Double Bootstrap Research Articles

An application of data envelopment analysis with the double bootstrapping technique to analyze cost and technical efficiency in aquaculture: Do credit constraints matter?

For a long-term growth in shrimp farming, a significant increase in shrimp output can only be fully realized by improving both technical and cost efficiency. This study employs data envelopment analysis with the double bootstrapping technique to analyze cost and technical efficiency for aquaculture. This procedure allows statistical inferences about cost and technical efficiency to be made. The case of intensive white-leg shrimp farming in Ninh Thuan, Vietnam is used for analysis. The result shows that the bias-corrected cost and technical efficiency were 0.533 and 0.723, respectively. From a policy perspective, it means that there is considerable room for improvement in cost and technical efficiency in the case of intensive white-leg shrimp farming. Such gains are certainly greater than for those using conventional data envelopment analysis, which has been widely adopted in the aquaculture literature. Regression analysis reveals that credit constraints for operational cost have a statistically negative influence on both cost and technical efficiency in this type of aquaculture.

Agricultural Management Practices and Factors Affecting Technical Efficiency in Zimbabwe Maize Farming

Integrating best management practices and improving the technical efficiency of smallholder maize farmers is critical in raising overall agricultural output. However, there is not much information, besides marginalization and high rehabilitation and maintenance costs, which adequately explains why productivity of smallholder irrigation farmers drop at very high rates. Therefore, this study measured technical efficiency, identified factors affecting technical efficiency, and identified best management practices adopted by smallholder maize irrigation farmers at Tokwane-Ngundu (Zimbabwe). The Data Envelopment Analysis, Double Bootstrap Approach in a Principal Component Regression was used. Primary data were gathered using a questionnaire. Empirical results revealed that the mean technical efficiency of the respondents was 77%, which indicated a potential for them to increase their efficiency by 30%. The factors that increased technical efficiency included human capital, extension contacts and compliance with best management practices. The policy implication of this study is the need for robust group incentive schemes to promote farmer-to-farmer skills transfer to boost the technical efficiency of smallholder maize irrigation farmers in Zimbabwe.

Generalised regression estimation via the bootstrap

SummaryA generalised regression estimation procedure is proposed that can lead to much improved estimation of population characteristics, such as quantiles, variances and coefficients of variation. The method involves conditioning on the discrepancy between an estimate of an auxiliary parameter and its known population value. The key distributional assumption is joint asymptotic normality of the estimates of the target and auxiliary parameters. This assumption implies that the relationship between the estimated target and the estimated auxiliary parameters is approximately linear with coefficients determined by their asymptotic covariance matrix. The main contribution of this paper is the use of the bootstrap to estimate these coefficients, which avoids the need for parametric distributional assumptions. First‐order correct conditional confidence intervals based on asymptotic normality can be improved upon using quantiles of a conditional double bootstrap approximation to the distribution of the studentised target parameter estimate.

Bootstrap-based testing inference in beta regressions

We address the issue of performing testing inference in small samples in the class of beta regression models. We consider the likelihood ratio test and its standard bootstrap version. We also consider two alternative resampling-based tests. One of them uses the bootstrap test statistic replicates to numerically estimate a Bartlett correction factor that can be applied to the likelihood ratio test statistic. By doing so, we avoid estimation of quantities located in the tail of the likelihood ratio test statistic null distribution. The second alternative resampling-based test uses a fast double bootstrap scheme in which a single second level bootstrapping resample is performed for each first level bootstrap replication. It delivers accurate testing inferences at a computational cost that is considerably smaller than that of a standard double bootstrapping scheme. The Monte Carlo results we provide show that the standard likelihood ratio test tends to be quite liberal in small samples. They also show that the bootstrap tests deliver accurate testing inferences even when the sample size is quite small. An empirical application is also presented and discussed.

Sumca: Simple, Unified, Monte-Carlo-assisted Approach to Second-Order Unbiased Mean-Squared Prediction Error Estimation

SummaryWe propose a simple, unified, Monte-Carlo-assisted approach (called ‘Sumca’) to second-order unbiased estimation of the mean-squared prediction error (MSPE) of a small area predictor. The MSPE estimator proposed is easy to derive, has a simple expression and applies to a broad range of predictors that include the traditional empirical best linear unbiased predictor, empirical best predictor and post-model-selection empirical best linear unbiased predictor and empirical best predictor as special cases. Furthermore, the leading term of the MSPE estimator proposed is guaranteed positive; the lower order term corresponds to a bias correction, which can be evaluated via a Monte Carlo method. The computational burden for the Monte Carlo evaluation is much less, compared with other Monte-Carlo-based methods that have been used for producing second-order unbiased MSPE estimators, such as the double bootstrap and Monte Carlo jackknife. The Sumca estimator also has a nice stability feature. Theoretical and empirical results demonstrate properties and advantages of the Sumca estimator.

Industrial policy, energy and environment efficiency: Evidence from Chinese firm-level data

Based on a unique micro-level dataset of 30,689 mining enterprises from 2008 to 2011, this paper uses the non-radial directional distance function (NDDF) to calculate the unified efficiency index (UEI) and energy-environmental performance index (EEI) for China's mining enterprises. The double bootstrap method is then used to test how tax incentive policies affect the UEI and EEI of China's mining enterprises. The results show that: (1) the UEI and EEI of Chinese mining enterprises first decreased and then increased over the sample period; overall, Chinese mining enterprises had low energy and environmental efficiency, especially the coal mining enterprises, private mining enterprises and mining enterprises in the central and western regions. (2) Tax incentives positively affect the energy and environmental efficiency of mining enterprises, especially the efficiency of coal mining enterprises, non-state-owned mining enterprises and mining enterprises in the central and western regions. Our results remain robust after using the propensity score matching estimator (PSM). (3) There is a positive feedback between tax incentives and energy and environmental efficiency, more efficient mining enterprises receive more government incentives. Further analysis shows that although tax incentives do not reduce the total energy consumption of enterprises, reducing the energy consumption of enterprises can improve their UEI and EEI. In addition, R&D investment, profitability and resource taxes all contribute to improving the UEI and EEI of mining enterprises.

Data envelopment analysis for analyzing technical efficiency in aquaculture: The bootstrap methods

The paper adopts the bootstrap procedures for data envelopment analysis to analyze the technical efficiency in the Vietnam’s intensive white-leg shrimp farming. The results demonstrate that the null hypothesis of constant returns-to-scale is rejected in favor of variable returns-to-scale for the production technology. Moreover, the potential improvement is greater using bootstrapping than that achieved by using the conventional data envelopment analysis, which has been widely adopted in the aquaculture literature for technical efficiency estimation. By adopting the double bootstrap proposed by Simar and Wilson, the bias-corrected technical efficiency is 0.69 and at the 95% confidence interval, with the lower limit of 0.65 and the upper limit of 0.75. In addition, statistically positive influences on technical efficiency in this type of farming are larger farm size and having access to formal credit, with cultivation period in the production year being a negative influence.

Conventional procedurevis-à-visbootstrap-based corrections of efficiency analyses

PurposeThis paper aims to evaluate the performance of basic metals industry in India and analyze its determinants, using data envelopment analysis (DEA) method. It also intends to compare the results through conventional two-stage and bootstrap-based inferences.Design/methodology/approachConsidering technical efficiency as a measure of performance, this paper specifically investigates whether the participation of a firm in the global market affects its performance. The conventional two-stage procedure is used to test the export intensity and firm performance nexus. The bootstrap-based algorithms (by Simar and Wilson, 2007) are used to correct the bias and serial correlation issues involved in the conventional approach.FindingsThe result shows a negative relation between export intensity and firm performance while following the conventional procedure. Even after accounting for serial correlation, the relation remains more or less similar to that of conventional analysis. However, a strong negative relation between export intensity and firm performance is not observed in a more reliable inference obtained after correcting for possible bias as well as serial correlation.Research limitations/implicationsThis paper is based on cross-sectional analysis, and a more reliable result can be obtained by considering a larger sample and longer period.Originality/valueThis paper shows how the conventional two-stage procedure may result in misleading inferences due to bias in the estimation of efficiency scores and the serial correlation during the second stage inferential analysis. This paper also empirically exemplifies how the double bootstrap DEA procedure can overcome these limitations of the conventional two-stage approach.

Assessing the Efficiency of Countries in Making Progress Towards Universal Health Coverage: A Data Envelopment Analysis of 172 Countries

Background: Maximising efficiency of resources is critical to progressing towards Universal Health Coverage (UHC) and therefore the Sustainable Development Goal (SDG) for Health. This study estimates the technical efficiency of national health spending in achieving UHC and its determinants. Methods: A two-stage efficiency analysis using Simar and Wilson’s double bootstrap data envelopment analysis investigates how efficiently countries convert health spending into UHC outputs (measured by service coverage and financial risk protection) for 172 countries. We use World Bank and World Health Organization data from 2015. Thereafter, the environmental factors affecting efficient progress toward UHC goals are identified. Findings: The mean bias-corrected technical efficiency score across 172 countries is 85·7% (68·9% for low income and 95·5% for high income countries). High-achieving middle- and low-income countries such as El Salvador, Colombia, Rwanda and Malawi demonstrate that peer-relative efficiency can be attained at all incomes. Governance capacity, income and education are significant determinants of the efficiency score. Sensitivity analysis suggests that results are robust to changes. Interpretation: We provide a 2015 baseline for cross-country UHC technical efficiency scores. If countries wish to improve their UHC outputs within existing budgets, they should identify their current efficiency and try to emulate more efficient peers. Policy-makers should focus on strengthening institutions, improving data availability on UHC indicators and implementing known best practices to replicate efficient systems. Using resources more efficiently is likely to positively impact UHC coverage goals and health outcomes, and without addressing gaps in efficiency progress towards achieving the SDGs will be impeded. Funding Statement: None. Declaration of Interests: We declare no competing interests.

Efficiency of university–industry collaboration and its determinants: evidence from Chinese leading universities

ABSTRACT This paper applies the two-stage double bootstrap DEA to evaluate the university–industry (U–I) collaboration efficiency of 71 Chinese universities. In the first stage, we evaluate unbiased efficiency scores by using the bootstrap DEA method. Results show that the U–I collaboration efficiency of leading universities has fluctuated slightly from 2007 to 2015. In the second stage, we perform truncated regressions to explore underlying determinants of estimated efficiency. We found that the U–I collaboration efficiency is determined by a university’s characteristics, scientific research fund from the government, and regional economic status. We also found heterogeneous effects across samples.

Measuring and explaining the production efficiency of Spanish universities using a non-parametric approach and a bootstrapped-truncated regression

The identification of environmental factors that explain differences in efficiency is essential for improving the results of public universities. A two-stage, semi-parametric approach with the single and double bootstrap procedure (Algorithm #1 and Algorithm #2) proposed by Simar and Wilson (J Econom 136(1):31–64, 2007) was used in this article for making valid inferences about the impact of environmental factors on university efficiency. A data envelopment analysis (DEA) efficiency estimator was used in the first stage to estimate technical efficiency scores for Spanish public universities. It is common to explore the determinants of (in)efficiency in a second stage. To provide valid inference, Simar and Wilson (2007) suggested a parametric bootstrap of the truncated regression (Algorithm #1). Alternatively, they recommended a bootstrap procedure to obtain bias-corrected technical efficiency scores used in the second-stage truncated regression; valid inference can be obtained by using a second bootstrap procedure applied to the truncated regression (Algorithm #2). Under both algorithms, three environmental factors were statistically significant predictors of efficiency. Our results confirmed that universities with a higher percentage of academics with tenure, outgoing Erasmus students, and state grantees tend to be less inefficient.

Assessing the effects of exogenous factors for benchmarking hospitals with double bootstrapping

PurposeData envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.Design/methodology/approachFirst, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.FindingsThe authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.Research limitations/implicationsThe study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.Practical implicationsIn this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.Originality/valueThe study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.

Will structure-environment-fit result in better port performance? —An empirical test on the validity of Matching Framework Theory

Although Matching Framework Theory (MFT) was introduced to explain inconsistencies in the port performance literature more than two decades ago, empirical studies to test its validity are absent. In this paper, we conduct an empirical study—focusing on one dimension of MFT, structure-environment-fit—and its impact on port performance. We deliberately select the Chinese container terminal industry (2004–2014) to test the proposed theory. We assume that the 2008 Financial Crisis in the middle of our observation period significantly changed the original structure-environment-fit state in the context, thereby resulting in a different impact on port performance. The event study research design also enables us to examine the influence of structure-environment-fit on port performance, while leaving other variables relatively stable. A multivariate analysis model in the truncated double bootstrapping method, median regression and a meta-frontier analysis are applied to prove our hypotheses. The results obtained based on data of 56 Chinese container terminals from 2004 to 2014 confirm that structure-environment-fit results in better port performance.

Application of Non-Parametric Bootstrap Confidence Intervals for Evaluation of the Expected Value of the Droplet Stain Diameter Following the Spraying Process

In the era of sustainable agriculture, the issue of proper and precise implementation of agrotechnical operations, without harmful effects on the natural environment, begins to play an important role. Statistical tools also become important, for example, when assessing the malfunction of plant cultivation equipment. The study presents a comparison of six nonparametric bootstrap methods used for construction of confidence intervals for the expected value of an average diameter of droplet stains following the spraying process. The simulation tests were carried out based on experiment with nozzle sprayer Lechler 110-03 using two spray nozzles: a new one and an old one. It was assumed that the distribution of the droplet stain size was consistent with the lognormal distribution. The paper considers the influence of the sample size, mean value and standard deviation of the droplet stain diameter on the interval range as well as on the estimated coverage probabilities of the confidence intervals. It was shown that in general these methods can be applied for this purpose. For the double bootstrap method and the studentized method, the empirical confidence levels of the constructed intervals turned out to be less distinct than the assumed level but the lengths of these intervals were greater than the lengths of intervals obtained using the other four methods.

The impact of the economic crisis on the (in)efficiency of public Higher Education institutions in Southern Europe: The case of Spanish universities

The evaluation of university efficiency in Europe began timidly when the European Higher Education Area (EHEA) was created. However, this issue is currently becoming increasingly important in Southern European countries, where the limitation of public funding following the economic crisis in 2008 has put greater pressure on their public universities to achieve excellence and improve competitiveness. In this context, the goals of this paper are: first, to measure the relative technical (in)efficiency of Spanish public Higher Education Institutions in the period 2002–03 to 2012–13, comparing the situation before and during the last economic crisis; and, second, to analyze the determinants of university (in)efficiency and, especially, the direct impact of the crisis. After applying the two-stage double bootstrap DEA methodology, the results show that Spanish public universities have become more efficient during the crisis than before it. In fact, the regression analysis confirms that the “crisis” variable has had a statistically significant positive impact on university efficiency. We also find that age has favorably influenced how these institutions utilize their resources to produce teaching and research outputs, but technical specialization has had a negative effect. Moreover, the regional location of public universities has been also a crucial determinant of their efficiency level. Our findings are therefore relevant for political and academic decision-makers in order to know if public universities have been adequately managed in the crisis period and to identify factors that could improve their efficiency, and hence to help them to enhance their international competitiveness in the future.

Investigating the efficiency of greenhouse production in Oman: A two-stage approach based on Data Envelopment Analysis and double bootstrapping

This paper investigates the performance of greenhouse production in the Sultanate of Oman in two-stages. In the first stage, Data Envelopment Analysis is used to estimate the efficiency of greenhouse farmers and pinpoint the wasted resources. The efficiency results show that 79% of greenhouse farmers are technically inefficient, with an average efficiency score of 73%. Moreover, slack analysis indicates that water and electricity consumption both exceed the efficiency norm by up to 54% and 46%, respectively. The second stage aims to identify the contextual (socioeconomic and environmental) factors that influence the efficiency of greenhouse farms. This is accomplished using a truncated regression model along with a double bootstrapping mechanism found in the greenhouse production literature. Results reveal that ground water salinity is the environmental factor that has the most negative impact on greenhouse efficiency. The socioeconomic factor that has the most positive effect on greenhouse efficiency is the secondary occupation of the farmer; especially when that occupation pertains to the government agricultural sector. To enhance efficiency of greenhouse farms two main courses of action are recommended. On the greenhouse farmer’s side, training programs are needed to educate the farmers on methods that promote more rational resource consumption. For policies and regulations, a regulated water framework is essential, as well as an appropriate revision of the government’s subsidy policies.

Brand strategy scope and advertising spending: The more the better?

Given the need to justify business management expenses, firms are very interested in measuring marketing performance. The objective of this article is to analyze mass media advertising investment from an efficient point of view in hotel chains. To accomplish the objective, this article applies a two-stage double bootstrap data envelopment analysis to the monetary resources allocated to the different advertising media by the main companies in the Spanish hotel sector. The authors further investigate the determinants of hotel advertising efficiency in terms of the number of brands in the hotel portfolio and the combination of advertising media used (i.e. Internet advertising). The results show a certain level of waste of advertising spending by hotel chains and that both brand portfolio scope and Internet advertising positively affect efficiency.

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

AbstractThis paper applies bootstrap methods to LM tests (including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu (2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests isO((NT)−2) and that of fast double bootstrap LM tests isO((NT)−5/2). Extensive Monte Carlo experiments suggest that, compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level. Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our investigation is based on three well-known real datasets, i.e., Wolf’s sunspot data, Canadian lynx data and, Malaysia ringgit/United States dollar exchange rate data. Statistical analysis on SSE, MSE, RMSE, MAE, MAPE and VAF is then conducted to verify that the proposed models are better than previous ARIMA, ANN and ANN–ARIMA models. The empirical results show that, compared with ARIMA, ANNs and ANN–ARIMA models, the proposed models generate smaller values of SSE, MSE, RMSE, MAE, MAPE and VAF for both training and testing datasets. In other words, the proposed models are better than those that we compare with. Their forecasting values are closer to the actual values. Thus, we conclude that the proposed models can be used to generate better forecasting values with higher degree of accuracy, efficiency and, precision in forecasting time series results becomes a priority.

Role of Bank Regulation on Bank Performance: Evidence from Asia-Pacific Commercial Banks

The banking industry is an essential financial intermediary, thus the efficient operation of banks is vital for economic development and social welfare. However, the 2008 global financial crisis triggered a reconsideration of the banking systems, as well as the role of government intervention. The literature has paid little attention to the banking industry in the Asia-Pacific region in the context of bank efficiency. This study employs double bootstrap data envelopment analysis to measure bank efficiency and examine the relationship between regulation, supervision, and state ownership in commercial banks in the Asia-Pacific region for the period 2005 to 2014. Our results indicate that excluding off-balance sheet activities in efficiency estimations lead to underestimating of the pure technical efficiency, while overestimating the scale efficiency of banks in the Asia-Pacific region. Cross-country comparisons reveal that Australian banks exhibit the highest levels of technical efficiency, while Indonesian banks exhibit the lowest average. Our bootstrap regression results suggest that bank regulation and supervision are positively related to bank technical efficiency, while state ownership is not significantly related to bank efficiency. Furthermore, our findings show that tighter regulation and supervision are significantly related to higher efficiency for small and large-sized banks.