# **5. Results**

Due to differences between life and non-life insurance markets, we separate the analysis between life and non-life IP. In Table 2 we present the estimates of Equation 1 using all countries in our sample. Estimates in odd-numbered columns do not include country time trends, while in even-numbered columns we include these trends. In some cases, especially when we use data with smaller coverage of countries and time (i.e. Financial Reform data and Findex data), including country time trends may be too demanding due to small sample sizes, so our preferred specifications do not include country time trends. In all columns we include GDP per capita in both linear and non-linear fashion.

In Columns 1 and 2 of Table 2 we regress IP on GDP per capita. In Columns 3-10, we add the other variables of interest, one at a time (always controlling for GDP and GDP<sup>2</sup> ). We prefer to add each one at a time for two reasons: (i) as mentioned in Section 4, since income per capita, the governance index and our measures of financial markets development are arguably correlated, we want to avoid collinearity issues; and (ii) the geographical and time coverage of the Abiad, Detragiache and Tressel (2010) and of the Findex data is far more limited. In Columns 3 and 4 we add our governance index, while in Columns 5-6, 7-8 and 9-10 we include domestic credit to private sector (as % of GDP), the Financial Reform Index, and the number of account holders (as % of population) from Findex, respectively. Due to sample size restrictions, our preferred specification when using the last two variables does not include country time trends. Appendix Table 2 shows analogous estimates using total (sum of life and non-life) IP.

In order to quantify the magnitude of our estimates, we ask what the predicted increase in IP would be if a country were to move from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution of each one of the predictors. For example, what would be the predicted change in life IP if a country moved from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution of institutional quality (i.e., governance index). The numbers are shown in Table 3a. Appendix Table 3a shows analogous comparative statics using estimates of total IP from Appendix Table 2.

In Table 3a, we can see that the estimates of our preferred specification (from Column 2 of Table 2) imply that an increase in GDP per capita that moves a country from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of income per capita distribution (roughly a USD 20,000 increase, or one standard deviation) is associated with an increase in life IP of 141 percent over an average of 1.2% of GDP. The same increase in GDP per capita is associated with an increase of 62 percent in non-life IP, over an average of 1.6% of GDP. Interestingly, changes in institutional quality of the same magnitude as changes in income per capita (around 1.5 SD of the governance index, which moves a country from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution) are associated with similar changes in life and non-life IP (125 and 54 percent, respectively). Thus, our estimates suggest that, even controlling for income per capita, institutional quality is strongly correlated with insurance market development, as much as income per capita per se.

Changes in credit to the private sector as percent of GDP and the Financial Reform Index are not as strongly associated with changes in insurance penetration. Moving a country from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of the distribution of the first variable is associated with an increase in life and non-life IP of 49 percent and 18 percent over the same averages of 1.2% and 1.6% of GDP, respectively. Increases in the level of financial liberalization are associated only with 26 percent and 8 percent higher life and non-life IP over the same averages, respectively. Despite being available only for 2011 and 2014, the Findex data on account holders as percent of the adult population seem to be a strong predictor of insurance penetration. Moving a country from the 25<sup>th</sup> to the 75<sup>th</sup> percentile of account holding 10