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The evolving role of agricultural technology indicators and economic growth in rural poverty: has the ideas machine broken down?

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Abstract

The objective of the study is to examine the impact of technical progress in agriculture on changes in rural poverty in Pakistan by using annual data from 1975–2011. Data is analyzed by the set of sophisticated econometric techniques i.e., cointegration theory, Granger causality test and variance decomposition, etc. The results reveal that agricultural technology indicators act as an important driver to alleviate rural poverty in Pakistan. Granger causality test indicate that causality runs from technological indicators to rural poverty but not vice versa. However, agricultural irrigated land and industry value added, both does not Granger cause rural poverty, which holds neutrality hypothesis between the variables. Variance decomposition analysis shows that among all the technological indicators, agricultural machinery in form of tractors have exerts the largest contribution to changes in rural poverty in Pakistan. The study concludes that agricultural technology indicators are closely associated with economic growth and rural poverty in Pakistan. Technology in Pakistan has a low pace but still old technology continuously contributed towards poverty reduction. The question whether idea machine is broken down or not? Still need further exploration.

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Notes

  1. If the series are integrated with the mixture of order of integration i.e., I(0) and I(1), it implies bonds testing approach which was proposed by Pesaran et al. (2001).

References

  • ACIAR.: The contribution of agricultural growth to poverty reduction. ACIAR impact assessment series 76. The Australian Centre for International Agricultural Research (ACIAR), Canberra, Australia. (2012)

  • Alene, A.D., Coulibaly, O.: The impact of agricultural research on productivity and poverty in sub-Saharan Africa. Food Policy 34(2), 198–209 (2009)

    Article  Google Scholar 

  • Ali, A., Sharif, M.: Impact of farmer field schools on adoption of integrated pest management practices among cotton farmers in Pakistan. J. Asia Pac. Econ. 17(3), 498–513 (2012)

    Article  Google Scholar 

  • Alston, J.M., Martin, W.J., Pardey, P.G.: Influences of agricultural technology on the size and importance of food price variability. In: NBER Conference on Economics of Food Price Volatility, Seattle, Washington, August 15–16, 2012 (2012)

  • Asfaw, S., Kassie, M., Simtowe, F., Lipper, L.: Poverty reduction effects of agricultural technology adoption: a micro-evidence from rural Tanzania. J. Dev. Stud. 48(9), 1288–1305 (2012a)

    Article  Google Scholar 

  • Asfaw, S., Shiferaw, B., Simtowe, F., Lipper, L.: Impact of modern agricultural technologies on smallholder welfare: evidence from Tanzania and Ethiopia. Food Policy 37(3), 283–295 (2012b)

    Article  Google Scholar 

  • Davis, K., Nkonya, E., Kato, E., Mekonnen, D.A., Odendo, M., Miiro, R., Nkuba, J.: Impact of farmer field schools on agricultural productivity and poverty in East Africa. World Dev. 40(2), 402–413 (2012)

    Article  Google Scholar 

  • Dickey, D., Fuller, W.: Distribution of the estimators for autoregressive time-series with a unit root. J. Am. Stat. Assoc. 74(2), 427–431 (1979)

    Google Scholar 

  • Dickey, D., Fuller, W.: Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49(1), 1057–1072 (1981)

    Article  Google Scholar 

  • Ding, S., Meriluoto, L., Reed, W.R., Tao, D., Wu, H.: The impact of agricultural technology adoption on income inequality in rural China: evidence from Southern Yunnan Province. China Econ. Rev. 22(3), 344–356 (2011)

    Article  Google Scholar 

  • Engle, R.F., Granger, C.W.J.: Co-integration and error-correction: representation, estimation and testing. Econometrica 55(2), 251–276 (1987)

    Article  Google Scholar 

  • FAO.: Contribution of agricultural growth to reduction of poverty, hunger and malnutrition. Food and Agriculture Organization, United Nations. http://www.fao.org/docrep/016/i3027e/i3027e04.pdf (2012). Accessed 15 Oct 2012

  • GoP.: Government of Pakistan, Economic Survey of Pakistan (2010–11). Government of Pakistan, Finance Division, Economic Advisor’s Wing, Islamabad (2011)

  • GoP.: Government of Pakistan, Economic Survey of Pakistan 2011–12. Government of Pakistan, Finance Division, Economic Advisor’s Wing, Islamabad (2012)

  • Granger, C.W.J.: Causality, cointegration and control. J. Econ. Dyn. Control. 12, 551–559 (1988)

    Google Scholar 

  • Hassine, N.B.: Trade liberalization, agricultural productivity and poverty in the Mediterranean region. Eur. Rev. Agric. Econ. 36(1), 1–29 (2009)

    Article  Google Scholar 

  • Headey, D., Alauddin, M., Rao, D.S.P.: Explaining agricultural productivity growth: an international perspective. Agric. Econ. 41(1), 1–14 (2010)

    Article  Google Scholar 

  • Hussain, A.: Agriculture growth and poverty reduction: a policy perspective. Paper presented at the international seminar on management of the Pakistan economy, Lahore School of Economics, Pakistan (2005)

  • IFPRI.: Estimating the impact of agricultural technology on poverty reduction in rural Nigeria. The International Food Policy Research Institute, IFPRI Discussion Paper 00901. http://www.ifpri.org/sites/default/files/publications/ifpridp00901.pdf (2009). Accessed 15 Dec 2012

  • Janvary, A., Sadoulet, E.: Agricultural growth and poverty reduction: additional evidence. World Bank Res. Obs. 25(1), 1–20 (2010)

    Article  Google Scholar 

  • Javed, Z.H., Farooq, M., Ali, H.: Technology transfer and agricultural growth in Pakistan. Pakistan J. Agric. Sci. 47(1), 82–87 (2010)

    Google Scholar 

  • Johansen, S.: Statistical analysis of cointegrating vectors. J. Econ. Dyn. Control 12, 231–254 (1988)

    Article  Google Scholar 

  • Johansen, S., Juselius, K.: Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxf. Bull. Econ. Stat. 52(1), 169–210 (1990)

    Google Scholar 

  • Kassie, M., Shiferaw, B., Muricho, G.: Agricultural technology, crop income, and poverty alleviation in Uganda. World Dev. 39(10), 1784–1795 (2011)

    Article  Google Scholar 

  • Khan, R.E.A., Bashir, H.N.: Trade, poverty and inequality nexus: the case of Pakistan. World Appl. Sci. J. 18(5), 722–726 (2012)

    Google Scholar 

  • MacKinnon, J.G.: Numerical distribution functions for unit root and cointegration tests. J. Appl. Econ. 11, 601–618 (1996)

    Article  Google Scholar 

  • Mendola, M.: Agricultural technology adoption and poverty reduction: a propensity-score matching analysis for rural Bangladesh. Food Policy 32(1), 372–393 (2007)

    Article  Google Scholar 

  • Pesaran, M.H., Shin, Y., Smith, R.: Bounds testing approaches to the analysis of level relationships. J. Appl. Econ. 16(3), 289–326 (2001)

    Google Scholar 

  • Simtowe, F., Kassie, M., Asfaw, S., Shiferaw, B., Monyo, E., and Siambi, M.: Welfare effects of agricultural technology adoption: the case of improved groundnut varieties in rural Malawi. Paper presented at the International Association of Agricultural Economists (IAAE) triennial conference, Foz do Iguaçu, Brazil, 18–24 August, 2012 (2012)

  • Suryahadi, A., Hadiwidjaja, G., Sumarto, S.: Economic growth and poverty reduction in Indonesia before and after the Asian financial crisis. Bull. Indones. Econ. Stud. 48(2), 209–227 (2012)

    Article  Google Scholar 

  • The Economist.: Innovation pessimism: has the ideas machine brokendown? http://www.economist.com/news/briefing/21569381-idea-innovation-and-new-technology-have-stopped-driving-growth-getting-increasing (2013). Accessed 14 Jan 2013

  • Toda, H.Y., Yamamoto, T.: Statistical inference in vector autoregressions with possibly integrated processes. J. Econ. 66, 225–250 (1995)

    Google Scholar 

  • World Bank.: Agriculture and poverty reduction. World Development Report-2008. http://siteresources.worldbank.org/SOUTHASIAEXT/Resources/223546-1171488994713/3455847-1192738003272/Brief_AgPovRedctn_web.pdf (2008). Accessed 21 Sept 2012

  • World Bank.: Agriculture: an engine for growth and poverty reduction. International Development Association (IDA). http://siteresources.worldbank.org/IDA/Resources/IDA-Agriculture.pdf (2009). Accessed 25 June 2012

  • World Bank.: Food price watch, World Bank 2011, Washington, D.C. http://www.worldbank.org/foodcrisis/food_price_watch_report_feb2011.htm (2011). Accessed 21 July 2012

  • World Bank.: World Development Indicators–2012. World Bank, Washington D.C. (2012)

  • Wu, H., Ding, S., Pandey, S., Tao, D.: Assessing the impact of agricultural technology adoption on farmers’ well-being using propensity-score matching analysis in rural China. Asian Econ. J. 24(2), 141–160 (2010)

    Article  Google Scholar 

  • Zaman, K., Khilji, B.: The relationship between growth and poverty in forecasting framework: Pakistan’s future in the year 2035. Econ. Model. 30(1), 468–491 (2013)

    Article  Google Scholar 

  • Zaman, K., Khan, M.M., Ahmad, M., Rustam, R.: The relationship between agricultural technology and energy demand in Pakistan. Energy Policy 44(1), 268–279 (2012a)

    Article  Google Scholar 

  • Zaman, K., Khan, M.M., Ahmad, M.: The relationship between foreign direct investment and pro-poor growth policies in Pakistan: the new interface. Econ. Model. 29(4), 1220–1227 (2012b)

    Article  Google Scholar 

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Khan, M.A., Khan, M.Z., Zaman, K. et al. The evolving role of agricultural technology indicators and economic growth in rural poverty: has the ideas machine broken down?. Qual Quant 48, 2007–2022 (2014). https://doi.org/10.1007/s11135-013-9877-6

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