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2 Characteristics of the population and dwellings Microcensus 2016
2 Characteristics of the population and dwellings
Microcensus 2016
Budapest, 2018
2 Characteristics of the population and dwellings
Microcensus 2016
© Hungarian Central Statistical Office, 2018
ISBN 978-963-235-494-1ÖISBN 978-963-235-531-3
Responsible publisher:Dr. Gabriella Vukovich president
Responsible editor:Marcell Kovács head of department
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Contents
Greetings to the Reader ..................................................................................4Summary ........................................................................................................51 Number and characteristics of the population ........................................... 10 1.1 Population number, population density .............................................. 10 1.2 Age structure, number of men and women .......................................... 11 1.3 Citizenship ........................................................................................ 12 1.4 Marital status ..................................................................................... 12 1.5 Educational attainment ...................................................................... 14 1.6 Economic activity ............................................................................... 152 Households, families ................................................................................. 18 2.1 Number and composition of households ............................................. 18 2.2 Size and age structure of households ................................................... 19 2.3 Number and composition of families .................................................. 20 2.4 Size of families, number of children .................................................... 213 Characteristics of the housing stock ........................................................... 23 3.1 Number of dwellings .......................................................................... 23 3.2 Walling of dwellings ........................................................................... 24 3.3 Dwellings by ownership and tenure status ........................................... 25 3.4 Size of dwellings: floor space, number of rooms ................................... 26 3.5 Equipment and comfort level of dwellings ........................................... 27 3.6 Dwellings and their occupants, density standard ................................. 29Methodological guide ................................................................................... 30 Concept of microcensus ............................................................................ 30 Most important features of the 2016 microcensus ...................................... 30 Length of data collection, mode of implementation ................................... 30 Sampling .................................................................................................. 31 Sample of dwellings and holiday homes ..................................................... 31 Sample of institutions ............................................................................... 31 Weighting ................................................................................................ 32 Non-response adjustment ......................................................................... 32 Calibration ............................................................................................... 32List of Detailed Tables.................................................................................. 33
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GREETINGS TO THE READER,
Dr. Gabriella VukovichPresident
Hungarian Central Statistical Office
In October/November 2016 the Hungarian Central Statistical Office conducted a microcensus based on a 10% sample of households. During this ’lesser census’ we approached approximately 440 thousand households in 2,148 localities in the country in order to gather relevant information regarding our society’s current characteristics.
A microcensus occurs traditionally between two censuses usually at half time. The first one in our country took place in 1963; this was the time when, due to the social processes’ acceleration, it became necessary to have at our disposal census data even between two population censuses. Last year’s, the 7th microcensus occurred exactly after five years following the last population census, creating the opportunity to present the changes of the recent period. Data are accessible in a more detailed territorial breakdown – even on district level at some relevant indicators – due to the 10% – larger than all previous – sample size. The larger sample also made possible the addition of 5 supplementary surveys to the basic sampling through different sub-samples. These sub-samples studied relevant social phenomena from the users’ needs point of view like social stratification, occupational prestige, subjective well-being, limitations due to health problems and international migration.
A significant innovation of the 2016 microcensus was its exclusively electronic completion, avoiding paper based forms, using only online completable questionnaries and registering interviews by electronic devices. This method made possible not only the avoidance of questionnaire printing, data processing also became faster and the results are available for publishing by little more than 6 months after finishing the data collection.
Our first publication regarding the 2016 microcensus shares the most important data from the main questionnaire with the reader. We present our population’s most relevant demographic, educational, employment characteristics, households’, families’ living conditions and housing conditions. In addition to this publication illustrated with figures and maps, the HCSO homepage holds approximately one thousand tables for download arranged by counties, the most relevant data being detailed on district level as well.
We are going to publish the results of the microcensus’ supplementary survey, as well as more detailed information from the main questionnaire in the coming months.
I would kindly recommend the census homepage for all interested (www.ksh.hu/mikrocen-zus2016) where one can access our present publication and learn about further data releases.
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1 Number and characteristics of the population
1.1 Population number, population density•On1October2016,Hungary’sresidentpopulationwas 9,803,837; the population decreased by 134thousandinthefiveyearssincethe2011populationcensus.
• Due to the population decline, the populationdensity continued to decrease: while the numberofpeoplepersquarekilometrewas107in2011;in2016 itwasonly105.ThemostdenselypopulatedsettlementwasBudapestwheretheaveragenumberof people living in a square kilometre was 3,360.The most densely populated counties were Pest(196)andKomárom-Esztergom(131),andtheleastpopulatedoneswereSomogy(50),aswellasTolna,Bács-KiskunandBékés(60)counties.
1.2 Age structure, number of men and women•On 1 October 2016, 15% of the population werechildren,67%wereofworkingage(15–64year-old)and19%wereaged65yearsandover.
• 26thousandfewerchildrenand308thousandfewer15–39 year-olds lived in Hungary than five yearsearlier. By contrast, the number of 40–64 year-oldsgrewby56thousand,andthatofpeopleaged 65yearsandoverby144thousand.
•Thenumberofwomenperthousandmenwas1,096in2016asopposedto1,106in2011.
1.3 Citizenship•Theoverwhelmingmajorityofthecountry’sresidentpopulation are Hungarian citizens. On 1 October2016,149,111non-HungariancitizenslivedinHun-garywhichaccountfor1.5%ofthepopulation.
• Among non-Hungarian citizens living in Hunga-ry, thereweremoremen thanwomen.Theyweretypicallyofworkingage,more than threequartersofthembelongedtothe15–64agegroup.
1.4 Marital status•The proportion of married people continued todeclineslightlyinthepastfiveyears.
•The increase in the proportion of never marriedwomenandmencontinued,butataslowerpace.
1.5 Educational attainment• In the population aged 15 years and over, theproportion of those with educational attainmentlower than the eighth grade inprimary (general)school decreased to 3.2%. By 2016, more thanhalf of the adult population had secondary levelwith final examination or higher educationalattainment.Theproportionofpeoplewithtertiaryeducation increased from 17% to 20% over fiveyears.
•Theregionaldifferencesintheproportionofpeoplewithtertiaryeducationcontinuedtoincrease:theirproportionwas41%inthecapitalandonly13%inNógrádcounty.
Summary
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1.6 Economic activity• 46% of the total population worked, 2.6% wereunemployed, 28% were economically inactivereceivingbenefitand24%weredependents.
• In the past five years, the number of people ofworking age continued to decrease somewhat;however, partly due to the rise in the retirementage, the actual number of the economically activepopulationincreased.
•The proportion of economically inactive peoplereceiving benefit, including mainly pensionersand recipients of social benefits, aswell as that ofdependentsbecamesmaller.
•The age structure of the economically activepopulationhassomewhatchanged:theheadcountincreased significantly in the youngest (15–25year-old) and the oldest (60–65 year-old) agegroups.
•The activity rate continued to be the highest in Budapest (52%). Among counties, it was 50% inGyőr-Moson-Sopron, Vas, Komárom-EsztergomandFejérandonly45–46%inBorsod-Abaúj-Zemp-lén,NógrádandTolnacounties.
2 Households, families
2.1 Number and composition of households• Familieslivedin66%ofthe4million21thousandhouseholdsandtheotherthirdconsistedmostlyofpersonslivingalone.
•Thevastmajorityof familyhouseholdswereone-family households, it rarely occurred that morefamilieslivedinacommonhousehold.
•The number of persons living alone decreased,theirnumberwas1million217thousandin2016.
2.2 Size and age structure of households•Thenumberofpersonsperhundredhouseholdswas238,whilethisfigurewas236in2011and257in2001.
• Untilthe2001census,theproportionoftwo-personhouseholdswasthehighest,butin2011,one-personhouseholdsrepresentedthelargestnumber.By2016,thenumberoftwo-personhouseholdsbecameagainhigherthanthatofone-personhouseholds.
•Thenumberof two-personhouseholdswas1mil- lion 261 thousand, and that of households largerthanthathasbeendecreasingsteadilysince1980.
2.3 Number and composition of families•Thenumberoffamilieswas2million743thousandin2016.
• 82%offamilies(2million240thousand)werebasedon relationship. Within this, consensual unionsgained more and more space, their number wasmorethan483thousand.
• In503thousandfamilies,oneparentlivedwithhis/herchildorchildren.
2.4 Size of families, number of children•Theaveragenumberoffamilymembersperhundredfamilieswas283in2016and287fiveyearsearlier.
• Inmarriedcouplefamilies,thenumberofpersonsperhundred familieswas294,while itwas287 inconsensualunionfamilies.
•More than half of the 1 million 716 thousandfamilieswithchild(ren)raisedonechild,onethirdraised two children, and the proportion of thoseraisingatleastthreechildrenwas12%.
3 Characteristics of the housing stock
3.1 Number of dwellings• In2016,thenumberofhousingunitswas4,404,518.• 21%ofhousingislocatedinBudapest,52%inothertownsand28%invillages.
3.2 Walling of dwellings•Nearlytwo-thirdsofoccupieddwellingshavebrick,14%panel,and13%adobewalls.
3.3 Housing stock by ownership and tenure status• 98%ofoccupieddwellingswereownedbyprivateindividuals,1.3%bylocalgovernmentsand0.5%byotherinstitutions,organizations.
• In Budapest, county seats and towns of countyrank,theshareofnon-privatelyowneddwellingsisslightlyhigherthanaverage,theirsharewas3.3%inBudapest.
• In 2016, 90% of occupants were owners, 8.3%tenantsand1.4%occupantswithotherlegaltitle.
3.4 Size of dwellings: floor space, number of rooms• 6.6%ofoccupieddwellingshadone,32%two,33%threeand29%fourormorerooms.
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• In 2016, occupied dwellings had an average floorspaceof82m2,4m2morethanin2011.
•The proportion of dwellings with a floor space ofmore than 100 m2 has continued to grow, every third to fourth occupied dwelling belongs to thiscategory.
3.5 Equipment and comfort level of dwellings• 99%ofoccupieddwellingshadpipedwater,withinthis97%communityschemepipedwater.
• 87% of occupied dwellings were connected to thepublicsewagefacilitynetwork.
• Despiteanincreaseinaccesstopublicutilities,morethan 50,000 homes did not have piped water and
sewagefacilitywasnotsolvedinabout70thousandhomes. In addition, there were no flush toilets in153,000 homes, and 116,000 homes lacked hotwater.Theproportionofworst-equippeddwellingsishighest inBorsod-Abaúj-ZemplénandSzabolcs-Szatmár-Beregcounties.
3.6 Dwellings and their occupants, density standard• In2016,249peoplelived,onaverage,inahundredoccupied dwellings. As a result of a declinein population and in the number of occupieddwellings, density standard did not changesubstantially.
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1.1 Population number, population density
On1October2016,theresidentpopulationofHun-gary was 9,803,837. The population decline lastingfrom 1980 continued. In the five years since the2011population census, thepopulationofHungarydecreasedby134thousand.
The degree and direction of change in thepopulation number is different in the regions ofthe country. In Central Hungary, the increase inthe population number continued – by 2.3% inthe past five years –, while the other regions werecharacterizedbyadecline.ThedecreaseaffectedWest- ern Transdanubia the least (–0.3%) and NorthernHungarythemost(nearly–5%).
The population of Pest county was more than1 million 200 thousand at the time of the 2011population census, and grew by another 2.7% in thepastfiveyears.ThepopulationofGyőr-Moson-SoproncountyandBudapestalsoincreased(by2.4%and2.0%,respectively),whileitdecreasedinalltheotherregions.ThedeclinewasthehighestinBékés(–5,7%),Nógrád(–5,3%)andBorsod-Abaúj-Zempléncounties(–5.1%).
Due to the population decline, the populationdensity continued to decrease: while the numberof people per square kilometre was 107 in 2011, itwas only 105 in 2016.Themost densely populated
Figure 1.1.2 Change in the resident population between 1 October 2011 and 1 October 2016
(–5.7) – (–5.1)(–5.0) – (–4.1)(–4.0) – (–2.1)
%
(–2.0) – (–0.1)(–0.0) – (–2.7)
1 Number and characteristics of the population
09 5009 6009 7009 8009 900
10 00010 10010 20010 30010 400
1990
10 375
2001
10 198
2016
9 804
2011
9 938
10 500Thousand
Figure 1.1.1 Change in the population number
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settlement remained Budapest where the averagenumber of people living in a square kilometre was3,360.ThemostdenselypopulatedcountieswerePest(196) andKomárom-Esztergom(131), and the leastpopulatedoneswereSomogy(50),aswellasTolna,Bács-KiskunandBékés(60).
1.2 Age structure, number of men and women
On 1 October 2016, 15% of the population werechildren,67%wereofworkingage(15–64year-old)and19%wereaged65yearsandover.Sincethe2011census, the ageing of the country’s population hascontinued.On1October 2016,nearly 26 thousandfewerchildrenand144thousandmorepeopleaged65 years and over lived inHungary thanfive yearsearlier.Inthepopulationofworkingage,thenumberof15–39year-oldsdecreasedby308thousand,whilethatof40–64year-oldsgrewby56thousand.
Within thepopulationunder15yearsofage, thepopulation number decreased in each five-year agegroup in thepastfiveyears, and thedeclinewas thehighest(17thousand)amongchildrenundertheage
of 5.The number of the population of working age(15–64 year-olds) has fallen by 252 thousand since2011.Amongtheelderly,theincreaseinthenumberofthe65–69agegroupwasespeciallyhigh(70thousand).The number of 70–79 year-olds grew by nearly 62 thousand and that of 80 year-olds and older byabout13thousandcomparedto2011.Thegrowthratewassimilarforbothsexes.Inthecountry’spopulation,oneineveryeightpeoplewas70year-oldorolder.
In addition to Pest county with increasingpopulationnumber, theproportionof the child-agepopulation was above average in Szabolcs-Szatmár-Bereg and Borsod-Abaúj-Zemplén counties. Peopleofworkingageaccountedfor68%ofthepopulationinSzabolcs-Szatmár-BeregandGyőr-Moson-Sopron counties, while their proportion was only 65% in Heves county. Compared to their total population,mostelderlypeoplelivedinBékésandZalacounties,where the proportion of people aged 65 years andoverwas21%.
Figure 1.2.1 Population number by sex and age groups
Years
100 80 60 2040 0thousand
100thousand806020 400
04812162024283236404448525660646872768084889296100–
Males Females
2011 2016 20112016
Table 1.2.1 Distribution of the population by age groups and counties, 2016
(%)
Territorial unit–14 15–64 65–
Totalyears
Budapest 13.2 67.4 19.4 100.0Bács-Kiskun 14.4 66.2 19.4 100.0Baranya 13.7 67.1 19.3 100.0Békés 13.2 65.7 21.1 100.0Borsod-Abaúj-Zemplén 15.8 66.2 17.9 100.0Csongrád 13.3 67.2 19.5 100.0Fejér 14.5 67.6 17.9 100.0Győr-Moson-Sopron 14.7 68.0 17.3 100.0Hajdú-Bihar 14.9 67.8 17.2 100.0Heves 14.3 65.5 20.2 100.0Jász-Nagykun-Szolnok 14.8 65.7 19.5 100.0Komárom-Esztergom 14.7 67.2 18.1 100.0Nógrád 14.1 65.6 20.3 100.0Pest 16.8 66.7 16.6 100.0Somogy 13.7 65.9 20.4 100.0Szabolcs-Szatmár-Bereg 16.2 68.3 15.5 100.0Tolna 13.9 66.1 20.0 100.0Vas 13.2 67.7 19.0 100.0Veszprém 14.0 66.6 19.4 100.0Zala 12.9 66.5 20.6 100.0Total 14.5 66.9 18.6 100.0
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Thenumberofwomenperthousandmenwas1,106in2011and1,096in2016,i.e.thesurplusofwomenslightlydecreased.Thedistributionofthepopulationby sex is only slowly changing.More boys are bornthan girls, but the ‘male surplus’ at birth disappearswithincreasingage.Accordingtothedataofthe2016microcensus,thesexratiobecameequalisedattheageof47,andinthepopulationolderthanthat,a‘femalesurplus’wasobserved.Accordingly,intheareaswitholder age structure, the proportion of women wasabove average. Among counties, the sex ratio wasthemostequalisedinFejérandGyőr-Moson-Soproncounties, and the ‘female surplus’was thehighest inBudapest,aswellasinHevesandBaranyacounties.
1.3 Citizenship
Theoverwhelmingmajorityofthecountry’sresidentpopulationareHungariancitizens.Accordingtothedataofthe2016microcensus,149,111non-HungariancitizenslivedinHungary,6thousandmorethanfiveyearsearlier.Theirproportionwasonly1.5%whichhardlyexceededthe1.4%measuredfiveyearsbefore.
Among non-Hungarian citizens living in Hun-gary, there were more men than women. Theirage structure also differed from that of Hungariancitizens: they were typically of working age, morethan three quarters of them belonged to the 15–64agegroup.TheproportionofchildrenwasonlyhalfashighasamongHungariancitizens,andthenumberoftheelderlywasloweramongthem.
1.4 Marital status
In2016,themaritalstatusofthelargestshare(44%)ofthepopulationaged15yearsandoverweremarried,although their proportion slightly decreased inthe past five years.Theproportion ofwidowed anddivorcedpeoplefellaswellandwasaround11%each.At the same time, the proportion of nevermarriedmen and women continued to increase, but at adeceleratingrate,andin2016,theyalreadyaccountedformorethanonethirdofthepopulation.
Peoplegetmarriedatlateragesordonotmarryatall.Thewillingnessofmenunder30yearsofagetomarryispersistentlylow,in2016(similarlyto2011),95%ofthemwerenevermarried.Among30–39year-oldmen,theproportionofnevermarriedgrewfrom49%to57%andamongthe40–49year-oldsfrom23%to30%.
Despite the increase inthenumberofmarriages,theproportionofmarriedmenunder30yearsofagedidnotchangesignificantlyandwas5%in2016.Theproportionofmarriedmenfell to38%inthe30–39agegroupandto56%amongthe40–49year-olds.
Thevastmajorityofwomen,89%under30yearsofagewerelivingaloneand42%ofthe30–39year-oldshavenevermarriedyet.Between2010and2016,the proportion ofmarriedwomen fell from 11% to10% amongwomen under 30 years of age, and theproportionofmarriedwomenaged30–39yearswas53%in2011andonly49%fiveyearslater.In2016,itwascharacteristiconlyoftheagegroupsovertheageof40thatthemajoritywasmarried.
In the population aged 15 years and over, theproportionofnevermarriedmenandwomenwas
Figure 1.2.2 Proportion of people aged 65 years and over, 2016
15.5–17.417.5–18.418.5–19.4
%
19.5–20.420.5–21.1
Table 1.4.1 Distribution of men and women aged 15 years and over by marital status
(%)
Marital statusMen Women Together
2011 2016 2011 2016 2011 2016
Never married 38.8 40.8 27.0 28.6 32.6 34.3
Married 47.2 46.5 41.9 41.7 44.4 44.0
Widowed 3.8 3.7 18.2 17.5 11.5 11.0
Divorced 10.1 9.0 12.8 12.2 11.6 10.7
Total 100.0 100.0 100.0 100.0 100.0 100.0
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between 30% and 38% in the counties of Hun-gary, and was the highest in Csongrád and Haj-dú-Bihar counties after Budapest and the lowestin Nógrád county. The proportion of marriedpeoplewasbetween40%and48%,ahighvaluewascharacteristic of Győr-Moson-Sopron, Pest, Sza-bolcs-Szatmár-Bereg and Vas counties and a low
oneofBudapestandCsongrádcounty.Thenumberof widows and widowers was the highest inNóg-rádandHevescountiesandthelowestinthecapitaland Pest county. Divorced people accounted forthe largest proportion in Budapest and Csongrádcounty and for the smallest in Szabolcs-Szatmár-Beregcounty.
Figure 1.4.3 Distribution of 30–39 year-old men and women by marital status, 2016
Never married
Married
Widowed
Divorced
57.2%38.4%
0.1%
4.3%
42.1%
49.5%
0.4%8.0%
Males Females
Figure 1.4.1 Population number by marital status and sex, 2011
Males Females
Never married Married Widowed Divorced
thousand
Years
thousand020406080100 0 20 40 60 80 100
100–X
05
101520253035404550556065707580859095
Figure 1.4.2 Population number by marital status and sex, 2016
100–X
05
101520253035404550556065707580859095
Males FemalesYears
Never married Married Widowed Divorced
thousand thousand020406080100 0 20 40 60 80 100
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1.5 Educational attainment
Inthepopulationaged15yearsandover,theproportionof people who completed at most the 8th grade ofprimaryschoolhascontinuedtodecreasesince2011,andinparallelwiththis,theproportionofthosewithsecondaryortertiaryeducationhasbeenincreasing.
The proportion of those with educationalattainment lower than the 8th grade in primary(general)schoolhasdecreasedto3.2%sincethelastpopulation census. Thenumber of peoplewhodidnotcompletethefirstgradeofprimaryschoolwaslowinallagegroups,andtheirproportionwithinthetotalpopulationwas around0.5%.Therewere also fewerpeoplewithatmostprimaryeducationalattainment,theirproportionwas23%inthetotalpopulationandhigherthanthisintheolderagegroups.
The number of those who obtained secondaryeducationallevelwithoutfinalexamination,withfinalvocationalexamdecreasedinthelastfiveyears(by3%),whichwasthefirsttimeafterthesetypesofqualificationhadbeenintroducedintheschoolsystem.Thereasonfor this was that the popularity of ‘only’ vocationalqualifications fell among young people intending tocontinue their studies and the efforts to acquire thefinalexaminationbecamecommonplace.Obtainingaqualificationinanapprenticeorvocationalschoolwasthemostcharacteristicofpeopleaged40–64years.
In the observed period, the proportion of peoplewithsecondaryeducationallevelwithfinalexaminationslightly increased.The highest level of education of33% of the population aged 18 years and over was
secondarylevelwithfinalexamination.Theproportionofpeoplewithsecondaryeducational levelwithfinalexaminationwas thehighest (62%) in the20–24agegroup, some ofwhomwere still studying in tertiaryeducation,whileitwaslowerintheolderagegroups.
In half a decade, the number of people withuniversityorcollegedegreecontinuedtogrow.Theirproportionincreasedfrom17%to20%andwasthehighest(34%)inthe30–34agegroup.
Amongmen, the proportion of those with lowereducational attainment or secondary level with finalexaminationcontinuedtobelowerthanamongwomen,and the proportion ofmenwho obtained secondaryeducationallevelwithoutfinalexamination,withfinalvocationalexamwashigherthanthatofwomen.Thenumberofthosewithuniversityorcollegedegreewasstillhigheramongwomen.Theproportionofwomenwith tertiary educational attainment has increasedfrom18%to22%sincethelastpopulationcensus.
In2016,morethanhalfoftheadultpopulationhadsecondaryeducationallevelwithfinalexaminationorhighereducationalattainment.
Thedynamicgrowthinthenumberandproportionof people with secondary educational level with finalexamination observed in the past decades continuedatamoremoderatepace.Aboutonethirdofthepopu-lation aged 18 years and over had at least secondaryeducationallevelwithfinalexaminationin1990,nearlyonehalfin2011,andtheirproportionwas55%in2016.Theirproportionwasespeciallyhighamongthe20–34year-olds, it nearly reached the average of the totalpopulation in the 45–49 age group, but it wasmore
Figure 1.5.1 Distribution of the population aged 15 years and over by the highest level of education completed and sex
0102030405060708090
1990 2001 20162011
100%
0102030405060708090
1990 2001 20162011
100%
General (primary) school less thanthe 8th grade completed
General (primary) school, 8th grade completed Secondary level without �nal examination, with �nal vocational exam
Secondary level with �nal examination University, college, etc. with degree
Males Females
17.27.7 3.1 2.1
34.3
31.624.1 20.4
21.8
26.5
29.328.7
16.822.5
27.729.9
9.8 11.6 15.8 19.0
26.114.2 6.5 4.2
36.8
35.7
29.2 25.8
8.9
11.6
14.213.9
20.428.1
32.134.3
7.8 10.418.0 21.8
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andmorelaggingbehindtheaverageintheolderones.Only30%ofpeopleolderthan75yearshadsecondaryeducationallevelwithfinalexamination.
Theregionaldifferencesineducationalattainmenthardlychangedinthelastfiveyears.Theproportionof people with at least secondary educational levelwithfinalexaminationwasstillextremelyhighinthecapital (76%) and in the county seats (65%), but itwasonly38%invillages.InadditiontoBudapest,the
proportionofthosewithsecondaryeducationallevelwith final examination was the highest in Pest andCsongrádcountiesandthelowestinNógrádcounty.
Inthepopulationaged25yearsandover,thenationalproportionofpeoplewithuniversityorcollegedegreewas23%.Theirproportionwasextremelyhigh in thecapital,lowerbutmuchabovethenationalaverageinthecountyseats,whileitwasonly12%invillages.Amongcounties,Nógrádcounty,aswellasSzabolcs-Szatmár-Bereg,Békésand Jász-Nagykun-Szolnokcountieshadthelowestvalues(13%and15%,respectively).
1.6 Economic activity
Thecompositionofthepopulationbyeconomicactiv-ity has changed favourably since the last populationcensus: the number and proportion of economicallyactivepeopleincreased,withinthis,thoseofthepersonsin employment grew and those of the unemployeddecreased.Inconnectionwiththis, theproportionofeconomically inactives receiving benefit1, includingmainlypensionersandrecipientsofsocialbenefits,aswellasthatofdependentsbecamesmaller.
In the observed five years, the number of peopleof working age continued to decrease somewhat,however,theactualnumberoftheeconomicallyactive
Figure 1.6.1 Distribution of the population by economic activity
0
10
20
30
40
50
60
70
80
90
2001 20162011
100%
Person in employment
Unemployed
Economically inactive receiving bene�t
Dependent
36.2 39.7 45.9
4.15.7
2.6
32.429.7 27.8
27.3 24.9 23.7
Figure 1.5.2 Proportion of people with at least secondary educational level with final examination in the population aged 18 years and over, 2016
42.8–44.945.0–49.950.0–54.9
%
55.0–59.960.0–76.4
Table 1.5.1 Population aged 15 years and over by highest education completed, 2016
(%)
Type of settlement
Population aged
15 years and over
18 years and over
25 years and over
the proportion of those who
completed at least the 8th grade of general (primary)
school
completed at least
secondary level
with final examination
completed university or college, etc. with degree
Capital 98.7 76.4 40.7County seat 98.1 65.2 29.1Other town with county
right 97.2 58.2 22.2Other town 96.5 50.9 18.8Towns together 97.5 61.4 27.2Villages 95.0 37.8 11.8Country, total 96.8 54.6 22.8
1Inactiveearnersatthetimeofthe2011censusandearlier.
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population increased.The increase in retirementagehasalsocontributed to thegrowthof the latter, as aresult of which, a large proportion of people aged60–64were still in the labourmarket as opposed to2011.Thislargeagegroup,borninthefirsthalfofthe1950s, contributed significantly to the expansion oftheeconomicallyactivepopulation.Althoughtheagegroupunder30wasbasicallystillcharacterizedbythetrendbeginninginthe1990s,i.e.thatduetoprolongingtheperiodofstudying,theirmuchsmallerproportionappears in the labour market and they appear onlylater, the proportion of economically active 15–19year-oldsgrewnow.Theincreaseintheemploymentoftheyoungestagegroupmaybeinconnectionwithreducingthecompulsoryschoolingageto16years.
The number of the active population, includingthe persons in employment and the unemployed,was 4 million 754 thousand in October 2016,which represented an increase of 5.4% since 2011.Economicallyactivepeopleaccountedfor48%ofthetotal population.Their age structure has somewhatchanged:themostsignificantincreaseoccurredintheproportionoftheyoungest(15–25year-old)andtheoldest(60–65year-old)agegroups.Inthe15–19agegroup,14thousandmoremenand7thousandmorewomenandinthe60–64agegroup,99thousandmoremenand52thousandmorewomenwerepresent inthelabourmarketin2016thanfiveyearsearlier.
The proportion of economically active personswas 55% among men and by 13 percentage pointslower than that among women (42%). Since 2011,theparticipationrateofbothmenandwomeninthelabour market has increased, the difference in theactivityofthetwosexeshashardlychanged.
Regardingtheeconomicactivityofthepopulation,there are significantdifferences among thedifferentareasofthecountry.Amongtheregions,theactivityrate was outstandingly high in Central Hungary(51%),whileNorthernHungarywasattheotherend
Figure 1.6.2 Proportion of the economically active population, 2016
45.1–45.946.0–46.947.0–47.9
%
48.0–48.949.0–49.950.0–52.1
Table 1.6.1 Population by economic activity and age groups (thousand persons)
Age group, years
2011 2016
person in employment unemployed
economically inactive
receiving benefit
dependent total person in employment unemployed
economically inactive
receiving benefit
dependent Total
–14 – – – 1 448 1 695 – – – 1 422 1 42215–19 19 9 9 556 669 36 15 9 439 49820–24 252 73 39 255 809 293 42 36 231 60225–29 424 77 69 42 787 472 33 60 53 61730–34 555 76 106 28 701 483 26 81 24 61335–39 619 81 86 30 609 634 29 74 28 76540–44 556 70 61 27 709 702 31 49 29 81145–49 458 59 61 24 825 611 26 44 26 70750–54 467 59 107 25 705 491 20 55 21 58955–59 423 54 277 27 609 450 17 138 23 62960–64 108 8 530 8 535 257 10 442 21 73065– 62 2 1 606 8 1 546 76 0 1 741 4 1 821Total 3 943 568 2 950 2 477 10 198 4 503 250 2 728 2 322 9 804
17
ofthescalewitha5percentagepointslowerrate.TheactivityratecontinuedtobethehighestinBudapest(52%),andamongcounties,itwas50%inGyőr-Mo-son-Sopron,Vas,Komárom-EsztergomandFejérandonly45–46%inBorsod-Abaúj-Zemplén,NógrádandTolnacounties.
In2016,thenumberofpersonsinemploymentwas4million503 thousandandthatof theunemployedwas 250 thousand. The number of the persons inemployment has increased by 14% and that of theunemployedhasfallenby56%sincethelastpopulationcensus.Theproportionofunemployedpeopleinthetotalpopulationwas5.3%asopposedto13%in2011.
The composition of the employed and theunemployed according to educational attainmentvaries considerably. Among employed people,11% completed atmost primary school, 61% hadsecondaryandmorethanonequarterhadtertiaryeducation, while among unemployed people, theproportion of those having completed at mostprimaryschoolwas28%andonly12%ofthemhaduniversityorcollegedegree.
The number of economically inactives receivingbenefit, the other large groupof thepopulationhasdecreasedby7.5%since2011,andon1October2016,their number amounted to 2million 728 thousandandtheirproportioninthetotalpopulationwas28%.Thenumberofpensionersrepresentingthelargerpartofthisgroupwasreducedbytheriseinretirementage
andtheprolongationoftheactiveperiod.Thenumberofthoseonchild-careleave,i.e.theotherlargegroupofeconomicallyinactivesreceivingbenefitdecreasedsomewhat. There was a significant decline in thecategory of other economically inactives receivingbenefit,socialassistanceandsupport:thenumberofpeoplebelongingtothisgroupfellbyabouthalf.
Due to the higher presence of the elderly, theproportion of economically inactives receiving benefitwasthehighestinNógrád,TolnaandBékéscountieswheretheyaccountedfor32%ofthepopulation,whileitwasthelowestinPestcountywhereonequarterofthepopulationbelongedtothisgroup.
The third large group of the population consistsof the dependents. In 2016, their proportion was24%, somewhat lower than five years earlier. 61% ofdependentswere children and another 29%were15–24year-olds.Intheolderagegroups,thecategoryof dependents has almost disappeared, and withpension rights and pension-type benefits becominggeneral, the share of not studying dependents wasonly1%inallagegroupsalreadyin2011.
The proportion of the dependent populationis the lowest in Western Transdanubia and thehighest in Northern Great Plain. Among counties,the proportion of dependents was 25–26% in Pest, Szabolcs-Szatmár-Bereg, Hajdú-Bihar and Borsod-Abaúj-Zemplén counties. InZala andVas counties,only21%ofthepopulationbelongedtothiscategory.
Table 1.6.2 Economic activity of men and women by settlement types, 2016 (%)
Type of settlement
Men Women
person in employ-
mentunemployed
economically inactive
receiving benefit
depen-dent Total
person in employ-
mentunemployed
economically inactive
receiving benefit
depen-dent Total
Capital 55.3 2.8 17.8 24.1 100.0 44.9 2.1 32.0 21.0 100.0
County seat 52.1 2.8 19.3 25.9 100.0 41.6 2.0 33.9 22.5 100.0
Other town with county right 53.0 2.4 20.6 24.0 100.0 40.7 2.0 35.4 21.9 100.0
Other town 51.4 3.1 20.8 24.7 100.0 39.7 2.2 35.4 22.8 100.0
Towns together 52.6 2.9 19.7 24.8 100.0 41.6 2.1 34.1 22.2 100.0
Villages 50.9 3.3 21.2 24.6 100.0 37.3 2.2 36.6 23.9 100.0
Country, total 52.1 3.0 20.1 24.8 100.0 40.3 2.1 34.8 22.7 100.0
18
2 Households, families
2.1 Number and composition of households
In2016,98%ofthepopulationlivedinprivatehouse-holds and 2% (217 thousand people) in institutionalhouseholds(e.g.students’hostels,homesfortheaged,prisons). In recentdecades, theproportionofpeoplelivinginprivatehouseholdshasbarelychanged.
The number of private households grew earlier,reachingmorethan4millioninthe2011populationcensus. After that, however, the expansion did notcontinue, and the fragmentation of householdsseemstostop.At thetimeof the2016microcensus,
the number of private households was 4 million 21thousand,84thousandfewerthan5yearsearlier.The decrease was mainly due to the decline in thenumberofone-personhouseholdsandpersonslivingalone.
In 2016, the vast majority of family householdswere one-family households, it rarely occurred thatmorefamilieslivedinacommonhousehold(2.5%).Insomeareasofthecountry,familyhouseholdsconsistalmost exclusively of one family.The proportion ofhouseholdswithmorefamilieswasexceptionallylowin thecapital,but itwasmuchbelowtheaverage inBékés and Csongrád counties as well. Most house-holds with more families were enumerated in Somogy and Szabolcs-Szatmár-Bereg counties, buttheirproportiondidnotreach4%.
In themajority of one-family households (82%),couples(marriedorconsensualunion)livedtogetherwith one or more children2 or without child(ren).Among them, married couple households werestill in majority, but the proportion of one-familyhouseholds based on consensual unionwas already17%.Atthesametime,theproportionofhouseholdswhere one parent lived alone with his/her child orchildrendecreased.In16%ofone-familyhouseholds,one parent lived with his/her child or children in2001,whilethisproportionreached20%in2011anddecreasedto18%by2016.
0
2 000
4 000
6 000
8 000
10 000
1990
10 124
3 890
9 945 9 697 9 587
4 106 4 0213 863
2001 20162011
12 000Thousand
Number of people living in households Number of households
Figure 2.1.1 Number of households and people living in the households
2Inprocessinghouseholdandfamilydata,anevermarriedchildisconsideredachildirrespectiveofage.Thus,achildlivinginthefamilycanbeofadultageaswell.
19
The vast majority of non-family householdsconsistedofpersonslivingalone,whileasmallerpartof them were households with other composition,consisting of relatives or unrelated persons. In1990 and 2001, less than 30% of households werenon-family households, then their proportion grewsignificantly to 35% by 2011 due to the high andincreasingnumberofpersonslivingalone.Inthepastfiveyears,thenumberandproportionofhouseholdswithout family decreased.Only one person lived inoneineveryfourhouseholdsin1990andoneineverythreehouseholdsin2016.Thismeans1million217thousandpersonslivingalone.
In the capital, the proportion of non-familyhouseholds,andwithinit,thatofone-personhouse- holds was especially high (40%). Their proportionwas above average in the counties of Southern GreatPlainand inBaranyacounty,while itdidnotevenreach24% inPestandSzabolcs-Szatmár-Beregcounties.
2.2 Size and age structure of households
The average number of persons per hundredhouseholdswas236in2011,and238in2016,sothesizeofhouseholdsincreasedslightly.
In2016,onepersonlivedin30%andtwopersonsin 31% of households. Until the 2001 census, theproportion of two-person households was alwaysthe highest, but in 2011, one-person householdsrepresentedthelargestnumber.Duetothedecreaseinthenumberofone-personhouseholds,thenumberof two-person households became again higherthan that of one-person households by 2016. Theproportion of households larger than two-personhouseholds has been decreasing for decades. In2016,18%ofhouseholdshadthreeand13%hadfourmembers, and the proportion of households withmorememberswaslessthan7%.
Due to the high proportion of persons livingalone, the number of smallest households was stillthehighestinthecapitalandinCsongrádandBékéscounties,whilethemostpopulatedhouseholdslivedinSzabolcs-Szatmár-BeregandPestcounties.
Due to the ageing of the population, the agestructureofhouseholdsischangingaswell.Theshareofhouseholds consistingofonlyolderpeople (aged
Table 2.1.1 Number of households and people living in the households
Type of settlementHouseholds, 2016 People living in households, 2016 Number of people per one hundred
households
number, thousand households
as a percentage of the 2011 census
number, thousand households
as a percentage of the 2011 census 2011 2016
Capital 835 101.9 1 718 102.2 205 206County seat 737 97.4 1 650 97.7 223 224Other town with county right 109 98.1 256 98.8 234 236Other town 1 257 97.5 3 122 98.6 246 248Towns together 2 938 98.7 6 746 99.3 228 230Villages 1 084 96.0 2 840 97.9 257 262Country, total 4 021 97.9 9 587 98.9 236 238
Figure 2.1.2 Distribution of households by household composition
0
10
20
30
40
50
60
70
80
90
2001 20162011
100%
Households based on relationship
Households with two or more families
Lone parent family households
Non-family households
58.7 57.0 50.8 53.1
10.6 10.712.9 11.7
2.6 3.21.2 1.7
28.2 29.1 35.2 33.5
1990
20
60 years and over) continued to grow. In 2016, theproportionofhouseholdsofyoungpeople(youngerthan30 years)was5.4%, and in18%ofhouseholdsonlymiddle-aged and in 29% only old-aged peoplelived.Householdswithanagestructurecharacteristicof households with children, i.e. those consistingof young and middle-aged people account for thelargestshareofallhouseholds,andtheirproportionwas 31% in 2016, 1.8 percentage points lower thanfive years earlier. At the same time, the proportionof households consisting of middle- and old-agedpeople has increased somewhat, and more than10%ofhouseholdswere such.Mostly, they are alsohouseholdswithchildren,buthereanadultchildliveswithhis/herold-agedparent(s).Commonhouseholdsofthreegenerationswerealsomorefrequentthanfiveyears earlier, and in 2016, young, middle-aged andelderlypeoplelivedtogetherin5%ofhouseholds.
2.3 Number and composition of families
Thenumberof familieswas2million743thousandin2016,mostofthemwerebasedonrelationshipwithorwithoutchild(ren).Theproportionoffamiliesbasedonrelationship in all families was nearly 82%,while 18%of families consisted of one parent and one or morenevermarriedchild(ren).Themajorityofrelationships
werebasedonmarriage,in2016thenumberofmarriedcoupleswas1million757thousand.Consensualunionsgainmoreandmorespace,theirnumberwasmorethan483thousand,andoneineverysixrelationshipswassuch.
Thenumberofloneparentfamilieswithchild(ren)was503thousandinOctober2016.Withinallloneparentfamilies, the proportion of lone mother families withchild(ren) approached86%and theirnumber reachedalmost431thousand.Inoneineverysevenloneparentfamilies,thefatherlivedtogetherwithhischild(ren).
Thenumberof familieshasbarelychangedsincethe 2011 population census. Among the differentfamily types, there was a significant increase in thenumberoffamiliesbasedonconsensualunion(19%).In1990andinthepreviousperiods,consensualunionwas commonmostly amongdivorced andwidowedpeople,whilein2016,itwasthemostcharacteristicofnevermarriedpeople.
The occurrence of different family types showsdifferences by counties.The proportion of marriedcouple families was the highest in Győr-Moson-Sopron, Szabolcs-Szatmár-Bereg and Vas counties(68–69%)andthelowestinthecapitalandCsongrádcounty (61%). Living in consensual union was themost common in Komárom-Esztergom, Jász-Nagy-kun-Szolnok and Csongrád (20–21%) and the leastfrequentinNógrádandVascounties(14%).
Table 2.2.1 Age structure of households, 2016(thousand households)
Household composition
Only Young and middle-
aged
Young Middle-aged
Young, middle-
agedTotal
young middle-aged elderly and elderly
person(s)
Family household 94 287 455 1 236 44 359 198 2 673One-family household 94 287 454 1 208 43 354 165 2 605Married couple 24 180 419 732 20 206 102 1 684Consensual union 60 85 29 222 3 30 22 451Together 84 265 448 954 24 236 123 2 134Lone parent with child(ren) 10 22 6 254 19 119 41 471Household with more families 0 1 0 28 1 5 33 68Non-family household 122 444 710 10 10 51 2 1 349One-person household 105 424 688 – – – – 1 217Other composition 17 20 22 10 10 51 2 131Total 216 731 1 165 1 245 54 410 199 4 021
21
Theothermainfeatureoffamiliesiswhethertheyareraisingchildren inthe familyornot. In2016,atleastonechildwasraisedin1million716thousandfamilies,innearlytwothirdsofallfamilies.Inslightlymorethanonethirdoffamilies,marriedorcohabitingcouples livedwithoutchild(ren).Familieswhere thechildwho livedearlier in the familybutalready lefttheparentalhomeandstarted to live independentlyalso belong to this category. Out of one hundredfamiliesbasedon relationship, 54werewith and46werewithout child(ren).The proportion of familieswith child(ren) decreased in the last five years bothin case of married couples and consensual unions,butitwasstillhigher(by3percentagepoints)amongmarriedcouplefamilies.
Childless families were the most common inthecapital (41%). In smaller townsandvillages, thenumberoffamilieswithchild(ren)wasaboveaverage.Thedifferencebythetypeofsettlementwasthemostspectacular in case of consensual unions: 63% ofcohabiting partners lived without child(ren) in thecapital,whiletheirproportionwas52%intownsand40%invillages.
Theproportionoffamilieswithchild(ren)wasthehighestinPestandSzabolcs-Szatmár-Beregcounties(67%).
2.4 Size of families, number of children
In 2016, 7 million 768 thousand people lived infamilies.Inthepastfiveyears,thenumberofpersonsper hundred families continued to decrease. Asopposedto287in2011,thevalueoftheindicatorfellto283in2016.Marriedcouplefamilieswerestillthemostpopulated,thenumberofpersonsperhundredfamilies was 294 in these families. In hundredconsensual union families 287 and inhundred loneparentfamilies240personslived.
Two or more children were raised more oftenin married or consensual union families than inlone parent families. Among families based onrelationship, thenumberofmarried couple familieswithonechildwaslowerandthosewithtwochildrenwashigherthanincaseofconsensualunionfamilies.Theproportionoffamilieswithfourormorechildrenwas 3.0% amongmarried couple families and 4.9%amongconsensualunionfamilies.
Theproportionof childless families increasedby 3percentagepointsinthelastfiveyears.Onthewhole,
Table 2.3.1 Number of families and persons living in families
Type of settlementFamilies, 2016 People living in families, 2016 Number of persons per one hundred
families
number, thousand families
as a percentage of the 2011 census
number, thousand people
as a percentage of the 2011 census 2011 2016
Capital 469 103.6 1 263 102.9 271 269County seat 477 100.2 1 307 98.4 279 274Other town with county right 74 100.0 208 99.2 282 280Other town 910 101.1 2 602 99.6 290 286Towns together 1 931 101.4 5 380 100.1 282 279Villages 812 100.4 2 387 99.2 297 294County, total 2 743 101.1 7 768 99.8 287 283
Figure 2.3.1 Number of families by the composition of families
0
500
1 000
1 500
2 000
2 500
2001 20162011
3 000Thousand families
Married couples
Father with child(ren)
Consensual unions
Mother with child(ren)
1990
2 3212 125 1 772 1 757
125272
405 483
89 5872 72
361 413465 431
22
theproportionoffamilieswithoneortwochild(ren)decreased to the same extent. The proportion offamilieslargerthanthishashardlychanged.
The composition of families by the number ofchildren varies by types of settlement. In Buda-pestandinlargertowns,theproportionoffamiliesraising one child is higher, while in smaller townsand villages, familieswithmore children aremorecommon.InPestcounty,theproportionoffamilieswithmorechildrenexceededtheonecharacteristicof the country in each childnumber category, andamong counties, the proportion of families withtwo childrenwas also the highest here (25%).Thenumber of families raising three children was thehighestinSzabolcs-Szatmár-BeregandBorsod-Aba-új-Zempléncountieswhereoneineverytenfamilieswassuch.
Among children living in families, 70%weredependent, their number was nearly 1 million960 thousand in 2016, i.e. fewer than five yearsearlier.
In 2016, the number of children was 101 perhundred families, 162 per hundred families withchildren, 171 per hundred married or consensualunion families with children and 140 per hundredloneparentfamilies.
Figure 2.4.1 Number of family members per hundred families by family types
0
100
150
200
250
300
2001 20162011
350Persons
Families, total
Lone parent with child(ren)Families based on relationship
1990
50
292 291 287 283301 302 297 293
243 240 245 240
23
3 Characteristics of the housing stock
3.1 Number of dwellings
On1October 2016, the total number of dwellingsandoccupiedholidayhomes–thesizeofthehousingstock–was4,404,518, i.e. 14,000more thanat thetimeofthe2011census.Overthelastfiveyears,thehousingstockgrewlesscomparedtoperiodsbetweenprevious censuses due to a significant decline inhousingconstruction.
Examiningchangesinthehousingstockbytypeofsettlement,citiesandtownswereusuallycharacterizedbyaslightincrease,whilevillagesbyasmalldecreaseinthenumberofdwellings.
Among regions, the number of dwellings hasgrown in Central Hungary, Central Transdanubia,Western Transdanubia and Southern Transdanubiaanddeclinedinotherregionssincethe2011census.
Therewere fewer dwellings in nearly half of thecounties (Bács-Kiskun, Békés, Borsod-Abaúj-Zemp-lén, Heves, Jász-Nagykun-Szolnok, Komárom-Esz-tergom,Nógrád,Szabolcs-Szatmár-BeregandTolna)than five years earlier. In these counties, the smallnumberofnewlybuiltdwellingscouldnotoffsetthatofliquidateddwellings.Housinggrowthexceeded2%onlyinGyőr-Moson-SopronandPestcounties.
More than20%of thehousing stock is inBuda-pest,52%inruraltownsand28%invillages.
ExceptforBudapest,boththenumberandshareofunoccupieddwellingsgrewinalltypesofsettlementsandinallcounties.
In2016,morethan12%ofthehousingstockwasunoccupied, i.e. used for other purposes, seasonallyoccupiedorvacant.Thissharewas11%in2011and9.2% in 2001.This phenomenonwas influenced byseveral factors showing territorial features. Loss ofpopulation, aging population in small settlements,migration fromvillagesdue to lackof employment,offices and businesses in city dwellings as well ashomesusedonlyseasonally,e.g.forholidaypurposesallincreasethenumberofunoccupieddwellings.
Theshareofunoccupieddwellingswithinhousingunits increased themost inBorsod-Abaúj-Zemplén,Komárom-Esztergom and Jász-Nagykun-Szolnokcounties.
Figure 3.1.1 Changes in the housing stock
0
2 000
2 500
3 000
3 500
4 000
1990 2001 20162011
4 500
Thousand dwellings
3 853 4 065
4 390 4 405
500
1 000
1 500
24
Compared to the results of the recent census,therewasaslightdecreaseinthenumberofoccupieddwellings,whichalmostappliesfortheentirecountry.
The number of occupied dwellings decreasedbyover4%in thecountiesofBékés,Borsod-Abaúj-Zemplén,Heves, Jász-Nagykun-Szolnok,Komárom-EsztergomandTolna.OnlyBudapest,PestandGyőr-Moson-Soproncountieshadmoreoccupieddwellingsthanatthetimeofthelastcensus.
3.2 Walling of dwellings
Typical building materials for dwellings varyaccordingto theperiod inwhichthegivendwellingwasconstructed.99%ofdwellingsbuiltbefore1960were built of brick or adobe. As a result of large-scale housing construction started in the 1960s, theproportion of prefabricated homes (withmiddle orlargeblocksorpanelwalling)amongdwellingsbuilt
Table 3.1.1 Occupied and unoccupied dwellings, 2016
Type of settlementOccupied Unoccupied Total Occupied Unoccupied Total
dwellings, thousand dwellings dwellings as a percentage of the 2011 census
Capital 801 107 908 101.7 91.0 100.3County seat 709 88 797 98.0 121.0 100.1Towns of county rank 105 11 116 99.2 107.7 99.9Other town 1 206 163 1 368 98.1 129.1 101.0Towns together 2 820 369 3 189 99.1 112.8 100.5Villages 1 034 182 1 216 97.0 120.0 99.8Total 3 854 550 4 405 98.5 115.1 100.3
Table 3.2.1 Occupied dwellings by year of construction and walling, 2016
Capital, countyBrick, stone,
manual walling element
Middle or large block, cast concrete
Panel Adobe, mud Wood, other, not known Total
Budapest 69.6 4.7 24.7 0.3 0.8 100.0Bács-Kiskun 49.3 7.7 8.4 32.7 1.9 100.0Baranya 63.9 3.0 19.2 12.6 1.3 100.0Békés 55.0 5.2 6.5 31.1 2.2 100.0Borsod-Abaúj-Zemplén 60.3 11.3 17.1 10.1 1.2 100.0Csongrád 54.8 3.5 17.4 22.7 1.5 100.0Fejér 57.0 5.6 19.8 14.9 2.6 100.0Győr-Moson-Sopron 78.6 2.8 14.9 1.7 2.0 100.0Hajdú-Bihar 59.0 3.8 15.4 19.8 2.0 100.0Heves 63.9 10.8 5.5 16.9 2.8 100.0Jász-Nagykun-Szolnok 50.1 5.2 6.4 37.0 1.4 100.0Komárom-Esztergom 70.2 8.9 14.5 5.0 1.5 100.0Nógrád 58.7 14.7 9.0 16.2 1.5 100.0Pest 74.4 3.4 4.7 14.5 3.0 100.0Somogy 80.1 2.6 6.9 9.1 1.3 100.0Szabolcs-Szatmár-Bereg 61.2 5.2 7.8 23.1 2.7 100.0Tolna 58.2 7.3 8.8 23.9 1.9 100.0Vas 83.9 5.3 6.7 2.6 1.5 100.0Veszprém 79.4 3.4 13.5 2.5 1.2 100.0Zala 80.2 9.6 4.6 4.3 1.3 100.0Total 65.9 5.6 13.8 13.0 1.7 100.0
25
in this decadewasnearly 20%.Theuse of adobe asa building material gradually diminished. Amongdwellingsbuiltinthenextdecade,theproportionofhomesbuiltfrompanelwasalready40%,whilethatofbrickdwellingsdroppedto53%.Fromthe1990s,theshareofprefabricateddwellings (withpanelwallingandmiddleorlargeblocks)hasbecomelessandlesspronouncedinhousingconstruction.
Among occupied dwellings, nearly two-thirdsweremadeofbrick,14%paneland13%adobe.Mostpaneldwellingsare located inBudapest,where25%ofoccupieddwellingsarelikethis.Baranya,Borsod-Abaúj-Zemplén,CsongrádandFejércountiesfollowthis(17–20%).
Theproportionofadobedwellingsisparticularlyhigh (23–37%) in the four Great Plain counties ofBács-Kiskun,Békés,Jász-Nagykun-SzolnokandSza-bolcs-Szatmár-BeregandinTolnaCounty.
3.3 Dwellings by ownership and tenure status
In 2016, 98% of occupied homes were owned byprivate individuals, 1.3% by local governments andless than1%byother institutionsandorganizations.Theownershipstructureofdwellingswascompletelytransformedbytheearly2000s.Theproportionoflo-calgovernmenthousing,whichwasmorethan25%inthe1970sand1980s,decreasedtoaminimumby2016.
Since the census of 2011, there has been nosubstantialchangeintheownershipstructureofthehousingstock.Theshareofoccupiedhomesownedbyprivateindividualsincreasedbyanother2percentagepoints.
Inmost counties, the ownership structure is thesameas thenational average.The shareofprivatelyowneddwellingsfromhousingassetswashighest inPestCounty(99%)andlowestinVasCounty(97%).
Ownership types of occupied dwellings andchangesthereofarereflectedbychanges indwellinguse. In 2016, the share of owner-occupants, tenantsand other occupants was 90%, 8.3% and 1.4%respectively.Over thepastfiveyears, thenumberofdwellingsoccupiedbytenantshasincreasedby45,000,notresultinginasubstantialchangeindwellinguse,aschangeintheshareofownersortenantsdoesnotevenreach1.5percentagepoints.
Thelowshareoftenantsshowsthatrentingisstillunattractive,whichisduetoahighrenttoearnings
ratio and an almost total lack of local governmentrentalhousing.
The share of tenants is highest in Budapest aswell as in county seats and towns of county rank.In other towns and villages, the share of owners isabovenationalaverage,whilethatoftenantsismuchsmaller.
Figure 3.3.1 Number of occupied dwellings by type of ownership
0
1 500
2 000
2 500
3 000
3 500
2001 20162011
4 000
Private person Local government, other institution, organization
500
1 000
3 5153 769 3 786
176
144 68Thousand dwellings
Figure 3.3.2 Number of occupied dwellings by title of use
0
1 500
2 000
2 500
3 000
3 500
2001 20162011
4 000
Thousand dwellings
Owner Tenant
500
1 000
3 401 3 582 3 477
271277 322
1853 56
Other
26
3.4 Size of dwellings: floor space, number of rooms
In2016,6.6%ofdwellingshadone,32%two,33%threeand 29% four or more rooms.The typically more-room newly built dwellings shift the compositionof housing units in the direction of more-roomdwellings.Thedecreaseinthenumberandproportionofone-andtwo-roomdwellingsaswellastheincreaseinthenumberandproportionoffourandmoreroomdwellingscontinuedinthepastfiveyears.
According to legal status of settlements, theproportionofmore-roomdwellingsrisesifwemovetowardsvillages.
In the capital city, 14% of dwellings have onlyone-room,which is7percentagepointshigher thanthe national average. The proportion of one-roomdwellingsisclosetoaverageincountyseatsandtownsofcountyrankandonly4.4%and3.2%respectivelyinother towns and villages.Two-roomdwellingshavea higher than national average share in Budapest,countyseatsandtownsofcountyrank.Inothertownsandvillages,morethan60%ofdwellingshadthreeormorerooms.
Mostdwellingshavethreeroomsinthemajorityof counties and within this two rooms in Baranya, Fejér, Jász-Nagykun-Szolnok and Komárom-Eszter-gom counties and four or more rooms in Pest, Somogy,TolnaandVeszprémcounties.
In 2016, the average floor space of occupieddwellingswas82m2,4m2morethanat thetimeofthe2011census.Theaveragefloorspaceofdwellingsandthenumberofroomsgrowduetothenewlybuiltlargerdwellings.
Larger-sizeddwellingsgainedgroundagainst thesmaller-sized ones. Particularly noteworthy is theincrease in the proportionof dwellingswith a floorspace of more than 100 m2, every third or fourthdwellingbelongstothiscategory.
In Budapest, the proportion of dwellings with afloor spaceof less than40m2 is 16%which is al-most three times higher than the national average,whiletheshareofdwellingswithafloorspaceofover 80 m2 is lower than half the national average. Insmallertownsandvillages,however,largerdwellingsare more frequent. More than 70% of dwellings invillagesare largerthan80m2andmorethanhalfofthemarelargerthan100m2.
The average floor space of occupied dwellings ishighest in Pest County (93 m2), which is followedby Tolna (90 m2) as well as Győr-Moson-Sopron,
Figure 3.4.1 Distribution of occupied dwellings by number of rooms
9.1%
37.3%
32.6%
20.9%
6.6%
31.7%
33.1%
28.6%1
2
3
4 or more
2016
2011
Table 3.4.1 Occupied dwellings by floor space, 2016
Type of settlement
–39 40–59 60–79 80–99 100–
Total
–39 100–
floor space of dwelling, m², thousand dwellingsfloor space of dwelling, m², as a percentage of the 2011
census
Capital 124 301 185 82 108 801 89.6 118.2County seat 55 279 155 83 136 709 78.7 120.2Towns of county rights 6 38 22 16 24 105 77.2 121.9Other town 35 240 256 272 403 1 206 66.5 119.9Towns together 220 858 618 453 672 2 820 81.9 119.7Villages 10 78 198 296 453 1 034 45.5 116.9Total 229 936 817 748 1 125 3 854 79.3 118.6
27
Somogy, Szabolcs-Szatmár-Bereg and Zala counties(88m2).Thesmallestdwellingswithanaveragesizeof67m2areinthecapitalcity.
3.5 Dwelling equipment and comfort level
Thesupplyofdwellingswithutilitieshas continuedto improve over the past five years. In 2016, nearly99%ofoccupieddwellingshadrunningwater,withinthis 97%were connected to the community schemepipedwater,and1.7%hadprivatesourcepipedwater.Theshareofdwellingsconnectedtothewatermainsnetwork has continued to increase since the lastcensus.
97%ofoccupiedhomeshavehotwater,whichisalsohigherthanfiveyearsearlier.
98%ofalloccupieddwellingswereconnectedtoaseweragenetwork.Thebiggestprogresswasmadeindevelopingseweragenetworks:fiveyearsago77%ofoccupieddwellingswerepublicsewered,by2016thisratioincreasedto87%andtheshareofdwellingswithprivatesewercontinuedtofallto12%.
The share of dwellings with flush toilet hascontinuedtoriseto96%overthepastfiveyears.
Along with improving coverage indicators, more than 50 thousand dwellings have no pipedwater and thenumberofdwellingswithunsolvedsewage disposal exceeds 70,000. There are noflush toilets in more than 150,000 homes, and116,000homeslackthehotwatersupply.Theshare of worst-equipped dwellings is highest in Bor-sod-Abaúj-Zemplén and Szabolcs-Szatmár-Beregcounties.
Coverage differences are well visible betweenthe eastern and western regions of our country. InCentral Hungary, Central Transdanubia andWest-ernTransdanubia,allformsofequipementareabovenationalaverage.SouthernTransdanubiahasabetterthan average supply of community scheme pipedwater,buttheshareofdwellingswithhotwatersupplyandpublic sewerage in the region is belownationalaverage.Eachregionintheeasternpartofthecoun-try has a lower than average supply of communityscheme piped water, hot water, flush toilets andsewerage.
Outside Budapest, Veszprém, Győr-Moson- Sopron, Vas and Zala counties have the best watersupplynetworks,where99%ofdwellingshavepublic
Figure 3.5.1 Share of occupied dwellings with piped water, sewage disposal and flush toilet
0
40
50
60
70
80
1990 2001 20162011
100%
10
20
30
90
Community scheme piped water Private source piped waterPublic sewer Private sewer Flush toilet
Table 3.5.1 Share of occupied dwellings with piped water and sewage disposal, 2016(%)
Type of settlement
Share of dwellings with piped water from
Together
Share of dwellings with
Togethercommunity
scheme private source public sewage facility
private sewage facility
Capital 99.9 0.1 100.0 98.8 1.2 100.0County seat 98.6 0.9 99.6 96.7 2.9 99.6Towns of county right 99.3 0.4 99.7 97.1 2.5 99.7Other town 96.7 1.8 98.5 91.1 7.1 98.3Towns together 98.2 1.1 99.3 94.9 4.2 99.1Villages 93.0 3.6 96.6 64.1 31.4 95.6Total 96.8 1.7 98.5 86.7 11.5 98.2
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watersupply.Outsidethecapital,theshareofsewereddwellings is highest inKomárom-Esztergom (95%),Győr-Moson-Sopron(94%)andZalacounties(92%).
Differences between types of settlements aredeclining, but the coverage level is still significantlydifferentbetweenbigcitiesandvillages.Communityscheme piped water supply, which is essentiallycomplete in Budapest, 99% in smaller towns and98% in villages, shows the smallest difference. Pub-lic sewerage isworst in rural townsandvillages (91and 64% respectively).The equipment of dwellingswith hot water supply and flush toilet correspondsto the national average in smaller towns and a fewpercentagepointslowerinvillages.
Thepastfiveyearssawarearrangementinheatingmodes,theshareofdwellingswithheatingseparatelyfor eachplace continued todecrease and theuseofcentral heating systems from an installation in thebuildingorinthedwellingincreased.
Central heating systems from an installation inthe building or in the dwelling, which are used bymore than half of all dwellings, represent themosttypical heatingmode inoccupieddwellings. 16%ofall dwellingsuse central heating froma communityheating centre and 33% heating separately for eachplace.
The share of homes centrally heated from acommunityheatingcentreis29%inthecapital,34%incountyseats,one fourth in townsofcountyrankandhardlyonetenthinothertowns.
In Transdanubia, central heating from aninstallation in thebuildingor in thedwelling is the
most popular, the proportion of this heatingmodeisdoublethatofdwellingsheatedseparatelyforeachplace. In regions of the eastern part of the country,the proportion of homes heated separately for eachplaceisroughlythesameasthatofhomeswithcentralheatingsystemfromaninstallationinthebuildingorin the dwelling.Central heating from a communityheatingcentreisusedmorethanthenationalaveragein Central Hungary, Central Transdanubia and SouthernTransdanubia,whileitsuseisthelowestinSouthernGreatPlain.
Compared to the 2011 census, the comfort levelof the housing stock continued to improve due tobetter equipped newly built dwellings and housingimprovementsimplementedinrecentyears.
In 2016, 66% of all dwellings were with allamenities, 29%with principal amenities, 2.6% withpartofamenitiesandonly2.5%withoutcomfortoremergencyandotherdwellings.
Theshareofdwellingsinthetwohighestlevelsofcomforthasbarelychangedover thepastfiveyears,whilenearly50% fewerdwellingsbelong to the twolowestcategories.
The comfort level of dwellings reflects territorialdifferences in equipment and heating mode ofdwellingsasequipmentofdwellingsandtheheatingmode used are basic criteria for classification intocomfortlevels.Thereisamarkeddifferencebetweentheeasternandwesternpartsofourcountry.Theshareofdwellingswithallamenitieswasaboveaverageandthat of other lower-grade housing below average in
Figure 3.5.2 Distribution of occupied dwellings by level of comfort
0
50
60
70
80
90
2001 20162011
100%
With all amenities With principal amenitiesWith part of amenitiesWithout comforts, emergency and other dwelling
1990
10
20
30
40
39.951.7
61.465.8
30.6
30.2
31.028.4
39.951.7
61.4 66.3
30.6
30.2
31.0 28.77.6
5.02.7 2.6
21.913.1
4.92.5
Table 3.5.2 Occupied dwellings by type of heating, 2016
Type of settlement Central heating
Of which: from a
community heating centre
Heating separately
for each place
Total
Capital 613 230 187 801County seat 541 243 169 709Towns of county rights 81 27 24 105Other town 790 106 416 1 206Towns together 2 025 606 795 2 820Villages 572 2 462 1 034Total 2 597 609 1 257 3 854
(thousand dwellings)
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Central Hungary, Central Transdanubia andWest-ern Transdanubia. In the eastern part of the coun-try,therewerefewerdwellingswithallamenities,butmoredwellingswithprincipalamenities,sotheshareofdwellingswithpartofamenities,withoutcomfortoremergencyandotherdwellingsisnotsignificantlyhigherthanthenationalaverageevenintheseregions.
TheshareofdwellingswithoutcomfortishighestinSzabolcs-Szatmár-BeregandBorsod-Abaúj-Zemp-léncounties.
3.6 Dwellings and their occupants, density standard
In2016,249peoplelived,onaverage,inonehundredoccupieddwellings. In2011, theirnumberwas 248.Duetoadeclineinpopulationandinthenumberofoccupieddwellingsdensitystandardslightlyincreasedoverthelastfiveyears.
Density standard is smallest in Budapest, where215peopleliveinonehundreddwellings,ifwemovetowards settlements with a smaller population thedensitystandardisincreasingandreaches275peopleperhundreddwellingsinvillages.
Density standard was highest in Szabolcs-Szat-már-Bereg (282), Pest (281) and Borsod-Abaúj-Zemplén(264)countiesandlowestinBudapest(215)Békés (234),Csongrád (237)andBács-Kiskun(244)counties.
Figure 3.6.1 Changes in the number of occupants per hundred occupied dwellings
0
50
100
150
200
250
1990 2001 20162011
300Persons
280 269248 249
Figure 3.6.2 Number of occupants per 100 occupied dwellings, 2016
215–244245–249250–254
Persons
255–259260–282
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Methodological guide
Concept of microcensus
Amicrocensusisapopulationcensusthatmonitorssocial processes between two full-scale censusesusuallyathalf-timeusingasampledatacollection.It is also referred to as ’lesser census’. Similarly tocensuses,themicrocensusisprescribedbylaw.
InHungary,thefirstmicrocensuswasconductedby theHungarianCentral StatisticalOffice in 1963.The2016datacollectionwastheseventhinthelineofHungarianmicrocensuses.
Most important features of the 2016 microcensus
The2016microcensuscovered10%ofhouseholds.In 2,148 settlements, approximately 440,000addresses were contacted. In addition to privatehouseholds, there were nearly 500 residentialinstitutions,so-calledinstitutionalhouseholds(e.g.students’ hostels, homes for the elderly), in theobservedsample.
Duetothelargesamplesize,themostimportantdataarereliableevenatdistrict level. Inadditiontothebasicquestionnaire, the10%sampleallowedthemicrocensus to collect data on social stratification,occupationalprestige,subjectivewell-being,disabilitystemming from health problems and internationalmigration within the framework of five additionalsurveys.
Basicquestionnairesofthemicrocensus(dwellingquestionnaire, personal questionnaire) followed thethematicstructureofthe2011censusandmodifieditaccordingtocurrentrequirements incaseofcertaindatagroups.
Length of data collection, mode of implementation
Thereferencedateofthissurvey-thepointintimeforwhichthequestionshadtobeanswered-wasthestart(00:00)of1October2016,exactlyfiveyearsafterthereferencedateofthelastcensus.
Themicrocensuswasconductedbetween1Octo-berand8November2016.
Datawerecollectedintwophases:• between 1 and 9 October questions could be
answeredonline,• between10Octoberand8Novemberenumer-
ationofficerscollecteddatathroughinterviews.Themost important technological innovation of
the microcensus was that it was carried out solelyin electronic form without paper questionnaires –through self-completedquestionnaires on the inter-netandelectronicdevicesincaseofinterviews.
19% of contacted private households completedthe questionnaire online.The proportion of onlinerespondentswas29%inthecapital,butonly14%inBorsod-Abaúj-ZemplénCountyand13%inSzabolcs-Szatmár-BeregCounty.
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Sampling
Themicrocensushada10%sample,whichconsistedof subsamples for the institutionalized and non-institutionalized population. The most importantrequirement was to provide reasonably accurateestimatorsforsomemainindicatorsatdistrict level.Duetopractical(datacollection),organizationalandbudgetaryreasonsroughly2,000settlementscouldbeinvolved.
Sample of dwellings and holiday homes
Thesample consistsof 197district level subsampleswiththefollowingfeatures:
1.Thesamplingframewastheupdatedregisterofaddresses.
2.Biggersettlementswereselectedwithprobability1 (self-representing settlements). Within thesecertainty PSUs3 dwellings were selected in onestagewhileintherestofthepopulationastratifiedtwo-stageselectionwasappliedwheresettlementswere selected with probability proportional tosize.Withineachselectedsettlements,dwellingswere selected with systematic random methodresultinginabalancedterritorialcoverage.
3.Theplannedundercoveragewasminimal.Focus- ing on accuracy only 42 of the smallest settle-ments were excluded from the frame whichconstitutes0.1%ofthetargetpopulation.
4.Finally2,148settlementsandinmostofthematleast50dwellingswereselected.The1,409self-representing settlements covered more than90%ofthetargetpopulation.
5.The sample was not self-weighting. Districtswere various in size, settlements’ structureand population composition. The need foraccuratedistrict-levelestimatorsyieldedhighersampling rates in smaller or heterogeneousdistricts.
6.The sampling rate for the holiday homes(unoccupiedin2011)wasmuchlower.Thefinalsample size for dwellings and holiday homeswere430618and9484,respectively.
Sample of institutions
Duetotheabovementionedreasons,notmorethan500institutionscouldbeselected.
1.Thesamplingframewasbasedoninstitutionsincensus2011andupdatedwithnewlyestablishedones.
2.Being rarely populated, numerous accommo-dation establishments were excluded from theframe.
3.Toselectinstitutionsastratifiedone-stagesam-pling and systematic random selectionmethodwasapplied.Stratificationfactorsareasfollows:a.old(existingin2011)ornewb.sizecategoryc.functionofinstitution.
Table 1 Distribution by method of data supply among private households by county and settlement type
Capital, county
Share of addresses, with
Totalonline interview based
data supplyBudapest 28.6 71.4 100.0Bács-Kiskun 15.7 84.3 100.0Baranya 16.2 83.8 100.0Békés 16.6 83.4 100.0Borsod-Abaúj-Zemplén 14.3 85.7 100.0Csongrád 16.8 83.2 100.0Fejér 20.0 80.0 100.0Győr-Moson-Sopron 22.6 77.4 100.0Hajdú-Bihar 16.0 84.0 100.0Heves 16.4 83.6 100.0Jász-Nagykun-Szolnok 15.3 84.7 100.0Komárom-Esztergom 20.9 79.1 100.0Nógrád 16.0 84.0 100.0Pest 22.3 77.7 100.0Somogy 14.8 85.2 100.0Szabolcs-Szatmár-Bereg 12.7 87.3 100.0Tolna 18.3 81.7 100.0Vas 23.4 76.6 100.0Veszprém 21.5 78.5 100.0Zala 16.7 83.3 100.0Country total 19.4 80.6 100.0Of which:
towns without Budapest 20.4 79.6 100.0villages 14.2 85.8 100.0
3Primarysamplingunits.
32
4. The largest institutions were selected withcertainty. Within these PSUs4 every tenthperson was interviewed. Within smallerinstitutions’stratasystematicrandomsamplingwasappliedgiventheframesortedbyregions.Ineachselectedsmallerinstituteseverypersoninscopehadtobeinterviewed.
Weighting
Weightingwascarriedoutintwobasicsteps:1.Non-responseadjustmentofdesignweights.2.Somefurtheradjustmentinorderthatsamplefits
knownpopulationdistributions(calibration).
Non-response adjustment
91% of the selected dwellings were successfullyenumerated5. Besides, information on occupancycouldbegatheredinanother6.4%oftheoverallcasesandtherewerenorelevantinformationonoccupancyavailablein2.6%only.
Non-response adjustment was carried outseparatelyinthefollowingfoursub-samples:holidayhomes,dwellingsbuiltaftercensus2011,oldhousingunits,institutions.
1.Basedonnon-responsecode informationonly,a multi-step simple correction was made insub-samples of holiday homes andnewly builtdwellingswithineachdistricts.
2.Probabilitytoresponsewasestimatedusingcensus2011datainsub-sampleofoldhousingunits.
3. Incaseofinstitutionsprimaryweightwasgivenby the ratio of the number of residents andrespondents.
Calibration
Since estimates with primary weights were notreliable, calibrationwas applied6with the followingcontroltotals:
1.populationbygenderandfive-yearagecategoriesatdistrictlevel;
2. number of newly built dwellings, old housingunitsandholidayhomesatdistrictlevel;
3.numberofforeignnationalsatcountylevelDuring calibration, special attentionwas paid to
avoidlargeweightadjustment.Therangeofthefinalestimationweight isquite
large,partlyduetonon-self-weightingsamplewherethesamplingratesarequitedifferentbydistrictsoreven within a given district. Some outlier weightswere created in the first step of weighting, wherenon-response adjustment in a few districts couldbedonewith ahigher correction factordue to thelimitednumberofcases involved.Since theseweremainly in sub-samplesofholidayhomesoramongunoccupieddwellingsweightswerenotboundedatthisstage.
Standard error estimates can be found on theHCSOwebsitenexttothemicrocensusdatasheets.
Methodological notes and concepts for theinterpretationofmicrocensusdataarealsoavailableonthewebsite.
4Primarysamplingunits.5Responsecases:occupiedorunoccupieddwellingswithfilledquestionnaire,unoccupiedholidayhomes,emptyplot, shop-office,institutionaddress.Theseweregivenfinalestimationweight.
6MihályffyL.:Meghiúsulások kompenzálása lakossági felvételekben: egy speciális lineáris inverz probléma.Szigma,XXV.évf.,191–202.
33
List of detailed tables available from the HCSO website
(http://www.ksh.hu/mikrocenzus2016)
1. Retrospective data (national and by counties)1.1 Number and characteristics of the population
1.1.1 Population, population density, increase of population1.1.2 Population by age group and sex1.1.3 Population aged 15 years and over by marital status and age group1.1.4 Population by citizenship and sex
1.2 Educational attainment1.2.1 Males by education and age group1.2.2 Female by education and age group1.2.3 Population by education and age group1.2.4 Population aged 7 years and older by highest education completed and sex
1.3 Economic activity1.3.1 Population by economic activity and sex1.3.2 Population by sex, age group and economic activity
1.4 Households, families1.4.1 Households and persons living in household by household composition1.4.2 Households by household composition and age composition of persons living in the household1.4.3 Households by household composition and economic activity composition1.4.4 Families and persons living in families by family composition and average size of the family1.4.5 Families by family composition and number of children1.4.6 Families by family composition and number of children under 15 years
1.5 Dwelling stock1.5.1 Type of housing units, number of occupants and density standard1.5.2 Occupied dwellings by type of ownership and tenure status1.5.3 Occupied dwellings by equipment and number of rooms1.5.4 Occupied dwellings by level of comfort and number of rooms 22.12.2017
2. Detailed data2.1 Number and characteristics of the population
2.1 Number and characteristics of the population2.1.1 Population by age and sex, sex ratio, 20162.1.2 Population aged 15 years and over by marital status, age group and sex, 20162.1.3 Population by citizenship, age group and sex, 2016
2.2 Educational attainment2.2.1 Population aged 7 years and over by education, age group and sex, 20162.2.2 Population aged 7 years and older by highest education completed, age group and sex, 2016
2.3 Economic activity2.3.1 Population by marital status, economic activity and sex, 20162.3.2 Population by highest education completed, economic activity and sex, 2016
34
2.3.3 Population by highest education completed, economic activity and marital status, 20162.3.4 Economically inactives receiving benefit by period of time passed since last job, age groups, education and sex, 20162.3.5 Unemployed by period of time passed since last job, age group, education and sex, 2016
2.4 Households, families2.4.1 Households by household composition, number of household members, number of persons living
in household and number of persons per one hundred households, 20162.4.2 Households by household composition and age composition of persons living in the household, 20162.4.3 Households by household composition and economic activity composition, 20162.4.4 Families by family composition, total number of children, number of children under 15 years and
number of dependent children, 20162.4.5 Families by age group of husband (male partner), wife (female partner) and father and mother, by type of family, 20162.4.6 Families by highest education completed of husband (male partner), wife (female partner) and father and mother, by type of family, 2016
2.5 Dwelling stock2.5.1 Occupied dwellings by type of ownership, tenure status, number of rooms, level of comfort, total floor space and type of heating, 2016 22.12.20172.5.2 Occupied dwellings by tenure status, number of rooms, level of comfort, total floor space and type of heating, 2016 22.12.20172.5.3 Occupied dwellings by number of rooms, total floor space, level of comfort, equipment and type of heating, 2016 22.12.20172.5.4 Occupied dwellings by total floor space, level of comfort, equipment, material of outer walls and number of occupants, 2016 22.12.20172.5.5 Occupied dwellings by equipment, type of ownership, tenure status, total floor space and level of comfort, 2016 22.12.2017
3. Data on counties/Data on districts3.1 Number and characteristics of the population
3.1.1 Population by age group and sex, 20163.2 Educational attainment
3.2.1 Population aged 7 years and older by highest education completed and sex, 20163.3 Economic activity
3.3.1 Population by economic activity, 20163.4 Households, families
3.4.1 Main data of households, 20163.4.2 Main data of families, 2016
3.5 Dwelling stock3.5.1 Type of housing units, 20163.5.2 Occupied dwellings by number of rooms and type of ownership, 2016
Compiled by:Anasztázia Bojer, Orsolya Eszenyi, Hajnalka Hluchány,
Eleonóra Nagy Forgács, Éva Simor, Noémi Szabó-Jankovics
Contributors:Karolina Bartha, Gábor Csordás, István Ecseri, Virág Erdei, Zita Ináncsi, Benedek Kovács, Marcell Kovács, Mária Nagy,
Márta Varga Loch, Mónika Vörös
Translators:Zsuzsa Radnóti, Péter Strömpl
Layout editor:Zita Dobróka
Table program prepared by:Márton Papp
More information: Marcell KovácsPhone: (+36-1) 345-6309, e-mail: [email protected]
Internet: www.ksh.hu/[email protected]
(+36-1) 345-6789 (phone), (+36-1) 345-6788 (fax)
Cover photo: Fotolia
