Silviase/jgov_v1.4_full_ann huggingface dataset:
sample or separately by employment status, using a dummy variable that indicates whether or not the ownership is public. We then run the same regression by type of facilities (public and private) to describe the wage-age profile to examine the difference in the steepness.

### 4. Results

Tables 2-1, 2-2, and 2-3 show the estimated coefficients of simple semi-logarithmic human capital wage function, for hourly, daily, and monthly wages, respectively. Since regular and non-regular workers are paid by different wage settings, we need these three types of wages to standardize the results. Note that all the regression results are controlled by area and facility dummies to adjust for the differences in government subsidies. Since the results are not different among these wage settings, we will focus on hourly wages in this section (Table 2-1). At a 5-percent-level significance, for a full pooled sample and regular workers, a public dummy has a statistically significant and positive effect on hourly wages by 19.3 percentage points compared to private status. In real value, the adjusted difference of full samples between the public and private sectors is 355 yen for hourly wages. After adjusting for various employee and facility characteristics, the effects of public status still remain significantly definite, and public workers enjoy much higher wages than private workers do, as unadjusted comparisons between the two sectors, shown in Table 1. We use a simple semi-logarithmic human capital wage function without controlling for sectoral selection bias, since we did not find the evidence on sample selection between the public and private sectors using the Heckman two-stage procedure. Also, we will show later graphical analyses separately by potential major factors causing sample selection bias, firm settings, and employment status to avoid such a statistical problem.

First, we would like to focus on individual characteristics. The results also show that various factors associated with human capital—such as the fact that being a male, older, and more experienced employee with a higher degree of education and qualifications seems
to be more appreciated in the child care market. All of those characteristics statistically significant for the full sample and regular workers. However, this is not the case for non-regular workers, implying that human capital indicators do not seem to be significant factors in determining wage rates for non-regular status.

Further, minus coefficients of squared years of age and experience in these tables designate a familiar pattern of wage profile that does not linearly increase along with age and market experience. Thus, the rate of increase in return for workers' human capital would be marginally diminishing along with age and market experience. The increase rate in wages among regular workers that is significantly affected by factors associated with human capital is seen to be noticeably diminishing—with the fastest in companies with market experience—compared to other employment statuses. However, the magnitude is not large at all. For example, in real value, the growth rates of regular worker wages along with age would be decreasing by 0.02 yen (-0.02 percent) for hourly wages, correspondingly. Also, those with years of market experience would be declining by 0.34 yen (-0.02 percent) for hourly wages. The results represent that the age and experience wage profile in the child care industry seems to strongly reflect the seniority mechanism in Japanese labor market.

Focusing on full samples, as we expected, employment status has a large effect on the wage profile. Compared to non-regular employees with shorter hours of work who are treated as a reference in the regression, the regular status has approximately a 50 percentage-points-higher positive impact on hourly wage rates. Also, non-regular workers who work long hours are more likely to enjoy higher wage rates than are referred workers, but lower rates than regular workers. These results are partially accounted for by the different implications of human capital between regular and non-regular workers, as we previously discussed. Individual human capital is valued for regular workers, but not appreciated for non-regular workers in the labor market.

Next, we will look at the impact of facility characteristics on wages. Considering the status of land tenure as a fixed cost, the fixed costs of centers whose lands are owned by someone other than the public sector are expected to be higher than those renting lands.
from local governments or social welfare cooperation. This is especially true in urban areas, where renting land is extremely expensive. Therefore, the centers operating on rented land might be inclined to keep wages lower (to meet their budgets) than those using publicly owned lands. We found that non-regular workers at centers on land other than the public sectors are paid at much less hourly. Moreover, the effect of firm size on wages will presumably increase gradually along with the number of employees, which is consistent with previous studies demonstrating the positive relation between them. We found the effects of providing special care on wage rates to be deviate, and not very significant.

Consequently, we found public status to be a significant determinant in child care worker wages; it influences the worker wages profile considerably. Also, our findings show that, for regular workers, various indicators associated with their human capital—such as being a male, older, and more experienced employee with higher degree of education and qualifications leads to higher wages—are principal factors in determining wages. These factors are not significantly correlated with non-regular workers' wage rates. Further, the rate of increase in return for worker human capital will marginally diminish, along with age and market experience, but the magnitude is negligible. Therefore, we conclude that the age and experience wage profiles in the child care industry strongly reflect the seniority mechanism in Japanese labor market. Employment status also has a large effect on wage profiles. Both regular and non-regular employees who work long hours enjoy enormously higher wages than do non-regular workers who work shorter hours. The result might be partially accounted for by the implications of human capital such that they are valued for regular workers, but not appreciated for non-regular workers in the labor market.

To verify wage profiles influenced by public-private status, years of age and experience, and employment status, Figures 1-1~1-4, Figures 2-1~2-4, and Figures 3-1~3-2 demonstrate hourly, daily, and monthly wages. Figures 1-1, 2-1, and 3-1, contrasted against Figures 1-3, 2-3, and 3-3, represent age-wage profiles in publicly owned facilities versus licensed private centers. On the other hand, Figures 1-2, 2-2, and 3-2 versus Figures 1-4, 2-4, and 3-4 represent market experience-wage profiles in publicly owned facilities versus licensed private centers. Each figure shows a simple least square estimate using age and
squared age as well as experience and squared experience as explanatory variables by employment status. The estimation was run separately by types of facility. These figures basically replicate our regression results in Table 2-1, 2-2, and 2-3. First, the wage profile along with age and experience is most likely to draw the identical outline. Second, as we explained above, in both public and private child care centers, human capital including age and experience, is not an important factor in determining the wages of non-regular workers, shown by almost horizontal fixed lines in these figures. Third, the status of regular workers seems to have a keen impact on wage profiles in the public sector more clearly than in the private sector. The slopes of regular workers' wage profiles in the public sector are much larger compared to the ones in the private sector. Also, the rates of increases in return for worker human capital shown by coefficients of squared indicators are diminishing much faster in private centers than the ones in public facilities. Finally, the magnitudes of diminishing rates in the public sector are negligible, which is consistent with our previous findings, while the diminishing rates in private facilities are not insignificant enough to be ignored. Thus, we can verify that the age and experience wage profile in the child care market firmly reflects the seniority system in Japan.

### 5. Conclusion

Due to strict entry regulation to firms and high costs of service, households with preschool children have been experiencing an excessive undersupply of child care, especially in Japan’s urban areas. Since child care is a labor-intensive product, high costs are most likely to reflect high wage rates. Therefore, research on wage determination and profiles, along with age and experience in the market, seems to be urgent for improving efficiency in child care management and breaking through the bottleneck of child care supply.

In this paper, we investigate various factors that might determine child care worker wage profiles, along with different types of management, age, and market experience, using
the simple semi-logarithmic wage equation frequently used in labor literature. Publicly owned and privately licensed centers, both of which are considered not-for-profit organizations, are dominant in Japan's child care industry. Thus, focusing on worker wages in these nonprofit sectors will provide a comprehensive picture of wage profiles with respect to human capital and center characteristics.

We present four major empirical findings. First, we found public status to be a significant determinant of child care worker wages; it influences worker wage profiles considerably. Second, for regular workers, various indicators associated with their human capital, such as the fact that being a male, older, and more experienced employee with a higher degree of education and qualifications tends to lead to higher wages, are principal factors in determining wages. These factors, however, are not significantly correlated with the wage rates of non-regular workers. Thus, we can conclude that the age and experience wage profiles for regular workers in the child care market strongly reflect the seniority system in Japan. At the same time, differences in the wage age profile could not totally be attributed to differences in types of management. Third, the rates of increases in return for worker human capital are diminishing much faster in private centers than the ones in public facilities are. The magnitudes of diminishing rates in the public sector are negligible, while the diminishing rates in private facilities are not insignificant enough to be ignored. Finally, employment status also has a large effect on wage profiles. Both regular and non-regular employees who work long hours of work enjoy enormously higher wages than do non-regular workers who work shorter hours. The result might be partially accounted for by the implications of human capital, such that they are valued for regular workers, but not appreciated for non-regular workers in the labor market.

We conclude that much higher wages and steeper age-wage profiles in public centers cannot be justified unless such high costs are closely related with the quality of care in the public sector, where it tends to be higher, but that might not be the case, since we observe smaller differences in the age profiles for non-regular workers in both sectors. The current wage-age profiles for regular workers in the public sector follow the same pattern for general officials; they are not designed for workers with specific skills, which significantly
skews wages from labor productivity. In other words, wage determination does not depend on worker productivity. One favorable policy direction, as implemented in several local governments, is to apply a specific age profile for child care workers, which is more closely linked with their productivity.
## Appendix

Appendix tables and figures show the results based on a sample that includes child care workers in for-profit organizations that are subsidized by local governments. Consequently, the difference of wage profiles between nonprofit (including public and private centers) and subsidized for-profit centers is much steeper than the differences within the not-for-profit sector.
Appendix Table 1 shows that unadjusted hourly wages in nonprofit centers are higher by 50.8 percentage points than subsidized for-profit centers (the mean hourly wage is 1,033 yen in for-profit centers) $ ^{8} $ . Both mean years of age and market experience are much higher in the nonprofit centers than they are in the subsidized for-profit centers (mean age is 33.2 and mean years of experience is 5.6). It would be interesting if we found out that for-profit facilities are more likely to hire college graduates than private settings are. Like private centers, for-profit facilities tend to hire child care workers in their twenties, and the share of regular workers is slightly higher at for-profit facilities (83.8 percent) than at publicly owned centers. As regards facility-related characteristics, for-profit centers have a relatively shorter history of operation than public centers do; most for-profit facilities use land tenured by organizations other than public ones; more than 90 percent hire a very small number of care workers; they are inclined to provide temporary and post-acute care than are the other two settings; and they provide extra hours of care by 0.12 hours longer than public centers do.
Appendix Tables 2-1~2-3 show the estimated coefficients of simple semi-logarithmic human capital wage function using a subsidized for-profit dummy as a reference indicator for a full sample, including 136 workers in this setting, for hourly, daily, and monthly wages, respectively. Regression results confirm the basic statistics. At a 5-percent-level significance, for a pooled full sample and regular workers, public and private dummies always have a statistically significant and positive effect on hourly wages;
by 59.7 percentage points compared to the for-profits. The adjusted difference of full samples between the private and for-profit sectors is 553 yen per hour.

Appendix Figures 1-1~1-6 show the age-wage and years of experience-wage profiles for subsidized for-profit workers. Age and market experience would not affect the wage profiles of for-profit workers, which are almost flat in any type of wage setting.

All regression results are controlled by area and facility dummies to adjust for the amounts of government subsidies. Therefore, to some extent, the wage profiles of workers in non-licensed facilities reflect labor productivity, and the results would replicate the unfairly preferable difference of wage profiles to workers in nonprofit settings, although these nonlicensed facilities are subsidized by the local government.
## References

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Borjas, George J., Frech III, H.E., and Ginsburg, Paul B. (1983). “Property Rights and

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Brown, C. and Medoff, James (1989). “The Employer Size Wage Effect.” Journal of Political Economy, vol.101, pp483-496

Cabinet Office. (2003). Report on the Child Care Market in Japan. Government of Japan.

Frank, Richard G. and Salkever, David S. (1994). “Nonprofit Organizations in the Health Sector.” Journal of Economic Perspective, vol.8, pp129-144.

Feldstein, Martin (1971). The Rising Cost of Hospital Care. Information Services Press. Washington D.C.

Hansmann, Henry (1980). “The Role of Nonprofit Enterprise.” Yale Law Journal, vol.89, pp835-901.

Holtmann, A.G, and Idson, Todd L. (1993). “Wage Determination of Registered Nurses in Proprietary and Nonprofit Nursing Homes.” Journal of Human Resources, vol.28, pp55-79.

Idson, Todd, and Daniel J. Feaster (1990) “A Selectivity Model of Employer-Size Wage Differentials.” Journal of Labor Economics, 8(1), pp99-122.

Lakdawall, Darius and Philipson, Tomas (1998). “Nonprofit Competition and Production.”
NBER Working Papers, no.6377.

Leete, Laura (2001). “Whither the Nonprofit Wage Differential? Estimates from the 1990 Census.” Journal of Labor Economics, vol.19, pp136-170.

Mocan, H. Naci and Deborah Viola (1997). “The Determinants of Child Care Worker’s Wages and Compensation: Sectoral Difference, Human Capital, Race, Insiders and Outsiders,” NBER Working Papers, no.6328.

Mocan, H. Naci and Tekin, Erdal (2000). “Nonprofit Sector and Part-Time Work: An Analysis of Employer-Employee Matched Data of Child Care Workers.” NBER Working Papers, no.7977.

Montgomery, Mark and Cosgrove, James (1995). “Are Part Time WomenPaid Less? A Model with Firm Specific Effects.” Economic Inquiry, vol.38, pp119-133.

Noguchi, Haruko and Satoshi Shimizutani (2002). “Earnings and Quality Differentials in For-Profit versus Nonprofit Long-Term Care: Evidence from Japan's Long-Term Care Market.” ESRI Discussion Paper Series No.17, Cabinet Office, Government of Japan

Noguchi, Haruko, Satoshi Shimizutani and Wataru Suzuki (2003a). “The Quality of Child Care in Japan: Evidence from Micro-level Data”. mimeo, Economic and Social Research Institute, Cabinet Office, Government of Japan.

Noguchi, Haruko, Satoshi Shimizutani and Wataru Suzuki (2003b). “Nonprofit Wage Premium in the Japanese Child Care Market: Evidence from Employer-Employee Matched Data.” mimeo, Economic and Social Research Institute, Cabinet Office, Government of Japan.
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Ruhm, Christopher J., and Borkoski, Carey (2000). “Compensation in the Nonprofit Sector.” NBER Working Papers, no.7562.

Troske, Kenneth R. (1999). “Evidence on the Employer Size-Wage Premium from Worker-Establishment Matched Data.” Review of Economics and Statistics, vol.81, pp15-26.

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(参 考)

昭和52年度各省各庁債権現在額報告書
## 目次

## 一 般会計債権現在額報告書

頁 1
総 理 府 所 管 2
法 務 省 所 管 5
外 務 省 所 管 7
大 蔵 省 所 管 8
文 部 省 所 管 12
厚 生 省 所 管 13
農 林 省 所 管 16
通 商 産 業 省 所 管 17
運 輸 省 所 管 19
労 働 省 所 管 21
建 設 省 所 管 22
自 治 省 所 管 24

一般会計債権現在額報告書(徵収停止分)

裁 判 所 所 管 25
総 理 府 所 管 26
法務省所管 27
大蔵省所管 28
厚生省所管 30
農林省所管 31
通商産業省所管 32
運輸省所管 33
建設省所管 34
(各特別会計の債権現在額報告書は債権現在額総計算書とその内容が同一であるので添付しない。)
昭和52年度裁判所所管一般会计債権現在額報告書
区分及び債権の種類	本年度発生債権分	前年度以前発生債権分	合計	備考
履行期限到來額(円)	履行期限未到来額(円)	計(円)	履行期限到來額(円)	履行期限未到来額(円)	計(円)	履行期限到來額(円)	履行期限未到来額(円)	計(円)
歳 入
雑 収 入
諸 収 入	27,260,527	7,761,364	35,021,891	53,302,542	82,870,225	136,172,767	80,563,069	90,631,589	171,194,658
弁償及返納金	27,260,527	1,260,426	28,520,953	52,028,599	67,219,864	119,248,463	79,289,126	68,480,290	147,769,416
費用弁償金債権	19,368,562	794,305	20,162,867	5,746,962	3,106,188	8,853,150	25,115,524	3,900,493	29,016,017
返納金債権	0	0	0	58,302	0	58,302	58,302	0	58,302
弁償金債権	0	0	0	328,695	0	328,695	328,695	0	328,695
損害賠償金債権	7,891,965	466,121	8,358,086	45,894,640	64,113,676	110,008,316	53,786,605	64,579,797	118,366,402
雑 入	0	6,500,938	6,500,938	1,273,943	15,650,361	16,924,304	1,273,943	22,151,299	23,425,242
延滞金債権	0	6,500,938	6,500,938	1,273,943	15,430,361	16,704,304	1,273,943	21,931,299	23,205,242
利息債権	0	0	0	0	220,000	220,000	0	220,000	220,000
歳 入 計	27,260,527	7,761,364	35,021,891	53,302,542	82,870,225	136,172,767	80,563,069	90,631,589	171,194,658
昭和52年度総理府所管一般会計債権現在額報告書
区分及び債権の種類	本年度発生債権分	前年度以前発生債権分	合計	備考
履行期限到來額(円)	履行期限未到来額(円)	計(円)	履行期限到來額(円)	履行期限未到来額(円)	計(円)	履行期限到來額(円)	履行期限未到来額(円)	計(円)
歳 入
官業益金及官業收入
官 業 収 入
病 院 収 入
病院等療養費債権	0	17,781,191	17,781,191	0	0	0	0	17,781,191	17,781,191
政府資産整理収入	0	10,016,500	10,016,500	0	66,594,000	66,594,000	0	76,610,500	76,610,500
国有財産処分収入
国有財産売払収入
不動産売払代債権	0	7,990,000	7,990,000	0	66,500,000	66,500,000	0	74,490,000	74,490,000
回収金等収入
貸付金等回収金収入
自衛隊学資貸与金債権	0	2,026,500	2,026,500	0	94,000	94,000	0	2,120,500	2,120,500
雑 収 入	9,235,841	4,365,835	13,601,676	2,107,483,935	66,450,063	2,173,933,998	2,116,719,776	70,815,898	2,187,535,674
国有財産利用収入	6,663,438	1,438,200	8,101,638	34,658,835	904,115	35,562,950	41,322,273	2,342,315	43,664,588
国有財産貸付収入	6,663,438	0	6,663,438	34,603,540	0	34,603,540	41,266,978	0	41,266,978
物件貸付料債権	419,461	0	419,461	63,646	0	63,646	483,107	0	483,107
物件使用料債権	6,243,977	0	6,243,977	34,539,894	0	34,539,894	40,783,871	0	40,783,871
利 子 収 入
利 息 債 権	0	1,438,200	1,438,200	55,295	904,115	959,410	55,295	2,342,315	2,397,610
諸 収 入	2,572,403	2,927,635	5,500,038	2,072,825,100	65,545,948	2,138,371,048	2,075,397,503	68,473,583	2,143,871,086
受託調査試験及役務収入
受託調査及び試験手数料債権	0	156,931	156,931	0	0	0	0	156,931	156,931
総理府所管 一般会計 債権現在額報告書 （3）

区分及び債権の種類	本年度発生債権分	前年度以前発生債権分	合計	備考
履行期限到來額(円)	履行期限未到來額(円)	計(円)	履行期限到來額(円)	履行期限未到來額(円)	計(円)	履行期限到來額(円)	履行期限未到來額(円)	計(円)
弁償及返納金	2,546,576	1,871,235	4,417,811	2,056,133,439	57,799,989	2,113,933,428	2,058,680,015	59,671,224	2,118,351,239
返納金債権	36,007	0	36,007	10,706,893	3,475,199	14,182,092	10,742,900	3,475,199	14,218,099
弁償金債権	0	0	0	3,545,028	0	3,545,028	3,545,028	0	3,545,028
損害賠償金債権	2,510,569	1,871,235	4,381,804	2,041,881,518	54,324,790	2,096,206,308	2,044,392,087	56,196,025	2,100,588,112
物品売払収入
不用物品売払代債権	0	39,574	39,574	371	0	371	371	39,574	39,945
雑入
延滞金債権	25,827	4,911	30,738	2,609,886	3,408,085	6,017,971	2,635,713	3,412,996	6,048,709
特別収入	0	854,984	854,984	14,081,404	4,337,874	18,419,278	14,081,404	5,192,858	19,274,262
返納金債権	0	0	0	7,181,690	0	7,181,690	7,181,690	0	7,181,690
延滞金債権	0	0	0	255,822	1,047,883	1,303,705	255,822	1,047,883	1,303,705
損害賠償金債権	0	0	0	6,643,892	2,383,278	9,027,170	6,643,892	2,383,278	9,027,170
利息債権	0	854,984	854,984	0	906,713	906,713	0	1,761,697	1,761,697
歳入計	9,235,841	32,163,526	41,399,367	2,107,483,935	133,044,063	2,240,527,998	2,116,719,776	165,207,589	2,281,927,365
歳入外
歳入外債権
前渡資金返納金債権
前渡資金返納金債権
返納金債権	170,868	0	170,868	0	0	0	170,868	0	170,868
歳入外計	170,868	0	170,868	0	0	0	170,868	0	170,868
資金
特別調達資金債権	3,404,821,292	74,209,636	3,479,030,928	37,430,612	0	37,430,612	3,442,251,904	74,209,636	3,516,461,540
調達資金受入金債権
合衆国政府受入金債権
合衆国政府受入金債権	3,404,519,139	73,875,294	3,478,394,433	33,983,016	0	33,983,016	3,438,502,155	73,875,294	3,512,377,449
諸収入債権
諸収入債権	302,153	334,342	636,495	3,447,596	0	3,447,596	3,749,749	334,342	4,084,091
( 4 ) 総理府所管 一般会計 債権現在額報告書

区分及び債権の種類	本 年 度 発 生 債 権 分	前 年 度 以 前 発 生 債 権 分	合 計	備 考
履行期限到來額(円)	履行期限未到来額(円)	計(円)	履行期限到來額(円)	履行期限未到来額(円)	計(円)	履行期限到來額(円)	履行期限未到来額(円)	計(円)
保険料被保険者負担金債権	105,990	240,003	345,993	229,168	0	229,168	335,158	240,003	575,161
返 納 金 債 権	196,163	94,339	290,502	790,575	0	790,575	986,738	94,339	1,081,077
損害賠償金債権	0	0	0	2,427,853	0	2,427,853	2,427,853	0	2,427,853
資 金 計	3,404,821,292	74,209,636	3,479,030,928	37,430,612	0	37,430,612	3,442,251,904	74,209,636	3,516,461,540
合 計	3,414,228,001	106,373,162	3,520,601,163	2,144,914,547	133,044,063	2,277,958,610	5,559,142,548	239,417,225	5,798,559,773