Data

The main dataset is the special license version of the UK Labour Force Survey (LFS) from 2003 to 2013. The special license version of the LFS is only available since 2003. The sample is limited to employed individuals between 20 and 59 years of age. The information on country of birth and location is used to construct an indicator of the immigrant (i.e. foreign-born) share of the population by local authority.

The ISCO-88 classification and the General Index for Job Demands in Occupations constructed by Kroll (2011) is used to create a variable (1 to 10 metric) for the average physical burden of a given job. The factors determining the physical burden of a job include considerations such as having to lift and/or carry heavy loads, bend, kneel or lie down, working in the presence of smoke, dust, gases, vapours, working in cold, heat, or wet conditions, etc. We also created two indicators for jobs with high physical burden (above median) and very high physical burden (highest quartile). Workers are also classified according to occupations (1-digit) and blue- and white-collar status following standard OECD classifications.

The special license of the LFS is combined with the standard version to measure work-related risks. There is no information on work-related injuries in the special license of the LFS.Footnote 4 This information is available in the standard version, but this version does not include information on the individual’s local authority of residence. In order to analyse the relation between immigration and actual injury rates, we constructed a time-varying index of occupational risk based on injury rates by occupation and year. Injury rates are calculated as the share of individuals in a given occupation which reported accidents resulting in injury at work or in the course of work in the last 12 months. Those occupations with an injury rate above the median are categorised as risky. Examples of occupations with high/low physical burden and injury rate are reported in Table 12.

We also explore the impact of immigration on natives with different levels of education. Natives are divided in three educational groups. The “high education” group refers to those with a university degree or equivalent. The “medium education” group refers to those with a high school degree or equivalent, including GCE, A-level and GCSE grades A* to C. Finally, the “low education” category refers to those natives with no qualifications or qualifications below the ones included in other categories.

Descriptive statistics for the outcomes and covariates are reported in Table 1. On average, immigrants are more likely to work in jobs with a higher physical burden, but the injury rate is similar across the two groups. Immigrants are also younger than natives and more likely to be concentrated in the higher or lower educational groups.

Table 1 Descriptive statistics Full size table

We also present evidence exploiting retrospective information on worker’s occupational characteristics. Since 2003, the first quarter of the standard LFS collects information on respondents’ occupation in the previous year. This allows us to analyse the effects of immigration on occupational changes at the individual level. By removing any individual time invariant characteristics and following the worker wherever he/she moves, we can address the concern about the potential spillovers on other labor markets due to spatial arbitrage (Borjas 2003).

Table 2 reports immigrant-native differences in the likelihood of working in physically intensive jobs (1 to 10 metric) by gender. All estimates include standard demographic controls (a quartic in age, marital status and number of children), year and local authority fixed effects. Previous studies suggest that as immigrants are often positively selected on health, they have incentives to self-select into more strenuous jobs (Giuntella and Mazzonna 2015) and are more likely to hold risky jobs (Orrenius and Zavodny 2012). The estimates in Table 2 support this dynamic. Immigrants are significantly more likely to hold jobs characterised by higher physical burden (column 1). With respect to the mean, immigrants are 11% more likely to hold jobs in the upper quartile of the physical burden index distribution (physical burden> 7, see column 3). The coefficients are smaller, but the differences remain significant when controlling for socio-demographic characteristics (columns 2 and 4). With respect to the mean, immigrants are 5% more likely to hold high physical burden jobs than natives with similar characteristics.

Table 2 Immigrant-native differences in average physical burden Full size table

The native-immigrant difference is also present for women. With respect to the mean of the dependent variable, foreign-born women are 53% more likely to be employed in physically high-intensive occupations. However, it is worth noting that in general, women are less likely to work in physically demanding jobs (only 12% of native women work in physically high-demanding jobs vs. 30% of native men). For this reason, in our analysis, we focus primarily on native men.

Table 3 shows differences in occupational risk and individual likelihood of experiencing an injury between natives and immigrants. The sample is smaller as the information on occupational injury rate is not available for all the occupations in every year.Footnote 5 In the first two columns, we estimate the native-immigrant difference in occupational risk (continuous variable and above median indicator). Given the higher share of immigrants in physical demanding jobs (see Table 1), it is unsurprising that we find that immigrants are 10% more likely to work in occupations with a higher injury risk (column 2). At the same time, using information on self-reported injuries, we show that immigrants are 5% less likely to report an injury (column 3) and that this result holds when we compare immigrants and natives in the same occupational category (column 4). It is possible that immigrants are less likely to officially report injuries compared to natives (Orrenius and Zavodny 2012). However, we employ self-reported data and this could mitigate this bias. A possible explanation for the lower injury rates observed by immigrants in a given occupational category is that immigrants are typically healthier than natives (Giuntella et al. 2018) and the ability to cope with physical stress and risk is a function of health capital.

Table 3 Immigrant-native differences in occupational risk and individual injuries Full size table

Empirical specification

To identify the effect of immigration on job physical burden and occupational risk, we exploit variation over time in the share of immigrants living in each local authority between 2003 and 2013. The estimated empirical model is as follows:

$$ Y_{ilt} = \alpha + \beta S_{lt} + X'_{ilt}\gamma+ Z'_{lt} \lambda + \mu_{l} +\eta_{t} +\epsilon_{ilt}, $$ (1)

where Y ilt is a metric of job physical burden or occupational risk of individual i, in local authority l at time t; S lt is the share of immigrants in local authority l at time t; X ilt is a vector of individual characteristics; Z lt is a vector of time-varying characteristics at the local authority level (share of White, Asian and Black population, share of individuals with low, medium and high education, share of female population, log of average gross income, local authority employment rate and share of individuals claiming unemployment benefits) and μ l and η t are local authority and year fixed effects, respectively; and 𝜖 ilt captures the residual variation.

Immigrants might endogenously cluster in areas with better economic conditions and have an impact on natives’ internal mobility (e.g., Borjas et al. 1996, Borjas2003). We adopt the traditional “shift share” instrumental variable approach (Altonji and Card 1991; Card 2001; Bell et al. 2013; Sá 2015) to address this endogeneity. This approach exploits the fact that immigrants tend to locate in areas with higher densities of individuals from their same country of origin.

The annual national inflow of immigrants from each country across local authorities is distributed according to the concentration of foreign-born individuals in the 1991 UK Census, reducing the bias from endogeneity.

We define F ct as the total population of immigrants from country c residing in England and Wales in year t and s cl1991 as the share of that population residing in local authority l in the year 1991. We then construct \(\hat {F}_{clt}\), the imputed population from country c in local authority l in year t, as follows:

$$ \hat{F}_{clt} = s_{cl1991}* {\Delta} F_{ct}+ F_{cl1991} $$ (2)

and the imputed total share of immigrants \(\hat {S}_{lt}\) in local authority l in year t will be

$$ \hat{S}_{lt}=\sum\limits_{c}{\hat{F}_{clt}/P_{l,1991}} $$ (3)

where P l,1991 is the total population in local authority l in 1991. Thus, the predicted number of new immigrants from a given country c in year t in local authority l is obtained by redistributing the national inflow of immigrants from country c based on the distribution of immigrants across local authorities in 1991. Adding data for all countries of origin, it is possible to obtain a measure of the predicted total immigrant inflow in each local authority and use it as an instrument for the actual share of immigrants. We consider nine foreign regions of origin: Africa, Americas and Caribbean, Bangladesh and Pakistan, India, Ireland, EU-15, Poland and other countries.

One potential threat to the validity of this approach is that the instrument cannot credibly address the resulting endogeneity problem if the local economic shocks that attracted immigrants persist over time. However, this problem is substantially mitigated by including local authority fixed effects and by controlling for time-varying characteristics at the local authority level. Thus, it is reasonable to assume that past levels of concentration of immigrants are not correlated with current unobserved local shocks that might be correlated with a job’s level of physical burden and occupational risk. In other words, the exclusion restriction holds under the assumption that—after controlling for local authority and year fixed effects, and local authority time-varying characteristics—the imputed inflow of immigrants is orthogonal to the local specific shocks and trends in labor market conditions.

We test the robustness of our results to a change in the geographical unit using a higher level of aggregation to address the concern that our results may be biased by the effects of immigration on native internal mobility (Borjas et al. 1996). We also show that our results are robust to the inclusion of local authority specific time trends. Finally, a placebo test is conducted to analyse the effects of immigration on past trends in physical burden associated with a given occupation and injury risk and find there is no evidence of significant correlations.