Determinants of Human Development: Insights from State-Dependent Panel Models

In this paper, we study economic development in a panel of 84 countries from 1970 to 2005. We focus on characterizing heterogeneities in the development effects of macroeconomic policies and on comparing the development process as measured by GDP to that measured by the Human Development Index (HDI). We do so within a novel dynamic panel modelling framework that can account for crucial aspects of both the cross-sectional and intertemporal features of the observed process of economic development, and that can capture the dependence of the development effects of macroeconomic policies on differences in countries' persistent characteristics, such as their social norms and institutions. Among our findings are that macroeconomic policies affect economic development with less delay than suggested by conventional econometric frameworks, yet impact HDI with longer delay and overall less strongly than GDP. Differences in countries' persistent characteristics may even affect the sign of the long-run development effects of a given macroeconomic policy: Fiscal stimuli in the form of government consumption positively affect GDP in countries with low institutional quality, but negatively affect long-run GDP in countries with high institutional quality.human development, institutions and social norms, dynamic panel modelling.

Determinants of Human Development: Capturing the Role of Institutions

In this paper, we study development in a panel of 87 countries from 1970 to 2005. We focus on characterizing institutionally driven heterogeneities in the development effects of macroeconomic policies and on comparing the development process as measured by GDP to that measured by the Human Development Index (HDI). We do so within a novel dynamic panel modelling framework that can account for crucial aspects of both the cross-sectional and intertemporal features of the observed process of development, and that can capture the dependence of the development effects of macroeconomic policies on differences in countries’ persistent characteristics, such as their institutions. Among our findings are that macroeconomic policies affect development with less delay than suggested by conventional econometric frameworks, yet impact HDI with longer delay and overall less strongly than GDP. Differences in countries’ persistent characteristics may even affect the sign of the long-run development effects of a given macroeconomic policy: Fiscal stimuli in the form of government consumption expansions positively affect long-run GDP in countries with low institutional quality, but negatively affect long-run GDP in countries with high institutional quality.human development, institutions, dynamic panel modelling

Has the Preston Curve Broken Down?

Three apparently contradictory stylized facts characterize the relationship between per capita incomes and life expectancy: (i) the existence of a strong correlation between the level of life expectancy and the level of per capita income, (ii) the absence of a significant correlation between changes in per capita income and changes in life expectancy, and (iii) the persistence of twin peaks in the distribution of life expectancy, despite their progressive disappearance from the income data. This paper seeks to reconcile these apparently contradictory findings. We argue that a data generating process in which there is a relationship between income and life expectancy for high levels of development but not for low ones can explain these stylized facts, while models that apply a uniform relationship to all countries cannot. We also argue that the slope of the relationship between income and life expectancy is significantly overestimated by standard cross-sectional estimates, with the true slope being much lower for some countries and not statistically significantly different from zero for others. Lastly, we provide evidence from an error-correction model showing that the elasticity of life expectancy to incomes has been declining both for countries at high and low levels of development. We suggest that these results can be interpreted as showing that income matters only for countries that are close enough to the world health technological frontier.Life expectancy, income growth, Preston curve, health determinants, Monte Carlo experiments.

SiS at CLEF 2017 eHealth tar task

This paper presents Strathclyde iSchool's (SiS) participation in the Technological Assisted Reviews in Empirical Medicine Task. For the ranking task, we explored two ways in which assistance to reviewers could be provided during the assessment process: (i) topic models, where we use Latent Dirichlet Allocation to identify topics within the set of retrieved documents, ranking documents by the topic most likely to be relevant and (ii) relevance feedback, where we use Rocchio's algorithm to update the query model for subsequent rounds of interaction. A third approach combines the topic and relevance feedback to quickly identify the relevant abstracts. For the thresholding task, we apply a score threshold, and exclude documents which did not exceed the threshold given BM25

The panel conditionally homogenous vectorautoregressive model

In the panel conditionally homogenous vectorautoregressive model, the cross-sectional units' dynamics are generally heterogenous, but homogenous if units share the same structural characteristics. The panel conditionally homogenous vectorautoregressive model thus allows (i) to account for heterogeneity in dynamic panel data sets, (ii) to nevertheless exploit the panel nature of the data, and (iii) to analyze the relationship between the units' observed heterogeneities and structural characteristics. I show how standard least squares estimation can be applied, how impulse responses can be computed, how multivariate conditioning is implemented, and how polynomial order restrictions can be incorporated. Finally, I present an easy-to-use Matlab routine which can be used to estimate the panel conditionally homogenous vectorautoregressive model and produce impulse responses as well as forecast error variance decompositions

The panel conditionally homogenous vectorautoregressive model

In the panel conditionally homogenous vectorautoregressive model, the cross-sectional units' dynamics are generally heterogenous, but homogenous if units share the same structural characteristics. The panel conditionally homogenous vectorautoregressive model thus allows (i) to account for heterogeneity in dynamic panel data sets, (ii) to nevertheless exploit the panel nature of the data, and (iii) to analyze the relationship between the units' observed heterogeneities and structural characteristics. I show how standard least squares estimation can be applied, how impulse responses can be computed, how multivariate conditioning is implemented, and how polynomial order restrictions can be incorporated. Finally, I present an easy-to-use Matlab routine which can be used to estimate the panel conditionally homogenous vectorautoregressive model and produce impulse responses as well as forecast error variance decompositions