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Gender Differences in Political Knowledge: Distinguishing Characteristics-Based and Returns-Based Differences

Abstract

This study assesses whether gender-based differences in political knowledge primarily result from differences in observable attributes or from differences in returns for otherwise equivalent characteristics. It applies a statistical decomposition methodology to data obtained from the 1992–2004 American National Election Studies. There is a consistent 10-point gender gap in measured political knowledge, of which approximately one-third is due to gender-based differences in the characteristics that predict political knowledge, with the remaining two-thirds due to male–female differences in the returns to these characteristics. The methodology identifies the relative contribution of the predictors of political knowledge to each portion of the gap, and then uses this information to elucidate the underlying sources of the political knowledge gender gap and its prognosis. Education is the characteristic that most clearly enlarges the gap, with men receiving significantly larger returns to political knowledge from education than women. Group membership reduces the gap as women obtain gains in political knowledge from belonging to organizations that do not accrue to men. However, these gains are not sufficient to significantly reduce the gap.

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Notes

  1. 1.

    Although there is debate over whether the schools do so effectively (Niemi and Junn 1998; cf. Galston 2004).

  2. 2.

    This is by construction: “IQ” tests indicate academic ability, and contain few instruments that produce significant gender-based advantages or liabilities.

  3. 3.

    In addition, the ANES interviewer assessments also show a statistically significant difference in perceived respondent intelligence by gender. Men score higher than women on this assessment in each of the 1992–2004 studies.

  4. 4.

    Speaking of political knowledge, Jennings and Niemi (1974, p. 97), write that “We are arguing that homes where parents have higher levels of information are likely to be homes where the atmosphere is conducive for the child’s acquisition of political facts.” The same is most certainly true for political interest as well (see, for example, Jennings 1981, Table 4.1).

  5. 5.

    Specifically, if women are more likely to respond “don’t know” when presented a question to which they do not know the answer, while equally uninformed men are more likely to guess, a knowledge measure created from a summation of correct responses with no adjustment for guessing may produce higher scores for men when there is no underlying difference in knowledge. This conclusion, however, has not gone unchallenged. The question of how to treat “don’t know” responses in the measurement of political sophistication is the subject of an active debate (Barabas 2002; Luskin and Bullock 2005, 2006; Mondak 2001). While the details of these exchanges are not central to this study, I believe the weight of evidence supports coding these responses as incorrect answers.

  6. 6.

    Lupia (2006) cautions that responses to factual political knowledge questions such as those used in the ANES are sensitive to interviewing protocols, and may produce underestimates respondent’s actual knowledge. However, the dependent variable can withstand this scrutiny because it does not exclusively rely on factual knowledge questions of the type described by Lupia (e.g. “How long is the term of office for a US Senator?”). The dependent variable is also constructed in a manner similar to those used in the literature, so the findings may be easily compared with those from previous studies.

  7. 7.

    There is also concern that the ANES questions may focus on political figures and policies that are of more interest to men than women, with attendant consequences for measured political knowledge. There is evidence, for example, that women are more likely than men to correctly identify members of the local school board (Delli Carpini and Keeter 1996, pp. 207–208). However, more generally, there is little evidence that women are more knowledgeable about so-called “women’s issues” than men, and even if this were the case, it would do little to attenuate the importance of the knowledge-gap revealed by the ANES questions.

  8. 8.

    Race is not included as a variable because African American respondents are dropped from the sample. Race is a significant predictor of political sophistication, and it is customary to conduct analysis separately for racial groups (e.g. Burns et al. 2001, Table C11.10.1). Since my focus is on gender, the sample is limited to non-African American ANES respondents.

  9. 9.

    The occupational prestige scores are the raw scores that are redacted from current ANES releases, but available through institutional research board request. For details on their construction, see Nakao et al. (1990). “Computing 1989 Prestige Scores,” Chicago: NORC, 1990. GSS Methodological Report No. 70.

  10. 10.

    The mean personal income levels recovered from the 2000 ANES are less than those in the remaining years because this study uses a different income scale than in the previous or following years (compare ANES variables v960701 and v000997). This has few implications for the statistical analysis and interpretation because I am only interested in relative, not absolute, income.

  11. 11.

    I use the test statistic outlined in Gujarati (2003, pp. 306–310). The Chow test assumes the female and male regressions have equal error variances. Regression diagnostics reveal that these are approximately equal, and retesting using Wald statistics, which allows for unequal variances, revealed no significant differences in the statistical inferences.

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Acknowledgments

I thank Kenneth Troske, Jeff Milyo and the anonymous reviewers for helpful comments on this study. I am responsible for all errors.

Author information

Correspondence to Jay K. Dow.

Appendix

Appendix

ANES Instruments used to create political knowledge measure: 1992–2004

  1992 1996 2000 2004
Ideological Self-placement × × × ×
Candidate placement × × × ×
Party placement × × × ×
Objective knowledge Vice president × ×   ×
Chief justice supreme court × × × ×
Russian PM × ×   
British PM    × ×
House of representatives speaker × ×   ×
Senate majority leader    ×  
Attorney general    ×  
Issue placement Services and spending—candidate × × × ×
Services and spending—party × × × ×
Defense spending—candidate × × × ×
Defense spending—party × × × ×
Jobs and Std of living—candidate × × × ×
Jobs and Std of living—party ×   × ×
Abortion—candidate × × × ×
Abortion—party   ×   ×
Independent variables: coding
Age Age in years
Married Binary: 1 (married), 0 (not married)
N child Number of children living in the household more than half time
Education Education level on ANES 7 Point Scale
PID strength O (independent) to 3 (strong partisan) scale
Political interest 1 (low) to 3 (high) scale
Working Binary: 1(employed outside the home), 0 (not employed outside the home)
Education Education level on ANES 7 Point Scale
PID strength O (independent) to 3 (strong partisan) scale
Political interest 1 (low) to 3 (high) scale
Working Binary: 1(employed outside the home), 0 (not employed outside the home)
Hours worked Number of reported working hours
Occupational prestige NORC Occupational Prestige Scores: 15 (low) – 85 (high). See note 7
Personal income Personal income on ANES scale. See note 8
Group membership Reported membership in community, philanthropic or work related groups that hold regular meetings or face-to-face activities
Religious service Religious service attendance coded on a 0 (never attends services) to 4 (attends weekly services) scale

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Dow, J.K. Gender Differences in Political Knowledge: Distinguishing Characteristics-Based and Returns-Based Differences. Polit Behav 31, 117–136 (2009). https://doi.org/10.1007/s11109-008-9059-8

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Keywords

  • Gender
  • Political knowledge
  • Blinder–Oaxaca decomposition