Understanding the Perception of the ‘Migrant Work Ethic’

Abstract

Over the last decade, the UK has experienced unprecedented increases in migration associated with the 2004 A8 expansion of the European Union. These migrant workers have been praised by managers in the UK, who have frequently stated that they perceive these workers to have a strong ‘work ethic’ when measured on aspects such as absence from work rates. This article examines this perceived migrant ‘work ethic’ by analysing worker absence data from the UK Quarterly Labour Force Survey for the period 2005–2012. Regression analysis reveals that when A8 migrant workers first arrive in the UK, they record substantially lower absence than native workers, but that these migrant absence levels assimilate within two to four years. If employers use this information to make hiring decisions, this may have negative implications for native workers, but, importantly, only in the short run.

Keywords

absence from work migration UK work ethic

Introduction

On 1 May 2004 the European Union was expanded to include the A8 nations of Central and Eastern Europe. Since their entry into the United Kingdom labour market, this new group of workers has been lauded by employers as having what has been described as a stronger ‘work ethic’ than workers from the UK. Within the academic literature, recent qualitative evidence studying managers’ views of the migrant work ethic, particularly through observations on sickness absence, suggests that the work ethic of migrants was perceived by these managers to be higher than that of native workers (see, for example, MacKenzie and Forde, 2009; Matthews and Ruhs, 2007; Tannock, 2013). Despite these voluminous qualitative findings, however, quantitative evidence that substantiates these employer perceptions of a distinctive migrant work ethic is scant.

Using worker absence data from the UK Quarterly Labour Force Survey (QLFS) covering the period 2005 to 2012, this article presents the first quantitative investigation into the migrant work ethic. While the literature recognizes that work ethic is a multidimensional concept, absence from work has previously been identified by managers within the qualitative research literature as being an important measure of work ethic (MacKenzie and Forde, 2009; Tannock, 2013). Absence from work has also been extensively used as a measure of work effort within the labour economics literature, especially when examining the increased levels of effort (i.e. lower absence) that temporary workers exert in order to increase their chances of being offered a permanent contract (see Bradley et al., 2014; Engellandt and Riphahn, 2005).¹

The theoretical framework presented in this article asserts that recent A8 migrants face certain disadvantages in the UK labour market relative to comparable natives, despite their higher levels of human capital, as evidenced, for example, with their higher levels of qualifications (Hopkins and Dawson, 2016; Wadsworth, 2015). It is argued that these disadvantages weaken the labour market power of A8 migrants (Vershinina et al., 2011), providing them with an incentive to exert more work effort. Firstly, recent migrants have limited labour market information about the host country; while, on the demand side, UK employers are unaware of the value of migrant characteristics, such as education and other work-related characteristics, if obtained outside of the UK (Clark and Drinkwater, 2008). This latter factor has also been found in workplace studies (Hopkins et al., 2016), and is the result of both the diversity of qualifications across eight different educational systems, and also the lack of information provided to businesses because of the initially low predictions of the number of additional migrants that would enter into the UK (as also found in Hopkins, 2017). Secondly, many recent migrants possess low levels of English language proficiency which will hinder their labour market outcomes, as these migrants are unable to obtain employment that adequately reflects their particular skills.² In this view, language skills are seen as complementary to job-related skills and both are needed in order to match workers with jobs that reflect their skill set (Dustmann et al., 2013; Eckstein and Weiss, 2004). A particular consequence of these disadvantages and information asymmetries is that migrants are unable to signal ex ante (i.e. when applying for a job) their underlying productivity to employers through the traditional channels, such as education (Spence, 1973) and labour market experience. As such, this article argues that recent migrants have an incentive to find new ex post (i.e. after being employed) methods of signalling productivity to employers in order to progress from low-skilled, low-paying roles and into employment positions that better reflect their skill sets. In this view, migrant workers signal productivity through a stronger work ethic and, within the context of this study, through lower absenteeism. This signalling of effort will be over and above that required to signal underlying productivity when UK employers are fully informed about migrant characteristics.

According to the migrant assimilation model pioneered by Chiswick (1978), the employment outcomes of migrants (e.g. their earnings from work) will converge with those of natives as migrants acquire language skills, labour market information and skills specific to industries in the host nation over the years following arrival. In line with the predictions of the ‘assimilationist’ model, this article also assesses the assimilation of the migrant work ethic. In short, if a longer residency in the UK improves the employment outcomes of migrants, then these migrants will no longer have an incentive to signal productivity through behaviours associated with a stronger work ethic.

Background

The current UK context following the A8 EU expansion of 2004 makes the UK a suitable arena for the study of the links between migration and perceived work ethic (Anderson, 2010). The issue of migrant labour has become particularly important in the UK following the A8 expansion of 2004, where eight Central and Eastern European (CEE) nations joined an expanded EU (Ciupijus, 2011). These countries are the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia. Whereas other member states imposed restrictions of up to seven years, Sweden, the Republic of Ireland and the UK were the only three EU member states to allow full access to workers from the A8 nations to work without restriction. The UK government’s decision was influenced by an original predicted figure of increased migration as a result of the expansion of between 8000 and 13,000 (Dustmann et al., 2003), and as such the only requirement for A8 migrants to take work in the UK was to register on the Worker Registration Scheme (WRS). By the time this scheme was closed in April 2011, seven years since the A8 expansion, over a million people had registered. Clark and Drinkwater (2008) show that these changes saw the proportion of the total number of migrants and immigrants to the UK from the A8 countries rise from 4.1 per cent of the total in 2000–2003 to 36.5 per cent of the total in 2004–2007. While there are different reasons to migrate, as examined by Eade et al. (2007) and Hopkins et al. (2016), in-depth qualitative studies have found that a recurring story among A8 migrants is of highly qualified people taking lower-skilled roles. These migrants are found in sectors such as hospitality (Alberti, 2014; McDowell et al., 2008) and manufacturing (Hopkins et al., 2016; Tannock, 2013), particularly in roles where interaction with customers is not required (for example, in distribution warehouses or back of house roles in hospitality). A notable feature of these sectors is the use of deskilled work practices, which demotes the importance of English language proficiency. A further recurring theme is that of managers comparing these migrant workers to those from the UK using the term ‘work ethic’ as a differentiator, which we now examine in greater detail.

Theoretical framework and empirical literature

One of the key themes that has emerged from workplace studies of migrant workers is a preference among managers for A8 migrant workers over native workers (see, for example, MacKenzie and Forde, 2009; Tannock, 2013). One explanation for this may be the opportunity to pay lower wages to migrant workers. However, in the context of minimum wage legislation that limits this opportunity, managers cite the stronger migrant work ethic as a key reason for choosing migrants over native workers. But what is this work ethic? Managers repeatedly pointed to low absence in particular, with Hopkins’s (2014) study of absence management finding that managers consider A8 migrants to take less sickness absence than their UK colleagues. Hopkins (2014) also examined the influences of factors other than ill health on absence, following Edwards and Scullion’s (1982) view that absence must also be considered as a response to managerial control. Qualitative evidence from workplace studies, such as those of MacKenzie and Forde (2009) and Tannock (2013), reveals that managers propose a link between this work ethic and migration and, as a result, they prefer migrant to native workers. Matthews and Ruhs (2007) suggest that in lower-skilled roles employers will actually prefer a ‘good work ethic’ over more recognizable qualifications or skills. This creates complex hierarchies among potential recruits, where ‘workers are often – and in some cases primarily – distinguished and recruited on the basis of their nationality’ (Matthews and Ruhs, 2007: 29). These findings in the UK match with previous research in other countries; for example, that of Chiswick (1978) and Waldinger and Lichter (2003), who find a preference for migrant workers among US managers. Waldinger and Lichter (2003: 176) find that managers reported that they preferred Latino migrants as they ‘liked to work’, while African Americans were reported to be too ‘Americanized’ and thus more likely to demand higher wages and better conditions.

Within the context of this article, it is argued that the stronger migrant work ethic is directly linked to migrant labour market power and, in particular, low levels of English language proficiency and issues around the portability of qualifications. This is despite the higher levels of human capital among this group, as evidenced by higher levels of qualifications (Hopkins and Dawson, 2016; Wadsworth, 2015). Firstly, migrants may endeavour to negate these issues by being more compliant to the demands of employers, which is often termed by these employers as the ‘migrant work ethic’ (see, for example, MacKenzie and Forde, 2009). Secondly, a particular consequence of lower migrant labour market power is that migrants are forced to supply labour in low-paying, low-skilled employment positions, which do not adequately reflect their particular skills (McCollum and Findlay, 2015). More specifically, Clark and Drinkwater (2008) find that recent migrants from the A8 countries have the lowest returns for their skills, and relate this to the issue of English language proficiency. Related to this, Dustmann and Faber (2005) find that language proficiency is lowest among those groups that have the largest disadvantages in the labour market. In conjunction with language proficiency, Friedberg (2000) argues that another reason for this poor ability to obtain higher skilled roles is a lack of portability of skills and qualifications between countries, with managers unaware of the value of these if they are earned outside the host nation (see also Clark and Drinkwater, 2008; Dustmann and Faber, 2005; Dustmann et al., 2013; Eckstein and Weiss, 2004). Although this would also be the case for migrant workers from other EU nations, workplace studies have confirmed the diversity of qualifications across the eight new accession states as contributing to the lack of portability (Hopkins, 2017). This is a contributory factor in the majority of recent migrant workers in the UK taking low-skill jobs (Alberti, 2014), despite their relatively high levels of formal education (Hopkins and Dawson, 2016). Wadsworth (2015) finds that the immigrant workforce in the UK is better qualified than the native workforce; for example, 46 per cent of the UK-born workforce left school aged 16 or younger, compared to 8 per cent of the A8 migrant workforce.

Within this context, a stronger migrant work ethic, for example through lower work absence, enables migrants to signal their underlying productivity to employers. While signalling usually takes place ex ante (e.g. Spence, 1973), as UK employers are not fully informed of the value of migrant qualifications (Clark and Drinkwater, 2008; Friedberg, 2000) then migrants will have to find an alternative way of demonstrating their skills and commitment ex post, and signalling this through, in particular, lower levels of absence. As a comparison, Bradley et al. (2014) show that those on a probationary contract will demonstrate a superior work ethic through lower absence in an attempt to increase their chances of gaining a permanent contract. This signalling can therefore be seen as an attempt by individuals to overcome asymmetries of information and, for high-productivity migrant workers, to demonstrate to employers that they truly are more productive in order to be reallocated into more highly skilled roles which adequately reflect their particular skill sets.

While recent A8 migrants are likely to face disadvantages in the UK labour market relative to comparable natives, the pioneering work of Chiswick (1978) showed that, although immigrants earn less than natives when first arriving, there was equality of earnings for immigrants in the US 10–15 years following their arrival (although this varied across ethnicities, with Mexican-born immigrants performing less well). The ‘assimilationist’ interpretation of this finding is that, after arrival, migrants will accumulate language skills, labour market information, and other skills specific to industries in the host country. Moreover, employers may have greater information concerning the work-related characteristics of these migrant workers. The accumulation of these skills is expected to increase the labour market power of migrants, leading to better employment prospects and to the assimilation of migrant wages. Empirical evidence consistent with this assimilation is provided by Clark and Lindley (2009) and Dickens and McKnight (2008). As this labour market assimilation process occurs, migrants will no longer have an incentive to signal productivity through additional effort and therefore their reliance on signalling through, for example, lower absence, will lessen.

Data and descriptive statistics

Data source and sample

In order to study the A8 migrant work ethic, data drawn from the October to December rounds (fourth quarter) of the QLFS for the years 2005–2015 are utilized. The QLFS is particularly rich in information concerning working hours and absence from work and this information can be used to construct absence measures that proxy the work ethic of individuals working in the UK. The QLFS has a rotating panel structure, where each household member of the sample is interviewed for five consecutive quarters/waves. Wave five responses are excluded from the final sample to avoid duplicate observations for respondents that were observed in their first wave in the previous year.³ The sample is also further restricted to employees that are either UK nationals or A8 migrants. In the latter group, only migrants that arrived in the UK in or after 2004 are included, in order to specifically study the wave of migration from the A8 countries after the enlargement of the EU in that year. Workers that are full-time students, those under 16 years of age or above the state pension age (64 for men, 59 for women) and those that report over 90 usual weekly hours (to remove extreme and/or invalid information) are also excluded. Finally, as the QLFS allows interviewers to collect information by proxy (i.e. from another related adult in the household), we exclude these responses from our analysis owing to potential measurement error.

In the estimations, four work absence measures are considered as dependent variables: (1) the sickness absence probability; (2) the sickness absence rate; (3) the overall absence probability; and (4) the overall absence rate. To construct these measures, the procedure outlined in Barmby et al. (2004) has been followed. First, an absence rate is calculated for each individual in the sample as follows: let UH_i denote the usual hours the employee i works in a week, excluding any overtime work. This is assumed to correspond to the hours the individual is contracted to work. AH_i denotes the actual hours the same employee worked in the reference week of the survey, again excluding any overtime. Those respondents who reported working fewer hours than usual during the reference week were asked a follow-up question regarding the reason for this. The exact wording of the QLFS question is as follows:

What was the main reason that you did fewer hours than usual/were away from work in the week ending Sunday the …?

1. Number of hours worked/overtime varies

2. Bank holiday

3. Maternity or paternity leave

4. Parental leave

5. Other leave/holiday

6. Sick or injured

7. Attending a training course away from own workplace

8. Started new job/changed jobs

9. Ended job and did not start new one that week

10. Laid off/short time/work interrupted by bad weather

11. Laid off/short time/work interrupted by labour dispute at own workplace

12. Laid off/short time/work interrupted by economic and other causes

13. Other personal/family reasons

14. Other reasons

(Source: QLFS questionnaire, 2012)

A dummy variable $s_{i}^{j}$ is then created, which takes the value of ‘1’ if the individual’s response was j = sick or injured (option 6) in the question above, and 0 otherwise. For the case of overall absence, $s_{i}^{j}$ = 1 if the individual’s response was j = sick or injured (option 6), or other personal/family reasons (option 13), or other reasons (option 14), and 0 otherwise.⁴

By using all the above variables, the sickness or overall absence rate, $R_{i}^{j}$ , for each individual i is constructed as follows:

R_{i}^{j} = \frac{(U H_{i} - A H_{i}) s_{i}^{j}}{A H_{i} (1 - s_{i}^{j}) + U H_{i} s_{i}^{j}}

(1)

where 0 ⩽ $R_{i}^{j}$ ⩽ 1 for each i. This variable measures the proportion of weekly hours lost owing to the reasons mentioned above and is the sickness absence rate or overall absence rate, depending on how $s_{i}^{j}$ is calculated. By using this rate, we can also construct our sickness and overall absence probability measures. These are discrete variables taking the value of ‘1’ if the respective absence rate is positive (and 0 otherwise) and they effectively measure the incidence of at least one hour of absence in the reference week.

In the multiple regression analysis, linear models for the four dependent variables are estimated to investigate whether A8 migrants record more or less absence from work than UK nationals.⁵ As well as including in the model a dummy variable indicating whether the individual is an A8 migrant, an interaction of this with a variable that measures the number of years an A8 migrant has resided in the UK since migration is also included. The coefficient of the A8 dummy, therefore, measures the absence differential between a UK national and an A8 migrant who arrived in the UK in the same year as the one he/she is observed in the QLFS, while the coefficient of the interaction term measures the rate of absence assimilation as residency in the UK lengthens for A8 migrants.⁶

In order to account for the heterogeneity in both personal and labour market circumstances between the A8 migrants and UK nationals, a standard barrage of control variables is included in the regression models. Basic demographic variables include gender, age (and its square), education (in years),⁷ marital status, number of dependent children under 16 years old, and age of the youngest dependent child. Health status, an important variable in all work absence studies (see, for example, Leigh, 1991), is also included and is captured by two dummies indicating (1) whether the respondent suffers from a long-term health problem, and (2) if that problem affects the amount of work for the employee. A series of region of residence and year dummies are also included to control for regional variations in weather conditions and other relevant variations by place and time. Finally, housing tenure and receipt of any state benefits or tax credits are included in the models in order to capture access to the welfare state.⁸ These are important controls since A8 migrants were not eligible for tax credits before registering with the WRS, while they also could not claim any income-related benefits before having worked continuously for one year (Dustmann et al., 2010: 6). This limited access to the welfare state is, in turn, expected to affect migrant work effort (see also Hansen and Lofstrom, 2011). To account for possible differential effects of benefits receipt on UK nationals and A8 migrants, an interaction term is added in the models.

Labour market heterogeneity is captured through a series of control variables including: usual basic weekly hours worked; paid and unpaid overtime hours; whether the employee works in the public sector; has a second job; a permanent contract; a managerial or supervisory status; whether the employee works at home (or in the same building as his/her home); tenure with current employer; establishment size (see, for example, Barmby and Stephan, 2000); trade union status (see, for example, Allen, 1984); and flexible working arrangements (see, for example, Heywood and Miller, 2015). A series of occupational and industry dummies are also included in the models.⁹ Finally, since job dissatisfaction is a much studied variable in the work absence literature (see Steers and Rhodes, 1978), it is proxied here by the following variables: (1) a variable that captures dissatisfaction with current working hours (‘Fewer hours desired’); (2) a dummy indicating whether the employee is looking for an extra job; and (3) a dummy taking the value of ‘1’ if the respondent is looking for a new job.

All the above variables are included in the final models, with their corresponding sample means available in Table A1 of Appendix 1. A final sample of 113,804 observations is obtained after dropping individuals with missing observations for any of the dependent or independent variables. A total of 112,408 of these (98.8% of the total) correspond to UK nationals and 1396 (1.2%) to A8 migrants. The average UK residency of A8 migrants in the final sample is approximately 3.1 years. The full distribution of migrant UK residency is presented in Figure 1.

Figure 1.

Distribution of A8 migrants by years of migrant residency in the UK.

Descriptive evidence

Before presenting the ordinary least squares (OLS) coefficient estimates, the raw differences in work absence between the native and migrant samples are briefly considered. Table 1 presents the relevant sample means. Crucially, A8 migrants are less likely to be absent from work and also record lower levels of absence than UK nationals. For the difference in means between the groups t-tests are performed; they are highly significant, confirming the differences in each case. These differences are not small; in particular, all absence sample means are 70–90 per cent higher for UK nationals, providing prima facie evidence in favour of a better work ethic among A8 migrant workers.

Table 1.

Absence from work by nationality.

Variable	UK nationals	A8 migrants
Sickness absence probability	0.043	0.024^a
Sickness absence rate	0.031	0.018^a
Overall absence probability	0.053	0.028^a
Overall absence rate	0.034	0.019^a

Observations	112,408	1396

Notes: Numbers in the table are sample means; ^at-test significant at 1%.

Source: UK QLFS 2005–2012.

Table A1 in Appendix 1 also shows that A8 migrants have on average two more years of education than their UK national counterparts. Migrants are also younger, healthier, more likely to work in a temporary full-time job, less likely to have managerial or supervisory duties in their job, less likely to be unionized and have on average a shorter tenure with their current employer than UK nationals. Consistent with the literature on the disadvantages faced by A8 migrants in the UK labour market, A8 migrants face a substantial hourly wage penalty relative to natives and their work is heavily concentrated in low-skilled occupations. Around 63 per cent of A8 migrants work as plant and machine operatives or in elementary occupations, while the corresponding percentage for UK nationals is only around 16 per cent. A8 migrants also exhibit a lower amount of dissatisfaction with current working hours, although they work substantially more hours (basic and paid overtime) than UK nationals.

Regression results

A8 migrants and work ethic

In view of the above differences in personal and job characteristics between the two groups, regression analysis is employed in order to control for these differences and therefore to compare the work ethic of observationally similar UK national and A8 migrant workers. Table 2 presents the estimates. As the main focus of the article involves the comparison of absence levels between natives and migrants and, furthermore, the assimilation of these levels as UK residency increases, only these core results are presented in the table.

Table 2.

A8 migrants and work absence – regression results.

	(1)	(2)	(3)	(4)
	Sickness absence probability	Sickness absence rate	Overall absence probability	Overall absence rate
A8	–0.0333^a	–0.0211^a	–0.0354^a	–0.0202^a
	[0.0058]	[0.0045]	[0.0069]	[0.0049]
A8*(years in UK)	0.0080^a	0.0063^a	0.0072^a	0.0059^a
	[0.0021]	[0.0018]	[0.0022]	[0.0018]

Control variables	Yes	Yes	Yes	Yes

Notes: Sample size for all models is 113,804 observations; OLS estimates; Huber–White (robust) standard errors in brackets; all models include controls for gender, age, age squared, marital status, health, education, number of dependent children, age of youngest dependent child, usual basic hours of work, paid and unpaid overtime hours, public sector, permanent contract, holding a second job, working from home, managerial status, looking for new job, looking for extra job, number of fewer working hours desired, job tenure, establishment size, trade union status, flexible working arrangements, housing tenure, claiming any benefits and its interaction with A8 migrant status, industry, occupation, region of residence and survey year; full results are available from the authors upon request. ^aSignificant at 1%.

Source: UK QLFS 2005–2012.

A common pattern can be observed in all the estimated models in Table 2, confirming the conclusions drawn from the descriptive analysis presented above: A8 migrants record substantially lower absence incidence and rates than natives. In particular, the A8 migrant sickness absence probability is 3.3 percentage points smaller than for an observationally similar UK national. Relative to the mean level of a UK national’s sickness absence probability (4.3%), this difference corresponds to a more than three times lower probability of absence for A8 migrants. Substantial (and of similar magnitude) differences are also estimated for the rest of the absence measures.

However, as mentioned above, owing to the presence of the interaction of the A8 dummy with the variable measuring the length of residence in the UK, the above differences correspond to the comparison of a UK national with an A8 migrant that arrived in the UK in the same year as the one observed in the QLFS. Evidence in favour of an assimilation pattern in work ethic is, thus, given by the positive and statistically significant coefficient of the interaction term in all estimated models. Specifically, the results indicate that the UK-A8 absence differential reduces in size with length of residency in the UK and becomes statistically insignificant after three or four years of residence, depending on the model. A similar pattern is observed in Figure 2, which plots the relevant coefficients and the corresponding confidence intervals from regression models with a more flexible functional form, where separate coefficients are estimated for A8 migrants for each year of length of residency observed in the data.¹⁰ The results here indicate that after two to four years in the UK, A8 migrants record similar levels of sickness and overall absence to observationally equivalent UK nationals.

Figure 2.

A8-UK absence gaps by years of migrant residency in the UK.

Work ethic and labour market power

The migrant assimilation model suggests that migrants acquire UK-specific skills over the years following arrival, leading to the assimilation of migrant employment prospects and wages. While the data presented in Table A1 in Appendix 1 reveal that A8 migrants do indeed face a substantial wage penalty relative to natives despite their higher levels of education (reflecting their lower levels of labour market power), do the labour market outcomes of A8 migrants assimilate and, thus, help also explain the assimilation of the migrant work ethic? In addressing this question, the wage assimilation of migrants is now examined. It should be noted here that earnings are not the only indicator of migrants’ labour market assimilation. Previous studies (Chiswick et al., 1997; Clark and Lindley, 2009) have investigated the assimilation of migrants’ ability to find employment relative to natives. Wages are, however, likely to give a more complete picture, as earnings capture the quality of employment found by migrant workers.

Examining the wage assimilation of A8 migrants using the baseline sample presents, however, important technical difficulties. Owing to the availability of earnings information only for employees that are observed in their first (and fifth, which has been excluded from the analysis) wave in the QLFS, only around 25 per cent of the cases in the baseline sample (about 29,000 observations) can be used for the study of wages. This seriously reduces the number of A8 migrants observed with earnings information (425 observations). For this reason, two relatively parsimonious model specifications are estimated in order to examine the wage assimilation of A8 migrants. The migrant variables are constructed now by splitting the A8 sample in only two groups: those with up to three years of residency in the UK and those with four to eight years of residency. This is done to ensure that each group consists of a sufficient number of A8 migrants’ observations for the A8-UK wage gap to be precisely estimated. The dependent variable in both specifications is the log of the real hourly wage. In the first specification, the unadjusted wage gaps are estimated, including no further controls in the model apart from the two migrant dummies. However, since wages are determined by a number of factors that may themselves be correlated with migrant status or years of residency in the UK, the results from a multiple regression model of assimilation are also presented. This latter model assumes wages are determined by the following influences: gender, current age in quadratic form, marital status, education, health, and a set of survey year and region of residence dummy variables. Job characteristics such as industry and occupation are not included as these will capture part of the process through which A8 migrants increase their earnings.¹¹

Table 3 presents the results. It shows that newly arrived migrants (0–3 years in the UK) face a substantial wage penalty when compared to UK natives. Importantly, there is also some evidence of a reduction in this penalty, since the coefficient for migrants with longer UK residency (4–8 years) is smaller in (absolute) size, and significantly so. This result holds for both specifications.

Table 3.

Wage regression results.

	(1)	(2)
A8 0–3 years in UK	–0.5213^a	–0.5633^a
	[0.0294]	[0.0321]
A8 4–8 years in UK	–0.4376^a	–0.4812^a
	[0.0242]	[0.0249]

Control variables	No	Yes
F-test for equality of A8 dummies (p-value)	0.0266	0.0400

Notes: Sample size for all models is 28,521 observations; dependent variable in both models is the log of the real hourly wage; OLS estimates; Huber–White (robust) standard errors are in brackets; controls include: female, age, age squared, education, marital status, health, survey year and region of residence dummies; full results are available from the authors upon request. ^aSignificant at 1%.

Source: UK QLFS 2005–2012.

While A8 migrants’ wages may not have fully assimilated to those of UK natives, these migrants may also have assimilated in other unobservable ways which may help to explain the assimilation of the migrant work ethic. Firstly, migrants may culturally assimilate into their host nation, by adopting social norms and subsequently behaving more like natives. For example, the general manager at MacKenzie and Forde’s (2009: 150) workplace study of ‘Glassfix’ stated that ‘There was a tail off in the Balkan staff, then all of a sudden there was an influx of Poles … 50 percent of the accession nationalities are Poles. They are very good’, with the managing director agreeing that ‘they have a good work ethic’. However, as the Balkan staff had been in the UK for a longer period of time, the managing director of ‘Glassfix’ found that ‘The Balkan workers were sharper when it came to money and benefits, a lot of the Kosovans had been around a long time, they were more “Westernized”. They knew about benefits, knew it was better to draw off benefits – like the English.’ In addition, Waldinger and Lichter (2003: 176) found that US managers reported that they felt African Americans were too ‘Americanized’, and thus more likely to demand higher wages and better conditions (see also Chiswick, 1978).

Secondly, migrant wage expectations may assimilate, consistent with the dual labour market hypothesis and the migrant ‘frame of reference’ (Piore, 1979). More specifically, suppose that everyone initially sets reservation wages with the belief that they will most likely earn the average of those with the same educational background and other observable characteristics. However, when migrants are added to the UK labour supply, their ‘frame of reference’ is usually the labour market in their home countries, meaning that their wage expectations are significantly lower than those of native workers. So, while recent A8 migrants face a substantial wage penalty relative to natives, wages may still remain significantly above expectations and may therefore be associated with higher worker effort according to a ‘fair wage-effort’ hypothesis (Akerlof and Yellen, 1990), which states that workers should withdraw effort if their actual wage falls short of what they consider a fair wage. This is especially likely for the most recent of A8 migrant workers who are less embedded in the UK labour market. Accordingly, as migrants’ experience and knowledge of the UK labour market increase, their ‘frame of reference’ is likely to shift and, as such, wage expectations will adjust accordingly.

Discussion and conclusions

The UK has experienced unprecedented increases in migration in recent years, primarily associated with the entry of the A8 accession countries into the EU. These migrant workers arriving in the UK have been praised by employers as having a stronger work ethic than native workers. Using worker absence data from the QLFS for the period 2005–2012, and considering migrants from the recent 2004 A8 expansion, this article provides quantitative evidence that substantiates these perceptions of this distinctive migrant work ethic. The key finding of this article is that A8 migrants record around three times lower worker absence than natives in their first year of residency in the UK. What is a particularly interesting result is that it takes migrants between two and four years to eradicate this difference.

It has been argued throughout this study that this migrant work ethic is a result of the lower levels of labour market power faced by this recent group of migrants, despite their higher levels of human capital, as evidenced through their higher qualification levels than UK natives (Wadsworth, 2015). In particular, low levels of English language proficiency see migrants moving into lower-skilled roles that are not customer facing, frequently on non-standard contracts and crowded into a small range of sectors (Hopkins and Dawson, 2016). It is this low labour market power that provides the incentive for migrant workers to exert increased effort through reduced absence, in order to signal to employers their underlying productivity. In line with this view and that of the traditional migrant assimilation model, it was proposed that the observed assimilation of the migrant work ethic would reflect the increasing levels of labour market power and employment prospects of more embedded migrants. Consistent with this, the modelling of the wage assimilation of A8 migrants suggested that these migrant workers face a substantial wage penalty relative to natives when first arriving in the UK, despite their higher levels of education. Importantly, this wage penalty was found to decline in magnitude as migrant residency in the UK increased. However, while A8 absence levels were found to fully assimilate to those of natives, wages do not. Migrants, thus, may have assimilated in other ways, particularly through adopting the social norms of natives or via converging wage expectations. It is left for future work to analyse whether A8 migrants do eventually assimilate in terms of their labour market outcomes, given a longer time frame.

While the evidence in this study points to the assimilation of migrant effort levels, there are, however, several limitations of the data that should be noted. Principally, the use of cross-sectional data in studies of migrant wage assimilation has been criticized owing to both cohort effects and the attrition of ‘below-average’ migrants (Borjas, 1985). The former criticism reflects the changing quality of migrants into the host nation. If, for example, there was an improvement in the quality of A8 migrants entering the UK over time, we may expect this to impact upon work effort levels observed in the sample. It does, however, seem unlikely that cohort effects are important within the context of this study, since only a short wave of migration is investigated. Furthermore, if ‘below-average’ A8 migrants are the first to return home, it is not obvious how this migrant attrition may bias the absence results. On the one hand, ‘better than average’ migrants may have more perseverance, which is likely to be associated with reduced absenteeism. On the other hand, ‘better than average’ migrants may be those that are more able to adapt their skill sets to the UK labour market and, therefore, will have a weaker incentive to signal to employers their underlying productivity through reduced absenteeism. Only with the use of appropriate longitudinal data, which can track effort convergence for particular migrants, could these methodological issues be addressed.¹²

If the estimates presented above are taken at face value, then our findings are in line with qualitative evidence based upon managers’ perceptions of the migrant work ethic. If managers use this information upon which to base their hiring decisions, then this may have negative implications for unusually productive native workers. These conditions seem likely to hold particularly in low-skilled, low-paying industries where employers regularly use deskilled work practices which demote the importance of English language proficiency. The raw data presented within this study (see Table A1 in Appendix 1) report a large concentration of A8 migrants within elementary occupations, and therefore these migrants will be more likely to operate in roles where employers have a preference for a ‘good work ethic’ over more recognizable qualifications or skills (Matthews and Ruhs, 2007). While managers may distinguish among workers on the basis of readily available information such as nationality, the assimilation of the migrant work ethic, however, will lead employers to switch away from more established migrants towards newer groups. Indeed, findings from qualitative investigations of A8 migrant workers in the UK report managers stating that ‘We are looking forward to the next round of accession states’ and ‘We’ll be picking up new nationalities’ (MacKenzie and Forde, 2009: 149), showing that this perception of work ethic among different nationalities affects organizations’ recruitment behaviours (MacKenzie and Forde, 2009; Tannock, 2013). These findings imply that this form of ‘statistical discrimination’ may have adverse effects for native workers in the short run, but, importantly, this may only be a short run effect.

Footnotes

Appendix 1

Table A1.

Sample means for all variables by nationality.

Variable	UK nationals	A8 migrants
Demographic characteristics
Female	0.535	0.451^a
Age	42.526	31.112^a
Education (years)	11.905	14.044^a
Long-term health problem	0.262	0.092^a
Long-term health problem limits work	0.059	0.029^a
Married or cohabiting	0.687	0.633^a
Number of dependent children <16 years old	0.592	0.473^a
Dummy variables for age of youngest dependent child:
1. 0–2 years	0.082	0.146^a
2. 3–4 years	0.045	0.044
3. 5–9 years	0.101	0.083^b
4. 10–15 years	0.133	0.061^a
5. 16–18 years	0.046	0.014^a
6. No child	0.592	0.652^a
Job characteristics
Dummy variables for usual basic hours:
1. 1–15	0.058	0.015^a
2. 16–29	0.178	0.099^a
3. 30–35	0.159	0.082^a
4. 36–40	0.489	0.620^a
5. 41–48	0.072	0.134^a
6. 48+	0.043	0.050
Public sector	0.326	0.055^a
Paid overtime hours	1.143	2.384^a
Unpaid overtime hours	1.792	0.214^a
Holding second job	0.043	0.026^a
Working from home or same building	0.061	0.021^a
Permanent job	0.962	0.892^a
Manager/foreman/supervisor	0.419	0.150^a
Fewer hours desired (no. of hours)	1.049	0.261^a
Looking for new job	0.056	0.090^a
Looking for extra job	0.008	0.015^a
Dummy variables for establishment size:
1. Size 1–24	0.324	0.255^a
2. Size 25–49	0.137	0.135
3. Size 50–499	0.344	0.465^a
4. Size 500+	0.195	0.145^a
Dummy variables for one-digit occupation:
1. Managers and senior officials	0.156	0.028^a
2. Professionals	0.161	0.039^a
3. Asst professional and technical	0.157	0.042^a
4. Administrative and secretarial	0.142	0.051^a
5. Skilled trades	0.070	0.117^a
6. Personal services	0.090	0.068^a
7. Sales and customer services	0.068	0.030^a
8. Plant and machine operatives	0.062	0.226^a
9. Elementary	0.094	0.400^a
Tenure dummies:
1. 0–3 months	0.036	0.091^a
2. 3–6 months	0.034	0.109^a
3. 6–12 months	0.052	0.138^a
4. 12–24 months	0.091	0.218^a
5. 24–60 months	0.211	0.359^a
6. 60+ months	0.575	0.085^a
Dummy variables for industries:
1. Agriculture and fishing	0.006	0.024^a
2. Energy and water	0.016	0.011
3. Manufacturing	0.127	0.297^a
4. Construction	0.049	0.047
5. Distribution, hotels and restaurants	0.160	0.261^a
6. Transport and communications	0.076	0.112^a
7. Banking, finance and insurance	0.154	0.112^a
8. Public admin., education and health	0.368	0.105^a
9. Other services	0.044	0.031^b
Dummy variables for trade union status:
1. Covered member	0.242	0.076^a
2. Covered non-member	0.123	0.100^a
3. Not covered member	0.085	0.031^a
4. Not covered non-member	0.549	0.794^a
Dummy variables for flexible working arrangements:
1. Flexitime	0.140	0.045^a
2. Annualized hours contract	0.049	0.029^a
3. Term time working	0.054	0.001^a
4. Other flexible arrangement	0.028	0.014^a
5. No flexible arrangement	0.073	0.910^a
Hourly wage
Real hourly wage	11.806	6.627^a
Log of real hourly wage	2.303	1.816^a
Housing tenure and benefits
1. Outright owner	0.172	0.007^a
2. Owned with mortgage	0.621	0.080^a
3. Private renter	0.114	0.812^a
4. Social housing	0.093	0.101
Claims any benefits	0.306	0.251^a
Observations	112,408	1,396

Notes: Numbers in the table are sample means; the total sample size is 113,804 observations; the sample size for average wage calculations is 28,521; ^aSample mean difference significant at 1%; ^bat 5%.

Source: UK QLFS 2005–2012.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

Chris Dawson is currently Associate Professor in Business Economics at the School of Management, University of Bath. He is a labour economist, with a focus on the causes and consequences of atypical employment. He has also undertaken research funded by the Low Pay Commission.

Michail Veliziotis is currently a Lecturer in Human Resource Management within the Southampton Business School at the University of Southampton. His areas of expertise include employment studies, employee relations and applied statistics. His articles have appeared in journals such as the Economic and Industrial Democracy, European Journal of Industrial Relations and The International Journal of HRM. He has also undertaken research funded by the Low Pay Commission and the Joseph Rowntree Foundation.

Benjamin Hopkins is Lecturer in Work and Employment at the University of Leicester. His research interests include non-permanent work, migration, and the causes and consequences of wage inequality.

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