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2 Introduction	The production of quality reports is part of the implementation of the EU-SILC instrument. In order to assess the quality of data at national level and to make a comparison among countries, the National Statistics Institutes are asked to report detailed information mainly on: the entire statistical process, sampling and non-sampling errors, and potential deviations from standard definition and concepts. This document follows the ESS standard for quality reports structure (ESQRS), which is the main report structure for reference metadata related to data quality in the European Statistical System. It is a metadata template, based on 13 main concepts, which can be used across several statistical domains with the purpose of a better harmonisation of the quality reporting requirements in the ESS. For that reason the template of this document differs from that one stated in the Commission Reg. 28/2004. Finally it is the combination of the previous intermediate and final quality reports therefore it is worth mentioning that it refers to both the cross sectional and the longitudinal data. The Romanian survey on income and living conditions, named Quality of life survey, represents the implementation of EU-SILC survey in Romanian statistical system. The main goal of this survey is to produce data regarding the income and living conditions in a standardized manner, in order to produce comparable estimates at EU level. In this way, the survey is the reference source for comparative statistics on income distribution and social exclusion in European Union. The survey implemented the methodology described in the EU-SILC Regulation (EC) no 1177/2003 of the European Parliament and of the Council concerning Community Statistics on Income and Living Conditions. We designed this survey as a new harmonized survey in order to meet all EU-SILC requirements. An integrated design with a rotational sample was applied, in which the sample is divided in sub-samples, each of them similar in size and design and representative for the whole population. From one year to another three sub-samples are retained, one is dropped and one new sub-sample is included in the survey. In this way, the cross-sectional and longitudinal statistics are produced from the same set of sample observations. This document provides common cross-sectional EU indicators based on the cross-sectional component of EU-SILC, a description of the accuracy, precision, the comparability and the coherence of the Romanian SILC 2012 survey.

Metadata File Name	SILC_ESQRS_A_RO_2012_0000 - Version 2
TimeDimension	2012-A0
DataProvider	RO1
DataFlow	SILC_ESQRS_A

1 Contact
2 Introduction
3 Quality management - assessment
4 Relevance
5 Accuracy and reliability
6 Timeliness and punctuality
7 Accessibility and clarity
8 Comparability
9 Coherence
10 Cost and Burden
11 Confidentiality
12 Statistical processing
13 Comment

Related Metadata

Annexes

1 Contact	Individual or organizational contact points for the data or metadata, including information on how to reach the contact points is listed below.
1 . 1 Contact organisation	National Institute of Statistics
1 . 2 Contact organisation unit	Social Statistics
1 . 3 Contact name	Paula Luysterman
1 . 4 Contact person function	Senior Expert at Department of Social Statistics
1 . 5 Contact mail address	-
1 . 6 Contact email address	Paula.Custura@insse.ro
1 . 7 Contact phone number	-
1 . 8 Contact fax number

2 Introduction	The production of quality reports is part of the implementation of the EU-SILC instrument. In order to assess the quality of data at national level and to make a comparison among countries, the National Statistics Institutes are asked to report detailed information mainly on: the entire statistical process, sampling and non-sampling errors, and potential deviations from standard definition and concepts. This document follows the ESS standard for quality reports structure (ESQRS), which is the main report structure for reference metadata related to data quality in the European Statistical System. It is a metadata template, based on 13 main concepts, which can be used across several statistical domains with the purpose of a better harmonisation of the quality reporting requirements in the ESS. For that reason the template of this document differs from that one stated in the Commission Reg. 28/2004. Finally it is the combination of the previous intermediate and final quality reports therefore it is worth mentioning that it refers to both the cross sectional and the longitudinal data. The Romanian survey on income and living conditions, named Quality of life survey, represents the implementation of EU-SILC survey in Romanian statistical system. The main goal of this survey is to produce data regarding the income and living conditions in a standardized manner, in order to produce comparable estimates at EU level. In this way, the survey is the reference source for comparative statistics on income distribution and social exclusion in European Union. The survey implemented the methodology described in the EU-SILC Regulation (EC) no 1177/2003 of the European Parliament and of the Council concerning Community Statistics on Income and Living Conditions. We designed this survey as a new harmonized survey in order to meet all EU-SILC requirements. An integrated design with a rotational sample was applied, in which the sample is divided in sub-samples, each of them similar in size and design and representative for the whole population. From one year to another three sub-samples are retained, one is dropped and one new sub-sample is included in the survey. In this way, the cross-sectional and longitudinal statistics are produced from the same set of sample observations. This document provides common cross-sectional EU indicators based on the cross-sectional component of EU-SILC, a description of the accuracy, precision, the comparability and the coherence of the Romanian SILC 2012 survey.

2 Introduction

The production of quality reports is part of the implementation of the EU-SILC instrument. In order to assess the quality of data at national level and to make a comparison among countries, the National Statistics Institutes are asked to report detailed information mainly on: the entire statistical process, sampling and non-sampling errors, and potential deviations from standard definition and concepts.

This document follows the ESS standard for quality reports structure (ESQRS), which is the main report structure for reference metadata related to data quality in the European Statistical System. It is a metadata template, based on 13 main concepts, which can be used across several statistical domains with the purpose of a better harmonisation of the quality reporting requirements in the ESS.

For that reason the template of this document differs from that one stated in the Commission Reg. 28/2004.

Finally it is the combination of the previous intermediate and final quality reports therefore it is worth mentioning that it refers to both the cross sectional and the longitudinal data.

The Romanian survey on income and living conditions, named Quality of life survey, represents the implementation of EU-SILC survey in Romanian statistical system. The main goal of this survey is to produce data regarding the income and living conditions in a standardized manner, in order to produce comparable estimates at EU level. In this way, the survey is the reference source for comparative statistics on income distribution and social exclusion in European Union. The survey implemented the methodology described in the EU-SILC Regulation (EC) no 1177/2003 of the European Parliament and of the Council concerning Community Statistics on Income and Living Conditions. We designed this survey as a new harmonized survey in order to meet all EU-SILC requirements. An integrated design with a rotational sample was applied, in which the sample is divided in sub-samples, each of them similar in size and design and representative for the whole population. From one year to another three sub-samples are retained, one is dropped and one new sub-sample is included in the survey. In this way, the cross-sectional and longitudinal statistics are produced from the same set of sample observations.

This document provides common cross-sectional EU indicators based on the cross-sectional component of EU-SILC, a description of the accuracy, precision, the comparability and the coherence of the Romanian SILC 2012 survey.

3 Quality management - assessment	-

4 Relevance	-
4 . 1 Relevance - User Needs	-
4 . 2 Relevance - User Satisfaction	-
4 . 3 Completeness	-
4 . 3 . 1 Data completeness - rate	-

5 Accuracy and reliability

The concept of accuracy refers to the precision of estimates computed from a sample rather than from the entire population. Accuracy depends on sample size, sampling design effects and structure of the population under study. In addition to that, sampling errors and non sampling errors need to be taken into account. Sampling error refers to the variability that occurs at random because of the use of a sample rather than a census and non-sampling errors are errors that occur in all phases of the data collection and production process.

5 . 1 Accuracy - overall

In terms of precision requirements, the EU-SILC framework regulation as well the Commission Regulation on sampling and tracing rules refers respectively, to the effective sample size to be achieved and to representativeness of the sample. The effective sample size combines sample size and sampling design effect which depends on sampling design, population structure and non-response rate.

5 . 2 Sampling error

EU-SILC is a complex survey involving different sampling design in different countries. In order to harmonize and make sampling errors comparable among countries, Eurostat (with the substantial methodological support of Net-SILC2) has chosen to apply the "linearization" technique coupled with the “ultimate cluster” approach for variance estimation. Linearization is a technique based on the use of linear approximation to reduce non-linear statistics to a linear form, justified by asymptotic properties of the estimator. This technique can encompass a wide variety of indicators, including EU-SILC indicators. The "ultimate cluster" approach is a simplification consisting in calculating the variance taking into account only variation among Primary Sampling Unit (PSU) totals. This method requires first stage sampling fractions to be small which is nearly always the case. This method allows a great flexibility and simplifies the calculations of variances. It can also be generalized to calculate variance of the differences of one year to another .

The main hypothesis on which the calculations are based is that the "at risk of poverty" threshold is fixed. According to the characteristics and availability of data for different countries we have used different variables to specify strata and cluster information. In particular, countries have been split into four groups:

1)BE, BG, CZ, IE, EL, ES, FR, IT, LV, HU, NL, PL, PT, RO, SI, UK and HR whose sampling design could be assimilated to a two stage stratified type we used DB050 (primary strata) for strata specification and DB060 (Primary Sampling Unit) for cluster specification;

2) DE, EE, CY, LT, LU, AT, SK, FI, CH whose sampling design could be assimilated to a one stage stratified type we used DB050 for strata specification and DB030 (household ID) for cluster specification;

3) DK, MT, SE, IS, NO, whose sampling design could be assimilated to a simple random sampling, we used DB030 for cluster specification and no strata;

In case Eurostat methodology is not accepted by your country, please describe the methodology used at national level for computing the estimates.

Nr crt	Subpopulation	est	stat_se	kish	n
1	HCR	0,2311	0,0110	1,1905	17749
2	HCR, after social transfers: Age 0-17	0,3479	0,0201	1,0762	2201
3	HCR, after social transfers: Age 18-24	0,2960	0,0200	1,1201	1386
4	HCR, after social transfers: Age 25-49	0,2226	0,0129	1,1359	5607
5	HCR, after social transfers: Age 50-64	0,1662	0,0097	1,1718	4315
6	HCR, after social transfers: Age 65+	0,1582	0,0106	1,2250	4240
7	HCR, after social transfers: Male	0,2234	0,0114	1,1864	8562
8	HCR, after social transfers: Female	0,2382	0,0113	1,1941	9187
9	HCR, after social transfers: Male Age 0-17	0,3401	0,0231	1,0771	1145
10	HCR, after social transfers: Male Age 18-24	0,3011	0,0244	1,1359	759
11	HCR, after social transfers: Male Age 25-49	0,2181	0,0137	1,1431	2782
12	HCR, after social transfers: Male Age 50-64	0,1741	0,0115	1,1518	2036
13	HCR, after social transfers: Male Age 65+	0,0958	0,0099	1,2274	1840
14	HCR, after social transfers: Female Age 0-17	0,3560	0,0221	1,0741	1056
15	HCR, after social transfers: Female Age 18-24	0,2906	0,0227	1,1008	627
16	HCR, after social transfers: Female Age 25-49	0,2268	0,0136	1,1285	2825
17	HCR, after social transfers: Female Age 50-64	0,1589	0,0099	1,1918	2279
18	HCR, after social transfers: Female Age 65+	0,2054	0,0131	1,2227	2400
19	HCR, after social transfers: Male Age 18+	0,1948	0,0104	1,1904	7417
20	HCR, after social transfers: Female Age 18+	0,2128	0,0100	1,1941	8131
21	HCR, after social transfers: Male Age 18-64	0,2176	0,0121	1,1632	5577
22	HCR, after social transfers: Female Age 18-64	0,2149	0,0112	1,1705	5731
23	HCR, after social transfers: Male Age 0-64	0,2460	0,0130	1,1577	6722
24	HCR, after social transfers: Female Age 0-64	0,2457	0,0128	1,1664	6787
25	HCR, after social transfers: One person hh under 65 years	0,2172	0,0175	1,1318	878
26	HCR, after social transfers: One person hh 65 years and over	0,2624	0,0204	1,2005	1375
27	HCR, after social transfers: One person hh male	0,2104	0,0186	1,1805	806
28	HCR, after social transfers: One person hh female	0,2627	0,0207	1,1760	1447
29	HCR, after social transfers: One person hh total	0,2445	0,0165	1,1779	2253
30	HCR, after social transfers: 2 adults, no dependant children, both adults under 65 years	0,1352	0,0114	1,1165	2606
31	HCR, after social transfers: 2 adults, no dependant children, at least one adult 65 years or more	0,0823	0,0106	1,1725	2460
32	HCR, after social transfers: Other hh without dependant children	0,1146	0,0129	1,0786	2726
33	HCR, after social transfers: Single parent hh, one or more dependant children	0,4006	0,0613	1,1363	281
34	HCR, after social transfers: 2 adults, one dependant child	0,1804	0,0166	1,1303	2022
35	HCR, after social transfers: 2 adults, two dependant children	0,2712	0,0248	1,0563	1708
36	HCR, after social transfers: 2 adults, three or more dependant children	0,5980	0,0516	1,0743	493
37	HCR, after social transfers: Other hh with dependant children	0,2862	0,0229	1,1006	2961
38	HCR, after social transfers: Hh without dependant children	0,1391	0,0081	1,1423	10045
39	HCR, after social transfers: Hh with dependant children	0,2879	0,0163	1,1109	7465
40	HCR, after social transfers: Accommodation tenure status:Owner or rent free	0,2233	0,0109	1,1933	17065
41	HCR, after social transfers: Accommodation tenure status:Tenant	0,1184	0,0297	1,1215	220
42	HCR, after social transfers: Main activity status: Employed	0,1884	0,0134	1,1411	6693
43	HCR, after social transfers: Main activity status: Unemployed	0,5202	0,0402	1,1604	278
44	HCR, after social transfers: Main activity status: Retired	0,1218	0,0073	1,2133	5837
45	HCR, after social transfers: Main activity status: Other inactive	0,3532	0,0183	1,1442	2430
46	HCR, after social transfers: Main activity status: Employed, Male	0,2064	0,0143	1,1520	3823
47	HCR, after social transfers: Main activity status: Unemployed, Male	0,5752	0,0475	1,1618	197
48	HCR, after social transfers: Main activity status: Retired, Male	0,0858	0,0086	1,2038	2542
49	HCR, after social transfers: Main activity status: Other inactive, Male	0,2980	0,0263	1,0994	703
50	HCR, after social transfers: Main activity status: Employed, Female	0,1656	0,0140	1,1273	2870
51	HCR, after social transfers: Main activity status: Unemployed, Female	0,4035	0,0499	1,1309	81
52	HCR, after social transfers: Main activity status: Retired, Female	0,1495	0,0088	1,2179	3295
53	HCR, after social transfers: Main activity status: Other inactive, Female	0,3755	0,0188	1,1581	1727
54	HCR, after social transfers: Work intensity: hh without dependent children, w=0	0,0615	0,0091	1,0573	1728
57	HCR, after social transfers: Work intensity: hh with dependent children, w=0	0,0974	0,0121	1,0659	2752
60	HCR, before social transfers including pensions: Age 0-17	0,4262	0,0188	1,0893	2201
61	HCR, before social transfers including pensions: Age 18-24	0,3675	0,0201	1,1226	1386
62	HCR, before social transfers including pensions: Age 25-49	0,2696	0,0125	1,1462	5607
63	HCR, before social transfers including pensions: Age 50-64	0,2336	0,0104	1,1800	4315
64	HCR, before social transfers including pensions: Age 65+	0,1835	0,0111	1,2321	4240
65	HCR, before social transfers including pensions: Male	0,2801	0,0115	1,1931	8562
66	HCR, before social transfers including pensions: Female	0,2924	0,0106	1,1984	9187
67	HCR, before social transfers including pensions: Male Age 0-17	0,4243	0,0226	1,0944	1145
68	HCR, before social transfers including pensions: Male Age 18-24	0.2924	0.0106	1.1984	9187
69	HCR, before social transfers including pensions: Male Age 25-49	0.4243	0.0226	1.0944	1145
70	HCR, before social transfers including pensions: Male Age 50-64	0,3643	0,0260	1,1363	759
71	HCR, before social transfers including pensions: Male Age 65+	0,2646	0,0147	1,1546	2782
72	HCR, before social transfers including pensions: Female Age 0-17	0,2487	0,0121	1,1701	2036
73	HCR, before social transfers including pensions: Female Age 18-24	0,1133	0,0104	1,2153	1840
74	HCR, before social transfers including pensions: Female Age 25-49	0,4283	0,0219	1,0836	1056
75	HCR, before social transfers including pensions: Female Age 50-64	0,3710	0,0220	1,1047	627
76	HCR, before social transfers including pensions: Female Age 65+	0,2742	0,0119	1,1378	2825
77	HCR, before social transfers including pensions: Male Age 18+	0,2194	0,0109	1,1895	2279
78	HCR, before social transfers including pensions: Female Age 18+	0,2367	0,0141	1,2347	2400
79	HCR, before social transfers including pensions: Male Age 18-64	0,2447	0,0108	1,1947	7417
80	HCR, before social transfers including pensions: Female Age 18-64	0,2631	0,0095	1,2000	8131
81	HCR, before social transfers including pensions: Male Age 0-64	0.2751	0.0124	1.1701	5577
82	HCR, before social transfers including pensions: Female Age 0-64	0.2708	0.0100	1.1744	5731
83	HCR, before social transfers excluding pensions: Age 18-24	0.3097	0.0131	1.1668	6722
84	HCR, before social transfers excluding pensions: Age 25-49	0.3051	0.0114	1.1699	6787
85	HCR, before social transfers excluding pensions	0.5044	0.0095	1.2037	17749
86	HCR, before social transfers excluding pensions: Age 0-17	0.4956	0.0176	1.0992	2201
87	HCR, before social transfers excluding pensions: Age 18-24	0.4289	0.0196	1.1226	1386
88	HCR, before social transfers excluding pensions: Age 25-49	0.3454	0.0108	1.1493	5607
89	HCR, before social transfers excluding pensions: Age 50-64	0.5212	0.0128	1.2387	4315
90	HCR, before social transfers excluding pensions: Age 65+	0,8747	0,0088	1,3810	4240
91	HCR, before social transfers excluding pensions: Male	0,4957	0,0103	1,1994	8562
92	HCR, before social transfers excluding pensions: Female	0,5125	0,0099	1,2082	9187
93	HCR, before social transfers excluding pensions: Male Age 0-17	0.4856	0.0210	1.1039	1145
94	HCR, before social transfers excluding pensions: Male Age 18-24	0.4260	0.0259	1.1370	759
95	HCR, before social transfers excluding pensions: Male Age 25-49	0.3529	0.0134	1.1570	2782
96	HCR, before social transfers excluding pensions: Male Age 50-64	0,4920	0,0145	1,2226	2036
97	HCR, before social transfers excluding pensions: Male Age 65+	0,8989	0,0097	1,3273	1840
98	HCR, before social transfers excluding pensions: Female Age 0-17	0.5061	0.0213	1.0938	1056
99	HCR, before social transfers excluding pensions: Female Age 18-24	0.4319	0.0223	1.1042	627
100	HCR, before social transfers excluding pensions: Female Age 25-49	0,3384	0,0108	1,1417	2825
101	HCR, before social transfers excluding pensions: Female Age 50-64	0,5486	0,0144	1,2605	2279
102	HCR, before social transfers excluding pensions: Female Age 65+	0,8564	0,0103	1,4045	2400
103	HCR, before social transfers excluding pensions: Male Age 18+	0,4981	0,0101	1,2071	7417
104	HCR, before social transfers excluding pensions: Female Age 18+	0,5139	0,0089	1,2171	8131
105	HCR, before social transfers excluding pensions: Male Age 18-64	0,4056	0,0114	1,1781	5577
106	HCR, before social transfers excluding pensions: Female Age 18-64	0,4146	0,0097	1,1724	5731
107	HCR, before social transfers excluding pensions: Male Age 0-64	0,4242	0,0115	1,1715	6722
108	HCR, before social transfers excluding pensions: Female Age 0-64	0,4345	0,0111	1,1682	6787
109	Median equivalised disposable income	8925,0000	4517,8872	1,2024	17749
110	At-risk-of-poverty threshold	5355,0000	107,6953	1,2024	17749
111	At-risk-of-poverty threshold, one person hh	4836,0000	137,1741	1,1937	2253
112	At-risk-of-poverty threshold, hh 2 adults 2 dependent children	4708,1739	134,0567	1,1208	1708
113	S80/S20	6,8338	0,2933	1,2339	17749
114	Relative median at-risk-of-poverty gap	0,3300	0,0127	1,2024	3984
115	Relative median at-risk-of-poverty gap: Age 017	0,3377	0,0140	1,1000	806
116	Relative median at-risk-of-poverty gap: Age 1824	0,3421	0,0653	1,1246	419
117	Relative median at-risk-of-poverty gap: Age 2549	0,3464	0,0170	1,1727	1323
118	Relative median at-risk-of-poverty gap: Age 5064	0,3733	0,0125	1,2311	775
119	Relative median at-risk-of-poverty gap: Age 65+	0,1995	0,0132	1,2076	661
120	Relative median at-risk-of-poverty gap: Male	0,3328	0,0150	1,2006	1870
121	Relative median at-risk-of-poverty gap: Female	0,3277	0,0308	1,2040	2114
122	Relative median at-risk-of-poverty gap: Male Age 0-17	0,3375	0,0150	1,1067	418
123	Relative median at-risk-of-poverty gap: Male Age 18-24	0,3369	0,0161	1,1388	231
124	Relative median at-risk-of-poverty gap: Male Age 25-49	0,3457	0,0249	1,1796	648
125	Relative median at-risk-of-poverty gap: Male Age 50-64	0,3633	0,0239	1,2245	401
126	Relative median at-risk-of-poverty gap: Male Age more then 64	0,1979	0,0159	1,2037	172
127	Relative median at-risk-of-poverty gap: Female Age 0-17	0,3382	0,0245	1,0928	388
128	Relative median at-risk-of-poverty gap: Female Age 18-24	0.3629	0.0237	1.1064	188
129	Relative median at-risk-of-poverty gap: Female Age 25-49	0.3471	0.0374	1.1656	675
130	Relative median at-risk-of-poverty gap: Female Age 50-64	0,3912	0,1267	1,2369	374
131	Relative median at-risk-of-poverty gap: Female Age more then 64	0,2031	0,0135	1,2105	489
132	Median income below the at-risk-of-poverty threshold	3587,7419	56,1729	1,2024	3984
133	Dispersion around the risk-of-poverty threshold -40%	0,1134	0,0080	1,1973	17749
134	Dispersion around the risk-of-poverty threshold -50%	0.1691	0.0095	1.1933	17749
135	Dispersion around the risk-of-poverty threshold -70%	0.2991	0.0093	1.1885	17749
136	Gini coefficient	0.3277	0.0057	1.2542	17528
137	Mean equivalised disposable income	10251.8445	207.1656	1.2420	17528

5 . 2 . 1 Sampling error - indicators

	AROPE			At risk of poverty (60%)			Severe Material Deprivation			Very low work intensity
	Ind. value	Stand. errors	Half CI (95%)	Ind. value	Stand. errors	Half CI (95%)	Ind. value	Stand. errors	Half CI (95%)	Ind. value	Stand. errors	Half CI (95%)
Total	41.7	1.1	±2.2	22.6	0.9	±1.8	29.9	1.1	±2.1	7.4	0.5	±1.0
Male	40.7	1.2	±2.3	21.9	1.0	±1.9	29.8	1.2	±2.3	6.5	0.5	±1.0
Female	42.6	1.1	±2.3	23.2	0.9	±1.8	30.0	1.2	±2.3	8.3	0.6	±1.1
Age0-17	52.2	1.9	±3.6	34.6	1.9	±3.7	37.9	1.8	±3.8	5.1	0.8	±1.5
Age18-64	40.2	1.2	±2.4	21.0	1.0	±2.2	27.9	1.1	±2.2	8.1	0.5	±1.0
Age 65+	35.7	1.3	±2.6	15.4	0.9	±1.8	28.6	1.3	±2.6

5 . 3 Non-sampling error

Non-sampling errors are basically of 4 types:

Coverage errors: errors due to divergences existing between the target population and the sampling frame.
Measurement errors: errors that occur at the time of data collection. There are a number of sources for these errors such as the survey instrument, the information system, the interviewer and the mode of collection
Processing errors: errors in post-data-collection processes such as data entry, keying, editing and weighting
Non-response errors: errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:

– Unit non-response: refers to absence of information of the whole units (households and/or persons) selected into the sample

– Item non-response: refers to the situation where a sample unit has been successfully enumerated, but not all required information has been obtained

Due to the lack of appropriate information, the new dwellings, built after 2002 Census of the Population and Dwellings, that could possibly constitute a sampling frame of the new dwellings, have not been taken into account. Thus, an updates has be done for the PSU included in EMZOT in 2007 year, on the basis of a micro-census type survey. The micro-census has aimed in particular the updating of the addresses of the dwellings.

5 . 3 . 1 Coverage error

Coverage errors include over-coverage, under-coverage and misclassification:

Over-coverage: relates either to wrongly classified units that are in fact out of scope, or to units that do not exist in practice
Under-coverage: refers to units not included in the sampling frame
Misclassification: refers to incorrect classification of units that belong to the target population

Over-coverage rate was estimated on the basis of the survey sample, as ratio between number of not-eligible dwellings (not-existing addresses, or being non-residential or unoccupied or not the main addresses) and number of sampled dwellings (all addresses selected). Over-coverage rate was 2.61%.

Under-coverage rate was estimated as the ratio between number of new dwellings, built in the period end of 2002 year (the year of the census)- end of 2010 year and number of dwellings at the end of 2010 year (Source: Romanian Statistical Yearbook, 2011). Thus, it was assumed that the proportion of the new dwellings in total dwellings should be the same in the master sample. Under-coverage rate was 4.24%.

5 . 3 . 1 . 1 Over-coverage - rate

Main problems

Size of error

Cross sectional

data

·Over-coverage

·Under-coverage

·Misclassification

2.61%

4.24%

5 . 3 . 2 Measurement error

Cross sectional data

Source of measurement errors

Building process of questionnaire

Interview training

Quality control

As in any other survey, there are 3 main sources of measurement errors:

- the questionnaires (1)

- the interviewers (2)

- the respondents (3)

We used three types of questionnaires:

- the household file;

-the household questionnaire, with the detailed questions regarding the household;

- the individual questionnaire, which was fulfilled for each person 15 years or more, in order to record better the incomes of the people less than 16 years.

The questionnaires were up-dated with the improvements based on the 2011
survey conclusions and the 2012 secondary module.
The structure of questionnaires was the following:

The household file included:

-identification data;

- the household composition

- name, identificator, date of birth, sex, the relatives’ code (mother’s, father’s and husband’s/wife’s), sample-person or co-resident, person’s mobility compared with first wave, month and year when the current person left the household/came into the sampled household (if was the case), economic status during the income reference period etc.;

- some questions about household identification; the household file is design and used all four years a person is included in the survey.

The household questionnaire included:

-identification data;

-data regarding child care for all the children less than 12 years;

-questions regarding economic situation of the household (housing and

non-housing related arrears, non-monetary household deprivation questions); endowment with durable goods;

-housing conditions including questions regarding the 2012 secondary module - (information about dwelling installations and facilities, accessibility of basic needs, change of the dwelling, dwelling and dwelling environment, housing cost, amenities in the dwelling);

-taxes paid at household level for the year 2011;

-household incomes in 2011;

The individual questionnaire:

-identification data;

-questions regarding de jure and de facto marital status; first and second citizenships; country of birth; year of immigration in Romania;

-questions regarding the health status; limitations in activities due to a medical problem; unmeet need for medical, respectively dental consultation; reasons for the unmeet need for medical and dental consultation;

-level of education questions (the school attended currently, the highest level of education attended and the year when the person graduated this level);

-questions regarding detailed information about employment/non-employment;

-questions regarding the 2012 secondary module (2012 Module on housing conditions);

-individual incomes achieved in 2011.

In order to help the data collection activities, other materials were designed by the
methodological team:
-the letter for the households – a paper sheet in which the objectives of the EU-SILC survey is presented, the importance of the people participation is highlighted and the confidentiality of the data is guarantied.

-the list of the dwelling and households included in the sample (LG) is a document with two parts: first one included the exact addressees selected to carry-out the interviews. The second part included the situation found on the field for each address. This document is very useful for the interviewers and supervisors in order to check the integrity of the data collected.

-the tracing file, was a paper sheet designed in order to identify households/persons which moved from the initial addresses from the first wave. The paper sheet fulfilled by the county from which they left were sent to the NIS methodological team and they sent again in the county where the information collected show they moved in. These counties proceeded to follow-up and interviewed them, in the case they founded.

The main challenge for the interviewers in the sixth wave was to administer the tracing rules. Beside this, the recording of the accurate incomes was the second very difficult task. A handbook was prepared with all the information available to help the interviewers in the fields work activities. Explanations for a big number of questions from all the questionnaires were included. Aspects related to the follow-up of households/persons and the construction of identifiers was explained in this handbook also. A special section included some recommendations about the behavior in the respondents’ presence and the way the interviewers should convince population to participate to this survey. Other aspects:

Some interviewers used very seldom some household identification numbers for the households and individuals from the new sub-sample, which were overlapped with some old households from the sub-samples which left the survey in 2009 and 2010; all these identification numbers were corrected.

5 . 3 . 3 Non response error

Non-response errors are errors due to an unsuccessful attempt to obtain the desired information from an eligible unit. Two main types of non-response errors are considered:

1) Unit non-response which refers to the absence of information of the whole units (households and/or persons) selected into the sample. According the Commission Regulation 28/2004:

Household non-response rates (NRh) is computed as follows:

NRh=(1-(Ra * Rh)) * 100

Where Ra is the address contact rate defined as:

Ra= Number of address successfully contacted/Number of valid addresses selected

and Rh is the proportion of complete household interviews accepted for the database

Rh=Number of household interviews completed and accepted for database/Number of eligible households at contacted addresses

Individual non-response rates (NRp) will be computed as follows:

NRp=(1-(Rp)) * 100

Where Rp is the proportion of complete personal interviews within the households accepted for the database

Rp= Number of personal interview completed/Number of eligible individuals in the households whose interviews were completed and accepted for the database

Overall individual non-response rates (*NRp) will be computed as follows:

*NRp=(1-(Ra * Rh * Rp)) * 100

For those Members States where a sample of persons rather than a sample of households (addresses) was selected, the individual non-response rates will be calculated for ‘the selected respondent’, for all individuals aged 16 years or older and for the non-selected respondent.

2) Item non-response which refers to the situation where a sample unit has been successfully enumerated, but not all the required information has been obtained.

5 . 3 . 3 . 1 Unit non-response - rate

Cross sectional data
Address contact rate (Ra)*		Complete household interviews (Rh)*		Complete personal interviews (Rp)*		Household Non-response rate (NRh)*		Individual non-response rate (NRp)*		Overall individual non-response rate (NRp)*
A*	B*	A*	B*	A*	B*	A*	B*	A*	B*	A*	B*
99.63%	98.60%	95.62%	87.69%	99.64%	99.76%	4.73%	13.54%	0.36%	0.24%	5.08%	13.75%

* All the formulas are defined in the Commission Regulation 28/2004, Annex II

A* = Total sample; B = * New sub-sample

5 . 3 . 3 . 2 Item non-response - rate

The computation of item non-response is essential to fulfil the precision requirements concerning publication as stated in the Commission Regulation No 1982/2003. Item non-response rate is provided for the main income variables both at household and personal level.

We have no item non-response due to the checking programs used at the county level which show these missing data and the supervisors have to solve it: first of all, the questionnaire is checked in order to find if it is an operator’s mistake and secondly, the household is asked again if the information was not supplied from the beginning. Finnaly, item non-response imputation is applied, if it is the case.

5 . 3 . 3 . 2 . 1 Item non-response rate by indicator

	Total hh gross income (HY010)	Total disposable hh income (HY020)	Total disposable hh income before social transfers other than old-age and survivors benefits (HY022)	Total disposable hh income before all social transfers (HY023)
% of household having received an amount
% of household with missing values (before imputation)
% of household with partial information (before imputation)

	Imputed rent (HY030)	Income from rental of property or land (HY040)	Family/ Children related allowances (HY050)	Social exclusion payments not elsewhere classified (HY060)	Housing allowances (HY070)	Regular inter-hh cash transfers received (HY080)	Interest, dividends, profit from capital investments in incorporated businesses (HY090)
% of household having received an amount
% of household with missing values (before imputation)
% of household with partial information (before imputation)

	Cash or near-cash employee income (PY010)	Other non-cash employee income (PY020)	Income from private use of company car (PY021)	Employers social insurance contributions (PY030)	Cash profits or losses from self-employment (PY050)	Value of goods produced for own consumption (PY070)	Unemployment benefits (PY090)	Old-age benefits (PY100)	Survivors benefits (PY110)	Sickness benefits (PY120)	Disability benefits (PY130)	Education-related allowances (PY140
% of household having received an amount
% of household with missing values (before imputation)
% of household with partial information (before imputation)

5 . 3 . 4 Processing error

Data entry and coding

Editing controls

During the field work period and data processing period several checks were done. Data editing and cleaning was done in two steps: firstly, at the level of each county and secondly, after the counties’ files will be sent to INS team, a second check was done by EU-SILC central team. At the county level, after data collection, supervisors had the duty to check the integrity of the questionnaires (one household file and at least one household questionnaire per household and as many personal questionnaires as household members 16 years and more exists). During data entry, checking software was applied at county level. The counties sent the files at central level and a new check was done on the national files. The checking software included 3 types of checks: checks at each questionnaire level (household and personal questionnaires), checks for the correlation between the information included in household and personal questionnaires, and a third type of checks, integrity checks, if all the addresses included in the sample were visited (if questionnaires completed exist for each address included in the sample). Inside each type of questionnaire there were 2 types of logical conditions: to see if all the compulsory questions were fulfilled and to check if the answers were correct (for quantitative variables minimal and maximal limits were established, and for qualitative variables logical conditions were tested). After the data files in the EUROSTAT format were obtained, a third data check was done, using the EUROSTAT software available on Circa user group. The process of cleaning the data took a long time and imposed special efforts both from the county teams and central metodological team in order to obtain the 4 micro-data files in Eurostat format, due to the big number of variables and numerous corelations between them. A special kind of difficulties were related to the special codification of the split-of/moved hoseholds/persons in the original files.

5 . 3 . 4 . 1 Imputation - rate

5 . 3 . 4 . 2 Common units - proportion

5 . 3 . 5 Model assumption error

5 . 3 . 6 Data revision

5 . 3 . 6 . 1 Data revision - policy

5 . 3 . 6 . 2 Data revision - practice

5 . 3 . 6 . 3 Data revision - average size

5 . 3 . 7 Seasonal adjustment

6 Timeliness and punctuality	-
6 . 1 Timeliness	-
6 . 1 . 1 Time lag - first result	-
6 . 1 . 2 Time lag - final result	-
6 . 2 Punctuality	-
6 . 2 . 1 Punctuality - delivery and publication	-

7 Accessibility and clarity	-
7 . 1 Dissemination format - News release	-
7 . 2 Dissemination format - Publications	-
7 . 3 Dissemination format - online database	-
7 . 3 . 1 Data tables - consultations	-
7 . 4 Dissemination format - microdata access	-
7 . 5 Documentation on methodology	-
7 . 5 . 1 Metadata completeness - rate	-
7 . 5 . 2 Metadata - consultations	-
7 . 6 Quality management - documentation	-
7 . 7 Dissemination format - other	-

8 Comparability

According to the Regulation (EC) No 1177/2003 of the European Parliament and of the Council concerning EU-SILC: "Comparability of data between Member States shall be a fundamental objective and shall be pursued through the development of methodological studies from the outset of EU-SILC data collection, carried out in close collaboration between the Member States and Eurostat".

Although the best way for keeping the comparability of data is to apply the same methods and definitions of variables, small departures of the definitions given by Eurostat are allowed in EU-SILC. In this way, the mentioned Regulation in its article 16th says: "Small departures from common definitions, such as those relating to private household definition and income reference period, shall be allowed, provided they affect comparability only marginally. The impact of comparability shall be reported in the quality reports."

8 . 1 Comparability - geographical

8 . 1 . 1 Asymmetry for mirror flow statistics - coefficient

8 . 1 . 2 Reference population

Reference population	Private household definition	Household membership
The reference population is all private households and their current members residing in the territory of the Romania at the time of data collection. Persons living in collective households and in institutions are excluded from the target population.	Household is defined as a person living alone or a group of persons who live together in the same dwelling and share expenditures including the joint provision of the essentials of living.	We used the same household membership definition as the Eurostat recommended in the document EU-SILC 065.

8 . 1 . 3 Reference Period

Period for taxes on income and social insurance contributions	Income reference periods used	Reference period for taxes on wealth	Lag between the income ref period and current variables
No departure from the common definition. The repayments and receipts for tax adjustment referring to the income taxes recalculated for the global income gained in 2010 and they were collected if there were paid/received during the calendar 2011.	No departure from the common definition. We used a fixed income reference period of twelve-month, more exactly the previous calendar year (January – December 2011).		No departure from the common definition.

8 . 1 . 4 Statistical concepts and definitions

Total hh gross income (HY010)	Total disposable hh income (HY020)	Total disposable hh income before social transfers other than old-age and survivors' benefits (HY022)	Total disposable hh income before all social transfers (HY023)
F	F	F	F

Imputed rent (HY030)	Income from rental of property or land (HY040)	Family/ Children related allowances (HY050)	Social exclusion payments not elsewhere classified (HY060)	Housing allowances (HY070)	Regular inter-hh cash transfers received (HY080)	Interest, dividends, profit from capital investments in incorporated businesses (HY090)	Interest paid on mortgage (HY100)	Income received by people aged under 16 (HY110)	Regular taxes on wealth (HY120)	Regular inter-hh transfers paid (HY130)
F	F	F	F	NC	F	F	F	F	F	F

Cash or near-cash employee income (PY010)	Other non-cash employee income (PY020)	Income from private use of company car (PY021)	Employers social insurance contributions (PY030)	Cash profits or losses from self-employment (PY050)	Value of goods produced for own consumption (PY070)	Unemployment benefits (PY090)	Old-age benefits (PY100)	Survivors benefits (PY110)	Sickness benefits (PY120)	Disability benefits (PY130)	Education-related allowances (PY140)	Gross monthly earnings for employees (PY200)
F	F	F	NC	F	NC	F	L	F	F	F	F	NC

The source or procedure used for the collection of income variables

The form in which income variables at component level have been obtained

The method used for obtaining target variables in the required form

The source for the collection of income variables was paper and pencil interviews
for all income variables, including the money drawn out of business by the self-
employed. We did not used administrative records.
The use of the justificative documents regarding the incomes was the respondents’
decision.

The majority of income components were recorded net and the gross variables were obtained by adding at the net values, the value of income tax retained at source and social contributions paid (in the case of wages, we add the value of other sums retained at source, too).

The only income components calculated in the process of data editing were:

- the value of income tax retained at source for salaries (we have a flat rate of 16% for income tax), the respondents being asked only if they paid or not the income tax for wage;

- the exact value of the social insurance contribution retained at source for salaries, if this was declared in the form of an interval.

- the value of income tax retained at source and social insurance contributions for pensions (if the pension was bigger than 1000 lei);

- the interest for dividends and money withdrawn from the banks;

8 . 2 Comparability - over time

A very exact comparison between incomes from HBS and EU-SILC data is not possible due to some methodological differences, more exactly, differences at the level of income elements collected and included in the EU-SILC.

The differences between these two surveys it is possible to be due to the greater value of the income taxes and social insurance contributions for wages, own account activities and pensions in EU-SILC, where these elements are automatical calculated (if the person declared there were paid). In HBS the person should declare himself the value of these components in the diary.

A better comparison can be made between at-risk-of-poverty indicators calculated from both surveys.

	2012
	HBS	EU-SILC
Poverty threshold - lei, for one person annually	5684	5382
At-risk-of-poverty rate (after all social transfers) -%	21.5	22.6
Dispersion around the poverty threshold -%
- at-risk-of-poverty rate at 40% of median	8.8	10.7
- at-risk-of-poverty rate at 50% of median	14.7	16.6
- at-risk-of-poverty rate at 70% of median	29.1	29.6
Relative median risk-of-poverty gap -%	27.3	30.9
At-risk-of-poverty rate before social transfers -%
- including pensions	48.0	50.1
- excluding pensions	25.5	28.0
S80/S20 quartile share ratio	5.6	6.3
Gini Coefficient -%	32.0	33.2

8 . 2 . 1 Length of comparable time series

8 . 3 Comparability - domain

9 Coherence	The coherence of two or more statistical outputs refers to the degree to which the statistical processes, by which they were generated, used the same concepts and harmonised methods. A comparison with external sources for all income target variables and the number of persons who receive income from each ‘income component’ will be provided, where the Member States concerned consider such external data to be sufficiently reliable.
9 . 1 Coherence - cross domain	A very exact comparison between incomes from HBS and EU-SILC data is not possible due to some methodological differences, more exactly, differences at the level of income elements collected and included in the EU-SILC. The differences between these two surveys it is possible to be due to the greater value of the income taxes and social insurance contributions for wages, own account activities and pensions in EU-SILC, where these elements are automatical calculated (if the person declared there were paid). In HBS the person should declare himself the value of these components in the diary. A better comparison can be made between at-risk-of-poverty indicators calculated from both surveys.
9 . 1 . 1 Coherence - sub annual and annual statistics	-
9 . 1 . 2 Coherence - National Accounts	-
9 . 2 Coherence - internal	-

10 Cost and Burden	-

11 Confidentiality	-
11 . 1 Confidentiality - policy	-
11 . 2 Confidentiality - data treatment	-

12 Statistical processing

Detailed information concerning sampling frame, sampling design, sampling units, sampling size, weightings and mode of data collection can be found in this section. Such information is mainly used for the computation of the accuracy measures.

12 . 1 Source data

In the first stage, a stratified random sample of 780 areas, Primary Sampling Units (PSUs), was designed after the 2002 Census. The PSUs were sampled with probability proportional to the size (number of permanent dwellings). This is the Multifunctional Sample of Territorial Areas, so called the master sample EMZOT. The EMZOT sample has 427 PSUs selected from urban area and 353 PSUs selected from rural area. In the second stage, a fix number of dwellings are systematically selected from each PSU of EMZOT. All households within each dwelling are included. EMZOT was up-dated in 2007.

12 . 1 . 1 Sampling design and procedure

Type of sampling design

The sampling plan is a two-stage probability sampling of housing units (dwellings).

Stratification and sub stratification criteria

Stratification concerns only the first stage sampling. There are 88 strata, the criteria used being the area where a certain PSU is located (urban or rural area) and county (NUTS 3 level).

Sample selection schemes

The survey uses the integrated four-years rotational panel design, in which one-fourth of the sample is replaced each year. In 2012, one sub-sample, S5, left the survey and a new one (S9) entered for the first time. The total sample for the year 2012 is made by the sub-samples S6, S7, S8 and S9.

Sub-samples	Years
	2007	2008	2009	2010	2011	2012
	S1
	S2	S2
	S3	S3	S3
	S4	S4	S4	S4
		S5	S5	S5	S5
			S6	S6	S6	S6
				S7	S7	S7
					S8	S8
						S9

Sample distribution over time

The sample is not distributed over time.

12 . 1 . 2 Sampling unit

The Primary Sampling Unit, corresponding to the selection of the master sample, is a group of Census sections (census enumeration areas EAs).

The Secondary (ultimate) Sampling Unit, corresponding to the selection of the survey sample, is the dwelling.

12 . 1 . 3 Sampling rate and sampling size

Concerning the SILC instrument, three different sample size definitions can be applied:

- the actual sample size which is the number of sampling units selected in the sample

- the achieved sample size which is the number of observed sampling units (household or individual) with an accepted interview

- the effective sample size which is defined as the achieved sample size divided by the design effect with regards to the at-risk-of poverty rate indicator

Given that the effective sample size has been already treated in the section dealing with sampling errors, in this section the attention focuses mainly on the achieved sample size.

Actual and achieved sample size

Obs	Actual S_Size	Achieved_S
1	8187	7598

Achieved sample size

Obs

number_of_hh

2011

number_of_hh

2012

percent1

persons_16_

over_2012

last_rot_

group

num_of_

rot__hh_2012

percent2

7675

7598

0.99

15856

1859

24.47

12 . 2 Frequency of data collection

Frequency of data collection is annually.

12 . 3 Data collection

Data collection period was 9 – 29 May 2012.

Mode of data collection

The method of data collection was face-to-face personal interviews, using paper questionnaires. The interviewers visited the addresses selected in the sample and fulfilled the questionnaires, based on the interviews. The household questionnaire was fulfilled by interview with the household head and individual questionnaire by interview with each household member 16 years old and more.

Distribution of households members 16 years old and over by data status

Number			%
Total		15913	100.0
Information of interview completed		15856	99.64
- information completed only from interview (RB250=11)		15856	99.64
-information completed only from registers (RB250=12)		na	na
-information completed both from interview and registers		na	na
(RB250=13)
Interview not completed, though contact made		26	0.16
-individual unable to answer and no proxy possible
(RB250=21)
-failed to return the self-administrated questionnaire		na	na
(RB250=22)
-refusal to cooperate (RB250=23)		26
Individual not contacted because:		31	0.2
-person temporarily away and no proxy possible (RB250=31)		20	0.13
-	no contact for other reasons (RB250=32)	9	0.06
Information not completed, reason unknown (RB250=33)		2	0.01

Distribution of household members by the respondent status

Number		%
Total	17749	100.0
- Current household member aged 16 years and over (RB245=1)	15913	89.66
- Selected respondent (RB245=2)	na	na
- non-selected respondent (RB245=3)	na	na
- not eligible respondent (RB245=4)	1836	10.34

Distribution of households members aged 16 years old and over by the type of interview

Number		%
Total	15856	100.0
Questionnaire completed –face-to-face interview PAPI (RB260=1)	13510	85.20
Questionnaire completed –face-to-face interview CAPI (RB260=2)	na	na
Questionnaire completed –CATI (RB260=3)	na	na
Self-administrated by respondent (RB260=4)	na	na
Proxy interview (RB260=5)	2346	14.80

Obs	RB010	proxy	total	proxy_rate
1	2012	2346	15856	14.8

A description of the mode of data collection used in your country. Please mention if you use mixed mode of data collection.

1-PAPI (% of total)	2-CAPI (% of total)	3-CATI (% of total)	4-Self administrated (% of total)
100.0	-	-	-

The mean interview duration

The mean interview duration per household is calculated as the sum of the duration of all household interviews plus the sum of the duration of all personal interviews, divided by the number of household questionnaires completed. Only households accepted for the database have to be considered.

Average interview duration = 29.4 minutes.

Obs	duration_12	duration_11	duration_10	duration_09
1	29.4	30.8	30.6	29.9

12 . 4 Data validation

12 . 5 Data compilation

12 . 5 . 1 Weighting procedure

Design factor

Non-response adjustments

Adjustment to external data

Final cross sectional weights

Wave 1(subsample selected in 2012)

The design factor of the household is the inverse of inclusion probability. The design factor for households and for individuals are the same, because in each selected dwelling, all persons are selected for the survey.

In case of the households at the second, third and four wave, an indirect sampling of households is done through the panel (of persons aged 14+ at the time of the panel selection). In this case, the inclusion probabilities cannot be calculated. Then, the solution consists of applying the Weight Share Method.

Wave 2(subsample selected in 2011)

The design factors of households are calculated through the individual base weights. The individual base weights are obtained from cross-sectional weights calculated in previous year 2011 inflated with attrition. Co-residents are given zero base weight.

Wave 3(subsample selected in 2010)

There are two situations:

b. The sample person was a non-respondent in 2011 (re-entrees) In this case the base weight is obtain taking into account the cross-sectional weight RB050 calculated in 2010 corrected for the attrition between 2010-2012. For co-residents the weight is equal with zero.

Wave 4(subsample selected in 2009)

The approach is similar with the previous wave and two cases are distinguished, too:

a. The sample person was a respondent in 2011. The base weight is calculated
taking into account the base weight of previous year and then corrected both: attrition
between 2011 and 2012 and compensation of the re-entrees.

b. The sample person was a non-respondent in 2011.
In this case the base weight is obtain taking into account the base weight calculated in
2010 corrected for the attrition between 2010-2012.
For co-residents the weight is equal with zero.

We applied an integrative calibration that means that we used both households and personal variables in the procedure. The calibration is performed at the household level using the household variables and individual variables in their aggregate form as calibration variables. This technique ensures that all members in the same household receive the same weight. Adjustments were made using the SAS macro CALMAR. Calibration variables were: “distribution of the population by age group (0-15; 16-24; 25-34; 35-49; 50-64; 65-74; 75 and over), area of residence (urban\ rural) and gender” using Romanian Population Estimates at the end of the income reference period and ’’households totals by region’’.

In order to contra balance the non-respondent households, it is proceed at a re-weighting, by adjusting the weights of the respondent households with the inverse of the response rate.

The non-response are not globally adjusted, at the entire sample level, but separately-at wave level, on groups of households, groups generated by the variables considered as explicative of the non response. This correspond to the so-called 'response-homogenous groups’ method, which assumes that in a certain group all the units have the same probability. For wave 1 we used as explicative variables for non-response region (NUTS II level) and area of residence (urban / rural) and for the second, third and fourth wave - the region. In order to minimize the effects induced by the presence of non-response another adjustment is done: re-weighting by calibration of the weights.

Three cross-sectional weights were calculated: 1) Household cross-sectional weight (DB090) 2) Personal cross-sectional weight for all household members (RB050) 3) Personal cross-sectional weight for all household members aged 16 and over (PB040)

12 . 5 . 2 Estimation and imputation

Imputation procedure used

Imputed rent

Company car

The value of imputed rent was estimated at the household level (and included in the personal file for only one person per household) from the household budget survey (HBS), using the stratification method. The HBS includes arround 37000 households and it is conducted continuosly during each year.

The following information was collected in the individual questionnaire:
-the type of the car;
-the model;
-the registration year;

-number of months in 2011 the car was at the disposal of the person for private use;

The company car value was calculated as:
Company car value = number of months*selling price*[1 – 100*(2012- registration year)/10]/12

The selling prices of the cars by type of car and producer were taken from the List of manufactures recommended retail prices of the Competition DG report.

12 . 6 Adjustment

13 Comment	-

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