Ref. Ares(2016)6874852 - 09/12/2016

Final Report on "Improvement of the use of administrative sources (ESS.VIP ADMIN WP6 Pilot studies and applications)" - -

Application of 11/06/2015 Ares (2015) 2487712

August 2016

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List of Contents I. About the project ...... 3 II. Actions executed ...... 3 1. Identification of main challenges and action ...... 3 2. Review of previous examinations in the field of register based census methodology ...... 3 3. Review of legal background ...... 4 4. Identifying potential partners among administrative data owners ...... 5 Main findings ...... 7 5. Defining the evaluation methodology ...... 8 6. Contact with most important partners ...... 9 Lessons learned from the consultations ...... 11 Guidelines on the involvement of a new secondary data source for the production of official statistics ...... 12 7. Preparation for the data linkage stage ...... 13 8. Data takeover – KARAT system ...... 13 9. Record linkage ...... 13 Conceptual checks ...... 14 Data matching: steps and tools ...... 16 10. Distribution of aggregated estimates of demographic data between Census data and the Population and Address Register’s data ...... 22 Distribution of basic demographics based on Census without imputation ...... 23 Logical correction of Population and Address Register’s data ...... 23 Calculation of age groups ...... 24 Main results and analysis of the comparisons at the aggregated level ...... 25 III. Conclusions and future actions ...... 40 ANNEX 1 ...... 41 List of Figures ...... 43 List of Tables ...... 43

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I. About the project On April 20, 2015 Hungary submitted a proposal in response to the invitation ref no. 967785 of 13/03/2015 "Improvement of the use of administrative sources (ESS.VIP ADMIN WP6 Pilot studies and applications)". As stated in the proposal, the main objective of the project is to examine the possibilities of using administrative data sources in order to develop a cost- effective way of executing Census 2021 and producing census type data in an annual basis that are less burdensome for the respondents. The analysis of the possibilities of use covers the aspects of data content (the correspondence and the difference of concepts and time of reference), IT issues (IT system of registers, the possible ways of data transfer), legislation (availability of legal permission to use the data) as well as methodology (method of linkage). Clarifications were requested on technical, budgetary and administrative aspects – among them an explanation about the differences with the actions financed by the grant "Usual residence population definition: feasibility studies" – that were supplied by June 11, 2015. We requested a starting date earlier than the recommended 1 October, 2015 to reduce as much as possible the overlapping with the 10 percent microcensus to be carried out by the reference date of October 1, 2016. On August 27, 2015 the contract was signed, establishing September 1, 2015 as a starting date.

II. Actions executed

1. Identification of main challenges and action The project team consists of colleagues from the Census Department and the Methodological Department of the Hungarian Central Statistical Office. First the project team was set up and a detailed work plan was created with particular concerns to the actions identified as of high risk. The team comprises statisticians from different fields with different backgrounds: census experts, IT experts, methodological experts, etc. For this reason it was important to get to know in the first meetings each other’s knowledge and specialties in detail to utilize human resources in the optimal way.

2. Review of previous examinations in the field of register based census methodology Before starting to work first we reviewed in detail the results of the 2007-2008 Transition Facility Project, 4. Preparation for 2011 Population Census on the analysis of the possibilities of using administrative data sources in Census 2011. We examined the related Hungarian as well as the international best practices. Furthermore, we revised the methodologies of the register based and combined censuses. The experts of the Census Department shared this

3 information with the others while the Methodological Department shared the HCSO’s previous experience on data matching/linkage.

3. Review of legal background The legal background of population censuses was already reviewed in 2007 in the framework of the Transition Facility Project, 4. Preparation for 2011 Population Census. However, as the legal environment has been changed since 2008, the anew revision and description of the current situation was the first step of the present project. Legal acts regulating the population censuses, the use of administrative data and the issue of interoperability together with their relation to Regulation (EC) No 223/2009 on European statistics (and to Regulation (EU) 2015/759 of the European Parliament and of the Council of 29 April 2015 amending Regulation (EC) No 223/2009 on European statistics) were reviewed in detail. The most important related information was summarized in the working document titled “The legal background of population censuses in Hungary and the use of administrative data for census purposes”. An important deficiency of the current Statistical Law (Act No. XLVI of 1993) in Hungary is that although it discusses data transmission closely but it is the object of the individual law of a certain register to stipulate the circumstances of access for statistical purposes. This legal background makes the data transmission rather difficult. At the moment bilateral agreements with register owners are always necessary. The Act CXII of 2011 on Informational Self-determination and Freedom of Information also mentions statistics as an important field of the use of personal data. This law gives permission to the use of personal data for statistical purposes. However in case of all those special acts that regulate a certain administrative register permitting the statistical use of non-identifiable data only, these acts require amendment. As the standardised use of addresses is a substantial issue of the use of administrative data in census, the governmental decree No. 345/2014. (XII. 23.) about the Central Address Register must be mentioned here. The decree established the central, uniform and complete address register (in development at the moment) as a certified database in order to create the uniform legal environment of address management in Hungary. The decree defined the technically uniform standard of addresses and appointed the Central Office for Administrative and Electronic Public Services as responsible body. Important changes in the legal situation regarding the use of administrative data are expected from the amendment of the Act No. CCXX of 2013 regulating the interoperability of administrative registers on one hand, and the adoption and entry in force of the new act on statistics on the other. Sectoral laws are currently being reviewed, those inconsistent with Regulation 223/2009 will be amended. As the adoption of these changes and amendments are in progress this document should not be considered as finalized. Therefore the actualization of the review of the legal background remains constantly on the agenda. EU legal obligations regarding census data may also have influence on the statistical use of administrative data. The requirements of the EU census regulations for the 2021 census are expected to be along the same lines as of the previous census round. However after 2021 a

4 serious change may take place with a shift to a yearly census exercise. Annual “census” data will be possible to produce only by using administrative sources.

4. Identifying potential partners among administrative data owners As a starting point for the analysis of registers and other administrative data sources, a broad list of potential partners was assembled based on data takeover from administrative sources in all statistical domains of HCSO. Besides the population and address register of the Central Office for Administrative and Electronic Public Services already mentioned as key register in our proposal, we checked all data transmissions from administrative registers to the statistical office. Based on basic checks of the content and target population of registers we selected those which could be useful for census purposes. We added other sources which were missing from the existing list and which can be used for census purposes. The most important sources of a register based census in the future can be the following (focusing on personal data): 1. Register of Personal Data and Addresses – Central Office for Administrative and Electronic Public Services Containing data about Hungarian citizens living in the territory of Hungary, persons of immigrant and settled status as well as persons recognised as asylum seekers, as well as persons with the right of free movement and residence. Data content: name, sex, immigrant status, place and date of birth, name of mother, personal identifier, place and date of death, place of residence, place of stay, legal marital status, place of marriage. Historical data contains all changes in the registered information. For statistical use access to data is possible by law without personal identification except census related tasks, which make possible the use of personal identification. 2. National Health Insurance Fund Database - National Health Insurance Fund of Hungary Data content by partial registers: 2.1 Social Security Number Register Containing data of persons living in 1995 and of persons registered since. Data content: name, birth name, name of mother, date and place of birth, sex, legal marital status, citizenship, place of residence, social security number. For statistical use access to data is possible by law with personal identification. 2.2 Register of legal relationship data of persons Containing data of persons in legal relationship on the 31th of Jan 1998 and of all legal relationships generated since 31th of Jan 1998. Data content: name, social security number, name, seat, register number of the employer, title of legal relationship, code of occupation by Hungarian Standard Classification of Occupations, period of legal relationship, weekly working hours. For statistical use access to data is possible by law with personal identification.

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2.3 Register of data related to the management of benefits Containing data about persons receiving benefits (child benefit, sick allowance etc.) Data content: name, birth name, name of mother, date and place of birth, data of children, citizenship, place of residence or place of stay, social security number, data about the employer, data about income. For statistical use access to data is possible by law with personal identification. 3. Central Administration of National Pension Insurance Database – National Tax and Customs Administration of Hungary Containing data about persons about persons having insurance or receiving pension. Data content by partial registers: 3.1 KELEN (Central Electronic Registration System) Data content: social security number, name, date of birth, legal marital status, citizenship, place of residence (place of stay), occupation, place of work, earnings, income, paid contributions, length of service. 3.2 NYUGDMEG (Pension Claim Consideration Assistant System) Data content: social security number, name, date of birth, legal marital status, citizenship, place of residence (place of stay), occupation, place of work, earnings, income, paid contributions, length of service, degree of disability, degree of health damage, data about capacity of work, possibility of rehabilitation, state of health (in case pension is considered with regard to state of health); data about cohabiting partner and dependent relations (in case pension is considered based on cohabiting partner, dependent relations). 3.3 NYUFUR (System Containing Data Related to Payment) Data content: social security number, name, date of birth, legal marital status, citizenship, place of residence (place of stay), occupation, place of work, earnings, type of pensions, amount of pensions, length of service, year of retirement. For statistical use access to data is possible by law without personal identification. 4. Tax Database – National Tax and Customs Administration Containing data about persons having registered place of residence or place of stay in Hungary or just staying there, in case of having any property or income, profit, Data content: individual identifier, name, name of mother, date of birth, citizenship, settlement of place of residence, date of registration. For statistical use access to data is possible by law in non-identifiable way. 5. KIR (Educational Information System) – Educational Authority in Ministry of Human Capacities Data content by registers: 5.1 Educational Information System Containing data about (1) teachers and (2) students.

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Data content: (1) name, date and place of birth, individual identifier, data about level of education and qualification, address, type and identifier of place of work, (2) name, date and place of birth, individual identifier, name of mother, place of residence, place of stay, citizenship, number on student card, data of educational institution. 5.2 Higher Education Information System Containing data about (1) teachers and (2) students. Data content: (1) name, sex, date and place of birth, individual identifier, data about labour relations, identification data of required documents. (2) name, sex, date and place of birth, citizenship, in case of foreigners the status of stay in Hungary and identification data of the relating document, data of student status, data of current studies, period and place of foreign studies, identifier and type of student documents, subject, date and result of final exam, data of certificate, data of following the career. 5.3 Student Card System Containing data of persons having student card. Data content: date and place of birth, place of residence, palace of stay, citizenship, individual identifier, name and address of school. For statistical use access to data is possible by law without personal identification.

Other administrative sources containing census relevant information:  Register of Foreign Nationals – Office of Immigration and Nationality  Asylum Register – Office of Immigration and Nationality  Tax Database of Foreigners – National Tax and Customs Administration of Hungary  E-Registry – Central Office for Administrative and Electronic Public Services  Social Register – Treasury  Register of Jobseekers – Ministry for National Economy

The situation regarding dwelling data is less favourable. Basic dwelling data are not available at the moment from administrative sources. A part of census relevant dwelling information is contained in the registers of Land Offices, but data queries are very costly so statistical use has not been achieved yet. To have reliant information about the content and quality of the data it is necessary to analyse closely these databases. This can be the aim of another project. The results of our current analysis about possible administrative sources are similar to those of the 2007-2008 Transition Facility Project, 4. Preparation for 2011 Population Census. The group of relevant administrative sources remained the same with no favourable changes in data content or scope.

Main findings Administrative registers in Hungary contain relevant census information but using them as direct substitutes of a traditional census data collection would provoke the following problems:

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 no data about persons without registered address,  no occupation data about persons without registered work,  no education data about the whole population,  no data about family and household structure, cohabiting partnerships,  no data about such traditional Hungarian census topics as mother tongue, nationality/ethnicity, religion while data about disability are only partial,  no data of sufficient quality and coverage about the dwelling stock. A serious limitation of the administrative sources is that data of registered place of residence often don’t reflect the real situation. Because of this the use of administrative data has the risk of a biased population number and regional composition with no reliable measure of the bias. The bias can differ by type of settlement, and can result in difference not only in population number but also in regional indicators of education, economic activity or family composition.

5. Defining the evaluation methodology We reviewed domestic and international methodological reports on the quality assessment of administrative data. Based on this review we started to create a quick methodology to measure the quality of administrative data. We should add here that the HCSO’s established quality assessment methods are based on the Dutch checklist for the Quality evaluation of administrative data sources. Based on a short checklist we realized the evaluation of the most important potential administrative sources.

Quality assessment of potential data sources After we overviewed all data sources in our data source register and selected those which can be potentially used for census purposes, a quick assessment was carried out based on a short checklist. Short checklist for assessing quality and usability of administrative data sources for census purposes: 1. Target population of the administrative register 2. Variables in the administrative register 3. Existence of unique identifiers 4. Availability of unique identifiers 5. Existence of meta data 6. Availability of meta data (especially compatibility of concepts) 7. Timeliness (Frequency of update of the administrative register) 8. Legal background – laws defining the creation, content and maintenance of the administrative register 9. Length of time series (first year of the register) 10. Coverage The most important problem of the datasets was the lack of unique identifiers and the aggregation level of the data (in many cases, aggregated data are transmitted to HCSO).

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Identified already as one of the most important partners, we contacted the Central Office for Administrative and Electronic Public Services. Two other data owners were selected and contacted, but we did not receive data sets (not even a sample for assessment and for testing) from them (detailed in the next chapter).

6. Contact with most important partners Central Office for Administrative and Electronic Public Services In order to analyse the content of the register we requested a data set to be compared with data from Census 2011. The requested data set was chosen to reflect possible differences between population data from register and census. The 314 thousand record personal data set contains the census relevant variables of sex, date of birth, place of birth, legal marital status, date of registration of legal marital status, registered place of residence, registered place of stay. The data set contains data of 28 complete settlements and one district of the capital, all chosen based on characteristics considered problematic from a census point of view. A request of a complete dataset for the whole country from the Office seemed to be risky based on previous experience. We presumed that if the methods – to be introduced in detail later – work well for the chosen 28 settlements plus one capital district or the emerging problems can be solved then these methods will work in a larger scale as well.

The list of the settlements contained in the dataset: , District 07 (population, 2011: 56 093) – a capital district, (population, 2011: 168 048) – a large with sizeable student population, (population, 2011: 40 545) – a town from the agglomeration area of the capital, Karcag (population, 2011: 20 632) – a town with a large outskirt area, Hegyhát district, containing the settlements Ág, Alsómocsolád, Bakóca, Baranyajenő, Baranyaszentgyörgy, Felsőegerszeg, Gerényes, Gödre, , Kishajmás, , Mágocs, Mekényes, Meződ, , Nagyhajmás, Palé, Sásd, Szágy, Tarrós, Tékes, Tormás, Varga, Vásárosdombó, Vázsnok (total population, 2011: 12 744) – a rural district with very small settlements. The data set was requested by the census reference date situation of the register. Due to that the contact with the register owner had been considered risky, we scheduled this data takeover to be at an early stage. The requested data set was provided as planned.

Central Administration of National Pension Insurance “The central office performing the central administration of statutory social and pension insurance is the Central Administration of National Pension Insurance, controlled and

9 supervised by the Government via the Minister for social and labour affairs who is in charge of political issues regarding retirement. The Central Administration of National Pension Insurance manages the Pension Insurance Fund supervised by the Government and controls the performance of administrative and official tasks set forth by legislation, as well as the altogether seven regional pension insurance directorates executing these tasks, the Pension Payment Directorate and the Directorate for Central Pension Record and Informatics.” (Source: https://www.onyf.hu/en/, 2016.08.30.) We contacted the Central Administration of National Pension Insurance (CANPI) and requested data set on pension contributions with unique identifiers. According to Act LXXXI of 1997 on social security pension benefits, data of CANPI can be used for statistical purposes and can be transmitted for statistical use, but only without unique identifiers. CANPI was open to discuss the possibility of transmission of data and metadata, but as far as the data are concerned, they can be transmitted only without unique identifiers, according to the law. A meeting was organised with the participation of experts from HCSO and from CANPI. The main goals of the grant and the reasons of statistical use of administrative data in general were presented by HCSO. CANPI presented its system, the maintenance of their register and the main data sources used. The data base of CANPI on contributions is based on registers transmitted by other bodies of the public administration. The main data source is a data set transmitted by the National Tax and Customs Administration. After the personal meeting, the communication on the possibilities of cooperation continued by e-mail. CANPI is willing to participate in the quality assessment of administrative data sources.

National Health Insurance Fund of Hungary “The National Health Insurance Fund (OEP), as a central agency, performs the functions set out in legislation, carries out the tasks relating to the management of the National Health Insurance Fund, the maintenance of records, keeping financial accounts and fulfilling the reporting obligation. The National Health Insurance Fund is supervised by the Government of Hungary, the central official organ of health insurance is managed by the Government through the Minister of Human Resources.” (Source:http://www.oep.hu/felso_menu/rolunk/kozerdeku_adatok/tevekenysegre_mukodesre_ vonatkozo_adatok/a_szerv_feladata_alaptevekenysege_es_hatarkore/en_a_szerv_alapteveken yege_feladata_es_hatarkore, 2016.08.30.) As the National Health Insurance Fund OEP registers data on the economic activity, employment, profession and the NACE classification of the workplace on the whole population in Hungary with unique identifiers, we contacted OEP and requested data with unique identifiers and metadata for the grant project according to Act LXXX of 1997 on persons entitled to social security provision and private pensions and on funding these services. This Act determines who the insured parties are, the extent of contributions, the method of contribution payments and obligations to keep records and supply data. According to the Act data can be transmitted to HCSO for statistical purposes with unique identifiers.

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We asked for a personal meeting to discuss the details of the project and of the data transmission. The OEP asked for detailed specification, therefore we selected the variables relevant for census purposes and sent the list of requested data and repeated our request for metadata and also our willingness to held a face-to-face meeting. The request variables were the following: Personal data (name, maiden name, mother’s name, place of birth, year, month and day of birth);

a. Legal marital status; b. Citizenship; c. Address of place of (permanent) residence, address of place of stay (temporary residence); d. Profession, workplace, activity; e. Degree of health injury, other additional data on rehabilitability, on state of health, on familial status; f. Income data; g. Hungarian social security number.

OEP refused to transmit data to HCSO referring to point f on health injury, because confidentiality measures of Act XLVI of 1993 on statistics pronounce that health data cannot be transmitted only with the permission of the person concerned or by the power of the law. OEP refused the whole data transmission not only the transmission of data related to point f. HCSO did not intend to contest on this issue, but we made a last attempt to receive a minimum set of data and metadata which would have been useful and usable in the grant project. We also asked for a face-to face meeting, but did not get any answer to our last contact. Lessons learned from the consultations The very first step in the involvement of a new data source in the statistical production process is to check if there is a cooperation between the statistical institute and the data owner of the new data source. In HCSO there is a data source register (which is part of the META system) where all primary and secondary data sources are described. Statisticians can check in the data source register if the owner of the new data source is our partner related to other data transmissions. An existing cooperation between the data owner and the statistical institute influences the process of the involvement of a new data source to the statistical production process. If there is a good cooperation and regular communication with the data owner, the involvement of a new data source can be made in the course of the annual revision of cooperation agreement. The latter case is typical of the cooperation between the HCSO and the National Tax and Customs Administration. There is a cooperation agreement between the two institutes and it is revised once a year. In the course of the negotiations, new needs are formulated, the detailed content, form, frequency and all technical aspects of the data transmission are discussed and fixed in the cooperation agreement. 1. Commitment and involvement of the top management is crucial if there is no existing cooperation between the statistical institute and the data owner of the new data set or if the cooperation is cumbersome. If the first contact is made by the head of the

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statistical institute and a positive answer is received, the discussion and cooperation of the experts from the two institutes are easier. 2. Knowledge of the legal background (including special laws and regulations referring to the data owner). 3. Proactive approach on the part of the statistical institute is indispensable. Besides the presentation of general goals and advantages of statistical use of administrative data, existence of a specific goal and work plan can facilitate the cooperation between the institutions. Participation in a common project enables experts from different institutes to work together and to know the most important aspects of each other. 4. In general, cooperation is more important for the statistical institute than for the data owner, it is our role to push on the cooperation. 5. In case of a project it is important that potential stakeholders (data owners of administrative registers) be inform about and involved in the planning of the work before the beginning of the project.

Guidelines on the involvement of a new secondary data source for the production of official statistics Based on our experiences related to the cooperation with data owners we started to create a set of guidelines on the involvement of a new secondary data source for the production of official statistics. We tried to identify the main steps of the involvement process. Further elaboration and fine-tuning of the guidelines is needed as far as detailed description of each step. Preparatory phase – before the 1st contact: 1. Commitment of the top management 2. Collection of all possible information on the data owner and on the potential data sets(s) 3. Knowledge of the legal background 4. Preparation of a data transmission work plan Involvement of the new data source 5. Contacting the data owner. 6. Personal meeting of experts from the statistical institute and the data owner (general presentation of goals and advantages of statistical use of administrative data, presentation of the specific work plan) 7. Transmission of metadata 8. Analysis of metadata 9. Transmission of a test data set 10. Analysis of the test data set 11. Creation of a test process – involvement of the new data source in the statistical production process in a testing area 12. In the case of a successful testing: conclusion of a cooperation agreement 13. Realisation of a regular data transmission 14. Incorporation of the new data set in the statistical production process 15. Regular review of the cooperation agreement 16. Feedback on the quality of administrative data set to the data owner

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17. Consultation and cooperation on the development and discontinuation of the administrative register (as foreseen in amended Regulation 223/2009) 18. Cooperation between the statistical institute and the data owner towards the improvement of the quality of administrative data

6. Preparation for the data linkage stage We reviewed domestic and international methodological literature about data linkage. The necessary IT infrastructure (SAS) has been prepared. As the linkage was planned to be performed by colleagues from the Methodology Department, they had to be informed about the special characteristics of the census data set. As the requested data set had been provided by the Central Office for Administrative and Electronic Public Services, we started the preparation for data linkage, checking first the completeness of the administrative dataset.

7. Data takeover – KARAT system The number of administrative and other secondary data sources used for the production of official statistics in the HCSO has increased in the past few years. In the past, data transmissions were managed differently throughout the HCSO with different security, metadata- and database-management. In order to address this issue, the KARAT system – a new integrated data transmission system – was developed in 2013-2014 assuring a secure channel for the data takeovers. It has a number of automatic, proactive functions to support the tasks of the data masters and data providers of the secondary sources. KARAT also has monitoring functions to manage the whole data transfer procedure. The quality of the data transmission processes can be measured with a series of indicators. The new system contributes to the improvement, standardization and monitoring of the process quality related to the transmission of secondary data and data requests. The dataset provided by the Central Office for Administrative and Electronic Public Services (see above) was uploaded into this system and accessed by the General Address Editor Application – GAEA – through it.

8. Record linkage Once the HCSO obtained the requested dataset, the preparation for the data integration began. Deterministic record linkage methods were chosen as a mean of testing the accuracy of the Population and Address Register provided by the Central Office for Administrative and Electronic Public Services, in comparison to that of Census data. The comparison was made on the assumption that Census data is more accurate (regarding the place of residence on settlement level), thus any differences and records not linked would indicate the errors and inaccuracy of register data. One of the most important methodological challenges we had to face was the lack of names and unique identifiers to make record linkage easier. Instead, a series of common variables were used to identify and match records. This suggests that previous conceptual checks of the common variables were needed before commencing the data linkage. It should be added, that the preparation for the data linkage, as well as the data

13 linkage processes themselves, were guided by the data integration practices established by the HCSO (see 1. Figure).

1. Figure - The Record Linkage scheme established by the HCSO

In the next sections, first we describe the preparation processes and our experiences upon carrying out the data linkage together with the IT tools we used for it. These will be followed by the main results of the analysis.

Conceptual checks As it was explained in previous sections, in its request for administrative data HCSO specified the need for register data that correspond to the reference date of Census 2011 (1st October, 2011). Due to that the Census in Hungary is anonymous, does not contain names and surnames, the data taken over from the Central Office for Administrative and Electronic Public Services contain exclusively the variables that are also present in the Census: a) sex; b) date of birth (YYYYMMDD); c) place of birth; d) nationality; e) legal marital status; f) address of place of (permanent) residence1 (postal code, settlement, district, type of public place – street, avenue, square, etc. – name of public place, house number, building number, stairway, floor, door);

1 Please note that Census data reflects the usual place of residence where the respondent independently of whether it is the permanent or a temporary address.

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g) address of place of stay (temporary residence), if available (postal code, settlement, district, type of public place – street, avenue, square, etc. – name of public place, house number, building, stairway, floor, door). With the aim of comparing conceptually these variables with those of the Census, the HCSO also demanded the metadata and the descriptions of them. However, the Central Office for Administrative and Electronic Public Services only have provided the possible values of variables that respondents could choose in some cases, such as for example the marital status. Some variables do not need any further explanation, e.g. sex, where the values in both data sources are male and female. The date of birth was recorded in different formats in the data sources, that is, a simple transformation of this variable was needed to make comparable the register and Census data. While the format of the date of birth in the Population and Address Register was YYYYMMDD, in the census this was coded in three, separated variables: a) year of birth (YYYY); b) month of birth (MM); c) day of birth (DD). To resolve this, we decided to divide the date of birth variable used in the register into the three used in the Census. The place of birth was coded by settlements in both data sources, however, an incongruence appeared in the case of birthplaces abroad: the Census used the name of the country in these cases, while the Population and Address Register contains the names of foreign cities. Nationality can be interpreted self-evidently in both datasets. The possible values of marital status differed across the sources (see 1. Table).

1. Table - Values of the variable “legal marital status” in Census 2011 and in the Population and Address Register

Census Population and Address Register (1) never married never married (2) married married (3) widowed widowed (4) divorced divorced (5) in registered cohabiting partnership in registered cohabiting partnership (6) widowed in registered cohabiting - partnership (7) divorced in registered cohabiting divorced in registered cohabiting partnership partnership (8) - marriage broken off (9) - unknown

Table 2 and Table 3 shows the legal marital status frequencies in the Census and in the Population and Address Register.

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2. Table - Marital status frequencies from the Census

Legal marital Cumulative Cumulative Frequency Percent status Frequency Percent Never married 140767 46.78 140767 46.78 Married 100108 33.27 240875 80.05 Widowed 25343 8.42 266218 88.47 Divorced 34670 11.52 300888 100.00 In registered cohabiting partnership 10 0.00 300898 100.00

3. Table - Legal marital status frequencies from the Population and Address Register

Cumulative Cumulative Legal marital status Frequency Percent Frequency Percent In registered cohabiting partnership 29 0.01 29 0.01 Divorced 37520 11.96 37549 11.97 Divorced in registered cohabiting partnership 2 0.00 37551 11.97 Marriage broken off 5 0.00 37556 11.98 Married 107733 34.36 145289 46.33 Unknown 4870 1.55 150159 47.88 Never married 139475 44.48 289634 92.36 Widowed 23953 7.64 313587 100.00

The received metadata from the Central Office for Administrative and Electronic Public Services was not consistent with the values within the database.2 Fortunately the value labels define exactly the categories of legal marital status. Most of these values are easily paired. Regarding the values that are not present in both datasets, these are indifferent from the point of view of the data linkage due to the low number of records in these categories (widowed in registered cohabiting partnership in Census: 0; marriage broken off in the Population and Address Register: 5). For the sake of simplicity, we decided to include the records with marriage broken off in the unknown category. (This category is not recognised in Hungarian law as a legal marital status.) Finally, the details of the addresses were similar in the Census and register data, and their treatment should be considered as the first step of the data matching. Data matching: steps and tools The data matching was carried out in two main steps: a) matching addresses and b) matching persons.

2 Value labels of marital status in received metadata list: 1= never married; 2= married; 3= widowed; 4= divorced; 5= marriage broken off; 6= unknown.

16 a) For matching addresses, we used the General Address Editor Application (GAEA) developed by the HCSO. This application treats and edits address data, providing a permanently updated list of the addresses in the Hungarian national territory. The application also considers and stores the changes of addresses through time. Another important feature of it is that it identifies addresses with a unique address identifier: the serial number of addresses accessed during the Census 2011. Thus, we carried out the first step – the address data matching – using the GAEA: first, the erroneous addresses from the register were corrected; and second, where possible, unique Census address identifiers were assigned to them. (On the details of the address level data matching see 2. Figure.)

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2. Figure– GAEA – Record linkage by address level

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Settlement ∑ No. of Households Public domain % of Public domain Building site % of Building site Sub-deposit % of Sub-deposit Census ID % of Census ID Aba 8 8 100 7 87,5 7 87,5 7 87,5 Abádszalók 44 44 100 41 93,18 40 90,909 34 77,273 Szerző: 9 Percentage7 of finded 77,78 7 Szerző:77,78 7 Szerző:77,778 6 66,667 Abasár 4 domains.4 100 4 Percentage100 of finded 4 Percentage100 of finded4 100 building sites. sub-deposits. Abaújalpár 1 1 100 1 100 1 100 1 100 Abaújkér 1 1 100 1 100 1 100 1 100 Abaújszántó 9 9 100 7 77,78 5 55,556 4 44,444 Abda 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 43 42 97,67 41 95,35 39 90,698 38 88,372 Ábrahámhegy 3 3 100 1 33,33 1 33,333 0 0 Ács 8 8 100 8 100 8 100 6 75 1 1 100 1 100 1 100 1 100 Acsád 1 1 100 1 100 1 100 1 100 Ácsteszér 1 1 100 1 100 1 100 1 100 Adács 9 9 100 9 100 7 77,778 7 77,778 Ádánd 2 2 100 2 100 2 100 2 100 6 6 100 6 100 6 100 4 66,667 Adorjánháza 1 1 100 1 100 1 100 1 100 Ág 215 215 100 215 100 202 93,953 202 93,953 Ágasegyháza 4 4 100 4 100 4 100 4 100 Ágfalva 2 2 100 2 100 2 100 2 100 3 3 100 3 100 3 100 3 100 Agyagosszergény 2 2 100 2 100 2 100 2 100 6 6 100 6 100 6 100 6 100 65 60 92,31 60 92,31 48 73,846 47 72,308 Aka 1 1 100 1 100 1 100 0 0 Akasztó 17 17 100 17 100 17 100 17 100 Alap 1 1 100 1 100 1 100 1 100 Alattyán 14 14 100 14 100 14 100 14 100 55 54 98,18 49 89,09 46 83,636 43 78,182 Alcsútdoboz 3 3 100 1 33,33 1 33,333 1 33,333 Aldebrő 1 1 100 1 100 1 100 1 100 Algyő 616 577 93,67 565 91,72 543 88,149 513 83,279 Almamellék 1 1 100 1 100 1 100 1 100 Almásfüzitő 3 3 100 3 100 0 0 0 0 Almáskamarás 4 4 100 4 100 4 100 4 100 Almáskeresztúr 1 1 100 1 100 1 100 1 100 Álmosd 2 2 100 2 100 2 100 2 100 Alsóberecki 3 3 100 3 100 3 100 3 100 Alsóbogát 1 1 100 1 100 0 0 0 0 Alsógagy 1 1 100 1 100 1 100 1 100 Alsómocsolád 388 384 98,97 381 98,2 372 95,876 365 94,072 Alsónána 6 6 100 3 50 3 50 3 50 Alsónémedi 9 9 100 7 77,78 6 66,667 6 66,667 Alsónemesapáti 2 2 100 2 100 2 100 2 100 Alsónyék 2 2 100 2 100 2 100 2 100 Alsóörs 18 17 94,44 17 94,44 17 94,444 9 50 Alsópáhok 4 4 100 4 100 4 100 4 100 Alsópetény 8 8 100 8 100 8 100 8 100 Alsószuha 1 1 100 1 100 1 100 1 100 Alsóújlak 1 1 100 1 100 1 100 1 100 Alsózsolca 7 7 100 7 100 7 100 6 85,714 Ambrózfalva 15 15 100 15 100 15 100 15 100 2 2 100 2 100 2 100 2 100 8 8 100 8 100 5 62,5 5 62,5 Andornaktálya 6 6 100 6 100 6 100 5 83,333 Andrásfa 2 2 100 2 100 2 100 2 100 Annavölgy 4 4 100 4 100 4 100 4 100 Apácatorna 1 1 100 0 0 0 0 0 0 5 5 100 5 100 5 100 5 100 4 4 100 4 100 4 100 3 75 5 5 100 5 100 5 100 5 100 Apátfalva 69 69 100 67 97,1 66 95,652 62 89,855 Apc 12 12 100 12 100 12 100 12 100 Áporka 3 3 100 3 100 3 100 3 100 5 5 100 5 100 5 100 5 100 Aranyosapáti 3 3 100 3 100 3 100 3 100 Aranyosgadány 5 5 100 5 100 5 100 5 100 Arló 3 3 100 3 100 3 100 3 100 Arnót 9 9 100 9 100 9 100 9 100 Árpádhalom 7 7 100 7 100 7 100 4 57,143 Ártánd 1 1 100 1 100 1 100 1 100 Ásotthalom 352 351 99,72 238 67,61 235 66,761 216 61,364 Ásványráró 3 3 100 3 100 2 66,667 2 66,667 Aszaló 2 2 100 2 100 2 100 2 100 Ászár 3 3 100 3 100 3 100 3 100 Aszód 24 24 100 24 100 19 79,167 11 45,833 Aszófő 2 2 100 1 50 1 50 1 50 Áta 1 1 100 1 100 1 100 1 100 Atkár 3 3 100 3 100 3 100 2 66,667 Attala 4 4 100 4 100 3 75 3 75 1 1 100 1 100 1 100 1 100 Babócsa 4 4 100 4 100 4 100 4 100 Bábolna 8 7 87,5 5 62,5 3 37,5 2 25 Bábonymegyer 1 1 100 1 100 1 100 1 100 Bácsalmás 150 143 95,33 140 93,33 122 81,333 107 71,333 Bácsbokod 26 26 100 26 100 26 100 25 96,154 Bácsborsód 4 4 100 4 100 4 100 4 100 Bácsszentgyörgy 1 1 100 1 100 1 100 1 100 Bácsszőlős 6 6 100 6 100 6 100 6 100 12 12 100 9 75 9 75 6 50 Badacsonytördemic 5 5 100 4 80 4 80 3 60 Bag 13 13 100 13 100 11 84,615 11 84,615 2 2 100 2 100 2 100 2 100 Bágyogszovát 1 1 100 1 100 1 100 1 100 Baj 3 3 100 3 100 3 100 3 100 Baja 459 456 99,35 447 97,39 373 81,264 309 67,32 Bajna 5 4 80 4 80 4 80 4 80 Bak 1 1 100 1 100 1 100 1 100 Bakóca 346 346 100 346 100 304 87,861 290 83,815 1 1 100 1 100 1 100 1 100 Bakonybánk 2 2 100 2 100 2 100 2 100 Bakonybél 3 3 100 3 100 3 100 2 66,667 Bakonyjákó 1 1 100 1 100 1 100 1 100 Bakonykoppány 2 2 100 2 100 2 100 2 100 2 2 100 2 100 2 100 2 100 Bakonyszentiván 1 1 100 1 100 1 100 1 100 Bakonyszentkirály 3 3 100 3 100 3 100 3 100 Bakonyszentlászló 4 4 100 4 100 4 100 4 100 Bakonyszombathely 2 2 100 2 100 2 100 2 100 Bakonyszücs 1 1 100 1 100 1 100 1 100 Bakonytamási 1 1 100 1 100 1 100 1 100 109 104 95,41 103 94,5 101 92,661 90 82,569 Baksa 1 1 100 1 100 1 100 1 100 Baktakék 1 1 100 1 100 1 100 1 100 Baktalórántháza 11 11 100 11 100 11 100 10 90,909 88 87 98,86 86 97,73 64 72,727 53 60,227 Balástya 224 223 99,55 202 90,18 202 90,179 196 87,5 2 2 100 2 100 2 100 2 100 8 8 100 6 75 6 75 3 37,5 13 13 100 13 100 12 92,308 7 53,846 Balatonalmádi 87 87 100 84 96,55 78 89,655 61 70,115 Balatonberény 3 3 100 3 100 3 100 2 66,667 Balatonboglár 44 41 93,18 37 84,09 35 79,545 28 63,636 Balatoncsicsó 2 2 100 2 100 2 100 1 50 3 3 100 3 100 3 100 1 33,333 Balatonendréd 4 4 100 4 100 4 100 4 100 12 12 100 11 91,67 11 91,667 6 50 Balatonfőkajár 4 4 100 4 100 4 100 3 75 Balatonföldvár 24 24 100 21 87,5 19 79,167 15 62,5 Balatonfüred 120 120 100 106 88,33 78 65 50 41,667 Balatonfűzfő 30 30 100 30 100 27 90 22 73,333 Balatongyörök 11 10 90,91 7 63,64 6 54,545 6 54,545 1 1 100 1 100 1 100 0 0 19 19 100 18 94,74 17 89,474 13 68,421 Balatonkeresztúr 3 3 100 2 66,67 1 33,333 0 0 27 26 96,3 25 92,59 21 77,778 20 74,074 Balatonmagyaród 1 1 100 1 100 1 100 1 100 Balatonmáriafürdő 9 7 77,78 7 77,78 7 77,778 3 33,333 Balatonőszöd 5 5 100 5 100 5 100 2 40 9 9 100 9 100 9 100 9 100 Balatonszárszó 14 14 100 13 92,86 12 85,714 8 57,143 21 21 100 20 95,24 18 85,714 14 66,667 Balatonszentgyörgy 3 3 100 2 66,67 2 66,667 2 66,667 2 2 100 2 100 2 100 1 50 Balatonszőlős 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 0 0 Balatonújlak 1 1 100 1 100 1 100 1 100 Balatonvilágos 15 14 93,33 13 86,67 13 86,667 5 33,333 Balinka 1 1 100 1 100 1 100 1 100 Balkány 10 10 100 10 100 10 100 8 80 Ballószög 7 6 85,71 6 85,71 6 85,714 6 85,714 Balmazújváros 29 29 100 29 100 28 96,552 28 96,552 Balogunyom 1 1 100 1 100 1 100 1 100 Balotaszállás 26 24 92,31 23 88,46 23 88,462 23 88,462 Bálványos 3 3 100 3 100 3 100 3 100 Bana 2 2 100 2 100 2 100 2 100 Bánd 1 1 100 1 100 1 100 1 100 Bánhorváti 2 2 100 0 0 0 0 0 0 Bánk 8 8 100 8 100 8 100 7 87,5 Bánokszentgyörgy 1 1 100 1 100 1 100 0 0 Bánréve 4 4 100 4 100 4 100 2 50 Bár 1 1 100 1 100 1 100 1 100 Barabás 2 2 100 2 100 2 100 2 100 6 3 50 3 50 3 50 3 50 10 10 100 9 90 9 90 8 80 Báránd 5 5 100 5 100 5 100 5 100 Baranyajenő 541 541 100 541 100 519 95,933 514 95,009 Baranyaszentgyörgy 167 167 100 167 100 144 86,228 143 85,629 1 1 100 1 100 1 100 1 100 61 61 100 60 98,36 59 96,721 37 60,656 Báta 6 5 83,33 5 83,33 5 83,333 5 83,333 Bátaapáti 2 2 100 2 100 2 100 2 100 Bátaszék 24 24 100 23 95,83 23 95,833 22 91,667 Baté 4 4 100 4 100 4 100 4 100 Bátmonostor 9 9 100 9 100 9 100 9 100 Bátonyterenye 48 47 97,92 47 97,92 41 85,417 38 79,167 Bátor 1 1 100 1 100 1 100 0 0 Bátorliget 1 1 100 1 100 1 100 1 100 185 185 100 181 97,84 170 91,892 160 86,486 Bátya 5 5 100 5 100 5 100 5 100 Bázakerettye 6 6 100 6 100 2 33,333 2 33,333 3 3 100 3 100 2 66,667 1 33,333 5 5 100 5 100 5 100 4 80 3 3 100 3 100 3 100 3 100 Bedő 1 1 100 1 100 0 0 0 0 Bejcgyertyános 2 2 100 2 100 2 100 2 100 Békás 1 1 100 1 100 1 100 1 100 9 9 100 9 100 9 100 9 100 Békés 132 131 99,24 124 93,94 123 93,182 112 84,848 Békéscsaba 763 761 99,74 744 97,51 648 84,928 482 63,172 Békéssámson 48 46 95,83 46 95,83 44 91,667 43 89,583 Békésszentandrás 23 23 100 23 100 23 100 23 100 Bekölce 4 4 100 4 100 4 100 4 100 Bélapátfalva 9 8 88,89 7 77,78 6 66,667 6 66,667 4 4 100 4 100 4 100 0 0 3 3 100 3 100 3 100 3 100 1 1 100 1 100 1 100 0 0 Bélmegyer 8 8 100 7 87,5 7 87,5 7 87,5 1 1 100 1 100 1 100 1 100 Bénye 3 3 100 3 100 3 100 3 100 Bér 3 3 100 3 100 3 100 3 100 Bérbaltavár 2 2 100 2 100 2 100 2 100 15 15 100 14 93,33 14 93,333 12 80 Beregdaróc 2 2 100 0 0 0 0 0 0 Berekböszörmény 11 11 100 11 100 10 90,909 10 90,909 Berekfürdő 89 89 100 87 97,75 86 96,629 50 56,18 7 7 100 7 100 7 100 7 100 4 4 100 3 75 2 50 1 25 Beret 3 3 100 3 100 3 100 3 100 Berettyóújfalu 63 63 100 63 100 57 90,476 44 69,841 9 9 100 9 100 6 66,667 6 66,667 4 4 100 4 100 4 100 4 100 4 4 100 4 100 4 100 4 100 5 5 100 5 100 5 100 3 60 Bernecebaráti 8 8 100 8 100 8 100 8 100 Berzék 1 1 100 1 100 1 100 1 100 8 8 100 8 100 8 100 7 87,5 Besenyőtelek 4 4 100 4 100 4 100 4 100 Besenyszög 12 12 100 12 100 12 100 12 100 Besnyő 5 5 100 5 100 5 100 5 100 6 6 100 1 16,67 1 16,667 1 16,667 Biatorbágy 70 69 98,57 63 90 52 74,286 48 68,571 Bicsérd 3 3 100 2 66,67 2 66,667 2 66,667 55 55 100 52 94,55 49 89,091 48 87,273 14 11 78,57 11 78,57 10 71,429 10 71,429 7 4 57,14 4 57,14 4 57,143 4 57,143 9 9 100 8 88,89 7 77,778 7 77,778 14 14 100 13 92,86 12 85,714 11 78,571 Biri 1 1 100 1 100 1 100 1 100 Bocfölde 3 3 100 3 100 3 100 3 100 Boconád 4 4 100 4 100 4 100 4 100 Bócsa 4 4 100 4 100 4 100 4 100 13 13 100 13 100 13 100 13 100 Boda 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 3 100 Bodolyabér 11 11 100 11 100 9 81,818 9 81,818 3 3 100 3 100 3 100 3 100 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 3 3 100 3 100 3 100 3 100 5 5 100 5 100 5 100 4 80 5 5 100 5 100 5 100 5 100 Bódvarákó 1 1 100 1 100 1 100 1 100 Bódvaszilas 6 6 100 5 83,33 5 83,333 5 83,333 Bogács 3 3 100 3 100 3 100 2 66,667 Bogád 4 4 100 3 75 3 75 3 75 Bogdása 2 2 100 2 100 0 0 0 0 Bogyiszló 2 2 100 2 100 2 100 2 100 Bogyoszló 1 1 100 1 100 1 100 1 100 Bokod 3 3 100 2 66,67 2 66,667 2 66,667 Boldog 2 2 100 2 100 2 100 2 100 2 2 100 2 100 2 100 2 100 Boldogkőváralja 4 4 100 4 100 3 75 2 50 2 2 100 2 100 2 100 2 100 Bolhó 4 4 100 4 100 4 100 4 100 Bóly 14 14 100 14 100 12 85,714 12 85,714 1 1 100 1 100 0 0 0 0 Bonyhád 69 69 100 63 91,3 56 81,159 48 69,565 Bordány 283 283 100 269 95,05 265 93,64 262 92,58 Borjád 3 3 100 3 100 3 100 3 100 7 7 100 7 100 7 100 7 100 2 2 100 2 100 2 100 2 100 Borsodbóta 3 3 100 3 100 3 100 3 100 1 1 100 1 100 1 100 1 100 Borsodivánka 5 5 100 5 100 5 100 4 80 Borsodnádasd 6 6 100 6 100 5 83,333 4 66,667 Borsosberény 1 1 100 1 100 1 100 1 100 Borzavár 3 3 100 3 100 3 100 1 33,333 Botpalád 5 5 100 5 100 5 100 5 100 4 4 100 4 100 4 100 1 25 Bózsva 2 2 100 2 100 2 100 2 100 Böde 1 1 100 1 100 1 100 1 100 Böhönye 4 4 100 4 100 3 75 3 75 Bököny 5 5 100 5 100 4 80 4 80 Bölcske 11 11 100 10 90,91 10 90,909 9 81,818 Bőny 2 2 100 2 100 2 100 2 100 Börcs 1 1 100 1 100 1 100 1 100 Bősárkány 4 4 100 4 100 4 100 4 100 Bucsa 89 89 100 88 98,88 88 98,876 85 95,506 Bucsu 2 2 100 2 100 2 100 2 100 Búcsúszentlászló 3 3 100 3 100 3 100 1 33,333 Budajenő 9 9 100 9 100 9 100 9 100 Budakalász 104 103 99,04 96 92,31 59 56,731 46 44,231 115 114 99,13 105 91,3 84 73,043 49 42,609 198 198 100 179 90,4 153 77,273 126 63,636 Budapest 01. ker. 396 396 100 367 92,68 239 60,354 208 52,525 Budapest 02. ker. 1181 1181 100 1124 95,17 805 68,163 630 53,345 Budapest 03. ker. 1686 1683 99,82 1622 96,2 1463 86,773 1234 73,191 Budapest 04. ker. 2216 2208 99,64 2147 96,89 1765 79,648 1636 73,827 Budapest 05. ker. 624 604 96,79 597 95,67 457 73,237 425 68,109 Budapest 06. ker. 1277 1275 99,84 1237 96,87 864 67,659 797 62,412 Budapest 07. ker. 65929 65794 99,8 65515 99,37 62673 95,061 62120 94,223 Budapest 08. ker. 1863 1861 99,89 1799 96,56 1314 70,531 1160 62,265 Budapest 09. ker. 1195 1194 99,92 1158 96,9 823 68,87 669 55,983 Budapest 10. ker. 1159 1159 100 1041 89,82 889 76,704 694 59,879 Budapest 11. ker. 2686 2682 99,85 2513 93,56 2143 79,784 1567 58,34 Budapest 12. ker. 901 876 97,23 830 92,12 579 64,262 410 45,505 Budapest 13. ker. 2448 2446 99,92 2325 94,98 2048 83,66 1909 77,982 Budapest 14. ker. 2497 2497 100 2389 95,67 1903 76,211 1580 63,276 Budapest 15. ker. 1351 1351 100 1313 97,19 1055 78,09 949 70,244 Budapest 16. ker. 739 735 99,46 705 95,4 560 75,778 461 62,382 Budapest 17. ker. 620 619 99,84 580 93,55 486 78,387 442 71,29 Budapest 18. ker. 713 709 99,44 673 94,39 551 77,279 461 64,656 Budapest 19. ker. 577 577 100 565 97,92 395 68,458 352 61,005 Budapest 20. ker. 637 637 100 606 95,13 416 65,306 333 52,276 Budapest 21. ker. 603 601 99,67 581 96,35 465 77,114 406 67,33 Budapest 22. ker. 414 412 99,52 379 91,55 287 69,324 231 55,797 Budapest 23. ker. 147 147 100 130 88,44 92 62,585 67 45,578 12 12 100 12 100 11 91,667 11 91,667 Bugacpusztaháza 1 1 100 1 100 1 100 1 100 4 4 100 4 100 3 75 3 75 4 4 100 4 100 4 100 4 100 Buják 13 13 100 13 100 13 100 12 92,308 Buzsák 1 1 100 1 100 1 100 1 100 Bük 13 12 92,31 12 92,31 9 69,231 5 38,462 Bükkábrány 6 6 100 6 100 6 100 6 100 Bükkaranyos 3 3 100 3 100 3 100 3 100 Bükkmogyorósd 1 1 100 1 100 1 100 1 100 Bükkösd 9 9 100 9 100 9 100 8 88,889 Bükkszék 1 1 100 1 100 1 100 1 100 Bükkszenterzsébet 2 2 100 2 100 2 100 2 100 Bükkszentkereszt 4 4 100 4 100 4 100 3 75 Bükkszentmárton 1 1 100 1 100 1 100 1 100 Bükkzsérc 4 4 100 4 100 4 100 4 100 Büssü 1 1 100 1 100 1 100 1 100 Cece 6 6 100 6 100 6 100 6 100 Cégénydányád 5 5 100 4 80 4 80 4 80 Cegléd 202 202 100 181 89,6 159 78,713 146 72,277 Ceglédbercel 16 16 100 15 93,75 14 87,5 14 87,5 Celldömölk 22 22 100 22 100 19 86,364 19 86,364 4 4 100 4 100 4 100 4 100 Cibakháza 12 12 100 11 91,67 10 83,333 9 75 Cigánd 15 15 100 15 100 15 100 15 100 Csabacsűd 7 7 100 6 85,71 6 85,714 6 85,714 4 4 100 4 100 4 100 4 100 11 11 100 11 100 11 100 11 100 2 2 100 2 100 2 100 2 100 Csajág 1 1 100 1 100 1 100 1 100 Csákánydoroszló 2 2 100 2 100 2 100 0 0 Csákberény 1 1 100 1 100 1 100 1 100 Csákvár 15 13 86,67 13 86,67 6 40 6 40 Csanádalberti 12 12 100 12 100 12 100 12 100 Csanádapáca 24 24 100 24 100 23 95,833 22 91,667 Csanádpalota 129 128 99,22 127 98,45 123 95,349 115 89,147 Csány 1 1 100 1 100 1 100 1 100 Csányoszró 1 1 100 1 100 1 100 1 100 Csanytelek 64 64 100 59 92,19 59 92,188 55 85,938 Csárdaszállás 1 1 100 1 100 1 100 1 100 Csarnóta 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 Császár 2 2 100 2 100 2 100 2 100 Császártöltés 22 22 100 22 100 22 100 21 95,455 Császló 2 2 100 2 100 2 100 2 100 Csátalja 12 12 100 11 91,67 11 91,667 11 91,667 Csávoly 18 18 100 18 100 18 100 17 94,444 Csécse 1 1 100 1 100 1 100 1 100 Csemő 12 12 100 11 91,67 11 91,667 10 83,333 63 53 84,13 52 82,54 52 82,54 50 79,365 12 12 100 12 100 11 91,667 10 83,333 Csengőd 26 26 100 26 100 26 100 25 96,154 Csép 2 2 100 2 100 2 100 2 100 Csépa 23 23 100 23 100 23 100 17 73,913 Csepreg 9 9 100 9 100 8 88,889 7 77,778 4 4 100 4 100 4 100 4 100 Cserénfa 1 1 100 1 100 1 100 1 100 Cserépfalu 2 2 100 2 100 2 100 1 50 Cserháthaláp 1 1 100 1 100 1 100 0 0 Cserkeszőlő 10 10 100 10 100 10 100 9 90 Cserkút 5 5 100 5 100 5 100 5 100 3 3 100 3 100 3 100 1 33,333 4 4 100 4 100 4 100 4 100 Csertő 9 9 100 9 100 9 100 3 33,333 2 2 100 2 100 2 100 2 100 Csetény 1 1 100 1 100 1 100 1 100 Csévharaszt 4 4 100 4 100 4 100 2 50 Csibrák 2 1 50 1 50 0 0 0 0 Csikéria 19 19 100 19 100 19 100 18 94,737 Csikóstőttős 25 25 100 25 100 25 100 25 100 Csipkerek 3 3 100 3 100 3 100 3 100 Csitár 1 1 100 1 100 1 100 1 100 Csobád 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 Csobánka 22 20 90,91 17 77,27 16 72,727 16 72,727 Csókakő 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 3 100 Csokvaomány 2 2 100 2 100 2 100 2 100 Csolnok 6 6 100 6 100 6 100 6 100 Csólyospálos 92 92 100 90 97,83 90 97,826 89 96,739 Csoma 4 4 100 3 75 3 75 3 75 Csomád 18 18 100 18 100 18 100 16 88,889 Csongrád 336 336 100 311 92,56 267 79,464 230 68,452 Csonkahegyhát 1 1 100 1 100 1 100 1 100 12 12 100 12 100 12 100 5 41,667 Csór 2 2 100 2 100 2 100 2 100 17 17 100 17 100 16 94,118 15 88,235 Csorvás 44 41 93,18 40 90,91 39 88,636 35 79,545 Csögle 1 1 100 1 100 1 100 1 100 Csökmő 16 9 56,25 9 56,25 9 56,25 8 50 Csököly 5 5 100 5 100 5 100 5 100 Csömör 63 63 100 62 98,41 60 95,238 45 71,429 Csörög 21 21 100 20 95,24 19 90,476 19 90,476 Csörötnek 3 3 100 3 100 3 100 3 100 Csővár 4 4 100 4 100 4 100 4 100 Csurgó 22 22 100 22 100 21 95,455 11 50 Cún 1 1 100 1 100 1 100 1 100 Dabas 60 58 96,67 54 90 45 75 38 63,333 Dad 2 2 100 2 100 2 100 2 100 Dág 3 3 100 3 100 3 100 3 100 Dáka 2 2 100 2 100 2 100 2 100 5 5 100 4 80 4 80 4 80 Dámóc 1 1 100 1 100 1 100 1 100 Dánszentmiklós 9 9 100 9 100 9 100 8 88,889 Dány 16 16 100 15 93,75 14 87,5 13 81,25 Darány 2 2 100 2 100 2 100 2 100 Darnózseli 4 4 100 4 100 4 100 4 100 2 2 100 2 100 2 100 2 100 Dávod 9 9 100 8 88,89 8 88,889 8 88,889 1231 1230 99,92 1131 91,88 987 80,179 791 64,257 Debréte 1 1 100 1 100 1 100 1 100 13 13 100 12 92,31 12 92,308 10 76,923 Dédestapolcsány 7 7 100 7 100 7 100 7 100 Dég 4 4 100 4 100 4 100 4 100 Dejtár 10 10 100 9 90 9 90 9 90 Délegyháza 13 12 92,31 10 76,92 10 76,923 8 61,538 5 5 100 5 100 5 100 5 100 Demjén 1 1 100 1 100 1 100 1 100 17 15 88,24 15 88,24 15 88,235 15 88,235 Derekegyház 31 31 100 31 100 31 100 26 83,871 384 353 91,93 344 89,58 330 85,938 318 82,813 7 7 100 7 100 7 100 7 100 Dévaványa 58 58 100 57 98,28 51 87,931 47 81,034 2 2 100 2 100 2 100 1 50 1 1 100 1 100 1 100 1 100 Diósd 57 57 100 48 84,21 38 66,667 31 54,386 Diósjenő 26 26 100 24 92,31 21 80,769 20 76,923 Dióskál 1 1 100 1 100 1 100 1 100 Diósviszló 7 7 100 7 100 4 57,143 3 42,857 Doba 2 2 100 2 100 2 100 1 50 23 23 100 23 100 23 100 22 95,652 Dóc 48 48 100 48 100 48 100 41 85,417 Domaháza 1 1 100 1 100 1 100 1 100 Domaszék 492 490 99,59 456 92,68 455 92,48 437 88,821 Dombegyház 67 59 88,06 58 86,57 52 77,612 49 73,134 15 15 100 15 100 15 100 15 100 Dombóvár 437 425 97,25 417 95,42 380 86,957 362 82,838 Dombrád 10 10 100 9 90 5 50 5 50 9 9 100 9 100 9 100 9 100 Domoszló 5 3 60 3 60 3 60 3 60 Dormánd 1 1 100 1 100 1 100 1 100 37 37 100 36 97,3 30 81,081 28 75,676 Dorogháza 3 3 100 3 100 3 100 3 100 Döbörhegy 1 1 100 1 100 0 0 0 0 Döbrököz 17 17 100 17 100 16 94,118 16 94,118 Döge 1 1 100 1 100 1 100 1 100 Dömös 3 3 100 3 100 3 100 3 100 Dömsöd 31 31 100 27 87,1 25 80,645 22 70,968 Dörgicse 1 1 100 1 100 1 100 1 100 Drágszél 6 6 100 6 100 6 100 6 100 Drávafok 2 2 100 2 100 2 100 2 100 Drávagárdony 4 4 100 4 100 1 25 1 25 Drávapalkonya 1 1 100 1 100 0 0 0 0 Drávaszabolcs 2 2 100 2 100 2 100 2 100 Drégelypalánk 11 11 100 11 100 10 90,909 10 90,909 Dudar 2 2 100 2 100 2 100 2 100 Dunaalmás 5 5 100 5 100 4 80 4 80 Dunabogdány 9 9 100 8 88,89 8 88,889 7 77,778 Dunaegyháza 6 6 100 6 100 6 100 6 100 7 7 100 7 100 7 100 7 100 Dunaföldvár 28 27 96,43 27 96,43 25 89,286 23 82,143 100 100 100 90 90 81 81 71 71 Dunakeszi 47355 47130 99,52 44588 94,16 35235 74,406 30778 64,994 3 3 100 3 100 3 100 3 100 16 16 100 16 100 16 100 16 100 4 4 100 4 100 4 100 3 75 Dunaszekcső 2 2 100 2 100 2 100 2 100 4 4 100 4 100 4 100 4 100 Dunaszentgyörgy 10 10 100 10 100 10 100 10 100 Dunaszentpál 1 1 100 1 100 1 100 1 100 4 4 100 3 75 3 75 2 50 Dunatetétlen 3 3 100 3 100 3 100 2 66,667 Dunaújváros 277 269 97,11 253 91,34 219 79,061 209 75,451 Dunavarsány 27 27 100 25 92,59 24 88,889 21 77,778 18 17 94,44 16 88,89 16 88,889 14 77,778 18 18 100 18 100 18 100 18 100 5 5 100 5 100 5 100 4 80 Écs 1 1 100 1 100 1 100 1 100 7 7 100 7 100 7 100 7 100 3 3 100 3 100 3 100 3 100 4 4 100 4 100 4 100 4 100 1 1 100 1 100 1 100 1 100 33 33 100 31 93,94 29 87,879 28 84,848 Edelény 8 8 100 8 100 7 87,5 7 87,5 424 420 99,06 410 96,7 338 79,717 271 63,915 Egerág 2 2 100 2 100 2 100 2 100 Egerbakta 1 1 100 1 100 1 100 1 100 Egerbocs 10 10 100 5 50 5 50 5 50 Egercsehi 2 2 100 1 50 1 50 1 50 1 1 100 1 100 1 100 1 100 Egerlövő 1 1 100 1 100 1 100 1 100 Egerszalók 2 2 100 1 50 1 50 1 50 Egerszólát 2 2 100 2 100 2 100 2 100 Egervár 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 25 25 100 25 100 22 88 21 84 Egyházasdengeleg 2 2 100 2 100 2 100 2 100 Egyházashetye 1 1 100 1 100 1 100 1 100 Egyházashollós 1 1 100 1 100 1 100 1 100 Egyházaskozár 18 18 100 18 100 18 100 18 100 Egyházasrádóc 3 3 100 3 100 3 100 3 100 19 19 100 18 94,74 18 94,737 15 78,947 Előszállás 2 2 100 2 100 1 50 1 50 Emőd 8 8 100 8 100 8 100 8 100 13 11 84,62 11 84,62 11 84,615 10 76,923 6 6 100 6 100 6 100 6 100 Endrőc 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 16 16 100 16 100 14 87,5 14 87,5 2 2 100 2 100 2 100 0 0 3 3 100 3 100 3 100 3 100 Eplény 1 1 100 1 100 1 100 1 100 Epöl 1 1 100 1 100 1 100 1 100 30 30 100 29 96,67 28 93,333 27 90 Érd 341 341 100 323 94,72 293 85,924 278 81,525 Erdőbénye 2 2 100 2 100 2 100 2 100 Erdőhorváti 2 2 100 2 100 2 100 2 100 Erdőkertes 66 66 100 59 89,39 59 89,394 53 80,303 Erdőkürt 1 1 100 1 100 1 100 1 100 Erdőtarcsa 3 3 100 3 100 3 100 3 100 Erdőtelek 9 9 100 8 88,89 8 88,889 8 88,889 Erk 5 5 100 5 100 5 100 4 80 Érpatak 3 3 100 3 100 3 100 3 100 Érsekcsanád 14 14 100 14 100 14 100 14 100 Érsekhalma 1 1 100 1 100 1 100 1 100 Érsekvadkert 9 9 100 9 100 9 100 9 100 Értény 2 2 100 2 100 2 100 2 100 Esztár 2 2 100 2 100 2 100 2 100 Esztergályhorváti 1 1 100 1 100 1 100 1 100 157 156 99,36 148 94,27 128 81,529 99 63,057 Ete 1 1 100 1 100 1 100 1 100 13 13 100 13 100 9 69,231 9 69,231 Fábiánháza 5 5 100 5 100 4 80 4 80 Fábiánsebestyén 25 25 100 24 96 24 96 23 92 Fadd 10 10 100 9 90 9 90 9 90 Fajsz 9 9 100 9 100 9 100 9 100 Farkasgyepű 1 1 100 1 100 1 100 1 100 2 2 100 2 100 0 0 0 0 22 22 100 20 90,91 19 86,364 19 86,364 Fegyvernek 32 32 100 31 96,88 31 96,875 30 93,75 Fehérgyarmat 15 13 86,67 12 80 12 80 11 73,333 Fehérvárcsurgó 8 8 100 7 87,5 7 87,5 7 87,5 Felcsút 9 9 100 7 77,78 7 77,778 7 77,778 Feldebrő 1 1 100 1 100 1 100 1 100 Felgyő 16 16 100 16 100 16 100 16 100 Felpéc 1 1 100 1 100 1 100 1 100 Felsődobsza 2 2 100 1 50 1 50 1 50 Felsőegerszeg 154 154 100 145 94,16 120 77,922 120 77,922 Felsőlajos 3 3 100 2 66,67 2 66,667 2 66,667 Felsőnyárád 2 2 100 2 100 2 100 2 100 Felsőnyék 3 3 100 3 100 3 100 3 100 Felsőörs 8 8 100 3 37,5 3 37,5 2 25 Felsőpáhok 2 2 100 2 100 2 100 2 100 Felsőpakony 18 18 100 18 100 18 100 16 88,889 Felsőpetény 5 5 100 5 100 5 100 4 80 Felsőszentiván 19 19 100 19 100 19 100 18 94,737 Felsőszentmárton 1 1 100 1 100 1 100 1 100 Felsőtárkány 5 5 100 5 100 4 80 3 60 Felsőtelekes 2 2 100 2 100 2 100 2 100 Felsővadász 3 3 100 3 100 3 100 3 100 Felsőzsolca 12 12 100 12 100 12 100 12 100 Fényeslitke 3 3 100 3 100 3 100 3 100 Ferencszállás 59 59 100 59 100 58 98,305 57 96,61 Fertőboz 1 1 100 1 100 1 100 1 100 Fertőd 3 3 100 3 100 3 100 3 100 Fertőhomok 2 2 100 2 100 2 100 2 100 Fertőrákos 1 1 100 1 100 1 100 1 100 Fertőszentmiklós 6 6 100 6 100 6 100 4 66,667 Fertőszéplak 1 1 100 1 100 1 100 1 100 Foktő 10 9 90 9 90 9 90 9 90 Folyás 1 1 100 1 100 1 100 1 100 Fonó 1 1 100 1 100 1 100 1 100 Fony 2 2 100 2 100 2 100 2 100 Fonyód 33 33 100 33 100 33 100 24 72,727 Forráskút 143 143 100 143 100 143 100 139 97,203 Forró 15 15 100 14 93,33 14 93,333 14 93,333 Fót 425 416 97,88 385 90,59 349 82,118 283 66,588 Földeák 66 66 100 65 98,48 65 98,485 64 96,97 Földes 7 7 100 7 100 7 100 7 100 Füle 2 2 100 2 100 2 100 2 100 Fülöp 2 2 100 2 100 2 100 2 100 Fülöpháza 7 7 100 4 57,14 4 57,143 4 57,143 Fülöpjakab 5 5 100 5 100 5 100 5 100 Fülöpszállás 11 11 100 11 100 9 81,818 9 81,818 Füzér 2 2 100 2 100 2 100 2 100 Füzérkajata 1 1 100 1 100 1 100 1 100 Füzérkomlós 1 1 100 1 100 1 100 1 100 Füzérradvány 1 1 100 1 100 1 100 1 100 Füzesabony 19 19 100 19 100 19 100 19 100 Füzesgyarmat 43 43 100 43 100 41 95,349 40 93,023 Gáborján 1 1 100 1 100 1 100 1 100 Gacsály 1 1 100 1 100 1 100 1 100 Gadány 1 1 100 1 100 1 100 1 100 Gádoros 26 24 92,31 23 88,46 23 88,462 21 80,769 Gagyvendégi 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 8 8 100 8 100 8 100 8 100 Galgagyörk 11 11 100 11 100 10 90,909 10 90,909 Galgahévíz 12 12 100 12 100 12 100 12 100 Galgamácsa 12 12 100 9 75 9 75 9 75 Gálosfa 1 1 100 1 100 1 100 1 100 Ganna 2 2 100 2 100 2 100 1 50 Gánt 5 5 100 5 100 5 100 3 60 Gara 8 7 87,5 7 87,5 6 75 6 75 Garáb 1 1 100 1 100 1 100 1 100 1 1 100 0 0 0 0 0 0 Gárdony 78 78 100 74 94,87 71 91,026 52 66,667 Gátér 3 3 100 3 100 3 100 3 100 Gávavencsellő 8 8 100 8 100 7 87,5 7 87,5 1 1 100 1 100 1 100 1 100 Géderlak 5 5 100 5 100 5 100 4 80 Gégény 4 4 100 4 100 4 100 4 100 Gelénes 3 3 100 3 100 3 100 3 100 Gellénháza 6 6 100 6 100 4 66,667 4 66,667 2 2 100 2 100 2 100 2 100 Gerde 1 1 100 1 100 0 0 0 0 Gerendás 21 21 100 18 85,71 18 85,714 17 80,952 Gerényes 306 305 99,67 305 99,67 293 95,752 292 95,425 1 1 100 1 100 1 100 1 100 Gersekarát 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 Gibárt 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 3 100 5 5 100 5 100 5 100 5 100 2 2 100 2 100 2 100 2 100 495 492 99,39 455 91,92 426 86,061 378 76,364 Gödöllő 406 405 99,75 266 65,52 231 56,897 198 48,768 Gödre 986 986 100 982 99,59 909 92,191 897 90,974 Gölle 5 5 100 5 100 5 100 5 100 Gönc 10 10 100 10 100 8 80 8 80 Gönyű 3 3 100 3 100 3 100 3 100 Görbeháza 4 4 100 4 100 4 100 4 100 Görcsöny 3 3 100 3 100 3 100 2 66,667 Grábóc 4 4 100 4 100 4 100 3 75 Gutorfölde 2 2 100 2 100 2 100 2 100 Gyál 138 138 100 125 90,58 118 85,507 111 80,435 Gyékényes 2 2 100 2 100 2 100 2 100 Gyenesdiás 9 9 100 9 100 9 100 9 100 Gyermely 11 11 100 11 100 11 100 11 100 Gyód 1 1 100 0 0 0 0 0 0 Gyomaendrőd 116 113 97,41 110 94,83 106 91,379 100 86,207 Gyömöre 2 2 100 2 100 2 100 2 100 Gyömrő 66 66 100 63 95,45 50 75,758 47 71,212 Gyöngyös 219 202 92,24 196 89,5 186 84,932 159 72,603 Gyöngyösfalu 1 1 100 1 100 1 100 1 100 Gyöngyöshalász 9 9 100 9 100 9 100 9 100 Gyöngyösmellék 4 4 100 4 100 1 25 1 25 Gyöngyösoroszi 2 2 100 2 100 2 100 2 100 Gyöngyöspata 2 2 100 2 100 2 100 1 50 Gyöngyössolymos 16 16 100 16 100 15 93,75 15 93,75 Gyöngyöstarján 2 2 100 2 100 2 100 2 100 Gyönk 6 6 100 6 100 6 100 6 100 Győr 587 585 99,66 572 97,44 512 87,223 313 53,322 Győrasszonyfa 1 1 100 1 100 1 100 1 100 Györe 1 1 100 1 100 1 100 1 100 Györgytarló 6 6 100 6 100 6 100 6 100 Györköny 7 7 100 7 100 5 71,429 5 71,429 Győrladamér 1 1 100 1 100 1 100 1 100 Győrság 1 1 100 1 100 1 100 0 0 Győrsövényház 1 1 100 1 100 1 100 1 100 Győrújbarát 6 6 100 5 83,33 5 83,333 5 83,333 Győrújfalu 5 5 100 5 100 5 100 5 100 Győrvár 1 1 100 1 100 1 100 1 100 Győrzámoly 3 3 100 3 100 3 100 3 100 5 5 100 5 100 5 100 3 60 Gyula 338 337 99,7 316 93,49 280 82,84 242 71,598 Gyulaháza 3 3 100 3 100 2 66,667 2 66,667 1 1 100 1 100 1 100 1 100 Gyúró 4 4 100 4 100 4 100 4 100 Gyüre 2 2 100 2 100 2 100 2 100 Hács 1 1 100 1 100 1 100 1 100 Hahót 4 4 100 4 100 4 100 4 100 Hajdúbagos 1 1 100 1 100 1 100 1 100 Hajdúböszörmény 38 38 100 38 100 37 97,368 27 71,053 Hajdúdorog 20 20 100 20 100 19 95 16 80 Hajdúhadház 4 4 100 4 100 4 100 4 100 Hajdúnánás 36 36 100 36 100 36 100 31 86,111 Hajdúsámson 8 8 100 8 100 6 75 5 62,5 Hajdúszoboszló 97 97 100 91 93,81 79 81,443 73 75,258 Hajdúszovát 4 4 100 4 100 4 100 4 100 Hajmás 2 2 100 2 100 2 100 2 100 Hajmáskér 4 4 100 4 100 4 100 3 75 Hajós 14 14 100 13 92,86 13 92,857 10 71,429 Halászi 5 5 100 5 100 5 100 4 80 Halásztelek 47 45 95,74 44 93,62 38 80,851 32 68,085 7 7 100 7 100 7 100 7 100 3 3 100 3 100 3 100 3 100 1 1 100 0 0 0 0 0 0 1 1 100 1 100 1 100 1 100 4 4 100 4 100 4 100 4 100 Harkakötöny 13 13 100 13 100 13 100 12 92,308 Harkány 15 14 93,33 14 93,33 12 80 10 66,667 Háromhuta 2 2 100 2 100 2 100 2 100 Harsány 1 1 100 1 100 1 100 1 100 Hárskút 2 2 100 2 100 2 100 2 100 Harta 12 12 100 12 100 12 100 12 100 94 94 100 88 93,62 79 84,043 70 74,468 Hédervár 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 7 7 100 7 100 5 71,429 3 42,857 Hegyfalu 2 2 100 2 100 2 100 2 100 Hegyhátmaróc 2 2 100 2 100 1 50 1 50 Hegyhátszentjakab 1 1 100 1 100 1 100 1 100 Hegykő 5 5 100 5 100 5 100 2 40 3 3 100 2 66,67 2 66,667 2 66,667 1 1 100 1 100 1 100 1 100 Héhalom 5 5 100 5 100 5 100 5 100 1 1 100 1 100 1 100 0 0 Hejőbába 1 1 100 1 100 1 100 1 100 Hejőkeresztúr 1 1 100 1 100 1 100 1 100 Hejőpapi 4 4 100 4 100 4 100 4 100 Hejőszalonta 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Helvécia 12 12 100 12 100 12 100 10 83,333 2 2 100 2 100 1 50 1 50 6 6 100 6 100 5 83,333 4 66,667 Hercegszántó 23 23 100 23 100 23 100 21 91,304 Heréd 6 6 100 6 100 6 100 6 100 Herencsény 4 4 100 4 100 4 100 4 100 8 8 100 8 100 7 87,5 7 87,5 Hernád 20 20 100 20 100 18 90 18 90 Hernádkak 5 5 100 5 100 5 100 5 100 Hernádkércs 1 1 100 1 100 1 100 1 100 Hernádnémeti 5 5 100 5 100 5 100 5 100 Hernádszurdok 3 3 100 1 33,33 1 33,333 1 33,333 Hernádvécse 4 4 100 4 100 4 100 4 100 Hernyék 1 1 100 1 100 1 100 1 100 Hetefejércse 1 1 100 1 100 1 100 1 100 7 7 100 6 85,71 4 57,143 4 57,143 32 32 100 32 100 30 93,75 28 87,5 Hevesaranyos 1 1 100 1 100 1 100 1 100 Hevesvezekény 2 2 100 2 100 2 100 2 100 Hévíz 39 38 97,44 35 89,74 34 87,179 28 71,795 Hévízgyörk 5 5 100 5 100 5 100 5 100 11 11 100 11 100 10 90,909 10 90,909 Hidasnémeti 1 1 100 1 100 1 100 1 100 Hidegség 1 1 100 1 100 1 100 1 100 Himesháza 2 2 100 2 100 2 100 2 100 3 3 100 3 100 3 100 3 100 Hodász 9 9 100 8 88,89 8 88,889 8 88,889 Hódmezővásárhely 1256 1252 99,68 1191 94,82 1048 83,439 919 73,169 Hollád 1 1 100 1 100 1 100 1 100 Hollókő 2 2 100 2 100 2 100 2 100 Homokbödöge 1 1 100 1 100 1 100 1 100 Homokmégy 11 11 100 11 100 11 100 11 100 Homokszentgyörgy 2 2 100 1 50 1 50 1 50 Homorúd 9 9 100 9 100 9 100 9 100 3 3 100 3 100 3 100 3 100 Hont 5 5 100 5 100 5 100 5 100 Hort 5 5 100 5 100 5 100 5 100 Hortobágy 5 5 100 3 60 3 60 2 40 Horváthertelend 1 1 100 1 100 0 0 0 0 Horvátzsidány 2 2 100 2 100 1 50 0 0 Hosszúhetény 18 18 100 16 88,89 16 88,889 16 88,889 Hosszúpályi 7 7 100 7 100 7 100 7 100 Hosszúpereszteg 3 3 100 3 100 3 100 3 100 Hosszúvíz 1 1 100 0 0 0 0 0 0 Hőgyész 11 11 100 11 100 7 63,636 4 36,364 9 9 100 7 77,78 7 77,778 7 77,778 Husztót 5 5 100 5 100 4 80 3 60 Ibrány 12 12 100 11 91,67 10 83,333 10 83,333 Igal 7 7 100 7 100 7 100 6 85,714 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Ikervár 2 2 100 2 100 2 100 2 100 8 7 87,5 7 87,5 7 87,5 7 87,5 Ikrény 2 2 100 2 100 2 100 2 100 3 3 100 3 100 3 100 3 100 8 8 100 4 50 4 50 4 50 Inárcs 11 11 100 11 100 11 100 10 90,909 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Ipolydamásd 3 3 100 3 100 3 100 3 100 Ipolyszög 1 1 100 1 100 0 0 0 0 Ipolytölgyes 19 19 100 19 100 18 94,737 18 94,737 4 4 100 4 100 4 100 4 100 15 15 100 15 100 15 100 13 86,667 51 49 96,08 48 94,12 43 84,314 41 80,392 Ispánk 1 1 100 1 100 1 100 1 100 5 5 100 5 100 5 100 5 100 Iszkaszentgyörgy 3 3 100 2 66,67 2 66,667 2 66,667 Iszkáz 1 1 100 1 100 1 100 1 100 Iván 4 4 100 4 100 3 75 3 75 Iváncsa 9 9 100 9 100 8 88,889 8 88,889 Izsák 31 31 100 30 96,77 30 96,774 28 90,323 Izsófalva 4 4 100 4 100 4 100 4 100 Jágónak 19 19 100 19 100 14 73,684 14 73,684 Ják 1 1 100 1 100 1 100 1 100 Jakabszállás 11 10 90,91 10 90,91 10 90,909 10 90,909 Jánd 3 3 100 3 100 3 100 3 100 Jánkmajtis 2 2 100 2 100 2 100 2 100 Jánoshalma 89 89 100 88 98,88 84 94,382 80 89,888 Jánosháza 2 2 100 2 100 1 50 1 50 Jánoshida 13 13 100 13 100 13 100 11 84,615 Jánossomorja 5 5 100 5 100 3 60 3 60 Járdánháza 2 2 100 2 100 2 100 2 100 Jármi 2 2 100 2 100 2 100 2 100 Jászágó 3 3 100 3 100 3 100 3 100 Jászalsószentgyörgy 8 8 100 8 100 8 100 8 100 Jászapáti 58 58 100 57 98,28 56 96,552 54 93,103 Jászárokszállás 33 33 100 33 100 32 96,97 31 93,939 Jászberény 143 143 100 140 97,9 123 86,014 112 78,322 Jászboldogháza 2 2 100 2 100 2 100 2 100 Jászdózsa 15 15 100 15 100 13 86,667 13 86,667 Jászfelsőszentgyörgy 4 4 100 3 75 3 75 3 75 Jászfényszaru 26 26 100 26 100 25 96,154 25 96,154 Jászivány 1 1 100 1 100 1 100 1 100 Jászjákóhalma 5 5 100 4 80 4 80 4 80 Jászkarajenő 12 12 100 12 100 10 83,333 7 58,333 Jászkisér 21 21 100 21 100 21 100 21 100 Jászladány 30 30 100 30 100 29 96,667 29 96,667 Jászszentandrás 6 6 100 6 100 6 100 6 100 Jászszentlászló 23 21 91,3 21 91,3 20 86,957 20 86,957 Jásztelek 3 3 100 3 100 3 100 3 100 Jenő 1 1 100 1 100 1 100 1 100 Jobaháza 2 2 100 2 100 2 100 2 100 Jobbágyi 12 12 100 12 100 11 91,667 11 91,667 Jósvafő 1 1 100 1 100 1 100 1 100 4 4 100 4 100 4 100 4 100 Kaba 12 12 100 12 100 12 100 12 100 Kács 2 2 100 2 100 2 100 2 100 Kadarkút 2 2 100 2 100 2 100 1 50 Kajárpéc 1 1 100 1 100 1 100 1 100 Kajászó 6 6 100 6 100 6 100 6 100 1 1 100 1 100 1 100 1 100 4 4 100 4 100 4 100 2 50 Kákics 2 2 100 2 100 2 100 2 100 6 5 83,33 5 83,33 3 50 3 50 Kál 25 25 100 24 96 21 84 20 80 Kalaznó 4 4 100 4 100 4 100 3 75 Káld 3 3 100 3 100 3 100 3 100 Kálló 5 5 100 5 100 5 100 5 100 Kállósemjén 5 5 100 5 100 4 80 4 80 180 178 98,89 176 97,78 156 86,667 146 81,111 Káloz 2 2 100 1 50 1 50 1 50 2 2 100 2 100 2 100 2 100 Kamut 4 4 100 4 100 4 100 4 100 Kántorjánosi 3 3 100 3 100 3 100 3 100 1 1 100 1 100 1 100 1 100 Kápolna 6 6 100 6 100 6 100 5 83,333 Kápolnásnyék 11 11 100 11 100 10 90,909 10 90,909 6 6 100 6 100 6 100 6 100 Kaposfő 1 1 100 1 100 1 100 1 100 2 2 100 1 50 1 50 1 50 Kaposkeresztúr 6 6 100 6 100 5 83,333 5 83,333 Kaposmérő 6 6 100 6 100 6 100 6 100 3 3 100 3 100 3 100 3 100 Kaposszekcső 47 47 100 47 100 43 91,489 42 89,362 3 3 100 3 100 3 100 3 100 Kaposújlak 4 4 100 4 100 1 25 1 25 Kaposvár 371 371 100 346 93,26 298 80,323 244 65,768 Káptalanfa 3 3 100 3 100 3 100 3 100 Káptalantóti 1 1 100 0 0 0 0 0 0 Kapuvár 21 21 100 21 100 19 90,476 18 85,714 Kára 3 3 100 3 100 3 100 3 100 Karácsond 7 6 85,71 6 85,71 6 85,714 6 85,714 Karád 7 5 71,43 5 71,43 5 71,429 5 71,429 4 4 100 4 100 4 100 4 100 Karancsberény 1 1 100 1 100 1 100 1 100 6 6 100 6 100 6 100 6 100 Karancslapujtő 1 1 100 1 100 1 100 1 100 Karancsság 2 2 100 2 100 2 100 2 100 Kárász 1 1 100 1 100 1 100 1 100 Karcag 24452 24437 99,94 23488 96,06 22208 90,823 21431 87,645 2 2 100 2 100 1 50 1 50 3 3 100 2 66,67 2 66,667 2 66,667 Kardoskút 11 11 100 11 100 11 100 11 100 2 2 100 2 100 2 100 2 100 Károlyháza 1 1 100 1 100 1 100 1 100 Kartal 15 15 100 14 93,33 14 93,333 12 80 Kaskantyú 5 5 100 5 100 5 100 5 100 Kastélyosdombó 1 1 100 1 100 1 100 0 0 18 14 77,78 14 77,78 13 72,222 13 72,222 Katymár 23 23 100 22 95,65 19 82,609 18 78,261 Káva 6 6 100 6 100 6 100 6 100 Kazár 7 3 42,86 3 42,86 3 42,857 3 42,857 113 109 96,46 97 85,84 90 79,646 85 75,221 Kázsmárk 3 3 100 3 100 3 100 3 100 65 65 100 62 95,38 62 95,385 61 93,846 Kecskéd 7 7 100 7 100 7 100 7 100 Kecskemét 1045 1017 97,32 952 91,1 799 76,459 612 58,565 Kék 4 4 100 4 100 4 100 4 100 Kékcse 7 7 100 7 100 7 100 7 100 77 77 100 76 98,7 74 96,104 70 90,909 Kéleshalom 1 1 100 1 100 1 100 1 100 9 9 100 9 100 9 100 9 100 Kemence 11 11 100 11 100 11 100 11 100 Kemenesmagasi 1 1 100 1 100 1 100 1 100 Kemenesmihályfa 1 1 100 1 100 1 100 1 100 Kemenespálfa 1 1 100 1 100 1 100 1 100 Kemenessömjén 1 1 100 1 100 1 100 1 100 Kemenesszentpéter 3 3 100 3 100 3 100 3 100 Keménfa 1 1 100 1 100 1 100 1 100 Kémes 1 1 100 1 100 1 100 1 100 Kenderes 43 43 100 42 97,67 31 72,093 25 58,14 Kenézlő 6 6 100 6 100 6 100 6 100 Kengyel 15 15 100 15 100 15 100 15 100 Kenyeri 1 1 100 1 100 1 100 1 100 2 2 100 2 100 1 50 1 50 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Kerekegyháza 27 27 100 26 96,3 26 96,296 24 88,889 3 3 100 3 100 2 66,667 2 66,667 Kerékteleki 2 2 100 2 100 2 100 2 100 84 84 100 80 95,24 75 89,286 69 82,143 Kérsemjén 1 1 100 1 100 1 100 1 100 Kertészsziget 2 2 100 2 100 2 100 2 100 5 5 100 4 80 4 80 4 80 Kesznyéten 1 1 100 1 100 1 100 1 100 Keszőhidegkút 1 1 100 1 100 1 100 1 100 110 110 100 106 96,36 88 80 78 70,909 Kesztölc 7 7 100 6 85,71 6 85,714 6 85,714 Keszü 3 3 100 3 100 3 100 3 100 Kétbodony 5 5 100 3 60 3 60 3 60 Kétegyháza 35 32 91,43 32 91,43 32 91,429 25 71,429 Kéthely 3 3 100 3 100 3 100 3 100 Kétsoprony 7 7 100 5 71,43 5 71,429 5 71,429 Kétújfalu 1 1 100 1 100 1 100 1 100 Kétvölgy 1 1 100 1 100 1 100 1 100 21 19 90,48 19 90,48 19 90,476 17 80,952 5 5 100 5 100 5 100 5 100 Kincsesbánya 1 1 100 0 0 0 0 0 0 Királd 1 1 100 1 100 1 100 1 100 Királyhegyes 13 13 100 13 100 13 100 8 61,538 5 2 40 2 40 2 40 2 40 Kisar 8 8 100 8 100 8 100 8 100 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Kisbárapáti 1 1 100 1 100 1 100 1 100 Kisbér 7 7 100 5 71,43 5 71,429 4 57,143 Kisbeszterce 101 101 100 101 100 81 80,198 80 79,208 2 2 100 2 100 2 100 2 100 Kiscsősz 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 Kisdombegyház 13 13 100 13 100 13 100 13 100 4 4 100 4 100 3 75 3 75 1 1 100 1 100 1 100 1 100 Kishajmás 264 263 99,62 262 99,24 254 96,212 253 95,833 Kisharsány 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 Kiskorpád 1 1 100 1 100 1 100 1 100 Kisköre 8 8 100 8 100 8 100 7 87,5 Kiskőrös 114 113 99,12 112 98,25 106 92,982 100 87,719 Kiskunfélegyháza 336 336 100 328 97,62 250 74,405 201 59,821 373 372 99,73 350 93,83 298 79,893 271 72,654 Kiskunlacháza 27 27 100 27 100 27 100 21 77,778 213 213 100 209 98,12 194 91,08 183 85,915 Kisláng 3 3 100 3 100 3 100 3 100 Kisléta 1 1 100 1 100 1 100 1 100 Kislőd 2 2 100 2 100 2 100 2 100 Kismányok 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 20 20 100 18 90 17 85 15 75 Kisnamény 2 2 100 2 100 2 100 2 100 Kisnána 4 4 100 4 100 4 100 4 100 Kisnémedi 10 8 80 8 80 7 70 6 60 7 7 100 5 71,43 5 71,429 3 42,857 Kispáli 1 1 100 1 100 1 100 1 100 Kisszállás 56 54 96,43 53 94,64 52 92,857 48 85,714 Kisszékely 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Kisszentmárton 6 6 100 6 100 6 100 6 100 Kisszőlős 1 1 100 1 100 1 100 1 100 77 77 100 75 97,4 59 76,623 51 66,234 406 406 100 394 97,04 378 93,103 334 82,266 1 1 100 1 100 1 100 1 100 Kistolmács 1 1 100 1 100 1 100 1 100 Kistótfalu 1 1 100 1 100 1 100 1 100 Kisújszállás 157 157 100 151 96,18 131 83,439 93 59,236 Kisunyom 1 1 100 1 100 1 100 0 0 Kisvárda 58 58 100 56 96,55 52 89,655 47 81,034 Kisvarsány 1 1 100 1 100 1 100 1 100 Kisvaszar 387 387 100 363 93,8 337 87,08 335 86,563 160 160 100 160 100 158 98,75 150 93,75 Klárafalva 80 80 100 79 98,75 78 97,5 78 97,5 2 2 100 2 100 2 100 2 100 Kocsér 7 7 100 7 100 7 100 7 100 8 8 100 7 87,5 7 87,5 6 75 1 1 100 1 100 1 100 1 100 Kóka 25 25 100 23 92 23 92 19 76 1 1 100 1 100 1 100 1 100 Komádi 31 30 96,77 30 96,77 27 87,097 23 74,194 Komárom 58 58 100 54 93,1 43 74,138 39 67,241 Komjáti 1 1 100 1 100 1 100 1 100 Komló 622 614 98,71 569 91,48 476 76,527 432 69,453 Komoró 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 3 100 Kondó 6 6 100 6 100 6 100 6 100 Kondorfa 2 2 100 2 100 2 100 2 100 32 28 87,5 28 87,5 28 87,5 26 81,25 Kóny 1 1 100 1 100 1 100 1 100 Konyár 2 2 100 2 100 2 100 2 100 Kópháza 5 5 100 5 100 5 100 5 100 Koppányszántó 2 2 100 2 100 2 100 2 100 Korlát 1 1 100 0 0 0 0 0 0 Koroncó 2 2 100 2 100 1 50 1 50 12 12 100 11 91,67 11 91,667 11 91,667 Kóspallag 4 4 100 1 25 1 25 1 25 Kótaj 1 1 100 1 100 1 100 1 100 Kozárd 2 2 100 2 100 2 100 2 100 Kozármisleny 12 12 100 11 91,67 7 58,333 7 58,333 Köblény 4 4 100 4 100 4 100 4 100 Kökény 2 2 100 2 100 2 100 2 100 Kőkút 2 2 100 2 100 2 100 2 100 Kölcse 2 2 100 2 100 2 100 2 100 Kölesd 10 10 100 10 100 10 100 10 100 Kölked 1 1 100 1 100 1 100 1 100 Kömlő 6 6 100 6 100 6 100 6 100 Kömlőd 2 1 50 1 50 1 50 1 50 Kömpöc 27 27 100 26 96,3 25 92,593 25 92,593 Körmend 12 11 91,67 11 91,67 10 83,333 10 83,333 Környe 8 8 100 8 100 8 100 7 87,5 Köröm 1 1 100 1 100 1 100 1 100 Kőröshegy 9 9 100 8 88,89 7 77,778 6 66,667 Körösladány 31 30 96,77 30 96,77 30 96,774 30 96,774 Körösszakál 2 2 100 2 100 2 100 2 100 Körösszegapáti 1 1 100 1 100 1 100 1 100 Köröstarcsa 13 13 100 13 100 13 100 13 100 Kőröstetétlen 2 2 100 2 100 2 100 2 100 Körösújfalu 3 3 100 3 100 3 100 3 100 Kőszárhegy 2 2 100 2 100 2 100 2 100 Kőszeg 44 44 100 43 97,73 30 68,182 14 31,818 Kötcse 2 2 100 2 100 2 100 1 50 Kötegyán 9 9 100 9 100 9 100 9 100 Kőtelek 9 9 100 9 100 9 100 9 100 Kővágóörs 5 5 100 5 100 5 100 3 60 Kővágószőlős 6 6 100 2 33,33 1 16,667 1 16,667 Kővágótöttös 1 0 0 0 0 0 0 0 0 Kövegy 15 15 100 15 100 15 100 15 100 1 1 100 1 100 1 100 1 100 12 12 100 10 83,33 10 83,333 10 83,333 9 9 100 5 55,56 5 55,556 5 55,556 Kunágota 44 43 97,73 43 97,73 42 95,455 42 95,455 47 47 100 47 100 47 100 46 97,872 3 3 100 3 100 3 100 2 66,667 Kuncsorba 1 1 100 1 100 1 100 1 100 Kunfehértó 30 30 100 30 100 29 96,667 28 93,333 Kunhegyes 90 90 100 84 93,33 76 84,444 66 73,333 Kunmadaras 127 127 100 125 98,43 119 93,701 116 91,339 Kunpeszér 1 1 100 1 100 1 100 1 100 Kunszállás 4 4 100 3 75 3 75 3 75 Kunszentmárton 104 102 98,08 99 95,19 93 89,423 80 76,923 Kunszentmiklós 33 33 100 33 100 27 81,818 27 81,818 Kup 1 1 100 1 100 1 100 1 100 Kupa 1 1 100 1 100 1 100 1 100 Kurd 8 6 75 6 75 5 62,5 5 62,5 Kurityán 4 4 100 4 100 3 75 3 75 6 6 100 6 100 6 100 6 100 Kübekháza 214 210 98,13 202 94,39 201 93,925 186 86,916 Külsővat 1 1 100 1 100 1 100 0 0 Lábatlan 13 13 100 12 92,31 9 69,231 8 61,538 Lábod 1 1 100 1 100 1 100 1 100 Lácacséke 10 10 100 10 100 10 100 3 30 Ladánybene 3 2 66,67 2 66,67 2 66,667 2 66,667 Lajoskomárom 3 3 100 3 100 3 100 1 33,333 42 42 100 40 95,24 38 90,476 37 88,095 26 26 100 26 100 25 96,154 24 92,308 Lakócsa 1 1 100 1 100 1 100 1 100 Lánycsók 14 14 100 14 100 13 92,857 13 92,857 Lápafő 1 1 100 1 100 1 100 1 100 5 5 100 4 80 4 80 4 80 Látrány 2 2 100 2 100 2 100 2 100 Lázi 2 2 100 2 100 2 100 0 0 Leányfalu 29 29 100 26 89,66 26 89,655 23 79,31 Leányvár 13 13 100 13 100 13 100 13 100 Lébény 2 2 100 2 100 2 100 2 100 Legénd 4 4 100 4 100 4 100 4 100 Legyesbénye 1 1 100 1 100 1 100 1 100 Léh 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 3 100 17 17 100 17 100 17 100 9 52,941 Lengyeltóti 7 7 100 6 85,71 6 85,714 6 85,714 25 25 100 23 92 23 92 23 92 Lepsény 15 15 100 14 93,33 13 86,667 12 80 Lesenceistvánd 1 1 100 1 100 1 100 1 100 Létavértes 8 8 100 8 100 8 100 8 100 6 6 100 6 100 5 83,333 5 83,333 Letkés 5 5 100 5 100 5 100 5 100 Levél 5 5 100 5 100 5 100 5 100 5 5 100 5 100 5 100 5 100 6 4 66,67 4 66,67 4 66,667 4 66,667 Lipót 4 4 100 0 0 0 0 0 0 Lippó 1 1 100 1 100 1 100 1 100 Lispeszentadorján 6 6 100 6 100 6 100 6 100 Liszó 1 1 100 1 100 1 100 1 100 Litér 6 6 100 6 100 6 100 6 100 Lónya 1 1 100 1 100 1 100 1 100 Lórév 2 2 100 2 100 2 100 2 100 Lovas 2 2 100 0 0 0 0 0 0 Lovasberény 1 1 100 1 100 1 100 1 100 Lőkösháza 18 18 100 18 100 18 100 17 94,444 Lőrinci 23 23 100 21 91,3 14 60,87 14 60,87 Lövőpetri 3 3 100 3 100 3 100 2 66,667 1 1 100 1 100 1 100 1 100 Ludányhalászi 6 6 100 6 100 6 100 6 100 2 2 100 2 100 2 100 2 100 Mád 11 11 100 11 100 11 100 11 100 37 37 100 34 91,89 33 89,189 31 83,784 3 3 100 3 100 3 100 3 100 Maglód 68 68 100 66 97,06 61 89,706 59 86,765 Mágocs 2656 2656 100 2619 98,61 2469 92,959 2409 90,7 Magyaralmás 3 3 100 3 100 2 66,667 2 66,667 Magyaratád 3 3 100 3 100 3 100 3 100 Magyarbánhegyes 18 18 100 18 100 18 100 18 100 Magyarcsanád 52 52 100 52 100 51 98,077 47 90,385 Magyardombegyház 3 1 33,33 1 33,33 1 33,333 1 33,333 6 6 100 6 100 6 100 6 100 1 1 100 1 100 1 100 1 100 Magyarföld 1 1 100 1 100 1 100 1 100 Magyargéc 4 3 75 3 75 3 75 3 75 17 17 100 17 100 16 94,118 14 82,353 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Magyarnándor 2 2 100 2 100 2 100 2 100 Magyarpolány 2 2 100 2 100 2 100 2 100 Magyarszecsőd 3 3 100 3 100 3 100 3 100 Magyarszék 12 12 100 11 91,67 11 91,667 10 83,333 Magyarszentmiklós 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 Majosháza 8 8 100 7 87,5 7 87,5 6 75 1 1 100 1 100 1 100 1 100 Makád 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 Maklár 7 7 100 7 100 7 100 7 100 Makó 816 815 99,88 721 88,36 655 80,27 608 74,51 Mályi 17 17 100 17 100 16 94,118 16 94,118 Mándok 6 6 100 6 100 6 100 5 83,333 Mánfa 20 20 100 20 100 20 100 19 95 Mány 8 8 100 7 87,5 6 75 6 75 29 29 100 28 96,55 24 82,759 23 79,31 Marcaltő 1 1 100 1 100 1 100 1 100 Máriahalom 1 1 100 1 100 0 0 0 0 Máriakálnok 2 2 100 2 100 2 100 2 100 Máriakéménd 2 2 100 2 100 2 100 2 100 Márianosztra 10 10 100 9 90 7 70 6 60 Máriapócs 5 5 100 5 100 5 100 5 100 Markaz 5 5 100 5 100 5 100 4 80 Márkháza 1 1 100 1 100 1 100 1 100 Márkó 1 1 100 1 100 1 100 1 100 Markotabödöge 2 2 100 2 100 2 100 2 100 Maróc 1 1 100 1 100 1 100 1 100 Márok 1 1 100 1 100 1 100 1 100 73 73 100 73 100 69 94,521 69 94,521 Mártély 19 19 100 19 100 19 100 17 89,474 Martfű 54 54 100 54 100 37 68,519 36 66,667 Martonvásár 29 29 100 26 89,66 24 82,759 22 75,862 Mátészalka 48 48 100 48 100 44 91,667 43 89,583 Mátételke 7 7 100 7 100 7 100 7 100 Mátraderecske 5 5 100 5 100 5 100 5 100 Mátramindszent 1 1 100 1 100 1 100 1 100 Mátranovák 5 5 100 5 100 3 60 3 60 Mátraszentimre 14 14 100 9 64,29 9 64,286 6 42,857 Mátraszőlős 2 2 100 2 100 2 100 2 100 Mátraterenye 6 6 100 6 100 6 100 6 100 Mátraverebély 3 3 100 3 100 3 100 3 100 Mátyásdomb 5 5 100 5 100 5 100 5 100 Máza 3 3 100 3 100 2 66,667 2 66,667 Mecseknádasd 1 1 100 1 100 1 100 1 100 Mecsekpölöske 11 11 100 11 100 10 90,909 10 90,909 Medgyesbodzás 21 21 100 21 100 20 95,238 20 95,238 Medgyesegyháza 38 38 100 37 97,37 37 97,368 36 94,737 Meggyeskovácsi 1 1 100 1 100 1 100 1 100 Megyaszó 3 3 100 0 0 0 0 0 0 Méhkerék 11 11 100 11 100 11 100 11 100 Méhtelek 1 1 100 1 100 1 100 1 100 Mekényes 345 345 100 337 97,68 336 97,391 335 97,101 Mélykút 135 121 89,63 121 89,63 120 88,889 112 82,963 4 4 100 4 100 4 100 1 25 Mende 15 15 100 14 93,33 14 93,333 12 80 1 1 100 1 100 1 100 1 100 Mersevát 7 7 100 7 100 7 100 7 100 Mesterháza 1 1 100 1 100 1 100 1 100 Mesteri 3 3 100 3 100 3 100 1 33,333 Mesterszállás 4 4 100 4 100 4 100 3 75 Meszlen 2 2 100 2 100 2 100 2 100 Mesztegnyő 3 3 100 3 100 3 100 3 100 Mezőberény 83 83 100 82 98,8 77 92,771 59 71,084 Mezőcsát 6 6 100 6 100 5 83,333 5 83,333 Mezőcsokonya 4 4 100 4 100 4 100 4 100 Meződ 153 153 100 153 100 140 91,503 135 88,235 Mezőfalva 2 2 100 2 100 2 100 1 50 Mezőgyán 3 3 100 3 100 3 100 3 100 Mezőhegyes 196 190 96,94 188 95,92 107 54,592 95 48,469 Mezőkeresztes 7 7 100 7 100 7 100 7 100 Mezőkomárom 3 3 100 3 100 3 100 3 100 Mezőkovácsháza 140 140 100 138 98,57 124 88,571 122 87,143 Mezőkövesd 53 53 100 52 98,11 47 88,679 45 84,906 Mezőladány 3 3 100 3 100 3 100 2 66,667 Mezőlak 1 1 100 1 100 1 100 1 100 Mezőnagymihály 2 2 100 2 100 2 100 2 100 Mezőnyárád 4 4 100 19 4 100 4 100 4 100 Mezőörs 1 1 100 1 100 1 100 1 100 Mezőpeterd 1 1 100 1 100 1 100 1 100 Mezőszemere 5 5 100 5 100 5 100 5 100 Mezőszentgyörgy 1 1 100 1 100 1 100 1 100 Mezőszilas 5 5 100 5 100 5 100 5 100 Mezőtárkány 9 9 100 9 100 8 88,889 8 88,889 Mezőtúr 225 214 95,11 202 89,78 137 60,889 104 46,222 Mezőzombor 2 2 100 2 100 2 100 2 100 Miháld 2 2 100 2 100 2 100 2 100 Mihályfa 2 2 100 2 100 2 100 2 100 Mihálygerge 1 1 100 1 100 1 100 1 100 Mihályháza 1 1 100 1 100 1 100 1 100 5 5 100 4 80 4 80 4 80 Mikepércs 2 2 100 2 100 2 100 2 100 Miklósi 1 1 100 1 100 1 100 1 100 Mikófalva 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 118 118 100 118 100 117 99,153 113 95,763 Mindszentgodisa 972 971 99,9 968 99,59 939 96,605 915 94,136 Mindszentkálla 2 2 100 2 100 2 100 1 50 17 17 100 17 100 17 100 17 100 696 658 94,54 639 91,81 547 78,592 405 58,19 1 1 100 1 100 1 100 1 100 Mocsa 3 3 100 3 100 3 100 3 100 Mogyoród 57 57 100 52 91,23 47 82,456 44 77,193 Mogyorósbánya 1 1 100 1 100 1 100 1 100 Moha 1 1 100 1 100 1 100 1 100 Mohács 82 82 100 73 89,02 64 78,049 54 65,854 Molnári 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 3 100 66 66 100 58 87,88 52 78,788 50 75,758 Monorierdő 18 18 100 16 88,89 16 88,889 16 88,889 Mónosbél 4 4 100 4 100 3 75 3 75 Monostorpályi 2 2 100 2 100 2 100 2 100 Mór 32 32 100 32 100 26 81,25 24 75 Mórágy 1 1 100 1 100 1 100 1 100 Mórahalom 347 334 96,25 330 95,1 322 92,795 302 87,032 Móricgát 5 5 100 5 100 5 100 4 80 Mórichida 2 2 100 2 100 2 100 2 100 Mosdós 10 10 100 6 60 5 50 4 40 Mosonmagyaróvár 89 88 98,88 85 95,51 56 62,921 48 53,933 Mosonszentmiklós 1 1 100 1 100 1 100 1 100 3 3 100 2 66,67 2 66,667 2 66,667 2 2 100 2 100 0 0 0 0 Mozsgó 2 2 100 2 100 2 100 0 0 Mőcsény 3 3 100 3 100 2 66,667 2 66,667 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 Múcsony 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Murakeresztúr 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 8 8 100 8 100 8 100 8 100 1 1 100 1 100 1 100 1 100 Nádasdladány 1 1 100 1 100 1 100 0 0 Nádudvar 24 24 100 24 100 23 95,833 23 95,833 Nágocs 2 2 100 2 100 2 100 2 100 Nagyatád 42 42 100 42 100 34 80,952 33 78,571 2 2 100 2 100 2 100 2 100 4 4 100 4 100 4 100 4 100 Nagybánhegyes 25 24 96 23 92 23 92 21 84 7 7 100 7 100 6 85,714 5 71,429 3 3 100 3 100 3 100 3 100 Nagyberény 3 3 100 3 100 2 66,667 2 66,667 6 6 100 6 100 6 100 5 83,333 Nagybörzsöny 22 22 100 22 100 22 100 17 77,273 2 2 100 2 100 2 100 2 100 Nagycsécs 2 2 100 2 100 2 100 2 100 4 4 100 3 75 3 75 3 75 5 5 100 5 100 5 100 5 100 10 10 100 10 100 10 100 8 80 7 7 100 7 100 5 71,429 5 71,429 Nagyér 15 15 100 15 100 15 100 15 100 Nagyesztergár 1 1 100 1 100 1 100 1 100 Nagyfüged 8 8 100 8 100 6 75 6 75 Nagyhajmás 431 431 100 431 100 411 95,36 400 92,807 Nagyhalász 14 14 100 13 92,86 8 57,143 8 57,143 Nagyharsány 3 3 100 3 100 3 100 3 100 3 3 100 3 100 3 100 3 100 Nagyigmánd 6 6 100 6 100 5 83,333 5 83,333 Nagyiván 15 15 100 15 100 15 100 15 100 Nagykálló 30 30 100 22 73,33 19 63,333 16 53,333 Nagykamarás 23 23 100 23 100 19 82,609 19 82,609 136 124 91,18 116 85,29 97 71,324 82 60,294 Nagykarácsony 4 4 100 4 100 4 100 4 100 Nagykáta 55 55 100 51 92,73 48 87,273 47 85,455 8 8 100 8 100 8 100 5 62,5 Nagykeresztúr 1 1 100 1 100 0 0 0 0 1 1 100 1 100 1 100 1 100 Nagykónyi 8 8 100 8 100 7 87,5 7 87,5 Nagykovácsi 37 37 100 34 91,89 31 83,784 23 62,162 Nagykozár 6 6 100 6 100 6 100 5 83,333 Nagykökényes 1 1 100 1 100 1 100 1 100 Nagykőrös 131 128 97,71 128 97,71 113 86,26 108 82,443 Nagykörű 7 7 100 6 85,71 6 85,714 6 85,714 43 38 88,37 35 81,4 30 69,767 28 65,116 Nagylóc 9 9 100 9 100 9 100 9 100 Nagylók 2 2 100 2 100 2 100 2 100 Nagymágocs 36 36 100 30 83,33 29 80,556 29 80,556 Nagymányok 11 11 100 11 100 11 100 11 100 43 40 93,02 34 79,07 32 74,419 30 69,767 Nagymizdó 1 1 100 1 100 1 100 1 100 Nagynyárád 1 0 0 0 0 0 0 0 0 11 11 100 9 81,82 8 72,727 8 72,727 Nagypáli 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 1 50 Nagyrábé 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Nagyrákos 1 1 100 1 100 1 100 1 100 Nagyréde 6 6 100 6 100 6 100 6 100 Nagyrév 8 8 100 8 100 7 87,5 7 87,5 Nagyrozvágy 1 1 100 1 100 1 100 1 100 Nagysáp 1 1 100 1 100 1 100 1 100 Nagysimonyi 3 3 100 3 100 3 100 3 100 Nagyszakácsi 5 5 100 5 100 5 100 5 100 Nagyszékely 12 12 100 11 91,67 11 91,667 11 91,667 2 2 100 2 100 2 100 2 100 Nagyszénás 48 46 95,83 46 95,83 46 95,833 45 93,75 3 3 100 3 100 3 100 3 100 Nagytálya 1 1 100 1 100 1 100 1 100 18 18 100 17 94,44 12 66,667 12 66,667 Nagytőke 3 3 100 3 100 3 100 3 100 Nagyút 2 2 100 2 100 2 100 2 100 Nagyvarsány 2 2 100 2 100 2 100 2 100 Nagyváty 1 1 100 1 100 1 100 0 0 Nagyvázsony 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 6 6 100 6 100 5 83,333 5 83,333 Nagyvisnyó 4 4 100 4 100 4 100 4 100 Nak 3 3 100 3 100 3 100 3 100 8 8 100 8 100 8 100 7 87,5 Négyes 2 2 100 2 100 2 100 1 50 Nekézseny 1 1 100 1 100 1 100 1 100 1 0 0 0 0 0 0 0 0 Nemesbük 3 3 100 3 100 3 100 3 100 Nemesgörzsöny 3 3 100 3 100 3 100 3 100 Nemesgulács 1 1 100 1 100 0 0 0 0 Nemeshetés 1 1 100 1 100 1 100 1 100 Nemeskeresztúr 2 2 100 1 50 1 50 1 50 Nemesnádudvar 8 8 100 8 100 8 100 8 100 Nemesnép 2 2 100 2 100 2 100 2 100 Nemesszalók 1 1 100 1 100 1 100 1 100 Nemesvámos 2 2 100 2 100 2 100 2 100 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 Németkér 4 4 100 4 100 4 100 4 100 1 1 100 1 100 1 100 1 100 Neszmély 4 4 100 4 100 4 100 3 75 Nézsa 12 12 100 10 83,33 8 66,667 8 66,667 Nick 1 1 100 1 100 1 100 1 100 11 11 100 11 100 11 100 11 100 Nógrádkövesd 7 7 100 7 100 7 100 6 85,714 Nógrádmarcal 3 3 100 3 100 3 100 3 100 Nógrádmegyer 2 2 100 2 100 2 100 2 100 Nógrádsáp 5 4 80 4 80 4 80 4 80 Nógrádszakál 5 5 100 5 100 5 100 5 100 3 3 100 3 100 3 100 3 100 2 2 100 2 100 2 100 2 100 Nova 4 4 100 4 100 4 100 4 100 Novajidrány 5 5 100 5 100 5 100 5 100 Nőtincs 7 7 100 7 100 7 100 7 100 1 1 100 1 100 1 100 1 100 Nyáregyháza 13 13 100 12 92,31 12 92,308 11 84,615 Nyárlőrinc 9 9 100 9 100 9 100 9 100 Nyársapát 4 4 100 4 100 4 100 4 100 Nyékládháza 9 9 100 7 77,78 7 77,778 5 55,556 Nyergesújfalu 21 18 85,71 15 71,43 11 52,381 9 42,857 Nyésta 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 2 66,667 Nyírábrány 1 1 100 1 100 1 100 1 100 Nyíracsád 4 4 100 4 100 4 100 4 100 Nyirád 2 2 100 2 100 2 100 2 100 Nyíradony 8 8 100 6 75 5 62,5 5 62,5 Nyírbátor 31 31 100 31 100 28 90,323 23 74,194 Nyírbéltek 4 4 100 4 100 4 100 4 100 Nyírbogát 3 3 100 3 100 3 100 3 100 Nyírbogdány 2 2 100 2 100 2 100 2 100 Nyírcsaholy 2 2 100 2 100 2 100 1 50 Nyírderzs 2 2 100 2 100 2 100 2 100 Nyíregyháza 440 432 98,18 405 92,05 359 81,591 298 67,727 Nyírgyulaj 1 1 100 1 100 1 100 1 100 Nyíri 2 2 100 2 100 2 100 2 100 Nyíribrony 1 1 100 1 100 1 100 1 100 Nyírkarász 5 5 100 5 100 5 100 5 100 Nyírlugos 4 4 100 4 100 4 100 4 100 Nyírmada 3 3 100 3 100 3 100 3 100 Nyírmártonfalva 1 1 100 1 100 1 100 1 100 Nyírmeggyes 2 2 100 2 100 2 100 2 100 Nyírmihálydi 1 1 100 1 100 1 100 1 100 Nyírpazony 8 8 100 8 100 8 100 8 100 Nyírpilis 4 4 100 4 100 3 75 3 75 Nyírtass 3 3 100 3 100 3 100 3 100 Nyírtelek 7 7 100 7 100 7 100 7 100 Nyomár 1 1 100 1 100 1 100 1 100 Nyúl 9 9 100 9 100 9 100 7 77,778 Óbarok 1 1 100 1 100 0 0 0 0 Ócsa 39 39 100 39 100 36 92,308 31 79,487 Ócsárd 1 1 100 1 100 1 100 1 100 Ófehértó 3 2 66,67 2 66,67 2 66,667 2 66,667 Óföldeák 7 7 100 7 100 7 100 7 100 Okány 12 12 100 12 100 12 100 12 100 Olaszfa 1 1 100 1 100 1 100 1 100 3 3 100 3 100 3 100 3 100 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 0 0 Old 2 2 100 2 100 2 100 2 100 Onga 14 14 100 14 100 12 85,714 12 85,714 Ónod 2 2 100 2 100 2 100 2 100 Ópályi 4 4 100 4 100 4 100 4 100 Ópusztaszer 127 127 100 125 98,43 86 67,717 84 66,142 3 3 100 3 100 3 100 3 100 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 Orfű 5 5 100 4 80 4 80 4 80 Orgovány 9 9 100 9 100 9 100 9 100 Ormosbánya 3 3 100 2 66,67 2 66,667 2 66,667 Orosháza 538 525 97,58 515 95,72 480 89,219 441 81,97 Oroszlány 64 64 100 62 96,88 59 92,188 54 84,375 Oroszló 16 16 100 16 100 11 68,75 11 68,75 1 1 100 1 100 1 100 1 100 Ostffyasszonyfa 3 3 100 3 100 3 100 3 100 4 4 100 4 100 4 100 4 100 Oszkó 3 3 100 3 100 3 100 3 100 Ózd 103 103 100 99 96,12 78 75,728 75 72,816 Ózdfalu 5 5 100 5 100 5 100 5 100 Ozmánbük 2 2 100 2 100 2 100 2 100 Ozora 7 7 100 5 71,43 2 28,571 2 28,571 Öcs 1 1 100 1 100 1 100 1 100 Őcsény 7 7 100 7 100 7 100 7 100 Öcsöd 27 27 100 27 100 23 85,185 21 77,778 Ököritófülpös 2 2 100 1 50 1 50 1 50 Ölbő 2 2 100 2 100 2 100 2 100 Őr 1 1 100 1 100 1 100 1 100 Őrbottyán 57 57 100 49 85,96 47 82,456 38 66,667 Öregcsertő 6 6 100 6 100 6 100 6 100 Öreglak 4 4 100 4 100 4 100 4 100 Őrhalom 4 4 100 4 100 4 100 4 100 Őrimagyarósd 1 1 100 1 100 1 100 1 100 Őriszentpéter 1 1 100 1 100 1 100 1 100 Örkény 15 14 93,33 14 93,33 14 93,333 14 93,333 Örményes 3 3 100 2 66,67 2 66,667 2 66,667 Őrtilos 3 3 100 3 100 3 100 3 100 Örvényes 5 5 100 5 100 5 100 1 20 Ősagárd 2 2 100 2 100 2 100 2 100 Ősi 2 2 100 2 100 2 100 2 100 Öskü 3 3 100 3 100 3 100 3 100 Öttevény 1 1 100 1 100 1 100 1 100 Öttömös 69 69 100 60 86,96 60 86,957 43 62,319 Ötvöskónyi 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 Páhi 9 9 100 9 100 9 100 8 88,889 Páka 4 4 100 4 100 4 100 4 100 Pákozd 2 2 100 2 100 2 100 2 100 135 134 99,26 127 94,07 90 66,667 68 50,37 Palé 123 123 100 123 100 123 100 123 100 Pálfa 2 2 100 2 100 2 100 2 100 Pálháza 8 8 100 8 100 8 100 8 100 2 2 100 2 100 2 100 2 100 Pálmajor 3 3 100 3 100 3 100 3 100 Pálmonostora 36 36 100 36 100 36 100 34 94,444 Pálosvörösmart 3 3 100 3 100 3 100 2 66,667 2 2 100 2 100 2 100 2 100 Palotás 2 2 100 2 100 1 50 1 50 4 4 100 1 25 1 25 1 25 Pánd 4 4 100 4 100 3 75 3 75 43 43 100 43 100 43 100 42 97,674 3 3 100 3 100 3 100 3 100 3 3 100 3 100 3 100 3 100 87 86 98,85 86 98,85 69 79,31 56 64,368 Pápadereske 1 1 100 1 100 1 100 1 100 Pápakovácsi 1 1 100 1 100 1 100 1 100 Pápateszér 3 3 100 3 100 3 100 3 100 2 2 100 2 100 2 100 2 100 Páprád 2 2 100 0 0 0 0 0 0 Parád 12 12 100 12 100 12 100 11 91,667 Parádsasvár 2 2 100 2 100 2 100 2 100 2 2 100 2 100 2 100 2 100 Pári 1 1 100 1 100 1 100 1 100 Pásztó 60 60 100 59 98,33 58 96,667 47 78,333 Patak 4 4 100 4 100 4 100 4 100 1 1 100 1 100 1 100 1 100 Pátka 1 1 100 1 100 1 100 1 100 Pátroha 4 4 100 4 100 4 100 4 100 Páty 56 52 92,86 48 85,71 46 82,143 38 67,857 Pátyod 1 1 100 1 100 1 100 1 100 Pázmánd 4 4 100 1 25 1 25 1 25 Pázmándfalu 1 1 100 1 100 1 100 1 100 Pécel 63 63 100 62 98,41 57 90,476 49 77,778 Pecöl 1 1 100 1 100 1 100 1 100 Pécs 1738 1735 99,83 1587 91,31 1360 78,251 1081 62,198 Pécsdevecser 1 1 100 1 100 1 100 1 100 Pécsely 1 1 100 1 100 1 100 1 100 Pécsvárad 30 30 100 30 100 11 36,667 10 33,333 Pellérd 5 5 100 3 60 2 40 2 40 Pély 7 7 100 7 100 7 100 6 85,714 8 8 100 8 100 8 100 8 100 Penészlek 2 2 100 2 100 2 100 2 100 4 4 100 4 100 4 100 4 100 Perbál 13 13 100 13 100 11 84,615 9 69,231 Perenye 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 0 0 Perkáta 7 6 85,71 5 71,43 5 71,429 5 71,429 Perőcsény 5 5 100 4 80 4 80 1 20 3 3 100 3 100 3 100 3 100 Péteri 7 7 100 6 85,71 6 85,714 6 85,714 Pétervására 6 5 83,33 5 83,33 5 83,333 4 66,667 Pétfürdő 17 17 100 17 100 8 47,059 4 23,529 Pethőhenye 1 1 100 1 100 1 100 1 100 Petneháza 2 2 100 2 100 2 100 2 100 Petőfibánya 9 9 100 9 100 4 44,444 3 33,333 Petőfiszállás 7 6 85,71 6 85,71 6 85,714 5 71,429 1 1 100 1 100 1 100 1 100 45 45 100 30 66,67 29 64,444 27 60 Pilisborosjenő 32 31 96,88 26 81,25 26 81,25 20 62,5 54 53 98,15 50 92,59 46 85,185 35 64,815 Piliscsév 8 8 100 8 100 8 100 8 100 Pilisjászfalu 9 9 100 7 77,78 7 77,778 3 33,333 Pilismarót 5 5 100 3 60 3 60 3 60 Pilisszántó 11 11 100 7 63,64 7 63,636 7 63,636 Pilisszentiván 18 18 100 15 83,33 10 55,556 9 50 22 20 90,91 20 90,91 20 90,909 11 50 Pilisszentlászló 3 3 100 3 100 3 100 3 100 Pilisvörösvár 61 61 100 53 86,89 48 78,689 40 65,574 12 12 100 12 100 12 100 12 100 2 1 50 1 50 1 50 1 50 Pirtó 5 5 100 5 100 5 100 5 100 54 54 100 54 100 54 100 52 96,296 2 2 100 2 100 2 100 2 100 Pócsmegyer 11 10 90,91 8 72,73 8 72,727 6 54,545 Pócspetri 2 2 100 2 100 2 100 2 100 Pogány 2 2 100 2 100 2 100 2 100 Pókaszepetk 1 1 100 1 100 0 0 0 0 Polány 1 1 100 1 100 1 100 1 100 Polgár 13 13 100 13 100 13 100 13 100 Polgárdi 8 8 100 7 87,5 6 75 6 75 Pomáz 124 124 100 113 91,13 92 74,194 75 60,484 2 2 100 2 100 2 100 2 100 Poroszló 12 12 100 11 91,67 11 91,667 10 83,333 1 1 100 1 100 1 100 1 100 Pötréte 1 1 100 1 100 1 100 1 100 Prügy 9 9 100 9 100 9 100 9 100 Pula 3 3 100 2 66,67 2 66,667 2 66,667 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 Pusztaföldvár 24 22 91,67 22 91,67 22 91,667 22 91,667 1 1 100 1 100 1 100 1 100 Pusztakovácsi 1 1 100 0 0 0 0 0 0 Pusztamérges 131 131 100 127 96,95 127 96,947 82 62,595 Pusztamonostor 1 1 100 1 100 1 100 1 100 1 1 100 1 100 0 0 0 0 26 25 96,15 22 84,62 21 80,769 17 65,385 Pusztaszentlászló 1 1 100 1 100 1 100 1 100 71 71 100 71 100 71 100 65 91,549 Pusztavacs 4 4 100 3 75 3 75 2 50 Pusztavám 5 5 100 3 60 3 60 3 60 Pusztazámor 1 1 100 1 100 1 100 1 100 10 10 100 9 90 8 80 7 70 Püspökhatvan 5 5 100 5 100 5 100 5 100 Püspökladány 110 110 100 102 92,73 96 87,273 89 80,909 Püspökszilágy 4 4 100 4 100 4 100 4 100 Rábacsécsény 1 1 100 1 100 1 100 1 100 Rábagyarmat 1 1 100 1 100 1 100 1 100 Rábakecöl 1 1 100 1 100 1 100 1 100 Rábapatona 1 1 100 1 100 1 100 0 0 Rábapaty 2 2 100 2 100 2 100 2 100 Rábapordány 1 1 100 1 100 1 100 1 100 Rábaszentandrás 1 1 100 1 100 1 100 1 100 Rábatamási 1 1 100 1 100 1 100 1 100 Rácalmás 13 13 100 10 76,92 10 76,923 9 69,231 Ráckeresztúr 12 12 100 12 100 11 91,667 11 91,667 Ráckeve 51 50 98,04 49 96,08 36 70,588 29 56,863 Rád 10 10 100 9 90 9 90 9 90 Ragály 2 2 100 2 100 2 100 2 100 1 1 100 1 100 1 100 1 100 2 2 100 2 100 2 100 2 100 5 5 100 5 100 5 100 5 100 Rákóczifalva 23 23 100 23 100 23 100 23 100 Rákócziújfalu 6 3 50 3 50 3 50 3 50 Ráksi 1 1 100 1 100 1 100 1 100 Ramocsaháza 1 1 100 1 100 1 100 1 100 Rásonysápberencs 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 11 11 100 11 100 10 90,909 8 72,727 Réde 2 2 100 2 100 2 100 1 50 Rédics 2 2 100 2 100 2 100 2 100 Regéc 1 1 100 1 100 1 100 0 0 1 1 100 1 100 1 100 1 100 Regöly 6 6 100 6 100 6 100 6 100 Rém 3 1 33,33 1 33,33 1 33,333 1 33,333 Remeteszőlős 2 2 100 2 100 2 100 2 100 Répcelak 2 2 100 2 100 1 50 1 50 Répceszentgyörgy 1 1 100 1 100 1 100 0 0 Rétalap 1 1 100 1 100 1 100 1 100 Rétközberencs 1 1 100 1 100 0 0 0 0 Rétság 21 21 100 19 90,48 17 80,952 17 80,952 Révfülöp 20 20 100 14 70 14 70 10 50 Révleányvár 1 1 100 1 100 0 0 0 0 5 5 100 2 40 2 40 2 40 4 4 100 4 100 4 100 4 100 Rigács 1 1 100 1 100 1 100 1 100 Rimóc 2 2 100 2 100 2 100 2 100 Rinyakovácsi 3 3 100 3 100 3 100 3 100 Rinyaújlak 1 1 100 1 100 1 100 1 100 1 1 100 1 100 1 100 1 100 Romhány 12 12 100 11 91,67 11 91,667 11 91,667 1 1 100 1 100 1 100 1 100 Rozsály 11 11 100 11 100 10 90,909 10 90,909 Rózsaszentmárton 7 7 100 7 100 7 100 7 100 Rönök 2 2 100 2 100 2 100 2 100 Röszke 346 344 99,42 339 97,98 325 93,931 300 86,705 Rudabánya 5 5 100 5 100 3 60 3 60 4 4 100 3 75 3 75 3 75 Rum 1 1 100 1 100 1 100 0 0 197 197 100 110 55,84 108 54,822 84 42,64 Ságújfalu 8 7 87,5 7 87,5 7 87,5 7 87,5 Ságvár 3 3 100 3 100 3 100 3 100 Sajóbábony 13 13 100 13 100 10 76,923 10 76,923 Sajóecseg 3 3 100 3 100 3 100 3 100 Sajóhídvég 2 2 100 2 100 2 100 2 100 Sajóivánka 1 1 100 1 100 1 100 1 100 Sajókaza 3 1 33,33 1 33,33 1 33,333 1 33,333 Sajókeresztúr 1 1 100 1 100 1 100 1 100 Sajólád 1 1 100 1 100 1 100 1 100 Sajólászlófalva 3 3 100 3 100 2 66,667 2 66,667 Sajóörös 1 1 100 1 100 1 100 1 100 Sajópetri 3 3 100 3 100 3 100 3 100 Sajópüspöki 1 1 100 1 100 1 100 1 100 Sajósenye 1 1 100 1 100 1 100 1 100 Sajószentpéter 15 12 80 12 80 6 40 6 40 Sajószöged 2 2 100 2 100 2 100 2 100 Sajóvámos 7 7 100 7 100 7 100 7 100 Sajóvelezd 1 1 100 1 100 1 100 1 100 Salgótarján 217 209 96,31 202 93,09 170 78,341 134 61,751 The GAEA was created in order to provide edited, cleaned address data for companion registers. The application’s logical functions also support the checking and the control of the Address Register and indirectly also support the efficient data processing.

The GAEA’s Graphical User Interface (GUI) appears in a user friendly form of executable processes that supports the assessment of similarity between the mass loaded records, and also supports the address cleaning/editing, the error analysis and the comparison of address information by pairs. Also it provides a single, descriptive checking and proper control of addresses syntactic elements in HCSOs systems. In this way, GAEA does not form a part of any statistical register or any management application, however it is a service that underlies many of the HCSO’s computer applications. For these purposes the GAEA mainly uses: 1. the tables of the HCSO’s Address Register as reference or correcting tables; 2. the nomenclatures of HCSO’s META system. The System Architecture of GAEA is shown in 3. Figure. 3. Figure - System Architecture of GAEA

b) During the data matching on the personal level we encountered a series of difficulties, being the most important of them the lack of unique identifiers to be used. For this reason, in this step we used the variables sex, date of birth (year, month and day separated), place of birth, legal marital status, citizenship and data on the addresses. For the latter we used the results of the first step, that is, the Census address identifiers assigned to the permanent and temporary addresses, and – where no Census identifiers were found – the settlement. The data matching was carried out using the SAS Enterprise Guide software and was organized in 24 rounds. As it can be observed in 4. Figure, we used the above mentioned socio-demographic variables according to a pre-defined priority order,

20 leaving out the less informative one in every second round. On the other hand, we checked whether the Census identifier found for the permanent addresses - together with the socio-demographic variables - assured the pairing of records from datasets, and if it did not, we checked the same variables and the Census identifier of temporary addresses (1-12 rounds). After each round, we eliminated all the paired records from the sources and used only the rest in the next round. The rounds 13-24 refer to those records where no Census identifier were found in the first step, however, in these rounds we used the same reasoning and left out one variable in each steps. The results of the data linkage are resumed in 4. Table. As it can be observed, 10 607, that is 3.38% of the records could not be matched (Round 0), while 96.62% were matched in any of the 24 rounds, being the first round the one with the highest proportion of linked records (29.1% and 91 198 records). As regards the permanent or temporary character of addresses, it is clear that the rounds where permanent addresses were used were more successful: in any of the impair rounds a higher rate of records were matched than in the corresponding even rounds that used the Census identifier or the settlement of the temporary addresses. The second most successful round was Round 11, that only considered the correspondence of the variables sex, year of birth and the Census identifier for permanent addresses (82 340 matched records, 26.28% of the total). Another important feature of the results is that 60.69% of the records were matched in the Rounds 1-12 where Census address identifiers were available, and 35.92% in Rounds 13-24 where Census address identifiers were not accessible.

21

4. Figure – Record Linkage in 24 Steps

1.Round 2.Round 3.Round 4.Round 5.Round 6.Round 7.Round 8.Round 9.Round 10.Round 11.Round 12.Round

Sex x x x x x x x x x x x x Date of birth (Year) x x x x x x x x x x x x Date of birth (Month) x x x x x x x x - - - - Date of birth (Day) x x x x x x ------Place of birth x x x x x x x x x x - - Marital status x x x x ------Nationality x x ------

Census permanent for identifier

addresses

x - x - x - x - x - x -

Census identifier for temporary Census temporary for identifier

addresses

- x - x - x - x - x - x

13.Round 14.Round 15.Round 16.Round 17.Round 18.Round 19.Round 20.Round 21.Round 22.Round 23.Round 24.Round

Sex x x x x x x x x x x x x Date of birth (Year) x x x x x x x x x x x x Date of birth (Month) x x x x x x x x - - - - Date of birth (Day) x x x x x x ------Place of birth x x x x x x x x x x - - Marital status x x x x ------Nationality x x ------

Settlement of permanent

address X - X - X - X - X - X -

Settlement of temporary Settlement of temporary

address - X - X - X - X - X - X

22

4. Table – Frequency and proportions of matched records by rounds

Round of Frequency Percent Cumulative Cumulative DL Frequency Percent 0 10607 3,38 10607 3,38 1 91198 29,1 101805 32,49 2 5178 1,65 106983 34,14 3 311 0,1 107294 34,24 4 10 0 107304 34,24 5 2905 0,93 110209 35,17 6 159 0,05 110368 35,22 7 2034 0,65 112402 35,87 8 138 0,04 112540 35,91 9 932 0,3 113472 36,21 10 117 0,04 113589 36,25 11 82340 26,28 195929 62,52 12 4842 1,55 200771 64,07 13 38304 12,22 239075 76,29 14 1421 0,45 240496 76,74 15 142 0,05 240638 76,79 16 6 0 240644 76,79 17 1572 0,5 242216 77,29 18 78 0,02 242294 77,32 19 14986 4,78 257280 82,1 20 929 0,3 258209 82,4 21 11359 3,62 269568 86,02 22 1275 0,41 270843 86,43 23 42520 13,57 313363 100 24 11 0 313374 100

Taking into account that the rounds consider different sets of key variables for the data matching – that obviously influence the strength of the relationship among the records of the datasets – we decided to check the found relationships from a probabilistic point of view, to have an insight on the quality of the above detailed data linkage processes. For this, we calculated a score for each variable using the Fellegi-Sunter method3 (see 5. Table) and assigned a weight, compounded of the agreement and disagreement field weights of corresponding variables, to all rounds (see 6. Table). In accordance with these calculations, those records that were matched in round 23 and round 24 should not be considered identical. However, the proportion of matched records is still high: 83.04%. That is, in our interpretation, the high level of corresponding records throughout the data sources – achieved with both methodologies – in the case of the

3 For more details on the Fellegi-Sunter method, see in Fellegi & Sunter, Vol. 64, No. 328. (Dec., 1969) and Statistics New Zeland (2006):Data integration manual, Wellington, 2006.

23

29 most problematic settlements from the point of view of the Census, signs that data from the Population and Address register for the whole Hungarian population would approximate quite well the results of a full-scope population Census.

5. Table - Calculated scores for each variable using the Fellegi-Sunter method

number u m log2((m/u) log2(1-m)/(1- of values (agreement u)) field weight) (diseagreement field weight) Census identifier 179720 5,56421E-06 0,95 17,381391 -4,321920067 for permanent addresses Census identifier 10444 9,57488E-05 0,95 13,276386 -4,321789952 for temporary addresses Sex 2 0,5 0,95 0,9259994 -3,321928095 Date of birth 110 0,009090909 0,95 6,7073591 -4,308752706 (year) Date of birth 12 0,083333333 0,95 3,5109619 -4,196397213 (month) Date of birth 31 0,032258065 0,85 4,7197311 -2,689659879 (day) Place of birth 2970 0,0003367 0,95 11,462247 -4,321442257 Marital status 5 0,2 0,70 1,8073549 -1,415037499 Nationality 113 0,008849558 0,70 6,3056058 -1,724141554 Settlement of 29 0,034482759 0,65 4,2364926 -1,4639471 permanent address Settlement of 29 0,034482759 0,65 4,2364926 -1,4639471 temporary address

24

6. Table – Sum of agreement and disagreement field weights (Fellegi-Sunter Scores) of corresponding variables Round of ∑ of Fellegi- DL Sunter Scores

0 1 61,1781578 2 57,0731531 3 44,7909024 4 40,6858977 5 41,56851 6 37,4635053 7 34,159119 8 30,0541143 9 26,4517599 10 22,3467552 11 10,668071 12 6,56306631 13 39,6757515 14 39,5602743 15 31,6460041 16 31,5305269 17 28,4236117 18 28,3081345 19 21,0142208 20 20,8987436 21 13,3068617 22 13,1913844 23 -2,47682723 24 -2,59230445

9. Distribution of aggregated estimates of demographic data between Census data and the Population and Address Register’s data As an additional exercise, to compare the Census and register data at an aggregated level, the Methodology Department two series of tables by settlements, the Hegyhát region and the total of all accessed settlements. In the first series, the aggregated data were compared by sex and age group, while in the second by sex, age group and marital status. Apart from the comparison of the two basic datasets – Census 2011, Population and Address register (permanent addresses) – we also considered two datasets derived from the former: Census 2011 without imputed records and the Population and Address register corrected by the temporary addresses where available. (See Annex II.) However, the preparation of these tables has set some methodological problems.

25

Distribution of basic demographics based on Census without imputation For the first time in the history of Hungarian censuses, in 2011 the HCSO used administrative data sources for the imputation of the missing records and/or basic variables such as the most important socio-demographic data. This administrative source was the Population and Address Register provided by the Central Office for Administrative and Electronic Public Services. This register based data imputation was carried out using donors. On the frequencies and proportions of the imputed and not-imputed records in Census 2011, see Table 7. 7. Table - The frequencies and proportions of the imputed and not-imputed records in Census 2011

Frequency Percent Cumulative Cumulative Frequency Percent No. of Not- 287259 95,47% 287259 95,47 % imputed records No. of imputed 13639 4,53% 300898 100,00 % records

We should add that in the analysed dataset only 9 settlements needed imputation: 1. Budapest district VII, 2. Dunakeszi, 3. Gödre, 4. Karcag, 5. Mindszentgodisa, 6. Mágocs, 7. Szeged, 8. Sásd, 9. Vásárosdombó. The column “Census 2011 without imputed records” was only included in the aggregate tables in the case of these settlements.

Logical correction of Population and Address Register’s data As regards the correction of the register data on permanent addresses with the temporary ones, we had the preliminary hypothesis that temporary addresses would predict more accurately the factual usual place of residence of the population. For this reason, we elaborated the following correction strategy:

26

Calculation of age groups In the Census, the date of birth was coded in three, separated variables: 1. The year of birth (YYYY), 2. The month of birth (MM), 3. The day of birth (DD). The Census Department only uses the first one to calculate the age and age groups. Due to this, the variables “month of birth and day of birth” were not further edited. In the present project, we used another method for the calculation of the age and age groups. First we integrated the three variables of birth into one common variable (YYYYMMDD) and counted the difference expressed in days between the new variable and the date of data collection of the Census (2011.10.01). After this, we divided the results with 365 and calculated the exact age of respondents rounding downwards the outcomes. In the case of the register data we used a similar calculation method. At this point, we experienced two challenges: 1. Missing values of the day of birth (DD). (N=1045); 2. Fictious/inconsistent dates of birth, e.g. 1946.06.31.; 1993.02.29.; 2006.11.31. etc. (N=33). The first problem was resolved as follows: ( ) ( )

Our strategy to resolve the second problem was to keep the month of birth and to correct the day to the nearest valid value: ( ) ( ) ( ) ( ) ( ) ( ) ( )

( ) ( )

After these corrections we defined the exact age and the exact age group to which respondents belong.

27

Main results and analysis of the comparisons at the aggregated level Despite our hypothesis, the register data corrected with the temporary addresses underestimates in all cases the values calculated from the Census data, than that of the uncorrected values based on permanent addresses. On the other hand, the aggregated results are consistent with the results of data matching, that is, the estimations based on the permanent addresses of the register fit better to the Census data. In the case of the male as well as the female population in Szeged, we found greater differences in relation to the 15-19 and especially to 20-24 age groups between the Census and the uncorrected register data. In these age groups, the Census found much more subjects than the Population register (see 5. Figure, 6. Figure, 7. Figure). In relation to the small of the Hegyhát region, we found the opposing situation. Our hypothesis is that the population in these age groups moves to the cities – such as Szeged – with the aim of studying in secondary schools and universities. In Budapest, the data from both datasets move together, there are no outstanding differences. Typically in the case of the 35-39 age group, the uncorrected register data surpass the Census data (see 9. Figure, 10. Figure, 11. Figure ) The case of Dunakeszi illustrates quite well that the uncorrected register data is apt for the estimations of the basic socio-demographic variables (see 12. Figure, 13. Figure, 14. Figure). In summary, as regards the total of the datasets the estimations at the aggregated level based on the register’s permanent addresses, overestimate the population with more than 30 years up to 59while, on the contrary, between 14-30 the register data underestimate the Census data in a small scale

28

5. Figure - The total population of Szeged according to the Hungarian Census (2011) and the Population and Address Register

Szeged - Total 18000

16000 15882 15249 14534 14000 13941 13452 13702 1296413018 1265712846 12710 12358 12304 12000 11896 11895 11497 11650 11664 11711 11106 11260 11156 11224 11008 10775 1077610966 10569 10483 10195 10072 10000 9871 9765 9385 9381 9128 8795 8991 8950 8523 8496 8770 85518762 8000 748975007662 7409 7097 71537365 721072307255 6863 6966 6746 639765486677 6000

4808484949495019 4000 3658374238043823 28812928 25302811 2000

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

29

6. Figure - The male population of Szeged according to the Hungarian Census (2011) and the Population and Address Register

Szeged - Male 8000 7616 7304 7174 7000 6903 6758 6810 63666448 64326436 6080 6000 5900 5700 5698 5716 5537 5624 5562 5351 5395 5438 5339 5218 5339 5000 5142 4793 4732 4660 4626 4373 4430 4376 43624487 4330 4238 4205 4000 4027 4142 38333.915 3939 3816 3.82637053801 372637413759 37023819 3619 3551 3589 35603612

3000 2581 242424842541 2000 1669167817021759 115811771181 1000 1137 689741753794

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

30

7. Figure - The female population of Szeged according to the Hungarian Census (2011) and the Population and Address Register

Szeged - Female 9000

8266 8000 7945

7360 7038 7148 7000 6892 6965 6753 6694 6586 6528 6431 62916398 6278 62896340 6121 6000 5960 5950 59665996 5995 5930 5755 5718 56135636 5557 5535 5389 5309 5230 5139 5176 51505248 5000 4955 4991 4753 4831 4422 4469 4193 41364165 4000 39734064 3.747 36563684 35643.583 3478 3448 3377348434893496 3312 318032473260 3000 3130 2521258426272642 2000 207021282134 1841

1000

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

31

8. Figure - The total population of Szeged according to the Hungarian Census (2011) and the Population and Address Register

Szeged - Total 18000

16000 15882 15249 14534 14000 13941 13452 13702 1296413018 1265712846 12710 12358 12304 12000 11896 11895 11497 11650 11664 11711 11106 11260 11156 11224 11008 10775 1077610966 10569 10483 10000 10195 10072 9871 9765 9385 9381 9128 8795 8991 8770 87628950 8523 8496 8551 8000 75007662 7489 73657409 72307255 7097 7153 69667210 6863 66776746 63976548 6000

4808484949495019 4000 3658374238043823 281128812928 2530 2000

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

32

9. Figure – The male population of Budapest district VII. according to the Hungarian Census (2011) and the Population and Address Register

Budapest district VII. - Male 3500

3000 3044 3043 2937

2701 2737 2589 2500 2405 2378 2317 2333 2222 2198 2121 2000 2050 2041 1865 1794 1848 1675 1690 15691594 1500 1481 1505 14691522 1334 1343 1262 1274 1315 1195 1246 1239 1225 1000 1.0361050 1061 10121065 937 979 973 909 875 870 909 752 736740777 746 774 668699712 706 656713 573 500 510546 439488 463 395423 377 356 318340376

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

33

10. Figure - The female population of Budapest district VII. according to the Hungarian Census (2011) and the Population and Address Register

Budapest district VII. - Female

3500 3273 3000 29322966 2847 2868 2691 2683 2500 2537 25402563 2397 2364 2291 2174 2236 2000 2009 1999 1989 1983 1919 1780 1824 1714 1688 16941696 1755 1640 16101656 1591 1532 1500 1458 1446 1472 1369 1307 1298 1303 1182 1224 1184 1179 1199 1162 1163 10931121 11031109 11241136 1000 1009 10451072 1004 936978 996 927 959 906 852 922 750 741743799 667688692 712 500

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputatuion Population and Address Register (2011) Population and Address Register (Corrected) (2011)

34

11. Figure - The total population of Budapest district VII. according to the Hungarian Census (2011) and the Population and Address Register

Budapest district VII. - TOTAL 7000

6317 6000 6009 5869 5569 5428 5164 5272 5000 47854873 4802 4658 4742 4372 4332 4000 4033 4084 3583 3645 3618 3699 3363 34413468 3283 3201 3121 3070 3000 2925 3037 2771 2631 2704 2671 26222721 2502 2436 2271 2381 2000 20142059 20512106 2077 1873 1866 1928 1937 1815 1727 1819 1730 1576 1577 15811631 15321642 1576 14041502 141814771483 1498 14761500 13351387 1366 1278 1277 1000

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputatuion Population and Address Register (2011) Population and Address Register (Corrected) (2011)

35

12. Figure - The male population of Dunakeszi. according to the Hungarian Census (2011) and the Population and Address Register

Dunakeszi - Male 2500

2251 22092243 2151 21062114 2000

166116781691 15681593 1589 1528 1564 1500 1484

12391255 11991215 11981205 1222 1158 1175 1142 1103 10951109 103810431055 104410651072 1062 1062 1000 980 996 9931006 983 937945951 973 968969 891 838 778781789 681 698 641 651 567572573 500 508

351352356 298 164172175 9395103 0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

36

13. Figure - The female population of Dunakeszi according to the Hungarian Census (2011) and the Population and Address Register

Dunakeszi -Female 3000

2500 2382 23242333 2218 21462174 2000

1613 155515771583 1500 14771502 1476 1416 14471455 1358 1349 130713101335 124112471254 11391160 114211571167 11481151 1117 1092 1131 1000 1041 100210191024 101810281030 964976991 970 954 984 884 889 887 887 841873875 749751760 705 659 682 569570573 500 501 393396411416 299309327335

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

37

14. Figure - The total woman population by Hungarian Census (2011) and the Population and Address Register

Dunakeszi -Total 5000

4500 4533 4469 44384439 43834389

4000

3500 3274 3202 32163255 3095 3000 3045 2944 2913 2842 267727022715 2500 248525122533 23382375 2309 2275 22372266 22152237 2144 2154 21412157 2210 2046 204620842096 2104 2000 20022019 1966 1949 1869 181018201835 1792 179918061819 1732 1682 1500 1538 1340 1346 131813221332 1190 1000 920922929 799 583591 500 557568 394402430438

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

38

15. Figure - The male population by Hungarian Census (2011) and the Population and Address Register

TOTAL - Male 16000

14000 1314213275 13380 12717 12000 12174 12339 12054 11602 11210 11.398 10.797 11032 10527 10367 10000 10144 10151 9.883 9883 9705 9.3139231 9295 87119007 8.344 8450 8304 8000 7939 79588125 7591 7843 7.820 7631 722472807.288 7.2897512 7502 6949 6663 6.666 63656.5596598 64186433 6309 6000 62006.290 6.293 6.192 60006271 5.583 44494479 4000 4.0514286

2.806292630343033 2000 1.895197920262078 1.251133213871418 0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

39

16. Figure - The female population by Hungarian Census (2011) and the Population and Address Register

TOTAL - Female

16000

14000 13508 13506 13286 13210 12746 12597 12418 12000 12083 1208412263 11604 11586 11.278 10933 10771 10.636 1069810749 10.39610554 10487 10300 10000 9.810 9996 10.014 9456 9549 9145 9179 9411 9.282 87288963 8971 88438854 8.360 8343 8537 8000 7.906 72787.340 7.399 6911 69837048 66226.8376847 6.710 6812 61866313 6.445 6000 59706.165 5.946588960766079 550756125682 5.177 464546894797 4.362 4000 389539234032 3.448

2000

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

40

17. Figure - The total population by Hungarian Census (2011) and the Population and Address Register–

TOTAL 30000

26886 2635226783 25460 25463 25000 24757 24199 24137 22814 22676 22414 21965 21789 21298 21433 20698 20854 20881 20000 20279 19879 19123 18463 18410 1870718823 17856 17853 17834 17931 17413 17254 16704 16667 16473 15934 15948 1512515152 15000 1462914790 141251412714135 14537 13571 13591 13489 127241278412976 13003 12335 12089122361249412512 110981143211527 10496 10000 843386458716 7983 67156875 62576624 522753415419 5000 4699

0 –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

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18. Figure - The total population according to the Hungarian Census (2011) and the Population and Address Registers

Total - percentage 10,00% 8,46% 9,18%

8,76% 9,14% 9,00% 8,62% 8,40% 8,46%8,42% 8,82% 8,33% 8,00% 7,58% 7,88% 7,41% 7,27%7,65% 7,43% 7,50% 7,24% 6,93%6,92% 7,00% 7,07% 6,93% 6,14%6,22% 6,49% 6,22%6,24% 6,43% 6,28% 6,20% 6,00% 5,73%5,73% 6,09% 5,54%5,55% 5,94% 5,69% 5,49% 5,03%5,06% 5,28% 5,17%5,24% 5,00% 4,70%4,72% 4,95% 5,05% 5,06% 4,82% 4,76% 4,29% 4,25% 4,43%4,15% 4,21% 4,27% 3,83%3,86% 4,08% 4,00% 3,90%

2,94% 2,90% 3,10% 3,00% 2,95% 2,28%2,31% 2,43% 2,29% 2,00% 1,80%1,82% 1,82%1,83%

1,00%

0,00% –4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–

Census 2011 Census 2011 - Without Imputation Population and Address Register (2011) Population and Address Register (Corrected) (2011)

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III. Conclusions and future actions

With most of the aimed tasks performed in the project we have to integrate our achievements into the preparation work for future censuses and also into the broader works in progress of HCSO. As the requirements from censuses in the near future are expected to increase the 10 year frequency to annual exercises, it will be necessary to change from the traditional census method to the use of administrative sources. The analysis of the results of the project will be continued with modelling census data production from the Population register with focus on special population groups (by census characteristics such as young people, students, special employed groups). The 10 percent sample microcensus to be executed in October 2016 will produce a database based on which the methodology designed in the project can be used for further analysis. The Central Office for Administrative and Electronic Public Services has legal obligation to provide population register data for the settlements covered in the sample. Although the microcensus has a diffused sample which makes the estimations more problematic but the takeover of the new register database will be indisputably useful technically and in terms of content. Further task in the future is to test our results not only on a sample, but on the whole data base of the Central Office for Administrative and Electronic Public Services. As the dataset analysed contained settlements chosen to be the most problematic from a census point of view, we expected that the analysis for the whole country will produce the same or better results. The on-going approval of the act on statistics and as a consequence changes in sectoral legislation, conditions to access to register data will improve. Therefore contacting the register owners will continue based on the guidelines formulated in the project. Further elaboration of the guidelines on the involvement of a new secondary data source for the production of official statistics is needed as far as detailed description of each step. Introduction of a standard cooperation agreement scheme is an ongoing work in HCSO such as the renewal of the existing cooperation agreements. In the course of negotiations the aspect of census will be taken into considerations. The next step in the use of administrative data should be the linkage of different registers. When HCSO will have access to more registers, we can start work on formulating the methodology of this linkage. Further analysis of population data from different sources can be performed based on the methodology designed in the project. Based on the lessons learned we can create a plan to produce census results from administrative sources simultaneously with the 2021 census (expected to be a traditional census). This “test” – similar to Spain’s earlier practice and what the UK also plans to do in 2021 – can be the base of a future register based census.

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ANNEX I.

Workplan Task Deadline Comment Review of legal background 31 Dec 2015 (223 Reg, Review from Legal Affairs HUN Interoperability Department Act.) 30 June (HUN Stat Act.) Contact with Central Office for 31 Oct 2015 With contribution from Administrative and Electronic Methodology Department Public Services Data set from Central Office for 31 Dec 2015 Administrative and Electronic Public Services taken over List of potential partners 15 Jan 2016 Short description of registers 31 Jan 2016 Methodology of evaluation and other sources designed with contribution from Methodology Dept Interim report 29 Febr 2016 With contribution from Methodology Department Description and quality 15 March 2016 As only one potential data set was evaluation of potential registers transmitted to HCSO, this 2 points Detailed description of the were combined register to be used for the 22 March 2016 linkage Preparation and execution of 15 May 2016 With contribution from Census data linkage Dept. Personal contact with register Continually With contribution from Census owners Dept. Guidelines to involving new 15 August 2016 With contribution from data sources Methodology Department. The main steps of the involvement process were defined, but detailed description of each step is needed in the future. Final report 31 Aug 2016 With contribution from Methodology Department

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ANNEX II.

19. Figure - Sex_age_group and marital status_at the aggregated_level_in all datasets

Microsoft Excel 97–2003-as munkalap

20. Figure - Sex_and_age_group _at the aggregated_level_in the settlements_with imputation

Microsoft Excel 97–2003-as munkalap

21. Figure - Sex_and_age_group _at the aggregated_level_in in all datasets

Microsoft Excel 97–2003-as munkalap

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List of Figures 1. Figure - The Record Linkage scheme established by the HCSO ...... 14 2. Figure– GAEA – Record linkage by address level ...... 17 3. Figure- System Architecture of GAEA ...... 18 4. Figure – Record Linkage in 24 Steps ...... 19 5. Figure - The total population of Szeged according to the Hungarian Census (2011) and the Population and Address Register ...... 26 6. Figure - The male population of Szeged according to the Hungarian Census (2011) and the Population and Address Register ...... 27 7. Figure - The female population of Szeged according to the Hungarian Census (2011) and the Population and Address Register ...... 28 8. Figure - The total population of Szeged according to the Hungarian Census (2011) and the Population and Address Register ...... 29 9. Figure – The male population of Budapest district VII. according to the Hungarian Census (2011) and the Population and Address Register ...... 30 10. Figure - The female population of Budapest district VII. according to the Hungarian Census (2011) and the Population and Address Register ...... 31 11. Figure - The total population of Budapest district VII. according to the Hungarian Census (2011) and the Population and Address Register ...... 32 12. Figure - The male population of Dunakeszi. according to the Hungarian Census (2011) and the Population and Address Register ...... 33 13. Figure - The female population of Dunakeszi according to the Hungarian Census (2011) and the Population and Address Register ...... 34 14. Figure - The total woman population by Hungarian Census (2011) and the Population and Address Register ...... 35 15. Figure - The male population by Hungarian Census (2011) and the Population and Address Register ...... 36 16. Figure - The female population by Hungarian Census (2011) and the Population and Address Register ...... 37 17. Figure - The total population by Hungarian Census (2011) and the Population and Address Register– ...... 38 18. Figure - The total population according to the Hungarian Census (2011) and the Population and Address Registers ...... 39 19. Figure - Sex_age_group and marital status_at the aggregated_level_in all datasets ...... 42 20. Figure - Sex_and_age_group _at the aggregated_level_in the settlements_with imputation ...... 42 21. Figure - Sex_and_age_group _at the aggregated_level_in in all datasets ...... 42

List of Tables 1. Table - Values of the variable “legal marital status” in Census 2011 and in the Population and Address Register ...... 15 2. Table - Marital status frequencies from the Census ...... 16 3. Table - Legal marital status frequencies from the Population and Address Register ...... 16 4. Table – Frequency and proportions of matched records by rounds ...... 20 5. Table - Calculated scores for each variable using the Fellegi-Sunter method ...... 21

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6. Table – Sum of agreement and disagreement field weights (Fellegi-Sunter Scores) of corresponding variables ...... 22 7. Table - The frequencies and proportions of the imputed and not-imputed records in Census 2011 ...... 23

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