Without reference to Beck and Webb (2003), Erbas and Sayers (2006) focus their research on the link between non-life insurance development and institutional quality. Based on experimental findings by other authors, they argue that institutional quality is a good indicator of uninsurable uncertainty, which is detrimental to insurance demand. This is because a country’s institutional framework determines the reliability of norms, which in turn determine the subjective evaluation of economic risks, for example the risk that a legitimate insurance claim will be denied without recourse. With 1994-2003 data from 70 countries and the World Bank Governance indicators, they find that institutional quality, and the levels of transparency and uncertainty it entails, explains insurability (which they measure as insurance penetration) better than income level for non-life insurance, and suggest that “an examination of life insurance penetration across countries along the lines of the present paper is left for future research.” This is the point of departure for the present paper, which establishes correlations of the different drivers of insurance market development discussed in the literature with insurance penetration for both life and non-life insurance based on a data set that is much more extensive in terms of countries and years of observation.

# **3. Data**

We use 4 main sets of cross-country data in our analysis: income per capita, insurance market data, national governance indices and financial market development measures. All the data are available yearly and most of the variables span roughly 180 countries for a period of 20 years. Two of the three measures of financial market development are more limited in terms of geographical and time coverage, as described below. Summary statistics are presented in Table 1. Online Appendix Table 1 shows summary statistics for low and lower-middle income countries and higher-middle and high income countries separately.

<u>Income.</u> The income measure used is annual GDP per capita provided by the World Bank for 194 countries for the period of 1996-2016. The values are in U.S. dollars of 2011 in PPP terms to allow for comparisons across countries. The average GDP per capita in our sample is almost USD 18,000 and the median is around USD 10,500. When splitting the sample between low and lower-middle income countries and higher-middle and high income countries, we use the World Bank income categories.

<u>Insurance markets. The measure of insurance market development, that is, the main dependent variable,</u> is life and non-life insurance written premiums as percent of GDP, or insurance penetration (IP). The data are collected by AXCO, a British company that provides extensive and detailed information on insurance markets across the globe. AXCO gets the data from the insurance supervisor in each country, or from the insurance association in situations where the supervisor does not provide data, or the data are outdated. Non-life insurance includes motor, machinery, bonds and health insurance, among others. See Section OA1 of the Online Appendix for the all the insurance classes available in the AXCO data.

Insurance penetration data are available for 180 countries, spanning the years of 1996 to 2016. Insurance premiums add up to a relatively small fraction of the GDP. As seen in Table 1, the mean of total (i.e. adding life and non-life) insurance penetration is 2.9% of GDP and most of the countries in our sample have IP lower than 5% of GDP (75<sup>th</sup> percentile is 4.1%). Total insurance penetration in higher-middle and high income countries is almost 4 times higher than total IP in low and lower-middle income countries (see OA Table 1).

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