Given that migration behaviour is in fact far from homogeneous, there are at least four implications for us. The first two implications relate to policy and arise because the heterogeneous migration behaviour results in different individuals bearing a different cost of adjustment.
The first implication relates to the share of the cost of adjustment. When a region experiences an adverse shock, for example, there needs to be some adjustment. This cost is spread across the heterogeneous population. More mobile people (i.e. those with a lower cost of moving) can capture the gains92 (avoid the costs93) of regional shocks.
The second implication is that the size of the cost of adjustment (due to a negative shock) may be magnified if there is “cumulative causation”. “Cumulative causation” implies that once a region becomes uncompetitive, it is very difficult to return to its original condition, in level terms. In other words, there is a downward spiral. This could happen for various reasons. For example, it could be due to the interaction between economic performance and the creation of human capital (see for example, Taylor (2000)). If this is the case, one has to consider the impact of the migration flow of particular subgroups on the origin and destination communities. For example, a continuous outward migration of young and highly skilled from declining regions may have negative effects of deskilling the regional population and further weakening the regions’ growth potential. The theoretical approach proposed by Roy (1951) argues that the size and direction of migration flows depend on regional differences in the returns to skills (as well as regional differences in mean income). Regions that pay higher returns to skills would attract more skilled workers than regions that pay lower returns. If this is the case, the economic impact of migration depends on which people move as well as on how many people move. Therefore, one has to consider the spillover and neighbourhood effects of migration patterns.
These first two implications can be thought of as the consequences of migration (see for example Greenwood (1997)). The consequences of migration can be addressed at two levels. One refers to the performance of migrants in their new locations relative to a benchmark, such as their presumed performance in their previous place of residence had they not moved. The second deals with the impacts that migrants have on others in origin and destination areas.
The third and fourth implications relate to further empirical work. The third one is that any modelling of migration needs to control for differences in population compositions. The fourth and final implication is that one has to take into account the selectivity bias. The migration decision in fact separates the population into those who expect to gain
92
In the case of a positive regional shock, the more mobile people are the ones benefiting. For example, if the government introduces assistance programs in particular regions, in-migrants will benefit from them.
93
On the other hand, when there is an adverse shock to a region, the more mobile people are able to leave while those remaining are the ones who have to bear the cost of lack of adjustment. For example, if a factory closes down, the more mobile people are able to move to other regions to look for jobs, but those unable to leave will remain unemployed in their region.
by moving, and those who expect to gain by staying. If we ignore this selection process when we estimate the earnings equation (or any other labour market variables) of the two sub-groups, then we may wrongly find more gains to migration than there actually are. Therefore, we have to consider the selectivity bias problem when we conduct our empirical work.
6.2 How?
This subsection discusses how researchers identify the heterogeneous migration behaviour, as well as the implications of this heterogeneity on regional labour market adjustment. Before we proceed, it is worth highlighting three main ways in which the approaches used are different.
The first distinction is between univariate (using a single characteristic to explain migration behaviour) and multivariate studies (using more than one characteristic). The second distinction lies in the unit of analysis (essentially the type of data used). Some studies use area (or group) characteristics to explain migration rates of the area (or group). These are generally called aggregate studies. On the other hand, there are studies that use not only area characteristics, but also individual characteristics. These studies have access to micro-level data (be it from the census, or survey data, along the lines proposed by Statistics New Zealand as discussed in Section 4.2). Finally, the third distinction is between studies that use a short time series and those that use a long time series. The minimum time variation in order to be able to say anything about the implications of who moves on regional labour markets, is one time window (i.e. a snapshot of the world at two points in time). The New Zealand census is one example of such a data set94. With a longer time series, there is much more information that we can infer from the data about the implications on regional labour market, because the adjustment process would have had enough time to work itself out. An example of a data set that provides such long time series is the National Longitudinal Survey of the Labor Market Experience of Youth (in short, National Longitudinal Survey of Youth), in the United States. McCarthy (1992) used this data set to assess the unemployment and wage motivations for migratory behaviour among young males. Given these distinctions, we shall discuss in more detail the range of approaches that researchers have used to examine the patterns of migration behaviour.
6.2.1 Identifying heterogeneity
When identifying personal characteristics that influence mobility patterns, one can focus on a single dimension (univariate studies) or look at the interaction of multiple dimensions (multivariate studies). We shall begin with a discussion on univariate studies, which is the easiest to carry out.
Univariate and simple descriptive studies
Studies of this type have typically used descriptive techniques, such as cross tabulations, simple descriptive statistics and bar charts, to infer about the links between personal characteristics and migration.
A few examples are worth mentioning here (as cited from Herzog, Schlottmann et al. (1993)). Saben (1964) inferred from cross tabulations that unemployed workers in the United States are more likely to migrate than are the employed. Meanwhile, Lansing
94
A good discussion of the scope, limits and reliability of using the New Zealand census data on internal migration is provided by Poot (1986a).
and Mueller (1967) document the important link between personal unemployment and migration, to the extent that such causation can be revealed within summary tables. However, this approach has a few limitations. Firstly, such an approach does not serve to formally quantify the relationships between migrant and area characteristics, and migration propensities. Secondly, it (wrongly) assumes that there is causality running from one variable to another (e.g. unemployment driving migration decisions).
Thirdly, such descriptive techniques do not adequately control for other personal and place characteristics that may affect migration. In other words, the univariate approach focuses on one characteristic alone, in isolation of, or ignoring other characteristics (i.e. only a partial analysis). Without such controls, one may well reach the wrong conclusions. For example, the stereotypical image in Australia and some (naïve) stylised facts may suggest that indigenous Australians are more mobile than the rest of the population (as cited from Taylor and Bell (1996)). However, these observations do not consider variations in age structure across different ethnicity groups. The higher mobility rate among indigenous Australians could be due to the greater concentration of indigenous people in the more mobile, youthful age groups. By standardising the data for the effects of variable age structure, Taylor and Bell (1996) find that the mobility rates for the two groups are essentially the same.
Therefore, it is important to consider the interaction between and among different characteristics that are expected to influence migration behaviour. One can of course extend the descriptive analysis to two or more personal characteristics. However, descriptive studies still face other limitations, some of which were discussed above. This leads us to seek multivariate approaches, which (as the name implies) permit the researcher to examine the relative contribution of a range of characteristics on migration behaviour.
Multivariate studies
A simple multivariate approach is the shift-share technique. As discussed in Section 4.2, the shift-share method is basically a decomposition technique. This is one way for decomposing the change in a particular variable in a region (e.g. change in migration flow) to changes in the variable of interest (change in migration behaviour or mobility) and other influencing variables such as demographic effects (e.g. age). In principle, one can of course control for other dimensions of interest, for example skill, provided that there are sufficient data available. However, the shift-share technique is still very much a descriptive technique. It does not explain why certain regions have locational advantages, or why mobility patterns have changed as they did.
An example of the use of the shift-share technique is provided by Green (1994). Green (1994) provides an illustration of the use of the shift-share approach to examine the role of migration in bringing labour supply and demand into balance (or in mitigating imbalance) in Britain in the 1980s. The author decomposes the change in the number of migrants between a base period and a terminal period into three additive components: the population base component (the ‘age composition’ effects), the mobility component (the age-specific rates of inter-area mobility), and the geographic- distribution component (the change in attractiveness of specific destinations for out- migrants from specific origins). The identity equation is as below:
ijk ijk ijk ijk A B C M = + + ∆
where ∆Mijkis the change in the number of migrants from origin i to destination j in age group k during the intervening period.
ijk
A is the population base component, which is a purely demographic supply measure, encompassing ‘age composition’ effects.
ijk
B is the mobility component.
ijk
C is the geographic-distribution component, that is, the portion of the change in migration by persons of age k from i to j that is attributable to increases or decreases in the destination-specific flow after the effects of the population base (A ) and mobility ijk (B ) components are removed. ijk
Although the shift-share technique can help control for some characteristics of interest, while analysing people’s propensities to move, it can only do so to a certain extent. The number of dimensions one can control for are limited to usually three or four; the analysis gets rapidly more complex with more variables.
A more sophisticated and elaborate multivariate approach is the regression technique. The regression technique allows one to assess the impact of a particular characteristic on migration behaviour, controlling for other characteristics (i.e. under true ceteris paribus conditions). The regression technique usually uses the ordinary least squares (OLS) estimator and variants of it. Since these have been covered earlier in Section 4.2, we shall only highlight issues that are particularly relevant to this key question, for example the use of unit record level data.
Aggregate studies
The regression analysis could be based on average area characteristics (or composition of the area). Such aggregate studies formed the bulk of early research. These studies basically modelled the number of individuals moving across areas as a function of distance, and area characteristics (e.g. the unemployment rate and the average wage level at the origin and destination). Later studies extended the explanatory variables to include demographic characteristics of the migration streams such as the average age and education levels. However, these studies are still of an aggregate nature.
Studies of this category have already been discussed in Chapter 4. However, it is worth noting a recent example of such a study in the New Zealand context. Kerr, Maré et al. (2001) attempted to identify the characteristics that push people to move and/or pull them to a new location. Their paper investigates the effect that the characteristics of a community have on the likelihood of people leaving and/or travelling to the community. The data are aggregated to an Area Unit (equivalent to a suburb); therefore, the smallest definable community is an Area Unit. They use geographical information system (GIS) tools to define variables based on aggregations of meshblocks around the area units of interest.
Such aggregate studies have been criticised on several grounds, three of which are particularly noted here. Firstly, the theoretical foundation on which most of these studies were based is Sjaastad’s (1962) human capital approach and variants of it, which focuses on the behaviour of individuals. In order to analyse the individual’s migration decision, strictly speaking one needs to have individual characteristics as well as area characteristics, rather than area characteristics alone. A related criticism
of aggregate studies is that a high qualification area may have a high mobility but this does not necessarily mean that it is the high-qualification people who are moving. Secondly, aggregate studies fail to adequately capture the heterogeneity of the stream of individuals moving from one area to another. Aggregate data is likely to conceal differences in the underlying determinants of migration of various population subgroups. Thirdly, there is still the selectivity bias problem, as discussed in Section 4.2. While it is noted that one necessarily requires unit record data to control for selectivity bias, using microdata in itself does not necessarily absolve one from the selectivity bias problem.
Individual (microdata) studies
There is quite an extensive list of studies employing individual data in countries such as the United States and Britain (see for example, Böheim and Taylor (2000), Gardner, Pierre et al. (2000), Enchautegui (1997), and Day (1992)). In the New Zealand context, there have been few such studies.
However, one such study is by Vaithianathan (1995) who used micro level data (1991 census data) to model the Maori and non-Maori males regional migration decision. The author employed a binary logit model (move/stay) to assess the effect of observed regional and personal characteristics on the probability that an individual leaves an area.
Longitudinal data
Furthermore, there is a strand of literature that takes into account a number of life-cycle considerations that are potentially important in an individual’s or a family’s decision to migrate. For example, a migration decision is likely to be made in conjunction with other events such as marriage or divorce, birth and ageing of children, graduation from school, episodes of unemployment and finding new jobs, entering the workforce and retirement. These studies employ event history data or longitudinal data (which tracks the same individuals or families over time). Alternatively, where no “pure” longitudinal data set is available, one can use pooled cross-sectional microdata (i.e. “synthetic” longitudinal data). The advantages of using longitudinal data and some key references (e.g. Odland (1997)) have already been discussed in Chapter 4.
As far as we are aware, little work has been done in New Zealand to capture such dynamic effects (e.g. migration history). The only example we have come across is Heenan (1999). This paper is based on autobiographical details of residential mobility for a small sample of New Zealand male career professionals collected in 1981. Classified by cohort and employment sector, the information is used to examine migration in the context of personal career development. Aspects explored include age of entry to the workforce, frequency of movement, migration and career promotion, and the spatial structure of migration. However, this study has a small sample size. Despite this caveat, this New Zealand study is nevertheless a good attempt to account for the context and individual decision-making processes involved.
One reason for the scarcity of longitudinal studies in New Zealand is because we do not have a suitable longitudinal data set. We only have cross-sectional data (based on the national census), which are available over several five-year periods. Based on such data, one can only use either aggregate studies (impact of “area” characteristics, or “average among individuals in the area” characteristics, on migration flows) or individual studies (if unit-record data is available).
Case studies
Another approach that has been used to identify sub-groups within the population who are bearing the cost of adjustment or the lack of adjustment (i.e. identifying heterogeneity in migration flows) is using case studies. This approach contrasts with the other approaches discussed earlier because it focuses on a particular location and has advantages particularly when mobility patterns and their dynamics differ across locations. Le Heron and Pawson (1996) and Peck (1985) provide a good starting point for thinking about possible case studies in New Zealand to address our key questions. 6.2.2 Implications of heterogeneity on regional labour market adjustment
As discussed earlier, the consequences of migration can be addressed at two levels – one deals with the migrants themselves, and the other deals with migration’s impact on others in the origin and destination areas.
In general, although there has been much research on the determinants of migration, little research has focused on the consequences of migration (see Greenwood, Mueser et al. (1991) and Greenwood (1997)). However, the first type of consequence (i.e. impact on migrants themselves) has been studied relatively more than the second (i.e. impact on origin and destination). As Greenwood (1997, p.650) puts it, “… the second [consequence], which although potentially important, has not been studied in sufficient depth regarding internal migration”. Greenwood, Mueser et al. (1991) argues that this is because micro and panel data, while valuable for studying the behaviour of individuals (rather than streams of aggregate movement) and on decision-making processes, “…do not lend themselves to an analysis of the consequences of migration, except the personal consequences”.
Many studies, however, put forward speculative and often untested statements that imply that the question of who moves have some impact on origin and/or destination areas. As an illustration, the quote below is taken from Green (1994):
“The concerns of many policymakers with retaining more highly qualified young adults in their origin areas to contribute to local economic development would appear to be well placed.” (p. 1576)
Of those studies that have in fact looked at assessing the effects of shocks and migration flows on the community, case studies are probably the most common approach used. One could focus, for example, on the impact of industrial closedowns and mass redundancies. As mentioned earlier, Le Heron and Pawson (1996) and Peck (1985) provide a good starting point for thinking about possible case studies in New Zealand to address this second part of the third key question.
6.3 Findings
This subsection summarises the findings from research that seeks to identify the heterogeneous migration behaviour, and the impacts of this heterogeneity. For the latter, the discussion will be divided into two parts; one looks at the impact on individuals themselves, and the other focuses on the link between who moves (i.e. the heterogeneous pattern) and the effects on the origin and destination communities. The first part effectively covers the share of the cost of adjustment while the second looks at the size of the adjustment cost.
6.3.1 Identifying heterogeneity
The conclusion from this subsection is that migrants’ behaviour is far from homogeneous. It depends on factors such as qualifications, family structure, gender, home ownership, ethnicity, etc. And this is true for New Zealand and for other countries. Generally, there is evidence that younger, highly skilled, single males, who