نشریه نامه انجمن جمعیت شناسی ایران
پیاپی 38 (پاییز و زمستان 1403)

Forecsting the death rate accurately is of paramount importance. The purpose of this research is to select the best stochastic mortality model in Iran based on a comparison between Lee-Carter models and the age-at-death distribution model (maximum entropy) for the male and female populations separately. It is worth mentioning that one of the strengths of modeling using age-at-death distribution is that it effectively represents the age-specific mortality pattern in Iran and answers the question of whether the mortality pattern in Iran is stable. Considering any model that reports the lowest error based on evaluation criteria, the life table for the year 1400 in Iran has been introduced and compared with the mortality tables TD88-90 in France and ILT1400 in Iran. In this regard, real mortality data in Iran for the years 2011-2019 for ages 0-100 years have been used. Ultimately, it was concluded that the Lee-Carter model performed better for the male population and the Currie model for the female population in Iran. It was also concluded that the existing tables in Iran report a higher mortality rate, and the life table presented in this article may be suitable for pricing insurance products and for more accurate calculation of actuarial indicators, such as the probability of death.

The study of ethnic differences in childbearing preferences and behaviors has attracted demographers and policy makers, due to its impact on future social and demographic compositions and trends. The aim of this article is to explore ethnic differences in childbearing ideals and behaviors in Iran using data from the National Family Survey (2018). The sample size includes 1302 married women aged 15 to 44, living in urban areas of the country. The results showed that childbearing ideals and behaviors vary significantly among different ethnic groups. Baloch women have the highest number of children ever born, ideal family size, and desire to have (another) child, while Gilak/Mazandarani women have the lowest. Multivariate analysis showed a significant effect of ethnicity on childbearing ideals and behaviors. However, socioeconomic and demographic variables slightly reduced the role of ethnicity, supporting the ethnic/cultural hypothesis. When controlling for other variables, Gilak/Mazandarani women exhibit fewer childbearing preferences and behaviors compared to Persian women. Conversely, Kurdish, Arab, and particularly Baluch women have more childbearing preferences and behaviors. Therefore, ethnicity plays a crucial role in fertility preferences and behaviors. Consequently, it is essential to consider ethnic diversity in population policies and programs when addressing fertility.

Over the past half-century, Iran has undergone significant shifts in fertility behaviors, transitioning from large to smaller families due to cultural and social transformations. With the enactment of the Law on Population Rejuvenation and Family Support, the national media has played a crucial role in shaping public attitudes toward childbearing. This study employs Fairclough's three-stage discourse analysis framework to investigate how childbearing is represented in Iranian media, with a focus on television programs from 2020 to mid-2024. The findings reveal that a mono-discursive environment regarding childbearing dominates national media. This means that the national media predominantly and unilaterally emphasizes the importance and necessity of increasing fertility, presenting this discourse as the sole legitimate and desirable narrative to its audience. While this one-sided approach may influence the attitudes of certain segments of society in the short term, it could lead to audience resistance or indifference in the long term. Therefore, it is recommended that national media, taking into account the diversity of perspectives and needs within society, adopt a more varied and balanced range of discourses on this issue, grounded in specialized and more comprehensive viewpoints, to achieve a more sustainable impact on audiences and society as a whole.

Various studies have examined the transformations of marriage at the national and regional levels, but there are still unknowns at the provincial level within the country. The purpose of this article is to investigate the universality of women's marriage in the provinces of the country using the survival random forest method, one of the common algorithms in machine learning. Using the household expenditure and income survey data from 2008 to 2019, the general status of women's marriage was investigated by defining a threshold level (marriage rate less than 95% for women aged 45-49). The findings of this research indicate that, among the research variables, high school and university education levels had the greatest impact on reducing the universality of women's marriages. The proportion of urbanization in each province is another influential variable in reducing the universality of women's marriages. Out of the total of 31 provinces of the country, 11 provinces still had the universality of marriage until the end of the study period, but the marriage rate of women aged 45-49 in other provinces has decreased to below 95%. The high marriage rates in most of the provinces that do not enjoy the universality of marriage now indicate the existence of a suitable platform for facilitating marriage and expanding its universality in the country. Otherwise, it is expected that this trend will continue to decrease in the future.

Childbearing is one of the human needs and one of the reasons for the tendency of young people to live together. In some cases, couples are not able to have children naturally and need medical help. A group achieves fertility with medical assistance, while others are left unable to benefit from this gift, which can cause certain psychological and family problems. The purpose of this article was to compare marital commitment among infertile couples, couples fertilized with artificial insemination, and those with natural fertility. The research method was causal-comparative, and the statistical population included all the couples referred to the hospitals and fertility and infertility clinics of Tehran. A sample size of 50 infertile couples, 50 couples fertilized with artificial insemination, and 50 natural fertile couples who referred to Arash Women's Hospital in Tehran between May and June 2022 were selected using available sampling method. The DCI Marital Commitment Scale was used to collect data. The results of multivariate analysis of variance showed that there is a significant difference in marital commitment and its components among infertile couples, couples fertilized with artificial insemination, and those with natural fertility. Infertile couples and those fertilized with artificial insemination, marital commitment exhibit lower marital commitment compred to naturally fertile couples. Based on this research, infertility and the inability to have natural fertility affects marital relationships, and as a result, reduce marital commitment.

This study investigated the chance of leaving married life using Cox proportional hazards models with random effects, estimating the influence of demographic variables such as age at marriage, gender, place of residence, and province. The analysis is based on marriage and divorce records from the civil registration organization, covering the period from March 21, 2022, to March 20, 2023. A total of 412,918 records were included in the survival analysis, after excluding records with errors or missing data. The results indicated that the likelihood of men leaving married life is 1.54 times that of women, which is significant. Additionally, younger ages at marriage are associated with a higher probability of divorce compared to older ages. Specifically, the chances of leaving married life are 31.3, 11.7, 3.9, and 1.3 times higher, respectively, for the age groups 10-20, 20-30, 30-40, and 40-50 years, compared to those in the 50+ age group. The province variable also significantly affects the duration of marriage. Examining the factors that influence the length of married life provides valuable insights for timely interventions aimed at enhancing marital stability. These findings are relevant for couples, families, psychologists, and social workers at the micro level, as well as for sociologists and policymakers at the macro level.

The desire to migrate and its patterns reflect broader changes in the fabric of society. This study examines the desire to migrate and its underlying factors among the youth of Abdanan City in the Ilam province of Iran. A sample of 376 young people from Abdanan in Ilam Province was selected for this investigation. The findings reveal that only 8.9% of respondents had not considered migrating from the city in the past year. Specifically, 22% expressed a desire to migrate within their home province, 52.6% between provinces, and 16.5% indicated a desire to emigrate internationally. The primary reasons for the desire to migrate include job searching, a lack of educational, recreational, and health facilities, and an uncertain future in the city. These reasons were cited by both those desiring internal migration and those willing to migrate internationally. However, for those inclined towards international migration, access to greater freedom and security—in its various social, economic, political, and judicial dimensions—also emerged as a significant factor alongside an uncertain future. Multivariate analysis demonstrated that, after controlling for all independent variables, employment status and the level of perceived security significantly influenced the desire to migrate both internally and internationally. The probability of international migration among the unemployed was approximately 3.9 times higher than among the employed. Additionally, as the overall sense of security increased, the tendency towards international migration decreased. The study concludes that policymakers can reduce youths' desire for international migration by enhancing their overall sense of security—economic, political, judicial, and social—and by creating sustainable employment opportunities.

Considering the significant role of big cities in Iranian internal migration and their importance in migration policies, this article employs spatial statistics to analyze migration patterns between these cities based on geographical distance, population size, and the influence domain (ID) during 2011-2016. The study identified three migration clusters based on the Getis-Ord Index method and EMR index, two of which are migrant-sending clusters, while the third is a migrant-reception cluster. The migrant-sending clusters are located in Khuzestan and West Azarbaijan provinces, while the migrant-reception cluster comprises a significant number of big cities mainly distributed in the Tehran metropolitan area. Tehran and Karaj cities rank highest among big cities, as evaluated by hierarchical cluster analysis and the integration of EMR and ID indices, respectively. The variable of geographical distance, according to the Pearson correlation coefficient, showed no significant correlation with migration. The results of the geographic regression model also revealed that the two variables, city population and ID, explain 87% - 93% of the changes in immigration and 52%-89% of the changes in EMR.

Reports and statistics on brain drain from Iran indicate a surge in the tendency of highly skilled individuals to emigrate following the COVID-19 pandemic. According to the World Bank, Iran loses an estimated $50 billion annually due to this phenomenon. This study aims to identify the root causes of brain drain and propose solutions to mitigate its impact. Employing a meta-synthesis approach, the researchers analyzed relevant Persian and English research articles published between the start of the pandemic (2019) and March of 2024. The extracted factors contributing to brain drain and corresponding countermeasures were categorized into six themes: economic factors, social and cultural factors, political factors, educational system issues, urban infrastructure issues, and attractiveness of destination countries. The findings of this meta-synthesis reveal two crucial points: 1) Social scientists have made minimal contributions to research in this critical social domain, especially during the post-COVID-19 era. 2) The pandemic, which has profoundly impacted global social life, has further intensified the brain drain phenomenon. Furthermore, a comparison of the meta-synthesis results with previous meta-analyses reveals that additional factors, including intensifying financial instability, economic deterioration, country risk, human development, and globalization, have emerged as key drivers of brain drain from Iran.

Due to the increased emigration from Iran over the last decade, it is necessary to examine the factors that influence it. Since most emigrants are young and educated individuals, their departure can cause economic, social, and demographic challenges. Therefore, in this research, we examine the most important factors influencing migration from Iran to its main destination countries, emphasizing three key factors: inflation, unemployment, and political stability. Using the two-stage generalized moment method, factors affecting international migration from Iran between 2010 and 2021 were estimated. According to the results, inflation, unemployment, political instability in Iran, and economic growth in the destination countries serve as motivational factors for migration. In contrast, improvement in the human development index and increase in per capita income function as deterrents. Policy makers should recognize that inflation not only leads to the devaluation of the national currency but also results in job losses for businesses unable to withstand inflationary pressure, ultimately causing  widespread unemployment and driving human capital out of the country. This migration threatens economic growth and accelerates the closure of the population's golden window by changing the age structure. Financial policymakers are encouraged to adopt appropriate strategies to create a stable political environment and improve labor market conditions through education, ultimately reducing the outflow of human capital and preventing the waste of valuable resources.

The aim of this study is to examine the impact of income inequality on women's economic participation across 31 provinces of Iran during the period 2011-2020. To this end, the Gini coefficient has been used as an indicator of income inequality, and fertility rate, women's literacy rate, and the ratio of urban women to the total female population have been included as control variables in the model. The results from applying panel data econometric techniques indicate that income inequality has a positive and significant effect on women's economic participation across Iran's provinces. Additionally, the results show that as the literacy rate and fertility rate increase, women's economic participation decreases across Iran's provinces. In contrast, the ratio of urban women to the total female population has a positive and significant effect on women's economic participation. Given these results, the implementation of redistributive policies by the government aimed at improving infrastructure and public services can not only reduce income inequality but also increase women's economic participation in Iran's provinces.

The present study aims to model the factors affecting formal and informal sector employment in Iran, taking into account the unique conditions of this market. The research spans the period from 1991 to 2020. In this study, 43 variables influencing formal and informal sector employment were incorporated into a Bayesian averaging model. Results indicate that among the BMA, TVP-DMA, TVP-DMS, BVAR, and OLS models, the BMA model was identified as the most efficient. Based on the BMA model, 11 non-fragile variables affecting formal and informal sector employment were identified. The findings revealed that diverse factors influence employment in both sectors.  For the formal sector, the most impactful variables include education level, service sector value-added, economic growth, structural change index, real wages, consumer price inflation, government current expenditures, privatization, minimum wage, oil revenues, and sanctions. In the informal sector, the business environment index, service sector value-added, business cycles, economic globalization index (KOF), producer price inflation, labor productivity, interest rates, government construction expenditures, oil revenues, and sanctions emerged as the most significant determinants of employment.