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Determinants of Corporate Savings in Germany

  • Marcus Jüppner EMAIL logo
Published/Copyright: October 19, 2021
Jahrbücher für Nationalökonomie und Statistik
From the journal Jahrbücher für Nationalökonomie und Statistik Volume 241 Issue 5-6

Abstract

From the early 2000s onwards, the rise in net lending of the nonfinancial corporate sector has contributed substantially to the increase in the German current account surplus. The main driving factor behind the development in the nonfinancial corporate sector’s net lending was a rise in savings (retained earnings) while business investment was comparatively stable. To shed light on the determinants of corporate savings, this study presents results from an analysis using firm-level data from the recently developed Bundesbank database Janis. The descriptive analysis suggests that the increase in corporate savings was a widespread development across the corporate sector in Germany. In line with aggregate results, also the median firm saw an increase in profitability while dividends were subdued. Empirical results show that firms with initially higher leverage ratios increased their saving ratios more after the year 2001, providing arguments for a role of a desire to deleverage in explaining increased corporate savings. No (unanimous) evidence is found that increases in uncertainty and market power of firms affect corporate savings positively.

Keywords: corporate saving; deleveraging; intangible capital; market power; tax reforms
JEL classification: D40; E22; G30; H25
Corresponding author: Marcus Jüppner, Deutsche Bundesbank and the Faculty of Economics and Business Administration, Goethe University Frankfurt, Frankfurt, Germany, E-mail:
The views expressed herein are solely those of the author and do not necessarily reflect the views of the Deutsche Bundesbank (or the Eurosystem).

Acknowledgment

I would like to thank the editor, Peter Winker, the guest editor and three anonymous referees for their helpful comments and suggestions. I am also grateful to my colleagues Philipp Meinen and Arne Nagengast for their advice and support.

A Appendix

A.1 Variables

This section provides details on the definition of variables used in this study and how they are constructed using the items in the dataset Janis. Variables are defined following the definition of items from extrapolated results from financial statements of German enterprises, published by the Bundesbank (see Special Statistical Publication 5).

  1. Equity (adjusted) is defined as

E q u i t y   ( a d j u s t e d ) = B o o k   e q u i t y + o t h e r   e q u i t y   t e r m s − a d j u s t m e n t s ,

where other equity terms (item P21200) include equity-equivalent partner loans or partner loans with a subordinate claim, equity-equivalent profit participation capital, equity-equivalent contributions by silent partners, and reserves (book equity including other equity terms is P21000 + 0.5*P22000 + P23000). Adjustments include deficit not covered by equity (A17000), outstanding contributions to subscribed capital (A11000), treasury shares (A14320), deferred tax assets (A16000), business start-up and expansion expenses (A12000), discount (A15010), self created industrial and similar rights and assets (A13150), and other adjustments (A18000). The equity ratio is calculated as

E q u i t y   r a t i o = e q u i t y   ( a d j u s t e d ) a s s e t s   ( a d j u s t e d ) .

  1. Assets (adjusted) are given by book assets (A10000) minus adjustments (see above):

A s s e t s   ( a d j u s t e d ) = b o o k   a s s e t s − a d j u s t m e n t s .

  1. Gross value added is given by gross profits (G36000):

G r o s s   v a l u e   a d d e d = G r o s s   p r o f i t s = t o t a l   o u t p u t + o t h e r   o p e r a t i o n a l   i n c o m e − c o s t   o f   m a t e r i a l s ,

where total output (G33000) includes sales (G30000), increase or decrease in finished and unfinished goods inventories and work in progress (G31000), and other own work capitalized (G31000). Cost of materials (G35000) includes cost of raw materials, consumables and supplies (G35100), and of purchased merchandise and services (G35200).

  1. Net savings are defined as

N e t   s a v i n g s = i n t e r n a l   f u n d s − d e p r e c i a t i o n       = c a p i t a l   i n c r e a s e   f r o m   p r o f i t s + Δ p r o v i s i o n s ,

where Δ is the change from previous year. Provisions include provisions for pensions and similar obligations (P24100), provisions for taxes (P24200), and other provisions (P24300), but also the proportionate special tax-allowable reserve (0.5*P22000), and deferred income (P26000) less prepaid expenses (A15000-A15010). Capital increase from profits (retained earnings) is defined as

C a p i t a l   i n c r e a s e   f r o m   p r o f i t s = Δ E q u i t y   ( a d j u s t e d ) − i n c r e a s e   i n   c a p i t a l   o f   c o r p o r a t i o n s .

The increase in capital of corporations is given by

I n c r e a s e   i n   c a p i t a l   o f   c o r p o r a t i o n s = Δ ( s u b s c r i b e d   c a p i t a l / c a p i t a l   s h a r e s   ( P 21100 ) + c a p i t a l   r e s e r v e s   ( P 21500 ) + o t h e r   e q u i t y   i t e m s   ( P 21200 ) − o u t s t a n d i n g   c o n t r i b u t i o n s   t o   s u b s c r i b e d   c a p i t a l   ( A 11000 ) − t r e a s u r y   s h a r e s   ( A 14320 ) )

  1. Labor costs are given by personnel expenses (G37000) which include wages and salaries as well as social security, post employment and other employee benefit costs.

  2. Depreciation (total) includes depreciation of fixed assets (amortization and write-downs of intangible fixed assets, depreciation and write-downs of tangible fixed assets and amortization of capitalized business start-up and expansion expenses), write-downs of current assets to the extent that they exceed the write-downs that are usual for the corporation (G38000), and also write-downs of long-term financial assets and securities classified as current assets (G50000).

  3. Interest costs are given by interest and similar expenses (G52000).

  4. Investment income is defined as

I n v e s t m e n t   i n c o m e = i n c o m e   f r o m   p a r t i c i p a t i n g   i n t e r e s t s   ( G 46000 ) + i n c o m e   f r o m   p r o f i t   t r a n s f e r s   ( p a r e n t   c o m p a n y )   ( G 47000 ) + i n c o m e   f r o m   s e c u r i t i e s   a n d   l e n d i n g   o f   f i n a n c i a l   a s s e t s   ( G 48000 ) + i n t e r e s t   a n d   o t h e r   i n c o m e   ( G 49000 ) − c o s t s   a r i s i n g   f r o m   l o s s   t r a n s f e r s   ( p a r e n t   c o m p a n y )   ( G 51000 ) .

  1. Other costs (net of other income) are defined as

O t h e r   c o s t s   n e t   o f   o t h e r   i n c o m e = o t h e r   o p e r a t i o n a l   c h a r g e s   ( G 39000 ) + e x t r a o r d i n a r y   c o s t s   ( G 58000 ) − E x t r a o r d i n a r y   i n c o m e   ( G 57000 ) .

  1. Total taxes include taxes on income (G60000) and earnings and other taxes such as operating taxes and purchase tax (G61000).

  2. The effective tax rate (ETR) is given by

E T R = t a x e s   o n   i n c o m e   a n d   e a r n i n g s e a r n i n g s   b e f o r e   t a x e s   ( E B T ) ,

where earnings before taxes (EBT) are calculated as

E a r n i n g s   b e f o r e   t a x e s   ( E B T ) = N e t   i n c o m e / n e t   l o s s   f o r   t h e   f i n a n c i a l   y e a r   ( E A T )   ( G 65000 ) − p r o f i t   a n d   l o s s   t r a n s f e r   ( s u b s i d i a r y )   ( G 62000 ) + t a x e s   o n   i n c o m e   a n d   e a r n i n g s   ( G 60000 ) .

  1. The dividend payout ratio is defined as

D i v i d e n d   r a t i o = d e s i g n a t e d   d i v i d e n d E A T .

Since the designated dividend (P21C10) is not a mandatory field and only reported by a fraction of companies. However, it is not possible to distinguish between actual zero values and missing values.

  1. Firm size is measured by the natural logarithm of assets (adjusted) deflated by the Consumer Price Index (CPI).

  2. As profitability measure, returns on earnings (ROA) are used, which are calculated as

R O A = e a r n i n g s   b e f o r e   i n t e r e s t   a n d   t a x e s   ( E B I T ) a s s e t s   ( a d j u s t e d ) ,

where earnings before interest and taxes are given by operating result (G45000).

  1. Dividend payer is a dummy variable which equals one if a company pays out cash in the given period and zero otherwise. To construct this variable, a payout proxy is calculated as

p a y o u t = n e t   i n c o m e − r e t a i n e d   e a r n i n g s ,

where retained earnings are given by the capital increase from profits.[42]

  1. Intangibles are defined as book intangible assets (A13100) minus goodwill (A13120) and internally generated industrial rights and similar rights and assets (A13150). The intangible share is calculated as

I n t a n g i b l e   s h a r e = i n t a n g i b l e s f i x e d   a s s e t s ,

where fixed assets include tangible assets and intangible assets (A13200).

  1. Cash flow is calculated as

C a s h f l o w = N e t   i n c o m e + d e p r e c i a t i o n   ( t o t a l ) + Δ ( p r o v i s i o n s   f o r   p e n s i o n s + o t h e r   p r o v i s i o n s + s p e c i a l   t a x − a l l o w a b l e   r e s e r v e + d e f e r r e d   i n c o m e − p r e p a i d   e x p e n s e s ) .

  1. Industry sigma (cashflow risk) is the standard deviation of industry cash flow to assets (adjusted). Standard deviation of cash flow to assets is computed for every firm-year using data over the previous five years. Cash flow standard deviations are then averaged each year over all firms within an industry.

  2. The interest rate of firms paid is calculated as

I n t e r e s t   r a t e = i n t e r e s t   c o s t s i n t e r e s t − b e a r i n g   d e b t ,

where the interest-bearing debt is defined as total liabilities (P25000) minus payments received on account (P25300) and liabilities arising from goods and services (P25400).

  1. The (real) wage rate is defined as

W a g e   r a t e = r e a l   l a b o r   c o s t s n u m b e r   o f   e m p l o y e e s ,

where real labor costs are labor costs deflated by the CPI. Since the number of employees (EMPL) is not a mandatory field, the wage rate is only observed for a fraction of companies.

  1. Sales growth is defined as the log-difference of sales deflated by the Consumer Price Index (CPI).

  2. Total debt (P25000 + A14140 + A14214) includes liabilities, provisions, deferred income, and the proportionate special tax-allowable reserve.

  3. Bank debt is given by liabilities to credit institutions (P25200).

  4. Cash is defined as cash, Bundesbank balances, balances at credit institutions and cheques (A14400) plus current asset securities excluding own shares (A14300-A14320).

  5. Capital expenditures or gross investments (Capex) are defined as

C a p e x = Δ ( t a n g i b l e s + i n t a n g i b l e s + d e p r e c i a t i o n   o f   f i x e d   a s s e t s ) .

  1. Depreciation of fixed assets is depreciation on intangible assets and tangible assets as well as on capitalized start-up and business expansion expenses (G38100).

  2. Acquisitions (of other long-term equity investments) are defined as

A c q u i s i t i o n s = Δ ( o t h e r   l o n g − t e r m   e q u i t y   i n v e s t m e n t s ) + w r i t e − d o w n s   o f   l o n g − t e r m   f i n a n c i a l   a s s e t s   a n d   s e c u r i t i e s ,

where other long-term equity investments are given by other long-term equity investments plus shares in affiliated companies and goodwill (A13340 + A13310 + A13120). Acquisitions are normalized by assets (adjusted).

  1. Economic policy uncertainty is measured by the Economic Policy Uncertainty Index (PU) from Baker et al. (2016).

A.2 Variables for Computing Firm-Level Markups

For computing markups, the input factors capital, labor, and materials (intermediate goods) are used. The firm-specific wage rate is chosen as the input price to identify the gross output production function.

  1. The real capital stock is generated following Eslava et al. (2004) and given by:

(A.1) K i , t = ( 1 − δ t ) K i , t − 1 + I i t D t ,

where K it is firm i’s capital stock, δ t is the depreciation rate, D t is a deflator for capital goods, and I it are firms i’s gross investments. The capital stock series is initialized in the year a firm enters the sample by deflating the book value of total fixed assets with ( D t + D t − 1 / 2 ).

  1. The cost of materials comprises cost of raw materials, consumables and supplies, and of purchased merchandise and services.

  2. The number of employees defines the labor input. This variable is only observed for a fraction of firms, since it is not a mandatory field. Therefore, the sample used to calculate markups is limited to firms who report the number of employees.

  3. The wage rate as input price for labor is calculated as described in Section A.1.

Materials, wages, and gross value added are deflated using an industry-specific intermediate goods price index, the CPI, and an industry-specific price index, respectively. The depreciation rate is calculated each year using aggregate data on depreciation and capital stock from National Accounts.

A.3 Descriptives

Figure A1: Median net income of German non-financial corporations (in % of GVA). Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.
Figure A1:

Median net income of German non-financial corporations (in % of GVA).

Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

Figure A2: Wage and interest rate payments of German non-financial corporations. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.
Figure A2:

Wage and interest rate payments of German non-financial corporations.

Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

Figure A3: Median dividend payout and equity ratio of German non-financial corporations. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.
Figure A3:

Median dividend payout and equity ratio of German non-financial corporations.

Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

Figure A4: Mean and median intangibles as a share of total fixed assets of German nonfinancial corporations. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.
Figure A4:

Mean and median intangibles as a share of total fixed assets of German nonfinancial corporations.

Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

A.4 Determinants of Corporate Savings: Deleveraging

A.4.1 Main Results: Different Window Sizes

Table A.1:

DID regressions: debt (three year window).

(1) (2) (3) (4) (5)
Binary Continuous Controls Placebo EB
DID −0.044*** (0.008) −0.161*** (0.032) −0.044*** (0.008) −0.041*** (0.009) −0.004 (0.018)
Placebo −0.009 (0.008)
N 46,086 46,086 46,086 46,086 46,086

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Table A.2:

DID regressions: debt (five year window).

(1) (2) (3) (4) (5)
Binary Continuous Controls Placebo EB
DID −0.015** (0.006) −0.061** (0.031) −0.016** (0.006) −0.018** (0.008) −0.001 (0.010)
Placebo 0.004 (0.006)
N 48,140 48,140 48,140 48,140 48,140

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Table A.3:

DID regressions: bank debt (three year window).

(1) (2) (3) (4) (5)
Binary Continuous Controls Placebo EB
DID 0.019** (0.008) 0.056*** (0.018) 0.017** (0.008) 0.022** (0.009) 0.001 (0.008)
Placebo −0.010 (0.008)
N 46,086 46,086 46,086 46,086 46,086

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Table A.4:

DID regressions: bank debt (five year window).

(1) (2) (3) (4) (5)
Binary Continuous Controls Placebo EB
DID 0.010 (0.006) 0.036** (0.014) 0.009 (0.006) 0.009 (0.008) 0.002 (0.006)
Placebo 0.000 (0.006)
N 48,140 48,140 48,140 48,140 48,140

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Figure A5: Determinants of corporate savings: Generalized difference-in-differences. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.
Figure A5:

Determinants of corporate savings: Generalized difference-in-differences.

Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

Figure A6: Determinants of corporate savings: Generalized difference-in-differences. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.
Figure A6:

Determinants of corporate savings: Generalized difference-in-differences.

Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

A.4.2 Additional Results

To study more closely the impact of the corporate tax reform 2001 and the role of intangibles, additional estimations were performed. According to Givoly (1992), firms with a high marginal effective corporate tax rate will change their capital structure more than firms with a low marginal effective corporate tax rate in response to the same change in the statutory corporate tax rate. Hence, one would expect that firms with high marginal tax rates increased their saving ratios more than firms with low marginal tax rates in response to the tax rate reductions by the reform (see also Jüppner and Nagengast 2018). In the employed dataset, there is no data on firm-specific marginal tax rates. To evaluate the tax channel, one may follow Givoly (1992) by using tax payments over earnings before taxes as a proxy for the marginal tax rate of firms. Using this approach, however, no significant and robust evidence is obtained that firms with high income tax rates increased their saving ratio after 2001 more strongly than firms with low income tax rates (see Table A.5). Since, the employed proxy for marginal tax rates is rather crude, the effects can only be measured with a considerable degree of uncertainty in this approach. Results could therefore also indicate that the effective tax rate is not a suitable proxy in this case. A similar problem may arise when looking at the share of intangible assets in total fixed assets. Also in this case, there is no significant evidence that firms with higher intangible shares increased their saving ratios more compared to firms with lower intangible shares (see Table A.6). As pointed out by Crouzet and Eberly (2019), using the book value of intangibles may be problematic. To properly analyze the effects of intangible assets on corporate savings, a deeper analysis using a different measure for intangible capital may be needed.[43]

Table A.5:

Determinants of corporate savings: income tax (four year window).

(1) (2) (3) (4) (5)
Binary Continuous Controls Placebo EB
DID 0.007 (0.007) 0.008 (0.017) 0.011 (0.007) 0.007 (0.008) 0.006 (0.007)
Placebo 0.000 (0.007)
N 44,544 44,544 44,544 44,544 44,544

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Table A.6:

Determinants of corporate savings: intangibles (four year window).

(1) (2) (3) (4) (5)
Binary Continuous Controls Placebo EB
DID −0.001 (0.006) 0.016 (0.041) 0.002 (0.006) −0.002 (0.007) 0.001 (0.006)
Placebo 0.002 (0.006)
N 42,808 42,808 42,808 42,808 42,808

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

A.4.3 Robustness Checks

When studying deleveraging processes, one needs to distinguish between the motive/desire for deleveraging and the actual increase in equity ratios as a use of net saving. The previous analysis showed that firms with higher leverage prior to 2001 subsequently increased saving ratios more. Instead of a desire to deleverage, it is also plausible that firms with higher leverage ratios benefited more from the decline in interest rates and associated reductions in debt servicing costs, which boosted their profits and net savings. At the firm-level, Figure 2 shows a sizable decrease in interest costs for the median firm and panel results from Section 6.2 show that interest rates are significantly negatively correlated with net savings.[44] However, this is already indirectly taken into account in the estimation using Eq. (3) by including profitability as additional control variable. Deleveraging results are still significant in this case and they also remain significant (for all window sizes) when interest costs over interest-bearing debt (as interest rate proxy) are included as a separate control variable (see Table A.7).

Table A.7:

DiD regressions: debt (controlling for interest costs).

(1) (2) (3)
Three-year window Four-year window Five-year window
DID 0.043*** (0.008) 0.028*** (0.007) 0.017*** (0.006)
Firm size −0.119*** (0.013) −0.080*** (0.010) −0.068*** (0.009)
Profitability −0.134*** (0.038) −0.069** (0.031) −0.046* (0.026)
Interest rate −0.061 (0.113) −0.163** (0.067) −0.186*** (0.068)
N 45,720 45,200 47,740

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

To further account for decreasing interest costs as source of higher savings, a triple DID approach can be considered. This approach tests whether the DID deleveraging effect is different for firms with high interest rates (defined as interest costs relative to interest-bearing debt) compared to firms with low interest rates. In addition to the initial leverage ratio, the initial interest rate is used as a second treatment variable:

(A.2) N S i t G V A i t = γ 6 + ϕ 6 ⋅ d p o s t − 2001 ⋅ d 1 h i g h p r e + φ 6 ⋅ d p o s t − 2001 ⋅ d 2 h i g h p r e     + κ 6 ⋅ d p o s t − 2001 ⋅ d 1 h i g h p r e ⋅ d 2 h i g h p r e + α 6 i + ν 6 t + ϵ 6 i t .

where d1 is the first treatment variable (leverage ratio) and d2 is the second treatment variable (interest rate). The treatment variables are again defined on the median. The coefficient of interest is κ 6, i.e. the coefficient on the interaction term between the two treatment variables and the post-2001 dummy. Results from Table A.8 suggest that the deleveraging effect on saving ratios of firms with high interest rates was not significantly different compared to the deleveraging effect for firms with low interest rates (i.e. the triple DID coefficient is insignificant). Results hold when using different window sizes for the panel regression (not shown here but available upon request). Overall, findings suggest that the general deleveraging process is also driven by a desire to deleverage and not only the result of increased profits.

Table A.8:

Triple DID regressions: debt and interest costs (four year window).

Deleveraging motive
Post × Treated_Interest −0.013 (0.012)
Post × Treated_Debt 0.024*** (0.008)
T-DiD 0.010 (0.013)
N 45,184

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Since this study analyzes in detail the deleveraging motive for increased corporate savings, a thorough investigation of the actual use of savings is beyond the scope of this paper. However, to provide more evidence on the deleveraging hypothesis, one can use a simple regression to show that savings were indeed used to increase equity ratios. Computing mean saving and equity (leverage) ratios for the post-2001 and pre-2001 periods, the change in equity ratios (post- vs. pre-2001) can be regressed on the post-2001 saving ratio, controlling for the pre-2001 leverage ratio. Results show that controlling for initial leverage, subsequent increases in savings lead to a significantly higher speed at which firms deleverage (see Table A.9). To provide further support for actual deleveraging, one can test whether firms with initially high leverage ratios decreased their leverage ratios more. When performing a simple DID estimation as in Eq. (1) but using the equity ratio as dependent variable, results show that firms with initially high leverage ratios in the pre-2001 period increased their equity ratios more (see Table A.10). Actually, such an analysis has already been done by Deutsche Bundesbank (2019b) considering changes of the equity ratio around the year 1998 but showing the same pattern. In fact, due to mean reversion of leverage ratios, which is widely documented in the literature (see e.g. Lemmon et al. 2008), one can generally observe that firms with initially lower equity ratios subsequently increase their equity ratios more.[45] However, looking at additional Placebo regressions for years before 2001, the magnitude of the effect is largest in 2001 (see Table A.10).

Table A.9:

Speed of delivering.

Three-year window Four-year window Five-year window
Pre-2001 mean equity ratio −0.081*** (0.009) −0.115*** (0.012) −0.137*** (0.014)
Post-2001 mean saving ratio 0.072*** (0.008) 0.121*** (0.016) 0.119*** (0.020)
N 7681 5700 4814

  1. Robust standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Table A.10:

DiD regressions: equity ratio as dependent variable (four year window).

(1) (2) (3)
1991 1996 2001
DID 0.026*** (0.002) 0.016*** (0.002) 0.032*** (0.003)
N 103,328 71,688 55,472

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

Dao and Maggi (2018) find that the trend towards higher corporate saving is concentrated among large publicly listed firms. In a final robustness test, another triple DID estimation is performed investigating whether the deleveraging motive was more relevant for increased savings of large publicly listed firms. In this case, the second treatment variable is a dummy variable showing whether a firm is large and publicly listed:

(A.3) N S i t G V A i t = γ 7 + ϕ 7 ⋅ d p o s t − 2001 ⋅ d 1 h i g h p r e + φ 7 ⋅ d p o s t − 2001 ⋅ d 2 l a r g e p u b l i c + κ 7 ⋅ d p o s t − 2001 ⋅ d 1 h i g h p r e ⋅ d 2 l a r g e p u b l i c + α 7 i + ν 7 t + ϵ 7 i t .

where, d1 is the first treatment variable (leverage ratio) and d2 is the second treatment variable (referring to firm size and legal form). The first treatment variables areagain defined on the median. The second treatment variable equals one if a firm is large and publicly listed and zero otherwise. The coefficient of interest is κ 7, i.e. the coefficient on the interaction term between the two treatment variables and the post-2001 dummy. Results from Table A.11 suggest that the deleveraging effect on saving ratios of large public firms was not significantly larger compared to the deleveraging effect for other firms (i.e. the triple DiD coefficient is insignificant but also negative). Results hold when using different window sizes for the panel regression (not shown here but also available upon request). This result is in line with the observation that compared to large firms, small and medium sized firms in Germany had significantly lower equity ratios during the 1990s and have increased their equity base more strongly since then (see e.g. Deutsche Bundesbank 2018a, 2019b). One would therefore expect that the deleveraging motive is not stronger for large publicly listed firms.

Table A.11:

Triple DiD regressions: debt and firm type (four year window).

Deleveraging motive
Post × large public firm 0.006 (0.013)
Post × Treated_Debt 0.027*** (0.007)
T-DiD −0.022 (0.030)
N 45,600

  1. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Sources: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, Ustan (1987–2017), retrieved December 11, 2019, own calculations.

A.5 Determinants of Corporate Savings: Firm-Level Markups

This section presents the empirical strategy for estimating firm-level markups.

A.5.1 Empirical Methodology

A.5.1.1 Deriving Markups

Firm-level markups are estimated following the methodology of Loecker and Warzynski (2012), which builds on the work of Hall (1988). This approach only assumes cost-minimizing firms and the presence of at least one variable input that is free of adjustment costs (see also Meinen 2016a,b). Cost minimization and optimal input demand imply that the output elasticity of any variable input is equal to its input cost share:

(A.4) P i t X X i t λ i t Q i t = ∂ Q i t ∂ X i t X i t Q i t ,

where X it is the choice of input X by firm i in year t, P i t X is the price of that input, λ i t are marginal production costs and Q it is the total output of the firm. The right-hand side of Eq. (A.4) represents the output elasticity of the input X, which, in the following, is denoted by ϵ i t X . The markup is defined as the price-marginal cost ratio ( μ i t = P i t λ i t ) . Rearranging Eq. (A.4) yields:

(A.5) μ i t P i t X X i t P i t Q i t = ϵ i t X .

Assuming imperfect competition, i.e. revenue shares are typically lower than output elasticities, a firm’s markup can be recovered from

(A.6) μ i t = ϵ i t X α i t X ,

where α i t X = P i t X X i t P i t Q i t . While, the cost share α i t X is typically observed in firm-level datasets such as Janis/Ustan, the output elasticity ϵ i t X needs to be estimated in order to obtain the markup μ i t . This study follows the methodology of Ackerberg et al. (2015) and estimates a gross output production function using the output elasticity of materials to recover firm-level markups. Given the rather rigid labor market in Germany,[46] it is more reasonable to consider materials as the variable input that is free of adjustment costs instead of labor. For identification of the gross output production function, following the insight of Collard-Wexler and Loecker (2016); Gandhi et al. (2020), lagged wages are used as control variables in the first stage regression. In the following subsection, the estimation of the output elasticity is outlined.

A.5.1.2 Estimating Output Elasticities and Markups

The estimation procedure consists of two steps and follows Ackerberg et al. (2015). As is standard in the literature, a translog production function is used to estimate the output elasticities. The gross output translog production function is given by:

(A.7) y i t = α l l i t + α l l l i t 2 + α k k i t + α k k k i t 2 + α m m i t + α m m m i t 2 + α l k l i t k i t   + α l m l i t m i t + α k m k i t m i t + α l k m l i t k i t m i t + ω i t + ϵ i t ,

where all variables in Eq. (A.7) are presented in lower-case letters which indicate logs. y it denotes real value added of firm i in year t, l it , k it , and m it are firm-level inputs of labor, capital, and materials, respectively. ω i t is a measure of unobserved firm-level productivity (TFP) that captures a constant term ϵ i t is noise capturing measurement error and unanticipated shocks to output.

In the first stage, the following equation is estimated:

(A.8) y i t = ϕ i t ( l i t , k i t , m i t , z i t ) + ϵ i t ,

where estimates of expected output ( ϕ ˆ i t ) and an estimate for ϵ i t are obtained. z i t is a vector including additional control variables. In this case, year- and (four-digit) sector fixed effects are included as well as lagged wages to identify the gross output production function. Expected output is given by

(A.9) ϕ i t = α l l i t + α l l l i t 2 + α k k i t + α k k k i t 2 + α m m i t + α m m m i t 2 α l k l i t k i t     + α l m l i t m i t + α k m k i t m i t + α l k m l i t k i t m i t + h t ( m i t , k i t , l i t , z i t ) ,

and ω i t = h t ( m i t , k i t , l i t , z i t ) , obtained from inverting the material demand equation. The first stage allows computing productivity for any parameter value α = ( α l , α l l , α k , α k k , α m , α m m , α l k , α l m , α k m , α l k m ) as

(A.10) ω i t ( α ) = ϕ ˆ i t − ( α l l i t + α l l l i t 2 + α k k i t + α k k k i t 2 + α m m i t + α m m m i t 2 + α l k l i t k i t + α l m l i t m i t       + α k m k i t m i t + α l k m l i t k i t m i t )

The second stage provides estimates for all production function coefficients by relying on the law of motion for productivity which is modeled as a first-order Markov process:

(A.11) ω i t = g ( ω i t − 1 ) + ν i t ,

where g t ( ⋅ ) is some polynomial function. The innovation to productivity given α, ν i t ( α ) , can be obtained as the residual from nonparametrically regressing ω i t ( α ) on its lag ω i t − 1 ( α ) . Finally, moments can be formed to obtain the estimates of the production function where E [ ν i t ( α ) d i t ] = 0 is used to estimate the production function parameters using standard GMM techniques.[47] The production function is estimated separately for industry and service firms. Using the estimated production function parameters, the output elasticity of materials is computed as

(A.12) ϵ i t M = α m + 2 α m m m i t + α l m l i t + α k m k i t + α l k m l i t k i t .

To finally compute markups, the input share is adjusted for potential measurement error and unanticipated shocks to production, as suggested by Loecker and Warzynski (2012). Specifically, the input share is computed as:

(A.13) α i t X = P i t X X i t P i t Q i t e x p ( ϵ i t ) ,

where ϵ i t is obtained from Eq. (A.8).

For the estimation, firms with less than 10 employees are excluded. After estimation, observations with implausible values for the output elasticity of materials are excluded, i.e. for ϵ i t M < 0 and ϵ i t M > 1 . Observations with e x p ( ϵ i t ) > 1.5 and e x p ( ϵ i t ) < 0.5 are excluded as well. Finally, observations with implausible material shares are excluded, i.e. when α i t X > 1 .

A.5.1.3 Summary statistics

Table A.12:

Markups: summary statistics.

Count Mean SD p1 p5 p10 p25 p50 p75 p90 p95 p99
Markup 149,977 1.23 0.28 0.86 0.92 0.96 1.04 1.17 1.35 1.57 1.79 2.32

  1. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

A.6 Determinants of Corporate Savings: Precautionary Motives and Other Channels

Table A.13:

Determinants of corporate savings: weighted panel regressions.

(1) (2) (3) (4) (5) (6) (7)
Intangibles Taxes Uncertainty Policy uncertainty Input prices All Sales growth
Size −0.057*** (0.003) −0.057*** (0.003) −0.060*** (0.003) −0.055*** (0.003) −0.070*** (0.005) −0.092*** (0.006) −0.092*** (0.006)
Equity ratio −0.097*** (0.008) −0.097*** (0.008) −0.105*** (0.009) −0.090*** (0.008) −0.134*** (0.014) −0.185*** (0.018) −0.163*** (0.017)
Profitability 0.062*** (0.009) 0.062*** (0.009) 0.059*** (0.009) 0.062*** (0.009) 0.089*** (0.015) 0.098*** (0.018) 0.088*** (0.018)
Dividend payer −0.104*** (0.002) −0.104*** (0.002) −0.111*** (0.002) −0.104*** (0.002) −0.080*** (0.003) −0.086*** (0.003) −0.078*** (0.003)
Intangible share −0.040*** (0.014) −0.041*** (0.014) −0.047*** (0.016) −0.040*** (0.014) −0.064** (0.025) −0.089*** (0.033) −0.043 (0.027)
Effective tax rate 0.002 (0.004) −0.002 (0.005) 0.001 (0.004) 0.005 (0.006) −0.001 (0.009) 0.010 (0.007)
Industry cash flow volatility −0.168 (0.193) −0.911** (0.364) −0.789** (0.326)
Policy uncertainty 0.000*** (0.000)
Interest rate −0.083*** (0.022) −0.088*** (0.023) −0.067*** (0.020)
Wage −0.000 (0.000) −0.000 (0.000) −0.000 (0.000)
Sales growth 0.024** (0.010)
N 713,810 706,722 609,072 706,722 246,167 171,295 157,062

  1. Regressions are weighted by the log of firm-specific average real gross value added. Standard errors are corrected for clustering at the firm-level. Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

Table A.14:

Determinants of corporate savings: weighted cross-sectional regressions.

(1) (2) (3) (4) (5)
Intangibles Taxes Input prices Sales growth Uncertainty
Size 0.007*** (0.001) 0.008*** (0.001) 0.004*** (0.001) 0.003*** (0.001) −0.000 (0.001)
Equity ratio 0.134*** (0.009) 0.132*** (0.009) 0.128*** (0.009) 0.133*** (0.009) 0.116*** (0.010)
Profitability 0.115*** (0.017) 0.098*** (0.017) 0.098*** (0.017) 0.072*** (0.018) 0.019 (0.017)
Dividend payer −0.006* (0.003) −0.011*** (0.003) −0.011*** (0.003) −0.009*** (0.003) −0.001 (0.004)
Intangible share 0.038* (0.021) 0.039* (0.021) 0.012 (0.020) −0.002 (0.020) −0.010 (0.020)
Effective tax rate 0.054*** (0.007) 0.050*** (0.007) 0.045*** (0.007) 0.030*** (0.009)
Interest rate −0.045* (0.027) −0.025 (0.027) −0.025 (0.030)
Wage 0.001*** (0.000) 0.001*** (0.000) 0.001*** (0.000)
Sales growth 0.180*** (0.025) 0.256*** (0.032)
Firm-specific cash flow volatility −0.259*** (0.030)
N 25,321 25,321 25,321 25,321 18,152

  1. In specifications (1)–(4), firms with less than three years of observations are excluded. In specification (5), firms with at least five years of observations enter the sample. Regressions are weighted by the log of firm-specific average real gross value added. Robust standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

A.7 Determinants of Corporate Cash Holdings

In order to investigate which firm characteristics are positively and negatively associated with corporate cash holdings, the following dynamic panel model is estimated:

(A.14) C i , t A i , t = ρ C i , t − 1 A i , t − 1 + β X i , t − 1 + α i + ν t + u i , t ,

where C i , t A i , t are cash holdings as a share of total assets and X i,t−1 are firm characteristics which are lagged by one period due to endogeneity concerns. Regressions include firm- and year-fixed effects.[48] For firm characteristics, standard determinants of cash holdings from the literature are used (see e.g. Falato et al. 2013). Equation (A.14) is estimated using three different dynamic panel estimators that take into account the Nickell bias. For specifications (1) and (2), the Blundell and Bond (1998) System GMM estimator is used. More specifically, the two-step estimator is used and it is assumed that firm characteristics are predetermined. Specification (3) and (4) uses the long differencing (LD) estimator by Hahn et al. (2007). It is implemented using the code from Elsas and Florysiak (2015) with three iterations and a differencing window of 4 years in order to exclude the fewest observations, given the unbalanced panel structure of Janis. For specification (4) and (5), the doubly censored Tobit estimator for dynamic panel estimation with a fractional dependent variable and unobserved heterogeneity (DPF) by Elsas and Florysiak (2015) is used. This estimator takes into account that the cash ratio is bounded between 0 and 1. Results are displayed in Table A.15. The median cash ratio of German nonfinancial corporations is shown in Figure A7.

Figure A7: Median liquid assets in % of assets of German nonfinancial corporations. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.
Figure A7:

Median liquid assets in % of assets of German nonfinancial corporations.

Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

Table A.15:

Determinants of corporate cash holdings: dynamic panel regressions.

(1) (2) (3) (4) (5) (6)
System GMM System GMM LD LD DPF DPF
Cash ratio 0.484*** (0.006) 0.498*** (0.006) 0.549*** (0.004) 0.558*** (0.005) 0.590*** (0.002) 0.592*** (0.002)
Intangibles −0.002 (0.004) 0.008* (0.004) 0.003 (0.003) 0.003 (0.003) −0.000 (0.002) −0.000 (0.002)
Industry sigma 0.396*** (0.033) 0.017 (0.022) −0.177*** (0.040) −0.154*** (0.029) 0.013 (0.033) −0.169*** (0.024)
Size −0.012*** (0.001) −0.015*** (0.001) −0.006*** (0.001) −0.004*** (0.001) −0.009*** (0.000) −0.008*** (0.000)
Cash flow 0.011*** (0.002) 0.009*** (0.002) 0.002 (0.002) 0.002 (0.002) −0.008*** (0.001) −0.009*** (0.001)
Capex −0.019*** (0.003) −0.023*** (0.003) −0.044*** (0.003) −0.045*** (0.003) −0.012*** (0.002) −0.013*** (0.002)
Acquisitions −0.012* (0.007) −0.011 (0.007) −0.043*** (0.009) −0.043*** (0.009) −0.014** (0.006) −0.016** (0.006)
Dividend payer −0.001* (0.000) −0.001* (0.000) −0.000 (0.000) −0.001 (0.000) 0.001*** (0.000) 0.001*** (0.000)
PU 0.000*** (0.000) 0.000*** (0.000) 0.000*** (0.000)
Firm FE YES YES YES YES YES YES
Year FE YES NO YES NO YES YES
N 454,669 454,669 148,200 148,200 454,669 454,669

  1. Standard errors are corrected for clustering at the firm-level, except for specifications (5) and (6). Standard errors are reported in parentheses. *, **, and *** indicate statistical difference from zero at the 10, 5, and the 1% level, respectively. Source: Data and Service Centre (RDSC) of the Deutsche Bundesbank, Janis (1997–2017), retrieved November 07, 2019, own calculations.

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Article Note

This article is part of the special issue “Market Power and Concentration and Developments: Evidence and Implications for Germany and Europe” published in the Journal of Economics and Statistics. Access to further articles of this special issue can be obtained at www.degruyter.com/journals/jbnst.

Received: 2020-08-04
Accepted: 2021-08-23
Published Online: 2021-10-19
Published in Print: 2021-11-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Market Power and Concentration Developments: Evidence and Implications for Germany and Europe
  4. Special Issue Articles
  5. The Fall and Rise of Market Power in Europe
  6. Industry Concentration and Profitability in Europe: The Case of Norway
  7. Digitalization, Industry Concentration, and Productivity in Germany
  8. Markups and Concentration in the Context of Digitization: Evidence from German Manufacturing Industries
  9. Markups for Consumers
  10. Determinants of Corporate Savings in Germany
  11. Data Observer
  12. The Top 100 Companies Panel Database
  13. Business Concentration Data for Germany
  14. Miscellaneous
  15. Annual Reviewer Acknowledgement
https://doi.org/10.1515/jbnst-2020-0031
Keywords for this article
corporate saving; deleveraging; intangible capital; market power; tax reforms

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Market Power and Concentration Developments: Evidence and Implications for Germany and Europe
  4. Special Issue Articles
  5. The Fall and Rise of Market Power in Europe
  6. Industry Concentration and Profitability in Europe: The Case of Norway
  7. Digitalization, Industry Concentration, and Productivity in Germany
  8. Markups and Concentration in the Context of Digitization: Evidence from German Manufacturing Industries
  9. Markups for Consumers
  10. Determinants of Corporate Savings in Germany
  11. Data Observer
  12. The Top 100 Companies Panel Database
  13. Business Concentration Data for Germany
  14. Miscellaneous
  15. Annual Reviewer Acknowledgement
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