Examining the determinants of efficiency using a latent class stochastic frontier model

Abstract

In this study, we combine the latent class stochastic frontier model with the complex time decay model to form a single-stage approach that accounts for unobserved technological differences to estimate efficiency and the determinants of efficiency. In this way, we contribute to the literature by estimating “pure” efficiency and determinants of productive units based on the class structure. An application of this proposed model is presented using data on the Ghanaian banking system. Our results show that inefficiency effects on the productive unit are specific to the class structure of the productive unit and therefore assuming a common technology for all productive units as is in the popular Battese and Coelli model used extensively in the literature may be misleading. The study therefore provides useful empirical evidence on the importance of accounting for unobserved technological differences across productive units. A policy based on the identified classes of the productive unit enables a more accurate and effectual measures to address efficiency challenges within the banking industry, thereby promoting financial sector development and economic growth.

Keywords:

• latent class stochastic frontier model
• technology differences
• inefficiency effects

JEL classification:

• D24 ·047· 055

Public Interest Statement

In this paper, we attempt to show that it is important to account for the differences in technology of productive units in order to accurately estimate their efficiency and the determinants of efficiency. In doing this, we use a latent class stochastic frontier model that account for underlying technology differences to derive efficiency as well as determinants. Applying the model using data on the Ghanaian banking system, our results show that inefficiency effects on the productive unit are specific to the class structure of the productive unit and therefore assuming a common technology for all productive units as is normally done in the literature may be misrepresentative. The study therefore provides useful empirical evidence on the importance of accounting for technological differences across productive units. A policy based on the identified classes of the productive unit enables a more accurate and effectual decision-making.

Notes

1. Kumbhakar, Ghosh, and McGuckin (1991), Reifschneider and Stevenson (1991) and Huang and Liu (1994) are some of the earlier studies that presented models to overcome this problem in the two-stage approach (i.e. the first stage assumes that inefficiencies are independent and identically distributed, while the second stage contradicts the identical distribution assumption of the first stage) by estimating both the frontier and efficiency effects in one stage.

2. We apply the Cobb-Douglas specification because the estimated variance matrix of the flexible translog specification was singular.

3. Greene (2011) also notes that no accepted approach for estimating unbiased efficiency estimates with endogeneity is currently available for SFA.

4. Following the work by Greene (2003), it is worth noting that the posterior class probabilities do not depend only on the estimated δ parameters above but also on the vector parameters from the production frontier.

Additional information

Funding

Funding. The authors received no direct funding for this research.

Notes on contributors

Michael Danquah

Michael Danquah is an economist and lecturer at the Department of Economics, University of Ghana, Legon. He holds a PhD in Economics from Swansea University (UK). His research interests include informality, inclusive growth and stochastic frontier modelling. He has published in journals such as Economic Modelling and Empirical Economics among others. In 2015, he was interviewed by BBC World Service on the Live 8, G8 and the making of Poverty History program.

Peter Quartey

Peter Quartey holds a PhD in Development Economics from the University of Manchester (UK). He is an associate professor in Development Economics and currently the head, Department of Economics, University of Ghana. He is also an economist with the Institute of Statistical, Social and Economic Research, University of Ghana. He was formerly the deputy director, Centre for Migration Studies. He has published extensively and his research interests are private sector development, development finance, migration and remittances and poverty analysis.