Farm-level efficiency and productivity measurement using panel data: wool production in south-west Victoria

In this article we explore some issues surrounding the use of farm-level efficiency and productivity estimates for benchmarking studies. Using an eight-year balanced panel of Victorian wool producers we analyse annual variation between estimates of farm-level technical efficiency derived using Data Envelopment Analysis and Malmquist estimates of Total Factor Productivity. We find that farms change their relative rank in terms of efficiency across years. Also, unlike aggregate studies of Total Factor Productivity, we find at best erratic and modest growth, a worrying result for this industry. However, caution is needed when interpreting these results, and for that matter, benchmarking analysis as currently practised when using frontier estimation techniques like Data Envelopment Analysis.

Carbon dioxide emissions and economic growth : panel data evidence from developing countries

In this paper we test the Environment Kuznet's Curve (EKC) hypothesis for 43 developing countries. We suggest examining the EKC hypothesis based on the short- and long-run income elasticities; that is, if the long-run income elasticity is smaller than the short-run income elasticity then it is evident that a country has reduced carbon dioxide emissions as its income has increased. Our empirical analysis based on individual countries suggests that Jordan, Iraq, Kuwait, Yemen, Qatar, the UAE, Argentina, Mexico, Venezuela, Algeria, Kenya, Nigeria, Congo, Ghana, and South Africa—approximately 35 per cent of the sample—carbon dioxide emissions have fallen over the long run; that is, as these economies have grown emissions have fallen since the long-run income elasticity is smaller than the short-run elasticity. We also examine the EKC hypothesis for panels of countries constructed on the basis of regional location using the panel cointegration and the panel long-run estimation techniques. We find that only for the Middle Eastern and South Asian panels, the income elasticity in the long run is smaller than the short run, implying that carbon dioxide emission has fallen with a rise in income.

Research of the construction industry efficiency in China-based on DEA panel data approach

The efficiency of the construction industry is analyzed based on provinces panel data in China in this paper. The Mean Number of Employee and the Mean Completed Investment are used as inputs. The Mean Actual Sales of Commercial Houses and the Mean Net Profit are used as outputs. Data Envelopment Analysis (DEA) model is used to measure the efficiency of the construction industry. Shanghai and Zhejiang are found technically efficient. Shandong is scale efficient but technology efficiency is lower. There are two provinces are decreasing returns to scale and other provinces are increasing returns to scale. On the whole, the technology efficiency of the construction industry of China is lower. Based on the conclusions, the paper proposes some suggestions to improve the efficiency of the construction industry in China.

A non-stationary panel data investigation of the unemployment–crime relationship

Many empirical studies of the economics of crime focus solely on the determinants thereof, and do not consider the dynamic and cross-sectional properties of their data. As a response to this, the current paper offers an in-depth analysis of this issue using data covering 21 Swedish counties from 1975 to 2010. The results suggest that the crimes considered are non-stationary, and that this cannot be attributed to county-specific disparities alone, but that there are also a small number of common stochastic trends to which groups of counties tend to revert. In an attempt to explain these common stochastic trends, we look for a long-run cointegrated relationship between unemployment and crime. Overall, the results do not support cointegration, and suggest that previous findings of a significant unemployment–crime relationship might be spurious.

The impact of OFDI on economic growth countries. An econometric approach using panel data and time-series evidence

The thesis at hand adds to the existing literature by investigating the relationship between economic growth and outward foreign direct investments (OFDI) on a set of 16 emerging countries. Two different econometric techniques are employed: a panel data regression analysis and a time-series causality analysis. Results from the regression analysis indicate a positive and significant correlation between OFDI and economic growth. Additionally, the coefficient for the OFDI variable is robust in the sense specified by the Extreme Bound Analysis (EBA). On the other hand, the findings of the causality analysis are particularly heterogeneous. The vector autoregression (VAR) and the vector error correction model (VECM) approaches identify unidirectional Granger causality running either from OFDI to GDP or from GDP to OFDI in six countries. In four economies causality among the two variables is bidirectional, whereas in five countries no causality relationship between OFDI and GDP seems to be present.

Poverty dynamics in Costa Rica with panel data from cross-sections

Includes bibliography

China Urban Competitiveness in Industrialization: Based on the Panel Data of 25 Cities in China from 1990 to 2009

There is a consensus in China that industrialization, urbanization, globalization and information technology will enhance China's urban competitiveness. We have developed a methodology for the analysis of urban competitiveness that we have applied to China's 25 principal cities during three periods from 1990 through 2009. Our model uses data for 12 variables, to which we apply appropriate statistical techniques. We are able to examine the competitiveness of inland cities and those on the coast, how this has changed during the two decades of the study, the competitiveness of Mega Cities and of administrative centres, and the importance of each variable in explaining urban competitiveness and its development over time. This analysis will be of benefit to Chinese planners as they seek to enhance the competitiveness of China and its major cities in the future.

Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patternsof this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.

Long-term relationship between energy consumption and GDP in Latin America: an empirical assessment using panel data

The main objetive of this research is to evaluate the long term relationship between energy consumption and GDP for some Latin American countries in the period 1980-2009 -- The estimation has been done through the non-stationary panel approach, using the production function in order to control other sources of GDP variation, such as capital and labor -- In addition to this, a panel unit root tests are used in order to identify the non-stationarity of these variables, followed by the application of panel cointegration test proposed by Pedroni (2004) to avoid a spurious regression (Entorf, 1997; Kao, 1999)

Output and labor productivity in organized manufacturing: a panel cointegration analysis for India

One of the policy puzzles faced in India during the last two and half decades has been the weak association between output and labor markets, particularly in the manufacturing sector. In this research, we investigate the long-run relationship between output, labor productivity and real wages in the case of organized manufacturing. We adjust the measure of labor productivity incorporating bottlenecks, such as lack of infrastructure, access to external finance, and labor regulations, which all may influence labor market outcomes. Using panel data from seventeen manufacturing industries, we establish long-run dynamics for the output-labor productivity-real wages series over a period of nearly three decades. We employ recently developed panel unit root and cointegration tests for cross-sectional dependence to incorporate heterogeneity across industries. Long-run elasticities are generally found to be low for labor productivity compared to real wages due to the changes in manufacturing output. There are variations across industries within the manufacturing sector for the effects of the labor market on manufacturing output. In some industries, lower wages are associated with higher output, and the reason for the positive relationship in other industries could be due to workers' bargaining power.

Determinants of Terminal Value in the Evaluation of Companies: A Panel Data Approach to the Context of European Companies.

The uncertainty of the future of a firm has to be modelled and incorporated into the evaluation of companies outside their explicit period of analysis, i.e., in the continuing or terminal value considered within valuation models. However, there is a multiplicity of factors that influence the continuing value of businesses which are not currently being considered within valuation models. In fact, ignoring these factors may cause significant errors of judgment, which can lead models to values of goodwill or badwill, far from the substantial value of the inherent assets. Consequently, these results provided will be markedly different from market values. So, why not consider alternative models incorporating life expectancy of companies, as well as the influence of other attributes of the company in order to get a smoother adjustment between market price and valuation methods? This study aims to provide a contribution towards this area, having as its main objective the analysis of potential determinants of firm value in the long term. Using a sample of 714 listed companies, belonging to 15 European countries, and a panel data for the period between 1992 and 2011, our results show that continuing value cannot be regarded as the current value of a constant or growth perpetuity of a particular attribute of the company, but instead be according to a set of attributes such as free cash flow, net income, the average life expectancy of the company, investment in R&D, capabilities and quality of management, liquidity and financing structure.

Performance determinants For Swedish Financial Monetary Institutions: Panel Data evidence between 2010-2014.

Due to the rapid changes that governs the Swedish financial sector such as financial deregulations and technological innovations, it is imperative to examine the extent to which the Swedish Financial institutions had performed amid these changes. For this to be accomplish, the work investigates what are the determinants of performance for Swedish Financial Monetary Institutions? Assumptions were derived from theoretical and empirical literatures to investigate the authenticity of this research question using seven explanatory variables. Two models were specified using Returns on Asset (ROA) and Return on Equity (ROE) as the main performance indicators and for the sake of reliability and validity, three different estimators such as Ordinary Least Square (OLS), Generalized Least Square (GLS) and Feasible Generalized Least Square (FGLS) were employed. The Akaike Information Criterion (AIC) was also used to verify which specification explains performance better while performing robustness check of parameter estimates was done by correcting for standard errors. Based on the findings, ROA specification proves to have the lowest Akaike Information Criterion (AIC) and Standard errors compared to ROE specification. Under ROA, two variables; the profit margins and the Interest coverage ratio proves to be statistically significant while under ROE just the interest coverage ratio (ICR) for all the estimators proves significant. The result also shows that the FGLS is the most efficient estimator, then follows the GLS and the last OLS. when corrected for SE robust, the gearing ratio which measures the capital structure becomes significant under ROA and its estimate become positive under ROE robust. Conclusions were drawn that, within the period of study three variables (ICR, profit margins and gearing) shows significant and four variables were insignificant. The overall findings show that the institutions strive to their best to maximize returns but these returns were just normal to cover their costs of operation. Much should be done as per the ASC theory to avoid liquidity and credit risks problems. Again, estimated values of ICR and profit margins shows that a considerable amount of efforts with sound financial policies are required to increase performance by one percentage point. Areas of further research could be how the individual stochastic factors such as the Dupont model, repo rates, inflation, GDP etc. can influence performance.

Sustainable subdivisions – ventilation data analysis

This project, as part of a broader Sustainable Sub-divisions research agenda, addresses the role of natural ventilation in reducing the use of energy required to cool dwellings