First draft: December 1st 2008 This draft: April 20th 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 Independence of a country‟s banks from both government and business families explains cross country differences in banking system efficiency. In fact, fraction of state-controlled banks and family controlled banks are associated with roughly similarly inefficient capital allocation. Unlike state control, family control is also associated with lower economic growth and financial instability. These findings are consistent with theories that elite-capture of countries‟ financial system embeds “crony capitalism”. 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]. We are grateful for comments from Stijn Claessens, Mara Faccio, Ross Levine, Enrico Perotti, Alex Philipov, Andrei Shleifer, Fei Xie, seminar participants at the AFA 2009 meetings, Arizona State University, Darden International Finance Conference and George Mason University. Ching-Hung Chang, Minjeong Kang, Zhen Shi and Xiaowei Xu provided excellent research assistance.
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”. Fogel (2006) shows that economies whose business sectors are dominated by family controlled
1
King and Levine 1993a; Demirguc-Kunt and Levine 1996; Levine 1996; Levine and Zervos 1998; Rajan and Zingales 1998; Demirguc-Kunt and Maksimovic 1998; Beck et al. 2000; Levine et al. 2000; Beck and Levine 2002b
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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). In addition to limiting capital to potential entrants families may favor related firms and engage in risk shifting activities. Dysfunctional and undeveloped financial markets - stock markets, bond markets, and the like, thus could be 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). Thus, business family control of a country‟s banks may result in inefficient allocation of its capital, especially absent alternative sources of capital. Consistent with previous efforts, e.g., Caprio et al. (2007) and La Porta et al. (2002), we also include government bank ownership in our study. We find that fraction of the banking system controlled by elite business families is correlated with banking inefficiency measures such as capital misallocation, measured by Wurgler‟s (2000) cross-industry correlations of growth in capital spending with value-added or by the fraction of non-performing loans, and the number of banking crises . Interestingly, while both family and state control over banks have similarly economically significant negative implications to capital allocation efficiency, only the former is correlated with lower GDP per capita growth, productivity growth, capital accumulation growth and higher volatility of growth. 2
These results persist after controlling for development of the financial system and are robust to controlling for reasonable sources of endogeneity. Our findings are consistent with the crony capitalism literature (Murphy et al. 1991, 1993; Shleifer & Vishny 1998b; Rajan & Zingales 2004). Family control of banks is correlated with other predictions of the literature such as higher income inequality and higher barriers to entry.
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 economic entrenchment 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, and economy stability – are not fully explored. We therefore correlate these economylevel 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 see Perotti and Volpin (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 rentseeking – 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. Statecontrolled 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 statecontrolled 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 long8
term perspective on borrower quality, and avoid short-term gains that carry imprudent longerterm 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 oldmoneyed 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. These should result in slower economic growth. 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 9
for the country overall (Fogel et al. 2008). Family-controlled banks might perform well because of their monopoly power against borrowers, monopsony power against depositors, or rentseeking 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 economy level measures – along with that of our measures of bank system control, and the various control variables – is explained in the next section.
3.
Sample, Data, and Variable 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
6
Including 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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countries, 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. 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. (2002). 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.
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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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 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 as follows:
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]
ln
I ict V c c ln ict ict I ict 1 Vict 1
with i denoting industry, t time, I fixed capital investment, and V is industry value added. 12
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 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 aˆ ranging across the real line. That is, we transform a [0,1) into:
[2]
a aˆ ln , 1 a
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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). Nevertheless the caveat is that 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 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.
Banking Crises Our third banking efficiency measure, the number of banking crises the economy experiences, is also directly tied to the governance and control of the banking system. Banking crises could happen for various reasons that are not directly related to the entrenchment argument above as in 2008 banking crises in US. However, ceteris paribus, if capital is not allocated efficiently and, particularly if families engage in risk shifting activities – channel lending money to risky business connected to the family and leave the government to bail out banks when the downside takes place – we expect higher number of banking crises in countries with higher family control. We obtain the number of banking crises 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 14
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 and ending at 2000. In our main analysis, we use the recent period (1993 to 2000), which could be more relevant for our bank control variables. In robustness tests, we use the longer period (1981 to 2000), which could provide better estimate of the prevalence of rare events such as banking crises.
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 more stability across time (Drèze & Sen 1989). We therefore use two 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
[3]
Yt AK t L1t ,
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
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World Tables, and using logarithms of first differences in time, we estimate the rate of change of 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, K t (1 ) K t 1 I t
[4]
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 growth volatility as a measure of macro economic volatility. This variable is the standard deviation of per capita GDP growth estimated from the same data used to generate our real per capita GDP growth rate estimates. In other words, it is equal to the standard deviation of log first differences in real per capita GDP from 1993 through 2003 for each country.
3.5 Economic Entrenchment Measures 16
We introduce variables that are related to predictions of the economic entrenchment literature. The first set of variables tries to measure economic inequality. The second set of variables measures whether elites introduce barriers to entry other than capturing the financial system.
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 familycontrolled 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 diverting capital from banks they control to other family controlled businesses and away from their competitors or upstarts. Indeed, countries where economic entrenchment persists we also expect wealth to be collected at the hands of the well connected elite resulting in skewed income distribution. We therefore consider several measures of 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). 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 17
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.
Regulation of Entry Families may use other means 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. Note also that state control of banks may indicate government activism leading to possibly greater regulatory burdens. 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. Number of procedures measures the 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. Time measures the number of business days it takes to obtain legal status to operate a firm. Cost measures the cost of obtaining legal status to operate a firm as a share of per capita GDP in 1999 and includes all identifiable official expenses.
3.6
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
18
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), 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. In our robustness tests, we also control for inflation, size of the government, black market premium, average number of coups and average number of assassinations (Beck et al. 2000).
3.7
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 two unfortunate consequences. First, we cannot run lead and lag causality tests
19
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 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.
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. 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
3.8
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]
4.
Empirical Findings
We examine the correlations between our indices of bank control and various measures of economic performance, including the efficiency of the banking system, the pace of growth, macroeconomic stability, and other measures that are correlated with crony capitalism.
[Tables 4 about here]
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. 21
[Figure 1 about here]
Third, more family-control of banks is associated with more non-performing loans, greater number of bank crises, slower economic growth, including both capital accumulation and productivity growth, and greater macro volatility while greater incident of independent private sector bank has the exact opposite pattern of associations. Greater level of state bank control is only negatively associated with more non-performing loans and slower capital accumulation.
4.2
Regressions
Figure 1 graphs the capital allocation efficiency measure against the fraction of top banks that are controlled by “families,” “states,” and “independent.” The figure 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 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 and second four columns show that capital allocation efficiency, measured as in Wurgler (2000) and for either the limited time period (1993-2003) or the extended time period (1963 – 2003), is clearly correlated with the control of banks. Countries whose banking system are more independent exhibit more efficient capital allocation. Countries that entrust their banking systems to either families or states, exhibit less efficient capital allocation.13
[Tables 5 about here]
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
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 next four 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. Finally we test the relationship between bank control and the health of the banking system: the number of banking crises the country experienced after 1993. The results are reported in the last four columns in Table 5. Banking 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 statecontrolled banks seem uncorrelated with the incidence of crises. State controlled banks could be enjoying the implicit guarantee of the state. 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. 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 allocation by 25% and increases the fraction of non-performing loans to total loans by about 23
20%. In addition, we find that the greater the extent banks are controlled by families, the more frequent bank crisis takes place.
[Table 5 about here]
Economic Growth Given that the control structure of the banking system is associated with the capital allocation efficiency and measures of banking system efficiency, we expect bank control to be correlated with economic growth as well. Especially, given that value-added growth across all industries, by definition, sums to GDP growth, capital allocation efficiency should be correlated with economic growth14. Table 6 therefore regresses economic growth – per capita income growth, TFP growth, and per capita capital accumulation – 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). The regression relations between the variable and TFP growth and capital accumulation have a negative sign but are not significant. 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. 15 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). 14
Several papers have argued that financial development is important for country growth mainly though its role in efficiently allocating capital: 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, Durnev, Li, Morck, Yeung 2004. 15 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).
24
Finally we check whether family control of banks helps to stabilize the economy given that family banks may establish more stable lending relationships with related firms. We find the opposite relationship. The standard deviation of the country‟s real per capita GDP growth rate is positively associated with family control over banks (p = 0.01). Note that in all these specifications the state‟s control of bank is not statistically significantly related to the various measures of growth and to the standard deviation of growth. Independent control of banks has a positive and significant relationship (robust to outliers) with TFP growth, capital accumulation, and a negative one with the standard deviation of growth.
[Table 6 about here]
In summary, table 6 shows that family control is correlated with economic growth and volatility. 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%) and 26% larger standard deviation of growth. The collection of results in this section is consistent with the economic entrenchment argument about the impact of family ownership of banks but is not supportive of the efficiency arguments.
4.3
Endogeneity
Although the above results are consistent with the predictions of the entrenchment literature, they could also be explained by latent variables that drive both the economic results and control structure of banks. We are especially concerned about the 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, Levine et al 2000) and used as an exogenous instrument in explaining relationship between financial development and economic 25
growth (Beck et al 2000). 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 “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 7 panel C2 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.
[Table 7 about here]
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 7 reports the coefficients of bank control variables as well as the first stage LR statistics, 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. The exogenous component of family control results in less efficient capital 26
allocation, higher fraction of non-performing loans, slower economic growth, higher growth volatility and larger number of banking crises. Also, independent control of banks results in the opposite while state control of banks only results in more non-performing loans and slower capital accumulation. Overall, the results indicate that latent variables do not explain our results.
4.4
Robustness
Our results survive wide range of robustness tests. We examine robustness against reasonable definition of the bank control variables, standard econometric concerns like outliers, and explore alternative controls. 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.
Integrity of the bank control variables Our first step is to ascertain that our results are robust against reasonable variations in the construction of the bank control variables.
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. A key concern is that there are many cases of bank “deregulation.” Many state owned banks are privatized; they become independently controlled banks or family controlled banks. If corrupt and incompetent governments pass state controlled banks to rich families for reasons not related to aggregate economic performance and such incidents are prevalent in our sample, what we document is not that family control leads to economic malfunctioning, but that family control of banks is a consequence of corrupt and incompetent government and economic malfunctioning. 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 27
polarized ways of accounting for privatization transactions that change bank controls. 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 A1 of Table 8 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 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 8 Panel A2, 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 A3 of Table 8 shows that using this generates qualitatively similar results. Raising the threshold to 20% (not shown) likewise produces qualitatively similar results.
Family Control of Firms 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 28
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.16 We first use this information to perform a robustness check by assuming that banks controlled by families which 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 8
Panel A5 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 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.
16
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.
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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 A4 of Table 8 shows that qualitatively similar results again ensue.
Econometric concerns
We have already examined the endogeneity issue in Section 4.3. Here, we examine other standard econometric concerns.
Heteroskedasticity The statistical tests in all our regressions control for heteroskedasticity. Using standard OLS regression coefficients and t-tests, as reported in Table 8 panel B1, 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 8 panel B2 precludes influence due to outliers by presenting qualitatively similar results.17
Controlling for Other Determinants of Growth The economic growth literature has identified several other factors that could be important in explaining cross-country differences in growth. In Table 6 we control for development of both
17
The regression results for the banking crises are excluded because the iteratively reweighted least squares algorithm does not converge.
30
banking and stock markets, initial GDP per capita, average years of schooling and trade openness. However, we cannot possibly control for all factors that could affect growth given that we have only 43 observations. Regardless, we attempt to control few other factors including inflation, size of the government, black market premium, average number of coups and average number of assassinations in addition to our controls in Table 6. Table 8 Panel C shows that family control is always significantly negatively correlated with income growth regardless of the specification, while government control and independent have insignificant coefficients most of the time.
4.4 Consistency with Crony Capitalism We advocate that the correlations between family control of banks and financial system efficiency and economic growth are consistent with the entrenchment argument which some call crony capitalism (Murphy et al. 1991, 1993; Shleifer & Vishny 1998b; Rajan & Zingales 2004). 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. To strengthen our case, we check the consistency of our empirical results with other predictions of crony capitalism. Crony capitalism predicts higher income inequality and barriers to entry. We check the correlations of bank control with these variables. 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 9, Columns 1 and 2). In contrast, state-controlled banks weakly significantly correlate with greater egalitarianism in income distribution and so do independently controlled banks. 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. The results are reported in Table 9 Columns 3 and 4. Unsurprisingly, family-controlled business empires are more important in
31
countries whose banking systems are more extensively controlled by families or more extensively controlled by the states. As a third measure of equality, we use consumption items that could proxy for the importance of the country‟s middle class: personal computers (Table 9 columns 5 and 6)18. Countries with more family-controlled banking systems have poorer and/or smaller middle income classes.
[Table 9 about here]
The last six columns of Table 9 show the relationship between bank control and regulation of entry. All three measures number of procedures, time and cost required to setup a new company 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. This provides further evidence that our results are correlated with the predictions of the crony capitalism literature.
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
18
As alternative variables use include telephone lines per 1000 people, internet user per 100 people and passenger cars per 1000 people in Table 8 Panel D.
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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, more frequent bank crises, greater macro volatility, slower capital accumulation and slower income and productivity growth. Moreover, family control of banks is correlated with other predictions of the economic entrenchment literature such as income inequality and higher barriers to entry. 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.
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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 Argentina Australia Austria Brazil Canada Chile Colombia Denmark Egypt Finland France Germany Greece Hong Kong India Indonesia Ireland Israel Italy Japan Jordan Kenya Korea Malaysia Mexico Netherlands Norway Pakistan Peru Philippines Portugal Singapore South Africa Spain Sri Lanka Sweden Switzerland Taiwan Thailand Turkey United Kingdom United States Venezuela Zimbabwe
Code AR AU AT BR CA CL CO DK EG FI FR DE GR HK IN ID IE IL IT JP JO KE KR MY MX NL NO PK PE PH PT SG ZA ES LK SE CH TW TH TR GB US ZM ZW
Family 0.44 0.01 0.00 0.61 0.00 0.71 0.41 0.01 0.02 0.00 0.00 0.14 0.36 0.76 0.00 0.04 0.00 0.48 0.11 0.00 0.91 0.03 0.03 0.93 0.57 0.00 0.00 0.04 0.49 0.68 0.43 1.00 0.64 0.34 0.00 0.30 0.09 0.17 0.54 0.48 0.21 0.02 0.76 0.00
State 0.56 0.00 0.00 0.31 0.00 0.29 0.18 0.00 0.98 0.00 0.00 0.24 0.56 0.24 1.00 0.91 0.00 0.43 0.00 0.22 0.09 0.83 0.38 0.00 0.00 0.22 0.43 0.96 0.19 0.21 0.29 0.00 0.01 0.01 0.59 0.00 0.21 0.74 0.46 0.32 0.00 0.00 0.00 0.00
34
Independent 0.00 0.99 1.00 0.08 1.00 0.00 0.41 0.99 0.00 1.00 1.00 0.62 0.08 0.00 0.00 0.05 1.00 0.09 0.89 0.78 0.00 0.15 0.59 0.07 0.43 0.78 0.57 0.00 0.33 0.11 0.29 0.00 0.34 0.65 0.41 0.70 0.70 0.09 0.00 0.21 0.79 0.98 0.24 1.00
# of Banks 4 11 6 10 9 5 4 9 9 1 8 8 10 6 13 12 7 8 9 7 8 5 9 6 2 3 9 4 4 13 7 2 5 14 6 4 9 14 7 11 6 10 2 2
Table 2 Descriptive Statistics of Main Variables Sample is the countries listed in Table 1; variables are defined in Table 3. Standard Mean Median Deviation Panel A. Bank control indexes 1 Family 2 State 3 Independent Panel B. Financial system efficiency 4 Capital allocation efficiency, „63-„03 5 Capital allocation efficiency, „93-„03 6 Non-performing loans 7 Banking crises, „93-„03 Panel C. Economic growth 8 Real GDP growth 9 TFP growth 10 Capital accumulation 11 Growth rate volatility Panel D. Crony capitalism 12 Income inequality 13 Oligarchy 14 Computers 15 Number of Procedures 16 Time 17 Cost Panel E. Main controls 18 Initial income 19 Stock market size 20 Banking system size 21 Trade openness 22 Human capital
0.29 0.27 0.44
0.17 0.21 0.41
Maximum
Minimum
0.31 0.31 0.39
1.00 1.00 1.00
0.00 0.00 0.00
0.54 0.43 8.12 0.23
0.54 0.46 6.16 0.00
0.28 0.42 7.42 0.48
1.12 1.32 27.43 2.00
-0.03 -1.02 0.45 0.00
0.02 0.02 -0.01 0.03
0.02 0.02 -0.01 0.02
0.02 0.01 0.01 0.02
0.07 0.07 0.01 0.08
-0.02 -0.01 -0.04 0.01
38.88 0.62 165.84 2.10 3.38 6.75
36.03 0.66 104.72 2.20 3.61 6.69
9.57 0.33 151.88 0.54 0.98 1.23
59.08 1.00 462.72 2.89 4.85 8.87
24.70 0.00 3.53 0.69 0.69 4.21
8.66 3.92 4.41 4.13 2.05
9.01 3.93 4.48 4.10 2.05
1.41 0.82 0.58 0.62 0.30
10.45 5.68 5.68 5.94 2.54
5.78 2.15 2.98 2.96 1.49
35
Table 3 Variable 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 The efficiency of capital allocation is the estimated elasticity of manufacturing investment allocation to value added, estimated as in Wurgler (2000). Note: Two versions of this variable are efficiency used, one using all available data and the other using data for 1993 through 2003 only. Nonperforming 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).
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).
Panel C. Economic Growth Income Real per capita GDP growth is the coefficient in an OLS regression of log real per capita growth 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.
Growth rate volatility
Standard deviation of real GDP per capita growth, 1993-2003. Source: Calculated from World Penn Tables data.
36
Panel D. Crony Capitalism Income Average Gini coefficient, or deviation of income distribution from uniformity, (Gini 1921) inequality 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 privatesector 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.
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. 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.
Cost
Panel E. 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.
Financial development
Log sum of average stock market capitalization to GDP and average credit outstanding 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
37
Inflation
Inflation rates are calculated using average annual CPI data from the International Financial Statistics. Source Beck et al (2000).
Size of the government
Real general government consumption as the share of real GDP. Source: Beck et al (2000).
Black market premium
Source: Beck et al (2000).
Average number of coups
Source: Beck et al (2000).
Average number of assassinations
Source: Beck et al (2000).
Panel F. Instrumental Variables Catholic, Shows fraction of Catholics, Protestants, Muslims, Buddhists and Other religions‟ Protestant, followers in a country. The values are from World Christian Encyclopedia (1995) as Muslim, reported by Stulz and Williamson (2003). Buddhist, Others 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.
38
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. 1 1.00
2
State
-0.23 (0.13)
1.00
3
Independent
-0.62 (0.00)
-0.62 (0.00)
1.00
4
Capital allocation efficiency, „63-„03
-0.20 (0.22)
-0.65 (0.00)
0.69 (0.00)
1.00
5
Capital allocation efficiency, „93-„03
-0.26 (0.14)
-0.27 (0.13)
0.44 (0.01)
0.54 (0.00)
1.00
6
Non-performing loans
0.30 (0.05)
0.58 (0.00)
-0.70 (0.00)
-0.63 (0.00)
-0.37 (0.03)
1.00
7
Banking crises, „93-„03
0.29 (0.06)
0.03 (0.84)
-0.26 (0.09)
-0.26 (0.11)
0.02 (0.91)
0.32 (0.04)
1.00
8
Real GDP growth
-0.27 (0.07)
0.02 (0.91)
0.21 (0.18)
0.16 (0.32)
0.13 (0.48)
-0.44 (0.00)
-0.30 (0.05)
1.00
9
TFP growth
-0.20 (0.19)
0.10 (0.51)
0.08 (0.600
0.05 (0.76)
0.05 (0.780
-0.31 (0.04)
-0.30 (0.05)
0.97 (0.00)
1.00
10
Capital accumulation
-0.33 (0.03)
-0.32 (0.03)
0.52 (0.00)
0.43 (0.01)
0.31 (0.08)
-0.55 (0.00)
-0.07 (0.66)
0.33 (0.03)
0.09 (0.57)
1.00
11
Growth rate volatility
0.47 (0.00)
0.06 (0.69)
-0.43 (0.000
-0.37 (0.02)
-0.31 (0.08)
0.48 (0.00)
0.36 (0.02)
-0.20 (0.20)
-0.14 (0.36)
-0.24 (0.120
1.00
12
Initial income
-0.13 (0.40)
-0.60 (0.00)
0.58 (0.00)
0.66 (0.00)
0.51 (0.00)
-0.73 (0.00)
-0.23 (0.15)
0.18 (0.25)
0.06 (0.70)
0.49 (0.00)
-0.40 (0.01)
1
Family
2
3
4
39
5
6
7
8
9
10
11
12
1.00
TABLE 5 Bank Control and Financial System Efficiency The first 12 columns show cross-country OLS regressions with robust standard errors. In the next four columns the coefficients are from negative binomial 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 Independent
0.275 (0.07)
Efficiency of Capital Allocation 1963-2003
0.276 (0.05)
0.353 (0.00)
Non Performing Loans / Total Gross Loans
0.352 (0.00)
-1.39 (0.00)
Number of Banking Crises (1993- )
-1.29 (0.00)
-1.72 (0.03)
Family
-0.350 (0.05)
-0.338 (0.06)
-0.295 (0.00)
-0.288 (0.00)
1.45 (0.00)
1.22 (0.00)
2.41 (0.03)
2.31 (0.02)
State
-0.115 (0.60)
-0.136 (0.22)
-0.480 (0.00)
-0.497 (0.00)
1.29 (0.01)
1.46 (0.00)
0.735 (0.49)
0.824 (0.40)
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)
-0.410 (0.57)
-0.886 (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)
-0.602 (0.17)
-0.368 (0.40)
Financial Development GDP per capita Constant R2 N
0.0907 (0.05) -0.856 (0.29)
0.167 (0.15) 0.0843 (0.06) -0.967 (0.24)
0.34 33
0.34 33
0.0699 (0.17) -0.998 (0.20)
0.164 (0.26) 0.0690 (0.15) -1.07 (0.14)
0.33 33
0.33 33
0.0380 (0.23) 0.175 (0.51)
0.0701 (0.27) 0.0337 (0.28) 0.115 (0.27)
0.61 39
0.61 39
0.0538 (0.06) -0.292 (0.20)
0.081 (0.27) 0.0491 (0.12) -0.441 (0.08)
0.59 39
0.58 39
40
-0.399 (0.00) -0.661 (0.54)
-0.0588 (0.74) -0.376 (0.00) -0.116 (0.90)
0.68 43
0.65 43
-0.388 (0.00) 0.648 (0.45)
-0.0709 (0.70) -0.392 (0.00) 1.40 (0.05)
0.67 43
0.65 43
-1.78 (0.02)
-0.0779 (0.78) 1.95 (0.40)
-1.10 (0.01) -0.0503 (0.85) 2.95 (0.18)
0.155 (0.38) 2.86 (0.15)
-1.26 (0.01) 0.178 (0.30) 3.53 (0.08)
0.20 43
0.20 43
0.17 43
0.18 43
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 Robust to Outliers Independent Family State Human capital Trade openness Banking system size Stock market size GDP per capita Constant
R2 N
OLS
0.0101 (0.16) -0.0263 (0.00) -0.0096 (0.29) 0.00323 (0.79) 0.00442 (0.24) 0.00117 (0.84) 0.00631 (0.16) -0.0051 (0.08) 0.0190 (0.46) 0.33 43
0.0170 (0.18) 0.00237 (0.54) 0.00557 (0.33) 0.00125 (0.77) -0.0049 (0.08) -0.0176 (0.47) 0.20 43
TFP Growth Robust to Outliers
0.00820 (0.28) -0.0154 (0.09) 0.00169 (0.87) 0.0173 (0.21) 0.00519 (0.22) 0.00329 (0.60) 0.00242 (0.63) -0.0030 (0.36) -0.0324 (0.26) 0.26 43
0.0232 (0.09) 0.00375 (0.37) 0.00652 (0.28) -0.0005 (0.92) -0.0048 (0.11) -0.0329 (0.20) 0.17 43
Capital Accumulation OLS
0.0145 (0.02) -0.0223 (0.00) -0.0057 (0.50) 0.00293 (0.80) 0.00594 (0.09) -0.0001 (0.99) 0.00616 (0.14) -0.0054 (0.05) 0.0235 (0.32) 0.34 43
0.00955 (0.38) 0.00449 (0.19) 0.00259 (0.60) 0.00333 (0.38) -0.0066 (0.01) 0.0102 (0.63) 0.27 43
Robust to Outliers
0.00477 (0.51) -0.0122 (0.16) 0.00552 (0.57) 0.0161 (0.23) 0.00700 (0.09) 0.00210 (0.73) 0.00197 (0.68) -0.0032 (0.30) -0.0268 (0.33) 0.23 43
41
0.0222 (0.09) 0.00550 (0.18) 0.00546 (0.35) -0.0010 (0.82) -0.0051 (0.08) -0.0236 (0.34) 0.18 43
OLS
0.0114 (0.03) -0.0116 (0.06) -0.0106 (0.13) 0.0132 (0.17) -0.0052 (0.07) 0.00575 (0.19) 0.00088 (0.80) -0.0011 (0.63) -0.0297 (0.13) 0.48 43
0.0148 (0.09) -0.0053 (0.06) 0.00595 (0.13) 0.00074 (0.81) -0.0014 (0.47) -0.0414 (0.02) 0.49 43
Standard Deviation of Growth Robust to Outliers
0.0114 (0.04) -0.0105 (0.12) -0.0128 (0.09) 0.00420 (0.68) -0.0060 (0.06) 0.00395 (0.38) 0.00149 (0.68) 0.00082 (0.73) -0.0184 (0.38) 0.43 43
0.00341 (0.72) -0.0058 (0.06) 0.00352 (0.41) 0.00188 (0.57) 0.00107 (0.62) -0.0309 (0.10) 0.43 43
OLS
-0.0140 (0.00) 0.0173 (0.00) 0.00860 (0.13) 0.00297 (0.70) 0.00009 (0.97) -0.0089 (0.02) 0.00009 (0.97) 0.00286 (0.12) 0.0214 (0.19) 0.44 43
0.00122 (0.87) 0.00062 (0.79) -0.0110 (0.00) 0.00096 (0.71) 0.00415 (0.02) 0.0308 (0.04) 0.42 43
-0.0094 (0.30) 0.0221 (0.04) -0.0083 (0.48) 0.00452 (0.77) 0.00647 (0.18) 0.00465 (0.52) -0.0034 (0.55) -0.0073 (0.05) 0.0442 (0.18) 0.40 43
-0.0059 (0.71) 0.00903 (0.07) -0.0011 (0.88) 0.00178 (0.75) -0.0039 (0.27) 0.0399 (0.19) 0.31 43
TABLE 7 Instrumental Variable Regressions The table reports the results of instrumental variable regressions. Panel A, B replicate the regressions in Tables 5 and 6 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 C1 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. Panel C2 summarizes the control structure of banks by a country‟s legal origin, largest religion and capital city latitude. The numbers reported 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. Panel A: Bank Control and Financial System Efficiency Capital Allocation Capital Allocation Non Performing Efficiency Efficiency Loans / Total 1993-2003 1963-2003 Gross Loans Independent 0.733 0.592 -4.74 (0.14) (0.04) (0.00) Family -0.668 -0.378 3.14 (0.07) (0.06) (0.00) State 0.327 -0.501 3.81 (0.65) (0.16) (0.01) Panel B: Bank Control and Economic Growth Real GDP per Productivity Capital Capita Growth Growth Accumulation Independent 0.0666 0.0536 0.0487 (0.00) (0.01) (0.01) Family -0.421 -0.312 -0.291 (0.01) (0.03) (0.01) State -0.00816 0.00425 -0.0829 (0.79) (0.88) (0.00) Panel C: Relevance of Instrumental Variables Independent Family C1: LR Statistics of First Stage Regressions Eff. of Capital Allocation 1963-2003 30.94 34.61 All Other Dependent Variables 31.51 39.73 C2: What Explains Bank Control Legal system British 0.427 0.291 origin French 0.308 0.409 German 0.629 0.072 Scandinavian 0.816 0.076 Largest religion Catholic dummy 0.524 0.331 Muslim dummy 0.053 0.405 Protestant dummy 0.703 0.125 Others dummy 0.245 0.331 Latitude Lowest quartile 0.168 0.498 Second quartile 0.260 0.348 Third quartile 0.505 0.244 Highest quartile 0.832 0.068
42
Number of Banking Crises (1993- ) -6.45 (0.04) 3.90 (0.08) 0.798 (0.89) Standard Deviation of Growth -0.0521 (0.08) 0.0274 (0.09) 0.0608 (0.07) State 38.52 27.25 0.282 0.283 0.299 0.108 0.144 0.542 0.172 0.424 0.334 0.392 0.251 0.100
TABLE 8 Robustness Tests In Panels A and B we replicate the regressions in Tables 5 and 6 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 A1 adjust the banking control variables using privatization transactions from Megginson(2004) assuming that banks that are privatized between 1992-2003 are state owned. In Panel A2 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 A3 banks assumed to have a controlling shareholder if the shares controlled is larger than 15% instead of the 10% used before. Panel A4 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 A5 family banks are considered independent if the family that control the bank does not control other firms. In Panel B we try alternative regressions models. Panel B1 reports the results of a simple OLS regression without controlling for heteroskedasticity. Panel B2 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 C includes additional control variables to Table 6 income growth regressions. Panel D uses various alternate left hand side variables, but the same control variables. 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. Banking Efficiency Economic Growth Capital Non Number of Income Standard Allocation Performing banking Crises Growth Deviation of Efficiency Loans / Total (1993- ) Growth Gross Loans Panel A: Various Ways of Constructing Bank Control Variables A1: Privatizations Independent 0.325 -1.86 -2.19 0.0170 -0.0225 (0.00) (0.00) (0.06) (0.07) (0.00) Family -0.314 1.86 2.95 -0.0299 0.0300 (0.01) (0.00) (0.04) (0.00) (0.00) State -0.346 1.86 1.36 -0.0140 0.00922 (0.02) (0.00) (0.36) (0.16) (0.39) A2: Bank Control Weighted by Total Assets Independent 0.343 -1.42 -1.54 0.00770 -0.0147 (0.00) (0.00) (0.05) (0.31) (0.07) Family -0.284 1.41 2.08 -0.0205 0.0255 (0.01) (0.00) (0.05) (0.01) (0.01) State -0.481 1.44 -0.864 -0.00556 -0.0046 (0.00) (0.00) (0.44) (0.59) (0.71) A3: Control Assumed at 15% Independent 0.353 -1.39 -1.72 0.0101 -0.012 (0.00) (0.00) (0.03) (0.16) (0.14) Family -0.295 1.45 2.41 -0.0263 0.0245 (0.00) (0.00) (0.03) (0.01) (0.01) State -0.480 1.28 0.735 -0.00960 -0.00901 (0.00) (0.01) (0.49) (0.29) (0.46) A4: Dropping Countries with Fewer than 3 Banks Independent 0.309 -1.56 -1.94 0.0149 -0.0202 (0.02) (0.00) (0.04) (0.09) (0.02) Family -0.250 1.55 3.76 -0.0199 0.0350 (0.07) (0.01) (0.05) (0.06) (0.00) State -0.407 1.57 1.03 -0.00999 -0.000842 (0.02) (0.01) (0.38) (0.36) (0.99)
43
TABLE 8 (Continued)
Capital Allocation Efficiency
Banking Efficiency Non Number of Performing banking Crises Loans / Total (1993- ) Gross Loans
Economic Growth Income Standard Growth Deviation of Growth
A5: Family Control of Firms
Independent Family State
Independent Family State
Independent Family State
Inflation Size of the government Black market premium Average number of coups Average number of assassinations All controls together
Independent Family State
0.320 -1.31 -0.941 0.00748 (0.00) (0.00) (0.29) (0.30) -0.258 1.39 1.45 -0.237 (0.00) (0.00) (0.21) (0.01) -0.436 1.18 0.151 -0.00607 (0.00) (0.01) (0.89) (0.50) Panel B: Alternative Regression Methods B1: OLS Regressions (Without Correcting for Heteroscedasticity) 0.353 -1.42 -0.314 0.00820 (0.00) (0.00) (0.17) (0.28) -0.295 1.45 0.509 -0.0154 (0.01) (0.00) (0.05) (0.09) -0.480 1.29 -0.0303 0.00169 (0.00) (0.02) (0.92) (0.87) B2: Iterative Estimation Robust to Outliers 0.343 -1.65 Not Applicable 0.0101 (0.00) (0.00) (0.16) -0.298 1.68 Not Applicable -0.0263 (0.02) (0.00) (0.00) -0.462 1.51 Not Applicable -0.00960 (0.01) (0.01) (0.29) Panel C: Alternative Determinants of Growth Independent Family State -0.00099 -0.0282 -0.00303 (0.92) (0.01) (0.79) 0.00385 -0.0254 -0.00314 (0.47) (0.01) (0.77) 0.0217 -0.0170 -0.0332 (0.00) (0.04) (0.00) -0.00373 -0.217 0.00864 (0.67) (0.01) (0.34) 0.00374 -0.0270 0.00032 (0.65) (0.00) (0.98) -0.00813 -0.0375 0.0109 (0.47) (0.00) (0.25) Panel D: Alternative Left Hand Side Variables Cars Telephone Internet Total banking crises 197 9.17 7.06 -0.820 (0.00) (0.01) (0.00) (0.06) -228 -17.3 -9.72 1.13 (0.00) (0.00) (0.00) (0.03) -144 4.29 -2.68 0.279 (0.02) (0.39) (0.35) (0.65)
44
-0.0115 (0.13) 0.0251 (0.00) -0.0937 (0.44)
-0.0124 (0.17) 0.0245 (0.01) -0.00900 (0.44) -0.0139 (0.00) 0.165 (0.00) 0.00838 (0.13)
TABLE 9 Consistency with Crony Capitalism The table shows cross-country OLS regressions with robust standard errors. Dependent variables are in columns and independent variables are in rows. Variables are as defined in Table 3. P values are in parentheses. Income Distribution Regulation of Entry Income Inequality
Oligarchy
-0.266 (0.07)
Independent
PCs
-1.89 (0.01)
Number of Procedures
113 (0.00)
Time
-0.710 (0.00)
Cost
-1.07 (0.02)
-1.18 (0.01)
Family
0.599 (0.00)
1.98 (0.01)
-170 (0.00)
0.933 (0.00)
1.37 (0.00)
1.15 (0.04)
State
-0.308 (0.10)
1.68 (0.04)
-12.1 (0.80)
0.317 (0.30)
0.554 (0.20)
1.24 (0.03)
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)
0.284 (0.06)
0.174 (0.16)
0.196 (0.45)
0.0514 (0.83)
0.563 (0.13)
0.579 (0.11)
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)
-0.442 (0.00)
-0.338 (0.00)
-0.639 (0.00)
-0.503 (0.01)
-0.663 (0.02)
-0.678 (0.00)
GDP per capita (1992)
-0.140 (0.01) 1.02 (0.01)
-0.0977 (0.07) 0.967 (0.03)
-0.359 (0.04) 2.83 (0.36)
-0.335 (0.03) 4.36 (0.06)
74.5 (0.00) -518 (0.00)
64.0 (0.00) -551 (0.00)
-0.019 (0.78) 2.39 (0.00)
0.022 (0.72) 2.79 (0.00)
-0.125 (0.14) 5.56 (0.00)
-0.0716 (0.44) 6.22 (0.00)
0.830 (0.00) -0.978 (0.41)
0.824 (0.00) 0.251 (0.80)
R2
0.59
0.41
0.63
0.63
0.85
0.80
0.42
0.37
0.49
0.46
0.55
0.55
N
42
42
27
27
43
43
43
43
43
43
43
43
Constant
45
Figure 1. Capital Allocation Efficiency and the Control of Banks The y axis is the capital allocation efficiency (1963-2003), and x axis is the fraction of familycontrolled, state-controlled, and independent banks as in Table 3. Country codes are in Table 1. Panel A. Family-controlled banks 1.2 DK
1
ZA
GB
FR AT JP 0.8 FI US KR ZW CA 0.6 NO NL AU IE EG
0.4
IT
SE ES PE
GR
PT IL
LK IN
0.2
SG
JO CL
CO DE
PH TR
KE ID
0
MY
ZM
MX
TH
PK
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-0.2
Panel B. State-controlled banks 1.2 DK ZA GB IT AT SE FR 0.8 US ZW FI ES CA 0.6 AU SG MY IE ZM 0.4 MX JO
1
JP PE
KR NO
NL
GR
PT
EG
IL CL
CO
LK
DE
0.2
PH
IN
TR
KE ID
TH
0
PK
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-0.2
Panel C. Independent banks 1.2 1
IE
CA
AU
FI ZW FR US
AT IT
0.8
NL
GB
JP SE
DE
0.6
ES CO
0.4 0.2
KE
-0.2
PH
0
TH
IL CL
JO EG
0.2
0.4
-0.2
46
PE
PT
ZM
IN
0
KR
LK
TR
ID
PK
NO
MX
GR
MY SG
0.6
0.8
1
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