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The aim of this paper is to use stochastic frontier regression to measure the effect of the introduction and expansion of prevailing wage requirements on the cost efficiency of public school construction in British Columbia. The results provide evidence from a specific sector and location that contributes to the accumulated evidence on the topic.

Stochastic frontier regression is used to measure the effect of the introduction and expansion of prevailing wage requirements on the cost efficiency of public school construction in British Columbia. The results of a likelihood ratio test indicate that stochastic frontier regression is preferred to OLS cost estimation because of the presence of statistically different cost inefficiencies across a sample of covered and uncovered projects.

Specifically, projects covered by the introductory stage of the wage requirements are significantly less cost efficient compared to other public school projects. However, by the time of the expansion of the wage policy 17 months later, covered projects were no more cost inefficient than other projects. The results indicate that if prevailing wage laws are associated with changes in the cost efficiency of construction, stochastic frontier regression is the preferred estimation technique.

This is the first study of its kind with results specific to school construction in British Columbia. Further research based on data from other projects may/may not confirm the existence of differences in construction cost inefficiencies for projects covered by construction wage policies.

By implication, the results suggest that if the cost inefficiency of prevailing wage laws is short‐lived, the impact on the total cost of construction may similarly be ephemeral. The results imply that prevailing wage regulations do not contribute to long‐run construction cost inefficiency.

This is the first study to use stochastic frontier regression to examine the cost inefficiencies associated with prevailing wage laws. Previous studies that examine the effect of the wage policy on construction costs are based on OLS estimation.

The purpose of this paper is to estimate the China’s outward FDI efficiency and it determinants in 69 countries along the Belt and Road over the period of 2003-2013.

This paper defines the extent of the Belt and Road in terms of geographical boundaries, justifying the application of the stochastic frontier gravity model to the FDI analysis, and then constructing a frontier regression model to assess the China’s outward FDI efficiency and it determinants in countries along the Belt and Road.

Regarding the core gravity parameter estimates, China’s outward FDI was highly consistent with the gravity model. As far as policy parameters are concerned, China’s outward FDI was significantly restricted by some man-made barriers in host countries. According to the estimated FDI efficiency scores, China has huge outward FDI potential in countries along the Belt and Road. In general, China’s outward FDI efficiency demonstrated a consistent uptrend from the perspectives of both FDI flows and stocks over the period of 2003-2013. Although China’s outward FDI performance indicated a very uneven pattern across different countries and periods, there were no significant performance differences between the Road and Belt.

The Belt and Road initiative can be largely beneficial to China’s outward FDI, but the specific framework of cooperation should be designed on the basis of determinants of China’s outward FDI. The regional cooperation with the Road countries should mainly focus on the removal of business barriers and financial barriers. The regional cooperation with the Belt countries should mainly concern the improvement of local intellectual property protection, the reduction of local tax burden, and removal of business barriers and financial barriers.

To the authors’ best knowledge, no existing literature has specifically examined the efficiency of China’s outward FDI in the countries along the Belt and Road and its determinants.

Using a panel of 1,122 UK firms listed on the London Stock Exchange over the period of 1981-2009, corporate efficiencies are predicted in this paper as inverse proxies of agency cost and the agency cost hypotheses are tested. The paper aims to discuss this issue.

Stochastic frontier analysis is used to estimate corporate efficiency of firms, but from two different perspectives. The long-run and short-run corporate efficiencies are predicted focussing on modern approach of value maximization and traditional approach of profit maximization, respectively.

The estimation results reveal that, an average firm in the sample achieves 74.5 percent of its best performing peer's market value and 86.6 percent of its best performing peer's profit and both of them are highly significant in the analysis. The long-run market value efficiency supports the agency cost of outside equity and the short-run profit efficiency supports the agency cost of outside debt hypothesis. Also there is a positive rank correlation between these two efficiencies which confirms that an average firm in the UK suffers from inefficiency or agency conflicts to a certain extent, no matter whether the firm is driven by short-run or long-run growth perspectives.

The predicted broad measures of agency costs in the paper have wider implications in enhancing the understanding of the UK firms’ corporate performance especially when they operate under a relatively free and market based governance and financial system.

The work is distinguished by the large panel of UK firms and a long period of time that is considered. Emphasizing on the empirical implications of the distinctions between short-run and long-run efficiency is also novel.

The purpose of this paper is to measure technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from actual inefficiency using comprehensive household-level panel data.

This paper estimates technical efficiency based on the true random-effects stochastic production frontier estimator with a Mundlak adjustment. By utilising comprehensive panel data with 4,694 observations from 39 districts of four major maize-producing regions in Ethiopia, the author measures technical efficiency and examine its determinants while disentangling unobserved time-invariant heterogeneity from technical inefficiency. By using competing stochastic production frontier estimators, the author provides insights into the influence of farm heterogeneity on measuring farm efficiency and the subsequent impact on the ranking of farmers based on their efficiency scores.

The study results indicate that ignoring unobservable farmer heterogeneity leads to a downwards bias of technical efficiency estimates with a consequent effect on the ranking of farmers based on their efficiency scores. The mean technical efficiency score implied that about a 34% increase in maize productivity can be achieved with the current input use and technology in Ethiopia. The key determinants of the technical inefficiency of maize farmers are the age, gender and formal education level of the household head, household size, income, livestock ownership, and participation in off-farm activities.

While the findings of this study are critical for informing policy on improving agricultural production and productivity, a few important things are worth considering in terms of the generalisability of the findings. First, the study relied on secondary data, so only a snapshot of environmental factors was accounted for in the empirical estimations. Second, there could be other sources of unmeasured potential sources of heterogeneity caused by persistent technical inefficiency and endogeneity of inputs. Third, the study is limited to one country. Therefore, future research should extend the analysis to ensure the generalisability of the empirical findings regarding the extent to which unmeasured potential sources of heterogeneity caused by persistent technical inefficiency, endogeneity of inputs and other unobservable country-specific features – such as geographical differences.

This paper contributes to the literature on agricultural productivity and efficiency by providing new evidence on the influence of unobservable heterogeneity in a farm efficiency analysis. While agricultural production is characterised by heterogeneous production conditions, the influence of unobservable farm heterogeneity has generally been ignored in technical efficiency estimations, particularly in the context of smallholder farming. The value of this paper comes from disentailing producer-specific random heterogeneity from the actual inefficiency.

The purpose of this paper is to provide evidence concerning wage differentials and the existence of gender pay discrimination in France, Germany, Italy, Spain and the United Kingdom.

The authors used the stochastic frontier approach as an alternative method to measure the relevance of gender discrimination. This methodology allows them to explain the differences between the potential and the observed wage that an individual could obtain, given his or her human capital endowment.

The authors found support for wage discrimination against women. The results show that a significant part of the gender wage gap in all the countries analysed is not attributable to differences in human capital endowment or personal and job‐related characteristics.

The definition of education in the data set is modest and broadly defined.

This paper contributes to the debate on gender discrimination by analysing the determinants of the gender gap using an innovative methodology in the gender gap literature: the stochastic frontier approach.

A firm is technically efficient when it produces the maximum level of output for a given level of input on the assumption that technology is fixed. Although the above definition of technical efficiency has been around for decades, economists have, for the most part, been estimating average production functions (i.e. production functions that assume that all firms are technically efficient except for random noise), and then proceeding to make inferences regarding the potential of firms from this average production function.

The purpose of this paper is to analyze the relationship between farm size and agricultural production efficiency from the aspects of output and profit in order to find an optimal farm size that achieves both output and profit efficiency in agricultural production in China.

This study uses the 2012 China Family Panel Studies survey data and employs the stochastic frontier analysis (SFA) models to investigate empirically the relationship between farm size and agricultural production efficiency.

The study finds that there is an inverted-U curve relationship between farm size and output efficiency and a U-shaped curve relationship between farm size and profit efficiency in agricultural production in China. Based on the empirical results, the study estimates that the appropriate farm size is around 10–40 mu and the optimal farm size is around 20–40 mu both in terms of output efficiency and profit efficiency in Chinese agricultural production under the current agricultural technology and land management system.

The findings of this study suggest that appropriate land consolidation will bring more benefits to farmer households and agricultural production efficiency. There are some policy implications. First, governments should give long term and more stable land using rights to farmers through extending the period of land contract and verifying land using rights. Second, governments should encourage transfers of land using rights and promote land consolidation. But the implementation of this policy should consider regional differences and not be used for blindly pursuing increasing land size. Third, land consolidation should be accompanied with the development of specialized agricultural services.

The paper makes two major contributions to the literature. First, the authors use the SFA model to investigate the relationship between land size and agricultural production efficiency. Second, the authors establish two SFA models – the stochastic frontier output analysis model and the stochastic frontier profit analysis model – to estimate the optimal land size to achieve both output and profit efficiency of agricultural production in China.

The purpose of this paper is to improve the estimation of the production frontier in cases where outliers exist. We focus on the case when outliers appear above the true frontier due to measurement error.

The authors use stochastic data envelopment analysis (SDEA) to allow observed points above the frontier. They supplement SDEA with assumptions on the efficiency and show that the true frontier in the presence of outliers can be derived.

This paper finds that the authors’ maximum likelihood approach outperforms super-efficiency measures. Using simulations, this paper shows that SDEA is a useful model for outlier detection.

The model developed in this paper is original; the authors add distributional assumptions to derive the optimal quantile with SDEA to remove outliers. The authors believe that the value of the paper will lead to many citations because real-world data are often subject to outliers.

The purpose of this paper is to consider a new application of stochastic frontier analysis, in which the method is applied to demand data for a food product category, in an attempt to benchmark category consumption and segment food consumers.

In a unified, two‐stage approach, a stochastic frontier model is first estimated and subsequently deviations from the demand frontier are regressed on customer characteristics. The method is illustrated in scanner panel data.

A frontier demand function estimated in scanner data of a frequently‐bought food category has significant and consistent parameters. Specific descriptor variables can explain excessive category demand and profile customers with considerable sales potential.

More work is needed to generalise the usefulness of the proposed model in different food categories. Future research may employ alternative functional specifications and explanatory variables.

The empirical identification of salient characteristics improves consumer understanding and can assist in the design of data‐driven marketing action. Applied researchers can use marketing and demographic variables that are found in standard consumer panels to estimate frontier models.

The paper introduces stochastic frontier analysis as a means to determine consumer differences in food demand. This is an important area for retailers, producers and researchers.

The purpose of this paper is to estimate the degree of technical efficiency, determinants of technical inefficiencies and driving forces behind the production growth for a panel data set collected during the 1998/1999 and 2004/2006 Kharif cropping season, from 452 small-scale rice farming households in the Giridih and Purulia districts of Eastern India.

The estimations of technical efficiency utilize stochastic frontier production function with a sub-model of inefficiency effects at both aggregated farm level and disaggregated plot level where traditional varieties (TVs) and high-yielding varieties (HYVs) are differentiated. The output growth decomposition analysis identifies the main contributor to the total rice production growth.

The results indicate that the sampled farms are operated at moderate levels of technical efficiency. The production of HYV rice is associated with higher technical efficiency compared to TV rice. Farming experience, education attainment, landholding size, the share of non-agricultural income and the share of land in the lower terraces account for the differences in technical inefficiencies across the sampled farms. The decomposition analysis suggests that as technical efficiency decreased, technical change is the main source of production growth during the survey period.

The small sample size applied in the analysis will result in an insufficient representativeness of the study area.

This paper fills the literature gap as estimations of technical efficiency that account for subtle differences in adopted rice varieties are still rare in India.