Bank Control, Capital Allocation, and Economic Performance

Randall Morck University of Alberta M. Deniz Yavuz Oblin, Washington University in St Louis Bernard Yeung NUS

Financial System Efficiency

• Is the control structure of the banking system related to capital allocation efficiency and economic outcomes?

– family, state, and independent control of banks • Result: – Fraction of banks control by business families is correlated with • less efficient allocation of capital, • more non-performing loans, • more bank crisis, • more macro volatility, and • slower growth – Opposite is true for independent bank control – State control is correlated only with less efficient allocation of capital and more NPL

Motivation: Type of wealth and growth

The Cross-Country Relationship Between Growth in Real

Per Capita

GDP and Capital Ownership, Controlling for Current per Capita Income, Capital Investment Rate, and Level of Education (from Morck, Stangeland, and Yeung, 2000) .

.

.

Business Entrepreneur Billionaire Wealth Over GDP Heir Billionaire Wealth Over GDP I .

.

.

.44

(.00) II .

.

.

.37

(.00) III .

.

.

.42

(.00) IV .

.

.

.37

(.00) V .

.

.

.50

(.00) VI .

.

.

.38

(.00) VII .

.

.

.45

(.00) VIII .

.

.

.37

(.00) -.29

(.03) -.17

(.10) -.27

(.03) -.16

(.09) -.41

(.01) -.19

(.09) -.33

(.01) -.17

(.08) Definition of ‘heir’ H 1 H 2 H 3 H 4 H 5 H 6 H 7 H 8 R squared .519

.488

.531

.489

.545

.491

.536

.491

H 1

includes only the wealth of billionaires known positively to be heirs, politicians or politicians wealth of billionaires who are probably heirs.

H 3

includes

H 1

= relations.

H 2

also includes the plus fortunes jointly controlled by a founder and his heirs.

H 4

includes all the above.

H 5

through

H 8

are analogous to

H

,

H 2

,

H 3

and

H 4

but do not include politician billionaires and their relations.

• •

Why such results?

– Around the world firms have “controlling owners,” LLSV 1999 a large fraction of these dominant owners are wealthy families.

Via pyramids, etc., these wealthy families gain control of a large fraction of corporations and corporate assets + more micro efficiency among family controlled set of firms • Overcome inefficiency in using the market – due to information asymmetry and regulatory/bureaucratic burdens – what is good for a set of firms can be bad for the economy • Intrinsic results due to biased resources allocations • Economic entrench – rent seeking – – Lobby for entry barriers (Fogel 2006) Capture the capital market

Why capture the financial market?

• Developed financial markets encourage “creative destruction” and promote competition. (Schumpeter, 1942) – Not always in the best interest of elite/powerful families to promote efficient financial systems. • Capture the institutional development – save capitalism from capitalist – Morck, Stangeland, Yeung, 2000, 2002, 2005; Rajan and Zingales 2003, 2004;….Perotti and Vorage 2008 etc – Morck et al 2002, Rajan and Zingales 2003 • An economy may have no stock markets, but, cannot do without banks – Capturing the banking system • Family bank ownership was first looked at Caprio et al JEI 2007

Let us not presume

• The positive and negative consequences of family control of banks – Efficiency view (mostly applies at the micro level): • Interest alignment and mitigating information asymmetry (Diamond 1984; Hoshi

et al.

1991; Khanna

et al.

2000; Fisman & Khanna 2004; Almeida & Wolfenzon 2006, Caprio et al 2007, etc) – Entrenchment view (has macro level implications): • Family banks may favor related firms (see, e.g., La Porta

et al.

2003), limit capital to potential competitors, • ATM – Engage in risk shifting, government bails out ( see, e.g., La Porta

et al.

2003, Haber 2002 ).

• Add state ownership – LLSV, “Government Ownership of Banks,” JFE, 2002

Empirical investigation of ….

• Relationship between bank ownership structure and

– Efficiency in capital allocation – NPL, Bank Crisis – Macro Volatility – Growth • Efficiency vs. economic entrenchment?

Dataset and Ownership Variables

• Starting sample is the 10 largest banks in 44 countries (Banker 2001).

• Caprio et al. (2007) provide control structure of 244 public banks.

• We add the ownership structure of private banks (total 318 banks).

– Most of the data come from Bankscope 2001.

– Controlling shareholder = largest ultimate shareholder with stake ≥ 10%.

– Controlling classifications: State, family, independent.

• Bank Control variables: by state, family and independent banks weighted by total loans. – Same if weighted by assets Fraction of the banking system controlled

Capital Allocation Efficiency Measure 1

• Elasticity of capital spending with respect to the value added, by industry.

ln

I ict I ict

 1  

c

 

c

ln

V ict V ict

 1  

ict

as in Wurgler (2000) –Version 1: UNIDO (UN General Industrial Statistics) Data for 1993 through 2003.

–Version 2: UNIDO (UN General Industrial Statistics) Data for 1963 through 2003.

Capital Allocation Efficiency Measure 2 • Follow Rajan & Zinglaes (1988)

– US industries’ external financing dependence • [(capital expenditure – cashflow from operation)/ capital expenditure], average over 80-90 – Dummy = 1 if above median – Or just the raw data

Measures of Capital Allocation Efficiency • Regress

growth

i

,

c



k



f

 ,

s

,

a

k w



i



k

,

c



b

s

s

i

,

c



b

c

d

c



b

i

d

i

 

i

,

c

–

growth i,c

is annualized compound value-added growth rate from 1993 to 2003 of industry

i

in country

c

; –

δ i

= 1 if industry – the  k,c

i

is more dependent on external financing than the median industry are the fractions of country c’s banking system controlled by families (

k

=

f

), state controlled (

k

=

s

), or widely held (

k

=

w

) – –

s i,c

d

c

is the share of industry and

d

i i

in country

c

are country and industry fixed effects

Data and econometric problems

• Please hold your questions on – Data timing (contemporary data on ownership (2001 2003) and target dependent variables (1993 – 2003) – Causality?

– Endogeneity?

• Will deal with them after providing a snap shot of the results

Other Critical variables

• Nonperforming loans /Total loans outstanding.

– WDI site – World Development Indicators (World Bank) – Average between 1993 and 2003.

– Do a logistical transformation

• Bank crisis Dell’ Ariccia et al 2008

– Deposit runs, bank holidays or nationalization, fiscal cost of bank rescues > 2% GDP, NPL > 10% bank assets – 1993 – 2003 • Add 2008 – IMF, direct government rescue attempt 2008

• • • •

Other Critical variables

Macro variables – Beck et al 2003, Beck et al 2000, Penn world Tables,

Real per capita GDP growth

– 1993 through 2003 and 1963 to 2003 – Estimate as the time trend TFP Growth – 1993-2003 – –

Y t

 

AK t

 = 30%

L t

1   – using logarithms of first differences for Y, K, and L to estimate the rate of change of the parameter A = TFP growth Capital accumulation – Beck et al (2000), 1993 - 2003 – –

K t

 (   7% 1   )

K t

 1 

I t

– 1964 as the starting points – Forward iteration to get capital stocks – Calculate the rate of change • Economic stability, 1993 - 2003 – the standard deviation of log first differences in real per capita GDP

Table 4 Main Variables: Simple Cross-sectional Correlation Coefficients

Variables are as defined in Table 3. Numbers in parentheses are probability levels for rejecting the null hypothesis of a zero correlation. Boldface indicates significance at ten percent or better.

1 2 3 4 5 6 7 8 9 10 7 8 9 1 2 3 4 5 6 11 12 10

Family State Widely held Capital allocation efficiency, ‘63-‘03 Capital allocation efficiency, ‘93-‘03 Non-performing loans Banking crises Income growth TFP growth Capital accumulation Growth rate volatility Initial income 1.00

-0.66 (0.00) -0.65 (0.00)

-0.24 (0.18)

0.58 (0.00)

0.01 (0.95) 0.03 (0.87) 0.13 (0.40)

-0.40 (0.01)

0.08 (0.60)

-0.59 (0.00)

1.00 -0.20 (0.19)

-0.60 (0.00)

-0.21 (0.19)

-0.30 (0.09) 0.30 (0.05) 0.35 (0.02) -0.25 (0.10)

-0.20 (0.20)

-0.26 (0.09) 0.51 (0.00)

-0.21 (0.19) 1.00

0.54 (0.00) -0.63 (0.00)

-0.26 (0.11) 0.16 (0.32) 0.05 (0.76)

0.43 (0.01) -0.37 (0.02) 0.66 (0.00)

1.00

0.70 (0.00) 0.43 (0.01) -0.70 (0.00) -0.28 (0.07)

0.17 (0.27) 0.04 (0.78)

0.52 (0.00) -0.45 (0.00) 0.63 (0.00)

1.00

0.32 (0.04) -0.44 (0.00) -0.31 (0.04) -0.55 (0.00) 0.48 (0.00) -0.73 (0.00)

1.00

-0.37 (0.03)

0.02 (0.91) 0.13 (0.48) 0.05 (0.78)

0.31 (0.08) -0.31 (0.08) 0.51 (0.00)

1.00

-0.30 (0.0)5 -0.30 (0.05)

-0.07 (0.66) 0.36 (0.02) -0.23 (0.15) 1.00 0.09 (0.57) -0.14 (0.36) 0.06 (0.70) 1.00

0.97 (0.00) 0.33 (0.03)

-0.20 (0.20) 0.18 (0.25) 1.00

-0.40 (0.01)

11

1.00 -0.24 (0.12)

0.49 (0.00)

So …

• Family ownership of banks is associated with – Less allocation efficiency – more NPL and bank crisis, – more macro volatility – slower growth • Opposite result for independently owned banks • State ownership is associated with – Less allocation efficiency – More NPL • Implications – Economic entrenchment, not efficiency argument – Previous results on the effect of state owned bank may need refinement

Table 5 Bank Control and Capital Allocation Efficiency

Each row in Panel A summarizes a country-level regressions explaining capital allocation quality with banking system control measures. All are OLS except for that of the numbers of banking crises, which is negative binomial, and the number of banking crises including 2008, which is a Poisson regression (as the negative binomial does not converge). Each row of Panel B summarizes a multi-country industry-level regression of value-added growth on interactions of industry external finance dependence with banking system control measures. All variables are as in Table 3. P-levels for robust standard errors are in parentheses.

PANEL A

Widely held Family State Banking system size Stock market size Initial income R 2 N

-0.372

-0.088 0.067 0.086

0.093

0.35 33 Capital Allocation Efficiency (1993-2003)

0.262 (0.04)

(0.67) (0.78) 0.119 (0.40) 0.045

(0.04)

0.073 0.33 33

(0.08)

(0.63) (0.64) (0.16) Capital Allocation Efficiency (1963-2003) Non performing Loans Banking Crises Banking Crises, including 2008 Growth Rate Volatility

0.353 (0.00) -1.312 (0.00) -2.32 (0.01)

-0.836 (0.19) -0.015 (0.12)

-0.284 (0.00) 1.287 (0.01) 3.152 (0.00) 1.239 (0.09) 0.028 (0.01) -0.462 (0.00) 1.349 (0.01)

1.085 (0.32) 0.197 (0.80) -0.004 (0.74) 0.003 (0.98) -0.03 (0.71) 0.356 (0.26) 0.364 (0.20) 0.073 (0.92) -0.496 (0.38) -0.013 (0.98) -0.189 (0.68) 0.002 (0.70) -0.002 (0.71) 0.053 (0.30) 0.083 (0.16) -0.266 (0.24) -0.274 (0.20)

-0.774 (0.09)

-0.454 (0.29) -0.237 (0.48) -0.117 (0.36) 0.001 (0.68) 0.005 (0.18) 0.043 (0.15)

0.055 (0.05) -0.398 (0.00) -0.401 (0.00)

-0.105 (0.69) 0.16 (0.37) 0.138 (0.48) 0.199 (0.18)

-0.006 (0.09)

-0.004 (0.26) 0.61 0.59 0.65 0.65 0.21 0.16 0.03 0.02 0.37 0.25 39 39 43 43 43 43 43 43 43 43

PANEL B

External finance dependence interaction with Industry Value Added Growth Widely held

0.055 (0.07)

Family

-0.093 (0.01)

State -0.013 (0.71) Industry share

-0.530 (0.06) -0.536 (0.07)

Industry dummies yes yes Country dummies yes yes R 2 0.32 0.31 N 446 446

Table 6 Bank Control and Economic Growth

The table shows results of cross-country OLS regressions with robust standard errors. Dependent variables are in columns and independent variables are in rows. Variables are as defined in Table 3. P values are in parentheses. Income Growth TFP Growth Capital Accumulation Widely held Family State Human capital Trade openness Banking system size Stock market size Africa dummy Initial income square Initial Income Constant R 2 N

-0.035 (0.01)

-0.003 (0.78) 0.012 (0.19) 0.003 (0.49) 0.006 (0.28)

0.005 (0.24) -0.023 (0.07) -0.004 (0.09)

0.064 (0.12) -0.280 (0.13) 0.41 43 0.015 (0.16) 0.015 (0.18) 0.003 (0.59) 0.008 (0.17) 0.002 (0.69)

-0.024 (0.05)

-0.001 (0.60) 0.011 (0.76) -0.077 (0.62) 0.31 43

-0.030 (0.03)

0.002 (0.82) 0.011 (0.19) 0.005 (0.26) 0.004 (0.43) 0.005 (0.25)

-0.022 (0.08) -0.004 (0.10)

0.058 (0.13) -0.252 (0.14) 0.39 43 0.010 (0.34) 0.014 (0.16) 0.005 (0.33) 0.006 (0.29) 0.001 (0.76)

-0.023 (0.06)

-0.001 (0.73) 0.004 (0.91) -0.041 (0.77) 0.28 43 -0.015 (0.19)

-0.016 (0.05)

0.004 (0.77)

-0.006 (0.02)

0.005 (0.27) 0.001 (0.69) -0.004 (0.53) -0.001 (0.57) 0.021 (0.57) -0.094 (0.54) 0.45 43

0.016 (0.00)

0.004 (0.77)

-0.006 (0.02)

0.005 (0.32) 0.001 (0.66) -0.004 (0.54) -0.001 (0.24) 0.023 (0.24) -0.120 (0.17) 0.45 43

Robustness – Bank control data

•

Different ways of constructing bank control variables:

– Calculate bank control variables by using total assets as weights. – Control assumed at 15% or 20% instead of 10%.

– Ignoring countries with number of banks < 3.

Robustness – Bank control data

• Contemporary data – ownership and the target variables – Should use lagged bank control variables • Cannot get them – May not be that important if control structure changes slowly. • Only 14 banks (4.4% of the total 318) switch category between 2001-2007.

• Still, many bank privatizations between 1993-2001 from Megginson (2004). – 14 (out of 324) banks change control from 2001 to 2007 • 2 family banks  state controlled, 4 becomes widely held • 4 state controlled  • 2 widely held  widely held family owned, and 2 become state controlled – Beware of the “Chen” factor – • incompetent and corrupt government passes the control of state owned banks to rich families – Result = corrupt and incompetent government – Do the data both way • Move all family owned back to state owned • Or, like we did before, keep them as family owned • Or, take a weighted average for each – Identical results – So, the mis-classification not a factor

Robustness

• • •

Alternative Regressions:

– Regression robust to outliers.

– Weighted least squares regressions.

– Correcting for arbitrary heteroscedasticity vs simple OLS

Expanding the sample to 2007 (GDP data

– no change )

Expanding the crisis data

– E.g., use both Dell’ Ariccia (2006) , result the same

et al

(2008) and Demirguc-Kunt

et al

Robustness in the interpretation: using banks to entrenchment

• Proxying for under development in financial markets?

– Control for capital market development – In the Rajan and Zingales regression (panel B table 5), we put in cross terms between external finance dependence and capital market development, • Same result • Just due to “oligarchic” behavior?

– Put in an oligarchy variable as an additional control – Or form another cross term in the R& Z regression • same result

Causality and endogeneity

• Reverse causality – poor environment and therefore own banks?

– Cannot do Granger • Limited sample size and data • Instrument? – done that, works, but, not really credible because there could be many hidden channels these instruments affect growth or stability – Still, the R&J regression results • Fixed industry and country effects included, results therefore not due to latent variables at the industry or country level • If reverse causality, families own banks to support their firms, their bank ownership should mitigate the slower growth of external financing dependent industry in poor environment. – Found the opposite – Clinical country level exists – Haber (2004) – Shall give more results

Instrumental variable

• To deal with endogeneity using time invariant instrumental variables: – legal origin (La Porta et al. 1998, 2008) – financial development – religion (Stulz and Williamson, 2003) – credit rights – latitude (Hall and Jones, 1999; Acemoglu et al. 2001) – Western influence – Use them to predict ownership (Tobit) • French legal origin = family ownership most prevalent • Protestant countries = independently owned banks most prevalent • Highest latitude = independently owned banks most prevalent • Instrumental variable result = table X – Plead guilty – legal origin and religion are exogenous, but, they may have a relationship with our target performance variables

Independent Family State Independent Family State

TABLE X Instrumental Variable Regressions Panel A: Bank Control and Financial System Efficiency

Capital Allocation Efficiency 1993-2003 Capital Allocation Efficiency 1963-2003 Non Performing Loans / Total Gross Loans 0.733 (0.14) -0.668 (0.07) 0.327 (0.65) 0.592 (0.04) -0.378 (0.06) -0.501 (0.16) -4.74 (0.00) 3.14 (0.00) 3.81 (0.01)

Panel B: Bank Control and Economic Growth

Real GDP per Productivity Capital Capita Growth Growth Accumulation 0.0666 (0.00) -0.421 (0.01) -0.00816 (0.79) 0.0536 (0.01) -0.312 (0.03) 0.00425 (0.88) 0.0487 (0.01) -0.291 (0.01) -0.0829 (0.00) Number of Banking Crises (1993- ) -6.45 (0.04) 3.90 (0.08) 0.798 (0.89) Standard Deviation of Growth -0.0521 (0.08) 0.0274 (0.09) 0.0608 (0.07)

More on robustness in the economic interpretation • Incompetent heir effect?

– Step one, improve the bank ownership data • Get ownership data to identify families that do and do not own other business – “Orbis” to identify other business owned – Use also the internet • Reclassify those with no “known other business” as independent • Same result

• Step two

– Dig up alternative stories • Incompetent family?

– Find that family ownership is highly related to “entry barriers » Number of procedures, » Time and costs to set up a firm

• Aside – family bank ownership is related to more income inequality and a smaller middle class too

Table 7 Consistency with Crony Capitalism

The table shows cross-country OLS regressions with robust standard errors. Dependent variables are in columns and independent variables are in rows. Variables are as defined in Table 3. P values are in parentheses. Equality of Outcomes Equality of Opportunity Income Inequality (Gini coefficient) PCs per thousand Population Difficulty starting a new company Number of procedures Time Cost Widely held

-0.266 (0.08) 118 (0.00) -0.785 (0.00) -1.136 (0.02) -1.39 (0.00)

Family State Banking system size Stock market size Initial income

0.629 (0.00) -0.275 (0.10) -0.174 (0.10)

0.103 (0.13)

-0.130 (0.01) 0.924 (0.01) -0.303 (0.01) 0.229 (0.01) -0.098 (0.07) 0.947 (0.03) -189 (0.00)

-11.5 (0.82) -37.5 (0.17)

63.0 (0.00) 72.9 (0.00) -493 (0.00)

-12.1 (0.61)

39.9 (0.04) 63.0 (0.00) -537 (0.00) 1.028 (0.00)

0.415 (0.17)

0.242 (0.08) -0.382 (0.00)

0.001 (0.98)

2.132 (0.00)

0.154 (0.20)

-0.302 (0.00)

0.036 (0.58)

2.662 (0.00) 1.440 (0.00)

0.674 (0.15) 0.130 (0.59)

-0.545 (0.00)

-0.102 (0.25)

5.261 (0.00)

0.020 (0.93)

-0.446 (0.01)

-0.059 (0.55)

6.078 (0.00) 1.277 (0.02) 1.571 (0.00)

0.506 (0.17)

-0.583 (0.02) 0.880 (0.00)

-1.570 (0.17) 0.548 (0.13)

-0.621 (0.00) 0.863 (0.00)

-0.054 (0.96) Constant R 2 N 0.60 42 0.40 42 0.86 43 0.80 43 0.44 43 0.39 43 0.49 43 0.46 43 0.57 43 0.57 43

Conclusion

• Control of the banking system is important for efficiency of capital allocation.

– Family and state control over banks have similarly economically significant and negative implications for capital allocation efficiency. – However, family control is also correlated with lower economic growth, elevated macroeconomic instability, and more income inequality.

• Implication: – Do not let families capture the banking system! – Multiple governments (e.g., Singapore, Canada) actually stipulate against bank owning families from holding non-banking business • "Measures to separate financial and non-financial activities of banking groups“ – Speech by DPM Lee Hsien Loong At The Association Of Banks in Singapore (ABS) 21 June 2000 –

“

with banking and non-banking activities inter-meshed within a conglomerate, there will be a strong tendency to stretch any safety net intended for the banking system also to cover non bank operations in the group.”

• Why is family ownership of banks common?

– Political Economy Outcome.

• Rajan and Zingales (2004); Morck, Wolfenzon, Yeung(2005); Stulz (2005); Perotti and Volpin(2007); Haber and Perotti(2008); Acemoglu, Johnson and Robinson (2008).

• Families are the highest bidders because their efficiency gains are the highest.