Analysis of effective factors and forecasting of labor demand in the perspective of 2026 Iranian agriculture

Document Type : Research Paper

Authors

1 Ph.d Student, Department of Agricultural Economics, Faculty of Agricultural Engineering, shiraz University,, Iran

2 Ph.d Student, Department of Agricultural Economics, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Mazandaran, Iran

3 Associate Professor, Department of Agricultural Economics, Ardakan University, Ardakan, Iran

4 Ferdowsi University Mashhad

Abstract

The purpose of this study is to investigate the effect of imports in the agricultural sector on the demand of agricultural labor (employment) in Iran for the period 1978-2019 and forecast employment in this sector for the outlook 2026. In the present study, labor demand in the agricultural sector is a function of wages, value added in the agricultural sector, capital stock and imports. To estimate the aggregate relationship and short-term and long-term dynamics, vector autoregression patterns and vector error correction model were used. Also, the neural network designed due to greater efficiency than other models was used to predict employment in the agricultural sector of Iran for the years 2020 to 2026. The results showed that in the long run, value added in the agricultural sector has a positive effect and capital stock, wages and imports have a negative effect on employment in the agricultural sector.
The results also showed that the trend of reducing the share of employment in the agricultural sector of Iran in relation to the total economy until 2026, will continue as in previous decades and from an annual growth of 1.03 percent to 0.77 percent. Ultimately, government policies must be implemented with the aim of increasing the value added of the agricultural sector in order to provide new jobs, food security and self-sufficiency.

Keywords

References

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Agricultural Economics
Volume 14, Issue 3 - Serial Number 3
December 2020
Pages 131-155

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