THE NEAR FUTURE PROGRESS OF EU S ECONOMY AND ITS IMPACT ON THE CHEMICAL INDUSTRY Šimon Buryan 1. Czech Republic.

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1 THE NEAR FUTURE PROGRESS OF EU S ECONOMY AND ITS IMPACT ON THE CHEMICAL INDUSTRY Šimon Buryan 1 1 University of Economics, Prague, Faculty of International Relations, W. Churchill Sq. 4; Prague 3 Czech Republic. simon.buryan@vse.cz Abstract: This paper analyses the current and near future development of the economy of European Union (EU) and the dependence of EU chemical industry on the EU economy. The EU economy is experiencing a slow recovery from the financial and debt crisis. Many issues have to be tackled in the EU in order to improve long-term prospects, especially low levels of investment. The first aim of this paper is to predict the future economic development of the EU economy. According to the estimated ARIMA model of quarterly data on GDP of EU28, the EU economy should grow by 1,9 % in 2015 and 2,16 % in This estimate is in line with the predictions of both OECD and IMF. The second aim of this paper is to predict the near future total production of chemical industry in the EU. If current dependence of this sector on the economy performance remains, the industry production should either stagnate or decline by 1,5 % in 2015 and 2016 as well. Another finding according to the estimated model is that the regulation on chemicals REACH, which is in force since 2007, would cause decrease of the production of chemicals in the range of 375 billion Euro between 2010 and Keywords: economy dynamics, economic growth forecast, European Union, chemical industry, REACH regulation JEL classification: L50 1. Introduction This paper analyses the current and near future development of the economy of European Union (EU) and the dependence of EU chemical industry on the EU economy. The first aim of this paper is to predict the future economic development of the EU economy. In the near future EU economy is expected to recover slowly. The estimated ARIMA model projects an economic growth of 1,9 % in 2015 and 2,16 % in The EU economy is still slowly recovering from the effects of economic crises and the resulting debt crisis. Gross Domestic Product (GDP) growth in the EU has consistently been slower compared to last decades. EU economy failed to generate strong growth in order to get back to the pre crisis GDP levels. This resulted in an ongoing economic stagnation with increased unemployment and stagnant living standards. (OECD 2015) According to the IMF (2015) there are signs of a pickup in near future due to the lower oil prices and supporting financial environment. However the risks of prolonged slow growth and near zero inflation are remaining in the EU. The next step by the EU governing bodies on the Union as well as member states level cover a boost of growth and inflation by a comprehensive approach that involves quantitative easing and using the available fiscal space, especially for investment. As the EU economy slowed down, the EU chemical industry remained low as well. This sector is strongly dependent on the EU s economy performance. Chemical industry products are sold to consumers as well as manufacturing companies. As the EU s consumption and manufacturing output both stagnated, the chemical industry output failed to grow. The second aim of this paper is to predict the near future total production of chemical industry in the EU using a regression model, which explains the total value of production of chemicals as a function of growth of the total economy and regulatory influence of the REACH (Regulation EC No. 1907/2006 on Registration, Evaluation, Authorisation and Restriction of Chemicals) on the industry

2 According to the developed regression model European chemical industry production is strongly dependent on the on the EU economy growth. OECD as well as IMF predicted that EU economy should be growing slowly in the years 2015, 2016 and onwards, but according to the model this estimated growth won t be strong enough to increase the output of chemical industry. Moreover the chemical industry growth was to some extent influenced negatively by the REACH regulation. It is currently the most ambitious legislative framework for chemicals in the world. The regulation aims for ensuring the health and environmental safety of chemical substances. It requires chemical companies to register their products and to submit detail information of their chemicals. Depending on the total sold volume on the EU market of a given chemical, REACH demands toxicological test results. The total testing and registration fees of a single substance mount up to about Euros. So far there were two REACH registration rounds in November 2010 and May According to the estimated model, the first deadline led to a decrease of quarter growth rates of chemicals production by 0,62 % (in every quarter of ), the 2013 deadline caused a quarter growth rate decrease by 1,57 % (in every quarter of ). These results, as it will be discussed in chapter 4, are statistically significant. If the same market dynamics endure, we can predict the impact of the May 2018 deadline. The next REACH registration round could reduce the quarterly growth rate in the year 2018 by 7,25 %. The estimated loss of the volume of chemicals in EU due to the REACH three registration rounds between 2010 and 2018 could be up to cca. 375 billion Euros. To put this value into perspective, this sum was 76 % of EU chemical industry production in Expected economic development in the EU The economic recovery from the 2008 crisis has been unusually weak. The USA recovered faster compared to the EU (see figure 1). In 2009 USA and EU experienced a stern economic decline. EU s GDP declined by 4,4 %; 1,6 % more compared to the USA. Both countries experienced economic growth in 2010 and 2011, but EU s economy fell back into stagnation in 2012 and 2013 due to the debt crisis in the EU. There was some growth in EU in 2013 and 2014; however the volume of GDP of 2014 was still lower than the volume of (OECD 2015) Fig. 1. Real GDP growth rate - volume, change on previous year [%] Source: Eurostat

3 The debt crisis of large EU member states caused cuts in government expenditures, which led to decline in GDP. Another factor adding to economic stagnation has been lower private investment. Investment was kept low due to the subdued (actual and expected) demand, both at home and globally, which inhibited improvements of employment, wages, and consumption. (OECD 2015) Labour market in 2014 remained low in the euro area. While the German labour market was strong, unemployment was 11,2 % in the euro area and 13,5 % not counting in the German economy. (OECD 2015) On the supply side, low investment has undermined the rate of growth of potential output via a lower rate of increase of the capital stock as well as a slowdown in the diffusion and embodiment of technical innovation. The some extent, global economy is in the same situation. (OECD 2015) Improving growth prospects of the EU will require a leap to higher growth equilibrium with more investment, leading to improvements in job creation and consumption. These developments then induce an increase of investment rate causing a benign supply side outcome. (OECD 2015) The IMF (2015) foresees a positive momentum in the euro area. Low prices of oil and supportive financial conditions would improve the growth prospects, however the risks of prolonged low growth and low inflation is still in place. The main target is to boost growth and inflation through measures such as a quantitative easing, comprehends fiscal expansion, an increase of investment, structural reforms enhancing productivity and measures strengthening bank balance sheets. Growth is stronger in European advanced economies outside the euro area; however some of these countries may need to tighten their macro prudential policies if housing-related risks do not pass away. In January 2015 the European Central Bank (ECB) introduced a comprehensive asset purchase program to address persistently low inflation in the euro area, as both the core as well as headline inflation have been below the ECB s medium-term price stability objective for some time. ECB has increased its purchases to include bonds issued by euro area central governments, agencies and European institutions as well. (ECB 2015, IMF 2015) Since late 2014 EU was pushing forward on policies to promote completion of the comprehensive assessment of banks, start of the Single Supervisory Mechanism, announcement of plans for a European Fund for Strategic Investments. Then a new guidance to enhance flexibility under the Stability and Growth Pact for member states improves undertaking structural reforms or investment in the current situation. (IMF 2015) IMF (2015) predicts an increase of economic growth to 1,5 % in 2015 from 0,9 % in The increased growth in 2015 reflects stronger growth momentum at the end of 2014, wage increases, a supporting decrease of oil prices, and the ECB s steps to improve financial conditions. After 2015, growth in euro area is expected to fluctuate around 1,25 %, which reflects the demand as well as supply side bottlenecks. Inflation is forecasted at circ. 0,1 % in 2015 and is forecasted to stay under the ECB s medium term price stability objective due to persistent low dynamics of the EU economy. (IMF 2015) Emerging and Developing European economies show slower growth as well. These countries experienced a weaker demand from abroad. Quite a few economies slipped into deflation in According to the IMF external demand will remain low and high corporate debt will continue to lower the investment. In case that emerging and developing European economies had a monetary policy space, they should aim for a boost in domestic demand. (IMF 2015) 2.1 Prediction for the future development of the EU economy Both OECD and IMF predicted that EU economy will grow by about 1,5 % in 2015 and 2016 forecast estimate about 2 % growth in 2016 (see Table 1). To reveal the current dynamics and estimate the possible economic growth in time period , a statistical time series model of EU s GDP was estimated as a time series ARIMA process. The time series for GDP of 28 member states of the -774-

4 EU (EU28) from the Eurostat (2015) database was modelled. The modelled time interval was 2009 Q1 up to 2015 Q1. The seasonality was filtered out by Census X12 application in statistical programme EViews 7. Table 1. Gross domestic product annual growth, constant prices [%] Year OECD 1,4 2, IMF 1,847 1,949 1,907 1,876 1,877 1,852 Source: OECD 2015, IMF The time series data of GDP EU28 was stationarized by the first difference. The estimated model is ARIMA (2,1,3,)c. There are two autoregressive (AR) terms in the equation, three moving average (MA) terms and a constant of the volume of 1, There is then the residuals term, which is a t in given quarter t. The visualisation of the model is in figure 2. The curve representing model residuals stays most of the time within the shown interval of significance. The curve with dots highlights the actual data and the plain curve represents the estimated GDP of EU28 according to the model. We can clearly see that the model explains the data quite well. Fig. 2. ARIMA(2,1,3)c model of GDP of EU28 [in Euros] The model equation is as follows: -775-

5 GDP EU 28, t 1,346 a 1,96 10 t ,903 a 0,997 GDP t 3 EU 28, t 1 0,416 GDP EU 28, t 2 a t 1,376 a t 1 (1) The statistical output of the model can be seen in Annex 1.1. All the AR and MA terms of the model are significant at the level of 1 % apart from the second AR term ( 0,416 GDP EU 28, t 2 ) which is significant at the level of 10 %, still this level of significance is often considered satisfactory in econometrical praxis. The model according to the coefficient of determination explains 78,85 % of the original data variability. According to the F test, model is significant at the level of 1 %. There is not autocorrelation present in the model on the level of significance of 5 % according to the Breusch Godfrey Serial Correlation LM Test (p. value is 20,4 %), as it can be seen in annex 1.2. Heteroscedasticity is not present in the model at the level of significance of 5 % as well (see annex 1.3). The model residuals were tested by the unit root test and they are stationary as well as the original dataset. So we might conclude that the model is statistically significant. This time series model was used to predict the expected future values of GDP of EU28. The result of this prediction is visualised in figure 3. Economy of EU28 is expected to grow steadily in time period Fig. 3. Forecast of GDP of EU28 [in Euros] The estimates were used to calculate the annual growth in The EU28 GDP is expected to grow by more than 2 % in the time period (see table 2). Table 2. Gross domestic product annual growth Year Predicted growth [%] 1,90±0,67 2,16±0,02 2,13±0,01 2,07±0,

6 3. Chemical industry in the EU The chemicals industry has a significant role in the EU s economy. It belongs to the largest European manufacturing sectors. EU chemical industry produces high end products, which support EU s competitiveness on the global market. This industry provides solutions for various social challenges, as it belongs to the heart of the EU manufacturing. It supplies 66 % of its production to other European manufacturing industries. (European Commission 2015) 3.1 Importance of the chemical industry to the EU The EU chemicals industry combines 7 % of EU industrial production and stands for 1,1 % share of EU GDP. The sales of chemicals in 2013 reached 527 billion Euros, which was circ. 17 % of world sales of chemicals. About 1,15 million highly skilled persons were employed in the industry in The industry has the second highest figure of added value per employee; first being the after pharmaceutical industry. The sector has to deal with significant challenges such as increased competition from abroad and rising costs. The economic crisis of 2008 and 2009 led to substantial decrease of industry turnover; however the industry recovered quickly and has remained more or less stable in terms of total sales. (European Commission 2015) EU chemical industry output is strongly dependent on the EU. EU companies export about a quarter of their total chemical production abroad. Approximately 20 % of chemicals production is sold in the company home markets and the majority (55 %) of chemical production is sold on the market of other EU member states. (CEFIC 2015b) Because the chemical industry output is so dependent on the EU economy it is reasonable to study their interdependence using a regression model. The estimated model is in the chapter Influence of REACH regulation on EU chemical companies The REACH regulation is applicable to all chemical sold on the EU market as well as in other European Economic Area Member States. REACH created a single, comprehensive, over arching system of chemicals oversight. The regulation established a high standard ensuring safety management of risks associated with chemicals through all parts of the supply chain and to some extent even disposal of chemicals. (Bergkamp 2013) REACH raised many challenges for the industry in many areas, including compliance, management as well as strategy. (Bergkamp 2013) It entered into force on the 1 st June There are three stages, during which regulation applies to individual products. By 30 th November 2010 all the chemicals which are sold in the EU in volumes above tonnes per annum, had to be registered at the European Chemicals Agency (ECHA). The deadline for chemicals of volume was 31 st May The last deadline 31 st May 2018 is aimed at chemicals of volume (Bergkamp 2013) According to Foth and Hayes (2008) REACH deadline in 2010 was expected to apply to 8,5 % of all the products and the 2013 deadline applied to another 13,1 % of substances. The 2018 deadline is staged for the remaining substances sold in EU in the volume greater than 1 ton per year. Together with establishment of the regulation the ECHA was created. Its responsibility is to receive registration dossiers, manage the chemicals information database, issuing guidance to assist the industry and etc. (Bergkamp 2013) Companies submit information about their products to ECHA. The number of tests, as can be seen in table 3, can reach 59. These include tests on physical, toxicological and eco toxicity features. (Feurborn 2011) The necessary cost of a single registration can be substantial, up to almost per a chemical. These substantial costs are carried by the companies and then they are usually passed to the end user of the given substance. (Scott 2007) -777-

7 Table 3. Reach laboratory costs Chemical production Number of tests Testing cost per each product [tonnes per year] [Euros] Source: Scott 2007 Companies can share the costs among consortia, in case they register the same product. (Bergkamp 2013) But the 2018 registration round might prove to be very disadvantageous for small and medium sized enterprises (SMEs), as these companies are operating with small turnovers, which makes it hard for them to bare the registration costs, and they usually specialize in production of special chemical substances, which fit the 1 10 tonnes per annum volume. So from the nature of this produce, there are a few or just a one producers of a given substance, so the number of parties, which could share the costs, are very low. (Stuchlík 2013) In Commission consultation the REACH regulation was identified as the number 1 burdensome EU legislation for SMEs. According to the 2013 REACH review, the benefits of REACH ought to be large, but their materialisation would be clearly visible only in ten to twenty years. (Gubbels et al. 2013) Some experts even forecast that substantial share of SMEs would have to leave the market due to REACH incompliance. (Stuchlík 2013) The Centre for European Policy Studies (CEPS) conducted in 2013 a series of interviews with chemical SMEs. CEPS concluded that REACH so far did not alter the business models of SMEs, however REACH was considered a constrain for innovation negatively affecting time to market and limiting the possibility to test new uses or substances. Companies reported that they have discarded more new projects than before, as testing the real market potential would automatically need a registration according to REACH. About 15 % of interviewed companies raised their prices due to REACH in the range of 3 5 %, in some cases up till 25 %. About a third of responders indicated they observed withdrawals of substances after REACH introduction and another third expect that to happen in the future. Recent economic crisis limited the availability of finance to fund REACH testing and several SMEs were diverting funds for research and development to REACH compliance. These implications resulted in lowering innovation and decreasing the potential future growth of companies. (Gubbels et al. 2013) The European Commission acknowledge that the registration costs are above the capacities of SMEs to bear them. So it proposed a decrease of costs for SMEs. The ECHA, agreed to reduce the burden for SMEs. In March 2013, the ECHA announced fee reduction of % for registrations and % for authorisations for SMEs. The Commission is also considering some form of financial assistance to companies in the form of subsidized loans to ensure that the registration requirement will be financially managed and SMEs will stay solvent after 2018 registration. (Davis 2013) The extent of this support only point out to the fear of many experts, which predicted in 2007 that REACH would have a significant restructuring effect on the chemical industry in the EU. This finding was affirmed by the Directorate general for Internal Policies (2013) in their report on impact of REACH for SMEs. It is surprising that many companies are still unaware of the 2018 REACH deadline and believe it should hold to the big corporations. (Stuchlík 2013) So to conclude this part, we might expect that the REACH regulation has had a substantial impact on the EU chemical industry. Companies have to invest substantial resource to comply with the new regulation and for some enterprises these cost might prove to be too much

8 The companies, which have the financial capacity to withstand the necessary REACH fees, quite often face other set of problems. ECHA conducted a revision of enterprises of four size categories in 2013 and found out that 67 % of screened companies violated to various extents the provisions of the chemicals legislation. These cases of mismanagement consisted of failures of sufficiently informing the downstream users on risks of hazardous chemicals, registration and notification contraventions as well as deficient implementation of risk management measures. (ECHA 2013) 4. Expected production of chemicals in the EU In this section a model is estimated in order to analyse the dynamics of chemicals production in relationship to changes of the level of GDP in the EU. This regression model was estimated with the standard ordinary least squares method. 4.1 Used data sets All the time series data was quarterly and it was seasonally adjusted by the Census X12. The estimated model was using the data on GDP from the Eurostat database. The data for production of chemicals were calculated from the CEFIC (2015a) report and from the Eurostat database in order to check their consistency. 4.2 Production of chemicals in the EU after 2009 and the role of REACH In order to get a better model more suitable for good predictions, model was estimated using not so distant time period 2009 Q1 until 2015 Q1. Altogether there were 24 observations after adjustment used. The model estimates the quarterly growth coefficient of production of chemicals (as a total production value in quarter t divided by the value from the last quarter) gprod in the given quarter t as a function of two lagged values of the quarterly growth coefficient of production of chemicals, quarterly growth index of GDP in EU a constant and the variable representing the effect of the REACH regulation. The REACH variable was constructed in the logic of Foth and Hayes (2008 p. 460) that the 2010 REACH round aimed at 8,5 % of the chemicals sold in the EU, the 2013round affected next 13,1 % of the sold substances and by 2018 round all the chemicals would have to comply with the regulation. So the REACH variable value starts at zero in the beginning of the studied time period and it sums up all the fractions in given periods until it reaches value of 1 in Then there is a constant in the model present and the term u t is the model residual value in quarter t. The model equation is as follows: gprod EU 28, t 0,073 REACH 0,28 u 0,23 gprod EU 28, t 1 0,31 gprod EU 28, t 2 t 1,36 ggdp (2) The visualisation of the model can be seen in figure 4. The curve representing the model residuals remains most of the time within the interval of significance. The curve highlighting the actual values of growth coefficient of chemicals production is fitted quite well with the curve representing the growth coefficient on the basis of the original given function 2. The statistical model output can be seen in detail in annex 2.1. All the terms in the model apart the constant are significant (at level of significance at least 5 %). The constant itself would be significant at level of significance of 24 % (its p value is 23,6 %), but if we delete the constant from the model, we would get almost the same result. The constant is kept in the model in order to calculate the overall F test of the model. The model explains 84,3 % of variance of growth of production. According to the F test, model is significant at level of significance lower than 1 %. Model residuals are not auto correlated (at level of -779-

9 significance at least 5 %) according to the Breusch Godfrey Serial Correlation LM Test (see annex 2.2) and residual variability are not heteroskedastic (at level of significance at least 5 %) according to the ARCH test (see annex 2.3). Fig. 4. Production of chemicals in the EU after 2009 [quarterly growth] 4.3 Model results The dependence of production of chemicals on the performance of the economy was expected in advance. This relationship is positive, so the more economy grows, to more the production of chemicals would increase. However the significant negative influence of the REACH and the scope of it was not expected in the beginning of this study. This variable measures the share of all EU substances, which are in the given quarter subjected to the regulation. If we delete the REACH variable the model coefficient of determination decreases by 6 %, so this variable explains that amount of variance of growth of production. The coefficient value is 0,0725, that means that if the variable would grow by 10 % the quarterly growth of production would decline by 0,725 %. In the 2018 round, the regulation will apply to all the chemical substances sold in the EU and it would according to the model decrease the quarterly growth by 7,25 %. We could tell that in the period 2010 Q4 till 2014 Q4 the regulation caused a decrease of production of chemicals in the amount of 21,41 billion Euros. When the REACH regulation was originally proposed, the European Commission estimated the direct total industry costs at 2,3 billion Euros over 11 years ( ). According to European Commission (2003) estimated costs to downstream users of chemicals were estimated at 2,8 3,6 billion Euros over a period of 11 and 15 years

10 However these are the direct affects. The industry feared the indirect costs which would be caused by REACH, such as tight standards and measures of compliance, withdrawer of products from the market, probable cancellation of new products and significant administrative costs. (Stuchlik 2013) The regulation might have a negative effect on the diversion of research and development funds towards reach compliance according to the Gubbels (et al. 2013). These measures would cause extra costs, which will be accommodated in an increase of the price. As the price of this products increases, demand would then naturally to some extent decrease. The decrease of demand could then lead to decline of the production. This might be the explanation for the negative influence of REACH on production of chemicals in the EU. 4.4 Prediction for the near future We can estimate the near future development of chemical production using the estimated model, which is given by the equation 2. The exact estimate largely depends on the values of used GDP growth. There were three predicted values of GDP used, first being Author s prediction from the chapter 2.1, second being from the OECD (2015) and third from the IMF (2015). Then there is as fourth a prediction on the production of chemicals by the American Chemistry Council (ACC). The results can be seen in annex 3. According to the model, the expected GDP growth would be not strong enough to increase the chemicals production. In these cases the black curves in annex 3 represent the forecasts of quarterly production of chemicals. The industry production should either stagnate or decline by about 1,5 % in 2015 and 2016 as well. The future impact of REACH on chemicals production depends on the future economic growth. The dotted grey curves in annex 3 highlights the possible production, if negative effect of REACH wouldn t be present. From these predictions it seems that in the current economic environment REACH is lowering the chemical industry to grow. ACC on the other hands predicts that the industry production should grow, however only in the range of 1 % in 2015 with expected slow increase in (IHS Chemweek. 2015) This prediction is highlighted in annex 3 together with a scenario without the REACH effects as well In all the case apart from the ACC case, there is expected a slight decline of production of chemicals, the reason for this might be slow decline of demand for chamicals, because the economic growth is not strong enough. All the cumulative predicted losses due to REACH are highlighted in table 4 bellow. Most of the predictions indicate that the expected losses could be circ. 375 billion Euros in time period 2015 Q1 till 2019 Q1. Table 4. Expected loss due to REACH in 2015 Q1 to 2019 Q1 [millions of Euros] Used data Lower Estimate Medium Estimate High Estimate ACC IMF Prediction OECD Prediction Author's Prediction Conclusion This paper studied the current development of the EU economy and its impact on the chemical industry. It was found that EU economy faces many difficulties, which cause current slow economic growth. One of these main issues is low levels of investment. According to the first estimated model, EU economy should grow by 1,90 % in 2015 and 2,16 % in These estimates are relatively close to the predictions made by the OECD and the IMF

11 Regarding the impact of the economic development on the chemical industry, the second estimated model revealed that there are strong ties between the economic growth and growth of chemicals production. It is expected that the chemicals production would stagnate or grow very slowly in 2015 and 2016, because the economic growth is not strong enough in order to increase the demand for chemical substances. The second model also revealed a negative influence of the REACH regulation on the production of chemicals. According to the model, REACH caused a cumulative loss of production of chemical in time period 2010 Q4 till 2014 Q4 in the amount of 21,41 billion Euros and in 2015 Q1 till 2019 Q1 the cumulative loss is expected to be circ. 375 billion Euros. So the overall loss caused by REACH is expected to be 400 billion Euros, which was in % of the total EU chemicals production 6. Acknowledgement This research was financially supported by the research grant of University of economics: VŠE Praha F2/7/2014 Nové směry governance ve světle finanční a dluhové krize. References [1] Bergkamp, L The European Union REACH Regulation for Chemicals: Law and Practice. Oxford: Oxford University Press. 1. ed. ISBN: [2] CEFIC. 2015a. Chemicals Trends Report: Monthly summary March 2015 [online]. [cit ] Available at: hemicals%20trends%20report.march% pdf. [3] CEFIC. 2015b Chemical Industry Profile. [online]. [cit ]. Available at: [4] Eurostat. 2015a. Database. [online]. [cit ]. Available at: [5] Davis, N SMEs Could See REACH Costs to Fall. ICIS Chemical Business. Feb 25, 2013, 283, 7, p. 11. ISSN: [6] Directorate general for Internal Policies The Consequences of REACH for SMEs. [online]. [cit ]. Available at: [7] European Commission. Chemicals: Commission presents proposal to modernize EU legislation. [online] October [cit ]. Available at: [8] European Central Bank (ECB), Directorate General Communications Press Release: 22 January ECB announces expanded asset purchase programme [online]. [cit ]. Available at: [9] ECHA Forum REACH-EN-FORCE 2 Project Report Obligation of downstream users -formulators of mixtures. [online]. [cit ]. Available at: [10] European Chemical Industry Council (CEFIC) Chemical Industry Profile. Chemical Industry Profile: EU chemical industry sales - structure and destination [online]. [cit ]. Available at:

12 [11] European Commission Chemicals: Importance of the EU Chemicals Industry [online]. [cit ]. Available at: [12] Eurostat. 2015b. Real GDP growth rate - volume: code: tec00115 [online]. [cit ]. Available at: [13] Feurborn H. J Coal Combustion in Europe - an update on Production and Utilisation, Standardisation and Regulation. World of Coal Ash (WOCA) Conference. May 2011, Denver, USA. [online]. [cit. 2015] Available at: [14] Foth H, Hayes A. W Background of REACH in EU regulations on evaluation of chemicals. Human & Experimental Toxicology, vol. 27 (2008) 27: p [online]. [cit ]. Available at: [15] Gubbels I., Pelkmans J., Schrefler L REACH: A killer whale for SMEs? In: CEPS Policy Brief No. 307, 9 December [online]. [cit ]. Availabe at: [16] International Monetary Fund (IMF) World Economic Outlook: Uneven Growth: Shortand Long-Term Factors [online]. [cit ]. Available at: [17] IHS Chemweek Forecast: Can demand shrug off crude price shock? [online]. [cit ]. Available at: 0.html. [18] Organization for Economic Co-operation and Development (OECD) Economic Outlook: June 2015 [online]. [cit ]. Available at: bplmmxs.pdf?contenttype=%2fns%2fstatisticalpublication%2c%2fns%2foecdbook%2c%2f ns%2fbook. [19] Scott A Rough Seas Ahead. IHS Chemical week. [online]. [cit ]. Available at: [20] Stuchlik J REACH může do pěti let zničit většinu malých chemických firem. In: E15.cz. [online]. [cit ] Available at: l=http%3a%2f%2fzpravy.e15.cz%2fbyznys%2fprumysl-a-energetika%2freach-muze-do-pet i-let-znicit-vetsinu-malych-chemickych-firem &ei=bxlpvy6wk4ggyaoohydaaq& usg=afqjcnfddxwj1y-vbmplielt9fcn6htdqa&sig2=bdolfuqatlrwdokqxrkcwg&b vm=bv ,d.bgq&cad=rja

13 Annex Annex 1.1 ARIMA(2,1,3)c Model of GDP of EU28 Dependent Variable: D(GDPEU28_SA) Method: Least Squares Date: 06/28/15 Time: 01:45 Sample (adjusted): 2009Q1 2015Q1 Included observations: 25 after adjustments Convergence achieved after 66 iterations MA Backcast: 2008Q2 2008Q4 Variable Coefficient Std. Error t-statistic Prob. C 1.96E E AR(1) AR(2) MA(1) MA(2) MA(3) R-squared Mean dependent var 1.64E+10 Adjusted R-squared S.D. dependent var 3.98E+10 S.E. of regression 2.06E+10 Akaike info criterion Sum squared resid 8.03E+21 Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) Inverted AR Roots i i Inverted MA Roots i i Annex 1.2 Breusch-Godfrey Serial Correlation LM Test of the Model of GDP of EU28 F-statistic Prob. F(2,17) Obs*R-squared Prob. Chi-Square(2) Annex 1.3 Heteroskedasticity Test: ARCH of the Model of GDP of EU28 F-statistic Prob. F(1,22) Obs*R-squared Prob. Chi-Square(1)

14 Annex 2.1 Model Dependent Variable: EU_PROD_GROWTH Method: Least Squares Date: 06/26/15 Time: 21:09 Sample (adjusted): 2009Q1 2014Q4 Included observations: 24 after adjustments Variable Coefficient Std. Error t-statistic Prob. EU_PROD_GROWTH(-1) EU_PROD_GROWTH(-2) EU_GDP_GROWTH REACH C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) Annex 2.2 Breusch-Godfrey Serial Correlation LM Test of the Model of GDP of EU28 F-statistic Prob. F(2,17) Obs*R-squared Prob. Chi-Square(2) Annex 2.3 Heteroskedasticity Test: ARCH of the Model of GDP of EU28 F-statistic Prob. F(1,21) Obs*R-squared Prob. Chi-Square(1)

15 Annex 3 Historic and predicted quarterly production of chemicals [Billion of Euros] -786-