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Measuring Poverty by Group

Local Area Knowledge


Local knowledge has it's place in terms of establishing local poverty. However, it carries considerable limitations and should be used to enhance more robust data rather than as a main data source. Adding qualitative information about peoples' personal experiences can make a powerful case when dealing with anti poverty activity.


·Time. At face value, this approach would appear to be less time-consuming than research-based alternatives, as it draws upon existing knowledge.

·Cost. Drawing upon existing knowledge could appear to be more cost-effective than research-based alternatives, as there is no need to cover additional research costs.

·Local Credibility. As this approach represents the common understanding (perception) of local poverty, it is likely to be viewed locally as an accurate description.


·Reliability of local knowledge  when places are constantly changing. Local knowledge is most robust when local poverty is less changeable. Changes in population, area regeneration or local economy may impact on the distribution and nature of local poverty and therefore local knowledge. 

·Precision of Poverty Knowledge. Local knowledge is qualitative information. It does not provide detailed estimates or comparisons to assist with policy decisions.

·Transparency. The accuracy of information based on personal experience or third party knowledge is hard to examine or confirm.

·Lack of Comparability. The knowledge of local community members and practitioners may only cover a certain area or population group in detail. Therefore knowledge may be 'patchy' and lack robust comparison.

·Practicality.  Using local knowledge to define poverty may create practical challenges for a robust research methodology.

·Time and Cost  There would be resource requirements (time and cost) in collecting and presenting local opinion in a form that could be used in local policy-making.  


Local Area Modelling


Local area modelling is a useful contribution to local poverty measurement and understanding for large organisations with the capacity to undertake advanced statistical analysis. In particular, a comprehensive poverty analysis may find value in comparing projected local poverty rates (estimated through local area modelling) with actual local poverty rates.  This may provide insight into the nature of the drivers of local poverty.


·Availability of data on poverty 'risk rates'. UK data on the risk of poverty by population groups is published on an annual basis. More limited Scottish data on the risk of poverty by age group is also published on an annual basis.

·External comparisons. Using risk rate data to estimate local poverty allows local and national comparison. It may also allow comparisons between local areas e.g. comparing local poverty in the electoral wards of Dundee and Glasgow.

·Internal comparisons. Local modelling could be applied to all localities within an area, allowing for systematic comparison e.g. comparing local poverty in all datazones in East Ayrshire.

·Time-series analysis. Annually updated figures allow for trends to be established.


·Availability of technical expertise. Not all local organisations will have the technical expertise or resources to undertake local area modelling.

·Availability of local population data. The ability to undertake local area modelling depends on the availability of accurate local population data.  The National Census can provide this information. However, the accuracy will decrease over the ten year period between surveys.  

·Availability of information on local drivers of poverty. The theory of modelling assumes that certain factors will have the same impact where ever they are. The local drivers of poverty may imply that the population at risk of poverty in a locality does not reflect that for Scotland or the UK as a whole.

·  Accuracy of model for smaller geographical areas. Local area models may be less robust for smaller geographical areas as less local population data are available compared to large geographical areas.   


Material deprivation


Material deprivation is a non income based measure which captures the wider impact of living in poverty. The key measure of material deprivation is "would like, but cannot afford", as opposed to "do not have".  Material deprivation can be used in bespoke local analyses of poverty. However, it can be fairly demanding in terms of data collection and processing,  In particular, it is possible to replicate the indicators used to measure child poverty in the UK to allow a direct comparison of local and national poverty.   The results of the UK Government's annual material deprivation analysis is reported in Households Below Average Income survey.There are specific measures for child material deprivation and pensioner material deprivation .  


·Established Methodology. Considerable body of research-based evidence exists to advise researchers of appropriate indicators to use.

·National Comparability. Material deprivation indicators are available at the national level.

·Comparable within Scotland. Information on some aspects of deprivation is readily available for Scottish local authorities.

·Useful for Population Surveys. Can be easily incorporated into local population surveys

·Common sense. Common-sense understanding suggests that areas of local poverty are likely to comprise populations who are materially deprived.


· Time and cost.  In general, there is a need to incorporate a suite of indicators to adequately measure deprivation. This can be demanding in terms of time (and cost) if incorporating into a local population survey.
· Limited usefulness of indicator sets across the population. Different indicator sets are most appropriate for different groups in the population.
· Material bias. Focus on material deprivation may imply that what matters most to people is their ability to consume. However, opinion surveys and qualitative research consistently show that the deprivations that matter most to people are the 'soft' deprivations surrounding quality of relationships.
· Skill required in survey administration and analysis. The most useful material deprivation data demands careful data collection and analysis, in order to avoid classifying people as deprived, when they have chosen not to buy something (but not on the grounds of cost).
· Differing access to goods within the same household.  A household having a material good might not reflect equal access of all people. For example, parents have been known to forego material goods to ensure that their children have greater access to them.  Care needs to be taken in interpreting material deprivation data for households with children.


Local income


Profiling local income means finding out the amount of disposable household income (adding up all of the income from all household members, after deductions) and comparing this to a threshold measure of typical income.  If household income is below 60% of typical household income, then that household is judged to be living in poverty.

Income based measures of poverty are widely accepted as the most robust measures. However, their lack of availability of income data at the local level has led to the search for alternative measures of local poverty. Recent methodological developments are raising the prospects for utilising local income-based measures of poverty.


· Comparisons.   It allows a direct comparison of local poverty to national levels of poverty (Scotland and the UK) using the key measures of poverty in the UK 
· Availability for larger Geographical Areas. The Scottish Government is currently working to develop poverty estimates for Scottish local authorities . The estimates currently published on the website are a step toward an estimate that will be quality assured. At present, these data should be understood to be 'data under development'. Information can be accessed from commercial companies for smaller geographical areas (see sources)
· Useful for Small Households.  Processing of household income data into a measure of poverty is less problematic for smaller households and, in particular, for households without children.


· Lack of availability for smaller areas. There is a lack of income data available at local level.
· Cost. There may be an associate cost of procuring income data estimates for local areas from commercial companies.
· Quality and uncertainty of using estimates. Commercially provided income data are estimates and not measures of income.
· Quality assurance of survey data. It is difficult to be assured of the quality of income data collected from local area surveys, particularly from larger households and households with more complex earning patterns.
· Access to resources within the household.  As a household based measure of poverty it assumes everyone has access to an equal share of household resources and/or benefit equally from them.
· Debt and Disposable Income. Disposable income is not necessarily an accurate measure of the income that is actually at the disposal of the household. For example, households that are servicing debt may find that they have less income to spend than their disposable income suggests.

Service Users


Two main approaches can be used to identify clusters of service users. Service use can be taken as an indication of poverty (for example, entitlement to free school meals) or lack of access can be used as an indication of poverty (for example, lack of access to key services).

Service user data may be a readily available source of poverty information, particularly for the organisation that is providing the service.  However, it is likely to be less robust than income-based indicators and care must be taken when assessing whether data should be used as a poverty indicator.

Service use data tend to be used within measures of multiple deprivation, although where the services that are being accessed are those which largely service people experiencing poverty there is the potential to use this information as an indicator of local poverty in its own right.


· Useful for Population Surveys.  Service use indicators can be incorporated easily into local population surveys (particularly when determining either the presence or absence of service use).
·Administrative Data.  These data may be collected routinely as part of the administration process.
· Easy to compare locally. Where data are administered across an area, data will be available to compare across localities within that area. 


· Availability. Robust measures of geographical access to key services are readily available and have been incorporated in the Scottish Index of Multiple Deprivation.
· Calculating travel or geographical access.  Critics of the measure feel that differences between public and private transport are not adequately accounted for e.g no account for the frequency of public transport.
· Easy to make assumptions.  Service access is not only determined on the basis of where you live. There can be significant variations in access across populations (for example, between car owners and non car owners)
· Service Uptake. Not all of those who are entitled to services (and who would be defined as living in poverty) would access the services.
· Limitations in target audience. If a service is targeted at one section of the population e.g. the unemployed, it may fail to capture poverty among the under-employed or low paid.
· Broad Base of Service Users.  Service users may extend beyond the population who are living in poverty so they may be hard to distinguish.
· Limitations to national comparisons. If eligibility is determined locally, it is impractical to use service use data to compare poverty more widely as other areas may have different eligibility criteria.

Fuel Poverty


As one of the Local Outcome Indicators, fuel poverty is widely accepted as a particular measure of poverty.  The twin-definition of fuel poverty and extreme fuel poverty is also helpful as a measure of poverty intensity.  It has, however, tended to be used just as a measure of fuel poverty and not as a more general indicator of poverty in its own right.

The Scottish Government has set a target to tackle fuel poverty. The term 'Fuel Poverty' refers to the situation where a household must spend more than 10% of their disposable income on heating their home to an adequate level. 'Extreme Fuel Poverty' is defined as a household having to spend more than 20% of its disposable income on fuel. Local Outcome Indicator 54 (version 5.1) concerns fuel poverty.  Measuring fuel poverty is demanding in that it requires information on both income and expenditure. The strengths and weaknesses below can also be relevant for other measures of consumtion.


·  Established Measure.  The national status of fuel poverty means that this is a widely understood measure of poverty.
·  National Comparablity. Scottish Household Condition Survey data are available for Scottish local authorities, allowing comparative analysis.
·  Useful for Population Surveys. A simplified measure of fuel poverty could be easily used in local population surveys, e.g. Do you spend more than 10% of your disposable income on household fuel?
·  Common sense. Common-sense understanding suggests that areas of local poverty are likely to comprise populations who are materially deprived.


·  Demanding in terms of data required.  Data are required on both income and expenditure.
·  Limited focus.  Fuel poverty only concerns one aspect of poverty.
·  Local influences on fuel costs. Local variations in the factors that determine fuel costs (quality of local housing stock, cost of fuel) will influence the proportion of income that is expended on fuel. Fuel poverty data may not be directly equivalent to poverty defined on the basis of the income that is available to a household.

Welfare Benefit


The ready availability, income-base and regularity of data series mean that welfare statistics are a valuable means of estimating local poverty. Particularly valuable is the availability of such data for smaller geographical areas. Welfare benefits can be a useful proxy indicator of poverty levels as welfare benefit populations are known to receive a similar low level of income in order to qualify. However,  changes to welfare entitlement over time and the transitions involved in the wider Welfare Reform implies that long term analysis or comparison must be handled with care and is not as reliable when used in isolation.


·  Regular data. Data are updated monthly, meaning that welfare data are not as dated as other poverty indicators can be.
·  Availability of data.  Data are readily available. A data tabulation tool enables local level analysis and the production of a user-defined report. Welfare statistics are also incorporated in wider indices of deprivation and can be accessed through SIMD and Scottish Neighbourhood Statistics .
·  Easy to measure. The single straightforward indicator approach to measuring poverty is relatively easy to apply and comprehend.
·  Easy to compare nationally and regionally. The results would be directly comparable to national data. It is also likely that the local results in one area, could be compared to the local results in other areas, e.g. comparing local poverty in the electoral wards of Fife and East Ayrshire.
·  Easy to compare within a Local Authority. The results could be applied to all localities within an area, allowing for systematic comparison across an area, e.g. comparing local poverty in all datazones in Aberdeenshire.


·  Changing entitlement to welfare. Changes to welfare entitlement over time implies that long term analysis or comparison must be handled with care.
·  Welfare Reform. The scale of the changes to the current Welfare reforms will present a series of issues. In this transitional period, reliance on welfare data alone may be inadequate as a means to estimate local levels of poverty. For example, changes proposed to Housing Benefit may have an impact on the threshold household income levels that are used to determine poverty. 
·  Expert knowledge of welfare system. Although it is planned to simplify the system in future years, at present there is a need for expert knowledge of the system in order to understand how benefit entitlement can be used as a measure of poverty. The DWP have published a user guide to welfare statistics.
·  Inaccurate assumptions.  Some people are missed from the claimant count as they may live below the income threshold but don't quality or because they do not claim. Alternatively there are concerns over welfare abuse (claims made by those who are not entitled to support).
·  Working poor.  Unless tax credits are considered, there is a risk that welfare statistics will not capture the significant proportion of those living in poverty who are working.  Some welfare measures may underestimate the extent of poverty in areas with a higher level of in-work poverty, e.g. rural Scotland.
·  Population dependent.  The use of individual benefits, or entitlement to all benefits for a defined age group, means that welfare statistics are often only relevant to a limited range of the population.




The Scottish Index of Multiple Deprivation (SIMD) ranks the 6505 datazones in Scotland from most to least deprived in terms of concentration of poverty. The 15% Most Deprived datazones has become a key threshold that is widely used by local government and the Third Sector as an estimate of relative need.

SIMD is an excellent resource for Scotland as a means to identify small pockets of multiple deprivation. Rural local authorities or local authorities with a mix of urban and rural areas may choose to analyse datazones outwith the 15% most deprived, for example to identify the most deprived datazones in their area or selecting a higher cut-off using available guidance.


·  Familarity. Users and stakeholders are now familiar with the SIMD.
·  Availability. SIMD data are readily available from both the SIMD home pages and Scottish Neighbourhood Statistics.
·  Cost. These data are made available by the Scottish Government to all users without charge.
·  Disaggregation and data on Income Deprivation.  The SIMD can be disaggregated into 'domains' e.g. Housing Deprivation, Health Deprivation, etc. In particular, the Income Deprivation domain has value as a close proxy of income poverty.
·  Time Series Analysis. Although not all sections are updated annually, the stable methodology, combined with the updates to the Index, means that time series analysis is possible. (The Income and Employment domains are updating annually)
·  Comparisons.  The national focus means that the SIMD is very useful for comparison across datazones (and local authorities).
·  Flexible Geographies.  The building block of the datazone means that it would be possible to use the SIMD to generate a range of area profiles at larger geographical scales.


·  Urban / rural. The SIMD is concerned with areas of concentrated multiple deprivation. Some rural areas may also experience poverty and deprivation that isn't as concentrated geographically so tend not to rank as highly within the scale. However, there is clear guidance and advise on how SIMD can be best used as a measure for rural areas. 
·  Not Measuring Poverty.  Although small area multiple deprivation and poverty are related, they are not interchangeable.  Sensitivity is required in using the SIMD to inform understanding of poverty in Scotland.
·  Area based. The SIMD is an area-based measure; it is not designed to estimate multiple deprivation for particular population groupings.
·  Data Quality. Not all of the data that is used in the SIMD is of an equivalent standard. In particular, data for the Housing Domain relies on the National Census and can therefore become dated a few years after the last Census has elapsed.