7 październik, 2015 spatial aspects of well...
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Seminarium PTS
Oddział Warszawski 7 październik, 2015
SPATIAL ASPECTS OF WELL-BEING /PRZESTRZENNE ASPEKTY DOBROSTANU
INFORMACJA NT. SESJI NA 60-TYM
WORLD STATISTICS CONGRESS
W RIO DE JANEIRO 2015.
Włodzimierz Okrasa
Główny Urząd Statystyczny,
Uniwersytet Kardynała Stefana Wyszyńskiego
Special Topic Session /STS – strona z informacją o sesji STS081
Sesja STS081
Spatial Aspects of Well-Being.
A Cross-National Comparative Perspective Organizer: Włodzimierz Okrasa
Chair: Graham Kalton
o A nationwide study on Subjective Well-being Scale for Chinese Citizens
Xing Zhanjun, Liqing Huang
o Research of Subjective Well-being in Ukraine
Yulia Muzychenko, Oleksandr Osaulenko
o Price and quality of life
Vasily M. Simchera
o Individual and Community Well-Being in Transborder Areas: an exercise from Poland and Ukraine
Wlodzimierz Okrasa, Marek Cierpiał-Wolan, Sebastian Wójcik
Discussant: Graham Kalton
Державна служба статистики України National Academy of Statistics, Accounting and Audit
Key Sources of Information
on Household Living Conditions
Three population sample surveys (household living conditions, economic
activity and agricultural activity of population in rural areas),
Thematic modular sample surveys of population for subjective evaluations of
living standards. Annual effective sample of household living conditions amounts to about 10,000
households.
Research of Subjective Wellbeing in
Ukraine
Державна служба статистики України National Academy of Statistics, Accounting and Audit
The System of Indicators to Characterize the Self-
evaluation of the Achieved Well-being and the Degree of
Satisfaction of Basic Living Needs
subjective determination of the adequacy of annual household income,
consumption abilities of certain groups of households,
self-identification of households as representatives of certain population
groups,
the level of accessibility of medical aid for household members, ability to
purchase medicines and medical supplies if such needs emerge (share of
households which needs were satisfied),
distribution of households, which did not satisfy these needs, by reasons they
were not available,
distribution of households by the degree of satisfaction with their living
conditions.
Державна служба статистики України National Academy of Statistics, Accounting and Audit
Interrelation of Objective and Subjective Poverty
Evaluations in 2013
Державна служба статистики України National Academy of Statistics, Accounting and Audit
Conclusions
• new approaches to subjective evaluations and estimates of material
deprivation;
• identification of the most problematic and vulnerable population groups
on the basis of multidimensional analysis and combined estimates,
obtained by different factors and criteria (for example, poor
concurrently by income and deprivation, etc.);
• focus on factorial analysis of key indicators of subjective well-being and
material deprivations of the population living in rural areas, especially in
the context of the task, relevant for Ukraine, to optimize the
development of rural areas;
• introduction of the study of the regional differences and territorial
determinants in the material deprivations of the population.
Vasily M. Simchera ( Russia):
Price and quality of life
In the contemporary world we observe incremental growth of price rather than of quality of life. The main, though not used for this purpose, indicator of steadily decreasing level of quality of life is social inflation index, which - in contrast to economic inflation, being measured on a regular basis - is growing in geometric fashion.
The paper presents a framework for measuring social inflation in order to characterize the process of relative impoverishment of the deprived groups of population, along with suggestions on how to use it for calculation and communication of quality of life index, in the international comparative context.
Włodzimierz Okrasa*
Central Statistical Office, Poland
Marek Cierpiał-Wolan
Sebastian Wojcik
Statistical Office in Rzeszow, Poland
Struktura prezentacji
I. Problem, dane i miary Wstępna eksploracja relacji pomiędzy miarami
(niedo)rozwoju lokalnego i indywidualnego dobrostanu subiektywnego (Correspondence Analysis)
II. Dobrostan indywidualny a dobrostan lokalny (Community Well-Being)
analiza wpływu między-poziomami (gospodarstwa domowe i społeczność lokalna)
- interakcje czynników ‘obiektywnych’ i ‘subiektywnych’
III. Aspekty przestrzenne zależności między-poziomowej
analiza przestrzenna efektu ‘transgraniczności’ (transborder effect) dla poziomu i zróżnicowania dobrostanu subiektywnego
o model ‘opóźnienia przestrzennego’ (przykład)
Podsumowanie i wnioski 14
Problem - Dane – Miary (P-D-M) Problem: Q1 (badawcze): whether and how SW-B, its main dimensions - overall SW-B,
satisfaction from everyday life activities, important aspects of life – relate to important factors of quality of life at the local community (commune) level?
what is their way of operation - in separation or confluence /interaction with household level factors, and which ones?
Q2 (metodologiczne): given different forms of spatial interactions across different levels of the hierarchy - individual, household, community - how to model them adequately?
Q3 (pragmatyczne/policy) *): how to explain variability in individual SW-B and identify patterns of relationship between individual (subjective) well-being and community well-being in a way informing policy, esp. for local development purposes?
o (q3.1) can we use, and rely upon the knowledge of the relevant community-level factors of SW-B in assessment of its level and predicting changes in it?
o (q3.2) how important is the role of place and the effect of neighbourhood in defining the way the community-level factors affect SW-B?
15
*) [Dolan Layard, Metcalfe (2010): The measurement of wellbeing is central to public policy for three main reasons:
1) monitoring progress; 2) informing policy design; and 3) policy appraisal. (p.1)];
US CNSTAT-Panel: research, policy, and general information purposes
Wymiary dobrostanu subiektywnego i źródła danych - typologia zorientowana na potrzeby polityki (policy)
(Dolan et al., 2010; OECD, 2013; Stone and Mackie, 2014)
Source: Dolan, Layard, Metcalfe, 2010
National Well-Being in 28 EU Cs (Randall and Corp, 2014)
Randall and Corp, 2014, p. 4
Dane: ▪ GD/poz. indywidualny: pilotaż – quasi-losowa random /celowa próbka g.d. – kwestionariusz indywidualny ▪ Gmina /poz. lokalny: Pl – Bank Danych Lokalnych (DDL) UA – Baza Danych Regionalnych (Rayon Database)
Obszary Transgraniczne Central region
Number PL – Subacarpatian
UA - Lviv PL - Masovian
Households (respondents)
410
382
400
(commune) PL - Gmina UA - Local (village) Council
13
8
9
(county) PL - Powiat UA – Rayon
4
8
6
Warning!: Due to differences in size of spatial units (‘samples’) the issue of adjusting for heterogenous variance arises in the spatial analysis (eg., computing Moran’s I, below).
▪ Instrumenty – Miary ► Miary poziomu lokalnego
PL - Wielowymiarowy Indeks Deprywacji Lokalnej (WIDL/MILD)
Zastosowano FA (wersja konfirmacyjna – dla 11 wyróżnionych dziedzin) dla
identyfikacji – na podstawie ładunków czynnikowych - zmiennych/cech w
bazie danych (poddanych standaryzacji i agregowanych w obrębie dziedzin,
oraz normowanych dla włączenia w indeks złożony deprywacji lokalnej).
UA – Indeks Niedorozwoju Lokalnego (INRL)*) -- miara opracowana na podstawie danych BDR za pomocą FA w zakresie 5-ciu dziedzin: - dochód (przeciętny) - rynek pracy - usługi społeczne - emerytury (przeciętne) - budownictwo *) Indeks (jego sktruktura) przypomina kanadyjski The Community Well-Being Index: income, education, housing conditions and labour force activity.
Wielowymiarowy Indeks Deprywacji Lokalnej
Instrumenty i miary –cd.
► Poz. Indywidualny i gosp. domowego (7 miar) *)
• Dobrostan Subiektywny Ogółem:
Overall, how satisfied are you with your life nowadays?
Overall, how happy did you feel yesterday?
Overall, how anxious did you feel yesterday?
Overall, to what extent do you feel the things you do in your life are worthwhile?
• Skale zadowolenia – wybrane aspekty dobrostanu subiektywnego (Skala 5–punktowa: Całkowicie zadowolony – Całkowicie niezadowolony)
─ satisfaction from everyday (‘yesterday’) life activities (10 items)
─ feeling of discomfort or health problems (5 items) ─ satisfaction from job and living environment (9 items) ─ satisfaction from social and intellectual dimensions of life (12 items) ─ life (important aspects of) satisfaction (10 items) ─ feeling of belonging to local community / Subjective Community Well-Being (SCW-B), (12 items)
*) Konstrukcja oparta na Survey Modules for the Oxford Quality of Life Index and Dashboard (OXQOL) - Anand et al. (2010)
(I) Eksploracja danych – wyniki wstępne na pyt. o ‘trafność
prognostyczną’ (czy rozkład dobrostanu subiektywnego może być ‘wyjaśniony’/przewidziany na podstawie znajomości poziomu deprywacji/(niedo) rozwoju społeczności lokalnej?) Correspondence Analysis (CORA) dla:
1. Overall Subjective Well-Being
2. Satisfaction from everyday life activities (EDLA)
3. Satisfaction from different aspects of life
4. Feeling of belonginess / ‘Sense of Community’ / SCW-B
przykłady
22
I
Satysfakcja z czynności ‘codziennych’/życiowych (work, transportation to
work, housework, education, caring children, volunteering, eating, social meeting, hobby)
wg poziomu deprywacji lokalnej
23
Dobrostan subiektywny ogółem (satisfaction from life, happy/unhappy
yesterday, sensof life activities) wg poziomu indeksu niedorozwoju – Lwów rejon
24
Zadowolenie z różnych aspektów życia (health, job, sleep, leisure time, family life, social life, housing conditions, personal income, life
prospect) wg poziomu deprywacji lokalnej – Podkarpacie
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Tab. 1 Podsumowanie wyników CORA
Dobrostan subiektywny wg poziomu deprywacji lokalnej
Dobrostan Subiektywny
Wzory powiązań:
YES - clear /unambiguous Yes – a weak tendency No – lack of tendency
HH w/n 50 km HH out 50km PL central
Kraj /region PL
Podkarpacie
UA Lwów
PL Podkarpacie
UA
Lwów
Mazowsze
Dobrostan subiektywny ogółem YES No No YES YES
Zadowolenie z czynności życiowych YES No YES No Yes
Zadowolenie z różnych aspektów życia YES No YES YES YES
Poczucie przynależności do społ. Lokalnej ('sense of community’)
Yes
YES
No
No
YES
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Obserwacje/komentarz:
Według CORA, istnieje istotna widoczna zależność rozkładu subiektywnych miar dobrostanu od poziomu deprywacji lokalnej. Jest to szczególnie widoczne w przypadku DobSub ogółem i zadowolenia z życia (dwóch kluczowych miar). W przypadku niektórych –np. satysfakcja z codziennych czynności i poczucie przynależności do SL- zależności te znacząco odmienne (tzn. są modyfikowane) po uwzględnieniu odległości 50 km od granicy.
Odległość od granicy wydaje się odgrywać generalnie większą rolę po polskiej stronie granicy; dla gospodarstw polskich w obrębie 50 km od granicy poziom deprywacji lokalnej wpływa w bardziej przejrzysty i istotny sposób na postrzeganie i odczuwanie w zakresie aspektów ważnych w życiu niż w przypadku mieszkańców spoza tego obszaru. Ale odwrotny wzór zależności występuje po stronie UA/rej .lwowskiego – stąd obserwacja ta nie może być uogólniona bez dalszych analiz z perspektywy porównawczej.
27
Exploration of data – cont. Next to examining the relevance of the community level of (under)development for distribution of subjective well-being, an analogous issue needs to be explored with respect to household level characteristics: to what extend can distribution of subjective well-being be explained by the
knowledge of the relevant household characteristics?
Household level characteristics included into the CORA for checking association between the selected raw-variables:
- main source of HH income
- type of HH
- HH financial/living conditions
- years of living in the community
and the selected subjective well-being (column-variables): overall SW-B - satisfaction from life – feeling of/’sense of community’ (SCW-B)
Exploration of data - results:
(series 2) well-being data distribution by the
household level characteristics
Appendix 1B
/on request
Table 2. Results of a preliminary data exploration: self-reported well-being data distribution by selected household characeristics:
Correspondence Analysis
of subjective well-being by household level characterisitcs
Indicators of subjective well-being
by selected characteristics of household
Pattern of relations:
YES - clear /unambiguous; Yes – a weak tendency; None
Country / region / rayon
Subcarpathian UA_Lviv
All Hh
w/n 50km All Hh w/n
50km
Overall SW-B o HH living conditions Yes YES YES YES
o type of household Yes Yes Yes YES
Life satisfaction o main source of income
Yes YES Yes YES
Feeling of belonging to community /SCW-B
o years of living in local community
Yes
Yes
No
Yes
Individual level : Overall SW-B and SCW-B
Overall SW-B o Subjective CW-B YES YES
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Subjective Well-Being by Subjective Community Well-Being
• Following the so-called spillover theory (Bernini et al.,, 2013) - according to which the environment features in which individuals live are one of the main domains affecting overall SWB (others are the individual personal life and people’s leisure activities) - the figures below confirm the usefulness (and even external validity) of the Subjective Community Well-Being in measuring SWB.
• Sociologists also show growing interest in the role played by space and place/neighbourhood in analysis of different aspects (domains) of people’s well-being – eg. Sampson (2003):
„We need to treat community contexts as important units of analysis in their own right, which in turn calls for new measurement strategies as well as theoretical frameworks that do not simply treat the neighborhood as a “trait” of the individual.” (p.S53).
Dobrostan Subiektywny wg
Subiektywnego Dobrostanu Lokalnego (Subjective Community Well-Being)
Lwowskie Podkarpacie
32
Observations/comments: Practically all the above results (figures) confirm expectations concerning association between the HH level raw-variables and subjective measures of individual well-being as column-variables. It does prove usefulness of the patterns emerging from CORA-type analysis both for policy purposes and for further exploration of explanatory models through preliminary identification of the potential factors of changes in such measures. Several detail observations are worthwhile mentioning . For instance, different role of income source, such as earning, that seems more conducive for high SW-B in PL than in UA (except for HH w/n 50 km), but very much similar, low position of pensioners on SW-B in both regions (countries); also, more happy seems to be a couple without kids in Pl while in UA it is a couple with children. Mixed pattern is shown by Subjective Community Well-Being (‘sense of community’) in relation to the years of living in the community – but in both regions the least satisfied from their communes are likely to be people living in them for relatively longest time (more than 18 years). It is also worth to notice very consistent pattern of association between Overall SW-B and SCW-B: in all cases these two measures goes together (high-high, low-low). This implies some suggestions for substitution in practical situation and/or for empirical verification of some model (as below).
This also agrees with above mentioned afforts in the lterature to build individiual SW-B measure through incorporating in it residents’ satisfaction with environment and community (so-called DEA-Like Model - Bernini et al., 2013).
Model 1: między-poziomowa interakcja (gosp. domowe – społ. Lokalna) :
IWB = f (CID, HV, HV*CID) where:
IWB - Individual Well-Being: overall subjective well-being (SW-B), or life satisfaction, etc.
predyktory:
▪ poziom indywidualny: SCW-B / poczucie przynależności do SL
▪ poz. Gosp. Dom.: status zagrożenia g.d. (vulnerability status), typ g.g..,
▪ community level
─ CID, community index of deprivation
-- WILD –Podkarpacie
-- INRL Indeks niedorozwoju lok., - Lwów
34
II. Dobrostan Lokalny i Dobrostan Indywidualny ► rodzaje wpływu – kontekst czy interakcja? Two versions of the model of influence – with and without a subjective measure (a pseudo-well-being equation approach different from Well-Being or Life Satisfaction Equations (e.g., Blanchflower and Oswald (2004)
Summing-up (Model I): results of the OLS regression of subjective measures of
well-being on selected ‘objective’ characteristics of household and community – comparing transborder regions (PL and UA)
Influence on/ - predictors Similarities Differences
Overall SW-B: -relatively strongest influence on overall SW-B in both regions of the HH vulnerability status; -CID (local deprivation /underdevelopment) weak and negative, and also the interaction term (CID & HH vulnerability); -marital status and kids at home generally positive and significant
– big negative impact of HH-vulnerability, in all sections (w/n and out/50 km); generally weak but negative impact of the interaction term (CID & vulnerability); positive pattern of influence for couple w/kids
- weak impact of CID except for w/n 50 km in Lviv region, where it is significant and negative, but the interaction term is positive but for out/50km; to be a couple w/t children in PL is negative for overall SWB
Life satisfaction: -HH vulnerability status strong and negative; local deprivation (CID) rather mixed - generally negative; - interaction term also mixed as regards direction and strength; - type of HH predominantly negative in PL but positive in UA.
- In each case vulnerable HH have the same pattern (strong and negative), followed by similar one for local deprivation but HH w/n 50km; Only Married couple w/children have clear positive impact in both regions, other mixed effect.
- Local deprivation (CID) weak and positive only in the case of HH w/50km; interaction term positive for w/n 50km in UA and for out/50km in PL (for negative but weak); Married w/t kids in areas out 50km most different.
Dobrostan Lokalny i Dobrostan Indywidualny - cd
Model 2: Subjective well-being equation with subjective community well-being (also in the interaction term):
IWB = f (CID, SCWB, SCWB*CID, HH) (where variable symbols – as above)
[The growing importance of SCW-B: conceptualization after Chavis at al. (1986 / 2008) - involving
sociological ideas from Tönnis and Durkheim- with strong arguments for such a type of variable in community-focused analysis and policy from „psychology of community” (e.g., .Pretty et al., 2006)]
Box. 1 [Sense of Community (SCI), Chavis et al., (2008):
Community members and I value the same things. Being a member of this community makes me feel good. People in this community have similar needs, priorities, and goals. I can recognize most of the members of this community. Most community members know me. I care about what other community members think of me. I have influence over what this community is like. If there is a problem in this community, members can get it solved. It is very important to me to be a part of this community. I expect to be a part of this community for a long time. Members of this community have shared important events together, such as holidays, celebrations, or disasters. Members of this community care about each other.]
Summing-up (Model II): results of the OLS regression of subjective measures of
well-being on selected ‘objective’ and ‘subjective’ household- and community-level variables – comparing transborder regions (PL and UA).
Influence on/ - predictors Similarities Differences
Overall SWB: -HH vulnerability status consistently impacts the overall SW-B in relatively strongest negative way; CID (local deprivation influences it in generally negative way; SCW-B positive but for PL outside 50 km; the interaction term (CID & SCW-B) shows a mixed pattern (positive in PL); Regardless marital status kids at home are positive in each case.
– impact of HH vulnerability coherent across all sections but weaker than in Model I; SCW-B positive in HHs except for Pl-HH out/50km; but interaction term positive (small) in this case in both regions; positive pattern of influence for all types of HHs, esp. strong for Couple w/kids in HH in areas out/50km .
- Weak positive impact of CID for HHs w/n 50 km in Pl makes the only difference in this respect; SCW-B negative only for PL-HH out/50 km where positive initeraction term contrasts with that for borderland w/n 50km, in both regions.
Life satisfaction (LS): -The HH vulnerabiliyt affects LS in negativel but stronger way than OSWB; local deprivation (CID) has negative impact on LS for all cases except for HHs out/50km in Lviv-rayon; the interaction term is low but positive in each case; type of HH positively affects UA-HHs, but in PL it has a mixed pattern.
-The HH vulnerability status impacts LS in strong and negative way; the same pattern shows CID (local deprivation) and SCW-B except for UA-HHs in areas out 50km; but effect of interaction term (CIS*CWB) is everywhere positive, thought near zero in size.
- Local deprivation (CID) weak and positive only in the case of HH w/50km; interaction term positive for w/n 50km in UA and for out/50km in PL (for negative but weak); Married couple w/kids in HHs w/n 50km and Couple w/t kids in out/50km in PL-HHs show negative impact - other positive
Contrasting profiles of influence (‘factors’) for Low vs. High scored respondents - Multinomial Logistic Regression: measures of subjective well-being : ─ overall subjective well-being (SW-B) ─ satisfaction from everyday life activities ─ feeling of discomfort or health problems ─ satisfaction from job and living environment ─ satisfaction from social and intellectual dimensions of life ─ life (important aspects of) satisfaction ─ feeling of belonging to local community / Subjective Community Well-Being (SCW-B)
selected characteristics of household and community:
─ main source of income
─ type of household ─ level of local deprivation (MILD, ILUd)
─ HH vulnerability status *location (w/n or out/50 km from border)
Multinomial Logistic Regression – results for PL-Subcarpathian
Appendix 2A /on request
Summing up MLR1– salient ‘factors’ of influence, contrasts. Subcarpathian
Low profile holders High profile holders
• overall subjective well-being (SW-B)
(1) Earners and Pensioners have lower relative risk of being in ‘L’ than Self-employers, compared to Agriculture (↑) .(2) Couples w/t kids and Married w/ kids less (↓) likely than Others. (3) Living in more deprived areas less likely (↓). (4) Vulnerable HH w/n 50km most likely (↑).
(1) Similar to ‘low’ (2) Others (single per., one-parent) more likely (↑) than Couples w/t kids and Married w/kid. (3) Mixed pattern-in more deprived areas (↑). (4) Members of not-vulnerable HH much more likely (↑) esp. HHs in areas out /50 km from border.
• satisfaction from life (important aspects of)
(1) Earners and Self-employers have lower relative risk of being in ‘L, ’ compared to Agriculture (↑). (2) Other HHs more likely to be ‘L’ (↑). (3) All categories of local deprivation but highest more conducive for ‘L’ (↑). (4) Not vulnerable HHs either w/n or out of 50km distance from border have much lower relative risk of being ‘L’.
(1) Pensioners least likely to be ‘H’ while Self-employers and Earners (↑). (2) Couples wt/ kids and Married w/kids (↓) compared to Others. (3) Mixed but higher local deprivation more conducive (↑) for ‘H’. (4) Not vulnerable HHs w/n or out of 50km from border likely (↑) to
be in ‘H’
• feeling of belonging to local community / (SCW-B)
(1) Compared to Agriculture, HHs of all
3categories of income have bigger relative risk (↑) to be in ‘L’. (2) Married w/kids have higher relative risk (↑) to be in „L” (compared to Other). (3) Biggest relative risk of being in „L” have HHs in most deprived areas. (4) Vulnerable HHs w/n 50km have highest relative risk to be in ‘L’.
(1)Less likely to be in „H’ compared to Agriculture HHs are Earners and Self-employers. (2) Both Couples w/t kids and Married w/kids likely (↑) to be in „H”,
compared to Other HHs. (4) Lower local deprivation is more conducive to being in „H”. (4) Not vulnerable HHs w/n 50km are
relatively (↑) to be in „H’(vs. vulnerable).
Multinomial Logistic Regression – results for UA – Lviv region
Appendix 2B /on request
Summing up MLR2 – salient ‘factors’ of influence, contrasts: UA Lviv
Low profile holders High profile holders
• overall subjective well-being (SW-B)
(1) Earners, Pensioners and Self-employers have much lower relative risk of being in ‘L’ compared to Agriculture (↑) .(2) Couples w/t kids and Married w/ kids less (↓) likely than Others. (3) Extremely low relative risk of being in „L” for members of HH in not-underdeveloped areas -other groups (↑) . (4) Vulnerable HH w/n 50km most likely (↑).
(1) Compared to Agriculture, all other income sources likely to be in ‘H’ (↑) (2) Couples w/t kids and Married w/kid more likely (↑) than Others. (3) HHs in not-underdeveloped areas likely to in ‘H” compared to highly deprived (↓) . (4) Compared to vulnerable HHs w/n 50 km from border, all HHs likely in ‘H’(↑) .
• satisfaction from life (important aspects of)
(1) Earners and Self-employers have lower relative risk of being in ‘L, ’ compared to Agriculture (↑). (2) Other HHs more likely to be ‘L’ (↑). (3) All categories of local deprivation but highest more conducive for ‘L’ (↑). (4) Not vulnerable HHs either w/n or out of 50km distance from border have much lower relative risk of being ‘L’.
(1) Pensioners least likely to be ‘H’ while Self-employers and Earners (↑). (2) Couples wt/ kids and Married w/kids (↓) compared to Others. (3) Mixed but higher local deprivation more conducive (↑) for ‘H’. (4) Not vulnerable HHs w/n or out of
50km from border likely (↑) to be in ‘H’
• feeling of belonging to local community / (SCW-B)
(1) Compared to Agriculture, HHs of all 3 categories of income have bigger relative risk (↑) to be in ‘L’. (2) Married w/kids have higher relative risk (↑) to be in „L” (compared to Other). (3) Biggest relative risk of being in ‘L’ have HHs in most deprived areas. (4) Vulnerable HHs w/n 50km have highest relative risk to be in ‘L’.
(1) Less likely to be in „H’ compared to Agriculture HHs are Earners and Self-employers. (2) Both Couples w/t kids and Married w/kids likely (↑) to be in „H”,
compared to Other HHs. (4) Lower local deprivation is more conducive to being in „H”. (4) Not vulnerable HHs w/n 50km are relatively
(↑) to be in ‘H’(vs. vulnerable).
Comparing profiles of influence on well-being in transborder areas:
Pl_Subcarpathian and UA_Lviv
Aspects of comparison
Pl_Subcarpathian vs. UA_Lviv
Re.Overall SubjectiveW-B
(1) Much stronger contrast between Low and High profile holders in Lviv (UA) than in Subcarpathian (PL), though the patterns of influence of main source of HH income are similar (biggest relative risk of being low on SW-B face members of Agriculture-based HHs .) (2) Different pattern for HH-type: while in both PL and UA Couples w/ and w/t kids have lower relative risk to be in L-group on SW-B, the opposite is true about the H-groups (HHs in Pl (↑) to be in it). (3) More clear pattern for UA due to reverse profile of L- (not-underdvlpd (↓)) and H-groups (not-underdvlpd (↑)) – rather mixed pattern in Pl). (4) The most similar patterns for PL and UA HHs due to dominance of vulnerable HHs w/n 50 km from border in terms of the relative risk of being in L-group (↑) and in H-group (↓) .
Re. Satisfaction from life
(aspects of)
(1) Practically the same pattern of effects of HH’s source of income-only Pensioners face bigger relative risk of being in L-group (↑) than Agriculture (who are consistently in the most disadvantageous position, in UA and Pl). (2) While Others are most likely to be in L-group both in PL and UA , only in UA they are more likely to be in H-group. (3 Somewhat opposite profile of local deprivation in UA: (↑) to be in ‘L’ for HH in most deprived areas, while (↓) in PL, and vice-versa for being in ‘H’ . (4) In PL all other HHs than vulnerable w/n 50 km from border face lower relative risk (↓) to be in l-group, but in UA (↑) HH vulnerable out/50 km; H-groups identical.
Re. Feeling of belonging /(SCW-B)
(1) Compared to Agriculture, in PL members of all other 3 groups of HH income source face bigger relative risk of being low on the SCW-B, but in UA also Self-employers who are at the same time (↓) to be in H-group; in PL only Pensioners (Earners and Self-employers (↓). (2) In PL Other HHs are less likely to be either in L- (↓) and in H-group (↓) , while in UA only Married w/kids (↑) to be ‘L’, but both them and Couple w/t kids are less likely to in H-group. (3) Contrasting profiles of relative risk to be in ‘L’ – in PL HHs in highly deprived areas (↑) but in UA (↓); a reversed pattern for being in H-group: in lower deprived areas (↑) in UA and (↓). (4) compared to vulnerable HHs w/n 50 km from border, reverse pattern of relative risk of being in L-group; in UA HHs in ‘out/50km’ (↑), in PL, L-(↓); while not-vulnerable HHs (↑) in both regions (w/n or out/50km).
III. Zależności przestrzenne i efekt ‘opóźnienia’ przestrzennego w analizie lokalnego wymiaru
dobrostanu subiektywnego
Spatial dependence and spatial (lag) effect in the
analysis of community dimensions of subjective well-being
Autokorelacja przestrzenna i autoregresja przestrzenna miar dobrostanu subiektywnego w
kontekście lokalnym
III. Spatial aspects of between level relationships The structure of relationship between variables of the three levels of units – individuals, households and communes – presents a complex architecture. When cross-level interactions are to be recognized and accounted for in analysis of spatial dependencies, there should be three types of influences involved:
(i) individual level spatial unit’s attributes (e.g., of local deprivation);
(ii) an effect of hierarchical location (regional), and
(iii) an interaction between the units/neighbuors with their properties that depends on distance (and on spill over externalities across space)
Hypotheses underlying this part of analysis stress that not only ‘location’ (place) and its characteristics are important for how people perceive and feel about their life and its determining factors. But that spatial relations, proximity or distance in terms of both the dependent variable (a measure of SW-B) and of certain sources of influence on it can jointly impact the ultimate level of life satisfaction or happiness. The appropriate approach to deal with such a multifaceted social phenomenon would be multilevel modelling while accounting explicitly for spatial effect. However, given the data constraints, only a part of this complex problem is possible to be analytically addressed here, confining ourselves diagnostic and assessing the effect of spatial dimensions on
subjective well-being.
The chief null hypothesis on the lack of spatial dependence (or clustering ) and spatial (lag) effect is being handled on using ESDA framework and GeoDa software (Anselin et al., 2010).
III. Spatial aspects … contin
Two-step spatial analysis: (1) checking a tendency to clustering among ‘spatial units’ with respect
to selected measures – subjective and objective – using Moran’ I (global):
; i ≠ j
where:
xi, xj - values of a measure at each location;
W is the spatial weights matrix.
III. Spatial aspects … cont. (2) Estimation of the spatial regression model parameters: (notation for individual observation i):
yi = ρ ∑n
j=1 Wij yj + ∑k
r=1 Xir βr + εi
where: yi – the dependent variable for observation i; Xir k – explanatory variables , r = 1, …, k with associated coefficient βr ; εi is the disturbance term; ρ is parameter of the strength of the average association between the dependent variable values for region/observations and the average of them for their neighbors (eg., LeSage and Pace, 2010, p. 357)
The above specification of the spatial regression model assumes that εi is meant as the the spatially lagged term – versus spatial error formulation - for the dependent variable (wich is correlated with the dependent variable), that is:
εi = ρ Wi.yi + Xi. β + ϵi These types of models allow us to examine the impact that one observation has on other, proximate observations.
ρ e.g., HHs in gminas the neighbors of which are on average higher on a subjective well-being measure are expected to have also (on avergae) higher position on it.
Observations on autocorrelation
There is a low overall tendency to dependence (i.e., similarity of values at location to the spatial proximity of the location - e.g., Aldstadt, 2010, p.280), esp. among the first category of units: subjective well-being of individuals in selected HHs - except for relatively high value of autocorrelation among the members of UA-HHs in the transborder areas (w/n 50 km from border). Meaning that observations from one location are among those HHs generally more similar to those from nearby location: all five measures have significant Moran’s I value, particularly overall SW-B, life satisfaction and material (job and location-related) aspects of life. But this cannot be attributed to the type of area - proximity within narrowly defined borderland - because no such a tendency to autocorrelation occurs among PL-HHs. [Of course, the latitude-longitude coordinates for HHs are defined as centroid points, using ESDA.]
Some tendency to clustering in space is shown by the households of similar levels of living conditions, however, among HHs located in different types of areas on both sides of the border – w/n 50 km among UA-HHs and out/50 km among PL-HHs.
Relatively the most clear pattern of autocorrelation emerges among the local territorial units – i.e., among gminas with respect to their level of deprivation (Pl-MILD) and Village-Councils (UA) with respect to their underdevelopment index (ILUd); in both cases there is a tendency to spatial (global) clustering.
Such a preliminary knowledge of (on general rather low) ‘sensitivity’ of the involved individual variables – subjective measures vs. objective spatial units’ characteristics (development) – allows for selection of predictors for the spatial regression (lag) model.
(Preliminary) Conclusions The results of spatial lag model provide a bit mixed picture. First of all, spatial autoregressive coefficient rho is generally big and positive (except for life satisfaction in Pl, where is negative and low). This confirms spatial effect for overall SW-B and life satisfaction among UA-HHs and the same in the case of overall SW-B and social and intellectual dimensions of life in PL among HHs residing within the narrowly defined borderland. [There was no possibility to conduct such analysis for the UA-HHs beyond 50 km belt because of their concentration in the city of Lviv.]
From among explanatory variables practically only one – feeling of belonging to community or SCW-B – shown to be consistently significant, in all cross-sections. This leads to rather a reserved type of conclusion that the structure of the data used in these computations (which did not meet assumptions concerning randomization and normality, while showing heteroscedasticity problem) allow us for demonstrating an approach to the tasks of taking into account the community dimensions of subjective well-being in analysis. Without drawing substantive conclusions for research and policy purposes, however. Therefore, given methodological feasibility of the demonstrated approach to this task, it may be concluded that such a strategy deserves implementation on the ground of the appropriate data (as it is planned).
Conclusions This paper addresses the issue of community dimensions of individual (subjective) well-being – whether and how they interact in both ‘hierarchical’ and ‘horizontal’ aspects of possible complex relationships. It does it in the comparative, transborder perspective, using data for two (Polish and Ukrainian) bordering regions (PL-Subcarpathian voivodship and UA-Lviv region), contrasted occasionally in the analysis with data from a central region (PL_Masovian); altogether 1,192 HHs*).
The four stage strategy employed in this study encompasses:
(i) preliminary exploration of the distribution of subjective measures of well-being by selected features of the living environment/communes (using Correspondence Analysis); overall SW-B and life satisfaction showed to be relatively most dependent on the overall quality /deprivation of local community (esp. in the Polish part of the borderland, w/n 50 km);
(ii) search for the pattern of (cross-level) influence of the level of community (under)development in interaction with objective (HH-vulnerability) followed by interaction with Subjective Community Well-Being.
______________
*) Since the data come from an extended pilot-type study – not from a representative sample of HHs or communes - the presented results have only a preliminary and illustrative rather than a conclusive character. The study has focused on methodological adequacy of the employed strategy - including validity of measures and efficiency of analytical techniques – rather than on generalization of the substantive results.
Conclusions … cont.
The results of the OLS regression allowed to identify some patterns of influence while indicating on household and individual levels variables - the HH-vulnerability status and SCW-B , respectively – as the potentially best predictors of changes in the subjective well-being;
(iii) recognition of the profiles of factors (specific categories of selected features of all the three levels: individual, HH and commune) affecting the subjective measures of well-being in relatively most noticeable way – such as generally bigger difference between facing relative risk of being in Low group (less satisfied or happy) vs. High-group in UA than in PL, or more similar positions of Pensioners of the two regions (countries) than Earners or Self-employed, etc.
(iv) at the last (fourth) stage of the study – devoted to the spatial aspects of well-being - some methodological lessons have been drawn: (a) positive one, concerning the efficacy of the approach focused on spatial dependence and spatial effect in assessing ‘true’ influence of the factors of subjective well-being in the spatial/community context, followed by (b) cautionary one, stressing the quality of data as a precondition for using such powerful tool as spatial analysis applied to hierarchical data structure, along with multilevel modelling as the most promising approach (to be continued - beyond a pilot-type study).
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References
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