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Innovation and internationalization through exports

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Abstract

Successful product innovation leads to the decision by small and medium enterprises (SMEs) to enter the export market. We argue that in addition to a direct effect of innovation on exports, product innovation, through its effect on firm productivity, increases the likelihood of the firm entering the export market. Using a panel of Spanish manufacturing firms, we show that the strong positive association found between firm productivity and exports in the literature relates to the firm’s earlier innovation decisions, and that, when controlling for product innovation, the relationship between productivity and exports vanishes for these innovating firms.

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Notes

  1. Recent studies report evidence in favor of learning-by-exporting. De Loecker (2007), for a sample of Slovenian firms, finds that export entrants become more productive once they start exporting. Van Biesebroeck (2005) finds evidence of exports increasing firm productivity for a sample of sub-Saharan African firms. Salomon and Shaver (2005) and Salomon and Jin (2008), for a sample of Spanish firms, find that exporting is associated with the post-entry increase in patent applications.

  2. We consider the self-selection mechanism of more productive firms into exports, confirmed by most of the empirical studies, as the main explanation for the correlation between productivity and exports. However, we do not exclude the possibility of learning-by-exporting. Our assumption is that the effect of learning-by-exporting is less pronounced than that of self-selection, which has also been confirmed by most of the empirical findings. Nevertheless, in the discussion we argue that a focus on non-innovating firms might be more appropriate to uncover evidence from learning-by-exporting.

  3. The proportion of the firms in year t that continue in the survey in year t+1 is approximately 90% for the 1990–1998 sample period. Among the firms that exited the sample, approximately 2.2% disappeared and approximately 7.7% stopped collaborating.

  4. A less restrictive definition of foreign-owned companies will also include firms with more than 10% and less than 50% of foreign capital. Only about 1.5% of SMEs in our sample are with foreign ownership between 10% and 49% of foreign capital.

  5. Pla-Barber and Escriba-Esteve (2006) show the existence of a group of firms that follow a relatively faster internationalization process compared with the traditional sequential internationalization. These firms begin to export at an early stage (1–6 years from founding) and devote substantial efforts to the international arena, thus complying with the definition of born global firms. Therefore their results suggest that firms from a late-investor country do not necessarily need to follow a slow, sequenced pattern to enter the international markets effectively.

  6. In addition, comparison of the variance measure of export and product innovation variables shows that the variation of innovation activity is higher than that of exports.

  7. Table A1 in the Appendix provides definitions of all the variables used in the analysis.

  8. The ESEE survey uses the following definitions of product and process innovation. For product innovation, the firms were asked whether they obtained a product innovation in a given year. Product innovation was defined as new products, or products with new features that were different from those that a firm had produced in previous years. If the answer to this question was “yes”, the type of modification was asked: (1) whether the innovation incorporated new materials; (2) whether the innovation incorporated new components or intermediate products; (3) whether the innovation incorporated new design or presentation; and (4) whether the new product performed new functions. For process innovation, the question was whether a firm introduced an important modification in the current production process. If the answer was “yes”, the type of product innovation was asked: (1) whether it was related to the introduction of the new machinery; (2) whether it was related to the introduction of new methods of production organization; and (3) whether it was both types of process innovation.

  9. As innovation does show some persistence, we experimented with various lag specifications. Our basic result is robust to these alternative specifications. See the “Robustness and sensitivity analysis” section.

  10. Syverson (2010) indicates that the empirical facts on productivity documented in the literature show little sensitivity to the actual choice of productivity measures. Van Biesebroeck (2007) argues that the index measures of productivity are well suited for the type of analysis we perform.

  11. Typically popular in the labor economics literature, this non-parametric technique has been also used by MacGarvie (2006) to test the direction of causality between exporting, importing and innovation measured by patent citations, and by De Loecker (2007) to examine the causal link between exports and productivity.

  12. What makes a variable “relevant” and appropriate is the extent to which the variable affects the probability of being subject to treatment.

  13. We estimate a matching estimator using a STATA routine developed by Abadie et al., 2001.

  14. This result needs careful interpretation. Process innovation usually involves changes in the production process aimed at improving production efficiency, thus presumably having a direct effect on firm's productivity. We do not observe such an effect in our data. The possible explanation could be that, very often, process innovation is incorporated in the capital investment, as suggested by Crespi, Criscuolo, and Haskel (2007): process innovation “is a combination of advanced capital investment and process organizational change”. As we already control for capital investment in our productivity measure (TFP), we should not expect a significant effect of process innovation on productivity measured as TFP.

  15. We have tested the model for the different lag structures of product innovation: product innovation at least once in two previous years (Table 8); product innovation in both year t−1 and t−2; product innovation in year t−2; and product innovation once in 3 years. Our finding on the moderating effect of product innovation on the export – productivity association is robust to these alternative specifications. We report only the results for the product innovation measured as innovating at least once in the two previous years.

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Acknowledgements

We are grateful for the comments received from José Manuel Campa, seminar participants at the Jornadas de Economia Industrial in Barcelona, the EARIE conference in Amsterdam, and the editor and three anonymous reviewers. This paper was circulated previously under the title “Innovation and the export – productivity link”. We acknowledge financial support from the Anselmo Rubiralta Center for Globalization and Strategy and the Public – Private Sector (SP – SP) Research Center at IESE Business School, the Spanish Ministry of Education and Science and Technology for projects no. SEJ2006-11833/ECON and no. ECO2009-13169, and Catalan Government Grant no. 2009-SGR919.

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Authors and Affiliations

  1. IESE Business School, Barcelona, Spain

    Bruno Cassiman

  2. Department of Managerial Economics, Strategy and Innovation, K.U. Leuven, Leuven, Belgium

    Bruno Cassiman

  3. CEPR, Barcelona, Spain

    Bruno Cassiman

  4. Department of Organization & Strategy, CIR and CentER, Tilburg University, Tilburg, The Netherlands

    Elena Golovko

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  1. Bruno Cassiman
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Correspondence to Bruno Cassiman.

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Accepted by Arjen van Witteloostuijn, Area Editor, 25 April 2010. This paper has been with the authors for one revision.

APPENDIX

APPENDIX

See Table A1.

Table a1 Variable definitions
Full size table

Calculation of the Total Factor Productivity (TFP) Index

The productivity index is computed as a multilateral index, developed in Caves et al. (1982) and extended in Good et al. (1997). In calculating the TFP index we follow Aw et al. (2000, 2007) and Delgado et al. (2002). The TFP index measures the proportional difference in TFP for firm i in year t relative to a hypothetical reference firm in the same industry r, that is, each firm's output, inputs and productivity for each year are measured relative to this hypothetical firm in the same industry. This reference firm is defined as follows:

  1. 1)

    The output is equal to the geometric mean of outputs over all observations in industry r.

  2. 2)

    Inputs are equal to the geometric means of inputs over all observations in industry r.

  3. 3)

    Cost shares are the arithmetic means of cost shares over all observations in industry r.

Firms with positive residuals are relatively more productive, and firms with negative residuals are relatively less productive We calculate the index as the logarithm of the firm's output minus a cost-weighted sum of the logarithms of the three inputs in the production process (labor, capital and raw materials). More specifically, the TFP index for firm i (i=1, …, N) from industry r (r=1, …, R) in year t (t=1, …, T) is computed using the formula

where Yitr is the output of firm i in year t; Xitrj is an input j () of firm i in year t; Sitrj is a cost-based share of input j of firm i in year t; and

and

are the same variables for the reference firm.

The ESEE data provide information on the output and input variables needed to measure TFP at the firm level. We model each firm as using three inputs in its production function: labor, capital and material input. The labor input is measured as the number of total effective working hours per year. The measure of capital input is the capital stock, calculated using the formula

where I represents investment in equipment in year t, d the depreciation rates in year t, and P the price indexes for equipment in year t. The material input includes raw materials, fuel and electricity costs, and other services bought by a firm. The material expenditures are deflated using the firm-specific price indexes for each of the inputs provided in the ESEE survey.

Firm output is defined as total firm sales deflated by a producer price index defined at the two-digit NACE industry level. The information on depreciation rates, price indexes for equipment, producer price index and consumer price index is taken from the Instituto Nacional de Estadística de España (www.ine.es). Cost-based input shares are calculated as the costs of each input in total input costs. The total input cost is the sum of the labor cost, material cost and the cost of capital. Labor costs are measured as total salaries to employees, deflated by the consumer price index. Capital cost is computed using an estimation of the user cost of capital for each firm. User cost of capital is calculated as the sum of the cost of long-term debt and depreciation rates less the variation of the price index for capital goods. The cost share of materials is calculated as the residual after subtracting the expenditures on labor and capital.

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Cassiman, B., Golovko, E. Innovation and internationalization through exports. J Int Bus Stud 42, 56–75 (2011). https://doi.org/10.1057/jibs.2010.36

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