Search results1 – 3 of 3
This chapter presents a model of distribution dynamics in the presence of measurement error in the underlying data. Studies of international growth convergence generally…
This chapter presents a model of distribution dynamics in the presence of measurement error in the underlying data. Studies of international growth convergence generally ignore the fact that per capita income data from the Penn World Table (PWT) are not only continuous variables but also measured with error. Together with short-time scale fluctuations, measurement error makes inferences potentially unreliable. When first-order, time-homogeneous Markov models are fitted to continuous data with measurement error, a bias towards excess mobility is introduced into the estimated transition probability matrix. This chapter evaluates different methods of accounting for this error. An EM algorithm is used for parameter estimation, and the methods are illustrated using data from the PWT Mark 6.1. Measurement error in income data is found to have quantitatively important effects on distribution dynamics. For instance, purging the data of measurement error reduces estimated transition intensities by between one- and four-fifths and more than halves the observed mobility of countries.
This paper considers the distributional dynamics of a well‐known corruption index. Specifically, we are interested in evaluating whether corruption is best characterized as multimodal (i.e. pointing to clusters of countries with persistently different levels of corruption) and whether there have been significant changes (i.e. convergence or divergence) in the distribution of the perception of corruption across countries and over time. Using non‐parametric kernel density methods, our findings lend support to concerns expressed in the theoretical literature – namely, that corruption can be highly persistent, and characterized by multiple equilibria. This highlights and corroborates the conclusion that anti‐corruption campaigns must be sustained to be effective.