1

THE HETEROGENOUS EFFECTS OF THE COMPENSATION

THESIS:

A COMPARATIVE STUDY ON THE MICRO LEVEL

Word count: 22.120

Leon Tristan de Jong Student number: 0110782

Supervisor(s): Prof. Dr. Glenn Rayp, Prof. Dr. Dirk Van der Gaer

A dissertation submitted to Ghent University in partial fulfilment of the requirements for the

degree of Master General Economics

Academic year: 2016 - 2017

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Nederlandse samenvatting

Dit onderzoek gaat over de relatie tussen overheidsconsumptie, in de vorm van sociale

welvaartsvoorzieningen en de openheid van de economie. In navolging van Rodrik (1998)

onderzoek ik deze relatie, die tot compensatie thesis is gedoopt (Liberati, 2007).

Hiervoor heb ik een aantal objectieve maatstaven gevonden om openheid te meten.

Dit doe ik aan de hand van buitenlandse directe investeringen en handel, waaraan ik zelf

migratie heb toegevoegd. Hiermee hoop ik een recent fenomeen in het publieke debat te

vangen in het onderzoek naar compensatie mechanismen. Daarnaast gebruik ik een

parameter om ook subjectieve

Dit doe ik aan de hand van twee casussen. De Nederlandse casus vertegenwoordigt de

kleine open economie, terwijl het Verenigd Koninkrijk, met haar grote economie en relatief

beperktere handelsafhankelijk een goede tegenpool vormt. Ook qua sociale welvaarstaat zien

we grote verschillen. Niet alleen empirisch en theoretisch, maar ook op het vlak van survey

onderzoek.

Al deze verschillende facetten worden samen in een meervoudige binair logaritmisch

regressiemodel gebracht. Hierin vinden we overeenkomende en verschillende resultaten.

Handel speelt een ambigue rol, terwijl voor FDI weinig duidelijk wordt verschaft. Voor migratie

zien we echter wel interessante resultaten, evenals voor enkele andere variabelen.

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Preface

With great joy have I written this dissertation, but obviously I couldnot have done it all by

myself. Special thanks to my parents, who I can call whenever necessary. I love them deeply.

Nick Houttekier, a philosopher with experience in writing dissertations, and much

more understanding of statistical economics as I. Not only worth mentioning here, because of

his capabilities and fruitful assistance, but also for never ending discussions about politics,

economics and philosophy, It is a shame that we have lost you to the Peruvian cause, but I

won a friend for life.

Also to professor Rayp. I could not wish a better promoter. Responses within the day

and always willing to have face to face meetings for discussions about opportunities and

barriers with every step of this research.

Special thanks also, to Rutger Ruitinga, for always having my back. Without him, I

would not be standing where I am now. Also, thank you for after work dinners!

Thank you, University of Gent and all of its personal, for having me. I felt always

welcome! I will never forget that.

And last, but certainly not least, Lisa de Roeck, the love of my life, who helped me even

at the latest of hours with a smile. What would I do without you?

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Table of Content

List of abbreviations ..................................................................... 10

Table of Figures ............................................................................ 11

Content of tables .......................................................................... 12

Introduction ................................................................................. 13

The Cameron claim ....................................................................... 14

Introduction .......................................................................................................................14

The theory ........................................................................................................................14

His findings .......................................................................................................................15

Retesting the Cameron claim ............................................................................................16

The compensation thesis .............................................................. 18

Introduction .......................................................................................................................18

The origins of the compensation thesis .............................................................................18

Results .............................................................................................................................19

Integrating external risk .....................................................................................................19

Other findings in favor of the compensation thesis ............................................................20

Methodological criticism of the compensation thesis .........................................................21

The efficiency hypothesis ............................................................. 25

Introduction .......................................................................................................................25

The idea............................................................................................................................25

The empirical framework ...................................................................................................26

A critical view on the efficiency hypothesis ........................................................................26

The micro-economic level ............................................................. 29

Introduction .......................................................................................................................29

Ricardo-Viner model .........................................................................................................30

Heckscher Ohlin model .....................................................................................................30

Tradables and non-tradables ............................................................................................31

The empirical framework ...................................................................................................32

Integrating risk ..................................................................................................................32

The model .........................................................................................................................33

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The simplified model .........................................................................................................35

The alternative model .......................................................................................................37

Migration openness ..................................................................... 40

Empirical relevance ..........................................................................................................40

Theoretical relevance ........................................................................................................42

Social welfare systems in perspective ........................................... 44

Empirical evidence ............................................................................................................44

Theoretical classifications .................................................................................................45

Summary and hypotheses ............................................................ 47

Methodology ............................................................................... 48

Introduction .......................................................................................................................48

The outcome variable .......................................................................................................49

The predictor variables .....................................................................................................49

The model .........................................................................................................................52

Netherlands 2010 ......................................................................... 54

Introduction .......................................................................................................................54

Economical context ...........................................................................................................54

Operationalizing the outcome variable ..............................................................................55

Operationalizing the predictor variables ............................................................................56

Results without FDI ...........................................................................................................58

Multicollinearity .................................................................................................................60

Results with FDI ................................................................................................................61

Netherlands 2004 ......................................................................... 63

Economical context ...........................................................................................................63

Introduction to the data .....................................................................................................63

Multicollinearity .................................................................................................................65

Results with FDI ................................................................................................................66

United Kingdom 2010 ................................................................... 67

Economical context ...........................................................................................................67

Operationalizing the outcome variable ..............................................................................67

Operationalizing the predictor variables ............................................................................68

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Results without FDI ...........................................................................................................72

Results with FDI ................................................................................................................72

United Kingdom 2004 ................................................................... 73

Economical context ...........................................................................................................73

Introduction to the data .....................................................................................................73

Results without FDI ...........................................................................................................75

Results with FDI ................................................................................................................75

Synthesis and literature ................................................................ 76

Economic insecurity ..........................................................................................................76

Trade openness ................................................................................................................77

FDI openness ...................................................................................................................78

Migration openness ..........................................................................................................79

Control variables ...............................................................................................................79

Conclusions and discussion ........................................................... 81

Appendix A .................................................................................. 94

Determents of demands for compensation: Netherlands 2010, without FDI, with scale

variables ...........................................................................................................................94

Appendix B................................................................................... 95

Determents of demands for compensation: Netherlands 2010, without FDI, without scale

variables ...........................................................................................................................95

Appendix C ................................................................................... 96

Correlation table: Netherlands 2010, without FDI ..............................................................96

Appendix D .................................................................................. 97

Determents of demands for compensation: Netherlands 2010, with FDI and scale variables

.........................................................................................................................................97

Appendix E .................................................................................. 99

Determents of demands for compensation: Netherlands 2010, with FDI, without scale

variables ...........................................................................................................................99

Appendix F ................................................................................... 97

Determents of demands for compensation: Netherlands 2004, without FDI, with scale

variables ...........................................................................................................................97

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Appendix G .................................................................................. 98

Determents of demands for compensation: Netherlands 2004, without FDI and scale

variables ...........................................................................................................................98

Appendix H ................................................................................ 100

Determents of demands for compensation: Netherlands 2004, with FDI and scale variables

....................................................................................................................................... 100

Appendix I .................................................................................. 101

Determents of demands for compensation: Netherlands 2004, with FDI, without scale

variables ......................................................................................................................... 101

Appendix J ................................................................................. 103

Determents of demands for compensation: United Kingdom, 2010 without FDI, with scale

variables ......................................................................................................................... 103

Appendix K ................................................................................. 104

Determents of demands for compensation: United Kingdom 2010, without FDI, without scale

variables ......................................................................................................................... 104

Appendix L ................................................................................. 106

Determents of demands for compensation: United Kingdom 2010, with FDI, with scale

variables ......................................................................................................................... 106

Appendix M ............................................................................... 107

Determents of demands for compensation: United Kingdom, 2010, with FDI, without scale

variables ......................................................................................................................... 107

Appendix N ................................................................................ 108

Determents of demands for compensation: United Kingdom 2004, without FDI, with scale

variables ......................................................................................................................... 108

Appendix O ................................................................................ 109

Determents of demands for compensation: United Kingdom 2004, without FDI, without scale

variables ......................................................................................................................... 109

Appendix P ................................................................................. 110

Determents of demands for compensation: United Kingdom 2004, with FDI, with scale

variables ......................................................................................................................... 110

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Appendix Q ................................................................................ 111

Determents of demands for compensation: United Kingdom 2004, with FDI, without scale

variables ......................................................................................................................... 111

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List of abbreviations

BMD Benchmark Definition of Foreign Direct Investment

CBS Centraal Bureau voor de Statistiek [Central Bureau for Statistics]

ESS European Social Survey

FDI Foreign Direct Investment

GDP Gross Domestic Product

HO model Heckscher-Ohlin theorem

IMF International Monetary Fund

ISIC International Standard Industrial Classification

NACE Nomenclature statistique des Activités économiques dans

la Communauté Européenne [Statistical Classification of Economic

Activities in the European Community]

N.S. Not Shown

NRC Nieuwe Rotterdamse Courants (New Rotterdam Paper)

OECD Organization of Economic Cooperation and Development

ONS Office for National Statistics (UK)

P.S. Personal communication

PPP Purchasing Power Parity

PVV Partij voor de Vrijheid (Party for Freedom)

RV model Ricardo Viner model

SBI Standaard Bedrijfsdeling (Standard Industrial Classification)

SIC Standard Industrial Classification

WVS World Value Survey

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Table of Figures

Figure 1. Globalization and the Welfare State Expansion 34

Figure 2. Globalization and demands for compensation 37

Figure 3. Diagrammatic presentation of argument 38

Figure 4. Immigration among the public’s most important issues 41

Figure 5. Revised hypothesized trajectories of change in the rich democracies 45

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Content of tables

Table 1. Evolution public’s most important issues. 42

Table 2. Cases in perspective. 44

Table 3. Demands of compensation, Netherlands 2010 55

Table 4. Binary demands of compensation, Netherlands 2010 56

Table 5. Economic Insecurity, Netherlands 2010 57

Table 6. Binary Economic Insecurity, Netherlands 2010 58

Table 7. Binary Economic Insecurity, Netherlands 2004 64

Table 8. Demands of compensation, United Kingdom 2010 67

Table 9. Binary demands of compensation, Netherlands 2010 68

Table 10. Economic Insecurity, United Kingdom 2010. 71

Table 11. Binary Economic Insecurity, United Kingdom 2010 71

Table 12. Economic Insecurity, United Kingdom 2004 74

Table 13 Binary. Economic Insecurity, United Kingdom 2004 74

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Introduction

‘’The election of Donald Trump demands a reevaluation of the future of globalization and our

earlier optimism that the open global economic order will endure’’ (Brookings, 2016). This is

perhaps the best description of current affairs. Globalization is again a hot topic, as we have

seen during the American presidential elections and in the build up to Brexit.

This dissertation will focus on the relationship between social welfare systems and

economic openness. I will describe briefly the broad theoretical discussion on the

macroeconomic level and then explicate recent developments on these topics.

The starting point will be Rodrik’s theory, which has been known as the compensation

thesis (Liberati 2007). We will follow chronologically the evolution of and critics on this theory,

which states that economic openness leads to more external risks for a nations citizens and

therefore demands compensation.

We will then investigate recent research, which is focusses on the micro level. Using

databases as the European Social Survey or the World Value Survey, socio-economic

characteristics of respondents and their demands for compensation.

Here we can find distinction in objective measurements, which I will call individual

effects on globalization following (Walter, 2010) and a subjective indicator of economic

insecurity.

This in turn will be applied for two very dissimilar cases. A small open economy, the

Netherlands, will be compared to a much larger and relatively more closed economy, the

United Kingdom. Both are members of the Organization of Economic Cooperation and

Development.

It is focused on FDI and Trade openness, but I will also introduce a new concept,

migration openness, to capture labor market competition, due to interconnecting nation

states.

In the end, we hope to find a relationship between these indicators of openness and

the demands for compensation. Also, it hopefully brings us insights if we should rethink our

thoughts on globalization, just like Brookings say we should.

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The Cameron claim

Introduction

The origins of the debate about whether economic openness and government size are related,

can be traced back to Cameron's paper ‘‘The expansion of the public economy: A comparative

analysis’’ (Cameron, 1978). In this work, Cameron tries to find an explanation for growing

governments during the ‘60’s and first half of the ‘70’s (Cameron, 1978). He defines

‘government size’ as “the ratio of all governmental revenues to gross domestic product’’

(Hereafter: GDP) (Cameron, 1978, p. 1244).1

This includes all direct and indirect taxes, all social welfare contributions by employers

and employees, and all other fees, rents and revenues. Also, he uses GDP, as a mean to

compare his sample, which exists of eighteen OECD countries (Cameron, 1978). Then he

identifies five possible explanations for the growing revenues of the government.

The theory

First of all, there is the economic argument of the Wagner rule (1883), which stated that

European countries have a tendency of growing governments, due to growing income levels.

Secondly, there could be a fiscal explanation, because ‘’fiscal illusions’’ are created, to

mislead the electorate. Additional policies are paid for by the citizens in the form of indirect

taxations. This, in turn, leads to higher revenues (Cameron, 1978).

1 While, Cameron uses government's revenues, Rodrik uses government consumption from the Penn World

Tables as an indicator for government size, while: “they are available for a much larger group of countries than

contained in the World Bank data. In addition, they are free of biases arising from cross-country differences in the

relative price of government purchases. Two countries with identical levels of real government purchases will

appear to have very different shares of government in GDP if the price index for such purchases relative to the

GDP deflator differs.” (Rodrik, 1998: 1001). According to Rodrik (1998), Cameron’s conclusions still hold, when

using government consumption instead of revenues. By reason of comparability, other authors have used

consumption as well. Thus, whenever I speak about government size, government's consumption will be its

indicator, with the exception of the disquisition about Cameron’s findings, or when explicitly mentioned.

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A possible third explanation is a political one. Cameron looks for a relationship between left-

wing parties and government expenditures, since left-wing parties have a tendency of creating

more social policies and therefore need to collect more revenues.

The fourth is considered to be an institutional one, which he tries to examine the

relationship between government spending and its own structure (Cameron, 1978). According

to Cameron (1978) empirical evidence has been found that government spending increases,

when a state is more decentralized and/or the formal relationship between governmental

bodies are less defined. When government spending rises, it must be compensated by

increasing revenues (Cameron, 1978).

The fifth and last explanation is of the international kind. Cameron states that the

amount of openness can relate to government spending, while governments have a habit of

introducing and/or spending more on social policies, when they integrate further into the

world economy. He operates the concept of openness by using ‘’the ratio of imports and

exports as a proportion of GDP" (Cameron, 1978, p. 1251). This is also called trade dependency

(Cameron, 1978).

His findings

After researching data of eighteen OECD countries and the 1960-1975 period, Cameron (1978)

finds two relationships. First, countries where the political power of left-wing parties is higher,

governments are bigger. When controlled for bigger countries as Germany or Britain the

results still hold. ‘’In Britain, for example, the change was positive in every year in which the

Labour party was in power (1964-70, 1974-75), and was negative in five of the seven years

during which the Conservatives governed. Similarly, in Germany (...)’’ (Cameron, 1978, p.

1254).

This does not mean it is a necessary condition, since countries as Canada, Ireland, the

Netherlands or Belgium, had increasing public expenses even when no left-wing parties were

part of the government. However, these four countries share a relatively high trade-

dependency (Cameron, 1978).

Secondly, this trade dependency, or economic openness is strongly correlated with

government revenues (Cameron, 1978). This means that public expenditure rises when trade

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dependency is higher. Rodrik refers to these findings as the Cameron claim (Rodrik, 2011) and

I will use these findings as the starting point of my research.

Furthermore, these results imply that the amount of openness is the determinant here

and not the change in openness. The degree of the relationship is much higher, than that of

the relationship between left-wing party power and government revenues. Intuitively, this

finding is surprising, because a government is supposed to be less effective, when exposed to

international trade (Rodrik, 1998). But more on this later.

However, he cannot find a relationship between the Wagner ‘’rule’’ and governments

revenues, nor with the fiscal explanation and governments revenues. Also, his results suggest

that there is no relationship between the institutional explanation and government revenues.

On the contrary, centralized or unitary countries are much more likely to have higher public

expansions (Cameron (1978).

In conclusion, Cameron researched five possible explanations for growing government

revenues. He founded two relationships, of which trade-dependency has much more

influence. Later on, his research has been redone, criticized and refined by different authors,

but he has start an ongoing debate about government size and international trade. This has

become more and more relevant now globalization is also a hot topic in the public debate.

Retesting the Cameron claim

For now, I want to explicate Bullmann's (2008) research. He has retested Cameron’s findings

using the same sample and the same explanatory variables, and came to the same

conclusions. But he makes some interesting notes.

Firstly, international trade has been liberalized, expanded and diversified during the

‘80’s and thereafter. And as we will see, capital flows will play an important role in discussing

the relationship between government size and the openness of a country (Bullmann, 2008).

Especially as we will see, when examining the efficiency hypothesis.

Secondly, the relationship does not hold when we test for changes in trade or

government revenues. On the contrary, however not significant, the relationship turns into a

negative one (Bullmann, 2008). Testing for change, instead of levels is important, because

globalization is a dynamic process, meaning that analyzing by using (aggregated) levels,

perhaps does not capture the development of a country's openness (Garrett, 2001).

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Thirdly, government's revenues as a percentage of GDP has not grown for the OECD countries

since the 1980’s, which is an essential fact for proponents of the efficiency thesis (supra)

(Bullmann, 2008).

Finally, in a second testing Bullmann (2008) extends the observation period to 2006.

He also uses a fixed effects regression model and a panel instead of Cameron first differences

approach (Bullmann, 2008). This leads to different results, which I will show when discussing

the efficiency hypothesis.

Obviously, there is much more to say about his work, but for a more detailed

description about Cameron’s variables, methodology and literature used, I would like to refer

to his paper.

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The compensation thesis

Introduction

The relationship between economic openness and government size (1978) has been

developed in the late 90’s by Rodrik (1998). In his paper ‘‘Why do more open economies have

bigger governments?’’ (Rodrik, 1998), Rodrik lays the groundwork for, what has later been

known as the compensation thesis (Liberati, 2007).

In other literature (Ehrlich & Hearn, 2014), Rodrik’s theory also has been referred to

as the ‘embedded liberalism thesis’. This is often related to Ruggie’s contribution in the

‘‘International Organization 2(36)’’ (Ruggie, 1982). Ruggie introduced the term ‘embedded

liberalism’ as the first scholar and saw it as a compromise between free trade liberalism

favored by the USA and (almost) all other countries who supported a more impeded

multilateralism (Ruggie, 1982).

With nationalistic Europe during the 1930’s in mind, motivated by their shared goal for

domestic stability, both movements came to some kind of compromise: “Unlike the economic

nationalism of the thirties, it would be multilateral in character; unlike the liberalism of the

gold standard and free trade, its multilateralism would be predicated upon domestic

interventionism.” (Ruggie, 1982, p. 393).

The origins of the compensation thesis

The compensation thesis, as explicated by Rodrik (1998), exists of two components. Firstly,

Rodrik (1998) endorses Cameron’s findings and acknowledges the relationship between

openness and government size. However, as stated above, government size is in Rodrik's

findings consumption instead of government revenues.

Secondly, Rodrik (1998) integrates (external) risk as an explanatory factor for

increasing government's expenditures. Furthermore, he sees it as a causal relation instead of

a correlation, which is of crucial importance. To put it in other words “His basic argument is

that the increased volatility brought about by growing exposure to, and dependence on,

developments in the rest of the world creates incentives for governments to provide social

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insurance against internationally generated risk and economic dislocations.” (Monala, 2004,

Non-Technical Summary)

Following general economic theories, we can also describe the compensation thesis in

terms of supply and demand (Tridimas and Winer, 2005). When a country integrates further

into the world economy, a society faces more inequality and is exposed to more external risk

(Rodrik, 1998). This leads to growing demands for compensation mechanisms. The supply side,

which is the government, should satisfy these needs, which in turn leads to increasing

governmental expenditure (Liberati, 2007). Especially where it concerns social security and

welfare (Rodrik, 1998)

Results

Thus Rodrik (1998) uses Cameron’s work (1978) as his framework and also acknowledges the

link between an expanding government and the openness of the economy. On the other hand,

Rodrik (1998) expands the sample to over 100 countries, instead of the 18 OECD countries

Cameron (1978) used.

Looking at the results Rodrik presents, we can see that openness is an important

determinant to explain increasing government consumption. Also, the amount of openness

during the ‘60’s is a significant explanation for growing government consumption in the

following three decades. Last, but not least, he could not find a reverse relationship between

government consumption and the openness of the economy, meaning that openness

influences government expenses, not the other way around.

Checking for robustness, Rodrik looks at other possible explanations that influences

this relationship. He checks for country size, population, possibility from external sources such

as the financial sector, inflation, trade as a way of taxation, export as a way to collect revenues

by installing tariffs and of course possible outliers. Rodrik concludes that none of these

variables are changing the results (Rodrik, 1998). He also states that when government

consumption instead of transfers, the results still hold (Rodrik, 1998).

Integrating external risk

The second part of his groundwork, consists of exploring external risk as a possible explanation

for growing government consumption in the form of social welfare. Rodrik (1998) sees the

20

government as the safe sector “in terms of employment and purchases from the rest of the

economy” (Rodrik, 1998, p. 12).

According to Rodrik (1998), a representative household will be more stable, when the

government is bigger, while this household total income will derive more from the

government. Mainly because portfolio diversification is absent according to Rodrik (1998):

In principle, external risk should be diversifiable for small countries through

participation in international capital markets. In practice, this does not appear to be

the case. Karen Lewis summarizes the literature on international portfolio

diversification thus (1995, 1914): "recent evidence shows that domestic investors

continue to hold almost all of their wealth in domestic assets." (Rodrik, 1998, p.13)

But does exposure to external risk lead to an increase of the aggregate risk? Of course,

the world economy is much less volatile than that of a single country. But in line with Ricardo’s

comparative advantage, small open countries have tendency of specializing (Rodrik 1998).

Furthermore, there is a lack of portfolio diversification.

Therefore Rodrik (1998) concludes that external risk is positively and significantly

related to income and thus for the household.

Other findings in favor of the compensation thesis

Alesina & Wacziarg (1998) studied the relationship between small countries and openness

from a different angle. They retest Rodrik’s (1996) findings, and also conclude that there is a

relationship, between openness and government size. However, they contest Rodrik’s

conclusion that this relationship is not driven by country size, as they “found this rejection to

be sensitive to small changes in the sample, the specification or the definition of the control

variables. “(Alesina & Waziarg, 1998, p. 317).

They conclude that country size does play a role when examining government size,

while large countries already benefit from a big domestic market, which lowers the need for

international trade (Alesina & Waziarg, 1998) and can afford smaller governments because

“when you can share the costs of partially or completely non-rival public goods over larger

population, per capita expenditure on these goods is lower” (Alesina & Waziarg, 1998, p. 306).

21

However, if government size is driven by country size or the other way around is hard to say

(Alesina & Waziarg, 1998).

Nonetheless, Ram (2009) recently criticized Alesina & Waziarg (1998) findings, while

his results show that there is no negative covariation between country size and trade

openness nor government size, and concludes that “These estimates thus do not support (...)

that the positive covariation between trade openness and share government consumption in

GDP is due to the mediating role of country size” (Ram, 2009, p. 213). By using data from 154

during 1960-2000 period, he finds that the compensation thesis mainly hold.

Busemeyer (2009) also finds a relationship between openness and government

expenditure of open economies (such as the Netherlands, or Belgium), but states that this

decays during the ‘90. Also, because of the regression models Rodrik uses, he cannot

distinguish between correlation and a causal relation, which is an important feature to the

compensation thesis (Busemeyer, 2009).

Other authors have looked for this relationship for developing countries. Jeanneney &

Hua (2004), reviewed this relationship by taking the Chinese provinces as a sample, while the

budgetary autonomy in China is relatively high and is growing since the early 80’s. They take

levels for the 1996-98 period, while “the reform of 1994 implemented a transparent and

objective fiscal policy (...)” (Jeanneney & Hua, 2004, p.529).

By using the same variables as Rodrik, they explore the relationship of trade openness

and government size, and find positive and significant results in favor of the compensation

thesis. However, whether this growing budgetary expense is directly related to social security

and welfare spending remains unclear (Jeanneney & Hua, 2004).

Other point of concern is the fact that capital flows in the form of Foreign Direct

Investment (FDI) are not included in the variable openness. This is not surprising, while FDI’s

does not play a significant role in the Chinese economy until the mid-1990’s (Jeanneney &

Hua, 2004).

Methodological criticism of the compensation thesis

The compensation thesis has been tested and retested in the past 40 years by multiple authors

as we have seen above. However, other findings or opposite results have also been found.

22

Globally, we can distinguish two broad criticisms: on the one hand a theoretical one, in the

form of the efficiency hypothesis (Liberati 2007)., which will be discussed in detail below. On

the other hand, we can find some different methodological criticisms, which I will discuss

briefly.

Fiercely criticized is Rodrik’s use of cross-country data instead of time-series.2 This is

debatable while, following Birk (1971), the evolution of government size is a dynamic

procedure.

Furthermore, the liberalization waves of the 80’s and 90’s has not only changed

international trade, but also accelerated trade (Busemeyer, 2009). Busemeyer therefore

states that “if the process of economic internationalisation only gained momentum starting in

the 1980s, as is often posited in popular and academic debates, the long-term effects of

globalization cannot show up in these studies” (Busemeyer 2009, p.458).

Also, Garrett (2001) concludes that regression methods like Rodrik used, do not

capture the dynamics of globalization. He also states that multivariate techniques are needed,

to control for population or country size (Garrett, 2001). We can find the same criticism with

Alesina & Wacziarg (1998), as described above.

Garrett (2001) also argues for more dynamic indicators for better understanding of

the relationship between openness and governments size, such as market integration instead

of trade openness and growth rates of government spending instead of aggregate levels. He

finds, by using time series, that countries that integrate faster in the world economy, are

having slower growth of government size. This challenges the robustness of Rodrik's findings

and contests the compensation thesis.

Following Garrett (2001), Monala et al. (2004) see the relationship as a dynamic

process “”and therefore may not be best captured by static regressions based on cross-country

data which is averaged over a number of years” (Monala et al., 2004, p. 7). They show that

2 In his paper, Rodrik (1998) uses cross-country data to investigate the relationship between trade openness and

government size. Cross country is: “a sample of observational units all drawn at the same point in time” (Green,

2008: pp. 1020). Opposite of cross country analysis, is analysis through time series. It is supposed to be ‘random’,

while the features of this particular time period can happen only once or as Green (2008) puts it: “at least in

economics, the process could not be repeated” (Green, 2008, p. 630).

23

“the dynamics of the relationship between trade-openness and government size varies

considerably across countries.” (Monala et al.,2004, p. 8).

They also try to find a relationship between openness in trade and government size,

using a sample of 23 OECD countries and time-series in the form of annual data over a period

of 50 years, starting in 1948. They think their sample is more homogeneous, while the

similarities between OECD countries are much higher than the 100+ sample Rodrik uses.

Therefore, the need for country-specific control variables is absent. Moreover, by using time-

series they also include the time dimension (Monala et al., 2004).

Other than Rodrik, Molana et al. (2004) cannot find strong convincing evidence for the

compensation thesis. Only three out of 23 countries have some kind of relationship between

the two variables. Six countries have an inverse relationship and five countries have a causality

relationship that works both ways. Also, there are countries that have a negative relationship

between the two.

Therefore, it is difficult to distinguish between a correlation or a causal relation

(Garrett, 2001). However, causality is a crucial feature to the compensation thesis

(Busemeyer, 2009). Moreover, Garrett (2001) suggest that possible multicollinearity, meaning

the interdependency of the independent variables, corrupts the output.

Another point of concern is the second part of Rodrik's (1998) paper. Here he

integrates risk as the explanatory variable for rising government sizes. Furthermore, openness

leads to higher external risk, which the government wants to mitigate (Rodrik, 1998).

However, Islam (2004) claims that government size is not related to greater risk

created by more openness. By using country-specific measures averaging over time, important

data, such as heterogeneity and short-term dynamics can be lost (Islam, 2004). He studies six

dissimilar OECD countries by using annual data, but concludes also that there is no relationship

between risk and openness except for Australia:

24

“Therefore, data does not lend support to the mechanism through which external risk

is supposed to affect government size. The results partially support the hypothesis that

government size is affected by the degree of openness and terms of trade volatility.

However, the impact is idiosyncratic, and country specific heterogeneity may be

important in determining the nature of the relationship between government size,

openness, and volatility.” (Islam 2004, p. 999)

And last but not least, Rodrik (1998) does not include capital flows in his analyses,

which has grown a lot during the liberalization waves of the ‘80s and ‘90s as we’ve seen earlier

(Garrett, 2001).

In conclusion, a lot has been written about the compensation thesis. Garrett (2001)

ends his paper with advice for future research on the compensation thesis and globalization

in general. He states “that empirical analysts need to be very careful in the future when they

are thinking about how to measure globalization and its impact on domestic politics and

policy” (Garrett, 2001, p 27)

25

The efficiency hypothesis

Introduction

According to the compensation thesis, higher levels of trade leads to more external risks and

therefore creates a demand for social policies, as we’ve seen above. Opposing this view, we

can find the efficiency hypothesis, or conventional wisdom (Liberati, 2007). Capital flows has

increased during the ‘80’s and ‘90’s and beyond (Garrett, 2001), but are not taken into account

by Rodrik, nor Cameron (Liberati, 2007). However, these capital flows have played an

increasing role in world trade and are central in the efficiency hypothesis (Garrett, 2001).

The idea

The efficiency hypothesis states that, because of capital mobility, the possibilities for taxations

by governments decrease (Liberati, 2007). Capital is always trying to find the highest possible

rates of return, all other things equal. Obviously, other factors such as political and/or financial

stability, the term of investment or interest rates can also play an important role, but they

influence the achievable rate of return (or efficiency) of capital. So, for capital to be efficient,

the government must not or little intervene (Bullmannn, 2008).

During the liberalization waves of the late 20th century, capital mobility has

exponentially increased, meaning that it is much easier to move capital from one country to

another. This in turn, leads to decreasing taxation abilities for a government, while high(er)

taxes on capital can lead to capital flight and thus “following this logic, the potential increasing

demand for additional expenditures induced by the compensation hypothesis could not easily

matched by an increasing supply” (Liberati, 2007, p. 2).

This is not only due to decreasing taxing abilities, but also because it is getting harder

for governments to have high budgetary deficits (Liberati, 2007). Indeed, euro countries for

example, are not allowed to have a deficit of more than three percent, as agreed by the nation

states, to prevent financial instability (European Union, 1992).

Hence, we can see two opposite forces in the debate about government size and

openness. It is important to note that Liberati (2007) does not deny the demand for

26

compensation in the form of social policies, but his focus lays on the inability of the

government to fulfill its role as a supplier.

The empirical framework

Liberati (2007) examines the levels of trade as used by Rodrik (1998) and on the other hand

tries to find a relationship between capital flows and government expenditures. Capital flows

are measured by two distinct measures. Namely “the algebraic sum of inward and outward

foreign direct investments (FDI) on GDP; the algebraic sum of inward and outward portfolio

investments (PI) on GDP. The first measure will be referred to as FDI openness; the second as

PI openness.” (Liberati, 2007, p. 8).

FDI can be seen as a more permanent flow, while PI’s lean on the short term. Also,

Liberati (2007) includes inflows as well, while a country can be seen as closed when it only

attracts a lot of foreign capital.

He finds a negative correlation between government size and capital openness. His

data set considered democratic and developed countries with big governments, where almost

70 and 80 percent of the world's capital flows can be found (Liberati, 2007).

The results also imply low elasticities between FDI flows and governments

expenditures: “which means that the effect of FDI, as expected, has not been disruptive on the

size of the government until now, (...)’’ (Liberati 2007, p. 15).

A critical view on the efficiency hypothesis

So, the efficiency hypothesis sees a negative correlation between capital flows and

government size and claims that because of the growing importance of FDI’s, governments

are losing taxation abilities or are unable to have huge budgetary deficits.

Indeed, when capital mobility is high, it is easier to move capital around the globe.

Even more, when financial taxes are high or political instability due to rising deficits grows, the

chance of capital flight increases. This leads to, according to proponents of the efficiency

hypothesis, increasing pressure on government expenditures. This process is often referred to

as a race to the bottom (Busemeyer, 2009). But this is too simplistic:

27

“Policy making is a complex process and takes time. Policy makers are confronted with

a multitude of demands from various constituencies, with owners of mobile capital

being only one of them. The notion of the ‘race to the bottom’ as developed in the

‘efficiency’ camp implies a radically simplified and thus inadequate conception of the

political process. In the political arena, demands from business have to compete with

a variety of other demands. (...) In addition, policy makers have to weigh the diffuse

exit threat of capital against concrete demands from other constituencies. Then, even

if they decide to lower rates of corporate taxation, the lengthy process of tax reform

might come too late to prevent the hemorrhaging of capital. Plus, corporate taxes are

only one element in the decision-making process of firms. Firms might suffer more from

increases in consumption taxes and non-wage labour costs necessary to finance

reductions in corporate taxation, depending on their mix of production factors and their

position in the market. (Busemeyer, 2009, p. 459-460).

Kimakova (2009) contests the efficiency hypothesis fiercely. She states that the highest

volatility due to financial openness are countries at the intermediate level of development

(Aghion et al. (2004) in: Kimakova (2009)).

This can be a possible explanation for the negative correlation between government

size and the amount of openness, while the need for risk-mitigating by the government is

more absent, when volatility is less high. Thus, while Liberati (2007) and Bretschger & Hettich

(2002) take OECD countries as their sample, she states that:

“Such a limited sample is problematic, especially since the richest OECD countries

included in the analyses have not been exposed to a great deal of financial crises until

recently and it is therefore not surprising that Liberati (2007) did not find empirical

support for Rodrik’s (1998) compensation hypothesis transposed to financial

openness“. (Kimakova, 2009, p. 395)

Also, she is not convinced by the way these studies incorporate measures of financial

integration (Kimakova, 2009). Liberati (2007) works with the sum of inward and outward

28

foreign direct investments (FDI) on GDP, or respectively the PI on GDP, while Bretschger and

Hettich (2002) work with the outflows of FDI.

Instead Kimakova (2009) uses gross private capital flows as a percentage of GDP, and

expends the sample to all countries except: those of small countries less than 2 million;

countries with trade openness of over 200%; and those countries with an unorthodox policy

mix, such as China and Cuba (Kimakova 2009). Still she sticks to government size as defined by

Rodrik (1998) (Kimakova 2009).

Her results are in line with the compensation thesis. Moreover, she finds a positive

correlation, significant at the one percent level, between financial openness and government

size. However, she also finds that the relationship between financial openness and per capita

GDP is highly significant.

In conclusion, we can state that there is evidence for a negative correlation between

government size and financial openness, but it looks like this can only be said for countries

with a high GDP, such as OECD countries. Indeed, when extending the sample, we can also

find results in favor for the compensation thesis, even when measuring for financial openness

instead of trade openness. However implicitly, Liberati (2007) described this as well in his

conclusions:

“The system of OECD countries is therefore rather close and previous data suggest that

if capital openness is to play any role in shaping government size it is likely to play it

where capital actually flows. This might also explain why across-the-world conclusions

may be difficult to achieve and why, when extending the number of countries, the

effects of capital openness tend to dilute.” (Liberati, 2007, p. 23)

29

The micro-economic level

Introduction

The fiercely debated discussion has we’ve seen above, is primarily contested by arguments on

the macroeconomic level. However, in the last fifteen years the focus has been slowly drifting

towards the micro economic level. One of the first and serious testings of the compensation

thesis at the micro-level, can be traced back to Hays, Ehrlich and Peinhardt (2005).

In their paper, they research the supply side of the compensation thesis. They state

that when people are demanding compensation they will do it more likely in the form of trade

tariffs than in the form of social welfare compensation (Hays, Ehrlich and Peinhardt (2005).

However, with the establishment of the World Trade Organization in 1994, and the

‘’Technical Barriers to Trade (TBT) Agreement’’ which ‘’aims to ensure that technical

regulations and standards (…) and procedures for assessment of conformity with technical

regulations and standards do not create unnecessary obstacles to trade’’ (World Trade

Organization, 1994, p.1), the government loses possibilities to maintain, let aside install new

trade barriers.

Therefore Hays, Ehrlich and Peinhardt, (2005) see social welfare compensation as a

compromise to make room for the government to build support in favor of free trade. This

reminds us of the embedded liberalism thesis introduced by Ruggie (1982). Indeed, Hays,

Ehrlich and Peinhardt (2005) see this as the first micro testing of the embedded liberalism

thesis.

Now we are entering a new dimension regarding the compensation thesis. While we

started by looking at the relationship between trade-dependency or economic openness and

social welfare policies on the aggregate level, we are now focusing on individual

redistributional preferences (Rehm, 2009). Therefore, I will describe some basic economic

theories to shed some light on distinction mentioned above, before I will deepen the

microlevel testings of the compensation thesis.

30

Ricardo-Viner model

This model, also known as the specific factors model (Sanz, Martinez & Steinberg, 2009),

distinguishes between sectors with and without comparative advantage (Scheve & Slaughter,

2001).

According to Ricardo’s comparative advantage theory, countries export goods and

services in which it has a comparative advantage, meaning it can produces these goods and

services at a relatively lower cost than the importing country (Cuyvers et al., 2012).

Furthermore, a country without absolute cost advantages can still engage in international

trade, if it can produce one or more goods and services relatively cheaper. (see for an example

Cuyvers et al., 2012, p. 60-63).

Relating comparative advantage to individual preferences towards international trade,

we would expect that workers in the export-sector will mostly benefit from free trade,

whereas employees of the import sector will mostly be harmed, because “Sectors with price

declines - presumably comparative-disadvantage sectors - realize income losses for their

specific factors while sectors with prices increases - presumably comparative-advantage

sectors - realize income gains for their specific factors.“ (Scheve & Slaughter, 2001, p. 272).

Moreover, in the Ricardo-Viner model (some) factor mobility is absent (Scheve &

Slaughter, 2001). This is a crucial assumption, while this explains the importance of sectors in

regard to individual preferences towards international trade, because: “the income of specific

factors is linked to their sector of employment.” (Scheve & Slaughter, 2001, p. 272).

Heckscher Ohlin model

The RV-model can be seen as a short-run model of the Heckscher-Ohlin Model (Scheve &

Slaughter, 2001). This theorem (Hereafter: HO model) states that a country, engaging in

international trade, will specialize according to the factor of production it uses most

intensively, and is most abundant with. This does not mean a country will fully specialize

(Cuyvers et al., 2012).

Moreover, the Stolper-Samuelson theorem deepens the HO model by focusing on the

change of the relative prices of the factors of production. (Cuyvers et al., 2012). It states that

international trade leads to a raise in the rate of return of the abundant factor of production

31

in every country, relative to a decrease of the rate of return of the scarce factor of production

(Cuyvers et al., 2012: pp. 101).

Finally, the HO model make the assumptions that factors can move free and costless

(Scheve & Slaughter, 2001). This is obviously theoretically. Even in the European Union, where

free mobility of persons, capital and goods and services is one of the pillars, perfect factor

mobility is absent, due to market failures, language barriers, cultural aspects, transaction costs

and so on. This does not mean it is impossible for factors to move within or between borders.

Thus, international trade leads to countries specializing in sectors where the factors of

production are most abundant and which uses the factors of production most intensively. This

leads to higher rate of returns for these factors of production. The focus on factors is the

reason this model is also known as the factor endowment model (Sanz, Martinez and

Steinberg, 2009). Also, factors of production can move freely (Scheve & Slaughter, 2001).

When we relate these to individual preferences towards international trade, we should

expect that HO models evaluate preferences by using factor types. To give the example of the

United States, where high skilled workers are abundant “more skilled workers should support

freer trade while less skilled workers should oppose it” (Scheve & Slaughter, 2001, p. 273).

This is logical, while international trade leads to higher return rates of the abundant

factor of production, which is in the United States high skilled labor. More skilled workers

should therefore prefer international trade, at least according to the HO model.

Tradables and non-tradables

The third theoretical distinction we can find in the literature is between tradables versus non-

tradables. According to Zeugner (2013) this distinction can be made in two ways. Firstly, by

classification. A traditional example of a non-tradable is a service like a haircut. Whilst

tradables are mostly utilities like manufacturing or agriculture according to tradition

classification. (Zeugner, 2013).

However, a second and more dynamic approach is looking at export intensity. Then we

can find 100% domestic demand on one side and 100% foreign demand on the other

(respectively: non-tradable, tradable) (Zeugner, 2013).

If we relate this to the Hekscher-Ohlin model nothing changes due to costless

interindustry factor mobility. However, in the Ricardo-Viner model, where (some) factor

mobility is absent, people can hardly change between tradable and non-tradable sectors.

32

There we expect that, when we take into account that free trade leads to higher national

income, the demand for nontraded sectors will also increase and thus “we predict that in an

RV model workers in nontraded sectors should support freer trade. However, (...) less strongly

than do comparative-advantage-sector workers.” (Scheve & Slaugther, 2001, p. 272).

The empirical framework

So, in the theoretical description above we can find relevance for sectors in the RV-model, for

skill in the HO-model and the sectoral distinction between tradables and non-tradables.

Hays, Ehrlich and Peinhardt find in their research (2005) confirmation that the

tradable/non-tradables and import/export distinctions are significant. Furthermore, they

conclude that “the movement of workers from tradable to non-tradable sectors will increase

support for trade” (Hays, Ehrlich and Peinhardt, 2005, p. 486). However, only when imports

rise during the transformation from an industrial, to a post-industrial economy (Hays, Ehrlich

and Peinhardt, 2005).

In their research, their focus on short-term is important to emphasize, because this

can be traced back to the RV model. When (some) factor mobility is absent, preferences

towards international trade are influenced by sector more than by skill (Hays, Ehrlich and

Peinhardt, 2005).

Moreover, postindustrial economies are characterized by a large service economy,

while industrial economies are, as the term suggests, characterized by sectors producing

tradables and “Indeed, the short-term effect of increasing a country’s import is larger for

industrial than postindustrial economies” (Hays, Ehrlich and Peinhardt, 2005, p. 490).

Rehm (2009) implies a similar effect after studying mainly European countries covering

the 2002-2006 period. He states that “it may be (…) that the industry-level variables played an

important role in shaping redistributional preferences in earlier years” (Rehm, 2009, p. 872).

Hence, before or during the transition from industrial to postindustrial economies (Rehm,

2009).

Integrating risk

Rehm (2009) argues that the studies on micro level until then, such as Hays, Ehrlich and

Peinhardt (2005) research, are too much focused on sectors and industries, instead of

33

individual’s occupation “especially risk exposure related to someone's occupation” (Rehm,

2009, p. 856).

Rehm (2009) therefore, looks at the uncertainty of future income. This implies a

negative correlation between income and support for redistribution. On the other hand, this

does not mean that people with high incomes cannot support redistribution. It all depends on

their future income3 and their rational consideration. This also means that we are testing for

the compensation thesis instead of the embedded liberalism thesis (Rehm, 2009).

His results show that there is more than one variable, which influences the

redistribution preferences (Rehm, 2009). For example, income, education, gender and union

membership all influences these demands for redistribution. However, church attendance is

insignificant and “age is never a significant predictor of redistributional demands.” (Rehm,

2009, p. 867).

Furthermore, as a predictor, occupation is much more important than industry.

Moreover Rehms (2009) conclude that: “Neither industry unemployment rates, nor an

industry’s (dis)advantage, nor its involvement in international markets is a meaningful direct

predictor of redistribution in demand.” (Rehm, 2009, p. 871). Instead, the risk exposure levels

of the occupational level are much better predictors of an individual’s redistributional

preferences. This is in line with theoretical speculations, you suspect (see: Rehm, 2009, p.

861).

Sanz, Martinez and Steinberg (2009) also point out that individual skill level

determinants the redistributional preferences of the individuals. They break up social welfare

spending in different subcategories and find higher levels of support for free trade, when there

are higher levels of unemployment and pension spending. This let them to believe that

“People seem willing to intensify the globalisation process if, in exchange, they are protected

from greater levels of economic volatility, especially employment insecurity, that globalization

entails” (Sanz, Martinez and Steinberg 2009, p. 13)

The model

This brings us to the model, that I will use. The model (see: figure 1) is presented by Walter

(2010). She tested the underlying micro foundations of the compensation thesis for

3 See Heylen et.al, 2012, p. 172 for a detailed discussion about Friedman’s permanent income hypothesis.

34

Switzerland by using the theoretical assumptions as described above (Walter 2010).

Therefore, the focus will be the individual effect of globalization instead of the aggregate

effects. This I will do for the Netherlands, see further.

Obviously, the individual effects of globalization will be regarding the openness of the

economy. Walter (2010) operationalizes this by looking at trade openness and FDI openness.

These variables capture the sectoral dimension, while both categories are in her research

related to the respondent’s sector of employment. Also, trade openness is an indicator for the

distinction between tradables and non-tradables.

Lastly, the skill endowment model, is captured by the introduction of a new indicator

called ‘’offshorability’’ (Walter, 2010). However, skill will not be a part of this research,

therefore I would like to refer to Walters paper for more detailed information.

She identifies different causal links, which she analysis by using logistic ordinal

stepwise regression (Walter, 2010). First, growing openness leads to rising economic

insecurity as proposed by Rodrik (1998) and is the first of three-causal links (Walter, 2010).

Furthermore, rising economic insecurity leads to increasing demands for compensation in the

form of social welfare, which is the second causal link (Walter, 2010).

Figure 1. Globalization and the Welfare State Expansion. Reprinted from: Globalization and the welfare state

expansion: Testing the micro-foundation of the compensation hypotheses, by S Walter, 2010, International Study

Quarterly, 54, p. 406). Reprinted with permission. Copyright, 2010 International Studies Association.

35

In her analysis, Walter (2010) does not divide social welfare in different facets, like Sanz,

Martinez and Steinberg (2009) do. This is mainly due to fact that Walter (2010) uses the World

Value Survey (Hereafter: WVS) to retrieve indicators for economic job insecurity, demands for

compensation and preference for left parties. The methodological problem is that the WVS

does not gauge for different parts of social welfare preferences, but only takes in account the

general preferences towards social welfare.

Finally does these increasing demands for compensation leads to affinity with left-wing

parties, while these parties intend to acknowledge these demands (Walter, 2010). We have

already seen that left-wing parties play a role in Cameron’s (1978), and Rodrik’s (1998)

research. However, I have intentionally left this out of the literature study, while describing

left and right is far more difficult as it seems. Especially for a proportionally party system as in

the Netherlands, where thirteen parties have been elected, after the last elections of 2017,

(NRC, 2017b).

So, we have seen that individual effects of globalization, affect economic insecurity,

and in turn the demands for compensation. This affects individual preferences towards left

wing parties. These links are causal, but are indirect effects of globalization. However, she also

acknowledges direct effects of globalization on compensation demands and voting

preferences, but that these effects are very hard to analyze, to conclude that further research

is necessary (Walter, 2010).

The simplified model

In this study a simplified model method will be used. I will only investigate the direct links

between globalization and demands for compensation, since this is an unexplored field

(Walter 2010). Furthermore, doing a stepwise regression as Walter (2010) did, can have some

major methodological complications as we will see further.

Also, I will not examine the welfare state expansion, nor the preference for left wing

parties. In line with the efficiency thesis, it is possible that the government is unable to expand

in a time of accelerating (financial) globalization. Moreover, there can be a whole lot of other

reasons involved that leads to the government inability to expand its social welfare system,

such as decision-making procedures, law, or interest groups (Buseymer, 2009). I do not want

36

to get bogged down in a discussion about policy making, which I think can be at least a thesis

in itself.

The same applies for preference for left wing parties. The discussion about what is left

and right is ongoing in literature and popular debate. Take the Partij voor de Vrijheid (PVV)

[Party for the Freedom] in the Netherlands for example. They are viewed as a right-extreme

movement in the (international) media, because of their views on immigrants. However, they

want to reverse privatizations in healthcare and energy sector and plead to take better care

for old people in the form of expanding social welfare. Also, they can be seen as anti-globalists

like some left-wing movements and parties (NRC, 2017a).

In the end, it is also a matter of interest. I would like to research people’s views towards

demands for compensation in regards with their socio-economic position instead of the

possible outcomes of these demands.

This leads to an abridged model as presented below. As we can see, demands for

compensation has two predictor4 variables that seems independent from each other.

The individual effects of globalization are threefold. Obviously, trade openness is part

of the model. Also, I have included FDI openness, while the Netherlands and Britain are both

OECD countries, which means that FDI could play a role in demands for compensation

(Liberati, 2007).

In contrary to the original model presented by Walter (2010) I have left the indicator

offshorability out, due to the limited amount of time available and the time-consuming nature

of the activity. Furthermore, offshorability and the (non)-tradable are somewhat related

(Rayp, G., Personal communication, 2017). Moreover, I also wanted to introduce a new

concept to the model, migration openness.

The relevance of this concept finds itself in the (perception of) growing migration flows

towards Europe, due to climate change, political instability in Northern Africa and the Middle

East and, paradoxical, growing household incomes in sub-Sahara Africa, which leads to

financial opportunities for a way out. I will discuss this briefly further.

4 Following Field (2005) I will label independent as the predictor variable and the dependent as the outcome

variable while: ‘’correlation research by its nature seldom controls the independent variables to measure the

effect on a dependent variable. Instead, variables are measured simultaneously and without strict control. It is,

therefore, inaccurate to label regression variables in this way (Field, 2005: p. 144).

37

So, the individual effects of globalization exist of the three indicators, FDI, trade and migration

openness, which can be seen as ‘objective’ measurements of globalization risks.

These exist next to the subjective variable economic insecurity, that I have retrieved

due to the respondents own perception of job insecurity. I will discuss this more deeply in the

methodology.

Individual

effects of

globalization

Economic

insecurity

Demands

For

Compensation

Figure 2: Globalization and demands for compensation: objective and subjective predictors.

The alternative model

In 2009, before Walter’s paper (2010) was presented, Ruoff & Schaffer presented a different

model (figure 2) in their paper ‘’Adding Another Level: Individual Responses to Globalization

and Government Welfare Policies’’ (Ruoff & Schaffer, 2009). They criticize the lack of

integrated research between the macro - micro relationship (arrow 1), which is respectively

the welfare state expansion and preferences towards globalization and the micro level (arrow

3), existing of the relationship between economic insecurity and preferences for social

protection or compensation. Therefore, they use a two-step hierarchical estimation strategy

(Ruoff & Schaffer, 2009).

38

Also, they argue that preferences were mostly indirectly tested. Therefore, they use a

survey of the Eurobarometer to test for individual preferences and if individuals think

globalization is good for their country (Ruoff & Schaffer, 2009). Due to their results, they

contest the compensation thesis, but I will not discuss their findings. I would rather challenge

their methodology and argue why I will use Walter’s (2010) model.

Figure 3. Diagrammatic presentation of argument. Reprinted from: by G. Ruoff, & L. Schaffer, 2009, NBER, p. 8.

Reprinted with permission.

First of all, they operationalize openness by looking to trade and FDI openness only.

Furthermore, they define trade openness in general terms, whereas Walter (2010) looks to

trade shares relative to the total economy. The same applies for FDI.

Second, their distinctions in occupation seems arbitrary to me. Of course, this is due

to the fact that they follow the categorization of the Standard Eurobarometer5. However, the

use of this categorization has multiple problems. For example, the category ‘other white collar

worker’ exists of both desk and non-desk employees, while the letter can be a salesman or

driver (Eurobarometer, 2004). But non-desk, such as a secretary on the one hand, and desk

employees on the other hand, may, work in totally different sector or need whole different

5 The occupation scale of the Standard Eurobarometer exists of the following categories: Self- employed,

Managers, Other White collar workers, Manual workers, House persons, Unemployed, Retired, Students

(Eurobarometer, 2004).

39

skills, which means that the individual effects of globalization may not be the same. At least,

according to the H-O model. This ‘’small disadvantage’ is also acknowledged by Ruoff &

Schaffer (2009, p. 13)

Furthermore, using a more standardized categorization, like Walter (2010) did, we can

research more in detail by linking trade and FDI openness to each of the economic sectors.

Therefore, it seems to me that this model is not properly fit for the research I want to

do. Especially, while I just want to focus on the micro side of the compensation thesis.

However, there are interesting facets of Ruoff and Schaffers (2009) model, such as the two-

step hierarchical method. I will expand this method to a three-step hierarchical method.

40

Migration openness

Empirical relevance

As a third indicator for the individual effects of globalization, I have chosen migration

openness. The relevance of the concept is clear. First of all, immigration was a major theme

during the campaign in the build op to the EU-referendum. The Independent wrote that

‘’Britain’s vote to leave the EU was the result of widespread anti-immigration sentiment, rather

than a wider dissatisfaction with politics, according to a major survey of social attitudes in the

UK’’ (Independent, 2017).

Secondly, the European faced an international migration crisis in 2015, while hundreds

of thousands, mainly Syrian and Iraqi civilians, tried to escape the escalating Syrian civil war

(World Economic Forum, 2015).

However, I am researching the years 2004 and 2010. Nonetheless, we can see from

figure 4 that immigration was also a major theme in the first decennium for UK citizens.

Especially after the 9/11 terror attacks, the importance rises to a peak just after the 2005

London metro bombing, to descend steeply during the outburst of the financial crisis in 20076.

However, as discussed above the theme grows in significance in the build up to the

referendum.

We can find similar figures for the Netherlands. I have found numbers for different

years in Eurobarometer surveys (2005, 2011, 2015). The question ‘’what are the two most

important issues your country is facing today’’ has been the same over the years, but

unfortunately, methodologies differ, which leads to missing numbers (Eurobarometer, 2005,

2011, 2015).

6 Not part of this paper, but interesting to see is that the growth of the theme immigration rises hand in hand with growth of terrorism, but after the financial crisis their growth tends to be divergent.

41

Figure 4. Immigration among the public’s most important issues. Reprinted from the Migration Observatory at

the University of Oxford. Retrieved, 15 august, 2016, from: http://www.migrationobservatory.ox.ac.uk/wp-

content/uploads/2016/04/Briefing-Public_Opinion_Immigration_Attitudes_Concern.pdf. Copyright: Issue-MorI

I Issue Index

However, we can find the same tendency for ‘economic situation’ for the Netherlands as for

the UK. Also, terrorism has been descending since the violent years after the 9/11 terror

attacks, with the murder of anti-immigrant politician Pim Fortuyn by an animal activist and

the murder of director Theo van Gogh by a Muslim extremist (Guardian, 2004). Finally,

immigration has been doubled between 2005 and 2015. However, these are figures from the

period before the European migration crisis, so ratios could have changed.

Obviously, migration is an international phenomenon. According to a report, we can

see a permanent and increasing flow of migrations to the Organization of Economic Co-

operation Development (Hereafter: OECD) countries (OECD, 2014). However, until the

migration crisis of 2015, the influx dropped to levels below 2003 for the UK. It is therefore

interesting to see that migration is also a matter of perception, while the importance of the

issue and the real influx have a negative correlation for this period.

42

Table 1. Evolution public’s most important issues. Adapted from: Public Opinion in the European Union, by

European Union, 2005; 2011; 2015. Copyright European Union 2005; 2011; 2015 Eurobarometer.

Theoretical relevance

I have thought of a way to relate the concept of migration openness to existing literature.

However, a lot of research related to my subject focusses on perception towards migration.

For example, hostilities towards immigrants can come from generous immigration

policies. Consequently, these higher influxes of migrant leads to labor market competition,

whereas immigrants are perceived as competitors in the social economic spheres (Burns &

Gimpel, 2000). Similar results can be found with Mewes & Mau (2012), who researched

subjective economic insecurity. However, their focus was the relationship between people’s

(subjective) threatened social-economic position and their view towards immigration (Mewes

& Mau, 2012).

Other research also investigates the relationship between migration and economic

insecurity, but focus mainly on migrants, or foreign-born citizens, as an objective definition of

job insecurity (Blomberg et al., (2012), in: Chung & Mau, 2014). However, not significant

0

10

20

30

40

50

60

70

Evolution public's most important issues

2005 2011 2015

43

(Marx, 2014). Or when they focus on subjective economic insecurity and the relationship, it is

the subjective economic insecurity of migrants (Chung & Mau, 2014; Marx, 2014).

In contrary to the above, I have defined migration openness as the amount of migrants

divided by the total amount of workforce in the sector, to get the relative share of migrants

by industry. Hence, I therefore capture the risk of globalization, in the form of expended

labour market competition, next to trade openness and FDI openness and exists next to the

subjective variable of economic insecurity.

This means that, all other things equal, this will be the first introduction of migration

openness in relationship with demands for compensation.

44

Social welfare systems in perspective

Empirical evidence

I will end this chapter with a brief introduction to classifications of social welfare systems. To

test the compensation thesis on the micro level, I will do a comparative case study between

the Netherlands and the UK, for several reasons.

First of all, I will use a model that has been used to test for Switzerland, see further. I

wanted a similar case study of a small open country. Therefore, I have chosen to take the

Netherlands, while it is also a small open economy.

Switzerland The Netherlands United Kingdom OECD average

GDP7 (PPP) 8 373,533 711,982 2.156.000,000 -

GDP per capita (PPP) 9 49.467,221 43.462.049 35.151.341 33.994.966

Trade as % GDP10 132 112 55 53

Net social expenditure as %

GDP11

16.3 19.9 17.7 18.0

Table 2. Cases in perspective. Adapted from: World Bank, 2017a, 2017b, 2017c & OECD, 2017c.

In the table above, we find that GDP per capita is comparable for both the Netherlands and

Switzerland. Those income levels are much higher than of the United Kingdom, as well as of

the OECD average. Furthermore, trade dependency is more than double of the UK and the

7 Figures are in current dollars (World Bank, 2017a).

8 ‘’Purchasing power parity (PPP) is a price index very similar in content and estimation to the consumer price index, or CPI.

Whereas the CPI shows price changes over time, a PPP provides a measure of price level differences across countries’’

(Vogel, n.d., p.1)

9 Figures are in current dollars (World Bank, 2017b).

10 Also known as trade dependency (Cameron, 1978). Figures are in current dollars (World Bank, 2017c).

11 Organization of Economic Cooperation and Development, 2017c

45

OECD average. Also, net social expenditure at for all three countries around OECD average.

However, for the Netherlands it is slightly higher.

In contrary to small open economies as the Netherlands and Switzerland, I have chosen

for the UK. Which is a much bigger country as shown by GDP. However, GDP per capita and

trade dependency are much lower.

Theoretical classifications

Classifying social welfare systems is an ongoing debate in the political, sociological and

economical literature (Thelen, 2012). A good and interesting introduction is Thelen’s ‘’Varities

of capitalism: Trajectories of liberalization and the new politics of social solidarity’’ (Thelen,

2012). Here, she summarizes the distinguishes in the literature, but adds the dimension of

liberalization’s, which started in the 1980’s (Busemeyer, 2009).

Figure 5. Revised hypothesized trajectories of change in the rich democracies. Three ideal-typical trajectories of

liberalization might be: deregulation, often associated with liberal market economies; liberalization as

dualization, associated especially with continental European political economies like Germany; and liberalization

through socially embedded flexibilization, typically associated with the Scandinavian cases. Reprinted from

‘’Trajectories of liberalization and the new politics of social solidarity’’, by Thelen, K. (2012), Annual Review of

Political Science, 15, p. 146. Copyright 2012 by Annual Review of Political Science

( )

( )

( )

46

I am not going to recite this paper, but present a visualization (figure 5). Hence, besides a scale

of equality and one for the amount of bi- or triparty coordination, she introduces liberalization

and identifies three possible outcomes (Thelen, 2012).

The Anglo-Saxon liberal market economy, such as the UK, tend to have the lowest

equality and also the lowest cooperation between unions, employers-organizations and/or

the state. They are also facing deregulations, which lead to even lower levels (Thelen, 2012).

Indeed, the British labour market is the most deregulated of Europe (Centre for European

Reform, 2015).

The Dutch are somewhere between the highly egalitarian social welfare system of the

Scandinavian countries and the coordinated market economies, such as Germany. As we will

see, trade unions still play role in shaping demands for compensation in the Netherlands. Also,

flexibilization in turn has been one of the policies the Dutch government as implemented

during the first decennium of the 21th century (International Monetary Funds, 2005).

47

Summary and hypotheses

So according to the compensation thesis, trade openness leads to external risk and therefore

an increase in demands for compensation (Rodrik, 1998). We have seen that capital openness

also plays a role (Kimakova, 2009), but these findings also have been contested (Buysemeyer,

2009) and theoretically criticized (Liberati, 2009). This was all on the macro-economic level.

On the micro-economic level, the compensation thesis also has been tested (Rehm,

2009; Walter, 2010) leading to dissimilar findings. I have sketched three theories that could

have an influence on people’s demands for compensation. Also, I have added migration

openness as a third objective measurement of globalization, next to trade openness and FDI

openness and the subjective indicator for economic insecurity. This leads to the following

hypotheses, when keeping in mind that we are testing for effect between these indicators and

demands for compensation, meaning a two-tailed method model.

First regarding the individual effects of globalization:

• H1 = Trade openness has an effect on demands for compensation

• H2 = FDI openness has an effect on demands for compensation

• H3 = Migration openness has an effect on demands for compensation

Second in regard to economic insecurity:

• H4 = (subjective) Job insecurity has an effect on demands for compensation

With:

• H0 = The indicator(s) (trade/fdi/migration/job insecurity) do not have an effect on

demands for compensation

48

Methodology

Introduction

In the theoretical framework (infra) we have seen a lot of methods to (re)investigate the

relationship between social policy preferences and an individual’s socio-economic position. In

line with recent scholars, I have chosen to perform an empirical research on the micro-level.

Walter (2010) analyzes the same relationship for Switzerland and presents a model, I will

partly use (infra).

Whereas Walter (2010) used the World Social Survey (hereafter: WVS), in her more

recent work (2017) she uses the European Social Survey (hereafter: ESS), “while ESS is the only

cross-national survey that simultaneously contains detailed information on individuals’ degree

of exposure to the international economy, labor market risk perceptions and policy

preferences.” (Walter, 2017: p. 62).

Hence, data from the ESS will be the pillar stone for the following empirical research.

Situated in Norway, the European Social Survey, describes itself as:

“An academically driven cross-national survey that has been conducted across Europe

since its establishment in 2001. Every two years, face-to-face interviews are conducted

with newly selected, cross-sectional samples. The survey measures the attitudes, beliefs

and behaviour patterns of diverse populations in more than thirty nations’’ (European

Social Survey, n.d.).

Indeed, every two year the ESS publishes reports about topics, such as labor, health, safety

and happiness, which are called rounds. Originally, the idea was to take the Netherlands at

two different periods in time. Moreover, I wanted to take a year before the Maastricht Treaty,

and consequently the European Monetary Union, came into force (European Union, 2002).

However, the ESS published its first round in 2002.

Therefore, the time gap between the two rounds is shorter than I would have wanted.

In addition to the the first round has some practical methodological complications as we will

see further.

49

The other case is situated in the year 2010, because this is the last time the indicator for my

outcome variable was part of the questionnaire. This means that the time gap between those

two cases is six years. However, a lot can change in six years, as we already know.

The outcome variable

Following Walter (2017), Wren & Rehm (2013) and Marx (2014), I also take the following

question as indicator for my outcome variable demands for compensation:

“The government should take measures to reduce differences in income levels.”

(European Social Survey, 2010: B30)

This is measured on a five-point scale with higher values denoting stronger agreement with

the statement. However, I have chosen to do a binary logistic regression analysis, which means

that I have recode the variable into a binary variable. The operationalization will be clarified

by using the 2010 ESS data (supra).

The more or less comparable indicator in the WVS and used by Walter in 2010 is less

specific and therefore less suitable:

“The state should take more responsibility to ensure that everyone is provided for –

People should take more responsibility to provide for themselves”

(World Value Survey, 2014)

This is measured on a ten-point scale with lower values denoting stronger agreement with

more (or full?) state responsibility and higher with more individual (or community?)

responsibility.

The predictor variables

In the model we can distinguish four different predictor variables, of which three of them are

indicators for individual effects of globalization. The first being trade openness, which I have

calculated by the sum of import and export of goods and services divided by the total supply

50

in real prices and in millions of euro’s. This leads to a number between 0 and 1, whereas 0

stands for a fully-closed sector and 1 represents fully openness in trade.

I have found these numbers for the Netherlands on the website of the Centraal Bureau

voor de Statistiek [Central Bureau for Statistics] (Hereafter: CBS) (CBS, 2017a). For the United

Kingdom, I have found these on the website of the OECD (OECD, 2017a) and the website of

the Office for National Statistics (Office for National Statistic, 2011; 2016c).

Each of these numbers are linked to the individual’s sector of employment, which is

found due to the following question:

“What does/did the firm/organization you work/worked for mainly make or do?”

(European Social Survey, 2010, F31)

The answer is coded by a standard European scheme, which is called Nomenclature

statistique des activités économiques dans la Communauté Européenne [Statistical

Classification of Economic Activities in the European Community) (Hereafter: NACE)

(European Community, 2002). The coding of these findings will be explained with every case.

The second predictor variable is FDI openness. This has been a very inconsistent

indictor since its introduction. However, I have chosen for stock levels, while these are more

stable than FDI flows and therefore are of better use for this comparable case study

(Kimakova), 2009).

De Nederlandsche Bank [Dutch Central Bank] did not monitor these figures in detail

until 2013. Therefore, I have used figures from the OECD STAT database online (OECD, 2017b).

However, these are coded by using the Benchmark Definition of Foreign Direct Investment

(BMD) third edition for this period, which is not fully applicable to the coding schemes of the

ESS as we will see further.

Nonetheless, I have calculated the sum of inward and outward stocks and divided this

by the total supply in real prices and in millions of euro’s. This leads to a scale variable just as

trade openness. However, in contrary to trade openness, this number can be smaller than 0,

thus negative, but also larger than 1.

51

The third predictor variable is migration. I have found different figures for this indicator in the

CBS database and divided the number of migrants per sector by the number of total

employees in the sector. Also, this is a scale variable running from 0-1 (CBS, 2017b).

The fourth and last predictor variable is economic insecurity. Again, we can find

indictors in both the ESS and the WVS. But again, the indicator in ESS is more specific and

captures, besides labor market risk, also the element of income inequality. Or as Walter puts

it: “this question gauges both the ease of moving to a different employer (which arguably

strongly reduces the risk of unemployment) and the ease of obtaining an equally or better

paying job (which gets at the wage issue)” (Walter, 2017, p. 62).

So, job insecurity will be the indicator of economic insecurity and is formulated as

follows:

“How difficult or easy would it be for you to get a similar or better job with another

employer if you had to leave your current job?”

(European Social Survey, 2010, G40)

It is measured on a ten-point scale with higher values denoting higher levels of easiness.

However, just like the outcome variable, this variable must be either interpreted as a scale

variable or transformed into a binary variable to be useful for binary logistic regression. This

also will be explained by using the 2010 data from the Netherlands.

Again, to indicate differences with the WVS, I have put the comparable indicator as

formulated in the WVS for the same period below:

“Now I would like to ask you something about the things which would seem to you,

personally, most important if you were looking for a job. Here are some of the things

many people take into account in relation to their work. Regardless of whether you're

actually looking for a job, which one would you, personally, place first if you were

looking for a job (read out and code one answer):

52

1 A good income so that you do not have any worries about money

2 A safe job with no risk of closing down or unemployment

3 Working with people you like

4 Doing an important job that gives you a feeling of accomplishment”

(World Value Survey, 2014, p. V49)

This is followed by a follow-up question (World Value Survey, 2014, p. V50) where the

respondent has to choose one of the ’things’ on a second place. Aside from the fact it is less

specific nor directly formulated, it also has a methodological problem, while an aggregate of

both questions is necessary to receive a complete view. But more importantly, the variable

can only be distinguished in three categories (first, second, neither) instead of the initial four

possibilities. These leads to some major methodological complications when turning these

into a binary variable.

The model

The considerations described above lead to the following model. Keep in mind that whereas

Walter (2010) researches an indirect link between economic insecurity and demands for

compensation, I will consider this as a direct one.

Therefore, I do not have to do a stepwise regression, while a stepwise method: ‘‘takes

many methodological decisions out of the hands of the researcher. What’s more, the derived

model (..) often takes advantage of random sampling and so decisions about which variables

should be included will be based upon slight differences (…) [and] may contract dramatically

with the theoretically importance of a predictor to the model.’’ (Field, 2005, p.161).

Instead, I used three-step block entry method where I would put in the known

predictors, FDI and trade openness first, followed by the experimental predictor migration. I

end the regression with entering the control variables (Field, 2005).

On that note, because predictor FDI openness leads to a serious drop in the number of

researched cases, I have decided to run two regressions: one with and one without FDI

openness. This means that trade openness and job insecurity, will be entered into the model

first, followed by migration openness.

53

Lastly, FDI openness will be entered into the first block next to trade openness and job

insecurity, after which the model will run again. Hence, the model can be written into the

following equation:

𝑃(𝑌) =1

1 + 𝑒(𝛽0+𝛽1𝑋1+ 𝛽2𝑋2+ 𝛽3𝑋3+𝛽4𝑋4+𝛽5𝑋5+𝛽6𝑋6+𝛽7𝑋7+𝛽8𝑋8+𝛽9𝑋9+𝜀𝑖)

𝛽0 = 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 𝑋1 = 𝑇𝑟𝑎𝑑𝑒 𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠 𝑋2 = 𝐹𝐷𝐼 𝑜𝑝𝑒𝑛𝑛𝑒𝑠𝑠

𝑋3 = 𝑚𝑖𝑔𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑝𝑒𝑛𝑒𝑠𝑠 𝑋4 = 𝐽𝑜𝑏 𝑖𝑛𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑦 𝑋5 = 𝐷𝑢𝑚𝑚𝑦 𝑟𝑒𝑙𝑖𝑔𝑖𝑜𝑛

𝑋5 = 𝐷𝑢𝑚𝑚𝑦 𝑔𝑒𝑛𝑑𝑒𝑟 𝑋7 = 𝐷𝑢𝑚𝑚𝑦 𝑢𝑛𝑖𝑜𝑛 𝑋7 = 𝐴𝑔𝑒

𝑋8 = 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 𝑋9 = 𝐼𝑛𝑐𝑜𝑚𝑒 𝜀𝑖 =Error

Indeed, the equation does not represent a simple linear or multiple regression, but is instead

a logarithmic regression, where ‘’instead of predicting the value of a variable Y (…) we predict

the probability of Y occurring (…).” (Field, 2005: p. 219-220).

To do a simple linear regression, my outcome variable must be a scale variable. Indeed,

my ordinal variable is measured on a Likert scale, but I thought it was not without risk to take

a non-parametric ordinal variable as a scale variable. Therefore, I have transformed my

outcome variable demands for compensation is to dichotomous variable which means that:

“the assumption [of linearity] is usually violated” (Field, 2003, p. 220) and chosen for a binary

logistic regression.

54

Netherlands 2010

Introduction

The starting point of the empirical research will be the Netherlands in 2010. I have chosen for

the Netherlands, because it is in line with Rodrik’s research ‘Why do more open economies

have bigger governments’ (Rodrik, 1998). Also, as discussed earlier, Switzerland and the

Netherlands are somewhat similar in GDP per capita, trade dependency and social welfare

system.

Economical context

According to the annual IMF rapport was the Dutch economy slightly recovering from a deep-

recession in mid-2009. I do not think it has relevance to explain the financial crisis in detail,

but according to the IMF, the economic downfall of the Netherlands was ‘’caused by adverse

trade and financial spillover from the global crisis, which also forced large public intervention

in the financial sector’’ (IMF, 2011, p.1).

However, due to strong exports, the Dutch economy was recovering. Also,

unemployment had only risen slightly and external competitiveness looked adequate. Even

for the housing market the econometric did not signal any structural problems or acute

dangers (IMF, 2011).

Looking at the labor market, which is interesting for job insecurity, the IMF makes

recommendations regarding the ageing population. These recommendations are essentially

related to female full-time employment, elderly work and the retirement age. Also, according

to the IMF, are unemployment’s benefits “fairly generous and discourage job search’’ (IMF,

2011: p. 6). I will not discuss this in detail, but consider a minimal understanding of these

issues, while these are common to the current state of Western-European economies.

In general, the economy was performing under its potential, but great structural

budgetary deficits or a sharp rise in financial debt have been avoided. Especially in comparison

with other European countries, such as Greece, was the Dutch economy performing good

under the circumstances.

55

Operationalizing the outcome variable

As described above, I have used data form the European Social survey. The sample for 2010

exists of a total of 1823 cases. However, the indicator inequality policy of my outcome variable

demands for compensation has a total of 14 missing cases, which leads to a maximum of 1815

observable cases. The answers of the respondents have been put in the frequency table

below:

Frequency Percent Valid Percent Cumulative

Percent

Valid Agree strongly 299 16.3 16.5 16.5

Agree 761 41.6 41.9 58.4

Neither agree nor disagree 312 17.1 17.2 75.6

Disagree 385 21.0 21.2 96.8

Disagree strongly 58 3.2 3.2 100

Total 1815 99.2 100.0

Missing Refusal 2 .1

Do not know 12 .7

Total 14 .8

Total 1829 100.0

Table 3. Demands of compensation, Netherlands 2010. Adapted from: ESS, 2016b

To do a binary logistic regression I had to make this indicator dichotomous, meaning changing

it into a binary variable. I have chosen to take the first two categories, ‘agree strongly’ and

‘agree’, together which led to a total of 1016 cases (see table 4). The other category consists

of the three remaining categories. Obviously, this decision is arguable. However, would I have

left out disagree strongly, clearly an outlier, I would not have opposing categories. Leaving out

the middle category would have led to a drop in the observable cases by 312 and, as we will

see further, the N is already doubtfully low. In the end, it was a choice between three evils,

which led to the following dichotomous and thus my outcome variable:

56

Frequency Frequency Percent Valid

Percent

Cumulative

Percent

Valid Agree (strongly) 1060 58 58.4 58.4

Neutral or disagree

(strongly)

755 41.3 41.6 100

Total 1815 99.2 100

Missing Refusal 2 .1

Do not know 12 .7

Total 14 .8

Total 1829 100

Table 4. Binary demands of compensation, Netherlands 2010. Adapted from: ESS, 2016b

As reference category for the outcome variable I took the second category ‘neutral or disagree

strongly’. Consequently, the regression coefficient is linked to ‘agree strongly’, meaning that

a positive regression coefficient12 means that the variable is positively correlated with

demands for compensation.

Operationalizing the predictor variables

The first of my predictor variables is trade openness, which I have created by calculating the

sum of import and export of goods and services divided by the total supply in real prices and

in millions of euro’s. These numbers, GDP by industry and the import and export by industry,

can be found on the website of the CBS and have been coded by using the Standaard

Bedrijfsindeling [Standard Business Classification] (Hereafter: SBI) 2008 coding scheme. This is

the latest version of SBI and is based on NACE (CBS, n.d.).

The ESS uses the NACE revision 2 for this sample and codes the valid answers to the

second digit. These 99 codes are identical to the figures found in the CBS database.

12 Actually, when doing a binary logistic regression, regression coefficients are smaller than 1 (but larger than zero), 1 or larger than 1, see further.

57

For migration openness, this is not the case. I could only find figures for the first digit, which

means that the information is not as detailed13. For example, the main category

manufacturing, which exists of a total of around 25 industries, going from food products, to

chemicals to furniture, all get the same number. However, the introduction of the variable

has not been done before and can be seen as exploratory.

FDI openness has been found by combining the OECD database and the CBS figures as

described earlier. All three of these variables are scale variables. I have coded the public sector

as missing leading to a drop of around 600 cases.

The last of my predictor variables is the subjective indicator of economic insecurity.

However, binary logistic regression does not only need for the outcome variable to be

dichotomous. Predictor variables need to be scale or binary variables as well. My initial

indicator for economic insecurity is measured on a ten-point scale. Thus, the variable is an

ordinal variable, but could have been interpreted as a scale variable.

However, as the results show in the figure below, these are far from normal

distributed. They are in fact extremely negatively skewed (Field, 2005).

Table 5. Economic Insecurity, Netherlands 2010. Adapted from: ESS, 2016b

13 I have had contact with the CBS, but I could not get more detailed information for migrants by industry, while they did not monitor it in detail for this period..

15 1132 27 40 44 54

140

232

115

72

0

50

100

150

200

250

Freq

uen

cy

Answer

58

Therefore, I have chosen to make this variable binary, whereas the middle has been left out.

This leaves us with two categories, of which the former exists of the initial five categories (0-

4) and the latter of the remaining five (6-10):

Table 6. Binary Economic Insecurity, Netherlands 2010. Adapted from: ESS, 2016b

The number of observable cases dropped to a maximum of 694. Partly because I have dropped

44 cases myself, but mostly because the researchers from ESS have had a total of 1047 missing

cases for this particular question. This is not a coincidence as we will see further.

Controlling for the regression I will focus on more mainstream variables such as

dummies for gender and religion. I also made a dummy for trade union membership, by taking

past and present membership together and called it trade union affinity. Reference categories

are respectively man, religious, trade affinity. Also, I will use scale variables age, gross pay and

years of education (Ram, 2009).

Results without FDI

The results for the Netherlands are shown in appendix A. It seems that job insecurity is always

significant. However, trade openness is never. Neither is migration openness. Furthermore,

309

385

0

50

100

150

200

250

300

350

400

450

Difficult Not difficult

Freq

uen

cy

Category

59

the expected B shows a negative relationship for these three variables and demand for

compensation.

Keep in mind, that the reference category of my outcome variable is the category ‘neutral and

disagree strongly’. This means that the regression coefficients are related to the category

‘agree (strongly)’. Furthermore, a regression coefficient of below 1, means that the odds of

the outcome variable decreases while, the expected B is the: “change in odds resulting from

a unit change in the predictor” whereas the odds: “are defined as the probability of an event

occurring divided by the probability of that event not occurring” (Field, 2005, p. 225):

∆odds =odds after a unit change in the predictor

orginal odds

(Field, 2005, p. 225):

In plain English, this means that an increase in trade openness by one, leads to a decrease in

the chance of having more demands for compensation. The same implies for migration

openness, FDI openness and the scale variables age, pay and education.

However, for dummy variable this a bit more difficult. The regression coefficient is

related to both categories. This can be illustrated by looking at the other dummy variables.

For religion, the exp B is bigger than 1 (app. 1,5) meaning that people who are not religious

[in reference to those who are] tend to have more demands for compensation.

Or to put in other words, non-religious people tend to have more affinity with

government compensation. The same applies for people who do not have affinity with trade

unions, while the exp B shows a negative relationship between not or never members of trade

unions [in reference to those who are or were] and demands for compensation.

Nonetheless, for job insecurity the negative relationship actually implies a positive one.

The reference category for job insecurity is more ‘difficult[ies]’ to change to another or better

job. This means that the regression coefficient is related to ‘not difficult’. As we already now,

the regression coefficient for the outcome variable demands for compensation is related to a

rise in demands for compensation.

So, a negative relationship between job insecurity and demands for compensation,

means that when people perceive less barriers to changes to similar or better jobs, their

60

demands for compensation tends to decline. Or, the other way around, when it tends to be

more difficult, their demands for compensation rise.

Most striking is that trade openness is not significant. This can mean one of three things. First

of all, the data can be misinterpreted. The number of observable cases have been dropped to

a total of 410, which means that a lot of information has been lost. Secondly, it could be that

there is no relationship between trade openness and demand for compensation. This has been

argued by defenders of the efficiency hypothesis. However, we can find counter arguments

for different periods of time and with different samples.

So thirdly, there could be multicollinearity, meaning that two or more variables are

(highly) correlated and therefore leading to misleading information. Therefore, I have run a

collinearity diagnostics, using linear regression, while SPSS does not have the possibility to

check for multicollinearity for logistic regressions. The results are presented in appendix C.

Multicollinearity

According to Field (2005) there are no hard and fast rules for analyzing these values. However,

there is a universal agreement that the eigenvalues have to be somewhat similar for the model

to be accurate. The second column shows the condition index and ‘’is the squared root of the

ratio of the largest eigenvalue to the eigenvalue of interest’’ (Field, 2005 p.261). Both columns

show that these values tend to be dissimilar towards the end. This means that ‘’the solutions

of the regression parameters can be greatly affected by small changes in the predictors or the

outcome’’ (Field, 2005 p.261).

The rest of the table exists of the variance proportions of each variable for every

eigenvalue, which can be more easily understand when multiplied by a hundred and

interpreted as a percentage. We can see that more than 90 percent of the variances of the

variables age and education are present in the aberrant eigenvalues 8 and 9. This could

indicate there is some form of multicollinearity. However, it does not say anything about the

direction of this relationship or causality in general (Field, 2005).

Therefore, I ran a correlation matrix (not shown), which showed that both variables

were significantly correlated with every other variable. Moreover, income is also significantly

correlated with many of the variables. Also, I did a logistic regression without one or more of

61

these variables, which showed that trade openness was significant, but only when income is

not included.

The results are presented in appendix B. I have also included the normal B and lower and

upper bounds for the 95% level. When these bounds are near the exp B, it is an indication that

the exp B is a good indicator for generalizations. (Field, 2005). When these parameters cross

1, it means that it is unclear whether the relationship is a positive or a negative one.

This can be illustrated by the data for migration openness. The lower bound is a nearly

perfect negative relationship, while the upper bound exceeds the exp b by far. This means

that it is unclear which direction the relationship tends to.

The table also shows that the constant and my predictor variables are (highly)

significant. Again, trade openness is only significant when income is not included. When (both)

age or education are included, it leads to moderate differences from the figures presented

(n.s.). Nonetheless, I ran a pearson r test (n.s.) and find a less than moderate (app. 0.15) and

not significant relationship between those variables. However, trade openness is not

parametric while it is linked to sector industrial employment. Therefore, I did a non-

parametric correlation test in the form of Spearman’s rho, which shows a significant positive

relationship (0.108) between those variables.

Before I proceed, I need to stress that dismissing one, in this case even three variables,

as being omitted is a bold move. Another option is to gather more data, which I’ve done by

analyzing data for the Netherlands in 2004. But first I will present briefly the data for the

Netherlands in 2010 with inclusion of FDI openness.

Results with FDI

The tables (Appendix D & E) are presented below. As described above, FDI has been a

challenging indicator to integrate in this framework. This is also reflected in the number of

observed cases which dropped to 159 cases, which is less than ten percent of the initial

sample. However, because job insecurity only has 650 observable case, the number of cases

has dropped to around a quarter.

With the inclusion of FDI, only religion is still significant. Moreover, for all the variables

except income it is unclear if the relationship between them and the output variable are either

62

positive or negative, while their lower and upper bound cross 1 (n.s.). Especially for migration

openness, which has a lower bound of 0, meaning a perfect negative relationship and an upper

bound in the billions – what looks like a way for SPSS to express infinity.

Therefore, I run a regression without migration openness (n.s.) which led to better results,

however still with inconsistencies. Nonetheless trade affinity became significant again.

I have also run a regression without the three possible omitting scale variables (infra),

but with inclusion of migration openness and which led to the second table. Here also, I have

presented the lower and upper bounds for each variable, which shines light on what I

mentioned above.

The table (Appendix E) is reasonably comparable to the table (Appendix B) without FDI.

The introduction of migration openness leads to trade openness losing its significance.

However, introducing the control variables undoes this again. Most of the lower and upper

bounds are comparable, except for trade openness which upper bound doubles and for

migration openness which tends to rise exponentially.

What is most interesting is that the introduction of FDI openness leads to job insecurity

losing its significance. Moreover, I ran a Spearman rho correlation test, which shows a

negative but almost negligible non-significant relationship between these variables (app. -.04).

Hence, where the introduction of income could indicate some form of multicollinearity,

correlation between FDI and job insecurity seems absent.

This could mean that the drop in observed cases has led to less accurate calculations,

or that the corrupted data leads to misleading conclusions. Another possibility is that FDI is a

better predictor of demands for compensation. Lastly, FDI openness leads to less demands for

compensation, but the strength is less than for trade openness14.

14 This has been pretty fast forward, but I wanted to make clear the difficulties and the uncertainties this research faces,

while not so much is known about these processes and not much field work has been done. I will summarize the highlights

after showing the results for the UK, but first I will present the Dutch survey of 2004 to compare those findings with the

above.

63

Netherlands 2004

Economical context

The IMF wrote in their conclusions of their annual report that ‘’The Netherlands hit a difficult

stretch going into the new millennium’’ (IMF, 2005, p.1). The economy was stagnating in the

2000-2003 period and even turned ‘’negative on a year-average basis in 2003 for the first time

in twenty years’’ (IMF, 2005, p.1). This was due to labor market tightness and a weakened

international environment as a result of a rise in the euro and an increasing inflation, which

deteriorated the competitiveness of the Dutch economy (IMF, 2005).

Tax and subsidiaries reforms and also structural adjustment to tackle problems in

regard to the ageing population, are some of the recommendations the IMF makes. I would

like to refer to their paper for deeper understanding of these issues. Worth highlighting is two

recommendation they make with reference to the tightened labor market (IMF, 2005).

First of all, they notice a considerable labor reserve that is unused, which was

‘’especially the case with respect to disability, a long acknowledged haven of hidden

unemployment’’ IMF, 2005: p.1). Moreover, they urge the Dutch government to support

legislation in favor of deregulation and flexibility of the labor market to stimulate labor

mobility, while ‘’ [the employment protection legislation] is (…) considered among the most

restrictive in Europe, acts as a tax on hiring and can stand in the way of the reallocation process

and entrepreneurial activity’’ (IMF, 2005: p.1).

Finally, innovation falls behind and improvement of innovation needs to go hand in

hand with more flexibility. Whether or not these improvements have led to innovation is not

part of this thesis, but the Dutch government released a statement las year that the

Netherlands transformed from an innovation follower to one of the innovation leaders in

Europe, according to the European Innovation Scoreboard (Rijksoverheid, 2016).

Introduction to the data

The same transformation of the data has been used as for the 2010 survey. Also, most of the

data sources are the same for the data of 2004 and 2010. However, the classification used by

ESS for the 2004 study is NACE 1.1 instead of NACE 2. Therefore, I have recoded the data for

64

trade openness, which led to a slightly different categorization and a loss of a few more cases.

Also, FDI has been recoded differently, which led to more differences than for trade openness.

For migration openness, this has not been a problem, while I found data matching the

industrial code NACE 1.1 used by ESS.

The frequencies of the variables are slightly different than for the 2010 survey. For

most variables, the differences were between the three percent error margin. For example,

the respondents were slightly older in 2010 and on average had have more education. Both

are not surprising when keeping in mind that also the Netherlands have a tendency towards

population ageing (IMF, 2005) and low-educated people, meaning just primary school, are

becoming more scarce. Remarkable is the fact that the outcome variable demands for

compensation is almost identical to the 2010 survey. Either before and after recoding it into a

binary variable.

On that note, a few differences were interesting. The data of the ESS survey contains

more male respondents than the 2004 data (45,7 to 41,7 percent). Also, I have classified up to

six percent point more people as being religious in the 2004 data. Most interesting was the

fact that the predictor variable job insecurity was mirrored in comparison to the 2010 survey,

see figure. The same variable had respectively 309 and 385 in the 2010 data. Which means

that people felt more difficulties to change to a better or similar job in 2004 than in 2010,

which feels odd, while we would expect that during the crisis the possibilities of changing

careers would be abated.

Table 7. Binary Economic Insecurity, Netherlands 2004. Adapted from: ESS, 2016a

372

308

0

50

100

150

200

250

300

350

400

Difficult Not difficult

Freq

uen

cy

Category

65

Nonetheless, a possible explanation can be found in the rigid labor market and the barriers

common to the Dutch economy for this period that discouraged labor mobility as described

by the IMF (IMF, 2005).

Results without FDI

The results are shown in appendix F. The results differ from the 2010 results. None of the

predictor variables is significant. Remarkable is also that trade openness has a slightly positive

relationship with demands for compensation. I would emphasize slightly while it is very close

to one (1.042). Also, the lower and upper bounds are extremely narrow, but crossing 1

meaning that it is unclear whether the relationship is either positive or negative (n.s.). The

same applies for job insecurity.

Just like the results for 2010, migration openness is not significant. Still the exp B is

exceptionally high and not only crosses the lower and upper bound 1, but again are covering

a wide range (n.s). Moreover, trade openness is not significant as we have seen before, but

the introduction of migration openness leads to a drop in the expect B. Also for migration and

trade openness variables can we find a significant correlation in the 2004 data, but curious is

the fact that this relationship is negative in contrary to the 2010 survey.

When controlled for the other variables including the scale variables, we find that

dummy variables religion and union affinity are once again significant correlated. Also, the

relationships tend to have a comparable, for religious almost identical strength.

Finally, age and education are significant. Moreover, their bounds seem very strict.

Multicollinearity

I also ran a regression without the scale variables. The results are shown in appendix H. The

first step is more or less comparable with the 2010 output, except for trade openness. But the

introduction of migration openness leads to some interesting results.

First of all, trade openness is not significant anymore as we have seen before.

Moreover, and for the first time migration openness becomes significant. Still, lower and

upper bounds are again very much divergent and crossing 1. Also, the relationship is extremely

negative.

66

The introduction of the categorical control variables changes the relationships again. Trade

openness becomes significant, but still is negative correlated. Migration openness is not

significant anymore (significance: 0,111). Trade affinity is significant, but religion is not. Also,

gender becomes significant.

I ran both regressions again without migration openness but the results were

comparable with the tables aforementioned.

Results with FDI

The N drops to 178 observed cases (Appendix I). Again, the results are similar and dissimilar

at the same time. With inclusion of the scale variables only job insecurity is significant. This is

contrary to the 2010 survey, where job insecurity was not significant, but religion was. Trade

openness keeps its positive correlation and also FDI is positively correlated with demands for

compensation. However, both are close to 1.

Furthermore, almost every lower and upper bound cross 1 and somehow the

relationship between religion and the outcome variable is mirrored in comparison with the

2010 results. Here we find that non-religious people have less demands for compensation

than religious people have.

Without the scale variables, trade openness is significant even with the introduction

of migration openness. Also, union affinity is significant again.

The addition of FDI also means that migration openness is in neither of the regressions

significant. Furthermore, the exp B of trade openness remains around 1.05, even when I ran

the regression without migration openness. Also, without migration openness job insecurity

stays significant in both regressions (with and without scale variables). However, trade

openness is only significant in the regression without the scale variables.

Lastly, I have run a linear regression analysis to test for multicollinearity. The results

are not shown, but were reasonable comparable with the table for the 2010 data.

67

United Kingdom 2010

Economical context

The UK also experienced a downturn of the economy due to the financial crisis. However,

growth was still around two percent and projection of the early years in the second decennium

were just slightly lower. Furthermore, most policies recommended by the IMF, are related to

the banking sector. Obviously, this is due to the fact that the UK government has one of the

biggest financial districts in the world and therefore was facing one of the greatest possible

risk, in regard to the financial world. Other recommendations are related to what we have

seen with the Netherlands, mostly in the form of structural reforms to catch potential harm

of an ageing society (IMF, 2010b).

Operationalizing the outcome variable

As for the Netherlands, the indicator for the outcome variable demands for compensation can

be found in the European Social Survey. However, more than four times the amount of people

has failed to answer the question ‘’Should the government reduce difference in oncome

levels’’. Still, with 2344 out of 2422 observable cases it is still a good result.

The answers have been presented in the frequency table below:

Frequency Percent Valid percent Cumulative

percent

Valid Agree strongly 366 15.6 15.6 15.6

Agree 1090 45 46.5 62.1

Neither agree nor

disagree

426 17.6 18.2 80.3

Disagree 387 16 16.5 96.8

Disagree strongly 75 3.1 3.2 100

Total 2344 96.8 100

Missing Do not know 78 302

Total 2422 100

Table 8. Demands of compensation, United Kingdom 2010. Adapted from: ESS, 2016b

68

It is interesting to see that most of these results are comparable to the Dutch survey.

Nonetheless, it seems the British have tendency towards more government intervention to

reduce the income inequality.

When we transform the variable into a binary variable we get the following

classification:

Frequency Percent Valid

Percent

Cumulative

percent

Valid Agree

(strongly)

1456 60.1 61.5 62.1

Neutral or

disagree

(strongly)

888 36.7 37.9 100

Total 2344 96.8 100

Missing Do not know 78 3.2

Total 2422 100.0

Table 9. Binary demands of compensation, Netherlands 2010. Adapted from: ESS, 2016b

Hence, whereas the Dutch binary variable was coded 58.4/41.6, we notice that for the British

in the same period the balance is almost two-third/one-third.

As for the Netherlands, the reference category for this variable is the second one,

meaning that the regression coefficients are directly linked to the category ‘agree strongly’,

thus demands for compensation. So, an exp B greater than 1 indicates a positive relationship

and an exp B smaller than 1 indicates a negative one, as we have seen before.

Operationalizing the predictor variables

The three scale predictors have been formulated identical to the predictor variables earlier.

However, the British does not seem to be as disciplined as the Dutch when it comes to

presenting basis economic statistics – or they have other things on their mind.

69

In the end, I have used numbers of GDP by industry from the website of the OECD

(OECD 2017a; 2017, b), which were coded by ISIC revision 4. Trade in goods have been found

in the same database, but they were presented in dollar, so I have used an estimate for the

exchange. However, for trade in services I have used the Pink Book, which is an annual report

of the UK government in the form of time series for the balance of payments (ONS, 2012) and

the Detailed tables by service category: 2006-2010 (OECD & Eurostat, 2012) and Detailed

tables by service category: 2002-2008 (OECD & Eurostat, 2010).

Trade openness is calculated by the sum of import and export of goods and services

divided by the total supply in real prices and in millions of euro’s. But, due to merging of

different database, much of the manufacturing sectors had an openness of above 1. I have

coded these as 1.

Migration openness was also a tricky indicator. It was unclear for me whether the UK

government monitors migrants in the workforce periodically. I have filled in a form, but did

not receive any response. What I could find were figures for 2007 and 2014 from so called

user requested data. Also, I found overall figures of the British labor force including migrants

for the year 2010 (ONS, 2015).

It is interesting to see that the number of migrants from outside the European Union

entering the British labor force remains stable and is relatively even declining. The inflow of

migrants from within the European Union on the other hand is steeply rising. This is especially

true for people from Eastern Europe, such as Poland. Furthermore, the labor force

experienced a decline in the second half of the first decennium.

Therefore, and because a serious drop in the first two years of the Financial Crisis, the

labor force was about as large in 2005 as it was in 2014. However, the number of migrants

was not.

For this reason, I have calculated the average annual growth rate for migrants from the

European Union for the period 2007-2014. This was a surprisingly 11% per year. Nonetheless,

I am interested in the total share of migrants. So, due to the high inflow of European migrants,

the overall amount of migrants grew annually on average with 3,5%. Both numbers have been

controlled for by the overall figures of 2010.

70

I have applied the growth rate in reverse on the 2007 data per industry to get an

estimated amount of migrants per sector in the UK for 2005. Also, I have applied the difference

between 2005 and 2014 in overall labor force, for every sector in the figures of per sector

workforce for 2005. The difference was less than 1%. In this way, I also estimated the numbers

for 2010.

The last predictor is job insecurity, the indicator for economic insecurity. As I did for

the Netherlands, I have produced a diagram to present the answers of the respondents. The

total amount of valid responses is 949, which means that about 40% of the initial 2422 sample

remained.

Moreover, where the results of the Netherlands in 2010 were negatively skewed, we

see that for the UK this is almost normally distributed. Consequently, this means that leaving

out the middle category (5) to obtain the same binary variable, the number of observable

cases drops with 134 cases. This is another 14% of the 949 valid cases for this variable, in

contrary to ‘just’ 6% for the Netherlands in 2010.

Also for this binary variable the reference category is the first category ‘difficult’.

Therefore, a positive regression coefficient means that when people perceive its more easy

[relative to those who do not] to change to another similar or better job, their demands in

compensation rise. Obviously, this is contrary to our intuition and the empirical results we’ve

found earlier. Therefore, we hope to find a negative relationship, meaning a regression

coefficient smaller than 1 and thus that when people feel it is difficult to change to a better

or similar job, their demands for compensation rise.

71

Table 10. Economic Insecurity, United Kingdom 2010. Adapted from: ESS, 2016b

Table 11. Binary Economic Insecurity, United Kingdom 2010. Adapted from: ESS, 2016b

39

69

98 10084

134

102119 126

39 39

0

20

40

60

80

100

120

140

160

Freq

uen

cy

Category

390425

0

50

100

150

200

250

300

350

400

450

Difficult Not difficult

freq

uen

cy

Category

72

Results without FDI

As for the Netherlands, I have run four regressions, one including FDI and one without FDI and

both with and without the scale variables.

The results without FDI (Appendix J, K)) show that trade openness is significant in every

step and, in contrary to the Netherlands, even when the scale variables, thus income, are

included. Furthermore, the regression coefficient is again below 1, meaning that trade

openness is negatively correlated with demands for compensation. Moreover, the lower and

upper bounds do not cross 1, which means that the results are not ambiguous.

Another interesting result is the fact that income is significant, but does not seem to

affect the significance of trade openness. Remarkable is also the fact that none of the dummy

variables are significant in neither of the regressions. Moreover, the upper and lower bounds

of every variable crosses 1, except for trade openness, where either lower and upper bound

are also below 1 (0.2-0.9).

This is especially true for migration which upper and lower bound are again very

divergent. Additionally, the results show a positive correlation between job insecurity and

demands for compensation, meaning that lower job insecurity leads to higher demands for

compensation. This is contrary to the intuition and the overall results of the Netherlands.

Results with FDI

The results with FDI are not that different from the above. Again, trade openness is significant

and negatively correlated. Also, income is significantly correlated. Furthermore, all the other

variables have non-significant relationships and their lower and upper bounds, as well as their

regression coefficients, are almost identical to the results above.

73

United Kingdom 2004

Economical context

In contrary to the Dutch economy, the British were growing fast into the new millennium. The

IMF seems impressed with the figures the UK economy can present. Recommendations are

related to high conjuncture, such as preventing housing market bubble. Worth mentioning

though, is the low productivity of labor in the United Kingdom, which is on average 10 percent

lower than in surrounding economies. Also, reforms regarding pension and retirement age are

encouraged (IMF, 2004)

Introduction to the data

As for the Netherlands, we can find similarities and differences between the 2004 and the

2010 data. First of all, the sample is significantly lower for the 2004 data. The survey consists

of 1897 observable cases in contrary to the 2010 survey, which consisted of 2422 cases. The

percentage of woman and man is almost identical to the 2010 survey. The same applies for

the amount of people who are or were member of any trade union. Moreover, the results for

the outcome variable is almost identical to the 2010 survey. This was also the case for the

Netherlands, where we could find almost identical findings for the question in both surveys.

However, where only 45.3% of the people thought of itself as belonging to a religion

or denomination in 2010, this is 50.7% in the 2004 survey. Furthermore, the predictor variable

job insecurity is also different in comparison to the 2010 data. Apparently, people thought it

was more easy to change to similar or better jobs in 2004 as it was in 2010.

All of the variables have been coded the same way as it was for the 2010 study and for

both surveys of the Netherlands.

74

Table 12. Economic Insecurity, United Kingdom 2004. Adapted from: ESS, 2016a

Table 13 Binary. Economic Insecurity, United Kingdom 2004. Adapted from: ESS, 2016a

1434

4759 50

110

71

138115

60 51

0

20

40

60

80

100

120

140

160

Freq

uen

cy

Category

204

435

0

50

100

150

200

250

300

350

400

450

500

Difficult Not difficult

Freq

uen

cy

Category

75

Results without FDI

For the first regression, which includes the scale variables, the results show again a significant

correlation between income and the outcome variable (Appendix N). Furthermore, the results

show a positive and significant correlation between migration openness and demands for

compensation, meaning that more migration openness leads to rise in demands for

compensation. Even when controlled for the control variables including the scale variables.

This in contrary for the Netherlands 2004 results, where migration openness was once

significant, but only without FDI and any of the control variables (Appendix G).

However, without the scale variables, migration openness is (just) not significant

anymore. Furthermore, only gender is significant and positively correlated. The reference

category for this dummy variable was man, which means that a positive correlation indicates

that women have more demands for compensation.

Last but not least, trade openness is not significant. Even more, the lower and upper

bound of the regression without the scale variables are more divergent and crossing 1

extensively, meaning that the relationship is ambiguous. However, the results for trade

openness with the scale variables are more in line with the 2010 results of the United

Kingdom.

Results with FDI

In every step of both regressions including FDI, is migration significant. Also, the relationship

is clearly positive, while the lower and upper bounds are above 1. However, they are

extremely divergent.

FDI is not in any of the regression significant. Neither are the dummy variables.

Nonetheless, income is significant, with again very strict bounds. Also, trade openness is

significant, but only when not controlled for any of the control variables. However, and in

contrary to the Netherlands, trade openness is significant next to migration openness.

76

Synthesis and literature

Economic insecurity

In contrary to the original model, I have tested for job insecurity as an independent variable

next to the objective individual effects of globalization. The results were inconclusive;

however, some conclusions can be drawn.

For the 2010 data of the Netherlands, we see that job insecurity is significant,

whenever FDI was absent. As mentioned before, an indicator for FDI was hard to find.

Furthermore, FDI in the public sector is absent. Therefore, I coded these as missing. This could

have influenced the indicator for job insecurity, while people working in the public sector tend

to have high job security relative to those working in the private sector. However, why this

does not affect the indicator in the 2004 period remains unclear.

Scheve and Slaughter (2004) were the first and until recently the only one, who

analyzed the relationship between FDI and economic insecurity. They concluded that FDI had

some negative influence on economic insecurity, but results were mixed. Nonetheless, the

relationship between the variables in the 2010 survey were negative, but not significant.

Besides, we found a negative relationship between FDI and demands for compensation, but a

positive one between job insecurity and demands for compensation.

For the 2004 it is a different story. Job insecurity is always significant, except for the

regression without FDI and with scale variables. However, as aforementioned, a possible

explanation is the multicollinearity that exists. Also, this is the first and only time when the

scale variables income and age are significant.

Moreover, job insecurity is not only influenced by external, or foreign factors as

mentioned by Scheve and Slaughter (2004). Also, domestic elements such as legislatures play

a role as we have seen (IMF, 2005). This is especially true for the first years of this millennium,

and consequently during the period stretching the 2004 survey. The rigid labor market and

the lack of labor mobility for this period could therefore be put forward as an explanation why

job insecurity is a better predictor than during the 2010 survey.

All the above is in contrary to the United Kingdom. Never, not even almost, is job

insecurity a significant predictor, however ‘’according to the OECD’s indices, employment

77

protection legislation is only slightly more restrictive in the UK than it is in the US or Canada,

and less so than in Australia. It is, of course, much less restrictive than in continental European

countries like France or Spain’’ (Centre for European Reform, 2016).

However, Walter (2010) found a significant relationship for her cross-country study

between economic insecurity and demands for compensation, but results differ between low-

and high-skilled workers. The same can be found with Chung & Mau (2014).

This could mean that the relationship is not only influence by institutions and different

facets of openness, but also between workers with a different skillset. This is also in line with

the Heckscher-Ohlin theorem. However, this was not part of this research.

Trade openness

The most debated concept in the literature is probably trade openness. Unfortunately, the

results are as inconclusive as its debate.

The results show a significant result between trade openness and demands for

compensation, but only when income is not included. Therefore, income could be a better

predictor for demands for compensation, but it is almost never significant. This is also

suggested by Garret (2001) on the macro-level. Another possibility is that the significant

correlation between income and trade openness (as to almost every other variable) leads to

misleading information. It is therefore difficult to draw hard conclusions.

Interesting though is the fact that regressions with FDI lead to the same results as

without. Critics of the compensation thesis have a tendency of replacing trade openness for

FDI openness, but it seems that both predictors can exist together.

Most strikingly however, is the fact that for the 2010 survey, trade openness is

negatively related to demands for compensation for both the UK as for the Netherlands.

Moreover, for the UK, trade openness is always negatively correlated.

This is not the first time that we have seen these results. Nonetheless this is contrary

to the 2004 data, but could for the Netherlands be explained by the financial crisis.

The IMF reports that the Dutch economy’s performance was under its potential during

this period of time. However, the export due to its relative competitiveness was making up for

some of this downturn. Therefore, it is possible that employees working in tradables are

78

perhaps exposed to more external risks, but perceived a better performance relative to other

sectors in the Dutch economy and therefore needed less compensation.

I have found figures on the World bank (2017c) website that shows the percentage

share of trade in the country’s overall GDP. Even during the first few years of the crisis, trade

shares were growing for both countries. This is also in line with the Ricardo-Viner model,

where we saw that especially for postindustrial countries, people in comparative advantage

sectors are more in favor with free trade.

This asks for further research, possible in the form of time-series to capture these

issues. However, trade openness seems to have a relationship with demands for

compensation, but in contrary to the compensation thesis its negative.

FDI openness

I have run separate regressions for both countries and both periods with and without FDI. For

the Netherlands 2010 survey we can find a slightly negative and significant relationship

between FDI and demands for compensation. This is in line with the efficiency hypothesis, that

states that government are unable to expand their social welfare system due to financial risks.

We can find similar regression coefficients for the 2004 survey and the UK. However,

none of these relationships were a significant predictor for the outcome. Interesting though,

is the regression coefficients are less strong in the 2004 than in the 2010 crisis. Moreover, FDI

is even significant for the Netherlands 2010 survey.

Nonetheless, FDI was problematic to find. To find data for the 2004 and 2010 survey, I

have uses the OECD stat database, where I find figures coded as BM3. However, there were

more mismatches for the 2004 survey than for the 2010. Furthermore, for a part of the sectors

figures were not able to find, which means that this has led to some major methodological

issues as described above.

Therefore, concluding for the data used and the results presented, I am unable to find

a causality between FDI openness and demands for compensation. However, there are

indications of some sort of relationship. Further research on the micro level is therefore

needed, to gain a better picture. Lastly, standardization of the concept and its indicator would

benefit the research, while the lack of standardization leads to difficulties for overall

comparison. I would suggest a more stable indicator than in some previous research in the

form of, but necessary, FDI stocks.

79

Migration openness

The introduction of migration openness has led to some interesting results as we have seen.

For the 2010 data, migration does not seem to be a significant predictor of the outcome

variable. Possible due to the financial crisis, which led to a downturn of the economy and

consequently household’s income. However, for the 2004 period, it looks totally different.

This applies for both the Netherlands and the UK.

Whereas for the Netherlands, migration was almost significant and even was without

control variables. For the UK, it was at almost every step. In the literature, we can see why this

is not surprisingly, whilst people’s most important issues were immigration and terror, after

healthcare in the first years after the 9/11 terror attacks. This changed dramatically after the

outburst of the financial crisis in 2007 (Figure 4).

Nonetheless, while the UK went in a more stable phase economically speaking,

immigration came back on the agenda and was one of the main themes during the campaign

in the build towards the 2016 referendum about ‘Brexit’ (Independent, 2017).

The same applies for the Netherlands, where immigration became again a theme

during the so-called migration crisis of 2015. Therefore, it is interesting if the results of 2004

also apply for this period. However, the ESS does not use the same questionnaire after the

2010 survey and so I was unable to do the same analysis for this period.

Nonetheless, a similar case study should be possible and I would support this

thoroughly. Especially a more detailed research in regard to sectoral classifications.

Control variables

The control variables seem to correlate in the same way as we have seen before (Rehm, 2009;

Walter, 2010). Union membership and religion play a significant role in demands for

compensation, unless the fact the variable religion is sometimes ambiguous. At least for the

Netherlands. For the UK, it does not, which perhaps could be explained by their social welfare

system and the fact that the influence of the trade unions has serious declined during the

Thatcher Era. (Thelen, 2012)

Furthermore, income plays also an important role in shaping preferences towards income

equality. Surprisingly though, education did not so much. Age, as we have seen with Ram

(2009) almost never plays a role, as did gender.

80

However, the regression coefficients are especially for the dummy variables, as we see

with Walter (2010). Woman, often in economic weaker position (Walter, 2010) have a

tendency towards more demands for compensation. The same applies for people who are or

were member of trade unions.

81

Conclusions and discussion

In this dissertation, I have tried to explicate the compensation thesis as proposed by Rodrik

(1998) on the micro level. Where I started with the idea of testing this brilliant and simple

theory, I ended up looking at individual preferences towards demands for compensation on

the border of politics and economics. This has shed some insights.

First of all, it looks like trade openness does not have a positive effect on demands for

compensation. Or at least, for the aggregate of demands for compensation per sector in

postindustrial countries. In fact, it looks that, especially during periods of crisis, the

relationship is significant but negatively correlated. This could not only be linked to other

empirical findings, but also to the literature. Especially to the Ricardo-Viner model.

Secondly, job insecurity also plays a role in shaping demands for compensation.

Interesting is the fact that during the financial crisis, people did not feel worse off than before.

This is not in term of income, but in term of labor market mobility. Further research is

necessary for better understanding of these issues and to distinguish between low, middle

and high-skilled workers.

Thirdly, it seems that labor market competition between native and migrant workers,

also play a role in shaping demands for compensation. Especially for the UK. This could

possibly be traced back to perceptions towards immigration. However, more research is

necessary whether perceptions play a role, while this dissertation only highlights the

importance of the issue in general.

Lastly, the role of FDI. It seems that its role in the literature is ambiguous and fiercely

contested. I have found several indications for some form of correlation, but results were

inconclusive. Perhaps, this was due to the lack of cases. However, I still support a more stable

indicator for FDI, than we have seen in previous research.

Some critical notes in regard to my own dissertation. The lack in distinctions between

workers could have been an interesting facet. However, I have chosen to do a comparative

study between the United Kingdom and the Netherlands and was afraid that adding more

features would have overcomplicated things, and would draw the attention away from the

original concept. With the introduction of migration openness, I already tried to renew old

82

concepts and integrate a relevant issue of the present. Nonetheless, a more diversified sample

would have my utmost support.

There are other facets of recent work that also could integrate in further research. For

example, controlling for the public sector or unemployment as Walter did (2010). Moreover,

to extend the research from OECD countries, to middle or low income countries can also shed

light on theoretical differences.

In general speaking, the compensation thesis is possible not applicable as a universal

rule, but is in the origin a simple and brilliant idea where many facets are undiscovered. And

with the new possibilities in regard to big data can only be encouraged to be researched.

83

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Appendix A

Determents of demands for compensation: Netherlands 2010, without FDI, with scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI fr boundaries is 95% R1= 2.4% R2= 2.5% R3= 6% N = 410

Variable Step 1 Step 1 Step 2 Step 3

Dichotomous Job Insecurity 0.628** (0.203) 0.621** (0.204) 0.659** (0.214)

Trade openness 0.406 (0.620) 0.628 (0.628) 0.376 (0.669)

Migration openness 0.274 (2.218) 0.582 (2.290)

Religious 0.484* (0.222)

Man 0.883 (0.217)

Affinity Union 0.564** (0.229)

Age in years 0.998 (0.009)

Years of education 0.986 (0.029)

Income 1.000 (0.000)

Constant 1.135 (0.099) 1.596*** (0.163) 1.696*** (0.193) 2.579 (0.731)

95

Appendix B

Determents of demands for compensation: Netherlands 2010, without FDI, without scale

variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 3.2%, R2= 3.2 %, R3= 5.7% N = 581

B (SE)

Lower

Exp B

Upper

Step 1

Constant

0.541**** (0.136)

1.718

Dich Job Insecurity

-0.532*** (0.171)

0.420

0.587

0.820

Trade openness

-0.986** (0.488)

0.143

0.373

0.972

Step 2

Constant

0.567**** (0.158)

1.763

Dich Job Insecurity

-0.535*** (0.171)

0.419

0.586

0.819

Trade openness

-0.952* (0.498)

0.145

0.386

1.025

Migration openness

-0.602 (1.857)

0.014

0.548

20,845

Step 3

Constant

0.785**** (0.250)

0.456

Dich Job Insecurity

-0.491*** (0.173)

0.436

0.612

0.859

Trade openness

-1.126** (0.525)

0.116

0.324

0.907

Migration openness

-0.025 (1.892)

0,024

0.976

39,770

Religious

0.229 (0.182)

0,879

1.257

1.797

Man

-0.124 (0.178)

0,623

0.884

1.253

Affinity Union

-0.530*** (0.182)

0.413

0.589

0.841

96

Appendix C

Correlation table: Netherlands 2010, without FDI

Model Eigen

value

Condition

index

Constant Job

Insecurity Religious Man Trade

openness

Migration

openness Age Education Income

1 5,568 1,000 0,00 0,01 0,01 0,01 0,01 0,01 0,00 0,00 0,00

2 0,979 2,385 0,00 0,00 0,00 0,07 0,19 0,01 0,00 0,00 0,51

3 0,918 2,463 0,00 0,00 0,00 0,03 0,36 0,03 0,00 0,00 0,41

4 0,502 3,330 0,00 0,16 0,00 0,02 0,18 0,62 0,00 0,00 0,00

5 0,396 3,747 0,00 0,45 0,00 0,43 0,20 0,03 0,00 0,00 0,02

6 0,342 4,037 0,00 0,16 0,36 0,30 0,02 0,17 0,01 0,00 0,00

7 0,216 5,078 0,01 0,09 0,59 0,07 0,02 0,08 0,08 0,03 0,02

8 0,064 9,341 0,00 0,10 0,00 0,01 0,00 0,01 0,44 0,47 0,01

9 0,017 18,281 0,99 0,03 0,03 0,07 0,03 0,05 0,46 0,50 0,01

97

Appendix D

Determents of demands for compensation: Netherlands 2010, with FDI and scale variables

Variable

Step 1

Step 2

Step 3

Dichotomous Job Insecurity

0.303 (0.327)

0.695 (0.330)

0.646 (0.366)

Trade openness

0.151 (0.765)

0.459 (0.789)

0.055 (0.856)

FDI Openness

0.271 (0.141)

0.824 (0.144)

0.583 (0.152)

Migration openness

0.000 (8.702)

0.000 (9.374)

Religious

1.255** (0.390)

Man

0.323 (0.388)

Affinity Union

0.253 (0.055)

Age in years

0.966 (0.016)

Years of education

0.874 (0.055)

Income

1.000 (0.000)

Constant

1.788 (0.295)

3.192** (0.505)

0.388 (0.731)

98

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 3.8%, R2= 5.5%, R3= 16.6% N=159

99

Appendix E

Determents of demands for compensation: Netherlands 2010, with FDI, without scale

variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1 = 5.9% R2= 6.2% R3= 12.9 N =221

B (SE)

Lower

Exp B

Upper

Step 1

Constant

0.807****(0.254)

2.241

Dichotomous Job Insecurity

-0.372(0.279)

0.399

0.689

1.191

Trade openness

-0.986* (0.626)

0.090

0.307

1.048

FDI

-0.245**(0.115)

0.782

0.782

0.981

Step 2

Constant

0.989*** (0.158)

2.689

Dichotomous Job Insecurity

-0.380 (0.280)

0.396

0.684

1.183

Trade openness

-1.027 (0.662)

0.098

0.358

1.310

FDI

-0.259**(0.117)

0.613

0.771

0.971

Migration openness

-3.929 (5.692)

0.000

0.020

1374.946

Step 3

Constant

0.973** (0.472)

2.646

Dichotomous Job Insecurity

-0.383 (0.297)

0.381

0.682

1.221

Trade openness

-1.575** (0.173)

0.050

0.207

0.851

FDI Openness

-0.278** (0.122)

0.596

0.757

0.962

Migration openness

0.025 (1.857)

0.000

1.051

109805.125

Religious

0.723**(0.182)

1.106

2.061

3.840

Man

-0.099 (0.178)

0.492

0.905

1.665

Affinity Union

-0.780***(0.182)

0.244

0.458

0.860

97

Appendix F

Determents of demands for compensation: Netherlands 2004, without FDI, with scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 1.6% R2= 1.9% R3= 7.1% N = 438

Variable

Step 1

Step 1

Step 2

Step 3

Dichotomous Job Insecurity

0.719* (0.193)

0.721* (0.193)

0.823 (0.202)

Trade openness

1.040 (0.031)

1.037 (0.031)

1.042 (0.032)

Migration openness

0.089 (2.166)

0.259 (2.261)

Religious

1.514** (0.208)

Man

1.260 (0.205)

Affinity Union

.687* (0.206)

Age in years

1.021** (0.010)

Years of education

0.991 (0.029)

Income

1.000* (0.000)

Constant

1.179* (0.096)

1.357** (0.136)

1.998* (0.373)

0.696 (0.750)

98

Appendix G

Determents of demands for compensation: Netherlands 2004, without FDI and scale variables

B (SE)

Lower

Exp B

Upper

Step 1

Constant

0.363*** (0.115)

1.438

Dichotomous Job

Insecurity

-0,398** (0.166)

0,485

0,672

0,930

Trade openness

0,049* (0.29)

0,993

1,050

1,112

Step 2

Constant

0,915***(,323)

2,498

Dichotomous Job

Insecurity

-0,394**(,167)

0,486

0,674

0,935

Trade openness

0,045 (,029)

0,988

1,046

1,109

Migration openness

-3,451*(1,882)

0,001

0,032

1,267

Step 3

Constant

0,799**(,348)

2.224

Dichotomous Job

Insecurity

-0,384**(,169)

0,489

0,681

0,949

Trade openness

0,050*(,030)

0,991

1,051

1,115

Migration openness

-3,076 (1,930)

0,001

0,046

2,026

99

Religious

0,284 (,174)

0,946

1,329

1,868

Man

,334* (,170)

1,000

1,396

1,950

Affinity Union

-0,488**(,174)

0,436

0,614

0,863

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 2.3%, R2= 3.0%, R3= 5.9%, N =596

100

Appendix H

Determents of demands for compensation: Netherlands 2004, with FDI and scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 7.6%, R2= 7.6%, R3= 11.6%, N =178

Step 1

Step 1

Step 2

Step 3

Dichotomous Job Insecurity

0.426*** (0.313)

0.429*** (0.314)

0.481** (0.327)

Trade openness

1.045 (0.032)

1.046 (0.033)

1.045 (0.033)

FDI openness

0.994 (0.095)

0.994 (0.095)

1.012 (0.99)

Migration openness

0.805 (4.087)

7.017 (4.260)

Religious

0.808 (0.338)

Man

0.870 (0.357)

Affinity Union

.855 (0.332)

Age in years

1.015 (0.015)

Years of education

0.948 (0.054)

Income

1.0 (0.000)

Constant

1.070 (0.150)

1.505* (0.244)

1.282 (0.692)

1.572 (1.294)

101

Appendix I

Determents of demands for compensation: Netherlands 2004, with FDI, without scale variables

B (SE)

Lower

Exp B

Upper

Step 1

Constant

0.450** (0.207)

1.569

Dichotomous Job

Insecurity

-0.744***

(0.264)

0.283 ,475 ,798

Trade openness

0.053* (0.030)

0.994 1,055 1,119

FDI

-0.245

(0.073)

0.783 ,904 1,044

Step 2

Constant

0.305

(0.573)

1.356

Dichotomous Job

Insecurity

-0,739***

(0.265)

0,284

0,478

0,803

Trade openness

0,055*

(0.031)

0,995

1,056

1,121

FDI

-0,100

(0.073)

0,783

0,904

1,045

Migration openness

0,917

(3.373)

0,003

2,501

1859,115

Step 3

Constant

0.498

(0.597)

1.645

Dichotomous Job

Insecurity

-,707**

(0.267)

,292 ,493 ,833

102

Trade openness

,059*

(0.31)

,998 1,061 1,127

FDI Openness

-,104

(0.074)

,779 ,902 1,043

Migration openness

1,336

3.496

,004

3,803

3599,436

Religious

-,047

(0.268)

,564

,954

1,614

Man

,063

(0.287)

,606

1,065

1,870

Affinity Union

-,471*

(0.269)

,369

,624

1,057

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 7.5%, R2= 7.6%, R3= 9.1%, N =254

103

Appendix J

Determents of demands for compensation: United Kingdom, 2010 without FDI, with scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 1.6%, R2= 2.0% R3= 4.9% N = 507

Step 0

Step 1

Step 2

Step 3

Dichotomous Job Insecurity

1.153 (0.181)

1.127 (0.182)

1.129 (0.188)

Trade openness

0.497** (0.305)

0.468 (0.310)**

0.467** (0.336)

Migration openness

16.175 (2.510)

9.140 (2.608)

Religious

1.156 (0.188)

Man

0.874 (0.205)

Affinity Union

0.800 (0.196)

Age in years

0.990 (0.008)

Years of education

0.992 (0.027)

Income

1.000 (0.000)**

Constant

1.305*** (0.090)

1.365** (0.141)

1.086* (0.247)

2.755 (0.664)

104

Appendix K

Determents of demands for compensation: United Kingdom 2010, without FDI, without scale variables

B (SE)

Lower

Exp B

Upper

Step 1

Constant

0.309** (0.125)

1.362

Dichotomous Job

Insecurity

0.197(0.161)

(0.264)

0.888

1.218

1.671

Trade openness

-0.537** (0.261)

0.305

0.585

0.976

Step 2

Constant

0.089 (0.222)

(0.573)

1.093

Dichotomous Job

Insecurity

0.172 (0.163)

(0.573)

0.863

1.187

1.633

Trade openness

-0.594**(0.266)

(0.265)

0.238

0.552

0.930

Migration openness

2.743 (2.290)

(3.373)

0.174

15.528

1382.156

Step 3

Constant

0.016 (0.271)

0.984

Dichotomous Job

Insecurity

0.179 (0.164)

0.868

1.197

1.650

Trade openness

-0.539** (0.282)

0.318

0.553

0.960

Migration openness

2.950 (2.307)

0.208

19.097

1757.410

105

Religious

0.290 (0.165)

0.892

1.232

1.702

Man

0.056 (0.173)

0.753

1.058

1.487

Affinity Union

-0.120 (0.165)

0.642

0.887

1.226

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R1= 1.2% R2 = 1.5% R3= 1.9% N =637

106

Appendix L

Determents of demands for compensation: United Kingdom 2010, with FDI, with scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R3 = 4% N=485

STEP 3

B (SE)

Lower

Exp B

Upper

Dichotomous Job Insecurity

0.156 (0.192)

-0.8

0.802

1.169

1.703

0

Trade openness

-0.807** (0.365)

0.218

0.466

0.912

FDI openness

-0.002 (0.142)

0.755

0.998

1.319

Migration openness

2.100 (2.685)

0.042

8.165

1576.046

Religious

0.152 (0.193)

0.798

1.164

1.698

Man

-0.152 (0.208)

0.587

0.882

1.326

Affinity Union

-0.176 (0.201)

0.565

0.839

1.244

Age in years

-0.010 (0.008)

0.974

0.990

1.007

Years of education

-0.009 (0.027)

0.939

0.991

1.046

Income

0.000 (0.000)*

1.000

1.000

1.000

Constant

0.943 (0.667)

2.569

107

Appendix M

Determents of demands for compensation: United Kingdom, 2010, with FDI, without scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R3 = 2, 6 % N = 608

STEP 3

B (SE)

Lower

Exp B

Upper

Dichotomous Job Insecurity

0.233 (0.169)

0.907

1.263

1.757

Trade openness

-0.640** (0.133)

0.285

0.527

0.974

FDI openness

-0.111 (0.121)

0.706

0.895

1.136

Migration openness

3.167 (2.382)

0.223

23.733

2528.082

Religious

0231 (0.170)

0.903

1.260

1.757

Man

-0.016 (0.179)

0.693

0.984

1.397

Affinity Union

-0.086 (0.170)

0.657

0.918

1.281

Constant

0.012 (0.725)

1.012

108

Appendix N

Determents of demands for compensation: United Kingdom 2004, without FDI, with scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R3= 7.8% N=345

STEP 3

B (SE)

Lower

Exp B

Upper

Dichotomous Job Insecurity

-0.063 (0.243)

0.583

0.939

1.519

Trade openness

-0.433 (0.387)

0.304

0.649

1.386

Migration openness

11.884** (6.798)

0.237

144901.408

8.856E+10

Religious

-0.035 (0.221)

0.627

0.996

1.489

Man

0.229 (0.228)

0.804

1.258

1.967

Affinity Union

-0.196 (0.229)

0.525

0.822

1.286

Age in years

0.000 (0.001)

0.980

1.000

1.021

Years of education

-0.002 (0.041)

0.921

0.998

1.080

Income

0.000** (0.000)

1.1000

1.000

1.000

Constant

0.069 (0.958)

1.071

109

Appendix O

Determents of demands for compensation: United Kingdom 2004, without FDI, without scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R3= 2.3 N = 492

STEP 3

B (SE)

Lower

Exp B

Upper

Dichotomous Job Insecurity

-0.030 (0.209)

0.644

0.970

1.462

Trade openness

-.0336 (0.346)

0.363

0.715

1.409

Migration openness

9.491* (5.871)

0.133

13234.542

1316122794

Religious

-0.013 (0.187)

0.683

0.987

1.425

Man

0.323 (0.195)

0.942

1.382

2.026

Affinity Union

-0.179 (0.193)

0.573

0.837

1.221

Constant

-0.189 (0.422)

0.828

110

Appendix P

Determents of demands for compensation: United Kingdom 2004, with FDI, with scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R3= 7.8% N=393

STEP 3

B (SE)

Lower

Exp B

Upper

Dichotomous Job Insecurity

-0.142 (0.260)

0.521

0.868

1.446

Trade openness

-0.602 (0.410)

0.245

0.548

1.223

FDI Openness

-0.284 (0.279)

0.436

0.753

1.301

Migration openness

17.119** (7.627)

8.759

27202290.70

8.448E+13

Religious

-0.006 (0.235)

0.627

0.994

1.575

Man

0.173 (0.248)

0.732

1.189

1.932

Affinity Union

-0.303 (0.224)

0.548

0.739

1.192

Age in years

0.001 (0.011)

0.797

1.001

1.024

Years of education

-0.008 (0.044)

0.910

0.992

1.082

Income

0.000** (0.000)

1.0000

1.000

1.000

Constant

-0.093 (1.022)

0.962

111

Appendix Q

Determents of demands for compensation: United Kingdom 2004, with FDI, without scale variables

*P ≤ 0.1, **P ≤ 0.05, ***P ≤ 0.01, ****P ≤ 0.001.. Between parentheses is the standard error. CI for boundaries is 95% R = 3.9% N =492

STEP 3

B (SE)

Lower

Exp B

Upper

Dichotomous Job Insecurity

-0.122 (0.244)

0.570

0.885

1.373

Trade openness

-0.524 (0.362

0.291

0.592

1.204

FDI openness

-0.262 (0.229)

0.491

0.770

1.206

Migration openness

14.140** (6.562)

3.591

1382717.120

5.324E11

Religious

0.030 (0.200)

0.696

1.030

1.524

Man

0.224 (0.210)

0.830

1.251

1.887

Affinity Union

-0.250 (0.205)

0.521

0.779

1.164

Constant

-0.288 (0.444)

0.750