Non-Bank Financial Institutions (NBFIs) play important dual roles in a financial system. They complement the role of commercial banks by filling in financial intermediation gaps by offering a range of products and services. They also compete with commercial banks, forcing the latter to be more efficient and responsive to their customers needs. NBFIs’ state of development is usually a good indicator to the state of development of a country’s financial system as a whole. The importance of investigating the efficiency and productivity of Malaysian NBFIs could be best justified by the fact they play important roles in complementing the facilities offered by the commercial banks, as well as being key players in the development of the capital markets. As sophisticated and well-developed as capital markets are considered to be as the hallmark for a market-based economy worldwide, such a study of this nature is particularly important as the health and development of the capital market relies largely upon the performance of NBFIs. Hence, efficient and productive NBFIs are expected to enhance the Malaysian capital markets in its pursuit to move towards a full market based economy.

Despite the significant, economic developments of the NBFI sector, studies that attempt to investigate this issue are relatively scarce. Over the years, while there have been extensive literature examining the productivity and efficiency of banking industries in various countries, empirical works on NBFIs’ productivity & efficiency are still in its infancy. To the best of our knowledge, there has been no microeconomic study performed with respect to NBFIs. The study therefore aims to fill a demanding gap in that case. Nevertheless, the study will also be the first to investigate the sources of NBFIs’ productivity changes in developing economies. Section 2 will provide a brief overview of the Malaysian financial system with reviews of related studies. Section 3 will outline the approaches to the measurement and estimation of efficiency change, while Section 4 will discuss the results. Naturally, Section 5 will conclude the paper.

The Malaysian financial system can be broadly divided into the banking system and non-bank financial intermediaries. The banking system is the largest component, accounting for approximately 70 percent of the financial system’s total assets. The banking system can be further divided into three main groups, namely the commercial banks, financial companies, and merchant banks. The commercial banks are the main players in the banking system. They are the largest and most significant providers of funds in the banking system, enjoying the widest scope of permissible activities, those of which are able to engage in a full range of banking services. Financial companies formed the second largest group of deposit taking institutions in Malaysia. Traditionally, financial companies specialize in consumption credit, comprising mainly of hire purchase financing, leasing, housing loans, block discounting, and secured personal loans. Merchant banks emerged in the Malaysian banking scene in 1970, marking an important milestone in the development of the financial system, alongside Malaysian corporate development. They play a role in the short-term money market and capital raising activities such as financing, syndicating, corporate financing, and management advisory services that arrange for the issue and listing of shares, as well as managing portfolios. The Malaysian financial system’s assets and liabilities continued to be highly concentrated at the commercial banking sector with total assets and liabilities amounting to RM 761,254.8 billion (or 3.05 times the national GDP at the end of 2004). Prior to the Asian Financial Crisis in 1997/98, financial companies’ assets and liabilities were seen increasing from only RM531 million (or 0.05 times the national GDP in 1970) to a high of RM 152.4 billion (or 0.77 times in 1997). The ratio however, has gradually declined to RM 123.6 billion (or 0.60 times in 1998) to RM 109,409.8 billion (or 0.52 times the GDP in 2000), before increasing again in year 2001, to reach a post crisis high of RM 141,911.0 billion (or 0.61 times the GDP in 2003). Due to further consolidation in the Malaysian financial sector, financial companies’ assets as a ratio of the national GDP declined again to reach a low of

0.27 times in 2004. As for the merchant banks, a similar trend is observed where their assets and liabilities (as a ratio of the national GDP) have been increasing since 1971, reaching a peak of RM 44.3 billion or 0.23 times GDP in 1997 (before the Asian financial crisis). During the post crisis period, the merchant banks’ assets and liabilities continued to remain stable at 0.17 to 0.22 times the national GDP. A combination of both financial companies and merchant banks’ total assets reveal that the non-bank financial sector commanded approximately 22.8 percent of the banking system’s total assets and liabilities.1

such as frequent changes in technology required for modern banking, increasing competition, rising customer expectations, etc. Hence, the efficiency and productivity issues have become a major area of concern for the banks’ management. In fact, productivity is an important criterion to measure the performance of banks in addition to profitability, financial, and operational efficiency. An efficient management of banking operations aimed at increasing the efficiency and productivity of the financial sector requires up to date knowledge. The figure is at end-2003, prior to the consolidation of financial companies into their respective commercial banking parents.

A lot of research work has so far taken place concerning the views about the role of financial & banking developments in economic growth [McKinnon (1973); Shaw (1973); Rajan & Zingales (1998); Levine (2004); Singh (2005)], as well as banking efficiency and productivity [(Das & Ghosh (2006); Sufian (2007); and Weill (2007)].2 Similarly, some studies have been undertaken for measuring the productivity and efficiency of banks in Malaysia [most notably, Katib & Matthews (2000) and Okuda & Hashimoto (2004)]. Concerning our information, despite NFBIs’ significance towards economic development, studies that attempt to investigate this issue are relatively scarce. Over the years, while there have been extensive literature examining the productivity & efficiency of banking industries in various countries, empirical works on NBFIs’ productivity & efficiency are still in its infancy.

A non-parametric Data Envelopment Analysis (DEA) is employed with a variable return to scale assumption, measuring Malaysian NBFIs’ input-oriented technical efficiencies. DEA involves constructing a non-parametric production frontier based on the actual input-output observations in the sample, relative to the measured efficiency of each firm in the sample (Coelli, 1996). Let us give a short description of the Data Envelopment Analysis3. Assume that there is data on K inputs and M outputs for each N NBFI. For the ith NBFI, these are represented by the vectors xi and yi, respectively. Let us introduce the K x N input matrix, X, and the M x N output matrix, Y. To measure the efficiency for each NBFI, we calculate a ratio of all inputs, such as (u’yi/v’xi), where u is an M x 1 vector of output weights, and v is a K x 1 vector of input weights. To select optimal weights, we specify the following mathematical programming problem: min (u’yi /v’xi), u,v

The above formulation has a problem of infinite solutions; therefore we impose the constraint v’xi = 1, which leads to:

min (μ’yi), μ,ϕ

See Berger & Humphrey (1997) for an excellent review. Good reference books on efficiency measures are Coelli et al. (1998), Cooper et al. (2000), and Thanassoulis (2001).

where we change notation from u & v to μ & ϕ, respectively, in order to reflect transformations. Using the duality in linear programming, an equivalent envelopment form of this problem can be derived:

min θ, θ, l yi + Yλ > 0 θxi - Xλ > 0 λ>0 (3)

where θ is a scalar representing the value of the efficiency score for the ith decision-making unit, which will range between 0 and 1. λ is a vector of N x 1 constants. The linear programming has to be solved N times, once for each NBFI in the sample. In order to calculate efficiency under the assumption of variable returns to scale, the convexity constraint (N1'λ=1) will be added to ensure that an inefficient NBFI is only compared against NBFIs of similar size; thus providing the basis for measuring economies of scale within the DEA concept. For the empirical analysis, all Malaysian NBFIs would be incorporated. The annual balance sheets and income statements used to construct the variables for the empirical analysis are sourced from published balance sheet information in the annual reports. Due to scarce data from M & A activity, the final sample was an unbalanced panel sample of 92 NBFI observations. There are two main approaches that exist in banking theory literature to define the banking function: the production and intermediation approaches [Sealey & Lindley (1977)]. Under the production approach, which was pioneered by Benston (1965), a financial institution is defined as a producer of services for account holders. That is, they perform transactions on deposit accounts and process documents such as loans. The intermediation approach on the other hand, assumes that financial firms act as an intermediary between savers and borrowers, hypothesizing total loans and securities as outputs; whereas deposits with labor and physical capital are defined as inputs. For the purpose of this study, a variation of the intermediation approach or asset approach originally developed by Sealey and Lindley (1977) will be adopted in the definition of inputs and outputs used. Given the sensitivity of efficiency estimates to the specification of outputs and inputs, we have estimated two alternative models. In DEA Model A, we model Malaysian NBFIs as multi-product firms, producing two outputs by employing two inputs. Accordingly, Total Deposits (x1) include deposits from customers and other banks. Fixed Assets (x2) are used as input vectors to produce Total Loans (y1), which include loans to customers and other banks. Investments (y2) include investment securities held for trading, investment securities available for sale (AFS), and investment securities held to maturity. To assess the importance of risk and lending quality problems in explaining the efficiency of Malaysian NBFIs, following the approach by the likes of Drake and Hall (2003) and Charnes et al. (1990), Loan Loss Provisions (x3) is incorporated as an input variable in DEA Model B.

to construct the efficiency frontier. During the period of study, it is apparent that the financial companies were almost three times larger (in terms of asset size) and commanded higher market share in terms of loans & deposits, compared with their merchant bank peers. On the other hand, although the merchant banks were smaller, they seem to have produced a higher amount of investments with lower amounts of defaulted loans. The differences are further confirmed by a series of parametric (t-test) and non-parametric (Kruskal-Wallis and Mann-Whitney [Wilcoxon Rank- Sum] tests), which suggest that the differences in the mean are significant for all variables at the 1 per cent level of significance4.

In this section, we will discuss the technical efficiency change (TE) of the Malaysian NBFI sector, measured by the Data Envelopment Analysis (DEA) method, along with its decomposition into pure technical efficiency (PTE) and scale efficiency (SE) components. With the existence of scale inefficiency, we will attempt to provide evidence on the nature of returns to scale of Malaysian NBFI. The efficiency of Malaysian NBFIs was first examined by applying the DEA method for each year under investigation by employing the traditional input-output variables. We extend the analysis to examine the merchant banks and financial companies’ efficiency results derived from an alternative model, which incorporates a non-discretionary, input variable.

The preferred, non-parametric Data Envelopment Analysis (DEA) methodology allowed us to distinguish between three different types of efficiency: technical, pure technical, and scale efficiencies. During the period of study, the results suggested that the Malaysian merchant banks exhibited a mean technical efficiency of 69.6 percent, while the financial companies have exhibited a lower mean technical efficiency of 44.7 percent. Overall, the results suggest that scale inefficiency dominates pure technical inefficiency effects in determining Malaysian NBFIs’ total technical inefficiency. The findings also seem to suggest that scale efficiency tends to be much more sensitive to the exclusion of risk factors, implying that potential economies of scale may be overestimated when risk factors are excluded. The empirical findings clearly demonstrate the importance of risk in explaining financial institutions’ efficiency, particularly scale efficiency. If anything could be deduced from the results, the exclusion of risk factors may significantly overestimate the financial institutions potential economies of scale, which could result in bias conclusions and policy recommendations. The findings are important for policy makers in its quest to consolidate the banking system further to achieve greater economies of scale and efficiency. As the actual potential economies of scale may significantly be lower than initially expected, policy makers should be more cautious in promoting mergers as a mean to achieve greater efficiency by attaining better economies of scale.

Author statement: Fadzlan Sufian is affiliated with the research department of a local bank, the CIMB Bank Berhad and is a staff member of the The University of Malaysia. E--mail: fadzlan14@gmail.com.