International migration & low-skill labour markets: an agent based approach

Annual Review of Work 2012-2013

Huw Vasey & Yaojun Li, Institute for Social Change, University of ManchesterRuth Meyer, Centre for Policy Modelling, Manchester Metropolitan University

Contents:

About the SCID Project 2

Why study migrant labour markets as complex adaptive systems? 3

Model 1 – A highly descriptive approach to modelling the whole UK labour

market

7

Model 2 – A problem-based approach to using ABMs to study low-skilled

migrant labour markets

9

The ‘Other Half’ model: international migration, social networks

and the emergence of labour market segmentation in LA

9

The ‘LaMESt’ model: post-Accession migration and labour market

segmentation in Bristol

16

Future directions 20

Bibliography 22

1

About the SCID project:

This project integrates two very different disciplines – social science and complexity science – in

order to gain new understanding of complex, social issues. It does this by building a series of

computer simulation models of these social processes. One could think of these as serious

versions of the Sims computer games, programmes that track the social interactions between

many individuals. Such simulations allow ‘what if’ experiments to be performed so that a deeper

understanding of the possible outcomes for the society as a whole can be established based on

the interactions of many individuals, as well as examining social phenomena which are difficult

to understand using existing social science methods.

However, a difficulty with the computer simulation of complex systems is that if they are made

realistic (in the sense of how people actually behave) they become very complicated, making the

simulation hard to understand, whilst if they are made simple enough to understand and

rigorously analyse they can be too abstract to provide findings which are useful to our

understanding of ‘real world’ social situations.

This project aims to get around this by making “chains” of related models, starting with a

complicated, ‘descriptive’ model and then simplifying in stages, so that each simulation is a

model of the one “below” it. The simpler models help us understand what is going on in the

more comprehensive ones, whilst the more complicated models reveal the ways in which the

less elaborate ones are accurate as well as how they may over-simplify. In this way this project

will combine the relevance of social science with the rigour of the “hard” sciences, but at the

cost of having to build, check and maintain whole chains of models. Building on an established

collaboration between social and complexity scientists in Manchester, this project will integrate

the two disciplines to produce new insights, techniques and approaches for policy makers and

their advisors.

The social scientists will develop ways of relating these kinds of models to the rich sources of

social data that are available. They will also ensure that the modelling results are interpreted

meaningfully and usefully, in particular guarding against over-interpretation.

For more information and background to the SCID project, please visit our webpages at:

2

http://scid-project.org

Why study migrant labour markets as complex adaptive systems? An introduction to ‘employment’ modelling in the SCID project

Introduction – complexity, agent-based modelling and social science:Over the past decade social scientists have become increasingly interested in what insights the

science of complex physical and biological systems seem to provide for our understanding of

social systems and practices. Authors have explored this in regards to policy making (Geyer and

Rihani 2010; Room 2011), intersectionality (Walby 2009) social theory (Law and Urry 2004;

DeLanda 2006; 2011) and even social research methods (Law 2004).

Whilst there are striking similarities between complex physical and biological systems and

social systems, the connections between the socially complex and complexity science has largely

remained at the level of analogy. So, whilst concepts such as emergence and path dependency

have been useful aids to conceptualising the functioning of society, there has been less success

in routinely incorporating the insights of complexity science into the fabric of how social

processes are investigated, analysed and interrogated. Social complexity approaches are

generally only incorporated post hoc, and as a theoretical-analytical approach rather than as a

method in itself.

The SCID project has attempted to integrate the apparent advances of complexity science

approaches through the development of Agent-based models (ABMs) of the complex social

phenomena under considerations. Whilst ABMs have been used in social science research since

at least the 1970’s (see, e.g. Schelling 1971), their use has undergone a resurgence in recent

years. Part of this has been the ability of ABMs to create behaviour which is qualitatively similar

to complex systems – that is, multiple agents interacting with each other, which often produce

non-linear and emergent behaviour. The ‘rules’ by which these agents interact are often

extremely simple, but their collective behaviour may be neither simple, nor directly predicted

from their initial states (Gilbert and Troitzsch 2005).

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Migrant labour markets as complex social systems:Migrant labour markets appear to exhibit many features of a complex adaptive system. To

illustrate this, we can use Geyer & Rihani’s (2010: 29) ‘golden rules’ of complex systems in

which the agents are self-aware (‘conscious’ in the author’s terminology):

Partial order: phenomena can exhibit both orderly and chaotic behaviours; e.g. migration flows

often remain stable for many years, before rapidly shifting to a period of alternative order, or

apparent disorder.

Reductionism and holism: some phenomena are reducible, others are not; e.g. Rational Choice

Theory can explain some elements of the behaviour of economic migrants, but not all. Nor do

predictions based on such behaviours tend to produce accurate predictions of global

behaviours; like many reductionist approaches they do not ‘scale’ well.

Predictability and uncertainty: phenomena can be partially modelled, predicted and controlled;

e.g. as indicated above, many models of migration and the economy work well in highly

prescribed circumstances, but rarely extend well onto a larger scale (temporally, geographically

or socially).

Probabilistic: there are general boundaries to most phenomena, but within these boundaries

exact outcomes are uncertain; e.g. economic migration occurs within certain defined and

undefined limits (i.e. it is not a free-for-all). However, within these parameters the range of

variation of possible forms of organisation is significant and rarely predictable.

Emergence: they exhibit elements of adaptation and emergence; e.g. migration flows and labour

market niches grow, coalesce and fade in ways which are not predictable from initial conditions.

Interpretation: the actors in the system can be aware of themselves, the system and their history

and may strive to interpret and direct themselves and the system; e.g. agents react to known

and assumed properties of the system, learn from the results of past behaviours to guide future

activity, and may seek to alter the system to meet their own needs (e.g. in the case of

immigration policy).

For Geyer and Rihani, such ‘golden rules’ illustrate the ways in which a complexity-based

approach to social phenomena act as a synthesis between orderly rationalist paradigms and

disorderly post-modern approaches. That is, complex systems, such as migrant labour markets,

exhibit both general, predictable, characteristics, and unexpected emergent ones. In essence, we

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can understand this in terms of post-Accession migration to the UK in that we may have been

able to predict there would be flows of migrants to the UK from certain countries (e.g. Poland),

but we weren’t able to predict (or, indeed, effectively control) where they went, what work they

did, or the numbers that arrived. Nor have we been able to create adequate post-hoc

reconstructions of all the reasons for what happened. Thus, whilst post-Accession migration to

the UK could be understood in a rational, ordered, manner to a limited degree, the forms this

order took were emergent and not predictable from initial conditions.

However, once we have conceptualised migrant labour markets as complex adaptive systems,

we are not immediately confronted with an obvious solution as to how this translates into

researching such phenomena. We may take an extreme variant of what Kwa (2002) terms

‘baroque’ complexity and argue that, because outcomes are not predictable from initial

conditions, then the processes that lead to them must be unknowable; or, rather, that such a

concept as complexity is both fluid and never more than analogous to ‘reality’. This is very close

to the position Deleuze often finds himself in (see, e.g. 1988, 1993) and one which many social

theorists have tended to abide by when they utilise the notion of social complexity (see, e.g.

Law, 2004; Urry, 2007). Conversely, many agent-based modellers with an interest in complexity

have illustrated how complex phenomena can arise from very simple rules of interaction (see,

e.g. Simon, 1996; Waldrop, 1992). A ‘classic’ example of such a process is the ‘Schelling’ model,

which illustrates how residential segregation can develop from very simple rules regarding the

number of non-similar neighbours an agent will tolerate before looking to move (Schelling,

1971).

You can find a version of the Schelling model here: http://ccl.northwestern.edu/netlogo/models/Segregation

This would suggest that it is not the social processes themselves which are unknowable, rather it

is the results of repeatedly activating such inter-related processes which is unpredictable. Thus

small differences in initial conditions can produce vastly different outcomes (whilst even the

same initial conditions will often produce differing results). This insight is what makes using

agent-based models of migrant labour markets so appealing – if we can get the basic rules of

agent behaviour correct, we should be able to provide real insights into the often confusing and

hidden processes which produce and sustain migrant labour markets.

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Whilst social scientists with an interest in migrant labour markets have been highly successful

in illustrating the inequities in the labour market, the tightly intertwined webs of actors,

networks and institutions which make up the landscape of work and employment have made

definitive statements about why such differences emerge and persist in such an environment all

but impossible. Much work in quantitative sociology has helped to provide convincing evidence

as to both the existence of inequities in the labour market and their impact. Meanwhile,

qualitative researchers have provided rich and nuanced accounts of how such inequity is played

out in everyday settings. However, both approaches have struggled to provide us with satisfying

accounts of how processes of differentiation and advantage emerge, become ‘successful’ and are

sustained. Social theories too have struggled to bridge this gap, with both agency- and structure-

based approaches proving unsatisfactory to a growing number of researchers. Indeed, the

growth in recent years of social science approaches inspired by complexity theory (Law and Mol

2002; Walby 2009; Geyer and Rihani 2010; Room 2011) seems indicative that now may be the

time to explore this in more sustained depth.

Indeed, the potential of complex, descriptive ABMs to illustrate the hidden processes of social life

is one of their most-attractive features. By ensuring that a deep representation of the multiple

social interactions which make up our everyday lives is represented in the models, we are

producing a simulation of the labour market which allows us to uncover the interactive

processes of inequality which are normally hidden to us. Furthermore, by manipulating the

parameters of the model, we can envision what small changes in one area may produce

elsewhere. Therefore, this approach allows us both to ask why certain outcomes are more

commonplace than others, but also what if circumstances were different. The advantages of

being able to ask such questions is clear, both for a greater understanding of the work and

employment, and for providing more satisfying answers as to how we can change the labour

market for the better.

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Model 1 – A highly descriptive approach to modelling the whole UK labour market: Lessons learnt

Our first attempt at using ABM approaches in the ‘employment’ strand of the SCID project was

to explore ethnic occupational attainment in the UK labour market. The idea here was to

develop a highly descriptive model of the UK labour market which we could use to explore

issues around differences in occupational attainment. Whilst this fitted well with both SCID’s

stated aims and current research on work, ethnicity and immigration, it proved an extremely

tough challenge.

The original conceptualisation of this approach was, “to i) explore the potential of adapting

techniques from complexity science towards the purpose of expanding the tools at the disposal

of the social researcher as outlined above, and ii) to use such innovative techniques to

investigate occupational attainment in the labour market with particular reference to ethnic

diversity and immigration.” Furthermore, we were also keen to attempt an alternative approach

to building ABMs of complex social phenomena, beginning with a highly descriptive ‘close to

reality’ model, before simplifying it in stages, so that each simulation is a ‘model’ of the one

below it.

Our initial approach to resolving this dilemma was to model the demographics, behavioural

rules and interactive behaviour of the whole UK labour market, with the proviso that this ultra-

complicated model could then be iteratively simplified. However, we soon ran into two related

problems: i) too many processes and ii) too little information about their functioning.

If we think of all the possible processes which may impact on the occupational attainment of

different ethnic groups in a population, we will rapidly uncover an ever growing list. Some of

these will be demographic – age, gender, education etc… – of which we often have very reliable

sources of information. Others will be life-course events with their own complicated causes and

effects, such as marriage, having children and ill-health. Others still will be hugely important,

but little understood, processes such as direct and indirect discrimination. Furthermore, for

each complication added to the model, several highly interdependent processes were revealed,

complicating the model further in turn.

This provided us with a very practical problem – to study the issue of occupational attainment

in the manner we had original envisaged required a complete model of the labour market.

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However, the more we progressed, the further away completion appeared to be and the harder

it was to keep track of all the inter-related pieces of the model. This issue revealed a truth about

contemporary social science academia – spending four or five years on a project which may

eventually fail to produce the desired results was simply impossible. Funders and employers

(current and potential) are rarely impressed by such an endeavour, such is the pressure to

produce papers and results for academics at all levels. Furthermore, gaining a coherent

understanding of a system as complicated as a labour market is something academics spend

their lives working towards and rarely achieve – indeed, those who believe they have achieved

it, are generally proved wildly wrong by their colleagues. To attempt such a feat within the

timescale of a funded project is, at best, highly taxing.

So whilst a highly descriptive and complicated model would appear theoretically to be the right

approach, it currently appears an impractical way to use ABMs to advance our sociological

understanding of contemporary labour markets.

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Model 2 – A problem-based approach to using ABMs to study low-skilled migrant labour markets

The problems outlined above raise the question as to whether we can utilise the promise of

ABMs in the study of complex social systems in a manner which is both practical for

contemporary social science academics and provides genuine insights into our subjects of study,

without reducing them to abstract ‘toy’ models?

In light of this, we are currently exploring what we term a ‘problem-based’ approach to utilising

ABMs in social science – an approach with parallels to Edmund Chattoe-Brown’s recent

overview of the potential of ABMs for theory building and testing in sociology (Chattoe-Brown,

2013). In this approach, the initial ‘target system’ to be modelled is determined by the problem

under exploration, which itself is drawn from a close reading of contemporary sociological

research in the area of concern. Thus the aim moves from building a ‘whole world’ model which

can be used to test and build sociological understandings of the world, to creating a scenario in

which specific scenarios and interdependent relationships can be explored and tested. This

allows model builders to pare down the breadth of processes to be modelled, with the downside

of moving further away from ‘reality’. However, by basing such abstractions on pre-existing

theoretical and empirical findings it is possible to retain relevance to contemporary sociological

debates. Furthermore, such foundational models can be made increasingly intricate through

successive iterations, allowing researchers to develop more nuanced and sophisticated

understandings of sociological processes. It is hoped that such an approach will allow us to

create models of complex social systems which will be practical to produce, yet will retain a

relevance to our understanding of contemporary sociological problems.

The ‘Other Half’ model – international migration, social networks and the emergence of ethnic labour market segmentation in Los Angeles

Given our stated interest in the occupational attainment of ethnic minorities, what would be a

sensible sociological problem to initially focus on? What, in essence, could we conceptualise as

the basis of current work on the sociology of ethnic labour markets? Of course, every researcher

in the field would able to supply a different answer, but we decided to concentrate on a notion

that, implicitly or explicitly, is widely supported by social scientists working in this area – that

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social networks matter. And, if this was to be our starting point, Waldinger & Lichter’s (2003)

How the Other Half Works seemed an eminently sensible place to start.

In this seminal study of immigration and the social organisation of labour in Los Angeles,

Waldinger & Lichter not only provide a neatly defined area of study – LA in the 1990’s – but also

supply some highly testable scenarios which are interesting, not only from a sociological

perspective, but also from a complexity one. For the former, the question could be stated as, ‘can

social networks alone drive ethnic segmentation in a low-skill labour market?’ For the latter, we

can extend this to include whether such processes are emergent and whether they lock-in over

time. These are important questions in the sociology of immigration and work, because whilst

we are well aware such segmentation happens (often rapidly), there is less clarity about the

processes that drive it. Is it simply an effect of the functioning of social networks, or do we need

to take into account other socially embedded processes, such as habitus (Bauder, 2006), dual

interpellation (McDowell et al, 2007), discrimination and job queuing (Wills at al., 2010), or the

structural forms of the labour markets in global cities and their tributaries (Sassen, 1998)?

Indeed, even if we agree that many of the factors do have an impact, the extent of the effect

social networks have is difficult to gauge by other sociological methods; perhaps an ABM could

cast light on this?

Waldinger & Lichter’s conceptualisation of how social networks impact on labour market

segmentation is clearly expressed in the chapter entitled, ‘Networks, bureaucracy and exclusion’

(2003: 83-99) and is summarised below:

Q) ‘Why do social networks so heavily influence the way workers find jobs and bosses

find help?’ (ibid: 83)

Assumption 1: ‘Most job-seekers activate their social connections to find jobs’ (ibid)

Assumption 2: ‘Employers use ties linking the workers whom they know to the new people

they may like to hire’ (ibid)

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Note: The problem here is that the question stated above appears to have

been answered by the two assumptions presented in the same initial

paragraph of the chapter – if assumptions 1 & 2 are correct then the answer

to the question posed is implicit. However, the mechanics of this are not

immediately clear. Therefore, I suggest reframing the question as:

How do social networks so heavily influence the way workers find jobs

and bosses find help?

Waldinger & Lichter suggest four elements to consider (according to their interpretation of the

current sociological literature):

1) Networks provide information – ‘telling job seekers about opportunities and informing

employers about the characteristics of applicants’

2) Networks are instruments of influence –‘allowing job seekers to put themselves on the

inside track by proxy’

3) Networks can be used to enforce obligations – ‘so that the employer is assured that the

favors he or she does for the job-seeker and his or her accomplices will be repaid’

4) Networks can cement implicit contracts – because networks are carriers of information

and obligation, they can be used to impose the ‘rights and responsibilities of each party

of the employment exchange.’ Furthermore, ‘[t]o the extent that a group of workers feels

bound by these understanding, the employer can count of on its exercise of social

control to keep recalcitrant fellows in line’ (ibid).

This suggests two types of agent to be modelled:

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1) Worker agents – they look for jobs and are enmeshed in social networks. They can pass

informationabout jobs through these networks, and also appear to get a boost to their

likelihood of getting a job they have heard about through such a connection (influence).

However, getting a job through such a network implies responsibilities to both the

employer (implicit contracts) and their social network (obligation), suggesting that

social networks increase your chance of getting a job, but decrease your scope for

resistance to negative actions by your employer (such as decreased pay, poor conditions

etc…).

2) Employer agents – they give out jobs and use the social networks of their existing

workforce for recruitment. They have a preference for recruiting workers in the same

social networks as their existing workforce (influence), as this ensures new recruits are

trustworthy and effective (obligations). However, it is also implied that they can use

these same networks to constrain workers’ rights (such as poor pay and conditions,

breaking off of privileged access to the social networks of recalcitrant workers).

Additionally, the following are also required:

3) Social networks – these connect groups of workers. They provide information about

available jobs at the employers of social network group members (information). They

provide an unspecified advantage to getting a job where other network members are

already employed in an organisation (influence). They also act as conduits for

obligations and implicit contracts (though it is not immediately clear how this occurs).

The model is initiated in a scenario similar to the early 1950’s in LA – low-skilled jobs are

arranged in a variety of small, medium and large organisations (based on Table A9, pg. 251).

Most of these jobs are initially filled by a pre-existing majority population (invisible in the

model) who slowly vacate the jobs as they retire and do not compete for newly vacated jobs –

equivalent to a ‘white’ working class moving up the labour market hierarchy in the post-war era

(ibid: 9). The rest of the filled jobs are initially taken by a ‘native’ minority group (eth0 –

coloured orange in the model), equivalent to US-born African-Americans. A small number of

seed-corn agents are also present – equivalent to ‘pioneer’ Mexican and ‘Asian’ immigrants –

who visit organisations looking for work. All agents are initiated with, on average, 3 network

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ties with agents in geographic proximity who share their ethnicity. Agents are then provided

information regarding job vacancies at the organisations of linked agents.

Employers collect ‘job applications’ every tick (2 weeks) and employ their chosen agent (they

have a preference for those with social links to pre-existing employees). Employed agents may

then form new links with co-ethnics with whom they work – though agents do not form cross-

ethnic links in this model (mimicking initial linguistic barriers).

The immigration rate of the non-native ethnic groups (eth1 and eth2) depends on the labour

market success of co-ethnics already in the model – the lower the unemployment rate, the

higher the immigration rate. However, once employment falls below a certain level, immigration

will cease until that level is again exceeded (this is subject to a 2-tick information delay) (see,

e.g. Stalker, 2000).

Model outcomes:

It is clear that segmentation within the model can occur – some organisations become

dominated by one of the three ‘ethnic’ groups. Furthermore, it is clear that this process is

emergent, i.e. it cannot be predicted from the initial conditions.

The still of the ‘mature phase’ model (Figure 2) shows an organisation (circled in yellow),

dominated by eth1; the numbers under the blue organisation symbol show numbers of eth0,

eth1 and eth2 employed there in that order. The graph overleaf (Figure 1) shows the process of

segmentation in a similar scenario in a large organisation.

Such scenarios are consistent with Waldinger & Lichter’s hypothesis about the development and

persistence of labour market segmentation in low-skill work – once a successful labour market

niche is formed, social network processes will tend to reinforce that nascent advantage, leading

to the employment of more co-ethnics.

Therefore, it is plausible that social networks lend themselves to the emergence of locked-in

processes of labour market segmentation in low-skill work.

Figure 1 – An example of a segmenting organisation in the ‘Other Half’ model:

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However, as the organisations circled in purple in Figure 2 (overleaf) illustrate, it is also

possible for organisations dominated by a pre-existing ethnic group to ‘lock-out’ immigrant

groups. This is also a feature described in Waldinger & Lichter’s work, whereby certain

organisations (particularly those with more bureaucratic hiring regulations) tend to remain

dominated by African-American workers. Indeed, it is this process we are looking to develop in

the next iteration of the model – introducing linguistic skills and bureaucratic hiring processes

to replicate a more nuanced labour market simulation.

Figure 2 – A still of the ‘Other Half’ model after 25 years:

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Whilst we are developing a number of ways in which to validate the model both qualitatively

and quantitatively, we will focus here on unemployment rates. Figure 3 illustrates US Census

data for working-age populations in LA county equivalent to our model agents. Figure 4

provides a 5-run mean of unemployment rates in the model.

Considering the current model has no exogenous shocks (such as recessions) and can therefore

be expected to be more stable, the range of unemployment rates seem a reasonable fit to ‘real

world’ data.

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Figure 3: Unemployment rates by ethnicity 1950 - 2000 for ages 20-65 in LA County without

High School-level qualifications

1950 1960 1970 1980 1990 20000%

5%

10%

15%

20%

25%

Black US born Hisp f born Asian f born

Year

Une

mpl

oym

ent r

ate

Source: US Bureau of Labor Statistics

Figure 4 – Unemployment rates in the ‘Other Half’ Model (a 10-run average):

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Thoughts and comments:

Though the ‘Other Half’ model is clearly a highly abstracted version of the low-skill labour

market in Los Angeles, it does illustrate how ABMs can be used to discuss issues of fundamental

importance to our understanding of the sociology of immigration and work. Furthermore, it

demonstrates that social networks alone can produce emergent labour market segmentation

based on ethnically homogenous social networks. This segmentation may not be as extreme as

that described in other studies (see, e.g. Wills et al., 2010), but it does indicate that labour

market segmentation (and its persistence) is not only a matter of negative discrimination

against immigrants groups, but may also be partially explained by positive discrimination in

favour of known and trusted immigrant networks, reinforced by the use of social ties as the pre-

eminent source of information about job vacancies in the low-skilled sectors of the labour

market. Thus some degree of labour market segmentation persists even when socially

discriminatory processes, such as habitus, dual interpellation or job queuing, are discounted.

The ‘LaMESt’ Model – post-Accession migration and labour market segmentation in Bristol:

Keen to test the ‘Other Half’ model in a UK setting, we decided to shift our focus to the UK and

specifically Bristol. Bristol was chosen because we wanted to develop the model in an area for

which we had plenty of qualitative as well as quantitative data and which had seen a recent

influx of migration after the accession of countries such as Poland to the EU in 2004. Whilst

London was tempting, its position as a super-diverse global city arguably made it both atypical

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and difficult to model. Bristol was a more manageable target and we had the bonus of Huw

Vasey’s PhD research in the area to use as supporting qualitative data. Thus the Labour Market

and Ethnic Segmentation (LaMESt) model was born.

However, transferring a model to a new temporal and geographic setting is never easy, even

when the model is as abstracted as the ‘Other Half’. Bristol in 2001 (when the model was

initialised) was very different from LA in either the 1950s (the start point of the ‘Other Half’

model) or the 1990’s (its endpoint). Whilst many of the demographic changes were relatively

straightforward to adapt (such as birth rates, age ranges and ethnic composition), other factors

were surprisingly complicated – for example, getting the right balance and range of large,

medium and small organisations was far more of a modelling challenge than anticipated.

Therefore, whilst we have tried to keep this initial LaMESt model as close as possible to the LA

model, it has been necessary to incorporate a number of changes:

Worker agents – The two immigrant groups, plus one ‘native’ ethnic minority setup of the LA

model has been replaced by single ‘native’ and ‘immigrant’ categories of agent. Secondly, there

is no longer an invisible retreating majority population leaving the model as they retire. The

rationale for these changes is that the conditions which produce a labour market expansion, and

the resultant upward mobility of the majority ethnic population in 1950s LA were not relevant

to 21st century Bristol. Secondly, though Bristol has a number of settled ethnic minority

populations (most notably those from Caribbean or Pakistani backgrounds), including these

groups would not reveal much about the initial development of labour market niches, because

they had already gone through this process. Replicating any pre-existing niches would make it

difficult to provide any useful insights into the growth of new niches in the labour market after

the EU accessions in 2004.

Demographic data for these groups were updated from 2001 Census data (with the exception of

fertility rates, which were drawn from 2001 ONS data). The ‘Other: White’ category was used to

represent the small number of A8 migrants in Bristol in 2001 – replicating the role of ‘seed corn’

agents in the LA model.

Agents used the same processes to form social ties and search for jobs as in the previous model.

Employer agents – Unlike the ‘Other Half’ model we had no survey data regarding the types

and sizes of potential migrant-heavy organisations to draw on in Bristol. However, we did have

plenty of data about organisation size and industrial classification (SIC code), and employee

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socio-economic classifications (NS-SEC codes) at the level of the Unitary Authority. Scaling and

replicating the proportions of low-skilled jobs in organisations was a challenge which involved

incorporating numerous data sets, and producing a pool of ‘realistic’ organisations which would

provide a reasonable facsimile of the low-skilled labour market in Bristol in 2001. When the

model is initiated approximate proportions of large, medium and small organisations are

selected from the pool until there are sufficient jobs.

Employers then use the same ‘rules’ to select employees as in the previous model, but the

‘churn’ rate (i.e. how many employees leave by choice or otherwise) is higher (4.2% - taken

from LFS data) than in the previous model to ensure the model has a realistic turnover of staff.

Social network formation and development remains unchanged.

Initial findings:

It may initially appear logical that we would see very similar outcomes in our simulated Bristol,

as we did in our model of Los Angeles; after all, we are using the same rules of behaviour to

govern how our agents act. However, the changes outlined above produce an intriguing scenario

where labour market segmentation and niche formation happens, but with a much delayed

onset to the previous model. For example, the organisation in Figure 5 (below) is typical in that

the migrating group does not become dominant in the workplace until a full two decades into

the model run, whereas this occurred much faster (between 3-5 years) in the previous model.

Figure 5 – An example of segmentation in an organisation in the LaMESt model

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Why the change? The most obvious candidate is the removal of the openings caused by our

upwardly mobile majority ethnic group leaving the LA model (to pursue unmodelled higher

level jobs). This means the number of available slots are reduced substantially, making it much

harder for new migrants to gain a foothold in any one organisation and thus reducing the level

of immigration (because current migrants are largely unsuccessful) and delaying any emergent

segmentation between organisations for a considerable time.

It is also striking that this is nothing like what we observed in Bristol (and the UK in general),

after the accession of Poland to the EU in 2004, where we saw a rapid concentration of

Accession-state nationals in certain occupations in the low-skilled labour market (Bryant et al,

2006). These occupations were poorly paid, of low status and generally suffered from very high

staff turnover (ibid). Furthermore, they were heavily reliant on the use of employment agencies

to service their staffing needs – the same agents who quickly became central to the employment

of newly arrived economic migrants to the UK (see, e.g. Garapich, 2008; McDowell et al., 2008).

Add to this that many new migrants were keen to get into work as soon as possible, rather than

waiting for a ‘good’ job to come along (see, e.g. Ellis et al., 2007), and we would appear to have

the conditions for the rapid emergence of ethnic niches in the low-skilled labour market.

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Future directions – extending the modular problem-based approach

So whilst ethnic social networks go a long way to explaining the emergence of labour market

niches in the conditions described by Waldinger & Lichter in Los Angeles, they are far more

constrained in the more restricted labour market environment of a simulated Bristol. This

provides us with the interesting – but hardly shocking – observation that labour market niches

are much more likely to occur where there is a pre-existing labour shortage. However, in order

to gain a more nuanced insight into the processes of labour market segmentation in early 21 st

century Bristol, we need to add to our model.

Our initial findings from the LaMESt model, when taken alongside the existing literature on

post-Accession labour migration, imply that we first need to concentrate on two areas – poorly-

paid, low status jobs with a high turnover of staff (such as food production and processing), and

labour market intermediaries. In the second part of the workshop, we would like you to help us

develop these elements, as well as discussing and suggesting other future developments.

However, I will briefly outline our initial thoughts on developing the model below:

‘Undesirable’ jobs – we know from Worker’s Registration Scheme (WRS) data that most A8

migrants worked in poorly paid, manual occupations, often in the food sector. In essence, they

were working in jobs the ‘native’ population were unwilling to do (Wills et al., 2010).

Furthermore, such jobs often seem to be concentrated in the same organisations in the same

sectors (such as food processing). This would suggest we need to develop some way of

distinguishing between desirable and undesirable jobs. However, this isn’t simply a matter of

money – there are many poorly paid roles in the labour market, which this generation of

migrants did not initially colonise (such as shop work) – so adding wage levels would not

appear to be a satisfactory solution. In lieu of having data on the perceived desirability of

different jobs in the low-skill labour market, we can either try to find and use data on ‘hard-to-

fill’ vacancies in low-skill work, or we can design another way of denoting the relative

desirability of different workplaces in the model (and the relative tolerance of ‘native’ and

‘migrant’ agents to taking less desirable roles).

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Labour market intermediaries – it has been widely argued (not least at the previous review of

work for this model) that labour market intermediaries are central to the functioning of post-

Accession migration to the EU, as they acted as a conduit controlling and directing the flow of

new migrants into local low-skill labour markets in the UK. These intermediaries may have been

resisted, disliked and distrusted, but their role is undeniable. However, the exact processes by

which this role functioned is less clear – we have found little data about the way in which these

intermediaries controlled access to the labour market. In one recent study of post-Accession

migration to the south-west of England, most interviewees had found their first working role in

the UK through an employment agency, regardless of their linguistic ability, or their use of other

forms of job searching behaviour (Vasey, 2011). Additionally, the importance of such

intermediaries seems to cut across Waldinger & Lichter’s notion of the importance of ethnic

social networks in the process of approaching and vetting potential new recruits. Should we

assume this process is simply outsourced to intermediaries? Does this mean that employers no

longer accept ‘walk-up’ applications?

Language and skills – many theorists, particularly those from a human capital background,

stress the importance of linguistic ability to the job prospects of recent migrants (Dustmann &

Fabbri, 2003; Duvander, 2001). At the very least we can assume a reasonable proficiency in the

language of the host country allows for a widening of social networks and job prospects. Indeed,

many migrants stress that the inability to improve their language skills (because of the lack of

opportunities for practice in a workplace dominated by co-ethnics) is a major barrier to labour

market advancement (Vasey, 2011). Thus, a future iteration of the project will seek to model

how language skills develop amongst migrants and what effects this has on job advancement

and labour market niches. However, in order to do this, we will need to provide more nuanced

differentiation between jobs – there is no point in introducing a skill (such as language ability) if

employers do not value it. This would imply a substantial expansion of the model to incorporate

higher level jobs and the modelling of both pre-existing language skills of migrants and the

process of language learning once in the country (for which there is relatively little source

material).

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