Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely Data envelopment Analysis-, Corrected ordinary least squares-, and Stochastic Frontier Analysis-based Malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes.

The measurement and decomposition of economy-wide productivity growth:assessing the accuracy and selecting between different approaches

Productivity at the macro level is a complex concept but also arguably the most appropriate measure of economic welfare. Currently, there is limited research available on the various approaches that can be used to measure it and especially on the relative accuracy of said approaches. This thesis has two main objectives: firstly, to detail some of the most common productivity measurement approaches and assess their accuracy under a number of conditions and secondly, to present an up-to-date application of productivity measurement and provide some guidance on selecting between sometimes conflicting productivity estimates. With regards to the first objective, the thesis provides a discussion on the issues specific to macro-level productivity measurement and on the strengths and weaknesses of the three main types of approaches available, namely index-number approaches (represented by Growth Accounting), non-parametric distance functions (DEA-based Malmquist indices) and parametric production functions (COLS- and SFA-based Malmquist indices). The accuracy of these approaches is assessed through simulation analysis, which provided some interesting findings. Probably the most important were that deterministic approaches are quite accurate even when the data is moderately noisy, that no approaches were accurate when noise was more extensive, that functional form misspecification has a severe negative effect in the accuracy of the parametric approaches and finally that increased volatility in inputs and prices from one period to the next adversely affects all approaches examined. The application was based on the EU KLEMS (2008) dataset and revealed that the different approaches do in fact result in different productivity change estimates, at least for some of the countries assessed. To assist researchers in selecting between conflicting estimates, a new, three step selection framework is proposed, based on findings of simulation analyses and established diagnostics/indicators. An application of this framework is also provided, based on the EU KLEMS dataset

Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely Data envelopment Analysis-, Corrected ordinary least squares-, and Stochastic Frontier Analysis-based Malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes

Unpaid Overtime: Measuring its Contribution to the UK Industries’ Output

Unpaid overtime in Britain has been excessive. The article measures the contribution of unpaid overtime in relation to UK industries economic output (Gross Value Added-GVA) for the period 2002-2012, using the Labour Force Survey (LFS) and the Office for National Statistics (ONS-Blue Book), capturing the different patterns before and after the 2007-8 crisis. Measuring unpaid overtime’s contribution and the other parts of working day has important implication on labour’s remuneration. The paper adopts an out-put-based approach evaluation of unpaid labour. A decomposed working day is therefore examined by employing statistical regression methods (Pooled OLS,LASSO and FGLS) to account for unpaid overtime’s contribution to the UK industries’ output (GVA). The results display a strong link between unpaid overtime and GVA, and particularly its post-crisis contribution to GVA is significant in contrast to the weak pre-crisis relationship

Selecting between different productivity measurement approaches: An application using EU KLEMS data

Over the years, a number of different approaches were developed to measure productivity change, both in the micro and the macro setting. Since each approach comes with its own set of assumptions, it is not uncommon in practice that they produce different, and sometimes quite divergent, productivity change estimates. This paper introduces a framework that can be used to select between the most common productivity measurement approaches based on a number of characteristics specific to the application/dataset at hand; these were selected based on the results of previous simulation analysis that examined the accuracy of different productivity measurement approaches under different conditions. The characteristics in question include input volatility through time, the extent of technical inefficiency and noise present in the dataset and whether the parametric approaches are likely to suffer from functional form miss-specification and are examined using a number of well-established diagnostics and indicators. Once assessed, the most appropriate approach can be selected based on its relative accuracy under these conditions; accuracy can in turn be assessed using simulation analysis, either previously published or designed specifically to emulate the characteristics of the application/dataset at hand. As an example of how this selection framework can be implemented in practice, we assess the productivity performance of a number of EU countries using the EU KLEMS dataset

Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely Data envelopment Analysis-, Corrected ordinary least squares-, and Stochastic Frontier Analysis-based Malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes