Driving factors on unemployment in Iran’s provinces: Do temporal and spatial dynamics matter?

Abstract

Unemployment is an important index describing an economic situation and the development of societies. The existence of unemployment means a loss of production factor that is not savable and irrecoverable. Therefore, it is essential to proper planning and policymaking to control and reduce unemployment, and it is necessary to conduct extensive studies on unemployment. Since there is a probability of autocorrelation between the unemployment rate of provinces, it is necessary to pay attention to spatial dynamics. In addition, controlling unemployment requires a long‐run policy, and it cannot be reduced over periods as short as 1 year. Correspondingly, unemployment in a province can be expected to be dynamic by nature and dependent on the unemployment in the previous period. Thus, temporal dynamics should be considered when examining unemployment’s driving factors. To do that, this study seeks to identify the driving factors of unemployment by emphasizing temporal and spatial dynamics in 28 of Iran’s provinces for the period 2001–2019 using the dynamic SAR model. The result indicates that the lag time of unemployment positively affects unemployment in the current time. Additionally, the unemployment of neighbors has a negative and statistically significant impact on the unemployment of provinces. The effects of GDP per capita, government budget per capita, human capital, inflation rate, industrial concentration, the share of the added value by agriculture in GDP, and the share of value added by services in GDP on the unemployment rate are negative and statistically significant.