Relationship Between Oil Price and Unemployment Rate in Different Counties of Norway

RELATIONSHIP BETWEEN OIL PRICE AND UNEMPLOYMENT RATE IN DIFFERENT COUNTIES OF NORWAY

Master thesis

Ramunas Meironas

Faculty of Social Sciences

UNIVERSITETET I OSLO

2019, SPRING

RELATIONSHIP BETWEEN OIL PRICE AND UNEMPLOYMENT RATE IN DIFFERENT COUNTIES OF NORWAY

III

RELATIONSHIP BETWEEN OIL PRICE AND UNEMPLOYMENT RATE IN DIFFERENT COUNTIES OF NORWAY

Ramunas Meironas http://www.duo.uio.no/

Trykk: Reprosentralen, Universitetet i Oslo

Abstract

This thesis investigates causal relationship between real oil prices, real interest rate and unemployment rate in Norway and different counties of Norway (Akershus, Buskerud, Hordaland, Troms, Oslo and Rogaland). By applying Toda-Yamamoto causality test and using monthly data from period of 1999:1 to 2014:12, study concluded that there is no causal relationship between real oil price and overall unemployment level in Norway. Nonetheless, analysis on a county level indicated unanimous causal relationship of real oil price and unemployment rate in all tested regions. The relationship was unidirectional in all cases, declaring that changes in real oil prices Granger causes unemployment. Additionally, Norway and Buskerud exhibited unidirectional causal relationship between real interest and unemployment: changes in real interest rates Granger causes unemployment. Results are partially consistent with previous researches, as well as partially consistent with wage efficiency model. The largest surprise was the non-causality between real oil price and overall unemployment, and substantial causality between real oil price and unemployment in various counties of the country. This phenomenon is proposed to be studied in more depth.

Keywords: oil prices; interest rates; unemployment rate; causal relationships; Toda- Yamamoto

Acknowledgement

Studying at University of Oslo was a huge step forward in my life. It inspired me in a lot of different ways.

I would like to thank to all the teachers and especially to my supervisor Marcus Hagedorn and Anders Grøn Kjelsrud, who guided me through preparation and undertaking of this thesis, and supported me with priceless feedback.

Additionally, I would like to also thank to my fiancé Gintare Rabikauskyte, my parents and my friends who always motivated and supported me. I want to give my special thanks to my friend Gediminas Meskauskas who helped me at most critical point of my life. Your invaluable help with everything is highly appreciated.

Ramunas Meironas

11th May, 2019

VII

VIII

Table of Contents Abstract ...... V Acknowledgement ...... VII Table of Figures ...... XI 1 Motivation ...... 2 2 Introduction ...... 3 2.1 Descriptive Analysis ...... 4 2.2 Approach ...... 5 2.3 Format ...... 6 3 Background ...... 7 4 Literature Review ...... 11 4.1 Oil price and unemployment ...... 11 4.2 Oil price and GDP ...... 14 4.3 Exchange rate and Unemployment ...... 15 4.4 Real interest rate and unemployment ...... 16 4.5 Dutch disease ...... 17 4.5.1 Resource Movement ...... 18 4.5.2 Spending effect...... 19 4.6 Dutch Disease and Norway ...... 19 5 Factor Discussion...... 22 5.1 Unemployment ...... 22 5.2 Oil price ...... 23 5.2.1 Brent crude price ...... 23 5.2.2 Oil price effects to Norway...... 25 5.3 Real interest rate ...... 26 5.4 Exchange rates ...... 28 6 Methodology ...... 30 6.1 Data Overview ...... 30 6.1.1 Unemployment Rate ...... 30 6.1.2 Real Oil Price ...... 31 6.1.3 Real Interest Rate ...... 31 6.2 Vector Autoregressive Model (VAR) ...... 32 6.3 Toda-Yamamoto Procedure ...... 32 6.4 Serial Correlation ...... 34 6.5 Efficiency Wage Model ...... 34

7 Hypotheses...... 36 8 Analysis ...... 37 9 Discussion of Findings ...... 43 10 Conclusion ...... 45 11 Suggestions for Further Research ...... 46 12 Bibliography ...... 47 13 Appendix ...... 49

Table of Figures

Equation 1. Total Labor Force ...... 30 Equation 2. Unemployment Rate ...... 30 Equation 3. Real Oil Price ...... 31 Equation 4. Real Interest Rate ...... 32 Equation 5. VAR Model ...... 32 Equation 6. Augmented VAR ...... 34 Equation 7. Profit Function in Perfectly Competitive Economy ...... 34 Equation 8. Equilibrium Unemployment...... 35 Equation 9. Augmented VAR equation for 4 lags (k+d=4) ...... 39 Equation 10. Augmented VAR equation for 5 lags (k+d=5) ...... 39

Exhibit 1. Granger causality test results for Norway ...... 39 Exhibit 2. Granger causality test results for Oslo ...... 40 Exhibit 3. Granger causality test results for Akershus ...... 40 Exhibit 4. Granger causality test results for Buskerud ...... 40 Exhibit 5. Granger causality test results for Hordaland ...... 41 Exhibit 6. Granger causality test results for Rogaland ...... 41 Exhibit 7. Granger causality test results for Troms ...... 42 Exhibit 8. Descriptive Statistics ...... 49 Exhibit 9. Serial Correlation Tests Summary ...... 50

Figure 1. Employment Oil & Gass Sector, Norway 1972-2017 ...... 4 Figure 2. Crude Oil Exports, Norway 1972-2017 ...... 5 Figure 3. The timeline displays with the year of when the oil field was discovered (in brackets) and the year when the extraction from the field has started. Source: Norsk Petroleum (2019)...... 10 Figure 4. Population Growth in Norway, 1962-2019 ...... 22 Figure 5. Overall Unemployment Level in Norway, 1999-2014 ...... 23 Figure 6. Nominal vs. Real Oil Price, 1960-2014 ...... 24 Figure 7. Employed people in oil industry in Norway (1000 people per unit). Source: Norsk petroleum ...... 25 Figure 8. Real interest rates vs. inflation, Norway 1982-2014 ...... 27 Figure 9. Real interest rate and unemployment in Norway. Source: SSB ...... 27 Figure 10. VAR Residual Serial Correlation LM Tests, Unemployment Norway ...... 38

Table 1. Results from ADF and KPSS tests ...... 37 Table 2. Lag length selection based on LR, FPE & AIC ...... 38

1 Motivation

Country’s welfare and economy is one of the most important topics within economics field. It was always interesting for me to understand how and why some of the countries had a better or worse economy than the others, and what are the reasons behind that. Many people like to outline that Norway is blessed with its oil fields and government, which were some of the main underlying reasons for such an unprecedented growth and prosper.

At the beginning of master studies, I was wondering whether the oil indeed have had such a remarkable impact on the economy of Kingdom of Norway; or were there other factors that were influencing the extraordinary growth. By residing in this country, I have been bombarded with massive amounts of contradicting information about nearly every aspect of the country. Various newspapers daily printing articles about oil crisis, oil price shocks, layoffs, the dream of a better future and the idea that one day this fantasy will shatter. For a student in economics, this was always a struggle to distinguish what is true, and what is fake news, and what is down right nonsense. Therefore, in order to figure out the truth about the actual impact of the oil, and whether it would actually be so catastrophic on Norway – or even the world, I decided to take control in my own hands, and perform an in-depth analysis. Since the industry is so vast, I had to narrow down the scope of the research, which led to analysis of two most interesting topics for me – Oil and Unemployment.

2 Introduction

Historically crude oil has been used for thousands of years, however the age of oil is considered to have started around 1800’s, with the introduction of internal combustion engine and the advancements in drilling techniques. Initially, oil production was controlled by seven American companies: Anglo-Iranian, Gulf Oil, Royal Dutch Shell, Standard Oil Company of California, Standard Oil Company of New Jersey, Standard Oil Company of New York and Texaco, which were known as Seven Sisters. Not long after, other countries and continents joined the ever-expanding industry, with Russia, Persia, Britain, Holland and others taking its part of the pie. While other companies started operations, Standard Oil company was the main and largest player in the market, controlling supply - and thereby prices not only in America, but also worldwide. In response to drastic oil price reduction by Standard Oil in 1960’s, the major oil exporting countries gather for a meeting, forming OPEC1. The organization was formed by Iran, Venezuela, Saudi Arabia, Kuwait, Iraq and Qatar (which at the time controlled around 80% of oil supply worldwide), with two main goals in mind - to defend posted oil price and prevent unilateral pricing decisions by western multinational oil companies. Initially the organization was not recognized by the western world, and consequently not treated as threat or with any importance whatsoever. After less than half a century (1973-1978) it became the world’s main power, with such an influential extent, that it could decide whether it would be and inflation or depression in the economy. While the organization no longer possesses such influence, it still wields significant influence over the oil prices and politics surrounding petroleum to date.

Norway, as well as the whole world, has seen significant changes in the oil industry in the last couple of centuries. Worldwide production of oil has risen substantially, from less than 0,5 million barrels a day in early 1900’s to more than 90 million barrels a day in 20122. This has led in substantial demand for labor in the particular sector. US alone employed 1,4 million people in Oil industry in 20153, up from 475 thousand people in 1979 (Rosen, 1984). This represents nearly threefold increase in industry employment in just 35 years, and underlines the severity that oil industry can have on the unemployment level. Norway is not an exception, with 6,2% of the total labor force employed in this industry. The underlying fact

1 Organization of Oil Exporting Countries 2 Historical Energy Production Statistics. Source: http://www.tsp-data-portal.org/Energy-Production-Statistics#tspQvChart 3 The Richest Countries in The World. Source: https://www.worldatlas.com/articles/the-richest-countries-in-the-world.html

3 provides a premise that changes in the oil industry can have a crucial effect on employment in Norway or even the whole world. Therefore, this thesis will analyze the extents that petroleum industry has on unemployment rate.

2.1 Descriptive Analysis

Norway, a late entrant to the oil market, has managed to stun the world with its rapid growth, decision making and strategies. Starting with the very first drillings in 1969, when the world was already dominated by OPEC, Norway managed to climb its way up. As of 2017, Norway was ranked 8th by crude oil exports and 9th by refined oil exports worldwide4. This rapid growth had a significant impact on economy, living standards and employment. In 1970, Norwegian GDP per capita was just below OECD average, and in less than half a century Norway was ranked 6th worldwide, only falling behind Ireland, Brunei, Singapore, Luxembourg and Qatar. It is no coincidence, that oil played a big role in this swift movement up the ladder. By early 1980’s, export of oil and gas represented nearly 15% (Fageberg, et al., 1992) of Norwegian GDP, and by 2013 it grew to 21% (Cappelen, et al., 2014). As a consequence of rapid growth in the oil sector, the demand in the labor Total Employment in Oil & Gass Sector, Norway 1972-2017, in 000's 70 market grew at an increasing rate. In 60 1974 there were barely 1000 people 50 employed at this particular sector, 40 while by the end of 2014, this 30 Nr. of People, in 000's number has reached to more than 20

65’000 (Figure 1). As of 2017, there 10 were 170 200 people who worked 0 directly or indirectly in the 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 Figure 1. Employment Oil & Gass Sector, Norway 1972-2017 petroleum sector5 representing close to 6,2% of the total labor force.

4 The Richest Countries in The World. Source: https://www.worldatlas.com/articles/the-richest-countries-in-the-world.html

5 Norskpetroleum. Source: https://www.norskpetroleum.no/en/economy/employment/ 4

Over the years, oil industry and its products became the main exports for Norway. For several decades, crude oil exports accounted for more than 30% of the total Norwegian exports, reaching a peak of just north of 50% in 2001 (Figure 2). Such dependence on one industry can come with strings attached, and in case of crisis can have a substantial impact on employment.

Crude Oil Exports, Norway 1971-2017 Crude oil Exports Crude Oil as % of Overall Exports

500 60,00 %

450

50,00 % 400

350 40,00 %

300

250 30,00 %

200 % of Overall Exports Exports in 000's NOK 20,00 % 150

100 10,00 %

0 0, 00 %

1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 2. Crude Oil Exports, Norway 1972-2017 2.2 Approach

One of the aspects that the study will focus on will be the direct impact of changes in oil prices and real interest rates on the unemployment in Norway and its’ different counties. With such a high dependency on oil sector, employing more than 6% of the total labor force, various fluctuations in the market can have a substantial impact. Additionally, while the various booms and busts in the market can affect the employment in a direct way, A. Carruth et. al (1998) have developed an alternative approach to tackle this issue. A creation of Efficiency Wage Model assumed raw materials, such as oil, as part of production process, and thereby changes in such factors was hypothesized to lead to changes in employment. This premise was analyzed by the authors and confirmed to be statistically significant. Therefore, for the purpose of this study, it will be analyzed whether this hypothesis raised by A. Carruth et. al (1998) holds in Norwegian market. Additionally, it will be analyzed whether any significant differences can be observed between the country wide unemployment, and unemployment in different regions.

2.3 Format

This thesis consists of two parts. First part covers the theoretical aspect and qualitative analysis of Norwegian oil history (section 3), literature review (section 4), relevant factors and discussion (section 5), methodology that will be applied for analysis (section 6) and hypotheses for the research (section 7). Second part covers empirical analysis (section 8) and discussion of the findings comparing with previous researches results that were covered in literature review (section 9), conclusion of the research (section 10) and suggestions for the further research (section 11).

3 Background

Oil industry in Norway - a short summary

Pre-Oil

As of 1970, GDP per capita in Norway was below the OECD average. Norway has exported various of raw products such as fish, pulp, paper, together with chemical products, aluminum, ammonia and others. After World War II one of the main growth factors in Norway was a shipbuilding industry.

1962-1965

1962 marks the start of Norwegian oil history. In October of this year, an American oil company Phillips first asked for permission to begin explorations of what was soon to become a Norwegian continental shelf. In 1963, negotiations regarding the border disputes has started between Norway, Denmark, Britain, Iceland, Greenland and Soviet Union. Preparations for allocating concessions started during 1964, after last and final agreement with Britain was signed. In 1965, the allocations were finished and comprised seventy-nine blocks ready for explorations and drilling. The concession round for these territories was dominated by foreign companies, with Norwegians represented in only twenty-nine, and with minority shares.

1966-1970

First drillings took place in 1966, during which a presence of hydrocarbons was confirmed. Only three years later, an oil field was discovered in the southwestern corner of Norwegian shelf. After deeper investigations, it was confirmed that the newly discovered field - Ekofisk field, was a massive one, containing 534 million Sm3 of oil6 and 158 billion Sm3 of gas. The discovery made clear that there could be much more oil further north. This led to an immediate agreement between Norwegian politicians, that participation in the industry was important. In order to achieve more power, a move by center-right government was made to secretly secure more than 50% of Norsk Hydro and provide conditions for it to become the dominant Norwegian oil company.

6 1 Sm3 = 6,39 US Barrels 7

1971 - 1989

In March of 1971 Labor party took over center-right and politicians Finn Lied and Arve Johnsen took over of the Ministry of Industry. A new direction for a state-owned oil company was chosen, dismissing the initial plans for Norsk Hydro. This marked the start of Statoil (State Oil) and on 14th of June 1972, this entity was legally established. Several months later Norwegian parliament declined membership in EEC (now EU), and Labor party left government. Shortly after this, Arve Johnsen became president of newly established Statoil. In 1977 Norwegian Petroleum Consultants (hereafter NPC) was founded, due to efforts of A. Johnsen and Statoil, and A. Johnsen’s desire for a private Norwegian engineering company. This was done in order to reduce the discovery and extraction costs, which were comparably higher in contrast foreign companies. Additionally, there were no Norwegian companies capable to undertake such projects as Ekofisk, neither in size, nor skills.

1981 marked a new political era, when conservative party took over social democratic government. Kåre Willoch, leader of conservatives, did not change the approach towards foreign investments, and the Norwegianization persisted. The only notable change implied by K. Willoch was to reduce power and influence of Statoil, by strengthening Norsk Hydro - a semi state-owned company, and establishing direct state-ownership of most productive fields. This movement converged to creation of State’s Direct Financial Interest (hereafter SDFI) in 1984, after which the state was controlling oil reserves three times the size of Statoil. Eventually SDFI became effective means for collecting resource rent for the country. In 2001, it became state company known as Petoro.

Oil prices drop in 1986 pushed towards more open oil policy. Labour government introduced significant tax reductions for foreign companies and in 1987 foreign companies were dismissed from exploration phase taxes, however the state maintained the 50% of property rights.

In 1989 Norway along with other European members of EFTA, started the negotiation process with European Union (hereafter EU). The main goal was to become a part of internal EU market, without being a direct member of EU.

1990 - 2000

In the 1990s, under leadership of Harald Norvik, Statoil was depoliticized. Additionally, in 1990 Statoil and British Petroleum agreed on comprehensive alliance in order to develop a key player in the market. In order to further decrease costs and provide local subcontractors with an advantage, Minister for Oil and Energy announced an introduction of own standards, which became known as NORSOK. However, not long after BP-Amoco merger in 1998, the alliance between Statoil and BP was dissolved.

As the oil export caused economic surpluses, in 1990 The Government Pension Fund of Norway (Oil Fund) was established. One of the fundamental goals of the fund was to provide government with more flexibility regarding fiscal policy. Norges Bank Investment Management was responsible for managing the fund on behalf of the Ministry of Finance7. The newly established fund grew from 172 billion NOK in 1998, to 386 billion NOK in 2000, which represented more than 200% growth.

Another important factor that shaped the whole path of Norwegian oil industry was becoming a member of EEA in 1994. This move allowed foreign companies to operate in Norway, as long as it’s registered in EEA. This also opened free labor movement between Norway and EEA. Moreover, upon joining EEA, Norway was no longer allowed to give prioritization and preferences to national companies in licensing rounds.

2001 - 2006

In 2001, shortly after depoliticization, Statoil was partially privatized. While the state maintained majority of the shares outstanding - up to 67%, it did not interfere in the internal matters, and the steering of company was in the hands of shareholders. While in the early 2000s Statoil could show profits at the same level as other major international players - and much better than any other Norwegian company, subsequent years revealed that this was in large due to former privileged position provided by the state.

In 2003, Petroleum Directorate opened free share trading in the Norwegian sector. Additionally, a tax reform in 2004 was introduced, providing a tax deduction of up to 78% for exploration companies. One of the main reasons was a fading belief that more oil fields can be discovered, since there was no significant one’s post 1984 (Figure 3).

7 Norges Bank Investment Management. Source: https://www.nbim.no/en/the-fund/about-the-fund/ 9

Figure 3. The timeline displays with the year of when the oil field was discovered (in brackets) and the year when the extraction from the field has started. Source: Norsk Petroleum (2019).

2007 - 2016

In 2007 Statoil has merged with Norsk Hydro’s oil department, making it a dominant operator with approximately 80% of the oil and gas production in Norway. While the decision came from the leadership of both companies, the underlying political factor was non-deniable, as this merger was approved by the parliament. One of the main goals of the merger was to create a more vigorous entity for bolder international expansion. After the merger, new company became known as StatoilHydro. On 2009, the company was renamed back to Statoil, and as of 2012 Statoil still controlled around 70% of Norwegian oil production.

4 Literature Review

Petroleum industry has been studied by many researchers. Different angles and perspectives were analyzed, trying to bind movements in the particular industry to economic events. Oil, the so called black-gold, is often tied to prosperity, and more often than not, categorized as wealth creator. Researchers have tried to find correlations between the natural resource and various factors that impacts every household. Economic factors such as gross domestic product (hereafter GDP), exchange rate, interest rates, unemployment and many more were studied, trying to find correlations and dependencies. On the other hand, some of the analysts were not all positive, claiming that some nations might suffer from sudden discovery of precious natural resources. The most famous of such cases is called Dutch disease, where discovery of natural gas led to disastrous outcome for other internal industries in Netherlands, and huge rise in unemployment of more than 300%. In this section, an overview of such researches and the theoretical frameworks will be provided; examining most notable factors, relations, different models and the prerequisites that are needed for conducting the analysis and tests for the topic of this thesis.

4.1 Oil price and unemployment

Relationship between oil price fluctuations and its effect on the unemployment have been analyzed by many studies, including a range of different factors, and subsequently leading to a range of different findings. A relatively recent study performed by F. Bahadorkhah and A. Aminifard (Bahadorkhah & Aminifard, 2014) analyzed the relationship between unemployment and real oil price in Iran, taking real interest rate into account. The research was performed in the range between 1973 and 2012, and have concluded that there is a direct and positive relationship between real oil price and unemployment rate. Authors have extrapolated that this is due to labor being a substitute factor of production. While this might hold in developing countries like Iran, contradictory outcomes can present themselves during analysis of advanced economy like Norway.

Research by Alan Carruth and Andrew J. Oswald (Carruth & Oswald, 1998) analyzed the impact that changes in oil prices and interest rates could have on unemployment in U.S. The study was based on efficiency-wage model, and was analyzed in light of perfectly competitive market. By obtaining such assumption, researchers have argued that oil, as an input to

11 production, might have a direct link to unemployment. As the oil price increase, final product price increases - thereby deviating from the zero-sum profits of perfectly competitive market. In order to correct this effect, and resume to zero-profits, companies would either lower wages or decrease employments. Both of these actions would lead to increase in unemployment. Therefore, the changes in oil prices should have a direct positive relationship with unemployment in the region. With these assumptions in mind, A. Carruth and J. Oswald (1998) conducted the research, finding strong evidence of oil price change impacting the unemployment rate. Additionally, real interest rates were also identified to impact the unemployment, however at lesser extent and slightly weaker. In order to differentiate between the oil crises and recessions, authors have also analyzed different time spans. The results, however, were the same, identifying that oil price and real interest rates Granger causes unemployment. A different angle of unemployment was analyzed by Brown and Yucel (2002), from the perspective of labor immovability. Similarly, to A. Carruth and J. Oswald (1998), oil price was analyzed as an input, thereby affecting marginal production costs in the economy. However, they hypothesized that increase in unemployment under oil price shocks can also be attributed to issues with highly specialized labor. Relocation of such labor from one industry to another is costly, and workers do not relocate immediately. As an aftermath, aggregate employment declines. This theory supports the findings of A. Carruth and J. Oswald (1998), furthermore elaborating different perspectives, which can explain the causality between the factors.

Another research of oil prices and unemployment conducted by F. Ahmad (Ahmad, 2013) have found similar findings to A. Carruth and J. Oswald (1998) and F. Bahadorkhah and A. Aminifard (2014). The study was performed in Pakistan, analyzing time span between 1990- 2011. In addition to oil prices and unemployment, real interest rates were also added, similarly to above reviewed analysis. Findings of both studies provided same results - positive relationship between oil price and unemployment. Ahmad F. (2013) argued that this is due to rise in oil prices, which increases production costs, thereby leading to decrease in labor force employment and subsequently raising unemployment in the country.

A very similar research to F. Bahadokkhah et al. (2014) and F. Ahmad (2013) was performed by E. Papapetrou (Papapetrou, 2001), analyzing Greece’s economy and the impact of oil price changes on employment. The analysis was performed using multivariate Autoregressive

Model (VAR), studying time period between 1989 to 1999. Author have concluded that oil prices have a significant negative relationship with employment. Additionally, oil price shocks have been identified to negatively affect stock prices, and the economy. These findings are in line with the conclusions of F. Bahadokkhah et al. (2014) and F. Ahmad (2013), who have found positive relationship between the two variables. Most importantly, the largest similarities can be identified between Greece and Pakistan. Both of the countries are oil importers, and mostly do not produce it on their own, therefore one would expect similar tendencies and correlations between the oil price and employment level. While the two countries are at different stages of development, we can see that the impact of changes in oil prices have very similar effects on the unemployment in both of them.

Another interesting research was performed by B. Altay, M. Topcu and E. Erdogan (Altay, et al., 2013), analyzing Turkish economy and the correlation between oil prices, employment and real output. The study was based on Vector Error Correction Model, and took into consideration time span between 2000 to 2012. Findings of the research were contradictory to the three previously discussed studies, as the authors have identified significant negative relationship between oil price changes and unemployment rate. The significance, however, was only notable in the short run, and did not indicate any long-term causality. These findings oppose the conclusions drawn by F. Bahadokkhah et al. (2014), where the authors have identified a significant positive relationship between the two variables. As both countries are oil producers, it could be hypothesized that the impact should be similar, or at least same directional. However, Iran is a net exporter country and Turkey is net importer country, it might be one of the main reasons for such differences. On the other hand, the two countries not only are situated at different development stages, but also play different roles in politics and are situated in economically different regions, which might have substantial influence for the results.

Lastly, a research performed by P. Senzangakhona (Senzangakhona, 2015), analyzing oil price changes and its’ impact on unemployment in South Africa between 1990 and 2010. Authors have employed Vector Autoregressive Model (VAR). The most notable aspect in this analysis is the development stage of the country. While previously reviewed studies could be relatively easy classifiable to either a developing or developed countries segment, South Africa is somewhere in between the two. This is also easily observable from the findings.

Similarly, to B. Altay, M. Topcu and E. Erdogan (2013), P. Senzangakhona (2015) have identified a significant negative relationship between the changes is oil price and unemployment in the short run. However, counterintuitively, in South Africa, authors have also identified a statistically significant positive relationship between the variables in the long run, which were insignificant in Turkey.

In light of all of these studies, we can see significant differences between the findings. While some studies find positive relationship between the unemployment and changes in oil prices, others draw completely opposite conclusions. Additionally, there can be observed a misalignment in the significances in short- and long-term impacts. It could be argued that there are many country-specific factors that influences these inconsistencies, such as geopolitical location, development stage of the country, importance of the raw material on the overall economy, whether the country is net importer or exporter, and more. All of these factors can play a significant role on the impact and thereby affect the findings. This paper will take into account the conclusions of the abovementioned studies, and compare them in light of Norwegian economy.

4.2 Oil price and GDP

One of the main factors representing well-being of a country is its Gross Domestic Product or GDP. It provides easily understandable output, which can be used in many various occasions; from assessing living standards - to determining investment opportunities. By being such an important factor, it can be influenced by a whole range of nuances, some of which are out of control by the country. Oil price is highly argued to be one of these factors - especially in Norway. Large number of nations are directly impacted by the changes of oil prices, with no means of influencing it. In this part, it will be reviewed various researches that have analyzed the particular factor - oil price, and its effects on the economy.

A research performed by Jiménez-Rodríguez, Rebeca and Sánchez, Marcelo (Jiménez- Rodríguez & Sánchez, 2004) analyzed oil price shocks and its impact on real GDP growth of OECD countries for period 1972 - 2001. The study was performed using multivariate VAR analysis and using linear and non-linear models. Authors have identified that oil price hikes have a significant negative effect on GDP of oil importing economies except for Japan. Unexpectedly, UK, a net-exporter of oil was affected in the same way as importing countries,

14 experiencing a decrease in GDP growth during price hikes. This was argued to be the cause of sharp appreciation of currency. On the other hand, Norway, the other European net-exporter of oil experienced a significant positive relationship between oil price changes and real GDP growth. Additionally, study has uncovered that oil price increase has a larger impact on GDP growth, while a decrease in the price has no statistical significance in most cases. A research conducted by Khan Z. et al (Khan, et al., 2017) supports findings of Jiménez-Rodríguez, Rebeca and Sánchez, Marcelo (2004). Khan Z. et al. (2017) analyzed whether the crude oil prices had any effect on economic growth in Pakistan during 2000 and 2015. The results revealed an existing significant negative relationship meaning that a rise of crude oil price makes GDP to decline. Authors tested it by using a structural break dummy inside the model and distinguishing the periods into pre-and post-oil price shocks in 2008. The model identified high significance, with 80% of the variation in economic growth relatable to oil price changes. Pakistan, a net-importer of oil, upholds the conclusions drawn by Jiménez- Rodríguez, Rebeca and Sánchez, Marcelo (2004).

Trang et al. (Trang, et al., 2017) performed a research of the changes in oil price and its impact to various macroeconomic variables in Vietnam. Authors have investigated the period from 2000 to 2015. VAR econometric model was employed in a research. It was concluded that a rise in oil price would lead to higher inflation and budget deficit in Vietnam, however GDP and unemployment rate in short terms effect was positive, but insignificant. This somewhat contradicts with Jiménez-Rodríguez, Rebeca and Sánchez, Marcelo (2004) and Khan et al. (2017) findings, where researchers concluded a negative impact of oil price hikes for oil importing countries, and a positive impact to oil exporting countries. However, the issue is that Vietnam cannot be clearly defined as net-importer, nor net-exporter of oil. While the country exports crude oil, it imports petroleum. This might be one of the main reasons behind the insignificant findings regarding GDP and unemployment, and the contradiction with previous researches.

4.3 Exchange rate and Unemployment

Exchange rates play a crucial role in the development in the country. It influences a range of various factors, such as foreign direct investments, imports and exports, labor intensity and more. Indirectly, by affecting the mentioned aspects, it is impacting the employment rate in the country. Strong currency might decrease willingness of foreign investors to invest in the

15 country and incentivize governments to allocate more money to foreign direct investments. On the contrary, weakening currency might discourage governments from foreign investments, and allow to invest more in internal infrastructure, thereby creating more work places and lowering unemployment. There can be found many similar examples of direct and indirect effect of exchange rate to unemployment, and some more important cases will be reviewed in this section.

A research performed by R. Frenkel and J. Ros (Frenkel & Ros, 2006) have analyzed how real exchange rate (hereafter RER) affects unemployment rate in Latin America. The study contained 17 countries, in a time span between 1990 to 2002. Authors have identified a significant negative relationship between RER and employment, confirming their hypothesis that RER has an important influence on unemployment. They have outlined that a 10% appreciation of the RER leads to 5,6% increase in unemployment rate two years later. E. M. Atya (Atya, 2017) has performed a similar research, supporting the findings of R. Frenkel and J. Ros (2006). Research by E. M. Atya (2017) on Egypt’s economy found supporting evidence, claiming that appreciation in RER has a significant positive relationship with unemployment rate. The study was conducted for time periods 1985-2015, and found that a single percent depreciation in RER will lead to decrease in unemployment by 15,8% (according to ARDL) and 7,75% (according to FMOLS). Lastly, yet another research by Z. Bakshi et al. (Bakhshi & Ebrahimi, 2016) analyzed Iran’s economy during the period 1981- 2012. The findings were somewhat contradictory to previous two studies. Authors have identified a negative relationship between unemployment and RER. Appreciation of exchange rate by one unit, decreased the unemployment by 0,032% in the short term, and by 0,064% in the long term.

Most of the researched discussed above concentrated on the developing countries, while this paper will analyze Norway, an advanced economy, and will try to identify whether RER has any effect on unemployment in a developed country.

4.4 Real interest rate and unemployment

Many various crises have affected the worldwide economy in the last half a century. Most notable ones, such as energy crisis of 1979, Black Monday of 1987, financial crisis of 2007 and many more, have impacted the interest rates and employment drastically. While

16 researchers are careful with pertaining the inter causality between the two factors, matter of a fact is that both of them seem to go hand-in-hand. H. Fieldmann (Fieldmann, 2013) has clearly identified correlation between the components, both in the short-run and in the long run. The most important aspect of the study was that H. Fieldmann (2013) has analyzed not one particular country, or a cluster or similar countries, but a group of 92 countries from all around the world. Not only that he has confirmed the correlation between the factors, the findings stressed a causal correlation between the two. Moreover, H. Fieldmann (2013) has outlined a distinction, where changes in interest rates affects younger population to a higher extent, as opposed to general population. Findings of G. H. Doğrul and U. Soytas (Doğrul & Soytas, 2010) were consistent with H. Fieldmann’s (2013), outlining that real interest rate Granger causes unemployment in Turkey in long-term. However, the study failed to identify significant short-term relationship between the two, outlining that, while the general worldwide trend might identify general causal relationship, neither of the factors can explain the whole variance.

A similar research was performed by O. Blanchard and J. Wolfers (Blanchard & Wolfers, 2000), analyzing unemployment and various shocks in 20 OECD countries. The research has studied various factors and their impact or correlation with unemployment rate. The main distinction in the analysis was the quality of labor market institutions within the sample. Findings confirmed the initial hypothesis, that the level of impact of various factors is directly related to the quality of such institutions. One of the factors analyzed by the study was real interest rates, which was confirmed to have statistical correlation with the unemployment rate. However, the significance could only be identified in the long run. An increase of 5 percentage points in the real interest rates led to increase in unemployment at the magnitude of 2,5%. Lastly, the real interest rates were found to have somewhat smaller effects on the unemployment, when compared to other factors. These findings were consistent with H. Fieldmann (2013) and G. H: Doğrul G. H. & U. Soytas (2010).

Based on the research findings discussed above, these shocks in the economy should have impacted the unemployment levels, at least in the long run. Therefore, in order to confirm or reject this hypothesis, this paper will analyze the relationship between the real interest rate in Norway, and the possible effects it might have had on the unemployment level in the country.

4.5 Dutch disease 17

There are many various variables and aspects that influences country’s economy. Some of the factors are rather straightforward and can be easily measured and manipulated, while others could be slightly harder to interpret, and even so, to measure. Information about factors such as oil price or exchange rate can be easily gathered and analyzed, immediately pertaining their impact on the variable of interest. On the other hand, there are forces that influences the economy, which are not so obvious and can be often overlooked. One infamous example of such matter is so-called Dutch Disease.

The Dutch disease refers to the negative effects of natural gas discoveries on the Netherlands manufacturing in 1960’s, mainly through the significant appreciation of the Dutch real exchange rate (Corden, 1984). As the export of natural gas has strengthened Dutch currency, it had negative long-term consequences. Other export sectors competitiveness has suffered. Non-gas export sectors started to shrink and became less and less appealing, while imports increased drastically (Cohen, 2013). In addition, the proceeds from gas revenues were used to increase local demand in the country, which eventually led to rise in prices of land and local services (restaurants, hospitality, etc.). As a consequence, wages rose substantially, further damaging competitiveness of exports. All of this led to a substantial increase in unemployment, as industry employment has fell by more than 24% from 1964 to 1986 (Rudd, 1996).

The Dutch Disease case was analyzed by many different researchers, with first model described in 1982 by W.M. Corden and J.P. Neary (1982). Authors assumed a small open economy partitioned into 3 separate sectors – a non-tradable and two tradable sectors; booming and lagging one. The non-tradable sector represents the domestic supply of services, retail trade or construction. Booming export sector defined exports of natural resources or crops. The lagging sector was the traditional export sector, usually producing agricultural or manufacturing products. The model assumes that labor is mobile between all 3 sectors and that wages are equal, while the capital is not. Additionally, all goods are for final consumption, trade is balanced and prices are not distorted. The two-main analysis’ areas of Dutch Disease model are: (i) Resource Movement effect and (ii) spending effect.

4.5.1 Resource Movement

Resource Movement can be explained as a case when the booming sector starts attracting the resources from the lagging and non-tradable sectors (Corden, 1984). W.M. Cordon and J.P. 18

Neary (1982) also referred to it as a direct de-industrialization (Corden & Neary, 1982). As a result, it reduces the output in other parts of economy. Reduced non-tradables sectors output can cause the prices of non-tradables sector to increase relative to the price of tradables and to those set in the world markets (Brahmbhatt, et al., 2010). Resource Movement effect eventually leads towards higher employment within booming sector, and “...higher real product wage as measured by the nominal wage relative to the price of the lagging sector” (Cappelen & Eika, 2017). The higher nominal wages within booming sector becomes much more attractive for those within lagging and non-tradable sectors, which eventually leads to lower employment in both of them.

4.5.2 Spending effect

Spending effect is caused by higher incomes due to increased revenues from resource discover. Higher incomes lead to higher expenditures in all sectors, however, while tradable sector is determined outside of the country’s scope, the non-tradable sector gets impacted directly. Due to higher demand, prices for non-traded goods increases, thereby contracting the lagging sector. With the increase in prices, wages rise in line, consequently attracting labor from lagging sector. This shift from lagging sector to non-tradable is defined as indirect- deindustrialization.

The two effects combined causes real appreciation of currency and a contraction of employment and output in the non-booming tradable sector.

4.6 Dutch Disease and Norway

Norway’s oil discovery and fast economic growth might at first glimpse look similar to Netherlands case with gas. Due to the fact, many studies have been performed, in order to check whether Dutch Disease can be identified in Norwegian economy (Bjørnland & Thorsrud, 2013) have analyzed this but with no success - no direct evidence was found. On the contrary, authors have identified that booms in the energy sector have substantial productivity spillovers on the non-oil sectors, effects that have not been captured in previous analysis.

The analysis of H. C. Bjørnland and L. A. Thorsrud (2013) was performed using Bayesian Dynamic Factor Model (BDFM), and evaluated oil price shocks on GDP, employment, wages and more. Additionally, they have distinguished two different cases of price shocks and the effects: (i) Price shocks due to global activities; and (ii) Price shocks due to increase in oil supply. In the former case, positive effects were observed on GDP, productivity and employment. In the latter case, however, no effects on productivity were discovered. GDP and employment were affected positively, nonetheless, in this case exchange rate have appreciated sharply. Additionally, both of the cases have identified that booms in the energy sector stimulates investments, production, employment and wages in nearly all other non-oil industries. Moreover, authors have uncovered evidence of a two-speed economy, with non- tradables growing a much faster pace. This particular finding contradicts Resource Movement theory covered by W.M. Corden (1984), which states that booming sector attracts resources from lagging and non-tradable sectors - one of the main causes of Dutch Disease in Netherlands. Due to their findings, H. C. Bjørnland H. and L. A. Thorsrud (2013) have concluded that there is no evidence of Dutch Disease in Norway.

Another research was performed by C. Å. Cappelen and T. Eika (Cappelen & Eika, 2017), where the authors have analyzed the relationship between Dutch Disease and immigration in Norway. Study has analyzed time period between 2004 and 2013, providing significant findings. Authors have spotted evidence indicating a faster economic growth as a consequence of resource boom. More accurately, resource boom was related to 2% increase in population in Norway. C. Å. Cappelen C. and T. Eika (2017) described this as the effect of resource boom to immigration. The boom in itself was not the only factor contributing to the population growth, as explained by authors, as other factors could have had influence to it. EU-enlargement of May 2004, where more liberal migration regime was implemented, was outlined as a significant factor, related to immigration to Norway, and consequently a population increase. Higher immigration decreased the effect of unemployment, and thereupon might have disguised the resource movement between sectors. The resource boom, contradictory to W.M. Corden’s (1984) Resource Movement effect, did not affect lagging sector in any negative aspect. On the contrary, an increase in domestically manufactured goods was observed. By performing this research, C. Å. Cappelen C. and T. Eika (2017) have supported H. C. Bjørnland and L. A. Thorsrud (2013) findings, rejecting Dutch Disease clause in Norway.

In the analysis part, both researches will be taken into consideration and analyzed on a county basis as opposed to country wide level. This might lead to identification of different conclusions due to smaller sample size and more sensitive nature of the particular regions that will be analyzed.

5 Factor Discussion

5.1 Unemployment

Unemployment in the country can be affected by a range of various factors, from rise in population, to changes in education level, to increase in cost of living. Employment - as an integral part of economy, life and even health, has been studied by hundreds of researchers, and was analyzed from nearly every imaginable aspect. Researchers studied topics like: the impact of unemployment on mental health (Warr, 1987); Unhappiness and unemployment (Clark & Oswald, 1994); Inflation impact on employment (Friedman, 1977); Exchange rate relationship with unemployment (Gordon, 1981); Time-series evidence of the effect of the minimum wage on youth employment and unemployment (Brown, et al., 1981); Relationship between output and unemployment (Noor, et al., 2007) and many, many more. Undeniably, unemployment is an extraordinarily broad subject, and can be approached from a range of different directions. Therefore, in order to perform a therall analysis, this paper will concentrate on one particular independent factor - oil price changes. In addition to the prices, the study will take advantage by implementing a multi-factor analysis, which will include real interest rates. With help of these elements, a research will try to identify the relationship between these factors and unemployment in selected regions in Norway.

As statistics indicate, the population level in Norway has increased steadily (Figure 4); Additionally, cost of living has been Total Population, Norway 1962-2019, in 000s 5 500 rising sharply. Until 2018, CPI8 has

5 000 increased by more than 81% since 1990. Such factors suggest that the 4 500 unemployment should have risen 4 000 significantly, however the numbers Total Population in 000's

3 500 are not inline. The unemployment has been fluctuating substantially, 3 000

19631965196719691971197319751977197919811983198519871989199119931995199719992001200320052007200920112013201520172019 and as it can be identified from Figure 4. Population Growth in Norway, 1962-2019 Figure 5, with very seasonal traits.

8 Consumer Price Index in Norway. SSB. Source: https://www.ssb.no/en/kpi

These fluctuations can identify various anomalies in the economy, governmental influences or even various booms and shocks.

Overal Unemployment in Norway, 1999-2014

5, 5 %

5, 0 %

4, 5 %

4, 0 %

3, 5 %

3, 0 %

2, 5 % Unemployment Level, in %

2, 0 %

1, 5 %

1, 0 % 1999M01 1999M06 1999M11 2000M04 2000M09 2001M02 2001M07 2001M12 2002M05 2002M10 2003M03 2003M08 2004M01 2004M06 2004M11 2005M04 2005M09 2006M02 2006M07 2006M12 2007M05 2007M10 2008M03 2008M08 2009M01 2009M06 2009M11 2010M04 2010M09 2011M02 2011M07 2011M12 2012M05 2012M10 2013M03 2013M08 2014M01 2014M06 2014M11

Figure 5. Overall Unemployment Level in Norway, 1999-2014

5.2 Oil price

5.2.1 Brent crude price

Oil market is pretty much is like any other market. By big degree oil price is being controlled by supply and demand in the long run. Demand that increases with economic and population growth and can reduce with the declining economic growth. Supply that is being controlled by extracting countries, which have power to manage their oil reserves more and more independently (Zietlow, 2015). For one, Organization of the Petroleum Exporting Countries (OPEC) is one of the biggest worlds crude supplier in the world that controlled 45% of crude oil supply in 2008 and 76% in 2009. This gives them a big power to influence the crudes oil price in the market. (Merz, 2016). Crude oil by default is a raw material for oil products which are consumed by end-users and that creates the demand for it. The price of oil can be influenced by many different factors.

Figure 69 indicates a growing trend with similar patterns in fluctuations. However, as mentioned before oil price can be changing due to different factors. First oil price shock that happened in 1973, was due to OPEC proclaimed embargo. A reduced oil supply had increased the oil price more than 300% at the time. The next oil price shock in 1979 is related to a revolution in Iran when Iran’s oil production almost stopped and therefore a price increase can be observed. The decline in oil price in period of 1985-1986 was due to both non-OPEC and OPEC countries increasing the oil supply. The major Brent crude oil price increase in period of 1999-2008 was due to rapid demand increase in growing countries such as India or China and production cuts by OPEC, drove the prices to incredible heights. Thereafter in period of 2008-2009 the prices started to drop, due to collapse in demand which was caused by global financial crisis. Aftermath of the financial crisis led back to post-crisis level prices. As one of the possible reasons why there is a sharp fall in 2014, was due to increasing oil supply in the global market (Ellwanger, et al., 2017).

Therefore, this paper will study whether oil price shocks between period of 1999:1 and 2014:12 have had any influence on variables of interest.

Comparison of Real Oil Price vs. Nominal Oil Price, 1960-2014

Real Oil Price (NOK) Nominal Oil Price (NOK) Nominal Oil Price (USD)

2000 140

1800 120 1600

1400 100

1200 80 1000 60 800 Oil Oil Price, NOK Oil Price, USD

600 40

400 20 200

0 0

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Figure 6. Nominal vs. Real Oil Price, 1960-2014

9 Real oil price adjusted to the base year, 2014 24

5.2.2 Oil price effects to Norway

Oil sector has experienced very sharp growth in Norway, affecting employment in the sector, and consequently its influence on the overall employment of the country. Figure 7 outlines the employment growth in the particular sector in the early 2000’s, and a sharp drop during 2014. As being one of the most important industries in Norwegian economy, this industry might have had a direct or indirect impact on the unemployment.

Many previous researches have Total Employment in Oil & Gass Sector, Norway 1972-2017, in 000's 70 analyzed the relationship between 60 the two factors, with various 50 findings. Literature review has

40 outlined very contradictory results

30 portrayed by various researches. Nr. of People, in 000's 20 Some of the studies have identified 10 a significant positive relationship 0

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 between oil price changes and the Figure 7. Employed people in oil industry in Norway (1000 people per unemployment level in the country unit). Source: Norsk petroleum - Iran (Bahadorkhah & Aminifard, 2014); Pakistan (Ahmad, 2013); Greece (Papapetrou, 2001) while others have determined the opposite - negative relationship between the two. All of the analyzed studies have been selected to represent a different instance in either geopolitical aspect, different country development state and whether the country is a net importer or exporter of oil. Surprisingly, neither of the instances have been particularly supportive of the relationship. Geopolitical aspect appeared to have little influence, as Iran, Pakistan and Greece exhibited positive association between the oil price changes and unemployment, while Turkey and South Africa negative. Additionally, while Turkey and Iran are both producers of oil, they shown completely opposite reflection of the oil price influence. One possible aspects of this counter intuitiveness could be the fact that Turkey is a net-importer of oil, while Iran is a net-exporter. Lastly, Turkey and Greece are both classified as developed countries, however once again the relationship between the underlying factors was opposite.

Varying conclusions of the researches do not portray any consistent tendencies. Neither geopolitical aspect, development level of the country, nor net importer/exporter status exhibits causal relationship between oil price changes and unemployment, making it harder to make any predictions on Norway. Some similarities, nonetheless, can be found. Since Norway is a net exporter like Iran, one would expect positive correlation between oil price changes and unemployment. However, as the development level of the countries are quite different, it makes it hard to hypothesize the same effects. On the other hand, Norway is a developed country with a good geopolitical location, which would direct the expectations towards findings of Turkish, Greek or South African analysis. In order to determine which of the findings are closest to Norway, a comparison of the cases will be performed after the analysis of Norwegian economy.

In addition to a general country analysis, a regional study will be performed, in order to determine whether there can be identified any differences between country level and county level.

5.3 Real interest rate

Real interest rate is one of the main macroeconomic variables. Real interest rate can play an essential role for changing private and public-sector decisions, and thereby influencing lives of individuals. In Norwegian economy, the last 40 years have brought 4 major interest-rate affecting events. Time periods of 1979, 1987-1991, 1998-2001 and 2007 have outlined the major shifts in the real interest rates in the country (Figure 810). Some of these shocks could be attributed to monetary policy aimed at exchange rate stabilization against European (western) currencies, which was applied from 1972 (Akram, 2000). Ever since the implementation of the policy, Norway has suffered from high inflation nearly for one and a half decade (1972-1986) after Bretton Woods regime. In 1986 due to persistent downward pressure and devaluation on the krone, the inflation was on a declining a trend under system where krone exchange rate was fixed against trade weighted basket of currencies (1986- 1990). Then in 1990-1992 there was a short attempt of fixing the exchange rate against European currency unit (hereafter ECU). The Norwegian krone was floating from 1992 until 1994 following the strains on European Monetary System (hereafter EMS). In May of 1994, Norway has changed to a managed-float system that aimed at stability against European

10 Sources: SSB and Norges Bank 26 currencies (Soikkeli, 2002). Finally, in 2001 a switch from an exchange rate target to an inflation target in Norway took place. Floating exchange rate allowed monetary policy to have a higher influence towards stabilization policy. Norway’s central bank was responsible for an implementation of monetary policy in the country.

Real Interest Rates vs. Inflation, Norway 1982-2014

15,0

13,0

11,0

9, 0

7, 0

5, 0 Percentages 3, 0

1, 0

-1,0

-3,0 1982M04 1983M01 1983M10 1984M07 1985M04 1986M01 1986M10 1987M07 1988M04 1989M01 1989M10 1990M07 1991M04 1992M01 1992M10 1993M07 1994M04 1995M01 1995M10 1996M07 1997M04 1998M01 1998M10 1999M07 2000M04 2001M01 2001M10 2002M07 2003M04 2004M01 2004M10 2005M07 2006M04 2007M01 2007M10 2008M07 2009M04 2010M01 2010M10 2011M07 2012M04 2013M01 2013M10 2014M07

Inflation Real Interest

Figure 8. Real interest rates vs. inflation, Norway 1982-2014

As discussed in literature review, many various researchers have identified direct correlation between the real interest rates and unemployment. H. Fieldmann (2013) even predicted a causal relationship between the two. In Norway, however, the relationship might not be very clear from the first look. As it can be observed in Figure 9, there is no clear Real Interest rates vs. Unemployment, Norway 1982-2016 9, 0 % pattern between the variables, and it 7, 0 % might seem that the unemployment moves are barely correlated with the 5, 0 % changes in interest rates. In order to 3, 0 % Percentages clarify this initial intuition, this paper 1, 0 % will analyze the interrelations of the -1,0 % 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 factors and compare the findings to

-3,0 % previous studies. Real int rate Unemployment

Figure 9. Real interest rate and unemployment in Norway. Source: SSB

5.4 Exchange rates

Exchange rates can have a substantial impact not only on employment in a country, but also on the entire economy. Norway had pursued fixed-exchange-rate from World War II until 1999. Within period of 1996 and 1998 Norway’s economy has experienced a bigger growth as the oil price was high and Norwegian Krone appreciated (Beier, et al., 2003). Monetary policy was one of a possible solution as of to keep the inflation rate stable and keeping exchange rate floating. Officially Norway had an annual inflation targeting applied of annual inflation rate with 2.5%11 from 2001, but Norges Bank had started applying inflation targeting from 1999, allowing exchange rate to float and pursuing of inflation level to be the same as in euro area (Beier, et al., 2003). The inflation targeting policy was applied together with a fiscal rule called Handlingsregelen. The rule says structural, that government spending’s of oil revenues cannot be greater than 4%12. Oil price shock in 2014 had severely impacted Norwegian currency, which fell to lowest rates in the preceding decade. As one Norwegian newspaper reported, tens of thousands of people13 have lost their jobs as an aftermath of this event. However, these skilled individuals have shortly been employed in other industries, somewhat negating the effect. Additionally, the very same drop in exchange rate meant that Norwegian Krone revenues in other exporting industries have risen and expansion could be expected, as they became more competitive. As this event indicate, changes in exchange rate can have various effects on the economy of a country, and on its employment status. Many researchers have analyzed the relationship between exchange rate variations and employment, concluding on significant findings. R. Frenkel & J. Ros (2006). and E. M. Atya (2017) have identified very significant positive relationship between appreciation of the currency and the unemployment, leading towards conclusions that strengthening currency is bad for employment. On the other hand, conclusions drawn by Z. Bakshi et al. (2016) suggests the complete opposite - appreciating currency improves employment. While the exchange rate on its own might not directly impact the employment, and there are other forces affecting it, it is undeniably a significant one. In light of these findings, this study will incorporate exchange rate through oil prices by converting Brent crude oil price in U.S. dollars into Norwegian

11 Norges Bank, 2017 (Bank, 2017) 12 Was reduced to 3% in 2017 13 Aftenposten. Source: https://www.aftenposten.no/okonomi/i/bGxB/27000-jobber-har-forsvunnet-fra-oljebransjen-pa-to-ar

28 kroners, and will analyze whether real oil prices together with real interest rate have had any influence on the unemployment rate in Norway.

6 Methodology

This section will outline various data selection criteria, factor definitions, models and tests, which will be employed in analysis part for hypothesis testing. Moreover, ambiguous terminologies will be presented and clarified. Descriptive statistics of all variables can be investigated in Exhibit 8, in Appendix.

6.1 Data Overview

6.1.1 Unemployment Rate

This study employs monthly unemployment rates data published by SSB14, collected between

15 1999:01 to 2014:12 . Unemployment rate (!") is calculated by dividing total labor force (L) by total number of unemployed individuals (U) (Equation 2). The total labor force (L) is consequentially calculated by adding total employed labor force (E) with total unemployed labor force (U) (Equation 1).

Equation 1. Total Labor Force

Equation 2. Unemployment Rate

Since the definition of employed labor force and unemployed labor force can vary between countries, this study will employ the following definitions used by Statistics Norway:

“Employed persons are persons aged 15-74 who performed work for pay or profit for at least one hour in the reference week, or who were temporarily absent from work because of illness, holidays, etc. Conscripts are classified as employed persons. Persons engaged by government measures to promote employment are also included if they receive wages. Persons laid off 100 per cent with a continuous duration of until three months are defined as employed, temporarily absent.

14 Statistics Norway / Statistic Sentralbyrå, www.ssb.no 15 Source: https://www.ssb.no/en/arbeid-og-lonn/statistikker/aku

Unemployed persons are persons who were not employed in the reference week, but who had been seeking work during the preceding four weeks, and were available for work in the reference week or within the next two weeks (in 1996-2005 one should be available within two weeks following the time of interview, and until 1996 one should be able to start working in the reference week). Persons laid off 100 per cent are defined as unemployed after three continuous months of leave.” (Statistics Norway, 2019).

One important aspect to take into consideration, and which might significantly affect the findings or even contradict previous studies, is that person is not considered unemployed before three months has passed after a lay off (Statistics Norway, 2019). This implies that, if a person manages to get hired during these three months, it will never be registered as unemployed. This assumption might affect the data comparison and potentially cause Type I error – not assuming the unemployment when person is unemployed.

6.1.2 Real Oil Price

Nominal oil prices denominated in USD were extracted from financial data website Quandl16 for the same period as unemployment – 1999:1 to 2014:12. In order to convert the nominal prices into real, a CPI deflator and appropriate exchange rates were applied. CPI deflator was retrieved from Statistics Norway17 and exchange rates from Norwegian central bank18. By applying these two variables the nominal oil prices were transformed into real oil prices expressed in Norwegian krone (NOK) (Equation 3).

Equation 3. Real Oil Price

Where $%&',) is real oil price at time t expressed in Norwegian krone, *+,-,) is nominal oil

0123 price at time t expressed in USD, .%&'/+,-,) is exchange rate at time t, and is a deflator. 0124

6.1.3 Real Interest Rate

16 Quandl. Brent crude oil price. Source: https://quandl.com/data/ODA/POILBRE_USD-Brent-Crude-Oil-Price 17 CPI & Inflation. SSB. Source: https://www.ssb.no/statbank/table/03013/ 18 Exchange rates. Norges bank. Source: https://www.norges-bank.no/tema/Statistikk/Valutakurser/?id=USD 31

In order to obtain real interest rates, nominal interest rates were downloaded from Norwegian central bank19, and reduced by the underlying inflation rate. The inflation rates as were used from the same source as for real oil price determination.

Equation 4. Real Interest Rate

Where 5)is real interest rate at time t, 6)is nominal interest rate at time t and 7)is inflation rate at time t.

6.2 Vector Autoregressive Model (VAR)

Vector Autoregression (hereafter VAR) model was introduced by C.A. Sims (1980). VAR is a system regression model (e.g. there can be more than 1 dependent variable) and can explain the model variables by the equation which includes its own lag and lagged value of other variables and error term. VAR models are easier for multiple series analysis as one does not need to specify whether variables are endogenous or exogenous. However, one of the drawbacks of VAR is that it requires all variables in the system to be stationary (Brooks, 2008). For small sample sizes this can be crucial implication, as the degrees of freedom is already limited. For this study, a relatively small sample size is present, partially preventing implementation of standard VAR model (Equation 5). Therefore, Toda-Yamamoto procedure is employed, which does not require the variables to be stationary, as well as providing several other simplifications such as disregard for integration and cointegration.

Equation 5. VAR Model

Where 8) are level variables, 8)9: are lagged variables, ; are constants, < are estimates and

=) are white noise residuals.

6.3 Toda-Yamamoto Procedure

19 Key policy rates. Norges Bank. Source: https://www.norges-bank.no/en/topics/Statistics/Interest-rates/Key-policy-rate-monthly/ 32

This study takes advantage of a relatively new procedure for causal relationship developed by Hiro Y. Toda and Taku Yamamoto (Toda & Yamamoto, 1995). While the method by H. Y. Toda and T. Yamamoto (1995) (hereafter TY) is built upon a standard VAR model, it overcomes some of its’ problems. Contrarily to standard VAR model, TY procedure is valid for tests of variables with different order of integration. Moreover, this procedure avoids biases associated with unit roots. Unlike the general VAR model, where long-term information is often sacrificed, TY augmented VAR modeling allows testing of samples with different order of integration and does not require all series to be cointegrated. As long as the order of integration does not exceed the true lag length, modified Wald statistic is valid for this procedure (Toda & Yamamoto, 1995).

Four steps involving TY procedure are: (i) determining maximum order of integration (>?@A) of all series lag length; (ii) analyzing optimal lag length in VAR model (k); (iii) estimating modified order VAR (k+>?@A); (iv) Wald test for Granger causality.

1) In order to determine the maximum order of integration (>?@A), various stationarity tests can be applied. This study applied two different tests – Augmented Dickey-Fuller (hereafter ADF) test for unit-root and Kwiatkowski–Phillips–Schmidt–Shin (hereafter KPSS) test for stationarity. The two tests have opposite null hypothesis, thereby allowing for a cross check. The maximum level of differencing required for

stationarity assumption to hold will be used as maximum order of integration (>?@A).

2) For determination of an optimum lag length (k), vector autoregressive model (VAR) was built using level data of the time series. Thereafter, three different criteria were used for optimum lags: Likelihood Ratio (hereafter LR), Final Predictor Error (hereafter FPE) and Akaike Information Criterion (hereafter AIC).

3) Ensuring no serial correlation in VAR residuals by performing Langrage Multiplier (hereafter LM)-test.

4) Conducting Wald test on augmented VAR (k+>?@A) model (Equation 6). The test statistic follows an asymptotic Chi-square distribution with k-degrees of freedom under the null-hypothesis of non-causality. Thereby, any variable rejecting null- hypothesis supports Granger causality.

Equation 6. Augmented VAR

Where BC are variables, ;C are constants,

6.4 Serial Correlation

Serial Correlation (or Autocorrelation) can cause several issues, such as exaggerating goodness of fit, enlarging t-statistic or even exhibiting false-positive significant coefficients – assuming significance where there is none. Over the years, many various ways for determining serial correlation have been developed, however, since this analysis is based on lagged variables, test like Durbin-Watson Statistics cannot be applied. Therefore, this study will employ a Lagrange Multipliers test. LM test statistic is based on the derivatives of the log-likelihood function evaluated at θ̂R, the maximum likelihood estimator of θ obtained under restrictions. Under the null hypothesis the distribution of the LM test statistic in a large sample is chi-square, with degrees of freedom equal to the number of restrictions. Hence, null hypothesis implies no serial correlation between the residuals, and allow as to discard the variables if it is rejected.

6.5 Efficiency Wage Model

As discussed in the literature review part, Efficiency Wage Model builds on the premise that raw materials are taken as inputs for production. In a market with perfect competition, this would imply that an increase in input prices leads to deviation from equilibrium. The equilibrium must hold such that:

Equation 7. Profit Function in Perfectly Competitive Economy

Where p is profit, R is total revenue and C total production costs.

Taken that everything else is held constant and cost function assumes labor and materials as inputs, rise in raw material prices would affect the profit function. Since perfectly competitive market would require profit function to hold, and companies are price takers, there would be only one variable for manipulation – costs. Since the wages for employees are contractual and cannot be re-adjusted at short periods, the wages won’t fall, leading to layoffs and rise in unemployment.

Carruth et. al. (1998) outlined the functionality of such model by taking real interest rates and oil prices as inputs. Authors have showed that the Equilibrium unemployment rate can thereby be expressed as:

Equation 8. Equilibrium Unemployment

Where !∗is equilibrium unemployment, r is real interest rate, ep energy price (oil price), b(µ) is unemployment benefits expressed thru technology, f is effort and s is the probability of shirking successfully.

By analyzing this equation, authors (Carruth & Oswald, 1998) have successfully proven the causal relationship between real oil price, real interest rates and unemployment in their study. Therefore, this study will employ the same logical reasoning of the two variables – real oil price and real interest rate and will analyze them with respect to unemployment in Norway.

7 Hypotheses

Literature review and factor analysis has laid the premise that all of discussed variables can have an impact on the unemployment. Oil price, as a main factor in question, has been concluded to have a significant explanatory power over unemployment rate by A. Carruth and J. Oswald (1998), F. Bahadorkhah & A. Aminifard (2014), F. Ahmad (2013), E. Papapetrou (2001), B. Altay, M. Topcu and E. Erdogan (2013). P. Senzangakhona (2015), Jiménez- Rodríguez, Rebeca and Sánchez, Marcelo (2004) and many more. However, the direction of impact was not unanimous. Therefore, the main goal of this study will be to analyze whether changes in oil price have any effect on unemployment rate in Norway, as well as to determine what kind of relationship it is. In addition to changes in oil price, this paper will employ real interest rates. Both of these variables were concluded to have a significant correlation and causal relationship with the unemployment in previous researches. Based on the analyzed literature, it is hypothesized that changes in oil price and real interest rates causes changes in unemployment in Norway:

Hypothesis A: Changes in real oil price and real interest rates cause changes in unemployment rate in Norway.

In addition to Hypothesis A, this study will analyze the different Norwegian regions in order to check the theory of labor immovability. Based on previous findings, regions with higher industrial employment should experience shocks of higher extent, while areas with lower industrialization should not experience impacts at the same magnitude. Therefore, it is hypothesized that different regions in Norway experience oil price shocks at different significances:

Hypothesis B: Changes in real oil price and real interest rates cause changes in unemployment rate in different regions in Norway.

8 Analysis

Based on factor discussions and literature review, in this section Toda-Yamamoto (hereafter TY) procedure for long term Granger causality will be employed. In order to test for Hypothesis A, the study will analyze relationship between real oil prices, real interest rates and unemployment in Norway. In addition to overall country analysis, the study will test Hypothesis B for causality effects between the real oil prices, real interest rates and unemployment in various counties in Norway.

In order to perform TY procedure, two variables must be identified: (i) Maximum Order of Integration (hereafter MOI) and (ii) Optimum Lag Length (hereafter OLL). To determine MOI, Augmented Dickey-Fuller (ADF) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests were performed. Based on the results in Table 1, although somewhat conflicting, the tests have identified that the maximum order of integration is 2 (d=2).

Unemployment Variable RIR ROP Norway(2) Oslo(2) Akershus(3) Buskerud(3) Hordaland(2) Rogaland(2) Troms(2) Level -2,508 -2,9874 -2,867* -4,9852*** -3,798** -3,7401** -3,2771* -2,9489 -3,5311** 1st Difference -9,9173*** -10,5178*** -2,5998* -2,5169 -1,9497 -2,3009 -2,0892 -2,1249 -2,5386 ADF 2nd Difference -7,1058*** -10,5416*** -11,4425*** -3,9462** -8,8048*** -10,8183*** -10,3675*** -9,6694*** -11,1816*** Level 1,4219 0,0667*** 0,1458* 0,152* 0,1324* 0,0685*** 0,1506* 0,1644* 0,1564* 1st Difference 0,0535** 0,0565*** 0,073*** 0,0649*** 0,1382* 0,0685*** 0,0785*** 0,1049*** 0,0576***

KPSS 2nd Difference 0,5 0,3275 0,1572* 0,0545*** 0,0664*** 0,0773*** 0,071*** 0,128* 0,0616*** (Number) by the name of the variable identifies OLL Significance: * (10%); ** (5%); *** (1%) RIR - Real Interest Rate ROP - Real Oil Price Table 1. Results from ADF and KPSS tests

In order to determine the OLL, three different tests were performed: Likelihood Ratio (LR); Final Prediction Error (FPE) and Akaike Information Criterion (AIC). All of the tests presented unanimous suggestions for each of the variable (Table 2). For testing unemployment causality on the whole Norway, proposed OLL was 2 (k=2). The same proposal followed for Oslo, Hordaland, Rogaland and Troms. OLL with a magnitude of 3 (k=3) was suggested for Akershus and Buskerud.

Lag LogL LR FPE AIC 0 -848.6495 NA 1.727146 9.060101 1 -120.0549 1426.185 0.000818 1.404839 2 -98.01126 42.44569* 0.000712* 1.266077*

Norway 3 -93.45096 8.635466 0.000747 1.313308 4 -88.24424 9.693365 0.000778 1.353662 * indicates optimum lag length

Lag LogL LR FPE AIC Lag LogL LR FPE AIC 0 -840.5332 NA 1.584274 8.973757 0 -920.4506 NA 3.707331 9.823943 1 -98.49232 1452.505 0.000650 1.175450 1 -149.8835 1508.344 0.001123 1.722165 2 -79.07023 37.39787 0.000582 1.064577 2 -129.8457 38.58341* 0.000999* 1.604742* Oslo 3 -68.44040 20.12882* 0.000572* 1.047238* 3 -121.3308 16.12396 0.001004 1.609902 Akershus 4 -66.23951 4.097409 0.000615 1.119569 4 -119.8962 2.670954 0.001089 1.690385

Lag LogL LR FPE AIC Lag LogL LR FPE AIC 0 -822.8324 NA 1.312351 8.785451 0 -849.8708 NA 1.749732 9.073094 1 -145.8364 1325.184 0.001076 1.679110 1 -158.4131 1353.492 0.001230 1.812905 2 -125.8099 38.56169 0.000957 1.561807 2 -136.7310 41.74953* 0.001075* 1.677989* 3 -115.9843 18.60580* 0.000949* 1.553025* 3 -128.8225 14.97572 0.001088 1.689601 Buskerud 4 -112.7099 6.095997 0.001009 1.613935 Hordaland 4 -120.1815 16.08690 0.001092 1.693420

Lag LogL LR FPE AIC Lag LogL LR FPE AIC 0 -890.6675 NA 2.700589 9.507101 0 -837.3544 NA 1.531594 8.939940 1 -125.2909 1498.184 0.000865 1.460541 1 -212.9442 1222.250 0.002197 2.393024 2 -104.1172 40.77049* 0.000760* 1.331034* 2 -192.9158 38.56546* 0.001954* 2.275699*

3 -97.76097 12.03634 0.000782 1.359159 Troms 3 -185.9786 13.13628 0.001998 2.297645 Rogaland 4 -94.06278 6.884920 0.000827 1.415562 4 -180.8878 9.477547 0.002083 2.339232 Table 2. Lag length selection based on LR, FPE & AIC

In order to safely claim that the variables are efficient, Lagrange Multiplier test for serial correlation was performed20. As observable from Figure 10 (and Exhibit 9 for the analyzed counties in Appendix), serial correlation is rejected in and between all of the lags, thereby concluding that selected variables will not overestimate results.

Null hypothesis: No serial correlation at lag h Lag LRE* stat df Prob. Rao F-stat df Prob. 1 8,864636 9 0,4499 0,987049 (9, 433,4) 0,4499 2 8,825028 9 0,4536 0,982594 (9, 433,4) 0,4536 3 8,332481 9 0,501 0,927228 (9, 433,4) 0,501 4 7,233264 9 0,6128 0,803894 (9, 433,4) 0,6129

Null hypothesis: No serial correlation at lags 1 to h Lag LRE* stat df Prob. Rao F-stat df Prob. 1 8,864636 9 0,4499 0,987049 (9, 433,4) 0,4499 2 18,38028 18 0,4309 1,02347 (18, 495,5) 0,431 3 27,85182 27 0,4186 1,034223 (27, 503,0) 0,419 4 34,64491 36 0,533 0,962607 (36, 500,1) 0,5336 * Edgeworth expansion corrected likelihood ratio statistic. Figure 10 . VAR Residual Serial Correlation LM Tests, Unemployment Norway 20 For overall unemployment in Norway 38

Once both of the variables - maximum order of integration and optimum lag length were determined, a Granger causality test and a modified Wald test was performed. The equations were examined on number of time lags (k+d), since the modified Wald test follows Chi- square distribution asymptotically, and degrees of freedom are equal to k+d. Since OLL varies, two different equations were estimated:

Equation 9. Augmented VAR equation for 4 lags (k+d=4)

Equation 10. Augmented VAR equation for 5 lags (k+d=5)

Where G)=(HIH), HJ$), !KLM*NO8MLKPQRSCTD,)); ;) is a vector of constants, <) are coefficient matrices and EV) denotes residuals. Summary of tests’ results for each region is outlined in Exhibits 2-7.

Region: Norway From To Test Statistic p-value Real Oil Price Unemployment 3,8312 0,1473 Real Interest Rate Unemployment 4,6321 0,0987* Real Interest Rate Real Oil Price 0,4953 0,7806 Unemployment Real Oil Price 2,5001 0,2865 Real Oil Price Real Interest Rate 0,1734 0,9169 Unemployment Real Interest Rate 1,2406 0,5378 Degrees of Freedom = 2 Significance: * (10%); ** (5%); *** (1%)

Exhibit 1. Granger causality test results for Norway Exhibit 1 outlines the results for overall unemployment in Norway. As it can be inferred from the table, only unidirectional relationship between real interest rates and unemployment can be observed, and it can be stated that real interest rates Granger causes unemployment. However, the relationship is only significant at 10% confidence interval. Real oil price does not exhibit any causal effects on the overall Norwegian unemployment. No other factors exhibit any significant causal relationship.

Region: Oslo From To Test Statistic p-value Real Oil Price Unemployment 7,3010 0,0260** Real Interest Rate Unemployment 1,6174 0,4454 Real Interest Rate Real Oil Price 0,2168 0,8973 Unemployment Real Oil Price 1,6557 0,4370 Real Oil Price Real Interest Rate 0,3744 0,8293 Unemployment Real Interest Rate 1,9002 0,3867 Degrees of Freedom = 2 Significance: * (10%); ** (5%); *** (1%) Exhibit 2. Granger causality test results for Oslo Exhibit 2 outlines results for Oslo region. Contrariwise to overall Norway, real interest rates do not seem to exhibit any significant impact on unemployment. In addition, real oil price becomes very significant (5%). The effect is once again unidirectional, stating that real oil prices Granger causes unemployment in Oslo.

Region: Akershus From To Test Statistic p-value Real Oil Price Unemployment 13,7368 0,0033*** Real Interest Rate Unemployment 6,2072 0,1020 Real Interest Rate Real Oil Price 1,3587 0,7152 Unemployment Real Oil Price 3,8512 0,2780 Real Oil Price Real Interest Rate 0,5936 0,8979 Unemployment Real Interest Rate 2,7769 0,4273 Degrees of Freedom = 3 Significance: * (10%); ** (5%); *** (1%) Exhibit 3. Granger causality test results for Akershus Exhibit 3 outlines results for Akershus region. Similarly, to Oslo, real interest rates do not seem to exhibit any significant impact on unemployment or vice versa. Moreover, real oil price is even more suggestive determinant of unemployment, with significance level of 1%. The effect is once again unidirectional, stating that real oil prices Granger causes unemployment in Akershus.

Region: Buskerud From To Test Statistic p-value Real Oil Price Unemployment 14,4208 0,0024*** Real Interest Rate Unemployment 7,6285 0,0543* Real Interest Rate Real Oil Price 1,1738 0,7593 Unemployment Real Oil Price 0,7860 0,8528 Real Oil Price Real Interest Rate 0,4494 0,9299 Unemployment Real Interest Rate 3,9759 0,2641 Degrees of Freedom = 3 Significance: * (10%); ** (5%); *** (1%) Exhibit 4. Granger causality test results for Buskerud

Exhibit 4 outlines results for Buskerud region. Similarly, to overall Norway and conversely to Oslo and Akershus, real interest rates exhibit somewhat significant impact on unemployment. The causality is unidirectional and significant at 10% confidence interval. Moreover, real oil price is also significant causal factor for unemployment, with significance level of 1%. The effect is once again unidirectional. From the results, it can be concluded that, real interest rates and that real oil prices Granger causes unemployment in Buskerud.

Region: Hordaland From To Test Statistic p-value Real Oil Price Unemployment 11,5591 0,0031*** Real Interest Rate Unemployment 3,2669 0,1953 Real Interest Rate Real Oil Price 0,2846 0,8674 Unemployment Real Oil Price 2,0357 0,3614 Real Oil Price Real Interest Rate 0,1698 0,9186 Unemployment Real Interest Rate 0,7458 0,6887 Degrees of Freedom = 2 Significance: * (10%); ** (5%); *** (1%) Exhibit 5. Granger causality test results for Hordaland Exhibit 5 outlines results for Hordaland region. Similarly, to Oslo and Akershus, real interest rates do not exhibit any significant impact on unemployment or vice versa. Real oil price is also significant at 1% confidence interval. The effect is once again unidirectional, stating that real oil prices Granger causes unemployment in Hordaland. No other variables exhibit any significances.

Region: Rogaland From To Test Statistic p-value Real Oil Price Unemployment 8,8131 0,0122** Real Interest Rate Unemployment 1,6406 0,4403 Real Interest Rate Real Oil Price 0,3044 0,8588 Unemployment Real Oil Price 2,4825 0,2890 Real Oil Price Real Interest Rate 0,1929 0,9080 Unemployment Real Interest Rate 0,5254 0,7690 Degrees of Freedom = 2 Significance: * (10%); ** (5%); *** (1%) Exhibit 6. Granger causality test results for Rogaland Exhibit 6 outlines results for Rogaland region. Similarly, to Oslo, Hordaland and Akershus, real interest rates do not exhibit any significant impact on unemployment or vice versa. Real oil price is same as Oslo, significant at 5% confidence interval. The effect is once again unidirectional, stating that real oil prices Granger causes unemployment in Rogaland. No other variables exhibit any significances.

Region: Troms From To Test Statistic p-value Real Oil Price Unemployment 10,5354 0,0052*** Real Interest Rate Unemployment 0,9836 0,6115 Real Interest Rate Real Oil Price 0,2268 0,8928 Unemployment Real Oil Price 0,9289 0,6285 Real Oil Price Real Interest Rate 0,1811 0,9134 Unemployment Real Interest Rate 1,5394 0,4631 Degrees of Freedom = 2 Significance: * (10%); ** (5%); *** (1%) Exhibit 7. Granger causality test results for Troms Exhibit 7 outlines results for Troms region. Similarly, to Oslo, Hordaland, Rogaland and Akershus, only real oil price suggests any significance. The effect is once again unidirectional, stating that real oil prices Granger causes unemployment in Troms. No other variables exhibit any significances.

9 Discussion of Findings

Based on the findings from Exhibit 1, we have to partially reject Hypothesis A, and conclude that only real interest rates have any causal effects on the unemployment rate on overall unemployment in Norway. These findings are consistent with H.G. Drogrul and U. Soytas (2010), H. Fieldmann (2013) as well as A. Carruth and J. Oswald (1998), all of whom have identified significant causality of real interest rates on unemployment. Real oil price changes, on the other hand, do not pertain any significant causal relationship with unemployment. These findings contradict conclusions drawn by nearly every researcher, that were discussed in literature review. However, Norway has very different treatment of unemployed people as compared to other countries. Once a person misses a job, it does not get registered as unemployed immediately. As defined in Methodology part, after losing a job, person has three months paid support from the state, and is not treated as unemployed. During these three months, majority of people manages to find another occupation, and gets back to labor force, without any record of them being officially unemployed. Such system might have affected the overall results, favoring the non-causality in the results of the research.

On the other hand, high significance was observed between real oil prices and unemployment in various regions in Norway. Exhibits 2-7 outline that real oil price Granger causes unemployment in every studied county. These findings partially accept Hypothesis B, since the significance of the impact varies between the regions. Additionally, only Buskerud region exhibits any significance with respect to changes in real interest rates. Such findings contradict the labor immobility theory. The fact that real oil price causes changes in employment in different regions, without affecting the overall unemployment of the country, suggests that the labor is very mobile. Upon various shocks in economy, people are willing to relocate to different areas and/or to undertake different positions. Such movements might increase unemployment in one region – i.e. the Capital of the country, while reducing unemployment in the regions that are most affected by the shock. A deeper study of this anomaly is suggested for significant claim.

Additionally, no significant evidence with regards to Dutch Disease were identified. In case of Dutch Disease, the study would have uncovered significant change in unemployment upon oil price shocks, as a consequence of labor movement from lagging and non-tradable sectors to booming sector (Resource Movement Effect). These findings contradict W.M Corden

(1984), however they are consistent with findings from H. Bjørnland and L.A. Thorsrud (2013) analysis of Norway. H. Bjørnland and L.A. Thorsrud (2013) hypothesized that booms in energy sector stimulates investments, production, employment and wages in nearly all other non-oil industries. Findings from this research supports these suggestions.

10 Conclusion

The aim of this study was to analyze the hypothesized relationship between real oil price, real interest rates and unemployment in Norway, and in different Norwegian counties. In order to perform the study, Toda-Yamamoto procedure was employed. Findings from analysis concluded no causal relationship between real oil price and overall unemployment in Norway, however a weak causality of real interest rates to unemployment was discovered. The causality was unidirectional – real interest rates Granger causes unemployment, but not vice versa. Overall unemployment in Norway did not cause changes in real oil prices, nor in real interest rates.

Analysis of different counties identified a strong unidirectional causality between real oil price and unemployment in studied counties – changes in real oil price Granger causes unemployment. Unemployment in analyzed counties did not exhibit any significance in causing changes in real oil prices or real interest rates. Unemployment in one of the counties – Buskerud, was determined to be caused by changes in real interest rates. The relationship was of weak significance and unidirectional – changes in real interest rates Granger causes unemployment in Buskerud county.

11 Suggestions for Further Research

The study concluded that changes in real oil prices causes unemployment in different counties but do not cause changes in overall unemployment in Norway. This suggests that labor in Norway is very mobile, and can swiftly adjust to shocks, thereby providing a zero-sum change in overall unemployment. A deeper study of this anomaly is suggested for significant claims.

12 Bibliography

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13 Appendix

Mean Std. Error Median Std. Deviation Minimum Maximum Observations Real Oil Price (NOK) 447,2798 12,2196 452,8627 169,3204 107,0752 771,3183 192 Real Interest Rate 3,0887 0,1645 2,5500 2,2797 -1,0100 8,1000 192 UN_Norway 0,0351 0,0004 0,0350 0,0056 0,0210 0,0500 192 UN_Akershus 0,0209 0,0004 0,0220 0,0056 0,0100 0,0320 192 UN_Oslo 0,0340 0,0006 0,0330 0,0083 0,0190 0,0540 192 UN_Buskerud 0,0249 0,0004 0,0250 0,0049 0,0130 0,0360 192 UN_Rogaland 0,0243 0,0006 0,0220 0,0084 0,0100 0,0420 192 UN_Hordaland 0,0270 0,0005 0,0260 0,0070 0,0140 0,0430 192 Unemployment UN_Troms 0,0274 0,0004 0,0260 0,0062 0,0160 0,0440 192 Exhibit 8. Descriptive Statistics

VAR Residual Serial Correlation LM Tests VAR Residual Serial Correlation LM Tests Oslo Akershus Null hypothesis: No serial correlation at lag h Null hypothesis: No serial correlation at lag h Lag LRE* stat df Prob. Rao F-statdf Prob. Lag LRE* stat df Prob. Rao F-statdf Prob. 1 12,92573 9 0,166 1,445972 (9, 433,4) 0,166 1 12,92705 9 0,1659 1,446122 (9, 433,4) 0,166 2 14,39028 9 0,1091 1,612523 (9, 433,4) 0,1091 2 19,03227 9 0,0249 2,144121 (9, 433,4) 0,0249 3 2,164148 9 0,9886 0,239126 (9, 433,4) 0,9886 3 2,865688 9 0,9694 0,316897 (9, 433,4) 0,9694 4 14,32646 9 0,1112 1,605253 (9, 433,4) 0,1112 4 10,9347 9 0,2802 1,220443 (9, 433,4) 0,2802

Null hypothesis: No serial correlation at lags 1 to h Null hypothesis: No serial correlation at lags 1 to h Lag LRE* stat df Prob. Rao F-statdf Prob. Lag LRE* stat df Prob. Rao F-statdf Prob. 1 12,92573 9 0,166 1,445972 (9, 433,4) 0,166 1 12,92705 9 0,1659 1,446122 (9, 433,4) 0,166 2 17,87263 18 0,4641 0,994697 (18, 495,5)0,4642 2 23,50022 18 0,1721 1,31528 (18, 495,5)0,1722 3 30,64493 27 0,286 1,141056 (27, 503,0)0,2863 3 34,33676 27 0,1565 1,283156 (27, 503,0)0,1567 4 57,52416 36 0,0128 1,634598 (36, 500,1)0,0129 4 62,6375 36 0,0039 1,788893 (36, 500,1)0,0039 * Edgeworth expansion corrected likelihood ratio statistic. * Edgeworth expansion corrected likelihood ratio statistic.

VAR Residual Serial Correlation LM Tests VAR Residual Serial Correlation LM Tests Buskerud Hordaland Null hypothesis: No serial correlation at lag h Null hypothesis: No serial correlation at lag h Lag LRE* stat df Prob. Rao F-statdf Prob. Lag LRE* stat df Prob. Rao F-statdf Prob. 1 14,07096 9 0,1198 1,576162 (9, 433,4) 0,1198 1 9,737848 9 0,3721 1,085366 (9, 433,4) 0,3721 2 15,80847 9 0,071 1,774334 (9, 433,4) 0,071 2 11,46508 9 0,2452 1,28042 (9, 433,4) 0,2452 3 2,113192 9 0,9895 0,233482 (9, 433,4) 0,9895 3 10,711 9 0,296 1,195169 (9, 433,4) 0,2961 4 13,63367 9 0,136 1,52641 (9, 433,4) 0,136 4 20,86753 9 0,0133 2,355857 (9, 433,4) 0,0133

Null hypothesis: No serial correlation at lags 1 to h Null hypothesis: No serial correlation at lags 1 to h Lag LRE* stat df Prob. Rao F-statdf Prob. Lag LRE* stat df Prob. Rao F-statdf Prob. 1 14,07096 9 0,1198 1,576162 (9, 433,4) 0,1198 1 9,737848 9 0,3721 1,085366 (9, 433,4) 0,3721 2 20,33304 18 0,3144 1,134417 (18, 495,5)0,3145 2 16,23943 18 0,5758 0,902329 (18, 495,5)0,5759 3 33,28026 27 0,188 1,242387 (27, 503,0)0,1882 3 36,77671 27 0,0993 1,377629 (27, 503,0)0,0995 4 71,40558 36 0,0004 2,057045 (36, 500,1)0,0004 4 88,61401 36 0 2,596733 (36, 500,1)0 * Edgeworth expansion corrected likelihood ratio statistic. * Edgeworth expansion corrected likelihood ratio statistic.

VAR Residual Serial Correlation LM Tests VAR Residual Serial Correlation LM Tests Rogaland Troms Null hypothesis: No serial correlation at lag h Null hypothesis: No serial correlation at lag h Lag LRE* stat df Prob. Rao F-statdf Prob. Lag LRE* stat df Prob. Rao F-statdf Prob. 1 9,910919 9 0,3577 1,104876 (9, 433,4) 0,3578 1 10,98816 9 0,2765 1,226485 (9, 433,4) 0,2765 2 8,873719 9 0,449 0,988071 (9, 433,4) 0,449 2 9,858791 9 0,362 1,098999 (9, 433,4) 0,3621 3 5,831475 9 0,7567 0,64706 (9, 433,4) 0,7567 3 2,161296 9 0,9887 0,23881 (9, 433,4) 0,9887 4 16,74666 9 0,0528 1,881668 (9, 433,4) 0,0528 4 13,01015 9 0,1621 1,455558 (9, 433,4) 0,1622

Null hypothesis: No serial correlation at lags 1 to h Null hypothesis: No serial correlation at lags 1 to h Lag LRE* stat df Prob. Rao F-statdf Prob. Lag LRE* stat df Prob. Rao F-statdf Prob. 1 9,910919 9 0,3577 1,104876 (9, 433,4) 0,3578 1 10,98816 9 0,2765 1,226485 (9, 433,4) 0,2765 2 13,37321 18 0,7689 0,740948 (18, 495,5)0,769 2 13,38809 18 0,768 0,741783 (18, 495,5)0,7681 3 24,60122 27 0,5968 0,910618 (27, 503,0)0,5971 3 29,25795 27 0,3485 1,087933 (27, 503,0)0,3488 4 52,78362 36 0,0351 1,49291 (36, 500,1)0,0353 4 81,58846 36 0 2,374227 (36, 500,1)0 * Edgeworth expansion corrected likelihood ratio statistic. * Edgeworth expansion corrected likelihood ratio statistic. Exhibit 9. Serial Correlation Tests Summary