Silviase/jgov_v1.4_full_ann huggingface dataset:
Table 6: Average Ranges of 90% Credible Interval of Structural Shocks over the entire sample periods

Structural Shocks	Case A	Case B	Case C	Case D
TFP	0.635	0.353	0.528	0.539
Preference	1.593	1.633	1.058	0.824
Corp. Net Worth	0.141	0.148	0.246	0.216
Bank Net Worth	1.902	1.433	0.886	0.907
Government Expenditure	2.207	2.018	0.417	0.322
Investment	0.983	0.236	0.575	1.107
Labor Supply	2.516	3.133	1.447	1.430
Monetary Policy	0.121	0.178	0.127	0.126

Note: This table reports the average of the difference between the upper and the lower bounds of 90% credible interval of the structural shock over the entire sample periods (1985Q2-2012Q2), depicted in Figures 1 and 2.

Table 7: Average Ranges of 90% Credible Interval of Stochastic Volatilities in the entire sample periods

Structural Shocks	Case C	Case D
TFP	0.498	0.384
Preference	0.906	0.857
Corp. Net Worth	0.243	0.219
Bank Net Worth	1.043	0.908
Government Expenditure	0.709	0.769
Investment	0.604	0.592
Labor Supply	1.743	1.378
Monetary Policy	0.095	0.095

Note: This table reports the average value in the entire sample periods (1985Q2-2012Q2) of the difference between the upper bound and the lower bound of 90% credible interval on the stochastic volatility for the structural shock depicted in Figure 3.

Table 8: Leverage Effect of Structural Shocks: Correlation between the Sign of Shock and its Volatility

Structural Shocks	Case C	Case D
TFP	0	0
Preference	+	+
Corp. Net Worth	+	0
Bank Net Worth	0	0
Government Expenditure	0	0
Investment	0	0
Labor Supply	0	0
Monetary Policy	0	+

Note: The mark “-” indicates negative of $ \rho_{\sigma} $ (leverage effect) at 90% credible degree of posterior probability, while the mark “+” does positive of $ \rho_{\sigma} $ (opposite leverage effect) in similar way. The mark “0” implies that we do not judge the sign of $ \rho_{\sigma} $ and leverage effect of each shock because zero is within 90% credible interval of $ \rho_{\sigma} $ .
Table 9: Posterior Estimates: Case A and Case B

Case A	Case B
Key Structural Parameters
Parameter	Mean	SD	90% CI	Mean	SD	90% CI
$ \kappa $	0.614	0.043	[0.547, 0.689]	0.877	0.038	[0.818, 0.938]
h	0.464	0.045	[0.396, 0.537]	0.597	0.040	[0.535, 0.661]
$ \sigma_{C} $	1.628	0.036	[1.578, 1.688]	1.404	0.032	[1.356, 1.451]
$ \sigma_{L} $	0.939	0.071	[0.819, 1.052]	0.417	0.072	[0.323, 0.524]
$ \varphi $	0.027	0.002	[0.024, 0.030]	0.025	0.001	[0.023, 0.026]
$ \iota_{P} $	0.521	0.027	[0.478, 0.566]	0.358	0.017	[0.330, 0.386]
$ \iota_{W} $	0.422	0.009	[0.408, 0.437]	0.450	0.007	[0.440, 0.459]
$ \theta_{P} $	0.854	0.026	[0.811, 0.895]	0.374	0.041	[0.305, 0.440]
$ \theta_{W} $	0.589	0.037	[0.531, 0.649]	0.428	0.048	[0.351, 0.500]
$ \rho_{R} $	0.670	0.055	[0.581, 0.758]	0.643	0.038	[0.582, 0.707]
$ \mu_{\pi} $	2.805	0.025	[2.767, 2.842]	2.820	0.018	[2.790, 2.848]
$ \mu_{Y} $	0.006	0.005	[0.000, 0.014]	0.010	0.007	[0.000, 0.020]
Persistence Parameters for Structural Shocks
Parameter	Mean	SD	90% CI	Mean	SD	90% CI
$ \rho_{A} $	0.975	0.007	[0.964, 0.986]	0.975	0.005	[0.966, 0.983]
$ \rho_{C} $	0.636	0.093	[0.504, 0.788]	0.088	0.053	[0.004, 0.166]
$ \rho_{K} $	0.391	0.044	[0.323, 0.462]	0.998	0.001	[0.996, 0.999]
$ \rho_{E} $	0.907	0.022	[0.873, 0.944]	0.976	0.012	[0.959, 0.996]
$ \rho_{F} $	0.031	0.024	[0.000, 0.064]	0.016	0.011	[0.000, 0.031]
$ \rho_{G} $	0.798	0.047	[0.733, 0.864]	0.671	0.012	[0.652, 0.686]
$ \rho_{L} $	0.933	0.041	[0.876, 0.995]	0.967	0.009	[0.953, 0.982]
Standard Errors for Structural Shocks
Parameter	Mean	SD	90% CI	Mean	SD	90% CI
$ e_{A} $	0.564	0.043	[0.492, 0.629]	0.398	0.030	[0.347, 0.447]
$ e_{C} $	1.475	0.161	[1.242, 1.716]	1.729	0.189	[1.441, 1.986]
$ e_{K} $	0.787	0.072	[0.689, 0.918]	1.423	0.042	[1.358, 1.491]
$ e_{E} $	0.238	0.016	[0.212, 0.265]	0.286	0.020	[0.254, 0.318]
$ e_{F} $	0.757	0.057	[0.690, 0.843]	0.890	0.058	[0.811, 0.979]
$ e_{G} $	0.520	0.050	[0.439, 0.603]	0.895	0.119	[0.751, 1.102]
$ e_{L} $	0.881	0.110	[0.722, 1.060]	1.383	0.040	[1.325, 1.448]
$ e_{R} $	0.228	0.016	[0.201, 0.255]	0.245	0.019	[0.215, 0.274]

Note: 300,000 iterations are implemented using MH within Gibbs described in Section 4. We sample one draw out of every 10 replicates and discard first 10,000 samples. The remaining 20,000 samples are used for calculating moments of the posterior distributions. Items SD and 90% CI denote the standard deviations and 90% credible intervals of the posterior distributions of the structural parameters, respectively.
Table 10: Posterior Estimates: Case C and Case D

Case C	Case D
Key Structural Parameters
Parameter	Mean	SD	90% CI	Mean	SD	90% CI
$ \kappa $	0.564	0.041	[0.498, 0.632]	0.562	0.058	[0.470, 0.661]
$ h $	0.334	0.038	[0.271, 0.396]	0.221	0.038	[0.161, 0.282]
$ \sigma_{C} $	1.630	0.012	[1.613, 1.649]	1.605	0.017	[1.574, 1.627]
$ \sigma_{L} $	0.819	0.021	[0.786, 0.855]	0.597	0.017	[0.569, 0.626]
$ \varphi $	0.039	0.004	[0.032, 0.045]	0.041	0.003	[0.036, 0.046]
$ \iota_{P} $	0.397	0.014	[0.376, 0.422]	0.503	0.009	[0.490, 0.520]
$ \iota_{W} $	0.475	0.002	[0.472, 0.479]	0.489	0.001	[0.487, 0.491]
$ \theta_{P} $	0.804	0.025	[0.763, 0.846]	0.760	0.038	[0.697, 0.822]
$ \theta_{W} $	0.623	0.049	[0.544, 0.703]	0.516	0.039	[0.452, 0.580]
$ \rho_{R} $	0.653	0.029	[0.605, 0.698]	0.632	0.026	[0.590, 0.675]
$ \mu_{\pi} $	2.989	0.006	[2.979, 2.998]	2.986	0.006	[2.977, 2.995]
$ \mu_{Y} $	0.006	0.006	[0.000, 0.013]	0.008	0.007	[0.000, 0.018]
Persistence Parameters for Structural Shocks
Parameter	Mean	SD	90% CI	Mean	SD	90% CI
$ \rho_{A} $	0.989	0.006	[0.981, 0.999]	0.956	0.014	[0.933, 0.979]
$ \rho_{C} $	0.819	0.037	[0.757, 0.877]	0.909	0.025	[0.868, 0.952]
$ \rho_{K} $	0.127	0.050	[0.038, 0.202]	0.776	0.056	[0.682, 0.864]
$ \rho_{E} $	0.333	0.131	[0.107, 0.518]	0.918	0.036	[0.867, 0.971]
$ \rho_{F} $	0.192	0.011	[0.174, 0.209]	0.167	0.012	[0.151, 0.191]
$ \rho_{G} $	0.655	0.006	[0.646, 0.664]	0.619	0.005	[0.612, 0.627]
$ \rho_{L} $	0.924	0.053	[0.844, 0.991]	0.982	0.012	[0.965, 0.998]

Note: 300,000 iterations are implemented using MH within Gibbs described in Section 4. We sample one draw out of every 10 replicates and discard first 10,000 samples. The remaining 20,000 samples are used for calculating moments of the posterior distributions. Items SD and 90% CI denote the standard deviations and 90% credible intervals of the posterior distributions of the structural parameters, respectively.
Table 11: Posterior Estimates of Parameters of SVs: Case C and Case D

Case C	Case D
Parameter	Mean	SD	90% CI	Mean	SD	90% CI
Parameters of Stochastic Volatilities for TFP Shock
σA	0.373	0.099	[0.215, 0.500]	0.338	0.120	[0.158, 0.500]
ρσA	0.059	0.307	[-0.490, 0.507]	0.347	0.390	[-0.186, 0.989]
φA	0.737	0.168	[0.530, 0.986]	0.509	0.184	[0.213, 0.810]
μA	0.442	0.035	[0.378, 0.491]	0.429	0.049	[0.349, 0.501]
Parameters of Stochastic Volatilities for Preference Shock
σC	0.479	0.027	[0.456, 0.500]	0.476	0.023	[0.447, 0.500]
ρσC	0.357	0.190	[0.043, 0.658]	0.481	0.141	[0.226, 0.696]
φC	0.934	0.059	[0.854, 0.990]	0.958	0.037	[0.919, 0.990]
μC	0.656	0.075	[0.544, 0.764]	0.933	0.055	[0.844, 1.026]
Parameters of Stochastic Volatilities for Corporate Net Worth Shock
σE	0.434	0.053	[0.357, 0.500]	0.412	0.073	[0.303, 0.500]
ρσE	0.418	0.221	[0.066, 0.785]	0.280	0.329	[-0.217, 0.869]
φE	0.803	0.124	[0.627, 0.990]	0.758	0.186	[0.493, 0.990]
μE	0.149	0.007	[0.139, 0.162]	0.194	0.013	[0.173, 0.212]
Parameters of Stochastic Volatilities for Bank Net Worth Shock
σF	0.450	0.034	[0.404, 0.500]	0.445	0.041	[0.395, 0.500]
ρσF	0.161	0.231	[-0.216, 0.543]	0.218	0.199	[-0.132, 0.498]
φF	0.854	0.121	[0.685, 0.990]	0.894	0.066	[0.804, 0.990]
μF	0.665	0.051	[0.598, 0.769]	0.893	0.050	[0.783, 0.959]
Parameters of Stochastic Volatilities for Government Expenditure Shock
σG	0.266	0.050	[0.182, 0.327]	0.440	0.048	[0.373, 0.500]
ρσG	0.384	0.350	[-0.152, 0.990]	0.044	0.367	[-0.536, 0.670]
φG	0.663	0.286	[0.178, 0.990]	0.517	0.246	[0.071, 0.891]
μG	0.505	0.028	[0.461, 0.548]	0.570	0.031	[0.519, 0.627]
Parameters of Stochastic Volatilities for Investment Specific Technology Shock
σK	0.449	0.038	[0.399, 0.500]	0.452	0.063	[0.335, 0.500]
ρσK	0.304	0.318	[-0.228, 0.841]	0.128	0.246	[-0.215, 0.540]
φK	0.450	0.257	[0.001, 0.838]	0.219	0.214	[0.000, 0.548]
μK	0.496	0.023	[0.457, 0.528]	0.406	0.049	[0.324, 0.476]
Parameters of Stochastic Volatilities for Labor Supply Shock
σL	0.446	0.043	[0.386, 0.500]	0.482	0.016	[0.458, 0.500]
ρσL	-0.163	0.308	[-0.781, 0.254]	0.232	0.178	[-0.071, 0.517]
φL	0.779	0.263	[0.291, 0.990]	0.903	0.084	[0.813, 0.990]
μL	1.010	0.116	[0.870, 1.207]	1.461	0.078	[1.351, 1.580]
Parameters of Stochastic Volatilities for Monetary Policy Shock
σR	0.422	0.045	[0.355, 0.493]	0.464	0.034	[0.407, 0.500]
ρσR	0.156	0.238	[-0.268, 0.520]	0.479	0.211	[0.122, 0.797]
φR	0.763	0.109	[0.589, 0.948]	0.727	0.122	[0.540, 0.941]
μR	0.099	0.006	[0.089, 0.106]	0.112	0.013	[0.092, 0.131]

Note: 300,000 iterations are implemented using MH within Gibbs described in Section 4. We sample one draw out of every 10 replicates and discard first 10,000 samples. The remaining 20,000 samples are used for calculating moments of the posterior distributions. Items SD and 90% CI denote the standard deviations and 90% credible intervals of the posterior distributions of the structural parameters, respectively.
(b) Case B (Red)

Figure 1: Structural Shocks with i.i.d. Normal in Cases A and B

Note: Deep blue line and blue shaded area are posterior mean and 90% credible interval of structural shocks in Case A, respectively. And deep red line and red shaded area are posterior mean and 90% credible interval of structural shocks in Case B.
(b) Case D

Figure 2: Structural Shocks with SV in Cases C and D

Note: Deep blue line and blue shaded area are posterior mean and 90% credible interval of structural shocks in Case C, respectively. And deep red line and red shaded area are posterior mean and 90% credible interval of structural shocks in Case D.
(a) Case C (Blue)

(b) Case D (Red)

Figure 3: Stochastic Volatilities of Structural Shocks in Cases C and D

Note: Deep blue line and blue shaded area are posterior mean and 90% credible interval of structural shocks in Case C, respectively. And deep red line and red shaded area are posterior mean and 90% credible interval of structural shocks in Case D. Black dashed lines are constant volatilities estimated in Case A and B.
(a) Case A

(c) Case C

(b) Case B

(d) Case D

Figure 4: Historical Decomposition of Real GDP

Note: Case A; 11 observable variables and structural shocks with i.i.d. Case B: 41 observable variables and structural shocks with i.i.d. Case C: 11 observable variables and structural shocks with SV. Case D: 40 observable variables and structural shocks with SV.
(a) Case A

(c) Case C

(b) Case B

(d) Case D

Figure 5: Historical Decomposition of Gross Private Domestic Investment Note: Case A; 11 observable variables and structural shocks with i.i.d. Case B: 41 observable variables and structural shocks with i.i.d. Case C: 11 observable variables and structural shocks with SV. Case D: 40 observable variables and structural shocks with SV.
(a) Case A

(b) Case B

(c) Case C

(d) Case D

Figure 6: Historical Decomposition of Moody’s Bond Index (Corporate Baa) Note: Case A; 11 observable variables and structural shocks with i.i.d. Case B: 41 observable variables and structural shocks with i.i.d. Case C: 11 observable variables and structural shocks with SV. Case D: 40 observable variables and structural shocks with SV.
(a) Case A

(c) Case C

(b) Case B

(d) Case D

Figure 7: Historical Decomposition of Commercial Bank Leverage Ratio Note: Case A; 11 observable variables and structural shocks with i.i.d. Case B: 41 observable variables and structural shocks with i.i.d. Case C: 11 observable variables and structural shocks with SV. Case D: 40 observable variables and structural shocks with SV.
# Quality Assurance Framework of Higher Education in Japan

2009

Higher Education Bureau

Ministry of Education, Culture, Sports,

Science and Technology
## Contents

Outline of the Quality Assurance Framework in Japan 1
Classification of the Standards for Establishing Universities (undergraduate courses) 6
Illustration of Quality Assurance Framework in Japan 7
Standards for Establishing Universities (ordinance of MEXT, No.28 of October 22, 1956) 8
References
School System in Japan 53
Establishment type and Number of Higher Education Institutions 54
Student Enrollment by Major (FY 2008) 55
Outline of Related Institutions:
Council for Establishment-Approval System 56
Certified Agencies Conducting Accreditation Process 57
University Council 58
National Institution for Academic Degrees and University Evaluation 59
### 1. Introduction

## (1) Quality assurance framework as based on prior regulations

In Japan, the quality assurance framework worked as prior regulations, through applying the Standards for Establishing University (SEU) and operating the establishment-approval system (EAS). The intention here is to respect the independence and autonomy of universities, and thus assume that universities which are once approved would be ready to autonomously assure their own quality. Since the SEU and the EAS were introduced, this system has played a very significant role in assuring appropriate levels of quality in all universities.

On the other hand, there have been concerns about assuring the quality of every educational activity with only this kind of prior regulations, whose function is only to indicate what is needed to conduct various activities and not to check the outcome afterwards. As higher education became more diversified, reflecting the rise in enrollment rates and the maturity of society, more people posed concerns that dependence on the prior regulations would cause over-regulation and restraint against innovative projects. Thus, the government began considering a new system that would be checking afterwards, while maintaining the independence and autonomy of universities.

(2) Introduction of a new system integrating prior regulations with checking afterwards

The government amended the regulations such as creating more flexibility in applying the SEU, revising the EAS process, and introducing self-examination and evaluation as compulsory in all universities. Still, as the need for change from prior regulations to checking afterwards through examining current regulations became more widespread throughout the Japanese administrative system, the School Education Law was amended in 2002 to introduce the Quality Assurance and Accreditation System (QAAS), which came into effect from April 2004. Concurrently, the EAS was converted in 2003 so that all universities would be approved as long as they meet the conditions stipulated in the concerned law. At this stage, requirements were reduced and standards were simplified, while a new notification system in which universities could make minor organizational changes without approval was introduced. As this new system of integrating prior regulations with checking afterwards was introduced, the quality assurance framework in Japan has both the advantage of the prior regulations.