Containing urban sprawl—Evaluating effectiveness of urban growth boundaries set by the Swiss Land Use Plan

Urban containment strategies are one of the best-known planning instruments for managing urban sprawl. The two main types of such strategies are greenbelts and urban growth boundaries. The boundaries set by the Land Use Plan in Switzerland have been applied since 1970. We evaluate the effect and success of this strategy by analysing expansion of developed land, change in number of buildings, and building density within and outside building zones in four Swiss municipalities 1970–2000. Our results show that the boundary restricted most development to building zones and promoted increased building density. Generally, building density increased in the building zones but decreased outside of building zones. For more efficiency and success, the political institutions should specify explicit goals for development inside and outside of building zones, and develop new instruments for guiding development outside of building zones.     

Robust Methods for the Analysis of Income Distribution, Inequality and Poverty

Income distribution embeds a large field of research subjects in economics. It is important to study how incomes are distributed among the members of a population in order for example to determine tax policies for redistribution to decrease inequality, or to implement social policies to reduce poverty. The available data come mostly from surveys (and not censuses as it is often believed) and often subject to long debates about their reliability because the sources of errors are numerous. Moreover the forms in which the data are availabe is not always as one would expect, i.e. complete and continuous (microdata) but one also can only have data in a grouped form (in income classes) and/or truncated data where a portion of the original data has been omitted from the sample or simply not recorded.
Because of these data features, it is important to complement classical statistical procedures with robust ones. In tis paper such methods are presented, especially for model selection, model fitting with several types of data, inequality and poverty analysis and ordering tools. The approach is based on the Influence Function (IF) developed by Hampel (1974) and further developed by Hampel, Ronchetti, Rousseeuw & Stahel (1986). It is also shown through the analysis of real UK and Tunisian data, that robust techniques can give another picture of income distribution, inequality or poverty when compared to classical ones.     

Poverty measurement with contaminated data: A robust approach

We examine the sensitivity of poverty indices to data contamination using the concept of the influence function, and demonstrate that an important commonly used subclass of poverty measures will be robust under data contamination. This is illustrated using simulations. In this respect poverty and inequality indices have fundamentally different robustness properties. We investigate both the case where the poverty line is exogenously fixed and where it must be estimated from the data.