Polarimetry at the ILC

At the ILC, the luminosity-weighted average polarization at the IP needs to be determined at the permille-level. In order to reach this goal, the combined information from the polarimeter and the collision data is required. In this study, a unified approach will be presented, which for the first time combines the cross section measurements with the expected constraints from the polarimeters. Hereby, the statistical and systematical uncertainties are taken into account, including their correlations. This study shows that a fast spin flip frequency is required because it easily reduces the systematic uncertainty, while a non-perfect helicity reversal can be compensated for within the unified approach. The final goal is to provide a realistic estimation of the luminosity-weighted average polarization at the IP to be used in the physic analyses.Comment: 8 pages, conference proceedings LCWS 2016 Morioka Japa

Open For Business? Institutions, Business Environment and Economic Development

Recent years have seen a significant focus in the literature on growth and development on the idea that legal and political institutions are the key determinant of economic development. The main finding of this paper is that the focus on the primacy of legal and political institutions may be misplaced and that business-friendly economic policies (proxied for here by the World Bank’s Doing Business indicator) are the key determinant of the level of income per capita. We find that a country’s Doing Business rank dominates a range of measures of legal and political institutional quality as an explanatory variable for income per capita. We also find the Doing Business rank to be a key explanatory variable for economic growth and that previous findings assigning a significant role to educational attainment are not robust to the inclusion of this new indicator in growth regressions.

Is There a Bubble in the Housing Market?

This paper looks for evidence of a bubble in U.S. housing prices. It analyzes quarterly state-level data over 1985-2002, focusing on the relationship between home prices and selected fundamental variables. Income per capita alone largely explains price changes in all but eight states; in the latter, large price movements are observed unrelated to the fundamentals. Results from a new survey of recent homebuyers in the Los Angeles, San Francisco, Boston, and Milwaukee metropolitan areas are reported. This survey replicates an almost identical 1988 survey and finds, as before, that buyers in most of these markets perceive little risk in their housing investment, have unrealistic expectations about future price increases, and hold economically implausible beliefs about home price behavior—findings consistent with a bubble. Prices in such markets could stall or decline, but only if such declines are simultaneous or spread to other markets are significant effects on the national economy likely.macroeconomics, Bubble, Housing Market

Forecasting Prices and Excess Returns in the Housing Market

The U. S. market for homes appears not to be efficient. A number of information variables predict housing price changes and excess returns of housing relative to debt over the succeeding year. Price changes observed over one year tend to continue for one more year in the same direction. Construction cost divided by price, the change in per capita real income, the change in adult population are all positively related to price changes or excess returns over the subsequent year. The results are based on time-series cross section regressions with quarterly data 1970-1 to 1987-3 and for cities Atlanta, Chicago, Dallas, and San Francisco.

The Efficiency of the Market for Single-Family Homes

Tests of weak-form efficiency of the market for single family homes are performed using data on repeat sales prices of 39,210 individual homes, each for two sales dates. Tests were done for Atlanta, Chicago, Dallas, and San Francisco/Oakland for 1970-86. While evidence for seasonality in real housing prices is weak, we do find some evidence of inertia in housing prices. A city-wide real log price index change in a given year tends to be followed by a city-wide real log price index change in the same direction (and between a quarter to a half as large in magnitude) in the subsequent year. However, the inertia cannot account for much of the variation in individual housing real price changes. There is so much noise in individual housing prices relative to city-wide price index changes that the R[squared] in forecasting regressions for annual real price change in individual homes is never more than .04.