Trade, R&D spending and financial
development
Yuanchen Chang
a,*, Mao-Wei Hung
band Chiuling Lu
ca
Department of Finance, National Chengchi University, 64, Chi Nan Rd., Sec. 2 Mucha, Taipei, Taiwan, ROC
b
Graduate Institute of International Business, National Taiwan University, Taiwan, ROC
c
Department of Finance, Yuan Ze University, Taiwan, ROC
This paper assesses the importance of financial development and R&D spending for exports using both a theoretical model and econometric test-ing. It is shown that financial development and R&D expenditures are positively related to exports and the balance of manufactured goods. The results suggest that countries that want to increase their exports should invest more in R&D activities and increase the priority of their financial reforms.
I. Introduction
Economists develop economic growth theory from various perspectives and one of their viewpoints is the impact of financial development on economic growth (Goldsmith, 1969). This possible relevance has been examined recently in many studies (De Gregorio, 1998; Habibullah, 1999; Beck and Levine, 2001; Beck et al., 2001a,b; Leahy et al., 2001; Hu, 2002). With different samples and indicators as proxies for finan-cial development, these papers have established the link between the level of financial development and economic growth. Their results support that financial intermediaries ameliorate the allocation of resource across space and time in an uncertain environment to stimulate economic growth (Goldsmith, 1969).
Other studies use the neoclassical growth model, which emphasizes the important role played by tech-nical progress in economic growth (Solow, 1956; Lucas, 1988). Some researchers also argue that suc-cessful R&D activities have heavily influenced eco-nomic growth through technology progress. In the era of the knowledge-based economy, Porter (1999) indicates that countries with more innovative capaci-ties tend to achieve higher levels of GDP per capita.
Cameron (1998) and Temple (1999) review a large number of studies that give support to the effect of R&D on total factor productivity. Rebelo (2001) extends the neoclassical growth model by considering endogenous technological progress. However, to the best of the present authors’ knowledge, no paper has investigated the role of both financial development and technology progress.
In this paper an attempt is made to fill this gap by exploring the possible relevance of financial devel-opment and R&D activities in promoting interna-tional trade. Kletzer and Bardhan (1987) construct a theoretical model to show that countries with less transaction costs have a comparative advantage to develop industries that rely more on external finance. Beck (2002) extends this framework and provides evidences that countries with a relatively well-devel-oped financial sector have a comparative advantage in manufacturing industries. Grossman and Helpman (1991) construct a series of theoretical models to integrate the theory of international trade with the theory of economic growth, with special focus on the economic determinants of technological progress. They show that technological innovations provide a comparative advantage to export-oriented countries. * Corresponding author. E-mail: yccchang@nccu.edu.tw
Applied Financial EconomicsISSN 0960–3107 print/ISSN 1466–4305 online # 2005 Taylor & Francis Group Ltd 809 http://www.tandf.co.uk/journals
International trade is used as a proxy for economic growth for the following reasons. First of all, many export-oriented countries serve trade as an engine of growth. For example, South Korea, Singapore, and Taiwan have larger ratios of exports in GDP than other countries. At the same time, some of these countries have needed to undergo financial reform following the Asian financial crisis. If any relation-ship is found between financial development and exports, then the results would suggest that govern-ments should increase the priority of their financial reforms if they want to increase their exports. In addition, these high-tech industries usually require a large number of R&D activities to sustain their com-parative advantages in the world market. Hence, if evidence is found to support the link between R&D spending and a country’s exports, then it would sug-gest that countries that want to increase their exports should invest more in R&D activities.
Following Beck (2002), a theoretical model is derived incorporating effects of R&D activities and financial development on exports and trade balance of manufacturing goods. The model assumes that investments in R&D have a successful probability and a failing probability 1 . With the expected productivity, both the agricultural and manu-facturing sectors show increasing return-to-scale. Entrepreneurs of both sectors are able to allocate their endowments, have capacity to transfer capital into producing goods, and provide loans through financial intermediaries to maximize their profits. Financial intermediaries serve as a channel to transfer deposits from savers to entrepreneurs.
The model consists of three markets: debt market, production market, and goods market. All three markets have to clear in the long-run equilibrium. The theoretical model implies that a country with a higher level of financial development has a compara-tive advantage to increase its export shares. It is also shown that manufacturing sectors can benefit more from sound financial development than agricultural sectors. Successful R&D activities can also enhance the productivity of a country. Countries with more resources invested in R&D will become exporters of manufacturing goods and importers of agricultural goods.
The empirical results obtained from 16 countries between 1991–1999 provide supports to the theoreti-cal inference. Finance activity defined by Beck
et al. (2001b) is used to proxy for financial develop-ment and R&D activities defined in the OECD Basic Science and Technology Statistics database as the independent variables. The dependent variable, a ratio of total exports in GDP, measures the exports in a country. In order to control the specific charac-teristics for each country, the link between these vari-ables is estimated using panel regressions. The results suggest that higher levels of financial development and R&D spending are positively related to exports. R&D activities tend to have positive and lagged effects on exports and trade volumes.
The rest of this paper is organized as follows. Section II describes a theoretical model which incor-porates sectors of consumption, production, and financial intermediaries. In Section III, data and hypotheses are discussed. Section IV presents the empirical results and Section V concludes this paper.
II. Theoretical Model
The theoretical model extends the models proposed by Kletzer and Bardhan (1987) and Beck (2002) in several ways. Both the influence of R&D spending and finan-cial developments on exports are considered. It is assumed that successful R&D can increase the com-parative advantage of a country by increasing its total factor productivity. In addition, the link between financial development and international trade through two sectors is explored depending on external finance. It is assumed that both manufacturing and agricul-tural sectors experience increasing return-to-scale. The manufacturing sector requires a higher level of external finance so that the producers of manufac-tured goods benefit from a well-functioning financial system more than agriculturists.1
This model starts with overlapping generations and each generation has a representative agent who can live for two periods. They are given endowments of capital k and productive ability to turn capital into manufactured goods or agricultural goods. The first generation is denoted as an entrepreneur and the second generation as savers in this economy. Therefore, entrepreneurs can enlarge their equity using external finance from financial intermediaries.
First, the model in the goods market as how agents arrange their two-period consumption bundles is pre-sented in the following sections. Second, the decision
1
Rajan and Zingales (1998) estimate that external finance in electric machinery is about 0.77, that in office and computing products is 1.06, and that in professional and scientific goods is 0.96. However, external finance in tobacco is about 0.45 and that in food products is 0.14.
made by producers in both the manufacturing and agricultural sectors is described by incorporating their R&D expenditures. Third, the role of financial intermediaries and the general equilibrium in the open economy are discussed.
Consumers’ decisions
It is assumed that representative agents live only two periods and have identical logarithmic preferences. The problem they face is to maximize their two-period utilities at the beginning of their lifetime:
U ¼max ln C ð Þ þln C 0 ð1Þ subject to the budget constraint:
s:t: C0¼ðqk CÞR ð2Þ
where C and C0 represent consumption in the first and second period, respectively; denotes the subjec-tive discount factor and is a constant term; q describes return on equity of their elaborated firms; and k is the capital representing the agent’s endow-ments. The first generation manages business by endowed capital and obtains qk returns in the first period. In the beginning of the second period, they deposit their savings with financial intermedi-aries and have interest R ¼ 1 þ iD. They consume all of their deposits and bank interest at the end of their life.
Equations 1 and 2 imply that the intertemporal consumption in the first period is equal to:
C ¼ qk
1 þ ð3Þ
and the second consumption is
C0¼ qk
1 þ
R ð4Þ
The optimal intratemporal consumption bundles can be presented as follows:
C ¼ x Z! j¼0 yjdj 1= 0 < < 1 ð5Þ where x is a homogeneous good of agriculture and yj denotes various manufactured goods. It is assumed that the price of agricultural goods equals one and the relative price of manufactured good j is Pj. Because Equation 5 is a Cobb–Douglas consump-tion funcconsump-tion, the agent will spend the ratio of his budget on agricultural goods and 1 on manufac-tured goods. The assumption of 0<<1 states that agents prefer a variety of manufactured goods to
homogeneous ones. It is seen that the optimal condition implies that the demand functions for agri-cultural and for manufactured goods are respectively:
x ¼ C þ C 0 ð6Þ yj¼ 1 ð ÞC þ C0 P1=1j Z! i¼0 P=1i di ð7Þ
where ! denotes how many different manufactured goods a consumer can choose. Since the demand functions of a variety of manufactured goods are symmetrical, Pj¼Pis in equilibrium.
Producers’ decisions
To explore the influence of R&D on comparative advantage, the productivity parameters are modified to include increasing return-to-scale characteristics in both sectors. It is assumed that x denotes the pro-ductivity in the agricultural sector and yin the manu-facturing sector. Both x and yare assumed to be greater than one, which implies that both the manu-facturing and agricultural sectors have increasing return-to-scale in the modified model.
Following Beck’s model, it is assumed that the production of the manufacturers is always more efficient than that of agricultural products, suggesting yx. Furthermore, it is assumed that entrepre-neurs in both the agricultural and manufacturing sectors invest " units in R&D activities. The probabil-ity of a successful R&D activprobabil-ity is , and the failing probability is 1 . In an agricultural firm, if R&D activities happen to be successful, then the produc-tivity of that sector becomes xþx("). However, the productivity parameter remains to be x when R&D activities fail. Likewise, yþy(") is the succes-sful productivity parameter and y implies R&D activities that are unsuccessful in any manufacturing firm. Also, the function of (") is given to be xð"Þ ¼ yð"Þ ¼ "=. This assumption implies that an
R&D activity with a lower probability of success will increase to higher productivity once the R&D activity is successful. Moreover, it is taken that one unit invested in R&D activities will equally strengthen productivity in the agriculture and manufacturing sectors from the function of ("), such that 0yð"Þ ¼
0
xð"Þ ¼1= will hold if R&D activities
are successful. Hence, the expected productivity equations are obtained as follows:
Lx¼xþ" ð8Þ
where Lxand Lyare expected productivity in agricul-ture and manufacturing firms, respectively, and LyLx.
It is further assumed that entrepreneurs are free to choose between production of agricultural and manu-factured goods with their endowments. Thus
x ¼ Lxz kð xþlxÞ " ð10Þ
yj¼Lyz kyþly
T " ð11Þ
where l are loans that financial intermediaries provide for entrepreneurs and T denotes the sunk costs. Term z is a firm-specific shock with a uniform distri-bution, F(z) ¼ z/b ranging between 0 and b, and the expected value of z is R0bzdFðzÞ ¼ b=2. Since manu-facturing entrepreneurs can differentiate their goods without additional costs and all manufactured pro-ducts have the same demand function, the model shows that each manufacturing firm will specialize in a specific product. It is assumed that an entrepre-neur needs more capital to run a manufacturing firm so that the size of equity in a manufacturing firm is larger than that of an agricultural firm, k ky> kx. For simplification, it is also assumed
that more capital is needed to run a manufacturing firm, Pj 1, and producers of both sectors prefer to
produce rather than leave the market.
Financial intermediaries and the optimal debt contracts
Following Goldsmith (1969) and Levine (1997), the second generation is isolated so that they have no information about firm-specific shocks and the prob-ability of successful R&D activities. Only through a verification process can savers observe firms’ pro-duction information. In addition, entrepreneurs and savers both face searching costs when they attempt to deal with each other. Because of asymmetric informa-tion costs, financial intermediaries play an important role between savers and entrepreneurs in diversifying their risk in a perfectly competitive environment. It is assumed that every saver can be a financial inter-mediary as desired.
It is assumed that entrepreneurs and financial inter-mediaries will sign a contract before production, which specifies the amount of the loan, the verifica-tion process, and the repayment that the entrepreneur should pay back in two states. When the entrepreneur borrows amount lx from an intermediary, the first state is that he will repay rx per unit of debt if a positive shock or a successful R&D activity occurs in the agricultural sector; that is, z > rx. Otherwise, he will pay back z(1 ) if z rx.
Manufacturing entrepreneurs also face similar choices. With this contract, the firm will maximize its profits and the intermediary will hold a zero-profit condition. Therefore, the maximization problem that the agriculturists face is:
Max LxðkxþlxÞ b rx ð Þ2 2b " ð12Þ s:t: LxðkxþlxÞ r2xð1 Þ 2b þ rxðb rxÞ b " # R lx 1 s ð13Þ where R, ", and kx are given. Term Lxrx denotes the fixed repayment per unit in non-verified states. The fraction in Equation 12 expresses the expected value of (z rx) from rx to b multiplying the probability of the state z > rx. Term describes the verification cost, while R is the gross interest rate that the entrepreneur gives to financial intermediaries for the loan.
Since the s ratio of deposits is used as search costs, the interest rate R should be divided by (1 s).
The first term in the second parentheses of
Equation 13 denotes the verified state, whereby the repayment rx per unit the intermediary receives is subtracted from the verification cost . The second term in these parentheses indicates the fixed return per unit in the non-verified state. Hence the financial intermediary’s expected return per unit is defined as follows: LxJ rð Þ x Lx r 2 xð1 Þ 2b þ rxðb rxÞ b " # ð14Þ Equation 13 can then be rewritten as:
LxðkxþlxÞJ rð Þ x R lx
1 s ð15Þ
The manufacturing entrepreneur has a similar max-imization problem: Max Lykyþlyp b ry 2 2b T " ð16Þ s:t: Ly kyþly pJ ry R ly 1 s ð17Þ
where R, ", and ky are given. The intermediary’s expected return per unit is:
LyJ ry Ly r2yð1 Þ 2b þ ry b ry b " # ð18Þ There exists a unique rxthat solves the optimal loan contract between the intermediary and the agricul-tural firm. The loan size lx is linear in equity. Because of the symmetric assumption, manufacturing
firms show a similar solution as that of agricultural firms.
Equilibrium in the loan sector
In order to derive the equilibrium in the loan sector, is defined as the aggregate loan share:
L
L þ K¼
lxþly
kxþkyþlxþly
ð19Þ In particular, the debt ratio in the agricultural and manufacturing sectors are denoted as x and y,
respectively. Two conditions should be satisfied in equilibrium. First, the non-profit condition for finan-cial intermediaries in Equations 15 and 17 should hold in equality. Second, the interest rates for both the agricultural and manufacturing sectors should be the same.
The equilibrium in the debt market can now be described as follows: x lx kxþlx ¼LxJ rð XÞð1 sÞ Rxð Þrx ð20Þ y ly kyþly ¼LypJ ry 1 s ð Þ Ry ry ð21Þ Rxð Þ ¼rx Ry ry ð22Þ xþð1 Þy¼ ð23Þ
where is agricultural sector’s asset as a share of total asset in the economy. Therefore, the returns on equity, qx and qy, in the two sectors can be written as: qx¼Lx b rx ð Þ2 2b 1 1 x " kx ð24Þ qy ¼Lyp b ry 2 2b 1 1 y T ky " ky ð25Þ where qx and qyare increasing in equity kx and ky, respectively. If the sunk cost T is sufficiently small, then some agents will prefer being a manufacturing entrepreneur to being an agriculturist. Moreover, producers in both sectors will always prefer to have leverage in their productions.
General equilibrium
Debt, production, and goods markets in a closed economy have to clear in equilibrium. Since the equi-librium condition in the loan market have been described, only the equilibria in production and goods markets are presented as follows.
In the production market, there is only one kind of goods in the agricultural sector, and therefore agriculturists face perfect competition. Their mar-ginal costs have to equal their price:
MCx¼ð1 xÞqxþxLxJ ð xÞ ¼1 ð26Þ
It is assumed that the manufacturing producers can shift to different goods without any costs, and thus they face monopolistic competition and marginal revenue equals marginal costs:
p ¼1 yqyþyLypJ y
ð27Þ The assumption of free entry in the markets makes the non-profit condition in equilibrium and average costs equal price in the manufacturing sector:
p ¼ 1 y qyþyLypJ y þT y þ " y ð28Þ
Finally, return on equity should be indifferent between producing in the agricultural or manufactur-ing sector:
qx¼qy ð29Þ
Equations 26–29 present the long-term equilibrium in the production market.
The equilibrium condition in the goods market is that the relative demand for goods should be equal to the relative supply:
x y¼
p
1 ð30Þ
When the debt, production, and goods markets all clear, the closed economy should be in an equilibrium state. Also assumed in a world with only two open countries and the difference between them is either the level of financial development or the level of expenditure on R&D. Therefore, there are two sets of equilibrium conditions in the debt and production markets. Supply and demand in the goods market of the world have to be equal in the equilibrium:
x þ x y þ y ¼
p
1 ð31Þ
where * denotes the foreign country.
As shown in the Appendix, it is proven that @qy=@" @qx=@" >0 and @qy=@s
@q x=@s in a closed economy. The reduction in searching costs in a country with a better-developed financial market provides the comparative advantage for increasing firms’ return on equity. Beck (2002) proves that pro-ducers of the goods with increasing returns to scale profit from a higher level of financial development more than producers of other goods, since a higher level of external finance allows them to exploit scale economies. This results in a higher production and
trade balance of this good in total output in econo-mies with a better-developed financial system. The theoretical model also indicates that R&D expendi-tures in the form of higher return on equity will shift the production incentives in favour of manufactured goods resulting in a larger manufacturing sector. These results provide the following testable hypoth-eses: Everything else equal, a higher level of external finance and R&D expenditure results in a higher export share and trade balance of goods that have relatively high scale economies.
III. Data Description
Empirical samples consist of data for 16 countries from 1990 to 1999. Data is collected from various sources, including Beck et al. (2001a), China Science and Technology Statistics (2000), International Financial Statistics (IFS), Indicators of Science and Technology Republic of China (2001), OECD Basic Science and Technology Statistics, Taiwan Economic Journal (TEJ), and World Development Indicators (WDI). Since the theoretical model attempts to explore the comparative advantage between two open economies resulting from well-functioning financial markets and R&D activities, total exports are used as a percentage of GDP (EXR) as the depen-dent variable to measure a country’s comparative advantage. In the following, the measure of financial development and R&D expenditure that are used as independent variables in the regression models are described.
Financial development
One of the main focuses in this model is the role played by financial intermediaries in transferring savers’ money into entrepreneurs’ investments. The proxy for financial development using finance-activity (FA)2 is chosen, which is defined as the product of private credit by financial institutions and total value traded on the stock market as a share of GDP. As shown in the literature, finance activity is expected to have a positive contribution to export and the trade balance of manufactured goods.
R&D activities
The relationship between R&D activities and exports is also explored in this paper. The ratio of domestic expenditure on R&D, as defined by OECD Basic Science and Technology Statistics, is used to measure R&D activities. The proxy for the expenditure on R&D (RDG) as a share of GDP is defined as gross domestic expenditure on R&D divided by GDP.
It is assumed that successful R&D inputs can enhance firm’s comparative advantage, so a country with more R&D expenditure will increase the ratio of its total exports. In addition, the theoretical model shows that manufacturing sector has more returns than the agricultural sector does when equivalent " units are invested in R&D activities in both sectors. This suggests that RDG will increase exports of manufacturing-based countries more significantly than agriculture-based countries.
Table 1 summarizes the descriptive statistics of these variables. Panel A shows that there is a consid-erable variation in exports as a share of GDP, ran-ging from a low of 7.55% in the USA to a high of 46.87% in Netherlands. The ratios of total exports in GDP for Canada, Finland, Netherlands, South Korea and Taiwan are much higher than the rest of the world. This implies that exports play an impor-tant role in these countries. Financial development is measured by finance activity, which is defined as the log of the product of private credit by financial institutions and total value traded on the stock market as a percentage of GDP.
The third column in Panel A shows substantial variance among the countries in the financial devel-opment indicator. For example, Taiwan had the pri-vate credit and value traded equal to almost 3.4, while Turkey’s private credit and value traded aver-aged 0.04. The financial development indicators for Netherlands, Korea, USA and UK are much higher than the rest of the countries. The country with the largest ratio of expenditure on R&D is Japan (2.86%) while that with the lowest is Italy. Countries with a higher ratio of R&D tend to be classified as main producers of high-tech goods, such as the UK,
USA, Japan, South Korea, Canada, Demark,
Germany and Taiwan. Variations in RDG are rela-tively small in each country, suggesting stable inputs of R&D activities.
2
Goldsmith (1969) uses the ratio of financial intermediary assets to GNP as a measurement. Habibullah (1999) uses three measures for financial development: ratio of money stock to income, ratio of total deposits to income, and total domestic credit to income. Recently, Beck et al. (2001b) measure financial development by four indicators: finance activity, finance size, finance efficiency, and finance aggregate.
Panel B shows that the correlation between exports and finance activity is about 0.36, while the correla-tion between EXR and RDG is 0.06. Whereas the correlation between exports and R&D in Demark and Italy is respectively 0.31 and 0.83, the corre-lation in Austria and Canada is respectively 0.94 and 0.92.3
IV. Empirical Results
In this section the theoretical hypotheses is tested with a nine-year panel for 16 countries.4 Speci-fically, the relationship between the level of financial
development, R&D activities, and total exports is focused on.5 Rajan and Zingales (1998) is followed to split the sample into two subsamples according to export shares above and below the median for a coun-try, which correspond to manufacturing-based and agriculture-based countries.6 The empirical results are obtained from random effect panel regressions.7
Empirical results with pooling cross-section data The empirical results use data of 16 countries from 1991 to 1999. To capture possible lagged impact of financial activity and R&D expenditures on a coun-try’s exports, one-period lagged financial activity 3Correlations between variables in each country are not reported in this paper. Countries with a negative correlation between total exports and R&D expenditure are Denmark, France, Italy, Spain, USA and China. Results are available on request. 4
They are Austria, Canada, Denmark, Finland, France, Germany, Italy, Netherlands, Spain, UK, USA, Japan, China, South Korea, Taiwan and Turkey.
5
The FA variable is included in logs for the regression analysis. 6
The ratio of manufacturing exports in each country is from World Development Indicators and the database of Directorate General of Budget Accounting and Statistics Executive Yuan, ROC.
7
The results from fixed effect panel regressions provide similar conclusions and are available upon request. Table 1. Summary statistics
EXR FA RDG Mean % Standard deviation Mean % Standard deviation Mean % Standard deviation Panel A: Descriptive statistics
Austria 25.74 3.08 7.10 3.35 0.12 0.01 Canada 29.90 5.39 48.01 12.40 1.68 0.06 Demark 27.51 0.50 6.92 3.10 1.84 0.16 Finland 28.49 5.01 14.40 14.14 0.42 0.07 France 18.74 1.72 21.24 12.29 0.35 0.01 Germany 22.50 2.21 38.71 24.44 1.20 0.05 Italy 18.62 2.64 7.53 6.98 0.00 0.00 Netherlands 46.87 3.79 131.24 146.83 0.90 0.02 Spain 15.73 3.40 41.79 47.20 0.01 0.00 UK 19.62 1.64 76.66 31.03 1.94 0.11 USA 7.55 0.47 110.15 90.86 2.56 0.09 Japan 8.95 0.60 46.54 9.37 2.86 0.07 China 19.14 2.08 28.39 31.45 0.70 0.08 Korea 28.06 6.33 81.50 77.34 2.38 0.26 Taiwan 41.03 3.02 336.78 169.12 1.83 0.11 Turkey 11.99 2.36 4.78 3.33 0.48 0.08
Panel B: Correlations between variables
EXR FA RDG
EXR 1.00 – –
FA 0.36 1.00 –
RDG 0.06 0.54 1.00
Notes: For each country means and standard deviations of three variables are calculated. EXR indicates total exports as a percentage of GDP. Financial development is measured by finance activity (FA), which is defined as the log of the product of private credit by financial institutions and total value traded on the stock market as a share of GDP. RDG indicates R&D expenditure as a share of GDP. All three variables are counted in US dollars. The period of yearly data is from 1991 to 1999. Data is from Beck et al. (2001a), China Science and Technology Statistics (2000), IFS, Indicators of Science and Technology Republic of China(2001), OECD Basic Science and Technology Statistics, TEJ, and WDI. Descriptive statistics are reported in Panel A and Panel B reports correlations between variables.
and R&D spending variables are included in the model. The following panel regression is estimated:
EXRit¼iþ1FAitþ2FAit1þ3RDGit
þ4RDGit1þ"it ð32Þ
Results in Panel A of Table 2 indicate that countries with better-developed financial markets have larger shares of total exports. The estimated parameters of the financial development variables are positive and significant at the 5% level in the regression of total exports for total sample countries.
Table 2. Relationship between financial development, R&D expenditure and trade Panel A
Total samples Total samples
FAt 0.02** 0.02** (6.65) (3.04) FAt 1 – 0.01 (1.51) RDGt 0.10** 0.10** (6.28) (2.45) RDGt 1 – 0.20** (5.22) R2 0.95 0.96 Numbers of observations 144 128 Panel B Manufacturing-based countries Manufacturing-based countries FAt 0.02** 0.02** (4.25) (2.34) FAt 1 – 0.01 (0.98) RDGt 0.09** 0.11** (4.95) (2.19) RDGt 1 – 0.21** (4.42) R2 0.91 0.97 Numbers of observations 81 72 Panel C Agriculture-based countries Agriculture-based countries FAt 0.02** 0.01 (5.55) (1.36) FAt 1 – 0.01 (1.45) RDGt 0.09** 0.07 (3.45) (1.18) RDGt 1 – 0.16** (2.61) R2 0.97 0.98 Numbers of observations 63 56
Notes: Panel A reports the link between financial development, R&D expenditure and trade for the full sample. The dependent variable in all regressions is a ratio of total exports to GDP in each country (EXR). Random effects panel regressions include independent variables: finance activity (FA), FAt 1, R&D expenditure (RDGt), and RDGt 1. Panels B and C report empirical results with samples that are separated according to the median of the ratio of manufacturing exports in merchandise exports and the ratio of manufacturing exports in merchandise exports. In Panel B, a country with a ratio greater than the median of the ratio of manufacturing exports in merchandise exports in classified as a manufacturing-based country. The rest of the countries in the sample are classified as agriculture-based countries in Panel C. *Significant at the 10% level and **indicates significance at the 5% significant level.
The results show that a 1% increase in financial devel-opment implies a 0.02% increase in export shares for the full sample. However, the lagged impact of financial-activity is not significant at the 5% level.
With respect to the impact of R&D expenditure on export share, the results show that the parameters of RDGtare positive and significant at the 5% level, indi-cating that RDGtcontributes to the ratio of exports in our samples. A country that sees a 1% increase in R&D expenditure will have the export share decrease about 0.1% at the same year. When the lagged RDGt 1 variable is added in the regression model, the parameter for the contemporary R&D expendi-tures (RDGt) is negative and significant. In addition, parameter for the lagged R&D expenditures is positive and significant, which suggests that R&D expendi-tures have a lagged impact on a country’s export.
Manufacturing-based and agriculture-based countries
To explore the impact of R&D activities in manufac-turing- or agriculture-based exporters, the sample is divided into manufacturing-based or agriculture-based countries according to the median of the ratio of manufacturing exports in merchandise exports.8 A country with a ratio greater than the median of the ratio of manufacturing exports in merchandise exports is classified as a manufacturing-based country; otherwise, it is classified as an agriculture-based country.
Two subsamples are examined using the random effects panel regression in Equation 32 and the results are presented in Panel B and C of Table 2. In Panel B, the parameter for financial development is positive and significant at the 5% level for manufacturing-based countries. R&D expenditure shows a signifi-cant contribution to the ratio of exports in manufac-turing-based countries. A 1% increase in R&D expenditure decreases exports by 0.11% in the same year but increases exports by 0.21% in the next year. Results are similar for the agriculture-based countries. In Panel C of Table 2, it is found that increase in the priority financial activity indicators are not significant at the 5% level. The lagged R&D parameter is positive and significant at the 5% level but it is smaller than the result in Panel B of Table 2. This is consistent with the theoretical model, which shows that R&D activity will increase exports more significantly for manufacturing-based countries than agriculture-based countries.
The economic implications in the analysis are that countries with a higher ratio of manufacturing exports experience more benefit from financial devel-opment and expenditure on R&D. The contribution from R&D inputs exhibits similar patterns. In sum-mary, a manufacturing-based country will benefit more from a better financial market and more R&D expenditures. In the case of manufacturing exporters, especially in East Asian countries, govern-ments should increase the priority of their financial reforms to increase their exports. With more comparative advantages, the increased export will contribute to these countries’ GDP growth.
V. Conclusions
This paper explores both the link between the level of financial development, international trade, and the contribution from R&D activities. Following Beck (2002), a theoretical framework with two sectors is developed to show this possible linkage. The model indicates that a country with a well-functioning finan-cial market will have a comparative advantage in exports. In addition, the amount of R&D inputs ben-efit the manufacturing sector more than the agricul-tural sector, so a country with a higher expenditure on R&D could experience a larger ratio of manufac-turing exports.
Empirical results obtained from 16 countries between 1991–1999 support the conclusions of the theoretical model. Countries with a higher level of financial development do experience higher shares of total exports in their GDP. These results hold when the full sample is divided into manufacturing-based and agriculture-manufacturing-based countries. Furthermore, countries with investments in R&D expenditure in manufacturing-based countries result in a higher ratio of total exports than agriculture-based countries. The conclusion in the paper emphasizes both the importance of a better financial market and the role of R&D activities. It is shown that the linkage between financial development and R&D expenditures are positive related. Though R&D expenditures have a lagged impact on export growth, the results suggest that lagged financial development indicator do not have a significant impact on export growth.
Acknowledgements
Financial support from MOE program for promoting academic excellence of universities under the grant 8
number j-91-H-FA08-1-4 is gratefully
acknowl-edged. Pei-Ju Liao and Leh-chyan So provided excellent research assistance.
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Appendix
Proof: @qy=@" @qx=@" >0
@qy=@" >0 and @qx=@" >0 will be proved followed by
@qy=@" @qx=@".
Return on equity for a firm in a manufacturing sector can be written as follows:
qy¼Lyp b ry 2 2b 1 1 y T ky " ky ð25Þ Thus: @qy @"¼ 0 yð Þp" J ry b ry bJ0 r y þ b ry 2 2b " # 1 ky ðA1Þ Insert 0yð"Þ ¼1= and y¼ly=kyþly into Equation
A1 and rewrite as: @qy @" ¼ 2J ry þJ0 ry b ry J0 r y b ry p b ry 2 2b 1 kyþly " # ðA2Þ Since ½2JðryÞ þJ0ðryÞðb ryÞ=J0ðryÞðb ryÞ>0, then
@qy=@" >0 if and only if ðkyþlyÞ is sufficiently
large. That is, if total capital of a firm in a manufac-turing sector is large enough, then other things being equal, it can be concluded that the firm has a higher return on equity due to more R&D inputs.
Return on equity in the agricultural sector is similarly shown as:
qx¼Lx b rx ð Þ2 2b 1 1 x " kx ð24Þ
It can be proved that: @qx @" ¼ 2J rð Þ þx J0ð Þrx ðb rxÞ J0r x ð Þðb rxÞ ðb rxÞ 2 2b 1 kxþlx ð Þ " # >0
if and only if (kxþlx) is sufficiently large. Furthermore, if it is defined that:
f rð Þ i J rð Þi ðb riÞ bJ0r i ð Þ þ b ri ð Þ2 2b " # i ¼ x, y ðA3Þ and Q @qy @" @qx @" ðA4Þ then: Q ¼ pf ry f rð Þ x 1 ky 1 kx ðA5Þ Since J(ri) is defined in Equations 14 and 18, the partial derivation becomes:
f0ð Þ ¼ ri
rið2b þ riþriÞ
2 b þ rð iþriÞ2
>0 0 < < 1,
0 rib ðA6Þ
Equation A6 implies f(ri) is an increasing function in ri. By assumptions, ryrx, P 1, and kykx, the Q 0 is verified.
Proof: j@qy=@sj j@qx=@sj Following Equations 24 and 25:
@qx @s ¼ 2J rð ÞRx xrx ð Þ J0 r x ð Þ2bð1 sÞ2 ðA7Þ @qy @s ¼ 2J ry Ry ry J0 r y 2 bð1 sÞ2 ðA8Þ
can be derived. Since J(ri) is an increasing function in ri, J0ðriÞ is decreasing in ri. Because RxðrxÞ is
equal to RyðryÞ in equilibrium, j@qy=@sj j@qx=@sj is
