Using Personal Car Register for Measuring Economic Inequality in Countries with a Large Share of Shadow Economy: Evidence for Latvia

We suggest using information from the state register of personal cars as an alternative indicator of economic inequality in countries with a large share of shadow economy. We illustrate our approach using the Latvian pool of personal cars. Our main finding is that the extent of household economic inequality in Latvia is much larger than officially assumed. According to Eurostat, the officially published estimate of the Gini coefficient for Latvia is 0.374 for 2009, which is much higher than the Gini coefficient value reported for all the 27 EU member countries (0.304), but significantly lower than 0.48 according to our results.     

Does aging influence structural change? Evidence from panel data

Our study represents a first attempt to single out the effects of aging on the entire structure of the economy that is approximated by employment shares in different sectors. We find that even after controlling for the effects of other relevant factors - e.g., income per capita, share of trade in GDP, government consumption share in GDP, population size - aging does have a statistically significant differentiated impact on the employment shares. In particular, we find that an increase in aging exerts a statistically significant adverse effect on the employment shares in agriculture, manufacturing, construction, and mining and quarrying industries. At the same time, an increasing share of the elderly (decreasing share of the youth) in society positively affects employment shares in community, social, and personal services as well as in the financial sector.     

Think national,forecast local: a case study of 71 German urban housing markets

In this article, we examine whether the local indicators are able to predict the city-level housing prices and rents better than national indicators. For this purpose, we assess the forecasting ability of 126 indicators and 21 types of forecast combinations using a sample of 71 large German cities. There are several predictors that are especially useful, namely price-to-rent ratios, national-level business confidence, and consumer surveys. We also find that combinations of individual forecasts are among the top forecasting models. On average, the forecast improvements attain about 20%, measured by a reduction in root mean square error, compared to the naive models.