More on the influence of gender equality on gender differences in economic preferences

Cerioli, Sara; Formozov, Andrey Article More on the influence of gender equality on gender differences in economic preferences Journal of Economics and Statistics Provided in Cooperation with: De Gruyter Brill Suggested Citation: Cerioli, Sara; Formozov, Andrey (2024) : More on the influence of gender equality on gender differences in economic preferences, Journal of Economics and Statistics, ISSN 2366-049X, De Gruyter Oldenbourg, Berlin, Vol. 244, Iss. 1/2, pp. 131-148, https://doi.org/10.1515/jbnst-2022-0072 This Version is available at: https://hdl.handle.net/10419/333277 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Sara Cerioli* and Andrey Formozov* More on the Influence of Gender Equality on Gender Differences in Economic Preferences https://doi.org/10.1515/jbnst-2022-0072 Received December 5, 2022; accepted January 16, 2024 Abstract: This study replicates and extends the work of Falk and Hermle (2018. “Relationship of Gender Differences in Preferences to Economic Development and Gender Equality.”Science 362 (6412): eaas9899), who hypothesized that gender differences in economic preferences (patience, altruism, willingness to take risks, negative and positive reciprocity, and trust) were related to economic development and gender equality. While we were able to replicate their main results, we found that a number of methodological choices called for reexamination. Specifically, the use of an ad hoc gender equality index built by the authors lacked systematic justification, which led us to employ solely well-established indexes from gender studies in the subsequent analysis. This new analysis confirmed a positive and statistically significant association between aggregated gender differences in economic preferences and economic development conditional on gender equality. However, in contrast to the original article, the evidence of the relationship between gender differences and gender equality conditional on economic development was weak. We also investigated the relationships for the separate economic preferences and found that economic development predicts gender differences in all six preferences, whereas gender equality seems to have a negligible or null influence on most of them. Our findings provide a more nuanced view of the gender differences in economic preferences, with possible implications for policy-making. Keywords: replication study; gender differences; economic preferences; crosscountry variation JEL Classification: C19; D01; C91; D63; D64; D81; F0 Article Note: This article is part of the special issue “Gender Economics”published in the Journal of Economics and Statistics. Access to further articles of this special issue can be obtained at www.degruyter.com/jbnst. *Corresponding authors: Sara Cerioli, Independent Researcher, Mannheim, Germany, E-mail: [email protected]; and Andrey Formozov, Department of Neurophysiology, MCTN, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany, E-mail: formozoff@gmail.com. https://orcid.org/0000-0001-8935-8612 (A. Formozov) Journal of Economics and Statistics 2024; 244(1–2): 131–148 Open Access. © 2024 the author(s), published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License. 1 Introduction Published findings on gender differences in human perceptions and behaviors, such as happiness (Schneider et al. 2012), competition (Croson and Gneezy 2009; Gneezy, Leonard, and List 2009; Klonner, Pal, and Schwieren 2021; Niederle and Vesterlund 2007), and work preferences (Beblo and Görges 2018) and their relation to gender inequality are frequently used to influence decisions and policy-making, both in the public and private sectors (World Bank 2012). Furthermore, issues related to gender inequality are becoming a more integral part of the agenda for many institutions and organizations, and stakeholders need to reveal, estimate, monitor, and prevent gender inequalities on individual, group, and nationwide levels (Dei et al. 2021; World Bank 2012). The study of behavioral gender differences on a world scale is challenging. One challenge hampering progress in the field is the lack of large and homogeneous data sets across different social groups and countries. In an influential article published in the Quarterly Journal of Economics (Falk et al. 2018), a world scale data set on economic preferences, the Global Preference Survey within the Gallup World Poll 2012, was analyzed. The study focused on general questions about the distributions of economic preferences –in particular, patience, altruism, willingness to take risks, negative and positive reciprocity, and trust –in different countries, relating them to several variables from the Gallup World Poll, such as age, gender, education level, and others. The subsequent article (Falk and Hermle 2018a), which we abbreviate in the following text as FH, used the same data set but focused explicitly on the gender differences highlighted in the previous study and reported evidence for the relationships of gender differences in economic preferences with economic development and gender equality. Following the approach described in FH and analyzing the same data set, we were able to replicate their work and found very similar results in terms of the magnitude and significance of the regression coefficients, as described below. Although statistically significant and socially relevant, FH’sfindings raise additional questions that the authors gave only a cursory treatment and that we focus on in our extended analysis in this paper. The first relevant issue we address is the robustness of empirical findings to the choice of indexes for gender equality, as several indexes have been used in FH study. In this regard, one point of concern is FH’s introduction of a customized index with insufficient justification and limited interpretability. In our analysis, we provide a list of possible shortcomings in this custom aggregated index and some of its components. We then restricted our analysis exclusively to those indexes widely used by academic, governmental, and other institutions (World Economic Forum 2006, 2015). 132 S. Cerioli and A. Formozov A second issue is that FH’s main focus was hypothesis testing on the aggregated gender differences by using dimensionality-reduction techniques to combine the results into one single variable. However, investigating in detail these gender differences, specifically by examining individual preference measures rather than combined ones, enables the identification of potential significant factors contributing to these differences in relation to economic development and gender equality. Moreover, from the perspective of policy-making and follow-up studies, it is more informative to measure the magnitude of the differences (McCartney and Rosenthal 2000) rather than just conducting hypothesis discrimination. The present study aims to fill this gap. The article is structured as follows: Section 2 presents the summary of FH’s original article, while Section 3 contains our replication of FH’smainfindings. The first part of Section 4 is dedicated to the issues related to measuring gender inequality –particularly the custom index FH built for this purpose –while the second part reports the magnitude of the effects of gender differences on aggregated and separate preferences. Finally, in the Discussion and Conclusions (Section 5), we evaluate the relevance of our results for further studies and practical use in various areas. The code used to perform the analysis, the input, and the output data are publicly available (or referenced to be downloaded) at https://github.com/scerioli/GlobalPreferences-Survey. 2 Summary of the Original Article In this section, we summarize the analysis and main findings of the original article (Falk and Hermle 2018a). FH used the Gallup World Poll 2012 Global Preference Survey to measure gender differences in economic preferences across 76 countries, representing nearly 90 % of the world population, with a total of almost 80,000 people surveyed and each country having around 1000 participants. Survey participants were asked to answer qualitative and quantitative questions, and their score on each preference was assigned based on a weighted mean of the answers given (for more details, we refer to Falk and Hermle 2018b, section “Extended Materials and Methods”). Therefore, for each person in the data set, each of the six economic preferences was scored. Additional individual-level variables indicating age, sex, education level, subjective math skills (as a proxy for cognitive skills), and household income quintile were collected. FH proposed two competing hypotheses to be tested (Falk and Hermle 2018a, page 1): 1. Social role hypothesis: “Following social role theory, one may hypothesize that gender differences in preferences attenuate in more developed, gender-egalitarian Gender Differences in Preferences 133 countries […]. As a consequence, according to the social role hypothesis, higher economic development and gender equality (and the associated dissolution of traditional gender roles) should lead to a narrowing of gender differences in preferences.” 2. Resource hypothesis: “In contrast [to hypothesis 1], there is reason to expect that gender differences in preferences expand with economic development and gender equality […]. In sum, greater availability of material and social resources to both women and men may facilitate the independent development and expression of gender-specific preferences and, hence, may lead to an expansion of gender differences in more developed and gender-egalitarian countries.” To test these hypotheses, FH first performed an ordinary least-squares regression using the preferences as predictor variable and the gender indicator as independent variable, controlling for other effects such age, age squared, sex, education level, subjective math skills, and household income quintile. From it, they performed the dimensionality-reduction technique of principal component analysis (Jolliffe 2002), also known as PCA, on the gender coefficients. The first component of the PCA was then used as a summary index for gender differences in economic preferences. The logarithm of GDP per capita (Log GDP p/c) was used as a proxy for the economic development of the countries under study, while for gender equality, FH constructed a customized gender equality index, which they called the Gender Equality Index. This index was built using the first component of a PCA applied to four different indexes for gender equality: the World Economic Forum Gender Global Gap Index (WEF GGGI), the United Nations Development Programme Gender Inequality Index (UNDP GII), the ratio of female to male labor force participation (F/M LFP), taken from the World Bank database, and the Time Since Women’sSuffrage (TSWS), from the Inter-Parliamentary Union Website. The study reported a positive, large, and statistically significant correlation between gender differences in economic preferences and Log GDP p/c (r= 0.67, p-value < 0.0001), and between gender differences in economic preferences and the custom Gender Equality Index (r= 0.56, p-value < 0.0001), when performing a simple linear regression between variables. FH also conducted a conditional analysis to isolate the impact of economic development and gender equality. In this instance, they reported the regression coefficient being large and statistically significant (slope coefficient = 0.53, p-value < 0.0001) when gender differences were related to Log GDP p/c conditioned by Gender Equality Index, while moderately weak and statistically significant (slope coefficient = 0.32, p-value = 0.003) when relating to Gender Equality Index and controlling for Log GDP p/c. Based on this evidence, FH concluded that higher levels of economic development and gender equality favor the manifestation of gender differences in 134 S. Cerioli and A. Formozov preferences across countries, “highlighting the critical role of availability of material and social resources, as well as gender-equal access to these resources, in facilitating the independent formation and expression of gender-specific preferences”(Falk and Hermle 2018a). 3 Replication of the Original Analysis In this section, we describe the methodology used to replicate the analysis in FH, and we compare our results to theirs. Additionally, we make use of robust linear regression on the same data to take into account the non-normality of the data set. 3.1 Data To conduct the replication, we downloaded the Gallup World Poll 2012 Global Preferences Survey data set from the briq –Institute on Behavior & Inequality website. The full data set is under restricted access, and education level and household income quintile are not available in the open-access version on the individual level (for more information, see Supplementary Material “Data Collection, Cleaning, and Standardization”). In Falk and Hermle (2018b), FH provide a complementary analysis where all the independent variables (except for gender) are dropped, and the results are coherent with what was found in their main analysis. Therefore, we decided to continue the replication study without having access to education level and income quintile. 3.2 Methods and Results Following the analysis conducted by FH, we built a multilinear regression model to assess the relationship between each of the six economic preferences, standardized at the global level to exhibit a mean of 0 and a standard deviation of 1, and the independent variables associated to the individuals across countries: preferencec i=βc 1femalei+βc 2agei+βc 3age2 i+βc 4subjectiveMathSkillsi+ϵi(1) The subscript iis the index of a survey participant and cis the index for a country. Thus, for each country, this results in six models –one for each economic preference –with four coefficients. The coefficient for the dummy variable female,βc 1,is used as a measure for gender difference, and it illustrates the extent to which men Gender Differences in Preferences 135 and women differ in terms of a specific economic preference in a particular country, measured in standard deviations. We performed PCA on the six coefficients for gender differences of the separate preference measures and used the first component to obtain a single measure for gender differences. FH referred to this summarized index as “average gender differences.”We find this nomenclature potentially confusing; therefore, we refer to it as “aggregated index,”rather than “average.”The PCA technique has also been applied to the four gender equality indexes to get the customized index (Gender Equality Index) already described in Section 2 of this paper. The competing hypotheses proposed by FH described in Section 2 can be formally written using the following multilinear model: Aggregated Gender Diff =βEcon Develop Econ Develop+βGender Equality Gender Equality +ϵ (2) Where the variable Econ Develop is always Log GDP p/c, while Gender Equality can be either the Gender Equality Index or one of its subindexes (WEF GGGI, UNDP GII, F/M LFP, and TSWS). All the variables were standardized at the global level to show a mean of 0 and a standard deviation of 1. After standardization, Aggregated Gender Diffshows how many standard deviations away is a certain country from the global average gender difference. From the equation above, we would expect that: 1. If the social role hypothesis is correct, we will have negative coefficients for economic development and gender equality. 2. If the resource hypothesis is correct, then we will have positive coefficients for economic development and gender equality. Any other scenario (for example, when one coefficient in the model is positive and the other is negative) is left out of FH’s original research design and would require the formulation of additional hypotheses and further studies. Since the correlation between economic development and gender equality has been previously documented (Duflo 2012; World Economic Forum 2015), we checked the correlation between their proxies, regressing Log GDP p/c on the Gender Equality Index built by FH. The correlation found is moderately strong (r= 0.54) and statistically significant (p-value < 0.0001), as one can see in our Supplementary Material, Figure 3. The multilinear regression takes into account this correlation, and the theorem from Frisch-Waugh-Lovell (Frisch and Waugh 1933; Lovell 1963) guarantees that the coefficients found are the same as those in the residual analysis, as performed in FH. 136 S. Cerioli and A. Formozov In Table 1, we summarize the comparison of our results to those presented by FH in Figure 2A–F. The results found are all in agreement with those of the original study (although with some differences in p-values), except for the coefficient for TSWS. The difference is not surprising, as TSWS was one of the most difficult indicators to replicate because of a lack of clear instructions in FH (see also our Supplementary Material, “Data Collection, Cleaning, and Standardization”). Note that the coefficients for economic development conditional on the four single indexes for gender equality are not provided in FH’s original analysis. 3.3 Robust Linear Regression Within the Global Preference Survey, economic preferences were measured with both qualitative and quantitative responses. For all the economic preferences, a qualitative question based on a Likert scale between 0 and 10 was used, while a quantitative measurement was performed for every preference, excluding trust (Falk and Hermle 2018b). This mixed approach of semi-continuous and ordered categorical variables has led us to the question of the appropriateness of the OLS method for the data analysis. A diagnostic test on the data for each preference and each country, carried out using a Shapiro–Wilk test, indicated the presence of non-normality for all the measured economic preferences. Table :Comparison of the conditional analysis results from FH study (where OLS was used) and our replication using the OLS and the RLR. Coefficient Regression on Conditional on FH (OLS) Replication (OLS) Replication (RLR) β Econ Develop Log GDP p/c GEI .*** . (.)*** . (.)*** β Econ Develop Log GDP p/c WEF GGGI –. (.)*** . (.)*** β Econ Develop Log GDP p/c UNDP GII –. (.)* . (.)* β Econ Develop Log GDP p/c F/M LFP –. (.)*** . (.)*** β Econ Develop Log GDP p/c TSWS –. (.)*** . (.)*** β Gender Equality GEI Log GDP p/c .** . (.)*** . (.)** β Gender Equality WEF GGGI Log GDP p/c .** . (.)* . (.)* β Gender Equality UNDP GII Log GDP p/c . . (.). (.) β Gender Equality F/M LFP Log GDP p/c .*. (.)** . (.)* β Gender Equality TSWS Log GDP p/c .** . (.)* . (.)* Reported are the coefficients of the linear regressions and the corresponding p-values. In parenthesis, we indicate the standard error of the coefficient. Note that the errors related to FH study are missing because they were not reported in the article. Significance levels: ≤. (***), ≤. (**), ≤. (*). Gender Differences in Preferences 137 Based on this outcome, we ran the previous analysis with robust linear regression (Fox 2015) instead of ordinary linear regression to mitigate potential biases introduced by outliers. The results obtained with the robust linear regression did not differ significantly from the original and the replication analysis (see Table 1). 4 Additional Analysis of Established Gender Equality Indexes and Separate Preference Measures This section brings attention to various unresolved concerns regarding the Gender Equality Index built by FH, especially the lack of interpretability associated with this index. We then extend the analysis to state-of-the-art indexes such as WEF GGGI, UNDP GII, and UNDP Gender Development Index (GDI). We also delve deeper into the relationship between the separate preference measures and gender equality to better understand the source of the largest differences. Doing so further illuminates the associations found and renders new research questions. 4.1 Gender Equality Indexes and Potential Issues In this subsection, we discuss potential issues related to the gender equality indexes that we considered worthy of analyzing further. One concern is the way FH built their Gender Equality Index and why they propose it as a measure of gender equality. The justification for using this custom index rather than internationally recognized, studied, adopted, and already available indexes was omitted in FH’s study. To characterize the structure of the Gender Equality Index, we visualized its composition with the diagram shown in Figure 1. We briefly summarize the main issues found below. As one can see in Figure 1, the components of the Gender Equality Index used in FH contain repetitions. The two indexes, WEF GGGI and UNDP GII, share three subindexes, indicated here with different colors: ratio of female to male labor force participation (purple), the share of seats in parliament (green), and enrollment into secondary education (blue). As a third variable to construct the Gender Equality Index, FH used the ratio of female to male labor force participation, already included in the previous two indexes, as a weighted subindex. While the PCA technique in some cases permits the aggregation of variables even in the presence of large correlations among the inputs, in the present case, such a procedure may lead to an 138 S. Cerioli and A. Formozov only in a preprocessed form, which is partially restricted. Nevertheless, we were able to replicate the analysis and obtained results similar to those in FH’s original article. In addition, we ran the same analysis using robust linear regression instead of ordinary linear regression to take into account the non-normality and outliers observed in the data. However, no significant changes in the results were observed. We then investigated the indexes used to estimate gender equality and their relationship with economic development. We analyzed the Gender Equality Index built by FH and its components. Some methodological issues were identified, and we concluded that FH’s use of this custom index over more established, balanced indexes lacks justification. Therefore, we conducted further analyses based on separate, widely accepted indexes of gender equality used in FH’s original article (WEF GGGI and UNDP GII), as well as an additional index –the UNDP GDI. We examined gender differences in economic preferences and their relationship with economic development and gender equality using the above-mentioned indexes. Performing a conditional analysis, we found a positive, strong, and statistically significant correlation between the aggregated gender differences in economic preferences and economic development when we controlled for WEF GGGI and UNDP GDI; controlling for UNDP GII yielded a somewhat milder correlation. However, when controlling for economic development, no correlation between UNDP GII or UNDP GDI and the aggregated gender differences in economic preferences was found. We did find a statistically significant but weak correlation between aggregated gender differences in economic preferences and WEF GGGI. Therefore, the dependency of gender differences in economic preferences on gender equality cannot be consistently supported when only established, commonly recognized indexes are used. This lack of consistency in the results leads us to the conclusion that either there is a weak correlation between gender differences in economic preferences and gender equality or that the correlation does not exist at all. In the latter case, the set of hypotheses should not be limited to the two alternatives that were proposed as the main hypotheses in FH. We additionally analyzed how gender differences in separate preference measures are related to economic development and gender equality. Interestingly, we observed contrasting patterns in the regression coefficients between gender differences in each separate economic preference and both Log GDP p/c and gender equality indexes. Specifically, we found positive and statistically significant coefficients when examining the relationship between gender differences in separate preference measures and Log GDP p/c, when controlling for WEF GGGI and UNDP GDI. When controlling for UNDP GII, only one economic preference (altruism) showed a statistically significant coefficient, while for the other preferences, the coefficients were not statistically significant. Meanwhile, among the six economic preferences studied in relationship with gender equality indexes, the only Gender Differences in Preferences 145 statistically significant coefficients were for altruism (which exhibited an association with WEF GGGI) and risk taking (with an association with UNDP GII). These findings align with our results obtained from the aggregated gender differences. However, the dependencies of gender differences in economic preferences on economic development and gender equality becomes more nuanced once analyzed for separate economic measures, with a more differentiated picture emerging. This differentiation is particularly important in the realms of decisionmaking and policy formulation. 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