Dynamic Productivity Growth Research Articles

Robust nonparametric analysis of dynamic profits, prices and productivity: An application to French meat-processing firms

AbstractAppropriately considering adjustment costs, this paper develops a robust nonparametric framework to analyse profits, prices and productivity in a dynamic context. Dynamic profit change is decomposed into a dynamic Bennet price indicator and a dynamic Bennet quantity indicator. The latter is decomposed into explanatory factors. It is shown to be a superlative indicator for the dynamic Luenberger indicator. The application focuses on 1,638 observations of French meat-processing firms for the years 2012–2019. Using m-out-of-n bootstrapped data envelopment analysis, we obtain robust estimates and confidence intervals. The components of dynamic productivity growth fluctuate substantially. However, these fluctuations are often statistically insignificant.

Innovation and Dynamic Productivity Growth in the Indonesian Food and Beverage Industry

This paper examines the relationship between innovation and dynamic productivity growth in the Indonesian food and beverage industry. Dynamic productivity growth is calculated using a Luenberger indicator, and innovation is represented by a process innovation. This research uses firm-level data for the period 1980–2015 sourced from the Indonesian Central Bureau of Statistics. This research uses a panel data regression model to estimate the relationship between innovation and dynamic productivity growth. This research finds that innovation is relatively low in the Indonesian food and beverage industry. Dynamic productivity growth declines steadily during the period of estimation. This research also found that innovation positively affected dynamic productivity growth only after the introduction of the competition law in Indonesia.

Dynamic sustainable productivity growth of Dutch dairy farming.

The economic, environmental and social sustainability of Dutch dairy farms have attracted increasing societal concern in the past decades. In this paper, we propose a recently developed dynamic Luenberger indicator based on the by-production model to measure dynamic productivity growth in the economic, environmental and social dimensions of sustainability of Dutch dairy farms. Subsequently, we investigate the statistical associations between productivity growth and socio-economic factors using the OLS bootstrap regression model. We find that dairy farms have suffered a decline in dynamic sustainable productivity growth, especially in the environmental dimension where it is more pronounced than in the economic and social dimensions. Furthermore, we find that both technical and scale inefficiency change contribute to the decline of environmental productivity growth. Specialization and government support are associated with a higher economic and environmental sustainability productivity growth, and with, a decreased growth of social sustainable productivity. We found no significant association between the age of the oldest entrepreneur, financial structure, farm size or cost of advisory service and dynamic productivity growth in the three sustainability dimensions. The results provide insights into potential pathways towards improving the three pillars of sustainability.

The effect of cow longevity on dynamic productivity growth of dairy farming

Cow longevity is recognized as an important trait to improve farm economic performance while concurrently reducing environmental and social impacts. However, there is an economic trade-off between longevity and herd genetic improvement, which may influence the dairy farms’ efficiency and productivity growth over time. This study used a panel data of 723 Dutch specialized dairy farms over 2007-2013 to empirically measure the effect of longevity on dynamic productivity change and its components. First, the productivity growth estimates were obtained using the Luenberger dynamic productivity indicator. Then, the estimates were regressed on longevity and other explanatory variables using dynamic panel data model. Results show that the average dynamic productivity growth was 1.1% per year, comprising of technical change (0.5%), scale inefficiency change (0.4%) and technical inefficiency change (0.2%). Longevity is found to have a statistically significant positive association with productivity growth and technical change, implying that farms with more matured cows were also those farms that recorded increased productivity through technical progress. However, it has a negative association with technical inefficiency change, which might follow from the reduced milk productivity of old cows per unit of inputs used. Dutch dairy farms have a potential to raise productivity growth by reducing technical inefficiencies associated with input utilization.

The effect of farm genetics expenses on dynamic productivity growth

Genetic improvement of animals has been an important source of productivity growth in dairy farming. Studying the effect of genetic progress on productivity growth of farms requires a long-term dynamic perspective due to the long generation interval of dairy animals, and the slow, persistent and cumulative effects of genetics. It is also essential from a farm decision-making perspective to disentangle overall productivity growth in relation to each variable input and investment in quasi-fixed input while accounting for adjustment costs associated with the slow changes in quasi-fixed inputs. This paper contributes to the literature by combining input- and investment-specific dynamic productivity growth analysis with impulse response analysis. The application focuses on panel data of Dutch specialized dairy farms over 2007–2013. The results show that farms that adopt improved genetic materials, as proxied by farm expenses on artificial insemination and breeding stock investment spike, achieved higher input- and investment-specific productivity growth in the first two years after the year of the expenses/spike. That is, farms that produce more efficiently after adopting quality genetics are also those farms that utilise their resources efficiently. The positive relationships suggest a potential positive spill-over effect from using high quality genetics on managerial efficiencies.

Productivity Growth in Dynamic Factor Adjustment for the Japanese Manufacturing Industry

ABSTRACT By utilising a dynamic adjustment-cost framework, this study analyses dynamic productivity growth in the Japanese manufacturing industry. Empirical results show that labour and capital are very slow in converging toward the long-run equilibrium, and that output supply and factor demand elasticities vary greatly, depending on the time horizon considered. The results also show that disequilibrium effects of quasi-fixed factors are positively biased toward productivity growth measured in a static equilibrium model. The bias results largely from negative adjustment costs related to the decreasing investment in the factors. There is an almost steady decrease in returns to scale, causing negative scale effects on productivity growth.

Dynamic productivity growth and its determinants in the Indonesian food and beverages industry

ABSTRACTThis research investigates dynamic productivity growth and its determinants in the Indonesian food and beverages industry decomposing dynamic productivity growth into the contributions of dynamic technical inefficiency change, dynamic technical change, and dynamic scale inefficiency change. The empirical application employs unbalanced panel data of 44 subsectors in the Indonesian food and beverages industry over 1990–2014. To estimate dynamic productivity growth, this research uses a Luenberger indicator accounting for the presence of adjustment costs. The results show that dynamic productivity growth exhibits a decreasing trend. Dynamic technical inefficiency change and dynamic scale inefficiency change contribute positively to dynamic productivity growth, while dynamic technical change contributes negatively. Dynamic productivity growth is affected by the change in industrial concentration, the growth rate of capital intensity, the growth rate of exports, the growth rate of foreign direct investment, and location.

Dynamic cost productivity and economies of scale of Ghanaian insurers

Previous insurance efficiency studies have focused on cost efficiency or static and dynamic technical productivity and therefore ignored dynamic cost productivity. Previous studies have also failed to consider economies of scale at the firm level. This study employs a panel data of insurers to assess the dynamic cost productivity growth in Ghana from 2005 to 2014. We also explore the determinants of cost productivity growth in the Ghanaian insurance industry. We find that the introduction of the Insurance Act of 2006 saw some large cost productivity growths; however, since 2012 the industry has been facing some marginal cost productivity decline. The cost improving policies in the Act that encouraged cost efficiency must be revisited by regulators, as it seems that the industry is going back to the pre-regulation cost environment. Additionally, not many insurers have been operating at the optimal production scale over the period.

The impact of the 2008 financial crisis on dynamic productivity growth of the Spanish food manufacturing industry. An impulse response analysis

The emergence of a financial crisis is an event that can impact the fortunes of nearly all economic agents. The focus here is on the 2008 financial crisis and how firms’ productivity growth was impacted by this crisis in the years that followed. This article focuses on dynamic productivity growth and its components using a firm-level data set of Spanish meat processing, dairy processing, and oils and fats firms. The impulse response analysis shows that the impact of the crisis on dynamic productivity growth is negative and persistent in the oils and fats industry, initially positive but then negative in the meat processing industry, and positive in the dairy processing industry. The observed magnitudes of change in indicator are between 2% and 5% for oils and fats industries, and of 1% in both dairy and meat industries. Our analysis further confirms that firms’ size is an important factor in explaining how crisis impacts dynamic productivity growth and its components, while we find only slight evidence regarding the firms’ experience in the market.

Impacts of technological change on energy use efficiency and GHG mitigation of pomegranate: Application of dynamic data envelopment analysis models

Greenhouse gas (GHG) mitigation in agriculture should be approached, among others, through changes in farm management and technology use. The application of new GHG mitigation technologies are essential to promote a cleaner environment. This is critical since not all new technologies are environmentally friendly. To understand whether and to what extent such technologies mitigate GHG, this survey study was conducted to measure the amount of the dynamic energy consumption and productivity growth of pomegranate production during 2009–2015 in the Fars province, Southwest Iran. Dynamic DEA models were employed for the efficiency analyses. The data were gathered from pomegranate gardeners through questionnaires by face-to-face interviews. The validity and reliability of the questionnaires were tested and confirmed respectively through a pilot study and Cronbach’s alpha. Using simple random sampling method, 55 pomegranate gardeners were chosen for the survey. The dynamic DEA revealed that our gardeners had the best performance in 2013, 2014 and 2015. The average energy consumption was 42,230 MJ/ha to produce 17,300 kg/ha pomegranate. The highest output-input energy ratio was also obtained in 2013, 2014 and 2015 of 1.013. The most efficient gardeners consumed more renewable energy especially in the form of farm yard manure. Accordingly, the gardeners with the least efficiency should enhance pomegranate yield and reduce the extra energy inputs of around 10,000 kg/ha (58% of average) and 25,000 MJ/ha (59% of the average), respectively. Malmquist index showed that the best efficient gardeners had annual positive technology change of 8.3%. Statistical analyses confirmed that each positive percentage of technology change reduces the energy input by 710 times and CO2 emission mitigation by 634 times meaning that the gardeners used some energy efficient technologies that lead to lower GHG emissions. The technology change can be predicted by some of the socio-demographic attributes of the gardeners such as farming experience, net income and participating in extensional classes. The study concludes that highly efficient pomegranate production besides new technology use policies would ultimately contribute to the robust climate change mitigation from this sector.

Investment Age and Dynamic Productivity Growth in the Spanish Food Processing Industry

This article analyzes the relation between investment age, measured as the number of years since investment spike, and dynamic productivity growth and its components, which include dynamic technical change, dynamic inefficiency change, and dynamic scale inefficiency change. The empirical application focuses on firm‐level data for the Spanish food processing industry covering the period from 1996 to 2011. This investigation of the impact of firms' investment decisions on productivity growth employs a dynamic production framework and analyzes the impact of these decisions on the components of dynamic productivity growth. Our findings show that dynamic productivity growth is negatively affected by investment spikes in both the meat processing and oils and fats industries, and that dynamic inefficiency change initially falls just after the infusion of large investment for oils and fats firms, but then grows as the firms acquire experience with this investment. We further find that investment spikes lead to improvements in dynamic technical change and worsening in dynamic technical inefficiency change in the meat processing industry, while dynamic scale inefficiency change was negatively impacted in both industries.

Effect of Food Regulation on the Spanish Food Processing Industry: A Dynamic Productivity Analysis.

This article develops the decomposition of the dynamic Luenberger productivity growth indicator into dynamic technical change, dynamic technical inefficiency change and dynamic scale inefficiency change in the dynamic directional distance function context using Data Envelopment Analysis. These results are used to investigate for the Spanish food processing industry the extent to which dynamic productivity growth and its components are affected by the introduction of the General Food Law in 2002 (Regulation (EC) No 178/2002). The empirical application uses panel data of Spanish meat, dairy, and oils and fats industries over the period 1996-2011. The results suggest that in the oils and fats industry the impact of food regulation on dynamic productivity growth is negative initially and then positive over the long run. In contrast, the opposite pattern is observed for the meat and dairy processing industries. The results further imply that firms in the meat processing and oils and fats industries face similar impacts of food safety regulation on dynamic technical change, dynamic inefficiency change and dynamic scale inefficiency change.

Analyzing the impact of investment spikes on dynamic productivity growth

Firm-level data usually show that a large portion of firm-level investment takes place in a few investment episodes. This paper assesses productivity growth and its components in production framework that accounts for the dynamics of capital adjustment and relates this to investment spikes using firm-level data of the Spanish meat processing industry over the period 2000–2010. Using the method of impulse responses by local projections, it is shown that investment spikes produce a significant productivity change loss of 0.7% in the first year after the spike. The worsening of technology is the main cause of the reduction of productivity growth in the first year. Technology then improves in the fifth year as a result of investment spike, resulting in the U-shape pattern of relationship. Scale inefficiency significantly improves by 0.4% and 0.5% in the first and second year after the spike has occurred, respectively. All these effects, however, largely depend on the firms׳ size. In particular, it is shown that the loss of technology in the first year mainly pertains to smallest firms, while larger firms experience a negative impact on the contribution of technical inefficiency change to productivity growth.

Primal and dual dynamic Luenberger productivity indicators

This paper develops primal and dual versions of the dynamic Luenberger productivity growth measures that are based on the dynamic directional distance function and intertemporal cost minimization, respectively. The empirical application focuses on panel data of Dutch dairy farms over the period 1995–2005. Primal dynamic Luenberger productivity growth averages 1.5 percent annually in the period under investigation, with technical change being the main driver of annual change. Dual dynamic Luenberger productivity growth is −0.1 percent in the same period. Improvements in technical inefficiency and technical change are partly counteracted by deteriorations of allocative inefficiency, with large dairy farms presenting a slightly higher productivity growth than small dairy farms.

Analysis of Static and Dynamic Productivity Growth in the Spanish Meat Processing Industry

This paper estimates static Malmquist and dynamic Luenberger productivity growth measures and decomposes these to identify the contributions of technical change, technical efficiency change and scale efficiency change. The Malmquist and Luenberger productivity growth measures are estimated using Data Envelopment Analysis. The empirical application uses data on Spanish meat processing firms over the period 2000-2010. The dynamic Luenberger indicator and the static Malmquist index show, in the period under investigation, a productivity decrease of, on average, 0.3% and 1% respectively. In both measures, the technical regress is the main driver of change, despite the technical and scale efficiency growth.

Is growth of services an obstacle to productivity growth? A comparative analysis

The relationship between economic structure and productivity growth has been a subject of increasing interest over recent decades. The innovative focus of this paper concerns the role of the service sector in this relationship. Services play a core role in advanced economies, both from a quantitative and a strategic point of view. However, empirical research in this area lies considerably behind the research into the agricultural and manufacturing sectors. This paper focuses on the impact of tertiarisation on overall productivity growth, using a sample of 37 OECD countries in the period between 1980 and 2005. The results partially refute traditional knowledge on the productivity of services. Contrary to what conventional theories suggest, this research demonstrates that several tertiary activities have shown dynamic productivity growth rates, while their contribution to overall productivity growth plays a more important role than was historically believed.

Harmonizing Antitrust Worldwide by Evolving to Michael Porter's Dynamic Productivity Growth Analysis

Harmonizing Antitrust Worldwide by Evolving to Michael Porter's Dynamic Productivity Growth Analysis