Multilevel Ordered Logit Models Research Articles

Population decline and public attitudes toward multicultural immigration policies in South Korea

AbstractPopulation decline due to low fertility and aging causes socioeconomic challenges such as a shrinking labour force and regional decline. In response to these challenges, there is a growing discussion about accepting immigrants to mitigate the side effects of population decline. This implies the importance of analyzing local peoples' perceptions as a basis of policy and planning in anticipation of demographic transitions toward a multicultural society. While there are many studies on local problems caused by depopulation and perceptions of immigrants based on contact theory and group threat theory, there has been relatively little research on the relationship between depopulation and individual perceptions of immigrants. This study fills this gap by using survey data of Seoul, Korea, the city experiencing both population decline and an increase in immigrants. It employs the multilevel ordered logit model to explore how the decrease in local population associates with individuals' perception on the need for multicultural immigration policies. The results suggest that individuals are more likely to have a positive view of multicultural immigration policies if their region is experiencing a higher population decline. This finding is significant as it demonstrates that a shift to an unfavourable demographic structure can have a positive impact on perceptions of immigrant acceptance, offering a new perspective on the relationship between immigrants and native residents.

Receipt of core antenatal care components and associated factors in Ethiopia: a multilevel analysis.

Despite recent promising progress, maternal morbidity and mortality are still unacceptably high in Ethiopia. This is partly attributed to the lack of quality health services. Pregnant women may not receive adequate services that are essential to protect the health of women and their unborn children. This study aimed to examine the extent of receiving prenatal care components and associated factors in Ethiopia. It also assessed prenatal service use inequality between urban and rural residents. The analysis was carried out using the 2016 Ethiopian Demographic and Health Survey (EDHS), which is nationally representative survey data. A weighted sample of 4,772 women nested within 595 communities who had live births five years preceding the survey was included in the study. Necessary adjustments were made to account for the design of the survey, and sampling weights were used to adjust for nonproportional allocation of the sample to strata. Bivariate and multivariable multilevel ordered logit models were used to analyze factors associated with receiving comprehensive ANC contents. Statistically significant predictors were identified at p value ≤ 0.05. Among those women who had at least one ANC visit, only 15% (95% CI: 13, 16) received six core elements of antenatal care. The proportion of mothers who had essential prenatal components in rural areas was less than 13 percentage points. Approximately 43% of women did not receive at least two doses of tetanus toxoid vaccines to protect them and newborn infants against this life-threatening disease. Moreover, the majority of them, particularly those in rural Ethiopia, were not informed about pregnancy danger signs. Mothers who had at least four ANC visits received more types of prenatal components compared to those who had fewer ANC visits. The multilevel regression analysis revealed that receiving adequate ANC content is positively associated with having more frequent ANC visits, attaining a higher education level, being a member of a household in the highest wealth quintile and residing in urban areas. The evidence implies that the quality of maternal health services needs to be improved. Health programs and interventions should also give priority to rural areas where the majority of Ethiopian women reside.

Artificial intelligence and customers’ intention to use robo-advisory in banking services

Research background: Robo-advisory is a modern and rapidly developing area of implementing artificial intelligence to support customer decision-making. The current significance of robo-advisory to the financial sector is minor or marginal, and boils down to formulating recommendations and implementing investment strategies. However, the ongoing digital transformation of the economy leads us to believe that in the near future this technology will also be much more widely used with banking products. This makes it necessary for banks and other financial institutions to be prepared to offer this service to their customers. Purpose of the article: The aim of this paper is to identify factors significantly influencing bank customers’ intention to use robo-advisory. Identification of robo-advisory acceptance factors may increase the effectiveness of banks' promotional activities regarding such a service. Methods: Empirical data was obtained through a survey conducted on a representative sample of 911 Polish respondents aged 18–65. Using a multilevel ordered logit model and methods based on machine learning algorithms, the authors identified variables relating to the demographic and socio-economic characteristics, behaviors, and attitudes of consumers that primarily determine respondents’ adoption of robo-advisory. Findings & value added: The results of the study indicate that the variables regarding the respondents' attitude towards the use of artificial intelligence in banking services turned out to be the most important from the point of view of acceptance of robo-advisory. Next in terms of importance were the variables presenting respondents' assessments of the ethics of financial services. An important finding is that experience in using basic financial services is not a significant factor when accepting robo-advisory. From the practical perspective, the article provides recommendations on the use of artificial intelligence technology in finance and ethical aspects of the provision of such services by banks.

Threats for workers or opportunities for consumers? The impact of the Great Recession on perceived trade threat in 21 countries

Two competing perspectives have been offered to explain how the Great Recession (GR) impacted citizens’ perspectives on international trade. The citizens-as-workers perspective maintains that anti-trade attitudes increase during economic downturns because higher imports of foreign products pose a severe threat to workers’ job security, whereas the citizens-as-consumers perspective suggests that favorable attitudes toward international trade increase as imports provide better opportunities for cheaper consumer goods. In this article, we examine how the dual identities of citizens—as workers and consumers—play a role in shaping how the GR affects perceived trade threat. Using multilevel ordered logit models, we analyze responses from 19,982 respondents nested within 21 countries in the 2013 International Social Survey Program survey. We find that the GR exacerbated perceived trade threat. We also find that the GR intensified pro-trade attitudes in countries with fast-growing unemployment. The results suggest that anti-trade sentiments of citizens-as-workers were dominant after the GR, but pro-trade sentiments of citizens-as-consumers were also present as a countervailing force against a protectionist backlash.

From quantity to quality: Effects of urban greenness on life satisfaction and social inequality

Although a burgeoning literature has focused on greenspace quantity and relevant contribution to life satisfaction, little effort has been devoted to greenspace quality and nuanced differences in shaping the nexus of urban greenness and life satisfaction across social groups. Taking Beijing as an example, this study attempts to (1) explore the effects of both the quantity (availability and accessibility) and the quality (attractiveness and natural aesthetics) of neighborhood greenspaces on residents’ subjective life satisfaction, and (2) examine the heterogeneity in these effects across different social groups defined by household registration and housing status. Combining geo-coded, street-level residential greenness exposure data and city-scale social survey data in a multilevel ordered logit model, the results reveal that greenness quality play more pronounced roles, while both quantity and quality features could contribute positively to life satisfaction. When interacting quantity dimensions with quality ones, evident complementary roles of greenspace quality are detected. The impacts of four greenspace dimensions on life satisfaction are stronger for residents with Beijing hukou than those without. While availability becomes insignificant for residents in private-housing, and natural aesthetics is insignificant for those in social-housing. This study enriches our understanding of the nexus between multi-dimensions of urban greenspaces and life satisfaction, and complements existing research on green inequality to how unequal greenness in terms of quantity and quality affects individuals’ life satisfaction across different social strata. Our findings offer useful insights for fleshing out urban greening as central to crafting livable cities that can improve population life satisfaction and narrow inequalities.

Feeling insecure and excluding immigrants: Relationship between subjective risks and welfare chauvinism

AbstractWe argue that subjective insecurity plays an important role in explaining welfare chauvinism, which is defined as the restriction of immigrants' access to social benefits and public services. Additionally, macroeconomic performance and welfare regime are closely related to opinions about the social rights of migrant groups. We test these propositions, using a multilevel ordered logit model using the 8th wave of ESS. It is found that subjective unemployment and income risks are not overlapping with objective measures, and self‐assessed insecurity has a strong and positive effect on welfare chauvinism. Moreover, we demonstrate that, even for the most socio‐economically advantaged respondents, subjective risk increases the likelihood of chauvinistic welfare attitudes. At the macro level, higher rates of GDP per capita growth decrease welfare chauvinism, and Central and Eastern European welfare regime increases the likelihood of exclusionary attitudes in relative terms. The results are robust across different estimation techniques and inclusion of alternative contextual factors.

Do guests care more about hotel cleanliness during COVID-19? Understanding factors associated with cleanliness importance of hotel guests

PurposeGrowing health concerns amid the COVID-19 pandemic have led guests to focus on various aspects of hotel cleanliness. This study aims to investigate whether customers’ perceived importance of hotel cleanliness during their stay depends on local pandemic severity and moderators of the pandemic–cleanliness relationship.Design/methodology/approachBased on TripAdvisor data from 26,519 reviews in 2020 for 2,024 hotels across the USA, this study evaluated the importance of hotel cleanliness using the estimated coefficient of the cleanliness score in a regression of overall hotel rating scores.FindingsResults of a multilevel ordered logit model confirmed that a more difficult local pandemic situation rendered cleanliness more important during hotel stays. Additionally, the effect of the pandemic was more pronounced among specific groups: men and travelers with more expertise, and guests staying in hotels without COVID-19 protocols for linen cleaning, with a lower average rating, with a larger size and in a more urbanized location.Originality/valueThis study represents a pioneering effort to assess how pandemics shape people’s (perceived) importance of cleanliness during hotel stays based on revealed data. Despite potential managerial relevance, a number of the moderating variables included in this study, such as traveler expertise and hotel location, have never been studied within the context of cleanliness perceptions during a pandemic.

How Communities Benefit from Collaborative Governance: Experimental Evidence in Ugandan Oil and Gas

Abstract This paper reports the results of a field experiment to assess the collaborative effects of community participation in the Ugandan oil and gas sector. Our research design assesses collaborative impacts as relational between community members and different decision-makers in the sector and measures these impacts from the point of view of local people. Local people often face power imbalances in collaborative governance. Decision-makers are increasingly attempting to mitigate such imbalances to improve outcomes for communities, but little experimental evidence exists showing the impact of such efforts. Using multilevel ordered logit models, we estimate positive treatment effects, finding that encouraging the equitable participation of communities improves collaboration with other actors. Next, we use machine-learning techniques to demonstrate a method for targeting communities most likely to benefit from the intervention. We estimate that purposefully targeting communities that would benefit most yields a treatment effect about twice as large, relative to pure random assignment. Our results provide evidence that interventions mindful of community needs can improve collaborative governance and shows how such communities can be most effectively targeted. The experiment took place across 107 villages (53 treatment and 54 control) and the unit of statistical analysis is the household, where we report outcomes measured from 6,062 household surveys (approximately half at baseline and half at endline).

Why do hotels go green? Understanding TripAdvisor GreenLeaders participation

PurposeMore hotels are beginning to embrace green practices given increasing awareness of sustainable development. The purpose of this study is to examine factors contributing to hotels’ participation in TripAdvisor’s GreenLeaders program.Design/methodology/approachBased on a sample of 48,064 hotels from 328 destinations in 29 countries, the authors leverage a multi-level logit model to examine antecedents of GreenLeaders participation. A multi-level ordered logit model is then estimated to uncover factors influencing the ranking of this participation.FindingsEmpirical results indicate that hotels with a larger size, a higher class, a better online reputation, greater reliance on business travelers, fewer neighboring hotels and a more long-term-oriented culture are more apt to join the program. Online reputation factors, hotel size and the number of neighboring hotels explain GreenLeaders hotels’ rankings. A series of robustness checks reinforces the results.Practical implicationsThe results shed light on green program design and promotion. These findings can help hotel practitioners identify ideal target markets and better use their organizational resources to establish green programs. Several strategies can be implemented to promote hotels’ commitment to sustainability and to encourage guests’ awareness of and involvement in green practices.Originality/valueThis study enriches knowledge of sustainable hospitality and tourism. The findings of this study also address corporate social responsibility by analyzing factors that can promote and inhibit GreenLeaders program participation. Further, as a complement to hotel- and location-specific factors, the authors scrutinize the effects of cultural features in shaping green strategies.

The relationships between school children's wellbeing, socio-economic disadvantage and after-school activities: a cross-sectional study

BackgroundLower socioeconomic status is associated with poorer wellbeing among children. Identifying how children participate in after-school activities and how after-school activities are associated with wellbeing may inform interventions to improve wellbeing among children from low socioeconomic backgrounds. This study explored whether children’s after-school activities varied by socioeconomic status and examined the associations between after-school activities and wellbeing in low socioeconomic status children.MethodsThis study analysed cross-sectional data from 61,759 school students in years 4 to 9 who completed the 2018 South Australian Wellbeing and Engagement Collection. Students reported the number of days per week they participated in 12 activities (after-school care, homework, music lessons or practice, youth organisations, sports, television, videogames, social media, reading, chores, arts and crafts, and socialising with friends) during the after-school period (3-6 pm) and their wellbeing (happiness, sadness, worry, engagement, perseverance, optimism, emotion regulation, and life satisfaction). Socioeconomic status was measured by parents' highest education level obtained from school enrolment data. Linear multilevel models were used to examine whether frequency of after-school activities varied by socioeconomic status. Multilevel ordered logit models were used to analyse the association between after-school activities and wellbeing amongst participants in the low socioeconomic status category.ResultsAfter-school activities differed according to socioeconomic status; high socioeconomic status children did more frequent sport, homework, and reading and low socioeconomic status children did more frequent screen-based activities (TV, videogames and social media). Among children from low socioeconomic status backgrounds, higher wellbeing was associated most consistently with more frequent sports participation, homework, reading and spending time with friends and less frequent videogames, social media and after-school care.ConclusionsChildren's wellbeing is positively associated with socioeconomic status. Amongst children from disadvantaged backgrounds, participating in sport, spending time with friends and getting less screen time may be protective for wellbeing. The results suggest that programming targeted at increasing sports participation and reducing screen time amongst children from low socioeconomic status backgrounds may support their wellbeing.

Urban greenness, mixed land-use, and life satisfaction: Evidence from residential locations and workplace settings in Beijing

China’s unprecedented urbanisation in the last several decades has significantly transformed its urban built environment. On the one hand, such an urbanisation has brought about improvement in infrastructure, access to various public facilities, and opportunities for social connectivity, all of which would benefit citizen’s wellbeing. On the other hand, rapid, unplanned, and unregulated urbanisation may lead to air pollution and water pollution, compact neighbourhoods, traffic noise and congestion, and lack of natural amenity, which pose various threats to urban dwellers’ wellbeing and life satisfaction. Therefore, how to build liveable cities has become one of the key goals and top priorities of urban planning in China and other developing countries. This study investigates the effect of urban greenness and mixed land-use, two key dimensions defining urban liveability, on residents’ life satisfaction at both residence and workplace settings in Beijing. Three big geo-coded datasets are combined, including a social survey about residents’ subjective life satisfaction and demographic characteristics, eye-sensored street greenness data extracted from online platform through machine learning, and fine-grain land-use data based on point-of-interest entropy, and then taken into a Bayesian multilevel ordered logit model. The empirical results reveal that (1) street view greenness could enhance life satisfaction at residence, but depress life satisfaction at workplace; (2) mixed land-use could positively contribute to life satisfaction at both residence and workplace; and (3) there exist positive interactions between greenness and mixed land-use. These empirical findings provide practical implications for planning and constructing liveable cities in China and other countries where both urban greening and mixed land-use are promoted and embraced as core elements of the compact city and smart city ideal.

What impact do private vehicle restrictions in urban areas have on car ownership? Empirical evidence from the city of Madrid

City characteristics and transport policies influence individuals' decisions to own and use private vehicles, thereby impacting on externalities and quality of life. This research explores to what extent car ownership is influenced by the implementation of transport policies aimed at improving urban sustainability in the city of Madrid. Through the calibration of a Multilevel Ordered Logit Model, household car ownership is explained in terms of sociodemographic characteristics, city built-environment variables, transport network attributes, and policy-related variables. The results indicate that built environment factors play a key role in explaining car ownership trends. Moreover, ceteris paribus policies aimed at deterring private vehicles' use seem to discourage households from purchasing cars.

The Determinants of Subjective Well-Being in a Developing Country: The Ecuadorian Case

In this study, we identify some individual and contextual factors potentially affecting subjective well-being in developing countries and check their effect in the Ecuadorian case. Ecuador is an oil country where attempts have been made to overcome deep social and territorial inequalities by placing human well-being at the core of public policy through the National Plan for Good Living. By means of ordinary least squares and ordered logit with clustered standard errors, as well as multilevel ordered logit models, we find that oil-dependent territories negatively affect well-being. Moreover, women and indigenous people report lower well-being, while personal income, education, housing quality, institutional trust, health insurance and social relationships can improve it. From a policy perspective, we find that basic unmet needs still need to be fulfilled to increase well-being, and further improvements of the institutional framework, redistributive system and inclusion policies are required. In this respect, we observe that Good Living-based policy accurately addresses these elements and, therefore, has great potential for application in other countries with similar characteristics.

Subjective social class and individual preferences for redistribution

PurposeThe recent increase in economic inequalities in many countries heightened the debates about policy preferences on income distribution. Attitudes toward inequality vary greatly across countries and numerous explanations are offered to clarify the factors leading to support for redistribution. The purpose of this paper is to examine the link between subjective social class and redistributive demands by jointly considering the individual and national factors. The author argues that subjective measures of social positions can be highly explanatory for preferences about redistribution policies.Design/methodology/approachThe author uses data from 48 countries gathered by World Values Survey and empirically tests the impact of self-positioning into classes by multilevel ordered logit model. Several model specifications and estimation strategies have been employed to obtain consistent estimates and to check for the robustness of the results.FindingsThe findings show that, in addition to objective factors, subjective class status is highly explanatory for redistributive preferences across countries. The author also exhibits that there is interaction between self-ranking of social status and national context. The author’s estimations from the multilevel models verify that subjective social class has greater explanatory power in more equal societies. This is in contrast to the previous studies that establish a positive link between inequality and redistribution.Originality/valueThe paper contributes to the literature by introducing subjective social class as a determinant. Self-ranked positions can be very relieving about policy preferences given the information these categorizations encompass about individuals’ perceptions about their and others’ place in the society.

Handgrip Strength Is Associated with Poorer Cognitive Functioning in Aging Americans.

Measures of handgrip strength may show promise for detecting cognitive erosion during aging. To determine the associations between lower handgrip strength and poorer cognitive functioning for aging Americans. There were 13,828 participants aged at least 50 years from the 2006 wave of the Health and Retirement Study included and followed biennially for 8 years. Handgrip strength was assessed with a hand-held dynamometer and cognitive functioning was assessed with a modified version of the Mini-Mental State Examination. Participants aged <65 years with scores 7- 11 had a mild cognitive impairment, ≤6 had a severe cognitive impairment, and ≤11 had any cognitive impairment. Respondents aged ≥65 years with scores 8- 10 had a mild cognitive impairment, ≤7 had a severe cognitive impairment, and ≤10 had any cognitive impairment. Separate covariate-adjusted multilevel logistic models examined the associations between lower handgrip strength and any or severe cognitive impairment. A multilevel ordered logit model analyzed the association between lower handgrip strength and poorer cognitive functioning. Every 5-kg lower handgrip strength was associated with 1.10 (95% confidence interval (CI): 1.04, 1.15) and 1.18 (CI: 1.04, 1.32) greater odds for any and severe cognitive impairment, respectively. Similarly, every 5-kg lower handgrip strength was associated with 1.10 (CI: 1.05, 1.14) greater odds for poorer cognitive functioning. Measurement of handgrip strength is a simple, risk-stratifying method for helping healthcare providers determine poorer cognitive functioning. Interventions aiming to prevent or delay cognitive dysfunction should also implement measures of handgrip strength as an assessment tool for determining efficacy.

Happiness in urbanizing China: The role of commuting and multi-scale built environment across urban regions

Although the relationship between commuting time and residents’ happiness has been widely investigated, few studies have examined how the connection varies with urbanization levels. Using 7,774 individual samples across China, this study employs a multilevel ordered logit model to examine how commuting time is correlated with residents’ happiness. The effects of both the neighborhood and city-level built environment are revealed. Our findings show that commuting time is an essential factor correlated with residents’ happiness and the correlation varies across urbanization levels. People from cities with high urbanization rates (>70%) have a strong mental endurance of commuting time, whereas commuting time is more influential on residents’ happiness in cities with low urbanization rates (<50%). For the effects of the built environment, residents’ happiness is significantly associated with both levels of built environment in cities with urbanization rates higher than 50%. Conversely, residents’ happiness is only significantly correlated with the neighborhood built environment in cities with urbanization rates lower than 50%. Hence, to enhance residents’ happiness, urban planners and policy makers should consider multi-scale built environment characteristics and improve the public transport services according to local urbanization levels.

Impact of roadway geometric features on crash severity on rural two-lane highways

This study examines the impact of a wide range of roadway geometric features on the severity outcomes of crashes occurred on rural two-lane highways. We argue that crash data have a hierarchical structure which needs to be addressed in modeling procedure. Moreover, most of previous studies ignored the impact of geometric features on crash types when developing crash severity models. We hypothesis that geometric features are more likely to determine crash type, and crash type together with other occupant, environmental and vehicle characteristics determine crash severity outcome. This paper presents an application of multilevel models to successfully capture both hierarchical structure of crash data and indirect impact of geometric features on crash severity. Using data collected in Illinois from 2007 to 2009, multilevel ordered logit model is developed to quantify the impact of geometric features and environmental conditions on crash severity outcome. Analysis results revealed that there is a significant variation in severity outcomes of crashes occurred across segments which verifies the presence of hierarchical structure. Lower risk of severe crashes is found to be associated with the presence of 10-ft lane and/or narrow shoulders, lower roadside hazard rate, higher driveway density, longer barrier length, and shorter barrier offset. The developed multilevel model offers greater consistency with data generating mechanism and can be utilized to evaluate safety effects of geometric design improvement projects.

An analysis on perceived crowding level reported by domestic visitors of South Korean National Parks: a multilevel ordered logit approach

ABSTRACTPerceived Crowding Level (PCL) is an indicator of the social carrying capacity of recreational sites such as National Parks. Using a sample of national-level visitor survey data across 21 National Parks in South Korea, this paper aims to apply a multilevel ordered logit model as a method in testing for statistical relationships between the PCL and the covariates, accounting for site- and individual-specific heterogeneity. The results hint that levels of perceived crowding in South Korean National Parks are related to factors such as the behaviour of other users and the quality of park facilities such as comfort in accommodation. The measured level of heterogeneity can provide useful information about the level (nation-wide or park-specific) of intervention policies and management strategies.

Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions

This study analyzes 86,622 commercial motor vehicle (CMV) crashes (large truck, bus and taxi crashes) in South Korea from 2010 to 2014. The analysis recognizes the hierarchical structure of the factors affecting CMV crashes by examining eight factors related to individual crashes and six additional upper level factors organized in two non-nested groups (company level and regional level factors). The study considers four different crash severities (fatal, major, minor, and no injury). The company level factors reflect selected characteristics of 1,875 CMV companies, and the regional level factors reflect selected characteristics of 230 municipalities. The study develops a single-level ordinary ordered logit model, two conventional multilevel ordered logit models, and a cross-classified multilevel ordered logit model (CCMM). As the study develops each of these four models for large trucks, buses and taxis, 12 different statistical models are analyzed. The CCMM outperforms the other models in two important ways: 1) the CCMM avoids the type I statistical errors that tend to occur when analyzing hierarchical data with single-level models; and 2) the CCMM can analyze two non-nested groups simultaneously. Statistically significant factors include taxi company’s type of vehicle ownership and municipality’s level of transportation infrastructure budget. An improved understanding of CMV related crashes should contribute to the development of safety countermeasures to reduce the number and severity of CMV related crashes.

Impact of Income Inequality on Workers’ Life Satisfaction in the U.S.: A Multilevel Analysis

Over the last two decades, the issue of income inequality has taken center stage in discussions of the economy and how workers’ perceptions of their relative pay affect their overall life satisfaction. Using National Study of Changing Workforce data from 1997 to 2008, this study examined how income inequality affects workers’ life satisfaction in the U.S. Income inequality was measured by calculating each state’s Gini coefficient, a representative inequality index. Results from analysis of multilevel ordered logit models suggest a significant association between income inequality and workers’ self-rated life satisfaction. As income inequality in the U.S. grew dramatically between 1997 and 2008, workers’ self-reported life satisfaction decreased by 33.8 %. Interestingly, this negative impact of income inequality is significant for men, but not for women when analyzed separately. Implications for policy and practice are discussed.