Technological progress and its implications for the labour market

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Technological Progress and its Implications for

the Labour Market

Marco van Loenen 9-feb-2016 10445358 University of Amsterdam Nicoleta Ciurila Economics & Business

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Statement of Originality

This document is written by Marco van Loenen who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Introduction

During the past decades there have been significant changes in the structure of developed economies. Among these changes are changing wage structures, sectoral shifts and changing labour demands. The causes of these changes have been wildly debated and often attributed to technological progress. Sectoral shifts and changing labour demands are said to be the cause of rising unemployment rates. Since technological progress doesn’t show any signs of slowing down, it is important to understand its effects on the labour market. By creating an understanding and exploring possibilities for consensus about the effects, it becomes possible to find solutions to potential future problems. Past research has mainly focused on specific labour markets, neglecting the differences between labour markets. This paper tempts to fill this gap by analysing similar labour markets of multiple economies. Matching results could indicate changes induced by technological progress. To examine the effects of technological change on labour markets this paper uses data from a range of different economic variables. First a detailed analysis on the U.K. labour market is performed. After which the German, French, Italian and Dutch labour markets are more briefly (examining less economic variables) analysed. The analyses are performed by generating multiple graphs and examining long-term trends. The reason for analysing these countries is that they are all developed economies and members of the European Union (EU). For this reason they have the possibility to have similar economic structures and so, a good comparison is possible. This paper finds pervasive evidence of a sectoral shift. In time labour has significantly moved away from the industry and agriculture sectors towards service sectors. This is caused by technological progress but does not necessarily imply any problems. In terms of rising unemployment and inactivity rates the results are ambiguous across the different labour markets. This does not indicate that technological progress creates higher unemployment or changing inactivity rates. The structure of the remainder of this paper is as follows. In the first chapter the existing literature is reviewed by presenting different theories about the effect of technological progress on the labour market. The second chapter contains the results and the analysis for the U.K. labour market. After which in the third chapter the results for the German, French, Italian and Dutch labour markets are presented and analysed. In the fourth chapter the results of the second and third chapter are compared. In the fifth chapter the obtained results are summarized and the conclusion is formulated. Chapter six contains shortcomings of this paper and presents further research suggestions.

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1. Literature Review

Over the past decades a vast amount of literature has been written about the changing labour markets due to technological progress. The existing literature discusses different effects including job polarization, jobless recoveries, skill-biased technological change and sectoral shift. The literatures don’t unanimously show the same results about the effects. This chapter covers these theoretical insights and discusses the different findings in the literature.

1.1. Job Polarization

Research suggests that in the past 25 to 30 years jobs in the middle-wage level have been disappearing, while jobs in the highest- and lowest-wage classes have been increasing (Jaimovich, 2012). This phenomenon is referred to as Job Polarization. Goos and Manning (2003) describe the reason by explaining that technological progress leads to a rising relative demand in the highest-wage jobs (these typically require non-routine cognitive skills) and the lowest-wage jobs (these typically require non-routine manual skills), this is accompanied by a falling relative demand in the middle-wage jobs (these typically require routine manual and cognitive skills). They state in their paper that there is strong evidence to suggest that jobs are polarizing in the U.K.; they use the Autor et al. (2003) “routinization” hypothesis as an explanation. This hypothesis allows technological progress to result in routine task jobs to be replaced by computers. Computerization results in lower prices of computer capital (so investment in computer capital relatively increases) and leads to a decrease of demand of labour performing routine tasks (these are typically middle-wage class jobs). According to the hypothesis computers complement high-wage class jobs and increase their productivity. Autor et al. (2006) have researched the matter for the U.S. labour market. Their conclusion is similar to other researches performed on job polarization and confirms the phenomenon that is taking place since the last 25 years. In Germany the trend has been analysed by Spitz-Oener (2006). She concludes that the skill requirement has changed and jobs require more skills due to technological change. According to Spitz-Oener (2006) the change is strongest for middle-wage class jobs. Her conclusion is slightly different from previous researches discussed since it does not address the effect of technological progress for low-wage class jobs. It is tempting to generalise the past researches globally and conclude that job polarization is the reason for the changing labour market, and so the rise in wage inequality.

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Although it seems obvious that it is part of the reason for the change, it is not the sole reason defined to explain the changing labour markets and rising wage inequality.

1.2. Jobless Recoveries

Typically when economies pass the trough mark of a recession it implies the rising phase of the business cycle and so, a rise in aggregate output. This in turn results in a rise in aggregate employment. When the improvement in employment is sluggish or non-existent, it is referred to as a jobless recovery (Schreft 2003). This trend has been observed following the last three recessions of 1991, 2001 and 2009, although aggregate employment did recover following the three prior recessions in 1970, 1975 and 1982 (Jaimovich, 2012). This suggests that jobless recoveries are rather new trends that have a substantial negative impact on the current labour market. During a speech in 2003 Bernanke has spoken about his concerns regarding this trend in the U.S., saying that the weak labour market and its continued failure to improve could threaten the sustainability of economic recoveries. The reasons for jobless recoveries have been researched and debated in different studies. The hypothesis researched by Groshen and Potter (2003) is that structural change– permanent shifts in the distribution of workers throughout the labour market - is the main reason for the jobless recoveries. They find that permanent job layoffs were greater than temporary job layoffs. The permanent character of job losses during recent recessions could explain the slow recovery. The permanently laid off workers have to reallocate in different firms or industries. Because creating jobs is riskier than recalling employees to their old jobs as well as it is more expensive, job reallocation could be the cause of slow employment recovery (Groshen, Potter 2003). This is rejected by the paper of Aaronson et al. (2004). In their research they do not find evidence that supports the theory of job reallocation across industries after the recessions of 1991 and 2001 (the two recessions researched). This indicates that no consensus has been reached about whether jobless recoveries are the reason for the changing labour market.

1.3. Skill-Biased Technological Change (SBTC)

Skill-biased technological change (SBTC) is not focused on the employment dynamics as much as job polarization and jobless recoveries do. The main attention for SBTC is the rising wage inequality (Acemoglu, 1999). This is not the focus of this research; nevertheless it is useful to briefly discuss SBTC. This gives a better insight of the changing labour market. SBTC

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predicts that demand for skilled labour is rising relative to unskilled labour, which results in a rising wage inequality (Goos, Manning, 2003). Author et al. (2003) find supportive evidence that attribute STBC to the wage changes in the U.S. during the 1980’s. According to Goos and Manning (2003) improvement in technology is the main cause. Technology is supposed to be biased in favour of skilled labour opposed to unskilled labour. Job polarization and SBTC both explain the changing labour market due to technology improvement. The important difference is that within job polarization, unskilled jobs do not disappear when they are non-routine jobs. Berman et al. (1998) investigate if the SBTC that has occurred in the U.S. is common across several developed economies. They find that the same industries that biased towards skilled labour in the U.S., during the 1970-90 period, did so in the researched developing countries as well. They provide SBTC as the major explanation for the change in skill demands. Although they do mention that it is difficult to attribute these findings solely to SBTC. A reason is that it is hard to distinguish the effects of a general increase in the quality of skilled labour from the SBTC effects. Another problem in researching SBTC is identifying the skill-level in labour; this proves difficult (Gera, Gu, Lin, 2001). Gera et al. (2001) challenge the effects of SBTC. They have investigated the effects of STBC for the Canadian labour market for the same period as Berman et al. (1998). They have attempted to eliminate the problem in skill measurement by implementing two classification schemes. These take important characteristics of skills such as education, training and experience in consideration instead of using solely the education level or production/non-production worker distinction. Their findings do not have similar supportive results as for the U.S. labour market in terms of SBTC. They also find that the relative change of skilled workers remained practically unaltered, indicating that there is no evidence for SBTC. Card et al. (2002) find similar results. Whilst their research is based on the U.S. they find that evidence-linking SBTC to wage inequality is rather weak. This is odd since this result is the opposite of the result of Autor et al. (2003). The fact that the period of their research is from 1960 to 1998 could be the reason for the difference. But this reasoning is not obvious since the researched period of Card et al. (2002) also incorporates the fully researched period of Autor et al. (2003) Also Balleer and Rens (2013) challenge the effects of SBTC. This implies that results about SBTC are not pervasive. SBTC will remain a matter of discussion and there has yet to be waited for conclusive results.

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1.4. Sectoral shift

During the last decades there have been significant changes in the structure of developed economies. In particular the reallocation of the manufacturing sector towards service sector (Bonatti, Felice, 2007). Multiple researches have been performed on this matter. These researches provide different explanations and different implications of a sectoral shift. Sasaki (2007) investigates the role of sectoral shift on the productivity growth when services are used for both the intermediate input and final consumption. Other researches have focused on the structural change and its impact on the labour market regarding the unemployment rates, regional labour market performance and the changing wage structure. Chiarini and Piselli (2000) state that the fluctuation in labour demand across sectors is a substantial cause for the variation in unemployment. In 1982 Lilien has written a prominent paper, researching the sectoral shift of the U.S. labour market. In this paper he advocates that it takes time for reallocating workers to find matching jobs, creating an always-present rate of unemployment. The process of finding new jobs due to a sectoral shift tends to be slow and typically induces significant unemployment rates before the labour market fully adjust to the shift (Lilien, 1982). Prasad (1997) has researched the effect of structural change on wages for the Japanese economy. He finds that wage growth in the service sector has been relatively high in the post-1973 period. Nevertheless wage levels in the service sector are still lower than in the manufacturing sector. Prasad’s (1997) result implicates that labour demand, rather than labour supply has played an important role in the sectoral reallocation of labour. In the U.K. Robson (2009) has performed a research on the regional labour market effects due to structural changes. He finds that structural change has regional and not nationwide effects. This is due to regional differences in the industry sector. Multiple reasons explain the emergence and dynamics of structural changes. This includes the effects of changing consumer preferences due to rising incomes and competition from low-wage developing economies (Robson, 2009). This in line with the research performed by Buera and Kabobski (2012). They indicate that sectoral reallocations of production closely are related with consumption. Changing consumer preferences can indirectly be caused by technological progress. A faster growing productivity in the manufacturing sector compared to the overall economy in the U.K. has resulted in job losses in the manufacturing sector (Coutts, Glyn, Rawthorn, 2007). Schettkat and Yocarini (2003) have performed a literature review about the structural shift towards the service sector. To the previous reasons for sectoral shift discussed earlier they add the classification of workers. Workers are not classified according to the nature of their activity but on the basis

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of the sector of their workplace. They take managers performing the exact same tasks as an explanation. One manager works for a specialized marketing firm whereas another works in a car factory. The first manager is classified working in the service sector while the latter is classified as working in manufacturing sector. As manufacturing firms outsource their service activities, the fraction of service sector employment will increase, although it is only a reallocation of activities. In short, workers that were first classified in the manufacturing sector (because they performed service work in e.g. a factory), are now classified in the service sector (since manufacturing firms outsource their service activities to specialized service firms) although the actual work did not change, only the classification did.

2. Data analysis

This chapter explains the methodology used to analyse the U.K. labour market. Further, it presents a number of graphs that analyse UK data and it discusses the results by comparing the figures presented in the data section.

2.1. Methodology

The goal of this paper is to research if technological progress results in significant changes in the labour market by creating higher unemployment rates, prompting employees to switch sectors or inducing agents to exit the labour market. For the U.K. labour market this is done by analysing data solely from the Office of National Statistics (ONS). The reason is that measurements and results can differ across databases. This creates the risk of having measurement errors when interchangeably using multiple databases. In order to see the trend in the labour market for the U.K., a number of relevant graphs will be presented. All data used is already seasonally adjusted by the ONS.

2.2. Data

In this section the data collected for the U.K. labour market is presented. The period of the data can differ across variables due to availability. The figures reflect all available data. 2.2.1. Employment by industry The workforce data is used instead of the employment data. The ONS measures the workforce as the number of filled jobs instead of the number of people employed: this

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means people can have multiple jobs. The fact that workforce statistics are more detailed in terms of industry and sector compared to employment statistics is an important reason for its use. Since the workforce gives a good description of the employment level,

there are enough reasons to use these statistics. In the remainder of this paper, the workforce data will be denoted by “employment”. Given the high number of sectors in the ONS employment data, the results will be separated in two different parts. The first contains all the service related sectors (to make the analysis orderly, the sectors that are unlikely to be affected by technological progress are omitted). These sectors are: “accommodation & foodservice activities”, “real estate activities” and “arts, entertainment & recreation”. The second part contains the industry related sectors including agriculture. Figures 1, 2 and 3 contain data on the service related sectors, whereas figures 4, 5 and 6 contain data on the industry related sectors, including agriculture. Figure 1 displays the percentage change in employment. Figure 2 represents the absolute change in employment. The logic behind displaying the absolute values is that the percentage change in employment could be large, although, the absolute amount in employment could be relatively small. Figure 3 shows the percentage change over time; this gives an indication of the fluctuation in employment. The goal is to examine the change in overall fluctuation of employment per period and not the change per sector. So, the lack of clarity of this figure is of no concern. For the figures 4, 5 and 6 the same reasoning as figures 1, 2 and 3 applies. Figure 4 displays the percentage change in employment. Figure 5 presents the absolute change in employment. Figure 6 shows the percentage change per period. Figure 1: Percentage Change in Employment for Service Related Sectors (base 100 in 1978). Note: Data from the Office for National Statistics, Summary labour statistics.

60 100 140 180 220 260 300 Information & communication Financial & insurance activities Professional scientific & technical activities

Administrative & support service activities

Public admin & defence; compulsory social security1 Human health & social work activities

Wholesale & retail trade; repair of motor vehicles and motorcycles Transport & storage

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Figure 2: Absolute Change in Employment for Service Related Sectors (in thousands). Note: Data from the Office for National Statistics, Summary labour statistics.

Figure 3: Percentage Fluctuation in Employment for Service Related Sectors (in percentages).

Note: Data from the Office for National Statistics, Summary labour statistics.

By analysing the trends in figure 1 it becomes clear that most of the service sectors have witnessed a rise in employment. Except for “public administrative & defence” sector; this trend follows an opposite path. The declining interest in the “defence” sector for the U.K. could be an explanation for the decline. Also, higher efficiency in the “public administration” sector could be an explanation. The latter can be a result of technological progress. The “health care” sector has seen a significant rise in terms of both the percentage and absolute employment. A possible explanation is that the social security systems in developed economies have become increasingly important over the last century. Also the aspiration of making health care available to all layers of society is probably a large contribution to this trend. These changes in social structure have created a strong rise in demand, and so, in employment. Although this change is remarkable, technological progress 600 1,100 1,600 2,100 2,600 3,100 3,600 4,100 4,600 5,100 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Information & communication

Financial & insurance activities Professional scientific & technical activities

Wholesale & retail trade; repair of motor vehicles and motorcycles Transport & storage

-8 -6 -4 -2 0 2 4 6 8 10 Information & communication Financial & insurance activities Professional scientific & technical activities

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The fluctuations in employment are considerably small and stay within the -6% and 6% range (figure 3). It can also be seen that the fluctuations in employment over time have risen since the mid 90’s. That could be due to loosening of redundancy regulations, making it easier to hire and lay-off participants in the labour market. This results in a more cyclical labour market and creates bigger fluctuations in employment. This is in line with the results obtained by Groshen and Potter (2003). The biggest rise in employment fluctuation is observed during the great recession. The economical recovery was uncertain. For that reason it could be that the labour market mostly attracted temporary employment. This could explain the rise in employment fluctuation. The changing sizes of fluctuations do not affect the trend in employment. For that reason it is not a matter of importance for the analysis. Figure 4: Percentage Change in Employment for Industry Related Sectors, Including Agriculture (base 100 in 1978. Note: Data from the Office for National Statistics, Summary labour statistics 10 25 40 55 70 85 100 115 130

Agriculture, forestry & fishing Mining & quarrying Manufacturing

Electricity, gas, steam & air conditioning supply Water supply, sewerage, waste & remediation activities Construction

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Figure 5: Absolute Change in Employment for Industry Related Sectors, Including Agriculture (in thousands). Note: Data from the Office for National Statistics, Summary labour statistics.

Figure 6: Fluctuation in Employment for Industry Related Sectors, Including Agriculture (in percentages). Note: Data from the Office for National Statistics, Summary labour statistics.

The industry related sectors show a downward trend in employment. From figures 4 and 5 it can be concluded that, if there was growth, it was poor and decline is more substantial. In contrast, the sectors experiencing a decline mostly account for a rather small absolute portion of employment. This means it does not have a significant effect on the labour market. This is not the case for the “manufacturing” sector. This sector has experienced a substantial downward percentage and absolute change in employment. An explanation for this decrease is outsourcing. Most manufacturing from developed economies has been outsourced to developing economies. The reason is the cheaper 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

Agriculture, forestry & fishing Mining & quarrying Manufacturing

Electricity, gas, steam & air conditioning supply

Water supply, sewerage, waste & remediation activities Construction -30.00 -25.00 -20.00 -15.00 -10.00 -5.00 0.00 5.00 10.00 15.00 20.00 25.00 30.00 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Agriculture, forestry & fishing

Mining & quarrying Manufacturing

Electricity, gas, steam & air conditioning supply

Water supply, sewerage, waste & remediation activities

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by strong decreases in transportation costs (containers shipping and bulk shipping). Another possibility might be the high increase in productivity resulting in less need of workers to attain a similar output. Higher productivity is a consequence of technological progress. Better machines can improve production while decreasing labour needs. This is further examined in section 2.2.6. The “mining & quarrying” sector has experienced a dramatic decrease in percentage change in employment. This decline was caused by Margaret Thatcher (prime minister of U.K. from 1979 to 1990) who closed most of the mines. This is purely political, although technological progress might have contributed to the decline. The fluctuations are rather large compared to those in the service sectors. They lie in the range of -15% and 15%. Also, for the industry related industries the fluctuations in employment have started increasing since the mid 90’s. Possibly for the same reasons as for the higher fluctuation in the service related industry. An explanation for the substantially larger fluctuations is that industry related sectors tend to react faster to economical changes and are more cyclical than service industries. This makes the employment in those sectors more vulnerable to short term changes in economic growth. 2.2.2. Unemployment Figure 11 displays the unemployment for the age group of 16 to 64. Figure 12 displays the unemployment for the different age groups: 18-24, 25-49 and 50-over. The reason is to observe possible changes in unemployment for different age groups. A difference can be the basis for an interesting discussion. Figure 11: Unemployment for Age group 16 to 64 (in percentages) Note: Data from the Office for National Statistics, Summary labour statistics. 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 Ma r-M a y 1 9 9 2 De c-Fe b 1 9 9 3 Se p -N o v 1 9 9 3 Ju n -Au g 1 9 9 4 Ma r-M a y 1 9 9 5 De c-Fe b 1 9 9 6 Se p -N o v 1 9 9 6 Ju n -Au g 1 9 9 7 Ma r-M a y 1 9 9 8 De c-Fe b 1 9 9 9 Se p -N o v 1 9 9 9 Ju n -Au g 2 0 0 0 Ma r-M a y 2 0 0 1 De c-Fe b 2 0 0 2 Se p -N o v 2 0 0 2 Ju n -Au g 2 0 0 3 Ma r-M a y 2 0 0 4 De c-Fe b 2 0 0 5 Se p -N o v 2 0 0 5 Ju n -Au g 2 0 0 6 Ma r-M a y 2 0 0 7 De c-Fe b 2 0 0 8 Se p -N o v 2 0 0 8 Ju n -Au g 2 0 0 9 Ma r-M a y 2 0 1 0 De c-Fe b 2 0 1 1 Se p -N o v 2 0 1 1 Ju n -Au g 2 0 1 2 Ma r-M a y 2 0 1 3 De c-Fe b 2 0 1 4 Se p -N o v 2 0 1 4

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Figure 12: Unemployment for Different Age Groups (in percentages) Note: Data from the Office for National Statistics, Summary labour statistics.

Figure 11 illustrates a rather high unemployment rate in the first period (1992). This is explained by the U.K. recession of 1992 caused by high inflation rates, declining real estate prices and an overvalued exchange rate (Pettinger, 2015). The recovery is strong and fast, unemployment rates fall to 4.8%. The following recession in 2008 shows an obviously large increase in unemployment and the recovery is rather slow. This is in line with the theory of “jobless recoveries” (Jaimovich, 2012): no recovery or a remarkably slow recovery of the unemployment rate after a recession. The unemployment during the great recession was mostly affected for the age group of 18 to 24 years. For the age groups above 24 years unemployment did not substantially rise (figure 12). This is remarkable and could indicate the fact that there weren’t a substantial amount of redundancies during the great recession. Employers might have been reluctant to hire new entrants in the labour market due to the uncertain economic climate. 2.2.3. Population inactivity Population inactivity displays the amount of people that do not register as unemployed and therefore do not wish to participate in the labour market. There is a possibility that people get discouraged searching for a job. This can be due to negative economic periods or they receive sufficient government aid (which is subjective) created by social security systems for example. These people simply do not register and so will not be accounted in unemployment statistics. For this reason unemployment can be a deceiving statistic. Figure 13 shows the inactivity of the population of 16 to 64. 0.0 5.0 10.0 15.0 20.0 Ma r-M a y 1 9 9 2 Ma r-M a y 1 9 9 3 Ma r-M a y 1 9 9 4 Ma r-M a y 1 9 9 5 Ma r-M a y 1 9 9 6 Ma r-M a y 1 9 9 7 Ma r-M a y 1 9 9 8 Ma r-M a y 1 9 9 9 Ma r-M a y 2 0 0 0 Ma r-M a y 2 0 0 1 Ma r-M a y 2 0 0 2 Ma r-M a y 2 0 0 3 Ma r-M a y 2 0 0 4 Ma r-M a y 2 0 0 5 Ma r-M a y 2 0 0 6 Ma r-M a y 2 0 0 7 Ma r-M a y 2 0 0 8 Ma r-M a y 2 0 0 9 Ma r-M a y 2 0 1 0 Ma r-M a y 2 0 1 1 Ma r-M a y 2 0 1 2 Ma r-M a y 2 0 1 3 Ma r-M a y 2 0 1 4 Ma r-M a y 2 0 1 5 18-24 25-49 50 & Over

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Figure 13: Inactivity Level from Population Group 16-64 (in percentages) Note: Data from the Office for National Statistics, Summary labour statistics.

By looking at figure 13 the inactivity level is stable since 1993 and even decreasing since 2010. The level of unemployed decreased even though more people were looking for jobs. This indicates that the number of jobs available, and labour participants, increased during the period of 1992 to 2013.

2.2.4. Full-time/Part-time employment This paper focuses on examining trends in the labour market, and not in quarterly period comparisons. The overlapping periods in the data do not create a deficiency in examining the trend for full-time/part-time employment. So there is no need to make adjustment for the periods. Figure 7 displays the percentage change in full-time/part-time employment. Figure 8 shows the absolute change. Figure 7: Percentage Change in Full-time/Part-time employment (base 100 in 1992) Note: Data from the Office for National Statistics, Summary labour statistics.

21.0 22.0 23.0 24.0 25.0 26.0 Ja n -Ma r 1 9 7 1 Ma y-Ju l 1 9 7 2 Se p -N o v 1 9 7 3 Ja n -Ma r 1 9 7 5 Ma y-Ju l 1 9 7 6 Se p -N o v 1 9 7 7 Ja n -Ma r 1 9 7 9 Ma y-Ju l 1 9 8 0 Se p -N o v 1 9 8 1 Ja n -Ma r 1 9 8 3 Ma y-Ju l 1 9 8 4 Se p -N o v 1 9 8 5 Ja n -Ma r 1 9 8 7 Ma y-Ju l 1 9 8 8 Se p -N o v 1 9 8 9 Ja n -Ma r 1 9 9 1 Ma y-Ju l 1 9 9 2 Se p -N o v 1 9 9 3 Ja n -Ma r 1 9 9 5 Ma y-Ju l 1 9 9 6 Se p -N o v 1 9 9 7 Ja n -Ma r 1 9 9 9 Ma y-Ju l 2 0 0 0 Se p -N o v 2 0 0 1 Ja n -Ma r 2 0 0 3 Ma y-Ju l 2 0 0 4 Se p -N o v 2 0 0 5 Ja n -Ma r 2 0 0 7 Ma y-Ju l 2 0 0 8 Se p -N o v 2 0 0 9 Ja n -Ma r 2 0 1 1 Ma y-Ju l 2 0 1 2 Se p -N o v 2 0 1 3 Ja n -Ma r 2 0 1 5 95 100 105 110 115 120 125 130 135 140 Ma r-M a y 1 9 9 2 Ja n -Ma r 1 9 9 3 No v-Ja n 1 9 9 4 Se p -N o v 1 9 9 4 Ju l-Se p 1 9 9 5 Ma y-Ju l 1 9 9 6 Ma r-M a y 1 9 9 7 Ja n -Ma r 1 9 9 8 No v-Ja n 1 9 9 9 Se p -N o v 1 9 9 9 Ju l-Se p 2 0 0 0 Ma y-Ju l 2 0 0 1 Ma r-M a y 2 0 0 2 Ja n -Ma r 2 0 0 3 No v-Ja n 2 0 0 4 Se p -N o v 2 0 0 4 Ju l-Se p 2 0 0 5 Ma y-Ju l 2 0 0 6 Ma r-M a y 2 0 0 7 Ja n -Ma r 2 0 0 8 No v-Ja n 2 0 0 9 Se p -N o v 2 0 0 9 Ju l-Se p 2 0 1 0 Ma y-Ju l 2 0 11 Ma r-M a y 2 0 1 2 Ja n -Ma r 2 0 1 3 No v-Ja n 2 0 1 4 Se p -N o v 2 0 1 4

Total people working full-time

Total people working part-time

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Figure 8: Absolute Change in Full-time/Part-time Employment (in thousands) Note: Data from the Office for National Statistics, Summary labour statistics.

Full-time/part-time employment could mitigate low unemployment levels. A relatively large increase in part-time compared to full-time employment could result in a low unemployment rate. But in reality mean that the number of available working hours is actually decreasing. Part-time employment has increased faster than full-time employment. It could be that full-time employment transformed in part-time employment. During the great recession full-time employment decreased while part-time employment increased. Although both have increased in the long run, it may imply that the available working hours decreased while employment increased. Technological progress and therefore higher productivity could be the reason for fewer labour hours demanded. This can be the reason for the increasing amount of part-time employment. Another reason could the change in preferences of economic agents. Preferring more time for leisure or family (e.g.) in exchange for lower wage compensation. 2.2.5. Public/private sector employment The U.K. government has nationalised multiple corporations and banks since 2002. Due to this some private employment has become public (ONS, 2015). The data used for this subject has been measured on a consistent basis to remove the effects of major

reclassifications. In figure 9 the percentage change is presented. Figure 10 represents the fraction change of public compared to private employment. 5,000 7,000 9,000 11,000 13,000 15,000 17,000 19,000 21,000 23,000 Ma r-M a y 1 9 9 2 Fe b -Ap r 1 9 9 3 Ja n -Ma r 1 9 9 4 De c-Fe b 1 9 9 5 No v-Ja n 1 9 9 6 O ct -D e c 1 9 9 6 Se p -N o v 1 9 9 7 Au g -O ct 1 9 9 8 Ju l-Se p 1 9 9 9 Ju n -Au g 2 0 0 0 Ma y-Ju l 2 0 0 1 Ap r-Ju n 2 0 0 2 Ma r-M a y 2 0 0 3 Fe b -Ap r 2 0 0 4 Ja n -Ma r 2 0 0 5 De c-Fe b 2 0 0 6 No v-Ja n 2 0 0 7 O ct -D e c 2 0 0 7 Se p -N o v 2 0 0 8 Au g -O ct 2 0 0 9 Ju l-Se p 2 0 1 0 Ju n -Au g 2 0 11 Ma y-Ju l 2 0 1 2 Ap r-Ju n 2 0 1 3 Ma r-M a y 2 0 1 4 Fe b -Ap r 2 0 1 5

Total people working full-time

Total people working part-time

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Figure 9: Percentage Change of Public/Private Employment (in percentages)

.

Note: Data from the Office for National Statistics, Summary labour statistics.

Figure 10: Fraction Change of Public/Private Employment (in percentages). Note: Data from the Office for National Statistics, Summary labour statistics. When low unemployment rates seem oddly low there could be a suspicion that these rates are in some way manipulated. The expectation would be that the employment in the public sector would increase. In other words, the government could increase employment in the public sector to “cover up” high unemployment rates. As seen in figures 9 and 10 these fractions fluctuate over the years. Since 2009 the fraction of public employment is greatly reduced. Even considering the U.K. government has nationalised several corporations and banks since 2002, which should actually increase the public employment rate. In 2009 the conservatives, known to be at the right side of the political spectrum, won the government elections. It is also well known that right wing politics are not in favour of public employment. This could be an explanation for the declining fraction of public sector employment. This political choice is not affected by technological progress.

2.2.6. Productivity Productivity is a reflection of the technological progress. The goal of technological progress is to produce faster, using less labour and/or with higher quality. These are factors denoting 85.0 90.0 95.0 100.0 105.0 110.0 Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c Ju n De c 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 20132014 Public sector excluding

effects of major reclassifications Private sector excluding effects of major reclassifications 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0 Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep Ma r Sep 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Public sector excluding

effects of major reclassifications Private sector excluding effects of major

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productivity; so productivity is used as a measure of technological progress. Figure 14 shows the percentage change in productivity. The classification is equal to the industries in the employment section although not all industries have available data. For this reason not all the same industries are displayed in the graph for productivity. Figure 14: Percentage Change of Productivity Across Industries (base 100 in 1079) Note: Data from the Office for National Statistics, Labour Productivity, Q3 2015 Dataset.

The “manufacturing” sector has experienced a large decline in employment while productivity grew extensively. Technological progress is likely to have large influence in this trend. Less labour is needed to create similar output and so the demand for labour has decreased in this sector. The “info & comms service” sector shows the largest growth in productivity while employment grew over the last 25 years. This indicates that the demand for “info & comms service” has largely increased.

3. General European Union Data analysis

First this chapter explains the methodology used for a general analysis of different European Union (EU) labour markets. Further it presents and examines the results acquired through the presented figures.

3.1. Methodology

An important question is whether the change in the U.K. labour market is a general trend for developed EU economies. Or whether there are differences between these countries. If 80 100 120 140 160 180 200 220 240 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Output per job: Ag, forestry and

fishing

Output per job: Manufacturing Output per job: Construction Output per job: Wholesale & retail services

Output per job: Transport & storage services

Output per job: Info & comms services

Output per job: Finance & insurance services

Output per job: Admin & support services

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there are, can technological progress be accounted for it? This is examined by analysing the German, French, Italian and Dutch labour markets. The logic behind choosing these countries is that they all are developed EU economies and so, are similar to the U.K. All the results originate from the Eurostat database. For the same reason as for the U.K. labour market analysis the same database is used for the entire analysis.

3.2. Data European Union labour market

In this section the data obtained for the EU labour market is presented. The period of the data differs across different variables due to availability. The figures reflect all available data. 3.2.1. Employment Figure 15 reflects the change in total employment. The Eurostat statistics show the fraction of the service, industry and agriculture sector of total employment. These are shown in figures 16, 17 and 18 respectively. The Dutch data for the years 1984 and 1986 is missing. To be able to correctly observe the trend for the missing years, the averages from previous and following periods were taken. Figure 15: Change in Total Employment (base 100 in 1992) Note: Data from Eurostat Database, employment growth and activity branches. Figure 16: Fraction of Employment in Service Sector (in percentages) Note: Data from Eurostat Database, employment growth and activity branches.

90 100 110 120 130

Germany (until 1990 former territory of the FRG) France Italy Netherlands 60.0 65.0 70.0 75.0 80.0 85.0 Germany (until 1990 former territory of the FRG)

France Italy Netherlands

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Figure 17: Fraction of Employment in Industry Sector (in percentages) Note: Data from Eurostat Database, employment growth and activity branches.

Figure 17: Fraction of Employment in Agriculture Sector (in percentages) Note: Data from Eurostat Database, employment growth and activity branches.

The EU countries show similar results as the U.K. with respect to a sectoral shift. It is clear that employment has moved away from the industry and agricultural sectors towards the service sector. Italy shows a rather sluggish decline in the fraction of the industry sector employment. On one hand the reason could be that Italy has a highly competitive industry sector with high productivity. This is making it attractive to produce in Italy. On the other hand Italy could experience problems increasing their productivity. For this reason a relatively large amount of labour is needed In order to reach their output levels. It is assumed that the latter is the case. Also the relatively high agriculture employment could be attributed to this reason. This could be due to a slow reaction of technological progress in Italy. This has made Italy relatively less productive compared to other EU countries. Since the great recession Italy is also the worst performer in terms of total employment. A more positive view for the employment levels is seen by the German trend. Their employment levels are sharply increasing since 2005 and continue to do so despite the great recession. The highest employment levels are observed for the Netherlands with high growths in two different epochs. The first period of growth abruptly stabilised in 2001, after which a period of moderate growth occurred. This was due to low investments, low growth of 10.0 15.0 20.0 25.0 30.0 35.0 40.0 Germany (until 1990 former territory of the FRG) France Italy Netherlands 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Germany (until 1990 former territory of the FRG)

France Italy Netherlands

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consumer spending and a relative decrease in competitiveness (Morks, 2003). In 2004 the employment levels sharply increased again until the start of the great recession. Since then the Dutch labour market has experienced a moderate decline in employment levels. The epochs of moderate growth or declining employment levels occurred due to external factors. Technological progress cannot be accounted for it.

3.2.2. Unemployment Figure 19 reflects the unemployment rates analysed for the four different European countries. Since there is no data of unemployment in Germany for the periods previous to 1991, the German trend line start in 1991. Figure 19: Unemployment Rates (in percentages) Note: Data from Eurostat Database, Unemployment rates by sex, age and nationality.

Currently Italy has the highest unemployment rate of the analysed countries, exceeding 12%, which is alarming. Before the crisis Italy had the second lowest unemployment rate. The question is, what are the reasons for this development? Technological progress is not the guaranteed cause. Technological progress has been advancing since more than 50 years and is not a sudden shock. Throughout the whole data presented, Italy already had high unemployment rates, except just before the great recession. France has experienced a stable but also rather high unemployment rate in the last 30 years. This is not in conjunction with the rising employment levels. Also in the Dutch labour market the employment and unemployment levels do not show opposite paths. You would expect that growing employment levels indicate decreasing unemployment levels and vice versa. Changing levels in part-time/full-time employment and/or activity rates could be the reason for this occurrence. These trends are further examined in sections 3.2.3. and 3.2.4. respectively.

2 4 6 8 10 12 14 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 201 1 2012 2013 2014 Germany (until 1990 former territory of

the FRG) France Italy Netherlands

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3.2.3. Part-time Employment In figure 18 the part-time employment as percentage of the total employment is presented. Figure 18: Part-time Employment as Percentage of the Total Employment (in percentages) Note: Data from Eurostat Database, Part-time employment as percentage of the total employment, by sex and age

The Netherlands has experienced a large increase in the fraction of part-time employment. This could explain why the Dutch labour market has experienced the strongest growth in employment. More participants in the labour market do not necessarily mean that more labour hours are available. As analysed in section 2.2.4. this could mitigate the high employment or low unemployment levels. The remaining countries show similar trends in the fraction of part-time employment although, less drastic. Possible reasons for the increase in the fraction of part-time employment are previously discussed in section 2.2.4.

3.2.4. Inactivity rates In figure 20 the inactivity rates for the populations from 16 to 64 are presented. For the Netherlands the data for the years 1984 and 1986 is missing. To be able to correctly see the trend for the missing years, the averages from previous and following periods were taken. Figure 20: Inactivity Rates for Population from 16 to 64 Years (in percentages) Note: Data from Eurostat Database, active population by sex, age and citizenship.

0.0 10.0 20.0 30.0 40.0 50.0 60.0 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 201 1 2012 2013 2014 Germany (until 1990 former territory of

the FRG) France Italy Netherlands 20.0 25.0 30.0 35.0 40.0 45.0 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 201 1 2012 2013 2014 Germany (until 1990 former territory of

the FRG) France Italy Netherlands

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In the French and Italian labour markets the inactivity rates have slightly decreased over the years. On the other hand the German and Dutch have experienced are large drop of inactivity rates. Economic agents have been increasingly willing to participate in the labour market. The low unemployment rate in conjunction with the low inactivity could explain the rise in the fraction of part-time employment in the Netherlands.

4. Comparing the analyses

The aim of this paper is to examine if technological improvement could trigger changes in the labour markets. This was mostly done using U.K. data, but as a comparison also other European countries were analysed. One general trend, which has been observed for every country, is the sectoral shift away from the agriculture and industry sector towards the service sector. For some the shift is bigger than others, but it is evidently there. When comparing both analyses we can conclude that the U.K. labour market is performing generally well. It is odd that the differences between labour markets can be as large. It assumed that each country has the same economic possibilities. Members of the EU have the same access to physical, capital and intellectual resources. This is because between EU members there is free movement of labour and free trade of capital and goods. Also most of the EU trade is intra-trade, meaning the economies and labour markets have the possibility to be very much alike (http://ec.europa.eu/, 2015).

5. Conclusion

This paper has researched the effect of technological progress on changes in the labour market. This was done by extensively analysing the U.K. labour market, after which the German, French, Italian and Dutch labour markets have been analysed as a benchmark. The main, general, result that was found through our data analysis is that there is a sectoral shift in the labour market away from the agriculture and industry sector towards the service sector. The change in the fraction of part-time employment could also be caused by technological progress, but this result is more doubtful.

N

o general trends between countries have been observed in terms of unemployment and inactivity rates. That implies that countries have different economic structures, but most important of all, that technological improvement cannot be accounted for the difference in unemployment and inactivity rates. If technological improvement would have created these changes in the labour markets, the analysed markets should have showed similar trends for the unemployment and inactivity rates. Also, the unemployment and inactivity rates should

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have risen steadily since technological progress is a rather constant and not volatile factor. So except for a reallocation of workers across sectors, there probably won’t be any major changes in the labour market following the process of labour automization.

6. Shortcomings & Further Research Suggestions

In this paper the general trends of various labour markets were examined. There was not a focus on specific job sectors, which is also important. For this reason it cannot be concluded that there is job polarization or that there is SBTC due to technological improvement. Another problem is analysing jobless recoveries, with the data used it cannot be analysed when GDP was starting to pick up after the last three recessions. Therefore it is not observable if employment recovers slowly. The root for the large differences in the analysed variables through different economies is not researched in this paper. These are probably caused by differences in tax structures, regulation, social security, and rigidities of labour markets. These are all laws, formulated by governments, and not caused by a change in technological improvement. As H.J. Chang advocates: in an economy there are a lot of different influences that may have different outcomes. Past research focuses on the changing labour markets and change in jobs due to technological improvements. But it can be interesting to investigate the difference in economic structure between countries and their reaction on technological improvements. This way it can be researched why performances differ across similar economies.

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References

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Data

Eurostat (2015). Database – Unemployment rate by sex and age – annual average, % (une_rt_a) Eurostat (2015). Database - Employment (main characteristics and rates) – annual averages (lfsi_emp_a).

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Eurostat (2015). Database - Part-time employment as percentage of the total employment, by sex and age (%)(lfsa_eppga). Eurostat (2015). Database – Inactive population as a percentage of the total population, by sex and age (%) (lfsa_ipga) ONS (2015). Database - http://www.ons.gov.uk/ons/publications/re-reference- tables.html?edition=tcm%3A77-367096.

Technological progress and its implications for the labour market