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The need for random assignment in sexual offending programme evaluation is clear. Decades of high dependence on weak-inference methodology, that of observational studies, has inhibited professional agreement regarding the effects of programmes. Observational studies have a place in evaluation research when more rigorous scientific designs precede them, as occurs in neighbouring fields of drug development and health. If, however, observational studies remain the only method used to evaluate sexual offending programmes, the field will continue to endure uncertainty with confident causal inferences regarding their effects remaining elusive. The paper aims to discuss these issues.

The paper takes the form of a literature review and discussion.

The case for random assignment is made alongside a rebuttal of arguments against their use.

This is an original look at the need for random assignment in sexual offending programme evaluation taking into account existing studies and discussion topics.

This paper aims to evaluate the technical efficiency of the health-care systems in 21 selected middle-income countries during the period (2000–2017) and determine the source of inefficiency whether it is transient (short run) or persistent (long run).

The study uses the stochastic frontier analysis technique through employing the generalized true random effects model which overcomes the drawbacks of the previously introduced stochastic frontier models and allows for the separation between unobserved heterogeneity, persistent inefficiency and transient inefficiency.

Persistent efficiency is lower than the transient efficiency; hence, there are more efficiency gains that can be made by the selected countries by adopting long-term policies that aim at reforming the structure of the health-care system in the less efficient countries such as South Africa and Russia. The most efficient countries are Vietnam, Mexico and China which adopted a social health insurance that covers almost the whole population with the aim of increasing access to health-care services. Also, decentralization in health-care has assisted in adopting health-care policies that are suitable for both the rural and urban areas based on their specific conditions and health-care needs. A key success in the implementation of the adopted long-term policies by those countries is the continuous monitoring and evaluation of their outcomes and comparing them with the predefined targets and conducting any necessary modifications to ensure their movement in the right path to achieve their goals.

Although several studies have evaluated the technical efficiency both across and within countries using non-parametric (data envelopment analysis) and parametric (stochastic frontier analysis) approaches, to the best of the authors’ knowledge, this is the first attempt to evaluate the technical efficiency of selected middle-income countries during the period (2000–2017) using the generalized true random effects stochastic frontier analysis model.

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 examine the potential biases in research designs used to assess the efficacy of sex offender treatment.

In all, 50 treatment studies (n=13,886) were examined using a random effects meta-analysis model.

Results indicated a positive effect of treatment for both sexual (OR=0.58, 95%, CI 0.45-0.74, p < 0.0001), and general recidivism (OR=0.54, 95%, CI 0.42-0.69, p < 0.0001), indicating that the likelihood of being reconvicted after treatment was around half compared to no treatment. RCTs showed no significant effect for sexual or general, recidivism. Significant effects were found for non-RCT designs (i.e. incidental cohort, completers vs non-completers designs). Assignment based on need (i.e. giving treatment to those who were high-risk) indicated a negative effect of treatment.

The results highlight the importance of considering study design when considering treatment efficacy.

The current research reports studies identified up until 2009, and examined both published, and unpublished, research originating from a variety of samples employing a random effects model. Consequently, it can be argued that the results are both original and are reflective not only of identified studies, but are also representative of a random set of observations drawn from the common population distribution (Fleiss, 1993). The results of the study suggest that what is required in future research is methodological rigour, and consistency, in the way in which researchers measure the effectiveness of sexual offender treatment.

Fertilizer overuse is regarded as one of the main contributors to agricultural pollution and environmental problems in China. The purpose of this paper is to evaluate technical efficiency (TE) and fertilizer overuse index (FOI) with respect to China’s arable agricultural production and examine regional variations in terms of fertilizer overuse.

The maximum likelihood random effects–time varying inefficiency effects model was applied to estimate TE, fertilizer use efficiency (FUE) and FOI.

Over the study period (2011–2015), TE steadily increased for each individual province. Overall, mean annual TE was 0.811, implying that, on average, Chinese provinces could increase output by 18.9 per cent given unchanged levels of inputs and technology. Mean annual FOI ranged from 0.008 to 3.139, with a mean of 0.685, suggesting that there is fertilizer overuse in almost all provinces, and that large regional variation exists. Coastal provinces were found to have the highest TE scores, while the central region showed the highest degree of fertilizer overuse.

The results indicate that fertilizer use has had a significant positive impact on production in the China’s arable agricultural sector. High TE was not necessarily associated with low FUE.

This paper evaluates the importance of both liquidity and solvency risk factors on variations in efficiency measures of US commercial and domestic banks from 2005 to 2017.

The analysis is conducted using the true random effect stochastic cost model to examine the role of both liquidity and solvency risk factors. To this end, the author uses the exponential stochastic cost function and adds new variables, including bank size, crisis as an indicator of the financial crisis and the Dodd–Frank Act and Basel II Accord as regulatory dummies.

The results show that both liquidity and solvency risk factors positively affect the variance of cost inefficiency measures and thus have negative impact on the cost efficiency measures. In addition, banks increased the cost of financial intermediation during the financial crisis, whereas regulatory factors of Basel II Accord and Dodd–Frank Act play a crucial role in explaining the cost efficiency measures.

These results are the first to quantify the impact of both liquidity and solvency on the variations in cost efficiency measures.

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.

Recent advances in stochastic frontier analysis (SFA) suggest two alternative approaches to account for unobserved heterogeneity and to distinguish between persistent and transient inefficiency. The first approach is the generalized true random effects (GTRE) model, and the second approach is an autoregressive inefficiency (ARI) model. This study compares them to highlight whether they capture similar inefficiency aspects.

Using recent methodological advances in SFA, the authors estimate the GTRE and the ARI models using a Monte Carlo experiment and two real datasets from two industries (banking and agriculture).

The authors find that the two models provide quite different results in terms of inefficiency persistence and overall inefficiency (combination of transient and persistent inefficiency), regardless of the dataset considered.

The study findings suggest that researchers should be careful when referring to these two models because they do not capture the same inefficiency aspects, even though they have the same conceptual basis. This work is a warning about the empirical aspects of the persistent and transient efficiency framework, in order to convey a consistent story to the reader on firms' performance.

Even though they are used in a large number of studies, the present paper contributes to the productivity and efficiency literature by providing the first comparison of the GTRE and the ARI models.

Faced with contradictory outcomes in empirical studies on the relation between democracy and income inequality, this paper attempts to provide empirical relations between democracy and income inequality. In particular, the authors seek to find if any curvilinear relation exists as in the Kuznets hypothesis.

Given elusiveness in empirical relations, the authors will consider several specifications using different estimation methods such as ordinary least squares (OLS), panel data estimation and performing statistical tests to determine the best specification for the relation between income inequality and democracy. Once the authors choose the specification, then the authors will apply this specification to the different groups of data to find any meaningful implications.

Using the unbalanced panel of 136 countries spanning from 1980 to 2018, the authors found an inverse U-shaped relation, called a political Kuznets curve – income inequality increases first and then decreases later as more democracy is achieved. By quantifying the curve, the authors find that the direct impact of democracy on income inequality is small and that the incremental impact of democracy on income inequality is smaller in a semi-democracy while relatively larger in a full democracy and autocracy.

From the study’s findings, the following policy implications can be considered. First, any change in income inequality caused by democratization should not be concerning as the impact of democracy on income inequality is measured to be very small. Second, the largest factor reducing income inequality is real GDP per capita. Third, the authors find that an impact of government expenditure on income inequality is also inversely U-shaped.

This chapter synthesized some of the published literature comparing the cognitive functioning of children with math disabilities (MD) with (1) average achieving children, (2) children with reading disabilities (RD), and (3) children with comorbid disabilities (RD+MD). Twenty-one studies, which yielded 194 effect sizes (ESs), indicated that average achievers outperformed children with MD on measures of verbal problem solving (M=−0.58), naming speed (M=−0.70), verbal (M=−0.70) and visual-spatial working memory (WM, M=−0.63), and long-term memory (LTM, M=−0.72). The results further indicated that children with MD outperformed children with combined disabilities on measures of literacy (M=0.75), visual-spatial problem solving (M=0.51), LTM (M=0.44), short-term memory (STM) for words (M=0.71), and verbal WM (M=0.30). Children with MD could only be clearly differentiated from children with RD on measures of naming speed (−0.23) and visual-spatial WM (−0.30). The magnitude of ESs was persistent across age and severity of math disability. Hierarchical linear modeling (HLM) indicated that the magnitude of ES in overall cognitive functioning between MD and average achievers was due to verbal WM deficits when the effect of all other variables (e.g., age, IQ, reading level, other domain categories) were partialed out. The results are discussed within the context of defining MD by level of severity of WM abilities.