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The nexus between labor diversity and firm’s innovation

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In this paper, we investigate the nexus between firm labor diversity and innovation by using data on patent applications filed by firms at the European Patent Office and a linked employer–employee database from Denmark. Exploiting the information retrieved from these comprehensive data sets and implementing proper instrumental variable strategies, we estimate the contribution of workers’ diversity in cultural background, education and demographic characteristics to valuable firm’s innovation activity. Specifically, we find evidence supporting the hypothesis that ethnic diversity may facilitate firms’ patenting activity in several ways by (a) increasing the propensity to (apply for a) patent, (b) increasing the overall number of patent applications, and (c) by enlarging the breadth of patenting technological fields, conditional on patenting. Several robustness checks corroborate the main findings.

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  1. See next section for a brief overview of the literature.

  2. Part of the statistics in REGNSKAB refers to selected firms for direct surveying: all firms with more than 50 employees or profits higher than a given threshold. The rest is recorded in accordance with a stratified sample strategy. The surveyed firms can choose whether to submit their annual accounts and other specifications or to fill out a questionnaire. In order to facilitate responding, questions are formulated in the same way as required in the Danish annual account legislation.

  3. The access to these data has been made possible, thanks to the Centre for Economic and Business Research (CEBR), an independent research center affiliated with the Copenhagen Business School (CBS).

  4. More details concerning the construction and composition of the data set can be found in Kaiser et al. (2008).

  5. The industries include agriculture, fishing, and quarrying; electricity, gas and water supply; sale and repair of motor vehicles; hotels and restaurants; transports; and public services.

  6. See Appendix 1 for more details about the countries belonging to each ethnic category.

  7. A previous literature argues that linguistic distance serves also as a proxy for cultural distance (Guiso et al. 2009; Adsera and Pytlikova 2012).

  8. Specifically, we use the official language spoken by majority in a given country of origin to link the country into groups by family of languages.

  9. The linguistic classification is more detailed than the grouping by nationality categories. Specifically, we group countries (their major official language spoken by the majority in a particular country) by the third linguistic tree level, e.g., Germanic West vs. Germanic North vs. Romance languages. The information on languages is provided by Adsera and Pytlikova (2012) based on the encyclopedia of languages Ethnologue: Languages of the World; see the Appendix 1 section for more details about the list of countries and the linguistic groups included. Furthermore, we adjust the index to take account of the firm size. Specifically, we standardize the index for a maximum value equal to \(\left (1-\frac {1}{N}\right )\) when the total number of employees (N) is lower than the number of linguistic groups (S).

  10. Section 4.3 describes how the discounted stock of patent applications is calculated.

  11. The Ministerial Order on the Statistics Denmark Act requires every employer in Denmark to report annually an occupational classification code for each of its full-time employees, following the DISCO. The DISCO is the Danish version of the ILO’s International Standard Classification of Occupations (ISCO). Normally, the DISCO code reporting to Statistics Denmark takes place directly through the company’s electronic salary systems. Over the sample period, the DISCO codes have been updated regularly, with some codes being eliminated and some new codes being created. Of obvious concern is therefore the possibility of spurious changes in the DISCO codes assigned to workers who experience no real change in their occupations. We believe, however, that our analysis is largely free from such spurious changes in the DISCO codes, as we base our main analysis on one-digit classification. As shown by Frederiksen and Kato (2011), over the 1992–2002 period, reassuringly at the one-digit level, there was no new code added.

  12. In the aggregate specification, for example, the correlations between ethnic (demographic) diversity and the shares of workers from each ethnic (demographic) group are always below 0.30. The correlations between educational diversity and the shares of workers with either tertiary or secondary education are below 0.20.

  13. The so-called functional economic regions or commuting areas are identified using a specific algorithm based on the following two criteria: firstly, a group of municipalities constitute a commuting area if the interaction within the group of municipalities is high compared to the interaction with other areas; secondly, at least one municipality in the area must be a center, i.e., a certain share of the employees living in the municipality must work in the municipality, too (Andersen 2000). In total, 104 commuting areas are identified.

  14. Unfortunately, in our data set, it is not possible to observe in which area each establishment of a multi-establishment firm is located. For multi-establishments firms, the information about the location is only provided at the headquarter level. However, we do not think that this represents a serious problem as multi-establishments firms constitute only 11 % of our sample. It is also important to note that most of firms included in our estimation sample remain in the same commuting area over the estimation period (1995–2003).

  15. We chose the year 1990 as a historical base for our predictions because we believe that the lag of 5–13 years should be a sufficient lag for the purposes of our IV construction. In addition, the development in immigration to Denmark also supports the choice. The 1980s and 1990s were characterized by rather restrictive immigration policy with respect to economic migrants from countries outside the European Union (EU), which made it rather difficult for firms in Denmark to hire applicants from the international pool of applicants (due to consequences of the Oil Crisis). Immigration to Denmark from those countries during the 1980s until mid-1990s was rather characterized by immigration on the basis of humanitarian reasons and family reunion. However, Denmark since then has further tightened its immigration policy (even laws concerning family reunification and asylum). In particular, since the 2001 election, in which the right-wing Danish People’s Party (DF) with its anti-immigration agenda acquired a significant political power, the immigration policy in Denmark became one of the strictest in the world. For firms, it meant almost no possibilities to hire international workers from countries outside the EU, which has often been criticized by the Confederation of Danish Industry (DI). Given those historical developments, we decided to use shares of immigrants from 1990 as a base for our predictions.

  16. In the case of Denmark, there was also a special dispersal policy implemented for refugees between years 1986 and 1998 by the Danish authorities. The dispersal policy implied that new refugees were randomly distributed across locations in Denmark (see, e.g., Damm (2009)). This fact as well supports the validity of our instrument because the refugees, as a part of international migrants to Denmark, were not driven by the firm innovation outcomes when settling, but by those dispersal policies or by the migrant networks. In addition, the inflows of economic migrants are driven by push and pull factors of destination and origin countries, costs of migration and other bilateral relationships between the origins and destinations (Pedersen et al. 2008; Ortega and Peri 2009). We believe that those migration determinants are unlikely to be correlated with a firm’s innovation outcomes.

  17. The error term u i t is added to allow for endogeneity. It also induces overdispersion, so that the Poisson model and the negative binomial model are empirically equivalent.

  18. This assumption means that ε is a common latent factor that affects both diversity and patent applications and is the only source of dependence between them, after controlling for the influence of the observed variables.

  19. The index of occupational diversity is constructed on the first-digit classification code of occupation (DISCO) and is based on the Herfindahl index. Its sample mean and standard deviation are 0.692 and 0.229.

  20. These figures are obtained using the average probability of innovating. From the estimates in Table 2, the average probability of innovating is around 0.03. Therefore, the changes in the probability of innovating, in percentage terms, are (0.2/0.03) \(=\) 6.66.

  21. Results obtained from the specifications with single diversity dimensions are very similar to the ones reported in columns 5, 6, and 7 and are available on request from the authors.

  22. Negative binomial models provide very similar results which are available on request from the authors.

  23. The results from the first stage are reported in Table 9 of Appendix 3.

  24. We have also investigated whether the effects of a particular dimension of diversity can be influenced by other forms of labor heterogeneity by inclusion of all possible interaction couples between the diversity indexes. Furthermore, driven by the hypothesis that there might be complementarities among different skills and demographic groups, in particular young and educated workers together with a more diverse workforce that can stimulate innovation and creativity, we have augmented our models with interactions between diversity indexes and shares of highly skilled and younger workers. Nevertheless, none of the interactions turned out to be statistically significant. Figures showing marginal effects of the interactions are available from the authors upon request.

  25. From the estimates in Table 4, the average probability of patenting in different technological areas is around 0.18. Therefore, the changes in the corresponding probability, in percentage terms are (14/0.18) = 77.

  26. The results using the aggregate indexes are qualitatively similar to the detailed categorization and are reported in Tables 10, 11, and 12 of Appendix 3.

  27. The \(\chi ^{2}\) (p value) are, respectively, 29.64 (0.000), 4.79; 0.047: 34.78 (0.000) in the regressions for firm probability to innovate, number of patent applications, and firm probability of applying for a patent in different technological areas.

  28. We use the following formula: \(d_{ij}=6378.7*a\cos \{\sin ({\rm {lat}}_{i}/57.2958)*\sin ({\rm {lat}}_{j}/57.2958)+ +\cos ({\rm {lat}}_{i}/57.2958)*\cos ({\rm {lat}}_{j}/57.2958)*\cos ({\rm {long}}_{j}/57.2958-lon_{i}/57.2958)\).


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We thank Guglielmo Barone, Tor Eriksson, Hideo Owan, Pekka Ilmakunnas, Michael Rosholm, Chad Syverson, and Mns Soderbom (alphabetical order) for helpful suggestions. In addition, we appreciate comments from participants at seminars organized by the Copenhagen Business School, University of Bergamo, Aarhus School of Business, University of Lausanne; and from participants at the following conferences: ESPE 2010, The 5th Nordic Summer Institute in Labor Economics, The 2010 Ratio Young Scientist Colloquium, ESEM 2010, CAED/COST 2010 in London, the 2010 International Symposium on Contemporary Labor Economics at WISE, Xiamen, and EALE 2011 in Paphos. We also thank Ulrich Kaiser and Cedric Schneider for graciously providing us the data on patent applications. Pierpaolo Parrotta acknowledges the financial support from the Swiss National Centre of Competence in Research LIVES and Graduate School for Integration, Production and Welfare. Mariola Pytlikova gratefully acknowledges funding from the NORFACE programme on “Migration in Europe—Social, Economic, Cultural and Policy Dynamics” (project MI3, “Migration: Integration, Impact and Interaction”) and funding from the Czech Science Foundation (project No. 402/11/2464 “Measuring Discrimination by Gender”). The usual disclaimer applies.

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Correspondence to Mariola Pytlikova.

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Appendix 1: Groups included in the measure of ethnic diversity

1. The citizens in the different nationality groups are the following: Danish: Danish native including second-generation immigrants; North America and Oceania: USA, Canada, Australia, New Zealand; Central and South America: Guatemala, Belize, Costa Rica, Honduras, Panama, El Salvador, Nicaragua, Venezuela, Ecuador, Peru, Bolivia, Chile, Argentina, and Brazil; formerly communist countries: Armenia, Belarus, Estonia, Georgia, Latvia, Lithuania, Moldova, Russia, Tajikistan, Ukraine, Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovakia, Albania, Bosnia and Herzegovina, Croatia, Rep. of Macedonia, Montenegro, Serbia, and Slovenia; Muslim countries: Afghanistan, Algeria, Arab Emirates, Azerbaijan, Bahrain, Bangladesh, Brunei Darussalem, Burkina Faso, Camoros, Chad, Djibouti, Egypt, Eritrea, Gambia, Guinea, Indonesia, Iran, Iraq, Jordan, Kazakstan, Kirgizstan, Kuwait, Lebanon, Libyan Arab Jamahiriya, Malaysia, Maldives, Mali, Mauritania, Morocco, Nigeria, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Senegal, Sierra Leone, Somalia, Sudan, Syria, Tadzhikstan, Tunisia, Turkey, Turkmenistan, Uzbekistan, and Yemen; East Asia: China, Hong Kong, Japan, Korea, Dem. People’s Rep. of Korea, Macao, Mongolia, and Taiwan; Asia: all the other Asian countries not included in both East Asia and Muslim country categories; Africa: all the other African countries not included in the Muslim country; Western and Southern Europe: all the other European countries not included in the formerly communist country category.

2. Using linguistic grouping: Germanic West (Antigua Barbuda, Aruba, Australia, Austria, Bahamas, Barbados, Belgium, Belize, Bermuda, Botswana, Brunei, Cameroon, Canada, Cook Islands, Dominica, Eritrea, Gambia, Germany, Ghana, Grenada, Guyana, Haiti, Ireland, Jamaica, Liberia, Liechtenstein, Luxemburg, Mauritius, Namibia, Netherlands, Netherlands Antilles, New Zealand, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and Grenadines, Seychelles, Sierra Leone, Solomon Islands, South Africa, St. Helena, Suriname, Switzerland, Trinidad and Tobago, Uganda, UK, USA, Zambia, and Zimbabwe), Germanic Nord (Denmark, Iceland, Norway, and Sweden), Slavic West (Czech Republic, Poland, Slovakia), Slavic South (Bosnia and Herzegovina, Croatia, Serbia, and Slovenia), Slavic East (Belarus, Georgia, Mongolia, Russian Federation, and Ukraine), Baltic East (Latvia and Lithuania), Finno-Permic (Finland and Estonia), Ugric (Hungary), Romance (Andorra, Angola, Argentina, Benin, Bolivia, Brazil, Burkina Faso, Cape Verde, Chile, Columbia, Costa Rica, Cote D’Ivoire, Cuba, Djibouti, Dominican Republic, Ecuador, El Salvador, Equatorial Guinea, France, French Guina, Gabon, Guadeloupe, Guatemala, Guinea, Guinea Bissau, Holy See, Honduras, Italy, Macau, Martinique, Mexico, Moldova, Mozambique, Nicaragua, Panama, Peru, Portugal, Puerto Rico, Reunion, Romania, San Marino, Sao Tome, Senegal, Spain, Uruguay, and Venezuela), Attic (Cyprus and Greece), Turkic South (Azerbaijan, Turkey, and Turkmenistan), Turkic West (Kazakhstan and Kyrgystan), Turkic East (Uzbekistan), Gheg (Albania, Kosovo, Republic of Macedonia, and Montenegro), Semitic Central (Algeria, Bahrain, Comoros, Chad, Egypt, Iraq, Israel, Jordan, Kuwait, Lebanon, Lybian Arab Jamahiria, Malta, Mauritiania, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, Yemen, and United Arabs Emirates), Indo-Aryan (Bangladesh, Fiji, India, Maldives, Nepal, Pakistan, and Sri Lanka), Mon-Khmer East (Cambodia), Semitic South (Ethiopia), Malayo-Polynesian West (Indonesia and Philippines), Malayo-Polynesian Central East (Kiribati, Marshall Islands, Nauru, Samoa, and Tonga), Iranian (Afghanistan, Iran, and Tajikistan), Betai (Laos and Thailand), Malayic (Malasya), Cushitic East (Somalia), Viet-Muong (Vietnam), Volta-Congo (Burundi, Congo, Kenya, Lesotho, Malawi, Nigeria, Rwanda, Swaziland, Tanzania, and Togo), Barito (Madagascar), Mande West (Mali), Lolo-Burmese (Burma), Chadic West (Niger), Guarani (Paraguay), Himalayish (Buthan), Armenian (Armenia), Sino Tibetan (China, Hong Kong, Singapore, and Taiwan), Japonic (Japan, Republic of Korea, and Korea D.P.R.O.).

Appendix 2: External knowledge indexes

The main literature on agglomeration economies emphasizes the importance of firm’s local environment, which may reflect information advantages, labor, or other inputs pooling and further beneficial network effects aimed at alleviating the burden represented by fixed costs. A seminal contribution in this field is due to Audretsch and Feldman (1996), who find that industries characterized by elevated R&D intensity or particularly skilled labor forces present a greater degree of geographic concentration of production. Other relevant studies like those of Wallsten (2001) and Adams and Jaffe (1996) provide evidence of the geographic extent of knowledge spillovers by computing the distance in miles between each firm pair. However, the geography is not the only dimension of the external knowledge. In fact, there exists at least another approach which focuses on the concept of technological proximity (Jaffe 1986; Adams 1990). Specifically, the idea that the technology developed by a firm can affect other firms, even though they are not geographically close or no transactions of goods occur between them, has led to the definition of technological proximity as closeness between firm-pairs’ technological profiles.

Following both the cited approaches, we construct two indexes of knowledge spillovers. These are weighted sums of firms’ codified knowledge proxied by the discounted stock of patent applications. The weighting function for the first index refers to the geographical distance between pairs of workplaces’ municipalities and is computed by using the firms’ latitude and longitude coordinates (the address of their headquarters). Specifically, assuming a spherical earth of actual earth volume, this method allows us to measure the distance in kilometers between any pair of firms i and j.Footnote 28 The first knowledge spillover index is then computed as follows:

$$ K\_{\rm{geo}}_{it}=\frac{1}{e^{{\rm{dist}}_{ij}}}\sum\limits_{j\neq i}^{I}{\rm{disc}}\_{\rm{stock}}_{jt}\:. $$

The second index is instead based on the technological proximity. Following Adams (1990), we use the shares of differently skilled workers to define our alternative weighting function \(\psi _{ij}\) that is the uncentered correlation:

$$ \psi_{ij}=\frac{f_{i}f_{j}^{'}}{\left[\left(f_{i}f_{i}^{'}\right)\left(f_{j}f_{j}^{'}\right)\right]^{1/2}}\:. $$

The components of the generator vector f reflects firm’s workforce composition in terms of skills using the disaggregated categorization as described in Section 3.1. The second measure of knowledge spillover pool is therefore defined as follows:

$$ K\_{\rm{tech}}_{it}=\psi_{ij}\sum\limits_{j\neq i}^{I}{\rm{disc}}\_{\rm{stock}}_{jt}\:. $$

Thus, both K_geo it and K_tech ij contain weighting functions that might capture the so-called firm’s absorptive capacity, which is the ability to identify and exploit the knowledge externally produced (Cohen and Levinthal 1990).

Appendix 3: Additional results

Table 8 SUR estimates of the IV first step for the probability to innovate
Table 9 Estimates of the IV first step for the number of patent applications
Table 10 The effects of labor diversity on firm innovation and the mechanisms involved
Table 11 The effects of labor diversity on firm innovation and robustness checks
Table 12 The effects of labor diversity on firm innovation and further robustness checks

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Parrotta, P., Pozzoli, D. & Pytlikova, M. The nexus between labor diversity and firm’s innovation. J Popul Econ 27, 303–364 (2014).

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