From Efficiency Analyses to Policy Implications: a Multilevel Hierarchical Linear Model Approach

Dao, Thuy T. T.; Tian, Xuan; Ngo, Tuan; Le, Tu; Ho, Huong

doi:10.1080/13571516.2021.1981750

Cited by 4 publications

(7 citation statements)

References 64 publications

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“…In line with the literature on evaluating the efficiency of manufacturing firms, such as Verschelde et al (2016), Ngo et al (2019a), andSahoo et al (2021), we examined the Vietnamese MSMEs in terms of three important inputs, namely labour (proxied by the number of employees, x 1 ), capital (proxied by the value of total assets, x 2 ), and materials (proxied by the amount of materials, x 3 ), to produce a single output (total revenue, y). This information was extracted from the annual surveys of Vietnamese enterprises conducted by the national General Statistics Office (GSO, 2016); such data are popular in many studies (Dao et al, 2021;Le et al, 2018;Rand & Tarp, 2020). Since we focused our study on MSMEs only, we followed the IFC (2012) and filtered out firms with more than 250 employees, resulting in 37,557 firm-year observations for the Vietnamese MSMEs operating during the 2010-2016 period.…”

Section: Data and Variable Selectionmentioning

confidence: 99%

“…For MSMEs, such innovations do not require many resources and are feasible. Because most Vietnamese MSMEs operate in the garment, textile, and footwear sector (Dao et al, 2021;Pham et al, 2010), where the productivity of female employees is still low, it is reasonable to see that firms with a higher female employee ratio tend to have lower efficiency. In contrast, the participation of foreign investors allows the firms to possess more advanced technologies and management knowledge and hence, improve their performance (Huang & Yang, 2016;Ngo et al, 2019a).…”

Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...mentioning

confidence: 99%

“…Similarly, MSMEs involved in export activities can benefit from a learning-by-exporting effect because they are more exposed to foreign technology and competition (Amiti & Konings, 2007;Baldwin & Gu, 2004; Pilar , 2018). The MSMEs also benefit from operating in large municipalities because of the effects of firm selection and agglomeration economies (Combes et al, 2012;Le et al, 2018;Vu et al, 2016), having a good provincial business environment (Ngo et al, 2019a;Dao et al, 2021;VCCI & USAID, 2022), and economies of scale (Bačić et al, 2018;Ngo et al, 2019a), to further improve their efficiency. These findings were robust across different models, including Tobit, truncated, and LASSO regressions.…”

Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...mentioning

confidence: 99%

“…These explanatory variables are deemed to affect firm efficiency. For more details, seeAnh and Gan (2020),Dao et al (2021), andSahoo et al (2021).…”

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confidence: 99%

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Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

Boubaker

Ngo

et al. 2023

Ann Oper Res

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Micro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010‒2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management.

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Section: Data and Variable Selectionmentioning

confidence: 99%

Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...mentioning

confidence: 99%

Section: Second-stage Analytics: Predicting the Vietnamese Msmes' Per...mentioning

confidence: 99%

“…These explanatory variables are deemed to affect firm efficiency. For more details, seeAnh and Gan (2020),Dao et al (2021), andSahoo et al (2021).…”

mentioning

confidence: 99%

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Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

Boubaker

Ngo

et al. 2023

Ann Oper Res

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“…Banking efficiency is a long-term topic for many researchers (Tripe 2010;Aiello and Bonanno 2016;Boubaker et al 2022;Le et al 2022b). While it is important to analyse how efficient a bank or banking system is; however, it is even more important to examine the determinants that can influence such efficiency and performance (Simar and Wilson 2007;Gardener et al 2011;Dao et al 2021). In modern banking, the role of information, technology, and communication (ICT) is becoming more important and thus, ICT is receiving more attention (Berger and DeYoung 2006;Salim et al 2010;Arora and Arora 2013;Girmaye 2018).…”

Section: Introductionmentioning

confidence: 99%

ICT as a Key Determinant of Efficiency: A Bootstrap-Censored Quantile Regression (BCQR) Analysis for Vietnamese Banks

Ngo

et al. 2022

IJFS

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There is evidence that ICT developments can improve bank efficiency and performance. Previous studies often employ data envelopment analysis (DEA) to first examine bank performance and then use a second-stage regression to explain the influences of other environmental factors, including ICT, on such efficiency. Since DEA efficiency scores are bounded between the (0, 1] intervals, Tobit and truncated regressions are commonly used in this stage. However, none has accounted for the skewness characteristic of DEA efficiency. This paper applied a bootstrap-censored quantile regression (BCQR) approach to triply account for the issues of a small sample (via bootstrap), bounded intervals (via censored regression), and skewness (via quantile regression) in DEA analysis. We empirically examined the efficiency and performance of 27 Vietnamese commercial banks in the 2007–2019 period. The efficiency scores derived from our first stage revealed that they are skewed and thus, justify the use of the BCQR in the second stage. The BCQR results further confirmed that ICT developments could enhance bank efficiency, which supports the recent policy to restructure the Vietnamese banking sector toward innovation and digitalization. We also examined the impacts of other factors such as bank ownership, credit risk, and bank size on efficiency.

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The trade-off frontier for ESG and Sharpe ratio: a bootstrapped double-frontier data envelopment analysis

Boubaker¹,

Le²,

Manita³

et al. 2023

Ann Oper Res

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The trade-off between the returns and the risks associated with the stocks (i.e., the Sharpe ratio, SR) is an important measure of portfolio optimization. In recent years, the environmental, social, and governance (ESG) has increasingly proven its influence on stocks’ returns, resulting in the evolvement from a two-dimensional (i.e., risks versus returns) into a multi-dimensional setting (e.g., risks versus returns versus ESG). This study is the first to examine this setting in the global energy sector using a (slacks-based measures, SBM) ESG-SR double-frontier double-bootstrap (ESG-SR DFDB) by studying the determinants of the overall ESG-SR efficiency for 334 energy firms from 45 countries in 2019. We show that only around 11% of our sampled firms perform well in the multi-dimensional ESG-SR efficient frontier. The 2019 average (in)efficiency of the global energy sector was 2.273, given an efficient level of 1.000. Besides the differences in the firm’s input/output utilization (regarding their E, S, G, and SR values), we found that the firm- (e.g., market capitalization and board characteristics) and country-level characteristics (e.g., the rule of law) have positive impacts on their ESG-SR performance. Such findings, therefore, are essential not only to the (responsible) investors but also to managers and policymakers in those firms/countries.

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From Efficiency Analyses to Policy Implications: a Multilevel Hierarchical Linear Model Approach

Cited by 4 publications

References 64 publications

Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

ICT as a Key Determinant of Efficiency: A Bootstrap-Censored Quantile Regression (BCQR) Analysis for Vietnamese Banks

The trade-off frontier for ESG and Sharpe ratio: a bootstrapped double-frontier data envelopment analysis

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