dN

OL*LC

FD

eductibility

asw

ella

sth

eot

her

cont

rolv

aria

bles

and

fixed

effec

ts.

All

regr

essi

ons

incl

ude

coun

try-

year

and

indu

stry

-yea

rfix

edeff

ects

(whe

reth

ein

dust

ries

are

defin

edus

ing

four

-dig

itN

AC

Ecl

assi

ficat

ions

).St

anda

rder

rors

are

repo

rted

inpa

rent

hese

san

dar

ecl

uste

red

atth

efir

mle

vel

and

the

coun

try-

year

leve

l.**

*,**

,an

d*

deno

test

atis

tica

lsi

gnifi

canc

eat

the

1%,

5%,

and

10%

leve

ls(t

wo-

taile

d),r

espe

ctiv

ely.

49

Table 7: Productivity Outcomes of Group Support and Loss Tax Shields

(1) (2) (3) (4)

Dep. Var.: Zombie TFP (Cobb Douglas)

Groups Standalones Groups Standalones

NOL(t-1) -10.850*** -8.002*** 0.227*** 0.014(1.157) (1.120) (0.039) (0.014)

NOL(t-1)*LCF Deductibility 5.040*** 4.244*** -0.149*** -0.022**(0.981) (0.990) (0.029) (0.010)

� NOL(t-1) 2.848*** -0.213***� NOL(t-1)*LCF Deductibility -0.796 0.127***

Obs. 2,788,425 22,526,981 2,574,335 19,308,912Adj. R-squared 0.412 0.354 0.672 0.653Firm Controls Yes Yes Yes YesFirm FE Yes Yes Yes YesCtry-Year FE Yes Yes Yes YesInd-Year FE Yes Yes Yes Yes

Notes: This table presents estimation results from regressions of firm-level productivity outcomes on afirm’s NOL position and several firm-level control variables conditional on firms not defaulting in a givenyear. The outcome variable Zombie is a binary variable (multiplied by 100) indicating whether a firmhas been relatively unproductive, i.e., non-performing, based on five cumulative financial characteristicsfor at least two years. The outcome variable TFP (Cobb Douglas) is firm-level total factor productivitybased on residual values from a regression based on a Cobb Douglas production function estimated atthe country-industry-year level. NOL is a firm’s accumulated net operating loss position (accumulatedEBIT since 2000 multiplied by -1 and set to zero if negative) scaled by total assets. LCF Deductibility

is the share of pre-tax profits that can be netted against accumulated losses (NOL) in a given year in agiven country (i.e., the immediate tax shield of NOL ranging from 0 to 1). Regressions in columns (1)and (3) are based on the subsample of group member firms only and regressions in columns (2) and (4)are based on the subsample of all standalone firms with available data only. The difference betweenthe coefficients (�) on NOL(t-1) and NOL(t-1)*LCF Deductibility in columns (1) and (2) ((3) and(4)) are obtained from an auxiliary regression based on the combined sample. All regressions includefirm, country-year, and industry-year fixed effects (where the industries are defined using four-digitNACE classifications). Standard errors are reported in parentheses and are clustered at the firm leveland the country-year level. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels(two-tailed), respectively.

50

Figure 5: Productivity Outcomes of Group Support and the Value of Loss Tax Shields -Marginal Effects of NOL on Default(t+ 1) Depending on LCF Deductibility and Tax Rate

(1) Group Firms: Zombie (2) Standalone Firms: Zombie

(3) Group Firms: TFP (Cobb Douglas) (4) Standalone Firms: TFP (Cobb Douglas)

Notes: This graph plots the marginal effects of NOL on the linear prediction of Zombie and TFP

(Cobb Douglas) for different values of the share of future pre-tax profits that can be netted againstNOL (LCF Deductibility) over different values of the corporate income tax rates (Tax Rate, abbreviatedas CIT in the graphs’ legends). The results are based on the linear panel fixed effects regressions thatextend the models in columns (1) and (3) for group firms and (2) and (4) for standalone firms of Table7 by including the triple interaction term NOL*LCF Deductibility*Tax Rate and controlling for theinteraction term NOL*Tax Rate.

51

Table 8: Market-Level Outcomes of Group Support and Loss Tax Shields

(1) (2) (3) (4)

TFP MarketDep. Var.: Zombies (Cobb Douglas) Output Concentration

Ind. Group NOL (t-1) 5.026** 0.888** 0.358 1.591(2.355) (0.443) (0.837) (4.001)

Ind. Group NOL (t-1)*LCF Deductibility -7.687*** -1.107** -0.514 -3.070(2.483) (0.539) (0.959) (4.221)

Ind. Group NOL (t-1)*Tax Rate 0.143*** 0.006 0.001 0.055(0.054) (0.016) (0.019) (0.097)

Ind. ROA -0.004* 0.000 -0.001 0.003(0.002) (0.001) (0.001) (0.003)

Ind. Leverage 0.000 -0.000 -0.000 0.001(0.001) (0.000) (0.000) (0.001)

Ind. Debt Coverage -0.008*** 0.001 0.000 0.003(0.003) (0.001) (0.001) (0.004)

Ind. ln(1+Total Assets) 0.083* 0.006 0.467*** -0.288***(0.046) (0.017) (0.022) (0.098)

Ind. ln(1+Comp. Expense) 0.034** 0.006 0.069*** -0.331***(0.015) (0.008) (0.008) (0.039)

Ind. ln(1+Cash) -0.032 -0.002 0.177*** -0.581***(0.041) (0.013) (0.018) (0.088)

Ind. Capital Intensity 0.018 -0.009*** 0.001 0.003(0.019) (0.003) (0.004) (0.015)

Ind. Age 0.012 0.000 -0.006 0.002(0.009) (0.003) (0.006) (0.028)

Obs. 14,257 13,346 14,252 14,257Adj. R-squared 0.449 0.496 0.861 0.499Ind FE Yes Yes Yes YesCtry-Year FE Yes Yes Yes Yes

Notes: This table presents estimation results from regressions of market-level outcomes on aggregatedgroup member firms’ NOL positions and several market-level control variables. All dependent andcontrol variables are based on firm-level observations used in the main regression analysis and areaggregated at the market level classified as the two-digit NACE industry in a given country in a givenyear. The outcome variable Zombies is the ratio of zombie to all firms, the outcome variable TFP(Cobb

Douglas) is the unweighted average total factor productivity of firms, the outcome variable Output isthe natural logarithm of the sum of sales of all firms, and the outcome variable Market Concentration

is the average squared market share of firms (Herfindahl Index) in a given industry, respectively. Ind.

Group NOL(t-1) is the sum of accumulated losses of group member firms only scaled by the sum oftotal assets of all group member and standalone firms within a given industry. LCF Deductibility is theshare of pre-tax profits that can be netted against accumulated losses (NOL) in a given year in a givencountry (i.e., the immediate tax shield of NOL ranging from 0 to 1). All other variables are constructedusing the full sample of group member and standalone firms with available data in a given industry.All regressions include industry and country-year fixed effects (where the industries are defined usingtwo-digit NACE classifications). Standard errors are reported in parentheses and are clustered at theindustry-country level. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels(two-tailed), respectively.

52

Table 9: Resource Shifting to Non-defaulting Group Member Firms

(1) (2) (3) (4) (5) (6)

Dep. Var.: Sales to Group-Sales Assets to Group-Assets Abnormal ROA

NOL(t-1) -0.002*** 0.007** 0.002*** 0.007** 0.016*** 0.070***(0.001) (0.003) (0.001) (0.003) (0.001) (0.008)

NOL(t-1)*LCF Deductibility -0.006*** -0.004* -0.027***(0.002) (0.002) (0.006)

Obs. 3,042,019 3,019,024 3,049,557 3,026,561 3,049,557 3,026,561Adj. R-squared 0.830 0.830 0.827 0.827 0.255 0.256Firm Controls Yes (t-1) Yes (t-1) Yes (t-1) Yes (t-1) Yes (t-1) Yes (t-1)Firm FE Yes Yes Yes Yes Yes YesCtry-Year FE Yes Yes Yes Yes Yes YesInd-Year FE Yes Yes Yes Yes Yes Yes

Notes: This table presents estimation results from regressions of member firm-level outcomes on afirm’s NOL position and several firm-level control variables conditional on firms not defaulting in agiven year. The outcome variable Sales to Group-Sales (Assets to Group-Assets) is a member firm’sshare in overall sales (total assets) of the business group measured as a firm’s sales (total assets) dividedby the sum of all other group firm’s unconsolidated sales (total assets) as available in the database.The outcome variable Abnormal ROA is the firm’s ROA minus the average ROA of all firms belongingto the same business group. NOL is a firm’s accumulated net operating loss position (accumulatedEBIT since 2000 multiplied by -1 and set to zero if negative) scaled by total assets. LCF Deductibility

is the share of pre-tax profits that can be netted against accumulated losses (NOL) in a given year ina given country (i.e., the immediate tax shield of NOL ranging from 0 to 1). Other control variablesare reported and defined in Appendix A. All regressions include firm, country-year, and industry-yearfixed effects (where the industries are defined using four-digit NACE classifications). Standard errorsare reported in parentheses and are clustered at the firm level and the country-year level. ***, **, and* denote statistical significance at the 1%, 5%, and 10% levels (two-tailed), respectively.

53

Table 10: Evidence from RDD - Resource Shifting and LCF Limitation in Spain after 2010

Panel A (1) (2) (3) (4)

Dep. Var.: Sales to Group-Sales

Treatment -0.033*** 0.034 0.127*** 0.137***(0.011) (0.021) (0.038) (0.046)

Orig. Obs. 147,244 75,637 75,637 75,637Bandwidth 8.64 6.87 8.08 8.08Obs. in Bandwidth 16,960 5,101 6,129 6,129

Panel B (1) (2) (3) (4)

Dep. Var.: Assets to Group-Assets

Treatment 0.055*** 0.144*** 0.188*** 0.184***(0.018) (0.032) (0.039) (0.047)

Orig. Obs. 147,253 75,646 75,646 75,646Bandwidth 3.23 3.11 7.36 7.36Obs. in Bandwidth 5,710 2,108 5,530 5,530

Panel C (1) (2) (3) (4)

Dep. Var.: Abnormal ROA

Treatment 0.001 0.011 0.022* 0.017(0.005) (0.009) (0.012) (0.014)

Orig. Obs. 147,253 75,646 75,646 75,646Bandwidth 6.08 5.02 9.02 9.02Obs. in Bandwidth 11,205 3,522 7,007 7,007

Sample Full NOL NOL NOLPolynomials Linear Linear 3 3Firm Controls Yes Yes Yes YesInd FE No No No YesYear FE No No No Yes

Notes: This table presents the results of estimating several specifications of the RDD based on thesample of Spanish group member firms in the period 2011 to 2013. The outcome variable Sales to

Group-Sales in Panel A (Assets to Group-Assets in Panel B) is a member firm’s share in overall sales(total assets) of the business group measured as a member firm’s sales (total assets) divided by thesum of all other member firm’s unconsolidated sales (total assets) within the group as available inthe database. The outcome variable Abnormal ROA in Panel C is the member firm’s ROA minus theaverage ROA of all member firms belonging to the same business group. All specifications include thesame control variables as in the main tests reported in Table 9 based on fiscal year end 2010 (i.e., pre-treatment). The sample is either the full sample of Spanish group member firms within the bandwidth(column (1)) or consists only of Spanish group member firms with non-zero values for NOL in a givenyear. The models in columns (1) and (2) are based on nonparametric local linear polynomial regressions.Models in columns (3) and (4) are based on nonparametric third order polynomial regressions. Mean-squared error optimal bandwidths are calculated and selected based on Calonico et al. (2014). Thespecification reported in column (4) uses optimal bandwidths from column (3) and further includesindustry (NACE section) and year fixed effects. Robust standard errors are reported in parentheses.In Columns (1) to (3), standard errors are bias-corrected following Calonico et al. (2014). ***, **, and* denote statistical significance at the 1%, 5%, and 10% levels (two-tailed), respectively.

54

Figure 6: Robustness Tests - Covariate Balance around LCF-limitations

Notes: This graph plots the standardized differences in means of NOL and the control variables basedon the sample of group member firms used in the empirical analysis when the sample is split accordingto firm-years in which LCF Deductibility is equal to 1 or smaller than 1. Values of the standardizeddifference in means below 0.25 indicate that firm-year observations are fairly comparable in countrieswith versus without limitations on the LCF deductibility throughout the sample period (Imbens &Wooldridge 2009).

55

Table 11: Robustness Tests - Potential Endogeneity of Tax Policy and NOLs

(1) (2) (3) (4) (5) (6)

Dep. Var.: LCF Deductibility

Ctry. ln(1+Abs. NOL) -0.017* 0.013 -0.034 0.002 -0.010 -0.049(0.009) (0.019) (0.020) (0.042) (0.031) (0.039)

ln(GDP) 0.228 0.019 0.063 -0.651*(0.153) (0.291) (0.176) (0.335)

GDP Growth -0.003 0.001 -0.002 0.004(0.004) (0.002) (0.003) (0.003)

Short-term Interest Rate -0.011 0.003 -0.040*** 0.015(0.009) (0.009) (0.012) (0.010)

Unemployment 0.001 -0.008 -0.007 -0.017**(0.007) (0.008) (0.005) (0.007)

Average Wages -0.000 -0.000 0.000 0.000(0.000) (0.000) (0.000) (0.000)

Ctry. ROA -1.590* -0.138(0.808) (0.477)

Ctry. Leverage 0.334 0.520(0.300) (0.369)

Ctry. Debt Coverage 0.381** -0.262(0.174) (0.265)

Ctry. ln(1+Total Assets) -0.073 0.093(0.098) (0.104)

Ctry. ln(1+ Comp. Expense) -0.035 0.081(0.024) (0.052)

Ctry. ln(Cash) 0.093 -0.030(0.082) (0.147)

Ctry. Capital Intensity -0.208 -0.219(0.398) (0.673)

Ctry. Age 0.003 0.023*(0.006) (0.012)

Obs. 330 330 253 253 253 253Adj. R-squared 0.097 0.192 0.186 0.223 0.409 0.362Ctry FE No Yes No Yes No YesYear FE Yes Yes Yes Yes Yes Yes

Notes: This table presents estimation results from regressions of the tax policy variable LCF De-

ductibility on accumulated losses of the sample firms aggregated at the country level and severalmacroeconomic country and aggregated firm characteristics. Ctry. ln(1+Abs. NOL) is the naturallogarithm of the sum of accumulated losses of all group member and standalone firms in the samecountry in a given year. ln(GDP) is the natural logarithm of gross domestic product per capita, GDP

Growth is the annual growth rate in GDP, Short-term Interest Rate is the rate demanded for short-terminterbank lending in the country, Unemployment is the share of unemployed workforce, and Average

Wages is the annual average wage for full-time employees in 2017 nominal values. This country-leveldata is from the OECD’s Main Economic Indicator database. All other control variables refer to thefirm-level data used in the main analysis but aggregated at the country level (Ctry.). Ratios and Age

are the unweighted means per country. All remaining variables are constructed by aggregating theabsolute values on the country-year level and taking the natural logarithm. Columns (2), (4), and (6)also include country fixed effects. Standard errors are clustered on the country level and reported inparentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels (two-tailed),respectively.

56

Table 12: Robustness Tests - Validation of NOL Variable

(1) (2) (3) (4) (5)Dep. Var.: Predicted Default (Odds Ratio)

NOL 0.547*** 0.412*** 0.545*** 0.406*** 0.411***(0.001) (0.001) (0.001) (0.001) (0.001)

Obs. 40,602,642 41,624,206 40,602,642 41,624,206 39,439,234Adj. R-squared 0.163 0.059 0.188 0.089 0.477Industry FE Yes . Yes . .Firm FE No Yes No Yes YesYear FE No No Yes Yes .Ctry-Year FE No No No No YesInd-Year FE No No No No Yes

Notes: This table presents estimation results from regressions of the sample firms’ predicted default(Default(t+1)) on a firm’s NOL position. Predicted Default (Odds Ratio) is the predicted likelihoodof a firm’s default expressed in Log-Odds-Ratios based on a discrete-time hazard default predictionmodel as recently tested in Beaver et al. (2019). This default prediction model regresses Default(t+1)

on a firm’s return on assets, a binary variable indicating loss-years, size, leverage, debt coverage ratio,and the one-digit SIC industry-year average default ratio. The log odds ratios for sample firms in thispaper are obtained from the logit regression based on the specification presented in column (1) of TableA.13. The model in column (1) only includes industry fixed effects (where the industries are definedusing four-digit NACE classifications). Model (2) includes firm fixed effects. Model (3) ((4)) includesindustry (firm) and year fixed effects. Model (5) includes firm, country-year, and industry-year fixedeffects. Robust standard errors are clustered on the firm level and reported in parentheses. ***, **,and * denote statistical significance at the 1%, 5%, and 10% levels (two-tailed), respectively.

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Online Appendix

Loss or Lost? Economic Consequences of

Internal Capital Markets in Business Groups

Marcel Olbertolbert@uni-mannheim.de

University of Mannheim - Business School

December 4, 2019

The Online Appendix is available at www.marcelolbert.com/research/.

A.1