Asia Small and Medium-Sized Enterprise Monitor 2020: Volume IV

Technical Note—Designing a Small and Medium-Sized Enterprise Development Index

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• Publisher: Asian Development Bank
• Year: 2020
• Print ISBN: 9789292625535
• eBook ISBN: 9789292625542
• Topic: Business
• Subtopic: Small Business
• Index: Business

This final volume of the Asia Small and Medium-Sized Enterprise Monitor 2020 sets out ADB's work so far on a new composite index called the Small and Medium-Sized Enterprise Development Index (SME-DI) to help guide support for the development of micro, small, and medium-sized enterprises (MSMEs). The SME-DI aims to measure MSME development and access to finance using multivariate analysis of national MSME data. The volume includes the results of testing a pilot index using data from Southeast Asia.

Estimation Results

By using the two datasets, we conduct the test-run of the two-stage PCA to measure selected dimensions for the regional and country (Cambodia) SME-DI. The main objective of this exercise is to test the feasibility of SME-DI and extract issues to improve and develop the index.

1. Regional SME Development Index Exercise Based on Aggregate MSME Data

First-Stage Principal Component Analysis

This exercise based on aggregate MSME data with time series focuses on three dimensions of the Nonfinance Sub-Index (scale of MSMEs, employment, and productivity) and one dimension of the Finance Sub-Index (financial depth) under the SME-DI. Due to limited data availability, we restructured and simplified factors affecting MSME development into four dimensions: (i) macro conditions, (ii) bank financing, (iii) nonbank financing, and (iv) equity financing (Figure 1).

Figure 1: Factors Affecting MSME Development

MSME = micro, small, and medium-sized enterprise; Z=principal component.

Source: Authors.

For the first stage, we organize the 12 variables into four likely factors. Indicators on “inflation” and “lending rate” are extracted from International Monetary Fund/International Financial Statistics (IMF/IFS) end-of-year time series data. Other indicators are explained in the abovementioned “data selection.”

• Macro conditions (Macro): Number of MSMEs to total number of firms (“num”), MSME employees to total employees (“emp”), MSME contribution to GDP (“gdp”, and the annual inflation rate (“inf”).

• Bank financing (Bank): MSME bank loans to total (“bkc”), MSME bank loans to GDP (“bkc_gdp”), MSME nonperforming loans to total MSME bank loans (“bkc_npl”, and commercial bank lending rate (“len_r”).

• Nonbank financing (Nonbank): NBFI financing to total bank loans (“nbkc”), NBFI financing to GDP (“nbkc_gdp”), and NBFI nonperforming loans to total NBFI financing (“nbkc_npl”).

• Equity financing (Equity): Market capitalization to GDP (“m_cap”).

In equation form, we have the following (Huh and Park, 2017):

where Z(a)_i is the i ^th principal component on macro conditions (Macro), Xa₁ the number of MSMEs to total number of firms (“num”), Xa₂ denotes MSME employees to total employees (“emp”), Xa₃ denotes MSME contribution to GDP (“gdp”), and Xa₄ is inflation (“inf”).

where Z(p)_i is the i ^th principal component on bank financing (Bank), Xp₁ denotes MSME bank loans to total (“bkc”), Xp₂ denotes MSME bank loans to GDP (“bkc_gdp”), Xp₃ denotes MSME nonperforming loans to total MSME bank loans (“bkc_npl”), and Xp₄ is the commercial bank lending rate (“len_r”).

where Z(s)_i is the i ^th principal component on nonbank financing (Nonbank), Xs₁ denotes NBFI financing to total bank loans (“nbkc”), Xs₂ denotes NBFI financing to GDP (“nbkc_gdp”), and Xs₃ denotes NBFI nonperforming loans to total NBFI financing (“nbkc_npl”).

Table 6 shows eigenvalues of each component under each indicator (dimension)—Macro (D1), Bank (D2), and Nonbank (D3). Based on the results, we use the first two components for each dimension. Each eigenvalue is 0.95 or higher, with the exception of “emp” in D1 which has an eigenvalue of only 0.70. Because it adds another 0.17 to the cumulative, raising it from 0.69 to 0.86, we opted to include it in the analysis.

Table 6: Results of Principal Component Analysis, Regional SME-DI (First Stage)

obs = observation, SME-DI = Small and Medium-Sized Enterprise Development Index.

Source: Calculation based on ADB Asia SME Monitor 2020 database.

From this, by dimension, we calculated squared loadings of each variable in the selected principal components (Z₁, Z₂,…, Z_i) (Table 7).

Table 7: Squared Loadings, Regional SME-DI (First Stage)

Dimension	Z1	Z2
D1: Macro (74 obs.)
PC1: num	0.7044	0.0008
PC2: emp	0.7567	0.1100
PC3: gdp	0.8495	0.0555
PC4: inf	0.4381	0.5294
D2: Bank (88 obs.)
PC1: bkc	0.9206	0.0003
PC2: bkc_gdp	0.6524	0.0839
PC3: bkc_npl	0.0514	0.7467
PC4: len_r	0.2983	0.5761
D3: Nonbank (60 obs.)
PC1: nbkc	0.8709	0.0264
PC2: nbkc_gdp	0.8727	0.0244
PC3: nbkc_npl	0.0980	0.9019

obs = observation, SME-DI = Small and Medium-Sized Enterprise Development Index.

Source: Calculation based on ADB Asia SME Monitor 2020 database.

The sum of squared loadings in each Z_i is λ (λ₁, λ₂,… λ_i). We calculated Θ as the proportion of respective λ (λ₁, λ₂,… λ_i) to the total λ (λ₁ + λ₂ + …+ λ_i). Then, “squared loadings to the unity sum (η)” (Huh and Park, 2017) were calculated as the proportion of the squared loading to the λ in each component (Z_i). In the next step, we calculated weights of respective variables of the dimension as the sum of “η by Θ” of each component (Z_i). Table 8 shows the weights (scores) of each dimension obtained through the first-stage PCA.

Table 8: Weights for Index, Regional SME-DI (First Stage)

Dimension	Weight
Macro
PC1: num-MSMEs to total (%)	0.2047
PC2: emp-MSME employees to total (%)	0.2516
PC3: gdp-MSME contribution to GDP (%)	0.2627
PC4: inf-Annual inflation rate (%)	0.2809
Bank
PC1: bkc-MSME bank loans to total (%)	0.2766
PC2: bkc_gdp-MSME bank loans to GDP (%)	0.2211
PC3: bkc_npl-MSME nonperforming loans to total MSME bank loans	0.2397
PC4: len_r-Annual commercial bank lending rate (%)	0.2626
Nonbank
PC1: nbkc-NBFI financing to bank loans (%)	0.3211
PC2: nbkc_gdp-NBFI financing to GDP (%)	0.3210
PC3: nbkc_npl-NBFI nonperforming loans to total NBFI financing	0.357...

Table of contents

• Front Cover
• Title Page
• Copyright
• Contents
• Tables, Figures, and Boxes
• Foreword
• Acknowledgments
• Abbreviations
• Executive Summary
• Introduction
• MSME Landscape in Southeast Asia
• Exploration of Index
• Methodology
• Estimation Results
• Conclusion
• References
• Annex: Profile of Cambodian Enterprises
• Footnotes
• Back Cover