Japanese Minimum Wage Institutional Aspects

Minimum Wage in Japan institutional aspects and its consequences Ryo Kambayashi Institute of Economic Research, Hitotsubashi University Workshop on minimum wages Lessons from recent experiences and European perspectives 20thOct. 2017, Paris

What I will talk today (A) Institutional aspects of Japanese minimum wage (B) Its consequences Employment loss? Association with polarization Some evidence on wage compression (C)Lessens from the Japanese case

(A) Institutional aspects (a)Universality (b)Geographical zoning (c) Centralized bargaining result in (d)Various bites between groups

(A) Institutional aspects (a) Universality [almost] No exemption ANY employment contract in Japan must satisfy the minimum wage. Contractors and self-employment is NOT covered. No automatic exemption Even trial jobs or apprenticeship are subject to mw as long as they are employed. Employers of disable worker can apply individually for the exemption [14,619 in 2012]

(A) Institutional aspects (b) Geographical zoning Determined by Prefecture-level 47 prefecture The border of prefecture is not likely to be artificial. Most of them has been kept since feudal era. Cf.) Negligible industry-level minimum wage Potentially covers only 3.1 million workers in 2015 (less than 3% of total number of employee)

(A) Institutional aspects (c) Centralized bargaining Two stage bargaining Central Council for Minimum Wage determines four standards of minimum wage hike. 6 university professors, 6 union representatives, 6 employer association representatives. They classify 47 prefectures into 4 areas (rank A to D), and determine the standards of mw hike for each area. For example, on 27thJuly 2017, the central council determines that +26 (JPY) for rank A, +25 for rank B, +24 for rank C, and +22 for rank D.

(A) Institutional aspects (c) Centralized bargaining Two stage bargaining Prefectural Council for Minimum Wage determines the actual minimum wage hike. Usually only accept its standard that the central council had determined for (almost independent from local economic conditions). Cf.) Econometrically, the Japanese institution provides the exogenous variation of mw.

nominal GDP growth in 2014 (%) 1.1 0.7 2.5 4.1 0.0 0.6 2.9 1.7 0.1 2.6 0.5 1.3 0.9 0.0 0.2 1.9 0.8 -1.1 1.6 2.4 2.0 -1.2 1.7 0.0 nominal GDP growth in 2014 (%) 0.1 3.0 2.0 3.3 0.7 -0.3 0.5 2.7 -0.3 4.1 3.4 2.2 0.8 1.1 1.9 1.5 1.9 -1.3 0.5 1.9 1.5 1.1 3.5 mw in 2014 (JPY) mw in 2015 (JPY) mw in 2014 (JPY) mw in 2015 (JPY) mw (JPY) "standar ds" (JPY) mw (JPY) "standar ds" (JPY) Rank Prefecture Rank Prefecture C D D C D D D B B C B A A A C B C C C B C B A B Hokkaido Aomori Iwate Miyagi Akita Yamagata Fukushima Ibaraki Tochigi Gunma Saitama Chiba Tokyo Kanagawa Niigata Toyama Ishikawa Fukui Yamanashi Nagano Gifu Shizuoka Aichi Mie 748 679 678 710 679 680 689 729 733 721 802 798 888 887 715 728 718 716 721 728 738 765 800 753 764 695 695 726 695 696 705 747 751 737 820 817 907 905 731 746 735 732 737 746 754 783 820 771 16 16 17 16 16 16 16 18 18 16 18 19 19 18 16 18 17 16 16 18 16 18 20 18 16 16 16 16 16 16 16 18 18 16 18 19 19 19 24 18 16 16 16 18 16 18 19 18 B B A B C C D D C B C D C D D C D D D D D D D Shiga Kyoto Osaka Hyogo Nara Wakayama Tottori Shimane Okayama Hiroshima Yamaguchi Tokushima Kagawa Ehima Kochi Fukuoka Saga Nagasaki Kumamoto Oita Miyazaki Kagoshima Okinawa 746 789 838 776 724 715 677 679 719 750 715 679 702 680 677 727 678 677 677 677 677 678 677 764 807 858 794 740 731 693 696 735 769 731 695 719 696 693 743 694 694 694 694 693 694 693 18 18 20 18 16 16 16 17 16 19 16 16 17 16 16 16 16 17 17 17 16 16 16 18 18 19 18 16 16 16 16 16 18 16 16 16 16 16 16 16 16 16 16 16 16 16

(A) Institutional aspects (d) Different bites Proportion of MW to Median Wage Level Higher in Female than in Male Higher in Big-City than in Country-side Time series trend The timing of increasing trend is earlier in Country-side than in Big-City. Recent increasing trend is much faster in Big-City than in Country-side (which comes from the 2017 amendment).

(A) Institutional aspects (a)Universality (b)Geographical zone (c) Centralized bargaining result in (d)Various bites between groups = Advantageous for economic analysis [because of its exogeneity], but we should be careful about negative consequences of minimum wage hike.

(B) Its consequences (a)Employment loss? (b)Relation to polarization (c) Wage compression

(B) Its consequences (a) Employment loss? Data limitation The sample size of LFS (CPS equivalent) is too small to produce prefecture-level employment sizes (0.05% of population on average). Use Employment Status Survey (ESS) 1% from population (over 1M in a year) But Once five year Usual base questionnaire (not actual base) {Mainly working, Mainly Schooling/Housework, Non-employed}

(B) Its consequences (a) Employment loss? Outcome variables Employment Usual employment status Job quality [Training (Hara, 2017, Labour Economics)] Econometric Model ?= ?? ????+ ?????_????? ?+ ?????_????+?????? ? ?????????

?= ?? ????+ ?????_?????+ ?????_??? Prefecture j : 1-47 Year t: 1997, 2002, 2007, 2012 Group k: gender*education*age (2*2*11) ????? gender and education group Male Female University Grad. and more less than University Grad. University Grad. and more less than University Grad. coeff. s.e. p-value coeff. s.e. p-value coeff. s.e. p-value coeff. s.e. p-value age group 15-19 0.171 -0.080 0.201 0.399 0.113 0.48 20-24 0.022 0.059 -0.009 0.034 0.052 0.68 0.049 0.243 0.045 0.835 0.048 0.48 25-29 -0.028 0.009 0.027 0.063 0.031 0.37 0.037 0.815 0.056 0.624 0.042 0.14 30-34 -0.007 -0.032 0.057 0.015 0.021 0.75 0.041 0.440 0.067 0.403 0.056 0.79 35-39 -0.003 0.009 0.038 0.021 0.021 0.87 0.017 0.589 0.090 0.676 0.039 0.59 40-44 -0.015 -0.010 -0.017 0.041 0.017 0.38 0.023 0.647 0.075 0.822 0.031 0.20 45-49 -0.027 0.013 -0.060 0.039 0.022 0.21 0.019 0.513 0.073 0.412 0.018 0.03 50-54 0.005 0.028 0.008 0.013 0.021 0.83 0.020 0.173 0.078 0.921 0.019 0.48 55-59 -0.002 0.011 -0.201 0.014 0.031 0.96 0.026 0.673 0.172 0.247 0.032 0.67 60-64 0.044 0.052 -0.005 -0.025 0.060 0.47 0.036 0.149 0.123 0.969 0.026 0.34 65- -0.050 -0.001 -0.135 0.000 0.050 0.32 0.016 0.935 0.110 0.225 0.021 0.99 mean of mw in 1997= 599 mean of mw in 2012= 691

?= ?? ????+ ?????_?????+ ?????_??? Prefecture j : 1-47 Year t: 1997, 2002, 2007, 2012 Group k: gender*education*age (2*2*11) ????? gender and education group Male Female University Grad. and more less than University Grad. University Grad. and more less than University Grad. age group 15-19 20-24 + + 25-29 + + 30-34 + 35-39 40-44 + 45-49 50-54 55-59 60-64 65- mean of mw in 1997= 599 mean of mw in 2012= 691

?= ?? ????+ ?????_?????+ ?????_???+ ?????? ????? Prefecture j : 1-47 Year t: 1997, 2002, 2007, 2012 Group k: gender*education*age (2*2*11) gender and education group Male Female University Grad. and more less than University Grad. University Grad. and more less than University Grad. coeff. s.e. p-value coeff. s.e. p-value coeff. s.e. p-value coeff. s.e. p-value age group 15-19 -0.182 0.062 0.166 0.28 0.093 0.51 20-24 -0.030 -0.056 -0.011 -0.041 0.058 0.61 0.058 0.34 0.053 0.84 0.043 0.35 25-29 0.032 -0.027 0.010 -0.103 0.036 0.37 0.041 0.52 0.055 0.86 0.048 0.04 30-34 0.010 0.022 -0.063 -0.001 0.022 0.66 0.037 0.57 0.088 0.48 0.072 0.99 35-39 0.009 -0.011 -0.078 -0.046 0.025 0.72 0.016 0.51 0.080 0.34 0.038 0.22 40-44 0.010 0.005 0.096 -0.055 0.019 0.60 0.022 0.82 0.114 0.41 0.037 0.15 45-49 0.023 -0.021 0.082 -0.006 0.019 0.24 0.020 0.30 0.079 0.30 0.026 0.81 50-54 -0.011 -0.022 -0.056 0.023 0.019 0.58 0.020 0.29 0.096 0.56 0.027 0.40 55-59 -0.003 -0.012 0.095 -0.015 0.035 0.94 0.026 0.64 0.142 0.51 0.029 0.60 60-64 -0.036 -0.040 -0.119 0.007 0.060 0.55 0.027 0.15 0.121 0.33 0.025 0.79 65- 0.033 -0.011 -0.003 -0.010 0.041 0.42 0.018 0.53 0.079 0.97 0.012 0.42 mean of mw in 1997= 599 mean of mw in 2012= 691

?= ?? ????+ ?????_?????+ ?????_???+ ?????? ????? Prefecture j : 1-47 Year t: 1997, 2002, 2007, 2012 Group k: gender*education*age (2*2*11) gender and education group Male Female University Grad. and more less than University Grad. University Grad. and more less than University Grad. age group 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65- mean of mw in 1997= 599 mean of mw in 2012= 691

?= ?? ????+ ?????_?????+ ?????_???+ ?????? ???????? Prefecture j : 1-47 Year t: 1997, 2002, 2007, 2012 Group k: gender*education*age (2*2*11) gender and education group Male Female University Grad. and more less than University Grad. University Grad. and more less than University Grad. age group 15-19 0.171 -0.080 0.201 0.399 0.113 0.48 20-24 0.022 0.059 -0.009 0.034 0.052 0.68 0.049 0.243 0.045 0.835 0.048 0.48 25-29 -0.028 0.009 0.027 0.063 0.031 0.37 0.037 0.815 0.056 0.624 0.042 0.14 30-34 -0.007 -0.032 0.057 0.015 0.021 0.75 0.041 0.440 0.067 0.403 0.056 0.79 35-39 -0.003 0.009 0.038 0.021 0.021 0.87 0.017 0.589 0.090 0.676 0.039 0.59 40-44 -0.015 -0.010 -0.017 0.041 0.017 0.38 0.023 0.647 0.075 0.822 0.031 0.20 45-49 -0.027 0.013 -0.060 0.039 0.022 0.21 0.019 0.513 0.073 0.412 0.018 0.03 50-54 0.005 0.028 0.008 0.013 0.021 0.83 0.020 0.173 0.078 0.921 0.019 0.48 55-59 -0.002 0.011 -0.201 0.014 0.031 0.96 0.026 0.673 0.172 0.247 0.032 0.67 60-64 0.044 0.052 -0.005 -0.025 0.060 0.47 0.036 0.149 0.123 0.969 0.026 0.34 65- -0.050 -0.001 -0.135 0.000 0.050 0.32 0.016 0.935 0.110 0.225 0.021 0.99 mean of mw in 1997= 599 mean of mw in 2012= 691

?= ?? ????+ ?????_?????+ ?????_???+ ?????? ???????? Prefecture j : 1-47 Year t: 1997, 2002, 2007, 2012 Group k: gender*education*age (2*2*11) gender and education group Male Female University Grad. and more less than University Grad. University Grad. and more less than University Grad. age group 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65- mean of mw in 1997= 599 mean of mw in 2012= 691

(B) Its consequences (a) Employment loss? Minimum wage hike does not always decrease jobs in prefecture. If there be job loss effects, it may be concentrated in lower educated female people. Further discussion is needed because of the inconsistency between employment-loss and discouraged effect

(B) Its consequences (b) Relation to polarization ESS provides industry * occupation information Define 3,483 jobs by industry (3 digit) * occupation (3 digit) (average # of observation: 293.2) Classify each job into three bins by 2002 (national) median wage of job. Measure the growth of each bin between 2002 and 2012 by prefecture.

(B) Its consequences (b) Relation to polarization ESS provides industry * occupation information Define 3,483 jobs by industry (3 digit) * occupation (3 digit) (average # of observation: 293.2) Classify each job into three bins by 2002 (national) median wage of job. Measure the growth of each bin between 2002 and 2012 by prefecture. Regress each bin growth on mw hike. ???? ?= ?????_????+ ? ???+ ?????????? ?

?= ?????_????+ ? ???+ ?????????? ? ???? sample bin by prefecture growth rate of employment dependent variable coeff. p-value coeff. p-value coeff. s.e. s.e. s.e. p-value lower one-third 0.116 0.116 0.228 0.012 0.00 0.010 0.00 0.033 0.00 upper one-third 0.199 0.199 0.283 0.012 0.00 0.010 0.00 0.033 0.00 lower one-third * MW -1.433 0.404 0.00 upper one-third * MW -1.067 0.404 0.01 MW 1.202 2.035 0.172 0.00 0.286 0 constant (= middle one-third) -0.183 -0.277 -0.342 0.008 0.00 0.015 0.00 0.023 0.00 # of observation is 141 = 3*47 mean of MW = 0.078 s.d. of MW = 0.024

(B) Its consequences (b) Relation to polarization Rising minimum wage may be associated with mitigating polarization. though still needs to be investigated (Remember, we did not control for prefecture-specific trend in this section).

(B) Its consequences (c) Wage compression Aomori, Female 1994 2003 8 6 4 2 0 Percent 0 1000 2000 3000 2012 8 6 4 2 0 0 1000 2000 3000 hourly wage (JPY) Graphs by year

(B) Its consequences (c) Wage compression Aomori, Male 1994 2003 2 1.5 1 .5 0 Percent 0 1000 2000 3000 2012 2 1.5 1 .5 0 0 1000 2000 3000 hourly wage (JPY) Graphs by year

(B) Its consequences (c) Wage compression Tokyo, Female 1994 2003 3 2 1 0 Percent 0 1000 2000 3000 2012 3 2 1 0 0 1000 2000 3000 hourly wage (JPY) Graphs by year

(B) Its consequences (c) Wage compression Tokyo, Male 1994 2003 2 1.5 1 .5 0 Percent 0 1000 2000 3000 2012 2 1.5 1 .5 0 0 1000 2000 3000 hourly wage (JPY) Graphs by year

(B) Its consequences (c) Wage compression Apparent compression of wage distribution due to minimum wage hike. During 1990s, only in female & low-rank prefecture Recently, even in male & Tokyo Implies some spillover effect by mw

(B) Its consequences (c) Wage compression Quantify the wage compression by David Lee s method (Kambayashi, Kawaguchi, and Yamada, 2013, Labour Economics) ? ??? j: prefecture (1-47) t: year (1994-2012) p: percentile (10-90) wjtp: log of hourly base wage at percentile p in prefecture j in year t 50= ?????? ??? 50+ ?????+ ???+ ?????? ???

???????:10? ?????????? 0.657 (0.025)???? ??? 10 ??? 50= 50+ ?????+ ???+ ?????? ??? In 1994, average relative 10thpercentile (w101994-w501994) was -0.323. Since, between 1994 and 2012, average change in relative MW (MWt-w50t) was +0.128, the compression effects: 0.657*0.128=+0.084 Median

Median

Median

(B) Its consequences (a)Employment loss? Slightly YES, but we do not find major employment loss. (b)Relation to polarization Rising mw is associated with a transformation from polarization to up-grading in terms of job quality. (c) Wage compression Rising mw is strongly associated with the compression of lower tail of wage distribution.

(C) Lessens from the Japanese case Given the Japanese labor market situation, Minimum Wage hike does not conclude the apparent negative consequences. Next question: is it due to the low level of mw (the relative position was 0.3 to 0.4) Sufficient informal sector (the employee s ratio in population was over 10% even during 1990s)