Two-stage Bootstrap Data Envelopment Analysis Research Articles

Measuring Local Economy Efficiency With Two-Stage Bootstrap DEA

With increasing fiscal decentralization and the growth in GDP per capital around the world, local government expenditures have been on a rise. This study examines the financial efficiency of Gyeonggi Province's municipal currency through a 2-stage network data envelopment analysis (DEA) using the 31 municipalities as the decision-making units (DMUs). Then, the authors identify environmental variables that affect efficiency of the municipal currency through a Tobit regression and discuss the policy implications of these findings to provide helpful insight for decision-makers in establishing future policies on municipal currencies.

Determinants of transit-oriented development efficiency focusing on an integrated subway, bus and shared-bicycle system: Application of Simar-Wilson's two-stage approach

This research explores the efficiency of Transit-Oriented Development (TOD) at transit stations in Seoul using a two-stage bootstrap Data Envelopment Analysis with truncated regression. The ridership, in terms of the bus, subway, and shared bicycle usage within the transit stations' impact area, is judged as an immediate output of TOD performance, and indicators relating to the core concepts of TOD are selected as inputs. In the first-phase, TOD efficiency estimates are computed and regressed on environmental variables in the subsequent phase to identify contributory factors of TOD efficiency. Assessing the performance of TOD at the transit station revealed that the stations located around the CBD where commercial activities highly concentrated and transit stations are connected were more efficient compared to those located at the periphery of the city. The second-stage results indicate that a rise in pedestrian volume, the number of shared-bicycle stations and bus stops, commercial floor area, and traffic congestion around a transit station improves the efficiency of TOD. Our study makes a significant contribution to the literature by employing a more robust approach to explore TOD efficiency at transit stations. The findings of this study provide useful implications and insights for urban planners and policymakers across cities.

Drivers of financial and operational efficiency of MFIs: empirical evidences from Eastern Europe and Central Asia

PurposeThe study attempts to examine the bias-adjusted financial and operational efficiency estimates of microfinance institutions (MFIs) operating in the Eastern Europe and Central Asia (ECA) region during the financial year 2017–2018. In addition, the study also identifies the responsible factors determining the financial and operational performances of MFIs operating in the ECA region.Design/methodology/approachThe study employs two-stage bootstrap data envelopment analysis (DEA). In the first stage, the authors incorporate the bootstrap procedure in the DEA framework as suggested by Simar and Wilson (2000) to estimate the bias-corrected efficiency scores of 67 sample MFIs. In order to identify the drivers of efficiency level, the study deploys the bootstrap truncated regression model following the Simar and Wilson (2007) guidelines in the second stage of analysis.FindingsThe authors note from the empirical results that MFIs operating in the ECA region are relatively more financially efficient (0.588) than socially efficient (0.496). However, none of the MFIs were found to be operating at best-practice frontier while considering the bias-adjusted efficiency estimates. Further, the results of second stage of analysis confirm that corporate governance, that is, board size has positive and statistically significant impact on MFIs’ performances. In addition, the bad credit quality deteriorates both financial revenue and operational efficiency. Moreover, the MFIs’ size, profit status and debt-to-equity ratio were also found to be statistically significant to determine the operational and financial efficiency of MFIs in the ECA region.Practical implicationsThe study provides the robust efficiency estimates and factors responsible to determine the financial and operational efficiency of MFIs operating in the ECA region. Further, the empirical results of the study provide the inputs and further direction to the policymakers, regulators, practitioners and managers in framing the policy and optimal operating strategies for ECA MFIs industry.Originality/valueThe study extends the DEA analysis by incorporating the bootstrap procedure in DEA model to estimate the bias-adjusted efficiency scores which are more reliable and robust. In addition, bootstrap truncated regression has been applied to identify the drivers of efficiency. Moreover, in the literature there is no single study which has deployed the double bootstrap DEA framework to examine the financial and operational efficiency estimates and its drivers.

Two-Stage Double Bootstrap Data Envelopment Analysis for Efficiency Evaluation of Shared-Bicycle Stations in Urban Cities

To optimize the performance and operation of shared-bicycle systems, this study aims to evaluate the efficiency of shared-bicycle stations and to find factors affecting their efficiency scores. We analyzed the efficiency of 1,260 shared-bicycle stations in the City of Seoul using shared-bicycle rental and trajectory data as of June 2018. In this study, the two-stage bootstrap data envelopment analysis, which is a non-parametric frontier technique, was applied to estimate each shared-bicycle station’s efficiency. In the first stage, we evaluated efficiency scores by employing the number of bicycle racks and bicycle path ratio as input variables, and bicycle turnover rate and balancing rate as output variables. The efficiency scores were regressed on potential covariates using a bootstrapped truncated regression in the second stage. From our results, the efficiencies of shared-bicycle stations were found to be diverse depending on the nature of land use around the station location. The results present evidence to show that shared-bicycle stations located in residential and school-dominated areas are likely to be efficient, whereas those in semi-industrial areas, commercial, and business districts are generally inefficient. Furthermore, the effect of variables like the commuting population, the number of registered vehicles, and the number of bicycle-related accidents per year were statistically significant, thus affecting shared-bicycle station performance. This study offers essential insights into the efficiency of shared-bicycle stations, which could be incorporated into shared-transportation strategies to improve mobility in urban cities.

Analyzing the variations in cost-efficiency of marine cage lobster aquaculture in Vietnam: A two-stage bootstrap DEA approach

ABSTRACTThis study measures cost-efficiency and its determinants for 353 marine cage lobster farms in Vietnam using a two-stage bootstrap data envelopment analysis (DEA). In the first stage, the bootstrap DEA approach was used to obtain bias-corrected technical, allocative cost-efficiencies for the farms. Then, the bias-corrected cost-efficiency scores were regressed on a set of explanatory variables using a bootstrapped truncated regression. The results show that substantial cost-inefficiency occurs in lobster aquaculture due to the overuse of inputs, which, given the prevailing input prices, are also used in inappropriate ratios. Cost-efficiency is shown to be significantly impacted by the age of the farmer, total cage volume and by variables affecting the production environment such as the cage cleaning, distance to the nearest farms and the presence of other discharges into the marine environment.

An empirical study of comparing DEA and SFA methods to measure hospital units' efficiency

The paper aims to examine the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) results in order to facilitate a common understanding about the adequacy of these methods, defining any differences in healthcare efficiency estimation. A two-stage bootstrap DEA method and the Translog formula of the SFA were performed. Multi-inputs and multi-outputs were used in both of the approaches assuming two scenarios either including environmental variables or not. Thirty-two Greek public hospital units constitute the sample. DEA and SFA were found to yield divergent efficiency estimates due to many factors such as the nature of the environmental variables, the measurement error and other random factors. Environmental variables being hospital status and geographical position were found significantly correlating with inefficiency. The analysis concludes that the choice of the appropriate mathematical form depends on the expertise of the researcher and the purpose of the evaluation.