Gender quotas: Moral Credentialling

Moral credentialling (Monin & Miller, 2001) is a phenomenon where individuals are more likely to be discriminatory if they have previously proved to their own satisfaction that they are not prejudiced. For example, giving people the chance to endorse the election of Barack Obama as US President led to their subsequently describing a job as less suitable for Black people (Effron, Cameron, & Monin, 2009). People are motivated to self-enhance and show themselves that they are fair and just, so non-discriminatory behaviour licenses subsequent discrimination and makes it psychologically easier (Monin & Miller, 2001). This is supported by experimental evidence, where people were more likely to discriminate against women or Black people after they had already hired a well-qualified Black person (Monin & Miller, 2001). Quotas are likely to cause moral credentialling because businesses will feel that their “token” female employees prove that they are not discriminatory, and therefore in subsequent hiring decisions may feel licensed to hire men over women. Unfortunately, this effect can also occur vicariously – when members are exposed to the prior moral and egalitarian behaviour of the in-group, they are more likely to subsequently show prejudice (Kouchaki, 2011). Quotas may therefore increase discrimination and handicap moves to address wider gender imbalances in the organisation. However, other studies argue that there is still a positive long-term benefit to having disadvantaged group members in high-status positions. For instance, long-term exposure to Obama created positive exemplars and decreased implicit racial bias (Plant et al., 2009). On this basis, even “token” appointments from the disadvantaged group into high-status positions may help that group’s position in the longer term.

Effron, D. A., Cameron, J. S., & Monin, B. (2009). Endorsing Obama licenses favoring whites. Journal of Experimental Social Psychology, 45(3), 590–593. doi: 10.1016/j.jesp.2009.02.001.
Kouchaki, M. (2011). Vicarious moral licensing: The influence of others’ past moral actions on moral behavior. Journal of Personality and Social Psychology, 101(4), 702–715. doi: 10.1037/a0024552.
Monin, B., & Miller, D. T. (2001). Moral credentials and the expression of prejudice. Journal of personality and social psychology, 81(1), 33–43. doi: 10.1037//0022-3514.8I.I.33.
Plant, E. A., Devine, P. G., Cox, W. T., Columb, C., Miller, S. L., Goplen, J., & Peruche, B. M. (2009). The Obama effect: Decreasing implicit prejudice and stereotyping. Journal of Experimental Social Psychology, 45(4), 961–964. doi: 10.1016/j.jesp.2009.04.018.

Gender quotas: Threat

Integrated Threat theory (Stephan & Stephan, 2000) states that a dominant group (men in this instance) will oppose policies such as gender quotas that cause them to feel threatened. Women who are quota beneficiaries pose realistic threats because they are seen as competition for jobs, promotions, and potential income, and quota policies themselves pose a symbolic threat because they threaten existing work values and ideas of meritocracy (Renfro, Duran, Stephan, & Clason, 2006). This is the case even if men are already advantaged: the English judiciary is highly
unbalanced in terms of gender (only 24% of judges are women), but one British supreme court judge, commenting on the possible introduction of gender quotas for the judiciary, said that male candidates might feel that “the cards are stacked against them” (Proudman, 2015). This is supported by findings that people oppose gender quotas when their in-group has something to lose, but base their opinions on fairness when their in-group has nothing to lose (Lowery, Unzueta, Knowles, & Goff, 2006). Moreover, perceived reverse discrimination against the dominant group (men) in favour of the disadvantaged group (women) increases tension and intolerance towards women (Crosby et al., 2006). Quotas may therefore increase workplace hostility and exacerbate prejudice due to perceived competition for powerful positions, thus failing to reduce imbalance in the long term.

Crosby, F. J., Iyer, A., & Sincharoen, S. (2006). Understanding affirmative action. Annual Review of Psychology, 57, 585–611. doi: 10.1146/annurev.psych.57.102904.190029.
Lowery, B. S., Unzueta, M. M., Knowles, E. D., & Goff, P. A. (2006). Concern for the in-group and opposition to affirmative action. Journal of Personality and Social Psychology, 90(6), 961–974. doi: 10.1037/0022-3514.90.6.961.
Renfro, C., Duran, A., Stephan, W. G., & Clason, D. L. (2006). The role of threat in attitudes toward affirmative action and its beneficiaries. Journal of Applied Social Psychology, 36(1), 41–74.
Stephan, W. G., & Stephan, C. W. (2000). An integrated threat theory of prejudice. In S. Oskamp (Ed.), Reducing Prejudice and Discrimination (pp. 23–45). Mahwah: Laurence Erlbaum.

Gender quotas: Collective Action

Another issue is the effect of quotas on collective action. According to Social Identity theory (Tajfel & Turner, 1979) people have a sense of belonging to distinct social groups. The ability for individuals to move between different groups depends on the permeability of the group boundaries (is it easy to move into the higher status group?). When group boundaries are permeable, it suggests that individual merit will achieve advancement (Ellemers, Knippenberg Wilke, 1990). The possibility of improving one’s own status may reduce willingness to engage in collective action, where a disadvantaged group seeks to improve the whole group’s status (Taylor & McKirnan, 1984). This individual mobility also leads to a decrease in identification with the disadvantaged ingroup, which is crucial for collective action (Mummendey, Klink, Mielke, Wenzel, & Blanz, 1999). The first two studies are based on experimental groups, so may have limited validity, but the latter derives from a more valid field study. The implications are that because quotas cause group boundaries to be highly restrictive but permeable, they will reduce collective action. Additionally, seeing women in high-status positions encourages the belief that general inequality is not an issue, and that a woman’s individually disadvantaged status is due to her inadequate personal attributes and achievements (Wright & Taylor, 1999). In turn, this causes even less support for collective action. Therefore, it follows that the success of individual women because of quotas may demotivate others from engaging in collective action, which is detrimental to the possibility of social change for gender equality.

Ellemers, N., Knippenberg, A., & Wilke, H. (1990). The influence of permeability of group boundaries and stability of group status on strategies of individual mobility and social change. British Journal of Social Psychology, 29(3), 233–246.
Mummendey, A., Klink, A., Mielke, R., Wenzel, M., & Blanz, M. (1999). Socio-structural characteristics of intergroup relations and identity management strategies: Results from a field study in East Germany. European Journal of Social Psychology, 29(2-3), 259–285.
Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The Social Psychology Of Intergroup Relations (pp. 33–47). Monterey: Brooks-Cole.
Taylor, D. M., & McKirnan, D. J. (1984). Theoretical contributions: A five-stage model of intergroup relations. British Journal of Social Psychology, 23(4), 291–300.
Wright, S. C., & Taylor, D. M. (1999). Success under tokenism: Co-option of the newcomer and the prevention of collective protest. British Journal of Social Psychology, 38(4), 369–396.

Gender quotas: System Justification

Institutional changes via quotas may also hasten the narrowing of the workplace gender gap due to system justification (Jost & Banaji, 1994) whereby people are motivated to defend the status quo regardless of whether it is unfair or illegitimate. Therefore, it follows that if the status quo becomes inclusive of females in high-status roles, people will begin to defend this new system. A related concept is injunctification, where people perceive the status quo as the most reasonable and desirable system (how things are is how things should be). Kay et al. (2009) ound that when people were told that many women were in politics, they tended to rate female MPs as more desirable – the more people see women occupying high-status positions, the more they accept that women should be occupying such positions. Quotas would therefore harness the system justification motive to encourage equality.

On the other hand, system justification can reinforce the gender imbalance – to defend the system that they would like to perceive as fair, people believe that fewer women are in high-status positions because they are less well-qualified, not because of prejudice (Crosby et al., 2006). Quotas can therefore cause the beneficiaries to be seen as incompetent (if they were adequately qualified, they would not need to get the job via quotas) – they become stigmatised, and lose credibility (Heilman, Block, & Lucas, 1992). This not only increases workplace tension and hostility towards beneficiaries, but damages women’s self-image and lowers their assessment of themselves – they devalue their performance and leadership skills, and feel more inadequate (Heilman, Simon, & Repper, 1987). They also have lower job commitment and satisfaction, perhaps because they do not feel personally important to the organisation (Chacko, 1982). Women who know they owe their position to quotas make more timid and limited decisions (Heilman & Alcott, 2001). The same study, however, also found that quota women who are confident of their ability make ambitious decisions in order to make a good impression, so in practice, these negative effects of quotas may not apply. This is supported by the fact that being hired via quotas can be beneficial for some women – one female movie executive described it as her first “big break” (Martinson, 2015). Furthermore, provided that promotion is not obviously based on gender, reaching powerful positions increases womens’ self-esteem because they feel their success is based on achievement (Unzueta, Gutiérrez, & Ghavami, 2010).

Chacko, T. I. (1982). Women and equal employment opportunity: Some unintended effects. Journal of Applied Psychology, 67(1), 119–123.
Heilman, M. E., & Alcott, V. B. (2001). What I think you think of me: Women’s reactions to being viewed as beneficiaries of preferential selection. Journal of Applied Psychology, 86(4), 574–582.
Heilman, M. E., Block, C. J., & Lucas, J. A. (1992). Presumed incompetent? Stigmatization and affirmative action efforts. Journal of Applied Psychology, 77 (4), 536–544.
Heilman, M. E., Simon, M. C., & Repper, D. P. (1987). Intentionally favored, unintentionally harmed? Impact of sex-based preferential selection on self-perceptions and self-evaluations. Journal of Applied Psychology, 72(1), 62–68.
Jost, J. T., & Banaji, M. R. (1994). The role of stereotyping in system-justification and the production of false consciousness. British Journal of Social Psychology, 33, 1–27.
Kay, A. C., Gaucher, D., Peach, J. M., Laurin, K., Friesen, J., Zanna, M. P., & Spencer, S. J. (2009). Inequality, discrimination, and the power of the status quo: Direct evidence for a motivation to see the way things are as the way they should be. Journal of Personality and Social Psychology, 97(3), 421–434.
Unzueta, M. M., Gutiérrez, A. S., & Ghavami, N. (2010). How believing in affirmative action quotas affects White women’s self-image. Journal of Experimental Social Psychology, 46 , 120–126. doi: 10.1016/j.jesp.2009.08.017.

Gender quotas: gender roles

Although the Sex Discrimination Act (1975) made it unlawful to purposefully discriminate against women, imbalances may arise from hiring and promotion decisions unconsciously based on stereotypes about inherent differences between men and women’s traits and abilities. These stereotypes create gender roles which prescribe types of work that men and women are suitable for. Role Incongruity theory (Eagly & Karau, 2002) posits that people associate leadership with men, and that the feminine gender role is perceived as a poor fit with leadership requirements.  This leads to a preference for men in high-status roles, and even when women reach these, their behaviour is negatively evaluated, although identical behaviour might be praised in a man (Eagly & Karau, 2002). Quotas could therefore increase diversity by bypassing this prejudiced selection process (Crosby, Iyer, & Sincharoen, 2006).

Furthermore, quotas may eliminate these gender roles altogether, because experience of working with women who disprove the stereotype of poor leadership tends to reduce people’s biases (Dasgupta & Asgari, 2004). One ecologically valid study (De Paola et al., 2010) looked at the effects of applying gender quotas to elections for Italian municipalities – such a policy was in place for two years before being repealed. Representation of women in politics increased in municipalities with elections during the quota period, and this effect persisted for years after the abolition of quotas. A similar reduction in stereotypes has also been shown in Indian village councils (Beaman, Chattopadhyay, Duflo, Pande, & Topalova, 2009). This suggests a long-term benefit from quotas.

Beaman, L. A., Chattopadhyay, R., Duflo, E., Pande, R., & Topalova, P. (2009, November). Powerful women: Does exposure reduce bias? Quarterly Journal of Economics, 124(4), 1497–1540.
Crosby, F. J., Iyer, A., & Sincharoen, S. (2006). Understanding affirmative action. Annual Review of Psychology, 57, 585–611. doi: 10.1146/annurev.psych.57.102904.190029.
Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of Experimental Social Psychology, 40(5), 642–658. doi: 10.1016/j.jesp.2004.02.003.
De Paola, M., Scoppa, V., & Lombardo, R. (2010). Can gender quotas break down negative stereotypes? Evidence from changes in electoral rules. Journal of Public Economics, 94(5), 344–353.
Eagly, A. H., & Karau, S. J. (2002). Role congruity theory of prejudice toward female leaders. Psychological Review, 109(3), 573–598. doi: 10.1037//0033-295X.109.3.573.

Calls for gender quotas

Gender quotas are the requirement that women should form a particular percentage of the sample of individuals under consideration: organisations or businesses must employ a certain percentage of female employees, or appoint a certain number of women on their board of directors; political parties must ensure that a proportion of their candidates are women, and legislatures must set aside a certain percentage of seats for female representatives.

Gender quotas are currently illegal in the UK because of claims of reverse discrimination against men (ie. that less-qualified women will get the job just because they are women).

However, there has recently been a lot of coverage in the media about the possibility of introducing gender quotas to increase diversity and female representation in high-status jobs.  For example:

  • An LSE report states that quotas should be mandatory across business sectors in Britain, as well as in political parties.
  • Others have called for them in the judiciary, because on 24% of judges are women.  However, one supreme court justice says that it would be unfair to men.

One amazing finding is that companies perform less effectively when they have male-only boardrooms – in the UK, they lose up to $74billion of profit compared to boards with at least one female executive (Lagerberg, 2015).

The next couple of posts will analyse why gender quotas could have both positive and negative effects, based on psychological theory and research.  So stay tuned!

SPSS, part 6: Regressions

Regression can be used to predict variables:

  • IV: x axis (predictor)
  • DV: y axis (criterion / outcome)

Analyze -> Regression -> Linear

  •  move variables into corresponding boxes
  • ensure “Method = Enter”
  • in SPSS
    • raw equation: DV = (B [slope] x IV) + constant [intercept]
    • standardised: ZDV = β [beta] x ZIV (standardised variables to Z scores)
    • standardised is a better indicator of strength and measure independent of units
  • in multiple regression (more than one IV) β measures unique effect of IV – no shared variance
  • look in last table for β value (the correlation) and B and constant
  • look in ‘model summary’ table for R2 (proportion of variance explained by all IVs)
  • look in ‘ANOVA’ table for F value (to check if R2 predicts DV better than chance – if F is significant then R2 is better)
  • R2: need to multiply by 100 to get %

 

 

 

SPSS, part 5: Correlations

Bivariate = linear relationship between TWO variables.

Pearson correlation = r = parametric, assume normal distribution (more powerful)

Spearman’s = ρ (rho) = nonparametric ranked data

Analyze -> Correlations -> Bivariate

  • Move variables into box on right
  • Tick “Pearson” or “Spearman”
  • In output look at the 2 numbers that are the same and significant.

Scattergram: to check it’s linear

Graphs -> Legacy Dialogs -> Scatter/Dot

  • Click “Simple Scatter” and move the variables to the axes (it doesn’t matter which one goes where)
  • To get line of best fit, right-click -> edit content -> Elements -> fit line at total

Phi and Cramer’s V = only look at phi (φ) = both variables dichotomous (2 categories)

Analyze -> Descriptive Stats -> Crosstabs

  • Click “Stats” and choose “Phi and Cramer’s V”
  • Click “Cells” and tick “Observed” and “expected”
  • Check how variables are coded because a negative correlation could mean a positive relationship if coded non-intuitively (low numbers = high levels of variable).

SPSS, part 4: Graphs and tables

Chart Builder – doesn’t calculate mean scores for ordinal variables, so you have to change “measure” to “scale”, but it is easier to use quickly.

Graphs -> Legacy Dialogs -> (choose graph)

Simple Bar Chart: tick “summaries of separate variables”

  • Right-click on chart in output, click “Edit content”
  • Can add title (above the chart), footer (below the chart), and change pattern of bars by clicking properties -> double-clicking on the bar -> “fill and border”

Clustered Bar Chart: tick “summaries of groups of cases”

  • Tick what the bars represent (eg mean) and put in the variable
  • Move that variable to “Category axis”
  • Move the variable you want to split the file by to “Define clusters by”

Multiple Line Graph: tick “summaries for groups of cases”

  • Tick what the lines represent (N for frequency/mean) and put in the variable (if mean)
  • Move the variable to “Category axis”
  • Move the variable you want to split the file by to “Define lines by”
  • Can edit in output through right-clicking

Analyze -> Table -> Custom Tables

If trying to find mean of categorical variables, need to change measure to “Scale” in variable view.

  • Move categorical variables to left (rows)
  • Move variables you want to split the file by to top (columns)
  • Click variable on left -> click “Summary stats” at bottom left
  • Change Format to “nnnn” and Decimals to “2”
  • To add SD or anything else: “Summary stats” -> Move SD from “Statistics” box to box on the right
  • Same with cumulative %: Move “Column %”
  • To add Total: Click on the variable you want the total for -> Click “Categories and Totals” at bottom left -> Tick “Total” on right

Categorical Tables: frequencies, so change measure back to “ordinal”

  • Need to use custom tables when splitting files by TWO variables.

Pivot Tables: Put the second splitting variable into the “Layers” box on the right.

  • Can move things around by double-clicking on the table in the output.

Boycotting sexist music

Happy New Year!

Here’s an interesting question that I think may have a range of opinions – should we boycott music/artisits that we deem to have sexist music videos and sexist language?

So many songs include sexist lyrics and videos (mostly sexually objectifying women).  However… they are usually very catchy tunes.  Is it possible to ignore the sexism and just appreciate the music?  I can’t.  I try, but I think it’s impossible – I’ve noticed it too many times that now I check for it in every song.  I stop listening to songs that I deem derogatory because it upsets me.  It upsets me that little boys and girls listen to those songs and that however much they may not notice the lyrics or however much they know that women should not be treated like objects, I feel like the lyrics must enter some part of their brain, and it must be processed somewhere, unconsciously.

It might seem like overreacting, but I can’t stand hearing that he “fucked two bitches before [he] saw you”, or that she doesn’t let herself have a choice: ” When you need that I’mma let you have it”.  I can’t stand hearing that women are passive characters in sexual interaction – men fuck, nail, and screw; women get fucked, get nailed, get screwed.  I’m sick of women being disrespected by being called sluts, whores, bitches etc – it’s highly dehumanising.  As a matter of fact, psychological literature suggests that when people dehumanise others, they are more likely to be violent towards them.

So, I asked my boyfriend to stop listening to this music too.  Or at least to put his headphones in, so I don’t have to suffer it.  He says he doesn’t listen to lyrics, only to the song.  I think they’re both one and the same.

Lois.

PS. On a brighter note – Rizzle Kicks is safe from all derogatory language (I think) so continue to listen to them all you like!  I’m sure there are others too :) I like Alessia Cara at the moment.