Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation

The determinants of non-life insurance expenditure in a panel data set covering 36 developed countries and 31 developing countries for the period 2000–2011 are analysed. Results of our instrumental variable analysis indicate that economic freedom, income, bank development, urbanization, culture and law systems are the key drivers of the non-life insurance expenditure across countries. However, their impacts differ significantly between the groups of developed and developing countries, suggesting that the heterogeneity among countries in terms of the level of development plays an important role. The global financial crisis is also found to influence the direction of those effects, especially in developed countries. The article yields useful policy and economic implications for governments and multinational non-life insurance companies with regard to the development of the non-life insurance sector, an important engine for economic growth and prosperity.

Testing for predictability in panels of any time series dimension

The few panel data tests for the predictability of returns that exist are based on the prerequisite that both the number of time series observations, T, and the number of cross-section units, N, are large. As a result, it is impossible to apply these tests to stock markets, where lengthy time series of data are scarce. In response to this, the current paper develops a new test for predictability in panels where N is large and T≥. 2 can be either small or large, or indeed anything in between. This consideration represents an advancement relative to the usual large-. N and large-. T requirement. The new test is also very general, especially when it comes to allowable predictors, and is easy to implement. As an illustration, we consider the Chinese stock market, for which data are available for only 17 years, but where the number of firms is relatively large, 160.

Performance assessment, vulnerability, human capital, and the allocation of aid among developing countries

Developing country performance with respect to economic policies and institutional behavior is a common criterion for the allocation of aid among recipient countries. This paper examines how performance is used, arguing that performance is too narrowly defined. A more appropriate definition is one that controls for the economic vulnerability and human capital of developing countries. Econometric analysis of cross-section and panel data is presented that supports this contention. The paper also contends that performance and exogenous economic shocks are likely to be pro-cyclical. This implies a double punishment when aid is allocated according to performance. Evidence of such punishment is also provided. The paper concludes by arguing that economic vulnerability and human capital variables should augment performance measures in aid allocation decision-making.

Economic growth and carbon emissions

In this paper, we investigate the dynamic relationship between economic growth and carbon dioxide (CO2) emissions for 181 countries. We propose a new approach based on the cross-correlation estimates to understand how economic growth and CO2 emissions are related. Our proposal is that if there is a positive cross-correlation between the current level of income and the past level of CO2 emissions and a negative cross-correlation between the current level of income and the future CO2 emissions, then CO2 emissions will decline with an increase in income over time, consistent with the environmental Kuznets curve (EKC) hypothesis. Our main findings can be summarized as follows. First, for 21 out of 181 countries (12%), there is clear evidence supporting the EKC hypothesis. Second, we ask whether a rise in income reduces emissions in the future. We find that for 49 countries (27%), income growth will reduce emissions in the future.

Error correction testing in panels with common stochastic trends

This paper develops panel data tests for the null hypothesis of no error correction in a model with common stochastic trends. The asymptotic distributions of the new test statistics are derived and simulation results are provided to suggest that they perform well in small samples. Copyright © 2015 John Wiley & Sons, Ltd.

What firm characteristics determine women's employment in manufacturing? evidence from Bangladesh

The purpose of this paper is to investigate the principal determinants of women's employment in the manufacturing sector of Bangladesh using a firm-level panel data from the World Bank's "Enterprise Survey" for the years 2007, 2011 and 2013. The paper sheds light on the demandside factors, mainly firm-level characteristics, which also influence this decision. Design/methodology/approach - The authors estimate a fractional logit model to model a dependent variable that is limited by zero from below and one from above. Findings - The results indicate that firm size, whether medium or large, and firms' export-oriented activities, have an important impact on women's employment in the manufacturing sector in Bangladesh. Moreover, the authors find that women are significantly more likely to work in unskilledlabour- intensive industries within the manufacturing sector. Research limitations/implications - The research is limited to Bangladesh; however, much of the evidence presented here has implications that are relevant to policymakers in other developing countries. Practical implications - The study identifies factors that affect female employment, that is, where the main constraints to increase female labour force participation. The study focuses on the demand-side factors, which has been somewhat neglected in recent years. As such, it has practical policy implications. Social implications - Focusing on female employment in Bangladesh also sheds light on the nexus between labour market opportunities and social change within a country that is characterised by extreme patriarchy, which has wide-reaching implications. Originality/value - This is an original and comprehensive paper by the authors.

Decomposition of temporal changes in construction labour productivity

Construction productivity is recognized as an indicator reflecting the performance efficiency and competitiveness of the industry. A large amount of research has been carried out focusing on the decomposition of the influential factors and the temporal trends of construction productivity changes, respectively. However, the decomposition of the temporal changes in construction labour productivity has not yet been explored. Analogous to the framework of the productivity frontier, this research argues for a four-component decomposition of the temporal changes in construction labour productivity, including technology, technology-utilization efficiency, the capital-labour ratio and production capacity. An error correction model is subsequently estimated using the panel data regression method to investigate the effects of these components on the temporal changes in construction productivity across a sample of the Australian construction industry. The empirical results con?rm that the effects of the four components on the temporal changes in construction productivity changes vary over the observed time periods. From the aggregate level, the technology-utilization efficiency and capital-labour ratio across the regions are found to be barriers to growth in Australian construction productivity. Nevertheless, the effects of technology-utilization efficiency and production capacity varied significantly over the three sub-periods, when innovative national economic systems were introduced.

Analysis of stamping production data with view towards quality management

A quality analysis trial was undertaken at Ford Geelong Stamping Plant on a press line that was fitted with standard press sensors to measure press and binder force over the stamping cycle for each panel. The quality of randomly sampled panels was measured by obtaining the panel thicknesses at five points, for 135 panels in total. These points were chosen such that they exhibited different forming modes. This paper analyses the input force data and the output quality data from the trial to determine any potential relationships. The analysis of the production data was performed using statistical correlation techniques to determine initial potential relationships between input and output variables. An Active Shape Model was used to extract features when identifying the major sources of variation within the input data. However, the initial analysis of the data elicited no direct relationship between the input variables measured and the panel thicknesses. This result is significant as the data collected is from a standard sensor configuration found in many press lines through-out the world. The reason for the lack of a direct relationship is believed to come from the lack of sensitivity in the force measurements which are not able to identify small changes in the process, whereas gross geometric variations have in previous studies shown an obvious relationship with changes in the force press profile. This means that existing force sensors require augmentation by additional sensors if a detailed automatic quality control system for the press lines based on input sensors alone.

Testing the psychometric properties of Clary's volunteer motivation scale with online panel members

Academic researchers, industry and policy-makers are increasingly using online panels as a means for data collection for their research. Online panels are frequently constituted by volunteers who can choose to accept offers to participate in research (cf. voluntary opt-in Cooper 2000). This research focuses on the importance volunteering as the primary reason as to why individuals decide to become panel members. In so doing, we tested the psychometric properties of Clary et al. 's (1998) Volunteer Functions Inventory (VFI) making use of confirmatory factor analyses and ascertained that the dimensions exhibit adequate levels of reliability and validity in an online panel member's milieu.

Modelling money demand for a panel of eight transitional economies

In this article, we estimate money demand functions for a panel of eight transitional economies, using quarterly data for the period 1995:01 1995 to 2005:03. We find that real M1 and real M2 and their determinants, namely real income and short-term domestic interest rate, are cointegrated, both for individual countries as well as for the panel. Long-run elasticities suggest that consistent with theory, real income positively and nominal interest rate negatively impact real money demand. Our test for panel Granger causality suggests short-run bidirectional causality between M1 and M2 and their determinants. Finally, our tests for stability of the money demand functions reveal more cases of unstable money demand functions when M2 is used as a proxy for money demand.

Non-standard work schedules and health outcomes in Australia : some evidence from the HILDA Panel

What does the around-the-clock economic activity mean for workers' health? Despite the fact that non-standard work accounts for an increasing share of the job opportunities, relatively little is known about the potential consequences for health and the existing evidence is ambiguous. In this paper I examine the associations between non-standard job schedules and workers' physical and mental health outcomes using longitudinal data from the Household, Income and Labour Dynamics in Australia (HILDA). Specifically, the four health indicators considered are self-rated health and the SF-36 health indices for general health, mental health and physical functioning. Overall results generally suggest a negative relationship between non-standard work schedules and better health for both males and females. Regarding the statistical significance and magnitudes of the associations, however, we observe apparent differences between males and females. Among females, most of the coefficients in all models are statistically insignificant, which implies very small magnitudes in terms of the correlation between non-standard working hours and health. These results apply uniformly to all health measures investigated. Among males, on the other hand, the negative relationship is more noticeable for self-rated health, general health and physical functioning than for mental health. The pooled OLS and random effects coefficients are usually larger in magnitude and more significant than the fixed effects parameters. Nonetheless, even the more significant coefficients do not imply large effects in absolute terms.

Testing for unit roots in panel time-series models with multiple level breaks

This paper proposes two new unit root tests that are appropriate in the presence of an unknown number of structural breaks in the level of the data. One is based on a single time series and the other is based on a panel of multiple series. For the estimation of the number of breaks and their locations, a simple procedure based on outlier detection is proposed. The limiting distributions of the tests are derived and evaluated in small samples using simulation experiments. The implementation of the tests is illustrated using as an example purchasing power parity.

Analysing building energy use using sub metering and external weather data

The Intergovernmental Panel on Climate Change and the McKinsey Greenhouse Gas abatement studies have highlighted reduction of building energy consumption as a primary cost-effective element in the abatement of Global Warming. Nevertheless, the energy investigation in most of our existing building stock remains at a novice level at best. Building sub-metering, by which we mean any secondary, hourly, metering (after the main) of various circuits, provides substantial information on when and where energy is used in specific buildings. Furthermore, combining this information with external weather data provides information beyond basic metering results. This paper discusses three case studies and explains how sub-metering, augmented by external solar and temperature data, benefits energy management and identified problems. It explains how different methods of analysing energy usage allowed: justifiable sizing of a solar photovoltaic system, with a calculated Cooling Degree Unit, identified the absence of savings from a proprietary chiller controller, and the energy variation due to user schedules and external conditions indicated anomalies in energy use. The advantages of wireless access are noted. Extracting information in graphical formats suggests better strategies to understand and control energy use.

A panel vector error correction approach to forecasting demand in regional construction markets

Reliable forecasting as to the level of aggregate demand for construction is of vital importance to developers, builders and policymakers. Previous construction demand forecasting studies mainly focused on temporal estimating using national aggregate data. The construction market can be better represented by a group of interconnected regions or local markets rather than a national aggregate, and yet regional forecasting techniques have rarely been applied. Furthermore, limited research has applied regional variations in construction markets to construction demand modelling and forecasting. A new comprehensive method is used, a panel vector error correction approach, to forecast regional construction demand using Australia’s state-level data. The links between regional construction demand and general economic indicators are investigated by panel cointegration and causality analysis. The empirical results suggest that both long-run and causal links are found between regional construction demand and construction price, state income, population, unemployment rates and interest rates. The panel vector error correction model can provide reliable and robust forecasting with less than 10% of the mean absolute percentage error for a medium-term trend of regional construction demand and outperforms the conventional forecasting models (panel multiple regression and time series multiple regression model). The key macroeconomic factors of construction demand variations across regions in Australia are also presented. The findings and robust econometric techniques used are valuable to construction economists in examining future construction markets at a regional level.