Allocation of Time and Household-level Consumption Equivalent Welfare: A Case of South Korea

ABSTRACT

Using “2014 Time Usage and Quality of Life” of 17th KLIPS (Korean Labour and Income Panel Study), the first and most detailed time use survey of its kind in South Korea, we first document the patterns of time use in market work, nonmarket work (household work), child care, and a variety of definitions in leisure. We find that, while men work longer hours, men’s additional market work is well compensated by more leisure and less hours in nonmarket work and child care. We also find within-household unequal distribution of time use in nonmarket work, child care, and leisure in favour of men. Consistent with the cases of the US and other advanced economies, high-income earners tend to enjoy less hours of leisure while they spend more money on leisure activities. Secondly, we calculate the household-level consumption equivalent measure that considers consumption, leisure, life expectancy, and uncertainty. We find that taking into account non-separable preference over leisure and consumption and the quality of leisure is important and household-level welfare measures based solely on income or consumption are both incomplete and misleading.

Keywords:

• Inequality
• consumption equivalent welfare
• household production
• leisure
• non-separable preference

Subject classification codes:

• D13
• I31
• J22

Acknowledgement

The authors gratefully acknowledge the suggestions and comments from Jinil Kim and participants of 2017 IARIW-BOK Special Conference “Beyond GDP: Past Experiences and Future Challenges in the Measurement of Economic Well-Being.” All remaining errors are ours. The usual disclaimers apply.

Notes

1 See Aguiar et al. (2012) and Aguiar and Hurst (2016) for a survey on the economics of time use and descriptions on databases of time use. The Centre for Time Use Research based in the University of Oxford’s Department of Sociology manages MTUS (Multinational Time Use Study) to collect and provide data on time use of 60 datasets from 25 countries (https://www.timeuse.org/mtus).

2 Strictly speaking, 2014 Additional Survey is not the first survey on time use in South Korea. The main theme of 2004 Additional Survey is on leisure and work hours. However, it provides too limited information and it seems more appropriate to regard 2014 survey as the first dataset on time use comparable to ones in other advanced countries. We discuss the differences between 2004 and 2014 surveys and document trends in comparable time use categories in Section 3.3.

3 Lucas (1987) uses the consumption equivalent welfare to calculate the welfare cost of business cycles.

4 As of 2010, women spend 175 and 140 minutes more daily in housework in Italy and Spain, respectively. In Scandinavian countries, the gender gap is only one hour on average.

5 The same numbers increase to 92% and 83% for single-income family.

6 A large volume of literature has studied the role of increases in labour taxes and transfers on the evolution of hours of work across OECD countries. A notable exception is Rogerson (2016), who develops a two-sector model to analyse the effect of publicly financed health care on labour supply.

7 Issues of labour supply are at the heart of macroeconomic studies because they help understand large economic fluctuations. See Chang and Kim (2016) for a nice summary of issues related to labour supply elasticity.

8 We make the fitted values bounded within the minimum and maximum values of each category.

9 The numbers based on method 1 and 3 are available upon request to the authors.

10 The OECD statistics of ‘average annual hours actually worked per work’ is obtained by dividing the total number of hours worked over the year by the average number of people in employment. Note that the data are intended for comparisons of trends over time, rather than for cross-country comparisons because the sources of data are different. The related link is https://stats.oecd.org/Index.aspx?DataSetCode=ANHRS

11 The Survey can be found at the following link: http://kosis.kr/eng/statisticsList/statisticsList_01List.jsp?vwcd=MT_ETITLE&parmTabId=M_01_01.

12 In this regard, the question of how to promote work-life balance (WLB) in practice is an important question. However, there are few economic researches related to the issue. Hudgins and Gevrek (2016) use an optimal tracking control framework to design a dynamic labour schedule that determines the optimal balance between labour, leisure, and consumption in the context of Japan’s labour market.

13 In terms of converting daily hours into weekly hours, as discussed in Section 3.1.2, 2004 survey questionnaire is more convenient for research purpose.

14 It would be important to understand how people allocate their market/nonmarket hours in response to an exogenous shock such as the Global Financial Crisis. By examining how people adjust their hours when labour market conditions deteriorate and market income falls, we can enhance our understanding on the behaviour of labour supply (both in terms of extensive margin and intensive margin) and relative shadow prices of men/women’s leisure. Unfortunately, we have only two data points of 2004 and 2014 for the case of South Korea. In case of the United States, we examine the ATUS from 2003 to 2010 and find that, during the period of GFC, men work less hours and women work more hours in the market. While women’s nonmarket hour decreases, their total hours of market/nonmarket work increases. Berik and Kongar (2012) also report that the market work hours of mothers and fathers converge as men experienced disproportionate job losses during the period.

15 However, our household-level approach has some limitations. For example, adult kids that work and make money may affect the time use of their parents. In addition, since we exclude the group of single family that earns relatively less income, our sample based on couples would represent relatively richer people.

16 We apply the KRW/USD exchange rate of 1,050.

17 See the link at https://www.bls.gov/news.release/atus.nr0.htmfor more detail.

18 We find that inequality is severe only in top 20% and bottom 20% income group. The Gini coefficients of disposable income for income quintiles are 0.17 (top 20%), 0.05, 0.04, 0.05, and 0.15 (bottom 20%), respectively.

19 See the Appendix for the details and calculation of .

20 According to Kim et al. (2017), life expectancies differ by income quintiles in South Korea. Life expectancy rises as income increases. Their study shows that life expectancies by income quintiles are 70.9, 75.2, 77.0, 77.4, and 78.8 for men while 80.3, 82.7, 83.1, 83.7, and 84.1 for women, as q = 1, 2, 3, 4, 5, respectively.

21 The difference may result from the difference in sample household characteristics between ours and Jones and Klenow (2016). Our sample contains workers aged 21–65 whereas Jones and Klenow (2016) only consider prime-age worker (25–55 years old) by which real wage rate would be higher in their analysis.

22 While we regard non-separable preference as a more appropriate specification in measuring welfare, one advantage of separable preference is that it is possible to decompose the consumption equivalent welfare measure into each component of consumption, leisure, life expectancy, and uncertainty (inequality in a cross-country study). We can break up log of consumption equivalent welfare measure where [1], [2], [3] and [4] show gaps in consumption, leisure, life expectancy, and uncertainty, respectively.[1] [2] [3] [4]

23 Note that leisure is a normal good with non-separable preference due to substitutability between leisure and consumption.

24 We check the sensitivity of our results to different values of γ in Table 10.

25 With , is calibrated as , which can be simplified as when

26 While we introduce household production only for leisure, one can model both consumption and leisure are a product of household production that combine hours and goods. One example can be found in the Appendix. However, more research is needed for functional forms and parameter values.

27 As Aguiar and Hurst (2016) emphasize, the long-run effect of child care time on children’s labour market outcome can be an important research topic.

28 27See Rupert et al. (1995), Aguiar and Hurst (2007a), Gonzalez Chapela (2011), and Gelber and Mitchell (2012) for this line of research.