A comparison of the technical efficiency of accounting firms among the US, China, and Taiwan under the framework of a stochastic metafrontier production function

Chang, Bao‐Guang; Huang, Tai–Hsin; Kuo, Chin-Hwa

doi:10.1007/s11123-014-0397-8

Cited by 22 publications

(22 citation statements)

References 21 publications

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“…3. Recently, the new metafrontier model, developed by Huang, Huang, and Liu (2014), has been used by, for example, Chang, Huang, and Kuo (2015), Huang, Chiang, and Tsai (2015), and Lee and Huang (2017). two-step approach lies in the second step, where the new approach suggests using SFA, rather than linear or quadratic programming techniques, to estimate the metafrontier.…”

Section: Introductionmentioning

confidence: 99%

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What Causes the Efficiency and the Technology Gap Under Different Ownership Structures in the Chinese Banking Industry?

Lee

Huang

2018

Contemporary Economic Policy

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This paper estimates and compares the cost efficiency of the Chinese banking industry among different ownership types for the period 2003–2014, using the stochastic metafrontier model. We find that foreign banks have the lowest cost frontier, while state‐owned commercial banks undertake the least sophisticated technology. Moreover, the results of the upward trend in the technology gap ratio (TGR) and in metafrontier cost efficiency support that a more open financial market is able to enhance banking efficiency. As for the role of environmental conditions, off‐balance sheet items, non‐performing loans, and financial market structure significantly impact the TGRs of different bank types. (JEL C51, G21, D24)

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Section: Introductionmentioning

confidence: 99%

“…Recently, the new metafrontier model, developed by Huang, Huang, and Liu (), has been used by, for example, Chang, Huang, and Kuo (), Huang, Chiang, and Tsai (), and Lee and Huang ().…”

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confidence: 99%

What Causes the Efficiency and the Technology Gap Under Different Ownership Structures in the Chinese Banking Industry?

Lee

Huang

2018

Contemporary Economic Policy

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“…To address this issue, Battese et al [25] redefined the metafrontier to be an envelope of the deterministic parts of the groupfrontiers, which was further confirmed and developed in O' Donnell et al [26]. However, the deterministic metafrontier function may have some limitations in terms of computation: (i) it is hard to provide a meaningful statistical interpretation to the calculated metafrontier function due to the unclear statistical properties of the estimated parameters; (ii) the mathematical programming techniques are unable to incorporate random shocks, which may result in a relatively inefficient calculation of metafrontier energy efficiency; and, more seriously, (iii) the calculation in the second step applies the estimated group frontiers rather than the theoretical ones such that the degree of bias is unknown (Huang et al [44]; Chang et al [56]). Consequently, Huang et al [44] introduced a new two-step stochastic metafrontier approach, in which SFA is also utilized for the calculation of metatechnology ratio (PEEI) in the second step.…”

Section: Model Specification and Estimationmentioning

confidence: 99%

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

2017

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Under the concept of metafrontier, technology gap ratio is alternatively interpreted as potential energy efficiency. Combined with Malmquist index framework and Shephard energy distance function, we then develop a metafrontier Malmquist energy productivity index to analyze the total-factor energy productivity growth with four specific components: groupfrontier efficiency change index, groupfrontier technological change index, efficiency catch-up index and technological catch-up index. Methodologically, a newly developed two-step stochastic metafrontier analysis is applied to address the potentially biased estimation problems in the previous mixed approach. This analytical framework is used to evaluate the energy productivity growth of China's 35 sub-industries in industrial sector from 2001 to 2015. The main empirical results show that: (1) In terms of cumulative metafrontier Malmquist energy productivity growth, China's overall industry has witnessed a 25% growth and a U-shaped growing trend bottoming out in 2006; meanwhile, 19 sub-industries have suffered an energy productivity loss and the remaining 16 sub-industries have experienced an energy productivity gain. (2) From the technology heterogeneity perspective, light industry outperforms heavy industry in metafrontier Malmquist energy productivity growth, while the intra-group and inter-group energy productivity develops roughly in balance for overall industry. (3) The change of metafrontier Malmquist energy productivity is mainly driven by technological change components rather than efficiency change components. On average, groupfrontier technological change makes the biggest contribution to energy productivity growth, followed by technological catch-up, then efficiency catch-up, and groupfrontier efficiency change is last. (4) The metafrontier Malmquist energy productivity growth has shown a significant convergence in heavy industry and light industry, as well as overall industry.

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“…Numerous studies have analyzed whether changes in accounting systems, regulations, and environments increase or decrease the productivity or efficiency of accounting firms (Banker, Chang, & Cunningham, 2003;Banker, Chang, & Natarajan, 2005;Banker, Chang, & Natarajan, 2007;Chang, Choy, Cooper, & Parker, 2009a;Chang, Choy, Cooper, & Parker, 2009b;Chang, Huang, & Kuo, 2015;Hwang, Kang, & Hur, 2015;Hwang, Kim, & Jeon, 2005;Lee & Kim, 2001). Lee and Kim (2001) analyzed the relationship between the human resources input and revenue of Korean accounting firms from 1997 to 1999 by using a Cobb-Douglas production function.…”

Section: Previous Studiesmentioning

confidence: 99%

Effect Of Changes In The Korean Accounting Environment On The Productivity Of Accounting Firms

Kang

Hwang

Hur

2017

JABR

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To investigate how changes in the accounting environment in Korea affect firm productivity, this study analyzes productivity by firm size and labor type from 2000 to 2014, using a Cobb–Douglas production function. We find that (1) the greater the management advisory (tax) revenue, the greater the total revenue in large (small) accounting firms; and (2) marginal revenue is greatest for partners, followed by certified public accountants and general employees. In particular, partners’ contribution to large accounting firms improved after 2007, whereas general employees made a significant positive contribution to total revenue before 2007.

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A comparison of the technical efficiency of accounting firms among the US, China, and Taiwan under the framework of a stochastic metafrontier production function

Cited by 22 publications

References 21 publications

What Causes the Efficiency and the Technology Gap Under Different Ownership Structures in the Chinese Banking Industry?

What Causes the Efficiency and the Technology Gap Under Different Ownership Structures in the Chinese Banking Industry?

China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach

Effect Of Changes In The Korean Accounting Environment On The Productivity Of Accounting Firms

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