Impact of Monetary Policy on Loan Availability to Small and Medium Enterprises in Pakistan

Chapter 1: Introduction

1.1 Background

In terms of population, Pakistan is the 6th largest country. It is replete with substantial natural endowments, advantageous climatic conditions, and a large amount of skilled and unskilled workforce. In spite of all this factor availability, Pakistan still struggles to join the group of economically developed nations. The major obstacle towards economic development has been the use of a western approach based on capital-intensive mechanisation. Successive governments over the years have implemented policies of capital accumulation, but this has only resulted in massive levels of poverty, unemployment, and huge current account and budget deficits, resulting in alarming debt levels. On the other hand, the most appropriate approach towards economic growth may be the promotion of small and medium enterprises (SMEs), which has been largely ignored during previous years (Metha, 2009).

The western developed nations and Asian tigers (Malaysia, South Korea, Singapore and Japan) owe much of their economic success to the SME sector (Kader & Ibrahim, 2007; Esra, 2007). Government attention and favourable policies over the years have led to rapid economic growth of these nations. Entrepreneurship is asserted to be the main force behind economic success in capitalistic societies and a tool of social change in some developing nations (Andreas and Roy, 2007). According to Nails (2002) and Acs (2004), entrepreneurship provides a significant contribution to economic success of entrepreneurial economies by permeating the knowledge filter and developing ideas as businesses.

In spite of the fact that SMEs can play a vital role in the economic growth of the country, their contribution is just 7% to total GDP in Pakistan (GEM, 2007). The growth of the SME sector in Pakistan has remained quite dormant due to policy bias. As a result, a large number of SMEs are involved in traditional businesses, lacking access to technology, with low levels of productivity and almost sub-standard products. Therefore, there is a major need to restore this sector.

One of the major obstacles towards SME growth around the world is access to finance. SMEs around the world list lack of finance as a major constraint to their growth (World Bank, 2011). According to Nasir (2008), an effective financial system and access to finance are keys to economic development and growth. Limited access to finance can severely constrain the growth of small and medium enterprises. Therefore, investigation of financing channels to the SME sector is essential.

A major factor affecting all kinds of credit supply is monetary policy. In this study, we investigate how changes in monetary policy can affect loans to SMEs in Pakistan. The monetary policy transmission mechanism is the procedure through which monetary policy changes affect output and inflation — the primary goals of monetary policy. In order to study transmission of monetary policy's impact on credit availability to SMEs in Pakistan, a distinction is needed between the following two channels of monetary policy transmission.

The interest rate channel is the most widely recognised channel of monetary transmission and relates to the demand side of the credit market. Transmission through this channel depends upon changes in the policy rate and short-term real interest rates, resulting in changes in the cost of financing. On the other hand, monetary policy affects the supply side of the credit market through the credit channel of monetary policy transmission. The credit channel transmits these changes by affecting the firm's ability to borrow and the bank's capacity to lend. The balance sheet channel and bank lending channel compose the credit channel. The bank lending channel indicates if monetary policy affects total credit supply, whereas the balance sheet channel determines whether monetary policy redistributes credit supply from risky to less risky borrowers — in our case, from SMEs to large corporations and governments.

1.2 Motivation of the Study

The primary motivation behind this study is that Pakistan has a huge workforce but lacks capital. Therefore, the most appropriate and quickest strategy to achieve economic growth is to promote the SME sector. SMEs contribute to GDP, provide employment to the working-age population, ensure income equality, help in reducing poverty, and improve the living conditions of the general population. However, time series data as well as literature is very limited for the SME sector in Pakistan.

The environment available to entrepreneurs in Pakistan is not very conducive for the growth of SMEs due to certain social and economic factors. The entrepreneurial class is concentrated in a few rich families in Pakistan, and the majority of businessmen are too small to grow into SMEs. Numerous formal institutions are established to finance SMEs in Pakistan, but their role has not been very commendable. Most small-scale entrepreneurs in Pakistan find it impossible to obtain credit from formal institutions due to lack of collateral, leaving a large number of SMEs deprived of access to easy finance.

Numerous studies have been conducted around the world at regional and country level to investigate the financing of SMEs. However, due to the unique environment in Pakistan for SMEs, these studies generally become quite irrelevant. Therefore, there is an urgent need to study the factors leading to the availability of credit to SMEs in Pakistan, which in turn determines the economic growth of the country.

1.3 Objectives of the Study

The primary objectives of the study are:

1. To establish a relationship between changes in monetary policy and credit to SMEs.
2. To investigate the credit channel of monetary policy transmission and to examine how it affects credit supply to large enterprises and SMEs.
3. To investigate whether changes in government borrowing crowd out credit to the private sector (SMEs).

1.4 Significance of the Study and Contribution

This study is devoted to explaining the relationship between monetary policy and credit availability to SMEs in Pakistan. Literature on this topic in Pakistan is almost nonexistent and almost the same is the case with the availability of time series data. According to Snage and Nam (2005), studies available on SMEs in Pakistan are quite insufficient for policy-making purposes. Hence, it is highly desirable to address the issues of SMEs and provide guidelines for the design of effective policies. Investigation of the credit channels of the monetary policy therefore contributes to the existing literature and adds to the significance of the study.

The study is organised into 6 chapters. Chapter 2 contains a literature review. Chapter 3 deals with data and its sources. The analytical framework is given in Chapter 4. Chapter 5 deals with empirical results. Finally, Chapter 6 concludes the study and outlines policy implications.

Chapter 2: Literature Review

2.1 Introduction

Roman (2010) defines an enterprise as a common setup or organisation composed of one or more people that perform economic activities utilising multiple goods and economic methods to derive profit. When it comes to defining SMEs, numerous views exist. Literature available on SMEs does not provide any single or universally accepted definition. Several approaches are available for categorising enterprises as small and medium enterprises, with differences in the economic, cultural and social aspects of countries around the world reflected in their definitions.

Various criteria are based upon number of employees, annual sales, turnover, and total assets. In the USA, a company is considered an SME if it has at most 500 employees (Small Business Administration). The World Bank defines SMEs as those businesses with at most 300 employees, $15 million in annual revenues, and $15 million in assets. According to the OECD, SMEs are non-subsidiary, autonomous enterprises which employ less than a given number of employees, with the most commonly used upper limit being 250. In Pakistan, a firm is considered an SME if it employs less than 250 people, has paid-up capital below 25 million rupees, and annual revenue up to 250 million RS (SMEDA, 2012).

2.2 SMEs and Economic Growth

With the increase in the spread of globalisation and capitalism, entrepreneurship has earned significant importance, and there is a vital relationship between the economic growth of a country and its entrepreneurial activity (Wigwan and Venter, 2004; GEM, 2002). According to GEM (2002), entrepreneurial activity can explain one third of variation in the economic growth of a country, while Metha (2009) explains that the best solution to increase a country's economic growth is to increase the number of entrepreneurs in the society.

In the Netherlands, SMEs constitute 98.8% of all the private sector, add 31.6% to GDP, and provide employment to 55% of the total workforce (EIM Business & Policy Research, 1999). SMEs provide 35 million dollars in exports and provide employment to 22 million people in Italy (Patrianila, 2003), while in Indonesia, SMEs are numbered at 42.4 million and contribute 56.7% of GDP (Blenker & Nielson, 2003). In the European Union, SMEs form 98% of total enterprises and provide employment to 65 million people (Kader & Ibrahim, 2007; Esra, 2007). In the UK, SMEs provide employment to 59% of the total workforce and form 99% of all businesses. According to Esra (2007), almost 80% of employment in Japan and South Korea is based on SMEs.

SMEs are responsible for the creation of two thirds of new jobs. Small and medium enterprises can create jobs at a lower cost compared to large enterprises. More than 95% of enterprises around the world are SMEs and employ 60–70% of the workforce (OECD). On average there are 31 SMEs per 1,000 individuals globally. In Japan and China, 60% of the addition to GDP comes from SMEs; in the USA that percentage rises to 65%; and in the EU, SMEs generate 52% of GDP (The Steering Group, 2011).

Dellis and Karkalakos (2015), using time series data from OECD countries, examine the interrelationship between entrepreneurship, unemployment and economic growth. The study finds a positive relationship for entrepreneurial activity and GDP per capita growth, with unemployment in OECD countries significantly decreasing due to the opening of new entrepreneurial ventures.

2.3 Small and Medium Enterprises in Pakistan

According to Snage and Nam (2005), literature available on SMEs in Pakistan is extremely inadequate, with most available literature composed of studies of large corporations (Beaver, 2007). SMEs compose 85% of the private sector in Pakistan and provide employment to 78% of the non-agricultural workforce. However, these businesses are mostly traditional businesses with lack of technological innovation, resulting in low levels of productivity and sub-standard products (GEM, 2007). As a result, the contribution of these SMEs to GDP is only 7%.

In Pakistan there are 3.2 million enterprises, out of which 99% are SMEs (Bianchi, Parrilli, 2002). Majority of these enterprises have tremendous potential for job creation. They can use techniques suited to the local conditions in developing countries as compared to the techniques used by large enterprises. It is important that 87% of the industrial sector in Pakistan consists of small and medium enterprises.

Ali (2013) provides an empirical relationship between small and medium enterprises and poverty alleviation in Pakistan using time series data for the period 1972–2008. The study shows that SMEs play a significant role in economic development and have a strong impact on poverty alleviation. The major obstacles faced by SMEs in Pakistan include law and order, credit availability, political instability, energy shortage, labour market issues, taxation problems, and lack of information and coordination among different government institutions (Subhan et al., 2014).

2.4 Credit as a Constraint to SME Growth

The focus of this study is on credit as an impediment towards SME growth and how availability of credit to SMEs is affected by monetary policy. Ayyagari et al. (2006) discuss that although political instability, crime, and finance affect the growth rate of firms, the credit constraint is the most significant variable. Similarly, Abor and Quartey (2010) highlight the socio-economic significance of SMEs.

Developing countries in general are replete with financial constraints, but these constraints particularly affect SMEs in developing countries due to high collateral requirements, increased administrative costs, and absence of knowledge within financial intermediaries. Availability of credit enables SMEs to make investment for future expansion. According to Maximillian (2013), SMEs depend upon finance to uplift their productivity.

SMEs usually find it difficult to obtain loans as compared to larger firms. According to Beck, Demirgüç-Kunt, and Maksimovic (2005), SMEs perceive cost of credit and access to it as a greater obstacle compared to large enterprises, and this factor also constrains SME growth. According to Beck et al. (2005), financing constraints have almost twice the effect on growth of small enterprises compared to large enterprises.

2.5 Monetary Policy and Credit to SMEs

Unlike Bernanke and Blinder (1992) and Christiano et al. (1994), who use the central bank discount rate (Federal Funds Rate) as an indicator of monetary policy stance, we use the monetary base as a variable for monetary policy. This implies that we interpret changes in the monetary base as shocks to monetary policy, and the response of other variables to changes in the monetary base as a response to unanticipated changes in monetary policy.

The conventional view is that monetary policy affects the real economy via the demand side of the economy. Variations in short-term real interest rates affect the cost of capital, which consequently affects business investment and demand for consumer durables. These variations in aggregate demand consequently affect the level of production (Bernanke and Blinder, 1992).

An important variable which affects the availability of loans to private sector businesses is government borrowing. High government borrowing or expansionary fiscal policy can result in crowding out of private sector investment. According to Das (2010), whenever expansionary fiscal policy is discussed there is always a question of trade-off between deficit-financed government expenditure and private investment because the investible resources in an economy are limited.

During times of tightened monetary policy, banks make comparatively more secure loans (Nakamura, 1995). According to Black and Rosen (2011), contractionary monetary policy causes banks to shift their credit from small firms to large firms. This implies that banks may shift credit towards less risky and transparent firms during times of tight monetary policy, as predicted by the balance sheet channel. Alternatively, if smaller firms are most vulnerable to a credit crunch, then easing of monetary policy remedies the credit problems.

Khan (2012) analyses the role of monetary policy on the credit availability to SMEs in Pakistan. Using multiple regression analysis, the study explores a significantly negative relationship between credit availability to SMEs and large enterprises, suggesting that financial sectors in Pakistan consider small and medium enterprises less credible and highly risky.

2.6 Conclusion

Overall, the literature review documents a negative impact of monetary policy on credit availability to SMEs. A negative relationship is also established between credit to large enterprises and that to SMEs. In the context of Pakistan, there is a large body of empirical work devoted to checking the relationship of SMEs with other factors affecting growth. But the relationship between monetary policy and credit to SMEs remains very limited for Pakistan. The present study contributes to the literature by analysing credit to SMEs and factors affecting it — namely credit to large enterprises, government borrowing, monetary policy, and bank spread linkages for Pakistan.

Chapter 3: Theoretical Framework and Variables

3.1 Introduction

The study aims to investigate the impact of monetary policy on credit to SMEs in Pakistan. Time series monthly data is used, covering the period from January 2012 to February 2018. To meet the objectives of the study, secondary data is drawn from different reliable sources, while variables are constructed according to economic theory.

3.2 Theoretical Framework — Monetary Policy

In order to establish a relationship between monetary policy and credit availability to SMEs, we study the supply side of the credit market through the credit channel of monetary policy transmission. The external finance premium — the difference in cost between funds raised externally and funds generated through internal mechanism — augments the effect of monetary policy on interest rates. A variation in monetary policy that raises or lowers interest rates results in the external finance premium moving in the same direction (Bernanke, 1995).

The credit channel of monetary policy transmission is further divided into the bank lending channel and the balance sheet channel. According to the bank lending channel, contractionary monetary policy raises banks' external finance premium, which results in banks reducing their loan supply (Stein, 1998). Alternatively, monetary tightening can increase the agency cost in lending by reducing the net worth of banks and borrowers, which may cause banks to redirect their loan supply towards larger firms (Gertler, 1996). This balance sheet branch shows how monetary policy affects the allocation of resources across firms of different sizes.

3.2.1 Credit to Large Enterprises

Credit to large enterprises affects loan availability to small and medium enterprises. During times of tightened monetary policy, banks make comparatively more secure loans (Nakamura, 1995). According to Black and Rosen (2011), contractionary monetary policy causes banks to shift their credit from small firms to large firms, implying a shift towards less risky and transparent firms, as predicted by the balance sheet channel.

3.2.2 Government Borrowing

An important variable which affects loan supply to SMEs is government borrowing. Higher government borrowing can result in reduced supply of loans to SMEs. According to Das (2010), whenever expansionary fiscal policy is discussed there is always a question of trade-off between deficit-financed government expenditure and private investment, because the investible resources in an economy are limited. Government borrowing also represents an alternative, more secure lending option for banks during times of tighter monetary policy.

3.2.3 Bank Spread

Bank spread is the difference between average lending and average deposit rates. It represents the overall credit risk in the economy and is an important variable in determining the effect of monetary policy on loan availability to SMEs. According to the literature, banking spread has a negative relationship with credit to the private sector.

3.3 Sources of Data

As this research relates to monetary policy, the majority of data was extracted from the State Bank of Pakistan. Monetary policy statements, economic data archives, and development finance reviews from the SBP were used as sources for the majority of data.

3.3.1 Dependent Variable: Loans to SMEs

This includes the share of loans to SMEs out of loans given to all private sector businesses. The data for loans to SMEs has been taken from SBP economic archives and SBP economic development reviews. Some missing data points were generated through trending.

3.3.2 Independent Variable: Monetary Base

The monetary base is the amount of currency held by the public or by the central bank in the form of commercial bank reserves. Monthly data for the monetary base has been extracted from SBP economic data archives.

Additional variables include Government Borrowing (all loans to government from SBP and commercial banks), Loans to Private Businesses (used as a proxy for loans to large enterprises), Bank Spread (difference between weighted average lending rate and weighted average deposit rate), and SME Non-Performing Loans (SME loans in default for more than 90 days).

Chapter 4: Analytical Framework

4.1 Introduction

In this chapter, a brief description of estimation methodology is presented to empirically examine the impact of monetary policy on credit to small and medium enterprises. This study employs time series data that cannot be handled as ordinary data due to time trends in the series, which can cause regression results to become spurious. The following strategy is pursued: first, time series properties of the variables are tested (section 4.2); the Johansen cointegration technique is discussed in section 4.3; the Vector Auto Regressive (VAR) model is discussed in section 4.4; and the Vector Error Correction Model (VECM) is discussed in section 4.5.

4.2 Unit Root Test

To examine the impact of monetary policy on credit to SMEs, time series data has been taken, which usually encounters non-stationarity issues that can lead to spurious results. This issue arises when the mean and variance of the data are not constant over time. Therefore, it is necessary to look into the true order of integration of the time series data.

The key arguments for testing non-stationarity are: (i) the behaviour and characteristics of time series data are profoundly influenced by stationarity; (ii) high R² values for variables trending over time can lead to spurious regression results even if they are totally unrelated; and (iii) with non-stationary variables, the assumption of asymptotic analysis becomes invalid.

To handle this issue, the Unit Root Test is widely used. The objective of the test is to check for the hypothesis \(\phi = 1\) in the following equation:

4.2.1 Augmented Dickey-Fuller Test (ADF)

Named after David Dickey and Wayne Fuller, this test checks whether a unit root is present in an autoregressive model. The Dickey-Fuller (DF) test is based on the assumption that error terms are serially uncorrelated, which is generally violated in complex economic models. This study incorporates the augmented version (ADF) which relaxes this assumption. The three basic models for testing unit root are:

4.2.2 Phillips-Perron (PP) Test

Developed by Phillips and Perron (1988), this test is also adjusted for serial correlation of error terms. The only distinction from ADF is that ADF uses lagged difference terms, whereas the PP test uses nonparametric statistical methods to handle auto-correlated residuals. The edge of the PP test over the ADF test is that the estimates are robust to heteroskedasticity in the error term.

4.2.3 Choice of Lag Length

The choice of lag length is very important in the case of time-dependent data. It helps to achieve normally distributed error terms with no autocorrelation and heteroskedasticity problems. Several ways exist to choose the optimum number of lags, such as the Akaike Information Criterion (AIC) and the Schwarz Bayesian Information Criterion (SBC). The model with minimum AIC or SBC or maximum R² is chosen for analysis.

4.3 Johansen Cointegration Method

In economic theory, variables are cointegrated if they have a long-run relationship between them (Rao, 2007). This cointegrating relationship can be studied using the Johansen technique, Engle-Granger technique, or autoregressive distributed lag (ARDL) method, depending on the order of integration of the variables.

If variables are integrated of order one [I(1)], the Johansen or Engle-Granger method is used. The Johansen test, named after its pioneer Søren Johansen (1991), allows for more than one cointegration relationship, making it more practical compared to the Engle-Granger test. Johansen's technique has two maximum likelihood ratio tests: the maximum Eigenvalue test and the Trace test.

In this study, we consider Case 2 (error correcting equation includes an intercept but no trend; VAR model includes no intercept or trend) and Case 3 (error correcting equation includes an intercept but no trend; VAR model includes an intercept but no trend). The number of lags is selected using the Schwarz Bayesian Criterion (SBC), with tests conducted at lag length of four.

4.4 Vector Auto Regressive Model (VAR)

Sims (1980) introduced the Vector Auto Regressive model to investigate the linear interdependencies among multiple time series. VAR models are perfect instruments for forecasting because the present values of a set of variables can be interpreted by older values of the variables involved. Forecast error variance decompositions, historical decompositions, impulse response analysis, and analysis of forecast scenarios are used for disentangling relationships between variables in a VAR model.

4.5 Vector Error Correction Model

Vector Error Correction Models are used to test whether the long-run established equilibrium is stable or not. This analysis incorporates the restricted VAR technique, in which variables are regressed on their own lags and on the lags of variables incorporated in the analysis, to measure the speed at which the dependent variable returns to equilibrium after a variation in other variables. In other words, VECM measures the speed of convergence.

Chapter 5: Results and Discussion

5.1 Introduction

This chapter describes empirical results to explain the relationship between credit to SMEs and monetary policy with reference to Pakistan. Section 5.2 contains results for the Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test. Section 5.2 discusses the results of the Johansen cointegration test. The results for VAR and VECM are discussed in sections 5.3 and 5.4, respectively.

5.2 Unit Root Test Results

ADF and PP tests are applied to investigate the order of integration of our data series. All variables are found to be integrated of order I(1) by including the trend and intercept term.

Both the ADF and PP tests confirm that the variables are integrated of order one, providing ground for the application of Johansen's cointegration technique.

5.2 Johansen Cointegration Results

Since each of our time series is stationary at first difference, our variables are cointegrated of order one. According to Engle-Granger (1987), there exists a long-run relationship or equilibrium between time series if they are integrated of the same order. We therefore apply the Johansen and Juselius (1990) cointegration technique.

The null hypothesis of no cointegration is conclusively rejected in both Case 2 and Case 3, implying at least one cointegrating vector. The results for the long-run relationship are presented in Table 7.

Empirical results show a positive relationship between monetary policy (MB) and credit to SMEs. A coefficient value of 6.12 shows that one unit increase in the monetary base increases credit to SMEs by 6.12 units, indicating that expansionary monetary policy leads to an increase in the level of credit availability to SMEs.

A negative coefficient for LPB and GB reinforces the existence of the credit channel in Pakistan. The negative coefficient for NPL shows that banks reduce their credit supply to SMEs when non-performing loans are increasing. The negative coefficient for government borrowing strengthens the stance that in times of tightened monetary policy, banks transfer their loanable funds from risky borrowers (SMEs) to relatively safer lending options (government and large enterprises), confirming crowding out of private sector investment by government.

5.3 VAR Results

Vector autoregression (VAR) is often utilised for forecasting interrelated time series and studying the impact of random disturbances on the system of variables. The VAR model treats each endogenous variable as a function of lagged values of every endogenous variable in the model.

The results of vector autoregression show that past values of credit to SMEs affect the current level both positively and negatively. Both one- and two-month lagged values of MB (monetary policy) affect the current value of credit to SMEs positively with significant t-statistics. The one-month lagged value of loans to large enterprises affects current credit to SMEs negatively (significant), whereas the two-month lagged value has a positive impact.

5.4 VECM Results

As our variables are integrated of order I(1), there exists a long-run equilibrium relationship between them. According to the Engle-Granger theorem, if variables are cointegrated, the relationship between them can be expressed as an Error Correction Model (ECM), which measures the speed of convergence and short-run relationship between cointegrating variables.

According to the theory of error correction model, the sign of the ECM lagged term should be negative and significant. The ECM(-1) coefficient of -0.1904 is a clear evidence that if the time series diverges from its equilibrium path, it will swiftly return with an adjustment speed of 19.04% per month.

5.5 Conclusion

The empirical results match the predictions of economic theory. The unit root test showed that all variables were integrated of order one. Johansen cointegration tests confirmed long-run cointegrating relationships. The negatively valued error correction term establishes that the system of variables is stable. The results show a positive relationship between monetary policy and credit to SMEs, and also establish the presence of the balance sheet channel of monetary policy transmission in Pakistan.

Chapter 6: Conclusion and Policy Recommendations

The important feature of the study is that monetary policy has a positive effect on credit availability to SMEs. An increase in the monetary base — expansionary monetary policy — results in increased credit to SMEs in Pakistan. The results also establish the existence of the credit channel of monetary policy transmission in Pakistan. A contractionary monetary policy results in decreased credit to SMEs because banks transfer their loanable funds from risky borrowers (SMEs) to relatively safer lending options (government and large enterprises). This confirms the existence of the balance sheet channel (part of the credit channel) in Pakistan. The study also establishes a negative relationship between credit to SMEs and government borrowing, reflecting the crowding out of private sector investment by the government sector.

In the wake of turbulent economic conditions in Pakistan, this paper is an effort to study the credit market through channels that have been slightly ignored until now. The growth of the SME sector and its contribution to GDP in Pakistan has been deficient due to shortage of credit. A policy of easy monetary policy would go a long way in ensuring adequate credit availability to small and medium enterprises in Pakistan. Formal institutions should be set up by federal and provincial governments to ensure access of smaller entrepreneurs to cheap credit. Adequate credit to SMEs would result in their growth, which in turn would affect the overall growth of the economy.

Similarly, government — being a relatively safer lending option for banks compared to small and medium enterprises — takes away a major chunk of credit from the latter. There is a great need to formulate policy that prevents crowding out of private sector investment by the government sector.

Large corporations find it relatively easy to obtain credit, but their benefits to the economy are far less compared to a thriving SME sector. Policies should be enacted that ensure large corporations do not take away all the credit available to the private sector.

Finally, the government should work on improving the entrepreneurial environment that surrounds SMEs in Pakistan. Incubation centres should be established around the country with sufficient funding to promote entrepreneurial culture in Pakistan. With its huge workforce, Pakistan is destined for economic prosperity with the help of a thriving SME sector.

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