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This study aims to investigate if the level of economic freedom matters for how corruption affects per capita income in US states.

Using a new (and novel) index of corruption, which is based on Associated Press news wires, the author estimates the long-run cointegrating relationship between corruption, economic freedom and per capita income with fully modified ordinary least squares (FMOLS) following Pedroni (2000).

The author finds that there is a threshold level of economic freedom that determines if corruption reduces the per capita income in a state. According to the FMOLS estimations, the negative effects of corruption on income decrease as economic freedom increases, and they eventually disappear.

This is the first study investigating the intricate relationship between corruption, economic freedom and economic performance using data from US states. The study uses a news-based measure of corruption constructed by Dincer and Johnston (2017), which has several advantages over the convictions-based measure used in previous studies analyzing the relationship between corruption and growth using US data. The study takes into account the integration and cointegration properties of the data and estimates the relationship among the cointegrated variables using FMOLS following Pedroni (2000).

This study seeks to provide answers to the following questions: Is there a relationship between poverty and income inequality in the short run/long run? Is the relationship unidirectional from income inequality to poverty as the previous studies assume, or is it bidirectional?

The paper investigates the causality between income inequality and poverty within a multivariate framework using a panel data set of 50 US states over the period 1980 to 2004.

The results reveal that a bidirectional relationship exists between poverty and income inequality both in the short run and in the long run. With respect to the short‐run dynamics associated with poverty, both income inequality and the unemployment rate have a positive and statistically significant impact on poverty, a negative and statistically significant impact for real per capita personal income and level of education, while corruption is insignificant. In terms of the short‐run dynamics associated with income inequality, poverty, the unemployment rate, real per capita personal income, and the level of education have a positive and statistically significant impact, while corruption has a statistically insignificant impact on income inequality. With regard to the long‐run dynamics, the statistically significant error correction terms indicate the presence of a feedback relationship between poverty and income inequality.

This study contributes to the existing empirical literature on several fronts. First, while previous studies rely on cross‐country data, both the cross‐sectional and time series variation of a panel data set of all 50 US states spanning the period 1980 to 2004 is exploited. Second, while the previous studies implicitly assume unidirectional causality from income inequality and poverty in their model specifications, the short‐run and long‐run causal dynamics between poverty and income inequality within a panel vector error correction model are explicitly examined.

The purpose of this paper is to investigate the relationships between measures of ethnic and religious heterogeneity on the one hand and measures of inequality and redistribution on the other, using state‐level US data.

The relationship between ethnic/religious heterogeneity and inequality/redistribution is estimated, first, with ordinary least squares (OLS) and then with generalized method of moments (GMM). The principal explanatory variables for inequality and redistribution in the study are ethnic and religious polarization and fractionalization measures.

It is found that inequality is positively associated with polarization and has an inverse‐U relationship with fractionalization. These findings are bolstered by findings of indirect effects of ethnic and religious heterogeneity on inequality through their influence on welfare programs.

This is the first paper investigating the magnitude of possible direct and indirect effects of ethnic and religious heterogeneity on income inequality using US data.

The current Industry 4.0 era is considered not only as a process that dominates technological developments but also as a process that influences the leadership styles. Management 4.0 is essential for businesses to find and apply the appropriate technologies in the age of Industry 4.0. The leadership styles that business managers will adopt in order to be successful in this process and to survive in an intensely competitive environment can play an important role. At this point, a significant problem arises: identifying leadership styles that will bring success. In this context, the primary purpose of this chapter is to explain the modern leadership styles that business managers can adopt or follow in the age of Industry 4.0. In line with this purpose, the chapter first describes the historical development of leadership, leadership theories and modern leadership styles, such as transactional, transformational, technological, strategic, visionary and agile leadership, and all these concepts are discussed based on the Industry 4.0 perspective.

Public spaces, which offer opportunities for social, cultural and recreational activities, enhance urban life quality (ULQ). Thus, this study aims to investigate the impact of public spaces and physical-environmental criteria affecting the usability of ULQ in Adana city, Turkey.

The study method consists of three stages. Firstly, public spaces and physical-environmental criteria that can be effective in ULQ were determined. Secondly, the effect of the determining criteria on ULQ was evaluated through a 5-point Likert scale questionnaire (1 = very negative, 5 = very positive). The survey was conducted with 601 people in the four central districts of Adana, including Çukurova, Seyhan, Sariçam and Yüregir. Participants evaluated ULQ for both the residence district and Adana city. Lastly, factors affecting ULQ were determined using exploratory factor analysis (EFA). In addition, MANOVA was used to determine the changes in factors according to socio-demographic characteristics.

Based on the EFA, the results show that the criteria affecting the ULQ are grouped into four factors, including (1) open spaces, (2) cultural, sports and recreation, (3) environmental and (4) transportation. In evaluating these factors, while gender does not affect the perception of ULQ, residence districts show a statistically significant difference in the perception of ULQ. Cultural and transportation factors show statistical differences according to education and age.

This study has a limitation in that it relies solely on the quantitative perceptions of residents with varying demographics, such as age, gender and educational level, to evaluate public spaces and physical environment criteria. While these perspectives are valuable, they may not necessarily reflect the qualitative reality of the urban environment. Therefore, future studies combining quantitative and qualitative data could provide a more comprehensive understanding of the factors affecting ULQ in urban areas.

The implementation of the survey showed the subjective perception of ULQ in Adana city. Urban green spaces, including cultural, sports and recreational areas, should be improved in areas with insufficient facilities that affect the quality of urban life. Additionally, the impact of climate conditions on the quality of life should be taken into account when designing the city to ensure maximum utilization of public spaces. Furthermore, safe cycling transportation networks should be developed.

The novelty of this study lies in its unique approach to investigating the effects of public spaces and physical environmental criteria on ULQ based on combining residents' perceptions, literature review and data analysis. The study provides a valuable perspective often overlooked in urban planning research, especially in developing countries like Turkey. Additionally, the study's findings can inform the development of strategies to enhance ULQ.

This study aims to present the financial performance of companies and investment areas in the real estate investment trust (REIT) industry.

A fuzzy model for financial performance measurement (FM-FPM) was proposed through the collaboration of fuzzy axiomatic design (FAD) and fuzzy entropy weighting (FEW). For the data, financial ratios were used, and their importance and functional requirements were collected via a questionnaire survey.

The FM-FPM is a beneficial model to be used for a REIT industry based on the structured procedures of FAD and FEW techniques. It can be suitable to regularly evaluate the performance of REITs and their investment areas in financial means, especially in today’s turbulent business environment. The Turkish market that was considered to show the practical applicability of the FM-FPM demonstrated specifically that diversified real estate was found to rank first, followed by mixed-buildings, warehouses, shopping malls and hotels, respectively.

The FM-FPM can be employed for REIT industries in other countries and adapted to different industries. However, more respondents or a different set of criteria might lead to different outputs.

The FM-FPM may guide REIT managers and investors while making their decisions and controlling the performance of REITs and investment areas.

The FM-FPM may encourage low- and middle-income investors to make good use of their savings.

The research is first (1) to offer a FPM model in order to determine investable areas in a REIT industry and (2) to employ multiple criteria decision-making tools in order to measure the financial performance of individual companies and investment areas in a REIT industry.

This paper aims to investigate the impact of oil price shocks on the Turkish sovereign yield curve factors.

To extract the latent factors (level, slope and curvature) of the Turkish sovereign yield curve, we estimate conventional Nelson and Siegel (1987) model with nonlinear least squares. Then, we decompose oil price shocks into supply, demand and risk shocks using structural VAR (structural VAR) models. After this separation, we apply Engle (2002) dynamic conditional correlation GARCH (DCC-GARCH (1,1)) method to investigate time-varying co-movements between yield curve factors and oil price shocks. Finally, using the LP (local projections) proposed by Jorda (2005), we estimate the impulse-response functions to examine the impact of different oil price shocks on yield curve factors.

Our results demonstrate that the various oil price shocks influence the yield curve factors quite differently. A supply shock leads to a statistically significant increase in the level factor. This result shows that elevated oil prices due to supply disruptions are interpreted as a signal of a surge in inflation expectations since the cost channel prevails. Besides, unanticipated demand shocks have a positive impact on the slope factor as a result of the central bank policy response for offsetting the elevated inflation expectations. Finally, a risk shock is associated with a decrease in the curvature factor indicating that risk shocks influence the medium-term bonds due to the deflationary pressure resulting from depressed economic conditions.

Our results provide new insights to understand the driving forces of yield curve movements induced by various oil shocks to formulate appropriate policy responses.

The study contributes to the literature by two main dimensions. First, the recent oil shock identification scheme of Ready (2018) is modified using the “geopolitical oil price risk index” to capture the changes in the risk perceptions of oil markets driven by geopolitical tensions such as terrorism and conflicts and sanctions. The modified identification scheme attributes more power to demand shocks in explaining the variation of the oil price compared to that of the baseline scheme. Second, it provides recent evidence that distinguishes the impact of oil demand and supply shocks on Turkey's yield curve.