The psychology of poverty and life online: natural experiments on the effects of smartphone payday loan ads on psychological stress

Figures & data

Figure 1. Example of application switching quantification.

Figure 2. One participant’s smartphone use during pre- and post- payday loan ad exposures.

Note. White circles indicate the percentage of the dependent variables in each screenshot. Blue triangles indicate percentage of the dependent variables for each session (i.e., intervals when the screen is on). Note that application switching was computed only for sessions (not for each screenshot), and thus, there are no screenshot-level observations indicated in the figure. For more information about operationalization of each dependent variable, see Methods.

Table 1. Smartphone applications where payday loan ads appeared.

Household Income	Category	Application	Percent	Obs.
Lower Income (Under $30,000)	Social media	Facebook	54.4%	62
	Email	Gmail	23.7%	27
	Email	Yahoo Mail	20.2%	23
	Email	Samsung Email	0.9%	1
	Web browser	Google Chrome	0.9%	1
	Number of observations			114
Higher Income ($100,000 or more)	Email	Gmail	52.0%	39
	Email	Yahoo Mail	40.0%	30
	Social media	Instagram	4.0%	3
	Email	Samsung Email	2.7%	2
	Social media	Facebook	1.3%	1
	Number of observations			75

Note. Number of observations refers to the number of screenshots.

Table 2. Results of generalized multilevel models examining differences in smartphone use between pre- and post- payday loan ad exposure periods.

	Future-oriented words			Application switching			Negative emotional words			Positive emotional words
	B	SE	P	B	SE	P	B	SE	P	B	SE	P
Fixed effects
Intercept, β000	−3.75	0.91	<.001	0.17	1.74	.92	−4.28	0.76	<.001	−2.05	0.97	.04
Lower Income, β001	0.70	0.88	.43	0.19	1.76	.91	0.42	0.75	.57	−1.15	0.96	.23
Ad Exposure, β010	−0.05	0.19	.78	0.66	0.42	.11	−0.06	0.17	.73	−0.08	0.17	.63
Social Safety Net, β002	0.54	0.36	.13	0.13	0.69	.85	−0.28	0.32	.37	−0.15	0.38	.69
Lower Income $\times$Social Safety Net, β003	−1.40	0.50	.01	−1.37	1.12	.22	−0.60	0.41	.14	0.20	0.55	.72
Lower Income $\times$ Ad Exposure, β011	−0.91	0.48	.06	−3.53	1.46	.02	−1.17	0.47	.01	0.25	0.45	.57
Ad Exposure$\times$Social Safety Net, β012	−0.07	0.23	.78	0.31	0.54	.57	−0.27	0.22	.23	0.57	0.21	.01
Lower Income $\times$Ad Exposure $\times$ Social Safety Net, β013	1.02	0.52	.049	2.04	1.53	.18	1.39	0.50	.006	−0.71	0.48	.14
Age, β004	−0.02	0.02	.26	−0.02	0.03	.65	−0.01	0.01	.68	−0.02	0.02	.22
Gender (Ref. Male), β005	−0.19	0.33	.58	1.39	0.61	.02	0.21	0.28	.45	0.38	0.36	.29
Race (Ref. Not Hispanic), β006	−0.15	0.57	.79	−0.74	1.10	.50	−0.32	0.48	.50	−0.61	0.62	.33
Education (Ref. Some college or above), β007	0.40	0.63	.52	1.00	1.23	.42	0.37	0.54	.50	1.08	0.68	.11
Random effects
Variance in intercept (experiments), σ^2v0ji	0.11			0.13			0.04			0.18
Variance in intercept (persons), σ^2υ00i	< 0.0001			< 0.0001			< 0.0001			< 0.0001
Overdispersion, σ^2etji	64.6			2.28			92.8			32.4
Fit Indices
AIC/BIC	−3,585.9/ −3,522.7			610.1/ 733.7			−3,937.4/ −3,874.1			−2,273.9/ −2,210.7
Conditional R^2/Marginal R^2	.16/ .05			.15/ .13			.10/ .06			.32/ .10
Number of observations	501			524			501			501

B = estimates; SE = standard error; P = p-value; AIC = Akaike information criterion; BIC = Bayesian information criterion.

Note. A zero-inflated beta mixed model was used for testing ‘Application switching’ due to excessive zero instances observed in the data. Table 2 reports the zero-inflation model coefficients. Since variance in smartphone use among participants was minimal in our data, conditional R² (variance explained by both fixed and random effects) and marginal R² (variance explained by only fixed effects) are reported for the models only having experiment intercept in the random effect structures. Number of observations = up to 524 sessions nested within 100 natural experiments nested within 14 persons. For the analyses of the ‘Future-oriented words’ and ‘Negative emotional words’ variables, we excluded non-text sessions (e.g., image-only or video-only sessions) because these measures were computed based on text.

Figure 3. Model implied group differences in smartphone use during pre- and post- payday loan ad exposures.