SFeDu
of.211, Gorgoko St., 88 
Rostov-on-Don
Russia
344002
+7 (863) 250-59-54
This email address is being protected from spambots. You need JavaScript enabled to view it.
te@sfedu.ru

TERRA ECONOMICUS

  • Home
  • Issues
  • 2023
  • No 4
  • Application of ARDL model to capture the differences in causal relations operating in crop production across geographic regions: A study on Latin America and South Asia

Application of ARDL model to capture the differences in causal relations operating in crop production across geographic regions: A study on Latin America and South Asia


TERRA ECONOMICUS, , Vol. 21 (no. 4),

The identification of the causal relations between the determinants of crop production and the crop production index has serious policy implications. Given the importance attached to agriculture in Latin America & Caribbean Islands and Southern Asia, understanding the differences in the causal relations operating in crop production in both the regions is necessary, especially to capture the policy differentials, if any required, to combat underdevelopment. With this backdrop, the present study aims to figure out the differences between both the regions in the nature of causal relations existing among select factors associated with the crop production. The study is based on the World Bank data. The model used in the present study consists of a dependent variable in the form of crop production index that has dependence on its lagged values. The dependent variable is also influenced by the lagged values of a set of independent variables – share of permanent crop land in total land available in the region, fertilizer usage, carbon dioxide emission and GDP growth per capita. The study uses auto regressive distributed lag (ARDL) model for data analysis. The study finds that in Latin America & Caribbean Region no long run relations exist between the dependent and the independent variables, while in South Asia significant long run relations exist between them.
Citation: Neogi D. (2023). Application of ARDL model to capture the differences in causal relations operating in crop production across geographic regions: A study on Latin America and South Asia. Terra Economicus 21(4), 106–122. DOI: 10.18522/2073-6606-2023-21-4-106-122


Keywords: causal relations; crop production; carbon dioxide emission; GDP growth rate per capita; fertilizer

JEL codes: C01; N50; N56

References:
  • Ayyildiz, M., Erdal, G. (2021). The relationship between carbon dioxide emission and crop and livestock production indexes: A dynamic common correlated effects approach. Environmental Science and Pollution Research 28, 597–610. DOI: 10.1007/s11356-020-10409-8
  • Blyde, J., Fernandez, A. (2004). Why does Latin America grow more slowly? Inter-American Development Bank, Economic and Social Study Series, October. https://publications.iadb.org/publications/english/document/Why-does-Latin-America-Grow-More-Slowly.pdf
  • Devarajan, S., Nabi, I. (2006). Economic growth in South Asia: Promising, unequalizing, sustainable? Economic and Political Weekly 41(33), 3573–3580. https://www.jstor.org/stable/4418586
  • Fukuyama, F., Marwah, S. (2000). Dimensions of development. Journal of Democracy 11(4), 80–94. Project MUSE, doi:10.1353/jod.2000.0076.
  • Kohli, A. (2012). Coping with globalization: Asian versus Latin American strategies of development, 1980–2010. Brazilian Journal of Political Economy 32(4). DOI: 10.1590/S0101-31572012000400001
  • Kripfganz, S., Schneider, D. (2018). ARDL: Estimating autoregressive distributed lag and equilibrium correction models. London Stata Conference 2018, Stata Users Group. http://repec.org/usug2018/uk18_Kripfganz.pdf
  • Lin, C.Y (1988). East Asia and Latin America as contrasting models. Economic Development and Cultural Change 36(3), Supplement: Why does overcrowded, resource-poor East Asia succeed: Lessons for the LDCs? (Apr., 1988), S153–S197. https://www.jstor.org/stable/1566542?seq=1#metadata_info_tab_contents
  • Mulatu, D., Eshete, Z., Gatiso, T. (2016). The impact of CO2 Emissions on agricultural productivity and household welfare in Ethiopia: A computable general equilibrium analysis. Environment for Development Discussion Paper Series, EfD DP 16-08, March. https://www.efdinitiative.org/sites/default/files/publications/efd-dp-16-08.pdf
  • Nissanke, M., Thorbecke, E. (2010). The Poor under Globalization in Asia, Latin America, and Africa. University Press Scholarship Online. DOI: 10.1093/acprof:oso/9780199584758.001.0001
  • Pesaran, M., Shin, Y., Smith, R. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16(3), 289–326. http://www.jstor.org/stable/2678547
  • Sah, D., Devakumar, A. (2018). The carbon footprint of agricultural crop cultivation in India. Carbon Management 9(3). DOI: 10.1080/17583004.2018.1457908
  • Shrestha, M., Bhatta, G. (2018). Selecting appropriate methodological framework for time series data analysis. The Journal of Finance and Data Science 4, 71–89. DOI: https://doi.org/10.1016/j.jfds.2017.11.001
  • Tsunekawa, K. (2019). Emerging states in Latin America: How and why they differ from their Asian counterparts. In: Tsunekawa, K., Todo, Y. (eds.) Emerging States at Crossroads. Emerging-Economy State and International Policy Studies. Singapore: Springer. DOI: 10.1007/978-981-13-2859-6_4
  • Wiebe, K., Sulser, T., Dunston, S., Rosegrant, M., Fuglie, K., Willenbockel, D., Nelson, G. (2021). Modeling impacts of faster productivity growth to inform the CGIAR initiative on Crops to End Hunger. PLoS ONE 16(4), e0249994. DOI: 10.1371/journal.pone.0249994
  • Zettelmeyer, J. (2006). Growth and reforms in Latin America: A survey of facts and arguments. IMF Working Paper WP/06/210. https://www.imf.org/external/pubs/ft/wp/2006/wp06210.pdf
Publisher: Southern Federal University
Founder: Southern Federal University
ISSN: 2073-6606