Journal of Productivity Analysis最新文献

In practical applications of data envelopment analysis, inputs and outputs are often stated as ratio measures, including various percentages and proportions characterizing the production process. Such ratio measures are inconsistent with the basic assumptions of convexity and scalability required by the conventional variable and constant returns-to-scale (VRS and CRS) models. This issue has been addressed by the development of the Ratio-VRS (R-VRS) and Ratio-CRS (R-CRS) models of technology, both of which can incorporate volume and ratio inputs and outputs. In this paper, we provide a detailed standalone development of the special case of the R-CRS technology, referred to as the F-CRS technology, in which all ratio inputs and outputs are of the fixed type. Such ratio measures can be used to represent environmental and quality characteristics of the production process that stay constant while simultaneously allowing the scaling of the volume of production. We illustrate the use of the F-CRS technology by an application in the context of school education.

Using rich firm-level data of around 12,000 firms over 2004–2016, this study attempts to identify the factors responsible for the slowdown in gross investment and productivity in Indian manufacturing post-Global Financial Crisis. Our analysis reveals that the decline in investment is more pronounced for firms with higher productivity. Furthermore, we find evidence indicating a slowdown in the flow of capital and labor from less productive to more productive firms post-Global Financial Crisis. This indicates that a part of the fall in investment can be attributed to a decline in allocative efficiency, which is likely to have an impact on both aggregate productivity and income. Moreover, we probe into the causes behind the slowdown in the relationship between firm productivity, investment, capital and labor growth. We find that credit misallocation, financial constraints, age, and firm size played key roles in the investment slowdown. Finally, we present a counterfactual scenario by analyzing the extent of extra output and aggregate productivity that could be generated in the absence of misallocation.

National Statistical Institutes (NSIs) in advanced countries have generally adopted chain-linking in their national accounts. The United States uses a chained Fisher, an example of a superlative index number, in its national accounts. However the Fisher is only one of an infinite number of superlative index numbers. So an important issue is how sensitive are the estimates of output growth to the choice of index number. This issue is analysed by examining data from the BEA/BLS industry-level integrated production account, 1987–2020. Estimates of superlative and other index numbers are presented for this dataset. The sensitivity of real GDP growth to the value of the crucial parameter in a superlative index number is tested. The extent to which the desirable characteristics of value consistency and aggregation consistency are satisfied for different superlative index numbers is also analysed. The desirability of chain-linking does not follow automatically just from the use of superlative indices. So I also compare chained and unchained versions of these same index numbers. Finally, Europe uses a different approach to output measurement to the US, chained Laspeyres versus chained Fisher. I look at how different US estimates would be if they employed European methodology.

In this paper, we introduce an optimization model to quantify the trade-off between resource capacity utilization and disposition time for the caseload of courts of justice. The optimization model takes into account the impact of an increase in demand that may arise when disposition time is reduced. We employ the model to measure the impact of various policy reform scenarios on the length of trials, both at the court and system level. We do so by taking into account the potential reallocation of resources, using the population of Italian courts of justice over the 2005–2012 period. Our results show that if all policy scenarios we discuss were to be implemented, the average length of trials for civil cases would be more than halved, from the current 15.5 months to about 7 months. Implementing best practices, the single most effective policy would be equivalent to a 25% increase in the number of judges (which would otherwise cost around 100 million euros per year).

Nonparametric methods have been commonly used to assess the performance of both private and public organizations. Among them, the most popular ones are envelopment estimators such as Free Disposal Hull (FDH) or Data Envelopment Analysis (DEA), which estimate the attainable sets and their efficient boundaries by enveloping the cloud of observed units in the appropriate input-output space. However, these nonparametric envelopment techniques do not provide estimates of marginal products and other coefficients of economic interest. This paper presents a new approach that provides local estimates of all the desired partial derivatives and economic coefficients, which complement and complete the analysis based on nonparametric envelopment estimators. We improve nonparametric estimators by estimating nonparametrically smoothed efficient boundaries and providing derivatives and other coefficients without having to assume any parametric structure for the frontier and the inefficiency distribution. Our approach offers several advantages, such as a flexible nonparametric adjustment of the efficient frontier based on local linear models; a general multivariate efficiency model based on directional distances where one can choose the desired benchmark direction; the possibility of assessing the impact of external-environmental variables; a bootstrap-based statistical inference for deriving confidence intervals on the estimated coefficients for nonparametric and robust frontier approximations; the possibility of including factors aggregating inputs or outputs and recovering the estimated coefficients in the original units. To demonstrate the usefulness of the proposed approach, we provide an illustration in the field of education, where economic coefficients are important but the parametric assumptions have been questioned.

Innovation is often seen as crucial for firm survival and as a way for firms to differentiate themselves from their rivals. However, innovation studies are vague about the actual importance of different innovation strategies. In this study, we distinguish between pure product, process, organizational, and marketing innovations and their combinations. We use a (balanced) panel data set with more than 15,000 firm-year observations for manufacturing firms to analyze the performance of firms with different innovation strategies. Additionally, we investigate from a societal perspective whether innovation facilitates less efficient firms to catch up, or whether firms already utilizing best practices are the main beneficiaries. Using Data Envelopment Analysis (DEA), we find the highest increase in firms’ performance among the firms with innovation strategies that combine product innovation with other innovation types. This finding applies to both the short and the longer term. We also conclude that catch-up is strongest among the firms adopting pure process innovation, whereas the other innovation strategies are primarily associated with frontiers shifts.

Innovation is a key driver of productivity growth. This paper proposes a novel methodology in order to explore the impact of farm-level innovations on farm productivity and its components (i.e. technology, efficiency and scale) using representative data from Irish dairy farms. We measure innovation by an index based on employed production practices, continuous innovation activity and knowledge weighted by expert opinions. The results suggest that more innovative Irish dairy farmers are more productive. Specifically, all farmers improve their production technology and efficiency through their use of innovations, but farmers at specific levels of innovativeness may experience a decrease in productivity due to the small scale at which they operate. This indicates that innovation has a non-linear effect on productivity. We discuss the policy implications for reducing the unequal gains of innovation across farmers.

We measure the cost of technical inefficiency for local electricity distribution firms in Sweden using Stochastic Frontier Analysis, and explore how small-scale generation, the number of electric vehicles and the introduction of dynamic pricing schemes affects the transient inefficiency and efficiency scores. Our results show little to no effect of these environmental variables on the cost of technical inefficiency of electricity distribution grids in Sweden.

Fixed-effects modeling has become the method of choice in several panel data settings, including models for stochastic frontier analysis. A notable instance of stochastic frontier panel data models is the true fixed-effects model, which allows disentangling unit heterogeneity from efficiency evaluations. While such a model is theoretically appealing, its estimation is hampered by incidental parameters. This note proposes a simple and rather general estimation approach where the unit-specific intercepts are integrated out of the likelihood function. We apply the theory of composite group families to the model of interest and demonstrate that the resulting integrated likelihood is a marginal likelihood with desirable inferential properties. The derivation of the result is provided in full, along with some connections with the existing literature and computational details. The method is illustrated for three notable models, given by the normal-half normal model, the heteroscedastic exponential model, and the normal-gamma model. The results of simulation experiments highlight the properties of the methodology.

Stochastic frontier models commonly assume positively skewed inefficiency. However, if the data speak against this assumption, sample-failure problems are often cited, but less attention is paid to economic reasons. We consider this phenomenon as a signal of distinctive population characteristics stemming from the inefficiency component, emphasizing its potential impact on evaluating market conditions. Specifically, we argue more generally that “wrong” skewness could indicate a lack of competition in the market. Moreover, endogeneity of model regressors presents another challenge, hindering the identification of causal relationships. To tackle these issues, this paper proposes an instrument-free estimation method based on Gaussian copulas to model the dependence between endogenous regressors and composite errors, while accommodating positively or negatively skewed inefficiency through simultaneous identification. Monte Carlo simulation experiments demonstrate the suitability of our estimator, comparing it with alternative methods. The contributions of this study are twofold. On the one hand, we contribute to the literature on stochastic frontier models by providing a comprehensive method for dealing with “wrong” skewness and endogenous regressors simultaneously. On the other hand, our contribution to an economic understanding of “wrong” skewness expands the comprehension of market behaviors and competition levels. Empirical findings on Vietnamese firm efficiency indicate that endogeneity hinders the detection of “wrong” skewness and suggests a lack of competitive market conditions. The latter underscores the importance of policy interventions to incentivize firms in non-competitive markets.