E42 -Monetary Systems|Standards|Regimes|Government and the Monetary System < , F33 -International Monetary Arrangements and Institutions < , F15 -Economic Integration

Keywords: Intra-Industry Trade, ASEAN, Economic Integration, Business Cycle Harmonisation Intra-Industry Trade, Business Cycle Harmonization, Economic Integration, Asean. JEL Classifications: F15, F33, E42

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Introduction

The Association of Southeast Asian Nations or ASEAN was established in Bangkok in 1967 and even if it seems unquestionable that it has been successful in containing intra-ASEAN conflicts and in providing a forum for the discussion of regional matters, it also seems consensual that ASEAN has failed in asserting itself as a political force on the world stage and has been disappointing in terms of tangible economic benefits for its members 1 . This has led some authors to describe ASEAN as an enigma in Asia because of its longevity as a trading block which is always at the crossroads in the sense that "it fails to deliver and periodically something always needs to be done to revitalize the integration process". 2 Recently, however, a new resolve for both increased economic integration and monetary and exchange rate cooperation has started to emerge, especially since the 1997-1998 Asian financial crisis. In fact, and paradoxically, the Asian financial crisis increased economic disparities within the region making monetary integration more difficult while at the same time, by showing the flaws of unilateral exchange rate pegging, worked as a "wake up call for ASEAN" 3 which increased the interest in a common currency arrangement for the region 4 . In fact, a full currency union in ASEAN has become an inevitability for some of the most 'OCA-philes', at least in the long run 5 . The recent popularity of the 'hollowingout' hypothesis seems to leave no choice for ASEAN but to decide between fully flexible exchange rates or a common currency 6 . F o r P e e r R e v i e w 3 Even though there has been a large increase in intra-regional trade in ASEAN since the beginning of the 90s it is not clear that it occurred as a direct effect of the tariff reduction or a more general trend in the world markets 7 . It does, nevertheless, raise the question of whether the large increased Intra-Asean Trade in recent years is creating more harmonized business cycles amongst its members since in light of the existing literature on optimum currency areas (OCA) these are two of the most important criteria on the suitability of adopting a currency union (or other fixed or semi-fixed currency arrangements).

The degree of trade integration is believed to be an important OCA argument since it affects the likelihood of asymmetric shocks and their transmission between countries. The effect of more trade between two countries on the harmonization of business cycles is not, however, clear cut in the existing economic theory. [START_REF] Kenen | The Theory of Optimum Currency Areas: An Eclectic View[END_REF] was the first to suggest that well diversified economies, having a large share of intra-industry trade (IIT) in their total trade, will experience less asymmetric shocks. Conversely, [START_REF] Krugman | Geography and Trade[END_REF][START_REF] Krugman | Lessons from Massachusetts for EMU[END_REF] warned that the potential for asymmetric shocks increases with greater integration among countries (and regions) since it increases their specialisation. These two opposing views on what would be the effect of closer integration on regional specialisation (and thus on the costs and benefits of joining an OCA) are what came to be known as 'The European Commission View' and 'The Krugman View' 8 .

The European Commission view states that closer integration will lead to a situation whereby asymmetric shocks will occur less frequently. The reasoning is

F o r P e e r R e v i e w
4 that since most trade between European countries is intra-industry trade, the more integrated they are, the more similarly they will be affected by disturbances and therefore the more synchronised their business cycles will be. Conversely, Krugman's view, taking the United States as an example, is that increased integration leads to increased regional concentration of industries (in order to profit from economies of scale) and thus more trade will lead to more divergence between countries.

The ambiguity in the economic theory on this matter has made this an essentially empirical matter. In two seminal papers, Frankel andRose (1997, 1998) argue that closer trade relations result in a convergence of business cycles, i.e., that both international trade patterns and international business cycles correlations are jointly endogenous and thus that any monetary union creates ex-post an optimum currency area 9 . Frankel and Rose report a significant and positive correlation between trade intensity and the correlation of business cycles as measured by four separate indicators of economic activity in a cross-section of OECD countries between 1959 and 1993. [START_REF] Kenen | Currency Areas, Policy Domains and the Institutionalization of Fixed Exchange Rates[END_REF] argues that Frankel and Rose's results should be interpreted cautiously. He shows in a framework of the Keynesian model that the correlation between two countries' output changes increases unambiguously with the intensity of trade links between these countries but this does not necessarily mean that asymmetric shocks are reduced as well.

A number of recent empirical studies seem to confirm a positive correlation between intra-industry trade and business cycles synchronisation, and that increased trade itself does not necessarily lead to business cycle harmonisation. [START_REF] Firdmuc | The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement[END_REF] found that when Frankel and Rose's model was augmented to include intra-industry trade there was no relation between business cycles and F o r P e e r R e v i e w 5 trade intensity. Intra-industry trade, however, was found to have a positive and significant relationship with business cycles for the OECD countries between 1990 and 1999. [START_REF] Shin | Trade Integration and Business Cycle Synchronisation in East Asia[END_REF], applying a model which included a larger set of explanatory variables found that intra-industry trade is the major channel through which the business cycles of 12 East Asian economies become synchronised and that increasing trade itself does not necessarily lead to greater synchronisation of business cycles. [START_REF] Gruben | How Much Does International Trade Affect Business Cycle Synchronization[END_REF] show the instrumental variables used by Frankel and Rose in their study to be inappropriate and to result in inflated results. They develop an OLS-based procedure adding structure-of-trade variables to the model to separate the effects of intra-and interindustry trade and to include a number of omitted variables for the countries.

Their findings are consistent with Frankel and Rose's and conclude that specialisation does not asynchronise business cycles between the OECD countries.

These recent empirical contributions suggest that the effect of more trade between two countries on the harmonization of business cycles depends not only on the intensity of trade links but on the structure of that trade. If more trade means more intra-industry trade, we should expect more common shocks and thus, more business cycle harmonization. If, however, more trade means more specialization, we should expect more idiosyncratic shocks.

The contribution of this paper is to test this hypothesis in the special case of ASEAN, that is, to investigate whether the recorded increase in intra-ASEAN trade in recent years, measured at the highly disaggregated 4-digit industry level, is leading the ASEAN members to closer economic integration and thus creating F o r P e e r R e v i e w 6 better preconditions for policy integration and the creation of a common currency area. As will be discussed below, there is a lack of consensus on the correct methodology to use for this purpose and therefore several methods are employed.

The paper is structured as follows: The next section will explain the data and empirical methodology and present the empirical results. Finally, the last section concludes the paper.

Data, Empirical Methodology and Results

To measure output co-movements, annual data on real GDP was collected for the ASEAN5 countries over the period 1962-1996 from the IMF International Financial Statistics CD-ROM. The period after 1997 is excluded because the data is likely to be distorted by the 1997-1998 Asian Financial Crisis 10 . Data on the other ASEAN countries was not available and therefore these countries were excluded from this study.

Intra-Industry Trade in ASEAN was measured using the traditional [START_REF] Grubel | Intra-Industry Trade[END_REF] Index. The IIT indexes were computed for all industries over the period 1962[START_REF] Pomfret | ASEAN -Always at the Crossroads?[END_REF]using the 'World Trade Flows, 1962[START_REF] Kenen | Currency Areas, Policy Domains and the Institutionalization of Fixed Exchange Rates[END_REF]' data complied by Feenstra, Lipsey, Deng and Ma (2005) at the four-digit industry classifications following the Standard International Trade Classification, revision 2 11 .

Since there is no consensus on the correct methodology to apply, several models will be tested. Firstly, the variation of [START_REF] Frankel | Is EMU more justifiable ex post than ex ante?[END_REF] model first applied by [START_REF] Firdmuc | The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement[END_REF] will be estimated:

F o r P e e r R e v i e w 7 ε β α + + = ij j i IIT Q Q Corr ) , ( (1)
where Corr(Q i ,Q j ) stands for the correlation of de-trended real GDP and IIT ij denotes the average four-digit level of intra-industry trade index between ASEAN5 countries i and j in each period and is the error term 12 . The sign of the coefficient if negative will indicate that the specialisation effect dominates in ASEAN ('Krugman View') and if positive will mean that more intra-industry trade leads to more output synchronisation in that region (European Commission View). As stated above, most empirical evidence to date seems to be consistent with the latter possibility so that we expect a positive coefficient for IIT 13 .

Frankel and Rose (1997) note that countries are likely to orient their monetary policy and fix exchange rates towards their most important trade partners. In the case of ASEAN it is well known that the US dollar has a large weight in the exchange rate policies leading them to pursue broadly similar monetary policies.

As noted by [START_REF] Firdmuc | The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement[END_REF], it is quite possible that bilateral trade reflects the adoption of a common exchange rate policy and not vice-versa. This suggests the need to instrument the regressions by exogenous determinants of intra-industry trade. The instruments normally chosen for the two-stage least squares (TSLS) are the ones provided by the gravity models and include the log of distance between countries and a dummy for geographic adjacency 14 . However, Gruben, Koo and Millies (2003) suggested that these instruments might be inappropriate and result in inflated results. However, the authors also find when using an alternative OLS-

F o r P e e r R e v i e w 8
based approach, that their results are consistent with those of Frankel and Rose's model. Accordingly, the results for both OLS and TSLS are presented for (1).

Following [START_REF] Frankel | Is EMU more justifiable ex post than ex ante?[END_REF], the whole sample period is divided into four sub-sample periods : 1962-70, 1971-79, 1980-88 and 1989-96 in order to access time-series changes in intra-industry trade patterns and business cycles correlations. As there is no consensus on the proper de-trending method to apply, the four alternative methods of de-trending real GDP first applied by [START_REF] Frankel | Is EMU more justifiable ex post than ex ante?[END_REF] namely, first-differencing, HP-filtering, quadratic de-trending and HP-filtering on the residual of a regression of the real GDP on a constant and 5year period dummies, were used 15 . Since the sample includes 5 countries, the number of observations will be 40 (10 country pairs each with four period observations).

Table 1 reports the results of eight separate specifications, corresponding to the four de-trending methods discussed above, applied to both OLS and TSLS estimations 16 .

< Table 1

around here>

The results are very weak. Even though IIT yielded the expected sign in all specifications, it was found to be significant (and only marginally so) in only one case.

Also, as expected, the TSLS versions of (1) generate more robust results than the OLS estimates. However, the question of whether the variables used as In order to investigate this matter, a procedure developed by [START_REF] Baum | Instrumental Variables and GMM: Estimation and Testing[END_REF] is applied that allows for the determination of the Hansen test of overidentifying restrictions in TSLS 17 . The results are presented in Table 2.

<Table 2 around here>

The Hansen test included a specification that takes into account the possibility that observations might not necessarily be independent within the group of countries under analysis. As the null hypothesis of the Hansen test is that the instruments are valid, i.e., that the instruments are uncorrelated with the error term, the instruments can reasonably be accepted as being valid in all four specifications.

Once again only one specification was found to be significant but in this case corresponds to the estimation of (1) using first-differenced de-trended data (specification (1)) instead of the estimation using quadratic de-trending data (specification (3)) in Table 1. To all effects, the size of the estimated coefficient (0.01) is much smaller than the results reported by [START_REF] Firdmuc | The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement[END_REF] for the OECD countries (0.175) 18 The division of the sample period into sub-periods in (1) raises a number of important issues. First, by creating sub-periods, we are in fact using small period averages of the variables which greatly reduces the number of observations in the estimations and its explanatory power which creates an error in variable (EIV) problem, especially when using annual data. Second, the division of the whole sample period into four more or less arbitrary periods raises the question of whether these smaller periods are able to capture the business cycles. Finally, as the analysis below will demonstrate, the explanatory variable is non-stationary and since this issue is not addressed in (1) the results may in fact be spurious.

In order to try to overcome these problems, the following model is estimated:

ε β α + ∆ + = ∆ - ∆ ij j i IIT Q Q 2 ) ( (2)
where Q i , Q j , IIT ij and assume the same meaning as in (1). This alternative model has the great advantage of using yearly data and therefore of greatly increasing the number of observations. Since (2) is to be estimated using panel data, for the results to be valid both the dependent variable and the regressor need to be stationary. As there seems to be no agreement on the validity of panel unit root tests, ADF and PP tests were also conducted for all individual IIT time-series in firstdifferences. The tests were conducted with one lag and a constant and a trend in the test regressions for the cases where a trend was found to be significant and only a constant for the remainder cases. Table A-2 of Appendix A presents a summary of the results and shows all series to be integrated of order 1 at the 1% level of confidence in at least one of the tests. The results presented in Tables A-1

and A-2 show that we can reasonably assume the first-difference of the variable IIT to be stationary.

Next, we look at the dependent variable. Once again both the ADF test and the PP test were regressed for all individual series of the dependent variable, using three alternative data de-trending methods, namely, HP-filtering, quadratic-de-trending and HP-filtering on the residual of a regression of the real GDP on a constant and 5-year period dummies 19 . The results are presented in Table A-3 of Appendix A.

The dependent variable was found to be stationary in two data de-trending methods, namely quadratic de-trending and HP-filtering on the residual of a regression of the real GDP and 5-year period dummies. However, the dependent variable de-trended by HP-filtering was found to be non-stationary in both tests in at least three individual time series. Accordingly, regressions of (2) will only be conducted using the two series found to be stationary. Due to the construction of the model, the sign of now assumes the opposite significance of the previous models, that is, a negative sign implies that an increase in intra-industry trade will reduce differences in the growth rate of business cycles across ASEAN countries.

As stated before, OLS estimations of (2) may be inappropriate in this case.

Therefore, the regressions of (2) will be estimated by TSLS using the same instruments used in (1) as they proved to be valid in that case. Table 3 shows the results for the TSLS estimates of (2) which also included a specification that takes into account the possibility that observations might not necessarily be independent within the group of countries under analysis.

<Table 3 around here>

The coefficient of IIT yielded the expected sign in both specifications suggesting that the increase in intra-ASEAN trade has led to more synchronised business cycles amongst its members. The coefficients for IIT were not, however, were not found to be very significant with only one (specification 1) being significant at the 10% level.

As before, the Hansen test was estimated and included a specification that takes into account the possibility that observations might not necessarily be independent within the group of countries under analysis. The results show that once again the instruments used can be considered valid as we cannot reject the null hypothesis that the instruments are uncorrelated with the error term. In order to further test the robustness of the results, the same two specifications of (2) were estimated using as instruments not only the log of distance and a dummy for common border but also dummies for each period (minus one) of the whole sample period. Table 4 presents the results.

<Table 4 around here>

The results are identical with those of Table 3. Once again, the coefficients for IIT was not found to be significant.

Finally, in order to access the possible influence of one individual country in the results of the whole group, (2) was estimated excluding all the data involving each of the countries with the remaining pairs, that is, instead of including all of the 5 countries (10 pairs) in the sample, 5 separate regressions using the data of four countries (6 pairs) were computed. In these TSLS estimations, apart from the log of distance and a dummy for land border, dummies for each year (minus one) of the data sample were also included. The results are presented in Table 5.

<Table 5 around here> Excluding one country from the sample does not significantly change the previous outcome with one notable exception. When Indonesia is excluded from the sample, the coefficient of IIT becomes significant at the 1% level in both specifications. The explanation for this result might be that because Indonesia is Furthermore, these results also show that the recorded increase of intra-industry trade amongst Malaysia, the Philippines, Singapore and Thailand has led to the synchronisation of business cycles among its members. This result is consistent with previous empirical studies in confirming the 'European Commission View' 20 .

Final Remarks

Frankel and Rose (1997,1998) found that business cycles synchronisation increases with trade intensities leading them to conclude that these two important OCA criteria -trade links and similarity of business cycles -are jointly endogenous. This argument is a source of contention and can be interpreted as an invitation to disregard the 'static' OCA theory and encourage the early introduction of a monetary union since a country is more likely to satisfy the

[OCA] criteria for entry into a currency union ex post than ex ante due to lowered asymmetrical shocks.

Recent empirical studies have shown, however, that increasing trade itself does not necessarily lead to more synchronisation of business cycles. The effect of more trade between two countries on the harmonization of business cycles depends not only on the intensity of trade links but on the structure of that trade.

More trade will mean more synchronised business cycles only if it is of the intraindustry type, as we should expect more common shocks across countries. This paper sets to test whether the recorded increase in intra-ASEAN trade is leading the ASEAN members to closer economic integration and thus to better satisfy the criteria for a common currency. Two separate models are estimated for that purpose. Firstly, a variation of the model of [START_REF] Frankel | Is EMU more justifiable ex post than ex ante?[END_REF] first used by [START_REF] Firdmuc | The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement[END_REF] was estimated for the ASEAN members. Following [START_REF] Frankel | Is EMU more justifiable ex post than ex ante?[END_REF] four alternative data de-trending techniques were applied in both OLS and TSLS regressions. The results were very weak, with only one specification out of eight being statistically significant even if all the results yielded the expected positive relation between intra-industry trade and the synchronization of business cycles. Furthermore, this methodology has some flaws which may invalidate the results.

Therefore, a new methodology was implemented. Unlike previous studies, our own panel data model uses the whole sample data instead of dividing it into subgroups which greatly increases the number of observations in the regressions. The results with our own model for ASEAN5, using two alternative data de-trending techniques suggested a positive correlation between intra-industry trade and business cycle synchronization in ASEAN but were not very significant.

However, when excluding Indonesia from the sample, the result becomes highly significant for both data de-trending methods. The results are very robust even when using the highly disaggregated SITC fourth-digit industry data for all reported trade unlike most previous studies that either use the three-digit level of (Frankel andRose (1997,1998), [START_REF] Gruben | How Much Does International Trade Affect Business Cycle Synchronization[END_REF], [START_REF] Firdmuc | The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement[END_REF]) or a limited number of industries [START_REF] Shin | Trade Integration and Business Cycle Synchronisation in East Asia[END_REF]). Also, it was shown that the instruments used in the two-stage least squares of both models included in this paper -log of distance and a dummy for a geographic adjacency -to be valid, which further strengthens our conclusions. This outcome contrasts with Gruben, Koo and Millies (2002) which report the instrumental variables used by Frankel and Rose in their study to be inappropriate and to result in inflated results.

These results have important implications for the prospects of the creation of a common currency in ASEAN. As intra-industry trade leads to business cycle synchronization with respect to Malaysia, the Philippines, Singapore and Thailand, the costs of joining a currency union in ASEAN will diminish when intra-industry trade is dominant. Therefore, even if we take the endogenous OCA criteria hypothesis as valid -that a monetary union creates ex-post an OCA -the traditional OCA theory is still relevant since observing the initial conditions for a potential monetary union will give us an idea of how costly it would be for each member and how the economic policy can decrease the adjustment costs.

F

(Q it ,Q jt )= + IIT itj + ijt - (1
) to (4) correspond to regressions of alternative data de-trending tecniques, namely, first-differencing, HP-filtering, quadratic de-trending and HP-filtering on the residual of a regression of the real GDP on a contant and 5-year period dummies.

-Intrumental variables for Intra-Industry Trade (TSLS results) are log of distance and dummy variable for common border.

-Absolute value of t-values with robust standard errors in parenthesis.

-Bilateral annual data from ASEAN5 countries, from 1962 to 1996 split into four sub-periods. IITij is the bilateral average fourth SITC intra-industry trade in each sub-period.

(1) (2) (3) (4) Notes: -Model: Corr( Q i, Q j ) = + IIT itj + ijt -(1) to (4) and IIT assume the same meaning as in Table 1.

-Intrumental variables for Intra-Industry Trade (TSLS results) are log of distance and dummy variable for common border.

-Absolute value of t-values with robust standard errors to both heteroskedasticity and arbitrary intra-group correlation in parenthesis. 1) and (2) correspond to regressions of two alternative data de-trending tecniques, namely, quadratic-detrending and HP-filtering on the residual of a regression of the real GDP on a constant and 5-year period dummies.

Model: ( Q it -Q jt ) 2 = + IIT itj + ijt -(
-Intrumental variables for Intra-Industry Trade are log of distance and a dummyfor common border.

-Absolute value of t-values in parenthesis with robust standard errors to both heteroskedasticity and arbitrary intra-group correlation. 2) assume the same meaning as in Table 3.

-Intrumental variables for Intra-Industry Trade are log of distance, a dummy for common border and dummies for each year (minus one) of the sample data.

-Absolute value of t-values with robust standard errors in parenthesis.

Table 5: Estimations for (2) using alternative combinations of 4 of the ASEAN5.

( 2) assume the same meaning as in Table 3. Constants not reported.

-Intrumental variables for Intra-Industry Trade are log of distance and a dummy for common border and dummy variables for each year (minus one) of the sample data.

-Absolute value of t-values in parenthesis with robust standard errors to both heteroskedasticity and arbitrary intra-group correlation. -PP = Phillips-Peron test statistics.

-The estimations included a trend in the cases when a trend was found to be significant at the 5% level.

-* = rejection of hypothesis of unit root at 1% critical level -the estimations included trend in the cases where a trend was found to be significant at the 5% level.

-ADF = Augmented Dickey-Fuller test statistics and PP = Phillips-Peron test statictics.

-(1) to (3) correspond to regressions of the dependent variable using alternative data de-trending tecniques, namely, HP-filtering, quadratic-HP-filtering, detrending and HP filtering on the residual of a regression of the real GDP on a constant and 5-year period dummies.

-significance level at which the null hypothesis is rejected: *, 1%, **, 5% and ***, 10%.

( A recent paper by [START_REF] Lim | Convergence and Catching Up in ASEAN: A Comparative Analysis[END_REF], for example, using several different techniques did not find clear evidence of any income convergence and catching up in ASEAN suggesting that the existing gaps are not closing with time. It must be said, however, that since their data only covers the years from 1966 to 1992, that the opposite might be true after that period, especially since the introduction of AFTA.

2 [START_REF] Wilson | Prospects for Asian Monetary Cooperation after the Asian Financial Crisis: Pipedream or Possible Reality[END_REF], p. 6. [START_REF] Pomfret | ASEAN -Always at the Crossroads?[END_REF] is the author of the 'always at the crossroads' argument. The original five members of ASEAN or ASEAN5, Indonesia, Malaysia, Philippines, Singapore and

Thailand have since been joined by Brunei Darussalam (1984), Vietnam (1995), Laos and Myanmar (1997) and Cambodia (1999).

3 Yong ( 2004), p2.

4 Notable initiatives to promote regional financial stability and monetary policy cooperation include the establishment of 'Manila Framework Group' in 1997, the 'ASEAN Surveilance

Process' in 1998 and the 'Chiang Mai Initiative' in 2000. Recent initiatives to promote economic integration include the ASEAN Free Trade Area (1992) and the adoption of the so-called "ASEAN's Vision 2020" in 1997 where a timetable was established to create an ASEAN Economic Region.

5 Recently [START_REF] Mundell | Poverty, Growth and the International Monetary System[END_REF], defended that Asia eventually needs a common currency even though it recognised that it cannot at present have a single currency, p.18.

6 See [START_REF] Eichengreen | Toward a New Financial International Architecture: A Practical Post-Asia Agenda[END_REF] and Wyplosz (2001).

7 [START_REF] Sharma | ASEAN: Economic Integration and Intra-Regional Trade[END_REF] found empirical evidence that the "ASEAN integration scheme did not increase intra-ASEAN trade" and that "increase in ASEAN countries trade occurred with members of a wider APEC group", p. 167. A more recent study by [START_REF] Elliot | AFTA and the Asian Crisis: Help or Hindrance to ASEAN Intra-Regional Trade?[END_REF] reinforce these findings and even come to the conclusion that the degree of trade creation in the years immediately after the signing of the AFTA agreement in 1993 was actually lower than for the preceding period of 1988-1992.

8 De Grauwe (1997) was the first to use these denominations. The first accrues from European Commission (1990) and the second from [START_REF] Krugman | Geography and Trade[END_REF][START_REF] Krugman | Lessons from Massachusetts for EMU[END_REF]. [START_REF] Patterson | Adjustment to Asymmetric Shocks, Directorate-General for Research Working Paper[END_REF] quote [START_REF] Peters | European Monetary Union and Labour Markets: What to expect?[END_REF] as dividing the same opposite approaches as the 'Convergence School' and the 'Divergence School'. (1998, p. 1010). 10 In any case, a recent study by Zhang, Sato and McAleer (2004) found evidence that the Asian Financial crisis has increased the degree of supply, demand and monetary shock correlation among ASEAN countries. Therefore, the exclusion of this period from the analysis should not overstate the results.

11 Originally, this study intended to include not only the [START_REF] Grubel | Intra-Industry Trade[END_REF] intra-industry trade index but also the measures developed by Abd-el-Rahman (1991) and [START_REF] Fontagné | Intra-Industry Trade: Methodological Issues[END_REF] for vertical and horizontal intra-industry trade. That was not possible; however, as the sample included a significant number of zero observations which would greatly limit the analysis.

12 Originally, Frankel and Rose (1997) used the model Corr(Q i ,Q j ) = + TI ij + , where TI ij stands for trade intensity between countries i and j. They used four de-trending methods for real GDP and three other measures of economic activity and three measures of trade intensity, defined in relation to exports, imports and trade turnover.

13 This is especially true as the specialisation effect is more likely to exist in terms of inter-industry trade than intra-industry trade.

14 These two variables are known to be highly correlated with intra-industry trade (see for example [START_REF] Loertscher | Determinants of Intra-Industry Trade: Among Countries and across Industries[END_REF] and [START_REF] Hummels | Monopolistic Competition and International Trade: Reconsidering the Evidence[END_REF]). Both shorter distance and common border are expected to increase intra-industry trade for three main reasons, lower transportation costs, cultural similarities and similar resource bases which increases the likeliness of countries to participate in the same industries.

15 Unlike [START_REF] Frankel | Is EMU more justifiable ex post than ex ante?[END_REF] the data frequency in the present study is annual. Therefore, some adjustments needed to be made, namely, first differencing instead of fourth-differencing and the use of 5-year period dummies instead of quarterly dummies for the quadratic de-trending and HP-filtering of a regression of real GDP on a constant and period dummies. 16 All estimations were conducted with Stata version 8.2. 17 Baum, Schaffer and Stillman (2003) [START_REF] Firdmuc | The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement[END_REF] however, uses quarterly instead of annual data which might account for some of the difference. Also, in his study the IIT indexes were computed for three-digit SITC commodity groups. Immediate conclusions should, therefore, be avoided.

  instruments, i.e., uncorrelated with the error term, might cast some doubt on the results.

  relatively more closed economy of the group it is less integrated with the rest of ASEAN than its smaller and more open partners.

  trade might mean more specialization, and we should expect more idiosyncratic shocks.

  For that purpose, several alternative unit root tests were conducted for both variables. The results are presented in Appendix A. in the panel is stationary. Table A-1 of Appendix A reports the results. The results show that we cannot reject the hypothesis that all 10 individual time series contain unit roots.

	least one series
	F o r
	P e e
	r
	R e v
	i e
	w
	First, a Fisher type unit root test for panel data, developed by Madalla and
	Shaowen (1999) was conducted for the variable IIT using both an augmented
	11

Dickey-Fuller (ADF) test and a Phillips-Peron (PP) test. This test assumes that all series are non-stationary under the null hypothesis against the alternative that at

Table 2 :

 2 Hansen Tests to the Validity of the Instruments of Model (1)

		(1)	(2)	(3)	(4)
		TSLS	TSLS	TSLS	TSLS
	Contant	0.231486	0.4719848	0.2604285	0.829763
		(3.78)	(4.95)	(1.46)	(16.49)
	IIT	0.0100578	0.0093942	0.0312887	0.0080749
		(2.34)	(1.21)	(1.68)	(1.51)
	Hansen J Statistic	1.467	1.036	2.492	2.221
	Chi-Square(1) P-Val	0.226	0.308	0.114	0.136
	no. Obs.	40	40	40	40

Table 3 :

 3 Intra-Industry Trade and Business Cycles in

		(1)	(2)
	Contant	13.83819	37.08033
		(2.64)	(2.23)
	IIT	-16.03203	-45.78226
		(1.92)	(1.75)
	Hansen J Statistic	0.165	0.218
	Chi-Square(1) P-Val	0.68	0.64
	no. Obs.	340	340
	Notes: -

Table 4 :

 4 Estimations for Model (2) with Year Dummies as Instruments

		(1)	(2)
		TSLS	TSLS
	Contant	9.212954	25.49597
		(0.38)	(0.37)
	IIT	-16.03203	-45.78226
		(1.82)	(1.66)
	no. Obs.	340	340
	Notes: -Model: ( Q it -Q jt ) 2 = + IIT itj + ijt
	-(1) and (
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Table A -

 A 1: Fisher Test for Panel Unit Root on variable IIT in levels Table A-2: Unit root tests for all individual IIT series in first-differences

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	APPENDIX A: Unit Root Tests
	Pair:	ADF	PP
	Indonesia-Singapore Indonesia-Malaysia F Indonesia-Phillipines Indonesia_Thailand o Malaysia-Phillipines r -6.334* -7.292* -3.975* -2.736 5.495* Singapore-Malaysia -6.144*	-11.325* -6.970* -5.564* -5.530* -8.532* -7.472*
	Thailand-Malaysia Thailand-Phillipines Thailand-Singapore Singapore-Phillipines	-3.860 -4.101* P e -4.709* -7.626* -2.343 -4.635* e -3.899* -8.374*
		r
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			w
	Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK	23

Notes: -ADF = Augmented Dickey-Fuller test statictics.

Table A

 A

	-3: Unit root tests for all individual series of depended variable of (2)
		ADF	PP	ADF	PP	ADF	PP
	Pair:
	Indonesia-Singapore	-1.207	-1.641	-5.057* -3.636** -3.827** -3.900*
	Indonesia-Malaysia	-3.515** -4.336*	-3.732*	-4.176*	-3.899*	-5.584*
	Indonesia-Phillipines	-1.207	-1.641	-3.487** -4.627* -3.519** -5.501*
	Indonesia_Thailand	-3.332** -4.202*	-3.729*	-5.717*	-5.083*	-6.541*
	Malaysia-Phillipines	-3.062	-4.985* -3.547** -5.556*	-3.760*	-5.234*
	Singapore-Malaysia	0.212	-1.643	-2.336	-4.180* -2.961*** -3.183**
	Thailand-Malaysia	-3.674** -3.674** -2.845*** -4.506* -2.992** -4.743*
	Thailand-Phillipines	-3.162** -3.419** -3.986*	-5.176*	-4.588*	-6.071*
	Thailand-Singapore	4.005**	-6.660* -2.823*** -5.446* -3.724** -6.595*
	Singapore-Phillipines -3.237** -5.194*	-3.811*	-5.454* -3.650** -5.051*

Notes:

  Page 24 of 26 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK They conclude that "a naïve examination of historical data gives a misleading picture of a country's suitability for entry into a currency union, since the OCA criteria are endogenous",

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  developed a STATA module called ivreg2 for extended TSLS estimation and instrument validity testing.

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Page 25 of 26

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

First-differencing of the data was excluded as it did not, in this case, remove the trend in the data.

It should be noted that this is not necessarily a rejection of 'Krugman's View'. The specialisation effect is more likely to exist as regards to inter-industry trade than for intra-industry trade.

Acknowledgements

The author is grateful to Francisco Veiga and Miguel Ângelo of The University of Minho and John Maloney, Malcolm Macmillen and Cherif Guermat of The University of Exeter for useful comments and suggestions. The usual disclaimer applies.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Julio Pindado

Rafael Rabelo

Keywords: trade credit, agency theory, adverse selection, moral hazard trade credit, agency theory, adverse selection, moral hazard JEL classification: M21, G39

come

An agency model to explain trade credit policy and empirical evidence

Introduction

Trade credit is a very important source of financing for firms. Although it is an old practice, it is not completely understood. Regarding trade credit, there are two strands of literature. The first focuses on studying the demand for trade credit, which is closely related to lending relationships and lines of credit. The papers in this strand of literature are mainly based on the argument that firms would increase the level of trade credit used when their alternative sources of finance are limited, in that trade credit is an important alternative for short and long term bank debt (see, for instance, [START_REF] Petersen | The benefits of lending relationships: evidence from small business data[END_REF][START_REF] Petersen | The effect of credit market competition on lending relations[END_REF][START_REF] Deloof | Trade credit, corporate groups, and the financing of Belgian firms[END_REF][START_REF] Danielson | Bank loan availability and trade credit demand[END_REF][START_REF] Alphonse | When trade credit facilitates access to bank finance: evidence from US small business data[END_REF][START_REF] Niskanen | The determinants of corporate trade credit policies in a bankdominated financial environment. The case of Finnish small firms[END_REF]. The second strand of literature studies the supply of trade credit (see, for instance, [START_REF] Long | Trade credit, quality guarantees, and product marketability[END_REF][START_REF] Deloof | Trade credit, product quality, and intragroup trade: some European evidence[END_REF]Wei and Zee,1997;[START_REF] Ng | Evidence on the determinants of credit terms used in interfirm trade[END_REF][START_REF] Pike | Trade credit terms: asymmetric information and price discrimination evidence from three continents[END_REF]. There are also papers such as that of [START_REF] Petersen | Trade credit: theories and evidence[END_REF] and [START_REF] Marotta | When do trade credit discounts matter? Evidence from Italian firm-level data[END_REF], which study the trade credit from both points of 3 adverse selection phenomenon in explaining trade credit policy, but none of them have considered the moral hazard phenomenon. Consequently, from our point of view, the main contribution of this paper is to show the importance of the moral hazard phenomenon and it suggests that both phenomena together could explain trade credit policy.

According to our argument, we have tested an agency model whose explanatory variables are those that explain the adverse selection and moral hazard phenomena. The empirical evidence is provided from a sample of UK companies, where, according to previous evidence, trade credit is highly relevant and represents more than 62% of firms' total debt [START_REF] Bevan | Capital structure and its determinants in the UK -a decompositional analysis[END_REF] 1 .

Moreover, the estimation methodology used in this paper is panel data in order to control for individual heterogeneity.

The results support our agency model. According to the adverse selection phenomenon, we find that smaller firms, those with a smaller proportion of fixed assets, and those that are less profitable, tend to extend more trade credit. Regarding the moral hazard phenomenon, our empirical evidence reveals that the higher the proportion of variable costs and the higher the percentage of bad debts, the less trade credit offered. Concerning traditional models, our paper finds empirical evidence against tax and transactions costs theories. Furthermore, as in [START_REF] Marotta | When do trade credit discounts matter? Evidence from Italian firm-level data[END_REF], our results do not support the liquidity argument that links trade credit and credit rationing.

The remainder of the paper is organized as follows. Section 2 focuses on the theories on trade credit policy. Our agency model explaining trade credit policy is developed in Section 3, and Section 4 describes the data set and methodology used. Section 5 shows our interpretations of the estimation results of our agency model and also tests traditional models. Finally, the conclusions are presented in Section 6.

1 These authors have made a decompositional analysis of capital structure for UK companies. According to them, determinants of gearing depends on the measure used to proxy it and consequently depend on which component of debt is being analysed. Their evidence shows that results are very sensitive to whether or not trade credit is included. Trade credit is one of the oldest forms of corporate financing and it continues to be very important at present; it refers to the financing provided by a seller to the client [START_REF] Wei | Trade credit as quality signal: an international comparison[END_REF]. To understand the concept of trade credit it is important to know the range of alternative credit arrangements that can occur in trade. Depending on the type of credit policy, payment can be made at different times. It can occur before delivery, on delivery or after delivery. In the last case, the seller may or may not offer discounts for prompt payment, depending on trade arrangements.

When payment does not occur before or on delivery, trade credit is being extended and the seller assumes the credit risk. Otherwise, trade credit is not being offered and the buyer assumes the risk that the product may be of low quality.

Although trade credit is a very useful source of resources for different kinds of firms, there is no clear explanation of it yet, as pointed out by [START_REF] Long | Trade credit, quality guarantees, and product marketability[END_REF]. In the last three decades, several theories and models have appeared to explain trade credit. Most of these theories rely on market imperfections, such as the existence of taxes, transactions costs and asymmetric information, as shown in Figure 1.

[INSERT FIGURE 1 ABOUT HERE]

Tax theory

The decision whether or not to accept a trade credit depends on the ability to access other sources of funds. A buyer should compare different financing alternatives to find out which choice is the best. In trade between a seller and a buyer a post payment may be offered, but it is not free, there is an implicit interest rate which is included in the final price. Therefore, to find the best source of financing, the buyer should check out the real borrowing cost in other sources of funds. [START_REF] Brick | Taxes and the theory of trade debt[END_REF] suggest that the tax effect should be considered in order to compare the cost of trade credit with the cost of other financing alternatives. The main reason for this is that if buyers and sellers are in different tax brackets, they have different borrowing costs, since interests are tax deductible. The authors' hypothesis is that firms in a high tax bracket tend to offer more trade credit than those in low tax brackets. Consequently, only buyers in a lower tax bracket than the seller will accept credit, since those in a higher tax bracket could borrow more cheaply directly from a financial institution. Another conclusion is that firms allocated to a given industry and placed in a tax bracket below the industry average cannot profit from offering trade credit.

Therefore, [START_REF] Brick | Taxes and the theory of trade debt[END_REF] suggest that firms cannot both use and offer trade credit.

Transactions costs theory

First developed by [START_REF] Schwartz | An economic model of trade credit[END_REF], this theory conjectures that suppliers may have an advantage over traditional lenders in checking the real financial situation or the credit worthiness of their clients. Suppliers also have a better ability to monitor and force repayment of the credit. All these superiorities may give suppliers a cost advantage when compared with financial institutions. Three sources of cost advantage were classified by [START_REF] Petersen | Trade credit: theories and evidence[END_REF] as follows: information acquisition, controlling the buyer and salvaging value from existing assets.

The first source of cost advantage can be explained by the fact that sellers can get information about buyers faster and at lower cost because it is obtained in the normal course of business. That is, the frequency and the amount of the buyer's orders give suppliers an idea of the client's situation; the buyer's rejection of discounts for early payment may serve to alert the supplier of a weakening in the credit-worthiness of the buyer, and sellers usually visit customers more often than financial institutions do. In his model, [START_REF] Smith | Trade credit and informational asymmetry[END_REF] concludes that in two-part credit terms with a high interest rate, those buyers that do not choose to take advantage of the discount can be identified as high risks, because they may be having financial difficulties.

Recently, [START_REF] Burkart | In-kind finance: a theory of trade credit[END_REF] argued that the suppliers' monitoring advantage applies exclusively to input transactions. They posit that the source of suppliers' advantage is the input transaction itself. According to the authors, an input supplier does not incur in monitoring costs to know that an input transaction has been completed, but other lenders do. The main argument supporting the authors' proposition is the difference between cash and input. While the 6 former is easily diverted, i.e., its use does not maximize lenders' expected return, the latter is not easily diverted and its illiquidity facilitates trade credit.

The second source of cost advantage arises from the power of the seller to threaten buyers. In other words, in some cases there are only a few alternative suppliers for the product needed and, consequently, buyers have very restricted choice. In this case, suppliers can threaten to cut off future supplies if they note a reduction in the chances of repayment. Compared with suppliers, financial institutions do not have the same threatening power. This advantage can become stronger when either the buyers represent only a small part of the supplier's sales or the supplier is part of a network and future community sanctions can be made by a group, which makes this threat much stronger [START_REF] Kandori | Social norms and community enforcement[END_REF]. Empirical evidence supporting this hypothesis can be found in [START_REF] Mcmillan | Interfirm relationships and informal credit in Vietnam[END_REF]. Another interesting finding in this strand of literature was provided by [START_REF] Petersen | Trade credit: theories and evidence[END_REF], whose empirical results suggest that debtors are less willing to repay a distressed seller. Their argument is that threats of cutting off future supplies made by a supplier with financial problems are not so credible. The seller's ability to salvage value from existing assets is the third source of cost advantage.

In the case of buyer default, the seller can seize the goods that are supplied, of course financial institutions can reclaim the firm's assets as well. The difference between them is that since the firms trading are very often from the same industry, the supplier already has a network to sell the goods and consequently repossessing and resale costs would be lower. [START_REF] Mian | Accounts receivables management policy: theory and evidence[END_REF] and [START_REF] Petersen | Trade credit: theories and evidence[END_REF] provide two interesting approaches related to this cost advantage. The former obtain evidence supporting the idea that the more durable the goods, the better collateral they provide and the greater the credit offered by the suppliers. The latter point out that the extent to which the customers transform the product is also very important. The less they are transformed, the easier it will be for the supplier to repossess and sell the asset using the same channel.

Another important point refers to the relative value of the goods. [START_REF] Ng | Evidence on the determinants of credit terms used in interfirm trade[END_REF] consider that the value of a product differs between firms and financial institutions; i.e., if a product has 7 more value as collateral to a seller than to a financial institution, the seller may have a cost advantage in recuperating the product and selling it again. In this situation, suppliers tend to offer cheaper credit than financial institutions because of the reduction of the credit risk.

Another paper related to transactions costs is [START_REF] Emery | An optimal financial response to variable demand[END_REF]; this author hypothesizes that there is a positive relation between demand variability and credit offered. This hypothesis is based on the following argument. When demand fluctuates, a firm has two traditional reactions (production or price adjustment); however, both are very costly and a better decision could be taken in that the seller could change trade credit terms according to demand. Terms can be relaxed when demand drops and tightened when demand increases. In this case, trade credit can be seen as an operational tool. [START_REF] Long | Trade credit, quality guarantees, and product marketability[END_REF] obtain empirical evidence supporting this hypothesis. Their results show that firms with variable demand extend more trade credit than firms with stable demand.

Liquidity theory

This theory, first suggested by [START_REF] Emery | A pure financial explanation for trade credit[END_REF], proposes that credit rationed firms use more trade credit than those with normal access to financial institutions. The central point of this idea is that when a firm is financially constrained the offer of trade credit can make up for the reduction of the credit offer from financial institutions. In accordance with this view, those firms presenting good liquidity or better access to capital markets can finance those that are credit rationed.

Several approaches have tried to obtain empirical evidence in order to support this assumption.

For example, Nielsen (2002), using small firms as a proxy for credit rationed firms, finds that when there is a monetary contraction, small firms react by increasing the amount of trade credit accepted.

As financially unconstrained firms are less likely to demand trade credit and more prone to offer it, a negative relation between a buyer's access to other sources of financing and trade credit use is expected. [START_REF] Petersen | Trade credit: theories and evidence[END_REF] obtained evidence supporting this negative relation. The trade credit relation gives rise to two problems. On the one hand, sellers do not usually know the real credit-worthiness of their buyers and; on the other, buyers do not properly know the quality of the product that is being acquired. To solve the first problem, [START_REF] Smith | Trade credit and informational asymmetry[END_REF] suggests a model where sellers offer two-part credit terms because they can recognize potential defaults faster than financial intermediaries. And, as commented in Section 2.2, there are many other arguments supporting the idea that suppliers have cost advantages in acquiring knowledge about a buyer's financial situation. Regarding the second problem, [START_REF] Smith | Trade credit and informational asymmetry[END_REF] also claims that with asymmetric information about product quality, sellers offer trade credit to allow buyers to verify product quality before payment.

Other options to reduce the cost of the above-mentioned problem is to offer money-back guarantees and warranties. Trade credit has some advantages when compared with these two. First, in a case of money-back or warranties, if the seller is not in business any more, the buyer can be damaged. Second, when payment is made at the time of sale, a client who wants to obtain the advantages of the money-back system must try to convince the seller that the quality of the product is not as promised.

As pointed out by [START_REF] Smith | Trade credit and informational asymmetry[END_REF], one of the major purposes of trade credit is to allow clients to assess product quality prior to payment; however, this is not true for some categories of product.

Therefore, this theory works better in some industries whose product quality is unknown at the moment of purchase. According to this argument, sellers will extend more trade credit when selling products where quality is indefinite at a prior moment and the purchase is not frequent. On the contrary, sellers will extend less trade credit when trading perishable items where acquisition is very frequent.

Many financial scholars have studied trade credit from this point of view (see, for instance, [START_REF] Lee | Product risk, asymmetric information, and trade credit[END_REF][START_REF] Long | Trade credit, quality guarantees, and product marketability[END_REF][START_REF] Deloof | Trade credit, product quality, and intragroup trade: some European evidence[END_REF][START_REF] Wei | Trade credit as quality signal: an international comparison[END_REF][START_REF] Pike | Trade credit terms: asymmetric information and price discrimination evidence from three continents[END_REF]. In summary, the main results of these authors are as follows. i) Small firms tend to offer 9 more trade credit than large firms, since small firms still have to establish their reputation about product quality. ii) Firms with longer production cycles prolong their collection period, since they produce high-quality goods. iii) Firms selling products whose quality is difficult to measure extend more trade credit because customers must have enough time to assess quality. iv) Sellers of low quality goods may try to pass them off as high-quality goods. In this case, as the cost of extending trade credit increases, these firms will have less incentive to cheat on the information on quality.

What do these theories not explain?

Although many theories have attempted, in different ways, to explain the existence of trade credit, they cannot provide a complete explanation of the topic. While some of the models are more consistent with the case of certain industries or categories of products, others work better in a financially constrained environment. According to [START_REF] Frank | Trade credit, collateral, and adverse selection[END_REF] "the existing theories show effects that may be important in specific circumstances, but they do not capture what seems to be central for explaining the wide-spread use of trade credit and the empirical patterns of its use". Let us check theory by theory and identify some inconsistencies or situations that are not very well explained.

The tax theory suggests that firms in high tax brackets tend to offer credit to those in low tax brackets. Some research studies have found empirical evidence to support this, but this explanation does not seem to be enough since it cannot explain trade credit between firms situated in the same tax bracket.

If trade credit is an operational tool and exists to minimize transactions costs, as [START_REF] Ferris | A transactions theory of trade credit use[END_REF] suggested, a reduction in the level of trade credit used would be expected since many improvements in transaction technologies have taken place. However, this reduction has not been observed in recent years.

The liquidity theory supposes that credit constrained firms use more trade credit than those with easier access to financial intermediaries. This may be an explanation, but once more, it does The product quality theory argues that trade credit is offered to allow clients to check the real quality of the goods before payment, but it does not explain why firms selling some products and services do not offer credit at all; some firms even require clients to pay in advance.

An agency model explaining trade credit policy

The main difference between our agency model and other models that attempt to explain trade credit by assuming the existence of asymmetric information is the inclusion of the moral hazard phenomenon into our model. According to [START_REF] Jensen | Theory of the firm: managerial behavior agency costs and ownership structure[END_REF] the agency problem could be extended to the relation between a firm and its clients. This agency relation is characterized by two phenomena: adverse selection and moral hazard. Adverse selection occurs when there is exante asymmetric information between sellers and buyers. In this case, clients do not know ex-ante the characteristics and quality of the goods that are being acquired. As described in Section 2.4, this phenomenon has already been studied by other authors, but not moral hazard, which consists of the possibility of the contractual relationship not being carried out by the client, facilitated by the ex-post asymmetric information. This phenomenon has an important consequence in that it could lead to clients not paying for the goods bought when the payment is due, giving rise to bad debts. Consequently, we define a function to explain trade credit as follows: DSO=φ(ϖ,µ), where DSO is the day of sales outstanding, ϖ stands for the agency costs arising from the adverse selection phenomenon, while µ represents the agency costs caused by the moral hazard phenomenon. According to our model, the greater the adverse selection costs the more trade credit provided to clients. In addition, the greater the moral hazard costs the less the trade credit offered to clients. Therefore, we expect ϖ to be positively related to DSO and µ to be negatively related.

Consequently, our model explains the days of sales outstanding (DSO), which is measured as the natural logarithm of the accounts receivable divided by daily sales. The literature has argued that several variables should be taken into account to proxy the adverse selection phenomenon. First, the goods sold by technical industries need a significant amount of time to verify their quality level, hence a high adverse selection cost is associated with these goods. To classify each firm in our sample as part of the technical industry or not, we follow [START_REF] Titman | The determinants of capital structure choice[END_REF], defining technical industries as those in SIC codes between 3400 and 3999. Other authors that have used the same classification include [START_REF] Long | Trade credit, quality guarantees, and product marketability[END_REF]. Additionally, there are no adverse selection costs when buying some products which are easy to observe (perishable). Following [START_REF] Long | Trade credit, quality guarantees, and product marketability[END_REF], we consider perishable products to be those sold by firms in SIC codes between 2000 and 2199. Therefore, we have constructed the Product Quality Level variable (PQL) in order to measure the product quality level combining these two characteristics. We also base the construction of the product quality level variable, PQL, on the argument that if the reputation of the firm selling the product is already known, as is the case of large firms, less time is needed to verify the quality. Therefore, the PQL variable takes into account both the kind of product and the size of the firm. This variable takes higher values when the period needed to verify the quality of the product is longer, and vice versa 2 . As a result, the product quality level should be positively related to DSO. Second, the firm's reputation considerably reduces the adverse selection costs. The firm's reputation is proxied by the variable SIZE, measured as the natural logarithm of a firm's total assets. The expected relationship between DSO and SIZE is negative. Third, [START_REF] Prowse | Institutional investment patterns and corporate financial behavior in the US and Japan[END_REF] argues that the greater the proportion of fixed assets in a firm, the smaller the asymmetric information problem. Following [START_REF] Prowse | Institutional investment patterns and corporate financial behavior in the US and Japan[END_REF], we compute the variable fixed assets, FIX, as 1-(Fixed Assets/Total Assets). This variable provides a measure that is inverse to the fixed assets of a firm; hence we expect FIX to be directly related to DSO. Fourth, the negative consequences of the problem of adverse selection to the buyer are mitigated by the 2 We calculate the PQL variable as follows: PQL=(30/(1+SIZE)) for technical industries, PQL=(0.5/(1+SIZE)) for perishable industries, and PQL=(2/(1+SIZE)) for the remaining firms. The figures 30, 2 and 0.5 have been chosen in order to give more power to the first characteristic related to industry, as suggested in financial literature. However, note that the second characteristic (size) plays an important role, since it distributes the values within each kind of industry and provides variability to PQL, which is necessary when using panel data methodology. More details will be provided by the authors upon request. supplier's profitability. To proxy for supplier's profitability we use the return on assets variable (ROA), calculated as earnings before interest and taxes divided by total assets. This variable is expected to be negatively correlated to DSO. Fifth, the last variable we use to proxy the adverse selection is the days to pay accounts payable (DPA), calculated as in Compustat Global Vantage as follows: accounts payable times 360 divided by the sum of inventories plus cost of goods sold plus depreciation and amortization minus the previous value for inventories minus depreciation and amortization. This variable is entered into the model because high quality firms are prone to increase the trade credit offered to their clients, and they use this argument to require more trade credit from their suppliers. Therefore, the expected relation between days to pay accounts payable and DSO is positive.

We base our variables to proxy the moral hazard phenomenon on [START_REF] Oh | Opportunity cost in the evaluation of investment in accounts receivable[END_REF]. In accordance with this author, the decision to change a current credit policy to a new one depends on whether the marginal profitability is greater than the associated marginal opportunity cost. Therefore, when a firm decides to extend its credit policy it is probably expecting a sales increase and, hence, the firm is prepared to assume new costs such as collection costs, bad debt losses and variable costs. Thus, the credit policy decision involves a tradeoff between profits from marginal sales and their marginal costs.

According to trade credit literature, the more extensive the credit offered, the greater the amount of bad debt losses the seller will support. To mitigate this problem, firms are motivated to offer bigger cash payment discounts. [START_REF] Marotta | When do trade credit discounts matter? Evidence from Italian firm-level data[END_REF] argues that there is a strong relation between cash discounts and debt periods, specifically when creditors' rights protection is more effective. These discounts must be attractive enough to convince even the risky buyer to pay sooner, because if they do, it may reduce the possibility of bad debt losses inasmuch as it restricts the amount of time available to buyers to develop more problems. We proxy this problem (the cost of bad debt that may arise when trade credit is extended) by using the bad debt provision variable (BDP). This variable is calculated as the Provision-other item from Compustat Global Vantage divided by total assets. Consequently, the expected relationship between bad debts provision and DSO is negative.

When trade credit is extended, if the buyer does not pay, the supplier will support all costs generated by the sales. Therefore, the damage caused by the moral hazard problem will be greater the higher the percentage of variable costs of the goods sold. As a result, we expect that the greater the variable costs, the more rigid the credit policy will be. Consequently, an additional variable to proxy the extent of the moral hazard problem is the variable costs variable (VCO), calculated as the cost of goods sold divided by total assets. Therefore, a negative relationship between the variable costs and DSO is expected.

To test the expected relation between trade credit policy and both phenomena, we propose an agency model incorporating all the variables that we have considered as characterizing the adverse selection and moral hazard problems. This model would be as follows:

it it it it t i t i it it DPA ROA FIX SIZE PQL BDP VCO DSO ε β β β β β β β β + + + + + + + + = 7 6 5 4 3 2 1 0 (1)
The explanatory variables and their expected sign according to our previous argument are displayed in Table 1.

[INSERT TABLE 1 ABOUT HERE]

Data and methodology

We decided to restrict our sample to manufacturing firms because it is in this category where trade credit takes place in its habitual form. Consequently, our sample covers active manufacturing companies in the UK (SIC 2000 through 3999) from the Compustat Global Vantage database for a four-year period ending in 2002. As a result, we have obtained an unbalanced panel comprising 336 companies and 1,162 observations. The observations in our sample are industry dispersed, i.e., the sample does not appear to be more concentrated in any industry. 3. The days of sales outstanding (DSO) is around 70 days. Note that correlation coefficients are moderate and do not violate the assumption of independence between explanatory variables.

[INSERT TABLE 3 ABOUT HERE]

To test the hypotheses related to the proposed model, we use panel data methodology because, unlike cross-sectional analysis, it allows us to control for individual heterogeneity. This heterogeneity could lead us to biased results (see, for instance, [START_REF] Moulton | Random group effects and the precision of regression estimates[END_REF][START_REF] Moulton | Diagnostics for group effects in regression analysis[END_REF]. Therefore, in our paper we control for heterogeneity by modelling it as an individual effect, η i . Consequently our model in Equation 1 would be as follows:

it i it it it t i t i it it DPA ROA FIX SIZE PQL BDP VCO DSO ν η β β β β β β β β + + + + + + + + + = 7 6 5 4 3 2 1 0 (2)
where ν it is a random disturbance. As a result of the possible correlation between the individual effect and the explanatory variables, we have estimated the model by using the fixed effects estimator, since this estimator is unbiased even if the above-mentioned correlation is present.

Results

Estimation results of the agency model

Table 4 (see column I) provides the results from estimating the model in Equation (2). Our findings show evidence supporting the hypothesis that the greater the adverse selection problem the more trade credit offered. The SIZE variable is negatively related to DSO, which is in agreement with the adverse selection phenomenon, related to the product quality guarantee argument. As a result, smaller firms, with their reputation not yet built, extend more trade credit to allow clients to check product quality and build their status as high quality firms. The FIX variable reveals additional support to the abovementioned hypothesis since there is a positive relationship between FIX and DSO. According to the construction of the variable, the greater its value, the smaller the proportion of fixed assets of a firm. Thus, trade relations between buyers and suppliers with a high proportion of fixed assets suffer from smaller problems of adverse selection, which facilitate the extension of trade credit. The ROA variable is negatively related to DSO. This relationship implies that the more firms earn, the less credit offered. These results support the product quality argument in that the greater the seller's profitability the smaller the adverse selection problem suffered by buyers, and consequently the less trade credit required by them 3 .

[INSERT TABLE 4 ABOUT HERE]

Regarding the moral hazard phenomenon, the regression results are partially in accordance with the arguments presented in Section 3. First, we have found that VCO is negatively related to DSO. Therefore, variable costs negatively influence the length of the trade credit extended, because sellers with a high level of variable costs will be more strongly affected in case of buyer default than those with lower levels and, consequently, they will extend less credit. Second, the unexpected positive coefficient for the BDP variable could be related to the distress situation of risky clients. Our argument to explain this result is that firms whose clients are considered "high risk", i.e. those presenting high levels of BDP, should raise the cash discounts offered in order to encourage risky clients to pay early and thus avoid bad debts. Unfortunately, these risky clients cannot make use of the discounts offered probably because of their distress situation and then they pay the total value on the net date. Note that this argument is similar to that provided by [START_REF] Fazzari | Investment-cash flow sensitivities are useful: A Comment on Kaplan and Zingales[END_REF] to explain the very different results obtained by [START_REF] Kaplan | Do investment-cash flow sensitivities provide useful measures of financing constraints?[END_REF] when using a subsample of those firms facing a distress situation in the sample corresponding to the [START_REF] Fazzari | Financing constraints and corporate investment[END_REF] seminar paper. Additionally, [START_REF] Pindado | How does financial distress affect small firms financial structure?[END_REF] show that when firms face a distress situation they lose their normal patterns of behavior. Our argument also complements [START_REF] Marotta | When do trade credit discounts matter? Evidence from Italian firm-level data[END_REF], who argues that the effectiveness of the decision to offer cash discounts in order to diminish credit periods will depend on the existence of penalties for ex post delays and on the extent to which they are enforced. We add that the success of this decision will also depend on the financial situation of the client. 3 The above-mentioned variables related to the adverse selection phenomenon show the expected relationships with the dependent variable; however, the PQL and DPA variables are not significant. Therefore, results from regressions do not allow us to affirm that firms categorized as high tech producers extend more credit than others and that high quality firms are prone to increase the trade credit offered to their clients in order to use this argument to require more trade credit from their suppliers. To distinguish between the firms whose clients are distressed firms from the others, we have constructed a dummy variable, DBDP, that takes the value of 1 if the bad debt provision of the firm is higher than the mean and zero otherwise. We have then interacted this dummy variable with the BDP variable and incorporated this term into the model. Therefore, the new model would be as follows:

it i it it t i t i it it it DPA ROA FIX SIZE PQL BDP DBDP VCO DSO ν η β β β β β γ β β β + + + + + + + + + + = 7 6 5 4 3 1 2 1 0 ) ( (3)
This new model, whose results are provided in Table 4 (see column II), allows us to test the negative relation expected between trade credit and moral hazard, controlling for the distress situation of the clients. Regarding the BDP variable, the results indicate two different facts. First, when the firm has a bad debt provision higher than the mean, the dummy variable takes the value of 1 and, consequently, the coefficient is β 2 +γ 1. We have performed a linear restriction test whose null hypothesis is H 0 = β 2 +γ 1 in order to know if the coefficient β 2 +γ 1 =1.0772 is significantly different from zero. As the t-value is 3.3346, we can reject the null hypothesis and conclude that the coefficient is significantly different from zero. This result supports our explanation that when clients present a very high level of risk, seller's efforts to diminish the average collection period are never enough and, in this case, a positive relation between trade credit and bad debt provision arises. Second, when the firm has a bad debt provision lower than the mean, the dummy variable takes the value of zero and the coefficient is β 2 = -4.3698. Therefore, when controlling for the distress situation the bad debt provision is negatively related to DSO. This result added to the negative relationship found between the variable costs and DSO supports that the greater the moral hazard problem, the less trade credit offered.

Note that the results provided in column II show the same results on the adverse selection phenomenon as those commented above. As a result, we conclude that trade credit policy is explained by a trade-off between the adverse selection and moral hazard phenomena. Therefore, these findings support our model based on agency theory.

Estimation results of the traditional models

As a robustness check for our model, we test the alternative models described in financial literature. The results show that our model works better in explaining trade credit policy than the traditional models. Specifically, the results do not support the tax, liquidity and transactions costs theories. Furthermore, the results only partially support the product quality theory.

To test the tax argument, we use the total debt variable, TD 4 . The idea behind this theory is as follows: if sellers have a high level of debt, they are less likely to obtain another loan with tax deductible interest which would reduce the cost of borrowing. Therefore, we expect the total debt variable to be negatively related to DSO. This model also includes profitability (ROA) as an independent variable, since the interest cannot be tax deductible if the seller does not obtain positive earnings before taxes. Our findings (see column III of Table 4) show that only the ROA variable is significant. Therefore, this result does not support the tax theory, showing that trade credit policy cannot be explained by tax motives.

According to the liquidity theory, the DSO is expected to be directly related to the size (SIZE) and profitability (ROA) variables, and inversely related to the days to pay accounts payable variable (DPA). As can be seen in column IV of Table 4, our findings do not support the liquidity theory, since the ROA and DPA variables show non-significant coefficients and SIZE is negatively related to the dependent variable.

To test the operational argument of transactions costs theory, we regress the DSO using as the explanatory variable SDS (standard deviation of sales). As this variable is constructed by using the standard deviation of total revenue over the four-year period ending in 2002, we only have cross-sectional data. Therefore, we use the ordinary least squares method to estimate the model, since it was not possible to use panel data methodology. This theory predicts that DSO is positively related to SDS. The results (provided by the authors upon request) reveal that the coefficient of the 4 This variable is calculated as the sum of long term debt and debt in current liabilities as a percentage of total assets. The product quality theory is tested by using the same explanatory variables incorporated in the agency model to proxy the adverse selection phenomenon. The results (see column V of Table 4) are similar, in terms of sign and significance of coefficients, to those obtained when testing our agency model for the variables capturing the adverse selection phenomenon.

Furthermore, the specification tests could also help us to compare our agency model with the traditional model. Except for the product quality model, the F statistic shows that the null hypothesis that all variables are jointly equal to zero cannot be rejected; therefore these models do not offer a good explanation of the trade credit policy. Although the F statistic for the product quality model allows us to reject the null hypothesis, the explanatory power of this model is lower than that of our agency model, as can be seen if we compare the values for the R 2 between displayed in Table 4. Note that by using panel data methodology the R 2 between is the directly relevant measure of goodness of fit. This greater explanatory power is due to the fact that the product quality model does not include the moral hazard phenomenon, thus providing a limited explanation of trade credit policy, since the risk of buyers not paying when payment is due is crucial when making trade credit decisions.

Conclusions

This paper proposes an agency model to explain trade credit offered by firms. This model is based on the two phenomena (adverse selection and moral hazard) arising from the agency relation between sellers and buyers. According to our argument, firms make their decisions related to trade credit by taking into account the trade-off between adverse selection (since clients do not know the characteristics and quality of the goods bought) and moral hazard (in that the buyer might not pay for the goods bought when the payment is due). Our findings strongly support our model to explain trade credit policy. Additionally, our results show that our model works better at explaining trade credit policy than the traditional models. In fact, our results do not support the tax, liquidity and transactions costs theories, while the product quality theory is only partially supported. Therefore, a model accounting for both adverse selection and moral hazard phenomena is needed in order to properly explain how firms make their trade credit decisions. First, the adverse selection phenomenon explains why sellers need to offer trade credit to buyers in order to mitigate the ex-ante asymmetric information caused by the unknown characteristics and quality of the goods bought. Therefore, smaller firms, those with a smaller proportion of fixed assets, and those that are less profitable tend to extend more trade credit in order to mitigate the adverse selection problem. Second, the moral hazard phenomenon explains why some firms do not offer trade credit in order to avoid the negative consequences arising when the clients do not pay for the goods bought. Therefore, the higher the proportion of variable costs the less trade credit offered since the consequences of the moral hazard phenomenon would be more negative. Additionally, the higher percentage of bad debts a firm has, the less trade credit offered in order to encourage their clients to pay when payment is due. This table provides the mean, the standard deviation, the median, and the Pearson's correlations for the 1,162 observations. DSO denotes the natural logarithm of days of sales outstanding, VCO is the cost of goods sold divided by total assets, BDP stands for the bad debt provision divided by total assets, PQL denotes the product quality level, SIZE is the natural logarithm of total assets, FIX is the fixed assets proportion of total assets, ROA stands for the return on assets, and DPA denotes the days to pay accounts payable.

  be enough since it does not explain why financially unconstrained firms also use trade credit.

  the variables used in our analysis and Pearson´s correlations are in Table

Table 2

 2

	shows the distribution

Page 17 of 23 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	variable SDS is not significant; hence the operational argument of the transactions costs theory is
	not supported.
	F o
	r
	P
	e e r
	R e v i e w
	18

Table 1

 1

	. Variables and their expected sign
	Phenomenon	Variables	Expected Sign
	Adverse Selection	Product quality level (PQL)	+
	Adverse Selection	Size (SIZE)	-
	Adverse Selection	Return on assets (ROA)	-
	Adverse Selection	Fixed assets (FIX)	+
	Adverse Selection	Days to pay accounts payable (DPA)	+
	Moral Hazard	Variable costs (VCO)	-
	Moral Hazard	Bad debts provision (BDP)	-

This table contains all the explanatory variables entered into our agency model and their expected sign.

Table 2

 2 This table shows the distribution by industry for the 1,162 observations.

	Page 21 of 23	Submitted Manuscript
		. Sample distribution by industry
	SIC	Industry	Number %
	20 Food and Kindred Products	98	8.43
	21 Tobacco Products	9	0.77
	22 Textile Mill Products	38	3.27
		Apparel and Other Finished Products Made from Fabrics and Similar	42	3.61
	23	Materials
	25 Furniture and Fixtures	24	2.07
	26 Paper and Allied Products	38	3.27
	27 Printing, Publishing, and Allied Industries	92	7.92
	28 Chemicals and Allied Products	131	11.27
	29 Petroleum Refining and Related Industries	8	0.69
	30 Rubber and Miscellaneous Plastic Products	44	3.79
	31 Leather and Leather Products	10	0.86
	32 Stone, Clay, Glass, and Concrete Products	72	6.20
	33 Primary Metal Industries	32	2.75
		Manufactured Metal Products, Except Machinery and Transport	33	2.84
	34	Equipment
	35 Industrial and Commercial Machinery and Computer Equipment	110	9.47
		Electronic and Other Electrical Equipment and Components, Except	170	14.63
	36	Computer Equipment
	37 Transport Equipment	68	5.85
		Measuring, Analyzing, and Controlling Instruments; Photographic,	103	8.86
	38	Medical and Optical Goods; Watches and Clocks
	39 Miscellaneous Manufacturing Industries	40	3.44
	Total	-	1,162 100.00

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 3 .

 3 Descriptive statistics and Pearson's correlations Mean S. Deviation Median LNDSO VCO BDP PQL

	SIZE FIX	ROA DPA

Table 4

 4 The dependent variable is the natural logarithm of days of sales outstanding, and the details about the independent variables are in Table3. The rest of the information needed to read this table is: i) Standard error in parentheses; ii) *, ** and *** indicate significance at the 1%, 5% and 10% level, respectively.

			Submitted Manuscript		Page 22 of 23
	. Estimation results
	Explanatory Variables	(I)	(II)	(III)	(IV)	(V)
	BDP it	1.145*	-4.326**
		(3.54)	(-2.15)
	DBDP it *BDP it		5.401*
			(2.76)
	VCO it	-0.455*	-0.460*
		(-10.89)	(-11.04)
	ROA it	-0.083*	-0.084*	-0.067*	-0.047	-0.087*
		(-2.91)	(-2.95)	(-2.32)	(-1.57)	(-2.89)
	TD it			-0.112
				(-1.14)
	SIZE it	-0.149*	-0.149*		-0.037***	-0.073*
		(-5.60)	(-5.63)		(-1.66)	(-2.64)
	DPA it	-9.14e-07	-9.20e-07		-9.48e-07	-8.27e-07
		(-0.87)	(-0.88)		(-0.83)	(-0.73)
	PQL it	-0.025	-0.024			-0.018
		(-1.41)	(-1.33)			(-0.92)
	FIX it	0.275*	0.273*			0.329*
		(4.82)	(4.79)			(5.42)
	Const.	5.237*	5.250*	4.270*	4.442*	4.535*
		(29.44)	(29.62)	(201.45)	(37.94)	(25.60)
	R 2 -within	0.166	0.174	0.007	0.010	0.044
	R 2 -between	0.126	0.126	0.033	0.023	0.040
	R 2 -overall	0.144	0.144	0.038	0.023	0.054
	F	23.41	21.60	3.00	2.71	7.62

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

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Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

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