From Basic Research to Competitiveness: An Econometric Analysis of the Global Pharmaceutical Sector
Abstract
:1. Introduction
2. Literature Review
3. Hypothesis Development
4. Methodology
4.1. Databases
4.2. Models Applied
5. Results and Discussion
6. Conclusions
- The boosting effect of the state is a necessary development of R&D activity even in branches dominated by large-scale enterprises.
- The optimal resource utilization for R&D requires international cooperation because the time between the achievement of academic results and economic advances is rather long and the risk is relatively high—there is no straightforward relationship between intellectual success and international competitiveness.
- The efficiency of resources allocated to R&D activity can be evaluated and measured by economic indicators only in a long-range perspective.
- Currently, increasingly complex regulation related to the introduction of new pharmaceutical products increases the time-gap between the conceptualisation of new innovation and its introduction to the market.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Source | Method | Results |
---|---|---|
Coe and Moghadam [43] | Aggregate production function for a 20-year period of the economic development of France. | Government infrastructure, business sector capital, residential capital and research and development capital have a significant influence on aggregate production function. |
Coe and Helpman [44] | 21 Organisation for Economic Co-operation and Development (OECD) countries and Israel, economic data from 1971-1990. | Foreign R&D has a positive effect on domestic factor productivity, but this depends on the openness of the economy. High rates of return of R&D in domestic output and international spillovers. |
Thirtle et al. [45] | Ten EU countries and the USA, agriculture, cointegration. | Total Factor Productivity (TFP) calculations, returns to R&D are seriously biased if spillovers are ignored. |
Funk [46] | Trade patterns and international spillovers of OECD countries and Kao et al. [47] panel cointegration model. | There is no significant relationship between import patterns and R&D spillovers, exporters receive significant R&D spillover from customers. |
Edmond [48] | Pedroni’s test for panel cointegration to determine the coefficients, estimated in Coe and Helpman [44] model. | Cointegration coefficients are less robust when more heterogeneity is allowed; the elasticity coefficient of productivity on foreign R&D is unstable. |
del Barrio-Castro et al. [49] | OECD database. | Average years of schooling influence the effect of international R&D. |
Gutierrez and Gutierrez [50] | Panel data on TPF productivity from 47 countries in a 32-year period. | TFP is influenced by domestic and international R&D, a significant role of geographical location. |
Liu [51] | Cointegration analysis on R&D input intensity and independent innovation ability of Chinese enterprises on the basis of enterprise-level data from between 1991 and 2003. | Bi-directional mutual relationship and stable long-term equilibrium between R&D intensity and innovation. |
Yoo [52] | Long- and short-run causality between public and private R&D expenditure in Korea. | Bi-directional causality between private and public R&D. |
Bottazzi and Peri [53] | Employment in R&D and patent applications in OECD countries. | Knowledge spillovers are sufficiently strong to create long-run endogenous growth. |
Coe and Helpman [44] | 21 OECD countries and Israel, economic data from 1971–2004. | TFP, domestic and foreign R&D capital are cointegrated; human capital is cointegrated with Total Factor Productivity (TFP); considerable differences between countries. |
Frantzen [54] | Panel cointegration test on the effect of domestic and foreign R&D on the productivity base of 22 manufacturing sectors of 14 OECD countries between 1972 and 1994. | Log of TFP and logs of domestic and foreign R&D are cointegrated. Dynamic Vector Autoregressive (VAR) model suggests that, in the majority of sectors, causation runs from the R&D are cointegrated. A dynamic VAR model suggests that, in the majority of sectors, causation runs from the R&D variables to TFP, a long-run causation in nature. |
Teixeira and Fortuna [55] | Portugal data from 1960 to 2001, cointegration. | Significant effect of human capital and R&D efforts on TFP. |
Cho et al. [56] | Oil prices, energy consumption and R&D in EU countries, cointegration. | Significant role of R&D on renewable energy consumption. |
Voutsinas and Tsamadias [57] | Greek economic data 1987–2007 to determine effect of public and private R&D on TFP. | A 1% increase in the total R&D capital increases TFP by 0.038%, whereas a 1% increase in the public R&D capital raises TFP by 0.075%. |
Khan and Salim [58] | Australian country level data on R&D and TFP between 1953 and 2009. | Cointegration between R&D and productivity growth, unidirectional causality from R&D to TFP. |
Sussex et al. [59] | Medical research costs in UK between 1982 and 2012. | A 1% increase in public sector expenditure is associated with a 0.81% increase in private sector expenditure. |
Meyer and Meyer [60] | Brazil, Russia, India, China and South Africa (BRICS) data on relationships between economic growth, employment and established business ownership | Established business ownership is a significant predictor of employment |
Countries | Pharm R&D | Pharm Export | Pharm Patents | Publications | |
---|---|---|---|---|---|
Australia | X | X | X | X | |
Austria | X | X | X | ||
Belgium | X | X | X | X | |
Canada | X | X | X | ||
Chile | X | ||||
Czech Republic | X | X | X | ||
Denmark | X | X | X | ||
Estonia | X | X | |||
Finland | X | X | X | ||
France | X | X | X | X | |
Germany | X | X | X | X | |
Greece | X | X | X | ||
Hungary | X | X | X | X | |
Iceland | X | X | X | ||
Ireland | X | X | |||
Israel | X | X | X | X | |
Italy | X | X | X | X | |
Japan | X | X | X | ||
Korea | X | X | X | ||
Luxemburg | X | X | |||
Mexico | X | X | X | ||
Netherlands | X | X | X | ||
New Zealand | X | X | |||
Norway | X | X | X | ||
Poland | X | X | |||
Portugal | X | X | |||
Slovak Republic | X | ||||
Slovenia | X | X | X | ||
Spain | X | X | X | ||
Sweden | X | X | X | ||
Switzerland | X | X | X | ||
Turkey | X | X | |||
United Kingdom | X | X | X | ||
United States | X | X | X |
Variables | LLC | IPS | ADF-Fischer Chi-Square | PP-Fischer Chi-Square | |
---|---|---|---|---|---|
RD (level) | Individual intercept | −5.234 *** [0.000] | −1.544* [0.061] | 49.333 [0.148] | 84.399 *** [0.000] |
Individual intercept and trend | −2.695 *** [0.003] | −1.562 * [0.059] | 66.904 *** [0.004] | 149.502 *** [0.000] | |
None | 7.160 [1.000] | 7.156 [1.000] | 6.810 [1.000] | ||
D(RD) (first difference) | Individual intercept | −18.925 *** [0.000] | −19.217 *** [0.000] | 342.403 *** [0.000] | 361.865 *** [0.000] |
Individual intercept and trend | −15.693 *** [0.000] | −18.439 *** [0.000] | 333.197 *** [0.000] | 638.538 *** [0.000] | |
None | −18.571 *** [0.000] | 374.320 *** [0.000] | 448.052 *** [0.000] | ||
PUBL (level) | Individual intercept | −3.061 [1.000] | 0.112 [0.544] | 54.506 * [0.062] | 66.279 * [0.005] |
Individual intercept and trend | −2,870 *** [0.002] | −2.665 *** [0.004] | 72.375 *** [0.001] | 123.359 *** [0.000] | |
None | −5.658 [1.000] | 2.273 [1.000] | 2.084 [1.000] | ||
D(PUBL) (first difference) | Individual intercept | −2.695 *** [0.003] | 22.810 *** [0.000] | 405.735 *** [0.000] | 615.071 *** [0.000] |
Individual intercept and trend | 7.160 [1.000] | −20.744 *** [0.000] | 390.392 *** [0.000] | 480.876 *** [0.000] | |
None | −18.925 *** [0.000] | 505.227 *** [0.000] | 615.071 *** [0.000] | ||
PAT (level) | Individual intercept | −14.903 *** [0.000] | −11.680 *** [0.000] | 209.145 *** [0.000] | 260.677 *** [0.000] |
Individual intercept and trend | −3.065 *** [0.001] | −2.240 ** [0.012] | 96.586 *** [0.000] | 127.755 *** [0.000] | |
None | 3.602 [0.998] | 19.257 [0.997] | 33.986 [0.736] | ||
D(PAT) (first difference) | Individual intercept | −10.599 *** [0.000] | −17.094 *** [0.000] | 297.268 *** [0.000] | 407.650 *** [0.000] |
Individual intercept and trend | −13.129 *** [0.000] | −23.971 *** [0.000] | 524.793 *** [0.000] | 2196.161 *** [0.000] | |
None | −21.375 *** [0.000] | 463.554 *** [0.000] | 601.177 *** [0.000] | ||
RCA (level) | Individual intercept | −9.789 *** [0.000] | −12.650 *** [0.000] | 230.343 *** [0.000] | 250.925 *** [0.000] |
Individual intercept and trend | −15.050 *** [0.000] | −13.905 *** [0.000] | 237.923 *** [0.000] | 288.850 *** [0.000] | |
None | −2.232 ** [0.012] | 233.120 *** [0.000] | 262.656 *** [0.000] | ||
D(RCA) (first difference) | Individual intercept | −37.299 *** [0.000] | −33.381 *** [0.000] | 589.096 *** [0.000] | 379.076 *** [0.000] |
Individual intercept and trend | −33.893 *** [0.000] | −31.705 *** [0.000] | 598.819 *** [0.000] | 4538.01 *** [0.000] | |
None | −35.068 *** [0.000] | 858.194 *** [0.000] | 4366.23 *** [0.000] | ||
TSC (level) | Individual intercept | −5.214 *** [0.000] | −4.060 *** [0.000] | 95.287 *** [0.000] | 132.095 *** [0.000] |
Individual intercept and trend | −0.446 [0.327] | -0.538 [0.295] | 59.588 ** [0.023] | 173.970 *** [0.000] | |
None | 3.969 *** [0.000] | 109.026 **** [0.000] | 115.136 *** [0.000] | ||
D(TSC) (first difference) | Individual intercept | −18.533 *** [0.000] | −19.119 *** [0.000] | 330.858 *** [0.000] | 333.604 *** [0.000] |
Individual intercept and trend | −15.846 *** [0.000] | −19.541 *** [0.000] | 390.267 *** [0.000] | 904.160 *** [0.000] | |
None | −23.366 *** [0.000] | 507.458 *** [0.000] | 759.408 *** [0.000] | ||
VRCA | Individual intercept | −2.623 *** [0.004] | −2.536 *** [0.005] | 66.398 *** [0.005] | 83.500 *** [0.000] |
Individual intercept and trend | −5.116 *** [0.003] | −5.928 *** [0.000] | 125.040 *** [0.000] | 360.909 *** [0.000] | |
None | 1.198 [0.115] | 51.183 [0.110] | 61.339 ** [0.016] | ||
D(VRCA) | Individual intercept | −25.151 *** [0.000] | −25.665 *** [0.000] | 462.757 *** [0.000] | 489.980 *** [0.000] |
Individual intercept and trend | −25.945 *** [0.000] | −29.320 *** [0.0000] | 749.123 *** [0.0000] | 2409.670 *** [0.000] | |
None | −30.301 *** [0.000] | 747.591 *** [0.000] | 1137.38 *** [0.000] |
Hypothesis | W-Statistics | Z-bar Statistics | Probability |
---|---|---|---|
PAT→PUB | 15.16 | 2.29 | 0.02 |
PAT→RCA | 20.24 | 4.46 | 0.00 |
PAT→RD | 10.86 | 0.45 | 0.65 |
PAT→TSC | 11.56 | 0.75 | 0.45 |
PAT→VRCA | 13.90 | 1.75 | 0.08 |
PUB→PAT | 12.67 | 1.22 | 0.22 |
PUB→RCA | 12.67 | 1.23 | 0.22 |
PUB→RD | 8.90 | −0.38 | 0.70 |
PUB→TSC | 16.66 | 2.93 | 0.00 |
PUB→VRCA | 16.29 | 2.77 | 0.01 |
RCA→PAT | 20.83 | 4.71 | 0.00 |
RCA→PUB | 15.53 | 2.44 | 0.01 |
RCA→RD | 17.34 | 3.22 | 0.00 |
RCA→TSC | 20.77 | 4.68 | 0.00 |
RCA→VRCA | 11.19 | 0.60 | 0.55 |
RD→PAT | 15.93 | 2.62 | 0.01 |
RD→PUB | 20.85 | 4.72 | 0.00 |
RD→RCA | 17.43 | 3.26 | 0.00 |
RD→TSC | 14.54 | 2.02 | 0.04 |
RD→VRCA | 12.92 | 1.33 | 0.18 |
TSC→PAT | 13.20 | 1.45 | 0.15 |
TSC→PUB | 9.95 | 0.07 | 0.95 |
TSC→RCA | 16.71 | 2.95 | 0.00 |
TSC→RD | 12.19 | 1.02 | 0.31 |
TSC→VRCA | 11.38 | 0.67 | 0.50 |
VRCA→PAT | 13.79 | 1.70 | 0.09 |
VRCA→PUB | 10.46 | 0.28 | 0.78 |
VRCA→RCA | 11.36 | 0.67 | 0.51 |
VRCA→RD | 14.18 | 1.87 | 0.06 |
VRCA→TSC | 20.18 | 4.43 | 0.00 |
Independent Variables | Dependent Variables | ||
---|---|---|---|
Balassa Index of Competitiveness | Trade Specialisation Coefficient | Vollrath Index of Competitiveness | |
Variables in the Level | |||
Resources for research and development | 0.236 | −1.38 | 1.29 |
Number of publications | 0.385 | 1.96 * | 2.72 ** |
Number of patents | 0.141 | 2.08 ** | 0.94 |
Constant | 0.90 | −3.66 | −6.59 |
Coefficients of Fixed Effect Model | Coefficients of Random Effect Model | Difference | Standard Error | |
---|---|---|---|---|
Dependent variable: RCA, level Chi square: 9.05** | ||||
RD | 0.25 | 0.22 | 0.03 | 0.03 |
PUB | −0.10 | −0.24 | 0.14 | 0.05 |
PAT | 0.11 | 0.14 | −0.03 | 0.01 |
Dependent variable: TSC, level Chi square: 9.44** | ||||
RD | −0.03 | −0.01 | −0.02 | 0.01 |
PUB | 0.06 | 0.03 | 0.03 | 0.01 |
PAT | 0.01 | 0.02 | 0.00 | 0.00 |
Dependent variable: VRCA, level Chi square: 19.94* | ||||
RD | 0.02 | 0.01 | 0.00 | 0.00 |
PUB | 0.12 | 0.09 | 0.02 | 0.01 |
PAT | 0.01 | 0.02 | -0.01 | 0.00 |
Balassa Index of Revealed Competitive Advantages | ||||
Coefficient | Std.error | Z | P > |z| | |
LGRD | 0.222 | 0.078 | 2.860 | 0.004 |
LGPUBL | 0.236 | 0.111 | 2.120 | 0.034 |
LGPAT | 0.139 | 0.045 | 3.090 | 0.002 |
CONST | −0.114 | 0.264 | −0.430 | 0.665 |
Trade Specialization Coefficient | ||||
Coefficient | Std.error | Z | P > |z| | |
LGRD | −0.009 | 0.027 | −0.340 | 0.734 |
LGPUBL | 0.031 | 0.039 | 0.790 | 0.431 |
LGPAT | 0.048 | 0.015 | 3.200 | 0.000 |
CONST | −0.451 | 0.098 | −4.620 | 0.000 |
Vollrath Index of Competitive Advantages | ||||
Coefficient | Std.error | Z | P > |z| | |
LGRD | 0.014 | 0.023 | 0.630 | −0.030 |
LGPUBL | 0.094 | 0.032 | 2.880 | 0.030 |
LGPAT | 0.018 | 0.013 | 1.420 | −0.007 |
−1.062 | 0.093 | −11.460 | −1.244 |
Dependent Variable | Monetary Resources, Allocated for R&D | |||||
---|---|---|---|---|---|---|
Independent Variable | Balassa Index of Competitiveness | Trade Specialisation Coefficient | ||||
Chi-Square | Degrees of Freedom (df) | Prob. | Chi-Square | df | Prob. | |
Greece | 4.8385 | 2 | 0.089 * | 5.8715 | 2 | 0.091 * |
Hungary | 11.251 | 5 | 0.046 ** | 13.4157 | 5 | 0.008 ** |
Mexico | 16.527 | 2 | 0.000 *** | 17.0123 | 2 | 0.000 *** |
Sweden | 7.5469 | 2 | 0.023 ** | 8.1547 | 2 | 0.009 *** |
Switzerland | 13.618 | 4 | 0.008 *** | 14.872 | 4 | 0.000 *** |
Countries | Dependent Variable: Balassa Index of Competitiveness | Trade Specialisation Coefficient | ||||
---|---|---|---|---|---|---|
Independent Variable: Number of Patents | Independent Variable: Number of Patents | |||||
Chi-Square | Chi-Square | df | Chi-Square | Chi-Square | df | |
Austria | 19.461 | 8 | 0.012 ** | 22.214 | 8 | 0.000 |
Australia | 16.159 | 8 | 0.056 * | 17.221 | 8 | 0.022 ** |
Denmark | 14.235 | 6 | 0.027 ** | 14.005 | 6 | 0.020 |
Finland | 16.052 | 8 | 0.041 ** | 16.987 | 8 | 0.038 ** |
France | 8.0202 | 8 | 0.431 | 9.222 | 8 | 0.412 |
Germany | 12.726 | 6 | 0.047 ** | 12.213 | 6 | 0.057 * |
Greece | 36.338 | 8 | 0.000 *** | 39.477 | 8 | 0.000 *** |
Ireland | 36.263 | 8 | 0.000 *** | 38.997 | 8 | 0.000 *** |
Italy | 13.787 | 8 | 0.087 * | 14.12 | 8 | 0.075 * |
Netherlands | 12.805 | 6 | 0.046 ** | 11.128 | 6 | 0.053 * |
Norway | 19.559 | 6 | 0.003 *** | 21.125 | 6 | 0.001 *** |
Spain | 21.520 | 8 | 0.005 *** | 22.547 | 8 | 0.002 ** |
Switzerland | 15.667 | 6 | 0.015 ** | 18.142 | 6 | 0.000 *** |
United Kingdom | 21.056 | 8 | 0.007 *** | 20.145 | 8 | 0.009 ** |
United States | 17.171 | 8 | 0.028 ** | 16.125 | 8 | 0.0312 ** |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Lakner, Z.; Kiss, A.; Popp, J.; Zéman, Z.; Máté, D.; Oláh, J. From Basic Research to Competitiveness: An Econometric Analysis of the Global Pharmaceutical Sector. Sustainability 2019, 11, 3125. https://doi.org/10.3390/su11113125
Lakner Z, Kiss A, Popp J, Zéman Z, Máté D, Oláh J. From Basic Research to Competitiveness: An Econometric Analysis of the Global Pharmaceutical Sector. Sustainability. 2019; 11(11):3125. https://doi.org/10.3390/su11113125
Chicago/Turabian StyleLakner, Zoltán, Anna Kiss, József Popp, Zoltán Zéman, Domicián Máté, and Judit Oláh. 2019. "From Basic Research to Competitiveness: An Econometric Analysis of the Global Pharmaceutical Sector" Sustainability 11, no. 11: 3125. https://doi.org/10.3390/su11113125