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Record W119479649

Mobilizing South Korea's Women

2001· article· en· W119479649 on OpenAlexaboutno aff
Jungkiu Choi, Wook‐Jin Chung, Su-Kyeong Kim

Bibliographic record

VenueThe McKinsey Quarterly · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Industrial and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceGross domestic productPurchasing power parityPer capitaLegislationPolitical sciencePurchasing powerEmployment protection legislationEconomic growthDemographic economicsChinaBusinessDevelopment economicsEconomicsDemographyPopulationSociologyUnemploymentLawExchange rate
DOInot available

Abstract

fetched live from OpenAlex

Too few college-educated women participate in South Korea's workforce, a factor that is likely to affect the country's prospects for long-term economic growth. Educated women must therefore play a larger role if South Korea is to become one of the world's most economically advanced nations. That goal may be a stretch. South Korea already belongs to the Organisation for Economic Co-operation and Development (OECD). But a McKinsey study has found that if the country is to become one of the OECD's top ten members (ranked by gross domestic product per head) as of 2010, its GDP per capita (purchasing-power-parity basis) would have to grow by 6.1 percent annually. [1] Such high growth would generate 3 million new jobs, at least 1.2 million of them for professionals. But as things stand today, South Korea wouldn't be able to fill those jobs only with men, since more than 90 percent of its college-educated men already participate in the labor force. Although nearly half of today's college graduates in South Korea are women, only 54 percent of its female college graduates participate in the labor force -- the lowest such rate of any member of the OECD. By contrast, the corresponding rate for South Korean men nearly matches the rates for men in Sweden and the United States (Exhibit 1). The McKinsey study identified ten obstacles for women in the South Korean workforce, including discriminatory hiring policies, ineffective legislation on working women's rights, and social prejudice. It recommended several remedies, from launching equal-opportunity programs to reinforcing employment-discrimination laws to using the mass media for a campaign against sex bias. The way to get women into the workforce, the study found, was to relieve them of the burden of childcare, which the respondents to a 1998 survey of nearly 40,000 South Korean women perceived as the biggest obstacle to employment -- more serious than prejudice. Their perception is grounded in reality: the labor force participation rate is particularly low for women in their mid-20s and early 30s. This so-called M-curve contrasts with the reverse U-curve of countries such as Canada and Sweden (Exhibit 2). In practice, it means that women leave the workforce during their peak learning years because there is no one to take care of their children. This phenomenon gives rise to discriminatory human-resources policies --companies don't think it worthwhile to invest in the careers of women who are destined to leave the workforce -- and those policies are responsible for the concentration of women in lower-status jobs and for career ceilings. South Korea isn't the only Asian country that fails to make full use of its highly educated women. In Japan, 98 percent of college-educated men participate in the labor force, compared with only 68 percent of college-educated women; in the Philippines, those figures are 83 and 47 percent, respectively. In some parts of Asia (including Hong Kong, Japan, and Singapore), women also leave the workforce temporarily in their mid-20s and early 30s--or even permanently when they marry or give birth (Exhibit 3, on the next page). In South Korea, childcare problems manifest themselves in two ways: inadequate maternity benefits and poor day-care options. Maternity leaves will probably be raised to 90 days in November, from 60 days--a very large step for South Korea. …

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.260
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2001
Admission routes1
Has abstractyes

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