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Determinants of city quality-of-life perception: the interaction between city and life satisfaction 12 th of May, 2014. Dmitriy Potapov, HSE-Perm Anastasiya.

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Presentation on theme: "Determinants of city quality-of-life perception: the interaction between city and life satisfaction 12 th of May, 2014. Dmitriy Potapov, HSE-Perm Anastasiya."— Presentation transcript:

1 Determinants of city quality-of-life perception: the interaction between city and life satisfaction 12 th of May, 2014. Dmitriy Potapov, HSE-Perm Anastasiya Bozhya-Volya, HSE-Perm Irina Shafranskaya, HSE-Perm Higher School of Economics, 2014 www.hse.ru XV April International Academic Conference on Economic and Social Development

2 Higher School of Economics - Perm Motivation and Purpose photo HSE, 2014 In knowledge economy human capital become the main asset of the regions and cities People choose place for living, they decide to stay or to leave (migration flows in the city of Perm increased twice from 2008 till 2011). People with high human capital are more likely to change place for living Local authorities can influence people choice throw urban services (satisfaction of public needs) The purpose of the paper is to measure how citizens satisfaction over different urban services (education, healthcare, safety, etc.) influence overall city satisfaction taking into account personal life satisfaction and happiness.

3 Higher School of Economics - Perm Theoretical Background photo HSE, 2014 Subjective vs objective city quality-of-life approach (Tesfazghi, 2010: Obulicz – Kozaryn, 2013) City quality-of-life could be considered as the individual’s subjective experience of dealing with different urban services (Diener and Suh, 1997; Kahneman and Kruger, 2006) Subjective measures of city and life satisfaction inter-correlate (Cummins, 2000) All this raises the task of separation of residents’ overall satisfaction with the city, urban services quality and personal happiness perception (Jeffres and Dobos, 1995)

4 Higher School of Economics - Perm 1. Linear Regression Model photo HSE, 2014 Look for the effect of different public needs satisfaction (urban services) on the overall city satisfaction From M1 to M4 include control variables to «purify» the effects Measure only direct effects

5 XV April International Academic Conference photo HSE, 2014 2. Path Analysis Model Measure direct and indirect effects

6 Higher School of Economics - Perm Data Collection photo HSE, 2014 Door-to-door poll of more than 2000 inhabitants of Perm city (Russia). City population is around 1 million people. Sample is representative over Gender Age City districts (7 areas) Questionnaire contained 35 composite questions covering satisfaction and attitude to different aspects of life in the city (i.e. education, safety, etc.) and overall city satisfaction, happiness and well-being. Survey (sponsored by local authorities) was conducted in August-September, 2012

7 Higher School of Economics - Perm Data Preparation photo HSE, 2014 Drop out observations with many (more than 2/3 in parcel) missings (controlled for systematic bias) Implemented parcel approach (Coffman, MacCallum, 2005) to convert different measures of a construct into one index Imputed missings in parcels as a prediction on the basis of linear regression, where dependent variables include other questions from the same parcel and social-demographic variables Generating parcel scores (indexes) as weighted sum of variables with equal weights

8 Higher School of Economics - Perm Data Description - 1 photo HSE, 2014 VariableDescription # of indicators Type, Scale# obsMeanS.d.MinMax Personal happiness/satisfaction CitySat_INDCity satisfaction5Likert (1…7)17815.11.41.07.0 LifeSatLife satisfaction1Likert (1…7)17433.91.31.07.0 Happiness 1Likert (1…7)17745.61.51.07.0 JIIncSatIncome satisfaction1Likert (1…7)17543.61.51.07.0 Urban services satisfaction indexes Cu_INDCulture satisfaction index7Likert (1…7)17815.01.11.07.0 Edu_INDEducation satisfaction index11Likert (1…7)17814.31.0 7.0 Env_INDEnvironment satisfaction index7Likert (1…7)17813.61.0 6.9 HC_INDHealth care satisfaction index5Likert (1…7)17813.51.20.97.0 SS_IND Social security satisfaction index 6Likert (1…7)17813.50.91.06.5 Saf_INDSafety satisfaction index6Likert (1…7)17814.01.11.07.0 Sport_INDSport satisfaction index6Likert (1…7)17813.41.10.85.8

9 Higher School of Economics - Perm Data Description - 2 photo HSE, 2014 VariableDescription # of indicators Type, Scale# obsMeanS.d.MinMax Social-demographic characteristics SD_AgeGrAge group- Ordered (6 groups) 17813.71.31.06.0 SD_AgeGr_114-17 years-Dummy17810.10.20.01.0 SD_AgeGr_218-21 years-Dummy17810.10.30.01.0 SD_AgeGr_322-35 years-Dummy17810.30.50.01.0 SD_AgeGr_436-49 years-Dummy17810.30.40.01.0 SD_AgeGr_550-65 years-Dummy17810.20.40.01.0 SD_AgeGr_665+ years-Dummy17810.10.30.01.0 SD_CivStCivil status- Categorical (4 groups) 17381.91.0 4.0 SD_CivSt_1Married-Dummy17380.5 0.01.0 SD_CivSt_2Not married-Dummy17380.30.50.01.0 SD_CivSt_3Widowed/Divorced-Dummy17380.20.40.01.0 SD_CivSt_4With partner-Dummy17380.10.30.01.0

10 Higher School of Economics - Perm Data Description - 3 photo HSE, 2014 VariableDescription # of indicators Type, Scale# obsMeanS.d.MinMax Social-demographic characteristics SD_EduGrEducation group- Ordered (5 groups) 17653.31.31.05.0 SD_EduGr_1Incomplete second. edu-Dummy17650.10.30.01.0 SD_EduGr_2Second. edu-Dummy17650.20.40.01.0 SD_EduGr_3Second. prof. edu-Dummy17650.30.50.01.0 SD_EduGr_4Incomplete higher edu-Dummy17650.10.30.01.0 SD_EduGr_5Higher edu-Dummy17650.30.40.01.0 SD_HealthHealth group- Ordered (4 groups) 17511.70.91.04.0 SD_Health_1No restrictions-Dummy17510.5 0.01.0 SD_Health_2Few restrictions-Dummy17510.30.40.01.0 SD_Health_3Signif. restrictions-Dummy17510.10.30.01.0 SD_Health_4Strong restrictions-Dummy17510.10.20.01.0 SD_GenderGender-Dummy17810.5 0.01.0 SD_WorkWorking status-Dummy17810.60.50.01.0

11 Higher School of Economics - Perm Linear Regression Models – OLS Estimation (we report B - standardized β) photo HSE, 2014 FactorsModel1Model2Model3Model4 Personal happiness/satisfaction LifeSat 0.12***0.020.05 (0.03) Happ 0.24***0.27*** (0.02) JIIncSat 0.05** (0.03) Urban services satisfaction indexes Cu_IND 0.17*** 0.13***0.10*** (0.04) (0.03) Edu_IND 0.16***0.15***0.14***0.11*** (0.04) Env_IND -0.02 0.010.00 (0.04) HC_IND 0.13***0.10***0.08**0.10*** (0.04) (0.03) SS_IND -0.04-0.07-0.03-0.02 (0.05) (0.04) Saf_IND 0.22***0.21***0.20***0.16*** (0.04) Sport_IND 0.050.040.020.07** (0.04) (0.03)

12 XV April International Academic Conference Linear Regression Models – OLS Estimation (continuation) photo HSE, 2014 FactorsModel1Model2Model3Model4 Social-demographic characteristics SD_AgeGr 0.27*** (0.03) SD_CivSt_2 -0.04 (0.08) SD_CivSt_3 -0.05 (0.09) SD_CivSt_4 0.12 (0.12) SD_EduGr -0.21*** (0.02) SD_Health_2 -0.03 (0.07) SD_Health_3 0.12 (0.10) SD_Health_4 -0.01 (0.13) SD_Gender -0.01 (0.06) SD_Work -0.15** (0.07) _cons 2.24***2.12***1.35***0.86*** (0.20) (0.21)(0.27) Number of obs1636 Adj R-squared0.15 0.210.28 * p<0.1, ** p<0.05, *** p<0.01

13 FactorsCitySat_INDLifeSatHappiness Personal happiness/satisfaction LifeSat 0.05 (0.03) Happiness 0.27***0.19*** (0.02) JIIncSat 0.05**0.42***0.17*** (0.03)(0.02)(0.03) Urban services satisfaction indexes Cu_IND 0.10***0.05**0.19*** (0.03) (0.04) Edu_IND 0.11***0.000.07 (0.04)(0.03)(0.04) Env_IND 0.000.04-0.09** (0.04)(0.03)(0.04) HC_IND 0.10***0.07***0.08** (0.03) (0.04) SS_IND -0.020.17***-0.10** (0.04)(0.03)(0.05) Saf_IND 0.16***0.040.10** (0.04)(0.03)(0.04) Sport_IND 0.07**0.010.06 (0.03) (0.04) Higher School of Economics - Perm Path Analysis Model. DIRECT EFFECTS – ML Estimation (we report B - standardized β) photo HSE, 2014

14 FactorsCitySat_INDLifeSatHappiness Social-demographic characteristics SD_AgeGr 0.27***-0.05*-0.18*** (0.03)(0.02)(0.04) SD_CivSt_2 -0.040.09-0.41*** (0.08)(0.06)(0.09) SD_CivSt_3 -0.05-0.12*-0.32*** (0.09)(0.07)(0.10) SD_CivSt_4 0.12-0.08-0.20 (0.12)(0.09)(0.13) SD_EduGr -0.21***0.05**0.04 (0.02) (0.03) SD_Health_2 -0.03-0.05-0.32*** (0.07)(0.06)(0.08) SD_Health_3 0.12-0.23***-0.31*** (0.10)(0.08)(0.11) SD_Health_4 -0.01-0.15-0.32** (0.13)(0.10)(0.15) SD_Gender -0.01-0.02-0.03 (0.06)(0.05)(0.07) SD_Work -0.15**-0.07-0.21*** (0.07)(0.05)(0.08) Number of obs1636 R-squared0.290.520.16 * p<0.1, ** p<0.05, *** p<0.01 XV April International Academic Conference Path Analysis Model. DIRECT EFFECTS – ML Estimation (continuation) photo HSE, 2014

15 FactorsDIRECTINDIRECTTOTAL Personal happiness/satisfaction LifeSat 0.05 (no path) 0.05 (0.03) Happiness 0.27***0.01***0.28*** (0.02)(0.00)(0.02) JIIncSat 0.05**0.07***0.12*** (0.03)(0.02) Urban services satisfaction indexes Cu_IND 0.10***0.05***0.16*** (0.03)(0.01)(0.04) Edu_IND 0.11***0.020.13*** (0.04)(0.01)(0.04) Env_IND 0.00-0.02*-0.03 (0.04)(0.01)(0.04) HC_IND 0.10***0.03**0.12*** (0.03)(0.01)(0.03) SS_IND -0.02 -0.04 (0.04)(0.01)(0.04) Saf_IND 0.16***0.03**0.19*** (0.04)(0.01)(0.04) Sport_IND 0.07**0.020.08** (0.03)(0.01)(0.04) Higher School of Economics - Perm Path Analysis Model. CitySat equation – ML Estimation (we report B - standardized β) photo HSE, 2014

16 FactorsDIRECTINDIRECTTOTAL Social-demographic characteristics SD_AgeGr 0.27***-0.05***0.21*** (0.03)(0.01)(0.03) SD_CivSt_2 -0.04-0.11***-0.15* (0.08)(0.03)(0.09) SD_CivSt_3 -0.05-0.10***-0.14 (0.09)(0.03)(0.10) SD_CivSt_4 0.12-0.060.06 (0.12)(0.04)(0.12) SD_EduGr -0.21***0.01*-0.19*** (0.02)(0.01)(0.03) SD_Health_2 -0.03-0.09***-0.12 (0.07)(0.02)(0.08) SD_Health_3 0.12-0.10***0.02 (0.10)(0.03)(0.10) SD_Health_4 -0.01-0.10**-0.10 (0.13)(0.04)(0.14) SD_Gender -0.01 -0.02 (0.06)(0.02)(0.07) SD_Work -0.15**-0.06***-0.21*** (0.07)(0.02)(0.07) Number of obs1636 R-squared0.29 * p<0.1, ** p<0.05, *** p<0.01 XV April International Academic Conference Path Analysis Model. CitySat equation – ML Estimation (continuation) photo HSE, 2014

17 Higher School of Economics - Perm Basic results photo HSE, 2014 1.Life satisfaction doesn’t influence City satisfaction, Happiness does 2.Culture, Education, Safety, Healthcare, Sport influence City satisfaction; Environment and Social security doesn’t 3.Path Analysis Model is more appropriate to identify influence of urban services on overall city satisfaction than Linear Regression Model, because we observe significant indirect effects 4.Perm inhabitants are happy people

18 Higher School of Economics - Perm Implementation Issues photo HSE, 2014 The results could be used to put some priorities especially under the pressure of limited budget to promote some urban services in more appropriate way

19 You can contact me via abozhya-volya@hse.ru Higher School of Economics - Perm, 2014 www.hse.ru

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