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Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

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 traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

While firms in developing countries are increasingly adopting lean practices of inventory management, there is limited evidence showing the impact of lean practices on firm performance in countries such as India. Lean practices improve the financial performance of the firms through superior cost-reduction measures and operational efficiencies. This paper examines the impact of inventory leanness in Indian manufacturing firms on their financial performance.

The authors measure inventory leanness based on stochastic frontier analysis (SLA), apart from using conventional measures available in the literature. The authors analyze the impact of inventory leanness on the financial performance of firms by examining data for 12,334 unique Indian manufacturing firms for the period 2009–2018. The authors present a comparative analysis using different methods of inventory leanness and study the effects on firm performance.

First, the authors find that only 68 industries out of 411 industries follow lean practices, i.e. most industries do not follow lean practices. Second, the estimation results show that there exists a positive relationship between inventory leanness and firm performance. The results suggest that an inverted U-shaped relationship exists between inventory leanness and firm performance for the entire sample. In particular, 17% of the industries in the sample exhibit such a relationship, and it is sufficiently strong to show up in the average regression results for the entire sample.

The authors introduce a novel measure of inventory leanness named stochastic frontier leanness based on the SFA method used in production economics. It measures leanness by benchmarking the inventory levels against the industry “frontier”. Furthermore, the authors conduct an empirical study of the lean-financial performance relationship with a large panel dataset of Indian firms instead of the survey-based methods that were previously used in the literature.

This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin.

This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin.

Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield.

The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13.

This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision.

This study aims to analyse the efficiency effects of institutional distance on Chinese outward foreign direct investment (FDI) in Africa.

The study utilised the true fixed-effect stochastic frontier analysis (SFA) model. Data from 2003 to 2016 (14 years) were acquired from 42 targeted African countries, which are included in the analysis.

The results reveal that FDI flow efficiency can be maximised with a high institutional distance between China and African countries. Contrariwise, comparable institutional distance, measured by the rule of law, regulatory quality and government effectiveness between the host and home countries, reflected a significant positive impact for Chinese outward foreign direct investment (OFDIs), indicating Chinese MNEs can invest directly in a country with comparable institutional characteristics.

There have been limited exceptional studies that assessed the effect of institutional distance between emerging countries. However, none of these studies investigated the effect of institutional distance between China and Africa at a national level. Using the advantage of the SFA model, this study assesses the efficiency effects of institutional distance between the host and home country.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

This paper aims to study the underpricing phenomenon of initial public offerings (IPOs) of 355 Indian companies issued from 2007 to 2019. The research question this paper empirically examines is whether Indian corporate executives deliberately underprice IPOs from its fair value to attract investors, thereby causing an abnormal spike in the prices on the listing day. The findings of this study challenge a commonly held notion of leaving money on the table by IPO issuing companies. Of the overall average listing day returns of 17%, the deliberate premarket underpricing component is found to be mere 5.3%, while the remaining price fluctuation is, inter alia, a result of market momentum along with the unmet demands of impatient investors.

Following Koop and Li (2001), this study uses Stochastic frontier model (SFM) to study a routine anomaly of disparity between the primary market price (i.e. IPO issue price) and the secondary market price (listing price). The jump in the issue price observed on a listing day is decomposed into deliberate premarket underpricing component that reflects the extent of managerial manipulation and the after-market misvaluation component attributable to information asymmetry and prevailing market volatility.

This paper uses SFM to bifurcate initial returns into deliberate underpricing by managers and after-market mispricing by noise traders. This study finds that a significant part of the initial return is explained through after-market mispricing. This study finds that average initial returns are 17%, deliberate premarket underpricing is 5.3% and after-market mispricing averages 11.9%.

This study can isolate underpricing done at the premarket by estimating a systematic one-sided error term that measures the maximum predicted issue price deviation from the offered price. Consequentially, the disaggregation of initial returns may be especially informative for retail investors in planning their exit strategy from an IPO by separating the strength of the firm's fundamentals and its causal relationship with the initial returns. Substantial proportion of after-market mispricing implies that future research should focus on factors causing after-market mispricing. As underlying causes are identified, tailor-made policy responses can be formulated to benefit investors.

This paper has empirically validated that initial return is a mix of both components, i.e. deliberate underpricing and aftermarket mispricing. This disaggregation of initial returns can prove helpful for investors in planning their exit strategy. This study can help investors to become more aware of the importance of the fundamentals of the firm and its causal relation with the initial returns. This information in turn can help reduce the information asymmetry amongst investors and help them lessen the costs of adverse selection.

A large number of research studies on IPO pricing find overwhelming evidence of underpricing in public issues. This research attempts to decompose the extent of underpricing into deliberate underpricing and after-market mispricing, thereby supplementing the existing literature on the IPO pricing puzzle. To the best of the authors’ knowledge, this study is the first contribution to the literature on initial return decomposition for the Indian capital markets.