Malmquist productivity index decompositions: a unifying framework

Zofío, José L.

doi:10.1080/00036840600606260

Cited by 111 publications

(47 citation statements)

References 29 publications

(14 reference statements)

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“…Moreover, the capacity of comparing a DMU's efficiency between two consecutive periods makes MI as the most useful tool in efficiency measurement (Zofio 2007;Bassem 2014). Bassem (2014) examined 33 Middle East and North Africa (MENA) MFIs using MI.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficiency analysis of major microfinance institutions in Bangladesh: a Malmquist index approach

et al. 2015

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Microfinance institutions (MFIs) have dual objectives-reaching under privileged households for poverty easing and achieving financial sustainability. This paper investigates the efficiency of major 15 MFIs in Bangladesh using Malmquist index (MI) over the period 2008-2012. A balanced panel dataset of 75 observations is used. The results from MI are decomposed into three efficiency scores-technical efficiency (TCH), scale efficiency (SE) and pure efficiency (PE). The empirical findings indicate that MFIs are experienced an excellent annual efficiency progress (93.5 %) which was mainly because of PE. PE measures the capacity of optimizing minimum input to produce maximum output by reducing production wastage. This outstanding growth was an outcome of country's ever best economic settings before 2008. However, from 2009 onward, a substantial efficiency regresses is observed. The results also signify that the major MFIs have less capacity to work at their optimal scales. The contribution of SE is very low (only 2.2 %) compared to total efficiency progress. Findings from TCH indicate that the overall frontier is moving relatively slow in an outward direction (only 3.7 % of TCH progress).

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Section: Literature Reviewmentioning

confidence: 99%

Efficiency analysis of major microfinance institutions in Bangladesh: a Malmquist index approach

et al. 2015

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“…In fact, although several studies support the computation of technical change using VRS technology, they are all in the context of Malmquist index; these include Simar and Wilson (1998), Zofio and Lovell (1998), Wheelock and Wilson (1999) and Zofio (2007). This point leads to the further decomposition of dynamic technical change component T in (4) into technical change under VRS (TV) and the residual measure that can be called the scale change of dynamic technology (ST) (that is, T = TV +ST ).…”

Section: Dynamic Luenberger Productivity Growth Indicator and Its Decmentioning

confidence: 99%

Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union

Kapelko

2017

Ann Oper Res

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This paper extends the measurement of dynamic productivity change over time to provide its full decomposition into economically meaningful components in the Data Envelopment Analysis framework. The dynamic approach accounts for dynamics of production decisions via adjustment costs and is visualized as a dynamic Luenberger productivity change indicator. The paper also estimates the dynamic productivity change and its components for a large dataset of European food companies from 2004 till 2012, grouped into Eastern, Southern, and Western regions. The study reveals three main results. First, the overall trend of dynamic technical regress and positive dynamic technical inefficiency change across almost all regions and sectors was found. Second, some differences for this general pattern were found for the bakery industry and for Eastern European firms. Thirdly, there are also some remarkable changes in indicators observed during the periods related to the financial crisis and the volatility of agricultural commodity prices.

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“…To compare the decomposition in expression (33) with the Ray and Desli decomposition (28), Zofío (2007) proposed to split the second factor of the latter decomposition into two components, resulting iň…”

Section: The Base Period Viewpointmentioning

confidence: 99%

The Many Decompositions of Total Factor Productivity Change

Balk

Zofío

2018

SSRN Journal

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Total factor productivity change, here defined as output quantity change divided by input quantity change, is the combined result of (technical) efficiency change, technological change, a scale effect, and input and output mix effects. Sometimes allocative efficiency change is supposed to also play a role. Given a certain functional form for the productivity index, the problem is how to decompose such an index into factors corresponding to these five or six components. A basic insight offered in the present paper is that meaningful decompositions of productivity indices can only be obtained for indices which are transitive in the main variables. Using a unified approach, we obtain decompositions for Malmquist, Moorsteen-Bjurek, price-weighted, and share-weighted productivity indices. A unique feature of this paper is that all the decompositions are applied to the same dataset of a real-life panel of decision-making units so that the extent of the differences between the various decompositions can be judged.

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Malmquist productivity index decompositions: a unifying framework

Cited by 111 publications

References 29 publications

Efficiency analysis of major microfinance institutions in Bangladesh: a Malmquist index approach

Efficiency analysis of major microfinance institutions in Bangladesh: a Malmquist index approach

Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union

The Many Decompositions of Total Factor Productivity Change

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