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Kristin Davis, E. Nkonya, Edward Kato, Daniel Mekonnen, Martins Odendo, R. Miiro, Jackson Nkuba (2012)
Impact of Farmer Field Schools on Agricultural Productivity and Poverty in East AfricaWorld Development, 40
M. Pesaran, Y. Shin, Richard Smith (2001)
Bounds testing approaches to the analysis of level relationshipsJournal of Applied Econometrics, 16
Rana Khan, Hafiza Bashir (2012)
Trade, Poverty and Inequality Nexus: The Case of Pakistan
S. Johansen, K. Juselius (2009)
MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEYOxford Bulletin of Economics and Statistics, 52
B. Grewal, Helena Grunfeld, P. Sheehan (2012)
The Contribution of Agricultural Growth to Poverty Reduction
N. Hassine, Magda Kandil (2009)
Trade liberalisation, agricultural productivity and poverty in the Mediterranean regionEuropean Review of Agricultural Economics, 36
(1988)
Statistical analysis of cointegrating vectors
D. Gollin (2009)
Agriculture as an Engine of Growth and Poverty Reduction
Z. Javed, M. Farooq, H. Ali (2010)
TECHNOLOGY TRANSFER AND AGRICULTURAL GROWTH IN PAKISTANPakistan Journal of Agricultural Sciences, 47
K. Zaman, Muhammad Khan, Mehboob Ahmad (2015)
The relationship between foreign direct investment and pro-poor growth policies in Pakistan: The new interface
A. Suryahadi, Gracia Hadiwidjaja, S. Sumarto (2012)
Economic growth and poverty reduction in Indonesia before and after the asian financial crisisBulletin of Indonesian Economic Studies, 48
K. Zaman, Muhammad Khan, Mehboob Ahmad, Rabiah Rustam (2012)
The relationship between agricultural technology and energy demand in PakistanEnergy Policy, 44
(2012)
The Australian Centre for International Agricultural Research (ACIAR), Canberra, Australia
Haitao Wu, Shijun Ding, S. Pandey, D. Tao (2010)
Assessing the Impact of Agricultural Technology Adoption on Farmers' Well-being Using Propensity-Score Matching Analysis in Rural China*Asian Economic Journal, 24
Hiro Toda, Taku Yamamoto (1995)
Statistical inference in vector autoregressions with possibly integrated processesJournal of Econometrics, 66
F. Simtowe, M. Kassie, S. Asfaw, B. Shiferaw, E. Monyo, M. Siambi (2012)
Welfare Effects of Agricultural Technology adoption: the case of improved groundnut varieties in rural Malawi
D. Dickey, W. Fuller (1979)
Distribution of the Estimators for Autoregressive Time Series with a Unit RootJournal of the American Statistical Association, 74
(2012)
Government of Pakistan, Economic Survey of Pakistan 2011-12. Government of Pakistan, Finance Division, Economic Advisor's Wing
K. Zaman, Bashir Khilji (2013)
RETRACTED: The relationship between growth and poverty in forecasting framework: Pakistan's future in the year 2035Economic Modelling, 30
D. Dickey, W. Fuller (1981)
LIKELIHOOD RATIO STATISTICS FOR AUTOREGRESSIVE TIME SERIES WITH A UNIT ROOTEconometrica, 49
Food price watch
M. Kassie, B. Shiferaw, G. Muricho (2011)
Agricultural technology, crop income, and poverty alleviation in UgandaWorld Development, 39
R. Engle, C. Granger (1987)
Co-integration and error correction: representation, estimation and testingEconometrica, 55
David Currie, David Hendry, Frank Srba (2007)
Co-Integration and Error Correction : Representation , Estimation , and Testing
Lena Osterhagen (2010)
World Development Indicators 2010
R. Stott (1999)
The World BankBMJ, 318
C. Granger (1988)
Causality, cointegration, and controlJournal of Economic Dynamics and Control, 12
D. Headey, M. Alauddin, D. Rao (2010)
Explaining agricultural productivity growth: an international perspectiveAgricultural Economics, 41
Shijun Ding, Laura Meriluoto, W. Reed, D. Tao, Haitao Wu (2011)
The impact of agricultural technology adoption on income inequality in rural China: Evidence from southern Yunnan ProvinceChina Economic Review, 22
(2012)
Contribution of agricultural growth to reduction of poverty , hunger and malnutrition
J. Alston, W. Martin, P. Pardey (2014)
Influences of Agricultural Technology on the Size and Importance of Food Price Variability
S. Asfaw, M. Kassie, F. Simtowe, L. Lipper (2012)
Poverty Reduction Effects of Agricultural Technology Adoption: A Micro-evidence from Rural TanzaniaThe Journal of Development Studies, 48
S. Asfaw, B. Shiferaw, F. Simtowe, L. Lipper (2012)
Impact of modern agricultural technologies on smallholder welfare: Evidence from Tanzania and EthiopiaFood Policy, 37
K Zaman, B Khilji (2013)
The relationship between growth and poverty in forecasting framework: Pakistan’s future in the year 2035Econ. Model., 30
M. Mendola (2007)
Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural BangladeshFood Policy, 32
J. MacKinnon (1996)
Numerical Distribution Functions for Unit Root and Cointegration TestsJournal of Applied Econometrics, 11
A. Janvry, E. Sadoulet (2010)
Agricultural Growth and Poverty Reduction: Additional EvidenceWorld Bank Research Observer, 25
Akhter Ali, M. Sharif (2012)
Impact of farmer field schools on adoption of integrated pest management practices among cotton farmers in PakistanJournal of the Asia Pacific Economy, 17
(2005)
Agriculture growth and poverty reduction: a policy perspective. Paper presented at the international seminar on management of the Pakistan economy
A. Alene, O. Coulibaly (2009)
The impact of agricultural research on productivity and poverty in sub-Saharan AfricaFood Policy, 34
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.
Quality & Quantity – Springer Journals
Published: May 26, 2013
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