UMBR ReadMe
Version 1.1;
Alex Fenton, September 28 2012
Introduction
UMBR, the Unadjusted Means-tested Benefits Rate, is a proxy measure for income poverty within small geographic units in Great Britain. It is particularly designed to work for small areas (such as Lower-level Super Output Areas, or LSOAs) and to permit comparison over time. The accompanying file, umbr_hh-esw-2001-11.csv, contains poverty proxy rates for all small areas (LSOAs in England and Wales, Datazones in Scotland) for all years from 2001 to 2011. It is suitable for describing the spatial distribution of poverty within cities and regions, and for analysing how that has changed over time. 
UMBR design
The principal UMBR poverty proxy rate in each area at any point in time is the total number of claimants of major means-tested benefits, divided by the estimated number of households. It is not a direct estimate of the rate of income poverty, but a proxy for that rate.
The means-tested benefits included in the count of claimants are:
•	Income Support
•	Job Seeker’s Allowance (Income-Based and Contribution-Based)
•	Pension Credit, Guarantee Element (2003 onwards)
•	Employment and Support Allowance, Income-Based and Contributory (2008 onwards)

The counts of these benefits are extracted from NOMIS, averaged over four calendar quarters to give a total Combined Means Tested Benefits Count.
The number of households in an area in each year is estimated from the small-area population estimates that are published by ONS (England and Wales) and GROS (Scotland). The estimates are made by multiplying the number of people in households in broad age/sex bands by the rate at which those age/sex groups are ‘household reference persons’ (HRPs). The household estimates and the underlying population data are constrained so they are consistent with local authority mid-year estimates produced by ONS, GROS for population, and the separate national governments for households. 
Uses
Some things that UMBR can be used for:
•	To make statements about relative poverty rates with different parts of a wider area (a city, a region). For LSOAs and Datazones, small differences of <10% should not be considered as indicating significant differences in the underlying poverty rate.
•	To assess whether poverty rates appear to have been changing over time in an area or a group of areas over time
•	To judge how much any such change might be attributed to falls in absolute numbers of poor households, or to increases in the household population
•	To aggregate to higher geographic units (such as MSOAs, Wards) to make comparisons at that level
Some things that UMBR should not be used for:
•	To assert that the actual poverty rate (as it is operationalised in, for example, Households Below Average Income) is x, where x is an UMBR rate. UMBR is not a direct estimate of poverty.
•	To make direct comparisons between very dissimilar areas (for example, highly rural and inner urban areas) or to make comparisons between different neighbourhoods in different regions (for example, between London and Scotland)
•	To make statements about the shape of the statistical distribution (e.g. skewness, variance) or spatial distribution (e.g. segregation, autocorrelation) except within a single country (England, Wales or Scotland) at a time. The base areal units in Scotland (Datazones) are smaller than those in England and Wales and defined by a different algorithm. Greater variance in UMBR in Scotland therefore does not necessarily indicate greater spatial variation in poverty rates.
Usage
The accompanying file is in comma-separated value format, and can be read by statistics packages such as SPSS, Stata or R, or by GIS packages such as Arc, Mapinfo or GRASS. It can also be opened in Excel, although as it is a large file, it may be hard to work with in Office packages.
The file contains one row per-year per-area, giving a total of around 450,000 rows. For convenience, it may be easier to extract a subset of areas or dates to work with into a separate file. Each row has the following fields:
Variable	Description	
year	The calendar year to which the row pertains	
geogcode	The area code for the LSOA or Datazone to which the row relates	
Households	The estimated count of households in the area that year	
IncomeSupport	The four-quarter mean average count of Income Support claimants	
JobSeekers	The four-quarter mean average count of JSA claimants	
EmploymentSupport	The four-quarter mean average count of ESA claimants	
PensionCredit.GE	The four-quarter mean average count of Pension Credit claimants receiving either the Guarantee Element or the Guarantee and Savings Elements (but excluding those receiving only the Savings element)	
All.MTB	The sum of the four benefit claimant counts	
UMBR.HH	The Unadjusted Means-tested Benefits Rate for Households, i.e. All.MTB divided by Households	

Note that in a small number of cases (n < 100), the combined number of benefit claimants is greater than the number of households. This can be because of 
•	error in the household estimates, 
•	because some households may contain more than one means-tested benefit claimant, 
•	or because some benefit claimants are people not in households – they are residents of communal establishments such as nursing homes
In such cases, the UMBR for that area is given as 1.
In a smaller number of cases – two Datazones in Scotland, the estimated population, and hence estimated households are zero in some years. In these case, the UMBR is given as NA (Not Available).
For further information
A detailed discussion of the methodological principles underlying UMBR, and the empirical work behind its design are contained in a forthcoming working paper from the Centre for Analysis of Social Exclusion (CASE) at the LSE.
http://sticerd.lse.ac.uk/case/_new/publications/series.asp?prog=CASE
UMBR was produced as part of the Social Policy in a Cold Climate (SPCC) research programme, which is funded by the Joseph Rowntree Foundation, the Nuffield Foundation and the Trust for London.
http://sticerd.lse.ac.uk/case/_new/research/Social_Policy_in_a_Cold_Climate.asp
For further information on UMBR and SPCC, contact Ruth Lupton, CASE, London School of Economics (r.lupton@lse.ac.uk)