On Ordinary Least Squares Research Articles

Accessibility and Older and Foreign Populations: Exploring Local Spatial Heterogeneities across Italy

The interplay between accessibility and population change is a relatively new subject in Italian academic research. Along with social and economic factors such as regional economic prosperity, the ease of movement inside and outside an area can play a pivotal role in shaping population dynamics. This study seeks to explore the spatial distribution and spatial relationships of three indicators, including one related to real accessibility (RAI) and two others related, respectively, to the shares of the older population (SOP) and of the foreign population (SFP). An exploratory spatial data analysis is, therefore, conducted at the local level using Italian municipalities as the statistical units for the empirical analysis. Local univariate spatial autocorrelation analysis is used together with a regression analysis based on ordinary least squares (OLS) and geographically weighted regression (GWR) models. The results provide valuable insights into the local heterogeneity that characterizes the distribution of each indicator and the local relationship between them, highlighting the importance of thinking locally in quantitative social sciences.

Pengaruh Pendapatan Biaya Komisi Provisi Dan Vakuta Asing Pada Return On Aset (ROA) Pada Bank Mauamalat Priode 2015-2022

The purpose of this study was to examine the effect of fee-based income and foreign exchange fees on return on assets at Muamalat bank for the 2015-2020 period. There are two types of fee-based income studied, namely fee and commission income, and foreign exchange transaction service income. The sample is using Quarterly financial reports for the 2015-2020 period in this study. Multiple regression based on ordinary least squares (OLS). The results showed that the three types of fee-based income had a positive and significant effect on return on assets. However, total assets have no significant effect. Overall, the results suggest that banks should increase their fee-based income activities to improve profitability.

The impact of fiscal opacity on business confidence: empirical investigation from an emerging economy

PurposeThe relevance of transparency related to public finances is considered fundamental for good economic policy management. An environment of greater fiscal transparency allows the private sector greater predictability, improving the entrepreneur’s decision-making ability. This study empirically analyzes fiscal opacity’s effect on business confidence in an emerging economy.Design/methodology/approachWe use monthly data from the Brazilian economy from January 2010 to March 2023. Based on Ordinary Least Squares (OLS) and the Generalized Method of Moments (GMM) regressions, we analyze whether fiscal opacity, measured by the signal-to-noise ratio, affects business confidence. Moreover, to evaluate the duration of a shock transmitted by the fiscal opacity on business confidence, we consider an impulse-response function generated by a Vector Auto-Regressive (VAR).FindingsWe found that fiscal opacity resulting from the lack of information to anticipate the budgetary result of the public sector deteriorates business confidence.Practical implicationsWe present robust empirical evidence that allows us to assume that using a strategy to reduce fiscal opacity through mechanisms that provide reliable economic data and fiscal forecasts is essential for fiscal policy to affect business confidence positively. Reducing fiscal opacity provides greater clarity regarding the budget outcome, reduces economic uncertainty and improves the fiscal policy expectation channel.Originality/valueThis paper is the first to analyze how the lack of information for market agents to anticipate the government’s budget execution accurately (fiscal opacity) affects business confidence.

Integration of immigrant women in Europe: a multifaceted approach

PurposeThis work analyzes female immigrants’ integration in the dimensions of education, labor market participation and fertility in 15 European countries, considering individual characteristics, including cultural background, host countries’ attitudes towards immigrants, the role of women in the family and country-specific integration policy. All these aspects taken together are crucial to understand the main patterns of integration focusing on gender differences.Design/methodology/approachWe focus on second- and first-generation male and female immigrants between the age of 25 and 41, with a length of stay of at least ten years. Enrollment ratios for tertiary education in parents’ countries, the total fertility rate and the female labor force in the mother’s country represent ethnic background. Diversity in the destination regions is captured by local attitudes towards immigrants, the perceived role of women and national policies to integrate migrants [Migrant Integration Policy Index (MIPEX)]. The data are drawn from the European Social Survey (ESS) for 2010–2018. Our results are based on ordinary least squares (OLS) and logit estimates; multilevel analysis was conducted.FindingsWe find significant evidence of gender role transmission from mother to daughter; age at immigration seems to be crucial to examine the importance of the culture of origin among immigrants. However, females are responsive to attitudes toward immigrants and gender equality in receiving societies, while integration policies, by defining the set of opportunities, may contribute to both genders’ tertiary education and women’s probability of being in the labor force.Social implicationsThis work underlines that integration policies favoring equal rights as nationals may contribute to both women’s tertiary education and their probability of being in the labor force.Originality/valueWe explore female integration in Europe in the dimensions of education, labor market, fertility and the role of both immigrants’ cultural heritage and specific aspects of destination countries. Previous research, particularly in the USA, has generally focused on some of these features at the expense of a more comprehensive approach. This study builds upon the existing literature and contributes to it by taking a multifaceted approach to female integration in Western Europe, which presents not only an institutional context different from the USA but also some heterogeneity with respect to integration policies and socioeconomic factors.

Founder ownership concentration and risk disclosures: an emerging economy view

PurposeThis paper examines the impact of founder ownership concentration (FOC) on risk disclosures. It further investigates the moderating role of risk governance in the association between FOC and risk disclosures.Design/methodology/approachWe use data from the top 200 Indian listed firms as our sample and rely on ordinary least squares (OLS) for our results. In addition, we use the propensity score matching, Heckman selection model and instrumental variable estimates for robustness checks.FindingsWe find that FOC decreases the risk disclosures. However, the effectiveness of risk management committee composition (risk governance) mitigates the negative influence of FOC on risk disclosures.Research limitations/implicationsThe paper is built on the agency theory. Based on the agency theory, the ownership concentration has two implications: first, it reduces the conflicts between managers and shareholders. Here, the managers act in favour of shareholders and therefore, brings more risk disclosers. Second, it invites conflicts between controlling and minority shareholders. The study is, therefore, interesting to see the cost and benefits of FOC on risk disclosures.Practical implicationsThe study has practical implications for the regulatory bodies to encourage risk disclosures and benefit the outsiders of the firm. It also has implications for the companies to see the benefits of risk management committee as improved risk governance.Originality/valueIt contributes to the literature of risk disclosures and risk governance in emerging economies. It is the first study to investigate the role of risk governance in mitigating the adverse effects of founder’s ownership on risk disclosures in developing economies. It also contributes to the theory of agency cost and information asymmetry.

Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering.

The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an of 0.721, a of 0.046, and an of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.

Digital Maturity of Forecasting and its Impact in Times of Crisis

AbstractEspecially in times of crisis, reliable predictions about probable future developments are difficult, but critical for successfully managing business operations. At the same time, it remains unclear what constitutes a good forecasting process during crises. The aim of this study is to analyze whether and how digital transformation can enhance forecasting processes and enable firms to better deal with crises. To do so, we refer to the concept of digital maturity, i.e., the extent to which digital transformation is adopted in internal processes, studied at the practice of forecasting. Specifically, we analyze whether digitally more mature forecasting processes positively influence (1) satisfaction with forecasting during crises, (2) the effectiveness of countermeasures, and (3) the economic situation during crises. We conduct a cross-sectional survey among 195 medium-sized and large companies in Germany to shed light on the forecasting process and its digital maturity as well as on the impact of the COVID-19 economic crisis on companies. Based on ordinary least squares (OLS) regression, we find that digitally more mature forecasts increase satisfaction with forecasting and the effectiveness of countermeasures. Overall, this study provides new insights into relevant aspects of forecasting to support successful crisis management, and it highlights the importance of advancing digital transformation in forecasting, especially to successfully deal with crises.

Intra-urban differences underlying leprosy spatial distribution in central Brazil: geospatial techniques as potential tools for surveillance.

This ecological study identified an aggregation of urban neighbourhoods spatial patterns in the cumulative new case detection rate (NCDR) of leprosy in the municipality of Rondonópolis, central Brazil, as well as intra-urban socioeconomic differences underlying this distribution. Scan statistics of all leprosy cases reported in the area from 2011 to 2017 were used to investigate spatial and spatiotemporal clusters of the disease at the neighbourhood level. The associations between the log of the smoothed NCDR and demographic, socioeconomic, and structural characteristics were explored by comparing multivariate models based on ordinary least squares (OLS) regression, spatial lag, spatial error, and geographically weighted regression (GWR). Leprosy cases were observed in 84.1% of the neighbourhoods of Rondonópolis, where 848 new cases of leprosy were reported corresponding to a cumulative NCDR of 57.9 cases/100,000 inhabitants. Spatial and spatiotemporal high-risk clusters were identified in western and northern neighbourhoods, whereas central and southern areas comprised low-risk areas. The GWR model was selected as the most appropriate modelling strategy (adjusted R²: 0.305; AIC: 242.85). By mapping the GWR coefficients, we identified that low literacy rate and low mean monthly nominal income per household were associated with a high NCDR of leprosy, especially in the neighbourhoods located within high-risk areas. In conclusion, leprosy presented a heterogeneous and peripheral spatial distribution at the neighbourhood level, which seems to have been shaped by intra-urban differences related to deprivation and poor living conditions. This information should be considered by decision-makers while implementing surveillance measures aimed at leprosy control.

An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares

Many birds in the natural world are capable of engaging in sustained soaring within thermal updrafts for extended periods without flapping their wings. Autonomous soaring has the potential to greatly improve both the range and endurance of small drones. In this paper, the extended Kalman filter (EKF) thermal updraft center prediction method based on ordinary least squares (OLS) is proposed to develop the autonomous soaring system for small drones, and an adaptive step size update strategy is incorporated into the EKF. The proposed method is compared with EKF thermal updraft prediction methods through simulated experiments. The results indicate that the proposed prediction method has low computational complexity and fast convergence speed and performs more stably in weak thermal updrafts. The above advantages stem from the OLS providing an approximate distribution of the thermal updraft around the drone for the EKF. This empowers the EKF algorithm with ample information to dynamically update the thermal updraft center in real time. The adaptive step size update strategy further accelerates the convergence speed of this process. In addition, flight experiments were conducted on the Talon fixed-wing drone platform to test the autonomous soaring system. During the flight experiment, the drone successfully engaged in static soaring within thermal updrafts, effectively hovering and gaining energy. Throughout the approximately 40 min flight duration, the drone only utilized its propulsion for about 8 min. This demonstrated the effectiveness of the autonomous soaring system using the EKF thermal updraft center prediction method based on OLS. Finally, by analyzing and discussing the differences between the simulation experiment results and the flight experiment results, some improvement strategies for the current work are proposed.

The impact of mode of delivery on parent-infant-bonding and the mediating role of birth experience: a comparison of mothers and fathers within the longitudinal cohort study DREAM

BackgroundThe association between mode of delivery (MOD) and parent-infant-bonding has only been studied in mothers and findings have been inconclusive. The aim of this study was to prospectively investigate how MOD relates to postpartum parent-infant-bonding in both mothers and fathers and whether these associations are mediated by birth experience.MethodsThis study is part of the prospective cohort study “Dresden Study on Parenting, Work, and Mental Health” (DREAM). Our sample comprised N = 1,780 participants who completed quantitative questionnaires during pregnancy as well as 8 weeks and 14 months postpartum. MOD was dummy coded, contrasting spontaneous vaginal delivery against vaginal delivery induced by drugs, operative vaginal delivery, planned, and unplanned cesarean section. Parent-infant bonding and birth experience were assessed using validated scales. A moderated mediation analysis based on ordinary least square (OLS) regression and bootstrapped estimates was conducted, considering relevant confounding variables.ResultsCompared to spontaneous vaginal delivery, all categories of MOD predicted more negative birth experiences in both parents. A more positive birth experience predicted stronger parent-infant-bonding at 8 weeks, but not at 14 months postpartum. Mothers who delivered via cesarean section (planned or unplanned) reported stronger parent-infant-bonding at 8 weeks and 14 months postpartum. In fathers, only unplanned cesarean section was associated with stronger parent-infant-bonding at 8 weeks postpartum. At 8 weeks postpartum, birth experience mediated the association between a vaginal delivery induced by drugs and a planned cesarean section and mother-infant-bonding and between a vaginal delivery induced by drugs, an operative vaginal delivery, and planned cesarean section and father-infant-bonding. At 14 months postpartum, birth experience mediated the association between a vaginal delivery induced by drugs, operative vaginal delivery, and planned cesarean section and parent-infant-bonding in both parents.ConclusionsThe results emphasize the importance of the birth experience for parent-infant-bonding in both mothers and fathers. Further research should address the mechanisms by which parents with an unplanned cesarean section establish stronger parent-infant-bonding compared to parents whose baby was delivered via spontaneous vaginal delivery, despite their overall more negative birth experiences.

Consumer acceptance under hydrogen energy promotion policy: Evidence from Yangtze River Delta

Hydrogen fuel cell vehicle (HFCV) as an emerging industry with great potential have received great attention in the Yangtze River Delta, China. Under government's promotion of hydrogen energy, whether HFCV can be accepted by consumers is an important topic for future policymaking. Therefore, this study takes consumers' willingness to consume HFCV as the dependent variable and collects questionnaires from 21 cities in the Yangtze River Delta from 2020 to 2021. Based on Ordinary Least Squares (OLS) and Logit Regression, the evaluation was conducted from four perspectives: personal and family situation, environmental awareness, energy attitude and local product confidence. The results show that gender, age and income differences are not necessarily determinants of HFCV purchase, but educational level is a significant factor. Consistent with social-psychological studies, personal awareness of environmental protection and energy attitudes are the key factors that are significant affect HFCV purchase. Lastly, it is found that in the Yangtze River Delta, consumers' confidence in Chinese local hydrogen products is also a significant factor. This paper confirms that HFCV consumers have commonalities with other new energy consumers. However, due to the expectation of greater local production and development, enhancing the social recognition and confidence of local hydrogen technology may be one of the promotion approaches neglected.

A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

The effect of incontinence on depression among older adults: a longitudinal study in China

ObjectivesPrevious studies have found that incontinence was positively associated with depression among older adults in Western countries; however, the cross-sectional design of these studies constrains the ability to infer the impact of incontinence on depression. Moreover, few studies investigated the underlying mechanism linking incontinence and depression in Chinese context. This study aims to explore the effect of incontinence on depression and the underlying mechanism in China. Study designThis study used a longitudinal research design. MethodsThe data used in this study were from the 2014 and 2018 waves of Chinese Longitudinal Healthy Longevity Survey. Among the sample, 1225 (49.04%) were females, and 1273 (50.96%) were males. The mean age was 88.146 years, and the standard deviation was 8.607. After eliminating samples with missing values, this study finally included 2498 samples. This study used Stata version 16.0 to conduct the statistical analysis. Based on Ordinary Least Squares (OLS) model, this study investigated the relationship between incontinence and depression. Using bootstrap method, this study tested the mediating effect of activity of daily living (ADL) on the above relationship. ResultsMultivariate regression analysis revealed that incontinence was a significant risk factor for depression (coefficient = 2.261, P < 0.05). After heterogeneity analysis, this effect was significant for non-solo-living older adults (coefficient = 2.950, P < 0.05) and insignificant for solo-living older adults. Moreover, the mediation effect analysis suggested that ADL was a complete mediator between incontinence and depression (indirect effect = 1.083, 95% confidence interval [0.450, 1.716]). Notably, the indirect effect was quite large (47.92%). ConclusionsIncontinence was a significant risk factor associated with depression among older adults in China, especially for non-solo-living older adults. The effect of incontinence on depression was completely mediated by ADL. The research provided possible interventions for alleviating depression among older adults suffering from incontinence.

Polynomial Estimation of Flux Linkage for Predictive Current Control in PMSM

This article presents an automatic parameter identification method based on ordinary least square (OLS) algorithm for deadbeat predictive current control (DPCC) to fulfill the purpose of high steady and dynamic performance in permanent magnet synchronous motor (PMSM) drives. Different from the conventional model, a novel model based on the polynomial estimation of flux linkage is constructed which takes the cross-saturation effects into account. First, the conventional mathematical model of PMSM and the proposed model are presented, respectively, and the inverter considering nonideal factors is also analyzed. Then the parameter identification method is investigated, in which the measurement procedure is delimited in advance and the automatic identification method is developed. Furthermore, a voltage reconstruction algorithm is applied to solve the inverter nonlinearities and the digital time-delay effect, so as to improve the accuracy of the identified parameters. Accordingly, a novel DPCC algorithm is presented based on the proposed model to achieve better performance. Finally, based on the concept and principles mentioned before, a detailed comparison of the DPCC based on the classical model considering the parameters’ variation and the modified model of the proposed method is carried out on a laboratory prototype.

Analisis Faktor-Faktor Yang Mempengaruhi Generasi Z Dalam Konsumsi E-Wallet Saat Pandemi Covid-19 (Studi Pada Mahasiswa Aktif Program Sarjana Di Kota Malang)

This study was conducted to determine the effect of convenience, promotion and lifestyle variables on generation Z e-wallet consumption (active undergraduate students) during the COVID-19 pandemic in Malang City. The analytical model used in this study is multiple linear regression analysis based on Ordinary Least Squares (OLS) using the SPSS Statistics 26 application which uses primary data obtained from the distribution of online questionnaires and then processed first from ordinal data using the Method of Successive Interval (MSI) into interval data with a quantitative research approach. Produced an analysis that the convenience, promotion and lifestyle variables had a positive and significant influence on the consumption of e-wallet generation Z (active undergraduate students) during the covid-19 pandemic in Malang City.Keywords: E-Wallet Consumption, Generation Z, Covid-19 Pandemic.

Landsat NDVI-based vegetation degradation dynamics and its response to rainfall variability and anthropogenic stressors in Southern Bui Plateau, Cameroon

• Long-term vegetation degradation is assessed by means of time-series Landsat NDVI. • Mean annual NDVI and rainfall anomaly index (RAI) were compared. • Anthropogenic stressors are primarily responsible for vegetation degradation. • Vegetation degradation leads to declining land productivity and ecosystem services. Land degradation is a serious problem affecting the livelihoods of people leaving in marginal lands. Its assessment has been made easy by a plethora of remote sensing techniques. This study seeks to establish the spatiotemporal trends in vegetation degradation and its response to climate change and anthropogenic stressors in Southern Bui Plateau, Cameroon. The study used mean annual Landsat-derived NDVI of cloud-free months to model vegetation degradation dynamics over 37 years (1984–2021) based on Ordinary Least Square (OLS) Regression and Pearson's Product Correlation of NDVI with Rainfall Anomaly Index (RAI) on a pixel-by-pixel basis. Areas undergoing significant degradation are estimated at 10.81% (1469.08 km 2 ) and slight degradation, 23.57% (3202.39 km 2 ). Total degraded lands accounted for 34.38% (either 4671.47 km 2 ). Areas with slight improvement in vegetation cover accounted for 24.88%, while 9.69% area showed significant improvement. NDVI-Rainfall relationship revealed that areas significantly impacted by human activities and pressures ( r ≤ -0.50) driving vegetation changes covered 24.67% (either 3352.03 km 2 ), while those under climatic variability and change influence ( r ≥ 0.50 ≥ 0.90) accounted for 55.84% (either 7587.26 km 2 ), under both climatic and human stressors ( r ≥ 0.50 < 0.70), 13.09% (either 1779.01 km 2 ) and areas not significantly impacted, i.e., somewhat stable nature over the years accounted for 6.40% (869.04 km 2 ). Areas under human vegetation pressures occupy the heavily grazed landscapes, the transhumance paths, and forested areas of the Kilum-Ijim forest. The practice of sustainable land management and landscape restoration initiatives are key to achieving land degradation neutrality at watershed scale.

Information and Communication Technology and Environmental Degradation Global Perspective: A Panel Data Analysis

The linkage between ICT and environmental degradation has an important role in today’s environmental debates. This research empirically scrutinizes the ecological dimension of ICT at the global level by using different key indicators. This paper conducts an analysis to provide comprehensive documentation regarding ICT impact on environmental degradation from 1984 to 2019. The empirical findings are based on Ordinary Least Square (OLS), Pooled OLS, Two-Stage Least Squares (2SLS), and Generalized Method of Moment (GMM) techniques. By applying cross-sectional and panel data, this research found evidence that ICT is an effective tool to alleviate environmental degradation. The study points out many policy implications drawn from its findings.

COMPANY SIZE AS A MODERATING VARIABLE ON ENTERPRISE RISK MANAGEMENT DISCLOSURE OF BANKING COMPANIES IN INDONESIA

Purpose: This research aims to review the relationship of the board of commissioners, the ownership concentration, the audit committee meetings, and the risk management committee with enterprise risk management disclosure moderated by the company size.Method: The population for this study was 46 banking companies listed on the Indonesia Stock Exchange (IDX) from 2017 to 2019. The sample selection was carried out using the purposive sampling technique which resulted in 39 companies with 117 analytical units performed using documentation techniques. Data were analyzed using moderating regression analysis based on Ordinary Least Square (OLS).Findings: The outcome of this research showed that the board of commissioners and audit committee meetings had a positive effect on the enterprise risk management disclosure while the ownership concentration and risk management committee did not affect the enterprise risk management disclosure. The company size can moderate the influence of the board of commissioners and audit committee meetings but is not able to moderate the influence of the ownership concentration and risk management committee. The findings prove companies that have a good board of commissioners and a competent audit committee can trigger companies to disclose enterprise risk management broadly.Novelty: This study adds company size as moderating variable. Based on the investment concept, namely high-risk high return, and low-risk low return. When the company wants to get big returns, the company will face big risks as well. This is directly proportional to companies with large sizes. The bigger company so the higher the risks faced by the company. This is also related to the disclosure of the risks.

Overeducation wage penalty among Ph.D. holders: an unconditional quantile regression analysis on Italian data

PurposeThe wage effect of job–education vertical mismatch (i.e. overeducation) has only recently been investigated in the case of Ph.D. holders. The existing contributions rely on ordinary least squares (OLS) estimates that allow measuring the average effect of being mismatched at the mean of the conditional wage distribution.Design/methodology/approachThe authors implement a recentered influence function (RIF) to estimate the overeducation gap along the entire hourly wage distribution and compare Ph.D. holders who are overeducated with those who are not on a specific sample of Ph.D. holders in different fields of study and European Research Council (ERC) categories. Moreover, the authors compare the overeducation gap between graduates working in the academic and non-academic sector.FindingsThe results reveal that overeducation hits the wages of those Ph.D. holders who are employed in the academic sector and in non-research and development (R&amp;D) jobs outside of the academic sector, while no penalty exists among those who carry out R&amp;D activities outside the academia. The size of the penalty is higher among those who are in the mid-top of the wage distribution and hold a Social Science and Humanities specialization.Practical implicationsTwo policies could reduce the probability of overeducation: (a) a reallocation of Ph.D. grants from low to high demand fields of study and (b) the diffusion of industrial over academic Ph.Ds.Originality/valueThis paper observes the heterogeneity of the overeducation penalty along the wage distribution and according to Ph.D. holders' study field and sector of employment (academic/non-academic).

Measuring Spatial Distribution in the Banking System in Hungary

Over the past decade, due in part to the global economic crisis, a significant part of the bank branches have been closed in the European banking system, but in Hungary this proportion has been significantly higher than the European average. Therefore, the aim of the present study is to explore what aspects of commercial banks are taken into account when deciding where to be present within bank branches. This will also reveal the spatial dimension of public access to financial services. The present study seeks to answer the question of which socio-economic factors and in what form they affect the spatial structure of bank branches. The settlement-level examination can also provide additional indication of which settlements may be affected by further bank branch closures. Linear regression based on Ordinary Least Squares (OLS) parameter estimation was used to explore the factors influencing the location of bank branches. In addition, the possible clustering of bank branches was observed, i.e., whether spatial autocorrelation was present at certain stages of the analysis. Geographically Weighted Regression (GWR) was also estimated in the present study. Based on the results of the research, the resident population, the proportion of enterprises per capita, the average income, the number of neighbouring bank branches and the type of settlement all proved to be significant factors that may encourage decision-makers to establish a bank branch.