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Endogenous intangible resources and their place in the institutional hierarchy

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Abstract

This paper seeks to find evidence for the impact of local culture on the share of intangible investments and the results for productivity per worker in the EU15 countries at the NUTS2 level during the period 2000–2008. The main scope of the paper is to explore the nature and consequences of intangible resources and their hierarchical relationship with culture, thus seeking to identify the genuine endogenous sources of local productivity beyond the standard Romer-type of models. In our study we use basic economic and social indicators from two main sources: the EUROSTAT Regional Database and the European Social Survey (ESS). Regression analysis, based on a pooled cross-section and on a balanced panel through a generalized method of moments (GMM) approach, shows evidence in support of: (i) the classical Tiebout hypothesis on cultural dependence of local public goods in the case of three different types of intangible investments: education, health care, and research and development; (ii) the impact of intangible investments on local productivity which confirms their treatment as investments rather than their usual treatment as a spending category in the national accounts. Clearly, the data set used has its limitations, but what remains most noticeable is the added value of the methodologically alternative measures of local culture employed in our analysis, i.e. a novel cultural attitudes Herfindahl Index, a cultural attitudes Segregation Index, and a CBD (Culture-Based Development) two-vector approach (living culture and cultural heritage). The results generated depict a confirmative picture of the hierarchical relationship between intangible investments, culture and productivity at the regional scale in Europe.

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Notes

  1. In another stream of literature this was explained as the result of a Median Voter preference in the locality, i.e. a locally-specific aggregate choice.

  2. This dependence is captured in the CBD notion of cultural gravity. For more details, please see Tubadji and Nijkamp (2015c).

  3. The CBD framework, in general and in this particular case, is always an augmentation and opening of the neo-classical paradigm to additional important previously under-considered or completely overlooked factors. Therefore, we use a model for tapping on tangible capital, by adding to it the tapping on intangible capital as well. Thus a complete, more realistic model is achieved.

  4. The list of intangible investment starts with these three most essential types but is by no means exhausted by them. As clarified in the Introduction, intangible investment, is defined by any public good or mixed-good creating investment. As Sect. 2 demonstrated however, even these three main types of intangible resources, though more or less established as understanding to be essential, are far from being dealt with in a systematic structured way together as intangible investments as special type of investment. The aim of our current work is to illustrate conceptually and empirically the hierarchy between culture and intangible investments per se, by approximating it with the hierarchical relationship between culture and these three main types of intangible investments. This we hope both fills in a gap in the literature in viewing them together as a common type of investment and also paves the road for the understanding of the dependence and treatment of such types of investments in relation to culture.

  5. Like World Value Survey, the ESS dataset has a wealth of cultural attitudes addressed in its questionnaires. The ESS data however, is gathered every second year and continues to augment as a database ever since 2000. Therefore, in spite of its limitations of being representative only on national level, with careful statistical transformations, this data can be brought to being useful for certain quantitative approximation of cultural attitudes across the European regions. In our study, we acknowledge the limitations of the data and do not overemphasize on its interpretative significance. Yet, we use it to demonstrate the operationalization of our CBD conceptual propositions and in specific the novel methodological approach for computing a cultural Herfindahl Index and a cultural Segregation Index.

  6. Clearly, the best variable here would be wages. This data however is not available. The only available closest approximation, with all theoretical and empirical limitations that it is subject to, is this variable—income.

  7. The shares are formed by the available number of people sharing a particular attitude and the total number of people interviewed on NUTS2 level in the locality. The limitations of this approximation are accounted for and therefore, we do not involve the cultural variables in the second causal inquiry part of the study.

  8. We also considered the exploration of two additional measures of cultural diversity—called the fault lines approach (see Dawson 2010) and the Hurst Index (as used in a Tiebout Hypothesis estimation context in Kubiliusa and Mishurab 2012). For both measures, however, no adequate data was found to be available.

  9. As the GMM estimation can handle only models of one equation, we have to express the simultaneous equation model (5) into a model of one equation that is a most close approximation of it. To do so, we take the lagged Y variable from the first equation in model (5) which brings an important endogeneity component in it and has also a logical place from estimation methodology point of view as a lagged outcome variable. We also do not have the cultural variables here. So we do not face collinearity, as the intangible investment II is dependent exogenously only on the cultural factor. Moreover, we do not have a variable to approximate the quality of human capital, therefore we substitute it in equation three with its formants from equation two of model (5). Namely, we use II and W as regressants in our model (10) to express the QHC, which is otherwise unquantifiable with our data. Finally, actually we u the standard model for tapping on local economic capitals—equation three of model (5) and just augment it with the relevant variables from equation one—the lagged Y, and from Eq. 2– the formants of QHC—II and W and thus we arrive at our testable with GMM method model (10). K is not available as a measure, but our control variable for economic structure are used to counter balance for that.

  10. Cambridge mixed method for triangulation. See: Downward and Mearman (2007).

  11. See Appendix (Table 8) for a summary table of the variables used in the estimations that follow.

  12. The Hurst Index was already employed in the context of the Tiebout Hypothesis in Kubiliusa and Mishurab (2012).

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Authors and Affiliations

  1. University of the Aegean, Chios, Greece

    Annie Tubadji &  Vassilis Angelis

  2. VU University, Amsterdam, The Netherlands

    Peter Nijkamp

  3. A. Mickiewicz University, Poznan, Poland

    Peter Nijkamp

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  1. Annie Tubadji
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  2. Vassilis Angelis
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  3. Peter Nijkamp
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Correspondence to Annie Tubadji.

Appendix

Appendix

Table 8 Table of main dependent and explanatory variables used in the empirical analysis
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Tubadji, A., Angelis, V. & Nijkamp, P. Endogenous intangible resources and their place in the institutional hierarchy. Rev Reg Res 36, 1–28 (2016). https://doi.org/10.1007/s10037-015-0097-5

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