A. Al-Badawi, S., T. Al-Wasity, R. (2023). AN ECONOMIC ANALYSIS OF THE MOST IMPORTANT VARIABLES AFFECTING AGRICULTURAL EMPLOYMENT IN IRAQ FOR THE PERIOD (1998 - 2019). Alustath, 21(1), 224-249. doi: 10.32649/ajas.2023.179764
S. A. Al-Badawi; R. T. Al-Wasity. "AN ECONOMIC ANALYSIS OF THE MOST IMPORTANT VARIABLES AFFECTING AGRICULTURAL EMPLOYMENT IN IRAQ FOR THE PERIOD (1998 - 2019)". Alustath, 21, 1, 2023, 224-249. doi: 10.32649/ajas.2023.179764
A. Al-Badawi, S., T. Al-Wasity, R. (2023). 'AN ECONOMIC ANALYSIS OF THE MOST IMPORTANT VARIABLES AFFECTING AGRICULTURAL EMPLOYMENT IN IRAQ FOR THE PERIOD (1998 - 2019)', Alustath, 21(1), pp. 224-249. doi: 10.32649/ajas.2023.179764
A. Al-Badawi, S., T. Al-Wasity, R. AN ECONOMIC ANALYSIS OF THE MOST IMPORTANT VARIABLES AFFECTING AGRICULTURAL EMPLOYMENT IN IRAQ FOR THE PERIOD (1998 - 2019). Alustath, 2023; 21(1): 224-249. doi: 10.32649/ajas.2023.179764

AN ECONOMIC ANALYSIS OF THE MOST IMPORTANT VARIABLES AFFECTING AGRICULTURAL EMPLOYMENT IN IRAQ FOR THE PERIOD (1998 - 2019)

ANBAR JOURNAL OF AGRICULTURAL SCIENCES
Volume 21, Issue 1, June 2023, Pages 224-249    PDF (1021.09 K)
Document Type: Research Paper
DOI: 10.32649/ajas.2023.179764
Authors
S. A. Al-Badawi1; R. T. Al-Wasity2
1Planet Protection Dep, Iraqi Ministry of Agric., Baghdad, Iraq
2Coll. of Agric. Engin. Sci. University of Baghdad
Abstract
The research aims to investigate the most important consequences of the economic shocks that Iraq has suffered in the past decades through some agricultural indicators with employment levels in the agricultural sector (agricultural wages, agricultural investment, agricultural output and agricultural mechanization). To discuss the problem during the period 1998-2019 the authors adopted set of methodology based on an important tool in economic studies at present days, which is the analysis of the structural vector autoregression – SVAR model and the subsequent analysis of structural variance decomposition and structural impulse response, in order to investigate the most important shocks to the economy and the impact on the development of indicators and their impact on these shocks among themselves. The research reached several results in this regard, including the most important and strongest variable shock contributed to the development of indicators and their impact on these shocks among themselves. The agricultural wages (the first shock) is the most influencing variable that effect in variance decomposition of agricultural employment, where the shock effect was 82% in the first year and slowly declined to 69.7% at the end of the period, which means the importance of increasing wages in stimulating the offer of work and thus employment in the agricultural sector, and this variable has a permanent and strong impact in the long run.   
Keywords
SVAR; Accumulation impulse respond; Structural variance decomposition
References
  1. Al-Atabi, H. A M. (2022). An economic analysis of the impact of economic shocks on some agricultural indicators in Iraq for the period (1990-2019). PhD dissertation. University of Baghdad/ College of Agricultural Engineering Sciences - Department of Agricultural Economics.
  2. Al-Sudani, A. H. (2020). An analytical economic study of the impact of some economic variables on the productivity and structure of agricultural labor in Iraq for the period 1990-2017. PhD thesis. University of Baghdad, College of Agricultural Engineering Sciences, Department of Agricultural Economics.
  3. Ajmair, M., Gilal, M. A., Hussain, K., and Iqbal, Z. (2018). Determinants of sectoral growth in Pakistan: An analysis of SVAR. The Pakistan Journal of Social Issues, 9(1): 10-18.
  4. Arab Organization for Agricultural Development. (2019). Arab Agricultural Statistics Yearbook - Volume 40.‏
  5. Asaleye, A. J., Maimako, R. F., Lawal, A. I., Inegbedion, H., and Popoola, O. (2021). Monetary policy channels and agricultural performance: evidence from Nigeria. Asian Economic and Financial Review, 11(3): 205-218.
  6. Blanchard, O., and Perotti, R. (2002). An empirical characterization of the dynamic effects of changes in government spending and taxes on output. the Quarterly Journal of economics, 117(4): 1329-1368.
  7. Dada, Eme A. (2018). Jobless Growth in Nigeria: Determining Employment Intensive Sectors, Journal of African Development, African Finance and Economic Association (AFEA), 20(2): 69-79.
  8. Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a): 427-431.
  9. Gottschalk, J. (2001). An introduction into the SVAR methodology: identification, interpretation and limitations of SVAR models (No. 1072). Kiel working paper.
  10. Gujarati, D. N., Porter, D. C., and Gunasekaran, S. (2012). Basic econometrics. Tata McGraw-Hill education.
  11. Kunst, R. M. (2011). Vector autoregressions - Based on the book ‘New In-troduction to Multiple Time Series Analysis’ by Helmut Lutkepohl. University of Vienna and Institute for Advanced Studies Vienna. Download: https://homepage.univie.ac.at/robert.kunst/varpres2.pdf.
  12. Lee, J. H., Lim, E. S., and Hwang, J. (2012). Panel SVAR model of women's employment, fertility, and economic growth: A comparative study of East Asian and EU countries. The Social Science Journal, 49(3): 386-389.
  13. Nelson, C. R., and Plosser, C. R. (1982). Trends and random walks in macroeconmic time series: some evidence and implications. Journal of monetary economics, 10(2): 139-162.
  14. Phillips, P. C., and Perron, P. (1988). Testing for a unit root in time series re-gression. Biometrika, 75(2): 335-346.
  15. Ravnik, R., and Žilić, I. (2011). The use of SVAR analysis in determining the effects of fiscal shocks in Croatia. Financial theory and practice, 35(1): 25-58.
  16. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, 48: 1-48.
Statistics
Article View: 122
PDF Download: 114


Journal Management System. Powered by ejournalplus.com