A Probabilistic Approach for Modeling the Empirical Economic Loss Impacts of Data Surveys on Small and Medium-Sized Enterprises; A case Study of the November 2016 Sarpol-e Zahab Earthquake

Severe earthquakes have profound economic impacts that extend beyond direct physical damage, significantly affecting businesses especially small and medium-sized enterprises. The previous study has indicated that these indirect losses (including interruption of productivity, services and goods) can exceed direct damage costs, highlighting the needs for accurate assessments of after-disasters economic impacts. This paper introduces a new methodology for estimating such economic impacts using the case study of Sarpol-e Zahab earthquake in November 2016, with a magnitude of 7.3 Mw. We developed an empirical model using surveyed data from 71 small and medium-sized enterprises across five industrial zones at different distances from the epicenter. Data were collected through face-to-face interviews, capturing both direct physical damage and business interruption losses, the latter was surveyed monthly from sales amounts for a post-earthquake 24-month period. A probabilistic model combining logarithmic regression and normal distribution fitting was employed to analyze the impacts based on organizational activity sectors and structural types. Statistical analysis was used to estimate downtime/restoration curves and economic recovery rates, providing a comparative assessment of economic resilience including internal and external economic resilience. The additional results for predicting normalized direct and business interruption losses versus a range of pga demonstrate that the internal resilience have about 10 percent effectiveness while external resilience have approximately 30 percent effectiveness on economic recovery process. This methodology will be useful for policy makers and governments to support individual businesses by make them more resilient against earthquakes.