تأثیر بهره وری نیروی انسانی بر رشد اقتصادی: یک رویکرد بین نسلی (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
در این مقاله، با بهره گیری از مدل نسل های همپوشان سه دوره ای، تأثیر بهره وری نیروی انسانی بر رشد اقتصادی ایران بررسی شده است. مسئله ی اصلی پژوهش، چگونگی تأثیرگذاری انتقالات بین نسلی بر بهره وری منابع انسانی و در نتیجه رشد اقتصادی کشور است. این پژوهش با روش تحلیلی-کمی و با استفاده از داده های فصلی از سال ۱۳۷۰ تا ۱۴۰۰ انجام شده است. برای برآورد مدل، از روش تعادل عمومی پویای تصادفی ( DSGE ) بهره گرفته شده است. با استفاده از داده های حساب های ملی و بودجه خانوار، و ایجاد ارتباط میان آن ها، میزان بهره وری نیروی انسانی در نسل های مختلف استخراج گردید. نتایج نشان می دهد انتقالات مربوط به خانواده در بخش های مصرف، آموزش و بهداشت، نقش مؤثری در افزایش بهره وری داشته اند. همچنین، بیشترین تأثیر بر رشد اقتصادی از سوی گروه سنی ۲۵ تا ۶۴ سال بوده که با نظریه چرخه عمر سازگار است.The Effects of Human Resource Productivity on Economic Growth - An Intergenerational Approach
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. Introduction The 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 Material This 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 Discussion Although 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 Parameters Parameter Name Symbol Prior Distribution Posterior Distribution Distribution Type Time Preference Rate γ 0.968 0.967 Gamma Labor Force Growth Rate β 0.035 0.04 Beta Social Security Tax Rate γ 0.32 0.35 Gamma Intertemporal Substitution Elasticity γ 0.92 0.95 Gamma Intra-period Substitution Elasticity γ 0.79 0.81 Gamma Leisure Preference Rate (Age 1–30) γ 0.29 0.31 Gamma Leisure Preference Rate (Age 31–55) γ 1 1 Gamma Productivity Growth Rate β 0.015 0.02 Beta Capital Share in Production γ 0.53 0.61 Gamma Private Household Consumption Transfer Rate β 0.34 0.32 Beta Private Household Health Transfer Rate γ 0.18 0.19 Gamma Private Household Education Transfer Rate β 0.24 0.23 Beta Public Consumption Transfer Rate γ 0.29 0.28 Gamma Public Health Transfer Rate γ 0.37 0.38 Gamma Public Education Transfer Rate γ 0.44 0.39 Gamma Source: Research Study The 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 Shocks Shock Type Age 0–24 Age 25–64 Age 65+ All Age Groups Household Consumption Transfer Positive impact throughout Positive impact throughout Negative impact throughout Positive impact throughout Household Health Transfer Initially negative, then positive Positive throughout Positive throughout Positive throughout Household Education Transfer Negative throughout Positive throughout Negative throughout Positive throughout Public Consumption Transfer Negative throughout Positive throughout Positive throughout Positive throughout Public Health Transfer Initially negative, then positive Initially negative, then positive Negative throughout Positive throughout Public Education Transfer Negative throughout Initially negative, then positive Negative throughout Positive throughout The 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. Conclusion National 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: <strong>Enhancing Public Transfer Efficiency</strong><strong>:</strong>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. <strong>Facilitating Private Transfers</strong><strong>:</strong>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. <strong>Extending the Demographic Dividen:</strong><strong> </strong>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. <strong>Harnessing Youth Potential</strong><strong>:</strong>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. <strong>Aligning with Macro-Level Population Policies</strong><strong>:</strong>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. Acknowledgments The 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.
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