0.03

0.04

0.03

0.03

0.03

0.03

Good

0.33

0.33

0.33

0.34

0.33

0.38***

Normal

0.53

0.50*

0.53

0.50*

0.53

0.50*

Bad

0.10

0.12*

0.10

0.12

0.10

0.09+

Very bad

0.01

0.01

0.01

0.01

0.01

0.002**

Marital status: Married=1 Not=0

0.59

0.61*

0.59

0.61+

0.59

0.60

Living area: urban=1 rural=0

0.73

0.54***

0.73

0.62***

0.73

0.51***

From the Table 4, representing descriptive statistics of cross section in 2016, we can conclude that 9.2% of the interviewees from the monitored sample were dissatisfied with high prices, 4.2% were dissatisfied with quality of the goods, and 5.7% were dissatisfied with assortment. Probably, the proportion has changed from 2011 because more observations have been collected. There are 56% (2% less than in 2011) of women and 58.7% were married individuals in the sample. 10% of observants have finished 9 grades in school, 59% have secondary education, 27% of individuals have higher education, 3% have no education. Comparing education, we can assume that 1% changes in numbers of secondary and education was caused by growing demand for it in society, because better education gives higher opportunity to get a better job and increase life conditions. Also, it may be caused by bigger sample and higher average age, which had been 46 and became 48.5. 69% of interviewees were living in cities of towns in 2016, and 31% were living in rural areas. 3.7% of interviewees has estimated their health as very good, 34% as good, 50% as normal, 10% as bad and 1.1% as very bad. These numbers are a little bit higher than in 2011.

Table 4

Descriptive statistics for cross section of 2016

Variable	Observations	Mean	Std. Dev.
Dissatisfaction
High Prices: yes=1	107,679	0.091782	-
Quality: yes=1	102,149	0.042614	-
Assortment: yes=1	103,738	0.057279	-
Gender: Woman=1 Man=0	134,852	0.559124	-
Age	112,040	48.55403	17.89481
Education
Basic	112,040	0.099572	-
Secondary	112,040	0.594984	-
Higher	112,040	0.274848	-
No basic	112,040	0.030596	-
Health
Very good	111,885	0.0375475	-
Good	111,885	0.345614	-
Normal	111,885	0.50206	-
Bad	111,885	0.103285	-
Very bad	111,885	0.011494	-
Marital status: Married=1 Not=0	111,058	0.587216	-
Living area: urban=1 rural=0	134,852	0.686352	-

Table 5

T-test results for 2016

2016	Price (y1)	Quality(y2)	Assortment(y3)
	0	1	0	1	0	1
	Mean	Mean	Mean	Mean	Mean	Mean
Gender: Woman=1 Man=0	0.57	0.62***	0.57	0.61***	0.57	0.61***
Age	48.82	47.59***	48.82	45.79***	48.82	44.10***
Education
Basic	0.10	0.11***	0.10	0.10	0.10	0.09
Secondary	0.59	0.63***	0.59	0.59	0.59	0.59
Higher	0.28	0.23***	0.28	0.29*	0.28	0.30**
No basic	0.03	0.03	0.03	0.02***	0.03	0.02***
Health
Very good	0.04	0.03***	0.04	0.04	0.04	0.03**
Good	0.35	0.32***	0.35	0.36	0.35	0.40***
Normal	0.50	0.53***	0.50	0.49+	0.50	0.48***
Bad	0.10	0.11*	0.10	0.10	0.10	0.08***
Very bad	0.01	0.01+	0.01	0.01*	0.01	0.01*
Marital status: Married=1 Not=0	0.59	0.60**	0.59	0.62***	0.59	0.61***
Living area: urban=1 rural=0	0.72	0.46***	0.72	0.55***	0.72	0.44***

Student's t-test have shown the statistically significant differences of means for most of the variables, as it is seen from Table 5, thus we can build a probit model to estimate which characteristic variables were more susceptible to each reason of dissatisfaction and how it has changed after sanctions.

The probit model is the model of binary choose.

(3)

In case of this paper six probit models are needed to be build for each dissatisfaction reasons in 2011 and 2016, and three more with interaction terms between all independent variables and dummy of the year (YEAR2011=1 in 2011 and YEAR2011=0 in 2016). Probit model predict the probability of the dependent valuable to express a positive outcome, rather than to predict its actual value. Predicted values say how likeable individual to be dissatisfied with a certain reason rather than satisfied.

(4)

where: biniary dependent variable;

regressor;

control variables.

The coefficients are calculated by the maximum likelihood estimation. The coefficients illustrates the “reaction” of regressors on the equality of the dependent variable to one, but they cannot be interpreted directly. Coefficients show the direction of probability, but not the probability itself. Control variables are regional dummies to alleviate the effect of differences between regions of Russian Federation, when some regions are much poorer, some located too far from big cities, that can significantly change the reasons of dissatisfaction and consequently the coefficients in models.



Results

1 Time series.

Firstly, the regressions on lags was built to check if there are significant lags. The Table 6 represents the autoregressions of differences of Retail trade volume indexes from 1^st to 12^th lags, the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) illustrates the quality of models. Which model has smaller criteria, that model is better, because criteria shows the approximate amount of lost information by a model. The more lags are included in the model, the better it explains the dependent variable in this case, what is concluded from coefficients of determination (R-squared) values, and less information is lost according to AIC and BIC. However, only several lags' coefficients are significant according to p-values; 3^rd lags' coefficients in regressions containing from one to eleven lags are significant on 90% level, 11^th lag's coefficient is significant on the 95% level and 12^th lag's coefficient is significant on the 99% level. If the values of adjusted R-squared, AIC, BIC and p-values are taken together and analyzed, the regression with twelve lags is the best from the list. The difference between models with 11 and 12 lags is bigger than between others if the upper mentioned criteria are compared.

As the adjusted R-squared notably increased between those models, that 12^th lag is more “powerful” than other. To check this hypothesis, the regression only with 12^th lag was built (R13). It is represented in Table 7 in comparison with regression with 12 lags. The difference in BICs is

significant, 653.8 has fallen to 610.4, and despite the model R13 contains only one regressor adjusted R-squared has not fallen considerably, which leads to conclusion that the role of other lags too insignificant, and they should not be included in the regression with CPI. Moreover, the 12^th lag's coefficient became significant on 99.9% level. The seasonal factor is eliminated with inclusion of 12^th lag into the regression.

												Table 6
			Autoregregressions on lags for Retail trade volume index
	R1	R2	R3	R4	R5	R6	R7	R8	R9	R10	R11	R12
LD.Retail trade volume index	0.110	0.0971	0.0791	0.0762	0.0777	0.0758	0.0741	0.0727	0.0751	0.0647	0.0584	0.0386
	(0.0769)	(0.0773)	(0.0770)	(0.0788)	(0.0792)	(0.0796)	(0.0801)	(0.0808)	(0.0808)	(0.0816)	(0.0807)	(0.0799)
L2D.Retail trade volume index		0.116	0.0994	0.0999	0.110	0.112	0.115	0.117	0.125	0.129	0.116	0.0931
		(0.0773)	(0.0768)	(0.0777)	(0.0794)	(0.0797)	(0.0801)	(0.0808)	(0.0810)	(0.0813)	(0.0805)	(0.0783)
L3D.Retail trade volume index			0.142+	0.142+	0.144+	0.145+	0.145+	0.147+	0.149+	0.156+	0.153+	0.127
			(0.0770)	(0.0777)	(0.0785)	(0.0802)	(0.0806)	(0.0812)	(0.0813)	(0.0819)	(0.0808)	(0.0786)
L4D.Retail trade volume index				-0.00501	-0.00268	-0.00687	-0.0194	-0.0209	-0.0273	-0.0260	-0.0145	0.000981
				(0.0783)	(0.0789)	(0.0797)	(0.0814)	(0.0819)	(0.0820)	(0.0825)	(0.0816)	(0.0792)
L5D.Retail trade volume index					-0.0251	-0.0283	-0.0361	-0.0425	-0.0429	-0.0489	-0.0335	-0.00771
					(0.0787)	(0.0793)	(0.0801)	(0.0820)	(0.0820)	(0.0824)	(0.0814)	(0.0792)
L6D.Retail trade volume index						0.0418	0.0359	0.0349	0.0553	0.0554	0.0558	0.0587
						(0.0791)	(0.0797)	(0.0806)	(0.0820)	(0.0823)	(0.0813)	(0.0789)
L7D.Retail trade volume index							0.0699	0.0693	0.0817	0.0991	0.0912	0.0735
							(0.0795)	(0.0802)	(0.0807)	(0.0824)	(0.0812)	(0.0789)
L8D.Retail trade volume index								0.00617	0.0150	0.0244	0.0314	0.0346
								(0.0801)	(0.0804)	(0.0811)	(0.0816)	(0.0790)
L9D.Retail trade volume index									-0.110	-0.103	-0.0854	-0.0317
									(0.0801)	(0.0806)	(0.0800)	(0.0790)
L10D.Retail trade volume index										-0.0764	-0.0638	-0.0374
										(0.0809)	(0.0799)	(0.0778)
L11D.Retail trade volume index											-0.160*	-0.146+
											(0.0799)	(0.0775)
L12D.Retail trade volume index												-0.229**
												(0.0784)
Constant	-0.0480	-0.0447	-0.0556	-0.0599	-0.0533	-0.0447	-0.0459	-0.0515	-0.0482	-0.0438	-0.0656	-0.0552
	(0.129)	(0.130)	(0.129)	(0.130)	(0.131)	(0.132)	(0.133)	(0.134)	(0.134)	(0.135)	(0.134)	(0.130)
Observations	169	168	167	166	165	164	163	162	161	160	159	158
AIC	656.9	653.7	647.0	646.0	644.6	643.0	641.3	640.1	637.0	634.9	627.1	614.0
BIC	663.1	663.1	659.5	661.6	663.3	664.7	666.0	667.9	667.9	668.7	663.9	653.8
R-squared	0.01	0.03	0.04	0.04	0.05	0.05	0.05	0.05	0.07	0.07	0.10	0.15
Adjusted R-squared	0.01	0.01	0.03	0.02	0.02	0.01	0.01	0.01	0.01	0.01	0.03	0.08
F-stat	2.06	2.15	2.54	1.83	1.53	1.36	1.27	1.11	1.20	1.19	1.48	2.08

Standard errors in parentheses ; + p<0.10, * p<0.05, ** p<0.01, *** p<0.001Table 7

Autoregressions of total Retail trade index with 12 and 12^th lags

	(1)	(2)
	R12	R13
LD.Retail trade volume index	0.0386
	(0.0799)
L2D.Retail trade volume index	0.0931
	(0.0783)
L3D.Retail trade volume index	0.127
	(0.0786)
L4D.Retail trade volume index	0.000981
	(0.0792)
L5D.Retail trade volume index	-0.00771
	(0.0792)
L6D.Retail trade volume index	0.0587
	(0.0789)
L7D.Retail trade volume index	0.0735
	(0.0789)
L8D.Retail trade volume index	0.0346
	(0.0790)
L9D.Retail trade volume index	-0.0317
	(0.0790)
L10D.Retail trade volume index	-0.0374
	(0.0778)
L11D.Retail trade volume index	-0.146+
	(0.0775)
L12D.Retail trade volume index	-0.229**	-0.271***
	(0.0784)	(0.0748)
Constant	-0.0552	-0.0748
	(0.130)	(0.130)
Observations	158	158
AIC	614.0	604.3
BIC	653.8	610.4
R-squared	0.15	0.08
adjusted R-squared	0.08	0.07
F-stat	2.08	13.13

Standard errors in parentheses

+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001

The next step is to check the significanse of different lags of the main regressor, which is differences of CPI. The Bayesian information criterion fluctuating between regressions is not significantly changes, thus it is more reasonable to choose model with less variables. Taking into account that only 1^st lags' coefficients are significant on the 90% level (for regressions till 6 lags) and current meaning is significant on 99% level in R1 model, it is reasonable to choose this CPI meanings for regression. Comparing BICs R1 has 3^rd smallest meaning, 638.1, and its regressors explain the dependent variable on 16%. Other models also have lags with significant coefficients, but BIC is not improving noticeably and most of the coefficients are still not significant.

Summarizing everything upperstated, the 12^th lag, taken from autoregression of total Retail trade volume index, the current period total CPI and couple of lags were combined to the final regression. The Table 9 represents 3 alternatives.

													Table 8
				Regressions on Consumer price indexes' lags
	R0	R1	R2	R3	R4	R5	R6	R7	R8	R9	R10	R11	R12
D.Consumer price index	-0.972***	-0.655**	-0.627*	-0.592*	-0.597*	-0.607*	-0.629*	-0.633*	-0.639*	-0.647*	-0.748**	-0.762**	-0.814**
	(0.175)	(0.243)	(0.254)	(0.255)	(0.256)	(0.257)	(0.260)	(0.257)	(0.259)	(0.264)	(0.265)	(0.281)	(0.282)
LD.Consumer price index		-0.461+	-0.583+	-0.665+	-0.642+	-0.628+	-0.611+	-0.562	-0.556	-0.549	-0.406	-0.429	-0.505
		(0.243)	(0.331)	(0.345)	(0.348)	(0.350)	(0.353)	(0.350)	(0.352)	(0.358)	(0.364)	(0.367)	(0.365)
L2D.Consumer price index			0.143	0.206	0.154	0.162	0.161	0.100	0.107	0.0999	-0.0161	0.0659	0.142
			(0.253)	(0.343)	(0.360)	(0.364)	(0.366)	(0.363)	(0.366)	(0.369)	(0.372)	(0.377)	(0.373)
L3D.Consumer price index				-0.0448	0.0325	-0.0184	-0.0492	-0.0152	-0.0259	-0.0241	0.0239	-0.0558	-0.115
				(0.255)	(0.348)	(0.365)	(0.368)	(0.364)	(0.368)	(0.370)	(0.367)	(0.374)	(0.374)
L4D.Consumer price index					-0.0754	0.0824	0.161	0.225	0.231	0.225	0.265	0.315	0.353
					(0.256)	(0.349)	(0.367)	(0.365)	(0.367)	(0.371)	(0.367)	(0.369)	(0.370)
L5D.Consumer price index						-0.178	-0.313	-0.541	-0.530	-0.521	-0.596	-0.584	-0.603
						(0.257)	(0.352)	(0.364)	(0.368)	(0.370)	(0.368)	(0.367)	(0.365)
L6D.Consumer price index							0.137	0.679+	0.638+	0.634+	0.688+	0.668+	0.663+
							(0.259)	(0.349)	(0.367)	(0.370)	(0.366)	(0.367)	(0.363)
L7D.Consumer price index								-0.588*	-0.494	-0.499	-0.446	-0.446	-0.458
								(0.257)	(0.351)	(0.370)	(0.367)	(0.369)	(0.365)
L8D.Consumer price index									-0.102	-0.0494	-0.275	-0.236	-0.192
									(0.259)	(0.357)	(0.371)	(0.372)	(0.368)
L9D.Consumer price index										-0.0648	0.498	0.402	0.363
										(0.262)	(0.360)	(0.374)	(0.370)
L10D.Consumer price index											-0.604*	-0.484	-0.474
											(0.263)	(0.362)	(0.370)
L11D.Consumer price index												-0.107	0.0859
												(0.278)	(0.361)
L12D.Consumer price index													-0.278
													(0.279)
Constant	-0.0926	-0.103	-0.107	-0.127	-0.135	-0.134	-0.125	-0.137	-0.140	-0.136	-0.143	-0.159	-0.147
	(0.119)	(0.119)	(0.120)	(0.119)	(0.120)	(0.121)	(0.122)	(0.122)	(0.123)	(0.124)	(0.123)	(0.124)	(0.123)
Observations	170	169	168	167	166	165	164	163	162	161	160	159	158
AIC	633.4	628.7	627.3	622.2	621.0	619.6	618.1	611.9	611.1	610.1	603.3	600.3	593.4
BIC	639.6	638.1	639.8	637.8	639.7	641.3	642.9	639.8	642.0	644.0	640.2	640.2	636.3
R-squared	0.15	0.17	0.18	0.19	0.19	0.19	0.19	0.22	0.22	0.22	0.25	0.25	0.26
adjusted R-squared	0.15	0.16	0.16	0.17	0.16	0.16	0.16	0.18	0.17	0.17	0.19	0.19	0.19
F-stat	30.75	17.44	11.75	9.26	7.38	6.18	5.34	5.41	4.75	4.24	4.46	4.03	3.90

Table 9

Variations of final regression for total indexes

	(1)	(2)	(3)
	R1	R2	R3
L12D.Retail trade volume index	-0.157*	-0.138+	-0.141+
	(0.0737)	(0.0740)	(0.0742)
D.Consumer price index	-0.892***	-0.603*	-0.551*
	(0.184)	(0.243)	(0.254)
LD.Consumer price index		-0.437+	-0.596+
		(0.243)	(0.330)
L2D.Consumer price index			0.179
			(0.252)
Constant	-0.101	-0.105	-0.102
	(0.121)	(0.120)	(0.121)
Observations	158	158	158
AIC	583.9	582.6	584.1
BIC	593.1	594.9	599.4
R-squared	0.20	0.22	0.22
adjusted R-squared	0.19	0.20	0.20
F-stat	19.29	14.12	10.68

Standard errors in parentheses

+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001

The R1 regression was chosen to be the final, because both coefficients (of 12^th lag and CPI) are statistically significant on thr 95% level (CPI even 99.9%), while the R2 and R3 have less significant coefficients of 12^th lag of Retail trade volume index (90% level) and CPI (95% level) and its lags (90% level), which make them worse comparing to R1. Moreover, the BIC of R1 is the lowest and adjusted R-squared is 1% less than in R2 and R3, which is provided by extra 2 and 3 variables. The coefficients of (differences) total CPI and 12^th lag have negative sign, since in the model differences were used, when they increase, the original meaning decrease. In this case, when CPI falls, the Retail trade index (dependant variable) grows.

For food and nonfood categories the same manipulations with choosing lags were processed in Stata, and results are presented in Appendix 2-7. For food category only the 12^th lag's coefficient is significant at the 99.9%. The model of autoregression with 12 lags shows the best BIC, AIC and adjusted R-squared, but as only the last coefficient is significant, the autoregression with 12^th lag only was built. As in the case with total indexes, this model showed the best results, thus the 12^th lag was included to the final regression for food goods. Absolutely the same situation was with nonfood Retail trade volume index's autoregression. The only 12^th lag was added to final model of this category. The regressions with regressors food CPI and its lags have statistically significant coefficients (at 99.9% level) only for the current meanings of CPI. Since, the Final model represented in Table 10 includes only two regressors; 12^th lag of dependent variable and CPI. Interpretation of the coefficients preserves the same as for total indexes, but they have become statistically significant on 99.9% level.

Table 10

Final linear regression for food indexes and comparative regression.

	Final	Comparative
L12D.Retail trade volume index (food)	-0.255***	-0.261***
D.Consumer price index (food)	(0.0731)	(0.0735)
	-0.406***	-0.335*
LD.Consumer price index (food)	(0.0956)	(0.131)
		-0.185
L2D.Consumer price index (food)		(0.166)
		0.191
Constant		(0.129)
	-0.0775	-0.0741
	(0.112)	(0.112)
Observations	158	158
AIC	558.8	560.5
BIC	568.0	575.8
R-squared	0.21	0.22
adjusted R-squared	0.20	0.20
F-stat	20.77	10.96

However, for nonfood categories regressions with CPI and lags had different results (Appendix 7). The current meaning of CPI (differences) was significant only in case when it was the only regressor in the model. Adding lags to the model has shown that 1^st,2^nd ,3^rd and 12^th lags have statistically significant coefficients (at 99.9%, 99.9%, 90% and 95% level respectively). As the chosen boarder for this study is 95%, thus 3^rd lag was excluded from final regression. The Table 11 represents the variations of final models for nonfood category. According to BIC and AIC the best model is R4, which includes upper mentioned lags of CPI (without 3^rd) and include 12^th lag of Retail trade volume index. Adding one lag has decreased both criteria quite noticeably, and adjusted R-squared has grown to 38%, which is the highest among the final models of all categories.

Table 11

Variations final regressions for nonfood indexes

	R1	R2	R3	R4
L12D.Retail trade volume index (nonfood)	-0.223**	-0.140*	-0.140*	-0.231**
	(0.0710)	(0.0697)	(0.0695)	(0.0719)
D.Consumer price index (nonfood)	-1.665***	-0.185
	(0.314)	(0.455)
LD.Consumer price index (nonfood)		-2.944***	-3.135***	-3.501***
		(0.652)	(0.450)	(0.446)
L2D.Consumer price index (nonfood)		1.585***	1.658***	1.938***
		(0.462)	(0.425)	(0.417)
L12D.Consumer price index (nonfood)				-1.111***
				(0.314)
Constant	-0.0983	-0.0918	-0.0911	-0.131
	(0.165)	(0.156)	(0.155)	(0.150)
Observations	158	158	158	158
AIC	681.2	665.4	663.6	653.2
BIC	690.4	680.7	675.8	668.5
R-squared	0.26	0.34	0.34	0.39
adjusted R-squared	0.25	0.33	0.33	0.38
F-stat	26.68	20.03	26.79	24.72

After the final regressions for total, food and nonfood categories were chosen, the tests on structural break were conducted, and the Hypothesis 1 were tested. The traditional test on structural break is Chow test, but the exact date should be known to divide the time series beforehand. We do not have this date, thus the estat sbsingle test were processed in Stata, this is supremum Wald test for unknown break date. Test is robust to heteroscedasticity. The results for total indexes' regression indicate structural break in January 2015, rejecting null hypothesis (no break) at the 0.026% level. However, the tests for food and nonfood categories failed to reject null hypothesis. This can be interpreted by the error of the test, or for total indexes the significant drop was detected. However, the regression of nonfood category without CPI lags was tested, and the null hypothesis was rejected at the 0.0157% level, but this regression has worse (higher) BIC comparing to the final chosen, so we cannot rely on that. To sum up, the significant drop in 2015 have leveled up and the general declining trend of consumption volume has preserved, which is illustrated by the Figures in Methodology section.

2 Cross sections

For each of cross sections of 2011 and 2016, were build three probit models for each reason of dissatisfaction price (y1), quality (y2) and assortment (y3). The purpose is to identify the direction of reaction's probability of different independent variables on equality of dependent variable to zero. Table 12 and 13 show the results.

First (binary) variable is gender, female = 1 and male = 0, thus the coefficients shows that women are more likely to be dissatisfied with price, quality and assortment of products than man. The coefficients in all three models are statistically significant on 99.9% level. The coefficient for variable Age is statistically significant at the 95% level in Assortment model and have negative sign. Since, we found that older citizens were less likely to be dissatisfied with assortment of goods in 2011 than younger. For other two models Age coefficients are insignificant, thus we cannot make any conclusions. The coefficients for variable Secondary education (ed=2) are statistically significant at the 90% level in model Price, at 99% level in model Quality and at 95% level in model Assortment and their signs are positive. This means that people with secondary education are likely to be more dissatisfied with price, quality and assortment of goods. However, we have several variables describing education. Comparing coefficients of Higher education (ed=3) variable and Secondary education we can state that citizens with Higher education are more likely to be dissatisfied. The coefficients of Higher education are statistically significant at higher level comparing to secondary (99.9% level in Quality and Assortment, 95% level in Price model). We can assume that people Higher education earns more, thereby they are more demanding to products they buy. Good, Normal and Bad health coefficients are statistically insignificant, while Bad Health coefficient are significant at the 90% level in Price model and at the 90% level in Quality product and their signs are positive. Since, individuals with bad health are more sensitive to price level than other categories. The marginal effects of this probit models and the rest are in Appendix 8-9.

Table 12

Dissatisfaction reasons

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	Price 2011	Quality 2011	Assortment 2011
	(y1=1)	(y2=1)	(y3=1)
Gender (female=1, male=0)	0.141***	0.118***	0.129***
	(0.0299)	(0.0343)	(0.0344)
Age	-0.00124	-0.00437	-0.0119*
	(0.00481)	(0.00570)	(0.00569)
Age squared	-0.0000205	-0.00000227	0.0000214
	(0.0000493)	(0.0000589)	(0.0000594)
Secondary education (ed=2)	0.104+	0.205**	0.130*
	(0.0548)	(0.0670)	(0.0657)
Higher education (ed=3)	0.151*	0.284***	0.256***
	(0.0602)	(0.0725)	(0.0710)
School (ed=1)	0.113	0.115	0.0670
	(0.0953)	(0.127)	(0.123)
Good health (health=2)	-0.112	0.0414	-0.0666
	(0.0802)	(0.105)	(0.0987)
Normal health (health=3)	-0.0380	0.0160	-0.00684
	(0.0827)	(0.108)	(0.101)
Bad health (health=4)	0.155+	0.192	0.109
	(0.0939)	(0.120)	(0.115)