Good for girls or bad for boys? Schooling, social inequality and intrahousehold allocation in early twentieth century Finland


Apart from the commonly emphasized historical or cultural explanations, was there an economics behind the early, extensive schooling of girls in Europe’s Nordic periphery? This article analyses factors behind the emerging female majority in secondary schooling in early twentieth century Finland through resource allocation within households. We argue that a significant part of the female educational advantage can be explained with a classic unitary Beckerian schooling investment model. We apply an Engel specification widely used in development economics to a household budget dataset from the 1920s to estimate the effect of the age and gender of children on schooling investment across social groups. We find a pro-girl bias among households of low socio-economic status, explained primarily by the sizable penalty to boys caused by opportunity costs and expected returns. Worker boys could generate significant income from an early age, making their education initially expensive for cash-constrained families. Contrary to previous claims, the dropout rates of boys were also higher than those of girls. Together with a propensity to leave home earlier, this lowered the expected net returns to schooling. Meanwhile, the expansion of modern services created attractive job opportunities for secondary educated girls. We find no evidence of intrahousehold bargaining. The findings resemble certain cases in development economics and the economic history of advanced countries including the USA. Rather than matching with patterns of anti-girl discrimination in many developing countries, our results highlight the prehistory of the currently emerging pattern of female educational advantage—and male disadvantage—in OECD countries.

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  1. 1.

    Exceptions include Rahikainen’s work (e.g., 1996) linking schooling and child labour markets in the early twentieth century, and the studies by Pekkarinen (2008), Pekkarinen et al. (2009) on the econometrics of the comprehensive school reform of the 1970s.

  2. 2.

    Kansa means the (common) people in the Finnish language, while koulu means school.

  3. 3.

    An estimate based on the occupations of “heads of households” has 11.8 % of the population classified as industrial in 1920 and 12.8 % in 1930; with construction and miscellaneous labour added, the figures are 17.2 and 20.3 %, respectively (Pitkänen 1982, p. 200; Computations from Tilastokeskus 1979, pp. 5–10). Both rates of growth are clearly below that for pupils from worker families in secondary schools in this period.

  4. 4.

    State share of female students was 41 % in 1920–1921, slightly over 43 % in 1927–1928 and 44 % in 1937–1938.

  5. 5.

    Available at

  6. 6.

    Over 15 years.

  7. 7.

    Compared with the cases in the “advanced countries” group of the Barro–Lee data, both men and women had less average years of schooling only in Portugal and Turkey. Finnish women were on average better educated than women in Greece, Italy and Spain, but Finnish men fared worse than their counterparts in these countries.

  8. 8.

    Male dominance at the top was particularly clear looking at completed tertiary education, where the ratio of male to female percentages ranged from 1.34 (USA) to 4.75 (Germany). In Finland, the ratio was 2.6.

  9. 9.

    Measured including all those who never started secondary education.

  10. 10.

    Examples of similar data used in research literature include, e.g., a survey conducted by the US Bureau of Labour in various countries in 1889–1890 (Horrell and Oxley 1999, pp. 497–498, 2000, pp. 38–39), a cost of living survey by the US Bureau of Labor Statistics 1917–1919 (Moehling 2001, pp. 932–933; Emery 2010, p. 76) and the New Survey of London Life and Labour conducted in 1929–1930 (Baines and Johnson 1999a, pp. 950–952; b, pp. 696–697).

  11. 11.

    The term, translated as “elite”, in fact literally means “civil servant”, but as this group included top-earner private sector employees as well, that translation would not be appropriate.

  12. 12.

    This is essentially similar to the simple two-period model of returns to education presented by Becker (1964, pp. 59–61), with the addition of the parameters p.

  13. 13.

    The same concern applies to a Tobit specification. The application of models separating enrolment and expenditure decisions, such as hurdle or Heckman, is precluded by the lack of separate information on enrolment in the source.

  14. 14.

    The summary cards passed through analysts correcting errors in classification in the original weekly account books. Leakage between schooling expenditure and general expenditures on food, clothing and books has been explored econometrically. None of these variables reaches significance when entered on the right hand side of the schooling expenditure regressions, with the exception of writing equipment—a class of goods nearly identical to those consumed as school materials—where there is some evidence of correlation.

  15. 15.

    The variables have been defined both allowing a state girl school to define the lowest fee and restricting the identification of lowest fee to the most inexpensive coeducational or all-male institution due to a sometimes notable difference. The findings are robust to this. Observation of Epanechnikov kernel density graphs for the underlying variables seems to indicate a break in the densities close to value 1, suggesting the cut-off for the dummies is appropriate. This was also done relating the expenditure per lowest fee separately to the number of boys or girls of relevant age in the households. While the densities by gender were predictably different, there appeared to be no difference in the position of the break, suggesting the results are not driven by, e.g., scholarships disproportionately targeting worker boys. The break appeared further below 1 for the group of workers than for the data as a whole for the per capita-based variables, indicating a higher possibility of errors of exclusion.

  16. 16.

    In case of families with low socioeconomic status, this source of bias might have existed within localities where the most inexpensive institution was not open for girls. However, the cheapest option was typically a girl school.

  17. 17.

    Using specifications with dummies based on schooling expenditure per capita for the relevant age group further diminishes the sample, as only 415 households both had children in this age and resided in localities where school fee data could be constructed. With such specifications, the pro-boy difference would disappear, but the pro-girl difference among workers is always highly significant (p < 0.001).

  18. 18.

    This is robust to entry as sum rather than income share; as a dummy, the variable acquires marginal (p < 0.1) but negative significance in some specifications.

  19. 19.

    Around 5 % of the industrial workers of Helsinki in mid-1920s were under 18 (Rahikainen 1996, p. 334).

  20. 20.

    The presence and contributions of older male children were also influenced by mandatory military service, which lasted 12 months and was usually performed at around year 20. The children’s contribution Engel curves still indicate significant returns to households on the relevant male age category. The effect of the service on human capital formation is unclear, but selection to superior ranks tended to replicate attained civilian levels of education. In the late 1920s, around a third of conscripts were rejected, primarily on medical grounds, but to a highly disputed degree possibly also on grounds of perceived loyalty issues related to the legacy of the civil war of 1918. Both causes were likely to affect workers more than others (Ahlbäck 2010, pp. 77–78, 236–237; Ylikangas 2009).

  21. 21.

    A gender difference in the propensity to postpone the start of secondary school also biases the Engel estimates slightly downwards for girls when standard age categories are applied, but this pattern was less pronounced in 1928.

  22. 22.

    The direct connections between secondary schooling, social groups and gender also make it improbable that the household budget data results were driven by investment into other types of education, like vocational training.

  23. 23.

    Logan (2007) has suggested seemingly egalitarian allocations might reflect parental uncertainty over future returns in absence of devices for making binding commitments, and argues similarly with Horrell and Oxley (1999) that children of the sex more likely to leave home early were treated preferentially to influence the decision.

  24. 24.

    Measured including all those who never started secondary education.


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Funding from the Academy of Finland is acknowledged. The authors would like to thank the participants of the “Gary Becker Revisited” workshop at the EUI, Florence, March 25th 2011, particularly Susan Carter, the participants of the economic history seminar of the Paris School of Economics on November 7th, 2011, particularly Denis Cogneau, the participants of the session “Families and Daughters” at the Social Science History Association’s 36th annual conference in Boston, November 19th, 2011, particularly Stacey Jones, the participants of the Nordic Centre of Excellence NordWel symposium “Intervention and Deprivation: Long-Run Perspectives on Policy and Poverty”, Helsinki, June 8th–9th, 2011, particularly Juho Härkönen, the participants of the session “Global Crisis in the Periphery” at the Social Science History Association’s 39th annual conference in Toronto, November 7th, 2014, particularly Paul Sharp, as well as Tuomas Pekkarinen, Marjatta Rahikainen, Leonid Borodkin and anonymous referees for many useful comments and observations on early versions of the paper.

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See Tables 11 and 12.

Table 11 The social background of secondary school students, 1920–1938
Table 12 Retention and dropout rates of secondary school students by sex, 1922–1937

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Saaritsa, S., Kaihovaara, A. Good for girls or bad for boys? Schooling, social inequality and intrahousehold allocation in early twentieth century Finland. Cliometrica 10, 55–98 (2016).

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  • Gender
  • Education
  • Labour
  • Intrahousehold allocation
  • Engel model

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