Health Insurance and Household Savings: Evidence from Morocco

Health Insurance and Household Savings: Evidence from Morocco | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Health Insurance and Household Savings: Evidence from Morocco Sara Loukili, Patti Fisher This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6145707/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper investigates the relationship between health insurance and household savings. It first demonstrates that the precautionary motive dominates the saving behavior of Moroccan households, primarily due to the widespread exposure to uncertainty, including frequent income fluctuations, health risks, and limited social insurance coverage. Next, using an instrumental variable strategy, the study examines how access to health insurance affects household saving behavior. The identification strategy leverages the variation in exposure to health risks, driven by chronic disease prevalence, as an exogenous factor influencing saving and consumption decisions. The results reveal two key impacts of health insurance: it crowds out precautionary savings and alters household expenditure patterns. While further research is needed to fully understand these dynamics, policymakers must carefully balance the provision of essential health coverage—particularly with the ongoing expansion of the "Assurance Maladie Obligatoire"—and the need to encourage households to maintain sufficient savings for future needs. JEL Classification Codes: I13, D14, I18 Health Insurance Household Savings Precautionary Motive Morocco Chronic Disease Social Insurance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Savings are a potential source of investment, and therefore of economic growth, and play a role in the monetary transmission mechanism (Beckmann, Hake, & Urvova, 2013 ). Savings is also useful for households and can provide, for example, protection against life’s hazards such as unemployment and unexpected expenses (precautionary saving). It can be used to plan short- and medium-term expenditures, such as financing children's education or purchasing property, particularly in the case of cash-strapped households (Browning & Lusardi, 1996 ). Thus, the study of saving behavior is important for assessing households’ exposure to risk and uncertainty, as well as their ability to cope with economic and financial shocks (Kulikov, Paabut, & Staehr, 2007 ). To date, there have been few studies of household saving behavior in North Africa, Morocco in particular. In 2010, Abdelkhalek et al. conducted a micro-econometric analysis of the determinants of household savings in a poor region of Morocco, Essaouira. To our knowledge, this is the only study to have adopted such a microeconomic approach. Our contribution to the literature is twofold. Firstly, we study the saving behavior of Moroccan households, which we find to be essentially precautionary, at a microeconomic level using nationally representative household data. Secondly, to better understand the role of uncertainty in the formation of precautionary savings, we propose an analysis of the relationship between health insurance and household saving behavior. The study contributes to the literature on household saving behavior in developing economies, and its results are useful for including household behavior in macroeconomic analyses and better-guiding policymaking. Most research into household saving behavior is based on the permanent income and life-cycle hypothesis, which in its original formulation assumes that consumption and saving are based on perfect anticipation of the future and ignores the role of uncertainty. Responding to a medium- to long-term precautionary motive, liquid savings, or contingency funds, act as a buffer against medium-term fluctuations in disposable income (Deaton, 1991). The precautionary motive encourages thus the acquisition of greater wealth to compensate for background risk. As such, health status is often considered a significant measure of background risk in many studies suggesting that anticipated medium-term health expenditure can increase savings (Palumbo 1999 ; Dynan, Skinner, and Zeldes 2004 ). Access to medical insurance can, however, mitigate the burden of medical expenditure. In other words, insurance coverage affects risk preference through income and substitution effects (Hubbard et al., 1995 ; Carroll, 1997 ), crowding out savings by reducing background health risk (Browning & Lusardi, 1996 ). While this relationship has been extensively studied and proven in the case of developed economies, with their more extensive medical coverage systems, the effect of health insurance in developing countries has not been so thoroughly studied. Figure 1 illustrates the relationship between public health expenditure and gross savings worldwide, revealing a U-shaped curve: higher public health expenditure is typically associated with lower savings (likely precautionary savings) in middle-income countries, and with higher savings in high-income countries (suggesting non-precautionary savings). In the case of Morocco, this dynamic is particularly relevant given the ongoing political and economic reforms aimed at expanding compulsory health insurance (Assurance Maladie Obligatoire, AMO). As these reforms unfold, understanding how health insurance influences household saving behavior becomes crucial for assessing their potential impact on both household financial security and broader economic stability, making this research highly pertinent to policymakers and the central bank in guiding monetary and fiscal policies, such as adjusting interest rates, shaping savings incentives, and ensuring the stability of financial systems in response to evolving household saving patterns and consumption preferences. Several recent studies (Atella et al. (2005) for Italy; Chou et al. (2003) for Taiwan; Wagstaff and Pradhan, ( 2005 ) for Vietnam; Zang et al. (2012) and Cheung and Padieu (2015) for China) reveal economically significant effects of extending social insurance programs on household consumption and savings. For example, Zang et al (2012) find that the introduction of basic medical insurance increases urban households' non-medical consumption by 13%. Similarly, in Taiwan, Chou et al. (2003) found that the generalization of national health insurance contributed to a decrease in household savings. The results of these and other studies support the precautionary savings hypothesis and suggest that studying the impact of expanding compulsory health insurance in Morocco is particularly important, as it could similarly alter household saving behavior. Morocco’s health care system is currently characterized by significant disparities in access and quality. Although the government implemented some health initiatives, a large portion of the population remains uninsured or underinsured, particularly in rural and underserved areas. As a result, many families face substantial out-of-pocket expenses for medical care, which contributed to financial insecurity. According to the World Bank, approximately 60% of health care spending in Morocco is financed through private out-of-pocket payments, leading to increased household vulnerability and limited access to necessary services. In response to these challenges, the Moroccan government has initiated a reform aimed at achieving universal health insurance coverage. This reform is designed to expand access to health services and reduce the financial burden on households. We argue that the universal coverage proposed by this reform can significantly alter household behavior regarding saving and consumption. As families gain access to health insurance coverage, they are likely to experience a reduction in health-related financial background risks. Consequently, the need for precautionary savings—traditionally set aside for unexpected medical expenses—may decline. Moreover, with increased financial security, households might redirect their resources toward consumption, investing more in education, housing, and durable goods, which contributes to enhanced well-being. This shift can stimulate economic growth as higher consumer spending promotes demand for local products and services, making the study of household saving behavior a timely and important undertaking to better guide policy makers. To study the potential effect on household saving behavior in Morocco, we propose an instrumental variable approach that exploits differences between households with and without access to health insurance in a setting prior to the introduction of the ongoing AMO reform. Controlling for the health status of the head of household, we find evidence of a crowd-out effect of health insurance on savings across all income quintiles. The coefficient magnitudes are greater for the poorest households. We also find a positive distributional relationship between access to health insurance and household consumption. By reducing their exposure to health risks, health insurance increases household spending on welfare improving expenditures such as education. These results testify to the potential positive impact of the extension of health insurance coverage on households’ well-being, thanks to the overall reduction in risk exposure, and particularly for poorer households. The remainder of this paper is organized as follows. Section 2 presents summary data and descriptive statistics. Section 3 establishes the precautionary nature of households’ saving behavior in Morocco. Section 4 describes the empirical and identification strategy proposed to study the relationship between health insurance and households’ savings. Results and a discussion of channels and mechanisms are presented in Section 5 while section 6 concludes. 2. Data and summary statistics The analysis presented in this paper is based on data from the National Survey of Household Consumption and Expenditure, a nationally representative household survey carried out by the Haut Commissariat au Plan. To process the data, we begin by examining the distributions of savings rates. The calculation of household savings rates revealed extremely low observations, which are often explained in the empirical literature by households under-reporting their incomes for various reasons. To avoid arbitrarily deleting observations, we eliminate outliers above the 97.5th percentile and below the 2.5th percentile. Subsequently, our sample consists roughly of 6,357 households representing various social strata and regions of Morocco. In our analysis, we use several socio-demographic variables, such as the age of the household head, marital status, employment status, education level, and household size. Table 1 presents summary statistics for the full sample and by access to health insurance. In this table and in the rest of the document, households are represented by the head of the family. By age group, heads of households aged between 40 and 54 are the majority, at around 42% of our sample. The proportion of other age groups is around 20% each, giving a balanced representation of households across the life cycle. Household heads aged between 40 and 54 are more likely to have health insurance, particularly if they are males. Over two-thirds of the sample (80%) are married heads of household, followed by widowed heads of household (11%), mainly females, and then single and divorced heads of households, with 3% and 2%, respectively. The average household size is around 5 people, with rural households being larger than urban ones. In terms of average level of education, heads of households with no education, as opposed to those with an average or higher level of education, account for over 50% of the sample, followed by those with an elementary diploma (low level of education), who represent around 29% of the sample. There are significant differences between the groups studied, as heads of households who hold health insurance tend to be better educated and are around 15% more likely to hold higher levels of education. Regarding work-related variables, we observe that salaried and self-employed heads of households make up the bulk of the sample, representing up to 73%. We also examine the sample by income quintiles, where households are divided into five classes of almost equal proportion in our sample. Moreover, as an indicator of assets and wealth accumulation, we consider the home ownership and tenure status of the households. More than 75% of households own their home, 4% own it with a mortgage, and 15% of households are renters. Household wealth is also indicated by housing type. Most of those surveyed live in traditional or modern Moroccan houses 1 , around 61%, while 28% live in rudimentary housing conditions, such as shanty towns, and less than 10% live in apartments. Table 1 Sample summary statistics (1) (2) (3) (4) Full sample Health insurance No health insurance (3)–(2) A. Head of household characteristics Age 15–24 0,009 0,006 0,009 0,181 Age 25–39 0,195 0,167 0,202 -0,030** Age 40–54 0,416 0,481 0,401 0,080*** Age 55–64 0,189 0,201 0,187 0,271 Age 65+ 0,191 0,145 0,202 -0,056*** Male 0,834 0,893 0,820 0,073*** Income quintile. Bottom quantile 0,172 0,044 0,201 -0,157*** Second quantile 0,224 0,131 0,246 -0,115*** Third quantile 0,221 0,210 0,223 0,290 Fourth quantile 0,218 0,284 0,203 0,081*** Top quantile 0,165 0,331 0,126 0,205*** Marital status. Single 0,031 0,030 0,031 0,942 Married 0,837 0,892 0,824 0,067*** Divo. / Sep. 0,025 0,012 0,027 -0,015*** Widowed 0,108 0,066 0,118 -0,051*** B. Head of household's labor market characteristics Qualification (education proxy) No education 0,593 0,284 0,664 0,00*** Low education 0,292 0,351 0,278 -0,380*** Med. Education 0,073 0,194 0,045 0,072*** High education 0,043 0,172 0,014 0,149*** Employment status Inactive 0,254 0,348 0,232 0,00*** Active/ employed 0,726 0,645 0,745 0,116*** Active/ unemployed 0,020 0,007 0,024 -0,099*** D. Assets Characteristics (real estate as proxy) Housing tenure Homeowner 0,710 0,576 0,741 -0,164*** Homeowner with mortgage 0,039 0,105 0,024 0,080*** Use residence for free 0,099 0,095 0,100 0,612 Renter 0,152 0,224 0,135 0,088*** Property type Villa 0,013 0,034 0,008 0,025*** Appartment 0,090 0,231 0,058 0,173*** Moroccan house 0,611 0,672 0,597 0,074*** Basic housing 0,279 0,056 0,331 -0,274*** Geographic lcoation Rural 0,394 0,118 0,458 -0,339*** Property amenities Basic amenities (water, electricity) 0,548 0,867 0,475 0,391*** Number of rooms 3 3 3 0,0003** N 6 357 1 192 5 165 3973 Source: Authors’ calculations. The HCP 2014 National Survey of Household Consumption and Expenditure. Notes: Unweighted statistics. The first column reports summary statistics for the whole sample of surveyed heads of households. The following two columns report the same statistics when the sample is restricted to households with (column 2) or without (column 3) access to a health insurance. Column 4 reports the differences and p-values from the t-tests on the quality of means reported in columns 2 and 3. 3. The precautionary nature of household saving behavior in Morocco In this section, we aim to explore the key determinants of household saving behavior in Morocco, focusing on the precautionary motives that influence families' financial decisions. By examining various factors such as income, education, and household composition, we seek to understand the primary drivers of saving patterns within Moroccan households, shedding light on how these factors contribute to the formation of precautionary savings. 3.1. Literature review Household saving involves the decision to increase asset accumulation or limit current consumption to achieve financial goals (Chang, 1994 ). Saving and asset accumulation, which are almost universally considered desirable goals (Beverly, McBride, and Schreiner, 2003 ), have been the subject of a considerable number of theoretical and empirical studies in the literature (Canova, Rattazzi and Webley, 2005 ). The empirical literature supports a number of variables that influence household savings behavior (Fisher & Montalto, 2011 ). Many of the determinants of saving included in empirical studies at the household level are household demographics and wealth (Aghevli et al., 1990 ). Home ownership and income were found to have a positive association with saving in Australia (Harris, Loundes and Webster, 2002 ). In the same study, more children in a household and having a mortgage were negatively associated with saving. Again, based on Australian data, Finley and Price (2014) found that education and income were positively related to saving. Research generally shows a positive relationship between income and saving (Avery & Kennickell, 1991 ; Bosworth, Burtless, & Sabelhaus, 1991 ; Kulikov et al., 2007 ). In a study using data from Portugal, Alves and Cardoso ( 2010 ) found a positive association between home ownership and education, on the one hand, and savings, on the other. A negative relationship was found between household size and savings, as well as between unemployment and savings. Education has also been shown to be positively related to saving behavior in the US and Korea, respectively (Hefferan, 1982 ; Hong, Sung and Kim, 2002 ), as has home ownership (Fisher and Montalto, 2011 ). Le Blanc, Porpiglia, Teppa, Zhu and Ziegelmeyer ( 2016 ) investigated the determinants of saving using Household Finance and Consumption Survey data for eurozone countries and found that larger households are less likely to save, as are those who are self-employed, unemployed, or retired. Based on Irish data, Le Blanc ( 2016 ) found that larger households or those with an unemployed head are more likely to report negative savings. Beckmann et al. ( 2013 ) found that the young and elderly are less likely to save than the middle-aged, but that the propensity of older respondents to dissave is lower than would be predicted by the life-cycle hypothesis. Beckman et al. (2013) also found a significant positive association between education and saving, as well as between being employed or self-employed and saving. Age is positively associated with saving, while age squared is negatively associated with saving. Bosworth et al. ( 1991 ) found that savings rates in the U.S. rise until the mid-to-late 60s, after which savings rates fall. According to Fuchs-Schündeln, Masella, and Paule-Paludkiewicz ( 2020 ), cultural factors are also important determinants of savings behavior in different countries. There are few empirical studies on savings behavior in Morocco. In their study of households in a poor region of Morocco, Abdelkhalek et al. ( 2010 ) found that income, household size, gender of the household head, and education of the household head were closely related to savings behavior. Studying rural and urban households separately, the researchers found that income was an important determinant for urban households, while education of the household head was more determinant than income for rural households. Household size had a negative relationship with savings for urban households but was not significant for rural households. 3.2. A setting for investigating the characteristics of household saving behavior The theoretical framework guiding this study is based on the life-cycle hypothesis proposed by Modigliani and Brumberg ( 1954 ) and the permanent income hypothesis presented by Friedman ( 1957 ). The life-cycle framework is vast and includes many possible empirical models (Browning & Crossley, 2001 ). It states that consumption is a function of wealth, expected lifetime earnings and the number of years to retirement. To study household saving behavior in Morocco, we specify a regression model controlling for the sociodemographic characteristics of the household and its head (base model) and adding controls for additional sensitivity checks. As we are unable to verify precisely which household characteristics are more likely to be correlated with income underreporting, we prioritize analysis of the probability of having a positive savings rate. We therefore estimate a logit model in which the dependent variable takes the value 1 if a household declares that it has consumed less than its income (i.e., a positive savings rate), and zero otherwise. The probability of saving for household i is written as follows: $$\:{Y}_{i}^{*}={X}_{i}\beta\:+{\epsilon\:}_{i}$$ 1 $$\:{Y}_{i}=\:\left\{\begin{array}{c}1\:if\:{Y}_{I}^{*}\ge\:\:0\\\:0\:if\:{Y}_{i}^{*}<\:0\end{array}\right.$$ where \(\:{Y}_{i}\) is the observed binary dependent variable which takes the value of 1 if the household has a positive saving rate, 0 otherwise, \(\:{Y}_{i}^{*}\) is the underlying latent variable that indexes the household’s saving status, \(\:{X}_{i}\) is row vector of values of K regressors of the ith saving household, \(\:\beta\:\) is a vector of parameters to be estimated, and \(\:{\epsilon\:}_{i}\) is an error term which is assumed to have standard normal distribution. We include in the vector \(\:{X}_{i}\) the characteristics of the household and its head that are derived from theoretical and empirical literature on lifecycle and permanent income hypothesis. They include age cohorts, total household income, head of household's employment status, marital status and education level, as well as other controls for household demographic structure and wealth indicators (i.e., home ownership, living conditions, etc.). The empirical and theoretical literature relates most recorded savings motives to the life-cycle model, primarily the old-age provision motive. Thus, including the age of the head of household captures saving behavior changes over their lifecycle, and we keep the age group 40–54 as a benchmark because this group typically has higher savings rates and income and can operate significant behavior corrections as retirement age looms closer. The basic lifecycle hypothesis has been extended to include many savings motives among which we mainly find the precautionary motive (Gourinchas and Parker, 2002) posed by the longevity risk or out-of-pocket expenses related to health risk among other life-cycle events (Hubbard, Skinner, and Zeldes, 1995 ). As income is known to be strongly correlated with savings, we include income quintiles, with the third quintile serving as the benchmark. The education level of the household head adds an additional control for income, as educational attainment is correlated with income and income-risk (e.g., lower educational attainment is strongly correlated with informality and income fluctuation). Since wealth and assets are not available in the survey data, we also include household ownership status, as a proxy for assets or wealth accumulation, which has been shown in the literature to have a relationship with saving behavior (Turner & Luea, 2009 ; Fisher & Montalto, 2010; Harris et al., 2002 ). Property type and property amenities are included as additional proxies for wealth accumulation, and we also adjust for household size in the empirical model. 3.3. The lifecycle hypothesis partially explains household saving behavior The results of the baseline model are consistent with economic theory (Table 2 ; e.g., Friedman, 1957 ; Modigliani & Brumberg, 1954 ), but we note some specificities of the Moroccan context. First, income is strongly correlated with household saving behavior. We find differences in saving rates between the extreme income quintiles, invalidating the assumption of homogeneity in savings behavior in the permanent income hypothesis. The higher the income, the higher the saving rate. This finding is consistent with several empirical studies showing heterogeneity in savings behavior according to income level and supporting the precautionary savings argument. A closer look shows that the data are only partially supportive of the lifecycle hypothesis. Relative to households with heads aged between 40 and 54 years, the probability of saving in Moroccan households is increasingly important for older households. One of the explanations, also widely advanced by the literature, is that liquidity constraints raise the saving rate for older households in view of constraining credit regulation (Browning & Lusardi, 1996 ). Still, testing the life-cycle hypothesis is difficult because cross-sectional data may confuse an “age effect” with a “generation effect”. In a developing economy, such as Morocco, the younger generations enjoy a higher average standard of living than the generations that preceded them, increasing their consumption relative to their savings. On the other hand, it seems that households do reason in terms of lifecycle, as shown by the saving rates during working ages (40 to 54 age group). Households might also have a desire to pass on an inheritance and/or smooth retirement consumption, which also contributes to explaining the high saving rates at older ages. Finally, this high saving rate among older households might also be explained by income uncertainty because very few have a pension income and rather rely on informal sources of retirement insurance. To better understand the relationship between age and savings, we have sought to characterize the behavior of working-age households headed by people aged between 29 and 59. We test the hypothesis that savings rates differ according to whether household income comes mainly from capital or labor. To do this, we control for the employment status of the household head: inactive, employed, and unemployed. Compared to households headed by employed persons, households headed by unemployed persons are, on average, 4.7 percentage points more likely to save, which can be explained by the build-up of buffer savings in anticipation of income variations, particularly in the case of seasonal workers and the self-employed. All other things being equal, this observation is reversed for the economically inactive. Taking employment status into account therefore provides us with two characteristics of Moroccan household saving behavior. The first stems from the previous results, namely the evidence of behavior closer to the lifecycle hypothesis at working age than at retirement age. The second is linked to the Kaldorian hypothesis that savings rates are differentiated according to the nature of resources (income from work, annuities, capital, and even replacement income when the entrepreneur receives a pension). Real estate, and especially the main dwelling, makes up the bulk of household assets. Certainly, for the same income level, a household that owns its home has a higher standard of living than a renter. Similarly, homeowners as opposed to owners with a mortgage are at different standards of living, all things being equal. The acquisition of a home plays a fundamental role in the constitution of savings and the financial effort dedicated to it modifies households’ wealth structure. Compared to a household that owns its home, renters or homeowners with a mortgage are on average less likely to build up savings. This is because renter households are forced to cut back on consumption to pay rent. Looking at housing characteristics, we find that lower quality housing encourages the formulation of savings that are more likely to be precautionary. Facilitating access to home ownership, and more so to decent housing, seems needed as it should support lower-income households. The values of the AUROC curves, situated well above the commonly accepted threshold of 0.50, indicate that the estimated models effectively capture the association between household characteristics and the probability of saving. This aligns with findings in the literature, such as those by Duflo and Saez ( 2003 ), who demonstrated that demographic factors significantly influence saving behaviors. The strong AUROC values suggest that these models not only distinguish between savers and non-savers but also highlight the critical role that specific household characteristics play in shaping saving behavior. 3.4. Income related uncertainty explains an important part of households’ propensity to precautionary saving To assess the sensitivity of our estimates, we provide additional controls for employment status, focusing on self-employment, and geographic location of residence (urban vs. rural). 3.4.1. Controlling for self-employment In our baseline specification, we control for employment status based on three conditions: inactive, active-employed, and active-unemployed. However, among employed heads of households, the self-employed could be more exposed to income variation risk than employees. Thus, savings might be used as a substitute for income variation or even for the unemployment insurance system. Indeed, several studies 2 have shown that savings can be a medium-term substitute for unemployment insurance systems, allowing the transfer of resources from a period of employment to a period of unemployment (Deaton, 1997 ; Dercon, 2002 ; Brunet & Lesueur, 2004). Similarly, the self-employed could have a desire to accumulate buffer savings in anticipation of income shocks. Compared to employees, households with a self-employed head are on average 1.26 percentage points less likely to save (Table 2 – column 2). A potential explanation is the association of business and personal finances by the self-employed especially since they are, for the most part, uneducated (70.5% of the self-employed are estimated to have no diploma (HCP). Savings of the self-employed may very well be explained by transitory rather than permanent income (Kulikov et al., 2007 ). The self-employed also tend to be active in the informal sector where income is, often, underreported. The absence of statistical significance of the marginal effects coefficient for self-employment, however, suggests that our baseline specification is already a good fit. 3.4.2. Controlling for geographic location of residence Both urban and rural households face significant common and specific risks, resulting in large and, often, consistent income fluctuations. The need for safety nets manifests itself primarily in the need to ensure purchasing power during crises, especially for rural households dependent on agriculture (smallholders or seasonal workers) and for rural and urban self-employed workers. Climatic risks, economic fluctuations, and many specific shocks make these households vulnerable to shocks and savings can constitute a self-insurance mechanism. As a sensitivity check, we re-estimate our model and introduce a control for area of residence to our baseline specification (Table 3 – column 3). Ceteris paribus , rural households are, on average, 7 percentage points less likely to save than urban ones, which might be explained by their constant exposure to income shocks with agriculture being their main economic activity. One of the limitations of this analysis is the difficulty in identifying the hazards faced by rural households. In an environment where climate-related events are the main cause of economic shocks, affecting income, other common or specific shocks may also intervene in the labor market. Indeed, rural households are often involved in a variety of activities ranging from on-farm and off-farm work to seasonal migration to diversify income sources (Rosenzweig & Binswanger, 1993 ). Given the limited available data, our conclusions, specifically related to the development of social safety nets and labor insurance policies that would contain the vulnerability of rural households to income variations, remain unresolved. From an empirical point of view, a quasi-experimental approach could be a useful way to analyze the relationship of occupational status and sources of income with the effects of uncertainty on household saving behavior. Table 2 – Household Savings Baseline with Robustness Checks for Employment Status and Residence (1) Saver (baseline) (2) Saver (self-employment) (3) Saver (rural vs. urban) Income of the household Bottom quintile -0.3499667*** -0.3507391*** -0.3462863*** Second quintile -0.192295*** -0.1927524*** -0.1921535*** Third quintile (benchmark) Fourth quintile 0.1823014*** 0.1826594*** 0.182701*** Top quintile 0.3892981*** 0.3898275*** 0.387916*** Life cycle: age of household head Age 15–24 0.0343729 0.0340597 0.0366976 Age 25–39 -0.0210025 -0.0214594 -0.0206282 Age 40–54 (benchmark) Age 55–64 0.0167671 0.0179534 0.0178889 Age 65+ 0.0420044* 0.0437783* 0.0446289* Employment status of household head Inactive -0.0977868*** -0.1041461*** -0.1055993*** Active/ employed (benchmark) Active/ self-employed -0.0126023 -0.0042984 Active/ unemployed 0.0479484 0.0462612 0.0449162 Education of household head No education 0.1677726*** 0.1697697*** 0.1709546*** Low education 0.0890553** 0.0903811** 0.0892798** Med. Education (benchmark) High education -0.0832552* -0.0843645* -0.0839871* Marital status of household head Single 0.1585919*** 0.1594368*** 0.1587643*** Married (benchmark) Divo, / Sep, 0.1032579* 0.1035318* 0.0967769* Widowed 0.1125659*** 0.1130082*** 0.1119524*** Housing tenure Homeowner ( benchmark ) Homeowner with mortgage -0.0083503 -0.009844 -0.0133047 Uses residence for free -0.0005712 -0.002219 -0.0024685 Renter -0.0687585*** -0.0699466*** -0.0754682*** Property type Villa -0.1191308* -0.1200158* -0.1256468* Apartment -0.04671* -0.0473561* -0.050021* Moroccan house (benchmark) Basic housing 0.0090073 0.0097831 0.0225817 Geographic location Rural -0.070857** Property amenities Basic amenities (water, electricity) -0.0595469** -0.0611949*** -0.0969593*** Number of rooms -0.0695427*** -0.0691807*** -0.0661044*** Household size Number of family members -0.0173719*** -0.0173603*** -0.0172581*** Observations 6,357 6,357 6,357 Log-Likelihood -3716,7107 -3716,4374 -3711,1266 Notes: This table presents baseline and robustness checks specifications. Marginal effects are reported, where Model (1) is the baseline specification for household saving, (2) includes a control for self-employment, and (3) includes a separate control for residence in rural vs. urban geographic location. ***, **, and * indicate significance at the 1%, 5% and 10% levels. 4. Identifying the relation between health insurance and household saving behavior Establishing the dominance of the precautionary motive in household saving behavior can be further backed by the analysis of the effect of a risk-mitigating tool, such as access to health insurance. In this section, we assess the saving behavior response of households to holding a health insurance. A potential challenge in identifying a relationship between access to health insurance and saving behavior is the endogeneity of health insurance on household consumption decisions. For this, an instrumental variable approach is proposed. 4.1. A Naïve approach Let \(\:{Insurance}_{i}\) be a dummy variable for whether the household is covered by health insurance 3 . The effect of access to health insurance on the outcome \(\:{Savings}_{i}\) , the saving rate of the household, can be estimated using the following specification: $$\:{Savings}_{i}={\beta\:}_{0}+{\beta\:}_{1}{Insurance}_{i}\:+\:{\beta\:}_{2}{C}_{i}+\:{\beta\:}_{3}{H}_{i}+{\epsilon\:}_{i}$$ 2 Where \(\:{C}_{i}\) is a set of characteristics belonging to the head of the household, the main decision maker, namely gender, age, education level, and status of employment. \(\:{H}_{i}\) is a vector of household level controls linked to a household’s income quintile, household size, wealth holdings, proxied by home ownership status, housing tenure, and property type and amenities. This approach, while containing sufficient information that controls for household income and expenditures and, thus, the savings potential, can yield inconsistent results, that can under or overestimate the effect of health insurance coverage on household saving behavior due to the endogeneity of health insurance with household consumption decisions. 4.2. The endogenous nature of access to health insurance Although many potential confounding factors affecting household consumption and savings decisions are controlled for, some unobservable characteristics influencing the use of health insurance benefits may distort the estimates. First, the issue of self-selection into health insurance schemes based on the health status of the head of the household or their risk appetite is not relevant in the Moroccan context, primarily due to the structure of the social protection system. In Morocco, access to health insurance is closely tied to employment status, with formal sector workers typically covered by social security, while informal workers often lack coverage. This framework reduces the likelihood of self-selection based on individual health needs and risk appetite, as many households do not have the option to self-select into a health insurance scheme. Moreover, endogeneity may arise from preferences related to income availability and exposure to risks, which in turn influence household saving rates. Households at different income levels demonstrate distinct saving behaviors shaped by their financial situations and health expenditure preferences. Fonseca et al. ( 2021 ) illustrate that the growth of healthcare spending in the U.S. from 1965 to 2000 is largely driven by increases in disposable income within a lifecycle model that incorporates endogenous medical expenditures. Furthermore, out-of-pocket healthcare costs often rise due to lifecycle events, such as childbirth and the number of young children in a household (McDonald, 1990; Percival & Harding, 2007). While these expenses tend to be transitional, they can significantly impact a household’s ability to save. As a result, the decision to increase health expenditures—often guided by personal preferences rather than solely by access to health insurance—can lead to biased ordinary least squares (OLS) estimates, obscuring the true nature of the relationship between savings and health spending. In Morocco, a similar trend is evident, as household expenditures on health and other well-being-related expenses have increased significantly in recent years (see Fig. 4 ). This rise can be attributed to heightened demand driven by rising income levels and an aging population, alongside a gradual shift of these expenses from the public sector to households. Notably, health expenditures now account for an increasing share of total consumption expenditures, particularly among lower-income households. This growing trend reinforces our intuition regarding the potential existence of endogeneity in household saving behaviors, as financial circumstances and preferences increasingly dictate how families allocate their resources toward health and well-being-related expenditures. To address the endogeneity of healthcare expenditure, we employ an instrumental variable (IV) approach. We exploit the degree of exposure of households to health risks by identifying those who face a permanent risk linked to chronic diseases. Chronic illness can serve as a valid instrument since it affects household expenditures and is naturally randomly assigned (i.e., a random natural treatment exogenous to household choices). The identification strategy is predicated on two key assumptions: firstly, household heads suffering from chronic illnesses are more likely to decrease their expenditures and increase their precautionary savings (buffer savings) to cover future health expenses. Secondly, access to health insurance reduces precautionary savings by covering expected or unexpected health expenses. Based on our initial analysis, we observed that, in the absence of health risks (i.e., chronic disease), households with insurance exhibit a significantly lower probability of saving across all income quintiles, while uninsured households tend to accumulate buffer savings against unexpected expenses (see Fig. 5 – (a)). This suggests the existence of a crowd-out effect of health insurance on savings. However, the presence of chronic illness alters this dynamic (see Fig. 5 – (b)). While uninsured households may initially have a higher probability of saving as a precautionary measure, the risk of chronic illness significantly diminishes their ability to save, as these buffer savings are continuously constituted but often redirected to cover necessary healthcare expenditures. Given these observations, chronic illness emerges as a potential valid instrument for understanding the relationship between health insurance and household saving behavior. Furthermore, Fig. 6 illustrates the prevalence of chronic illness among heads of households, categorized by age and differentiated by health insurance coverage within the study sample. The data reveals a consistent distribution across all age groups, highlighting similar patterns of exposure to chronic illness risk between those with and without health insurance. This finding is particularly relevant for our analysis, as it suggests that chronic illness is not biased by age or insurance status and is rather random, allowing us to examine more reliably the relationship between health insurance access and household saving behavior without confounding effects from these factors. Thus, the relationship between chronic illness and health expenditures is exploited as an identification strategy where chronic illness is instrumented as follows: $$\:{Insurance}_{i}={\propto\:}_{0}+{\propto\:}_{1}{Chronic}_{i}+\:{\propto\:}_{2}{C}_{i}+\:{\propto\:}_{3}{H}_{i}+{\nu\:}_{i}$$ 3 Here \(\:{Chronic}_{i}\) is a binary variable indicating whether the head of the household has a chronic illness. The results of the first stage, presented in Table 3 , show that the presence of chronic disease is negatively associated with access to health insurance. This suggests that households with chronic illness are less likely to have health insurance, and importantly, that they do not self-select into health insurance as a means to mitigate risk. This relationship holds consistently across all income quintiles, which suggests that the prevalence of chronic illness is not influenced by income levels. The Kleibergen-Paap F-statistic for the excluded instrument exceeds the rule-of-thumb threshold of 10, indicating that chronic illness is a strong instrument. Additionally, the F-statistics from the Montiel-Olea and Pflueger (2013) weak instrument test exceed the critical values at both the 5% and 10% significance levels, confirming that chronic illness is a valid and strong instrument for health insurance. Table 3 – First stage estimates (1) (2) (3) (4) (5) (6) All income quintiles Bottom quintile Second quintile Third quintile Fourth quintile Top quintile Chronic illness -0.127*** -0.117*** -0.079** -0.157*** -0.122*** -0.178*** (0.0119) (0.0233) (0.0239) (0.0273) (0.026) (0.0301) Kleibergen-Paap F-statistic 56.41 13.38 6.02 16.64 10.54 17.58 Kleibergen-Paap p-value 0.000 0.000 0.0028 0.000 0.001 0.000 N 6 357 1 092 1 427 1 403 1388 1047 R² 0.2359 0.0693 0.0639 0.1509 0.2223 0.3381 Lifecycle controls √ √ √ √ √ √ Household level controls √ √ √ √ √ √ Income and wealth controls √ √ √ √ √ √ Montiel Olea and Pflueger [2013] test for weak instruments Effective F-statistic 62.616 17.277 7.862 18.353 11.062 18.825 Critical value for tau = 5% 21.318 25.950 24.819 21.174 20.565 20.675 Critical value for tau = 10% 13.377 16.157 15.477 13.291 12.927 12.993 Notes: This table presents first-stage estimates analyzing the effect of chronic illness across income quintiles. The Kleibergen-Paap F-statistic confirms the strength of the instruments used, while the Montiel Olea and Pflueger (2013) test indicates valid instrument assumptions. All models control for lifecycle, household, and income/wealth factors, ensuring comprehensive analysis. ***, **, and * indicate significance at the 1%, 5%, and 10% levels. The second stage of our analysis builds upon the first stage by estimating the effect of health insurance on household saving behavior. Using the predicted values of health insurance from the first stage, we estimate Eq. ( 2 ) to examine the relationship between health insurance and household savings. This approach allows us to explore how access to health insurance may influence saving behavior, while accounting for potential confounding factors. By investigating this relationship, we aim to gain a better understanding of the role health insurance plays in shaping household savings decisions in the context of Morocco’s evolving health insurance system. The next section presents the results of this analysis, offering insights into the dynamics of health insurance and savings behavior. 5. The well-being effect of health insurance on households In this section, we present the results of the instrumental variable regression analyzing the relationship between access to health insurance and household saving behavior, followed by an analysis of the well-being effects of health insurance on household expenditures. We further decompose household responses by income and expense category to understand how it effects overall financial resilience. The findings provide important policy insights, particularly for designing strategies to enhance household financial security and ensure equitable access to essential services, especially in the context of ongoing health insurance reforms. 5.1. Access to health insurance significantly lowers precautionary savings The results presented in Table 4 show that access to health insurance is negatively associated with household saving rates across all income quintiles, with an average decrease of approximately 13.8% (Panel A, column 1). When instrumenting access to health insurance using the prevalence of chronic disease, the effect on savings becomes much stronger. In this case, health insurance reduces precautionary savings by about 58% on average (Panel B, column 1). Disaggregating by income quintile, the results reveal that poorer households experience the most substantial reductions in savings. Specifically, for the bottom quintile, access to health insurance is associated with a decrease in savings of about 66% (Panel B, column 2), and the second quintile shows a reduction of approximately 73% (Panel B, column 3). These findings suggest that health insurance significantly reduces precautionary savings of insured households, particularly among lower-income households. Table 4 Effect of having access to health insurance on household savings, by income quintile (1) (2) (3) (4) (5) (6) All income quintiles Bottom quintile Second quintile Third quintile Fourth quintile Top quintile Panel A. OLS Access to health insurance -0.138*** -0.247*** -0.122*** -0.153*** -0.150*** -0.0861** (0.0131) (0.0547) (0.0341) (0.0261) (0.0236) (0.0279) R² 0.193 0.058 0.051 0.039 0.094 0.086 Panel B. 2SLS Access to health insurance -0.580*** -0.661** -0.732* -0.459** -0.532** -0.473** (0.0974) (0.250) (0.334) (0.163) (0.200) (0.161) Kleibergen-Paap F-statistic 56.412 13.384 6.018 16.641 11.528 19.755 Kleibergen-Paap p-value 0.000 0.000 0.000 0.000 0.000 0.000 N 6 357 1 092 1 427 1 403 1 388 1 047 Notes: This table presents second-stage estimates analyzing the effect of access to health insurance on households’ saving decision and across income quintiles. ***, **, and * indicate significance at the 1%, 5%, and 10% levels. This pattern suggests a potential crowd-out effect of health insurance on savings, particularly for lower-income households, where access to health insurance can reduce the need for precautionary savings. Research by Finkelstein et al. (2012) provides support for this idea, showing that the expansion of Medicaid significantly reduced financial burdens for lower-income families, which may have contributed to enhanced overall economic well-being. Households without health insurance typically rely on buffer savings to protect themselves against unexpected health-related expenses, highlighting the potential link between health insurance access and saving behavior. Additionally, other studies support that health insurance could influence household financial decision-making. For instance, Duflo ( 2003 ) discusses how interventions related to enhanced health insurance coverage may shift household behaviors by offering a safety net, potentially leading to greater investment in health and education. Similarly, Karlan et al. (2016) suggest that access to health insurance can encourage households to invest more in income-generating activities and durable goods. Furthermore, Schmeiser and Carretta (2017) point out that health insurance could facilitate consumption smoothing while also influencing saving and investment decisions. This idea is reflected in studies on universal health coverage in developing countries, where similar dynamics have been observed in Taiwan (Chou et al., 2003), Vietnam (Wagstaff & Pradhan, 2005 ), and China (Zang et al., 2012). All in all, these findings indicate that health insurance may play an important role in shaping household financial decisions, particularly in lower-income contexts. Understanding these potential dynamics is crucial for policymakers aiming to enhance household resilience against health-related shocks. As noted by Barnett and Mahajan (2021), the relationship between health security and economic stability suggests that health insurance expansion could be a valuable tool for improving household financial well-being. 5.2. Access to health insurance has a positive distributional effect on expenditures In addition to reducing the cost of accessing medical care, universal health coverage offers financial protection against “impoverishing payments” and “catastrophic payments” (Wagstaff et al., 2015). An effect often highlighted in the literature is that of an increase in consumption linked primarily to the purchase of durable goods. In general, research findings on the subject confirm that universal health insurance coverage increases well-being by reducing the likelihood of calling in sick at work (Wagstaff & Manachotphong, 2012 ), increasing the likelihood of using preventive healthcare (Ghilslandi et al., 2015) or reducing overall healthcare expenditure (Limwattananon et al., 2015 ). Table 5 The distributional effect of having access to a health insurance on household expenditures I. By income quintile (1) (2) (3) (4) (5) (6) All income quintiles Bottom quintile Second quintile Third quintile Fourth quintile Top quintile Access to health insurance 0.689*** 0.526* 0.620* 0.476** 0.868*** 0.716*** (0.0974) (0.250) (0.334) (0.163) (0.200) (0.161) Kleibergen-Paap F-statistic 56.412 13.384 6.018 16.641 10.539 17.577 Kleibergen-Paap p-value 0.000 0.000 0.000 0.000 0.000 0.000 N 6 357 1 092 1 427 1 403 1 388 1 047 II. By expense category (7) (8) (9) (10) (11) (12) Total consumption Health Food Education Housing Transportation Access to health insurance 0.689*** 2.799*** 0.00311 1.190*** 1.584*** 0.202 (0.107) (0.409) (0.108) (0.325) (0.186) (0.261) Kleibergen-Paap F-statistic 56.412 57.874 56.412 43.293 56.412 56.272 Kleibergen-Paap p-value 0.000 0.000 0.000 0.000 0.000 0.000 N 6 357 6 357 6 357 6 357 6 357 6 357 Notes: This table presents estimates analyzing the distributional effects of access to health insurance on households’ expenditures across income quintiles and by expense categories. ***, **, and * indicate significance at the 1%, 5%, and 10% levels. Using the same instrumental variable approach, we examine the effect of health insurance on household consumption. The results, presented in Table 5 , show that access to health insurance has a distributive effect on household consumption across all income quintiles. By expenditure category, we find a significant association between access to health insurance and an increased spending on health, education, and housing – expenditures that are associated with increased well-being. These results show that the implications of health insurance go beyond reducing healthcare costs and crowding out precautionary savings as it can contribute to increasing overall household well-being. These findings corroborate those of Gonzalez et al. (2016), who demonstrated that insured individuals are more likely to adopt preventive healthcare behaviors, ultimately reducing the incidence of illnesses and associated costs. Policy recommendations. The results of this analysis underscore the critical role that health insurance plays in improving household well-being and fostering economic resilience. To maximize these benefits, policymakers can take a comprehensive approach that integrates health insurance with targeted financial support. As Meyer and Sullivan (2012) note, policies that simultaneously improve access to health insurance and provide direct financial support can create a comprehensive safety net, particularly for vulnerable households. For example, linking health insurance reforms with subsidies or transfers for essential goods, such as housing, education, and transportation, can help households reallocate funds previously spent on necessary medical expenses. This reallocation would enhance household financial resilience, particularly for those in lower-income brackets. Moreover, to further encourage households to maintain savings, incentives, such as tax-advantaged savings accounts or matching programs, can be introduced. Given that Morocco already has a strategy for financial inclusion, these initiatives could complement existing efforts by fostering better savings habits and increasing household financial resilience. The rollout of universal health coverage should be accompanied by educational campaigns that emphasize the importance of preventive healthcare and financial planning. These campaigns would not only encourage healthier lifestyles but also equip households with the knowledge to manage their resources more effectively. Limitations. While this study provides valuable insights into the relationship between health insurance and household behavior, it is important to acknowledge its limitations. The analysis is based on cross-sectional data, which, while informative, restricts our ability to capture the long-term effects of health insurance on households’ saving and consumption behavior. Given that Morocco's universal health coverage reform, the AMO, is still being rolled out, comparing the current data with earlier periods may not have been particularly relevant either, as no specific policy interventions related to universal health insurance were implemented before this reform. Therefore, the study highlights the key structural determinants of household saving behavior and their relationship to access to health insurance, providing useful conclusions for understanding the potential effects of the ongoing reform and informing policy-making ex-ante. Future research incorporating longitudinal data will be crucial for capturing the evolving impacts of universal health insurance on household consumption and saving behavior. Such studies would offer deeper insights into how the expansion of the AMO influences long-term economic resilience across different income groups. By analyzing the dynamic interactions between health coverage and financial decision-making over time, future research could provide a more nuanced understanding of how access to health insurance affects both immediate and long-term household well-being. These insights will be valuable for policymakers seeking to enhance household financial security as the reform progresses and will help to design more effective policies that support health and economic resilience across all income brackets. 6. Conclusion This paper presents new evidence on the relationship between health insurance and household savings in Morocco. First, it shows that the saving behavior of households is dominated by the precautionary motive because of the exposure of a large part of the population to uncertainty linked, among other things, to frequent income fluctuations, health risk, and limited social insurance coverage. Second, exploiting exposure to chronic disease as an exogenous source of variation in health risk, it points to two positive effects of health insurance: a crowd-out of households’ precautionary savings and a distributional effect on household expenditures. The crowd-out effect downwardly influences household savings due to a perceived reduction in risk that decreases the prevalence of the precautionary motive, as households feel more secure in their financial planning due to the availability of health insurance coverage. The crowd-out effect does not uniformly impact all income quintiles, as low-income households experience a more significant outcome. High income households may be less sensitive to this effect, maintaining some of their initial savings’ strategies. The implications of crowd-out on household savings carry significant policy relevance. Chetty et al. ( 2014 ) suggest that understanding crowd-out effects is crucial for designing health insurance programs that minimize adverse impacts on saving behavior. Thus, policymakers are ought to consider the balance between providing necessary health coverage and encouraging families to maintain adequate savings for future needs. As Morocco introduces the generalization of the AMO health insurance coverage, experimental or quasi-experimental research is essential to understand these dynamics better and inform policies that support both health coverage and financial resilience of households. As for the distributional effects of health insurance on household expenditures, they reveal substantial benefits, particularly for low-income households. Health insurance not only alleviates direct medical costs but also facilitates better financial planning and investment in non-health related goods. Continued research in this area is essential for informing policy decisions aimed at improving health equity and economic resilience across different income quintiles. Declarations Author Contribution S.L. and P.F. jointly conceived the study and designed the research methodology. S.L. conducted the empirical analysis, interpreted the results, and drafted the main manuscript text. P.F. refined the analytical approach, and provided critical revisions to the manuscript. Both authors reviewed and approved the final version of the manuscript. Data Availability declaration : The data used in this study are sourced from the High Commission for Planning (HCP) of Morocco. Due to institutional restrictions, the datasets are not publicly available. However, researchers may request access to the data through the High Commission for Planning’s official website (www.hcp.ma) or by directly contacting the institution. Where applicable, the code and methodological documentation used for data processing and analysis are available upon request from authors. Competing Interests: The authors declare that there are no competing interests related to this research. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. References Abdelkhalek, T., Arestoff, F., El Mekkaoui de Freitas, N.E., & Mage, S. (2010). 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The effect of urban resident basic medical insurance on household consumption. Economic Research Journal 7, 75–85 (in Chinese). Footnotes In Morocco, typical two-story houses are often designed to accommodate extended families, with multiple generations living together. This setup can provide financial benefits by pooling resources and sharing living costs, which helps mitigate income fluctuations and economic uncertainty. For more information on Moroccan households’ living conditions, visit the regularly updated HCP website at https://www.hcp.ma/Conditions-d-habitation_r458.html . Literature on "consumption smoothing" and "risk sharing" is very extensive and work on developing countries has strongly influenced the mainstream research literature. For an extensive review of the literature, see Townsend (1995), Deaton ( 1997 ), Dercon ( 2002 ), and Morduch (2004). Note that dependent family members benefit from the coverage of the health insurance that the head of the household has access to. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6145707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440812053,"identity":"c20294e4-4935-4e7a-8d9e-8d53ea649060","order_by":0,"name":"Sara Loukili","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYNCCAgYGCWbmhgMf2IAcCcYGIrQYgLQwNhycQZoWBsYGZh6wFgKK5dvPPvzAYGCTOLOdsfGwTZld4nbp5rYHHxhsEnHZZXAm3ViCwSAtcTbQYYdzziUn7pxzsN1wBkMabi0MaUCXGBxOnAfSktvGnLjhRmKbNA/DYZxa5PufMf+Aa7Fsq4do+cPwH6cWhhtpbGBbwA5jbDsM0cLAcAC3w248Y7NIMEgzntkMDOSec8eNd85IbJPsMUg2xu2wNOYbHypsZGecP3z4w4+yatntEunPJH5U2MnidBgIJKAGCIIkEpCkeBSMglEwCkYEAADeQ1qy0kdRnwAAAABJRU5ErkJggg==","orcid":"","institution":"Bank AL-Maghrib (Central Bank of Morocco)","correspondingAuthor":true,"prefix":"","firstName":"Sara","middleName":"","lastName":"Loukili","suffix":""},{"id":440812054,"identity":"30377a0e-5724-451c-a7ce-ffe0729209a0","order_by":1,"name":"Patti Fisher","email":"","orcid":"","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Patti","middleName":"","lastName":"Fisher","suffix":""}],"badges":[],"createdAt":"2025-03-03 11:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6145707/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6145707/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81015515,"identity":"52ee159e-7885-4393-a8f7-ce1b5b6b60df","added_by":"auto","created_at":"2025-04-21 08:51:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between health expenditures and gross savings, (% GDP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors’ elaboration. Note: Used data comes from the latest available World Bank national accounts data (2021). Are included 95 countries that are classified by high-income, upper-middle-income, lower-middle-income and low-income groups based on the latest World Bank country classifications by income (2022-2023).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6145707/v1/b6f715e90d536180106d4b5a.jpg"},{"id":81015516,"identity":"794ca39b-ccc3-4379-bbb7-135e45fabf6a","added_by":"auto","created_at":"2025-04-21 08:51:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve for the baseline specification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: The grey 45-degree dash line traces the reference ROC for a coin toss. Observations higher than the reference cutoff set to 0.50 are considered successful.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6145707/v1/fbded18db733456197d87687.jpg"},{"id":81015517,"identity":"16bf1e19-e65e-40cb-a08b-90a6fe1bcc10","added_by":"auto","created_at":"2025-04-21 08:51:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve for the baseline vs. robustness checks specifications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotes: The grey 45-degree dash line traces the reference ROC for a coin toss. Observations higher than the reference cutoff set to 0.50 are considered successful.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6145707/v1/d2810480e70346635aafd2d2.jpg"},{"id":81016437,"identity":"1f195694-9629-4ae2-a950-34b88a006f95","added_by":"auto","created_at":"2025-04-21 08:59:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50582,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolution in shares of non-food expenditures (in %) by product group in the non-food budget\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors’ elaboration. Note: Used data comes from the High Commission for Planning: results from the National Survey on Household Consumption and Expenditures, the latest round being 2014 (1985, 2001, 2007, 2014).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6145707/v1/a9263e326277be3861980db3.jpg"},{"id":81015521,"identity":"8e0053cc-220b-43d2-9cc6-63224ae03012","added_by":"auto","created_at":"2025-04-21 08:51:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHousehold saving behavior by income quintile and health status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors’ elaboration based on the studied sample of households. Notes: these figures plot the marginal effects of the probability of saving based on estimates from equation (1) presented in Table 2 (baseline).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6145707/v1/5f05155f5e85a7468ce6dfc0.jpg"},{"id":81015523,"identity":"782c9e75-1639-4ebf-bdfd-b412495401ac","added_by":"auto","created_at":"2025-04-21 08:51:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":46205,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOccurrence of chronic illness by age and access to health insurance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors’ elaboration based on the studied sample of households.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6145707/v1/08c857dc6f8360f1e17bb354.jpg"},{"id":84699297,"identity":"2f25f9e0-091d-47eb-8eb6-805878c82d6d","added_by":"auto","created_at":"2025-06-16 11:09:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2061913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6145707/v1/d4662b2f-4941-4fa4-8053-01ee5de0f2b5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Insurance and Household Savings: Evidence from Morocco","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSavings are a potential source of investment, and therefore of economic growth, and play a role in the monetary transmission mechanism (Beckmann, Hake, \u0026amp; Urvova, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Savings is also useful for households and can provide, for example, protection against life\u0026rsquo;s hazards such as unemployment and unexpected expenses (precautionary saving). It can be used to plan short- and medium-term expenditures, such as financing children's education or purchasing property, particularly in the case of cash-strapped households (Browning \u0026amp; Lusardi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Thus, the study of saving behavior is important for assessing households\u0026rsquo; exposure to risk and uncertainty, as well as their ability to cope with economic and financial shocks (Kulikov, Paabut, \u0026amp; Staehr, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To date, there have been few studies of household saving behavior in North Africa, Morocco in particular. In 2010, Abdelkhalek et al. conducted a micro-econometric analysis of the determinants of household savings in a poor region of Morocco, Essaouira. To our knowledge, this is the only study to have adopted such a microeconomic approach.\u003c/p\u003e \u003cp\u003eOur contribution to the literature is twofold. Firstly, we study the saving behavior of Moroccan households, which we find to be essentially precautionary, at a microeconomic level using nationally representative household data. Secondly, to better understand the role of uncertainty in the formation of precautionary savings, we propose an analysis of the relationship between health insurance and household saving behavior. The study contributes to the literature on household saving behavior in developing economies, and its results are useful for including household behavior in macroeconomic analyses and better-guiding policymaking.\u003c/p\u003e \u003cp\u003eMost research into household saving behavior is based on the permanent income and life-cycle hypothesis, which in its original formulation assumes that consumption and saving are based on perfect anticipation of the future and ignores the role of uncertainty. Responding to a medium- to long-term precautionary motive, liquid savings, or contingency funds, act as a buffer against medium-term fluctuations in disposable income (Deaton, 1991). The precautionary motive encourages thus the acquisition of greater wealth to compensate for background risk. As such, health status is often considered a significant measure of background risk in many studies suggesting that anticipated medium-term health expenditure can increase savings (Palumbo \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Dynan, Skinner, and Zeldes \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Access to medical insurance can, however, mitigate the burden of medical expenditure. In other words, insurance coverage affects risk preference through income and substitution effects (Hubbard et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Carroll, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), crowding out savings by reducing background health risk (Browning \u0026amp; Lusardi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). While this relationship has been extensively studied and proven in the case of developed economies, with their more extensive medical coverage systems, the effect of health insurance in developing countries has not been so thoroughly studied.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the relationship between public health expenditure and gross savings worldwide, revealing a U-shaped curve: higher public health expenditure is typically associated with lower savings (likely precautionary savings) in middle-income countries, and with higher savings in high-income countries (suggesting non-precautionary savings). In the case of Morocco, this dynamic is particularly relevant given the ongoing political and economic reforms aimed at expanding compulsory health insurance (Assurance Maladie Obligatoire, AMO). As these reforms unfold, understanding how health insurance influences household saving behavior becomes crucial for assessing their potential impact on both household financial security and broader economic stability, making this research highly pertinent to policymakers and the central bank in guiding monetary and fiscal policies, such as adjusting interest rates, shaping savings incentives, and ensuring the stability of financial systems in response to evolving household saving patterns and consumption preferences.\u003c/p\u003e \u003cp\u003eSeveral recent studies (Atella et al. (2005) for Italy; Chou et al. (2003) for Taiwan; Wagstaff and Pradhan, (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) for Vietnam; Zang et al. (2012) and Cheung and Padieu (2015) for China) reveal economically significant effects of extending social insurance programs on household consumption and savings. For example, Zang et al (2012) find that the introduction of basic medical insurance increases urban households' non-medical consumption by 13%. Similarly, in Taiwan, Chou et al. (2003) found that the generalization of national health insurance contributed to a decrease in household savings. The results of these and other studies support the precautionary savings hypothesis and suggest that studying the impact of expanding compulsory health insurance in Morocco is particularly important, as it could similarly alter household saving behavior.\u003c/p\u003e \u003cp\u003eMorocco\u0026rsquo;s health care system is currently characterized by significant disparities in access and quality. Although the government implemented some health initiatives, a large portion of the population remains uninsured or underinsured, particularly in rural and underserved areas. As a result, many families face substantial out-of-pocket expenses for medical care, which contributed to financial insecurity. According to the World Bank, approximately 60% of health care spending in Morocco is financed through private out-of-pocket payments, leading to increased household vulnerability and limited access to necessary services. In response to these challenges, the Moroccan government has initiated a reform aimed at achieving universal health insurance coverage. This reform is designed to expand access to health services and reduce the financial burden on households.\u003c/p\u003e \u003cp\u003eWe argue that the universal coverage proposed by this reform can significantly alter household behavior regarding saving and consumption. As families gain access to health insurance coverage, they are likely to experience a reduction in health-related financial background risks. Consequently, the need for precautionary savings\u0026mdash;traditionally set aside for unexpected medical expenses\u0026mdash;may decline. Moreover, with increased financial security, households might redirect their resources toward consumption, investing more in education, housing, and durable goods, which contributes to enhanced well-being. This shift can stimulate economic growth as higher consumer spending promotes demand for local products and services, making the study of household saving behavior a timely and important undertaking to better guide policy makers.\u003c/p\u003e \u003cp\u003eTo study the potential effect on household saving behavior in Morocco, we propose an instrumental variable approach that exploits differences between households with and without access to health insurance in a setting prior to the introduction of the ongoing AMO reform. Controlling for the health status of the head of household, we find evidence of a crowd-out effect of health insurance on savings across all income quintiles. The coefficient magnitudes are greater for the poorest households. We also find a positive distributional relationship between access to health insurance and household consumption. By reducing their exposure to health risks, health insurance increases household spending on welfare improving expenditures such as education. These results testify to the potential positive impact of the extension of health insurance coverage on households\u0026rsquo; well-being, thanks to the overall reduction in risk exposure, and particularly for poorer households.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section 2 presents summary data and descriptive statistics. Section 3 establishes the precautionary nature of households\u0026rsquo; saving behavior in Morocco. Section 4 describes the empirical and identification strategy proposed to study the relationship between health insurance and households\u0026rsquo; savings. Results and a discussion of channels and mechanisms are presented in Section 5 while section 6 concludes.\u003c/p\u003e"},{"header":"2. Data and summary statistics","content":"\u003cp\u003eThe analysis presented in this paper is based on data from the National Survey of Household Consumption and Expenditure, a nationally representative household survey carried out by the Haut Commissariat au Plan. To process the data, we begin by examining the distributions of savings rates. The calculation of household savings rates revealed extremely low observations, which are often explained in the empirical literature by households under-reporting their incomes for various reasons. To avoid arbitrarily deleting observations, we eliminate outliers above the 97.5th percentile and below the 2.5th percentile. Subsequently, our sample consists roughly of 6,357 households representing various social strata and regions of Morocco. In our analysis, we use several socio-demographic variables, such as the age of the household head, marital status, employment status, education level, and household size. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents summary statistics for the full sample and by access to health insurance. In this table and in the rest of the document, households are represented by the head of the family.\u003c/p\u003e \u003cp\u003eBy age group, heads of households aged between 40 and 54 are the majority, at around 42% of our sample. The proportion of other age groups is around 20% each, giving a balanced representation of households across the life cycle. Household heads aged between 40 and 54 are more likely to have health insurance, particularly if they are males. Over two-thirds of the sample (80%) are married heads of household, followed by widowed heads of household (11%), mainly females, and then single and divorced heads of households, with 3% and 2%, respectively. The average household size is around 5 people, with rural households being larger than urban ones. In terms of average level of education, heads of households with no education, as opposed to those with an average or higher level of education, account for over 50% of the sample, followed by those with an elementary diploma (low level of education), who represent around 29% of the sample. There are significant differences between the groups studied, as heads of households who hold health insurance tend to be better educated and are around 15% more likely to hold higher levels of education.\u003c/p\u003e \u003cp\u003eRegarding work-related variables, we observe that salaried and self-employed heads of households make up the bulk of the sample, representing up to 73%. We also examine the sample by income quintiles, where households are divided into five classes of almost equal proportion in our sample. Moreover, as an indicator of assets and wealth accumulation, we consider the home ownership and tenure status of the households. More than 75% of households own their home, 4% own it with a mortgage, and 15% of households are renters. Household wealth is also indicated by housing type. Most of those surveyed live in traditional or modern Moroccan houses\u003csup\u003e1\u003c/sup\u003e, around 61%, while 28% live in rudimentary housing conditions, such as shanty towns, and less than 10% live in apartments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample summary statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealth insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3)\u0026ndash;(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA. Head of household characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 25\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,030**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 40\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,080***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,073***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIncome quintile.\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBottom quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,157***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,115***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,081***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop quantile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,205***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMarital status.\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,067***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivo. / Sep.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,015***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,051***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB. Head of household's labor market characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQualification (education proxy)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,00***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,380***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMed. Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,072***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEmployment status\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,00***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive/ employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,116***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive/ unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,099***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD. Assets Characteristics (real estate as proxy)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHousing tenure\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeowner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,164***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeowner with mortgage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,080***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse residence for free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eProperty type\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,025***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,173***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoroccan house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,274***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGeographic lcoation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0,339***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eProperty amenities\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic amenities (water, electricity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,391***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of rooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,0003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Authors\u0026rsquo; calculations. The HCP 2014 National Survey of Household Consumption and Expenditure. Notes: Unweighted statistics. The first column reports summary statistics for the whole sample of surveyed heads of households. The following two columns report the same statistics when the sample is restricted to households with (column 2) or without (column 3) access to a health insurance. Column 4 reports the differences and p-values from the t-tests on the quality of means reported in columns 2 and 3.\u003c/p\u003e"},{"header":"3. The precautionary nature of household saving behavior in Morocco","content":"\u003cp\u003eIn this section, we aim to explore the key determinants of household saving behavior in Morocco, focusing on the precautionary motives that influence families' financial decisions. By examining various factors such as income, education, and household composition, we seek to understand the primary drivers of saving patterns within Moroccan households, shedding light on how these factors contribute to the formation of precautionary savings.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Literature review\u003c/h2\u003e \u003cp\u003eHousehold saving involves the decision to increase asset accumulation or limit current consumption to achieve financial goals (Chang, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Saving and asset accumulation, which are almost universally considered desirable goals (Beverly, McBride, and Schreiner, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), have been the subject of a considerable number of theoretical and empirical studies in the literature (Canova, Rattazzi and Webley, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The empirical literature supports a number of variables that influence household savings behavior (Fisher \u0026amp; Montalto, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Many of the determinants of saving included in empirical studies at the household level are household demographics and wealth (Aghevli et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Home ownership and income were found to have a positive association with saving in Australia (Harris, Loundes and Webster, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In the same study, more children in a household and having a mortgage were negatively associated with saving. Again, based on Australian data, Finley and Price (2014) found that education and income were positively related to saving. Research generally shows a positive relationship between income and saving (Avery \u0026amp; Kennickell, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Bosworth, Burtless, \u0026amp; Sabelhaus, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Kulikov et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a study using data from Portugal, Alves and Cardoso (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found a positive association between home ownership and education, on the one hand, and savings, on the other. A negative relationship was found between household size and savings, as well as between unemployment and savings. Education has also been shown to be positively related to saving behavior in the US and Korea, respectively (Hefferan, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Hong, Sung and Kim, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), as has home ownership (Fisher and Montalto, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Le Blanc, Porpiglia, Teppa, Zhu and Ziegelmeyer (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) investigated the determinants of saving using Household Finance and Consumption Survey data for eurozone countries and found that larger households are less likely to save, as are those who are self-employed, unemployed, or retired. Based on Irish data, Le Blanc (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that larger households or those with an unemployed head are more likely to report negative savings.\u003c/p\u003e \u003cp\u003eBeckmann et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found that the young and elderly are less likely to save than the middle-aged, but that the propensity of older respondents to dissave is lower than would be predicted by the life-cycle hypothesis. Beckman et al. (2013) also found a significant positive association between education and saving, as well as between being employed or self-employed and saving. Age is positively associated with saving, while age squared is negatively associated with saving. Bosworth et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) found that savings rates in the U.S. rise until the mid-to-late 60s, after which savings rates fall. According to Fuchs-Sch\u0026uuml;ndeln, Masella, and Paule-Paludkiewicz (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), cultural factors are also important determinants of savings behavior in different countries.\u003c/p\u003e \u003cp\u003eThere are few empirical studies on savings behavior in Morocco. In their study of households in a poor region of Morocco, Abdelkhalek et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that income, household size, gender of the household head, and education of the household head were closely related to savings behavior. Studying rural and urban households separately, the researchers found that income was an important determinant for urban households, while education of the household head was more determinant than income for rural households. Household size had a negative relationship with savings for urban households but was not significant for rural households.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. A setting for investigating the characteristics of household saving behavior\u003c/h2\u003e \u003cp\u003eThe theoretical framework guiding this study is based on the life-cycle hypothesis proposed by Modigliani and Brumberg (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1954\u003c/span\u003e) and the permanent income hypothesis presented by Friedman (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). The life-cycle framework is vast and includes many possible empirical models (Browning \u0026amp; Crossley, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). It states that consumption is a function of wealth, expected lifetime earnings and the number of years to retirement.\u003c/p\u003e \u003cp\u003eTo study household saving behavior in Morocco, we specify a regression model controlling for the sociodemographic characteristics of the household and its head (base model) and adding controls for additional sensitivity checks. As we are unable to verify precisely which household characteristics are more likely to be correlated with income underreporting, we prioritize analysis of the probability of having a positive savings rate. We therefore estimate a logit model in which the dependent variable takes the value 1 if a household declares that it has consumed less than its income (i.e., a positive savings rate), and zero otherwise. The probability of saving for household i is written as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{i}^{*}={X}_{i}\\beta\\:+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{i}=\\:\\left\\{\\begin{array}{c}1\\:if\\:{Y}_{I}^{*}\\ge\\:\\:0\\\\\\:0\\:if\\:{Y}_{i}^{*}\u0026lt;\\:0\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the observed binary dependent variable which takes the value of 1 if the household has a positive saving rate, 0 otherwise, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}^{*}\\)\u003c/span\u003e\u003c/span\u003eis the underlying latent variable that indexes the household\u0026rsquo;s saving status, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is row vector of values of K regressors of the ith saving household, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is a vector of parameters to be estimated, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e is an error term which is assumed to have standard normal distribution. We include in the vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e the characteristics of the household and its head that are derived from theoretical and empirical literature on lifecycle and permanent income hypothesis. They include age cohorts, total household income, head of household's employment status, marital status and education level, as well as other controls for household demographic structure and wealth indicators (i.e., home ownership, living conditions, etc.).\u003c/p\u003e \u003cp\u003eThe empirical and theoretical literature relates most recorded savings motives to the life-cycle model, primarily the old-age provision motive. Thus, including the age of the head of household captures saving behavior changes over their lifecycle, and we keep the age group 40\u0026ndash;54 as a benchmark because this group typically has higher savings rates and income and can operate significant behavior corrections as retirement age looms closer. The basic lifecycle hypothesis has been extended to include many savings motives among which we mainly find the precautionary motive (Gourinchas and Parker, 2002) posed by the longevity risk or out-of-pocket expenses related to health risk among other life-cycle events (Hubbard, Skinner, and Zeldes, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). As income is known to be strongly correlated with savings, we include income quintiles, with the third quintile serving as the benchmark. The education level of the household head adds an additional control for income, as educational attainment is correlated with income and income-risk (e.g., lower educational attainment is strongly correlated with informality and income fluctuation). Since wealth and assets are not available in the survey data, we also include household ownership status, as a proxy for assets or wealth accumulation, which has been shown in the literature to have a relationship with saving behavior (Turner \u0026amp; Luea, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Fisher \u0026amp; Montalto, 2010; Harris et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Property type and property amenities are included as additional proxies for wealth accumulation, and we also adjust for household size in the empirical model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The lifecycle hypothesis partially explains household saving behavior\u003c/h2\u003e \u003cp\u003eThe results of the baseline model are consistent with economic theory (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; e.g., Friedman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Modigliani \u0026amp; Brumberg, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1954\u003c/span\u003e), but we note some specificities of the Moroccan context. First, income is strongly correlated with household saving behavior. We find differences in saving rates between the extreme income quintiles, invalidating the assumption of homogeneity in savings behavior in the permanent income hypothesis. The higher the income, the higher the saving rate. This finding is consistent with several empirical studies showing heterogeneity in savings behavior according to income level and supporting the precautionary savings argument.\u003c/p\u003e \u003cp\u003eA closer look shows that the data are only partially supportive of the lifecycle hypothesis. Relative to households with heads aged between 40 and 54 years, the probability of saving in Moroccan households is increasingly important for older households. One of the explanations, also widely advanced by the literature, is that liquidity constraints raise the saving rate for older households in view of constraining credit regulation (Browning \u0026amp; Lusardi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Still, testing the life-cycle hypothesis is difficult because cross-sectional data may confuse an \u0026ldquo;age effect\u0026rdquo; with a \u0026ldquo;generation effect\u0026rdquo;. In a developing economy, such as Morocco, the younger generations enjoy a higher average standard of living than the generations that preceded them, increasing their consumption relative to their savings. On the other hand, it seems that households do reason in terms of lifecycle, as shown by the saving rates during working ages (40 to 54 age group). Households might also have a desire to pass on an inheritance and/or smooth retirement consumption, which also contributes to explaining the high saving rates at older ages. Finally, this high saving rate among older households might also be explained by income uncertainty because very few have a pension income and rather rely on informal sources of retirement insurance.\u003c/p\u003e \u003cp\u003eTo better understand the relationship between age and savings, we have sought to characterize the behavior of working-age households headed by people aged between 29 and 59. We test the hypothesis that savings rates differ according to whether household income comes mainly from capital or labor. To do this, we control for the employment status of the household head: inactive, employed, and unemployed. Compared to households headed by employed persons, households headed by unemployed persons are, on average, 4.7 percentage points more likely to save, which can be explained by the build-up of buffer savings in anticipation of income variations, particularly in the case of seasonal workers and the self-employed. All other things being equal, this observation is reversed for the economically inactive. Taking employment status into account therefore provides us with two characteristics of Moroccan household saving behavior. The first stems from the previous results, namely the evidence of behavior closer to the lifecycle hypothesis at working age than at retirement age. The second is linked to the Kaldorian hypothesis that savings rates are differentiated according to the nature of resources (income from work, annuities, capital, and even replacement income when the entrepreneur receives a pension).\u003c/p\u003e \u003cp\u003eReal estate, and especially the main dwelling, makes up the bulk of household assets. Certainly, for the same income level, a household that owns its home has a higher standard of living than a renter. Similarly, homeowners as opposed to owners with a mortgage are at different standards of living, all things being equal. The acquisition of a home plays a fundamental role in the constitution of savings and the financial effort dedicated to it modifies households\u0026rsquo; wealth structure. Compared to a household that owns its home, renters or homeowners with a mortgage are on average less likely to build up savings. This is because renter households are forced to cut back on consumption to pay rent. Looking at housing characteristics, we find that lower quality housing encourages the formulation of savings that are more likely to be precautionary. Facilitating access to home ownership, and more so to decent housing, seems needed as it should support lower-income households.\u003c/p\u003e \u003cp\u003eThe values of the AUROC curves, situated well above the commonly accepted threshold of 0.50, indicate that the estimated models effectively capture the association between household characteristics and the probability of saving. This aligns with findings in the literature, such as those by Duflo and Saez (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), who demonstrated that demographic factors significantly influence saving behaviors. The strong AUROC values suggest that these models not only distinguish between savers and non-savers but also highlight the critical role that specific household characteristics play in shaping saving behavior.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Income related uncertainty explains an important part of households\u0026rsquo; propensity to precautionary saving\u003c/h2\u003e \u003cp\u003eTo assess the sensitivity of our estimates, we provide additional controls for employment status, focusing on self-employment, and geographic location of residence (urban vs. rural).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. Controlling for self-employment\u003c/h2\u003e \u003cp\u003eIn our baseline specification, we control for employment status based on three conditions: inactive, active-employed, and active-unemployed. However, among employed heads of households, the self-employed could be more exposed to income variation risk than employees. Thus, savings might be used as a substitute for income variation or even for the unemployment insurance system. Indeed, several studies\u003csup\u003e2\u003c/sup\u003e have shown that savings can be a medium-term substitute for unemployment insurance systems, allowing the transfer of resources from a period of employment to a period of unemployment (Deaton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Dercon, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Brunet \u0026amp; Lesueur, 2004). Similarly, the self-employed could have a desire to accumulate buffer savings in anticipation of income shocks. Compared to employees, households with a self-employed head are on average 1.26 percentage points less likely to save (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026ndash; column 2). A potential explanation is the association of business and personal finances by the self-employed especially since they are, for the most part, uneducated (70.5% of the self-employed are estimated to have no diploma (HCP). Savings of the self-employed may very well be explained by transitory rather than permanent income (Kulikov et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The self-employed also tend to be active in the informal sector where income is, often, underreported. The absence of statistical significance of the marginal effects coefficient for self-employment, however, suggests that our baseline specification is already a good fit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. Controlling for geographic location of residence\u003c/h2\u003e \u003cp\u003eBoth urban and rural households face significant common and specific risks, resulting in large and, often, consistent income fluctuations. The need for safety nets manifests itself primarily in the need to ensure purchasing power during crises, especially for rural households dependent on agriculture (smallholders or seasonal workers) and for rural and urban self-employed workers. Climatic risks, economic fluctuations, and many specific shocks make these households vulnerable to shocks and savings can constitute a self-insurance mechanism. As a sensitivity check, we re-estimate our model and introduce a control for area of residence to our baseline specification (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026ndash; column 3). \u003cem\u003eCeteris paribus\u003c/em\u003e, rural households are, on average, 7 percentage points less likely to save than urban ones, which might be explained by their constant exposure to income shocks with agriculture being their main economic activity.\u003c/p\u003e \u003cp\u003eOne of the limitations of this analysis is the difficulty in identifying the hazards faced by rural households. In an environment where climate-related events are the main cause of economic shocks, affecting income, other common or specific shocks may also intervene in the labor market. Indeed, rural households are often involved in a variety of activities ranging from on-farm and off-farm work to seasonal migration to diversify income sources (Rosenzweig \u0026amp; Binswanger, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Given the limited available data, our conclusions, specifically related to the development of social safety nets and labor insurance policies that would contain the vulnerability of rural households to income variations, remain unresolved. From an empirical point of view, a quasi-experimental approach could be a useful way to analyze the relationship of occupational status and sources of income with the effects of uncertainty on household saving behavior.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Household Savings Baseline with Robustness Checks for Employment Status and Residence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003cp\u003eSaver (baseline)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003cp\u003eSaver (self-employment)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003cp\u003eSaver (rural vs. urban)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eIncome of the household\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBottom quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3499667***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3507391***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3462863***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.192295***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1927524***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1921535***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quintile (benchmark)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1823014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1826594***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.182701***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTop quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3892981***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3898275***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.387916***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLife cycle: age of household head\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0343729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0340597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0366976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 25\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0210025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0214594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0206282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 40\u0026ndash;54 \u003cem\u003e(benchmark)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0167671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0179534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0178889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0420044*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0437783*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0446289*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eEmployment status of household head\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0977868***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1041461***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1055993***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive/ employed \u003cem\u003e(benchmark)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive/ self-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0126023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0042984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive/ unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0479484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0462612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0449162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eEducation of household head\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1677726***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1697697***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1709546***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0890553**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0903811**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0892798**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMed. Education (benchmark)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0832552*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0843645*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0839871*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMarital status of household head\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1585919***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1594368***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1587643***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried \u003cem\u003e(benchmark)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivo, / Sep,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1032579*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1035318*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0967769*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1125659***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1130082***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1119524***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eHousing tenure\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeowner (\u003cem\u003ebenchmark\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeowner with mortgage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0083503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.009844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0133047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUses residence for free\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0005712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0024685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0687585***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0699466***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0754682***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eProperty type\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVilla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1191308*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1200158*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1256468*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04671*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0473561*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.050021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoroccan house \u003cem\u003e(benchmark)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0090073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0097831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0225817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eGeographic location\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.070857**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eProperty amenities\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic amenities (water, electricity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0595469**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0611949***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0969593***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of rooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0695427***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0691807***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0661044***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eHousehold size\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of family members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0173719***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0173603***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0172581***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-Likelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3716,7107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3716,4374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3711,1266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: This table presents baseline and robustness checks specifications. Marginal effects are reported, where Model (1) is the baseline specification for household saving, (2) includes a control for self-employment, and (3) includes a separate control for residence in rural vs. urban geographic location. ***, **, and * indicate significance at the 1%, 5% and 10% levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Identifying the relation between health insurance and household saving behavior","content":"\u003cp\u003eEstablishing the dominance of the precautionary motive in household saving behavior can be further backed by the analysis of the effect of a risk-mitigating tool, such as access to health insurance. In this section, we assess the saving behavior response of households to holding a health insurance. A potential challenge in identifying a relationship between access to health insurance and saving behavior is the endogeneity of health insurance on household consumption decisions. For this, an instrumental variable approach is proposed.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. A Na\u0026iuml;ve approach\u003c/h2\u003e \u003cp\u003eLet \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Insurance}_{i}\\)\u003c/span\u003e\u003c/span\u003e be a dummy variable for whether the household is covered by health insurance\u003csup\u003e3\u003c/sup\u003e. The effect of access to health insurance on the outcome \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Savings}_{i}\\)\u003c/span\u003e\u003c/span\u003e, the saving rate of the household, can be estimated using the following specification:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{Savings}_{i}={\\beta\\:}_{0}+{\\beta\\:}_{1}{Insurance}_{i}\\:+\\:{\\beta\\:}_{2}{C}_{i}+\\:{\\beta\\:}_{3}{H}_{i}+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a set of characteristics belonging to the head of the household, the main decision maker, namely gender, age, education level, and status of employment. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of household level controls linked to a household\u0026rsquo;s income quintile, household size, wealth holdings, proxied by home ownership status, housing tenure, and property type and amenities. This approach, while containing sufficient information that controls for household income and expenditures and, thus, the savings potential, can yield inconsistent results, that can under or overestimate the effect of health insurance coverage on household saving behavior due to the endogeneity of health insurance with household consumption decisions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The endogenous nature of access to health insurance\u003c/h2\u003e \u003cp\u003eAlthough many potential confounding factors affecting household consumption and savings decisions are controlled for, some unobservable characteristics influencing the use of health insurance benefits may distort the estimates. First, the issue of self-selection into health insurance schemes based on the health status of the head of the household or their risk appetite is not relevant in the Moroccan context, primarily due to the structure of the social protection system. In Morocco, access to health insurance is closely tied to employment status, with formal sector workers typically covered by social security, while informal workers often lack coverage. This framework reduces the likelihood of self-selection based on individual health needs and risk appetite, as many households do not have the option to self-select into a health insurance scheme.\u003c/p\u003e \u003cp\u003eMoreover, endogeneity may arise from preferences related to income availability and exposure to risks, which in turn influence household saving rates. Households at different income levels demonstrate distinct saving behaviors shaped by their financial situations and health expenditure preferences. Fonseca et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) illustrate that the growth of healthcare spending in the U.S. from 1965 to 2000 is largely driven by increases in disposable income within a lifecycle model that incorporates endogenous medical expenditures. Furthermore, out-of-pocket healthcare costs often rise due to lifecycle events, such as childbirth and the number of young children in a household (McDonald, 1990; Percival \u0026amp; Harding, 2007). While these expenses tend to be transitional, they can significantly impact a household\u0026rsquo;s ability to save. As a result, the decision to increase health expenditures\u0026mdash;often guided by personal preferences rather than solely by access to health insurance\u0026mdash;can lead to biased ordinary least squares (OLS) estimates, obscuring the true nature of the relationship between savings and health spending.\u003c/p\u003e \u003cp\u003eIn Morocco, a similar trend is evident, as household expenditures on health and other well-being-related expenses have increased significantly in recent years (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This rise can be attributed to heightened demand driven by rising income levels and an aging population, alongside a gradual shift of these expenses from the public sector to households. Notably, health expenditures now account for an increasing share of total consumption expenditures, particularly among lower-income households. This growing trend reinforces our intuition regarding the potential existence of endogeneity in household saving behaviors, as financial circumstances and preferences increasingly dictate how families allocate their resources toward health and well-being-related expenditures.\u003c/p\u003e \u003cp\u003eTo address the endogeneity of healthcare expenditure, we employ an instrumental variable (IV) approach. We exploit the degree of exposure of households to health risks by identifying those who face a permanent risk linked to chronic diseases. Chronic illness can serve as a valid instrument since it affects household expenditures and is naturally randomly assigned (i.e., a random natural treatment exogenous to household choices). The identification strategy is predicated on two key assumptions: firstly, household heads suffering from chronic illnesses are more likely to decrease their expenditures and increase their precautionary savings (buffer savings) to cover future health expenses. Secondly, access to health insurance reduces precautionary savings by covering expected or unexpected health expenses.\u003c/p\u003e \u003cp\u003eBased on our initial analysis, we observed that, in the absence of health risks (i.e., chronic disease), households with insurance exhibit a significantly lower probability of saving across all income quintiles, while uninsured households tend to accumulate buffer savings against unexpected expenses (see Fig.\u0026nbsp;5 \u0026ndash; (a)). This suggests the existence of a crowd-out effect of health insurance on savings. However, the presence of chronic illness alters this dynamic (see Fig.\u0026nbsp;5 \u0026ndash; (b)). While uninsured households may initially have a higher probability of saving as a precautionary measure, the risk of chronic illness significantly diminishes their ability to save, as these buffer savings are continuously constituted but often redirected to cover necessary healthcare expenditures. Given these observations, chronic illness emerges as a potential valid instrument for understanding the relationship between health insurance and household saving behavior.\u003c/p\u003e \u003cp\u003eFurthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the prevalence of chronic illness among heads of households, categorized by age and differentiated by health insurance coverage within the study sample. The data reveals a consistent distribution across all age groups, highlighting similar patterns of exposure to chronic illness risk between those with and without health insurance. This finding is particularly relevant for our analysis, as it suggests that chronic illness is not biased by age or insurance status and is rather random, allowing us to examine more reliably the relationship between health insurance access and household saving behavior without confounding effects from these factors.\u003c/p\u003e \u003cp\u003eThus, the relationship between chronic illness and health expenditures is exploited as an identification strategy where chronic illness is instrumented as follows:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Insurance}_{i}={\\propto\\:}_{0}+{\\propto\\:}_{1}{Chronic}_{i}+\\:{\\propto\\:}_{2}{C}_{i}+\\:{\\propto\\:}_{3}{H}_{i}+{\\nu\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Chronic}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a binary variable indicating whether the head of the household has a chronic illness. The results of the first stage, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, show that the presence of chronic disease is negatively associated with access to health insurance. This suggests that households with chronic illness are less likely to have health insurance, and importantly, that they do not self-select into health insurance as a means to mitigate risk. This relationship holds consistently across all income quintiles, which suggests that the prevalence of chronic illness is not influenced by income levels. The Kleibergen-Paap F-statistic for the excluded instrument exceeds the rule-of-thumb threshold of 10, indicating that chronic illness is a strong instrument. Additionally, the F-statistics from the Montiel-Olea and Pflueger (2013) weak instrument test exceed the critical values at both the 5% and 10% significance levels, confirming that chronic illness is a valid and strong instrument for health insurance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; First stage estimates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll income quintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBottom quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSecond quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThird quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourth quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTop quintile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.127***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.117***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.079**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.157***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.178***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.0301)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap F-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLifecycle controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold level controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome and wealth controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026radic;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eMontiel Olea and Pflueger [2013] test for weak instruments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffective F-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical value for tau\u0026thinsp;=\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical value for tau\u0026thinsp;=\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: This table presents first-stage estimates analyzing the effect of chronic illness across income quintiles. The Kleibergen-Paap F-statistic confirms the strength of the instruments used, while the Montiel Olea and Pflueger (2013) test indicates valid instrument assumptions. All models control for lifecycle, household, and income/wealth factors, ensuring comprehensive analysis. ***, **, and * indicate significance at the 1%, 5%, and 10% levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe second stage of our analysis builds upon the first stage by estimating the effect of health insurance on household saving behavior. Using the predicted values of health insurance from the first stage, we estimate Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to examine the relationship between health insurance and household savings. This approach allows us to explore how access to health insurance may influence saving behavior, while accounting for potential confounding factors. By investigating this relationship, we aim to gain a better understanding of the role health insurance plays in shaping household savings decisions in the context of Morocco\u0026rsquo;s evolving health insurance system. The next section presents the results of this analysis, offering insights into the dynamics of health insurance and savings behavior.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. The well-being effect of health insurance on households","content":"\u003cp\u003eIn this section, we present the results of the instrumental variable regression analyzing the relationship between access to health insurance and household saving behavior, followed by an analysis of the well-being effects of health insurance on household expenditures. We further decompose household responses by income and expense category to understand how it effects overall financial resilience. The findings provide important policy insights, particularly for designing strategies to enhance household financial security and ensure equitable access to essential services, especially in the context of ongoing health insurance reforms.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Access to health insurance significantly lowers precautionary savings\u003c/h2\u003e \u003cp\u003eThe results presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that access to health insurance is negatively associated with household saving rates across all income quintiles, with an average decrease of approximately 13.8% (Panel A, column 1). When instrumenting access to health insurance using the prevalence of chronic disease, the effect on savings becomes much stronger. In this case, health insurance reduces precautionary savings by about 58% on average (Panel B, column 1).\u003c/p\u003e \u003cp\u003eDisaggregating by income quintile, the results reveal that poorer households experience the most substantial reductions in savings. Specifically, for the bottom quintile, access to health insurance is associated with a decrease in savings of about 66% (Panel B, column 2), and the second quintile shows a reduction of approximately 73% (Panel B, column 3). These findings suggest that health insurance significantly reduces precautionary savings of insured households, particularly among lower-income households.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of having access to health insurance on household savings, by income quintile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll income quintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBottom quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSecond quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThird quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourth quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTop quintile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel A. OLS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.138***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.247***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.122***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.153***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0861**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.0279)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel B. 2SLS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.580***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.661**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.732*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.459**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.532**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.473**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap F-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: This table presents second-stage estimates analyzing the effect of access to health insurance on households\u0026rsquo; saving decision and across income quintiles. ***, **, and * indicate significance at the 1%, 5%, and 10% levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis pattern suggests a potential crowd-out effect of health insurance on savings, particularly for lower-income households, where access to health insurance can reduce the need for precautionary savings. Research by Finkelstein et al. (2012) provides support for this idea, showing that the expansion of Medicaid significantly reduced financial burdens for lower-income families, which may have contributed to enhanced overall economic well-being. Households without health insurance typically rely on buffer savings to protect themselves against unexpected health-related expenses, highlighting the potential link between health insurance access and saving behavior.\u003c/p\u003e \u003cp\u003eAdditionally, other studies support that health insurance could influence household financial decision-making. For instance, Duflo (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) discusses how interventions related to enhanced health insurance coverage may shift household behaviors by offering a safety net, potentially leading to greater investment in health and education. Similarly, Karlan et al. (2016) suggest that access to health insurance can encourage households to invest more in income-generating activities and durable goods. Furthermore, Schmeiser and Carretta (2017) point out that health insurance could facilitate consumption smoothing while also influencing saving and investment decisions. This idea is reflected in studies on universal health coverage in developing countries, where similar dynamics have been observed in Taiwan (Chou et al., 2003), Vietnam (Wagstaff \u0026amp; Pradhan, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and China (Zang et al., 2012). All in all, these findings indicate that health insurance may play an important role in shaping household financial decisions, particularly in lower-income contexts. Understanding these potential dynamics is crucial for policymakers aiming to enhance household resilience against health-related shocks. As noted by Barnett and Mahajan (2021), the relationship between health security and economic stability suggests that health insurance expansion could be a valuable tool for improving household financial well-being.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Access to health insurance has a positive distributional effect on expenditures\u003c/h2\u003e \u003cp\u003eIn addition to reducing the cost of accessing medical care, universal health coverage offers financial protection against \u0026ldquo;impoverishing payments\u0026rdquo; and \u0026ldquo;catastrophic payments\u0026rdquo; (Wagstaff et al., 2015). An effect often highlighted in the literature is that of an increase in consumption linked primarily to the purchase of durable goods. In general, research findings on the subject confirm that universal health insurance coverage increases well-being by reducing the likelihood of calling in sick at work (Wagstaff \u0026amp; Manachotphong, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), increasing the likelihood of using preventive healthcare (Ghilslandi et al., 2015) or reducing overall healthcare expenditure (Limwattananon et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe distributional effect of having access to a health insurance on household expenditures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eI. By income quintile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll income quintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBottom quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSecond quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThird quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFourth quintile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTop quintile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.689***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.526*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.620*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.476**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.868***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.716***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.161)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap F-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eII. By expense category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHousing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTransportation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to health insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.689***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.799***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.190***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.584***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(0.261)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap F-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKleibergen-Paap p-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6 357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: This table presents estimates analyzing the distributional effects of access to health insurance on households\u0026rsquo; expenditures across income quintiles and by expense categories. ***, **, and * indicate significance at the 1%, 5%, and 10% levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the same instrumental variable approach, we examine the effect of health insurance on household consumption. The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, show that access to health insurance has a distributive effect on household consumption across all income quintiles. By expenditure category, we find a significant association between access to health insurance and an increased spending on health, education, and housing \u0026ndash; expenditures that are associated with increased well-being. These results show that the implications of health insurance go beyond reducing healthcare costs and crowding out precautionary savings as it can contribute to increasing overall household well-being. These findings corroborate those of Gonzalez et al. (2016), who demonstrated that insured individuals are more likely to adopt preventive healthcare behaviors, ultimately reducing the incidence of illnesses and associated costs.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePolicy recommendations.\u003c/b\u003e The results of this analysis underscore the critical role that health insurance plays in improving household well-being and fostering economic resilience. To maximize these benefits, policymakers can take a comprehensive approach that integrates health insurance with targeted financial support. As Meyer and Sullivan (2012) note, policies that simultaneously improve access to health insurance and provide direct financial support can create a comprehensive safety net, particularly for vulnerable households. For example, linking health insurance reforms with subsidies or transfers for essential goods, such as housing, education, and transportation, can help households reallocate funds previously spent on necessary medical expenses. This reallocation would enhance household financial resilience, particularly for those in lower-income brackets.\u003c/p\u003e \u003cp\u003eMoreover, to further encourage households to maintain savings, incentives, such as tax-advantaged savings accounts or matching programs, can be introduced. Given that Morocco already has a strategy for financial inclusion, these initiatives could complement existing efforts by fostering better savings habits and increasing household financial resilience. The rollout of universal health coverage should be accompanied by educational campaigns that emphasize the importance of preventive healthcare and financial planning. These campaigns would not only encourage healthier lifestyles but also equip households with the knowledge to manage their resources more effectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations.\u003c/b\u003e While this study provides valuable insights into the relationship between health insurance and household behavior, it is important to acknowledge its limitations. The analysis is based on cross-sectional data, which, while informative, restricts our ability to capture the long-term effects of health insurance on households\u0026rsquo; saving and consumption behavior. Given that Morocco's universal health coverage reform, the AMO, is still being rolled out, comparing the current data with earlier periods may not have been particularly relevant either, as no specific policy interventions related to universal health insurance were implemented before this reform. Therefore, the study highlights the key structural determinants of household saving behavior and their relationship to access to health insurance, providing useful conclusions for understanding the potential effects of the ongoing reform and informing policy-making ex-ante.\u003c/p\u003e \u003cp\u003eFuture research incorporating longitudinal data will be crucial for capturing the evolving impacts of universal health insurance on household consumption and saving behavior. Such studies would offer deeper insights into how the expansion of the AMO influences long-term economic resilience across different income groups. By analyzing the dynamic interactions between health coverage and financial decision-making over time, future research could provide a more nuanced understanding of how access to health insurance affects both immediate and long-term household well-being. These insights will be valuable for policymakers seeking to enhance household financial security as the reform progresses and will help to design more effective policies that support health and economic resilience across all income brackets.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis paper presents new evidence on the relationship between health insurance and household savings in Morocco. First, it shows that the saving behavior of households is dominated by the precautionary motive because of the exposure of a large part of the population to uncertainty linked, among other things, to frequent income fluctuations, health risk, and limited social insurance coverage. Second, exploiting exposure to chronic disease as an exogenous source of variation in health risk, it points to two positive effects of health insurance: a crowd-out of households\u0026rsquo; precautionary savings and a distributional effect on household expenditures.\u003c/p\u003e \u003cp\u003eThe crowd-out effect downwardly influences household savings due to a perceived reduction in risk that decreases the prevalence of the precautionary motive, as households feel more secure in their financial planning due to the availability of health insurance coverage. The crowd-out effect does not uniformly impact all income quintiles, as low-income households experience a more significant outcome. High income households may be less sensitive to this effect, maintaining some of their initial savings\u0026rsquo; strategies. The implications of crowd-out on household savings carry significant policy relevance. Chetty et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) suggest that understanding crowd-out effects is crucial for designing health insurance programs that minimize adverse impacts on saving behavior. Thus, policymakers are ought to consider the balance between providing necessary health coverage and encouraging families to maintain adequate savings for future needs. As Morocco introduces the generalization of the AMO health insurance coverage, experimental or quasi-experimental research is essential to understand these dynamics better and inform policies that support both health coverage and financial resilience of households.\u003c/p\u003e \u003cp\u003eAs for the distributional effects of health insurance on household expenditures, they reveal substantial benefits, particularly for low-income households. Health insurance not only alleviates direct medical costs but also facilitates better financial planning and investment in non-health related goods. Continued research in this area is essential for informing policy decisions aimed at improving health equity and economic resilience across different income quintiles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.L. and P.F. jointly conceived the study and designed the research methodology. S.L. conducted the empirical analysis, interpreted the results, and drafted the main manuscript text. P.F. refined the analytical approach, and provided critical revisions to the manuscript. Both authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability declaration\u003c/strong\u003e: The data used in this study are sourced from the High Commission for Planning (HCP) of Morocco. Due to institutional restrictions, the datasets are not publicly available. However, researchers may request access to the data through the High Commission for Planning\u0026rsquo;s official website (www.hcp.ma) or by directly contacting the institution.\u003c/p\u003e\n\u003cp\u003eWhere applicable, the code and methodological documentation used for data processing and analysis are available upon request from authors.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare that there are no competing interests related to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelkhalek, T., Arestoff, F., El Mekkaoui de Freitas, N.E., \u0026amp; Mage, S. (2010). A microeconometric analysis of household savings determinants in Morocco. 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Health Economics 18 (2), S7\u0026ndash;S23.\u003c/li\u003e\n\u003cli\u003eWagstaff, Adam, Yip, Winnie, Lindelow, Magnus, Hsiao, William C., 2009b. China\u0026rsquo;s health system and its reform: a review of recent studies. Health Economics 18 (2), S7\u0026ndash;S23.\u003c/li\u003e\n\u003cli\u003eZang, Wenbin, Liu, Guoen, Xu, Fei, Xiong, Xinjun, 2012. The effect of urban resident basic medical insurance on household consumption. Economic Research Journal 7, 75\u0026ndash;85 (in Chinese).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e In Morocco, typical two-story houses are often designed to accommodate extended families, with multiple generations living together. This setup can provide financial benefits by pooling resources and sharing living costs, which helps mitigate income fluctuations and economic uncertainty. For more information on Moroccan households\u0026rsquo; living conditions, visit the regularly updated HCP website at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hcp.ma/Conditions-d-habitation_r458.html\u003c/span\u003e\u003cspan address=\"https://www.hcp.ma/Conditions-d-habitation_r458.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Literature on \"consumption smoothing\" and \"risk sharing\" is very extensive and work on developing countries has strongly influenced the mainstream research literature. For an extensive review of the literature, see Townsend (1995), Deaton (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), Dercon (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and Morduch (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Note that dependent family members benefit from the coverage of the health insurance that the head of the household has access to.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Health Insurance, Household Savings, Precautionary Motive, Morocco, Chronic Disease, Social Insurance","lastPublishedDoi":"10.21203/rs.3.rs-6145707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6145707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates the relationship between health insurance and household savings. It first demonstrates that the precautionary motive dominates the saving behavior of Moroccan households, primarily due to the widespread exposure to uncertainty, including frequent income fluctuations, health risks, and limited social insurance coverage. Next, using an instrumental variable strategy, the study examines how access to health insurance affects household saving behavior. The identification strategy leverages the variation in exposure to health risks, driven by chronic disease prevalence, as an exogenous factor influencing saving and consumption decisions. The results reveal two key impacts of health insurance: it crowds out precautionary savings and alters household expenditure patterns. While further research is needed to fully understand these dynamics, policymakers must carefully balance the provision of essential health coverage—particularly with the ongoing expansion of the \"Assurance Maladie Obligatoire\"—and the need to encourage households to maintain sufficient savings for future needs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification Codes: \u003c/strong\u003eI13, D14, I18\u003c/p\u003e","manuscriptTitle":"Health Insurance and Household Savings: Evidence from Morocco","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 08:51:06","doi":"10.21203/rs.3.rs-6145707/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88845740-f7ac-4b23-a784-92d97574f4ee","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T11:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 08:51:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6145707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6145707","identity":"rs-6145707","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}