Single mother families and employment,race, and poverty in changing economic times

Using American Community Survey data from 2001, 2005, and 2010, this paper assesses the relationships between employment, race, and poverty for households headed by single women across different economic periods. While poverty rates rose dramatically among single-mother families between 2001 and 2010, surprisingly many racial disparities in poverty narrowed by the end of the decade. This was due to a greater increase in poverty among whites, although gaps between whites and Blacks, whites and Hispanics, and whites and American Indians remained quite large in 2010. All employment statuses were at higher risk of poverty in 2010 than 2001 and the risk increased most sharply for those employed part-time, the unemployed, and those not in the labor force. Given the concurrent increase in part-time employment and unemployment between 2000 and 2010, findings paint a bleak picture of the toll the last decade has had on the well being of single-mother families.     

Who gets evicted? Assessing individual,neighborhood, and network factors

The prevalence and consequences of eviction have transformed the lived experience of urban poverty in America, yet little is known about why some families avoid eviction while others do not. Applying discrete hazard models to a unique dataset of renters, this study empirically evaluates individual, neighborhood, and social network characteristics that explain disparities in displacement from housing. Family size, job loss, neighborhood crime and eviction rates, and network disadvantage are identified as significant and robust predictors of eviction, net of missed rental payments and other relevant factors. This study advances urban sociology and inequality research and informs policy interventions designed to prevent eviction and stem its consequences.     

Doin’ Meth or Doin’ Math: What Client Constructions of Social Class Mean for Social Work Practice

The voices of people experiencing poverty are underrepresented in social work research, public policy development, and practice interventions. This study explored the social class attributions of clients receiving poverty-related services through qualitative interviews. Findings reveal dynamic contributions of individual, environmental, and structural factors of social class positioning and significant stress and stigmatization associated with experiencing economic hardship. Participants indicate a sense of lived contradiction, viewing social class to be the result of fate while simultaneously endorsing individualistic attributions of poverty. Results have implications for social work research and practice, as well as poverty-related policy and program development.     

Generalized correlation and kernel causality with applications in development economics

New generalized correlation measures of 2012, GMC(Y|X), use Kernel regressions to overcome the linearity of Pearson's correlation coefficients. A new matrix of generalized correlation coefficients is such that when |r*_ij| > |r*_ji|, it is more likely that the column variable X_j is what Granger called the “instantaneous cause” or what we call “kernel cause” of the row variable X_i. New partial correlations ameliorate confounding. Various examples and simulations support robustness of new causality. We include bootstrap inference, robustness checks based on the dependence between regressor and error, and on the out-of-sample forecasts. Data for 198 countries on nine development variables support growth policy over redistribution and Deaton's criticism of foreign aid. Potential applications include Big Data, since our R code is available in the online supplementary material.     

The social benefits of water and sanitation projects in Northern Colombia: Cost‐Benefit Analysis,the Water Poverty Index and beyond

Water, and Sanitation projects play an important role in alleviating water‐related poverty in developing countries. There are several methodologies that will not only assess the performance of these projects but will also help to better identify the problem and its characteristics, and to improve the efficiency of the investment. The Water Poverty Index is one of them. It is a very useful tool that helps to measure water stress at the household level in a holistic way, and to identify priorities. It is important, however, to complement it with a Cost‐Benefit Analysis that will take into account the costs of the project. A case study in Northern Colombia illustrates this point.     

性别视角下的农村丧偶独居老人生存状态研究——以吉林省东丰县某村为例

通过对吉林省东丰县横道村99例老人的问卷调查以及7例丧偶独居老人的个案访谈,从性别角度对农村丧偶独居老人的生存状态进行了分析,研究发现：老年女性的贫困化程度较男性高、生活适应性更差,老年男性的心理适应性较差。据此提出了应从政策、社团组织等四个方面改善农村独居老人生存状态的建议。     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

Individual and institutional determinants of multidimensional poverty: A European comparison

In this article we evaluate to what extent between-country differences in the probability of being ‘multidimensional’ poor can be explained by a range of ‘domain-specific’ indicators of welfare regime arrangements. To this end, a so-called micro-macro model is estimated, testing the ‘independent’ effect of institutions, as opposed to alternative explanations such as between-country differences in population composition and economic affluence. Although we conclude that institutional arrangements do influence the risk of multidimensional poverty in the expected direction, we also find that bringing the ‘economy’ back into the analyses has a non-trivial impact. Our results point at several avenues for further discussion and research. First, although the more elaborate welfare regimes generally do a better job in preventing poverty, the level of transfers is not always ‘proportional’ to the general standard of living in these countries. Second, we only find partial confirmation for the often cited ‘negative’ impact of labour market flexibility and the related equality-jobs trade-off. While stricter employment regulations do reduce the poverty risk (be it only after controlling for economic affluence), flexibility in terms of the availability of fixed-term labour seems to be preferable to unemployment, even if at the individual level, labour market flexibility increases the likelihood of being poor quite severely.

Measuring the extent, depth, and severity of food insecurity: an application to American Indians in the USA

Within the extensive food insecurity literature, little work has been done regarding (a) the depth and severity of food insecurity and (b) the food insecurity of American Indians. This paper addresses both these topics with data from the 2001 to 2004 Core Food Security Module of the Current Population Survey. To measure food insecurity, three axiomatically derived measures of food insecurity are used. As expected, given the worse economic conditions facing American Indians, their food insecurity levels are generally higher than non-American Indians. However, the magnitude and significance of these differences differ depending on the choice of food insecurity measure.