Sexual orientation and self-reported lying

Abstract

This paper examines empirical links between sexual orientation and self-reported lying using data collected in several waves of Georgia Institute of Technology’s World Wide Web Users Survey. The data include questions about sexual orientation, lying in cyberspace, and a broad range of demographic information. According to the theoretical framework of Gneezy (Am Econ Rev 95: 384–395, 2005) on the economics of deception, individuals conceal or falsify information when the expected benefit of lying exceeds its costs in terms of psychic disutility. If non-heterosexuals expect to benefit more by falsifying information, then this theory predicts higher rates of lying among non-heterosexuals. The data show that gays and lesbians do indeed report lying more often than heterosexuals, both unconditionally in bivariate correlations and after controlling for demographic and geographic differences. These empirical results are consistent with the conclusion that non-heterosexuals expect higher benefits from concealing personal information because of anti-homosexual discrimination.

Appendices

Appendix A

Logit model of self-reported lying

	Men		Women
	Coeff	t	Coeff	t
Non-Heterosexual	−0.255	−0.5	1.326	1.4
Age	−0.104	−11.2	−0.065	−4.5
Age-Squared	0.001	5.5	0.000	1.8
Age × Non-Heterosexual	0.013	0.5	−0.036	−0.6
Age-Squared × Non-Heterosexual	0.000	−0.2	0.000	0.2
Years On Internet	0.245	14.1	0.205	8.5
Completed High School	−0.443	−1.7	−0.659	−1.6
Some College	0.100	1.5	0.105	1.1
Completed College	0.100	2.6	0.110	2.0
Completed Graduate Degree	−0.006	−0.1	0.165	2.5
Works in Software or Computer Biz	0.267	4.6	0.290	3.9
Works in Education	−0.027	−0.4	−0.073	−1.0
Works as a Manager	0.105	1.6	0.067	0.8
Works as Other Professional	0.031	0.5	0.066	0.9
Non-Windows OS User (Mac/Linux)	−0.005	−0.1	0.010	0.2
Disabled	−0.016	−0.2	0.024	0.3
Household Income in $1000 units	0.001	0.9	0.001	0.7
Native Language Non-English	−0.036	−0.5	0.074	0.5
Asian	−0.222	−2.3	0.115	0.7
Black	−0.124	−0.9	−0.031	−0.2
Hispanic	−0.135	−1.1	−0.087	−0.5
Latino but not Hispanic	−0.109	−0.5	0.362	1.1
Indigenous Person	0.458	1.9	0.494	1.5
Other Race	0.057	0.5	−0.148	−0.8
Africa	−0.183	−0.8	−1.087	−1.4
Asia	0.285	1.8	0.252	0.8
Canada	0.047	0.7	−0.025	−0.2
Central America	−2.123	−2.9	−0.919	−0.8
Europe	0.226	3.0	0.089	0.6
Middle East	0.249	0.9	0.664	1.4
Oceania	−0.159	−1.7	0.314	2.1
South America	−0.776	−3.5	−2.037	−1.9
West Indies	−0.118	−0.3	0.776	0.9
8th-Wave Survey	0.093	2.3	0.206	3.6
9th-Wave Survey	0.286	7.4	0.242	4.4
Constant	2.084	6.7	0.905	1.9
p-Value for H0: Non-Hetero coeffs = 0*	0.026		0.000
Pseudo R 2	0.0957		0.0575
Unconditional rate of self-reported lying	0.430		0.325
Sample Size	18,597		9,354

1. * The p-value is for the joint test that the three coefficients Non-Heterosexual, Age × Non-Heterosexual and Age Squared × Non-Heterosexual are all zero

Appendix B

Previous work emphasizes the importance of jointly accounting for misreporting and non-response when using data with self-reported sexual orientation (Berg 2005; Berg and Lien 2006). This appendix provides additional detail on how the data were cleaned and how item non-response correlates with important demographic characteristics.

Recall that the data consists of three waves of surveys. These data contain responses from a total of 34,498 individuals aged 19 and older, but only 27,951 provided valid responses to all variables used in the empirical model. The other 6,547 (=34,498 − 27,951) individuals non-responded to at least one sample item, raising the question of systematic inclusion or exclusion from the sample due to correlations between the event of non-response and other variables in the model.

There were two non-response possibilities for the dependent variable regarding the frequency of falsifying information: those who left the item blank, referred to as “Not Say;” and those who responded that the question was “Not Applicable,” perhaps because these responders never faced a website that requested personal information. Eliminating these invalid dependent-variable responses led to 3,913 individuals being dropped. These individuals are not included in any of the reported results here, and the “non-responders” label is defined to refer only to respondents who have a valid dependent variable observation but at least one missing response among the other variables in the empirical model listed in Table 1.

Rates of lying are 0.395 among responders and 0.421 among non-responders, are not very different. Regarding rates of self-reported sexual orientation, only 28.1% of those in the non-responder category non-responded to the sexual orientation sample item. Among the 71.9% of non-responders who did provide a valid response to the sexual orientation sample item (but non-responded to a sample item other than the sexual orientation), 5/71.9 = 7.0% report their status as non-heterosexual, roughly 1 percentage point lower than the responder sample.

The sample item with the highest rate of non-response was Household Income, with 70.1% of non-responders having left this item blank. Average Household Income among non-responders with a valid Household Income response is $42,730, which is significantly lower than mean Household Income among responders. Other interesting correlations are as follows. Those who work in education are more likely to non-respond, as are those whose native language is non-English. Those who report having a disability are more than twice as likely to be non-responders. More than 11% of non-responders refused to identify their ethnicity, although a large majority of both responders and non-responders who did provide ethnicity are white. Few systematic differences in geography between responders and non-responders were apparent, and both groups are roughly 60% male.

As for why non-responders choose to non-respond, there is one survey item that provides some information. Respondents were asked to choose from a list of 15 issues which was the “most important issue facing the Internet.” Privacy concerns are noticeably higher among non-responders, with 33% of non-responders versus 26% of responders saying that privacy is the most important issue facing the Internet.