Austerity stats: Making sense of cuts and changes to official statistics under the coalition

More than four years into the coalition, an interesting picture of “austerity statistics” is emerging. Deciding which statistics might be superfluous involves debates about what official statistics should be measuring and why and for whom they are prepared. This in turn raises question for the role of traditional statistical and social-scientific expertise in government, writes Alex Fenton

Gentrification in London: a progress report, 2001-2013

This paper investigates changes in the composition and spatial distribution of income poverty in London from 2001 to 2013, and considers them as evidence of gentrification. It is first argued that income poverty measures address some of the shortcomings of conventional occupational class statistics in gentrification research. The empirical analysis, using poverty proxies and spatial microsimulation income estimates, show that in the poorest, eastern parts of inner London, poverty rates fell. Here there was intense development and valorisation of land and housing around the financial districts, rapid population growth, and absolute falls in the numbers of the out-of-work poor. Poverty rates rose in the relatively disadvantaged parts of outer London. This is accounted for partly by rises in out-of-work poverty, but predominantly by the impoverishment of low-income workers through their wages becoming insufficient relative to housing costs. The paper thus confirms broad changes in the spatial distribution of poverty identified in recent studies, while pointing to the exploitation of labour and land as central mechanisms in explaining patterns of gentrification and proletarianisation in the city

Spatial microsimulation estimates of household income distributions in London boroughs, 2001 and 2011

Spatial microsimulation (SMS) is a range of techniques for estimating the local distribution of a variable – here, household income – by combining social survey microdata with Census or administrative population totals. This paper makes a case for the value of these methods in social policy analysis of spatial economic differences because unlike other methods and sources, they permit distributional analysis of income, encompass both market outcomes and secondary distribution through taxes and transfers, and measure income poverty in standard national terms. As a demonstration of spatial microsimulation by iterative proportional fitting (IPF), the household income distribution in London's 33 boroughs in 2001/02 and 2011/12 is estimated in this paper. The coherence and plausibility of the results in comparison to other official statistics is examined in some detail. Two refinements to standard IPF methods are presented, including "multi-level IPF", which allows the use of both person- and household-level data; this is found to improve the estimation of poverty rates. The paper confirms the value of SMS for synchronic spatial analysis, and argues for its hitherto little-explored use in modelling spatial differences in the effects of fiscal and welfare policy changes

Social Media

In this chapter you will: 1) understand the dynamics of social media-based communities, their composition and behaviours 2) learn to conduct a social media audit including competitor analysis 3) recognise the importance of a blog as a centre of digital presence development 4) use amplification strategies to develop brand presence explore the concept of social capital and its relationship to brand engagement activities 5) develop tactics for measuring social media based, their quality and quantity

Unadjusted Means-Tested Benefits Rate (UMBR), 2001-2013

The accompanying package contains an updated version of the Unadjusted Means- Tested Benefits Rate (UMBR) data set. UMBR is a proxy measure of income poverty for small geographic areas in England, Scotland and Wales. It provides a singlenumber household poverty rate for somewhat over 40,000 small area units annually from 2001 to 2013. UMBR is produced from public data sources by the Centre for Analysis of Social Exclusion at the London School of Economics, as part of the Social Policy in a Cold Climate (SPCC) research programme. UMBR is suitable for a variety of purposes, including analysis of the local distribution of poverty over time, and the coding of other individual or area data-sets with an income poverty indicator