Bank lending to small business in India: Analyzing productivity and efficiency

Accepted Manuscript Title: Bank Lending to Small Business in India: Analyzing Productivity and Efficiency Author: Harri Ramcharran PhD PII: DOI: Reference:

S1062-9769(16)30031-X http://dx.doi.org/doi:10.1016/j.qref.2016.06.003 QUAECO 940

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Please cite this article as: Ramcharran, H.,Bank Lending to Small Business in India: Analyzing Productivity and Efficiency, Quarterly Review of Economics and Finance (2016), http://dx.doi.org/10.1016/j.qref.2016.06.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Highlights: Ms Ref. No.: QUAECO-D-15-00192. Harri Ramcharran  Methodology, based on “money as an input in the production function”, we estimate the efficiency of bank lending to SME in India.  The results have important policy implications regarding capital allocation and risk

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management since bank loans are the main source of financing SME.

 The results indicate increasing productivity (output elasticity) of bank credit from 0.76 to

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1.23; the productivity of labor is negative, but increases from -1.57 to -0.628.

 The SME sector’s efficiency improved from returns to scale of -0.89 to 0.607 mainly due

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to the increasing productivity of bank credit. The decrease in the number of “sick” units

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with large outstanding debt apparently contributes to this improvement.

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Harri Ramcharran Ph.D* Professor of Finance and International Business Department of Finance College of Business University of Akron Akron, OH 44325 USA

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Bank Lending to Small Business in India: Analyzing Productivity and Efficiency

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Phone: (330) 972-6882 Fax: (330) 972-5970 E-mail: [email protected]

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*Corresponding Author

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Bank Lending to Small Business in India: Analyzing Productivity and Efficiency Abstract

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This study extends the literature of the performance of Small and Medium Size Industries (SME) by empirically estimating the efficiency of bank loans to the SME sector of India using data from 1979 to 2013. We use a production function methodology, specifically estimating a non-homogeneous production function which, unlike other specifications, provides time varying econometric estimates of productivity, this helps to detect the impact of policy changes on efficiency overtime. The theoretical rationale for this approach is the “money as an input in the production function” argument. The results have important policy implications regarding capital allocation and risk management since bank loans are the main source of financing SME. The results indicate increasing productivity (output elasticity) of bank credit from 0.76 to 1.23; the productivity of labor is negative, but increases from -1.57 to -0.628. The sector’s efficiency improved from returns to scale of -0.89 to 0.607 mainly due to the increasing productivity of bank credit. The decrease in the number of “sick” units with large outstanding debt apparently contributes to this improvement. Current problems facing the banking sector could increase borrowing cost, decrease credit availability and thus the productivity of SME. Policies to improve the skills of workers and to reduce the number of sick units are important for improving efficiency. Keywords: bank loan, small scale industry, production efficiency, returns to scale

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JEL Classifications:C22, C52, E50, G21,

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Bank Lending to Small Business in India: Analyzing the Productivity and Efficiency 1. Introduction The growing significance of Small and Medium size Enterprises (SME) for employment and income

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generation has led various governments and international organizations to support their development and expansion. The World Bank (2010) provides data to illustrate the growth of Micro, Small, and Medium size

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Enterprises (MSME) worldwide. There is a growing literature (Ayyagari et al 2007; Tybout 2000; Schiffer and

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Weder 2011) on different aspects of SME operation and performance. Several studies focus on the pattern of financing, for example, Ayyagari et al (2012), Calice, et.al (2012), Beck et al, (2008, 2011), Tybout (2000) and

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Clarke, et.al (2005). Tybout (2000) notes that in studies of microenterprises, scale economies based on estimated production functions are consistently missing; this research contributes significantly to an under-investigated

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area of the literature.

Several researchers have addressed the efficiency aspect of SME, they contend that the performance of

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SMEs has to be efficiency driven in order to achieve economic growth, employment creation, and poverty reduction. Mead and Liedholm (1998) articulate the necessity of containing production costs and of achieving

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economic efficiency to foster the growth of SMEs, while Dewatripoint and Maskin (1995) emphasize “credit efficiency” as a goal for lending to SME. Tybout (2000) lists several factors that may prevent scale efficiency in

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SME; some are (i) surplus unskilled labor and a lack of long term financing, (ii) poor infrastructure, including communication facilities and transportation network, and (iii) volatility in business environment which discourages mass production techniques. Several authors (Beck, et al 2011, Ayyagari, et al 2012) document the growing importance of bank financing for SMEs around the world. Beck, et al (2011) also emphasise the necessity of research of the supply side of bank financing. The efficient use of bank loans has not been investigated despite various reports of their significant role in SME financing; the findings of such research have important policy implications related to efficient capital allocation, managing risk at the firm level and policies to improve productivity and performance.

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This research, using a methodology derived from recent advances (Fried, et. al, 2008) in production economics, estimates the efficiency of bank loans to the SME sector of India using data from 1978-79 to 201213. We use a non-homogeneous production function (NHPF), developed by Vinod (1972), analyzed by Bairam (1997) and Intrilligator (1978), in which bank loans to the SME sector and the level of employment are used as

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inputs... The rationale for using labor is the labor intensity of the production process and also the government priority to protect labor employment through rigid labor laws, e.g. making retrenchment difficult during

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economic downturn. Two streams of studies provide the rationale for the methodology. First, the issue of the efficiency of financial institutions (commercial banks, saving and loans, credit unions, and insurances firms) is

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well-documented in several studies (Berger and Humphrey 1997; Berger, Hunter, and Timme, 1993). Recent studies have used different estimation techniques including parametric frontier analysis with different

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specifications of cost, profit/revenue and production functions. Second, several theoretical and empirical models, incorporating the role of “real money balances” (bank loans, financial assets) as a factor of production, have been

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advanced in various studies, e.g. Laumas and Mohabbat (1980), Finnerty (1980), Sinai and Stokes (1981), Khan

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and Ahmad (1984), and Hasan and Mahmud (1993).

The SME sector of India provides an interesting case study for examining the efficiency of bank lending.

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This sector has grown significantly in terms of increasing value added, the level of employment, the number of units and loans received from the government. From 1993 to 2005, production growth from this sector

sector.

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averages about 8.38% annually; this is higher than the 5.8% from the overall (economy wide) industrial The sector relies heavily on external financing mainly from the central government. Lending to this

sector is mandated by the Reserve Bank of India Priority Sector Lending Program that requires every commercial bank to allocate 40% of these loans to what is defined as priority sectors (SME and Agriculture). Preferential treatment is also provided to this sector in the form of low interest rate on bank credit. 1 The NHPF specification has several advantages, unlike linear homogenous production functions (CobbDouglas and Constant Elasticity of Substitution) which assume constant productivity estimate at all levels of output, it provides parameter estimates of efficiency that vary with output and factor (input) proportions. Timevarying parameters enable an examination of the pattern of productivity/efficiency changes over the period of 2

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study, as well as making inferences about policies implemented to impact the performance of this sector, for example, the economic reform / liberalization policies during the 1990s.2 The efficiency parameters estimated and analyzed are: (i) the output elasticity of labor, (ii) the output elasticity of bank loans, and (iii) returns to scale (RTS). The model specification and estimation technique are fully described in another section of the paper. In

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addition to regression analysis, we perform several diagnostic tests to ensure the structural stability of the model and the reliability of the results; the tests are for (i) the stationarity of the data, and (ii) the cointegration of the

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variables.

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The results have practical policy implications for both government and business. Signs of weakness in the economy and banking sector could result in a credit crunch and increasing borrowing cost which could impact

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the performance of this sector. Despite some impressive performance data, a major problem with this sector is the large number of “sick” units, defined as units with outstanding accounts/debt remained overdue for a period

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of more than 2.5 years. Recent data indicate that about 10 percent of the units are classified as “sick”. Previous studies (Ramaswamy, 1994; Bhavani, 1991) of India’s SME sector use the stochastic production frontier

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technique to estimate RTS in different industries; they have not examined issues related to the productivity of bank credit (the country’s scarce resource) and of labor (the abundant factor). Moreover, performance resulting

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from recent changes in economic policies targeting this sector should be addressed. As a key part of India’s manufacturing sector, its performance requires a fundamental understanding of the productive/efficiency

expand it.

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characteristics regarding factor productivity and RTS since the Government continues to commit resources to

The rest of the paper is structured as follows: (i) an overview of SSI in India, (ii) financing small businesses in developing countries, relevant literature, (iii) literature on “money as an input in the production function” (iv) model specification and data, (v) discussion of results, and (vi) conclusion. 2. An overview of the SME sector of India The Government of India (GOI) has always recognized that SME will contribute to the economic progress of the country with its labor intensive (capital scarce) factor endowment. The arguments for its expansion are: 3

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(i) small firms would create additional employment opportunities, (ii) small firms are capable of producing large quantities of consumer goods and increasing demand and income would generate new investment in heavy basic industries, (iii) SME could produce a wide range of products with technology varying from traditional to state of art.3

In India, the definition of SME is in terms of cumulative investment in plant and machinery; Table 1A

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shows the changing sizes in terms of fixed asset over past years. 4 The sector mainly relies on bank finance for its operation, thus adequate flow of credits has been an overriding policy objective of the government. The growth

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of this sector has been spectacular; from 1979-2013, (i) the number of units of SME increased from 0.73 million to 46.75 million, see Figure 1 (ii) production increased from 58,200 crore (582,000 million) ₹ to 1,809,976 crore

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(18,099,760 million) ₹ at constant price, see Figure 2 (iii) employment increases from 6.38 million to 106.14 million, see Figure 3 (iv) export from this sector increased from 11 billion crore (11,000 million) ₹ or $1,303

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million to 6,973.18 billion ₹ (6.97318 million) or $128,162 million.5 Recent data from Reserve Bank of India (2006) attest to the economic importance of this sector, it accounts for: (i) 95% of industrial units in the country,

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(ii) 39.92% of value added in the manufacturing sector, (iii) 34.29% of national exports, (iv) 6.86% of Gross Domestic Product (GDP), and (v) employment of 19.3 million persons. Government policy to increase the size of

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this sector involves the passage of the Micro, Small, and Medium Enterprises Development (MSMED) Act of 2006; with the passage of this Act, the aggregate number of units increased from 12.34 million in 2006 to 46.75

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million in 2013. Table 2 A highlights some of the provisions. The economic liberalization process that began in

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1991 aimed at deregulating the economy to achieve greater competition in the domestic markets and openness to international trade and investments also included specific policies that target the development of the SME sector (Ahluwalia, 1994). Foremost was the removal of industrial licensing to create new investments; a controversial aspect of the “delicensing” policy is the restriction of large firms from producing selecting manufactured products “reserved” for SME.

The sector mainly relies on bank finance for its operation, thus adequate flow of credits has been an overriding policy objective of the government. Beginning 1969, within nationalization of the banking industry, banking policy mandated that 40% of banks’ credit should be allocated to “priority” sectors which include SME and agriculture. Preferential treatment includes low interest rates. The Small Industries Development Bank 4

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(SIDB) was established in 1990 as the premier financial institutions for promoting and developing of this sector. Bank loans (advances) to the SME sector increase steadily from 22.32 billion ₹ in 1979 to 6,872 billion in 2013 (see Figure 4) Delinquent loans (loans outstanding for more than 2.5 years) constitute a major problem affecting this sector; however, the number of units has been decreasing since 2001. Figure 5 shows the pattern of “sick”

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units with outstanding loans. [Figure 1 here]

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[Figure 2 here]

[Figure 4 here]

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[Figure 5 here]

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[Figure 3 here]

Table 3 A provides a summary of recent targets for priority sector lending by commercial banks.6 Banks have

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been advised to achieve a 20% annual growth to SMEs; the allocation of 60 percent of the advances is to be achieved in three stages: 50 percent in the year 2010-11, 55% in the year 2011-12, and 60 percent in the year

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2012-13. Over 70% of loans to the SME sector are from public sector banks, next of importance are private sector banks, while foreign banks provide less than 5%. Table 4A provides data on bank loans from 2008 to

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2011. Kamesam (2003) identifies other problems faced by this sector, some include the following: (i) a limit for collateral free loans, many SME entrepreneurs are facing difficulties in providing collateral security (ii) high

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borrowing cost for loans, and (iii) considerable delay in the settlement of dues/payment of bills, and (iv) marketing, small SSI have to sell output individually. A number of measures have been taken to address these problems to promote sustainable growth of this sector. 3. Financing Small Business, Review of the Literature A growing literature on the financing of SME includes a seminal paper by Ayyagari, et.al (2012) who examine financing pattern of 99 countries based on institutional and economic factors; they analyze the pattern of financing based on the following stylized facts about firms : (i) concentration of ownership, (ii) capital structure choice, (iii) bank versus market sourcing, (iv) access to foreign capital, (v) cross border mergers, (vi) 5

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productivity, (vii) industry structure and entrepreneurship, (viii) the role of small firms, and (ix) informality. The important findings are (i) debt financing (bank loans) is the major source of external financing, (ii) foreign bank entry has potential of increasing lending, (iii) labor productivity is low because of a mix of financial and organization factors ( poor access to finance and poor management), and (iv) informal firms (unregistered )

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account for up to half of all economic activity in developing countries; also informal financing channels play any important loan in facilitating access to finance. Calice (2012) use some of these facts to analyze African

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countries. With the presence of foreign banks in developing countries because of financial liberalization, Clarke et al (2005) find that foreign banks play a significant role in lending to firms in some Latin America countries.

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Beck et al (2005, 2008) and De la Torre et al (2010) find that the sources (and pricing) of lending to SMEs in developing countries include large, small, private, government-owned, and foreign banks. This is beyond

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“relationship” lending, the type of financing based on “soft” information generated by loan officers through direct and personalized contacts with owners of SMEs. Beck et al (2008) also find that size matters, small firms

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in countries with poor institutions use less external finance. A recent study by Beck et al (2011) indicates the following variables significant in bank financing of SME, (i) ownership types, (ii) foreign banks, (iii) domestic

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banks, (iv) different lending technology, (v) organization structure.

Tybout (2000) lists other

cultural/institutional factors affecting the financing of small firms, some are (i) policies tends to favor large firms

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because they are low risk and cheaper to service, (ii) private sector credit is relatively scarce, (iii) information

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networking are poorly developed, (iv) binding interest rate contracts are very common, (v) some small producers operate partly or wholly outside the realm of government regulation and rely heavily on the informal credit market.

Serval studies investigate the impact of bank loans on economic growth. King and Levine (1993a, 1993 b) and Levine (1997) articulate a positive relationship between financial development and economic growth. Ranjan and Zingales (1998) provide evidence that firms/ industries with external financing grow faster. Fishman and Love (2009) provide modified technique to investigate this issue. Levine and Zervos (1998) examine the impact of financing development on economic growth using the amount of bank loans available to firms as one of the indicators of financial development. 6

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4. Money as an input in the production function: review of the literature. The production approach methodology to examine the impact of real balances (loans and credit) on output was stimulated by Friedman’s (1969) theory of the optimal amount of money stating that money should be treated as a productive input similar to capital and labor in explaining the behavior of firms. Finnerty (1980)

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provides several arguments why real money balances should be included in the firms production function, he contends (page 671), “we have also shown that, in general, the firms expansion path will not be identical with or

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without cash balances as a factor input, which suggests that including cash balances in the production function

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may be the more appropriate formulation.” Laumas and Mohobbat (1980) note that if real balances are used as an input, then it must have productivity characteristics (e.g. marginal and average productivity) similar to physical

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inputs. They use a three factor (capital, labor, and real money balances) aggregate Cobb-Douglas (CD) production function to estimate factor output elasticity for the French economy; real balances are defined as

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currency outside banks plus demand deposit, the results show the significance of the output elasticity of real balances. Sinai and Stokes (1991) suggest that the CD specification may have errors; instead, they test a three

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factor Constant Elasticity of Substitution (CES) model with different measures (M1, M2, and financial assets held by non-financial corporations) of real balances.

Khan and Ahmad (1984) applied a three input (capital, labor,

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and real money balances) CD production technique to large scale manufacturing sector of Pakistan. They use a multi equation framework and find the coefficient of real money balance statistically significant. Hassan and

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Mahmud (1989) use a translog cost function with the following independent variables, capital, skilled labor, unskilled labor, and real balances (measured by cash and marketable securities).They estimate the following elasticities (i) own price , (ii) cross price , and (iii) substitution; all the elasticities are statistically significant. Falls and Natke (1988) delineate the demand approach to analyze scale efficiency; it emphasizes the firm’s transaction demand for liquid assets (money). Their model includes liquid assets as the dependent variables and the following independent variables (i) a scale factor, measured by sales, (ii) ownership, indicated by foreign or domestic and (iii) different industry classifications. Falls and Natke (2010) extend their model to examine the difference in scale elasticity in money holding between small firms and large US corporations; scale is measured by sales or assets. The results show small firms having higher scale elasticity. 7

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5. Methodology The literature on estimating production efficiency (Intrigillator 1978; Chung 1994; Bairam 1997; Fried et al 2008), lists several techniques. Two common functional forms used in early studies are: (i) the Cobb-Douglas (C-D) and (ii) the Constant Elasticity of Substitution (CES). Their specifications contain several problems; one

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of which is the restriction on the value of the parameter measuring RTS. Both models assume that RTS is fixed (constant) at all levels of output;7 later studies recognize the possibility of variable RTS with changing input

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(capital/labor) ratio. Revanker (1971) develops a variable elasticity of substitution (VES) specification in which

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factor productivity varies with the input ratios. Christensen et al (1973) articulate the importance of developing empirical tests of production theory employing the non-constant scale elasticity (non-homogeneity); they

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develop the transcendental logarithmic (translog) production function which has been applied by others, including Kim (1992).8

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The NHPF model that we apply is a special case of a log-quadratic production function used by Vinod (1972) to estimate factor intensity and returns to scale. The virtues of this model are: (i) no restriction is imposed

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on the data, (ii) the specification is flexible, and (iii) it estimates the properties of production, for example, output elasticity and return to scale with different factor proportion, these estimates vary over the period of study and

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(v) the model is linear in its parameters and can be estimated by OLS.

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This specification enables the estimation of productivity parameters that vary overtime with the level of output and factor proportion, this helps to analyze the variation in efficiency and to relate it to change in specific policies. The data on the SME of India show significant variation in output, amount of bank loans, number of units of SME, and the level of employment overtime, thus a flexible model specification is appropriate to estimate production efficiency. Additionally, there have been various public policies enacted by the government to enhance the performance of this sector. Ramcharran (2001, 2011, and 2012) has applied this methodology to analyze production efficiency in different industries. The model specification is:

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a0

Q=e

CR

a1+a3lnL a2

L .

(1)

The NHPF model includes multiplicative input combination to assess their joint contribution to productivity. Since Eq. (1) is multiplicative, it can be written in double logarithmic format as ln Qt = a0+a1 lnCRt + a2 lnLt + a3 (lnCR*lnL)t,

(2)

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where: Q = Production at constant prices (millions of rupees) CR= the amount of bank credit (millions of real rupees)

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L= number of employees (millions)

The main restriction on the model is that if a3 is not statistically significant from zero (say at the 5% level), we

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may reject the non-homogeneous formulation (Eq. 2) and use for example a CD formulation.

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The elasticity output of the amount of credit (ECR) and of labor (EL) is respectively: ECR = ∂lnQ/∂lnCR = a1 + a3 lnL,

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The scale elasticity is expressed as RTS

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EL= ∂lnQ/∂lnL =a2 + a3 lnCR.

RTS = (ECR + EL), or a1+ a2 + a3 ln(CR*L).

(3) (4)

(5)

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Note that the productivity measure of each input is related to the productivity of the other input and the level of

6. Data

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output; these are consistent with some of the fundamental assumptions of the NHPF.

The sources of the data are: (i) Handbook of Statistics on the Indian Economy, 2012-2013 (Reserve Bank of India) and (ii) Ministry of Micro, Small, and Medium Enterprises, Annual Report (2002, 2006, and 2013), Government of India. The variables used are (i) L, employment in millions, (ii) CR, the amount of loans in millions of real rupees, and (iii) Q, is output at constant price (millions of rupees). 6.1 Descriptive statistics. Descriptive statistics on the distributional properties of the variables are presented (actual values) in Table 1. Test for normality based on the coefficient of skewness indicates non-normality, all 9

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three variables are positively skewed; the coefficient of kurtosis also indicates non-normality, it is greater than three (leptokurtic) for all three variables. The values of the Jarque- Bera statistics also indicate the rejection

of the null hypothesis of normality (skewness = 0, excess kurtosis = 0) for the variables at α = 0.10; these values are higher than the critical values of the Chi-square distribution with 2 degrees of freedom

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[Table 1 here]

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(Brooks, 2002). The low values of ρ also indicate rejection of the null hypothesis.

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6.2 Test of Unit Root. To avoid the problems of “spurious regression” in empirical studies using time series data, we test for the stationarity of the data, using the ADF (Augmented Dickey- Fuller) test which corrects for

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uncorrelated error terms. There are several tests discussed in the literature (Gujarati and Porter 2009; Enders 1995), however, the unit root test is very prominent. The results, shown in Table 2, indicate that for the variables L and Q the null hypothesis of the existence of unit root (non-stationarity of the data) is rejected at the first

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difference level and in the three cases that allow for (i) an intercept, (ii) an intercept and deterministic (linear)

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trend, and (iii) none. For the variable CR, the null hypothesis is rejected only for the first case.

6.3 Test of cointegration

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[Table 2 here]

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The importance of a long run stable relationship among the variables used in time series econometric models is widely documented in the literature (Maddala and Kim 1998; Enders 2010, Johansen 1988). The results of a model derived from cointegrating variables are stable over the period analyzed and are valid for statistical inferences. Granger (1986) avers that the test of co-integration can be thought of as a pre-test to avoid the problems of ‘spurious regression’. We examine two versions of the unrestricted rank test using (i) “trace” statistics test, and (ii) Max-Eigen statistic under the assumption of no determining trend. The results, presented on Table 3, indicate cointegrating relationship among all three variables L, CR, and Q expressed logarithmic transformation (lnL, lnCR, and lnQ) based on the “rank” test. [Table 3 here] 10

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Despite some evidence of non-normality in the distribution of the data, the existence of stationarity and cointegration of the variables ensure the reliability of the estimated results.

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7. Discussion of regression results

ln Qt = 11.66 + 0.5229 ln CRt -2.743 ln Lt + 0.1723 (lnCR*lnL)t (3.319)* (-3.117)* (2.199)*

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The regression results Of Eq.2 (with t values in parentheses) are:

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R2 = 0.885, Ṝ2 = 0.874, DW = 0.523, F = 80.8, Prob (F-statistic) = 0.00, * denotes significance at α = 0.05 level and less

All three coefficients, based on the t-values, are statistically significant at the 1% level; the coefficient of CR is

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positive and of L negative. Importantly, the significance of the coefficient a3 justifies the relevance of the NHPF technique. An adjusted Ṝ2 of 0.87 indicates a high explanatory power of the model; the F statistic is also

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significant. The DW statistic indicates no evidence of positive autocorrelation at α = 0.10.Since R2 < DW, there is no reason to suspect that the estimated results are spurious (Granger and Newbold 1974), this is supportive of

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the results of the unit root test and the cointegration test. The productivity estimates based on the above results

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are presented in Table 4. Figure 6 shows the graphical pattern of productivity estimates. [Table 4 here] [Fig. 6 here]

The results indicate improved efficiency in the operation of SME over the period studied an increase in RTS from -0.809 to 0.607 mainly due to the increasing productivity of bank loans. From 1978-79 to 1997-98 RTS is negative; however in the post 1998-1999 period RTS is positive and operation is at decreasing RTS (RTS < 1). The improvement is apparently due to the effects of the special preferential treatment to this sector under economic liberalization reforms during the 1990s. The other studies, Bhavani (1991),Ramaswamy (1994), and Little et al (1987) find RTS less than unity using different production specification and data from an earlier period. The output elasticity of labor (EL) is negative throughout the period but increases from -1.5 to -0.728; the 11

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primary cause is the low level of skills. However, the increase could be attributed to the easement of some of the rigid labor laws during this period; Ahluwalia (1994) and Aiyar (2011) have noted that India’s restrictive labor laws (which make it difficult to retrench workers in small companies)and prevent flexibility in input choice (production technique). Other studies (Bloom and Van Reenen 2007; Bloom et al 2010) also report very low

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productivity of labor in firms in developing countries. The important aspect of our findings is the positive and increasing productivity of bank loans, ECR

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increases from 0.76 to 1.23. The possible contributory factors are: (i) increasing allocation to the SME sector

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(see Figure 2), and (ii) the decrease in the number of “sick” units beginning 2000 (see Figure 3). Several studies (Mester 1996, 1997; Berger and De Young 1996) have documented the impact of problem loans on the

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efficiency of financial institutions.9 Other studies (Mel et al 2008, Mckenzie and Woodruff 2005) also report high return on capital investment in small firms in developing countries. Since RTS is the sum of ECR and EL, this

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improved overall efficiency is attributed to the positive and increasing value of ECR. The negative EL has important implications for policy making since one of the goals of SME development is the efficient use of the

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country’s abundant factor; a negative EL does not bode will with this objective. Tybout (2000), in analyzing the role of skilled workers in efficiency, notes that flexibility in the production process and the ability to absorb new

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technology are positively related to the stock of human capital.

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8. Policy Implications and Conclusion

Many studies have shown that access to finance is closely linked with the performance and efficiency of SME in developing countries. Within the framework of “money as an input in the production function”, the NHPF estimates indicate improvement in scale efficiency the SME sector of India mainly due to the positive and increasing productivity of bank loans. Other studies discussed in this paper before also report a positive impact of external financing on growth. Future lending to this sector will depend on: (i) the rising cost of credit, (ii) economic recession that could slow the demand for output from this sector, and (iii) banks’ compliance with international regulations to increase capital. Most of the loans to SME are from state owned banks which account for about 75 percent of the banking assets of the country. The prospects of financing from other sources are not promising because of the following: (i) venture capital is primarily for firms with high growth potential, and (ii) 12

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the national equity markets will not accommodate small firms. A recent proposal to reform the banking industry has the potential of increasing loans to the SME sector from private and foreign banks. The new measures will allow new licenses to increase foreign investment and allow private companies to transform their financial services department to full-fledged banks.10 Additionally, a recent report indicates that there is a growing

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number of bad loans owed to government banks by the top Indian companies; this is considered a significant risk factor to the financial system.11

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Some analysts have expressed reservations about “credit efficiency” regarding the priority lending

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mandate to SME in light of the high number of “sick” units.Tybout (2000) indicates that the potential gains from scale economy exploitation may be dampened by policies that prop up “sick” firms and thereby saturating the

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market with inefficient producers and discouraging better firms from entering. Many studies (Boot et al 1993 and Ranjan and Zingales 1999) have favored “market” lending since private banks have better screening and

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monitoring methods; this could possibly eliminate the existence of many “sick” banks. Labor productivity, though it increases, is still negative. Business policies to address the low level of

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skills and surplus workers are needed to improve efficiency. It is estimated that by 2016, 50 per cent of India’s labor force will be in the age group of 15-25 years; thus, there will be tremendous pressure for this group to be

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efficiently employed. There are also complementary /external factors to be addressed; these include the nonavailability of some raw materials, power shortages, transport and financial bottlenecks, production disruption

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and demand shortfalls.

This research adds significantly to the empirical literature on the effects of bank lending to SMEs; the estimation technique enables us to identify the sources of efficiency/inefficiency. This enables the formulation of appropriate policies to enhance productivity. The slight limitation of the model, like other studies, is some aggregation bias since there are firms (units) with different sizes, ages, and operating in different locations. Systematic published data at the disaggregate level are not published and unavailable, plus the latest data are not very timely.

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Notes: 1. Kamesam (2003) lists some of the various measures implemented by the GOI for the development of SME, they include: (i) product reservations, (ii) fiscal concessions, (iii) preferential allocations of credit and interest subsidy in a credit rationing framework, (iv) extension of business and technical services, (v) preferential procurement by the government, and (vi) the provision of lower interest rates as well as requirement for a minimum credit allocation from the commercial banks.

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2. An important aspect of the reform program includes policies targeting the performance of the SME sector: (a) industrial policy that remove barriers to industrial licensing to enable the creation of new investment and expand existing capacity,(b) labor market reforms that gradually remove some of the restrictive labor laws by creating a safety net (National Renewal Fund) , to deal with problems of displaced workers, and (c) financial sector (banking) reform to enable the financial sector to mobilize savings and allocate resources towards more efficient use in restructuring the real economy. All these three polices have resulted in a vast increase in the number of units of enterprise, the level of employment and funds (bank loans) available to this sector.(See Ahluwalia, 2002, 1994)

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3. Kumar (www.preservearticles.com) also notes the following important roles played by SME: (i) mobilization of resources and entrepreneurial skill, (ii) promoting equitable distribution of income, (iii) enabling regional dispersal of industries (iv) providing opportunities for the development of technology, (v) indigenization of production, (vi) export promotion, (vii) supports the growth of large industries through linkages, and (viii) better industrial relations and social inclusion. 4. The definition of SME is by the value of fix assets; in other countries it is defined by the level of employment or by sales (World Bank 2010).

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5. The data are from Handbook of Statistics of India Economy (RBI, 2010-201) 6. See “Master Circulation- Lending to Priority Sector”, Reserve Bank of India, July 2012 and Chakaraborty (2012)

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7. This results in the long run average total cost curve (LRATC) being either constantly rising (decreasing return to scale), horizontal (constant returns to scale), or constantly falling (increasing returns to scale).

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8. A main problem with the translog production function is the possibility of multi-collinearity arising from using the squared value of each input and the product of two inputs as independent variables. For a twoinput case, Q = f (X1, X2), the translog specification is ln Q = a0 + a1 ln X1 + a2 ln X2 + a3 (ln X1)2 + a4 (ln X2)2 + a5 (ln X1 * ln X2) If a3, a4, and a5 are not statistically significant, the C-D specification is appropriate (see Intrilligator (1978) Chap. 8). 9. We could not control for the problems of bad loans on efficiency, for example, by including it as an independent variable because of the lack of systematic published date on bad loans. 10. “India Eases Its Hold on Bank Lending”, Financial Times, J. Carbtree, December 20, 2012 11. “Bad Loans at State- Run Banks Add to India’s Woes” by Vikas Bajaj. New York Times, March 15, 2012

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Upto Rs.5.0 lakh in fixed assets

1960 1966 1975 1980 1985 1991 1997 1999 2001

Upto Rs.5.0 lakh in fixed assets Upto Rs.7.5 lakh in Plant & Machinery Upto Rs.10 lakh in Plant & Machinery Upto Rs.20 lakh in Plant & Machinery Upto Rs.35 lakh in Plant & Machinery Upto Rs.60 lakh in Plant & Machinery Upto Rs.300 lakh in Plant & Machinery Upto Rs.100 lakh in Plant & Machinery Upto Rs.100 lakh in Plant & Machinery

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Annual Report 2002-2003, Government of India, Ministry of Small Scale industries

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Source:

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1950

Additional Conditions Less than 50/100 persons with or without power No Condition No Condition No Condition No Condition No Condition No Condition No Condition No Condition No Condition

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Investment Limits

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Year

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Table 1A Definition of SSI Since 1950

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Table 2A provisions of MSMED Act 2006 A.1. The Government of India has enacted the Micro, Small and Medium Enterprises Development (MSMED) Act, 2006 in terms of which the definition of micro, small, and medium enterprises is as under:

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(a) Enterprises engaged in the manufacture or production, processing or preservation of goods as specified below:

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(i) A micro enterprise is an enterprise where investment in plant and machinery does not exceed Rs. 25 lakh; (ii) A small enterprise is an enterprise where the investment in plant and machinery is more than Rs.25 lakh but does not exceed Rs. 5 crore; and (iii) A medium enterprise is an enterprise where the investment in plant and machinery is more than Rs. 5 crore but does not exceed Rs. 10 crore.

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(b) Enterprises engaged in providing or rendering of services and whose investment in equipment ( original cost excluding land and building and furniture, fitting and other items not directly related to the service rendered or as may be notified under the MSMED Act, 2006, are specified below.

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(i) A micro enterprise is an enterprise where the investment in equipment does not exceed Rs. 10 lakh, (ii) A small enterprise is an enterprise where the investment in equipment is more than Rs. 10 lakh but does not exceed Rs. 2 crore, and (iii) A medium enterprise is an enterprise where the investment in equipment is more than Rs. 2 crore but does not exceed Rs. 5 crore.

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Source : Annual Report (2005-06), Government of India Ministry of Small Scale Industry

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Note: 1 crore = 10 million, 1 lakh = 100, 000

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Table 3A Targets for Priority Sector Lending by Domestic Commercial Banks

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A. The domestic commercial banks are expected to enlarge credit to priority sector and ensure that priority sector advances (which include the micro and small enterprises (MSE) sector) constitute 40 per cent of Adjusted Net Bank Credit (ANBC) or credit equivalent amount of Off Balance Sheet Exposure, whichever is higher. B. In terms of the recommendations of the Prime Minister’s Task Force on MSMEs, banks are advised to achieve a 20 per cent year on year growth in credit to micro and small enterprises and a 10 per cent annual growth in the number of micro enterprise accounts C. In order to ensure that sufficient credit is available to micro enterprises within the MSE sector, banks should ensure that: (a) 40 percent of the total advance to MSE sector should got to micro (manufacturing) enterprises having investment in plant and machinery up to Rs. 5 lakh and micro (service) enterprises having investment in equipment up to Rs. 2 lakh; (b) 20 per cent of the total advances to MSE sector should go into micro (manufacturing) enterprises with investment in plant and machinery above Rs. 5 lakh and up to Rs. 25 lakh and micro (service) enterprises with investment in equipment about Rs. 2 lakh and up to Rs. 10 lakh. Thus, 60 per cent of MSE advances should go to the micro enterprises. (c) While banks are advised to achieve the 60% target as above, in terms of the recommendations of the Prime Minister’s Task Force, the allocations of 60% of the MSE advances to the micro enterprises is to be achieved in stages viz. 50 % in the year 201011, 55% in the year 2011-12 and 60% in the year 2012-13.

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Source: Micro & Small Enterprises (MSE) Information Kit: Central Bank of India Priority Sector Department

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All SCBs Amount 2135.386 2561.280 3622.907 4785.270

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Foreign banks Amount % 154.892 7.25 180.634 7.025 211.470 5.83 209.810 4.38

% 100 100 100 100

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BIS central bankers speeches: " Address by Dr K C Charkrabarty, Deputy Governor of the Reserve Bank of India, at the " SME Banking Conclave 2012, Mumbai, 4 Feburary 2012"

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Source:

Private sector banks Amount % 469.118 20.96 466.563 18.21 648.247 17.89 881.160 18.41

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2008 2009 2010 2011

Public sector banks Amount % 1511.374 70.77 1914.083 72.73 2763.189 76.27 3694.300 70.2

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Year

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Table 4A Outstanding credit to the SME sector by Scheduled Commercial Banks( in Rs billion)

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Reference: Ahluwalia, M. S. (2002) “ Economic reforms in India since 1991: Has gradualism worked?” Journal of Economic Perspectives, 16:67-88

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Ahluwalia, M. S. (1994), “ India’s quiet economic revolution” The Columbia Journal of World Business, Spring 1-12 Aiyar, S. S. A. (2011) “ The elephant that became a tiger”, CATO Institute, Center for global liberty and prosperity, No.13, July 20

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Annual Report (2002-2003), Government of India, Ministry of Small Scale Industries

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Annual Report (2005-2006), Government of India, Ministry of Small Scale Industries

Ayyagari. M, T. Beck, A. Demirguc-Kunt, (2007), “ Small and medium enterprises across the global”, Small Business Economics, 29, 415-434,

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Ayyagari, M, Asli Demirguc-Kunt, V. Maksimovic, (2012), “ Financing of firms in Developing countries” Policy research working paper 6036, The World Bank

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Bairam, E.I.(1997). Homogenous and Nonhomogeneous Production Functions, Avebury Press Bajaj, V. (2012). “Bank Loans at State-Run Banks Add to India’s Woes”, New York Times, March 15

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Beck, T., Demirguc-Kunt, A., Maksimovic. V. (2005). “ Financial and Legal Constraints to Growth : Does Firm Size Matter?” Journal of Finance 60 (1), 137-177 Beck, T., A. Demirguc-kunt, and M.S. Martinez Peria, (2011). “ Bank Financing for SMEs: Evidence Across Countries and Bank Ownership Types”, Journal of Financial Services Research 39, 34-34

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Beck, T., A. Demirguc-kunt, and V. Maksimovic (2008) “ Financing patterns around the world : are small firms different?” Journal of Financial Economics, 89, for 467-487

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Beck, T., Demirgu-Kunt, A., L. Laeven, and R. Levine (2008) “Finance, Firm Size and Growth” Journal of Money Credit and Banking, 40, 1379-1405 Berger, A.N, and D. B. Humphery (1997), “Efficiency of Financial Institutions: International Survey and Directions for Future Research”, The Wharton Financial Institution Center Berger A.N, W.C. Hunter, and S.G. Timme, (1993) “The efficiency of Financial Institutions: A Review and Preview of Research Past, Present, and Future”, Journal of Banking and Finance, 17: 221-49 Berger A.N, and R. DeYoung (1996), “Problem loans and cost efficiency in commercial banks” Working paper, Broad of Governor of the Federal Reserve’s System, Washington. D.C. August. Bhavani, T.A. (1991). “ Technical Efficiency in Indian Modern Small Scale Sector: An Application of Frontier Production Function,” Indian Economic Rev., 31, pp. 149-66 Bloom, N. and Van Reenen, J. (2007). “ Measuring and Explaining Management Practices Across Firms and Countries”. The Quarterly Journal of Economics , MIT Pres, VOL.122(4), pages 1351-1408, November.

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Calice, P., V. Chando, S. Sekioua (2012), “ Bank financing to small and medium enterprises in East Africa: finding of a survey in Kenya, Tanzania, Uganda and Zambia” , Working Paper Series, Working Paper No. 146, African Development Bank Group,

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Chakrabarty, K.C (2012) “ Empowering MSMEs for financing inclusion and growth-the role of banks and industry associations”, Mumbai, February 4th , BIS central bankers’ speeches

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Christensen, L., D. Jorgenson, and L. Lau, (1973). Transcendental Logarithmic Production Frontiers, Review of Economics and Statistics, 55, February, 28-45.

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Chung, J. W. (1994). Utility and Production Functions, Blackwell Publishers.

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Clarke, G, R. Cull, Maria Soledad Martinez Peria and Susana M. Sanchez, (2005) “Bank Lending to Small Businesses in Latin America: Does Bank Origin Matter?”, Journal of Money, Credit, and Banking, 37, 93-118 Crabtree, J (2012), “India Eases Its Hold on Bank Lending”, Financial Times, December 20

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De la Torre, A., M.S. Martinez Peria, and S.Schmukler, (2010). “ Bank Involvement with SMEs: Beyond Relationship Lending,” Journal of Banking and Fiancing 34, 2280-2293 Dewatripont, M., and E. Maskin, (1995). Credit efficiency in centralized and decentralized economics. Review of Economic Studies 62, 541-555

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McKenzie, D., Woodruff, C.(2005). “ Experimental Evidence on Returns to Capital and Access to Finance in Mexico.” World Bank Economic Review 22(3): 457-82 Mester, L.J. (1996), “A study of bank efficiency taking into account risk-preferences”, Journal of Banking and Finance, 20, 1025-45

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Reserve Bank of India (2012), Master Circular- Lending to priority sector, July 02

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Revankar, N.S. (1971). A Class of Variable Elasticity of Substitution Production Functions, Econometrica, 39 (1): 61-71. Schiffer, M. and B. Weder, (2011). “ Firm size and the Business Environment: Worldwide Survey Results”. IFC Discussion Paper, 43. The World Bank, Washington DC

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Sinai, A and H. H, Stokes (1981), “ Money and production function: a reply to Boyes and Kavanaaugh” The Review of Economics and Statistics, Vol. 63, No. 2, 313-318

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Tybout, James R (2000), “ Manufacturing firms in developing countries: How well do they do and why?” , Journal of Economic Literature, March 2000, pp. 11-44 Vinod, H.D. (1972). Non-homogeneous Production Functions and Applications To Telecommunications, Bell Journal of Economics and Management Science, 3 (2): 531-543. World Bank (2010), “ Micro, small and medium enterprises around the world : How many are there and what affects the count?”, by Kushnir. K, M, Mirmulstein, and R. Ramalho,

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Figure 6: Productivity Estimates

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Figure 4: Productivity Estimates

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L

CR

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

1611624. 1181000. 5329790. 342000.0 1284048. 1.464307 4.224861

17.33486 15.83000 62.63000 3.970000 13.48860 1.887274 6.978293

35131.49 17938.00 213539.0 904.0000 44537.94 2.207891 8.683047

Jarque-Bera Probability

14.69572 0.000644

43.85797 0.000000

75.53604 0.000000

Sum Sum Sq. Dev.

56406830 5.61E+13

606.7200 6186.036

1229602. 6.74E+10

Observations

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Table 1 Descriptive Statistics

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Table 2 Results of ADF Test of Unit Roots Test in

Included in Test

Coefficient

t(tau) Value

Prob

Decision*

LQ

1st Diffrence

Constant Constant & Trend None

-0.9639 -0.966 -0.783

-5.367 -5.292 -4.5156

0.0000 0.0000 0.0000

Reject Ho Reject Ho Reject Ho

LL

1st Difference

Constant Constant & Trend None

-1.066 -1.087 -0.719

-5.9729 -5.963 -4.2333

0.0000 0.0000 0.0000

Reject Ho Reject Ho Reject Ho

LCR

1st Difference

Constant Constant & Trend None

-0.609 -0.597 -0.056

2.699 2.4311 -0.5182

0.1012 0.0212 0.6079

Reject Ho Do not reject Ho Do not reject Ho

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Variable

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*Ho: unit root exists. Decision is based on the Augmented Dicky-Fuller test statistic, MacKinnon (1996)

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Table 3 Johansen Cointegration, Test Trend assumption: No deterministic trend, Series: LL LQ LCR Lags interval (in first differences): 1 to 1

Hypothesized No. of CE(s) Eigenvalue

Trace Statistic

0.05 Critical Value Prob.**

None At most 1 At most 2

20.56092 2.614336 0.216396

24.27596 12.32090 4.129906

0.419483 0.070088 0.006536

0.1371 0.8931 0.6987

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Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Max-Eigen Statistic

0.05 Critical Value Prob.**

None * At most 1 At most 2

17.94658 2.397940 0.216396

17.79730 11.22480 4.129906

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Hypothesized No. of CE(s) Eigenvalue

0.0475 0.8748 0.6987

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0.419483 0.070088 0.006536

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Unrestricted Cointegration Rank Test (Trace)

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Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

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-1.5701 -1.5460 -1.5282 -1.4944 -1.4609 -1.4068 -1.3819 -1.3159 -1.2949 -1.2625 -1.2292 -1.1981 -1.1698 -1.1449 -1.1111 -1.0903 -1.0752 -1.0552 -1.0458 -1.0282 -1.0052 -0.9714 -0.9438 -0.9249 -0.8938 -0.8728 -0.8558 -0.8467 -0.8278 -0.8341 -0.8176 -0.7901 -0.7569 -0.7174 -0.6283

0.7605 0.7636 0.7856 0.7996 0.8136 0.8423 0.8507 0.8607 0.8702 0.8791 0.8901 0.9016 0.9127 0.9222 0.9314 0.9408 0.9506 0.9989 1.0071 1.0160 1.0235 1.0316 1.0374 1.0442 1.0502 1.0561 1.0626 1.0713 1.0793 1.0869 1.0943 1.0988 1.1061 1.2270 1.2360

-0.8096 -0.7824 -0.7427 -0.6948 -0.6473 -0.5645 -0.5311 -0.4551 -0.4247 -0.3833 -0.3390 -0.2965 -0.2570 -0.2228 -0.1797 -0.1494 -0.1246 -0.0563 -0.0387 -0.0122 0.0184 0.0603 0.0936 0.1193 0.1565 0.1833 0.2068 0.2246 0.2514 0.2528 0.2767 0.3087 0.3493 0.5096 0.6076

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1978-79 1979-80 1980-81 1981-82 1982-83 1983-84 1984-85 1985-86 1986-87 1987-88 1988-89 1989-90 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13

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Table 4: Estimates of Elasticity and RTS

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