Transportation Disadvantage and Neighborhood Sociodemographics: A Composite Indicator Approach to Examining Social Inequalities

Transportation disadvantage refers to the barriers to or limits on the participation in daily socioeconomic and political life due to the reduced physical accessibility to services and opportunities. The transportation disadvantaged groups not only face the problems of social exclusion in location, but also experience greater exposure to a diversity of social and environmental threat. Under this context, identifying the transportation disadvantaged neighborhoods is of great interest for urban planners. This paper examines the transportation disadvantage in association with neighborhood sociodemographics in Shenzhen, China. A set of multidimensional indicators are initially selected to measure the transportation disadvantage (physical conditions, public transport services, education opportunity, employment opportunity, medical care opportunity, food opportunity, and commercial opportunity). Based on these indicators, principal component analysis produces four composite indicators of different types of transportation disadvantage: transportation disadvantage in physical infrastructure and public services, transportation disadvantage in food and education accessibility, transportation disadvantage in medical care accessibility, and transportation disadvantage in commercial and employment accessibility. In space, the transportation disadvantaged neighborhoods are generally located in the outskirts. Transportation disadvantage is more likely to be observed in neighborhoods that are economically, educationally, residentially and occupationally disadvantaged. These findings reveal significant inequalities in transportation opportunity.     

Area Deprivation and Liver Cancer Prevalence in Shenzhen,China: A Spatial Approach Based on Social Indicators

Cancer has become an alarming threat to human health and well-being worldwide. Examining the social determinants of cancer prevalence should effectively inform the practices and strategies on cancer treatment and prevention. However, rather few studies have conducted in this regard for developing countries. This paper attempts to characterize the association between area deprivation and liver cancer prevalence using a case of Shenzhen, China. Data from 2009 to 2011 provided by Shenzhen’s Health Information Center are used to calculate the incidence rate of liver cancer at district level. An area deprivation index (ADI) at district level is established by integrating 13 indicators of 5 domains (income, employment, education, housing and demography). The weight for each indicator is determined by two typical subjective methods (AHP and fuzzy AHP) and two common objective methods (Entropy and Coefficient Variation). Quantitative comparisons indicate that the four methods are highly consistent though they assign different weight to the indicators. Spatial lag regression identifies significantly positive linear relationships between ADIs and liver cancer incidence rate from 2009 and 2010. It suggests that greater possibility of liver cancer prevalence would be expected in districts of higher social deprivation. The results also denote that the obtained relationships are insensitive to weight determination method and temporal dimension. Our study demonstrates that spatial autocorrelation should be incorporated for better understanding the association between area deprivation and liver cancer prevalence at district level. This paper provides some new insights into social indicators research.     

Big Data and Social Indicators: Actual Trends and New Perspectives

Big Data are a top subject in international research articles and a vast debate is taking place on their actual capability of being used to complement or even substitute official statistics surveys and social indicators in particular. In this paper we analyse the metadata of the Scopus database of academic articles on Big Data and we show that most of the existing and intensively growing literature is focused on software and computational issues whilst articles that are specifically focused on statistical issues and on the procedures to build social indicators from Big Data are a much smaller share of this vast production. Nevertheless the works that focus on these topics show promising results because in developed countries Big Data seem to be a good information base to create reliable proxies of social indicators, whereas in developing countries their use (for instance using satellite images) may be a viable alternative to traditional surveys. However, Big Data based social indicators deeply suffer of a number of open issues that affect their actual use: they do not correspond to any sampling scheme and they are often representative of particular segments of the population; they generally are private process-produced data whose access by national statistical offices is rarely possible although the intrinsic value of the information contained in Big Data has a social importance that should be shared with the whole community; Big Data lack the socio-economic background on which social indicators have been founded and their help to policy makers in their decision process is a fully open point. Therefore Big Data may be a big opportunity for the definition of traditional or new social indicators but their statistical reliability should be further investigated and their availability and use should be internationally coordinated.     

Spatial Disaggregation of Social Indicators: An Info-Metrics Approach

In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale. The main novelties of the technique presented are that (i) the small-scale mapping produced is perfectly consistent with the aggregates -regional or national- observed in the sample, and (ii) it does not require imposing strong distributional assumptions. The methodology suggested here follows the basics presented on Golan (2018) by adapting a cross-moment constrained Generalized Maximum Entropy (GME) estimator to the spatial disaggregation problem. This procedure is compared with the equivalent methodology of Tarozzi and Deaton (2009) by means of numerical experiments, providing a comparatively better performance. Additionally, the practical implementation of the methodology proposed is illustrated by estimating poverty rates for small areas for the region of Andalusia (Spain).

     

Indicators of Social Isolation: A Comparison Based on Survey Data from Germany

The paper presents a data-based comparison of three indicators of social isolation that are frequently used in contemporary social research: (a) low frequency of social contact with friends, relatives, and neighbors (social contact indicator); (b) absence of a discussion network (discussion network indicator); (c) absence of social support (support indicator). All three indicators are in line with an understanding of social isolation as the absence of close personal relationships. However, as shown in this paper, they are correlated only weakly to each other and lead to different results concerning the overall share of social isolation, age disparities, and gender differences of social isolation. Against this background, the paper proceeds with considerations on the content validity and criterion validity of the three indicators. Different versions of each indicator are scrutinized to gain insights on content validity. Conclusions on criterion validity are drawn from an examination of the correlations between the indicators and health, poverty, satisfaction with family life, and satisfaction with social life. Findings suggest a higher validity of the social contact indicator compared to the other two indicators. Data source is the German Socio-Economic Panel. Discussions of content validity are additionally based on analyses using the German Family Survey and the German Aging Survey.     

Measuring Substance Abuse Treatment Need Among Adults in Florida: A Social Indicators Approach

Determining the size and demographiccharacteristics of substance abuse populationsis extremely important for implementing publicpolicies aimed at the control of substanceabuse. Such information not only assists in theallocation of limited treatment resources bythe state, but also in the monitoring ofsubstance abuse trends over time and in theevaluation of innovative policy initiatives. Inthis study, we develop three composite measuresof treatment need. We then use these measuresto estimate treatment need for alcohol abuseand for controlled substance abuse within eachof Florida's 67 counties. This study providesan important empirical component of communityplanning, quantifying and, to a limited degree,specifying the level of need for the substanceabuse treatment of community residents. Anadditional benefit is the development of a costeffective and unobtrusive methodology fordetermining empirically when levels of need arechanging so that treatment levels can beadjusted accordingly. With proper use,policymakers can readily employ the methodologydeveloped in this study in Florida andelsewhere to make better-informed decisions inthe allocation of finite substance abusetreatment resources.     

Neighborhood Concentrated Disadvantage and Adult Mortality: Insights for Racial and Ethnic Differences

While racial and ethnic differences in mortality are pervasive and well documented, less is known about how mortality risk varies by neighborhood socioeconomic status across racial and ethnic identity. We conducted a prospective analysis on a sample of adults living at or below 300% poverty with 8 years of the National Health Interview Survey (N = 159,400) linked to 11,600 deaths to examine the association between neighborhood disadvantage and mortality for non-Hispanic whites, non-Hispanic blacks, and U.S.- and foreign-born Hispanics. Using multilevel logistic regression, we find that the probability of death from any cause for lower-income adults is higher in more-disadvantaged neighborhoods, compared to less-disadvantaged neighborhoods, but only for whites. The adjusted likelihood of death for blacks and foreign-born Hispanics is not associated with neighborhood disadvantage, and the likelihood of death for U.S.-born Hispanics is lower in more-disadvantaged neighborhoods. While future research and policy should focus on improving health-promoting resources in all communities, care should be given to better understanding why race/ethnic groups have differential mortality returns with respect to area-specific socioeconomic conditions.     

Clustering of the Italian Regions Based on Their Equitable and Sustainable Well-Being Indicators: A Three-Way Approach

Social Indicators Research - The aim of this study is to provide an analysis of the Italian regions according to their equitable and sustainable well-being indicators pertaining to several...     

Paternal Incarceration and Adolescent Social Network Disadvantage

Previous research has suggested that adolescent peers influence behavior and provide social support during a critical developmental period, but few studies have addressed the antecedents of adolescent social networks. Research on the collateral consequences of incarceration has explored the implications of parental incarceration for children’s behavioral problems, academic achievement, health, and housing stability, but not their social networks. Using network data from the National Longitudinal Study of Adolescent Health, I find that adolescents with recently incarcerated fathers are in socially marginal positions in their schools and befriend more-marginal peers than other adolescents: their friends are less advantaged, less academically successful, and more delinquent than other adolescents’ friends. Differences in network outcomes are robust to a variety of specifications and are consistent across race and gender subgroups. This study advances the social networks literature by exploring how familial characteristics can shape adolescent social networks and contributes to the collateral consequences of incarceration literature by using network analysis to consider how mass incarceration may promote intergenerational social marginalization.     

Comparative Perspectives on Social Indicators

Social Indicators Research -     

Social Monitoring and Reporting: A Success Story in Applied Research on Social Indicators and Quality of Life

From its beginnings research on social indicators was not primarily considered as pure, but rather applied research in terms of the regular monitoring of and reporting on quality of life. Thus, the successes—but eventually also failures—of social indicators research may first of all be visible in its most important field of application. Social monitoring and reporting activities, which can be traced back to the early 1970s provide quantitative information and empirically based analytical knowledge on well-being and progress in a single society or groups of societies to be used for different purposes, including policy making. Providing an overview over the variety of social monitoring and reporting projects emerging from social indicators research is supposed to be important with a view to form a more solid fundament for present and future discourses and initiatives in the field of measuring and monitoring well-being and progress. The article looks back to this field of applied social indicators research and—with a focus on Europe—identifies patterns and recent trends in this sort of activities. By looking forward, it finally discusses selected issues that are considered to be crucial for further improvements in this field.     

Social Capital and Subjective Wellbeing in Europe: A New Approach on Social Capital

This paper aims to analyze the relationship between the various dimensions of social capital and subjective wellbeing. Data used in this study come from the fourth wave of the European Social Survey and different measures of wellbeing are used to take account of both the cognitive and affective processes of individual wellbeing (i.e. life satisfaction, happiness, and subjective wellbeing). A factor analysis is performed to summarize information coming from a large set of variables into different components corresponding to each dimension of social capital (i.e. networks, norms, and trust). Among the results, we find that the impact of social capital on subjective wellbeing differ depending on the component of social capital which is under analysis. In particular, social networks, social trust and institutional trust are the components that show a higher correlation with subjective wellbeing. Furthermore, in addition to the positive effects of the individual variables, our results suggest that social capital at the aggregate level positively correlates with individual wellbeing, thus pointing to an external or environmental effect of social capital.     

Social Indicators of Substance Abuse Prevention: A Need-Based Assessment

We combine a variety of census and archival data to assess the relationships between social area characteristics and substance use. We found a substantial association between social area characteristics and substance use. We focus on intra and inter community, family, school and individual domain correlations to produce a prevention need-based score to rank communities from high to low need areas. Our analyses support the general risk and protective factor model advocated by Hawkins et al. (1992). As an important outcome of this work we suggest an objective criterion for selecting variables for the final model of the social indicators; suggest a set of weights for the selected factors and validate the selected factors with regard to substance abuse by linking the social indicator database to substance abuse outcome measures from another independent database. The selection criterion was based on intra domain correlations and proved to be robust. The weights were proportional to the information conveyed by each extracted factor. Analyses of intra-domain correlations are useful in identifying the types of prevention programs needed for each municipal area. Analyses of inter-domain correlations resulted in producing an overall need score based on social area characteristics. Many of the domains revealed more than one dimension, especially the school and the community/environment domains. Our analysis supports the use of social area characteristics in predicting the need for substance abuse prevention and treatment. It also suggests cautious and well informed decisions in selecting the indicators for a need based model.     

Social Indicators and Voting: The Turkish Case

The aim of this article is to set out the various explanations of voting behaviour in Turkey. Its principal intellectual motivation is to present the political and social indicators that shape voting in such a way as to explain how an activity which is so crucial for democratic life is also based upon voters’ values and surrounding political context. Although the number of empirical work on voting behaviour in Turkey is increasing there is a considerable room for testing alternative variables and research tools in order to expand the academic knowledge on the problematic. Findings ratified that the political and personal values coupled with the other social factors have considerable impact on the voting behaviour in Turkey. Additionally this research also proved that these factors work differently for the competing political parties. Hence the contribution of this study is the introduction of an explicit analysis on the impact of different social and political factors that are salient in voting behaviour and magnitudes of these factors on different political parties by using a public database. Thus this study will not only uncover the relevancy of the variables in the literature for Turkish voters but also it will highlight the meaning and significance of these factors for different political parties.     

An Empirical Approach to the Poverty Indicators Based on Revealed Parameters: The Case of Spain

This article investigates the issue of measuring the proportion of the population affected by the phenomenon of poverty in Spain on the basis of the relationship between the severe material deprivation rate and the at-risk-of-poverty rate by regions. The current definitions of equivalence units and equivalent income were analysed and an innovation to allow the introduction of a parametric model, based on the revealed annual consumption of the main different type of households and on the regional purchase power parities, was subsequently proposed. In the case of Spain it was discovered empirically that the equivalence units are not constant, as maintained until now, but that they vary over time. An improvement between the relations of the two rates was obtained by means of the application of these revealed scales. The regional composition was also improved. The results could serve as a base for future studies to provide more detailed information about the breakdown of the relative poverty within each country and, thereby, to meet the relevant information requests, at national and international level, to support the implementation of public policies in this area.     

Social Indicators Movement and Human Agency: Comment on Land and Michalos

This paper offers a critical comment on Land and Michalos (Soc Indic Res, 2017) and suggests a relay perspective to see the past achievements and future directions in the social indicators movement. In accordance with Land and Michalos, it suggests bringing the issue of human agency to the foreground, and offers an example using an index of the mother’s wellbeing for global comparison.     

居住空间、邻里交往与社会距离--基于CSS2011的城镇居民与农民社会距离的影响因素研究

利用CSS2011调查数据,探讨了当前我国城镇居民与农村居民社会距离的影响因素。研究发现：个人经济地位越高,与农民的社会距离越近,家庭经济地位与个人文化地位越高,与农民的社会距离越远;居住空间条件越好,与农民的社会距离越远;与邻居互惠性交往程度越高,与农民的社会距离越近。研究认为,在城镇现实的体制和制度环境下,以下两个方面的努力可以缩减城镇居民与农民的社会距离,提升他们对农民的心理接纳程度：一是通过制定各种政策措施,遏制并缩小目前城镇内部和城乡之间的住房不平等程度;二是创造理想的社会交往环境,促进城镇居民与包括邻居在内的社区其他居民和进城农民之间的互惠性交往。     

Nowcasting Indicators of Poverty Risk in the European Union: A Microsimulation Approach

The at-risk-of-poverty rate is one of the three indicators used for monitoring progress towards the Europe 2020 poverty and social exclusion reduction target. Timeliness of this indicator is critical for monitoring the effectiveness of policies. However, due to complicated nature of the European Union Statistics on Income and Living Conditions (EU-SILC) poverty risk estimates are published with a 2–3 years delay. This paper presents a method that can be used to estimate (“nowcast”) the current at-risk-of-poverty rate for the European Union (EU) countries based on EU-SILC microdata from a previous period. The EU tax-benefit microsimulation model EUROMOD is used for this purpose in combination with up to date macro-level statistics. The method is validated by using EU-SILC data for 2007 incomes to estimate at-risk-of-poverty rates for 2008–2012 and to compare the predictions with actual EU-SILC and other external statistics. The method is tested on eight EU countries which are among those experiencing the most volatile economic conditions within the period: Estonia, Greece, Spain, Italy, Latvia, Lithuania, Portugal and Romania.