Economics - Research Publications

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    Low-Quality Patents in the Eye of the Beholder: Evidence from Multiple Examiners
    de Rassenfosse, G ; Griffiths, WE ; Jaffe, AB ; Webster, E (OXFORD UNIV PRESS INC, 2021-11-01)
    Abstract A low-quality 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 low-quality 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)
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    Posterior Probabilities for Lorenz and Stochastic Dominance of Australian Income Distributions*
    Gunawan, D ; Griffiths, WE ; Chotikapanich, D (WILEY, 2021-08-25)
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    A note on inequality measures for mixtures of double Pareto–lognormal distributions
    Griffiths, W ; Chotikapanich, D ; Hajargasht, G (Wiley, 2022)
    Formulas are derived for the Gini, Theil and Pietra coefficients for a population-weighted 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 large-scale projects that utilise dPLN distributions or their special cases.
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    Bayesian assessment of Lorenz and stochastic dominance
    Lander, D ; Gunawan, D ; Griffiths, W ; Chotikapanich, D (Wiley, 2020-05-01)
    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 p-values 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 Monte-Carlo 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.
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    Bayesian weighted inference from surveys
    Griffiths, 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.
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    Minimum distance estimation of parametric Lorenz curves based on grouped data
    Hajargasht, G ; Griffiths, WE (Taylor & Francis, 2020-01-01)
    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 well-developed 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.