impact of the romanian industrial sector on the country's economic ...
IMPACT OF THE ROMANIAN INDUSTRIAL SECTOR ON THE COUNTRY’S ECONOMIC PERFORMANCE MIHAI NAGHI1, LEVENTE SZÁSZ2,* 1 Babeş-Bolyai University, Cluj-Napoca, [email protected] 2 Babeş-Bolyai University, Cluj-Napoca, [email protected] * The authors wish to thank for the financial support provided from programs co-financed by The SECTORAL OPERATIONAL PROGRAMME HUMAN RESOURCES DEVELOPMENT, Contract POS DRU 6/1.5/S/3 – „DOCTORAL STUDIES, A MAJOR FACTOR IN THE DEVELOPMENT OF SOCIO-ECONOMIC AND HUMANISTIC STUDIES”.
considered by researchers and specialists as being the motor of overall economic development. There are mainly two reasons behind this statement: • A strong relationship can be observed between the industrial performance and economic development of a country (Lall et al., 2004, Kjöllerström and Dallto, 2007). • The industrial sector is responsible for manufacturing those physical goods (and for providing associated after-sales services) which are necessary for other economic sectors, like services or commercial sector, in order to operate efficiently. Using econometric modeling, in this paper, we provide evidence that in the long-term there is a strong correlation between the performance of the Romanian industrial sector and the economic development of the country. In order to achieve this, in the first part of the paper we provide a theoretical and methodological background for analyzing the industrial sector of Romania. Based on statistical data, the second part of this article analyzes past development of the industrial sector of Romania in the last two decades. The third part of this paper we develop a linear regression model, in order to quantify the impact, the direction and the strength of the relationship between the industrial sector and the economic performance of the country. In the same part we also identify the manufacturing (processing) industry as being a major determinant of the development of the country’s economy.
ABSTRACT During the recent global economic and financial crisis the evolution of the industrial performance of Romania has received an increased attention by the analysts. In the present paper we will attempt to identify the relationship between the performance of the industrial sector and the country’s economic performance. Based on statistical data, in the first part of the paper we will analyze the current state and stage of development of the industrial sector, considering also its past and recent evolution. Having these data, in the second part of the paper we build an econometric model, by defining a linear regression model, in order to quantify the impact, the direction and the strength of the relationship between the industrial sector and the economic performance of the country. In the third part of the paper we extend our analysis further to examine the role of major industrial subsectors, identifying possible critical subsectors which can have a major impact on future economic development of the country. KEY WORDS Industrial sector, economic regression, industrial subsectors.
performance,
linear
JEL classification codes: O14, O11, L60, C10
1. Introduction The present paper aims to analyze the current state and past development of the industrial sector of the Romanian economy, and to provide useful information for macroeconomic decision-makers and managers on the microeconomic level regarding the impact of industrial performance on Romania’s economic development. Although the tertiary and quaternary sector (commerce, services, intellectual services) are receiving a continuously increasing attention by researchers (not only in developed, but also in developing countries) mainly because their growing share in many country’s GDP (Kniivilä, 2007), industrial enterprises of the secondary sector remain a major determinant of a country’s economic performance. According to this, the industrial sector of a country is often
2. Definition and structure of the Romanian industrial sector By definition, the industrial sector represents an important component of the national economy and it is composed by those enterprises which deal with the exploitation of natural resources and with their transformation into capital and consumer goods. From a methodological point of view, the National Institute of Statistics of Romania (from now on NIS) specifies the industrial sectors as being comprised of the mining and quarrying industry, manufacturing industry and the industry for production, 135
handling and distribution of electricity, gas and water (Romanian Statistical Yearbook, 2008). For the sake of simplicity, in the following, we will denote the industry for production, handling and distribution of electricity, gas and water as energy industry, since the production of electricity and gas accounts for at least 95% of the industrial output of this subsector. Although the delimitation of the industrial sectors provided by the NIS seems to be precise, there are other approaches too, totaling three different points of view: 1. In a broad approach, the industrial sector contains besides mining and quarrying, manufacturing, electricity, gas and water, a major part of the construction industry too. This broad approach can be valid particularly if we refer to the four major sector of the economy: agriculture, industry, commerce and services. 2. According to the methodological notes of the NIS, the industrial sector is comprised of the mining and quarrying industry, manufacturing industry and the industry for production, handling and distribution of electricity, gas and water. 3. In a narrow approach, the industrial sector is only comprised by mining and quarrying industry and manufacturing. According to this approach, the energy industry, considering its national importance and strategic role, should be considered as being a detached, distinct industry. In the present paper we will mainly refer to the approach of the NIS, but where necessary we will also include into the analysis figures of the construction industry too. Taking a look at the structure of the Romanian industrial sector, we can pronounce that manufacturing has far the greatest share (in terms of yearly aggregate output, measured in monetary terms) and this dominant role seems to be constant over the last twenty years. On the other hand, the mining industry shows a slow but constantly decreasing tendency over this period, while the energy sector showed a strong growing tendency in the first half of the ‘90s and its share from the industrial output remained approximately constant over the last ten years. Figure 1 illustrates these tendencies.
As we have seen, the manufacturing industry (processing industry) accounts for a very large part of total yearly industrial output of the country. Figure 2 illustrates the major subsectors of the manufacturing industry, indicating their share in the total output (measured in monetary terms) of the manufacturing industry. The output data refers to the year 2008, as these are most recent validated and confirmed data provided by the NIS (2009 and 2010 data are provisional). Figure 2: Contribution of the major subsectors to the output (in monetary terms) of the manufacturing industry (2008)
According to figure 2, the major manufacturing subsectors in Romania are: coal and oil processing industry, metal processing industry, Food and beverage industry, machine industry and the industry of vehicles of transport. From the manufacturing subsectors presented above, in the last two decades the most spectacular decrease has produced in the textile- and clothing industry (1990: 12% - 2008: 4%), while coal and oil processing showed the most impressive development in the same period (1990: 8% - 2008: 18%). In the following parts of the paper, after analyzing the relationship between overall industrial performance and economic development in Romania, we will also take a closer look on the role of the different industrial subsectors, presented in this chapter.
3. Past development and current performance of the Romanian industrial sector
Figure 1: The evolution in the last two decades and current structure of the Romanian industrial sector
In order to thoroughly examine past development of the Romanian industrial sector, we will use, first of all, the industrial production index (IPI), which is a volume index and measures the evolution of industrial activities results from one period as against another one (Monthly Statistical Bulletin, 01/2010). The industrial production index is a Laspeyres-type volume index, which is calculated using the following formula (Andrei and Bourbonnais, 2008):
Lqt/ 0 =
∑p ∑p
0
⋅ qt
0
⋅ q0
, where
Lqt/ 0 is a Laspeyres-type volume index (e.g. IPI)
136
p0 is an element of the price vector of the goods
The following figure illustrates the evolution of the IPI in different industrial subsectors.
produced in the base period q 0 is an element of the volume vector of the goods
Figure 4: The evolution of the industrial production index in industrial subsectors (base year: 2000)
produced in the base period q t is an element of the volume vector of the goods produced in the current period The following figure illustrates the evolution of the Romanian industrial production index (calculated according to the formula above) in the last two decades. The base period is considered to be the year 2000, where the value of the index is equal to 100. Figure 3: The evolution of the Romanian industrial production index in the last twenty years (IPI, base year: 2000)
Another appropriate measure to illustrate the contribution of the industrial sector to the overall economic performance of the country, is the percentage share of the industrial sector in total gross value added (GVA) on country level. Figure 5: Contribution of the Romanian industrial sector to total Gross Value Added (1990-2008)
As it can be seen on the figure above, after 1989 Romania inherited a large, but inefficient industrial system, which resulted in a quick restructuring process in the first 3-4 years by closing a large number of inefficient plants and industrial units. Mainly due to the massive in-flow of foreign direct investment, the first positive signs appeared after year 1993. The upward tendency was only interrupted by the Russian crisis in 1997. The determining role of foreign direct investment (FDI) in early economic development and increase of industrial production in Romania has been proved by several researchers (Boscaiu et al., 2002, Boscaiu and Mazilu, 2001). The year 2000 represented the beginning of a dynamic development period until 2008, when the first effects of the global financial crisis started to influence the performance of the Romanian industrial sector. However, industrial subsectors showed a different contribution to the evolution of the Romanian IPI: while in the first decade the energy industry outperformed other subsectors, the industrial development of the last decade can mainly be attributed to the manufacturing industry. On the other hand, according to Figure 1, the strongest correlation can be identified between the evolution of the industrial production index of the manufacturing industry and that of the Romanian industrial sector.
The contribution of the industrial sector to total Gross Value Added showed a continuously decreasing tendency over the first ten years, mainly due to the increasing importance of the commercial and services sector. Following the year 2000, a stabilization process has been started, but this can be somewhat misleading, since this stabilization is mainly the result of the growing share of the construction industry (here included in the industrial sectors GVA). The share of the construction industry in total GVA has been doubled in the last 8 years, growing from only 6% to a major 12% in 2008. (Porter…) Following the numbers shown on figure 5, a similar decreasing tendency can be identified in the evolution of the number of employees in the industrial sector. Due to the massive closing process of ineffective industrial enterprises in the first decade and due to the automation of production processes (and introduction of high technology processes) the demand for manual workforce
Table 1: Correlation between the evolution of industry level and subsector level IPI
Mining industry Manufacturing industry Energy industry
Correlation with industry level IPI (R) 0,241999 0,982127 0,272925
137
has been almost continuously fallen in the industrial sector. Figure 6 illustrates this tendency.
The intuitive correspondence between the two variables is shown on Figure 8, where the coefficient of correlation equals a high value, precisely 0.905355.
Figure 6: The evolution of labor force employed in the Romanian industrial sector (1992-2009)
Figure 8: The evolution of yearly GDP index and yearly industrial production index (1990-2009)
Due to the more and more widespread automation of production processes in the industrial sector of Romania, together with the fall of the number of workers employed, the productivity of labor force has been steadily increased in the last decade. This constant increase of workforce productivity is valid not only for the industrial sector, but also for every industrial subsector, as it is shown on Figure 7. Statistical data measuring labor productivity is only available starting from year 2000.
In order to model the relationship between the two variables, first we identify the GDP index (dGDP) as being the explained (dependent) variable, while IPI will be the explanatory (independent) variable. In this case we are searching for a relationship, which can be written in the following form: (1) dGDPt = f ( IPI t ) Assuming that relationship (1) is not deterministic, but statistical (stochastic), it can be described exactly only in probabilistic terms, as follows: dGDPt = α + β ⋅ IPI t + u t (2), where
Figure 7: The evolution of labor productivity index in the Romanian industrial sector (base year: 2000)
α and β are called regression coefficients or regression parameters that we will estimate based on statistical data available, while ut is the error term (residual) of the model (Maddala, 2001), which includes the effect of all factors excluded from the model. To estimate the regression parameters we will use the Ordinary Least Squares (OLS) method, incorporated in GNU’s Gretl software. Using statistical data available from 1990 to 2009 (altogether 19 observations), the OLS method estimates the following parameters: dGDPt = 44.8232 + 0.570492 ⋅ IPI t + u t (3) In order to validate the model above, we need to perform three different tests: for the normality of the residual, the heteroskedasticity of the residual (different variances of the error terms) and the autocorrelation of the error terms. • The test for the normality of the residual shows that error is normally distributed, with a p-value of 0.77367 (the probability of mistake if we reject the null-hypothesis, which states that error is normally distributed). Normality of error terms is illustrated in Appendix 2. • To test heteroskedasticity we used White’s test, which showed that the error terms are homoskedastic, with a p-value of 0.298551 (the probability of mistake if we reject the nullhypothesis, which states that heteroskedasticity is not present)
4. Linear regression model 4.1. Relationship between industry level IPI and GDP growth In this chapter we develop an econometric model in order to quantify the effect of the industrial sectors performance on the economic development of Romania. This implies developing a linear regression model which describes the relationship between the following two variables: • dGDP – GDP index, which measures the yearly change of the gross domestic product of Romania; • IPI – industrial production index, which measures the yearly change in volume of the industrial output of the country;
138
•
appropriate to examine the impact of the manufacturing (procesing) industry on the evolution of overall economic performance of Romania (as suggested by Mereuţă, 2009). Building a similar model as described by equation (1) and (2) we change the meaning of the independent variable, substituting industry level IPI with the manufacturing industry’s IPI and we get following equation: dGDPt = α + β ⋅ IPIM t + u t (4), where
To test autocorrelation first we identifyied the value of the Durbin-Watson statistic, which is close to 2, having an exact value of 1.816940. The LM test also shows, that the error terms of the regression model are independent, with a pvalue of 0.839792 (the probability of mistake if we reject the null-hypothesis, which states that there is no autocorrelation) The three tests performed show that we have a valid econometric model and we can pronounce following: • A 1% change in the industrial production index results in average in a 0.5705% change in the GDP index. With a probability of 95% we can also affirm, that the value of this coefficient falls in the interval of (0.433566, 0.707419). • The adjusted R-squared value is 0.809060, which means that the variance of the independent variable (IPI) explains in an extent of 80,9% the variance of the dependent variable (GDPindex) The detailed results of the model can be found in Appendix 1 of this article. The relationship between the linear model developed (forecast) and real statistical data (dGDP), with 95 percent confidence intervals, is shown on Figure 9.
IPIM t denotes the industrial production index of the manufacturing (processing) industry in period t. Using statistical data available from 1990 to 2009 (altogether 19 observations), the OLS method estimates the following parameters: dGDPt = 50.7158 + 0.509013 ⋅ IPIM t + u t (5) Performing the same tests as in the previous chapter (4.1), we can validate the model built. The results of these tests show that the distribution of the residuals is normal and there is no heteroskedasticity or autocorrelation in the model. Detailed results of the model and validation tests can be found in Appendix 3 of this paper. According to the linear regression model developed, we can affirm following: • A 1% change in the industrial production index of the manufacturing industry results in average in a 0.5090% change in the GDP index. With a probability of 95% we can also affirm, that the value of this coefficient falls in the interval of (0.379574, 0.638452). • The adjusted R-squared value is 0.790299, which means that the variance of the independent variable (IPI of the manufacturing industry) explains in an extent of 79,03% the variance of the dependent variable (GDPindex) The relationship between this second linear model developed (forecast) and real statistical data (dGDP), with 95 percent confidence intervals, is shown on Figure 10.
Figure 9: Relationship between the model developed and actual data
Figure10: Relationship between the second model developed and actual statistical data
In conclusion, the model suggests a strong relationship between the evolution of industrial production in Romania and the economic development of the country, with a correlation coefficient of about 90%. This correlation stands not only in periods of economic development, but also in periods of recession. This result is proved by other researchers too (Mereuţă, 2007, Mereuţă, 2009), who determine a correlation coefficient of slightly above 85% and emphasize that in times of recession governance has to pay a greater attention to key industrial subsectors. 4.1. Relationship between manufacturing (processing) industry level IPI and GDP growth As we have showed in Chapter 3, there is a very strong relationship between the IPI of the manufacturing industry and that of the industrial sector (the value of the coefficient of correlation is 0,982127). Hence, it is 139
5. Conclusion and perspectives
References
In the present paper we analyzed current state and past development of the Romanian industrial sector and its most important subsectors. As the evolution of the industrial sector is often followed and thoroughly analyzed by researchers and practitioners, it is useful to determine the exact relationship between the evolution of the industrial sector of Romania and the economic development of the country. Based on a linear regression model we conclude that the evolution of the industrial production is a major determinant of GDP growth and its value adding potential deserves greater attention both in periods of recession and in period of economic growth. The second model developed shows us that, according to Mereuţă (Mereuţă, 2009) mainly subsectors of the manufacturing (processing) industry are responsible for the evolution of the country’s GDP. Besides the conclusions and advantages summarized above, a clear limitation of the two models developed is that they do not take into consideration the effect of other important economic sectors, like services or the commercial industry. Considering the perspectives of the research, one question still remains open. Is or could the industrial sector become the key force that can drive the country’s economy out of the current economic crisis? Many practitioners state that this is one plausible and likely way, but taking a look on Figure 9 and Figure 10, one can see that the largest discrepancy between predicted values and real data can exactly be observed in the period of the current financialeconomic crisis (even if they still fall inside the 95% confidence intervals). Similarly, in the period of the financial crisis GVA of the industrial sector shows a bigger variance, compared to the evolution of the country’s GDP (see Appendix 4). This raises the problem that solving this discrepancy involves the analysis of other economic sectors too.
[1] Andrei, T. and Bourbonnais, R. (2008), Econometrie, Editura Economică, Bucharest. [2] Boscaiu, V. and Mazilu, A. (2001), Foreign Direct Investment and Competitiveness in Romanian Manufacturing, working paper no. 29/november 2001, Romanian Center for Economic Policies. [3] Boscaiu, V., Munteanu, C., Liusnea, D., Puscoi, L. (2002), Impact of the FDI on Productivity in Romanian Manufacturing Industry, working paper no. 22/2002, Romanian Center for Economic Policies. [4] Kjöllerström, M. and Dallto, K. (2007). Natural resource-based industries in: United Nations, Industrial development for the 21st century: sustainable development perspectives, New York, 119-182. [5] Kniivilä, M. (2007). Industrial development and economic growth: Implications for poverty reduction and income inequality in: United Nations, Industrial development for the 21st century: sustainable development perspectives, New York, 295-332. [6] Lall, S., Albaladejo, M. and Moreira, M.M. (2004). Latin American industrial competitiveness and the challenge of globalization, Washington D.C.: InterAmerican Development Bank, Integration and Regional Programs department, INTAL-ITD Occasional PaperSITI-05. [7] Maddala, G.S. (2001), Introduction to Econometrics, John Wiley & Sons, New York. [8] Mereuţă, C. (2007), Analiza SWOT a industriei prelucratoare romanesti in perioada 1998-2004, din perspectiva cresterii economice, Working Papers of Macroeconomic Modelling Seminar, Institute for Economic Forecasting, Bukarest. [9] Mereuţă, C. (2009), 7 sectoare ale industriei prelucrătoare pot opri recesiunea în România, economie.hotnews.ro, 10.07.2009. [10] Monthly Statistical Bulletin (2010), 01/2010, available online at http://www.insse.ro/cms/files/ arhiva_buletine2010/bsl_1.pdf, last date of access: 02.06.2010. [11] Romanian Statistical Yearbook (2008), available online at http://www.insse.ro/cms/rw/pages/anuarstatistic 2008.ro.do, last date of access: 02.06.2010.
140
Appendix 1 Model 1: OLS, using observations 1991-2009 (T = 19) Dependent variable: dGDP
Const dIPI
Coefficient 44.8232 0.570492
Mean dependent var Sum squared resid R-squared F(1, 17) Log-likelihood Schwarz criterion Rho
Std. Error 6.47321 0.0648998
101.4474 131.7019 0.819667 77.27023 -45.35280 96.59448 -0.057758
t-ratio 6.9244 8.7903
S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn Durbin-Watson
Test for normality of residual Null hypothesis: error is normally distributed Test statistic: Chi-square(2) = 0.513219 with p-value = 0.77367 White's test for heteroskedasticity Null hypothesis: heteroskedasticity not present Test statistic: LM = 2.41763 with p-value = P(Chi-Square(2) > 2.41763) = 0.298551 LM test for autocorrelation up to order 1 Null hypothesis: no autocorrelation Test statistic: LMF = 0.0422201 with p-value = P(F(1,16) > 0.0422201) = 0.839792
Appendix 2
141
p-value
considered by researchers and specialists as being the motor of overall economic development. There are mainly two reasons behind this statement: • A strong relationship can be observed between the industrial performance and economic development of a country (Lall et al., 2004, Kjöllerström and Dallto, 2007). • The industrial sector is responsible for manufacturing those physical goods (and for providing associated after-sales services) which are necessary for other economic sectors, like services or commercial sector, in order to operate efficiently. Using econometric modeling, in this paper, we provide evidence that in the long-term there is a strong correlation between the performance of the Romanian industrial sector and the economic development of the country. In order to achieve this, in the first part of the paper we provide a theoretical and methodological background for analyzing the industrial sector of Romania. Based on statistical data, the second part of this article analyzes past development of the industrial sector of Romania in the last two decades. The third part of this paper we develop a linear regression model, in order to quantify the impact, the direction and the strength of the relationship between the industrial sector and the economic performance of the country. In the same part we also identify the manufacturing (processing) industry as being a major determinant of the development of the country’s economy.
ABSTRACT During the recent global economic and financial crisis the evolution of the industrial performance of Romania has received an increased attention by the analysts. In the present paper we will attempt to identify the relationship between the performance of the industrial sector and the country’s economic performance. Based on statistical data, in the first part of the paper we will analyze the current state and stage of development of the industrial sector, considering also its past and recent evolution. Having these data, in the second part of the paper we build an econometric model, by defining a linear regression model, in order to quantify the impact, the direction and the strength of the relationship between the industrial sector and the economic performance of the country. In the third part of the paper we extend our analysis further to examine the role of major industrial subsectors, identifying possible critical subsectors which can have a major impact on future economic development of the country. KEY WORDS Industrial sector, economic regression, industrial subsectors.
performance,
linear
JEL classification codes: O14, O11, L60, C10
1. Introduction The present paper aims to analyze the current state and past development of the industrial sector of the Romanian economy, and to provide useful information for macroeconomic decision-makers and managers on the microeconomic level regarding the impact of industrial performance on Romania’s economic development. Although the tertiary and quaternary sector (commerce, services, intellectual services) are receiving a continuously increasing attention by researchers (not only in developed, but also in developing countries) mainly because their growing share in many country’s GDP (Kniivilä, 2007), industrial enterprises of the secondary sector remain a major determinant of a country’s economic performance. According to this, the industrial sector of a country is often
2. Definition and structure of the Romanian industrial sector By definition, the industrial sector represents an important component of the national economy and it is composed by those enterprises which deal with the exploitation of natural resources and with their transformation into capital and consumer goods. From a methodological point of view, the National Institute of Statistics of Romania (from now on NIS) specifies the industrial sectors as being comprised of the mining and quarrying industry, manufacturing industry and the industry for production, 135
handling and distribution of electricity, gas and water (Romanian Statistical Yearbook, 2008). For the sake of simplicity, in the following, we will denote the industry for production, handling and distribution of electricity, gas and water as energy industry, since the production of electricity and gas accounts for at least 95% of the industrial output of this subsector. Although the delimitation of the industrial sectors provided by the NIS seems to be precise, there are other approaches too, totaling three different points of view: 1. In a broad approach, the industrial sector contains besides mining and quarrying, manufacturing, electricity, gas and water, a major part of the construction industry too. This broad approach can be valid particularly if we refer to the four major sector of the economy: agriculture, industry, commerce and services. 2. According to the methodological notes of the NIS, the industrial sector is comprised of the mining and quarrying industry, manufacturing industry and the industry for production, handling and distribution of electricity, gas and water. 3. In a narrow approach, the industrial sector is only comprised by mining and quarrying industry and manufacturing. According to this approach, the energy industry, considering its national importance and strategic role, should be considered as being a detached, distinct industry. In the present paper we will mainly refer to the approach of the NIS, but where necessary we will also include into the analysis figures of the construction industry too. Taking a look at the structure of the Romanian industrial sector, we can pronounce that manufacturing has far the greatest share (in terms of yearly aggregate output, measured in monetary terms) and this dominant role seems to be constant over the last twenty years. On the other hand, the mining industry shows a slow but constantly decreasing tendency over this period, while the energy sector showed a strong growing tendency in the first half of the ‘90s and its share from the industrial output remained approximately constant over the last ten years. Figure 1 illustrates these tendencies.
As we have seen, the manufacturing industry (processing industry) accounts for a very large part of total yearly industrial output of the country. Figure 2 illustrates the major subsectors of the manufacturing industry, indicating their share in the total output (measured in monetary terms) of the manufacturing industry. The output data refers to the year 2008, as these are most recent validated and confirmed data provided by the NIS (2009 and 2010 data are provisional). Figure 2: Contribution of the major subsectors to the output (in monetary terms) of the manufacturing industry (2008)
According to figure 2, the major manufacturing subsectors in Romania are: coal and oil processing industry, metal processing industry, Food and beverage industry, machine industry and the industry of vehicles of transport. From the manufacturing subsectors presented above, in the last two decades the most spectacular decrease has produced in the textile- and clothing industry (1990: 12% - 2008: 4%), while coal and oil processing showed the most impressive development in the same period (1990: 8% - 2008: 18%). In the following parts of the paper, after analyzing the relationship between overall industrial performance and economic development in Romania, we will also take a closer look on the role of the different industrial subsectors, presented in this chapter.
3. Past development and current performance of the Romanian industrial sector
Figure 1: The evolution in the last two decades and current structure of the Romanian industrial sector
In order to thoroughly examine past development of the Romanian industrial sector, we will use, first of all, the industrial production index (IPI), which is a volume index and measures the evolution of industrial activities results from one period as against another one (Monthly Statistical Bulletin, 01/2010). The industrial production index is a Laspeyres-type volume index, which is calculated using the following formula (Andrei and Bourbonnais, 2008):
Lqt/ 0 =
∑p ∑p
0
⋅ qt
0
⋅ q0
, where
Lqt/ 0 is a Laspeyres-type volume index (e.g. IPI)
136
p0 is an element of the price vector of the goods
The following figure illustrates the evolution of the IPI in different industrial subsectors.
produced in the base period q 0 is an element of the volume vector of the goods
Figure 4: The evolution of the industrial production index in industrial subsectors (base year: 2000)
produced in the base period q t is an element of the volume vector of the goods produced in the current period The following figure illustrates the evolution of the Romanian industrial production index (calculated according to the formula above) in the last two decades. The base period is considered to be the year 2000, where the value of the index is equal to 100. Figure 3: The evolution of the Romanian industrial production index in the last twenty years (IPI, base year: 2000)
Another appropriate measure to illustrate the contribution of the industrial sector to the overall economic performance of the country, is the percentage share of the industrial sector in total gross value added (GVA) on country level. Figure 5: Contribution of the Romanian industrial sector to total Gross Value Added (1990-2008)
As it can be seen on the figure above, after 1989 Romania inherited a large, but inefficient industrial system, which resulted in a quick restructuring process in the first 3-4 years by closing a large number of inefficient plants and industrial units. Mainly due to the massive in-flow of foreign direct investment, the first positive signs appeared after year 1993. The upward tendency was only interrupted by the Russian crisis in 1997. The determining role of foreign direct investment (FDI) in early economic development and increase of industrial production in Romania has been proved by several researchers (Boscaiu et al., 2002, Boscaiu and Mazilu, 2001). The year 2000 represented the beginning of a dynamic development period until 2008, when the first effects of the global financial crisis started to influence the performance of the Romanian industrial sector. However, industrial subsectors showed a different contribution to the evolution of the Romanian IPI: while in the first decade the energy industry outperformed other subsectors, the industrial development of the last decade can mainly be attributed to the manufacturing industry. On the other hand, according to Figure 1, the strongest correlation can be identified between the evolution of the industrial production index of the manufacturing industry and that of the Romanian industrial sector.
The contribution of the industrial sector to total Gross Value Added showed a continuously decreasing tendency over the first ten years, mainly due to the increasing importance of the commercial and services sector. Following the year 2000, a stabilization process has been started, but this can be somewhat misleading, since this stabilization is mainly the result of the growing share of the construction industry (here included in the industrial sectors GVA). The share of the construction industry in total GVA has been doubled in the last 8 years, growing from only 6% to a major 12% in 2008. (Porter…) Following the numbers shown on figure 5, a similar decreasing tendency can be identified in the evolution of the number of employees in the industrial sector. Due to the massive closing process of ineffective industrial enterprises in the first decade and due to the automation of production processes (and introduction of high technology processes) the demand for manual workforce
Table 1: Correlation between the evolution of industry level and subsector level IPI
Mining industry Manufacturing industry Energy industry
Correlation with industry level IPI (R) 0,241999 0,982127 0,272925
137
has been almost continuously fallen in the industrial sector. Figure 6 illustrates this tendency.
The intuitive correspondence between the two variables is shown on Figure 8, where the coefficient of correlation equals a high value, precisely 0.905355.
Figure 6: The evolution of labor force employed in the Romanian industrial sector (1992-2009)
Figure 8: The evolution of yearly GDP index and yearly industrial production index (1990-2009)
Due to the more and more widespread automation of production processes in the industrial sector of Romania, together with the fall of the number of workers employed, the productivity of labor force has been steadily increased in the last decade. This constant increase of workforce productivity is valid not only for the industrial sector, but also for every industrial subsector, as it is shown on Figure 7. Statistical data measuring labor productivity is only available starting from year 2000.
In order to model the relationship between the two variables, first we identify the GDP index (dGDP) as being the explained (dependent) variable, while IPI will be the explanatory (independent) variable. In this case we are searching for a relationship, which can be written in the following form: (1) dGDPt = f ( IPI t ) Assuming that relationship (1) is not deterministic, but statistical (stochastic), it can be described exactly only in probabilistic terms, as follows: dGDPt = α + β ⋅ IPI t + u t (2), where
Figure 7: The evolution of labor productivity index in the Romanian industrial sector (base year: 2000)
α and β are called regression coefficients or regression parameters that we will estimate based on statistical data available, while ut is the error term (residual) of the model (Maddala, 2001), which includes the effect of all factors excluded from the model. To estimate the regression parameters we will use the Ordinary Least Squares (OLS) method, incorporated in GNU’s Gretl software. Using statistical data available from 1990 to 2009 (altogether 19 observations), the OLS method estimates the following parameters: dGDPt = 44.8232 + 0.570492 ⋅ IPI t + u t (3) In order to validate the model above, we need to perform three different tests: for the normality of the residual, the heteroskedasticity of the residual (different variances of the error terms) and the autocorrelation of the error terms. • The test for the normality of the residual shows that error is normally distributed, with a p-value of 0.77367 (the probability of mistake if we reject the null-hypothesis, which states that error is normally distributed). Normality of error terms is illustrated in Appendix 2. • To test heteroskedasticity we used White’s test, which showed that the error terms are homoskedastic, with a p-value of 0.298551 (the probability of mistake if we reject the nullhypothesis, which states that heteroskedasticity is not present)
4. Linear regression model 4.1. Relationship between industry level IPI and GDP growth In this chapter we develop an econometric model in order to quantify the effect of the industrial sectors performance on the economic development of Romania. This implies developing a linear regression model which describes the relationship between the following two variables: • dGDP – GDP index, which measures the yearly change of the gross domestic product of Romania; • IPI – industrial production index, which measures the yearly change in volume of the industrial output of the country;
138
•
appropriate to examine the impact of the manufacturing (procesing) industry on the evolution of overall economic performance of Romania (as suggested by Mereuţă, 2009). Building a similar model as described by equation (1) and (2) we change the meaning of the independent variable, substituting industry level IPI with the manufacturing industry’s IPI and we get following equation: dGDPt = α + β ⋅ IPIM t + u t (4), where
To test autocorrelation first we identifyied the value of the Durbin-Watson statistic, which is close to 2, having an exact value of 1.816940. The LM test also shows, that the error terms of the regression model are independent, with a pvalue of 0.839792 (the probability of mistake if we reject the null-hypothesis, which states that there is no autocorrelation) The three tests performed show that we have a valid econometric model and we can pronounce following: • A 1% change in the industrial production index results in average in a 0.5705% change in the GDP index. With a probability of 95% we can also affirm, that the value of this coefficient falls in the interval of (0.433566, 0.707419). • The adjusted R-squared value is 0.809060, which means that the variance of the independent variable (IPI) explains in an extent of 80,9% the variance of the dependent variable (GDPindex) The detailed results of the model can be found in Appendix 1 of this article. The relationship between the linear model developed (forecast) and real statistical data (dGDP), with 95 percent confidence intervals, is shown on Figure 9.
IPIM t denotes the industrial production index of the manufacturing (processing) industry in period t. Using statistical data available from 1990 to 2009 (altogether 19 observations), the OLS method estimates the following parameters: dGDPt = 50.7158 + 0.509013 ⋅ IPIM t + u t (5) Performing the same tests as in the previous chapter (4.1), we can validate the model built. The results of these tests show that the distribution of the residuals is normal and there is no heteroskedasticity or autocorrelation in the model. Detailed results of the model and validation tests can be found in Appendix 3 of this paper. According to the linear regression model developed, we can affirm following: • A 1% change in the industrial production index of the manufacturing industry results in average in a 0.5090% change in the GDP index. With a probability of 95% we can also affirm, that the value of this coefficient falls in the interval of (0.379574, 0.638452). • The adjusted R-squared value is 0.790299, which means that the variance of the independent variable (IPI of the manufacturing industry) explains in an extent of 79,03% the variance of the dependent variable (GDPindex) The relationship between this second linear model developed (forecast) and real statistical data (dGDP), with 95 percent confidence intervals, is shown on Figure 10.
Figure 9: Relationship between the model developed and actual data
Figure10: Relationship between the second model developed and actual statistical data
In conclusion, the model suggests a strong relationship between the evolution of industrial production in Romania and the economic development of the country, with a correlation coefficient of about 90%. This correlation stands not only in periods of economic development, but also in periods of recession. This result is proved by other researchers too (Mereuţă, 2007, Mereuţă, 2009), who determine a correlation coefficient of slightly above 85% and emphasize that in times of recession governance has to pay a greater attention to key industrial subsectors. 4.1. Relationship between manufacturing (processing) industry level IPI and GDP growth As we have showed in Chapter 3, there is a very strong relationship between the IPI of the manufacturing industry and that of the industrial sector (the value of the coefficient of correlation is 0,982127). Hence, it is 139
5. Conclusion and perspectives
References
In the present paper we analyzed current state and past development of the Romanian industrial sector and its most important subsectors. As the evolution of the industrial sector is often followed and thoroughly analyzed by researchers and practitioners, it is useful to determine the exact relationship between the evolution of the industrial sector of Romania and the economic development of the country. Based on a linear regression model we conclude that the evolution of the industrial production is a major determinant of GDP growth and its value adding potential deserves greater attention both in periods of recession and in period of economic growth. The second model developed shows us that, according to Mereuţă (Mereuţă, 2009) mainly subsectors of the manufacturing (processing) industry are responsible for the evolution of the country’s GDP. Besides the conclusions and advantages summarized above, a clear limitation of the two models developed is that they do not take into consideration the effect of other important economic sectors, like services or the commercial industry. Considering the perspectives of the research, one question still remains open. Is or could the industrial sector become the key force that can drive the country’s economy out of the current economic crisis? Many practitioners state that this is one plausible and likely way, but taking a look on Figure 9 and Figure 10, one can see that the largest discrepancy between predicted values and real data can exactly be observed in the period of the current financialeconomic crisis (even if they still fall inside the 95% confidence intervals). Similarly, in the period of the financial crisis GVA of the industrial sector shows a bigger variance, compared to the evolution of the country’s GDP (see Appendix 4). This raises the problem that solving this discrepancy involves the analysis of other economic sectors too.
[1] Andrei, T. and Bourbonnais, R. (2008), Econometrie, Editura Economică, Bucharest. [2] Boscaiu, V. and Mazilu, A. (2001), Foreign Direct Investment and Competitiveness in Romanian Manufacturing, working paper no. 29/november 2001, Romanian Center for Economic Policies. [3] Boscaiu, V., Munteanu, C., Liusnea, D., Puscoi, L. (2002), Impact of the FDI on Productivity in Romanian Manufacturing Industry, working paper no. 22/2002, Romanian Center for Economic Policies. [4] Kjöllerström, M. and Dallto, K. (2007). Natural resource-based industries in: United Nations, Industrial development for the 21st century: sustainable development perspectives, New York, 119-182. [5] Kniivilä, M. (2007). Industrial development and economic growth: Implications for poverty reduction and income inequality in: United Nations, Industrial development for the 21st century: sustainable development perspectives, New York, 295-332. [6] Lall, S., Albaladejo, M. and Moreira, M.M. (2004). Latin American industrial competitiveness and the challenge of globalization, Washington D.C.: InterAmerican Development Bank, Integration and Regional Programs department, INTAL-ITD Occasional PaperSITI-05. [7] Maddala, G.S. (2001), Introduction to Econometrics, John Wiley & Sons, New York. [8] Mereuţă, C. (2007), Analiza SWOT a industriei prelucratoare romanesti in perioada 1998-2004, din perspectiva cresterii economice, Working Papers of Macroeconomic Modelling Seminar, Institute for Economic Forecasting, Bukarest. [9] Mereuţă, C. (2009), 7 sectoare ale industriei prelucrătoare pot opri recesiunea în România, economie.hotnews.ro, 10.07.2009. [10] Monthly Statistical Bulletin (2010), 01/2010, available online at http://www.insse.ro/cms/files/ arhiva_buletine2010/bsl_1.pdf, last date of access: 02.06.2010. [11] Romanian Statistical Yearbook (2008), available online at http://www.insse.ro/cms/rw/pages/anuarstatistic 2008.ro.do, last date of access: 02.06.2010.
140
Appendix 1 Model 1: OLS, using observations 1991-2009 (T = 19) Dependent variable: dGDP
Const dIPI
Coefficient 44.8232 0.570492
Mean dependent var Sum squared resid R-squared F(1, 17) Log-likelihood Schwarz criterion Rho
Std. Error 6.47321 0.0648998
101.4474 131.7019 0.819667 77.27023 -45.35280 96.59448 -0.057758
t-ratio 6.9244 8.7903
S.D. dependent var S.E. of regression Adjusted R-squared P-value(F) Akaike criterion Hannan-Quinn Durbin-Watson
Test for normality of residual Null hypothesis: error is normally distributed Test statistic: Chi-square(2) = 0.513219 with p-value = 0.77367 White's test for heteroskedasticity Null hypothesis: heteroskedasticity not present Test statistic: LM = 2.41763 with p-value = P(Chi-Square(2) > 2.41763) = 0.298551 LM test for autocorrelation up to order 1 Null hypothesis: no autocorrelation Test statistic: LMF = 0.0422201 with p-value = P(F(1,16) > 0.0422201) = 0.839792
Appendix 2
141
p-value
