Assessment and Evaluation of Iran's Labor Force Participation up to 2061 using a System Dynamics Approach

Abstract
This research employs the System Dynamics (SD) methodology with the objective of analyzing and forecasting the Labor Force Participation Rate (LFPR) in Iran up to the year 2061. The LFPR was recognized as a key indicator of the labor market, and the factors influencing it, including social, economic, and demographic variables, were identified and formulated into a conceptual and quantitative simulation model. Utilizing the Vensim software, the impacts of variables such as population age structure, investment levels, fertility rate, marital status, and the discouragement level from job searching on the LFPR were examined and simulated over the period from 2006 to 2061. The results derived from simulating various scenarios indicate a significant potential for the economic participation rate to grow up to 60%, leading to an estimated employed population of around 35 million under the ideal scenario. Conversely, the adverse conditions scenario shows a decline in the participation rate to below 40%, with the employed population estimated at approximately 18 million people. The findings of this study underscore precise and targeted policymaking aimed at effectively capitalizing on demographic opportunities (the demographic window), strengthening investment, improving the fertility rate, and mitigating the feeling of discouragement within the labor market.

Extended Abstract
Purpose: The main goal of this study is to construct a dynamic simulation framework that projects Iran’s LFPR to 2061 through an integrated analysis of demographic, economic, and institutional influences. The System Dynamics method was selected to address the inherent limitations of static models, which often overlook the interdependencies of population structure, investment behavior, and labor market participation.
By employing feedback modeling, this framework captures the nonlinear interactions among variables such as fertility, education, marital patterns, and capital accumulation. It also allows for scenario testing, enabling policymakers to evaluate the consequences of policy interventions, such as increasing female labor participation, modifying retirement age, or enhancing childcare support, over long-term horizons rather than in short snapshots.
This model thus serves as a decision-support tool for policy formulation, offering insights into how coordinated demographic and economic strategies can maximize labor engagement during Iran’s demographic window of opportunity. To this end, it also seeks to counterbalance the potential adverse effects of aging, declining fertility, and job-market discouragement on overall participation.

Methodology: The SD-based approach was implemented in the three main stages of conceptualization, model formulation, and validation through simulation.
Conceptualization: The process began with an extensive review of empirical studies and statistical sources to identify the variables with direct and indirect influence on the LFPR. These include population dynamics (youth share, aging, fertility), economic indicators (investment, employment elasticity, sanctions impact), educational attainment, and labor discouragement. Experts from economics, sociology, and demography contributed during workshops to determine the causal directions and strengths.
Model Formulation in Vensim: Within Vensim, the variables were organized into Causal Loop Diagrams (CLDs) and later translated into stock-and-flow structures. The stocks represented accumulative variables like the total labor force or population of working age, while the flows denoted transitions such as workforce entry, retirement, or exit due to discouragement. Where exact data were unavailable, the relationships were parameterized based on logical approximations and expert judgment to preserve internal coherence.
Validation and Scenario Design: Historical data covering 2006-2022 were used for calibration. The validation included checking the model’s structural integrity and comparing simulated trends with the observed LFPR data. Three primary scenarios were then designed, including a Baseline (where LFPR stabilizes around 45%), an Ideal/Optimistic scenario (targeting 60% participation under favorable socio-economic policies), and an Adverse/Pessimistic scenario (LFPR below 40%, reflecting economic stagnation and demographic decline). The horizon for simulation extended to 2061 to capture the delayed effects of fertility and investment.

Findings and Discussion: The simulation results demonstrated how intertwined demographic behaviors and economic performance determine labor participation. Under the Baseline Scenario, structural inertia keeps participation around 45%, supported by modest investment and gradual aging. The Ideal Scenario, reinforced by effective investment growth and proactive family policies, raises participation toward 60%, bringing the employed population close to 35 million by 2061. Conversely, the Adverse Scenario, driven by economic contraction and persistent discouragement, lowers participation below 40%, reducing the employed population to roughly 18 million.
Three categories of the leverage variables emerged as crucial:
Fertility and Marriage Patterns: Rising marriage age and low fertility shrink the potential workforce. Without addressing delayed family formation, the country risks losing its demographic dividend. Programs encouraging timely marriage and supporting childbearing would safeguard future labor supply.
Investment and Economic Climate: The continuation of sanctions and financial uncertainty suppresses productive investment. The model shows that even moderate improvement in investment inflows amplifies labor participation through a positive feedback loop of job creation, income stability, and reduced discouragement.
Discouragement Behavior: When job opportunities stagnate, discouraged individuals exit the labor market, forming a self-reinforcing negative loop that steadily reduces LFPR. Stimulating confidence through economic transparency and active employment programs is, therefore, essential for breaking this cycle.
The model further revealed indirect effects such as the role of education and gender composition in moderating feedback loops. They reinforce the importance of integrated cross-sectoral policies.

Conclusions and Policy Implications: Achieving sustained improvement in Iran’s LFPR depends on transforming negative loops (aging, discouragement, low fertility) into virtuous cycles that reinforce economic participation. The study recommends the following strategic priorities:
Demographic Stabilization: Introduce comprehensive family-support policies, including financial incentives for early marriage, affordable housing, and accessible childcare systems. These measures will help maintain a stable labor supply and ensure continuity through the demographic transition period.
Investment and Growth Strategy: To offset sanctions and volatility, adopt long-term de-risking mechanisms such as domestic investment guarantees, public-private partnerships, and knowledge-based enterprise incentives. Strategic investment in job-rich sectors (manufacturing, green energy, and services) will drive sustained participation growth.
Labor Market Activation: Redefine policy focus from unemployment mitigation to broader labor force re-engagement. Expanding vocational training, introducing skill-matching systems, and improving employment security can curb discouragement and consolidate participation trends.
Integrated Governance: Coordination among ministries of labor, economy, and social welfare is critical. The SD simulation results highlight that isolated measures lose efficiency; only synchronized demographic and economic policies can generate lasting structural improvement.
Transitioning from the current path (~45% participation) toward the ideal 60% outcome requires persistence, social consensus, and institutional continuity. Through maintaining demographic vitality, improving economic resilience, and fostering psychological confidence in the labor market, Iran can successfully harness its workforce potential up to 2061.