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Insurance Market and Economic Growth in an Information-Driven Economy: Evidence from a Panel of High- and Middle-Income Countries?

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Abstract

The main focus of this investigation is potential Granger causal relationships between the insurance market, Information and Communications Technology (ICT) infrastructure, and economic growth in a sample of high- and middle-income countries (H&MICs) from 1980 to 2019. We deployed a panel vector autoregressive model, and found that in the long run, the insurance market and ICT infrastructure Granger-cause economic growth. In the short run, we found robust causal links, but they vary in nature. The findings suggest that H&MICs should base ICT infrastructure planning on strategies that endorse economic growth and policies that may also promote insurance market development.

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Source: Modified from Benlagha and Hemrit (2020)

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Notes

  1. Insur-Tech is a set of new technologies which can potentially contribute to innovation in the insurance sector and alter the regulatory provisions regarding insurance markets (OECD, 2017).

  2. Some of these studies link ICT infrastructure with financial development in particular, but coverage of the ICT-insurance market nexus is more limited.

  3. Note that PCA is not intended to search for causal relationships; rather, it explores interdependence between variables, without identifying the nature of the causal relationships. We used it to reduce a large set of related variables into a smaller set of independent variables (Stock and Watson, 2002). Additionally, to remove the dynamic bias, we have used the time-demean approach of PCA by Bai and Ng (2013), and Bai and Li (2012). Hence, there is no possible bias in the formation of both CIN and CIC.

  4. This is a common problem in the panel data, because the countries are interlinked at regional and global levels. If we do not control for this problem, then the estimators will be inconsistent and biased (Phillips and Sul, 2003).

  5. We chose these panel unit root tests based on the CD test results, which reject the null hypothesis of no cross-sectional dependence at the 1% significance level (see Table 8), and confirm that cross-sectional dependence is present in the panel.

  6. We do not report the estimates of IRFs here for the sake of brevity, but they may be obtained from the authors on request.

References

  • Ahmad, M., Majeed, A., Khan, M.A., Sohaib, M., and Shehzad, K. (2021). Digital financial inclusion and economic growth: provincial data analysis of china. China Economic Journal (forthcoming).

  • Alhassan, A. L., & Biekpe, N. (2016). Insurance market development and economic growth: Exploring causality in 8 selected African countries. International Journal of Social Economics, 43(3), 321–339.

    Article  Google Scholar 

  • Anargiridou, D. C., Anargiridis, S. C., & Papadopoulos, D. L. (2007). A critical examination of the advantages and disadvantages of the E-Insurance and how they explain its small penetration in the Greek market. Operational Research, 7(3), 465–480.

    Article  Google Scholar 

  • Apergis, N., & Poufinas, T. (2020). The role of insurance growth in economic growth: fresh evidence from a panel of OECD countries. North American Journal of Economics and Finance, 53(C), 1–13.

    Google Scholar 

  • Appiah-Otoo, I., & Song, N. (2021). The impact of ICT on economic growth – comparing rich and poor countries. Telecommunications Policy, 45, 1–15.

    Article  Google Scholar 

  • Arena, M. (2008). Does insurance market activity promote economic growth? A cross-country study for industrialized and developing countries. Journal of Risk and Insurance, 75(4), 921–946.

    Article  Google Scholar 

  • Arvin, M. B., Pradhan, R. P., & Nair, M. (2021). Uncovering interlinks among ICT connectivity and penetration, trade Openness, foreign direct investment, and economic growth: The case of the G-20 countries. Telematics and Informatics, 60, 1–25.

    Article  Google Scholar 

  • Asongu, S. A., & Odhiambo, N. M. (2019). Enhancing ICT for Insurance in Africa. MPRA, 102060, 1–21.

    Google Scholar 

  • Asongu, S. A., & Odhiambo, N. M. (2020). Insurance policy thresholds for economic growth in Africa. European Journal of Development Research, 32, 672–689.

    Article  Google Scholar 

  • Bai, J., & Li, K. (2012). Statistical analysis of factor models of high dimension. Annals of Statistics, 40, 436–465.

    Article  Google Scholar 

  • Bai, J., & Ng, S. (2013). Principal Components Estimation and Identification of Static factors. Journal of Econometrics, 176, 18–29.

    Article  Google Scholar 

  • Balcilar, M., Gupta, R., Lee, C., & Olasehinde-Williams, G. (2020). Insurance and economic policy uncertainty. Research in International Business and Finance, 54, 1–13.

    Article  Google Scholar 

  • Bazini, E., & Madani, F. (2015). ICT application in the insurance industry: its impact in customer relationship management. Academic Journal of Interdisciplinary Studies, 4(3), 307–311.

    Google Scholar 

  • Benlagha, N., & Hemrit, W. (2020). Internet use and insurance growth: evidence from a panel of OECD countries. Technology in Society, 62, 1–13.

    Article  Google Scholar 

  • Chang, T., Lee, C. C., & Chang, C. H. (2014). Does insurance activity promote economic growth? Further evidence based on bootstrap panel granger causality test. European Journal of Finance, 20(12), 1187–1210.

    Article  Google Scholar 

  • Chen, P. F., Lee, C. C., & Lee, C. F. (2012). How does the development of life insurance market affect economic growth? Some international evidence. Journal of International Development, 24(7), 865–889.

    Article  Google Scholar 

  • Cronin, F. J., Parker, E. B., Colleran, E. K., & Gold, M. A. (1991). Telecommunications infrastructure and economic growth: An analysis of causality. Telecommunications Policy, 15(6), 529–535.

    Article  Google Scholar 

  • Dash, S., Pradhan, R. P., Jayakumar, M., Maradana, R., Zaki, D., Chatterjee, D. (2017). Insurance market penetration and economic growth in eurozone countries: Time series evidence on causality. Future Business Journal, 4(1), 50–67.

    Article  Google Scholar 

  • Dash, S., Pradhan, R. P., Maradana, R. P., Gaurav, K., & Jayakumar, M. (2020). Impact of banking sector development on insurance market-growth nexus: The study of Eurozone countries. Empirica, 47(2), 205–243.

    Article  Google Scholar 

  • Din, S. M. U., Regupathi, A., & Abu-Bakar, A. (2017). Insurance effect on economic growth—among economies in various phases of development. Review of International Business and Strategy, 27(4), 501–519.

    Article  Google Scholar 

  • Dutta, A. (2001). Telecommunications and economic activity: An analysis of granger causality. Journal of Management Information Systems, 17(4), 71–95.

    Article  Google Scholar 

  • Gabor, D., & Brooks, S. (2017). The digital revolution in financial inclusion: International development in the Fintech era. New Political Economy, 22(4), 423–436.

    Article  Google Scholar 

  • Gonzalez, M.R., Wegener, C., and Basse, T. (2021). Re-investigating the insurance-growth nexus using common factors. Finance Research Letters (forthcoming).

  • Gupta, R., Lahiani, A., Lee, C. C., & Lee, C. C. (2019). Asymmetric dynamics of insurance premium: the impacts of output and economic policy uncertainty. Empirical Economics, 57, 1959–1978.

    Article  Google Scholar 

  • Haiss, P. R., & Sumegi, K. (2008). The relationship between insurance and economic growth in Europe: A theoretical and empirical analysis. Empirica, 35(4), 405–431.

    Article  Google Scholar 

  • Han, L., Li, D., Moshirian, F. and Tian, Y. (2010) Insurance development and economic growth. The Geneva Papers on Risk and Insurance, 35(2), 183–199.

    Google Scholar 

  • Ibrahim, M., Sare, Y. A., & Adam, I. O. (2021). An application of frequency domain approach to the causal nexus between information, communication and technology infrastructure and financial development in selected countries in Africa. International Journal of Finance and Economics, 26, 1206–1235.

    Article  Google Scholar 

  • Lee, C. C., Chang, C. H., Arouri, M., & Lee, C. C. (2016). Economic growth and insurance development: the role of institutional environments. Economic Modelling, 59, 361–369.

    Article  Google Scholar 

  • Lee, C. C., Huang, W. L., & Yin, C. H. (2013a). The Dynamic Interactions among the Stock, Bond and Insurance Markets. North American Journal of Economics and Finance, 26, 28–52Lee, C.C., and Lee, C.C. (2020). Insurance Activity, Real Output, and Geopolitical Risk: Fresh Evidence from BRICS. Economic Modelling, 92, 207–215.

    Article  Google Scholar 

  • Lee, C. C., & Lee, C. C. (2020). Insurance activity, real output, and geopolitical risk: fresh evidence from BRICS. Economic Modelling, 92(207), 215.

    Google Scholar 

  • Lee, C. C., Lee, C. C., & Chiu, Y. B. (2013b). The link between life insurance activities and economic growth: Some new evidence. Journal of International Money and Finance, 32(3), 405–427.

    Article  Google Scholar 

  • Lee, S. T., Gholami, R., & Tong, T. Y. (2005). Time series analysis in the assessment of ICT impact at the aggregate level – lessons and implications for the new economy. Information & Management, 42(7), 1009–1022.

    Article  Google Scholar 

  • Lee, S. H., Levendis, J., & Gutierrez, L. (2012). Telecommunications and economic growth: An empirical analysis of Sub-Saharan Africa. Applied Economics, 44(4), 461–469.

    Article  Google Scholar 

  • Liu, G. C., Lee, C., & Lee, C. C. (2016). The nexus between insurance activity and economic growth: a bootstrap rolling window approach. International Review of Economics and Finance, 43, 299–319.

    Article  Google Scholar 

  • Mark, N., & Sul, D. (2003). Cointegration vector estimation by panel DOLS and long-run money demand. Oxford Bulletin of Economics and Statistics, 65(5), 655–680.

    Article  Google Scholar 

  • Mehmood, B., & Siddiqui, W. (2013). What causes what? Panel cointegration approach on investment in telecommunication and economic growth: Case of Asian countries. Romanian Economic Journal, 16(47), 3–16.

    Google Scholar 

  • Nair, M., Pradhan, R. P., & Arvin, M. B. (2020). Endogenous Dynamics between R&D, ICT and economic growth: Empirical evidence from the OECD countries. Technology in Society, 62(C), 1–12.

    Google Scholar 

  • OECD. (2017). Technology and Innovation in the Insurance Sector. OECD. Available at www.oecd.org/daf/fin/insurance.

  • Outreville, J. F. (2013). The relationship between insurance and economic development: 85 empirical papers for a review of the literature. Risk Management and Insurance Review, 16(1), 71–122.

    Article  Google Scholar 

  • Owusu-Agyei, S., Okafor, G., Chijoke-Mgbame, A. M., Ohalehi, P., & Hasan, F. (2020). Internet adoption and financial development in Sub-Saharan Africa. Technological Forecasting and Social Change, 161, 1–13.

    Article  Google Scholar 

  • Peleckienė, V., Peleckis, K., Dudzevičiūtė, G., & Peleckis, K. K. (2019). The relationship between insurance and economic growth: Evidence from the European Union countries. Economic Research-Ekonomska Istraživanja, 32(1), 1138–1151.

    Article  Google Scholar 

  • Phillips, P., & Sul, D. (2003). Dynamic panel estimation and homogeneity testing under cross section dependence. Econometrics Journal, 6, 217–259.

    Article  Google Scholar 

  • Pradhan, R.P., Bahmani, S., and Abraham, R. (2021b). Institutional quality, insurance markets, and economic growth: Evidence from pooled mean group estimation. Applied Economics Letters (forthcoming).

  • Pradhan, R. P., Arvin, M. B., Nair, M., Bennett, S. E. and Bahmani, S. (2022b). Some determinants and mechanics of economic growth in middle-income countries: The role of ICT infrastructure development, taxation and other macroeconomic variables. Singapore Economic Review (forthcoming).

  • Pradhan, R. P., Arvin, M. B., Bahmani, S., & Bennett, S. E. (2017a). Broadband penetration, financial development, and economic growth nexus: Evidence from the Arab league countries. Macroeconomics and Finance in Emerging Market Economies, 10(2), 151–171.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., Hall, J. H., & Bennett, S. E. (2018a). Mobile telephony, economic growth, financial development, foreign direct investment, and imports of ICT goods: The case of the G-20 countries. Economia e Politica Industriale, 45, 279–310.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., Hall, J. H., & Nair, M. (2016a). Insurance-growth nexus and macroeconomic determinants: evidence from middle-income countries. Empirical Economics, 52(4), 1337–1366.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., Nair, M., & Bennett, S. E. (2020). Unveiling the causal relationships among banking competition, stock and insurance market development, and economic growth in Europe. Structural Change and Economic Dynamics, 55, 74–87.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., Nair, M., Hall, J. H., & Bennett, S. E. (2021b). Sustainable economic development in India: The dynamics between financial inclusion, ICT development and economic growth. Technological Forecasting and Social Change, 169, 1–19.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., Nair, M., Hall, J., & Bennett, S. E. (2022a). Institutional development in an information-driven economy: Can ICTs enhance economic growth for low- and lower middle-income countries? Information Technology for Development, 28(3), 468–487.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., & Norman, N. R. (2015a). Insurance development and the finance-growth nexus: evidence from 34 OECD countries. Journal of Multinational Financial Management, 31(1), 1–22.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., & Norman, N. R. (2015b). The dynamics of information and communications technologies infrastructure, economic growth, and financial development: Evidence from Asian countries. Technology in Society, 42(1), 135–149.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., Norman, N. R., & Bele, S. K. (2014). Economic growth and the development of telecommunications infrastructure in the G-20 countries: A panel-VAR approach. Telecommunications Policy, 38(7), 634–649.

    Article  Google Scholar 

  • Pradhan, R. P., Arvin, M. B., Norman, R. R., Nair, M., & Hall, J. H. (2016b). Insurance penetration and economic growth nexus: Cross-country evidence from ASEAN. Research in International Business and Finance, 36, 447–458.

    Article  Google Scholar 

  • Pradhan, R. P., Dash, S., Maradana, R. P., Jayakumar, M., & Gaurav, K. (2017b). Insurance market density and economic growth in Eurozone countries: The granger causality approach. Financial Innovation, 3(17), 1–24.

    Google Scholar 

  • Pradhan, R. P., Mallik, G., & Bagchi, T. P. (2017). Information communication technology (ICT) infrastructure and economic growth: A causality evinced by cross-country panel data. IIMB Management Review, 30(1), 1–14.

    Google Scholar 

  • Pradhan, R. P., Mallik, G., & Bagchi, T. P. (2018b). Information communication technology (ICT) infrastructure and economic growth: A causality evinced by cross-country panel data. IIMB Management Review, 30, 91–103.

    Article  Google Scholar 

  • Pradhan, R. P., & Sahoo, P. P. (2020). are there links between financial inclusion, mobile telephony, and economic growth? Evidence from Indian states. Applied Economics Letters, 28(4), 310–314.

    Article  Google Scholar 

  • Rumson, A. G., & Hallett, S. H. (2019). Innovations in the use of data facilitating insurance as a resilience mechanism for coastal flood risk. Science of the Total Environment, 661, 598–612.

    Article  Google Scholar 

  • Salatin, P., Yadollahi, F., & Eslambolchi, S. (2014). The effect of ICT on insurance industry in selected countries. Research Journal of Economics, Business and ICT, 9(1), 1–5.

    Google Scholar 

  • Shiu, A., & Lam, P. L. (2008). Causal relationship between telecommunications and economic growth in China and its regions. Regional Studies, 42(5), 705–718.

    Article  Google Scholar 

  • Solomon, E. M., & van Klyton, A. (2020). The impact of digital technology usage on economic growth in Africa. Utilities Policy, 67, 1–12.

    Article  Google Scholar 

  • Stock, J. H., & Watson, M. W. (2002). Macroeconomic forecasting using diffusion indices. Journal of Business and Economic Statistics, 20, 147–162.

    Article  Google Scholar 

  • Vu, K. M. (2011). ICT as a source of economic growth in the information age: Empirical evidence from the 1996–2005 period. Telecommunications Policy, 35(4), 357–372.

    Article  Google Scholar 

  • Vu, K., Hanafizadeh, P., & Bohlin, E. (2020). ICT as driver of economic growth: A survey of the literature and directions for future research. Telecommunications Policy, 44, 1–20.

    Article  Google Scholar 

  • Ward, D., & Zurbruegg, R. (2000). Does insurance promote economic growth? Evidence from OECD countries. Journal of Risk and Insurance, 67(4), 489–506.

    Article  Google Scholar 

  • Ward, D., & Zurbruegg, R. (2002). Law, politics and life insurance consumption in Asia. Geneva Papers on Risk and Insurance-Issues and Practice, 27(3), 395–412.

    Article  Google Scholar 

  • Weisbart, S. (2018). How insurance drives economic growth. Working Paper, Insurance Information Institute, New York.

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Authors and Affiliations

  1. Vinod Gupta School of Management, Indian Institute of Technology Kharagpur, Kharagpur, WB, 721302, India

    Rudra P. Pradhan

  2. College of Business, University of Wisconsin-Parkside, Kenosha, WI, 53144, USA

    Sahar Bahmani

  3. College of Business and Entrepreneurship, Nova Southeastern University, Fort Lauderdale, FL, 33314-7796, USA

    Rebecca Abraham

  4. Department of Financial Management, University of Pretoria, Pretoria, 0028, South Africa

    John H. Hall

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  1. Rudra P. Pradhan
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  2. Sahar Bahmani
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Correspondence to Rudra P. Pradhan.

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Appendices

Appendix 1: The List of High- and Middle-Income Countries (H&MICs) in the Sample

1.1 Part A: High Income Countries

Australia, Austria, the Bahamas, Bahrain, Barbados, Belgium, Brunel Darussalam, Canada, Chile, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan. Kuwait, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, New Zealand, Norway, Oman, Poland, Portugal, Qatar, the Republic of Korea, Saudi Arabia, Singapore, the Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Taiwan, Trinidad and Tobago, the United Arab Emirates, the United Kingdom, the United States, and Uruguay.

1.2 Part B: Middle-Income Countries

Albania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Bhutan, Bolivia, Bosnia, Botswana, Brazil, Bulgaria, Cabo Verde, Cambodia, Cameroon, China, Colombia, Congo, Costa Rica, Cote d’Ivoire, Croatia, Cuba, Djibouti, the Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Fiji, Gabon, Georgia, Ghana, Guatemala, Guyana, Herzegovina, Honduras, India, Indonesia, Iran, Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, the Kyrgyz Republic, the Lao People’s Democratic Republic, Lebanon, Lesotho, Libya, Malaysia, the Maldives, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Montenegro, Morocco, Myanmar, Namibia, Nicaragua, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, the Philippines, Romania, the Russian Federation, Samoa, Sao Tome and Principe, the Solomon Islands, Serbia, South Africa, Sri Lanka, Sudan, Suriname, Swaziland, the Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Tunisia, Turkey, Ukraine, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, and Zambia.

Appendix 2: Devising Composite Index of ICT Infrastructure: Using PCA

Table 5 (Part A) shows that the maximum eigen value of the CIC index is 3.514 for the first factor, and it is 0.957, 0.795 0.394, 0.251 and 0.089 for the remaining five factors, respectively. The proportionate variation of the first factor is 58.6%, and it is 16.0%, 13.3%, 6.56%, 4.18%, and 1.48% for the remaining factors, respectively. Table 5, Part B, shows the eigen vectors signifying the six PC loadings (PC1–PC6). That means that the ICT indicator corresponding to PC1 is the only factor with an eigenvalue above 1, and it describes about 59% of the total variance. The remaining PCs’ contributions are fairly small, with eigenvalues much smaller than 1. Additionally, factor loadings are all positive in PC1, but have some negative values for the rest of the PCs. Hence, we considered PC1 factor loadings in building the CIC index. This is well-supported by the loading plots (see Figs. 4,

Fig. 5
figure 5

Scree Plot with Plots of Eigenvalue Difference and Eigenvalue Cumulative Proportion

Full size image

5).

Appendix 3: Devising the Composite Index of Insurance Markets Development: Using PCA

From Table 6 (Part A), we find that the maximum eigen value of the CIN index is 2.682 for the first PC, and it is 0.895, 0.348, and 0.076, for the remaining factors respectively. The proportionate variation of the first factor is 67.1%, and it is 22.4%, 8.69%, and 1.69%, for the remaining factors respectively. From Table 6, Part B, we note the eigen vectors demonstrating the eight PC loadings (PC1-PC4), which imply that the insurance indicator corresponding to PC1 (the only one with an eigenvalue above 1), and elucidate about 67% of the total variance. The residual PCs are not considered, since their contributions are moderately to small, which means that the corresponding eigenvalues are much smaller than 1. Additionally, the factor loadings are all positive in PC1, but the factor loadings have some negative values for the rest of the PCs. We therefore considered only PC1 in building the CIN index, as is well supported by the loading plots (see Figs. 

Fig. 6
figure 6

Loading plots

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6 and

Fig. 7
figure 7

Scree Plot along with Plots of Eigenvalue Difference, and Eigenvalue Cumulative Proportion

Full size image

7).

Appendix 4: Technical Hypotheses Tested in this Study

See Table 7.

Appendix 5: Additional tables for results

See Tables 8, 9 and 10

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Pradhan, R.P., Bahmani, S., Abraham, R. et al. Insurance Market and Economic Growth in an Information-Driven Economy: Evidence from a Panel of High- and Middle-Income Countries?. Asia-Pac Financ Markets 30, 587–620 (2023). https://doi.org/10.1007/s10690-022-09390-8

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