The motivating effect of monetary over psychological incentives is stronger in WEIRD cultures - PubMed (original) (raw)
The motivating effect of monetary over psychological incentives is stronger in WEIRD cultures
Danila Medvedev et al. Nat Hum Behav. 2024 Mar.
Abstract
Motivating effortful behaviour is a problem employers, governments and nonprofits face globally. However, most studies on motivation are done in Western, educated, industrialized, rich and democratic (WEIRD) cultures. We compared how hard people in six countries worked in response to monetary incentives versus psychological motivators, such as competing with or helping others. The advantage money had over psychological interventions was larger in the United States and the United Kingdom than in China, India, Mexico and South Africa (N = 8,133). In our last study, we randomly assigned cultural frames through language in bilingual Facebook users in India (N = 2,065). Money increased effort over a psychological treatment by 27% in Hindi and 52% in English. These findings contradict the standard economic intuition that people from poorer countries should be more driven by money. Instead, they suggest that the market mentality of exchanging time and effort for material benefits is most prominent in WEIRD cultures.
© 2024. The Author(s).
Conflict of interest statement
The authors declare no competing interests.
Figures
Fig. 1. Study 1, pooled monetary vs pooled non-monetary conditions in the United States and India.
Effects of pooled monetary incentives (green) and pooled non-monetary treatments (blue) in the United States (N = 5,526 participants on MTurk) and India (N = 768 participants on MTurk) from a previous study. a, The central tendency and distribution of effort by incentive type and country. The black line within each box represents the median; the red dot shows the mean; upper and lower bounds show the third and first quartiles, respectively; whiskers represent 1.5× the interquartile range, with black dots showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y axis. The interaction between country and incentive type in a multiple linear regression model is statistically significant (b = 170.56, t(6,287) = 2.92, P = 0.004, 95% CI 55.91–285.21). b, The money advantage, that is, how much more effective money is than the pooled non-monetary treatments in each country. In b, error bars are bootstrapped 95% CIs for the mean relative difference in the number of button presses in the pooled monetary vs non-monetary treatments.
Fig. 2. Study 2a, monetary vs pooled non-monetary conditions in the United Kingdom and China.
Effects of a monetary incentive (green) and pooled non-monetary treatments (flat fee and social norm; blue) in the United Kingdom (N = 1,067 participants recruited on Prolific) and China (N = 1,086 participants recruited on social media). a, The central tendency and distribution of effort by incentive type and country. The black line within each box represents the median and the red dot shows the mean; upper and lower bounds show the third and first quartiles, respectively; whiskers represent 1.5× the interquartile range, with black dots showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y axis. The interaction between country and incentive type in a multiple linear regression model is statistically significant (b = 38.40_, t_(2,145) = 9.65, P < 0.001, 95% CI 30.59–46.20). b, The money advantage, that is, how much more effective money is than the pooled non-monetary treatments in each country. In b, error bars are bootstrapped 95% CIs for the mean relative difference in the number of images rated in the monetary vs pooled non-monetary conditions.
Fig. 3. Study 2b, monetary vs pooled non-monetary conditions in the United States and Mexico.
Effects of a monetary incentive (green) and pooled non-monetary treatments (flat fee and social norm; blue) in Mexico (N = 1,053 participants recruited on Prolific) and two samples in the United States: one with the same nominal pay (N = 1,098 on Prolific) as in Mexico and one with the same subjective pay (N = 1,122 participants recruited on Prolific) as in Mexico. a, The central tendency and distribution of effort by incentive type and country. The black line within each box represents the median and the red dot shows the mean; upper and lower bounds show the third and first quartiles, respectively; whiskers represent 1.5× the interquartile range, with black dots showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y axis. The interaction between country and incentive type in a multiple linear regression model is statistically significant (b = 13.93_, t_(2,143) = 3.42, P < 0.001, 95% CI 5.94–21.92 for the comparison between Mexico and the US sample with the same nominal pay; b = 19.28, t(2,167) = 4.71, P < 0.001, 95% CI 11.26–27.30 for the comparison between Mexico and the US sample with the same subjective pay). b, The money advantage, that is, how much more effective money is than the pooled non-monetary treatments in each sample. In b, error bars are bootstrapped 95% CIs for the mean relative difference in the number of images rated in the monetary vs pooled non-monetary conditions.
Fig. 4. Study 2c, monetary vs pooled non-monetary conditions in the United States and South Africa.
Effects of a monetary incentive (green) and pooled non-monetary treatments (competition and charity; blue) in South Africa (N = 649 participants on Prolific) and two samples in the United States: one with the same nominal pay (N = 662 on Prolific) as in South Africa and one with the same subjective pay (N = 662 participants on Prolific) as in South Africa. a, The central tendency and distribution of effort by incentive type and country. The black line within each box represents the median and the red dot shows the mean; upper and lower bounds show the third and first quartiles, respectively; whiskers represent 1.5× the interquartile range, with black dots showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y axis. The interaction between country and incentive type in a multiple linear regression model is statistically significant (b = 7.98_, t_(1,303) = 1.97, P = 0.049, 95% CI 0.03–15.92 for the comparison between South Africa and the US sample with the same nominal pay; b = 15.40, t(1,303) = 3.74, P < 0.001, 95% CI 7.33–23.48 for the comparison between South Africa and the US sample with the same subjective pay). b, The money advantage, that is, how much more effective money is than the pooled non-monetary treatments in each sample. In b, error bars are bootstrapped 95% CIs for the mean relative difference in the number of images rated in the monetary vs pooled non-monetary conditions.
Fig. 5. Study 3a, norm vs minimal pay condition in India and the United States.
Effects of a minimal monetary incentive (of 1 cent per 20 image ratings; green) and a social norm condition (blue) in the United States (N = 382 participants recruited on Prolific) and India (N = 352 participants recruited on MTurk). a, The central tendency and distribution of effort by incentive and country. The black line within each box represents the median and the red dot shows the mean; upper and lower bounds show the third and first quartiles, respectively; whiskers represent 1.5× the interquartile range, with black dots showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y axis. The interaction between country and incentive in a multiple linear regression model is statistically significant (b = 13.77, t(726) = 2.31, P = 0.021, 95% CI 2.09–25.45). b. The money advantage, that is, how much more effective the minimal incentive is compared to the social norm condition in each country. c, The central tendency and distribution of cost-effectiveness (effort per dollar spent) of each incentive by country. Graph elements are analogous to those in a, with the width of each violin corresponding to the frequency of observations at any given level of cost-effectiveness (effort per dollar spent) rated on the y axis. Minimal monetary incentive is significantly more cost-effective than the social norm condition in the United States (two-sided Welch’s t(365.20) = 3.14, P = 0.002, _P_Bonf = 0.004, Meandifference = 13.00, d = 0.32, 95% CI 4.86–21.15) but not in India (two-sided Welch’s t(345.72) = −0.27, P = 0.785, _P_Bonf = 1.000, Meandifference = −1.06, d = −0.03, 95% CI −8.73 to 6.60). In b, error bars are bootstrapped 95% CIs for the mean relative difference in the number of images rated in the minimal-monetary-incentive vs social norm condition.
Fig. 6. Study 4, norm vs monetary condition in India, by language prime (English or Hindi).
Effects of a monetary incentive (green) and a social norm treatment (blue) in India (N = 2,065 participants recruited on Facebook), by assigned language (English or Hindi). a, The central tendency and distribution of effort by language and incentive conditions. The black line within each box represents the median and the red dot shows the mean; upper and lower bounds show the third and first quartiles, respectively; whiskers represent 1.5× the interquartile range, with black dots showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y axis. The interaction between language and incentive in a multiple linear regression model is statistically significant (b = 10.69, t(2,061) = 3.31, P = 0.001, 95% CI 4.35–17.02). b, The money advantage, that is, how much more effective the monetary condition is compared to the social norm condition. c, The central tendency and distribution of the cost-effectiveness (effort per dollar spent) by language and incentive. Graph elements are analogous to those in a, with the width of each violin corresponding to the frequency of observations at any given level of cost-effectiveness (effort per dollar spent) rated on the y axis. The monetary incentive is more cost-effective than the social norm condition in English (two-sided Welch’s t(919.21) = 4.37, P < 0.001, _P_Bonf < 0.001, Meandifference = 4.34, d = 0.27, 95% CI 2.39–6.28), but the two incentives do not significantly differ in their cost-effectiveness in Hindi (two-sided Welch’s t(915.62) = 0.30, P = 0.761, _P_Bonf = 1.000, Meandifference = 0.30, d = 0.02, 95% CI −1.64 to 2.24). In b, error bars are bootstrapped 95% CIs for the mean relative difference in the number of images rated in the norm vs monetary condition.
Extended Data Fig. 1. Study 1, Individual Conditions by Country.
Effects of individual monetary incentives (green) and non-monetary treatments (blue) amongst participants in the US (N = 5,526 on MTurk) and India (N = 768 on MTurk) from a prior study. Panels A (US) and B (India) show the central tendency and distribution of effort by incentive. Conditions within each panel (x-axis) are ordered from the lowest to highest mean effort in each country. The black line within each box represents a median, and the red dot shows a mean. Upper and lower bounds show the third and the first quartile, respectively. The whiskers represent the 1.5 times the interquartile range, with black points showing observations outside of this range. The width of each violin corresponds to the frequency of observations within each panel at any given number of button presses on the y-axis.
Extended Data Fig. 2. Study 2a, Monetary Versus Individual Non-Monetary Conditions in the UK and China.
Effects of the three individual conditions: monetary (green), social norm (dark blue) and flat fee (light blue) in the UK (N = 1,067 recruited on Prolific) and China (N = 1,086 recruited on social media). Panel A shows the central tendency and distribution of effort by incentive type and country. The black line within each box represents a median, and the red dot shows a mean. Upper and lower bounds show the third and the first quartile, respectively. The whiskers represent the 1.5 times the interquartile range, with black points showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y-axis. Panel B shows the money advantage—that is, how much more effective money is than each of the two non-monetary treatments in each country. Panel C shows the central tendency and distribution of cost-effectiveness (effort per dollar spent) by incentive type and country. Graph elements are analogous to those in Panel A, with the width of each violin corresponding to the frequency of observations at any given level of cost-effectiveness (effort per dollar spent) rated on the y-axis. The results for cost-effectiveness are summarized in the main text. In Panel B, error bars are bootstrapped 95% CIs for the mean relative difference in the number of images in the monetary and each of the non-monetary conditions.
Extended Data Fig. 3. Study 2b, Monetary Versus Individual Non-Monetary Conditions in the US and Mexico.
Effects of the three individual conditions: monetary (green), social norm (dark blue) and flat fee (light blue) in Mexico (N = 1,053 recruited on Prolific) and two samples in the US: one with the same nominal pay (N = 1,098 recruited on Prolific) as in Mexico and the other with the same subjective pay (N = 1,122 recruited on Prolific) as in Mexico. Panel A shows the central tendency and distribution of effort by incentive type and country. The black line within each box represents a median, and the red dot shows a mean. Upper and lower bounds show the third and the first quartile, respectively. The whiskers represent the 1.5 times the interquartile range, with black points showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y-axis. Panel B shows the money advantage—that is, how much more effective money is than each of the two non-monetary treatments in each sample. Panel C shows the central tendency and distribution of the cost-effectiveness (effort per dollar spent) by incentive type and country. Graph elements are analogous to those in Panel A, with the width of each violin corresponding to the frequency of observations at any given level of cost-effectiveness (effort per dollar spent) rated on the y-axis. The results for cost-effectiveness are summarized in the main text. In Panel B, error bars are bootstrapped 95% CIs for the mean relative difference in the number of images in the monetary and each of the non-monetary conditions.
Extended Data Fig. 4. Supplementary Study 3b, Minimal Pay Versus Points (Gamification) Condition in the US.
Effects of the minimal monetary incentive (1 cent per rating 10 images; in green) and a non-monetary gamification treatment (1 extra point per rating 10 images; in blue) in the US (N = 537 on Prolific). Panel A shows the central tendency and distribution of effort by incentive condition. The black line within each box represents a median; the red dot shows a mean. Upper and lower bounds show the third and the first quartile, respectively. The whiskers represent the 1.5 times the interquartile range, with black points showing observations outside of this range. The width of each violin corresponds to the frequency of observations at any given number of images rated on the y-axis. The difference in the number of images rated in the two conditions is statistically significant, Welch’s t(446.98) = 4.67, P < 0.001, _Mean_difference = 10.61, d = 0.40, 95% CI 6.15 to 15.08. Panel B shows the money advantage—that is, how much more effective the minimal monetary incentive is compared to the gamification condition. Panel C shows the central tendency and distribution of cost-effectiveness (effort per dollar spent) of each incentive. Graph elements are analogous to those in Panel A, with the width of each violin corresponding to the frequency of observations at any given level of cost-effectiveness (effort per dollar spent) rated on the y-axis. The minimal monetary incentive is more cost-effective than the gamification treatment, Welch’s t(471.93) = 4.37, P < 0.001, _Mean_difference = 7.15, d = 0.38, 95% CI 3.93 to 10.36. In Panel B, the error bar is a bootstrapped 95% CI for the mean relative difference in the number of images rated in the minimal-monetary-incentive versus social-norm condition.
Extended Data Fig. 5. Studies 2a–c, Mapping the Money Advantage on Cultural Distance from the US.
Mapping the difference in the effectiveness of monetary and non-monetary treatments (money advantage) from the samples with the same nominal pay in Studies 2a–c on the cultural distance from the US. Cultural distance scores are taken from a previous study.
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