Technological progress spillover effect in Lithuanian manufacturing industry

Research background: Various methods for technological progress assessment and evaluation exist in the context of economic development. Each of the methods possesses distinct advantages and disadvantages in analysis of technological progress fluctuations. For most neoclassical growth theories, technological progress measures are included as exogenous variables, thus excluding evaluation of factors influencing technological progress variation throughout time. Purpose of the article: The aim of this article is to offer improvements on classical technological progress evaluation methodologies for manufacturing industries, separating effect of intersectoral technological progress spillover effect from internal factors influencing technological progress growth and perform analysis in the case of Lithuanian manufacturing industry. Methods: Earlier research papers used linear time series regression and vector autoregression methods to assess technological progress values and define equations explaining effect of different manufacturing level indicators on technological progress measure growth. This research paper uses results of previously mentioned methods and performs simulation analysis applying agent-based modelling framework. Findings & value added: The conducted vector autoregression analysis has showed that two variables which influence technological progress most significantly are labor productivity measure and gross profit value. Sensitivity analysis emphasizes that effect of these two variables on technological progress growth is substantially different. Increase in gross profit value affects technological progress growth for wider range of sectors from Lithuanian manufacturing industry (15 out of 18 analyzed sectors? technological progress measure values are affected by changes in gross profit, while changes in labor productivity influence technological progress values in the case of 9 sectors). Rising gross profit values also produce intersectoral technological progress spillover effect more significantly, while growth in labor productivity measure has stronger effect on technological progress fluctuations for sectors which are able to exploit this effect. Presented research suggests improved methodology for intersectoral technological progress spillover effect assessment in the context of manufacturing industries.

The Impact of Trade Liberalization on the Mexican Automobile Industry: Evidence from the First 20 Years of NAFTA

This paper examines the impact of trade liberalization under NAFTA on the productivity of the Mexican automobile industry. Using a panel of establishments for the period 1994-2014, in a first stage a Cobb-Douglas production function is estimated by the Levinsohn and Petrin's (2003) method (in an alternative exercise by that of Ackerberg, Caves and Frazer, 2015) to obtain a productivity measure. In a second stage, a model is estimated by System GMM to analyze the effect of trade openness on the estimated productivity. The main results indicate that there exists a positive association between tradeliberalization and productivity for medium size establishments, but not for small or large establishments. This finding is consistent with that of other authors, who find that trade liberalization results in higher productivity for some firms, but not for all of them (e.g. Lileeva and Trefler, 2010; Bustos, 2011).

Unraveling News: Reconciling Conflicting Evidence

This paper addresses the lack of consensus in the empirical literature regarding the effects of technology diffusion news shocks. We attribute the conflicting evidence to the wide diversity in terms of variable settings, productivity series used, and identification schemes applied. We analyze the different identification schemes that have been employed in this literature. More specifically, we impose short- and medium-run restrictions to identify a news shock. The focus is on the medium-run identification maximizing at and over different horizons. We show that the identified news shock depends critically on the applied identification scheme and on the maximization horizon. We also investigate the importance of the information content of the model and of the productivity measure used. We find that models which either contain a large set of macroeconomic variables or include variables that are strongly forward looking deliver more robust results. Moreover, we show that the productivity series used may influence results, but there is convergence of findings for newer total factor productivity series vintages. Our conclusion is that news shocks have expansionary properties.

Assessing Efficiency of Public Poverty Policies in UE-28 with Linguistic Variables and Fuzzy Correlation Measures

The present study analyzes the efficiency of social expenditure by EU-28 countries within the period 2014–2018 to reduce poverty. The data are provided by programs European Union Statistics on Income and Living Conditions (EU-SILC) and European System of Integrated Social Protection Statistics (ESSPROS) of Eurostat. We first calculate the Debreu–Farrell (DF) productivity measure similarly to our previous work, published in 2020, for each EU-28 country and rank these poverty policies (PPPs) on the basis of that efficiency index. We also quantify the intensity of the relationship between efficiency and the proportion that each item of social expending suppose within the overall. When evaluating public policies within a given number of years, we have available a longitudinal set of crisp observations (usually annual) for each embedded variable and country. The observed value of variables for any country for the whole period 2014–2018 is quantified as fuzzy numbers (FNs) that are built up by aggregating crisp annual observations on those variables within that period. To rank the efficiency of PPPs, we use the concept of the expected value of an FN. To assess the relation between DF index and the relative effort done in each type of social expense, we interpret Pearson’s correlation as a linguistic variable and also use Pearson’s correlation index between FNs proposed by D.H. Hong in 2006.

Overview and classification of approaches to productivity measurement

PurposeProductivity is a multidimensional and context-dependent concept. Therefore, many different definitions and consequently, many different approaches to productivity measurement (PM) exist in the literature. As a result, the understanding of productivity and the appropriate use of PM approaches are at a low level. The literature provides some overviews, but these overviews consider only a few selected individual aspects. Therefore, the overviews do not allow a comprehensive comparison and evaluation of existing approaches. This paper aims to give an overview of existing approaches to PM and to classify them according to elaborated criteria based on the main characteristics of productivity.Design/methodology/approachLiterature review for existing approaches to PM using the following keywords: productivity, PM, productivity measure, labour productivity and labor productivity.FindingsA total of 38 approaches are identified and listed between 1955 and 2020. The main result is a systematic overview and classification of existing approaches to PM.Research limitations/implicationsResearchers can use the overview to determine the development over time, the current state of research in the field of PM and identify research gaps. The classification can also be used to classify new approaches.Practical implicationsCompanies can use the classification as a guide to identifying appropriate approaches to measuring productivity in corporate practice.Originality/valueThis paper enables a comprehensive comparison and evaluation of existing approaches to PM. Also, the understanding of the multidimensional character of the productivity concept is enhanced.

ТЕХНОЛОШКА СТРУКТУРА И ПРОДУКТИВНОСТ РАДА ПРЕРАЂИВАЧКЕ ИНДУСТРИЈЕ РЕПУБЛИКЕ СРБИЈЕ – НИВО ОБЛАСТИ

Labor productivity of the manufacturing industry is an important factor of economic growth and compatibility. The aim of the research is to point out the significance of conducting efficient structural and technological changes in the manufacturing industry of the Republic of Serbia and to examine their impact on the growth of labor productivity. Technological structure was examined according to the technological intensity and methodology of OECD. Labor productivity was analyzed by partial productivity measure, value added per employee from the aspect of impact of various factors on its growth, shift-share analysis. The results of the research show that labor productivity growth rates in the manufacturing industry are high and positive, that they are higher than gross value added, which is the result of change in the number of employees. Productivity growth is higher in areas that belong to high and medium-level technology and is based on the inter-sector effect. The results of this research are useful to the creators of industrial politics when initiating structural changes and relocating the factors that impact labor productivity towards more productive areas of the manufacturing industry.

The societal impact of obinutuzumab in the first-line treatment of patients with follicular lymphoma in Germany

In this study, we assessed the productivity gains associated with the use of obinutuzumab in combination with chemoimmunotherapy (G-chemo) in first-line treatment among follicular lymphoma patients. Health benefits, measured as an increase in progression-free survival, were translated into productivity gains in both paid and unpaid work using gross value added as productivity measure. From 2017 to 2030, 11,870 overall progression-free years can be gained by utilizing obinutuzumab. These progression-free years correspond to undiscounted productivity gains of about €187.9 million in paid work and about €535.9 million in unpaid work. Our study shows that the benefits of the use of obinutuzumab in the first-line treatment of follicular lymphoma extend beyond clinical advantages.

Estimating Forest Productivity Using Site Characteristics, Multipoint Measures, and a Nonparametric Approach

Understanding the productivity of forestland is essential in sustainable management of forest ecosystems. The most common measure of site productivity is breast height–age site index (BHASI). BHASI has limitations as a productivity measure and can compound error in predictive models. We explored the accuracy of productivity predictions using an alternative productivity measure (10-meter site index) and a nonparametric approach. An orthogonal sampling design ensured samples were collected across the range of conditions known to influence Douglas-fir (Pseudotsuga menziesii var. glauca) height-growth rates. Using climate, soil, and topographic data along with 10-meter site index measurements, we compared five possible models to estimate forest productivity. Model parameters, performance, and predictions were compared. Twelve validation sites were used to test the accuracy of model predictions. Model performance was significantly improved when smoothing span values were optimized and elevation was added as a predictor. A four-predictor nonparametric model with a bias-corrected Akaike information criterion–optimized smoothing span value produced the most accurate results and was used to produce forest productivity maps for the study area. The low resolution of currently available climatic data and the complex nature of the study area landscape necessitate a topographic variable for accurate productivity predictions. Study Implications Defining and understanding forest productivity is of interest to a wide variety of natural resource professionals including ecologists, climate change experts, forest biometricians, and forest managers. A new method of defining forest productivity using multipoint height-age pairs at 10 and 20 meters and calculated growth rates combined with an appropriate landscape-scale stratification and a nonparametric approach provides accurate productivity estimates. This method is more widely applicable and more precise for specific locations than previous productivity estimation methods. Better productivity and tree growth information will provide more accurate estimates of future forest condition and structure.

Relationship between residual feed intake classification as a heifer and lifetime productivity of beef cattle

The effect of residual feed intake adjusted for backfat thickness (RFIfat) on heifer pregnancy rate and subsequent lifetime productivity was examined in 867 beef females that were ranked as low, medium, or high RFIfat. Age at first calving, weaning weight of first calf, and most probable producing ability for birth weight (MPPAbw) and weaning weight (MPPAww) were calculated to assess first parity heifer productivity. The effect of heifer RFI adjusted for backfat (RFIfat; n = 532) on subsequent lifetime cow productivity (n = 415) was calculated based on kg of calf weaned per cow bred per year. A total lifetime productivity measure (n = 218) were also calculated as total calf weaning weight (kg) output per cow culled. RFI rank had no significant effect on pregnancy rate, when adjusted for season and site differences (P = 0.33). No significant correlations (P < 0.05) were observed between MPPAww and RFI, RFIfat, RFI adjusted for backfat and feeding event frequency (RFIfat & activity), or age at first calving. A negative trend (P < 0.10) between RFI, RFIfat, and MPPAbw calculated from first parity pregnancy rate and production traits was no longer apparent when adjusted for RFIfat & activity. These results suggest that selection for low RFI replacement heifers has no impact on their first parity pregnancy rate and productivity or on subsequent cow productivity.