The Impact of Truancy on Educational Attainment: A Bivariate Ordered Probit Estimator with Mixed Effects

This paper investigates the relationship between educational attainment and truancy. Using data from the Youth Cohort Study of England and Wales, we estimate the causal impact that truancy has on educational attainment at age 16. Problematic is that both truancy and attainment are measured as ordered responses requiring a bivariate ordered probit model to account for the potential endogeneity of truancy. Furthermore, we extent the 'naïve' bivariate ordered probit estimator to include mixed effects which allows us to estimate the distribution of the truancy effect on educational attainment. This estimator offers a more flexible parametric setting to recover the causal effect of truancy on education and results suggest that the impact of truancy on education is indeed more complex than implied by the naïve estimator.educational attainment, truancy, bivariate ordered probit, mixed effects

The econometric modeling of social Preferences

Experimental data on social preferences present a number of features that need to be incorporated in econometric modelling. We explore a variety of econometric modelling approaches to the analysis of such data. The approaches under consideration are: the random utility approach (in which it is assumed that each possible action yields a utility with a deterministic and a stochastic component, and that the individual selects the action yielding the highest utility); the random behavioural approach (which assumes that the individual computes the maximum of a deterministic utility function, and that computational error causes their observed behaviour to depart stochastically from this optimum); and the random preference approach (in which all variation in behaviour is attributed to stochastic variation in the parameters of the deterministic component of utility). These approaches are applied in various ways to an experiment on fairness conducted by Cappelen et al. (2007). At least two of the models that we estimate succeed in capturing the key features of the data set.Econometric modelling and estimation, model evaluation, individual behaviour, fairness

Assessing Multiple Prior Models of Behaviour under Ambiguity

The recent spate of theoretical models of behaviour under ambiguity can be partitioned into two sets: those involving multiple priors (in which the probabilities of the various events are not known but probabilities can be attached to the various possible values for the probabilities) and those not involving multiple priors. This paper concentrates on the first set and provides an experimental investigation into recently proposed theories. Using an appropriate experimental interface, in which the probabilities on the various possibilities are explicitly stated, we examine the fitted and predictive power of the various theories. We first estimate subject-by-subject, and then we estimateand predict using a mixture model over the contending theories. The individual estimates suggest that 25% of our 149 subjects have behaviour consistent with Expected Utility, 54% with the Smooth Model (of Klibanoff et al, 2005), 12% with Rank Dependent Expected Utility and 9% with the Alpha Model (of Ghirardato et al 2004); these figures are very close to the mixing proportions obtained from the mixture estimates. However, if we classify our subjects through the posterior probabilities (given all the evidence) of each of them being of the various types: using the estimates we get 38%, 19%, 28% and 16% (for EU, Smooth, Rank Dependent and Alpha); while using the predictions 36%, 19%, 33% and 11%. Interestingly the older models (EU and RD) seem to fare relatively better, suggesting that representing ambiguity through multiple priors is perceived by subjects as risk, rather than ambiguityAlpha Model, Ambiguity, Expected Utility, Mixture Models, Rank Dependent Expected Utility, Smooth Model.

Strategies in Social Network Formation

We run a computerised experiment of network formation where all connections are beneficial and only direct links are costly. Players simultaneously submit link proposals; a connection is made only when both players involved agree. We use both simulated and experimentally generated data to test the determinants of individual behaviour in network formation. We find that approximately 40% of the network formation strategies adopted by the experimental subjects can be accounted for as best responses. We test whether subjects follow alternative patterns of behaviour and in particular if they: propose links to those from whom they have received link proposals in the previous round; propose links to those who have the largest number of direct connections. We find that together with best response behaviour, these strategies explain approximately 75% of the observed choices. We estimate individual propensities to adopt each of these strategies, controlling for group effects. Finally we estimate a mixture model to highlight the proportion of each type of decision maker in the population.network formation, experiments, mixture models

The impact of truancy on educational attainment: A bivariate ordered probit estimator with mixed effects

This paper investigates the relationship between educational attainment and truancy. Using data from the Youth Cohort Study of England and Wales, we estimate the causal impact that truancy has on educational attainment at age 16. Problematic is that both truancy and attainment are measured as ordered responses requiring a bivariate ordered probit model to account for the potential endogeneity of truancy. Furthermore, we extent the naive bivariate ordered probit estimator to include mixed effects which allows us to estimate the distribution of the truancy effect on educational attainment. This estimator offers a more flexible parametric setting to recover the causal effect of truancy on education and results suggest that the impact of truancy on education is indeed more complex than implied by the naive estimator

A bivariate ordered probit estimator with mixed effects

In this paper, we discuss the derivation and application of a bivariate ordered probit model with mixed effects. Our approach allows one to estimate the distribution of the effect (gamma) of an endogenous ordered variable on an ordered explanatory variable. By allowing gamma to vary over the population, our estimator offers a more flexible parametric setting to recover the causal effect of an endogenous variable in an ordered choice setting. We use Monte Carlo simulations to examine the performance of the maximum likelihood estimator of our system and apply this to a relevant example from the UK education literature

Endogenous variables in non-linear models with mixed effects: Inconsistence under perfect identification conditions?

This paper examines the consequences of introducing a normally distributed effect into a system where the dependent variable is ordered and the explanatory variable is ordered and endogenous. Using simulation techniques we show that a naive bivariate ordered probit estimator which fails to take a mixed effect into account will result in inconsistent estimates even when identification conditions are optimal. Our results suggest this finding only applies to non-linear endogenous systems

Use of data on planned contributions and stated beliefs in the measurement of social preferences

In a series of one-shot linear public goods game, we ask subjects to report their contributions, their contribution plans for the next period, and their first-order beliefs about their present and future partner. We estimate subjects' preferences from plans data by a infinite mixture approach and compare the results with those obtained from contribution data. Our results indicate that preferences are heterogeneous, and that most subjects exhibit conditionally cooperative inclinations. Controlling for beliefs, which incorporate the information about the other's decisions, we are able to show that plans convey accurate information about subjects' preferences and, consequently, are good predictors of their future behavior

The econometric modeling of social preferences

Experimental data on social preferences present a number of features that need to be incorporated in econometric modelling. We explore a variety of econometric modelling approaches to the analysis of such data. The approaches under consideration are: the random utility approach (in which it is assumed that each possible action yields a utility with a deterministic and a stochastic component, and that the individual selects the action yielding the highest utility); the random behavioural approach (which assumes that the individual computes the maximum of a deterministic utility function, and that computational error causes their observed behaviour to depart stochastically from this optimum); and the random preference approach (in which all variation in behaviour is attributed to stochastic variation in the parameters of the deterministic component of utility). These approaches are applied in various ways to an experiment on fairness conducted by Cappelen et al. (2007). At least two of the models that we estimate succeed in capturing the key features of the data set