1. Andris Fisenko and Jānis Lapiņš
Use of Register Data in Latvian Household Finance and Consumption Survey
Andris Fisenko and Jānis Lapiņš
Workshop of the Baltic-Nordic-Ukrainian Network on Survey Statistics 2018
Hello!

2. Additional data sources Some results References
Outline
About the HFCS
Additional data sources
Some results
References
This is the outline of our presentation. It covers an introduction to Household Finance and Consumption Survey, administratve data sources used and some first results illustrating effectiveness of usage administrative data for editing purposes.

3. About the HFCS
The main aim of the HFCS is to gather micro-level structural information on euro area households' assets and liabilities
The survey also collects other information in order to analyse the economic decisions taken by households:
Person level data (demographics, employment, pensions and insurance policies, person level income)
Household level data (real assets and their financing, other liabilities and credit constraints, private businesses and financial assets, household level income, gifts and inheritances received, consumption)

4. About the HFCS
Joint project (1st wave – 15 countries; 2nd wave – 18 EA countries + HU, PL; 3rd wave all EA countries + HU, PL)
Sampling, data collection, editing, imputation done at country level
Probability sample design
Oversampling of wealthy HHs
Harmonized output (i.e. survey data), but not necessarily identical questionnaires
commonly agreed set of standardised output variables
harmonised methodology for coding the variables
multiple imputation for item non-responses

5. About the Latvian HFCS Latvia participates since 2014 (second wave)
Joint project of the Bank of Latvia and the Central Statistical Bureau of Latvia
Sample size households
CAPI, Questionnaire (in Latvian, Russian, English)
ECB questions related to the 1st and 2nd pension scheme not included in the Latvian HFCS_2017
Use of administrative data

6. Administrative data sources
State Revenue Service (SRS) data on all type of persons' income in 2016 and data on persons' participation in the voluntary (third level) pension schemes'
Land Cadastre's data on real estate properties that belong to the household members
Credit Register data on persons' mortgages, loans and/or leasing contracts
The State Social Insurance Agency data on persons' participation in the first and second level pension plans
Shortly about administrative data. From the State revenue service we obtained person level data on income in 2016 by the type of income, as well as, data on persons' participation in the voluntary (third level) pension schemes'.
From the Land Cadastre we have data on real estate properties that belong to household members including Cadastre value of property.
From the Credit Register we have data on household members' mortgages, loans and/or leasing contracts.
The State Social Insurance Agency data are about the household member participation in the first and the second level of pension scheme.
In the next slides we present the first results of usage of administrative data for editing of HFCS data. Currently, we have not started the imputation of missing HFCS data what has to be done when data editing will be finished.

7. First results – Income received (HFCS data)
In this table we summarise respondents answers on the type of the person level income that household members have received in 2016.
About each type of income respondent can use one of four answers: YES, NO, Don't Know , No answer ( I don't like to answer).
From the table we can see that only in a very few cases respondents used answer “Don't Know” or “No answer”.

8. Income received (weighted HFCS data)
The percentage of cases when respondent used one of answers “Don't Know” or “No answer” is small if we look at weighted figures, too. One tenth of percent is the highest value for indicator type question.
From the next slide we see that the situation is completely different when we consider what are the respondents’ answers to the question what was the amount of each type of income received in 2016.

9. Amount of employee income (HFCS data)
From this table we can see that out of 1361 cases when person reported that he/she received employee income in 2016 only in 422 cases (or 31% of cases) person reported amount of income received. In 825 cases respondent answered “Don't Know”, and in 114 – didn’t like to answer.
In 1003 cases the question what was amount of employee income in 2016 was not asked because respondent already answered that he/she didn’t have employee income in 2016 or respondent chooses one of answers “Don't Know” or “No answer”.

10. Amount of employee income (edited data)
In this table we compare the aggregated data on respondents’ employee income before and after the data editing. All respondents are subdivided in several groups.
One group (cells in green colour) corresponds to persons whose data on employee income was not changed during the data editing process.
In turn, cells in red colour correspond to persons who reported that they had no employee income, nevertheless from the State revenue service data we find out that these persons had employee income in Without access to administrative data, non-reported (133 cases) or under-reported (250 cases) amount of employee income of these persons would be lost. The difference between the edited amount of income and survey data for this group of persons is significant – almost six hundred millions of EUR (7% of edited total amount of employee income).

11. Amount of employee income (edited data)
The third group (cells in brown colour) corresponds to cases when editing based on administrative data allows to reduce the number of item non-response cases. Without access to administrative data we have to do the data imputation.
Finally, the forth group (cells in blue colour) correspond to cases when after finishing of the data editing we still have to do the data imputation.

12. Self-employment income (HFCS data)
Very similar situation we find out when considering data on amount of income for other types of income.
For self-employment income we can find that out of 157 persons reporting that they had income in 2016 only 48 persons reported amount of income received.
In 89 cases person answered that he/she does not know the amount of income, and in 20 cases respondent refuse to answer.

13. Self-employment income (edited data)
From this table we can see that data of 1039 respondents (cells in green colour) were not changed during the data editing.
In 400 cases amount of self-employment was non-reported (red colour, 380 cases) or under-reported (red colour, 20 cases).
The difference between the edited amount of income and survey data for this group of persons is significant – almost 235 millions of EUR (34.8% of edited total amount of self-employment income).

14. Self-employment income (edited data)
In 85 cases editing based on administrative data allows to reduce the number of item non-response cases for amount of received self-employment income, which would be necessary impute if we have not access to administrative data.
Still in 27 cases after data editing it is necessary to impute the amount of self-employment income.

15. Household level income received (HFCS)
When considering respondent answers if their household had a household level income in 2016 we can see that for all types of the household level income the percentage of answers “Don't Know” and “No answer” is negligible.

16. Income from public transfers

17. Income from public transfers (edited)
This table shows that 451 respondent reported that their household did not received income from public transfers in At the same time, from State revenue service data we can find that these households received income from public transfers in The weighted total amount of income from public transfers received in 2016 by these households in 2016 is 406 millions EUR. This amount would be lost in HFCS data if we would not have access to administrative data.

18. Income from public transfers (edited)
In 112 cases editing based on administrative data allows to reduce the number of item non-response cases for amount of income received from public transfers in 2016, which would be necessary impute if we have not access to administrative data.
Still in 2 cases after data editing it is necessary to impute the amount of income received from public transfers in 2016.

19. Income from real estate properties
Similar situation is also for other type of household level income. There is only a small percentage of households, which don’t like to answer if they have received certain type of household level income or say that they do not know if their household had received such income. At the same time, rather high is percentage of respondents, who gave wrong answer that their household did not receive income while from the State revenue service data we can find that household received income. High is also percentage of respondents who did not know what amount of income household received.
This and the following two tables illustrates this for household income received from the real estate properties.

20. Income from real estate properties (edited)

21. Income from real estate properties (edited)

22. Participation in the 3-rd level pension plans
In addition to HFCS data, we received State Revenue Service (SRS) data on participation of household members in the voluntary (so called, third level) pension plans.
From this chart we can see that only in less than 40% cases respondents reported participation in pension plans, and the reported total value is only 33% of the respective edited value.

23. First and second level pension plans
Experience from the second wave:
Many respondents don't know if they are participants of the pension scheme
Only few respondents know current balance of their pension plan account(s)
Data obtained from SSIA (participation, annual contribution, current value of pension plan)
No imputation needed
Already in HFCS_2014 we found out that:
Many respondents don't know if they are participants of the pension scheme
Only few respondents know current balance of their pension plan account(s)
Therefore, in the questionnaire of HFCS_2017 we did not include questions related to participation of household members in the 1st and 2nd pension plan.
Most important data (does person participate in the pension plan, the amount of annual contribution to pension plan, and the value of pension plan at the reference date December 31, 2016) were obtained from the State Social Insurance Agency.
Since State Social Insurance Agency data were obtained for all persons there was no need to make any imputation.

24. References
Household Finance and Consumption Network (2016) The Household Finance and Consumption Survey: methodological report for the second wave. ECB Statistical Paper Series, 17.
research/research- networks/html/researcher_hfcn.en.html
At this ECB web site you can find all information about survey also HFCS report on the results from the second wave and HFCS methodological report for the second wave

25. Thank you!