Yoboua Angoua Ensea

Determinants of insurance uptake in sub-Saharan developing countries: evidence from Côte d'ivoire

Keywords: Insurance uptake, socio-economic determinants, Sub-saharan region, Côte d'Ivoire

There have been several studies on the determinants of insurance consumption in developing countries and particularly in sub-Saharan African countries. Most of them focus on insurance consumption in rural areas and reach the conclusion that financial illiteracy and lack of liquidity constrain insurance consumption in rural area and mostly farmers. Given the proven growing African middle class, educated and with growing revenue, we focus in this study on the middle class with the aim to identify the determinants of insurance consumption. Using data from Harmonized Survey on Households Living Standards 2018-2019 in Côte d'ivoire, the study identifed that, the main determinants are the marital status, the literacy, the Socio-professional category and the employer, the revenue, the living area and also the holding of another insurance. Furthermore we highlight the under-consumption paradox of some type of insurance (life and household) given the identified determinant which need further analysis.

Introduction

As pointed out by [START_REF] Outreville | The Economic Significance of Insurance Markets in Developing Countries[END_REF], the United Nations Conference on Development and Trade recognized, during its founding conference in 1964, that a national insurance and reinsurance market is one of the essential pillars of economic growth. Analyzing data from 55 developing countries, Outreville [START_REF] Outreville | The Economic Significance of Insurance Markets in Developing Countries[END_REF]) demonstrated a relationship between the development of the property and casualty insurance market in developing countries and the overall level of global economic development. In their analysis of OECD countries, [START_REF] Ward | Does Insurance Promote Economic Growth? Evidence from OECD Countries[END_REF] conclusively affirmed the significant role of insurance in fostering economic development. Existing scholarly literature has delineated two pivotal mechanisms by which the growth of insurance within a nation propels economic advancement: financial intermediation and claims settlement. By instituting risk pooling [START_REF] Ward | Does Insurance Promote Economic Growth? Evidence from OECD Countries[END_REF]Kunreuther, Michel-Kerjan, and Tonn n.d.;[START_REF] Goetz | Unmitigated disasters[END_REF], the expansion of insurance markedly mitigates the adverse repercussions of unforeseen events on household financial stability (e.g., the demise or disability of the primary breadwinner or residential fires) and enhances corporate solvency (e.g., fires in crucial industrial facilities). This, in turn, encourages a more daring approach to risk and fosters investment, innovation, and overall economic activity. Furthermore, insurance firms function as vital financial intermediaries [START_REF] Ward | Does Insurance Promote Economic Growth? Evidence from OECD Countries[END_REF]. Their substantial cash reserves serve as a crucial source of financing for domestic economies, facilitating both public and private investments.

Hence, for developing countries, particularly those in Sub-Saharan Africa, fostering the growth of the insurance industry emerges as a pivotal goal that governments and states may prioritize in their pursuit of economic development. Two illustrative cases underscore this issue: [START_REF] Beck | Determinants of Life Insurance Consumption across Countries[END_REF] In 2018, due to a notable absence of domestic savings, the countries of the West African Economic and Monetary Union (WAEMU) were compelled to resort to external debt. This accumulation amounted to €34 billion (XOF 22,219 billion), equivalent to 32% of the region's annual GDP. (2) In 2019, the devastating IDAI cyclone struck Southern Africa, affecting Mozambique, Malawi, Zimbabwe, and Madagascar between March 11 and 16, 2019. The cyclone caused damages of at least $2 billion and resulted in the tragic loss of thousands of lives. This catastrophe accounted for 3% of the combined GDP of the four affected nations, with estimates from Swiss Re indicating that only 7% of these losses were covered by insurance.

However, the insurance penetration rate (measured as the ratio of premiums issued to GDP, used to assess the level of insurance development) remains notably low in countries across West and Central Africa. In 2021, this rate ranged from 0.4% in Nigeria to 1.2% in Kenya. In contrast, it stood at 6% in Germany, 9.6% in France, 12% in the United States of America, and as high as 14% in South Africa1 . Consequently, one might inquire about the factors that account for the limited adoption of insurance in these nations. There exists a substantial body of literature examining the determinants of life and non-life insurance consumption in both developing and developed countries [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF][START_REF] Beck | Determinants of Life Insurance Consumption across Countries[END_REF][START_REF] Njegomir | Determinants Of Nonlife Insurance Market Attractiveness For Foreign Investments: Eastern European Evidence[END_REF][START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF]. It is well-established that income and per capita income represent primary drivers of insurance consumption. Hence, the utilization of insurance as an instrument for promoting economic development within the countries of the CIMA zone becomes ensnared in a paradoxical situation. On one hand, the limited development of the insurance sector hampers the full realization of economic potential in these regions. On the other hand, the low level of insurance consumption can be attributed to the meager incomes of the populace. Overcoming this dialectic requires, beyond the liquidity and income constraints in the CIMA zone, that we identify all the obstacles to the development of the insurance sector and the potential growth drivers.

Numerous studies have investigated barriers to insurance adoption in underdeveloped countries. These studies consistently affirm that low household income and limited financial liquidity are significant obstacles. Furthermore, education emerges as a critical factor influencing insurance consumption [START_REF] Giné | Microinsurance: A Case Study of the Indian Rainfall Index Insurance Market[END_REF][START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF]). These findings find support in the work of [START_REF] Langat | Determinants of Insurance uptake in developing countries: Evidence from CIC insurance, Kericho Branch[END_REF], [START_REF] Casaburi | Time versus State in Insurance: Experimental Evidence from Contract Farming in Kenya[END_REF], and [START_REF] Belissa | Liquidity constraints, informal institutions, and the adoption of weather insurance: A randomized controlled Trial in Ethiopia[END_REF], who conducted research among rural populations in Ethiopia and Kenya, specifically focusing on the adoption of index-based agricultural insurance. It is worth noting that the majority of these studies primarily focus on rural populations. To our knowledge, there is a lack of similar research encompassing the populations of the CIMA countries. Furthermore, there is a significant scarcity of studies delving beyond insurance consumption habits among non-rural populations in developing nations. Additionally, despite the inherent challenges of quantification [START_REF] Němečková | Measuring the Middle Class in Africa -Income Versus Assets Approach[END_REF], existing research collectively suggests the emergence of an educated middle class in Sub-Saharan Africa, characterized by increasingly substantial incomes. This demographic potentially forms a significant market for insurance adoption. Nevertheless, to our knowledge, no comprehensive studies have been dedicated to exploring the insurance consumption patterns within this middle-class demographic. Key questions remain unanswered: What are the levels of insurance consumption within this group? What are the determinants influencing insurance consumption among this population?

In this paper, we aim to address these questions by analyzing the determinants of insurance product consumption in Côte d'Ivoire, particularly in urban areas.The dataset used in this study is extracted from the 2018 EHCVM2 of Côte d'Ivoire, which measures standard living conditions, particularly in terms of their manifestations in Côte d'Ivoire. It includes data on insurance consumption at the household level, as well as economic and socio-demographic data of household heads. The contribution of this paper lies firstly in the identification of the determinants of insurance consumption in both urban and rural areas within a developing African country. Furthermore, the paper characterizes the levels of consumption across various insurance products and highlights certain under-consumption paradoxes that require further explanation.

The paper is organized as follows: Section 2 reviews the literature. Section 3 describes the data and empirical strategies, while Section 4 presents the results, primarily focusing on the determinants of insurance consumption and the levels of consumption across these determinants.

Literature review

The goal of the current literature review is to emphasize the determinants of insurance demand in both the life and non-life insurance sectors. It will be structured as follows: in the first part, the focus will be on the socio-economic factors that drive insurance consumption across previous research works. In the second part, research papers on environmental factors (non-individual factors) will be discussed.

Depending on the context, "insurance consumption" can either refer to the propensity to acquire an insurance policy or to the amount of monetary units spent to acquire an insurance policy.

Individual related determinants of insurance consumption

In this section, we present previous works on individual characteristics, including socio-demographic and economic factors, that influence insurance consumption.

Income

The effects of income on insurance uptake have been widely analyzed at both the macroeconomic and microeconomic levels. Insurance uptake is positively associated with income. These findings hold true for both non-life insurance [START_REF] Beenstock | The Relationship between Property-Liability Insurance Premiums and Income: An International Analysis[END_REF][START_REF] Petkovski | AN ANALYSIS OF NON-LIFE INSURANCE DETERMI-NANTS FOR SELECTED COUNTRIES IN CENTRAL AND SOUTH EASTERN EUROPE: A CO-INTEGRATION APPROACH[END_REF][START_REF] Belissa | Liquidity constraints, informal institutions, and the adoption of weather insurance: A randomized controlled Trial in Ethiopia[END_REF]) and life insurance [START_REF] Fortune | A Theory of Optimal Life Insurance: Development and Test[END_REF][START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF][START_REF] Millo | Non-life insurance consumption in Italy: a sub-regional panel data analysis[END_REF][START_REF] Schulz-Nieswandt | Referent: Prof[END_REF], irrespective of the country's income level [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF]Lee, Kwon, and S. Y. Chung 2010;[START_REF] Petkovski | AN ANALYSIS OF NON-LIFE INSURANCE DETERMI-NANTS FOR SELECTED COUNTRIES IN CENTRAL AND SOUTH EASTERN EUROPE: A CO-INTEGRATION APPROACH[END_REF][START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF][START_REF] Trinh | Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation[END_REF].

The effect of income on insurance consumption may vary even among countries with the same level of development. [START_REF] Dragos | Life and non-life insurance demand: the different effects of influence factors in emerging countries from Europe and Asia[END_REF], while analyzing life and non-life insurance densities3 , of then 17 emerging countries of Asia4 and eastern Europe5 showed that the effect of income on non-life insurance demand is stronger within Central European countries. One explanation offered is that due to the planned nature of the Chinese economy, which is not entirely market-driven, the effect of income levels on non-life insurance demand is not significant among these emerging Asian countries.

Similarly, the effect of income varies among different types of insurance. For example, [START_REF] Browne | International Property-Liability Insurance Consumption[END_REF], using data from OECD countries, demonstrated that income variation has a stronger impact on motor insurance compared to general liability insurance.

At the individual level, [START_REF] Hammond | Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation[END_REF] and [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF] have found a non-linear effect of income on insurance consumption across different income levels. In the case of life insurance, when analyzing micro-level data of households in a developed country (USA), [START_REF] Hammond | Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation[END_REF] discovered that for both low and high-income groups, life insurance premium expenditures are inelastic in response to changes in income. However, for the middle-income group, premium expenditures are highly responsive to income changes, with a given percentage change in income resulting in a premium expenditure increase approximately one-and-a-half times as large.In contrast, for non-life insurance, [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF] found in Kenya that the increase in insurance consumption following an increase in income is higher for lower-income individuals.

Finally, [START_REF] Hammond | Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation[END_REF], [START_REF] Duker | Expenditures for Life Insurance among Working-Wife Families[END_REF][START_REF] Schulz-Nieswandt | Referent: Prof[END_REF] found that the effect of income on insurance consumption varied along with other determinants such as wealth, job and occupation, age, marital status, and the number of children or dependents, which will be described below.

Wealth

In the current study, wealth is defined as the accumulation of valuable economic resources that can be measured in terms of either real goods or monetary value. In contrast, income corresponds to valuable economic resources earned during a specific period, while wealth encompasses accumulated resources earned and not consumed from previous periods. There are notions that suggest wealth may serve as a substitute for insurance, implying that the wealthier you are, the less you might need insurance. However, in developing countries, wealth is positively associated with both life insurance (Schulz-Nieswandt 2017) and non-life insurance [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF][START_REF] Belissa | Liquidity constraints, informal institutions, and the adoption of weather insurance: A randomized controlled Trial in Ethiopia[END_REF]. In developed countries, the impact of wealth can vary. Several studies conclude that wealth is associated with higher consumption of both life and non-life insurance [START_REF] Duker | Expenditures for Life Insurance among Working-Wife Families[END_REF][START_REF] Petkovski | AN ANALYSIS OF NON-LIFE INSURANCE DETERMI-NANTS FOR SELECTED COUNTRIES IN CENTRAL AND SOUTH EASTERN EUROPE: A CO-INTEGRATION APPROACH[END_REF][START_REF] Millo | Non-life insurance consumption in Italy: a sub-regional panel data analysis[END_REF]. However, a limitation emerged with Hammond, Houston, and Melander 1967, which found a mixed effect between income and wealth: the impact of wealth on insurance consumption is only significant for low and high-income groups. Furthermore, Fortune 1973 and Browne and Kim 1993 found that wealth is negatively correlated with insurance consumption.

Education

Education has a positive effect on insurance consumption, and these results are consistent in both developed [START_REF] Duker | Expenditures for Life Insurance among Working-Wife Families[END_REF][START_REF] Browne | International Property-Liability Insurance Consumption[END_REF]Lee, Kwon, and S. Y. Chung 2010;[START_REF] Dragos | The nexus between the demand for life insurance and institutional factors in Europe: new evidence from a panel data approach[END_REF]) and developing countries [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF][START_REF] Mapharing | Determinants of Demand for Life Insurance: The Case of Canada[END_REF][START_REF] Langat | Determinants of Insurance uptake in developing countries: Evidence from CIC insurance, Kericho Branch[END_REF]. Furthermore, the positive effect of education on insurance consumption holds true for both life [START_REF] Ćurak | The Effect of Social and Demographic Factors on Life Insurance Demand in Croatia[END_REF][START_REF] Langat | Determinants of Insurance uptake in developing countries: Evidence from CIC insurance, Kericho Branch[END_REF] and non-life insurance [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF]. Education, however, appears to be less significant compared to income. The positive effect of education on insurance consumption can be explained by the fact that the better one understands insurance mechanisms, the higher their consumption tends to be.

Religion

Religion plays a limited role in insurance consumption. According to [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF], [START_REF] Park | The Impact of Culture on the Demand for Non-Life Insurance[END_REF][START_REF] Park | The Impact of Culture on the Demand for Non-Life Insurance[END_REF][START_REF] Trinh | Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation[END_REF][START_REF] Trinh | Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation[END_REF], countries with Islam as the dominant religion tend to have lower insurance consumption. [START_REF] Mapharing | Determinants of Demand for Life Insurance: The Case of Canada[END_REF] explained that religion is a determinant of life insurance consumption because the degree of risk aversion in a particular country is related to the predominant religion. At the individual level, Schulz-Nieswandt (2017) found that in Sri Lanka, among different religions (Hindus, Muslims, Christians, Buddhists), only Muslims have a lower adoption rate of life insurance, all else being equal.

Gender

There are discrepancies regarding whether gender is a significant determinant of insurance consumption. There are as many studies that found gender to be a determinant [START_REF] Langat | Determinants of Insurance uptake in developing countries: Evidence from CIC insurance, Kericho Branch[END_REF] as those that found the contrary ( Ćurak 2013).

Age

Age is associated with both life and non-life insurance, regardless of the level of development of the country. For instance, Langat, Naibei, and Getare 2017 found that Kenyans aged between 29 and 50 have a higher adoption of life and non-life insurance. Similarly, Duker 1969, Lee, Kwon, and S. Y. Chung 2010, and Ćurak 2013 also found that age is a determinant explaining insurance consumption in developed countries, namely the USA, Korea, and Croatia, respectively. In particular, Lee, Kwon, and S. Y. Chung 2010 found that non-life insurance consumption increases until age 47 and decreases afterwards, while Ćurak 2013 found that the uptake of life insurance is higher for the age group 44-56 followed by the age group 31-43. Finally, Hammond, Houston, and Melander 1967 showed that the effect of age on insurance consumption varies across income levels, with no significant effect for high-income groups, a negative effect for low-income groups, and a positive effect for middle-income groups.

Marital status

The effect of marital status on insurance consumption appears to be less unequivocal. Ćurak (2013), while studying the impact of social and demographic factors in a sample survey in developed countries, found no evidence that marital status is associated with life insurance consumption. On the other hand, [START_REF] Langat | Determinants of Insurance uptake in developing countries: Evidence from CIC insurance, Kericho Branch[END_REF], while exploring insurance consumption data in a developing country, concluded that married individuals tend to have a higher uptake of insurance (both life and non-life) compared to the single or widowed. Additionally, Abdul-Fatawu, Logubayom, and Abonongo (2019) found that married individuals have a higher adoption of insurance in a study in northern Ghana.

The discrepancies in these findings can be explained by the differentiated effect of marital status on insurance consumption, which depends on the income level and the number of dependents (children). Indeed, [START_REF] Hammond | Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation[END_REF], in their study on life insurance determinants in a developed country, showed that for low and high-income households, marital status has no significant effect on life insurance consumption, regardless of the number of children. However, for the middle-income group, married individuals with children spend more on insurance than those who are not married and those who are married without children.

Trust

Several studies have examined people's trust in insurance companies to explain low adoption rates, especially in developing countries. Thus, [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF], [START_REF] Casaburi | Time versus State in Insurance: Experimental Evidence from Contract Farming in Kenya[END_REF][START_REF] Casaburi | Time versus State in Insurance: Experimental Evidence from Contract Farming in Kenya[END_REF][START_REF] Belissa | Liquidity constraints, informal institutions, and the adoption of weather insurance: A randomized controlled Trial in Ethiopia[END_REF] demonstrated that the low adoption of non-life insurance products is partly explained by low trust in the insurance industry. Furthermore, they found that when agricultural insurance was sold through a trustworthy channel, such as a highly regarded person in society, uptake was higher. Similarly, when looking at developed countries, Millo and Carmeci 2011, using regional panel data, showed that Italian regions where people expressed a higher level of trust in others had a higher consumption of non-mandatory non-life insurance.

Occupation and job

Occupation, at the individual level, appears to be a significant determinant of insurance consumption, especially in life insurance, in developed countries ( Ćurak 2013). In Korea, Lee, Kwon, and S. Y. Chung (2010) have showed that, contrary to the employed, self-employed individuals tend to purchase more protection-like6 life insurance. [START_REF] Hammond | Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation[END_REF] found that depending on the income class, occupation tends to be significant: for middle and high-income groups, self-employed and professionals tend to purchase more life insurance.

Environmental determinants of insurance demand/non individual specific factors

In this section we review the environmental determinants of insurance.

Financial system and banking development

The financial system and banking development are other important determinants. In this context, studies primarily address the non-life insurance branch since the positive correlation between life insurance and financial system development seems straightforward [START_REF] Mapharing | Determinants of Demand for Life Insurance: The Case of Canada[END_REF]. Regarding non-life insurance consumption, [START_REF] Outreville | The Economic Significance of Insurance Markets in Developing Countries[END_REF] and [START_REF] Park | The Impact of Culture on the Demand for Non-Life Insurance[END_REF] found, when comparing levels of non-life insurance consumption among developed or developing countries, that the more developed the financial system of a country is, the higher the consumption of insurance. Cavalcante, Sobreiro, and Kimura (2018) also found that a developed financial system enhances non-life insurance consumption.

Judicial system and rule of law

Looking at earlier studies, the judicial system is a determinant of insurance consumption for both life and non-life insurance. [START_REF] Browne | International Property-Liability Insurance Consumption[END_REF] and [START_REF] Millo | Non-life insurance consumption in Italy: a sub-regional panel data analysis[END_REF] showed that an efficient judicial system is favorable for higher non-life insurance consumption in developed countries. However, [START_REF] Dragos | The nexus between the demand for life insurance and institutional factors in Europe: new evidence from a panel data approach[END_REF] showed that the effect of an efficient judicial system on life insurance consumption is greater in developing and emerging countries compared to developed countries. Additionally, there are findings that judicial systems based on common law have a positive impact on insurance consumption, while judicial systems based on Islamic law have a negative impact on insurance consumption [START_REF] Trinh | Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation[END_REF][START_REF] Browne | International Property-Liability Insurance Consumption[END_REF].

Population density/urbanisation

The level of urbanization or population density is also associated with insurance consumption. However, the impacts vary across different types of products and countries with different levels of development. For instance, [START_REF] Sherden | An Analysis of the Determinants of the Demand for Automobile Insurance[END_REF], while studying the consumption of several motor insurance products in a developed country (USA), found that consumption is high in areas with higher population density. In contrast, Lee, Kwon, and S. Y. Chung (2010), also in a developed country (Korea), found that the consumption of protection-type insurance7 is higher in rural areas with lower population density. [START_REF] Trinh | Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation[END_REF], in their study on determinants of non-life insurance expenditures, using a panel of both developed and developing countries, found that higher population density is associated with higher consumption in developing countries, whereas it is associated with lower consumption in developed countries. Finally, [START_REF] Dragos | Life and non-life insurance demand: the different effects of influence factors in emerging countries from Europe and Asia[END_REF] found that for life insurance, population density is significant for Asian emerging countries but not for central European emerging countries. On the other hand, for non-life insurance products, urbanization is significant for both Asian emerging countries and European emerging countries.

Insurance price

As expected, insurance prices have a negative correlation with insurance consumption for both life [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF]) and non-life insurance [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF]. Furthermore, [START_REF] Sherden | An Analysis of the Determinants of the Demand for Automobile Insurance[END_REF] showed that insurance price elasticity varies among different insurance products:

• Bodily injury8 coverage has a marginal price elasticity close to zero. An increase in price has a very small effect on reducing consumption since bodily injury coverage is considered a necessity by vehicle owners.

• Collision9 coverage has a higher marginal price elasticity that reaches 3 when the price is twice the average price. This means that an increase of 1 unit in price leads to a reduction of 3 units in insurance consumption.

Interest rates

According to previous studies, interest rates are determinants for both life insurance [START_REF] Mapharing | Determinants of Demand for Life Insurance: The Case of Canada[END_REF] and non-life insurance [START_REF] Beenstock | The Relationship between Property-Liability Insurance Premiums and Income: An International Analysis[END_REF][START_REF] Millo | Non-life insurance consumption in Italy: a sub-regional panel data analysis[END_REF][START_REF] Belissa | Liquidity constraints, informal institutions, and the adoption of weather insurance: A randomized controlled Trial in Ethiopia[END_REF], regardless of the level of development of the country.

Inflation

Most of the papers analyzing the impact of inflation on insurance consumption have focused on life insurance products and have shown that inflation has a negative impact on life insurance consumption in both developed countries [START_REF] Fortune | A Theory of Optimal Life Insurance: Development and Test[END_REF] and developing countries [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF].

Regarding non-life insurance, [START_REF] Petkovski | AN ANALYSIS OF NON-LIFE INSURANCE DETERMI-NANTS FOR SELECTED COUNTRIES IN CENTRAL AND SOUTH EASTERN EUROPE: A CO-INTEGRATION APPROACH[END_REF] found that inflation has a negative but not significant impact on insurance demand.

Summary

The table 1 sum up the main determinants of insurance consumption in developing countries for both life and non life insuranc products according to the previous works. Positive effect in developing countries in both life [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF][START_REF] Schulz-Nieswandt | Referent: Prof[END_REF] and non life insurance [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF][START_REF] Trinh | Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation[END_REF]. Varying Effects depending on the levels of revenue [START_REF] Casaburi | Time versus State in Insurance: Experimental Evidence from Contract Farming in Kenya[END_REF] and of other determinants [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF][START_REF] Hammond | Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation[END_REF] Negative effect on insurance consumption [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF] 3 Data and Methodology

Data

The dataset used in this study is extracted from the Living Standard Measurement Survey (EHCVM) 2018, which was a harmonized survey conducted in the member countries of the West African Economic and Monetary Union (WAEMU)10 to measure living conditions, particularly in terms of their manifestations. Our focus is specifically on the data from Côte d'Ivoire.

The EHCVM 2018 in Côte d'Ivoire is representative at both the national and regional levels. The measurement units include household members (individuals), households, and administrative regions. It consists of a two-stage stratified random survey, with the stratification variable being the administrative regions. In the first stage, 1,084 enumeration blocks are randomly selected in each stratum, with the number of enumeration blocks being proportional to the population size of each stratum. In the second stage, 12 households are randomly selected in each enumeration block selected at the first level. In total, 12,992 households were surveyed.

In our study, we are particularly interested in insurance consumption and the individual or environmental determinants of this insurance consumption. Since the data for insurance products (Health insurance, household insurance, and life insurance) is collected at the household level, our focus will be on household data and data related to household heads. However, it's worth noting that motor insurance is compulsory in Côte d'Ivoire for anyone driving a vehicle on public roads. As a result, we included motor insurance data in our analysis for comparison purposes, even though it may not be suitable for analyzing determinants in the same way as the other insurance products.

Definition of variables

In the current survey, consumption data were collected on several products and services, among which our variables of interest on insurance consumption. In the table 2, there is a description of the variables used in the current study. The majority of them are married, with 58% in monogamous marriages and 10% in polygamous marriages. As far as religion is concerned, Muslims (43% of the household heads) and Christians (40%) are the most highly represented.

Regarding literacy and education, almost 57% have no formal education. Furthermore, most of the household heads in the sample are self-employed (65%) and work in the primary sector, including farming, fishing, and breeding. The surveyed income in the sample encompasses annual earnings from wage-earning activities (as employees or trainees), annual revenue from fishing, farming, and breeding, income from non-farming activities (such as industry and trading), pension income for retirees, and any transfers received by the household. On average, the annual income is XOF 1.04 million. Finally, most of the surveyed household heads are living in rural areas (59%).

In order to evaluate correlation among socio-economical characteristic, we run pairwise chi-square test. Only with gender and type of employer we were not able to reject the hypothesis null. The correlation among the explanatory variables is high (see 4).

Insurance consumption

Table 3 provides the number of policyholders by type of insurance, along with statistics about household heads who own one or more insurance policies. Among the 12 thousands households surveyed, 644 households have at least one insurance policy (4.9% of the surveyed households). Only 10 household heads have three different types of insurance, and no household has all four types of insurance. The computed proportions of policyholders among household heads in both urban and rural areas for the four different types of insurance are displayed in Table 4. Overall, the proportion of insured individuals is consistent with the insurance penetration rates in CIMA countries, which are among the lowest in the world.

In the current sample, the highest average rate is observed for Motor insurance, with 5.2% of urban household heads declaring that they have insurance coverage for a vehicle, while 3.9% of rural household heads have motor insurance. It's worth noting that it's compulsory to have third-party liability insurance coverage in order to drive a vehicle in Côte d'Ivoire.

The second most commonly purchased insurance is health insurance: 1.4% of urban household heads are insured, while only 0.5% of rural household heads have health insurance. Finally, life and household insurances are held by less than one out of every 100 households.

It's important to notice that for all four types of insurance, the proportion of insured household heads is significantly higher among urban households compared to rural households. Since motor insurance is compulsory in Côte d'Ivoire, we will not include motor insurance in the analysis of the determinants. However, we will display the main results on motor insurance for benchmarking purposes.

Urbain

Rural

Insurance consumption and individual characteristics

For each type of insurance, we compare the proportion of different individual characteristics among policyholders on one side and among non-policyholders on the other side. The results are displayed in corresponding tables (see Table 8, Table 10, Table 11, Table 9).

For the different types of insurance, there is at least one modality within every individual characteristic that exhibits a statistically significant difference (significant at the 10% level) between the modality's proportion among policyholders and the modality's proportion among non-policyholders.

We highlight here some similarities and discrepancies between the results and the literature review.

Similarly to all previous work, we observe in our sample a significant difference in REVENUE between nonpolicyholders and policyholders, with the latter group having a higher proportion of household heads with higher revenue.

Thus, when considering the insured rate by class of REVENUE (see 1a), household heads with an annual revenue above XOF 6 million 11 have an insured rate varying between 5.7% for Health insurance and 2.2% for Household insurance, while household heads with annual revenue lower than XOF 720,000 12 display an average insured rate varying between 0.2% for health insurance and 0.1% for life insurance.

For the two following economic variables available in our sample, SOCIO-PROFESSIONAL CATEGORY and the SECTOR OF ACTIVITY, which are related to the determinant Occupation and Job in the literature, we observe, similarly to the literature, a significant difference between the distribution of household heads who are policyholders and non-policyholders for the different types of insurance (see [START_REF] Dragos | Life and non-life insurance demand: the different effects of influence factors in emerging countries from Europe and Asia[END_REF]10,11,9). Besides, we introduce in our study the Type of EMPLOYER, for which we also observe significant differences between the distribution of policyholders and nonpolicyholders: the highest insured rates are observed for household heads employed by the state and public companies (ranging from 11% for health insurance to 1.2% for household insurance, see 1b).

On contrary to the previous works(see [START_REF] Beck | Determinants of Life Insurance Consumption across Countries[END_REF], we observed a significant difference between the gender distribution of policyholders and the one of non policyholders, for the different types of insurance (see [START_REF] Dragos | Life and non-life insurance demand: the different effects of influence factors in emerging countries from Europe and Asia[END_REF]10,11,9). Especially, the rate of insured is higher among men household head (varying between 0.9% in Health insurance and 0.3% in household insurance) compared to women household (see 2a). For the AGE of the household head, in line with the literature review findings, there are significant differences between the proportions of different age groups among policyholders and non-policlyholders. The highest insured rates are observed for the ]35-54] age group (see 2b, from 1.3% in Health insurance to 0.4% in household insurance) likewise in literature.

LITERACY and LEVEL OF EDUCATION of the household show significant differences between the distribution of policyholders for different types of insurance and the distribution of non-policyholders. Additionally, a positive correlation is observed between the level of education and the rate of policyholders. Specifically, for household heads with the highest education level, the insured rate varies between 8.5% for health insurance and 1.2% for household insurance. In contrast, for those with no education, the insured rate ranges from 0.2% for health insurance to 0.08% for life insurance (see 3a).

In line with previous studies, we find that, for life, health, and household insurance, Muslim heads of households are significantly less represented among policyholders, while Christians are statistically more numerous among policyholders. However, for motor insurance, we observe the opposite trend, where the proportion of Muslims increases significantly among policyholders. This may be attributed to the high presence of this population segment in transport-related activities. This result contradicts the findings of most studies, where Islam is considered a limiting factor for insurance consumption (Abdul-Fatawu, Logubayom, and Abonongo 2019; Schulz-Nieswandt 2017).

Correlation among determinants

The Xhi square tests on the determinants variables highlights that most of the household heads characteristics analyses here are not independent at a statistical level of 1%. The only non dependant pair of household head characteristics is Religion and Age. So we will have to manage the high dependence among determinants in the estimation strategy.

Econometric setting

We adopt a glm setting with a Logit link function for the modeling. As, highlight in the literature review, the determinant might varied from one type of insurance to another. Thus we adopt for each insurance type a separate model.

The model is as follow, with Insured t i , representing dummy variables (1 if the household head i is a holder of the insurance type t and 0 otherwise):

11 approx € 9100 12 € 950 p = p t i = Pr(INSURED t i = 1 | Socio Economic characteristics) log p 1 -p = (β0 + β1GENDER + β2AGE + β3HOUSEHOLD SIZE + β4MATRIMONIAL STATUS + β5RELIGION + β6EDUCATION + β7SPC + β8EMPLOYER + β9REVENUE + β10REGION + β11OTHER INSURANCE) (1)
t holds for insurances Health, Life and Household. Since, the rate of success in our glm logit setting is very low (less than 1%), we will run in addition to the classic Logit a Firth logistic for rare event puhr˙firths˙2017).

Estimation results and discussion

Results

As stated earlier, since the motor insurance is compulsory in Côte d'Ivoire, the results analysis will be focus on the three other types of insurance.

The logistic regression ?? and the Firth's logistic regression for rare events 6 provide similar results (see 5). We check for the multicolinearity with the Generalized Variance Inflation Factors (see 12, 13 , 14, 15). In the four models, only the Socio professional category appears to have multicolinearity issue13 . Furthermore, the glm Logit and firth logistic models are significant when we consider the Likelihood Ratio Test.

Determinants of insurance consumption: overall analysis

When considering the demographic variables, the estimated results (see 5, 6) show that AGE is not significant for health, life, and household insurance, while GENDER is significant for life insurance only. In the case of gender, the literature is not equivocal, with some studies finding that gender is a determinant [START_REF] Langat | Determinants of Insurance uptake in developing countries: Evidence from CIC insurance, Kericho Branch[END_REF] and others not ( Ćurak 2013). For the AGE, our finding are opposite to previous works [START_REF] Hammond | Determinants of Household Life Insurance Premium Expenditures: An Empirical Investigation[END_REF] which found that AGE is a determinant. For the economic variables, REVENUE is significant for all three insurances, which aligns with previous works [START_REF] Browne | An International Analysis of Life Insurance Demand[END_REF], [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF]). The results show that the higher the revenue, the higher the probability of having insurance. Additionally, the variable SOCIO PROFESSIONAL CATEGORY (CSP) is significant for all three types of insurance, and the variable MAIN EMPLOYER is significant for health insurance. These results are consistent with the literature findings on occupation and job determinants ( Ćurak (2013), Lee, Kwon, and S. Y. Chung (2010)). Notably, this is the first time that the type of employer has been analyzed and found to be a determinant of insurance consumption. Is this result specific to Sub-Saharan African countries? One potential explanation may lie in the channel of acquiring insurance. In Côte d'Ivoire, employers often purchase life or health insurance for their employees.

For the sociological variables, on one hand, MARITAL STATUS and EDUCATION are shown to be significant determinants, as found in previous works (see [START_REF] Cole | Barriers to Household Risk Management: Evidence from India[END_REF], [START_REF] Langat | Determinants of Insurance uptake in developing countries: Evidence from CIC insurance, Kericho Branch[END_REF]). On the other hand, we found no indication that RELIGION is a determinant for health or life insurance, in contradiction to the existing literature [START_REF] Park | The Impact of Culture on the Demand for Non-Life Insurance[END_REF][START_REF] Trinh | Determinants of non-life insurance expenditure in developed and developing countries: an empirical investigation[END_REF]. Although RELIGION is a determinant for household insurance, it's worth noting that the Muslim religion is not a limiting factor, which contradicts previous research findings.

In addition, regarding the environmental variables, our study shows that the area of living of the household (region) is a significant determinant for health insurance. Finally, our study shows multi-holding of insurance is significant (i.e., having a life insurance contract is positively associated with having health insurance).

Determinants of insurance consumption: analysis by type of insurance

We analyze here the differences between the determinants of the three different types of insurance. We also compare these findings with Motor insurance, which is compulsory.

Health insurance consumption appears to have a higher number of determinants, including REVENUE, SOCIO PROFESSIONAL CATEGORY, EMPLOYER TYPE, REGION, EDUCATION, and finally, LIFE INSURANCE. We computed the odds ratios of the significant modalities (see Table 7). For instance, a household head with a higher level of education is 3.8 times more likely to have health insurance than a household head without education. Considering the employer's type, a household head employed by the private sector is 0.3 times less likely to have health insurance compared to a household head employed by the public sector. Furthermore, a household head holding a life insurance policy is 35 times more likely to have health insurance than a household head without a life insurance policy.

Life insurance consumption appears to be determined by GENDER, REVENUE, SOCIO PROFESSIONAL CAT-EGORY, MATRIMONIAL STATUS, HOUSEHOLD SIZE, and HEALTH INSURANCE. For instance, a household head who has health insurance is 30.1 times more likely to have life insurance than a household head who has no health insurance (see Table 7).

Household insurance consumption appears to be determined by GENDER, REVENUE, SOCIO PROFESSIONAL CATEGORY, HOUSEHOLD SIZE, and RELIGION. It's worth noting that, in contrast to health and life insurance, EDUCATION does not have a significant impact on household insurance consumption. Moreover, the possession of other insurance policies does not have any significant impact on household insurance consumption. The most significant odd ratio is with the Socio Professional category: an Owner is 5.2 times more likely to have a Household insurance, compare to the other Socio professional categories. Finally, contrary to previous works,

Predictive power of the determinants

To better assess the explanatory capacity of the identified determinants in our various models, we proceed to examine the predictive ability of policyholder status using the estimated models. To achieve this, we meticulously select the optimal cutoff values (as illustrated in Figure 6) for each model, guided by several accuracy measures14 . The tables (refer to Tables 21,22,23,24,25,26) present the confusion matrices derived from the different models. Additionally, Tables 16,17, and 18 provide insight into the quality of predictions for the various types of insurance using the optimal threshold.

For instance, in the case of health insurance, the models exhibit a sensitivity, or True Positive Rate, of 0.359. This means that we successfully predicted the insured status of 36% of the households that actually hold health insurance. Furthermore, the precision is 0.37, indicating that 37% of the predicted insured households indeed have health insurance. Similar sensitivity and precision values of approximately 0.26 are observed for life insurance. However, for household insurance, the sensitivity and precision are notably lower, at around 0.077.

Overall, despite our successful identification of significant determinants using the models, our ability to accurately predict households with insurance and those without remains limited, particularly in the case of household insurance.

Conclusion

This study aimed to identify the determinants of insurance consumption in both urban and rural areas of Côte d'Ivoire, a developing African country. The analysis utilized data from the Living Standard Measurement Survey (EHCVM) 2018, a two-stage stratified random survey covering 12,992 households. The focus was on three non-mandatory insurance products: Health, Life, and Household insurances.

Consistent with prior researches, we found that both REVENUE and Socio-Professional Category were significant determinants for all three insurances: Health, Life, and Household. However, the significance of other determinants varied among the insurance products. Specifically, Household size and EDUCATION emerged as significant factors for two out of the three insurances, with EDUCATION not influencing Household insurance and Household size not affecting Health insurance. Furthermore, we observed that certain determinants were significant for only one type of insurance. For instance, Gender and Marital status impacted Life insurance, while Employer type and Region influenced Health insurance, and Religion played a role in Household insurance. Interestingly, our findings contradicted previous studies, as we discovered that AGE had no effect on the consumption of all three insurances, and RELIGION yielded different results, with the Muslim religion not limiting insurance consumption. An original discovery in our study was that the type of employer significantly influenced the insurance consumption especially for Health insurance. Additionally, the possession of Health insurance increased the probability of having Life insurance (and vice versa) by more than 30 times. An intuitive explanation for these findings is the increasing prevalence in Côte d'Ivoire of employers providing health and life insurance coverage for their employees.

Despite identifying these determinants, our ability to accurately predict an individual's insurance status remained limited for various insurance products. On one hand, when considering the identified determinants and the modeling, 66% of those with Health insurance, 70% of those with Life insurance, and 88% of those with Household insurance were not correctly predicted as insured. On the other hand, with the identified determinants and the models, 68% predicted as Health insured, 74% as Life insured, and 91% as Household insured were not insured. This highlights the presence of additional determinants and limiting factors for insurance adoption that warrant further investigation. More broadly, this study aids in understanding the disparities in the adoption rates between Health insurance, on one hand, and Life and Household insurance, on the other. It underscores that the lower adoption rates of the latter two insurance types cannot be solely attributed to income and educational levels among the population in Côte d'Ivoire. Future research should explore additional factors, including individuals' motives for insurance adoption, the channels through which they acquire coverage, and modeling the decision-making process under uncertainty. Additionally, investigating behavioral biases in the decision-making process will further enhance our understanding.

Appendices

Figure 1

 1 Figure 1: Insurance consummation across economic variables

Figure

  Figure 2: Insurance consummation across demographic variables

Figure 3 :

 3 Figure 3: Insurance consummation across sociological variables

Figure

  Figure 4: Correlation

  (a) Logit for Health insurance (b) Rare logit for Health insurance (c) Logit for Life insurance (d) Rare logit for Life insurance

Table 1 :

 1 Determinants of insurance consumption in developing countries

	Type	Variable	Non life Insurance	Life Insurance
		Income
	Economic

Table 2 :

 2 Definition of variables

	Variable	Description
	Motor Insurance Home insurance Life insurance Health insurance	Dummy variable, 1 if the household has bought the service during the last 12 months, and also the amount spent.
	Demographic variables
	Gender	Dummy variable, 1 if the household head is a male
	Age	age of the household head
	Size of the household	the size of the household
	Sociological variables
	Religion	the religion of the household head
	Martial status	the marital status of the household head
	Alphabetisation	dummy variable, 1 if the household head is a literate
	Level of education	the school leaving qualification of the household head
	Economical variables
	Employer	the type of employer (public or private entity) of the household head
	socio-professional cate-gory	the socio-professional category of the household head
	Sector	the sector in which the household head is employed (primary, secondary. . . )
	Annual income	the annual income of the household head
	Savings
	Environmental variables
	Region	the region inhabited by the household head
	3.3 Characteristics of the sample

3.3.1 Socio-demographics and economic characteristics

Table 8

 8

displays the distribution of household heads across socio-demographic and economic variables. Approximately 82% of the household heads are male, and the largest age group among them (43%) falls between 35 and 54 years old.

Table 3 :

 3 Number of insured among household head

	Total

Table 4 :

 4 Proportion of insurances' policyholders

Source: Author' calculations using data from EHCVM2018

Table 5 :

 5 Glm Logistic Estimation results

	Terms	Health	Life	Household	Motor
	(Intercept)	-3.269*** ( 0.715)	-6.501*** ( 1.211)	-6.887*** ( 1.357)	-4.017*** (0.361)
	Gender:Female	-0.175 ( 0.374)	-1.179* ( 0.713)	-16.065 ( 571.832)	-1.631*** (0.279)
	Age:]25,35]	-0.552 ( 0.548)	-0.472 ( 0.881)	0.574 ( 1.076)	0.390* (0.224)
	Age:]35,54]	0.141 ( 0.540)	0.190 ( 0.854)	0.740 ( 1.069)	0.203 (0.227)
	Age:]More than 55	-0.316 ( 0.609)	0.064 ( 0.938)	0.707 ( 1.107)	0.059 (0.245)
	Household size	-0.022 ( 0.044)	0.117** ( 0.056)	0.095** ( 0.045)	0.041*** (0.015)
	Matrimonial status: Maried(Mono & Poly)	0.260 ( 0.308)	-1.198** ( 0.465)	0.005 ( 0.521)	0.425*** (0.145)
	Matrimonial status:Divorced or Widow	0.456 ( 0.605)	-0.295 ( 0.964)	0.582 ( 1.135)	-0.492 (0.430)
	Religion:Christian	-0.209 ( 0.261)	0.404 ( 0.434)	0.346 ( 0.352)	-0.481*** (0.106)
	Religion:Animist and others	0.057 ( 0.385)	-0.447 ( 0.747)	-1.781* ( 1.034)	-0.751*** (0.152)
	Education: Primary	-0.039 ( 0.524)	-0.111 ( 0.830)	0.247 ( 0.442)	0.167 (0.127)
	Education:Secondary	0.664* ( 0.367)	0.776 ( 0.604)	-0.125 ( 0.496)	0.320** (0.128)
	Education:Higher education	1.343*** ( 0.380)	1.849*** ( 0.634)	0.783 ( 0.604)	0.410** (0.196)
	SPC:Unskilled worker and familly helper	-0.684 ( 0.425)	0.712 ( 0.835)	0.519 ( 0.805)	-0.928*** (0.220)
	SPC:Self employed	-2.056*** ( 0.464)	0.673 ( 0.803)	0.985 ( 0.732)	-0.002 (0.177)
	SPC:Owner	-16.019 (404.650)	2.792*** ( 0.877)	1.649* ( 0.981)	0.365 (0.310)
	SPC:Retired,Inactive	-0.543 ( 0.891)	13.273 (735.626)	16.284 (2036.993)	-0.679 (0.506)
	Employer:Private company	-1.216*** ( 0.299)	-0.977 ( 0.595)	-0.964 ( 0.735)	-0.438** (0.203)
	Employer:Others	-0.822 ( 0.783)	-14.139 (735.625)	-16.293 (2036.993)	-0.299 (0.463)
	Revenue:(7.2e+05,1.44e+06]	-0.376 ( 0.477)	0.328 ( 0.653)	0.562 ( 0.464)	0.368*** (0.126)
	Revenue:(1.44e+06,6e+06]	0.692** ( 0.325)	0.507 ( 0.555)	0.906** ( 0.417)	0.812*** (0.113)
	Revenue:(6e+06,6e+07]	0.178 ( 0.505)	1.728** ( 0.687)	1.355** ( 0.634)	1.972*** (0.191)
	Region: Other Urban centers	-1.117*** ( 0.284)	-0.416 ( 0.474)	-0.324 ( 0.493)	0.786*** (0.190)
	Region:Rural	-0.536* ( 0.298)	-0.531 ( 0.538)	-0.655 ( 0.528)	0.540*** (0.197)
	Health Insured		3.406*** ( 0.484)	0.247 ( 0.867)	1.427*** (0.262)
	Life Insured	3.555*** ( 0.460)		-0.202 ( 1.175)	0.664 (0.415)
	Household Insured	-0.306 ( 1.028)	-1.202 ( 1.488)		0.978** (0.415)
	Motor:Insured	1.456*** ( 0.274)	0.615 ( 0.464)	0.986** ( 0.418)
	Observations	12,992	12,992	12,992	12,992
	Log Likelihood	-388.963	-166.627	-246.630	-2,070.379
	Akaike Inf. Crit.	831.926	387.255	547.260	4,194.758
	Note:

* * * p<0.01; * * p<0.05; * p<0.1

Table 6 :

 6 Firth Logistic Estimation results

	Terms	Health	Life	Household	Motor
	(Intercept)	-3.129*** (0.659)	-6.036*** (0.996)	-6.271*** (1.076)	-3.971*** (0.355)
	Gender:Female	-0.142 (0.348)	-0.998* (0.577)	-3.159** (1.287)	-1.596*** (0.271)
	Age:]25,35]	-0.591 (0.499)	-0.548 (0.706)	0.255 (0.810)	0.375* (0.221)
	Age:]35,54]	0.080 (0.491)	0.029 (0.684)	0.385 (0.806)	0.187 (0.223)
	Age:]Plus de 55	-0.350 (0.555)	-0.037 (0.756)	0.377 (0.843)	0.047 (0.241)
	Household size	-0.019 (0.041)	0.122*** (0.047)	0.099** (0.039)	0.041*** (0.015)
	Matrimonial status: Maried(Mono & Poly)	0.233 (0.288)	-1.189*** (0.398)	-0.089 (0.439)	0.417*** (0.143)
	Matrimonial status: Divorced or Widow	0.489 (0.551)	-0.192 (0.766)	0.809 (0.840)	-0.439 (0.413)
	Religion: Christian	-0.211 (0.245)	0.381 (0.371)	0.340 (0.309)	-0.478*** (0.105)
	Religion: Animist and others	0.077 (0.358)	-0.318 (0.614)	-1.400* (0.768)	-0.743*** (0.151)
	Education:Primary	0.023 (0.470)	0.024 (0.645)	0.277 (0.388)	0.170 (0.125)
	Education: Secondary	0.657* (0.340)	0.757 (0.501)	-0.077 (0.429)	0.322** (0.127)
	Education: Higher education	1.312*** (0.356)	1.761*** (0.541)	0.786 (0.521)	0.412** (0.193)
	SPC:Unskilled worker and familly helper	-0.650* (0.392)	0.714 (0.682)	0.498 (0.673)	-0.921*** (0.216)
	CSP:Self employed	-2.027*** (0.430)	0.554 (0.673)	0.853 (0.619)	-0.008 (0.175)
	CSP:Owner	-3.754** (1.579)	2.728*** (0.736)	1.705** (0.814)	0.381 (0.304)
	CSP:Retired,Inactive	-0.687 (0.782)	-0.015 (1.539)	0.810 (1.457)	-0.730 (0.486)
	Employer:Private company	-1.180*** (0.284)	-0.906* (0.520)	-0.890 (0.624)	-0.433** (0.200)
	Employer:Others	-0.613 (0.680)	-0.687 (1.396)	-0.761 (1.389)	-0.226 (0.444)
	Revenue:(7.2e+05,1.44e+06]	-0.321 (0.433)	0.366 (0.528)	0.584 (0.406)	0.370*** (0.125)
	Revenue:(1.44e+06,6e+06]	0.677** (0.302)	0.493 (0.465)	0.906** (0.368)	0.812*** (0.112)
	Revenue:(6e+06,6e+07]	0.195 (0.475)	1.659*** (0.588)	1.390** (0.552)	1.964*** (0.189)
	Region: Urban centers	-1.089*** (0.267)	-0.416 (0.409)	-0.346 (0.419)	0.773*** (0.187)
	Region: Rural	-0.525* (0.281)	-0.532 (0.464)	-0.691 (0.453)	0.527*** (0.194)
	Health Insured		3.212*** (0.418)	0.394 (0.705)	1.418*** (0.258)
	Life Insured	3.417*** (0.436)		-0.007 (0.909)	0.664 (0.406)
	Household Insured	-0.115 (0.905)	-1.062 (1.229)		1.004** (0.404)
	Motor Insured	1.430*** (0.261)	0.623 (0.410)	1.004*** (0.366)
	Note:			* * * p<0.01; * * p<0.05;

* p<0.1

Table 7 :

 7 Odd ratios of the significant determinants' modalities

	Terms

Table 8 :

 8 Proportion of motor insurance policyholder

	Labels	Whole Sample	Class	non policyholder	policyholder	Difference
	Gender	0.82	Male	0.8116 (0.0035)	0.9741 (0.0066)	0.1625*** (0.0164)
	Age class	0.09	]0,25]	0.0969 (0.0027)	0.0432 (0.0084)	-0.0537*** (0.0124)
		0.28	]25,35]	0.2812 (0.0040)	0.3022 (0.0191)	0.0210 (0.0191)
		0.43	]35,54]	0.4242 (0.0044)	0.4940 (0.0208)	0.0698*** (0.0210)
		0.20	]Plus de 55	0.1977 (0.0036)	0.1606 (0.0153)	-0.0371** (0.0169)
	Household size Class	0.68	[1,5]	0.6845 (0.0042)	0.5561 (0.0206)	-0.1284*** (0.0199)
		0.28	]5,10]	0.2765 (0.0040)	0.3679 (0.0200)	0.0914*** (0.0191)
		0.04	More than 10	0.0390 (0.0017)	0.0760 (0.0110)	0.0370*** (0.0084)
	Marital Status	0.24	Single	0.2426 (0.0038)	0.1226 (0.0136)	-0.1200*** (0.0181)
		0.68	Maried(Mono & Poly)	0.6712 (0.0042)	0.8653 (0.0142)	0.1941*** (0.0198)
		0.08	Divorced or Widow	0.0862 (0.0025)	0.0121 (0.0045)	-0.0741*** (0.0117)
	Religion	0.43	Muslim	0.4275 (0.0044)	0.5579 (0.0206)	0.1303*** (0.0211)
		0.40	Christian	0.4030 (0.0044)	0.3437 (0.0197)	-0.0593*** (0.0208)
		0.17	Animist and others	0.1695 (0.0034)	0.0984 (0.0124)	-0.0711*** (0.0158)
	Education	0.57	No education	0.5709 (0.0044)	0.4819 (0.0208)	-0.0891*** (0.0211)
		0.19	Primary	0.1893 (0.0035)	0.1641 (0.0154)	-0.0252 (0.0166)
		0.19	Secondary	0.1875 (0.0035)	0.2142 (0.0170)	0.0267 (0.0166)
		0.06	Higher education	0.0523 (0.0020)	0.1399 (0.0144)	0.0876*** (0.0098)
	Sector	0.54	Farming,fishing and breeging	0.5431 (0.0045)	0.4732 (0.0207)	-0.0698*** (0.0212)
		0.09	Industry and building	0.0916 (0.0026)	0.0708 (0.0107)	-0.0208* (0.0122)
		0.29	Services	0.2860 (0.0041)	0.4214 (0.0205)	0.1354*** (0.0193)
		0.08	None	0.0794 (0.0024)	0.0345 (0.0076)	-0.0448*** (0.0114)
	Main employer	0.04	State, public company	0.0390 (0.0017)	0.1434 (0.0146)	0.1044*** (0.0087)
		0.87	Private company	0.8677 (0.0030)	0.8117 (0.0162)	-0.0560*** (0.0145)
		0.09	Others	0.0933 (0.0026)	0.0449 (0.0086)	-0.0484*** (0.0122)
	Socio Prof. Cat	0.10	Manager and Skilled worker	0.0928 (0.0026)	0.2349 (0.0176)	0.1421*** (0.0127)
		0.16	Unskilled worker and familly helper	0.1620 (0.0033)	0.0725 (0.0108)	-0.0895*** (0.0155)
		0.65	Self employed	0.6525 (0.0043)	0.6287 (0.0201)	-0.0238 (0.0203)
		0.01	Owner	0.0134 (0.0010)	0.0294 (0.0070)	0.0160*** (0.0050)
		0.08	Retired,Inactive	0.0794 (0.0024)	0.0345 (0.0076)	-0.0448*** (0.0114)
	Revenue cat.	0.60	(0, 7.2e+05]	0.6095 (0.0044)	0.3575 (0.0199)	-0.2520*** (0.0208)
		0.19	(7.2e+05,1.44e+06]	0.1873 (0.0035)	0.1762 (0.0158)	-0.0111 (0.0166)
		0.20	(1.44e+06,6e+06]	0.1895 (0.0035)	0.3661 (0.0200)	0.1767*** (0.0169)
		0.02	(6e+06,6e+07]	0.0137 (0.0010)	0.1002 (0.0125)	0.0865*** (0.0056)
	Region	0.07	Abidjan (Capital)	0.0748 (0.0024)	0.0743 (0.0109)	-5e-04 (0.0112)
		0.33	Other Urban centers	0.3279 (0.0042)	0.4024 (0.0204)	0.0745*** (0.0200)
		0.59	Rural	0.5974 (0.0044)	0.5233 (0.0208)	-0.0740*** (0.0209)
	Car's holding	0.99	No	0.9918 (8e-04)	0.8722 (0.0139)	-0.1196*** (0.0049)
	Health Ins.	0.99	Non Insured	0.9938 (7e-04)	0.9413 (0.0098)	-0.0525*** (0.0039)
	Life Ins.	1.00	Non Insured	0.9978 (4e-04)	0.9775 (0.0062)	-0.0203*** (0.0024)
	Household Ins.	1.00	Non Insured	0.9973 (5e-04)	0.9845 (0.0051)	-0.0129*** (0.0024)
	Note:

* * * p<0.01; * * p<0.05; * p<0.1

Table 9 :

 9 Proportion of household insurance policyholder

	Labels	Whole Sample	Class	non policyholder	policyholder	Difference
	Gender	0.82	Male	0.8182 (0.0034)	1.0000 (0.0000)	0.1818*** (0.0595)
	Age class	0.09	]0,25]	0.0947 (0.0026)	0.0238 (0.0235)	-0.0709 (0.0452)
		0.28	]25,35]	0.2824 (0.0040)	0.2143 (0.0633)	-0.0681 (0.0696)
		0.43	]35,54]	0.4269 (0.0043)	0.5476 (0.0768)	0.1207 (0.0765)
		0.20	]Plus de 55	0.1960 (0.0035)	0.2143 (0.0633)	0.0183 (0.0614)
	Household size Class	0.68	[1,5]	0.6792 (0.0041)	0.5476 (0.0768)	-0.1316* (0.0722)
		0.28	]5,10]	0.2803 (0.0039)	0.3571 (0.0739)	0.0768 (0.0694)
		0.04	More than 10	0.0405 (0.0017)	0.0952 (0.0453)	0.0548* (0.0305)
	Marital Status	0.24	Single	0.2377 (0.0037)	0.1190 (0.0500)	-0.1186* (0.0658)
		0.68	Maried(Mono & Poly)	0.6792 (0.0041)	0.8571 (0.0540)	0.1779** (0.0721)
		0.08	Divorced or Widow	0.0831 (0.0024)	0.0238 (0.0235)	-0.0593 (0.0426)
	Religion	0.43	Muslim	0.4333 (0.0044)	0.4524 (0.0768)	0.0191 (0.0766)
		0.40	Christian	0.3999 (0.0043)	0.5238 (0.0771)	0.1239 (0.0757)
		0.17	Animist and others	0.1668 (0.0033)	0.0238 (0.0235)	-0.1430** (0.0576)
	Education	0.57	No education	0.5674 (0.0044)	0.4286 (0.0764)	-0.1388* (0.0766)
		0.19	Primary	0.1882 (0.0034)	0.1905 (0.0606)	0.0023 (0.0604)
		0.19	Secondary	0.1887 (0.0034)	0.1667 (0.0575)	-0.0221 (0.0605)
		0.06	Higher education	0.0557 (0.0020)	0.2143 (0.0633)	0.1586*** (0.0356)
	Sector	0.54	Farming,fishing and breeging	0.5403 (0.0044)	0.4286 (0.0764)	-0.1117 (0.0770)
		0.09	Industry and building	0.0908 (0.0025)	0.0476 (0.0329)	-0.0432 (0.0444)
		0.29	Services	0.2915 (0.0040)	0.4524 (0.0768)	0.1609** (0.0703)
		0.08	None	0.0774 (0.0023)	0.0714 (0.0397)	-0.0059 (0.0413)
	Main employer	0.04	State, public company	0.0432 (0.0018)	0.1667 (0.0575)	0.1234*** (0.0316)
		0.87	Private company	0.8656 (0.0030)	0.7619 (0.0657)	-0.1037** (0.0528)
		0.09	Others	0.0912 (0.0025)	0.0714 (0.0397)	-0.0198 (0.0445)
	Socio Prof. Cat	0.10	Manager and Skilled worker	0.0988 (0.0026)	0.2143 (0.0633)	0.1155** (0.0462)
		0.16	Unskilled worker and familly helper	0.1582 (0.0032)	0.0952 (0.0453)	-0.0630 (0.0564)
		0.65	Self employed	0.6517 (0.0042)	0.5714 (0.0764)	-0.0802 (0.0736)
		0.01	Owner	0.0140 (0.0010)	0.0476 (0.0329)	0.0336* (0.0182)
		0.08	Retired,Inactive	0.0774 (0.0023)	0.0714 (0.0397)	-0.0059 (0.0413)
	Revenue cat.	0.60	(0, 7.2e+05]	0.5993 (0.0043)	0.2857 (0.0697)	-0.3136*** (0.0758)
		0.19	(7.2e+05,1.44e+06]	0.1868 (0.0034)	0.1905 (0.0606)	0.0037 (0.0602)
		0.20	(1.44e+06,6e+06]	0.1967 (0.0035)	0.4048 (0.0757)	0.2081*** (0.0615)
		0.02	(6e+06,6e+07]	0.0172 (0.0011)	0.1190 (0.0500)	0.1018*** (0.0203)
	Region	0.07	Abidjan (Capital)	0.0744 (0.0023)	0.1667 (0.0575)	0.0922** (0.0406)
		0.33	Other Urban centers	0.3310 (0.0041)	0.3810 (0.0749)	0.0499 (0.0727)
		0.59	Rural	0.5945 (0.0043)	0.4524 (0.0768)	-0.1421* (0.0759)
	Motor Ins.	0.96	Non Insured	0.9560 (0.0018)	0.7857 (0.0633)	-0.1703*** (0.0319)
	Health Ins.	0.99	Non Insured	0.9916 (8e-04)	0.9524 (0.0329)	-0.0392*** (0.0142)
	Life Ins.	1.00	Non Insured	0.9970 (5e-04)	0.9762 (0.0235)	-0.0208** (0.0086)
	Note:

* * * p<0.01; * * p<0.05; * p<0.1

Table 10 :

 10 Proportion of health insurance policyholder

	Labels	Whole Sample	Class	non policyholder	policyholder	Difference
	Gender	0.82	Male	0.8182 (0.0034)	0.8919 (0.0295)	0.0737** (0.0367)
	Age class	0.09	]0,25]	0.0949 (0.0026)	0.0450 (0.0197)	-0.0499* (0.0279)
		0.28	]25,35]	0.2829 (0.0040)	0.1982 (0.0378)	-0.0847** (0.0429)
		0.43	]35,54]	0.4254 (0.0044)	0.6396 (0.0456)	0.2142*** (0.0472)
		0.20	]Plus de 55	0.1967 (0.0035)	0.1171 (0.0305)	-0.0796** (0.0378)
	Household size Class	0.68	[1,5]	0.6789 (0.0041)	0.6667 (0.0447)	-0.0122 (0.0445)
		0.28	]5,10]	0.2804 (0.0040)	0.2973 (0.0434)	0.0169 (0.0428)
		0.04	More than 10	0.0407 (0.0017)	0.0360 (0.0177)	-0.0046 (0.0188)
	Marital Status	0.24	Single	0.2375 (0.0037)	0.2162 (0.0391)	-0.0213 (0.0406)
		0.68	Maried(Mono & Poly)	0.6793 (0.0041)	0.7387 (0.0417)	0.0594 (0.0445)
		0.08	Divorced or Widow	0.0832 (0.0024)	0.0450 (0.0197)	-0.0382 (0.0263)
	Religion	0.43	Muslim	0.4347 (0.0044)	0.2793 (0.0426)	-0.1554*** (0.0472)
		0.40	Christian	0.3985 (0.0043)	0.6126 (0.0462)	0.2141*** (0.0467)
		0.17	Animist and others	0.1668 (0.0033)	0.1081 (0.0295)	-0.0587* (0.0355)
	Education	0.57	No education	0.5706 (0.0044)	0.1441 (0.0333)	-0.4265*** (0.0472)
		0.19	Primary	0.1894 (0.0035)	0.0450 (0.0197)	-0.1444*** (0.0373)
		0.19	Secondary	0.1881 (0.0034)	0.2523 (0.0412)	0.0641* (0.0373)
		0.06	Higher education	0.0519 (0.0020)	0.5586 (0.0471)	0.5067*** (0.0220)
	Sector	0.54	Farming,fishing and breeging	0.5437 (0.0044)	0.0991 (0.0284)	-0.4446*** (0.0475)
		0.09	Industry and building	0.0911 (0.0025)	0.0450 (0.0197)	-0.0460* (0.0274)
		0.29	Services	0.2878 (0.0040)	0.7838 (0.0391)	0.4960*** (0.0433)
		0.08	None	0.0774 (0.0024)	0.0721 (0.0245)	-0.0053 (0.0255)
	Main employer	0.04	State, public company	0.0390 (0.0017)	0.5856 (0.0468)	0.5466*** (0.0195)
		0.87	Private company	0.8699 (0.0030)	0.3243 (0.0444)	-0.5456*** (0.0326)
		0.09	Others	0.0911 (0.0025)	0.0901 (0.0272)	-0.0011 (0.0274)
	Socio Prof. Cat	0.10	Manager and Skilled worker	0.0935 (0.0026)	0.7477 (0.0412)	0.6542*** (0.0285)
		0.16	Unskilled worker and familly helper	0.1586 (0.0032)	0.0901 (0.0272)	-0.0685** (0.0348)
		0.65	Self employed	0.6562 (0.0042)	0.0901 (0.0272)	-0.5661*** (0.0454)
		0.01	Owner	0.0142 (0.0010)	0.0000 (0.0000)	-0.0142 (0.0112)
		0.08	Retired,Inactive	0.0774 (0.0024)	0.0721 (0.0245)	-0.0053 (0.0255)
	Revenue cat.	0.60	(0, 7.2e+05]	0.6020 (0.0043)	0.1712 (0.0358)	-0.4308*** (0.0467)
		0.19	(7.2e+05,1.44e+06]	0.1879 (0.0034)	0.0631 (0.0231)	-0.1248*** (0.0372)
		0.20	(1.44e+06,6e+06]	0.1935 (0.0035)	0.6486 (0.0453)	0.4552*** (0.0379)
		0.02	(6e+06,6e+07]	0.0167 (0.0011)	0.1171 (0.0305)	0.1004*** (0.0125)
	Region	0.07	Abidjan (Capital)	0.0727 (0.0023)	0.3063 (0.0438)	0.2336*** (0.0251)
		0.33	Other Urban centers	0.3311 (0.0041)	0.3423 (0.0450)	0.0112 (0.0449)
		0.59	Rural	0.5961 (0.0043)	0.3514 (0.0453)	-0.2448*** (0.0468)
	Motor Ins.	0.96	Non Insured	0.9577 (0.0018)	0.6937 (0.0438)	-0.2640*** (0.0197)
	Life Ins.	1.00	Non Insured	0.9984 (3e-04)	0.8198 (0.0365)	-0.1786*** (0.0053)
	Household Ins.	1.00	Non Insured	0.9969 (5e-04)	0.9820 (0.0126)	-0.0149*** (0.0054)
	Note:

* * * p<0.01; * * p<0.05; * p<0.1

Table 11 :

 11 Proportion of life insurance policyholder

	Labels	Whole Sample	Class	non policyholder	policyholder	Difference
	Gender	0.82	Male	0.8185 (0.0034)	0.9250 (0.0416)	0.1065* (0.0610)
	Age class	0.09	]0,25]	0.0947 (0.0026)	0.0500 (0.0345)	-0.0447 (0.0463)
		0.28	]25,35]	0.2826 (0.0040)	0.1500 (0.0565)	-0.1326* (0.0713)
		0.43	]35,54]	0.4266 (0.0043)	0.6500 (0.0754)	0.2234*** (0.0783)
		0.20	]Plus de 55	0.1962 (0.0035)	0.1500 (0.0565)	-0.0462 (0.0629)
	Household size Class	0.68	[1,5]	0.6790 (0.0041)	0.6000 (0.0775)	-0.0790 (0.0739)
		0.28	]5,10]	0.2805 (0.0039)	0.3000 (0.0725)	0.0195 (0.0711)
		0.04	More than 10	0.0405 (0.0017)	0.1000 (0.0474)	0.0595* (0.0313)
	Marital Status	0.24	Single	0.2371 (0.0037)	0.3000 (0.0725)	0.0629 (0.0674)
		0.68	Maried(Mono & Poly)	0.6799 (0.0041)	0.6500 (0.0754)	-0.0299 (0.0739)
		0.08	Divorced or Widow	0.0830 (0.0024)	0.0500 (0.0345)	-0.0330 (0.0437)
	Religion	0.43	Muslim	0.4338 (0.0044)	0.2750 (0.0706)	-0.1588** (0.0785)
		0.40	Christian	0.3996 (0.0043)	0.6500 (0.0754)	0.2504*** (0.0776)
		0.17	Animist and others	0.1666 (0.0033)	0.0750 (0.0416)	-0.0916 (0.0590)
	Education	0.57	No education	0.5683 (0.0044)	0.1500 (0.0565)	-0.4183*** (0.0785)
		0.19	Primary	0.1886 (0.0034)	0.0500 (0.0345)	-0.1386** (0.0619)
		0.19	Secondary	0.1886 (0.0034)	0.2000 (0.0632)	0.0114 (0.0620)
		0.06	Higher education	0.0545 (0.0020)	0.6000 (0.0775)	0.5455*** (0.0365)
	Sector	0.54	Farming,fishing and breeging	0.5411 (0.0044)	0.1750 (0.0601)	-0.3661*** (0.0789)
		0.09	Industry and building	0.0906 (0.0025)	0.1000 (0.0474)	0.0094 (0.0455)
		0.29	Services	0.2908 (0.0040)	0.6750 (0.0741)	0.3842*** (0.0720)
		0.08	None	0.0774 (0.0023)	0.0500 (0.0345)	-0.0274 (0.0423)
	Main employer	0.04	State, public company	0.0422 (0.0018)	0.5000 (0.0791)	0.4578*** (0.0324)
		0.87	Private company	0.8665 (0.0030)	0.4500 (0.0787)	-0.4165*** (0.0541)
		0.09	Others	0.0913 (0.0025)	0.0500 (0.0345)	-0.0413 (0.0456)
	Socio Prof. Cat	0.10	Manager and Skilled worker	0.0976 (0.0026)	0.6000 (0.0775)	0.5024*** (0.0473)
		0.16	Unskilled worker and familly helper	0.1583 (0.0032)	0.0750 (0.0416)	-0.0833 (0.0578)
		0.65	Self employed	0.6528 (0.0042)	0.2000 (0.0632)	-0.4528*** (0.0755)
		0.01	Owner	0.0139 (0.0010)	0.0750 (0.0416)	0.0611*** (0.0187)
		0.08	Retired,Inactive	0.0774 (0.0023)	0.0500 (0.0345)	-0.0274 (0.0423)
	Revenue cat.	0.60	(0, 7.2e+05]	0.5996 (0.0043)	0.1750 (0.0601)	-0.4246*** (0.0776)
		0.19	(7.2e+05,1.44e+06]	0.1871 (0.0034)	0.1000 (0.0474)	-0.0871 (0.0617)
		0.20	(1.44e+06,6e+06]	0.1965 (0.0035)	0.4750 (0.0790)	0.2785*** (0.0630)
		0.02	(6e+06,6e+07]	0.0168 (0.0011)	0.2500 (0.0685)	0.2332*** (0.0208)
	Region	0.07	Abidjan (Capital)	0.0740 (0.0023)	0.3000 (0.0725)	0.2260*** (0.0416)
		0.33	Other Urban centers	0.3311 (0.0041)	0.3750 (0.0765)	0.0439 (0.0745)
		0.59	Rural	0.5949 (0.0043)	0.3250 (0.0741)	-0.2699*** (0.0778)
	Motor Ins.	0.96	Non Insured	0.9563 (0.0018)	0.6750 (0.0741)	-0.2813*** (0.0327)
	Health Ins.	0.99	Non Insured	0.9930 (7e-04)	0.5000 (0.0791)	-0.4930*** (0.0146)
	Household Ins.	1.00	Non Insured	0.9968 (5e-04)	0.9750 (0.0247)	-0.0218** (0.0090)
	Note:

* * * p<0.01; * * p<0.05; * p<0.1

Table 12 :

 12 Motor's Generalized variance inflation factor

		GVIF	Df	GVIF^(1/(2*Df))
	Genre	1.11	1.00	1.06
	Age	1.36	3.00	1.05
	Household size	1.29	1.00	1.14
	StatutMatrimonial	1.31	2.00	1.07
	Religion	1.25	2.00	1.06
	Education	2.14	3.00	1.13
	CSP	12.21	4.00	1.37
	Principal employer	9.45	2.00	1.75
	Revenue	1.38	3.00	1.06
	Region	1.36	2.00	1.08
	Health Insured	1.28	1.00	1.13
	Life Insured	1.15	1.00	1.07
	Household Insured	1.01	1.00	1.01
	Table 13: Health's Generalized variance inflation factor
		GVIF	Df	GVIF^(1/(2*Df))
	Genre	1.25	1.00	1.12
	Age	1.86	3.00	1.11
	Household size	1.36	1.00	1.17
	StatutMatrimonial	1.68	2.00	1.14
	Religion	1.25	2.00	1.06
	Education	1.82	3.00	1.10
	CSP	11.11	4.00	1.35
	Principal Employer	7.98	2.00	1.68
	Revenue	1.79	3.00	1.10
	Region	1.34	2.00	1.08
	Assure Motor	1.14	1.00	1.07
	Life Insured	1.08	1.00	1.04
	Household Insured	1.05	1.00	1.03
	Table 14: Life's Generalized variance inflation factor
		GVIF	Df	GVIF^(1/(2*Df))
	Genre	1.30	1.00	1.14
	Age	1.79	3.00	1.10
	Household size	1.39	1.00	1.18
	StatutMatrimonial	1.98	2.00	1.19
	Religion	1.28	2.00	1.06
	Education	2.22	3.00	1.14
	CSP	4608104.97	4.00	6.81
	Principal employer	2548561.92	2.00	39.96
	Revenue	2.19	3.00	1.14
	Region	1.55	2.00	1.12
	Assure Motor	1.37	1.00	1.17
	Health Insured	1.85	1.00	1.36
	Household Insured	1.24	1.00	1.11

Table 18 :

 18 Cutoff and accuracy measures: Household

Source: Statistica.com

Enquête sur les Conditions de Vie des Ménages

ratio of gross written premium of non life or life insurance to total population

China, India, Indonesia, Malaysia, Philippines, Thailand and Vietnam

Bulgaria, Czech Republic, Estonia, Croatia, Hungary, Latvia, Poland, Romania, Slovenia and Slovakia

In contrast to saving-like insurance products

Protection-type insurance includes accident insurance, disease insurance, critical illness (CI) insurance, as well as term life insurance sold by either life or non-life insurers

Covers for bodily injuries following a responsible or non-responsible accident

Covers for car damages following a responsible or non-responsible accident

Benin, Burkina Faso, Guinea-Bissau, Côte d'Ivoire, Mali, Niger, Senegal, Togo

However once we remove Socio professional category, the models lost their predictive power, so we keep it

1.True Positive Rate (TPR) or sensitivity, it indicates how many positive values, out of all the positive values, have been correctly predicted,2.True Negative Rate (TNR) or specificity -It indicates how many negative values, out of all the negative values, have been correctly predicted, 3.False Positive Rate (FPR) -It indicates how many negative values, out of all the negative values, have been incorrectly predicted, 4.The False Negative Rate (FNR) -It indicates how many positive values, out of all the positive values, have been incorrectly predicted, 5.The Precision -indicates how many values, out of all the predicted positive values, are actually positive, 6. The F1-Score -it is the harmonic mean of precision and sensitivity.


Mathias Chauchat, professor of public Law, Université de la Nouvelle-Calédonie (LARJE) TOWARDS A POWER-SHARING AGREEMENT IN NEW CALEDONIA?

After three referendums on self-determination that left the country ethnically divided and without a strong majority, there is no doubt that we cannot be content with the provisions of the Nouméa Accord which states that "If the answer is [definitely] No, the "irreversibility" of the political organization is being constitutionally guaranteed." In concrete terms, the expression means that a revision of the French Constitution would necessarily be required to move on. It is therefore a difficult scenario.

The FLNKS (the Kanak Liberation National and Socialist Front) does not want to "get out" of the Nouméa Accord, which it sees as an impassable floor. On the other side, the French government does not want to unilaterally leave the Nouméa Accord by taking responsibility for breaking off the dialogue. That's why both sides are talking to each other.

What are the starting positions? Where do things stand? Where are we heading? 1.16

The starting positions

On the French government's side, the government guarantees "the irreversibility of transferred powers and the constitutional right to self-determination". However, the French government "rejects the partition of the territory" and wants to "partially open up the electorate for provincial elections". This is an initial relief for the FLNKS: the French government does not want to change much in the Nouméa Accord, unlike the Loyalists. The Minister suggested "a small agreement": maintain the right to self-determination and in exchange get the Kanaks to open up the electorate to French newcomers.

What does it mean by opening up the electorate? French citizens who settle in New Caledonia after 1998 can no longer become Caledonian citizens. Being a decolonization agreement, the Nouméa Accord put an end to French settlement, but did not prevent free movement and free establishment of the French people. So, the perimeter of the Caledonian people was fixed.

These seemingly modest proposals nonetheless go to the very heart of the Nouméa Accord. The irreversibility defined in the Constitution goes beyond "transferred powers" and relate to "political organization". What, in a nutshell, is political organization? It is the transformation of a people into institutions, so it encompasses the citizen electorate, the voting system, the number of seats and their distribution between Congress and provinces… On the pro-independence side, it should be noted that, in legal terms, the consultation is not a referendum that would be legally binding on the French government. This is why the FLNKS is calling for the signing of a "treaty between Kanaky-New Caledonia and the French Republic on accession to full sovereignty and freely defining interdependencies". This associated State's status is directly based on UN Resolution 1541 of 15 December 1960. At a so-called "partners' convention" held without the FLNKS in Paris in April, Prime Minister Elisabeth Borne tried to broaden the scope of the discussions and established 8 working groups beyond institutional issues. In so doing, the French government wanted to create "trilateral talks" bringing together the three former partners of the Accord: FLNKS, Loyalists and the French government. The FLNKS refused to take part in these meetings. It can be explained simply: with three successive "No" votes, the Loyalists refused to accept the building of a country together. So, the FLNKS is pursuing its quest for independence on its own and the "others" are lining up "behind their State of choice". The format is therefore that of "bilateral talks". 4.39

How did the discussion evolve?

After several visits by Minister of Overseas Darmanin, the French government succeeded in June 2023 in getting the FLNKS to discuss the "small agreement" enabling provincial elections to be held in May 2024. Roughly speaking, France recognizes the Kanak people's right to pursue the dream of independence on condition that they relinquish political power to the Loyalists.

The FLNKS added one point: the transfer of powers must continue as written in the Nouméa agreement. The Minister accepted the principle of discussing them.

This does not mean that the demand for a treaty of interdependence has been abandoned.

Discussions have therefore begun on three points:

-The new transfers of powers; -The right to self-determination; -The provincial electorate.

On new transfers of powers

The FLNKS firstly called for the Nouméa Accord to be completed before moving on to another stage: the transfers so-called "under Article 27" of the Organic Law (administrative and budgetary control, audiovisual and higher education and research) have firstly to be done. The FLNKS is also asking for more autonomy in terms of foreign relations. At this stage, this is more a symbolic amendment to the Nouméa Accord than a new deal.

On the right to self-determination

The FLNKS proposed a final transitional stage before independence that should take place during the next provincial term of office, i.e. five years. The FLNKS wants an automatic referendum during this period on a non-binary question: a question relating to an associated State with France. The government's counter-proposal is to introduce a period extending over one or two generations (20 to 40 years) burying the dream of independence. Moreover, the French government would like the referendum to be triggered by a two-third majority in Congress, so the FLNKS would ask the Loyalists for authorization to become independent...

On the provincial electorate

Seen from Paris, the electorate is "restricted". The word alone sounds like an exclusion. That is the French point of view. Seen from Nouméa by the FLNKS, it is conceived as an inclusion of "others" in the Caledonian people. The Kanak colonized people have accepted recognition of the right of others (the colonizing people) to live in the country as equal citizens: it is the "perimeter of the Caledonian people". First of all, there is a point of agreement: opening up the right to citizenship to all children born in the country when they turn 18, even if their parents are not citizens. People would become Caledonian citizens either because they were locally-born (so, by birth, jus soli), or because they were born elsewhere and have a parent with citizenship (so, by descent, jus sanguinis).

There is a deep disagreement on the other points. The minister suggested 7 years of residency before to be allowed to vote, then 10 years of residency. That's what we call a "sliding" electorate. The official objective is democratic by "relegitimizing the institutions".

The "sliding" electorate does not comply with UN Resolution 35/118 (1980), which calls for a freeze on immigration for a country in the process of decolonization. Moreover, a "sliding" electorate reopens the door to French colonization. Finally, a "sliding" electoral body would restore political power to the French of New Caledonia at the cost of the Kanak people.

According to information provided by the High Commission, opening up a "sliding" electorate to 10 years of residency would add more than 10% of the electorate in the Southern province, which would swing 2 to 3 seats at each election to the loyalist parties. This would prevent the FLNKS from retaining a majority in Congress for 10 years. That is the French government's main objective: to buy political time so that New Caledonia remains French. 9.17

Where are we heading?

The FLNKS has agreed to discuss without having approved any of the points. There is no certainty of success. There are still some very strong reservations.

The first reservation is mistrust between partners. The French government campaigned for the No vote in December 2021. It ignored the FLNKS's refusal and encouraged the "other" people to do without the Kanaks by going to the polls. France is not a referee, it is the colonizer.

The second reservation is that the French government's proposals would weaken the unity of the country. The increase in the electorate in the Southern province would favor the hold of the French people ("white in local parlance) over political life, whereas in the "Kanak" provinces, there would be no impact. The next move could be the "Mayotte coup", i.e. the partition.

The third reservation is to take into account the sensibility of the activist base, which is not inclined to compromise. Independence is a quest for dignity and in many ways is irrational. The Caledonian Union DNA (it is the main party of FLNKS) is more in refusal. Why sign a backward-looking agreement and not resist and mobilize? Electorally, by mobilizing, the pro-independence parties would limit their losses in the Southern province and guarantee the elimination of the Loyalists in the Northern and the Loyalty Islands province. The country would be ungovernable and, as a result of a certain amount of disorder, would lose its attractiveness to migratory flows.

Civil peace can never be taken for granted. Three ingredients are needed to build an agreement: respect, trust and hope. Let's ask ourselves: are these three ingredients present today?