D O C U M E N T O D E T R A B A J O

Instituto de EconomíaTESIS d

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GÍSTER

I N S T I T U T O D E E C O N O M Í A

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Impact of Financial Development on Economic Growth:An Empirical Assessement for Chile

Luis Ignacio Valenzuela.

2009

TESIS DE GRADO

MAGISTER EN ECONOMIA

Valenzuela Rivera, Luis Ignacio

Agosto 2009

PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE I N S T I T U T O D E E C O N O M I A M A G I S T E R E N E C O N O M I A

IMPACT OF FINANCIAL DEVELOPMENT ON ECONOMIC GROWTH:

AN EMPIRICAL ASSESSEMENT FOR CHILE

Luis Ignacio Valenzuela Rivera

Comisión

Juan Eduardo Coeymans

Luis Felipe Lagos

Agosto, 2009

ABSTRACT

This thesis evaluates whether Financial Development have had an impact on Chilean economic growth in

the last thirty years and in which way. Following literature regarding the issue, the relation is evaluated

for the main channels by which the financial sector is supposed to affect aggregated product: via Total

Factor Productivity and via physical capital accumulation. Empirically, the former is tested using a

Financial Development proxy in a typical aggregate production function where the proxy is included as a

determinant of Total Factor Productivity. The latter channel is evaluated using the same proxy but in an

investment function. Different variables are used as proxy for Financial Development, which different

results, very likely representing how diverse is indeed the financial sector as each would be representing

different aspects of it. Given the large literature (mainly cross-section) regarding the effects of the

financial sector on economic activity and the practically inexistent empirical evaluation of this hypothesis

for Chile, this study adds to the current literature about growth determinants for Chile. Results show that

Financial Development does have an important effect of GDP via the TFP channel, but sector has to

growth fast enough to give a clear boost on growth. There is a remaining uncertain growth component

which is not easy to detect given large colinearities present in the data, effect that may partly come from

the financial sector but also from other factors like education attainment. Regarding the physical capital

accumulation, this does not to respond to any Financial Development indicators, although there could be

some theoretical restrictions in this link given the inherent higher-frequency characteristics of the

investment function. Still, results are robust to different specifications and indicators. Overall, this study

supports existent empirical evidences in that Financial Development has a significant effect on GDP and

that the most important channel for this is TFP.

1. Introduction

The relationship between financial sector and economic activity has been focus of a growing

literature in economics, which first relevant lines started theoretically with Schumpeter (1911) and

empirically with Goldsmith (1969) and was fostered later in the empirical level with a series of works by

Jung (1986) in time series and King and Levine (1993a, b) in cross-section analysis. Since then, literature

developing formal models and testing different hypothesis for the link between financial development

(henceforth FD) and economic activity has not stopped to come, also using the last econometric

techniques available. An extensive survey on this area can be found in Levine (2005). Results on

evaluating the relation mentioned above are clear in terms of the important correlation between FD

(measured in many different ways) and economic growth, results mainly coming from cross-country

literature. Nonetheless, this correlation opens the question of causality, if any, between finance and

growth. Always is possible that empirical correlation is due not to causality in some direction but

because of a third variable in common fostering both FD and GDP. This question is crucial for policy

issues because it shed lights on the real relevance of the financial system on the long run output level

and whether policy makers has or not a relevant role to play here. Perhaps, as Lucas (1988) suggested,

economists are over-stressing the financial factors importance on economic growth.

Several ideas have been pointed out about the FD-GDP link. One line argue that financial sector

develops exogenously of the economic activity, promoted by reforms, institutional change and

innovations, thus creating more and better services to firms and entrepreneurships, and providing

financing to high yield – but riskier projects, so promote growth. Other line states that it is the demand

for financial services by firms what makes the sector evolve. Growth would impulse firms to demand

financial services as they need financing to their projects and as more firms and households put their

resources in the financial market. Some mix both ideas and/or add further complexities like

nonlinearities to the dynamic of the relation, for instance, that the strength of the link depends on the

income level of the countries. Regarding empirical evidence, it seems to be no conclusive for any

particular theory, with positive evidence for each link and sometimes contradicting other’s results1.

Even though literature suggests the existence of a two-way relationship, this paper focuses only on the

first link, which is from FD to GDP. The reasons behind this are two. First, this work is already long

1 Literature is susceptible to suffer from publication bias – this is tendency to publish more studies that provide

positive findings to a hypothesis. It is not very elegant to show studies with no “interesting” results, even though they are as important as the rest.

enough at evaluating the first relationship. Second, the first effect is quite more appealing in terms of

economic theory, welfare and policy issues. The impact on the size of the economy (GDP) of how

financial sector evolves is also interesting, but here it is only taken in consideration in order to avoid

endogeneity issues.

This paper studies the particular case of Chile in a time series context, for the 1980-2008 period2. The

FD-GDP link is tested in two different ways. The first one is to include a FD proxy in a typical aggregate

production function, as one of the many explanatory variables for Total Factor Productivity. This is a

direct channel and one of the most important according to the literature. Opposite to the mainstream

examples for Chile, no artificial TFP variable is created but regression is directly run on GDP. The second

approach is to test the impact of FD in physical capital accumulation, estimating a basic investment

function. This is an indirect channel since higher investment means higher levels of physical capital and

then an increase in GDP. Investment is regarded in literature as the second most relevant channel in the

FD-GDP link3.

Financial development is measured here with three indicators, following the general literature for this:

Private Credit, Liquid Liabilities and Traded Value. They are defined as a ratio to GDP and are described

later.

The importance of this study is to contribute to the time series literature regarding the causality issue

among finance and growth and to fill an existent gap for Chile with regard to this topic. Many papers

have studied growth determinants for Chile, yet almost none of them assessing empirically the effect of

financial sector on growth (either directly or indirectly). The closer analysis has been related to the

several economic effects of the Pension Reform of 1981, which had clear effect on the development of

the financial sector but does represent only a part of it. This paper also tries to make clear the

importance of country analysis, particularly regarding the different policy reforms and institutional

changes in which countries go through, issue that cross-section studies do not fully account.

Findings of this work are in line with what general literature suggests, and also with particular studies for

Chile that use a more or less similar methodology. With regards to the first channel evaluated here (FD

on GDP via TFP), model found shows an economically significant effect from the cyclical component of

2 Most of the variables evaluated here have data available before 1980 but economy has been by so many

disruptive changes previously that in order to avoid structural change problems, sample starts in 1980. 3 Other channels are for instance human capital accumulation and changes in savings rate. See Section 2 for more

details.

FD into growth, together with other traditional variables like education and government expenditures4.

Perhaps the most characteristic issue on this equation is the exogenous growth that could not be

explained, given by the trend included in the final model. Indeed, a large array of variables was tried as

potential determinants of TFP but in the end including a trend was the only way to find a good

specification, this is a stable model with reasonable coefficients and successful against the traditional

econometric tests5. The cost of this is that total effect of FD could not be measured as it is shown that

exogenous growth can be explained either by FD or by other variables like education level.

Notwithstanding this uncertainty, the certain effect that is found from the cyclical component of FD is

economically significant so it lets to move further. One true weakness of estimations here is that only

one FD indicator was found significant, while the rest did not replicate those good results. However, it is

argued then that this would be showing that FD indicators are indeed not the same and reflect different

aspects of the financial sector.

As regards to the indirect channel (investment), FD indicators showed no explanatory power in the final

equation, which is robust to different specifications and tests. It is also more or less in line with other

works. The drawback here is that there could be specification problems inherent to the investment

function in the sense that this should be better studied using higher frequency data (quarterly as other

authors do) instead of yearly as in here. This is also a cost to be paid given the nature of the question,

which is related to the FD effect on GDP (a long run relationship without doubts). This result is also

robust to the different FD indicators used here.

The paper continues as follows. In section 2, theory and evidence about the issue is revised. It includes a

general review and a particular for Chile. Section 3 focuses on the FD indicators generally used in

literature and describes the chosen here. Next section describes the model in which the estimation is

based, providing theoretical explanation for the specification and variables included. Section 5 explains

with some depth the econometric methodology together with the final definitions for the FD indicators.

Section 6 presents the empirical results for the three approaches used here. Final section concludes with

some thoughts about the findings, pointing out as well some shortcomings of this work.

4 FD variable had to be “detrended” in the final model. This is why the effect found from FD into GDP is for the cyclical component of the indicator. This will clarify in the results section. 5 Some of the specifications found by other authors were tested here as well without success. On behalf of these

works it can be said that period of estimation and some variables definitions are not exactly the same. Yet, some models did result as the original ones but were no robust to some important tests like cointegration and stability.

2. Theory and evidence

i) General literature

Financial sector has become a very relevant topic for economist on trying to understand economic

activity and growth. The sector’s size and complexity has been growing constantly since Industrial

Revolution (existing before though), deepen its links with the productive sector and therefore related to

both booms and bursts, as the so called Great Depression recently showed. Not many deny the

relevance of the financial system in the modern economies. Many theoretical works has been written in

this issue, starting as far as Schumpeter (1911) who emphasized that the services provided by financial

intermediaries are important for innovation and development because the financial sector successfully

identify and fund profitable projects. While empirically the positive correlation between FD and

economic growth is clear, the direction of causality (if any), maybe the most relevant issue on this topic,

has been constantly a cause of dispute among economists.

Generally speaking there are two lines of thoughts regarding FD-GDP link. Although many of the models

developed theoretically are in the context of a single country, their empirical testing has been far more

based on cross-section techniques than time series. Hence, most of the literature revised here is on that

line, opposite to the approach applied here.

The first and wide-spread hypothesis, the supply-leading financial development hypothesis has been

supported by many works like McKinnon (1973), Shaw (1973), and King and Levine (1993a, b) just to say

a few6. Here, causality goes from a wider, deeper and more efficient financial sector to higher growth

rates. The services that financial system provides to the economy may be grouped in these: 1) it

generates information about firms and projects, improving efficiency in the allocation of funds among

them and hence fostering economic growth; 2) corporate government and monitoring of firms is

boosted by the participation of financial institutions on the directories; 3) risk management is improved

with access to diverse diversification and trading opportunities of it; 4) because innovation is risky,

better risk management foster more and better innovative projects (this is projects with higher

productivity); 5) more savings channeled to both banks and non-bank institutions and markets and so

increasing investment; 6) finally, a better financial system implies lower transaction and informational

6 Agreement on the causality direction does not imply agreement on the channels by which finance impact growth

and its relative importance. This matter is also of constant debate. Some of the suggested channels are an increase in private savings rate, in factor accumulation (physical and maybe human capital) or via productivity change. For a recent analysis of this issue, see Beck, Levine and Loayza (2000b).

costs, thus facilitating specialization and exchange of goods and services. For an extensive review of

theoretical suggestions of these functions and their empirical assessments see Levine (2005). On this

line, the financial sector develops mainly exogenously through the improvement in institutions (property

rights protection, regulation and supervision, etc), informational and contractual framework, efficient

adaptation to changing environment (flexibility), and technological innovation, none of them directly

related to economic growth.

Empirical evidence of this relationship is plenty. In a cross section study, King and Levine (1993) use a

cross-section analysis regressing growth (1960-89) on previous financial depth (M2/Y in 1960) to avoid

endogeneity of contemporary M2/Y. Sample includes 77 OECD and developing countries. They show

that countries with an initially (1960) less developed financial system grew at slower rates than

countries with initial more developed financial systems. For example, the difference in yearly growth

between the top quartile and the bottom quartile of FD is 3.2% versus 1.2% per year.

In a very prominent paper, Rajan and Zingales (1998) find that industries more dependent on financial

sector grow at higher rates in countries with well-developed financial systems. This implies that causality

goes at least from financial deepening to economic growth. In another relevant study, Levine, Loayza

and Beck (2000) see that differences in legal origin among countries (civil, common, German or

Scandinavian-based legal system) predict differences in both financial level and growth between them.

They conclude that the effect of the legal origin on growth goes probably through FD, setting a clear

causality direction. Because simultaneity is a potential problem, other works like Beck, Levine and

Loayza (2000) propose the use of GMM dynamic panel estimators to run robust estimations. They find a

robust relation from finance development to economic growth, specifically through total factor

productivity growth. However, in this approach, the integration and cointegration properties of the data

are not considered, and therefore estimated panel models may represent a spurious relationship

between FD and output growth instead of a structural long run equilibrium one.

More in detail, literature points out four major channels by which the financial sector foster growth:

private saving rate, human capital accumulation, physical capital accumulation and TFP, although

empirical evidence generally highlights the last two7. Benhabib and Spiegel (2000) use both neoclassical

7 Beside these channels there exists another channel hard to test empirically: FD may provoke structural change in

the economy. In the context of a production function, this means that FD can change elasticities of labor, human capital and coefficients for other variables. For instance, is argued that macroeconomic instability in the way of inflation may have detrimental effects on economic activity. However, the better the financial sector (high

and endogenous growth models to show that TFP and investment channels exist, although the impact

differs with the financial indicator chosen. Beck, Levine and Loayza (2000) find robust positive links only

with the TFP channel. For the rest, the significance of the coefficients depends on the measure used for

FD. Rioja and Valev (2004b) extend Beck, Levine and Loayza´s work using a GMM estimator, finding that

both channels are important but varies with the income level of countries. Capital accumulation would

be the central channel in developing countries while productivity would be in more advanced

economies. This channel’s issue is very important to have in mind as different models helps to evaluate

different channels. It is not correct to say that one model or specification is able to enclose all kind of FD

effects on growth.

The second line of thoughts in the finance-growth link is the demand-following financial development

hypothesis. In this view, economic growth implies more demand for financial services and therefore,

exerting a force on the financial sector. Robinson (1952), Gurley and Shaw (1967), Goldsmith (1969) and

Ireland (1994) are some examples on this line. On the one side, economic activity itself uses the financial

sector in their normal transactions (excess funds, financing needs, portfolio management, transactions,

etc), fostering its development. On the other hand, considering that GDP is very much related to

payment to factors of production, part of this wealth is transferred to the financial system in terms of

savings. In this sense, pension funds, mutual funds, bank deposits and private credit are fostered by

wealth creation. Given the different needs of the each agent of the economy and their continuous

change, financial sector develops new instrument and securities like derivatives for instance, which have

boomed in this decade, both in the developed world and in Chile8. On another respect, given that

financial structure is costly in terms of information and transaction costs and very likely present

economies of scale, a larger economy improves the sector’s development supporting its efficiency.

Notice that what matter is not much the increase in the level of a FD proxy (a monetary aggregate or

credit for instance) but as a ratio to GDP, because it signals that the economy presents a larger financial

sector relative to the size of its economy.

diversity of financial instruments, nominal protection and proper international market integration) the less damage inflation may have. In other words, independently of how FD affects inflation, for the same level of inflation, damage on the economy may be lower the more developed the financial sector is. The latter means a change in inflation coefficient. Notice that this FD effect is possible to evaluate using the structural change techniques, although difficult to detect in short time periods. It is difficult also because there could be other factors generating structural change. An interacting term may be one solution to empirically test this structural change. 8 See any BIS derivatives report for measures of this sector’s size and its evolution around the world, and Orellana

and Rodriguez (2008) for Chile.

The hypotheses detailed above are basically assuming a linear relation among FD and GDP. Nonetheless,

the issue may be more complicated, especially in terms of bi-causality, thresholds and nonlinearities.

Greenwood and Jovanovic (1990) present a model in which both financial intermediation and growth

are endogenous. They show that there is a positive two-way causal relationship between economic

growth and FD. On the one hand, the process of growth stimulates higher participation in financial

markets via facilitating the creation and expansion of financial institutions. On the other hand, financial

institutions, by collecting and analyzing information from potential investors, allow investment projects

to be chosen more efficiently and, hence, stimulate investment and growth. Acemoglu and Zilibotti

(1997) is another example of theoretical justifications with regard to bi-causality.

Several empirical studies find this bi-causality, mainly testing for causality a la Granger. A recent paper

on this is Apergis et al. (2007). They employ dynamic panel data integration and cointegration for 15

OECD countries over the period 1975 to 2000. The main finding is a long-run bidirectional relationship

between financial deepening and economic growth.

Patrick (1966) was the first in formally considering those complexities and started what is called

sometimes the “theory of stages of financial development”. He suggested that probably the relation is

simultaneous and that depending on the stage of development of the country, one force is stronger

than the other. Because of several restrictions and inflexibility of financial markets in early stages of

development, demand for its services does not automatically imply the supply of them. There, policies

and institutional changes are the main force in the financial sector evolution which then creates a

facilitator effect for growth. In this process, the financial sector becomes more efficient and therefore

can provide better responses to the demand for its services (which always exist). At certain moment,

demand would be a strong force in shaping financial sector development than supply restrictions easing.

Patrick also considered that these stages could run at the industry level with different industries in

different stages depending on their level of financial restrictions. Calderon and Liu (2003) indirectly test

this hypothesis using a panel of 109 countries for 35 years. They find that, among other things, while

causality between FD and economic growth is in both directions, the link on finance to growth is

stronger in the less developed countries, supporting in some extent Patrick´s hypothesis.

From a methodological approach, there is certain criticism to the cross-country studies conducted on

this area. Assuming that the “beta” (FD coefficient in a GDP regression) is the same for all countries, no

matter their financial and income level, or that such thing like a “representative country” is valid is a

strong assumption in terms of no respect to heterogeneity in each particular growth process, no matter

how robust correlations are measured. This is particularly relevant regarding policy implications because

countries are at different stages in their development and the “representative country” may differ a lot

with them. Therefore, grouping all countries in the same sample can be a very dangerous process. For

instance, several studies have found that the relationship among FD and income growth depends on the

country group’s selection and samples used. Fernandez and Galetovic (1995) use King and Levine’s

sample to split it in two groups, OECD and non-OECD countries and show that for the former group the

finance-growth link is insignificant. De Gregorio and Guidotti (1995) use a sample of 98 countries from

1960 to 1985, merged into three groups regarding their initial income level, finding that the poorer the

country, the more significant the correlations are. They argue that a higher development of the non-

banking financial system in richer countries may explain this because they use a banking indicator as the

proxy for FD. Also they find a negative correlation for Latin America, probably showing the negative

effects of the strong financial liberalization without the proper regulation carried in these countries.

Rioja and Valev (2004a), using a GMM dynamic panel technique, find that there are three regions

regarding the effect of FD on growth, with two threshold with respect to financial level. The “low”

region shows an uncertain link, depending of the measure used for FD. Above the first threshold (the

“middle” region), they find the strongest positive relation of finance on growth. The higher region also

show a positive link but weaker. Authors mention a theoretical justification for this scale effects as

representing a low equilibrium (or poverty trap) for the lower income countries.

Evidence from time series studies is rather scarce regarding FD-GDP relationship. Although there is

abundance of papers for individual countries explaining factors behind GDP and Total Factor

Productivity growth, not many uses FD as a direct determinant of them. For Chile there are a few

papers, as mentioned later.

ii) Literature for Chile:

While in several of the cross-section and panel analyses in the literature revised above Chile is included,

is not desirable to conclude that they take the whole picture of Chilean growth and linkages with the

financial sector, especially considering the particularities of the Chilean economic history. These are

important factors that must be well thought-out for Chile, including policy reforms, external crises

impact, political instability, and so on.

There is an extensive literature regarding economic growth for Chile, using different methodologies,

variables and data. For an extensive review see on this see De Gregorio (2005) and Schmidt-Hebbel

(2006). The interest here is to focus on those studies that stress the impact of FD as a determinant of

growth, at least in the short-run (no steady state). The only paper found that evaluates a direct impact

of a FD proxy on GDP is Holzmann (1997). He empirically assess the effect of FD on TFP using two

different base indicators: one based on liabilities of the financial sector (including pension funds, mutual

funds and insurance companies) and the other based on the stock market. Author finds only a significant

effect for the former indicator although he point out two drawbacks: small sample (1979 to 1994) and

FD to be correlating financial liberalization and openness. He also does not take any analysis on

stationarity and cointegration of data, which shed further cautions on his results. No other paper is

found following the methodology used here.

Nonetheless, many papers evaluate the impact of particular Chilean reforms on growth or TFP. While

this evaluation is not directly considering a financial indicator, it is plausible to assume that some of the

reforms impact GDP through a better financial sector. Corbo and Schmidt (2003) for instance evaluate

the effect of pension reform on savings and investment, employment and labor productivity and in

capital markets. Then they link these effects to growth finding important effects of the reform mainly via

factor accumulation and their efficiency. Fuentes, Larrain and Schmidt-Hebbel (2006) use abroad index

of structural reforms, a method that was initiated for Latin America by Lora (1997). They use this index

as an explanatory variable for TFP, finding significant results. However, the construction of the Index is a

matter of criteria and hence opens to criticism. Indeed, it is not pretty clear what it is measuring because

they construct the index as a mix of five different indexes for trade reforms, financial liberalization, tax

reform and others. Hence, it may reflect impact of other reforms instead of those impacting the

financial sector. In a different approach, Bergoeing, Kehoe P., Kehoe T. and Soto (2001) compare the

behavior of Mexican reforms with Chilean ones in the impact on growth during the 80’s and 90’s. They

argue that the banking reform in Chile (privatization of banks, deregulation, foreign competition, low

reserves requirements) together with the 1982 bankruptcy law reform explains much of the differences

in growth, although they use growth accounting instead of an econometrical approach to support their

hypothesis.

With respect to the indirect effect of FD on investment, Vergara (2004) analyze the determinants of

private investment rate from 1975 to 2003 mainly focusing on the steep tax rate reductions occurring in

the 80s. He includes Private Credit in his equations, finding no significant effect from this variable on

investment9. Cerda and Larrain (2005) also study the tax effects on investment, including a FD proxy as

an interactive term with the tax rate in order to assess whether a more developed financial sector

alleviates credit restrictions to firms, finding positive evidence on this. Dominichetti and Roeschmann

(2006) also includes a FD proxy (credit ratio to GDP) when explaining investment using an error-

correction model with quarterly data. Although they find a positive effect of credit, the economic effect

is rather insignificant.

Therefore, this study is relevant because it fills a hole in Chilean literature regarding the FD link on

growth via TFP, and expands existent evidence regarding the second channel, via investment. The

former is perhaps the most important contribution since there large existent TFP literature for Chile

does not consider FD as a direct variable despite of the general literature which suggests this

extensively.

3. Financial development

i) Concepts

The concept of financial development is so wide and open to debate that a theoretical discussion of it is

out of the scope of this work. Still, some basic lines are drawn next on the concept in order to generate a

common ground on it, enough for this study.

Financial Development as a “stock” may be defined as the quality and quantity of services provided to

firms and households by the financial sector (banks, stock exchanges, mutual funds, pension funds, OTC

markets and so on) in order to help them optimize their decisions and expand their opportunities

available. Financial Development as a “flow” may be defined as the process of increase in those quality

and quantity of services. The interpretation used here is the first one, this is as the level of development

of the sector.

The main issue here is how to measure FD. From the above definition it is clear that a good indicator

should not only measure a size but also quality, flexibility, fragility, structure, efficiency and in general

the six functions of the financial system mentioned in Section 2. Moreover, financial sector includes

many markets (banks, stocks, bonds, money, etc) together with different intermediaries (public and

private, banks, pension funds, insurance companies and so).

9 He uses a different sample (here from 1981 to 2008), a different investment definition, and different regressors,

where Private Credit is also differently defined.

This study does not try to innovate with this regards and uses some of the traditional indicators in

literature. These are generally size-based and implicitly assume that size is positively and highly

correlated with quality. As many financial crises have shown, including the recent sub-prime crisis, size

may be a bad indicator of quality. Chile is not absent on this issues, as banking crisis of 1982 revealed.

However, due to complexity of measuring those other properties of the financial sector, it is assumed

that in a long run period, the size of a FD indicator is able to capture the rest of financial system

characteristics.

ii) Indicators

There are many possible candidates for FD indicators, all with their strengths and weaknesses. Here it

follows a short description of the main indicators used in literature. Notice that the following indicators

are proxies for FD as a “stock” and therefore their change is measuring the evolution of the financial

sector, that is FD as a “flow”.

A first type of indicator, very used in the literature is based on a monetary aggregate, usually M1 or M2.

While an aggregate is telling something about the level of liquidity for exchange and the saving services

provided by the financial sector, it is not necessary related to the efficiency of credit allocation or

improved investment opportunities. Moreover, a highly monetized economy may be showing that there

are not many alternatives to keep wealth or that financial sector is unable to provide enough financial

contracts and therefore imposing liquidity constraints to the economy. Bencivenga and Smith (1991)

show that a higher level of liquid assets relative to GDP may come from a low-developed financial sector

and not from a developed one because agents self-insure against liquidity risks. One way to decrease

these problems is using a broader definition of money (M3 if possible) where currency is a lower fraction

of it. Other authors directly remove currency to M2, indicator often called Bank deposit liabilities

(Demetriades and Hussein 1996, King and Levine 1993a).

Looking at the asset side of the balance sheet of financial institutions is another way to evaluate the

development of the sector. Some authors (King and Levine 1993a, Rioja and Valev 2004b) suggest using

the ratio of commercial banks (deposit money banks) domestic assets to the sum of commercial banks

and central banks’ domestic assets. This indicator measures the extent to which commercial banks

allocate society's savings. It is supposed that commercial banks are better in promoting efficient

investments and other financial services than central banks. This measure has some drawbacks like it

does not consider other financial institutions and cross claims with them, the government ownership of

some banks and firms, and, perhaps the most important drawback, that it does not consider to whom

credit is issued.

One indicator that overcomes some of these weaknesses is Private Credit to GDP, used in several recent

studies (Demetriades & Hussein, 1996; Beck et al, 2000; Levine et al, 2000; Rioja and Valev, 2004a). This

indicator considers the credit extended to the non financial private sector (not to government, public

enterprises or other financial institutions) only by the financial intermediaries (neither central bank,

public-owned banks and development banks are considered). The benefit of using only private credit is

that it supposes to increase efficient investments and productivity more than credits to the public sector

(Akinboade 1998, Levine and Zervos 1998). A shortcoming for private credit is that investment is not

only financed via domestic credit but also using foreign credit, a particularly important issue in

developing countries, whereas very likely Chile is not an exception. Therefore, this indicator is not

capturing all the credit impact on growth.

Regarding the capital market, many indicators are used for equity and bonds. Two characteristics

ascribed in literature as showing a more developed stock market are its size and liquidity. The former is

measured as the total market value to nominal GDP (market capitalization ratio) and the latter as the

value of the traded shares on domestic exchanges over the total market value (turnover ratio). Theory

suggests that securities markets are especially good in providing liquidity and helping investors to

diversify portfolios10. However, a problem of a stock size indicator is related to the future nature of stock

markets. In some extent they reflect expectations of future firms’ growth and, because market

capitalization is measured at the price of traded stocks, its change would be more related to price ups

and downs than to a more or less developed market, particularly marked in stock booms and crashes.

This price issue weakens the ability of the indicator to precisely represent how much firms use stock

market as a risk management instrument, an imputed benefit to it. One solution to this problem is to

adjust the indicator by the price index of the stock market, as suggested in Gallego and Loayza (2000).

The other main component of the capital market, i.e. bond market, has not received much attention in

the literature regarding its growth effects. Debt securities are an important source of corporation´s

financing too, hence also playing a role in fostering investment and better risk management. Herring and

Chatusripitak (2000) argues that the relative underdevelopment of bond market is due to its complexity

of pricing risks (default, liquidity, etc) and because of that, credit and stock markets are usually a better

10 For instance, see Beck and Levine (1991), Levine and Zervos (1998) and Rousseau and Wachtel (2000).

financing option if there is not a strong financial infrastructure (accounting practices, reliable bond

ratings and so on). Nonetheless, there is not perfect substitution between those alternatives and bonds

in terms of their functions (for instance, banks generally lend for much shorter periods or charge

relatively higher rates). If firms tend to match the maturity of their assets and liabilities as Caprio and

Demirgüç-Kunt (1997) states, investments will bias against long-term assets. They say also that the

absence of a bond market hinders the development of the derivatives markets due to lack of market-

determined term structure of interest rates, including risk-free ones (public bonds). Last but not least,

bonds enhance competition in the banking sector, which may otherwise extract rents to different agents

like ventures for example. Outstanding value (or new issuance) of domestic private and public bonds is a

possible indicators to proxy bond market development. One drawback of this indicator would be that

new issuance may be related to cycle (similar as initial public offerings in the stock market), as firms

would tend to issue bonds when interest rates are low and/or growth prospects are positive. Still this

pattern may be diminished using the indicator as a ratio to GDP.

This study uses three different indicators in order to compare results and provide robustness to the

empirical assessments. The first one is Private Credit (called RFDPC). It is measured as all the credit given

by the banking sector (which for Chilean case includes “Banco del Estado”, a state-owned bank but

which has been and still is a very big actor in the market). This measure includes only resident banks and

it includes credit that during the banking crisis was kept in Central Bank documents. It does not delete

the interbank credit due to the difficultness to find data on that. However, data available for recent

years indicates that its size is insignificant. This measure does not include only credit to private sector

but it is expected that credit to public sector or to abroad entities is rather low11.

The second indicator (called RFDLL) is a monetary aggregate, M7. This is the broadest definition for

Chile, and in some extent similar to the M3 definition used in developed countries12. Later, to confirm

results, this indicator is adjusted taking out currency in circulation from it. Variables are very similar.

These first two indicators have been corrected following the literature in order to solve the stock-flow

problem. This is, Private Credit an M7 are both measured in December and are deflated by the end-of-

11 Historical data does not present classification by sector so this is not a decision of the author.

12 See Arraño (2006) for detailed M7 definition and for comparisons with other countries.

the-year CPI. Then an average between two consecutive years is calculated, which is the resultant

variable (over real GDP then).

The third indicator is Traded Value (RFDTV), which is the value of all the shares transactions in the

Santiago’s Stock exchange13. This indicator is deflated by a stock’s price index (the general index of

stocks prices, IGPA in Spanish) in order to correct for the price effect on it, which would otherwise

generate a very endogenous indicator14.

Figure 3.1 shows these three variables.

13 There are two more Stocks Exchanges in Chile. However, the used here is by far the most important in transaction’s volume. Notice that it includes only transactions of shares, not bonds or other instruments traded in stock exchanges. 14

Endogeneity could persist due firms tend to put out new shares when the market is “bullish” and not “bearish”. All this implies that even deflating for a price index would generate a cyclical variable. Indeed, this is what RFDTV shows.

FIGURE 3.1

Three different Financial Development indicators used in this study: credit, M7 and Traded Value

Figure 3.1 shows that RFDPC increased quite significantly from 1980 up to the banking crisis of 1982

(variable started its growth in 1975, not shown). This credit boom is a complete change with respect to

prior years. Indeed, from 1960 to 1975 RFDPC remained relatively constant. The huge change in the

trend from 1975 should be related to the new economic orientation, fostered by financial reforms and

liberalization jointly with other structural reforms. These factors, together with a booming economy and

a very lax regulation fostered a credit boom that crashed around 1983. Economy also crashed, which

explains why the index does not fall in that period (yet stopped its growth). Indeed, it starts to decrease

around 1986, once the economy entered into a very rapid growth path. In this period, credit was not

growing enough, situation that changed then in 1993, when GDP is still growing very fast. Immediately

after the Asian crisis, pace of growth on RFDPC slows down to then accelerate fast until 2008. Look this

variable as a good proxy for FD? Perhaps the most relevant issue here is that the credit boom from 1975

to 1982 lead to a large economic depression later. If FD is to be taken as the quality of the financial

.3

.4

.5

.6

.7

.8

.9

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

RFDPC

.2

.3

.4

.5

.6

.7

.8

.9

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

RFDLL

.00

.04

.08

.12

.16

.20

.24

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

RFDTV

sector, then the proxy seems not good enough, at least in that period. Nonetheless, even though this

credit boom was related to bad regulation, its effect on activity cannot be discarded. In fact, the credit

boom was very likely related to the increase in the rate of investment around the period (see RINV

graph in Appendix 1) and therefore related to growth. Notice also that pension reform (1981) and

banking reform (1986) seems not to be captured by RFDPC. Effect from these on activity may be not

captured by this FD indicator. Perhaps this is not a good characteristic of the indicator.

Behavior of Liquid Liabilities is somehow different from credit. RFDLL shows an interesting path with a

particularly increasing similar rate before and after the banking crisis. From 2001 on the indicator

started to decrease, but recovering later. This proxy seems interesting in the sense that it may reflect

the many reforms on the financial sector, which foster a constant development of the financial sector.

Notice that the initial structural reforms of the Chilean economy in the second part of the 70’s seem not

to generate a large boom in LL indicators like before. This may be a better property of the indicator. The

decrease of RFDLL during the 00’s is however a strange issue with no clear explanation. Yet, this

indicator seems very good.

Turning to Traded Value, RFDTV, it comes out immediately the strong volatility it presents, beside its

slight upward trend. One explanation behind this would be no more than the dependency of stocks on

expected and present GDP. The index used to deflect the variable seems not to rule out its dependency

on the cycle. This issue makes RFDTV a very bad proxy for financial development.

Summarizing, it seems that RFDPC and RFDLL are the best indicators although they show some

departure from what it seems logical from the narrative financial sector development approach.

Interestingly, the different behavior of RFDPC and RFDLL would indicate that both reflect different parts

of the financial sector. For example RFDPC, as one particular instrument among others, it may show a

slowdown in its upward trend due to substitution effects, issue that LL does not show. Furthermore

RFDLL seems less dependent of the cycle than RFDPC and without showing a bubble on it. RDFTV look

definitively as the worse proxy.

4. The model

As already mentioned, this study evaluates two different channels by which FD may affect activity. First,

the effect of FD on GDP is tested using an aggregate Cobb-Douglas production function, where a FD

indicator is considered part of the Total Factor Productivity and hence has a direct effect on GDP

growth. Special care is taken in order to avoid potential simultaneity negative effects. Second, a basic

investment function is estimated including FD as a potential explanatory variable. This is an indirect

effect as FD would impact the evolution of the capital stock and through this impact output. The models,

equations and variables included in each one are described next.

i) Direct channel: Cobb-Douglas function

The econometric analysis here is based on estimating a traditional Cobb-Douglas production function for

GDP. Most of the growth literature for Chile does not use this approach but first generate a TFP series to

use it as the dependent variable. There is no consensus about the capital elasticity although 0.50 is a

typical assumption, which is around the average elasticity deducted from national accounts15. The

problem with that methodology is that it makes an unnecessary assumption, as elasticity can be

estimated from regressions. Also constant return to capital hypothesis can be evaluated there, instead

of considering it as a fact. The ex-ante assumption that is made here is that an aggregate production

function exists and following a Cobb-Douglas technology. This may be not obvious for several reasons.

First, aggregate economy is the sum of different sectors, each with different production functions and

with evolving contribution in activity. For instance, data from national accounts shows that

manufacturing sector has average “empirical” capital elasticity around 0.5; Retail sector capital elasticity

is around 0.75; and it is around 0.67 for Agriculture sector, all elasticities that also change yearly.

Moreover, when public administration and non-profit institutions are deleted from the elasticity

calculation (trying to capture the productive sector of the economy), average capital elasticity increases

to 0.56, reflecting that those sectors are more labor intensive (indeed, non-profit institutions have no

capital income by definition). All in all, the assumption of an aggregate Cobb-Douglas function is

necessary to the following analysis16.

In order to understand comprehensively the factors behind GDP movements and conduct a good

specification of the model, a simple analysis is done based on the classic microeconomic understanding

of a production function. First, let define a usual Cobb-Douglas function such as:

15

Capital elasticity for any year is calculated as the ratio between capital income and total factor’s income of that year. Theoretically this is correct for an economy that is in its frontier (optimum) and without any departure from perfect competition. 16 Perhaps the better way to understand aggregate growth is at the industry level. Very likely capital intensity, individual factor productivity and total factor productivity differ at that level. The variables used to explain TFP here are also likely to generate different impact on each industry. Technological transfer for instance would definitely have a larger effect in Mining sector than in Retail sector. The only paper found that use disaggregate sector analysis is Coeymans and Mundlak (1993).

tttttt ZhLKAGDP 1

(1)

where the dependent variable is real GDP; A is the “adjusted” Solow Residual; K is physical capital stock

measured at the end of the year t (and hence included with one lag); L is employment; h is a human

capital index; and Z represents the factor’s rate of utilization17. The latter effect accounts for the fact

that production can be suboptimal, this is the economy may not be producing on the function isoquant

but below it. This effect, rather strange in reviewed literature, is taken from Coeymans (1999a, b). He

argues that it is necessary to consider not only the level of factors available in the economy but also its

rate of use. Along the cycle, rate of use of factors change considerably as firms adjust production not

only through the amount of factors hired but also varying the hours and intensity these factors are used,

due to (fixed) costs of adjustment like hiring, firing or non-linearities in the cost of capital. This would be

more relevant in the recession side of the cycle.

Equation (1) says then that GDP moves by four forces: (i) changes in quantity of factors; (ii) changes in

quality of factors; (iii) changes in A (where FD is expected to play a role, among others); (iv) movements

to or from the optimal production frontier. Figure 4.1 shows these effects.

17 The traditional TFP definition is not equivalent to the term A in equation (1) but as all the other components

affecting GDP beside labor and physical capital:

tttt ZhATFP

FIGURE 4.1

Four different effects on aggregate output: quantity of factors, quality of factors, total productivity and rate of utilization

Axis for each graph is defined in terms of effective factor utilization, which is a combination of quantity,

quality and rate of use. So, effects (i) and (ii) looks the same. Effect (iii) implies a translation in the

isoquant map to the lower-left side of the graph: every same labor and capital combination produces

more output than before. Effect (iv) implies economy moves from an inner point (isoquant inside the

feasible area) to a point on the maximum isoquant, which is when all factors are used at their capacity.

Notice that the latter effect is quite important because there is an increase in activity that does not

come by the amount of effective factors used or by changes in total productivity.

In order to estimate equation (1), it is necessary to define exactly which variables will be used as proxy

for their theoretical counterpart. Regarding Labor, it is defined in terms of hours and not only as the

number of employees. This is because working hours varies a lot throughout the sample so it would not

be very representative to have only employment. The variable is combined then as employment

multiplied by hours, which gives total hours worked in the year by employed population. Adjusting for

hours also helps to explicitly consider the rate of utilization change mentioned before. For human

capital, the variable used is average education years of population. This one is taken as a lagged moving

average in order to capture the delayed and slow effects that education would have in production.

With respect to rate of use of factors (Z), Coeymans (1999a, b) shows that for Chile this is an

unavoidable issue and proposes a few alternative variables as proxy for this, variables very related to the

business cycle, as the rate of use also should be. Chilean economy generally tends to present cycles

related to external stocks, particularly shocks to terms of trade, external demand for Chilean exports

(copper, etc), interest rates, capital flights and so on. Hence, the author creates a variable, called FEC

(from foreign exchange constraints), that tries to capture that external vulnerability. FEC is a

combination of many variables: nominal exchange rate, exports, capital flights, financial payments,

international reserves, transfers and imports and exports deflators, defined as a ratio to lagged GDP18. A

higher FEC may imply a positive economic environment due to higher terms of trade, more capital

inflows or transfers, together with good internal capacity of payments maybe due to higher

international reserves of the banking system, less financial payments and/or an increase in exports. The

main advantage of using FEC instead of other like unemployment rate is its likely high exogeneity, as it is

basically reflecting external shocks. Nonetheless, it has a severe weakness which is its trend behavior (it

is trend-stationary indeed). Although FEC reflects very well cycles, its upward tendency prevents other

trended variables (like FD, openness, quality indexes, etc) to be significant, “stealing the show”19.

Because of this, and again following Coeymans (1999a,b), a new variable is created from FEC, called

RECEFEC, defined as the difference from the actual (year t) FEC’s value and the maximum value it have

achieved up to t (hence never positive). This has always non positive values and it is zero in periods of

high growth and good external conditions, which makes more sense with the idea of underuse of

factors, as continuous overuse of factors seems unreal. Finally note that RECEFEC should have a positive

sign because for instance, in a downturn, the amount of factors is overrepresented, and hence, their

effect on product should be lower. As RECEFEC decreases in periods of crises, its sign is expected

positive, in order to reduce factors effect in output. Notice that working hours is already incorporated in

18

Following Coeymans (1999a, b), FEC is:

1

1 )(

tIMP

tEXPtttttt

GDPP

FPPXTRKFIREFEC

See Appendix 2 for definitions. 19 Correlation is of 0.87 when comparing the cyclical components of both FEC and log of GDP, taken with HP filters. This makes FEC variable highly significant in the regressions, as it was found in preliminary regressions. Due to this high significance, other variables are not well accounted, even if they should, due to the artificial trend behavior of FEC. This of course is unwanted and then FEC was replaced by RECEFEC, which keeps cyclical behavior but has no trend. In fact, RECEFEC’s correlation with the cycle of FEC is 0.72 and with the cycle of GDP is 0.68, again based on HP filters.

the equation so RECEFEC is accounting for another factors related to utilization rate but beyond hours of

work adjustments.

Finally, the most critical variables are the included in the term A of equation (1). Here is where variables

other than quality of factors but related to total productivity should be included.

The first one and the crucial one on this study is a proxy for FD. Including a FD proxy in equation (1)

through the term A correspond to test the TFP channel described above. Let assume that:

tuFD

t eXecA )1)((

(2)

where c is a constant, X1 are the rest of variables affecting total productivity, and ut is a random error.

This particular definition (multiplicative) for A is chosen because when a logarithmic transformation is

applied to the final model, it turns linear, hence simplifying estimations. Thus, replacing (2) in (1) and

taking logs results in20:

tttt

tttt

uZhL

qKXFDcGDP

lnlnln

lnln1lnlnln 1

(3)

Notice that specification for A does not necessarily assume an exogenous growth (like a trend) as

sometimes is done in the TFP literature of Chile. Still, later a trend is included in the model as it helps to

define the final model with respect to the financial development indicators.

Regarding the variables included in X1, this is the most difficult part of the task, as literature suggest

dozens of variables that can be used here, many already tested in Chilean literature without consistent

results. Because of this, in the process of finding a good final model many of the variables suggested in

literature were tested, in different specifications, even replicating some of the same specifications

suggested for Chilean TFP literature. The final model found is the best attempt in the author´s opinion,

given the focus of this paper (not necessarily to explain all growth process but whether financial

development is part of it or not). In the end, only a few variables remained significant in the model but,

just to evidence the initial broad selection of variables tested as part of the X1 term of equation (3), here

it follows a short analysis of them.

20 In order to test potential non-linearities, square FD is later included in the model, although without success.

One of the most intuitive factors that would be behind productivity increases are related to new

technology. There is an important literature, both cross-section and time series that stress the

importance of the sometimes called “Information and Communication Technology” (ICT), a booming

phenomena in the last decades. This boom that brought computers, barcode systems, internet and so

many other improvements and standardizations to the productive sector, have been very well studied in

other economies like US, generally finding significant effects on growth21. Beyond the obvious effects of

ICT in capital accumulation (new equipment for instance), ICT has also effects on TFP. Jorgenson, Ho,

and Stiroh (2008) US calculations show that for the period 1973 to 1995, ICT explained 64% of TFP and

8% of GDP growth. For 1995 to 2000, ICT explained 59% of TFP and 12% of GDP growth, while for 2000

to 2006, ICT explained 41% of TFP and 13% of GDP growth. Although these numbers change across

studies, the general picture seems to be that so-called ICT have been contributing very importantly to

growth activities.

For a small open economy like Chile, where technological advance is mostly imported instead of created

in the country, technology incorporation is proxy with some variable related to trade. Openness is a

typical example, measured as the sum of export and imports to GDP. This transfer phenomenon may be

also related to “learning by doing” models and “knowledge convergence” models22. Because is

reasonably to expect that learning by exporting and importing takes time and does not decrease but just

slows (or stops) in recessions, some adjustment is made here to decrease or eliminate this cyclical

behavior. Following Coeymans (1999a,b), the new variable, called MAXOPEN, is defined as the peak

among all the past measures of openness calculated in the traditional way23. Hence, in recessions and

weak growth periods, when the traditional openness variable goes down, here it does not. Conversely,

in periods of high growth, MAXOPEN goes up. Hence, this definition diminishes potential endogeneity

that affects the original openness definition. Also it is included with some lag to account for the delay in

implementing new technology. Its coefficient is expected positive.

21 See for instance Nordhaus (2002), Gordon (2003), and Jorgenson, Ho, and Stiroh (2008). 22

Some learning by doing models states that the larger the size of the market (expanded by openness to trade), the more the knowledge acquired in production, this due to movements along the decreasing medium costs function. The knowledge acquisition convergence hypothesis stress that a country will tend to catch up the knowledge level of the most advanced countries, where the pace of convergence depends, ceteris paribus, of the openness of the economy. 23

Special awareness was taken with respect to periods with strong GDP falls, where openness may goes up. These cases were studied and fixed. For further explanation of MAXOPEN construction and these particular cases see Appendix 2.

Given that transfer of technology could be one of the most important variables behind TFP, three other

variables are tested as alternatives to MAXOPEN in order to find robust results and avoid problems with

a particular definition. These are: five year average of traditional openness variable (OPEN5YR), the ratio

of capital imports to total imports (IMPCAP), and the GDP gap between Chile and US (GAP)24. The

correlation of these alternatives with MAXOPEN is 0.99, -0.48 and -0.88 respectively.

Infrastructure is sometimes pointed out as affecting total productivity. Highways, water supply system,

energetic matrix, and many other that are product of government investment may have a positive effect

on aggregate productivity. Opposite to this positive effects would be the potentially negative crowding

out impact on private investment. As public investment may be classified without much controversy as

less efficient and less market-oriented, $1 plus invested by the government may impact GDP less than

the $1×b not invested by the private (where b is measuring the intensity of the crowding out effect).

Infrastructure is evaluated here using as a proxy the real (no financial) central government investment

ratio to GDP (named INVPUB). Given above arguments, coefficient is not clear, although a positive one

cast less doubts that a negative one, in author’s opinion.

Another effect from government activity is related to general government expenditures (or the

traditional government consumption aggregate of GDP). The size of the government has usually been

associated with negative effects on activity, as larger consumption would be related to higher taxes,

negative externalities and inflationary pressures, hampering efficiency in resources allocation (Barro,

1990; Allen and Ndikumana, 1998). Here this variable (defined as GOVEXP) is also tested, taken from

national accounts. It is defined as nominal current government expenditures over nominal GDP and

tested as a moving average in order to avoid endogeneity issues. The benefit of this definition is that

government expenditures take some time in affecting economy so that total effect of government

activity is not appreciated immediately, property quite more related to efficiency than the

contemporaneous variable. Also notice that current expenditures (opposite to capital expenditures) do

not include public investment and hence GOVEXP is different from INVPUB25. With regards to its

coefficient, it is expected negative as argued above.

The final variables tested as part of X1 correspond to macroeconomic stability and a reforms indicator.

These are the most complicated conceptual issues to proxy, given the high subjectivity associated with

24

It is calculated as the difference between US GDP and Chile GDP over the sum of both variables. Output is valuated in real US dollars with 2000 as base year. 25 Correlation of 0.19.

their definition. The idea here is not to innovate and to take existing solutions from the literature. What

is been trying to capture here is the positive productivity effect that pro-market institutions and

macroeconomic stability put on the firms and the economy as a whole.

Let consider first some of the “structural reforms” that Chile has been through in the sample period.

Three major waves of reforms can be more or less defined (author’s categorization). The first one, with a

high political stability component is the transition from a “toward socialism” experimental regime with

high rigidities and general government intervention to a liberal pro-market regime. In 1975, a new tax

law entered into force (incorporating the VAT), re-shaping the government financing. It seems logical

that part of the high growth rates observed from 1976 to 1981 is partly explained by an adjustment of

the economy from the old inefficiencies to the new economic orientation. The second wave can be

started with the Pension reform of 1981 and include all the efforts to reorganize the financial sector that

suffered heavy damages from the debt crisis. These efforts include the new banking law of 1986. The

impact of democracy (achieved in 1990) is less evident in the data, but all its implications may also be

contributing to the period high growth rate. Also in 1990 Central Bank became autonomous. The third

wave would be in the 2000’s basically including the new fiscal responsibility rule (pro-cyclical effective

budget) set up in 2000 and the inflation targeting scheme that Central Bank is explicitly following since

2003. A proxy for structural reforms that follows such a pattern could be regarded at least as

reasonable26.

Fuentes, Larraín and Schmidt-Hebbel (2006) use a structural reform index (SRI henceforth), which they

construct from 1960 to 2005 following other authors in the literature, particularly starting with Lora

(1997). Here that variable is expanded three years to 2008 trying to be as close as possible to authors’

criteria. The variable follows more or less the above description but it is still very subjective.

As regards to macroeconomic stability, another advocated factor behind general efficiency, it has usually

been associated in literature to both inflation size and volatility. Very likely some of the reforms

described above (particularly inflation targeting scheme) played a role in decreasing inflation so there is

a risk of overlapping with a reform index. However, a simple non ceteris-paribus look at growth

performance in Chile puts in evidence that macroeconomic stability in terms of inflation rate seems not

to be playing a very crucial role on growth rates. For instance, average inflation between 1976 and 1981

26

One problem when defining a proxy for institution and reforms is the likely non-linearities that these phenomenon would have. De Gregorio (2005) for instance states that policies in Chile present important complementarities and that the impact of some reforms have exhausted.

was 60%, while it was 15% between 1986 and 1997, both periods of very high growth rates (7% and

7.3% respectively). On the contrary, average inflation from 2000 to 2008 is 3.7%, a period to be

considered historically in terms of prices stability but where GDP only grew 4.2% in average. Are the

benefits of these reforms meaningless? On the contrary, would growth rates be worse without them?

This is not easy to answer. Still, it is clear that such macroeconomic stability did not produce a boom in

productivity. Therefore, if any effect can be expected, it should not be very high.

In order to test for this stability effect, the variable MINST is created, which is calculated as inflation rate

over one plus the inflation rate. This also follows Fuentes et al. (2006). For this variable a negative

coefficient is expected. Correlation of MINST with SRI is -0.75.

Beside the variables described above, others were tested as part of the long process to find a good

model. Some of these are also taken from Chilean literature like terms of trade, real exchange rate

misalignments, etc.

Table 4.1 summarizes main selection of variables tested in equation (3). Some of these are shown in

Appendix 1.

TABLA 4.1

THEORY VARIABLE PROXY

Factors Accumulation

Capital Stock K

Employment L

Education EDUCATION

Utilization Adjustment

Hours of work H

Capacity Utilization RECEFEC

Total Factor Productivity

Financial Development RFDLL, RFDPC, RFDTV

Technology/knowledge transfer MAXOPEN, OPEN5YR, IMPCAP, GAP

Infrastructure INVPUB

Government activity GOVEXP

Structural reforms SRI

Macroeconomic stability MINST

Exogenous factors Trend

Others Terms of Trade, RER, Tax rate, etc

Summarizing, here it follows the final GDP equation estimated, named Equation (a):

t

tttttt

vREST

EDUCATIONFDhLKGDP

ln)ln(lnln 543121

(a)

where REST consists in the other variables included as part of TFP besides FD. Notice that these other

variables are not evaluated all together but by blocks, trying to capture which would be the most

important, from a general to specific approach that even though is not statistically extent of problems,

these would be less danger than using the inverse method (specific to general).

It is clear from the Solow model that long run per capita economic growth (in the steady-state, this is

with constant stock of capital per capita) is given by human capital and TFP growth. RECEFEC in the long

run (no cycle) should be zero while hours should not vary too much in time or at least would have a

lower bound. Finally, notice that this specification has no dynamic, that is, it describes the long run

relationship among variables. Later a dynamic model (an error correction model but in the Banerjee-

Pesaran format27) is derived from the long run equation which is also estimated for Equation (a). See

Section 5 for more insights.

ii) Indirect channel: Investment function.

With regard to the second channel in the FD – GDP relationship, this is physical capital accumulation, it

has to be evaluated in an investment function. What is been captured in equation (a) is only FD impact

on GDP through TFP. This is because for the investment channel, FD does not impact the capital stock

but its flow (conversely, FD impacts the “stock” of TFP). Indeed, using basic formulas:

),()1(1 XFDfIwhereIKK tttt

is the depreciation rate and X is a set of other explanatory variables for investment. Taking logs, it is

obvious that28:

27 Instead of including the lagged error term from the long run regression in the reparametrized model, the Banerjee-Pesaran format directly incorporates the lagged long run equation in the regression. More details in Section 5.

28 Here it was assumed that

tt XFD

t eeI

. However, functional form of the investment function is irrelevant for

the argument.

tttttt XFDKIKK )]1(ln[])1(ln[ln 11

Hence, replacing lagged capital by the last expression is a mistaken procedure. Thus, it is not possible to

evaluate the FD effect on capital within the output equation. This channel can only be evaluated in an

investment function.

A typical neo-classical investment function has two variables: cost of capital and expected return rate.

However, other authors add more regressors to reflect investment restrictions, uncertainty and

structural conditions of markets.

Equation estimated here is the following:

ttttttt uDebtRFDROIRINTRINV 165

)2(

4321 _ (b)

where RINV is the investment rate, RINT is the real interest rate, ROI is the profitability rate, is inflation

(which is also tested squared), and R_Debt is the net external debt as a ratio to GDP, both in real

terms29. Real interest rate negatively affects investment rate as it reflects both the opportunity cost of

savings and the cost of borrowing (the former relevant when there are liquidity constraints). Expected

profitability rate of investment has a positive effect on the investment rate. Here it is used the “actual”

return rate instead of the expected one, assuming rational expectations30. Inflation is used as a proxy for

macroeconomic and political instability, likely relevant for investment decisions. Debt inclusion follows

Vergara (2004) who argues that for developing countries like Chile, liquidity constraints may exists in the

sense that debt burden decreases available funds for investment. It can also be a source of

macroeconomic instability. The lag is for endogeneity issues. The author also includes tax rate but here a

proxy for physical capital profitability (ROI) is used. Finally, Vergara also includes private credit (other

definition though) finding no significant impact on private investment.

29 Investment rate is real fixed gross capital formation, as a ratio to real GDP; RINT is defined as the real interest rate on bank deposits from 1 to 3 years; Inflation is defined as the average 12 months inflation; and net external debt is long run debt minus Central Bank reserves.

30 Rent is defined as:

K

taxGDPROI K

)1( This formula is similar to Coeymans (1999a). See Appendix 2

for construction details. Rational expectations indicate that the best proxy for expected value is the actual one due to non systematic errors. However, this normally distributed error could still be correlated with other variables, provoking some econometric problem.

Equation (b) is a long run equilibrium specification. Again, in order to check robustness of results, two

other models are estimated. One is the error correction model (Banerjee-Pesaran format). The other is a

simple lagged model, like an ADL(0,n), this is only with lags for regressors. Notice that Equation (a) is not

tested in the latter format because its many regressors would reduce the degrees of freedom

considerably. Here this does not happen.

5) Econometric methodology

Some comments about the econometric techniques used here. First, as already said equations (a) and

(b) are estimated in its “long run equilibrium” specification. This format only includes contemporaneous

or lagged variables but not both at the same time (exception is a moving average but this can be treated

as a new variable), which allows to easily test cointegration. This long run model is estimated using OLS

and TSLS methodologies in order to evaluate potential endogeneity of FD indicator. Particularly for

endogeneity caused by simultaneity, it is discarded if cointegration is found. However there could be

other endogeneity problems caused by measurement errors or omitted variables.

A whole bunch of other tests are conducted in order to check that classic Gauss-Markov assumptions

are fulfilled, tests related to serial correlation, normality of errors, stability in its many ways (CUSUM

tests, Chow tests, Ramsey RESET, and so). Particularly for RESET test, it evaluates the omission of

variables, wrong functional form and measurement errors, all important for OLS to be “BLUE” (best

linear unbiased estimator). Cointegration is tested here a la Engel and Granger, which is to run a unit

root test on the residuals of the long run equation. Notice that to run this test, first a pretest should be

done regarding integration order of each variable in the equation, which should be not higher than I(1).

Finding a stable and cointegrated long run model is however not enough. One problem with non-

stationary variables is the existence of spurious correlation, which many times is not ruled out by the

Engel and Granger cointegration test due to its relatively low power. Small sample issues may generate

problems on the long run estimations and tests due to their (potential) asymptotic properties if any

Gauss-Markov assumption is violated and not detected here. Hence, together with the long run model,

each equation is tested also in the error correction model (ECM hereafter), particularly in its Banerjee-

Pesaran format. This specification is a more powerful framework to rule out spurious correlation.

The Banerjee-Pesaran model puts together the long run equation, lagged once, with some dynamic

terms which are variables in differences, where the number of lags is chosen based on information

criterions like Akaike or Schwarz information criterions. Next a general ECM model is developed and its

properties highlighted.

Let’s start from a general long run model based on a stochastic Cobb-Douglas production function with

returns to scale, just as an example:

ttttt uLLnKLnALnGDPLn )()1()()()( 1 (i)

This equation is also called cointegration equation. The error term (ut) may follow or not a AR(1). Later,

evidence of this is found in some regressions, so the general case is presented here:

ttt euu 1 (ii)

where term et is a white noise.

By definition, 1 ttt XXX , which is equivalent to 1 ttt XXX

The latter transformation is introduced for every variable in equation (i). Also the error term is replaced

by (ii) and terms rearranged, letting to:

tttttt

tttt

euLLnKLnALnGDPLn

LLnKLnALnGDPLn

11211

1

)()1()()()(

)()1()()()(

(iii)

This format contains some dynamic terms (with Δ), where more lags can be introduced. The rest of the

terms in levels are no more than the lagged long run equation, equal to -ut-1. To check this, just take

equation (i) lagged once and rearrange terms a bit, like shown next:

11211 )()1()()()( ttttt uLLnKLnALnGDPLn

)()1()()()( 12111 ttttt LLnKLnALnGDPLnu

One format of the ECM is the Engel and Granger one, which replaces the lagged long run equation with

its lagged error, in equation (iii). The model is then estimated in two steps, first estimating the long run

model (i), generating the error series and using it lagged in the ECM. However, if there are econometric

problems in the long run model, these would invalidate the consecutive estimation of the ECM. An

alternative way is to do the opposite, which is to replace the lagged error present in equation (iii) (ut-1)

by the variables of the long run model. After doing this and rearranging terms, the subsequent equation

results:

ttttt

tttt

eLLnKLnALnGDPLn

LLnKLnALnGDPLn

))()1()1()()1()()1()()1(

)()1()()()(

1211

1

(iv)

This equation can be written in a more clear way:

ttttt

tttt

eLLnKLnALnGDPLn

LLnKLnALnGDPLn

)()1()()()()1(

)()1()()()(

1211

1

(v)

This is known as the ECM in its Banerjee-Pesaran format and just requires a single estimation.

Equation (v) has several properties. First, in order to be in presence of a cointegrated (so stable) model,

it is necessary for the autocorrelation coefficient to be between -1 and 1. In other words, ut should be

I(0). This assures the long run model to be cointegrated. This condition implies that the term )1(

of equation (v), known as the adjustment coefficient, is between -2 and 0. Importantly, the

autoregressive coefficient is directly estimated in this model, as is evident in equation (v). If the original

model presents no serial correlation, general results still maintain as the estimated adjustment

coefficient should be statistically similar to -1 ( 0 ). Second, the error term of equation (v) is a white

noise (if originally it was). This gives important robustness to empirical distributions of coefficients and

hence to tests. Third, observe that the term in brackets correspond to the lagged long run equation,

which can be then deducted from the model without estimating it directly. For this is better to use

equation (iv). See there that long run coefficients are calculated dividing each estimated level variable

coefficient by )1( (absolute value of the adjustment factor). Also note that the sign of the regressors

are the same that of the long run equation. This implicit long run equation is an interesting benchmark

to compare coefficients of the long run model estimated directly. However, it has the drawback that no

test can be directly conducted in its coefficients as these are deducted.

In the ECM, cointegration is tested a la Pesaran, Shin and Smith. This consists on running a Wald test of

conjunct significance for the variables in levels of equation (iv). The F-statistics is compared with the

tables provided by the authors, for 5 different cases, depending on trend and intercept restrictions.

Conversely to the Engel and Granger cointegration test, this does not require pretesting on integration

order of series. Their order (I(0) or I(1)) are deducted from the results of the test.

For all uni-equational models described in previous section, the Banerjee-Pesaran model is estimated in

order to compare how robust long run estimations are. What is trying to be found here is a stable

model, with cointegration hypothesis not rejected, where deducted cointegration equation is relatively

similar to the long run one or at least its coefficients are reasonably in line with expected values. If a

previous long run model found is not backed up by the ECM, then the most prudent conclusion is that

the long run model is spurious. This conclusion of course should be taken only once tests indicate that

Gauss-Markov assumptions are also fulfilled in the ECM. Therefore, all classic tests done in the long run

specification are also tested here.

Important prior to the estimations is to analyze the integration properties of the data in order to run the

cointegration tests, particularly the Engel and Granger test. Appendix 3 presents unit root tests for

selected variables of both GDP and investment equations. Tests used are the Augmented-Dickey-Fuller

test (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin test (KPSS). More tests exist but these two provide

sufficient support already to what is expected here. Generally speaking, most of the variables are found

to be integrated of first order. Most relevant exceptions are RECEFEC, GOVEXP, and INVPUB, although

not strange. This strong evidence of non-stationary data gives even more importance to a strict

econometric analysis, as spurious relations may flourish without difficulty.

Finally some words about correlations. Appendix 3 shows the correlation for selected variables of both

GDP and investment equations. The main issue to point out with respect to the first set of variables is

the high correlation of RFDLL with EDUCATION and each of them with respect to labor and capital. This

will explain some of the results later. MAXOPEN, SRI and MINST seems very correlated as well with

production factors, while RECEFEC and GOVEXP are not very much related with most of the variables, at

least at the magnitude the rest is.

Regarding correlation among the second set of variables, it shows that, also as above, RFDLL is the more

correlated indicator to the dependent variable and to the rest set of regressors. Signs of correlation are

the expected ones.

6) Results

i) Direct channel: Cobb-Douglas function.

The first empirical approach to Equation (a) is in its “long run” format (opposite to a dynamic one like an

ADL model or an ECM specification). As described before, there are many potential regressors to be

included as part of the TFP term, so the process used here is a general to specific in the sense that

specification included many variables, discarding the insignificant ones. Procedure is more complicated

as the final model should fulfill many econometric conditions based on specific tests. Overall, a model

has to be stable, present cointegration and be coherent with results found using a dynamic

transformation of it.

The final model found here presents the conditions mentioned above and was found after many

iterations and re-specifications. It includes, beside lagged capital and employment adjusted by hours,

the variable RECEFEC, a trend, and three variables as moving averages which are GOVEXP, Education,

and a FD indicator (RFDLL). Next the general analysis of the model is presented first, with the supporting

econometric evidence shown later.

Regarding to the FD indicator, only Liquid Liabilities resulted significant. Private Credit did not show any

interesting results, which is confirmed when both are put together in the equations (only RFDLL

remains). Traded Value, without surprises, was the worst indicator (with the opposite coefficient

indeed). Furthermore, as suggested when analyzing FD indicators, Liquid Liabilities can be a better

measure of sector’s development when taking out currency in circulation. This was also tried with same

results than using total Liquid Liabilities. This is because both series are practically the same as currency

is a low proportion of total indicator (from 10% in the beginning of 1980 to 4% in 2000’s). Finally,

Holzmann (1997), the only paper found using a similar methodology, finds that only the liabilities

indicators was significant (not same definition than here but related), with the stock related one

resulting insignificant.

With respect to moving averages, the number of periods was selected based on an information

criterion, although qualitative results remain in place when using other lags (coefficients change but not

their significance and general equation properties).

Table 6.1 presents the results for the first estimation of the final model31.

31 All the tables shown in this analysis are based on the E-Views output so variables definitions and codes are those from that software. Some clarifications:

- X(-t) means the variable X lagged t periods - @MOVAV(X(-t),n) means the n-periods moving average of the variable X(-t) - @TREND stands for a simple trend - D(X) means the delta operator on X, this is Xt – Xt-1 - AR(1) to AR(n) means an autoregressive structure of length n.

This estimation is included here just to show the starting point of the analysis as this equation is

transformed afterwards. All the coefficients show the expected sign with the exception of trend

coefficient, which results negative. This apparent theoretical mistake is later corrected once some

variables are redefined. Yet, at this point, Table 6.1 shows that constant return to scale hypothesis is not

rejected, so model is transformed using this restriction32. New estimation is presented in Table 6.2.

32 P-value of the hypothesis is 0.7296 according to a Wald test.

Dependent Variable: LOG(GDP)

Method: Least Squares

Sample: 1980 2008

Included observations: 29

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.  

LOG(K(-1)) 0.377224 0.132011 2.857528 0.0094

LOG(L_H) 0.688792 0.141709 4.860612 0.0001

@MOVAV(RFDLL(-2),5) 0.633731 0.09229 6.866749 0.0000

@MOVAV(GOVEXP(-1),3) -2.160709 0.158778 -13.60836 0.0000

@MOVAV(LOG(EDUCATION(-1)),5) 1.413224 0.315007 4.486328 0.0002

C -2.891063 3.963188 -0.729479 0.4738

RECEFEC 0.287393 0.056174 5.116148 0.0000

@TREND -0.022414 0.007485 -2.994581 0.0069

R-squared 0.999278     Akaike info criterion -5.45268

Adjusted R-squared 0.999037     Schwarz criterion -5.07549

S.E. of regression 0.014126     F-statistic 4149.457

Durbin-Watson stat 2.392153     Prob(F-statistic) 0.0000

Table 6.1

First estimation of final model - Row equation

This table shows also a negative coefficient for trend, with rest of coefficients presenting good behavior

in terms of sign. Of course, a negative trend is not a property of a good growth model so this issue has to

be solved. The wrong sign of the trend is due here to the very marked trend path that both RFDLL and

EDUCATION series present. What seems to be happening here is that when a trend is included in the

regression, it counterbalances the implicit trend effect present in RFDLL and EDUCATION, variables that

seems very significant in explaining growth even though when they are “de-trended”. To check this

hypothesis, both RFDLL and EDUCATION are redefined as they cyclical components. This is done in two

ways: i) taking the residual of a regression against a constant and a trend, and ii) taking the cyclical

component of a HP filter33. What is expected from this process is that when the cyclical components of

RFDLL and EDUCATION are included in the model instead of the whole variables, trend will result no

negative. As Table 6.3 and 6.4 shows, this happens indeed. Table 6.3 shows this for the first “de-

trending” method while Table 6.4 shows it for the second one.

33

Adjusting EDUCATION variable gives practically the same result under both methods so it was finally considered just the first method, this is the regression with respect to a trend (ne variable named RES_EDU). Conversely, both results for RFDLL are shown (named RES_RFDLL and RES_RFDLL2 respectively). Notice that the trend adjustment is conducted over the variables defined as moving averages, not as single year definition.

Dependent Variable: LOG(GDP)-LOG(L_H)

Method: Least Squares

Sample: 1980 2008

Included observations: 29

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.  

LOG(K(-1))-LOG(L_H) 0.344683 0.015408 22.37024 0.0000

@MOVAV(RFDLL(-2),5) 0.62381 0.079912 7.8062 0.0000

@MOVAV(GOVEXP(-1),3) -2.197628 0.062419 -35.20744 0.0000

@MOVAV(LOG(EDUCATION(-1)),5) 1.47978 0.201861 7.330687 0.0000

C -1.945298 0.370418 -5.251631 0.0000

RECEFEC 0.299704 0.037254 8.044856 0.0000

@TREND -0.020751 0.002044 -10.1513 0.0000

R-squared 0.997687 Akaike info criterion -5.51582

Adjusted R-squared 0.997056 Schwarz criterion -5.18578

S.E. of regression 0.013841 F-statistic 1581.656

Durbin-Watson stat 2.412681 Prob(F-statistic) 0.0000

Table 6.2

Second estimation of final model - Constant returns to scale

Obviously, Table 6.3 shows the same results than Table 6.2 with the exception of the trend coefficient34.

This turned positive, as expected, with an average yearly growth rate of 1.6%. When using the HP filter

the trend also changes to positive, with the rest of the coefficients varying as well. Hence this confirms

that initial negative trend was part of an over valuation of the implicit trend components from both FD

and Education variables.

34

New cyclical variables RES_RFDLL and RES_EDU are original ones minus a weighted trend, so in the end it is just a reallocation of terms.

Dependent Variable: LOG(GDP)-LOG(L_H)

Method: Least Squares

Sample: 1980 2008

Included observations: 29

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.  

LOG(K(-1))-LOG(L_H) 0.344683 0.015408 22.37024 0.0000

@MOVAV(GOVEXP(-1),3) -2.197628 0.062419 -35.20744 0.0000

RES_RFDLL 0.62381 0.079912 7.8062 0.0000

RES_EDU 1.47978 0.201861 7.330687 0.0000

C 0.367969 0.02728 13.48881 0.0000

RECEFEC 0.299704 0.037254 8.044856 0.0000

@TREND 0.015611 0.000434 35.93211 0.0000

R-squared 0.997687 Akaike info criterion -5.51582

Adjusted R-squared 0.997056 Schwarz criterion -5.18578

S.E. of regression 0.013841 F-statistic 1581.656

Durbin-Watson stat 2.412681 Prob(F-statistic) 0.0000

Third estimation of final model - adjustment method 1

Table 6.3

The root of this phenomenon is that it is very hard if not impossible to distinguish how that trend is

composed in the end. To support this last statement two more regressions are shown, adjusting only

one variable at the time (either RFDLL or EDUCATION), and eliminating the general trend from the

model. These regressions are shown in Tables 6.5 and 6.6.

Dependent Variable: LOG(GDP)-LOG(L_H)

Method: Least Squares

Sample: 1980 2008

Included observations: 29

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.  

LOG(K(-1))-LOG(L_H) 0.271548 0.023111 11.74983 0.0000

@MOVAV(GOVEXP(-1),3) -2.391095 0.145202 -16.4674 0.0000

RES_RFDLL2(-2) 0.978538 0.280382 3.490023 0.0021

RES_EDU 2.187573 0.30146 7.256597 0.0000

C 0.498842 0.066024 7.555451 0.0000

RECEFEC 0.213234 0.043686 4.881004 0.0001

@TREND 0.016807 0.000515 32.64122 0.0000

R-squared 0.996711 Akaike info criterion -5.16384

Adjusted R-squared 0.995814 Schwarz criterion -4.8338

S.E. of regression 0.016505 F-statistic 1111.281

Durbin-Watson stat 1.771921 Prob(F-statistic) 0.0000

Table 6.4

Third estimation of final model - adjustment method 2

This exercise shows that no matter which one is adjusted, both variables are significant and coefficients

remain more or less stable, very close to what is found when including a trend. In other words, these

results indicate, first, that cyclical component of both variables are significant in determining aggregate

Dependent Variable: LOG(GDP)-LOG(L_H)

Method: Least Squares

Sample: 1980 2008

Included observations: 29

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.  

LOG(K(-1))-LOG(L_H) 0.352712 0.019522 18.06772 0.0000

RES_RFDLL 0.70475 0.068886 10.2307 0.0000

@MOVAV(LOG(EDUCATION(-1)),5) 1.127487 0.039379 28.63165 0.0000

@MOVAV(GOVEXP(-1),3) -2.245192 0.082038 -27.36768 0.0000

C -1.571097 0.065641 -23.93459 0.0000

RECEFEC 0.289048 0.048606 5.946724 0.0000

R-squared 0.997519 Akaike info criterion -5.51466

Adjusted R-squared 0.99698 Schwarz criterion -5.23177

S.E. of regression 0.01402 F-statistic 1849.576

Durbin-Watson stat 2.217044 Prob(F-statistic) 0.0000

Table 6.5

Only RFDLL is "de-trended"

Dependent Variable: LOG(GDP)-LOG(L_H)

Method: Least Squares

Sample: 1980 2008

Included observations: 29

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.  

LOG(K(-1))-LOG(L_H) 0.342546 0.011892 28.80471 0.0000

@MOVAV(RFDLL(-2),5) 0.605967 0.012649 47.90724 0.0000

RES_EDU 1.503419 0.150254 10.00587 0.0000

@MOVAV(GOVEXP(-1),3) -2.194335 0.059184 -37.07622 0.0000

C 0.596626 0.027579 21.63304 0.0000

RECEFEC 0.295556 0.031705 9.321954 0.0000

R-squared 0.997685 Akaike info criterion -5.58374

Adjusted R-squared 0.997181 Schwarz criterion -5.30085

S.E. of regression 0.013544 F-statistic 1982.183

Durbin-Watson stat 2.413145 Prob(F-statistic) 0.0000

Table 6.6

Only EDUCATION is "de-trended"

output. Second, that it is not clear which variable is behind the trend component of growth. Moreover,

there could be another variables represented in the trend components of RFDLL and EDUCATION (due

to correlation), even with these two not relevant in the end. However, theoretically these two are big

candidates to be explaining partly or totally the average 1.6% growth rate found. In other words, the

underlying problem here is the heavy colinearity among variables, particularly between RFDLL and

EDUCATION (see Appendix 3 for correlations). While it is possible to identify the non-trend effect of

both variables, the rest is not identifiable due to colinearity, a property of the data.

Overall, evidence seems to indicate that Table 6.3 is the best model in terms of generality of results.

First, capital elasticity is around 0.35, at the lower end of the usual assumptions (0.33 to 0.55 in

reviewed literature). RECEFEC has a similar coefficient to what is found in Coeymans (1999a,b), the only

reference existent. EDUCATION coefficient represents an elasticity because the variable is defined as a

natural logarithm. It implies that an “over the trend” 1% increase in average education years turns to a

1.5% increase in growth rate, an economically important effect. It must be over the trend because that

is how the variable is re-defined (trend is 1.4% according to the adjustment equation). Given that there

is a 1.6% uncertain growth effect, it is not possible to say that the EDUCATION effect calculated above is

the total effect on growth. The right expression then must be this: when average education years grow

2.4%, GDP is boosted at least in a 1.5% although this effect could be higher, with 3.1% as the upper limit

(1.5% plus 1.6%).

GOVEXP coefficient (semi-elasticity) is -2.2, negative as expected. Here, when the ratio of central

government expenditures to GDP increases an average of 0.01 over a three years period (e.g. from 20%

to 21% in the long run), it has a negative effect on growth of 2.2%. This could be rather high but is

consistent across different moving average lengths. To avoid endogeneity, equation is estimated with

TSLS, without changing results. Standard error is low so there could be no help from this side.

One possible explanation for this high coefficient is that it accounts for a very turbulent period at the

beginning of 1980’s, where government took control over certain companies and intervened in the

economy, meaning quite disturbances in the market assignation and productivity. Yet, introducing a

dummy to GOVEXP variable (e.g. for the 1982-1987 period) changes the coefficient only to -2.08.

Another reason behind the high coefficient could be that Government expenditures ratio may be

working as a proxy for environmental conditions in the economy, very much related with government

intervention. In periods of high stress, adverse external environment and any internal situations

inducing government to increase expenditures as an expansionary measure, efficiency of firms may

decrease, either due to direct government intervention or due to environment conditions. Hence,

GOVEXP would not only be reflecting government effects on activity but also environment for business,

the latter affecting total productivity. Notice that this relationship may work also in good times, when

the economy is expanding and the government is accounting for relatively less of total activity. All in all,

the equation found has very good robustness properties as later described so GOVEXP coefficient

magnitude is not a very decisive matter.

Regarding to the main variable of interest here, that is RFDLL, the cyclical component of it has a

coefficient of 0.62, and it is also a semi-elasticity. This means that an over the trend average increase of

0.01 in the Liquid Liabilities ratio to GDP translates into a 0.62% growth effect. Trend coefficient for this

variable is 0.026, which is also the median of the distribution (standard deviation of 0.013) so a 0.01

over-the-trend growth is not uncommon. The coefficient found is indeed very interesting as it means

that there is an effect of financial sector on activity, at least when it grows above its historical average in

the long run. Nonetheless, the same uncertainty surrounding the final effect of EDUCATION is playing

here. Indeed, it could be that the unexplained 1.6% growth captured by the trend in the final model

could be due partial or totally to the trend component of the financial indicator. In fact, potential trend

effect can be calculated as 0.026 0.62, which gives 1.612%, so with the 0.62 coefficient found for

RFDLL, its trend can perfectly account for the unexplained GDP growth. Yet, what is clear is that there is

plenty of uncertainty with regard to the final effect of FD on growth, although evidence suggests at least

that it does exist and it would be economically significant, ranging from 0.62% to 2.2% when RFDLL

increases 0.036 points (e.g. from 50% to 53.6%)35.

In order to assume the model presented in Table 6.3 as final, it must have econometric robustness to

general tests and alternative specifications. This evidence is presented next.

First, notice the low standard errors of the regression (0.0138). Errors behave normally according to the

Jarque-Bera statistics (p-value of 0.79), even though one observation (1994) seems to depart from the

rest. Yet, according to the boxplot diagram, it is not an outlier.

There are some problems regarding serial correlation, particular of second order (so Durbin-Watson is

not informative). The Breusch-Godfrey serial correlation test (with two lags) is rejected at 5% but the

35

2.2% comes from adding 0.62% to the unexplained 1.6% while the 0.036 comes from adding 0.01 to 0.026, the average change in the total variable (not only the cyclical component).

model can be improved using an AR(2) re-parameterization, which is shown in Table 6.3b36. Notice that

most of the coefficients are very similar to the ones found without adjustments, falling for FD and

RECEFEC. This rather small change happens because the problem associated to serial correlation is

inefficiency and not bias37. Indeed, all coefficients presented in Table 6.3b does not reject null

hypothesis of being equal to coefficients in Table 6.3 at 5%, with the exception of RECEFEC.

Furthermore, given the very high significance of all variables, serial correlation is likely not a strong

problem so coefficient analysis above remains in place. Still, all the following tests are conducted with

the serial correlation adjusted model. Also to mention, inverse roots of the AR polynomial are inside the

unitary circle.

36 Using E-Views®, re-parameterization is done internally by the software but it is equivalent to

)X(-)X(-X 2-t21-t1t where X is the variable to adjust and i is the i-th AR coefficient. This model does

not reject null hypothesis at 5%. 37 There is no lagged term of the dependent variable so there is no bias and inconsistency of OLS estimates.

Dependent Variable: LOG(GDP)-LOG(L_H)

Method: Least Squares

Sample (adjusted): 1982 2008

Included observations: 27 after adjustments

Convergence achieved after 7 iterations

Newey-West HAC Standard Errors & Covariance (lag truncation=2)

Variable Coefficient Std. Error t-Statistic Prob.

LOG(K(-1))-LOG(L_H) 0.33033 0.00836 39.53549 0.0000

@MOVAV(GOVEXP(-1),3) -2.15948 0.04204 -51.36712 0.0000

RES_RFDLL 0.54461 0.05987 9.09675 0.0000

RES_EDU 1.50685 0.12694 11.87084 0.0000

C 0.35678 0.01532 23.28917 0.0000

RECEFEC 0.22613 0.04362 5.18398 0.0001

@TREND 0.01637 0.00034 47.79454 0.0000

AR(1) -0.29383 0.14489 -2.02802 0.0576

AR(2) -0.61851 0.173834 -3.558039 0.0022

R-squared 0.998866 Akaike info criterion -6.01681

Adjusted R-squared 0.998362 Schwarz criterion -5.58487

S.E. of regression 0.010484 F-statistic 1982.347

Durbin-Watson stat 2.253289 Prob(F-statistic) 0.0000

Inverted AR Roots -.15-.77i -.15+.77i

Table 6.3b

Serial correlation adjusted estimation

Ramsey RESET test performs well (either with 1 or 2 fitted terms), as also does the Chow Forecast test.

The latter is shown for the 2000 to 2008 period but results are the same for other years after 2000.

Cointegration is found when using the simple Engel-Granger methodology, with an ADF statistics of -

5.12 (null hypothesis rejected at 1%). Errors do not present evidence of unit root.

Plots for CUSUM and CUSUM-Q (which evidence whether model is stable or not), together with the

recursive coefficients plot indicate no evidence of structural change.

Ramsey RESET Test (two fitted terms):

F-statistic 0.962578 Probability 0.400716

Log likelihood ratio 2.743506 Probability 0.253662

Chow Forecast Test: Forecast from 2000 to 2008

F-statistic 0.831293 Probability 0.602944

Log likelihood ratio 14.00983 Probability 0.121976

Stability tests for Final Model (re-parameterizated)

Null Hypothesis: RES has a unit root

Exogenous: Constant

Lag Length: 1 (Automatic based on SIC, MAXLAG=6)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -5.11555 0.0004

Test critical values: 1% level -3.72407

5% level -2.986225

10% level -2.632604

*MacKinnon (1996) one-sided p-values.

Engel and Granger Cointegration test

-15

-10

-5

0

5

10

15

90 92 94 96 98 00 02 04 06 08

CUSUM 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

90 92 94 96 98 00 02 04 06 08

CUSUM of Squares 5% Significance

Also, the final model has interesting econometric robustness with respect to the ECM specification

(Banerjee-Pesaran format). The latter provides more strength to cointegration tests and evaluates short

run dynamic of the model, as specification of Table 6.3 is a long run one. Table 6.7 presents the result

for the ECM. On this format, null hypothesis of the Breusch-Godfrey serial correlation test is not

rejected. As shown in Section 5, this format automatically corrects autocorrelation conditional to the

normality of the residual of the re-parameterized model (condition that holds).

-0.4

0.0

0.4

0.8

1.2

1.6

1992 1994 1996 1998 2000 2002 2004 2006 2008

Recursive C(1) Estimates ± 2 S.E.

-2.8

-2.7

-2.6

-2.5

-2.4

-2.3

-2.2

-2.1

-2.0

-1.9

1992 1994 1996 1998 2000 2002 2004 2006 2008

Recursive C(2) Estimates ± 2 S.E.

-3

-2

-1

0

1

2

3

1992 1994 1996 1998 2000 2002 2004 2006 2008

Recursive C(3) Estimates ± 2 S.E.

-2

0

2

4

6

8

1992 1994 1996 1998 2000 2002 2004 2006 2008

Recursive C(4) Estimates ± 2 S.E.

-2

-1

0

1

2

3

4

1992 1994 1996 1998 2000 2002 2004 2006 2008

Recursive C(5) Estimates ± 2 S.E.

-.4

-.3

-.2

-.1

.0

.1

.2

.3

.4

.5

1992 1994 1996 1998 2000 2002 2004 2006 2008

Recursive C(6) Estimates ± 2 S.E.

-.01

.00

.01

.02

.03

.04

.05

.06

1992 1994 1996 1998 2000 2002 2004 2006 2008

Recursive C(7) Estimates ± 2 S.E.

For this format, standard errors are also very low. Jarque-Bera test indicates with a p-value of 0.95 that

errors distribute normally. Ramsey RESET test and Chow forecast test also behave as expected,

indicating strong structural properties of the model (again for one and two fitted terms for RESET test

and for 2000 and following years as breakpoint of the Chow test). Regarding Cointegration a la Pesaran,

Shin and Smith, this exist at the 1% of significance for cases IV and V, this is unrestricted intercept with

restricted trend and with unrestricted trend respectively. This is a strong support for the reliability of the

model. Finally, structural change tests like CUSUM, CUSUM-Q and recursive coefficients test are also

well enough to close confidence on the results.

Dependent Variable: D(LOG(GDP)-LOG(L_H))

Method: Least Squares

Sample (adjusted): 1981 2008

Included observations: 28 after adjustments

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.

D(LOG(K(-1))-LOG(L_H)) 0.37976 0.068873 5.513882 0.0001

D(RECEFEC) 0.223203 0.105596 2.113738 0.0517

D(RES_EDU) 1.917532 0.384737 4.984005 0.0002

D(RES_RFDLL) 1.074471 0.266193 4.036439 0.0011

D(LOG(GDP(-1))-LOG(L_H(-1))) 0.437747 0.107237 4.082053 0.0010

LOG(GDP(-1))-LOG(L_H(-1)) -1.755201 0.148338 -11.83241 0.0000

LOG(K(-2))-LOG(L_H(-1)) 0.711139 0.068677 10.35478 0.0000

@MOVAV(GOVEXP(-1),3) -3.539962 0.320866 -11.03252 0.0000

RES_RFDLL(-1) 0.804204 0.168317 4.777904 0.0002

RES_EDU(-1) 2.990913 0.224519 13.3214 0.0000

C 0.434893 0.051076 8.514698 0.0000

RECEFEC(-1) 0.299335 0.077848 3.845127 0.0016

@TREND 0.025036 0.002022 12.3802 0.0000

R-squared 0.950611 Akaike info criterion -5.81321

Adjusted R-squared 0.9111 Schwarz criterion -5.19468

S.E. of regression 0.010389 F-statistic 19.92306

Durbin-Watson stat 2.407964 Prob(F-statistic) 0.0000

Error Correction format of Final Model

TABLE 6.7

As a final exam to the specification found above, Table 6.8 presents the TSLS estimation of the model,

using as instruments some lags of the regressors together with real copper price and capital flights.

-12

-8

-4

0

4

8

12

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

CUSUM 5% Significance

-0.4

0.0

0.4

0.8

1.2

1.6

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

CUSUM of Squares 5% Significance

-.4

-.2

.0

.2

.4

.6

.8

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(1) Estimates ± 2 S.E.

-.4

-.2

.0

.2

.4

.6

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(2) Estimates ± 2 S.E.

-6

-4

-2

0

2

4

6

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(3) Estimates ± 2 S.E.

-2

-1

0

1

2

3

4

5

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(4) Estimates ± 2 S.E.

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(5) Estimates ± 2 S.E.

-2.8

-2.4

-2.0

-1.6

-1.2

-0.8

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(6) Estimates ± 2 S.E.

-0.4

0.0

0.4

0.8

1.2

1.6

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(7) Estimates ± 2 S.E.

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(8) Estimates ± 2 S.E.

-3

-2

-1

0

1

2

3

4

5

6

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(9) Estimates ± 2 S.E.

1.2

1.6

2.0

2.4

2.8

3.2

3.6

4.0

4.4

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(10) Estimates ± 2 S.E.

-0.4

0.0

0.4

0.8

1.2

1.6

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(11) Estimates ± 2 S.E.

-0.8

-0.4

0.0

0.4

0.8

1.2

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(12) Estimates ± 2 S.E.

.016

.020

.024

.028

.032

.036

97 98 99 00 01 02 03 04 05 06 07 08

Recursive C(13) Estimates ± 2 S.E.

Notice again the low standard errors and how similar coefficients are with respect to the ones found

before. This shows that endogeneity seems not to be a problem of the model. The use of moving

average may be a reason behind the robustness of the model, avoiding contemporaneousness. This is

particularly important for the FD indicator as literature stress from the theoretical and empirical field

the bi-causal relationship between FD and GDP.

For a more detailed comparison, Table 6.9 presents the coefficients derived from the ECM with those of

the long run model in its different specifications, this is unadjusted (Table 6.3), re-parameterized (Table

6.3b), and using TSLS estimation (Table 6.8). Interestingly, in the ECM capital elasticity increases to 0.405

while GOVEXP coefficient “increased” to -2.02. Education coefficient goes up with this specification to

1.7 while FD effect falls even more to 0.46. RECEFEC also falls with respect to adjusted to 0.17, while

Dependent Variable: LOG(GDP)-LOG(L_H)

Method: Two-Stage Least Squares

Sample (adjusted): 1982 2008

Included observations: 27 after adjustments

Convergence achieved after 7 iterations

Newey-West HAC Standard Errors & Covariance (lag truncation=2)

Instrument list: LOG(K(-2))-LOG(L_H(-1)) RES_EDU RES_RFDLL

@MOVAV(GOVEXP(-1),4) C @TREND RECEFEC() LOG(PCU

*FX/IPC03) FKR(-1)

Lagged dependent variable & regressors added to instrument list

Variable Coefficient Std. Error t-Statistic Prob.

LOG(K(-1))-LOG(L_H) 0.328597 0.009283 35.39909 0.0000

RES_EDU 1.508426 0.126922 11.88463 0.0000

RES_RFDLL 0.541432 0.05935 9.122763 0.0000

@MOVAV(GOVEXP(-1),3) -2.155897 0.042465 -50.76876 0.0000

C 0.35645 0.015366 23.19703 0.0000

RECEFEC 0.219876 0.045854 4.795141 0.0001

@TREND 0.016435 0.000378 43.42881 0.0000

AR(1) -0.285605 0.144051 -1.982672 0.0629

AR(2) -0.615775 0.173454 -3.55007 0.0023

R-squared 0.998822 Durbin-Watson stat 2.209749

Adjusted R-squared 0.998298 F-statistic 1905.572

S.E. of regression 0.010688 Prob(F-statistic) 0.0000

Inverted AR Roots -.14-.77i -.14+.77i

TSLS estimation of Final model, adjusted

Table 6.8

exogenous growth is estimated to be 1.43% this time. Given that magnitude of coefficients did not

change radically, main structure of the economic analysis remains at place.

Which coefficients should be taken as the most reliable ones is not totally clear, although they have

some anchor in order to still defend the hypothesis that FD measured as Liquid Liabilities has an

explanatory power in GDP and that its effect should not be ignored. Education confirms its importance

when it comes at an accelerated process while current government expenditures growth should not

diverge too much from GDP growth to ensure a stable ratio38. Regarding the 1.4% - 1.6% average growth

rate not explained here, it remains open to be part either of FD, education or other factors. Given the

large array of variables tried here, in author´s opinion the trend could be reflecting the undistinguishable

effect of human capital and financial sector performance. Other popular variables like reforms and

institutions could be in place as well, given their likely trended behavior. Yet, given high colinearity

present in the data, it is difficult to distinguish which variables are behind it.

At the beginning of this work it was suggested that relationship between FD and GDP may not be linear

but increasing or decreasing as a country gets richer. That hypothesis was tested including a quadratic

term for FD, without success as the variable was highly insignificant. Even though there is no evidence

here of a more complex relation, this should not be totally rejected because the sample used here could

not be long enough to capture such effects.

38

Whether it should be low or not it is a matter of another discussion. Let remember that GOVEXP is a I(0) variable, without a trend and therefore it is exogenous to long run growth rate so this work does not say nothing about its level. It does says still that significant changes in the government size with respect to the economy may provoke relevant growth effects.

Normal Adjusted TSLS

LOG(K(-1))-LOG(L_H(-1)) 0.3447 0.3303 0.3286 0.4052

@MOVAV(GOVEXP(-1),3) -2.1976 -2.1595 -2.1559 -2.0168

RES_RFDLL 0.6238 0.5446 0.5414 0.4582

RES_EDU 1.4798 1.5069 1.5084 1.7040

C 0.3680 0.3568 0.3565 0.2478

RECEFEC 0.2997 0.2261 0.2199 0.1705

@TREND 0.0156 0.0164 0.0164 0.0143

LONG RUN MODEL

Table 6.9

Comparison of long run coefficients between models

VARIABLE

ECM

MODEL

(Banerjee-

Pesaran)

ii) Indirect channel: Investment function

Investment equation (b) described in Section 4 is tested for the 1980 – 2007 period and for the three FD

indicators39. Estimations for equation (b) are easier than for equation (a) above, particularly given the

very few variables that the equation includes, contrasting with the dozen potential determinants of TFP.

Therefore, results here are more straightforward to show and understand so this section is presented as

simple as possible.

Conversely to previous results, this time FD indicators do not enter into the final model, neither RFDPC,

nor RFDLL, nor RFDTV. This insignificance is not even fixed when defining the variables as moving

averages, as it was done before. Table 6.10 shows the final model found in its long run definition (only

contemporaneous variables). As it can be seen, the final model includes the real interest rate (RINT), the

rate of return (ROI) and the debt ratio (R_DEBT). Inflation did not result significant.

Notice that RINT is highly insignificant, which is probably reflecting that a yearly frequency of data is not

the best approach to investment, neither an estimation of the long run equation. Yet, this model

performs very well in other formats, as shown later. ROI coefficient has the expected sign, and its

magnitude implies than a 0.01 point change in rate of return generates a change of 0.01 in investment

rate, a not absurd effect but lower than the 1.59 found in Coeymans (1999a), the reference for this

39

Year 2008 derives in a very large error (and in an important change in coefficients and standard errors), which according to the boxplot view is an outlier. Hence that year is discarded.

Dependent Variable: RINV

Method: Least Squares

Sample (adjusted): 1980 2007

Included observations: 28 after adjustments

Newey-West HAC Standard Errors & Covariance (lag truncation=3)

Variable Coefficient Std. Error t-Statistic Prob.  

C 0.072033 0.034772 2.071603 0.0492

RINT 0.002952 0.116807 0.025273 0.9800

ROI 1.057735 0.183447 5.765888 0.0000

R_DEBT -0.050437 0.019624 -2.570104 0.0168

R-squared 0.902974     Akaike info criterion -5.586165

Adjusted R-squared 0.890846     Schwarz criterion -5.39585

S.E. of regression 0.013873     F-statistic 74.45243

Durbin-Watson stat 1.641112     Prob(F-statistic) 0.0000

Table 6.10

Long run model for Investment

equation. R_DEBT coefficient implies than a 0.01 increase in debt ratio decreases investment rate by

0.0005, which seems economically insignificant at first. Even though this variable changes quite more

than that (standard deviation for D(R_DEBT) is 0.09), its effect would be still low40. Still, it is close to

what is found in Vergara (2004), a coefficient of -6.6 (-0.066 translated to definition here).

The model of Table 6.10 has good properties in terms of the usual tests: errors follows a normal

distribution, there is no evidence of serial correlation, RESET test null hypothesis is not rejected, Chow

test shows good forecast capacity (since 2000 onwards), CUSUM and CUSUM-Q shows no signs of

structural change, and neither does the recursive coefficients tests41. Finally, Engel and Granger

cointegration test rejects null hypothesis of unit root at 1%.

Table 6.11 shows the dynamic specification of the model, which takes into account the lag structure of

the variables. This structure is rather simple, with just one lag per variable being significant.

This time RINT is very significant, with a negative coefficient of -0.53. This departure from other results

in literature, like Coeymans (1999a), who finds a magnitude of -0.96, and Vergara (2004), which

estimated coefficient is -0.26. The number means that a 0.01 increase in the interest rate has a negative

impact on investment rate of half a percentage point. ROI effect fell with respect to the long run model,

40

If the variable changes in that amount, it means an impact of 0.0045 on the rate of investment, this is half a percentage point (0.09 x -0.05 is -0.0045). 41 Not shown but available upon request.

Dependent Variable: RINV

Method: Least Squares

Sample (adjusted): 1980 2007

Included observations: 28 after adjustments

Newey-West HAC Standard Errors & Covariance (lag truncation=2)

Variable Coefficient Std. Error t-Statistic Prob.  

C 0.152803 0.023529 6.494278 0.0000

RINT(-1) -0.53257 0.106807 -4.986281 0.0000

ROI(-1) 0.744381 0.117715 6.323578 0.0000

R_DEBT(-2) -0.06779 0.012428 -5.454803 0.0000

R-squared 0.930979     Akaike info criterion -5.899486

Adjusted R-squared 0.921976     Schwarz criterion -5.70751

S.E. of regression 0.011835     F-statistic 103.4103

Durbin-Watson stat 1.719667     Prob(F-statistic) 0.0000

Table 6.11

Dynamic model for Investment

now to 0.74. Debt coefficient remains very low and economically insignificant. As before, model behaves

very well in all the tests, from normality to stability tests (no cointegration test here)42.

Finally, Table 6.12 shows investment equation in the ECM (Banerjee-Pesaran format). Model includes

one term in differences (rest no significant) and the long run equation lagged.

This equation also performs very well in the whole battery of tests, this time including the cointegration

test a la Pesaran, Shin and Smith, which null hypothesis is rejected at 1% for cases II and III (no trend and

with or without restricting intercept)43. The derived long run model from the ECM is shown in Table

6.13, also comparing with the other two models.

42

Not shown. 43

P-values for the F-statistic are 11.8 and 13.2 respectively, while critical values are 3.99 and 4.68 for each case, at 1% of significance.

Dependent Variable: D(RINV)

Method: Least Squares

Sample (adjusted): 1981 2007

Included observations: 27 after adjustments

Newey-West HAC Standard Errors & Covariance (lag truncation=2)

Variable Coefficient Std. Error t-Statistic Prob.  

D(ROI) 0.455031 0.187579 2.425813 0.0244

RINV(-1) -0.843849 0.185298 -4.554003 0.0002

C 0.11958 0.029348 4.074574 0.0005

RINT(-1) -0.350771 0.130225 -2.693586 0.0136

ROI(-1) 0.654917 0.260173 2.517231 0.0200

R_DEBT(-1) -0.060607 0.015673 -3.867067 0.0009

R-squared 0.738179 Akaike info criterion -6.105596

Adjusted R-squared 0.675841 Schwarz criterion -5.817632

S.E. of regression 0.010376 F-statistic 11.84151

Durbin-Watson stat 1.929005 Prob(F-statistic) 0.00002

Table 6.12

Error Correction Model for Investment equation

In the ECM model, coefficient for RINT fell to -0.42 (in absolute terms). ROI coefficient increased slightly

to 0.77, while R_DEBT changed but seems to remain economically insignificant44.

Some remarks about these results for equation (b). First, it is clear that FD does not enter in this

specification, and that the model found is robust to different formats and tests. Existent problems like

RINT insignificance in the long run model and some departure from other literature estimations may

rest support to the findings here but overall, results still makes sense. It is necessary to notice that the

main problem with equation (b) is that by nature, it would be better studied on a higher frequency basis

(quarterly data for instance). The problem when using higher frequency data is that the long run

characteristic trying to be captured in the financial sector when using a proxy may turn just into an

indicator of the economic cycle, which would be very wrong for evaluating “true financial development”

effects on the economy. So perhaps problems found here with yearly data are the cost of using a

suboptimal frequency.

Second remark is that FD insignificance is in line with Vergara (2004), even though the sample,

investment definition and very likely credit definition are not the same. Dominichetti and Roeschmann

(2006) result is that FD (measured as credit to GDP) is statistically significant in his model but

economically insignificant. In the end, the latter is what matters. Regarding international evidence, this

is less clear as cross-section studies tend to indicate that capital accumulation is for developing countries

the main channel by which FD would impact growth.

Third, this FD insignificance is robust to different indicators and their definitions, hence supporting the

conclusions from this section.

44New calculation is 0.09 x -0.07 = -0.0063, still a bit low.

C 0.0720 0.1528 0.1417

RINT 0.0030* -0.5326 -0.4157

ROI 1.0577 0.7444 0.7761

R_DEBT -0.0504 -0.0678 -0.0718

*: insignificant

LONG RUN

MODEL

DYNAMIC

MODEL

ECM MODEL

(Banerjee-Pesaran)VARIABLE

Comparison of long run coefficients between models

Table 6.13

In conclusion, evidence here seems enough with respect to the second channel, given the shortcuts that

this estimation would have. Financial development would not be a relevant factor in terms of the local in

spurring capital accumulation. Why not? Perhaps companies’ investment may not face so many

restrictions in terms of financing, particularly given that the new economic orientation since 1974

implied openness and outward orientation in general, letting companies to get resources abroad if local

market was not enough for it. Since sample period is entirely included in this new economic paradigm, it

may not reflect past restrictions that certainly existed in the 1960’s and at the beginning of 1970’s.

7) Conclusions

This study attempted to capture the Financial Development effect on GDP via the two most important

channels regarded in literature. Even though link between FD and GDP may be bi-causal, here only one

direction is evaluated due to both attractiveness and extension. Endogeneity is a concern but it was

tried to be accounted when estimating the equations. Results are very interesting as regard to the topic

of this work, as in general there was found evidence of the significant effect of FD on activity, although

only through the TFP channel. Indeed, when FD measured as Liquid Liabilities grows 1% above its

general trend, GDP growth goes up a 0.62% approx (final effect depends of the model, and ranges from

0.46 to 0.62). These are long run effects and not year to year impacts, which requires a dynamic

approach. Given the characteristics of the processes trying to be captured here (financial sector

development), a short-run interpretation of coefficients is more problematic. Indeed, there could be the

risk of measuring the same GDP cycle reflected in the FD indicators. The FD growth effect found is the

lower bound of it, as there is a remaining 1.6% exogenous GDP growth, not explained in the model.

Given large colinearities present in the data, it is quite difficult to assess from where this exogenous

component comes from. Still, as argued here, human capital in terms of average education and FD may

be relevant factors behind this.

Therefore, the total effect of FD cannot be determined but the positive implication is that it could be

even higher than the 0.62% found. One drawback of this estimation is that Liquid Liabilities was the only

indicator that resulted significant. Still, as argued in Section 3, this is indeed the best indicator among

the selected here.

Conversely to the findings for the first channel, the investment equation showed no support for FD as a

factor in fostering investment. This is robust to the three different FD indicators, defined either as single

year or as moving averages. The model found seems to be enough in terms of stability, cointegration

and other tests, and despite the coefficients found are not very similar to other papers on this, they

magnitude is quite reasonable. As stated in that moment, specification problems related to data

frequency may be playing a role here.

Further analysis requires testing other channels by which FD may impact activity, together with

assessing the inverse link in order to get a broader picture of the FD-GDP link. Another improvement

could be in using extra FD indicators to give more support to results. Available historical data for assets

of financial institutions and bonds (two other indicators sometimes suggested in literature) is not long

enough and it would require more time to get (if) accrued data on those issues.

Generally speaking, even though the mentioned (and perhaps not mentioned) drawbacks of this study,

results support a minimum view of FD effects on the economic growth. Policy implications are clear in

terms of that financial sector (size and quality) should be taken seriously, but particular implications

should be not derived from this study. These should be searched on the inverse link, which is how the

financial sector develops in time. Then, taking the whole picture together will help to construct the best

policy measures in order to enhance long term growth.

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APPENDIX 1

0.4

0.5

0.6

0.7

0.8

0.9

1.0

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

MAXOPEN

.3

.4

.5

.6

.7

.8

.9

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

OPEN5YR

.12

.16

.20

.24

.28

.32

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

IMPCAP

0.972

0.976

0.980

0.984

0.988

0.992

0.996

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

GAP

-.20

-.16

-.12

-.08

-.04

.00

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

RECEFEC

.18

.20

.22

.24

.26

.28

.30

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

GOVEXP

.016

.018

.020

.022

.024

.026

.028

.030

.032

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

INVPUB

.45

.50

.55

.60

.65

.70

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

SRI

.00

.05

.10

.15

.20

.25

.30

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

MINST

.10

.15

.20

.25

.30

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

RINV

.00

.04

.08

.12

.16

.20

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

RINT

.04

.06

.08

.10

.12

.14

.16

.18

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

ROI

.0

.1

.2

.3

.4

.5

.6

.7

.8

.9

80 82 84 86 88 90 92 94 96 98 00 02 04 06 08

RDEBT

APPENDIX 2

FEC (foreign exchange constraints) is defined as:

1

1 )(

tIMP

tEXPtttttt

GDPP

FPPXTRKFIREFEC

where E is exchange rate (Chilean pesos per US$); IR are international reserves of Chilean Central Bank,

net of short run credits; KF are capital flows, defined here as the capital account result; TR are net

transfers; XPEXP are exports (quantity by price); FP are financial payments; and PIMP is imports price

deflator.

ROI (investment rate of return) is constructed like:

K

taxGDPROI K

)1(

where K is the capital elasticity (0.50 here), GDP is current GDP, K is current capital stock, tax is the

corporative tax rate and is depreciation rate (0.04 and later 0.045).

Source of data:

Private Credit: 1974 - 2006 data is from “Boletin estadistico, Superintendencia de Bancos e Instituciones

Financieras”; 2007 - 2008 is from “Sintesis Monetaria 2008, Banco Central de Chile”.

Liquid Liabilities: 1974 - 2000 data is from “Cuentas Nacionales, Banco Central de Chile”; 2001 – 2005 is

from “Agregados monetarios: nuevas definiciones. E. Arraño (2006), Banco Central de Chile”

Traded Value: 1974 – 2000 data is from “Cuentas Nacionales, Banco Central de Chile”; 2001 – 2008 is

from “Estadisticas Anuales, Bolsa de Comercio”.

Data for FEC variable is from Balance of Payments of Chile and National Accounts of Chile, both from

Central Bank of Chile.

The capital stock series is taken from Fuentes, Larrain y Schmidt-Hebel (2006) and updated to 2008.

Labor (employed people) is taken from both Central Bank of Chile and National Bureau of Statistics

(INE).

MAXOPEN is constructed based on data in US dollars, where exports (FOB) and Imports (CIF) are taken

from Central Bank of Chile.

There is an important issue to mention here: this index was corrected for the crisis periods, where both

trade and GDP decreases but the output fell was big enough to make the traditional openness index

raise. For instance, in the year 75, openness rose from 0.27 to 0.46, which is completely the opposite of

what really happened. In fact, imports and exports fell together more than $US 800 MM, a 21% with

respect to the 1974 value. Correction was made for 1975 (international oil crisis), 1983 and 1985 (debt

and banking crisis) and 2001 (Asiatic crisis). Those years the index was replaced by the prior one. This

index was calculated also using the World Bank database and the IMF’s International Financial Statistics

database (IFS). Results are quite similar in author’s opinion. Details are available upon request.

Data regarding imports and exports detailed accounts (manufactures, capital goods and primary

exports) are taken from “Cuentas Nacionales, Banco Central de Chile”.

Pension reform, structural reform index, human capital index and tax rate data sources are taken from

their references cited on the text.

Gross fixed capital investment, net external debt and real interest rate are from Central Bank of Chile.

APPENDIX 3

Unit root analysis of selected variables

Variable ADF, 5% KPSS, 5%

Ln(GDP) I(1) I(1)

Ln(L) I(1) I(1)

Ln(L_H) I(1) I(1)

Ln(K) I(2) I(1)

EDUCATION (and its Ln) I(1) I(1)

RFDPC I(1) I(1)

RFDLL I(1) I(1)

RFDTV I(1) I(1)

RECEFEC I(1) I(0)

GOVEXP I(0) I(0)

INVPUB I(0) I(0)

MAXOPEN I(2) I(1)

MINST I(1) I(2)

SRI I(0) I(1)

RINV I(1) I(1)

RINT I(1) I(1)

ROI I(1) I(1)

INF I(1) I(2)

R_DEBT I(0) I(0)

Correlations between GDP equation variables

RFDPC RFDLL RFDTV LOG(GDP) LOG(K(-1)) LOG(L_H) LOG(EDUCATION) RECEFEC GOVEXP

LOG(GDP) 0.22 0.96 0.62

LOG(K(-1)) 0.41 0.93 0.59 0.97

LOG(L_H) 0.12 0.95 0.63 0.95 0.87

LOG(EDUCATION) 0.29 0.97 0.56 0.98 0.97 0.94

RECEFEC -0.31 0.37 0.49 0.45 0.32 0.59 0.38

GOVEXP 0.32 -0.63 -0.59 -0.74 -0.61 -0.75 -0.65 -0.70

INVPUB -0.47 -0.38 -0.32 -0.39 -0.49 -0.40 -0.42 -0.29 0.19

MAXOPEN 0.41 0.88 0.65 0.95 0.99 0.85 0.94 0.35 -0.65

MINST -0.33 -0.88 -0.52 -0.88 -0.88 -0.80 -0.87 -0.14 0.51

SRI 0.21 0.94 0.61 0.92 0.87 0.97 0.92 0.58 -0.63

Correlations between Investment equation variables

RFDPC RFDLL RFDTV RINV RINT ROI

RINV 0.18 0.83 0.77

RINT -0.39 -0.65 -0.52 -0.51

ROI 0.20 0.88 0.77 0.90 -0.66

INF -0.33 -0.86 -0.50 -0.69 0.47 -0.78

R_DEBT 0.54 -0.46 -0.30 -0.63 -0.04 -0.54