Factors affecting micro, small, and medium-sized enterprise development in developing Asia: Findings from a probabilistic principal component analysis

Shinozaki, Shigehiro; Miyakawa, Daisuke; Arahan, Romeo Working Paper Factors affecting micro, small, and medium-sized enterprise development in developing Asia: Findings from a probabilistic principal component analysis ADB Economics Working Paper Series, No. 715 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Shinozaki, Shigehiro; Miyakawa, Daisuke; Arahan, Romeo (2024) : Factors affecting micro, small, and medium-sized enterprise development in developing Asia: Findings from a probabilistic principal component analysis, ADB Economics Working Paper Series, No. 715, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS240032-2 This Version is available at: https://hdl.handle.net/10419/298161 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADB ECONOMICS WORKING PAPER SERIES NO. 715 February 2024 Factors Affecting Micro, Small, and Medium-Sized Enterprise Development in Developing Asia Findings from a Probabilistic Principal Component Analysis To identify factors affecting micro, small, and medium-sized enterprise (MSME) development, this paper proposes a probabilistic principal component analysis method that works despite current data limitations. The estimation results suggest that sound MSME credit markets, diversified financing options, support for new businesses and job creation, and active MSME participation in global marketplaces play a critical role in ensuring a smooth business recovery from various crises and shocks affecting developing Asia and the Pacific. About the Asian Development Bank ADB is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 68 members —49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. FACTORS AFFECTING MICRO, SMALL, AND MEDIUM-SIZED ENTERPRISE DEVELOPMENT IN DEVELOPING ASIA FINDINGS FROM A PROBABILISTIC PRINCIPAL COMPONENT ANALYSIS Shigehiro Shinozaki, Daisuke Miyakawa, and Romeo Arahan ASIAN DEVELOPMENT BANK The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB Economics Working Paper Series Shigehiro Shinozaki, Daisuke Miyakawa, and Romeo Arahan No. 715 | February 2024 Shigehiro Shinozaki ([email protected]) is a senior economist and Romeo Arahan (rarahan.consultant@ adb.org) is a consultant at the Economic Research and Development Impact Department. Daisuke Miyakawa ([email protected]) is a professor at Waseda University, and chief economist of UTokyo Economic Consulting Inc. (UTEcon). Factors Affecting Micro, Small, and Medium-Sized Enterprise Development in Developing Asia: Findings from a Probabilistic Principal Component Analysis Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2024 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2024. ISSN 2313-6537 (print), 2313-6545 (electronic) Publication Stock No. WPS240032-2 DOI: http://dx.doi.org/10.22617/WPS240032-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB in preference to others of a similar nature that are not mentioned. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” inthis publication, ADB does not intend to make any judgments as to the legal or other status of any territory or area. This publication is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) https://creativecommons.org/licenses/by/3.0/igo/. By using the content of this publication, you agree to be bound bytheterms of this license. For attribution, translations, adaptations, and permissions, please read the provisions andterms of use at https://www.adb.org/terms-use#openaccess. This CC license does not apply to non-ADB copyright materials in this publication. If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Note: In this publication, “$” refers to United States dollars. ABSTRACT Limited data on micro, small, and medium-sized enterprises (MSMEs) make it difficult for governments to design appropriate MSME policies in Asia and the Pacific. To identify factors affecting MSME development and promote evidence-based policymaking, we propose a probabilistic principal component analysis method that works despite current data limitations. The study uses time-series MSME data collected from 25 developing member countries of the Asian Development Bank (ADB) through the Asia Small and Medium-Sized Enterprise Monitor project. The estimation results suggest that sound MSME credit markets, diversified financing options, support for new businesses and job creation, and active MSME participation in global marketplaces play a critical role in ensuring a smooth business recovery from various crises and shocks affecting developing Asia and the Pacific. Keywords: SME development, access to finance, financial inclusion, SME policy, probabilistic principal component analysis, Southeast Asia, South Asia, Central and West Asia, the Pacific JEL codes: D22, G20, L20, L50 1. Introduction Developing Asian economies continue to recover from the coronavirus disease (COVID-19) pandemic that began in March 2020, although economic growth differs by country. Continuous global economic uncertainty, however, has amplified downside risks—including high inflation, currency depreciation, and global supply chain disruptions accelerated by regional political turbulence. In Southeast Asia, a recovery in tourism partly contributed to the region’s 5.6% growth in 2022; but it is forecast to drop to 4.6% in 2023 given continued weak exports. In South Asia, economic and political crises in Pakistan and Sri Lanka pushed the region’s growth down from 6.7% in 2022 to 5.4% in 2023. In Central and West Asia, the ongoing impact from the Russian invasion of Ukraine helped lower the region’s growth from 5.1% in 2022 to a forecast 4.6% in 2023. In the Pacific, a strong post-pandemic tourism rebound energized the region’s sharp economic recovery to 6.1% growth in 2022; but it is forecast to slow to 3.5% in 2023 partly due to labor shortages accelerated by emigration from small island countries to Australia and New Zealand (ADB 2023a). Micro, small, and medium-sized enterprises (MSMEs) help drive growth across developing Asia and the Pacific, given their large share of business enterprises, job creation, and economic output. Given their impact, governments in the region have taken several policy measures to promote MSME development. They commonly promote entrepreneurial development (especially for youth and women), use of technology that encourages business innovation, expanded market access by internationalizing MSMEs, human capital and skills development, and better access to finance. But constraints on MSME development remain in most countries. These include a lack of an entrepreneurial culture, high dependence on cash transactions that stymie innovation, a large percentage of unregistered or informal businesses, limited exports or participation in global markets, skilled labor shortages, and structural problems limiting access to formal financial services for working and growth capital. This raises the question how governments can enhance policies and their implementation to promote MSME development toward more inclusive, resilient growth. Better understanding the MSME business environment and structural problems that inhibit growth is critical before designing a feasible policy framework on MSME assistance. However, the lack of data on MSMEs makes this extremely difficult. To help governments promote evidence-based MSME policymaking, the Asian Development Bank (ADB) has, since 2020, provided benchmark indicators on MSME development and access to finance through its annual Asia Small and Medium-Sized Enterprise Monitor (ASM). As of November 2023, the ASM covers MSMEs in 25 ADB developing members in Southeast Asia, South Asia, Central and West Asia, and the Pacific. Insufficient data, however, remains a major problem. A solid quantitative evaluation on MSME development using sufficient, accurate, and comparable data remains a challenge both nationally and regionally. Incomplete data on MSMEs led global institutions—such as the Organisation for Economic Co-operation and Development (OECD), the Economic Research Institute for ASEAN and East Asia (ERIA), and the International Trade Centre (ITC)—to propose a qualitative approach using assessment matrices for performance ratings or median comparisons based on available data to evaluate MSME development and competitiveness, both nationally and regionally. The ASM project has also explored a new way to quantitatively identify factors affecting MSME development through its ASM database. In 2021, it developed a new trial that deals with MSME data limitations—a variant of a standard principal component analysis (PCA) that supplements some missing MSME data—a probabilistic PCA (ADB 2022). The pilot test covered 15 countries 2 from Southeast Asia and South Asia along with a firm-level data analysis for Viet Nam. While this contributed to the new MSME development index, insufficient data limited the proposed model’s ability to estimate more accurately the factors that represent MSME activities. More test-runs for additional countries are needed to produce a reliable index conducive to evidence-based policy design on MSMEs in the region. In 2023, we successfully compiled time-series MSME data covering 25 countries. With this new dataset, this study re-estimates factors that explain the MSME development path by region and country and rethinks how to develop a quantitative approach to better assess MSME development. Section 2 summarizes the MSME landscape in developing Asia, extracted from ADB (2023b). Section 3 reviews global MSME data initiatives in Asia and the Pacific. Section 4 explains the methodology and dataset used for analysis. Section 5 discusses the estimation results in four groups—(i) all countries, (ii) Southeast Asia, (iii) South Asia, and (iv) Central and West Asia. This is followed by associated policy implications in Section 6 and conclusions in Section 7. 2. MSME Landscape in Developing Asia MSMEs dominate the private sector in Asia and the Pacific. According to ADB (2023b), based on available data in participating countries through 2022, MSMEs in Asia and the Pacific accounted for an average 96.6% of all enterprises, 55.8% of the total workforce, and 28% of a country’s economic output (Table 1). Data collected depend on the national MSME definition of each country. Most MSMEs serve small domestic markets, with many engaged in distributive trade and informal business. Cash dominates their business model and there is little incentive to grow further—categorized as “stability-oriented” firms. With a large base of informal businesses, the official MSME contribution to a country’s economic output is likely well below its actual impact. Nonetheless, “growth-oriented” and innovative firms that want to expand into global markets have gradually increased across the region, although they remain a small fraction of MSMEs. Based on available data through 2022, MSME exports accounted for an average 26.3% of total export value. And MSME export growth is slowing, mainly due to the weak export environment globally. Low business diversification limits a country’s growth potential, suggesting the need for creating more innovative and globalized small firms, startups, and an entrepreneurial base, both nationally and regionally. Limited access to finance remains a chronic barrier to MSME growth. The MSME credit market remains small in Asia and the Pacific. ADB (2023b) reported that bank loans to MSMEs averaged 10.6% of a country’s gross domestic product (GDP) and 22% of total bank lending. The pandemic response boosted commercial bank lending to MSMEs, provided government emergency financial assistance or strengthened new lending to MSMEs through subsidized loan programs, refinancing facilities, and special credit guarantees. Despite this, MSME nonperforming loans remained high, averaging 7.2% of total MSME bank loans in the region. The lack of alternative financing options beyond traditional bank credit limits innovation and business opportunities for viable MSMEs, startups, and entrepreneurs. 3 Table 1: MSMEs in Developing Asia and the Pacific (percentage share) All Countries Southeast Asia South Asia Central and West Asia MSME d evelopment  Number of MSMEs to total enterprises 96.6% 98.0% 99.6% 99.2%  MSME employees to total employees 55.8% 66.4% 76.6% 51.9%  MSME contribution to economic output 28.0% 41.2% 17.7% 41.5%  MSME exports to total export value 26.3% 13.3% 37.4% 28.3% Access to finance (bank credit)  MSME loans to national GDP 10.6% 13.3% 5.2% 11.1%  MSME loans to total bank loans 22.0% 12.3% 12.5% 33.1%  MSME NPLs to total MSME loans 7.2% 5.3% 12.1% 4.3% GDP = gross domestic product, MSME = micro, small, and medium-sized enterprise, NPL = nonperforming loan. Notes: Reporting countries only. Data based on latest available data until 2022. Data for all countries cover 25 countries: 10 from Southeast Asia; 5 from South Asia; 7 from Central and West Asia; and 3 from the Pacific. Source: Asia SME Monitor 2023 database . 3. Global MSME Data Initiatives Several global initiatives are developing indices to measure specific aspects of MSMEs—such as access to markets, infrastructure, finance, skills development, use of technology and innovation, business operations and administration, competitiveness, and policy and regulatory frameworks (Table 2). Multilateral organizations such as the OECD, ERIA, ITC, and World Bank Group have been using various analytical approaches to overcome the lack of sufficient MSME data. The OECD produces two related reports on SME development: (i) the SME and Entrepreneurship Outlook and (ii) Financing SMEs and Entrepreneurships (OECD Scoreboard). Launched in 2002, the Entrepreneurship Outlook reviews 6 dimensions with 29 subdimensions using cross sectional data for median comparison. Dimensions include (i) institutional and regulatory frameworks, (ii) market conditions, (iii) infrastructure, (iv) access to finance, (v) access to skills, and (vi) access to innovation assets (OECD 2023). The subdimensions include (i) regulations, courts and laws, land and housing, public governance, competition, and taxation; (ii) domestic markets, global markets, public procurement, and trade and investment; (iii) logistics, energy, research and development (R&D) and innovation, the internet and information and communications technology (ICT); (iv) self-funding, debt, the financial system, and alternative instruments; (v) adult literacy, the labor market, entrepreneurial culture, training, and education; and (vi) technology, R&D, organization and processes, marketing, and data. It covers OECD members, including, from Asia, Australia, Japan, New Zealand, and the Republic of Korea. The OECD Scoreboard, launched in 2012, is an annual report focusing on trends in SME financing and policies for 48 countries. In 2022, it included 11 countries from Asia—Australia, Georgia, Indonesia, Japan, Kazakhstan, Malaysia, New Zealand, the People's Republic of China (PRC), the Republic of Korea, Thailand, and Türkiye. It reviews 5 financial dimensions with 25 subdimensions (indicators): (i) allocation and structure of bank credit to SMEs; (ii) extent of public 4 support for SME finance; (iii) credit costs and conditions; (iv) nonbank sources of finance; and (v) financial health (OECD 2022). The OECD constructs the indicators mainly using supply-side data from standardized forms filled in by banks, other financial institutions, statistics offices, and government agencies. The core indicators include total lending (overall and SMEs), new lending (overall and SMEs), shortversus long-term SME loans, direct government SME loans, government loan guarantees, interest rates (overall and SMEs), collateral (SMEs), and bankruptcies (SMEs), among others. The OECD and ERIA produced an ASEAN SME Policy Index in 2014 and 2018 outlining the policy landscape for SME development. It evaluates the scope and intensity of SME development policies through 8 dimensions with 25 subdimensions: (i) productivity, technology, and innovation; (ii) environmental policies targeting SMEs; (iii) access to finance; (iv) access to markets and internationalization; (v) institutional framework; (vi) legislation, regulation, and taxes; (vii) entrepreneurial education and skills; and (viii) social enterprises and inclusive entrepreneurship (OECD and ERIA 2018). These are measured in three stages: (i) planning and design; (ii) implementation; and (iii) monitoring and evaluation. Participating governments share their SME data and assess SME policies. They also conduct surveys of key stakeholders and private sector representatives to help supply missing information needed for qualitative analysis. For each subdimension, respondents score the strengths and weaknesses of current SME policies on a scale from 1 to 6, with higher scores indicating a better level of policy development and implementation. The ITC’s SME Competitiveness Outlook annually reviews SME development and financing conditions in 85 countries including several from Asia (ITC 2022). It aims to facilitate implementation of United Nations Sustainable Development Goals 8 and 9. The report produces an SME Competitiveness Index based on 3 dimensions with 39 subdimensions: (i) firm capabilities (SME's ability to manage resources under its control); (ii) business ecosystem (resources and competencies needed to enhance a firm’s competitiveness); and (iii) national environment (government functionality and policy implementation). Each dimension is measured on three abilities: (i) capacity to compete (enterprise efficiency); (ii) capacity to connect (information and knowledge gathering/exploitation); and (iii) capacity to change (human and financial capital investments). The index assesses the competitive strengths and weaknesses by firm size on a 0–100 scale, analyzing time-series data obtained from secondary data including (i) the World Bank’s Enterprise Surveys, Ease of Doing Business Index, and Logistics Performance Index; (ii) the International Monetary Fund’s (IMF) World Economic Outlook; and (iii) the ITC’s Market Access Map. Firm size classifications use the definition from World Bank Enterprise Surveys.1 Strengths and weaknesses are measured based on a reference level of per capita GDP. As mentioned, the World Bank Group regularly releases three related reports: (i) the International Finance Corporation (IFC) MSME Finance Gap Report; (ii) the Enterprise Surveys; and (iii) the Doing Business report. The latest IFC report was released in 2017 (with updates as needed), covering 128 countries including 29 ADB developing members (IFC 2017). Data cover general indicators such as MSME landscape, bank lending, and nonbank finance data. It estimates the potential demand for financing in emerging economies compared with current supply, and calculates the “finance gap.” The report is considered a benchmark of MSME financing needs in 10 advanced economies. MSME categories include industry (manufacturing, services, or retail), 1 The Enterprise Surveys define small firms as having 5-19 employees, medium firms 20-99, and large firms 100+ employees. https://www.enterprisesurveys.org/en/methodology. 11 The second equality holds as (𝑥,𝑧) (𝑖 = 1,⋯,𝑛) is independent. In summary, the EM algorithm alternately repeats two steps: an expectation step for log 𝑝(𝑋,𝑍|𝜃) with regard to 𝑝(𝑍|𝑋,𝜃) and to calculate the k-th target function 𝑄(θ), and a maximization step to maximize it. Although there is no guarantee of obtaining a global optimal solution, convergence of its likelihood is guaranteed by its derivation. This method is particularly useful for estimating parameters in latent variable models where the optimization of simultaneous distributions is difficult. Applying the EM algorithm to the probabilistic PCA model, the simultaneous distributions can be written as follows: Then, the conditional distribution 𝑝(𝑧|𝑥,𝜃) of 𝑧 with k−th parameters is given by the following: This object means that the mean < 𝑧> and the covariance < 𝑧𝑧 > of 𝑧 under 𝑝(𝑧|𝑥,𝜃) can be written respectively as follows, with 𝑊 𝑊+ σ𝐼 denoted as 𝑀: By extracting terms which relate to 𝜃 from 𝑄, we get the k−th target function: 12 Finally, 𝜃 is calculated by differentiating the target function by (𝜇,𝑊,𝜎) and finding a unique stationary point: under the condition μ= 0. When the set of data 𝑋 is missing some values, each 𝑥 is decomposed into the following two terms for easier explanation: The two variables 𝑠 and 𝑡 correspond to the observed coordinates and missing coordinates, respectively. When 𝑥 consists of 𝑢 observed coordinates and v missing coordinates, (𝑠) is the 𝑗−th observed coordinates of 𝑥, and (𝑡) is the 𝑗−th missing coordinates of 𝑥. Therefore, 𝑠 and 𝑡 are 𝑢-dimensional and 𝑣-dimensional, while 𝐼 ∈ ℝ× and 𝐼 ∈ ℝ× are defined as follows: Then, the simultaneous distribution of 𝑠,𝑡,𝑧 under fixed 𝐼 and 𝐼 can be written as follows: Also, there are conditional distributions about 𝑧 and 𝑡 under the observed 𝑠: 13 We define and calculate 𝑚 and 𝐷 with the mean and variance of 𝑡 and 𝑧 under the fixed 𝑠. Using these distribution functions, we can derive the EM algorithm for data with missing values by regarding both 𝑡 and 𝑧 as latent variables. 5. Estimation Results This section presents the estimation results for (i) all 25 countries, (ii) Southeast Asia, (iii) South Asia; and (iv) Central and West Asia. A regional estimation for the Pacific was not conducted as there were only 3 countries included in the model. A robustness test was done by applying another variant of P-PCA to aggregate data of the 25 countries (Appendix 4). 5.1. All Countries The P-PCA was applied to country-level panel data of 25 countries to see the time-series dynamics of MSME development in Asia and the Pacific. Three factors were obtained—principal component (PC)1 to PC3 (Figure 1, Table 3). PC1 makes the largest contribution to the variation in country-level panel data (59%), followed by PC2 (15%) and PC3 (7%), explaining 80% in total (Table 4). Factor loadings are sorted in descending order (Table A3.1). A darker red color indicates a positive impact to the trend in the principal component, while a darker blue means a negative impact. Each factor is orthogonal to each other and related to each variable with specific factor loadings. Key factors that form each PC can thus be extracted. PC1-PC3 formed three different trend curves on MSME development (Figure 1). PC1 traces a low line until the middle of the sample period, then rises from 2015 until it slows after 2020. It suggests that MSMEs felt the effects of the aftermath of the 2008–2009 global financial crisis (GFC) until 2014, when recovery accelerated until development slowed after the COVID-19 pandemic spread in 2020. Overall, it indicates “a slow recovery against the shocks.” PC2 remains low until 2011, moves up until the 2015 peak, then declines afterward. It suggests that MSMEs made relatively rapid recovery from the GFC, then decelerated development around the latter part of the 2014–2016 Russian Financial Crisis (RFC), and shifted to the negative after 2019, accelerated by the 2020–2021 COVID-19 pandemic. It indicates “relatively fast recovery against the shocks but sensitive to the shocks.” The PC3 curve is more complicated, rising soon after the GFC with its first peak in 2011, bottoming out in 2016 (RFC), and rising again afterward. It suggests “a quick recovery against the shocks but very sensitive to the shocks.” In PC1 (slow recovery), the main factors slowing MSME development were nonperforming loans by banks, NBFIs, and for MSMEs (e.g., Pakistan, Kazakhstan, Brunei Darussalam, Malaysia, and Viet Nam). Negative factor loadings also indicated “MSME loans" (e.g., Tajikistan, Kazakhstan, Papua New Guinea, and Georgia), which means that increased MSME loans in some countries lowered the level of MSME development under PC1. This suggests that the delivery of low quality MSME loans with increased nonperforming loans in the countries mentioned likely contributed to MSMEs’ slow GFC recovery until 2014. In contrast, the main factors that boosted MSME development were (i) bank loans (e.g., the Philippines, Fiji, the Kyrgyz Republic, and India), (ii) number of MSMEs (e.g., Indonesia, Nepal, Georgia, Viet Nam, and the Kyrgyz Republic), and (iii) MSME output (e.g., Indonesia and Pakistan). This suggests that steady delivery of bank loans to businesses likely catalyzed the increase in number of MSMEs (new small business creation) and output, bringing MSMEs back to their growth path after 2015. In PC2 (relatively fast recovery), key drivers that lowered MSME development were also nonperforming loans by banks, NBFIs, and for MSMEs (e.g., Georgia, Fiji, Bangladesh, 14 Uzbekistan, the Philippines, Thailand, Papua New Guinea, the Kyrgyz Republic, Viet Nam, Cambodia, and Sri Lanka). On the other hand, factors that accelerated MSME development were (i) number of MSME employees (e.g., India, Uzbekistan, Viet Nam, Georgia, Tajikistan, Malaysia, Indonesia, and the Philippines), (ii) MSME output (e.g., Tajikistan, Uzbekistan, and Georgia), (iii) MSME loans (e.g., the Lao PDR, the Philippines, Thailand, and Malaysia), and (iv) equity market capitalization (e.g., the Lao PDR, Sri Lanka, and Pakistan). PC2 suggests that nonperforming MSME loans likely slowed MSME development after the GFC and the COVID-19 pandemic, while improved delivery of MSME loans and the recovery of market-based finance resulted in a better environment for new MSME jobs and enhanced output. This likely supported the relatively fast recovery and growth of MSME businesses after the GFC. In PC3 (quick but sensitive recovery), nonperforming loans by banks and for MSMEs (e.g., the Lao PDR, Indonesia, and Tajikistan) were again the main drivers slowing MSME development. Factors that raised the MSME development level were (i) bank loans (e.g., Malaysia, Thailand, Armenia, and India), (ii) market capitalization (e.g., Indonesia, Papua New Guinea, and Bangladesh), (iii) MSME output (e.g., Malaysia, Kazakhstan, Azerbaijan, Georgia, and the Kyrgyz Republic), and (iv) MSME exports and/or imports (e.g., Indonesia and the Kyrgyz Republic). Although PC3 showed a small contribution to explaining MSME development, it suggests that expanded bank lending and capital markets likely helped the rapid recovery of MSME exports and output and quickly accelerated MSME development. But due to weak financial markets and international trade for MSMEs, it remained highly sensitive to shocks like the GFC, RFC, and COVID-19 pandemic. Overall, the estimation results for all countries show that sound MSME credit markets, diversified financing options (market-based finance), support for new business development and job creation, along with active MSME participation in global markets play a critical role for the smooth recovery from crises and shocks in developing Asia and the Pacific. Sound, resilient finance sector development is indispensable for sustainable MSME growth nationally. Figure 1 : Time Series Plots of Estimated Principal Component s — All Countries PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 PC2 PC3 15 Table 3 : Time Series P l ots of Estimated Principal Components — All Countries PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. Table 4 : Contribution of Each Estimated Principal Component — All Countries PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. 5.2. Southeast Asia For Southeast Asia, three PC factors (PC1-PC3) were also obtained (Figure 2, Table 5). PC1 makes the largest contribution to the variation in country data (59%), followed by PC2 (16%) and PC3 (7%), explaining 82% in total (Table 6). Factor loadings are sorted in descending order (Table A3.2). PC1-PC3 in Southeast Asia followed similar trend curves as in “all countries” but with somewhat more complicated shapes (Figure 2). PC1 remains low until 2014, then rises to a 2017 peak, before decelerating growth with a drop in 2022. It indicates a slow recovery path from the GFC. PC2 also remains low until 2011, moves up until the peak in 2014, and then declines afterward— reaching its peak a year earlier than in “all countries.” It indicates a fast recovery from the GFC but sensitive to shocks such as the RFC and the pandemic. PC3 generated a complicated shape with frequent ups and downs during 2007–2022. It bottoms out in 2011, moves up to a peak in 2014, then falls until 2018 before rising again to a peak in 2021. It indicates that MSME development is very sensitive to shocks such as the GFC, RFC, and the pandemic, while also quickly recovering. In PC1 (slow recovery), the negative curve until 2014 is explained by negative factor loadings denoted by nonperforming loans by banks and for MSMEs (e.g., Brunei Darussalam, Malaysia, Viet Nam, and the Philippines). In contrast, the positive curve after 2015 is explained by positive factor loadings denoted by (i) bank loans and NBFI loans (e.g., the Philippines, Viet Nam, the Lao PDR, Thailand, and Singapore), (ii) number of MSMEs (e.g., Indonesia and Viet Nam), and (iii) MSME output (e.g., Indonesia, Brunei Darussalam, and Thailand). PC1 suggests that increased nonperforming loans likely contributed to their slow recovery from the GFC. But improved lending by banks and NBFIs likely facilitated new small business creation and a rebound in output, allowing a return to development growth after 2015. In PC2 (fast recovery), the negative curve until 2011 and after 2019 is also explained by negative factor loadings denoted by nonperforming loans by banks, NBFIs, and for MSMEs (e.g., Thailand, Viet Nam, Myanmar, Cambodia, and the Philippines). The positive curve peaking in 2014 is explained by positive factor loadings denoted by (i) MSME loans (e.g., the Lao PDR, the Philippines, Thailand, and Malaysia), (ii) market capitalization (e.g., the Lao PDR, Singapore, and Thailand), and (iii) number of MSME employees (e.g., Viet Nam, Malaysia, the Philippines, and Indonesia). PC2 suggests that the high level of nonperforming loans in the finance sector likely Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 -0.17 -0.21 -0.25 -0.27 -0.32 -0.30 -0.25 -0.17 0.03 0.19 0.26 0.30 0.33 0.31 0.27 0.19 PC2 -0.23 -0.25 -0.19 -0.17 -0.05 0.13 0.30 0.42 0.44 0.33 0.26 0.09 -0.05 -0.17 -0.25 -0.26 PC3 -0.42 -0.34 -0.31 0.28 0.37 0.35 0.20 0.03 -0.14 -0.22 -0.07 0.07 0.15 0.06 0.21 0.29 Item PC1 PC2 PC3 Contribution ratio 0.59 0.15 0.07 Cumulative contribution rate 0.59 0.74 0.80 16 impeded MSME development around the GFC and the pandemic, while expanded MSME lending, the capital market recovery (including dedicated MSME equity markets such as Catalist in Singapore and mai in Thailand), along with more MSME jobs likely helped the relatively fast MSME development post GFC. In PC3 (sensitive recovery), the negative curve around two points in 2011 and 2018 is explained by negative factor loadings denoted by (i) nonperforming loans by banks, NBFIs, and for MSMEs (e.g., the Lao PDR, Singapore, Brunei Darussalam, Thailand, and Viet Nam) and (ii) MSME loans (e.g., Thailand, the Philippines, and the Lao PDR). The positive curve around two points in 2014 and 2021 is explained by positive factor loadings denoted by (i) loans by banks, NBFIs, and MSME lending (e.g., Singapore, Brunei Darussalam, and Cambodia) and (ii) market capitalization (e.g., Malaysia [ACE and LEAP markets] and the Philippines [SME Board]). PC3 generated a different curve than for “all countries”, more pronounced in the effect of access to finance. It suggests that low quality MSME loans with increased nonperforming loans in countries such as Thailand and the Lao PDR likely kept MSME development suppressed in the region (especially in 2016–2018 amid the global economic slowdown). But diversified financing options from bank credit along with nonbank and market-based finance likely helped MSMEs recover from the shocks smoothly, while volatile financial markets held back resilience. Figure 2 : Time Series P l ots of Estimated Principal Components — Southeast Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. Table 5 : Time Series P l ots of Estimated Principal Components — Southeast Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 PC2 PC3 Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 -0.28 -0.28 -0.25 -0.27 -0.32 -0.29 -0.15 -0.07 0.07 0.26 0.31 0.27 0.30 0.27 0.27 0.17 PC2 -0.21 -0.23 -0.26 -0.12 -0.01 0.23 0.42 0.46 0.39 0.19 0.16 0.02 -0.12 -0.16 -0.24 -0.28 PC3 0.14 0.04 0.00 -0.26 -0.29 -0.20 0.16 0.30 0.28 -0.24 -0.32 -0.41 -0.20 0.23 0.31 0.26 17 Table 6 : Contribution of Each Estimated Principal Component — Southeast Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. 5.3. South Asia In South Asia, three factors were obtained from the P-PCA, but PC1 and PC2 trends were swapped (Figure 3, Table 7). PC1 makes the largest contribution to the variation in country data (59%), followed by PC2 (22%) and PC3 (8%), explaining 89% in total (Table 8). Factor loadings are sorted in descending order (Table A3.3). PC1-PC3 in South Asia generated different trend curves from “all countries” estimates (Figure 3). PC1 remains low until 2010, moves up until its 2014 peak, and then declines afterward. It indicates a fast recovery from the GFC (a year earlier than the trend in “all countries”) but was sensitive to the global economic slowdown and the pandemic. PC2 remains low until 2015, then rises through 2019, before declining until 2021. It indicates a slow recovery from the GFC and South Asia’s economic slowdown. The PC3 trend was somewhat reversed from the trend in “all countries.” It bottoms out in 2013, moves up to a peak in 2017, then drops afterward bottoming out in 2022. It indicates MSME development was sensitive to the region’s economic and political instability (e.g., India before the current administration started in 2014, and economic and political crises in Pakistan and Sri Lanka from around 2019) as well as the shock from the pandemic. In PC1 (fast recovery), the negative curve until 2010 and after 2019 is explained by negative factor loadings denoted by nonperforming loans by banks, NBFIs, and for MSMEs (e.g., India, Bangladesh, Pakistan, and Sri Lanka). MSME loans in India and Pakistan were also identified as negative factors, suggesting that the delivery of low quality MSME loans with high levels of nonperforming loans in these countries likely impeded MSME development in the region. The positive curve peaking in 2014 is explained by positive factor loadings denoted by (i) NBFI loans and market capitalization (e.g., Sri Lanka, Pakistan, and Bangladesh) and (ii) number of MSME employees (e.g., India and Nepal). A recovery in nonbank and market-based finance, along with increased MSME jobs, likely supported a smooth shift back to growth. But the MSME funding environment was likely sensitive to economic and political environment changes, especially after 2019. In PC2 (slow recovery), the negative curve until 2015 is explained by negative factor loadings denoted by MSME loans and nonperforming loans by banks, NBFIs, and for MSMEs in Pakistan and India, suggesting that increased MSME loans accompanying rising nonperforming loans in these countries likely made MSMEs recover slowly from the GFC and the region’s stagnant economic growth. The positive curve after 2016 is explained by positive factor loadings denoted by (i) loans by banks and for MSMEs (e.g., Bangladesh, Pakistan, Sri Lanka, and India), (ii) number of MSMEs (e.g., Nepal), and (iii) MSME output (e.g., Bangladesh and Pakistan). After 2016, improved bank lending and MSME loans along with an environment conducive to new small businesses and better productivity likely boosted MSME development. In PC3 (sensitive recovery), the negative, downward trend during 2010–2013 and after 2019 (economic crises in Pakistan and Sri Lanka) is explained by negative factor loadings denoted by Item PC1 PC2 PC3 Contribution ratio 0.59 0.16 0.07 Cumulative contribution rate 0.59 0.75 0.82 18 nonperforming loans by banks and NBFIs (e.g., Sri Lanka, Pakistan, and Bangladesh). The positive curve before the GFC and during 2016–2018 (linked to the new administration in India) is explained by positive factor loadings denoted by MSME loans and NBFI loans (e.g., Pakistan, Bangladesh, and India). PC3 was more affected by access to finance for MSME development. It suggests that high levels of nonperforming loans by banks and NBFIs likely led to a slowdown in MSME development. But once the MSME credit market and the nonbank finance industry expanded, MSME development quickly turned positive, although its growth pattern was likely highly sensitive to shocks, such as regional economic crises, political conditions, and the pandemic. Figure 3 : Time Series P l ots of Estimated Principal Components — South Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. Table 7 : Time Series P l ots of Estimated Principal Components — South Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. Table 8 : Contribution of Each Estimated Principal Component — South Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 PC2 PC3 Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 -0.22 -0.34 -0.26 -0.09 0.02 0.20 0.32 0.44 0.41 0.38 0.26 0.08 -0.004 -0.10 -0.14 -0.08 PC2 -0.08 -0.15 -0.30 -0.36 -0.35 -0.33 -0.23 -0.13 -0.006 0.08 0.22 0.27 0.36 0.27 0.24 0.27 PC3 0.44 0.26 0.08 -0.07 -0.14 -0.20 -0.22 -0.003 -0.001 0.25 0.37 0.28 -0.03 -0.33 -0.32 -0.35 Item PC1 PC2 PC3 Contribution ratio 0.59 0.22 0.08 Cumulative contribution rate 0.59 0.80 0.89 19 5.4. Central and West Asia As in other regions, three factors were obtained in Central and West Asia from the P-PCA (Figure 4, Table 9). PC1 makes the largest contribution to the variation in country data (63%), followed by PC2 (15%) and PC3 (8%), explaining 85% in total (Table 10). Factor loadings are sorted in descending order (Table A3.4). PC1-PC3 show similar trends on MSME development as those in “all countries” (Figure 4). PC1 remains low until 2014, rising in 2015–2018 before slowing afterwards, indicating a slow recovery from the GFC. PC2 remains low until 2010, rises to a 2015 peak, and then declines with a negative curve after 2019, indicating a relatively rapid recovery from the GFC but sensitive to shocks like the RFC and the pandemic. PC3 bottoms out in 2009 (GFC), peaks in 2011, then drops until 2016 (RFC). It rises afterward with erratic movement during the pandemic, suggesting a quick but very sensitive recovery from shocks. In PC1 (slow recovery), the negative curve until 2014 is explained by negative factor loadings denoted by (i) nonperforming bank loans (e.g., Kazakhstan, Azerbaijan, and Uzbekistan) and (ii) MSME loans (e.g., Kazakhstan, Tajikistan, and Georgia). The positive curve after 2015 is explained by positive factor loadings denoted by (i) bank loans and those for MSMEs (e.g., the Kyrgyz Republic, Armenia, and Georgia), (ii) number of MSMEs (e.g., Georgia, the Kyrgyz Republic, and Kazakhstan), and (iii) number of MSME employees (e.g., Azerbaijan, Kazakhstan, and Georgia). PC1 suggests that increased MSME lending with high levels of nonperforming loans in countries such as Kazakhstan likely slowed the recovery from the GFC. But improved bank lending likely supported creating new MSMEs and jobs in countries such as Georgia, helping them shift to growth. In PC2 (fast recovery), the negative curve until 2010 and after 2019 is explained by negative factor loadings denoted by nonperforming loans by banks, NBFIs, and for MSMEs (e.g., Uzbekistan, Georgia, and the Kyrgyz Republic). The negative curve largely reflected the trends in Uzbekistan. The positive curve during 2011–2018—peaking in 2015—is explained by positive factor loadings denoted by (i) number of MSME employees (e.g., Uzbekistan, Georgia, and Tajikistan), (ii) MSME output (e.g., Tajikistan, Uzbekistan, and Georgia), and (iii) MSME exports and/or imports (e.g., Uzbekistan and the Kyrgyz Republic). PC2 suggests that a high level of nonperforming loans likely impeded MSME development. Increased job creation, higher output, and internationalization of MSMEs likely helped drive MSME development. In PC3 (sensitive recovery), the downward curve around the GFC and RFC is also explained by negative factor loadings denoted by nonperforming loans by banks, NBFIs, and for MSMEs (e.g., Tajikistan, Uzbekistan, and Armenia). The negative curve in the PC3 largely reflected the trends in Tajikistan. The positive curve during 2010–2014 and after 2017 is explained by positive factor loadings denoted by (i) MSME exports and/or imports (e.g., the Kyrgyz Republic and Georgia), (ii) MSME output (e.g., Kazakhstan, Azerbaijan, Georgia, and the Kyrgyz Republic), and (iii) NBFI loans (the Kyrgyz Republic). PC3 suggests that MSMEs felt the hard impacts from financial crises (GFC and RFC) with poor access to quality bank credit and NBFI loans, more pronounced in Tajikistan. However, higher MSME foreign trade, output, and improved access to NBFI loans likely encouraged MSME development, yet it remained very sensitive to shocks like the GFC, RFC, and the pandemic. 20 Figure 4: Time Series Plots of Estimated Principal Components— Central and West Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. Table 9: Time Series Plots of Estimated Principal Components— Central and West Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. Table 10: Contribution of Each Estimated Principal Component— Central and West Asia PC = principal component. Source: Calculated based on ADB Asia SME Monitor 2023 database. -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 PC2 PC3 Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 PC1 -0.22 -0.23 -0.24 -0.25 -0.29 -0.28 -0.27 -0.19 0.05 0.15 0.23 0.34 0.32 0.30 0.27 0.23 PC2 -0.27 -0.28 -0.19 -0.17 0.03 0.10 0.23 0.36 0.41 0.34 0.30 0.12 -0.06 -0.19 -0.28 -0.29 PC3 -0.33 -0.25 -0.35 0.24 0.40 0.35 0.25 0.04 -0.30 -0.32 0.03 0.15 0.18 0.03 0.21 0.07 Item PC1 PC2 PC3 Contribution ratio 0.63 0.15 0.08 Cumulative contribution rate 0.63 0.77 0.85 27 Table A1.4: Pacific MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution; obs = observations; S.D. = standard deviation. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable Unit Obs Mean Median S.D. Min Max Bank loans outstanding $ million 16 2,571 2,641 783 1,566 3,568 Nonperforming bank loans $ million 16 109 99 69 28 282 MSME loans outstanding $ million 16 216 190 138 35 415 Market capitalization $ million 16 854 545 537 405 1,921 Bank loans outstanding $ million 14 4,535 5,420 2,170 494 6,273 Nonperforming bank loans $ million 14 166 160 123 0 365 MSME loans outstanding $ million 8 1,195 1,034 410 928 2,170 NBFI loans outstanding $ million 11 509 511 46 430 586 Nonperforming NBFI loans $ million 11 56 51 18 23 83 Market capitalization $ million 7 25,573 23,541 12,499 10,981 41,408 Number of MSMEs 11 4,472 4,614 585 3,277 5,218 Bank loans outstanding $ million 16 374 362 71 246 465 Nonperforming bank loans $ million 16 19 18 3 15 25 MSME loans outstanding $ million 11 128 123 42 63 200 Samoa Fiji Papua New Guinea 28 Appendix 2: Missing Data, 2007–2022 Table A 2.1: Southeast Asia Lao PDR = Lao People’s Democratic Republic; MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution; n/a., = not available. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 MSME output n/a., n/a., n/a., n/a., n/a., n/a., Bank loans outstanding n/a., n/a., n/a., Nonperforming bank loans n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., Nonperforming NBFI loans n/a., n/a., n/a., Bank loans outstanding Nonperforming bank loans NBFI loans outstanding Nonperforming NBFI loans Market capitalization n/a., n/a., n/a., n/a., n/a., n/a., Number of MSMEs n/a., n/a., n/a., n/a., n/a., n/a., Number of employees n/a., n/a., n/a., n/a., n/a., n/a., MSME output n/a., n/a., n/a., n/a., n/a., n/a., MSME exports n/a., n/a., n/a., n/a., n/a., n/a., Bank loans outstanding n/a., n/a., n/a., n/a., Nonperforming bank loans n/a., n/a., n/a., n/a., MSME loans outstanding n/a., n/a., n/a., n/a., Nonperforming MSME loans n/a., n/a., n/a., n/a., Market capitalization Bank loans outstanding Nonperforming bank loans n/a., n/a., n/a., n/a., n/a., n/a., n/a., MSME loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., Market capitalization n/a., n/a., n/a., n/a., Number of MSMEs n/a., n/a., n/a., n/a., n/a., n/a., n/a., Number of employees n/a., MSME output n/a., MSME exports n/a., n/a., n/a., n/a., Bank loans outstanding Nonperforming bank loans MSME loans outstanding Nonperforming MSME loans NBFI loans outstanding Market capitalization n/a., n/a., Number of MSMEs n/a., n/a., n/a., Bank loans outstanding n/a., n/a., n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Nonperforming NBFI loans n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Number of MSMEs Number of employees Bank loans outstanding n/a., Nonperforming bank loans n/a., MSME loans outstanding n/a., Nonperforming MSME loans n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., Market capitalization Number of MSMEs n/a., n/a., n/a., n/a., n/a., n/a., n/a., Number of employees n/a., n/a., n/a., n/a., n/a., n/a., n/a., MSME output n/a., n/a., n/a., n/a., n/a., n/a., n/a., Bank loans outstanding n/a., n/a., n/a., Nonperforming bank loans n/a., n/a., n/a., MSME loans outstanding n/a., n/a., n/a., Nonperforming MSME loans n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., Market capitalization n/a., Number of MSMEs Number of employees n/a., MSME output MSME exports MSME imports Bank loans outstanding Nonperforming bank loans n/a., n/a., MSME loans outstanding Nonperforming MSME loans n/a., n/a., Market capitalization Number of MSMEs n/a., Number of employees n/a., Bank loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., Nonperforming bank loans n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., n/a., n/a., Nonperforming NBFI loans n/a., n/a., n/a., n/a., n/a., n/a., Market capitalization n/a., n/a., Myanmar Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Philippines Singapore Thailand Viet Nam 29 Table A2.2: South Asia MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution; n/a., = not available. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 MSME output n/a., n/a., n/a., n/a., n/a., n/a., n/a., Bank loans outstanding n/a., n/a., n/a., Nonperforming bank loans n/a., n/a., n/a., MSME loans outstanding n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., n/a., n/a., Nonperforming NBFI loans n/a., n/a., n/a., n/a., n/a., Market capitalization Number of MSMEs n/a., n/a., n/a., n/a., n/a., Number of employees n/a., n/a., n/a., n/a., n/a., MSME output n/a., n/a., n/a., n/a., MSME exports n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Bank loans outstanding Nonperforming bank loans MSME loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Nonperforming MSME loans n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Nonperforming NBFI loans n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Market capitalization n/a., n/a., n/a., n/a., n/a., n/a., Number of MSMEs n/a., n/a., n/a., n/a., n/a., n/a., Number of employees n/a., n/a., n/a., n/a., Bank loans outstanding n/a., Market capitalization n/a., MSME output Bank loans outstanding Nonperforming bank loans MSME loans outstanding Nonperforming MSME loans NBFI loans outstanding Nonperforming NBFI loans Market capitalization Bank loans outstanding Nonperforming bank loans NBFI loans outstanding Nonperforming NBFI loans n/a., n/a., Market capitalization Bangladesh India Nepal Pakistan Sri Lanka 30 Table A2.3: Central and West Asia MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution; n/a., = not available. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Number of MSMEs n/a., Number of employees n/a., n/a., MSME output n/a., Bank loans outstanding Nonperforming bank loans NBFI loans outstanding Market capitalization Number of MSMEs n/a., Number of employees n/a., MSME output n/a., Bank loans outstanding Nonperforming bank loans NBFI loans outstanding n/a., n/a., n/a., n/a., n/a., Number of MSMEs n/a., Number of employees n/a., MSME output n/a., MSME exports n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., MSME imports n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Bank loans outstanding Nonperforming bank loans MSME loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Nonperforming MSME loans n/a., n/a., n/a., NBFI loans outstanding Market capitalization n/a., n/a., n/a., n/a., n/a., Number of MSMEs Number of employees MSME output Bank loans outstanding Nonperforming bank loans n/a., MSME loans outstanding NBFI loans outstanding Number of MSMEs n/a., Number of employees n/a., MSME output n/a., MSME exports n/a., MSME imports n/a., Bank loans outstanding Nonperforming bank loans n/a., MSME loans outstanding n/a., NBFI loans outstanding Market capitalization Number of MSMEs n/a., Number of employees n/a., n/a., MSME output n/a., n/a., Bank loans outstanding n/a., Nonperforming bank loans n/a., n/a., n/a., n/a., MSME loans outstanding n/a., n/a., n/a., n/a., n/a., Nonperforming MSME loans n/a., n/a., n/a., n/a., n/a., NBFI loans outstanding n/a., Nonperforming NBFI loans n/a., Number of MSMEs Number of employees MSME output MSME exports MSME imports Bank loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Nonperforming bank loans n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Nonperforming NBFI loans n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Market capitalization n/a., n/a., n/a., Georgia Armenia Azerbaijan Kazakhstan Kyrgyz Republic Tajikistan Uzbekistan 31 Table A2.4: Pacific MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution; n/a., = not available. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Bank loans outstanding Nonperforming bank loans MSME loans outstanding Market capitalization Bank loans outstanding n/a., n/a., Nonperforming bank loans n/a., n/a., MSME loans outstanding n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., NBFI loans outstanding n/a., n/a., n/a., n/a., n/a., Nonperforming NBFI loans n/a., n/a., n/a., n/a., n/a., Market capitalization n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., n/a., Number of MSMEs n/a., n/a., n/a., n/a., n/a., Bank loans outstanding Nonperforming bank loans MSME loans outstanding n/a., n/a., n/a., n/a., n/a., Samoa Fiji Papua New Guinea 32 Appendix 3: Factor Loadings Based on Probabilistic Principal Component Analysis Table A3.1: All Countries Continued on next page Country Variable PC1 Country Variable PC2 Country Variable PC3 Philippines Bank loans outstanding 0.98 Tajikistan MSME output 0.88 Malaysia Bank loans outstanding 0.76 Indonesia Number of MSMEs 0.97 India Number of employees 0.87 Indonesia Market capitalization 0.72 Nepal Number of MSMEs 0.96 Uzbekistan Number of employees 0.86 Malaysia MSME output 0.68 Indonesia MSME output 0.92 Azerbaijan Nonperforming bank loans 0.85 Papua New Guinea Market capitalization 0.66 Fiji Bank loans outstanding 0.92 Viet Nam Number of employees 0.82 Kazakhstan MSME output 0.63 Philippines NBFI loans outstanding 0.92 Lao PDR Market capitalization 0.80 Thailand Bank loans outstanding 0.57 Kyrgyz Republic Bank loans outstanding 0.91 Lao PDR MSME loans outstanding 0.76 Bangladesh Market capitalization 0.57 Georgia Number of MSMEs 0.88 Philippines MSME loans outstanding 0.76 Azerbaijan MSME output 0.53 Kyrgyz Republic MSME loans outstanding 0.87 Sri Lanka NBFI loans outstanding 0.76 Indonesia MSME exports 0.50 Cambodia NBFI loans outstanding 0.87 Thailand MSME loans outstanding 0.75 Kyrgyz Republic NBFI loans outstanding 0.50 Viet Nam Number of MSMEs 0.86 Pakistan Market capitalization 0.73 Armenia Bank loans outstanding 0.49 India Bank loans outstanding 0.84 Georgia Number of employees 0.72 Georgia MSME output 0.48 Viet Nam NBFI loans outstanding 0.84 Tajikistan Number of employees 0.70 Philippines Number of MSMEs 0.47 Kyrgyz Republic Number of MSMEs 0.82 Uzbekistan MSME output 0.70 Kyrgyz Republic MSME output 0.44 India Nonperforming bank loans 0.82 Tajikistan Nonperforming NBFI loans 0.70 India Bank loans outstanding 0.44 Nepal Number of employees 0.79 Georgia MSME output 0.68 Thailand Number of employees 0.39 Azerbaijan Number of employees 0.79 Papua New Guinea Bank loans outstanding 0.67 Kyrgyz Republic MSME imports 0.39 Lao PDR Bank loans outstanding 0.79 Malaysia MSME loans outstanding 0.67 Armenia NBFI loans outstanding 0.39 Pakistan MSME output 0.78 Singapore Market capitalization 0.67 Myanmar Nonperforming NBFI loans 0.39 Nepal Bank loans outstanding 0.78 Tajikistan Nonperforming bank loans 0.65 Viet Nam Nonperforming bank loans 0.38 Viet Nam Bank loans outstanding 0.77 Malaysia Number of employees 0.64 Samoa Bank loans outstanding 0.37 Pakistan Bank loans outstanding 0.77 Indonesia Number of employees 0.60 Sri Lanka Market capitalization 0.35 Sri Lanka Bank loans outstanding 0.77 Tajikistan Nonperforming MSME loans 0.59 Myanmar NBFI loans outstanding 0.34 Georgia Bank loans outstanding 0.77 Thailand Market capitalization 0.57 Philippines Number of employees 0.33 Fiji MSME loans outstanding 0.77 Philippines Number of employees 0.56 Nepal Bank loans outstanding 0.33 Kazakhstan Number of employees 0.76 Lao PDR Bank loans outstanding 0.56 Uzbekistan Number of MSMEs 0.32 Bangladesh MSME output 0.74 Uzbekistan MSME exports 0.54 Kyrgyz Republic Nonperforming bank loans 0.32 Sri Lanka Nonperforming NBFI loans 0.74 Sri Lanka Market capitalization 0.54 Viet Nam Number of MSMEs 0.31 Myanmar Number of MSMEs 0.72 Brunei Darussalam Nonperforming NBFI loans 0.52 Georgia Bank loans outstanding 0.31 Thailand Bank loans outstanding 0.72 Brunei Darussalam NBFI loans outstanding 0.48 Malaysia MSME loans outstanding 0.31 Armenia Bank loans outstanding 0.72 Azerbaijan Bank loans outstanding 0.45 Pakistan Nonperforming bank loans 0.30 Papua New Guinea Nonperforming bank loans 0.71 Pakistan MSME output 0.44 Nepal Number of employees 0.29 Bangladesh MSME loans outstanding 0.71 India Number of MSMEs 0.43 Georgia MSME imports 0.29 Kazakhstan Number of MSMEs 0.69 Pakistan Nonperforming bank loans 0.43 Papua New Guinea NBFI loans outstanding 0.29 Samoa Bank loans outstanding 0.69 Tajikistan Bank loans outstanding 0.41 Thailand MSME loans outstanding 0.28 Cambodia Bank loans outstanding 0.68 Kyrgyz Republic MSME imports 0.39 Viet Nam Number of employees 0.27 Armenia NBFI loans outstanding 0.68 Bangladesh Market capitalization 0.38 Uzbekistan MSME imports 0.27 Myanmar Nonperforming NBFI loans 0.68 Malaysia Bank loans outstanding 0.37 Sri Lanka Nonperforming bank loans 0.27 Viet Nam Market capitalization 0.68 Sri Lanka Bank loans outstanding 0.35 Thailand MSME imports 0.25 Bangladesh Bank loans outstanding 0.65 Georgia NBFI loans outstanding 0.35 Kyrgyz Republic Market capitalization 0.25 Uzbekistan MSME imports 0.64 Armenia Nonperforming bank loans 0.34 Indonesia MSME output 0.24 Myanmar NBFI loans outstanding 0.64 Azerbaijan NBFI loans outstanding 0.34 India MSME output 0.24 Kazakhstan NBFI loans outstanding 0.64 Samoa Bank loans outstanding 0.32 Georgia MSME exports 0.24 Armenia Nonperforming bank loans 0.63 Malaysia MSME output 0.31 Uzbekistan MSME exports 0.24 India Number of MSMEs 0.62 Fiji Bank loans outstanding 0.31 Philippines MSME loans outstanding 0.24 Thailand MSME output 0.62 Fiji MSME loans outstanding 0.31 Viet Nam Nonperforming NBFI loans 0.24 Singapore Nonperforming MSME loans 0.62 Philippines Number of MSMEs 0.30 Malaysia MSME exports 0.23 Fiji Market capitalization 0.62 Samoa Nonperforming bank loans 0.30 Kyrgyz Republic MSME exports 0.23 Lao PDR NBFI loans outstanding 0.61 Nepal Number of employees 0.29 Georgia NBFI loans outstanding 0.23 Singapore Bank loans outstanding 0.60 India Nonperforming bank loans 0.28 Kazakhstan Nonperforming bank loans 0.22 Brunei Darussalam MSME output 0.57 Tajikistan MSME loans outstanding 0.27 Cambodia Bank loans outstanding 0.20 Georgia Number of employees 0.55 Malaysia MSME exports 0.26 Azerbaijan Nonperforming bank loans 0.19 Indonesia Bank loans outstanding 0.55 Kyrgyz Republic MSME loans outstanding 0.24 India MSME loans outstanding 0.19 Thailand Nonperforming MSME loans 0.55 Pakistan NBFI loans outstanding 0.24 Georgia MSME loans outstanding 0.19 Pakistan NBFI loans outstanding 0.55 Indonesia Market capitalization 0.24 Nepal Number of MSMEs 0.18 Bangladesh NBFI loans outstanding 0.54 Thailand Bank loans outstanding 0.22 Georgia Number of employees 0.18 Kyrgyz Republic MSME output 0.51 Viet Nam NBFI loans outstanding 0.22 India Number of MSMEs 0.17 Philippines Number of employees 0.49 Georgia Market capitalization 0.22 Thailand MSME output 0.17 Indonesia Nonperforming bank loans 0.49 Indonesia Number of MSMEs 0.21 Uzbekistan MSME output 0.17 Thailand Number of MSMEs 0.48 Philippines Market capitalization 0.21 Uzbekistan Nonperforming bank loans 0.16 Papua New Guinea Bank loans outstanding 0.48 Georgia Number of MSMEs 0.20 Tajikistan MSME output 0.15 Sri Lanka NBFI loans outstanding 0.48 Viet Nam Number of MSMEs 0.18 Cambodia Nonperforming bank loans 0.15 Nepal Market capitalization 0.47 Singapore Number of employees 0.18 Singapore Market capitalization 0.15 Indonesia Number of employees 0.46 Thailand Number of employees 0.18 India MSME exports 0.14 Thailand Nonperforming bank loans 0.46 Kazakhstan Nonperforming bank loans 0.17 Azerbaijan Bank loans outstanding 0.14 Malaysia MSME loans outstanding 0.46 Pakistan Bank loans outstanding 0.16 Kyrgyz Republic Number of MSMEs 0.13 Indonesia Nonperforming MSME loans 0.45 Kazakhstan Number of employees 0.15 Fiji Nonperforming bank loans 0.13 Indonesia MSME exports 0.44 Singapore Bank loans outstanding 0.15 Papua New Guinea MSME loans outstanding 0.11 Thailand Number of employees 0.43 Kyrgyz Republic MSME output 0.12 Pakistan MSME output 0.10 Indonesia MSME loans outstanding 0.43 Azerbaijan Number of employees 0.11 Thailand MSME exports 0.10 Uzbekistan Number of MSMEs 0.42 India Bank loans outstanding 0.11 Myanmar Bank loans outstanding 0.10 Samoa Number of MSMEs 0.42 Pakistan Nonperforming MSME loans 0.11 Cambodia NBFI loans outstanding 0.10 Azerbaijan MSME output 0.42 Kyrgyz Republic NBFI loans outstanding 0.10 Kyrgyz Republic Bank loans outstanding 0.10 Uzbekistan Number of employees 0.40 Kazakhstan Number of MSMEs 0.08 Singapore MSME output 0.10 Philippines MSME loans outstanding 0.40 Singapore NBFI loans outstanding 0.08 Uzbekistan Number of employees 0.09 Cambodia Nonperforming NBFI loans 0.40 Kyrgyz Republic Bank loans outstanding 0.05 Philippines Nonperforming bank loans 0.09 Samoa MSME loans outstanding 0.39 Nepal Number of MSMEs 0.04 Brunei Darussalam Nonperforming bank loans 0.09 Cambodia Market capitalization 0.39 Kazakhstan Bank loans outstanding 0.03 Kyrgyz Republic MSME loans outstanding 0.08 Malaysia MSME output 0.38 Bangladesh Nonperforming bank loans 0.03 Kazakhstan Number of MSMEs 0.08 India MSME exports 0.38 Singapore Nonperforming bank loans 0.02 Sri Lanka Bank loans outstanding 0.08 Malaysia Bank loans outstanding 0.37 Brunei Darussalam MSME output 0.02 Georgia Nonperforming bank loans 0.08 Cambodia Nonperforming bank loans 0.36 Indonesia MSME output 0.02 Brunei Darussalam Bank loans outstanding 0.08 Singapore NBFI loans outstanding 0.36 Armenia NBFI loans outstanding 0.00 Pakistan Nonperforming NBFI loans 0.08 Georgia MSME output 0.35 Samoa Number of MSMEs -0.01 Kazakhstan Number of employees 0.08 Singapore Market capitalization 0.35 Tajikistan Number of MSMEs -0.03 Azerbaijan NBFI loans outstanding 0.07 Armenia Market capitalization 0.35 Kyrgyz Republic Number of MSMEs -0.03 Cambodia Nonperforming NBFI loans 0.07 India Number of employees 0.34 Kazakhstan MSME output -0.04 Georgia Market capitalization 0.07 Indonesia Market capitalization 0.34 Brunei Darussalam Nonperforming bank loans -0.05 Tajikistan MSME loans outstanding 0.07 Philippines Number of MSMEs 0.33 Armenia Number of employees -0.07 Georgia Number of MSMEs 0.07 Azerbaijan Number of MSMEs 0.32 India NBFI loans outstanding -0.08 Philippines Bank loans outstanding 0.07 Malaysia Number of MSMEs 0.31 Philippines Bank loans outstanding -0.09 Pakistan Nonperforming MSME loans 0.06 Thailand MSME loans outstanding 0.31 Armenia Bank loans outstanding -0.12 Kazakhstan NBFI loans outstanding 0.06 33 continued Lao PDR = Lao People’s Democratic Republic; MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution. Source: Authors’ calculation based on ADB Asia Small and Medium - Sized Enterprise Monitor 2023 database. Country Variable PC1 Country Variable PC2 Country Variable PC3 Georgia NBFI loans outstanding 0.31 Kyrgyz Republic MSME exports -0.13 Malaysia Number of employees 0.05 India Market capitalization 0.31 Malaysia NBFI loans outstanding -0.13 Sri Lanka Nonperforming NBFI loans 0.04 Kyrgyz Republic Market capitalization 0.31 Tajikistan NBFI loans outstanding -0.13 Kazakhstan Bank loans outstanding 0.04 Thailand Market capitalization 0.29 Philippines NBFI loans outstanding -0.14 Malaysia Market capitalization 0.03 Bangladesh Market capitalization 0.27 Georgia Nonperforming bank loans -0.15 Fiji MSME loans outstanding 0.03 Kyrgyz Republic Nonperforming bank loans 0.26 Nepal Market capitalization -0.18 Uzbekistan NBFI loans outstanding 0.03 Lao PDR Nonperforming bank loans 0.23 Papua New Guinea Nonperforming bank loans -0.19 Malaysia Nonperforming MSME loans 0.03 Viet Nam Nonperforming NBFI loans 0.23 Brunei Darussalam Bank loans outstanding -0.20 Fiji Market capitalization 0.02 Tajikistan MSME output 0.21 Myanmar Number of MSMEs -0.21 Thailand Number of MSMEs 0.01 Malaysia Market capitalization 0.20 Thailand MSME imports -0.21 Lao PDR Bank loans outstanding 0.01 Kazakhstan MSME output 0.19 Bangladesh MSME loans outstanding -0.22 Uzbekistan Bank loans outstanding 0.01 Uzbekistan MSME exports 0.16 Singapore Nonperforming MSME loans -0.22 Armenia MSME output 0.00 Singapore Nonperforming bank loans 0.16 Lao PDR Nonperforming bank loans -0.23 Indonesia Number of MSMEs -0.01 Philippines Nonperforming bank loans 0.15 Viet Nam Nonperforming bank loans -0.24 Myanmar Number of MSMEs -0.01 Bangladesh Nonperforming bank loans 0.15 Kazakhstan MSME loans outstanding -0.25 Singapore Number of MSMEs -0.02 Philippines Market capitalization 0.14 Malaysia Number of MSMEs -0.26 Sri Lanka NBFI loans outstanding -0.02 Papua New Guinea Nonperforming NBFI loans 0.13 Pakistan MSME loans outstanding -0.26 Papua New Guinea Bank loans outstanding -0.03 Sri Lanka Nonperforming bank loans 0.11 Uzbekistan MSME imports -0.27 Brunei Darussalam NBFI loans outstanding -0.03 Viet Nam Number of employees 0.09 Georgia Bank loans outstanding -0.27 Uzbekistan Market capitalization -0.03 Kyrgyz Republic Number of employees 0.08 Thailand Number of MSMEs -0.28 Singapore Number of employees -0.03 Singapore MSME loans outstanding 0.08 Thailand MSME output -0.28 Armenia Market capitalization -0.03 Georgia Nonperforming MSME loans 0.07 Indonesia Nonperforming MSME loans -0.30 India Number of employees -0.04 Tajikistan Nonperforming NBFI loans 0.05 Sri Lanka Nonperforming NBFI loans -0.31 Viet Nam Market capitalization -0.04 Georgia Nonperforming bank loans 0.05 Thailand MSME exports -0.32 Singapore Nonperforming bank loans -0.04 India MSME output 0.03 Armenia Market capitalization -0.32 Philippines Nonperforming MSME loans -0.05 Tajikistan Nonperforming bank loans -0.04 Papua New Guinea MSME loans outstanding -0.35 Papua New Guinea Nonperforming bank loans -0.05 Bangladesh Nonperforming NBFI loans -0.06 Armenia Number of MSMEs -0.36 Brunei Darussalam MSME output -0.06 Tajikistan Number of employees -0.09 Armenia MSME output -0.36 Thailand Market capitalization -0.07 Pakistan Market capitalization -0.09 Cambodia NBFI loans outstanding -0.36 Bangladesh Nonperforming bank loans -0.09 Kyrgyz Republic MSME exports -0.14 Bangladesh NBFI loans outstanding -0.36 Cambodia Market capitalization -0.09 Tajikistan Nonperforming MSME loans -0.15 Azerbaijan MSME output -0.37 Fiji Bank loans outstanding -0.10 Uzbekistan Bank loans outstanding -0.18 India Nonperforming MSME loans -0.37 Azerbaijan Number of MSMEs -0.12 Lao PDR Market capitalization -0.19 Pakistan Nonperforming NBFI loans -0.37 India Nonperforming bank loans -0.14 India Nonperforming MSME loans -0.21 India Nonperforming NBFI loans -0.38 Armenia Nonperforming bank loans -0.14 Philippines Nonperforming MSME loans -0.22 Georgia MSME loans outstanding -0.39 Tajikistan Number of employees -0.15 India NBFI loans outstanding -0.25 Papua New Guinea Market capitalization -0.39 Georgia Nonperforming MSME loans -0.15 Uzbekistan Nonperforming NBFI loans -0.25 Nepal Bank loans outstanding -0.41 India Nonperforming NBFI loans -0.15 Fiji Nonperforming bank loans -0.28 Myanmar Bank loans outstanding -0.43 India Nonperforming MSME loans -0.16 Uzbekistan NBFI loans outstanding -0.28 Samoa MSME loans outstanding -0.46 Lao PDR NBFI loans outstanding -0.19 India Nonperforming NBFI loans -0.28 Indonesia MSME exports -0.47 Kyrgyz Republic Number of employees -0.20 Uzbekistan Market capitalization -0.28 Malaysia Nonperforming MSME loans -0.48 Samoa Nonperforming bank loans -0.21 Kyrgyz Republic MSME imports -0.30 Myanmar NBFI loans outstanding -0.49 Philippines NBFI loans outstanding -0.21 Lao PDR MSME loans outstanding -0.30 Indonesia Nonperforming bank loans -0.50 Viet Nam Bank loans outstanding -0.22 Uzbekistan MSME output -0.31 Kazakhstan NBFI loans outstanding -0.50 Malaysia Nonperforming bank loans -0.22 Singapore Number of MSMEs -0.37 Myanmar Nonperforming NBFI loans -0.50 Singapore NBFI loans outstanding -0.22 Sri Lanka Market capitalization -0.37 Cambodia Bank loans outstanding -0.51 Uzbekistan Nonperforming NBFI loans -0.22 Malaysia Number of employees -0.43 India Market capitalization -0.51 Singapore MSME loans outstanding -0.23 Azerbaijan Nonperforming bank loans -0.44 Georgia MSME imports -0.52 Nepal Market capitalization -0.24 India MSME loans outstanding -0.44 Malaysia Nonperforming bank loans -0.52 Azerbaijan Number of employees -0.26 Brunei Darussalam Nonperforming NBFI loans -0.48 Cambodia Nonperforming bank loans -0.53 Brunei Darussalam Nonperforming NBFI loans -0.26 Brunei Darussalam Bank loans outstanding -0.50 Singapore MSME loans outstanding -0.55 Philippines Market capitalization -0.28 Pakistan MSME loans outstanding -0.50 Fiji Market capitalization -0.56 Bangladesh MSME output -0.29 Uzbekistan Nonperforming bank loans -0.51 Kyrgyz Republic Number of employees -0.58 Singapore Nonperforming MSME loans -0.30 Samoa Nonperforming bank loans -0.52 Bangladesh MSME output -0.58 Viet Nam NBFI loans outstanding -0.31 Singapore MSME output -0.57 Lao PDR NBFI loans outstanding -0.58 Pakistan Bank loans outstanding -0.32 Papua New Guinea Market capitalization -0.57 Kyrgyz Republic Market capitalization -0.58 Thailand Nonperforming bank loans -0.32 Singapore Number of employees -0.58 Viet Nam Bank loans outstanding -0.59 Bangladesh Nonperforming NBFI loans -0.34 Armenia Number of MSMEs -0.59 Bangladesh Bank loans outstanding -0.59 Singapore Bank loans outstanding -0.35 Papua New Guinea NBFI loans outstanding -0.59 Viet Nam Market capitalization -0.60 Thailand Nonperforming MSME loans -0.35 Tajikistan NBFI loans outstanding -0.64 Uzbekistan Nonperforming bank loans -0.61 Kazakhstan MSME loans outstanding -0.36 Malaysia MSME exports -0.65 Malaysia Market capitalization -0.61 Tajikistan Nonperforming NBFI loans -0.38 Georgia Market capitalization -0.65 Indonesia Bank loans outstanding -0.62 India NBFI loans outstanding -0.39 Malaysia NBFI loans outstanding -0.66 Sri Lanka Nonperforming bank loans -0.62 Papua New Guinea Nonperforming NBFI loans -0.39 Georgia MSME exports -0.67 Cambodia Nonperforming NBFI loans -0.63 Samoa MSME loans outstanding -0.40 Viet Nam Nonperforming bank loans -0.67 Papua New Guinea NBFI loans outstanding -0.64 Indonesia MSME loans outstanding -0.41 Myanmar Bank loans outstanding -0.68 Uzbekistan Number of MSMEs -0.66 Indonesia Number of employees -0.42 Tajikistan Number of MSMEs -0.69 Thailand Nonperforming MSME loans -0.66 Bangladesh Bank loans outstanding -0.42 Georgia MSME imports -0.69 Viet Nam Nonperforming NBFI loans -0.66 Indonesia Bank loans outstanding -0.45 Tajikistan Bank loans outstanding -0.69 Philippines Nonperforming bank loans -0.67 Lao PDR MSME loans outstanding -0.45 Pakistan Nonperforming bank loans -0.69 Kyrgyz Republic Nonperforming bank loans -0.67 Tajikistan Bank loans outstanding -0.46 Pakistan Nonperforming NBFI loans -0.69 Georgia MSME exports -0.68 Pakistan Market capitalization -0.46 Kyrgyz Republic NBFI loans outstanding -0.73 India MSME output -0.70 Armenia Number of employees -0.49 Malaysia Nonperforming MSME loans -0.75 Singapore MSME output -0.72 India Market capitalization -0.49 Brunei Darussalam NBFI loans outstanding -0.76 Indonesia MSME loans outstanding -0.72 Lao PDR Market capitalization -0.52 Thailand MSME imports -0.77 Papua New Guinea Nonperforming NBFI loans -0.72 Armenia Number of MSMEs -0.54 Malaysia Nonperforming bank loans -0.77 Cambodia Market capitalization -0.72 Bangladesh MSME loans outstanding -0.59 Azerbaijan Bank loans outstanding -0.78 Azerbaijan Number of MSMEs -0.73 Pakistan NBFI loans outstanding -0.59 Thailand MSME exports -0.79 Thailand Nonperforming bank loans -0.73 Tajikistan Nonperforming MSME loans -0.61 Georgia MSME loans outstanding -0.79 Philippines Nonperforming MSME loans -0.73 Indonesia Nonperforming bank loans -0.62 Papua New Guinea MSME loans outstanding -0.80 Uzbekistan Market capitalization -0.73 Tajikistan Number of MSMEs -0.65 Armenia Number of employees -0.81 India MSME exports -0.74 Pakistan MSME loans outstanding -0.65 Kazakhstan MSME loans outstanding -0.81 India MSME loans outstanding -0.78 Malaysia Number of MSMEs -0.67 Armenia MSME output -0.83 Uzbekistan Nonperforming NBFI loans -0.78 Tajikistan Nonperforming bank loans -0.67 Tajikistan MSME loans outstanding -0.86 Uzbekistan NBFI loans outstanding -0.85 Malaysia NBFI loans outstanding -0.67 Brunei Darussalam Nonperforming bank loans -0.87 Bangladesh Nonperforming NBFI loans -0.85 Indonesia Nonperforming MSME loans -0.68 Azerbaijan NBFI loans outstanding -0.91 Fiji Nonperforming bank loans -0.87 Lao PDR Nonperforming bank loans -0.70 Kazakhstan Nonperforming bank loans -0.93 Georgia Nonperforming MSME loans -0.87 Bangladesh NBFI loans outstanding -0.70 Pakistan Nonperforming MSME loans -0.93 Singapore Number of MSMEs -0.92 Tajikistan NBFI loans outstanding -0.71 Kazakhstan Bank loans outstanding -0.95 Uzbekistan Bank loans outstanding -0.93 Samoa Number of MSMEs -0.86 34 Table A3.2: Southeast Asia Lao PDR = Lao People’s Democratic Republic; MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable PC1 Country Variable PC2 Country Variable PC3 Indonesia Number of MSMEs 0.96 Lao PDR MSME loans outstanding 0.92 Singapore MSME loans outstanding 0.71 Philippines Bank loans outstanding 0.96 Lao PDR Market capitalization 0.89 Brunei Darussalam Bank loans outstanding 0.68 Indonesia MSME output 0.89 Viet Nam Number of employees 0.77 Philippines Nonperforming bank loans 0.68 Viet Nam NBFI loans outstanding 0.88 Brunei Darussalam Nonperforming NBFI loans 0.76 Malaysia Market capitalization 0.68 Philippines NBFI loans outstanding 0.87 Philippines MSME loans outstanding 0.74 Philippines Market capitalization 0.61 Viet Nam Number of MSMEs 0.83 Thailand MSME loans outstanding 0.72 Philippines Nonperforming MSME loans 0.55 Lao PDR Bank loans outstanding 0.79 Malaysia Number of employees 0.71 Singapore NBFI loans outstanding 0.54 Thailand Bank loans outstanding 0.73 Singapore Market capitalization 0.70 Cambodia Bank loans outstanding 0.49 Cambodia NBFI loans outstanding 0.73 Malaysia MSME loans outstanding 0.69 Viet Nam Nonperforming NBFI loans 0.45 Indonesia Number of employees 0.70 Brunei Darussalam NBFI loans outstanding 0.67 Cambodia Nonperforming bank loans 0.43 Viet Nam Bank loans outstanding 0.69 Philippines Number of employees 0.60 Malaysia Number of employees 0.42 Singapore Bank loans outstanding 0.67 Malaysia Bank loans outstanding 0.52 Cambodia Nonperforming NBFI loans 0.42 Singapore Nonperforming MSME loans 0.63 Malaysia MSME output 0.50 Indonesia Number of employees 0.38 Brunei Darussalam MSME output 0.60 Thailand Market capitalization 0.48 Brunei Darussalam NBFI loans outstanding 0.36 Myanmar Number of MSMEs 0.55 Indonesia Number of employees 0.46 Singapore Bank loans outstanding 0.32 Viet Nam Market capitalization 0.55 Lao PDR Bank loans outstanding 0.45 Thailand Market capitalization 0.30 Thailand MSME output 0.54 Malaysia MSME exports 0.42 Philippines Number of MSMEs 0.29 Indonesia Nonperforming MSME loans 0.52 Philippines Number of MSMEs 0.40 Singapore Nonperforming bank loans 0.25 Indonesia Nonperforming bank loans 0.51 Indonesia Market capitalization 0.30 Indonesia Nonperforming MSME loans 0.24 Indonesia Bank loans outstanding 0.50 Singapore Bank loans outstanding 0.28 Cambodia NBFI loans outstanding 0.23 Malaysia MSME loans outstanding 0.50 Thailand Bank loans outstanding 0.28 Singapore Number of MSMEs 0.18 Philippines Number of employees 0.49 Philippines Market capitalization 0.23 Singapore MSME output 0.18 Cambodia Bank loans outstanding 0.49 Viet Nam NBFI loans outstanding 0.20 Brunei Darussalam Nonperforming NBFI loans 0.18 Philippines MSME loans outstanding 0.48 Singapore Number of employees 0.19 Indonesia MSME loans outstanding 0.17 Lao PDR NBFI loans outstanding 0.47 Singapore NBFI loans outstanding 0.19 Malaysia NBFI loans outstanding 0.14 Thailand Nonperforming MSME loans 0.45 Thailand Number of employees 0.18 Viet Nam Bank loans outstanding 0.12 Myanmar NBFI loans outstanding 0.45 Indonesia Number of MSMEs 0.16 Myanmar NBFI loans outstanding 0.12 Myanmar Nonperforming NBFI loans 0.43 Viet Nam Number of MSMEs 0.15 Malaysia Bank loans outstanding 0.11 Thailand Number of employees 0.41 Malaysia Number of MSMEs 0.11 Indonesia Nonperforming bank loans 0.09 Singapore Market capitalization 0.41 Brunei Darussalam MSME output 0.11 Malaysia MSME loans outstanding 0.09 Indonesia Market capitalization 0.39 Brunei Darussalam Nonperforming bank loans 0.10 Singapore Market capitalization 0.07 Thailand Number of MSMEs 0.38 Singapore Nonperforming bank loans 0.09 Indonesia Bank loans outstanding 0.06 Malaysia Bank loans outstanding 0.37 Viet Nam Nonperforming bank loans 0.08 Philippines Bank loans outstanding 0.06 Thailand MSME loans outstanding 0.37 Indonesia MSME output -0.01 Malaysia Nonperforming bank loans 0.05 Malaysia MSME output 0.34 Brunei Darussalam Bank loans outstanding -0.03 Philippines NBFI loans outstanding 0.05 Singapore NBFI loans outstanding 0.33 Malaysia NBFI loans outstanding -0.07 Malaysia Number of MSMEs 0.04 Thailand Nonperforming bank loans 0.32 Thailand MSME imports -0.08 Thailand Bank loans outstanding 0.03 Thailand Market capitalization 0.32 Lao PDR Nonperforming bank loans -0.13 Philippines Number of employees -0.02 Indonesia MSME loans outstanding 0.31 Philippines Bank loans outstanding -0.20 Thailand Nonperforming bank loans -0.04 Philippines Number of MSMEs 0.28 Thailand MSME exports -0.21 Myanmar Nonperforming NBFI loans -0.05 Malaysia Number of MSMEs 0.27 Singapore Nonperforming MSME loans -0.24 Cambodia Market capitalization -0.06 Cambodia Nonperforming NBFI loans 0.20 Myanmar Number of MSMEs -0.25 Viet Nam Market capitalization -0.07 Viet Nam Number of employees 0.17 Philippines NBFI loans outstanding -0.26 Viet Nam NBFI loans outstanding -0.07 Cambodia Nonperforming bank loans 0.16 Indonesia Nonperforming MSME loans -0.27 Malaysia Nonperforming MSME loans -0.08 Singapore Nonperforming bank loans 0.15 Myanmar Bank loans outstanding -0.28 Lao PDR NBFI loans outstanding -0.09 Cambodia Market capitalization 0.10 Malaysia Nonperforming MSME loans -0.32 Lao PDR Market capitalization -0.10 Philippines Market capitalization 0.08 Thailand MSME output -0.36 Viet Nam Number of MSMEs -0.13 Lao PDR Market capitalization 0.07 Malaysia Nonperforming bank loans -0.37 Myanmar Bank loans outstanding -0.13 Indonesia MSME exports 0.03 Thailand Number of MSMEs -0.42 Indonesia Number of MSMEs -0.14 Viet Nam Nonperforming NBFI loans -0.01 Singapore MSME loans outstanding -0.44 Lao PDR Bank loans outstanding -0.16 Malaysia Market capitalization -0.04 Cambodia NBFI loans outstanding -0.49 Viet Nam Nonperforming bank loans -0.18 Singapore MSME loans outstanding -0.08 Malaysia Market capitalization -0.49 Thailand Nonperforming MSME loans -0.20 Philippines Nonperforming bank loans -0.09 Indonesia MSME exports -0.50 Lao PDR MSME loans outstanding -0.21 Lao PDR Nonperforming bank loans -0.13 Cambodia Nonperforming bank loans -0.50 Brunei Darussalam Nonperforming bank loans -0.23 Lao PDR MSME loans outstanding -0.20 Indonesia Nonperforming bank loans -0.53 Malaysia MSME output -0.24 Brunei Darussalam Nonperforming NBFI loans -0.30 Cambodia Bank loans outstanding -0.54 Thailand MSME exports -0.28 Malaysia Number of employees -0.36 Philippines Nonperforming bank loans -0.56 Indonesia MSME output -0.29 Philippines Nonperforming MSME loans -0.42 Philippines Nonperforming MSME loans -0.56 Thailand Number of MSMEs -0.30 Singapore Number of MSMEs -0.57 Myanmar NBFI loans outstanding -0.58 Singapore Number of employees -0.30 Brunei Darussalam Bank loans outstanding -0.58 Singapore MSME output -0.61 Viet Nam Number of employees -0.30 Singapore Number of employees -0.60 Cambodia Nonperforming NBFI loans -0.62 Thailand MSME output -0.30 Brunei Darussalam NBFI loans outstanding -0.63 Myanmar Nonperforming NBFI loans -0.62 Indonesia Market capitalization -0.30 Malaysia MSME exports -0.65 Viet Nam Nonperforming NBFI loans -0.62 Thailand MSME imports -0.33 Singapore MSME output -0.66 Lao PDR NBFI loans outstanding -0.65 Brunei Darussalam MSME output -0.38 Malaysia NBFI loans outstanding -0.74 Viet Nam Market capitalization -0.69 Philippines MSME loans outstanding -0.38 Viet Nam Nonperforming bank loans -0.80 Viet Nam Bank loans outstanding -0.70 Thailand MSME loans outstanding -0.44 Myanmar Bank loans outstanding -0.83 Indonesia Bank loans outstanding -0.72 Malaysia MSME exports -0.45 Thailand MSME imports -0.85 Thailand Nonperforming MSME loans -0.78 Singapore Nonperforming MSME loans -0.50 Thailand MSME exports -0.88 Singapore Number of MSMEs -0.79 Myanmar Number of MSMEs -0.57 Malaysia Nonperforming MSME loans -0.88 Indonesia MSME loans outstanding -0.80 Thailand Number of employees -0.59 Malaysia Nonperforming bank loans -0.90 Cambodia Market capitalization -0.80 Indonesia MSME exports -0.74 Brunei Darussalam Nonperforming bank loans -0.91 Thailand Nonperforming bank loans -0.84 Lao PDR Nonperforming bank loans -0.80 35 Table A3.3: South Asia MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable PC1 Country Variable PC2 Country Variable PC3 India Number of employees 0.95 Nepal Number of MSMEs 0.90 Pakistan NBFI loans outstanding 0.80 Sri Lanka NBFI loans outstanding 0.90 Bangladesh MSME loans outstanding 0.79 Pakistan MSME loans outstanding 0.66 Pakistan Market capitalization 0.74 India Nonperforming bank loans 0.78 Bangladesh NBFI loans outstanding 0.53 Sri Lanka Market capitalization 0.68 Bangladesh MSME output 0.75 India Nonperforming MSME loans 0.50 India Number of MSMEs 0.66 Pakistan Bank loans outstanding 0.74 India NBFI loans outstanding 0.50 Pakistan MSME output 0.64 Sri Lanka Bank loans outstanding 0.66 Pakistan Bank loans outstanding 0.49 Bangladesh Market capitalization 0.63 Nepal Number of employees 0.65 Pakistan Market capitalization 0.48 Sri Lanka Bank loans outstanding 0.50 Bangladesh Bank loans outstanding 0.65 Bangladesh Nonperforming bank loans 0.44 Pakistan Nonperforming bank loans 0.49 India Bank loans outstanding 0.63 India Nonperforming NBFI loans 0.37 Nepal Number of employees 0.46 Sri Lanka Nonperforming NBFI loans 0.63 India Market capitalization 0.29 India Bank loans outstanding 0.32 Pakistan MSME output 0.61 India Nonperforming bank loans 0.27 India Nonperforming bank loans 0.32 Nepal Bank loans outstanding 0.60 India Number of MSMEs 0.26 Pakistan Bank loans outstanding 0.16 Pakistan NBFI loans outstanding 0.49 India MSME output 0.25 Nepal Number of MSMEs 0.13 India MSME exports 0.45 Sri Lanka NBFI loans outstanding 0.13 Pakistan NBFI loans outstanding 0.13 Bangladesh NBFI loans outstanding 0.44 India MSME loans outstanding 0.12 Pakistan Nonperforming MSME loans 0.07 Nepal Market capitalization 0.36 India Number of employees 0.06 Sri Lanka Nonperforming NBFI loans -0.16 Sri Lanka NBFI loans outstanding 0.28 Pakistan Nonperforming MSME loans 0.03 Nepal Market capitalization -0.17 India Number of MSMEs 0.27 Nepal Market capitalization -0.02 Bangladesh MSME loans outstanding -0.18 India Number of employees 0.10 Bangladesh MSME loans outstanding -0.04 Bangladesh Nonperforming bank loans -0.23 India Market capitalization 0.05 Bangladesh Bank loans outstanding -0.17 Nepal Bank loans outstanding -0.28 Bangladesh Market capitalization 0.05 Pakistan Nonperforming bank loans -0.17 Sri Lanka Nonperforming bank loans -0.39 Sri Lanka Nonperforming bank loans 0.05 Nepal Number of employees -0.21 Pakistan Nonperforming NBFI loans -0.39 Bangladesh Nonperforming bank loans -0.07 Sri Lanka Bank loans outstanding -0.21 Pakistan MSME loans outstanding -0.45 India Nonperforming MSME loans -0.08 Pakistan MSME output -0.25 Bangladesh MSME output -0.55 Bangladesh Nonperforming NBFI loans -0.11 Bangladesh MSME output -0.26 Bangladesh Bank loans outstanding -0.60 India MSME loans outstanding -0.14 Sri Lanka Market capitalization -0.27 Bangladesh NBFI loans outstanding -0.65 India Nonperforming NBFI loans -0.20 Nepal Number of MSMEs -0.29 India MSME exports -0.66 Pakistan Market capitalization -0.23 Bangladesh Nonperforming NBFI loans -0.29 India Market capitalization -0.66 India MSME output -0.25 Pakistan Nonperforming NBFI loans -0.33 India NBFI loans outstanding -0.70 India NBFI loans outstanding -0.32 Bangladesh Market capitalization -0.37 India MSME output -0.71 Sri Lanka Market capitalization -0.50 India MSME exports -0.40 Bangladesh Nonperforming NBFI loans -0.82 Pakistan MSME loans outstanding -0.53 Sri Lanka Nonperforming NBFI loans -0.53 India Nonperforming MSME loans -0.84 Pakistan Nonperforming NBFI loans -0.73 India Bank loans outstanding -0.61 India Nonperforming NBFI loans -0.86 Pakistan Nonperforming bank loans -0.83 Nepal Bank loans outstanding -0.65 India MSME loans outstanding -0.97 Pakistan Nonperforming MSME loans -0.98 Sri Lanka Nonperforming bank loans -0.66 36 Table A3.4: Central and West Asia MSME = micro, small, and medium-sized enterprise; NBFI = nonbank finance institution. Source: Authors’ calculation based on ADB Asia Small and Medium-Sized Enterprise Monitor 2023 database. Country Variable PC1 Country Variable PC2 Country Variable PC3 Kyrgyz Republic Bank loans outstanding 0.95 Azerbaijan Nonperforming bank loans 0.81 Kyrgyz Republic NBFI loans outstanding 0.66 Kyrgyz Republic MSME loans outstanding 0.95 Uzbekistan Number of employees 0.80 Kyrgyz Republic MSME imports 0.57 Armenia Bank loans outstanding 0.94 Tajikistan MSME output 0.76 Kazakhstan MSME output 0.56 Georgia Number of MSMEs 0.93 Uzbekistan MSME output 0.70 Georgia MSME imports 0.50 Georgia Bank loans outstanding 0.93 Tajikistan Nonperforming NBFI loans 0.67 Azerbaijan MSME output 0.45 Kyrgyz Republic Number of MSMEs 0.92 Tajikistan Nonperforming MSME loans 0.63 Georgia MSME output 0.44 Kazakhstan Number of MSMEs 0.89 Georgia Number of employees 0.59 Kyrgyz Republic MSME output 0.43 Azerbaijan Number of employees 0.88 Tajikistan Nonperforming bank loans 0.58 Kyrgyz Republic MSME exports 0.38 Armenia NBFI loans outstanding 0.86 Tajikistan Number of employees 0.57 Kazakhstan Nonperforming bank loans 0.37 Kazakhstan Number of employees 0.85 Georgia MSME output 0.56 Georgia MSME exports 0.36 Uzbekistan MSME imports 0.81 Uzbekistan MSME exports 0.51 Georgia Market capitalization 0.33 Armenia Nonperforming bank loans 0.74 Azerbaijan Bank loans outstanding 0.39 Azerbaijan Nonperforming bank loans 0.28 Georgia Number of employees 0.71 Kyrgyz Republic MSME imports 0.37 Armenia Bank loans outstanding 0.25 Kyrgyz Republic MSME output 0.70 Tajikistan Bank loans outstanding 0.33 Armenia NBFI loans outstanding 0.23 Uzbekistan Number of MSMEs 0.67 Armenia Nonperforming bank loans 0.29 Azerbaijan Bank loans outstanding 0.19 Georgia NBFI loans outstanding 0.67 Azerbaijan NBFI loans outstanding 0.27 Kazakhstan Bank loans outstanding 0.19 Kazakhstan NBFI loans outstanding 0.66 Georgia NBFI loans outstanding 0.26 Uzbekistan MSME imports 0.18 Azerbaijan MSME output 0.65 Georgia Market capitalization 0.26 Uzbekistan MSME exports 0.17 Armenia Market capitalization 0.61 Tajikistan MSME loans outstanding 0.21 Uzbekistan Number of MSMEs 0.15 Kyrgyz Republic Market capitalization 0.60 Kazakhstan Nonperforming bank loans 0.12 Kyrgyz Republic Nonperforming bank loans 0.14 Uzbekistan Number of employees 0.56 Georgia Number of MSMEs 0.11 Armenia MSME output 0.13 Georgia MSME output 0.56 Kyrgyz Republic MSME loans outstanding 0.09 Georgia Number of employees 0.12 Kazakhstan MSME output 0.50 Kazakhstan Number of employees 0.08 Tajikistan MSME loans outstanding 0.12 Azerbaijan Number of MSMEs 0.45 Kyrgyz Republic MSME output 0.07 Kazakhstan NBFI loans outstanding 0.12 Tajikistan MSME output 0.45 Kazakhstan Number of MSMEs 0.05 Georgia Bank loans outstanding 0.10 Kyrgyz Republic Nonperforming bank loans 0.42 Kyrgyz Republic NBFI loans outstanding 0.03 Kyrgyz Republic Market capitalization 0.09 Uzbekistan MSME exports 0.39 Azerbaijan Number of employees 0.02 Tajikistan MSME output 0.07 Kyrgyz Republic Number of employees 0.26 Kyrgyz Republic Bank loans outstanding 0.01 Uzbekistan MSME output 0.06 Georgia Nonperforming bank loans 0.25 Armenia NBFI loans outstanding -0.04 Azerbaijan NBFI loans outstanding 0.05 Georgia Nonperforming MSME loans 0.21 Kazakhstan Bank loans outstanding -0.06 Kyrgyz Republic Number of MSMEs 0.03 Tajikistan Nonperforming NBFI loans 0.16 Tajikistan Number of MSMEs -0.07 Georgia Nonperforming bank loans 0.03 Tajikistan Nonperforming bank loans 0.13 Kyrgyz Republic Number of MSMEs -0.10 Georgia Number of MSMEs 0.02 Tajikistan Number of employees 0.11 Kyrgyz Republic MSME exports -0.11 Georgia MSME loans outstanding 0.01 Uzbekistan Bank loans outstanding 0.10 Kazakhstan MSME output -0.13 Kyrgyz Republic MSME loans outstanding 0.01 Kyrgyz Republic MSME exports 0.07 Armenia Bank loans outstanding -0.13 Uzbekistan Nonperforming bank loans -0.01 Uzbekistan NBFI loans outstanding 0.01 Georgia Nonperforming bank loans -0.16 Uzbekistan Number of employees -0.01 Tajikistan Nonperforming MSME loans 0.01 Armenia Number of employees -0.20 Uzbekistan Market capitalization -0.02 Uzbekistan Nonperforming NBFI loans 0.00 Uzbekistan MSME imports -0.24 Kyrgyz Republic Bank loans outstanding -0.05 Kyrgyz Republic MSME imports -0.09 Georgia Bank loans outstanding -0.25 Georgia NBFI loans outstanding -0.07 Uzbekistan Market capitalization -0.18 Tajikistan NBFI loans outstanding -0.28 Kazakhstan Number of employees -0.12 Uzbekistan MSME output -0.25 Kazakhstan MSME loans outstanding -0.31 Azerbaijan Number of MSMEs -0.13 Uzbekistan Nonperforming bank loans -0.25 Armenia Market capitalization -0.33 Uzbekistan Bank loans outstanding -0.14 Azerbaijan Nonperforming bank loans -0.38 Kazakhstan NBFI loans outstanding -0.37 Kazakhstan Number of MSMEs -0.16 Armenia Number of MSMEs -0.47 Azerbaijan MSME output -0.42 Kyrgyz Republic Number of employees -0.17 Tajikistan NBFI loans outstanding -0.52 Armenia MSME output -0.43 Georgia Nonperforming MSME loans -0.17 Georgia Market capitalization -0.56 Georgia MSME imports -0.45 Armenia Market capitalization -0.20 Georgia MSME exports -0.59 Armenia Number of MSMEs -0.52 Tajikistan Number of employees -0.22 Georgia MSME imports -0.59 Georgia MSME loans outstanding -0.54 Uzbekistan NBFI loans outstanding -0.23 Tajikistan Number of MSMEs -0.66 Kyrgyz Republic Market capitalization -0.57 Kazakhstan MSME loans outstanding -0.26 Tajikistan Bank loans outstanding -0.67 Kyrgyz Republic Nonperforming bank loans -0.63 Armenia Nonperforming bank loans -0.28 Kyrgyz Republic NBFI loans outstanding -0.68 Uzbekistan Number of MSMEs -0.64 Azerbaijan Number of employees -0.32 Georgia MSME loans outstanding -0.69 Kyrgyz Republic Number of employees -0.65 Uzbekistan Nonperforming NBFI loans -0.36 Armenia MSME output -0.76 Georgia MSME exports -0.69 Tajikistan Nonperforming NBFI loans -0.47 Kazakhstan MSME loans outstanding -0.79 Azerbaijan Number of MSMEs -0.72 Armenia Number of employees -0.49 Armenia Number of employees -0.81 Uzbekistan Nonperforming bank loans -0.80 Tajikistan Bank loans outstanding -0.54 Azerbaijan Bank loans outstanding -0.81 Georgia Nonperforming MSME loans -0.83 Armenia Number of MSMEs -0.56 Tajikistan MSME loans outstanding -0.85 Uzbekistan Nonperforming NBFI loans -0.84 Tajikistan Nonperforming MSME loans -0.57 Kazakhstan Nonperforming bank loans -0.88 Uzbekistan Market capitalization -0.88 Tajikistan Number of MSMEs -0.70 Azerbaijan NBFI loans outstanding -0.94 Uzbekistan NBFI loans outstanding -0.94 Tajikistan NBFI loans outstanding -0.70 Kazakhstan Bank loans outstanding -0.96 Uzbekistan Bank loans outstanding -0.97 Tajikistan Nonperforming bank loans -0.77