Measures Of Labor Productivity Research Articles

Productivity Indices and Efficiency Estimation of Indian Cement Industry

The term productivity was used for the first time over two centuries ago, in the Journal de l’ Agriculture (Tangen, S., 2005). It has been applied in many different circumstances, particularly in relation to economic system, at various levels of aggregation. Productivity is the ratio of what is produced by an operation of process to what is required to produce it, or put simply the ratio of actual output to input over a period of time. Inputs might include transforming and transformed resources (such as materials, equipment, customers and staff) and the outputs are goods and services (Schroeder, R.G., 1985; Slack, N et al, 2001). Measurement of productivity is associated with the extraction of the share of factor input from an increment in output which otherwise cannot be accounted for by mere increase in the quantity of input. Alternatively, productivity means a ratio of actual output to expect resources used. In simpler version, it can be understood as how much and how well one produces from the resources used. If more is produced or better goods are yielded from the same resources, increase in productivity is understood. Productivity varies due to differences in technology, differences in the efficiency in the production process and the differences in the environment in which the production unit operates. The usual method of analysis of productivity is based on the standard definition of production function where it is assumed that maximum output is attended at a given level of input. There are various productivity measures. The choice between them depends on the purpose of productivity measurement and, in many instances, on the availability of the data. Broadly, the measures can be classified as single factor productivity measures (relating to measure of output to a single measure of input) or Multifactor productivity measures (relating a measure of output to a bundle of inputs). Another distinction, of particular relevance at the industry or firm level is between productivity measures that relate some measure of gross output to one or several inputs and those which use a value added concept to capture movement of output. These are measures of labour and capital productivity, and multifactor productivity measures (MFP), in the form of capital- labour MFP, based on a value-added concept of output, or in the form of capital-labor-energy-materials MFP (KLEMS), based on a concept of Gross Output. Among those measures, value added based labour productivity is the single most frequently computed productivity statistics, followed by capital-labour MFP and KLEMS MFP. The Proposed paper is an attempt to measure the Productivity of Indian Cement Industry through the empirical study for a period of 33 Years starting from 1973-74. ASI data is been used with Price correction at WPI (2001-02) and CPI (2002-03) Bases. The productivity indices like Technological change measured through Elasticity of Substitution amongst core factor inputs like labour and capital, Total factor Productivity based on Solow Model on Technological Progress, Capital Intensity and DEA technique to measure Allocative and Technical Efficiencies with 18 decision making units (18 States) and Total Outputs-Net Value Added, Gross Capital Formation & Income are considered for analysis. 2004-05 ASI data is been used.

Labour Productivity Measures from the ABS: 1998 to 2009

This article compares micro and macro measures of productivity. Detailed industry-level productivity estimates are key to understanding how the aggregate economy is driven by the performance of particular sectors, and also how certain sectors perform relative to others. A detailed focus is all the more important in the aftermath of the UK's deepest recession since the 1930s. This article aims to aid the analysis of the economy over the recession and comment on which specific industries saw the biggest changes. The article updates the work of Long (2010) by adding two additional years of data from the Annual Business Survey (ABS), 2008 and 2009.

Labour Productivity Measures from the ABS: 1998 to 2009

Labour Productivity Measures from the ABS: 1998 to 2009

On-site Labour Productivity of New Zealand Construction Industry: Key Constraints and Improvement Measures

Productivity is key to the survival and growth of any organisation, industry or nation. Some factors constrain the achievement of the set project objectives in the New Zealand building and construction industry and are responsible for the reported steady decline of productivity and performance. This study aims to identify the key constraints to on-site labour productivity and improvement measures. Using the descriptive survey method, views of some project managers, contractors and subcontractors in New Zealand were canvassed via pilot interviews and questionnaire surveys at the qualitative and quantity data gathering stages, respectively. Multi-attribute technique was used to analyse the quantitative data. Results showed that the key external constraints to on-site labour productivity comprise, in order of decreasing impact, statutory compliance, unforeseen events and wider external dynamics. The internal constraints, which contribute 67 percent of the onsite productivity issues, comprise reworks, level of skill and experience of the workforce, adequacy of method of construction, buildability issues, and inadequate supervision and coordination. . The factors underlying each broad category of external and internal constraints are reported. The relative levels of impact of the identified constraints are expected to guide the project team in addressing the constraints in a cost-effective manner.

Labour productivity model for reinforced concrete construction projects

Purpose – This study aims to identify systematically the factors that can often influence labour productivity directly and indirectly, to build a model that can evaluate the significance of these factors. The model can be used as a tool for assisting field construction mangers responsible for productivity.Design/methodology/approach – The factors were first identified by undertaking a literature review. The scope and method for measuring labour productivity were then determined. The final analysis model was built through a statistical analysis conducted with the chosen factors.Findings – The results of the analysis indicate that the work management component (e.g. the manager's abilities) and the work technique component (e.g. work continuity) have greater impact than the worker component (e.g. the workers' capability) and the work characteristic component (e.g. work difficulty).Research limitations/implications – This research focuses on the qualitative perspective of site managers on labour productivity...

Knowledge Sharing through Face‐to‐Face Communication and Labour Productivity: Evidence from British Workplaces

AbstractWe investigate whether the adoption by workplaces of human resources management (HRM) practices that enhance face‐to‐face communication (FTFC) among employees is associated with productivity gains. The analysis is based on a nationally representative sample of over 500 British trading establishments drawn from the linked 2004 Workplace Employment Relations Survey and Annual Business Inquiry, for which objective measures of labour productivity (value added per employee) are available. We find a positive association between productivity and FTFC in problem‐solving groups, teams and meetings of senior or line managers and employees, provided that FTFC is adopted on a continuous basis. Our finding suggests that British workplaces in the trading sector could increase their productivity by implementing HRM practices in such a way as to enhance knowledge sharing through employees' personal interactions.

Labour productivity measures from the ABI: 1998 to 2007

This article presents current price labour productivity estimates using published data from the Annual Business Inquiry (ABI). The article updates the work of Goodridge (2007) and Daffi n and Lau (2002) in presenting estimates for the years 2006 and 2007. A new development presented in this article are constant price labour productivity measures which have been formulated using Producer Price Indices (PPI) and the experimental Services Producer Price Indices (SPPI) to deflate output. These enable a more accurate analysis of industry labour productivity trends over time. It is found that capital intensive industries generally exhibit both higher levels and growth in labour productivity over the sample period.

INTRODUCTION TO SPECIAL SECTION ON INTANGIBLE CAPITAL

INTRODUCTION TO SPECIAL SECTION ON INTANGIBLE CAPITAL

Technology shocks around the world

This article investigates the effects of a permanent technology shock on labor input in the major seven developed countries. The recent empirical literature which uses Structural Vector Autoregressions (SVAR) with long-run restrictions has argued that technology shocks lead to a persistent and significant decline in employment in most of the G7 countries. We claim that the international transmission of shocks prevents the use of existing SVAR models, but also suggests alternative specifications to properly uncover their effects. We show in a quantitative two-country model that a measure of labor productivity aggregated across countries is more immune to persistent, if not permanent, shocks and allows to accurately identify the responses of the labor input to a technology shock. Using labor productivity at the G7 aggregate level, we find that the response of employment changes critically in most of the major seven developed countries.

The Shimer Puzzle and the Identification of Productivity Shocks

Shimer (2005) argues that the Mortensen-Pissarides (MP) model of unemployment lacks an amplification mechanism because it generates less than 10 percent of the observed business cycle fluctuations in unemployment given labor productivity shocks of plausible magnitude. This paper argues that part of the problem lies with the identification of productivity shocks. Because of the endogeneity of measured labor productivity, filtering out the trend component as in Shimer (2005) may not correctly identify the shocks driving unemployment. Using a New-Keynesian framework to control for the endogeneity of productivity, this paper estimates that the MP model can account for a third, and possibly as much as 60 percent, of fluctuations in labor market variables.

Adjusting for Changes in Labour Composition in Statistics New Zealand's Productivity Series

In measuring labour productivity, a composition-adjusted series is generally considered to provide the most representative measure of labour input. The rationale for adjusting for changes in labour composition is that workers are not homogenous, and as such, have different skill levels. Not only should this provide a more accurate measure of labour input, but it can also provide insight into the effects that changes in labour composition have on productivity. In practice, this process is undertaken by cross-classifying labour by proxies for skill, such as educational attainment and experience. The relative skill levels of different groups are estimated via regression analysis, through differences in hourly wages. This paper evaluates the various theories behind compositional adjustment, and presents the results of applying such an adjustment to the Statistics NZ labour productivity series, under various conditions. The ultimate contribution of the paper is an experimental composition-adjusted labour productivity series, for which external feedback is welcomed.

The Shimer Puzzle and the Identification of Productivity Shocks

Shimer (2005) argues that the Mortensen-Pissarides (MP) model of unemployment lacks an amplification mechanism because it generates less than 10 percent of the observed business cycle fluctuations in unemployment given labor productivity shocks of plausible magnitude. This paper argues that part of the problem lies with the identification of productivity shocks. Because of the endogeneity of measured labor productivity, filtering out the trend component as in Shimer (2005) may not correctly identify the shocks driving unemployment. Using a New-Keynesian framework to control for the endogeneity of productivity, this paper estimates that the MP model can account for a third, and possibly as much as 60 percent, of fluctuations in labor market variables.

Measuring and Modeling Labor Productivity Using Historical Data

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual's experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.

Modes, weighted modes, and calibrated modes: evidence of clustering using modality tests

AbstractWe apply recent results from the statistics literature to test for multimodality of worldwide distributions of several (unweighted and population‐weighted) measures of labor productivity. Specifically, we employ Silverman (Bump) and Dip modality tests, calibrated to correct for their incorrect asymptotic levels. We show that test results are sensitive to the test statistic employed and to population weighting. But regardless of the statistical criterion used, multimodality is present throughout, or emerges during, our sample period (1960–2000). We also examine (a) movements of economies between modal clusters and (b) relationships between certain key development factors and multimodality of the productivity distribution. Copyright © 2008 John Wiley & Sons, Ltd.

Knowledge Sharing Through Face-to-Face Communication and Labour Productivity: Evidence from British Workplaces

We investigate whether the adoption by workplaces of human resources management (HRM) practices that enhance face-to-face communication (FTFC) among employees is associated with productivity gains. The analysis is based on a nationally representative sample of over 500 British trading establishments drawn from the linked 2004 Workplace Employment Relations Survey and Annual Business Inquiry, for which objective measures of labour productivity (value added per employee) are available. We find a positive association between productivity and FTFC in problem-solving groups, teams and meetings of senior or line managers and employees, provided that FTFC is adopted on a continuous basis. Our finding suggests that British workplaces could enhance their productivity by implementing HRM practices in such a way as to intensify knowledge sharing through employees' personal interactions.

Estimating GSP and Labor Productivity By State

In gauging the health of state economies, arguably the two most important series to track are employment and output. While employment by state is available about three weeks after the end of a month, data on output, as measured by Gross State Product (GSP), are only available annually and with a significant lag. This Policy Discussion Paper details how more current estimates of GSP can be generated using U.S. Gross Domestic Product and personal income along with individual states’ personal income. A straightforward share approach yields reasonable GSP estimates, but a more sophisticated econometric approach, at a cost of imposing more structure, yields even better ones. Both techniques are also applied to estimate nonfarm-business GSP in order to calculate a measure of labor productivity at the state level that follows as closely as possible the method used by the Bureau of Labor Statistics to calculate the national measure of labor productivity. We then briefly examine how labor productivity varies across states.

New labour productivity measures from the ABI – 1998 to 2005

This article presents new current price labour productivity data calculated using published data from the Annual Business Inquiry (ABI). This is an update of previous work by Daffin and Lau (2002) where results for 1998 to 2000 were provided. The results in this article contain revised data for this period1 and new data for 2001 to 2005. The rest of the article discusses the results, the quality of the estimates and also industry-specific data issues.

Regional economic performance in New Zealand: How does Auckland compare?

In this study we investigate Auckland's economic performance relative to other large cities, to medium‐sized urban centres and to small towns and rural area using data from the Income Survey to examine hourly earnings and other measures of labour productivity and utilisation. Our results tell a fairly consistent story. Auckland and Wellington have the highest levels of productivity performance based on almost all measures of earnings. In particular, both have significantly higher average levels of labour income, and wage rates than the three other comparison areas. Auckland has also experienced stronger growth in wages, in particular for wage/salary workers, than other regions.

Producing hours worked for the SNA in order to measure productivity: The Canadian experience

This paper provides a brief description of the methodology currently used to produce the annual volume of hours worked consistent with the System of National Accounts (SNA). These data are used for labour input in the annual and quarterly measures of labour productivity, as well as in the annual measures of multifactor productivity. For this purpose, hours worked are broken down by educational level and age group, so that changes in the composition of the labour force can be taken into account. They are also used to calculate hourly compensation and the unit labour cost and for simulations of the SNA Input-Output Model; as such, they are integrated as labour force inputs into most SNA satellite accounts (i.e., environment, tourism).

Stuck in the Slow Lane: Traffic Composition and the Measurement of Labor Productivity in the U.S. Trucking Industry

Mirroring the railroad industry of the 1940’s and 1950’s, the trucking industry today appears to be achieving impressive productivity gains. But it is easy to confuse true productivity advances in transportation industries with changes in ton-miles per unit of input that are due simply to changes in the composition of traffic, as initially happened with the mid-20th century U.S. railroads. This is due to the fact that transportation has vastly different productivities in different settings – for example, when moving long haul versus short haul traffic – and the measurement of changes in physical productivity can be overwhelmed by even subtle changes in the traffic mix. After controlling for endogenous changes in the composition of truck traffic, we find that trucking has in fact been a lagging sector of the U.S. economy over the period of our data, 1982-1997, with observed productivity changes much more likely due to changes in speed limits and the dimensions of vehicles than adoption of information technology. Our finding of a slow improvement in the physical productivity of trucking inputs does not deny the real improvements in the quality of trucking services (reliability, predictability, speed, order tracking, etc.) that have taken place in the last quarter century. But as in other service industries, true physical productivity improvements in trucking are hard to find.