99/19 pp rt r e o s
i i ees, a s Øs y, e e u am a Ma ek Ku k e wski
I e a Mig a io a egio a o u a io y amics i Eu o e o way Case S u y
S a is isk se a y å • S a is ics o way Os o-Ko gs i ge pp rt r I d nn r n p bl r t t t n l r t d - d llb r v l r fr d n lt f r n n - t t t råd r O å r lt t r v l n lt nd r- ø l r p bl r h r ft t d tf ll nd nt r r n l r
p rt h r nt n t t t l n l nd th d nd d l d r pt n fr th d ff r nt r r h nd t t t r lt f v r n l rv r l p bl h d h r ll th ppl nt r nt nd n l
© St t t ntr lb rå j l 1999 d br v t r l fr d nn p bl j n n v nnl t pp St t t ntr lb rå ld St nd rdt n t b ll r S b l n t bl S b l ll n f r C t r n t ppl bl IS -537- 7 3-9 Opp v n l r a a o a ai a e ISS - 5 Opp v n l r f r løp t n t t v l bl ll n ff ntl jør t f r p bl t n E n r pp ll i 9 M t d r d ll r d nt j n M ndr nn 5 ess a 5 o u i E n rd v d n br t nh t n pl d Inn nl nd fl tt n M ndr nn 5 ess a 5 o u i r v d n br t nh t n pl d n l b f l n n tv l n r løp t ll r v n l r pr l n r f r r dd d n l ddr tt r n r n th h n t f v rt l se ies n Enz n r n r dd d n v nnr tt r n r n th h n t f h r z nt l r r St t t ntr lb rå tt t d n f rr t v e ise si ce e e ious issue o o
t r ft t d r f n l r v n r f rh ld p bl r nt rn j n l f r t r v nt r å pr d nn nn p n å n j n lt t n vær r l å jør rb d t r ll nt t l j n l lv p bl - j n n båd r t tt W r n p r 9 / fr S h l f G r ph v d Un v r t f d d n r v dl på C -r t l Int rn l r t n nd r n l p p l t n d n n E r p nth p l t n t d 3 C n l f E r p bl h n Str b r 1999 En tt rl r r nn t l å tr n l n SS r t rt- n l r på n n vå r v r f r rt d t nn tr t v n v d Un v r t f d l r v d ndr l nd h r d lt tt pr j t t h r j rt d t på åt n ( r f r n l t n t l l tt pp rt n v l t r t d SS f r h n rb d d å få rt n å d d nå r M d nnt v t rt n r f r ndr t v pr t r nn r l t b r t p bl j n n n n ndr r tt n r r p bl j n n d nt d t v n fr Un v r t f d St t t ntr lb rå t r f r d t d rb d t r ndt n l n p bl r n n v r lt t n r f
h t d n n t n t d d th n th pr j t nt tl d "Int rn l M r t n nd n l p l t n n n E r p " h pr j t n t t d b th E r p n p l t n C tt (C O f th C n l f E r p In t t n n O t b r 199 th C O d d d t n n nv t t n th f b l t f p r t v t d f nt rn l r t n nd r n l p p l t n d n th n E r p n ntr h b - r nd t th pr j t t f ld r tl th r h d b n f r t r th r l ttl nt r t n th p rt f b th r r h r nd nt rn t n l r n t n r n n th f ld S ndl d r n r nt d d th r h b n n r l pr v nt f p p l t n t t t r E r p b t th h n t xt nd d t t t t n nt rn l r t n d p t th ntr d t n b E r t t f th r U S t f p r bl r n
r f r h l nd r M r K p z f th S h l f G r ph t th Un v r t f d rr d t h f b l t t d nd pr nt d t t th C O t t t n n n 1995 h r t d v r d ll ( t th t t b r t t f th C n l f E r p th r th n 1 ll n nh b t nt d n t nn r nt t ll r l v nt ntr th n l n th t n p t f v r n d t t t ld b nd l r b p bl t p rf r p r t v n l f th nd ( nd K p z 199
h C O d d d t r ees nd K p z t nd rt p r t v t d f nt rn l r t n nd r n l p p l t n d n d th r th C O l pp nt d Gr p f Sp l t th n n b r (r pr nt n th Cz h p bl E t n G r n It l th th rl nd r l nd rt l nd n h r d b Mr a s Øs y C O b r f r r h t r f r f r n f th t d r d f n d b th C O f ll ; (1 t nv t t th xt nt f r r l d p p l t n ( t n l th d r t h h th pr f rb n t n nt r rb n t n nd b rb n t n r n tr n nd (3 t d r b th p tt rn f nd tr nd n nt rn l r t n r h p r n f th t t n n th rl / d-19 th th t n th rl / d-199 t b rr d t
h th r ll xpr th r r t t d t Mr r n M ll h f th C n l f E r p f r th r nd tt nt n n d n th pr j t h E r p n C n r pr nt d n th C O b M I b ll d rb x t G Un t El t r t nt r t n th pr j t nd pr v d d - p n r- h p f 3 ECU n th f r t r E r t t h f ll d th pr j t thr h t t x t n nd h ppl d nf r t n n th d t l b nd r f r n t l p l n d t f r n p l t r 199 r ndl pr v d d b S ndr n f th p rt nt f S t E l St h l Un v r t M n r f th lt / rd n f th U E /G I - Ar nd l pr j t h h h n tr t d v r t f d t l p f r rth rn E r p
t l t d f n n nd th t v l bl th t d h d t r tr t t lf t th n n ntr r pr nt d n th Gr p f Sp l t n dd t n t th n lt nt ntr th Un t d K n d Ev n th th l t d v r th Gr p f Sp l t f nd th t d v r nt r t n ll tr t n th f ln f th nd f r -n t n l p r n h ntr t d l ll th th r r tt n b th n lt nt nd - th r d b th n t n l r pr nt t v n th Gr p f Sp l t
St t t r ld l t t th pp rt n t t p bl h n r th r lt f th n l f nt rn l r t n h r lt r lr d v l bl W r n p r 9 / fr S h l f G r ph Un v r t f d It ll l b n nn x n C - OM t Int rn l r t n nd r n l p p l t n d n n E r p nth p l t n t d 3 C n l f E r p bl h n Str b r 1999 A f rth r r n t pr nt th n l n n th t p n l n th l v l f th n p l t n d t b d n n l r p Un v r t f d ld n t ppl ll n t n l t d th th n b r f l r p n d d A n b r f p rt p t n ntr d d t n th ( l t f r f r n W nt t th n r t d f r h r th pr v n th p W th x pt n f th p f pr t l r n ll t d t th nd f th p bl t n nd n r d t r l h n th p bl t n d nt l th th n fr Un v r t f d St t t r nt t th n th Un v r t f f r fr tf l - p r t n n n l n nd p bl h n th r lt
p rt pr p r d f r th C n l f E r p ( r t r t f S l nd E n Aff r p l t n nd M r t n v n nd f r E r p n C n ( r t r t G n r l E pl nt Ind tr l l t n nd S l Aff r Un t El An l nd r h n th S l S t t n Ab tr t
i i ees , a s Øs yg, e e u am a Ma ek Ku is ewski 4 Int rn l M r t n nd n l p l t n n n E r p r C St d
p rt 99/19 • St t t r 1999
h p p r r p rt n nt rn l r t n nd r n l p p l t n d n n r It x n nt rn l r t n p tt rn nd tr nd n t r 19 nd 199 nd p r th
r p p l t n nt n r l t v l h h p p l t n r th b E r p n t nd rd f ll d b nt n n n t r l n r nd n t r t n fr t d th ntr Ab t h lf f r n p l t l t p p l t n n r t v r th 19 t 199 h n p l t r n ntr t d n th C ntr - rth nd nt r r f th rn r h r v d n th t n t th th l t d n t nd l t ntr l t r l n p p l t n thr h nt rn l r t n
Alth h th d r t n f r t n t rd d n r nd r ntr l pl th pr d t nl f th r t n f n p pl h n th r t n tr r br n d n b th r lt n t l h th t th l r t rb n r r xp r n n n t l fr ddl nd p rd h r l ttl d r t v d n f n t p t v r t n fl t r r l r t r f r th p p l t n h l M r t n fl t f th O l r n r t th r n p l t th n t n r n h d n ntr t n h ld th r f r b d nt f d xt nd d b rb n t n r th r th n nt r- rb n t n
hr h t th rr nt r p rt th r l f l f r t n nfl n n th d r t n f r t n h b n tr d M t ft n th v r ll p tt rn f p p l t n h ft n l v r d ff r nt fl tr t r f r f l r nt n d lt ld r r r r t r nd th ld rl In th r p t nt rn l r t n d n n r tr n l r bl th n th r W t E r p n ntr
E n f t r h v n p rt nt nfl n n r t n p tt rn M n p l t th n n n ntr t n n rv nd tr ttr t nt rn l r nt h l th p l z d n pr r nd tr ff r r t n tfl n nt n th d l n f r pr d t v t pr v nt n th r n t v t h r tr n r d nt f n r n n t tfl th n r n l v l f n pl nt
K rd Int rn l r t n r r n l p p l t n d n
i S h l f G r ph Un v r t f d d S 9 Un t d K n d
v n f r S l nd r ph r h St t t r O x 131- p 33 O l r
3 W ll C tt 1 h rp n C d S lb YO OSG Un t d K n d
S h l f G r ph Un v r t f d d S 9 Un t d K n d nd In t t t f G r ph nd Sp t l Or n z t n l h A d f S n rd 51/55 W r l nd
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Reports 99/19 I e a Mig a io o way
Co e s
is o a es
is o igu es
1 Co e 9
I e a mig a io a o u a io c a ge e iewe 1
3 a a a me o s use 1 3 1 e o u a io egis a io sys em 1 3 a ia es use 13 3 3 G r ph n t d pt d 1 3 C assi ica io s 15 3 5 Ma i g me o s 17
S a ia a e s o o u a io c a ge 1 1 o u a io s i s a com o e s o c a ge o egio s 1 e i e a a e e a mig a io a e s o egio s a cou ies 1 3 o u a io c a ge y mu ici a i y e o e a ic u e e i e a mig a io o mu ici a i ies Ge e a a e s 3 5 e e e a mig a io o mu ici a i ies Ge e a a e s 3 o u a io c a ge egimes 3 7 e i e a mig a io o mu ici a i ies i e cou se a e s 3
5 e a io s i s e wee o u a io y amics a e se eme sys em 5 5 1 e a io s i o e o uc io sys em 5 5 e a io s i o o u a io e si y 7 5 3 e a io s i o e eg ee o ce a i y 5 e a io s i o ge e a se eme y es 5 5 e a io s i e wee mig a io a u em oyme
C a gi g mig a io a e s 9 1 Mig a io ows e wee egio s 9 Mig a io ows e wee se eme y es 3
7 Sy esis a co c usio s 33 7 1 Ge e a c a ge 33 7 u a e o u a io 33 7 3 U a eco ce a io 33 7 Su u a i a io o cou e -u a i a io 33 7 5 e im o a ce o e i e cou se 33 7 e o e o eco omic ac o s 33 7 7 u u e e o u io 33
e e e ces 3
A e i 3
i ige e u gi å em eom å e 59
e sis u gi e u ikas o e e i se ie a o e
7 Int rn l M r t n r e o s
t f t bl
1 a o a ook u a e o co e i g 19 mu ici a i y i o ma io o 199 a eas 1 A ook u a e o isagg ega i g e 199 e ik s a mu ici a i y o e 199 a eas 15 3 S a is ics o way i us y i k c assi ica io o mu ici a i ies 15 S a is ics o way ce a i y c assi ica io o mu ici a i ies 1 5 S a is ics o way e si y c assi ica io o mu ici a i ies 1 S a is ics o way ge e a c assi ica io o mu ici a i ies 17 7 o u a io s e ce age s a es y age a c a ge o way egio s 19 a 199 19 Mig a io com o e s o c a ge y age o way egio s 19 a 199 9 e i e a mig a io a es y age o way egio s 19 a 199 1 e i e a a e e a mig a io a es y se o way egio s 19 a 199 1 11 o u a io c a ge a es y se o way egio s 19 -9 1 1 e i e a mig a io a es y age o way cou ies 19 a 199 13 e i e a mig a io a es y age o way i us y i k y es 19 a 199 1 e i e a mig a io a es y age o way e si y ca ego ies 19 a 199 15 e i e a mig a io a es y age o way ce a i y ca ego ies 19 a 199 7 1 e i e a mig a io a es y age o way ge e a se eme c asses 19 a 199 7 17 e i e a mig a io a es y age o way y u em oyme a 19 a 199 1 Co e a io o e i e a a e e a mig a io a es y age 199 wi u em oyme a es o mu ici a i ies 19 Mig a io ows e wee egio s o way 19 9 Mig a io ows e wee egio s o way 199 3 1 Mig a io ows e wee ge e a se eme c asses o way 19 31 Mig a io e wee mai mu ici a i y c asses 199 3
tt f f r
1 e egio s a cou ies o o way 3 e i us y i k c assi ica io o o wegia mu ici a i ies 37 3 e ce a i y c assi ica io o o wegia mu ici a i ies 3 e e si y c assi ica io o o wegia mu ici a i ies 39 5 e ge e a c assi ica io o o wegia mu ici a i ies o u a io c a ge a es o wegia mu ici a i ies 19 -9 a ages 1 7 e i e a mig a io a es o wegia mu ici a i ies 19 a ages e e e a mig a io a es o wegia mu ici a i ies 19 a ages 3 9 e i e a mig a io a es o wegia mu ici a i ies 199 a ages 1 e e e a mig a io a es o wegia mu ici a i ies 199 a ages 5 11 e We c assi ica io o o u a io c a ge o wegia mu ici a i ies 199 1 e mig a io a es o wegia mu ici a i ies 19 ages -1 7 13 e mig a io a es o wegia mu ici a i ies 199 ages -1 1 e i e a mig a io a es o wegia mu ici a i ies 19 ages 15- 9 9 15 e i e a mig a io a es o wegia mu ici a i ies 199 ages 15- 9 5 1 e i e a mig a io a es o wegia mu ici a i ies 19 ages 3 - 51 17 e i e a mig a io a es o wegia mu ici a i ies 199 ages 3 - 5
1 e i e a mig a io a es o wegia mu ici a i ies 19 ages 5-59 53 19 e i e a mig a io a es o wegia mu ici a i ies 199 ages 5-59 5 e i e a mig a io a es o wegia mu ici a i ies 19 ages -7 55 1 e i e a mig a io a es o wegia mu ici a i ies 199 ages -7 5
e i e a mig a io a es o wegia mu ici a i ies 19 ages 75+ 57
3 e i e a mig a io a es o wegia mu ici a i ies 199 ages 75+ 5
Reports 99/19 Int rn l M r t n r
1 C nt xt
This paper reports on migration patterns and popula- the Council of Europe, Rees and Kupiszewski (1996) tion change in Norway as part of a project on Internal concluded that reliable information was available from Migration and Regional Population Dynamics in Europe European National Statistical Offices to study popula- sponsored by the Council of Europe and the European tion dynamics at fine spatial scales. Building on that Commission. This project aims to build up a compar- knowledge this study describes population change and able picture of internal migration across the countries internal migration trends for Norway at municipality, of Europe. municipality type, and various regional scales.
In the 1990s the countries of Europe are collectively The report is divided into the following sections. engaged in what the German Chancellor, Helmut Kohl, Section 2 reviews knowledge about regional popula- has called "the European Project". This involves the tion change and internal migration in Norway. Section closer integration of countries in international organi- 3 describes the data available for analysing regional sations (such as the Council of Europe) or in multi- population dynamics in Norway and the classifications country institutions (such as the European Union or of municipalities, the territorial units used. Section 4 the European Economic Area to which Norway discusses patterns of population change and net inter- belongs) . Collective projects require an agreed and nal migration at municipal scale, while section 5 ana- comparable database of information about countries lyses both net internal migration for regions and for and their constituent regions. The Directorate of Social counties and using different official municipality and Economic Affairs of the Council of Europe has classifications. Two themes run through these analyses: been active in collating national statistics for over 30 the importance of life course stage in determining countries (Council of Europe 1997) . The Statistical migration directions and the changes in these direc- Office of the European Communities (EUROSTAT tions that are taking place over the 1984-94 decade. 1995a, 1995b) has been pursuing harmonisation of Section 6 examines flow patterns between regions, national and regional statistics for the member states counties and between different settlement types. of the European Union. Section 7 provides a synthesis of findings.
However, there is a major gap in these statistics with respect to internal migration and its role in regional population change. Considerable progress has been made by the European Commission and EUROSTAT in developing regional population projections for the European Union (see Rees 1996 and van der Gaag et al. 1997) . The primary aim of this work has been to incorporate internal migration data into multi-country, multi-regional population projection (see Van Imhoff et al. 1997 for a methodological report) . The EU regional projections are carried out for second level regions in the EUROSTAT statistical system, regions with average populations of 1.86 million people. Such regions are large spatial filters for understanding processes of population change within countries. Kupiszewski (1996) established for Poland that the surface of population change was virtually flat at Voivodship scale (49 units) while that at commune scale (4000 units) had lots of peaks and valleys. In a feasibility study for
9
I e a Mig a io o way p rt
I e a mig a io a o u a io c a ge e iewe
Norway has one of Europe's smaller populations, 4.393 Despite these individualities, recent demographic millions in 6, although in area it both large developments have followed the same path as in much ( 24,2 0 sq.km.) and extensive stretching over 600 of northern and western Europe. Mortality is low and kilometres from Lindesnes in the southwest to Nord- life expectancy high: .4 years for men and 8 . for kapp in the north. The north-eastern part of the women in 6 (Council of Europe . However, country has a common border with Russia, of almost fertility is comparatively high, even though the total 200 km. The easternmost town, Vardø, is well east of fertility rate has been below two since and close Istanbul. Its territory is rugged with mountains making to . for one decade. Demographic momentum (large up the interior of the country throughout and the relative numbers in the 2 to age range) has, how- coastline characterised by fjords and island clusters. It ever, kept natural increase positive and has been is also a recent creation, having gained its indepen- helped by net immigration from outside Norway since dence from Sweden in 0 . Natural resources (ore, the late- 60 . timber, waterpower, fish) have been the backbone of the economy in the past, although today these The spatial distribution of the country's population is industries employ only a small proportion of the profoundly affected by its geography. Hansen ( 8 workforce and service industries and occupations are refers to this as "one of vast peripheral or marginal dominant. The settlement pattern is more dispersed regions", with 0 per cent of its territory being eligible than in any other mainland European country. This for regional aid from the national government. Norway pattern is strongly supported by the majority of the has been late, in European terms in urbanising, and its political parties and the various governments, by rural population peaked around 0. This review of emphasising the values of small place living, by the evolution of the recent re-distribution of Norway's subsidies to remote districts, and by the election population relies heavily on Hansen ( 8 account, system. A significant proportion of the population, also which provides a comprehensive and accessible thesis. in the urban areas, recognises this settlement pattern This suggests that the long run trend towards greater as something that needs to be protected. population concentration through movement from rural areas to town and cities has dominated the post- Recent decades have seen considerable prosperity for war period and that the de-concentration of the 0 Norway as a result of the exploitation of oil and was both less marked than in other West European and natural gas resources in its sector of the North Sea and North American countries with the 80 seeing a Atlantic. The exploitation of these petroleum resources recession away from this de-concentration. have led to the development of an onshore support industry in south west Norway, centred on Stavanger, In the 0 , 60 and 0 rural population change including oil rig construction. The Norwegians have was negative and urban positive (Hansen 8 , Table always been a seafaring nation and shipbuilding and 6. . The proportion urban grew from 2 per cent in shipping are important industries and ones that take 0 to per cent in 80. There was in the 60 a Norwegians out of the country with later returns. strong positive relationship between centrality, as Against this background, the Norwegian people have measured by the size of the largest urban centre that twice rejected in referenda the opportunity to join the can be reached within a given travel time, and European Union. They have clearly been sceptical population change. However, in the following decade about the transfer of authority to a European bureau- the relationship was negative though moderate. In cracy, even more distant than the national one in Oslo. particular, the capital region centred on Oslo, which At the last referendum they were also sceptical about had experienced around one third of national the benefits to a rich country on the periphery of population growth in the 0 and 60 saw its Europe, feeling perhaps that they would lose more share fall to barely 10 per cent by . the end of than they would gain. the decade the population of the Oslo urban region
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Reports 99/19 Int rn l M r t n r had almost ceased growing. However, these counter- houses, and could supply themselves with products urbanising tendencies must be contrasted with those in from agriculture and fishing. In the late 1980s, a countries such as the United Kingdom in the same number of transfers were made to increase the attrac- period. Cities in Norway did not actually lose tiveness of living in remote regions, especially in the population; rural areas in the periphery continued to North. do so; counter-urbanisation was muted in form with population growth concentrated on intermediate size The labour market started to improve in 1992-1993, urban settlements of under 10,000 people. and net migration to the capital region increased again. The losses from the Northern periphery have The 1980s ushered in a partial reversal of this pattern never been as high as in 1996-97, and there is no with net in-migration to the East region (containing longer a big birth surplus to protect the population the capital) increasing rapidly and net out-migration numbers from declining. The population redistribution from the peripheral regions increasing in size as well of 1994, which will be described later in the paper, has (Hansen 1989, Figure 6.1) . The 1970s, suggests been increasing since then. Thus, conclusions drawn Hansen, were a decade of exception to the long run on the basis of migration pattern in two single years, concentration of population at regional and local will be very much dependent upon where these two levels. Population concentrated in the capital region, years are positioned on the "migration cycles" of the the interior East and the coastal East. The peripheral country. The three-four years following 1994 would all regions of the West, Trøndelag and the North returned have shown even stronger centralisation. to heavy losses. The South remaining a gaining region because of the employment opportunities afforded by The overall internal mobility (migrants per 1000 the oil industry centred on Stavanger and along the population) has not changed much in the port-war coast. This redistribution was effected both by internal period, and has had a declining trend in the last migration and by external. External migration gains decade. This figure is influenced by the ageing of the were highest in the capital region and the South but population, and by the reduced number of munici- also compensated a little for internal migration losses palities. Statistics Norway has tried to estimate the in the peripheral regions. mobility net of these effects. The age-specific mobility pattern in the early 1950s gave an expected number of Hansen (1989, Figure 6.3) also examines the pattern of moves across municipality boundaries of 4 for women inter-regional migration flows in four five year periods: and 3 for men. In 1996 it was around 2.5 for both 1966-70, 1971-75, 1976-80, 1980-85. The directions of sexes. This reduction is to a smaller degree influenced net flow were from periphery to the East throughout by the reduced number of municipalities, but the main the five year periods. What differed between them was effect is due to decline in intrinsic mobility. the volume of flows: high the later 1960s and the first half of the 1980s, but lower in between in the 1970s. The picture in the early and middle 1980s is of increas- ing growth of urbanisation in Norway, stagnation of middle rank towns away from the East core of the country and severe decline in peripheral rural areas. The diminution of natural increase means that this component can no longer compensate for rural popula- tion losses through migration. In more confident times (the 1960s and 1970s), public investment in schools, health, community and transport infrastructure was use to counterbalance the concentration tendency but Hansen anticipates a gloomy outlook for the periphery in demographic terms in the 1990s. This report picks the story where he left off and compares the situation of the mid-1980s (1984) with that in the mid-1990s (1994) .
During the last decade, after Hansen's report was completed, there have been two important shifts. In the late 1980s the country experienced significant unemployment for the first time since World War II. Net migration from remote to central regions came almost to a halt. The unemployment rate was as pronounced in the central as in the remote areas; those living in remote areas had on average rather cheap
11
Int rn l M r t n r Reports 99/19
3 t nd th d d
Norway is a country which has one of the most The registration is based on the use of a unique advanced demographic data collection systems in personal identification number (PIN) . Such a number Europe, to which methodological researchers often is allocated to every person registered in the CPR. It is turn for detailed life and migration history information kept unchanged throughout a person's lifetime, and it (Courgeau and Baccaïni 1997) . The first part of this is not "recirculated". This central registration system section describes the key features of the population with the PIN was introduced nationally in 1964, based registration system from which the data used in this on local registers from 1946 or earlier. Although study are drawn. The second part then describes the everyone has to inform the register about any change nature of population and migration information of residence, the data quality on within-municipal available for municipalities and the particular variables migrations are considered to be inferior, and such selected for use in this study. The third part discusses statistics are not produced on a regular basis. The the geographies used in the study and methods registration system provides a wide range of up to date employed to construct a geographically consistent data statistics on migration, both within the country (inter- series for municipalities for two years, 1984 and 1994, municipality) and for external movement. All other separated by ten years of considerable geographical aspects of population statistics are produced from the reorganisation. Because there are so many spatial units same system, and the PIN code is used in all kinds of involved it is necessary to develop and use various individual statistics on persons. Subject to the consent classification schemes which group municipalities into of the Data Inspectorate, a wide range of record link- classes. The fourth part of this report section reviews ages can be produced for statistical and analytical the classifications adopted. The final part briefly purposes. For the analysis of internal migration, indi- describes the source for the cartography employed in vidual migration biographies are constructed from the study and the mapping strategies employed. 1964. All biographies are linked to Census information 1960-1990, and to registers showing income, educa- 3 1 h p p l t n r tr t n t tion and labour force participation (as discussed in Norway maintains a population register through the Courgeau and Baccaïni 1996). requirement that all persons must register changes of address with their local kommune (municipality) office. Some minor problems affect the data, which are com- The records are collated nationally in a Central Popula- mon to many countries. The main principle in defining tion Register (CPR), and maintained in electronic form. place of residence is where "daily night rest" takes The register is established for administrative purposes, place, that is, your place of residence where you spend local and national, with the tax authorities as admini- most of the nights in the week. When changing resi- strators, on local as well as on central level. High dence for more than six months, you will be registered quality registers can be maintained only through as a migrant. Certain groups register, in accordance frequent and comprehensive use. It is difficult for with exceptions in the registration rules, as living in purely statistical registers to retain good quality for a locations where they do not spend most of their nights: longer period. As almost every contact with municipal unmarried students, for example, will normally remain and governmental administration involves your registered in their parental household even though register status, the quality of the register is supposed to they may reside elsewhere. The same goes for weekly be very high for statistical purposes (Statistics Norway commuters between place of work and the residence of 1994b) . The 1989 Statistical Act gives Statistics their family. Between the two years we will be study- Norway the right to exploit all administrative registers ing, the status of the growing number of asylum for purely statistical purposes, and they have also the seekers has changed. Since March 1987 they have been right to be consulted before any substantial changes viewed as in-migrants to Norway and hence as resi- are made in these registers. dents, while their applications for permanent stay are considered. In 1984, however, the number of asylum
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Reports 99/19 Int rn l M r t n r
applicants was negligible. The consequence is a major intra-municipal moves is probably improving, but data increase in the number of inhabitants (partially real, on such moves are not included in this report. partially apparent) for some municipalities that house reception centres for asylum seekers. In 1994, a Internal migration data are analysed in two forms: (1) decision was made not to include asylum seekers as total arrivals and departures by age and sex, and (2) before they were granted permit to stay, or had special as flows of persons between origin municipality and needs for a PIN, such as, for instance, health care or destination municipality. However, the migration data when they required an early permit for work. There is were conveniently supplied as records in a very large also the problem of failure of emigrants to de-register multidimensional table. Each record in the data file properly on embarkation for foreign countries. supplied (1) the code for the origin municipality, (2) Statistics Norway (1994b) suggests that at least 10 the code for the destination municipality, (3) a sex thousand immigrants are still on the register even code, (4) a five year age code and (5) a count of the though they have left the country, the majority from number of migrations (events) from origin and destina- Western countries. These numbers are to some extent tion. FORTRAN programs were written to transform balanced, however, by equivalent numbers of undocu- the data to a common geography and to aggregate to mented immigrants, estimated to number 4 to 5 thous- the standard set of six fifteen year age groups used in and by the police. Most of these illegal immigrants this analysis and that in other case studies: (1) 0-14 come from Third World countries, and are resident in years, (2) 15-29 years, (3) 30-44 years, (4) 45-59 Oslo. years, (5) 60-74 years and (6) 75 and over. These data were then used to produce total in- and outflows by 3.2. Variables used age for municipalities and higher aggregations, and The report concentrates on analysis of population tables of flows between areas or municipality types. change and change due to migration. Both types of The outputs from the FORTRAN programs were used data were supplied to the Council of Europe project by with the SPSS statistical package for further analysis. Statistics Norway at no cost; for which service we are None of the problems of aggregation arising from very grateful. having only knowledge of total inflows and outflows at the smallest spatial scale therefore arose (see Rees, 3.2.1. Population data Van Imhoff, Durham and Kupiszewski 1997 for a The population data used are for the 1st January in discussion) . As the data do not contain any other 1984 and the 1st January in 1994 for 454 and 435 information than sex, age and place of origin and kommuner respectively. We describe in section 3.3 destination, they were not subject to any confidentiali- what we did to convert these data to a comparable set ty protection device and so could be easily and directly of spatial units. The population counts for each compared with published counts. With respect to data municipality were broken down into five-year ages processing strategy, in retrospect, it would have been from 0-4 to 90-94 with a final age group of 95+ . more efficient to have written a simple computer Information was provided for both sexes. All of the program to expand the data set to a set of individual figures for aggregations of municipalities are built up records and to have used these directly in a statistical from this base, and agree with the counts published in package. the official handbooks (e.g. Statistics Norway 1994b), except where some minor interpolation was used to There are a couple of features of these migration and disaggregate one 1994 municipality population back to associated population data for municipalities, which its constituent municipal parts in 1990 for purpose of must be borne in mind which affect and restrict comparison and mapping. In general, we do not analysis. These features are (1) the treatment of age examine the variation in populations and migrations by when using populations at risk to compute migration sex, to keep the analysis within reasonable bounds. rates and (2) the effect of changes in municipal However, all analyses were prepared for males and boundaries on derived migration indicators. These females as well as persons, and a future report could features are discussed in turn. examine gender differences. Age definitions in the computation of migration rates. 3.2.2 Migration data Age is measured at the time of migration and so refers Migration available from the population registration to the period-age Lexis diagram (age-time) plan system come in three forms: intra-municipal migration, suitable for occurrence-exposure rate calculation. To which is a change of residence within a municipality; compute migration rates we need to adopt a computa- internal migration, which is change of residence across tion method for the population at risk. In the analysis a municipal boundary; and external migration, which of this report we use the start of the year start popula- between a municipality and a foreign country. The tions. Strictly speaking, the population at risk should focus in this report is on internal migration though we be defined as the average of start and end of year do use some external migration data (in all age populations. So a small upward bias may occur when aggregations) . The ability of the system to register the municipal population is increasing and the reverse
13 Int rn l M r t n r Reports 99/19
when it is declining. However, given the wide range of information was used to assign an old municipality net migration rates we report later in the paper, this that had "died" to the new municipality that had been should not be a major bias. "born" which gained the largest share of the old municipality's population. The resulting assignments in The effect of changes in municipal boundaries on the look up table are therefore "best fit" matches. migration indicators. As explained in section 3.3 below it is necessary to aggregate migration data for 1984 The 1984 to 1990 table lists the 454 municipalities and and 1994 to a common set of 1990 boundaries for provides codes and names for the corresponding 1990 mapping and temporal comparison. When munici- municipality. A majority of municipalities did not palities are subject to perfect aggregation (two or more change. A larger set of municipalities was amalgamated areas are merged to form a new aggregate area) then to form larger units. Table 1 provides selections of no bias in the resulting statistics occurs. However, municipalities in the county of Østfold from the look up where imperfect aggregation (a fraction of an area is table showing the different kind of changes that occur- added to another) is involved, estimation bias occurs. red. The municipality of Halden, code number 0101, is Fortunately, this problem was confined to five munici- an example of a municipality which does not change. Its palities in Østfold, which existed in 1990 but had been neighbouring municipality of Sarpsborg, code 0105, is in amalgamated by 1994. 1990 an amalgamation of 0102 Sarpsborg in 1984, 0114 Varteig, 0115 Skjeberg and 0130 Tune. A small 3 3 G r ph n t d pt d FORTRAN program was written that reads in the look To identify the processes of spatial redistribution, it up tables codes and then the 1984 population and was necessary to study population change and internal migration variables for 1984 municipalities and uses the migration on as fine a spatial scale as possible. The former to aggregate the latter. only practical candidate for geographic unit was the kommune or municipality, which is the smallest unit of The 1990 to 1994 look up tables lists the 439 munici- local government in Norway. This unit varies consider- palities in 1990 and provides codes and names for the ably in population size ranging from a maximum of corresponding 1994 municipality. However, in this case 477781 residents in 1994 in the municipality of Oslo a weight is added to the file to indicate the fraction of (and was over 500 000 in mid-November 1997) to a the 1994 municipality population that corresponds to minimum of 217 in the municipality of Utsira in the the 1990 unit when several units have been joined to- fylke (county) of Rogaland. Information exists at sub- gether. Table 2 shows the only entries from this look-up municipality level for total population by age and sex, table which were not unity. The weights, based on 1993 but is not easily available or with good enough quality populations of the municipalities, are used to break for migration analyses. down the 1994 populations into their 1990 municipality components. For example, 18.52 per cent of the 1994 Because of the ongoing process of municipal restruc- population of Fredrikstad, a municipality in Østfold, is turing, the total number of municipalities and/or the decomposed into the Borge municipality while other municipal borders change from year to year. On the shares are assigned to Fredrikstad (1990), Kråkerøy, whole, there is a trend towards reducing the number of Onsøy and Rolvsøy municipalities. Another FORTRAN municipalities, especially those surrounding cities with program was used to carry out the disaggregation. narrow borders: several small municipalities are merged with the central city into one large munici- pality. Between 1984 and 1994 the total number of bl 1 rt f l p t bl f r nv rt n 19 n - municipalities fell from 454 to 435. p l t nf r t n t 199 r 19 d 19 n 199 d 199 n In order to compare population redistribution n b r n b r processes in one year with another, it is necessary to 1 1 ld n 1 1 ld n 1 S rp b r 1 5 S rp b r adopt common spatial units. Because the digital 1 3 r dr t d 1 r dr t d boundaries available (see section 3.5) referred to the 1 M 1 M 439 municipalities in existence in 1990, it was decided 111 v l r 111 v l r to standardise on this geography and to convert the 113 r 113 r municipality statistics for 1984 and 1994 to 1990 11 rt 1 5 S rp b r 115 S j b r 1 5 S rp b r boundaries. To effect this conversion two look up 11 Ar r 11 Ar r tables were constructed: a 1984 to 1990 table and a 119 M r r 119 M r r 1994 to 1990 table, using Statistics Norway (1997a), 1 1 ø 1 1 ø which provided details of the amalgamation of 1 rø t d 1 rø t d municipalities. This publication contains dates of birth 1 3 Sp d b r 1 3 Sp d b r 1 A 1 A and death of municipalities and of boundary changes. 1 5 E d b r 1 5 E d b r In the case of boundary changes where the munici- 1 7 S ptv t 1 7 S ptv t pality was "split up", information on the population 1 t d 1 t d contained in the split sections is provided. This 13 n 1 5 S rp b r
1 Reports 99/19 I e a Mig a io o way
a e A ook u a e o isagg ega i g e 199 e ik- 3.4.2. Municipality classifications s a mu ici a i y o e 199 a eas We use 439 municipalities as the basic study unit in 199 199 this report. However, it is difficult to absorb informa- 199 ame Weig 199 ame co e co e tion, even when plotted on maps (as in section 4), for 1 e iks a 1 5 113 o ge so many units. To make sense of population redistribu- 1 e iks a 9 1 e iks a tion and internal migration, it is necessary to group 1 e iks a 11 133 K åke øy municipalities into significant classes. One of the most 1 e iks a 13 O søy 1 e iks a 91 131 o søy significant processes affecting population distribution t h ht b d n th 1993 p p l t n (t th n r t 1 over the century has been urbanisation, the concen- tration of people into towns and cities particularly the largest, followed in some countries by significant de- 3.4. Classifications concentration both locally (suburbanisation) and down Section 4 of the report presents the municipality the urban hierarchy (counterurbanisation) . Crosscut- patterns of population change and migration in detail. ting such size/density classifications are those based on However, to interpret these patterns we make sense of the economic functions of areas, reflecting how they the information by classifying municipalities in various earn their living. ways. The regional and county hierarchies employed in Norway to analyse population dynamics are discussed Norway is fortunate in having available several classifi- first. Then the official classifications developed over cations of its municipalities, which has been developed several decades by Statistics Norway are discussed. over several decades and draws heavily on census data. The classifications we use in this report are as follows. 3.4.1. The regional hierarchy Statistics Norway (1994a) uses three specialist classifi- Figure 1 shows the organisation of Norwegian regions cations: (1) Industry Link, (2) Density and (3) Cen- as used by Courgeau and Baccaïni (1997) . Official trality. Each municipality is assigned three correspon- statistics are normally provided by Statistics Norway ding codes. These are then synthesised into one over- for counties and regional classifications differ depen- all, general classification. ding on the analysis undertaken. Hansen (1989) also uses a five-region division but groups the capital region with East in many analyses and distinguishes Trønde- a e 3 S a is ics o way i us y i k c assi ica io o mu ici a i ies lag from the rest of the Centre-North region used in this report. Co e u a e A e ia io I e
Ag icu u e Ag icu u e 1 Ag icu u e is i g sea i g The main intermediate spatial unit in Norway is the w a i g Ag ic is i g fylke or county, of which there are nineteen. Each I Ag icu u e Ma u ac u i g Ag ic Ma u 3
A Ag icu u e Co s uc io Ag ic Co s county is assigned a code shown in Figure 1. The numbers range up to 20, because the number 13 is is i g sea i g w a i g is i g 5 is i g sea i g w a i g avoided. Ag icu u e is i g Ag ic is i g sea i g w a i g Ma u ac u i g is i g Ma u 7 The principal units of local government in Norway are is i g sea i g w a i g the Kommuner or municipalities (also referred to as A Co s uc io is i g Co s communes) . As mentioned previously, these units vary Ma u ac u i g Ma u ac u i g 9 I Ma u ac u i g Ag icu u e Ma u Ag ic 1 enormously in size and have been undergoing a con- Ma u ac u i g is i g tinuous process of consolidation, driven by the need to I sea i g w a i g Ma u is i g 11 make local government more efficient. The average IA Ma u ac u i g Co s uc io Ma u Co s 1 A Co s uc io Co s uc io 13 population of a municipality has increased from about A Co s uc io Ag icu u e Co s Ag ic 1 9.1 thousand inhabitants in 1984 to 9.7 thousand Co s uc io is i g sea i g residents in 1994. The median size is around 5 000 A w a i g Co s is i g 15 A Co s uc io Ma u ac u i g Co s Ma u 1 residents, 100 have less than 2 000 and 100 more than Se ices Ag icu u e Se Ag ic 17 10 000. By way of comparison, we note that the aver- Se ices is i g sea i g age population of the smallest units (wards/postal w a i g Se is i g 1 sectors) used in the United Kingdom case study were I Se ices Ma u ac u i g Se Ma u 19 A Se ices Co s uc io Se Co s around 5 thousand people in 1991 and the equivalent Se ices Se ices 1 average for Italian communes in 1994 was around 7 IE M n f t r n n l t r l Ma u u i a e a thousand. Norwegian municipalities resemble Dutch S r St t t r (199 and Italian communes in function and range of sizes t 1 C d = ff l St t t r d while UK wards/postal sectors were more uniform in r pt n f ll d t l n St t t r (199 size and subdivisions of larger local government units.
15 Int rn l M r t n r Reports 99/19
Industry Link. All information is based on the resident bl St t t r ntr l t l f t n f n - p l t population, so this link shows the industrial structure of those living in the municipality, not of those work- C d r pt n Ind x ing here, or of the enterprises registered there with v l 1 r n t th n 5 n t v l 3 n t O th n 15 n t 1 their main office or with their production. Table 3 lists v l 1 r n t th n 5 n t v l 3 th n the 22 categories that are set out in the 1994 classifica- OA 15 n t v l 1 r th n 5 n t v l 3 n t th n tion adopted for analysis in this report; Figure 2 maps 1 out the classification. They reflect the economic base of 15 n t 3 v l 1 r th n 5 n t v l 3 th n 15 1A each municipality: only a few examples exist of the n t v l r th n n t v l 3 n t th n most specialised categories (single letters L Agriculture, I Industry, A Construction) . In fact, just nine of the 22 15 n t 5 v l r th n n t v l 3 th n 15 types have more than 10 members and cover 94 per A n t cent of Norway's population (see Table 13) . Oslo and 3A v l 3 r th n 75 n t 7 its surrounding municipalities stand out as dominated S r St t t r (199 by services. There are also examples of this category in t 1 C d = ff l St t t r d northern Norway. r pt n f ll d t l n St t t r (199 Municipalities with a mixture of service and manufac- turing functions surround the principal service centres. bl 5 St t t r d n t l f t n f n - Manufacturing dominated municipalities are found p l t around the coast and in the outer parts of the Oslo Abbr - Gr p r pt n Ind x region. Municipalities where farming is dominant v t n generally occupy the interior of the country (northern -9 9% n d n l p p l t d r O 1 1 1 -19 9% n d n l p p l t d r 1 Hedmark and Oppland) and the Centre-North (Sør- - 9 9% n d n l p p l t d r 3 Trøndelag and Nord- Trøndelag) . 3 3 -39 9% n d n l p p l t d r M3 - 9 9% n d n l p p l t d r M 5 Centrality. This is a measure of a municipality's geo- 5 5 -59 9% n d n l p p l t d r M5 graphical position viewed in relation to a centre with - 9 9% n d n l p p l t d r M 7 7 7 -79 9% n d n l p p l t d r 7 higher order central functions are found. Urban centres - 9 9% n d n l p p l t d r 9 are divided into three levels: (1) on level 1 they 9 9 -1 % i e se y o u a e a eas 9 1 normally have between 5 and 15 thousand inhabitants, S r St t t r (199 (2) on level 2 between 15 and 50 thousand residents t and (3) on level 3 the centres house 50 000 people or 1 Gr p = ff l St t t r d r pt n f ll d t l n St t t r (199 more, although consideration is given to the type of 3 h bbr v t n d n l t r t bl functions that centres perform. The level 3 settlements are Oslo, Kristiansand, Stavanger, Bergen, Trondheim and Tromso. The level 2 settlements are Halden, Sarps- particular features of Norway's mountain, valley and borg, Fredrikstad, Moss, Hamar, Lillehammer, Gjøvik, fjord topography. It would not make sense to use crow Drammen, Kongsberg, Horten, Tønsberg, Sandefjord, flight distance as an accessibility index. Travel times Larvik, Porsgrunn, Skien, Arendal, Sandnes, Hauge- are based on the fastest means of surface transport. sund, Molde, Kristiansund, Ålesund, Bodo, Narvik, Mo i Rana and Harstad. There are some 50 Level 1 centres. Density. Table 5 lists the ten categories for this classifi- Municipalities are then classified according to the cation, all of which have reasonable numbers of muni- travel time incurred to centres of different levels as cipalities and Figure 3 shows the spatial distribution of specified in Table 4 while Figure 3 maps the classes. these density categories. Density is not treated as The centrality classification emphasises the accessibili- population divided by area because much of Norway's ty of municipalities clustered around the largest cities territory is devoid of habitation and density measures and towns of Norway - Oslo, Kristiansand, Stavanger, would depend on which municipalities encompassed Bergen, Trondheim and Tromsø. The accessibility is "empty" mountains and which did not. Rather, careful measured in two ways: for daily commuting trips attention is paid to the settlement nucleations in each (inside or outside commuting possibilities for centres municipality and the percentage of the population that on different levels, indicators 0-3) and for daily service lives in densely populated areas is computed and used trips (inside or outside travelling distance of 2 1/2 to form the classes. The density measure captures the hours, for Oslo 3 hours to a centre of level 3) . The degree to which population is concentrated in dense point of departure that the commuting distance is settlements rather than indicating the ratio of popula- much shorter than can be accepted for a service trip tion to land area. Norway is, on the latter measure, one that can be made in one day. This is a very sophisti- of the least populated countries in Europe, with an cated measure of accessibility to urban functions, average density of 14 persons per km2 (Statistics Nor- which is tailored, like the density measure, to the way, 1997, p.22) . The map shows that the densest
16 Reports 99/19 Int rn l M r t n r population concentrations are in the Oslo region and and compared using thematic maps. We acquired around the coast. It is of interest, however, to note that administrative area boundaries from the UNEP/GRID- municipalities in north Norway record dense urban Arendal project. In the maps in this edition, we applied concentrations - most people living in the small urban the UTM projection, considered to be the best for settlements with the rest of the municipality (almost) presenting Norway on maps. uninhabited.
The main classification. The three previous classifica- tions are used by Statistics Norway to compose a summary or synthetic classification (Table 5 and Figure 5) . The main classes are the first seven, with the last two being distinguished to identify vulnerable munici- palities dependent on a single industry or an activity dependent on fluctuating resources (fishing) . Class 1 consists of Primary industry municipalities; Class 2 is made up of Mixed agriculture and manufacturing municipalities; Class 3 are Manufacturing munici- palities; Class 4 comprise Less central, mixed service industry and manufacturing municipalities; Class 5 is made up of Central, mixed service industry and manu- facturing municipalities; Class 6 involves Less central service industry municipalities while Class 7 embodies Central service industry municipalities. Full details of the criteria for membership of the groups is provided in Statistics Norway (1994a) . Essentially, as one ascends the classification the economic structure becomes more advanced and less dependent on raw material har- vesting and processing. The spatial features of the three single dimension classifications are combined in Figure 5. The south eastern part of the country is domi- nated, for example, by the "Central service industry" type of municipality while remoter areas fall into more specialised categories where farming, fishing or forestry are dominant.
bl St t t r n r l l f t n f n - p l t
Co e esc i io n K1 ima y i us y mu ici a i ies 1 K Mixed agriculture and manufacturing mu ici a i ies K3 Ma u ac u i g mu ici a i ies 3 ess ce a mi e se ice i us y a K ma u ac u i g mu ici a i ies Ce a mi e se ice i us y a ma u ac u i g K5 mu ici a i ies 5 K ess ce a se ice i us y mu ici a i ies K7 Ce a se ice i us y mu ici a i ies 7 3E=Ma u ac u i g mu ici a i ies u i a e a i e K omi a e y o e i us y O e i c u e i K3 K9 1 = is e y mu ici a i ies o e i c u e i K1 9 S r St t t r (199 t 1 C d = ff l St t t r d r pt n f ll d t l n St t t r (199 3 k um = nd x d n S SS pr r h C d d n l t r t bl
3 5 M pp n th d The key indicators of population change and net internal migration for municipalities are considered
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Sp t l p tt rn f p p l t n h n
This section of the report begins our analysis of inter- only 42 000 in 1932). Migration balances some of this nal migration and regional population dynamics in the cohort effect in ensuring that these groups still grow in Norway by looking at population shifts and its compo- size in the Oslo and South regions. So the story is one of nents by age for 1984 and 1994 for the simplest divi- renewed centralisation coupled with resource led shifts. sion of the country into five regions. It is important to gain an understanding of age and cohort shifts. In the t nt rn l nd xt rn l r t n subsequent analysis we concentrate on net internal and p tt rn f r r n nd nt external migration, the key component for effecting 1 tt rn f r r n redistribution (though not necessarily absolute change) The main driver of departures from the national trend at successively smaller scales. We will remind the of population development is migration. Table 8 sets reader of the stochastic element when we are corn- out the absolute contributions of internal and external paring the situation in two single years, taken out of migration to population change, while Table 9 provides their historical context. the internal migration figures relative to the underlying population base, that is, the internal migration rates. 1 p l t n h ft nd p n nt f The top panel of Table 8 provides information on h n f r r n internal migration while the bottom panel shows the Table 7 sets out population numbers and percentage equivalent external migration figures. shares of the national population for the five regions. Oslo and the East (counties 1-8), which constitute the External migration provides positive additions to the core of the country, contain just under half of the populations of all regions and most ages, and is about Norwegian population. The rest of the country, peri- twice as high in 1994 as in 1984. Its positive contribu- pheral regions with some important urban centres like tion to the Centre-North region goes about half way to Kristainsand, Stavanger, Bergen, Trondheim and counterbalance the net internal migration losses. Exter- Tromso, make up the other half. All regions are nal migration gains are most pronounced in the ages growing in population still. The absolute and percen- below 45, although gains are evenly spread between tage shifts together with the change rates reported in the family/childhood ages and the late adolescent/ Table 11 suggest that Hansen was right in suggesting a young adult ages. renewed urbanisation and concentration of population in the capital region. Oslo's share of the Norwegian When internal migration is examined, we can see population increases by 1 percent overall between immediately in the tables that there are very consider- 1984 and 1994. The rest of the core loses share as do able differences between the life course stages in the the West and Centre-North. The gains of the South can directions of migration. The gains to the capital region be attributed to the employment generating and are made up almost entirely of gains in the 15-29 age migrant attracting role of the oil industry, the onshore group. Net losses characterise the other ages in 1994 bases for which are most important in that region. and all except the 30-44 age group in 1984. This pic- ture is mirrored by the profile of the East region that When the population picture is examined for the surrounds the capital and gains migrants from all age different age groups the picture changes somewhat groups except from those aged 15-29. There are heavy because of the effects of cohort replacement. So, for losses from almost all other regions in this age group, example, the first two age groups and the retirement directed towards the capital region. It is therefore un- ages experience loss due to replacement of the 1984 wise to talk just about de-concentration and concentra- population by smaller cohorts over the decade to 1994. tion of the population as a whole when the different Reduced numbers in retirement ages are due to the life course stages exhibit such different behaviour. effect of the significant interwar fertility decline (yearly Behind this pattern, we will find the effect of different number of births was more than 70 000 in 1920, and needs in the family life cycle.
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bl 7 p l t n p r nt h r b nd h n r r n 19 nd 199
A Gr p n Y r -1 15- 9 3 - 5-59 -7 75+ t l
p l t n (1 O l 19 1 5 19 1 1 1 55 7 199 1 197 1 155 11 9 7 Ch n 19 5 3 7 -1 7 E t 19 33 5 3 1 1 1 7 79 1175 199 1 5 5 9 17 9 1 Ch n -17 - 1 -13 17 7
S th 19 1 9 1 9 111 7 7 3 5 9 199 131 13 13 93 9 39 599 Ch n 7 19 17 -5 9 5 W t 19 1 3 173 1 1 1 5 9 7 199 157 171 1 1 97 59 7 7 Ch n - - 1 1 - 1 7 C ntr - rth 19 1 3 3 171 1 117 9 3 199 1 19 1 139 1 59 5 Ch n -15 -9 11 19 -9 1 13
O WAY 19 5 953 9 11 1 13 199 3 95 9 1 717 5 31 3 5 Ch n -1 -1 95 1 - 7 55 1
r nt h r 21.5O l 19 17 1 1 1 1 199 19 7 3 1 19 1 Ch n 1 1 5 - -1 5 1 E t 19 7 3 9 1 9 7 3 3 3 199 5 7 9 1 3 9 3 7 Ch n -1 5 - -1 3 - 3 1 - S th 19 15 1 13 5 13 1 1 5 1 1 11 5 13 3 199 15 7 1 3 13 13 1 1 3 13 Ch n 7 5 1 5 W t 19 19 1 1 17 17 1 17 1 17 9 199 1 1 17 1 7 17 1 7 17 7 Ch n - 3 - - - 1 - C ntr - rth 19 1 1 3 19 7 19 1 1 199 1 19 3 19 19 1 1 7 19 7 Ch n -1 3 - 9 - 9 - 3 - 1 - 7 O WAY 19 1 1 1 1 1 1 1 199 1 1 1 1 1 1 1
S r C p t d fr p p l t n t t t ppl d b St t t r
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Int rn l M r t n r e o s
bl M r t n p n nt f h n b r r n 19 nd 199
A Gr p n Y r -1 15- 9 3 - 5-59 -7 75+ t l
t nt rn l r t n O l 19 - 9 5 1 -77 - 5 -9 3 199 - 5 -1 9 -1 -3 1 -79 313
E t 19 1353 -1 9 933 39 57 1 15 7 199 1 3 - 3 5 1 171 31 7 133
S th 19 337 1 93 111 75 15 1 9 199 5 31 1 7 3 7
W t 19 -15 -1 3 - 1 -13 -3 - 3 -1791 199 -99 -119 15 - 3 -1 -1131
C ntr - rth 19 -1 5 -17 1 35 - 9 -153 - - 73 199 - -15 9 -73 -1 5 -51 -1 -3139
O WAY 19 199 -3 -1 - 5 -1 -13 5 -133 t xt rn l r t n O l 19 551 77 39 13 -53 - 15 199 579 1573 5 1 13
E t 19 177 1 3 3 17 -3 3 199 1 5 3 1 3 1 19
S th 19 3 5 3 9 - 1 11 199 1 15 - 9 - 5 1 71
W t 19 19 7 1 5 3 199 31 37 37 33 9
C ntr - rth 19 7 1 5 15 -15 - 5 - 1 199 51 5 171 57 -3 1 79 O WAY 19 13 15 7 -11 1 37 1 199 1 3 1 1 3 51 7353 S r C p t d fr p p l t n nd r t n t t t ppl d b St t t r
bl 9 t nt rn l r t n r t b r r n 19 nd 199
A n Y r -1 15- 9 3 - 5-59 -7 75+ t l
t nt rn l r t n r t p r 1 p p l t n O l 19 - 3 7 1 - -3 -1 7 7 199 - 7 - 5 - 9 - -1 3 3 5 E t 19 5 -7 3 9 3 1 1 3 199 - 3 1 7 1 S th 19 1 5 1 5 199 1 1 1 1 1 1 1 5
W t 19 - 9 -7 1 -1 5 -1 3 - 3 - 5 - 199 -5 - 7 1 - - -1 5
C ntr - rth 19 - - 7 -7 - -1 3 - 9 -5 199 - -7 - -1 - 5 - -3 7 O WAY 19 199 - - - 1 - - -
S r C p t d fr p p l t n nd r t n t t t ppl d b St t t r
Reports 99/19 Int rn l M r t n r
bl 1 t nt rn l nd xt rn l r t n r t b x r r n 19 nd 199
Int rn l Ext rn l n Y r M l l r n M l l r n
O l 19 7 7 7 1 5 1 1
199 3 7 3 3 5 7 3 5 3 1 E t 19 1 1 1 5 1 3 3 3 199 - 1 1 3 1 9 1
S th 19 1 1 7 199 1 3 1 1 5 9 5
W t 19 - 5 - - 199 -1 -1 -1 5 5 1
C ntr - rth 19 -5 - -5 199 - 1 -3 3 -3 7 1 3 O WAY 19 9 9 9 199 - - - 1 1 7 S r C p t d fr p p l t n nd r t n t t t ppl d b St t t r
bl 11 p l t n h n r t b x r r n tion rates for the counties of Norway (Table 12) . Note 19 -9 that our conclusions here are particularly dependent n p l t n h n r t on the years chosen for the comparison. The regional M l l r n generalisations of the previous section of the paper can
O l 1 3 5 9 5 be refined. The statistics confirm the enhanced position E t 9 1 of Oslo in 1994 compared with 1984. The county S th 91 5 1 9
W t 37 7 3 3 37 moves from net out-migration to in-migration; though
C ntr - rth 3 7 13 5 smaller net losses still characterise ages other than the
O WAY 5 5 1 15-29 and 45-59 age groups, these are counterbalan- ced by greater gains in the late adolescent and young adult ages. Counties surrounding Oslo in the rest of its Does this differentiation of behaviour extend to other region and in the East show both gains and losses but groups, such as men and women? Table 10 reports the the shift over the 1984-94 period is to lesser gains or all age internal migration rates for the two sexes while greater losses. Greater losses are characteristic of the Table 11 reports population change by sex for 1984- 15-29 age group in particular. The counties of the 94. There is relatively little in the way of differen- South have maintained positive net in-migration, tiation between men and women in these figures. A particularly Rogaland, the heart of the onshore oil little reflection suggests the reasons. For most of their service industry, which uniquely outside of Oslo experi- lives men and women are in partnership or in families ences gains in the 15-29 ages in both 1984 and 1994. together with person of the opposite sex. The sexes The beneficial features of oil related job development may differ in their occupations and achievements but may be behind the favourable shift in Hordaland's not much in their residential location. Even when decrease in net out-migration in the period. As one young and single there is little reason for men and moves north along the Norwegian coast the pattern of women to seek different destinations in migration and net loss comes to characterise more and more age most are not anxious to live in single sex neighbour- groups though there is some evidence of a lessening of hoods or institutions. Such preference can be safely left the rate of out-migration. The all ages rate for Troms to countries that cultivate celibate living or single sex county, for example, shifts from -8.6/1000 in 1984 to - boarding schools. In the remainder of the study, the 2.2 in 1994 and in particular the shift for the age analysis is targeted on the two sexes in combination. group 15-29 shows the effect of the growth in the new Before doing that, we will remind the reader that university town of Tromso. The Trøndelag counties earlier in the post-war era, young women on average and Finnmark maintain roughly the same position in had a shorter time in education than men. The labour the two years. The difference between the two counties market for unskilled women was in central regions, in Trøndelag shows the influence of the third largest whereas unskilled men could be absorbed in the local town in Norway, Trondheim, which has more than 50 labour market. Consequently, the mobility for women per cent of the population of Sør- Trøndelag, and has was higher, and more centrally directed. the national technical university. Perhaps we might suggest that the fears of Hansen (1989) about the 4.2.2. Patterns for counties future of the Norwegian periphery are still relevant, More detail of the pattern of migration in 1984 and although the speed of population loss may be slower 1994 is provided when we look at net internal migra- than initially feared.
21
Int rn l M r t n r Reports 99/19
bl 1 t nt rn l r t n r t b r nt 19 nd 199
A Gr p n C nt Y r -1 15- 9 3 -3 5-59 -7 75+ t l
OS O Ake us 19 1 1 7 13 7 1 1 9 1 7
199 11 -3 5 1 7 - -1 5 3 3
O l 19 - 7 33 -1 - - - 3 -
199 - 1 5 -1 1 7 - - 3
EAS Øs o 19 5 - 9 3 1 3 3 7 1
199 5 7 - 3 1 3 - 9
e ma k 3 9 19 5 9 -15 9 - 1 - 199 -17 -1 5 - -3 1
O a 19 3 9 -15 3 9 1 3 1 1 7 -1 3
199 1 -1 1 -1 3 - 7 9 3 -1 9
uske u 19 1 9 3 3 3
199 5 -1 9 3 5 1 - 1 1 7
es o 19 1 5 -3 9 9 1 3 5 3
-3 7 1 199 5 3 e ema k 19 - 9 1 1 -
199 9 -9 - 3 - 3 3 - 1 -1
SOU Aus -Ag e 5 3 19 - 1 3 3 1 199 - 5 5 3 1 1 1
es -Ag e -1 1 9 1 1 3 1 19 3 9 1 1 3 1 3 199 - 3 - oga a 19 9 5 1 1 - - 3 1 9
199 1 5 1 9 5 1
WES o a a 19 - 9 - -1 3 -1 - 5 - -1 3
199 -1 3 5 -1 7 - - - 5
Sog og o a e 19 - 1 -1 -1 - 5
199 - -1 7 -3 - 5 -1 5 -5 1
Mo e og oms a 19 -1 -1 1 - -1 7 - - - 3
199 3 5 -1 3 1 - 5 -1 5
CE E- So - o e a g 19 -3 9 - 9 - - 1 - 7 -1 -1 7 O 199 - - 5 -1 -1 -1 3 - 3 o - o e ag 19 -1 -13 9 - 5 - 3 1 - - -19 7 - -1 1 1 5 5 -5 o a 19 - 1 -13 3 - -3 -1 1 -1 - 199 -1 -1 - 1 -3 oms 19 -13 1 - 5 -1 1 -3 7 - -1 9 - 199 -5 - -3 - 9 -1 3 - 5 - i ma k 19 -1 5 -1 5 -1 - 3 - 7 -1 7 -11 1 199 -19 -1 -1 7 -5 7 -1 3 - 3 -1 S r C p t d fr p p l t n nd r t n t t t ppl d b St t t r t t r t n r t r xpr d p r 1 p p l t n
3 p l t n h n b n p l t h municipalities by shading class. All rates are expressed v r ll p t r per thousand population. Six classes are used on the We now turn to the patterns of population change at population change map: (1) from the minimum value the smallest spatial scale for which data are easily up to -100/1000, (2) from -100 up to -50/1000, (3) available in the Norway. To present statistics for a set from -50 up to 0/1000, (4) from 0 up to 50/1000, (5) of 439 municipalities necessitates use of maps. from 50 up to 100/1000, and (6) from 100 up to the maximum value. These classes are equivalent to the Figure 6 reports population change rates for all ages annual ones used in the net migration maps and are between 1984 and 1994. The population change rates the same as those used in other country studies. are computed by dividing the difference between the January 1st population in one year and that in the next As usual with maps at such fine scale, an intricate (1984, 1994) by the population in 1984. Note that this mosaic of population change is revealed, which shows is the ten-year rate rather than an average annual that considerable geographic variation occurs within equivalent rate. counties. Figure 6 shows a pattern of contrast between urban centres and rural hinterland in eastern Norway, It is important to note that, as with most shaded of contrast between coastal gains and interior losses in thematic maps, the figures give more prominence to southern Norway, of gain in municipalities along the rural areas with lower population densities. The map West coast but with losses in from the coast. Further legends give information about the distribution of north coastal municipalities also suffer loss along with
Reports 99/19 Int rn l M r t n r
interior kommuner. Occasional exceptions are urban their closing down can be seen in a number of munici- municipalities like Bodø in Nordland, Tromsø and Alta palities. Some immigrants (mainly Tamils) do also find municipality in Finnmark. a living in the fishing industry in the extreme north. The great majority of non-western immigrants, how- t nt rn l r t n f r n p l t ever, live in the capital region of Oslo. G n r l p tt rn The maps of overall net migration rates are displayed p l t n h n r in Figures 7 and 9 for 1984 and 1994 respectively. Figure 11 combines indicators of natural increase and Because the variable being plotted is net internal (total) net migration to provide a map of the eight migration, there is an even distribution of munici- demographic regimes defined by Webb (1963). palities around a mean of zero, because an internal out-migrant from one area is an internal in-migrant to 7 t nt rn l r t n f r n p l t another area. Internal migration is a zero sum game. f r p tt rn The patterns of gain and loss are varied and intricate; A succession of maps chart the progress of the Nor- in some cases we have stability between the two years wegian population through the life course and reveal and in others much change. The pattern has strong dramatically the differences in migration behaviour at regional components, as might be expected from the the different stages. We divide the life course into six preceding discussion. Most northern municipalities lose ages in this study and map net internal migration rates migrants in net terms. The gaining municipalities are for 1984 and 1994 for the following groups: concentrated in the Oslo, East and South regions but interior and remote coastal areas suffer net migration 1. the childhood ages, 0-14, mapped in Figures 12 and losses. The 1994 pattern shows a greater concentration 13; of migrant gains in fewer municipalities, again mainly 2. the adolescent and young adult ages, 15-29, mapped in the Oslo, East and South regions. There are also a in Figures 14 and 15; few gaining municipalities on the coast in the North, 3. the family ages, 30-44, mapped in Figures 16 and due to special circumstances in the fishing industry. 17; However, regional location alone is insufficient as a 4. the older working ages, 45-59, mapped in Figures 18 pattern descriptor. A small minority of municipalities and 19; in the West and Centre-North also gain. More munici- 5. the retirement ages, 60-74, mapped in Figures 20 palities are losing migrants than are growing; indi- and 21; and cating that population concentration through net 6. the elderly ages, 75 and over, mapped in Figures 22 migration is taking place. Many of the extreme and 23. numbers both in 1984 and 1994 are found in small municipalities, where small events can have a great 4.7.1. The childhood ages influence on the relative numbers. Figures 12 and 13 plot the net internal migration rates for the childhood ages. These two maps exhibit the 5 t xt rn l r t n f r n p l t same broad characteristics and resemble closely the G n r l p tt rn parental age maps. Oslo loses migrants, mainly to the The maps displayed in Figures 8 and 10 show the surrounding municipalities that have net migration lower but generally positive pattern of net external rates of 10 or more per 1000. Gains predominate in the migration. The net migration doubled from 3 800 (0.9 south of the country while losses are prevalent in many per thousand) to 7 400 (1.7 per thousand) in 1994. In municipalities in the interior of the country and in the 1984 the levels are rather low and vary only moderate- north. There are more gaining municipalities than ly across the country. Many gaining municipalities losing in the higher rate categories (e.g. 116 munici- were central cities, but also some tourist and hydro- palities have rates of 10+ in 1984, while only 91 have electricity regions in the mountains also gained. Almost rates of less than -10). There is some difference in the all municipalities have net external migration rates 1994 map, where the pattern is more varied and less between +5 and -5 per 1000 population. The range of regional, with a switch from gain to loss in the interior values in 1994 is greater, reflecting the higher level of of western Norway. Gaining municipalities still out- international migration, especially the higher number number losing ones. This indicates that, on average, of asylum seekers, but still much narrower than inter- the migration of this group for these years results in nal migration. A minority of municipalities experience de-concentration of population. losses through international migration. A majority shows, in 1994 particularly, a positive contribution to 4.7.2. The adolescent and young adult ages population change through external migration. Ex- The immediate impression on viewing Figures 14 and treme values, both positive and negative, are related to 15 is that the pattern of net internal migration for reception centres for asylum seekers. They were, while young adults is quite different from that of any of the waiting for a decision, often settled temporarily in other life course groups. The maps are a sea of losses available hostel accommodation located in mountain with just a few gains. Most smaller, remote and rural resort settlements. Both moves into these centres and kommuner lose migrants to the level 1 and level 2
3
I e a Mig a io o way Reports 99/19 municipalities. Young people, on leaving home for the 1994, compared to 114 in the older working ages. For first time, seek new experiences in other locations, some of the gaining municipalities, the gains are either in first jobs or increasingly in further or higher probably the result of return migration after the rural education. Oslo is a particularly attractive destination. exodus from the interior municipalities in South The process has intensified between 1984 and 1994. In Norway in the first decades after World War II. the latter year there were only 97 gaining munici- palities but 342 losing compared with 129 gainers and 4.7.5. The elderly ages 310 losers in 1984. The degree of loss to losing areas is Figures 22 and 23 map the net internal migration generally more intense than at other ages, with as patterns for the elderly. Migration activity is subdued many as 286 kommuner experiencing losses or gains of but shows the same pattern of more gaining munici- more than 10 net migrants per 1000 population, with palities than losing, in all sections of the country. The losses in 255 of them. Gaining municipalities outside pattern does not depart radically from that of the the big cities are often educational centres. retirement ages. The two older age groups do not show any significant relocation of the elderly population. 4.7.3. The family ages There is a very small "Florida" effect to be seen in the Figures 16 and 17 plot the net internal migration rates consistent net migration to the coastal municipalities for the family ages. These two maps exhibit the same west of Oslofjord, in the counties of Vestfold and Vest- broad characteristics and closely resemble the maps for Agder. the childhood ages. Northern municipalities and Oslo lose family migrants. More accessible municipalities in southern, eastern and south-west Norway and in some coastal areas gain migrants at a net migration rate of 10 or more per 1000. Gains predominate in the south of the country while losses are prevalent in the north of Norway, There is some difference in the 1994 maps, in which the pattern is more varied and less regional, although the number of gaining municipalities still exceeds the count of losing ones. On average, the migration of this group results in de-concentration of population.
4.7.4. The older working ages Figures 18 and 19 depict the structure of internal migration for the older working ages. At these ages migration activity is much reduced and most municipalities cluster in the categories that bracket migration balance (zero net migration) . There are more gaining municipalities than losing and the numbers of gainers are larger than in the family ages. The 1994 pattern is similar to that in 1984, except that there are fewer gainers and more losers. Families in this age group have reached the rather stable part of their life course: their work careers are settled, they have finished or are finishing the bringing up of their children, they are saving for their retirement, and some of them are contemplating relocation after that event. There are fewer gaining municipalities in the East part of the country in 1994, and more gaining in the north.
4.7.4. The retirement ages Figures 20 and 21 show the net internal migration patterns for the ages around retirement. The pattern shifts to one of more gaining municipalities than losing (280 gainers in 1984 and 159 losers; 265 gainers in 1994 and 174 losers) . The cities lose migrants at these ages and smaller municipalities gain but only some are chosen as destinations. The net effects are smaller than in the younger age groups, with only 73 municipalities outside the +/- 10 per 1000 band in this group in
24
Reports 99/19 Int rn l M r t n r
5 l t n h p b t n p p l t n d n nd th ttl nt t
To make sense of the intricate mosaics of the munici- Industry Link LA, Agriculture and Construction munici- pality maps, we employ the classifications of munici- palities. These lose migrants in all ages except the palities developed by Statistics Norway. Essentially, we retired and elderly and more in 1994 than in 1984. ask the following questions. Firstly, does the economic structure of places have an influence on the net Industry Link IL, Manufacturing and Agriculture munici- balance of people leaving and arriving? Secondly, does palities. These lose migrants, particularly in the 15-29 the degree of urbanisation represented by the age group and their position has worsened in 1994. Norwegian operational definition of density have an effect on the pattern of migration between places? Are Industry Link IA, Manufacturing and Construction people choosing one density of living over another, municipalities. These lose migrants in the same way as urbanity over rurality? Thirdly, is accessibility to cen- the previous group but the position in 1994 is little trally provided functions in an urban network an different from 1984 overall though the contrast be- important factor determining the flows of people into tween age groups has grown. and out of municipalities? Fourthly, does putting these separate dimensions of settlement variation together Industry Link TL, Services and Agriculture municipalities. afford a clearer picture of which are the gaining areas These lose migrants with the position worsening in and which are the losing? A fifth question, related to 1994. the first, asks whether the level of unemployment in a place is an important driver of the direction of internal Industry Link TF, Services and Fishing municipalities. migration. These lose migrants in both years but the position is better in 1994 with lower losses in the family ages in 5 1 l t n h p t th pr d t n t particular, due to the improved market and resource Table 13 sets out the net internal migration statistics situation. in the fisheries. for the main Industry link types. This typology is based on the resident population in the municipality. We Industry Link TI, Services and Manufacturing munici- have dropped from the table the categories with only a palities. These centres gain migrants in both years few municipalities in, leaving nine types in which though the 15-29 group is an exception. The gains reside 94 per cent of Norway's population in 1994. One shrink between the years. difficulty in using the classification is the clustering of the vast majority of municipalities into just two types: Industry Link TA, Services and Construction munici- Services and Manufacturing centres (dominated by the palities. These lose migrants in both years but the rate former) in which reside 34.8 per cent of Norway's of loss is much greater in 1994 than in 1984, especially population, and Services municipalities, which house in the family ages. 42.5 per cent of the 1994 population. This illustrates the increasing standardisation and modernisation of Industry Link TT, Services municipalities. These are the the industrial structure. main gainers in the decade moving to higher net in- migration in the 15-29 age group. However, the family Industry Link LI, Agriculture and Manufacturing munici- and an older age groups experience net migration loss. palities. These lose migrants on balance, particularly in the young adult ages, but do gain in the family ages in To sum up, there seems to be evidence of divergence in 1994. The 1994 position was worse than the 1984. net migration patterns from 1984 to 1994, but the all These municipalities are rather remote, dependent ages averages paint a misleading picture, and for each upon agriculture and forestry, and weekly commuting life course stage there is a different pattern of gain and to building sites in central towns, or to construction loss. sites related to hydro-electricity or oil extraction.
5 Int rn l M r t n r e o s
bl 13 t nt rn l r t n r t b r nd tr l n t p 19 nd 199
Ind tr A Gr p n t p ( p r nt p p Y r 199 -1 15- 9 3 - 5-59 -7 75+ t l A A r M n f 19 -11 - 1 7 - -1 3 ( 3% 199 5 - 3 1 1 9 - 1 -1 -3
I A r C n tr 19 1 5 -15 3 -5 5 1 7 1 -3 (1 % 199 -1 3 - 1 5 - 5 - 9 3 5 -5 9 I M n f A r 19 3 3 -9 5 1 - 3 3 3 - 7 - ( % 199 -5 - 1 - - 9 3 - 7 -
IA M n f C n tr 19 1 9 -1 3 -1 - 5 1 7 -1 - 5 (5 % 199 3 1 -1 - 3 1 - - 3 S rv A r 19 - -17 5 -1 - 1 3 - 1 - (1 9% 199 -1 1 - 3 - -1 - 1 3 - 9 S rv h n 19 -1 1 - 7 3 - 1 7 -1 3 1 -13 1 ( % 199 7 - 1 - 1 5 5 - 5 - 7 -3 1 I S rv M n f 19 5 5 - 3 5 1 7 1 5 (3 % 199 - 7 - 1 1 3
A S rv C n tr 19 7 -15 3 9 9 3 1 - ( 5% 199 - - 1 -1 3 - 1 - 1 - -9 1 S rv 19 -5 11 1 - -1 5 - 7 -1 ( 5% 199 -3 1 9 - -1 - 9 3 S r C p t d fr d t ppl d b St t t r t 1 Onl Ind tr l n t p th r th n 1 n p l t r r p rt d h Ind tr l n t p r p rt d n th t bl h 9 p r nt f r p p l t n n 199
bl 1 t nt rn l r t n r t b r d n t t r 19 nd 199
n t p r nt A Gr p dp C t r ( p r nt Y r p p 199 -1 15- 9 3 - 5-59 -7 75+ t l O -9 9 19 -1 -1 - 9 3 1 5 - - 9 (3 % 199 - - 7 1 -3 1 1 3 - - 1 1 1 -19 9 19 5 -1 5 3 3 5 9 - 3 - ( 3% 199 3 - 5 - -1 -5 5 - 9 9 19 7 -11 7 1 3 - 3 (3 9% 199 5 - 1 9 - 9 - 7 - 3 -3
M3 3 -39 9 19 5 -1 5 3 5 - (5 3% 199 1 5 - 1 -1 -1 1 1 - 5 M - 9 9 19 -1 1 1 3 1 1 -1 1 ( 1% 199 3 3 -17 7 - 9 -1 1 - 1 -3 M5 5 -59 9 19 1 -1 7 - 5 - 7 1 9 3 - 7 (5 7% 199 3 -11 1 - 1 - 5 -1 M - 9 9 19 -7 7 3 1 9 1 3 1 (9 7% 199 1 -11 1 - 9 9 -1
1 7 -79 9 19 7 - 5 5 3 1 (1 3% 199 3 3 -7 7 1 5 -1 9 1 - - 9 9 19 -1 3 1 (1 % 199 1 5 3 - 1 - 1 9
3 9 -1 19 -9 13 - -1 - 9 -1 7 - (37 % 199 -5 1 7 -1 5 1 3 -1 5 -1 7 S r C p t d fr d t ppl d b St t t r t 1 dp = d n l p p l t d r
Reports 99/19 Int rn l M r t n r
5 l t n h p t p p l t n d n t density falls. The later working ages have lower net Table 14 records the net migration rates by age for rates and a pattern that varies between years. The density categories. The distribution of the population retirement ages are characterised by loss from the across these categories is much more even than was densest settlements and mostly gains elsewhere. So the the case in the Industry classification. The gradients of association of the all age pattern with a neat density change in rate, once again, differ depending on the age gradient (the higher the density the greater the gain or group examined. The family ages show a pattern of net lower the loss) should not be taken too seriously. H3 migrant loss in the highest and lowest categories and municipalities (mostly central cities) show a pattern gains in between. The young adult ages see gains in almost consistently the opposite of all others. both 1984 and 1994 in the highest category and in 1994 in the second highest, with increasing losses as
bl 15 t nt rn l r t n r t b r ntr l t t r 19 nd 199
C ntr l t A Gr p C t r ( p r nt p p Y r 199 -1 15- 9 3 - 5-59 -7 75+ t l
O t n r 19 -5 5 -1 5 - -1 3 - 7
(11 9% 199 - 7 - 1 -7 1 - 1 -7
OA t l v l 1 nd 19 - -15 7 - 3 - -3
(3 3% 199 -1 - 1 - -3 5 7 - - 1
1 l v l 1 19 - - - - - 3 -1 -3 5
(3 9% 199 - - 9 - 1 -1 3 -
1A l v l 1 19 - 1 -1 7 -1 1 - 5
(3 7% 199 - -1 7 -3 1 1 - -3 9
A l v l - 1 -7 - -1 7 -1 5 - 19 - ( % 199 7 -1 1 - 1 -1 -1 9 l v l 19 5 -7 1 5 1 (17 3% 199 9 -5 3 1 3 1 3A l v l 3 19 - 1 1 9 - 1 -1 3 - 1 5 (51 9% 199 - 7 11 5 - 1 - 1 - 3 S r C p t d fr d t ppl d b St t t r t 1 S bl f r th f ll d f n t n f th ntr l t t r
bl 1 t nt rn l r t n r t b r n r l ttl nt l 19 nd 199
A Gr p Cl (% p p 199 Y r - 1 15 - 9 3 - 5-59 -7 75+ t l K1 r r Ind tr 19 - 1 -17 5 - 7 1 1 1 - 3 (3 % 199 -1 - 1 -7 - 1 1 1 1 1 -
0.5K M x d ag ic n f 19 3 -1 - 1 (5 1% 199 -1 - - 1 - 1 1 1 - 1 -5 3
K3 M n f 19 1.2 -1 1 -1 9 - - 5 (7 3% 199 -1 - 5 1 - 5 - 5
K ntr l x d 19 - 1 -15 -1 9 3 7 -3
(9 3% 199 - -17 7 - 9 -3 1 - - -5 5
K5 C ntr l x d rv 19 7 -1 3 5 3 3 3
( % 199 5 -3 3 5 1 1 1 3
K ntr l rv 19 - -9 -5 3 -1 1 - 5 -1 - 5 (7 % 199 - 3 -3 - 9 - 5 1 1 -1 9
K7 C ntr l S rv 19 - 1 - - 9 - 7 -1 7 1
(35 5% 199 -3 1 7 - - 5 -1 -1 3
K M n f n l t r l 19 -1 9 - - 5 -1 -3
( % 199 9 -15 - - 9 - 7 1 -3 9
K9 h r 19 -11 - 5 -1 - 9 -1 9 1 1 -11
(1 7% 199 - 3 - 1 - -5 S r ; C p t d fr d t ppl d b St t t r t 1 S bl f r th f ll d f n t n f th t r
7
Int rn l M r t n r Reports 99/19
5 3 l t n h p t th d r f ntr l t because of the net influx of young people, which The remarks made in connection with density exceeds the outflow of other ages. By way of contrast categories can be repeated in good measure for the Central Mixed Service municipalities, which also gain closely correlated centrality pattern. Table 15 reveals a overall, lose migrants in the young adult ages and gain strong relationship between the all age rate and in the other ages. All other types lose migrants in 1994. centrality, with net migration intensity rising with increasing centrality. This pattern is one that basically 5 5 l t n h p b t n r t n nd derives from the 15-29 age group whose high level of n pl nt activity dominate the picture. For groups aged 45 and Using unemployment rates for 1994 Table 17 establis- over the most central places are not the most attrac- hes, for Norway, a clear association between levels of tive. The second centrality class, especially regional unemployment and overall net migration gains and centres far away from the 6 top-level centres, is the losses. The areas with the highest unemployment rates one favoured by age groups other than young adults. lose migrants while areas with below average unem- ployment gain. The relationship is strongest for the 5 l t n h p t n r l ttl nt t p young labour groups but is not applicable to older When the three dimensions are combined we get the labour where a more complicated pattern is seen. patterns of Table 16. The same observation about the Table 18 reports the simple correlations between all age pattern hiding profound variation across the life unemployment rate in 1994 and other indicators: most course stages can be repeated. The Central Service have the expected signs but the magnitude is very low. municipalities stand out as overall gainers but this is
bl 17 t nt rn l r t n r t b r b n pl nt b nd 19 nd 199
Un pl nt b nd A Gr p Y r ( p r nt p p 199 -1 15- 9 3 - 5-59 -7 75+ t l
th n % 19 - 5 5 3 - 5 1 1 1 3 5
(19 1% 199 - 1 -1 - 1
- % 19 - 1 -1 - 5 - 9 - 5 - 1
199 - 9 5 5 -1 9 - - 5
- % 19 - 3 - 1 1 - 9
199 1 - 9 9 1 -
- 1 % 19 -1 5 -17 - -1 5 -3 3 - 5 -
199 - 3 -1 1 - 7 - 9 -
1 - 1 % 19 -9 5 - 9 -7 - -1 -1
199 - -- 3 3 -17 3 -3 - 5 - 7 -11
1 %+ 19 -1 -37 3 1 9 1 5 -1
199 - 3 - 1 13 7 -1
bl 1 C rr l t n f n t nt rn l nd xt rn l r t n r t b 199 th n pl nt r t f r n p l t
A Gr p Y r -1 15- 9 3 - 5-59 -7 75+ t l
M 19 - - 1 - 19 3 - 7 - 1 - 1
M 199 - 1 - 1 1 7 - 1 - C 9 - 3 E 1 9 - 9 E 199 - 5 t 1 M = t nt rn l r t n r t C = p p l t n h n r t 3 E = t xt rn l r t n r t
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Ch n n r t n p tt rn
So far in the report, we have analysed the patterns of Centre-North, while Hedmark and Oppland and the internal migration using net migration as our diagno- East region are together as East Norway. stic indicator. However, net migration values can result from widely different pairs of in- and out-flow compo- The structure of migration flows between regions nents. In this section, the gross flows between geo- remains the same in 1994 as it was in 1984. The Oslo, graphical regions and between the general munici- East and South regions are the net gainers in both pality types are examined. These flow tables have been years, while the West and Centre-North are the net aggregated from the inter-municipality migration losers. Between the two years migration flows arrays for 1984 and 1994. increased by 3.7 per cent but this was lower than the population increase of 4.6 per cent over the same time 1 M r t n fl b t n r n interval, indicating a small decrease in the rate of The regions used here are a slight aggregation of the inter-municipality migration. To assess the significance standard regionalisation shown in Figure 1. The of such a switch would, however, require further Trøndelag and North regions are grouped into the analysis of a time series of migration.
bl 19 M r t n fl b t n r n r 19
t n t n Or n O l E t S th W t C ntr - rth t l M r t n fl
O l 9 5 1 1 1 977 9 15 7
E t 9 399 79 15 7 1 1 5 S th 1 9 1 1 751 W t 3 77 3 7 51 15 9 13 C ntr - rth 5 11 1 3 3 13
t l 19 551 17 9 7 3 93 1 39 t r t n
O l - 5 - 3 -1 1 - 7 -3 E t 5 59 - -1 1 -1 5 7
S th 3 - 59 - 35 - 1 -1 9
W t 1 1 35 - 1 791 C ntr - rth 7 1 1 1 73
t l 3 1 5 7 1 9 -1 791 - 73 Eff t v n
O l - - - - -5 E t 7 -17 - -
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O igi s es i a io s
Os o Eas Sou Wes Ce e- o o a s Mig a io ows
Os o 9 9 1 3 3 9 1 1
Eas 9 77 1 9 1 1 9 9 1 7
Sou 395 1 7 3 15 1 7 7 5
Wes 3 31 59 595 95 9 9
Ce e- o 39 3 595 1 791 1 91
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Wes 951 115 1 -371 1 13
Ce e- o 1 59 9 519 371 3 1 9
o a s 3 15 -1 13 -3 1 9 Mig a io e ec i e ess Os o - - -19 - - Eas -3 -1 Sou - -9 -17 - Wes 19 3 9 -7 Ce e- o 1 17 7 5 o a s - -5 t h r n r d p f th f ll n nt O l A r h O l E t Ø tf ld d r Oppl nd r d tf ld l r S th A t-A d r t-A d r l nd W t rd l nd S n j rd n Mør d l C ntr - rth Sør- rønd l rd- rønd l rdl nd r nn r
The majority of inter-region flows increased when Mig a io ows e wee se eme 1994 and 1984 figures are compared with six notable y es exceptions. The outflows from the West and the Tables 22 and 23 collect together the migration flow Centre-North to the three other regions decreased statistics for municipalities grouped into the general between 1984 and 1994, while the corresponding classes used by Statistics Norway. Figure 5 presented inflows increased, accounting for the decreased net earlier showed that several classes of municipality have migration between the two sets in about equal geographically concentrated distributions (e.g. the measure. This may herald a re-assessment by migrants Central mixed service industry category clustered in of the relative attractiveness of the core regions of the Oslo region and its immediate surrounds) while Norway compared with the peripheral regions. Ex- others are dispersed (e.g. primary industry munici- changes of migrants between the peripheral regions palities are found in the interior of most of Norway's themselves increased a little between the two years. In regions) . this context, we will also remind the reader of the observations on the cyclical pattern of migration made The level of migration between these classes is higher at the end of section 2 and has increased more than the level of migration between regions, by 8.1 per cent compared with 3.7 The migration effectiveness measures indicate, in both per cent for inter-regional migration. Comparing 1984 years, that the greater the distance between regions and 1994 migration flows into all municipality classes the more "effective" the migration exchanges between increased. The largest increases were into the Central them. Regions which are close together (e.g. the Oslo service municipalities class (K7) of 3 688 migrations or region and East Norway) have flows and counterflows 11.1 per cent. Because out-migration levels for this which are close together in size while more distant class distinction did not increase as much, the net regions (e.g. the Oslo region and the Centre-North) migration increased into Central service municipalities. have less balanced flows. The flow to the Oslo region Out-migration behaved less uniformly over the period from the Centre-North was 26 per cent greater than than in-migration, with the Less central services (K6) the counterflow in 1984. However, the effectiveness and Fishery (K9) classes experiencing declines in the measures for most flow pairs decreased between 1984 level of out-migration. Net migration declined over the and 1994, suggesting that, at the regional scale, the 1984-94 comparison for seven of the nine municipality Norwegian migration system was closer to equilibrium classes. The Central service class saw larger net inflows in the latter year. while the Less central service and Fishery classes saw
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decreased net outflows. For the Fishery class one could ranked by increasing distance. The highest values are expect a varying pattern given its small size and its found in the first and last rows of the table, where the dependence on a fluctuating resource base. K1 class, Primary Industry municipalities and the K9 class, Fishery municipalities, have many effectiveness The effectiveness of flow exchanges between munici- indicators of greater than 10 per cent. A greater pality types does not show the same clear structure as proportion of out-migrants from these economically the inter-regional matrix (Tables 19 and 20) . The less sophisticated municipalities fail to be compensated municipality classes are not in any hierarchical order for the counterflow.
bl 1 M r t n fl b t n n r l ttl nt l r 19
t n t n Or n Cl K1 K K3 K K5 K K7 K K9 t l M r t n fl K1 r r nd tr 5 33 73 919 735 1 13 93 5 1 K M x d ag ic n f 391 7 9 1 5 3 1 7 9 13 5 9 7 K3 M n f 3 1 3 1 9 513 3 1 3 3 1 9 399 K ntr l x d rv n f 99 731 1 15 5 1 1 7 K5 C ntr l x d rv nd tr n f 7 1 79 97 19 5 1 19 15 759 1 7 1 9 K ntr l rv 57 77 313 13 71 3 5 11 K7 C ntr l rv 1 1 7 7 3 1 1 9 75 3 9 99 K M n f n l t r l 15 33 35 3 911 3 1 1 1 3 3 53 K9 h r 137 1 1 5 3 1 119 9 t l 315 5 9 11 3 7 9 9 5 5 3 3 13 1 3 1 1 t r t n 0K1 r r nd tr 3 39 177 7 - - 7 1 K M x d ag ic n f -3 7 -33 1 31 1 -11 -1 31 K3 M n f - - 7 175 3 1 -1 -3 599 K ntr l x d rv n f -39 33 - 5 3 171 1 1 -1 -1 1 53 K5 C ntr l x d rv nd tr n f -177 -1 -175 -5 3 -79 -1 577 -15 -1 1 -3 7 5 K ntr l rv - 7 -31 -3 -171 79 1 3 5 -11 19 1 K7 C ntr l rv - - 1 - 1 -1 1 1 577 -1 3 - 5 - 11 - 5 9 K M n f n l t r l 11 1 1 15 - 5 5 K9 h r 1 3 1 1 1 11 11 - 9 t l -7 1 - 31 -599 -1 53 3 7 5 -1 9 1 5 9 - -9 Eff t v n
0K1 r r nd tr 1 7 3 11 15 - -19 K M x d ag ic n f - 5 - 3 - - K3 M n f -1 -5 3 -1 -1 3 K ntr l x d rv n f -3 - 13 13 - - 3 5 K5 C ntr l x d rv nd tr n f -11 - -3 -13 - 5 -5 -9 - 7 - K ntr l rv - - - 7 -3 5 9 -1 K7 -15 C ntr l rv - - -13 5 - 1 -3 - K M n f n l t r l 1 9 -9 1 K9 h r 19 1 3 7 1 3 - 1 o a s -7 - -3 -5 -7 - -1 t S bl f r th f ll d r pt n f th n p l t l A r = r lt r ; n f = n f t r n
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bl M r t n b t n n n p l t l 199
t n t n Or n Cl K1 K K3 K K5 K K7 K K9 t l M r t n fl
K1 r r nd tr 37 1 1 7 1 93 1 3 11 5 5 3 5 K M x d ag ic n f 1 5 1 793 1 7 17
K3 M n f 3 1 3 375 571 3 3 7 3 1 153 K ntr l x d rv n f 71 7 1 1 1 753 5 1 11 5 97 3 3 K5 C ntr l x d rv nd tr n f 711 1 59 3 33 5 1 99 7 5 7 1 9
K 35 7 1 991 ntr l rv 75 51 3 1 5 3 5 K7 7 31 C ntr l rv 1 33 155 11 3 5 17 9 3 5 3 K 13 7 7 1 1 35 1 7 3 73 M n f n l t r l 3 1 3 79K9 h r 13 13 1 3 9 139 5 7 t l 375 7 9 1 11 9 9 3 1 5 3 11 3 39 1 5 113 3 3 t r t n K1 r r nd tr - -17 15 3 111 355 9 33 1 9 K M x d ag ic n f 1 5 173 5 - 1 1 1 9 K3 M n f 17 -1 13 -5 57 75 1 7 K ntr l x d rv n f -15 - -13 391 1 537 11 -3 9 K5 C ntr l x d se ice nd tr n f - 3 - 5 5 - -3 5 1 -15 -37 -1 9 K ntr l rv -111 -173 -57 -391 3 5 1 1 - 7 - 1 5 K7 C ntr l rv -355 -5 -75 -1 537 -1 -1 1 - -1 - 9 K M n f n l t r l -9 1 -11 15 7 -3 3 K9 h r -33 - - 1 3 37 1 1 3 3 t l -1 9 1 1 9 - 7 - 9 1 9 -5 9 - 3 - 3 Eff t v n K1 r r nd tr - -3 1 3 1 3 17 1 K M x d ag ic n f 1 1 - 15 K3 M n f 3 - -1 5 13 7 K ntr l x d rv n f -1 - - 13 1 1 -5 7 K5 C ntr l x d rv nd tr n f - 3 - 1 -13 -1 - - - K ntr l rv - -1 -5 - 1 15 - -1 K7 C ntr l rv -1 -1 -13 -1 -15 -1 -1 - K M n f n l t r l -3 -1 1 -13 K9 h r -17 -15 -7 5 1 1 13 t l -1 - - -7 - - - t S bl f r th f ll d r pt n f th n p l t l A r = r lt r ; n f = n f t r n
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7 Sy esis a co c usio s
7 1 Ge e a c a ge extended suburbanisation rather than counter- Norway's population maintains relatively high popula- urbanisation. tion growth by European standards, fuelled by con- tinuing natural increase and net migration from 7 5 e im o a ce o e i e cou se outside the country. The main period of external gain Throughout the current report the role of life course consequent on waves of out-migrants from countries in stage in influencing the direction of migration has been transition and the Third World fell between and after stressed. Most often the overall pattern of population the dates of this study. External migration makes the shifts conceal very different flow structures for family most significant impact in the Oslo region (Table 10) migrants, young adults, older workers, retirees and the where net immigration reinforces net internal in- elderly. In this respect internal migration dynamics in migration and the East of Norway where external gains Norway strongly resemble those in other West exceed internal. In the other parts of the country European countries (the United Kingdom, the external gains balance internal losses. Netherlands) .
7 u a e o u a io 7 e o e o eco omic ac o s About half of Norway's municipalities lost population These have an important influence on migration in aggregate over the 1984 to 1994 (Figure 6) . These patterns. Municipalities with an economic concen- municipalities are concentrated in the Centre-North tration in service industries attract internal migrants and interior of southern Norway. There is evidence while those specialized in primary industry suffer that communities with the lowest densities (Table 14) migration outflows consequent on the decline of or and least centrality (Table 15) are losing population productivity improvements in their economic activities. through internal migration. There is a strong gradient of increasing net outflows with increasing levels of unemployment. The internal migration losses from these remoter areas are dominated by the outflows of young persons (aged 7 7 u u e e o u io 15-29) . However, there are some small gains in the Mobility (total internal migration) has been declining retirement and elderly ages and in the family ages in since the 1960s in Norway and there is some evidence these rural municipalities. that in our study period the size of net exchanges of migrants reduced when 1994 is compared with 1984. 7 3 U a eco ce a io In this respect Norway resembles other West European Although the direction of migration is towards denser countries. Over the period studied, there was stability and more central places. This is a product mainly of in the patterns with no major transitions to new the migration of young people when the migration regimes of population shift. We would not anticipate streams are broken down by age, the resulting tales any dramatic changes to the current system of empha- show that the largest urban areas are experiencing net sis on central places and urban coastal regions in the losses from middle age and upwards, and also losses south, with some local deconcentration. for the family ages.
7 Su u a i a io o cou e -u a i a io There is little direct evidence of net positive migration flows to rural remote areas for the population as a whole. Migration flows out of the Oslo region are to other municipalities within commuting range. This deconcentration should therefore be identified as
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f r n
Council of Europe (1997) : Recent demographic Rees P and Kupiszewski M (1996) : Internal migration developments in Europe: 1997. Council of Europe and regional population dynamics: what data are Publishing and Documentation Service, Strasbourg. available in the Council of Europe Member States. Working Paper 96/1, School of Geography, University Courgeau D and Baccaïni B (1997): Analyse multi- of Leeds, Leeds UK. Report prepared for the Council of niveaux en sciences sociales. Population 52, 4, 831 -864. Europe. Submitted to the Journal of Official Statistics.
EUROSTAT (1995a) : Demographic Statistics 1995. Rees P, E Todisco, V Terra Abrami, H Durham and M Office for Official Publications of the European Kupiszweski (1997): Internal migration and regional Communities, Luxembourg. population dynamics in Europe: Italy case study. Report prepared for the Council of Europe (Directorate EUROSTAT (1995b): Regional Statistics 1995. Office of Social and Economic Affairs, Population and for Official Publications of the European Communities, Migration Division) and for the European Commission Luxembourg. (Directorate General V, Employment, Industrial Relations and Social Affairs, Unit El, Analysis and Hansen J C (1989): "Norway: the turnaround which Research on the Social Situation) . Also available as turned round." Chapter 6 in A T Champion (ed.) Working Paper 97/5, School of Geography, University Counterurbanization: the changing pace and nature of of Leeds. population deconcentration, Edward Arnold, London, 103-120. Rees P, E Van Imhoff, H Durham and M Kupiszewski (1997) : Internal migration and regional population Kupiszewski M (1996) Two maps of population change dynamics in Europe : Netherlands case study. Report in Poland. Paper presented to the Council of Europe, prepared for the Council of Europe (Directorate of Meeting of the Group of Specialists on Internal Social and Economic Affairs, Population and Migration Migration and Regional Population Dynamics, Division) and for the European Commission February 1996, Strasbourg. (Directorate General V, Employment, Industrial Relations and Social Affairs, Unit El, Analysis and Rees P (1996) "Projecting the national and regional Research on the Social Situation). populations of the European Union". Chapter 18 in Rees P, Stillwell J, Convey A and Kupiszewski M (eds.) Statistics Norway (1994a): Standard for kommune- (1996) Population migration in the European Union, klassifisering 1994 (Standard Classification of John Wiley & Sons, Chichester, 331-364. Municipalities 1994). NOS C192 (Official Statistics of Norway) . Rees P, H Durham and M Kupiszewski (1996) Internal migration and regional population dynamics in Europe: Statistics Norway (1994b): Befolkningsstatistikk 1994. United Kingdom case Study. Report prepared for the Hefte H Folkemengd 1 januar (Population Statistics Council of Europe (Directorate of Social and Economic 1994. Volume II Population 1 January, NOS C184 Affairs, Population and Migration Division) and for the (Official Statistics of Norway). European Commission (Directorate General V, Employment, Industrial Relations and Social Affairs, Statistics Norway (1997a) Endringer i kommuneinn- Unit El, Analysis and Research on the Social delingen 2. januar 1951-1. januar 1996. Unpublished Situation) . Also available as Working Paper 96/20, note, Sttistics Norway. School of Geography, University of Leeds.
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Statistics Norway (1997b) Statistical yearbook o Norway 1977. NOS C398 (Official Statistics of Norway) .
Van der Gaag N, E van Imhoff and L van Wissen (1997) Internal migration in the countries of the European Union. EUROSTAT Working Paper E4/1997- 5, The Statistical Office of the European Communities, Luxembourg.
Van Imhoff E, N van der Gaag, L van Wissen and P Rees (1997) The selection of internal migration models for European regions. International Journal o Population Geography, 3, 137-159.
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Norges offisielle statistikk (NOS) C488 Befolkningsstatistikk 1997. Oversikt
Economic Survey (ES) 2/99 Stambol L.S.: Interregional labour force mobility in Norway. Gross stream analysis and supply-side adjustment, 22-37.
Aktuelle befolkningstall (AB) 7/98 Flyttinger
3-5/99 Resultat av innenlandsk flytting gjennom første del av voksenlivet. Del 1-3. Kommuner på Østlandet, Sør- og Vestlandet og Trøndelag og Nord-Norge.
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Int rn l M r t n r Reports 99/19
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98/19 H.K. Reppen: Bruk av folkebibliotek 1998. 99/6 A.G. Hustoft, H. Hartvedt, E. Nymoen, 1998. 46s. 115 kr inkl. mva. ISBN 82-537-4586- M. Stålnacke og H. Utne: Standard for økono- 9 miske regioner: Etablering av publiseringsnivå mellom fylke og kommune. 1999. 76s. 140 kr 98/20 0. Landfald og M. Bråthen: Registerbasert inkl. mva. ISBN 82-537-4671-7 evaluering av ordinære arbeidsmarkedstiltak 1996: Overgang til jobb og utdanning. 1998. 99/7 T. Lappegård: Regionale variasjoner i 48s. 100 kr inkl. mva. ISBN 82-537-4596-6 fruktbarheten i Norge. 1999. 88s. 140 kr inkl. mva. ISBN 82-537-4673-3 98/21 J. Moen: Produktivitetsutviklingen i norsk industri 1980-1990 - en analyse av dynamikken 99/8 B. Halvorsen, B.M. Larsen og R. Nesbakken: basert på mikrodata. 1998. 85s. 115 kr inkl. Energibruk i husholdningene 1974-1995: En mva. ISBN 82-537-4597-4 dokumentasjon av mikrodata etablert for økonometriske formål innenfor prosjektet 98/22 K. Flugsrud og G. Haakonsen: Utslipp til luft fra "Fleksibel energibruk i husholdningene. 1999. utenlandske skip i norske farvann 1996 og 33s. 125 kr inkl. mva. ISBN 82-537-4676-8 1997. 1998. 37s. 100 kr inkl. mva. ISBN 82- 537-4599-0 99/9 H. Medin: Valg av måleenhet i verdsetting av miljøgoder: Empiriske eksempler. 1999. 45s. 98/23 E. Nørgaard: The Norwegian Balance of 125 kr inkl. mva. ISBN 82-537-4677-6 Payments: Sources and methods. 1998. 72s. 115 kr inkl. mva. ISBN 82-537-4600-8 99/10 R. Jensen: Kvadratmeterpriser for flerbolig-hus. 1999. 22s. 125 kr inkl. mva. ISBN 82-537-4679- 98/24 H. Hungnes: Imperfeksjoner i kapital-markedet. 2 1998. 37s. 100 kr inkl. mva. ISBN 82-537-4602- 4 99/11 T. Kalve: Innvandrerbarn i barnevernet. 1999. 29s. 125 kr inkl mva. ISBN 82-537-4680-6 98/25 T. Lowe: Levekår i landbruket: En studie av landbruksbefolkningens levekår. 1998. 181s. 99/12 A.S. Bye og K. Mork: Resultatkontroll jordbruk 220 kr inkl. mva. ISBN 82-537-4603-2 1999: Jordbruk og miljø, med vekt på gjennomføring av tiltak mot forurens-ninger. 99/1 A.C. Hansen: Fremskrivning av støybelastning 1999. 75s. 140 kr inkl. mva. ISBN 82-537-4683- for veitrafikk. 1999. 31s. 125 kr inkl. mva. ISBN 0 82-537-4659-8 99/13 D. Juvkam: Historisk oversikt over kommune- 99/2 T.W. Bersvendsen, J.L. Hass, K. Mork og B.H. og fylkesinndelingen. 1999. 90s. 140 kr inkl. Strand: Ressursinnsats, utslipp og rensing i den mva. ISBN 82-537-4684-9 kommunale avløpssektoren, 1997. 1999. 71s. 140 kr inkl. mva. ISBN 82-537-4663-6 99/14 J.-A. Jørgensen, B. Strøm og T. Åvitsland: Effektive satser for næringsstøtte 1996. 1999. 99/3 P. Boug: Modellering av faktoretterspørsel. 51s. 140 kr inkl. mva. ISBN 82-537-4685-7 1999. 60s. 140 kr inkl. mva. ISBN 82-537-4665- 2 99/15 J. Lyngstad og I. Øyangen: Sjung om studentens lyckliga dar: Studenters levekår 1998. 1999. 99/4 R. Jensen: Beregning av usikkerhet for 98s. 140 kr inkl. mva. ISBN 82-537-4690-3 boligprisindeksene på grunn av frafall. 1999. 25s. 125 kr inkl. mva. ISBN 82-537-4669-5 99/16 B. Aardal, H. Valen og I. Opheim: Valgunder -søkelsen 1997: Dokumentasjonsrapport. 1999. 99/5 K.E. Rosendahl: Vurdering av skadefunksjons- 109s. 165 kr inkl. mva. ISBN 82-537-4699-7 metoden til bruk på vegprosjekt - en case- studie. 1999. 38s. 125 kr inkl. mva. ISBN 82- 537-4670-9
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