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In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

The offence against femininity has not only destroyed India’s development but also its future. When it comes down to the most important factor like sex, the social evils like “sati” and “dowry” that had been plaguing our country have been banned in India. India is the most dangerous nation in regard to sexual violence against women, according to the summary of the Thomson Reuters Foundation, 2018. The purpose of this paper is to determine the relationship between the total populations of women with other different types of women crime in all states in India.

This paper will review existing panel data analysis literature and apply this knowledge in finding the highly occurred women crimes in India. Using R software the following models are analysed: pooled ordinary least squares, fixed effects models and random effects models for analysing the women crimes in India.

In this paper, the authors identify that the fixed effects model is more appropriate for the analysis of women crimes in India.

Violence against women is a social, economic, developmental, legal, educational, human rights and health issue. This paper can be used to find the importance of women crime types. Moreover, the police or legal department can take actions according to the crime types.

There is a lack of literature considering the crimes against women. This will help the society to understand women crime types because the only type of violence that has received much attention by the media is rape. But, through our panel data analysis, we conclude that kidnapping, abduction and dowry death are the most occurred crimes against women in India.

The main objective of this research is to investigate if there is a long-term relationship between “terrorism” and sustainable “economic growth” in Big Ten Countries.

The data was tested via Panel ARDL Analysis. The growth rate (GR) is the dependent variable, and the “Global Terror Index (GTI)” is the independent variable as the terror indicator. The ratio of Foreign Direct Investment (FDI) to the Gross Domestic Product (GDP), and the ratio of External Balance (EB) to Gross Domestic Product (GDP) are included in the model as the control variables due to their effect on the growth rate. A Panel ARDL analysis is conducted to examine the existence of long-term co-integration between terror and the economy. The planning of the study, the formation of its theoretical and conceptual framework, and the literature research were carried out in 2 months, and the collection of data, the creation of the methodology and the analysis of the analyzes were carried out in 2 months, the interpretation of the findings and the development of policy recommendations were carried out within a period of 1 month. The entire study was completed in a total of 5 months.

Results showed that “Terror” has a negative impact on “Growth Rate” in the long term while “External Balance” and “Foreign Direct Investment” positively affect the Growth Rate. The coefficients for the short term are not statistically significant.

The sample is only limited to Big Ten including China, India, Indonesia, South Korea, Argentina, Brazil, Mexico, Turkey, Poland and South Africa. The period for annual data collection covers the years between 2002 and 2019 and due to the unavailability of data.

Considering the risks and the mutual negative effect that turns into a vicious circle between terrorism and the economy, it is necessary to eliminate the problems that cause terrorism in the mentioned countries, on the one hand, and to develop policies that will improve economic performance on the other.

Trustful law enforcement bodies have to be established and supported by all technological means to prevent terror. The conditions causing terror have to be investigated carefully and the problems causing terror or internal conflict have to be solved. International cooperation against terrorism has to be strengthened and partnerships, information, experience sharing have to be supported at the maximum levels.

It is certain that terror might have a negative influence on the performance of economies. But the limited number of studies within this vein and the small size of their sample groups mostly including single-country case studies require conducting a study by using a larger sample group of countries. Big Ten here represents at least half of the population of the world and different regions of the Globe.

This chapter describes how to use popular software programs (Hierarchical Linear Modeling, LISREL) to analyze multiwave panel data. We review prevailing methods for panel data analyzes in strategic management research and identify their limitations. Then, we explain how multilevel and latent growth modeling provide more rigorous methodologies for studying dynamic phenomena. We present an example illustrating how firm performance can initiate temporal change in the human and social capital of members of Board of Directors, using hierarchical linear modeling. With the same data set, we replicate this test with first-order factor latent growth modeling (LGM). Next, we explain how to use second-order factor LGM with panel data on employee cognitions. Finally, we review the relative advantages and disadvantages of these new data-analytical approaches.

The purpose of this paper is to provide evidence that the U‐shaped relationship between intellectual property rights (IPRs) and per capita gross domestic product (GDP) observed in the past literature using a panel of data is not a consequence of longitudinal forces, as has been previously postulated, but instead a consequence of cross‐sectional influences.

Differences in the longitudinal and cross‐sectional relationship between IPRs and per capita GDP are analyzed through a variety of methods, including pooled regression analysis that isolates the regional differences that are critical in making an accurate longitudinal analysis from the panel data.

Analyzing the country data reveals that a longitudinal U‐shaped relationship is counterfactual, as countries generally do not weaken their IPRs once they are in place, barring a regime change or other alteration in their political economy. The significant U‐shape link between IPRs and per capita GDP empirically observed in preliminary analysis of the panel data is instead a result of cross‐sectional influences.

Making the distinction between the cross‐sectional and longitudinal relationship between IPRs and per capita GDP provides a more accurate insight about how IPRs change in a country as it develops.

The evolution of firm level practices over time has always been a keen area of interest for management scholars. However, in comparison to other social scientists, particularly economists, the relative dearth of firm level panel data sets has restricted the methodological options for exploring inter-temporal changes.

This paper applies a pseudo panel methodology to investigate the evolution of training spend at the firm level over time.

The analysis is framed within a varieties of capitalism lens and by adopting a more meaningful approach to examining changes over time it leads us to question some of the “truisms” linked to firms expected behaviours within different national institutional frameworks.

As with any large-scale quantitative analysis, it would always benefits from a larger number of observations and/or a longer time period, in this instance access to annual data rather than 4 or 5 year intervals would have been helpful.

By adopting a different, and more appropriate, approach to analysing existing cross-sectional data over time this empirical research helps to achieve a deeper understanding of the complex issues that influence decision making at the firm level.

At the firm level, in line with the practical implications above, this will enable decision makers to achieve a deeper understanding of the evolution of the external context in which they operate and the likely influence of that evolution within their own organisation.

This approach enables a more meaningful exploration of inter-temporal changes in situations where longitudinal data does not exist.

Purpose: The paper analyzes the variability of labor incomes in Argentina from mid-1980s to 2005. The magnitude of income instability and its determinants are evaluated under different macroeconomic contexts. It also analyzes how income fluctuations have influenced income distribution. Finally, the income convergence hypothesis is explored.

Methodology/approach: Different quantitative procedures are employed to measure mobility from dynamic information coming from the regular household survey. Four periods are distinguished that are relatively homogeneous. Dynamic pseudo-panels are also considered.

Findings: The growth in occupational instability registered since the mid-1990s led to a high variability of incomes despite the macroeconomic stability enjoyed throughout the nineties. Moreover, the panorama of growing inequality in the distribution of monthly income (the usual measure employed in Argentina) is also appropriate to describe what happened with the changes in the distribution of more permanent incomes. Finally, long-term income mobility in Argentina is scarce, indicating that the income path does not converge to the general mean.

Research limitations/implications (if applicable): Data refer only to Greater Buenos Aires since microdata are not available for the other areas covered by survey for the entire period under analysis. However, results are reasonably representative of the whole urban areas of the country.

Originality/value of paper: This research identifies the relative importance of labor market and macroeconomic factors in explaining income mobility. Moreover, it is for the first time in Argentina that dynamic information coming from panel data and pseudo-panels are analyzed together.

The purpose of this paper is to describe the further development of the previously introduced switch of brand (SB) model (presented in 2012) which helps to approximate variety-seeking behaviour (VSB) out of household panel data.

Based on existing theoretical variety-seeking models analysing household panel data, the further expansion of the variety-seeking model “Switch of Brands” (SB) is presented. In the last contribution in the British Food Journal the authors presented this simple but powerful tool to approximate VSB. The further developed model “Switch of Brands – Promotions” (SB _PR ) integrates relevant variables into one theoretical variety-seeking model (number of purchased brands, number of purchases, price promotions, etc.). In particular, price promotions were integrated into the SB model in order to deliver even more realistic approximations of households’ VSB.

The explanatory power of the model in view of brand loyalty is tested. The empirical analysis is conducted with scanner household panel data from Austria in three different product categories.

The data analysis shows that the model has an excellent explanatory power concerning brand loyalty, however, not better than the original SB model.

The SB _PR model allows interpretations for marketing purposes and brand management including marketing variables (here: price promotions). The model may be applied in any business field where panel data are available.

The model delivers a consistent theoretical framework for approximating VSB by means of purchase histories.

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.

The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.

The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.

The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

This study aims to measure the relationship between demand and exchange rate shocks in the tourism industry.

A panel data set is constructed covering the period between 1995 and 2017, and the data set includes the top 26 countries that host 10 million tourists and above in the world as of 2017. The standard errors of the series are used as an indicator of shocks. First, the cross-sectional dependency, stationarity and the homogeneity of the series are examined; second, a panel cointegration analysis is implemented; third, long-term panel cointegration coefficients are analyzed with Dynamic Common Correlated Effects (DCCE) approach; and, finally, Dumitrescu and Hurlin’s (2012) Granger non-causality test is used to detect the causality.

The preliminary analyses show that the variables are cross-sectional dependent and heterogeneous and are stationary in their first difference; hence, the effects of the shocks are temporary. On the other hand, as a result of the panel cointegration analysis, it is found that both series are cointegrated over the long-term. However, the long-term coefficients estimated with the DCCE approach are found not to be statistically significant. Finally, as a result of the Dumitrescu and Hurlin’s (2012) Granger non-causality test, it is concluded that there is a causality running from exchange rate shocks to demand shocks.

To the best of the authors’ knowledge, the cointegration between the tourism demand shocks and exchange rates shocks has not been investigated before, and therefore, this study is considered to be a pioneering study that will contribute to the literature.