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Financial Disruptions and BankProductivity Growth: Evidence fromthe Malaysian ExperienceFadzlan Sufian a ba Khazanah Research and Investment Strategy, Khazanah NasionalBerhad , Malaysiab Department of Economics, Faculty of Economics andManagement , Universiti Putra MalaysiaPublished online: 23 Sep 2009.

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International Economic JournalVol. 23, No. 3, 339–369, September 2009

Financial Disruptions and BankProductivity Growth: Evidence from

the Malaysian Experience

FADZLAN SUFIAN

Khazanah Research and Investment Strategy, Khazanah Nasional Berhad, Malaysia; andDepartment of Economics, Faculty of Economics and Management, Universiti Putra Malaysia

(Received 5 November 2007; final version received 30 October 2008)

ABSTRACT This paper examines, for the first time, the productivity of the Malaysianbanking sector around the Asian financial crisis 1997. The non-parametric Malmquist Pro-ductivity Index (MPI) is used to compute individual banks’ productivity levels. We findthat the Malaysian banking sector has exhibited productivity regress due to the decline inefficiency. The results seem to suggest that the domestic banks have exhibited productivityprogress attributed to technological change, while the foreign banks have exhibited pro-ductivity regress due to efficiency decline. We find that the large banks tend to experienceproductivity growth attributed to technological progress, while the small banks tend to expe-rience productivity decline due to technological regress. The empirical results suggest thatthe small banks with its limited capabilities are at a disadvantage compared with their largercounterparts in terms of technological advancements, thus, rejecting the divisibility theory.

KEY WORDS: Financial disruptions, bank productivity, Malmquist productivity index, MalaysiaJEL CLASSIFICATIONS: G21, G28

1. Introduction

Much has been written and revealed since the Asian financial crisis, which startedin July 1997. The Asian financial crisis began on 2 July 1997 with a devaluation of

Correspondence Address: Fadzlan Sufian, Khazanah Research and Investment Strategy, KhazanahNasional Berhad, Level 35, Tower 2, Petronas Twin Towers Kuala Lumpur City Centre,50088 Kuala Lumpur, Malaysia. Tel: 603-2034-0197; Fax: 603-2034-0035. Email: fadzlan.sufian@khazanah.com.my; fsufian@gmail.com1016-8737 Print/1743-517X Online/09/030339–29 © 2009 Korea International Economic AssociationDOI: 10.1080/10168730903119427

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Thai baht, and quickly spread to other Asian countries. The effects of the crisis oncountries differed in its intensity. For example, some Asian economies did not reapthe full whirlwind of dramatic consequences. According to some analysts, HongKong, Singapore and Taiwan did not suffer as much as other countries becauseof trade and current account surpluses, significant holdings of foreign exchangereserves, and the relative absence of ‘crony capitalism’ (Corsetti et al., 2001).

Efficiency and productivity analysis can be used to assess the impact of majoreconomic events, such as an economic crisis or financial liberalization, on theperformance of banking firms (e.g. Isik & Hassan, 2003a, 2003b; Kumbhakaret al., 2001; Leightner & Lovell, 1998). As in virtually all emerging financialmarkets, banks are the dominant financial institution in Malaysia. Thus, theirhealth is critical to the health of the general economy at large, as demonstrated bythe financial distress experienced by the country during the Asian financial crisis.However, despite its severity and deep influence on both the real and financialsectors, the impact of the 1997 crisis on the efficiency and productivity of theMalaysian banking sector has not been studied yet.

It is worth examining the 1997 Malaysian experience for policy and researchreasons. Mishkin (2006) among others pointed out that the initiation of the 1997crisis was mainly a product of the policy errors made by bank managements.1The results shed light on the behaviour and reaction of banking firms before,during, and after the crisis, which could help policy makers to detect what typesof banks (domestic or foreign banks, small or large banks) are more susceptibleto shocks. This in turn could induce policy makers to devise preventive strategiesto strengthen the durability of such banks against future shocks. This study willbe among the first empirical studies to link the productivity and efficiency offinancial institutions with financial disruption.

The paper is structured as follows. The following section identifies those factorsthat characterize the Asian financial crisis in general and the Malaysian crisis inparticular. Section 3 describes the data, sources and model specifications, whichare employed in the study. Section 4 presents the results using both parametricand non-parametric techniques. Finally, we conclude in section 5.

2. Asian Financial Crisis and the Malaysian Experience

The Asian financial crisis, started in Thailand in early 1997, and was rapidlytransmitted to Malaysia by mid-1997. The dramatic collapse of the Thai econ-omy raised serious concerns amongst foreign portfolio investors. In the case ofThailand, the failure of foreign analysts and even international rating agenciesto recognize the underlying real risks cast doubts on the subsequent perceptionof regional economies like Malaysia. As such, when the Thai Baht collapsed,investors’ confidence in the economies of the region took a serious beating.

1Interested readers are referred to the latest book by Frederick Mishkin. In his book, Mishkin(2006) provides a list of issues to ponder in pursuit of preventing a financial crisis from occurring.Among others, he suggests the importance of limiting currency mismatches, restricting connectedbank lending, ensuring that banks are well capitalized and that they do not take excessive risk,and encouraging disclosure. In addition, he also suggests regulatory forbearance, which essentiallyimplies allowing seriously troubled financial institutions to continue operating.

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Financial Disruptions and Bank Productivity Growth 341

The regional financial crisis hit the Malaysian currency, causing the Ringgitto depreciate sharply. It fell from RM2.48 against the US Dollar in March toRM2.57 in July, before depreciating further to RM3.77 by December. In tandemwith the sharp tumble of the Ringgit, the stock market collapsed. The KualaLumpur Stock Exchange (KLSE) composite index fell from 1,216.7 points as atend-January to 594.4 points in December (Ariff & Yap, 2000). Demonstrating typ-ical herding behaviour, investors began a massive sell-off of stocks and dumpingof the Ringgit. This panicky withdrawal by foreign and local investors eventuallyled to the creation of the ‘Twin Crises’, namely the currency and banking crises(Tsurumi, 2001).

These developments quickly took a toll on other sectors of the economy. TheConsumer Price Index (CPI) rose by 5.3%, which was the largest increase since1982. Unemployment increased from 2.6% in 1997 to 3.9% in 1998, while foreigndirect investments declined from US$6.5 billion to US$2.7 billion over the sameperiod (Athukorala, 1999). The Consumer Sentiments Index (CSI) also took abeating, as it declined sharply for four consecutive quarters, before hitting anall-time low in the second quarter of 1998 (Ariff & Yap, 2000). The financialinstitutions were also weakened by large-scale exposures to the property sector,many non-performing loans and short-term loans that were unhedged againstcurrency movements. Furthermore, the credit expansion especially to the privatesector was pronounced during 1995–1997, especially towards the real estate andproperty sectors.

Structural weaknesses in the financial system were accentuated by increasedfinancial liberalization without a commensurate improvement in prudentialregulation and supervision. This was evident in excessive bank lending to prop-erty and stock markets in the years leading up to the financial crisis. The increasedrisk rating aggravated the fragility of the banking sector. The vulnerabilities ofa fragmented banking system were also exposed by the crisis. The promotionof securities markets, elimination of controls on interest and credit allocationbefore an effective and mature banking system was well established diminishedthe franchise value of the banking system (Jomo & Kok, 2001).

Strong loan growth prior to 1997 had led to high loan exposure of the bankingsystem. As a result of the property market crash and substantial capital outflows,non-performing loans (NPLs) in the banking system began to escalate, resultingin the deterioration in quality of the asset portfolio of the banking institutions.The net NPLs to total loans ratio increased from 2% in July 1997 to 7.5% inDecember 1998. Banking institutions became reluctant to lend. Coupled withhigher interest rates, this resulted in a ‘credit crunch’ and squeeze in financing forindividuals and businesses.

High investment in non-tradables, along with private external debt and anunrestricted surge of portfolio investment inflows arising from financial liber-alization morphed into serious problems as the financial crisis unfolded. Theeffects on the economy and the financial system caused prolonged difficulties forsome banking institutions. In an unprecedented move that drew worldwide crit-icism and acclaim, the Malaysian government imposed capital control measuresin September 1998. To further preserve the stability of the banking system, thegovernment established various institutions, including Pengurusan DanahartaNasional Berhad (Danaharta), Danamodal Nasional Berhad (Danamodal) and

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the Corporate Debt Restructuring Committee (CDRC). Regardless of the causeof the Asian financial crisis, all economists have agreed that the crisis wassevere. Although initially only financial in nature, the crisis led to significantreal economic losses in the once fast growing developing economy.

At the onset of the crisis, the Malaysian banking system consisted mainly ofthree types of financial institution: commercial banks (domestic and foreign),finance companies, and merchant banks. The Malaysian financial system’s assetsand liabilities continued to be dominated by the commercial banking sector withtotal assets and liabilities amounting to RM761,254.8 billion or 3.05 times thenational GDP as at end 2004. Foreign commercial banks had control over 90%of the banking market in 1957 when Malaysia became independent. The marketshare of the foreign banks was relatively stable in the 1990s until the crisis. By1997, the foreign banks’ market share in terms of total assets declined to onlyabout 16.7%. The progressive decline of foreign banks was the result of a delib-erate government policy of developing the domestic financial sector, under whichforeign banks have been prohibited from opening new branches since 1971 andthe last licence to a foreign institution was granted in 1973. Domestic and foreigncommercial banks engaged in retail and corporate banking and were the onlyinstitutions authorized to take demand deposits.

Merchant banks have a minor presence in the Malaysian financial system.Their assets and liabilities as a ratio of the national GDP steadily increased from1971 to reach a peak of RM44.3 billion or 0.23 times GDP in 1997 before theAsian financial crisis. During the post crisis period, the merchant banks’ assetsand liabilities remained stable at 0.17 to 0.22 times of the national GDP. Thenumerous, relatively small finance companies, on the other hand, provided mainlyinstalment credit to consumers and small businesses, with funding provided fromtime and savings deposits. Prior to the Asian financial crisis in 1997–1998, thefinance companies’ assets and liabilities increased from only RM531 million or0.05 times of the national GDP in 1970 to reach a high of RM152.4 billion or0.77 times in 1997. The ratio, however, gradually declined to RM123.6 billionor 0.60 times in 1998 to RM109,409.8 billion or 0.52 times GDP in 2000, beforeincreasing again in year 2001, to reach a post crisis high of RM141,911.0 billionor 0.61 times of the national GDP in 2003.

Since the Asian financial crisis in 1997, many Asian countries have undergonemassive reforms in their financial sector. Consolidation of domestic banking insti-tutions in these countries is an essential concomitant of this strategy. To enhancecompetitiveness, the Malaysian government got local banking groups to consol-idate among themselves, rationalize common functions and operations acrossinstitutions, and outsource non-core activities. Due to further consolidation inthe Malaysian financial sector, the finance companies’ assets as a ratio of thenational GDP declined again to reach a low of 0.27 times in 2004.

3. Review of the Related Literatures

The literature examining the efficiency and productivity of financial institutionswith parametric and/or non-parametric frontier techniques has expanded rapidlyin recent times. The liberalization of the banking sector and the increasing number

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Financial Disruptions and Bank Productivity Growth 343

of bank failures in the 1980s and early 1990s contributed to an increasing academicinterest in the topic. Although, a large body of literature spanning a half-centuryexists on banking efficiency in the United States (see surveys in Berger et al., 1993;Berger & Humphrey, 1997; Berger & Mester, 2003; Berger, 2007 and referencestherein), the more recent studies examine several other countries such as India(Ataullah & Le, 2006), Hong Kong (Drake et al., 2006), Greece (Pasiouras, 2008),Singapore (Sufian, 2007), and Turkey (Isik, 2008).2

Berg et al. (1992) were among the first to investigate productivity change in thebanking industry. Using a sample of 346 banks in Norway over 1980–1989, theysuggest that productivity declined at the average bank prior to the period of expe-riencing deregulation but grew rapidly when deregulation took place. Fukuyama(1995) is among the first to examine bank productivity changes in Asia. He exam-ines the nature and extent of technical efficiency and productivity growth ofJapanese banks during the 1989 to 1991 period. He also investigates the relation-ship between efficiency measures, productivity indexes, organizational status, andbank size. During the early part of the studies he find that Japanese banks’ meanvalues of the three productivity change indexes are higher compared with thelatter part, attributed to the collapse of the bubble in the Japanese economy. Theempirical findings suggest that productivity gains are largely attributed to tech-nological rather than technical efficiency change. On the other hand, he suggeststhat technical efficiency decline, rather than technological regress, has resulted inproductivity losses.

Despite substantial studies performed in regard to the efficiency and produc-tivity of financial institutions in the US, Europe, and other Asia Pacific bankingindustries, empirical evidence on the East Asian countries is relatively scarce.Leightner and Lovell (1998) examine the performance of the foreign and domes-tic banks in Thailand. They find that the average Thai bank experienced fallingtotal factor productivity growth (TFP), while the average foreign bank experi-enced increasing TFP. In a study on the Malaysian banking sector, Sufian andIbrahim (2005) applied the Malmquist Productivity Index method to investigatethe extent of off-balance sheet (OBS) items in explaining the total factor pro-ductivity changes of Malaysian banks. They find that the inclusion of OBS itemsresult in higher productivity levels of all banks. They find that the impact is morepronounced on technological change rather than efficiency change.

The South Asian banking sectors have also been studied extensively. Sathye(2003) and Shanmugam and Das (2004) find that the public and foreign ownedbanks in India have exhibited higher technical efficiency levels compared withtheir privately-owned bank peers. Kumbhakar and Sarkar (2003) analyze thetotal factor productivity growth in the Indian banking industry during the period1985–1996. They find that the private sector banks have exhibited improvementsin their performance, while the public sector banks have not responded well tothe deregulation process. Iimi (2004) suggest that privatized banks in Pakistan are

2Apart from focusing on the banking sector, the Malmquist Productivity Index has also beenemployed to examine sources of productivity changes of other economic sectors worldwide, suchas insurance (e.g. Cummins & Xie, 2008), manufacturing (e.g. Lee & Kang, 2007), universities (e.g.Worthington & Lee, 2008), etc.

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the most efficient, followed by foreign and private banks, while the public banksare the least efficient. Hardy and di Patti (2001) investigate the effects of financialreforms on profitability, cost, and revenue efficiency of the Pakistan banking sectorduring 1981–1998. They show that financial liberalization has a positive impacton banks’ performance.

Jaffry et al. (2007) is among the few studies that have analyzed the efficiencyof the South Asian banking sectors in a multi-country setting. By employingthe Malmquist Productivity Index they examine the productivity of the India,Pakistan, and Bangladesh banking sectors during the period 1993–2001. Theyfind that technical efficiency both increases and converges across the Indian sub-continent in response to reform. India and Bangladesh experienced immediate andsustained growth in technical efficiency, whereas Pakistan endured a reductionin efficiency during the middle years of the study, before rebounding to levelscomparable to the rest of the sub-continent in the latter years of the study.

Although empirical research on bank efficiency and productivity is fast grow-ing, the study by Isik and Hasan (2003b) is the only empirical research to directlyexamine the impact of financial disruptions on bank productivity. By employ-ing a non-parametric Malmquist Productivity Index (MPI) approach, Isik andHassan (2003b) examine the impact of the financial crisis on different aspectsof the Turkish banking sector efficiency and productivity. They find a substan-tial productivity loss in 1994, which was mainly attributable to technical regressrather than efficiency decrease. They also examine the effect of the crisis ondifferent groups of banks operating in Turkey. The results suggest that whilethe foreign banks suffered the most from the crisis, the public banks apparentlypassed through the crisis unharmed, which could be explained by their relativelylow open positions in foreign exchange in the advent of the crisis and relativesoundness and safety in the event of the crisis. They find that even though thecrisis affected all sizes of banks dramatically, its adverse impact on small bankswas overwhelming.

To the best of our knowledge, no study has been undertaken to examine theimpact of the Asian financial crisis on the experience of the Malaysian bankingsector. In the light of these knowledge gaps, this paper seeks to examine theproductivity of the Malaysian banking sector in and around the Asian financialcrisis.

4. Methodology and Data

Three different indices are frequently used to evaluate technological changes: theFischer (1922), Tornqvist (1936), and Malmquist (1953) indexes.3 According to

3The Malmquist Productivity Index was not invented by Stan Malmquist. In his paper, Malmquist(1953) brought the input functions of distance into an analysis of consumption, developing a methodfor the empirical measurement of standard of living. The change in living standards is defined as theratio of two input functions of distance. Before the Malmquist paper, the input function of distancewas brought into a paper by Debreu (1951), and the output function of distance was introduced byShephard in his book (Shephard, 1953). The natural development of their papers was the definitionof the index of change of total factor productivity as the ratio of two input or output functions ofdistance. Some 31 years had to pass before it arrived. The Malmquist index of change in total factor

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Financial Disruptions and Bank Productivity Growth 345

Grifell-Tatje and Lovell (1996), the Malmquist index has three main advantagesrelative to the Fischer and Tornqvist indices. Firstly, it does not require the profitmaximization, or the cost minimization assumption. Secondly, it does not requireinformation on the input and output prices. Finally, if the researcher has paneldata, it allows the decomposition of productivity changes into two components(technical efficiency change or catching up, and technical change or changes inthe best practice). Its main disadvantage is the necessity to compute the distancefunctions. However, the Data Envelopment Analysis (DEA) technique can be usedto solve this problem.

Following Fare et al. (1994) among others, the output oriented Malmquistproductivity change index will be adopted for this study. Output orientation refersto the emphasis on the equi-proportionate increase of outputs, within the contextof a given level of input. The output-based Malmquist productivity change indexmay be formulated as:

Mt+1j (yt+1, xt+1, yt, xt) =

[Dt

j(yt+1, xt+1)

Dtj(y

t, xt)× Dt+1

j (yt+1, xt+1)

Dt+1j (yt, xt)

]1/2

(1)

where M is the productivity of the most recent production point (xt + 1, yt + 1)relative to the earlier production point (xt, yt). D’s are output distance functions.A value greater than unity indicates positive productivity growth between twoperiods. Following Fare et al. (1994) an equivalent way of writing this index is:

Mt+1j (yt+1, xt+1, yt, xt) = Dt+1

j (yt+1, xt+1)

Dtj(y

t, xt)

×[

Dtj(y

t+1, xt+1)

Dt+1j (yt+1, xt+1)

× Dtj(y

t, xt)

Dt+1j (yt, xt)

]1/2

(2)

orM = TE × TC

where

Technical Efficiency (TE) = Dt+1j (yt+1, xt+1)

Dtj(y

t, xt)(3)

Technical Change (TC) =[

Dtj(y

t+1, xt+1)

Dt+1j (yt+1, xt+1)

× Dtj(y

t, xt)

Dt+1j (yt, xt)

]1/2

(4)

where M is the product of a measure of technical progress TC as measured by shiftsin the frontier measured at period t + 1 and period t and a change in efficiencyTE over the same period.

productivity was proposed in a paper for the first time in Caves et al. (1982). Today these indices areentitled ‘partially oriented indices of change in total factor productivity.’ In the case of productiontechnology that satisfies the constant yields axiom, the indices are the same.

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In order to calculate these indices it is necessary to solve several sets of linearprogramming (LP) problems. We assume that there are N banks and that each varythe amounts of K different inputs to produce M outputs. The jth bank is thereforerepresented by the vectors xjyj and the K × N input matrix X and the M × Noutput matrix Y represent the data of all banks in the sample. The purpose is toconstruct a non-parametric envelopment frontier over the data points such that allobserved points lie on or below the production frontier. The calculations exploitthe fact that the input distance functions, D, used to construct the Malmquistindex are the reciprocals of Farrell’s (1957) output orientation technical efficiencymeasures.

Equations (5) and (6) are where the technology and the observation to beevaluated are from the same period and the solution value is less than or equal tounity. Equations (7) and (8) occur where the reference technology is constructedfrom data in one period, whereas the observation to be evaluated is from anotherperiod. Assuming a constant return to scale, the following output oriented LPsare used:

Dtj [yt, xt]−1 = max

θ ,λθ (5)

s.t.

− yjt + Ytλ ≥ 0

θxjt − Xtλ ≥ 0

λ ≥ 0

Dt+1j [yt+1, xt+1]−1 = max

θ ,λθ (6)

s.t.

− yjt+1 + Yt+1λ ≥ 0

θxjt+1 − Xt+1λ ≥ 0

λ ≥ 0

Dt+1j [yt, xt]−1 = max

θ ,λθ (7)

s.t.

− yjt + Yt+1λ ≥ 0

θxjt − Xt+1λ ≥ 0

λ ≥ 0

Dtj [yt+1, xt+1]−1 = max

θ ,λθ (8)

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Financial Disruptions and Bank Productivity Growth 347

s.t.

− yjt+1 + Ytλ ≥ 0

θxjt+1 − Xtλ ≥ 0

λ ≥ 0

This approach can be extended further by decomposing the constant returnsto scale technical efficiency change into scale and pure technical efficiency com-ponents. This involves calculating further LPs where the convexity constraintNjλ = 1 is introduced to equations (5) to (8). It is apparent that equations (6) and(7) give the Farrell efficiency scores and the programming problems are the dualform of the Charnes et al. (1978) DEA model. Solutions to these programmingmodels give us the efficiency scores of the jth bank in periods t and t + 1. By solv-ing the equations with the same data under constant returns to scale and variablereturns to scale, measures of overall technical efficiency, TE, and pure technicalefficiency, PTE, are obtained. Hence, dividing the overall technical efficiency, TE,by pure technical efficiency yields a measure of scale efficiency, SE.

By combining these models and the Fare et al. (1994) approach, it is thus possibleto provide four efficiency indices for each bank and a measure of technical progressover time. These are (i) Technical Efficiency Change (EFFCH), (ii) TechnologicalChange (TECHCH), (iii) Pure Technical Efficiency Change (PEFFCH), (iv) ScaleEfficiency Change (SECH) and (v) Total Factor Productivity Change (TFPCH).M indicates the degree of productivity change; M > 1 means that period (t + 1)productivity is greater than period t productivity, whilst M < 1 means produc-tivity decline and M = 1 corresponds to stagnation. An assessment can also bemade of the sources of productivity gains or losses by comparing the valuesof EFFCH and TECHCH. If EFFCH > TECHCH, then productivity gains arelargely the result of improvements in efficiency. Whereas if EFFCH < TECHCH,productivity gains are primarily the result of technological progress.

4.1 Data and Variables

This paper uses an unbalanced panel data of banks operating in Malaysia duringthe period 1995–1999. Our source of data is the balance sheets of the respectivebanks for the years included. The total number of banks operating in Malaysiavaried from 38 in 1995, 36 in 1996, and 33 in 1997, 1998 and 1999. The numberof observations varied across time due to bank entry and exit during the years.This gives us a total of 173 bank year observations, which represents 100% of thebanks operating in Malaysia during the period.

The DEA-based MPI requires bank inputs and outputs whose choice is alwaysan arbitrary issue (Berger & Humphrey, 1997). In the banking theory literature,there are two main approaches competing with each other in this regard: theproduction and intermediation approaches (Sealey & Lindley, 1977). Underthe production approach, pioneered by Benston (1965), a financial institutionis defined as a producer of services for account holders, that is, they per-form transactions on deposit accounts and process documents such as loans.

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Table 1. Summary statistics of the variables employed in the MPI model (in million of Ringgit)

Domestic Foreign

Mean S.D. Mean S.D.

Outputs1995Total Loans (y1) 6,698,619.36 10,764,078.09 3,074,048.64 3,045,451.19Non-Interest Income (y2) 2,164,237.82 4,145,746.47 748,109.07 927,110.461996Total Loans (y1) 8,777,875.91 12,289,630.21 3,678,635.64 3,771,251.40Non-Interest Income (y2) 2,633,861.95 4,128,688.09 1,152,298.93 1,250,438.521997Total Loans (y1) 10,653,761.35 14,483,251.96 4,808,010.00 4,945,479.99Non-Interest Income (y2) 3,192,063.90 5,216,868.65 1,526,211.15 1,672,466.02

1998Total Loans (y1) 12,837,021.75 17,465,110.83 4,866,902.62 5,117,604.04Non-Interest Income (y2) 3,839,504.45 5,678,962.98 1,454,305.00 1,699,703.811999Total Loans (y1) 12,950,538.40 17,567,260.76 4,763,643.77 4,793,420.95Non-Interest Income (y2) 3,361,879.60 5,762,771.47 2,290,445.62 4,436,039.60Inputs1995Total Deposits (x1) 9,471,984.95 14,879,614.66 3,587,924.64 3,771,896.61Fixed Assets (x2) 131,178.77 223,717.65 48,696.29 71,095.881996Total Deposits (x1) 11,572,336.64 16,480,767.31 4,267,051.00 4,442,634.90Fixed Assets (x2) 158,827.14 243,529.91 57,354.00 74,200.741997Total Deposits (x1) 14,488,070.90 20,297,632.11 5,989,143.46 6,283,218.28Fixed Assets (x2) 173,267.60 247,950.17 68,670.46 91,059.411998Total Deposits (x1) 16,321,158.45 21,224,855.54 5,737,325.92 6,214,965.73Fixed Assets (x2) 210,005.50 267,746.10 90,889.31 102,827.521999Total Deposits (x1) 17,366,798.65 22,747,379.77 6,021,941.46 6,453,176.25Fixed Assets (x2) 235,803.00 295,186.97 77,518.00 100,859.53

The table presents mean and standard deviation of Malaysian banks input and output variables used to constructthe MPI productivity frontiers during the period 1995, 1996, 1997, 1998, and 1999 respectively. Domestic andForeign denotes domestic and foreign banks respectively.Source: Individual Banks Annual Reports.

The intermediation approach on the other hand assumes that financial firms actas an intermediary between savers and borrowers and posits total loans and secu-rities as outputs, whereas deposits along with labour and physical capital aredefined as inputs. For the purpose of this study, a variation of the intermediationapproach or asset approach originally developed by Sealey and Lindley (1977)will be adopted in the definition of inputs and outputs used.4

4Humphrey (1985) presents extended discussions of the alternative approaches of what a bankproduces.

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The aim in the choice of variables for this study is to provide a parsimo-nious model and to avoid the use of unnecessary variables that may reduce thedegree of freedom.5 All variables are measured in million of Malaysian Ring-git (RM). Malaysian banks are regarded as intermediary between savers andborrowers, producing two outputs namely, Total Loans (y1), which include loansto customers and other banks, and Investments (y2), which include investmentsecurities held for trading, investment securities available for sale (AFS), andinvestment securities held to maturity. In performing its functions, we assumebanks employ three inputs, namely, Total Deposits (x1), which include depositsfrom customers and other banks, Capital (x2), measured as the book value ofproperty, plant, and equipment, and Labour (x3), which is inclusive of totalexpenditures on employees such as salaries, employee benefits and reserve forretirement pay.6

Table 1 presents the summary statistics of the output and input variables usedto construct the productivity frontiers. It is apparent that during the earlier periodof study, the domestic banks were almost three times larger (in terms of asset size),command higher market share, have greater intensity towards loans financing, andemployed more personnel relative to their foreign bank peers. However, during thelatter part of the study, i.e. after the Asian financial crisis, the foreign banks seemto have shifted their focus towards investments activities rather than the moretraditional loans-based financing relative to their domestic bank peers. FromTable 1 it is clear that the difference in the investments amount between thedomestic and foreign banks has significantly reduced to only 1.47 times in 1999compared with 2.89 times during the pre-crisis period.

5. Results and Discussions

In this section, we will discuss the productivity change of Malaysian banks,measured by the Malmquist Productivity Index (MPI) and assign the changein Total Factor Productivity (TFPCH) to Technological Change (TECHCH) andEfficiency Change (EFFCH). We will also attempt to attribute any change inEFFCH to change in Pure Technical Efficiency (PEFFCH) and Scale Efficiency(SECH). The summary of annual means of TFPCH, TECHCH, EFFCH, and itsdecomposition into PEFFCH and SECH for the years 1995–1999 are presented inTable 2. The MPI analysis is based on a comparison of adjacent years, i.e. indicesare estimated for 1995–1996, 1996–1997, 1997–1998, and 1998–1999. Becausethe year 1995 is the reference year, the MPI and its components takes an initialscore of 1.000. Hence, any score greater (lower) than 1.000 in subsequent yearsindicates an improvement (worsening) in the relevant measures. It is also worthmentioning that favourable efficiency change (EFFCH) is interpreted as evidenceof ‘catching up’ to the frontier, while favourable technological change (TECHCH)

5For a detailed discussion on the optimal number of inputs and outputs in DEA, see Avkiran (2002).6As data on the number of employees are not readily made available, personnel expenses are usedas a proxy measure.

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is interpreted as innovation (Cummins et al., 1999). Annual values of the indicesfor the industry and each banking group are provided in Table 2.7

5.1 Total Factor Productivity Change of the Malaysian Banking SectorAround the Asian Financial Crisis: An Analysis Based on the Levels

From Panel 1 of Table 2 it is observed that the mean TFPCH of the Malaysianbanking sector between 1995 and 1999 was 0.998. As for the TFPCH in eachyear, the results seem to suggest that Malaysian banks have exhibited TFPCHregress of 0.4% in 1996, before increasing by 2.1% and 3.1% in years 1997 and1998 respectively. It is clear that the mean TFPCH declined by 5.8% in 1999relative to 1998, a year after the Asian financial turmoil hit the Malaysian bankingindustry. The decomposition of TFPCH index into its EFFCH and TECHCHindices suggest that the regress in the TFPCH of Malaysian banks was mainlydue to the decline in EFFCH. It is also apparent from Table 2 that Malaysianbanks’ EFFCH of 1.278 during the pre-crisis was higher than 0.493 during thepost-crisis period. On the other hand, the TECHCH score of 1.909 during thepost-crisis period was higher than 0.779 recorded during the pre-crisis period. Itis apparent that the decline in the EFFCH of Malaysian banks was mainly due thedecline in SECH rather than PEFFCH. Although both the SECH and PEFFCHwere lower during the post-crisis compared to the pre-crisis period, it is clear thatthe decline in SECH was more abrupt.

Panel 2 of Table 2 presents the results for the domestic banks. It is observedthat the mean TFPCH of the domestic banks between 1995 and 1999 was 1.004.As for the TFPCH in each year, the domestic banks have exhibited productivityregress of 1.3% in 1997 and 6.8% in 1999, while the findings seem to suggestthat the domestic banks have exhibited 5.0% and 5.4% productivity progressduring the years 1996 and 1998 respectively. It is clear that the mean TFPCH ofthe domestic banks of 1.050 was higher during the pre-crisis period comparedto during the post-crisis period of 0.932. Similar results can be found when theTFPCH components of EFFCH and TECHCH are analysed. The findings seem tosuggest that the domestic banks have exhibited EFFCH decline of 56.4% duringthe post-crisis period compared to an increase of 41.9% during the pre-crisisperiod. On the other hand, it is clear that the domestic banks have exhibitedTECHCH progress during the post-crisis period compared to the TECHCHregress during the pre-crisis period. The decomposition of the EFFCH index into

7Following the procedures outlined in Aly et al. (1990), Elyasiani and Mehdian (1992), and Isikand Hassan (2002) among others, the null hypothesis of identical frontiers between the foreignand domestic banks’ productivity is examined by using a series of parametric (ANOVA and t-test)and non-parametric (Kolmogorov–Smirnov, Mann–Whitney [Wilcoxon Rank-Sum]), and Kruskal–Wallis tests. In general, both the parametric and non-parametric test statistics failed to reject thenull hypothesis at the 5% levels of significance that the domestic and foreign banks are drawn fromthe same population and have identical technologies, implying that there is no significant differencebetween the domestic and foreign banks’ technologies (frontiers). The results imply that we couldassume the variances among the domestic and foreign banks to be equal and it is appropriate toconstruct common frontiers by pooling data on both the domestic and foreign banks. For brevitypurposes, the results are not reported in this paper, but are available upon request from the authors.

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Table 2. Decomposition of Total Factor Productivity Change (TFPCH) in Malaysian banks

Indices

Pure Technical ScaleEfficiency Technological Efficiency Efficiency ProductivityChange Change Change Change Change

NBFI (EFFCH) (TECHCH) (PEFFCH) (SECH) (TFPCH)

Panel 1: ALL−BNKS1995 1.000 1.000 1.000 1.000 1.0001995–1996 1.278 0.779 1.096 1.167 0.9961996–1997 0.955 1.069 1.029 0.928 1.0211997–1998 1.146 0.900 0.942 1.216 1.0311998–1999 0.493 1.909 0.917 0.538 0.942Geometric Mean 0.928 1.074 0.995 0.933 0.998Min 0.493 0.779 0.917 0.538 0.942Max 1.278 1.909 1.096 1.216 1.031Std. Dev. 0.298 0.448 0.071 0.269 0.035

Panel 2: DOM−BNKS1995 1.000 1.000 1.000 1.000 1.0001995–1996 1.419 0.740 1.159 1.224 1.0501996–1997 0.982 1.004 1.029 0.955 0.9871997–1998 1.135 0.929 0.956 1.186 1.0541998–1999 0.436 2.138 0.942 0.463 0.932Geometric Mean 0.928 1.081 1.014 0.915 1.004Min 0.436 0.740 0.942 0.463 0.932Max 1.419 2.138 1.159 1.224 1.054Std. Dev. 0.358 0.556 0.087 0.304 0.050

Panel 3: FOR−BNKS1995 1.000 1.000 1.000 1.000 1.0001995–1996 1.084 0.845 1.003 1.081 0.9171996–1997 0.915 1.175 1.029 0.889 1.0751997–1998 1.162 0.859 0.922 1.259 0.9981998–1999 0.585 1.633 0.883 0.662 0.955Geometric Mean 0.924 1.069 0.966 0.957 0.988Min 0.585 0.845 0.883 0.662 0.917Max 1.162 1.633 1.029 1.259 1.075Std. Dev. 0.224 0.325 0.062 0.222 0.059

Note: The mean scores of the Total Factor Productivity Change (TFPCH) index and its components, Techno-logical Change (TECHCH) and Efficiency Change (EFFCH) that is further decomposed into Pure TechnicalEfficiency Change (PEFFCH) and Scale Efficiency Change (SECH), for All Banks (ALL−BNKS) and differentforms in the sample, Domestic Banks (DOM−BNKS) and Foreign Banks (FOR−BNKS).

its mutually exhaustive components of SECH and PEFFCH suggest that the SECHscore of the domestic banks was higher before the financial crisis than after thefinancial crisis period. Likewise, the PEFFCH of 1.159 during the pre-crisis periodwas lower than the 0.942 during the post-crisis period, albeit at a smaller degree.

The results for the foreign banks are presented in Panel 3 of Table 2. Theempirical findings seem to suggest that the foreign banks have exhibited meanTFPCH of 0.988 during the period under study. Similar to their domestic bankpeers, the results seem to suggest that the foreign banks have exhibited productiv-ity regress during the post-crisis period. However, it is apparent that the foreignbanks’ TFPCH regress during the post-crisis period was lower than that during

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the pre-crisis period. The decomposition of the TFPCH index into its mutuallyexhaustive components of EFFCH and TECHCH suggest that the regress inthe foreign banks’ TFPCH during the post-crisis period was solely due to the41.5% decline in EFFCH. Similar to their domestic bank counterparts, the resultsseem to suggest that the foreign banks have exhibited TECHCH progress of63.3% during the post-crisis period compared with a decline of 15.5% during thepre-crisis period. It is also clear from Panel 3 of Table 2 that the foreign bankshave exhibited an increase in PEFFCH of 0.03% during the pre-crisis, comparedwith a decline of 11.7% during the post-crisis period, while the foreign bankshave also exhibited a decline in SECH of 33.8% during the post-crisis comparedwith an increase of 8.1% during the pre-crisis period.

It is interesting to note that the foreign banks were also hit by the Asian financialcrisis. This comes as a surprise as earlier findings by, among others, Berger et al.(2005) suggest that foreign-owned banks from developed nations in developingcountries may have access to superior technologies, information technologiesfor collecting and assessing ‘hard’ quantitative information. Thus, the findingsfrom this study clearly suggest that the foreign banks may not be insulated fromunexpected events like the Asian financial crisis.

To examine the difference in the productivity of the Malaysian banking sectorbetween the two periods i.e. before and after the crisis, we perform a series ofparametric (t-test) and non-parametric (Mann-Whitney [Wilcoxon] and Kruskal-Wallis tests. The results are presented in Table 3. The results from the parametrict-test support the findings that the Malaysian banking sector has exhibited alower mean TFPCH during the post-crisis period (0.97658 < 1.05794) but is notstatistically significant at any conventional levels in all of the parametric andnon-parametric tests. The decomposition of TFPCH changes into its EFFCH andTECHCH components suggest that the decline in the Malaysian banking sector’sTFPCH post-crisis was mainly attributed to a lower EFFCH (0.52239 < 1.44847)and is statistically significant at the 1% level (p-value = 0.000). It is observed fromTable 3 that the decline in the EFFCH of the Malaysian banking sector duringthe post-crisis period was mainly due to the deterioration in SECH (0.56500 <1.15231) and is significant at the 1% level (p-value = 0.000). It is observed fromTable 3 that the results from the parametric t-test are further confirmed by thenon-parametric Mann–Whitney [Wilcoxon] and Kruskal–Wallis tests. Thus, weconclude that the Malaysian banking sector has exhibited a lower TFPCH duringthe post-crisis period mainly due to the decline in EFFCH. Likewise, the resultsfrom the parametric and non-parametric tests also confirm our earlier findingsthat the deterioration in the EFFCH of Malaysian banks was largely due to SECHrather than PEFFCH (statistically significant at the 1% level).

There are several general points regarding the results that are worth mention-ing. Firstly, the Malaysian banking sector was experiencing a lending boom priorto the Asian financial crisis (Frankel & Rose, 1996). Between 1992 and 1994, loanswere growing at an average rate of 12.2%, but, by 1995, the rate had risen dra-matically to 28.6%. In 1996, it eased slightly to 26.7%, but in early 1997 it hadincreased again to reach a worrisome level of close to 30.0%. As a percentage ofnominal GDP, the loans/GDP ratio increased strongly from 103% between 1992and 1994 to an excessively high level of 149% in 1997. The strong loans growth

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Table 3. Summary of parametric and non-parametric tests for the null hypothesis that pre-crisis(pre) and post-crisis (post) periods possess identical technologies (frontiers)

Test Groups

Parametric Test Non-Parametric Test

Mann-Whitney Kruskall-Wallis[Wilcoxon Rank-Sum] test Equality of

Individual Tests t-test MedianPre = MedianPost Populations test

Hypothesest (Prb > t) z (Prb > z) χ2 (Prb > χ2)

Test Statistics Mean t Mean Rank z Mean Rank χ2

EFFCHPre-CrisisPost-Crisis

1.448470.52239

5.820∗∗∗ 47.6418.16

−6.176∗∗∗ 47.6418.16

38.138∗∗∗

TECHCHPre-CrisisPost-Crisis

0.809531.95429

−14.756∗∗∗ 19.0051.42

−6.791∗∗∗ 19.0051.42

46.113∗∗∗

PEFFCHPre-CrisisPost-Crisis

1.152310.93839

2.400∗∗ 40.9925.89

−3.180∗∗∗ 40.9925.89

10.115∗∗∗

SECHPre-CrisisPost-Crisis

1.229190.56500

8.746∗∗∗ 47.6418.16

−6.176∗∗∗ 47.6418.16

38.139∗∗∗

TFPCHPre-CrisisPost-Crisis

1.057940.97658

0.903 37.1330.37

−1.415 37.1330.37

2.001

The table presents the results from the parametric (ANOVA and t-test) and non-parametric (Kolmogorov–Smirnov, Mann–Whitney and Kruskall–Wallis) tests. The tests are performed to test the null hypothesis thatthe domestic and foreign banks are drawn from the same population (environment). Test methodology followsamong others, Aly et al. (1990), Elyasiani and Mehdian (1992), and Isik and Hassan (2002).The numbers in parentheses are the p-values associated with the relative test.∗∗∗, ∗∗, ∗ indicates significant at the 1%, 5%, and 10% levels respectively.

could have explained the progress in the Malaysian banking sector’s TFPCHduring the pre-crisis period.

Secondly, the rapid growth of loans during the pre-crisis period was a preambleto the burgeoning NPLs level and losses on equity holdings, resulting in theerosion of the banking system’s capital base. This had severely constrained thebanks’ ability to lend to even solvent companies, due to the need to complywith international capital adequacy rules. This has resulted in loans growth todecelerate sharply and subsequently the decline in banks’ TFPCH during thepost-crisis period. Thus, if NPLs are taken into account, banks could exhibitproductivity regress during the pre-crisis period rather than productivity growth.8

Finally, it should be noted that banking sectors across the East Asian regionwere growing rapidly and increased sharply in size during the period immediately

8We would like to thank an anonymous referee for highlighting the limitations.

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before the Asian financial crisis. However, due to the sharp rise in NPLs andlosses on equity holdings, most banking institutions were more concentratedin preserving the quality of their balance sheets and coping with the erosionof capital, instead of generating new loans during the post-crisis period. Thiscould have explained the rapid decline in the banking sector’s SECH during thepost-crisis period.

While macroeconomic rebounds from the crisis tend to be sharp, as reflected inthe V-shaped patterns of GDP growth, financial system adjustments to financialcrisis tend to be spread over many years. Furthermore, Isik & Hassan (2003b)demonstrate that during the contraction of an economy due to a crisis, bankoutputs suffer more than bank inputs as banks follows defensive risk strategies,leading to lower bank productivity and efficiency.

5.2 Total Factor Productivity Growth of Malaysian Banks: An Analysis Basedon the Numbers

The analysis based on productivity levels of banks can be biased by a few observa-tions (Isik & Hassan, 2003a). Thus, it would be beneficial to perform an analysisbased on the number of banks, which is less sensitive to possible outliers. As arobustness check, Table 4 elaborates the productivity of Malaysian banks by sum-marizing the development in the number of banks, which experienced TFPCHprogress or regress. As the results in Panel 1 of Table 4 indicate, out of the total36 banks operating in Malaysia during the 1995–1999 period, 19 (52.78%) haveexperienced TFPCH progress in year 1996, declining to 15 (45.45%) in 1997,increasing to 20 banks (62.50%) in year 1998, before declining sharply to only 10banks (32.26%) during the year 1999, a year after the Asian financial crisis wasdeclared over.

It is also apparent from Table 4 that the decomposition of the TFPCH indexinto its TECHCH and EFFCH reveals that the number of banks that experiencedTECHCH progress increased from 8 (22.22%) in 1996 to 23 (69.70%) in 1997,declining substantially to only 2 (6.25%) banks in 1998 at the peak of the crisis,before improving again during the year 1999 with 30 (96.77%) banks recordingTECHCH progress. It is interesting to note that the number of banks that exhibitan EFFCH increase declined from 26 (72.22%) banks during the year 1996 to12 (36.36%) in 1997. Unlike the TECHCH index, the number of banks thatexhibit an EFFCH increase increased to 25 (78.13%) during 1998 before decliningsubstantially to only 1 (3.23%) in 1999, a year after the crisis was declared over.

The decomposition of EFFCH index into its PEFFCH and SECH componentssuggests that both the PEFFCH and SECH index followed a similar trend duringthe period of study. It is observed that the number of Malaysian banks that exhibita PEFFCH increase (decrease) fell (rose) from 22 (7) banks in 1996 to 15 (8) banksin 1999, while the number of Malaysian banks that exhibited a SECH increase(decrease) declined (increased) from 23 (11) banks in 1996 to 0 (28) banks in 1999.

As the results in Panel 2 of Table 4 indicate, 14, 5, 12, and 10 domestic banks haveexhibited productivity growth representing 63.64%, 27.78%, 6.16%, and 50.00%of the total population of domestic banks during the years 1995–1996, 1996–1997,1997–1998, and 1998–1999 respectively. The number of domestic banks that have

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rowth

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Table 4. Developments in the number (percentage) change of Malaysian banks with productivity progress (regress) and efficiency increase (decrease)

Productivity Change Technological Change Efficiency Change Pure Efficiency Change Scale Efficiency Change(TFPCH) (TECHCH) (EFFCH) (PEFFCH) (SECH)

Progress Regress No � Progress Regress No � Increase Decrease No � Increase Decrease No � Increase Decrease No �Period # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%)

Panel 1:ALL−BNKS

1995–1996 19 16 1 8 28 0 26 8 2 22 7 7 23 11 2(52.78) (44.44) (2.78) (22.22) (77.78) (0.00) (72.22) (22.22) (5.56) (61.11) (19.44) (19.44) (63.89) (30.56) (5.56)

1996–1997 15 18 0 23 10 0 12 19 2 14 9 10 9 21 3(45.45) (54.55) (0.00) (69.70) (30.30) (0.00) (36.36) (57.58) (6.06) (42.42) (27.27) (30.30) (27.27) (63.64) (6.09)

1997–1998 20 11 1 2 30 0 25 5 2 6 18 8 29 1 2(62.50) (34.38) (3.13) (6.25) (93.75) (0.00) (78.13) (15.63) (6.25) (18.75) (56.25) (25.00) (90.63) (3.13) (6.25)

1998–1999 10 21 0 30 1 0 1 27 3 15 8 8 0 28 3(32.26) (67.74) (0.00) (96.77) (3.23) (0.00) (3.23) (87.10) (9.68) (48.39) (25.81) (25.81) (0.00) (90.32) (9.68)

Panel 2:DOM−BNKS

1995–1996 14 8 0 5 17 0 18 4 0 16 4 2 16 6 0(63.64) (36.36) (0.00) (22.73) (77.27) (0.00) (81.82) (18.18) (0.00) (72.73) (18.18) (9.09) (72.73) (27.27) (0.00)

1996–1997 5 13 0 18 0 0 0 18 0 16 9 3 0 18 0(27.78) (72.22) (0.00) (100.00) (0.00) (0.00) (0.00) (100.00) (0.00) (88.89) (50.00) (16.67) (0.00) (100.00) (0.00)

1997–1998 12 6 1 1 18 0 17 2 0 3 13 3 18 1 0(63.16) (31.58) (5.26) (5.26) (94.74) (0.00) (89.47) (10.53) (0.00) (15.79) (68.42) (15.79) (94.74) (5.26) (0.00)

1998–1999 10 10 0 12 8 0 11 9 0 10 7 3 8 12 0(50.00) (50.00) (0.00) (60.00) (40.00) (0.00) (55.00) (45.00) (0.00) (50.00) (35.00) (15.00) (40.00) (60.00) (0.00)

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Table 4. Continued

Productivity Change Technological Change Efficiency Change Pure Efficiency Change Scale Efficiency Change(TFPCH) (TECHCH) (EFFCH) (PEFFCH) (SECH)

Progress Regress No � Progress Regress No � Increase Decrease No � Increase Decrease No � Increase Decrease No �Period # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%)

Panel 3:FOR−BNKS

1995–1996 5 8 1 3 11 0 8 4 2 6 3 5 7 5 2(57.14) (57.14) (7.14) (21.43) (78.57) (0.00) (57.14) (28.57) (14.29) (42.86) (21.43) (35.71) (50.00) (35.71) (14.29)

1996–1997 5 8 0 12 1 0 1 9 3 2 6 5 0 10 3(38.46) (61.54) (0.00) (92.31) (7.69) (0.00) (7.69) (69.23) (23.08) (15.38) (46.15) (38.46) (0.00) (76.92) (23.08)

1997–1998 8 5 0 1 12 0 8 3 2 3 5 5 11 0 2(61.54) (38.46) (0.00) (7.69) (92.31) (0.00) (61.54) (23.08) (15.38) (23.08) (38.46) (38.46) (84.62) (0.00) (15.38)

1998–1999 5 8 0 11 2 0 1 10 2 4 2 7 1 9 3(38.46) (61.54) (0.00) (84.62) (15.38) (0.00) (7.69) (76.92) (15.38) (30.77) (15.38) (53.85) (7.69) (69.23) (23.08)

Note: Malaysian banks are categorized according to the following. Productivity Growth: TFPCH > 1; Productivity Loss TFPCH < 1; Productivity Stagnation: TFPCH = 1;Technological Progress: TECHCH > 1; Technological Regress TECHCH < 1; Technological Stagnation: TECHCH = 1; Efficiency, Pure Technical and Scale increase: EFFCH,PEFFCH and SECH > 1; Efficiency, Pure Technical and Scale decrease: EFFCH, PEFFCH and SECH < 1; No Change in Efficiency, Pure Technical and Scale: EFFCH, PEFFCHand SECH = 1.

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seen progress in TECHCH declined from 5 (22.73%) banks in 1996 to 12 (60.00%)banks in 1999. It is also apparent from Panel 2 of Table 4 that the number ofdomestic banks that experienced an EFFCH increase decline from 18 (81.82%)in 1996 to 11 (55.00%) in 1999. The decomposition of the EFFCH index intoits PEFFCH and SECH components suggest that, the number of domestic banksthat exhibit a PEFFCH increase declined from 16 (72.73%) banks in 1996 to 10(50.00%) banks in 1999. Likewise, the number of domestic banks that exhibit aSECH increase also declined from 16 (72.73%) banks in 1996 to 8 (40.00%) banksin 1999.

The results in Panel 3 of Table 4 suggest that of the 13 foreign banks operatingin Malaysia during the period 1995 and 1999, 5 (8) foreign banks have expe-rienced productivity progress (regress) during the years 1996 and 1997, beforeincreasing (declining) to 8 (5) banks in 1998. The number of foreign banks thatexhibit TFPCH progress (regress) declined (increased) again during 1999 to 5 (8)banks, a year after the financial crisis ended. On the other hand, while only 3(21.43%) foreign banks experienced progress in TECHCH during 1996, with themajority 11 (78.57%) exhibiting TECHCH regress, the number of foreign banksthat exhibited TECHCH progress increased to 11 (84.62%) during 1999. It is alsoapparent from Panel 3 of Table 4 that the number of foreign banks that experi-enced an EFFCH increase (decrease) declined (increased) from 8 (4) in year 1996to 1 (10) during the year 1999. The decomposition of the EFFCH index into itsPEFFCH and SECH components suggest that the number of foreign banks thatexhibited a PEFFCH increase declined from 6 (42.86%) in 1996 to 4 (30.77%)in 1999. Similarly, the number of foreign banks that exhibited a SECH increase(decrease) declined (increased) from 7 (50.00%) in 1996 to only 1 (7.69%) bankin 1999.

5.3 Major Sources of Total Factor Productivity Growth of Malaysian Banks

Table 5 is constructed to examine the major sources of TFPCH progress (regress)and EFFCH increase (decrease) in the Malaysian banking sector during the 1995to 1999 period. The results given in Table 5 are simply a decomposition of Table 4.For instance, of those 19 banks that experienced TFPCH progress during the year1996 as shown in Panel 1 of Table 5, the majority, 17 (47.22%), were attributedto an EFFCH increase, while for 2 (5.56%) banks TFPCH progress was mainlyattributable to TECHCH progress. On the other hand, of the 16 banks thatexperienced productivity regress in 1996, the majority, 9 (25.00%), were due toregress in TECHCH, while for 7 (19.44%) banks TFPCH regress was due toEFFCH decline.

The results from Panel 1 of Table 5 indicates that of the 26 banks that expe-rienced an EFFCH increase during the year 1996, 4 (11.11%) banks experiencedthe increase in EFFCH attributed to the increase in PEFFCH, while the major-ity 22 (61.11%) banks experienced an EFFCH increase attributed to the increasein SECH. On the other hand, of the 8 banks that experienced efficiency lossduring the year 1996, 1 (2.78%) bank experienced the reduction in its EFFCHdue to a decline in its PEFFCH, whereas the majority, 7 (19.44%) banks faced thereduction in EFFCH mainly due to a decline in their SECH.

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Table 5. Major source of productivity progress (regress) and efficiency increase (decrease) in Malaysian banks

Productivity progress mainly Productivity regress mainly Efficiency increase Efficiency decreasedue to due to due to due to

EFFCH TECHCH EFFCH TECHCH No Productivity PEFFCH SECH PEFFCH SECH No EfficiencyIncrease Progress Decrease Regress � Increase Increase Decrease Decrease �

Period # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%) # (%)

Panel 1:ALL−BNKS1995–1996 17 2 7 9 1 4 22 1 7 2

(47.22) (5.56) (19.44) (25.00) (2.78) (11.11) (61.11) (2.78) (19.44) (5.56)1996–1997 10 5 15 3 0 9 3 4 15 2

(30.30) (15.15) (45.45) (9.09) (0.00) (27.27) (9.09) (12.12) (45.45) (6.06)1997–1998 18 2 9 2 1 1 24 4 1 2

(56.25) (6.25) (28.13) (6.25) (3.13) (3.13) (75.00) (12.50) (3.13) (6.25)1998–1999 0 10 20 1 0 1 0 2 25 3

(0.00) (32.26) (64.52) (3.23) (0.00) (3.23) (0.00) (6.45) (80.65) (9.68)Panel 2:

DOM−BNKS1995–1996 12 2 3 5 0 3 15 0 4 0

(54.55) (9.09) (13.64) (22.73) (0.00) (13.64) (68.18) (0.00) (18.18) (0.00)1996–1997 9 1 8 2 0 8 3 3 6 0

(50.00) (5.56) (44.44) (11.11) (0.00) (44.44) (16.67) (16.67) (33.33) (0.00)1997–1998 11 1 0 6 1 1 16 1 1 0

(57.89) (5.26) (0.00) (31.58) (5.26) (5.26) (84.21) (5.26) (5.26) (0.00)1998–1999 0 5 13 0 0 0 0 0 18 0

(0.00) (27.78) (72.22) (0.00) (0.00) (0.00) (0.00) (0.00) (100.00) (0.00)

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Panel 3:FOR−BNKS1995–1996 5 0 4 4 1 1 7 1 3 2

(35.71) (0.00) (28.57) (28.57) (7.14) (7.14) (50.00) (7.14) (21.43) (14.29)1996–1997 1 4 7 1 0 1 0 1 9 2

(7.69) (30.77) (53.85) (7.69) (0.00) (7.69) (0.00) (7.69) (69.23) (15.38)1997–1998 7 1 2 3 0 0 8 3 0 2

(53.85) (7.69) (15.38) (23.08) (0.00) (0.00) (61.54) (23.08) (0.00) (15.38)1998–1999 0 5 7 1 0 1 0 2 7 3

(0.00) (38.46) (53.85) (7.69) (0.00) (7.69) (0.00) (15.38) (53.85) (23.08)

Note: Malaysian banks are categorized according to the following. (1) Productivity Progress: TFPCH > 1, (2) Productivity Regress TFPCH < 1, (3) Productivity Stagnation:TFPCH = 1. (1) Technological Progress: TECHCH > 1, (2) Technological Regress TECHCH < 1, (3) Technological Stagnation: TECHCH = 1. (1) Efficiency, Pure Technicaland Scale increase: EFFCH, PEFFCH and SECH > 1, (2) Efficiency, Pure Technical and Scale decrease: EFFCH, PEFFCH and SECH < 1, (3) No Change in Efficiency, PureTechnical and Scale: EFFCH, PEFFCH and SECH = 1.

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The subgroup results in Panels 2 and 3 of Table 5 yield interesting findings.It is apparent that during the period of study both the domestic and foreignbanks’ TFPCH progress during the earlier years was attributed to the increasein EFFCH. However, during 1999, i.e. after the financial crisis was over, it seemsthat TECHCH has mainly been attributed to both the domestic and foreignbanks’ TFPCH progress. Likewise, while TECHCH decline has mainly resultedin the domestic banks’ TFPCH regress during the earlier year, it is apparent fromTable 5 that EFFCH decline has mainly resulted in the domestic and foreign banks’TFPCH regress during the latter year. It is also apparent from Panels 2 and 3 ofTable 5 that during the period of study the domestic and foreign banks’ EFFCHincrease was mainly attributed to the increase in SECH. Likewise, the domesticand the foreign banks’ EFFCH decline was largely due to the decline in SECH.

5.4 The Determinants of Malaysian Banks’ Productivity

An important understanding that arises after the calculation of the Malmquistproductivity indices is to attribute variations in productivity levels to bank specificcharacteristics and the environment in which they operate. The standard methodin the empirical bank studies is to estimate regression equations with pooledordinary least squares (OLS), which assumes that the omitted variables areindependent of the regressors and independently identically distributed (i.i.d.).However, such estimation can create problems of interpretation if bank specificcharacteristics, such as bank management, which affect performance are notconsidered. If those omitted bank specific variables (both observed and unob-served) correlate with the explanatory variables, then the pooled OLS producesbiased and inconsistent estimates (Hsiao, 1986). Using panel data, however, thefixed effect model produces unbiased and consistent estimates of the coefficients.As a robustness check, we have also computed the regressions based on the randomeffect model (REM).

The fixed effect model assumes that differences across banks reflect parametricshifts in the regression equation. Such an interpretation becomes more appropri-ate when the problem at hand uses the whole population, rather than a samplefrom it (Jeon & Miller, 2005). Since the sample considers all Malaysian banksover a particular time period and not a random draw, the fixed effect model isadopted in the analysis (Wooldridge, 2002). Using the productivity scores as thedependent variable, we estimate the following regression models:

BANPRODjt = β0 + β1�Bank_Characteristics

+ β2�Economic_Conditions + εjt

εjt = vj + uj

(9)

where, BANPRODjt is the jth bank TFPCH in period t derived from the MPI,Bank_Characteristics is a set of bank characteristics, Economic_Conditions is avector of economic conditions, and εjt is the disturbance term, with vj captur-ing the unobserved bank specific effect and uj is the idiosyncratic error and isindependently identically distributed (i.i.d.), eit ∼ N(0, σ 2).

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Seven independent variables that are widely followed by policymakers andpractitioners are examined. LOGTA (log of total assets) is used as a proxy ofbank size to capture the possible cost advantages associated with size (economiesof scale). In the efficiency literature, the relationship between size and efficiencyhas been mixed and in some cases, a U-shaped relationship is observed. LOGTAis also used to control for cost differences related to bank size and for the greaterability of large banks to diversify. In essence, LOGTA may lead to positive effectson bank performance if there are significant economies of scale. On the otherhand, if increased diversification leads to higher risks, the variable may havenegative effects.

The ratio of overhead expenses to total assets, NIE/TA, is used to provideinformation on variation in operating costs across the banking system. It reflectsemployment, total amount of wages and salaries, as well as the cost of runningbranch office facilities. A high NIE/TA ratio is expected to impact performancenegatively because efficient banks are expected to operate at lower costs. Further-more, the usage of new electronic technology, such as ATMs and other automatedmeans of delivering services, has resulted in a fall in the wage expenses (as capital issubstituted for labour). Therefore, a lower NIE/TA ratio may affect performancepositively.

Under Bank Risk Structure we have examined three independent variables,namely, LOANS/TA (total loans divided by total assets) as a proxy of lendingintensity, and LLP/TL (loan loss provisions divided by total loans) is used asa proxy measure for risk. Bank loans are expected to be the main source ofrevenue and are expected to impact performance positively. However, the loan-performance relationship depends significantly on the expected change of theeconomy. During a strong economy, only a small percentage of loans will default.On the other hand, a bank may adversely be affected during a weak economy,because borrowers are likely to default on their loans. Ideally, banks should capi-talize on favourable economic conditions and insulate themselves during adverseconditions. The coefficient of LLP/TL is expected to be negative because badloans reduce profitability.

EQUITY/TA (book value of stockholders’ equity divided by total assets) isincluded in the model because, as noted, domestic and foreign banks use differentdegrees of leverage. Furthermore, lower capital ratios in banking imply higherleverage and risk, and therefore greater borrowing costs. Berger and Mester (1997)pointed out that it is an important control variable used to account for differencesin risk among banking institutions.9 We expect EQUITY/TA to have a negativecoefficient because an increase in equity is a reduction in leverage, which reducesreturn on equity.

ROA (return on assets) is defined as net profit over total assets and is a financialratio used to measure the relationship between bank profits or returns and pro-ductivity. The variable is expected to take a positive sign as a more productive bankis expected to generate higher returns. NII/TA (non-interest income divided bytotal assets) is entered in the regression model as a proxy for bank diversificationinto non-traditional activities.

9See Berger and Mester (1997) for a detailed discussion of this point.

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The Economic Conditions variable is also included in the regression modelsto capture the relationship between economic growths and Malaysian banks’TFPCH. The LNGDP variable represents the growth rate of the country’s grossdomestic product and is used as a proxy for economic conditions. Favourableeconomic conditions will affect positively on the demand and supply of bankingservices, but will have either positive or negative influence on bank TFPCH.

To examine the impact of foreign ownership on bank productivity, DUMFORBis introduced in regression model 4. We expect the coefficient of the variable toexhibit a positive sign. The empirical evidence, particularly from the developingand transition countries, has generally found that foreign banks have succeededin capitalizing on their advantages and exhibit a higher level of efficiency andproductivity than their domestic bank peers (Bhattacharyya et al., 1997; Isik &Hassan, 2002; Hassan & Marton, 2003; Ataullah & Le, 2006; Havrylchyk, 2006).

Table 6 present the results derived from the GLS fixed-effects regression anal-ysis. The proxy of loans intensity, LOANS/TA, reveals a positive relationship(significant at the 1% level) with bank productivity levels for models 1 and 3(foreign banks are included in the regressions). The findings may imply that alter-native bank outputs such as investments and securities to be more highly valuedthan bank loans, particularly in the case of the foreign banks. LOGTA, as a proxyof banks’ size, shows negative coefficients for models 1 and 2 (domestic banksare included in the regression), suggesting that the smaller banks, particularly thedomestic banks, tend to be more productive. Thus, assuming that the average costcurve for the domestic Malaysian banks are U-shaped, the recent growth policiesof the medium and large domestic Malaysian banks seem to be inconsistent withthe drive to minimize costs. On the other hand, the results seem to suggest thatthe foreign banks may benefit from the increase in the scale of their operations.

It is observed from Table 6 that the proxy variable for risk, LLP/TL, is never sig-nificant in any of the regression models. The results seem to suggest that NII/TAconsistently possess a strong positive and with bank productivity level and is sta-tistically significant in regression models 1 and 3 (foreign banks are included in theregressions). The results imply that foreign banks that have a higher proportion ofincome emanating from non-interest sources are relatively more productive. Theempirical finding is consistent with earlier findings by, among others, Jeon andMiller (2005) who found that Korean banks with a high proportion of non-interestincome exhibits higher profitability levels.

The findings seem to suggest that expense preference behaviour as a proxyfor management quality measured by NIE/TA, has a negative and significantimpact on bank productivity levels in models 1 and 3 (foreign banks are includedin the regression). The results imply that the foreign banks have much to gainif they improve their managerial practices. In line with the findings of Akhigbeand McNulty (2005), EQUITY/TA seems to exhibit a positive relationship withbank productivity levels in regression model 2 (foreign banks are excluded fromthe regressions). The empirical findings seem to suggest that the more productivebanks, particularly the domestic banks, ceteris paribus, use more leverage (lessequity) compared to their peers. During the period of study, the findings seemto suggest that profitability, as measured by ROA, entered regression model 3(domestic banks are excluded in the regression) with a negative sign, suggesting

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Table 6. Results of pooled regression analysis

Model 1 Model 2 Model 3 Model 4(All Banks) (Domestic Banks) (Foreign Banks) (REM)

CONSTANT −3.580086∗∗∗ 4.801169∗∗∗ −17.01011∗∗∗ 2.187649(−2.856602) (2.770941) (−3.430020) (0.490821)

Bank CharacteristicsLOGTA −0.282097∗∗∗ −0.383534∗∗∗ 0.409632∗∗∗ −0.015086

(−6.179436) (−10.89276) (3.056224) (−0.722206)NIE/TA −22.53351∗∗ 11.63995 −28.39456∗∗∗ −5.630388

(−2.496245) (1.424038) (−5.709436) (−0.978149)LOANS/TA 1.947328∗∗∗ 0.069579 1.909558∗∗∗ 0.354096

(3.523008) (0.049477) (4.007739) (0.682848)LLP/TL 0.422060 0.224446 −2.522666 −0.284619

(0.981566) (0.842774) (−1.500210) (−0.750154)EQUITY/TA 1.998216 6.199666∗∗∗ −1.384986 (0.544212)

(0.946415) (3.410487) (−1.057611) (0.544212)ROA −0.025027 −0.056046 −0.087952∗∗∗ −0.009913

(−1.145854) (−1.519742) (−3.228155) (−0.339211)NII/TA 17.46637∗ 25.19912 33.60615∗∗∗ 13.18423

(1.975908) (1.339836) (9.997020) (1.490715)DUMFORB −0.132699∗∗∗

(−4.228765)Economic ConditionsLNGDP 0.726036∗∗∗ 0.140213 1.011204∗∗ −0.113474

(9.940191) (0.936878) (2.435122) (−0.280297)Hausman test χ2 0.000 0.000 0.000 0.000LM test χ2 70.969∗∗∗ 68.234∗∗∗ 32.794∗∗∗R2 0.445316 0.654259 0.608646 0.088704Adj. R2 0.174277 0.449637 0.343536 0.021478F−stat. 1.642996∗∗ 3.197403∗∗∗ 2.295820∗∗ 1.319482D.W. stat. 2.686222 2.329169 2.990249 1.949707No. of Observations 132 79 53 132

φjt = α + β1LOGTA + β2NIE/TA + β3LOANS/TA + β4LLP/TL

+ β5EQUITY/TA + β6ROA + β7NII/TA + β8DUMFORB

+ β9LNGDP + εj

The dependent variables are individual bank’s TFPCH scores derived from the MPI; LOGTA is the size ofthe bank’s total asset measured as the natural logarithm of total bank assets; NIE/TA is a measure of bankmanagement quality calculated as total non-interest expenses divided by total assets; LOANS/TA is a measureof bank’s loans intensity calculated as the ratio of total loans to bank total assets; LLP/TL is a measure ofbanks risk calculated as the ratio of total loan loss provisions divided by total loans; EQUITY/TA is a measureof banks leverage intensity measured by banks total shareholders equity divided by total assets; ROA is a proxymeasure for bank profitability calculated as bank profit after tax divided by total shareholders equity; NII/TA isa measure of bank’s diversification towards non interest income, calculated as total non-interest income dividedby total assets; DUMFORB is a dummy variable that takes a value of 1 for foreign bank, 0 otherwise; LNGDPis a natural log of gross domestic products.Values in parentheses are t- statistics.∗∗∗, ∗∗ and ∗ indicates significance at 1%, 5%, 10% levels, respectively.

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that the more productive foreign banks may not necessarily be the ones that aremore profitable.

The coefficient of LNGDP entered models 1 and 3 regressions with a positivesign and is statistically significant at the 1% and 5% levels respectively. Demandfor financial services tends to grow as economies expand and societies becomewealthier. The robust economic growth during the period under study could boostthe demand for financial services and improve the quality of loans, particularlythe foreign banks.

In order to check for the robustness of the results, we perform a number ofsensitivity analyses. First, we run similar regression models by using the RandomEffects Model (REM). We find that the results are robust with the coefficientsof the baseline variables staying mostly the same: they keep the same sign, thesame order of magnitude, they remain significant as they were so, in the baselineregression model (albeit sometimes at different levels), and with few exceptions,do not become significant if they were not in the baseline regression model.10

Second, we introduce DUMFORB in regression model 4 to examine the effectof bank ownership.11 It is interesting to note that the coefficient of DUMFORBentered regression model 4 with a negative sign and is statistically significant atthe 1% level. If anything could be found from the results, the empirical findingssuggest that the foreign banks are not immune to macroeconomic shocks. Despitetheir ability to capitalize on better risk management and operational techniquesprovided by their parent banks abroad, the empirical findings from this studysuggest that the foreign banks are also susceptible during period of crisis andfinancial instability, such as the Asian financial crisis in 1997. It is observed fromTable 6 that when we control for bank ownership, the coefficients of the baselineregression model variables loses its explanatory power and is no longer significantin the regression model.

6. Concluding Remarks and Directions for Future Research

The paper examines the sources of productivity changes of the Malaysian bank-ing sector around the Asian financial crisis of 1997. The productivity estimatesof the domestic and foreign banks are calculated by using the non-parametricMalmquist Productivity Index (MPI) method. During the period under study,the results suggest that the Malaysian banking sector has exhibited productivityregress. The decomposition of productivity change into its mutually exhaustivecomponents of technological change and efficiency change suggest that the lat-ter has played a greater influence in determining the productivity change of theMalaysian banking sector.

During the period under study, we find that the domestic banks have exhibiteda marginal productivity increase attributed to technological progress, while theforeign banks have exhibited productivity regress due to efficiency decline. The

10To conserve space the full regression results are not reported in the paper, but are available fromthe authors upon request.11In the fixed effects model, it is not possible to test for time invariant dummies such as type ofbanks.

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decomposition of the efficiency change index into its pure technical and scaleefficiency components suggest that the decline in both the domestic and foreignbanks’ efficiency were mainly due to the decline in scale efficiency. The empiricalfindings thus imply that size or the scale of operations rather than managerialissues played a critical role and have greater influence in determining Malaysianbanks’ efficiency levels during the period under study.

We have also explored the relationship between size and bank productivitylevels. We find a few interesting findings. First, the empirical results seem not tosupport the divisibility theory, as the majority of banks that exhibit productivityprogress attributed to technological progress were the large banks. Second, theempirical findings suggest that while the majority of Malaysian banks that experi-enced productivity growth were the medium and large banks group, the majorityof banks that experienced productivity regress were the small banks. The resultsimply that the small banks, with limited capabilities, have lagged behind theirlarger counterparts in terms of technological advancements.

A multivariate regression analysis is employed to analyse the determinantsof the domestic and foreign banks’ productivity. The empirical findings suggestthat the more productive banks have a higher proportion of income emanat-ing from non-interest sources and have greater loans intensity. We find thatthe more productive banks may not necessarily be the one that are more prof-itable. The results suggest that the domestic banks with a higher proportionof risky assets and which are better capitalized, exhibit higher productivitylevels. The empirical findings seem to support the expense preference behaviour,thus rejecting the ‘bad management’ hypothesis among the domestic banks.On the other hand, we find that foreign banks that incur higher operatingexpenditures are likely to be less productive. The findings suggest that risk andcapitalization are negatively related to the foreign banks’ productivity, whileloans intensity and size are positively related to the foreign banks’ productivitylevels.

The continued success of the Malaysian financial sector depends on its effi-ciency, productivity, and competitiveness. Furthermore, in view of the increasingcompetition resulting from the more liberalized banking sector, bank manage-ment as well as the policymakers will be more incline to find ways to obtain theoptimal utilization of capacities as well as making the best use of their resources,so that these resources are not wasted during the production of banking productsand services. From the regulatory perspective, therefore, the performance of thebanks will be based on their efficiency and productivity. Thus, the policy directionwill be towards enhancing the resilience and efficiency of the financial institutionswith the aim of intensifying the robustness and stability of the financial system(Bank Negara Malaysia, 2005).

The empirical findings of this study have considerable policy relevance. First, ifanything could be found from the results, the empirical findings suggest that theforeign banks are not immune to macroeconomic shocks. Despite their ability tocapitalize on better risk management and operational techniques provided bytheir parent banks abroad, the empirical findings from this study suggest that theforeign banks are also susceptible during period of crisis and financial instability,like the Asian financial crisis in 1997.

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Second, the empirical findings from this study clearly show the importanceof size in determining banks’ technological change growth rate. The empiricalfindings suggest that the small banks with capabilities are at a disadvantage com-pared to their large bank peers in terms of technological advancements. Thus,constant technological upgrades and investments in the state of the art technolo-gies should be an essential policy in order to improve the rate of total factorproductivity growth of the Malaysian banking sector. Within the context of theMalaysian financial sector, it could be argued that the more productive financialinstitutions will be able to offer more new products and services. To this end, therole of technology advancement is particularly important given that a financialinstitution with relatively more advanced technologies may have added advantageover its peers.

Third, the findings clearly suggest that the decline in the efficiency of the domes-tic and foreign banks was mainly due to scale. The results imply that the domesticand foreign banks are either too small to benefit from the economies of scale or toolarge to be scale efficient. Thus, from the policy-making perspective, the resultsimply that the relatively smaller banks, like the foreign banks, could raise theirefficiency levels by expanding, while the larger banks would need to scale downtheir operations to be scale efficient. However, Avkiran (1999) pointed out thatovercoming inefficiency due to scale may be more time consuming with in-marketmergers and business collaborations, compared to addressing purely technicalinefficiency in the short-term by ‘experimenting with new combinations of inputsand outputs observed from the operations of efficient peers’ within the sample.

Finally, from the economies of scale perspectives, mergers among the smallbanking institutions should be encouraged. This should entail the small banks toreap the benefits of economies of scale. The larger institutions will also have bettercapacity to invest in the state of the art technologies, which could enhance the rateof total factor productivity growth of the Malaysian banking sector. Furthermore,consolidation among the small banking institutions may also enable them tobetter withstand macroeconomic shocks such as the Asian financial crisis.

Due to its limitations, this paper could be extended in a variety of ways.First, the scope of this study could be further extended to investigate changesin Malaysian banks’ cost, allocative and technical efficiencies over time by usingthe DEA method. Second, future research into the productivity of Malaysianbanks could also consider the production function along with the intermediationfunction. Finally, the non-parametric frontier analysis used in this paper could becombined with the stochastic frontier analysis method of estimating the frontier.This should testify to the robustness of the results against alternative estimationmethods.

Despite these limitations, the findings of this study are expected to contributesignificantly to the existing knowledge on the operating performance of theMalaysian banking industry. Nevertheless, the study has also provided furtherinsight into bank-specific management as well as into the policymakers withregard to attaining optimal utilization of capacities, improvement in manage-rial expertise, efficient allocation of scarce resources and most productive scaleof operation of the banks in the industry. This may facilitate directions forsustainable competitiveness of future banking operations in Malaysia.

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Acknowledgement

The author would like to thank Muzafar Shah Habibullah, Law Siong Hook, Evan Lau, participants at the Uni-versiti Putra Malaysia, Faculty of Economics and Management Seminar 2006, the Malaysian Finance AssociationConference 2007, and two anonymous referees for many helpful comments and suggestions that considerablyimproved earlier versions of the paper. The usual caveats apply.

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