Relation of R&D expense to turnover and number of listed companies in all industrial fields
- Jun-Hwan Park1,
- Bangrae Lee1,
- Yeong-Ho Moon1,
- GyuSeok Kim2 and
- Lee-Nam Kwon1Email authorView ORCID ID profile
https://doi.org/10.1186/s40852-018-0093-4
© The Author(s). 2018
Received: 28 September 2017
Accepted: 14 March 2018
Published: 21 March 2018
Abstract
In this research, we studied the relation of research and development (R&D) investment to turnover and number of listed companies by using the financial information of publicly listed enterprises in all industrial fields of the world from 2007 to 2015. First of all, the present condition (as of 2017) of number and distribution of publicly listed enterprises was investigated. Secondly, the industrial areas having top 10 average turnovers and R&D expenses during 9 years (2007 ~ 2015) were analyzed by using their descriptive statistics and CAGR values. Finally, the analyses of correlation and linear regression were performed by using average R&D expense (independent variable) and average turnover or the number of listed enterprises (dependent variables). In other words, two models with different combination of independent and dependent variables (Model A: R&D expense and turnover, Model B: R&D expense and number of listed firms) were developed for the statistical analyses. As a result, it was confirmed that both the turnover and the number of listed companies were influenced by the R&D investment because the coefficients of determination for Model A and Model B were 0.686 and 0.612, respectively (both p-values < 2.2 × 10− 16). From the results of this study, it is expected that the unlisted firms (e.g., start-up companies) can build the basis of their growth and innovation when they invest in R&D higher inducing the increases in (1) turnover and (2) probability of becoming a listed firm. Thus, the financial information of enterprises can be utilized effectively as the quantitative evidence in order to develop the research model and methodology related to their growth and innovation.
Keywords
Introduction
Since industrial trends are being changed very quickly under globalization of world economy in recent years, it is very important to find the opportunities of growth and innovation through research for trends of various industrial areas and government support systems (Patra and Krishna, 2015; Kim et al., 2016; Jeong and Ko, 2016; Yun et al.,2016). In order to search for the promising industrial fields, the financial information of enterprise has achieved a great deal of attention recently (Carp and Mironiuc, 2015; Marilena and Alina, 2015). It is because the worth of business and the tendency of industry are very closely related to the financial data of companies including turnover, research and development (R&D) investment, R&D intensity, and so on. The R&D intensity is the ratio of R&D investment to turnover (Montresor and Vezzani, 2015). Recently, its level of enterprise or country and effect on the innovation performance is being studied (Goschin, 2014; Karahan, 2015; Doruk, 2015; Savrul and Incekara, 2015). It indicates that the study for relationship between R&D expenditure and turnover has received high research interests. Also, the relation between the business ability and the R&D expenditure is being investigated by many researchers (Bočková and Meluzín, 2016; Ugur et al., 2016a, b). In other words, the effect of R&D investment on the growth and the innovation of companies has been studied and it is very important. The financial information of enterprises in the whole world utilized in this study was obtained from the ORBIS database of Bureau van Dijk. The ORBIS database is being widely used to gain the enterprise data (Martin et al., 2014; Kwon et al., 2016; Faria and Andersen, 2017). Moreover, the forecast of market and technology opportunities/trends in the future based on the financial data of companies as the quantitative evidence is very significant because the enterprises are able to search for their proper industrial areas by utilizing the result of market/technology prediction.
In our previous work, the method for selecting industrial areas suitable to small and medium-sized enterprises (SMEs) in Korea was investigated (Park et al., 2016). The aim of present work is to study the relation of R&D expense to turnover and number of listed enterprises in all industrial fields of the world by using the financial information of enterprises as the quantitative evidence. We expect that the result of this study can be used for the unlisted companies (e.g., start-up firms) to build the basis for their growth and innovation accompanied by increasing in turnover and becoming a listed enterprise from higher investment in R&D. To achieve this goal, number, turnover, and R&D investment of publicly listed companies in the world by the US Standard Industrial Classification (US SIC) primary code from 2007 to 2015 were utilized as the company data. Then, the relationship of R&D investment to turnover and number of listed enterprises in all industries was studied by using the analyses of correlation and linear regression.
Literature review
Socea (2012) studied the role of financial data for decision-making by managers of companies. From this previous work, it was found that the financial information has the following effects on the managers: (a) assisting them in knowing the past and the present situations of their enterprises, (b) providing a quantitative overview of their companies, (c) preparing the activities and decisions for their future, and so on. Babkin et al. (2015) investigated the influence of innovation strategies and R&D investment on the performance of information technology (IT) enterprises. They discovered the correlation between innovation strategies and performance of enterprises. In addition, the result of their study showed that increase in R&D expense makes larger revenue for companies. Pilinkienė (2015) analyzed the effect of R&D expenditure on the competitiveness of companies in the Baltic States. This previous study obtained the correlation matrix between the chosen indicators including competitiveness index, GDP growth rate, and R&D investment in particular Baltic States. Also, they suggested with a policy perspective that the success in economic growth and the increase in country’s competitiveness for the Baltic States can be achieved by using R&D expense more efficiently. Malichová and Ďurišová (2015) estimated the financial performance of companies operating in the sector of IT using the setting of financial indicators such as return on assets (ROA), return on equity (ROE), return on sales (ROS), and so on. By utilizing those financial indicators, they investigated the operating results of enterprises and suggested the identification of important features to accomplish high performance with continuation of finding their changes of financial performance in the sector of IT. Lee and Choi (2015) studied the effect of the financial structure of pharmaceutical businesses on the R&D expense for making a profit source in the next generation or develop the cost-effective medicines for improving the value of company. From their work, the results for the influences of various indicators on the R&D expenditure were as follows: (a) a positive effect of the current ratio, (b) a negative influence of the debt ratio, and (c) little effect of the net sales growth rate. Kretschmer and co-workers (2016) used the financial data such as sales and assets of enterprises from the ORBIS database in order to study the cloud adaptiveness within the sectors of industrial fields by merging them with the technology information offered from the Harte Hanks technology database for 13 countries in Europe from 2000 to 2007. Castellani et al. (2017) investigated the influences of multinationality on the productivity of business by using the ORBIS database. In addition, they found a positive effect of multinationality on the worth of R&D intensity defined in their study as the ratio of R&D expense of company to the number of its workers. Braganza and co-workers (2017) utilized the ORBIS database for enterprises in the world as one of various data sources about businesses, venture capital transactions, academic papers, patents, and so on. They developed an archetype business procedure for the initiatives of big data and the parts needed to handle the resource of big data effectively.
As above-mentioned, many researchers are using the financial performance of enterprises in order to investigate its various effects on the activities of companies such as productivity and R&D. Also, some works mentioned in this section studied the correlation among the financial data of companies (e.g., profitability and R&D). However, in the most previous works, the influence of financial information for specific industries such as pharmacy and IT were studied. On the other hand, in this research, we analyzed the financial data for all industries of the world from a macro perspective and then the correlation and linear regression analyses were performed by developing two models with different combination of independent and dependent variables (1. R&D expense and turnover; 2. R&D investment and number of listed enterprise) to investigate the influence of R&D expense on turnover and number of listed companies.
Data and methodology
Data
The ORBIS database of Bureau van Dijk updated on 11 May 2017 was utilized in this study. It contains the business data such as financial information, trade description, products, and services in the world (about 200 million companies). In this research, the financial information of publicly listed enterprises was mainly focused for analyzing the relation of R&D investment to turnover and their number in all industrial fields of the world. The US Standard Industrial Classification (US SIC) is the systematic classification assigned by US government in order to identify the industrial activities of companies. It is comprised of total 11 divisions and they are divided into 83 major groups (2-digit level), 416 industry groups (3-digit level), and 1005 industries (4-digit level). Furthermore, the US SIC primary code of each company is decided by its first line of business (e.g., its business area generating the highest profit). The raw data from the ORBIS based on the list of individual enterprises in the 4-digit level of the US SIC primary code was merged into its 2-digit level to gain the figures related to (a) number of listed businesses (2017), (b) turnover (2007 ~ 2015), and (c) R&D expenditure (2007 ~ 2015). The turnover used in this study means the total operating revenues including net sales, other operating revenues, and stock variations. Also, it does not include value added tax (ORBIS database). The definition of R&D investment is the total amount of expenses on R&D activities (ORBIS database). Moreover, thousand US dollar (USD) was used as a currency unit in this study. The overall procedure to analyze the company-related data is composed of total 4 steps. First of all, the number and the ratio of listed businesses in each US SIC code were obtained in order to observe their distribution in all industrial fields. Secondly, the top 10 average turnovers with their compound annual growth rate (CAGR) figures and descriptive statistics for each US SIC primary code were gained to analyze the industrial trends in the viewpoint of market circumstance. Thirdly, the top 10 average R&D expenditures with their CAGR values and descriptive statistics were calculated to comprehend the trends of industry about research activities of listed enterprises. Finally, investigation into relation of R&D expense to turnover and number of listed companies in all industries was performed through correlation and linear regression analyses. In this step, among 83 major groups (2-digit level), total 6 codes were excluded in the viewpoint of independent variable (R&D expenditure from 2007 to 2015) by the following two reasons: (1) No R&D investment (43 - United States postal service, 81 - Legal services, 88 - Private households, 91 - Executive, legislative, and general government, except finance, and 99 - Nonclassifiable establishments) and (2) Very small average R&D expense (93 - Public finance, taxation, and monetary policy, 0.52 thousand USD).
Methodology
Schematic illustration for the goal of this study in the viewpoint of unlisted company
Search strategy in order to achieve the number of publicly listed enterprises in the world
No. | Search strategy for each step in the ORBIS | Search result (Number of enterprises) |
---|---|---|
1 | All companies | 204,523,868 |
2 | Active companies | 163,191,236 |
3 | Publicly listed companies | 65,639 |
4 | Consolidation code: C1 (companies with consolidated accounts only), C2/U2 (companies with both types of accounts), U1 (companies with unconsolidated accounts only) | 62,592 |
5 | Accounting practice: International Financial Reporting Standards (IFRS), Local Generally Accepted Accounting Principles (Local GAAP) | 62,592 |
6 | Accounting template: Industrial companies excluding branches | 59,573 |
7 | Publicly listed companies with US SIC primary code | 59,430 |
Also, correlation and linear regression analyses were performed by using R (an open source programming language, R Core Team, 2017) to study the relationship of R&D expense (one independent variable) to turnovers and number of listed companies (two dependent variables) in all industrial areas of the world. For statistical analysis, natural log values of average R&D expense (thousand USD), average turnover (thousand USD), and number of listed enterprises were used. Above all, we tried to confirm the correlation between (1) R&D investment and turnover and (2) R&D expense and number of listed enterprises. Then, the effects of change in the independent variable on two dependent variables were studied by using the linear regression analysis.
Results and discussion
Study for number and distribution of publicly listed enterprises
Number and distribution of listed companies based on the US SIC primary code
US SIC primary code - Text description | Number of listed companies | Ratio (%) |
---|---|---|
01 - Agricultural production-crops | 521 | 0.88 |
02 - Agricultural production-livestock and animal specialities | 234 | 0.39 |
07 - Agricultural services | 296 | 0.50 |
08 - Forestry | 63 | 0.11 |
09 - Fishing, hunting and trapping | 78 | 0.13 |
10 - Metal mining | 2475 | 4.16 |
12 - Coal mining | 203 | 0.34 |
13 - Oil and gas extraction | 1336 | 2.25 |
14 - Mining and quarrying of nonmetallic minerals, except fuels | 475 | 0.80 |
15 - Building construction-general contractors and operative builders | 840 | 1.41 |
16 - Heavy construction other than building construction-contractors | 668 | 1.12 |
17 - Construction-special trade contractors | 352 | 0.59 |
20 - Food and kindred products | 2267 | 3.81 |
21 - Tobacco products | 66 | 0.11 |
22 - Textile mill products | 1114 | 1.87 |
23 - Apparel and other finished products made from fabrics and similar materials | 536 | 0.90 |
24 - Lumber and wood products, except furniture | 279 | 0.47 |
25 - Furniture and fixtures | 208 | 0.35 |
26 - Paper and allied products | 565 | 0.95 |
27 - Printing, publishing and allied industries | 518 | 0.87 |
28 - Chemicals and allied products | 3889 | 6.54 |
29 - Petroleum refining and related industries | 223 | 0.38 |
30 - Rubber and miscellaneous plastics products | 766 | 1.29 |
31 - Leather and leather products | 157 | 0.26 |
32 - Stone, clay, glass and concrete products | 1077 | 1.81 |
33 - Primary metal industries | 1226 | 2.06 |
34 - Fabricated metal products, except machinery and transportation equipment | 1019 | 1.71 |
35 - Industrial and commercial machinery and computer equipment | 2519 | 4.24 |
36 - Electronic and other electrical equipment and components, except computer equipment | 3597 | 6.05 |
37 - Transportation equipment | 1166 | 1.96 |
38 - Measuring, analyzing and controlling instruments; photographic, medical and optical goods; watches and clocks | 1222 | 2.06 |
39 - Miscellaneous manufacturing industries | 557 | 0.94 |
40 - Railroad transportation | 75 | 0.13 |
41 - Local and suburban transit and interurban highway passenger transportation | 163 | 0.27 |
42 - Motor freight transportation and warehousing | 330 | 0.56 |
43 - United States postal service | 6 | 0.01 |
44 - Water transportation | 432 | 0.73 |
45 - Transportation by air | 204 | 0.34 |
46 - Pipelines, except natural gas | 57 | 0.10 |
47 - Transportation services | 511 | 0.86 |
48 - Communications | 1219 | 2.05 |
49 - Electric, gas and sanitary services | 1483 | 2.50 |
50 - Wholesale trade, durable goods | 2146 | 3.61 |
51 - Wholesale trade, nondurable goods | 1031 | 1.73 |
52 - Building materials, hardware, garden supply, and mobile home dealers | 44 | 0.07 |
53 - General merchandise stores | 293 | 0.49 |
54 - Food stores | 302 | 0.51 |
55 - Automotive dealers and gasoline service stations | 155 | 0.26 |
56 - Apparel and accessory stores | 167 | 0.28 |
57 - Home furniture, furnishings, and equipment stores | 206 | 0.35 |
58 - Eating and drinking places | 389 | 0.65 |
59 - Miscellaneous retail | 518 | 0.87 |
60 - Depository institutions | 1170 | 1.97 |
61 - Non-depository credit institutions | 3270 | 5.50 |
62 - Security and commodity brokers, dealers, exchanges and services | 856 | 1.44 |
63 - Insurance carriers | 137 | 0.23 |
64 - Insurance agents, brokers, and service | 100 | 0.17 |
65 - Real estate | 2113 | 3.56 |
67 - Holding and other investment offices | 2761 | 4.65 |
70 - Hotels, rooming houses, camps, and other lodging places | 670 | 1.13 |
72 - Personal services | 121 | 0.20 |
73 - Business services | 4524 | 7.61 |
75 - Automotive repair, services, and parking | 150 | 0.25 |
76 - Miscellaneous repair services | 38 | 0.06 |
78 - Motion pictures | 222 | 0.37 |
79 - Amusement and recreation services | 371 | 0.62 |
80 - Health services | 479 | 0.81 |
81 - Legal services | 14 | 0.02 |
82 - Educational services | 192 | 0.32 |
83 - Social services | 42 | 0.07 |
84 - Museums, art galleries, and botanical and zoological gardens | 7 | 0.01 |
86 - Membership organizations | 15 | 0.03 |
87 - Engineering, accounting, research, management, and related services | 1591 | 2.68 |
88 - Private households | 0 | 0.00 |
89 - Services not elsewhere classified | 199 | 0.33 |
91 - Executive, legislative, and general government, except finance | 4 | 0.01 |
92 - Justice, public order, and safety | 13 | 0.02 |
93 - Public finance, taxation, and monetary policy | 40 | 0.07 |
94 - Administration of human resource programs | 8 | 0.01 |
95 - Administration of environmental quality and housing programs | 18 | 0.03 |
96 - Administration of economic programs | 54 | 0.09 |
97 - National security and international affairs | 8 | 0.01 |
99 - Nonclassifiable establishments | 0 | 0.00 |
Total | 59,430 | 100.00 |
Investigation into industrial areas of top 10 average turnovers and their descriptive statistics from 2007 to 2015
Descriptive statistics of industrial areas having top 10 average turnovers (2007 ~ 2015)
Ranking | US SIC primary code - Text description | Average turnover (CAGR, %) | Median | Standard deviation | Minimum | Maximum |
---|---|---|---|---|---|---|
1 | 37 - Transportation equipment | 3,414,020,116 (1.88) | 3,425,822,911 | 339,938,866 | 2,916,833,631 | 3,808,847,394 |
2 | 13 - Oil and gas extraction | 2,816,148,067 (0.52) | 2,612,309,352 | 602,844,769 | 2,082,624,354 | 3,571,103,618 |
3 | 28 - Chemicals and allied products | 2,647,946,798 (4.48) | 2,854,291,561 | 406,439,457 | 2,051,240,171 | 3,083,769,070 |
4 | 49 - Electric, gas and sanitary services | 2,337,933,540 (3.34) | 2,317,151,073 | 343,131,652 | 1,782,134,510 | 2,837,146,655 |
5 | 29 - Petroleum refining and related industries | 2,213,020,301 (−1.82) | 2,381,386,062 | 418,156,592 | 1,609,563,019 | 2,639,850,252 |
6 | 36 - Electronic and other electrical equipment and components, except computer equipment | 2,178,651,683 (2.07) | 2,255,989,804 | 194,836,027 | 1,898,757,985 | 2,405,865,824 |
7 | 48 - Communications | 2,054,883,098 (2.88) | 2,169,861,470 | 195,042,643 | 1,759,189,019 | 2,240,045,754 |
8 | 35 - Industrial and commercial machinery and computer equipment | 1,881,408,977 (4.11) | 2,024,095,853 | 276,782,773 | 1,505,594,628 | 2,184,508,210 |
9 | 20 - Food and kindred products | 1,696,477,390 (5.69) | 1,797,364,959 | 283,201,167 | 1,204,070,732 | 2,024,704,606 |
10 | 73 - Business services | 1,439,930,594 (7.44) | 1,375,628,373 | 311,250,947 | 1,074,033,567 | 1,907,609,209 |
Research on industrial fields of top 10 average R&D investments and their descriptive statistics from 2007 to 2015
Descriptive statistics of industrial fields with top 10 average R&D investments (2007 ~ 2015)
Ranking | US SIC primary code - Text description | Average R&D investment (CAGR, %) | Median | Standard deviation | Minimum | Maximum |
---|---|---|---|---|---|---|
1 | 28 - Chemicals and allied products | 151,884,109 (4.39) | 154,019,882 | 16,847,820 | 125,179,857 | 176,548,957 |
2 | 36 - Electronic and other electrical equipment and components, except computer equipment | 115,438,474 (3.15) | 124,361,807 | 15,109,790 | 93,528,903 | 133,242,357 |
3 | 37 - Transportation equipment | 108,119,248 (2.06) | 112,322,852 | 10,067,455 | 92,306,185 | 119,505,243 |
4 | 73 - Business services | 56,528,208 (11.19) | 52,848,383 | 16,663,724 | 37,147,573 | 86,762,354 |
5 | 35 - Industrial and commercial machinery and computer equipment | 53,895,319 (4.74) | 55,258,316 | 6,953,883 | 43,909,638 | 63,622,179 |
6 | 38 - Measuring, analyzing and controlling instruments; photographic, medical and optical goods; watches and clocks | 25,523,664 (4.05) | 26,861,865 | 2,715,436 | 21,417,885 | 29,430,019 |
7 | 13 - Oil and gas extraction | 16,497,156 (7.23) | 18,152,769 | 3,401,947 | 11,045,634 | 19,951,396 |
8 | 48 - Communications | 12,563,872 (−1.05) | 12,504,317 | 1,080,100 | 10,715,087 | 14,265,049 |
9 | 29 - Petroleum refining and related industries | 10,699,576 (8.03) | 10,232,185 | 1,979,904 | 7,725,321 | 14,332,164 |
10 | 20 - Food and kindred products | 9,180,171 (3.95) | 9,860,196 | 1,257,921 | 7,231,432 | 10,202,145 |
Analyses of correlation and linear regression between R&D expense and turnover (model a)
Statistical values obtained from analyses of correlation and linear regression
Description and statistical values | ||
---|---|---|
Model | A | B |
Independent variable (xn) | Average R&D expense | Average R&D expense |
Dependent variable (yi,n) | Average Turnover | Number of listed companies |
Correlation coefficient (Ri) | 0.828 | 0.782 |
Slope (ai) | 0.583 | 0.390 |
Intercept (bi) | 11.379 | 0.883 |
Coefficient of determination (Ri2) | 0.686 | 0.612 |
p-value (pi) | < 2.2 × 10−16 | < 2.2 × 10−16 |
Residual standard error (RSEi) | 1.229 | 0.968 |
Linear regression analysis between average R&D expense and average turnover using their natural log values. *The subscripts “A” and “n” in this Figure mean the name of model and the US SIC primary code, respectively. * Currency unit: thousand USD
Regression equation from the Model A: yA,n = 0.583 × xn + 11.379 (n: US SIC code).
In other words, xn and yA,n mean the average R&D expense and the average turnover by the US SIC primary code, respectively. The coefficient of determination for the Model A (RA2) was 0.686. Thus, we can say that the change in the R&D expense affects the turnover in the level of industrial fields although they may not have causal relationship. For example, the increase in the turnover of specific US SIC code is related to the enhancement in its R&D investment. In addition, the residual standard error of the Model A (RSEA) was 1.229. In this work, the residual figure is the difference based on the natural log between the actual value and the estimated value obtained from the regression equation. The RSEA indicates that there is the error of 1.229 when the Model A is used to predict the turnover.
Analyses of correlation and linear regression between R&D investment and number of listed enterprises (model B)
Linear regression analysis between average R&D expense and number of listed company using natural log. *The subscripts “B” and “n” of regression equation and value in this Figure indicate the model name and the US SIC code, respectively. *Currency unit: thousand USD
Regression equation from the Model B: yB,n = 0.390 × xn + 0.883 (n: US SIC code).
The value of RB2 was 0.612 and this indicates that the number of listed enterprises is influenced by the change of R&D expense. In other words, there is the relationship between the higher R&D expenditure of specific industry and its more number of listed companies. Also, the RSEB was 0.968 and it is smaller than RSEA. This result is caused by the difference in the scale of dependent variables between the Model A (turnover) and the Model B (number of listed companies).
Conclusions and future work
In conclusion, the relation of R&D expense to the turnover and the number of listed companies in all industrial areas of the world from 2007 to 2015 was studied by using the analyses of correlation and linear regression based on the US SIC primary code. Through the overall procedure of this work, it was found that both the turnover and the number of listed enterprises were affected by the R&D investment. Therefore, we expect that the unlisted companies (e.g., start-up firms) are able to develop the basis of their growth and innovation as they invest in R&D higher which can induce the increases in (1) turnover and (2) probability of becoming a listed enterprise. However, the main limitations of this work are as in the followings: (1) using only three variables (turnover, R&D expense, and number of listed companies) and (2) performing only simple linear regression analysis. As the future research, it is needed to perform the multiple linear regression analysis using more kinds of financial information (e.g., cost of goods sold, gross profit, net income, and so on) for all listed enterprises in the whole world. In addition, the specific industry areas (3-digit or 4-digit level) included in the major groups of US SIC primary codes (2-digit level) showing the high figures of turnovers and R&D expenditures can be studied. Also, the comparative analysis of financial data among nations in a global sense can be performed. Therefore, it is expected that the above-mentioned future work can be performed effectively by using the results of this research in order to develop more advanced model and methodology based on the financial information of companies as the quantitative evidence.
Declarations
Acknowledgements
This research was supported by Korea Institute of Science and Technology Information (KISTI).
Authors’ contributions
All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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