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UDK: 330.34:314.118(439)
DOI: 10.5379/urbani-izziv-2016-27-02-002
Prejeto: 13. 4 . 2016 Sprejeto: 25. 8. 2016
Tamas EGEDY Annamaria UZZOLI
Mesta kot ključ do preživetja? Vpliv gospodarske krize na neenakosti v zdravju z vidika brezposelnosti in pričakovane življenjske dobe na Madžarskem
Avtorja v članku obravnavata prostorsko razsežnost neenakosti v zdravju na Madžarskem na podlagi povezave med brezposelnostjo in pričakovano življenjsko dobo na državni ravni ter vse do ravni mikroregij in naselij, pri čemer je poseben poudarek na mestih. Poudarjata povezavo med brezposelnostjo in povprečno pričakovano življenjsko dobo ob rojstvu na podlagi regionalnih statističnih podatkovnih baz, poleg tega predstavita tudi ugotovitve kvantitativnih vprašalnikov (n = 545) in kvalitativnih poglobljenih intervjujev (n = 26). Na tej podlagi obravnavata vpliv gospodarske krize na prebivalstvo večjih madžarskih mest. Povezava med brezposelnostjo in pričakovano življenjsko dobo se v času krize poveča.
Izsledki avtorjev kažejo, da sta položaj na trgu dela in raven prihodkov pomembna z vidika zdravja, raven in rast obstoječih družbenih in zdravstvenih neenakosti pa povečata tudi vpliv gospodarske krize na zdravje ljudi. Na Madžarskem neposredni vplivi gospodarske krize na neenakosti v zdravju še povečujejo regionalne razlike. Mesta lahko uravnovesijo neugodne vplive krize na zdravje in uspešno prispevajo k zniževanju neenakosti v zdravju.
Ključne besede: neenakosti v zdravju, gospodarska kriza, brezposelnost, pričakovana življenjska doba, mikroregije in mesta, Madžarska
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1 Uvod
Zdravstveno stanje prebivalstva na različnih geografskih območjih ter v različnih naseljih in družbenih skupinah določajo biološki, posameznikovi, okoljski, družbenogospodarski in družbeno-kulturni dejavniki ter dejavniki, povezani z življenjskim slogom (Marmot in Wilkinson, 2006). Na nastanek neenakosti v zdravju torej vpliva zapleten sistem povezav med številnimi sočasno delujočimi (odločilnimi in vplivnimi) dejavniki (Raphael, 2008a). Nekatere neenakosti v zdravju niso odvisne od družbenih in gospodarskih odnosov (na primer tiste, ki so povezane z genskimi dejavniki in starostnimi boleznimi), številne pa so odvisne od družbenogospodarskih neenakosti (Evans in Stoddart, 1990, in Elstad, 2005). Za najpomembnejše dejavnike, ki vplivajo na neenakosti v zdravju, se običajno uporablja angleška okrajšava »PROGRESS« - Place of residence, Race/ethnicity, Occupation, Gender, Religion, Education, Socioeconomic status and Social capital/resources (stalno prebivališče, rasa/narodnost, poklic, spol, veroizpoved, izobrazba, družbenogospodarski položaj in družbeni kapital/viri; Oliver idr., 2008). Pri teh osnovnih dejavnikih se kažejo pomembne razlike na globalni ravni (država in regije) in lokalni (naselja). Družbene in gospodarske razlike so hkrati vidne vertikalno in horizontalno, kar pomeni, da pomembnost prostora pri proučevanju neenakosti v zdravju ni sporna.
Avtorja v raziskavi proučujeta vpliv družbenih determinant (kot je brezposelnost) na neenakosti v zdravju v času krize ter povezavo med zdravjem in krajem, pri čemer uporabljata materialistično/strukturalistično pojmovanje. Ta pristop poudarja materialne pogoje, v katerih živijo ljudje, kar pomeni, da na njihove vedenjske izbire močno vplivajo materialne življenjske razmere (Bartley, 2003). Kot pravi Margaret Whitehead (1992: 243): »Dokazi še naprej potrjujejo, da imajo družbenogospodarske razmere pomembno vlogo pri razlikah v zdravju.«
V prvem delu članka je opisano metodološko ozadje raziskave, v osrednjem delu pa so predstavljeni empirični rezultati kvantitativnih statističnih analiz in kvalitativnih anket o povezavah med brezposelnostjo in povprečno pričakovano življenjsko dobo ob rojstvu ter o njunih prostorskih razlikah. Poleg tega so obravnavani vplivi trenutne gospodarske krize na mestne prebivalce. Avtorja domnevno povezavo med gospodarsko krizo in zdravjem razlagata na podlagi vplivov prostorskih vzorcev na neenakosti v zdravju ter poskušata ugotoviti, kakšen vpliv je gospodarska kriza imela na zdravstveni sistem in zdravstveno politiko. Na primeru Madžarske poskušata na novo opredeliti vlogo mest pri uravnoteženju zdravstvenih neenakosti v Vzhodni Evropi. Proučevanje vplivov gospodarske krize na neenakosti v zdravju na Madžarskem je še posebno zanimivo,
ker je povprečna pričakovana življenjska doba ob rojstvu v tej državi 5,6 leta krajša kot v zahodnoevropskih državah. Gospodarsko in družbeno tranzicijo v začetku 90. let 20. stoletja je spremljalo povečanje neenakosti v zdravju, kot ga država še ni doživela, čez nekaj let pa je svetovna gospodarska kriza še povečala in deloma preoblikovala obstoječe prostorske razlike v neenakosti v zdravju.
Neugodne razmere, ki jih je povzročila gospodarska kriza, ne izvirajo iz različnih bioloških značilnosti, ampak iz nepravičnosti in neenakosti, ki so posledica te krize, zato avtorja na podlagi madžarskih primerov proučujeta predvsem njen vpliv na neenakosti v zdravju. Raziskava je edinstvena v tem, da prikaže vpliv trenutne gospodarske krize na lokalni razvoj neenakosti v zdravju na Madžarskem ter vlogo mest pri dolgotrajnem razvoju teh neenakosti, ki imajo neposredne družbene in gospodarske posledice. V tem pogledu so v članku predstavljeni novi dokazi negativnih vplivov kriznih obdobij na neenakosti v zdravju, in sicer zlasti v srednje- in vzhodnoevropskih državah.
2 Teoretično ozadje
Z zdravstvenega vidika je pomembno razumeti, kako družbene determinante zdravja vplivajo na neenakosti v zdravju. Za razumevanje glavnih mehanizmov lahko uporabimo te okvire oziroma pristope: (a) kulturni/vedenjski, (b) materialistični/ strukturalistični in (c) neomaterialistični. S kulturnega oziroma vedenjskega vidika (pristop k preprečevanju bolezni na podlagi življenjskega sloga) naj bi bile za bolezni krive posameznikove izbire (na primer kajenje, pitje alkohola, fizična dejavnost, prehrana itd.; Raphael, 2008b). Povezava med življenjskim slogom in življenjskimi razmerami kaže, da del različne porazdelitve življenjskih slogov pravzaprav povzroča neenakomerna porazdelitev življenjskih razmer (Stronks idr., 1997). Materialistična oziroma strukturalistična razlaga poudarja materialne pogoje, v katerih ljudje živijo, in ti močno vplivajo na njihove vedenjske izbire. Izraz »družbene determinante zdravja« se nanaša na materialne pogoje, ki obstajajo v okoljih, v katerih se ljudje rodijo, živijo, delajo, se igrajo, molijo in se starajo, ti pa vplivajo na najrazličnejše izide in tveganja, ki so povezani s posameznikovim zdravjem, delovanjem in kakovostjo življenja (Graham, 2007). Neomaterialistična razlaga razširja materialistično analizo z vprašanjem, kako se narodi, območja in mesta razlikujejo glede na porazdeljenost gospodarskih in drugih virov med prebivalstvom. Neomaterialistični vidik torej usmerja pozornost na vplive življenjskih razmer in družbenih determinant zdravja na posameznikovo zdravstveno stanje (Bernard idr., 2007, ter Collins idr., 2009).
Povezava med enakimi možnostmi in zdravjem je v vsaki državi večinoma odvisna od makroekonomskih razmer. V ozadju
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neenakosti, povezanih z zdravjem, so gospodarske neenakosti, nepravična porazdelitev dohodka, ovire pri dostopu do izobraževanja in zdravstvene oskrbe, slabe stanovanjske razmere in življenjske okoliščine ter pomanjkanje možnosti za zdravo življenje (Benach idr., 2007). Družbenogospodarski trendi in dejavniki torej vplivajo na zdravstveno stanje, raziskave neenakosti v zdravju pa jasno poudarjajo pomen družbenega okolja (Braveman idr., 2005).
Družbenogospodarske determinante zdravja in dejavniki, ki vplivajo na neenakosti v zdravju, določajo, koliko bodo gospodarske krize vplivale na zdravstveno stanje prebivalstva (Marmot in Bell, 2009). Čeprav so vzročne povezave zapletene, je vloga negativnih procesov pri posameznikovem položaju na trgu dela ter spremembah in preobrazbah potrošniških navad gospodinjstev nedvoumna. Vse od začetka gospodarske krize se pojavljajo ugotovitve, ki potrjujejo povezavo med brezposelnostjo in dohodkom z neenakostmi v zdravju. Vzdolžne statistične analize trendov umrljivosti in zaposlovanja, ki so jih opravili David Stuckler idr. (2009), potrjujejo neposreden vpliv gospodarske krize na javno zdravje. Patricia A. Collins idr. (2009) so ugotovili, da lahko zaposlitev in dohodek pomembno napovesta zdravstveno stanje in da so razlike med družbenimi sloji pri samovrednotenju zdravja vidne tudi na območjih z majhnimi razlikami v prihodkih. Izsledki Dimitrisa Zavrasa idr. (2013) so potrdili, da je v času gospodarske krize večja verjetnost, da ljudje (zlasti brezposelni) svoje zdravje ocenijo kot slabo. Z družbenogospodarskim položajem pa so povezane veliko večje prostorske in regionalne razlike v neenakosti v zdravju in samoocenjenem zdravju, kot bi lahko pričakovali (Mackenbach idr., 2008).
Razlaga družbenih determinant neenakosti v zdravju odpira vprašanje, ali sta v času krize z vidika resničnega ali domnevnega zdravja pomembna samo položaj na trgu dela in raven prihodkov ali tudi stopnja in rast že obstoječih družbenih neenakosti in neenakosti v zdravju. Neugoden položaj na trgu dela ime negativen vpliv na enake možnosti, saj kot ugotavljata Petri Bockerman in Pekka Ilmakunnas (2008), prav brezposelnost najbolj vpliva na zdravstveno stanje. Zaradi nje ljudje dejansko zbolijo, saj vpliva na njihovo identiteto, čustva in samozavest. Poleg tega se zaradi brezposelnosti in tveganega vedenja (jemanje zdravil, alkoholizem in nenadzorovano kajenje) močno poveča tudi stres.
S prostorskega vidika bi bilo zanimivo raziskati razlike v neenakosti v zdravju ne samo z vidika mestnih in podeželskih območij, ampak tudi različnih ravni hierarhije naselij (razložena naselja, vasi in mesta). Ni naključje, da se je pomen kraja v akademskih raziskavah v zadnjih dveh desetletjih tako povečal (Diez Roux, 2001). Okrepilo se je zanimanje za proučevanje povezave med stalnim prebivališčem in zdravjem, deloma
tudi zato, ker različni konteksti, povezani s stalnim prebivališčem, prispevajo k razvoju lokalnih neenakosti v zdravju (Bernard idr., 2007, ter Macintyre idr., 2002).
Raziskave razlik v zdravstvenem stanju med mestnimi in podeželskimi območji so prišle v ospredje konec 80. in na začetku 90. let 20. stoletja (Eyles, 1987, ter Ricketts idr., 1994). Vse večji pomen te teme kaže, da se v zadnjih letih na vseh razvojnih polih svetovnega gospodarstva (to je v ZDA, Evropi in Aziji) vse več pozornosti posveča proučevanju neenakosti v zdravju na mestnih in podeželskih območjih (glej Fang idr., 2010; Pampalon idr., 2010; Gartner idr., 2011).
Že Richard Fearn in John Eyles (1987) sta opozorila na dejstvo, da kljub prednostim, ki jih imajo podeželska območja z vidika zdravega življenja (»zdravo podeželje«), v tem pogledu nikakor niso enaka. Številne raziskave so potrdile, da sta za podeželske prebivalce značilna boljše zdravstveno stanje in nižja stopnja umrljivosti kot za mestne (Shucksmith idr., 1996), pri čemer so se proučevali tudi geografski, družbeni in demografski razlogi za to ter razlogi, povezani z življenjskim slogom (Miller idr., 1987, ter Sobal idr., 1996). Številni strokovnjaki so opozarjali na veliko heterogenost podeželskih območij, kar povzroča ogromne razlike v kazalnikih zdravja (Schneider in Greenberg, 1992, ter Dolk idr., 1995). Postalo je jasno, da je treba pri analizah upoštevati geografsko okolje in sociodemografske dejavnike. Glede na velik pomen družbenih determinant Danielle C. Ompad idr. (2007) celo ugotavljajo, da je družbeno okolje ključno za razumevanje tega, kako različna območja vplivajo na zdravje prebivalcev.
Graham Bentham (1984) ter pozneje Peter Phillimore in Richard Reading (1992) so ugotovili, da je stopnja umrljivosti na podeželskih območjih odvisna predvsem od razdalje med mestnimi in podeželskimi območji (za bolj oddaljena podeželska območja je značilna višja stopnja umrljivosti), stopnja ruralnosti v kontekstu zdravja pa je močno odvisna od narave podeželskega območja. Z vidika stopnje družbene ogroženosti imajo lahko družbeno ogrožena mestna območja boljše kazalnike bolezni kot družbeno ogrožena podeželska območja (Congdon, 1995; Gartner idr., 2011), z večanjem družbene ogroženosti pa prednosti podeželskih območij z vidika zdravja postopoma izginejo (Phillimore in Reading, 1992). V zadnjih dveh desetletjih vse več raziskovalnih izsledkov kaže, da se razlike v zdravstvenem stanju ljudi na podeželskih in mestnih območjih zmanjšujejo (McLafferty, 1992; Maniecka-Bryla idr., 2012; Richardson idr., 2013). Gospodarska kriza je ta proces pospešila, mestnim območjem je celo koristila.
Med prostorsko podobo neenakosti v zdravju in konkurenčno sposobnostjo mestnih območij obstaja jasna povezava, saj je gospodarstvo (državno ali mestno) lahko konkurenčno samo,
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če ima na voljo visok delež produktivnih zaposlenih z visokimi prihodki, ki jim zagotovi visoko kakovost življenja (Frageberg, 1996, ter Atkinson idr., 1999). Ni naključje, da sta javno zdravje in učinkovitost trga dela tudi med stebri indeksa svetovne konkurenčnosti (Schwab, 2015). Kot navajata Tord Kjellstrom in Susan Mercado (2008), pa lahko urbanizacija na zdravje blagodejno vpliva samo, če države ključne družbene determinante neenakosti v zdravju na mestnih območjih rešujejo na vseh dohodkovnih stopnjah.
3 Metodološko ozadje
V skladu z glavnimi cilji raziskave so bila obravnavana ta raziskovalna vprašanja:
•	Ali je gospodarska kriza vplivala na neenakosti v zdravju na Madžarskem ter kako in koliko?
•	Koliko je položaj na trgu dela odločujoč in pojasnjevalen dejavnik pri razvoju neenakosti v zdravju? Kakšne so lokalne posledice tega?
•	Kakšna je vloga mest in mestnih območij pri oblikovanju neenakosti v zdravju v času krize ?
Povečanje neenakosti v zdravju povzroča predvsem rast lokalnih razlik. Kljub zmanjšanju proizvodnje, pomanjkanju naložb in rasti brezposelnosti so mesta in mestna območja v primerjavi z vasmi in razloženimi naselji tudi med krizo ohranila ugoden položaj z vidika zdravstvenega stanja ljudi.
Pri primerjalnih analizah in pregledih območnih pristopov sta se avtorja osredotočila na povezave med brezposelnostjo in povprečno pričakovano življenjsko dobo na eni strani ter njunimi prostorskimi vplivi na drugi. V številnih mednarodnih raziskavah je bila pričakovana življenjska doba ob rojstvu glavni kazalnik za merjenje neenakosti v zdravju zlasti v času gospodarske krize, saj makroekonomske spremembe neposredno vplivajo na pričakovano življenjsko dobo (na primer Goesling in Firebaugh, 2004, ter Jonker idr. 2013). Na podlagi stopnje brezposelnosti so proučevali družbene posledice krize, saj je brezposelnost najpomembnejši dejavnik tveganja z vidika zdravja in odločilno vpliva na spremembe neenakosti v zdravju. Najbolj ogroženi so moški srednjih let, ki izgubijo službo v zgodnji fazi gospodarske krize (Jozan, 1996), poleg tega pa ljudje zaradi brezposelnosti zbolijo, saj ta negativno vpliva na posameznikovo identiteto in samozavest (Kopp, 2007). Stopnja brezposelnosti kot gospodarski kazalnik in povprečna pričakovana življenjska doba ob rojstvu kot zdravstveni kazalnik sta primerni tudi za primerjavo stanja pred gospodarsko krizo in po njej ter njunih vplivov na neenakosti v zdravju.
Avtorja sta neenakosti v zdravju s prostorskega vidika proučevala na podlagi dihotomije med središčem in obrobjem na ravni mikroregij, pri čemer sta izvedla te naloge:
•	določila sta območja, ki na področju zdravja zaostajajo zaradi gospodarske krize;
•	proučila sta prostorske strukture neenakosti v zdravju z vidika nasprotja med mestnimi in podeželskimi območji;
•	pojasnila sta hierarhijo naselij v prostorskem vzorcu neenakosti v zdravju.
Uporabljene metode sta izbrala v skladu z raziskovalnimi cilji in vprašanji, pri čemer sta za proučevanje postavljene hipoteze uporabila kvantitativne in kvalitativne metode. Kvantitativni izračuni temeljijo na statističnih podatkih o mikroregijah iz uradnih virov (madžarskega centralnega statističnega urada in informacijskega sistema s področja razvoja nacionalnega ozemlja in državnega načrtovanja). Raven mikroregij je bila najnižja prostorska raven, za katero so bile statistične podatkovne baze prosto dostopne za analizo. Avtorja sta statistične podatke razdelila v tri skupine:
•	kazalnike za merjenje stopnje brezposelnosti (to je njena prostorska in časovna razčlenitev),
•	kazalnike za merjenje neenakosti v zdravju (to je prostorska in časovna razčlenitev povprečne pričakovane življenjske dobe ob rojstvu),
•	kazalnike za merjenje ozemeljskih/regionalnih neenakosti: register manjšin iz leta 2007 (175 mikroregij; to je raven LAU1), število mest in prebivalstva ter pravni status mest.
Pri proučevanju neenakosti sta avtorja pazljivo izbrala izhodiščno leto gospodarske krize. Vse raziskave in analize so bile opravljene za podatkovne registre iz let 2009 in 2010, saj je bil vpliv gospodarske krize na madžarski trg dela takrat najmočnejši. Na podlagi dobljenih rezultatov sta avtorja (v nasprotju z gospodarskimi raziskavami) za »krizno leto« izbrala leto 2010, saj je bila v večini primerov povezava med vrednostmi kazalnikov za leto 2010 višja kot za leto 2009. Poleg tega so se kazalniki brezposelnosti za mikroregije v neugodnem položaju poslabšali, trend izboljšanja pričakovane življenjske dobe ob rojstvu v mikroregijah v ugodnejšem položaju pa je postal zmernejši v primerjavi z letom 2009. Z vidika vzorca stopnje brezposelnosti in pričakovane življenjske dobe ob rojstvu je bila prostorskost kot pojasnjevalni dejavnik intenzivnejša v letu 2010.
Opravljena je bila primerjalna analiza Pearsonove korelacije, s katero sta avtorja opredelila in primerjala stanje pred gospodarsko krizo in po njej.
Kvantitativne analize (kot je analiza Pearsonovega koeficienta korelacije) sta dopolnila s kvalitativnimi raziskavami (vprašalniki in polstrukturirani poglobljeni intervjuji). Za analizo vplivov gospodarske krize na mestno prebivalstvo sta v madžarskih mestih izvedla pilotno anketo, s katero sta želela
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Slika 1: (a) Regije (NUTS2) in županije (NUTS3) na Madžarskem; (b) glavna mesta madžarskih županij (vir: avtorja)
ugotoviti, kako ljudje gledajo na krizo ter kakšne posredne in neposredne vplive je imela ta na njihovo mesto in vsakdanje življenje. Anketo z vprašalnikom sta izvedla med gospodinjstvi v Budimpešti, Szegedu in Gyoru (to je v najrazvitejšem središčnem naselju, v središču uspešne regije in središču manj razvite regije; glej sliko 1). Vprašalnik je vseboval 39 vprašanj pretežno zaprtega tipa, v vseh treh mestih pa so ga gospodinjstvom pomagali izpolniti študenti, ki sta jih najela avtorja. Razdeljen je bil v tri glavne sklope: prvi je vseboval nekaj splošnih vprašanj, katerih namen je bil ugotoviti, kako dobro so mestni prebivalci seznanjeni s svetovno gospodarsko krizo (kdaj, kje in zakaj se je začela in kakšne posledice je imela za madžarsko gospodarstvo na splošno); drugi sklop vprašanj se je osredotočal na vplive in posledice krize na lokalno gospodarstvo in konkurenčnost (na značilnosti in vidike lokalnega gospodarstva po končani gospodarski krizi), tretji slop pa je vseboval vprašanja o vsakdanjem življenju mestnih prebivalcev po začetku krize (finančni položaj, hipoteke, kakovost življenja in strahovi, povezani z gospodarsko krizo). Vprašalnik za gospodinjstva je bil objavljen tudi na internetu, tako da je bila osebna anketa septembra in novembra 2011 dopolnjena še s spletnim vprašalnikom (n = 352). Ob vprašalniku za gospodinjstva sta avtorja izvedla tudi ulično anketo, ki je vsebovala deset kratkih vprašanj (n = 193). Proučevala sta samo mnenja mestnega prebivalstva, kar pomeni, da pilotna anketa prinaša subjektivno oceno gospodarske krize. Da bi proučila strokovna mnenja o vplivih gospodarske krize na zdravstveno stanje ljudi, sta med januarjem in marcem 2013 opravila dvanajst poglobljenih intervjujev z madžarskimi strokovnjaki s področja zdravja. Intervjuji so se osredotočali na sistem povezav med gospodarsko krizo in zdravjem z vidika različnih strok na eni strani ter na podlagi empiričnih izkušenj praktično usmerjenih strokovnjakov na drugi.
4 Razmislek o vlogi mest pri neenakosti v zdravju
4.1 Vpliv gospodarske krize na brezposelnost in pričakovano življenjsko dobo na Madžarskem
Na začetku 90. let 20. stoletja se je brezposelnost na Madžarskem naglo povečevala. Vrhunec je dosegla leta 1993, ko je bila stopnja brezposelnosti 12,1 %, hkrati pa je bila stopnja umrljivosti najvišja (14,6 %, kar je pomenilo skupno skoraj 150.000 umrlih), povprečna pričakovana življenjska doba ob rojstvu pa najnižja (69,0 let za celotno prebivalstvo, 64,5 leta za moške in 73,8 leta za ženske). Leto 1993 lahko zato jemljemo kot prelomno leto v času tranzicije po padcu komunizma.
Sredi 90. let 20. stoletja so se začeli kazalniki ponovno zviševati in do leta 2001 je brezposelnost skoraj nepretrgano padala. Leta 2001 je bila vrednost kazalnika brezposelnosti najnižja (5,7 %), od takrat pa se zmerno, toda stalno zvišuje. Med letoma 2008 in 2009 se je število brezposelnih povečalo za 91.000, stopnja brezposelnosti pa je leta 2009 presegla 10 %. Mednarodne primerjave kažejo, da spada Madžarska med države, v katerih je gospodarsko krizo spremljala srednja rast brezposelnosti. Janos Kollo (2010) v raziskavi ugotavlja, da čeprav se je brezposelnost v primerjavi z bruto domačim proizvodom le malo znižala, je Madžarska med državami, ki so izgubile največ delovnih mest v regiji.
Z vidika regionalnih sprememb v stopnji brezposelnosti lahko glede na število brezposelnih in preteklo dinamiko brezposelnosti strogo ločimo gospodarsko uspešne regije (na primer Osrednje in Zahodno Čezdonavje) ter regije, ki se spopadajo s precejšnjimi strukturnimi in socialnimi problemi (na primer
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Slika 2: Stopnja brezposelnosti v madžarskih regijah med letoma 2005 in 2015 (vir: madžarski centralni statistični urad, 2005-2015)
17,0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
leto
Severna Madžarska in Južno Čezdonavje; slika 2). Zmanjševanje razlik v stopnji brezposelnosti v zadnjih nekaj letih je posledica državno financiranih programov javnih del, zato lahko trdimo, da gre za dokaj umeten proces.
Z vidika vplivov gospodarske krize na posamezne panoge v mestih se je zaposlenost najbolj zmanjšala v industriji (11 %), zlasti v gradbeništvu. Med prvim četrtletjem let 2008 in 2010 se je število zaposlenih v tej podpanogi zmanjšalo za 14,9 % (Balint idr., 2010), poleg tega je veliko ljudi izgubilo službo tudi v proizvodnem sektorju ter nepremičninskih in zasebnih podjetjih s področja izobraževanja. Največ delavcev so odpustila avtomobilska in elektronska podjetja v tuji lasti (v okviru kolektivnega, organiziranega in masovnega odpuščanja). Po začetku gospodarske krize leta 2009 se je število zaposlenih zmanjšalo za približno 100.000. Stopnja registrirane brezposelnosti je najvišjo vrednost (11,9 %) dosegla v prvem četrtletju leta 2010 (Kollo, 2010).
Stopnja umrljivosti je dosegla najvišjo vrednost leta 1993, od takrat pa lahko opazimo skromno izboljšanje. Kot posledica zmernejše stopnje umrljivosti po letu 1993 se je pričakovana življenjska doba po letu 1996 podaljšala na več kot 70 let. Leta 2009 se je zvišala na več kot 74 let in do leta 2013 dose-
gla 75,8 leta. To pomeni, da se je med letoma 1993 in 2013 podaljšala za 6,7 leta (preglednica 1). Kljub vsemu obstajajo precejšnje razlike med moškimi in ženskami. Največja razlika je bila leta 1994 (9,4 leta), nato pa se je do leta 2012 postopoma nižala (na 7,4 leta). Z vidika razlik med spoloma najbolj izstopa leto 2009, ko se je povprečna pričakovana življenjska doba madžarskih moških podaljšala na več kot 70 let.
Pri najnižje izobraženih ljudeh je treba upoštevati, da so številni izključeni iz sistema oskrbe, zato zanje ni na voljo natančnih podatkov. Po bolj optimističnih napovedih naj bi zaradi gospodarske krize povprečna življenjska doba ob rojstvu na Madžarskem stagnirala, vendar ne bo začela upadati. To pa lahko povzroči nadaljnje težave, saj je povprečna pričakovana življenjska doba od šest do sedem let krajša od zahodnoevropskega povprečja.
Za lažjo razlago vplivov gospodarske krize na neenakosti v zdravju sta avtorja primerjala razmere pred krizo in po njej (preglednica 2). Za primerjavo Pearsonove korelacije sta izbrala dve leti: leto 2001 je bilo kontrolno leto obdobja pred gospodarsko krizo, leto 2010 pa je bilo najprimernejše leto za razlago razmer po gospodarski krizi. Leta 2001 je bila stopnja brezposelnosti po prehodu Madžarske iz komunističnega siste-
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Preglednica 1: Povprečna življenjska doba ob rojstvu (v letih) v izbranih evropskih državah (1993, 2000 in 2013)
država	1993	2000	2013
Španija	77,2	79,3	83,2
Italija	77,4	79,9	82,9
Velika Britanija	75,7	78,0	81,1
Nemčija	75,7	78,3	80,9
Slovenija	73,1	76,2	80,5
Češka	72,6	75,1	78,3
Estonija	68,1	71,1	77,5
Poljska	71,6	73,8	77,1
Madžarska	69,1	71,9	75,8
Romunija	70,2	71,2	75,2
Vir: Eurostat (1993, 2000, 2013)
ma najnižja, pričakovana življenjska doba pa se je začela daljšati že leta 1996. Po finančni krizi med letoma 2008 in 2009 sta avtorja kot najprimernejše leto za analizo njenih posledic na neenakosti v zdravju izbrala leto 2010.
Med brezposelnostjo in pričakovano življenjsko dobo obstaja močna povezava, kar je zlasti vidno pri moških. Korelacija se je okrepila med letoma 2001 in 2010. Od padca komunizma je 60 % vseh brezposelnih na Madžarskem moških, z vidika pričakovane življenjske dobe pa izstopa zlasti njen upad pri moških srednjih let. Razlika v povezavi med stopnjo brezposelnosti in pričakovano življenjsko dobo pri moških in ženskah se je leta 2010 zmanjšala, kar pomeni, da je gospodarska kriza vplivala tudi na položaj žensk na trgu dela.
4.2 Regionalne povezave med brezposelnostjo in povprečno pričakovano življenjsko dobo
Z vidika geografskega vzorca brezposelnosti je bila najvišja stopnja registrirane brezposelnosti leta 2010 zabeležena v vhodnem in jugozahodnem delu države. V primerjavi z državnim povprečjem so bili na vzhodu Madžarske trendi brezposelnosti najbolj pozitivni v mikroregijah Szeged, Eger in Kecskemet (slika 3).
Prostorski vplivi gospodarske krize na pričakovano življenjsko dobo so postali vidni na dva načina. Po eni strani se pričakovana življenjska doba v mikroregijah, v katerih se je višala ali je bila že v izhodišču dobra, ni znižala niti v najslabših mikro-ekonomskih pogojih, po drugi strani pa se je v mikroregijah, v katerih se je pričakovana življenjska doba že tako zniževala ali ki so bile že na začetku v slabšem položaju, povprečna pričakovana življenjska doba ob rojstvu zaradi gospodarske krize znižala. Pri obeh skupinah proučevanih kazalnikov lahko določimo ta trend na ravni države: boljša pričakovana življenjska
Preglednica 2: Povezava med stopnjo brezposelnosti in povprečno pričakovano življenjsko dobo ob rojstvu glede na vrednost Pearso-novega koeficienta korelacije (r2), 2010
	stopnja brezposelnosti (%)	
	2001	2010
povpr. pričakovana življenjska doba ob rojstvu	-0,692	-0,727
povpr. pričakovana življenjska doba ob rojstvu, moški	-0,710	-0,722
povpr. pričakovana življenjska doba ob rojstvu, ženske	-0,637	-0,720
Vir: povzeto po Szilagyi in Uzzoli (2013) z dopolnitvami avtorjev
doba je značilna za glavna mesta županij in mesta druga mesta v njih ter sosednje mikroregije (slika 4).
Statistični podatki poleg tega kažejo jasne razlike v pričakovani življenjski dobi med mestnimi in podeželskimi območji (preglednica 3). Leta 2009 je povprečna življenjska doba ob rojstvu na madžarskih mestnih območjih znašala 70,4 leta, na podeželskih območjih pa samo 68,6 leta; razlika je bila torej 1,8 leta.
V	manjših naseljih je pričakovana življenjska doba krajša pri moških in ženskah. V primerjavi z Budimpešto je v najmanjših naseljih pričakovana življenjska doba pri moških krajša za 3,9 leta, pri ženskah pa za dve leti. Najugodnejše življenjske razmere nudijo naselja, v katerih živi med 50.000 in 100.000 ljudi, saj je pričakovana življenjska doba v tej kategoriji naselij najdaljša pri moških in ženskah (preglednica 3).
Stopnja brezposelnosti in povprečna pričakovana življenjska doba ob rojstvu imata podoben prostorski vzorec. Mikroregije z najboljšim položajem najdemo v aglomeraciji Budimpešte ter v Osrednjem in Zahodnem Čezdonavju, medtem ko so razmere najslabše v Severni veliki nižini in Južnem Čezdonavju. To pomeni, da je gospodarska kriza večinoma prizadela regije, ki so že prej veljale za gospodarsko najšibkejše. Razlike med vzhodom in zahodom, opisane zgoraj, lahko pojasnimo na podlagi prostorskega vzorca sistemov povezav med brezposelnostjo in pričakovano življenjsko stopnjo, vendar je treba poudariti, da tudi znotraj gospodarsko uspešnih osrednjih in zahodnih območij ter gospodarsko šibkejših vzhodnih območij obstajajo pomembne prostorske razlike, čeprav so manjše (Uzzoli, 2011). Poleg razlik med vzhodom in zahodom je pomembna tudi povezava med središčem in obrobjem. Vsekakor lahko ločimo regije z dobrim in slabim gospodarskim položajem, vendar med njimi ni mogoče potegniti jasne ločnice. V regijah z neugodnim gospodarskim položajem lahko opazimo izboljšanje na območjih, ki so bliže mest in središč županij.
V	vzhodnih madžarskih regijah se mikroregije z ugodnejšim gospodarskim položajem nahajajo v bližini glavnih mest županij (na primer Kecskemet, Szeged, Debrecen in Nyiregyhaza), mikroregije z najmanj ugodnim položajem pa vzdolž vzhodne in severne madžarske meje.
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stopnja brezposelnosti (državna raven = 100)
■	158-278 (53)
■	101-157 (52)
■	77-100 (34) □ 30-76 (35)
o mikroregija sedeža županije
Slika 3: Stopnja brezposelnosti (v odstotkih) v madžarskih mikroregijah v primerjavi z državnim povprečjem, 2010 (vir: povzeto po Szilagyi in Uzzoli, 2013, z dopolnitvami avtorjev)
povprečna življenjska doba ob rojstvu (%) (državna raven = 100)
■	101,40-106,00 (44)
■	100,01-101,39 (44)
■	98,93-100,00 (43)
■	95,00-98,92 (43)
o mikroregija sedeža županije
Slika 4: Povprečna življenjska doba ob rojstvu (v letih) v madžarskih mikroregiijah v primerjavi z državno ravnjo, 2010 (vir: povzeto po Szilagyi in Uzzoli, 2013, z dopolnitvami avtorjev)
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T. EGEDY, A. UZZOLI
Razlike med mikroregijami glede stopnje brezposelnosti in povprečne pričakovane življenjske dobe ob rojstvu opozarjajo na značilne prostorske vzorce. Na podlagi prostorske povezave med tema kazalnikoma lahko pri obeh opazimo le manjše poslabšanje in/ali izboljšanje položaja mikroregij med gospodarsko krizo. Mikroregije z najboljšim položajem najdemo v aglomeraciji glavnega mesta ali zahodnem delu države, mikroregije z najslabšim položajem pa na severovzhodu in/ali jugozahodu (slika 5).
Dejstvo, da višjo stopnjo brezposelnosti spremlja nižja povprečna pričakovana življenjska doba, pa ne velja za celotno državo. Položaj je najslabši v županijah Borsod-Abauj-Zemplen in Szabolcs-Szatmar-Bereg, kjer je bila brezposelnost visoka že pred gospodarsko krizo. V regiji Severna Madžarska je stopnja brezposelnosti višja od državnega povprečja, pričakovana življenjska doba pa je najkrajša v državi. V Budimpešti zaradi precejšnjega razvoja stopnja brezposelnosti manj odločilno vpliva na povprečno pričakovano življenjsko dobo ob rojstvu, tudi na zahodu in v osrednjem delu države ugodnejše družbenogospodarsko okolje omogoča daljšo pričakovano življenjsko dobo.
Čeprav lahko drugi vplivi (na primer rast stopnje izobrazbe prebivalstva) na srednji rok uravnotežijo zdravstvene vplive
Preglednica 3: Pričakovana življenjska doba ob rojstvu (v letih) glede na kategorijo naselij (2009)
kategorija naselij glede na število prebivalstva	povprečna pričakovana življenjska doba ob rojstvu (v letih)	
	moški	ženske
< 999	68,0	76,7
1.000-1.999	68,6	77,0
2.000-4.999	68,9	77,1
5.000-9.999	69,3	77,1
10.000-19.999	70,3	78,0
20.000-49.999	70,5	77,6
50.000-99.999	71,4	79,0
100.000-299.999	71,3	78,9
prestolnica (Budimpešta)	71,9	78,7
Madžarska	70,1	77,9
Vir: madžarski centralni statistični urad (2010)
gospodarske krize, jih raziskovalci izjemno težko izločijo. Prav zaradi tega kvantitativne statistične analize le težko razkrijejo sistem povezav med gospodarsko krizo in zdravjem. Avtorja sta zato morebitne povezave analizirala s pomočjo poglobljenih intervjujev s strokovnjaki in izvajalci v praksi. Na podlagi glavnih izsledkov intervjujev sta oblikovala model vplivov go-
stopnja brezposelnosti/povprečna življenjska doba ob rojstvu O nizka/visoka	(36)
O nizka/povprečna (18)
□	nizka/nizka	(4) O povprečna/visoka (21)
□	povprečna/povprečna (26) O povprečna/nizka (11) O visoka/visoka	(1) O visoka/povprečna (14) | visoka/nizka (43)
Slika 5: Povezava med stopnjo brezposelnosti (v odstotkih) in povprečno pričakovano življenjsko dobo ob rojstvu (v letih) v madžarskih mi-kroregijah v primerjavi z državno ravnjo, 2010 (vir: lastni izračuni na podlagi podatkov iz internet 1)
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na zdravstveno stanje
obolevnost
-	povečano število duševnih in psihosomatskih obolenj in tveganje za nastanek zasvojenosti
-	manjše možnosti za zdravo življenje
umrljivost
-	povečano tveganje za samomor, smrt zaradi srčno-žilnih obolenj in smrt v nesrečah
sprememba položaja na trgu dela med gospodarsko krizo
(npr. brezposelnost, zaposlitev s krajšim delovnim časom, nižji prihodki itd.)

na neenakosti v zdravju
povečanje brezposelnosti, revščine, družbenih razlik, razlik v dostopu do zdravstvene/ družbene oskrbe in ranljivosti

t
na zdravstveno politiko
večji stroški in nasprotna selekcija manj sredstev za zdravstveno zavarovanje, manj učinkovito dodeljevanje virov in manj podpore za preventivne programe marginalizacija interesov
dolgoročne posledice
na zdravstvo
večje izseljevanje zdravstvenih delavcev povečani družbeni konflikti v zdravniški oskrbi dezorganizacija manjša uporaba zasebnih zdravstvenih storitev večja uporaba javnih zdravstvenih storitev slabši kazalniki zdravja
Slika 6: Povezava med gospodarsko krizo in zdravjem na Madžarskem (vir: lastna anketa, empirični izsledki intervjujev s strokovnjaki)
spodarske krize na zdravstveni sistem, trg dela in neenakosti v zdravju (slika 6).
Različne oblike obvladovanja krize v bistvu dezorganizirajo zdravstveni sektor (na primer finančna podpora zdravstvu se zmanjša). Pomembno vprašanje je, koliko se uporabljajo sredstva oziroma zbiranje in dodeljevanje teh (če sploh) za reševanje tega problema. Poleg tega je treba upoštevati dejstvo, da so se zaradi gospodarske krize na Madžarskem povečali tudi učinki nasprotne selekcije (odseljevanje zdravnikov in degradacija zdravstvenih kadrov na trgu dela). Slab dostop do zdravstvenih storitev bi lahko bil vzrok za slabše zdravstveno stanje ljudi in večje neenakosti v zdravju, zlasti na podeželju. Gospodarska kriza je najbolj prizadela središčna območja, vendar vpliv gospodarstva na zdravje za zdaj še ni močno opazen. Zdravstveno stanje ljudi na najmanj ugodnih območjih (obrobjih) je predvsem posledica neugodnega izhodiščnega gospodarskega položaja teh območij in ne gospodarske krize. Prostorske razlike v
neenakosti v zdravju še vedno izhajajo iz družbenogospodarske preobrazbe po padcu komunizma. Neposrednih kratkoročnih vplivov gospodarske krize iz let 2008 in 2009 na neenakosti v zdravju še ni mogoče določiti, vendar pa lahko njeni naraščajoči vpliv še povečajo regionalne razlike.
4.3 Vplivi gospodarske krize na razvoj mest in dojemanje te med mestnimi prebivalci
Gospodarska kriza je madžarska mesta prizadela na različne načine in v različnem obsegu. Zaradi nje se je zmanjšala mednarodna konkurenčnost mest, kar dokazujejo tudi podatki indeksa svetovne konkurenčnosti, po katerih je Madžarska med letoma 2011 in 2015 z 48. mesta padla na 63. (Schwab, 2015). Avtorja sta proučila položaj madžarskih mest in ugotovila, da različno velika mesta z različnimi ravnmi poselitve začnejo v gospodarstvu konkurirati z različnimi izhodiščnimi možnostmi. Mednarodne izkušnje kažejo, da pri negativnih
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trg dela nepremičninski trg drugi dejavniki finance
gradnja, naložbe
družbeni položaj, življenjski standard
potrošnja, storitve mestna prenova infrastruktura

i i
■	celoten vzorec
□	Budimpešta
□	Szeged
□	Gyor
□	druga mesta
0,0	5,0	10,0	15,0	20,00 25,00 30,00 35,00 40,00
odstotek
Slika 7: Najbolj negativni vplivi gospodarske krize na življenje v madžarskih mestih, 2011 (vir: lastna anketa z vprašalniki, n = 352)
vplivih gospodarske krize nima ključne vloge velikost mest, ampak struktura lokalnega gospodarstva. Zaradi večjega trga dela večja naselja omogočajo boljši dostop delovni sili, poleg tega imajo tudi več podpornih panog (na primer logistiko in storitve) ter ponujajo več informacij, idej, ustvarjalnosti in inovativnosti (Turok, 2004). Začetni vpliv krize na lokalna gospodarstva je odvisen tudi od njihove gospodarske funkcije in sestave (Clark, 2009). Gospodarska kriza je bolj prizadela mesta z gospodarskimi panogami, ki so usmerjena v izvoz ter močno povezana z mednarodnimi trgi in trgovino.
Rezultati analize statistične podatkovne zbirke kažejo, da kriza ni pomembno vplivala na začetni ugodni položaj in konkurenčno prednost Budimpešte, je pa vidno okrepila konkurenco med regionalnimi središči. V Budimpešti so bili med negativnimi posledicami gospodarske krize kot najbolj pereči problemi navedeni upočasnjen infrastrukturni razvoj, vse večji družbeni problemi, obubožanje in slabši življenjski standard. Na podeželju so bili kot največji problemi poudarjeni nižji prihodki gospodinjstev ter upad potrošnje in različnih storitev (na primer izobraževanje, prosti čas in kulturne dejavnosti).
Podrobno kvantitativno analizo vloge brezposelnosti sta avtorja utemeljila z rezultati svoje kvalitativne raziskave. Kot ugotavljata Yamina Guidoum in Paul Soto (2010), glavni vpliv krize
na družbene razmere in kohezijo v mestih izvira neposredno s trga dela. Njune izsledke sta avtorja tega članka potrdila in dopolnila s svojimi ugotovitvami: po podatkih ankete mestni prebivalci menijo, da so največji negativni vplivi gospodarske krize vplivi na trg dela, problemi, povezani z rastjo brezposelnosti (ki so veliko intenzivnejši v večjih mestih), ter negativne spremembe na stanovanjskem in nepremičninskem trgu (slika 7). Skoraj polovica anketirancev je tudi osebno občutila posledice na trgu dela, saj so službo izgubili njihovi znanci (32 %) oziroma sorodniki ali prijatelji (16 %).
Gospodarska kriza je neposredno vplivala na stanovanjski trg, kar je imelo posledice tudi za duševno in telesno zdravje (Dunn, 2000). Gospodarska kriza torej zlasti prek stanovanjskih razmer in kakovosti stanovanj vpliva na zdravstveno stanje prebivalcev.
Negativne družbene posledice gospodarske krize se vidno izražajo tudi v subjektivnem mnenju mestnih prebivalcev. Kvalitativne raziskave kažejo, da se je po njihovem mnenju kakovost življenja po začetku krize poslabšala. Skoraj polovica vprašanih je zatrdila, da se je kakovost njihovega življenja v zadnjih letih nekoliko poslabšala, skoraj petina pa, da se je močno poslabšala. Posredni vplivi duševnega pritiska kot posledice slabše kakovosti življenja in vse večje zaskrbljenosti lahko povzročijo
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precejšnje spremembe v družbenem okolju. Veliko anketirancev meni, da slabšo kakovost življenja povzroča slabši finančni položaj, čeprav je pravi vzrok za to intenzivna polarizacija družbe na podlagi posameznikovega finančnega položaja, zaradi česar so se vse večjemu številu ljudi prihodki zmanjšali (Egedy, 2012). Razlike v prihodkih najbogatejših in najrevnejših so se povečale, kar pomeni, da so se zaradi gospodarske krize okrepile finančne razlike med gospodinjstvi.
Zmanjšanje prihodkov in povečanje zadolženosti sta imela odločilno vlogo pri finančnem položaju gospodinjstev. Na postopno povečevanje zadolženosti revnejših gospodinjstev kaže dejstvo, da je leta 2001 bančne kredite odplačevalo 14 % gospodinjstev v najnižjem dohodkovnem kvintilu, leta 2009 pa je bilo teh že več kot 40 %. Na začetku leta 2010 je bilo 43 % skupnega dohodka v najnižjem kvintilu porabljeno za odplačevanje dolgov, medtem ko je ta odstotek v najvišjem kvintilu znašal samo 15 % (Toth in Medgyesi, 2011). V zadnjih letih madžarski mediji vse pogosteje poročajo, da sta varčevalna zmožnost ljudi in njihova pripravljenost, da varčujejo, nizki: anketiranci (če imajo možnost) dajo na stran manj kot petino svojih mesečnih prihodkov. V številnih primerih lahko slabšo kakovost življenja pojasnimo z vse slabšim finančnim položajem gospodinjstev: ni naključje, da več kot četrtino gospodinjstev skrbijo finančne težave v prihodnosti (»ne bom mogel nahraniti družine«), nadaljnja petina anketirancev pa se boji, da bo izgubila službo ali da se ne bo mogla preživljati. Ichiro Kawachi in Bruce P. Kennedy (1999) sta ugotovila, da porazdelitev dohodka v družbi vpliva na zdravje in da je posameznikovo zdravstveno stanje boljše v družbah z enakomernejšo porazdelitvijo dohodka. Na podlagi njunih izsledkov lahko sklepamo, da je trenutna gospodarska kriza povečala neenakosti v zdravju med podeželskimi in mestnimi območji ter tudi znotraj metropolitanskih in mestnih območij.
Kljub vsem negativnim posledicam svetovne gospodarske krize, opisanim zgoraj, med prebivalci na splošno prevladuje pozitivno mnenje glede prihodnjega razvoja madžarskih mest. Čeprav četrtina mestnih prebivalcev pričakuje poslabšanje položaja in nadaljnja četrtina meni, da se bodo trenutne razmere stabilizirale, skoraj polovica pričakuje pozitivne spremembe. Avtorja sta v raziskavi ugotovila, da mestni prebivalci menijo, da kljub gospodarskim težavam procesi na splošno potekajo v pozitivno smer. Ta »optimistična podoba« ima obliko narobe obrnjene črke »V«, pri čemer zaupanje mestnih prebivalcev v prihodnji razvoj raste skupaj s hierarhijo naselij, vendar so prebivalci Budimpešte nekoliko manj optimistični kot prebivalci glavnih mest posameznih županij. Odgovori jasno kažejo, da so na višjih ravneh hierarhije naselij prebivalci bolj optimistični glede uspešnega okrevanja po krizi.
5 Sklep
Evropa se spopada s hudo finančno in gospodarsko krizo ter njenimi regionalnimi posledicami. Zlasti v Vzhodni Evropi bi morale države v tranziciji, kot je Madžarska, reševati posredne in neposredne družbene in zdravstvene posledice gospodarske krize, saj so se neenakosti v zdravju v teh državah še okrepile (na primer Billingsley, 2011). Slabo zdravstveno stanje prebivalcev tega območja, krajša pričakovana življenjska doba in neugodni kazalniki brezposelnosti v primerjavi z zahodnoevropskim povprečjem skupaj z dejavniki, kot sta zdravstvo, podedovano od prejšnjega komunističnega sistema, in njegovo nezadostno financiranje, povzročajo težave v zdravstveni politiki, za katere še po 25 letih od padca komunizma niso našli učinkovite rešitve. Poleg tega je treba upoštevati tudi dejstvo, da so se na Madžarskem povečali tudi učinki nasprotne selekcije (odseljevanje zdravnikov in degradacija zdravstvenih kadrov na trgu dela).
Zlasti na podeželskih območjih bi bil lahko slab dostop do zdravstvenih storitev vzrok za slabšanje zdravstvenega stanja ljudi in vse večjih neenakosti v zdravju. Gospodarska kriza je najbolj prizadela središčna območja, vendar vpliv gospodarstva na zdravje za zdaj še ni močno opazen. Zdravstveno stanje ljudi na najmanj ugodnih območjih (obrobjih) je predvsem posledica neugodnega izhodiščnega gospodarskega položaja teh območij in ne gospodarske krize. Prostorske razlike v neenakosti v zdravju še vedno izhajajo iz družbenogospodarske preobrazbe po padcu komunizma. Neposrednih kratkoročnih vplivov gospodarske krize iz leta 2008 na neenakosti v zdravju še ni mogoče določiti, vendar pa lahko njeni naraščajoči vplivi še povečajo regionalne razlike. Čeprav je kriza bolj prizadela gospodarsko uspešne regije (zlasti Osrednje in Zahodno Čez-donavje), gospodarski statistični podatki po letu 2010 kažejo, da županije v teh regijah hitreje okrevajo. V nasprotju z že prej slabše razvitimi mikroregijami na severu Madžarske in v Veliki severni nižini gospodarska kriza torej dejansko ne vpliva na pričakovano življenjsko dobo na tem območju.
Spremembe v strukturi brezposelnosti niso nujno povzročile kratkoročno in neposredno izmerljivega poslabšanja zdravstvenega stanja ljudi v trenutnem obdobju recesije. Med tistimi, ki so postali brezposelni, so se pojavile skupine z višjim družbenogospodarskim položajem in ugodnejšimi kazalniki zdravja. To pomeni, da ni prišlo do večjih sprememb v prostorski strukturi neenakosti v zdravju. Na Madžarskem lahko še vedno jasno ločimo med območji z boljšimi in slabšimi kazalniki zdravja.
Izsledki opravljene raziskave so pokazali, da imajo mesta boljše vrednosti proučevanih kazalnikov kot okoliška območja, kar
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pomeni, da naselja višjega razreda ponujajo boljše zaposlitvene možnosti in da je v njih pričakovana življenjska doba ob rojstvu daljša. Ob upoštevanju vseh vidikov torej mesta ponujajo ugodnejše življenjske priložnosti. To pomeni, da lahko selitev v mesto izboljša posameznikove življenjske razmere in zdravstveno stanje. Hierarhija naselij vpliva tudi na neenakosti v zdravju in na regionalne razlike, hkrati pa ima pozitiven učinek na zdravstveno stanje mestnih prebivalcev.
Negativni vplivi mest in metropol so že dolgo znani (na primer stres, onesnažen zrak, prah in hrup), vlogo mest pri neenakostih v zdravju pa bi bilo treba na novo interpretirati. V tem pogledu bi bilo zanimivo proučiti, ali imajo zdravstvene težave, ki jih povzroča življenje v mestih, in pozitivni vplivi mest na neenakosti v zdravju pomembnejšo vlogo v dolgoročnem zdravju prebivalcev. Izsledki predstavljene raziskave kažejo, da ti vplivi postajajo vse pomembnejši, kar pomeni, da bi lahko mesta prispevala k zmanjšanju neenakosti v zdravju na Madžarskem. Mesta z razvitejšimi zdravstvenimi storitvami lahko celo neposredno pomagajo zmanjšati te neenakosti, kar še zlasti velja za županije v regijah, ki po padcu komunizma zaostajajo za drugimi ali imajo neugoden družbenogospodarski položaj. V prihodnosti bi lahko mesta delovala kot »izhod v sili« in alternativa za ohranjanje zdravja. Čeprav lahko ta raziskava pomaga oblikovati novo razlago svetovnih procesov urbanizacije z vidika zdravja, so za razvoj teorije potrebne nadaljnje raziskave. Njeni izsledki kažejo, da mestni prebivalci menijo, da kljub gospodarskim težavam procesi na splošno potekajo v pozitivno smer. Za analizo »zdrave velikosti mesta« (to je najnižjega in največjega še primernega obsega naselja in števila prebivalstva) so potrebne nadaljnje raziskave, s katerimi lahko določimo točko, na kateri pozitivni vplivi na zdravje vidno prevladujejo oziroma negativni vplivi nad njimi še ne prevladajo.
Tamas Egedy
Geographical Institute, Centre for Astronomy and Earth Sciences, Hungarian Academy of Sciences, Budimpešta, Madžarska E-pošta: ege6727@mail.iif.hu
Annamaria Uzzoli
Institute for Regional Studies, Research Centre for Economic and Regional Studies, Hungarian Academy of Sciences, Budimpešta, Madžarska
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alth inequalities and its spatial dimension during the terms of crisis in Hungary after 1990]. Teruleti Statisztika, 53(2), str. 130-147.
Toth, I., in Medgyesi, M. (2010): Jovedelemeloszlas es megelhetesi nehezsegek a konszolidacios csomagok es a valsagok kozepette Ma-gyarorszagon [Income distribution and livelihood difficulties in consolidations phases and crises in Hungary]. V: Fazekas, K., in Molnar, G. (ur.): Munkaeropiaci tukor 2010 - Kozelkep: A valsag munka-piaci hatasai [Impact of crisis on labour-market in Hungary in 2010], str. 180-197. Budimpešta, MTA Kozgazdasagtudomanyi Intezet, Orszagos Foglalkoz-tatasi Kozalapitvany.
Uzzoli, A. (2011): The role of unemployment in the run of life chances in Hungary. International Journal of Population Research. DOI: 10.1155/2011/130318
Whitehead, M. (1992): The health divide. V: Townsend, P., Davidson, N., in Whitehead, M. (ur.): Inequalities in health: The black report and the health divide, str. 215-281. New York, Penguin.
Zavras, D., Tsiantou, V., Pavi, E., Mylona, K., in Kyriopoulos, J. (2013): Impact of economic crisis and other demographic and socio-economic factors on self-rated health in Greece. European Journal of Public Health, 23(2), str. 206-210. DOI: 10.1093/eurpub/cks143
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UDC: 330.34:314.118(439)
DOI: 10.5379/urbani-izziv-en-2016-27-02-002
Received: 13 Apr. 2016 Accepted: 25 Aug. 2016
Tamas EGEDY Annamaria UZZOLI
Cities as the keys to survival? The impact of the economic crisis on health inequalities in Hungary in terms of unemployment and life expectancy
This article interprets the spatial dimension of health inequalities in Hungary through the connection between unemployment and life expectancy from the national level to the level of micro-regions and settlements with a special emphasis on cities. The article highlights the correlation between unemployment and average life expectancy at birth based on regional statistical databases. The results of quantitative questionnaires (n = 545) and qualitative in-depth interviews (n = 26) are also presented in order to explore the effect of the crisis on urban populations residing in major Hungarian cities. The correlation between unemployment and life expectancy becomes stronger during crisis periods. Our findings indicate not
only that the labour-market position and level of income are important from a health point of view, but also that the level and growth of existing social and health inequalities strengthen the effects of the crisis on health. It is evident that, in Hungary, direct effects of the crisis on health inequalities exacerbate regional differences. Cities can counterbalance the unfavourable health effects of the crisis and they may successfully contribute to decreasing health inequalities.
Keywords: health inequalities, economic crisis, unemployment, life expectancy, micro-regions and cities, Hungary
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1 Introduction
The health status of populations in different geographical regions, different settlements and different social groups is determined by biological, individual, environmental, socioeconomic, sociocultural and lifestyle factors (Marmot & Wilkinson, 2006). Thus, the formation of health inequalities is determined by a complicated system of interrelations between many simultaneously effective (determinative and influential) factors (Raphael, 2008a). Some health inequalities are independent of social and economic relations (such as those connected with genetic factors and illnesses of old age), but a large number of them are dependent on socioeconomic inequalities (Evans & Stoddart, 1990; Elstad, 2005). The most important factors are mostly referred to by the acronym "PROGRESS": Place of residence, Race/ethnicity, Occupation, Gender, Religion, Education, Socioeconomic status and Social capital/resources (Oliver et al., 2008). These basic factors show significant differences at both the global level (countries and regions) and the local level (settlements). Social and economic inequalities are apparent at the same time not only vertically, but also horizontally, which means that the relevance of space in examinations of health inequity is unquestionable.
This study investigates the influence that social determinants (such as unemployment) have on health inequalities during a crisis period and examines the interrelationship between health and place. The study uses the materialist/structuralist conceptualisation. This approach emphasises the material conditions under which people live their lives, and so the behavioural choices are heavily influenced by the material conditions of life (Bartley, 2003). We refer to Margaret Whitehead: "The weight of evidence continues to point to explanations which suggest that socioeconomic circumstances play the major part in subsequent health differences" (1992: 243).
The first part of this article deals with the methodological background to the study. The main part highlights the empirical results of quantitative statistical analyses and qualitative surveys on interrelations and spatial differences in unemployment and average life expectancy at birth. It also explores the impacts of the current economic crisis on urban populations. In addition, we interpret the supposed relation between crisis and health through the effects of spatial patterns on health inequalities, and we specify the impact that the crisis has had on healthcare systems and health policies. This article reconsiders the role of cities in balancing health inequalities in eastern Europe through the example of Hungary. Examining the effects of the crisis on health inequalities in Hungary is especially interesting because the average life expectancy at birth in Hungary is 5.6 years less than in the western European countries. The
economic and social transition of the early 1990s went together with an increase in health inequalities never experienced before. A few years later, the global crisis further exacerbated and partly restructured the already existing spatial differences in health inequalities.
The disadvantageous situation caused by the crisis does not come from different biological features, but from injustices and inequities caused by the crisis, which is why Hungarian examples are used to primarily interpret the impact of the crisis on health inequalities. The uniqueness of this study is a demonstration of the impact of the current crisis on local formations of health inequalities in Hungary on the one hand, and the role of cities in the long-term formation of health inequalities with direct social and economic consequences on the other. In this sense, our article provides new evidence for the negative impacts of crisis periods on health inequalities, especially in central and eastern European countries.
2 Theoretical background
From a health point of view, it is important to understand how social determinants of health influence health inequities. To understand the primary mechanisms, we consider the following frameworks and approaches: a) cultural/behavioural, b) materialist/structuralist and c) neo-materialist. The cultural/behavioural explanation (in other words, "lifestyle" approaches to disease prevention) is that individuals' choices (e.g., smoking, drinking alcohol, drug addiction, physical activity, nutrition etc.) are responsible for diseases (Raphael, 2008b). The relationship between lifestyle and living conditions implies that part of the differential distribution of lifestyle is actually caused by the unequal distribution of living conditions (Stronks et al., 1997). The materialist/structuralist explanation emphasises the material conditions under which people live their lives, and so the behavioural choices are heavily influenced by the material conditions of life. The term "social determinants of health" are those material conditions that exist within the environments in which people are born, live, work, play, worship and age, and which affect a wide range of health, functioning and quality-of-life outcomes and risks (Graham, 2007). The neo-materialist explanation extends the materialist analysis by asking how nations, regions and cities differ with respect to how economic and other resources are distributed among the population. The neo-materialist view therefore directs attention to both the effects of living conditions and the social determinants of health on individuals' health conditions (Bernard et al., 2007; Collins et al., 2009).
The relationship between equal opportunity and health that is present in every country mostly depends on macroeconomic
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conditions. Behind inequalities related to health there are economic inequalities, injustices of distribution, obstacles in access to education and healthcare, poor housing and life circumstances, and a lack of opportunities for a healthy life (Benach et al., 2007). Thus, socioeconomic trends and factors basically influence the state of health, and the importance of the social environment is clearly highlighted by research on health inequalities (Braveman et al., 2005).
Socioeconomic determinants of health and the influential factors of health inequalities determine to what extent economic crises will affect the state of health of the population (Marmot & Bell, 2009). Although the causative interactions are complicated, the role of negative processes is unambiguous in the labour-market position of individuals and in the changes and transformations of household consumption habits. Since the beginning of the crisis, findings suggesting the association of unemployment and income with health inequalities have emerged. For instance, longitudinal statistical analyses of mortality and employment trends by David Stuckler et al. (2009) could provide evidence for the direct effects of the economic crisis on public health. Patricia A. Collins et al. (2009) demonstrated that employment and income are important predictors for health status, and that social gradients in self-rated health are observable in areas of even modestly contrasting income profiles. The findings of Dimitris Zavras et al. (2013) have confirmed that in times of economic crisis the probability of reporting poor self-rated health is higher, especially among the unemployed. However, there are much larger spatial and regional differences in health inequalities and self-assessed health associated with socioeconomic status than expected (Mackenbach et al., 2008).
The interpretation of social determinants of health inequalities raises the question whether, during times of crises from the point of view of real or supposed health, it is merely the labour market position and levels of income that count, or also the degree of and increase in the social and health inequalities that already exist. An unfavourable labour-market position has a disadvantageous influence on equal opportunities because, as Petri Bockerman and Pekka Ilmakunnas (2008) state, it is unemployment that affects the state of health the most. Unemployment truly makes one ill by affecting identity, emotions and self-esteem. Stress caused by unemployment and risky forms of behaviour (such as taking medicines, alcoholism and uncontrolled smoking) will increase significantly.
From a spatial point of view, it could be interesting to examine what differences can be recognised in health inequality not only in terms of urban and rural areas, but regarding various levels of settlement hierarchy (dispersed settlements, villages and towns). It is not by accident that the importance of lo-
cality in academic research has been appreciated in the past two decades (Diez Roux, 2001). The interest in studying the relationship between place of residence and health has grown, partly because different contexts in places of residence basically contribute to the local production of health inequalities (Bernard et al., 2007; Macintyre et al., 2002).
Research concerning differences in the state of health in urban and rural areas has come to the fore since the late 1980s and early 1990s (Eyles, 1987; Ricketts et al., 1994). The emerging significance of the topic shows that in the past few years, in all development poles of the global economy (i.e., America, Europe and Asia), increasing attention has been paid to investigating health inequalities in rural and urban areas (see Fang et al., 2010; Pampalon et al., 2010; Gartner et al., 2011).
Richard Fearn and John Eyles (1987) already called attention to the fact that, although rural areas have advantages in terms of healthy life ("the healthy countryside"), they are not at all uniform. There have been many studies that not only support the fact that a better state of health and lower mortality rate exists in rural populations in comparison to urban areas (Shuck-smith et al., 1996), but also study the geographical, social, lifestyle and demographic reasons behind these facts (Miller et al., 1987; Sobal et al., 1996). At the same time, many experts have called attention to the high heterogeneity of rural areas, resulting in enormous differences among health indicators (Schneider & Greenberg, 1992; Dolk et al., 1995). It has become evident that geographical settings and sociodemo-graphic factors are to be taken into consideration in the course of analyses. Considering the relevance of social determinants, Danielle C. Ompad et al. (2007) go so far as to suggest that the social environment is key to understanding how different areas affect the health of populations.
Graham Bentham (1984) and later Peter Phillimore and Richard Reading (1992) demonstrated that mortality rates of rural areas largely depend on the distance between urban and rural areas (remote rural areas have higher mortality rates), and measures of rurality in the context of health greatly depend on the nature of the rural area. Taking into consideration the extent of deprivation, it is possible that urban deprived areas have better illness indicator rates than deprived rural areas (Congdon, 1995; Gartner et al., 2011), and with the increase of the amount of deprivation rural health advantages will gradually disappear (Phillimore & Reading, 1992). In the past two decades, growing research findings have indicated that differences between the state of health in rural and urban areas are decreasing (McLafferty, 1992; Maniecka-Bryla et al., 2012; Richardson et al., 2013). The economic crisis has accelerated this process and has even benefited urban areas.
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There is a definite relationship between the spatial appearance of health inequality and urban competitive capacity, because any economy - be it a national economy or a local urban economy - can only be competitive if there is a high proportion of productive employees with high incomes and if the economy ensures them both a high level and high quality of life (Frageberg, 1996; Atkinson et al., 1999). It is not by chance that public health and labour-market efficiency are also among the pillars of the Global Competitiveness Index (Schwab, 2015). However, according to Tord Kjellstrom and Susan Mercado (2008), urbanisation can only be beneficial for health if key social determinants of health inequalities in urban areas are addressed in countries at all income levels.
3 Methodological background
In line with the main aims of this study, the research questions examined are as follows:
•	Did the economic crisis have any impact on health inequalities in Hungary and, if so, how and to what extent ?
•	To what extent is labour-market position a determining factor and an explanatory force in shaping health inequalities? What local consequences are there as a result of all of these ?
•	What are the roles of cities and urban areas in shaping health inequalities during crisis periods?
The increase of health inequality is caused above all by the growth of local differences. Nevertheless, cities and urban areas - in spite of the decrease of production, lack of investments, and growth of unemployment - have maintained their favourable position with regard to the state of health even during the period of crisis in comparison to villages and dispersed settlements.
In our comparative analyses and examinations of area-based approaches, we focused on the interconnections of unemployment and average life expectancy on the one hand, and their spatial effects on the other. In many international studies, average life expectancy at birth was a prioritised indicator to measure health inequalities mainly in crisis periods because macro-economic changes have direct impacts on life expectancy (e.g., Goesling & Firebaugh, 2004; Jonker et al. 2013). The unemployment rate was chosen in order to examine the social consequences of the crisis because unemployment is the most important risk factor for health and has a determining role in changes in health inequalities: middle-aged males are the most endangered stratum that becomes unemployed in the early phase of crisis periods (Jozan, 1996), and unemployment makes people sick because it has a negative effect on individuals' identity and self-esteem (Kopp, 2007). The unemployment rate as an economic indicator and average life
expectancy at birth as a health indicator are also suitable for comparing pre-crisis and post-crisis periods and their impacts on health inequalities.
The spatial examination of health inequalities was made using the centre-periphery dichotomy approach at the microregional level through the following tasks:
•	Circumscribing lagging areas in health processes because of the crisis;
•	Examining spatial structures of health inequalities from the point of view of the urban-rural division and
•	Interpreting settlement hierarchy in the spatial pattern of health inequalities.
The methods applied were chosen according to our aims and questions, and both quantitative and qualitative examination techniques were used to study our hypothesis. The quantitative calculations are based on micro-regional statistical data derived from official sources (Hungarian Central Statistical Office and the National Territory Development and Country Planning Information System). The micro-regional level was the lowest spatial level for which open-access statistical databases were available for analyses. The statistical data were divided into three groups:
•	Indicators to measure the unemployment level (i.e., spatial and temporal breakdown of unemployment rates);
•	Indicators to measure health inequalities (i.e., spatial and temporal breakdown of average life expectancy at birth);
•	Indicators to measure territorial/regional inequalities: the 2007 minority register (175 micro-regions; i.e., LAU1 level), number of towns/cities, population number and legal status of towns/cities.
The base year of the crisis was carefully chosen in the examination of inequalities. All of the examinations and analysis procedures were completed for the 2009 and 2010 data registers because the impact of the crisis on the Hungarian labour market was the strongest in these two years. On the basis of our results - contrary to economic studies - we chose 2010 as the "crisis year" because in most cases the correlation among the indicator numbers was higher in 2010 than in 2009. In addition, the unemployment indicators for micro-regions with unfavourable situations became worse, whereas the improving trend of average life expectancy at birth in micro-regions with a more favourable situation became more moderate in comparison to 2009 and, as far as the pattern of unemployment rate and average life expectancy at birth are concerned, spatiality as an explanatory factor was more intensive in 2010.
Comparative analysis of the Pearson correlation was carried out in order to define and compare the situation before and after the crisis.
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Figure 1: a) Regions (NUTS2) and counties (NUTS3) in Hungary; b) county capitals in Hungary (source: authors).
Quantitative analyses (such as Pearson's correlation coefficient) were complemented by qualitative examinations (questionnaires and semi-structured in-depth interviews). To analyse the effect of the crisis on an urban population, pilot research was used in Hungarian cities and towns concerning how people view the crisis, and what direct or indirect impacts it had on their city and their everyday life. To collect experiences, we administered household questionnaires in Budapest, Szeged and Gyôr (i.e., in the most developed core-settlement, in the centre of a prosperous region and in the centre of a less-developed region, respectively; Figure 1). The questionnaire contained thirty-nine (mostly closed) questions and it was completed with the help of students in all three cities. The household questionnaire is divided into three main sections: the first one contained a few general questions to measure city residents' knowledge of the global crisis (e.g., when, where and why the global crisis started, and its impacts on the Hungarian economy in general); the second group of questions investigated the effects and consequences of the crisis on the local economy and competitiveness (e.g., characteristics and perspectives of the local economy after the crisis) and the third part contained questions on the everyday life of urban residents since the beginning of the crisis (e.g., the financial situation, mortgages, quality of life and fears regarding the crisis). The household questionnaire was also posted on the internet, and so the personal surveys were expanded by using the internet version of the questionnaire in September and November 2011 (n = 352). The household questionnaire method was complemented using a street questionnaire containing ten short questions (n = 193). Only the opinion of the urban population was surveyed, and so the pilot survey is a subjective evaluation of the crisis. To examine the opinions of experts concerning the effects of the crisis on the state of health, twelve in-depth interviews were held with national health specialists between January and March 2013. The interviews focused on the system of relations between crisis and health through the diverse aspects of various disciplines on the one hand and
with the help of the empirical experience of practice-oriented experts on the other.
4 Reconsidering the role of cities in health inequalities
4.1 The impact of the crisis on unemployment and life expectancy in Hungary
Unemployment rose rapidly in Hungary in the early 1990s. The unemployment rate reached its peak in 1993 (12.1%), when, at the same time, the mortality rate was the highest (14.6%, almost a total of 150,000 deaths) and the average life expectancy at birth was the lowest (total population: 69.0 years, men: 64.5 years, women 73.8 years). This is why 1993 can be regarded as the iconic transition year following the collapse of communism.
Starting in the mid-1990s, there was an increase in the indicators, as a result of which, up until 2001, the decrease in unemployment was almost continuous. That was the point of the best indicator (5.7%), and since then it has moderately but continuously increased. Between 2008 and 2009, the number of unemployed increased by 91,000, and the rate passed 10% in 2009. International comparisons show that Hungary is among the countries where the crisis has been accompanied by medium growth in unemployment. In his study, Janos Kollo (2010) argues that, although employment decreased only to a lesser degree compared to the decrease in gross domestic product (GDP), on the basis of lost jobs Hungary is among the biggest losers in the region.
Regarding regional changes in unemployment, economically prosperous regions (e.g., Central and Western Transdanubia) and regions struggling with significant structural and social problems (e.g., Northern Hungary and Southern Transdanu-bia) can be sharply divided both in their volume and in their
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Figure 2: Unemployment rates in Hungarian regions, 2005-2015 (source: Hungarian Central Statistical Office, 2005-2015).
17,0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Year
history of unemployment (Figure 2). The convergence of unemployment rates characterising the past few years can be traced back to state-financed public work programmes, and it is thus a rather artificial process.
Regarding the sectoral effects of the crisis in cities, the largest decline in employment occurred in industry (11%), and more precisely within the construction industry. Between the first quarter of 2008 and 2010, the number of employees in this sub-sector decreased by 14.9% (Balint et al., 2010). In addition, a large number of losses in manufacturing took place, as well as in companies involved in real estate and private companies involved in education. The biggest redundancies (collective, organised and mass layoffs) took place in foreign-owned automotive and electronics companies. After the crisis began in 2009, the number of employees decreased by approximately one hundred thousand people. In Hungary, the registered unemployment rate in the first quarter of 2010 reached its highest level, at 11.9% (Kollo, 2010).
The mortality rate reached its peak in 1993, and since then a very modest improvement in mortality rates can be observed in Hungary. The moderation of the mortality rate after 1993 showed that life expectancy increased to over seventy years after 1996. The life expectancy increased to over seventy-four years in 2009, reaching 75.8 years in 2013. This means that
between 1993 and 2013 life expectancy grew by 6.7 years (Table 1). However, significant differences can be recognised in the breakdown by gender. The difference between the life expectancy of the two sexes was the highest in 1994 (9.4 years), and then it decreased gradually until 2012 (7.4 years). With regard to gender differences, 2009 stands out because the average life expectancy of Hungarian men increased to over seventy years.
In the case of the least-educated people, it must to be taken into consideration that many of these fall out of the supply system, and so no precise information is available about them. Because of this, more optimistic prognoses claim that the average life expectancy at birth will stagnate, but will not start to decrease in Hungary as a result of the crisis. This, then, may cause further problems in the future because the average life expectancy is six to seven years shorter than the western European average.
To help clarify the influence of the economic crisis on health inequalities, a comparative analysis of the situation before and after the crisis was conducted (Table 2). Two characteristic years were selected for this comparison of the Pearson correlation: 2001 was the control year of the pre-crisis period, and 2010 was the most appropriate year for explaining the post-crisis situation. In 2001, the unemployment rate was the lowest after the Hungarian transition, and starting in 1996
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Table 1: Average life expectancy at birth (in years) in selected Euro-
Italy	77.4	79.9	82.9
United Kingdom	75.7	78.0	81.1
Germany	75.7	78.3	80.9
Slovenia	73.1	76.2	80.5
Czech Republic	72.6	75.1	78.3
Estonia	68.1	71.1	77.5
Poland	71.6	73.8	77.1
Hungary	69.1	71.9	75.8
Romania	70.2	71.2	75.2
Source: Eurostat (1993, 2000, 2013).
there was a trend towards better life expectancy in Hungary. After the financial crisis of 2008/2009, we selected 2010 as the most suitable year to analyse the consequences on health inequalities.
There is a strong relationship between unemployment and average life expectancy, and this is especially characteristic for men. The correlation became stronger between 2001 and 2010. Since the collapse of communism in Hungary, 60% of the unemployed are men, and the decrease in life expectancy for middle-aged men in particular should be taken into consideration. The difference between the correlations of male and female life expectancy with the unemployment rate decreased in 2010, which means that the effects of the crisis on women's labour market position were also apparent.
4.2 The regional character of
interrelations of unemployment and average life expectancy
Regarding the geographical pattern of unemployment, the highest registered unemployment in 2010 was in the eastern and southwest part of the country. In comparison to the national average, in the eastern part of Hungary, the unemployment trends were the most positive in the micro-regions of Szeged, Eger and Kecskemet (Figure 3).
The spatial effects of the crisis then become noticeable in life expectancy in two ways. On the one hand, in micro-regions with increasing trends or others with originally good life expectancy, this does not decrease even under the worst macroeco-nomic conditions. On the other hand, in micro-regions with decreasing trends or in those that originally had a bad situation, the results of the crisis meant that the growth of average life expectancy at birth decreased. In both sets of indicators exam-
Table 2: Relationship between unemployment rate and average life expectancy at birth by Pearson's correlation coefficient (r2), 2010.
Unemployment rate (%) 2001 2010			
Average life expectancy at birth		-0.692	-0.727
Average life expectancy at birth, male		-0.710	-0.722
Average life expectancy at birth, female		-0.637	-0.720
Source: Based on Szilagyi & Uzzoli (2013) with own supplement.			
Table 3: Average life expectancy at birth (in years) by settlement category (2009).			
Settlement category	Average life expectancy at birth (year)		
by population	male		female
< 999	68.0		76.7
1,000-1,999	68.6		77.0
2,000-4,999	68.9		77.1
5,000-9,999	69.3		77.1
10,000-19,999	70.3		78.0
20,000-49,999	70.5		77.6
50,000-99,999	71.4		79.0
100,000-299,999	71.3		78.9
Capital (Budapest)	71.9		78.7
Hungary	70.1		77.9
Source: Hungarian Central Statistical Office (2010).
ined, the national trend can be recognised: that more positive life expectancy can be seen in county capitals and cities, as well as in the micro-regions neighbouring them (Figure 4).
Statistical data also show that there are clear differences between life expectancy in urban and rural areas in Hungary (Table 3). Average life expectancy at birth was 70.4 years in Hungarian urban areas in 2009, whereas it was only 68.6 years in rural areas: the difference is 1.8 years. In smaller settlements life expectancy is lower for both males and females. Compared to Budapest, the population in the smallest settlements has a life expectancy 3.9 years lower for males and two years lower for females. The most advantageous life circumstances are provided by settlements with a population between 50,000 to 100,000 because this settlement category has the highest life expectancies for males and females (Table 3).
Based on the unemployment rate and the average life expectancy at birth, a similar spatial pattern can be described. The best-situated micro-regions are found in the agglomeration of Budapest and in Central and Western Transdanubia, whereas the worst situation is in the Northern Great Plain and in Southern Transdanubia. This means that the consequences of the crisis have mostly affected the traditionally most depressed regions.
pean countries (1993, 2000, 2013). Country	1993	2000	2013
Spain	77.2	79.3	83.2
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Unemployment rate (%)	
(national level = 100)	
■	158-278 (53)
■	101-157 (52)
■	77-100 (34)
□	30-76 (35)
o County capital micro-region
Figure 3: Unemployment rate (in percentages) in Hungarian micro-regions in comparison to the national level, 2010 (source: based on Szi-lagyi & Uzzoli, 2013 with own supplement).
Average life expectancy at birth (%) (national level = 100)
■	101.40-106.00 (44)
■	100.01-101.39 (44)
■	98.93-100.00 (43)
■	95.00-98.92 (43)
o County capital micro-region
Figure 4: Average life expectancy at birth (in years) in Hungarian micro-regions in comparison with the national level, 2010 (source: based on Szilagyi and Uzzoli, 2013 with own supplement).
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The east-west relevance described above can be interpreted in terms of a geography of relational systems of unemployment and life expectancy. However, it should actually be emphasised that there are remarkable small-scale spatial differences within prosperous central and western and deprived eastern areas as well (Uzzoli, 2011). In addition to the east-west division, the centre-periphery relations also have an influential role in geographical developments. Regions of good and bad economic situations can definitely be separated, although no axial division can be identified. In regions that suffer from unfavourable conditions, improving trends can be seen in areas closer to the cities and county centres. In the eastern regions of Hungary, more favourable micro-regions are related to cities as county capitals (e.g., Kecskemet, Szeged, Debrecen and Nylregyhaza). Micro-regions with the least-favourable position are concentrated along the country's eastern and northern borders.
Micro-regional differences in the unemployment rate and average life expectancy at birth call attention to characteristic spatial patterns. On the basis of the spatial relation of unemployment and life expectancy, the loss of position and/or position gain of micro-regions can be noticed to a lesser degree in terms of both average life expectancy and unemployment during the crisis. Micro-regions with the best situations can
be found either in the capital agglomeration or in the western region, whereas micro-regions in the worst positions can be found in the northeast and/or southwest regions (Figure 5).
It is not true everywhere in the country that higher unemployment goes together with lower average life expectancy. The situation is worst in Borsod-Abauj-Zemplen County and Szabolcs-Szatmar-Bereg County, where unemployment was high even before the crisis. In Northern Hungary, unemployment above the national average goes together with the worst life expectancy. In Budapest, because of its significant development, the unemployment rate is less decisive in the average life expectancy at birth. In the western and central parts of the country, a more positive socioeconomic environment offers better life expectancy.
Although other effects (e.g., the growth of the educational level of the population) can balance the health effects of the crisis in the medium term, to filter these out is an enormous challenge for research. Precisely because of this, it is difficult for quantitative statistical analyses to reveal the system of interactions between the crisis and health. Therefore, possible connections were analysed with the help of in-depth interviews with experts and practitioners. Summing up the main outcomes of the in-
Unemployment rate / Average life	
expectancy at	birth
□ Low/High	(36)
EH Low/Average	(18)
□ Low/Low	(4)
□ Average/High	(21)
EH Average/Average	(26)
EH Average/Low	(11)
□ High/High	(1 )
EH High/Average	(14)
| High/Low	(43)
Figure 5: Connection between unemployment rate (in percentages) and average life expectancy at birth (in years) in Hungarian micro-regions in comparison to the national level, 2010 (source: own calculations based on the dataset of Internet 1).
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On health status
Morbidity
-	Increasing mental and psychosomatic diseases and risk of addiction
-	Decreasing chance of a healthy lifestyle
Mortality
-	Increasing risk of suicide, cardiovascular death and death by accidents
Change of labour market position during the crisis
(e.g., unemployment, parttime employment, decreasing incomes, etc.)

On health inequalities
Increasing unemployment, poverty, social inequalities, gap in access to healthcare /social care and vulnerability

t
On health policy
Increasing costs and contraselection Decreasing funding for health insurance, effectiveness of resource allocation and support for prevention programmes Marginalisation of interests
Long-term consequences
On healthcare
Increasing emigration of health workers Increasing social conflicts in medical care Disorganisation Decreasing use of private healthcare
Increasing use of public healthcare
Deterioration in health indicators
Figure 6: The connection between the economic crisis and health in Hungary (source: own survey, empirical results of expert interviews).
terviews, the following model can be outlined indicating the effects of crisis on the health system, labour market and health inequalities (Figure 6).
Different forms of crisis management basically disorganise the health sector (e.g., the advocacy of the health sector will decrease). An important question in this regard is the what extent to which funding (fund-raising and allocation) serves (if at all) to solve the problem. The fact should also be taken into consideration that contra-selective effects (the migration of doctors and the degradation of human resources in the health sector on the labour market) have also increased in Hungary due to the crisis. Lack of access to health services could be the cause of a worsening health state and the increase in health inequalities, especially in rural areas. The actual crisis hit central areas the hardest, but the impact of the economy in health processes cannot be strongly noticed yet. In terms of the state of health in the most unfavourable areas (peripheries), the disadvanta-
geous original economic situation is still the explanatory factor independent of the crisis situation. Spatial differences in health inequalities still derive from the socioeconomic transformation after the collapse of communism in Hungary. The direct effects of the 2008/2009 crisis on health inequalities cannot be demonstrated yet in the short term, but as a result of multiplicative effects it may exacerbate regional differences.
4.3 Effects of the crisis on urban development and perception of the crisis among urban residents
The crisis affected the cities in many ways and on very different scales. As a result of the crisis, the competitiveness of Hungarian cities and towns decreased internationally. This fact is supported by data from the Global Competitiveness Index, according to which Hungary fell from forty-eighth place to
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Labour market Real-estate market Other factors Finances
Developments, investment
Social situation, living standards
Consumption, services Urban regeneration Infrastructure
i I
□
■	Whole sample
□	Budapest
□	Szeged
□	Györ
□	Other towns
0.0	5.0	10.0	15.0	20.00 25.00 30.00 35.00
Per cent
40.00
Figure 7: The most negative effects of the crisis on Hungarian urban life, 2011 (source: own questionnaire, n = 352).
sixty-third between 2011 and 2015 (Schwab, 2015). Having examined the position of Hungarian cities, it can be stated that cities of different sizes and with different levels of settlement enter economic competition with different opportunities. International experience shows that, considering the negative effects of the crisis, it is not the city's size, but rather the composition of the local economy that has played major role. Due to a wider labour market, larger settlements provide better access for the labour force. In addition to these, in large cities there are more support sectors (e.g., logistics and services), and there is more information, ideas, creativity, and innovation (Turok, 2004). However, the initial impact of the crisis on local economies also seems to depend on their economic function and composition (Clark, 2009). Cities with export-oriented economic sectors deeply embedded in international markets and trade have been hit more by the crisis.
The results of the analysis of the statistical database prove that the crisis has not significantly influenced the earlier favourable position and the advantage in competitiveness of Budapest, but it evidently intensified competition among regional centres. In Budapest, among the negative experiences, the slowdown of infrastructural development, increasing social problems, impoverishment and a decline in living standards were listed as the most acute problems. In the countryside, lower household incomes, a decline in consumption and a decline in the level of various services (e.g., education, leisure time and cultural
opportunities) were mentioned as real problems by the participants.
The detailed quantitative analysis of the role of unemployment was supported by our qualitative research results. As Yamina Guidoum and Paul Soto (2010) have stated, the main impact of the crisis on social conditions and cohesion in cities seems to come directly from the labour market. Their results could be proved and completed by our outcomes: on the basis of the questionnaires, it is evident that urban people consider the most serious negative effects of the crisis to be the effects on the labour market, problems in connection with the growth of unemployment and the negative changes of the housing and real-estate market, respectively (Figure 7). Nearly half of the respondents also experienced labour-market effects personally when their acquaintances (32%), relatives or friends (16%) lost their jobs.
The economic crisis has had a direct impact on the housing market, which demonstratively affects mental and physical health (Dunn, 2000). Hence, the crisis - especially through the conditions and quality of housing - affects the state of health of the population.
The negative social consequences of the crisis are evidently reflected in the subjective opinion of urban people. Qualitative examinations prove that, according to urban people, the
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quality of life has declined since the crisis started. Almost half of the people questioned claimed that their quality of life has declined to some extent, and almost one-fifth of them said it has declined significantly during the past years. Indirect effects of mental pressure caused by a worse quality of life and growing anxiety can be significant in the changes in the social environment. Many of the respondents think the decline in quality of life derives from the decline in financial conditions. The real reason for this is intensive polarisation in society based on the financial situation, as a result of which a growing number of people have experienced a decrease in their incomes (Egedy, 2012). Income differences between the richest and the poorest have grown; that is, the crisis has intensified the financial differences between households.
The decrease in income and the increase in the level of indebtedness had a determining role in the financial situation of the households. The gradually increasing indebtedness of poorer households is indicated by the fact that 14% of households in the lowest income quintile were paying bank loan instalments in 2001; however, this figure climbed to over 40% in 2009. At the beginning of 2010, in the lowest income quintile 43% of total income was spent on debt repayment, whereas in the highest quintile it was only 15% (Toth & Medgyesi 2011). In recent years, increasingly more news noted in Hungary that saving ability and people's willingness to save is low: respondents (if they have a chance to save) set aside less than a fifth of their monthly income. In many cases, the deterioration in quality of life can be explained by the worsening financial conditions of households: it is no coincidence that more than a quarter of respondents are worried about their future income difficulties ("won't be able to feed the family"), and a further fifth of respondents are worried about their jobs or livelihoods. Ichiro Kawachi and Bruce P. Kennedy (1999) stated that the distribution of income within society affects health, and that an individual's health status is better in societies with a more equal distribution of incomes. Based on their findings, we can conclude that the current crisis exacerbated health inequalities not only between rural and urban areas but also within metropolitan and urban areas.
Despite all of the negative effects of the global economic crisis described above, generally a positive picture can be drawn among the residents with regard to the future development of cities in Hungary. Although a quarter of the urban respondents expect a downturn, a further quarter think the current situation will stabilise. However, nearly half of the respondents expect positive changes. A result of our research is that urban people think processes on the whole are going in a positive direction in spite of the economic difficulties. The "optimism figure" is an upside-down "V", where together with the level of settlement hierarchy the trust of
urban people in future development is growing, but the optimism of Budapest inhabitants is somewhat less than that of county capitals. The responses make it clear that at higher levels of the settlement hierarchy people see the possibility of recovery from the crisis more optimistically.
5 Conclusion
Europe is dealing with a severe financial and economic crisis and its regional consequences. Especially in east central Europe, transitional countries such as Hungary should be prepared to address the direct and indirect social, health and healthcare consequences of the crisis because health inequalities in these countries have become more acute (e.g., Billingsley, 2011). Poor health conditions of the region's population, shorter life expectancy and unfavourable employment indices compared to the western European average, as well as crisis factors of healthcare inherited from communism and the inadequate financing of healthcare system, combine to create problems for healthcare policy, which have not found an efficient solution even over twenty-five years since the collapse of communism. The fact should also be taken into consideration that contra-selective effects (the migration of doctors, and the degradation of human resources in the health sector on the labour market) have also increased in Hungary due to the crisis.
Lack of access to health services could be the cause for the worsening state of health and the increase in health inequalities, especially in rural areas. The actual crisis hit central areas the hardest, but the impact of the economy on health processes cannot be strongly noticed yet. In terms of the state of health in the most unfavourable areas (the peripheries), it is still the disadvantageous nature of the original economic situation that is the explanatory factor independent of the crisis situation. Spatial differences in health inequalities still derive from the socioeconomic transformation following the collapse of communism in Hungary. Direct effects of the crisis of 2008 on health inequalities cannot be demonstrated in the short term yet, but as a result of multiplicative effects it may exacerbate regional differences. Although the crisis hit economically prosperous regions more intensively (mostly Central and Western Transdanubia), economic statistical data after 2010 prove that there has been a faster recovery of counties in these regions. Life expectancy is therefore not really influenced by the crisis in this region in contrast to backwards deprived micro-regions of Northern Hungary and the Northern Great Plain.
The changes in the structure of unemployment resulted in the fact that the current recession has not necessarily been accompanied by a short-term and directly measurable decline in the state of health. Among those that became unemployed, groups with a higher socioeconomic status and more favour-
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able health indicators have appeared. Hence there is no considerable change in the spatial structure of health inequalities. There continues to be a definite division in Hungary between areas of worse and better position based on health indicators.
Our results demonstrated that cities have more positive indicator values than their surrounding areas, which leads to the conclusion that in higher-level settlements employment opportunities are more favourable and the average life expectancy at birth is higher. Cities, therefore, provide more favourable life opportunities if all aspects are considered. This means that moving into cities may increase life opportunities and improve one's health status. Settlement hierarchy also affects health inequalities and increases territorial or regional differences, but at the same time it moves urban people's state of health in a positive direction.
The negative effects of cities and metropolises have long been known (e.g., stress, air pollution, dust and noise). However, with regard to health inequalities, the role of cities has to be reinterpreted. It could be an interesting research topic in this respect to investigate whether the health problems generated by cities or their positive effects in health inequalities play an important role in the population's long-term state of health. Our results indicate that these latter effects become increasingly important, which means that towns or cities may contribute to decreasing health inequalities in Hungary. Cities with more developed health services can even directly contribute to a decrease in health inequalities. This is especially true for counties in regions that have lagged since the collapse of communism or that have an unfavourable socioeconomic position. In the future cities may play the role of "escape routes" and alternatives for maintaining health. Although our research may contribute to establishing a new, health-based explanation of global urbanisation processes, further research is needed to develop the theory. A result of our research is that urban people think that processes on the whole are going in a positive direction in spite of the economic difficulties. Further research is needed to analyse where (i.e., what settlement size and population number) the lowest and highest borders of "healthy city size" are in order to define the point where positive health effects evidently prevail but negative health effects do not yet outweigh these.
Tamas Egedy
Geographical Institute, Centre for Astronomy and Earth Sciences, Hungarian Academy of Sciences, Budapest, Hungary E-mail: ege6727@mail.iif.hu
Annamaria Uzzoli
Institute for Regional Studies, Research Centre for Economic and Regional Studies, Hungarian Academy of Sciences, Budapest, Hungary E-mail: uzzoli@rkk.hu
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