Journal of Productivity Analysis最新文献

In the Spatial Autoregressive (SAR) Stochastic Frontier (SF) model, the inefficiency term, which distinguishes it from the SAR model, can capture the effects of technical inefficiency. To determine whether inefficiency significantly exists in the cross-sectional SARSF model, this paper proposes a skewness-based test. This test does not rely on the normality assumption for the disturbances and allows inefficiency to follow an unknown one-sided distribution. We establish the asymptotic theory of the test statistic under spatial near-epoch dependent properties. Furthermore, we extend this test to the panel SARSF data model, accounting for both individual and time fixed-effects. Additionally, Monte Carlo simulations demonstrate the robustness of our test against non-normal disturbances and its satisfactory performance with different one-sided distributions for inefficiency. Finally, we provide an empirical application using data from 137 dairy farms in Northern Spain to illustrate the presence of technical inefficiency in production according to our test.

A competitive firm is considered to be inefficient when its observed profit falls short of the maximum possible profit at the applicable prices of inputs and outputs. Two alternative measures of performance of the firm are the ratio of the actual to the maximum profit and the difference between the maximum and the actual profit. The ratio measures its profit efficiency and is naturally bounded between 0 and 1 so long as the actual profit is strictly positive. However, the possibility of negative actual profit and zero profit at the maximum has prompted many researchers to opt for the difference measure of profit inefficiency, which is necessarily non-negative. For a meaningful comparison of performance across firms, however, the difference needs to be appropriately normalized to take account of differences in the scale of operation of firms. Three common variables used for normalization are the observed cost, the observed revenue, and the sum of revenue and cost. It is a common practice to measure the separate contributions of technical and allocative efficiencies to the overall profit efficiency of the firm. When the firm is not operating on the frontier of the production possibility set, there are many ways to project it on to the frontier. This leads to different decomposition of profit efficiency into technical and allocative components. In this paper, we consider McFadden’s gauge function and an endogenous projection based on the overall efficiency of the firm along with the usual input- or output-oriented distance functions, the graph hyperbolic distance function, and a slack-based Pareto Koopmans efficiency measure for technical efficiency. We show that in a multiplicative decomposition of profit efficiency, the technical efficiency component of profit efficiency is independent of input-output prices only from the projection based on the gauge function. Also, an empirical application using data from Indian banks shows that the alternative normalized difference measures of inefficiency generate different performance ranking of the firms. Thus, comparative evaluation of performance of firms depends on the analyst’s preference for a specific type of normalization.

In this paper, we incorporate spatial analysis to estimate industry-level productivity in the presence of inter-sectoral linkages. Since each industry plays a role in providing intermediate goods to other sectors, the interdependence of economic activities across industries is inevitable. We exploit the linkage patterns from the input-output relationship to define cross-industry dependencies in economic space. We propose a spatial stochastic frontier model, which extends the stochastic frontier model to a spatially dependent specification. The models are estimated using quasi-maximum likelihood methods. Applying the approach to U.S. industry-level data from 1947 to 2010, we find that sectoral dependencies are the consequences of indirect effects via the supply chain network of industries resulting in larger output elasticities as well as scale effects for the networked production processes. However, productivity growth is estimated comparably across different spatial and non-spatial model specifications.

Abstract

Adaption to prices is an important feature of productivity development. This paper proposes an extension of the StoNED model to accommodate estimation of allocative efficiency. It demonstrates how indirect production theory is suited for assessing allocative efficiency and helps alleviating the curse of dimensionality for stochastic nonparametric estimators compared to conventional measures of allocative efficiency. Furthermore, the paper elaborates on the appropriate cost of capital for the estimation of allocative efficiency. The proposed model framework is utilized to study allocative efficiency of Norwegian container ports, thereby adding to the literature on seaport terminal efficiency studies.

Abstract

Economists tend to believe that production technology should exhibit increasing returns to scale first and then constant and finally decreasing returns to scale, called regular variable returns to scale (RVRS) in this paper. Further, a special pattern of RVRS production technology when there is only one output is the production function that has an S-shaped curve along any ray of inputs from the origin. In the literature on efficiency analysis, the most frequently used empirical technology is the variable returns to scale (VRS) production technology. Although it exhibits RVRS, it is unable to model nonconvex production technologies, such as the S-shaped production function. Recently, a new empirical production technology has been introduced to capture RVRS with partial convexity. This paper explores its relationship with efficiency measurement. Furthermore, a novel empirical production technology that can better capture the characteristics of the S-shaped production function is proposed. These two new production technologies provide better alternatives to the commonly used Free Disposal Hull (FDH) production technology in non-convex production with RVRS. Our new production technology is illustrated using US manufacturing industry data. If one believes that the production technology is partially convex and exhibits RVRS, it is found that the conventional VRS production technology overestimates the technical inefficiency of small production units under this belief.

This study aims to analyze the impacts of improved agricultural productivity (IAP) on structural transformation and poverty in Guinea-Bissau (2014–2030) using a recursive dynamic computable general equilibrium (CGE) model. We found evidence of a positive effect of IAP on growth and sectoral output. Increased wealth accumulation and labor savings in agriculture have favored reinvestments in the manufacturing and services sectors and rural workers’ outflows from agriculture. There are long-term positive welfare effects from rising real income and household consumption. The poor in rural and urban settings benefited the most. We suggest an agricultural development agenda that may contribute to structural transformation in this country and improve the living standards of the poor.

There are still important theoretical and empirical gaps in understanding the role of best practices (BPs), such as quality management, lean and new product development, in generating firm’s performance advantage and overcoming trade-offs across distinct performance dimensions. We examine these issues through the perspective of performance frontiers, integrating in novel ways the resource-based theory with the emergent practice-based view. Hypotheses on relationships between BPs, performance advantage, and trade-offs are developed and tested with stationary and longitudinal (recall) data from a global survey of manufacturing firms. We use data envelopment analysis, which overcomes limitations of mainstream methods based on central tendency. Our findings support the view that BPs may serve as a source of enduring competitive advantage, based on their ability to lead to a heterogeneous range of dominant and difficult-to-imitate competitive positions. The study provides new insights on contemporary debates about the role of BPs in generating performance advantage and how practitioners can sustain internal support and extract benefits from them.

This paper investigates the contribution of industrial robots to labor productivity growth and cross-country economic convergence in a sample of 19 developed and 16 emerging countries over the period 1999 to 2019. To answer our research questions, we extend the non-parametric production frontier framework by considering industrial robots as a separate production factor. We find a positive contribution of robotization to labor productivity growth for all countries in our sample. In the period after the financial crisis (2009 to 2019) the contribution of robot capital deepening to productivity growth gained in importance. Over the period 1999 to 2019 we find some evidence of i) unconditional β-convergence (countries with lower initial productivity levels grow faster), ii) a reduction in the dispersion of productivity levels across economies (σ-convergence) and iii) a depolarization (shift from bimodal to unimodal distribution) of the labor productivity distribution in our sample. Accumulation of ‘traditional’ physical capital is the main driver of β-convergence. Robot capital deepening significantly contributed to economic convergence and the depolarization of the labor productivity distribution, but its effect on the entire shift of the labor productivity distribution is modest and dominated by other drivers of productivity growth such as ‘traditional’ physical capital deepening and technological change.