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With many countries having reached universal primary and secondary education, parents are increasingly investing in private tutoring as a means of ensuring that their children attend the best schools and universities. However, unlike the returns to years of schooling and effects of school quality on student achievement, the effects of spending on private tutoring have received limited attention. This chapter studies the impact of tutoring on higher educational outcomes using exogenous variation in tutoring expenditure caused by the imposition of a curfew on the operating hours of tutoring institutes in Korea. The estimated effects of the curfew highlight the severity of the college entrance rat race, with a 10 p.m. curfew constraining tutoring expenditure and increasing sleeping hours. I find diminishing marginal effects of tutoring on college entrance and positive effects on degree completion while the impact on college major followed varies across disciplines.

Evidence for adverse impact in applied, organizational settings can often depend on application of the “four-fifths rule.” We analyze the arithmetic four-fifths rule, its operationalization, and related statistical tests. We note that the rule has intuitive appeal and has arithmetic directness. On the other hand, the four-fifths rule contains many ambiguities because of the manner in which it is defined, as well as its use in practice. One purpose of this article is to discuss the arithmetic and statistical facets of the definition. A related purpose of this article is to demonstrate where the ambiguities (and possibly unintended consequences) with the four-fifths rule might arise when numerical interpretations are invoked. Implications for future research and academic dialogues are then noted.

This chapter proposes a new technique based on the data envelopment analysis (DEA) method to evaluate the scale efficiency with considering the environmental influences. Using this method, we can get the pure scale efficiency which has eliminated the environmental factors and random errors that might influence the production process. Our approach extends the three-stage-DEA model by Fried, Lovell, Schmidt, and Yaisawarng (2002) to the five-stage DEA model. Afterward, in order to measure the scale efficiency of the China’s universities more accurately, this chapter gives an empirical study on the scale efficiency of the top universities in China by applying the five-stage DEA model. The results show that the efficiency levels of many universities are indeed affected by external environmental variables and random factors. According to the levels of pure technical efficiency and scale efficiency, we divide China’s universities into four types, and we also propose some suggestions for the inefficient universities to improve their scale efficiency.