Companies' investment decisions in the NICS—evidence from Taiwan and South Korea

COMPANIES’

INVESTMENT

IN THE NICS-EVIDENCE TAIWAN AND SOUTH

DECISIONS FROM

KOREA

SHAW CHEN and COPA ~~O~~HU~Y

ABSTRACT This study considers the determinants of investment among manufacturing companies in two Newly Industrializing Countries, Taiwan and South Korea. Using company accounts data from the Pacific Basin Capital Markets Databases. traditional factors such as accelerator effects, liquidity constraints and cost of capital are considered, as well stock market influences embodied in Tobin’s Q. Diversity in companies’ investment behavior is also examined. Investment among Taiwanese companies appears to be p~ncipally affected by the tra~tionai factors, whereas stock market considerations appear to be more relevant for investment in South Korea. .iEL C~USS~~C~Z~~~: 016, G3ff

1. INTRODUCTION Over the last 30 years the economic performance of Newly Industrializing Countries (NICs) in the Pacific Basin has been one of rapid growth, embracing the development of a strong manufacturing base, geared towards the export market. Taiwan and South Korea are two such countries. The growth experiences of both countries have been similar in that exports of manufacturing goods have been the main engine of growth which in turn has been fueled by high levels of investment. Investment in Taiwan accounted for 30 percent of GDP in the early 80s Direct ulf

correspondence

Department,

Northeastern

to: Shaw Chen, Univeristy University, Boston, MA 021 IS.

-._..--

International Review of Economics ISSN: 1059-0560

and Fiance,

of Rhode Island;

Gopa Chowdhury,

Economics

-.-..._-_..-.___ 4(3): 283-298 Copyright 0 1995 by JAI Press Inc. All rights of reproduction in any form reserved. .~ _____

283

284

SHAW CHEN and COPA CHOWDHURY

falling to 21 percent in the late 80s and in South Korea averaged 28 percent of GDP throughout the 80s. Given the importance of investment in the manufacturing sector in both countries, it is interesting to consider the determinants of investment for manufacturing companies in Taiwan and South Korea at the empirical level. Taiwan and South Korea have very different company sectors which have been shaped by government policy (see for example Lau (1986) Collins (1990), Park (1990)). In South Korea the state has assumed a central role in directing industrial development. In the late 70s the government went for the “Big Push’ in selective heavy industries. This saw the rise of the huge conglomerates and government policy became one of coercion and preferential treatment of targeted industries. In 198 1 the government began liberalizing economy, including tax reform, promoting greater reliance on market incentives and cutting back favoritism on government loans. Nevertheless South Korean manufacturing remains very concentrated. In contrast, the government in Taiwan sought to remove barriers to commerce and fostered small businesses, making credit markets more accessible especially to smaller new enterprises. As a result Taiwan’s manufacturing sector comprises mostly small and medium size companies. Taiwan and South Korea both have emerging markets, so how does that characterization reflect in their investment functions? In deriving the most appropriate empirical investment equation for each country, this study asks the following: 1. 2. 3.

How relevant are changes in the company’s own operating environment and changes in the economy as a whole? How comparable are the respective investment functions--more specifically, is there a common model of investment behind the similar growth experiences? How do investment functions for these countries compare with functions obtained for mature industrialized countries?

The data set for this study is unique and has not been used before for analyses of this kind. The data comprise company accounts for Taiwanese and South Korean companies from the Pacific Basin Capital Markets Databases1’2 (PACAP). PACAP covers all companies listed on the stock exchanges. Annual data spanning the years 1980 to 1988 are available. A time-series cross-section approach to modeling is adopted because it yields more accurate and reliable results by controlling for observable and unobservable differences between companies. A disaggregated approach to investment functions in emerging market economies has not been reported before. Section II describes theoretical considerations and presents the model for this study. Section III describes the data and discusses estimation. Results are presented in Section IV and conclusions are stated in Section V.

II.

THEORY

AND MODEL

It is widely acknowledged that investment decisions are difficult to model theoretically and empirically. A variety of models exist, some conflicting. Most studies use aggregate data. Disaggregated data have been used to a much lesser degree and have focused on industrialized countries; for example, Kuh (1963) and Eisner (1978) for the US, Oudiz (1978) for

Company

Investment

Decisions

in the NlCs

285

French data and more recently, Devereux (1989), Blundell, Bond, Devereux, and Schianttereli (1987) for the UK, Schaller (1990) for the US and Hayashi and Inoue (1991) for Japan. A convenient starting point for empirical investment models is consider the actual capital stock, K,, as a weighted combination of previous levels of desired capital stock, c, because adjustment to the desired level is not instantaneous, reflecting order, delivery and gestation lags. Hence, K, = o(L)k*, where o(L) is a lag operator in L. Defining gross investment, I, as the total of net investment, c = Kt - K,_,, and replacement investment, Z: yields the basic equation for gross investment: I, = o(L)(K; - K*,,) + I;.

(1)

Theoretical investment models differ in their explanations of the desired capital stock, K*,, and of the replacement investment, Z:, and their specification of the adjustment process, o(L). In order not to pre-judge the nature of investment decisions in Taiwan and South Korea, we propose starting with a very general empirical investment function, which embraces several well known theoretical models: the Accelerator Model, the Liquidity Constraints Model, the Neoclassical Model and Stock Market Valuation Models. In the Accelerator Model investment relates to changes in output, with output typically proxied by a company’s sales. The Liquidity Constraints Model challenges the Modigliani-Miller (1958) result on the irrelevance of financial policy for investment; investment relates to the availability of internal funds, represented by profits (gross or net) or cash flow (after tax profits plus depreciation), because agency costs and information asymmetries characterize capital markets. In the Jorgenson (1963) Neoclassical Model the cost of capital and level of output are relevant for investment when firms maximize net worth (discounted future profits) under perfect competition. Stock market considerations were first formalized by Grunfeld (1960); a firm’s desired capital stock relates to its stock market value because that value represented future expected profits. More recently two schools of thought have popularized the role of the stock market. One views managers as being preoccupied with share price maximization; this reflects their desire to secure capital gains for stockholders and have their own rewards tied to stock price movements, and their fears of takeover. The other subscribes to the Tobin Q Model (Tobin, 1969). In the Tobin Model the rate of investment is determined by (marginal) Q, the ratio of the shadow value of installed capital to its after tax purchase price. Marginal Q is difficult to measure, so empirical studies have typically used average Q, the ratio of the market value of real capital assets to the replacement cost of those assets. In other studies empirical Q models at both the aggregate and micro levels have been disappointing, evidenced by equation misspecification, serial correlation in the error, and Q coefficients frequently becoming insignificant in the presence of more conventional variables, like acceleration terms and time trends (e.g., as reported in Mullins and Wadhwani’s (1989) aggregate study of industrialized countries and the Blundell et al. (1987) micro study of the UK). A general unrestricted model of investment is proposed, incorporating adjustment delays, response to changes in output, proxied by sales, finance availability, represented by gross profits, cost of capital, represented by its components: debt-equity ratio, long term borrowing rate, return on equity and the relative price of investment goods3 and stock market considerations represented by firm value or empirical measures of Tobin’s Q.

SHAW CHEN and COPA CHOWDHURY

Following

Mullins and Wadhwani (1989) our model has a very general form:

al (L)$

= a2 (L) CHSALj,+

a3 (L) PRF,, + a4 (L) Vit+ a5 (L) DEi,

+ a6 (L) rot + a7 (L) rEit + a8 (L) P/PG~ + 6, + Ujt

(2)

where for Period t, I is company i’s gross investment, CHSAL is the company’s change in sales from one period to the next, PRF is the flow of profits, V is either firm market value or Tobin’s Q, DE is the company’s debt to equity ratio, debt/(debt+equity), r. is the interest rate on long term borrowing, r, is the return on equity and pIIpc is the relative price of investment; investment goods deflator divided by the GDP deflator, al(L), . . ., ag(L) are lag operators, creating lags in accompanying variables. Variables I, CHSAL, PRE v Q4 and DE relate to companies’ own environments and are drawn from PACAP company accounts, whereas, rD and pI/pG relate to specific economy wide influences, common to all firms, and are taken from other published sources.’ The return on equity could fall into either category, so for r, we consider both a micro and a macro return on equity. The 6’s are time dummies to represent other economy wide influences (such as tax policy, general price movements). The composite error term, LQ= fi +Q, comprises an unobservable time invariant company effect, fi (possibly reflecting managerial skill, risk taking and so forth) and a zero mean time and company varying error, Q. We adopt a general to specific modeling strategy; we start from this general function and with appropriate econometric tests determine the specific function for Taiwan and South Korea. Many of the theoretical considerations for investment were developed for industrialized economies, so it is unclear a priori what would be appropriate for NICs such as Taiwan and South Korea. Nevertheless the following considerations are of interest. Traditional views are that the stock market may be less important as a source of finance in South Korea than in Taiwan, because South Korean companies tend to be more leveraged, and that private (unofficial) funding sources have been important in Taiwan and dominate commercial borrowing. Anecdotal evidence suggests casino like characteristics for the Taiwan stock exchange during the 80s. One might therefore question the relevance of stock market influences on investment in both countries and also the cost of capital for Taiwan. In 1981 South Korea launched a program of financial liberalization (Kwack & Chung, 1986) comprising the transfer of ownership of the major commercial banks to the private sector, and a series of measures to increase the efficiency of financial institutions in order to enhance the allocation of capital through the market mechanism. Companies were exhorted to make dividend payments to encourage the development of the stock market. In 1980 Taiwan took more limited steps to liberalize its financial system, focusing on the interest rate and foreign exchange systems (Emery, 1988). Also noteworthy is that, although the number of companies listed on the stock exchanges in both countries has risen significantly since 1984, South Korea has had three times the number of listings of Taiwan. Do the empirical investment functions reflect any of these phenomena?

III.

DATA AND ESTIMATION

The data comprise annual company accounts from the PACAP data set for manufacturing companies in Taiwan and South Korea, with economy-wide data drawn from published

Company Investment Decisions

in the N/G

287

sources. The Taiwanese sample consists of 53 companies, covering 11 manufacturing industries for the period 1980 to 1988. The South Korean sample consists of 165 companies covering 19 more disaggregated sectors from 1981 to 1988. The samples were obtained by selecting companies which were continuously active for these periods and which did not have missing item codes or irregular values reported in their account&. Summary statistics for sample are in the appendix. In the Taiwanese sample the most represented sectors are, in descending order, textiles, foods, and electric products. Textiles are the most dominant manufacturing sector in the whole of Taiwan and also the most dominant in our sample in terms of investment expenditures, sales, profits, firm value and capital stock. There are considerable variations and positive skews in the company variables. The long term borrowing rate for Taiwan is proxied by the six-month Treasury Bill rate. The return on equity, represented by the dividend yield, is not a published rate in either Taiwan or South Korea on an economy-wide basis. We computed the dividend yield for each company for a micro return on equity and derived a macro version by averaging across companies to obtain annual averages. It was observed that many Taiwanese companies did not pay any dividends at all. Between 1980 and 1982 only a quarter of the Taiwanese companies in the sample paid dividends, but from 1983 this proportion rose to one half. In the South Korean sample the most numerous sectors are chemicals, textiles and electronics-electrical products. The dominant sectors in the sample in terms of investment, sales, profits, and capital stock are machinery, electronics, motors, chemicals, and textiles. Company variables have considerable variation with positive skews. In contrast to Taiwan all the companies in the Korean sample paid dividends throughout the period. For South Korea the published Government bond yield is taken to represent the long-term borrowing rate. Estimation of the investment equation in equation (2) depends on the characterization of companies in the sample. Given the variability of data it is plausible that there is systematic heterogeneity across companies in which case company specific variables need to be deflated by some measure of company size. We deflate by capital stock; company specific non-ratio variables (investment, change in sales, profits, firm value) are divided by company capital stock. It is plausible that unobservable heterogeneity exists across companies, stemming from differences in managerial skill and risk taking in the investment decision. This is reflected in the time invariant company effect,f. Equation 2 is therefore a dynamic panel model with individual company effects. The usual Covariance and GLS estimators for panel models control for company effects in static models but yield inconsistent estimates when lagged dependent variables are present. Instrumental variables (IV) yield consistent estimates when instruments are legitimate (that is, they satisfy the required orthogonality condition with the equation error). The need for IV stems from the correlation betweenf and the lagged dependent variable (investment) and also the possible correlation betweenfand the other exogenous regressors (Mundlak, 1978), for example, a company’s profits may be correlated with its managerial skill). The traditional approach to IV for dynamic panel models is described by Anderson and Hsiao (1982); the model is differenced to eliminatef, followed by IV on the differenced model, using distant lags of the dependent variable as instruments for the differenced lagged dependent variable regressor, provided the error is iid, otherwise instruments must come

288

SHAW CHEN and COPA CHOWDHURY

from the past values of the exogenous regressors. More recently Arellano and Bover (in press) suggested an alternative approach, assuming regressors have constant correlation withf; instrumental variables on levels model with instruments being the first differences of the respective regressors, provided the error is iid, otherwise instruments for the lagged dependent variable must come from more distant differences of the exogenous regressors. The levels-IV approach appears to have some advantages over the difference-IV approach in certain contexts; the possibility of retaining more time periods and identifying the effects of regressors which are time invariant or have little variation over time. The general to specific modeling strategy entails starting from the general investment function in (2) and deriving the specific empirical function by applying a series of tests; specification tests for company effects, modified LM tests for serial correlation, nested tests for individual or joint significance of regressors, non-nested tests for competing models of investment and post sample predictive tests to assess model adequacy. The usual specification test for company effects (as in Hausman (1978), is not valid for a dynamic model.7 Holtz-Eakin (1988) suggests a test based on assessing the validity of a sequence of orthogonality conditions implied by the absence off. Since this test requires an arbitrary decision on the number of orthogonality conditions to be tested, we suggest a much simpler test based on a comparison of a suitable IV estimates and OLS estimates of the model in, yielding a x2 specification test*. In the interest of parsimony, we report the specific empirical investment functions and the test results supporting our claims.

Iv.

RESULTS

Given the stated advantages of the levels-IV approach, we present those results for Taiwan and South Korea in Tables 1 and 2 respectively. Specific functions and actual instruments are described in the Tables. For Taiwan the investment function is clearly dynamic in terms of the presence of lagged investment. However, AZit_l as an instrument for lit_1 yielded implausible results, indicating serial correlation in the error. This necessitated the use of lags of differenced exogenous variables as instruments for lit_1, the results of which are presented in Table 1. There are no clear signs of unobservable company effects. While the suitability of the x2 test comparing IV estimates in Column 1 with the corresponding OLS estimates may be questioned in view of the signs of serial correlation in the error, our alternative F-type test for company intercepts was insignificant: F(52,258) = 0.663 (a likelihood ratio test for company intercepts in the same framework yielded an insignificant ~25~= 39.87. Two sets of IV estimates are presented in Table 1. While both instrument Iit_ for serial correlation, Column 1 also instruments for company effects, whereas the Column 2 does not. The two sets of estimates are very similar, further indicating the possible absence of company effects. This suggests that Taiwanese companies are possibly more homogeneous, which is plausible given the small enterprise character of the manufacturing sector in Taiwan. We find evidence of second order serial correlation in the error. Modified LM tests for second order serial correlation are ~22 = 16.60 and x ‘2 =15 .82 for Columns 1 and 2 respectively. We present more accurate t-ratios for the IV estimates by estimating standard errors adjusted for arbitrary second order serial correlation (extending the Newey & West (1987) estimator to panels).

Company

Investment

Table 1.

Decisions

Investment

289

in the NlCs

Equations

for Taiwanese Manufacturing

Column I Explanatory Variable

Companies

Column

IV(‘)

2

IV(‘)

Coefficied2)

(f)(4)

C*efficied3’

(tp’

Ii,-I

0.805

(5.W

0.833

(7.00)

CHSAL,

0.112

(2.87)

0.101

(2.87)

PRF,,

0.237

(2.02)

0.303

(2.80)

AL%2

-0.352

(2.04)

-0.324

(1.98)

‘01

-0.037

(4.43)

-0.037

(4.25)

*‘E,;,-1

-0.006

(1.43)

-0.006

(1.37)

fl,‘PG,

-1.691

(2.03)

1

R2 p2(5) Notes:

1.804

0.619

0.619

0.619

0.622

(2.24)

I. N = 53 companies, T = 6 years: 1983- 1988; intercept included, but not reported. 2. Instruments: (ACHSAL ,,_,, APRF,,,, APRF,,2. rD1_,,ArE,rr_2,AP,lP,& for I ,,_,; ACHSAL,, for CHSALi,; APRF,, for PRF,,; remaining regressors are their own instruments. 3. Instruments: (CHSAL,,~,, PRF,,,, PRF;,.z, r~,_,, ALE,,,_2, API/P& for Ilet; remaining regressors are their own instruments. 4. Absolute r-ratios in parentheses, allowing for arbitrary second order serial correlation in the error. 5. Square of correlation between actual and predicted I.

Table 2.

Investment

Equations

for South Korean Manufacturing

Column 1 Exulanatorv kriable .

IV(‘) Coejj%ent(2)

Companies

Column 2 (tp’

IV(‘) Co&cied3)

(t)(4)

Iit- 1

0.78 1

(8.21)

0.800

(8.50)

Q,,

0.084

(4.69)

0.062

(3.86)

CHSAL;,

0.004

(1.10)

PRFit.1

0.006

(0.22)

I

-0.050

(1.14)

‘DI

-0.001

(0.37)

*‘&t-z

-0.000

(0.54)

pl’pG,l

-0.127

(1.17)

DE;,

R2 X5) P

0.724

0.740

0.726

0.742 Wald(@ ~26 = 0.011

Notes:

1. N = 165 companies, T= Syears: 1984.1988; intercept included, but not reported. 2. Instruments A&,_,; for I,,; AQ,, for QiP 3. Instruments Al;,., for I,,.,; AQ,, for Q,,; ACHSALi, for CHSAL,,; APRF,,_, for PRF,,; ALEi,_, for DE,,.,; remainmg regressors are their own instruments. 4. Absolute f-ratms in parentheses, allowing for arbitrary heteroscedasticity to take account of unobservable heterogeneity. 5. Square of correlation between actual and predicted I. 6. Wald test for the exclusion of (CHSALi,, PRF,,.,, DE;,.,, rn,, ArE,,,.*, PIIPG,_,)using adjusted variances of the IV estimates, & = 5.85 otherwise.

The results in Table 1 show that for Taiwan adjustments towards the desired capital stock are not instantaneous, as indicated by the presence of lagged investment, and that traditional accelerator influences, embodied in the change of sales, and liquidity constraints, reflected in profits, are also important. Furthermore cost of capital considerations appear to

SHAW CHEN and COPA CHOWDHURY

significantly influence current investment. This reflects the relative openness of credit markets in Taiwan; helping businesses to start up and expand, as observed by Lau (1986). All these factors influence investment in Taiwan in the expected way: investment increases when output grows and cash flow improves, while a rise in the cost of capital reduces investment. Specifically, companies cut back investment when their debt-equity ratio rises, when the borrowing rate rises, when the cost of raising equity rises and when the relative price of investment goods rises. Among the components of the cost of capital the borrowing rate appears to be the most important and the cost of equity appears to be relatively less important. The importance of the borrowing rate seems to suggest that interest rate liberalization measures in Taiwan in 1980 may have been more successful than previously thought; realistic interest rates prevailed to which business decisions respond. The debt-equity ratio effect reflects the threat of bankruptcy; lenders in Taiwan appear to withhold credit when these ratios rise, forcing companies to cut back investment. Since we measure cost of equity with the dividend yield, the relative unimportance of the cost of equity is consistent with the fact that many of our Taiwanese companies do not pay dividends. Our results suggest that manufacturing investment in Taiwan does respond to market incentives, but is probably more short term in nature given its principal determinants, and that would be consistent with the relatively transient character of firms in Taiwan. Numerous firms start and fail every day. The results in Table 1 report the effect of the micro dividend yield, but using our macro dividend yield changed the results negligibly with Ar,,, becoming more insignificant. We considered debt-equity ratios measured at both book and market value but the results were very robust to the alternative measures (book value results are reported in Table 1). We also considered a relative price of investment based on the wholesale price index for the numerator (not reported) and this also did not change the results. Furthermore, our results are robust to using nominal or real borrowing rates and also to incorporating time effects (O-l time dummies) and trends to take account of general economy-wide movements over the sample period, such as tax policy and inflation. Time effects and trend effects were very insignificant. We conclude inflation considerations were not important during this period (in fact, price stability prevailed; details in Note 2), and significant effects of changes in tax policy were probably not evident (substantial tax exemptions for business investment characterize Taiwan (Lau, 1986). A post-sample predictive test for Taiwan’s investment function over 1987 and 1988 was insignificant at the five percent level of significance with, ~22 = 5.67 confirming the adequacy of the model. Direct stock market influences appear not to be important for investment in Taiwan. During the 80s the stock market in Taiwan was seen as highly speculative. This translates into myopia in the stock market and the real sector insulates itself from that consideration. Tobin’s Q variables worsened the fit of the model and their effects were statistically insignificant (for example, (i) Q additionally in Table 1 Column 1 has an effect of -0.029 (t = 0.67), (ii) a traditional static Q model has an R2 = 0.08 and (iii) a dynamic Q model has a Q effect of 0.051 (t = Q.45)). Firm value appeared to be highly correlated with the profits variable, rendering the latter influences insignificant. Stock market influences, if any, appear to work indirectly through the debt-equity ratio and, to a lesser extent, through the cost of equity. For South Korean companies the investment function is also dynamic, reflecting adjustment delays. We observed that AZitl is a satisfactory instrument for 1,-r, suggesting no

Company

investment

Decisions

in the NlCs

291

serial correlation in the error. Modified LM tests for serial correlation were insignificant. For example, tests for second order serial correlation were ~22 = 1.40 and x\ = 0.68, for Columns 1 and 2 respectively in Table 2. Our preferred specification for South Korea is a dynamic Tobin’s Q model of investment (Column 1, Table 2). Since Q incorporates dynamics relating to expectations lags, the lagged investment term may be capturing dynamics related to delivery and other lags. We find no evidence at all of more conventional factors such as accelerator effects, liquidity constraints or the cost of capital. Column 2, Table 2 shows the insignificance of the additional variables, with Q retaining its significance in their presence. A Wald-type test for the joint significance of the coefficients of CHSALi,, PRFir.1, DEit_1, rDt, ArE,ir_z, PI/PG,+I is ~26 = 0.11 (based on IV variances adjusted for heterogeneity, and ~26 = 5.85 without such adjustment). Furthermore, Davidson-Mackinnon non-nested tests supported the dynamic Q model in Column 1. The non-nested test we considered were: (a) dynamic Q (Zir_tjQi,) versus (b) dynamic accelerator-liquidity constraint-cost of capital (Zir.t, CHSALi,, PRFi,. (b), t(prediction IIDEit-llrD* hE,it-2y PI’PGJ-1). For the null of (a) against the alternative from (b)) = -0.13, and for the null of(b) against the alternative (a), t(prediction from (a)) = 3.08. These results are very robust to the use of book or market debt-equity ratio, use of the micro or macro return on equity, use of nominal or real borrowing rates and to the use of a relative price of investment goods based on the wholesale prices index. Furthermore, time effects and trends to control for general economy-wide phenomena had insignificant effects. As for Taiwan, we conclude inflation considerations were not important during this period (in fact, price stability prevailed; details in Note 2), and significant effectsof changes in tax policy were probably not evident. Unobserved company effects are evident for the South Korean companies in our sample. Our proposed x2 specification test is valid for South Korea since the error is not serially correlated and yields a significant ~23 = 15.39 for the dynamic Q model. To corroborate this finding, our alternative F-type test also yielded a significant Fc,64,658) = 2.24 for company intercepts. Unobservable heterogeneity is plausible because South Korean manufacturing appears to have a concentrated structure which is more likely to foster managerial differences. Given the presence of unobservable heterogeneity we report t-ratios in Table 2 which are consistent for unspecified heteroscedasticity. It is noteworthy that the IV estimates of the dynamic Q model yield a larger Q coefficient than OLS: 0.084 with IV against 0.03 1 with OLS; this demonstrates the biases typical of OLS on dynamic panels with company effects. Our results suggest that Tobin’s Q does influence investment in South Korea with investment increasing with Q. This further suggests that the South Korean government’s attempts to encourage greater reliance on the stock market as a source of funds has been successful. In fact, the number of listed companies in South Korea doubled from 350 in 1984 to 700 in 1993 (source: IFC 1994). Clearly our empirical results confirm the growing importance of equity finance in South Korea’s emerging stock market. In the sample, average debt-equity ratios fell consistently throughout the period; from 0.73 in 1981 to 0.65 in 1988 for book values, and similarly from 0.86 to 0.61 for market values. The importance of the stock market shown here challenges the previously held belief that the relatively higher leverage ratios among South Korean companies meant lesser reliance on the stock market. In the sample debt-equity ratios for South Korea are higher than those for Taiwan, yet the results show a significant negative effect for the debt-equity ratio on investment for Taiwan and

292

SHAW CHEN and COPA CHOWDHURY

none for South Korea. A probable explanation is the closer links banks and other commercial lenders have with companies in South Korea; a link fostered by strongly interventionist government policy (Balassa, 1991) and we note that the denationalization program for commercial banks began only in 198 1. Given how much lenders know about the companies, a rise in the debt-equity ratio in not necessarily taken to indicate greater risk of bankruptcy. Lenders play a much more active role when a company faces financial distress so debt effectively assumes many of the features of the equity relationship. In South Korea credit is much less naturally allocated on the open market than in Taiwan (Lau, 1986). This is reflected in the reported irrelevance of the interest rate for South Korea. The importance of Q also suggests that myopia in South Korean stock market is unimportant and that manufacturing investment is more long term and forward looking, which is consistent with South Korea’s more rigid corporate structures. While the current Q effect was significant, we found no evidence of the relevance of lags of Q. We also examined causality in the investment-Q relationship. With both Granger and Sims causality tests we found unidirectional causality from Tobin’s Q to investment, that is, Q precedes investment9. It is interesting to note that our micro Q model for South Korea fares better than those for other countries in existing studies (for instance, Blundell et al. (1987) for the UK with the Q coefficient ranging from 0.003 to 0.01 and Schaller (1990) for the US with the Q coefficient between 0.004 and 0.007 for competitive firms and between 0.016 and 0.035 for non-competitive firms). South Korea’s Q coefficient is fairly large,which implies a reasonable rate of adjustment,” unlike other studies which suggest implausibly slow rates of adjustment. More importantly, the Q coefficient does not become insignificant in the presence of more traditional influences of investment, trends and time effects, there is no evidence of serial correlation in the error and the model has a high measure of fit. This suggests our estimation approach has greater accuracy and reliability. While the traditional Q model is static, our empirical Q model for South Korea is undeniably dynamic. Omitting lagged investment yields a worse fitting model, with a poorly defined Q coefficient and strong serial correlation in the error. Lagged investment probably reflects dynamics not captured by Q. A post-sample predictive test for South Korea’s investment function over 1987 and 1988 was insignificant at the five percent level of significance with ~22= 3.26, confirming the adequacy of the model. We further investigated the differences in investment decisions across the industrial sector, splitting the sample into light and heavy manufacturingll. Tables 3 and 4 show the results for Taiwan and South Korea respectively. For the preferred model for Taiwan (as in Table 1) a test for the equality of coefficients12 between light and heavy sectors yielded an insignificant ~28 = 8.34, indicating no statistical difference. Nevertheless, comparing separate coefficient estimates, in light of manufacturing accelerator effects, debt-equity ratio, and cost of equity appear somewhat more important, whereas liquidity constraints and the relative price of investment goods matter marginally more in heavy manufacturing. For South Korea no statistical difference was observed in the dynamic Q models between light and heavy manufacturing (~23 = 0.004 and with unadjusted IV variances, ~23 = 1.704). Nevertheless, comparing coefficients Q appears somewhat more important in heavy manufacturing than in light, suggesting a greater dependence on the stock market in the heavy sector. This is consistent with the fact that in our sample heavy manufacturing companies in South Korea have, on average, higher dividend yields and hold more equity

Company Investment Decisions in the NlCs

Table 3.

Investment

Equations

293

by Manufacturing

Sector-Taiwan Column 2-Heavy

Column l-light Explanutory

Variable

IV(‘)

IV(‘)

CoefJicient(2)

(tp’

Coejjicied3)

(tp)

lit-1

0.630

(3.04)

0.698

(3.75)

CHSAL,,

0.124

(1.99)

0.027

(0.77)

PRF;,.I

0.171

(0.87)

0.428

(2.41)

D&z

-0.493

(1.77)

-0.239

(1.21)

‘Dl

-0.044

(3.07)

-0.031

(3.90)

A’E.ir- I

-0.013

(1.70)

-0.002

(0.53)

~I’PG,.1

-1.719

(1.23)

-1.191

(1.80)

R2 34)

P Equality(‘) Notes:

0.363

0.820

0.392

0.847

x; = 8.34

1. N = 26 companies and 27 companies for light and heavy manufacturing respectively, T= 6 years: 1983.1988; intercept Included. but not reported. 2. Instruments: (ACHSAL,,., , APRF,,.,, APRF,,,, rocl, AT,c,~_~,AP,IP,& for I,,,; ACHSAL,, for CHSALi,; APRF,, for PRF,,; remainmg regressors are their own instruments. 3. Absolute r-ratios in parentheses, allowing for arbitrary second order serial correlation in the error. 4. Square of correlation between actual and predicted I. 5. For equality of coeffkients between light and heacy manufacturing taking account of serial corrleation in the error; ~28 = 8.66 without adjustment for serial correlation.

Table 4.

Investment

Equations

by Manufacturing

Sector-South

Column 1 Explanatory Variable

Column

IV(‘) CoefJicient(2)

Korea 2

IV(‘) (p

CoefJicient(3)

(tp'

Iit-1

0.750

(5.81)

0.821

(6.00)

Q,

0.063

(2.47)

0.103

(4.07)

R2 Z(4)

0.751 0.763

P Wald(‘)

x; = 0.005

Equality(6) Notes:

0.686 0.687 ~26= 0.016

x; = 0.004

1. N = 91 companies and 74 companies in light and heavy manufacturing respectively, T= 5 years: 1984.1988; intercept included, but not reported. 2. Instruments Al;,., for I,,,; AQ,, for Q,, 3. Absolute t-ratios in parentheses, allowing for arbitrary heteroscedasticity to take account of unobservable heterogeneity. 4. Square of correlation between actual and predicted I. 5. Wald test for the exclusion of (CHSAL,,, PRF,,.,, DE,,,, rDr, ArE,i,_2,PIIpc,.,) using adjusted variances of the IV estimates, without adjustment ~26= 2.778.7.397 respectively for light and heavy manufacturing. 6. For equality of coefficients with adjusted IV variances, x: = 1.70 otherwise.

than light manufacturing. Accelerator effects, liquidity, and cost of capital considerations are unimportant in both sectors. Wald tests for the exclusion of these variables were insignificant for both light and heavy manufacturing (x26 = 0.005 and 0.016 respectively) and non-nested tests in the manner described earlier also rejected the traditional factors for both sectors.

294

SHAW CHEN and GOPA CHOWDHURY

V.

CONCLUSIONS

The data set used in this study is relatively new and unexplored. Our results are therefore among the first of their kind for NICs. Our estimation approach has yielded robust results. With disaggregated data from company accounts we are able to provide some important insights into companies’ investment decisions in Taiwan and South Korea. We find that models of investment behavior developed for established industrial nations can equally apply to NICs such as Taiwan and South Korea. However, there appears to be no common model of investment behind similar growth experiences. While manufacturing investment decisions in both countries appear to be dynamic, reflecting adjustment delays, other differences exist. These reflect the different structure of the corporate sector in the two countries; the predominantly small family owned firms in Taiwan and the conglomerates in South Korea, against the background of differing approaches of government intervention. Investment does appear to respond to market incentives in both countries, albeit in different ways. In Taiwan there is a greater willingness by government to let market forces take their natural course, so investment in Taiwan appears to respond to short term market signals, whereas investment in South Korea responds more to long term market signals. In Taiwan, companies appear to take account of both their own operating environment and economy-wide developments in determining investment expenditure; output growth, cash flow and cost of capital are important influences. The stock market appears to have no direct influences, which might be expected given the highly speculative nature of Taiwan’s stock market in the 80s. During this period equity finance was of little importance; in fact relatively few companies were listed on the stock exchange (about 100 in 1984 (source: IFC)). In contrast, in South Korea the stock market appears to be the main influence on investment through Tobin’s Q. This suggests the growing importance of equity finance in South Korea. We found no evidence to support a role for the more traditional factors such as accelerator effects, liquidity constraints or cost of capital. Disaggregated data also allowed us to examine diversity in companies’ investment decisions; unobservable heterogeneity related to, for instance, differences in risk-taking and managerial style and systematic diversity related to industrial sector. We did not find convincing evidence of unobservable heterogeneity among companies in Taiwan, which suggests that Taiwan’s manufacturing sector is reasonably homogeneous. This appears to be consistent with the small enterprise character of its manufacturing sector. In contrast, there appears to be unobservable heterogeneity among South Korean companies, which might be expected given the more concentrated industrial structure of South Korea. Although we found no statistical differences in investment decisions between light and heavy manufacturing in either country, some interesting points emerged. Light manufacturing in Taiwan places somewhat more emphasis on output growth, debt-equity ratios, and cost of equity, whereas profits and the relative price of investment are slightly more important in heavy manufacturing. In South Korea, investment in heavy manufacturing appears to be somewhat more sensitive to movements in the stock market than in light manufacturing. Finally, our study has also shed some light on the extent of the success of financial liberalization and discretionary government measures implemented in South Korea during the 80s and interest rate liberalization in Taiwan in 1980. We found the importance of the stock market in South Korea and interest rate sensitivity in Taiwan. As Taiwan moves towards

Investment Decisions in the NlCs

Company

fuller financial

liberalization,

295

starting with initiatives

to privatize the banking system in the

early 90s and as South Korea gradually achieves permanent deregulation of existing control mechanisms, we believe it would be interesting to re-examine the influences of investment, especially Tobin’s Q, in both countries in the late 90s. Both Taiwan and South Korea have emerging stock markets and in both countries the number of listings has doubled in the past 10 years (source: IFC 1994). How important will equity finance be for investment

in the future?

APPENDIX Summary

Statistics for Taiwan 1988 Values, $NT 000’S Except Ratios Minimum

Ma.ximum

Investment

1333507

921

7820832

1969700

Sales

7322885

5 14080

52702829

10770000

Variable

Mean

Profits

698819

Capital Stock Firm value Dividend yield Debt-equity

ratio

Tobin’s Q investment

rate

I/K

-134045

10502082

Standard Deviation

698820

6182953

266378

106666551

14954000

23374000

1137000

357200000

51615000

0.9%

0%

4.3%

1.1%

0.46 0.24

0.15 0.06

0.76 0.62

0.15(@ 0.13(b)

1.12

4.32

0.69

1.49

0.26

2.27 0.26

Summary

0.0002

Statistics for South Korea I988 Values, Won 000 000’S Except Ratios

Variable

Mean

Investment

Minimum

12430

Sales

1

202260

Profits

14844

7264 -1578

Maximum

Standard Deviation

22114

27497

3411146

438510

174376

25333

Capital Stock

104710

1650

15113191

211180

Firm value

213510

10740

3095000

453330

2.8%

0%

7.1%

1.2%

0.68 0.67

0.12 0.24

0.99 0.54

0.13(a) 0.14(b)

0.65

1.96

0.20

0.00003

0.41

0.09

Dividend yield Debt-equity

ratio

Tobin’s Q investment

rate

l/K Notes:

1.08 0.11

aAt book value. bAt market value.

NOTES 1. The PACAP data set is compiled and maintained by the Center for Pacific Basin Capital Markets Research at the University of Rhode Island Business School.

SHAW CHEN and GOPA CHOWDHURY 2. We are satisfied using company accounts data based on historic values because significant changes in the price level did not occur over the relatively short time period considered. The GDP deflator gradually increased 6.5 percent and 15.3 percent in Taiwan and South Korea respectively over their estimation periods, and the investment goods deflator likewise rose 3.9 percent and 12.8 percent. Furthermore, in the empirical section time effects which capture general price level changes were insi~ni~cant. 3. The approp~ate way of measu~ng the cost of capital is very controversi~, so we take the cautious (unrestricted) approach of entering its components into the model (Equation 2). 4. Three versions of Q were computed, using book values for components whose market value could not be ascertained. The versions are: (i) denominator at book value; (ii) denominator allowing for five percent depreciation and inflation adjusted with the GDP deflator; (iii) as for (ii) but with the Investments Goods deflator. The three versions are numerically very similar, but we report results associated with the third measure. 5. Sources include The Bank of Korea, the Statistical Year Book of the Republic of China, Economist Intelligence Unit Reports and IMF Financial Statistics. 6. Sample selection bias is unlikely to be a problem here. Indeed, if the probability of selection into the sample is constant over time, related to the company’s characteristics, then it may be subsumed by the company effect. 7. The usual test compares OLS and covariance estimators or compares covariance with GLS estimators, which is not appropriate since covariance and GLS estimators are inconsistent in a dynamic panel model. 8. It can be shown that the relevant test statistic, provided the error is iid, q’V1q which is asymptotically distributed as x2 with freedom equal to number of regressors, where q = i$, - 6,,,, Vq = Var~v - VaroLp The basis of the test is that OLS in consistent and asymptotic~ly efficient in the absence off. but inconsistent otherwise, whereas IV estimates are consistent in either case. If the error is serially correlated, we suggest an F-type test for individual company intercepts be performed--this can also be used to corroborate the findings of the proposed x2 test when the error is iid. 9. For example, with the Granger tests, we incorporated two lags of investment and Q in the equations for lit and for Qi,. Zero restrictions on the lags of Q in the investment equation yielded: ~22 = 51.12 and 8.24 for OLS estimates and IV estimates respectively. Similarly zero restrictions for the lags of investment in the Q equation yielded: & = 0.39 and 0.54 for OLS estimates and IV estimates. 10. In a Q model of investment, derived from minimizing a quadratic cost function, the Q coefficient reflects the speed of adjustment. 11, For Taiwan light m~ufact~ng comprises foods, textiles, electronics and glass products (26 companies), and heavy manufacturing covers plastics, electrical machinery, chemicals, pulp and paper, iron and steel, rubber products and motors (27 companies). For South Korea light manufacturing comprises foods, beverages, textiles, apparel and leather, wood and wood products, pharmaceuticals, watch making and electronics (91 companies), and heavy manufacturing covers paper and paper products, chemicals, rubber and tires, plastics, nonmetallic metals, iron and steel, nonferrous metals, fabricated metals, machinery and motors constituting heavy manufacturing (74 companies). This classification is appropriate for Taiwan and South Korea in the 80s. 12. This is a large sample Wald type test based on independent samples, with the test statistic m’V;%n which is asymptotically distributed as x2 with degrees of freedom equal

Company investment

297

Decisions in the WCs

to number of regressors, where m = i!&- Syrin V, = &&I light and heavy m~ufacturing respectively.

+ ~~~

and L and H denote

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298

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