Impact Of Household Characteristics Research Articles

Spatial clustering and impact of household characteristics on under-five mortality in India: A secondary data analysis

Background: Under-five mortality rate (U5MR) is one among the best social indicators that identifies social development and well-being. Various risk factors are seen contributing to its increase. Aim: The present correlational study aims to identify the spatial clustering of U5MR in different states and union territories (UTs) of India and determines its association with household characteristics. Patients and Methods: The present study incorporates primary data for 29 states and 2 UTs (Delhi and Chandigarh) of India from the National Family and Health Survey-4 (2015–2016) for secondary data analysis. The data include the outcome variable which was the U5MR and predictor variables such as households with electricity; improved source of drinking water; toilet facility; solid fuels being used for cooking; anyone smoking and living in a pucca house. Primary data were analysed using GeoDa software, employing Univariate Local Indicators of Spatial Association, Spearman's correlation coefficient, ordinary least square (OLS) and spatial error model (SEM). Results: The study showed significant spatial clustering of high U5MR in six states and one UT, namely Rajasthan, Madhya Pradesh, Chhattisgarh, Uttar Pradesh, Bihar, Jharkhand and Delhi and clustering of lower U5MR in southern states of Tamil Nadu and Karnataka. The Spearman's correlation showed a significant positive association of U5MR with households using solid fuel for cooking and negative association with households using electricity, with toilet facility and Living in a pucca house. The OLS and SEM spatial regression models model showed an association between households with toilet facility and in which anyone smokes at home with under-five mortality. Conclusions: U5MR shows a significant clustering geographically. This mortality indicator is influenced by the external environment such as household characteristics.

Wealth Index as Substitute to Income and Consumption: Assessment of Household Poverty Determinants Using Demographic and Health Survey Data

ABSTRACTWe deviate from standard literature on assessment of household poverty. Contrary to standard practice of using household income and consumption, we use Wealth Index, calculated through Principal Component Analysis (PCA), as measure of poverty. Data from Pakistan Demographic and Health Survey (PDHS) 2006–07, gathered from female respondents, suggests an interplay of several socio-demographic factors underlying the incidence of household poverty. The multivariate analysis shows that household size, dependency ratio, and sex and age of the head of household are significantly associated with poverty status. Higher education lowers the likelihood of poverty while the opposite is true for the dependency ratio. Most interestingly, male headed households are more likely to have poverty-lower wealth index-compared to households headed by female. We also provide evidence that the impact of household characteristics on poverty is sensitive to the area of residence, rural or urban, and different regions. We conclude that DHS data provides an assessment of household poverty which goes beyond economic factors and single dimension measures.

A longitudinal study of deprivation in European countries

Purpose– Poverty is one of the most significant economic and social problems that European countries have to face. In recent years, it has become widely accepted that poverty is a multidimensional concept and now many studies use indicators of deprivation to examine the phenomenon. The focus on financial resources alone does not capture people’s quality of life as being poor means a lack of access to resources enabling a minimum standard of living and participation in the society within which one belongs. The paper aims to discuss this issue.Design/methodology/approach– Using a longitudinal component (2006-2010) of EU Statistics on Income and Living Conditions data on 26 European countries, the author apply a second-order confirmatory factor analysis to estimate deprivation. To describe the patterns of change over time and to evaluate the role of household characteristics in deprivation level, the author employ a set of multilevel growth curve models.Findings– Three findings clearly stand out from my analysis. First, there is great variability in deprivation between European countries. Second, European countries show various patterns of change in deprivation over time. Third, households with different characteristics have quite different deprivation levels; moreover, the impact of household characteristics on deprivation can vary over time and between countries.Originality/value– This paper sheds light on the importance of analysing deprivation from a longitudinal perspective and that financial resources alone does not capture people’s quality of life.

Demand or supply for schooling in rural India?

Is the poor human capital investment of rural India n families primarily a supply side or a demand side issue? Can time use data help analyze some of the hidden dimen sions of development? We examine school attendance and total human capital investment time (time in school plus travel time plus in-home instructional time) using the Indian Time Use Survey of 1998-1999 and the 7th All India School Education Survey (AISES). Probit and sample selection bias regression estimates indicate th at the influence of supply side factors (school qua lity and availability) is large relative to the impact of househo ld characteristics (e.g. low income). We discuss th e policy implications and illustrate the advantages of time us e data in analysis of development. JEL-Codes: I2, I24, I25, O15

An Examination of Retirement Income Adequacy Measures and Factors Affecting Retirement Preparedness

This article uses a consistent methodology to estimate several retirement income adequacy measures for households that participate in two national surveys. In addition, we examine the impact of a variety of factors on retirement preparedness. We find that Survey of Consumer Finances households have higher income replacement rates, but lower consumption replacement rates, than Health and Retirement Survey households. We also find that replacement rate estimates are sensitive to the treatment of home equity and assumed retirement age. Finally, the impact of household characteristics on the likelihood of being better prepared for retirement differs depending on the survey data and retirement income adequacy measure used.

Sample selectivity models for away from home expenditures on wine and beer

The impact of household characteristics on away from home wine and beer weekly expenditures in the USA is examined. Results imply that higher income households without children and headed by an older, white, and higher educated individual spend more on wine away from home than do others. On the other hand, smaller midwestern and western households without children and headed by a younger and white individual spend more on beer away from home than do others.

The demand for small cars in the United States: Implications for energy conservation strategies

The purpose of this paper was to investigate the impact of household characteristics and preferences for Japanese cars on the demand for small cars in the United States. Two stage probit analysis was used to examine the impact of various explanatory variables on the purchase decision. The results indicated that preferences for Japanese cars, income, price and several household characteristics had a significant impact on the probability of purchasing a small car. The results of this study provide support for freer trade in automobiles and higher gasoline taxes as energy conservation strategies.

Child survival, height for age and household characteristics in Brazil

The impact of household characteristics on child survival and height, conditional on age, is examined using household survey data from Brazil. Parental education is found to have a very strong positive effect on both outcomes and this is robust to the inclusion of household income and also parental heights, which partly proxy for unobserved family background characteristics. We find that income effects are significant and positive for child survival but insignificant for child height although the latter depends on identification assumptions. Parental height has a large positive impact on child height and on survival rates even after controlling for all other observable characteristics.