Efficiency Measurement With Unbalanced Panel Data: Evidence from Tunisian Textile, Clothing and Leather Industries

Mohamed Goaïed and Rim Mouelhi

Journal of Productivity Analysis, 2000, vol. 13, issue 3, 249-262

Abstract: This paper is concerned with the estimation of stochastic frontier production functions with unbalanced panel data when unobservable firm efficiency levels are related to explanatory variables. ≪We use the weighted-means≫ Instrumental Variables method acknowledged to R. Gardner (1998) which is a modification of the Hausman-Taylor (1981) procedure adapted for unbalanced panel data. The estimation method is used to examine technical efficiency in Tunisian textile, clothing and leather (TCL) industries during the period 1983–1994. Further, we assume a Translog production frontier where input uses are expressed in efficiency units and adjusted for the age of capital and types of labor. Firm-specific time-invariant technical efficiency is obtained using Schmidt and Sickles (1984) approach. The results suggest that the Instrumental Variables method produces more accurate estimates of the unknown firm level technical efficency. Mean efficency scores resulting from the MHT method is of 66.5%. Copyright Kluwer Academic Publishers 2000

Keywords: stochastic frontiers; technical efficiency; unbalanced panel data; instrumental variables; textile industry; Tunisia (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1007875009531 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:13:y:2000:i:3:p:249-262

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11123/PS2

DOI: 10.1023/A:1007875009531

Access Statistics for this article

Journal of Productivity Analysis is currently edited by William Greene, Chris O'Donnell and Victor Podinovski

More articles in Journal of Productivity Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().