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Malmquist Productivity Analysis using DEA Frontier in Stata

Malmquist Productivity Analysis using DEA Frontier in Stata. 14-15 July, 2011 Stata Conference Chicago 2011 Choonjoo Lee 1 , Kyoung-Rok Lee 1 , Byung-Ihn Lim 2 sarang90@kndu.ac.kr, bloom.rampike@gmail.com Korea National Defense University 1 , Nextree Soft 2. Contents.

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Malmquist Productivity Analysis using DEA Frontier in Stata

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  1. Malmquist Productivity Analysis using DEA Frontier in Stata 14-15 July, 2011 Stata Conference Chicago 2011 Choonjoo Lee1, Kyoung-Rok Lee1, Byung-Ihn Lim2 sarang90@kndu.ac.kr, bloom.rampike@gmail.com Korea National Defense University1, Nextree Soft2

  2. Contents • Malmquist Productivity Index • Malmquist Index using DEA Frontier • The User Written Command “malmq” • Notes and Examples • References

  3. Malmquist Productivity Index • Productivity = Output / Input • Productivity (Growth) Index measures the Productivity changes over Time • Malmquist (Productivity Growth) Index measures the productivity changes along with time variations and can be decomposed into changes in efficiency and technology.

  4. Malmquist Productivity Index • Productivity Index = (4/4)/(1/2) = 2 ☞ Productivity is improved by 100% Output A2(4,4) A1(2,1) 0 Input

  5. Malmquist Productivity Index • Malmquist Productivity Index Where Input based distance function at time t is defined by for Production Possibility Set Input vector Output vector ☞ is measured by production possibility set at time t.

  6. Malmquist Productivity Index • Malmquist Productivity Index And accordingly, for cross period distance function. Further, can be defined as

  7. Malmquist Productivity Index Output(y) • Productivity Change = =(4/1)*(2/4)=2 y6 y5 y4 At+1(4,4) y3 y2 y1 At(2,1) 0 x1 x2 x3 x4 x5 x6 Input(x)

  8. Malmquist Productivity Index • Malmquist Productivity Index

  9. Malmquist Index using DEA Frontier • Concepts of Malmquist Index using CRS Frontier

  10. Malmquist Index using DEA Frontier The input oriented CRS Malmquist Index using the observations at time t and t+1. The Geometric mean of two input oriented CRS Malmquist Indices

  11. Malmquist Index using DEA Frontier Decomposition of the input oriented geometric mean of Malmquist index using the concept of input oriented efficiency change and input oriented technical change ♨ Malmquist Index can be obtained from the DEA measure

  12. The User written command “malmq” • Program Syntax malmq ivars = ovars [if] [in] [, ort(in | out) period(varname) trace saving(filename)] • ort(in | out) specifies the orientation. The default is ort(in), meaning input-oriented DEA. • period(varname) identifies the time variable. • trace specifies to save all the sequences displayed in the Results window in the malmq.log file. The default is to save the final results in the malmq.log file. • saving(filename) specifies that the results be saved in filename.dta. • See “malmq.ado” file for the details

  13. Notes and Examples • Notes • Updated “dea.ado”, “malm.ado” files • In terms of accuracy and computational efficiency? Current version is more focused on ‘accuracy’ • Tested for 365DMU data set for dea.ado command and compared with other DEA programs.

  14. Notes and Examples • Example • Data : see “365dmu.dta” for dea command and “panel_data_for_malmquist_dea.dta” for malmq command. • Try the following commands • dea i_total = o_licnese o_sic o_nsic o_dpatent o_fpatent, rts(crs) ort(i) • malmq i_AC = O_SPI O_CPI, ort(i) period( period)

  15. Notes and Examples • Result • For dea: Results including the messages “No Solution(LOOP grather than maxiter):[DMUi=119][LOOP=16001]CRS-IN-SI-PII”. • See “dea.log” file for details • Compare with results by other programs • For malmq • see “malmquist.log” file for details • Compare with results by other programs

  16. References • Lee, C., & Lee, K.(2010). “An Efficient Data Envelopment Analysis with a large data set in Stata”, Boston10 Stata Conference. • Ji, Y., & Lee, C. (2010). “Data Envelopment Analysis”, The Stata Journal, 10(no.2), pp.267-280. • Lee, C., & Ji, Y. (2009). “Data Envelopment Analysis in Stata”, DC09 Stata Conference. • Fare, R., Grosskopf, S., Norris, M. & Zhang, Z. (1994). “Productivity Growth, technical progress and efficiency change in industrialized countries”, American Economic Review, 84(no.1), pp.66-83. • Lee, J., & Oh, D.,(2010). “Efficiency Analysis Methodology: Data Envelopment Analysis”, IB Book(in Korean).

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