Forecasting changes in agricultural employment rate in Gilan Province using some economic indicators

Document Type : Research Paper

Authors

1 Assistant Professor and Ph.D. Candidate, Agricultural Development, Abo Alisina University, Hamedan, Iran

2 Associate Professor, Faculty of Economics and Silence of Society, Abo Alisina University, Hamedan, Iran

Abstract

The overall aim of the present study was to estimate the time series of agricultural employment in the Gilan province during 1976-2011, and modeling and forecasting employment using artificial neural networks for years 2012-2019. For this purpose, employment series calculated by interpolation and input variables selected based on previous theoretical and empirical research. Finally, number of agricultural work force predicted through designing and training of different neural networks architectures. Required data obtained through population and housing report of 1976-2011 and provincial statistical year books. Results showed that number of employees during the period from 2012-2014 will be lower than in 2011 and then during 2015 to 2019 will be increased. Due to lack of time series data of economic variables at the regional level, this research is an essential and primary step to achieve reliable statistics of number of agricultural employment at provincial level that Provide required data for future studies on labor market and can be used for planning and policy making by related authorities.

Keywords

References

Abdi, E. (2003). Forecasting Foreign Turism Demand Using Artificial Neural Network and Fuzzy Regression. MS dissertation. Isfahan University of Technology. Iran. (In Farsi).
Amini, A., Nahavandi, M. &  Saffaripour, M. (1998). Estimation of the Statistics and Capital Stocks time Series in Iran's Economic sector. The Journal of Planning and Budgeting, 3(7,8), 69-97. (In Farsi).
Amini, A. (1999). Estimation and Analysis of Demand Function of the Agricultural Workforce and Forecasting it for the Third Development Plan Period. The Journal of Planning and Budgeting, 4(4,5),3-29. (In Farsi).
Amini, A. (2000). Estimating the Estatistics of Employment Time series in the Iran Economy during the period 1966-1996. The Journal of Planning and Budgeting, 5(3), 35-68. (In Farsi).
Amini, A., Neshat, H. &  Eslahchi, M. (2007). Reviewing the Estimation of Employees' Population time Series in Iran's economic sector. The Journal of Planning and Budgeting, 12(1), 47-97. (In Farsi).
Balli, S. & Tarımer. I. (2013) An Application of Artificial Neural Networks for Prediction and Comparison with Statistical Methods. ELEKTRONIKA IR ELEKTROTECHNIKA, 19(2), 101-105.
Baseri, B. & Jahangard, E. (2007). An Examination of Job Creation Capacity in Iranian Agriculture. Agricultural Economics and Development, 15(4), P 119-146. (In Farsi).
Basheer, M., Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43, 3–31.
Chen, C. F., Lai, M. C. and Yeh, C. C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26, 281–287.
Cheong, D., Jansen, m. and Peters, R. (2013).Shared Harvests: Agriculture, Trade and Employment.International Labour Office and United Nations Conference on Trade Development - Geneva: ILO and UNCTAD.
Claveria, O & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. time series models. Economic Modelling, 36, 220–228.
Dethier, J. and Effenberger, A. (2012). Agriculture and development: A brief review of the literature. Economic Systems, 36, 175–205.
Ghadimi, M. and Moshiri, S. (2002). Modeling and Forecasting Iranian Economic Growth Using Artificial Neural Networks. Iranian Economic Research, 12, 1-33. (In Farsi).
Karbasi, A., Asnaashari, H. and Aghel, H. (2009). Forecasting Agricultural Sector Employment in Iran. Journal of Economic and Agriculture Development, 22(1), 31-43. (In Farsi).
Keikha, A. (2014). Prediction of the Surplus Rate of Rural Labor Force in Iran from 2012 to 2040. Transaction on Economic Research, 1(1), 22- 26.
Khashei, M., Bijari, M. (2010). Gold Price Forecasting Using Hybrid Artificial Neural Networks with Fuzzy Regression Model. Journal of Industrial Engineering, 44(1), 39-47. (In Farsi).
Khayyati, M. (2006). Gilan Land Use Plan. Gilan Management and Planning Organization. (In Farsi).
Mbarek, M. & Feki, R. (2013). GDP and Unemployment Rate in Turkey: An Empirical Study using Neural Networks. International Journal of Economics, Finance and Management, 2(1), 154-159.
Li, G., Xu, S. and Li, Z. (2010). Short-Term Price Forecasting For Agro-products Using. International Conference on Agricultural Risk and Food Security, Beijing, China. 278–287
Lu, C., Chiu, C. and Yang, J. L. (2009). Integrating Nonlinear Independent Component Analysis and Neural Network in Stock Price Prediction. 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE), Tainan, Taiwan. 623-614.
Najafi, B. and Tarazkar, M. (2006). Forecasting Export of Pistachio From Iran: Application of Artificial Neural Networks. Iranian Journal of Trade Studies, 10(39),191-214. (In Farsi).
Najafi , B., Zibaei , M. Sheikhi , M. H. and Tarazkar, M. H. (2007). Forecasting Price of Some Crop Products in Fars Province: Application of Artificial Neural Network . Journal of Science and Technology of agricultural and Natural Resource, 11(1),501-512. (In Farsi).
Patuelli, R., Reggiani, A., Nijkamp, P. and Blien, U. (2006). New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets. Spatial Economic Analysis, 1, 7-30.
Samani, M., Gohari-Moghadam, M. and Safavi, A.A. (2007). A simple neural network model for the determination of aquifer parameters. journal of Hydrology, 340, 1– 11.
Sobhani, H. & Azizmohammadloo, H. (2005). An Analysis of the investment Expenditures Contribution to Job Creation in Manufacturing Subsectors in Iran. Quarterly Iranian Economic Research, 24, 1-31. (In Farsi).
Thammano, A. and Ruxpakawong, P. (2009). Feed-forward Neural Network with Multi-valued Connection Weights. 6th International Symposium on Neural Networks (ISNN), Wuhan, China. 229- 237.
Zara-Nejad, M. and Hamid, S. (2009). Prediction of Inflation Rates in Iran Using Dynamic Artificial Neural Networks (Time Series Approach). Quarterly Journal of Quantitative Economics, 6(1), 145-167. (In Farsi).
Zhang, Q., Sun, S. (2009). Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification. International Conference on Neural Information Processing (ICONIP). Part I. 349-353.
Zhang, G. P. and Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160, 501–514.
 
Iranian Journal of Agricultural Economics and Development Research
Volume 45, Issue 4 - Serial Number 4
January 2015
Pages 651-661

Files

Share

How to cite

Statistics