Back Matter
Author: Mr. Nicolas R Blancher, Maximiliano Appendino, Aidyn Bibolov, Mr. Armand Fouejieu1, Mr. Jiawei Li, Anta Ndoye2, Alexandra Panagiotakopoulou, Wei Shi, and Tetyana Sydorenko
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund
  • | 2 https://isni.org/isni/0000000404811396, International Monetary Fund
Publication Date:
12 Feb 2019
Language:
English
Keywords:
DPPP; DP; SME financing; capital market; SME access; bank credit; Fintech company; SME lending; SME credit; banking product; credit guarantee; SME financing needs; SME equity issuance; SME productivity; Small and medium enterprises; Financial inclusion; Credit; Capital markets; Middle East and Central Asia; Global; Middle East; North Africa; Central Asia and the Caucasus
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Annex 1. Methodology for the SME Financial Inclusion Index

The SME financial inclusion index reduces multidimensional data from the World Bank Enterprise Survey to a summary index using the following steps: (1) normalization of variables; (2) aggregation of normalized variables into sub-indices by principal component analysis, using the first component; and (3) aggregation of the subindices into the final index. Several choices need to be made in constructing the index. In the World Bank Enterprise Survey, several questions are designed to evaluate financial conditions for firms. From these, the variables most relevant to bank financing conditions were chosen (listed below) and divided into categories of access and usage. This index is available for 119 countries worldwide, of which 20 are in the MENAP and CCA regions. The index captures the observed SME financial inclusion that reflects the equilibrium of supply and demand for financial services for SMEs. As shown in the figures below, it correlates strongly with alternative measures of SME financial inclusion, with the share of partially of fully credit-constrained SMEs (Kuntchev and others, 2014), and with the share of SMEs with rejected loans (which highlights the importance of supply-side constraints that are the main focus of the paper).

Annex Figure 1.1.
Annex Figure 1.1.

SME Financial Inclusion Index

Citation: Departmental Papers 2019, 002; 10.5089/9781484383124.087.A999

Sources: World Bank Enterprise Surveys, IMF Staff calculations.
Annex Figure 1.2.
Annex Figure 1.2.

SME Financial Inclusion, Credit Constraints and Rejected Loan

Citation: Departmental Papers 2019, 002; 10.5089/9781484383124.087.A999

Sources: Kuntchev and others (2014), World Bank Enterprise Surveys, IMF Staff calculations.

Annex 2. SME Financial Inclusion Gap

Financial inclusion is driven mainly by macroeconomic and institutional fundamentals. In the following table, financial inclusion is linked to these fundamentals:1

  • Economic development (income per capita), which captures country characteristics such as quality of infrastructure, education, and health

  • Governance (control of corruption)

  • Credit information availability (coverage of credit registries)

  • Economic competition (proxied by the share of small firms in the private sector)

  • Business environment, including contract enforcement

The SME financial inclusion gaps are calculated as the difference between countries’ actual financial inclusion level and that of the country at the 90th percentile.

Annex Table 2.1.

SME Index

article image
Source: IMF staff calculations.Note: Standard errors are in parentheses. PPP = purchasing power parity.***p <0.01; **p <0.05; *p <0.1.

Annex 3. Growth and Employment Benefits of Increased Access to Financing for SMEs

The relationships between SME access to financing, unemployment, and growth are examined using static and dynamic panel regression frameworks.

Real GDP growth is linked to SME financial inclusion (financial inclusion index and SME bank loans) while controlling for other factors that are likely to affect growth, human capital, the macroeconomic environment, and the quality of institutions. The impact of financial inclusion on unemployment is estimated using a similar framework.

The following equations were estimated:

(1) Growthit = α+ βFIit + λXit + γi + φi + εit

(2) Unemplit = α+ βFIit + λXit + γi + φi + εit

where Growth is real GDP, Unempl is unemployment, FI is the measure of SME financial inclusion (the financial inclusion index or bank loans to SMEs),1 X is a vector of control variables, γi and φi are country and time fixed effects (respectively), and εit is the error term. i and t indicate country and year, respectively.

Equations (1) and (2) were first estimated using ordinary least squares panel fixed effects and generalized least squares (GLS) estimators. GLS with AR(1) correction take into account the possibility of a strong autocorrelation between unemployment and GDP growth data. To overcome a potential endogeneity bias affecting these estimates, dynamic general method of moments estimations (Arellano and Bond 1991; Blundell and Bond 1998) were also performed. Given the limited availability of external instruments, this estimation method relies on an internal instrumentation approach in which the endogenous variables are instrumented with their lags. To further check the robustness of the results, some specifications include private credit to GDP as an additional control variable. This helps separate more precisely the impact of SME lending from that of lending to the broader private sector.

Annex Table 3.1.

Employment Benefits of SME Financial Inclusion

article image
Source: IMF staff calculations.Note: Standard errors are in parentheses. Country and year fixed effects are included but not reported. Generalized least squares (GLS) estimates incorporate AR(1) correction. The list of instruments for generalized method of moments (GMM) is limited to a maximum of four lags to avoid using too many instruments. P values of Hansen test of overidentifying restrictions (to test the null hypothesis that the instruments are valid) are reported. FE = fixed effects. ***p <0.01; **p <0.05; *p <0.1.
Annex Table 3.2.

Growth Benefits of SME Financial Inclusion

article image
Source: IMF staff calculations.Note: Standard errors are in parentheses. Country and year fixed effects are included but not reported. Generalized least squares (GLS) estimates incorporate AR(1) correction. The list of instruments for generalized method of moments (GMM) is limited to the maximum of four lags to avoid using too many instruments. P values of Hansen test of overidentifying restrictions (to test the null hypothesis that the instruments are valid) are reported. ***p <0.01; **p <0.05; *p <0.1.

Annex 4. Benefits from Relaxing Constraints on SME Financial Inclusion: Country-Specific Analysis

Using the dynamic stochastic general equilibrium (DSGE) model in Dabla-Norris and others (2015b), we analyzed specific financial inclusion constraints facing individual countries and the possible macroeconomic impact of their relaxation.1

Sample: Based on World Bank Enterprise Survey data, the sample covers six MENAP countries (Egypt, Jordan, Lebanon, Morocco, Pakistan, Tunisia) and seven CCA countries (Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic, Tajikistan, Uzbekistan).

Overview of simulated results: Limited financial inclusion could weigh on economic prosperity by discouraging productive firms or forcing them to operate below optimal scale owing to insufficient financing. These are the key variables used to assess constraints to SME financial access:

  • Fixed financial access cost (ψ) incorporates several factors that prevent entrepreneurs from accessing credit, including asymmetric information; higher cost of serving the SME sector; and limited financial literacy, which affects SME credit demand. Relaxing these constraints could significantly boost the share of firms with access to credit and raise economic potential. Countries such as Egypt, Pakistan, and Uzbekistan could improve long-term output by several percentage points by mitigating such constraints.

  • Collateral requirements (λ) limit borrower moral hazard and contribute to greater financial stability. However, they may also force small entrepreneurs (with few resources of their own to put down as collateral) out of the market or to operate at a suboptimal scale. In countries such as Armenia and Georgia, policies to loosen collateral constraints could help reap significant benefits from greater SME financial inclusion.

  • Monitoring cost (χ) captures how efficiently banks can assess credit risk (including collateral recovery), which contributes to the margin between interest rates applied to highly leveraged borrowers and the cost of funding (savings rate). Low interest rate margins encourage highly productive SMEs to expand their production toward the optimal scale. However, they may also lead to excessive risk taking and rising nonperforming loans. This channel appears to have a relatively small macroeconomic impact for countries in the sample.

Annex Figure 4.1.
Annex Figure 4.1.

Stylized Facts and Model Simulation

Citation: Departmental Papers 2019, 002; 10.5089/9781484383124.087.A999

Sources: World Bank Enterprise Survey, World Development Indicators, and IMF staff calculation.Note: ISO country codes are used for abbreviations. MENAP = Middle East, North Africa, Afghanistan, and Pakistan; CCA = Caucasus and Central Asia; MCD = Middle East and Central Asia.1 World average is calculated based on information for all country-year combination for which an enterprise survey has been conducted. The survey covers mainly emerging markets and low-income countries.2 MCD aggregate is constructed using simulated comparative statics for six MENAP countries (Egypt, Jordan, Lebanon, Morocco, Pakistan, and Tunisia) and seven CCA countries (Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic, Tajikistan, and Uzbekistan). Simulation is done using the most recent World Bank Enterprise Survey data. Individual country outcome is weighted by relative GDP share computed using 2013 nominal GDP in U.S. dollar.3 Horizontal axis shows the magnitude of three financial frictions, normalized to be between 0 (least constraining case) and 1 (most constraining case). Parameter for monitoring cost (χ) ranges from 0.5 (implying a lending-deposit spread as high as 60 percent in some countries) and 0 (implying almost zero spread). Collateral requirement is captured by leverage ratio (λ)-calculated inclusive of collateral which varies between 1 (no borrowing) and 2 (borrowing allowed up to the amount of collateral). Financial access cost (ψ) varies from 3 (share of firms with credit approaching 0 percent) to 0 (share of firms with credit approaching 100 percent).

Annex 5. Access to Financing and Firm-Level Employment, Sales, and Productivity Growth

To assess the impact of access to financing on firm-level employment and labor productivity growth in MENAP and CCA countries, we employ the following specification, as in Ayyagari and others (2016), using data from the World Bank Enterprise Surveys: 1

ΔEijt=αFijt+BXijt+Zji+Cj+Yt+ϵijt,

where ∆Eijt is the annual employment (or labor productivity) growth for firm i in country j in year t; Fijt is an indicator variable capturing whether a particular firm in a particular country surveyed in a particular year had a formal loan outstanding; Xijt and Zjt are firm-level and country-level controls, respectively; Cj and Yt are country and year fixed effects. We estimated separately for (1) SMEs and large firms to test whether employment gains from access to financing are larger for smaller firms, and for (2) SMEs only. The results are consistent with our hypotheses. The point estimates for α are positive and statistically significant with p-values below 10 percent for each of them, and their relative magnitudes are consistent with our hypotheses regarding firm size as well (see Figure 3).

We used the point estimates for SMEs to estimate macroeconomic gains from SME financial inclusion through a simple growth accounting exercise. This suggests that the additional 1.3 percent in SME employment growth, added to a gain from augmented labor productivity of 2.3 percent (with a labor share equal to two-thirds) implies an additional 1 percent of GDP growth. Such an increase in employment represents about 16.5 million new jobs in MENAP and CCA countries (14.3 million in MENAP and 2.3 million in CCA) by 2025 above the baseline employment projection that follows the average annual growth rate from 2012 to 2017 (using employment data from the International Labour Organization).

Finally, we explore the introduction of credit bureaus as a supply-side policy change following Ayyagari and others (2016). For this, we replace Fijt in the above equation with CBjt, a proxy for SME credit bureau coverage based on the share of adults covered by credit bureaus (using data from the World Bank’s Doing Business Indicators). The results are consistent with the finding that financial inclusion has a positive impact on employment growth, with SMEs being a key driver.2 We used the point estimates of α to calculate the average employment growth gains from closing the credit bureau coverage gap with respect to the average emerging market and developing economy and to the average advanced economy for countries in our sample that are below each of these thresholds (see Figure 8).

Annex 6. SME Financial Inclusion and Macroeconomic Policy

To test the link between macroeconomic policies and SME financial inclusion, we separate MENAP and CCA countries into those with high and low SME financial inclusion, estimate two separate panel vector autoregressions at the country level, and test if the estimates for those with higher SME financial inclusion present dynamics consistent with more effective policy. We include the countries for which we have the needed annual macroeconomic time series from 1990 to 2017, using Haver Analytics data and the SME financial inclusion index as in Annex 1.1

In the case of fiscal policy, we explore the link between SME financial inclusion and the efficiency of tax collection. We estimate the following panel vector autoregression separately for firms in the top and bottom quartiles of SME financial inclusion:

Yit=AYit−1+ui+νt+eit

where Y={TaxGDP,outputgap,inf⁡lation}, which gives the recursive ordering for identifying structural shocks. We measure the efficiency of tax collection as the impulse response of TaxGDP to a structural positive shock to the output gap. The result confirms the hypothesis that SME financial inclusion makes tax collection more effective: high-financial-inclusion countries present a statistically significant (at 10 percent level) first lag response, but the same parameter is not statistically significant for low-financial-inclusion countries.2

We test the strength of monetary policy transmission estimating the same panel vector autoregression following Mehrotra and Yetman (2014) and IMF (2018a), with the same equation as above but with Y = {output gap, inflation, nominal interest rate}. In this case, the recursive ordering for identifying structural shocks allows us to measure the strength of monetary transmission by the magnitude of the impulse response of the output gap to a 100 basis point structural shock to the nominal interest rate, which, as in the case of fiscal policy, is statistically significant at the 10 percent level only for the high-financial-inclusion countries. The ratio of the variance of the output gap to inflation is higher for the group of countries with higher SME financial inclusion, providing evidence in support of the hypothesis that SME financial inclusion strengthens monetary policy effectiveness (see Figure 4).

Annex 7. Drivers of SME Financial Inclusion

This analysis aims to (1) identify the main determinants of SME access to formal (bank) financial services and (2) provide evidence about key constraints to SME financial inclusion in MENAP and CCA countries specifically. The empirical tests rely on the following two equations:

(1) FIit = α + βXit + λregion_dummy + ρzit + εit

(2) FIit = α + βXit + λregion_dummy + δregion_dummy*zit + ρzit + εit

where the dependent variable (FI) is the composite index of SME financial inclusion. X is a vector of control variables that includes total investment (in percent of GDP), inflation, SME share of employment (in percent of total employment), and the level of economic development. The region dummy is either a CCA or MENAP dummy variable, which takes the value of 1 for MENAP or CCA countries and 0 otherwise. Both equations are estimated with CCA and MENAP dummies, separately. The baseline specification is extended to control successively for additional characteristics (z) capturing1

  • Broader macroeconomic environment: diversification, informality, competition, quality of infrastructure, saving behavior, interest rate restrictions, public investment (percent of total investment), fiscal balance, and an oil-exporter country dummy.2

  • Quality of institutions: voice and accountability, political stability, government effectiveness, and control of corruption.

  • Banking sector characteristics: return on equity, asset quality (nonperforming loan ratios), bank deposits, banking sector stability, and banking sector concentration.

  • Business environment: taxation (percent of profit), cost of starting a business and registering property, time to enforce a contract, public credit registry coverage, and property rights.

Equation (1) describes a linear relationship between financial inclusion and its determinants, while equation (2) explores potential nonlinearities, especially with respect to the MENAP region. Both equations are estimated using ordinary least squares. Annex Figure 7.1 provides some preliminary stylized facts on the governance and structural characteristics across regions. Overall, the MENAP and CCA regions generally perform poorly compared with their peers. Annex Figures 7.2 and 7.3 report the estimated coefficients (statistically significant at the 10 percent level or lower) for the full sample as well as specifically for the MENAP and CCA regions.

Annex Figure 7.1.
Annex Figure 7.1.

Selected Governance and Structural Indicators

Citation: Departmental Papers 2019, 002; 10.5089/9781484383124.087.A999

Sources: Worldwide Governance Indicators; Global Competitiveness Index, OECD—latest available data.
Annex Figure 7.2.
Annex Figure 7.2.

The MacroFinancial Environment and SME Financial Inclusion

Citation: Departmental Papers 2019, 002; 10.5089/9781484383124.087.A999

Annex Figure 7.3.
Annex Figure 7.3.

Institutions, Business Environment, and SME Financial Inclusion

Citation: Departmental Papers 2019, 002; 10.5089/9781484383124.087.A999

Source: IMF staff estimates.Note: Coefficient estimates from equations (1) and (2), based on OLS panel fixed effects. The coefficients are statistically significant at a minimum level of 10%, with robust standard errors.

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This Middle East and Central Asia (MCD) departmental paper was prepared by a team under the supervision of Aasim Husain, led by Nicolas Blancher, and including: Max Appendino, Aidyn Bibolov, Jiawei Li, Anta Ndoye, Alexandra Panagiotakopoulou, Wei Shi, and Tetyana Sydorenko (MCD); Armand Fouejieu (EUR); and Mishel Ghassibe and Samir Elsadek (summer interns). It incorporates input from staff in other IMF departments, as well as experts from the World Bank, International Finance Corporation, Asian Development Bank, and International Organization of Securities Commissions, including via an iLab brainstorming session.

1

The Middle East and Central Asia region refers to 31 countries in the Middle East, North Africa, Afghanistan, and Pakistan (MENAP) and in the Caucasus and Central Asia (CCA). Financial inclusion is defined as the access to, and use of, formal financial services.

2

Statement by the International Monetary Fund, Arab Fund for Economic and Social Development, and Arab Monetary Fund, Conference on Promoting Growth, Jobs, and Inclusiveness in the Arab World, January 30, 2018.

3

Specifically, Islamic finance, correspondent banking relationships, leasing and factoring, microfinance, and informal finance are not addressed in depth, either due to data limitations or because their macroeconomic relevance is less pronounced or they are separately subject to in-depth analytical and policy work. For instance, see Ensuring Financial Stability in Countries with Islamic Banking, IMF 2017, and Recent Trends in Correspondent Banking Relationships: Further Considerations, IMF 2017 (2017a, 2017e).

1

These constraints, which may force SMEs to operate at a suboptimal scale due to limited access to credit, include asymmetric information, the higher costs of serving SMEs, limited financial literacy, and insufficient collateral requirement and recovery frameworks.

2

In the MCD region, Arab countries have the highest level of youth unemployment in the world (25 percent on average), and more than 27 million young people will enter those countries’ labor market in the next five years. See Baduel and others (2018).

1

See Annex 6. The empirical analysis covers a large sample of countries, including from the MENAP and CCA regions. Data limitations allow to identify correlations but not causal effects. While the focus of this paper is primarily on supply-side determinants of bank credit, low SME credit may reflect broader characteristics of the economy that depress SME demand for credit, such as informality, low financial literacy, and market regulations or concentration issues that affect SME investment. For more on the role of these demand-side factors in the Middle East and Central Asia, see IMF (2018c).

2

The share of government investment is used as a proxy for the size of the public sector in the economy. Fiscal balance, another proxy for private sector crowding out, is not significantly correlated with SME financial inclusion.

4

Love and Martinez Peria (2015).

5

This is also consistent with the Basel Core Principles for Effective Banking Supervision, especially Principle 20, which focuses on abuses and potential conflicts of interest arising from transactions between related parties that may be prone to lack of due diligence or market-related terms.

3

Fintech is defined by the Financial Stability Board as “Technologically-enabled financial innovation that could result in new business models, applications, processes or products with an associated material effect on financial markets, financial institutions and the provision of financial services” (FSB, 2017). See also Mills and McCarthy (2017).

5

Fintech-based credit scoring is used in mobile-data -based lending in Africa (Zoona), peer-to-peer SME lending in South Africa (Rainfn), and e-commerce lending in China (Zhima Credit). ShoBadge is an application that uses blockchain technology to provide enterprise-level identity authentication and an ecosystem for SME data management.

6

U.K. Government, SME finance: help to match SMEs rejected for finance with alternative lenders, 2014.

9

Green shoots include Bahrain-based PayTabs and Jordan-based ProgressSoft and eFAWATEERcom, which provide digital payment solutions for banks and SMEs. United Arab Emirates (UAE)–based Beehive and Eureeca, Lebanon-based Zoomaal, and Jordan-based Liwwa provide crowdfunding and peer-to-peer lending in the region.

10

For a comprehensive discussion on fintech credit and risks, see FSB (2017) and BIS (2018).

11

For example, the Abu Dhabi Global Market (ADGM) financial free zone in the United Arab Emirates (UAE) works with start-up companies for two years on a limited license before they can apply for a full license.

12

As of August 2018, the Global Financial Innovation Network (GFIN) is comprised of 12 regulatory bodies across the world, including the Abu Dhabi Global Market, Central Bank of Bahrain, and the Dubai Financial Services Authority.

2

Calice (2016), Chatzouz and others (2017). OECD (2017) provides a comprehensive survey of the empirical literature and finds that credit guarantee schemes have a positive additional impact on firm finance and employment, but that the impact on firm performance is mixed, and that there are increased default risks.

4

Outreach measures the credit guarantee scheme’s (CGS) capacity to meet the demand by SMEs for guaranteed loans. It could be based at a minimum on the number of guarantees issued and on the total amount of outstanding guarantees. Additionality refers to financial additionality (extra credit extended) and economic additionality (extra employment, investment, and growth obtained). In practice, additionality is difficult to measure. Financial sustainability refers to the CGS’s capacity to maintain an adequate capital base relative to its liabilities.

5

Melecky and Podpiera (2018). Malaysia is an example where an SME agency was given strong coordination powers and was able to reach across jurisdictions to gain consensus on policy priorities.

1

Indicators of governance (rule of law) and financial openness should be interpreted with caution due to a limited number of respondents, limited geographical coverage, and standardized assumptions on business constraints and information availability. They indicators may also not reflect more recent structural changes.

A more detailed discussion is provided in Appendino and others (IMF Working Paper, forthcoming).

1

Data on loans to SMEs offer a longer time series (compared with the financial inclusion index), which allows for implementation of the dynamic generalized method of moments.

1

Results from the model should be interpreted carefully. For instance, the model applies the same probability of failure (p) to all entrepreneurs, irrespective of their talent or firm size. As a result, greater financial inclusion does not lead to riskier credit portfolios, which in some cases may not be a realistic assumption.

1

The data cover Armenia, Azerbaijan, Djibouti, Egypt, Georgia, Iraq, Jordan, Kazakhstan, Kyrgyz Republic, Lebanon, Morocco, Tajikistan, Tunisia, Uzbekistan, and Yemen, for different years from 2008 to 2016.

2

The α point estimate for SMEs is similar to that for the overall sample, with similar statistical significance.

1

Countries covered are Armenia, Azerbaijan, Egypt, Georgia, Iraq, Jordan, Kazakhstan, the Kyrgyz Republic, Lebanon, Morocco, Tajikistan, Tunisia, Uzbekistan, and Yemen.

2

Top and bottom halves did not present statistically significant results.

A more detailed discussion is provided in Ndoye and others (IMF Working Paper, forthcoming).

1

A larger set of controls was tested. This appendix only reports only on the variables that were found to have a statistically significant relationship with SME financial inclusion.

2

Diversification is proxied by the Economic Complexity Index (OECD); infrastructure by the share of telephone lines in the population, and, informality by the share of the shadow economy (in percentage of total GDP).

Cover Departmental Papers
Financial Inclusion of Small and Medium-Sized Enterprises in the Middle East and Central Asia
Author: Mr. Nicolas R Blancher, Maximiliano Appendino, Aidyn Bibolov, Mr. Armand Fouejieu, Mr. Jiawei Li, Anta Ndoye, Alexandra Panagiotakopoulou, Wei Shi, and Tetyana Sydorenko
Volume/Issue:
Volume 2019: Issue 002
Publisher:
International Monetary Fund
ISBN:
9781484383124
ISSN:
2616-5333
Pages:
73
DOI:
https://0-doi-org.library.svsu.edu/10.5089/9781484383124.087

SME Financial Inclusion Index

SME Financial Inclusion, Credit Constraints and Rejected Loan

Stylized Facts and Model Simulation

Selected Governance and Structural Indicators

The MacroFinancial Environment and SME Financial Inclusion

Institutions, Business Environment, and SME Financial Inclusion