Banking& Insurance Growth

7/31/2019 Banking& Insurance Growth

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THE EFFECT OF BANKING AND INSURANCE ON THE GROWTH OF

CAPITAL AND OUTPUT

BY

IAN P.WEBB

INTERNATIONAL INSURANCE FOUNDATION

MARTIN F.GRACE

GEORGIA STATE UNIVERSITY

HAROLD D.SKIPPER

GEORGIA STATE UNIVERSITY

March 2002

CENTER FOR RISK MANAGEMENT AND INSURANCE WORKING PAPER 02-1

ROBINSON COLLEGE OF BUSINESS

GEORGIASTATE UNIVERSITY

POBOX 4036

ATLANTA,GA30302-4036

Contact author is: Ian P. Webb, Suite 202, 1233 20th St., NW, Washington, DC, 20036;

Phone: 202 296-2424; email: [email protected].


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THE EFFECT OF BANKING AND INSURANCE ON THE GROWTH OF

CAPITAL AND OUTPUT

ABSTRACT

Banks and insurers should contribute to economic growth by facilitating the

efficient allocation of capital. To test their roles in growth, a Solow model with

a set of productivity parameters is estimated. Identified endogeneity is

controlled for using an iterated three stage least squares simultaneous

estimation with exogenous instruments as key variables. The exogenous

components of banking and life insurance penetration are found to be robustly

predictive of increased productivity across 55 countries for the 1980-1996

period, after controlling for the impact of education, exports, government

displacement of the private sector, and investment on growth. The results also

suggest that higher levels of banking and insurance penetration jointly produce

a greater effect on growth than would be indicated by the sum of their

individual contributions.

INTRODUCTION

Among emerging market economies, we observe countries that are rich in natural resources or

blessed with high savings rates, yet with unimpressive economic growth rates. This fact points to the

now widely accepted premise that capital itself is insufficient for economic growth. Institutions and

environmental conditions that affect resource allocation appear also to be critical factors. If developing

countries fail to create favorable conditions or to promote institutions that permit resources to flow to

projects and industries promising the highest social return, their growth potential will be unrealized.

Development theory, consequently, is today according greater attention to institutions that promote

more efficient allocations of production factors.


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Financial intermediaries are widely credited with improving resource allocation. Banks and

insurers help mobilize and allocate savings, monitor investment projects and credit risk, and mitigate the

negative consequences that random shocks can have on capital investment. The roles of these two

types of financial intermediaries over different stages of growth, however, are poorly understood.

The neoclassical Solow-Swan model has been a cornerstone of growth theory since its

development in the 1950s [Solow (1956) and Solow (1957)]. Estimations using the models original

specifications of capital, labor, and technology have consistently explained major components of

growth across countries. These estimations, however, have also consistently left unexplained a residual

that accounts for 20 to 40 percent of growth.1

Variables for human capital, exports, and technology have been added to the Solow-Swan

framework in an effort to explain this productivity residual, but with only partial success. 2 The role of

financial institutions has not yet been analyzed. This paper takes advantage of new cross-country data

on insurance activity to explore the effects that banks and insurers separately and jointly have on

economic growth. With the help of the Swiss Reinsurance Company's Economic Research and

Consulting Department, a new data set was put together using official published statistics from national

supervisory authorities over a 16 year period. The new data set extends significantly beyond previous

cross-sectional and panel studies the coverage of countries and time periods studied.

We introduce country-specific intermediary activity in the Solow-Swan framework,

hypothesizing that is represents a measure of the efficiency with which capital is employed in

1For one of the most comprehensive studies using this model, see Mankiw, Romer, and Weil (1992). They use an

augmented Solow growth model incorporating human capital investment on a 98 country sample, increasing the

power of the regression from 60% to 80%.2

See Ram (1987), Hsing and Hsieh (1997), and Mankiw, Romer and Weil (1992).


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economies. Growth dynamics within the model are explored, and predictions of the relationship

between banking and insurance activity and growth rates of capital and output are generated.

Classical linear models and simultaneous systems of equations are specified to test various

hypotheses. We include economic and financial variables for 55 countries over the period 1980

through 1996. Other variables are included to control for omitted variable bias and to generate a

better understanding of the entire growth equation. The robustness of these results is evaluated using

control variables and alternative specifications of the model.

We find that the exogenous components of banking and life insurance penetration are robustly

predictive of increased productivity across the 55 countries. The results also suggest that higher levels

of banking and insurance penetration produce greater benefits together than would be indicated by the

sum of their individual contributions.

The paper is organized as follows. We introduce background, including a literature review, on

the financial intermediation process and resource allocation. We then explore the possible interaction

between banks and insurers in contributing to economic growth. The Solow-Swan model is then

introduced, followed by our revision of that model to account for financial activity of banks, life

insurers, and property/liability insurers. Results for two models follow.

FINANCIAL INTERMEDIATION AND RESOURCE ALLOCATION

Arrow (1974) summarized many of the contributions that financial institutions make to an

economy. The idealized Arrow-Debreu economy has perfect competition (including perfect

information and credible contract enforcement) as well as unrestricted lending and borrowing at

appropriately risk-adjusted interest rates. Such ideal states do not exist in reality, because economic


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agents cannot observe the true risks of investments or the behavior of contracted agents nor costlessly

diversify their resources through a multitude of contracts. Financial institutions, therefore, are created

to reduce transactions costs in meeting liquidity and risk preferences. By reducing frictions, financial

markets and intermediaries allow agents and economies to more efficiently to allocate income between

consumption and savings and to allocate savings across investments.

If financial intermediaries can achieve these allocation goals, they increase the effective level of

capital in an economy. They also enable entrepreneurs and individual savers to invest in riskier but

potentially more productive technologies. The liquidity, risk pooling, and project monitoring provided

by banks and insurers, consequently, may all contribute to more efficient capital allocation.

Financial intermediaries provide economic agents with additional liquidity and risk preferences.

Banks provide this liquidity to clients through interest-bearing deposits and loans, commercial paper,

and letters of credit, among others.3 In short, by promising liquidity and return, banks alter the

composition of savings from cash holdings, household and farm inventory, and jewelry and other

physical property to more productive forms of investment.4

Banks also possess comparative advantages over individual savers in collecting information

and monitoring investments. Funds are thereby channeled to a portfolio of investment projects offering

the highest marginal returns for their risk profiles. Through pooling, entrepreneurs and individual savers

can invest in riskier but potentially more productive technologies.

The role of insurance companies in the allocation of resources has not been studied as

extensively as that of banks. Skipper (1997) provides an overview of the various means by which

3For a survey of the literature describing, see Levine (1996).

4It can be argued that the convenience of a payment system attracts deposits as much as the comb ination of

liquidity and return on deposits.


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insurers may contribute to economic growth. These include: 1) promoting financial stability, 2)

facilitating trade and commerce, 3) mobilizing savings, 4) allowing risks to be managed more efficiently,

5) encouraging loss mitigation, and 6) fostering a more efficient allocation of capital. He notes that the

liquidity guaranteed by insurance coverage promotes greater financial and legal stability. Distress costs

and capital waste are minimized by insurance coverage that manages shocks to stocks of physical and

human capital.5 Trade and commerce are facilitated when transportation, payment, and goods are

insured. Life insurers, in particular, channel significant amounts of savings into capital markets.

Life insurance reduces the demand for liquidity in the form of money and durable goods, and

shifts the composition of individuals portfolios of savings to more productive assets. Life insurance

may shift the demand for liquidity through relatively unproductive assets (such as cash and jewelry) to

more productive forms. This effect mirrors that which banks have on the quality of investments, as

discussed by Pagano (1993) and Bencivenga and Smith (1991).

Among other benefits, property/liability insurers reduce the likelihood of distress liquidation of

firms in the face of catastrophic losses. Mayers and Smith (1982) reason that risk-neutral shareholders

have an interest in insuring against losses to avoid bankruptcy costs. These costs may collectively

have measurable effects on an economy. With insufficient risk-financing choices in an economy, the

potential for losses that destroy much of the built-up value of equity can affect initial and reinvestment

decisions.

Additionally, if insurers can lower the costs of risk financing, they boost the expected return on

projects. Lower costs could result because insurers: 1) excel in offering risk-pooling services through

the identification of standardized risks and simplification of contracts, 2) provide optimal investments

5The idea of health as a stock of human capital was discussed by Grossman (1972). Health insurance may help


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and asset-liability matching, 3) provide valuable and cost-effective administrative services related to

risk management and claims payments, and 4) offer products that are tax-deductible business expenses

in many markets.

INTERACTION OF BANKS AND INSURERS IN PROMOTING ECONOMIC GROWTH

Banks and insurers arguably complement each other in their intermediation functions. Retail

and investment banks excel in identifying and providing financing for investment-worthy small and large

businesses, respectively. Life and property/liability insurers, on the other hand, typically invest in

corporate and government bonds, commercial mortgages, and equity. Life insurers emphasize long-

term investments; banks short-term. As a result, their affect on emerging market economies may have

something to do with the relative importance of the type of financing they provide during different

stages of development. A collection of empirical studies forms a patchwork of generally supportive

evidence that banking, stock market, and financial sector activity all have a strong correlation with

economic growth [see, e.g., Fritz (1984) and Jung (1986)].

The services of banks and insurers may be interdependent to some degree. Banks, for

example, may more readily offer credit when insurance is present. Loans for residential

purchase/construction and new cars may require insurance on the collateral. Insurance requires

effective payment systems, so its growth may be facilitated by a strong banking sector.

Banks and life insurers both intermediate personal savings, providing important sources for

short- and long-term funds in an economy. Services offered to savers, however, differ enough to

suggest that they may be distant rather than close substitutes. The immediate liquidity of banking

reduce waste arising by ensuring prompt attention and preventive medicine for illnesses and injuries.


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deposits is uncharacteristic of endowment, whole life, and other savings-related insurance policies.

Liquidity and payment system needs probably are prime motivators for bank deposits. Although banks

offer fixed-term savings products, such as certificates of deposit, these often are of shorter duration

than life investment products.

Banks and property/liability insurers may be relatively close substitutes in very low- income

countries. Poor individuals and firms may not be able to afford insurance and decide instead to rely on

precautionary bank savings. The affordability of insurance is an objective matter in part because there

is a minimum level of coverage that makes its economically sound in most lines. Low-income

individuals needing low levels of coverage face comparatively higher unit insurance prices, because

insurer overhead, marketing, and servicing costs are large in relation to the actuarially fair price.

Affordability is also a function of risk aversion. The risk tolerance of individuals and firms may

change as their personal wealth rises, as does the nature of loss exposures. How risk aversion changes

with level of income/wealth is unsettled. If low-income individuals/firms have higher risk tolerances,

their demand for insurance could be lower. This could help explain the lower insurance penetration in

low-income countries.

It is reasonable also to expect banks to be comparatively more attractive providers of liquidity

in countries with inefficient insurance industries. These inefficiencies are more likely to exist in low-

income countries, many of which have a history of regulatory constraints, financial repression, and poor

infrastructure.

THE SOLOW-SWAN MODEL


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The Solow-Swan neoclassical model has enjoyed a resurgence in development economics.

Several studies have reiterated its strengths in light of the challenges posed by endogenous growth

models. For example, cross-country studies have estimated the value of broad capital (human and

physical) to be quite different from that implied by endogenous growth [Romer (1987) and Englander

and Mittelstadt (1988)]. Rather than an expected finding that the share of broad capital in empirical

estimates is unity, these authors found it to fall between 0.4 and 0.6, closer to values consistently

estimated with the neoclassical model. Explicit in the neoclassical model, moreover, is the notion of

diminishing returns to physical capital, a premise widely accepted in the field and so desirable as a

growth dynamic.

Further, the neoclassical models convergence prediction is defensible if interpreted as implying

conditional rather than absolute convergence. Conditional convergence requires augmenting the model

to account for differences in productivity across countries and over time. One way to account for

these differences is to measure country-specific differences in savings rates and/or institutional factors.

These factors should be able to shift the production function outward and so explain increases in

national output. Financial intermediation is a likely candidate to explain differences in investment as

well as productivity. For this reason, we consider it a shift variable in our revision to the Solow-Swan

framework.

Under the Solow-Swan model, production is organized by firms that hire the services of

workers and rent the services of capital. Households are endowed with units of labor that they

inelastically supply to firms at the prevailing wage rate (w). All capital is owned by households and

supplied to firms at the prevailing rental rate (r). The marginal products of labor and capital equal the


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interest rate (r) and the wage rate. Factors receive their marginal products and, assuming a constant

rate of substitution, capitals share in national income always will be and labors share will be (1- ).

Savings rates and population growth are taken as exogenous. Output saved is available to

augment the stock of capital owned by the household sector. Capital depreciates at a constant,

exogenous rate ( ).

Growth over time in the number of households and, equivalently, the supply of workers occurs

at a constant, exogenous rate (n). Employment, therefore, is governed byL nL

= , and so

L t L e

nt

( ) ( )= 0 , n 0 (1)

Assuming a Cobb-Douglas linearly homogenous function, production is governed by

Y t A t K t L t ( ) ( ) ( ) ( )= 1 0


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In this revised model,Z(t) measures the aggregate of the weighted financial activities of three

financial institutions: banks (B), property/liability insurers (PL), and life insurers (LF). Each financial

activity is weighted by the size of its monetary measure relative to output.

The aggregate of the three weighted financial activities makes up a productivity multiplicator in

this revised model, as follows:

Z t Z B PL LFit it i t ( ) ( ) exp( )= + +0 (4)

where subscripts refer to country i and time period t, and

Y t Z t A t K t L t ( ) ( ) ( ) ( ) ( )= 1 (5)

Z(t) is a multiplicative exponent that shifts the production function. 6 When some institutional factor or

other change in the economy shifts the production function outward, the economy produces more at

any level of capital and labor. With the case of financial intermediation, this shift presumably occurs

because capital is directed towards more productive ends.7

This model also assumes diminishing returns to capital and labor such that

dY/dK > 0, dY/dL > 0, d2Y/K


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gains come from improvements in the quality of investment or capital stock and not just increases in the

level of investment. Some authors [e.g., Eltis (1973)] argue that any new investment carries with it

innovations in the organization of capital, as in the replacement of old capital stock with new and so

more efficient/effective stock. Others argue that new investment in itself is insufficient evidence of

improvement in quality of capital stock and that the nature of the investment needs to be examined.

Eltis (1973) suggests that the rate of investment drives technical progress by creating an

externality from learning by watching. Scott (1992) supports Eltiss emphasis on investment, arguing

that the rate of inventions depends on the rate of investment, and so the rate of investment determines

technical progress. According to this story, innovation builds upon innovation, providing the rest of the

market with the opportunity to learn by watching. As other firms watch this process of innovation from

new investment, positive externalities are generated.

It does not seem plausible, however, that the level of gross investment itself drives

improvement in capital stock quality. This hypothesis appears to echo the neoclassical stance that the

level of capital stock itself is the fundamental determinant of growth. Consequently, it would be

desirable if any study examining the role that financial intermediaries have on growth could distinguish

their impact on the level from the quality of investment.

The stances of Scott and Eltis, moreover, appear to be driven as much by data limitations as

by the strength of their theoretical positions. Scott (1992) claims that a distinction between ordinary

investment, which merely reduplicates existing assets, and investment on research or educational

expenditures, which results in innovation, has not been made operational for a theory of economic

growth. As a result, he concludes that gross investment, and not any particular types of investment


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which differentiates between the productivity of human, R&D, or other capital, should be the

appropriate measure for productivity.

Distinguishing between the impact on level and quality of capital stock is particularly

challenging. Measuring the value-added and so productivity of new investment arguably requires

disaggregated data on quantity and type of investment, information that is not easily obtained for large

cross-country samples.

Due to these data issues, it is difficult to discern directly between the impact that banking and

insurance have on the level and quality of investment. Differentiating between these two effects of

financial intermediation is, however, important. This paper considers some indirect evidence that

shows the degree to which banking and insurance appear to stimulate economic growth through levels

of gross domestic investment.

Exogenous Financial Intermediary Variables

Studies employing the neoclassical model customarily construct a capital stock series or

assume that gross domestic investment (GDI) approximates the change in this stock. Because of the

inherent difficulties and resulting arbitrariness injected by methodological decisions that have to be

made in the construction of such a series, this paper uses GDI as a proxy.

Empirical studies employing the neoclassical framework commonly ignore the potentially

endogenous relationship between capital stock and output in the estimations. In our Model 1, we

assume that neither GDI nor financial variables are endogenous in the growth equation. To reduce the

possibility that endogeneity is an issue, we construct variables in accordance with the method used in

what might be coined the classic growth equation.8

8The use of average levels of independent variables against average growth rates has long been common in growth


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This approach takes average levels of financial intermediary activity and regresses them against

average growth rates of economies. Theory suggests that economic growth induces growth in the

financial system, and thus one would expect growth rates of the real and financial sectors to be

correlated. This has no implications, however, concerning the size of the financial system relative to

GDP. The financial variables used in this study measure this latter effect.9

Taking average levels of financial intermediary activity and regressing them against average

growth rates of the economy, consequently, is a better test of the causality running from the financial to

the real sector. It is also a better way to control for possible endogeneity between these two sectors in

growth equations, as any simultaneous determination of the real and financial sectors is more likely to

create highly correlated growth rates than correlation between average levels of financial penetration

and the growth rate of the economy.10

equations and is stylized by Barro and Sala-I-Martin (1995) in their textbook on economic growth.

9De Gregorio and Guidotti (1996), p. 252, explain why this specification of the growth equation is commonly used to

measure causality.10

Penetration refers to size of financial activity relative to GDP. Thus, life insurance penetration is gross life

premium divided by GDP.

TABLE 1

Specification of the Production Function Equation Assuming Exogeneity of FinancialIntermediary Variables

Variable Variable Definition

0 slope coefficient

yit

= average growth rate of real per

capita gross domestic product (RGDPc)

LnRGDPc LnRGDPc

n

endperiod begperiod

kit

= average growth rate of capital stock per

capita

LnGDIc LnGDIc

n

endperiod begperiod

Bit

= average level of banking activity

=t

n it

it

BankCredit

GDP

n

0

PLit

= average penetration of property/

liability insurance activity0

n itt

it

Property Liability Premium

GDP

n

=

LFit= average penetration of life insurance

activity 0n it

tit

Life Premium

GDP

n

=

EXGit= average level of exports as a share

of GDP

=t

n it

it

Exports

GDP

n

0

GOVGit = average government expenditure as

a share of GDP

=t

n it

it

Gov tExpenditures

GDP

n

0

'

EDU % population over 25 who have

completed primary school

GDPo natural log of initial real GDP per capita


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Dividing equation 5 by L, yields the intensive form of the growth equation,

y t Z t A t k t( ) ( ) ( ) ( )= . (5a)

An empirical specification is developed by taking natural logs and derivatives with respect to time. The

addition of control variables results in

y L nZ k Xit j it j

it j it

j

it

=

=

= + + + + 01

3

4

8

(7),

where change in output intensity is yit

= [the growth rate of real GDP per capita]. The change in

capital intensity is kit

= [gross domestic investment]. The change in financial intermediary activity with

respect to time is LnZ B PL LF

= + + , and the exogenous change in technology A is

represented by the slope coefficient. The variables included to control for other influences on

productivity areXit= [education enrollment, government expenditure as share of GDP, and log of initial

real GDP per capita]. Ln refers to the natural logarithm of a variable.

Following common practice in economic growth studies, this paper takes period averages; 8

and 16 year averages in this case. Variable definitions are shown in Table 1. Average growth rates of

GDP per capita and capital stock are measured as differences of logs divided by years in the period.

This produces a rough compound rate of growth over the entire period.

Our methodology dictates that average differences should be taken of the financial intermediary

variables. A slight alteration of the model could dictate that growth rates be taken of the financial

variables. Indeed, for financial (and all control) variables used in this study, either average differences

or average growth rates produce higher correlations with average growth rates of GDP per capita.11

11A yearly, pooled time-series also could be estimated with the models. However, by taking averages over a time

period, the model can ignore a variety of other potential dynamics that might condition the relationship between


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We adopt the practice common in growth studies of using average levels of explanatory variables of

interest, because of it being a more rigorous assessment of the causal relationship with economic

growth. The results using average levels, moreover, are just as strong as using average differences.12

Description of Control Variables

Technology Growth

A(t)represents worldwide improvement in productivity. If there is worldwide improvement infinancial transaction capacity, represented perhaps by new payments system technology or some other

innovation that can be transmitted to all countries, this term will pick up some of the impact that might

otherwise be attributable to the efficacy of the financial sector. The slope coefficient is expected to be

positive, reflecting a measurable level of productivity increase over time that is constant across

countries.

Banking

Banking activity is an important medium of external finance for entrepreneurs and capital

projects of all sizes. The measure of banking used in this study, following King and Levine (1993a),is

the ratio of the claims on the nonfinancial private sector by deposit money banks to GDP (BankCredit,

or as they name it, PRIVY). A financial system that simply funnels credit to the government or state-

these two in the short -run. These short -term influences include yearly macroeconomic shocks, any particular lag-

structure that might exist between the real and financial sector, and shocks exclusively affecting the insurance or

banking industries. By smoothing these out, a clearer picture is obtained about the long-term relationship between

financial intermediary activity and economic growth. A time-series analysis with yearly observations would requiresome hypotheses as to how particular time regimes, regulatory regimes, and short and long-term economic shocks

might affect the variables differently in the countries studied. An expanded dataset that includes information on

such factors would be particularly useful for such further research.12

Levine and Zervos (1998), pp. 543-4, discuss the practice of using averaged levels of growth indicators in cross-

country regressions. The average level of financial activity is the mean of financial penetration over the entire

period; that is, the sum each years penetration divided by the number of years in the period. The average

difference of financial activity is the mean increment of financial penetration; that is, the ending year level less the

beginning year level, divided by number of years. The use of average differences of financial activity produced

results that were almost identical to those using average levels of financial activity. This more stringent assessment

of causality running from the financial to the real sector produces no substantial difference in the findings.


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owned enterprises may not be evaluating managers, selecting investment projects, pooling risk, and

providing financial services to the same degree of effectiveness as a private sector orientation.

In many developing countries, government guarantees of loans to public sector projects have

created moral hazard problems with poor repayment rates. In such cases, government intervention into

the credit decisions of banks has interfered with their funneling funds to their most productive uses.

Consequently, credit provided to the private sector is used in this banking measure as an indicator of

that banking activity that is addressing the resource allocation needs of the economy. The coefficient

on BankCredit is expected to be positive to the extent that private sector use of banking is closely

related to growth of the gross domestic product.

Life Insurance

Life insurers mobilize funds through attractive medium and long-term savings products. Long-

term finance provided by life insurers may have a particularly important role in economies that need

such financing for infrastructure development. Long-term equity positions by life insurers also can have

a beneficial impact on private sector capital projects. The coefficient on LF is expected to be positive.

Property/Liability Insurance

Property/liability insurers do not mobilize medium and long-term savings to the extent that life

insurers do. Their products are characterized by a short- to medium-term intermediation of funds. As

a result, they channel funds from individuals and firms into short- and medium-term capital projects.

Property/liability insurers also can reduce costly interruption and even the entire liquidation of firms.

Trade and commerce in activities with otherwise troublesome risks can be facilitated. The analysis of

risks that accompanies an active insurance market, moreover, provides investors with information on


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the probabilities of loss/failure that allows better resource allocation. The coefficient on PL is expected

to be positive.

Education

Various authors have found that education is strongly related to economic growth.13 They have

reasoned that value added to production can be a function of the education level of the workforce.

High-technology industries have much to benefit from more educated workers. Small- and medium-

size entrepreneurs also can improve their productivity if they can educate themselves as to the latest

technological advances as well as to the local and international market conditions affecting their

products. The service industry, particularly, can ascribe much of its added value directly to the skills

and education of its workforce.

Education is measured in many ways, the most common being initial level of primary or

secondary enrollment. This study uses educational data compiled by Barro and Lee (1994) to

measure the percentage of the population over age 25 with primary educational attainment. The

expected sign is positive.

Government Consumption

The degree to which the public sector dominates the economy is believed by many to be an

indicator of the crowding out of the more efficient private sector. Private-sector investment is driven

by profit concerns, while government investment is directed to social or political concerns. Government

expenditure as a percent of GDP is expected to be negatively correlated with economic growth.

Exports

13For cross-country studies measuring the impact of education and other variables on growth, see Mankiw,

Weil, and Romer (1992) and Barro and Sala-I-Martin (1995).


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A vibrant export industry has been associated with faster economic development [McNab and

Moore (1998), Balassa (1985), Feder (1983), and Barro (1991)]. Exports can increase capacity

utilization, allow a country to take advantage of scale economies, and promote technical change. The

measure used in this study is total exports to GDP. The expected sign is positive.

Initial GDP

Cross-country empirical studies regularly include a measure of initial GDP to control for the

convergence effect of economies. Some low-income countries with initially low levels of capital stock

experience high growth rates that generally are not characteristic of developed nations. We control for

the initial level of income to reduce the possibility that the convergence effect biases the coefficients on

other variables. The log of initial real GDP per capita is used, following the example of Barro and

Sala-I-Martin (1995) and King and Levine (1993a). The expected sign is negative.

Capital Stock

Capital stock is the primary component of the production function in this model. Changes in

the capital stock level are expected to account for most of output variation. Measuring capital stock is

problematic, as comparable figures in national accounts do not exist. Rather than construct a capital

stock series, this paper follows the common practice of using GDI as a proxy for change in capital

stock. The expected sign is positive.

Joint Banking/Insurance

As suggested earlier, the coexistence of banking and insurance may create greater depth in the

financial sector, allowing for a greater menu of financing options for entrepreneurs and public-sector

projects and affecting an improved allocation of resources. If banking and insurance merely

complement each other, interaction terms between property/liability and life insurance and banking


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should have coefficients equal to zero. If these three intermediaries contribute to each other by creating

financial sector synergy, the coefficients on banking/insurance interaction terms added to the model

should be positive.

RESULTS

The 55 countries included in our study are listed in Appendix I. These countries represent the

great majority worldwide of both economic output and financial services production. Appendix II

indicates the sources of our data.

Assuming Exogenous Financial Variables: Model 1

Model 1 assumes no endogenous relationship between financial intermediary, investment, and

economic growth variables. Results using ordinary least squares are presented in Table 2


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Banking credit to the private sector, initial GDP per capita, and governments share in GDP

are all significant. Initial GDP per capita has its expected negative sign. Exports, investment, the

intercept, and the other financial intermediary variables do not enter significantly.

Various authors have suggested no a priori way to know the direction of causality between

financial activity and economic expansion. If causality does not run in one direction only, as Model 1

assumes, but rather in two directions, the models estimation may not produce reliable results.

Endogenous variables could produce contemporaneous correlation between regressors and the

disturbance term. This could lead to bias in the OLS estimator, even asymptotically.

TABLE 2Model 1 Results: Regression Assuming Exogeneity of Financial Intermediary Variables, Dependant

Variable is Growth Rate of Real GDP Per Capita.

GDPa

Population

Intercept -0.490(0.881)

GDP 1980

Population 1980

-0.000*b

(0.000)

GDI

Population

0.000

(0.000)

Bank Credit

GDP

4.060**

(1.35)

Life Premium

GDP

29.000

(23.331)

PL Premium

GDP

-6.700

(45.074)

% Pop 25+

Primary Education 1980

0.021

(0.018)

Exports

GDP

-3037.203

(4578.001)

Govt Expenditure

GDP

35230.278*

(1799.289)

Adj. R2

0.388

N 55

aThe GDP/Population variable is average growth rate of real GDP per capita over 1980-1996.

All other variables represent averages of yearly levels over 1980-1996 or, if indicated, 1980 values.b

Standard errors are in parentheses; and * and ** indicate significance at the 10 and 5 percent

level, respectively.


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21

Endogeneity between Gross Domestic Product, Banking, and Life Insurance

The fact that endogeneity may exist between financial variables and GDP growth complicates

the specification of empirical growth models incorporating the role of the financial sector. Barro and

Salai-I-Martin (1995) discuss the possibility that financial activity may be a product rather than a cause

of economic growth. To examine whether banking and investment are causes or consequences of

growth, they regress each separately against GDP per capita in non-structural growth regressions,

using instrumental variables to control for endogeneity. They find no clear evidence indicating the

direction of causation.

King and Levine (1993a, 1993b, 1993c) also examine the relationship between financial

activity and growth. They use GDP per capita and investment as dependent variables in separate

estimations. Their approach is tailored to recognize the possibility that banking and investment might

be endogenously related (as might occur if banking activity stimulated the level of investment). If this

were true, the significance of banking could be obscured or distorted if investment were included with it

as a regressor in the same equation. The disadvantage of this approach is that it risks producing an

under-specified model by excluding investment from the growth equation.

Gregorio and Guidotti (1996) presume that banking drives investment to some degree. They

compare the coefficients on the banking variable when investment is first excluded then added as a

regressor to a growth equation. By examining the resulting reduction in economic magnitude of the

banking coefficient, they conclude that approximately one-quarter to one-third of bankings influence

on output is due to its effect on the level of investment, the rest being due to its influence on

productivity.


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22

Endogenous Variables and Simultaneous Equations: Model 2

Financial services may not be supply leading, as assumed in the specification of Model 1, but

rather demand-following [Patrick (1966)]. This would imply that economic growth pulls along financial

activity, making financial intermediation an accompaniment rather than a stimulant to growth. If this

were true, the endogenous relationship between financial activity and GDP might bias the results of

Model 1.

There is also the possibility that investment is determined by economic growth. The precise

relationship between investment and growth of GDP has not been defined in the growth literature.

Barro and Sala-I-Martin (1995), for example, found a positive correlation between the investment

ratio (GDI/GDP) and GDP in their expansive cross-country growth study. Their results, however,

suggested that this correlation reflected a reverse causation from growth to investment, rather than from

investment to growth.

Finally, either banking or insurance might work directly through investment to affect output. In

this case, stimulation of capital growth would not exhibit a lagged effect due to the positive impact on

the productivity of capital itself, but rather an immediate impact as it would comprise a significant

portion of increase in investment. If so, an endogenous relationship would exist between either banking

or insurance and GDI.

Following the example of McNab and Moore (1996) who use simultaneous equations to

better address the issue of causality between export expansion and economic growth, Model 2

identifies exogenous variables that explain variations in banking and insurance activity. The following

simultaneous equations recognize the bi-directional causality between financial activity and GDP using

exogenous components of financial intermediary activity:


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23

it it it Y ENG FREN GERM SCAND= + + + + + +

0 1 2 3 4 5 1 (8)

PL Y CORRUPT BUREAUQLIT it it= + + + +

0 1 2 3 2 (9)

LF Y CATH MUSL PROTESTit it it= + + + + +

0 1 2 3 4 3 (10)K UGDIGoit it

= + + 0 1 4 (11)They are estimated simultaneously using 3SLS with equation (7), as,

y L nZ k Xit j it j

it j it

j

it

=

=

= + + + + 01

3

4

8

(12)

This method estimates all identified structural equations together as a set.

The specification of Model 2, which includes the measures used to describe the exogenous

components of financial and capital stock variables, is described in Table 3.

TABLE 3

Model 2 Specification: Assuming Endogeneity of Financial Intermediary Variables

Variable Variable Definition

ENG English Common Law

FREN French Commercial Code

GERM German Commercial Code

SCAND Scandinavian Commercial Code

SOC Socialist/Communist Law

CATH % population Catholic

MUSL % population Muslim

PROTEST % population Protestant

OTHDEN % population other denomination

CORRUPT Measure of corruption

BUREAQL Measure of bureaucratic quality

UGDIGo

Initial value of GDI per capita GDP (1980 value)


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24

Not all measures of religious composition and legal origin are used simultaneously in the

estimation, as this would produce singular matrices. Consequently, OTHDEN and SOC are omitted in

the estimation of Model 2.

Employing the above simultaneous system requires finding variables that account for significant

exogenous components of the endogenous variables in question. Research suggests that differences in

legal and accounting systems help explain differences in financial development.14 The theory here is

that legal and regulatory systems that give high priority to creditors receiving the full value of their claims

and to contract enforcement should have better functioning financial intermediaries than countries

whose systems provide weaker support to creditors and contract holders. Issues of contract

enforcement and creditor rights have direct relevance to the use of bank services. Consequently, the

origin of a countrys legal code (LEG) is tested as an identifying restriction of banking activity.

Property/liability insurance depends on the moral fabric of a society as well as the enforcement

of property rights. If moral hazard problems, including fraud, are prevalent, the insurance mechanism

can become prohibitively expensive for large population segments or even break down entirely.

Adequate property rights enforcement ensures that those responsible for damage to anothers

property are held accountable. Such rights are also fundamental to consumer confidence in the

performance of insurance contracts. Enforcement of property rights can be measured by the quality of

a nations justice system and efficiency of government. To measure these environmental factors

influence on property/liability insurance markets, theInternational Country Risk Guide measures of

14Thus, LaPorta, et al. (1997), in comparing 49 countries, show that legal rules of English, French, German, and

Scandinavian origin each relate differentially to the size of national capital and debt markets. Levine, Loayza and

Beck (1998) show that legal origin differences are associated with differences in financial intermediary activity.


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25

corruption and bureaucratic quality are used as proxies for the social ethic and strength of enforcement

of property rights.

Previous studies have found that national religious composition can have a significant effect on

life insurance demand (Browne and Kim: 1993). Many Muslims believe that the purchase of life

insurance is inconsistent with the Koran. Protestant populations generally hold no such beliefs.

Estimation of religious composition corroborates its suitability as an instrumental variable for life

insurance.

Identifying restrictions are found and tested for each financial intermediary variable and for

GDI. An F-test for the joint significance of these restrictions in explaining the variation of each financial

intermediary variable reveals all to be satisfactory (at the 5 percent level). Legal origin, religion, and

corruption are found to be good instrumental variables for banking, life insurance, and property/liability

insurance development, respectively.

While these instrumental variables are interesting from a theoretical perspective, as they point

to exogenous determinants of financial intermediary penetration, they are not used in the estimations.

Initial values of the financial variables, which also serve as good instruments (all meet the 5 percent

level of significance for model fit), do better at facilitating convergence of the simultaneous estimations.

For this reason, they are selected over legal origin, religion, corruption, and bureaucratic quality. Initial

values are commonly used as instruments to control for endogeneity [see Barro and Salai-I-Martin

(1996) and Levine, Loayza and Beck (2000)]. Results are presented in Table 4.


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26

The use of cross-country as opposed to panel data precludes definite conclusions about

causality. Nevertheless, these results suggest that higher levels of banking and life insurance

penetration predict higher growth rates across the sample of countries. This growth prediction holds

even after controlling for the role of investment, education, exports, and government intrusion in the

economy. Equally interesting, it is the exogenously determined components of banking and life

insurance penetration that predict economic growth.

TABLE 4

Simultaneous Equations Results: Interrelationships between Financial IntermediaryPenetration and Growth of GDP per Capita

Dependant Variables

GDP a

Population

GDI

Population

Bank Credit

GDP

Life Premium

GDP

PL Premium

GDP

Independent Variables

GDP

Population

231.091**b

(80.754)

0.022

(0.019)

0.004*

(0.001)

0.001

(0.001)

Intercept

-0.14

(0.925)

-359.820*

(184.321)

0.067

(0.043)

-0.004

(0.003)

0.004

(0.003)

GDP 1980

Population 1980

-0.000

(0.000)

GDI

Population

0.000

(0.000)

Bank Credit

GDP

4.239*

(2.108)

Life PremiumGDP

56.873**(21.572)

PL Premium

GDP

-63.148

(59.764)

% Pop 25+


0.023*

(0.014)

Exports

GDP

996.439

(3508.906)

Govt Expenditure

GDP

54574.754

(39433.930)

GDI 1980

Population 1980

1.036**

(0.034)

Bank Credit 1980

GDP 1980

0.921**

(0.091)

Life Premium 1980

GDP 1980

1.302**

(0.138)

PL Premium 1980

GDP 1980

0.641**

(0.083)

System Weighted R2 = 0.847

N = 55a GDP/Population is the average growth rate of real GDP per capita over 1980-1996. All other variables are average levels


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27

Table 4 shows a one-way relationship between banking penetration and GDP growth, and a

two-way relationship between life insurance penetration and GDP growth The coefficient on the life

insurance variable (first column in Table 4) suggests that a 2 percent increase in life insurance

penetration will be accompanied by a 1.12 percent increase in average GDP per capita.15 Concerning

the other direction of the relationship, the coefficient on the GDP variable (second column in Table 4)

indicates that a 1 percent increase in average GDP per capita growth will be accompanied by a 0.4

percent increase in life insurance penetration.

The coefficient on the banking variable (first column in Table 4) indicates that an increase of 10

percent in banking credit to the private sector as a share of GDP will be accompanied by a 0.42

percent increase in average GDP per capita growth.16 GDP per capita does not predict growth in

banking in this model.

From the estimation of Model 2, it can also be seen that the investment variable does not

explain GDP growth, but GDP does explain increases in investment. This finding corroborates that of

Barro and Salai-I-Martin (1996) who suggest that one of the principal reasons for the association

between growth and investment is the ability of growth to explain investment but not vice-versa.

The education variable is significant, consistent with the findings of other studies. The

government consumption variable is not significant. This result is not unexpected as two other studies

[King and Levine (1993a) and Levine, Loayza, and Beck (2000)],showed similar results.

15The average life penetration over 1980-1996 for the 55 countries ranges from 0 to 0.07. Consequently, a 2 percent

increase would move a country along approximately one-third of the range from the lowest to the highest

penetration. For example, if a countrys average life penetration over the 1980-1996 period is 2.5 percent and its

average GDP per capita growth rate is 3 percent, the model predicts that an increase in average life penetration to 4.5

percent would be accompanied by an increase in average GDP per capita growth rate to 4.12 percent.

16The average banking penetration over 1980-1996 for the 55 countries ranges from 0 to 1.0.

Consequently, a 10 percent increase would move a country along approximately one-tenth of the entire range.


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28

The export variable proves insignificant as a predictor of economic growth. Other studies

[McNab and Moore (1998) and Balassa (1985)] have found exports to be a significant and robust

indicator of economic growth. This discrepancy in results may be due to the use of different data sets

or to variation across studies in the construction of the export variable.

Each of the financial institution variables is significant when introduced into Model 2

individually. However, when added together, only life insurance and banking retain their significance,

possibly due to multicollinearity.

Integrated versus Independent Contributions to Growth

That property/liability insurance is robbed of its explanatory power by another financial

institution hints at some possible overlap between their roles in the economy. To explore this

possibility, we turn to factor analysis to determine whether banking, life insurance, and property/liability

insurance have shared roles in economic growth.

Banking, life insurers, and property/liability insurers offer distinct financial services. They

spread risks over time and across people in somewhat different ways. They are similar, however, in

that all are conduits for substantial amounts of investment. One factor, therefore, likely represents the

impact of this financial intermediation on the economy.

The factor thought to represent financial intermediation is labeled F[Bank_Life_PL] and is

included in Model 2.17 The results of this estimation are shown in Table 5.

17Estimated values of banking, life insurance, and property/liability insurance formed from instruments for these

variables are used to calculate the single factor, F[Bank_Life_PL].


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29

The financial intermediation factor accounts for 65 percent of these variables cumulative

variance and is statistically significant. It captures the major part of these financial institutions

contribution to economic growth.

Synergies

Financial institutions may both share some common role in stimulating economic growth and

function better collectively than separately. Thus, the more efficient are banks payment systems, the

lower are attendant insurer administrative costs. Property/liability insurance protects banks loan

collateral. Also, the growth of one type of financial intermediary in a society can have positive spillover

effects on the demand for the services offered by other financial intermediaries as consumer

sophistication grows.

TABLE 5Simultaneous Equations (Model 2): Financial Intermediation Factor (F[Bank_Life_PL])Extracted from Three Financial Intermediary Variables

GDPa

Population

GDI

Population

GDP

Population

196.342**

(57.428)

Intercept 1.782*

(0.729)

-335.112**

(140.798)

GDP 1980

Population 1980

-0.000*

(0.000)

GDI

Population

0.001

(0.000)

% Pop 25+


0.024*

(0.014)

Exports

GDP

-1661.642

(3495.749)

Govt Expenditure

GDP

-24249.909*

(13504.343)GDI 1980

Population 1980

1.031**

(0.034)

F[Bank_Life_PL]c1.358**

(0.369)

System weighted R2 = 0.923, N = 55a GDP/Population is the average growth rate of real GDP per capita over 1980-1996. All other variables are average levels

over 1980-1996; or, if indicated, 1980 values. The system weighted R 2 is reported for t he entire set of simultaneous

equations.b Standard errors are in parentheses; and * and ** indicate significance at the 10 and 5 percent level, respectively.c F[B_LF_PL] represents Factor 1 that was calculated for all three financial institution variables.


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30

To test the hypothesis that synergies exist between the three financial intermediaries, interaction

terms between these intermediaries are added to the simultaneous model.18 Results are presented in

Table 6.19 The interaction terms between bank and PL and bank and life are significant at the 10 and 5

percent levels, respectively. The strength of these interaction terms points to a mutually constructive

relationship between banking and insurance.

For greater clarity, the results of Model 2, with and without interaction terms, are presented in

Table 7. Banking and life insurance are significant in the absence of the interaction terms. When these

terms are added, the interaction terms are seen to dominate the explanatory power of the individual

financial institution terms.

18Interaction terms are also created by multiplying two financial intermediary terms taken from estimated values; that

is, they are the product of two estimated values calculated from instrumental variables.19

Testing these interaction terms one at a time in Model 2 does not alter this finding.

TABLE 6

Simultaneous Equations (Model 2): Interaction Terms among Financial IntermediaryVariables

Dependant Variables

GDPa

Population

GDI

Population

Bank Credit

GDP

Life Premium

GDP

PL Premium

GDP

Independent Variables

GDP

Population

218.773**b

(68.142)

0.025

(0.017)

0.005**

(0.001)

0.002

(0.001)

Intercept -0.712

(1.105)

-338.598**

(166.112)

0.069

(0.041)

-0.005

(0.003)

0.005*

(0.003)

GDP 1980

Population 1980

-0.000

(0.000)

GDI

Population

-0.000

(0.000)

Bank Credit

GDP

2.455

(1.946)

Life Premium

GDP

17.442

(29.500)

PL Premium

GDP

-12.031

(65.893)% Pop 25+


0.028**

(0.014)

Exports

GDP

-1970.909

(3712.989)

Govt Expenditure

GDP

16546.232

(36589.934)

GDI 1980

Population 1980

1.032**

(0.034)

Bank Credit 1980

GDP 1980

0.904**

(0.088)

Life Premium 1980

GDP 1980

1.235**

(0.146)PL Premium 1980

GDP 1980

0.629**

(0.081)

Bank Credit * LifePremium

GDP GDP

0.814**

(0.277)

Bank Credit * PLPremium

GDP GDP

111.651*

(63.067)

PLPremium * LifePremium

GDP GDP

0.043

(0.303)

System weighted R2 = 0.874

N = 55a GDP/Population is the average growth rate of real GDP per capita over 1980-1996. All other variables are average levels

over 1980-1996; or, if indicated, 1980 values. The system weighted R 2 is reported for the entire set of simultaneous

equations.b Standard errors are in parentheses; and * and ** indicate significance at the 10 and 5 percent level, respectively.

TABLE 7

Simultaneous Equations (Model 2): Comparison of Results with and without Interaction

TermsDependant Variables

GDPa

Population

GDPa

Population

Independant Variables

Intercept -0.141

(0.925)

-0.712

(1.106)

GDP 1980Population 1980

-0.000(0.000)

-0.000(0.000)

GDI

Population

0.000

(0.000)

-0.000

(0.000)

Bank Credit

GDP

4.230*b

(2.108)

2.455

(1.946)

Life Premium

GDP

56.870**

(21.572)

17.442

(29.500)

PL Premium

GDP

-63.141

(59.769)

-12.031

(65.899)

% Pop 25+


0.023*

(0.014)

0.028**

(0.014)

Exports

GDP

996.444

(3508.537)

-1970.989

(3712.982)

Govt Expenditure

GDP

54574.371

(39433.936)

16546.256

(36589.959)

Bank Credit * LifePremium

GDP GDP

0.814**

(0.277)

Bank Credit * PLPremium

GDP GDP

111.653*

(63.067)

PL Premium * LifePremium

GDP GDP

0.043

(0.303)

System weighted R2 = .834 = .879

N = 55 = 55a GDP/Population is the average growth rate of real GDP per capita over 1980-1996. All other variables are average levels

over 1980-1996; or, if indicated, 1980 values. The system weighted R 2 is reported for the entire set of simultaneous

e uations.


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31

An F-test is used to determine if the marginal effects of banking, life insurance, and PL

insurance are statistically different from zero. None of these financial intermediary variables have

statistically significant marginal effects when expressed as a function of both individual and interaction

terms measured at the means. However, as is clear from Tables 6 and 7, the interaction terms

themselves are statistically significant, and so are likely driving the model. In summary, results suggest

that the joint development of banking and property/liability insurance and of banking and life insurance

have more to do with economic expansion than the development of either banking or insurance

individually.

CONCLUSIONS

Using a Solow model, we examined whether banks, life insurers, and property/liability insurers

individually and collectively contribute to economic growth by facilitating the efficient allocation of

capital. Even controlling for the traditional variables believed to explain growth, we find that the

exogenous components of banking and life insurance penetration are robustly predictive of increased

productivity across our sample of 55 countries for the 1980-1996 period. We also find evidence of

synergy between banks and insurers, thus producing greater benefits jointly than indicated by the sum

of their individual contributions.

These study findings are consistent with the traditional economic arguments that

markets including financial markets that are permitted to develop under liberal conditions

are more likely to lead to greater social welfare. Conversely, financial markets whose

development is hindered by unnecessary government and other barriers to entry and anti-

competitive policies deny their country additional economic growth potential and


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32

development.


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33

BIBLIOGRAPHY

Arrow, K. J., 1974, Essays in the Theory of Risk Bearing. Chicago: Markham Publishing Co.

Balassa, Bela, 1985, Exports, Policy Choices, and Economic Growth: Further Evidence,Journal of

Development Economics, 18(May-June): 23-35.

Barro, Robert J., 1991, Economic Growth in a Cross Section of Countries, Quarterly Journal of

Economics 106: 407-433.

Barro, Robert J. and Jong-Wha Lee, 1994, Sources of Economic Growth, Carnegie-Rochester

Conference Series on Public Policy. 40:1-46.

Barro, Robert J. and Xavier Sala-I-Martin, 1995, Economic Growth. New York:McGraw-Hill,

Inc.

Bencivenga, Valerie, and Bruce D. Smith, 1991, Financial Intermediation and Endogenous Growth,

Review of Economic Studies, 58(2): 195-209.

Browne: Mark, and Kihin Kim, 1993, An International Analysis of Life Insurance Demand ,Journal

of Risk and Insurance 60(December): 616-34

Eltis, W.A., 1973, Growth and Distribution, United Kingdom: Macmillan Press Ltd.

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Feder, Gershon 1983, On Exports and Economic Growth,Journal of Development Economics 12

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Fritz, R.G., 1984, Time Series Evidence on the Causal Relationship between Financial Deepening and

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Gregorio, Jose De and Pablo E. Guidotti, 1996, Financial Development and Economic Growth, in

Road Maps to Prosperity: Essays on Growth and Development., ed. Andres Solimano,

University of Michigan Press.

Grossman, Michael, 1972, On the Concept of Health Capital and the Demand for Health. Journal of

Political Economy, 80: 223-255.

Hsing, Yu and Wen-Jen Hsieh, 1997, Testing the Augmented Solow Growth Model: The Case of

Taiwan,RISEC, 44(3): 601-606.

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Jung, W. S. 1986, Financial Deepening and Economic Growth: International Evidence,Economic

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Levine, Ross, 1996, Financial Development and Economic Growth: Views and Agenda, Washington,

DC (September), World Bank Mimeo.

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Quarterly Journal of Economics, 108(3): 717-738.

King, Robert G. and Ross Levine, 1993b, Finance, Entrepreneurship and Growth: Theory andEvidence,Journal of Monetary Economics, 32(3): 513-42.

King, Robert G. and Ross Levine, 1993c, Financial Intermediation and Economic Development, in

Capital Markets and Financial Intermediation. eds. Colin Mayer and Xavier Vives.

Cambridge: Cambridge University Press.

La Porta, Rafael, Florencio Lopes-De-Silanes, Andrei Shleifer, and Robert Vishny. 1997. Legal

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Mankiw, Gregory N., David Romer and David N. Weil, 1992, A Contribution to the Empirics ofEconomic Growth, The Quarterly Journal of Economics 107: 407-437.

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APPENDIX I:

Countries (55) used in the 1980-1996 estimationsAlgeria Australia Austria Belgium Brazil Canada

Chile China Ivory Coast Cameroon Colombia Costa Rica

Cyprus Denmark Dom. Rep Ecuador Egypt Finland

France Greece Guatemala H. Kong Iceland India

Indonesia Ireland Israel Italy Japan Kenya

S. Korea Luxembourg Morocco Mexico Malaysia Netherlands

New Zealand Nigeria Norway Pakistan Peru Philippines

Portugal Singapore South Africa Spain Sweden Switzerland

Thailand Tunisia UK USA Venezuela W. Germany Zimbabwe


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37

APPENDIX II:

Sources of Data

Income is measured as gross domestic product (GDP). GDP figures are taken from the

International Financial Statistics (IFS) line 99b.a

Data for capital stock do not exist in national

accounts. GDI, commonly used as a measure of the yearly change in capital stock, is used as a proxy

in this study. GDI figures are taken from the World Bank Development Indicators Database. Exports

as a share of GDP is calculated from IFS, lines 90c and 99b. Government consumption as a share of

GDP is calculated from IFS, lines 91F and 99b.

The financial activity of deposit money banks is measured as the bank credit extended to the

private sector, line 32D, as a share of GDP, line 99b. The financial activity of life and property/liability

insurers is their corresponding insurance penetration, defined as gross premiums written as a percent of

GDP. Gross premium data are extracted from various issues ofSigma, Swiss Reinsurance Company.

LFit

measures change life insurer penetration and PL

it

measures change in property/liability insurance

penetration.

These growth rates approximate the growth of services provided by these institutions. Actual

investment channeled, especially by life insurers, is greater in most cases than premiums collected in

one year would suggest, as insurers accumulate investments from earlier periods. Life insurer reserves

would be a more accurate approximation of the investment function but these data are unavailable

internationally.

aLine numbers refer to entries in the International Financial Statistics, as compiled by the International Monetary

Fund.


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Data for legal origin and religious composition of the population are taken from La Porta,

Lopez-de-Silanes, Shleifer, and Vishny (1998). The corruption and bureaucratic quality measures are

taken from theInternational Country Risk Guide 1999.

Documents

Banking& Insurance Growth