by Anttonen Jetro (November, 2018)
In this paper a Bayesian vector autoregressive model for nowcasting the seasonally non-adjusted unemployment rate in EU-countries is developed. On top of the official statistical releases, the model utilizes Google search data and the effect of Google data on the forecasting performance of the model is assessed. The Google data is found to yield modest improvements in forecasting accuracy of the model. To the author’s knowledge, this is the first time the forecasting performance of the Google search data has been studied in the context of Bayesian vector autoregressive model. This paper also adds to the empirical literature on the hyperparameter choice with Bayesian vector autoregressive models. The hyperparameters are set according to the mode of the posterior distribution of the hyperparameters, and this is found to improve the out-of-sample forecasting accuracy of the model significantly, compared to the rule-of-thumb values often used in the literature.
by Francesco Andreoli (November, 2018)
The neighborhood inequality (NI) index measures aspects of spatial inequality in the distribution of incomes within the city. The NI index is defined as a population average of the normalized income gap between each individual’s income (observed at a given location in the city) and the incomes of the neighbors, living within a certain distance range from that individual. This paper provides minimum bounds for the NI index standard error and shows that unbiased estimators can be identified under fairly common hypothesis in spatial statistics. These estimators are shown to depend exclusively on the variogram, a measure of spatial dependence in the data. Rich income data are then used to infer about trends of neighborhood inequality in Chicago, IL over the last 35 years. Results from a Monte Carlo study support the relevance of the standard error approximations.
by P.Dolton, D.Nguyen, H. Rolfe, M. Castellanos (November, 2018)
The UK’s H&SC workforce is under considerable strain to provide services for an ageing population with increasingly complex needs. While many of the problems supplying new recruits into the sector pre-date the 2016 Brexit referendum, the vote to leave the European Union (EU) has added another layer of challenge and uncertainty for planning this future workforce.
This report examines recent trends in the UK’s H&SC workforce and the critical role of EEA nationals within it. This is a vital issue because the vote to leave the EU and ongoing uncertainty regarding any deal between the UK and EU, will undoubtedly impact on their decision whether or not to stay with significant implications for the sector. It can also impact the decision of EEA nationals to move to the UK in the future.
Below we present a number of key findings and recommendations. These are designed to ensure that Brexit works in the interests of patient care, and to make sure that the H&SC sector is able to secure the skills and people it needs to continue to provide good care going forward.
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Time and place: 08/01/2019 in The Hague, Netherlands
In January 2006, the Dutch government implemented a pension reform that substantially reduced the public pension wealth of workers born in 1950 or later. At the same time, a tax-facilitated savings plan was introduced that substantially reduced the saving costs of all workers, irrespective of birth year. This paper uses linked administrative and survey data … Continued
Time and place: 17/12/2018 in The Hague, Netherlands
Labor market deregulation, intended to boost productivity and employment, is one plausible, yet little studied, driver of the decline in labor shares that took place across most advanced economies since the early 1990s. This paper assesses the impact of job protection deregulation in a sample of 26 advanced economies over the period 1970-2015, using a … Continued
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