# On Market Share Drivers in the Swiss Mandatory Health Insurance Sector

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## Abstract

**:**

## 1. Introduction

## 2. The Swiss Health Insurance Market

#### 2.1. Market Structure

#### 2.2. Premium Development 1998–2015

#### 2.3. Switching Behaviour of the Policyholders

## 3. Research Questions, Available Data and Model Assumptions

#### 3.1. Research Questions and Variables

**$RDP{M}_{i,t,c}$**. The first question hence is:

**$DRP{C}_{i,t,c}$**as the absolute difference between the insurer’s relative annual change in the premium and the one of the market premium. We assume a positive relationship between premium increase and switching decisions:

**Satisfaction:**Although a satisfied customer is not necessarily a loyal customer, Berry (1995) claims that a good service level promotes the relationship with customers in service companies such as insurance. Anderson et al. (1994) find a positive effect of satisfaction and performance. We define an explanatory binary variable for the satisfaction of the policyholders $S{L}_{i,t}$. Our third research question is:

**Group:**Companies that are members of a group have advantages such as knowledge sharing, reduced administrative expenses from scale effects and stronger marketing. Following various research findings such as Cummins and Xie (2008), we want to test whether belonging to a group supports the insurer and allows to attract and retain more customers. We define a binary variable ($G{R}_{i,t}$) representing if the insurer is part of an insurance group or not. Our fourth question is:

**Canton:**As discussed in Section 2.3, the switching behaviour of the policyholders is related to demographic parameters such as language and education level. The language spoken is strongly linked to the cantons, i.e. we are interested in the following question:

#### 3.2. Available Data

#### 3.2.1. Data Description

- The Market Share ($M{S}_{i,t,c}$) per insurer, year and canton as the size of an insurer’s customer base divided by the population in the canton.
- The Market Premium ($M{P}_{i,t,c}$) for insurer i as the weighted average premium (with market shares) of all the other insurers in the market per year and canton.
- The absolute year-to-year change in market share $DM{S}_{i,t,c}$= $M{S}_{i,t,c}-M{S}_{i,t-1,c}$ as the difference between the current year market share and the one of the previous year per insurer, year and canton.
- The relative difference between the insurer’s premium and the market premium ($RDP{M}_{i,t,c}$) as the difference between the current year premium (${P}_{i,t,c}$) and the current market premium ($M{P}_{i,t,c}$) divided by the current market premium, i.e. $RDP{M}_{i,t,c}$ = $({P}_{i,t,c}-M{P}_{i,t,c})/M{P}_{i,t,c}$.
- The difference between the relative annual change of the insurer’s premium and the relative annual change in the market premium ($DRP{C}_{i,t,c}$) per insurer, year and canton, which is defined by$$DRP{C}_{i,t,c}=[({P}_{i,t,c}-{P}_{i,t-1,c})/{P}_{i,t-1,c}]-[(M{P}_{i,t,c}-M{P}_{i,t-1,c})/M{P}_{i,t-1,c}].$$

- Information about market events such as mergers and acquisitions are not included.
- Our data includes only the total number of insureds per year per insurer per canton mixing all available insurance models so we use the market share ($M{S}_{i,t,c}$) as an estimation of the insurers’ cantonal market share. This is relevant since for the premium level we remain with using the standard model premium for reference.
- The yearly switching rates between insurers are unknown and there is no information about the number of policyholders that change their insurance model within the same insurer.

#### 3.2.2. Descriptive statistics

#### 3.3. Regression Models

## 4. Testing the Regression Models and Results

- ${\mathbf{RDPM}}_{\mathbf{i},\mathbf{t},\mathbf{c}}$, the relative difference between the insurer’s and the market premium, is the most significant explanatory variable with very high significance level in all cantons and with the aggregated data for all Switzerland in both regressions. The coefficient values of $RDP{M}_{i,t,c}$ are always negative which indicates the negative relation between the dependent variable $DM{S}_{i,t,c}$ and the explanatory variable $RDP{M}_{i,t,c}$. The coefficient values with normal residuals are always higher (in absolute value) than the ones with the two-sided lognormal residuals.
- ${\mathbf{DRPC}}_{\mathbf{i},\mathbf{t},\mathbf{c}}$, the difference between the insurer’s and market annual premium change, is a significant explanatory variable with negative coefficient values, meaning that a larger value of the difference between the relative change in annual premium and the relative change of the market premium causes a diminution in market share. However, for the model with two-sided lognormal residuals, in AG the $DRP{C}_{i,t,c}$ is not a significant explanatory variable and the coefficient value is only −$0.0033$ compared to −$0.0195$ under the assumption of normal residuals.
**The intercept**of the multivariate regression ($R2$) has positive values in GE and VD and negative values in the German cantons. As the number of insurers in GE and VD reduced along the years of our study, naturally, even with zero values of the explanatory variables, the insurers’ market shares grow and vice-versa for the German cantons.

**The relative difference between the insurer’s and the market premium**is the most significant variable and the coefficients of $RDP{M}_{i,t,c}$ have negative value. A lower premium than the market premium can hence be seen as a significant driver of increasing market share, cf. (Q1). Further

**the difference between the relative annual change in the insurer’s premium and the relative annual change in the market premium**is a significant explanatory variable with negative coefficient values. Higher relative annual increase in premium than the market premium thus significantly leads to decreasing market shares, giving an affirmative answer to (Q2) for the data set under study. As for (Q3), the study on our data suggest that

**satisfaction level**is not a significant variable in this context. Concerning (Q4),

**group affiliation**does not seem to affect the dependent variable. Finally, as a response to (Q5), the regression results differ among the cantons which suggests that the canton is a relevant factor.

## 5. Concluding Remarks

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A. A Regression Model with Two-Sided Lognormal Residuals and Its Estimation

#### Appendix A.1. Testing Significance between Nested Models

**Algorithm**

**1.**

- Fit the two-sided lognormal regression model (${M}_{1}$) with p slope parameters to the data ($x,y$). Let the resulting maximum likelihood estimator be denoted by$$({\widehat{\beta}}_{1},{\widehat{\mu}}_{1})=({\widehat{\beta}}_{1}\left(1\right),\dots ,{\widehat{\beta}}_{1}\left(p\right),{\widehat{\mu}}_{1}).$$
- Fit the two-sided lognormal regression model (${M}_{0}$) with $p-1$ slope parameters which is formed by deleting the i-th slope parameter from the model in the previous step to the data ($x,y$). Let the resulting maximum likelihood estimator be denoted by$$({\widehat{\beta}}_{0},{\widehat{\mu}}_{0})=({\widehat{\beta}}_{0}\left(1\right),\dots ,{\widehat{\beta}}_{0}(p-1),{\widehat{\mu}}_{0}).$$
- Simulate ${N}_{\mathrm{sim}}$ times from model ${M}_{0}$, that is, create$${y}_{j}^{\mathrm{sim}}={\widehat{\beta}}_{0}\phantom{\rule{0.166667em}{0ex}}{x}_{\setminus i}+{\epsilon}_{{\widehat{\mu}}_{0}},\phantom{\rule{1.em}{0ex}}{x}_{\setminus i}:=x\setminus {x}_{i},\phantom{\rule{1.em}{0ex}}j=1,\dots ,{N}_{\mathrm{sim}}.$$
- Fit ${N}_{\mathrm{sim}}$ p-dimensional (full) two-sided lognormal regression models to each simulated response, resulting in the replicated estimators $({\widehat{\beta}}_{1}^{j},{\widehat{\mu}}_{1}^{j}),\phantom{\rule{3.33333pt}{0ex}}j=1,\dots ,{N}_{\mathrm{sim}},$ and denote the empirical distribution function of ${\widehat{\beta}}_{1}^{j}\left(i\right),\phantom{\rule{3.33333pt}{0ex}}j=1,\dots ,{N}_{\mathrm{sim}},$ by ${\widehat{F}}_{{\beta}_{1}\left(i\right)}\left(x\right)$, $x\in \mathbb{R}$.
- Define the p-value of the parameter ${\widehat{\beta}}_{1}\left(i\right)$ as $p={\widehat{F}}_{{\beta}_{1}\left(i\right)}(-|{\widehat{\beta}}_{1}\left(i\right)\left|\right)+1-{\widehat{F}}_{{\beta}_{1}\left(i\right)}\left(\right|{\widehat{\beta}}_{1}\left(i\right)\left|\right).$

#### Appendix A.2. Goodness of Fit

Year | Number of Insurers | Average Number of Insureds (1000) | Average Premium Per Insured (CHF) | Gross Expenses Per Insured (CHF) | Total Premium (CHF mio.) | Gross Expenses (CHF mio.) | Total Operating Result (CHF mio.) |
---|---|---|---|---|---|---|---|

1998 | 118 | 7247 | 1754 | 1935 | 12,708 | 14,024 | −0.03 |

1999 | 109 | 7271 | 1793 | 2011 | 13,034 | 14,621 | −49.39 |

2000 | 101 | 7265 | 1850 | 2131 | 13,442 | 15,478 | −305.95 |

2001 | 99 | 7301 | 1917 | 2244 | 13,997 | 16,386 | −789.7 |

2002 | 93 | 7345 | 2091 | 2328 | 15,355 | 17,096 | −223.67 |

2003 | 93 | 7373 | 2281 | 2431 | 16,820 | 17,924 | 399.73 |

2004 | 92 | 7384 | 2442 | 2592 | 18,030 | 19,140 | 514.14 |

2005 | 85 | 7436 | 2487 | 2736 | 18,496 | 20,348 | 171.42 |

2006 | 87 | 7478 | 2583 | 2755 | 19,315 | 20,603 | 490.95 |

2007 | 87 | 7538 | 2612 | 2863 | 19,689 | 21,579 | 178.66 |

2008 | 86 | 7616 | 2586 | 2984 | 19,692 | 22,722 | −755.32 |

2009 | 81 | 7709 | 2611 | 3069 | 20,125 | 23,656 | −471.61 |

2010 | 81 | 7780 | 2834 | 3123 | 22,051 | 24,292 | 224.51 |

2011 | 63 | 7863 | 3005 | 3171 | 23,631 | 24,932 | 587.67 |

2012 | 61 | 7953 | 3075 | 3257 | 24,458 | 25,901 | 915.88 |

2013 | 60 | 8046 | 3105 | 3471 | 24,984 | 27,926 | −141.2 |

2014 | 60 | 8147 | 3172 | 3515 | 25,845 | 28,639 | 295.74 |

2015 | 58 | 8245 | 3289 | 3653 | 27,119 | 30,122 | −606.89 |

**Table A2.**Basic statistics of the variables: $DM{S}_{i,t,c}$, $RDP{M}_{i,t,c}$, $DRP{C}_{i,t,c}$, $S{L}_{i,t}$ and $G{R}_{i,t}$.

Year | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

$M{S}_{i,t,c}$ | ||||||||||||||

Mean | 0.063 | 0.061 | 0.060 | 0.058 | 0.057 | 0.055 | 0.053 | 0.053 | 0.052 | 0.055 | 0.054 | 0.053 | 0.053 | 0.054 |

StD | 0.072 | 0.069 | 0.068 | 0.066 | 0.065 | 0.063 | 0.061 | 0.060 | 0.059 | 0.061 | 0.060 | 0.060 | 0.059 | 0.060 |

$DM{S}_{i,t,c}$ | ||||||||||||||

Min | −0.057 | −0.051 | −0.039 | −0.030 | −0.052 | −0.041 | −0.034 | −0.046 | −0.069 | −0.068 | −0.042 | −0.025 | −0.017 | −0.056 |

Mean | −0.001 | −0.001 | 0.000 | −0.001 | 0.000 | −0.001 | 0.000 | 0.001 | −0.001 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 |

StD | 0.010 | 0.008 | 0.009 | 0.006 | 0.007 | 0.006 | 0.009 | 0.011 | 0.013 | 0.012 | 0.007 | 0.005 | 0.005 | 0.008 |

Max | 0.067 | 0.063 | 0.066 | 0.044 | 0.031 | 0.026 | 0.073 | 0.064 | 0.093 | 0.093 | 0.031 | 0.020 | 0.021 | 0.038 |

$RDP{M}_{i,t,c}$ | ||||||||||||||

Min | −0.254 | −0.226 | −0.209 | −0.182 | −0.203 | −0.196 | −0.210 | −0.220 | −0.224 | −0.216 | −0.194 | −0.207 | −0.236 | −0.250 |

Mean | −0.011 | −0.007 | −0.003 | −0.010 | −0.009 | −0.006 | −0.011 | −0.008 | −0.004 | 0.006 | 0.003 | 0.003 | 0.001 | 0.007 |

StD | 0.099 | 0.086 | 0.080 | 0.080 | 0.084 | 0.090 | 0.093 | 0.098 | 0.099 | 0.108 | 0.104 | 0.099 | 0.096 | 0.099 |

Max | 0.338 | 0.348 | 0.394 | 0.352 | 0.421 | 0.434 | 0.435 | 0.420 | 0.317 | 0.375 | 0.358 | 0.365 | 0.337 | 0.410 |

$DRP{C}_{i,t,c}$ | ||||||||||||||

Min | −0.258 | −0.115 | −0.177 | −0.073 | −0.135 | −0.140 | −0.127 | −0.083 | −0.245 | −0.064 | −0.102 | −0.069 | −0.092 | −0.225 |

Mean | 0.006 | 0.013 | 0.010 | −0.004 | 0.004 | 0.006 | 0.003 | 0.008 | 0.011 | 0.013 | 0.007 | 0.005 | 0.003 | 0.008 |

StD | 0.052 | 0.050 | 0.041 | 0.031 | 0.036 | 0.031 | 0.024 | 0.032 | 0.044 | 0.044 | 0.035 | 0.031 | 0.026 | 0.033 |

Max | 0.220 | 0.206 | 0.172 | 0.178 | 0.174 | 0.152 | 0.073 | 0.151 | 0.156 | 0.203 | 0.191 | 0.321 | 0.088 | 0.203 |

$S{L}_{i,t}$ | ||||||||||||||

High | 0.773 | 0.822 | 0.823 | 0.816 | 0.814 | 0.806 | 0.779 | 0.799 | 0.743 | 0.850 | 0.829 | 0.771 | 0.803 | 0.764 |

$G{R}_{i,t}$ | ||||||||||||||

Yes | 0.526 | 0.538 | 0.568 | 0.577 | 0.589 | 0.595 | 0.605 | 0.622 | 0.625 | 0.686 | 0.707 | 0.714 | 0.732 | 0.731 |

Observations | 388 | 398 | 412 | 418 | 431 | 439 | 458 | 458 | 456 | 420 | 457 | 462 | 467 | 453 |

AG | BE | GE | TI | VD | ZH | CH | |
---|---|---|---|---|---|---|---|

$RDP{M}_{i,t,c}$ | −0.0285 *** (0.0039) | −0.0343 *** (0.0044) | −0.0734 *** (0.0101) | −0.0338 *** (0.0049) | −0.0542 *** (0.0078) | −0.0380 *** (0.0034) | −0.0381 *** (0.0011) |

Intercept | −0.0006 (0.0004) | −0.0005 (0.0004) | 0.0025 *** (0.0009) | −0.0004 (0.0004) | 0.0021 *** (0.0007) | −0.0006 ** (0.0003) | −0.0003 *** (0.0001) |

Adjusted ${R}^{2}$ | 0.1552 | 0.1681 | 0.2220 | 0.1523 | 0.1730 | 0.3215 | 0.1759 |

$DRP{C}_{i,t,c}$ | −0.0304 *** (0.0115) | −0.0384 *** (0.0118) | −0.1512 *** (0.0238) | −0.0861 *** (0.0127) | −0.0815 *** (0.0202) | −0.0414 *** (0.0090) | −0.0616 *** (0.0029) |

Intercept | 0.0001 (0.0004) | 0.0001 (0.0004) | 0.0009 (0.0009) | 0.0002 (0.0004) | 0.0010 (0.0007) | 0.0002 (0.0003) | 0.0002 ** (0.0001) |

Adjusted ${R}^{2}$ | 0.0207 | 0.0320 | 0.1775 | 0.1478 | 0.0634 | 0.0698 | 0.0704 |

Satisfaction Level (baseline: Low) | |||||||

High | 0.0010 ** (0.0005) | 0.0009 ** (0.0005) | −0.0006 (0.0011) | 0.0003 (0.0006) | −0.0005 (0.0009) | 0.0004 (0.0004) | 0.0010 *** (0.0001) |

Intercept | 0.0005 * (0.0005) | 0.0003 (0.0005) | −0.0002 (0.0011) | 0.0001 (0.0006) | 0.0003 (0.0009) | 0.0003 (0.0009) | 0.0004 *** (0.0001) |

Adjusted R${}^{2}$ | 0.0118 | 0.0108 | −0.0039 | −0.0032 | −0.0033 | −0.0001 | 0.0080 |

Group affiliation (baseline: No) | |||||||

Yes | −0.0004 (0.0008) | −0.0015 * (0.0008) | 0.0001 (0.0021) | 0.0012 (0.0009) | −0.0012 (0.0016) | −0.0007 (0.0006) | −0.0014 *** (0.0002) |

Intercept | 0.0002 (0.0007) | 0.0008 (0.0006) | 0.0001 (0.0018) | −0.0010 (0.0008) | 0.0014 (0.0013) | 0.0005 (0.0005) | 0.0007 *** (0.0002) |

Adjusted ${R}^{2}$ | −0.0028 | 0.0081 | −0.0055 | 0.0025 | −0.0019 | 0.0009 | 0.0062 |

Observations | 283 | 293 | 184 | 259 | 228 | 268 | 6117 |

AG | BE | GE | TI | VD | ZH | CH | |
---|---|---|---|---|---|---|---|

$RDP{M}_{i,t,c}$ | −0.0275 *** (0.0040) | −0.0328 *** (0.0044) | −0.0652 *** (0.0093) | −0.0285 *** (0.0047) | −0.0496 *** (0.0078) | −0.0360 *** (0.0033) | −0.0348 *** (0.0011) |

$Std.\beta $ | −0.3832 | −0.3948 | −0.4224 | −0.3326 | −0.3845 | −0.5388 | −0.3831 |

$DRP{C}_{i,t,c}$ | −0.0195 *** (0.0108) | −0.0270 ** (0.0109) | −0.1293 *** (0.0214) | −0.0721 *** (0.0122) | −0.0583 *** (0.0189) | −0.0274 *** (0.0077) | −0.0448 *** (0.0027) |

$Std.\beta $ | −0.1000 | −0.1322 | −0.3650 | −0.3255 | −0.1860 | −0.1792 | −0.1932 |

Intercept | −0.0004 (0.0004) | −0.0003 (0.0004) | 0.0030 (0.0010) | 0.0000 (0.0004) | 0.0020 (0.0010) | −0.0004 (0.0003) | 0.0000 (0.0001) |

Adjusted R${}^{2}$ | 0.1620 | 0.1824 | 0.3496 | 0.2519 | 0.2029 | 0.3503 | 0.2118 |

Observations | 283 | 293 | 184 | 259 | 228 | 268 | 6117 |

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**Figure 9.**The relationship between DMS and explanatory variables. (

**a**) RDPM

_{i,t,c}versus DMS

_{i,t,c}; (

**b**) DRPC

_{i,t,c}versus DMS

_{i,t,c}.

**Figure 12.**Group distribution and satisfaction distribution. (

**a**) Satisfaction distribution per canton; (

**b**) Group distribution per year.

**Figure 13.**Distribution of the dependent variable $DM{S}_{i,t,c}$ with normal (blue) and two-sided lognormal (green) fit.

Canton | AG | BE | GE | TI | VD | ZH | CH |
---|---|---|---|---|---|---|---|

Residents (2016) | 663,462 | 1,026,513 | 489,524 | 354,375 | 784,822 | 1,487,969 | 8,419,550 |

Urban population in % (2016) | 85.1 | 74.6 | 100.0 | 92.0 | 89.6 | 99.3 | 84.6 |

Unemployment rate in % (annual average 2017) | 3.15 | 2.59 | 5.28 | 3.38 | 4.52 | 3.54 | 3.19 |

Physicians in private practice per 100 000 people (2017) | 167 | 220 | 376 | 219 | 244 | 257 | 219 |

Hospital beds per 1000 people (2017) | 4.5 | 4.8 | 5.2 | 5.4 | 4.8 | 4.2 | 4.5 |

Debt of the cantons in CHF per inhabitant (2016) | 3542 | 6735 | 31,504 | 10,300 | 4382 | 5236 | 7640 |

Social assistance rate (2016) | 2.2 | 4.2 | 5.7 | 2.8 | 4.8 | 3.2 | 3.3 |

Higher education rate (2016) | 22.6 | 24.0 | 28.0 | 29.1 | 25.9 | 22.6 | 28.7 |

Number of customers | Up to 5000 | 5001–10,000 | 10,001–50,000 | 50,001–100,000 | 100,001–500,000 | Over 500,000 | Total |
---|---|---|---|---|---|---|---|

1996 | 90 | 14 | 20 | 6 | 12 | 3 | 145 |

1997 | 76 | 14 | 20 | 4 | 11 | 4 | 129 |

1998 | 64 | 13 | 21 | 6 | 10 | 4 | 118 |

1999 | 56 | 12 | 19 | 8 | 10 | 4 | 109 |

2000 | 48 | 11 | 19 | 9 | 10 | 4 | 101 |

2001 | 43 | 9 | 23 | 9 | 11 | 4 | 99 |

2002 | 33 | 10 | 25 | 9 | 13 | 3 | 93 |

2003 | 32 | 10 | 26 | 9 | 12 | 4 | 93 |

2004 | 32 | 11 | 24 | 9 | 12 | 4 | 92 |

2005 | 27 | 13 | 20 | 8 | 13 | 4 | 85 |

2006 | 28 | 14 | 20 | 7 | 14 | 4 | 87 |

2007 | 27 | 13 | 21 | 8 | 14 | 4 | 87 |

2008 | 26 | 13 | 19 | 9 | 15 | 4 | 86 |

2009 | 22 | 10 | 20 | 8 | 17 | 4 | 81 |

2010 | 19 | 10 | 22 | 9 | 16 | 5 | 81 |

2011 | 13 | 10 | 14 | 3 | 18 | 5 | 63 |

2012 | 14 | 8 | 13 | 3 | 18 | 5 | 61 |

2013 | 14 | 8 | 10 | 5 | 18 | 5 | 60 |

2014 | 14 | 8 | 10 | 5 | 18 | 5 | 60 |

2015 | 11 | 9 | 10 | 6 | 17 | 5 | 58 |

$\Delta $ 1996–2015 | −88% | −35% | −50% | 0% | 42% | 67% | −60% |

Variable | Description | Type |
---|---|---|

$DM{S}_{i,t,c}$ | Absolute year-to-year difference in market share | Number |

$RDP{M}_{i,t,c}$ | Relative difference between the insurer’s premium and the market premium | Number |

$DRP{C}_{i,t,c}$ | Difference between the insurer’s and the market’s relative annual change of the premium | Number |

$S{L}_{i,t}$ | Satisfaction level of the customers | Binary: low, high |

$G{R}_{i,t}$ | Group affiliation | Binary: yes, no |

AG | BE | GE | TI | VD | ZH | CH | |
---|---|---|---|---|---|---|---|

$RDP{M}_{i,t,c}$ | −0.0150 *** | −0.0140 *** | −0.0412 *** | −0.0101 *** | −0.0192 *** | −0.0204 *** | −0.0164 *** |

$DRP{C}_{i,t,c}$ | −0.0033 | −0.0215 *** | −0.0925 *** | −0.0337 ** | −0.0236 ** | −0.0195 *** | −0.0175 *** |

Intercept | −0.0008 | −0.0006 | 0.0015 | −0.0006 | 0.0004 | −0.0006 | −0.0006 |

Observations | 283 | 293 | 184 | 259 | 228 | 268 | 6117 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Daily-Amir, D.; Albrecher, H.; Bladt, M.; Wagner, J.
On Market Share Drivers in the Swiss Mandatory Health Insurance Sector. *Risks* **2019**, *7*, 114.
https://doi.org/10.3390/risks7040114

**AMA Style**

Daily-Amir D, Albrecher H, Bladt M, Wagner J.
On Market Share Drivers in the Swiss Mandatory Health Insurance Sector. *Risks*. 2019; 7(4):114.
https://doi.org/10.3390/risks7040114

**Chicago/Turabian Style**

Daily-Amir, Dalit, Hansjörg Albrecher, Martin Bladt, and Joël Wagner.
2019. "On Market Share Drivers in the Swiss Mandatory Health Insurance Sector" *Risks* 7, no. 4: 114.
https://doi.org/10.3390/risks7040114