Random Effects Panel Data Model Research Articles

Modelling the Effect of Police Motivating Factors on Effective Crime Management in Nigeria

This study investigates the relationship between police motivating factors' and the crime rate in Nigeria. The fixed effect panel data model with geopolitical zones and states as dummy variables results indicated the significance of motivating factors aside from logistics and insurance with multiple R-squared values of 76.65%. The random effect panel data model showed every motivating factor was significant with multiple R-squared values of 53.81%. Therefore, based on multiple R squared values, the fixed effect panel data model explained the variations in crime rate better—random effect study. Conclusively, police motivating factors have a significant impact on the crime rate in Nigeria.

Influence of COVID-19 on Consumer Preference for Turfgrass Attributes in the Southern United States

This study estimates the influence of the coronavirus disease 2019 (COVID-19) pandemic on consumer preferences for turfgrass attributes by analyzing data from two surveys conducted in Jan 2019 and Apr 2021. First, the study estimated a mixed logit model to account for individual heterogeneity in preferences. Subsequently, estimates of the willingness to pay (WTP) were compared between periods before and after the pandemic. To show the impact of consumers’ risk attitudes with respect to climate change on their preference for turfgrass attributes, we re-estimated the model according to the risk attitude groups (i.e., risk-seeking vs. risk-averse). Finally, to examine how consumers’ demographic characteristics and risk attitudes are related to their WTP for improved turfgrass attributes, we estimated a random-effect panel data model for each attribute. Our results showed that, overall, consumers’ WTP increased during the COVID-19 pandemic. We also found that the WTP of risk-averse consumers were mostly higher than those of risk-seeking consumers during both time periods. Furthermore, the increase in the WTP observed among the risk-averse group was greater than the increase of the WTP of the risk-seeking group. Our findings imply that the demand for drought-tolerant and stress-resistant turfgrasses would increase with possible future climate changes and infectious disease outbreaks.

Financial determinants of environmental, social and governance performance: Empirical evidence from India

The present study aims to examine the firm-level financial determinants of ESG performance. It elucidates what financial resources it takes to enable the integration of ESG practices and improve a firm’s ESG scores, based on a sample of 94 Indian firms listed on the National Stock Exchange of India between 2015 and 2020. Econometrically, the study employs fixed effects and random effects panel data models as an appropriate methodology. The findings show that firm size, asset intangibility, analyst coverage, and operating cash flow influence firms’ ESG scores positively, whereas leveraging and strategic holding impact them negatively. In addition to the mentioned variables, cash holdings positively influence firms’ environmental, social, and governance scores. While dividend yield does not contribute to combined ESG and governance scores, it has a positive impact on a firm’s environmental and social scores. This is the first study examining the determinants of firm-level ESG performance in an emerging market. Results endorse the interaction of legitimacy theory and slack resource theory in determining a firm’s ESG performance. AcknowledgementThe infrastructural support provided by FORE School of Management, New Delhi, India in completing this paper is gratefully acknowledged.

Cross-section asymptotic for random-effects panel data models with autoregressive errors

A useful strategy to examine serial correlation in panel regression models is to adopt serially correlated error terms. The random effect is also present to address the individual-specific heterogeneity. Familiar transformations can restructure the AR(1) process into a serially uncorrelated dynamic model. It may result in unfitting estimation results once the statistical inference is carried on the conditional likelihood. We discuss in the article the consequence of this action. In particular, in the case of large cross-section units and short time-sequences, we provide analytical expressions for the asymptotic bias of maximum likelihood estimates when the analyst naïvely neglects the initial conditions problem. Under several conditions, we compute the size of biases and measure the mean squared errors to illustrate this violation. We also construct a joint regression model of the initial and subsequent responses to handle the initial conditions. Then, we conduct simulation studies to determine the asymptotic behavior.

Profit Persistence: is There A Conglomerate Effect? The Case of Banking and Insurance in Morocco

This paper analyses profit persistence and its determinants in Morocco's banking and insurance sectors. The solid and increasing interdependencies between both actors are becoming a matter of concern for the regulatory authorities, as they threaten the country's financial stability. Using both static and dynamic hypotheses of the competitive environment model, we study the determinants of profit persistence in eight banks and eight insurance companies for 13 years, using a random-effects panel data model. While previous articles studied banks and insurers separately, we fill the gap in the literature by exploring the profit persistence as a result of their increasing capitalistic interdependence. Our results show a very low-profit persistence level, which signals a competitive financial sector. The main determinants of this profit persistence are diversification, efficiency, size, solvency, risk, and entry barriers. However, we find no evidence of any conglomerate effect, eliminating any synergy premium through cross participation between banks and insurance companies.

Studying the relationship between women and the environment in developing countries

PurposeThe purpose of this study is to examine whether there is a unidirectional or a bidirectional relationship between women and the environment, and to further study the effect of women on environmental quality.Design/methodology/approachTo achieve this purpose, a Granger causality test and a random effects panel data model are used to study women–environment relationship in developing countries. Error correction model (ECM) is the chosen estimation technique. A Granger causality test is used because of its frequent use in examining the existence of a unidirectional or a bidirectional relationship between two or more variables. A random effects panel data model is used as it has proven to be more efficient than the fixed-effects panel data model.FindingsWomen Granger-cause environmental quality while the opposite is not true in developing countries in the long run. This indicates the existence of a unidirectional relationship between women and the environment when the long-run relationship is considered. However, when considering the long- and short-run relationship together, the results indicate the presence of a bidirectional relationship. The empirical results of the random effects panel data model through ECM estimation indicate the positive effect of women on improving environmental quality as illustrated by the coefficient of the current change of women. This shows that women are concerned about environmental degradation. In addition, the empirical results highlight the persistence of CO2 emissions. Results also confirm that foreign direct investment inflows lead to further environmental degradation. However, education and trade openness coefficients are found insignificant at the current period.Research limitations/implicationsThe research results have great implications on women empowerment, the reduction of gender bias and the increase in government expenditure on women’s education and health because of women’s positive effect in improving environmental quality.Originality/valueTo the best of the author’s knowledge, this is the first paper that examines the two-way relationship between women and the environment and, hence, it fills the gap present in the literature.

Broadband Speed and Firm Entry in Digitally Intensive Sectors: The Case of Croatia

We explore how improvements in digital infrastructure contribute to digital transformation of the Croatian economy. More specifically, we investigate under what conditions improvements in broadband speed are conductive for firm entry in digitally intensive sectors at the local level (cities and municipalities; LGUs) during the period 2014–2017. The results of the benchmark random effects panel data model suggest that a 10 percent increase in broadband speed increases the number of new digitally intensive firms by 0.68. Two-way interactions between explanatory variables indicate that improvements in broadband infrastructure yield the greatest number of new firm entries in densely populated LGUs, and in LGUs with a higher quality of human capital and greater public investment in physical infrastructure. Using the spatial Durbin panel method, we find improvements in broadband infrastructure exhibit positive firm entry effects both within and between cities and municipalities.

Audit Committee Characteristics and Earnings Management: Evidence from Jordan

This study investigates whether earnings management is influenced by the audit committee characteristics. This study uses a secondary-data and the population study's consists of all industrial firms listed on Amman Stock Exchange (ASE) over the period 2009-2013. Using a random effect panel data model, the results reveal that audit committee independence and audit committee financial expertise are negatively associated with earnings management at significant level. Moreover, the results indicate that a significant relationship between audit committee size and earnings management but positive direction. These results have implications for policy makers and regulatory bodies in Jordan since they highlight the need to improve the good corporate governance practices and attempt to constrain the incidence of earnings management in Jordanian firms.

On Labor Productivity in OECD Countries: Panel Data Modeling

Labor productivity is a key measure of economic performance and an essential factor of improving the living standards. High labor productivity growth rate can reflect increased use of capital, and/or a decrease in the employment of low-productivity workers. Thus, it can be said that labor productivity indicators reflect the effectiveness of labor utilization, labor cost, and revenue accumulation. This paper studies the impact of labor market conditions, economic integration, market size, and institutional quality on labor productivity in Organization for Economic Co-operation and Development (OECD) countries during the period from 2005 to 2017 by using panel data technique. The study finds that the random effects panel data model is the appropriate model to fit this data. The results suggest that average annual hours worked, labor force participation rate, and inflation rate have a negative significant impact on labor productivity in OECD countries. However, annual growth rate of GDP per capita, value added of industry, and control of corruption have a positive significant impact on labor productivity.

Impacts of COVID-19 on public transit ridership

In this paper, a national-wide study is conducted to investigate the impacts of COVID-19 on the public transit ridership in the top twenty metropolitan areas in the U.S. At first, COVID-19 composite index was developed to qualitatively measure the level of public fear toward COVID-19 in different metropolitan areas. After that, to analyze the impact of COVID-19 and some socioeconomic factors on transit ridership reduction during the COVID-19 pandemic, a random-effects panel data model was developed and the traditional correlation analysis was also conducted. According to the results of both analyses, it was found that the areas with higher median household income, a higher percentage of the population with a Bachelor’s degree or higher, higher employment rate, and a higher percentage of the Asian population are more likely to have more reductions in public transit ridership during the COVID-19 pandemic. On the other side, the areas with a higher percentage of the population in poverty, and a higher percentage of the Hispanic population are more likely to experience smaller reductions in public transit ridership.

ROBUST PARAMETER ESTIMATION FOR RANDOM EFFECT PANEL DATA MODEL IN THE PRESENCE OF HETEROSCEDASTICITY AND INFLUENTIAL OBSERVATIONS

Panel data estimators can strongly be biased and inconsistent in the presence of heteroscedasticity and anomalous observations called influential observations (IOs) in Random effect (RE) panel data model. The existing methods (LWS, WLSF, WLSDRGP) address only the problem of IO but fail to remedy the combine problem of heteroscedasticity and IOs. Therefore, in this research we develop a method that will remedy the combine problem of heteroscedasticity and IOs based on robust heteroscedasticity consistent covariance matrix (RHCCM) estimator and fast improvised influential distance (FIID) weighting method denoted by WLSFIID. The simulation and numerical evidences show that our proposed estimation method is more efficient than the existing methods by providing smallest bias, and smallest standard error of HC4 and HC5

Agricultural labour in transition: An update

The economic transformation process in Europe and Asia differed remarkably across countries, last but not least, with respect to agricultural labour use. While the structural change in some countries followed theoretical expectations and was characterised by a drastic reduction in agricultural employment, other countries experienced an increase in agricultural labour force. Against this background, the paper aims at analysing the determinants of the change of agricultural employment across a panel of formerly centrally planned economies with a particular focus on institutional factors. The analysis builds upon the theories of structural change and new institutional economics and relies on a set of econometric methods. To explain annual intersectoral labour flows, random-effects panel data models are used. Sectoral labour adjustment is measured by the difference between growth rates of agricultural and non-agricultural employment between 1990 and 2019 for a panel of 31 transition countries. The authors direct particular attention to the role of land ownership and transfer rights, which is operationalised by an updated and extended indicator of land relations. Similar to previous studies the classical determinants, such as the ratio of average income per worker in the non-agricultural sector over agriculture, the relative size of the agricultural sector or the development of relative prices, are positively correlated with a shift of labour out of agriculture. The findings suggest furthermore that occupational migration increased with a liberalisation of land transfer rights, in particular during the first two decades of transition. Land rental or sales agreements allow land owners to earn an income from their asset while working outside of the agricultural sector. Contrary to expectations based on economic theory, improved ownership rights seem to reduce labour outflow from agriculture. The results underline that institutional factors play a role in structural change. Deeper analyses of the incentives related to improved tenure rights for occupational change require individual level data.

ROBUST PARAMETER ESTIMATION FOR RANDOM EFFECT PANEL DATA MODEL IN THE PRESENCE OF HETEROSCEDASTICITY AND INFLUENTIAL OBSERVATIONS

Panel data estimators can strongly be biased and inconsistent in the presence of heteroscedasticity and anomalous observations called influential observations (IOs) in Random effect (RE) panel data model. The existing methods (LWS, WLSF, WLSDRGP) address only the problem of IO but fail to remedy the combine problem of heteroscedasticity and IOs. Therefore, in this research we develop a method that will remedy the combine problem of heteroscedasticity and IOs based on robust heteroscedasticity consistent covariance matrix (RHCCM) estimator and fast improvised influential distance (FIID) weighting method denoted by WLSFIID. The simulation and numerical evidences show that our proposed estimation method is more efficient than the existing methods by providing smallest bias, and smallest standard error of HC4 and HC5.

The role of high-speed rail and air travel in the spread of COVID-19 in China

BackgroundPublic transportation is a major facilitator of the spread of infectious diseases and has been a focus of policy interventions aiming to suppress the current COVID-19 epidemic. MethodsWe use a random-effects panel data model and a Difference-in-Differences in Reverse (DDR) model to examine how air and rail transport links with Wuhan as well as the suspension of these transport links influenced the development of the epidemic in China. ResultsWe find high-speed rail (HSR) and air connectivity with Wuhan resulted in 25.4% and 21.2% increases in the average number of daily new confirmed cases, respectively, while their suspension led to 18.6% and 13.3% decreases in that number. We also find that the suspension effect was dynamic, growing stronger over time and peaking 20–23 days after the Wuhan lockdown, then gradually wearing off. It took approximately four weeks for this effect to fully materialize, roughly twice the maximum incubation period, and similar dynamic patterns were seen in both HSR and air models. Overall, HSR had a greater impact on COVID-19 development than air transport. ConclusionsOur research provides important evidence for implementing transportation-related policies in controlling future infectious diseases.

Exploring the correlation between ride-hailing and multimodal transit ridership in toronto

Ride-hailing (RH) services have been growing rapidly and gaining popularity worldwide. However, many transit agencies are experiencing ridership stagnation or even decline. Understanding the correlation between RH trips and transit ridership has become an urgently important matter for transit agencies. This study aimed to explore the relationship between RH and public transit ridership and provide a starting point for future studies. This study benefitted from having access to detailed data on trip-level RH trips, transit supply and transit ridership in Toronto for three years (2016–2018). With this dataset, the study utilized random-effects panel data models and log–log regression models to estimate the correlation of RH pickup/drop-off counts with subway station and surface transit route (buses and streetcars) ridership within transit catchment areas, broken down into five different periods of a non-summer weekday. The results show that RH services generally have a positive association with subway station ridership while negatively correlating with surface transit route ridership. The positive relationship between RH and subway station ridership is the strongest during the mid-day and early evening. In contrast, the negative relationship between surface transit routes and RH ridership is the highest during peak commuting hours. Additionally, RH trip volume is more positively related to ridership at terminal/transfer subway stations in Toronto’s city centre while more negatively associated with routes with relatively poor services (e.g., low on-time performance, low vehicle running speed and low frequency) in the city centre where traffic congestion can be severe. According to the above findings, the degree of the relationship between RH and public transit demand tends to be mixed, varying by transit mode, time of day and transit level-of-service. The gained knowledge about RH and transit can provide insights for transit agencies to improve transit services, which are discussed in this paper.

Information asymmetry in fire insurance: a frontier approach

The composed error stochastic frontier model is used to separate random variations in fire insurance losses from systematic unexpected losses due to adverse selection or moral hazard. Net premiums are an ex-ante predictor of losses, based on information available to insurers. Losses due to information unknown to the insurer are picked up by the half-normal part of the error term. Loss data for 275 stock fire companies operating in 51 states and territories in 1917 are analyzed in a random effects panel data model. The mean cost per insurer of adverse selection and moral hazard amounts to 15% of fire losses. This is an upper bound estimate because some portion of this cost may be due to failure to operate efficiently rather than incomplete information.

The pluviosity effect on occupational diseases in Colombia during 2010–2014, a quasi–experimental approach

In this work, we provide a formal approach to the econometric relationship between the volume of rainfall and occupational diseases in Colombia for the period 2010 – 2014. Our main result is that, with a random effects panel data model at the municipal level with month – by – month and year – by – year effects, we obtained evidence of an increase in occupational diseases due to an increasing volume of rainfall. Finally, we bring outlines for future research in this topic.

Investors' preferences and the factors affecting investment in the Indian stock market: an industry view

PurposeThe authors examine the industrywise investment preferences of foreign portfolio investors (FPIs), domestic institutional investors (DIIs) and retail investors in the Indian context. They also investigate the factors influencing their preferences.Design/methodology/approachUsing the quarterly shareholdings and returns data of the Indian market from March 31, 2009 to March 31, 2018, the authors employ analysis of variance to study investors' preferences and a random effect panel data model to examine the factors that influence these preferences.FindingsFPIs hold proportionally more stocks in service-oriented industries and large-cap firms, DIIs hold proportionally large numbers of shares in paper industries and retail investors hold proportionally more shares in chemicals and textiles. FPIs prefer stocks with a high export-to-sales ratio and firms registered on a foreign stock market. Domestic investors, especially retail investors, prefer small-cap stocks and firms whose operations require local knowledge. In addition, industry heterogeneity determines investment decisions. Firm-specific and macroeconomic factors that influence investment decisions differ across industries. Finally, government policies and reforms also play a key role in attracting investors.Practical implicationsPolicymakers can identify the key variables that influence investment, which can help direct and regulate investment in India and similar emerging markets.Originality/valueThis study fills a research gap by addressing how industry-level heterogeneity affects investors' preferences in terms of the industrywise preferences of different types of investors and the factors that influence their preferences.

Factors influencing the hub connectivity of Beijing Capital Airport in its international markets

This paper investigates factors influencing the hub connectivity of Beijing Capital Airport (PEK) in its domestic to international markets between 2014 and 2018 based on a random-effect panel data model. The results show that variables representing the temporal and spatial characteristics of connecting flights have significant impacts on the hub connectivity of PEK. In specific, the changeable threshold of maximum connecting time based on different international destination regions has a negative impact. This paper identifies different timetable coordination modes at PEK and shows that the number of indirect connections under adjacent mode is significantly less than that of distant mode. Circuity and directionality are proven to have significant impacts. In addition, factors representing airport characteristics and heterogeneity of international destination regions also affecting the hub connectivity of PEK.

The Impact of Public Health Expenditure on Health Component of Human Capital

The analysis of the concept of human capital shows that this form of capital includes a set of different components that are used in economic activities and generates different kind of benefits. Health is one of the main components of human capital. Hence, considering its importance authors of this article tried to investigate the impact of public health expenditure on the health component of human capital. Ordinary least square, fixed and random effects panel data models for 28 European Union countries were used to reach this purpose. The main variables used in this study is life expectancy at birth (related to the health component of human capital) and general government health expenditure. It is known that there are other factors affecting health and human capital in general. Hence variables such as GDP grow, GINI coefficient, education level, alcohol consumption, old age dependency and urbanization rate were added in the models as explanatory variables. The results of the performed study show that public health expenditure has a positive and significant impact on the improvements of life expectancy. Selected fixed-effects panel data models also show significant and positive effects of the GDP growth, old age dependency while a negative effect is identified by alcohol consumption. The existing close relationship between health and education also is confirmed by this study. Results suggest that higher education level is positively and significantly related to life expectancy while a lower education has a negative impact on life expectancy.