Bank Control, Capital Allocation, and Economic Performance · 2009. 4. 10. · Bank Control, Capital Allocation, and Economic Performance Randall Morck, a M. Deniz Yavuz, b and Bernard

First draft: December 1st 2008 This draft: March 1th 2009 Very preliminary and not for quotation

Bank Control, Capital Allocation, and Economic Performance

Randall Morck, a M. Deniz Yavuz, b and Bernard Yeung c

Abstract More efficient capital allocation correlates with the independence of a country’s banks from both government and business families. In fact, state-controlled banks and family controlled banks are associated with roughly similarly inefficient capital allocation. However, family control also correlates with elevated wealth and income inequality, growth volatility and probability of financial crises. These findings are consistent with theories that elite-capture of countries’ financial system embeds “crony capitalism”, with negative economic and social consequences.

KEYWORDS: banks, ownership structure, capital allocation, economic growth, income distribution, family control, state control, independent banks.

a. Stephen A. Jarislowsky Distinguished Professor of Finance and University Professor, The University of Alberta Business School, Edmonton AB Canada T6E 2T9; Research Associate, National Bureau of Economic Research. Phone: +1(780)492-5683. E-mail: [email protected].

b. Assistant Professor of Finance, W.P. Carey School of Business, Arizona State University, PO Box 873906 Tempe, AZ 85287. Phone: (480) 965-7281 E-mail: [email protected]

c. Dean and Stephen Riady Distinguished Professor of Finance, National University of Singapore Business School, Biz 2 Building Level 6, 1 Business Link, Singapore, 117592. +65 6516 3075, [email protected] and Stern, NYU.

We are grateful for comments from Mara Faccio, Ross Levine, Enrico Perotti, Alex Philipov, Andrei Shleifer, Fei Xie, seminar participants at the AFA 2009 meetings, Arizona State University and George Mason University. Ching-Hung Chang, Minjeong Kang, Zhen Shi and Xiaowei Xu provided excellent research assistance.

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1. Introduction Economic growth is highly correlated with financial development.1 Financial systems, in turn,

are more developed where corruption and the returns to political rent-seeking are less (La Porta

& et al. 1997; La Porta 2000; La Porta & et al. 2002; La Porta et al. 2006, 2008). Financial

development is neither inevitable nor irreversible. Many countries never sustained dynamic

financial systems and, more surprisingly, many that once did ceased doing so (Rajan & Zingales

2003).

One explanation for such financial atavism is that financial development allows an initial

cadre of tycoons to grow rich and powerful during an initial burst of economic growth (Rajan &

Zingales 2004). Once established, this first generation of business moguls and their descendents

fear displacement by upstart entrepreneurs. To entrench their elite economic and social status,

these families must somehow erect barriers to entry against such upstarts (Morck et al. 2005b).

Since upstarts, by definition, begin without fortunes of their own; their success depends

upon mobilizing capital. Enduringly dynamic financial systems provide capital to successive

waves of upstart entrepreneurs in highly developed economies (Schumpeter 1912). In faster

growing countries, economic dominance is thus transient (Fogel et al. 2008). There is an elite

class, but its membership changes continually as new entrepreneurs displace bumbling scions

(Schumpeter 1951).

One obvious way for an otherwise ephemeral elite to slow, or stop, its passing, and

become a permanent oligarchy is to take control of the country’s financial system. Morck,

Stangeland and Yeung (2000) find that economies with more inherited billionaire wealth, scaled

by GDP grow slower, spend less on innovation, and erect more entry barriers. Based on this,

they propose that entrenched elites with inherited wealth sustain the values of their assets via

political rent-seeking directed at depriving upstarts of capital. Rajan and Zingales (2004) argue

such elite capture of financial systems explains the reversals in countries’ financial development

they observe, and argue that legal and regulatory measures to safeguard the independence of a

country’s private sector financial system are necessary to “save capitalism from the capitalists”.

1 King & Levine 1993a; Demirguc-Kunt & Levine 1996; Levine 1996; Levine & Zervos 1998; Rajan & Zingales 1998; Beck et al. 2000b; Levine et al. 2000; Beck & Levine 2002b

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Fogel (2006) shows that economies whose business sectors are dominated by family controlled

pyramids have more corrupt government, less efficient judiciaries, and more bureaucratic red

tape – all barriers to entry.

Controlling the financial system is arguably the simplest way for established business

families to preserve their advantageous status. Without control of the financial system, continual

rent-seeking to undermine upstarts would be needed. Once the financial system is captured, the

entry of upstarts is at the discretion of the established business families (La Porta et al. 2003;

Perotti and Vorage, 2008).

Dysfunctional financial markets - stock markets, bond markets, and the like, are thus a

potential symptom of such economic entrenchment (Morck, Wolfenzon, and Yeung, 2005; Stulz,

2005; Perotti and Volpin 2006; and others). Stock markets are important sources of capital in

only a few highly developed economies. Most developing economies and many highly

developed economies have small stock markets that play little or no economically significant

role in capital allocation (Beck & Levine 2002a). Banks, in contrast, are important in most

economies. Consequently, the corporate governance and control of banks, and the political

economy forces that determine these, merit study. One promising avenue posits that politicians

cede control of banks directly to powerful business families in return for favors, and that this is

most evident in countries where a medial level of accountability exists (Perotti and Vorage

2008).

Once the financial system is captured the families may favor related firms, limit capital to

potential entrants and engage in risk shifting activities. Thus, family control of a country’s banks

may result in inefficient allocation of its capital, especially absent alternative sources of capital.

Consistent with this, we find that fraction of the banking system controlled by elite business

families is correlated with capital misallocation, measured by Wurgler’s (2000) cross-industry

correlations of capital spending with value-added and by the fraction of non-performing loans. A

country that better focuses investment in higher value-added sectors or accumulates lower

fraction of non-performing loans is thus taken as allocating its people’s savings more efficiently.

These results persist after controlling for equity market size and in specifications that control for

plausible sources of endogeneity.

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Interestingly, while our regressions show that both family and state control over banks to

have similarly economically significant negative implications to capital allocation efficiency,

only the former is correlated with lower economic growth, elevated macroeconomic instability

and greater income inequality. Our findings are consistent with the crony capitalism literature

(Murphy et al. 1991, 1993; Shleifer & Vishny 1998b; Rajan & Zingales 2004). Entrusting the

governance of large banks to elite business families appears to be correlated with all the

enhanced inefficiency consequences of state-controlled banks, while missing any equality

consequences.

2. Motivation The role of the financial system is to allocate the economy’s savings to its highest value uses

(Tobin 1989; Wurgler 2000), including new firms (Schumpeter 1912; King & Levine 1993a).

Consequently, how well firms in the financial sector, especially banks, are governed affects not

just those firms, but the efficiency of capital allocation across the entire economy.

State-controlled banks are known to underperform (Megginson et al. 2004; Boubakri et

al. 2005) and allocate capital inefficiently (Wurgler 2000), hampering economic performance

(La Porta et al. 2002). These findings implicitly imply that while government control is bad

private control is generally beneficial. However, some single country explorations (e.g., La Porta

et al. 2003) suggest that family control of banks could also have negative economic impacts.

Consistent with the arguments in the crony capitalism literature banks that are captured by elite

families may not be as efficient as their independent counterparts. The effect of family controlled

banks on bank performance has been investigated (Caprio et al. 2007), but the effect of a family

controlled banking system on economy performance - capital allocation, economic growth,

economy stability, income distribution, and the like – remains incompletely explored. We

therefore correlate these economy-level variables with measures of the overall control of

countries’ banking systems, focusing on the implications of family-control of banks.

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2.1 The Worldwide Prevalence of Family Business We start with family control of corporations. Many countries entrust the governance of vast

swaths of their big business sectors to remarkably tiny handfuls of families. Each such family

often controls many firms via pyramiding: the family firm holds controlling equity blocks in a

first tier of listed firms, each of which controls members of a larger second tier of listed firms

and so on (Morck, Stangeland, and Yeung 2000, Bebchuk et al. 2000). Control blocks need not

be majorities because small public shareholders seldom vote at shareholders meetings; and

because super-voting shares, special directors, and other such mechanisms secure control via

small actual ownership blocks. In most countries, pyramiding magnifies large, but relatively

modest, family fortunes into control over large business groups, some with corporate assets

comprising substantial fractions of national economies (Morck et al. 2005b).2

This describes the economies of East Asia (Claessens et al. 2000; Claessens & et al.

2002), India (Khanna & Palepu 2000; Bertrand et al. 2002), Latin America (Hogenboom 2004;

Rogers et al. 2007; Adolfo 2008; Cueto 2008), Turkey (Ararat & Ugur 2003; Orbay & Yurtoglu

2006), and most other developing economies. Large family-controlled business groups are

important in developed economies too; including Canada (Morck et al. 2005a), continental

Europe (Faccio & Lang 2002), Israel (Daniel 1999), Japan (Nakamura 2002), and others. In the

United Kingdom (Franks et al. 2005) and United States (Villalonga & Amit 2008) family firms

are typically relatively small and family business groups are of marginal or no importance.3 But

overall, family-controlled business groups characterize big business organization in most

countries (La Porta et al. 1999; Khanna et al. 2000; Khanna & Yafeh 2005b).

2.2 Explaining Family Control. Family groups may be prevalent both for efficiency and entrenchment reasons. Efficiency

arguments propose that family control is an efficient solution, or at least a feasible second best

2 Pyramiding is rare in the United States, in part for tax reasons (Morck 2005), and in the United Kingdom because of takeover regulations that prevent dominant control blocks (Franks et al. 2005). 3 Studies of U.S. founding families are remarkable for the relative insubstantiality of the ties they retain to their forbearers’ firms (Anderson et al. 2003; Anderson & Reeb 2003).

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solution, to information asymmetry and agency problems (Shleifer & Vishny 1986), especially

absent legal systems that reliably enforce arm’s-length contracts and protect passive investors

(Burkart et al. 2003). Entrenchment arguments propose that widespread family control reflects

entrenched elites preserving their wealth and status, perhaps at the expense of broader economic

growth (Morck, Stangeland, Yeung 2000, Bebchuk et al. 2000; Morck et al. 2005b). Empirical

evidence suggests both sets of explanations be taken seriously.

Efficiency arguments are most persuasive for economies in early stages of

industrialization, where a common controlling shareholder directing firms in many different

industries can coordinate the growth of complementary industries, much as a state central

planner would (Morck & Nakamura 2007). The controlling family can transfer capital from

profitable firms to firms with growth opportunities (Hoshi et al. 1991; Almeida & Wolfenzon

2006), spread risk across many firms (Hoshi et al. 1990; Khanna & Yafeh 2005a), lend the

family’s good name to otherwise mistrusted firms (Khanna & Yafeh 2007), transfer resources

between firms without relying on dysfunctional domestic markets (Khanna et al. 2000; Fisman &

Khanna 2004), counter predatory governments more effectively (Fisman & Khanna 2004), and

perhaps facilitate development in other ways.4

However, these explanations lose traction in explaining long-lived pyramidal business

groups, for all these efficiency arguments evaporate as the economy develops and the state

acquires a large enough tax base to support an efficient judiciary, independent regulators, and

other background institutions necessary for efficient contracting and specialized professional

management to take over (Schumpeter 1918). To the extent that great business families

accelerate development, they should undermine their own raison d’être as developed economies

construct better-functioning market institutions. Why then does dynastic family wealth control

such large swaths of so many economies’ business sectors for so long (Morck et al. 2000)?

Entrenchment argument can potentially explain this puzzle. Business families throughout

the world are remarkably politically well-connected (Faccio 2006, Faccio et al. 2006). Families

may use their connections to impede entry and competition and therefore have superior

4 These efficiency arguments apply mostly to firms inside groups. For the economy as a whole, these efficiency arguments are not necessarily valid, see, e.g., Morck et al 2005 and Almeida and Wolfenzon 2005.

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performance. Perhaps, not surprisingly, this is more evident in developing countries (Khanna &

Rivkin 2001; Khanna & Yafeh 2005a, 2007). Large family-controlled business groups may thus

persist because they are better at political rent-seeking (Morck and Yeung 2004).

Haber et al. (2003) proposes a yet closer embrace, noting that predatory government

deters private business investment. Since the only credible protection against official predation is

to control the government, either business families must control the state or politicians must

control big businesses. Either way, a snug elite of politically powerful business families emerges.

Once ensconced, these families understandably bias state policy to preserve their wealth and

status.

Rajan and Zingales (2004) suggest that this situation need not be confined to countries

with insecure private property rights. Rather, sound economic institutions may allow a first

generation of tycoons to accumulate great wealth. They or their heirs might then invest in

political influence to bias state policies so as to preserve their economic and social positions.

Acemoglu, Johnson, Robinson (2005) argue that groups with economic power gains de facto

political power which they will turn into de jure political power to preserve both their economic

and political status quo.5 Yet, wealthy family business may still have other paths to preserve their

status quo.

2.3 Preserving a Patrimony under Different Banking Systems Free markets let creative upstarts with new products or processes arise and destroy established

businesses via what Schumpeter (1942, p. 84) calls a “perennial gale of creative destruction”.

Locking in the economic power and positions of a set of great business family requires a

windbreak – a barrier to entry that shelters the family patrimony from Schumpeter’s gale.

Were multigenerational family businesses highly competitive in freely contestable

markets, economic selection would suffice. However, as noted above, this is not observed. If the

country’s business and political elites merge (Haber et al. 2003), such barriers are likely to

emerge as core policy objectives, at least implicitly.

5 For a summary of supporting empirical literature, see Morck, Wolfenzon and Yeung (2005), and for a formal model Perotti and Volpin see (2006).

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A wide range of policies can erect effective barriers to entry. Thickets of costly

regulation (Djankov et al. 2000), tax disincentives against entrants (William & Hubbard 2000),

subsidies or regulatory favors to established businesses (Krueger 2002), trade barriers (Krueger

1974; Krueger 2004), and many other windbreaks are effective.

However, new entrants’ most critical need is typically capital (Schumpeter 1912; Levine

1991, 1992; King & Levine 1993a, b; Beck et al. 2000a). Consequently, policy changes

depriving entrants of capital promise the safest haven from the Schumpeterian gale (Rajan &

Zingales 2004). Different approaches to biasing capital allocation by banks arise, depending on

who controls the banks.

Independent banks

If the economy’s banks are predominantly independent and professionally-managed, business

families seeking to bias capital allocation in their favor would have to rely on political rent-

seeking – lobbying regulators and lawmakers. Weak property rights for small investors, official

corruption, inattention to the rule of law, and inefficient courts all can stunt a country’s banking

system and eliminate stock markets altogether as important players in capital allocation (La Porta

& et al. 1997; La Porta et al. 1998; La Porta et al. 2008). These institutional infirmities are

commonplace throughout the world, despite abundant evidence of the economic harm they

cause; motivating the argument that it might be a deliberate policy to entrench established

business elites (Rajan & Zingales 2004).

A country can do without a stock market, but not banks. Consequently, rent-seekers bent

on using political influence to bias capital allocation can seek to cripple or destroy stock markets

once and for all, but must exert continual lobbying effort to prevent the banking system from

capitalizing upstarts. The historical evidence is not inconsistent with this, for many countries had

substantially larger stock markets and banking systems in earlier decades than in the mid and late

20th century (Rajan & Zingales 2003). Moreover, this atavism seems to reflect clear policy

choices (Rajan & Zingales 2004). However, sustaining a biased capital allocation in the long-run

with independent banks might be difficult.

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State-controlled banks

If an economy’s banks are primarily state-controlled, they too can be pressured via political

influence into lending to established families’ businesses on artificially good terms. State-

controlled banks can rise above market forces, so “cronies can be favored through the granting of

domestic credit when that credit is allocated at rates significantly below market” (Krueger 2002,

p. 15).

However, state-controlled banks are vulnerable to a wide range of political pressures,

from arguably socially constructive pressure to promote equality or employment (La Porta et al.

2002) to more questionable pressure to concealing politicians’ fiscal irresponsibility via

evergreen loans to state organs (Krueger 2002). Many of these pressures could be antithetical to

the needs of established business families, though an entirely dysfunctional banking system

works to the advantage of those who already have money (Rajan & Zingales 2004). But state-

controlled banks can become tools of populist political entrepreneurs, who may have little

sympathy for old business families (Dornbusch & Edwards 1992). State-controlled banks are

prone to failure of government to allocate resources to their best uses and therefore affects the

overall economy adversely by compromising future growth prospects (Shleifer & Vishny 1998a).

Family-controlled Banks

Another option for wealthy family businesses is to directly control banks. If established business

families wished to entrench the status quo, this option would be superior, especially absent active

stock markets. Direct control over banks neatly avoids all the above-listed uncertainties inherent

in continually lobbying politicians to control the bias in capital allocation by independent or

state-controlled banks.

As with family firms in general, family banks might predominate because of either

efficiency or these entrenchment effects. Indeed, efficiency explanations might be more salient

for banks than for other sorts of family businesses because mitigating information asymmetry is

especially critical in bank governance (Diamond 1984). A well-connected family might, through

an extensive network of longstanding business relationships, have a genuine information

advantage in screening out risky or dishonest borrowers. Well-governed banks also take a long-

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term perspective on borrower quality, and avoid short-term gains that carry imprudent longer-

term risks. Family firms are alleged to specialize in sectors where long-term planning is more

important and risk-taking less important (Miller & Le Breton-Miller 2005). Such considerations

might let family-controlled banks allocate capital more efficiently than could independent

professionally-run banks

Such efficiency arguments are, again, especially plausible in developing economies,

where cross-industry mismatches in economies of scale, complementary goods production, and

the like can undermine uncoordinated growth by independent firms. However, banking is unique

in the importance it attaches to accurately screening borrowers and managing risk, so even if old-

moneyed families fail to provide superior governance in other firms, they still might have an

edge in banking. A prevalence of family control in banks thus need not signal entrenchment.

If the entrenchment arguments dominate family banks can be a means to ensure the

dominance and the well being of family businesses. This may result in inefficient capital

allocation for the entire economy, especially in countries with little direct means to raise capital.

For example, family banks may favor related firms, e.g., charge below market rates in lending

and hold inadequate collaterals. They can also limit capital to potential competitors regardless of

efficiency considerations. In addition, families can shift risks to governments. This is clearest in

countries with generous government deposit insurance, but contagion concerns let governments

everywhere justify bank bailouts more readily than bailouts in other sectors, thus facilitating the

socialization of risk in banking. Family control over banks can thus direct depositors’ savings

into risky family businesses in other sectors, letting the family claim any profits while leaving

losses to taxpayers (Faccio et al. 2006). All else equal, this should increase the prevalence of

banking crises, and render economy growth rates more volatile.

2.4 Family Banks and the Economy Which set of the above arguments predominates is thus an empirical question. Examining the

performance of individual banks, while useful in examining other issues, does not resolve this

question. What is good for a country’s major corporations, including its banks, need not be good

for the country overall (Fogel et al. 2008). Family-controlled banks might perform well because

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of their monopoly power against borrowers, monopsony power against depositors, or rent-

seeking expertise that lets them capture subsidies, tax breaks, or other advantages unrelated to

efficient capital allocation. Indeed, a lack of competition might well permit banks to allocate

capital inefficiently, yet post impressive profits. Perfect competition drives economic profits to

zero, so efficient capital allocation might well correlate with narrower bank profit margins.

Addressing the issues raised in the previous section requires examining economy-level

capital allocation quality and performance. If economies that entrust the governance of more of

their banking sectors to wealthy business families do better, family control might provide a

genuine efficiency edge in banking. If they do worse, we must give entrenchment explanations

more weight. We construct economy level measures to test whether entrenchment or efficiency

arguments better explain the implications of control structure of banks. The construction of

these variables – along with that of our measures of bank system control and the various control

variables – is explained in the next section.

3. Sample and Data Construction 3.1 Sample We start with the 2001 global sample of 244 banks Caprio et al. (2007) use to link banks’ market

valuations and equity ownership structures. Although this covers 83 percent of the total banking

assets of 44 large economies, it omits unlisted banks – a potentially important subsample

especially likely to be family or state-controlled.

We therefore augment these data to include all of each country’s ten largest banks, listed

and unlisted, ranked by 2001 assets in The Banker (2001).6 If The Banker lists fewer than ten

large banks in a country, we add more banks, beginning with the largest, by assets, not already

included but available from Bankscope.7 This yields 427 banks from 44 countries. In some

cases, we still have fewer than ten banks, and in others we have more than ten banks after

merging our data with the Caprio et al. (2007) original sample.

6Including smaller banks would be desirable, but greatly magnified the data collection problems. Since we seek to explain macroeconomic growth rates and cover large fraction of the banking assets in most countries, this is defensible as a first pass. 7 Van Dijk Electronic Publishing, see www.bankscope.com.

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We then identify the controlling shareholder, if any, for each bank. Caprio et al. (2007)

detail the control structures of the 244 banks also in that sample, so we need to fill in control data

only for the additional banks. Bankscope provides this information – in many cases for 2001, but

more comprehensively for 2002 and subsequently. A controlling owner is identified by 2001 for

81% of our sample and by 2003 for 95% of the sample. This leaves us with a grand total of 318

listed and unlisted banks for which we can identify controlling owners.

3.2 Defining and Classifying Banks’ Controlling Owners We ascertain each bank’s controlling owner, if any, as in Caprio et al. (2007) and La Porta et al.

(1999). We first identify all shareholders who vote blocks of five percent or more. If these are

state organs, individuals or families, we call them ultimate owners. But more often, they are

corporations. We therefore identify their owners, their owners’ owners, and so on until finally

reaching state organs, widely held organizations or biological persons, whom we designate as

ultimate owners.

At each level in a control chain we take the largest owner who controls more than 10

percent of the vote as the controlling owner. We then sum all voting blocks with common

ultimate owners, assuming family members act in concert and state organs are under a single

authority and decide on the ownership category based on the largest controlling owner. We

define a firm as controlled by an ultimate owner if that ultimate shareholder commands at least a

ten percent voting block and no other ultimate shareholder commands a larger voting block.

Since transparency of ownership structures are different across countries, this mechanical

procedure is not always accurate.8 We are thus more likely to underestimate the prevalence of

control blocks in countries with less stringent reporting requirements.

After determining the largest ultimate controlling owners we assign banks to one of three

categories. We say a bank is state-controlled if it has the government as its controlling

shareholder, and family-controlled if its controlling shareholder is a family. Banks that have

8 Different countries have different ownership reporting thresholds. In the United States, securities laws require all insider stakes and all stakes of five percent or more be disclosed. In the United Kingdom, the threshold is three percent. In Canada, it is twenty percent, which is also the definition of control in the country’s federal Business Corporations law; though separate rules mandate lower thresholds for chartered banks.

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neither as a controlling shareholder are called independent. Independent banks include banks

with no controlling shareholder and banks with ten percent plus blockholders that are

cooperatives or corporations with dispersed ownership. We classify banks controlled by widely

held firms as independent because our focus is the economic results of elite business families’

bank control, in particular, if such control is related to capital allocation efficiency. If

corporation control and family control are intrinsically similar, we are less likely to find

significant results.

Finally, we construct three country-level bank control indexes: fractions of the banking

system, weighted by total net credits or assets, that are state-controlled, family-controlled, and

independent banks. Table 1 displays these indexes, and Table 2 displays their simple descriptive

statistics.

[Tables 1 and 2 about here]

3.3 Financial System Efficiency Measures We estimate efficiency of a country’s financial system in three ways. These are:

Capital allocation efficiency

Following Wurgler (2000), we associate more efficient capital allocation with greater investment

in industries with higher value-added growth. We operationalize this technique by estimating a

simple elasticity of gross fixed capital formation to value added growth for each country using

its industry-level data. That is, country c’s elasticity is the coefficient ηc in the regression

[1] ictict

ictcc

ict

ict

VV

II

εηα ++=−− 11

lnln

with i denoting industry, t time, I fixed capital investment, and V is industry value added.

Comparable industry-level investment and value-added data are from United Nations'

General Industrial Statistics (UNIDO) database, which had data up to year 2003 at the time we

started this project. We estimate each country’s capital allocation efficiency twice. Our first

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estimate uses data for 1993 through 2003 – the ten years closest to our observation of the bank

control (2001-2003). We would ideally like to use capital allocation efficiency measurements

subsequent to our 2001 bank control cross section, which is simply not possible. Our second

capital allocation efficiency measure uses all available UNIDO data (1963 through 2003). The

longer window raises the number of countries with enough data to estimate the coefficient η

from 33 to 39 and permits more precise estimates if capital allocation efficiency changes little

through the window. If not, the first version is preferable. Table 4 shows that the two measures

are highly correlated.

Since value-added growth across all sectors, by definition, sums to GDP growth, this

measure gauges the strength of the link between capital spending in each industry and that

industry’s contribution to overall economic growth. Its weakness is that it fails to capture

investments that respond to new growth opportunities yet have to show up on change in value

added. To the extent that such mis-gauging is more important in economies with more efficient

capital allocation, the index tends to under-estimate capital allocation efficiency.

Nonperforming Loans

A more efficient banking system, all else equal, should carry fewer nonperforming loans. We

therefore use nonperforming loans, measured as a fraction of total gross loans outstanding, to

gauge the banking system’s ability to pick winners, or at least avoid losers. These data are from

the World Development Indicators database, provided by the World Bank, and are averaged

across 1993 through 2003 to yield one observation for each country – both to smooth out cyclical

variations and to be consistent with the time frame of the capital allocation efficiency measure.

In our regressions, we logistically transform each dependent variable a bounded within

the unit interval to a ranging across the real line. That is, we transform a ∈ [0,1) into:

[2] ⎟⎠⎞

⎜⎝⎛−

=a

aa1

lnˆ ∈ ℜ,

The fraction of non-performing loans is an imperfect proxy for the efficiency of capital

allocation. Some nonperforming loans are inevitable because screening can never be perfect, and

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because changing conditions can undermine previously plausibly profitable ventures. No

nonperforming loans at all would be equally consistent with perfectly prescient bankers and with

an inefficiently conservative banking system. In addition, differences in financial reporting

practices across countries may introduce noise and bias results down if family and government

control is more likely to be correlated with non-transparent reporting practices.

An efficiently-run banking system should finance borrowers with profitable investment

opportunities. That is, all else equal, well-governed banks should have less loan defaults.

However, state banks pressured by politicians into lending to financially unqualified but

politically favored borrowers often run up huge nonperforming loan problems. Banks controlled

by oligarchic families can get into very similar problems by lending to related parties who,

despite their pedigrees, are ill qualified managers (Krueger 2002).

3.4 Economic Performance Measures Economic performance is most commonly measured by growth in per capita income,

productivity, or capital. These are important metrics, but economies can also be plausibly

described as better-performing if they generate less inequality across individuals and more

stability across time (Drèze & Sen 1989). We therefore use three constellations of performance

measures:

Economic Growth

Our first set of performance measures capture the pace of economic growth following the

methodology in Beck et al. (2000). These are:

Real per capita GDP growth is the arithmetic mean of log differences in per capita GDP,

from the Penn World Tables, from 1993 through 2003 for each country. This is obtained by

regressing the country’s log real per capita GDP on a constant and a time trend, and taking the

time trend as its real per capita GDP growth rate.

TFP growth is the economy’s total factor productivity (TFP) growth rate: the growth rate

in the value of the outputs it can generate from inputs of a fixed value. To estimate this, we

assume output in each economy obeys the aggregate production function

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[3] αα −= 1ttt LAKY ,

with Yt, Kt, and Lt its GDP, capital stock, and labor force, respectively at time t; and with the

capital share, α, assumed to be 30% for all countries. Taking 1993 to 2003 data from the Penn

World Tables, and using logarithms of first differences in time, we estimate the parameter A for

each country and interpret this as its TFP growth rate.

Capital accumulation is the rate at which the economy’s aggregate stock of capital assets

grows through time. To estimate this, we assume its real capital stock at time t, denoted Kt, is its

previous year’s capital stock, allowing for depreciation at rate δ, plus its new capital investment,

It. That is,

[4] ttt IKK +−= −1)1( δ

We assume all capital to depreciate at seven percent per year, and assume 1964 capital

stocks as starting points. We then apply [4] repeatedly to generate subsequent years’ capital

stocks recursively moving forward, as in Beck et al. (2000).

Economic Instability

Rapid, but highly volatile economic growth might be less socially desirable than slower, but

steadier growth. A country’s banking system is a fundamental channel through which monetary

variables affect the real economy. Consequently, macroeconomic stability might be correlated

with the control of the banking system. Banking systems that allocate capital less efficiently

should be more vulnerable to negative economic shocks, and should curtail credit more sharply

in response. This might magnify the effect of economic shocks on the overall economy. On the

other hand, family control of banks might enhance an economy’s stability if this ensures

continued credit to other family controlled firms, moderating economic downturns.

We consider two measures of the economy’s aggregate instability. The first measure of

growth volatility is the standard deviation of per capita GDP growth estimated from the same

16

data used to generate our real per capita GDP growth rate estimates. This variable is thus the

standard deviation of log first differences in real per capita GDP from 1993 through 2003 for

each country.

Our second instability measure, the number of banking crises the economy experiences,

is more directly tied to the banking system. We obtain this from Dell'Ariccia et al. (2008), who

count episodes of bank distress as “crises” if at least one of the following conditions holds: there

were extensive depositor runs; the government took emergency measures, such as bank holidays

or nationalizations, to protect the banking system; the fiscal cost of bank rescues was at least two

percent of GDP; or non-performing loans reached at least ten percent of bank assets. Using these

counts, we construct two variables: the number of banking crises a country experienced after

1993 and the total number of banking crises Dell'Ariccia et al. (2008) record for the country

starting as early as 1981. In our main analysis, we use the recent period, which could be more

relevant for our control variables. In robustness tests, we use the longer period, which could

provide better estimate of the prevalence of rare events such as banking crises.

Economic Inequality

Rapid economic growth whose benefits accrue to tiny elite might be less socially desirable than

slower growth whose fruits are more evenly distributed across the population. State or family-

controlled banks might distribute wealth more evenly than independent banks if the bureaucrats

or families place social goals ahead of profits. Alternatively, either state or family control might

distribute wealth less evenly if bureaucrats or families restrict financing to cronies. Entrenched

business families might divert capital from banks they control to other family controlled

businesses and away from their competitors or upstarts, inducing an inefficient distribution of

capital across the economy.

We therefore consider several measures of the economic inequality. We gauge income

inequality by a country’s average Gini coefficient from 1993 through 2003. This measures the

deviation of the country’s income distribution from a uniform distribution, with a zero Gini

coefficient indicating a perfectly equal income distribution, and larger coefficients indicating

more extreme inequality (Gini 1921).

17

Another measure of inequality is the concentration of economic power in the hands of a

small oligarchy. This is defined as the fraction of a country’s ten largest (ranked by total

employees) domestic private-sector businesses controlled by families, and is from Fogel (2006).

In regressions, we normalize both the income inequality and oligarchy variables using [2].

Many authors argue that the life cycle-permanent income hypothesis implies that

consumption distributions are better measures of equality than income distributions (see Gordon

and Dew-Becker 2007 for this debate). Consumption inequality data is not available for a cross-

section of countries in our sample. Therefore we use consumption per capita statistics of goods

that could be correlated with size and well-being of the middle income class. We use personal

computers per thousand population in the main analysis and number of alternatives such as car

ownership, internet access and telephone lines per population in the robustness tests. The

argument is that countries with larger and wealthier middle income classes should have a larger

fraction of their population able to afford cars, computers and telephone lines after controlling

for per capita GDP .

3.5 Control Variables. Our regressions make use of a collection of control variables to isolate the effect of control

structure of the banking system on the variables above. This section explains the purpose,

construction, and sources of each.

Initial general development, gauged by the logarithm of the country’s per capita GDP in

1992, appears in all of our regressions. Initial economic development is often associated with

capital deepening. In addition, richer countries might have higher quality institutions (North

1981, La Porta et al. 1999), which could limit the effects of family control on capital allocation.

In growth regressions this variable controls for standard of living because countries already

sustaining very high standards of living have less scope for additional growth (Solow 1956;

Mankiw et al. 1992).

We also want to control for the development of the financial system directly–more

financing activity reflects a more effective financial system with better institutions. Following

Goldsmith (1969), King and Levine (1993), La Porta et al. (1997), Rajan and Zingales (1998),

18

and Wurgler (2000), we control for the country's equity and credit markets relative to its GDP as

a proxy for its financial development. Another reason for controlling the size of the stock

markets is to account for the fact that a country’s stock market is an alternative to its banking

system for allocating capital (Levine 2002). Consequently, a country with a large stock market

might be able to allocate capital efficiently regardless of what sort of banking system it has.

Banking system size is the total bank credit outstanding as a fraction of GDP, averaged

across 1993 through 2003. Stock market size is the country’s total stock market capitalization as

a fraction of GDP, averaged across 1993 through 2003. We average over years to smooth out

any cyclical variations.

In growth regressions, in addition to the above variables we also control for human

capital (Barro 2001) and trade openness (Krueger 1998), which are shown to be important for

economic growth.

3.6 Data Limitations It is ideal to use ownership data from a time period without major crises. During unusual times,

banks get nationalized and then privatized. Therefore using the banking control structure in 2001

is advantageous because that is the earliest available and sensible point after the Asian Crisis

resolved itself.

However, our dependent variables are generally estimated using data windows ending in

2003. This has few unfortunate consequences. First, we cannot run lead and lag causality tests

between bank control and economy performance.9 Second, bank control data do not proceed the

period in which we observe economy level performance.

This timing mismatch is important if bank control changes frequently, but less so if bank

control is highly persistent. To check this, we scan BankScope data from 2001 through 2007 for

bank control changes. Although banks’ controlling shareholders and the sizes of their control

blocks both change during this period; family controlled banks tend to remain family controlled,

state-controlled banks tend to remain state controlled, and independent banks tend to remain

9 Unfortunately, key right-hand side variables are unavailable for 2001-2007. UNIDO data exist only through 2003, and the Penn World Tables end in 2004. Some WDI data are also unavailable for recent years: for example Gini coefficients are missing after 2001 for most countries.

19

independent. Indeed, we were able to identify only 14 banks (4.4% of the total 318) switching

category.10

Still bank control can significantly change especially during privatization episodes. We

have data on bank privatizations and therefore we can control whether there were any significant

changes in the banking control structure prior to 2001. We use privatization data from

Megginson (2004), who documents 283 bank privatizations across the world. For example,

Italy’s state controlled Banco Nazionale del Lavoro was privatized in November 1998, and is

labeled independent in our data. Matching these data to ours reveals 16 changes in bank

control.11 Later, we directly control for the effect of privatizations.

These analyses are descriptive in nature but they indicate that our bank control category

variables appear to be highly persistent. Therefore, timing mismatch does not appear to be an

important problem. Nonetheless, bank control can change because of significant economic or

financial shocks, as for example, in 2008. 12 We must therefore control for endogeneity

problems, such as latent factors affecting both control over banks and banking sector or overall

economy performance.

3.7 Descriptive Statistics. Table 3 summarizes the definitions and sources of all our main variables; and Table 2 present

simple descriptive statistics for each.

[Tables 3 about here]

10 Two family controlled banks becomes state controlled and four become widely held. Four state controlled banks become widely held. Two widely held banks becomes family controlled and two becomes state controlled. 11 Although Megginson (2004) covers most countries in our sample the number of privatization transactions that affect control is low due to following reasons: In many privatization transactions the state maintains control, there are several observations with multiple transactions, i.e. privatization happens in multiple transactions, Megginson covers transition economies with many privatization transactions such as Poland, Czech Republic, Hungary etc. which are not in our sample, many banks that are privatized are small and not in our sample. 12 We explore this elsewhere.

20

4. Empirical Findings We examine the correlations between our indices of bank control and various measures of

economic performance, including the quality of capital allocation, the pace of growth,

macroeconomic stability, and measures of equality.


4.1 Simple Correlations Table 4 presents simple correlation coefficients of each main variable with all the others. Several

interesting patterns emerge. First, the three bank control indexes sum to unity, so each should

correlate negatively with the other two – purely as an algebraic artifact. However, the

magnitudes are of interest nonetheless. Countries tend to have less family control over their

banks if more state control is evident, however the correlation is small (–0.23) and not

significant. In contrast, countries that have more independent banking systems tend to have

markedly less state control and family control over their banks: both correlation coefficients are

more than twofold larger and both are significant with probability levels below one percent.

Thus, the primary difference across countries seems to be independent banks on the one hand

versus state or family controlled banks on the other.

Second, capital allocation efficiency is negatively correlated with state-control of the

banking system, replicating the finding of Wurgler (2000). However, efficient capital allocation

is positively and significantly correlated with a greater incidence of independent private sector

banks and negatively but insignificantly correlated with a greater incident of family-controlled

private sector banks. These patterns are illustrated in Figure 1.

[Figure 1 about here]

4.2 Regressions Figure 1 shows the general trends in the data clearly enough, indicated by the solid lines, but

they are surrounded by substantial scatter. This suggests other variables at work in the

21

background. We therefore turn to more formal multivariate tests to clarify the patterns in the

data.

Capital Allocation

Our first question is whether or not bank control correlates with the efficiency of capital

allocation. Table 5 reports these results. The first four columns show that capital allocation

efficiency, measured as in Wurgler (2000), is clearly correlated with the control of banks.

Countries whose banking system are more independent exhibit more efficient capital allocation,

measured across either of the two time frames. Countries that entrust their banking systems to

families exhibit less efficient capital allocation.13


The scatter in Figure 1 is considerably reduces in the table. The regression R2 statistics

range from 33% to 61% – indicating that the variables in the regression now explain a substantial

fraction, by the standards of cross-section regression analysis, of the variation in capital

allocation efficiency across countries.

The last two columns in Table 5 regress nonperforming loans on the bank control

variables. After controlling for the banking and stock market sizes and initial per capita GDP,

independent banking system is negatively correlated with the ratio of non-performing loans (over

total loans outstanding) while state or family controlled banking systems are positively

correlated.

In summary, table 5 shows that capital allocation is more efficient in countries with more

independent banks, and less efficient in countries with large fraction of the banks are controlled

either by families or state. These results are also economically significant. One standard

deviation increase in the fraction of family controlled banks decreases efficiency of capital

13 These results are robust to using weighted least squares regressions, which weights each observation by the standard error of capital allocation efficiency estimate (not reported).

22

allocation by 25% and increases the fraction of non-performing loans to total loans by about

20%.

[Table 6 about here]

Growth

We are only able to show correlations but, intuitively, if the control structure of the banking

system affects the efficiency of capital allocation, this should, in turn, affect economic growth.

Table 6 therefore regresses economic growth on our country level bank control measures. The

OLS regressions reveal a marginally statistically significant correlation between more family

control over banks and slower real per capita GDP growth (p = 0.09).

A close examination of data reveals that two African countries Zimbabwe and Kenya are

extreme outliers. These countries experience negative economic growth during this time period.

One approach to controlling for outliers is using an iteratively reweighted least squares

algorithm, which is robust to outliers.14 These “robust” regressions reveal a much stronger

negative relationship between family control of banks and real per capita GDP growth (p = 0.00),

productivity growth (p=0.00) and capital accumulation (p = 0.06). We repeat the same analysis

for all of our regressions to understand whether outliers affect our other results; in other

regressions outliers do not play an important role (see table 11).

In summary, table 6 shows that family control is correlated with economic growth. These

results are economically significant; a one standard deviation higher family control corresponds

to 43% lower real GDP per capita growth (the average growth rate in our sample is 1.92%).

Stability

Our first measure of economic stability is a direct measure of the health of the financial system:

the number of banking crises the country experienced after 1993. Table 7 shows that banking

14 We use rreg in Stata. The starting value of the iterative algorithm is taken to be a monotone M-estimator with Huber function(weights). Observations with a Cook distances larger than 1 receive a weight of zero. In later rounds Tukey Biweight loss function is used (Verardi and Croux, 2008). 16 The regression results for the banking crises are excluded because the iteratively reweighted least squares algorithm does not converge.

23

crises are more common in countries whose banking systems are more predominantly family-

controlled (p=0.03). In contrast, independent banks are negatively correlated with the number of

banking crises and state-controlled banks seem uncorrelated with the incidence of crises.

We also attempt to incorporate the recent financial crises into our analysis by assuming

that countries that had more than 30% loss in their banking industry index between 10/2/08

(marking the sharp drop in the US market) and 11/20/08 experienced financial crisis. We obtain

banking industry returns from WorldScope/DataStream for 39 out of 44 countries and add one to

the Dell'Ariccia et al. (2008) crises count if the country experienced a crisis. According to our

definition most developed countries had banking crises including US and UK. Our results are

qualitatively similar; family ownership has a coefficient of 0.51 with a p-value of 0.067 and

independent banks have a coefficient of -0.44 with a p-value of 0.097.

A second measure of macroeconomic instability, the standard deviation of the country’s

real per capita GDP growth rate is also positively associated with family control over banks (p =

0.01); while state-controlled and independent banks are insignificant.


In summary, Table 7 shows that family-control over banks is correlated with banking

system and macroeconomic instability. These effects are also economically significant. A one

standard deviation higher fraction of family-control is associated with on average 50% increase

in the number of banking crises after 1993, and with a 26% larger standard deviation in

macroeconomic growth volatility.

Equality

Our first measure of inequality, countries’ Gini coefficients, reveal significantly less egalitarian

income distributions in countries whose banking systems are more extensively controlled by

families (Table 8). In contrast, state-controlled banks weakly significantly correlate with greater

egalitarianism in income distribution.

24


As a second measure of inequality, we use the business family-controlled fraction of the

country’s ten largest domestically controlled private-sector businesses or business groups,

including listed and unlisted firms, ranked by employees. Unsurprisingly, family-controlled

business empires are more important in countries whose banking systems are more extensively

controlled by families.

As a third measure of equality, we use consumption items that could proxy for the

importance of the country’s middle class: personal computers in Table 8 and internet, cars and

telephone lines per people in Table 11. Countries with more family-controlled banking systems

have poorer or smaller middle income classes.

4.3 Endogeneity Section 4.2 reveals family control of banking systems negatively correlated with capital

allocation efficiency, economic growth, economic stability and economic equality. Although

these correlations are consistent with the predictions of the crony capitalism literature the results

could also be explained by latent variables that drive both the economic results and control

structure of banks. We are especially concerned about this endogeneity problem because of the

mismatch in the timing of macro economic variables and bank control measures. To address this

concern we employ instrumental variables regressions with historical instruments that do not

change over time. We have three potential instrumental variables; legal origin, religion and

latitude.

Legal origin, introduced by La Porta et al. (1998), has been shown to be relevant for the

development of the financial system (La Porta et al 1997) and widely used as an exogenous

instrument in cross country studies. In addition, we use religion dummies from Stulz and

Williamson (2003), who shows that a country’s principal religion explains cross country

variance in creditor rights.

Finally, we include latitude, distance of a country from the equator, as an exogenous

proxy for the social infrastructure. Hall and Jones (1999) argue that latitude is correlated with

25

“western influence” which leads to good institutions. Acemoglu et al. (2001) supports the

validity of this instrument by showing that latitude does not have an independent effect on

economic performance.

Table 9 panel A shows that fraction of the banking system that is independent is lowest in

countries with French civil code legal systems. These countries also exhibit the highest ratio of

family control (41%). Religion also seems to explain large cross-country variation in the fraction

of independent banks. Independent banks constitute 70 percent of the banking system in

Protestant countries but only 5 percent in Muslim countries. These differences are large and

statistically significant. The effect of latitude is also in the predicted direction. Countries that are

in the highest quartile of absolute value of latitude have the highest fraction of independent

banks and lowest fraction of family controlled and government controlled banks.


Given that legal origin, religion, and latitude seem to be relevant in explaining banks

control we use these plausibly exogenous variables to “predict” banking system control in a first

stage estimation using Tobit regressions. Then we use these “exogenous components” of our

banking system control indexes, rather than the raw variables, and rerun the regressions in

section 4.2 as a second stage estimation.

Table 10 reports the coefficients of bank control variables as well as first stage likelihood

ratio p-levels, which uniformly exceed the thresholds normally used to reject weak instruments.

Our instruments are thus sufficiently strong to render our second stage regressions useful in

inferences about causality.

The coefficients of the bank control variables are consistent with the previous

regressions with one exception: Family control loses statistical significance in explaining

“oligarcy” (p=0.11). On the other hand, the exogenous component of family control results in

less efficient capital allocation, higher fraction of non-performing loans, slower economic

growth, higher growth volatility, larger number of banking crises, greater income inequality and

26

lower number of PCs. Overall, the results indicate that latent variables do not explain our

results.


4.4 Robustness Our results survive wide range of robustness tests including different ways of calculating bank

control variables, accounting for past privatizations, various samples, different econometric

approaches and alternative left hand side variables. Where identical patterns of signs and

significance for the family control of banks ensue, we say the findings are qualitatively similar.

In other cases, we describe how the results differ.

Privatizations

Ideally, we would use variables based on past data to estimate variables based on present data.

Unfortunately, our bank control data are unavailable prior to 2001, and the construction of key

variables we posit to be affected by bank control requires windows of data that must include

years prior to 2001. However, we do have some information about prior bank control from

Megginson (2004), who documents 283 bank privatizations, which we match to our data by

banks’ names. There are two extreme ways of accounting for privatization transactions that

change category. The first approach is to use the new control category after privatization, which

we did in our main analysis. The second approach is to assume that these banks are state

controlled for the entire time period. Using the second approach, Panel A of Table 11 shows that

results are qualitatively similar. Therefore we can conclude that bank control changes due to

privatizations are not important enough to affect our results.

Measuring bank importance

We calculate country level bank control ratios weighting banks by their total net credit. Credit

issued by a bank relative to total credit issued the country’s banking system is a plausible proxy

for the importance of the bank as an allocator of capital. However, bank size is an obvious

27

alternative weight. Using total banks assets as a fraction of total banking system assets to weight

the importance of banks in calculating bank control variables, shown in Table 11 Panel B1,

yields qualitatively similar results.

Bank control thresholds

Following Caprio et al. (2007) and La Porta et al. (1999), we assumed that a 10% equity block

confers control. We raise this to 15% and recalculate the bank control variables. Panel B2 of

Table 11 shows that using this generates qualitatively similar results. Raising the threshold to

20% (not shown) likewise produces qualitatively similar results.

Countries with few banks

In some of the countries, we identify controlling shareholders for only few banks. Consequently,

our bank control variables may be less precise for these countries. We therefore repeat our tests

after eliminating countries represented in our data by fewer than three banks. The countries

dropped are Finland, Mexico, Singapore, Venezuela, and Zimbabwe. Panel B3 of Table 11

shows that qualitatively similar results again ensue.

Heteroskedasticity

The statistical tests in all our regressions control for heteroskedasticity. Using standard OLS

regression coefficients and t-tests, as reported in panel C1, also generates qualitatively similar

results.

Outliers

In the economic growth regressions we showed that two outliers were adversely affecting our

results. In order to address the concern that our regressions with significant results are not

positively affected by outliers we repeat our tests using an iteratively reweighted least squares

algorithm, which is robust to outliers. Table 11 panel C2 precludes influence due to outliers by

presenting qualitatively similar results.16

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Alternative Variables

First we use alternative proxies for the size and purchasing power of the middle income class.

These include car ownership, telephone lines and internet penetration. In each case, family bank

control is again negatively correlated with these proxies while independent banks are positively

correlated with them (Table 11 Panel D).

Finally we use the total number of banking crises from Dell'Ariccia et al. (2008)

starting as early as 1981. The longer estimation period could provide better estimate of the

prevalence of rare events such as banking crises. Table 11 Panel D shows that results are

qualitatively similar.

4.5 Consistency with Entrenchment The correlations between family control of banks and weak economy performance are thus both

statistically robust and consistent with the entrenchment hypothesis we advance. However, some

of our results could also be explained by alternative arguments. For example if managers in

family banks are simply incompetent, this could potentially explain the negative correlation

between family control and efficiency of capital allocation. One way of ruling out alternative

explanations and checking the consistency of entrenchment hypothesis with our empirical results

is to see if family control of banks correlates with other implications of this hypothesis.

Regulation of Entry

We propose that entrenched elite families may use their banks to erect barriers to entry that

protect them from upstart rivals. If this is an important motivation for the family control of

banks, we might see more regulations that hinder entry in economies where family control of

banks is more prevalent. To investigate this, we use the measures of each country’s regulation of

entry designed by Djankov et al. (2002). These are number of procedures, time and cost required

to setup a new company. Table 11 Panel F1 shows that all three measures are highly positively

correlated with family controlled banks and negatively correlated with independent banks. In

contrast, state control of banks is insignificant except in explaining cost of setting up a new firm.

29

Family Control of Firms in General

We already showed that family control of banks is highly positively correlated with oligarchy,

defined as the fraction of the top ten largest non-financial businesses controlled by families as in

Fogel (2007). We posit that control of banks by wealthy families could impede efficient capital

allocation because those families might limit capital to competitors, direct capital to their other

firms at concessionary prices, or engage in risk shifting. These problems could arise even if the

families that control the banks do not control other firms, for Faccio (2006) and others reveal

numerous connections between wealthy families. However, these problems may well be more

serious where a smaller number of families control the country’s banking system and large

corporations.

To check this, we use the Orbis dataset to identify other companies owned by our

banking families. We augment this with an extensive online media search using the family name

and bank name to find news articles mentioning other firms controlled by these families. This

admittedly crude approach doubtless underestimates the non-banking interests of these families,

but nonetheless confirms that 90% of the families that control banks in our data also control

other firms.17

We first use this information to perform a robustness check by assuming that banks

controlled by families who do not control other firms are equivalent to independent banks. Under

this definition, 100% of Mexican banks are classified as independent – an estimate of family

power over banking many students of the Mexican economy might find low. Table 11 Panel F2

shows that all our previous results persist, except for the correlations of family control with post

1993 banking crises, which lose statistical significance.

Family banks that allocate capital efficiently and independent banks that allocate capital

inefficiently should both bias our coefficients of interest down. For example, American Express

Company is controlled by Warren Buffet (through Berkshire Hathaway Inc.) and is classified as

family controlled by Caprio et al. (2007). It seems implausible that Buffet presses American

Express to allocate capital inefficiently; so classifying it as family controlled should bias our

17 Orbis data sometimes provide too many names that match the family last name, raising the specter of false positive matches, which we do not use. In few other cases, no family name is provided in the Caprio et al. (2007) dataset, precluding this exercise.

30

results against finding inefficiency in capital allocation. Likewise, an independent professionally

managed bank, like Citigroup, might allocate capital inefficiently if it has conflicted interests in

other sectors. Others, like Svenske Handelsbanken, an independent Swedish bank, can have

extensive control blocks in industrial firms and might be prone to some of the same

misallocation problems we attribute to family banks. Again, such banks should becloud our

findings of differences between independent and family banks in the efficiency of capital

allocation.


5. Conclusions This paper examines the correlation of the control of the banking system and several macro

variables including capital allocation efficiency. Banks are vital intermediaries allocating capital

for an economy; in many economies they are the only intermediaries available. Naturally, the

ownership and control of them could have critical economic implications.

The literature suggests that government control of banks negatively affect economic

performance. We focus on family control of banks. On the one hand, family control could have

positive consequences on efficiency because the dominant owners should have stronger incentive

to make banks perform. On the other hand, the entrenchment argument suggests the opposite.

Rich families could use banks they control to favor family owned businesses and to discriminate

against competition that threatens their economic dominance. They can also use their banks to

siphon an economy’s resources to enrich themselves, e.g., channeling gains from risk taking to

family business but leaving losses for the government to absorb when a government offers public

deposit insurance.

Focusing on the largest banks in each country in a cross country study and controlling for

capital market development, we find robust results that support the entrenchment argument: high

family control of banks is associated with less efficiency of capital allocation, greater incident of

nonperforming loans, slower capital accumulation and slower income and productivity growth.

31

Moreover, family control of banks is also associated with greater macro volatility, more frequent

bank crises, and more income inequality.

A rather popular economic proposition about rich economies that stagnated and failed to

develop further is that individuals who got rich early succeeded in conducting “economic

entrenchment” (Morck, Stangeland, Yeung 2000, Rajan and Zingales, 2003, Morck, Wolfenson,

Yeung 2005, Acemoglu, Johnson, Robinson, 2006). They use their economic power to sway

institutional evolution to their favor at the expense of the rest of the economy. Morck,

Stangeland, and Yeung (2000) specifically suggest that they use their control of the finance

system as an entry barrier to upstarts and to preserve their economic dominance. Our regression

results are descriptive in nature but they do provide robust support to the argument that rich

families may achieve economic entrenchment via control of banks.

Our results have important implications. First, from a policy perspective, allowing

wealthy families to control banks could have undesirable economic consequences as we have

shown in this paper. Second, the passing of bank control to wealthy families is likely an

endogenous outcome. In many countries, politicians and regulators often have significant

control over who can own banks (Perotti and Vorage, 2008). While politicians and regulators

may recognize the economic entrenchment motives, they can be bribed. For personal gains or

for enhancing short term government revenues, they might consciously auction off control of

banks to the highest bidders, which are likely to be wealthy families who also control other

businesses.

32

Table 1 : Control Structure of Banks Across Countries Family, state and independent measure the fractions of banks (weighted by total credit) controlled by family groups, governments and neither, respectively. Control is presumed to lie with the largest voting block of ten percent or more. If no such block exists, we classify the bank as independent. Banks is the number of banks in the country for which we have ownership data. Code abbreviates the country’s name in the graphs. See Table 3 for variable definitions and data sources.

Country Code Family State Independent # of Banks Argentina AR 0.44 0.56 0.00 4 Australia AU 0.01 0.00 0.99 11 Austria AT 0.00 0.00 1.00 6 Brazil BR 0.61 0.31 0.08 10

Canada CA 0.00 0.00 1.00 9 Chile CL 0.71 0.29 0.00 5

Colombia CO 0.41 0.18 0.41 4 Denmark DK 0.01 0.00 0.99 9

Egypt EG 0.02 0.98 0.00 9 Finland FI 0.00 0.00 1.00 1 France FR 0.00 0.00 1.00 8

Germany DE 0.14 0.24 0.62 8 Greece GR 0.36 0.56 0.08 10

Hong Kong HK 0.76 0.24 0.00 6 India IN 0.00 1.00 0.00 13

Indonesia ID 0.04 0.91 0.05 12 Ireland IE 0.00 0.00 1.00 7 Israel IL 0.48 0.43 0.09 8 Italy IT 0.11 0.00 0.89 9

Japan JP 0.00 0.22 0.78 7 Jordan JO 0.91 0.09 0.00 8 Kenya KE 0.03 0.83 0.15 5 Korea KR 0.03 0.38 0.59 9

Malaysia MY 0.93 0.00 0.07 6 Mexico MX 0.57 0.00 0.43 2

Netherlands NL 0.00 0.22 0.78 3 Norway NO 0.00 0.43 0.57 9 Pakistan PK 0.04 0.96 0.00 4

Peru PE 0.49 0.19 0.33 4 Philippines PH 0.68 0.21 0.11 13

Portugal PT 0.43 0.29 0.29 7 Singapore SG 1.00 0.00 0.00 2

South Africa ZA 0.64 0.01 0.34 5 Spain ES 0.34 0.01 0.65 14

Sri Lanka LK 0.00 0.59 0.41 6 Sweden SE 0.30 0.00 0.70 4

Switzerland CH 0.09 0.21 0.70 9 Taiwan TW 0.17 0.74 0.09 14

Thailand TH 0.54 0.46 0.00 7 Turkey TR 0.48 0.32 0.21 11

United Kingdom GB 0.21 0.00 0.79 6 United States US 0.02 0.00 0.98 10

Venezuela ZM 0.76 0.00 0.24 2 Zimbabwe ZW 0.00 0.00 1.00 2

33

Table 2

Descriptive Statistics of Main Variables

Sample is the countries listed in Table 1; variables are defined in Table 3.

Mean Median Standard Deviation Maximum Minimum

Panel A. Bank control indexes 1 Family 0.29 0.17 0.31 1.00 0.00 2 State 0.27 0.21 0.31 1.00 0.00 3 Independent 0.44 0.41 0.39 1.00 0.00

Panel B. Financial system efficiency 4 Capital allocation efficiency, ‘63-‘03 0.54 0.54 0.28 1.12 -0.03 5 Capital allocation efficiency, ‘93-‘03 0.43 0.46 0.42 1.32 -1.02 6 Non-performing loans 8.12 6.16 7.42 27.43 0.45

Panel C. Economic growth 7 Real GDP growth 0.02 0.02 0.02 0.07 -0.02 8 TFP growth 0.02 0.02 0.01 0.07 -0.01 9 Capital accumulation -0.01 -0.01 0.01 0.01 -0.04

Panel D. Economic instability 10 Growth rate volatility 0.03 0.02 0.02 0.08 0.01 11 Banking crises, ‘93-‘03 0.23 0.00 0.48 2.00 0.00 Panel E. Economic inequality 12 Income inequality 38.88 36.03 9.57 59.08 24.70 13 Oligarchy 0.62 0.66 0.33 1.00 0.00 14 Computers 165.84 104.72 151.88 462.72 3.53 Panel F. Controls 15 Initial income 8.66 9.01 1.41 10.45 5.78 16 Stock market size 3.92 3.93 0.82 5.68 2.15 17 Banking system size 4.41 4.48 0.58 5.68 2.98 18 Trade openness 4.13 4.10 0.62 5.94 2.96 19 Human capital 2.05 2.05 0.30 2.54 1.49

34

Table 3

Main Variables’ Definitions and Sources

Panel A. Bank control Family Total 2001 credit-weighted fraction of listed and unlisted banks controlled by an

individual or family. Control is imputed to the largest blockholder whose voting control, direct and indirect, sums to at least 10% for 2001 or the nearest year with data. Indirect control is inferred using the “weakest link” method, as in La Porta et al. (1999). Sources: Caprio et al. (2007), BankScope.

State Total credit-weighted fraction of banks controlled by state organs. Constructed analogously to Family.

Independent Total credit-weighted fraction of banks with no controlling shareholder. Constructed analogously to Family.

Panel B. Financial system efficiency Capital allocation efficiency

The efficiency of capital allocation is the estimated elasticity of manufacturing investment to value added, estimated as in Wurgler (2000). Note: Two versions of this variable are used, one using all available data and the other using data for 1993 through 2003 only.

Non-performing loans

Ratio of non performing loans as a fraction of total gross loans, averaged over 1993 through 2003. In regressions and correlations, this variable is log normalized by the formula: normalized (x) = ln(x/(1-x)). Source World Development Indicators (WDI).

Panel C. Economic Growth Income growth

Real per capita GDP growth is the coefficient in an OLS regression of log real per capita GDP time trend and intercept as in Beck et al. (2000). Data are for 1993 through 2003, and are from Penn World Tables.

TFP growth Each country’s total factor productivity (TFP) growth is A in the production function Y = A Kα L1-α, with Y, K, and L the country’s GDP, capital stock, and labor force, respectively; and with capital share α = 0.03 as in Beck et al. (2000). Data are for 1993 through 2003, and are from Penn World Tables.

Capital accumulation

Average growth rate in capital stock from 1993 to 2003, assuming 1964 capital stocks are in steady state and using aggregate real investment and 7% depreciation recursively to generate capital stock estimates going forward, as in Beck et al. (2000). Data are from World Penn Tables.

Panel D. Economic Instability Growth rate volatility

Standard deviation of real GDP per capita growth, 1993-2003. Source: Calculated from World Penn Tables data.

Banking crises Number of episodes of “bank distress”, defined as systemic crises featuring: extensive depositor runs; an emergency measure (e.g. bank holiday or nationalization); bank rescues costing ≥ 2% of GDP; or non-performing loans ≥ 10% of bank assets. Source: Dell'Ariccia et al. (2008).

35

Panel E. Economic Inequality Income inequality

Average Gini coefficient, or deviation of income distribution from uniformity, (Gini 1921) from 1993 though 2003. In regressions and correlations, this variable is log normalized by the formula: normalized (x) = ln(x/(1-x)). Source: WDI.

Oligarchy Fraction of the top ten largest (according to number of employees) non-financial private-sector domestically-controlled freestanding businesses or business groups, including listed and unlisted firms, controlled by business families in 1996. In regressions and correlations, this variable is log normalized by the formula: normalized (x) = ln(x/(1-x)). Source: Fogel (2006)

PCs Personal computers (PCs) per thousand people, averaged over 1993-2003. Personal computers are defined as self-contained and designed for use by one person. Source: International Telecommunication Union, World Telecommunication Development Report and database. Downloaded from WDI.

Cars Passenger cars per 1000 people, average over 1993-2003. Passenger cars refer to road motor vehicles, other than two-wheelers, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). International Road Federation, World Road Statistics and data files. Downloaded from WDI.

Telephone Telephone lines per 1000 people, average over 1993-2003. Telephone mainlines are fixed telephone lines connecting a subscriber to the telephone exchange equipment. Source: International Telecommunication Union, World Telecommunication Development Report and database. Downloaded from WDI.

Internet Internet user per 100 people, average over 1993-2003. Internet users are people with access to the worldwide network. International Telecommunication Union, World Telecommunication Development Report and database. Downloaded from WDI.

Panel F. Controls Initial income Logarithm of 1992 per capita GDP in US dollars at purchasing power parity. Source: Penn

World Tables.

Banking system size

Log average credit outstanding to GDP averaged across 1993-2003.. Source: World Development Indicators, World Bank.

Stock market size

Log of average stock market capitalization to GDP averaged across 1993-2003. Source: World Development Indicators, World Bank.

Human capital Log of average schooling years in total population aged 15 or over, 1990. Source: World Development Indicators, World Bank.

Trade openness

Log of trade/GDP: the sum of exports and imports of goods and services measured as a share of gross domestic product, over GDP. Source: World Bank national accounts data, OECD National Accounts data

36

Panel G. Instrumental Variables Catholic, Protestant, Muslim, Buddhist, Others

Shows fraction of Catholics, Protestants, Muslims, Buddhists and Other religions’ followers in a country. The values are from World Christian Encyclopedia (1995) as reported by Stulz and Williamson (2003).

British, French, German, Scandinavian

These are dummy variables, which indicate the legal origin of countries. La Porta et al. (1998)

Latitude Absolute value of average latitude of countries. Source CIA Factbook.

Panel H. Regulation of Entry Variables

Number of Procedures

Log number of different procedures that a start-up has to comply with in order to obtain a legal status, i.e. to start operating as a legal entity. Source Djankov et al. 2002.

Time Log time it takes to obtain legal status to operate a firm, in business days. A week has five business days and a month has twenty two. Source Djankov et al. 2002.

Cost Log cost of obtaining legal status to operate a firm as a share of per capita GDP in 1999. It includes all identifiable official expenses (fees, costs of procedures and forms, photocopies, fiscal stamps, legal and notary charges, etc). The company is assumed to have a start-up capital of ten times per capita GDP in 1999. Source Djankov et al. 2002.

37

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. Point estimates significant at ten percent or better in two-tailed t-tests are indicated in boldface.

1 2 3 4 5 6 7 8 9 10 11 12 1 1.00

Family (0.00)

2 –0.23 1.00

State 0.13 (0.00)

3 –0.62 –0.62 1.00

Independent (0.00) (0.00) (0.00)

4 –0.20 –0.65 0.69 1.00

Capital allocation efficiency, ‘63-‘03 (0.22) (0.00) (0.00) (0.00)

5 –0.26 –0.27 0.44 0.54 1.00

Capital allocation efficiency, ‘93-‘03 (0.14) (0.13) (0.01) (0.00) (0.00)

6 0.30 0.58 –0.70 –0.63 –0.37 1.00

Non-performing loans (0.05) (0.00) (0.00) (0.00) (0.03) (0.00)

7 0.47 0.06 –0.43 –0.37 –0.31 0.48 1.00

Growth rate volatility (0.00) (0.69) (0.00) (0.02) (0.08) (0.00) (0.00)

8 0.29 0.03 –0.26 –0.26 0.02 0.32 0.36 1.00

Banking crises (1993 – 2003) (0.06) (0.84) (0.09) (0.11) (0.91) (0.04) (0.02) (0.00)

9 0.64 –0.05 –0.49 –0.25 –0.36 0.35 0.40 0.44 1.00

Income inequality (0.00) (0.76) (0.00) (0.12) (0.04) (0.02) (0.01) (0.00) (0.00)

10 0.28 0.59 –0.64 –0.39 –0.22 0.60 0.35 0.33 0.40 1.00

Oligarchy (0.15) (0.00) (0.00) (0.07) (0.39) (0.00) (0.07) (0.09) (0.04) (0.00)

11 –0.34 –0.48 0.66 0.59 0.49 –0.75 –0.44 –0.34 –0.59 –0.72 1.00

PCs (0.03) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00) (0.00)

12 –0.13 –0.60 0.58 0.66 0.51 –0.73 –0.40 –0.23 –0.48 –0.65 0.85 1.00

Log per capita GDP, 1992 (0.40) (0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.15) (0.00) (0.00) (0.00) (0.00)

38

TABLE 5 Bank Control and Capital Allocation

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. Efficiency of Capital

Allocation 1993-2003 Efficiency of Capital Allocation 1963-2003

Non Performing Loans / Total Gross Loans

Independent 0.275 (0.07)

0.353 (0.00)

-1.39 (0.00)

Family -0.350 (0.05)

-0.295 (0.00)

1.45 (0.00)

State -0.115 (0.60)

-0.480 (0.00)

1.29 (0.01)

Banking system size

0.0690 (0.78)

0.122 (0.62)

0.0162 (0.85)

-0.0259 (0.75)

0.438 (0.16)

0.409 (0.12)

Stock market size

0.0902 (0.41)

0.0458 (0.64)

0.0444 (0.39)

0.0828 (0.16)

-0.383 (0.07)

-0.356 (0.07)

GDP per capita (1992)

0.0907 (0.05)

0.0699 (0.17)

0.0380 (0.23)

0.0538 (0.06)

-0.399 (0.00)

-0.388 (0.00)

Constant -0.856 (0.29)

-0.998 (0.20)

0.175 (0.51)

-0.292 (0.20)

-0.661 (0.54)

0.648 (0.45)

R2 0.34 0.33 0.61 0.59 0.68 0.67 N 33 33 39 39 43 43

39

TABLE 6

Bank Control and Economic Growth

The table shows results of cross-country regressions robust to outliers (an iteratively reweighted least squares algorithm) and OLS regressions. 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

Robust to Outliers OLS Robust to Outliers OLS Robust to Outliers OLS Independent 0.0101

(0.16) 0.00820

(0.28) 0.0145

(0.02) 0.00477

(0.51) 0.0114

(0.03) 0.0114

(0.04) Family

-0.0263 (0.00)

-0.0154 (0.09)

-0.0223 (0.00)

-0.0122 (0.16)

-0.0116 (0.06)

-0.0105 (0.12)

State -0.00960 (0.29)

0.00169 (0.87)

-0.00571 (0.50)

0.00552 (0.57)

-0.0106 (0.13)

-0.0128 (0.09)

Human capital 0.00323 (0.79)

0.0170 (0.18)

0.0173 (0.21)

0.0232 (0.09)

0.00293 (0.80)

0.00955 (0.38)

0.0161 (0.23)

0.0222 (0.09)

0.0132 (0.17)

0.0148 (0.09)

0.00420 (0.68)

0.00341 (0.72)

Trade openness 0.00442 (0.24)

0.00237 (0.54)

0.00519 (0.22)

0.00375 (0.37)

0.00594 (0.09)

0.00449 (0.19)

0.00700 (0.09)

0.00550 (0.18)

-0.00524 (0.07)

-0.00532 (0.06)

-0.00600 (0.06)

-0.00580 (0.06)

Banking system size

0.00117 (0.84)

0.00557 (0.33)

0.00329 (0.60)

0.00652 (0.28)

-0.0000912 (0.99)

0.00259 (0.60)

0.00210 (0.73)

0.00546 (0.35)

0.00575 (0.19)

0.00595 (0.13)

0.00395 (0.38)

0.00352 (0.41)

Stock market size

0.00631 (0.16)

0.00125 (0.77)

0.00242 (0.63)

-0.000468 (0.92)

0.00616 (0.14)

0.00333 (0.38)

0.00197 (0.68)

-0.00103 (0.82)

0.000877 (0.80)

0.000738 (0.81)

0.00149 (0.68)

0.00188 (0.57)


-0.00509 (0.08)

-0.00491 (0.08)

-0.00295 (0.36)

-0.00482 (0.11)

-0.00543 (0.05)

-0.00662 (0.01)

-0.00320 (0.30)

-0.00514 (0.08)

-0.00105 (0.63)

-0.00140 (0.47)

0.000817 (0.73)

0.00107 (0.62)

Constant 0.0190 (0.46)

-0.0176 (0.47)

-0.0324 (0.26)

-0.0329 (0.20)

0.0235 (0.32)

0.0102 (0.63)

-0.0268 (0.33)

-0.0236 (0.34)

-0.0297 (0.13)

-0.0414 (0.02)

-0.0184 (0.38)

-0.0309 (0.10)

R2 0.33 0.20 0.26 0.17 0.34 0.27 0.23 0.18 0.48 0.49 0.43 0.43 N

43

43

43

43 43 43

43 43

43 43

43 43

40

TABLE 7 Bank Control and Stability

The first two columns show cross-country OLS regressions with robust standard errors. Second and third columns show results of negative binomial regressions. Dependent variables are in columns and independent variables are in rows. Variables are as defined in Table 3. P values are in parentheses. Standard Deviation of

Growth Number of Banking Crises

(1993- )

Independent -0.0124 (0.14)

-1.72 (0.03)

Family 0.0245 (0.01)

2.41 (0.03)

State -0.00901 (0.46)

0.735 (0.49)

Banking system size

0.00405 (0.54)

-0.00194 (0.76)

-0.410 (0.57)

-0.886 (0.12)

Stock market size

-0.000850 (0.81)

0.00480 (0.25)

-0.602 (0.17)

-0.368 (0.40)


-0.00670 (0.05)

-0.00448 (0.20)

-0.0779 (0.78)

0.155 (0.38)

Constant 0.0675 (0.01)

0.0630 (0.02)

1.95 (0.40)

2.86 (0.15)

R2 /pseudo R2

0.36 0.24 0.20 0.17

N

43 43 43 43

41

TABLE 8

Bank Control and Wealth Distribution

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. Income Inequality

Oligarchy PCs


-1.89 (0.01)

113 (0.00)

Family 0.599 (0.00)

1.98 (0.01)

-170 (0.00)

State -0.308 (0.10)

1.68 (0.04)

-12.1 (0.80)

Banking system size -0.133 (0.24)

-0.295 (0.01)

0.700 (0.36)

0.706 (0.34)

-43.7 (0.13)

-15.4 (0.51)

Stock market size 0.0528 (0.46)

0.214 (0.02)

-0.844 (0.05)

-0.824 (0.06)

72.7 (0.00)

46.0 (0.01)

GDP per capita (1992) -0.140 (0.01)

-0.0977 (0.07)

-0.359 (0.04)

-0.335 (0.03)

74.5 (0.00)

64.0 (0.00)

Constant 1.02 (0.01)

0.967 (0.03)

2.83 (0.36)

4.36 (0.06)

-518 (0.00)

-551 (0.00)

R2

0.59 0.41 0.63 0.63 0.85 0.80

N

42 42 27 27 43 43

42

TABLE 9

What Explains Control Structure

Table summarizes the control structure of banks by a country’s legal origin, largest religion and capital city latitude. The numbers are the fraction of the top ten banks classified as independent, family, and state owned. Religion dummies are equal to 1 if the religion is the most commonly practiced one in that country. Latitude dummies represent the quartile of the country according to absolute value of average country latitude (latitude 1 is the lowest). Variables are as defined in Table 3.

Independent Family State British 0.427 0.291 0.282 French 0.308 0.409 0.283 German 0.629 0.072 0.299

Legal system origin

Scandinavian 0.816 0.076 0.108 Catholic dummy 0.524 0.331 0.144 Muslim dummy 0.053 0.405 0.542 Protestant dummy 0.703 0.125 0.172

Largest religion

Others dummy 0.245 0.331 0.424 Lowest quartile 0.168 0.498 0.334 Second quartile 0.260 0.348 0.392 Third quartile 0.505 0.244 0.251

Latitude

Highest quartile 0.832 0.068 0.100

43

TABLE 10 Instrumental Variable Regressions

The table reports the results of instrumental variable regressions. Panel A, B, C and D replicate the regressions in Tables 5,6,7 and 8, respectively. Coefficients from the second stage regressions come from two separate regressions. In one regression independent is the explanatory variable in the second regression family and state are explanatory variables. In rows, the first number shows the coefficient and the second number shows the P values in parenthesis. In Panel E we report the LR statistics of the first level Tobit regressions (censored between 0 and 1). LR statistics for the efficiency of capital allocation between (1963-2003) is slightly different because we use a different combination of legal origin, religion and latitude. Variables are as defined in Table 3.

Panel A: Bank Control and Capital Allocation Efficiency of Capital

Allocation 1993-2003 Efficiency of Capital Allocation 1963-2003

Non Performing Loans / Total Gross Loans


0.592 (0.04)

-4.74 (0.00)

Family -0.668 (0.07)

-0.378 (0.06)

3.14 (0.00)

State 0.327 (0.65)

-0.501 (0.16)

3.81 (0.01)

Panel B: Bank Control and Economic Growth

Real GDP per Capita Growth Productivity Growth

Capital Accumulation


0.0438 (0.02)

0.0535 (0.00)

Family -0.0394 (0.00)

-0.0280 (0.03)

-0.0362 (0.00)

State -0.000397 (0.99)

0.0139 (0.62)

-0.0905 (0.00)

Panel C: Bank Control and Stability

Standard Deviation of Growth

Number of Banking Crises (1993- )


-6.45 (0.04)

Family 0.0471 (0.02)

3.90 (0.08)

State 0.0451 (0.27)

0.798 (0.89)

Panel D: Bank Control and Wealth Distribution

Income Inequality Oligarchy PCs Independent -1.04

(0.04) -3.03 (0.13)

292 (0.01)

Family 0.834 (0.00)

2.11 (0.11)

-204 (0.00)

State -2.62 (0.00)

-0.605 (0.85)

127 (0.48)

Panel E: LR Statistics of First Stage Regressions

Family State Independent Efficiency of Capital Allocation 1963-2003 34.61 38.52 30.94 All Other Dependent Variables 39.73 27.25 31.51

44

TABLE 11 Robustness Tests

We replicate the regressions in Tables 5,6,7 and 8 using various robustness tests. Coefficients of independent and family-state are from two separate regressions. In one regression independent control is on the right hand side and in the second regression family and state control are on the right hand side. Panel A adjust the banking control variables using privatization transactions from Megginson(2004) assuming that banks that are privatized between 1992-2003 are state owned. Panel B reports robustness tests with different ways of constructing bank control variables. In Panel B1 the control structure at the country level is calculated as the weighted average of control of individual banks using total assets as weights. In Panel B2 banks assumed to have a controlling shareholder if the shares controlled is larger than 15% instead of the 10% used before. Panel B3 reports results after we drop countries that have less than 3 banks in our sample, which are Finland, Singapore, Zimbabwe, Mexico and Venezuela. In Panel C we try alternative regressions models. Panel C1 reports the results of a simple OLS regression without controlling for heteroskedasticity. Panel C2 reports the results of a regression that uses an iteratively reweighted least squares algorithm, which is robust to outliers This regression does not converge if the left hand side variable is number of banking crises. Panel D uses various alternate left hand side variables, but the same control variables as used elsewhere. In Panel F1 left hand side variables are regulation of entry variables. In Panel F2 family banks are considered independent if the family that control the bank does not own other firms. Efficiency of Capital Allocation is measured between 1963 and 2003, which provides higher number of countries. Variables are as defined in Table 3. P values are in parentheses.

Efficiency of Capital

Allocation

Non Performing

Loans / Total Gross Loans

Standard Deviation of

Growth

Number of banking Crises

(1993- ) Income

Inequality PCs Income Growth

Panel A: Privatizations Independent 0.325

(0.00) -1.86 (0.00)

-0.0225 (0.00)

-2.19 (0.06)

-0.392 (0.03)

149 (0.00)

0.0170 (0.07)

Family -0.314 (0.01)

1.86 (0.00)

0.0300 (0.00)

2.95 (0.04)

0.637 (0.00)

-193 (0.00)

-0.0299 (0.00)

State -0.346 (0.02)

1.86 (0.00)

0.00922 (0.39)

1.36 (0.36)

-0.00031 (0.99)

-71.5 (0.15)

-0.0140 (0.16)

Panel B: Various Ways of Constructing Bank Control Variables B1. Bank Control Weighted by Total Assets


-1.42 (0.00)

-0.0147 (0.07)

-1.54 (0.05)

-0.281 (0.05)

121 (0.00)

0.00770 (0.31)

Family -0.284 (0.01)

1.41 (0.00)

0.0255 (0.01)

2.08 (0.05)

0.596 (0.00)

-170 (0.00)

-0.0205 (0.01)

State -0.481 (0.00)

1.44 (0.00)

-0.0046 (0.71)

-0.864 (0.44)

-0.266 (0.21)

-31.63 (0.98)

-0.00556 (0.59)

B2. Control Assumed at 15% Independent 0.353

(0.00) -1.39 (0.00)

-0.012 (0.14)

-1.72 (0.03)

-0.266 (0.07)

113 (0.00)

0.0101 (0.16)

Family -0.295 (0.00)

1.45 (0.00)

0.0245 (0.01)

2.41 (0.03)

0.599 (0.00)

-170 (0.00)

-0.0263 (0.01)

State -0.480 (0.00)

1.28 (0.01)

-0.00901 (0.46)

0.735 (0.49)

-0.308 (0.10)

-12.1 (0.80)

-0.00960 (0.29)

B3: Dropping Countries with Fewer than 3 Banks Independent 0.309

(0.02) -1.56 (0.00)

-0.0202 (0.02)

-1.94 (0.04)

-0.276 (0.10)

160 (0.00)

0.0149 (0.09)

Family -0.250 (0.07)

1.55 (0.01)

0.0350 (0.00)

3.76 (0.05)

0.690 (0.00)

-236 (0.00)

-0.0199 (0.06)

State -0.407 (0.02)

1.57 (0.01)

-0.000842 (0.99)

1.03 (0.38)

-0.225 (0.27)

-55.4 (0.31)

-0.00999 (0.36)

45

TABLE 11 (Continued)


Allocation

Non Performing



Growth


(1993- ) Income

Inequality PCs Income Growth

Panel C: Alternative Regression Methods C1: OLS Regressions (Without Correcting for Heteroscedasticity)


-1.42 (0.00)

-0.0124 (0.17)

-0.314 (0.17)

-0.280 (0.11)

113 (0.00)

0.00820 (0.28)

Family -0.295 (0.01)

1.45 (0.00)

0.0245 (0.01)

0.509 (0.05)

0.599 (0.00)

-170 (0.00)

-0.0154 (0.09)

State -0.480 (0.00)

1.29 (0.02)

-0.00900 (0.44)

-0.0303 (0.92)

-0.308 (0.13)

-12.1 (0.78)

0.00169 (0.87)

C2: Iterative Estimation Robust to Outliers Independent 0.343

(0.00) -1.65 (0.00)

-0.0139 (0.00)

Not Applicable

-0.257 (0.16)

116 (0.00)

0.0101 (0.16)

Family -0.298 (0.02)

1.68 (0.00)

0.165 (0.00)

Not Applicable

0.578 (0.00)

-177 (0.00)

-0.0263 (0.00)

State -0.462 (0.01)

1.51 (0.01)

0.00838 (0.13)

Not Applicable

-0.470 (0.02)

-5.81 (0.91)

-0.00960 (0.29)

Panel D: Alternative Left Hand Side Variables Cars Telephone Internet Total


Independent 197 (0.00)

9.17 (0.01)

7.06 (0.00)

-0.820 (0.06)

Family -228 (0.00)

-17.3 (0.00)

-9.72 (0.00)

1.13 (0.03)

State -144 (0.02)

4.29 (0.39)

-2.68 (0.35)

0.279 (0.65)

Panel F: Consistency with the Entrenchment Argument F1: Regulation of Entry

Number of Procedures Time Cost


-1.07 (0.02)

-1.18 (0.01)

Family 0.933 (0.00)

1.37 (0.00)

1.15 (0.04)

State 0.317 (0.30)

0.554 (0.20)

1.24 (0.03)

F2: Family Control of Firms


Allocation

Non Performing



Growth


(1993- )

Income Inequality

PCs

Income Growth

Independent 0.320

(0.00) -1.31 (0.00)

-0.0115 (0.13)

-0.941 (0.29)

-0.200 (0.16)

92.8 (0.01)

0.00748 (0.30)

Family -0.258 (0.00)

1.39 (0.00)

0.0251 (0.00)

1.45 (0.21)

0.535 (0.00)

-149 (0.00)

-0.237 (0.01)

State -0.436 (0.00)

1.18 (0.01)

-0.0937 (0.44)

0.151 (0.89)

-0.307 (0.09)

-6.42 (0.90)

-0.00607 (0.50)

46

Figure 1. Capital Allocation Efficiency and the Control of Banks The y axis is the capital allocation efficiency, using 1963 – 2003 data, and x axis measures of the importance of family-controlled, state-controlled, and independent banks are as in Table 3. Country codes are in Table 1.

Panel A. Family-controlled banks

AU

AT

CA

CLCO

DK

EG

FI

FR

DE

GR

IN

ID

IE IL

ITJP

JO

KE

KR

MYMX

NLNO

PK

PE

PH

PT SG

ZA

ES

LK

SE

TH

TR

GB

US

ZM

ZW

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Panel B. State-controlled banks

AU

AT

CA

CLCO

DK

EG

FIFR

DE

GR

IN

ID

IE IL

ITJP

JO

KE

KR

MY

MX

NLNO

PK

PE

PH

PTSG

ZA

ES

LK

SE

TH

TR

GB

US

ZM

ZW

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Panel C. Independent banks

AU ATCA

CL

CO

EG

FIFR

DE

GRIN

ID

IE

IL

IT

JP

JO

KE

KR

MY

MX

NL

NO

PK

PE

PH

PT

SG

ES

LK

SE

TH

TR

GB

US

ZM

ZW

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

-0.2 0 0.2 0.4 0.6 0.8 1

47

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Bank Control, Capital Allocation, and Economic Performance · 2009. 4. 10. · Bank Control, Capital Allocation, and Economic Performance Randall Morck, a M. Deniz Yavuz, b and Bernard