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Australia Farewell: Predictors of Emigration in the 2000s

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Abstract

The factors leading individuals to immigrate to developed contexts are widely studied, but comparatively less is known about those who emigrate from them. In this paper, we use data from a nationally representative cohort of Australian adults to develop longitudinal measures of emigration and to assess how social ties and individual economic position predict emigration. Cox proportional hazards models indicate that the propensity to emigrate is particularly pronounced for those with relatively little social connectedness in Australia. Specifically, our results show that first-generation Australians, especially those with relatively short durations in the country, have substantially higher emigration rates than later-generation Australians. Similarly, having a partner with deeper generational roots in Australia strongly reduces the likelihood to emigrate. At the same time, our analysis also shows that economic position matters, with the not employed having higher risks of emigration. Perhaps most interestingly, estimates from our models reveal that those with university degrees are much more likely to emigrate than individuals with lower levels of education, a finding that is true for both first- and later-generation Australians.

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Notes

  1. Docquier and Rapoport (2012) review a primarily economics-based literature focusing on “brain drain” issues that has estimated emigration stocks and rates of emigration indirectly at the country level for over 195 countries in 1990 and 2000. For instance, a source country emigration stock is estimated by summing the stock of foreign-born residents of OECD countries using receiving countries’ census data in those years to estimate the source country’s emigration stock in that year by skill level, education, and gender. The source country’s rate of brain drain is then calculated using this value as the numerator and this value plus the source country’s census data for those years as the denominator. Parallel sociology-based literatures exist using this technique to focus on emigration from a country or group of countries to a specific country. See Thomas (2011) for a good example. He captures emigration of Africans to the United States using United States immigration information.

  2. In an Appendix available from the authors, we extend this table to the years 2006–2011. However, since 2006 the ABS has used a “12 out the next 16 months threshold” so these later year values are not consistent with those in Table 1. But they do show that emigration continues to be an important drag on immigration.

  3. Over the first decade of the 21st century, Australia’s average economic growth was 3.1 %, substantially higher than the OECD average of 1.7 %. Furthermore, unlike most other OECD countries, while Australia’s yearly economic growth slowed over the Global Finance Crisis, it always remained positive (OECD 2014).

  4. As Docquier, Lowell, and Marfouk (2009, p. 302) note, Australia is one of the few countries whose official statistics provide a realistic picture of emigration. Unlike the ABS, which at most only follows immigrants and emigrants for 16 months in defining them as transitory or permanent, we are able to determine how many of those we define as emigrants in our sample—those who we observed in the country in 2001 but not in Australia at some point later for two consecutive waves of the HILDA Survey data—do eventually return to Australia.

  5. If a person’s location in a given year (or streak of years) is “Unknown” but that person’s location in the following year is “Overseas,” we change that person’s location from “Unknown” to “Overseas.” That is, we assume that this person went overseas at the time the location is unknown. Subsequently, we exclude time points when a person’s location is still “Unknown” (14.4 % of all time points) or when a person is “Deceased” (3.5 % of all time points). Our survival analysis procedures treat these excluded time points as censored.

  6. HILDA only provides a true random sample of the Australian population in 2001 and we only focus on this population cohort. But to hold the number of years following an exit constant, we must use different years in each row and the maximum window is eight since our last wave of data is 2013.

  7. In an Appendix, available upon request, we also provide analyses using one-year, three-year, and four-year thresholds.

  8. A person contributes data as long this person is alive and their location is known; otherwise they are right-censored. The population in this last sample is smaller (n = 7653) than in our sample aged 25–54 since we only include those for whom we have complete information on all dependent variables.

  9. Since 1973, citizens of New Zealand and Australia have been allowed to travel, work, and live in either country without being required to apply for entry permission. This especially complicates the determination of temporary and permanent departures for this population of emigrants from Australia which we are unable to directly control in the HILDA data because we do not know emigrants’ destinations. See Sanderson (2009) for an analysis of the complex repeat and return migration patterns through June 2005 of New Zealand citizens who permanently arrived in Australia between August 1999 and July 2002 based on the old ABS “12 consecutive months in the country” definition. For a more general discussion of the theoretical problems related to prediction of circular migration, see Massey (1987) and Massey and Espinosa (1997) in the context of the United States and Mexico.

  10. In supplemental models, we considered an additional lag on employment and income to account for the possibility that individuals make labor market changes in anticipation of an international move. These models (in an Appendix available from the authors upon request) produce results that are substantively and statistical similar to those shown here.

  11. In addition, we calculated these values without population weights and using HILDA 2001–2012 longitudinal weights. The level of risk changed slightly (decreasing in the former and increasing in the latter case) but the relative risks remained approximately the same. More details are in an Appendix available from the authors upon request.

  12. We also find a significantly higher risk when we compare first- to second-generation Australians in this model.

  13. These two values are also significantly different from one another in this model.

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Acknowledgments

The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute).

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Authors and Affiliations

  1. Cornell University, 259 MVR Hall, Ithaca, NY, 14853, USA

    Richard V. Burkhauser & Matthew Hall

  2. University of Melbourne, Parkville, Australia

    Richard V. Burkhauser, Markus H. Hahn & Nicole Watson

Authors
  1. Richard V. Burkhauser
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  2. Markus H. Hahn
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  3. Matthew Hall
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Correspondence to Richard V. Burkhauser.

Additional information

This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The findings and views reported in this paper, however, are those of the authors and should not be attributed to either the Department of Social Services or the Melbourne Institute of Applied Economic and Social Research.

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Burkhauser, R.V., Hahn, M.H., Hall, M. et al. Australia Farewell: Predictors of Emigration in the 2000s. Popul Res Policy Rev 35, 197–215 (2016). https://doi.org/10.1007/s11113-016-9383-3

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