Growth and productivity paradox. An estimation in the form of state-space, with unobservable components, for the Argentine agricultural sector, 1955-2003

Authors

  • Luis Lanteri Banco Central de Argentina

DOI:

https://doi.org/10.24201/ee.v20i1.168

Keywords:

total factor productivity, technological change, latent variables

Abstract

We estimate the total factor productivity for argentine agriculture over the period 1955 to 2003. One method of quantifying the impact of productivity is the use of growth accounting index numbers (Divisia index). However, some papers (see, for example, Hsieh, 2000) show that if technological change is not Hicks neutral then conventional total factor productivity index is not a satisfactory measure of this indicator, since that observed cost shares conflates the contribution of factor accumulation to output growth with that of technological change. In this paper, cost share equation system in the form of state-space model with latent variables are used to detect technological bias and to estimate the total factor productivity adjusted.

Downloads

Download data is not yet available.

References

Ahearn, M. et al. (1998). Agricultural Productivity in the US, Agricultural Information Bulletin, núm. 740, USDA, Washington.

Archibald, S. y L. Brandt (1991). A Flexible Model of Factor Biased and Technological Change: An Application to Japanese Agriculture, Journal of Development Economics, (35), 127-145.

Arnade, C. (1997). Using a Programming Approach to Measure International Agricultural Efficiency and Productivity, Journal of Agricultural Economics, (49), 67-84.

Bailey, A., K. Balcombe y J. Morrison (2001). Modelling Technical Change Biases: A Comparison of Proxy Variable and Stochastic Latent Variable Approaches to the Measurement of Technology Biases in South African Agriculture, (mimeo).

Balcombe, K. et al (2000). Stochastic Biases in Technical Change in South African Agriculture, Agrekon, (39), diciembre.

Balcombe, K., X. Irz y A. Bailey (2003). An Application of the Stochastic Latent Variable Approach to the Correction of Sector Level TFP Calculations in the Face of Biased Technological Change, (mimeo).

Binswanger H. (1978). Measured Biases of Technical Change: The US, en H. Binswanger y V. Ruttan (comps.), Induced Innovation, Technology, Institutions and Development, Johns Hopkins University Press.

Binswanger H. (1974a). The Measurement of Technical Change Biases with Many Factors of Production, American Economic Review, (64), diciembre.

Binswanger H. (1974b). A Cost Function Approach to the Measurement of Elasticities of Factor Demand and Elasticities of Substitution, American Journal of Agricultural Economics, (56), 377-386.

Blackorby, C. y R. Russell (1989). Will the Real Elasticity of Substitution Please Stand Up? A comparison of the Allen-Uzawa and Morishima Elasticities, American Economic Review, (79), 882-888.

Chambers, R. (1988). Applied Production Analysis: A Dual Approach, Cambridge University Press.

Clark, S. y C. Youngblood (1992). Estimating Duality Models with Biased Technical Change: A Time Series Approach, American Journal of Agricultural Economics, (74), 353-360.

Elias, V. (1985). Government Expenditures on Agriculture and Agricultural Growth in Latin America, IFPRI núm. 50, International Food Policy Research Institute, Washington.

Esposti, R. y P. Pierani (1997). The Source of Technical Change in Italian Agriculture: A Latent Variable Approach, University of Wisconsin-Madison.

Felipe, J. y J. McCombie (2001). Biased Technical Change, Growth Accounting and the Conumdrum of the East Asian Miracle, Journal of Comparative Economics, (29), 542-565.

Ferguson, Ch. (1968). Neoclassical Theory of Technical Progress and Relative Factor Shares, Southern Economic Journal, (34), 490-540.

Ferguson, Ch. y J. Moroney (1969). The Sources of Change in Labors Relative Share: A Neoclassical Analysis, Southern Economic Journal, (35), 308-322.

Fulginiti, L. y P. Perrin (1999). Have Price Policies Damaged LDC Agricultural Productivity? Contemporany Economic Policy, (17), 469-475.

Fulginiti, L. y P. Perrin (1997). LDC Agriculture: Non-parametric Productivity Indexes, Journal of Development Economics, (58), 373-390.

Hayami, Y. y V. Ruttan (1985). Agricultural Development: An International Perspective, 2da. ed., Johns Hopkins University Press.

Hicks, J. (1932). The Theory of Wages, 1a. ed., Macmillan.

Hsieh Chang-Tai (2000). Measuring Biased Technology, Princeton University, http://www.wws.princeton.edu/chsieh/DIVISIA4.pdf.

Huang, K. (1991). Factor Demands in the US Food-Manufacturing Industry, American Journal of Agricultural Economics, (73), 615-620.

Hulten, Ch. (1973). Divisia Index Numbers, Econometrica, (41), 1017-1025.

Kohli, U. (1994). Technological Biases in US Aggregate Production, Journal of Productivity Analysis, (5), 5-22.

Lambert, D. y J. Shonkwiler (1995). Factor Bias under Stochastic Technical Change, American Journal of Agricultural Economics, (77), 578-590.

Leontief, W. (1947). Introduction to the Theory of the Internal Structure of Functional Relationship, Econometrica, (15), 361-373.

Leontief, W. (1943). A Note on the Interrelation of Subsets of Independient Variables of a Continuous Function with Continuous Partial Derivatives, Bulletin of the American Mathematical Society, (53), 343-350.

Lim, H. y R. Shumway (1997). Technical Change and Model Specification: US Agricultural Production, American Journal of Agricultural Economics, (79).

Mundlak, Y. y R. Domenech (1986). Estadísticas de la evolución económica de la Argentina, 1913-1984, Estudios, julio-septiembre.

Murgai, R. (2001). The Green Revolution and the Productivity Paradox: Evidence from the Indian Punjab, Agricultural Economics, (25), 199-209.

Nelson, R. y H. Pack (1999). The Asian Miracle and Modern Growth Theory, The Economic Journal, (109), 416-436.

Nghiep, L. T. (1979). The Structure and Changes of Technology in Prewar Japanese Agriculture, American Journal of Agricultural Economics, (61), 687-692.

Olmstead, A. y P. Rhodes (1993). Induced Innovation in American Agriculture: A Reconsideration, Journal of Political Economy, (101), 100-118.

Rodrik, D. (1997). TFP Controversies, Institutions and Economic Performance in East Asia, NBER, WP, 5914.

Ruttan, V. (2002). Productivity Growth in World Agriculture: sources and Constraints, Journal of Economic Perspectives, (16), 161-184.

Sharma, S. (2002). The Morishima Elasticity of Substitution for the Variable Profit Function and the Demand for Imports in the United States, International Economic Review, (43), febrero.

Solow, R. (1957). Technical Change and the Aggregate Production Function, Review of Economics and Statistics, (39), 312-320.

Stevenson, R. (1980). Measuring Technological Bias, American Economic Review, (70), 162-173.

Trueblood, M. y J. Coggins (2001). Intercountry Agricultural Efficiency and Productivity, (mimeo).

Downloads

Published

2005-01-01

How to Cite

Lanteri, L. (2005). Growth and productivity paradox. An estimation in the form of state-space, with unobservable components, for the Argentine agricultural sector, 1955-2003. Estudios Económicos De El Colegio De México, 20(1), 53–78. https://doi.org/10.24201/ee.v20i1.168
Metrics
Views/Downloads
  • Abstract
    437
  • PDF (Español)
    196

Issue

39-vol. 20, no. 1, january-june, 2005

Section

Articles

Metrics