A. A Foreign Liquidity Crisis May Lead to a Credit Crunch

The global financial crisis can be symbolized as a global event which was accompanied by a world-wide credit shock. The crisis emerged from the massive defaults of U.S. subprime mortgages, and eventually unfolded to full scale in September of 2008, when Lehman Brothers filed for bankruptcy. Subprime mortgages, which were real-estate-backed loans to households with relatively poor credit ratings, had been growing at a fast pace due to the prolonged accommodative monetary policy and the appreciation of house prices. Newly underwritten subprime mortgage loans accounted for nearly 8.6% of all mortgage lending in 2001, but they increased rapidly to 20.1% by 2006. However, after the middle of 2006, mortgage interest rates increased as the Federal Reserve increased rates, driving up the delinquency rate of subprime mortgage borrowers. As a result, the prices of financial derivatives related to the subprime loans, such as mortgage-backed securities (or MBS) and collateralized debt obligations (or CDOs) fell drastically. Financial institutions which had considerable exposure to these assets experienced massive losses, and the credit worthiness of Monoline, a U.S. CDO guarantor, was seriously downgraded. In March of 2008, the U.S. investment bank Bear Sterns declared bankruptcy, and the government sponsored entities Fannie Mae and Freddie Mac, which reinforced the credit of mortgage-backed securities, saw their stock prices plunge. Eventually, in September of 2008, Lehman Brothers, one of the largest investment banks in the U.S., filed for bankruptcy protection.

The Lehman bankruptcy alerted against counterparty risk, and the preference for risky assets evaporated quickly. Many financial institutions had difficulty securing liquidity, and the liquidity problem quickly spread from one institution to the others. Banks in emerging economies, which usually serve as intermediaries for funds from banks in developed countries to domestic firms and consumers, were at this point in peril because they borrowed short-term foreign currency while lending in long-term domestic currency. As a result, many emerging-market banks experienced their own foreign liquidity crises when their counterparties in developing countries stopped lending (or rolling over the loans). Korean banks were not an exception to this turmoil.

In the Korean domestic financial market, the volatility in exchange rates, stock prices and interest rates increased sharply (see Figure 1). As foreign bank branches stopped supplying short-term foreign currency loans (or rolling over these loans), domestic banks faced extreme foreign liquidity problems. As their demand for foreign liquidity skyrocketed, banks took large long positions in the FX spot market. As a result, won/dollar exchange rate soared. The exchange rate started to rise at a steep pace in September of 2008, reaching 1,513 won in November of 2008, thus marking a rise of nearly 38.9% in only two months. During this process, a vicious cycle emerged: as spot prices increased, the banks’ ability to repay the foreign-currency-denominated debt weakened and the creditworthiness of domestic banks was hampered, making the foreign banks even more hesitant to supply foreign currency loans to domestic banks. This caused the domestic bank’s demand for foreign currency to increase even more. Although the Korean economy had maintained a current account surplus before the crisis, many factors pushed the exchange rate surprisingly upwardly. Together with the foreign liquidity problem of domestic banks, we also observed a large counterparty risk premium to emerging countries and a sudden draw of short-term portfolio capital as one of the driving forces behind the exploding exchange rates. Meanwhile, the KOSPI index was at 938.75p in October of 2008, the lowest level of the year and marking a 50.3% decline compared to the highest level of 1,888.88p in May of 2008. Market interest rates also showed steep increases before the effect of the policy rate cut by the Bank of Korea was reflected. For instance, the corporate bond rate (AA-, three-year maturity) soared from 6.64% in January of 2008 to 8.56% in November of 2008.

FIGURE 1.

KOREA STOCK PRICE INDEX (KOSPI) AND THE WON/DOLLAR EXCHANGE RATE

Source: Bank of Korea

With regard to the impact of the global financial crisis on the real economy in Korea, we also observe that no major bankruptcy in the business sector was reported and that overall economic activity recovered in a relatively short period of time. However, this does not imply that the Korean economy did not suffer any negative influence of the liquidity shock. In fact, the following argument indicates that the credit crunch for financially constrained firms was real and affected them negatively. First, the foreign liquidity problem experienced by banks during the crisis contributed to the decrease in the credit supply to the business sector. As domestic banks found it more difficult to finance foreign currency, they sought alternative measure, such as selling their local currency assets and buying foreign currency on the spot market. As a result, households or firms found it more difficult to obtain new loans or were forced to repay their debts. Such a collapse of stock prices or asset prices can also affect the supply of credit negatively. The U.S. case can provide us valuable insight into this argument; as asset prices there significantly declined, bank balance sheets worsened due to losses from their exposure to the troubled assets, which triggered a massive retrieval of their exposure (or sales of assets) in an effort to maintain their capital requirements. In this process, the reduction of banks’ assets resulted in a decreased credit supply.

Figure 2 and Figure 3 show the results of a survey of loan officers and the net change in outstanding loans by banks, respectively. The net percentage of domestic banks tightening standards for industrial and household loans grew rapidly after mid-2008 (see Figure 2), and banks appeared to reduce their new lending during 2008, especially to SMEs (see Figure 3). This may reflect the domestic banks’ weakening balance sheets and their efforts to recover their capital. Firms can also experience a negative shock to their net worth due to asset price declines and sluggish demand during crises periods. As their net worth begins to contract, their creditworthiness is also aggravated such that financial institutions will hesitate to supply loans which otherwise are available to firms.

FIGURE 2.

FINANCIAL INSTITUTIONS’ATTITUDES TOWARD LENDING

Notes: A positive (+) lending attitude means that there are more financial institutions with an ‘easing’ attitude to lending than those with a ‘tightening’ attitude.

Source: Bank of Korea

FIGURE 3.

CHANGES IN OUTSTANDING LOANS BY BANKS

Source: Bank of Korea

In sum, domestic banks in Korea were affected by the sudden halt in short-term debt (or the stoppage of roll-overs) during the 2008 crisis, and the magnitude of the foreign liquidity problem does not appear to be less than that of the 1997 crisis. While the Korean economy rode out the crisis relatively well and showed a rapid recovery after the crisis in an ex-post sense, it is hard to say that firms were quarantined from the negative shock to the credit supply during the crisis period. From this perspective, we investigate whether the shock to the credit supply affected economic activity, especially firm investments during the crisis period. In the following subsection, we examine the relationship between pre-crisis cash reserves and corporate investment declines in a simple analysis of negative supply-side effects.

B. Corporate Investment during the Crisis and Pre-crisis Cash Reserves

In order to examine the relationship between pre-crisis cash and the corporate investment decline, we categorize our sample firms6 into three groups based on cash reserves (cash/total assets) as measured one year before the crisis year of 2008 and define low- and high-cash firms as the first and the third terciles, respectively. For both the low- and high-cash firms, we compare the sample means of corporate investment (capital expenditure/total assets) before and after the crisis and attempt to check whether high-cash firms show relatively low investment declines. Table 1 shows the differences in investments from 2007 to 2008 for both high- and low-cash firms. Before the crisis, the investment expenditure of an average firm matched 9.28% of its total assets, showing a decline by 0.99%p (10.6% relative to the pre-crisis investment level) to 8.29%, with the decline statistically significant at the 1% level. Meanwhile, when we compare the investment declines of low- and high-cash firms, the decline for low-cash firms was found to be 2.43%p, whereas it is only 0.20%p for high-cash firms, indicating that low-cash firms reduced their investments more than high-cash firms.

TABLE 1

CASH RESERVES AND POST-CRISIS INVESTMENTS: LARGE FIRMS AND SMES

Notes: 1) ‘Low’ and ‘High’ correspond to the first and third terciles, respectively. 2) The classifications of small and medium enterprises (SMEs) and large firms are based on KIS data. 3) The p-value is for the null hypothesis of an investment decline (B-A < 0).

Because SMEs are financially more constrained (or more exposed to the information asymmetry problem), one can expect that small firms experience greater investment declines even after controlling for pre-crisis cash reserves. In order to take into account the difference in financing constraints between large firms and SMEs, we divide the sample into two groups, e.g., large firms and SMEs, and perform the same analysis. For large firms, investments by low-cash firms drop by 1.92%p, while those of high-cash firms actually increase by 0.40%p. We find a similar relationship between cash and investment declines for SMEs, though the relationship between pre-crisis cash and the investment decline appears to be somewhat stronger than it is for large firms.

Figure 4 shows a scatterplot of the average pre-crisis cash reserves and the differences in investments for the 100th percentile cash group of large firms and SMEs. We confirmed that the negative relationship between pre-crisis cash and investment decline is greater for SMEs. Table 2 reports a simple regression result, showing the difference in corporate investments as the dependent variable and pre-crisis cash reserve as the independent variable. The estimated coefficients of cash for the pooled sample, for large firms, and for SMEs are all positive and statistically significant. However, the coefficient for the SMEs is more than two times greater than that of the large firms. For instance, SMEs with an additional 10%p of cash showed reduced investment drops by 1.01%p, while for large firms with 10%p more cash, this value was 0.48%p.

FIGURE 4.

PRE-CRISIS CASH AND CORPORATE INVESTMENT DECLINES

Source: KIS data, author’s calculations

TABLE 2

SIMPLE REGRESSION RESULTS FOR CORPORATE INVESTMENT DECLINES

Notes: 1) This result is based on a simple regression of the difference in corporate investments with regard to pre-crisis cash reserves. Cash reserves are the 100th percentiles of ‘cash and cash equivalents’ for fiscal year 2007, and the differences in investments are calculated for the corresponding percentile group from fiscal years 2007 to 2008. 2) The classifications of small and medium enterprises (SMEs) and large firms are based on KIS data. 3) Numbers in parenthesis are the standard errors from the ordinary least squares. 4) ***, **, or * indicate that the coefficient estimate is significant at 1%, 5%, or 10% level, respectively.

Overall, we find that firms with low amounts of cash before the crisis suffered more; their investment decline was greater than those of high-cash firms. Second, cash was more valuable to SMEs; their cash reserves played a greater role as an internal buffer in mitigating the negative shock to external finances as compared to those of large firms.

We will consider demand-side variables into our model, where unobservable firm-specific time-invariant effects are also considered.

A. Data

We use firm-level panel data on financial statements to construct our sample. The sample is extracted from the KIS VALUE database of the National Information & Credit Evaluation. In order to investigate corporate investments before and after the 2008 crisis, we use annual7 data for firms with the fiscal year closing at the end of December.

We pay strong attention to corporate investment expenditures between fiscal years 2007 and 2008, presuming that the crisis was mainly a financial phenomenon, which is usually short-lived. Hence, we exclude data from 2009 from our main sample.8 Because there may be concern over sample selection bias, we check whether the main results from our sample are robust. We find that our empirical findings stand still after adding two or three years of data to the pre- and post-crisis sample and after changing the dating of the crisis from 2008 to 2009. Our sample includes not only publicly traded firms (listed on either the KOSPI or KOSDAQ markets) but also firms with external audit requirements and general firms9 (see Table 3). The broad coverage of our dataset is helpful for examining whether SMEs were more vulnerable to the liquidity shock during the crisis. Typically, publically traded firms, which already meet stock market listing requirements such as years in operation, business size and performance level, are less exposed to the information asymmetry problem. Thus, they appear to be less financially constrained. However, small firms (or young firms) are more financially constrained such that their investment decline due to the credit supply shock is expected to be greater. By dividing our sample into large firms and SMEs, we can compare the negative supply-side effect on investment expenditures for the two different groups and find implications pertaining to the relationship between corporate investment decline and financial constraint.

TABLE 3

COMPOSITION OF FIRMS IN THE SAMPLE

Notes: 1) The table shows the composition of the sample after excluding outliers and those with missing values. 2) ‘KOSDAQ registered’ refers to the status in which a firm meets certain requirements of the Securities and Exchange Act while its stock is not yet traded in the market. 3) ‘Issues for administration’ refers to firms which may face a delisting from the stock market. 4) Firms can be counted twice if they changed their status regarding a stock market listing, e.g., the initial public offering of an external audit firm. For reference, the total number of firms in our sample is 12,614.

With regard to the SME sample, the following issues should be noted. First, we should consider a new proxy reflecting investment demand by individual firms, as the market value of non-public firms (and Tobin’s q) is unobservable in practice. In this study, we use the sales-to-capital ratio10 as a proxy for the marginal productivity of capital instead of Tobin’s q. Second, as Lim (2005) noted, we find that the financial statements of firms with external audit requirements or those of general corporations has many missing values about detailed items, compared to those of publicly traded firms, indicating that the credibility and usefulness of the included SME samples are relatively low.11

We measure corporate investment as cash outflows for items of purchase of tangible, intangible and leased asset on cash flow statements.12 Our measurement of corporate investment has the following features. First, as Lim (2005) noted, our measure is free from distortions such as depreciation and asset revaluations compared to the alternative investment measure of the difference in tangible assets. Because the increase in tangible assets reflects investment expenditures as well as the difference in accumulated depreciation, it may undervalue a firm’s investments. With regard to asset revaluations, tangible assets can increase even if there were no investment expenditures; in such a case, the alternative measure can overvalue corporate investments.

Second, our measure of corporate investment includes not only expenditures for property, plants, and equipment, which are typically termed ‘capital expenditures’, but also expenditures on land, buildings, and intangible assets (e.g., R&D expenditures). Hence, corporate investment in this paper is more comprehensive than capital expenditures. Practically, it is not very attractive or feasible to calculate capital expenditures in our case, as there are many missing values pertaining to expenditures for land, buildings, and intangible assets in our sample of SMEs.

Third, investment expenditures in our study did not adjust for an increase in cash due to asset sales, though Lim (2005) measures corporate investment as net cash outflows (cash outflows minus cash inflows) in items of purchases of tangible assets (excluding land and buildings). Because one of the objectives in his work was to compare corporate equipment investments as calculated from firm-level data with those from aggregate data, it appears to be reasonable to use the net cash decrease as equipment investments. However, our goal is to examine whether firms decrease investment expenditures in response to a negative credit supply shock. In this case, it is natural to focus on cash outflows.13

With regard to data purging, we exclude financial firms and utilities, defined as firms with Korea Standard Industry Classification (KSIC) codes 35-36 and 64-66 in the two-digit category. The financial statements of financial firms have different items and accounting standards; moreover, their investment expenditures (e.g., an increase in private loan) are not directly connected to real activities, which among the main variables of interest. Meanwhile, most utilities are accounted for in large public firms, where investment expenditures are related more to the government’s plan to supply electricity, gas, water, and other such utilities, indicating that the exclusion of these firms provides more accuracy in our empirical analysis. We also handle outliers in investments, cash reserves,14 and sales-to-fixed capital ratios by excluding extremely large or small firm-year observations15 compared to the empirical distribution of those variables in the largest sample from 1993 to 2011.16 For instance, we exclude observations with investments greater than the 99th percentile or smaller than the 1st percentile in all observations. Before calculating the percentile values, we exclude observations with variables which are nearly or precisely have a zero bound.17

Table 4 shows the summary statistics for of corporate investment, pre-crisis cash reserves, capital productivity, and cash flow amounts for the constructed sample. Except for the cash reserves, all of the variables are for the firm-year observations from 2007 to 2008. Large firms’ investment expenditures amount to is 6.33% of over total assets, which is less smaller than the value of 9.12% of SMEs’ investments. The average cash position of large firms is 12.75% of total assets, while that of SMEs was found to be 11.37%. Capital productivity, which is measured in terms of sales over tangible and intangible assets, is also shown to be greater in large firms. The average capital productivity of large firms is 9.33%, while that of SMEs is 8.09%. The average yearly cash flows are 9.71% and 8.53% (of total assets) for large firms and SMEs, respectively.

TABLE 4

SUMMARY STATISTICS

Notes: 1) ‘Investment’ denotes cash outflows due to the purchase of tangible, intangible and leased assets to the total assets ratio, ‘Cash’ is cash and cash equivalents over total assets during fiscal year 2004, ‘Capital productivity’ is the ratio of sales to tangible and intangible assets, and ‘Cash flow’ denotes operating income before depreciation and amortization over total assets. 2) The classifications of small and medium enterprises (SMEs) and large firms are based on KIS data.

B. Methodology

Mainly, we follow the empirical strategy in Duchin et al. (2010). One of the key identifying assumptions in their approach is that the significantly positive relationship between the corporate investment decline and pre-crisis cash reserves will reflect the negative supply-side effect during the crisis period. Before digging further into the details, we introduce the main features of their method here. The specifications of Duchin et al. (2010) are designed to test whether the decline in the credit supply during the financial crisis had a negative effect on corporate investments. Intuitively, the method is similar to the difference in differences method, in which mainly the treatment effect of pre-crisis cash reserves is examined. If the negative supply-side effect actually matters, the larger a firm’s cash holdings are, the smaller the constraint to investment would be, as cash holdings prior to a crisis can serve as an alternative funding source for investments. Hence, the regression model of investments by firms will exhibit a significantly positive coefficient of the interaction term between the crisis dummy and pre-crisis cash reserves, as far as the null hypothesis of the supply-side effect is evident.

Mainly, we follow the empirical strategy in Duchin et al. (2010). One of the key identifying assumptions in their approach is that the significantly positive relationship between the corporate investment decline and pre-crisis cash reserves will reflect the negative supply-side effect during the crisis period. Before digging further into the details, we introduce the main features of their method here. The specifications of Duchin et al. (2010) are designed to test whether the decline in the credit supply during the financial crisis had a negative effect on corporate investments. Intuitively, the method is similar to the difference in differences method, in which mainly the treatment effect of pre-crisis cash reserves is examined. If the negative supply-side effect actually matters, the larger a firm’s cash holdings are, the smaller the constraint to investment would be, as cash holdings prior to a crisis can serve as an alternative funding source for investments. Hence, the regression model of investments by firms will exhibit a significantly positive coefficient of the interaction term between the crisis dummy and pre-crisis cash reserves, as far as the null hypothesis of the supply-side effect is evident. investment demand. The significantly positive relationship between investment and cash reserves will only be observed in firms which experienced a negative external funding shock due to the decline in the credit supply or a loss of their net worth during the crisis period. Also, if corporate investment declines are mainly driven by demand-side effects, then the significantly positive relationship will disappear after including variables pertaining to investment opportunities. The baseline model of Duchin et al. (2010) can be described as follows:

Here, the subscripts i, t and τ are the firm, the year, and a fixed pre-crisis year, y_𝑖,𝑡 denotes investment expenditure over total assets, D_𝑡 is a crisis dummy, Cash_𝑖,𝜏 denotes pre-crisis cash reserves over total assets, X_𝑖,𝑡 represents variables which reflect investment opportunities, and ν_𝑖 indicates unobservable firm-specific fixed effects.

Though the methodology is simple and straightforward, the use of pre-crisis cash reserves in identifying supply-side effects requires much caution due to endogeneity and identification problem. Following Duchin et al. (2010), we provide a rationale for our selection of this instrument by comparing the estimation results. We also conduct various robustness checks of our baseline results. Specifically, the empirical strategy used here is as described below.

First, in order to address the possible endogeneity issue, we include lagged cash reserves of individual firms in our baseline specification. Our baseline specification can be expressed as follows:

Lagged cash balances of firms are known to be related to their investment activities; therefore, this may impose the endogeneity problem (a non-zero correlation between cash reserves and unexplained investment demands) on pre-crisis cash should it be missing. For example, firms which expect greater investment opportunities in the future may retain more profits to increase corporate savings. In such a case, the investment at time t will be correlated with cash reserves at t-1; moreover, the coefficient estimate of the interaction term between pre-crisis cash and the crisis dummy will be biased due to the endogeneity problem. In order to address the issue, we add lagged cash as an additional variable reflecting investment demand.18 Pre-crisis cash reserves represent valuable information regarding a firm’s financial buffer prior to the crisis, though it is not greatly correlated with unexplained investment opportunities, i.e., after controlling for the lagged cash variables. Therefore, this will contribute to reducing the upward bias from the endogeneity problem. In order to show that our specification is in fact distant from the endogeneity issue, we compare the estimation results with a different order of lagged cash reserves. If the endogeneity problem is still evident in our specification, the results with a higher order of lagged cash will be much different.

Second, in order to deal with the possible spurious correlation between pre-crisis cash and investment decline, we run the same analysis with samples from a financially stable period. If a significantly positive coefficient of the interaction term is observed from the financially stable period, the interpretation of the significantly positive relationship between pre-crisis cash and post-crisis investment given the existence of the negative supply-side effect will be misleading.

Third, we examine whether firms which are financially more constrained experience higher investment declines, as expected from the negative supply-side effects. If the negative shock to the credit supply in fact matters during the crisis period, we would then find a greater influence (or greater sensitivity) of cash for financially constrained firms on investment. In order to do this, we mainly categorize small and medium enterprises (SMEs) as financially more constrained firms. Alternatively, we also use leverage, short-term debt, and stock market listing as alternative method of identifying firm’s financial constraint.

Finally, we add recent firm-year observations to investigate whether the negative shock to external finance has any long-term effect on investment activity. Because our baseline model uses only two years (one pre- and one post-crisis observation for each firm) of observations, it is difficult to determine from our original sample whether the weak investment recovery thus far is related to supply-side effects. If the impact of the negative supply-side shock during the 2008 crisis still persists, then the estimated coefficient of interaction term between pre-crisis cash and time dummy for the recent years will also be significantly positive. On the other hand, if the negative effect is only short-lived, then the estimated coefficient of the interaction term would be insignificant or negative.

C. Results

1. Corporate investment and pre-crisis cash reserve

Table 5 presents the estimates of our baseline specification for cash reserves and corporate investment. We select the order of lagged cash reserves (or K in Equation (2)) as two years based on the information criteria19 and use cash reserves one year20 before the crisis (or 2007 cash) as pre-crisis cash. We also use capital productivity and cash flow as proxies for investment opportunities.

TABLE 5

BASELINE RESULTS

Notes: 1) ‘After’ is a dummy variable which is one for fiscal year 2008 and zero otherwise, ‘Cash’ is cash and cash equivalents over total assets during fiscal year 2004, ‘Capital productivity’ is the ratio of sales to tangible and intangible assets, and ‘Cash flow’ denotes operating income before depreciation and amortization over total assets. 2) The numbers in parenthesis are the Huber-White standard errors clustered at the firm level. 3) ***, **, or * indicate that the coefficient estimate is significant at 1%, 5%, or 10% level, respectively.

The results in Table 5 show that yearly investment expenditures decreased significantly after the global financial crisis. The coefficient estimate in the regression of corporate investment on the crisis dummy (see column (1)) shows a significantly negative value, and it remains close to this significantly negative value even if controlling for investment opportunities (see columns (2) to (6)). This implies that the investment decline after the crisis is significant even after we control for demand-side effects, such as the decline in investment opportunities. The results in column (6) show that the yearly corporate investment expenditure of assets on average for firms declined21 by 1.30%p following the global crisis period. This decline is 14.0% relative to the pre-crisis unconditional mean level of yearly investment expenditures of 9.28% (see Table 1).

More importantly, we find evidence that the negative supply-side effect is significant on post-crisis investments. The coefficient of the interaction term between the crisis dummy and cash reserves is estimated to be significantly positive with regard to the regression of corporate investments on the crisis dummy and the interaction term (column (2)); it also retains a significantly positive value even after we control for investment opportunities (columns (5) and (6)). This result coincides with our prediction that the investment decline will be positively correlated with pre-crisis cash if the negative credit supply shock matters during the crisis period. For example, as the supply of credit was reduced significantly following the onset of the crisis, firms with large amounts of cash could cope with the negative credit supply shock using their internal funds, resulting in less of an investment decline (or a positive relationship between pre-crisis cash and post-crisis investment). The empirical results show that the positive relationship between pre-crisis cash and investment remained even after controlling for investment demand, offering evidence that a significant portion of the investment decline was caused by the negative credit supply shock. From the result in column (6), a zero-cash firm suffered an investment decline of 1.30%p following the crisis, while for a mean-level (11.55%, see Table 4) cash firm, this value was 0.87%p; firms one standard deviation (12.77%) higher than the mean level cash firms saw an investment decline of only 0.39%p. A one-standard deviation increase in the cash balance softens the investment decline by 36.9% compared to the case of a zero-cash firm. Furthermore, cash reserves of 34.95% relative to total assets helped to eliminate the investment decline after the crisis, meaning that those who had the required cash balance to cancel out the investment decline perfectly was less than 5% of all firms.

2. Higher order lagged cash and financially stable periods

In order to investigate the endogeneity issue, we also additionally provide the estimation results from the specification with lagged cash up to order four (K=4). If the endogeneity problem resides in our baseline specification, a change in of unexplained investment opportunities will show a strong have large correlation with the pre-crisis cash variable. In this case, not only will the estimate be is very different from our earlier results, but it will also it will vary in its magnitude and sign depending upon the lag length.

The first four columns in Table 6 present the estimation results from the specification with a higher order of lagged cash reserves. First, it is important to note that the estimated coefficients of capital productivity and cash flow are very stable with regard to the choice of the lag length, implying that they are free from the endogeneity issue. The estimates for the interaction term are approximately 0.04 with lagged cash reserves of two years or more, and these values are all statistically significant. Specifically, they are not very different from the estimates with one and two years of lagged cash, as shown in Table 4-3, indicating that our baseline specification is not likely to be affected by the endogeneity problem caused by omitting the lagged cash variable. We also report the results from pre-crisis cash reserves measured during fiscal years 2005 and 2003. On the same token, the estimation results will be very different if our proxy for pre-crisis cash is contaminated by the endogeneity problem. However, we note that the estimates of both the crisis dummy and the interaction term are very stable. This implies that the inclusion of lagged cash effectively removes any possible endogenous changes in cash reserves from pre-crisis cash.

TABLE 6

HIGHER ORDER LAGGED CASH AND CASH RESERVES THREE AND FIVE YEARS PRIOR

Notes: 1) ‘After’ is a dummy variable which is one for the fiscal year 2008 and zero otherwise, ‘Cash’ is cash and cash equivalents over total assets for fiscal years 2003 (5-yr), 2004 (4-yr), 2005 (3-yr), 2006 (2-yr), and 2007 (1-yr), ‘Capital productivity’ is the ratio of sales to tangible and intangible assets, ‘Cash flow’ denotes operating income before depreciation and amortization over total assets, and ‘Cash (t-1)’ is one-period lagged cash and cash equivalents over total assets. 2) The numbers in parenthesis are the Huber-White standard errors clustered at the firm level. 3) ***, **, or * indicate that the coefficient estimate is significant at 1%, 5%, or 10% level, respectively. 4) The classifications of small and medium enterprises (SMEs) and large firms are based on KIS data.

With regard to the identification problem, one may be concerned about whether the significant relationship between pre-crisis cash and investment decline is spurious or driven by a demand-side effect. If the significantly positive relationship between pre-crisis cash reserves and post-crisis investments is a general feature of the data, it would be misleading to interpret our results as evidence of the existence of a negative supply-side effect. However, if the significantly positive relationship is only observed in the sample during a financially turbulent period, not in a financially stable period, we may argue that the findings in the previous analysis imply the existence of an investment constraint due to the credit supply shock.

In order to answer this question, we examine the coefficient estimate of the interaction term between cash reserves and the crisis dummy for 2000, for 2006 with placebo crises (stable periods), and during the 1998 currency crisis (a turbulent period). During stable periods, financial markets performed well; hence, investment expenditures by firms are less likely to be affected by the credit supply, resulting in a small or insignificant coefficient estimate of the interaction term. However, during the 1998 currency crisis, domestic banks underwent massive restructuring, and post-crisis investments by firms were more than likely constrained by the credit shortage. In that case, firms were forced to fund their investment via internal funds, implying that the coefficient estimate of the interaction term is significantly positive. We also provide estimation results for SMEs and for all firms because SMEs are considered to be more financially constrained. Thus, their reliance on internal funding will be more prevalent during this crisis period.

Table 7 presents the estimation results for the financially stable and turbulent periods. First, we confirm that pre-crisis cash reserves did not affect investment expenditures during financially stable22 periods. The interaction terms between pre-crisis cash and the time dummy are estimated to be 0.017 and 0.010 for 2000 and the 2006 placebo crisis, respectively, and they are both statistically insignificant. Although the estimates for the financially stable periods are in line with our expectations, but it is noteworthy that the estimates are small but positive, which implies that our findings are not as strong as those of Duchin et al. (2010).23

TABLE 7

FINANCIALLY STABLE VS. TURBULENT PERIOD

Notes: 1) ‘After’ is a dummy variable which is one for the fiscal year 2008 and zero otherwise, ‘Cash’ is cash and cash equivalents over total assets during fiscal year 2004, ‘Capital productivity’ is the ratio of sales to tangible and intangible assets, and ‘Cash flow’ denotes operating income before depreciation and amortization over total assets. 2) The numbers in parenthesis are the Huber-White standard errors clustered at the firm level. 3) ***, **, or * indicate that the coefficient estimate is significant at 1%, 5%, or 10% level, respectively. 4) The classifications of small and medium enterprises (SMEs) and large firms are based on KIS data.

With regard to the results for the 1998 currency crisis, we find that the estimated coefficients of the interaction terms for both all firms and SMEs are significantly positive, indicating that they experienced a shortage of credit supply following the currency crisis. All in all, the results from financially stable and turbulent periods suggest that our baseline specification can be used to identify negative supply-side effects.

3. Financial constraint and the supply-side effect

Financially constrained firms may suffer more from a negative shock to the credit supply during a global financial crisis period. Essentially, a financial constraint, which usually arises from information asymmetry, is known to amplify the negative impact on constrained firms, e.g., a greater decline in investment expenditures following the crisis in our case. Though there are several ways to identify financially more constrained firms,24 we use the classification as a small and medium enterprise (SME) for identifying financially more constrained firms. Together with the SME classification, we divide our sample into financially more and financially less constrained firms using alternative measures of financial constraint, in this case industry (three-digit) sales growth and ownership, and perform a similar analysis to test the robustness of our results.

The first two columns in Table 8 show the estimation results of our baseline specification for large firms and SMEs. The coefficient estimate of the crisis dummy is significantly negative for both large firms and SMEs. However, the post-crisis investment decline is greater for SMEs. For instance, the coefficient estimate of the crisis dummy for large firms is −0.99, while the estimate for SMEs is −1.36. The coefficient estimates of the interaction term between cash and the crisis dummy are positive for both large firms and SMEs. However, we find that the estimate for large firms is less significant (with 10% level)25 while the estimate for SMEs is much higher and significant at the 1% level. These results provide evidence that SMEs, which are more financially constrained due to information asymmetry, can experience greater investment declines following the onset of a financial crisis. Moreover, the results coincide with our previous analysis (in Section 3), which showed that pre-crisis cash has more of an impact on post-crisis investment levels for SMEs. When compared to the results for all firms, shown in Table 5, we find that the estimate of the interaction term for SMEs is 0.041, which is close to the estimate for all firms (0.037), implying that the overall estimate is largely driven by the SMEs in the sample. Compared to firms with no cash, the average cash holdings for large firms can mitigate an investment decline by 0.66%p, while the average cash holdings for SMEs can alleviate the decline by 0.89%p, implying that cash is more valuable for SMEs.

TABLE 8

FINANCIAL CONSTRAINTS AND POST-CRISIS INVESTMENT: ALTERNATIVE CLASSIFICATION

Note: 1) ‘Leverage’ is liabilities over the capital ratio, ‘Short-term Debt’ is current liabilities over the total liabilities ratio, ‘Stock listing’ refers to KOSPI-listed or KOSDAQ-listed (or registered) firms, and ‘Div. Payment’ denotes firms with non-zero dividend payments. 2) ‘After’ is a dummy variable which is one for the fiscal year 2008 and zero otherwise, ‘Cash’ is cash and cash equivalents over total assets during fiscal year 2004, ‘Capital productivity’ is the ratio of sales to tangible and intangible assets, and ‘Cash flow’ denotes operating income before depreciation and amortization over total assets. 3) The numbers in parenthesis are the Huber-White standard errors clustered at the firm level. 4) ***, **, or * indicate that the coefficient estimate is significant at 1%, 5%, or 10% level, respectively.

Table 7 (from column (3) to column (6)) presents the estimation results for financially more and less constrained firms by dividing the sample according to industry (three-digit) sales growth and ownership. We use industry sales growth one year prior to the crisis to capture the financial constraints and divide our sample into ‘high’ (above the third quantile) and ‘low’ (below the first quantile) groups. With regard to ownership, we classify KOSPI-listed, KOSDAQ-listed or registered firms as public firms, and others as private firms.

We find that the role of cash in mitigating the investment decline is greater for financially more constrained firms. The coefficient estimate of the interaction term for firms with low industry sales growth is 0.056, which is around twice the coefficient estimate for firms in the group showing high industry sales growth (0.024). Private firms also show greater sensitivity to of investment expenditures on cash reserves than public firms. Overall, the results imply that the corporate investment decline following the 2008 crisis is largely related to the level of financial constraint of a firm, supporting the view that negative credit supply-side effects on corporate investments were evident during the crisis period.

4. Why do corporate investments remain sluggish?

Related to recent investment activity in corporate sector, we observe that private fixed capital formation (in current prices) remains sluggish after showing an increase due to the base effect following the fourth quarter of 2009. In order to examine the impact of external finance shocks on recent corporate investments, we add recent firm-year observations26 to our original sample. Next, we include an additional year dummy variable (‘further after’ crisis dummy) for the years from the first to the third year following the 2008 crisis, as well as an interaction term between pre-crisis cash and the ‘further after’ crisis dummy. If the credit crunch during the crisis has had a negative effect on corporate investments persistently, we would observe that the interaction term (between pre-crisis cash and the ‘further after’ crisis dummy) is significantly positive with the coefficient estimate for the ‘further after’ crisis dummy being significantly negative. Meanwhile, if post-crisis investments are mainly affected by investment demand, such as depressed investment opportunities, we would find that the interaction term between the ‘further after’ crisis dummy and pre-crisis cash reserves loses its significance.

Table 9 shows the estimation results for our extended sample with large firms and SMEs. Interestingly, the ‘further after’ crisis dummy for SMEs is estimated to be significantly negative. Moreover, the estimated coefficient of the interaction term is significantly positive, implying that the more pre-crisis cash a firm has, the less its investment decreased during 2009-2011 compared to 2007. For large firms, the result is less suggestive for the supply-side effect on corporate investment; while the ‘further after’ crisis dummy has a negative coefficient, the estimated coefficient of the interaction term is not significant.

TABLE 9

LONG-TERM EFFECT OF A NEGATIVE SUPPLY SHOCK AND FINANCIAL CONSTRAINTS

Note: 1) ‘After’ is a dummy variable which is one for the fiscal year 2008 and zero otherwise, ‘Cash’ is cash and cash equivalents over total assets during fiscal year 2004, ‘Further after’ is a dummy variable which is one for fiscal years 2008-2011 and zero otherwise, ‘Capital productivity’ is the ratio of sales to tangible and intangible assets, and ‘Cash flow’ denotes operating income before depreciation and amortization over total assets. 2) The numbers in parenthesis are the Huber-White standard errors clustered at the firm level. 4) ***, **, or * indicate that the coefficient estimate is significant at 1%, 5%, or 10% level, respectively.

In summary, the investment decline of SMEs after the crisis remains significant even if we include recent firm-year observations and is related to their levels of pre-crisis cash reserves. This indicates that the additionally depressed investments of SMEs after the crisis (from 2009 to 2011) are associated with the credit supply shock during the 2008 crisis and that the funding problem may have had a long-last negative impact on the investment activities of SMEs. However, we could not find any evidence that investment declines in large firms are related to a negative shock to their external finances, especially over the long term.