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LABOUR INSTITUTE FOR ECONOMIC RESEARCH • * This study is a part of a project supported by the Academy of Finland (project number 41157).
** Labour Institute for Economic Research *** School of Business and Economics, University of Jyväskylä Abstract. Depressed regions typically lose a large number of migrants, but simultaneously are destination regions for some migrants. This paper analyses those people who decided to move to depressed regions in Finland in 1993-96. The analysis is based on a one-percent sample drawn from the Finnish longitudinal census. The results show that migration into depressed regions is also a selective process. However, the more educated an individual is, the more likely (s)he is to move to a prosperous region. The process of concentration of human capital is reinforced by in- Micro level investigation of migration essentially relates to the processes underlying the decision by a potential migrant either to remain in the current residence or to migrate elsewhere (Stillwell and Congdon 1991). Numerous migration studies based on micro data have dealt with out- migration, by analysing the characteristics of out-migrants and regions of origin. Typically, these studies do not account for why a particular region is chosen as the destination region. The rest of the country is treated as the single destination of all migrants from the region of origin. In a sense, the lack of destination region information assumes that a migration decision is based on back- ward-looking (origin region) considerations, in spite of the fact that theoretical models generally indicate that the attractive pull from the destination region is equally important. Because informa- tion about destinations is potentially important in decisions to migrate, it merits inclusion in this equation. One problem, however, is that the decision as to which of the alternative destination re- gions is chosen is a very complex one and hard to model, especially if the number of destinations is large and the number of migrants small.
There are only a limited number of studies which integrate an analysis of the decision to migrate from a region with a study of the destination choices made by regional migrants (see Hughes and McCormick 1994; Molho 1987; Mueller 1982). Our paper deals with the question of destination choice, though from a limited viewpoint. We do not analyse the destination choice-process, but ask instead: what people decide to move to declining regions? Do they differ from the in-migrants of prosperous regions? In that respect, our analysis resembles Haapanen’s (1998) study, which analysed those individuals who had a terminated spell of unemployment, by modelling their mi- gration behaviour between two destination alternatives, growth-centre regions and non-growth regions, as against the decision not to migrate.
As is well-known, the classical equilibrating theories of migration argue that workers move from depressed regions to prosperous regions. These models predict that interregional migration will help to bring about regional labour-market equilibrium. In reality, each region is always experi- encing both in- and out-migration, although migration to prosperous regions is consistently denser. In consequence, there is always a large group of workers who move in the “wrong“ direc- tion, i.e. into depressed regions. The question of the reasons for these apparently perverse migra- tion streams, as well as the question of their effects, is a largely neglected aspect of migration studies, especially from the point of view of the equilibrating process of regional labour markets.
In our analysis, we separate regions into different categories according to their unemployment level. We concentrate especially on analysing those who migrate to high-unemployment regions.
Previous research has shown that the characteristics of and reasons for moving are rather similar with respect to the place of origin (e.g. Ritsilä and Tervo 1999), but are they different with respect to the destination? This is one of the main questions in our paper. Another question relates to the effects of migration on depressed regions. Presumably, these effects are highly dependent on its selectivity. A well-known fact is that the migration process is selective of the young and more edu- cated part of the population, but is this also the case with the in-migrants of the depressed re- gions? If it is, i.e. that professional, managerial and skilled labour is also over-represented in the pool of in-migrants to depressed regions, the strangling effect of inter-regional migration is not that severe. If the in-migrants are as qualified as the out-migrants, the loss is quantitative rather than qualitative, the human capital loss mainly relating to the net migration loss.
Our analysis deals with migration streams of the working-age population in Finland in 1993-1996.
Inter-regional migration has accelerated in Finland in the 1990s. The main migration flows have been directed to urban areas, mainly located in the South. In addition to the established trend that rural areas lose population, several small towns and middle-sized urban areas are now also de- The data set is a one-percent sample drawn from the Finnish longitudinal census containing data on population, economic activity, dwelling conditions and family background. The census file is maintained and updated by Statistics Finland. Our analysis concerns the long-distance migration of the population aged between 18 and 75 (in 1996), which is determined to have taken place if an individual of working age moves from one province (NUTS 3-level regions, 19 in number) to an- other. In practice, a move is registered if the province of domicile in 1993 is different from that in The rest of the paper is structured as follows. Section 2 analyses in- and out-migration streams and their relationships in Finland, stressing the existence of the phenomenon of “perverse“ migra- tion. In section 3, we examine whether the in-migrants of different regions differ from each other, especially with respect to educational level and other characteristics. In the modelling of a worker’s decision to move into the depressed or prosperous regions as against the decision to not move, we exploit the multinomial logit method. In section 4, we analyse further the human capital content of in-migrants of depressed regions with a measure of educational level. Section 5 As each region is always experiencing both in- and out-migration, gross migration between re- gions far exceeds net migration, and a substantial amount of apparently perverse migration oc- curs. Many studies have even observed a strong positive relationship between out- and in- migration (e.g. Mueser and White 1989; Mueser 1997). In general, migration to declining areas follows from the fact that labour is not homogeneous. Individuals move between regions for a va- riety of reasons. Return migration may play an important role. Many may also move to depressed regions for individual advancement, as a part of a career plan or because of a company transfer The fact that each region is always experiencing both in- and out-migration can also easily be ob- served in Finland. Table 1 shows the out- and in-migration streams and rates between four catego- ries of local labour market areas, as classified according to their unemployment rate. The regions (local labour market areas) are divided into quartiles by the level of unemployment. In these analy- ses, a person is registered as a migrant if her/his province of domicile in 1996 is different from that in 1993. We have used data which are a one-percent sample drawn from the Finnish longitudinal census file. Our data only include those individuals who were residents of Finland in both 1993 and 1996 and who were aged between 18 and 75 in 1996.1 Table 1 suggests that net-migration rates behave as expected with regard to unemployment, viz.
net-migration is the greater, and out-migration is the smaller, the better is the unemployment situation in the region. These results confirm for their part the hypothesis that labour mobility is an important response mechanism with respect to regional unemployment disparities (Tervo 1997; Herzog et al. 1993; Pissarides and Wadsworth 1989; Herzog and Schlottman 1984).2 Contrary to 1 When analysing a comparatively long period as here (three years), there is the drawback that some movers may have migrated more than once. In our data, of the 1729 movers 84 (4.9%) have migrated twiceand 11 (0.6%) three times. In addition, 145 persons (0.4% of all persons included in the data ) have movedback to the province where they lived in 1993. These cases are not counted as movers, since their domicileof province is the same both in 1993 and 1996.
2 It should be noted that this result may be accounted for by both regional and personal unemplo- net- and out-migration rates, in-migration rates do not seem to behave consistently among our four regional categories, since in-migration to the second quartile of regions is higher than to the first quartile of regions. From our viewpoint, the most interesting finding relates, however, to the fact that even the most depressed regions are destination regions for some migrants. In fact, these regions simultaneously receive a large number of migrants, even though they lose a still larger number of residents and the net migration rate is negative. This fact has received only scant attention in empirical migration research.
Out- and in-migration in four categories of regions classified according to their Note: The data is a 1 percent sample of those people aged between 18 and 75 (in 1996) who were living inFinland in both 1993 and 1996. Migration relates to the period 1993-96. The regional break-down is basedon travel-to-work areas, which are divided into four equal-sized categories according to their unemploymentrates. The upper endpoints of the four categories were 18.1, 22.1, 24.2 and 35.1 in 1993 and 15.3, 19.9, 22.1and 40.4 in 1996.
Related to this, it is important to take into account the role played by return migration in migration flows into different regions. Table 2 below presents return migration flows into different regions divided into four categories by the level of unemployment. These reported return migration flows deal with migrations where a person moves back to a province where (s)he lived before.3 In our sample, the share of return migrants among all long-distance migrants is considerable, amounting to around one-third (29.9%). Of the migration to the most depressed regions, return migration ex- plains 34%. The corresponding shares of return migration to other regions are smaller, the lowest share of return migration being 27% to a low unemployment area. Although the observed regional differences are statistically significant (p=.04), they are not very great.
3 A migrant is defined as a return migrant, if (s)he moved in 1993-96 to a province where (s)he lived in one of the following years: 1970, 1975, 1980, 1985 or 1987-1992. In addition, a migrant is return migrant if(s)he moved to the province where (s)he was born.
Out- and in-return migration in four categories of regions classified accordingto their unemployment level Notes: The data is a 1 percent sample of those people aged between 18 and 75 (in 1996) who were living inFinland in both 1993 and 1996. Migration relates to the period 1993-96. Educational level is measured in1996. The regional break-down is based on travel-to-work areas, which are divided into four equal-sizedcategories according to their unemployment rates. The upper endpoints of the four categories were 18.1,22.1, 24.2 and 35.1 in 1993 and 15.3, 19.9, 22.1 and 40.4 in 1996. p-values show the lowest significancelevel at which the null hypothesis of equal educational levels can be rejected (one-way variance analysis).
In contrast, inspection of out-migration flows reveals greater regional differences in the shares of return migration. Up to 45% of all out-migration from regions with low unemployment can be la- belled as return migration. In other words, return migration takes place in nearly half of the migra- tions from prosperous, low unemployment regions. For all the other regional categories, return migration does not play as important a role in out-migration. The differences are highly statistically significant (p=.000). A typical return migrant moves back from a prosperous region to a de- pressed area. These people have perhaps failed to attach to their destination regions and move back, though unemployed, or they are retired people who want to go back to their native regions.
3. Do the in-migrants of depressed regions differ from The purpose of this section is to ascertain whether the in-migrants of the depressed regions sta- tistically differ from the stayers or from the migrants to other regions. Thus, in essence, we are interested in the influence of personal characteristics, family situations, labour market conditions and the characteristics of the regions where migrants originally were living on the decision to choose a particular region. We are especially interested in the impact of education.
In the analysis, we use a categorisation of destination areas into depressed areas and others which is carried out according to the level of unemployment. We divided destination areas into those characterised by a high unemployment rate and into other areas (with lower unemploy- ment). The regions characterised by high unemployment, i.e. depressed regions, constitute the fourth quartile in our regional breakdown (cf. Table1).
In the empirical analysis the decision to migrate to depressed or to other regions is modelled by the multinomial logit model. In our model, using the level of unemployment as the criterion for whether the destination area is depressed or not, we assume that the individual makes a choice from among the three following alternatives: Yi =0 if the individual does not migrate, Yi =1 if the individual migrates to a depressed region, Yi = 2 if the individual migrates to other region.
Thus the dependent variable in the model is Yi and can take values from 0 to 2.
The estimation of the multinomial logit model provides a set of probabilities for these three differ- ent destination choices of an individual with characteristics xi. These probabilities are given by : Prob(yi =j)= exp(βj’xi )/(1 + ∑ exp(βk’xi )), for j=1,2,…,J, where βj’s are unknown parameter vectors.
The method of estimation for our multinomial logit is maximum likelihood. The maximum likelihood estimates for βj‘s are difficult to interpret (Greene 916, 1997). Therefore rather than reporting thecoefficients from the multinomial model we prefer to report the marginal effects of the regressors on the probabilities ∂Pj/∂xi. These marginal effects can be calculated as ∂Pj/∂xi=Pij [βj - ΣPikβj ].
Our dependent variable is uneven in the sense that different migration categories have uneven number of observations. The greatest difference is between the non-migrant category, which acts as a reference group, and the other two groups. Only 4.7% of the individuals in our sample are registered as migrants. Of these migrants 20.7% had a high unemployment area as their destina- tion region and, respectively, 79.2% a lower unemployment area. Small migration likelihood has an influence on the calculated marginal effects for groups 1 and 2, which are bound to be smaller.
In addition to reporting the marginal effects, we also calculate log-odds ratios based on the model: Ln[Pij/Pik]=(βj’- βk’)xi. By assumption, the odds ratios in the multinomial logit model are independ-ent of the other alternatives. This property of Pj/Pk being independent of the remaining probabilitiesis called the independence of irrelevant alternatives (Greene 1997, 920). In the case of unbalanced data, log-odds are useful in the comparison of the odds of individuals with different characteris- tics. They provide perhaps more illustrative information on the migration probabilities of individu- als with different characteristics than the marginal effects. With the help of log-odds we can, for example, compare the odds of an individual with higher education versus an individual with inter- mediate level education to move to depressed regions or, alternatively, to move to lower unem- The employed explanatory variables can broadly be grouped into personal characteristics (age, sex, educational level), family and household characteristics (marital status, number of children under 18, home ownership), labour market characteristics (unemployed, student, pensioner) and regional characteristics of the area of origin (the local unemployment rate, number of residents).
The following table (Table 3) presents brief descriptions of the explanatory variables, their sample Descriptive statistics: definition and sample means value 1 if individual is under 30 years in1993 and 0 otherwise value 1 if individual is of age between 30and 45 years in 1993 and 0 otherwise value 1 if individual has an intermediatelevel education (classes 3-4, see Table 6) value 1 if individual has a higher educa-tion (classes 5-9, see Table 6) Marital status, a dummy variable, which is assigned value 1 if married or cohabitingand 0 otherwise value 1 if children under 18 years old (in1995) and 0 otherwise which is assigned value 1 if one ownshouse or owns shares in a housing cor-poration, 0 otherwise value 1 if individual is student and 0 oth-erwise value 1 if individual is pensioned and 0otherwise Size of municipality (number of residents The marginal effects (expressed as percentages) calculated from the multinomial logit model and their significance levels are given in Table 4. For comparison, we also report results from a simple bivariate logit estimation (Table 5) where the category of migrants to other areas (0) acts as a ref- erence group to those migrating to high unemployment areas (1). This provides us with a means to test whether the migrants to depressed regions differ statistically from other migrants. Other- wise, the results from the multinomial model are in accordance with those from the binomial Notes: t-values in brackets. Restricted log-likelihood (lnL0) is the maximized value of the log-likelihood func- tion computed with only the constant term lnL0. Likelihood ratio index corresponds to R2 in the normal re- gression and is calculated as LRI=1 - (lnL/lnL0). Marginal effects are expressed as percentage shares.
Table 5. Binary logit model: migration to high unemployment area (1) vs. other area (0) Notes: t-values in brackets. Restricted log-likelihood (lnL0) is the maximized value of the log-likelihood func- tion computed with only the constant term lnL0. Marginal effects expressed as percentage shares.
With regard to gender, the calculated marginal effects imply that women have a higher probability to migrate to both high unemployment areas and other areas than men do, but not at conventional significance levels. Continuing with personal characteristics, the impact of age was taken into ac- count in our model by two dummies, one denoting whether a person is under 30 years (YOUNG) and the other denoting whether a person is aged between 30 and 45 (MIDDLEAGED). The results suggest, in line with other studies, that persons under 30 years have a higher propensity to mi- grate than those over 30 years. The reasons for the lower incentive to migrate as one gets older are, among other things, a shorter expected working life over which to realise the advantages of migration, the increased importance of family ties and job security (Cadwallader 1992). As regards the destination of migration, according to the calculated marginal effects, persons under 30 years have a 2.8 percentage points higher migration probability to other areas as compared with high unemployment regions and, respectively, the middle-aged have a 1.2 percentage points higher probability. The calculated odds4 that a middle-aged person versus a young person will belong to 4 These odds are calculated as Prob(yj|middleaged=1)/Prob(yj|young=1).
migration category 1 (y=1) are 0.53, which exceeds the corresponding odds of 0.35 that the same individual will belong to group 2 (y=2). This suggests that middle-aged persons have a higher tendency to move to depressed regions. The logit-results, which directly compare the possibility of moving to depressed regions as against moving to other regions, show that young people, es- pecially, have a higher tendency to move to other than depressed regions. This result is statisti- cally significant. The estimated coefficient on the variable MIDDLEAGED is also negative, but not Typically, people with higher education tend to have a higher propensity to migrate. Our data also shows that this is the case, especially if the main direction of migration is towards other than de- pressed regions. There seem to be differences in the probabilities of choosing a certain destina- tion for persons with divergent education. Our results indicate that the probability to migrate to other than unemployment regions is around 0.5 percentage points higher for those who have an intermediate level education and 1.6 percentage points higher for those who have higher educa- The calculated odds for a person with an intermediate level education versus a person with higher education to belong to migration category 1 (y=1) are 0.80. The corresponding odds to belong to a group 2 (y=2) are 0.53. Therefore, on the basis of these calculations, it would seem that those with less education have a higher tendency to move to high unemployment regions. The binomial logit results (Table 4) also confirm this result.
We evaluated the impact of children on the choice of migration destination by including in the model a dummy variable for a person to have children under 18 years old or not. The calculated marginal effects imply that under 18-year-old children are a greater deterrent for those moving to other than depressed regions. However, when comparing the calculated odds for a person with children under 18 with the odds for a person without children under 18 to migrate to depressed regions and, alternatively, the odds on these two individuals, respectively, moving to other re- gions, the differences between these two groups are rather small (0.54 vs. 0.42). The logit-results do not show statistically significant differences either. With regard to the effect of marital status, the calculated marginal effects are the same for groups 1 and 2, but not significantly. As verified by many previous studies (e.g. Tervo 1997), home ownership influences negatively the decision to migrate, and this is the case in our model. However, the negative influence exerted by home own- ership is smaller when the individual’s destination of migration is a high unemployment region.
In the model we also surveyed the effects of labour market status on the probability to migrate to depressed versus other regions. Our results indicate that if person is unemployed, s(he) is en- couraged to migrate. The logit-results suggest that the effect of personal unemployment is stronger in the cases of moves to depressed regions (p=.07). Further, according to the results, students have a one percentage point higher migration probability to move to other than high un- employment regions. This is perhaps because most student places are situated at the regional centres of those provinces which are not usually among the highest unemployment regions. If person is retired, this will have a negative effect on his/her propensity to migrate, but this negative impact is smaller where the migration is to high unemployment regions.
4. The human capital content of in-migration to depressed re- Our results above confirmed the well-known fact that migration is selective of the more educated and skilled members of the labour force. The results indicated, however, that those moving to un- employment regions are less educated. Next we analyse more thoroughly the question of the hu- man capital content of “perverse“ migration, i.e. migration to depressed regions.
In the analysis of the educational level of migrants and non-migrants, we have exploited a measure based on the Finnish Standard Classification of Education by Statistics Finland which is a weighted average of the educational level of the people in question.5 Educational level is meas- ured in 1996 among those who migrated in 1993-96 as well as among those who stayed at their home regions. Theoretically, educational level measured in this way can range between 1.5 and 8.
In practice, the variation is much smaller. In Finland, the educational level of the working-age population varied between 2.70 and 3.38 by provinces in 1996, averaging 3.05 in the country as a Table 6 shows the distributions of educational level among the migrants and stayers. The measure of educational level obtains the value of 3.81 among the migrants and the value of 3.02 among the non-migrants.6 People with only basic education or the lower level of upper secondary education (categories 1.5 and 3) clearly move less frequently than people with the upper level of upper sec- ondary education or higher education (categories 4 to 8).
5 The formula for this measure is as follows: where fi is the number of people and xi is the level of education (from 1.5 to 8, see Table 6).
6 The above migration concerns long-distance migration, i.e. migration from one province to another.
It is interesting to note that among short-distance movers, i.e. among those who migrate between municipa-lities, but not to another province, the educational level is 3.36, which is lower than among long-distancemovers but higher than among stayers.
Educational level of migrants and non-migrants Undergraduate level of higher education 6 Note: The data is a 1 percent sample of those people aged between 18 and 75 (in 1996) who were living inFinland in both 1993 and 1996. Migration relates to the period 1993-96. Educational level is measured in1996.
Table 7 shows the results as to the educational level of migrants in local labour market areas clas- sified into categories according to their unemployment level. In this table, we exploit the same re- gional breakdown as above: travel-to-work regions are divided into four approximately equal-sized categories according to their unemployment rate so that, e.g., the first category includes those regions with the lowest unemployment rate and the fourth category those regions with the highest unemployment rate. The educational level of the people in these regions is measured among both in- and out-migrants as well as among stayers.
Educational level of out- and in-migrants in four categories of regions classi- fied according to their unemployment level Stayers Out-migrants In-migrants Indexes Notes: The data is a 1 percent sample of those people aged between 18 and 75 (in 1996) who were living inFinland in both 1993 and 1996. Migration relates to the period 1993-96. Educational level is measured in1996. The regional break-down is based on travel-to-work areas, which are divided into four equal-sizedcategories according to their unemployment rates. The upper endpoints of the four categories were 18.1,22.1, 24.2 and 35.1 in 1993 and 15.3, 19.9, 22.1 and 40.4 in 1996. p-values show the lowest significancelevel at which the null hypothesis of equal educational levels can be rejected (one-way variance analysis).
Table 7 reveals at least three interesting facts. First, the educational level of in-migrants varies sig- nificantly across regions. Those migrating to low unemployment regions are clearly more edu- cated than those migrating to high unemployment regions. In fact, the educational level of in- migrants is the lower, the higher the unemployment rate in the region. But if we compare the edu- cational level of in-migrants with that of the stayers (index C/A) we observe only small differences between the four regional categories. This means that the educational level of in-migrants is, more or less, in proportion to the prevailing educational level in the region. In particular, the in-migrants of the low unemployment regions do not have an especially high educational level compared with the in-migrants of other regions, rather the contrary, even though the in-migrants of these regions Second, there also seems to be some regional variation in educational level among the out- migrants. This variation is not, however, as great as among the in-migrants. The differences be- tween the educational level of out-migrants in the four regional categories are only indicatively sta- tistically significant (p = .083). Perhaps surprisingly, the educational level of out-migrants is not highest in the low unemployment regions, but in the intermediary regions in which unemployment is neither especially low nor especially high. Related to this finding, the index describing the rela- tionship between the educational level of out-migrants and stayers (index B/A) shows that the relative educational level of out-migrants is clearly lower in the low unemployment regions com- pared with all the other regions. The other regions do not differ very much from each other in this respect, even if the out-migrants of the most depressed regions are relatively highest educated (as compared with the population in the region of origin).
The third interesting finding concerns the relationship between the educational levels of in- and out-migrants (index C/B). This index shows that the educational level of in-migrants as compared with that of out-migrants is the higher, the lower the unemployment level. The disequilibrating na- ture of inter-regional migration is again observable here. In all, these results show that the more educated an individual is, the more likely she/he is to move to low unemployment regions. The most depressed regions receive those migrants who, on an average, are less educated, even if more educated than the original inhabitants. These regions also deliver up highly educated migrants to other regions. This finding is further strengthened if the educational level of out-migrants is compared with the educational level of stayers or in- migrants to these regions. The process of concentration of human capital is clearly reinforced by This paper analysed those people who decided to move to depressed regions in Finland in 1993- 96. The number of these people is large, even though the number of out-migrants is still larger. A considerable proportion of this “perverse“ migration consists of return migration. The share of re- turn migration is not, however, very much bigger among the in-migrants to depressed areas than among the in-migrants to other than depressed areas.
Our results showed that those moving to depressed areas differ in many respects from those staying in the region. In this sense, this migration is also selective. It is, however, worthy of note that the effect of education is not as clear as it is in the case of moves to more prosperous re- gions, although those moving to depressed areas are less educated. Actually, the more educated an individual is, the more likely (s)he is to move to a prosperous region. In addition, those moving to depressed areas are older and more often unemployed than those moving to other regions.
Furthermore, the out-migrants of the depressed regions are highly educated compared to the population in the region of origin. The process of concentration of human capital is clearly rein- forced by inter-regional migration. The exchange of population produced by inter-provincial mi- gration weakens the development potential of depressed areas both quantitatively (decrease in population) and qualitatively (decrease in human capital).
Cadwallader, M. 1992. Migration and residential mobility. Macro and Micro Approaches. Wiscon- sin: The University Press of Wisconsin Press.
Greene, W. 1997. Econometric analysis (3rd edition). New Jersey: Prentice Hall.
Haapanen, M. 1998. Internal migration and labour market transitions of unemployed workers. Vatt- discussion papers. Helsinki: Government Institute for Economic Research.
Herzog, H. and A. Schlottman. 1984. Labor force mobility in the United States: migration, unem- ployment, and remigration. International Regional Science Review 9:43-58.
Herzog, H., A. Schlottman and T. Boehm. 1993. Migration as a spatial job-search. Regional Stud- Hughes, G. and B. McCormick. 1994. Did migration in the 1980s narrow the north-south divide? Mueller, C. 1982. The economics of labor migration. A behavioral analysis. New York: Academic Mueser, P. 1997. Two-way migration in a model with identical optimizing agents. Journal of Re- Mueser, P. and M. White. 1989. Explaining the association between rates of in-migration and out- migration. Papers on the Regional Science Association 67:103-120.
Molho, I. 1987. The migration decisions of young men in Great Britain. Applied Economics 19:221- Pissarides, C. and J. Wadsworth. 1989. Unemployment and the inter-regional mobility of labour.
Ritsilä, J. and H. Tervo. 1999. Regional differences in the role of migration in labour-market ad- justment: the case of Finland. In Regional unemployment, job matching, and migration, ed. G.
Crampton. London: Pion Limited,166-182.
Stillwell, J. and P. Congdon. 1991. Migration modelling: concepts and contents. In Migration mod- els: macro and micro approaches, eds. J. Stillwell and P. Congdon. London and New York: Bell- Tervo, H. 1997. Long-distance migration and labour market adjustment: empirical evidence from Finland 1970-90. Working paper 168, School of Business and Economics. Jyväskylä: University of Palkansaajien tutkimuslaitos / Labour Institute for Economic ResearchTutkimusselosteita / Discussion Papers (ISSN 1236-7184) Kaj Ilmonen, Työmarkkinajärjestelmä, talouden kansainvälistyminen ja ay-liike, 1995.
Jukka Pekkarinen, Keynes ja velkadeflaatio, 1995.
125 Juhana Vartiainen, Can Nordic Social Corporatism survive? Challenges to the Labour Seppo Toivonen, Kiinteistöveron käyttö lähiöiden perusparannushankkeiden rahoituk- Hannu Piekkola, Taxation under economic integration, 1996.
Petri Böckerman, Tanskan pitkäaikaistyöttömyys ja sen hoitokeinot, 1996.
Mari Kangasniemi, Työmarkkinoiden polarisoituminen: Kirjallisuuskatsaus, 1996.
130 Petri Böckerman, Ansiotyösidonnaisista tukijärjestelmistä saadut kansainväliset koke- Petri Böckerman, Työsopimukset, organisaatiorakenne ja tuottavuus, 1996 131 Pekka Sauramo, The boom and the depression – A simple shock interpretation, Seija Ilmakunnas, Child care costs in labour supply models, 1996.
Eero Lehto, Group versus piece-rate contract, 1996.
135 Eero Lehto, Two-wage schemes, frequently observed output and a team contract, Petri Böckerman, Ansiosidonnainen tukijärjestelmä Suomen kannalta, 1997.
Katri Kosonen, House price dynamics in Finland, 1997.
Pasi Holm, Jaakko Kiander & Pekka Tossavainen, Rahastot ja EMU, 1997.
Katri Kosonen, Investment in residential building: A time-series, 1997.
140 Jukka Pekkarinen, Markan kelluttaminen talouspoliittisena vaihtoehtona EMUn toteu- 141 Pertti Haaparanta & Hannu Piekkola, Rent-Sharing Financial Pressures and Firm Petri Böckerman, Regional evolutions in Finland, 1998.
142 Pekka Sauramo, The Boom and the Depression: An Analysis within the Aggregate- Demand–Aggregate-Supply Framework, 1998.
Tuomas Pekkarinen, The Wage Curve: Finnish Evidence, 1998.
Petri Böckerman, Työn jakaminen ja työllisyys, 1998.
Petri Böckerman & Jaakko Kiander, Työllisyys Suomessa 1960–1996, 1998.
147 Pekka Sauramo, The Boom and the Depression: A Note on the Identification of Petri Böckerman & Jaakko Kiander, Has work-sharing worked in Finland?, 1998.
Petri Böckerman, Asuntomarkkinoiden toiminta ja työmarkkinoiden sopeutuminen, Petri Böckerman, Asuntokysyntä Suomessa. Poikkileikkaustarkastelu käyttäen varal- 151 Markus Jäntti & Sheldon Danziger, Income Poverty in Advanced Countries, 1999.
152 Jaakko Kiander, Työajan lyhentäminen ja työllisyys, 1999.
153 Petri Böckerman, Työn tarjonta ja työttömyys alue-ennusteessa, 1999.
154 Hannu Piekkola & Satu Hohti & Pekka Ilmakunnas, Experience and productivity in wage formation in Finnish industries, 1999.
155 Juhana Vartiainen, Job assignment and the general wage differential: Theory and evidence 156 Juhana Vartiainen, Relative wages in monetary Union and floating, 1999.
157 Petri Böckerman & Jaakko Kiander, Determination of average working time in Finland, 158 Kimmo Kevätsalo & Kaj Ilmonen & Kari Jokivuori, Sopiminen, luottamus ja toi- mipaikka- 159 Pekka Sauramo, Jobless growth in Finland? Evidence from the 1990s, 1999.

Source: http://www.labour.fi/tutkimusjulkaisut/tyopaperit/sel161.pdf


Frozen Shoulder If you’re having trouble lifting your arm above your head, reaching across your body or behind your back, you may have a problem with the range of motion in your shoulder. Limited motion is an early symptom of a frozen shoulder, which is a general term denoting all causes of motion loss in the shoulder. Who’s at risk?  Affects more women than men.  Usual onset be


DFCI US E ONLY: DFCI MRN #_____________ Protocol ID # _____________ Medical Questionnaire and Tissue Banking for Multiple Myeloma, Waldenström Macroglobulinemia, and Related Disorders DFCI Protocol Number: 09-233 Principal Investigator: Irene M. Ghobrial, MD Please complete and return in enclosed envelope to Irene Ghobrial at: Dana- Farber Cancer Institute, 450 Brookl

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