Demographic and Socioeconomic Determinants Use of Financial Products/Service

The use of financial products/services in Indonesia is growing. And is one of the factors of economic growth. This study aims to investigate the demographic and socio-economic factors that influence the use of financial products/services. The data used are based on the 2023 National Socio-Economic Survey. The object of analysis is the population aged 15 years and over who use at least one financial product/service. There were 241,075,975 respondents in this study, of which 23.17% used at least one financial product/service. The dependent variable is the use of financial products/services. The independent variables used are Gender, Education, Island of residence, Urban/rural, and Marital status. The analysis was carried out using bivariate and multivariate methods. Multivariate analysis using a binary logistic regression model was used in the analysis. The results showed that higher use of financial products/services was associated with being male, college graduates, living in Java, living in urban areas, and being married.

The digital divide in Indonesia is rather big. This study aims to investigate the demographic and socioeconomic determinants affecting the quantity of uses of the Internet in Indonesia. The data used are based on the National Socioeconomic Survey in 2021. The object of the analysis is population aged 15 years and above possessing at least one digital gadget. The dependent variable is the purpose of the Internet use. The independent variables included gender, age, number of household members, marital status, education, employment status, place of dwelling, island of residence, and number of types of digital gadgets owned. A binary logistic regression model was employed in the analysis. The results of the study indicate that a higher quantity of uses of the Internet is associated with a female sex, younger age, smaller number of household members, unmarried status, higher education, dwelling in urban areas, living in Java Island, and owning more than one type of digital gadgets. These findings suggest the need to improve access to the Internet among men, older people, ever-married individuals, rural dwellers, and Outer Java inhabitants, to promote family planning, to improve education and accessibility of more types of digital gadgets in order to bridge the digital divide and to boost the global competitiveness index 4.0 in Indonesia.

Studies on the nexus between socioeconomic and demographic factors and crime reporting are still limited in Indonesia. Therefore, this study aimed to investigate the socioeconomic and demographic causes of crime reporting in Indonesia using data from the results of the 2021 National Socioeconomic Survey. The data were analyzed using a binary logistic regression model. The unit of analysis was the population aged 15 years and older. The dependent variable was whether or not someone who experienced a crime reported it to the police. The independent variables were socioeconomic and demographic factors. The results of the study showed that a lower probability of reporting crime to the police was associated with being female, being younger, having higher educational attainment, living in urban areas, living in Java island, and working, implying the need to improve information, education, and communication on crime reporting among these groups.JEL Classification: A12, J19, K00, K39How to Cite:Rajagukguk, W. (2023). Socioeconomic and Demographic Causes of Crime Reporting in Indonesia. Signifikan: Jurnal Ilmu Ekonomi, 12(2), 413-424. https://doi.org/10.15408/sjie.v12i2.33436.

Objective: The main objective of this study was to examine the socio-economic, demographic and health determinants affecting the number of coronavirus cases, the number of coronavirus deaths, case fatality rates per 1,000 cases and coronavirus deaths per 1,000,000 population in OECD countries. Methods: The study was undertaken using 20 different independent variables and 4 different dependent variables, which were all obtained from the OECD and WHO databases. The study utilizes multiple linear regression statistical techniques to reveal the socio-economic, demographic and health determinants of the coronavirus pandemic. Results: The findings of the study show that higher shares of current expenditure on health in GDP, higher prevalence of obesity among adults, higher percentage of raised blood glucose levels among adults and the stringency index (which indicates the extent of the measures taken by the government related with the coronavirus outbreak) are influential on both the number of coronavirus cases and deaths in OECD countries. Increased case fatality rates seem to be closely related to the stringency index, higher share of current expenditure on health in GDP and higher percentage of tobacco users among adults in OECD countries. On the other hand, factors such as the stringency index, higher life expectancy at birth, higher use of tobacco and higher share of current expenditure on health in GDP are effective on the coronavirus death rate per 1,000,000 population. Conclusions: The demographic, economic, political and health factors that determine the current number of coronavirus cases and deaths indicate that the pandemic as a public health problem cannot be eliminated only with health interventions and that multi-dimensional policies are needed.

To identify mortality trends and risk factors associated with stillbirths and neonatal deaths 1982-2011. Population-based cross-sectional study based on reported pregnancy history in Iganga-Mayuge Health and Demographic Surveillance Site (HDSS) in Uganda. A pregnancy history survey was conducted among women aged 15-49 years living in the HDSS during May-July 2011 (n = 10 540). Time trends were analysed with cubic splines and linear regression. Potential risk factors were examined with multilevel logistic regression with adjusted odds ratios (AOR) and 95% confidence intervals (CI). 34 073 births from 1982 to 2011 were analysed. The annual rate of decrease was 0.9% for stillbirths and 1.8% for neonatal mortality. Stillbirths were associated with several risk factors: multiple births (AOR 2.57, CI 1.66-3.99), previous adverse outcome (AOR 6.16, CI 4.26-8.88) and grand multiparity among 35- to 49-year-olds (AOR 1.97, CI 1.32-2.89). Neonatal deaths were associated with multiple births (AOR 6.16, CI 4.80-7.92) and advanced maternal age linked with parity of 1-4 (AOR 2.34, CI 1.28-4.25) and grand multiparity (AOR 1.44, CI 1.09-1.90). Education, marital status and household wealth were not associated with the outcomes. The slow decline in mortality rates and easily identifiable risk factors calls for improving quality of care at birth and a rethinking of how to address obstetric risks, potentially a revival of the risk approach in antenatal care.

BackgroundWhile the rationale for integration of HIV and sexual and reproductive health (HIV and SRH) services is strong, there is paucity of information on which population groups most utilize these services. Such studies would inform policy and programs on integration of services. The overall objective of this assessment is to provide information to researchers, planners and policy makers on the best practices for integrated services in order to maximize feasibility of scaling up. Specifically, this research paper identifies demographic and socioeconomic factors that are most related to utilization of integrated services in Uganda.MethodsThis manuscript uses data from a sample of 9,691 women interviewed during the Uganda AIDS Indicator Survey (UAIS) of 2011. The selection criteria of the study respondents for this paper included women of reproductive age 15 – 49 years. The dependent variable is whether the respondent utilized integrated HIV and SRH services during pregnancy and delivery of the last child, while independent variables include; region of residence, age-group of woman, marital status, rural-urban residence, wealth indicator and educational level attainment. In the main analysis, a binary logistic regression model was fitted to the data.ResultsLog-odds of utilizing integrated services were significantly higher among those women with a primary education (OR = 1.2, 95 % CI = 1.0-1.4, p < 0.05) compared to those with no education. Women from the Central part of Uganda were more likely to utilize integrated HIV and SRH services (OR = 1.3, 95 % CI = 1.0-1.7, p < 0.05), further the log-odds of utilizing integrated HIV and SRH services were significantly higher among women residing in Northern region (OR = 1.6, 95 % CI = 1.2-2.2, p < 0.01). The odds of utilization of integrated HIV and SRH services were higher for currently married women (OR = 6.6, 95 % CI = 5.5-8.0, p < 0.01) and the formerly married (OR = 3.4, 95 % CI = 2.7-4.2, p < 0.01), compared to the never married group. The odds of utilizing integrated HIV and SRH services were higher for younger women of ages less than 35 years compared to older women aged 40 – 49 years.ConclusionsUtilization of integrated HIV and SRH services in Uganda is influenced greatly by demographic and socioeconomic characteristics. This study contributes to the current debate as it shows the on how best ways to improve HIV and SRH service delivery to the people.

Babies born with Low-birth weight are at increased risk for serious health problems which are accompanied by disabilities and even death. The purpose of this study was to determine socio-economic factors that lead to low birth weight of children in Kenya. Data used was from Kdhs 2003 and the significant effect of socio-economic determinants on low birth weight was examined using logistic regression analysis data is categorical and continuous in nature, where predictor variables being socio-economic determinants and birth weight being dependent variable. Results indicate that out of six socio-economic factors involved in the study, four (Religion, Time Wanted Pregnancy, Marital Status and Economic Status) revealed some significant effects on the children with low birth weight. Therefore Socio-economic determinants have a significant effect on Low birth weight which suggests a strong negative associated with infant survival in Kenya independent of other risk factors. The logistic function revealed a statistically significant association between the birth weight, Religion, Time Wanted Pregnancy, Marital Status and Economic Status. Predicted probability is 11.4% low birth weight. Researcher recommends that respondents should avoid conceiving unexpectedly since it was associated with high low birth weight. Also to effectively enhance normal birth weight in Kenya, then expectant mothers should keenly focus on the socio-economic determinants by avoiding marital problems like divorce.

Background:We compared the associations of socioeconomic factors with stress and depression among family members living with a dementia patient in urban and rural areas of South Korea.Methods:Data were collected from 9,730 (4,560 urban and 5,170 rural) participants in the Korean Community Health Survey from 2014–17. The variable of interest was the presence of a cohabitating dementia patient, and the dependent variables were stress and depression.Results:Family members living with a dementia patient in rural areas had a significantly lower socioeconomic status (education, household income, marital status, and employment) than those living in urban areas (p<0.001). In addition, family members living with a dementia patient in rural areas reported statistically significantly less stress and depression than did those in urban areas after adjusting for related factors(rural stress OR=0.87, 95% CI=0.80–0.95; rural depression OR=0.75, 95% CI=0.66–0.85). Female gender and a low family income were associated with stress and depression in both rural and urban areas. Age, educational attainment, number of family members, marital status, and employment status differed slightly between urban and rural areas.Conclusion:The socioeconomic factors associated with stress and depression differ slightly in the rural and urban areas of South Korea.

Background: Socioeconomic status and demographic factors, such as education, occupation, placeof residence, gender, age, and marital status have been reported to be associated with obesity. Weconducted a systematic review to summarize evidences on associations between socioeconomic factorsand obesity/overweight in Iranian population.Methods: We systematically searched international databases; ISI, PubMed/Medline, Scopus, andnational databases Iran-medex, Irandoc, and Scientific Information Database (SID). We refined datafor associations between socioeconomic factors and obesity/overweight by sex, age, province, andyear. There were no limitations for time and languages.Results: Based on our search strategy we found 151 records; of them 139 were from internationaldatabases and the remaining 12 were obtained from national databases. After removing duplicates,via the refining steps, only 119 articles were found related to our study domains. Extracted resultswere attributed to 146596 person/data from included studies. Increased ages, low educational levels,being married, residence in urban area, as well as female sex were clearly associated with obesity.Conclusion: Results could be useful for better health policy and more planned studies in this field.These also could be used for future complementary analyses.

This study aimed to estimate the prevalence of food insecurity and its association with demographic and socioeconomic factors that characterized elderly survey respondents in 48 developed countries. Cross-sectional data came from the Gallup World Poll for the years 2014 to 2017 based on respondents aged ≥60 years (n = 53,556). Food insecurity was assessed using the Food Insecurity Experience Scale, a direct measure of experience-based food insecurity. Descriptive statistics of target populations and the prevalence of food insecurity were produced. Demographic variables included age, gender, marital status, household size, education level, social support, and wellbeing. For socioeconomic variables, income, employment status, urbanicity, and the Community Basics Index were evaluated. A binary logistic regression model was used and the level of statistical significance was set at 5%. Respondents aged 60 to 69 years were the most food insecure. Food insecure individuals were more likely to live alone, not have a partner, and tended to have poorer scores for social support and wellbeing. Also, poor community infrastructure was associated with food insecurity of elderly people, and there were more food insecure elderly people in urban areas. At the multinational level, results indicated wide and statistically significant disparities among continents. The study concluded that not only personal factors but also social conditions could prevent the elderly from achieving full food security status.

This article attempts to analyse the composition and determinants of workforce participation among the females in Assam, a relatively less researched part of India. Unit-level data has been extracted from Periodic Labour Force Survey (PLFS), 2018–2019, and a binary logistic regression model has been used to identify some of the socio-economic and demographic factors that influence the odds of Assamese females being a part of the workforce in both the rural and the urban sectors along with the extent of their influence. The results show that age, educational qualification, marital status and social group have significant impact on workforce participation. The majority of Assamese women in the rural areas are found to be engaged in the growing of tea, while in the urban areas, they are primarily employed in the education sector. Moreover, though a bulk of the women workforce in Assam are engaged in regular wage/salaried forms of employment, more than one-third of them do not receive any social security benefit.

Homozygosity for killer immunoglobin-like receptor haplotype A predicts complete molecular response to treatment with tyrosine kinase inhibitors in chronic myeloid leukemia patients

Risk factors associated with cardiovascular diseases in Turkey: Evidence from National Health Survey

The effective use of digital devices has become an essential part of modern life. This study explores the socioeconomic determinants of the adoption of digital devices in Pakistan. The data has been obtained from the Pakistan Social Living Standard Measurement (PSLM) survey 2019–2020. A binary logistic regression model has been used. The dependent variable is the household adoption of digital devices i.e. computers, laptops, and tablets/iPads. Socioeconomic and demographic variables have been used as explanatory variables in the regression. The results conclude that the household adoption of digital devices is influenced by socioeconomic and demographic factors including gender, income, schooling years, and age. Receiving foreign remittances emerged as a positive predictor of the adoption of digital devices. Surprisingly, employed individuals are less likely to use digital devices. The study also highlighted a few behavioral factors that affect the adoption of digital devices. These factors i.e. lack of accessibility and affordability of devices, fears regarding security, and challenges associated with digital literacy, negatively affect the adoption of digital devices. Digital divide and digital inclusion have been studied for different economies due to their perceived benefits in modern lifestyles. As per our information, such household level analysis for Pakistan has not been performed. Pakistan has a lot of space for improvement in ICT infrastructure and accessibility. The study elucidates socioeconomic factors that can influence the implementation of digital technology, providing implications for policymakers, who seek to promote digital inclusion.

Household saving is the source of investment and is considered as an important factor in economic growth. The main objective of this study is to explore important determinants of household savings in both urban and rural areas of Pakistan. Purposive sampling technique was used to select the study areas in Khyber Pakhtunkhwa. The data were collected through a standardized questionnaire from 50 households. Through stratified random sampling, 25 urban and 25 rural household’s heads were interviewed. Simple multiple regression was employed to explore the relationship between a household saving and socio-economic determinants such as income, age, dependency ratio, education, employment status, and marital status. Results showed that overall model was a good fit in both urban and rural contexts. Income, age and employment had a significant and positive association with household savings in both urban and rural areas whereas education had significant negative association with household savings in both areas. However, dependency ratio with negative coefficient was only significant in rural areas. Contrarily, education was found as significant negative determinant of savings in urban areas.