Appendix B: Sampling Approach
The economic and data modelling informed the sample frame for the study.
The Labour Force Survey and the Annual Population Survey do not provide data on seasonal migrant workers as the surveys are based on samples of private households and therefore exclude those migrant workers living in hotels and caravan parks.
Therefore, to estimate the proportion of seasonal migrant workers in non-agricultural sectors in Scotland, a targeted sampling approach is likely to yield greater precision than a random sampling approach. However, given the limited availability of data on the seasonal migrant workforce, this will be indicative of the industry sectors where there may be a higher prevalence of seasonal migrant workers.
To inform the sampling frame, we have focused on APS data which indicates the industry sectors in which there is a higher prevalence of non-UK nationals working in low-skilled occupations on temporary contracts.
There are two assumptions underpinning the proposed sampling approach:
- that seasonal migrant workers may be overrepresented in low-skilled occupations;
- that seasonal patterns of employment may closely follow temporary patterns of employment.
Each of these is addressed in turn.
On the first assumption, that the sectoral pattern of non-UK nationals employed in lower skilled jobs provides a reasonable approximation for the sectoral pattern of employment for non-agricultural seasonal migrant workers.
While this is an assumption, and there is no direct evidence on this point due to a lack of data on seasonal working outside of agriculture, there is some existing evidence on the wider pattern of migrant employment which suggests that this is not an unreasonable assumption.
For example, there is a body of work stemming from EU Enlargement (when the so-called A8 countries joined the EU) in 2004 which looked at the labour market experiences of central and eastern European migrants (Anderson, 2006). While the focus of this work was on particular sectors rather than the whole labour market, it provides some important insight into employment in these parts of the economy.
The message from this work was that while there are sectoral differences as outlined below— much of this employment is temporary in nature and in lower skilled occupations, and in these sectors, some had a significant seasonal dimension to employment.
This research suggested that nearly a half of these workers were working without written employment contract, but that this was very sectorally varied with a concentration (among the four sectors considered in the au-pair sector (79%), relatively evenly split in the Hospitality and Construction sectors (44%/55%) and only 13% in the Agriculture sector.
This report also shows that only 9% of those migrant workers surveyed were in high skilled occupations, with 24% in medium skilled occupationsand 67% in "low skilled" occupations. These numbers are in line with an analysis for Scotland by Brown & Danson (2008).
While these numbers do not come from a random sample, and are somewhat dated, and the results are not sectorally defined as the LFS/APS survey data are, they do indicate that there is some prevalence of more fragile employment for migrants in the Hospitality and Construction sectors.
This report also documented the degree of seasonal work in these four sectors, with 86% of agricultural firms, and 53% of hospitality firms reporting seasonal fluctuations in their demand for workers.
Other later work, for example Rolfe & Hudson-Sharp (2016) which looked at migrant employment in the hospitality, food processing and construction sectors, reached similar conclusions regarding the seasonal nature of much of the work being undertaken by the migrants from A8 countries and the "low skilled" nature of much of this seasonal employment:
"Some general patterns can be identified across the case study sectors and firms. These are first the concentration of migrants in lower skilled posts, particularly in hospitality and food and drink, but also their recruitment for more skilled roles where shortages exist, as in construction engineering and professional services. The second pattern concerns the use of migrants to expand the workforce during busy times of year or when labour needs fluctuate. This was found across sectors: in hospitality because of the seasonal nature of tourism; in food and drink because of fluctuating and seasonal demands for products; and in construction because of the contract-driven nature of demand and large labour requirements for relatively short periods of time. Companies across sectors had much higher proportions of local workers among their permanent or core staff, and in some cases the seasonal workforce was very largely migrant." (Rolfe & Hudson-Sharp, 2016: p19—20)
This supports the suggestion that in thinking about seasonal migrant workers, it is not unreasonable to narrow our focus to "lower skilled" occupations. This is not to say that the migrants themselves are "low skilled", indeed there is evidence that they are more likely to be overqualified for the job that they do in the UK (Altorjai (2013), Johnston (2015).
Patterns of employment
Temporary migration is often used interchangeably with circular, seasonal, short-term and spontaneous migration. Temporary or circular migration is a move made for a short period of time with the intention of returning to the place of usual residence. An important group of temporary migrants consists of seasonal migrants, who combine activity at several places according to seasonal labour requirements (Keshri and Bhagat 2012).
Wider evidence suggests that new migrants are more likely to be employed in seasonal or temporary work (Demireva (2011), Matthews and Ruhs (2007); Ruhs (2006).
When comparing patterns of employment among non-UK nationals in "low skilled" occupations with non-UK nationals in "low skilled" occupations and temporary contracts, the results are broadly similar. One point of difference is that there is a higher share of non-UK nationals on temporary contracts in 'Banking, finance & insurance, etc' than the share of non-UK nationals working in low-skilled occupations in that sector generally, with a smaller proportion of those on temporary contracts in the 'Distribution, hotels and restaurant' sector than the total employed population of non-UK nationals in that sector.
Looking at those non-UK nationals on temporary contracts in lower skilled occupations, the general sectoral employment pattern does appear broadly similar, and in conducting the survey of firms in these sectors we will be able to verify these patterns in more detail.
Our sampling approach involved setting targets on industry sector, business size, and location. Sectoral targets were as follows:
Target sample frame for non-agricultural sectors
|Sector||% employed in each industry sector APS 2017||% non-UK nationals in "low skilled" occupations and temporary contracts employed in each sector||% difference||Interviews (n) in each industry sector|
|A: fishing and forestry (SIC 2007) - remove agri||1.7%||1%||1%||30|
|B, D, E: energy and water (SIC 2007)||4.2%||2%||-1%||30|
|C: manufacturing (SIC 2007)||7.8%||20%||10%||122|
|F: construction (SIC 2007)||7.2%||9%||2%||112|
|G, I: distribution, hotels and restaurants (SIC 2007)||18.8%||43%*||13%||375|
|H, J: transport and communications (SIC 2007)||7.4%||2%||-6%||30|
|K-N: banking, finance and insurance (SIC 2007)||15.2%||10%*||-13%||138|
|O-Q: public admin. education and health (SIC 2007)||31.4%||10%||-3%||122|
|R-U: other services (SIC 2007)||5.8%||3%||-3%||40|
While overarching targets are set on industry sectors, the survey included broad targets on business size within each industry sector. The reason why broad targets have been set rather than narrower business size targets is because the APS data on business size is likely to be unreliable as it is self-reported business size. However, by condensing size bands we are likely to minimise error.
IDBR data on business size shows a higher prevalence of smaller businesses across the range of industry sectors, however, as the survey is exploring variance in employment it is important to ensure businesses with a large number of employees are included. Data from the APS, which shows the employment of non-UK nationals employed in lower-skilled jobs by sector and size of business, shows a higher proportion of employees within a self-reported business size of 50+ employees than the population data. Therefore, broad survey targets have been set following the APS data to increase the proportion of larger businesses sampled in the survey as well as minimise error in self-reported business size.
Target sample frame for size of business
|Size of business (% employed in each)||% of interviews among businesses with 1 thru 49 employees||% of interviews among businesses with 50 thru 500 employees|
|A: fishing and forestry (SIC 2007) - remove agri||66%||34%|
|B, D, E: energy and water (SIC 2007)||25%||75%|
|C: manufacturing (SIC 2007)||22%||78%|
|F: construction (SIC 2007)||68%||32%|
|G, I: distribution, hotels and restaurants (SIC 2007)||66%||35%|
|H, J: transport and communications (SIC 2007)||33%||67%|
|K-N: banking, finance and insurance (SIC 2007)||68%||32%|
|O-Q: public admin. education and health (SIC 2007)||63%||47%|
|R-U: other services (SIC 2007)||73%||27%|
To ensure a geographic spread, we will set targets by the six-fold urban rural classification which reflects the population data of businesses as shown below. Data from the APS indicates that there is a higher proportion of seasonal migrant workers within rural areas – data on National Insurance registrations reflect a spread in Q3 and Q4 across certain local authorities such as Perth and Kinross, Angus, Edinburgh and Glasgow. These will fall out naturally in the urban-rural profiles of the businesses sampled in the survey.
Target sample frame by six-fold urban rural classification
|Location||Large urban||Other urban||Accessible small town||Remote small town||Accessible rural||Remote rural|
|A: fishing and forestry (SIC 2007) - remove agri||2%||8%||4%||4%||44%||39%|
|B, D, E: energy and water (SIC 2007)||25%||25%||5%||3%||23%||19%|
|C: manufacturing (SIC 2007)||27%||36%||8%||4%||15%||10%|
|F: construction (SIC 2007)||28%||32%||9%||4%||17%||10%|
|G, I: distribution, hotels and restaurants (SIC 2007)||34%||32%||8%||6%||11%||9%|
|H, J: transport and communications (SIC 2007)||43%||29%||7%||3%||12%||6%|
|K-N: banking, finance and insurance (SIC 2007)||40%||27%||9%||3%||14%||6%|
|O-Q: public admin. education and health (SIC 2007)||41%||30%||8%||4%||10%||7%|
|R-U: other services (SIC 2007)||37%||33%||7%||5%||11%||7%|
Achieved sample profile
The achieved sample profile is shown in the table below.
|A:fishing (SIC 2007)||42||30|
|B,D,E:energy and water (SIC 2007)||34||30|
|C:manufacturing (SIC 2007)||127||122|
|F:construction (SIC 2007)||103||112|
|G,I:distribution, hotels and restaurants (SIC 2007)||413||375|
|H,J:transport and communications (SIC 2007)||41||30|
|K-N:banking, finance and insurance (SIC 2007)||133||138|
|O-Q:public admin. education and health (SIC 2007)||127||122|
|R-U:other services (SIC 2007)||47||40|
|Sector by business size||1 to 49||50 to 500|
|A:fishing (SIC 2007)||21||20||5||10|
|B,D,E:energy and water (SIC 2007)||17||8||17||23|
|C:manufacturing (SIC 2007)||56||27||71||95|
|F:construction (SIC 2007)||59||76||44||36|
|G,I:distribution, hotels and restaurants (SIC 2007)||285||244||128||131|
|H,J:transport and communications (SIC 2007)||11||10||30||20|
|K-N:banking, finance and insurance (SIC 2007)||74||94||59||44|
|O-Q:public admin. education and health (SIC 2007)||45||77||82||45|
|R-U:other services (SIC 2007)||13||29||14||11|
|Sectors by location||Large urban||Other urban||Accessible small town||Remote small town||Accessible rural||Remote rural|
|A:fishing (SIC 2007)||0||1||4||2||0||1||1||1||20||13||17||12|
|B,D,E:energy and water (SIC 2007)||12||7||8||7||1||2||2||1||7||7||4||6|
|C:manufacturing (SIC 2007)||26||33||58||44||12||10||4||5||17||19||10||12|
|F:construction (SIC 2007)||33||31||32||36||11||10||4||5||19||18||4||11|
|G,I:distribution, hotels and restaurants (SIC 2007)||139||128||143||120||26||30||23||21||51||42||31||35|
|H,J:transport and communications (SIC 2007)||16||13||12||9||2||2||2||1||7||4||2||2|
|K-N:banking, finance and insurance (SIC 2007)||56||56||36||37||12||13||4||4||19||20||6||9|
|O-Q:public admin. education and health (SIC 2007)||63||50||30||37||9||10||4||5||20||12||1||8|
|R-U:other services (SIC 2007)||14||15||16||13||6||3||1||2||6||4||4||3|
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