The Information and Monitoring Role of Capital Markets ... · growth and capital market functions....

The Information and Monitoring Role of Capital Markets: Theoryand International Evidence

Solomon Tadesse*

The University of South Carolina

Abstract

Capital markets perform two distinct functions: provision of capital and facilitation ofgood governance through information production and monitoring. We argue that thegovernance function impacts on the efficiency with which resources are utilized withinthe firm. Based on industry level data across thirty-eight countries, we present evidencesuggesting a positive relation between market-based governance and improvements inindustry efficiency. The measures of governance are also positively correlated withproductivity improvements and growth in real output. The evidence underscores the roleof capital markets as a conduit of socially valuable governance services as distinct fromcapital provision.

JEL Classification: G3, G34, G14, E44, O16

Keywords: Corporate Governance, Information Aggregation, Monitoring, EconomicEfficiency, Productivity, Economic Growth

* Corresponding author: The Darla Moore School of Business, The University of South Carolina,

Columbia, SC 29208. Tele: (803) 777-4917; Fax: (803) 777-3609. E-mail: [email protected]. Iwould like to thank Steve Byers, Joshua Coval, Stijn Claessens, Marc Nerlove, Gordon Phillips,Raghuram Rajan, Lemma Senbet, Jeremy Stein, Alex Triantis, and Haluk Unal for comments andhelpful discussions. I also thank seminar participants at the American Finance Association,Western Finance Association, the International Monetary Fund, the Financial ManagementAssociation Meetings, Penn State University, and the University of South Carolina.

ii


Abstract

Capital markets perform two distinct functions: provision of capital and facilitation ofgood governance through information production and monitoring. We argue that thegovernance function impacts on the efficiency with which resources are utilized withinthe firm. Based on industry level data across thirty-eight countries, we present evidencesuggesting a positive relation between market-based governance and improvements inindustry efficiency. The measures of governance are also positively correlated withproductivity improvements and growth in real output. The evidence underscores the roleof capital markets as a conduit of socially valuable governance services as distinct fromcapital provision.

1


Why do we observe differences in economic performance among countries; across

industries in the same economy; and across firms belonging to the same industry? What could

be the role of the financial system in explaining cross-country and cross-industry variations in

economic performance? While determinants of cross-country economic growth have been of

great interest to development economists and growth economic theory, the role of financial

markets and institutions has traditionally received very little attention. Recent finance literature

reports strong relations between indicators of financial development and economic performance

in the real sector, indicating a positive role for capital markets and institutions (see, e.g., Levine

(1998), Levine and Zervos (1998); and Rajan and Zingales (1998)). Levine and Zervos (1998),

for example, find a strong correlation between financial development, and growth in per capita

GDP and productivity. Yet, there is still a lack of clear understanding of the manner in which the

financial system impacts on economic performance.

In this study, we utilize a corporate finance framework to investigate empirically the

finance-growth link through examining possible channels through which financial market

functions could influence economic performance at industry level. We begin from a premise

that financial markets and institutions play two critical roles in an economy: allocation of risk

capital through saving mobilization and risk-pooling and sharing; and promotion of responsible

governance and control through providing outside investors a variety of mechanisms for

monitoring inside decision makers. In its allocation function, the financial system helps transfer

resources from individual savers to agents with managerial and entrepreneurial talents with

investment opportunities and provides firms and investors risk-pooling and sharing facilities. As

its governance function, it provides monitoring and information production services by which it

2

helps mitigate the various agency problems of the firm resulting in better project evaluation and

selection even in the absence of external finance need. While recognizing the twin roles of the

financial system, modern corporate finance theory emphasizes the monitoring and information

production function; in contrast, recent finance-growth literature exclusively focuses on the

capital mobilization role. Bridging the gap, we argue that the two functions systematically

impact on differential sources of growth.

We argue that a country’s financial system positively contributes to economic

performance in the real sector of the economy. Previous literature (see, e.g., Levine and Zervos

(1998)) documents that financial development is correlated with growth in productivity. Noting

that productivity growth stems from efficiency improvements and technological advancement,

we identify two channels through which the twin functions of the financial system influence

economic performance: promoting technological innovations and inducing the efficiency with

which resources are utilized. Focusing on the governance role of the financial system, we argue

and empirically examine that information production and monitoring services contribute to

improvements in the relative economic efficiency with which the firm utilizes its resources.

The paper develops an empirical model of total factor productivity growth (as a

performance measure) which isolates the contribution of production and economic efficiencies

from the pure technological change effects. It estimates the relative production and economic

efficiencies for ten manufacturing industries across thirty-eight countries. We then empirically

relate these estimates of industry performance to measures of the governance functions of the

financial system, controlling for its capital mobilization role. Our hypothesis is that measures of

the governance function should have a positive impact on industry efficiency.

3

We find that measures of the information aggregation and monitoring functions of capital

markets are indeed positively related to improvements in efficiency. Moreover, control variables

for the capital mobilization function have little role in explaining variations in industry

efficiency. The results are economically meaningful as well. An increase in our proxies for the

governance function by one standard deviation would increase the rate of efficiency growth of

the average industry by 0.18 percent per year from the actual growth rate in economic efficiency

of 0.032 percent observed over the sample period.

The correlation between our measures of governance and efficiency is robust to

alternative model specification in which we use legal and institutional variables that are deemed

to be more exogenous as instruments, indicating that the relations identified could be causal.

The empirical result is also robust to alternative definitions of the focal constructs of ‘efficiency’

and ‘governance’, and alternative specifications of latent variables as random- or fixed-effects.

The evidence underscores the role of particularly the equity market as a conduit of

socially valuable governance services as distinct from capital provision. The value of this service

is economically large. An industry operating in a country with a stock market that is one

standard deviation above the mean of the proxy for governance function would have a growth

rate of 1.05 percent per annum in real output more than that for the average industry.

Cumulating over the sample period of 15 years, real output for such industry would have been

about 17 percent higher in 1995, the end of the study period.

The study is related to the large literature which identifies the role of financial markets as

information producers and monitors of management (see, e.g., Grossman (1976), Grossman and

Stiglitz (1980), Diamond and Verrechia (1981); and Holmstrom and Tirole (1993)). Stock

markets provide incentives to gather information that gets reflected in stock prices. Prices in

4

securities markets direct capital to its best use (the “prospective” role of stock prices), and

provide managers with feedback about how investors evaluate their performance (the

“retrospective” role) (Dow and Gorton (1997)). Financial intermediaries also serve as delegated

monitors and information producers (see Diamond, 1984)).

In addition to reducing the costs of information acquisition ex ante, financial contracts,

markets and institutions may arise to minimize the information acquisition and enforcement costs

of monitoring firm managers and exerting corporate control ex post. Shleifer and Vishney (1997)

provide an extensive review of this literature. Financial contracts could be designed to mitigate

agency problems (see Barnea, Haugen and Senbet (1985)). Financial intermediaries serve as

delegated monitors (Diamond (1984)). Financial markets allow efficient contracting for aligning

the interests of managers with those of owners (see Jensen and Meckling (1976)), and

information in stock prices allows effective managerial incentive schemes (Holmstrom and

Tirole (1993)). Moreover, financial markets facilitate corporate control through takeovers as a

way of disciplining (Scharfstein (1988) and Stein (1988)).

Recent literature relates this ability to produce information and facilitate monitoring and

control to improved resource allocation that may have growth implications (see, Allen (1993),

Holmstrom and Tirole (1993), Dow and Gorton (1997); and Titman and Subrahamanyam

(1999)). Dow and Gorton (1997) and Titman and Subrahamanyam (1999) present models in

which information produced by the stock market improves allocational efficiency through

guiding managerial decisions. In Holmstrom and Tirole (1993), the use of information for

incentive contracts improves managerial performance and this service of the stock market is both

privately and socially beneficial.

5

The present work extends the strands of arguments presented in these papers to suggest

that the information and monitoring role of markets and institutions has a distinct contribution to

real economic performance separate from the contribution of their capital provision function. It

argues that information aggregation and monitoring enhances economic efficiency within the

firm through the mechanisms identified by the literature.

The study complements the recent empirical literature examining the link between

financial development and economic growth. Levine and Zervos (1998) presents evidence of

correlation between indicators of banking and stock market developments and per capita GDP

growth, rate of capital accumulation and productivity growth. Rajan and Zingales (1998)

examine whether industries that are more dependant on external finance grow faster in more

developed financial markets than less in less developed markets. Similarly, using firm-level

data for 30 countries, Demirguc-Kunt and Maksimovic (1998) examine whether firms with

access to more developed financial markets grow at faster rates than those with access to less

developed markets

The paper differs from these studies in a number of ways. First, Rajan and Zingales

(1998) as well as Demirguc-Kunt and Maksimovic (1998)’s argument is that financial markets

matter only for firms or industries which rely on external finance. Our argument, by contrast, is

that it matters even for firms that rely on internal finance, though the channel of influence might

be different. Through their information aggregation and monitoring functions, financial markets

improve project evaluation and selection, raising economic. Second, unlike previous studies that

focus solely on the capital mobilization role of financial markets, we emphasize the latter’s

multiple functions. We trace the channel of influence of the many functions on the various

dimensions economic performance. In particular, the governance function should have an

6

impact of improving production and economic efficiency. The difference in terms of policy

implications between our functional perspective and previous studies is stark. Our major policy

message is that a mere launching of financial markets and institutions is not sufficient for

accelerating growth; what also matters is their efficient functioning. Third, we decompose

aggregate performance into its primitive sources distinguishing between those sources which are

under the control of inside decision makers (e.g. production efficiency) and those which are more

exogenous to the firm or the industry (e.g. technological change). By relating the various

dimensions of financial development to these disparate sources of growth, we underscore the role

of financial markets in impacting managerial behavior as distinct from its role as conduit of

capital provision.

The rest of the paper is organized as follows. Section I provides the theoretical

framework and develops the hypotheses to be investigated. We describe the data and

methodology in Section II. Section III examines the empirical relations between aggregate

performance and governance while Section IV investigates the relations between sources of

growth and capital market functions. Section V summarizes the results with policy implications.

I. Theoretical framework and hypotheses development

Corporate finance theory suggests that the link between finance and investment at the

micro level is a consequence of market and contractual imperfections. In fact, financial markets

and institutions arise to mitigate problems of informational and transactional frictions. To that

end, financial markets and institutions perform various functions. They aggregate and mobilize

capital, provide risk pooling and sharing services, assess and select projects and management

through producing information, and monitor inside decision making. These diverse services

could be classified into two analytically separable functions: capital allocation and governance.

7

The allocation function involves mobilizing savings from economic units with excess capital to

individuals with managerial and entrepreneurial talents with investment opportunities, as well as

providing risk pooling and sharing opportunities. The governance function refers to the role of

financial systems in alleviating agency problems that arise among stakeholders in the firm.

The degree to which the financial system influences real economic performance depends

on how effectively it carries out both its allocation and governance functions.

A. Governance via financial systems and economic performance: Theeconomic efficiency channel

A primary function of financial markets is one of facilitating responsible governance and

control within the firm. In a world of uncertainty and incomplete contracting, problems of

imperfect information and moral hazard may prevent the first-best value-maximizing investment

behavior. Investment and operations in the firm would be prone to agency problems in which

agents engage in value destroying behavior. Markets and institutions mitigate the consequences

of imperfect information and moral hazard through information production and facilitation of

monitoring. The effectiveness with which markets perform this governance function bears on

firm’s economic efficiency in the sense that alleviation of the contractual frictions engenders

convergence of the firm’s observed economic behavior to its optimum. Economic efficiency is

broadly defined as the degree to which observed economic behavior converges to the optimal

given the constraints of the underlying technologya.

a Assuming cost minimization as behavioral goal, for example economic efficiency could be operationalized as observed totalcost compared to the optimal given the level of output and input prices.

8

A.1 Information production and economic efficiency

A vital role of markets is processing of information. Stock markets influence the

incentive of agents to acquire and disseminate information about firms (see, e.g., Grossman and

Stiglitz (1980)). Trading activity among participants produce information that are conveyed

through price signals. This information role has consequences that have efficiency implications.

First, security prices formed in markets convey valuable information about the profitability of

current investment opportunities and thereby guide managerial decision-making (see Dow and

Gorton (1997), Bresnahaan, Milgrom and Paul (1992); and Titman and Subrahmanyam (1999)).

Managers’ investment decisions may be guided by price signalsb. Second, simply that based on

the information, bad firms (and management teams) or projects do not get funding, preventing

wastage of resource.

Financial institutions also provide an information production function. A large literature

identifies the role of financial intermediaries as information processing and monitoring (see, e.g.,

Diamond (1984), and Leland and Pyle (1977)). Instead of informed traders producing

information through trading and conveying it via prices, banks hire loan officers who produce

information while evaluating projects for loan financing. Dow and Gorton (1997) show that the

same information can be produced under the two mechanisms. As in the stock market, such

information guides managerial investment decision. Moreover, bad projects and firms get

screened out. In summary, the information production function and firm economic efficiency

are linked in that markets and institutions that generate better information enable firms to make

better decisions.

b In the finance literature, it is standard to assume that the information set of management is more finely partitioned than that ofoutsiders and thus information flows from the firm to the market. The reverse flow could be argued to be plausible in moderneconomies in which consensus are hard to achieve about the optimal managerial rule due to rapid technological change,constantly changing market conditions etc. Under these conditions, stock market prices will have a valuable role of aggregatingmany diverse opinions and convey the resulting information about the optimal decision rule to management (Allen(1993)).

9

A.2 Monitoring and economic efficiency

Contractual relationships are prone to severe agency problems due to conflicts of interest

among stakeholders in the firm, including those between management and shareholders that may

result in sub-optimal managerial actions. Markets and institutions provide an important function

in controlling and monitoring such sub-optimal behavior. First, markets generate information to

evaluate the quality of past managerial decisions (Kihlstrom and Matthews (1990), and Dow and

Gorton (1997)). Second, information in stock market prices allows effective managerial

incentive schemes (Holmstrom and Tirole (1993)). Third, the threat of takeover through the

facilitation of capital markets mitigates managerial inefficiencies (Grossman and Hart (1980),

Scharfstein (1988), and Stein (1988)). Fourth, the rich menu of contracts provided in the market

allow private work-outs of financial distress, easing the transfer of control to efficient

management as well as enabling the firm to avoid costly bankruptcy (Barnea, Haugen and Senbet

(1985)). The monitoring and control function can also be accomplished through a financial

intermediary which oversees managerial actions on behalf of numerous small investors (as

delegated monitor (Diamond (1984)) for whom it may not be economical to do the monitoring

by themselves.

The extent to which markets perform their monitoring services directly impacts on the

efficiency with which firms are run in the economy as the goal of monitoring is to induce agents

to perform as close to the value maximizing first best in their investment and operational

decisions. Efficiency can be achieved via markets and institutions in a number of ways.

Managerial incentives that use information in stock market prices reduce shirking. In general, it

aligns managerial interest to that of shareholders reducing potential under or over investment vis

a vis the optimum. Through the mechanism of the market for corporate control inefficient

10

& ( , )y g A G=

& & & ,y I TFP= +

management is forced out of office. More importantly, the threat of takeover induces managerial

discipline, preventing management from actions that waste firm resources.

Hence the economic efficiency of firms should be positively impacted by how well the

supporting financial system performs its information production and monitoring (i.e.

governance) function. To sum up, an index of “betterment” of an economic unit, extensively

used in the literature, is growth rate in some measure of output. We argue that how better-off an

economic unit will be, as measured by growth in output (y), is partly determined by the

effectiveness and efficiency of the financial system in delivering governance (G) and capital

allocation functions (A),

(1)

Furthermore, growth in output (y), can be decomposed into its primitive sources, which include

growth in constituent factors of production and changes in all other conditions that bear on the

production process. The latter represents what is called growth in total factor productivity (TFP),

and it accounts for all changes in output not accounted by growth in production inputs. Thus,

(2)

I denote growth rate in inputs. Physical capital being a factor, I also includes ‘rate of capital

accumulation’. Productivity (TFP) may change due to shifts in the underlying technology or

changes in the economic efficiency of the production process. Thus, denoting T to be

technological change and E to be changes in production efficiency,

ETPFT && += (3)

11

We trace a channel through which the financial system influences economic performance

whereby the degree to which it provides governance (G) effects improvement in the relative

economic efficiency (E) with which the firm utilizes its resources. Thus, our conceptual model

is,

),( GAE Φ=& (4)

and,

0>∂Φ∂G

II. Data and measurement of proxies

Our database combines country financial development indicators, industry production

characteristics and other country economic statistics that are assembled from various sources.

We use industry-level production data for ten manufacturing industries over the period 1980 to

1995 for thirty-eight countries from the United Nations Industrial Statistics database to estimate

measures of economic performance. We have data for measures of output, inputs and

investments. The financial development indicators include stock market capitalization, total

value traded and turnover ratio obtained from Emerging Markets Fact-book (various issues)

published by the World Bank. This data, for many countries, is available on a systematic manner

since 1980, thus limiting the study period. We obtain data on the size of domestic credit and size

of the private credit sector from the International Financial Statistics (IFS) published by the

International Monetary Fund. All other country-specific variables, including the Gross Domestic

Product, per capita GDP, exchange rates, the Producer Price Index, the Wholesale Price Index

and the Consumer Price Index are from the International Financial Statistics.

12

A. Measurement of capital market functions

Ideally, we would like to have a measure of the ability of firms to raise capital to meet

their financial needs and to benefit from the related governance services provided by financial

systems. We use measures of financial system size (relative to GDP) as proxies for the capital

mobilization function and measures of financial market activity variables as proxies for the

governance function. The size variables include (1) stock market capitalization to Gross

Domestic Product ratio; (2) domestic credit to GDP; and (3) claims against the private sector

relative to GDP.

We use two broad indicators of the degree of governance services provided by a country’s

financial system. The first is a measure of a country’s stock market activity for which we use the

ratio of total value of equity traded to stock market capitalization – turnover ratio.

There are strong theoretical reasons for using this variable as a measure of the

effectiveness of the governance function. First, greater market liquidity implies more and better

information— prices reflect information about the firm and its investment prospects more

accurately. Increased market activity induces more information-acquisition, which, in turn,

increases the information content of stock prices (see Holmstrom and Tirole (1993)). The more

shares of stock actively being traded and the more liquid the market, the easier it becomes for an

informed party to make a good return on investment (Kyle (1984)). The resultant increased

information flow into the market improves the information content of stock prices. Hence, a

measure of market liquidity is an indicator of the degree of “information aggregation”.

Second, informative security prices in liquid markets facilitate the monitoringc of

management, as well as the implementation of incentive-based compensation designed to align

c Effective monitoring and control could be exercised through other mechanisms such as via intermediaries and boardeffectiveness, which may not be captured by our proxy for governance.

13

management’s interests with those of outside shareholders. Incentive contracts in the form of

managerial option and equity related compensations are useful for reducing agency costs only to

the extent that the underlying equity prices are informative of firm performance. Holmstrom and

Tirole (1993) show how liquidity via increased informativeness of security prices enhances

monitoring.

Third, greater liquidity makes it easier for active shareholders to build positions so as to

effect changes in corporate policies. Bhide (1993) argues that more liquidity implies less

monitoring, since shareholders can dispose easily of positions if they disagree with

management’s policies. On the other hand, Maug (1998) shows that the benefits to shareholders

from building positions and effecting good governance is so significant that the impact of greater

market liquidity on effective monitoring is unambiguously positive.

Finally, the effective use of the secondary equity market for corporate-control activities

requires that the market be liquid. Takeovers require a liquid capital market— a market where

bidders can access a vast amount of capital on short notice. Therefore, with liquid markets,

investors who want to acquire a firm can do so.

An alternative way to measure the degree of information flow and the ease of monitoring

is to look at the accounting standards that reflect the relative transparency of the financial

system. Accounting standards determine the amount and quality of disclosure by firms trading

in the market, impacting the degree of information flow in the capital market. The Center for

International Financial Analysis and Research (CIFAR) creates an index of disclosure quality

across countries by rating the inclusion or omission of 90 reportable financial items for a sample

of at least three firms per country. The greater the level of disclosure, the higher the information

14

flow in the markets and the consequent ease and effectiveness of monitoring. So we use this

index as an alternative proxy for governance as well.

Table 1 presents a summary of the measures of capital market functions for the thirty-

eight countries in the sample. The table shows averages of the variables over the sample period

of 1980-1995 for each country. We observe a number of patterns. First, there is a wide

variation. For example, Germany has a turnover ratio of 1.0394 vis a vis Bangladesh’s 0.0327.

Second, stock market size does not necessarily go hand in hand with stock market activity. For

example, Chile has a relatively large market capitalization ratio (0.4717) and yet is one of the

thinnest markets with turnover of 0.0661. On the other hand, Turkey has one of the smallest

markets (capitalization equals 0.0624) and is relatively busy (with turnover of 0.5041). Third, in

all measures, developed countries have much more advanced financial systems than emerging

countries. The mean values of the proxies for the sub-sample of the nineteen advanced

economies are much higher than those for the sub-sample comprising the nineteen emerging

economies. The correlation’s (not reported) between log of real per capita income and each of

the financial development indicators is significantly positive (0.621 (significant at 10%) for

private credit, 0.484 (significant at 1%) for size of credit market and 0.350(significant at 5%) for

stock market turnover).

In general, the distribution of countries along the spectrum of the financial variables is

highly skewed in favor of developed economies. For example, 32% of emerging countries and

0% of developed countries fall in the lowest quartile of market turnover ratio; and over 40% of

emerging countries and only 5% of developed economies fall in the bottom quartile of stock

market capitalization. 45% of emerging economies and 0% of developed economies belong in

15

the bottom quartile on size of the credit market. In contrast, only 9% of emerging markets and

43 % of the advanced markets fall in the top quartile of market capitalization.

B. Measurement of economic performance

An aggregate index of improvement in an economic unit, extensively used in the

literature, is the growth rate in some measure of the output of the firm. Output growth could be a

result of either growth in component factors of production or improvements in productivity.

Furthermore, productivity gains in economic activities could be caused by two different factors:

adoption of technological innovations in products and processes (measured by the rate of

technological progress), and improvements in efficiency which reflects the capacity of insider

decision-makers to improve production, given inputs and available technology.

We define efficiency as the degree to which the firm’s observed attainment converges to

its optimal behavioral goal, under conditions of technological and market constraints. We

operationalize our sense of efficiency using Farrell’s (1957) concept of technical efficiency and

Leibenstein’s (1966) X-efficiency or economic efficiency. Technical efficiency reflects the

degree to which a producer achieves the maximum attainable quantity of output for a given

bundle of inputs. The optimum is in terms of production possibilities and as such efficiency is

defined in reference to the technical relations between observed and attainable quantities.

The optimum can also be defined in terms of some behavioral goal the producer is

assumed to pursue, such as cost minimization or profit maximization. Efficiency then refers to

the degree to which that assumed objective is achieved. In the cost minimization framework, an

empirical measure of efficiency would be the ratio of the minimum attainable cost for a given

level of output to the actual cost incurred by the producer. This is what Leibenstein (1966) call

“X-efficiency”. A firm can be technically efficient by obtaining the maximum output for

16

whatever bundle of inputs it chooses to employ. It may yet be inefficient if it purchases what is

not the best bundle of inputs given the input prices and their marginal productivities in

production. This latter concept of efficiency is called “price efficiency”, or ‘allocational

efficiency’. X-efficiency (or economic efficiency) subsumes both technical and price

efficiencies d.

Improvements in firm’s productivity are not the result of efficiency gains alone. They

may arise from adoption of technological innovations in processes and products that enable the

firm to achieve higher production quantities with lower input usage or equivalently the same

level of output at lower costs. While efficiency reflects managerial actions in reference to a

behavioral goal, this source of productivity reflects both the state of the available technology and

the ability of firms to acquire new technologies in their production processes.

Empirically, we measure technical efficiency based on a stochastic production frontier,

in which efficiency is measured as the proportion of actual output to the maximally attainable

one. We measure X-efficiency (economic efficiency) based on a stochastic cost frontier as the

proportion of the minimum attainable cost to actual cost. We measure the effect of technical

progress as the shift in the production frontier over time holding input quantities at the same

level or alternatively as the downward shift in the average cost of production over time holding

constant input prices and output level.

d The governance services provided by capital markets may have impacts on the degree of price efficiency in a number of ways.First, the first best value maximizing optimum (for example, in terms of input mix) may diverge from what is optimal tomanagement either due to managerial agency problems owing to managerial discretion or due to financial agency problems suchas risk shifting. In the presence of leverage, given relative prices, management may, in the interest of shareholders, opt to aninput mix that enhances risk shifting, trading the price advantage relative to firm value maximization with the ‘risk advantage’relative to shareholder value maximization. To the extent that capital markets alleviate these agency problems through incentivecontracts (for managerial agency), and through covenants and complex securities (for financial agency), they help improve afirm’s allocational efficiency. Second, prices in capital markets may convey information about marginal productivity.

17

ln ( ) ln ( ) ( ln ( )ln ( )

) ln ( ) ( ) ( )

,

( ) ( )

y t x t t x t x t

t x t t t t

where

t t

ci j cij

tj kj jkci

jci

k

tt jt cij

cij ci

ci c i ci

= + + +

+ + + +

= + +

∑ ∑∑∑β β β β

β β µ ε

ε α η ν

01

2

2

B.1 Empirical measure of production efficiency

We assume that there exists an unobservable function, a production frontier, representing

the maximum attainable output level for a given combination of inputs. We represent these best-

practice production technologies by a translog production function of the form e,

(5)

xcij(t) and xci

k(t) are production inputs j and k used in industry i of country c during period t. The

production inputs are capital (K) and labor (L) f. The variable t, an index of time, represents the

level of technology. µci(t) is a one-sided random variable and measures the degree of

inefficiency of industry i of country c in period t. The specification is a random-effects model

in which latent country and industry effects are specified as random variables. αc and ηi are the

random unobservable country-specific and industry-specific effects respectively, and νci(t) is the

usual white noise. The distributional assumptions on the error components are:

e Our choice of this particular functional form is dictated by its flexibility reducing the chance of inferring inefficiency when infact the problem is a poor fit to the data of a more restrictive form. Moreover, there is evidence that manufacturing production isnon-homothetic and exhibits scale economies, both of which are accommodated in the translog form.f In light of the importance of production data reliability particularly for the estimation of the stochastic frontier productionfunction, we need to ensure data quality to the extent we can. This is all the more important given the survey nature of the data.In that spirit, we implement specific data-editing rules to exclude observations that appear to be unreasonable. First, we excludeall industry-years that report zero values for measures of output and principal inputs. No manufacturing industry can be assumedto be in a steady state operation without reporting positive values for such variables. Accordingly, observations with zero valuesfor output are excluded from the analysis. Similarly, industries that report zero or negative values for value-added are alsoexcluded. Second, some countries report statistics for combination of two or more industries. While one can allocate thesevalues among the constituent industries based on some arbitrary scheme, we opted, in the interest of maintaining quality, toexclude such observations. Third, in constructing the sub-sample for estimating the production frontier, we exclude thoseindustry-years for which we have zero values or combined statistics for labor cost and or employment.

18

α ση σµ σ µν σ

α

η

µ

ν

c

i

ci ci

ci

iidN

iidN

t iid halfNormal and t

t iidN

≈≈

≈ − ≤≈

( , )

( , )

( ) ( , ), , ( )

( ) ( , )

0

0

0 0

0

2

2

2

2

)(tcit

ciePRODEFF µ−=

tPRODEFF

PRODEFF cit

∂∂ln=∆

The error components are independent among each other and independent of the X

variables in Eq. (5). We estimate the model by the method of maximum likelihood to obtain

unbiased and efficient estimates of the parameters. We then impute measures of changes in

efficiency and technological progress from the parameter estimates.

The predicted estimates of the inefficiency term ( µci(t)) are obtained from the parameter

estimates of the production function. The level of production efficiency of industry i of country

c during period t is then computed using the relationship Eq. (5) as:

(6)

PRODEFF represents the ratio of actual output to the maximum attainable output if the industry

were efficient holding the technology (i.e. the production frontier) and the level of input usage

constant. Its value ranges from 0 to 1(i.e.100% efficient). Improvements in efficiency for

industry i in country c over period (t) is then given by,

(7)

B.2 Empirical measures of economic efficiency

The efficiency estimate using the production function framework measures only technical

(production) efficiency. It does not account for the possible error of the firm in choosing an

appropriate input mix given relative prices (i.e. price inefficiency). Economic efficiency (X-

19

ln ( ) ln ( ) ln ( )

{ (ln ( )) (ln ( )) } ln ( ) ln ( )

ln ( ) ln ( ) ( ) ( )

,( ) ( )

C t K t Y t t

k t Y t t K t Y t

k t t Y t t t t

wheret t

ci k ci y ci t

kk ci yy ci tt ky ci ci

kt ci yt ci ci ci

ci c i ci

= + + + +

+ + + ++ + +

= + +

β β β β

β β β ββ β θ ε

ε α η ξ

0

2 2 212

efficiency) reflects both technical and price efficiencies and its estimation involves a reference of

an objective function that reflects both the production and market behaviour of the firm.

Employing the duality between the production function and the cost function in representing the

underlying technology, we derive estimates of X-efficiency in reference to a stochastic cost

frontierg. Duality theory suggests that under certain regularity conditions h, if producers pursue

cost minimizing objective, the production function can be uniquely represented by a dual cost

function i. We represent the underlying technology by a restricted cost function. We use a

translog functional form. After imposing homogeneity and symmetry conditions, the cost

function will have the following form:

(8)

θci(t) is a one-sided random variable denoting the degree of economic inefficiency. αc and ηi are

country specific and industry specific error components. ξci(t) is the usual disturbance term with

mean zero and standard deviation σξ . The error components and the disturbance term follow the

distributional assumptions in Eq. (5) above. ln Y is the log of output and ln K is the log of

capital stock. Also note that, with imposition of homogeneity, the input price of labor becomes a

g A cost function maps cost-minimizing points where relative prices are set to equal marginal productivities. This is a result of anoptimization problem in which the firm minimizes cost (choosing input levels) subject to the technological constraintsrepresented by the production function. Thus, a production unit on the cost frontier is both technically and allocatively efficient.The deviation of actual cost from the cost frontier, holding output level and input prices constant, would naturally measure theamount of total economic inefficiency.hTo be a valid representation of the technology, a cost function should be a non-negative, non-decreasing function of output y; anon-negative, non-decreasing concave function in input prices; and twice differentiable with respect to input prices.Furthermore, a restricted (variable) cost function should be a non-positive and convex function of quasi-fixed input quantities.i While the underlying production function merely reflects technical possibilities among inputs and outputs and nothing abouteconomic behaviour, the cost function summarizes optimizing behavior and captures all information about the underlying

20

ECONEFF t ecitci( ) ( )= − θ

ttECONEFF

tECONEFF cici ∂

∂ )(ln)( =Λ

numerier, effectively entering in the intercept term. Given the parameter estimates of equation

3.4, X-efficiency (economic efficiency) is given by,

(9)

Eq. (9) is the ratio of the minimum cost on the frontier to actual cost incurred and ranges in value

from 0 (inefficient) to 100% (efficient). Improvements in X-efficiency is then given by,

(10)

Table 2 presents a summary of the economic performance measures including growth in

output, productivity, economic efficiency and productive efficiency. There is wide variation in

the estimates across countries. Growth is slower in advanced countries than in emerging

economies, as would be expected, reflecting initial conditions. The growth rates in real value

added and in real output are strongly negatively correlated with per capita real GDP (correlation

of –0.47). Growth in efficiency does not appear to be related to countries’ level of economic

development (correlation with log of per capita GDP is not different from zero). The same is

true for productivity improvements. Average growth in economic efficiency and growth in

production efficiencies ranges from 0.93% and 0.54% in Korea to –0.74% in Bangladesh and -

0.87% in Peru respectively. However, an advanced country like the U.S. (0.11%) could have

realized growth in economic efficiency comparable to that for Chile (0.09%) and Colombia

(0.09%). We observe similar patterns in productivity growth. Realized productivity growth in

the U.S. (3.1% per annum) compares well with that of the Philippines (3.3%), the highest being

registered by industries in Korea (4.9%) and in Sri Lanka (5.4%).

technology relevant to economic behavior.

21

III. Market-based governance and economic performance

Table 3 summarizes both the financial variables and the economic performance variables

for the entire sample of 3605 industry-country-years j. There are wide variations in realized

performance measures. The median industry growth rates in real value added and output are

2.6% for the entire sample. Growth in economic efficiency has a median value of 0.032%, with a

range from -21.5% to 26.8%. The median growth in production efficiency is 0.008% with a

range of –20.1% to 29.1%. The average industry contributes about 5% of the manufacturing

sectors’ total real value added or real output.

These variations in performance appear to be closely associated with variations in the

measures of capital market functions across countries. Table 4 explores the relations between the

two sets of variables further by presenting difference-of-means test of the performance measures

across sub-samples formed on the basis of rankings in finance variables. We would like to see if

variations in the performance characteristics of the sub-samples represent genuine differences in

the underlying populations or merely reflect chance variations as expected of random samplings.

From panel (a), better capital mobilization by equity markets (as measured by stock market

capitalization) is weakly related to higher economic efficiency and aggregate measures of

performance, such as growth in value added and productivity. On the other hand, more active

equity markets (as measured by turnover ratio, our proxy for governance) are strongly associated

with higher efficiency, and productivity gains as well as with higher growth in value added (see

panel (b)). Better capital mobilization by the credit sector (as measured by size of domestic

credit) is strongly related to economic efficiency and productivity growth (see panel (c)).

j The figures for the financial variables in Table 2 are different from those reported in Table 1. The mean values reported inTable 1 are cross-country averages of country means. The mean values in Table 2 are averages over the entire panel.

22

GV F Zcitk

ctk

ct citk= + +∑ β γ ε

ε α η λ ν

α ση σλ σν σ

α

η

λ

ν

cit c i t cit

c

i

t

cit

where

IID

IID

IID and

IID

= + + +

≈≈≈≈

,

( , ),

( , ),

( , ),

( , ).

0

0

0

0

2

2

2

2

A. Governance and aggregate measures of performance

We begin our analysis of the relationship between financial markets and economic

performance, by examining in detail the link between capital market functions and aggregate

measures of industry performance. This would facilitate a comparison with the extant literature.

We use growth rate in real value added, and productivity growth as measures of how well off an

industry is. Our empirical model is a four-way error component (random effects) of the

following form:

(11)

∑ ++=k citct

kct

kcit ZFPG εγβ (12)

where, GVcit and PGcit are, respectively, annual compounded growth rate of real value- added

and growth in total factor productivity of industry i in country c over period t. c=1,… ,C;

i=1,… ,Ic ; and, t=1,… ,Tci. Fct k is the kth financial function indicator variable for country c in

period t. The financial function variables include: stock market capitalization to GDP ratio,

stock market turnover ratio, domestic credit to GDP ratio, and private credit to GDP ratio. The

control variable Zcit represents the relative significance of industry i in country c during period t.

We use the share of value added (output) of the industry in the total value added (output) of the

manufacturing sector of the country. The model is a four-way error-component (random effects)

specification with the following error structure:

23

αc, ηi, λt and νcit are independent from each other and also independent of the F and Z variables

in Eqs. (11) and (12) above. αc is unobservable time and industry invariant, country specific

effects; ηi is unobservable country and time invariant, industry effects; λt represents

unobservable country and industry invariant, time effects; and, νcit is a random disturbance term

Hence, we control for country, industry and time heterogeneity, thereby avoiding the risk

of bias in our estimates k l. Moreover, we treat these latent country, industry and time effects as

random variables rather than fixed parameters. Our random-effects specification has both

theoretical and empirical appeal for the research question we are addressing. First, the random

effects specification allows us to retain as much information on our main variables of interest,

the vector of finance variables, which are industry invariant but country specific variables. A

fixed-effects specification would result in loss of valuable information on such variables through

differencing. Second, the specification allows us to model correlations among observations that

share identical characteristics. Declaring country as a random effect, for example, allows

common correlations among observations that come from the same country. Such a structure is

reasonable in that industries in the same country, despite their differences, share the effects of

common country characteristics such as those of macro economic policies of the country.

Similarly, common characteristics of the technology of an industry across countries would justify

modeling intra-industry correlations.

k The model rests on the premise that a sensible representation of relationships among variables of interest across diversecountries, industries and time-periods cannot explicitly capture all important variables. These variables could be simply toomany to be included, since some may be un-measurable and others unobservable.l Variations in economic performance including efficiency gains are not exclusively attributable to variations in capital marketfunctions, and could be a result of non-finance factors such as the degree of competition in the industry. Ideally, one wouldcontrol for sources of variations through listing each potential variable in the empirical model. However, sources of variationsare numerous, vary from country to country as well as from industry to industry, and are generally difficult to exhaustivelyincorporate in a model. We adequately control for these extra sources of variations through the random country, industry andtime effects. For example, to the extent that industry competitiveness is a country characteristic, it would be reasonably reflectedas country effect. The competitiveness or lack of it of an industry, in some cases, could be a generic industrial characteristic,

24

Conceptually, the individual and time effects are meant to be related to a large number of

non-observable random causes. This intention is consistent with the random-effects

specification. Lastly, modeling the latent variables as random allows us to make inference about

the relationships of interest that would apply to the entire population from which the sample is

drawn rather than to the particular sample, which is the case in fixed-effects models. We estimate

the model by the method of maximum likelihood (ML) under the distributional assumption of

normality for the error components and the residual. The ML estimates are consistent and

asymptotically efficient, and have a known asymptotic sampling information matrix m.

A.1 Governance and industry growth in output

Table 5 presents estimates of the empirical model regressing industry’s growth in real

value added on proxies for the governance and allocation functions of financial markets. Panel

A uses growth in real value added, and Panel B shows growth in gross output as a measure of

aggregate performance. In all models, we control for unobservable country, industry and time

effects through random-effects specification n. The results indicate a very strong relationship

between the degree to which capital markets perform their governance functions and industry

aggregate performance. The coefficient estimates of market turnover ratio (our proxy for the

governance function) is positive (0.0570) and statistically significant at 1% level. Moreover, the

contributions of these services to industries’ growth in real value added based on our estimates

which would be reflected in the industry effect. (See also footnote 16).mAlternative estimation methods that include ANOVA type, ML, restricted maximum likelihood (REML), and MinimumQuadratic Unbiased Estimation (MINQUE) vary in the way the variances of the error components are estimated. SimpleANOVA type estimates no longer apply for unbalanced panel with three error-components. We use REML, a procedure inwhich variance components are estimated based on the portion of the likelihood function that depends on the error componentsalone. In a balanced data, the REML estimators of the variance components are identical to ANOVA estimators, which haveoptimal minimum variance properties. The results do not change when we estimate the models by ML, and by MINQUEprocedures.n For robustness, we also estimate the models in this section and the sections that follow as fixed effects specifications. Theresults are qualitatively similar to the ones under the error-components specification.

25

are economically very significant. For example, using the coefficient estimates, a one standard

deviation increase in the proxy for the governance function, the market turnover ratio (0.294)

would increase the growth rate in real value added of the average industry by about 1.68% per

annum.

While stock market size is not significant, the size of the credit market (our proxy for the

allocation function of credit markets) is positive (0.0706) and statistically significant.

Furthermore, a dollar of credit that is channeled through the public sector is different from that

channeled through the private sector: the former has no significant impact on industry growth,

while the latter is positively associated with growth. A one standard deviation increase in the

size of domestic credit, would increase the growth rate in real value added of the average

industry by 2.32% per annum, the same order of magnitude as the estimates of the effect of bank

development on per capita GDP (2.52%) in Levine(1998). Note, however, that a one standard

deviation (0.329) increase is approximately the difference in size of domestic credit between

India and the United States.

Industries that account for a larger portion of the country’s manufacturing have higher

growth rates. This may be a reflection of the effects of other sources of comparative advantage

(i.e. other than financial development). Developed countries have lower growth rates in

manufacturing (the coefficient of log per capita GDP (not reported) is significantly negative)

reflecting the convergence effecto. With respect to the latent variables, overall, the country,

industry and time effects are significant in explaining variations in industry growth.

Unobservable country factors appear to be relatively more important than the others. Country,

industry and time effects account for about 10% of the total unexplained variations in industry

o The results here and in the sections to follow are not sensitive to inclusion or exclusion of these variables.

26

growth, out of which country-specific (but industry and time invariant) factors account for more

than 50%.

In Panel B of Table 5, use of real gross output does not alter the results. The governance

function proxy (market turnover ratio) is positive and significant. The sizes of domestic credit

and private credit appear with positive sign and both are statistically significant at 1%.

A.2 Governance and productivity growth

Table 6 presents the relations between productivity growth, which is an amalgam of

technical change and efficiency gains, and the governance function. Models I through V

represent each variable’s relations with productivity growth individually. Models VI through

VIII examine the marginal impact of each of the variables on productivity controlling for at

least one of the other variables. Stock market turnover ratio (the governance function proxy)

positively impacts productivity growth with a statistical significance at 1%. On the other hand,

stock market capitalization is not significantly related to growth in productivity. The measure of

the size of the domestic credit sector is positively and significantly related to growth in

productivity. Including the proxies in combination (models VI through XIII), stock market

turnover ratio carries a strong positive coefficient (significant at 1%). Thus, even after

controlling for other aspects of the financial system, increases in the information production and

monitoring function of the stock market (using market activity as a proxy), are strongly

associated with improvements in productivity.

How large is the productivity impact of capital markets? The productivity consequences

of the information and monitoring role of markets are economically significant. To illustrate,

consider Mexico which has an average turnover of 0.5394 (Table 1) and a productivity growth of

27

∆PRODEFF F Zcitk

ctk

cit citk= + + +∑β β γ ε0

∆ECONEFF F Zcitk

ctk

citk cit= + + +∑β β γ ε0

0.9% (Table 2) over the sample period. Using our estimates, if Mexico were able to increase

market activity by a mere 10% of the present level, it would increase the rate of productivity

growth of the average industry to about 0.12% per annum.

IV. Governance and economic performance: The efficiency channel

Productivity growth is an amalgam of effects of technological innovations and

improvements in efficiency. This section explores in detail the impact of capital market

functions on efficiency. Our hypothesis is that financial markets through their information

aggregation and monitoring function induce economic efficiency within the firm. We use a four-

way error components (random effects) model of the following form:

(13)

(14)

where ∆PRODEFF is improvements in technical efficiency of industry i of country c in period t;

and, ∆ECONEFF is improvements in X-efficiency (economic efficiency) of industry i of country

c in period t. c=1,… ,C; i=1,… , Ic ; and, t=1,… ,Tci. Fct k is the kth financial development

indicator variable for country c in period t. The model is a four way random-effects model with

random country, industry, time effects as specified in Eqs. (11) and (12) above.

A. Governance and efficiency

Table 7 presents estimates of our empirical model regressing growth in industry’s

economic efficiency on proxies for the governance and allocation functions of financial markets.

Models I through V, includes each proxy separately. In the models VI through XIII, we include

28

combinations of the explanatory variables so as to evaluate the marginal contributions of the

variables of interest. In all models, we control for unobservable country, industry and time

effects, treating them as random variables. In addition, we include industry’s share in total

manufacturing of the country to control for characteristics common to both country and industry,

and average real per capita GDP. The former is meant to control for comparative advantage

differences across industries vis a vis countries p. The latter would control for the familiar

convergence effect from growth literature (or control for countries’ stage of economic

development). We expect the proxies for the governance function (market turnover ratio) to

enter the model with positive sign.

The results in Table 7 indicate a very strong association between the degree to which

capital markets perform their governance functions and improvements in industry economic

efficiency. In model I, the governance proxy (market turnover ratio) enters positively

(coefficient 0.0061), and is significantly different from zero at the level of 1%. The relationship

between stock market capitalization and efficiency, while positive, lacks statistical significance,

as does the relationship between size of domestic credit and efficiency. The latter is true for

credit channeled through the private sector as well as credit channeled through the public sector.

Interestingly, the coefficient on public sector credit is negative though statistically insignificant.

Higher turnover ratio is also strongly associated with larger efficiency improvements on

p It could be that an industry’s performance (in fact, its presence or absence in a country) may reflect comparative advantages ofthe country, other than financial development, in fostering a specific type of industrial activity. These may include advantagesrelated to natural endowments, better business environment in terms of less restrictive regulations, competition, trade etc.Sources of comparative advantages are numerous, vary from country to country as well as from industry to industry, and aregenerally difficult to exhaustively incorporate in a model. Omission of such variables would be of concern only to the extent thatthey could be correlated with our financial variables for which we do not have a priori reasons to suspect so. However, asstrength to our specification, we can adequately control for these variations. First, limiting our sample to only manufacturing (forexample, avoiding mining industries) eliminates natural resource endowment as a source of comparative advantage. Second, allunobservable industry and time invariant sources of comparative advantages are captured by the random country effect. Evenwithin the manufacturing sector, a country’s comparative advantage may not benefit all industries identically, however. Ourindex of the importance of an industry in a country’s manufacturing, inter alia, is meant to capture this type of variations incomparative advantages (industry-variant comparative advantages). The inclusion of such variables is made possible because ofthe random-effects specification.

29

the margin after controlling for the other financial proxies. In model VI, we include stock market

size in addition to turnover ratio, in order to gauge the marginal value of the information and

monitoring role of stock markets after controlling for its capital provision role. The coefficient

estimate of market turnover ratio is positive (0.0057) and significant at 1% level. Stock market

capitalization is not statistically different from zero. Similarly, in Models VIII and X, we include

alternatively domestic credit and credit to the private sector – proxies for capital mobilization

provided by the credit sector - in addition to stock market turnover ratio. Again, turnover is

positive (0.0063 and 0.0062) and significant at 1%. The coefficients on the other variables fall

sharply and remain statistically insignificant.

Finally, in model XII which include domestic credit in models that contain turnover and

stock market capitalization. The governance proxy is robustly positive (0.0060, significant at

1%); and none of the other variables are marginally significant. The same result holds when we

use size of private credit as proxy for the credit sector’s capital provision role in model XIII:

turnover has a coefficient of 0.0057 and is significant. Thus, even after controlling for the

capital provision services of both stock markets and the credit sector of the economy, our proxy

for the information and monitoring function of markets adds net value to efficiency growth.

Turning to the other variables, industries that account for a larger portion of the country’s

manufacturing have higher efficiency. This may be a reflection of the effects of other sources of

comparative advantage (i.e. other than financial development) on industry efficiency. Consistent

with the descriptive data, per capita GDP has no effect on variation in efficiency. With respect

to the latent variables, the estimates of the error components indicate that time effects are more

important than country or industry specific effects in explaining efficiency.

The evidence so far is consistent with the hypothesis that the information aggregation and

30

monitoring function of markets determines the relative efficiency with which firms utilize

resources. Increases in our proxy for the information and monitoring role increases industry

efficiency after controlling for unobservable country, industry and time effects and other services

provided by the financial sector. The contributions of these services to industry economic

efficiency based on our estimates are economically very significant. For example, using the

coefficient estimates in model I, a one standard deviation increase in the proxy for the

governance function, the market turnover ratio (0.294) would increase the growth rate in

economic efficiency of the average industry by about 0.18% per annum. Accumulating over the

15 years of the sample period, the average industry would have been about 3% more efficient in

1995, the end of the study period, compared to the actual fifteen years median of 0.032 percent.

The results based on growth in production efficiency, our alternative measure of

efficiency, are presented in Table 8. In the individual regressions, turnover ratio again has a

relatively large and positive effect on production efficiency (0.004 and significant at 1%). Stock

market capitalization has no effect on production efficiency. On the other hand, the coefficients

for domestic credit and credit to private sector are positive and significant in the individual

regressions; but these results loses significance and the coefficients fall sharply once turnover is

controlled for. Again, interestingly, credit channeled through the public sector enters with

negative sign although not statistically significant. The results from model versions VI to XIII

are closely similar to the results on economic efficiency in Table 7.

B. Accounting disclosure and efficiency

An alternative way to measure the degree of information flow in a capital market is to

look at the accounting standards that determine the amount and quality of disclosure by firms

trading in the market. We have an index of accounting reporting quality for different countries

31

created by the Center for International Financial Analysis and Research. The index rates the

annual reports (including income statements, balance sheets, cash flow statements, accounting

procedures, data on share of stock and general information about the firm) of at least three

companies in each country based on the inclusion or omission of 90 reportable items. The

sample of companies used in each country is designed to represent a cross-section of

representative industries. The index could be viewed as a measure of the degree of sophistication

and efficiency of the capital market in processing information. Also, more disclosure as

measured by the index, indicates the availability of public information that might be associated

with some of the governance functions of markets we intend to measure. Among others,

accounting information is used for market-based incentive contracts. The index ideally ranges

between 0 to 90, the higher score indicating more mandated public disclosure. For our sample

(we have the index only for 31 countries), the range goes from 24 to 83, the lowest registered by

Egypt and the highest by Sweden. The United States scores 71 on this index. More developed

countries, on average, have higher accounting standards (correlation with log per capita GDP is

0.56); yet, there are exceptions. The U.S. (score, 71), Norway (74) and Canada (74), Australia

(75), score less than that of Malaysia (78); whereas Philippines (65), Mexico (60) and Korea (62)

score as high as Italy (65), Japan (62) and Germany (62).

We use this index, instead of market turnover rat io, as our proxy for the information

production and monitoring function of capital markets. Table 9 presents the results. In Panel (a)

and Panel (b), the dependent variables are our two measures of efficiency. In model version I,

we include the index of accounting quality as a measure of the governance function. In models

II to VI, we include accounting standard with our measures of the capital mobilization (stock

market capitalization, domestic credit and credit to private sector) one at time so as to gauge the

32

effect of each variable controlling for the others. And, in models V and VI, we include all in one

together. In all the models, we include per capita GDP and a measure of the industry’s

importance in manufacturing. We control for unobservable country, industry and time factors

using a random-effects specification.

Table 9 indicates that governance as measured by the index of accounting quality is

strongly related with improvements in economic as well as production efficiency. The

coefficient estimates of the accounting quality index in panel a and in panel b are positive

(around 0.0001) and statistically significant at 5%). The index is also positive and significant on

the margin in models II to VI in which we control for the capital mobilization proxies. Thus,

after controlling for capital provision in equity market (model II and model V) and in credit

markets (model II, IV and VI), higher accounting standard quality is related to larger gains in

efficiency. Also, consistent with our earlier results, none of the proxies for capital mobilization

appear to be associated with efficiency gains. Thus, the governance services provided by capital

markets appear to be strongly related to firm efficiency, whether governance is measured in

terms of level of market activity or in terms of quality of accounting disclosure.

C. Causality issues

So far, we examined the association between indices of economic performance and the

degree to which capital markets discharge governance services. We measure the latter using

variables that we assume to be exogenous and predetermined. It may be argued that our proxies

for capital market functions are not exogenous enough in that capital market development may

simply be “a leading indicator rather than a causal factor”. In an attempt to isolate the

exogenous component of capital market functions, Table 10 uses two sets of variables as

instruments. These are indices of investor-protecting legal codes, and country of legal origin. La

33

Porta et al (1997) argues that legal protections afforded to investors and country’s legal origin

determine financial development, and that these, in turn, are primarily determined by a country’s

colonial history. Hence, the two sets of variables would be ideal instruments for capital market

functions in that while the variables are strongly correlated with our governance proxy, they do

not directly correlate with economic performance. Levine and Zervos (1998) use these variables

as instruments for financial development.

In Model I of Table 10, the component of turnover ratio predetermined by the extent of

legal protection afforded to investors has a positive, statistically large impact on growth in

economic efficiency (Panel A), and on growth in production efficiency (Panel B). The

exogenous component of the governance proxy is robustly positively related to growth in

economic and production efficiency on the margin after controlling for stock market

capitalization, and the size of the credit sector. In Model II, the component of the governance

proxy predetermined by legal origin has a significant positive impact on growth in economic

efficiency (Panel A), and on growth in production efficiency (Panel B). Hence, the relations

between governance and efficiency identified in this study are less likely to be explained by

endogeneity.

V. Conclusion

This study examines the causal relationship between capital market functions and firms’

real economic performance focusing on the information production and monitoring role of

markets. We begin from a premise that financial markets and institutions play two critical roles

in an economy: allocation of risk capital through saving mobilization and risk-pooling and

sharing; and promotion of responsible governance and control through providing outside

investors a variety of mechanisms for monitoring inside decision makers. The paper argues that

34

the two functions systematically impact on different sources of growth. Focusing on the

governance role of the financial system, we argue and empirically show that the information

production and monitoring services contribute to improvements in the relative economic

efficiency with which the firm utilizes its resources.

Using industry level data for ten manufacturing industries across thirty-eight countries

over the period 1980-1995, we find evidence consistent with our hypotheses. We find that

measures of the governance functions of capital markets are indeed positively related to

economic efficiency. The result holds both when the variables are treated individually as well as

in models where they enter in combination. Moreover, controlling for governance, measures of

the capital mobilization function have little role in explaining variations in industry efficiency.

The evidence is robust to outlier and influential observations, alternative model specifications

and choice of variables.

The evidence underscores the role of the equity market as a conduit of socially valuable

governance services as distinct from capital provision. The value of this socially desirable

service is economically large. An industry operating in a country with a stock market that is one

standard deviation above the mean of the proxy for information and monitoring function would

have a growth rate of 1.05 percent per annum in real output more than that for the average

industry. Cumulating over the sample period of fifteen years, real output for such industry

would have been about 17 percent higher in 1995, the end of the study period.

The finding of a strong association between economic performance and the effectiveness

of financial markets suggests the importance of financial development as a policy for

accelerating economic growth. It provides evidence that financial sector policies that promote

financial market’s functional capacities lead to better real economic performance. It points out

35

the incompleteness of traditional development strategies that exclusively focus on real-sector

reforms to induce economic development.

Furthermore, through linking the multiple functions of the financial system to sources of

economic performance, the study underscores the importance of a functional perspective in

guiding financial sector policies. The study documents that the different functions of the

financial system play distinct roles in the economic growth process. In particular, the

information and monitoring function promotes economic efficiency. The depth of the financial

infrastructure of an economy has to be judged in terms of the effectiveness and efficiency with

which it delivers these multiple functions. A mere launching of financial markets and

institutions is not sufficient for accelerating growth; what also matters is their efficient

functioning.

The prevailing policy discussions of financial markets and institutions, particularly in

developing countries, have focused on their role in mobilizing savings for industrialization.

Such emphasis on the capital provision role was inevitable given the dominant economic

thinking on the subject, the McKinnon-Shaw (see, McKinnon, 1973; and Shaw, 1973) paradigm,

which views the financial system as a mere conduit of capital provision. In this study, based on a

corporate-finance paradigm, we attempt to point out the shortcoming of such a policy perspective

by providing evidence that the value of financial markets lies in their governance services that

are distinct from capital mobilization.

36

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38

Table 1: Financial Function Proxy Variables: Averages over the period 1980-1995.

Market Capitalization to GDP is total market value of publicly traded equity at end of year as reported by IFC divided by the Gross Domestic Product ofthat year. Total Value Traded to GDP is the total value of publicly traded equity during the year divided by GDP of that year. The level of domesticcredit is the sum of assets held by the monetary authority and depository institutions excluding inter-bank deposits (i.e. IFS lines 32a-32f excluding 32e)divided by GDP. The ratio of private credit to domestic credit is the proportion of claims against the private sector (IFS line 32d) to total domesticcapital. Data on equity market capitalization and total equity value traded is obtained from various issues of the Emerging Market Fact-book publishedby IFC. GDP, domestic credit, private credit and exchange rates are obtained from the International Financial Statistics (IFS) issued by the IMF.

Countries Stock MarketCapitalization/GDP

Turnover Ratio Domestic Credit/GDP PrivateCredit/Domestic Credit

Emerging Markets

Bangladesh 0.0158 0.0327 0.2976 0.5819Chile 0.4717 0.0661 0.7692 0.7603Colombia 0.0725 0.0863 0.2091 0.7591Egypt 0.0442 0.0636 0.9532 0.2747Greece 0.0881 0.1218 0.7134 0.3479India 0.1460 0.4261 0.5075 0.5127Indonesia 0.0669 0.1855 0.2557 1.0383Jordan 0.5552 0.1571 0.8809 0.6875Korea 0.2710 0.8502 0.5470 0.9425Malaysia 1.2054 0.2392 0.7274 0.8746Mexico 0.1551 0.5394 0.3576 0.5475Pakistan 0.0945 0.1413 0.5135 0.5396Peru 0.0649 0.1630 0.1606 0.6351Philippines 0.2419 0.2161 0.3489 0.7292Portugal 0.0968 0.1537 0.9816 0.5647Sri Lanka 0.1333 0.0694 0.4171 0.5125Turkey 0.0624 0.5041 0.3672 0.5159Venezuela 0.0717 0.1275 0.2965 0.7659Zimbabwe 0.1705 0.0653 0.2849 0.4515

Mean 0.1918 0.2004 0.4587 0.5744Minimum 0.0158 0.0327 0.1606 0.2747Maximum 1.2054 0.8502 0.9816 1.0383

Developed MarketsAustralia 0.4712 0.2923 0.6133 0.8038Austria 0.0783 0.4422 1.1361 0.7619Belgium 0.2767 0.1202 0.9036 0.4394Canada 0.4687 0.3084 0.5763 0.8713Denmark 0.2386 0.2086 0.5974 0.7468Finland 0.1936 0.2019 0.6882 1.0263Germany 0.1995 1.0394 1.1282 0.7850Israel 0.3208 0.6492 1.2850 0.5334Italy 0.1285 0.2986 0.7939 0.4523Japan 0.7859 0.4329 1.2702 0.8470Kuwait 0.5051 0.2363 0.6614 0.9403Netherlands 0.4485 0.3656 0.9683 0.7952New Zealand 0.4242 0.1854 0.5030 0.8561Norway 0.1624 0.3265 0.6211 0.8300Singapore 1.3511 0.3254 0.6614 1.2949Spain 0.1966 0.2695 0.9965 0.6952Sweden 0.4141 0.2984 0.7613 0.5979United Kingdom 0.8100 0.3783 0.8814 0.8964United States 0.6273 0.5379 0.8337 0.8266

Mean 0.4396 0.3758 0.8755 0.7941Minimum 0.0783 0.1202 0.5030 0.4394Maximum 1.3511 1.0394 1.2850 1.2949

Entire SampleMean 0.3054 0.2787 0.6511 0.7110Standard Deviation 0.4305 0.2190 0.3148 0.2093Coefficient of Variation 1.2848 0.8418 0.4782 0.3006Minimum 0.0158 0.0327 0.1606 0.4394First Quartile 0.0790 0.0874 0.3830 0.5350Second Quartile 0.1820 0.2052 0.6171 0.7380Third Quartile 0.4424 0.3751 0.8691 0.8427Maximum 1.3511 1.0394 1.2850 1.2949

39

Table 2: Industry Performance Measures: Averages across Ten Industriesand over the Period 1980-1995

Country Log(Percapita)

Log(PerCapita)1980

Growth inValue Added

Growth inOutput

Growth inProducivity

Growth inEconomicEfficiency

Growth inProductionEfficiency

Level ofEfficiency

Australia 9.704 9.589 0.008 0.004 0.020 0.0003 -0.0000 66.257

Austria 9.856 9.739 0.019 0.014 0.028 0.0017 0.0007 65.607

Bangladesh 5.234 5.094 0.045 0.068 -0.064 -0.0074 0.0000 51.544

Belgium 9.791 9.700 -0.001 0.003 0.003 -0.0008 -0.0015 65.073

Canada 9.899 9.794 0.030 0.030 0.019 -0.0001 -0.0000 67.238

Sri Lanka 6.086 6.104 0.155 0.159 0.054 0.0048 0.0120 47.877

Chile 7.711 7.615 0.053 0.058 0.038 0.0009 0.0021 62.956

Colombia 7.096 7.014 0.039 0.033 0.011 0.0009 0.0014 60.821

Denmark 10.085 9.935 0.029 0.022 0.018 -0.0002 0.0002 67.774

Finland 10.081 9.941 0.000 0.000 0.021 0.0016 0.0005 67.383

Germany 9.963 9.881 0.025 0.012 0.022 0.0003 -0.0002 67.002

Greece 8.968 8.909 0.022 0.006 0.038 0.0022 0.0018 64.034

India 5.780 5.548 0.089 0.085 0.027 0.0040 0.0043 45.528

Indonesia 6.315 6.044 0.171 0.160 0.030 0.0033 0.0050 50.193

Israel 9.287 9.167 -0.019 0.023 -0.013 -0.0039 -0.0027 65.554

Italy 9.757 9.654 -0.015 0.010 -0.018 -0.0031 -0.0027 66.319

Japan 9.966 9.728 0.042 0.032 0.018 0.0002 -0.0003 66.261

Jordan 7.008 7.058 0.008 0.004 -0.021 -0.0049 -0.0045 69.962

Korea 8.527 7.921 0.113 0.098 0.049 0.0093 0.0054 60.954

Kuwait 9.632 9.951 0.030 0.034 0.007 -0.0018 0.0015 64.378

Malaysia 7.730 7.468 0.108 0.110 0.019 0.0000 0.0016 58.607

Mexico 7.975 7.988 0.021 0.023 0.009 -0.0015 -0.0006 63.841

Netherlands 9.786 9.690 0.017 0.009 0.016 0.0002 -0.0002 67.939

New Zealand 9.444 9.326 -0.016 -0.011 0.009 -0.0004 -0.0008 64.826

Norway 10.179 9.999 -0.002 0.001 0.024 0.0011 0.0000 67.299

Pakistan 5.794 5.569 0.075 0.089 -0.029 -0.0025 0.0005 59.835

Peru 7.524 7.660 -0.118 -0.119 -0.049 -0.0110 -0.0087 66.589

Philippines 6.566 6.660 0.017 0.009 0.033 0.0004 0.0017 54.884

Portugal 8.690 8.576 0.013 0.021 0.022 0.0024 0.0009 61.593

Singapore 9.422 8.947 0.040 0.031 0.012 0.0008 0.0003 64.242

Zimbabwe 6.496 6.401 0.027 0.028 -0.032 -0.0028 -0.0019 60.528

Spain 9.344 9.185 0.011 0.015 0.013 0.0002 0.0003 65.724

Sweden 10.123 10.023 0.008 0.006 0.018 0.0001 0.0001 67.731

Turkey 7.880 . 0.054 0.050 0.028 0.0019 0.0014 62.577

Egypt 6.984 . 0.029 0.024 0.013 -0.0051 -0.0006 52.481

United Kingdom 9.654 9.499 0.002 0.004 0.021 0.0010 0.0002 65.962

United States 9.949 9.840 0.033 0.025 0.031 0.0011 0.0004 67.702

Venezuela 7.876 7.985 -0.005 0.014 -0.005 -0.0053 -0.0037 60.982

40

Table 2 (Cont’d): Industry Performance Measures: Averages acrossThirty-Eight Countries Over the period 1980-1995.

Industry

ISIC Growth inValueAdded

Growth inOutput

Growth inProductivity

Growth inEconomicEfficiency

Growth inProductionEfficiency

Industryshare in

value added

Industryshare in

gross output

Food Products 311 0.036 0.029 0.012 -0.0008 0.0000 0.116 0.163Beverages 313 0.041 0.044 0.023 0.0010 0.0014 0.041 0.030Tobacco 314 0.014 0.014 0.014 0.0012 0.0005 0.028 0.018Textiles 321 -0.003 0.001 0.011 -0.0011 -0.0008 0.056 0.053Wearing Apparel 322 0.037 0.038 0.013 0.0003 0.0010 0.029 0.024Industrial Chemicals 351 0.041 0.026 0.030 0.0012 0.0011 0.050 0.052Rubber Products 355 0.006 0.002 0.006 -0.0010 -0.0008 0.015 0.014Plastic Products 356 0.062 0.058 0.015 0.0000 0.0011 0.021 0.020Iron and Steel 371 -0.008 -0.002 0.014 -0.0008 -0.0007 0.041 0.044Machinery, except Electrical 382 0.043 0.052 0.016 -0.0001 0.0010 0.067 0.057

41

Table 3: Summary Statistics

Industry growth in real value added is the annual compounded growth rate in real value added for each of the tenindustries in each of the forty-three countries over the period 1980 to 1995. Gross output includes values of intermediateinputs in addition to value added. The industry’s share in total value added (output) are computed by dividing the realvalue added (gross output) of the industry by the total real value added (gross output) of the manufacturing sector in thecountry in each year. Productivity and efficiency are computed based on parameter estimates of cross-country stochasticproduction and cost frontiers on the panel of industry production and cost data. Production efficiency is a Farrell (1957)output related measure of efficiency which measures the degree to which an industry diverges from the efficientproduction frontier. Economic efficiency measures the degree to which an industry diverges from the best practice costfrontier. Market Capitalization to GDP is total market value of publicly traded equity at end of year as reported by IFCdivided by the Gross Domestic Product of that year. Market Turnover Ratio is the total value of publicly traded equityduring the year divided by Market Capitalization. The level of domestic credit is the sum of assets held by the monetaryauthority and depository institutions excluding inter-bank deposits (i.e. IFS lines 32a-32f excluding 32e) divided by GDP.Domestic credit to GDP is domestic credit divided by GDP. Private credit to GDP is claims against the private sector(IFS line 32d) divided by GDP. Data on equity market capitalization and total equity value traded is obtained from variousissues of the Emerging Market Fact-book published by IFC. GDP, domestic credit, private credit and exchange rates areobtained from the International Financial Statistics (IFS) issued by the IMF.

Variable Mean Median Std Dev Minimum Maximum No. Obs.

Industry Growth in Real Value Added 0.027 0.026 0.205 -0.976 0.960 3301Industry Growth in Real Gross Output 0.027 0.024 0.175 -0.969 0.994 3301Productivity Growth 0.015 0.016 0.180 -1.076 0.950 3272Growth in Economic Efficiency -0.0001 0.00032 0.026 -0.215 0.268 3261Growth in Production Efficiency 0.00035 0.00008 0.022 -0.201 0.291 3272Production Inefficiency 0.384 0.364 0.075 0.226 0.816 3577Economic Inefficiency 0.473 0.434 0.117 0.261 0.999 3569Industry's Share of Total Value Added 0.047 0.031 0.044 0.001 0.326 3508Industry's Share of Total Gross Output 0.049 0.028 0.054 0.001 0.339 3508Stock Market Capitalization to GDP 0.272 0.140 0.345 0.001 3.5 3203Market Turnover Ratio 0.282 0.210 0.294 0.005 2.000 3420Domestic Credit to GDP 0.668 0.640 0.329 0.078 2.300 3558Credit to Private Sector to GDP 0.476 0.440 0.279 0.021 1.760 3558Log of Per Capita Income 8.477 9.344 1.527 5.234 10.179 38

42

Table 4: Inter-Quartile Mean Differences In Industry PerformanceSample is classified into quartiles based on stock market capitalization, turnover ratio, domestic credit, credit toprivate sector and average per capita GDP respectively over the period 1980-1995. Turnover Ratio is the value oftotal shares of stock traded divided by market capitalization. Stock Market Capitalization is the ratio of the totalmarket value of publicly traded equity to GDP. Domestic Credit is the sum of assets held by the monetary authorityand depository institutions excluding inter-bank transfer divided by GDP. Credit to Private Sector equals claimsagainst the private sector divided by GDP.

K-Sample Tests Bottom 25% vs Top 25%Variables

Bottom Middle Top 25% 50% 25% Kruskal-

WallisTest

MedianTest

WilcoxonTest

T test

Panel a: Ranking by Stock MarketCapitalization

0.0237

0.0307

-0.0006

-0.0030

0.0096

0.0282

0.0257

0.001

0.0013

0.0209

0.0334

0.0303

-0.00006

0.0004

0.0141

Panel b: Ranking by Market TurnoverRatio

0.0240

0.0330

-0.0002

-0.0025

0.0073

0.0176

0.0150

0.0002

0.0001

0.0128

0.0468

0.0443

0.0013

0.0020

0.0268

Panel c: Ranking by Domestic Credit toGDP

Growth in Value Added

Growth in Gross Output

Growth in ProductionEfficiencyGrowth in EconomicEfficiency

Growth in Productivity









0.0268

0.0254

-0.0003

-0.0021

0.0085

0.0287

0.0287

0.0007

0.0009

0.0151

0.0315

0.0313

0.0009

0.0002

0.0256

9.46b

11.3a

4.32

10.5b

5.76

36.9a

34.8a

11.89a

24.11a

18.04a

3.34

7.20c

4.31

13.4a

8.55b

5.44

8.88b

0.67

6.04

2.73

38.9a

40.2a

6.04

16.03a

14.65a

6.86c

13.4a

7.43c

13.6a

5.11

-1.85c

-0.29

-1.05

-2.54b

-1.62

-3.48a

1.87c

-2.81a

-4.21a

-3.77a

-0.24

0.41

-1.21

-2.57a

-2.82a

-0.89

0.05

-0.43

-2.28b

-0.46

-2.28b

-1.42

-1.37

-3.17a

-2.12b

-0.41

-0.64

-1.05

-1.59

-1.67c

a Significant at 1%; b Significant at 5%; c Significant at 10%

43

Table 4 (Cont’d): Inter-Quartile Mean Differences In IndustryPerformance

Sample is classified into quartiles based on stock market capitalization, turnover ratio, domestic credit, credit toprivate sector and average per capita GDP respectively over the period 1980-1995. Turnover Ratio is the value oftotal shares of stock traded divided by market capitalization. Stock Market Capitalization is the ratio of the totalmarket value of publicly traded equity to GDP. Domestic Credit is the sum of assets held by the monetary authorityand depository institutions excluding inter-bank transfer divided by GDP. Credit to Private Sector equals claimsagainst the private sector divided by GDP.

K-Sample TestBottom 25% vs Top

25%Variables Bottom Middle Top

25% 50% 25% Kruskal-Wallis Test

MedianTest

WilcoxonTest

T test

Panel d: Ranking by Credit to PrivateCredit to GDP

0.0396

0.0376

0.0013

-0.0008

0.0166

0.0240

0.0249

0.0002

0.0004

0.0155

0.0261

0.0260

0.0003

-0.0001

0.0164

Panel e: Ranking by average per capitaGDP



Growth in ProductionEfficiency

Growth in EconomicEfficiency




Growth in ProductionEfficiency

Growth in EconomicEfficiency


0.0427

0.0416

0.0008

-0.0015

0.0086

0.0230

0.0253

0.0002

0.0002

0.0131

0.0209

0.0159

0.0002

0.0007

0.0232

37.9a

27.01a

24.7a

31.27a

19.67a

26.7a

46.76a

6.43c

10.36b

15.23a

34.56a

20.04a

30.67a

28.59a

23.4a

31.3a

51.8a

7.84c

8.68b

7.22c

2.40b

2.72a

1.14

0.11

-0.46

2.89a

4.77a

-1.04

-2.22b

-3.23a

1.19

1.19

0.80

-0.53

0.02

2.05b

2.97a

0.52

-1.40

-1.51

a Significant at 1%; b Significant at 5% ; c Significant at 10%

44

Table 5 : Aggregate Performance and Capital Market FunctionsThe parameter estimates are maximum likelihood estimates of four-way error component models containing random country, industry and time effects. Thedependent variables are the annual compound growth rate in the real value added and the annual compound growth rate in the real gross output for each of theten industries over thirty-eight countries for the period 1980-1995. Turnover Ratio is the value of total shares of stocks traded divided by market capitalization.Stock Market Capitalization is the ratio of the total market value of publicly traded equity to GDP. Domestic Credit is the sum of assets held by the monetaryauthority and depository institutions excluding inter-bank transfer divided by GDP. Credit to Private Sector equals claims against the private sector divided byGDP. The variable Interaction represents the product of the two other variables in the respective model. Industry Share in Manufacturing is calculated bydividing the real output of the industry in the country by the total real output of the manufacturing sector of the country. Coefficients of the intercept are notreported. Asymptotic standard errors are given in parenthesis.DependentVariable

Panel A

Growth in Real Value AddedPanel B

Growth in Real Gross Output

IndependentVariables

I II III IV V I II III IV V

TurnoverRatio

0.0570a

(0.015)0.0358a

(0.012)

Stock MarketCapitalization

0.0161(0.016)

0.0100(0.013)

DomesticCredit

0.0706a

(0.021)0.0854a

(0.018)

Credit to PrivateSector

0.0940a

(0.024)0.1049a

(0.021)

Credit to PublicSector

0.0089(0.033)

0.0019(0.029)

Industry’Share inManufacturing

0.6178a

(0.097)0.6008a

(0.098)0.6159a

(0.102)0.6187a

(0.102)0.611a

(0.102)0.3830a

(0.081)0.3718a

(0.080)0.4085a

(0.087)0.4106a

(0.087)0.4031a

(0.087)

Error Componentsσα

2

ση2

σλ2

σν2

0.0018a

0.0007c

0.0008b

0.0307a

0.0013a

0.0007c

0.0009b

0.0317a

0.0020a

0.0006c

0.0009b

0.0365a

0.0015a

0.0006c

0.0009b

0.0366a

0.0020a

0.0006c

0.0009b

0.0366a

0.0014a

0.0005c

0.0006b

0.0215a

0.0011a

0.0004c

0.0007b

0.0213a

0.0019a

0.0004c

0.0005b

0.0263a

0.0015a

0.0004c

0.0006b

0.0263a

0.0019a

0.0004c

0.0006b

0.0265a


45

Table 6: Productivity Growth and Capital Market FunctionsThe parameter estimates are maximum likelihood estimates of four-way error component models containing random country, industry and time effects. Thedependent variable is the annual growth in total factor productivity for each of the ten industries over thirty-eight countries for the period 1980-1995. Economicefficiency is in terms of deviation of actual cost to the optimal minimum cost on a stochastic cost frontier. Turnover Ratio is the value of total shares of stockstraded divided by market capitalization. Stock Market Capitalization is the ratio of the total market value of publicly traded equity to GDP. Domestic Credit isthe sum of assets held by the monetary authority and depository institutions excluding inter-bank transfer divided by GDP. Credit to Private Sector equals claimsagainst the private sector divided by GDP. The variable Interaction represents the product of the two other variables in the respective model. Industry Share inManufacturing is calculated by dividing the real output of the industry in the country by the total real output of the manufacturing sector of the country.Coefficients of the intercept are not reported. Asymptotic standard errors are given in parenthesis.Variable I II III IV V VI VII VIII IX X XI XII XIII

TurnoverRatio

0.0389a

(0.011)0.0344a

(0.010)0.0393a

(0.014)0.0385a

(0.012)0.1073a

(0.027)0.0393a

(0.012)0.0933a

(0.025)0.0348a

(0.010)0.0335a

(0.011)


-0.0077(0.010)

-0.0087(0.009)

-0.0020(0.015)

-0.0087(0.009)

-0.0090(0.010)

DomesticCredit

0.0267b

(0.012)-0.0016(0.013)

0.0218(0.015)

-0.0093(0.011)


0.0426a

(0.015)-0.0079(0.017)

0.0222(0.020)

-0.0007(0.016)


-0.0010(0.017)

Interaction-0.0190(0.034)

-0.079a

(0.028)-0.0934b

(0.038)Industry’s Sharein Manufacturing

0.2431a

(0.072)0.2458a

(0.073)0.2486a

(0.072)0.2619a

(0.073)0.2476a

(0.072)0.2230a

(0.071)0.2245a

(0.071)0.2539a

(0.072)0.2495a

(0.071)0.2525a

(0.072)0.2562a

(0.072)0.2281a

(0.069)0.2290a

(0.070)


2

ση2

σλ2

σν2

0.0001<0.00010.0008b

0.0267a

0.0001<0.00010.0008b

0.0272a

<0.0001<0.00010.0009b

0.0300a

<0.0001<0.00010.0009b

0.0300a

<0.0001<0.00010.0009b

0.0300a

<0.0001<0.00010.0007b

0.0258a

<0.0001<0.00010.0007b

0.0258a

0.0001<0.00010.0008b

0.0266a

0.0001<0.00010.0008b

0.0266a

0.0001<0.00010.0008b

0.0266a

0.0001<0.00010.0008b

0.0266a

<0.0001<0.00010.0007b

0.0257a

<0.0001<0.00010.0007b

0.0257a


46

Table 7: Growth in Economic Efficiency and Capital Market FunctionsThe parameter estimates are maximum likelihood estimates of four-way error component models containing random country, industry and time effects. Thedependent variable is the annual compound growth rate in economic efficiency for each of the ten industries over thirty-eight countries for the period 1980-1995.Economic efficiency is in terms of deviation of actual cost to the optimal minimum cost on a stochastic cost frontier. Turnover Ratio is the value of total shares ofstocks traded divided by market capitalization. Stock Market Capitalization is the ratio of the total market value of publicly traded equity to GDP. DomesticCredit is the sum of assets held by the monetary authority and depository institutions excluding inter-bank transfer divided by GDP. Credit to Private Sectorequals claims against the private sector divided by GDP. The variable Interaction represents the product of the two other variables in the respective model.Industry Share in Manufacturing is calculated by dividing the real output of the industry in the country by the total real output of the manufacturing sector of thecountry. Coefficients of the intercept are not reported. Asymptotic standard errors are given in parenthesis.Variable I II III IV V VI VII VIII IX X XI XII XIII

TurnoverRatio

0.0061a

(0.002)0.0057a

(0.002)0.0077a

(0.002)0.0063a

(0.002)0.0181a

(0.004)0.0062a

(0.002)0.0152a

(0.004)0.0060a

(0.002)0.0057a

(0.002)


0.0003(0.002)

-0.0002(0.002)

0.0026(0.002)

-0.0001(0.002)

-0.0001(0.002)

DomesticCredit

0.0015(0.002)

-0.0020(0.002)

0.0021(0.002)

-0.0023(0.002)


0.0038(0.002)

-0.0013(0.003)

0.0038(0.003)

-0.0005(0.003)


-0.0019(0.003)


-0.014a

(0.004)-0.0154a


0.0236b

(0.011)0.0243b

(0.011)0.0283b

(0.011)0.0289b

(0.011)0.0283a

(0.010)0.0221b

(0.011)0.0226b

(0.011)0.0256a

(0.011)0.0251b

(0.011)0.0255b

(0.011)0.0260b

(0.011)0.0244a

(0.011)0.0245a

(0.011)


2

ση2

σλ2

σν2

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a

<0.001<0.001<0.001b

0.0006a


47

Table 8: Growth in Production Efficiency and Capital Market FunctionsThe parameter estimates are maximum likelihood estimates of four-way error component models containing random country, industry and time effects. Thedependent variable is the annual compound growth rate in production efficiency for each of the ten industries over thirty-eight countries for the period 1980-1995. Turnover Ratio is the value of total shares of stocks traded divided by market capitalization. Stock Market Capitalization is the ratio of the total marketvalue of publicly traded equity to GDP. Domestic Credit is the sum of assets held by the monetary authority and depository institutions excluding inter-banktransfer divided by GDP. Credit to Private Sector equals claims against the private sector divided by GDP. The variable Interaction represents the product ofthe two other variables in the respective model. Industry Share in Manufacturing is calculated by dividing the real output of the industry in the country by thetotal real output of the manufacturing sector of the country. Coefficients of the intercept are not reported. Asymptotic standard errors are given in parenthesis.

Variable I II III IV V VI VII VIII IX X XI XII XIII

TurnoverRatio

0.0040a

(0.001)0.0035a

(0.001)0.0047a

(0.002)0.0040a

(0.001)0.0120a

(0.003)0.0041a

(0.001)0.0099a

(0.004)0.0036a

(0.001)0.0034a

(0.001)


0.00002(0.001)

-0.0002(0.002)

0.0014(0.002)

-0.0003(0.001)

-0.0005(0.001)

DomesticCredit

0.0028c

(0.002)-0.0007(0.002)

0.0020(0.002)

-0.0007(0.001)


0.0041b

(0.002)-0.0014(0.002)

0.0018(0.003)

0.0008(0.002)


-0.0011(0.002)


-0.009a

(0.003)-0.0098b


0.0166b

(0.008)0.0177b

(0.008)0.0204b

(0.009)0.0215b

(0.009)0.0188b

(0.009)0.0147b

(0.008)0.0149b

(0.008)0.0180b

(0.008)0.0180b

(0.008)0.0178b

(0.008)0.0183b

(0.008)0.0162b

(0.008)0.0164b

(0.008)


2

ση2

σλ2

σν2

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a

<0.001c

<0.001<0.001b

0.0004a


48

Table 9: Accounting Disclosure and Economic PerformanceThe parameter estimates are maximum likelihood estimates of four-way error component models containing randomcountry, industry and time effects. Accounting standard quality is an index of the quality of company financialdisclosure across countries. Stock Market Capitalization is the ratio of the total market value of publicly tradedequity to GDP. Domestic Credit is the sum of assets held by the monetary authority and depository institutionsexcluding inter-bank transfer divided by GDP. Credit to Private Sector equals claims against the private sectordivided by GDP. The variable Interaction represents the product of the two other variables in the respective model.Industry Share in Manufacturing is calculated by dividing the real output of the industry in the country by the totalreal output of the manufacturing sector of the country. Coefficients of the intercept are not reported. Asymptoticstandard errors are given in parenthesis.

Variables I II III IV V VI

Dependent Variable: Growth in Economic EfficiencyAccounting Standard Quality(x100)

0.0113b

(0.005)0.0099b

(0.005)0.0108b

(0.005)0.0113b

(0.005)0.0093c

(0.005)0.0100b

(0.005)Market Capitalization (x100) -0.0854

(0.152)-0.0727(0.160)

-0.1162(0.166)

Domestic Credit (x100) -0.0715(0.199)

-0.0755(0.183)

Credit to Private Sector (x100) -0.0031(0.249)

0.0981(0.239)

Industry Share inManufacturing (x100)

1.883c

(0.989)1.849c

(1.000)2.023b

(0.997)2.032b

(0.997)1.995b

(1.008)2.017b

(1.008)

Dependent Variable: Growth in Production EfficiencyAccounting Standard Quality(x100)

0.0086b

(0.004)0.0069b

(0.003)0.0092b

(0.004)0.0084b

(0.004)0.0079b

(0.003)0.0073b

(0.003)

Market Capitalization (x100) -0.0787(0.107)

-0.1012(0.111)

-0.1373(0.118)

Domestic Credit (x100) 0.0970(0.047)

0.0888(0.0125)

Credit to Private Sector (x100) 0.0720(0.186)

0.1844(0.169)

Industry Share inManufacturing (x100)

1.671b

(0.759)1.607b

(0.762)1.785b

(0.765)1.782b

(0.765)1.728b

(0.767)1.745b

(0.768)a Significant at 1% ; b Significant at 5%; c Significant at 10%

49

Table 10: Governance and Economic Performance: InstrumentalVariables

The parameter estimates are maximum likelihood estimates of four-way error component models containing randomcountry, industry and time effects. In Model I, the instruments are index of shareholder’s legal rights provided inthe country’s legal codes, index of legal rights protecting debt holders, judicial efficiency and an index of rule of lawfrom La Porta et al (1998). In Model II, the instruments are the origin of a country’s legal system. The legal originvariables include dummy variables for “English origin”, “French origin”, “German origin”, and “Scandinavianorigin” from La Porta et al (1998). Industry Share in Manufacturing is calculated by dividing the real output of theindustry in the country by the total real output of the manufacturing sector of the country. Coefficients of theintercept are not reported. Asymptotic standard errors are given in parenthesis.

Variables I II

Panel (a): Dependent Variable: Growth in Economic EfficiencyTurnover Ratio 0.0187a

(0.007)0.0171b

(0.009)Market Capitalization 0.0008

(0.004)-0.0030(0.004)

Domestic Credit 0.0081(0.006)

-0.0099(0.009)

Credit to Private Sector

Industry’s Share inManufacturing

0.0177c

(0.010)0.0239b

(0.010)

Panel (b): Dependent Variable: Growth in Production Efficiency

Turnover Ratio 0.0105b

(0.005)0.0149c

(0.008)Market Capitalization 0.0018

(0.003)-0.0019(0.003)

Domestic Credit 0.0079(0.005)

-0.0108(0.008)

Credit to PrivateSectorIndustry’s Share inManufacturing

0.0159b

(0.008)0.0172b

(0.008)a Significant at 1% ; b Significant at 5%; c Significant at 10%

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