Voluntary IFRS adoption and accounting quality: Evidence from Japan

Figures & data

Table 1. Sample selection.

Listed companies for fiscal years 2010 to 2014	11,644
(less) Financial companies	−786
(less) Financial data unavailable	−2,634
(less) Stock data unavailable	−194
(less) Income smoothing and conservatism data unavailable	−1,243
(less) Sample unmatched	−2,373
Propensity score matched samples	4,414

Note: Downloaded data from the NEEDS Financial Quest database using the criteria: fiscal year-end at the end of March. Stock data contain information about stock price and market value. At least ten firms per year are required for each industry to be retained in the sample. For the PSM sample matched by the kernel method, we set the bandwidth for 0.01, and 0.05 of the treatment observations.

Source: Author’s calculation based on the NEEDS Financial Quest database.

Table 2. Descriptive statistics.

	JV_IFRS = 1 (N = 91)						JV_IFRS = 0 (N = 4,323)
Variable	Mean	SD	Q1	Median	Q3		Mean	SD	Q1	Median	Q3	Diff.	t-stat
VAR_NIG	0.003	0.007	0.001	0.001	0.003		0.003	0.006	0.000	0.001	0.003	0.001	1.16
NC_SCORE	1.236	1.458	0.759	1.253	1.661		0.872	0.785	0.514	0.794	1.114	0.364	4.27***
LEV	0.508	0.204	0.331	0.520	0.691		0.481	0.190	0.338	0.478	0.623	0.027	1.34
SIZE	13.316	1.575	12.193	13.112	14.500		11.267	1.501	10.243	11.043	12.082	2.049	12.88***
BANK	0.095	0.057	0.056	0.097	0.133		0.071	0.061	0.024	0.061	0.109	0.024	3.77***
EQU	1.101	2.020	0.286	0.687	1.074		0.597	0.755	0.271	0.421	0.669	0.504	5.94***
Q	1.187	0.587	0.877	0.968	1.298		1.012	0.558	0.781	0.915	1.078	0.175	2.95***
RD	0.032	0.039	0.000	0.017	0.053		0.020	0.027	0.000	0.012	0.029	0.012	4.33***
CURR	2.063	1.164	1.424	1.830	2.239		2.184	1.728	1.302	1.741	2.535	−0.121	−0.66
LOSS	0.143	0.352	0.000	0.000	0.000		0.163	0.369	0.000	0.000	0.000	−0.020	−0.51
EMP	9.509	1.403	8.502	9.678	10.318		7.597	1.505	6.538	7.388	8.500	1.911	12.00***
ROA	0.068	0.065	0.028	0.055	0.091		0.051	0.045	0.027	0.045	0.071	0.017	3.43***
GR_ASS	0.101	0.130	0.035	0.074	0.138		0.054	0.156	−0.001	0.040	0.090	0.047	2.84***
OPCF	0.086	0.060	0.041	0.077	0.120		0.063	0.059	0.034	0.062	0.091	0.023	3.75***
AGE	1.568	0.291	1.322	1.716	1.813		1.544	0.260	1.322	1.653	1.732	0.024	0.88
ROE	0.083	0.130	0.058	0.086	0.128		0.065	0.113	0.038	0.066	0.098	0.018	1.49
GR_SAL	0.075	0.174	−0.009	0.052	0.118		0.062	0.181	−0.009	0.043	0.106	0.013	0.68
FORSAL	0.068	0.224	0.000	0.000	0.000		0.058	0.174	0.000	0.000	0.000	0.010	0.53
TAGG	0.637	0.483	0.000	1.000	1.000		0.448	0.497	0.000	0.000	1.000	0.190	3.60***
THAV	0.319	0.744	0.000	0.000	0.000		0.021	0.203	0.000	0.000	0.000	0.297	12.36***
CLHC	4.132	2.386	2.000	4.000	6.000		1.396	1.734	0.000	1.000	2.000	2.736	14.76***
BIG4	0.967	0.180	1.000	1.000	1.000		0.789	0.408	1.000	1.000	1.000	0.178	4.15***
SCO_CHA	0.330	0.473	0.000	0.000	1.000		0.575	0.494	0.000	1.000	1.000	−0.245	−4.69***
POL_CHA	0.033	0.180	0.000	0.000	0.000		0.052	0.223	0.000	0.000	0.000	−0.019	−0.82
NEWAUD	0.011	0.105	0.000	0.000	0.000		0.048	0.214	0.000	0.000	0.000	−0.037	−1.64

Note: *, **, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, in two-tailed tests. See Appendix for definitions and measurements of the variables.

Source: Author’s calculation based on the NEEDS Financial Quest database.

Table 3. Distribution by year and industry.

Panel A: Distribution by year
					VAR_NIG		NC_SCORE
			JV_IFRS		JV_IFRS		JV_IFRS
	Full sample		= 0	= 1	= 0	= 1	= 0	= 1
Year	Obs.	Percent	Obs.	Obs.	Mean	Mean	Mean	Mean
2010	852	19.30	850	2	0.003	0.001	0.703	0.351
2011	871	19.73	866	5	0.003	0.007	1.033	−0.745
2012	894	20.25	879	15	0.003	0.006	0.539	0.502
2013	899	20.37	868	31	0.003	0.003	1.086	1.104
2014	898	20.34	860	38	0.002	0.002	1.001	1.940
Total	4,414	100.00	4,323	91	0.003	0.003	0.872	1.236
CAGR from 2010 to 2014			0.3%	108.8%
Panel B: Distribution by industry
					VAR_NIG		NC_SCORE
			JV_IFRS		JV_IFRS		JV_IFRS
	Full sample		= 0	= 1	= 0	= 1	= 0	= 1
Industry	Obs.	Percent	Obs.	Obs.	Mean	Mean	Mean	Mean
Chemicals	590	13.37	586	4	0.001	0.001	0.903	1.775
Electric appliances	727	16.47	706	21	0.003	0.008	0.843	1.114
Glass and ceramics products	149	3.38	144	5	0.002	0.001	0.809	0.524
Information and communication	380	8.61	372	8	0.004	0.002	0.959	2.300
Iron and steel	184	4.17	182	2	0.003	0.003	0.803	1.743
Land transportation	233	5.28	231	2	0.000	0.000	0.716	1.428
Machinery	597	13.53	592	5	0.004	0.002	0.882	1.326
Pharmaceutical	110	2.49	102	8	0.003	0.006	1.086	1.582
Rubber products	55	1.25	54	1	0.002	0.001	0.896	1.257
Services	323	7.32	318	5	0.003	0.003	1.095	0.595
Transportation equipment	359	8.13	347	12	0.003	0.002	0.806	1.416
Wholesale trade	707	16.02	689	18	0.003	0.000	0.799	0.782
Total	4,414	100.00	4,323	91	0.003	0.003	0.872	1.236

Note: This table presents the distribution, respectively, by year and industry. Panel A presents the annual descriptive statistics of the sample over five years. Panel B presents an industry breakdown of the sample on the basis of the TSE industry classifications. See Appendix for definitions and measurements of the variables.

Source: Author’s calculation based on the NEEDS Financial Quest database.

Table 4. Correlation coefficients between variables.

		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26
1.	VAR_NIG		−0.01	0.00	−0.01	−0.21	−0.07	0.05	0.04	0.22	0.14	0.20	−0.15	−0.01	0.00	0.00	−0.05	0.02	0.04	0.08	0.14	−0.02	0.13	−0.04	−0.05	0.04	0.06
2.	NC_SCORE	0.02		0.08	0.02	0.17	0.06	0.05	0.06	0.02	0.01	−0.01	0.17	0.03	0.07	0.05	0.02	0.06	0.01	−0.01	0.05	0.17	0.17	0.06	−0.07	−0.01	−0.02
3.	JV_IFRS	0.02	0.06		−0.09	−0.01	−0.04	0.16	0.07	0.01	0.07	−0.06	−0.02	0.16	0.15	0.07	−0.09	0.10	0.16	0.01	−0.04	−0.04	−0.02	0.01	−0.04	0.02	0.01
4.	LEV	0.01	0.02	−0.18		0.14	0.04	−0.54	0.19	−0.20	−0.86	0.14	0.15	−0.29	−0.09	−0.21	0.26	0.14	−0.02	−0.08	0.05	0.12	0.02	0.00	0.11	0.01	−0.01
5.	SIZE	−0.20	0.19	−0.07	0.17		0.45	0.11	0.26	0.19	−0.15	−0.12	0.85	0.10	0.09	0.14	0.35	0.17	0.04	0.06	0.06	0.19	0.50	0.20	0.35	0.08	−0.11
6.	BANK	−0.08	0.06	−0.05	0.02	0.41		0.08	0.12	0.19	−0.03	−0.06	0.41	0.06	0.06	0.08	0.32	0.10	0.07	0.03	0.05	0.11	0.35	0.08	0.15	0.03	−0.10
7.	EQU	0.14	0.09	0.26	−0.31	−0.03	0.03		0.63	0.28	0.49	−0.24	0.14	0.62	0.35	0.44	−0.20	0.37	0.21	0.07	−0.01	−0.06	0.16	0.05	0.06	0.02	−0.01
8.	Q	0.15	0.04	0.26	−0.05	0.04	0.04	0.84		0.14	−0.13	−0.11	0.27	0.45	0.22	0.30	−0.01	0.51	0.16	0.01	0.05	0.04	0.19	0.08	0.16	0.03	−0.03
9.	RD	0.10	0.07	0.02	−0.17	0.17	0.14	0.11	0.08		0.27	0.06	0.29	0.13	0.09	0.17	0.15	0.02	0.07	0.17	0.02	0.03	0.43	0.05	0.06	0.09	−0.03
10.	CURR	0.11	−0.01	0.10	−0.66	−0.12	−0.04	0.25	0.09	0.23		−0.08	−0.16	0.24	0.09	0.10	−0.26	−0.12	0.03	0.08	−0.04	−0.07	0.05	−0.03	−0.11	0.00	0.03
11.	LOSS	0.13	−0.01	−0.06	0.15	−0.11	−0.05	−0.11	−0.08	0.05	0.01		−0.07	−0.41	−0.23	−0.24	0.05	−0.33	−0.07	−0.02	0.05	0.02	−0.02	−0.05	−0.02	0.03	0.07
12.	EMP	−0.16	0.18	−0.07	0.17	0.88	0.40	−0.03	0.05	0.24	−0.16	−0.06		0.12	0.10	0.22	0.28	0.18	0.07	0.12	0.04	0.18	0.57	0.19	0.38	0.09	−0.09
13.	ROA	0.05	0.05	0.23	−0.26	0.04	0.05	0.59	0.53	0.05	0.04	−0.38	0.06		0.34	0.57	−0.21	0.78	0.32	0.12	−0.08	−0.06	0.13	0.05	0.04	−0.01	−0.03
14.	GR_ASS	0.10	0.04	0.07	−0.03	0.02	0.01	0.26	0.14	0.12	0.00	−0.14	0.03	0.21		0.25	−0.14	0.34	0.49	0.11	−0.01	0.03	0.15	0.01	0.08	0.03	−0.03
15.	OPCF	0.02	0.06	0.11	−0.19	0.09	0.06	0.40	0.33	0.10	−0.01	−0.22	0.16	0.60	0.25		−0.08	0.47	0.20	0.08	0.00	−0.04	0.13	0.07	0.06	0.00	−0.03
16.	AGE	−0.08	0.01	−0.13	0.23	0.32	0.29	−0.25	−0.18	0.07	−0.19	0.05	0.27	−0.24	−0.12	−0.11		−0.06	−0.09	−0.04	0.09	0.12	0.21	−0.04	0.05	0.05	−0.04
17.	ROE	0.02	0.02	0.08	−0.03	0.06	0.06	0.21	0.21	−0.02	−0.06	−0.31	0.06	0.56	0.19	0.35	−0.06		0.33	0.07	0.04	0.02	0.13	0.04	0.09	0.00	−0.03
18.	GR_SAL	0.08	0.01	0.07	−0.01	0.00	0.03	0.19	0.11	0.08	−0.03	0.01	0.02	0.21	0.66	0.21	−0.07	0.16		0.11	−0.01	0.02	0.13	0.00	0.04	0.02	−0.04
19.	FORSAL	−0.01	0.01	0.00	−0.07	0.09	0.02	0.02	0.00	0.14	0.03	0.01	0.16	0.06	0.05	0.04	−0.03	0.04	0.07		0.02	0.05	0.27	−0.01	0.03	0.01	0.03
20.	TAGG	0.16	0.05	−0.02	0.05	0.07	0.04	0.01	0.03	0.06	−0.01	0.05	0.05	−0.07	0.02	−0.02	0.08	−0.03	0.01	0.03		0.06	0.08	−0.01	0.01	0.04	0.01
21.	THAV	−0.02	0.18	−0.06	0.11	0.26	0.09	−0.04	−0.01	0.01	−0.04	0.00	0.22	−0.04	0.01	−0.03	0.10	0.00	0.00	0.03	0.07		0.21	0.05	0.06	0.00	−0.01
22.	CLHC	−0.03	0.22	−0.06	0.06	0.56	0.33	0.04	0.07	0.34	−0.03	−0.02	0.63	0.06	0.05	0.09	0.23	0.02	0.06	0.25	0.08	0.34		0.10	0.28	0.09	−0.06
23.	BIG4	−0.09	0.06	−0.01	0.00	0.21	0.08	0.03	0.04	0.07	0.00	−0.05	0.19	0.05	−0.02	0.08	−0.08	0.01	−0.01	0.00	−0.01	0.05	0.11		0.06	0.02	−0.24
24.	SCO_CHA	−0.04	−0.07	−0.04	0.11	0.34	0.14	0.02	0.07	0.05	−0.09	−0.02	0.37	0.03	0.07	0.05	0.03	0.04	0.04	0.04	0.01	0.05	0.28	0.06		0.05	−0.03
25.	POL_CHA	0.01	−0.01	0.04	0.01	0.08	0.03	0.03	0.04	0.07	0.00	0.03	0.09	0.00	0.02	0.00	0.04	0.02	0.02	0.02	0.04	−0.01	0.08	0.02	0.05		−0.02
26.	NEWAUD	0.11	−0.02	0.01	−0.01	−0.12	−0.10	−0.01	−0.01	−0.02	0.00	0.07	−0.10	−0.04	0.01	−0.04	−0.02	−0.02	−0.02	0.02	0.01	−0.01	−0.05	−0.24	−0.03	−0.02

Note: Correlations are based on 4,414 firm-year observations. Correlation coefficients in bold are statistically significant at the 0.10 level or better. Pearson (Spearman) correlations are in the lower (upper) diagonal. See Appendix for definitions and measurements of the variables.

Source: Author’s calculation based on the NEEDS Financial Quest database.

Table 5. Voluntary IFRS adoption and income smoothing.

	Dependent variable: VAR_NIG
	(A)		(B)		(C)		(D)
	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat
Test variable
JV_IFRS	0.002	3.04***	0.002	2.53**	0.002	2.49**	0.002	2.77***
Firm characteristics
LEV	0.006	7.58***	0.005	7.49***	0.005	7.49***	0.005	7.58***
SIZE	−0.001	−7.00***	−0.001	−7.38***	−0.001	−7.38***	−0.001	−6.81***
BANK	−0.001	−0.61	−0.001	−0.77	−0.001	−0.79	−0.001	−0.41
EQU	0.000	0.80	0.000	0.79	0.000	0.78	0.000	0.76
Q	0.001	3.48***	0.001	3.09***	0.001	3.09***	0.001	3.03***
RD	0.031	7.16***	0.028	6.43***	0.027	6.26***	0.028	6.46***
CURR	0.001	7.85***	0.001	7.86***	0.001	7.86***	0.001	7.95***
LOSS	0.002	6.14***	0.002	6.06***	0.002	6.06***	0.002	5.75***
EMP	0.000	0.14	0.000	0.29	0.000	0.18	0.000	−0.15
ROA	0.007	2.32**	0.010	3.24***	0.010	3.24***	0.010	3.23***
GR_ASS	0.003	5.37***	0.003	5.17***	0.003	5.17***	0.003	4.80***
OPCF	0.000	−0.05	−0.001	−0.27	−0.001	−0.27	0.000	−0.05
AGE	0.001	1.60	0.000	0.92	0.000	0.90	0.000	0.52
Tax incentives
TAGG			0.002	10.82***	0.002	10.79***	0.002	10.66***
THAV			0.000	0.41	0.000	0.32	0.000	0.37
Legal system
CLHC					0.000	0.32	0.000	0.19
Audit attributes
BIG4							−0.001	−2.81***
SCO_CHA							0.000	1.18
POL_CHA							0.000	0.77
NEWAUD							0.002	4.52***
Intercept	0.006	4.46***	0.006	4.57***	0.006	4.51***	0.006	4.40***
Year FE	Yes		Yes		Yes		Yes
Industry FE	Yes		Yes		Yes		Yes
Adjusted R^2	0.131		0.153		0.153		0.160
Observations	4,414		4,414		4,414		4,414

Note: *, **, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, in two-tailed test. This table presents the multivariate regression results for four different specifications by including various controls that affect income smoothing on a sample of 4,414 firm-year observations. All the regression models include industry fixed effects based on the TSE industry classifications and year fixed effects. See Appendix for definitions and measurements of the variables.

Source: Author’s calculation based on the NEEDS Financial Quest database.

Table 6. Voluntary IFRS adoption and conditional conservatism.

	Dependent variable: NC_SCORE
	(A)		(B)		(C)		(D)
	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat
Test variable
JV_IFRS	0.378	4.67***	0.407	4.98***	0.426	5.19***	0.437	5.25***
Firm characteristics
LEV	−0.985	−10.85***	−0.967	−10.63***	−0.967	−10.63***	−0.971	−10.67***
SIZE	−0.005	−0.31	−0.001	−0.06	0.000	0.02	−0.001	−0.03
BANK	−0.522	−2.53**	−0.515	−2.50**	−0.468	−2.26**	−0.450	−2.17**
EQU	−0.105	−3.41***	−0.106	−3.44***	−0.104	−3.37***	−0.106	−3.44***
Q	0.478	11.52***	0.479	11.55***	0.481	11.59***	0.481	11.60***
RD	−0.714	−1.29	−0.679	−1.22	−0.465	−0.82	−0.478	−0.84
CURR	−0.028	−3.08***	−0.027	−2.96***	−0.027	−3.00***	−0.027	−2.99***
LOSS	0.026	0.76	0.028	0.82	0.026	0.77	0.022	0.65
EMP	−0.024	−1.32	−0.024	−1.31	−0.013	−0.67	−0.014	−0.72
ROE	0.180	1.68*	0.176	1.65	0.167	1.56	0.160	1.50
GR_SAL	0.160	2.45**	0.161	2.47**	0.162	2.48**	0.160	2.46**
FORSAL	−0.050	−0.73	−0.042	−0.61	−0.022	−0.31	−0.021	−0.30
Tax incentives
TAGG			−0.027	−1.22	−0.025	−1.12	−0.028	−1.23
THAV			−0.113	−2.23**	−0.084	−1.61	−0.079	−1.50
Legal system
CLHC					−0.021	−2.20**	−0.022	−2.28**
Audit attributes
BIG4							−0.009	−0.32
SCO_CHA							0.000	0.02
POL_CHA							0.171	3.39***
NEWAUD							−0.005	−0.09
Intercept	1.144	8.05***	1.096	7.61***	1.013	6.81***	1.028	6.84***
Year FE	Yes		Yes		Yes		Yes
Industry FE	Yes		Yes		Yes		Yes
Adjusted R^2	0.172		0.173		0.173		0.175
Observations	4,414		4,414		4,414		4,414

Note: *, **, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, in two-tailed tests. This table presents the multivariate regression results for four different specifications by including various controls that affect conditional conservatism on a sample of 4,414 firm-year observations. All the regression models include industry fixed effects based on the TSE industry classifications and year fixed effects. See Appendix for definitions and measurements of the variables.

Source: Author’s calculation based on the NEEDS Financial Quest database.

Table 7. Cross-sectional analyses.

Panel A: Capital structure
	Dependent variable: VAR_NIG				Dependent variable: NC_SCORE
	LEV ≥ 0.478		LEV < 0.478		LEV ≥ 0.478		LEV < 0.478
	(A)		(B)		(C)		(D)
	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.002	3.08***	0.002	1.49	0.257	2.67***	0.619	5.45***
Controls	Yes		Yes		Yes		Yes
Year FE	Yes		Yes		Yes		Yes
Industry FE	Yes		Yes		Yes		Yes
Adjusted R^2	0.184		0.170		0.281		0.405
Observations	2,207		2,207		2,207		2,207
High versus Low in JV_IFRS
χ^2 (Prob > χ^2)	0.24 (0.622)				1.30 (0.254)
Panel B: Bank ownership
	Dependent variable: VAR_NIG				Dependent variable: NC_SCORE
	BANK ≥ 0.062		BANK < 0.062		BANK ≥ 0.062		BANK < 0.062
	(A)		(B)		(C)		(D)
	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.002	4.33***	0.001	0.63	0.375	3.99***	0.570	3.69***
Controls	Yes		Yes		Yes		Yes
Year FE	Yes		Yes		Yes		Yes
Industry FE	Yes		Yes		Yes		Yes
Adjusted R^2	0.197		0.197		0.281		0.140
Observations	2,207		2,207		2,207		2,207
High versus Low in JV_IFRS
χ^2 (Prob > χ^2)	0.54 (0.461)				0.23 (0.634)
Panel C: Tax incentive
	Dependent variable: VAR_NIG				Dependent variable: NC_SCORE
	ETR ≥ 0.369		ETR < 0.369		ETR ≥ 0.369		ETR < 0.369
	(A)		(B)		(C)		(D)
	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.000	0.21	0.002	2.54**	−0.019	−0.14	0.659	6.25***
Controls	Yes		Yes		Yes		Yes
Year FE	Yes		Yes		Yes		Yes
Industry FE	Yes		Yes		Yes		Yes
Adjusted R^2	0.126		0.185		0.198		0.186
Observations	2,207		2,207		2,207		2,207
High versus Low in JV_IFRS
χ^2 (Prob > χ^2)	4.12 (0.042)				5.85 (0.016)
Panel D: Investor protection
	Dependent variable: VAR_NIG				Dependent variable: NC_SCORE
	ADR ≥ 0.375		ADR < 0.375		ADR ≥ 0.375		ADR < 0.375
	(A)		(B)		(C)		(D)
	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.001	2.24**	0.002	1.14	0.343	4.66***	0.724	3.09***
Controls	Yes		Yes		Yes		Yes
Year FE	Yes		Yes		Yes		Yes
Industry FE	Yes		Yes		Yes		Yes
Adjusted R^2	0.189		0.188		0.272		0.133
Observations	2,238		2,176		2,238		2,176
High versus Low in JV_IFRS
χ^2 (Prob > χ^2)	0.08 (0.772)				0.25 (0.614)

Note: *, **, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, in two-tailed tests. This table presents the multivariate regression results on the test variable (i.e. JV_IFRS) based on the cross-sectional analyses from the perspective of capital structure, bank ownership, tax incentive and investor protection. All the regression models include industry fixed effects based on the TSE industry classifications and year fixed effects. See Appendix for definitions and measurements of the variables.

Source: Author’s calculation based on the NEEDS Financial Quest database.

Table 8. Robustness tests.

Panel A: Alternative dependent variables
	Dependent variable: VAR_NIG2		Dependent variable: BC_SCORE
	(A)		(B)
	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.002	2.03**	0.006	2.56**
Controls	Yes		Yes
Year FE	Yes		Yes
Industry FE	Yes		Yes
Adjusted R^2	0.118		0.533
Observations	4,414		4,414
Panel B: Alternative PSM samples (nearest neighbour matching with two counterparts)
	Dependent variable: VAR_NIG		Dependent variable: NC_SCORE
	(A)		(B)
	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.001	2.33**	0.378	2.45**
Controls	Yes		Yes
Year FE	Yes		Yes
Industry FE	Yes		Yes
Adjusted R^2	0.292		0.388
Observations	268		268
Panel C: Alternative PSM samples (radius matching with a radius caliper of 0.001)
	Dependent variable: VAR_NIG		Dependent variable: NC_SCORE
	(A)		(B)
	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.003	3.74***	0.269	3.17***
Controls	Yes		Yes
Year FE	Yes		Yes
Industry FE	Yes		Yes
Adjusted R^2	0.126		0.262
Observations	1,609		1,609
Panel D: Omitted variables
	Dependent variable: VAR_NIG		Dependent variable: NC_SCORE
	(A)		(B)
	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.002	2.89***	0.431	5.17***
USLIST	0.002	1.97**	−0.286	−2.57**
REPLAG	0.000	1.49	−0.001	−0.56
IND_EXP	0.000	−0.01	−0.018	−0.70
Controls	Yes		Yes
Year FE	Yes		Yes
Industry FE	Yes		Yes
Adjusted R^2	0.160		0.176
Observations	4,414		4,414
Panel E: Sample restrictions (a relatively constant number of observations for each year)
	Dependent variable: VAR_NIG		Dependent variable: NC_SCORE
	(A)		(B)
	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.001	2.07**	0.427	5.40***
Controls	Yes		Yes
Year FE	Yes		Yes
Industry FE	Yes		Yes
Adjusted R^2	0.152		0.222
Observations	4,256		4,256
Panel F: Sample restrictions (similar size firms)
	Dependent variable: VAR_NIG		Dependent variable: NC_SCORE
	(A)		(B)
	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.001	2.91***	0.432	5.18***
Controls	Yes		Yes
Year FE	Yes		Yes
Industry FE	Yes		Yes
Adjusted R^2	0.194		0.193
Observations	4,236		4,236
Panel G: Sample restrictions (outliers winsorized at the 1 and 99% levels)
	Dependent variable: VAR_NIG		Dependent variable: NC_SCORE
	(A)		(B)
	Coeff.	t-stat	Coeff.	t-stat
JV_IFRS	0.001	3.42***	0.380	6.01***
Controls	Yes		Yes
Year FE	Yes		Yes
Industry FE	Yes		Yes
Adjusted R^2	0.209		0.205
Observations	4,414		4,414

Note: *, **, *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively, in two-tailed tests. This table presents the multivariate regression results on the test variable (i.e. JV_IFRS) through a series of robustness tests. All the regression models include industry fixed effects based on the TSE industry classifications and year fixed effects. See Appendix for definitions and measurements of the variables.

Source: Author’s calculation based on the NEEDS Financial Quest database

Table

Variables	Definitions
Dependent variables
NC_SCORE	Firm-year conditional conservatism measure based on Lee et al.’s (2015) current and lagged earnings-changes model. Similarly, in order to let the NC_SCORE increases with conditional conservatism, the measures are multiplied by negative one.
VAR_NIG	The variance of the firm-year-level residuals from the net income growth model for a six-year period (t to t-5). The net income growth model is based on Ahmed et al. (2013) and Barth et al. (2008). Lower values of VAR_NIG indicate greater income smoothing.
Test variable
JV_IFRS	Indicator variable that equals 1 if a firm adopted IFRS voluntarily and 0 otherwise.
Control variables
AGE	Log number of years firm listed in the stock market.
BANK	The number of shares held by bank investors in the top five shareholders divided by total shares.
BIG4	Indicator variable that equals 1 if a firm selected a Big 4 auditor (i.e. Deloitte/EY/KPMG/PwC) and 0 otherwise.
CLHC	The number of common law countries in a firm’s FDI host country portfolio. Country-level information on legal systems is obtained from the CIA Factbook, and firm-year-level host country information is obtained from Toyo Keizai’s Overseas Japanese Companies database.
CURR	Current assets divided by current liabilities.
EMP	The natural logarithm of the number of employees.
EQU	The market value of equity divided by lagged total assets.
FORSAL	The ratio of foreign sales to net sales.
GR_ASS	The percentage change in total assets.
GR_SAL	The percentage change in total sales.
LEV	Total debt divided by total assets.
LOSS	Indicator variable that equals 1 if there was a reported loss for a three-year period (t to t-2) and 0 otherwise.
NEWAUD	Indicator variable that equals 1 if the auditor firm relationship is two years or less and 0 otherwise.
OPCF	Operating cash flow divided by total assets.
POL_CHA	Indicator variable that equals 1 if a firm changed in accounting policy and 0 otherwise.
Q	The total market value of firm divided by total asset value.
RD	Research and development expenses divided by lagged total assets.
ROA	Operating income divided by total assets.
ROE	Income before extraordinary items divided by shareholder's equity.
SCO_CHA	Indicator variable that equals 1 if a firm changed in scope of consolidation and 0 otherwise.
SIZE	The natural logarithm of total assets (in millions of yen).
TAGG	Indicator variable that equals to 1 if a firm was tax aggressive and 0 otherwise. Tax aggressiveness is defined as a firm with either a CAS_ETR or CUR_ETR in the lowest tertile by year and industry. CAS_ETR is cash effective tax rate of the six-year sum (t to t-5) of cash taxes paid to the six-year sum of pre-tax book income. CAS_ETRs are reset to 1 (0) if greater (less) than 1 (0). CUR_ETR is the current effective tax rate of the six-year sum (t to t-5) of current tax expense to the six-year sum pre-tax book income. CUR_ETRs are reset to 1 (0) if greater (less) than 1 (0).
THAV	The number of subsidiaries incorporated in a tax haven. Tax haven is based on the definition of tax haven (OECD) in Dharmapala (2008).
Variables added in additional and robustness tests
ADR	The proportion of countries with a higher level of anti-director rights in a firm’s FDI host country portfolio. Here, a higher level of anti-director rights is defined as anti-director rights that are higher than the upper quartile for all country observations. Anti-director rights are estimated following La Porta et al. (1998).
BC_SCORE	Firm-year conditional conservatism measure based on Lee et al.’s (2015) earnings per share model. Conditional conservatism is increasing in BC_SCORE.
ETR	Current tax expense to pre-tax book income. ETRs are reset to 1(0) if greater(less) than 1(0).
IND_EXP	Indicator variable that equals 1 if a firm was audited by an audit firm that had 25% or more market share in an industry in each year and 0 otherwise. An audit firm’s market share for an industry is calculated as the sum of sales of its individual clients in an industry divided by the sum of sales for all companies in the industry.
REPLAG	The number of days between a firm’s fiscal year-end date and the issuance date of the auditor's report.
USLIST	Indicator variable that equals 1 if a firm cross-lists in the U.S. and 0 otherwise.
VAR_NIG2	The variance of the firm-year-level residuals from the net income growth model for an eight-year period (t to t-7). The net income growth model is based on Ahmed et al. (2013) and Barth et al. (2008). Lower values of VAR_NIG2 indicate greater income smoothing.

Source: Made by the author with reference to related literature.

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