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)
I
II
III
IV
V
VI
VII
VIII
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.
.
.
Business Entrepreneur
Billionaire Wealth Over
GDP
.44
(.00)
.37
(.00)
.42
(.00)
.37
(.00)
.50
(.00)
.38
(.00)
.45
(.00)
.37
(.00)
Heir Billionaire Wealth
Over GDP
-.29
(.03)
-.17
(.10)
-.27
(.03)
-.16
(.09)
-.41
(.01)
-.19
(.09)
-.33
(.01)
-.17
(.08)
H1
H2
H3
H4
H5
H6
H7
H8
.519
.488
.531
.489
.545
.491
.536
.491
.
.
.
Definition of ‘heir’
R squared
H1 includes only the wealth of billionaires known positively to be heirs, politicians or politicians= relations. H2 also includes the
wealth of billionaires who are probably heirs. H3 includes H1 plus fortunes jointly controlled by a founder and his heirs. H4
includes all the above. H5 through H8 are analogous to H, H2, H3 and H4 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/powerfulfamilies 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: Fraction of the banking system controlled
by state, family and independent banks weighted by total loans.
– Same if weighted by assets
Capital Allocation Efficiency Measure 1
• Elasticity of capital spending with respect to the
value added, by industry.
I ict
Vict
ln
  c   c ln
  ict
I ict 1
Vict 1
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 growthi ,c 
a  
k  f ,s ,w
k
i
k ,c
 bs si ,c  b cd c  b i d i   i ,c
– growthi,c is annualized compound value-added
growth rate from 1993 to 2003 of industry i in
country c;
– δi = 1 if industry i is more dependent on external
financing than the median industry
– the k,c are the fractions of country c’s banking
system controlled by families (k = f), statecontrolled (k=s), or widely held (k=w)
– si,c is the share of industry i in country c
– dc and di are country and industry fixed effects
Data and econometric problems
• Please hold your questions on
– Data timing (contemporary data on ownership (20012003) 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
– Yt  AK t L1t
–  = 30%
– 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  (1   ) K t 1  I t
–   7%
– 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
1.00
2
State
-0.20
(0.19)
1.00
3
Widely held
-0.60
(0.00)
-0.66
(0.00)
1.00
4
Capital allocation efficiency, ‘63-‘03
-0.21
(0.19)
-0.65
(0.00)
0.70
(0.00)
1.00
5
Capital allocation efficiency, ‘93-‘03
-0.30
(0.09)
-0.24
(0.18)
0.43
(0.01)
0.54
(0.00)
1.00
6
Non-performing loans
0.30
(0.05)
0.58
(0.00)
-0.70
(0.00)
-0.63
(0.00)
-0.37
(0.03)
1.00
7
Banking crises
0.35
(0.02)
0.01
(0.95)
-0.28
(0.07)
-0.26
(0.11)
0.02
(0.91)
0.32
(0.04)
1.00
8
Income growth
-0.25
(0.10)
0.03
(0.87)
0.17
(0.27)
0.16
(0.32)
0.13
(0.48)
-0.44
(0.00)
-0.30
(0.0)5
1.00
9
TFP growth
-0.20
(0.20)
0.13
(0.40)
0.04
(0.78)
0.05
(0.76)
0.05
(0.78)
-0.31
(0.04)
-0.30
(0.05)
0.97
(0.00)
1.00
10
Capital accumulation
-0.26
(0.09)
-0.40
(0.01)
0.52
(0.00)
0.43
(0.01)
0.31
(0.08)
-0.55
(0.00)
-0.07
(0.66)
0.33
(0.03)
0.09
(0.57)
1.00
11
Growth rate volatility
0.51
(0.00)
0.08
(0.60)
-0.45
(0.00)
-0.37
(0.02)
-0.31
(0.08)
0.48
(0.00)
0.36
(0.02)
-0.20
(0.20)
-0.14
(0.36)
-0.24
(0.12)
1.00
12
Initial income
-0.21
(0.19)
-0.59
(0.00)
0.63
(0.00)
0.66
(0.00)
0.51
(0.00)
-0.73
(0.00)
-0.23
(0.15)
0.18
(0.25)
0.06
(0.70)
0.49
(0.00)
-0.40
(0.01)
1
Family
2
3
4
5
6
7
8
9
10
11
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.
Banking
Stock market
Initial
Widely held
Family
State
R2
N
PANEL A
system size
size
income
-0.088
0.067
0.086
0.35
33
-0.372
0.093
Capital
(0.67)
(0.78)
(0.40)
(0.04)
(0.04)
Allocation
Efficiency
0.119
0.045
0.073
0.33
33
0.262
(1993-2003)
(0.63)
(0.64)
(0.16)
(0.08)
Capital
Allocation
Efficiency
(1963-2003)
Nonperforming
Loans
Banking
Crises
-0.284
-0.462
0.003
0.053
0.043
(0.00)
(0.00)
(0.98)
(0.30)
(0.15)
0.353
-0.03
0.083
0.055
(0.00)
(0.71)
(0.16)
(0.05)
1.287
1.349
0.356
-0.266
-0.398
(0.01)
(0.01)
(0.26)
(0.24)
(0.00)
-1.312
0.364
-0.274
-0.401
(0.00)
(0.20)
(0.20)
(0.00)
3.152
1.085
0.073
-0.774
-0.105
(0.00)
(0.32)
(0.92)
(0.09)
(0.69)
-0.496
-0.454
0.16
(0.37)
-2.32
(0.38)
(0.29)
1.239
0.197
-0.013
-0.237
0.138
(0.09)
(0.80)
(0.98)
(0.48)
(0.48)
-0.836
-0.189
-0.117
0.199
(0.19)
(0.68)
(0.36)
(0.18)
(0.01)
Banking
Crises,
including
2008
Growth Rate
Volatility
PANEL B
-0.004
0.002
0.001
-0.006
(0.74)
(0.70)
(0.68)
(0.09)
-0.015
-0.002
0.005
-0.004
(0.12)
(0.71)
(0.18)
(0.26)
39
0.59
39
0.65
43
0.65
43
0.21
43
0.16
43
0.03
43
0.02
43
0.37
43
0.25
43
External finance dependence interaction with
Widely held
Industry
Value Added
Growth
0.028
(0.01)
0.61
Family
State
Industry share
Industry
dummies
Country
dummies
R2
N
-0.093
-0.013
-0.530
yes
yes
0.32
446
(0.01)
(0.71)
(0.06)
yes
yes
0.31
446
0.055
-0.536
(0.07)
(0.07)
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
Widely held
TFP Growth
0.015
(0.16)
Capital Accumulation
0.010
(0.34)
0.016
(0.00)
Family
-0.035
(0.01)
-0.030
(0.03)
-0.015
(0.19)
State
-0.003
(0.78)
0.002
(0.82)
-0.016
(0.05)
Human capital
0.012
(0.19)
0.015
(0.18)
0.011
(0.19)
0.014
(0.16)
0.004
(0.77)
0.004
(0.77)
Trade openness
0.003
(0.49)
0.006
(0.28)
0.003
(0.59)
0.008
(0.17)
0.005
(0.26)
0.004
(0.43)
0.005
(0.33)
0.006
(0.29)
-0.006
(0.02)
0.005
(0.27)
-0.006
(0.02)
0.005
(0.32)
Stock market size
0.005
(0.24)
0.002
(0.69)
0.005
(0.25)
0.001
(0.76)
0.001
(0.69)
0.001
(0.66)
Africa dummy
-0.023
(0.07)
-0.024
(0.05)
-0.022
(0.08)
-0.023
(0.06)
-0.004
(0.53)
-0.004
(0.54)
Initial income square
-0.004
(0.09)
-0.001
(0.60)
-0.004
(0.10)
-0.001
(0.73)
-0.001
(0.57)
-0.001
(0.24)
Initial Income
R2
0.064
(0.12)
-0.280
(0.13)
0.41
0.011
(0.76)
-0.077
(0.62)
0.31
0.058
(0.13)
-0.252
(0.14)
0.39
0.004
(0.91)
-0.041
(0.77)
0.28
0.021
(0.57)
-0.094
(0.54)
0.45
0.023
(0.24)
-0.120
(0.17)
0.45
N
43
43
43
43
43
43
Banking system size
Constant
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  widely held
• 2 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 et al (2008) and Demirguc-Kunt et al
(2006) , result the same
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
TABLE X
Instrumental Variable Regressions
Panel A: Bank Control and Financial System Efficiency
Independent
Family
State
Independent
Family
State
Capital Allocation Capital Allocation
Non Performing
Efficiency
Efficiency
Loans / Total
1993-2003
1963-2003
Gross Loans
0.733
0.592
-4.74
(0.14)
(0.04)
(0.00)
-0.668
-0.378
3.14
(0.07)
(0.06)
(0.00)
0.327
-0.501
3.81
(0.65)
(0.16)
(0.01)
Panel B: Bank Control and Economic Growth
Real GDP per
Productivity
Capital
Capita Growth
Growth
Accumulation
0.0666
0.0536
0.0487
(0.00)
(0.01)
(0.01)
-0.421
-0.312
-0.291
(0.01)
(0.03)
(0.01)
-0.00816
0.00425
-0.0829
(0.79)
(0.88)
(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
-0.266
(0.08)
Widely held
Difficulty starting a new company
Number of
procedures
Time
Cost
118
(0.00)
-0.785
(0.00)
-1.136
(0.02)
-1.39
(0.00)
Family
0.629
(0.00)
-189
(0.00)
1.028
(0.00)
1.440
(0.00)
1.277
(0.02)
State
-0.275
(0.10)
-11.5
(0.82)
0.415
(0.17)
0.674
(0.15)
1.571
(0.00)
Banking system size
-0.174
(0.10)
-0.303
(0.01)
-37.5
(0.17)
-12.1
(0.61)
0.242
(0.08)
0.154
(0.20)
0.130
(0.59)
0.020
(0.93)
0.506
(0.17)
0.548
(0.13)
Stock market size
0.103
(0.13)
0.229
(0.01)
63.0
(0.00)
39.9
(0.04)
-0.382
(0.00)
-0.302
(0.00)
-0.545
(0.00)
-0.446
(0.01)
-0.583
(0.02)
-0.621
(0.00)
Initial income
-0.130
(0.01)
-0.098
(0.07)
72.9
(0.00)
63.0
(0.00)
0.001
(0.98)
0.036
(0.58)
-0.102
(0.25)
-0.059
(0.55)
0.880
(0.00)
0.863
(0.00)
Constant
0.924
(0.01)
0.947
(0.03)
-493
(0.00)
-537
(0.00)
2.132
(0.00)
2.662
(0.00)
5.261
(0.00)
6.078
(0.00)
-1.570
(0.17)
-0.054
(0.96)
0.60
0.40
0.86
0.80
0.44
0.39
0.49
0.46
0.57
0.57
42
42
43
43
43
43
43
43
43
43
R2
N
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 nonbank 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.