Technical efficiency decomposed – The case of Ugandan referral hospitals

Abstract

Abstract:Aim: In an audit report provided to the Ugandan Parliament by the Office of the Audit General, Uganda, technical efficiency in Ugandan referral hospitals was measured and analysed. The audit report pointed out that there was a relatively low level of technical inefficiency, at least in comparison with other African countries. The purpose of this study is to look further into the issue of why there is inefficiency. Design / Research methods:We use a Data Envelopment Analysis framework and decompose longrun technical efficiency into short-term technical efficiency, scale efficiency and congestion. Conclusions / findings:Our results reveal that the source of the long-run inefficiency varies over the years. For 2012, more than 50% of the observed inefficiency relates to scale factors. However, in 2013 and 2014 the major contributor to the long-run inefficiency was input congestion.Originality / value of the article: Even though there are a substantial amount of research on efficiency in African hospitals, no other study have investigated existence of congestion. In that respect our research contributes to the existing research. Implications of the research:We recommend that inefficient hospitals should use efficient hospitals as benchmarks for improving their own efficiency. Further, since a large part of the technical inefficiency relates to congestion we recommend further investigation to identify factors in the production, or organisation that could be related to congestion.

Efficiency Estimates of Public Health Center II Facilities in Southwestern Uganda

“…Similarly in a study to estimate the efficiency of public health dispensaries in Kenya, Bundi (2018) found that 41% were inefficient with average VRS efficiency at 70%, the CRS and scale efficiencies averaged 55% and 80% respectively. In another technical efficiency study of referral health facilities in Uganda, Mulumba et al (2017) indicate that long-run inefficiency varied overtime and more than 50 percent of the inefficiencies that were observed are related to scale factors. The study recommended that inefficient health units should use efficient ones as benchmarks or role models to improve their efficiency.…”

“…However, the analysis explores efficiency only in a general sense using ratio indicators and ignores other factor inputs such as operational budget, essential medicines and health supplies (drugs), medical and non-medical staff used by health centres in the production of health outputs. In addition there are limited recently published health facility efficiency studies in Uganda (Mujasi et al, 2016;Mulumba et al, 2017).…”

The Remarkable Incidence of Congestion in Production: A Review, Empirical Illustration, and Research Agenda

“…Congestion measured using either decomposition ( 9) or ( 11) is the most important source of inefficiency at the sample level in at least eight articles we are aware of: and Byrnes, Färe, Grosskopf, and Lovell (1988) both analyse US surface coal mines, Ç akmak and Zaim (1992), Wu, Devadoss, and Lu (2003) and Zhengfei and Oude Lansink (2003) assess Turkish, American and Dutch agriculture respectively, Färe, Grosskopf, and Pasurka (1989) analyse US electric utilities, Mulumba, Nalubanga, Nankanja, Manasseh, Månsson, and Hollén (2017) assess Ugandan referral hospitals, and Odeck (2006) evaluates the Norwegian public bus companies. Just to offer some basic idea of the amount of waste involved, Table 1 summarises for each study the average amount of congestion efficiency as well as its incidence (% of sample).…”

“…Finally, reanalysing data on the Chinese automobile and textile industries in the period 1981-1997, Flegg and Allen (2009a) obtain in an intertemporal analysis that congestion is critical for both sectors in a single year over this time horizon (1989 and 1995 for automobile and textile industries respectively). Mulumba, Nalubanga, Nankanja, Manasseh, Månsson, and Hollén (2017) report that for the year 2012 congestion dominates for 5 out of 13 observations. Nasierowski and Arcelus (2003) find that the innovation system in about 30% of countries suffers from congestion and that it is a dominant source for some of these countries.…”