1. EXECUTIVE SUMMARY

This report synthesizes a critical linkage between Pakistan's monetary policy stance and the financing constraints faced by its Small and Medium Enterprises (SMEs). The core conclusion is that expansionary monetary policy (increase in monetary base) has a statistically significant positive effect on credit to SMEs. Crucially, the research empirically confirms the existence of the "Balance Sheet Channel" of monetary policy transmission in Pakistan. This channel explains how tightening monetary policy leads banks to reallocate scarce credit away from perceived riskier borrowers (SMEs) towards safer alternatives (large corporations and government debt), thereby "crowding out" the SME sector. The data underscores a systemic bias in credit allocation that stifles the growth potential of SMEs, which constitute over 90% of private enterprises and are vital for employment and economic diversification.

2. CONTEXT & PROBLEM STATEMENT: DATA-BACKED SIGNIFICANCE OF SMEs

• SMEs in Pakistan's Economic Structure:
• Share of Private Enterprises: ~99% (3.2 million out of 3.23 million enterprises).
• Employment Contribution: ~78% of the non-agricultural workforce.
• Contribution to GDP: A mere 7% (GEM, 2007), highlighting a severe underperformance relative to their population share.
• Contribution to Industrial Sector: Constitute 87% of all industrial establishments.
• The Global Benchmark (For Context):
• High-Income Countries: SMEs contribute, on average, ~50% to GDP and ~60% to employment (World Bank, 2011).
• USA/EU: SMEs contribute 52-65% to GDP.
• The Disparity: Pakistan's SME GDP contribution (7%) is a stark outlier, indicating profound structural and financial inefficiencies.
• The Primary Constraint - Access to Finance: Surveys indicate cost and access to finance is a major operational constraint for 28% of medium and 22% of small firms in lower-middle-income countries (World Bank). In Pakistan, this is exacerbated by informational asymmetries, high collateral requirements, and a risk-averse banking sector.

3. THEORETICAL FRAMEWORK & HYPOTHESES

The study tests the operation of the Credit Channel of monetary policy transmission, specifically contrasting two mechanisms:

1. Interest Rate Channel (Demand-Side): Traditional view. Policy rate changes affect the cost of capital, influencing investment demand from all firms.
2. Credit Channel (Supply-Side): The study's focus. Suggests monetary policy affects the supply of loans by altering banks' lending capacity and risk appetite. It has two sub-channels:

• Bank Lending Channel: Tight policy reduces bank reserves/deposits, shrinking overall loan supply.
• Balance Sheet Channel: Tight policy worsens the balance sheets of both banks and borrowers. Banks become more risk-averse and reallocate credit from "risky" SMEs to "safer" large enterprises and government securities.

Testable Hypotheses Derived from Theory:

• H1: An increase in the Monetary Base (expansionary policy) will increase the availability of credit to SMEs.
• H2: An increase in credit to Large Private Businesses (LPB) will decrease credit available to SMEs, evidencing credit reallocation.
• H3: An increase in Government Borrowing (GB) will decrease credit to SMEs, evidencing "crowding out."
• H4: An increase in Banking Spread (lending-deposit rate spread) or SME Non-Performing Loans (NPLs), indicators of risk, will decrease credit to SMEs.

4. METHODOLOGICAL RIGOR & DATA SPECIFICATIONS

• Data Type: Monthly Time Series Data.
• Time Period: January 2012 to February 2018 (74 observations).
• Variables & Definitions:

Variable

Symbol

Definition

Source

Credit to SMEs

CSME

Loans to SMEs by Scheduled Banks (PKR Millions)

SBP Economic Data Archives / Development Finance Review

Monetary Policy

MB

Monetary Base (Currency in Circulation + Bank Reserves) (PKR Millions)

SBP Economic Data Archives

Credit to Large Enterprises

LPB

Loans to Private Sector Businesses (Proxy for Large Firms) (PKR Millions)

SBP Economic Data Archives

Government Borrowing

GB

Credit to Government by SBP & Scheduled Banks (PKR Millions)

SBP Economic Data Archives

Banking Spread

SPREAD

Weighted Avg. Lending Rate - Weighted Avg. Deposit Rate (%)

SBP Economic Data Archives

SME Non-Performing Loans

NPL

SME loans in default for >90 days (PKR Millions)

SBP Development Finance Review

• Econometric Strategy: A multi-stage approach to ensure robust, non-spurious results:

1. Unit Root Tests: Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests confirmed all variables were Integrated of Order 1 [I(1)]—stationary only after first differencing.
2. Cointegration Analysis: Johansen's Maximum Likelihood procedure used to test for long-run equilibrium relationships among I(1) variables.
3. Short-Run Dynamics: Vector Error Correction Model (VECM) estimated to analyze short-term adjustments and the speed of convergence back to long-run equilibrium.

5. EMPIRICAL FINDINGS: QUANTITATIVE RESULTS

A. Long-Run Cointegrating Relationship (Johansen Test)

The trace and max-eigenvalue tests rejected the null hypothesis of "no cointegration" at the 1% significance level, confirming a stable long-run relationship among the variables.

Long-Run Coefficient Estimates:

Variable

Case 2 Coefficient

t-statistic

Economic Interpretation

Monetary Base (MB)

+6.1227

6.0486*

Positive & Significant. A 1 PKR million increase in MB increases credit to SMEs by 6.12 PKR million in the long run. H1 is strongly supported.

Loans to Large Pvt. Biz (LPB)

-7.9674

-5.7738*

Negative & Significant. Confirms credit reallocation. Increased lending to large firms crowds out SME credit. H2 is supported.

Government Borrowing (GB)

-1.8661

-3.4457*

Negative & Significant. Confirms "crowding out". Government borrowing competes for and displaces bank credit to SMEs. H3 is supported.

Banking Spread (SPREAD)

+2.0755

5.4680*

Positive sign, but requires context. May indicate banks charge higher spreads to SMEs to compensate for perceived risk when lending to them.

SME NPLs (NPL)

-10.8936

-3.6300*

Negative & Significant. A rise in SME defaults leads to a sharp contraction in future credit supply, reflecting bank risk aversion. H4 is supported.

*Note: * indicates significance at 1% level.*

Conclusion: The long-run model provides robust evidence for the Balance Sheet Channel. Credit to SMEs is not only a function of overall monetary easing but is negatively and significantly impacted by the credit demands of larger, safer entities (large firms and government).

B. Short-Run Dynamics & Speed of Adjustment (VECM Results)

• Error Correction Term (ECT): The coefficient on the lagged ECT( -1) was -0.1904 and statistically significant (t-stat: -5.00594).
• Interpretation: This negative and significant ECT validates the long-run model. It indicates that approximately 19% of any disequilibrium in SME credit is corrected within one month. The system shows a moderately swift return to long-run equilibrium following a shock.

C. Robustness Check - Vector Autoregression (VAR) Model

Short-run VAR results aligned with long-run findings:

• Laged MB positively affected CSME.
• Lagged LPB negatively affected CSME.
• Coefficients for GB, SPREAD, and NPL showed expected directional signs but with mixed short-term significance, highlighting that the reallocation and crowding-out effects are primarily long-run phenomena.

6. SYNTHESIS & POLICY IMPLICATIONS: A DATA-DRIVEN AGENDA

The empirical evidence leads to the following inescapable conclusions and policy prescriptions:

1. Monetary Policy is Not SME-Neutral: The State Bank of Pakistan's (SBP) monetary stance has a direct, quantifiable impact on SME financing. A tight policy stance, often aimed at curbing inflation or stabilizing currency, imposes a disproportionate cost on the SME sector through the credit reallocation mechanism.

2. The "Crowding-Out" Triple Threat is Real: SME credit faces competition from:
* The Government: Through high fiscal borrowing.
* Large Corporations: Due to their lower perceived risk.
* Bank Risk Aversion: Amplified by high NPLs in the SME portfolio.
This creates a systemic financial bias against small businesses.

3. Imperative for Targeted, SME-Sensitive Policy: Generic monetary or credit expansion is insufficient. Policy must be designed to insulate or preferentially channel credit to SMEs.

Detailed Policy Recommendations:

• For the State Bank of Pakistan (SBP):
• Mandatory SME Credit Quotas: Institute and strictly enforce regulatory requirements for banks to allocate a minimum percentage of their private-sector loan portfolio to SMEs (e.g., 20%).
• Refinance Schemes at Policy Rate: Dramatically expand the scope and scale of SBP refinance facilities for SMEs, providing banks with low-cost, long-term funding dedicated exclusively to SME lending.
• Risk-Weighting Adjustments: Adjust Capital Adequacy Regulations (Basel III) to assign lower risk weights to SME loans, reducing the capital cost for banks to lend to this sector.
• For the Federal Government:
• Fiscal Discipline to Reduce Crowding-Out: Commit to consolidating fiscal deficits to reduce domestic borrowing pressure on the banking system.
• Credit Guarantee Schemes: Capitalize a large, public-private Credit Guarantee Fund to cover a portion (e.g., 50-60%) of banks' losses on qualifying SME loans, directly addressing the risk aversion identified in the NPL variable.
• Digital Collateral Registry: Accelerate the implementation of a centralized, movable asset collateral registry (e.g., for inventory, machinery, receivables) to help SMEs use non-real estate assets to secure loans.
• For Commercial Banks:
• Develop SME-Specific Credit Scoring: Move beyond traditional collateral-based lending. Invest in technology and data analytics to develop cash-flow-based and behavioral scoring models for SMEs.
• Dedicated SME Banking Units: Establish specialized units with trained staff to understand SME business cycles and needs, moving away from a one-size-fits-all corporate lending approach.

7. CONCLUSION

This data-centric analysis moves the discourse on SME financing in Pakistan from anecdotal claims to empirical certainty. The thesis proves that the availability of credit to SMEs is a function not just of overall monetary policy looseness, but critically, of the distributional competition for credit within the financial system. The documented operation of the Balance Sheet Channel reveals a market failure where optimal credit allocation for national development (towards job-creating SMEs) is subverted by short-term bank risk management practices during policy tightening.

Addressing this requires deliberate, targeted structural intervention in the financial market to correct this bias. Investing in SME credit access is not a subsidy; it is a strategic investment in Pakistan's most abundant economic resource—its entrepreneurs—and is essential for achieving sustainable, inclusive, and high-employment economic growth. The data has spoken; policy must now respond with precision.