Place-based policy approaches to population challenges: Lessons for Scotland
This report by the independent Expert Advisory Group on Migration and Population analyses a range of place-based policy approaches to population challenges (including zonal approaches), and sets out lessons for Scotland.
Annex 4 Service provision, a key driver of differential population trends?
The report considers some of the issues involved in determining the geographical scale, number of areas and criteria for selection that could be the subject of zonal or place-based interventions aimed at addressing depopulation. These issues have largely concerned trends in population dynamics over time, at a variety of geographical scales.
Another set of issues that is likely to be material in determining the number and size of target areas is the sort of policy interventions to be implemented. Some types of interventions might be well-suited to implementation at a relatively small area level, defined by hard boundaries (such as for example tax reliefs of other fiscal incentives aimed at stimulating activity in particular areas). Other policy interventions might be more suited to broader geographical areas given the administrative challenges at smaller areas (such as for example a rural visa pilot). Others may not need to be defined by very specific boundaries (such as policies to promote or market the advantages of living in particular parts of the country).
The type of intervention to be implemented through a zonal or place-based approach is therefore likely to be material to the question of how to identify ‘target’ areas. Consideration will need to be given to ensuring that measures can be implemented locally in ways that appropriately address the complexity and nuance of population trends, their causes and consequences. Policy choices should therefore depend on what evidence says are material factors driving particular population trends or leading to a particular local population ‘outcome’.
The policies to be implemented within zones or places might feasibly include policy related to: job opportunities and access to those; housing access and affordability; infrastructure quality and suitability, particularly in relation to digital infrastructure; access to social and cultural services; access to and quality of public services.
We don’t have scope in this report to appraise the role and suitability of all these policy possibilities. But we do investigate the role of one specific factor that may influence population trends – the public’s perceptions of public services. In theory at least, it is possible that if public services are persistently perceived as being poor in certain areas, this could have a material influence on population decline.
Whilst perceptions of public services are clearly not the only factor that can influence individuals’ location or migration decisions, they are a particularly important indicator for policymakers to consider, since the quality and accessibility of public services can be influenced directly by policy. If residents’ perceptions of public services were notably worse in particular geographical areas, then in theory at least, one ‘place-based’ approach to addressing this would be to target additional funding or support at public services in those areas.
We used data from the Scottish Household Survey (SHS), an annual survey of approximately 10,000 Scottish households, to examine how public satisfaction with public services varies across different geographical areas in Scotland. SHS results from 2017, 2018 and 2019 were combined to produce a sample size of approximately 30,000.
An important question is then what level of geography to use when considering how perceptions of public services vary. Ideally, we might have considered grouping results according to the 8-fold urban/rural classification. Unfortunately, the public access version of the SHS does not include a variable identifying the location of households by the 8-fold urban/rural classification.
However, the SHS does identify the local authority area of each respondent, and the two-fold urban/rural classification of each respondent. We combine these two sets of information to identify four typologies of local authority, and within each of these four typologies, the two-fold urban/rural measure.
The four typologies of local authority were those specified by the Scottish Government’s RESAS (Rural and Environmental Science and Analytical Services) division and are reproduced in Table 1.
LA type | Local authority areas |
---|---|
Larger cities | Glasgow City, City of Edinburgh, Aberdeen City, Dundee City |
Urban with substantial rural | North Lanarkshire, South Lanarkshire, Fife, West Lothian, Renfrewshire, Falkirk, East Renfrewshire, Inverclyde, West Dunbartonshire, Midlothian, North Ayrshire, East Dunbartonshire, Stirling |
Mainly rural | East Ayrshire, Aberdeenshire, Clackmannanshire, East Lothian, South Ayrshire, Moray, Angus, Perth and Kinross, Highland, Dumfries and Galloway, Scottish Borders |
Islands and remote rural | Argyll and Bute, Shetland Islands, Orkney Islands, Na h-Eileanan Siar, |
We overlay these four LA types with the two-fold urban/rural classification. This approach leaves us with seven different types of geographical area (urban and rural parts of the four LA types - there are no rural areas in the ‘larger cities’). Our sample size for each of these seven areas is shown in Table 2.
Urban | Rural | Total | |
---|---|---|---|
Larger cities | 7,110 | - | 7,110 |
Urban LA with substantial rural | 11,255 | 1,089 | 12,344 |
Mainly rural LA | 5,786 | 3,231 | 9,017 |
Islands and remote rural LA | 1,125 | 2,062 | 3,187 |
Total | 25,276 | 6,382 | 31,658 |
To summarise, our seven types of geographical area are:
- The four major cities
- Urban areas in mainly urban local authorities
- Rural areas in mainly urban local authorities
- Urban areas of mainly rural local authorities
- Rural areas of mainly rural local authorities
- Urban parts of the island authorities and Argyll and Bute
- Rural parts of the island authorities and Argyll and Bute.
For shorthand, and to maintain some consistency with the RESAS categorisation in Table 1, we refer to the ‘island authorities and Argyll and Bute’ as ‘remote and island’ authorities. It is worth noting that of course that parts of Argyll and Bute are not particularly remote, and are sometimes less ‘remote’ than parts of some other authorities. This demonstrates some of the challenges in categorising areas. Nonetheless, we felt it was important to adopt a similar categorisation as used by the Scottish Government.
Summary of findings and discussion
The findings suggest that, in broad terms, satisfaction with public services tends to be higher in remote and island local authorities than in other areas of Scotland. This is true across a broad range of public services, even, perhaps paradoxically, when it comes to perceptions of public transport.
This general finding holds after controlling for observable characteristics of survey respondents, including age, income, employment status, education, and health. In general, the inclusion of socio-economic controls makes little difference to the observed relationships. This partly reflects relatively weak associations between some controls and perceptions of public services.
What we cannot observe are the attitudes and expectations of respondents. It is possible that people living in remote and island areas have different expectations of public services that act to frame their satisfaction with those services. In other words, satisfaction with public services might be higher in remote rural areas despite poorer public service provision in those areas, because expectations are low to begin with.
There is very mixed evidence on the extent to which public services are better or worse in remote and island areas compared to other parts of Scotland when assessed by objective measures. Take health services for example. On emergency department waiting times, the highland and island health boards tend to do very well. The proportion of emergency department referrals treated or discharged within four hours is well above 95% in the Island health boards, and 80% in NHS Highland, compared to 65% in Scotland as a whole. But on cancer referrals there is a very different story. The percentage of cancer patients beginning treatment within 62 days of referral is notably lower in NHS Orkney and NHS Shetland than nationally, whilst NHS Western Isles and NHS Highland are in line with the national average[16]. More generally, a recent report by Highlands and Islands Enterprise finds that a large proportion of residents of island local authorities, and the remoter parts of the Highland area, cannot access a range of health services (such as GP services, dentists, health visitor, and physiotherapists) within a 20-minute drive of their home[17].
Whilst satisfaction with public services in remote and island LAs is generally relatively high, this is not the case for predominantly rural LAs. Satisfaction with public services in predominantly rural LAs is sometimes relatively lower than in predominantly urban areas and major cities, particularly in relation to public transport, and the quality of local authority services in general (but not schools).
One obvious limitation of this analysis is that, by aggregating over broad geographical areas, it cannot say anything about how perceptions of public services may vary in specific localities. If it is the case that key public services – perhaps in relation to schools or health services – are perceived as being poor in particular localities (such as towns or islands), those perceptions could play a part in shaping population change in those specific localities over time.
Another limitation is that we only observe people residing in their current location. We do not know whether perceptions of what public services in particular areas might be like dissuade some people from moving into rural areas. We also do not know whether dissatisfaction with public services has been a factor in causing some people to migrate away from rural areas.
On the basis of the analysis here, however, there is no case for saying that satisfaction with public services is systematically weaker in remote and island Las. These are the areas that our analysis, presented in previous sections, are most likely to contain areas of shrinking population. In fact on average, perceptions of public services are more positive in those remote areas. And whilst perceptions of some local services are somewhat weaker in predominantly rural LAs (and rarely if ever more positive than average), it is not the case that satisfaction with public services is systematically lower in these areas.
A conclusion that population decline in some rural areas is unlikely to have been driven by poor perceptions of public services is probably not a big surprise. As we remarked above, depopulation has been occurring in some areas for many decades, and is driven by a complex range of factors. It is also often the outcome of demographic composition, the factors behind which were set in train many years or indeed decades ago.
Nonetheless, we hope this analysis is useful in stimulating further debate and analysis of the likely determinants of population change, and hence the type of policies that might help mitigate further change where that is deleterious to wider community viability. In surveys, including recent survey data from HIE, housing and employment opportunities are cited frequently as challenges for rural residents, whilst it is most often younger people that express interest in leaving those areas (half of 16-29 year olds think they will move away from their local area in the next five years). The types of policy that will most effectively mitigate these issues will have a bearing on the number, size and selection of areas or zones that are the focus for intervention.
Detailed results – charts
N = 28,457 (no controls); 26,494 (controls). Bars show 95% confidence intervals
N = 23,124 (no controls); 21,682 (controls). Bars show 95% confidence intervals
N = 20,810 (no controls); 19,537 (controls). Bars show 95% confidence intervals
N = 28,658 (no controls); 26,699 (controls). Bars show 95% confidence intervals
N = 17,371 (no controls); 16,491 (controls). Bars show 95% confidence intervals
N = 4,914 (no controls); 4,589 (controls). Bars show 95% confidence intervals
N = 25,000 (no controls); 23,393 controls). Bars show 95% confidence intervals
N = 27,089 (no controls); 25,289 (controls). Bars show 95% confidence intervals
N = 18,511 (no controls); 17,304 (controls). Bars show 95% confidence intervals
N = 31,669 (no controls); 27,126 (controls). Bars show 95% confidence intervals
N = 27,920 (no controls); 26,055 (controls). Bars show 95% confidence intervals
N = 26,404 (no controls); 24,751 (controls). Bars show 95% confidence intervals
N =26,850 (no controls); 25,137 (controls). Bars show 95% confidence intervals
Contact
Email: population@gov.scot
There is a problem
Thanks for your feedback