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Recent empirical studies have improved methodologies for identifying the causal effects of policies especially on a minimum wage hike. This study identifies causal effects of minimum wage hikes across 47 prefectures in Japan from 2008 to 2010 on employment, average hourly wage, work hours, full-time equivalent employment (FTE), total wage costs, average tenure, separation and new hiring in establishments using a micro dataset of business establishments in restaurant, accommodation, and food takeout and delivery industry. Various regression specifications including controls for time-varying regional heterogeneity are implemented by using the bite of the minimum wage in each establishment. First, this study finds that the effects of a revision of minimum wage on employment and FTE in the establishment are statistically insignificant, but the effects on hourly wages and total wage costs are statistically significant. Subsequently, it considers how the establishments react to the increase in total wage costs caused by the revised minimum wage, and finds that separation from the establishment may decrease, and average tenure of workers may increase.

Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual treatment effects are due to heterogeneity or a chance mechanism? (2) what is the best way to estimate the average treatment effects? Testing and aggregation methods are suggested. Monte Carlo simulations are also conducted to shed light on these two issues. An empirical analysis on the involvement of underground organization in China’s Peer-to-Peer (P2P) activities through the “anti-gang” campaign is also provided.

Land is an emotive issue for women in Kenya, majority of who still suffer the consequences of not having access to land, leading to economic insecurity. This paper aims at examining the effects of women access to land on household nutritional outcomes among smallholder farmers in Kenya.

The study uses primary data collected from a sample of 384 small-scale women farmers selected using multi-stage sampling technique. For data analysis, household nutritional outcomes were measured using Households Dietary Diversity Scores (HDDS) and Household Hunger Scale Scores (HHS). Stratification multilevel and matching-smoothing approach that controls for pre-treatment heterogeneity bias and treatment effect heterogeneity bias was used in estimating heterogeneous effects of women access to land.

The analysis reveals that women access to land has a significant positive effect on household nutritional outcomes. All households across all propensity scores strata benefited significantly but differently from women access to land in terms of nutritional outcomes.

Econometrically, propensity matching technique used in computing heterogeneity effects captures selection bias due to observable characteristics but it fails to capture selection bias due to unobservable factors. However, robust strategies were employed to ensure minimal estimation bias.

The paper provides insights on the determinants of women access to land and the influence women access to land has on household nutritional outcomes. In addition, by employing one of the conventional impact evaluation techniques, the paper contributes to knowledge by taking into accounts the heterogeneity in the effects of women access to land on household nutritional outcomes.

We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely data augmentation, the Gibbs sampler and the Metropolis-Hastings algorithm can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Conventional “treatment effects” such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are adapted for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects.

The purpose of this paper is to investigate the role of family structure on child labor by comparing children of nuclear families headed by the father with children of single-mother families headed by the divorced mother.

This paper uses data from Brazilian urban areas provided by the Brazilian Demographic Census of 2010. The empirical approach consists of the estimation of three treatment effect models: the Average Treatment Effect, IV Treatment Effect and Two-Stage Estimator proposed by Lewbel (2012).

The main findings show that children of single-mother families headed by divorced mothers are more likely to work, compared to children living with both parents. This paper found evidence of a direct effect of family structure parents’ determinant on child participation in labor. The main hypothesis is that the absence of the father paired with exposure to family stress arising from marital dissolution is an indicator toward child labor.

This study implies that in order to combat child labor effectively, it is important to understand deeply its several causes and consider ruptures in family structure, such as divorce, as one of these factors. In addition, location and family’s characteristics also play a role on the decision of child labor. For instance, boys living at metropolis areas have less chance to work. Family’s head education and non-work income affects positively the child well-being by reducing the probability of child labor. On the other hand, the number of siblings increases the chance of child labor. Finally, the results of this study suggest policies to raise awareness among parents about the negative effects of child labor on children during both childhood and adulthood, and that social policies need to act beyond legislation and enforcement, but including family mobilization.

This paper estimates the impact of family structure on child labor using an empirical approach to deal with the endogeneity problem of the treatment.

We study a microfounded search model of exchange in the laboratory. Using a within-subjects design, we consider exchange behavior with and without an intrinsically worthless token object. While these tokens have no redemption value, like fiat money they may foster greater exchange and welfare via the coordinating role of having prices of goods in terms of tokens. We find that welfare is indeed improved by the presence of tokens provided that the economy starts out with a supply of such tokens. In economies that operate for some time without tokens, the later surprise introduction of tokens does not serve to improve welfare. We also explore the impact of announced changes in the economy-wide stock of tokens (fiat money) on prices. Consistent with the quantity theory of money, we find that increases in the stock of money (tokens) have no real effects and mainly result in proportionate changes to prices. However, the same finding does not hold for decreases in the stock of money.

We present Bayesian models for finding the longitudinal causal effects of a randomized two-arm training program when compliance with the randomized assignment is less than perfect in the training arm (but perfect in the non-training arm) for reasons that are potentially correlated with the outcomes. We deal with the latter confounding problem under the principal stratification framework of Sommer and Zeger (1991) and Frangakis and Rubin (1999), and others. Building on the Bayesian contributions of Imbens and Rubin (1997), Hirano et al. (2000), Yau and Little (2001) and in particular Chib (2007) and Chib and Jacobi (2007, 2008), we construct rich models of the potential outcome sequences (with and without random effects), show how informative priors can be reasonably formulated, and present tuned computational approaches for summarizing the posterior distribution. We also discuss the computation of the marginal likelihood for comparing various versions of our models. We find the causal effects of the observed intake from the predictive distribution of each potential outcome for compliers. These are calculated from the output of our estimation procedures. We illustrate the techniques and ideas with data from the 1994 JOBS II trial that was set up to test the efficacy of a job training program on subsequent mental health outcomes.

This study aims to provide a thick description of a four-day bridging course in introductory accounting presented before the start of a student’s first year. The course aims to address the lack of prior accounting knowledge. The study also evaluates the effectiveness of the course using econometric techniques. Treatment effects are considered when interpreting the results.

This voluntary intervention used a quasi-experimental research design and quantitative techniques, including the application of propensity score matching (PSM), to isolate the treatment effect on the treated and untreated groups.

A positive and significant association is reported between attending the bridging course and performance in the first assessment.

A bridging course in accounting offers higher educators an opportunity to ensure that students are academically better prepared when entering university. This course provides adequate prior knowledge from which a student will benefit during the first assessments, which may contribute to increased self-efficacy and retention. This intervention has social implications for students as they can interact, participate and easily transition from school to university. Social implications include learning communities that are formed at the onset of their studies.

Bridging courses have been presented in other disciplines with positive results but not yet in accounting. Bridging courses in accounting are viable interventions to address gaps in prior knowledge and assist with the transition from school to university. This study expands literature by demonstrating the application and interpretation of PSM in quasi-experimental designs.

Reassesses the Minneapolis Domestic Violence Experiment (MDVE) data of the 1980s which said that “arrest works best”. Examines the potential effects of a small number of officers generating a relatively large number of MDVE cases. Expresses concern that a few officers submitting a high rate of cases might have an inordinately large effect on the experimental outcomes. Seeks to determine whether they followed the experimental protocol but finds instead that it was the low rate officers who failed to do so. Finds that officers who submitted experimental cases at a high rate were also different in other ways from the rest of the officers. Finally, excludes findings relating to lower case officers and thereby reveals that recidivism in domestic violence was not reduced by the arrests.

The purpose of this paper is to determine if neurolinguistic programming (NLP) tools and techniques were effective in alleviating the symptoms of posttraumatic stress disorder (PTSD) in clients from the Military and Emergency Services.

This project ran at the “Healing the Wounds” charity in Bridgend. All clients were opportunistic, having self-referred to a charity specifically set up to support Veterans from the Armed Forces. In total, 29 clients from an initial cohort of 106 clients provided pre and post data using Depression Anxiety Stress Scale (DASS) and the NLP Wheel of Life scale. Interventions included a range of NLP techniques, addressing self-reported symptoms.

Differences between DASS scores before and after treatment are very highly significant. t-test analysis infers that these results are indicative of the overall response from the clients in this study.

Limitations of the study include: client group; significant levels of incomplete data for the total study group; therapist effect and therapist training; treatment methodology.

Data suggest that NLP has potential as a therapeutic tool in the treatment of symptoms of anxiety and depression associated with a self-report of PTSD. An observation is proposed that these candidates experience an improvement in their emotional state when NLP is used which is statistically significant (p<0.001) both for overall DASS score averages and also for each of the three DASS categories (Depression, Anxiety and Stress). Stress was the highest scoring category prior to treatment for these clients; the reduction in their stress symptoms contributed most substantially to the overall reduction in average DASS score, indicating an improvement in their emotional state.