Esfandiar Jahangard; Mohammad Ghasemi Sheshdeh; Teymour Mohammadi; Farbod Jozani Kohan
Abstract
This study uses a three-period overlapping generations (OLG) model to investigate human capital productivity's impact on Iran's economic growth. The central question addresses how intergenerational transfers influence the productivity of human resources and, consequently, overall economic growth. ...
Read More
This study uses a three-period overlapping generations (OLG) model to investigate human capital productivity's impact on Iran's economic growth. The central question addresses how intergenerational transfers influence the productivity of human resources and, consequently, overall economic growth. Employing an analytical-quantitative approach, the study uses seasonal data from 1991 to 2021. The model is estimated within a Dynamic Stochastic General Equilibrium (DSGE) framework. By integrating data from national accounts and household budgets, the study derives productivity levels of human resources across generations. The findings reveal that intra-family transfers related to consumption, education, and healthcare significantly enhance productivity. Moreover, the age group 25 to 64 years shows the strongest impact on economic growth, which is consistent with the life-cycle hypothesis as formulated by Ando and Modigliani, as well as the theoretical perspective provided by the National Transfer Accounts (NTA) framework.IntroductionThe relationship between demographic dynamics and macroeconomic outcomes has become an increasingly important topic in economic research. Among the various mechanisms connecting these domains, the role of intergenerational transfers in shaping human capital productivity is especially significant. This study focuses on the Iranian context, where a shift in population structure, along with institutional and fiscal challenges, has made the efficient use of human capital a key policy priority.The central aim of this research is to assess intergenerational productivity through the lens of the National Transfer Accounts (NTA) framework. This approach allows for the quantification of how resources are allocated across age groups, highlighting differences in consumption, education, and healthcare. By identifying the age-specific patterns of resource use and output contribution, the study seeks to provide an empirical basis for measuring productivity across generations.Furthermore, to understand how productivity responds to macroeconomic fluctuations, the study incorporates a Dynamic Stochastic General Equilibrium (DSGE) model tailored to Iran’s economy. The model integrates shocks in key areas such as household consumption, educational investment, and health expenditures, and tracks their effects across different generational cohorts. Through this dual-layered approach—linking micro-level intergenerational data with macro-level modeling—the research aims to answer a critical question: Which generation is most responsive to shocks in ways that affect human capital productivity?Ultimately, the study provides not only a diagnostic tool for evaluating demographic-economic interactions but also a foundation for designing more targeted and effective policy interventions that consider the dynamic interplay between age structure, resource allocation, and economic growth.Methods and MaterialThis study employs a hybrid modeling framework that combines National Transfer Accounts (NTA), the Overlapping Generations (OLG) model, and a Dynamic Stochastic General Equilibrium (DSGE) structure to assess the intergenerational dynamics of human capital productivity in Iran. The research integrates demographic structure with macroeconomic modeling to trace the effects of economic shocks on various age cohorts in terms of their productivity levels.The OLG model used in this study features a three-period structure consisting of youth (15–24 years), working-age adults (25–64 years), and the elderly (65 years and older). This categorization aligns with the age classification defined in the NTA framework. Each cohort is subdivided by skill level, which determines their human capital endowments. Individuals enter the model at the age of 15 and progress through the stages of life, contributing to or benefiting from the economy through consumption, education, healthcare, and labor productivity.To operationalize the model, we derive age-specific indicators for consumption, education, and health expenditures from household budget survey data. These micro-level estimates are then scaled using aggregate national account data to compute public and private intergenerational transfers. For example, to determine the public health transfer received by each age group, the proportional share of health-related household spending is multiplied by total government health expenditure. A similar technique is used to calculate age-disaggregated values for other transfer categories such as education and consumption.The DSGE model is calibrated using quarterly macroeconomic data from 1991 to 2021. This framework enables us to incorporate random shocks to productivity, consumption, and fiscal policy, allowing for an analysis of the short- and long-term effects of these disturbances across different generations. The model builds upon the microfoundations of rational expectations and utility maximization, and follows the tradition established by Kydland and Prescott (1982), Clarida et al. (2002), and Smets and Wouters (2003). Technological shocks are modeled as a primary source of uncertainty, in line with the Real Business Cycle (RBC) literature.Despite some limitations—such as the complexity of infinite-horizon modeling and the challenge of solving nonlinear systems—DSGE models remain the gold standard for macroeconomic policy simulation. This study utilizes a finite-horizon version of the model to capture the productivity responses of distinct age cohorts to economic shocks. By integrating NTA data into a DSGE structure, the research bridges microeconomic resource allocation with macroeconomic performance. It further distinguishes itself by evaluating how generational productivity changes in response to policy-driven and exogenous shocks, providing a novel analytical tool for demographic-economic research.Results and DiscussionAlthough the calibration techniques of microeconomic and macroeconomic models slightly differ, the general approach in economic literature includes four key steps: selecting the model, defining the calibration objective, specifying the functional form, and adopting parameters estimated by other researchers or through original estimation. Table 2 presents the calibrated parameters and their estimation methods.One of the crucial outputs of the Dynare software is the Markov Chain Monte Carlo (MCMC) diagnostic test, which confirms that there is no issue with the model’s parameter estimations and that the estimates are reliable. Dynare performs several Metropolis-Hastings simulations, starting each time from a different initial point. If the chains behave similarly and converge toward one another, the results are considered trustworthy. Dynare provides three diagnostic indices—Interval, m2, and m3—which represent the 80% confidence interval, variance, and third moment of the parameters, respectively. These are visualized in multivariate diagnostic plots, illustrating the eigenvalue-based diagnostics of the variance-covariance matrix for each parameter. These charts provide evidence of convergence and stability across all parameter moments. The x-axis in each chart shows the number of Metropolis-Hastings iterations, and the y-axis indicates the parameter moments. A lack of similarity across plots suggests incorrect priors and may warrant re-estimation or more iterations.As shown in Figure 8, the curves converge toward each other, indicating a good model fit.Table 1. Calibrated ParametersParameter NameSymbolPrior DistributionPosterior DistributionDistribution TypeTime Preference Rateγ0.9680.967GammaLabor Force Growth Rateβ0.0350.04BetaSocial Security Tax Rateγ0.320.35GammaIntertemporal Substitution Elasticityγ0.920.95GammaIntra-period Substitution Elasticityγ0.790.81GammaLeisure Preference Rate (Age 1–30)γ0.290.31GammaLeisure Preference Rate (Age 31–55)γ11GammaProductivity Growth Rateβ0.0150.02BetaCapital Share in Productionγ0.530.61GammaPrivate Household Consumption Transfer Rateβ0.340.32BetaPrivate Household Health Transfer Rateγ0.180.19GammaPrivate Household Education Transfer Rateβ0.240.23BetaPublic Consumption Transfer Rateγ0.290.28GammaPublic Health Transfer Rateγ0.370.38GammaPublic Education Transfer Rateγ0.440.39GammaSource: Research StudyThe dynamic behavior of economic growth variables in response to various shocks was examined using impulse response functions. Table 3 summarizes how different generational cohorts respond to intergenerational transfers in key domains—household consumption, education, and healthcare, as well as public transfers.Overall, results indicate that household-based transfers are more effective and positively correlated with productivity and economic growth compared to public sector transfers. Households seem to allocate resources intergenerationally in a more optimal way, particularly in the domains of health and consumption. Conversely, public sector allocations often fail to produce the same economic impact, possibly due to inefficiencies in governance, planning limitations, and resource misallocation.Table 2. Production Response to Various Intergenerational Transfer ShocksShock TypeAge 0–24Age 25–64Age 65+All Age GroupsHousehold Consumption TransferPositive impact throughoutPositive impact throughoutNegative impact throughoutPositive impact throughoutHousehold Health TransferInitially negative, then positivePositive throughoutPositive throughoutPositive throughoutHousehold Education TransferNegative throughoutPositive throughoutNegative throughoutPositive throughoutPublic Consumption TransferNegative throughoutPositive throughoutPositive throughoutPositive throughoutPublic Health TransferInitially negative, then positiveInitially negative, then positiveNegative throughoutPositive throughoutPublic Education TransferNegative throughoutInitially negative, then positiveNegative throughoutPositive throughoutThe findings suggest that households tend to allocate intergenerational resources more efficiently, leading to higher productivity across most generations. The public sector, in contrast, appears less effective in aligning transfers with economic growth objectives. These discrepancies may be attributed to governance inefficiencies, widespread corruption, and the lack of long-term strategic planning.ConclusionNational Transfer Accounts (NTA) reflect the quantity and structure of economic flows across age groups and generations. These intergenerational flows are crucial as they embody a fundamental feature present in all societies. The findings of this study highlight that household-based transfers—particularly in consumption, education, and healthcare—are more effective than government-based transfers in enhancing human capital productivity across generations, thereby fostering economic growth.The results reveal that the age group of 25 to 64 years contributes most significantly to economic growth, consistent with the life-cycle theory as proposed by Ando and Modigliani and further supported by the intergenerational perspective of the NTA framework.Based on the empirical findings, the following policy recommendations are proposed:Enhancing Public Transfer Efficiency:Given that public transfers in consumption, education, and healthcare are generally less efficient—especially outside the 25–64 age range—it is recommended that the government allocate resources more effectively in accordance with the productivity levels of different generations. Such alignment could enhance the efficiency of public spending and improve intergenerational productivity outcomes.Facilitating Private Transfers:Since intergenerational transfers have a positive impact on labor productivity, and household-level transfers outperform public transfers in terms of effectiveness, it is recommended that the government minimize disruptions in private transfers by mechanizing and streamlining the transfer processes between households.Extending the Demographic Dividen: Considering the relatively limited contribution of the retired population to economic growth and their impact on both the first and second demographic dividends, policies should be designed to delay the depletion of these dividends. Potential strategies include promoting financial literacy in retirement, extending work life in low-intensity occupations, increasing human capital among the elderly, leveraging gender dividends by expanding female labor force participation, and improving consumption and healthcare patterns among retirees. Additionally, long-term strategic foresight in retirement policy is essential.Harnessing Youth Potential:On one hand, the young, educated population represents a latent advantage for growth and development; on the other hand, labor market limitations hinder their absorption, leading to rising unemployment and reduced youth productivity. Emphasis should be placed on fostering entrepreneurship among youth through legal, educational, and financial support. Encouraging youth-driven innovation, human resource planning, and investment in digital economic sectors—where younger generations demonstrate high adaptability—can help address this challenge.Aligning with Macro-Level Population Policies:At the macro policy level, these recommendations align with several of the Supreme Leader’s population policy guidelines, especially clauses 6, 8, and 10, which emphasize the importance of leveraging both demographic dividends. These include increasing life expectancy, promoting health and nutrition, empowering the working-age population through vocational and entrepreneurial training, and supporting rural and border populations through investment and job creation. Additionally, prioritizing knowledge-based economic development—consistent with successful global experiences—can enable Iran to fully capitalize on its second demographic dividend for achieving sustainable economic growth.AcknowledgmentsThe authors of this article sincerely express their gratitude to the editorial board and the esteemed members of the journal’s editorial team. Their support, attention to detail, and constructive guidance throughout the review and publication process have played a significant role in enhancing the scientific and editorial quality of this work. Undoubtedly, the valuable efforts of this dedicated team in advancing the journal’s academic mission and supporting researchers are worthy of appreciation and recognition.
Esfandiar Jahangard; Jamal Kakaie
Abstract
The COVID-19 pandemic became widespread in most countries of the world in late 2019. In addition to human casualties, it has also affected the economies of countries. According to IMF, the world economic growth in 2020 was -3.5 percent. However, the GDP’s growth rate of the Iran economy for ...
Read More
The COVID-19 pandemic became widespread in most countries of the world in late 2019. In addition to human casualties, it has also affected the economies of countries. According to IMF, the world economic growth in 2020 was -3.5 percent. However, the GDP’s growth rate of the Iran economy for 2020 has been positive and was reported as 1.5 percent, but there is evident that the economy has been affected by the coronavirus. In this paper, we evaluate the effect of the COVID-19 pandemic on the production, employment, and value-added of the economy of Iran by using the hypothetical extraction method of Dietzenbacher & Lahr (2013) and the Input-Output general equilibrium model of Zaytseva (2000). We have used the data from the input-output table of the Iran Central Bank in 2016 and the employment figures of the Statistics Center of Iran. The results show that the production and the value-added of the economy will decrease by 4.3 and 4 percent respectively. Among the value-added components, mixed-income and net operating surplus experienced the highest declining growth rate. Also, about 6.5 percent of the country's employees have been affected directly and indirectly by the Coronavirus.
