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Economics  Research Publications
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ItemNo Preview AvailableLowQuality Patents in the Eye of the Beholder: Evidence from Multiple Examinersde Rassenfosse, G ; Griffiths, WE ; Jaffe, AB ; Webster, E (OXFORD UNIV PRESS INC, 20211101)Abstract A lowquality patent system threatens to slow the pace of technological progress. Concerns about low patent quality are supported by estimates from litigation studies suggesting that most US patents granted should not have been issued. We propose a new model for measuring patent quality, based on equivalent patent applications submitted to multiple offices. Our method allows us to distinguish whether lowquality patents are issued because an office implements a low standard or because it violates its own standard. The results suggest that quality in patent systems is higher than previously thought. Specifically, the percentage of granted patents that are below each office’s own standard is under 10% for all offices. The Japanese patent office has a higher percentage of granted patents below its own standard than those from Europe, the USA, Korea, and China. This result arises from the fact that Japan has a higher standard than other offices. (JEL O34, K2, L4, F42)

ItemEstimation and efficiency measurement in stochastic production frontiers with ordinal outcomesGriffiths, W ; Zhang, X ; Zhao, X (SPRINGER, 20140801)We consider Bayesian estimation of a stochastic production frontier with ordered categorical output, where the inefficiency error is assumed to follow an exponential distribution, and where output, conditional on the inefficiency error, is modelled as an ordered probit model. Gibbs sampling algorithms are provided for estimation with both crosssectional and panel data, with panel data being our main focus. A Monte Carlo study and a comparison of results from an example where data are used in both continuous and categorical form supports the usefulness of the approach. New efficiency measures are suggested to overcome a lackofinvariance problem suffered by traditional efficiency measures. Potential applications include health and happiness production, university research output, financial credit ratings, and agricultural output recorded in broad bands. In our application to individual health production we use data from an Australian panel survey to compute posterior densities for marginal effects, outcome probabilities, and a number of withinsample and outofsample efficiency measures.

ItemPosterior Probabilities for Lorenz and Stochastic Dominance of Australian Income Distributions*Gunawan, D ; Griffiths, WE ; Chotikapanich, D (WILEY, 20210825)Using Household, Income and Labour Dynamics in Australia (HILDA) data for 2001, 2006, 2010, 2014 and 2017, we compute posterior probabilities for dominance for all pairwise comparisons of income distributions in these years. The dominance criteria considered are Lorenz dominance and first‐ and second‐order stochastic dominance. The income distributions are estimated using an infinite mixture of gamma density functions, with posterior probabilities computed as the proportion of Markov chain Monte Carlo draws that satisfy the inequalities that define the dominance criteria. We find welfare improvements from 2001 to 2006 and qualified improvements from 2006 to the later three years. Evidence of an ordering between 2010, 2014 and 2017 cannot be established.

ItemA note on inequality measures for mixtures of double Pareto–lognormal distributionsGriffiths, W ; Chotikapanich, D ; Hajargasht, G (Wiley, 2022)Formulas are derived for the Gini, Theil and Pietra coefficients for a populationweighted mixture of double Pareto–lognormal (dPLN) distributions; they are applied to South America for 2 years. Results are also provided for the special case Pareto–lognormal and lognormal distributions. The formulas are useful for measuring regional or global inequality in largescale projects that utilise dPLN distributions or their special cases.

ItemBayesian assessment of Lorenz and stochastic dominanceLander, D ; Gunawan, D ; Griffiths, W ; Chotikapanich, D (Wiley, 20200501)We introduce a Bayesian approach for assessing Lorenz and stochastic dominance. For two income distributions, say X and Y, estimated via Markov chain Monte Carlo, we describe how to compute posterior probabilities for: (i) X dominates Y, (ii) Y dominates X and (iii) neither Y nor X dominates. The proposed approach is applied to Indonesian income distributions using mixtures of gamma densities that ensure flexible modelling. Probability curves depicting the probability of dominance at each population proportion are used to explain changes in dominance probabilities over restricted ranges relevant for poverty orderings. They also explain some seemingly contradictory outcomes from the pvalues of some sampling theory tests. Résumé: Évaluation bayésienne des dominances stochastiques et de Lorenz. Dans cet article, nous présentons une approche bayésienne pour évaluer les dominances stochastiques et de Lorenz. Pour deux distributions de revenus estimées par la méthode de MonteCarlo par chaînes de Markov, X et Y par exemple, nous décrivons la fac¸on de calculer les probabilités à posteriori lorsque (i) X domine Y, (ii) Y domine X et (iii) ni Y ni X ne sont dominants. Nous avons appliqué l’approche proposée à la distribution des revenus en Indonésie en utilisant une variété de densités gamma pour garantir une modélisation flexible. Des courbes de probabilité illustrant la probabilité de dominance sur chaque proportion de population sont utilisées pour expliquer les changements de probabilité de dominance sur des fourchettes restreintes nécessaires à l’évaluation des niveaux de pauvreté. Ces courbes permettent également d’expliquer les résultats apparemment contradictoires des valeurs p de certains tests théoriques en matière d’échantillonnage.

ItemBayesian weighted inference from surveysGriffiths, W ; GUNAWAN, D ; Panagiotelis, A ; Chotikapanich, D (Wiley, 2020)Data from large surveys are often supplemented with sampling weights that are designed to reflect unequal probabilities of response and selection inherent in complex survey sampling methods. We propose two methods for Bayesian estimation of parametric models in a setting where the survey data and the weights are available, but where information on how the weights were constructed is unavailable. The first approach is to simply replace the likelihood with the pseudo likelihood in the formulation of Bayes theorem. This is proven to lead to a consistent estimator but also leads to credible intervals that suffer from systematic undercoverage. Our second approach involves using the weights to generate a representative sample which is integrated into a Markov chain Monte Carlo (MCMC) or other simulation algorithms designed to estimate the parameters of the model. In the extensive simulation studies, the latter methodology is shown to achieve performance comparable to the standard frequentist solution of pseudo maximum likelihood, with the added advantage of being applicable to models that require inference via MCMC. The methodology is demonstrated further by fitting a mixture of gamma densities to a sample of Australian household income.

ItemMinimum distance estimation of parametric Lorenz curves based on grouped dataHajargasht, G ; Griffiths, WE (Taylor & Francis, 20200101)The Lorenz curve, introduced more than 100 years ago, remains as one of the main tools for analysis of inequality. International institutions such as the World Bank collect and publish grouped income data in the form of population and income shares for a large number of countries. These data are often used for estimation of parametric Lorenz curves which in turn form the basis for most inequality analyses. Despite the prevalence of parametric estimation of Lorenz curves from grouped data, and the existence of welldeveloped nonparametric methods, a formal description of rigorous methodology for estimating parametric Lorenz curves from grouped data is lacking. We fill this gap. Building on two data generating mechanisms, efficient methods of estimation and inference are described; several results useful for comparing the two methods of inference, and aiding computation, are derived. Simulations are used to assess the estimators, and curves are estimated for some example countries. We also show how the proposed methods improve upon World Bank methods and make recommendations for improving current practices.

ItemBayesian inference for health inequality and welfare using qualitative dataGunawan, D ; Griffiths, WE ; Chotikapanich, D (ELSEVIER SCIENCE SA, 20180101)We show how to use Bayesian inference to compare two ordinal categorical distributions commonly occurring with data on selfreported health status. Procedures for computing probabilities for first and second order stochastic dominance and Sdominance are described, along with methodology for obtaining posterior densities for health inequality indexes. The techniques are applied to four years of data on Australian selfreported health status.

ItemUsing the GB2 Income DistributionChotikapanich, D ; Griffiths, WE ; Hajargasht, G ; Karunarathne, W ; Rao, DSP (MDPI AG, 20180601)To use the generalized beta distribution of the second kind (GB2) for the analysis of income and other positively skewed distributions, knowledge of estimation methods and the ability to compute quantities of interest from the estimated parameters are required. We review estimation methodology that has appeared in the literature, and summarize expressions for inequality, poverty, and propoor growth that can be used to compute these measures from GB2 parameter estimates. An application to data from China and Indonesia is provided.

ItemEstimation and testing of stochastic frontier models using variational BayesHajargasht, G ; Griffiths, WE (Springer Verlag, 201810)We show how a wide range of stochastic frontier models can be estimated relatively easily using variational Bayes. We derive approximate posterior distributions and point estimates for parameters and inefficiency effects for (a) time invariant models with several alternative inefficiency distributions, (b) models with time varying effects, (c) models incorporating environmental effects, and (d) models with more flexible forms for the regression function and error terms. Despite the abundance of stochastic frontier models, there have been few attempts to test the various models against each other, probably due to the difficulty of performing such tests. One advantage of the variational Bayes approximation is that it facilitates the computation of marginal likelihoods that can be used to compare models. We apply this idea to test stochastic frontier models with different inefficiency distributions. Estimation and testing is illustrated using three examples.
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