Sooner or later? The impact of child education on household consumption

We study the impact of one more year of child’s education on household (non-durable) consumption. We exploit an exogenous shock generated by a university reform in Italy in the early 2000s. We find that families responded in a way that is consistent with education as a production good. The higher child’s education produced household positive, permanent income innovations. Hence, family non-durable consumption increased. Our findings suggest that education can be an insurance device against adverse permanent income shocks. The 2001 reform not only positively affected offspring’s years of schooling, but it also had a positive effect to boost household consumption.

The Effect of General and Vocational High School Quality on Labor Market Outcomes in Indonesia

To improve the quality and competitiveness of human capital and correspond to the Sustainable Development Goals (SDGs) 4 especially target 4.3, to ensure equal access for all women and men to affordable and quality technical, vocational and tertiary education, Indonesia has been focusing on improving the quality of secondary education. However, empirical data and previous research showed that secondary school graduates in Indonesia face high unemployment and income differences, especially vocational school graduates. The quality of secondary high schools plays an important role in determining the years of schooling of the graduates and indirectly impacts labor market outcomes. Using longitudinal panel data at the individual level from the Indonesia Family Life Survey (IFLS) of 2000, 2007, and 2014; and applying education production function and Mincer earning equation, this study finds that the difference in wages between graduates of general and vocational high schools is not statistically significant despite the school quality. However, analyzing within the vocational high schools shows that better quality of vocational high schools increases years of schooling of its graduates as they can access tertiary education, and subsequently increases their performance in the labor market. This finding indicates that policies to improve school quality, especially vocational high schools, should be enhanced.

Iskolázottsági és keresetegyenlőtlenségek : Educational attainment inequalities and earnings inequalities

Az iskolázottság növekedése az előrejelzések szerint nem lassul. Ugyanakkor az iskolázottság növekedésével az iskolázottsági egyenlőtlenségek alakulása sajátos képet mutat. Az iskolázottság növekedésével az iskolázottsági egyenlőtlenségek a Kuznets-görbének nagyjából megfelelő lefutást mutatnak a magasabb iskolázottság időszakában, de az országcsoportok nagy részében ez kiegészül egy ellentétes görbületű résszel az alacsonyabb iskolázottság időszakában, s így egy hullámszerű alakulás jellemzi az iskolázottsági egyenlőtlenségeket az iskolázottsággal együtt vizsgálva. Az írás azt is megvizsgálja, hogy az iskolázottsági egyenlőtlenségek milyen kapcsolatot mutatnak a keresetegyenlőtlenségekkel.Educational attainment is growing and is not projected to slow down in the future. However, as educational attainment rises, the evolution of educational inequalities shows a particular pattern. As schooling increases, the inequalities in schooling follow a roughly Kuznets curve slope for the period with a higher number of years of schooling completed. But in most groups of countries, this is complemented by an opposite curvature in the period of lower education, and thus a wave-like trend characterizes educational inequalities when examined in conjunction with education. In addition to analysing educational inequalities, the paper also examines the relationship between educational inequalities and earnings inequalities.

How do the factors associated with frailty change with sex? An exploratory network analysis

Background It is important to study multiple social, physical and psychosocial factors associated with frailty in populations characterized by social and health disparities, such as men and women. Methods This was a cross-sectional population-based study with older adults ≥65 years from the FIBRA (Frailty in Brazilian Older Adults) 2008-2009 study. We carried out a comparative analysis of the factors associated with the frailty phenotype in older men (N=706) and women (N=1.251) using networks based on mixed graphical models (MGM) according to sex. Results In the male network, frailty was most strongly associated with years of schooling, overall satisfaction with life and falls; in the female network, the syndrome was associated with satisfaction with problem solving, depression and diabetes in addition to years of schooling. Furthermore, permutation tests showed that the networks for males and females were statistically different in terms of their structure, the global strength of the relationships and the strength of the relationships between frailty and diabetes; frailty and falls; frailty and depression; frailty and overall satisfaction with life; and frailty and satisfaction with problem solving (p<0.05). The walktrap network cluster detection algorithm revealed that in men, frailty was in a physical and social dimension while in women the syndrome was in a cardiometabolic and psychosocial dimension. Conclusions Network analysis showed that different factors are associated with frailty for each sex. The findings suggest that different strategies for dealing with frailty should be adopted for men and women so that care and prevention efforts can be directed appropriately.

Identification of Mean Years of Schooling as a Control for RPJMD: A Spatial Autocorrelation Approach

This article will identify the mean years of schooling in East Java as a control for achieving RPJMD. Inequality in the development of education leads to inequalities between the regions of East Java. This is due to the different regional characteristics, it is, therefore, necessary to respond to it by carrying out a regional mapping based on the education indicators listed in the RPJMD of each region using a statistical analysis approach, namely spatial autocorrelation. The variable that becomes the indicator in this study is the Mean Years of Schooling (MYS), the unit of observation being the regencies/cities of East Java. The results of the research that has been conducted can be concluded that the mean years of schooling for the population of East Java Province is seven years where urban areas have a better average length of schooling than in districts, and there are only nine areas in East Java that have MYS exceeding the RPJMD target. In the Global Moran's I test, there is a positive autocorrelation or cluster pattern that exhibits similar characteristics in adjacent locations, and the results of the local Morans’ show that there are nine regions that have spatial relationships with their most significant areas relatives based on the MYS indicator. These areas are Bondowoso Regency, Bangkalan Regency, Pamekasan Regency, Gresik Regency, Jember Regency, Probolinggo Regency, Sampang Regency, Sidoarjo Regency and Surabaya City

Supervised machine learning based prediction of 2021 cross-national COVID-19 prevalence rates using historical infection data and socioeconomic indicators. (Preprint)

BACKGROUND The COVID-19 pandemic represents the most unprecedented global challenge in recent times. As the global community attempts to manage the pandemic long-term, it is pivotal to understand what factors drive prevalence rates, and to predict the future trajectory of the virus. OBJECTIVE The aim of this study was to investigate whether socioeconomic indicators support in predicting year-on-year COVID-19 prevalence rates in a cross-sectional sample of 182 countries. Using a number of supervised machine learning techniques, results were evaluated and compared to determine the most accurate predictive algorithm. METHODS This research applied three supervised regression techniques: linear regression, random forest, and AdaBoost. Results were evaluated using k-fold cross validation and subsequently compared to analyse algorithmic suitability. The analysis involved two models. Firstly, the algorithms were trained to predict 2021 COVID-19 prevalence using only 2020 infection data. Following this, socioeconomic indicators were added as features and the algorithms were trained again. The Human Development Index metrics of life expectancy, mean years of schooling, expected years of schooling, and Gross National Income were used to approximate socioeconomic status. RESULTS Using 2020 infection prevalence rates as a lone predictor to predict 2021 prevalence rates, the average predictive accuracy of the algorithms was low (R2=0.562). When the socioeconomic indicators were added alongside 2020 prevalence rates as features, average predictive performance improved considerably (R2=0.724) and all error statistics decreased. This suggested that adding socioeconomic indicators alongside 2020 infection data optimised prediction of COVID-19 prevalence to a considerable degree. Linear regression was the strongest learner with R2=0.713 on the first model and R2=0.762 on the second model, followed by random forest (0.533 and 0.733) and AdaBoost (0.441 and 0.676). CONCLUSIONS Understanding the impact of socioeconomic status at national level will assist with future pandemic management. This paper puts forward new considerations about the application of machine learning techniques to understand and combat the COVID-19 pandemic.

An Economic Analysis on Years of Schooling of the Children Related to Financial Support from Family and Govt. & Non-Govt. Institutions

This study examined the relationship among father’s education, amount of father’s land (Dec), fathers occupation, any government help, and years of schooling in the rural area at Muktagacha Upazila in Mymensingh Division. Qualitative variables and variables which are quantitative in nature have been used for this study. We have chosen the years of schooling of the children of households as a dependent variable and the selected independent variables are father's education, fathers land amount, fathers occupation, male, rural, number of siblings, age antigovernment help. A convenient sampling procedure has been used in our research. Questionnaire and structured interview were the research instruments. Not only an urban area is counted for research but also rural households are counted for data collection about school-going children. We conducted our research by using primary data.

Realising the potential human development returns to investing in early and maternal nutrition: The importance of identifying and addressing constraints over the life course

The benefits of interventions which improve early nutrition are well recognised. These benefits, however, only accrue to the extent that later life circumstances allow. Consequently, in adverse contexts many of the benefits will never be realised, particularly for the most vulnerable, exacerbating inequality. Returns to investment in early nutrition could be improved if we identified contextual factors constraining their realisation and interventions to weaken these. We estimate cost and impact of scaling 10 nutrition interventions for a cohort of South African children born in 2021. We estimate associated declines in malnutrition and mortality, and improvements in years of schooling and future earnings. To examine the role of context over the life-course we estimate benefits with and without additional improvements in school quality and employment opportunities by socio-economic quintile. Scale up reduces national stunting (height for age < = -2SD) rates among children at 24 months by 3.18 percentage points, implying an increase in mean height for age z-score (HAZ) of 0.10, and 53,000 years of additional schooling. Quintile 1 (the poorest) displays the largest decline in stunting, and largest increase in mean HAZ. Estimated total cost of increasing coverage of the interventions for the cohort is US$90 million. The present value of the additional years of schooling is estimated at close to US$2 billion. Cost-benefit ratios suggest the highest return occurs in quintile 5 (1:23). Reducing inequality in school quality closes the gap between quintile 5 and the lower quintiles. If school quality and labour force participation were equal the highest returns are in quintile 1(1:31). An enabling environment is key to maximising human development returns from investing in early nutrition, and to avoid exacerbating existing inequality. Therefore, particularly for children in adverse conditions, it is essential to identify and implement complementary interventions over the life course.

Factors associated with cesarean delivery in adult nulliparous women

The aim of this study was to analyze the sociodemographic factors associated with cesarean section in adult women with conditions favorable for normal delivery and to identify the groups most likely to undergo this surgery in the state of Santa Catarina (SC). A case control study with microdata from the Sistema de Informação de Nascidos Vivos on 7,065 women for 2016 in SC. A relationship between cesarean section and sociodemographic variables was analyzed by logistic regression where we calculated the Adjusted Odds Ratio (AOR), confidence interval and p-value. The probability of cesarean section for each group of women (called "interaction") was also calculated. Among women with more favorable conditions for normal childbirth, the prevalence of cesarean section was 41.1%. Lower chance of cesarean section was found for women without partners (AOR: 0.79 [0.71-0.87]), up to 8 years of schooling (AOR: 0.56 [0.47-0.66]), with up to 2 prenatal visits (AOR: 0.46 [0.23-0.90]). The most likely group of women (51.4% [49.3-53.4]) to undergo cesarean section are women who perform 7 to 15 prenatal visits and have 12 or more years of schooling. A cesarean section occurs with women who have greater access to education and prenatal care and those who have partners, even though the aspects favor normal childbirth, suggesting that this does not seem to be a decision only by women.