Document Type : Research Article
Authors
1
Associate Professor, Department of Economics, Faculty of Economics and Social Sciences, Bu-Ali Sina University, Hamedan, Iran (Corresponding Author).
2
MSc Student, Department of Economics, Faculty of Economics and Social Sciences, Bu-Ali Sina University, Hamedan, Iran.
10.22084/csr.2024.28600.2253
Abstract
Abstract
Although marriage is a personal matter and one of the significant dimensions of social and human development, its economic aspect, particularly the provision of adequate accommodation, is regarded as a critical and fundamental factor in the stability and success of couples. Therefore, it is crucial to be aware of the economic and social factors, such as housing prices, that influence marriage and the formation of households, especially youthful households. This research investigates the impact of housing prices on marriage in Iran using the ARDL method and time series data from 1990 to 2021. The findings suggest that the marriage rate is significantly and negatively influenced by the real housing price, unemployment rate, and education level. Conversely, the marriage rate is significantly and positively influenced by the ratio of the urban population to the total population and the real GDP. In this study, the variables that had the greatest impact on marriage were housing price, urbanization, and education.
Keywords: Housing Economics, Marriage, Economic Factors, Housing Prices, ARDL.
1. Introduction
In numerous societies, housing provision is regarded as a determinant of social status, in addition to its economic implications. Traditionally, the main drivers of demand for property ownership have been young individuals who marry. The transition from renting to owning a residence is a significant milestone in the life cycle (Clark, 1994). Housing provision is not only a critical indicator of adulthood (Brownstein, 2015), but it also represents the largest share of the wealth in the majority of households at the micro level. The most costly and valuable asset for the majority of households is the acquisition of a residence (Brito et al., 2022). Before or during marriage, there are two primary motivations for purchasing a property that are significant and influential. One is the cultural norm that is prevalent in Asia, where young couples traditionally use the purchase of a residence as a symbol of their commitment to marriage (South & Spitze, 1986). The second rationale is that the acquisition of a residence is regarded as a component of the dowry. Sometimes parents assist their offspring in obtaining the necessary shelter by providing a deposit for a house. Intergenerational wealth transfer in the country’s housing market is also a consequence of such behavior, in addition to increasing the price of housing (Hui et al., 2016). These two reasons are of vital significance during the early stages of marriage formation and household agreement in a marriage. The analysis of family formation in Iran has been significantly influenced by economic challenges, despite the political support for lowering the age of marriage and increasing fertility. The primary factors that prevent numerous young Iranians from marrying and starting a family are high housing and rent prices (Eltejaei & Azizzadeh, 2016). Given that this issue has not been previously studied in Iran, this study endeavors to investigate the relationship between housing prices and marriage rates.
2. Materials and Methods
The data utilized in this study were obtained from scientific and reliable sources, including the Central Bank, Civil Registry, and Statistical Center of the Islamic Republic of Iran, between 1990 and 2020. Using Eviews software, the autoregressive distributed lag (ARDL) econometric model was implemented in the investigation.
Initially, in order to examine the short-term and long-term relationship between the variables of marriage rate, real housing price, education, unemployment rate, urban population ratio to total population, and real GDP, a stationary test was conducted on all variables to verify the absence of spurious regression phenomenon. Then, classical assumption tests were conducted, including the serial correlation, correct dependent form of the model, normality of residuals, variance heterogeneity, and convergence test (long-term relationship between variables). In order to establish dynamic short-term relationships, the error correction model was implemented. Lastly, the variable’s elasticity and ultimate effects were investigated. The conceptual expression of the variables and their abbreviations are illustrated in Table 1.
3. Discussion
The following is the forward research model, which is derived from Chang et al.’s (2020) model:
MR= f (PH, EDU, GDP, UR, UP)
As indicated by the error correction model estimation results, the error term coefficient is equivalent to -0.64 and is within the range of-1<ECM<0. Therefore, it is statistically significant and it can be inferred that according to the error correction term, 64 percent of the short-term disequilibrium is rectified in each period to achieve long-term equilibrium.
Equation 2 is employed to determine the model’s elasticity, while Equation 3 is employed to determine the ultimate effect:
Where ∈ is the elasticity, δ is the final effect, X is the mean of independent variable, and (LOGMR) is the mean of dependent variable.
If Log Ph increases by one percent, the short-term marriage rate decreases by 0.474 percent and the long-term marriage rate decreases by 2.976 percent. In the ultimate effect, if the real price of housing increases by one thousand rails, the marriage rate will decrease by 1.2 thousand individuals in the short-term and by 7.5 thousand individuals in the long-term.
With a one percent increase in LOG GDP, the short-term marriage rate increases by 0.424 percent and the long-term marriage rate increases by 1.4 percent. In the ultimate effect, if LOG GDP increases by one thousand rails, the marriage rate increases by 0.3 thousand people in the short-run and by 1.08 thousand people in the long-run.
If LOG EDU increases by one percent, the marriage rate will decrease by 1.059 percent in the short-term and by 6.7 percent in the long-term. In the ultimate effect, if the LOG EDU increases by one person, the marriage rate decreases by 0.8 individuals in the short-term and by 5.3 individuals in the long-term.
If LOG UR increases by one percent, the short-term marriage rate increases by 1.6147 percent and the long-term marriage rate increases by 3.0742 percent. The final effect also indicates that if LOG UR increases by 1,000 people, the marriage rate will decrease by 0.53 thousand people in the short-run and by 1.01 thousand people in the long-run.
If UP increases by one percent, the marriage rate decreases by 0.0389 percent in the short-run and by 0.19 percent in the long-run. Finally, a one percent increase in UP results in a 0.043 percent decrease in the short-term and a 0.215 percent decrease in the long-term in the marriage rate.
4. Conclusion
In this study, the relationship between marriage and economic factors was investigated in the 1990-2020. The findings indicated that the marriage rate is reduced in both the short- and long-term by unemployment, rising housing prices, and increased education. Additionally, the age of marriage for both husband and wife increases, while real GDP and urban population have a positive impact on the marriage rate, resulting in a decrease in the age of marriage. In light of the results, which indicate that the unemployment rate has a negative effect on the marriage rate, it is imperative to investigate the factors that contribute to the rise in the unemployment rate in Iran and implement suitable measures to regulate this variable. According to the results, the marriage rate is negatively affected by rising housing prices in both the short- and long-term. Therefore, it is important to acknowledge that housing establishes an equilibrium pattern and contributes to economic mobility throughout the country. Thus, the agenda of policymakers and the planning system should encompass the regulation or management of housing prices.
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