Regional Economic Disparities in Finland

Muistio | Brief | 68 25.6.2018 Regional economic disparities in Finland Abstract In this note, I study the Finnish regional dispersion of economic indicators such as the GDP per capita, labour productivity, the employment rate and the compensation of employees. Moreover, I examine the regional-level correlation between these variables. The Paolo Fornaro results are then compared with what has been found The Research Institute of the Finnish Economy for the German and Italian economies. [email protected] Finnish regional economies display substantial variation, but their GDP per capita, productivity and em- Suggested citation: ployment rate have converged. However, the com- Fornaro, Paolo (25.6.2018). pensation of employees has diverged. Compared to “Regional economic disparities in Finland”. Germany and Italy, the Finnish economy has a lower regional dispersion, with a similar convergence pro- ETLA Brief No 68. cess as in Germany. The correlation between region- https://pub.etla.fi/ETLA-Muistio-Brief-68.pdf al productivity and the employment rate is lower than what is found in Italy and Germany, and the same holds for productivity and wages. The picture gathered from this analysis is mixed. Con- vergence of economic conditions is certainly positive, but the divergence of the compensation of employees can be problematic for the long-term sustainability of the Finnish regional markets. If well-paid jobs con- centrate in richer regions, there will be higher incen- tives for young and well-educated workers to move away from peripheral (in economic terms) areas, which would be at risk of stagnation. Tiivistelmä Alueelliset erot Suomessa This brief is based on research conducted within the Tässä muistiossa tarkastellaan, millaista alueellista vaih- Tacking Biases and Bubbles in Participation (BIBU) telua Suomessa on esiintynyt bkt:n suhteessa asukasta project. The author is grateful for the funding provid- kohti, työn tuottavuudessa, työllisyysasteessa ja palkois- ed by the Strategisen tutkimuksen neuvos- sa. Lisäksi tutkitaan sitä, miten nämä tekijät ovat kor- to (STN). reloituneet keskenään. Suomen kehitystä myös verra- taan Saksaan ja Italiaan. Tämä muistio perustuu tutkimukseen, joka on osa kansalaisuuden kuilut ja kuplat -tutkimus- Suomen maakuntien väliset erot ovat olleet monessa hankkeesta (BIBU). Kiitään Strategisen tutkimuksen suhteessa suuret, mutta erot ovat kaventuneet merkit- neuvostoa (STN) tutkimuksen rahoituksesta. tävästi. Variaatiokertoimella mitattuna bkt per asukas, työn tuottavuus ja työllisyysaste vaihtelivat maakuntien välillä vähemmän vuonna 2015 kuin vuonna 2000. Sen Key words: Convergence, regional inequalities, sijaan palkkojen vaihtelu on kasvanut. Suomen alueel- productivity, wages liset erot ovat pienemmät kuin Saksassa ja Italiassa. Suomen alue-erojen kaventuminen on ollut samanlais- Avainsanat: Lähentyminen, alueellinen eriarvoisuus, ta kuin Saksassa. Tuottavuuden ja työllisyysasteen väli- tuottavuus, palkat nen korrelaatio Suomen maakunnissa on ollut pienem- pi kuin Saksassa ja Italiassa. JEL: O47, R11, R23 Tulokset tarjoavat kiinnostavan kuvan Suomen alue- kehityksestä. Elintason, työn tuottavuuden ja työllisyy- den erojen kaventumista voidaan pitää myönteisenä. Sen sijaan palkkatasojen erkaantuminen voi lopulta ai- heuttaa ongelmia. Jos korkeapalkkaiset työpaikat kes- kittyvät harvoille vauraille alueille, tämä voi kannustaa nuoria ja korkeasti koulutettuja lähtemään köyhemmis- tä maakunnista. Tämä saattaa jähmettää köyhempien kuntien kehityksen. 2 Regional economic disparities in Finland Introduction During the last 10 years, Finland has been hit by shocks with heterogeneous impacts on different areas. One can think of the crisis of the pulp and paper industry (data Regional economic inequalities have been widely dis- from the Finnish Forest Industries Federation indicate cussed during the years following the financial crisis, a roughly 25 percent drop in paper and pulp production especially at the European level. There are multiple from 2008 to 2017), or the closure of the Nokia plant reasons why this topic has been at the centre of the in Salo in 2012. Aside from sparse anecdotal evidence, public debate. First of all, economic differences among it is interesting to examine the dynamics of economic Member States and within individual nations can have (in)equality among Finnish regions. This note address- a strong impact on the EU economic governance (Gros es this issue by analysing the Finnish regional differenc- et al., 2017). Moreover, economic inequalities can al- es in GDP per capita, labour productivity, employment so have important consequences on the socio-political and wages, and how these have changed between 2000 landscape of a country (on this topic see, for example, and 2015, with a special focus on inter-regional disper- Piketty, 2018). sion (the so-called sigma-convergence). Long-term shifts in the economic structure, e.g. the in- creasing automation of low-skilled tasks in manufactur- ing processes (see the discussion in Bubbico and Freytag, Regional inequalities and 2018) and the creation of new jobs related to techno- logical advances, have favoured large urban areas and their dynamics damaged industrial hubs (Iammarino et al., 2017). In addition, regional endemic factors have affected the ca- I start the analysis by looking at how Finnish regions’ pacity of different areas to take advantage of new eco- GDP per capita for 2015 compares to the EU151 average, Regionalnomic trends. GDP per capita for 2015, in PPSin purchasing where power the standard EU15 (PPS). values A number is below100 Figure 1 Regional GDP per capita for 2015, in PPS where the EU15 values is 100 WHOLE COUNTRY Uusimaa Pohjanmaa Etelä-Karjala Satakunta Keski-Pohjanmaa Lappi Pirkanmaa Kymenlaakso Varsinais-Suomi Keski-Suomi Pohjois-Savo Kanta-Häme Päijät-Häme Pohjois Pohjanmaa Etelä-Pohjanmaa Etelä-Savo Pohjois-Karjala Kainuu 0 20 40 60 80 100 120 140 Source: Statistics Finland and author’s own calculations. Source: ????. 2 3 ETLA Muistio | ETLA Brief | No 68 100 indicates that the regional GDP per capita is lower the standard deviation among regions divided by the na- than the average of the EU15 countries and vice versa for tional average) for regional GDP per capita and GDP per values above 100. employee (i.e. labour productivity), for 2015 and 2000. The picture we get from Figure 1 is mixed. The 2015 GDP Figure 2 gives three major insights. Firstly, labour produc- per capita of Uusimaa is 30% larger than the EU15 aver- tivity dispersion across regions (measured by the coeffi- age, while the one of Kainuu is 30% lower than the EU15 cient of variation of GDP per employed person) is signifi- level. Apart from these two extremes, the GDP per cap- cantly lower than the one of GDP per capita. The second ita among regions is fairly uniform. aspect worth noting is that both coefficients of variation of have dropped substantially over the years 2000–2015, GDP per capita can be separated in two main compo- indicating a sigma-convergence for these two measures. nents, labour productivity and the employment rate, for- Finally, the drop in the regional variability is substantially mally: equal for both GDP per capita and per employed persons. Next, I report the values for the coefficients of variation of GDP per capita, GDP per employed person, compen- ܩܦܲ ܧ݉݌ ܩܦܲ݌ܿ ൌ sation of employees (per employee) and the employment where Emp denotes the number of employed persons.ܧ݉݌ ܲ݋݌As pointed out in Gros, Musmeci and Pilati rate (measured as the total number of employed persons (2017), the regional dispersion of GDPwhere and Empthe one denotes of productivity, the number in EU ofco untries,employed follow persons. different divided by the total population), together with their per- patterns. I verify this aspect by lookingAs atpointed the coef outficient in of Gros, variation Musmeci (i.e. the andstandard Pilati deviation (2017), among the centage changes between 2000 and 2015. regional dispersion of GDP and the one of productiv- regions divided by the national average) for regional GDP per capita and GDP per employee (i.e. labour ity, in EU countries, follow different patterns. I verify The results of Table 1 point toward a strong regional con- productivity), for 2015 and 2000. this aspect by looking at the coefficient of variation (i.e. vergence of the GDP per capita, GDP per employee and 0,25 the employment rate, with the latter showing an even stronger convergence compared to the other two. One Regional variability (coefficient of variation)possible explanation behind this convergence is related 0,2 Figure 2 Regional variability (coefficient of for GDPvariation) per capita for GDP and per capita labour and productivityto the internal migration toward richer regions. The cor- labour productivity relation between the average net migration rate (at the 0,15 regional-level) over the years 2000–2015, and the employ- GDP per capita ment rate in 2000 is more than 0,4 (0,3 correlation with 25 GDP per emp 0,1 GDP per capita in 2000). In other words, richer regions GDP per capita GDP per emp. have experienced a strongly positive net flow of popula- 0,05 20 tion from other regions. Another driving factor behind the convergence of the employment rate is the different 0 age structure characterizing Finnish regions. The share of 2000 2015 the population between 55 and 69 years old is significant- 15 ly larger in peripheral regions like Kainuu (21 percent) Figure 2 Regional variability (coefficient of variation) for GDP per capita and labour productivity. compared to Uusimaa (17 percent). The correlation be- Figure 2 gives three major insights. Firstly, labour productivity dispersion across regions (measured by tween the share of population above 60 (computed over the coefficient of variation of GDP per10 employed person) is significantly lower than the one of GDP per the years 2000 and 2015) and the change in the employ- capita. The second aspect worth noting is that both coefficients of variation of have dropped ment rate between 2015 and 2000 is 0,27, indicating that substantially over the years 2000-2015, indicating a sigma-convergence for these two measures.

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