The impact of immigration on the labour market outcomes of New Zealanders
2. Data and Sample Characteristics
2.1 Data sources and variable definitions
This paper uses unit record data for the entire usually resident New Zealand population from the 1996, 2001 and 2006 Census.[5] The Census collects information on each individual's country of birth and their year of first arrival in New Zealand. We restrict our analysis throughout to individuals aged 25-54 with non-missing year of first arrival, if foreign-born. We focus on this age group to exclude students and individuals nearing retirement. We classify individuals as being either New Zealand-born, a recent migrant or an earlier migrant, where recent migrants are all individuals who first arrived less than five years ago and earlier migrants are all other individuals born in a foreign country.
Information is also collected about the current usual residential location of each individual. This location information is coded to the census meshblock, allowing us to identify local labour market areas (LMAs), as well as other aggregated geographic areas. Our main estimates examine competition within 140 LMAs defined in Papps and Newell (2002) using an algorithm that ensures that most people who live in a LMA work in it, and most people who work in a LMA live in it.[6] Focusing on functional local labour market areas has major advantages over using administratively defined geographic areas, as migration between LMAs is typically related to employment mobility, whereas migration within a LMA more strongly reflects residential factors. We also estimate our regression models at more aggregated geographic levels, including 75 territorial authorities (TAs), 58 LMAs, and 16 regional councils (RCs) to test the robustness of our results to the critique of the 'area-analysis' approach discussed above.[7]
2.2 Sample characteristics
Table 1 presents the demographic characteristics of the three nativity groups (recent migrants, earlier migrants, New Zealand-born) in the 1996, 2001 and 2006 Census. Our analysis population is 1.45 million individuals in the 1996 Census of which 80% are New Zealand-born, 5% are recent migrants and 15% are earlier migrants. For the 2001 Census, our analysis population is 1.51 million of which 79% are New Zealand-born, 6% are recent migrants and 16% are earlier migrants and, for the 2006 Census, it is 1.59 million of which 74% are New Zealand-born, 9% are recent migrants and 17% are earlier migrants. As in most countries, recent migrants are younger than the non-immigrant population (for example, 45% are less than 35 years of age versus 31% of the New Zealand-born in 2006). But, unlike the United States where most immigrants are low-skilled, in New Zealand, recent migrants are much more qualified than the New Zealand-born, with 38% of recent migrants in 2006 (34% in 1996; 32% in 2001) having university degrees versus 17% of the New Zealand-born (9% in 1996; 12% in 2001). This is reflected throughout the qualification distribution, with few migrants having no qualifications compared to the New Zealand-born.[8] This occurs because New Zealand operates a structured immigration system that focuses mainly on higher-skilled migrants.
Table 1: Demographic characteristics of migrants and the New Zealand born in 1996, 2001 and 2006
The source country distribution of recent immigrants is fairly stable over the fifteen-year period examined here, but there is evidence that immigrants from the British Isles, South and Central Asia, and Sub-Saharan Africa (mainly South Africa) are becoming more common and those from North-East Asia are becoming less common.[9] Comparing recent migrants to earlier migrants, we can see that this reflects an ongoing evolution of migrant source countries. The relative strength of Asian immigration in the 1990s is reflected in a rising Asian share of earlier migrants, with less pronounced growth in the Asian share of recent migrants.
Table 2 presents the distribution of qualifications for recent migrants from different regions of birth in each year (as well as, the distribution for the New Zealand-born). The regions are ordered from the most to least common source areas of recent migrants. There is a large variation in the qualification distribution for recent migrants from different sources regions. For example, in 2006, 61% of recent migrants from the Pacific Islands have at most school qualifications and only 12% have university degrees, while only 22% of recent migrants from South and Central Asia have at most school qualifications and 63% have university degrees. These differences are largely related to the different immigration categories under which individuals from different countries enter New Zealand (mainly family versus skilled migration). Immigrants from different countries also are more or less likely to settle in different places in New Zealand. As will be discussed in more detail below, this variation allows us to create supply-pull instruments for where immigrants with different skills are most likely to settle.
Table 2: Qualifications for recent migrants by region of birth in 1996, 2001 and 2006
2.3 Defining skill-groups
Throughout this paper, we classify individuals into particular skill-groups and allow for substitutability both across and within these groups by nativity. One important question that we need to address is then how to define skill-groups. As in Cohen-Goldner and Paserman (2006), we consider multiple definitions. Our first definition follows the human capital approach taken in Borjas (2003) and creates 24 age/qualification skill-groups, using the six age categories shown in Table 1, and the four non-missing qualification groups.[10] This approach is appropriate if the productivity of different individuals is determined solely by their measured human capital. One potential problem with using age and qualifications to create skill-groups is that human capital acquired in foreign countries may not translate to similar skill levels in New Zealand.
Thus, our second definition follows the methodology first used in Card (2001) and creates five predicted occupation skill-groups defined as each individual's predicted probability of working in each of the following aggregated occupation groups: 1) Legislators, Administrators, and Managers; 2) Professionals; 3) Technicians, Associate Professionals; 4) Clerks, Agriculture, Fishery and Forestry Workers, Trades Workers, and Plant and Machine Operators; and 5) Service and Sales Workers and Elementary Occupations.[11] These predicted probabilities are calculated from a multinomial logit occupational choice model estimated at the national level for each census year separately by gender for the New Zealand-born and immigrants as a function of observed characteristics, such as education, age, ethnicity, years in New Zealand and region of birth.[12]
Predicted occupations are used to group individuals rather than actual occupations for two reasons. First, an individual's actual occupation is partially determined by the demand for different occupations in particular locations and the goal is to produce skill-groups that are exogenous to local demand patterns. Second, it would not be possible to assign a skill-group to individuals who are not currently employed without strong assumptions, such as that the unemployed complete directly with other unemployed regardless to other measures of their skill. The main downside in using predicted occupations is that they add noise to our estimates in the sense that some individuals are assigned to the wrong skill-group. The distribution of nativity groups across these five predicted occupational groups is summarised in the following section, together with the actual occupational distribution.
2.4 Labour market outcomes
Table 3 presents employment rates, and the industry and occupation mix for the three nativity groups in the 1996, 2001 and 2006 Census. Employment rates are much lower among recent migrants compared to both earlier migrants and the New Zealand-born, confirming earlier findings by Winkelmann and Winkelmann (1998) and Boyd (2006). Only 55% of recent migrants were employed in 1996 compared with 76% of earlier migrants and 78% of the New Zealand-born. This gap narrowed by 2006, with 73% of recent migrants employed versus 79% of earlier migrants and 82% of the New Zealand-born. Employed migrants and non-migrants work in similar occupations and industries (at a highly aggregated level). The only meaningful differences are that migrants are more likely to be in the Professional occupation and the Business and Property Services and the Accommodation, Cafes and Restaurants industries and are less likely to be in Agriculture, Fishery, or Forestry (occupation or industry) and other blue-collar professions (eg. Trades and Plant and Machine Operators) and industries (eg. Construction).
Table 3: Employment characteristics of migrants and the New Zealand born in 1996, 2001 and 2006
Table 3 also summarises the distribution of predicted occupations for each of the nativity groups, as defined in the previous section. These occupation-related skill groups are defined for all individuals, not only those who are employed at the time of the census. As expected from the different age, qualification, and other characteristics of the groups, the nativity groups have differing predicted occupation profiles. Recent and earlier migrants are much more likely to be in the Professional and Technicians/Associated Professional predicted occupational groups and less likely to be in the Clerks/Agricultural/Trades/Operator predicted occupational groups than the New Zealand-born. This is true in all three census years even though relatively more New Zealand-born individuals are in the Professional predicted occupational group in 2001 and 2006. Little differences in found in the proportion of each nativity group predicted to be employed in the three other occupational groupings.
Unfortunately, the Census does not directly collect wage data. However, it does collect (bracketed) total annual income on an individual basis. Since one of the goals of this paper is to estimate the impact of immigration on wages for different nativity-groups, we use a secondary dataset to impute wages for all employed individuals in the Census. The Income Survey (IS) has been run annually since 1997 as a supplemental questionnaire to the Household Labour Force Survey (HLFS) and directly collects wage data, as well as comprehensive demographic characteristics and total annual income using a question identical to the one in the Census (including having the exact same brackets). We run separate regressions stratified by gender and country of birth (New Zealand-born versus immigrant) using the 1997, 2001 and 2006 Income Surveys, where the dependent variable is each employed individual's log real hourly wage and the independent variables include indicator variables for each individual's total annual income bracket, their age, age-squared, ethnicity, marital status, qualifications, hours worked in the past week, occupation and geographic location (one of 12 local government regions), and, in addition, for migrants, a quadratic in years in New Zealand and indicator variables for whether they were born in Australia, the United Kingdom, Asia, the Pacific Islands or elsewhere (this is the finest coding available in the IS).[13] We then use the resulting coefficients from these regressions to predict the log real hourly wage for all employed individuals in each Census. This imputed wage rate is then used in the analyses throughout the remainder of this paper.[14]
Information on the average wages of individuals in each nativity-group and skill-group are summarised in Table 4.[15] Average real hourly wages are remarkably similar across the three nativity groups, varying by no more than 5% in any year. This lack of an overall difference in wage rates reflects the fact that immigrants are more likely to be in highly paid qualification groups but are paid somewhat less within qualification groups, possibly reflecting their younger age structure, lower New Zealand-specific human capital, or lower transferability of international qualifications. For example, recent migrants with either no qualifications or only school qualifications have wage rates that are about 90% of comparable non-migrant in 1996 and only 80% of comparable non-migrant in 2006. While the average wage rate for migrants with post-school qualified migrants is quite similar to that for similarly qualified non-migrants in 1996, it is only 86% of the comparable non-migrant rate in 2006. Wage rates for degree qualified recent migrants are 87% of that for comparable non-migrants rate in 1996 and 95% in 2001.
Table 4: Wages for migrants and the New Zealand born in different skill-groups in 1996, 2001 and 2006
In contrast to the wage differences by qualification group, migrants and non-migrants in the same predicted occupation group have quite similar wage rates, with wages for recent migrants 94-107% of the comparable non-migrant rate. This is true even though there is a large variation in the average wages for workers across the predicted occupation groups. For example, the average wage for New Zealand-born in the Professional predicted occupation is 26-32% more than the average wage for New Zealand-born in the Service and Sales/Elementary predicted occupation. This wage gradient across predicted occupation groups is even greater for migrants, with the highest paid predicted occupation group receiving, on average, 36-42% more than those in the lowest paid group. This compares to the wage gradient between individuals with no qualifications and those with university qualifications of 38-46%, which generally is the similar across nativity groups.[16]
Footnotes
[5] We also have access to the 1986 and 1991 Census data, but we do not use this data for three reasons: first, New Zealand underwent a period of comprehensive market-oriented economic reform from 1984-93 which complicates interpretation of any results from the early time-period (Evans et al. 1996); second, the occupational classification system was changed between the 1991 and 1996 Census in a way that makes it impossible to create a consistent series over time even at an aggregated level; and third, the 1991 Census did not ask foreign-born individuals their year of first arrival in New Zealand making it impossible to separate recent from earlier migrants in this Census.
[6] Labour market areas (LMAs) are created using travel-to-work data at area unit level drawn from the 1991 census. Two sets of labour market areas are defined – one with 140 areas and one with 58. The main differences are that the 140-area set provides greater disaggregation of some relatively small areas. The 140 LMAs are defined by enforcing a minimum employed population of 2,000 and 75% self-containment of workers (allowing for some trade-off between the two). These LMAs have an average size of approximately 1900 square kilometres. In main urban areas, LMAs generally encompass the urban area and an extensive catchment area. In rural areas, LMAs tend to consist of numerous small areas, each centred on a minor service centre. We drop a small number of individuals (less than 1%) from all analyses for whom the address recorded on the census form is not sufficient for assigning an LMA to the current residence.
[7] New Zealand geographically consists of two main islands separated by a three hour ferry ride or a plane flight, plus a third island that has a very minimal population (Stewart Island). Seventy-five percent of the working-age population lives on the North Island. Territorial authorities and regional councils are purely administrative areas.
[8] A large number of migrants have missing qualifications in 1996 because of the way that foreign qualifications were coded in this census. We generally drop these individuals from our econometric analyses, although we have also tested the robustness of our findings to treated them as a separate qualification group.
[9] The Pacific Islands include Melanesia, Micronesia, and Polynesia (excluding Hawaii); the British Isles include the United Kingdom and Ireland; Western Europe and North America includes all European countries not assigned to the British Isles or Eastern Europe, the United States, Canada and Bermuda; the Former Soviet Union and Eastern Europe includes Greece, Cyprus, the countries of the former Yugoslavia, all former Eastern Bloc countries and all former republics of the Soviet Union (including those in the Baltics, Caucasus, and Central Asia); the Americas, Africa and Middle East includes all countries in Central and South America, the Caribbean, North Africa, Sub-Saharan Africa, and the Middle East (including Turkey); South-East Asia includes Myanmar, Cambodia, Laos, Thailand, Viet Nam, Brunei, Indonesia, Malaysia, Philippines, Singapore, and East Timor; North-East Asia includes China, Hong Kong, Macau, Mongolia, Taiwan, Japan and the Koreas; and South Asia includes Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka.
[10] Borjas (2003) uses education and potential experience to define human capital groups. Because our data only identifies qualifications and not years of education, our groups will be the same whether we use age or potential experience to classify individuals.
[11] This particular aggregation was chosen by using cluster analysis to group occupations according to the similarity of the individuals employed in them across a wide variety of characteristics.
[12] Specifically, separate models are estimated for the New Zealand-born and migrants by gender for all individuals employed and reporting a non-missing occupation in each census year. The following covariates are included for the New Zealand-born models: qualifications, a quadratic in age, ethnicity, qualifications interacted with a quadratic in age, marital status, and household type (couple with or without children, single parent, or non-couple). For immigrants, the following additional covariates are included: a quadratic for years in New Zealand, indicators for whether the individuals moved to NZ earlier than at age 18 or at age 25, indicators for their region of birth and a quadratic for years in New Zealand interacted with qualifications and with region of birth. Predicted probabilities of working in each of the five occupations are then generated using the relevant model and each individual’s characteristics. These predicted probabilities are then totalled over each LMA and year to generate counts of the number of individuals predicted to be in occupation skill-group i in LMA j in year t.
[13] The IS sample is fairly large with 800-1,100 immigrants of each gender and 3,400-4,000 NZ-born of each gender in the sample in each year. The R-squared for the imputation regressions range from 0.47 for NZ-born women in 1997 to 0.79 for male immigrants in 2001. Besides annual income (strongly positively related to wage rates) and hours worked (strongly negatively related to wage rates controlling for annual income), few of the other control variables are significant or have large impacts on the predicted wage rates.
[14] We also estimate the regression models in the paper using average incomes for full-time wage and salary workers to instead proxy for the wages of particular migrant/skill-groups and get qualitatively similar results.
[15] For each predicted occupation skill-group, this is calculated by taking a weighted average of each employed individual’s wage where the weight is the predicted probability of a particular individual being assigned to a particular predicted occupation.
[16] Average wage rates for the missing qualifications group suggest that this group is mainly composed of low skilled workers. As noted previously, we exclude these individuals from our main estimates for human capital skill-groups, but also test whether our results are robust to including them as their own skill-group.