AIDS pandemic

Economics and Human Biology 30 (2018) 119–129

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Altruism in preventive health behavior: At-scale evidence from the HIV/AIDS pandemic Nicholas Wilson Office of Evaluation Sciences, The United States General Services Administration and Department of Economics, Reed College, 3203 SE Woodstock Blvd, Portland, OR, 97202, USA

A R T I C L E I N F O

Article history: Received 13 February 2018 Received in revised form 18 April 2018 Accepted 31 May 2018 Available online 3 July 2018 JEL classification: D64 D80 I12 I15 Keywords: Altruism HIV/AIDS Preventive health Sub-Saharan Africa

A B S T R A C T

Preventive behavior with regards to disease transmission offers a promising context in which to provide empirical evidence on altruism in human populations. I examine the association between HIV status, own knowledge about status, and preventive health behavior using household survey data from over 200,000 individuals in 25 sub-Saharan African countries. I find that individuals who are HIV positive and have taken a standard HIV test are much more likely to engage in efforts to prevent HIV transmission than are individuals who are HIV negative and have taken a standard HIV test. Moreover, this difference is greater than the difference between HIV positives and HIV negatives for individuals who have not taken a standard HIV test. Consistent with an altruistic motivation, this double-difference is larger for individuals who are married than for individuals who are not married. These results appear to be the first evidence on the change in risky sexual behavior associated with HIV testing at scale and are consistent with altruism dominating any self-interested response to HIV testing. © 2018 Elsevier B.V. All rights reserved.

1. Introduction A typical methodological assumption in economics is that individuals act in their own self-interest. Methodological assumptions like utility maximization are designed to facilitate the analysis of human behavior rather than judge the motivation of individuals (Becker, 1993). Utility maximization does not preclude recognizing that individuals often act in the interests of others and many versions of utility maximization include a role for altruism (e.g., Becker, 1976; Bernheim and Ray, 1987; Barro and Becker, 1989; Rabin, 1993; Khalil, 2004; Elster, 2006; Cox et al., 2008; Simon, 2016).1 Several economic studies have examined altruism in economic transfers within the household or across generations from parent to adult child, with some finding little evidence of altruism (e.g., Cox and Rank, 1992; Laitner and Justner, 1996; Altonji et al., 1997). Preventive behavior with regards to disease transmission offers another area in which to provide empirical

E-mail address: [email protected] (N. Wilson). Hamilton (1964) provides a biological basis for altruism by showing that kinship selection may be the mechanism promoting the propagation of altruistic genes. Becker (1976) shows that individual rationality can generate altruistic behavior through selection and that kinship selection is not a necessary condition for the propagation of altruistic genes.

evidence on this assumption and the role of altruistic behavior in utility maximization, yet there appear to be only a handful of studies on altruism in preventive health and these are almost exclusively on child health (Agee and Crocker, 1996; Dickie and Messman, 2004; Dickie and Gerking, 2007).2 These two sets of empirical analyses generally test for altruism by examining whether individuals make resource allocation decisions that are costly to the reference individual and increase the well-being of the individual to whom the reference individual may be altruistic. I follow this approach and examine altruism in preventive health behavior using household survey data on HIV prevention decisions from over 200,000 individuals in 25 sub-Saharan African countries. Pandemic diseases with a small set of readily controllable transmission mechanisms offer a promising context for examining the role of altruism in human behavior. Direct interpersonal contact drives the transmission of many diseases and individuals may engage in preventive behavior to reduce the likelihood of transmitting many of these diseases. For some diseases (e.g., the common cold), a large set of transmission mechanisms subject to

1

https://doi.org/10.1016/j.ehb.2018.05.004 1570-677X/© 2018 Elsevier B.V. All rights reserved.

2 Arrow (1963) notes that individuals may be more altruistic about health than about other aspects of individual welfare. Thus, finding evidence on altruism in preventive health behavior does not necessarily imply that individuals are altruistic toward non-health outcomes.

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N. Wilson / Economics and Human Biology 30 (2018) 119–129

limited direct personal control means that any particular preventive behavior offers a relatively low likelihood of curtailing transmission.3 For other diseases, a small set of transmission mechanisms typically subject to direct personal control means that preventive behaviors offer a high likelihood of curtailing transmission. This latter set of diseases provide a promising opportunity to investigate the role of own disease status and knowledge about own disease status in determining behavior to prevent transmission to other individuals.4 In turn, this should illuminate the empirical relevance of narrowly defined utility maximization compared to a more broadly defined utility maximization including altruism. HIV/AIDS in sub-Saharan Africa may be a particularly interesting setting because it is the leading cause of adult mortality in the region of the world with the poorest health status and seemingly has a low private cost of prevention relative to the social cost of transmission. Although several characteristics of the HIV/AIDS pandemic make it a particularly valuable setting for this research question, there is little at-scale evidence on whether individuals who have tested as part of the scale-up of HIV testing in sub-Saharan Africa and are HIV positive have responded by engaging in additional efforts to protect against disease transmission. Previous economic literature on HIV testing primarily has studied settings that simultaneously were not at-scale and were not standard voluntary counseling and testing. This set of not at-scale and not standard settings is: survey-based testing in rural Malawi (Thornton, 2008; Delavande and Kohler, 2012; De Paula et al., 2014; Beegle et al., 2015; Shapira, 2016), randomized home-based counseling and testing among never-married females age 13–22 in Malawi (Baird et al., 2014), and testing in a randomized controlled trial conducted at two sub-national study sites, Nairobi, Kenya and Dar Es Salaam, Tanzania, in the 1990s (Gong, 2015). This literature has provided valuable evidence on the change in average risky behavior in response to survey-based and randomized HIV testing, finding little-to-no change in average behavior.5 Individuals who select into truly voluntary counseling and testing programs at scale are likely to demonstrate a different behavioral response to testing than are individuals who test in atypical settings such as survey-based testing or in randomized testing programs, where testing take-up among those asked to test often approaches one-hundred percent. Individuals who test at scale (i.e. select into truly voluntary counseling and testing) are those with a larger expected behavior change in response to learning their status, suggesting that existing analyses that use survey-based or randomized testing, understate the behavioral

3 For example, an individual who has the common cold during the cold season can wash their hands to reduce the likelihood of transmitting their cold. However, the high likelihood of coughing/sneezing and the relatively low prevalence of consistent handwashing by other infected individuals mean that the marginal benefit of the reference individual engaging in preventive behavior is relatively low. In contrast, the low number of sexual partners for most individuals and the limited number of alternative transmission mechanisms mean that the marginal benefit of an individual engaging in correct and consistent condom use is large. 4 Although the scale-up of ART has reduced AIDS-related mortality (Bendavid et al., 2012) and morbidity (Lucas and Wilson, 2013, 2017), the health consequences of contracting HIV/AIDS remain severe and for females include a high likelihood of transmitting HIV to newborn children in the absence of prevention of mother-tochild transmission of HIV (Dabis and Ekpini, 2002). 5 Some of this literature disaggregates the behavioral response to testing by prior belief about HIV status. Although at least one study found no evidence of a differential response by prior belief (i.e. Thornton, 2008), several studies have found evidence that individuals whose HIV status does not match their prior beliefs are those who are likely to demonstrate the greatest behavior change (e.g., Baird et al., 2014; Gong, 2015; Wilson, 2016). The data in the current analysis (i.e. the Demographic and Health Surveys) do not include information on prior beliefs so I cannot disaggregate the response by prior beliefs.

response among those who test (Wilson, 2016). Truly voluntary testing and counseling programs test the vast majority of individuals who take a HIV test in sub-Saharan Africa, meaning that evidence on this research question is highly relevant to policymaking in the largest health expenditure area in this region of the world: HIV/AIDS. To shed light on the role of altruism in preventive health behavior, I examine the difference in HIV prevention behavior between HIV positive individuals who have taken a standard HIV test and HIV negative individuals who have taken a standard HIV test.6 A typical concern in economic analyses of HIV testing is simultaneity of the decision to take a HIV test and the decision to engage in HIV prevention behavior (i.e. using a condom).7 For example, individuals who have greater access to HIV/AIDS services or individuals with greater knowledge of HIV prevention simultaneously may be more likely to test and more likely to use a condom. By comparing HIV positive individuals who test to HIV negative individuals who test, I eliminate concern about unobserved differences between those who test and those who do not test.8 To address concerns about systematic differences in preventive behavior by HIV status, I compare this first difference to the difference between HIV positive and HIV negative individuals who have not taken a standard HIV test by using information from nondisclosed survey-based HIV tests. Thus, my primary empirical specification is a double-difference regression comparing the difference between HIV positive and HIV negative individuals who have taken a standard HIV test to the difference between HIV positive and HIV negative individuals who have not taken a standard HIV test.9 To further address possible endogeneity concerns, I include sub-national geographic fixed effects, time fixed effects, and examine the robustness of my results to controlling for knowledge of condoms as a HIV prevention method, self-reported access to condoms, and sociodemographic controls. As an additional test of altruism and to address remaining endogeneity concerns about the double-difference approach, I compare the differential behavior of individuals who are married to individuals who are not married in a triple-difference approach.10 My data include a survey-based HIV testing module that collected blood samples and tested the samples for HIV, but did not tell individuals the results of their test. They also include information on respondent standard HIV testing behavior outside of the survey test. Adult HIV prevalence in sub-Saharan Africa is approximately 5%, yielding sufficient statistical power in general population surveys to detect behavioral differences between HIV positive and HIV negative individuals.

6 The data I use in my analysis, the Demographic and Health Surveys (DHS), ask individuals about whether they have taken a standard HIV test and when they took this test. The DHS also ask individuals to consent to a DHS survey-based HIV test, the results of which are not disclosed to the respondent. The results of the (nondisclosed) DHS survey-based test allow me to know the individual’s HIV status, regardless of whether they have taken a standard HIV test prior to the survey. In Section 3, I discuss these data in more detail. 7 Pure reverse causality does not explain my findings. Reverse causality, in which decreased condom use causes a change in HIV status, would imply a negative association between being HIV positive and having used a condom, not the positive association that I find in my analysis. 8 For example, individuals who test may be those individuals who have a greater willingness-to-pay for their own health. 9 Equivalently, I am comparing the difference between HIV positive individuals who have taken a standard HIV test and HIV positive individuals who have not taken a standard HIV test to the same difference for HIV negative individuals. 10 A large body of economic literature has noted that an individual’s altruism is stronger for other individuals in their household than for individuals who are not part of that household (e.g., see Becker, 1976). Beginning with Hamilton (1964), the biology literature has argued that altruism is more likely in a related population.

N. Wilson / Economics and Human Biology 30 (2018) 119–129

I find that the difference in prevention behavior between HIV positive and HIV negative individuals is much greater for those who test than for those who do not test. For males, the doubledifference estimate is more than a 10 percentage point increase in the likelihood of using a condom.11 For females, the doubledifference estimate is also a more than 10 percentage increase in the likelihood of using a condom.12 These findings are robust to including additional demographic and socioeconomic controls, controlling for knowledge of condoms as a HIV prevention technology and access to condoms, restricting the sample of individuals who have tested to individuals who have gotten their test results, and to excluding individual countries. Regardless of which controls are included or how the regression sample is constructed, the double-difference estimate changes by less than 10%, suggesting that omitted variables bias, if any, is small relative to the estimated effects. This double-difference is larger for males who are married than for males who are not married, as is the double-difference for females although the triple-difference (i.e. HIV positive X Tested X Married) for females is not statistically significant. I find little support for alternative hypotheses and on the whole these findings suggest that altruism is driving the behavioral response to an individual learning that they are HIV positive. These results are broadly consistent with the existing economic literature on HIV testing, yet build on it in at least one key way. Previous economic literature found little evidence of a change in average risky behavior in response to HIV testing, possibly because individuals randomized into testing (or tested in survey-based testing with nearly universal take-up) are not as likely to demonstrate behavior change as those who select into HIV testing at scale. I provide what appears to be the first economic analysis of at-scale voluntary counseling and testing, finding evidence that it is associated with altruistic behavior by those who test. My results contribute to several bodies of economic literature outside of studies of HIV testing. First, by providing evidence on altruistic behavior toward adults in the use of preventive health inputs, I expand the small body of economic literature on altruistic behavior in preventive health, a literature that has focused on altruistic behavior toward child health (e.g., Agee and Crocker, 1996; Dickie and Messman, 2004; Dickie and Gerking, 2007). Second, I add to the broader body of economic literature on altruism in other health settings, such as in health provider behavior13 , blood/organ donations14 , research and development (R&D) in healthcare15 , and as an efficiency justification for government provision of health insurance.16 Third, by providing evidence on heterogeneity by marital status, I add to the large body

11 A common concern with self-reported risky behavior such as condom use is misreporting (Gersovitz et al., 1998), including social desirability bias. For misreporting to explain my results, misreporting must exist, it must vary by the interaction of HIV status and whether the individual has taken a standard HIV test, and it must vary by the interaction of HIV status, whether an individual has taken a standard HIV test and marital status. 12 I find similar effects for multiple partnerships and broadly similar effects for coital frequency. 13 For example, see Chou, (2002); Lu et al., (2003); Chalkley and Khalil, (2005); Jack, (2005); Olsen et al. (2009), Leonard and Masatu, (2010), Allard et al., (2011); Godager and Wiesen, (2013); Barham and Milliken, (2015); Brock et al., (2016); Kifmann and Siciliani, (2017); Lu, (2016), and Douven et al. (2017). 14 For example, see Byrne and Thompson (2001); Bergstrom et al. (2009); Wildman and Hollingsworth (2009); Li et al. (2013), and Schnier et al. (2018). 15 For example, see Jena et al. (2010). 16 For example, see Coate, (1995).

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of economic literature on altruistic behavior in intrahousehold resource allocation.17 The rest of the paper is organized as follows. Section 2 describes my empirical methods. Section 3 describes the data. Section 4 presents the results. Section 5 examines alternative hypotheses. Section 6 concludes. 2. Empirical methods My main empirical methodology is multivariate regression using a double-difference approach. A standard difference-indifferences regression compares the before-after difference for a “treatment” group to the before-after difference for a “control” group. Without classical longitudinal variation, I compare the cross-sectional difference (i.e. the difference between HIV positives and HIV negatives) for a “treatment” group (i.e. individuals taking a standard HIV test) to the cross-sectional difference (i.e. the difference between HIV positives and HIV negatives) for a “control” group (i.e. individuals not taking a standard HIV test). The identifying assumption is that in the absence of the test the difference in protective behavior between HIV positive and HIV negative individuals taking a HIV test would have been the same as the difference in preventive behavior between HIV positive and HIV negative individuals not taking a test. Limited evidence on the determinants of the HIV testing decision has identified three main determinants: (i) testing allows individuals who are HIV negative to “prove” their status to a potential partner (Philipson and Posner 1995), (ii) testing allows individuals to increase the precision of their belief about their own status and inform the optimal amount of risky sexual behavior (Wilson, 2016), and (iii) testing allows individuals who are HIV positive to access treatment and care for HIV/AIDS (Wilson, 2016). If a common factor such as belief about own HIV status is driving the HIV testing decision, then in the absence of the test HIV positive and HIV negative individuals who would have tested could be more similar to each other than to their respective counterparts who would not have tested. Under this scenario, the second difference (i.e. the difference between HIV positives who do not test and HIV negatives who do not test) may be an overly conservative second difference, making my estimates a lower bound on the effect of learning a HIV positive result on preventive behavior. I use ordinary least squares (OLS) regression analysis to estimate the following primary specification: condom usedircmt ¼ b0 þ b1 HIV positiveircmt  testedircmt 0 þ b2 HIV positiveircmt þ b3 testedircmt þ X ircmt þ mrc þ dm þ g t þ eircmt

ð1Þ

where condom usedircmt is an indicator variable equal to one if respondent i in subnational region r in country c interviewed in month m year t used a condom at last sex and zero otherwise, HIVpositiveircmt is an indicator variable equal to one if the result of the non-disclosed HIV test indicates that the individual is HIV positive and zero if it indicates that the individual is HIV negative,

17 For example see Becker (1981), Bernheim and Stark (1988), Barro and Becker (1989), Cox and Rank (1992), Hoddinott (1992), Nelson (1994), Bergstrom (1995), Laitner and Justner (1996), Tcha (1996), Altonji et al., 1997, Foster and Rosenzweig (2001), Posel (2001), Azam and Gubert, (2006), Page and Plaza, (2006); Das, (2007), Birchenall and Soares, (2009), Bruhin and Winkelmann (2009), Chang (2009), Li et al. (2010), Tenikue and Verheyden (2010), Schwarze and Winkelmann (2011), Akay et al. (2014), Grossbard (2014), Horioka (2014), Kalenkoski (2014), Molina (2014), Park (2014); Xiang et al. (2014), Holford (2015), Jiménez-Martín and Vilaplana Prieto (2015); Rieger (2015), Akresh et al. (2016), Aurino (2017), Cremer et al. (2017), Humphries et al. (2017), Klimaviciute et al. (2017) and Tirivayi and Groot (2018).

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and testedircmt is an indicator variable equal to one if the respondent reported taking a standard HIV test at any point prior to the survey. X 0 ircmt is a vector of demographic and socioeconomic controls (e.g., indicator variables for age in years, primary school completion, secondary school completion), mrc are subnational region fixed effects, dm are interview month fixed effects, and g t are interview year fixed effects. For all regressions, I first pool males and females and then I estimate Eq. (1) separately for females and for males. I estimate heteroscedasticity-robust standard errors clustered at the subnational region level. I interpret b1 as the difference in preventive behavior between HIV positive individuals who test and HIV negative individuals who test that is due to altruism. I take two approaches to defining testedircmt . First, I define testedircmt as equal to one if the respondent has ever tested. Second, I disaggregate ever tested into an indicator variable equal to one if the respondent’s most recent HIV test was in the past 12 months and an indicator variable equal to one if the respondent’s most recent HIV test was more than 12 months ago. As an additional test of altruism I estimate an expanded version of Eq. (1) that incorporates a third difference (i.e. married/not married) in a triple-difference approach allowing for heterogeneous estimates by marital status. Under the assumption that altruism is greater in related populations (e.g., Hamilton, 1964) this triple-difference estimate should further isolate altruism in preventive health behavior. I employ several additional strategies to further address endogeneity concerns. Knowledge about and access to condoms are determinants of condom use and likely are associated with the decision to take a standard HIV test. If this relationship varies differentially by HIV status then that is a threat to my identification assumption. To eliminate this possible source of bias, I estimate versions of Eq. (1) where I control for knowledge of condoms as a HIV prevention method and access to condoms. I also examine the robustness of my results to controlling for sociodemographic factors (e.g., household wealth, individual educational attainment, gender, and religion) often associated with tastes, income, and the relative price of risky sexual behavior. Finally, I examine robustness to restricting the regression sample to individuals who report getting their HIV test results, to using alternative measures of risky sexual behavior, and to excluding individual countries. 3. Data I use data from the Demographic and Health Surveys (DHS). The DHS are national household surveys conducted in low- and middle-income countries around the world that survey females age 15–49 and males age 15–59. I restrict my analysis to surveys from sub-Saharan Africa, the region of the world with the highest HIV prevalence. I use the most recent available Standard DHS survey round as of January, 2015 from each subSaharan African country with a HIV testing module. In total, my data come from 25 countries located throughout sub-Saharan Africa. As my main outcome of interest, I construct an indicator variable for whether the respondent reported using a condom at last sex in the past 12 months. Three variables form the regressors of interest. First, I construct an indicator variable for whether the DHS survey-based test indicated that the respondent was HIV positive. Consent and sample submission for the DHS surveybased HIV test is high. Across the most recent sub-Saharan African DHS surveys with HIV testing modules, nearly 93% of females and 95% of males successfully interviewed in the main DHS module and selected for the anonymous HIV testing module provided consent and submitted a blood sample to the DHS enumerator.

Consent rates in the existing economic literature on HIV testing range from 84% to 98%, with most studies reporting rates in the low 90 s. In the DHS and the surveys in most of the other studies, individuals are informed prior to consenting that they will not receive the result of this test or will not receive it under routine conditions. Second, I construct an indicator variable for whether the individual has taken a standard HIV test in the past 12 months. Third, I construct an indicator variable for whether the individual took a standard HIV test sometime prior to the past 12 months but not in the past 12 months. The DHS asks individuals when was the last time they took a HIV test and do not ask about repeat testing. I restrict the sample to individuals who report whether they used a condom at last sex in the past 12 months, whether they have taken a HIV test, and for whom a HIV test result is available.18 This yields a sample of 101,564 males and 119,476 females. Table 1 displays the countries, survey years, consent rates for the DHS anonymous HIV testing module, proportion of respondents taking a (non-DHS) HIV test, HIV prevalence as measured in the DHS survey-based HIV test, and condom use. HIV prevalence ranges from less than 1% (Niger) to 34% (Swaziland), having ever tested ranges from 13% (Guinea) to 94% (Rwanda), and the likelihood of condom use at last sex in the past 12 months ranges from 2% (Niger) to 49% (Namibia). Table 2 presents summary statistics in the pooled data for the main variables underlying my empirical strategy and several key demographic and socioeconomic characteristics. Most sex is unprotected sex and males appear to be more likely to report condom use than females, with 22% of males reporting condom use at last sex and 12% of females reporting condom use at last sex. Females are more likely to report having taken a standard HIV test than are males. Approximately two-thirds of respondents are married. 4. Results 4.1. Main results Table 3 presents the main results. Columns (1) and (2) display the results for the pooled sample (of males and females) without and with demographic/socioeconomic controls, respectively. Columns (3) and (4) repeat this analysis for males and Columns (5) and (6) repeat this analysis for females. All specifications include subnational region fixed effects and Columns (1) and (2) include an indicator variable for female. The coefficient estimates in the first row indicate that the difference between HIV positive and HIV negative individuals in the likelihood of condom use is approximately 10 percentage points larger for individuals who have taken a standard HIV test than individuals who have not taken a standard HIV test (statistically significant at the 1% level). This difference is large, a more than 50% increase relative to the mean condom use of 16%. Although they are not the main regressors of interest, Table 3 also reports coefficient estimate for the lower level variables in the double-difference regression (i.e. the coefficient estimates in the third through fifth rows). These coefficient estimates display some heterogeneity across males and females. For males who have never taken a standard HIV test (i.e. where the tester is offered the results), those who are HIV positive no more likely to use condoms than those who are HIV negative. For females who have never taken a standard HIV test (i.e. where the tester is offered the

18 I further restrict the sample to individuals with complete information for the other controls included in my analysis.

N. Wilson / Economics and Human Biology 30 (2018) 119–129

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Table 1 Countries and Survey Rounds. Country

Survey years

Observations

(1)

(2)

Burkina Faso Burundi Cameroon Congo Demographic Republic Cote d'Ivoire Ethiopia Gabon The Gambia Ghana Guinea Kenya Lesotho Liberia Malawi Mali Namibia Niger Rwanda Sao Tome and Principe Senegal Sierra Leone Swaziland Togo Zambia Zimbabwe

2010 2010–11 2011 2013–14 2011–12 2003 2012 2013 2014 2012 2008–09 2009–10 2013 2010 2012–13 2013 2012 2014–15 2008–09 2010–11 2013 2006–07 2013–14 2013–14 2010–11

HIV positive

(3)

Consented to DHS HIV test (4)

(5)

Tested ever (6)

Tested in past 12 months (7)

Tested more than 12 months ago (8)

22,098 5,163 10,741 13,882 7,040 17,912 9,176 4,679 6,219 5,736 4,943 5,101 6,849 10,266 6,352 5,795 6,701 7,857 3,678 6,131 11,408 5,287 6,687 21,900 9,439

0.95 0.90 0.93 0.95 0.80 0.86 0.95 0.78 0.93 0.96 0.83 0.92 0.90 0.90 0.86 0.79 0.85 0.99 0.79 0.81 0.91 0.82 0.94 0.87 0.75

Used condom (9)

0.01 0.02 0.05 0.01 0.04 0.02 0.05 0.02 0.02 0.02 0.08 0.25 0.02 0.11 0.01 0.15 0.01 0.04 0.02 0.01 0.02 0.34 0.02 0.14 0.18

0.30 0.48 0.54 0.17 0.34 0.44 0.58 0.43 0.40 0.13 0.59 0.54 0.39 0.54 0.15 0.81 0.21 0.94 0.61 0.28 0.37 0.39 0.44 0.81 0.61

0.12 0.21 0.25 0.07 0.14 0.24 0.28 0.16 0.13 0.06 0.33 0.39 0.18 0.34 0.08 0.50 0.08 0.43 0.34 0.14 0.15 0.23 0.16 0.50 0.35

0.19 0.27 0.29 0.10 0.20 0.20 0.30 0.27 0.28 0.08 0.26 0.15 0.21 0.20 0.07 0.31 0.14 0.51 0.27 0.14 0.23 0.16 0.28 0.31 0.26

0.15 0.04 0.25 0.08 0.20 0.06 0.34 0.10 0.12 0.12 0.16 0.37 0.12 0.14 0.04 0.49 0.02 0.14 0.19 0.11 0.06 0.41 0.17 0.20 0.20

Notes: Data come from the Demographic and Health Surveys. All variables are indicator variables.

Table 2 Descriptive Statistics by Gender. Sample:

Used condom HIV positive Tested ever Tested in past 12 months Tested more than 12 months ago Multiple partners Sex in past week Married Age in years Primary school completion Secondary school completion Total household consumer durables Urban Observations

Pooled

Males

Females

Mean (1)

Std. dev. (2)

Mean (3)

Std. dev. (4)

Mean (5)

Std. dev. (6)

0.16 0.06 0.47 0.24 0.23 0.11 0.47 0.66 31.95 0.40 0.15 2.06 0.35 221,040

0.37 0.24 0.50 0.43 0.42 0.32 0.50 0.47 10.07 0.49 0.36 1.56 0.48

0.22 0.05 0.41 0.21 0.19 0.21 0.48 0.62 34.30 0.47 0.20 2.09 0.36 101,564

0.41 0.22 0.49 0.41 0.40 0.41 0.50 0.49 10.99 0.50 0.40 1.56 0.48

0.11 0.07 0.52 0.27 0.25 0.03 0.47 0.69 29.96 0.35 0.11 2.04 0.35 119,476

0.32 0.26 0.50 0.44 0.44 0.16 0.50 0.46 8.74 0.48 0.32 1.57 0.48

Notes: Data come from Demographic and Health Surveys. All variables except age and total household consumer durables are indicator variables. Total household consumer durables is the sum of indicator variables for improved floor, refrigerator, television, radio, car, motorcycle, and bicycle.

results), those who are HIV positive are approximately 3–5 percentage points more likely to use a condom than those who are HIV negative (statistically significant at the 1% level).19 For HIV negative males, having tested is associated with a 4–6 percentage point increase in the likelihood of condom use (statistically significant at the 1% level). For HIV negative females, having tested is only weakly associated with increased condom use and this association disappears once I control for demographic/ socioeconomic characteristics.

19 This result seemingly is counterintuitive. Nonetheless, the coefficient may be positive because HIV+ women may be more likely to think that they are HIV+ than are HIV- women.

Table 4 examines whether the timing of testing matters. The question in the DHS about standard HIV testing behavior asks respondents when was the last time that they took a HIV test. For individuals who have taken a standard HIV test, I distinguish between testing in the past 12 months (i.e. the period over which condom use is measured) and testing more than 12 months ago. The results indicate that the double-difference estimate is approximately the same for individuals who recently tested and for individuals whose last HIV test was more than 12 months ago, consistent with a durable (not just a short-term) altruistic response. Table 5 presents the results of a heterogeneity analysis that allows the differential response to vary by marital status. In Columns (1), (3), and (5), I use the full sample of pooled individuals, males, and females, respectively. In Columns (2), (4), and (6), I

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Table 3 Testing, HIV Status, and Preventive Behavior. Dependent variable:

Used condom

Sample:

Pooled

HIV positive X Tested ever HIV positive Tested ever Demographic and socioeconomic controls Observations

Males

Females

(1)

(2)

(3)

(4)

(5)

(6)

0.115*** (0.011) 0.019** (0.009) 0.037*** (0.008) NO 221,040

0.123*** (0.009) 0.016* (0.008) 0.012** (0.005) YES 221,040

0.116*** (0.016) 0.022* (0.012) 0.061*** (0.012) NO 101,564

0.138*** (0.012) 0.012 (0.009) 0.040*** (0.006) YES 101,564

0.108*** (0.012) 0.052*** (0.011) 0.020*** (0.007) NO 119,476

0.113*** (0.011) 0.033*** (0.010) 0.002 (0.004) YES 119,476

Notes: Data come from the Demographic and Health Surveys (DHS). All specifications include the full set of indicator variables for subnational region. Columns (1) and (2) include an indicator variable for female. Demographic and socioeconomic controls are indicator variables for primary school completion, secondary school completion, married, urban, five-year age group, interview month, interview year, country-specific ethnicity, and country-specific religion, and a count of total consumer durables owned by the respondent's household. Parameters estimated using ordinary least squares (OLS) regression. Robust standard errors clustered at the subnational region level are in parentheses. *** Significant at the 1 percent level, ** Significant at the 5 percent level, * Significant at the 10 percent level.

Table 4 Testing Timing, HIV Status, and Preventive Behavior. Dependent variable:

Used condom

Sample:

Pooled

HIV positive X Tested in past 12 months HIV positive X Tested more than 12 months ago HIV positive Tested in past 12 months Tested more than 12 months ago Demographic and socioeconomic controls Observations

Males

Females

(1)

(2)

(3)

(4)

(5)

(6)

0.101*** (0.012) 0.130*** (0.013) 0.020** (0.009) 0.057*** (0.009) 0.018** (0.009) NO 221,040

0.117*** (0.010) 0.130*** (0.012) 0.016** (0.008) 0.019*** (0.005) 0.005 (0.005) YES 221,040

0.101*** (0.019) 0.131*** (0.018) 0.021* (0.012) 0.095*** (0.013) 0.027** (0.012) NO 101,564

0.134*** (0.015) 0.140*** (0.014) 0.013 (0.009) 0.058*** (0.007) 0.022*** (0.005) YES 101,564

0.097*** (0.013) 0.120*** (0.014) 0.053*** (0.011) 0.029*** (0.007) 0.012* (0.007) NO 119,476

0.107*** (0.013) 0.119*** (0.013) 0.032*** (0.010) 0.002 (0.004) 0.001 (0.004) YES 119,476

Notes: Data come from the Demographic and Health Surveys (DHS). All specifications include the full set of indicator variables for subnational region. Columns (1) and (2) include an indicator variable for female. Demographic and socioeconomic controls are indicator variables for primary school completion, secondary school completion, married, urban, five-year age group, interview month, interview year, country-specific ethnicity, and country-specific religion, and a count of total consumer durables owned by the respondent's household. Parameters estimated using ordinary least squares (OLS) regression. Robust standard errors clustered at the subnational region level are in parentheses. *** Significant at the 1 percent level, ** Significant at the 5 percent level, * Significant at the 10 percent level.

Table 5 Triple-Difference Specification with Heterogeneity by Marital Status. Dependent variable:

Used condom

Sample:

Pooled

Exclude individuals with multiple partners:

No (1)

Yes (2)

No (3)

Yes (4)

No (5)

Yes (6)

HIV positive X Tested ever

0.096*** (0.016) 0.043** (0.021) YES 221,040

0.102*** (0.017) 0.041* (0.023) YES 195,957

0.068*** (0.026) 0.104*** (0.034) YES 101,564

0.084*** (0.031) 0.108*** (0.040) YES 79,743 119,476

0.094*** (0.019) 0.024 (0.022) YES

0.092*** (0.019) 0.027 (0.023) YES 116,214

HIV positive X Tested ever X Married Demographic and socioeconomic controls Observations

Males

Females

Notes: Data come from the Demographic and Health Surveys (DHS). All specifications include the full set of lower-level interactions for the triple-difference variable and the full set of indicator variables for subnational region. Columns (1) and (2) include an indicator variable for female. Demographic and socioeconomic controls are indicator variables for primary school completion, secondary school completion, married, urban, five-year age group, interview month, interview year, country-specific ethnicity, and country-specific religion, and a count of total consumer durables owned by the respondent's household. Parameters estimated using ordinary least squares (OLS) regression. Robust standard errors clustered in the subnational region level are in parentheses. Columns (2), (4), and (6) restrict the regression sample to individuals who have not had multiple partners in the past 12 months. *** Significant at the 1 percent level, ** Significant at the 5 percent level, * Significant at the 10 percent level.

N. Wilson / Economics and Human Biology 30 (2018) 119–129

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Table 6 Robustness Checks - Knowledge of Condoms for HIV Prevention and Access to Condoms. Dependent variable:

Used condom

Sample:

Pooled

HIV positive X Tested ever Control for "Know condoms for HIV prevention" Control for "Know source for condoms" Demographic and socioeconomic controls Observations

Males

Females

(1)

(2)

(3)

(4)

(5)

(6)

0.121*** (0.012) YES YES NO 212,432

0.124*** (0.009) YES YES YES 212,432

0.125*** (0.017) YES YES NO 97,371

0.140*** (0.013) YES YES YES 97,371

0.115*** (0.013) YES YES NO 115,061

0.115*** (0.012) YES YES YES 115,061

Notes: Data come from the Demographic and Health Surveys (DHS). All specifications include the full set of lower-level interactions for the triple-difference variable and the full set of indicator variables for subnational region. Columns (1) and (2) include an indicator variable for female. Demographic and socioeconomic controls are indicator variables for primary school completion, secondary school completion, married, urban, five-year age group, interview month, interview year, country-specific ethnicity, and country-specific religion, and a count of total consumer durables owned by the respondent's household. Parameters estimated using ordinary least squares (OLS) regression. Robust standard errors clustered at the subnational region level are in parentheses. *** Significant at the 1 percent level, ** Significant at the 5 percent level, * Significant at the 10 percent level.

exclude individuals who report multiple partners in the past 12 months. Overall, the double-difference estimates are larger for individuals who are married than for individuals who are not married. The coefficient estimate in the first row of Column (1) indicates that the double-difference estimate for those who are not married is approximately 10 percentage points (statistically significant at the 1% level). The coefficient estimate in the second row of Column (1) indicates that the double-difference estimate is approximately 4 percentage points larger for individuals who are married (statistically significant at the 5% level). The results reveal some heterogeneity by gender. For females, the pattern is qualitatively similar as that for males, although the point estimate for the married interaction term is approximately one-quarter the magnitude for the married interaction term for males and is not statistically significant. Excluding individuals who report multiple partners in the past 12 months does not substantially affect the results.20 , 21 On the whole, these results suggest that on average altruism dominates any purely self-interested response to HIV testing. If a purely self-interested response dominated altruism, then the main double-difference would be associated with a lower likelihood of condom use, not a higher likelihood of condom use. Moreover, the stronger association for individuals who are married than for individuals who are not married further reinforces this interpretation. For an omitted factor (e.g., access to HIV/AIDS services or knowledge of HIV prevention technology) to be able to explain any triple-difference results, it would have to be differentially correlated for individuals who are HIV positive, who test, and who are married after controlling for systematic differences in behavior by HIV status, testing behavior, marital status, and the interactions therein.

20 The high prevalence of polygamy in this setting raises the question of whether there exist heterogeneous effects by polygamous versus monogamous marriage. In the data in the current analysis, approximately 14% of married males have multiple wives. When I restrict the regression sample to married males and test for heterogeneous effects by polygamous versus monogamous marriage, I do not find evidence of a heterogeneous effect. 21 Another heterogeneity analysis for the double-difference estimate that should illuminate the role of altruism is allowing the double-difference estimate to vary by local ART coverage. If altruism is the mechanism underlying the main result, then the double-difference should be smaller in locations with higher ART coverage. Regression estimates of a version of Eq. (1) that allows the response to vary by whether the respondent is in a country with above median ART coverage do not reveal large differences in preventive behavior by ART coverage. Without more granular data on ART coverage, this approach to testing for altruism may suffer from measurement-error related attenuation bias.

4.2. Robustness checks One concern about the results thus far is the bundling of HIV prevention resources. Under this view, individuals who take a HIV test may have greater access to, and knowledge of, condoms and it is these factors that may be driving the estimated response to HIV testing. Although this perspective suggests controlling for these factors is important for reducing omitted variables bias and the main specification should include these controls, these factors are also mechanisms by which voluntary counseling and testing (VCT) may affect risky sexual behavior. Table 6 presents the results after controlling for knowledge of condoms as a HIV prevention technology and for access to condoms. Controlling for knowledge and access does not substantially affect the point estimates for the double-difference terms and they remain statistically significant at the 1% level. Table 7 repeats the baseline specification and restricts the regression sample to individuals who have not tested and individuals who have tested and gotten their results, excluding individuals who have tested and not received their results. Although 95% of females and 95% of males who tested report getting their results, for individuals to engage in altruistic behavior in response to learning that they are HIV positive, they must receive their results. Excluding individuals who have tested and not gotten their results has little effect on the double-difference estimates, likely because most individuals report receiving the results of their standard HIV test. HIV positive individuals may have responded to testing by increasing condom use, but did they engage in preventive behavior on other margins? Table 8 presents results for two other main measures of risky sexual behavior commonly used in the economics literature. Panel A presents results where the outcome of interest is an indicator variable for multiple partners in the past 12 months. Panel B presents results where the outcome of interest is an indicator variable for sex in the past week. The double difference estimates for multiple partners look very similar to those for condom use, supporting the main findings in the current analysis. Among individuals who have taken a standard HIV test and after controlling for systematic differences between individuals who select into testing and those who do not, HIV positive individuals are less likely to have multiple partners than HIV negative individuals. An exploration of heterogeneity by gender of the respondent in Columns (3)-(6) reveals that it is males who are driving this result, with small and statistically insignificant effects for females, possibly because the baseline likelihood among females for multiple partners is very small (i.e. 3%, as displayed in Table 2). The double difference estimates for sex in the past week

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Table 7 Robustness Checks - Excluding Individuals Who Did Not Get Results of Standard HIV Test. Dependent variable:

Used condom

Sample:

Pooled

HIV positive X Tested ever Demographic and socioeconomic controls Observations

Males

Females

(1)

(2)

(3)

(4)

(5)

(6)

0.116*** (0.012) NO 220,230

0.124*** (0.009) YES 220,230

0.117*** (0.017) NO 101,154

0.138*** (0.013) YES 101,154

0.109*** (0.012) NO 119,076

0.114*** (0.011) YES 119,076

Notes: Data come from the Demographic and Health Surveys (DHS). All specifications include the full set of lower-level interactions for the triple-difference variable and the full set of indicator variables for subnational region. Columns (1) and (2) include an indicator variable for female. Demographic and socioeconomic controls are indicator variables for primary school completion, secondary school completion, married, urban, five-year age group, interview month, interview year, country-specific ethnicity, and country-specific religion, and a count of total consumer durables owned by the respondent's household. Parameters estimated using ordinary least squares (OLS) regression. Robust standard errors clustered at the subnational region level are in parentheses. *** Significant at the 1 percent level, ** Significant at the 5 percent level, * Significant at the 10 percent level.

Table 8 Robustness Checks - Other Outcomes. Sample:

Panel A: Multiple partners HIV positive X Tested ever HIV positive Tested ever Demographic and socioeconomic controls Observations Panel B: Sex in past week HIV positive X Tested ever HIV positive Tested ever Demographic and socioeconomic controls Observations

Pooled

Males

Females

(1)

(2)

(3)

(4)

(5)

(6)

0.016** (0.007) 0.029*** (0.006) 0.010*** (0.003) NO 221,040

0.016** (0.006) 0.026*** (0.006) 0.006** (0.002) YES 221,040

0.042*** (0.013) 0.043*** (0.011) 0.026*** (0.006) NO 101,564

0.040*** (0.012) 0.039*** (0.011) 0.020*** (0.004) YES 101,564

0.005 (0.005) 0.021*** (0.004) 0.001 (0.002) NO 119,476

0.004 (0.005) 0.015*** (0.004) 0.002 (0.001) YES 119,476

0.019 (0.011) 0.008 (0.010) 0.003 (0.008) NO 221,040

0.013 (0.011) 0.005 (0.009) 0.009** (0.005) YES 221,040

0.033** (0.016) 0.038*** (0.010) 0.020** (0.010) NO 101,564

0.031** (0.015) 0.001 (0.010) 0.004 (0.004) YES 101,564

0.001 (0.014) 0.047*** (0.013) 0.019** (0.008) NO 119,476

0.004 (0.014) 0.019 (0.012) 0.024*** (0.006) YES 119,476

Notes: Data come from the Demographic and Health Surveys (DHS). Multiple partners is an indicator variable equal to one if the respondent reported having multiple sexual partners in the past 12 months. Sex in the past week is an indicator variable equal to one if the respondent reported their most recent sexual activity as occuring in the past week. All specifications include the full set of indicator variables for subnational region. Columns (1) and (2) include an indicator variable for female. Demographic and socioeconomic controls are indicator variables for primary school completion, secondary school completion, married, urban, five-year age group, interview month, interview year, country-specific ethnicity, and country-specific religion, and a count of total consumer durables owned by the respondent's household. Parameters estimated using ordinary least squares (OLS) regression. Robust standard errors clustered at the subnational region level are in parentheses. *** Significant at the 1 percent level, ** Significant at the 5 percent level, * Significant at the 10 percent level.

are qualitatively similar to those for the other outcomes considered thus far. However, again it appears that males are driving this result. On the whole there appears to be evidence supporting the hypothesis that HIV positive individuals responded to taking a standard HIV test by engaging in safer behaviors on several margins of sexual behavior and not just condom use. In Table A1, I repeat the baseline specification (including the full set of demographic and socioeconomic controls) and exclude individual countries. The sample sizes for Burkina Faso, Ethiopia, and Zambia, for example, are much larger than for countries such as Sao Tome and Principe. Each of these larger samples are approximately 10% of the total sample, raising the possibility that a single country may be driving the main result. The analysis presented in Table A1 indicates that a single country is not driving my findings, with pooled doubledifference estimates ranging from a minimum of 0.120 to a maximum of 0.131 (always statistically significant at the 1% level), double-difference estimates for males ranging from 0.128 to 0.145 (always significant at the 1% level), and doubledifference estimates for females ranging from 0.104 to 0.119 (always significant at the 1% level).

5. Alternative hypotheses This section explores several key alternative hypotheses. One alternative hypothesis is that HIV positive individuals became safer in response to testing because infecting someone else would have made it more difficult to attract future partners. The fact that the double-difference estimate is greater for married individuals, who are less likely to be in the market for future partners, than for nonmarried individuals suggests that this alternative mechanism is not driving my results. A second alternative hypothesis is that HIV positive individuals became safer in response to testing because their partner pressured them to test and engage in safer behavior. The low degree of heterogeneity in the double-difference estimate by gender (e.g., see Columns (4) and (6) in Table 3) suggests that this is not the case. If partner pressure were the mechanism, then males (who seemingly have more control over household decisionmaking, including sexual behavior) should demonstrate a smaller reduction in risky behavior associated with testing than do females, not the approximately equivalent reductions found in Table 3 and throughout much of the rest of the analysis.

N. Wilson / Economics and Human Biology 30 (2018) 119–129

A third alternative hypothesis is that HIV positive individuals became safer in response to testing because of possible legal sanctions (Francis and Mialon, 2008), not because of altruism. To explore this hypothesis, I use data from UNAIDS on whether countries impose legal sanctions for HIV non-disclosure, exposure, or transmission (UNAIDS, 2014) to allow the estimated response to testing to vary by whether the respondent resides in a country with legal sanctions. The point estimate for the countries with legal sanctions (0.098, statistically significant at the 1% level) is smaller, not larger, than the point estimate for the countries without legal sanctions (0.132, statistically significant at the 1% level), suggesting that it is not legal sanctions that are driving the main results. A fourth alternative hypothesis is that HIV positive individuals became safer in response to testing because preventing HIV transmission to their partner increased household income, or provided some other tangible benefit, for the reference individual. The studies of altruism in preventive health behavior and in intrahousehold resource allocation cited in Section 1 test whether individuals make resource allocation decisions that increase the consumption (or income) of other household members and are subject to this criticism. In general, it is difficult to shut down the connection from the income (or material well-being) of a household member (or other loved one) to the consumption of the reference individual, making it very difficult to directly rule out this mechanism. Becker (1976) writes of this issue, “Even though an altruist gives away part of his income and refrains from some actions that raise his own income, his own consumption might not be less than that of an egoist because the beneficiaries of his altruism would consider the effect of their behavior on his consumption” (p. 820). If one of these four alternative mechanisms (or another alternative mechanism) were underlying the main result, then the effect through this mechanism must outweigh the selfinterested own health response. The most likely candidate to overwhelm any self-interested own-health response is the altruism mechanism, a mechanism that should generate a larger effect than legal sanctions (which likely are only weakly enforced), difficulty attracting future partners (which is a diffuse stream of discounted, future uncertain benefits), or household income (which is a diffuse stream of discounted, future uncertain benefits, of which the reference individual is only a partial claimant). 6. Conclusion The paper examines the role of altruism in preventive health behavior. I combine data on HIV status, own knowledge about

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status, and preventive health behavior from over 200,000 individuals from 25 sub-Saharan African countries. My results suggest that individuals who are HIV positive and have taken a standard HIV test are more likely to engage in preventive behaviors than are HIV negative individuals who have taken a standard HIV test and that this difference is larger than the difference between HIV positive and HIV negative individuals who have not taken a standard HIV test. This double-difference estimate is larger for individuals who are married than for individuals who are not married. These findings are robust to controlling for demographic and socioeconomic characteristics, knowledge of condoms as an HIV prevention method and access to condoms, restricting the sample to individuals who have received the result of their standard HIV test, using alternative measures of preventive behavior with respect to HIV transmission, and excluding individual countries. The findings are inconsistent with purely self-interested, nonaltruistic behavior. They suggest that altruism may drive individuals to engage in privately costly efforts to prevent disease transmission. This may mitigate some of the negative externalities otherwise associated with communicable diseases, which in turn would reduce the magnitude of the government response required to reduce their transmission. In the narrower context of HIV/AIDS policy, my findings support voluntary counseling and testing as a HIV prevention policy and help allay fears that HIV positive individuals who take a HIV test subsequently will not engage in preventive efforts. Future research should examine this behavioral question for other communicable diseases, explore whether human altruism for communicable diseases is stronger within related populations, and examine determinants of the magnitude of the altruistic response other than relatedness. Acknowledgements I thank three anonymous referees and Susan Averett for many helpful comments. I thank Willa Friedman and Jonathan Robinson for many stimulating discussions about the economics of HIV testing. I thank Keita Yagi for excellent research assistance. There are no funding sources or competing interests to report. The findings, interpretations, and conclusions expressed in this paper are those of the author and do not necessarily represent the views of the aforementioned individuals or agencies. All errors are my own. Appendix A

Table A1 Robustness Checks - Excluding Individual Countries. Reported independent variable:

HIV positive X Tested ever

Sample:

Pooled

Excluded country Burkina Faso Burundi Cameroon Congo Demographic Republic Cote d'Ivoire Ethiopia Gabon The Gambia Ghana Guinea Kenya Lesotho

Males

Females

Coefficient estimate (1)

Standard error (2)

Coefficient estimate (3)

Standard error (4)

Coefficient estimate (5)

Standard error (6)

0.130*** 0.126*** 0.131*** 0.125*** 0.127*** 0.128*** 0.124*** 0.126*** 0.125*** 0.126*** 0.127*** 0.128***

(0.008) (0.009) (0.008) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.008)

0.145*** 0.136*** 0.142*** 0.138*** 0.139*** 0.138*** 0.137*** 0.137*** 0.138*** 0.139*** 0.138*** 0.136***

(0.012) (0.012) (0.013) (0.012) (0.013) (0.013) (0.013) (0.012) (0.012) (0.012) (0.013) (0.013)

0.115*** 0.113*** 0.118*** 0.111*** 0.114*** 0.114*** 0.111*** 0.113*** 0.112*** 0.111*** 0.114*** 0.119***

(0.011) (0.011) (0.010) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.009)

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Table A1 (Continued) Reported independent variable: Sample:

Liberia Malawi Mali Namibia Niger Rwanda Sao Tome and Principe Senegal Sierra Leone Swaziland Togo Zambia Zimbabwe

HIV positive X Tested ever Pooled

Males

Females

Coefficient estimate (1)

Standard error (2)

Coefficient estimate (3)

Standard error (4)

Coefficient estimate (5)

Standard error (6)

0.125*** 0.129*** 0.125*** 0.126*** 0.126*** 0.121*** 0.125*** 0.126*** 0.128*** 0.120*** 0.125*** 0.123*** 0.121***

(0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.009) (0.008) (0.009) (0.009) (0.011) (0.010)

0.139*** 0.141*** 0.137*** 0.139*** 0.139*** 0.128*** 0.138*** 0.139*** 0.141*** 0.130*** 0.137*** 0.138*** 0.139***

(0.012) (0.013) (0.012) (0.013) (0.012) (0.011) (0.012) (0.012) (0.012) (0.013) (0.012) (0.016) (0.013)

0.112*** 0.116*** 0.111*** 0.113*** 0.112*** 0.112*** 0.112*** 0.112*** 0.115*** 0.108*** 0.112*** 0.109*** 0.104***

(0.011) (0.012) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.011) (0.012) (0.011) (0.013) (0.012)

Notes: Data come from the Demographic and Health Surveys (DHS). All specifications include the full set of lower-level interactions for the triple-difference variable, the full set of indicator variables for subnational region, indicator variables for primary school completion, secondary school completion, married, urban, five-year age group, interview month, interview year, country-specific ethnicity, and country-specific religion, and a count of total consumer durables owned by the respondent's household. Columns (1) and (2) include an indicator variable for female. Parameters estimated using ordinary least squares (OLS) regression. Robust standard errors clustered at the subnational region level are in parentheses. *** Significant at the 1 percent level, ** Significant at the 5 percent level, * Significant at the 10 percent level.

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