Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries, 1980-2000 *
By Tim Coelli, D. S. P. Rao. 2003
739 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/ce95212465373c1ec48d2630cd97e7324f359e39
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- K. Fuglie
- Thomas W. Hertel
- Nicholas E. Rada
- A. Alene
- C. O'Donnell
- K. Balcombe
- Philip G. Pardey
- Viet‐Ngu Hoang
In Management Science and Operations Research
- K. Fuglie
- Laure Latruffe *
- K. Balcombe
- C. O'Donnell
- Nicholas E. Rada
- Thomas W. Hertel
- A. Alene
- Patrick Meyfroidt
Citations in the corpus
- Productivity growth and technology capital in the global agricultural economy. (2012, background): “Coelli and Rao (2005) have also observed that the input shadow prices derived from the estimation of this model vary widely across countries and over time and in many cases are zero for major inputs like land and labor, which is not plausible.”
- Middle-range theories of land system change (2018, background): “factor productivity (TFP), which is the ratio of total output compared to all inputs compounded (Coelli and Rao, 2005; Fuglie, 2015).”
- Accounting for growth in global agriculture (2015, background): “Using information on output and input quantities and prices alone from two or more points in time, one can derive indexes of output and input growth, with the difference in their growth being defined as the growth in TFP.”
- The use of bootstrapped Malmquist indices to reassess productivity change findings: an application to a sample of Polish farms (2008, methodology): “Malmquist indices derived with the use of DEA have often been employed for investigating changes in productivity either at farm or sectoral level in agriculture (e.g. Coelli and Rao, 2003; Umetsu et al., 2003).”
- Efficiency, Productivity and Environmental Policy: A Case Study of Power Generation in the EU (2012, background): “We assume that the returns to scale are constant given that we focus on aggregate data at the sectoral level across different countries over time and as such assuming variable returns to scale doesn’t seem meaningful (See Coelli and Rao, 2005, for a more detailed discussion on this aspect).”
- Agricultural Productivity in the European Regions: Trends and Explanatory Factors (2007, background): “There is a vast literature on this issue, especially at the country level, some of the main works being Arnade (1998); Ball et al. (1999); Ball et al. (2001); McErlean and Wu (2003); and Coelli and Rao (2005).”
- Productivity growth and the effects of R&D in African agriculture (2010, background): “…productivity growth in developing countries in general (e.g., Fulginiti and Perrin, 1993, 1997, 1998; Trueblood and Coggins, 1997; Nin et al., 2003; Coelli and Rao, 2005) and in African countries in particular (e.g., Block, 1994; Frisvold and Ingram, 1995; Thirtle et al., 1995; Lusigi and Thirtle,…”
- Productivity Growth and Convergence in Crop, Ruminant and Non-Ruminant Production: Measurement and Forecasts (2006, background): “Rao and Coelli (2004) and Coelli and Rao (2005) find that countries that were less efficient in 1980 have a higher TFP growth rate than those countries that were on the frontier in 1980. They conclude that these results indicate a degree of catch-up due to improved technical efficiency along with gr”
- Measurement of agricultural total factor productivity growth incorporating environmental factors: A nutrients balance approach (2011, methodology): “First, the analysis involves the use of aggregate country-level data (Coelli and Rao 2005).”
- Is a slowdown in agricultural productivity growth contributing to the rise in commodity prices (2008, neutral): “But this methodology is sensitive to the set of countries included for comparison (Coelli and Rao, 2005) and the number of variables in the model, or the dimensionality issue (Lusigi and Thirtle, 1997).”