Convergence and Divergence among Technology Clubs

The paper investigates cross-country differences in technology in a large sample of developed and developing economies over the 1990s. The empirical analysis indicates the existence of three technology clubs with markedly different levels of technological development: advanced, followers and marginalized countries. The technology clubs also differ with respect to their dynamics over the 1990s. While the club of followers is characterized by a process of gradual convergence towards the technological frontier, the group of marginalized has experienced an increase in its gap in terms of innovative capabilities.Growth and development; technological change; convergence clubs; polarization

Convergence in Income Inequality: Further Evidence from the Club Clustering Methodology across States in the U.S.

This paper contributes to the sparse literature on inequality convergence by empirically testing convergence across states in the U.S. This sample period encompasses a series of different periods that the existing literature discusses -- the Great Depression (1929–1944), the Great Compression (1945–1979), the Great Divergence (1980-present), the Great Moderation (1982–2007), and the Great Recession (2007–2009). This paper implements the relatively new method of panel convergence testing, recommended by Phillips and Sul (2007). This method examines the club convergence hypothesis, which argues that certain countries, states, sectors, or regions belong to a club that moves from disequilibrium positions to their club-specific steady-state positions. We find strong support for convergence through the late 1970s and early 1980s, and then evidence of divergence. The divergence, however, moves the dispersion of inequality measures across states only a fraction of the way back to their levels in the early part of the twentieth century

The technology clubs: the distribution of knowledge across nations

The convergence clubs literature in applied growth theory suggests that countries that differ in terms of structural characteristics and initial conditions tend to experience diverging growth performances. What is the role of technological knowledge for the formation of clubs? The paper investigates this unexplored question by carrying out an empirical study of the cross-country distribution of knowledge in a large sample of developed and developing economies in the 1990s. The results indicate the existence of three technology clubs characterized by markedly different levels of development. The clubs also differ with respect to the dynamics of their capabilities over the decade, as the most advanced group and the intermediate one are found to be much more dynamic than the large cluster of less developed economies.National systems; knowledge creation and dissemination; convergence clubs; polarization

Growth Econometrics

This paper provides a survey and synthesis of econometric tools that have been employed to study economic growth. While these tools range across a variety of statistical methods, they are united in the common goals of first, identifying interesting contemporaneous patterns in growth data and second, drawing inferences on long-run economic outcomes from cross-section and temporal variation in growth. We describe the main stylized facts that have motivated the development of growth econometrics, the major statistical tools that have been employed to provide structural explanations for these facts, and the primary statistical issues that arise in the study of growth data. An important aspect of the survey is attention to the limits that exist in drawing conclusions from growth data, limits that reflect model uncertainty and the general weakness of available data relative to the sorts of questions for which they are employed.

Human capital, technological spillovers and development across OECD countries

In this paper, we study the relationship between the level of development of an economy and returns to different levels of education for the panel of OECD countries over the 1965-2004 period, in a club convergence framework. The connection between growth and human capital measures of primary, secondary and tertiary education in a multiple-club spatial convergence model with non linearities and spatial dependence is considered. By decomposing total schooling into its three constituent parts, we are able to evaluate their impact on regional growth without imposing homogeneous returns from each level of education. We contribute to the identification of two regimes for OECD countries, each characterized by different returns on physical and human capital accumulation and technological spillovers. We also find that the non-monotonic pattern of convergence is strongly influenced by human capital stocks and technology diffusion process is stronger in the club less close to the technological frontier.

No One True Path: Uncovering the Interplay between Geography, Institutions, and Fractionalization in Economic Development

Do institutions “rule” when explaining cross-country divergence? This paper finds that to a large extent they do. However, the role of ethno-linguistic fractionalization cannot be ignored. Sufficiently high-quality institutions are necessary if the negative impact on development from high levels of ethno-linguistic fractionalization is to be mitigated. Interestingly, I find no role for geographic factors; neither those associated with climate nor geographic isolation, in explaining divergence. There is also no evidence to suggest a role for religious fractionalization. Finally, my findings affirm earlier work in the literature that sets apart Sub-Saharan Africa’s development process from the rest of the world.Regression Trees, Economic Growth, Fundamental Determinants

Regional economic development and convergence clubs in Uruguay

El objetivo de este trabajo es analizar la inequidad territorial en el Uruguay a partir de la elaboración de un indicador sintético que refleje el nivel de desarrollo de los diferentes departamentos, identificando los factores que lo explican. El índice se construye a partir de distintas dimensiones, lo que permite realizar un análisis en profundidad de cada uno de los determinantes del desarrollo regional. Con ese fin, se construye una base de datos que permite la actualización anual de dicho indicador. Este trabajo contribuye a la literatura sobre desarrollo regional y realiza un análisis de convergencia aplicando la metodología de Philips y Sul (2007). El documento aporta al debate de políticas de desarrollo específicas para regiones (place based) versus políticas neutrales en términos de regiones (place neutral) en América Latina. El análisis de convergencia contrasta procesos de convergencia global y en clubes, de los departamentos del Uruguay en el período 2007-2015. Los hallazgos descartan la hipótesis de convergencia global para el período analizado, pero identifican la convergencia en clubes, distinguiendo 3 clubes con dinámicas de desarrollo específicas. [Resumen del autor

Spatial regression in large datasets: problem set solution

In this dissertation we investigate a possible attempt to combine the Data Mining methods and traditional Spatial Autoregressive models, in the context of large spatial datasets. We start to considere the numerical difficulties to handle massive datasets by the usual approach based on Maximum Likelihood estimation for spatial models and Spatial Two-Stage Least Squares. So, we conduct an experiment by Monte Carlo simulations to compare the accuracy and computational complexity for decomposition and approximation techniques to solve the problem of computing the Jacobian in spatial models, for various regular lattice structures. In particular, we consider one of the most common spatial econometric models: spatial lag (or SAR, spatial autoregressive model). Also, we provide new evidences in the literature, by examining the double effect on computational complexity of these methods: the influence of "size effect" and "sparsity effect". To overcome this computational problem, we propose a data mining methodology as CART (Classification and Regression Tree) that explicitly considers the phenomenon of spatial autocorrelation on pseudo-residuals, in order to remove this effect and to improve the accuracy, with significant saving in computational complexity in wide range of spatial datasets: realand simulated data