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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.

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.

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.

The purpose of this study is to analyse the trends regarding housing segregation over the past 10–20 years and determine whether housing segregation has a spillover effect on neighbouring housing areas. Namely, the authors set out to determine whether proximity to a specific type of segregated housing market has a negative impact on nearby housing markets while proximity to another type of segregated market has a positive impact.

For the purposes of this paper, the authors must combine information on segregation within a city with information on property values in the city. The authors have, therefore, used data on the income of the population and data on housing values taken from housing transactions. The case study used is the city of Stockholm, the capital of Sweden. The empirical analysis will be the estimation of the traditional hedonic pricing model. It will be estimated for the condominium market.

The results indicate that segregation, when measured as income sorting, has increased over time in some of the housing markets. Its effects on housing values in neighbouring housing areas are significant and statistically significant.

A better understanding of the different potential spillover effects on housing prices in relation to the spatial distribution of various income groups would be beneficial in determining appropriate property assessment levels. In other words, awareness of this spillover effect could improve existing property assessment methods and provide local governments with extra information to make an informed decision on policies and services needed in different neighbourhoods.

On housing prices emanating from proximity to segregated areas with high income differs from segregated areas with low income, policies that address socio-economic costs and benefits, as well as property assessment levels, should reflect this pronounced difference. On the property level, positive spillover on housing prices near high-income segregated areas will cause an increase in the number of higher income groups and exacerbate segregation based on income. Contrarily, negative spillover on housing prices near low-income areas might discourage high-income households from moving to a location near low-income segregated areas. Local government should be aware of these spillover effects on housing prices to ensure that policies intended to reduce socioeconomic segregation, such as residential and income segregation, produce desirable results.

Furthermore, a good estimation of these spillover effects on housing prices would allow local governments to carry out a cost–benefit analysis for policies intended to combat segregation and invest in deprived communities.

The main contribution of this paper is to go beyond the traditional studies of segregation that mainly emphasise residential segregation based on income levels, i.e. low-income or high-income households. The authors have analysed the spillover effect of proximity to hot spots (high income) and cold spots (low income) on the housing values of nearby condominiums or single-family homes within segregated areas in Stockholm Municipality in 2013.

The purpose of this study is to determine the extent to which R&D subsidy can affect the innovation process of manufacturing venture firms by examining the output additionality measured as both proximal indicators of innovation and distal indicators of growth. Further, the differences in output additionality between the clusters in the subcontracting regime were examined to investigate whether the effect of R&D subsidy can vary depending on subcontracting practices and structure among large enterprises and venture firms.

This study uses survey data of the Korea Venture Business Association conducted in 2012, 2013, 2014, 2015, and 2016 respectively, which selects a random sample from venture firms by stratified random sampling method based on the industry sector, size and location for each survey year. This study analyzed the data using an endogenous treatment effects model to estimate the average treatment effect of R&D subsidy, yielding more accurate estimates than a traditional treatment effects model by controlling the unobserved endogenous components.

This research found that R&D subsidy may not facilitate the process of transformation of innovation into financial growth even though R&D subsidy can facilitate the innovation process and contribute to producing new and improved products. This research also reveals that the relationship between R&D subsidy and innovation performance for firms heavily dependent on subcontracting is generally much weaker than those for independent subcontractors. Further, the present study exhibits that public R&D subsidy for independently subcontracting venture firms is more effective for the growth in both employment and sales than those for subcontracting with large enterprises or other subcontractors.

R&D subsidy for venture firms does not relieve the burden of liability of newness and smallness of venture firms, especially the disadvantage in market penetration and competition. In addition, venture firms subcontracting with large enterprises or other prime subcontractors tend to achieve incremental innovation with the help of the technology and competence of large companies and run stable businesses through a predetermined market.

R&D subsidy for venture firms does not relieve the burden of liability of newness and smallness of venture firms, especially the disadvantage in market penetration and competition. Further policy measures should be implemented so as to identify and eliminate barriers to market acceptance for new products of venture firms.

This research verifies that the effect of R&D subsidy may harmful to the sales growth of venture firms and the output additionality differs with the degree of dependency on subcontracting practices and structure.

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.

The innovative rural land property right mortgage loan program (RLPRMLP) provides a new channel for farmers to secure borrowing from microfinance institutions. Farmers’ land property right could be used as collateral to deal with moral hazard and adverse selection issues. The purpose of this paper is to document the effects of the RLPRMLP on households’ income using a unique data set from 1,279 households’ survey in Western China during 2012–2014.

At the first stage, the authors evaluate the impacts of RLPRMLP on households’ income to get the benchmark results when the authors control household’s observed and unobserved characteristics. To address the potential endogeneity issue resulting from the self-selection of farmers into the rural financial market, the authors apply the treatment effect model to identify the csusal effects of the innovative loan approach on a household’s income.

The empirical results favor the belief that participating in the RLPRMLP helps the households improve their total income (at least by 20.2 percent) and income per capita. This income-improving channel is only through agricultural sector, rather than through non-agricultural sector which potentially helps to deal with the inequality issue within poor regions. The results are robust when the authors control households’ characteristics, including observed and unobserved, and solve the endogeneity issue. Participating in the RLPRMLP could encourage farmers to invest more in the agricultural sector and increase agricultural productivity, which is the main mechanism of the income-improving effect of the RLPRMLP.

The innovative mortgage loan program provides a new channel for farmers to get loan. Land property right reform is being currently applied in rural China. Testing the effectiveness of combining land property right and microfinance loan method is necessary for the government policy making and development of rural areas. The findings are striking. The income improvement mechanism mainly works through agricultural sector, potentially because of the reform of land property, contributing to the increase of marginal product of land, i.e., the increase of agricultural productivity. These could help the development of microfinance theory, and the innovative loan method could be applied to other developing countries.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.