Economical and Ethical Implications of Off-Shoring Decisions from Germany to Romania

Economical and Ethical Implications of Off-shoring Decisions from Germany to Romania

Master Thesis

8/31/2015 Tudor Mihai Pavel - 4329910

Management of Technology Faculty of Technology, Policy and Management Delft University of Technology

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Economical and Ethical Implications of Off-shoring Decisions from Germany to Romania

MASTER THESIS

Student name: Tudor-Mihai Pavel

Student number: 4329910

Graduation date: 31 August 2015

Graduation Committee:

Chairman: Prof. Dr. C.P. (Cees) van Beers Professor of Management of Technical Innovation

First Supervisor: Dr. S.T.H. (Servaas) Storm Assistant Professor in the Section of Economics of Technology and Innovation

Second Supervisor: Prof. Dr. I.R. (Ibo) van de Poel Professor of Ethics and Technology

Executive Summary

With globalization, off-shoring has become a common practice for companies because of its cost saving advantages and ability to access needed human capital. However, off-shoring is often associated with the job loss and wage change in the domestic country. With the opening of the East European market, Germany has become one of the most important foreign investor in Romania. The most important objective of this master thesis is to see whether there is a correlation between the German off-shoring in Romania and the employment level and wage level in the two countries. More specifically, our interest is to see whether indeed the German off-shoring does lead to job loss and wage decrease in Germany as some studies suggest or there is an opposite effect that can be associated with the German off-shoring. In addition, the master thesis is to focus on the lower-wage county, Romania, as well and on whether there is a change in employment and wage levels in Romania that can be associated with the process of off-shoring.

The master thesis has an additional secondary research objective, naming to bring ethical discussion in the context of the off-shoring process. The econometrical analysis used to answer the main research question is to provide insights about the job displacement and change in the wage levels in the two countries, but the question remains whether the German off-shoring process is to be perceived as ethical or not. Therefore, two main ethical theories are to be used: the utilitarian framework that argues that an act should be followed as long as it brings the ‘greatest happiness for the greatest amount of people’ and the Rawlsian framework that suggests that an act should be taken as long as it is in the advantage of the people situated in the least advantageous position. By applying these two ethical frameworks, we should find out to what extent is the German off-shoring process to Romania an ethical one.

In order to attain the research objective and to answer the main research question, the first step is to derive a measure for the German off-shoring in Romania. With the help of Feenstra’s and Hanson’s method and the data available in the World Input-Output Database, the actual off-shoring index is derived for Romania and Germany that show the trend of German off-shoring to Romania during 1995 and 2011. We have limit ourselves to the 1995-2011 research period since the World Input-Output Database is limited to information on only those particular years.

The next step is to find an appropriate measure for the employment and wage levels in Romania and Germany. Using data available from the German Statistical Institute, the average real net wage for Germany is obtained as a measurement for the wage level in Germany. For the employment level in Germany, the total number of employees in Germany is used. Similarly, the Romanian Statistical Institute provides data for calculating the average real net wage in Romania. However, for reasons of controlling for different factors such as migrating population, the ratio of the number of total employees to the total population is used as a measurement for the employment level in Romania.

The actual off-shoring indexes for Romania and Germany are taken together with the data on employment and wage levels for Romania and Germany respectively. For each set of data a linear regression analysis is performed.

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The results of the linear regression analyses give us interesting insights on the effect of German off- shoring on the Romanian and German employment and wage levels. First there is a strong positive association between the German off-shoring and Romanian wages that can be explained by the fact that Romania was a closed economy during the communist period and the opening up of its market meant a slow process of wage adjustment towards market values. In addition, there is also a positive but not as strong correlation between Romanian jobs and the German off-shoring in Romania. The results show that although on the total economy, the German off-shoring to Romania is expected to create more job opportunities, the primary and secondary Romanian sectors are expected to lose jobs due to the off- shoring process. One explanation for this result is that the primary and secondary sectors in Romania were inefficient and the off-shoring process through the new technology it brought replaced part of the labour work with more efficient machine labour. This explanation is also supported by the increase in labour productivity that Romania went through that period.

On the other hand, the results for Germany give a different picture. The German wages are found to be negatively associated with the off-shoring process. One reason for such result is that while German jobs are moved elsewhere, the German supply of jobs is shrinking. This in turn affects the bargaining powers of the German employee who is forced to settle for smaller wages. In addition, the German off-shoring is positively correlated with the total number of jobs in Germany. The results show us that even though there are jobs lost in the primary and secondary sectors in Germany because of the off-shoring process, there are additional jobs created in the tertiary sector as other studies have suggested.

The results of the econometrical analysis showing the expected change in the number of jobs and in the wage levels in the two countries are then used for the ethical discussion. We have argued that the German off-shoring is meeting both the utilitarian and the Rawlsian principles from Romania’s perspective. However, from the ethical discussion, we have concluded that the utilitarian principle is not met from the German perspective. The Rawlsian principle is nevertheless met from the German perspective as well if we consider the people unemployed to be the ones in the least advantageous position.

Finally, the master thesis gives a couple of recommendations for the Romanian and German governments and for the German companies interested in the off-shoring process. We have argued that, through their roles in the society, governments should be more interested in helping the ones in the least advantageous position. Therefore, the Romanian and German governments should look at the German off-shoring process in a positive light. On the other hand, German companies are not responsible for the entire society, but we have argued that a company is behaving in a more ethical way as long as it also strives for creating a greater good for the society. Therefore, the German off-shoring process should be still pursued by the German companies as long as they redirect part of their money saved through the off-shoring process towards increasing the real wage of German employees. In this way, the negative effect of German off-shoring on German wages should be reduced and German companies could provide indeed a greater good for the society.

The master thesis has served its purpose of establishing the correlation between the German off-shoring and the employment level and wage level in Germany and Romania as well as of discussing the ethical

iv impact of the German off-shoring. However there are still some directions for research for the future. The first direction deals with finding out whether, aside from correlation, there is causality between German off-shoring and employment and wage levels in the two countries. Moreover, the results for Germany and Romania should be investigated if they could be generalized for a high-wage and a low- wage country respectively. Finally, the ethical discussion is not entirely complete without considering the diminishing marginal utility of money. Future research should find a way in taking into account this principle in the ethical discussion.

Keywords: Off-shoring, employment, wage, ethical, utilitarianism, Rawls

Acknowledgement

First of all, I am thankful for the great opportunity I had to further continue my studies within TU Delft. It has been an amazing experience for me in the last two years from which I have learned a lot and I have to thank all the staff from the Management of Technology program for sharing their knowledge and for opening my eyes in so many ways. Two years ago when I have started the master program I could not have imagine that I would end up in combining economic concepts, statistical analysis and ethical principles for my master thesis paper. However, this is what I have been working towards in the last six months and I hope that my work is going to be a useful tool for further discussion related to the off- shoring process.

I would like to thank especially the members of my graduation committee, who supported me in the last six months. Dr Servaas Storm has been an excellent supervisor who guided me throughout this entire period. I would also like to thank Prof Ibo van de Poel for his help and direction in developing an ethical framework and for his encouraging feedback. Finally, my gratitude goes also towards Prof Cees van Beers for his supervision over my entire graduation process.

Last but not least, I would like to thank all my family and friends for their support in this last two years. It would have been much more difficult without their constant encouragements and I will always be grateful for what they have done for me.

Tudor-Mihai Pavel

August 2015, Delft

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Contents

1. Introduction ...... 1 1.1. Problem identification ...... 1 1.2. Research objectives ...... 2 1.3. Research questions...... 3 1.4. Literature review ...... 3 1.4.1. Off-shoring ...... 3 1.4.2. Unemployment and wage level ...... 3 1.4.3. Ethics ...... 4 1.5. Conceptual model and hypotheses development ...... 4 1.6. Methodology ...... 5 1.7. Research design ...... 6 1. Literature review ...... 7 2.1. Macroeconomic context...... 7 2.2. Off-shoring ...... 12 2.3. Romania and Germany ...... 14 2.4. Ethical considerations ...... 18 3. Methodology ...... 22 3.1. World Input-Output Database ...... 22 3.2. Actual off-shoring index ...... 22 3.3. Employment in Romania ...... 23 3.4. Wage level in Romania ...... 24 3.5. Labour productivity in Romania ...... 26 3.6. Employment in Germany ...... 26 3.7. Wage level in Germany ...... 27 3.8. Aggregating economic activities ...... 28 3.9. Limitations of the data ...... 31 3.10. Statistical analysis ...... 32 4. Results ...... 34 4.1. Results for Romania...... 34 4.2. Results for Germany ...... 43

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5. Ethical Reflection ...... 49 5.1. Selected data ...... 49 5.2. Utilitarianism ...... 53 5.3. Theory of Justice ...... 56 5.4. Ethical conclusions ...... 58 6. Conclusion and discussion ...... 62 References ...... 66 Appendix ...... 71

1. Introduction

1.1. Problem identification

As the world is becoming more globalized, the different stages of production are becoming more and more fragmented and spread across different countries in what are called ‘global value chains’. This change in the production structure is merely due to outsourcing, which is defined as the procurement of external products or services (William M. Lankford, 1999), and to foreign direct investment.

Outsourcing is providing the company with several benefits as improved profitability, increased flexibility or competitive advantage (McCormack, 2010). The decision of outsourcing is made in terms of strategy, costs and environment. When considering outsourcing, firms investigate primary on how core is the competence or function they want to outsource, which is the gain in relative costs due to outsourcing and whether the necessary skills are readily available externally (O. I. T. Tibor Kremic, 2006). However, even though firms benefit from the outsourcing process, it may not be the same case for the overall economy and society. The effect on the welfare for the society is rather ambiguous (Chen, 2014). Moreover, in his book Palley argues that the financial crisis of 2008 was partly triggered by the trade deficit of the US that was favoured through the opening of new markets for companies through the NAFTA agreement, East Asian trade agreement and the establishing of permanent trade relationships with China (Palley, 2012b). These trading agreements resulted in massive off-shoring to low-wage countries which may have been partly responsible for the global financial crisis of 2008.

The Eastern European market is a preferred location for off-shoring production of different services due to its cheap labour work and skilled human capital resources. The Eastern European market was practically closed for any foreign investment until 1989. With the fall of the Soviet Union, the market opened up for foreign investments. As countries from the former communist block started joining the European Union in 2004 and 2007, the foreign investments and off-shoring practices increased in those countries since costs and risks have diminished. The transfer of business operations to Eastern European countries is conventionally seen as a loss of jobs in the Western European countries. However, outsourcing facilitated a better integration among the member states and limited the labour mobility towards higher-wage states (Stefanova, 2006). Another study suggest that the outsourcing towards Eastern Europe is not because cost effectiveness reasons, but for market seeking opportunities. In addition, firms are moving their skill intensive activities towards East because of a severe scarcity of human capital (Marin, 2010c). What most of the studies are not covering is the effect of off-shoring after the enlargement of the European Union of 2007 when Romania and Bulgaria entered the European Union. Moreover, most of the studies are mostly focusing on the impact of off-shoring on the high-wage country. There is little information on the benefits and costs, in terms of employment and wage levels, that the low-wage country, such as Romania, faces due to the process of off-shoring.

The majority of articles within the literature refer to the benefits and costs of the process of off-shoring from an economical perspective. However, in a world that tries to become more socially responsible, the ethical dimension of off-shoring should receive more attention. There is a number of articles that takes

1 into account the ethical perspective, by raising concerns on the benefits of society from the outsourcing process or by addressing the need of balancing the selflessness and self-interest decisions related to the outsourcing process (Wenzhong, 2013b; Zutshi A, 2012). Nevertheless, articles focusing on the ethical dimension of off-shoring are scarce and lack evidence of their applicability.

This paper focuses on the off-shoring process from Germany to Romania and the main practical problem that it tries to solve is to see how the off-shoring relationship between Germany and Romania has changed in the past twenty years and whether it had any impact on the employment rate and wage level in the two countries. The results obtained from the research could contribute towards future policies targeting unemployment levels and wage levels in Germany and Romania. In addition, off-shoring decisions might affect the reputation of a company through the job losses that it creates in the outsourcing country (Zutshi A, 2012). Therefore, these results might be, at least in a small proportion, relevant for German companies that are thinking of off-shoring production abroad. One theoretical problem is the availability of data in order to measure the German off-shoring to Romania. This problem is solved by using the data found in the World Input Output Database, which provides bilateral trade flows between the two countries in different industrial sectors. From this data, the actual off-shoring index is derived applying the approach developed by Feenstra and Hansen (Hanson, 1996) and this index is correlated with the employment level and wage level of the two countries to see whether there is any association between off-shoring and the two macro-economic variables in the two countries.

Nevertheless, the aim of this thesis is not to solely rely on mathematical models. As Thomas Sedlacek puts it in his book, economics is more than using mathematical models and should regain its lost ‘soul’ (Thomas Sedlacek, 2011). Therefore, the aim is to bring back ethical considerations in the economic environment; more specifically to assess whether off-shoring is an ethical process or not and to see to what extent the ethical dimension should be taken into consideration when talking about off-shore outsourcing.

1.2. Research objectives

The main goal of this master thesis is to focus on the outsourcing between Germany and Romania and to see how this affects Romania and Germany from an economical point of view. Previous studies on this topic rarely mention about the outsourcing occurring in Romania and if they do, they mainly focus on the period prior to the EU adherence of Romania. In addition, most of the studies dealing with the outsourcing topic and on the economic impact of this process focus more on the higher-wage country rather than on the lower-wage one. The main objective is to see whether there is any connection between the German off-shoring to Romania and the employment and wage levels in the two countries.

A second objective of this thesis is to reflect on the ethical nature of the outsourcing process. The matter of job displacement, job losses and related concepts should not be analysed only from an economical point of view, but also from a moral one. With the help of several ethical theories, a secondary aim of this paper is to determine whether the job displacements between the two countries and the changes in wage levels that are triggered by the German off-shoring to Romania are to be seen ethical or not.

1.3. Research questions

According to the research objectives, the main goal of the thesis is to see what the connection between the German off-shoring to Romania, employment and wage levels in the two countries is. Therefore, the main research question is:

What is the relationship between the German off-shoring in Romania and the employment and wage levels in the two countries?

The results obtained from answering the main research question will give insight on whether there is any job displacement within the two countries and whether there can be any association between the German off-shoring and the changes in the wage levels in Romania and Germany. According with the second research objective, the economic results are then examined, with the help of several ethical theories, in order to establish whether the German off-shoring can be seen as ethical or not. Therefore a secondary question that this master thesis is exploring is:

In the light of its effect on the German and Romanian employment and wage levels, to what extent one can conclude that the German off-shoring to Romania is an ethical process?

1.4. Literature review

1.4.1. Off-shoring

As the transportation and communication costs have become cheaper and with the opening of new markets, off-shoring has become a common practice for most of the companies ((UNDP), 1999). There are several reason why companies off-shore production. First of all, by moving the production to a low wage country, companies expect to save costs. A study has shown that, on average, a company is expected to save around 15% of its costs if it decides to off-shore production (Belcourt, 2006). In addition, the high-wage countries are lacking skilled human capital and therefore, through off-shoring, companies hope to compensate for this shortage of skilled workers (Marin, 2010b). There are other reasons why companies decide to off-shore production such as focusing on the core competence or reaching the local market just to name a few. However, the off-shoring process is not only a process from which companies benefit from. Off-shoring does have its risks and hidden costs. Through off- shoring, companies may lose control of one of their core function or see the morale of their employees decline (Overby, 2003). The main point is that companies that off-shore do experience costs aside the potential benefits that this process may bring.

1.4.2. Unemployment and wage level

What is then the link between off-shoring and employment and wage levels? Throughout the literature, off-shoring is often said to affect jobs in both the high-wage and low-wage countries. However, most of the literature focuses on the job displacement in the high-wage country. There is not a single opinion on how jobs are affected by off-shoring in the high-wage country. There are studies that argue that many jobs will be lost due to the off-shoring process (Overby, 2013). However, other studies argue that even though there are job lost, the money that are saved through the off-shoring process helps creating

3 additional jobs in other industry sectors (Global Insight, 2004). If a country experiences job displacement, then it is possible that there would be also a change in the wage level in that country. With the moving of jobs from the high-wage country, the job supply within the high-wage countries declines. A lower job supply is translated into less bargaining power for unions and therefore the employees have to adjust their demands (Reich, 2010).

1.4.3. Ethics

Aside from the economical dimension of outsourcing, there is also much debate on whether outsourcing is an ethical process. Some studies see that it is more ethical for a company to off-shore production and maintain its competitiveness than be forced to cease production (Harrison, 2004), while other studies regard outsourcing as a selflessness and self-interest behaviour (Zutshi A, 2012).

There are numerous ethical theories in literature, but few are applied for the specific case of outsourcing. The utilitarianism perspective emphasis on the fact that an act should be done as long as it gives ‘the greatest amount of happiness for the greatest amount of people’ (Royakkers, 2011). The job displacement and the change in the wage level triggered by the German off-shoring are measures of people’s “happiness”. The question then is whether the changes in these parameters do bring the “greatest happiness for the greatest amount of people”. A second perspective for defining an ethical act is given by John Rawls. He argues that an act is just as long as it provides equal rights for everybody. Nevertheless, an act can be ethical if inequalities are allowed, as long as the person in the worst position is better off in the unequal situation that in the initial equal one (Rawls, 1971). Depending on how off- shoring affects the employment and wage levels in Romania and Germany, it may be concluded or not that the German off-shoring meets one or both the above ethical theories.

Most of these ethical theories are rarely applied to the problem of off-shoring. There is a study applying the utilitarian framework and an ethical theory of rights to the outsourcing problem,, but the analysis is not based on any empirical results for the job displacement and wage levels (McGee, 2005).

Ethics and morality have used to be the main concerns for economic development; people that are now considered to be the founders of economics such as Adam Smith or John Stuart Mill put a high value on the ethical nature of economics. Their books were merely pure text and contained at most basic mathematical principles. Today, this model has been completely reversed; most economics books rely on complicated mathematical models that involve a certain number of more or less realistic assumptions and seem to have forgotten the ethical dimension (Thomas Sedlacek, 2011). Nevertheless, there are voices that suggest that economics should return to its ‘soul’ and therefore rely more on moral discussions aside from mathematical frameworks (Thomas Sedlacek, 2011).

1.5. Conceptual model and hypotheses development

The literature review shows that there is a connection between off-shoring production and the job displacement and wage levels in the corresponding two countries (for the purpose of this paper Romania and Germany). What this paper tries to find out is whether indeed there is such a relationship and what

4 is the direction of this relationship. The conceptual model used for answering this question is depicted in Figure 1:

Employment level Net trade inflow Wage level

Figure 1: Conceptual Model

In the conceptual model the independent variable is the actual off-shoring index as a measure of the off- shoring from Germany to Romania. It is expected that if there is a change in the actual off-shoring index, there should be a change in the level of employment and wage level in Romania and Germany. Therefore, the employment level and the wage level are set as dependant variables that are changing for any change in the independent variable.

1.6. Methodology

The main objective for the thesis is to find how off-shoring influences the employment and wage levels in Germany and Romania. The World Input-Output Database (WIOD) that provides globally consistent time-series data on national production networks integrated with bilateral trade flows is used to find a way of measuring the off-shoring from Germany to Romania. The WIOD is a huge database that shows the trade interdependencies between forty different countries (including Romania and Germany) on a number of thirty-five different sectors over a period of twenty years. Out of this huge matrix, only the data showing the interdependencies between the German and Romanian industries is used.

Out of the WOID, the actual off-shoring index (AOI) is to be computed using an approach developed by Feenstra and Hanson (Hanson, 1996). According to (Hanson, 1996), the AOI is a good approximation to determine the off-shoring occurring from one country to another, in this case from Germany to Romania. The actual off-shoring index is computed as the ratio of imported intermediate inputs from Germany to Romania to the total intermediate inputs in Romania. The actual off-shoring index computed in this way is relevant only from the Romanian perspective. From a German perspective the relevant AOI should have discarded the German companies that were already producing outside Germany, but decided to relocate to Romania. Consequently, for each country a different AOI is to be computed for all the years in between 1995 and 2011.

It is expected to see an increase in the value of the two different actual off-shoring indexes for each corresponding country during 1995 and 2011, partly because of the opening of Romania’s market after the communist regime felt in 1989 and partly because of the adherence of Romania to the European Union in 2007. If there is such a change, of interest would be to see whether there is a connection between the change in the German off-shoring pattern and the employment and wage levels in both Germany and Romania. Therefore, relevant data concerning the employment level and the wage level in the two countries have to be obtained from the National Statistical Institutes of Germany and Romania. These data is pulled along with the corresponding AOI for each country and statistical analysed using linear regression. The results of the linear regression are expected to show whether there exists a

5 correlation between the German off-shoring in Romania and the employment and wage levels in the two countries.

The final part should deal with the ethical dimension of off-shoring and has a rather reflective character. Using the insights from the statistical analysis, the aim of this section is to assess whether the German off-shoring in Romania is an ethical process. There are three different perspectives taken on the off- shoring process: the German, the Romanian and the combined Romanian-German perspective. For determining the ethical nature of German off-shoring, two ethical theories are used. First the utilitarianism theory is used that identifies an act as being moral as long as it provides the ‘greatest happiness for the greatest amount of people’. Parts of the statistical results are used within the utilitarian framework. The aim is to find out whether the German off-shoring does provide the ‘greatest good for the greatest amount of people’. The theory of justice developed by John Rawls is a second theory that is used for assessing the ethical nature of off-shoring. This framework identifies two principles of justice that should always be considered: an act is moral as long as it provides equal rights for everybody and if inequalities are accepted, then the person in the least off position should be better off than in the state of equality. As in the case of utilitarianism, this second theory is used for determining whether German off-shoring is an ethical process or not.

1.7. Research design

The research begins by addressing the problem that tries to be solved, followed by a brief literature review in order to see what has already been done. The research objectives and the research questions are afterwards derived based on the gap that was found in the literature review.

Since the data needed for conducting this research is readily available from the WIOD and other statistical information, the method of research will be the desk research. In order to analyse the raw data and find the corresponding AOI for each country, a couple of mathematical manipulations are used. Mathematics is also used in order to manipulate the data on the employment level and wage level in the desired manner. Once all the required data is available, the AOI set of data is linear regressed with the set of data of employment and wage levels respectively using SPSS. Finally, there will be a discussion of the ethical nature of the German off—shoring in Romania.

Research objectives Problem identification Literature review Research questions

Ethical reflection Statistical model Desk research

Figure 2: Research Design

2. Literature review

Globalization and the integration of the world’s economy have led to a massive transfer of production from advanced economies to less developed ones. This process is known as off-shore outsourcing. There are several reasons why companies decide to off-shore production such as cost saving advantages, availability of skilled human force or the need to focus only on the core competences. For many of these reasons, companies are forced to off-shore production in order to remain competitive on the market (Basics, 2009).

This section focuses on the available literature that deals with the off-shoring process and on how Germany and Romania are placed in the context of off-shoring. Therefore, the section starts by presenting several macro-economic factors that influenced the companies’ decision to off-shore. The following subsection is dedicated towards economic arguments in favour and against the off-shoring process. The third subsection focuses on the German and Romanian situation in relation to the off- shoring process and finally, the last section discusses about off-shoring from an ethical perspective.

2.1. Macroeconomic context

When there is debate regarding off-shore outsourcing on the macroeconomic level, it usually centers on whether domestic jobs should be protected (Gold, 2012b). However, such a debate is meaningless without fully comprehending the macroeconomic environment that currently defines the world that we live in. The majority of the literature that links off-shore outsourcing with the macroeconomic environment focuses on the US case. There are certain similarities between the US case and Western European one and therefore the US example would be still relevant in this discussion.

A significant development that challenges the economies of the developed countries deals with the aging population that is a consequence of the low fertility and longer life expectancy (Loichinger, 2013). The worrying impact of this phenomenon consists of the shrinking of the workforce population in the developed countries. (Freeman, 2006; Hayutin, 2007) shows that by 2050, the US, Europe and Japan workforce are expected to decrease in the context of a still worldwide growth of workforce facilitated by the growth in population in the least developed countries. Germany has lost 1.5 million of its inhabitants in the last years and its population is expected to decrease by 19% until 2060 (Kulish, 2013). One of the causes of the German shrinking population is its reduced fertility rate situated at a level of 1.4, which is below the 2.1 fertility rate that would keep the population stable (Kulish, 2013). The implications of the aging population could be overwhelming for the developed countries in terms of inability to sustain social security (Jakub Bijak, 2008) and lack of available skills and talent (Freeman, 2006). Consequently, various policies have been proposed to overcome these effects by increasing fertility rates, increasing immigration, investing in human capital or changing the labour market (Alin M. Ceobanu, 2013; Freeman, 2006; Jakub Bijak, 2008). In the German case, in order to increase the fertility rate, the German government has introduced a series of policies meant to increase monthly subsidies given to families with children, to give further benefits for parents who stay with their new-borns or to allow work payments for new fathers. However, these measures had little impact on the fertility rate (Markus Dettmer, 2013). Another set of measures introduced by Germany in this direction was to support parents

7 in dividing their time between career and family by expanding day care and after-school program. Even though Germany started a program guaranteeing day care for all children above 12 months old, there is still a lack of affordable facilities. In addition, since most schools finish at noon, there are still not enough after-school programs for children so that both parents could have a full-time job (Kulish, 2013). The German authorities tried to tackle the problem of the reduced workforce from a different perspective as well. One measure that they introduced was to gradually increase the retirement age to 67 years old and therefore increase the available workforce population (Kulish, 2013). However, there seems that there is a number of skilled jobs for which there is no available workforce and therefore there are voices that support the changing of the immigration laws to help companies find the needed workforce (Kulish, 2013). There is nevertheless concern on how the new foreign workers would be integrated within the German society.

Looking only at the aging population fails to bring the entire picture of the current environment we live in. Over the past years, computers and automation have gained more importance and have started substituting different type of jobs, especially the ones requiring low and medium skills (Bresnahan, 1999). This trend of substituting jobs by robots is predicted to continue in the future. A study on 702 different US jobs found that 47% of the jobs are at high risk of being replaced by robots (Osborne, 2013). The jobs in transport and logistics and office and administration are found to be among the ones with the highest risk of replacement (Osborne, 2013). In a similar study that focuses on Great Britain, it is predicted that one in three jobs would disappear in the next 20 years. Nevertheless, the study shows that even though jobs would be lost, higher skilled jobs will be created (Tovey, 2014). (McAfee, 2012) has a contradictory opinion stating that the technological advancement is destroying jobs at a faster pace than its ability to create new ones. An article that focuses on Japan suggests that the technological advancement could be the remedy for the aging population that Japan confronts with. The article points out that the improved technology may make labour tasks easier for the older population and therefore enable them to work more than their current retirement age (Hiyama, 2011). Hence, the decline in workforce that the develop countries experience today may be cancelled out by the displacement of jobs triggered by automation and technological advancement.

The last years were marked by increased unemployment throughout the world due to the financial crises that hit the US and Europe in 2008. In the US, although the median duration of unemployment has decreased lately it is still much above than the historical average (Louis, 2015), showing that there is a mismatch between the skills of those seeking jobs within the US and those offering jobs (Gold, 2012b). Different studies emphasis the fact that the US is lacking a low cost, high quality education system to provide the skills needed to occupy the skill intensive jobs (Freeman, 2006; Gold, 2012b). The share of university enrolments for US citizens has dramatically declined in the last decades whereas low wage countries have massively invested in higher education (Freeman, 2006). Western Europe is suffering of the scarcity of skilled intensive human capital as well. A study on Austria and Germany has concluded that one of the most important reasons of moving production from these two countries to Eastern European countries is the lack of human capital needed for performing the jobs in Austria and Germany (Marin, 2010b). The study shows that Austria and Germany are outsourcing the most skill intensive activities in Eastern Europe, since countries such as the Baltic States, Hungary and Russia have more

8 skilled labour than Germany and in particular Austria (Marin, 2010b).

After 1980, a new economic growth model has been adopted by the US and other developed countries (Palley, 2012a). This new growth model replaced the wage growth as being the element to trigger demand growth (Palley, 2012a). Even though wage compensation has remained relatively flat in the last decades, the corporate profits have been increasing significantly (Gold, 2012b). As a consequence, income inequality has risen in the most developed societies, leading to a dramatically increase of the share of the top 20 percentage of the population according to a study on the US economy (Palley, 2012a). The increase in income inequality is associated with change in the labour market (Arthur F. Jones Jr., 2000). The labour market has suffered changes from the 1980s by experiencing a shift from manufacturing industries that provided high wages for low skilled labour towards technical service industries that focus mainly on highly educated individuals in the most advanced economies (Arthur F. Jones Jr., 2000; Skills, 2014). The inequality trend is not only characteristic for the US case, but also for other OECD countries. A study on the European Union shows that inequality has risen in the last 30 years for the European Union as a whole. Although part of the increase may be explained due to the EU enlargement, this may not be seen as the sole reason since inequality, measured through the Gini coefficient, has risen in the first eight original EU countries. Having a value between 0 and 1, the Gini coefficients distinguish between an equal society, if the coefficient is close to 0, and an unequal one, if the coefficient is close to 1. In the case of Germany, the Gini coefficient has increased from 0.25 in the mid 1980 to 0.3 in 2008 (Fredriksen, 2012).

With the fall of the Iron Curtain, the Eastern European market has been opened for investors. The new market is characterized by low wage costs compared to the Western European countries. Figure 4 gives a comparison between the wage costs in 2012 between different Western and Eastern European countries including Romania and Germany (Schroder, 2013). The table shows that there are still big discrepancies between salaries in Eastern and Western Europe. To give an example, the average wage per hour in Germany is ten times larger than the average wage per hour in Romania. Therefore, Western European firms are confronted with adjustment problems to this new market and strive in remaining competitive in the face of the new low-wage competitors (Sinn, 2006).

Norway 57.85 Switzerland 46.55 Germany 36.98 France 36.77 Netherlands 33.69 Euro/Hour Czech Republic 10.15 Poland 6.65 Russia 5.87 Romania 3.78 Bulgaria 2.86

Source: (Schroder, 2013)

Figure 4: Wage expressed in Euros/hour in Europe in 2012

The discussion of the macroeconomic environment would not be complete without addressing some of the most important macroeconomic variables for Germany and Romania, which are the focus countries of this paper. As table 2 shows, the unemployment rate has experience a steady decline over the last twenty years in both countries reaching close to 5% in 2014. Germany experienced a low inflation rate throughout the entire 1995-2011 period, but Romania was characterized by higher inflation especially at the beginning of this period. This difference can be explained from the fact that Germany was a much stable country than Romania that went through a transition period after the fall of the communist regime. The GDP per capita has almost doubled for both countries during the 1995-2014 period. However, GDP per capita is still more than seven times smaller in Romania compared with Germany. The increase in the GDP per capita has stalled in the last years mainly due to the financial crises that hit Europe in 2008-2009. The difference in the average gross wage between Romania and Germany was huge in 1995, with a difference of more than 22 times between the average gross wage in Romania and the one in Germany. The last twenty years have seen an improvement of this gap between the average gross salaries; however by 2014 the average gross salary in Germany has still remained seven times higher than the one in Romania. One last macroeconomic parameter that is important to mention now, is the trade balance of the two countries. Table 1 shows that over the entire last twenty years period there was a trade surplus for Germany, while Romania experienced trade deficit over the whole period. Nevertheless, as the data shows, the trade deficit has become smaller for Romania in the last few years.

Germany Romania Variable 1995 2000 2005 2010 2014 1995 2000 2005 2010 2014 Unemployment (%) 8.13 7.76 11.17 7.07 4.98 9.5 10.5 5.9 6.87 5.29 GDP/capita (USD) 23116 26608 22114 39563 44203 3267.8 3253.99 4432.94 5654.66 6072.84 Inflation (%) 1.51 2 1.41 1.31 0.19 32.3 45.7 9.0 6.09 1.07 Average Gross Wage 2281 * 2551 2901 3227 3527 101.80 * 142.33 267.15 451.79 517.03 (EUR/month) Trade Balance (Million 43615 59128 158179 154863 216905 -1832 -2962 -10313 -9509 -6046 Euros) Table 1: Germany and Romania’s main macroeconomic variables

*- the numbers are derived using theoretical Euro exchange rates since the Euro was introduced as an official currency only in 1999

2.2. Off-shoring

Off-shore outsourcing has become a common tool for managers throughout the world in the last period. One of the most important factors that led to the increase use of this practice has to do with the globalized world that we now live in. Among other things, globalization is responsible for the opening up of new markets for companies to invest in, creating in this way global consumer markets ((UNDP), 1999). What is even more important in the context of off-shoring is that these new markets would not have been used as new investment opportunities if the costs were high. However, in the last period, due to the technological progress, there can be seen a decline in the cost of transport and communication, making it cheaper to off-shore now then earlier. The decreasing trend of the transportation and communication costs is depicted in Figure 5.

300

250 Sea freight( average ocean freight and port charges per ton) 200

Air transport(average

revenue per 100 passenger 150 mile)

USdollars Telephone call(3 minutes NY/London) 100

Computers (Index 1990=1) 50

0 1920 1930 1940 1950 1960 1970 1980 1990

Source: ((UNDP), 1999)

Figure 5: Decline of transportation and communication costs

The process of off-shore outsourcing is often associated with an effect on the employment levels and wages. However, there is no agreement on whether there is a positive or negative effect on these two macroeconomic parameters. A study on IT outsourcing reveals that 1.5 million jobs in this sector will be eliminated by 2017, both outsourcing and increased productivity contributing to this scenario (Overby, 2013). Another study on the US IT industry suggests that the global sourcing of US companies actually benefits the US economy, through job creation, higher real wages and higher GDP growth (Global Insight, 2004). The study suggests that there will be a total of 20.9 billion dollars saved due to off-shoring. Off- shoring is lowering costs and boosting productivity which in turn leads to lower inflation and interests rates, but a higher economic activity (Global Insight, 2004). With the increased economic activity, new jobs are being created both in IT and other industries (Global Insight, 2004). Consequently, even though jobs would be displaced by off-shoring, additional jobs would be created to compensate for this loss

(Global Insight, 2004). However, even though unemployment may not be affected, the off-shoring process may lead towards domestic employees settling for lower wages and benefits that would translate to reduced work satisfaction, increased poverty and widened inequalities (Reich, 2010).

There are several reasons why companies decide to move production abroad. One reason that is often mentioned in the literature is the shortage of skilled human capital within the developed countries (Gold, 2012b; Marin, 2010b; Privett, 2011). The developed countries suffer from the lack of available workforce in some of their skill intensive activities and therefore need to outsource to regions where there is abundant skilled labour. This shortage of available skilled labour in the advanced economies is widened by the aging population in the developed economies, the rise in income inequality and the underperforming education system. However, the process of outsourcing is frequently associated with the ability of firms to save costs. As the majority of companies are driven by profit-maximization, outsourcing gives the possibility of firms to reduce costs and increase profits. There are different numbers of the amount of money saved due to outsourcing, but, on average, around 15% of the costs are being saved due to outsourcing (Belcourt, 2006). There are two different possibilities through which firms can save costs by outsourcing production. First, an important part of the industries are sensitive on labour costs (Aspray, 2010). Since the labour costs in developing countries is much cheaper than in the develop countries, firms may off-shore production to save money on the labour costs (Aspray, 2010; Global Insight, 2004). The second method that firms can use to save money is by focusing on their core competence and consequently, by outsourcing the competencies that are not core to their business. Focusing on just the core competence can enable a company to achieve economies of scale which translates in reduced costs by spreading the fixed costs to more users (Belcourt, 2006).

Aside reduced costs, focus on core competences and access to skills, there are several other reasons from which companies benefit as a result of the outsourcing process. By outsourcing, companies can get access to the local market and understand its needs, making them more efficient and targeted when offering a product or a service to the new market (Aspray, 2010). In addition, off-shoring gives companies the opportunity to work continuously throughout the entire day, by passing the work on to the next office situated in a different location (Aspray, 2010). Nevertheless, managing virtual teams can be challenging and may not provide the intended initial results (Unit, 2009). Outsourcing production can lead to other benefits such as improved service/product quality, greater flexibility or access to latest technology or infrastructure (O. I. T. Tibor Kremic, Walter O. Rom, 2006).

Nevertheless, off-shoring is not a risk-free process. There are also threats and hidden costs when the decision to off-shore is made. If a company decides to off-shore production, the savings in labour costs will almost never represent the total savings for a company. There is a cost in selecting the most suitable vendor, which may also take considerable time (Marc J. Schniederjans, 2004). In addition, there is the risk to lose control over core functions if such functions are mistakenly outsourced (Overby, 2003). The suppliers are chosen by the vendor and therefore control over the suppliers is going to be weak which may also affect the quality of the outsourced products (Overby, 2003). Another category of important hidden costs are the transition costs. It takes time for the entire process to be transferred from the current location to another one, which may turn out to be costly for the company (Marc J. Schniederjans, 2004). There is sometimes an additional cost in laying-off the workers in the current location. A manager

13 wants to make sure that the transition to the off-shore location is made smoothly and that the knowledge is properly transferred, therefore the employees that need to be kept have to be paid additional bonuses (Marc J. Schniederjans, 2004). Moreover, there is always the risk that the people that survived from being laid-off may experience lower morale and hence lower productivity (Overby, 2003).

As the section shows, there are definitely certain advantages of off-shoring production to a foreign location, but one should not ignore the potential hidden costs and risks that the off-shoring production presents.

2.3. Romania and Germany

The previous sections have shown some of the benefits and costs of outsourcing production and some of its impacts in the current macroeconomic environment. This section is going to identify how Romania and Germany are integrating the process of off-shoring within their economies.

Germany has been characterized as being a ‘bazaar economy’ that sells high-quality products worldwide without being produced domestically (Emanuele Breda, 2010). This conclusion has been drawn after seeing that the increase in the German industrial production was higher than the increase of the real value added of German industry between 1995 and 2003. In addition, the increased German production should have meant an increase in the industrial employment, but in fact the industrial employment decreased during this period (Emanuele Breda, 2010). The ‘bazaar economy’ is characterized by international fragmentation of production in the German economy that has been accelerated with the opening of the Eastern European market (Emanuele Breda, 2010). Germany is becoming a “continuous- flow water heater for manufactured goods”, where the upstream production is moved abroad and the downstream production is kept within Germany (Sinn, 2006). In other words, Germany is relying more on the capital intensive activities, while the labour intensive ones are being moved abroad. The German manufacturing has therefore experienced a production depth decline over the last period with more than 90% of the decline being attributed to shifting intermediary production abroad (Sinn, 2006). There is a big debate on whether shifting to a “bazaar economy” is good or bad from a German perspective. There are voices that say that by relocating labour intensive jobs abroad more sophisticated jobs are being created in Germany. However, evidence shows that this might not be the case and on the contrary, German workers are losing jobs without being created any replacement ones. In this context, firms manage to remain competitive by gaining from the lower wages in Eastern Europe, but the German worker remains jobless and becomes dependent on the welfare state (Sinn, 2006). The German problem arises from its resistance to converge wages towards the factor price equilibrium created through globalization as a result of the single world labour market. With the help of strong unions and political support, the German wages are set above the factor price equilibrium and therefore lead to unemployment that is beyond the social optimum (Sinn, 2006). According to (Sinn, 2006), the situation would be only worsened with the introduction of trade protectionism or minimum wage and it would only benefit on the long-term with the improvement of the quality of education. The only solution that would bring results on the medium term would be to make the labour markets more flexible so that wages can adjust to their equilibrium values (Sinn, 2006). Nonetheless, in 2014 the German government has decided to introduce for the first time a minimum wage of 8.5 euros per hour worked applying from

2015 (Huggler, 2014). The introduction of the minimum wage would on the contrary make labour markets more rigid and more German companies may move production abroad to remain competitive.

The international fragmentation of production is seen both with negative and positive eyes within the economic literature. On one side, this process leads to delocalization of jobs that means that jobs are lost in the domestic economies. Other sources debate that by moving production elsewhere, firms have the opportunity to remain competitive and would eventually have a positive effect on employment. In addition, consumers would also benefit from the cheap imports (Affairs, 2005). Due to the off-shoring of production in Eastern Europe along with wage restraint policies, Germany managed to keep its unit labour cost low and remain competitive on the market (Marin, 2010a). Productivity gains from the off- shoring process are found to be responsible for the only minor job loss in Germany due to the opening up of Eastern Europe, German firms being able to cut costs by moving production to Eastern Europe and benefit from the pool of skilled workers. However, as the demand for skilled workers became lower in Germany, there has been a downward pressure on the skilled wages (Marin, 2010a). Its competitiveness on the market doubled by the healthy trade balance, enabled Germany to be one of the countries that have suffered the least from the effects of the financial crisis of 2008 ((UNDP), 1999).

During the Cold War, the markets in the Eastern Europe were practically closed for any foreign investment and there was little immigration to the Western countries (Freeman, 2006). But with the fall of the Soviet Union, along with the opening of the Chinese and Indian markets for foreign investments, the global economy has seen its workforce doubled in what has been called ’The Great Doubling’ (Freeman, 2006). As a result of the increase in workforce, the balance between capital and labour changed in the global economy. Companies become interested in hiring workers from these new open markets, benefiting from the low labour costs instead of hiring workers in the advanced economies. On the other hand, the newly entered countries in the global economy brought little capital since they were one of the lowest income countries (India or China) or were using out-dated technology, as was the case of the Eastern-European countries (Freeman, 2006). The Eastern European market benefited from the access to new capital and improved technology, whereas Western European manufactures have been profiting on the low wages in the Eastern Europe and started production there. In addition, having a new supply of lower wage workers gave firms the power to threat labour unions in advanced economies with moving production in case lower wage settings would not be accepted (Freeman, 2006). The Eastern- European countries are also said to benefit from the job displacement from the advanced economies. In one study, it has been found out that 37% of the total off-shoring is directed towards Eastern-Europe, with Romania having directed 8% of this share. However, an estimate of the number of jobs displaced due to off-shoring in Eastern Europe or Romania is not given (Mark Knell, 2009).

As being one of the countries behind the Iron Curtain until 1989, Romania has benefited in terms of foreign investment along with other former communist countries from the fall of the Soviet Union. The advanced economies found in Romania and other Eastern European countries low-wage skilled labour force that would help companies save costs and increase their profits. After the Iron Curtain fall, the foreign direct investments in Romania had experience an increase almost every year until 2006, having only a slight decrease in 2002 in comparison with the previous year (UNCTADstat, 2013).

The adherence of Romania to the European Union boosted foreign direct investments (FDI) even more reaching a record level of 9496 million Euros in 2008 ((BNR), 2009). After 2008, the amount of FDI in Romania has dropped significantly primarily due to the worldwide financial crisis that occurred in 2008. Figure 6 shows the evolution of the foreign direct investment from 2003 to 2011 using data given by the National Bank of Romania and the National Statistical Institute of Romania ((BNR), 2009).

10000

8000

6000 FDI 4000

Millions Euro Millions 2000

0 2003 2004 2005 2006 2007 2008 2009 2010 2011

Source: ((BNR), 2009)

Figure 6: Evolution of FDI in Romania

The total FDI was directed to various economic sectors, but the manufacturing and financial intermediaries sectors attracted the biggest share of the FDI ((BNR), 2009). From the manufacturing sector, the most FDI was directed towards the oil, chemical, rubber and plastic industry, transportation industry and metallurgic industry ((BNR), 2009). Figure 7 presents an accurate picture of how the FDI has been spread across the Romanian industries, whereas Figure 8 depicts how the FDI is spread across the manufacturing sector ((BNR), 2009) .

Manufacturing

15.9 Financial intermediaries 31.5 Commerce 5.4 Construction 7.9 Energy, gas, water

10.7 ICT 18.2 11.4 Other sectors

Source: ((BNR), 2009)

Figure 7: The division of FDI across Romanian economic sectors

Oil, chemicals, rubbe rand plastic 20 25 Transport equipment

Metallurgy

16.3 Food, beverages and 10.2 Tobacco Cement, glass and ceramics 13 15.5 Other activities

Source: ((BNR), 2009)

Figure 8: The division of FDI across the manufacturing sector

From the total of FDI in Romania, a high proportion of the capital comes from the Netherlands, Austria and Germany. German companies have been a constant investor in Romania’s economy in the last years. Figure 9 shows the percentage of the FDI that has been attributed to German companies in the 2004- 2011 period, period in which Germany has almost always been one of the top three investors in Romania ((BNR), 2009).

16 14 12 10 8 Share of German FDI 6

4 Percentage 2 0 2004 2005 2006 2007 2008 2009 2010 2011

Source: ((BNR), 2009)

Figure 9: Share of German FDI in Romania

Although Germany is not the top investor in Romania, a study on Germany and Austria reveals that over 60% of the investment of German firms in Romania represent off-shoring activities (Marin, 2005). In comparison, from the total Austrian FDI in Romania, less than 25% can be attributed to off-shoring activities (Marin, 2005). Moreover, the percentage of FDI that is resulted from off-shoring activities is greater for Germany than Austria in most of Eastern Europe. Table 2 gives the comparative values of the percentage of FDI that is determined by off-shoring activities for Germany and Austria. Consequently, off-shoring production of German companies is the most important activity in which FDI from Germany is directed and represents an important factor within the Romanian economy.

Austria Germany Romania 24.20 63.68 Eastern Europe average 17.27 45.44 Source: (Marin, 2005)

Table 2: Off-shoring in Romania and Eastern Europe

The off-shoring of production is found to be creating new jobs in the low-wage country (Affairs, 2005). A study on Romanian’s economy has found that there is a negative correlation between FDI and unemployment, meaning that an increase in FDI would lower the unemployment level in Romania (Lavinia Stefania Totan, 2013). However, the study does not make any distinction between low-skilled and high-skilled sectors, nor does it focus on the off-shoring share of the FDI. Moreover, a study on labour market impacts of FDI founds that FDI promotes higher wages for the country where these investments are directed (Arnal, 2008). However, a study on the Romanian economy finds that a Romanian region favoured from intense FDI has comparable wages with Romanian regions where FDI is almost lacking, concluding that FDI alone may have no positive effect on the wages in Romania (Paslaru, 2014).

The above discussion leads to a question to investigate in this paper: how does the off-shoring production of German companies to Romania affect the unemployment and wage levels in the two countries? The aim is not only to find an effect on the overall economy, but also try to distinguish between different industry sectors. This question comes in a time when the relations between Germany and Romania seem to be tighter than before, as the new Romanian president that was elected at the end of 2014 has German origins. Moreover, at the beginning of 2015, the German foreign minister met with the Romanian president in order to discuss new methods of cooperation between the two countries and identify ways to boost FDI from Germany in Romania (Steinmeier, 2015). In this context, establishing the relation between German off-shoring to Romania on the unemployment and wage levels would be of great importance.

2.4. Ethical considerations

Aside from the economical dimension, there is the question whether off-shoring production of German companies to Romania is ethical. In his book, Tomas Sedlacek argues that economics should not blindly rely on mathematical models, but return to its origins and take into account the ethical and moral dimension as well (Tomas Sedlacek, 2013). Economics has stopped being a normative science, in which the discussion focused on whether the proposed economic principle was ‘good or evil’ and transformed into a positive science that tries to depict the world as it is using mathematical models (Tomas Sedlacek, 2013). The problem is that the mathematical models are often based on unrealistic assumptions that have no connection with the real world. Sedlacek’s suggestion is that economics should return to its ‘soul’ and rely less on mathematical modelling and more on ethical principles (Tomas Sedlacek, 2013). This does not mean that mathematics should not be still used in economics, but attention should be given to morality and ethics as well. In addition, Sedlacek identifies another problem in today’s economic system. Today, the core value of the economic system is the endless need to grow. Our society is the richest ever, but it seems that nobody wants to stop and enjoy what has been achieve, but on the

18 contrary, grow even more leading to more working hours and increasing debts. In regard of this evidence, Sedlacek’s suggests that we should become more humble and be satisfied with what we have and try to limit our never ending desire of growth (Tomas Sedlacek, 2013). The insights that Sedlacek expressed in his book can be applied to the process of off-shoring. As seen previously in this chapter, the majority of the literature on off-shoring is focused on the economic costs and benefits of this process that are computed using different assumptions and mathematical approaches. What is often lacking is the inclusion of a discussion of the morality of this process, of whether off-shoring production is ‘good or evil’. Moreover, off-shoring is a way through which companies are expected to save costs which helps them remain competitive on the market and growth more. The question then comes whether this need of growth is not leading to unintended side-effects such as job loss and reduced wages and whether not the endless pursuit of growth, but other things should be the primarily values that a company should aim for.

There are several articles that tackle the ethical perspective of the outsourcing process. One article builds up a conceptual model that suggests that managers should balance the self-interest and selflessness issues in their decision making process. One limitation of this study though is the empirical evidence of whether managers do factor both the self-interest and selflessness in their decision making process (Ambika Zutshi, 2010). A second study uses a stakeholder analysis approach in order to establish the different interests of the involved parties from the outsourcing process. The article concludes that corporate social responsibility should be given more importance within the outsourcing process, but there also lacks empirical evidence that this happens in reality (Wenzhong, 2013a). The lack of corporate social responsibility is found to have a negative impact on the product quality and information security trust in a study that uses vignettes for empirical testing (Christopher J. Robertson, 2010).

A study on the ethics of outsourcing suggests that jobs need to be displaced in order for companies to remain competitive. This is also said to be economically efficient, since for every job lost two or three new jobs are created (McGee, 2005). Nevertheless, the study does not present any evidence of the number of jobs displaced and the number of new jobs created due to the outsourcing process. The study examines briefly the nature of outsourcing using well-known ethical theories: utilitarianism and theory of rights. Although there are several variants of the utilitarianism concept, the main objective of this theory is ‘doing the greatest good for the greatest amount of people’ (Bentham, 1776). On the other hand, the main argument of the rights theory suggests that rights are moral laws specifying what an individual should be free to do (Biddle, 2011). The article concludes that from both perspectives, outsourcing would be a good thing (McGee, 2005).

One of the main problems of the utilitarian perspective is that it does not take into account the distributive justice. From an utilitarian point of view, what is important is the maximization of the total ‘happiness’, disregarding any issues concerning the pattern of distribution of the ‘happiness’. Although, it may be true that more jobs are created than lost due to outsourcing, making it a positive sum gain, the utilitarianism in this form does not pay any attention on the people losing jobs and how are they reintegrated in society. In his book, Tandy Gold proposes a series of steps that should be assessed when implementing an off-shoring program in order to minimize impact on individuals and communities (Gold, 2012a). Gold argues that a company should invest part of the money saved into helping lay-offed

19 workers re-enter the market, either by providing financial compensation or training for requalification (Gold, 2012a). This cost should be factored in the offshoring program and the efforts of the company to help those laid-off should be communicated to the community in order to prevent any negative image associated with moving production (Gold, 2012a).

The conclusion whether outsourcing is ethical may be different when using the framework described in John Rawls’s book ‘A theory of justice’. The main idea of the book is that any decision should be based on two principles of justice: each person should have an equal right and if inequalities are accepted, then the inequality should be in the advantage of the one that is least off (Rawls, 1971). The first principle refers to the basic liberties of citizens such as the right to vote, freedom of speech or freedom of holding property, which in the view of Rawls should be equal to everybody in the society (Rawls, 1971). The second principle merely applies to the distribution of income and wealth. According to Rawls, the distribution of income should not be equal as long as the person that is the least off wins from such an unequal situation (Rawls, 1971). The establishment of just principles should be done in what Rawls calls ‘the original position’. The idea of the original position is to set up a fair procedure so that all the agreement reached would be fair (Rawls, 1971).Therefore, the pure procedural justice, in which there is no independent criteria for the right outcome, but the outcome is fair as long as the procedure is fair, is used as a basis of this theory (Rawls, 1971). Rawls believes that a fair procedure can only be achieved if the parties are situated under a ‘veil of ignorance’, so that they would not be tempted to use any social or natural circumstances in their own advantage (Rawls, 1971). The ‘veil of ignorance’ assumes that the parties have no knowledge about their place in the society and about their interests nor about their abilities and strengths. The only thing that the parties know is that the society is subject to the circumstances of justice. Therefore, by pursuing only the principles of justice and not any circumstances that would be exploited in their own advantage the parties would be able to reach a fair outcome (Rawls, 1971). The process of off-shoring production from Germany to Romania is not straight-forward beneficial to the people situated in the least advantageous position and often companies that are off- shoring are following their own interest in order to save money and increase their profits. Therefore, using John Rawls’s framework, the process of off-shoring may be regarded as an unjust procedure.

On the base of these theories and using the empirical evidence of how the off-shoring process from Germany to Romania affects the employment and the wage levels in the two countries, this paper questions whether the off-shoring process is an ethical one. The results of this discussion should make governments and managers more aware of the implications of off-shoring production and encourage them to behave in a more ethical way.

As this chapter has shown, there are a series of macro-economic developments that affect to some extent the process of off-shoring. There is a vast literature on the economic benefits and costs of off- shoring, but however there is not so much discussion when it comes to ethics in off-shoring. Evidence also shows that Germany, through its bazaar economy, is one of the most important foreign investor for the Romanian economy, which has been recently opened up after being closed for foreign investment during the communist regime.

With this evidence, the relationship between German off-shoring in Romania and the employment and

20 wage level in the two countries seem to be of interest. Moreover the ethical part of this paper should bring a different perspective on the off-shoring process that seems to be inexistent so far. The paper continues with explaining the methodology used for answering the research questions.

3. Methodology

The main purpose of the paper is to establish whether the off-shoring production from Germany to Romania has any influence on the employment levels and wage levels in the two countries. Based on the results, the paper should then reflect on whether the off-shoring process can be seen as an ethical process. This chapter contains a section explaining how the data relevant for the off-shoring process was collected and how this data has been used to show the level of off-shoring from Germany to Romania. Next part of the chapter deals with the data on employment, wage and labour productivity levels in the two countries and how these are used, together with the off-shoring data, to establish whether there is a correlation between offshoring and the change in the employment and wage levels.

3.1. World Input-Output Database

The data from the world input-output database (WIOD) is used to find the level of off-shoring occurring from Germany to Romania. The WIOD is a publicly available database that covers forty countries, including Romania and Germany, and thirty-five different industries (Erik Dietzenbacher & Vries, 2013). The database is available for seventeen years, from 1995 to 2011. The world input-output table gives information on the bilateral international trade flows for all forty countries showing the interdependencies of these countries for the different industries (M. P. Timmer, Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. , 2015). For the purpose of this paper, the WIOT presents the bilateral trade flow between Germany and Romania. Therefore, for each of the seventeen years for which there is available data, there is a 35 by 35 matrix covering the bilateral trade flows between the industries of the two countries. Each cell of the matrix represents the value of intermediary goods imported from the economic activity of one of the countries to another economic activity of the other country.

3.2. Actual off-shoring index

In order to establish the level of off-shoring for German companies to Romania, the actual off-shoring index is to be computed following the approach developed by Feenstra and Hanson (Hanson, 1996). In this context, the actual off-shoring index (AOI) is the ratio of imported intermediate inputs from Romania to Germany to the total intermediate inputs in Romania (Semih Akcomak, 2013). This concept is based on the assumption that the off-shored activity is imported back to the mother country where it will be used as an input in order to produce final goods. The AOI computed in this way is relevant for Romania since it is a good indicator showing whether there is a change in the off-shoring decision of German companies to Romania as well as whether German companies that were already off-shoring decided to relocate production to Romania. Nevertheless, whether German companies that were already off- shoring are relocating production to Romania is not of great importance for the German economy. From this perspective, the main objective is to see the trend of off-shoring production to Romania for firms that are producing internally. Consequently, a derived indicator will be used that measures the ratio of imported intermediate from Romania to Germany to the total intermediate inputs in Germany without taking into consideration imports of intermediary goods from other countries except Romania. This indicator will show the trend of off-shoring from Germany to Romania without being biased of any

22 relocation decisions to Romania of German companies that are already off-shoring elsewhere in the world.

The two indicators are computed for the seventeen years of our research period (1995 to 2011) for the overall economies of the two countries. For Romania, for each economic activity, the AOI is computed by dividing the sum of the value of the intermediary products produced in Germany and exported to Romania to the total value of intermediary consumption in Romania. This ratio is then multiplied by 100. The formula for the Romanian AOI is given by equation (1).

GerEX AOI  100 (1) Romania TIC

, where AOIRomania represents the actual off-shoring index of Romania, GerEX represents the sum of German intermediary products exports to Romania and TIC is the total intermediate consumption in Romania.

For Germany the AOI is computed similarly but the denominator has to be adjusted so that it contains only the intermediary products used for domestic production in Germany and the amount of intermediary products that are produced in Germany and exported to Romania. The AOI for Germany is given by equation (2).

GerEX AOI  100 (2) Germany GerEX GerDom

, where GerDom represents the sum of all the German intermediary products that are meant for domestic use.

3.3. Employment in Romania

The National Institute of Statistics (INS) in Romania provides data on the employment levels in Romania throughout the entire 1995-2011 period. The data contain information on both the overall level of employment and also for the employment levels in different economic activities in Romania.

Looking on the overall employment data provided by INS, there can be seen that the number of total employment has been experiencing a decline of about 25% in the period 1995-2011. According to official statistical data, the cause of this decline can be found from the negative birth rate that Romania has been experiencing over the last 20 years, but also because of the external migration of the active workforce. From 1992, the natural birth rate has been negative for all years until 2013 according to official statistical data. In addition, the net migration has been also negative for most of the period after 1990 (Bogdan Alexandru Suditu, 2012). However, as a study on the demographics of Romania shows, the official statistical data on migration captures only the permanent immigration and does not consider the effects of temporary immigration. According to the study, the temporary immigration is responsible for two thirds of the decrease in Romania’s population (Ghetau, 2007).

One of the main reasons why people immigrate relates to the economic conditions within the country. Living in poverty determines people to seek for stable earnings, better employment conditions and better life conditions in more developed and industrialized countries (Shah, 2008). With the fall of the communism in Romania, the country entered a transition period in which the needed reforms to switch from planned economy to a market-based one have been introduced with great difficulties. Therefore, the first years after the communism fall were marked by a decrease in the living standards for the Romanian population (Cerna, 2014).

As the INS data shows, the decline in the workforce population in Romania has experienced a brief pause during the 2000s. During this period, Romania experienced the biggest expansion and economic growth from 1990 and therefore it has been associated with prosperity by the Romanian population (Claudia Bentoiu, 2012). However, the period after 2008, Romania started feeling the effects of the global crisis and the government started to reduce its spending and introduced reforms that cut down public wages by 25% and increased the value-added tax on goods with 5% (Neagu, 2010; Pirvoiu, 2010). During this period, the workforce in Romania has again experienced a slight decrease presumably because of the worsening living conditions in the country.

Consequently, using the value of the employment level in Romania for answering the main research question might be biased because of the severe change in population due to immigration and negative birth rate that Romania experienced in the last 25 years. In order to overcome such bias, the value used for determining whether there is any effect of off-shoring from Germany to Romania on the Romanian employment level is going to be the share of employment relative to the population of Romania. Therefore, in a given year, the value used for a specific economic activity is given by equation (3).

Number_ of _ employees _ in_ the _ activity Emp  (3) Ro Total_ population

3.4. Wage level in Romania

The INS also provides data for the average nominal net salary in Romania for the 1995-2011 period. The data also differentiates between the average net salary on the total Romanian economy and the average net salary for different economic activities. The reason for choosing the net wage as a relevant figure for this paper’s purpose and not the gross wage comes from the fact that the net wage is not dependent on the fluctuation of the fiscal taxes. The fall of the communist regime left Romania in a transition period that had to switch from the former planned economy to a market based one. New laws concerning the new economy have been gradually introduced and were frequently changed with the coming of a new government. This is true for the fiscal laws as well. Before 2003, there were numerous laws and acts that were trying to establish a framework for the fiscal taxation. However, these laws and acts were specific to each governments and suffered frequent modifications (Medrega, 2013). Only in 2003, the first modern fiscal code was introduced in Romania. Even though this was an important achievement, in the next ten years the code suffered approximately one hundred major modifications that have changed some of the fiscal procedures in Romania (Medrega, 2013). Therefore, in order not to deal with the fiscal law instability that was marked in Romania for the desired research period, the net salary is going to be

24 used as a unit of measuring the wages for the Romanian economy in 1995-2011 period.

The data on the average nominal net salary cannot display though how the purchasing power of employees varied during the 1995-2011 period. This is because the data is not corrected for the inflation rate that occurred in the years of the research period. In order to overcome this problem, the INS provides data regarding the real wage index, which is computed by dividing the average nominal net salary to the consumer price index. The consumer price index (CPI) is the ratio of the cost of purchasing a basket of goods in one year to the cost of purchasing the same basket of goods in a different year (Anthony Boardman, 2014). By setting 1995 as the reference year, the real wage index for Romania, expressed in percentage, is given for each year in Table 3.

Years Reference year: 1995 1995 100 1996 138.8 1997 353.6 1998 562.6 1999 820.3 2000 1194.9 2001 1606.8 2002 1968.9 2003 2269.7 2004 2539.2 2005 2768.1 2006 2949.7 2007 3092.4 2008 3335.1 2009 3521.5 2010 3736.1 2011 3952.3 Table 3: Real wage index for Romania having 1995 as reference year

The real average net wage for one of the years 1995-2011 is then computed by dividing the average nominal net wage in a selected year by the corresponding real wage index of that year and finally multiplying the result by 100. The result is showing the real purchasing power of the Romanian employee in that sector, having year 1995 set as reference year. In a mathematical notation, the formula for the real average net wage is given by equation (4).

nij, 100 rij,  (4) rwii

,where rij, represents the real average net wage in year i for the economic activity j, nj represents the nominal average net wage for year i in the economic activity j and rwii is the real wage index corresponding to year i.

The average real net wage is expressed in Romanian’s local currency RON. However, the data until 2005 was found to be expressed in ROL, the currency that was in use before the denomination that occurred in July 2005. The denomination of 2005 meant cutting off four zeroes from the back of each ROL so that 1 RON would be equivalent to 10,000 ROL. Therefore, in order to express all the average real net wages in a single currency, all the values prior to 2005 that were expressed in ROL have been divided by 10,000. As a result, all the values of the average real net wage for the 1995-2011 have been expressed in RON.

3.5. Labour productivity in Romania

Labour productivity is the indicator that measures how much output has been produced in an hour of labour. There are several ways of computing this indicator; the Romanian Statistical Institute chose to measure the labour productivity as the value added by an activity divided to the total number of hours worked in that activity. The data for labour productivity for the 1995-2011 period is already given in the Romanian Statistical Institute’s databases for the whole Romanian economy. However, this indicator is needed for the three different sectors of the Romanian economy as well. In addition, the total labour productivity that is found in the Romanian Statistical Institute database relies on data of the value added expressed in current prices and not in real ones. Acknowledging these limitations, the method used is to separately determine the gross added value for all activities belonging to each sector and of the total number of hours worked in each sector respectively. The value added is to be transformed into real prices using Table 3 and using the same approach as for determining the real wages in Romania previously discussed. The way the different activities are aggregated for each sector is discussed further on during this chapter. For the time being, it is enough to say that the value added and the total number of hours worked is computed for each sector using the data available from the Romanian Statistical Institute database. The labour productivity for each sector is then computed by dividing the value added to the total number of working hours using the formula given in (5).

vai lpi  (5) hwi

, where lpi represents the labour productivity for one year in sector i, vai is the value added by sector i expressed in real terms and hwi is the total number of hours worked in sector i. The labour productivity is then computed for each sector for each year of the research period.

3.6. Employment in Germany

The data on the employment level for Germany can be found by searching through the databases offered by the German Statistical Institute. The data is available for the whole German economy as well as for the different economic sectors. There is also data divided on the German economic activity however, the data is not complete and not usable for reasons that will be explained further on during this chapter.

The German population has remained fairly constant over the 1995-2011 time frame. According to the statistical numbers, the maximum shift in the total population during the 1995-2011 period was around

1%. Although the fertility rate was below the optimum level of 2.1 that would keep the population constant, Germany profited by having a positive balance in term of migration on almost the entire research period. Consequently, during 1995-2011 Germany did not experience major shifts in its total population.

Taking this into consideration, for Germany’s case, the data of the total number of employees in a particular sector would solely reflect how the employment level fluctuated during the 1995-2011 period. This data is then simply taken from the German Statistical Institute database.

3.7. Wage level in Germany

The German Statistical Institute offers in its databases information about the average nominal net wage in Germany for the 1995-2011 period. The data is given for the whole economy as well as for different economic sectors and activities. For the same reasons as the ones explained in Romania’s case as well as due to uniformity concerns, the net wage was chosen in favour of the gross wage.

As in the case of Romania, the data on the nominal wages does not reflect how the purchasing power changed for the employees during 1995-2011. In order to reflect that the real wages should be used instead. The real wages are the nominal wages divide to the consumer price index, which reflects the cost of purchasing a basket of goods. Taking 2010 as reference, the CPI for Germany is given in Table 4.

Years Reference year: 2010 1995 80.7 1996 81.7 1997 83 1998 83.5 1999 84 2000 85.2 2001 86.8 2002 88 2003 88.9 2004 90.5 2005 92.2 2006 93.9 2007 96 2008 98.6 2009 98.9 2010 100 2011 102.5 Table 4: Real wage index for Germany having 1995 as reference year

Even though the year of reference is set to 2010 and not 1995 as in the Romanian case, the same formula (4) applies for transforming the nominal wage into real ones. One last remark concerning the data on the wage level in Germany: the EURO was introduced only in 2001, Germany having the German mark as official currency until then. Nevertheless, the German Statistical Institute managed to make the

27 data uniform by converting the amounts in German marks to Euro, therefore the data that it is dealt with is all expressed in Euro.

3.8. Aggregating economic activities

The classical approach is to divide the overall economy of one country into three main sectors: primary, secondary and tertiary. The primary sector is usually characterized by the activities that lead to the acquisition of raw materials, such as agriculture, hunting or mining. The secondary sector refers to the activities that process the raw materials into components and finite products. This sector is often called the manufacturing sector and it includes activities that lead to the development of textile, electrical or oil products. The tertiary sector, often called the service-based sector, include the activities that support the production process such as retail, insurance or tourism activities. For the purpose of this paper, Romania’s and Germany’s economies are to be divided in these three distinct sectors.

The input-output database contains data for the different industries as defined by the NACE rev 1 standard. The Romanian Statistical Institute also provides data for employment and wage level defined after the NACE rev 1 standard but only until 2008. From 2008, the standard used is the NACE rev 2. However, for aggregating the economic activities into the different economic sectors, the different standardization is not affecting the research. Table 5 shows how each field from the input-output database is aggregated into the three different economic sectors and also how the fields defined by the Romanian Statistical Institute are combined for both standards used.

Romanian activities according to Romanian activities according to NACE Sector World Input-Output Database NACE rev 1 rev 2 Agriculture, Hunting; Forestry; Agriculture, Hunting, Forestry and Fishing; Mining and Agriculture, Forestry and Fishing; Mining Primary Fishing; Mining and Quarrying Quarrying and Quarrying

Food, Beverages and Tobacco^;Textiles and Textile Products^; Leather, Leather and Footwear^;Wood and Products of Wood and Cork^;Pulp, Paper, Printing and Publishing^; Manufacturing*;Production of Electricity, Coke, Refined Petroleum and Nuclear Fuel^;Chemicals and Manufacturing*;Electricity, Gas Gas, Water and Air Conditioning; Secondary Chemical Products^;Rubber and Plastics^;Other Non- and Water Supply; Construction Distribution of Water, Garbage Metallic Mineral^;Basic Metals and Fabricated Management; Construction Metals^;Machinery, Nec^;Electrical and Optical Equipment^;Transport Equipment^;Manufacturing, Nec; Recycling^;Electricity, Gas and Water Supply; Construction

Sale, Maintenance and Repair of Motor Vehicles and Trade; Hotels and Restaurants; Retail and Wholesale Trade; Repair of Motorcycles; Retail Sale of Fuel; Wholesale Trade and Transport and Other Auxiliary Motor Vehicles and Motorcycles; Commission Trade, Except of Motor Vehicles and Services; Post and Transport and Other Auxiliary Services; Motorcycles; Retail Trade, Except of Motor Vehicles and Telecommunications; Financial Hotels and Restaurants; Information and Motorcycles; Repair of Household Goods; Hotels and Intermediaries; Real Estate Communication; Financial Intermediaries Restaurants; Inland Transport; Water Transport; Air Activities and Other and Insurances; Real Estate Activities; Transport; Other Supporting and Auxiliary Transport Tertiary Complementary Services; Public Professional, Scientific and Technical Activities; Activities of Travel Agencies; Post and Admin and Defence; Education; Activities; Administrative and Support Telecommunications; Financial Intermediation; Real Estate Health and Social Work; Other Service Activities; Public Admin and Activities; Renting of M&Eq and Other Business Activities; Community, Social and Personal Defence; Education; Health and Social Public Admin and Defence; Compulsory Social Security; Services Work; Other Community, Social and Education; Health and Social Work; Other Community, Personal Services; Social and Personal Services

^These economic activities are all part of the manufacturing industry

* The manufacturing data provided by the Romanian Statistical Institute is further divided on the different manufacturing industries similar to the ones provided by the World-Input Database. The data can be found in a different database.

Table 5: Division of economic activities for the different economic sectors

Using the division explained in Table 5 the wage level and employment level for the three different economic sectors in Romania are computed using a similar approach as was previously explained in this chapter. The actual off-shoring index for Romania is left unchanged as the one computed using (1) showing the amount of off-shoring from Germany to Romania.

For the employment level in Romania, the number of employees for each sector is computed in a similar way using the equation (3). The difference is that the denominator of equation (3) represents in this case the sum of all the employees in an economic activity that belongs to a specific sector, the primary, secondary or the tertiary one. The rest of the elements of equation (3) remain unchanged.

For computing the wage level in Romania for each of the three different economic sectors, a more complex approach has to be used. The average nominal net wage is computed by dividing the sum of the products between the average nominal net wage for an activity belonging to that economic sector and the number of employees working in that activity to the total number of employees working in that sector. The mathematical formula of the average nominal net wage for one sector is given in (6).

 Annwjj Number_ employees Annwi  (6) Total__ number employees i

The average nominal net wages that are computed using (6) for each of the three economic sectors are then transformed into real values using the same approach previously discussed in the chapter by using the mathematical formula defined in (4) and the data from Table 3.

There is a bit more work to do in the case of Germany. The actual off-shoring index though remains the same as the one computed for the whole economy using the equation (2).

One advantage that the German case provides is that the German Statistical Institute withholds data on the average nominal net wage and number of employees for the primary, secondary and tertiary sector. There is though a difference from the approach used so far: in their datasets, the German Statistical Institute includes the Mining and Quarrying activity in the secondary sector and not the primary one. To move the data of this economic activity from the secondary sector to the primary sector, the data on the average nominal net wage and on the number of employees for the Mining and Quarrying activity alone are needed. Fortunately, the German Statistical Institute does provide such data for this economic activity for the 1995-2011 period.

Consequently, the number of employees for the primary sector in Germany is computed by adding the values on the number of employees on the primary sector given by the German databases (which excludes the Mining and Quarrying activity) and the number of employees working in Mining and Quarrying. For the average nominal net wage, the same approach is used as in Romania’s case by using the formula (6).

The data given by the German Statistical Institute on the secondary sector includes the Mining and Quarrying activity. Therefore, in order to compute the number of employees for the secondary sector, from the number of employees on the secondary sector given by the German Statistical Institute, it has to be subtracted the total number of employees in the Mining and Quarrying economic activity. In addition the average nominal net wage for the secondary sector in Germany is

30 computed using the mathematical formula defined in (7).

Annws E s  Annw M  E M Annwsecondary  (7) Esecondary

,where AnnwsS E is the product between the average nominal net wage and the number of total employees in the secondary sector as found in the German Statistical Institute’s databases,

AnnwMM E is the product between the average nominal net wage and the number of employees in the Mining and Quarrying activity in Germany and Esecondary is the total number of employees in the German secondary sector as previously defined without containing the employees working in the Mining and Quarrying economic activity.

Lastly, the data for the tertiary sector provided by the German Statistical Institute is in accordance with Table 5 and can be used in the way it is given in the database. All the values for the average nominal net wage are transformed into real values by using the formula given by (4) and the data from Table 4.

3.9. Limitations of the data

As mentioned earlier in the chapter, the data for the number of employees and the wage level for each different economic activity can be only partly used for the purpose of this paper. The problem arises from the fact that during 1995 and 2011 two different standards for classifying the economic activities have been in place.

The world input-output database is composed of 35 different industries as defined by the NACE rev 1 standard (M. Timmer, 2012). Therefore the actual off-shoring index can be computed only for the industries as they are defined by the NACE rev 1 standard.

In the case of Romania, the number of employees and the average wage per industry in one year have been released until 2008 using the same NACE rev 1 standard. However, starting with 2008, the Romanian National Institute started to release the data following the new NACE rev 2 standard. Although, there are several sources that present the differences between the two standards, it is not feasible, for the purpose of this paper, to uniform the two different sets into a single set compatible throughout the 1995-2011 period for all the different industries. The Romanian Statistical Institute is working towards creating a single uniform set of data, but so far it is still unavailable. Moreover, even if this data would be available, it may be difficult to relate with the data derived from the world input-output table that is still using the NACE rev 1 standard. Therefore, in the light of these considerations, the analysis for the individual economic activities is going to use only data prior to 2008.

The situation is even less favourable in the case of Germany. The German Statistical Institute provides data on employment and wage levels divided on the three different economic sectors: primary, secondary and tertiary as well as for the whole economy. There is also data on the number of employees per economic activity during 1995-2011, however the data is divided on the economic activities using the NACE Rev 2 standard. Moreover, for the wage level in Germany, there is data that is missing for some economic activities before 2007. For example, the average wage in Construction

31 is not given prior to 2007. There is data only on sub-divisions of some economic activities prior to 2007 such as for the Wood constructions in the case of the Construction activities. In addition, the actual off-shoring index is computed on the economic activities as defined by the NACE Rev 1 standard in the case of Germany as well. Having these difficulties, the decision is to not use data on individual economic activities and only use the data on the three different economic sectors and for the whole German economy.

One last remark regarding the limitation of the data refers to the data for the labour productivity in Romania. Labour productivity has been computed, as previously mentioned, as the value added of an activity divided to the total number of hours worked for that activity. The data for the value added is available from the Romanian Statistical Institute’s database from 1995, but unfortunately, the series of the total number of hours worked in one activity only starts from 1999. Therefore, the analysis for Romania’s labour productivity is to start only from year 1999.

3.10. Statistical analysis

The main research question is whether the off-shoring of German production to Romania has any impact on the employment and wage levels in the two countries. For this purpose, linear regression analysis is used to see the relationship between the actual off-shoring index on one hand and the indicators for the employment and wage levels in Germany and Romania on the other hand. In this context, the independent variable is set to be the actual off-shoring index, the dependent variables are set to be the measures determining employment levels, wage levels or labour productivity levels in Romania and Germany and the linear regression analysis checks for the relationships between these variables. Figure 10 gives an overview of the model:

Employment level AOI Wage Level Labour Productivity

Figure 10: Conceptual model

From the linear regression analysis several parameters will be reported. To begin with, the F statistics of the model and its significance are reported which show that the model is significant or not, if the significance value is less than 0.05. Secondly, the values of the correlation coefficients give indications on the strength and direction of the relationship between the independent variable (AOI) and the dependent one (employment, wage or labour productivity). Moreover, the R square value gives an indication of the percentage of change in the dependent variable that is due to the change of the independent variable. Since the sample size for this paper is not that large (there are 17 years of observation), the adjusted R square will be reported as well. Finally, the last parameter that is mentioned is the coefficient B that shows what is the change in the dependent variable for one unit increase in the independent on. All the linear regression analysis are performed using the SPSS software program.

This chapter has dealt with describing the method used for the purpose of answering the main research question. The first step in the process uses the WIOD to compute the actual off-shore indexes for Germany and Romania. Then the data relevant for the employment level and wage level

32 is taken from the national statistical databases of the two countries respectively. Once all these data is obtained, the regression analysis method is used in order to see whether there is a relationship between the German off-shoring in Romania and the employment and wage levels in the two countries.

4. Results

Having defined a clear method for answering the research question, the next step is to apply the proposed method and to deliver the results. The first part of this chapter deals with the results for Romania. The linear regression analysis is used to see whether German off-shoring can be associated with changes in the employment and wage levels in this country. However, the results of these analyses do not tell anything about the causality between the variable; the analysis only tells whether there exists a relationship between the variables and the strength and direction of it if such relationship exists. The analysis is performed for both the whole economy and for individual sectors and industries.

Once the results for Romania are obtained and interpreted, the chapter continues with similar analyses for the German side. Because of the limitations expressed in the methodological section, the results for Germany focus only on the whole economy and on the individual sectors. As in the case of Romania, the results of the linear regression do not show a causality between the variables, but only an association between them.

4.1. Results for Romania

Is there a link between the wage growth in Romania and the off-shoring production from Germany to Romania? The first step in answering this question is to calculate the actual offshoring index for Romania and the average real net wage as defined in the Methodological section. As previously mentioned, the actual offshoring index is a good approximation of the total offshoring that occurs for a specific country. In this case, the actual offshoring index has been calculated so that it shows the total German offshoring in Romania. The average real net wage has been used as a measure of reference for the wage growth in Romania since it discounts the effect of inflation and of the different taxes from the wage level change as explained in the Methodological section. Consequently, Table 1 shows the actual offshoring index (AOI) and the average real net wage (Arnw) for the corresponding years of the 1995-2011 research period. The values of the Arnw are expressed in the current Romanian currency RON, the values prior to 2005 that were expressed in ROL being adjusted as described in the Methodological section.

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 AOI 0.0358 0.0348 0.0362 0.0386 0.0374 0.0475 0.0439 0.051 0.0595 Arnw 21.1 23.1 17.9 18.5 18.6 17.9 18.8 19.3 21.3 Year 2004 2005 2006 2007 2008 2009 2010 2011 AOI 0.0937 0.0935 0.1117 0.1191 0.1449 0.1393 0.1134 0.1352 Arnw 23.6 27 29.4 33.7 39.3 38.7 37.2 36.5 Table 6: AOI & Arnw for Romania for 1995-2011

Table 6 shows that the actual offshoring index has been increasing on almost the entire 1995-2008 period. Starting with 2009, the AOI has experienced a slight decline until 2011. The period in which the AOI declined coincides with the starting of the financial crisis, therefore it is expected to see fewer German investments in Romania during this period. The pattern that the AOI shows corresponds with the pattern of the FDI in Romania, shown in Figure 6, which has reached its maximum in 2008 and then experienced a steep decline for the following years. On the other hand,

34 the average real net wage has almost doubled over the 1995-2011 period, showing that during the communist period the wages were set below market values and the exit of Romania from a closed economy and the entering in an opened capitalist economy meant slowly adjusting its wages towards these market values. The trend in the average real wage in Romania reflects the political and economic instability that Romania went through during 1996-2000, followed by the biggest economic growth that Romania experienced until 2008 when the financial crisis hit the Romanian economy as well.

In order to see the strength and direction of the relationship between AOI and average net wage in Romania and also how much an increase in German offshoring in Romania translates into an increase of wages in Romania, a linear regression analysis has been applied. The correlation coefficient between AOI and average real wage in Romania was found to be 0.945, showing that there is a strong positive relationship between the two variables. In addition, the significance value is extremely small, much smaller than 0.05, implying that the correlation between AOI and average real net wage in Romania is statistically significant. Finally, the unstandardized coefficient B gives insight on how an 1% increase in AOI impacts the average real net wage in Romania on average. The table shows that for a 0.01% increase in AOI, it should be expected that the average real net wage to be higher by 1.83 RON. The results of the statistical analysis between the AOI and the Arnw are given in Table 7.

Sample Correlation R Adjusted Unstandardized Sector F Sig Size Coefficient R Square R Square Coefficient B Total 17 0.945 0.894 0.887 126.143 0.000 183.139 Table 7: Linear Regression between AOI and Arnw for Romania

The results show that there is a positive relationship between the level of offshoring from Germany to Romania and the wages in Romania. An increase in the offshoring of production from Germany to Romania is expected to bring therefore an increase of the real wages in Romania. An explanation of these results can be derived from the fact that until 1990, Romania has been under a closed and planned economy due to the communist regime, in which prices and quantities where set by a central authority. The opening up of the Romanian economy after the fall of the communist regime in 1990 gave foreign companies, including German ones, the possibility to invest in this new market. Hence, the AOI has been on a raising trend from the 1990s. Being in a closed economy for more than half a century, the Romanian wages and prices were set below the open market values. The opening of the Romanian market and the increase of foreign investment led to a process of adjustment of the wages towards their market values, values that are establish by the market forces of supply and demand and not by a central authority.

What is the link between off-shoring production from Germany to Romania and the employment level in Romania? The question can be answered by making use of the same actual off-shoring index described in the previous section as a measure of off-shoring production from Germany to Romania. As explained in the methodological section, for reasons of controlling for migration and low fertility rate, the unit of measurement for the employment level in Romania will be the share of total employment to total population. The National Statistics Institute of Romania distinguishes between total number of employees and total number of occupied population. The later term is a broader one, containing besides the total number of employees, the total number of company owners and

35 the total number of the self-employed. For the off-shoring process, it is enough to consider the number of total employees as being relevant. However, similar results can be found that have considered the share of total occupied population to total population. In Table 8, the data for measuring the employed population for each of the year from the 1995-2011 period can be found.

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 AOI 0.0358 0.0348 0.0362 0.0386 0.0374 0.0475 0.0439 0.051 0.0595 %ETP 0.266 0.260 0.239 0.230 0.207 0.207 0.206 0.211 0.215 %OTP 0.418 0.414 0.400 0.391 0.374 0.384 0.382 0.381 0.384 Year 2004 2005 2006 2007 2008 2009 2010 2011 AOI 0.0937 0.0935 0.1117 0.1191 0.1449 0.1393 0.1134 0.1352 %ETP 0.216 0.224 0.230 0.244 0.253 0.238 0.225 0.230 %OTP 0.383 0.392 0.398 0.413 0.424 0.411 0.412 0.414 Table 8: AOI & %ETP/%OTP for Romania for 1995-2011

The value for the share of the total employment to total population (%ETP) shows that there was an important decrease in the number of employees from 1995 to 1999. From 1999, the %ETP steadily increased until 2008, when the global financial crises occur, leading to a slight decrease of the %ETP. The significant decrease in the number of employees from 1995 to 1999 can be partly explained by looking at the political environment that Romania was experiencing during that time. As the data from the National Statistical Institute of Romania shows, a large part of the number of employee decrease comes from the manufacturing sector. During the communist regime, Romania experienced a massive forced industrialization that has been centrally planned. When the communism fell, Romania was left as a massive industrialized but underperforming country. The first post-communist governments avoided addressing this major issue of the underperforming industry and only in 1996, with the coming of a new government, the restructuring of the Romanian industry began. The next period was characterized by more or less successful privatizations of public companies and also by the closure of major industrial state companies such as Tractorul Brasov, SIDEX, Semanatoarea and many others (Adevarul, 2010). The immediate result of this set of measures was a massive increase in the unemployment rate, reaching a maximum of 11.8% in 1999, the biggest unemployment rate that Romania has seen since the fall of the communist regime (Adevarul, 2010). However, the restructuring of the industry, along with other measures, started to pay off in 2000, when, after three consecutive years of economic decline, Romania’s economy has grown with over 2% (Adevarul, 2010).

Consequently, in order to see whether the restructuring of Romania’s industry is a factor that affects the results, there have been two different linear regression analyses performed: one that takes into account all the 17 years of the research period and one that takes into account only the years after 1999. The results for the two analyses are given in Table 9 and Table 10 respectively.

Sample Correlation R Adjusted Unstandardized Sector F Sig Size Coefficient R Square R Square Coefficient B Total 17 0.188 0.035 -0.029 0.457 0.471 0.084 Table 9: Linear Regression between AOI and %ETP for Romania for 1995-2011

Sample Correlation R Adjusted Unstandardized Sector F Sig Size Coefficient R Square R Square Coefficient B Total 12 0.911 0.830 0.813 48.835 0.000 0.369 Table 10: Linear Regression between AOI and %ETP for Romania for 2000-2011

The results show that for the entire period 1995-2011 there is a negligible positive correlation between AOI and the number of employees in Romania. More important, the model for the entire period is statistically insignificant. However, if the first five years of the research period are discarded, the ones in which the restructuration of the Romanian industry occurred, there can be concluded that there is a strong positive correlation between AOI and total employment in Romania. In addition, the result of this model is statistical significant which means that an increase in the amount of off-shoring from Germany to Romania is associated with an increase of the number of total employment in Romania. Looking at the unstandardized coefficient B in Table 10, it can be concluded that for an increase of 0.01% in the AOI, it is expected to see an increase of 0.00369 of the share of total employees to the total population.

These results show that the off-shoring production from Germany to Romania contributes towards creating more labour opportunities for the Romanian population. The displacement of production from Germany to Romania translates therefore to more working places for the Romanian economy. For the 1995-1999 period, the effect of the offshoring on the employment level is cancelled out by the massive job cuts that are due to the restructuration of the Romanian industry. What is not clear is whether the offshoring has contributed towards the restructuration of the Romanian industry during the first five years of our research period. Following on this line of reasoning, it might be the case that offshoring would lead to fewer working places in one economical sector, but greater number of jobs in another one, having then a positive influence on the overall labour market. In order to examine these questions more thoroughly, the Romanian industry must be separated by the rest of the Romanian economy. Therefore, the Romanian economy is divided into the primary, secondary and tertiary economy and it is examined what the relation between the German off- shoring and the employment and wage levels in Romania is, taken the three economic sectors individually.

The primary sector consists of the activities that regard the extraction of raw materials such as agriculture and mining, whereas the secondary sector is composed of all manufacturing activities such as textile or metallurgic industries. Lastly, the tertiary sector is also referred as the service sector and it consists of the activities that mainly offer intangible goods than end products. Retail, sales and tourism are all examples of activities that belong to the tertiary sector of the economy.

In order to see how the offshoring from Germany to Romania affects the average real net wage and total employment in each of the three Romanian economic sectors, new values for the employment levels and for the wage levels have to be computed that are relevant for each sector in cause. The actual off-shoring index for the Romania remains the same as the one computed for the whole Romanian economy using the equation defined in (1). However, the average real net wage and the total employment level for each economic sector in Romania have to be computed separately using the approach discussed in the methodological sector. In this way, for each economic sector, only the activities belonging to that particular sector are taken into account. Table 11 contains an overview

37 of the average real net wage and of the share of employees to total population distributed on the three economic sectors in Romania.

What is important to remark from Table 11, is the different trend of the %ETP for the primary and secondary sectors of the Romanian economy compared with the %ETP computed for the whole Romanian economy. While %ETP on the whole economy, as well as for the tertiary sector, slowly increases after 2000 until the beginning of the economic crisis in 2008, the %ETP for the primary and secondary economic sectors in Romania is decreasing over the entire 1995-2011 period. In other words, over the entire research period, the job opportunities in the primary and secondary sectors have been decreasing. To find out whether the off-shoring of German production to Romania is associated with the change of employment levels in the three sectors, a linear regression analysis has been performed for each sector. In addition, a complementary analysis has been performed for each of the economic sector to see what the connection between German offshoring and wages in Romania for the primary, secondary and tertiary sectors is. In Table 12, the results of these analysis is summarized by showing the correlation coefficient R, the significance value for the models and the value of the unstandardized coefficient B, which tells the increase/decrease of the dependent variable for one unit change of the independent variable.

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 Arnw_P 23.3 25.2 19.2 20.4 20.8 21.5 23 23.9 25.8 Arnw_S 20.8 23.3 17.8 17.2 16.9 16.5 17.1 17.2 19.2 Arnw_T 21.0 22.7 17.7 19.2 19.3 18.3 19.4 19.9 22.0 %ETP_P 0.0298 0.0276 0.0209 0.0192 0.0155 0.0137 0.0131 0.0126 0.0123 %ETP_S 0.0960 0.0968 0.0873 0.0829 0.0738 0.0712 0.0721 0.0744 0.0736 %ETP_T 0.1405 0.1357 0.1309 0.1279 0.1179 0.1220 0.1205 0.1243 0.1293 Year 2004 2005 2006 2007 2008 2009 2010 2011 Arnw_P 27.8 30.2 33.3 38 45.5 44.3 42.8 41.9 Arnw_S 21.4 24 24.8 28.1 31.5 32.5 33.1 33.5 Arnw_T 24.4 28.2 31.1 35.8 41.9 40.4 38.2 37.2 %ETP_P 0.0120 0.0117 0.0105 0.0100 0.0090 0.0088 0.0079 0.0081 %ETP_S 0.0723 0.0696 0.0687 0.0692 0.0668 0.0552 0.0529 0.0547 %ETP_T 0.1319 0.1428 0.1518 0.1652 0.1791 0.1747 0.1650 0.1679 Table 11: AOI, Arnw and %ETP for the different economic sectors in Romania

Dependent Sample Correl R Adj R Unstandard Sector F Sig Variable Size Coeff R Square Square Coeff B Primary Arnw_P 0.955 0.912 0.906 155.800 0.000 211.806 Secondary Arnw_S 0.896 0.802 0.789 60.847 0.000 135.415 Tertiary Arnw_T 0.956 0.915 0.909 160.624 0.000 198.661 17 Primary %ETP_P -0.750 0.563 0.534 19.328 0.001 -0.117 Secondary %ETP_S -0.647 0.418 0.380 10.793 0.005 -0.197 Tertiary %ETP_T 0.932 0.868 0.859 98.409 0.000 0.402 Table 12: Linear regression results for the different economic sectors in Romania

The results from Table 12 show that there is a strong positive association between the German offshoring process and the average real net wage in Romania for all the three economic sectors. This is in accordance with the results on the whole economy that indicated that on the overall Romanian economy, an increase in German off-shoring is associated with an increase in the average real net

38 wage of Romania. Nevertheless, there is a different situation when looking at the effect of German offshoring on the number of employees on the three different sectors in Romania. According to the results, there is a strong positive relationship between offshoring and employment for the tertiary sector. However, the analysis gives a different result for the primary and secondary sectors; there is a medium negative correlation between offshoring and the number of employees on these two sectors.

There is a simple and straightforward explanation for these contradictory results. The communist regime left Romania as a country massively industrialized, but highly labour intensive. The industry was not competitive and was lacking the advanced technology the Western European countries were benefiting from. As foreign companies were beginning to make investments in Romania, they brought with them modern technology that would automate their production facilities and use little labour force, therefore reducing their cost. Therefore, in order to be competitive on the market, the Romanian industry had to adhere to the latest technology. As seen earlier in this chapter, from 1996 reforms have been implemented that would help to restructure the Romanian industry. During this process, many companies have been closed down therefore many working places have been lost. Moreover, some of the companies that have been privatized experience massive restructuration in terms of number of employees. For example, before being privatized, the biggest siderurgical plant in Romania had over 30,000 employees. However, by 2008 less than half of them still remained, the number dropping even greater by 2013 when the plant had only 7,300 employees. The reduced number of the employees came in the context of steep increase in revenues for this company. Likewise, the biggest oil refinery company of Romania experienced a drop of more than 60% of the number of employees from 2003 to 2013. These two examples are not exceptions, but a trend that occurred in Romania of replacing the labour intensive activities with machine intensive ones.

Similarly to the manufacturing sector, the primary sector has been exposed to the same process of replacing labour intensive activities. In the 1990s, most of the agricultural work has been done manually using an important number of labour force. However, the foreign direct investments in this sector brought new technology to this field and slowly the labour forced became replaced by machines. Therefore, there exists a negative association between the number of employees in this sector and the German offshoring process in Romania.

Table 13 and Table 14 show the correlation between German off-shoring and average real net wages and employment level respectively for Romania for different individual economic activities. The tables cover only data for the 1995-2008 period, excluding therefore years 2009, 2010 and 2011. This trade-off had to be made because the statistics in Romania defined a new procedure for mapping the activities from the national economy starting with 2009. Consequently, it is difficult to find an equivalence between some activities defined by the previous code and the ones defined by the new code. The decision was to leave out years 2009, 2010 and 2011 from these analyses assuming that the results for 1995-2008 would still be relevant for this particular case.

Activity Sector Sample Size Correlation Significance Coefficient Agriculture 0.900 0.000 Primary Mining 0.950 0.000 Food 0.764 0.001 Textiles 0.873 0.000 Leather 0.839 0.000 Wood 0.664 0.010 Pulp 0.877 0.000 Coke 0.954 0.000 Chemicals 0.948 0.000 Rubber 0.745 0.002 Secondary Non-metal 0.929 0.000 Metallurgic 0.900 0.000 Machines 0.924 0.000 Electrical equipment 0.945 0.000 Transport equipment 14 0.940 0.000 Recycling 0.836 0.000 Energy, water 0.931 0.000 Construction 0.805 0.001 Commerce 0.903 0.001 Hotels & Restaurants 0.910 0.000 Transport Services 0.909 0.000 Post & Telecom 0.943 0.000 Financial 0.973 0.000 Intermediary Tertiary Real Estate 0.884 0.000 Public Services 0.959 0.000 Education 0.969 0.000 Health 0.951 0.000 Rest of activities 0.969 0.000 Table 13: AOI & Arnw correlation for each activity in Romania

Activity Sector Sample Size Correlation Significance Coefficient Agriculture -0.691 0.018 Primary Mining -0.766 0.001 Food 0.033 0.911 Textiles -0.681 0.019 Leather 0.366 0.199 Wood 0.379 0.182 Pulp 0.385 0.174 Coke -0.803 0.001 Chemicals -0.728 0.003 Rubber 0.520 0.057 Secondary Non-metal -0.799 0.001 Metallurgic -0.623 0.017 Machines -0.795 0.001 Electrical equipment 0.762 0.002 Transport equipment 14 -0.511 0.062 Recycling -0.278 0.336 Energy, water -0.888 0.000 Construction 0.626 0.017 Commerce 0.935 0.000 Hotels & Restaurants 0.548 0.042 Transport Services -0.415 0.141 Post & Telecom -0.297 0.302 Financial 0.929 0.000 Intermediary Tertiary Real Estate 0.966 0.000 Public Services 0.972 0.000 Education 0.723 0.003 Health 0.882 0.000 Rest of activities 0.734 0.003 Table 14: AOI & ETP Correlation for each activity Romania

As the data from the Table 13 shows, there is a significant, positive correlation between off-shoring German production and wages in Romania for all the Romanian economic activities. There is a difference in strength for this relationship among the different economic activities, however all the different economic activities experience at least a medium strength for this relationship.

Looking at Table 14, there is an ambiguous effect of German off-shoring on the number of Romanian employees in the different economic activities. The effect of off-shoring on the employment level

41 ranges from a strong positive relationship, as in the case of the commerce activity, to a strong negative relationship, as in the case of energy and water supply activity. The ambiguity of these results can have as a possible explanation the fact that there could be two opposing effects that off- shoring exerts on the employment level in Romania. First, there is a positive effect since off-shoring German production implies that new jobs are created in Romania. However, as explained previously in this chapter, the new foreign investments bring modern technology in Romania, forcing the local industry to switch to the new technology in order to remain competitive in the market. Therefore, this process of restructuration and automation of the industry has a negative effect on employment since the industry becomes more machine and less labour dependent. Adding up these two effects can be a possible explanation for the wide range of the strength and direction of the relationship between German off-shoring and employment level in Romania for different economic activities. In some activities, the positive effect may be much greater than the negative one, whereas other activities may experience exactly the opposite. A proof for this explanation comes from the results on the tertiary economic sector. This sector was of little importance during the communist regime and started developing after 1990. Therefore, on most activities belonging to this sector the negative effect on employment should not be that significant. Indeed, looking at the results for the activities from the tertiary sector in Romania, it can be seen that almost for all of them there is a significant and positive association between German off-shoring and the employment level in Romania.

In order to support the claim that German off-shoring contributed to the restructuration and automation of the Romanian industry, the attention is shifted towards the change in labour productivity that Romania experienced during the research period. Labour productivity is defined in this circumstance as the ratio between the value added, expressed in real terms, of an activity and the total number of hours worked in that activity. Unfortunately, the labour productivity indicator has only been computed starting with 1999 by the Romanian authorities, therefore there is no data available for the period prior to 1999. In addition, the data has not been revised yet in order to be in accordance with the new code of classification of activities in the national economy. Consequently, the data that can be relied on at this moment refers only for the period 1999-2008. However, this data should be relevant to see what the trend in labour productivity in Romania is and whether there is a relationship between labour productivity and German off-shoring. Table 15 presents the values for the labour productivity, expressed in RON/hour worked, for the Romanian economy as a whole as well as for the three main economic sectors of Romania for the 1999-2008 period.

Sector 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Total 0.30 0.31 0.34 0.39 0.44 0.50 0.54 0.60 0.68 0.80 Primary 0.11 0.10 0.13 0.16 0.18 0.25 0.19 0.21 0.16 0.21 Secondary 0.37 0.39 0.45 0.46 0.50 0.55 0.62 0.69 0.78 0.95 Tertiary 0.52 0.54 0.54 0.55 0.65 0.67 0.76 0.81 0.98 1.09 Table 15: labour productivity in Romania during 1999-2008

The table shows that the labour productivity in Romania has increased almost 3 times over the 1999- 2008 period for the overall Romanian economy. According to Table 15, the largest increase in labour productivity occurred in the secondary sector, namely where all the manufacturing activities occur. Therefore, there is evidence that the Romanian industry went through a restructuration and automation process following the end of the communist regime. The remaining question is if the

42 increase in productivity can be associated with the increase of German off-shoring in Romania. To answer this question, several different linear regression analyses have been performed for the overall Romanian economy and for the individual sectors. The results of the relevant indicators for these analyses are given in Table 16.

Sample Correlation R Adjusted Unstandardized Sector F Sig Size Coefficient R Square R Square Coefficient B Total 0.983 0.966 0.962 228.599 0.000 4.381 Primary 0.699 0.488 0.424 7.636 0.025 0.913 10 Secondary 0.969 0.940 0.932 124.893 0.000 4.750 Tertiary 0.962 0.925 0.915 98.062 0.000 5.146 Table 16: AOI & Labour Productivity regression analysis

The results from Table 16 reveal that there is a strong positive correlation between the German off- shoring and labour productivity in Romania for the entire economy as well as for each individual economic sector. The results are significant for the four different analyses meaning that an increase in German off-shoring is associated with an increase of labour productivity in Romania. If the AOI increases by 0.01 then the labour productivity for the total Romanian economy is expected to grow, on average, by 0.04 RON per hour worked. The value of 0.04 RON is expressed having year 1995 as the reference year. The change in labour productivity differs for each individual sector, but all sectors are expecting an increase in the payment per hour worked if a 0.01% increase in the AOI occurs.

The result for the secondary sector, where all industrial activities are considered, is in accordance with the process of restructuration and automation occurring in Romania after 1990. The increase of offshoring German production can be then associated with the increase of labour productivity through the new technology that the foreign companies brought to Romania. In order to still remain competitive, the local industry in Romania had to restructure by switching to the new technology and by automating its processes. In this way, the labour productivity managed to increase almost 3 times during the 1999-2008 period.

The aim of this section was to see whether the German off-shoring is associated with changes in the employment and wage levels in Romania. The result obtained show that on the whole economy as well as for the individual sectors, there is a positive and strong relationship between off-shoring and wage level. The result for the relationship between off-shoring and employment in Romania is rather ambiguous because there seem to be two effects of off-shoring on employment: one negative for the primary and secondary sectors and one positive for the tertiary sector. In addition, German off- shoring can be associated with increases in labour productivity for the Romanian economy in the researched time frame.

4.2. Results for Germany

Now that the link between the German off-shoring and Romanian employment and wage growth level have been established, the attention is shifted towards the effect of off-shoring German production to Romania on the German economy. To begin the analysis, the first step is to compute the actual offshoring index for Germany. As explained in the Methodological section of this paper, the actual off-shoring index for Germany is computed in a similar way as the one for Romania. The

43 difference is that the actual off-shoring index that is computed for Germany excludes any German off-shoring activities in Romania that does not come directly from Germany. The wage level in Germany is first considered in order to see whether it can be associated with the German off-shoring in Romania. As explained in the Methodological section, the average real net wage (Arnw) is used to express the wage level in Germany for the 1995-2011 period. The nominal data obtained from the German Statistical institute is adjusted for inflation using the CPI with year 2010 as the base year. The difference between the Arnw in Germany and Romania is that the first one is expressed as an yearly wage, whereas the latter one is expressed as a monthly wage. In addition, for Germany’s case, all the values are expressed in Euro. Table 17 gives an overview of the AOI and Arnw for Germany for the 1995-2011 research period.

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 AOI 0.0421 0.0411 0.0433 0.0465 0.0452 0.0590 0.0546 0.0631 0.0738 Arnw 28499 28524 28108 28051 28690 29051 29037 29079 29126 Year 2004 2005 2006 2007 2008 2009 2010 2011 AOI 0.1180 0.1197 0.1460 0.1572 0.1929 0.1770 0.1481 0.1793 Arnw 28746 27724 26965 26633 26502 26489 26777 26690 Table 17: AOI & Arnw for Germany for 1995-2011

Table 17 shows that, as in the case of Romania, the actual off-shoring index has been increasing until 2008 with the exception of 2001 when there was a slight decrease in this index. After 2008, the actual off-shoring index started to decline for the next two years corresponding to the beginning of the financial crises and increased again in 2011 when the effects of the crises started to disappear. However, there is a major difference between Romania and Germany in terms of the average real net wage for this period. Whereas in the case of Romania the Arnw has increased on the entire period, the German Arnw stagnated or even decreased during this period. The trend of the Arnw in Germany found in Table 17 is in accordance with the findings of Karl Brencke and Christian Dustmann et all regarding the change in the German real wages after 1990. They suggest in their studies that the German real wage stagnated and even experienced a period of decline between 2004 and 2008 (Brencke, 2009; Dustmann, 2014).

One reason for the wage stagnation throughout this period may concern the German unification from 1990. After the fall of the Berlin wall, Western Germany had the difficult task of integrating a huge pool of new workers into its market economy, reducing therefore the bargaining power of workers. The more supply of workforce on the market, the lower the demands for the workers in terms of wages.

During the German transition period, wages in Western Germany stagnated or even declined in real terms, while the Eastern German ones slightly increased. However, the Eastern German wages did not manage to converge to the Western German ones as shown in (Johannes Gernandt, 2008). With the opening of the Romanian and of the whole Eastern European market, Germany may have faced a similar situation. As (Sinn, 2006) suggests, Germany may have faced a wage convergence between the German and Romanian wages meaning that as the Romanian wages are increasing the German

44 ones are increasing less or even declining. In order to see whether there is a relationship between the German off-shoring to Romania and the German wages, a linear regression analysis has been performed, where the AOI has been set as the independent variable and the Arnw in Germany as the dependent one. The results of this analysis are summarized in Table 18.

Sample Correlation R Adjusted Unstandardized Sector F Sig Size Coefficient R Square R Square Coefficient B Total 17 -0.865 0.749 0.732 44.651 0.000 -15727.051 Table 18: Linear Regression between AOI and Arnw for Germany

The results of the linear regression show that there is a strong negative correlation between the German off-shoring in Romania and the German wages. The model is significant meaning that an increase in the off-shoring of German production in Romania is associated with a decrease in the German wages. The opening up of new markets, such as the Romanian market, may have contributed therefore towards a slower increase in the nominal German wages (and to a decrease in the German real wages), while the Romanian wages are increasing in a more rapid pace. The fall of the communist regime in Romania opened a new market for German companies characterized by low labour costs. Off-shoring production to Romania meant labour costs savings for German companies but also a reduced supply of jobs in Germany. Consequently, as the supply for German jobs was decreasing, workers had to reduce their demands and become less powerful in their bargaining positions which led to a slowing down of the wage growth in Germany. However, there are also hidden costs of the off-shoring process (Overby, 2003), therefore one should not expect for the wages in the two countries to fully converge. But for now, the results of the linear regression analysis show that for an increase of 0.01% of German off-shoring in Romania, the average real wage in Germany is expected to decrease on average by 157.27 euros per year.

The second question, investigated from a German perspective, concerns the effect of German off- shoring on the German employment level. Is there a positive correlation between AOI and the employment level in Germany or is the off-shoring of German production responsible for job losses? For answering this question, the data relevant for measuring the employment level in Germany is used. In comparison with Romania’s total population, Germany’s total population experienced minor changes during the 1995-2011 period. Therefore, as explained in the Methodological section, the number of total employees is used to reflect the employment level in Germany. The data on the employment level, expressed in millions of people, in Germany is given in Table 19.

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 AOI 0.0421 0.0411 0.0433 0.0465 0.0452 0.0590 0.0546 0.0631 0.0738 Emp 34.161 34.115 34.036 34.447 35.046 35.922 35.797 35.570 35.078 Year 2004 2005 2006 2007 2008 2009 2010 2011 AOI 0.1180 0.1197 0.1460 0.1572 0.1929 0.1770 0.1481 0.1793 Emp 35.079 34.916 35.152 35.798 36.353 36.407 36.533 37.024 Table 19: AOI & Employment level for Germany for 1995-2011

The data from Table 19 shows that the total number of employees in Germany has increased overall on the 1995-2011 period, but the periods of increase alternated with periods of decrease for the total number of employees. The question investigated in this paper is whether the variance of the total number of employees in Germany can be related to the amount of off-shoring from Germany

45 to Romania. In order to answer this question, a linear analysis has been performed and the results of this analysis are given in Table 20.

Sample Correlation R Adjusted Unstandardized Sector F Sig Size Coefficient R Square R Square Coefficient B Total 17 0.741 0.548 0.518 18.215 0.001 11842.838 Table 20: Linear Regression between AOI and Total number of employees in Germany

The results from Table 20 suggest that there is a medium positive relationship between off-shoring German production to Romania and the total number of employees in Germany. The model is statistically significant meaning that an increase in German off-shoring is partly associated with an increase in the total number of employees. In addition, the Table 20 shows that with an increase of 0.01% of the AOI, it is expected to see an increase of 118428 employees on average. However, for Germany the association between the German off-shoring and the employment level is not as strong as we would have expected. Moreover, as it can be seen from Table 19, the total number of employees not fluctuating in a single direction over the 1995-2011 period. Is there a reason behind these facts?

The explanation for the weaker relationship between AOI and employment level in Germany could be explained in a similar way as for the case of Romania: there could be two opposing effects that the AOI triggers on the employment level. First, through the process of off-shoring, German jobs are displaced to Romania and therefore the number of employees is expected to be lower. However, through the cost-saving advantages of off-shoring, German companies can redirect some of the savings towards creating new jobs in other sectors of the economy. Therefore, new jobs are created in other sectors which can compensate for the direct loss of jobs due to off-shoring. In support of this argument is the data that reveals the division of employees on the three German sectors: primary, secondary and tertiary. According to the figures given by the German Statistical Institute, in 1995 32% of the employees worked in the manufacturing sector and 65.8% worked in the tertiary one. But in 2011 only 24.6% of the employees worked in the manufacturing sector and 73.8% of the employees worked in the tertiary economy (Freeman, 2006).

In order to further explore this matter, the question of how off-shoring relates with employment in Germany is explored for each economic sector in Germany. The three economic sectors are defined as in the case of Romania with the primary sectors consisting of the activities concerning the extraction of raw materials, the secondary sector incorporates all activities related with manufacturing and finally the tertiary sector consists of the service-based activities.

The next step is to compute the average real net wage (Arnw) and total number of employees (TNE) for that each of the three sectors similarly as it was done for Romania and using the same approach discussed in the methodological section. The values in matter are given in Table 21, where the total number of employees is expressed in thousands of employees. Similarly with Romania’s case, the actual off-shoring index remains the same as the one computed for the whole German economy using equation (2).

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 Arnw_P 31370 31382 30524 30097 30091 29926 28590 27972 27463 Arnw_S 42333 42696 42652 43138 43810 44645 44503 44523 45217 Arnw_T 34071 33906 33522 33546 33632 33378 33494 33547 33640 TNE_P 522 490 468 461 452 450 410 402 402 TNE_S 11268 10945 10686 10629 10536 10530 10343 10015 9676 TNE_T 22371 22680 22882 23357 24058 24942 25044 25153 25000 Year 2004 2005 2006 2007 2008 2009 2010 2011 Arnw_P 26106 25893 25627 25062 24804 24835 24913 25206 Arnw_S 45158 44752 45303 45095 44260 43548 44753 45283 Arnw_T 32965 32403 31886 31334 31340 31627 31945 32029 TNE_P 405 389 379 384 382 385 380 388 TNE_S 9477 9242 9140 9285 9461 9298 9188 9370 TNE_T 25197 25285 25633 26129 26510 26724 26965 27266 Table 21: AOI, Arnw and TNE for the different economic sectors in Germany

Table 21 shows that the average real net wage for Germany has dropped for the primary and tertiary sector in Germany during the 1995-2011 time period. However, the data shows that for the secondary sector, the average real net wage has increased on average. In addition, the number of total employees has fallen in the primary and secondary sectors, but has increased quite significantly in the tertiary sector. Therefore, there seems to be the case that there are jobs lost in the primary and secondary sectors, but new jobs are created in the tertiary sector. In order to see whether off- shoring German production is the reason of this phenomenon, several linear regression analyses are performed in order to see the relationship between AOI and Arnw and TNE for each different sector. The results of these analyses are given in Table 22.

Dependent Sample Correl R Adj R Unstandard Sector F Sig Variable Size Coeff R Square Square Coeff B Primary Arnw_P -0.938 0.879 0.871 109.317 0.000 -41518.845 Secondary Arnw_S 0.543 0.295 0.248 6.267 0.024 9627.563 Tertiary Arnw_T -0.959 0.920 0.915 173.171 0.000 -16270.058 17 Primary TNE_P -0.796 0.634 0.610 25.994 0.000 -625121 Secondary TNE_S -0.871 0.759 0.743 47.304 0.000 -1094884 Tertiary TNE_T 0.876 0.767 0.752 49.398 0.000 23416799 Table 22: Linear Regression analysis for Germany on the different economic sectors

The data from Table 22 shows that there is a strong negative correlation between the German off- shoring in Romania and the wages in the primary and tertiary sector. Interestingly, there is a weak but positive relationship between of-shoring and wages in the German manufacturing sector. According to the table, all the three results are significant which means that an increase of German of-shoring in Romania is associated with a decrease of the wage levels in Germany for the primary and tertiary sectors and with an increase of the wages in the manufacturing sector. Why is this difference between the different sectors?

To begin with, it is likely that the general explanation discussed previously in this chapter stands for all three sectors. As the companies off-shore production to Romania, there is lower supply of jobs making the unions to lower their demands and therefore accepting lower salaries. However, during the research period, an important part of the manufacturing jobs have been replaced with service-

47 based jobs. The share of the manufacturing jobs in Germany dropped from 32% in 1995 to less than 25% in 2011. According to (Arthur F. Jones Jr., 2000; Skills, 2014), the advanced economies are giving up the low skilled labour in favour of labour that requires higher skills. The obtained results show a similar pattern in Germany, where low skilled labour is outsourced to countries as Romania, leaving only the more specialized labour within the manufacturing sector in Germany. Therefore, by giving up the low skilled labour which is lower paid, the average real net wage for the manufacturing sector increases. Nevertheless, as discussed in (Marin, 2010b), Germany began outsourcing not only low skilled labour, but also lower skilled labour to countries such as Romania. Therefore, the supply of skilled jobs in the manufacturing sector is also going down, making unions to lower their expectations in this sector as well. The results show however that the effect of losing the low paid jobs is higher than the effect of lowering the higher paid jobs within the manufacturing sector and therefore off-shoring determines a weak increase in the average net wage during the 1995-2011 time frame.

Table 22 also provides information regarding the relationship between off-shoring German production to Romania and the employment level in Germany for the different economic sectors. According to Table 22, there is a medium negative relationship between offshoring and employment level in Germany for the primary and secondary sector, whereas for the tertiary sector the relationship is medium but positive. The results being significant, it means that an increase in the off-shoring of German activities to Romania is going to decrease the number of employees in the German primary and secondary sectors, but it increases the number of employees on the tertiary sector. There are studies that have argued that the loss of jobs due to outsourcing is to be compensated by the creation of additional jobs in other sectors (Global Insight, 2004). From the obtained results, the same conclusion can be derived. The process of off-shoring from Germany to Romania is displacing jobs from the primary and secondary sectors from Germany to Romania, but new additional jobs are being created in the tertiary sector. Therefore, although off-shoring displaces jobs from Germany, on the overall economy the number of new jobs rises.

This section has focused on the relationship between the German off-shoring and the employment and wage levels from a German perspective. The linear regression analysis identified a similar pattern in the association between off-shoring and the employment level as in the case of Romania. On the other hand, there is a different pattern in the relationship between off-shoring and wage level in Germany. The results show that the association between the two variable is negative: the increase in German off-shoring is associated with a decrease of German wages.

5. Ethical Reflection

5.1. Selected data

There is one more question that this paper tries to investigate. In the context of the job displacement and changes in the average real net wages in Romania and Germany, is the off-shoring process of German production to Romania ethically acceptable?

The general view on the off-shoring process is that it negatively impacts the domestic jobs. However, there are studies that suggest that the loss of domestic jobs is only part of the picture and that the whole, broader image should be taken into account (McGee, 2005). The narrow view only sees the jobs that are being lost due to the off-shoring process. However, by off-shoring productions, firms are able to save money and part of these savings is directed towards creating new jobs or towards making the products cheaper for the consumer (McGee, 2005). It is suggested that for every job off- shored, two or three new jobs are being created (McGee, 2005). One important drawback of the study is that it considers only the country from which the off-shoring is occurring from. It is not taking into account how off-shoring impacts the lower-wage country in terms of job creation and improved life conditions.

Therefore, the aim of this chapter is to use the results obtained previously from the econometrical analysis and see what the changes in terms of jobs and wages for Romania and Germany that can be attributed to off-shoring are. Three different perspectives are to be taken into account: the German perspective, the Romanian perspective and the combined German-Romanian perspective. These results are then put into the utilitarian and theory of justice frameworks in order to discuss whether the off-shoring process can be perceived as an ethical process or not.

To proceed with this analysis, it is required to use some data obtained in the results section. More specifically, the data needed is the unstandardized coefficient B that can be found in Tables 7,9,12,18,20 and 22 from the results section. This parameter indicates what the estimated change in the dependent variable (either employment level or wage level) is, when there is one unit increase in the independent one (in this case the actual off-shoring index). However, in the data obtained, the actual off-shoring index fluctuates with much less than 1% during the research period. Consequently, the unstandardized coefficient B is adjusted so that it shows the change in the dependent variable for a 0.01% change of the actual off-shoring index. Table 23 gives the modified unstandardized coefficients B for both Germany and Romania for the whole economies and different sectors for both the wage and employment levels.

Country Sector Modified B (employ.) Modified B (wage) Total 0.00084 1.83 Primary -0.00117 2.12 Romania Secondary -0.00197 1.35 Tertiary 0.00402 1.99 Total 118428 -157.27 Primary -6251 -415.19 Germany Secondary -109484 96.28 Tertiary 234168 -162.70 Table 23: Modified B corresponding to wage and employment levels in Germany and Romania

Before further discussing the parameters from Table 23, an essential remark has to be made. The unstandardized coefficient obtained from the regression analysis is simply a point estimate of the expected change in the dependent variable when there is a 1% change in the independent one. However, there is a standard error associated with each of these unstandardized coefficients. Using this standard error, we can find the 95% confidence interval for each of the estimates in Table 23. The confidence intervals for all the estimates are given in Table 24.

Dependent 95% Confidence Interval Country Sector variable Lower Bound Upper Bound Total 1.48 2.17 Primary 1.75 2.48 Wage Secondary 0.98 1.72 Tertiary 1.65 2.32 Romania Total -0.158 0.327 Primary -0.174 -0.060 Employment Secondary -0.325 -0.069 Tertiary 0.315 0.488 Total -207.43 -107.10 Primary -499.82 -330.54 Wage Secondary 14.30 17.82 Tertiary -189.05 -136.34 Germany Total 125675 255644 Primary -8864 -3637 Employment Secondary -143419 -75557 Tertiary 205813 358600 Table 24: 95% Confidence Interval for the Unstandardized B

For the following ethical discussion we are less interested in the magnitude of the expected change in the dependent variable, but more interested in the direction of this change. The discussion would be more complicated if the lower bound had a different sign than the upper bound for one variable, but fortunately, as Table 24 shows this is the case only for the total expected change in the employment level in Romania. However, as discussed in the results section, the period 1995-1999 was characterized of massive job restructuration and therefore, it is likely to assume that the more appropriate estimate for the expected change in the total wage level in Romania due to the German off-shoring is closer to the upper bound of the 95% confidence interval. Having this said, for the simplicity of the ethical discussion, we are going to use the results in Table 23 as relevant estimates on which our ethical discussion is going to be based on.

The data from Table 23 presents several issues that need to be taken care of. The main issue regards the lack of uniformity between the data corresponding to Germany and the one corresponding to Romania. Looking at the unstandardized coefficient corresponding to the wage level in the two countries, it can be seen that the values for Romania are given in RON and expressed on a monthly basis, whereas for Germany, the wages are expressed in Euro and on a yearly basis. Moreover, for Romania the base year for controlling for inflation was set to be 1995, whereas for Germany the base year was set to be 2010.

Therefore, in order to make the data corresponding to the wage level in Germany and Romania uniform, a couple of manipulations must be implemented. First, the data for Germany has to be

50 expressed in Euro per month. Therefore, all the values corresponding to Germany are divided by 12 as they are initially expressed on a yearly basis. Secondly, the values for Romania have to be converted to the same year as Germany, 2010. The values for Romania are therefore multiplied by the corresponding value for the year 2010 from Table 3. Finally, the Romanian values have to be converted into Euro. The National Bank of Romania provides an average exchange rate of 4.2099 RON for 1 euro. Using this exchange rate, the Romanian modified B values are transformed into Euro values. Table 25 gives the uniformed data for Germany and Romania, the values being expressed in Euro/month and having year 2010 as the reference year.

Country Sector Uniformed Modified B (wage) Total 16.2 Primary 18.8 Romania Secondary 12.0 Tertiary 17.7 Total -13.1 Primary -34.6 Germany Secondary 8 Tertiary -13.6 Table 25: The uniformed unstandardized coefficient for the wage levels in Germany and Romania

In a similar approach the modified unstandardized coefficient corresponding to the employment level has to be uniformed. In the case of Germany, the values in Table 23 represent the change in the number of employees when the AOI changes with 0.01%. On the other hand, for the reasons explained in the methodological section, the values for Romania represent the share of employees to the total Romanian population when there is a change of 0.01% in the AOI. Therefore, the values for Romania have to be brought in line with the values for Germany. In order to uniform the Romanian’s data with the German one, the values given in Table 23 corresponding to Romania have to be multiplied with the average total population of Romania between 1995 and 2011. The uniformed data corresponding to the employment level in the two countries is given in Table 26.

Country Sector Uniformed Modified B (employ.) Total 18192 Primary -25339 Romania Secondary -42665 Tertiary 87062 Total 118428 Primary -6251 Germany Secondary -109484 Tertiary 234168 Table 26: The uniformed unstandardized coefficient for the employment levels in Germany and Romania

Examining first the data from Table 26, it can be seen that the German economy is losing around 170000 jobs in the primary and secondary sector if the actual off-shoring index increases with 0.01%. However, if there is an increase of 0.01% in the AOI, the tertiary sector gains more than 280000 jobs. These findings are to some extent in accordance with (McGee, 2005), where it is suggested that for every off-shored job, two or three new jobs are created in the domestic economy. The findings from

51 this paper suggest that indeed there are more jobs created than are being lost, but the magnitude of the increase is a bit smaller; for every job lost an average of 1.6 new jobs are being created.

However, this is not the entire picture; the German off-shoring is also affecting the jobs in Romania. According to Table 26, there are a total of almost 20000 new jobs created in Romania if the AOI increases by 0.01%. Even though on the overall economy there is a positive relation between the number of employees in Romania and the German off-shoring in Romania, examining the individual economic sectors gives a different picture. As previously explained, the primary and secondary sectors in Romania went through a significant restructuring that allowed them to remain competitive in the market. The cost of the restructuring was the replacing of the inefficient labour force with machines and thus the number of employees in the two sectors during 1995-2011 has fallen. The process of off-shoring contributed to this phenomenon by bringing in Romania new technology that would make businesses more efficient. On the other hand, the tertiary sector, which was almost inexistent during the communist regime, did not need any restructuring and therefore the German off-shoring contributed only in bringing in more jobs for the Romanian economy.

The last perspective over the total number of employees is the combined German-Romanian perspective. This perspective shows that although there are jobs lost in the primary and secondary sectors of the two countries due to an increase of 0.01% of the AOI, the overall employment level grows by almost 140000 when such an increase of the AOI occurs. The trigger of this increase is the tertiary sector that replaces the jobs lost in the other two economic sectors.

Similarly, examining the data on Germany from Table 25, it can be seen that for a 0.01% increase in the AOI, it is expected to see an average decrease of 13.1 Euros for the monthly wages. The decrease in the average wage if a change of 0.01% in the AOI occurs is more important in the primary sector. In addition, for the reasons explained in the previous chapter, the average wage in the German secondary sector is slightly increasing when the AOI increases. However, as the tertiary sector is responsible for more than 75% of the German economy, the average wage decrease in the tertiary sector due to a 0.01% increase in the AOI is close to the total average wage for Germany.

The picture changes when looking at the Romanian wages. If the AOI increases by 0.01%, the Romanian average wage for the total economy is expected to rise with 16.2 Euros per month. There are slight differences between the Romanian primary, secondary and tertiary sectors in terms of the wage growth, but the common fact is that the average wages are increasing in all sectors if the AOI increases by 0.01%.

It is more difficult to use the combined Romanian-German perspective for the average real net wage context. Comparing just the values in Table 25, may not give a relevant image for understanding whether there is an overall loss or gain. It is therefore needed to compute the total loss for Germany and the total gain for Romania in terms of wages and then compute the difference between the two values. The total loss for Germany if a 0.01% increase occurs in the AOI is calculated by multiplying the average loss with the average number of employees for the 1995-2011 period. Similarly, the gain for Romania is computed by multiplying the average gain that can be found in Table 2 with the average number of employees in Romania. The average number of employees for the 1995-2011 period for Germany and Romania and the total amount expected to be lost or gained in the two countries is given in Table 27.

Germany Romania Average expected gain/loss (Euro) -13.1 16.2 Average number of employees 36,731,353 8,627,741 Total expected Gain/Loss (Euro) -481,180,724.3 139,769,404.2 Table 27: Total Loss/Gain in Romania and Germany

Table 27 shows that even though, the average wage in Romania gains more than the average wage in Germany, on the overall the loss is bigger for the German economy than the gain for the Romanian one. This results from the fact that there are more employees in Germany than in Romania and therefore there are more people experiencing a loss in Germany than people gaining from the increase in the average wage in Romania.

5.2. Utilitarianism

There are several variants of the utilitarian principle, but its core idea is that an act is ethical as long as it provides the greatest good (happiness) for the greatest amount of people. Although there are many critiques to the utilitarian theory regarding the impossibility of accurately quantifying happiness and good, it is reasonable to assume that, in the context of off-shoring, one person is happier or better off having a job than losing his/her job. Similarly, one can assume that a person is happier to have more money to spend than having his/her income reduced (in real terms). However, the problem of quantifying happiness exists for the off-shoring case as well. Is the happiness of earning more money for one person overcoming the sadness of another person losing his/her job? In addition, there are difficulties of quantifying the happiness for changes in the same variable. Thinking of the marginal utility of money, a poor person losing 20 euros from his/her income can be more unhappy than a wealthy person losing 1000 euros from his/her monthly salary. One more remark is that the general assumption in this case is that more money brings more happiness to one person, which may not be always the case.

Understanding these limitations, the utilitarianism framework is applied separately for the number of jobs affected by the off-shoring process and for the changes in the average wages in the two countries. An additional simplification is not to take the marginal utility of money into account and assume that each Euro lost or gained gives the same amount of happiness for everybody. As explained previously, the utilitarian framework is to be applied for the three distinct perspectives: German, Romanian and the combined perspective.

Taking first the Romanian perspective, the total results for the employment level in Table 26 is positive an equal to almost 20,000, meaning that for a 0.01% increase in the actual off-shoring index, it is expected to see an increase in the number of employees. Therefore, according to the utilitarian framework, the off-shoring process brings the greatest good for the greatest amount of people for Romania since new jobs are created that bring happiness for those unemployed. Similarly, Germany expects an increase in the number of employees if a 0.01% increase in the AOI occurs. Therefore, off-shoring does bring the greatest good for the greatest amount of people in the Germany case as well. Since off-shoring brings the greatest good for the greatest amount of people in terms of job offers for both countries, it is straight-forward that it would bring the same for the combined Romanian-German perspective.

This result along with the data in Table 26, reveal another limitation of the utilitarianism framework that is often discussed. Utilitarianism focuses solely on the entire society disregarding the individual level. It is easy to see from Table 24 that in fact the primary and secondary sectors from both Germany and Romania suffer a decrease in the number of employees, while only the tertiary sector sees an increase in the number of employees. The utilitarianism is not concerned of the fact that people are actually losing jobs in some sectors of the economy as long as these jobs are compensated by the creation of jobs elsewhere in the economy. This is a major difference between the utilitarian theory and the theory of justice developed by Rawls as it will be pointed out in the next section of this chapter.

The second set of analysis, where still the utilitarianism framework is applied, makes use of the data for the average wage given in Table 25. There is a positive association between the average wage and the AOI in Romania and according to Table 25, in Romania the average real net wage is expected to grow throughout all the Romanian economy for a 0.01% increase in the AOI. Since on average all the Romanian wages are growing, the off-shoring of German production brings greater happiness for the greatest amount of Romanians. They are better off in terms of real income than in the case when there would be no German off-shoring. Things get more complicated when considering the German perspective. Although on average the average wage is expected to decline when the AOI increases by 0.01%, the average wage in the secondary sector in Germany is expected to slightly grow. In order to be able to quantify the total happiness in terms of the change in the average wage, it is assumed that the happiness brought by 1 unit increase in one’s salary is cancelled out by the reduced happiness brought by a 1 unit decrease in another one’s salary. Therefore, the happiness brought by the increase in the average wage for the people working in the German secondary sector is cancelled out by the decrease in the average wage in the other sectors. In addition, as there are only around 20% of the German employees working in the secondary sector, there are more people losing money than people gaining. And as Table 25 shows, the loss in the tertiary and primary sector is bigger than the gain from the secondary one. Therefore, according to the utilitarianism framework, for Germany, the off-shoring process does not bring the greatest good for the greatest amount of people in terms of the average wage. Not only are there more people that see their average real wage reduced than the ones that see their average real wage increase, but also the decrease in the average real wage in the primary and secondary sectors is greater in absolute terms than the increase in the average real wage in the secondary sector. Therefore, the total happiness is expected to decrease for the greatest amount of people if an increase in the AOI occurs. Finally, the utilitarian framework is applied for the combined perspective of the two countries. The conclusion of the individual analysis was that off-shoring brings the greatest good for the greatest amount of people in Romania, but it does not in Germany’s case. What is the answer for the combined perspective then? As Table 27 suggests, Germany has a bigger number of employees than Romania and therefore the total loss expected for Germany for a 0.01% increase in the AOI is greater than the total gain for Romania. Therefore, in terms of the average real net wage, the off-shoring production from Germany to Romania does not bring the greatest happiness for the greatest amount of people considering Germany and Romania taken together. There is one remark that needs to be made here. The discussion so far does not take into account the notion of diminished marginal utility of money. However, the German average wage is more than ten times greater than the average Romanian wage. Although the two countries face different living expenses, it is still reasonable to assume that an extra Euro for a Romanian employee should weight more and bring more happiness

54 than an extra Euro for a German employee. In these conditions, our initial conclusion might be changed if the diminished marginal utility of money is taken into account. However, due to the complexity of measuring the marginal utility of money for the two countries, this notion was left out of our discussion at least for now.

As in the case of the discussion for the job displacement, the utilitarianism does not consider the individual level in this case as well. The secondary sector is benefiting from the off-shoring process through the increase of the real wages, but however this does not matter. Even though there is part of the German population benefiting, looking from the broad German society level, most of the people are worst off since they experience a decrease in their real wages. The second framework, the theory of justice developed by Rawls, gives more consideration on the individual level.

Due to the limitations described previously in the chapter, the utilitarian principle was applied for the results on employment and on the wage separately. It would be interesting if the utilitarian framework could be applied to the results on employment and wage levels taken together. At a first glance, from Romania’s perspective there is no issue in doing that. The previous discussion concerning Romania concluded that the off-shoring process does provide ‘the greatest happiness for the greatest amount of people’ from both an employment and a wage level perspective. Therefore, it is straight-forward to conclude that it provides ‘the greatest happiness for the greatest amount of people’ from a joint perspective. The matter is more complicated if the same thing should be done from a German and combined Romanian-German perspective. For these two latter cases, the German off-shoring is found to provide the ‘greatest happiness for the greatest amount of people’ from an employment perspective, but does not satisfy the utilitarian principle from the wage level perspective. The question is how the ‘happiness’ of getting a job can be compared with the decline in ‘happiness ’of having a lower wage? There is no easy answer to this question. One approach that can be applied is to convert the ‘happiness’ of getting a job into the amount of money one manages to earn from getting the new job. Of course, this approach can be rather simplistic since intangible values such as regaining respect or regaining self-esteem are not into account by using this approach. Still, this approach seems the best approximation that we can think so far. According to Table 26, the German economy is expected to earn 118428 new jobs if the AOI increases by 0.01%. The total loss in the German wages from Table 27 is divided to this number to see what should the minimum real average net wage be for the new employee in order to conclude that the German off- shoring is providing the ‘greatest good for the greatest amount of people’ from a joint employment and level perspective. The result of the division is 4077 Euros per month. However, during the 1995- 2011 the German average real wage was averaging 2327 Euros per month (expressed using 2010 as the year of reference). Therefore, the new worker should earn almost twice the average real wage in Germany in order to conclude that the German off-shoring satisfies the utilitarian principle. This having said, from a German perspective, off-shoring still does not provide the ‘greatest good for the greatest number of people’ if employment and wage terms are taken together. The same approach is used for the combined German-Romanian perspective. The difference of the gain and loss in the average wage for the two countries that can be computed using Table 27 should be compensated by the total wage received by the new employees. Assuming that the benefit of the new employees is the average real net wage, it means that there are 118428 jobs in Germany for an average of the average real net wage of 2327 Euro and 18192 jobs in Romania for an average of the average real net wage of 182 Euro. The total gain from the new employees is smaller than the lost incurred by the difference in loss and gain from the average real wage computed from Table 27. Therefore, it can be

55 concluded that, using this simplistic approach, the process of German off-shoring does still not satisfy the utilitarian principle when employment and wage are considered together.

5.3. Theory of Justice

The utilitarian approach has several problems in terms of the impossibility of accurately quantify happiness and of distributive issues that do not take into account the narrower, individual perspective. The theory of justice developed by John Rawls takes a slightly different approach, but manages to overcome these limitations. According to Rawls, an act is just as long as it is based on two principles of justice. The first principle states that all persons should have equal rights and it refers mainly to the basic liberties that each citizen should have. The second principle states that if inequalities in society are accepted, then the state of inequality should be in the benefit of the ones least off. For the purpose of this paper, the second principle of justice is to be used as a starting point. The off-shoring process clearly has advantages for some parties, but is it in the benefit of the least off? Is the least off person better after the off-shoring process than it would be without it? In this section, these questions are explored using the data from Table 25 and Table 26 of the expected change in the average wage and number of employees if an increase by 0.01% in the AOI occurs. As in the case of utilitarianism, there are three perspectives that are going to be taken into account: the German, the Romanian and the combined German-Romanian perspective.

To begin with, the principle of justice for the German off-shoring process is to be related with the number of employees that this process is expected to displace. According to Table 26, in Romania there is expected to see on average an increase in the number of employees if the AOI increases by 0.01%. Nevertheless, as the data from Table 26 shows, the increase in the total number of employees comes with the cost of the reduced number of employees in the primary and secondary sectors. Since the German perspective and the combined German-Romanian perspective are similar in this context with the Romanian case, the three cases are treated together.

Based on the data from Table 26, is the process of German off-shoring based on a just principle? Is the off-shoring process making the ones least off better through this process? It can be argued that yes it does. From the data from Table 26 it is expected to see an increase in the number of jobs with the increase of off-shoring from Germany to Romania. This means that more people are having opportunities to get a job. Assuming that a person is better off having a job than being unemployed and assuming that being unemployed is the worst position one can be in this context, it results that off-shoring indeed makes people in the worst possible position better off. However, as the data shows some people working in the primary and secondary sectors are losing their jobs. The number of people losing their jobs is smaller than the number of new jobs created. Therefore, it can be assumed that those losing a job in the primary and secondary sectors may find a job in the tertiary sector. Such an assumption depends to an important extent on whether the qualifications that the people laid off have match with the qualifications needed for getting one of the new jobs in the tertiary sector. This may not be always the case. But, as Tandy Gold suggested, companies have the responsibility to make sure that those who are laid off are properly compensated. The compensation can be a financial one, but also one aiming towards requalification, so that the laid off employee develops new skills that are useful for the new jobs created in the tertiary sector (Gold, 2012a). If

56 firms are to act in an ethical way, they should not just abandon the former employees when off- shoring their production elsewhere, but support them in finding replacement for the job they lost. If firms do not act in this way and do not care in supporting those laid off, some of the employees may end up in the worst possible position (being unemployed, having debts that cannot be repaid, damaging their health), which is not what Rawls’ principle is striving for. Therefore, in order to meet Rawls’ principle, firms do have to support the laid off employees in finding a potential new job in the tertiary sector. Looking from a different perspective on the effects of off-shoring on the employment level, without the loss of jobs in the primary and secondary sectors caused by the off-shoring process, companies would not save costs that would be redirected eventually towards creating new jobs in the tertiary sector as (McGee, 2005) suggests. Without off-shoring, companies may lose their competitiveness and may anyway need to lay off some of their employees. Therefore, the German off-shoring in this context is in the benefit of everyone in both German and Romanian economy and most important in the benefit of those situated in the least off position, meeting therefore Rawls’ principle.

The situation is different when it comes to analysing the three perspectives in terms of the changes in the average real net wage. Looking at Table 25, from Romania’s perspective all the wages are expected to raise on average if there is an increase in the AOI. Therefore, the process of German off- shoring would benefit all the employees in Romania as they would have more money to spend. According to Table 11, the average wage for the secondary sector is the lowest, so it can be assumed that the employees working in this sector are the worst off in terms of average net wage. The off- shoring of German firms is expected to increase the real wages for the employees working in the secondary sector as well and therefore it would make the people in the worst position better off.

In Germany’s case, although on the total economy there is expected to see a decrease of the real wage if the AOI increases, on the individual sectors this conclusion is not consistent as Table 25 shows. For the German secondary sector, it is expected to see an increase in the real wage when the AOI increases. In addition, according to Table 21 in the previous section, the secondary sector is the sector in which the real average wage is the highest in Germany. Therefore, it can be said that the other sectors which are worse off in terms of the real average wage are expected to experience a decrease if the AOI increases. This is contradictory with the principle of justice that John Rawls defined. In this case, the people in the worst position are even worse off than without the off- shoring process, whereas the additional wage inequality only benefits the people that are already better off. However, this conclusion stands to the extent that only the employed people are considered. In the case the unemployed people from Germany are also taken into consideration then the whole context changes. As previously seen, the German off-shoring in Romania is expected to bring additional jobs to the German economy. This would make the German unemployed better off since they would have more chances in getting a job. And since the unemployed people are the ones in the most disadvantageous position, the German off-shoring would then satisfy the Rawlsian principle even though there would be people that are expected to see their real wages decreased.

Applying the Rawls’s framework for the combined perspective for the average real wage seems to be more challenging. As Table 25 shows, the Romanian average wage is to increase on average for the both the whole economy and for the different sector if the AOI increases, whereas Germany sees its real average wage decrease throughout its economy except for the secondary sector. According to Rawls, inequalities should be permitted as long as the worst off will be better than in the initial

57 equalitarian position. However, as Table 11 and Table 21 of the previous section show, in this case the initial situation is the unequal one. In Romania the average real wage is much lower than Germany’s average real wage. Therefore, the Romanian employee is the one situated in the worst position in this instance, whereas the German employee is the one better off. In this case the German off-shoring is expected to contribute towards improving the situation for the Romanian employee at the cost of worsening the situation for the German employee. It can be argued that the off-shoring is expected to reduce the gap between the Romanian average wage and the German one and hence create more equality between the two countries. Since Rawls’s first principle state that all the people should have equal rights, it can be argued that the process of off-shoring is indeed striving towards this goal by closing up the gap between the wages in Germany and Romania. Of course it is improbable that the wages in the two countries would equal at some point due to various transaction costs, but the main point is that off-shoring limits the income gap between the employees in the two countries.

To conclude with, this section showed that from an utilitarian perspective, off-shoring is providing the greatest happiness for the greatest amount of people by creating additional new jobs in both Germany and Romania. The process of off-shoring is also based on just principles since it helps the people that are worst off (the ones unemployed) to have more opportunities in finding a job. However, from the average wage perspective, the German off-shoring is not providing the greatest good for the greatest amount of people since more people have to lose more money than the ones that have something to gain. Only for Romania taken alone, the utilitarianism argument stands since the average wages are increasing throughout the economy. Applying Rawls’s principles of justice for the average wage, the off-shoring process can be argued to be just for Romania and Romania and Germany combined since the employees paid the lowest see their wages increasing. On the other hand, Germany taken alone sees off-shoring as unjust since the lowest paid workers in Germany see their real wages diminished.

5.4. Ethical conclusions

The previous section analysed the German off-shoring to Romania from an ethical perspective using the utilitarian and Rawlsian frameworks. Depending on the different framework used and on the perspective taken, there are different conclusions on whether this process is an ethical one. Therefore, how should we perceive the German off-shoring taken into consideration the insights from the previous section? Should doing ‘the greater good for the greater amount of people’ be given more importance than doing an act that would benefit the people least off? This section is trying to answer these questions and give some ethical conclusions on the process of German off- shoring in Romania.

In order to answer this question, I think that we have to look at the matter from two different perspectives: the company and the governmental perspective. First, we look at the results from the government perspective. The question is what should the government take care of: the greater good for the greatest amount of people or making sure that the least off is getting more? I think that the government should give more weight to the latter one. In my view, the world that we now live in is characterized by wide inequalities between people. These inequalities are often triggered by the difference in chances that some people have and others do not. For example, getting a job is closely related to the level of education and skills a person has. But the quality of education is not the same

58 throughout all the areas of one country. The facilities of a school and the availability of qualified personal is limited in a school from a small Romanian village in comparison with one of the schools situated in the centre of a big city in Romania. Moreover, one child may be born in a poor family, meaning that he/she may have other responsibilities in the household and may not have the possibility to direct his/her full attention towards education. This also means that children from poorer families may not have the chance to be supported in continuing their higher education due to the lack of money. Not to mention that the family and the environment a child lives in contributes towards the importance that child gives to education. The point that I am trying to make is that there are extrinsic factors that may lead to someone being in the least off position. I think that the government is the most appropriate entity that can compensate for those kinds of inequalities. Even though the government has a responsibility towards all its citizens, it is the ones in the least off position that require the most attention. Consequently, I believe that the government should have the main focus in helping the ones least off do better and therefore help towards creating more equal opportunities for all its citizens. In this explicit case, the people unemployed are the ones that are situated in the most disadvantageous position. Should the government support for new jobs to be created even if this means that the average wage for the majority of people is expected to decrease? On the basis of the previous arguments regarding the inequality of chances between people, I think that even though more people would be worse off if their wages decline, the government should set as a priority helping the ones less fortunate through the new jobs creation. This is in accordance with Tomas Sedlacek arguing in favour of the redistribution of wealth principle that would make our world less unequal (Tomas Sedlacek, 2013). Although I think that this is the most ethical way in which the government should act, I think that it is unlikely for a government to follow this line of thinking in reality. The strongest argument in this direction is that government is thinking from a political perspective as well. Lowering the average wage, as in the case of Germany for the primary and tertiary sector, would make unhappy the vast majority of people which may not give their support for the party implementing such policies for the next elections. The potential lose in political support is far higher than the potential gain of opening around 180,000 of new jobs that are expected if there is an increase in the German off-shoring to Romania. Therefore, from a political perspective, the government will always choose doing the good for the greatest amount of people, even if this means that the least off are not helped at all. However, an ethical discussion is less interested in what happens in reality and more interested in what is the right thing to do. Even though reality shows that setting as a priority for the government in helping the ones least off might be improbable, in my view this is the way the government should act upon. The government is the most powerful entity that can compensate for inequalities between people and I think that it should act accordingly in the case of off-shoring as well.

On the other hand, from a company perspective, the situation might be somehow different. The major difference between the government and companies is that the latter ones do not have as a main purpose the well-being of the society, but are mostly driven by their selfish reason of increasing their profits. Following this line of thought, companies are not responsible for the well- being of all the citizens within the society; at most, they are responsible for the well-being of their employees. This means that companies do not aim in doing the ‘greatest good for the greatest amount of people’ nor they aim in helping the ones in the least off position as long as this does not lead to an increase in the companies’ profits. However, I am in accordance with Tandy Gold and I do believe that companies are responsible for their employees. Therefore, in this particular case, the

59 question is whether companies should strive for doing the ‘greatest good for the greatest amount of its employees’ or in following the Rawlsian principle that would aim in this scenario in helping the employees that are the least off? If a company refuses to off-shore some of its production on the base that it would make some of the employees worse off, it may end up losing its competitiveness on the market. This may actually lead to ceasing production and lying off more employees than in the case of off-shoring production. In other words, if a company would follow the Rawlsian principle, it may end up in doing more harm than good both for the least off and for the other employees. Therefore, I think that companies should act in terms of the ‘greatest happiness for the greatest amount of employees’. If some of the jobs need to be off-shored in order to continue production and keep the other jobs safe, than I believe that it is ethical for a company to do so. Of course, as Tandy Gold suggested, if off-shoring jobs leads to ‘the greatest good for the greatest amount of employees’ it shouldn’t mean that the laid off employees are abandoned; they should be supported by the companies to find new job opportunities or to be offered proper compensation (Gold, 2012a).

Nevertheless, the above considerations are drawn by assuming that the sole purpose of one company is to maximize its profits. This might be true, but I believe that companies should pursuit other goals aside from maximizing their profits. In my view, companies should strive for creating, through their products and services, an added value for the society. In other words, companies should create a greater good for the society and thus adhere to the utilitarian principle. The cost saving advantages is one of the most important reasons why companies decide to off-shore production. Whether the off-shoring provides the greatest good for the greatest amount of people depends on a high proportion on how the companies spend the extra money. Companies should not use the off-shoring decision solely as a reason to increase their profits, but use this extra profit to create a greater good for the society. In other words, companies should direct the money saved towards lowering the price of the products or services they offer, towards research and development in order to improve the existing products or services or create new ones or towards increasing the wages of the domestic employees whose jobs have not been off-shored. By acting in such a way, the expected negative effect of off-shoring on the German real wages should be diminished or even cancelled out. As a result, German off-shoring would be associated with additional jobs both in Romania and Germany and also with increased or almost constant real wages in both countries. Therefore, the company off-shoring production would bring a ‘greater good for the greatest amount of people’ in both the Romanian and German society. Of course, as previously mentioned, there will be people that would lose their jobs in Germany, but the company can still act in an ethical way by helping these employees in terms of offering training programs for acquiring new skills or by offering a financial compensation (Gold, 2012a).

In the light of these considerations, the Romanian government should support the German companies that want to off-shore production in Romania. Not only is the German off-shoring associated with increased real wages in Romania, but also the number of jobs is expected to increase. In this way, the unemployed people, the ones situated in the most disadvantageous position, are going to have more job opportunities and therefore more chances to get a job and end up in a better position. Similarly, the German government should not try to discourage German companies to off-shore production. The results show that the off-shoring production of German companies in Romania is expected to bring more jobs on the overall German economy. Therefore, in this case as well the German unemployed people, the ones who are least off, have more chances in finding a job and be better off than initially. One problem that the German government faces is that

60 the German off-shoring to Romania is associated with a decrease in the average real wage in Germany for most of the population. However, Germany has already taken some measures in this direction. Although a highly debated project, Germany has introduced in 2015 the mandatory minimum wage that may end up the decreasing or stagnating trend in the average German real wage. Moreover, as noted before, a company is to act ethically if at least a part of its increased profits that are due to the off-shoring process are redirected towards either lowering the cost of their products or services for the consumer or towards increasing the wages of the employees that still work in Germany. In other words, if firms are to act ethically, then the real average wage should see an increase from the off-shoring process. Therefore, the German government should find solutions in making the companies redirect part of the profits accordingly and not fully retain their increased profits. I believe that the introduction of the minimum wage in Germany is one method in achieving this goal.

Finally, from a company perspective, I think that companies should continue with their off-shoring practice, but should at least be concerned with the well-being of their employees. This means that if domestic employees have to be laid off, then the least the company can do is offer support in finding another job or offer proper compensation. Nevertheless, I would like to think that companies should have a bigger purpose aside from the one of making profit, which should end up in doing a greater good for our society. I believe that companies could achieve this if they make sure that the money saved through off-shoring are directed towards lowering the price of their products, towards R&D or towards increasing the wages of the domestic employees.

6. Conclusion and discussion

The aim of the paper was to answer the following main research question: what is the relationship between the German off-shoring in Romania and the employment and wage levels respectively in the two countries? For answering the research question, the approach developed by Feenstra and Hanson (Hanson, 1996) was followed in order to compute the actual off-shoring indexes relevant for measuring the German off-shoring in Romania. The data for computing the AOI was taken from the World Input-Output Database, while the statistical data relevant for the employment and wage levels in the two countries was taken from the National Statistical Database of each country. The obtained data was then analysed, using SPSS, in order to see whether there exists a relationship between the AOI and employment and wage levels in the two countries.

The results show that there is indeed a positive association between German off-shoring and the employment level in both Romania and Germany. They show that although there is a negative association between these variables on the primary and secondary sectors, on the overall economy an increase in the AOI is associated with an increase of the number of jobs. These findings are in accordance with other studies that suggest that although off-shoring results in losing some domestic jobs, it also triggers the creation of new jobs in other industries. Our results show that there is a strong positive correlation between off-shoring and the employment level on the tertiary sector, meaning that the new jobs are created within this sector. In Romania’s case, the negative correlation between the German off-shoring and the number of jobs on the primary and secondary levels has a possible explanation in the restructuring of the Romanian industry after the fall of communism. Therefore, although German jobs have been off-shored to Romania, the total number of jobs has been diminished by new and more effective technology that has replaced the labour force the old communist industry was based on.

The results for the relationship between the German off-shoring and the wage levels give opposing results for the two countries. For Romania’s case, there is a positive relationship between the two variables on all the different economic sectors, while for Germany the relationship is negative for the primary and tertiary sectors and positive for the secondary economic sector. However, for the whole German economy, there is a negative correlation between off-shoring and German wages. An explanation for this result is that as more jobs are off-shored outside of Germany, the supply for jobs decreases and therefore employees lose on their bargaining power and are being forced to accept lower wages. Again, the findings are consistent with other studies within the literature.

After establishing the relationship between off-shoring and employment and wage levels in the two countries, the paper continues with the secondary research question that tries to answer whether the off-shoring process is an ethical one. For answering the question, some of the results from the econometrical analysis have been used. The ethical nature of off-shoring has been analysed using two different frameworks: the utilitarian and the Rawlsian one. The analysis concluded that the process of off-shoring is ethical or not depending on the perspective taken. From Romania’s perspective the process of German off-shoring does meet the Rawlsian principle. This conclusion can be drawn from the fact that off-shoring is expected to bring more job opportunities for the Romanian economy and therefore help the unemployed Romanians, the one situated in the most disadvantageous position, do better. In addition, from the Romanian perspective, the German off- shoring meets the utilitarian principle as well since it provides the ‘greatest happiness to the

62 greatest amount of people’ through the expected new job opportunities and increased real wages. The German perspective on off-shoring draws slightly different conclusions. Using the Rawlsian framework, the German off-shoring does also help the German people that are least off do better through the expected creation of more job opportunities. However, our analysis shows that from a utilitarian point of view, the German off-shoring to Romania does not provide the ‘greatest good for the greatest amount of people’ for the German society. This is because, although there are new jobs created, the average real wage is expected to decrease for a huge majority of the German employees.

In the light of these insights, I have proposed a couple of recommendations for the Romanian and German governments as well as for the German companies willing to off-shore their production. I have argued that the government should put more value in helping the least off do better and therefore follow the Rawlsian principle. Therefore, the Romanian and German government should encourage the German off-shoring of production since this is expected to bring more job opportunities in both countries. In this way, the unemployed people, the ones least off, are to do better. The problem for the German economy is that the off-shoring is expected to lower the average real net wage. However, through off-shoring, firms are expected to save costs and may use the increased savings to cancel out such effect. Therefore, the German government should find ways in redirecting the extra money that the companies have towards increasing the average real wage. In my opinion, the introduction of the minimum wage in 2015 in Germany is one step towards reaching this goal. On the other hand, German companies are not responsible for the entire society and therefore do not have to meet the Rawlsian principle to act in an ethical way. However, I believe that companies should not only strive for maximizing their profits, but also towards creating a greater good for the society and therefore meeting the utilitarian principle. The German companies do create a greater good for the greatest amount of people in Romania since both the job opportunities and the average real wage are expected to grow, but fail to do that, according to our analysis, for the German society. In order to fulfil the utilitarian principle for the German society, firms should redirect part of their savings from the off-shoring process towards lowering the consumer prices or towards increasing the domestic employees’ wages. In this way, the German average real wages might stop their decline and the German off-shoring to Romania should lead to the greater good for the greatest amount of people for the German society as well.

From the ethical discussion, it is not intended to make any value statement on which of the two ethical theories is a better one. The reason for choosing two different theories is to show that there are several ways in which one can look at the process of German-offshoring. Each of the frameworks has its advantages and drawbacks, but the purpose of using them in this paper was to show that there should be other principles considered when talking about off-shoring aside the economic ones.

Although the research questions have been answered through this paper, the paper also presents a couple of weak points. To begin with, the data from the World Input-Output Table ends with the year 2011, therefore the last four years could not be included in the research. In addition, I have experience numerous setbacks in finding the right data for the employment and wage levels in the two countries. Either some databases started after 1995 or the methodology of defining the different industries changed during the 1995-2011 research period. Nevertheless, I have tried to do the research for the entire research period, but there were a couple of cases that I simply couldn’t find the right data. One more important limitation of the paper is that it only aims in identifying an

63 association between the independent and dependent variables. The paper therefore does not tell anything about the causality between the independent and dependent variables. Even though two variables are highly correlated it does not necessarily mean that, in our case, the actual off-shoring index is the one responsible for the change in the employment and wage level in the two countries. It may be the case that another variable is the one responsible for the change in the dependent variable such as economic growth. In order to see whether this is the case for the German off- shoring in Romania, this matter should be dealt with and investigated in further research.

The current research focuses on Germany, as the high-wage country, and Romania, as the country where German companies offshore production to. The economical results give interesting insights on how the German off-shoring is associated with the change in the employment and wage level in the two countries, but there is the question of how generalizable are these results. In other words are we expecting to see similar results in the off-shoring relationship of a different high-wage country and a different low-wage one? Therefore, for future research it should be investigated whether the same results are obtained if different high-wage and low-wage countries are considered and thus confirm whether the results that we have obtained are relevant for the off-shoring relationship between a low-wage and a high-wage country.

One last limitation of our research has to do with the difficulty of quantifying happiness when applying the utilitarian principle. Although such a difficulty is experienced by all situations when the utilitarian framework is applied, one additional assumption that we have made in order to be able to quantify the total happiness was neglecting the marginal utility of money. We have assumed that a poor person losing one unit of money experiences the same decrease in happiness as one rich person losing the same amount. However, in reality this is probably not the case since the marginal utility of money is decreasing as you are richer. Therefore, we should expect that a poor person losing one unit of money is less happy than a rich person losing the same amount. One way in dealing with this problem is to assign weights for the different values, but setting the corresponding weight may be just as complex. In addition, the calculation of the total happiness did not take into account the intangible effects of losing or getting a job. The loss of a job may have affect the morale, self-esteem and other such values of one employee that should be taken into account when calculating the total happiness. Unfortunately, I was unable to come up with a way that would reflect the marginal utility of money or the intangible values in the calculations of the total happiness. For future research, more time should be given in this direction so that the calculation of the total happiness due to the off-shoring process gives results that are more precise.

However, the paper does reaches its initial goal of establishing the relationship between the German off-shoring to Romania and the wage and employment levels in the two countries. It also uses a common concept for measuring the German off-shoring to Romania, but it uses a new database for computing the off-shoring indexes. The actual off-shoring index has given us insights of the trend of German off-shoring in Romania during the 1995-2011 period. We have seen that the German off- shoring in Romania has increased steadily until 2008 and then experienced a decrease that can be related to the global financial crisis. The actual off-shoring index was then correlated with the employment and wage levels in Romania and Germany. The results showed us that indeed, as other sources argue, the German off-shoring is beneficial for the employment level in both countries since more jobs are expected to be created. Moreover, the analysis shows that the increase of German off-shoring in Romania is associated with lower average real wages in Germany and higher ones in

Romania. In addition, the difference between this paper and others on the same topic is that it tries to make use of ethical concepts aside economical ones when discussing about the off-shoring process. As Tomas Sedlacek argues, economics has merely lost the morality and ethics from its discussions. I am in line with Tomas Sedlacek and I believe that ethics should not be omitted from any economical discussion. This paper has tried to show how economic and ethical reasoning can be combined when discussing about the effects of German off-shoring in Romania on the employment and wage levels in the two countries and make a couple of recommendations of how the Romanian and German government as well as German companies should be positioned in this context. Nevertheless, there are a couple of remaining issues that should be dealt with in further research.

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Appendix

Average Nominal Net Wage 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total 21 32 63 104 152 214 302 379 484 599 746 866 1042 1309 1361 1391 1444 Primary 23 35 68 114 171 257 369 470 586 705 835 982 1174 1518 1559 1600 1655 Romania (RON) Secondary 21 32 63 97 139 197 274 339 436 544 653 731 870 1051 1146 1237 1324 Tertiary 21 32 63 108 158 219 311 392 500 619 781 918 1106 1396 1422 1426 1470 Total 22999 23304 23329 23423 24100 24752 25205 25590 25893 26015 25561 25320 25568 26130 26197 26777 27357 Primary 25316 25639 25335 25131 25277 25497 24816 24616 24414 23626 23874 24064 24060 24457 24562 24913 25836 Germany (Euro) Secondary 34154 34883 35402 36020 36800 38038 38629 39180 40198 40868 41262 42539 43292 43640 43069 44753 46415 Tertiary 27495 27701 27823 28011 28251 28438 29073 29521 29906 29833 29876 29941 30081 30901 31279 31945 32830

Table A1: Average Nominal Net Wage in Romania and Germany (INS, Destatis)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Gross value Total 6026 6112 6628 6985 7797 8755 9276 10395 11958 14006 added in real Primary 988 873 1095 1018 1130 1355 1019 1073 828 1077 terms (thousands Secondary 1905 1909 2228 2437 2587 2938 3205 3667 4211 5134 RON) Tertiary 3134 3329 3305 3531 4080 4463 5053 5655 6919 7795 Total 20083160 19959764 19742440 17876158 17579561 17371250 17171308 17441747 17588499 17593169 Total number of Primary 8888230 8964165 8681525 6190064 6179838 5370599 5367943 5157563 5115056 5043151 worked hours Secondary 5158253 4849879 4904640 5304508 5125042 5322204 5170670 5325087 5423181 5429586 Tertiary 6036677 6145720 6156275 6381587 6274681 6678448 6632695 6959097 7050262 7120431

Table A2: Real gross value added and total number of hours worked in Romania (INS)

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Agriculture 17,2 18,5 13,8 14,2 15,0 13,7 14,2 14,5 16,7 19,0 19,1 21,0 24,1 27,8 Mining 33,6 35,1 27,6 29,9 28,8 30,8 32,6 34,0 36,1 38,4 45,0 51,4 58,3 68,6 Food 27,6 31,4 23,2 20,4 19,6 19,3 21,9 21,3 23,8 25,9 27,6 30,0 34,9 38,7 Textiles 19,4 21,6 17,3 15,7 15,8 15,6 16,0 15,8 18,5 21,4 21,9 22,8 26,7 29,4 Leather 19,3 21,9 16,4 14,9 14,4 13,8 15,1 15,4 18,3 20,1 21,6 22,2 25,9 29,2 Wood 21,7 24,4 17,5 14,6 14,8 14,4 14,2 14,1 16,7 18,3 20,4 20,5 25,1 29,0 Pulp 28,8 30,4 22,5 22,0 21,2 24,9 27,4 28,2 28,7 30,2 31,9 33,1 38,2 43,9 Coke 45,4 53,5 46,8 46,5 41,1 43,6 52,4 61,3 60,8 63,1 71,1 80,2 82,8 99,6 Chemicals 33,8 38,3 30,5 28,9 28,9 31,2 36,6 37,7 39,3 42,6 45,1 52,9 59,1 62,1 Rubber 30,7 33,8 25,2 22,7 22,4 21,7 25,8 25,9 27,2 29,4 29,2 32,1 36,3 40,2 Non-metal 28,0 31,5 24,1 23,1 22,2 23,7 25,0 26,5 28,7 32,6 34,9 38,2 45,1 50,5 Metallurgic 31,9 36,6 27,3 25,5 24,5 27,4 31,0 30,0 31,4 36,4 38,2 40,5 45,4 50,4 Machines 27,3 33,0 23,8 23,4 23,2 23,8 27,7 29,1 30,4 33,9 35,0 38,4 43,1 49,7 Electrical equipment 26,9 31,4 25,1 23,5 25,1 25,4 27,7 29,1 31,0 33,6 35,0 37,5 40,7 44,8 Transport equipment 31,2 36,0 26,9 26,1 25,3 26,8 30,3 32,7 35,8 38,6 41,5 45,3 51,4 57,3 Recycling 22,7 24,4 17,7 15,9 16,5 16,1 17,2 17,7 19,7 22,2 23,6 26,0 29,2 32,9 Energy, water 31,8 34,0 29,9 32,6 29,2 28,5 30,0 29,8 33,1 35,5 42,5 45,7 51,0 61,9 Construction 22,5 23,9 17,5 17,5 17,1 15,6 16,3 16,5 18,7 20,7 22,7 24,1 28,5 34,7

Average Average real wagenet Commerce 16,9 18,0 13,0 12,8 13,0 12,6 13,8 13,7 16,0 17,3 20,8 22,1 26,6 31,2 Hotels & Restaurants 14,5 15,6 11,7 11,8 11,5 11,6 13,1 12,4 14,4 16,2 16,4 18,1 21,1 23,2 Transport Services 25,3 28,7 21,9 21,7 22,0 21,7 22,7 23,2 26,1 28,5 30,2 32,2 37,6 44,6 Post & Telecom 26,6 27,8 25,6 29,6 30,9 30,0 33,6 37,8 40,3 40,3 49,3 47,9 48,2 54,7 Financial Intermediary 39,0 47,5 41,9 49,1 48,7 44,0 46,2 50,5 54,9 61,5 74,6 76,6 84,6 96,2 Real Estate 22,6 24,5 19,3 18,9 18,5 18,1 18,6 19,4 20,6 23,0 26,0 28,2 35,8 40,1 Public Services 22,6 21,9 17,2 24,4 26,1 25,5 26,1 26,0 30,5 33,3 42,0 53,4 64,6 71,5 Education 19,5 19,9 15,3 18,7 17,3 17,1 17,9 19,3 21,0 25,5 29,9 36,2 38,0 46,1 Health 16,1 16,6 13,1 15,1 18,4 14,8 16,3 16,2 18,2 20,5 24,4 27,9 30,7 38,5 Rest of activities 15,6 18,3 14,8 15,4 16,2 15,9 16,1 17,4 18,9 21,2 24,1 25,2 28,6 33,3 Agriculture 0,019 0,017 0,013 0,012 0,009 0,007 0,007 0,007 0,007 0,007 0,007 0,006 0,006 0,006 Mining 0,011 0,011 0,008 0,008 0,006 0,006 0,006 0,006 0,006 0,005 0,005 0,004 0,004 0,004 Food 0,015 0,013 0,016 0,013 0,013 0,020 0,013 0,015 0,020 0,030 0,031 0,036 0,035 0,069 Textiles 0,035 0,029 0,040 0,130 0,064 0,200 0,196 0,243 0,274 0,433 0,411 0,486 0,389 0,385 Leather 0,021 0,019 0,022 0,037 0,023 0,047 0,051 0,078 0,116 0,378 0,452 0,483 0,607 0,635 Wood 0,054 0,062 0,110 0,133 0,167 0,166 0,123 0,130 0,142 0,166 0,142 0,149 0,123 0,148 Pulp 0,022 0,021 0,024 0,022 0,020 0,022 0,018 0,023 0,026 0,033 0,033 0,041 0,045 0,059 Coke 0,038 0,041 0,012 0,012 0,010 0,057 0,007 0,007 0,011 0,028 0,027 0,049 0,029 0,075 Chemicals 0,038 0,035 0,039 0,037 0,030 0,037 0,030 0,030 0,034 0,048 0,048 0,062 0,081 0,104 Rubber 0,038 0,035 0,042 0,043 0,034 0,049 0,044 0,053 0,071 0,093 0,090 0,106 0,131 0,157 Non-metal 0,025 0,025 0,031 0,029 0,022 0,033 0,027 0,030 0,034 0,061 0,059 0,065 0,077 0,094 Metallurgic 0,120 0,110 0,116 0,094 0,083 0,093 0,080 0,081 0,096 0,155 0,145 0,206 0,169 0,216 Machines 0,066 0,064 0,063 0,065 0,063 0,082 0,083 0,093 0,111 0,152 0,166 0,201 0,218 0,271 Electrical equipment 0,059 0,058 0,058 0,067 0,075 0,103 0,107 0,122 0,146 0,182 0,225 0,285 0,290 0,358 Transport equipment 0,069 0,070 0,061 0,066 0,070 0,069 0,079 0,107 0,145 0,270 0,216 0,218 0,230 0,260 Recycling 0,052 0,055 0,075 0,089 0,087 0,104 0,090 0,098 0,121 0,170 0,166 0,185 0,175 0,212 Energy, water 0,008 0,008 0,008 0,008 0,008 0,008 0,007 0,007 0,006 0,006 0,006 0,006 0,006 0,006 Construction 0,019 0,019 0,017 0,015 0,013 0,014 0,013 0,014 0,015 0,015 0,017 0,018 0,020 0,022 Commerce 0,029 0,026 0,027 0,029 0,026 0,026 0,027 0,027 0,029 0,030 0,034 0,037 0,041 0,044 Hotels & Restaurants 0,005 0,005 0,005 0,004 0,004 0,004 0,003 0,003 0,004 0,004 0,004 0,005 0,005 0,006 Transport Services 0,018 0,017 0,016 0,014 0,012 0,012 0,012 0,012 0,012 0,011 0,012 0,012 0,013 0,013 Post & Telecom 0,004 0,004 0,004 0,004 0,004 0,004 0,004 0,004 0,004 0,004 0,004 0,004 0,004 0,005 Financial Intermediary 0,003 0,003 0,003 0,003 0,003 0,003 0,003 0,003 0,003 0,003 0,004 0,004 0,005 0,006 Real Estate 0,008 0,008 0,006 0,007 0,007 0,008 0,008 0,010 0,011 0,011 0,012 0,015 0,017 0,020 Public Services 0,006 0,006 0,006 0,006 0,006 0,007 0,006 0,007 0,007 0,007 0,008 0,009 0,010 0,011

Share of relative totalemployement to population Education 0,019 0,019 0,019 0,018 0,019 0,018 0,018 0,018 0,018 0,019 0,019 0,019 0,020 0,020 Health 0,015 0,015 0,014 0,014 0,012 0,014 0,014 0,015 0,015 0,015 0,016 0,016 0,017 0,019 Rest of activities 0,007 0,006 0,006 0,005 0,004 0,005 0,005 0,005 0,005 0,006 0,006 0,007 0,007 0,007 Table A3: Average real net wage and ETP for Romania (INS)