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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Public Health</journal-id>
<journal-title>Frontiers in Public Health</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Public Health</abbrev-journal-title>
<issn pub-type="epub">2296-2565</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpubh.2024.1480365</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Public Health</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Does obesity create a relative sense of excess poverty?</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Arbel</surname> <given-names>Yuval</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2623391/overview"/>
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<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Arbel</surname> <given-names>Yifat</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Kerner</surname> <given-names>Amichai</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Kerner</surname> <given-names>Miryam</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Sir Harry Solomon School of Economics and Management, Western Galilee College</institution>, <addr-line>Acre</addr-line>, <country>Israel</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Mathematics, Bar Ilan University</institution>, <addr-line>Ramat Gan</addr-line>, <country>Israel</country></aff>
<aff id="aff3"><sup>3</sup><institution>Faculty of Social Sciences, Banking and Finance Program, Bar Ilan University</institution>, <addr-line>Ramat Gan</addr-line>, <country>Israel</country></aff>
<aff id="aff4"><sup>4</sup><institution>The Ruth and Bruce Rapoport Faculty of Medicine, Technion &#x2013; Israel Institute of Technology</institution>, <addr-line>Haifa</addr-line>, <country>Israel</country></aff>
<aff id="aff5"><sup>5</sup><institution>Department of Dermatology, Emek Medical Center</institution>, <addr-line>Afula</addr-line>, <country>Israel</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0008">
<p>Edited by: Joao Sollari Lopes, National Statistical Institute of Portugal, Portugal</p>
</fn>
<fn fn-type="edited-by" id="fn0009">
<p>Reviewed by: Bo Zhou, University of North Texas Health Science Center, United States</p>
<p>Donna-Marie Palakiko, University of Hawaii at Manoa, United States</p>
<p>Limei Jin, Gansu University of Chinese Medicine, China</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Yuval Arbel, <email>YuvalAr@wgalil.ac.il</email>; <email>yuval.arbel@gmail.com</email></corresp>
<fn fn-type="other" id="fn0001a"><p><sup>&#x2020;</sup>ORCID: Yuval Arbel, <ext-link ext-link-type="uri" xlink:href="https://orcid.org/0000-0003-4365-6280">https://orcid.org/0000-0003-4365-6280</ext-link></p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>27</day>
<month>11</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>12</volume>
<elocation-id>1480365</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>08</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>08</day>
<month>11</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Arbel, Arbel, Kerner and Kerner.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Arbel, Arbel, Kerner and Kerner</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec id="sec1">
<title>Background</title>
<p>This study investigates the potential relationship between obesity and self-ranking of poverty, as a proxy for self-awareness and happiness. To the best of our knowledge, this issue has not been previously explored based on self-ranking of poverty when income is controlled.</p>
</sec>
<sec id="sec2">
<title>Method</title>
<p>Ordered Probit Regressions. We propose a new measure for the influence of western social values and norms associated with discrimination against obese women.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>Based on a follow-up survey after two years, findings demonstrate a <italic>drop</italic> in the projected probability of self-ranking as &#x201C;not poor&#x201D; with the <italic>BMI</italic> from 0.73 to 0.37 (females) &#x2013; 0.48 (males) when the level of income is controlled. Similar outcomes are obtained when the independent variables are lagged and thus avoid endogeneity concerns. Finally, additional outcomes support the conclusion that the lagged <italic>BMI</italic> Granger-cause self-ranking of poverty for women, but not for men. Findings support the awareness of more obese women to lower prospects of finding a job.</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>Since according to twin studies, approximately 80% of obesity emanates from genetic factors, research findings stress the need to educate the public against prejudices on the grounds of obesity. In particular, our study seeks to evoke awareness among potential employers, which, in turn, might motivate avoidance of, or at least reduction in, an implicit wage penalty against obese women.</p>
</sec>
</abstract>
<kwd-group>
<kwd>obesity</kwd>
<kwd>poverty</kwd>
<kwd>public health</kwd>
<kwd>a follow-up survey</kwd>
<kwd>lagged variables</kwd>
</kwd-group>
<counts>
<fig-count count="7"/>
<table-count count="9"/>
<equation-count count="1"/>
<ref-count count="62"/>
<page-count count="16"/>
<word-count count="10793"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Life-Course Epidemiology and Social Inequalities in Health</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec5">
<label>1</label>
<title>Introduction</title>
<p>Obesity is a global epidemic and a risk factor for non-communicable diseases and mortality. More than half of the population in 34 of the OECD countries are currently overweight, whereas one in four is obese. From 2010 to 2016, there was a 3 % increase in the number of people suffering from obesity, i.e., &#x2013; 50 million more people in the OECD countries.</p>
<p>One of the measures for the damages associated with obesity is the lost years of life (<xref ref-type="bibr" rid="ref1">1</xref>). Among the OECD countries, the life span in Mexico is shortened by 4.2&#x2009;years, in Poland and Russia by 3.9&#x2009;years, in the USA and Hungary 3.7&#x2009;years, in Romania 3.5&#x2009;years, in Israel&#x2014;3.4&#x2009;years, in Ireland 2.9&#x2009;years, in France 2.7, in South Korea 1.7 and in Indonesia 1.5&#x2009;years due to obesity. The list is closed by Japan &#x2013; where obesity shortens life by only one year. Diseases associated with obesity will claim the lives of approximately 90 million people in OECD countries over the next 30&#x2009;years.</p>
<p>Education and socio-economic background affect obesity. Reciprocally, obesity damages labor market outcomes that, in turn, contribute to reinforcing existing social inequalities (<xref ref-type="bibr" rid="ref2">2</xref>). Obese people have poorer job prospects compared to normal-weight people, they are less likely to be employed and have more difficulty re-entering the labor market (<xref ref-type="bibr" rid="ref3">3</xref>). Obese people are less productive at work due to more sick days and fewer worked hours, and they earn about 10% less than non-obese people. Addressing obesity and the associated negative labor market outcomes would help break the vicious circle of social and health inequalities.</p>
<p>Indeed, numerous studies have demonstrated the positive relationship between poverty and obesity. Yet, the extent to which obese people have an increased perception of being poor when the actual level of income is controlled remains an open question.</p>
<p>Previous studies have revealed the stigmatization of obese persons by society. Obese people have frequently been found to use language, which reflects poor self-identity following the perceived negative impact of their weight (<xref ref-type="bibr" rid="ref4">4</xref>). Likewise, obese persons seem to suffer more from poor self-esteem, as well as a higher level of vulnerability and a propensity to depression, particularly among women (<xref ref-type="bibr" rid="ref5">5</xref>).</p>
<p>Previous studies have also demonstrated wage and other penalties against obese people &#x2013; particularly women (<xref ref-type="bibr" rid="ref6 ref7 ref8 ref9 ref10 ref11 ref12 ref13 ref14">6&#x2013;14</xref>).</p>
<p>Yet, with one exception (<xref ref-type="bibr" rid="ref8">8</xref>); the question that remained open is the self-awareness of obese women and men to these penalties. Our study contributes by demonstrating this awareness while the income level is controlled. They support the awareness of more obese women, manifested by their subjective ranking as &#x201C;not poor,&#x201D; to lower prospects of finding a job.</p>
<p>The objective of the current study is to demonstrate that obese people suffer not only from poor self-esteem, but also from increased sense of subjective poverty among both genders, even when the actual level of income is controlled. The rationale of our study is the following. <italic>A-priori</italic>, compared to normal weight persons there is no reason that obese persons would have a sense of excess poverty where the level of income is controlled. Yet our study is the first to clearly demonstrate elevated awareness to weight discrimination among obese persons.</p>
<p>Indeed, many studies in the literature demonstrate wage discrimination against obese persons and particularly women. Based on panel setting, Caliendo and Gehrsitz (<xref ref-type="bibr" rid="ref12">12</xref>) suggest that for a 1-point <italic>BMI</italic> increase in Germany, wage drops by 0.6&#x2013;0.7% among women both in blue and white-collar professions (page 216). The authors mention the robust findings in the literature that unlike women, men are either not subject to weight penalties or premia in the labor market, or at least experience a diminished wage penalty [e.g., (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref16">16</xref>)]. Campos-Vazquez and Gonzalez (<xref ref-type="bibr" rid="ref17">17</xref>) show lower prospects of finding a job among obese women in Mexico, but not among obese men. Finally, Prioschery et al. (<xref ref-type="bibr" rid="ref18">18</xref>) demonstrate the importance of western values referring to the body silhouettes and obesity of South African urban females.</p>
<p>Puhl and Brownell (<xref ref-type="bibr" rid="ref19">19</xref>) argue that discrimination against obese persons can be documented in three important areas of living: employment, education, and health care. 28% of teachers in one study claimed that becoming obese is the worst thing that can happen to a person; controlling for income and grades, parents provide less college support for their overweight children than for their &#x201C;thinner&#x201D; children; 24% of nurses said that they are &#x201C;repulsed&#x201D; by obese persons.</p>
<p>Finally, B&#x00F6;ckerman et al. (<xref ref-type="bibr" rid="ref20">20</xref>), suggest that the outcomes obtained from economic models using the narrower genetic risk score as an instrument indicate 6.9% lower wages, 1.8% fewer years employed, and a 3-percentage point higher probability of receiving any social income transfers following a one-unit increase in BMI in Finland.<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> Note, however, that unlike B&#x00F6;ckerman et al. (<xref ref-type="bibr" rid="ref20">20</xref>), who discusses the impact of the genetic profile, the current paper discusses the socio-cultural factor, namely, the impact of the social stigma on the subjective sense of poverty among obese persons.</p>
<p>We propose a new measure for the influence of western socio-cultural values and norms associated with discrimination against obese women. Given the lower prospects among these women of finding a job, one would anticipate a positive relationship between obesity (represented by higher body-mass index <italic>BMI</italic><inline-formula>
<mml:math id="M1">
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula>) (where weight is measured in kilograms and height is measured in meters) and higher ranking of poverty.<xref ref-type="fn" rid="fn0002"><sup>2</sup></xref></p>
<p>This study is based on the longitudinal survey carried out by the Israeli Central Bureau of Statistics (ICBS) (<xref ref-type="bibr" rid="ref21">21</xref>) and based on a representative sample of the Israeli population. The survey reports the response of interviewers to the following question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; on a scale of 1&#x2009;=&#x2009;&#x201C;often&#x201D; to 4&#x2009;=&#x2009;&#x201C;never,&#x201D; as well as an objective measure of income [total gross annual income from all sources in NIS (the local Israeli currency, where 1 NIS&#x2009;&#x2248;&#x2009;$0.31)]. Additional recorded information is the weight and height of each individual, from which the <italic>BMI</italic> measure may be calculated.</p>
<p>Findings clearly demonstrate a <italic>drop</italic> in the projected probability of self-ranking as &#x201C;not poor&#x201D; with <italic>BMI</italic> ranging from 0.73 to 0.37 (females) &#x2013; 0.48 (males) when income is controlled. Similar outcomes are obtained when the independent variables are lagged and thus avoid endogeneity concerns. Finally, additional outcomes support the conclusion that the lagged <italic>BMI</italic> Granger-cause self-ranking of poverty for women, but not for men. Consequently, the outcomes of our study demonstrate awareness to the economic outcomes of discriminations against obese persons. These phenomena are plausible given the lower frequency of dates and jobs opportunities (<xref ref-type="bibr" rid="ref17">17</xref>, <xref ref-type="bibr" rid="ref22">22</xref>), which, in turn, diminishes the Social and Economic Status (SES) in the long run.</p>
<p>The contribution of this study lies in its focus on economic parameters. The focus of previous studies was obesity as a precursor of lack of self-confidence, as well as increased depression and vulnerability (<xref ref-type="bibr" rid="ref4">4</xref>, <xref ref-type="bibr" rid="ref22">22</xref>), or the genetic component of obesity as a precursor of lower wages, fewer years employed, and a higher probability of receiving any social income transfers (<xref ref-type="bibr" rid="ref20">20</xref>). Yet, to the best of our knowledge, the current study is the first to measure the subjective sense of poverty among obese persons (i.e., how often you consider yourself poor) when the level of income is controlled. Thus, the outcomes of our study can be interpreted as the degree of awareness of the social stigma concerning obese persons among potential employers, parents, and mates. In this context, a recent article by Campos-Vazquez &#x0026; Gonzalez (<xref ref-type="bibr" rid="ref17">17</xref>) indeed demonstrated a lower prevalence of job offers to obese women &#x2013; where all the other C.V. factors were controlled.</p>
<p>In sum, the contribution of this research lies in the new method proposed to assess the permanent income of obese persons. According to Friedman (<xref ref-type="bibr" rid="ref23">23</xref>) the permanent income hypothesis is a theory of consumer spending stating that people will spend money at a level consistent with their expected long-term average income. The outcomes of the current study indicate that the level of permanent income among obese people is lower than their current income.</p>
<p>According to twin studies, approximately 80% of obesity emanates from genetic factors (<xref ref-type="bibr" rid="ref24">24</xref>). Consequently, research findings stress the need to educate the public against prejudices on the grounds of obesity. In particular, our study seeks to evoke awareness among potential employers, which, in turn, might motivate avoidance of, or at least reduction in, an implicit wage penalty against obese women.</p>
<p>The implication of twin studies is the comparison between identical (monozygotic) twins. This is a conventional methodology in medical literature, particularly where the indication of genetic disorder emerges. In research, concordance is often discussed in the context of both members of a pair of twins. Twins are concordant when both have, or both lack a given trait.</p>
<p>One example is Ji and An (<xref ref-type="bibr" rid="ref25">25</xref>). Using the twin study design, and subsequent control for genetics and shared environmental effects, the authors found negative association between harsher parenting in communication and BMI in German twin families. Another example is Lietz&#x00E9;n et al. (<xref ref-type="bibr" rid="ref26">26</xref>), who studied the effects of regular exercise training on LFC, PFC, and metabolism in monozygotic twin pairs discordant for BMI.<xref ref-type="fn" rid="fn0003"><sup>3</sup></xref></p>
<p>The remainder of this article is organized as follows. Section 2 provides the description of data and methods. Section 3 gives the results. Finally, section 4 provides discussion and section 5 concludes and summarizes.</p>
</sec>
<sec id="sec6">
<label>2</label>
<title>Data and methods</title>
<sec id="sec7">
<label>2.1</label>
<title>Description of data</title>
<p>The data are obtained from the 2015 and 2016 waves of a longitudinal survey carried out by the Israeli Central Bureau of Statistics (ICBS) (<xref ref-type="bibr" rid="ref21">21</xref>). Given the conduct of the survey by ICBS &#x2013; a government agency &#x2013; supervised by the Organization of Cooperation and Economic Development (OECD), it is evident that rigorous measures were undertaken to ensure that the 2015 baseline is a representative sample of the Israeli population. A big advantage of governments authorities is their potential ability to enforce cooperation of the individuals randomly assigned to participate in the survey. In fact, many macro level outcomes reported as part of the national accounting of Israel are based on this sample rather than the whole population.</p>
<p>Within the framework of the survey, interviewers returned in 2016 to the same participants in 2015 and asked them the same questions. In the basic results sub-section only the 2016 wave is employed and analyzed. As a robustness test, and as explained below, in the robustness test sub-section, data from both waves are used, where the empirical model is based on lagged independent variables. This methodology prevents or reduces endogeneity concerns.<xref ref-type="fn" rid="fn0004"><sup>4</sup>
</xref></p>
</sec>
<sec id="sec8">
<label>2.2</label>
<title>Descriptive statistics</title>
<p><xref ref-type="table" rid="tab1">Table 1</xref> provides the descriptive statistics of the variables, which are later incorporated into the empirical model, and refers to the 2016 wave of the survey. While the <italic>Self_Rank_Poverty_</italic>2016 variable gives the subjective measure of poverty (self-ranking of poverty in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; on a scale of 1&#x2009;=&#x2009;&#x201C;often&#x201D; to 4&#x2009;=&#x2009;&#x201C;never&#x201D;), the total_inc2016 variable provides the objective proxy of income level (total gross annual income from all sources in NIS).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Descriptive statistics &#x2013; 2016 wave of ICBS longitudinal survey.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="2">A. Description of variables</th>
</tr>
<tr>
<th align="left" valign="top">Variable</th>
<th align="left" valign="top">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top"><italic>Self_Rank_Poverty_</italic>2016</td>
<td align="left" valign="top">Self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; in 2016. Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never.&#x201D;</td>
</tr>
<tr>
<td align="left" valign="top">Total_inc2016</td>
<td align="left" valign="top">Total gross annual income from all sources in NIS</td>
</tr>
<tr>
<td align="left" valign="top">BMI2016</td>
<td align="left" valign="top">Body mass index=<inline-formula>
<mml:math id="M2">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x003C;&#x2009;30 is overweight; BMI&#x2009;&#x2265;&#x2009;30 is considered obesity</td>
</tr>
<tr>
<td align="left" valign="top">Female2016</td>
<td align="left" valign="top">1&#x2009;=&#x2009;female; 0&#x2009;=&#x2009;male</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="5">B. Descriptive statistics (unweighted means)</th>
</tr>
<tr>
<th align="left" valign="top">Variable (<italic>N</italic>&#x2009;=&#x2009;4,017)</th>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">Std. Dev.</th>
<th align="center" valign="top">Pct</th>
<th align="center" valign="top">Median</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top"><italic>Self_Rank_Poverty_</italic>2016</td>
<td align="center" valign="top">3.295</td>
<td align="center" valign="top">1.112</td>
<td align="center" valign="top">100.00%</td>
<td align="center" valign="top">4.000</td>
</tr>
<tr>
<td align="left" valign="top">Often</td>
<td/>
<td/>
<td align="center" valign="top">14.190%</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Sometimes</td>
<td/>
<td/>
<td align="center" valign="top">8.890%</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Rarely</td>
<td/>
<td/>
<td align="center" valign="top">10.130%</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Never</td>
<td/>
<td/>
<td align="center" valign="top">66.790%</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Total</td>
<td/>
<td/>
<td align="center" valign="top">100.00%</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Total_inc2016</td>
<td align="center" valign="top">121,721 [117,867,125,574]</td>
<td align="center" valign="top">124,569</td>
<td/>
<td align="center" valign="top">84,969</td>
</tr>
<tr>
<td align="left" valign="top">BMI2016</td>
<td align="center" valign="top">25.616</td>
<td align="center" valign="top">4.272</td>
<td/>
<td align="center" valign="top">24.880</td>
</tr>
<tr>
<td align="left" valign="top">Gender</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top">Female2016</td>
<td/>
<td/>
<td align="center" valign="top">44.511%</td>
<td align="center" valign="top">0</td>
</tr>
<tr>
<td align="left" valign="top">Male2016</td>
<td/>
<td/>
<td align="center" valign="top">55.489%</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Total</td>
<td/>
<td/>
<td align="center" valign="top">100.00%</td>
<td/>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="4">C. Descriptive statistics (weighted means)</th>
</tr>
<tr>
<th align="left" valign="top">Variable (<italic>N</italic>&#x2009;=&#x2009;4,017)</th>
<th align="center" valign="top">Mean</th>
<th align="center" valign="top">Linearized standard error</th>
<th align="center" valign="top">95% CI</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top"><italic>Self_Rank_Poverty_</italic>2016</td>
<td align="center" valign="top">3.279</td>
<td align="center" valign="top">0.003</td>
<td align="center" valign="top">[3.273, 3.285]</td>
</tr>
<tr>
<td align="left" valign="top">Total_inc2016</td>
<td align="center" valign="top">122,507.2</td>
<td align="center" valign="top">1,959.858</td>
<td align="center" valign="top">[118,664.800, 126,349.600]</td>
</tr>
<tr>
<td align="left" valign="top">BMI2016</td>
<td align="center" valign="top">26.329</td>
<td align="center" valign="top">0.077</td>
<td align="center" valign="top">[26.178, 26.479]</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The table refers to the 2016 wave of the longitudinal survey carried out by the ICBS. The survey data analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_2016</italic>. The weight given to each observation is based on the inverse of the BMI variable. Based on the analysis the population size is 102,900.72. 95% confidence intervals are given in square parentheses.</p>
</table-wrap-foot>
</table-wrap>
<p>Referring to the ordinal <italic>Self_Rank_Poverty_</italic>2016 variable 14.19% of the respondents noted that they often considered themselves poor and 66.79% noted that they never considered themselves poor. These responses cover 80.98% of the sample of 4,017 subjects. The implication is left-skewed distribution of <italic>Self_Rank_Poverty_</italic>2016, which can also be inferred from the fact that the median of the <italic>Self_Rank_Poverty_</italic>2016 variable (=4) is higher than the mean (=3.295).</p>
<p>Referring to the total_inc2016 variable, the sample median is 84,969 NIS and the sample mean is 121,721 NIS. Given that the sample mean, affected by outliers, is greater than the sample median, the implication is right-skewed distribution. According to the ICBS (<xref ref-type="bibr" rid="ref27">27</xref>) press release from May 7, 2017, the gross monthly wage in December 2016 is 10,123 NIS, and the equivalent annual wage is 121,476 NIS. Based on the 95% confidence interval in the sample [117,867&#x2013;125,574], one cannot reject the null hypothesis that the sample mean equals to that of the population mean in December 2016.<xref ref-type="fn" rid="fn0005"><sup>5</sup></xref></p>
<p><xref ref-type="fig" rid="fig1">Figure 1</xref> describes the histograms of the variables <italic>Self_Rank_Poverty_</italic>2016 and total_inc2016. Indeed, as can be seen from <xref ref-type="fig" rid="fig1">Figure 1</xref>, while the distribution of 2016 poverty ranking is left-skewed, the distribution of 2016 total income from all sources is right-skewed. The implication is consistent with the findings of Stessman et al. (<xref ref-type="bibr" rid="ref28">28</xref>), who demonstrated that in the older population above 70&#x2009;years there is no overlapping between subjective and objective poverty. While the latter is measured based on the poverty line (below median net income),<xref ref-type="fn" rid="fn0006"><sup>6</sup>
</xref> the former is based on the subjective feeling of the individual that the net income is insufficient to cover monthly expenses. Moreover, higher levels of subjective poverty are associated with higher levels of depression and low self-assessment of health conditions (<xref ref-type="bibr" rid="ref28">28</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Histograms of self-ranked poverty and actual income in 2016. The histograms refer to 4,017 subjects. The skewness of <italic>Self_Rank_Poverty_</italic>2016 (total_inc2016) is &#x2212;1.219 (+2.895) and for both variables the null hypothesis of symmetrical distribution (zero skewness) is clearly rejected.</p>
</caption>
<graphic xlink:href="fpubh-12-1480365-g001.tif"/>
</fig>
</sec>
<sec id="sec9">
<label>2.3</label>
<title>Method</title>
<p>The conventional way to describe the empirical model is the following:</p><disp-formula id="E1">
<mml:math id="M3">
<mml:mtable columnalign="left">
<mml:mtr>
<mml:mtd>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mo>ln</mml:mo>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">Total</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>I</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>c</mml:mi>
<mml:mn>2016</mml:mn>
</mml:mrow>
</mml:mfenced>
</mml:mtd>
</mml:mtr>
<mml:mtr>
<mml:mtd>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:mi>B</mml:mi>
<mml:mi>M</mml:mi>
<mml:mi>I</mml:mi>
<mml:mn>2016</mml:mn>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:mo>&#x2208;</mml:mo>
</mml:mtd>
</mml:mtr>
</mml:mtable>
</mml:math>
</disp-formula>
<p>Where the dependent variable <inline-formula>
<mml:math id="M4">
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
</mml:math>
</inline-formula> is the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never&#x201D;; the independent variables are <inline-formula>
<mml:math id="M5">
<mml:mo>ln</mml:mo>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">Total</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>I</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>c</mml:mi>
<mml:mn>2016</mml:mn>
</mml:mrow>
</mml:mfenced>
</mml:math>
</inline-formula> and <inline-formula>
<mml:math id="M6">
<mml:mi>B</mml:mi>
<mml:mi>M</mml:mi>
<mml:mi>I</mml:mi>
<mml:mn>2016</mml:mn>
</mml:math>
</inline-formula>.<xref ref-type="fn" rid="fn0007">
<sup>7</sup>
</xref> <inline-formula>
<mml:math id="M7">
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mo>,</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:mo>,</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> are parameters and <inline-formula>
<mml:math id="M8">
<mml:mi>&#x03F5;</mml:mi>
</mml:math>
</inline-formula> is the classical random disturbance term.</p>
<p>One concern referring to the conventional model is the fact that the dependent variable <inline-formula>
<mml:math id="M9">
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
</mml:math>
</inline-formula> is ordinal, namely, the scale 1, 2, 3 and 4 has no definite quantitative interpretation. One could argue that a different scale could be employed. To address this concern, we use instead the ordered probit regression (for a detailed description see <xref ref-type="sec" rid="sec20">Appendix A</xref>). The estimation of the model yields projected probabilities of the choice <inline-formula>
<mml:math id="M10">
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>=</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>,</mml:mo>
<mml:mn>2</mml:mn>
<mml:mo>,</mml:mo>
<mml:mn>3</mml:mn>
<mml:mo>,</mml:mo>
<mml:mn>4</mml:mn>
</mml:math>
</inline-formula> as a function of the two independent variables <inline-formula>
<mml:math id="M11">
<mml:mi mathvariant="italic">Total</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>I</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>c</mml:mi>
<mml:mn>2016</mml:mn>
</mml:math>
</inline-formula> and <inline-formula>
<mml:math id="M12">
<mml:mi>B</mml:mi>
<mml:mi>M</mml:mi>
<mml:mi>i</mml:mi>
<mml:mn>2016</mml:mn>
</mml:math>
</inline-formula>.</p>
<p>This model is well established in empirical literature [e.g., (<xref ref-type="bibr" rid="ref29">29</xref>, <xref ref-type="bibr" rid="ref30">30</xref>)]. As Frey and Stutzer (<xref ref-type="bibr" rid="ref29">29</xref>) argue: &#x201C;Provided that reported subjective well-being is a valid and empirically adequate measure for human well-being, it can be modeled in a microeconometric happiness function <inline-formula>
<mml:math id="M13">
<mml:msub>
<mml:mi>W</mml:mi>
<mml:mi mathvariant="italic">it</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mi>&#x03B1;</mml:mi>
<mml:mo>+</mml:mo>
<mml:mi>&#x03B2;</mml:mi>
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi mathvariant="italic">it</mml:mi>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03F5;</mml:mi>
<mml:mi mathvariant="italic">it</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> that is estimated by ordered probit or logit&#x201D; (page 406).</p>
<p>The estimation results from this procedure are hard to interpret directly. Consequently, in subsequent sections, we provide for each Table the corresponding Figure, so that <xref ref-type="fig" rid="fig2">Figures 2</xref>&#x2013;<xref ref-type="fig" rid="fig7">7</xref> corresponds to column (1) in <xref ref-type="table" rid="tab2">Tables 2</xref>&#x2013;<xref ref-type="table" rid="tab7">7</xref>. While <xref ref-type="table" rid="tab2">Tables 2</xref>, <xref ref-type="table" rid="tab5">5</xref> reports the outcomes obtained from the pooled sample, <xref ref-type="table" rid="tab3">Tables 3</xref>, <xref ref-type="table" rid="tab4">4</xref>, <xref ref-type="table" rid="tab6">6</xref>, <xref ref-type="table" rid="tab7">7</xref> report the outcomes obtained for the sub-sample of females and males.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Pooled sample 2016. The figure is based on projected probabilities to report: &#x201C;I often consider myself poor&#x201D; (black line) and &#x201C;I never consider myself poor&#x201D; (grey line) as a function of the BMI level. The transformations to these projected probabilities are based on the outcomes reported on column (1) in <xref ref-type="table" rid="tab2">Table 2</xref>. The horizontal axis is the BMI level on a scale of between 18 to 50. <italic>BMI</italic> (=<inline-formula>
<mml:math id="M14">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
<mml:mi>&#x03B1;</mml:mi>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x003C;&#x2009;30 is overweight; <italic>BMI</italic>&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list).</p>
</caption>
<graphic xlink:href="fpubh-12-1480365-g002.tif"/>
</fig>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Females 2016. The figure is based on projected probabilities to report: &#x201C;I often consider myself poor&#x201D; (black line) and &#x201C;I never consider myself poor&#x201D; (grey line) as a function of the BMI level. The transformations to these projected probabilities are based on the outcomes reported on column (1) in <xref ref-type="table" rid="tab3">Table 3</xref>. The horizontal axis is the BMI level on a scale of between 18 to 50. <italic>BMI</italic> (=<inline-formula>
<mml:math id="M15">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
<mml:mi>&#x03B1;</mml:mi>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x003C;&#x2009;30 is overweight; <italic>BMI</italic>&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list).</p>
</caption>
<graphic xlink:href="fpubh-12-1480365-g003.tif"/>
</fig>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Males 2016. The figure is based on projected probabilities to report: &#x201C;I often consider myself poor&#x201D; (black line) and &#x201C;I never consider myself poor&#x201D; (grey line) as a function of the BMI level. The transformations to these projected probabilities are based on the outcomes reported on column (1) in <xref ref-type="table" rid="tab4">Table 4</xref>. The horizontal axis is the BMI level on a scale of between 18 to 50. <italic>BMI</italic> (=<inline-formula>
<mml:math id="M16">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x003C;&#x2009;30 is overweight; <italic>BMI</italic>&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list).</p>
</caption>
<graphic xlink:href="fpubh-12-1480365-g004.tif"/>
</fig>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Pooled sample 2015&#x2013;2016 (lagged variables). The figure is based on projected probabilities to report: &#x201C;I often consider myself poor&#x201D; (black line) and &#x201C;I never consider myself poor&#x201D; (grey line) as a function of the LAG (BMI) level. The transformations to these projected probabilities are based on the outcomes reported on column (1) in <xref ref-type="table" rid="tab5">Table 5</xref>. The horizontal axis is the BMI level on a scale of between 18 to 50. <italic>BMI</italic> (=<inline-formula>
<mml:math id="M17">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x003C;&#x2009;30 is overweight; <italic>BMI</italic>&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list).</p>
</caption>
<graphic xlink:href="fpubh-12-1480365-g005.tif"/>
</fig>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>Females 2015&#x2013;2016 (lagged variables). The figure is based on projected probabilities to report: &#x201C;I often consider myself poor&#x201D; (black line) and &#x201C;I never consider myself poor&#x201D; (grey line) as a function of the LAG(BMI) level. The transformations to these projected probabilities are based on the outcomes reported on column (1) in <xref ref-type="table" rid="tab6">Table 6</xref>. The horizontal axis is the BMI level on a scale of between 18 to 50. <italic>BMI</italic> (=<inline-formula>
<mml:math id="M18">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x003C;&#x2009;30 is overweight; <italic>BMI</italic>&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list).</p>
</caption>
<graphic xlink:href="fpubh-12-1480365-g006.tif"/>
</fig>
<fig position="float" id="fig7">
<label>Figure 7</label>
<caption>
<p>Males 2015&#x2013;2016 (lagged variables). The figure is based on projected probabilities to report: &#x201C;I often consider myself poor&#x201D; (black line) and &#x201C;I never consider myself poor&#x201D; (grey line) as a function of the LAG(BMI) level. The transformations to these projected probabilities are based on the outcomes reported on column (1) in <xref ref-type="table" rid="tab7">Table 7</xref>. The horizontal axis is the BMI level on a scale of between 18 to 50. <italic>BMI</italic> (=<inline-formula>
<mml:math id="M19">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x003C;&#x2009;30 is overweight; <italic>BMI</italic>&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list).</p>
</caption>
<graphic xlink:href="fpubh-12-1480365-g007.tif"/>
</fig>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Survey data and ordered probit regressions: pooled sample.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
</tr>
<tr>
<th/>
<th align="center" valign="bottom">SVY: ordered probit</th>
<th align="center" valign="bottom">Ordered probit</th>
</tr>
<tr>
<th align="left" valign="bottom">Variables</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
<th align="center" valign="bottom">
<italic>Self_Rank_Poverty_2016</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">ln (Total_inc2016)</td>
<td align="center" valign="bottom">0.179&#x002A;&#x002A;&#x002A; (0.0167)</td>
<td align="center" valign="bottom">0.174&#x002A;&#x002A;&#x002A; (0.0168)</td>
</tr>
<tr>
<td align="left" valign="top"><italic>BMI</italic>2016</td>
<td align="center" valign="bottom">&#x2212;0.0260&#x002A;&#x002A;&#x002A; (0.00454)</td>
<td align="center" valign="bottom">&#x2212;0.0257&#x002A;&#x002A;&#x002A; (0.00450)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M20">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M21">
<mml:mn>1</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>2</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.249 (0.212)</td>
<td align="center" valign="bottom">0.200 (0.216)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M22">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M23">
<mml:mn>2</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>3</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.594&#x002A;&#x002A;&#x002A; (0.213)</td>
<td align="center" valign="bottom">0.546&#x002A;&#x002A; (0.216)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M24">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M25">
<mml:mn>3</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>4</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.902&#x002A;&#x002A;&#x002A; (0.213)</td>
<td align="center" valign="bottom">0.856&#x002A;&#x002A;&#x002A; (0.217)</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">4,017</td>
<td align="center" valign="top">4,017</td>
</tr>
<tr>
<td align="left" valign="top">Population</td>
<td align="center" valign="top">102,900.72</td>
<td align="center" valign="top">-</td>
</tr>
<tr>
<td align="left" valign="top">Strata</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">-</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The estimation is based on the pooled sample of females and males obtained from the ICBS survey. The dependent variable (<italic>Self_Rank_Poverty_2016</italic>) is the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never.&#x201D; The black (grey) line in the corresponding <xref ref-type="fig" rid="fig2">Figure 2</xref> is the projected probability to answer 1&#x2009;=&#x2009;&#x201C;often&#x201D; (4&#x2009;=&#x2009;&#x201C;never&#x201D;) as a function of the BMI (=<inline-formula>
<mml:math id="M26">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:msup>
<mml:mi mathvariant="normal">meter</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x003C;&#x2009;30 is overweight; BMI&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list). Column (1) reports the outcomes of the survey data analysis. The analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_2016</italic>. The weight given to each observation is based on the inverse of the BMI variable. Column (2) reports the outcomes where equal weight is given to each observation. In column (1)/(2) The linearized/conventional standard errors are given in parentheses &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Survey data and ordered probit regressions: females.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
</tr>
<tr>
<th/>
<th align="center" valign="bottom">SVY: ordered probit</th>
<th align="center" valign="bottom">Ordered probit</th>
</tr>
<tr>
<th align="left" valign="bottom">Variables</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">ln(Total_inc2016)</td>
<td align="center" valign="bottom">0.162&#x002A;&#x002A;&#x002A; (0.0255)</td>
<td align="center" valign="bottom">0.157&#x002A;&#x002A;&#x002A; (0.0256)</td>
</tr>
<tr>
<td align="left" valign="top"><italic>BMI</italic>2016</td>
<td align="center" valign="bottom">&#x2212;0.0298&#x002A;&#x002A;&#x002A; (0.00661)</td>
<td align="center" valign="bottom">&#x2212;0.0285&#x002A;&#x002A;&#x002A; (0.00660)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M27">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M28">
<mml:mn>1</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>2</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">&#x2212;0.0963 (0.325)</td>
<td align="center" valign="bottom">&#x2212;0.122 (0.333)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M29">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M30">
<mml:mn>2</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>3</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.287 (0.326)</td>
<td align="center" valign="bottom">0.267 (0.333)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M31">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M32">
<mml:mn>3</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>4</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.634&#x002A; (0.327)</td>
<td align="center" valign="bottom">0.615&#x002A; (0.334)</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">1,788</td>
<td align="center" valign="top">1.788</td>
</tr>
<tr>
<td align="left" valign="top">Population</td>
<td align="center" valign="top">44,033.995</td>
<td align="center" valign="top">-</td>
</tr>
<tr>
<td align="left" valign="top">Strata</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">-</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The estimation is based on the sample of females obtained from the ICBS survey. The dependent variable (<italic>Self_Rank_Poverty_2016</italic>) is the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never.&#x201D; The black (grey) line in the corresponding <xref ref-type="fig" rid="fig3">Figure 3</xref> is the projected probability to answer 1&#x2009;=&#x2009;&#x201C;often&#x201D; (4&#x2009;=&#x2009;&#x201C;never&#x201D;) as a function of the BMI (=<inline-formula>
<mml:math id="M33">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:msup>
<mml:mi mathvariant="normal">meter</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x003C;&#x2009;30 is overweight; BMI&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list). Column (1) reports the outcomes of the survey data analysis. The analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_2016</italic>. The weight given to each observation is based on the inverse of the BMI variable. Column (2) reports the outcomes where equal weight is given to each observation. In column (1)/(2) The linearized/conventional standard errors are given in parentheses &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Survey data and ordered probit regressions: males.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
</tr>
<tr>
<th/>
<th align="center" valign="bottom">SVY: ordered probit</th>
<th align="center" valign="bottom">Ordered probit</th>
</tr>
<tr>
<th align="left" valign="bottom">Variables</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">ln(Total_inc2016)</td>
<td align="center" valign="bottom">0.193&#x002A;&#x002A;&#x002A; (0.0227)</td>
<td align="center" valign="bottom">0.189&#x002A;&#x002A;&#x002A; (0.0229)</td>
</tr>
<tr>
<td align="left" valign="top"><italic>BMI</italic>2016</td>
<td align="center" valign="bottom">&#x2212;0.0224&#x002A;&#x002A;&#x002A; (0.00650)</td>
<td align="center" valign="bottom">&#x2212;0.0228&#x002A;&#x002A;&#x002A; (0.00641)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M34">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M35">
<mml:mn>1</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>2</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.550&#x002A; (0.296)</td>
<td align="center" valign="bottom">0.498&#x002A; (0.300)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M36">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M37">
<mml:mn>2</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>3</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.868&#x002A;&#x002A;&#x002A; (0.297)</td>
<td align="center" valign="bottom">0.812&#x002A;&#x002A;&#x002A; (0.300)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M38">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M39">
<mml:mn>3</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>4</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">1.146&#x002A;&#x002A;&#x002A; (0.298)</td>
<td align="center" valign="bottom">1.091&#x002A;&#x002A;&#x002A; (0.300)</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">2,229</td>
<td align="center" valign="top">2,229</td>
</tr>
<tr>
<td align="left" valign="top">Population</td>
<td align="center" valign="top">58,866.729</td>
<td align="center" valign="top">-</td>
</tr>
<tr>
<td align="left" valign="top">Strata</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">-</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The estimation is based on the sample of males obtained from the ICBS survey. The dependent variable (<italic>Self_Rank_Poverty_2016</italic>) is the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never.&#x201D; The black (grey) line in the corresponding <xref ref-type="fig" rid="fig4">Figure 4</xref> is the projected probability to answer 1&#x2009;=&#x2009;&#x201C;often&#x201D; (4&#x2009;=&#x2009;&#x201C;never&#x201D;) as a function of the BMI (=<inline-formula>
<mml:math id="M40">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:msup>
<mml:mi mathvariant="normal">meter</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x003C;&#x2009;30 is overweight; BMI&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list). Column (1) reports the outcomes of the survey data analysis. The analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_2016</italic>. The weight given to each observation is based on the inverse of the BMI variable. Column (2) reports the outcomes where equal weight is given to each observation. In column (1)/(2) The linearized/conventional standard errors are given in parentheses &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Pooled sample 2015&#x2013;2016 (lagged variables).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
</tr>
<tr>
<th/>
<th align="center" valign="bottom">SVY: ordered probit</th>
<th align="center" valign="bottom">Ordered Probit</th>
</tr>
<tr>
<th align="left" valign="bottom">Variables</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">
<inline-formula>
<mml:math id="M41">
<mml:mo>ln</mml:mo>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">LAG</mml:mi>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">Total</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>i</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>c</mml:mi>
</mml:mrow>
</mml:mfenced>
</mml:mrow>
</mml:mfenced>
</mml:math>
</inline-formula>
</td>
<td align="center" valign="bottom">0.152&#x002A;&#x002A;&#x002A; (0.0194)</td>
<td align="center" valign="bottom">0.148&#x002A;&#x002A;&#x002A; (0.0187)</td>
</tr>
<tr>
<td align="left" valign="top"><italic>BMI</italic>2016</td>
<td align="center" valign="bottom">&#x2212;0.0221&#x002A;&#x002A;&#x002A; (0.00541)</td>
<td align="center" valign="bottom">&#x2212;0.0208&#x002A;&#x002A;&#x002A; (0.00517)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M42">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M43">
<mml:mn>1</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>2</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.0608 (0.244)</td>
<td align="center" valign="bottom">0.0456 (0.243)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M44">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M45">
<mml:mn>2</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>3</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.376 (0.245)</td>
<td align="center" valign="bottom">0.366 (0.243)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M46">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M47">
<mml:mn>3</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>4</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.695&#x002A;&#x002A;&#x002A; (0.246)</td>
<td align="center" valign="bottom">0.683&#x002A;&#x002A;&#x002A; (0.243)</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">3,083</td>
<td align="center" valign="top">3,083</td>
</tr>
<tr>
<td align="left" valign="top">Population</td>
<td align="center" valign="top">82,128.298</td>
<td align="center" valign="top">-</td>
</tr>
<tr>
<td align="left" valign="top">Strata</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">-</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The estimation is based on the sample of females obtained from the ICBS survey. The dependent variable (<italic>Self_Rank_Poverty_2016</italic>) is the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never.&#x201D; The black (grey) line in the corresponding <xref ref-type="fig" rid="fig5">Figure 5</xref> is the projected probability to answer 1&#x2009;=&#x2009;&#x201C;often&#x201D; (4&#x2009;=&#x2009;&#x201C;never&#x201D;) as a function of the BMI (=<inline-formula>
<mml:math id="M48">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:msup>
<mml:mi mathvariant="normal">meter</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x003C;&#x2009;30 is overweight; BMI&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list). LAG(Total_inc) and LAG(BMI) are the Total Income and BMI reported in the 2015 wave. The objective of this exercise is to avoid endogeneity problems. Column (1) reports the outcomes of the survey data analysis. The analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_2016</italic>. The weight given to each observation is based on the inverse of the LAG(BMI) variable. Column (2) reports the outcomes where equal weight is given to each observation. In column (1)/(2) The linearized/conventional standard errors are given in parentheses &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Females 2015&#x2013;2016 (lagged variables).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
</tr>
<tr>
<th/>
<th align="center" valign="bottom">SVY: ordered probit</th>
<th align="center" valign="bottom">Ordered probit</th>
</tr>
<tr>
<th align="left" valign="bottom">Variables</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">
<inline-formula>
<mml:math id="M49">
<mml:mo>ln</mml:mo>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">LAG</mml:mi>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">Total</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>i</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>c</mml:mi>
</mml:mrow>
</mml:mfenced>
</mml:mrow>
</mml:mfenced>
</mml:math>
</inline-formula>
</td>
<td align="center" valign="bottom">0.122&#x002A;&#x002A;&#x002A; (0.0284)</td>
<td align="center" valign="bottom">0.114&#x002A;&#x002A;&#x002A; (0.0284)</td>
</tr>
<tr>
<td align="left" valign="top"><italic>BMI</italic>2016</td>
<td align="center" valign="bottom">&#x2212;0.0320&#x002A;&#x002A;&#x002A; (0.00771)</td>
<td align="center" valign="bottom">&#x2212;0.0288&#x002A;&#x002A;&#x002A; (0.00748)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M50">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M51">
<mml:mn>1</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>2</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">&#x2212;0.570 (0.358)</td>
<td align="center" valign="bottom">&#x2212;0.573 (0.367)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M52">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M53">
<mml:mn>2</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>3</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">&#x2212;0.206 (0.359)</td>
<td align="center" valign="bottom">&#x2212;0.201 (0.367)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M54">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M55">
<mml:mn>3</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>4</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.142 (0.359)</td>
<td align="center" valign="bottom">0.143 (0.367)</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">1,366</td>
<td align="center" valign="bottom">1,366</td>
</tr>
<tr>
<td align="left" valign="top">Population</td>
<td align="center" valign="top">33,771.645</td>
<td align="center" valign="top">-</td>
</tr>
<tr>
<td align="left" valign="top">Strata</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">-</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The estimation is based on the sample of females obtained from the ICBS survey. The dependent variable (<italic>Self_Rank_Poverty_2016</italic>) is the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never.&#x201D; The black (grey) line in the corresponding <xref ref-type="fig" rid="fig6">Figure 6</xref> is the projected probability to answer 1&#x2009;=&#x2009;&#x201C;often&#x201D; (4&#x2009;=&#x2009;&#x201C;never&#x201D;) as a function of the BMI (=<inline-formula>
<mml:math id="M56">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:msup>
<mml:mi mathvariant="normal">meter</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x003C;&#x2009;30 is overweight; BMI&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list). LAG(Total_inc) and LAG(BMI) are the Total Income and BMI reported in the 2015 wave. The objective of this exercise is to avoid endogeneity problems. Column (1) reports the outcomes of the survey data analysis. The analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_2016</italic>. The weight given to each observation is based on the inverse of the LAG(BMI) variable. Column (2) reports the outcomes where equal weight is given to each observation. In column (1)/(2) The linearized/conventional standard errors are given in parentheses &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Males 2015&#x2013;2016 (lagged variables).</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">(1)</th>
<th align="center" valign="top">(2)</th>
</tr>
<tr>
<th/>
<th align="center" valign="bottom">SVY: ordered probit</th>
<th align="center" valign="bottom">Ordered Probit</th>
</tr>
<tr>
<th align="left" valign="bottom">Variables</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
<th align="center" valign="bottom"><italic>Self_Rank_Poverty_</italic>2016</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">
<inline-formula>
<mml:math id="M57">
<mml:mo>ln</mml:mo>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">LAG</mml:mi>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi mathvariant="italic">Total</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>i</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>c</mml:mi>
</mml:mrow>
</mml:mfenced>
</mml:mrow>
</mml:mfenced>
</mml:math>
</inline-formula>
</td>
<td align="center" valign="bottom">0.171&#x002A;&#x002A;&#x002A; (0.0273)</td>
<td align="center" valign="bottom">0.172&#x002A;&#x002A;&#x002A; (0.0255)</td>
</tr>
<tr>
<td align="left" valign="top"><italic>BMI</italic>2016</td>
<td align="center" valign="bottom">&#x2212;0.0136&#x002A; (0.00785)</td>
<td align="center" valign="bottom">&#x2212;0.0138&#x002A; (0.00745)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M58">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M59">
<mml:mn>1</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>2</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.542 (0.354)</td>
<td align="center" valign="bottom">0.555 (0.345)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M60">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M61">
<mml:mn>2</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>3</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">0.822&#x002A;&#x002A; (0.355)</td>
<td align="center" valign="bottom">0.835&#x002A;&#x002A; (0.345)</td>
</tr>
<tr>
<td align="left" valign="top"><inline-formula>
<mml:math id="M62">
<mml:mi>C</mml:mi>
<mml:mi>u</mml:mi>
<mml:msub>
<mml:mi>t</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> (<inline-formula>
<mml:math id="M63">
<mml:mn>3</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>_</mml:mo>
<mml:mn>2016</mml:mn>
<mml:mo>&#x2264;</mml:mo>
<mml:mn>4</mml:mn>
</mml:math>
</inline-formula>)</td>
<td align="center" valign="bottom">1.118&#x002A;&#x002A;&#x002A; (0.356)</td>
<td align="center" valign="bottom">1.132&#x002A;&#x002A;&#x002A; (0.345)</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">1,717</td>
<td align="center" valign="bottom">1,717</td>
</tr>
<tr>
<td align="left" valign="top">Population</td>
<td align="center" valign="top">45,429.417</td>
<td align="center" valign="top">-</td>
</tr>
<tr>
<td align="left" valign="top">Strata</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">-</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The estimation is based on the sample of females obtained from the ICBS survey. The dependent variable (<italic>Self_Rank_Poverty_2016</italic>) is the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never.&#x201D; The black (grey) line in the corresponding <xref ref-type="fig" rid="fig7">Figure 7</xref> is the projected probability to answer 1&#x2009;=&#x2009;&#x201C;often&#x201D; (4&#x2009;=&#x2009;&#x201C;never&#x201D;) as a function of the BMI (=<inline-formula>
<mml:math id="M64">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:msup>
<mml:mi mathvariant="normal">meter</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;BMI&#x2009;&#x003C;&#x2009;30 is overweight; BMI&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list). LAG(Total_inc) and LAG(BMI) are the Total Income and BMI reported in the 2015 wave. The objective of this exercise is to avoid endogeneity problems. Column (1) reports the outcomes of the survey data analysis. The analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_2016</italic>. The weight given to each observation is based on the inverse of the LAG(BMI) variable. Column (2) reports the outcomes where equal weight is given to each observation. In column (1)/(2) The linearized/conventional standard errors are given in parentheses &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</table-wrap-foot>
</table-wrap>
<p>In each of the subsequent sections, column (1) of each Table reports the outcomes of the survey data analysis. The analysis includes four strata based on the four categories of the variable <italic>Self_Rank_Poverty_</italic>2016. The weight given to each observation is based on the inverse of the BMI variable. Column (2) reports the outcomes where equal weight is given to each observation.</p>
<p>The remainder of the manuscript consists of two parts:</p>
<p>The basic results part refers to a cross section obtained from the 2016 wave. The dependent variable is <italic>Self_Rank_Povery_</italic>2016, the self-ranking of individuals in response to the question: &#x201C;During the last 15&#x2009;years, how often did you consider yourself poor?&#x201D; Possible answers are 1&#x2009;=&#x2009;&#x201C;often,&#x201D; 2&#x2009;=&#x2009;&#x201C;sometimes,&#x201D; 3&#x2009;=&#x2009;&#x201C;rarely,&#x201D; 4&#x2009;=&#x2009;&#x201C;never&#x201D;; The independent variables are <inline-formula>
<mml:math id="M65">
<mml:mo>ln</mml:mo>
</mml:math>
</inline-formula> (Total_Inc2016) (the natural logarithm of the total income); and BMI2016 (=<inline-formula>
<mml:math id="M66">
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="italic">kg</mml:mi>
<mml:mo>.</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="italic">mete</mml:mi>
<mml:msup>
<mml:mi>r</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula> where 18&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x2264;&#x2009;25 is normal weight; 25&#x2009;&#x2264;&#x2009;<italic>BMI</italic>&#x2009;&#x003C;&#x2009;30 is overweight; <italic>BMI</italic>&#x2009;&#x2265;&#x2009;30 is considered obesity &#x2013; see the links to the WHO websites in the reference list).</p>
<p>An important concern is the potential endogeneity problem between Total_Inc2016 / BMI2016 and <italic>Self_Rank_Povery</italic>_2016. Differently put, the chicken and egg problem might arise: Does reduced ranking motivate weight gain or vice versa (weight gain motivates lower ranking). The robustness test part investigates this problem by replacing the independent variables ln(Total_Inc2016) and BMI2016 by ln(LAG(Total_Inc))&#x2009;=&#x2009;ln(Total_Inc2015) and LAG(BMI)&#x2009;=&#x2009;BMI2015.</p>
<p>Finally, we run the Granger Causality test separately for females and males. This test permit testing whether lagged BMI Granger-cause different self-ranking of poverty.</p>
</sec>
</sec>
<sec sec-type="results" id="sec10">
<label>3</label>
<title>Results</title>
<sec id="sec11">
<label>3.1</label>
<title>Basic results</title>
<p>Based on the empirical model, <xref ref-type="fig" rid="fig2">Figures 2</xref>&#x2013;<xref ref-type="fig" rid="fig4">4</xref> correspond to <xref ref-type="table" rid="tab2">Tables 2</xref>&#x2013;<xref ref-type="table" rid="tab4">4</xref> and describe the projected probability to often (never) consider yourself poor as a function of the <italic>BMI</italic> when income is controlled. While <xref ref-type="fig" rid="fig2">Figure 2</xref> refers to the pooled sample, <xref ref-type="fig" rid="fig3">Figures 3</xref>, <xref ref-type="fig" rid="fig4">4</xref> are stratified by gender (females and males).</p>
<p>The Tables exhibit the &#x201C;correct&#x201D; signs of the two estimated coefficients of the independent variables. The sign of the ln(Total_Inc_2016) coefficient is positive and significant. Given that a <italic>rise</italic> in <italic>Self_Rank_Poverty_2016</italic> (the dependent variable) is associated with reduced subjective sense of poverty, the implication is that when income level <italic>rise</italic> and the model is BMI adjusted, the inclination to rank yourself as &#x201C;poor&#x201D; <italic>drops</italic>. In contrast, the sign of the <italic>BMI</italic>_2016 coefficient is negative and significant. The implication is that when the <italic>BMI</italic> variable <italic>increases</italic> and the model is income adjusted, the inclination to rank yourself as &#x201C;poor&#x201D; <italic>rises</italic>.</p>
<p>It is evident from the three figures that for the same level of income and for both genders, the projected probability of self-ranking as &#x201C;not poor&#x201D; <italic>drops</italic> from 0.73 where the <italic>BMI</italic> equals 18 to 0.37 (females) &#x2013; 0.48 (males) where the <italic>BMI</italic> equals 50. At the same time, the projected probability of self-ranking as &#x201C;poor&#x201D; <italic>rises</italic> from 0.09 (females) &#x2013; 0.11 (males) where the <italic>BMI</italic> equals 18 to 0.34 (females) &#x2013; 0.30 (males) where the <italic>BMI</italic> equals 50. Unlike Arbel et al. (<xref ref-type="bibr" rid="ref6">6</xref>), no gender differences were recorded in this section.</p>
</sec>
<sec id="sec12">
<label>3.2</label>
<title>Robustness tests</title>
<p>To address the potential endogeneity problem between the <italic>BMI</italic> and <italic>Self_Rank_Poverty_</italic>2016, we ran the same model based on a follow-up of two years (2015 and 2016), where the independent variables are the lagged <italic>BMI</italic> and the natural logarithm of lagged total income from all sources. This ensures that the independent variables are exogenous. The outcomes, given in <xref ref-type="table" rid="tab5">Tables 5</xref>&#x2013;<xref ref-type="table" rid="tab7">7</xref> and the corresponding <xref ref-type="fig" rid="fig5">Figures 5</xref>&#x2013;<xref ref-type="fig" rid="fig7">7</xref>, are robust to those obtained previously.</p>
<p>Finally, to investigate the casual relationships between the ranking of poverty and the independent variables, we ran the Granger causality test for the pooled sample, and separately for females and males [(<xref ref-type="bibr" rid="ref31">31</xref>, <xref ref-type="bibr" rid="ref32">32</xref>): 476&#x2013;477]. A detailed description of the test is given in <xref ref-type="sec" rid="sec20">Appendix B</xref>. Results of this test are reported in <xref ref-type="table" rid="tab8">Table 8</xref>.</p>
<table-wrap position="float" id="tab8">
<label>Table 8</label>
<caption>
<p>Granger causality test.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">Calculated</th>
<th align="center" valign="top">Calculated <italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Pooled Sample</td>
<td align="center" valign="top"><italic>F</italic>(1, 4,661)&#x2009;=&#x2009;6.52</td>
<td align="center" valign="top"><italic>p</italic> =&#x2009;0.0107</td>
</tr>
<tr>
<td align="left" valign="top">Females</td>
<td align="center" valign="top"><italic>F</italic>(1, 2,311)&#x2009;=&#x2009;6.62</td>
<td align="center" valign="top"><italic>p</italic> =&#x2009;0.0102</td>
</tr>
<tr>
<td align="left" valign="top">Males</td>
<td align="center" valign="top"><italic>F</italic>(1, 2,344)&#x2009;=&#x2009;1.33</td>
<td align="center" valign="top"><italic>p</italic> =&#x2009;0.2491</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The calculated F-statistics of the granger causality test is given by: <inline-formula>
<mml:math id="M67">
<mml:msub>
<mml:mi>F</mml:mi>
<mml:mi>C</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi>E</mml:mi>
<mml:mi>S</mml:mi>
<mml:msub>
<mml:mi>S</mml:mi>
<mml:mi>R</mml:mi>
</mml:msub>
<mml:mo>&#x2212;</mml:mo>
<mml:mi>E</mml:mi>
<mml:mi>S</mml:mi>
<mml:msub>
<mml:mi>S</mml:mi>
<mml:mi>U</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mfenced>
<mml:mo stretchy="true">/</mml:mo>
<mml:mn>1</mml:mn>
</mml:mrow>
<mml:mrow>
<mml:mi>E</mml:mi>
<mml:mi>S</mml:mi>
<mml:msub>
<mml:mi>S</mml:mi>
<mml:mi>U</mml:mi>
</mml:msub>
<mml:mo stretchy="true">/</mml:mo>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi>N</mml:mi>
<mml:mo>&#x2212;</mml:mo>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:mfenced>
</mml:mrow>
</mml:mfrac>
</mml:math>
</inline-formula>. Where <inline-formula>
<mml:math id="M68">
<mml:mi>E</mml:mi>
<mml:mi>S</mml:mi>
<mml:msub>
<mml:mi>S</mml:mi>
<mml:mi>R</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> <inline-formula>
<mml:math id="M69">
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi>E</mml:mi>
<mml:mi>S</mml:mi>
<mml:msub>
<mml:mi>S</mml:mi>
<mml:mi>U</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mfenced>
</mml:math>
</inline-formula> is the error sum of square of the restricted (unrestricted) model, and N is the number of observations. The unrestricted model is given by: <inline-formula>
<mml:math id="M70">
<mml:mi>A</mml:mi>
<mml:mi>v</mml:mi>
<mml:mi>g</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mi mathvariant="italic">LAG</mml:mi>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi>A</mml:mi>
<mml:mi>v</mml:mi>
<mml:mi>g</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
</mml:mrow>
</mml:mfenced>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:mi mathvariant="italic">LAG</mml:mi>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi>A</mml:mi>
<mml:mi>v</mml:mi>
<mml:mi>g</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>B</mml:mi>
<mml:mi>M</mml:mi>
<mml:mi>I</mml:mi>
</mml:mrow>
</mml:mfenced>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03BC;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula>. And the restricted model is given by: <inline-formula>
<mml:math id="M71">
<mml:mi>A</mml:mi>
<mml:mi>v</mml:mi>
<mml:mi>g</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mi mathvariant="italic">LAG</mml:mi>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mi>A</mml:mi>
<mml:mi>v</mml:mi>
<mml:mi>g</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Self</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Rank</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Poverty</mml:mi>
</mml:mrow>
</mml:mfenced>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03BC;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula>. Where each variable is manifested in terms of the difference from the respective mean; <inline-formula>
<mml:math id="M72">
<mml:mi>A</mml:mi>
<mml:mi>v</mml:mi>
<mml:mi>g</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi mathvariant="italic">Wealth</mml:mi>
</mml:math>
</inline-formula> <inline-formula>
<mml:math id="M73">
<mml:mi>A</mml:mi>
<mml:mi>v</mml:mi>
<mml:mi>g</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>B</mml:mi>
<mml:mi>m</mml:mi>
<mml:mi>i</mml:mi>
</mml:math>
</inline-formula> are the differences between the wealth divided by four (to scale the ordinal outcome between 0 and 1); the BMI and their respective means; the LAG operator is the variable lagged by one year; <inline-formula>
<mml:math id="M74">
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mo>,</mml:mo>
<mml:msub>
<mml:mi>&#x03B1;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:mo>,</mml:mo>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> are estimated parameters; and <inline-formula>
<mml:math id="M75">
<mml:msub>
<mml:mi>&#x03BC;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mo>,</mml:mo>
<mml:msub>
<mml:mi>&#x03BC;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:math>
</inline-formula> are the classical random disturbance term.</p>
</table-wrap-foot>
</table-wrap>
<p>The outcomes demonstrate that for women, the <italic>BMI</italic> Granger-cause the poverty subjective ranking at the 5% level. Yet, for the male group, there are no casual relationships between the <italic>BMI</italic> as a proxy for obesity and self-ranking of poverty. Several studies demonstrate that compared to men, women are penalized more severely due to obesity, including in prospects of finding employment [e.g., (<xref ref-type="bibr" rid="ref12">12</xref>, <xref ref-type="bibr" rid="ref17">17</xref>, <xref ref-type="bibr" rid="ref33 ref34 ref35 ref36 ref37 ref38 ref39 ref40 ref41 ref42">33&#x2013;42</xref>)]. In this context, Arbel et al. (<xref ref-type="bibr" rid="ref7">7</xref>) demonstrate that compared to men, female self-evaluation of housing prices is more conservative and less influenced by <italic>BMI</italic> changes. This outcome is obtained despite the fact that women are more susceptible to weight gain, particularly in western societies [e.g., (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref43">43</xref>, <xref ref-type="bibr" rid="ref44">44</xref>)].</p>
<p>To further explore the external validity, we employ an approach based on Friedman (<xref ref-type="bibr" rid="ref45">45</xref>) and machine learning (<xref ref-type="bibr" rid="ref46">46</xref>), consisting of the following steps:</p><list list-type="order">
<list-item>
<p>the cross-validation command in Stata. The command: (a) randomly assigns the sample to training (on-sample) group and test (off-sample) group and (b) generates a vector of predictions on the test group based on the outcomes obtained from the training group (Proj1).</p>
</list-item>
<list-item>
<p>Post-estimation results of projections obtained when the pooled sample is employed (Proj_ <italic>Self_Rank_Poverty</italic>).</p>
</list-item>
<list-item>
<p>Regression analysis between the vector of predictions obtained from step 1 and step 2.</p>
</list-item>
<list-item>
<p>If external validity exists, the null hypothesis of no constant and a slope of one (a <inline-formula>
<mml:math id="M76">
<mml:msup>
<mml:mn>45</mml:mn>
<mml:mo>&#x00B0;</mml:mo>
</mml:msup>
</mml:math>
</inline-formula> angle) should not be rejected.</p>
</list-item>
<list-item>
<p>The outcomes of this procedure are given in <xref ref-type="table" rid="tab9">Table 9</xref>. They indeed demonstrate that the null hypothesis of no constant and a slope of one cannot be rejected for both genders at the 1% significance level (<italic>p</italic>&#x2009;=&#x2009;0.0167&#x2013;0.0195).</p>
</list-item>
</list>
<table-wrap position="float" id="tab9">
<label>Table 9</label>
<caption>
<p>External validity test.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">Women</th>
<th align="center" valign="top">Men</th>
</tr>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top"><italic>Proj_Self_Rank_Poverty_</italic>2016</th>
<th align="center" valign="top"><italic>Proj_Self_Rank_Poverty_</italic>2016</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Constant</td>
<td align="center" valign="top">0.0004 (0.411)</td>
<td align="center" valign="top">0.0012 (0.158)</td>
</tr>
<tr>
<td align="left" valign="top">Proj1</td>
<td align="center" valign="top">0.9942&#x002A;&#x002A;&#x002A; (&#x003C;0.01)</td>
<td align="center" valign="top">0.9885&#x002A;&#x002A;&#x002A; (&#x003C;0.01)</td>
</tr>
<tr>
<td align="left" valign="top">Observations</td>
<td align="center" valign="top">1,788</td>
<td align="center" valign="top">2,229</td>
</tr>
<tr>
<td align="left" valign="top">F-test</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="top"><italic>F</italic>-value (Const&#x2009;=&#x2009;0; coef(Proj1))&#x2009;=&#x2009;1</td>
<td align="center" valign="top">4.10</td>
<td align="center" valign="top">3.94</td>
</tr>
<tr>
<td align="left" valign="top">d.f. numerator</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">2</td>
</tr>
<tr>
<td align="left" valign="top">d.f. denominator</td>
<td align="center" valign="top">1,786</td>
<td align="center" valign="top">2,227</td>
</tr>
<tr>
<td align="left" valign="top"><italic>P</italic>-value (Const&#x2009;=&#x2009;0; coef(Proj1))&#x2009;=&#x2009;1</td>
<td align="center" valign="top">0.0167</td>
<td align="center" valign="top">0.0195</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>p</italic>-values are given in parentheses. &#x002A;&#x002A;&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.01.</p>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec sec-type="discussion" id="sec13">
<label>4</label>
<title>Discussion</title>
<p>This study introduces a novel approach to examining the impact of societal norms on obese women, focusing on their economic self-perception. By controlling for income levels over a two-year period, the research reveals a significant decrease in the likelihood of obese individuals ranking themselves as &#x201C;not poor,&#x201D; particularly among women (from 0.73 to 0.37), and men (0.48). This underscores how weight biases influence economic self-assessment, independent of actual income variations.</p>
<p>Moreover, the study contributes to understanding the concept of permanent income among obese individuals, highlighting a discrepancy between their current and expected long-term income levels. Unlike prior research focusing mainly on self-esteem impacts, this study reveals a heightened perception of poverty among obese persons, indicating broader societal implications beyond health outcomes.</p>
<p>The findings also suggest a gender-specific effect, where lagged BMI significantly predicts self-ranking of poverty among women but not men, suggesting differential economic impacts based on weight.</p>
<p>Public policy implications are substantial, advocating for interventions to mitigate weight-based discrimination in employment and social settings. Efforts to enhance self-esteem and economic opportunities for obese individuals, similar to campaigns for other marginalized groups, are recommended.</p>
<p>Research findings support the awareness of more obese women, manifested by their subjective ranking as &#x201C;not poor,&#x201D; to lower prospects of finding a job.</p>
<p>This support is consistent with the results demonstrating a <italic>drop</italic> in the projected probability of self-ranking as &#x201C;not poor&#x201D; with the <italic>BMI</italic> from 0.73 to 0.37 among females. Differently put, the study reveals the inclination of more obese women to rank themselves as &#x201C;poor&#x201D; and less obese women to rank themselves as &#x201C;not poor.&#x201D; Given that the income level is controlled, under equal conditions, higher BMI will elevate the women&#x2019;s tendency to feel poorer compared to their less obese counterparts.</p>
<p>Research findings thus stress the need to educate the public against prejudices on the grounds of obesity, particularly given that 0.78&#x2013;0.81 of the weight gain is attributed to heritability (<xref ref-type="bibr" rid="ref24">24</xref>). In that context, Shugart (<xref ref-type="bibr" rid="ref47">47</xref>) demonstrate a shift in the American public opinion following <italic>The Oprah Winfrey Show,</italic> from an historical attribution of obesity to personal responsibility to cultural explanations. Ophra Winfrey is known for her own public struggle with obesity, which she often engages in on her show. This serves further to anchor the moral authority on the topic and the reflection of the fact that obesity is not necessarily the outcome of lack of will power. Consequently, there is no reason why employers should offer obese women less job opportunities compared to their normal weight counterparts [e.g., (<xref ref-type="bibr" rid="ref17">17</xref>)].</p>
<p>In particular, our study seeks to evoke awareness among potential employers, which, in turn, might motivate avoidance of, or at least reduction in, an implicit wage penalty against obese women (to which the women are aware of according to a possible interpretation of our findings).</p>
<p>This conclusion is further supported by &#x00C1;sgeirsd&#x00F3;ttir et al. (<xref ref-type="bibr" rid="ref8">8</xref>), Chung and Lim (<xref ref-type="bibr" rid="ref48">48</xref>), and Arbel et al. (<xref ref-type="bibr" rid="ref6">6</xref>, <xref ref-type="bibr" rid="ref7">7</xref>).</p>
<p>&#x00C1;sgeirsd&#x00F3;ttir et al. (<xref ref-type="bibr" rid="ref8">8</xref>) suggest that only females show a positive willingness to pay (WTP) for not being overweight. Based on their income level, and to achieve the same happiness level, the reduction of weight for overweight females is associated with WTP of $3,608&#x2013;$37,488 per year.</p>
<p>Based on Korea National Health and Nutrition Examination Survey (2010&#x2013;2014), Chung and Lim (<xref ref-type="bibr" rid="ref48">48</xref>) found that obesity prevalence more than doubled with a shift from less to more educated women. While 34.3% of the less-educated women were defined &#x201C;obese,&#x201D; this prevalence reduced to only 16.0% among the highly educated women. Given the return on higher human capital, higher education is also positively associated with income level. In this context Mathieu-Bolh (<xref ref-type="bibr" rid="ref49">49</xref>) suggests that among socio-economic characteristics poverty seems to be connected to obesity in rich countries, albeit this relationship might be more elusive than expected.</p>
<p>Arbel et al. (<xref ref-type="bibr" rid="ref6">6</xref>) demonstrate another aspect of the penalty against obese women: their need to compromise on men with shorter height as mates. The literature demonstrates positive association between the height of the men, and owning a car, having more children, and living in a single family detached unit. Stefanczyk et al. (<xref ref-type="bibr" rid="ref50">50</xref>), for instance, showed that shorter men are rated by others as less masculine, less physically attractive, of lower social and professional status, and less competent compared with taller men.</p>
<p>Finally, Arbel et al. (<xref ref-type="bibr" rid="ref7">7</xref>) explored the relationship between self-evaluation of apartments and obesity as a proxy for self-esteem, particularly among women. One would anticipate a lower self-evaluation of apartment value among obese women following the influence of western values and norms regarding a slim body image of women, namely, social obesity penalties. The authors demonstrate that for both genders, <italic>BMI</italic> is negatively correlated with self-evaluation of apartments. Yet, compared to males, the cognitive error in price evaluation is smaller among women.</p>
</sec>
<sec id="sec14">
<label>5</label>
<title>Summary and conclusions</title>
<p>The current study proposes and applies a new measure for the influence of western social values and norms associated with discrimination against obese women. Based on a follow-up survey of two years, we estimate the relationship between projected probability of self-ranking of poor individuals and obesity, when the income level is controlled. Findings clearly demonstrate a <italic>drop</italic> the projected probability of self-ranking as &#x201C;not poor&#x201D; with <italic>BMI</italic> from 0.73 to 0.37 (females) &#x2013; 0.48 (males) when income is controlled. Similar outcomes are obtained when the independent variables are lagged and thus avoid endogeneity concerns. Finally, additional outcomes support the conclusion that the lagged <italic>BMI</italic> Granger-cause self-ranking of poverty for women, but not for men.</p>
<p>The contribution of this manuscript lies in the new method proposed to assess the permanent income of obese persons. According to Friedman (<xref ref-type="bibr" rid="ref23">23</xref>) the permanent income hypothesis is a theory of consumer spending stating that people will spend money at a level consistent with their expected long-term average income. The outcomes of the current study indicate that the level of permanent income among obese person is lower than their current income.</p>
<p>Unlike previous studies, our manuscript shows that obese persons suffer not only from poor self-esteem (<xref ref-type="bibr" rid="ref4">4</xref>, <xref ref-type="bibr" rid="ref5">5</xref>, <xref ref-type="bibr" rid="ref22">22</xref>), but also from an increased sense of poverty among both women and men even, when the actual level of income is controlled. In response to the question&#x2014;how often do you consider yourself poor, a higher inclination among obese person to answer &#x201C;often&#x201D; was observed. Unlike B&#x00F6;ckerman et al. (<xref ref-type="bibr" rid="ref20">20</xref>), who demonstrate the impact of genetic factors, the current manuscript exemplifies the cultural factors of a social stigma in our thin-obsessed society (<xref ref-type="bibr" rid="ref22">22</xref>).</p>
<p>The outcomes of our research may be interpreted as the degree of awareness to the social stigma concerning obese persons among potential employers, parents, and mates. This pattern, in turn, adversely affects the Social and Economic Status (SES) in the long run. In this context, a recent article by Campos-Vazquez and Gonzalez (<xref ref-type="bibr" rid="ref17">17</xref>) indeed demonstrated a lower prevalence of job offers to obese women in Mexico&#x2013; where all the other C.V. factors were controlled.</p>
<p>Discrimination and prejudice may also be considered as irrational behavior. Preston and Szymanski (<xref ref-type="bibr" rid="ref51">51</xref>) demonstrated that increasing the proportion of black soccer players is expected to improve the ranking of English football teams. Goldin and Rouse (<xref ref-type="bibr" rid="ref52">52</xref>) provide evidence suggesting that the blind audition procedure fostered impartial hiring of musicians and increased the proportion of women in symphony orchestras.</p>
<p>The public policy repercussions of our study should be divided into two main components. The first relates to the work of professionals (psychologists, doctors, dietitians, personal trainers and social workers) with obese persons, in an effort to boost their self-esteem, provide motivation for development and assist them in maintaining appropriate nutrition and participating in physical activity programs. Undoubtedly, obesity is a medical problem and the risk factor of many diseases such as cardiovascular diseases (mainly heart disease and stroke), which were the leading cause of death in 2012; diabetes; musculoskeletal disorders (especially osteoarthritis &#x2013; a highly disabling degenerative disease of the joints); several cancer types (including endometrial, breast, ovarian, prostate, liver, gallbladder, kidney, and colon) (<xref ref-type="bibr" rid="ref53">53</xref>, <xref ref-type="bibr" rid="ref54">54</xref>). Solely from health considerations, obese persons should be monitored and encouraged to reduce weight.</p>
<p>The second component is the need for a broad-based public information campaign, explaining the genetic sources of obesity. About 60&#x2013;80% of obesity and severe obesity might be explained by genetic factors (<xref ref-type="bibr" rid="ref55">55</xref>, <xref ref-type="bibr" rid="ref56">56</xref>). Indeed, Shugart (<xref ref-type="bibr" rid="ref47">47</xref>) demonstrate a shift in the American public opinion following <italic>The Oprah Winfrey Show,</italic> from an historical attribution of obesity to personal responsibility to cultural explanations. Ophra Winfrey is known for her own public struggle with obesity, which she often engages on her show. This serves further to anchor the moral authority on the topic and the reflection of the fact that obesity is not necessarily the outcome of lack of will power. Consequently, there is no reason why employers should offer obese women less job opportunities compared to their normal weight counterparts [e.g., (<xref ref-type="bibr" rid="ref17">17</xref>)].</p>
<p>The strength of our study may be described as follows:</p><list list-type="order">
<list-item>
<p>The potential relationship between obesity and self-ranking of poverty has not been previously explored based on self-ranking of poverty when income is controlled. We thus propose a new measure for the influence of western social values and norms associated with discrimination against obese women.</p>
</list-item>
<list-item>
<p>Unlike previous studies, our manuscript shows that obese persons suffer not only from poor self-esteem (<xref ref-type="bibr" rid="ref4">4</xref>, <xref ref-type="bibr" rid="ref5">5</xref>, <xref ref-type="bibr" rid="ref22">22</xref>), but also from an increased sense of poverty among both women and men even, when the actual level of income is controlled.</p>
</list-item>
<list-item>
<p>In particular, our study seeks to evoke awareness among potential employers, which, in turn, might motivate avoidance of, or at least reduction in, an implicit wage penalty against obese women.</p>
</list-item>
</list>
<p>There are several weaknesses associated with this study. The study uses BMI as a measure of obesity. According to the World Health Organization, on the one hand, like any other measure BMI is not perfect indicator since it is only dependent on height and weight and it does not take into consideration different levels of adiposity based on age, physical activity levels and gender. For this reason it is expected that it overestimates adiposity in some cases and underestimates it in others (<xref ref-type="bibr" rid="ref53">53</xref>).</p>
<p>On the other hand, BMI is easy to measure and calculate and is therefore the most commonly used tool to correlate risk of health problems with the weight at population level. During the 1970s and based especially on the data and report from the Seven Countries study, researchers noticed that BMI appeared to be a good proxy for adiposity and overweight related problems (<xref ref-type="bibr" rid="ref53">53</xref>).</p>
<p>Other measures of obesity are also problematic. Association between waist circumferences (WC) and health risks is not a trivial exercise and should be undertaken scientifically using proper techniques (<xref ref-type="bibr" rid="ref53">53</xref>).</p>
<p>A further discussion of the advantages and disadvantages of BMI and other possible measures of obesity are given in Nirav and Braverman (<xref ref-type="bibr" rid="ref57">57</xref>), Antonopoulos et al. (<xref ref-type="bibr" rid="ref58">58</xref>), Bosello et al. (<xref ref-type="bibr" rid="ref59">59</xref>), Ga&#x017E;arov&#x00E1; et al. (<xref ref-type="bibr" rid="ref60">60</xref>), and Moltrer et al. (<xref ref-type="bibr" rid="ref61">61</xref>).</p>
<p>Another limitation that should be considered is the self-reported BMI in our sample. Yet, two points should be considered:</p><list list-type="order">
<list-item>
<p>The survey is an interview, so that the interviewer can get an impression whether the self-report is reasonable.</p>
</list-item>
<list-item>
<p>The figure given in <xref ref-type="sec" rid="sec20">Appendix C</xref> provides the prevalence of obesity based measured vs. self-reported BMI in OECD countries. As can be seen in this figure, unlike countries like Chile, Portugal, Australia, Turkey, Hungary and Ireland, in Israel there is almost no difference between the prevalence of obesity based measured vs. self-reported BMI.</p>
</list-item>
</list>
<p>Finally, an additional limitation is the possible extension of the proposed empirical model which will considers the potential non-linear effect of obesity. This is a subject for possible future research. Such research should employ parabolic models, which permit non-monotonic increase or decrease with obesity.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec15">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="sec" rid="sec20">Supplementary material</xref>, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="author-contributions" id="sec16">
<title>Author contributions</title>
<p>YuA: Conceptualization, Formal analysis, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. YiA: Conceptualization, Formal analysis, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. AK: Conceptualization, Formal analysis, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. MK: Conceptualization, Formal analysis, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<sec sec-type="funding-information" id="sec17">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<ack>
<p>The authors are grateful to Chaim Fialkoff for helpful comments.</p>
</ack>
<sec sec-type="COI-statement" id="sec18">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="sec19">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec20">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fpubh.2024.1480365/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fpubh.2024.1480365/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.pdf" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Data_Sheet_2.pdf" id="SM2" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Data_Sheet_3.pdf" id="SM3" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_1.xlsx" id="SM4" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_2.xlsx" id="SM5" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<title>Abbreviations</title>
<fn fn-type="abbr">
<p>BMI, Body Mass Index; ICBS, Israeli Central Bureau of Statistics; OECD, Organization for Economic Cooperation and Development; WHO, World Health Organization; WTP, Willingness to Pay.</p>
</fn>
</fn-group>
<fn-group>
<fn id="fn0001">
<p><sup>1</sup>It should be noted, however, that according to B&#x00F6;ckerman et al. (<xref ref-type="bibr" rid="ref20">20</xref>), the use of a newer, broader genetic risk score, changes this outcome to non-rejection of the null hypothesis of no effect of obesity on these factors.</p>
</fn>
<fn id="fn0002">
<p><sup>2</sup>BMI is formerly called the Quetelet index (named after the 19th century Belgian scientist &#x2013; Adolf Quetelet &#x2013; who was the first to formulate this measure). According to the World Health Organization (WHO) definitions (<xref ref-type="bibr" rid="ref53">53</xref>, <xref ref-type="bibr" rid="ref54">54</xref>), 18&#x2009;&#x2264; <italic>BMI</italic> &#x003C;&#x2009;25 is considered normal weight; 25&#x2009;&#x2264; <italic>BMI</italic> &#x2264;&#x2009;30 is considered overweight, and <italic>BMI</italic> &#x003E;&#x2009;30 is considered obesity &#x2013; see <ext-link xlink:href="https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi" ext-link-type="uri">https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi</ext-link> and <ext-link xlink:href="https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight" ext-link-type="uri">https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight</ext-link>.</p>
</fn>
<fn id="fn0003">
<p><sup>3</sup>In this context, Stunkard et al. (<xref ref-type="bibr" rid="ref56">56</xref>) suggested that: &#x201C;Classic twin methods estimated a high heritability for height, weight, and BMI, both at age 20&#x2009;years (0.80, 0.78, and.77, respectively) and at a 25-year follow-up (0.80, 0.81, and.84, respectively). Height, weight, and BMI were highly correlated across time, and a path analysis suggested that the major part of that covariation was genetic. These results are similar to those of other twin studies of these measures and suggest that human fatness is under substantial genetic control.&#x201D; (the Abstract)</p>
</fn>
<fn id="fn0004">
<p><sup>4</sup>Referring separately to the 2015 wave, and to the 2015&#x2013;2016 waves without lagged explanatory variables, results still remain robust. These complementary outcomes are available upon request.</p>
</fn>
<fn id="fn0005">
<p><sup>5</sup>Note the difference between the sample and population income. In the sample, the variable Total_inc refers to income from all sources (including self-employment, wage, royalties, babysitting and all other sources). In the population, the 121,476 NIS figure refers to annual income only from wage.</p>
</fn>
<fn id="fn0006">
<p><sup>6</sup>Sen (<xref ref-type="bibr" rid="ref62">62</xref>) criticized the conventional measures of poverty. Those are based either on the percent of households whose net income is below the poverty line <italic>z</italic> (<inline-formula>
<mml:math id="M77">
<mml:mi>H</mml:mi>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mi>q</mml:mi>
<mml:mi>N</mml:mi>
</mml:mfrac>
</mml:math>
</inline-formula> where <italic>q</italic> is the number of households whose income is below <italic>z</italic> and <italic>N</italic> is the total population) or by measuring the aggregated difference between the net income of the poor to the poverty line (<inline-formula>
<mml:math id="M78">
<mml:mi>I</mml:mi>
<mml:mo>=</mml:mo>
<mml:mo stretchy="true">&#x2211;</mml:mo>
<mml:mrow>
<mml:mfrac>
<mml:msub>
<mml:mi>g</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
<mml:mrow>
<mml:mi>q</mml:mi>
<mml:mi>z</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</inline-formula> where <inline-formula>
<mml:math id="M79">
<mml:msub>
<mml:mi>g</mml:mi>
<mml:mi>i</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> is the difference for individual <italic>i</italic>). Sen (<xref ref-type="bibr" rid="ref62">62</xref>) proposed a new measure based on the formula <inline-formula>
<mml:math id="M80">
<mml:mi>P</mml:mi>
<mml:mo>=</mml:mo>
<mml:mi>H</mml:mi>
<mml:mfenced open="[" close="]">
<mml:mrow>
<mml:mi>I</mml:mi>
<mml:mo>+</mml:mo>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mn>1</mml:mn>
<mml:mo>&#x2212;</mml:mo>
<mml:mi>I</mml:mi>
</mml:mrow>
</mml:mfenced>
<mml:mi>G</mml:mi>
</mml:mrow>
</mml:mfenced>
</mml:math>
</inline-formula> where <italic>G</italic> is the Gini coefficient.</p>
</fn>
<fn id="fn0007">
<p><sup>7</sup>To avoid outliers, the model incorporates the logarithmic transformation of the variable <inline-formula>
<mml:math id="M81">
<mml:mi mathvariant="italic">Total</mml:mi>
<mml:mo>_</mml:mo>
<mml:mi>I</mml:mi>
<mml:mi>n</mml:mi>
<mml:mi>c</mml:mi>
<mml:mn>2016.</mml:mn>
</mml:math>
</inline-formula></p>
</fn>
</fn-group>
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