Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming

This paper proposes an econometric framework for joint estimation of technology and technology choice/adoption decision. The procedure takes into account the endogeneity of technology choice, which is likely to depend on inefficiency. Similarly, output from each technology depends on inefficiency. The effect of the dual role of inefficiency is estimated using a single-step maximum likelihood method. The proposed model is applied to a sample of conventional and organic dairy farms in Finland. The main findings are: the conventional technology is more productive, ceteris paribus; organic farms are, on average, less efficient technically than conventional farms; both efficiency and subsidy are found to be driving forces behind adoption of organic technology.     

Accounting for growth in retail trade: an international productivity comparison

In this paper we argue that the standard approach for measuring output and productivity in the trade sector has become obsolete. The key problem is that changes in prices of goods purchased for resale are not accounted for. We outline a consistent accounting framework for measuring trade productivity and provide new estimates, taking into account purchase prices of goods sold in a double deflation procedure. We find strong productivity improvements in the UK and US compared to France, Germany and The Netherlands since the mid-1990s. This finding is robust for various productivity measurement models.

Efficiency,productivity and technology comparison for farms in Central and Western Europe: The case of field crop and dairy farming in Hungary and France

This paper investigates the differences in technical efficiency and productivity change, and the technology gaps, between French and Hungarian farms in the dairy and cereal, oilseed and protein crops (COP) sectors during the period 2001–2007. The analyses were performed with national Farm Accountancy Data Network (FADN) data and the Data Envelopment Analysis (DEA) approach under each country's respective frontier and under a metafrontier.Results reveal that French COP farms were on average more efficient under their own technology than Hungarian farms under theirs, but there was no difference between the two countries for dairy farms. However, metatechnology ratios calculated with the construction of the metafrontier indicate that Hungarian technology was the more productive in both the dairy and the COP sectors, but more noticeably in COP production.     

Multifactor productivity and its determinants: an empirical analysis for Mexican manufacturing

The aim of this paper is twofold. First, we estimate production functions for the Mexican manufacturing sector and for 14 comprehensive groups, allowing us to construct various measures of multifactor productivity. Second, we analyse some of the determinants of productivity growth. We find that, on the one hand, there is a positive relationship between market concentration and technology adoption; on the other hand, both technology adoption and human capital promote productivity, whilst market concentration exerts a negative influence on it. Our results suggest that, once controlling for the effect on technology adoption, more concentration impacts negatively productivity growth.     

Technical efficiency in competing panel data models: a study of Norwegian grain farming

Estimation of technical efficiency is widely used in empirical research using both cross-sectional and panel data. Although several stochastic frontier models for panel data are available, only a few of them are normally applied in empirical research. In this article we chose a broad selection of such models based on different assumptions and specifications of heterogeneity, heteroskedasticity and technical inefficiency. We applied these models to a single dataset from Norwegian grain farmers for the period 2004–2008. We also introduced a new model that disentangles firm effects from persistent (time-invariant) and residual (time-varying) technical inefficiency. We found that efficiency results are quite sensitive to how inefficiency is modeled and interpreted. Consequently, we recommend that future empirical research should pay more attention to modeling and interpreting inefficiency as well as to the assumptions underlying each model when using panel data.     

Parametric decomposition of the input-oriented Malmquist productivity index: with an application to Greek aquaculture

Using a stochastic frontier approach and a translog input distance function, this paper implements the input-oriented Malmquist productivity index to a sample of Greek aquaculture farms. It is decomposed into the effects of technical efficiency change, scale efficiency change, input-mix and, technical change, which is further attributed to neutral, output- and input-induced shifts of the frontier. Implementable expressions for the aforementioned components are obtained using a discrete changes-approach that is consistent with the usual discrete-form data. Empirical findings indicate that the productivity of the farms in the sample increased during the period 1995–1999 and it was shaped up primarily by the input mix-effect and technical change.     

Estimation and decomposition of inefficiency when producers maximize return to the outlay: an application to Norwegian fishing trawlers

This paper deals with estimation of a production technology where endogeneous choice of input and output variables is explicitly recognized. In particular, we assume that producers maximize return to the outlay (RO). For simplicity and tractability we start with a Cobb–Douglas transformation function with multiple inputs and outputs and show how the first-order conditions of RO maximization can be used to derive an estimating equation which is nothing but a partial input productivity equation. This equation does not suffer from the econometric endogeneity problem although the output and input variables are endogenous. First, we consider the case where producers are fully efficient allocatively but technically inefficient. The model is estimated using a single equation stochastic frontier approach. The model is then extended to allow allocative inefficiency and it is estimated as a system using generalized method of moment. Algebraic expressions are derived to decompose the effect of technical and allocative inefficiencies on RO. We also consider translog specifications that are estimated as (1) a single equation frontier model as well as (2) a system. We use a panel of Norwegian fishing trawlers data to estimate the model. Outputs are different species caught while inputs are labor and vessel size. We also control for number of days of operation, age of the vessel and year effects. Empirical results show that the average rate of RO is reduced by about 20 to 30 % due to technical inefficiency. On the other hand, average allocative efficiency is found to be about 78 %. The average overall efficiency is found to be around 60 %.     

Trade and product innovations as sources for productivity increases: an empirical analysis

Increases in total factor productivity (TFP) are commonly associated with technological innovations measured by the stock of R&D. Empirical evidence seems to corroborate this relationship. However, in trading countries like The Netherlands, productivity increases, even in industry, can also be the result of innovations in the way transactions are managed. These innovations reduce transaction costs and exploit the welfare gains from (further) international division of labour. Such innovations are only partly included in R&D data. Consequently there is not much attention for these ‘trade innovations’—as we label them—in policy. In an empirical analysis this paper compares the influence of trade innovations with the influence of the stock of R&D on TFP in The Netherlands. The regression results show that in this country trade innovations are as important for TFP as technological innovations which directly affect the efficiency of production, which we label ‘product innovations’.      

Malmquist productivity index for multi-output producers: An application to electricity generation plants

Different types of plants are used to generate electricity in the US: single-, multi-, and mixed-electricity plants. In this paper, we question the best type/design of plants for both renewable and non-renewable electricity. To do so, we suggest a new index that takes the form of a Malmquist productivity index. The specificity of our new index is that it offers the option to investigate the performances and the causes of the performance changes for each type of electricity separately; this is not possible when relying on more standard indexes. Moreover, our new index takes the links between the inputs and the outputs into account, and is nonparametric in nature. Using our index, we study the performances of more than 5000 plants for the period 2000–2012. Our findings reveal that single-electricity plants perform better for renewable electricity, while multi-electricity plants perform better for non-renewable electricity. This is coherent with the decreasing importance of multi-electricity plants in the US, and the increasing importance of single-electricity plants producing renewable electricity. Furthermore, our results do not suggest that combining renewable and non-renewable electricity generations within a plant improves the performance of the plants. Finally, we demonstrate that the reasons for the changes in performance are different for each type of electricity and plant.     

Technical efficiency in farming: a meta-regression analysis

A meta-regression analysis including 167 farm level technical efficiency (TE) studies of developing and developed countries was undertaken. The econometric results suggest that stochastic frontier models generate lower mean TE (MTE) estimates than non-parametric deterministic models, while parametric deterministic frontier models yield lower estimates than the stochastic approach. The primal approach is the most common technological representation. In addition, frontier models based on cross-sectional data produce lower estimates than those based on panel data whereas the relationship between functional form and MTE is inconclusive. On average, studies for animal production show a higher MTE than crop farming. The results also suggest that the studies for countries in Western Europe and Oceania present, on average, the highest levels of MTE among all regions after accounting for various methodological features. In contrast, studies for Eastern European countries exhibit the lowest estimate followed by those from Asian, African, Latin American, and North American countries. Additional analysis reveals that MTEs are positively and significantly related to the average income of the countries in the data set but this pattern is broken by the upper middle income group which displays the lowest MTE.

Accounting for technological innovation: an overview

Technology and, more specifically, significant technological innovation is increasingly being recognized by business executives as both an integral part of business strategy and either a major opportunity for, or strong threat to, their firm's development. A growing number of executives are giving equal attention to both process and product forms of technological innovation, and are scanning technological innovations in the international arena.This paper argues that R&D is an important but not exhaustive component of technological innovation. Its main conclusions from the viewpoint of accounting standards and practice are: - Net cash flow rather than net profit (or loss) is the most relevant financial criterion with which to judge the commercial value and results of innovation. - Both accounting practice and standards should be developed with the clear attempt to ensure international compatabilily. - Given the speed of change in technological innovation, accounting practices and standards should be regularly reviewed, and if necessary altered—two-yearly review seems a justifiable suggestion. - Several standards on accounting for the major fields of technology may need to be developed; however, a broad standard dealing with common issues may be possible. - The input of technologists, researchers and general managers is necessary to ensure that accounting practices and standards are conceptually sound and useful to business managers.     

Some cumulative logit models for panel-data analysis: an application to a dynamic analysis of personal efficacy

This paper introduces novel cumulative logit models for the panel-data analysis of transitions among ordered states of a polytomous dependent variable. The models differ from conventional cumulative logit models in that they can distinguish between covariate effects on the odds of having an upward transition and covariate effects on the odds of having a downward transition in the ordered states of the dependent variable. The new models are applied to panel data on personal efficacy and are used to identify asymmetric patterns in the effects of divorce and unemployment on changes in the level of personal efficacy. The effects of the two events on increasing the odds of having a downward transition are shown to be greater than their effects on decreasing the odds of having an upward transition. Some distinct characteristics of the effects of each event are also reported.     

Envelope condition method with an application to default risk models

We develop an envelope condition method (ECM) for dynamic programming problems – a tractable alternative to expensive conventional value function iteration (VFI). ECM has two novel features: first, to reduce the cost of iteration on Bellman equation, ECM constructs policy functions using envelope conditions which are simpler to analyze numerically than first-order conditions. Second, to increase the accuracy of solutions, ECM solves for derivatives of value function jointly with value function itself. We complement ECM with other computational techniques that are suitable for high-dimensional problems, such as simulation-based grids, monomial integration rules and derivative-free solvers. The resulting value-iterative ECM method can accurately solve models with at least up to 20 state variables and can successfully compete in accuracy and speed with state-of-the-art Euler equation methods. We also use ECM to solve a challenging default risk model with a kink in value and policy functions.     

Repertory grid analysis: an application to the measurement of destination images

The objective of this paper is to demonstrate and evaluate the use of repertory grid analysis (RGA) in the context of a study of destination brand images. The study explored the components of destination brand image from a business tourism perspective. RGA was used to identify the constructs used by events managers in a sample of 25 UK organisations to characterise the brand images of 15 UK towns and cities. The study identified eight categories of brand image attribute. Factor analysis reduced these to three higher level dimensions, an overall destination attractiveness dimension and two sub‐dimensions: functionality and ambience. The paper concludes with a discussion of the benefits and limitations of RGA as a technique for measuring destination brand images and argues that it offers significant advantages over alternative techniques with regard to image measurement. Copyright © 2004 Henry Stewart Publications     

Cluster Analysis for mixed data: An application to credit risk evaluation

Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.     

Using economic impact analysis for arts management: an empirical application to a music institute in the USA

Economic impact analysis can be used strategically by arts managers to provide information pertaining to making and justifying effective managerial and marketing decisions. The three aspects of economic impact analysis—direct, indirect and induced—reveal information such as arts participants' expenditure patterns, rationale for expenditure, relationships between participant demographics and expenditure, and amount of expenditure outside arts institutions. Arts managers can use the information and data to match product offerings of the institution with those of its participants; increase the participants' participation in, satisfaction with and loyalty to the institution; increase the stature and validity of the arts institution within the community; price the products of the arts institute; and increase community and financial support for arts institutions. This synergistic use of economic impact analysis information with marketing information is illustrated using a music institute in the USA. Copyright © 2003 Henry Stewart Publications     

A nonlinear multivariate error components analysis of technology and specific factor productivity growth with an application to the U.S. Airlines

This study outlines a nonlinear model of technology and specific factor productivity growth and uses the model to analyze a new panel of sixteen domestic carriers in the U.S. airline industry over 35 quarters from 1970.I to 1978.III. We outline mapping procedures which allow construction of the production surfaces implied by the translog cost function used in our analysis. To our knowledge this study is the first to estimate such a general nonlinear multivariate error components model using full-information maximum likelihood.     

Qualitative comparative analysis (QCA): an application for the industry

This article examines the management in Colombian industrial sectors using qualitative comparative analysis (QCA). This method conceptualizes cases as combinations of attributes and use Boolean algebra to derive simplified expressions of combinations that lead to a specific outcome. In this analysis, we show the value of this method for studying the management in the industrial from different approaches.     

Local influence analysis for Poisson autoregression with an application to stock transaction data

In statistical diagnostics and sensitivity analysis, the local influence method plays an important role and has certain advantages over other methods in several situations. In this paper, we use this method to study time series of count data when employing a Poisson autoregressive model. We consider case‐weights, scale, data, and additive perturbation schemes to obtain their corresponding vectors and matrices of derivatives for the measures of slope and normal curvatures. Based on the curvature diagnostics, we take a stepwise local influence approach to deal with data with possible masking effects. Finally, our established results are illustrated to be effective by analyzing a stock transactions data set.     

Cost Malmquist productivity index: an output-specific approach for group comparison

The cost Malmquist productivity index (CMPI) has been proposed to capture the performance change of cost minimizing Decision Making Units (DMUs). Recently, two alternative uses of the CMPI have been suggested: (1) using the CMPI to compare groups of DMUs, and (2) using the CMPI to compare DMUs for each output separately. In this paper, we propose a new CMPI that combines both procedures. The resulting methodology provides group-specific indexes for each output separately, and therefore offers the option to identify the sources of cost performance change. We also define our index when input prices are not observed and establish, in that case, a duality with a new technical productivity index, which takes the form of a Malmquist productivity index. We illustrate our new methodology with a numerical example and an application to the US electricity plant districts.