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Abstract

Australia offers regional visa schemes for skilled migrants who reside in nonmetropolitan Australia for a minimum of 2 years to alleviate nonmetropolitan depopulation and skills shortages. To assess the efficacy of this program in attracting and retaining migrants, we apply survival analysis to administrative longitudinal microdata from the Person Level Integrated Data Asset linked to census data from 2010 to 2020. We find that this program attracts few new migrants to regional Australia given that over 80% of regional visa holders applied onshore. Furthermore, close to 65% were already in a nonmetropolitan region for an average of 2 years while the remainder took on average 7 months to relocate to a nonmetropolitan area from overseas or from within Australia. While nonmetropolitan retention is high — 70% after 10 years — it is significantly lower in remote and very remote regions, and it is lower than the Australian population at large, suggesting limited policy efficacy in retaining rural populations in the long term. We also identify a negative selection, with migrants with low English proficiency being more likely to remain in nonmetropolitan regions, which suggests a possible segmentation of nonmetropolitan labor markets where migrants are concentrated in low-wage sectors. Two policy levers emerge to improve retention: (1) targeting co-ethnic communities and (2) focusing on young families with children. The mixed results from this policy evaluation highlight the difficulties of stimulating nonmetropolitan population growth via immigration.

Keywords

internal migration immigrants visa policies

Introduction

Australia is one of the top immigration countries in the world with 29% of its population born overseas and another 20% being second-generation immigrants (ABS, Australian Bureau of Statistics, 2022). The Australian population is unevenly distributed, being the fourth least densely populated country in the world and also one of the most spatially concentrated (Hugo 1999) with the majority of Australians clustered in the metropolitan areas along the south-eastern seaboard. This pattern even more pronounced among immigrants (Hugo 2011). In 2019, 85% of all new immigrants chose to settle in state/territory capitals with Sydney and Melbourne accounting for 75% of these moves (ABS, Australian Bureau of Statistics, 2019).

Prior to the World War II, immigrants played a key role in establishing new industries in nonmetropolitan Australia, particularly agriculture (Germans), mining (British, Chinese), and horticulture and viticulture (Italians, Greeks, and Germans) (Borrie 1954). In the early post-World War II years, a migration scheme for displaced persons allocated immigrants to designated areas with high labor shortages, which were often in remote locations, with a provisional 2-year bonding period (Kunz 1988). Such dispersal policies quickly faded away after the end of the Vietnam War (Nguyen 2022) and gave way to a focus on skills, which now form the cornerstone of the Australian's immigration policy through a raft of permanent and temporary skilled visa schemes (Hugo 2014b).

However, since the mid-1990s, the Australian Government has attempted to shape the spatial distribution of newly arrived immigrants to alleviate population pressures on the environment and infrastructure in cities (Withers and Powall 2003) and to fill labor shortages that have constrained the economic development of nonmetropolitan regions (Hugo 2008a). These ideas materialized in 1996 in the form of a state- and territory-specific and regional migration schemes to attract immigrants to nonmetropolitan regions that either lagged economically and/or faced high levels of internal out-migration (Hugo 1999). Since then, multiple visa schemes have been created (e.g., State/Territory Nominated, Regional Sponsored Migration Scheme, Designated Area Sponsored, Regional Established Business) that allow migrants to gain permanent residency after an initial 2-year bonding period in a designated nonmetropolitan area, after which migrants can move and settle freely across Australia. We refer to these visa schemes under the umbrella term of regional visa schemes hereafter. Similar emphasis on nonmetropolitan settlement has taken place in other high immigration countries, such as Canada, New Zealand, Sweden, the United Kingdom, and the Netherlands, although in Europe, these policies mainly focus on resettling asylum seekers and refugees, rather than skilled migrants, outside capital cities (Arnoldus, Dukes, and Musterd 2003; Stewart 2011; Haberfeld et al. 2019). Regional skilled visa requirements are legally valid in Australia despite freedom of movement being judicially interpreted as a constitutional right for citizens and permanent residents. The requirement of nonmetropolitan residence applies to temporary visa holders who agree to specific and temporary conditions that support legitimate public policy objectives, including regional development.

Since the inception of regional visa schemes in Australia, immigrant intakes associated with them have grown significantly, representing less than 3% of the total skilled migration annual intake in the mid-1990s but increasing to over 20% at their peak in 2013 (DHA, Department of Home Affairs, 2019). This new influx of immigrants has stimulated population growth and lowered the median age in South Australia (Hugo 2008a). In the Northern Territory, recent waves of immigration have slowed down population aging and reduced the male bias thanks to a new cohort of female migrants with high partnering rates (Taylor, Bell, and Gerritsen 2014). By design, regional visa immigrants are highly educated and thus have increased human capital in receiving nonmetropolitan regions (Krivokapic-Skoko and Collins 2016), with flow-on economic benefits for these areas.

However, the effectiveness of these visa schemes in retaining immigrants beyond the statutory 2 years remains largely unknown. This is because most studies to date either draw on cross-sectional data or use bespoke surveys in a specific regional area (Wulff and Dharmalingam 2008; Wickramaarachchi and Butt 2014; Sapeha 2017; Thurmer, Carson, and Taylor 2019) and thus do not offer a comprehensive nationally representative understanding of immigrants’ retention in nonmetropolitan areas. Retaining immigrants over the long term (Hugo, Khoo, and McDonald 2006; Wulff and Dharmalingam 2008), particularly for small and remote communities, can significantly improve the demographic and economic situation of local communities (Taylor 2018). Inversely, a growing number of immigrants coupled with a low retention rate could lead to high population turnover, which could be detrimental to social cohesion (Dennett and Stillwell 2008; Livingston, Bailey, and Kearns 2008; Lymperopoulou 2020).

In that context, the overall aim of this study is to establish the long-term levels and determinants of rural retention of skilled immigrants on regional visas. We proceed in four sequential steps using novel longitudinal administrative data from the Person Level Integrated Data Asset (PLIDA), which links multiple administrative datasets to census data from 2010 to 2020. First, we establish the level of nonmetropolitan retention of immigrants within visa-designated nonmetropolitan regions and compare retention rates across the urban hierarchy and by year of arrival. Second, we compare the nonmetropolitan retention of regional visa holders to that of (1) skilled migrants on non-regional visas and (2) the Australian population. Third, we establish the destination of migrants who leave nonmetropolitan regions. Finally, we identify the determinants of nonmetropolitan retention, paying attention to both individual and place-based characteristics, including co-ethnic networks, and explore their varying roles in immigrant retention, factoring in region of origin. This paper comes at a critical time, as the Australian Government has embarked on a reform of its migration program, calling for evidence-based planning and policy (Home Affairs 2023). The conjunction of heightened community concern about rapid population growth in major cities and an openness to policy reform provides a unique opportunity to assess the efficacy of regional skilled migrant schemes and identify policy levers to boost nonmetropolitan retention, with benefits beyond Australia.

With these objectives in mind, the next section provides an overview of policies on immigrants’ nonmetropolitan settlement and offers a synthesis of empirical and theoretical evidence on rural retention and its determinants. Section “Data and Methods” presents our longitudinal microlevel administrative dataset and introduces the methods, namely Kaplan–Meier survival analysis and a Cox proportional hazard regression model. Section “Descriptive Statistics” presents key descriptive statistics on the number, spatial distribution of immigrants in nonmetropolitan areas, and their migration pathways (offshore versus onshore applications). Section “Survival Analysis” reports Kaplan–Meier survival functions by year of arrival and migration pathway across the urban hierarchy before focusing on the determinants of retention from a Cox proportional hazard model, using the 2011 arrival cohort a as case study to provide a long-term perspective. Finally, we conclude by formulating policy recommendations to inform future nonmetropolitan settlement initiatives and discussing avenues for future research.

Literature Review

International Migration into Rural Areas: Evolving Theoretical Perspectives

Conventional approaches to migration theory tend toward an economic determinism regarding the “push” and “pull” factors that drive people to move from one place to another (Barcus and Halfacree 2018). In neoclassical economic models, migrants weigh up the income differentials between regions when deciding whether to remain at a location or migrate to another region to maximize their income. In the New Economics of Labor Migration model, the focus shifts from the individual to the family unit aiming to maximize returns and minimize risks. For example, a local income risk (e.g., a drought or factory closure) could prompt one member to find work in another country or region while sending remittances home to their family (Massey et al. 1993). The structuralist Marxist approach also considers income differentials, emphasizing the inequalities and inequities between the owners of the capitalist means of production and workers. For Harvey (1982), the class and structural differences between workers vis-à-vis entrepreneurs enforces worker flexibility and mobility: “The free geographical mobility of labour power appears as a necessary condition for the accumulation of capital” (Harvey 1982, 381), highlighting the need for workers, including immigrants to move internally.

Speaking directly to the topic of this paper — international migration into rural areas, frequently from lesser-developed countries — Immanuel Wallerstein's World Systems Theory conceived an increasingly global economy driven by highly unequal economic and political power relations. This theory highlights the role of historical and structural factors whereby a privileged subset of nations (i.e., “the core”) have the lead role in industrial and economic development, drawing lead firms and talent to their burgeoning industry sectors (e.g., banking, finance, accounting, information, and telecommunications), while remaining subsets (i.e., the so-called “semi-periphery” and “periphery”) are needed to provide cheap labor and endure unstable working conditions (e.g., seasonal work) to ensure low cost, natural resources are available to the previously mentioned subset. This “new international division of labor” (or NIDL as it came to be known) captured well the evolving, although in some cases deepening, economic inequality being structured into an increasingly globalized economy. Echoing Harvey's (1982) earlier comment, Hugo (2005) documented large and growing numbers of migrant from Asian nations recruited to perform so-called “3D” jobs (dirty, dangerous, and difficult) in the Middle East, Oceania, including Australia, and other Asian countries. Importantly, though, Hugo (2005) also noted the existence of a smaller but nonetheless significant migration current of highly skilled professionals from Asia to more developed countries in Europe, North America, and Oceania. This plays out in rural destinations, with migration toward nontraditional settlements being typically associated with low-skilled employment in primary and secondary industries, which reflects the economic structure of rural regions (Morken and Skop 2017).

While not denying the importance of financial incentives as a push and/or pull factor in migration decisions, behavioralist approaches more strongly emphasize the role of human agency and social factors in the migration decision-making process, albeit within an increasingly crowded and complex institutional context and sociocultural and economic landscape. For example, the Value Expectancy Theory recognizes affiliation and social networks as integral factors in the migration decision-making process since relocating near family and friends can minimize the costs and risks associated with migration (Haug 2008), as co-ethnic networks do by providing information and assistance (Hardwick 2003). However, rural settlement typically does not provide access to such social and kindship support (Morken and Skop 2017) or to co-ethnic communities (Flippen and Farrell-Bryan 2021), making new rural destinations often economically driven settlement choices or the result of government-led relocation programs.

Immigration to Rural Destination: A Review of Policies

Since the second half of the twentieth century, many rural areas in developed countries have faced demographic decline caused by ongoing net internal migration losses coupled with declining fertility rates and increasing mortality linked to population aging (Johansson 2014; Pinilla and Sáez 2017; Johnson and Lichter 2019). In that context, immigration has been proposed as a solution to rural depopulation and labor market shrinkage for numerous nations (Fonseca 2008; Hugo and Morén-Alegret 2008; Cameron 2011; Hedberg and Haandrikman 2014). There are examples of voluntary immigration flows to rural regions in response to labor shortages (Bayona-i-Carrasco and Gil-Alonso 2013), leading to population growth in some agricultural and peri-urban regions, particularly in the last two decades (Fonseca 2008; Janská, Čermák, and Wright 2014; Heider et al. 2020). For example, in Spain and Greece, international migration has played a crucial part in reviving agricultural regions suffering from long-lasting depopulation (Collantes et al. 2010), while in France, the immigrant proportion of the rural population has grown since the 1970s, contributing to spatial deconcentration and suburbanization (Fromentin 2022). More consumption-focused settlement associated with, for example, postretirement relocation and/or circular migration between a city primary resident and rural second home has boosted numbers in high amenity rural places. Similar experience has been recorded for other European nations; this research helping to spawn the “rural cosmopolitanism,” “super diversity,” and/or “new immigrant destinations” literatures (see Popke 2011; Vertovec 2006; McAreavey 2012). A substantial element of immigration into rural areas is often the product of dispersal policies that mandate migrants reside in nonmetropolitan regions via specific visa requirements (Hugo 2008a; Carter, Morrish, and Amoyaw 2008). Originally designed to either alleviate population pressure in metropolitan areas, limit migrants’ segregation, improve their integration, or boost the available labor for particular industries (Boswell 2003; Stewart 2011), dispersal policies have increasingly been used to tackle rural depopulation (Carter, Morrish, and Amoyaw 2008; Hugo 2008b; Fromentin 2022).

In the United Kingdom and Scandinavian countries, these policies are typically aimed at humanitarian migrants (Andersson 2003; Arnoldus, Dukes, and Musterd 2003; Robinson 2003; Wren 2003; Haberfeld et al. 2019), but in Australia, Canada, and New Zealand, these schemes have been extended to skilled migrants (Hugo, Khoo, and McDonald 2006; Carter, Morrish, and Amoyaw 2008; Akbari and MacDonald 2014) via a point-based visa system. While in Australia and Canada, regional policies are designed by the federal government in partnership with states/provinces, in New Zealand, they are implemented by local agencies and initiatives (Wulff et al. 2008). In Australia, regional migration schemes were initially available only in the permanent skilled stream in the mid-1990s, but in 2002, they were expanded to temporary skilled visas (subclass 457) by lowering the skill level requirements. In 2005, a regional variant of the Work and Holiday Makers visa was created to allow temporary migrants to extend their stay in Australia by 12 months after working for 3 months in remote parts of Australia (DIMA 2007), but this stream does not allow visa holders to transition to permanent residency. The same year, a regional scheme was implemented for humanitarian entrants with no family connections in Australia (Hugo 2008b) to be directed into nonmetropolitan regions (Wong, Perales and Bernard 2023), signaling growing interest by the Australian Government in nonmetropolitan settlement. In Canada and New Zealand, a similar shift toward temporary nonmetropolitan settlement has emerged thanks to dedicated visa schemes. These include the Regional Seasonal Scheme, established in 2007 in New Zealand, which allowed a set number of agricultural workers mainly from Pacific Island states to obtain short-term seasonal work visas (Keen 2009; Alam and Nel 2022) and the Mexican-Canadian Seasonal Agricultural Worker Program (Hennebry 2008).

Multiple studies have shown the positive effect of immigration on rural areas by filling labor shortages, stimulating economic growth, slowing down population aging, and increasing fertility and cultural diversity (Facchini and Lodigiani 2014; Hedberg and Haandrikman 2014; Taylor, Bell, and Gerritsen 2014). However, others have argued that these effects are mainly short term and that, in the long term, rural immigration does not rejuvenate nor stimulate demographic and economic development because (1) immigrants age at the same rate as the rest of the population, (2) their fertility levels converge over time to those of the native-born population, and (3) many eventually relocate to urban centers (Fonseca 2008; Hedlund et al. 2017). We investigate the latter issue, which is a topic of immediate policy concern.

Retaining Immigrants in Nonmetropolitan Areas

Immigrant attraction to and retention in rural regions and places are effectively two sides of the same international migration coin. The demographic, economic, and social success of nonmetropolitan regions hinges on their ability not only to attract but also to retain immigrants within designated areas. However, retention studies are comparatively meager compared to migrant recruitment research, due at least in part to the more complex and longitudinal data requirements of retention research. What work exists highlights the role of fast-tracked visa processing, three-way collaboration between government, employers and local communities, welcoming communities, lifestyle, and family-oriented factors (Garcea 1995; Akbari 2008; Fonseca 2008; Spoonley and Bedford 2008; Oliva 2010; Bayona-i-Carrasco and Gil-Alonso 2013; Hugo 2014a; Hedlund et al. 2017) in attracting immigrants to nonmetropolitan regions.

The internal migration literature shows that immigrants are highly mobile on arrival as they adjust their housing and labor market needs to their new surroundings (Hugo and Harris 2011; Silvestre and Reher 2014) and that it takes about a decade for their levels and patterns of internal migration to be similar to that of the native-born population (Bell and Cooper 1995), a process referred to as spatial assimilation (Raymer et al. 2018; Fromentin 2022). The initially high levels of mobility suggest that retention in regional areas may be limited, although levels and patterns of internal migration are known to vary by visa and country of origin. For example, in Australia, skilled migrants are the most mobile group. By contrast, family migrants are the least mobile, their spatial trajectories broadly mirroring those of the Australian population because this group reunites with already established migrants (Laukova, Bernard and Sigler 2020). In the Netherlands, asylum seekers are initially the most spatially diffused group, but over time, they show the highest propensity to move as they relocate to more ethnically concentrated neighborhoods (Zorlu and Mulder 2008). These differences are overlaid by distinct cultural preferences. For example, in Australia, retention in nonmetropolitan regions is particularly low for migrants from China and India, but much higher for migrants from the United Kingdom and New Zealand and on par with that of the Australian population (Raymer and Baffour 2018).

Variations in rural retention are in part shaped by local economic opportunities. This is why Canada, New Zealand, and Australia prioritize employer-sponsored schemes in their regional migration policies (Dalla, Ellis, and Cramer 2005; Derwing et al. 2005; Carter, Morrish and Amoyaw 2008; Hugo 2008b). Rural retention ultimately depends on a small number of key factors, including skill matching and quality of employment (Lewis 2010; Sapeha 2017); employment opportunities for spouses (Wickramaarachchi and Butt 2014; Sapeha 2017); and ready availability of affordable quality housing (Derwing et al. 2005; Spoonley and Bedford 2008; Taylor, Bell, and Gerritsen 2014), along with lifestyle (e.g., natural amenities) and quality of life, including good schools and training opportunities for children (Brochu and Abu-Ayyash 2006; Hugo 2008a; Spoonley and Bedford 2008). From a more behavioral perspective, Wulff and Dharmalingam (2008) have highlighted the importance of social connectedness in retaining immigrants in Australia, a finding that echoes the literature on ethnic networks (Hazebroek 1994; Le 2008; Haan, Li, and Finlay 2024). Ethnic networks facilitated through a place of worship, club, or cultural center can act as an “anchor” for immigrants in the new environment and increase retention levels (Krivokapic-Skoko and Collins 2016).

Thus, retention is a dynamic process as migrants constantly adjust their working and housing needs and preferences (Hof, Pemberton, and Pietka-Nykaza 2021) to the opportunities present in their new environment, highlighting the difficulty of designing policies that maximize rural retention. The dynamic nature of spatial integration calls for longitudinal studies. Yet, most Australian studies are descriptive, often cross-sectional and based on small-scale ad hoc surveys (Wulff and Dharmalingam 2008; Taylor, Bell, and Gerritsen 2014; Wickramaarachchi and Butt 2014; Sapeha 2017; Baffour and Raymer 2019). While longitudinal evidence is more readily available in Canada (Garcea 1995; Derwing et al. 2005; Brochu and Abu-Ayyash 2006; Akbari 2008; Haan, Li and Finlay 2024; Zhuang 2023) and Scandinavian countries (Andersen 2010; Hedlund et al. 2017), research on the role of targeted visa programs in Australia in retaining immigrants is critically lacking.

Data and Methods

The Person Level Integrated Data Asset

To quantify the nonmetropolitan retention of immigrants on regional visas in Australia, we draw on administrative longitudinal microdata from PLIDA, which brings together administrative datasets from multiple federal and state agencies linked to data from the quinquennial national census. PLIDA provides data on health, education, government payments, income, taxation, and employment combined with population characteristics including place of residence and movement in and out of Australia. First established in 2015, and further developed between 2017 and 2020, PLIDA is managed by the ABS, the agency that collects, combines, and provides access via its online platform (i.e., DataLab). The ABS vets all outputs before users can make them publicly available.

In order to maximize population coverage, these different datasets are linked via a unique identifier (i.e., the Spine ID) based on the integration of core datasets from three major federal agencies. These agencies are (1) Medicare, which is the public health insurance scheme available to all Australian residents, including permanent migrants; (2) Centrelink, a federal agency that provides social security payments to Australian citizens and eligible temporary visa holders; and (3) the Australian Taxation Office (ATO). Personal income tax data for everyone legally employed in the country are available in the database.

We use datasets from five different PLIDA modules: Travellers, Visa Applications, Combined Location, Deaths, and the 2011 Census. First, from the Visa Applications Module we select immigrants on skilled regional visas who are legally required to reside in nonmetropolitan regions for a minimum of 2 years before being eligible for permanent residence.¹ Our population at risk includes all immigrants who were granted a Regional Sponsored Migration Scheme or Designated Area Sponsored visa between January 1, 2010 and December 31, 2018 (n = 165,033).² We organize the data by year of arrival to track trends in the level of nonmetropolitan retention. Using the Deaths and Travellers modules, we remove individuals who died (n = 244) during the intervening period. We then merge our file with the Combined Location Module that triangulates address information from the three core datasets that underpin the Spine (i.e., data from Medicare, Centrelink, and ATO) and provides a full history of address changes since 2006. Our final analytical sample includes 144,641 immigrants on regional visas who settled in nonmetropolitan Australia.³ Our key variable of interest is duration of residence in nonmetropolitan Australia. In line with the visa requirements, all of Australia is considered nonmetropolitan except for Sydney, Melbourne, Brisbane, and the adjacent cities of Wollongong, Newcastle, and Gold Coast. This means that the capital cities of Perth, Adelaide, and Darwin are included in the nonmetropolitan category. We establish the start date of nonmetropolitan residence based on the first nonmetropolitan address after the regional visa grant date. The exit date is the start date of a first residence in metropolitan Australia or departure from Australia (n = 5,988). We track individuals until June 30, 2020, which was the latest date for which full residential histories were available at the time of writing.

While PLIDA is a rich dataset, many sociodemographic variables are only available in the census. To establish the determinants of nonmetropolitan retention, we then restrict the sample to visa holders who started living in nonmetropolitan areas between January 1, 2011 and the 2011 Census date, August 9, 2011 (n = 4,008). This is to ensure that sociodemographic variables obtained from the census are measured at the beginning of the observation interval. To limit the risk of reidentification, PLIDA allows longitudinal linkage to only one census, so we opted for the 2011 census to obtain the longest possible observation period and to establish the determinants of long-term retention.⁴

Methods

To quantify the retention of immigrants in nonmetropolitan areas, we use survival analysis, starting with Kaplan–Meier survival functions, which provide monthly “survival rates,” or the proportion of the immigrants that remain in nonmetropolitan Australia out of the total population “at risk” of leaving. When an individual leaves nonmetropolitan Australia either by relocating elsewhere in Australia or leaving the country, they exit the pool of individuals at risk of leaving and no longer factor into the estimation. To delve deeper into nonmetropolitan retention, we then establish retention within different remoteness levels (major city, inner regional, outer regional, remote, and very remote) because of well-known challenges in retaining populations in regions distant from metropolitan centers (Argent and Tonts 2015). Based on the first regional address, we assign migrants a remoteness status. If an individual leaves that remoteness level by moving up or down the urban hierarchy or emigrates, they are counted as not retained. Remoteness was determined using the Australian Statistical Geography Standard (ASGS) Remoteness Structure, which classifies regions and localities based on their proximity (via road distance) to other service centers within their respective urban hierarchies (ABS 2023).

We report Kaplan–Meier survival functions by year of arrival and migration pathway (offshore versus onshore applicants) and further distinguish onshore immigrants who already resided in a nonmetropolitan region before being granted a regional visa. Finally, to further assess the efficacy of the regional visa scheme, we also compare the retention of immigrants on regional visas (n = 11,429) to skilled migrants on non-regional visas (n = 22,267) and the Australian population (n = 1,550,815) who relocated to a nonmetropolitan region in 2011.

We then aggregate individual-level data to construct an origin-destination matrix from the Combined Location Module to establish the location of immigrants in nonmetropolitan Australia. We use the 2011 arrival cohort and report their destination across the urban hierarchy based on the remoteness status by June 30, 2020.

To control for possible confounders and establish the drivers of retention, we then estimate the risk of leaving nonmetropolitan Australia using a Cox proportional hazard model for the 2011 arrival cohort. Our estimation framework is as follows:

h_{i} (t) = h_{0} (t) \times \exp (b_{1} M + b_{2} R),

(1)

where

h_{i} (t)

denotes the risk of leaving nonmetropolitan Australia for individual i at time t, where time t is measured in months between January 1, 2011 and June 30, 2020. The dependent variable takes the value of 0 is an individual stays and the value of 1 if an individual leaves nonmetropolitan Australia. Individual- and household-level variables from the census 2011 are represented in vector M. These include age, sex, marital status, presence of dependent children, English proficiency, educational attainment, labor force status, occupation, household income, UN world region of birth, visa holder status (primary versus secondary applicant), and migration pathway (offshore versus onshore applicants). We also include region-level variables to establish the role of social and economic conditions in retaining immigrants. These are measured at the Spatial Area Four (SA4) geographical level, which is the first substate level of geography (n = 89) under the ASGS and broadly corresponds to labor market regions. They are captured in vector R. They include remoteness level,⁵ co-ethnic network size (proportion of people from the same country of birth because the Australian census does not collect information on ethnicity), unemployment rate, and average household weekly income. Descriptive statistics for all our variables can be found in Supplemental Appendix A. Of particular interest is the geographic origin of regional skilled migrants. Close to 65% come from Asia, particularly Southeast Asia and Southern Asia, followed by Northwest Europe at 14%. To capture possible variations in the role of co-ethnic networks in rural retention, we run a second model with an interaction term between region of birth and co-ethnic network.

In all our models, we cluster standard errors on family membership to account for potential interdependencies within family units. The b_s in Equation 1 represent estimated model parameters, expressed as hazard ratios. A hazard ratio greater (lower) than 1 indicates an increase (decrease) in the odds of leaving a nonmetropolitan area (remoteness level).

Descriptive Statistics

Before proceeding to survival analysis, we report key descriptive statistics to provide a background against which to interpret the results. Table 1 reports the annual number of permanent skilled visas granted from 2010 to 2018, distinguishing between non-regional and regional visas, the latter requiring a minimum of 2 years of residence in nonmetropolitan Australia before being granted permanent residency. Of the 884,591 permanent skilled visas granted over that period, close to 17% were regional visas. The share of regional visas gradually increased to peak at over 20% in 2012 and 2013. It reached a low of 11% in 2017 when Perth was reclassified as non-regional. In November 2019, Perth was reclassified as a regional area. The fact that the share of regional skilled migrants increased back to close to 15% in 2018 suggests the low share of regional migrants in 2017 was not a direct result of Perth's temporary change in classification.

Table 1.

Number and Percentage Distribution of Permanent Skilled Visa Granted from 2010 to 2018.

	Non-regional		Regional		Total
Year	Count	%	Count	%	Count	%
2010	69,527	85.57	11,729	14.43	81,256	100.00
2011	94,641	83.34	18,916	16.66	113,557	100.00
2012	80,928	77.17	23,943	22.83	104,871	100.00
2013	105,375	78.77	28,397	21.23	133,772	100.00
2014	95,141	83.01	19,474	16.99	114,615	100.00
2015	95,937	82.81	19,909	17.19	115,846	100.00
2016	96,393	86.32	15,282	13.68	111,675	100.00
2017	83,860	88.89	10,486	11.11	94,346	100.00
2018	97,726	85.26	16,897	14.74	114,623	100.00
Total	819,528	83.24	165,033	16.76	984.561	100.00

Source: Authors’ calculation from PLIDA.

We now turn our attention to the settlement destination in Figure 1, which reports the distribution across the urban hierarchy of migrants on regional visas, based on their first nonmetropolitan address and their migration pathways (i.e., whether they applied for the visa offshore — outside of Australia or onshore — in Australia). We further distinguish between onshore migrants depending on whether they were already staying in the nonmetropolitan region they chose to reside in while on a regional visa.

Figure 1.

First location of nonmetropolitan migrants by migration pathways, previous address and urban hierarchy, 2010–2018.

Two key findings emerge. First, 60% of regional migrants settled in inner regional areas and major cities i.e., Perth and Adelaide. In contrast, only 18% settled in remote and very remote areas, although this is significantly higher than the 6% of permanent skilled migrants who are not on a regional visa and settled in remote and very remote regions during the period. Second, over 80% of regional visa holders are onshore applicants who were already in Australia and three quarters of them were already in nonmetropolitan Australia for an average of 24 months. This raises questions about the efficacy of the scheme in bringing new immigrants to those regions given that in effect, 63% of all regional visa holders were already in nonmetropolitan regions. However, immigrants who were not already in nonmetropolitan Australia take about 7 months to relocate after their visa is granted. This is true for both onshore and offshore applicants. This can be seen in Table 2, which reports the average lag time between the visa grant date and first day of address in a nonmetropolitan area.

Table 2.

Duration in Days Between Regional Skill Visa Grant Date and First Regional Location.

Regional skilled migrants	Obs.	Mean	Std. dev.
Offshore	28,999	229.3	312.8
Onshore, not in nonmetropolitan area prior	28,358	261.4	338.1
Onshore, in nonmetropolitan area prior	87,284	0.0	0

Source: Authors’ calculations from PLIDA for all migrants who were granted regional visa between 2010 and 2018 and had an address in nonmetropolitan region.

Survival Analysis

Retention Levels

We now turn our attention to the retention of immigrants within nonmetropolitan areas as set out in the visa conditions; this effectively means residing outside of Sydney, Melbourne, Brisbane, and their satellite cities. Figure 2 reports Kaplan–Meier survival estimates by year of arrival in a regional area. The overall retention rate is high: about 70% after 10 years of settlement. Importantly, we do not observe a sharp drop in retention after the mandated minimum stay of 2 years. Instead, retention declines gradually with duration of residence. However, overall, there is no clear trend in retention among successive arrival cohorts. The 5-year retention for the 2010 cohort sat at around 85%, compared to 70% for the 2017 cohort, but retention bounced back for the 2018 cohort.

Figure 2.

Kaplan–Meier survival estimates of leaving nonmetropolitan area by year of arrival.

In Figure 3a–e, we report retention within each remoteness level. The retention level decreases significantly the further one moves down the urban hierarchy. For example, for the 2012 arrival cohort, the 5-year retention was 80% in major cities compared with only 45% in very remote areas. While retention has been broadly stable in major cities and inner regional Australia, it has somewhat declined in more remote regions, which display a noticeable drop in retention around the 2-year mark. The 5-year retention in major cities varies between 75% and 85% depending on the year. However, in remote areas, the 5-year retention rate dropped from 68% to just 30% between the 2010 and the 2013 arrival cohorts, but it bounced back up slightly for the 2018 cohort. In summary, there are no clear temporal trends in terms of retention level, but there are important spatial variations.

Figure 3.

(a–e) Kaplan–Meier survival estimates of leaving a remoteness zone by year of arrival.

Finally, using the 2011 arrival cohort by way of example, we compare the overall nonmetropolitan retention of regional migrants to that of Australian citizens and skilled migrants not on regional visas. Figure 4 shows comparable retention rates for all three groups for the first 60 months post-arrival. After settling in an approved regional area, regional skilled visa holders exhibit a higher retention rate than permanent skilled migrants not on a regional visa but a lower level of retention relative to Australian citizens.

Figure 4.

Kaplan–Meier survival estimates of leaving nonmetropolitan area by visa category, 2011 arrival cohort.

We next explore the destination of immigrants on regional visas by remoteness level by following the 2011 arrival cohort and reporting their destination at the end of the observation interval in 2020. Results in Figure 5 show that flows down the urban hierarchy are very small as most migrants move up the urban hierarchy, particularly to inner regional areas and major cities. As a result, only 11% are left in in remote and very remote areas after 9 years. The full origin-destination table is available in Supplemental Appendix C.

Figure 5.

Origin-destination matrix for immigrants on regional visa by remoteness for the 2011 arrival cohort.

Determinants of Retention

We next examine the determinants of nonmetropolitan retention. Results from the Cox proportional hazard regression model can be found in Table 3. The dependent variable takes the value of 0 if and individual stays in nonmetropolitan Australia and 1 if an individual leaves regional Australia. A hazard ratio greater (lower) than 1 indicates an increase (decrease) in the odds of leaving nonmetropolitan area (remoteness level). The results confirm a lower retention outside major cities. Importantly, economic factors, including the unemployment rate and average income, do not appear to foster retention.

Table 3.

Coefficients from a Cox Proportional Hazards Regression Model of Leaving Nonmetropolitan Australia, the 2011 Arrival Cohort.

Variables	Hazard ratio	95% CI
Age group (ref cat. 18–24)
25–34	0.88	0.72	1.07
35–44	0.70**	0.56	0.88
45–54	0.72*	0.54	0.97
55–64	1.31	0.73	2.36
Sex (ref cat. male)
Female	1.00	0.91	1.19
Social marital status (ref cat. married, registered or facto)
Not married	1.14	0.97	1.34
Dependent children (ref cat. no dependent children)
One or more dependent children	0.76**	0.64	0.90
English language proficiency (ref cat. very well)
Well	0.92	0.79	1.08
Not well	0.64**	0.48	0.86
Higher education (ref cat. less than bachelor)
Bachelor and higher	1.07	0.93	1.23
Visa holder (ref cat. secondary)
Primary	1.21	0.92	1.58
Migration pathway (ref cat. offshore)
Onshore	1.39*	1.06	1.83
Residence prior visa grant (ref cat. not the same region)
Same nonmetropolitan region	0.65***	0.55	0.78
Occupation (ref cat. technicians and trades workers)
Managers	0.93	0.72	1.20
Professionals	0.80*	0.65	0.98
Community and personal service workers	1.02	0.79	1.33
Clerical and administrative workers	1.31*	1.00	1.78
Sales workers	0.98	0.73	1.31
Machinery operators and drivers	1.08	0.77	1.53
Laborers	0.85	0.69	1.05
Unemployed, not in labor force	1.05	0.87	1.26
Remoteness (ref cat. major city)
Inner regional	1.55***	1.24	1.94
Outer regional	1.88***	1.50	2.36
Remote	2.13***	1.56	2.90
Very remote	1.84***	1.40	2.43
Region of birth (ref cat. Northwest Europe)
Oceania	0.73	0.40	1.34
Southern and Eastern Europe	1.05	0.68	1.64
North Africa and Middle East	1.19	0.71	1.97
Southeast Asia	0.73	0.56	1.01
Northeast Asia	1.90***	1.46	2.48
Southern and Central Asia	1.26*	1.00	1.60
Americas	1.47	0.99	2.14
Sub-Saharan Africa	0.69**	0.51	0.93
Co-ethnic network size (share of the population born in the same country	0.92*	0.82	0.98
Unemployment rate	1.05	0.97	1.15
Household income weekly	0.89	0.57	1.37

Note: N = 4,008. The dependent variable takes the value of 0 if and individual stays in nonmetropolitan Australia and 1 if an individual leaves regional Australia. A hazard ratio greater (lower) than 1 indicates an increase (decrease) in the odds of leaving nonmetropolitan area (remoteness level).

Source: Authors’ calculations from PLIDA.

***p < .001. **p < .01. *p < .05.

Instead, we find that sociodemographic characteristics have a significant influence. In particular, middle-aged migrants (35- to 54-year-old) with dependent children are significantly less likely to leave a regional area, which aligns with earlier findings and the longstanding concept of migration propensity being higher in younger ages (Rogers and Castro 1981; Bernard, Bell, and Charles-Edwards 2014). This finding also accords with the earlier point about lower levels of mobility existing among those embedded in local social and kinship networks, in this case through school. It is also the flipside of the Value Expectancy Theory explanation since having affiliations already present in the same region increases the costs and risks of relocating to an alternate region (Haug 2008). Similarly, professionals show a lower risk of leaving nonmetropolitan areas compared with technicians and trades workers, although the value of regression coefficients suggests that occupation plays a more limited role in retention decisions than other variables. The Marxian model explains this through structural inequalities whereby those least likely to own the means of production must remain flexible and mobile (Harvey 1982). Our results suggest that this holds for skilled migrants. Conversely, migrants with poor English proficiency are also less likely to leave regional areas, which suggests a possible negative selection that may reflect the more limited job opportunities for these migrants in larger urban centers (Nguyen et al. 2023) and the type of low-skilled manual labor jobs found in some rural regions (Dun, Klocker, and Head 2018).

Onshore migrants are generally at higher risk of leaving regional areas than offshore migrants, except for those who already resided in nonmetropolitan areas; this last mentioned group has a significantly lower risk of leaving regional areas. These migrants spent an average of 2 years in the same nonmetropolitan area prior to having been granted a regional visa and thus are likely to have established social networks in the community, which facilitates retention (Kaplan, Grünwald, and Hirte 2016). We also observe important variation in regional retention by birth region, with migrants from Northeast Asia and South and Central Asia being significantly more likely to leave than migrants from Northern and Western Europe, which suggests that sociodemographic differences are overlaid by cultural preferences. In contrast, migrants from sub-Saharan Africa are less likely to leave nonmetropolitan Australia than migrants from North and Western Europe.

We find a positive association between retention and the size of ethnic networks, measured as the proportion of the population born in the same country and currently residing within Australia. This finding aligns with the broader behavioral approach to migration and the literature on migrant retention. We explore this further by including an interaction term between the region of birth and the presence of ethnic networks in a separate model. Results in Table 4 show that the size of co-ethnic network increases the retention of migrants from Northeast Asia and Southern and Central Asia, whereas other groups are not responsive to those networks.

Table 4.

Coefficients from a Cox Proportional Hazards Regression Model (Interaction Terms).

Variables	Haz. ratio	95% CI
Region of birth (ref cat. Northwest Europe) # ethnic network
Oceania	1.00	0.94	1.06
Southern and Eastern Europe	0.59	0.21	1.68
North Africa and Middle East	2.90	0.01	2.01
Southeast Asia	1.10	0.82	1.47
Northeast Asia	0.78*	0.60	0.99
Southern and Central Asia	0.48***	0.37	0.62
Americas	0.09	0.00	2.04
Sub-Saharan Africa	0.74	0.39	1.40

Note: N = 4,008.

Source: Authors’ calculations from PLIDA.

***p < .001. **p < .01. *p < .05.

For ease of interpretation, we visualize these results as predicted probabilities of staying for the two regions of birth with statistically significant results (Figure 6).⁶ We can clearly observe a positive relationship between retention rates and the size of ethnic network for both groups. This is despite the relatively small size of regional co-ethnic networks when compared with metropolitan areas. This confirms existing findings in the literature indicating that the retention of Asian groups is very high in cities but low elsewhere, regardless of their visa status (Raymer and Baffour 2018).

Figure 6.

Predicted probability of staying by region of origin and co-ethnic network size, 2011 arrival cohort.

Discussion and Policy Implications

Immigrants’ nonmetropolitan retention is pivotal to the success of dispersal and retention policies. Despite the introduction of Australia's first regional visas more than 20 years ago, evidence on these programs’ relative success has been constrained by data availability and quality. By applying survival analysis to new administrative microdata (PLIDA), this study offers the first longitudinal and nationally representative insights into the nonmetropolitan retention of regional skilled migrants in Australia.

Four key findings have emerged. First, 80% of migrants on regional visas were already in Australia at time of their visa application and 63% of all migrants already resided in the nonmetropolitan region they elected to live in during the required 2 years of nonmetropolitan residence. This means that the regional visa scheme has enabled those who have a self-selected preference for regional living to extend their time in a nonmetropolitan setting rather than attract new skilled migrants. From 2010 to 2018, the scheme brought 49,000 (37%) immigrants who were not already living in nonmetropolitan Australia into this zone from an overall regional intake of 131,113 migrants. This suggests that regional visas are largely used as a means of transition from a temporary to a permanent residency status rather than a transition from metropolitan to nonmetropolitan region.

Second, overall retention outside Sydney, Melbourne, Brisbane, and their satellite cities is 70% 10 years after settlement. While this seems high, it is not higher than the retention of Australian citizens, a result that hints at the lack of efficacy of regional visa schemes in ensuring long-term population retention although their retention is higher than skilled migrants who are not on a regional visa. This finding speaks more broadly to the difficulty government’s face in more precisely directing migration outcomes. This is not an isolated situation, with pronatalist policies exhibiting limited success in lifting fertility rates (Gray et al. 2022). Our findings show that such a policy challenge extends to internal migration, reigniting a long-standing debate on whether governments should intervene in population processes or let self-regulating mechanisms operate (Demeny 1986). This issue also highlights the tension between the search for a better collective future and the upholding of individual preferences. Importantly, we found retention levels to gradually decrease with duration of residence but did not see a significant drop in retention after the two legally required years of nonmetropolitan settlement. This means that the 2019 increase in the duration of minimum residence to 5 years is likely to have a minimal impact on long-term retention.

Third, we found retention to decrease significantly in regions further down the urban hierarchy, particularly in outer regional, remote, and very remote areas, which consistently record a lower level of retention than inner regional Australia and major cities. For example, the 5-year retention rate in remote areas for the 2013 arrival cohort was only at 30% compared with 85% for Perth and Adelaide and 63% in inner regional areas. While the overall policy goal of retaining immigrants outside Australia's traditional gateway cities (Sydney, Melbourne, and Brisbane) is met, the regional visa scheme does not appear to be an effective mechanism to address rural depopulation in the long term. There is scope to extend this work by focusing on selected settlement regions to establish spatial variations in retention rate and identify possible “success story” for regions with retention rates higher than the benchmark provided in this study.

Fourth, retention is highly selective. Retention is higher among middle aged migrants with dependent children. This pattern of selectivity may be due to multiple factors, including the lower mobility of families (Cooke 2008), and it echoes the well-established age pattern of migration peaking at young adult ages and declining thereafter (Rogers and Castro 1981; Bernard, Bell, and Charles-Edwards 2014). It also highlights the role of school-aged children in building local social networks that may extend beyond ethnic networks. This in turn disincentivises outward migration that could sever ties with friendship circles, social capital, and the continuity of schooling (Tucker, Marx, and Long 1998), which our results suggest also applies to recently arrived migrants.

We also found a higher retention of immigrants with lower English proficiency, which signals a negative selection. Good English proficiency is vital in broadening employment opportunities and building a career in contemporary Australia, and so is often linked with higher mobility. Migrants with poor English levels are less likely to find employment and thus less likely to migrate internally (Hugo 2014a). This pattern of selectivity may also reflect the type of jobs in some nonmetropolitan regions. In that respect, the extension of the regional visa scheme to temporary working holiday makers in regional areas in 2005 and the introduction of an agricultural visa for low skilled workers do not appear misguided (Hugo 2014b). While high retention is a positive outcome from a policy perspective, poor English skills may lead to poorer labor market outcomes, lower social mobility, and lower life satisfaction, particularly given the limited size of co-ethnic networks in nonmetropolitan Australia. Ensuring access to English lessons for migrants in nonmetropolitan areas may be needed to ensure that regional visa policies do not foster a segmented nonmetropolitan labor market where migrants are overconcentrated in low-wage sectors like agriculture, construction, and service industries and where remote and very remote regions function as poverty traps. To better understand these processes, future work should examine trends in the industrial concentration of rural migrants and variation across space.

Ethnic networks have long been identified as a significant factor in attracting new immigrants (Hazebroek 1994; Le 2008) as they provide informal support during the initial settlement process. A recent study from Canada found that ethnic communities essential to foster nonmetropolitan retention (Haan, Li, and Finlay 2024). We find that co-ethnic networks improve the retention of groups with the lowest overall retention level, namely, migrants from Northeast Asia (China and South Korea) and Southern and Central Asia (India, Pakistan, Nepal, Bangladesh, Sri Lanka, and Afghanistan). While co-ethnic networks facilitate retention, their impact on labor market outcomes is unclear as Australian evidence remains contradictory (Nguyen et al. 2023). Yet, as nonmetropolitan co-ethnic networks progressively emerge as a result of these regional visa policies, retention levels may increase in the future. In the meantime, co-ethnic communities offer a policy lever to potentially boost nonmetropolitan settlement.

Despite a high overall retention of immigrants in nonmetropolitan areas, there are variations across the urban hierarchy, with remote regions struggling to retain immigrants in the long term. Locally tailored initiatives and programs, as done in New Zealand where local development agencies play the dominant role in the provision of migrant settlement services, might be a strategy to boost retention, which is an explicit policy aim in Australia (Home Affairs 2024). Alternatively, policymakers could target young families with children given their higher retention rather than younger and single migrants.

Most recent studies highlight the need to reevaluate the way rural retention is studied to objectively assess the success or failure of migration policies. They propose key benchmarks for successful regional policy to include social mobility, well-being, skill-matching, and fair-employment outcomes (Boese 2023). More information is needed about migrants’ lives post-migration and keeping this information flow continuous as retention is increasingly being viewed more dynamically as an ongoing process and not just a one-time decision (Hof, Pemberton, and Pietka-Nykaza 2021).

Supplemental Material

sj-docx-1-mrx-10.1177_01979183251330275 - Supplemental material for Are Visa-Based Dispersal Policies Effective in Attracting and Retaining Skilled Migrants in Rural Australia?

Supplemental material, sj-docx-1-mrx-10.1177_01979183251330275 for Are Visa-Based Dispersal Policies Effective in Attracting and Retaining Skilled Migrants in Rural Australia? by Dagamra Laukova, Aude Bernard, Tomasz Zając, Anthony Kimpton, Neil Argent and Thomas Sigler in International Migration Review

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the University of Queensland under its RTP scholarship and the Australian Research Council under its Discovery Project scheme (DP200100760).

ORCID iDs

Dagamra Laukova

Aude Bernard

Tomasz Zając

Anthony Kimpton

Neil Argent

Supplemental Material

Supplemental material for this article is available online.

Notes

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