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Effect of Formal and Informal Institutional Indicators on Innovation Activities: An Empirical Analysis for a Global Sample

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Abstract

In the current society, institutional quality is essential to innovation activity, as formal and informal institutions have a positive impact on innovation. Formal institutional quality establishes the intellectual property rights culture that lowers the transaction costs, required to stimulate the innovation process. Strengthen informal institutions inseminate mutual trust and cooperation, necessary to share the previous knowledge, which is mandatory to invent new ideas. Thus, this study aims to investigate the effect of formal and informal institutions and their indicators on innovation activities. We construct the indices to measure the quality of formal and informal institutions using data on 73 countries for the period 1981–2014. The system GMM estimation technique is used to predict the performance of formal and informal institutions on innovation activities. Besides, this study estimates the effect of each indicator of formal as well as informal institutions on innovations after controlling for FDI, human capital, trade and government size. The hypotheses of positive effects of formal and informal institutional quality is accepted. The size of the effect of different indicators of institutions are varying. The results are robust across developed and develo** countries. From a policy perspective, this study suggests that more focused monitoring of institutions is required to improve their quality that ultimately effects innovation activities.

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Notes

  1. The indicators of formal institutions are government stability, socioeconomic conditions, democratic accountability, bureaucracy quality, law & order and control on corruption. The indicators for informal institutions are trust, respect, self-control, thrift and internet users. These indicators are selected from theoretical literature and detail of each indicator and their method to construct are presented in Sect. 3.

  2. Acemoglu et al. (2003) identified the problems with empirical studies such as endogeneity, measurement errors and omitted variables bias that cannot be properly addressed by the OLS technique.

  3. For detail, see Williamson (1985), North (1991), Tebaldi and Elmslie (2013) and Lee and Law (2017).

  4. Web link to WVS is https://www.worldvaluessurvey.org/WVSEVSjoint2017.jsp, and WDI from World Bank is https://databank.worldbank.org/source/world-development-indicators#.

  5. Because WVS consists of 6 waves—covering the period of 1981–2014. A new 7th wave of WVS is expected to arrive in 2021 but it is unlikely to get a common sample from all waves. For example, we can find one variable from one wave but the similar variable is not available for the same group of countries in another wave.

  6. For detail, see Johnson and Wichern (2002). With reference to this study, the discussion about PCA and its procedure is added in the Appendix (see, Appendix 2).

  7. Web link to ICRG data source is https://guides.tricolib.brynmawr.edu/icrg#s-lg-box-5809747.

  8. For correlation efficient matrices, see Appendix Tables 8, 9 and 10.

  9. For detailed individual institutions’ indicators and sensitivity analysis, see Appendix Table 11.

  10. Countries are divided as per World Bank categorization; low income and lower-middle income countries are combined into one group-develo** countries, whereas upper middle income and high-income nations are grouped as developed countries.

  11. The system GMM estimates of control variables are not presented in the Tables 6 and 7, which are available on request.

  12. A new 7th wave of WVS is expected to arrive in 2021 but again it is unlikely to get a common set of variables for global sample. For example, we can find one variable from one wave but the similar variable is not available for the same group of countries in another wave.

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Authors and Affiliations

  1. Aarhus University, BTECH, Herning, Denmark

    Amjad Naveed

  2. Forman Christian College (A Chartered University), Lahore, Pakistan

    Ghulam Shabbir

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  1. Amjad Naveed
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Appendices

Appendix 1: Tables

See Tables 8, 9, 10 and 11.

Table 8 Correlation matrix informal institution and its sub-indicators
Full size table
Table 9 Correlation matrix of formal institution and its sub-indicators
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Table 10 Correlation matrix control variables
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Table 11 Impact of formal institution on innovation
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Appendix 2: Supplementary Material

2.1 PCA (Principle Component Analysis) Method

Principal Components Analysis (PCA) method has been used extensively to construct composite indices by grou** a large number of indicators (see e.g., Lai, 2003; Vyas & Kumaranayake, 2006; Havard et al., 2008). According to Johnson and Wichern (2002), PCA is constructed through the explanation of a variance–covariance structure of different variables that is based on linear combinations of these variables. Further they describes two purposes of PCA that are data reduction and interpretation. This study uses PCA technique to construct two composite indices for formal and informal institutional qualities, which are based on different indicators (as explained in Table 1 main text). Using standardized data of all the indicators, the principal components are obtained by a series of equations representing linear transformations of the original variables. The maximum variance of the original variables is explained with the first principal components that have highest eigenvalues for both formal and informal institutions and also these explain maximum variation of the original variables (for detail, see Johnson & Wichern, 2002).

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Naveed, A., Shabbir, G. Effect of Formal and Informal Institutional Indicators on Innovation Activities: An Empirical Analysis for a Global Sample. Soc Indic Res 164, 665–691 (2022). https://doi.org/10.1007/s11205-022-02975-w

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