Studying life satisfaction determinants of Brazilian workers using Wage Indicator Data Martin Guzi, Pablo de Pedraza Paulo do Valle & Mariana Rehder Amsterdam, August 2013

Studying life satisfaction using Brazilian Wage Indicator 1.- Working conditions and relative terms as SWB determinants 2.- Advantages, disadvantages and solutions of web surveys: The Wage Indicator Brazilian sample and the PSA 3.- Estimations and Results 4.- Future: Webdatanet and tapping in to web data in Applied Economics

Studying life satisfaction using Brazilian Wage Indicator 1.- Working conditions and relative terms as SWB determinants 2.- Advantages, disadvantages and solutions of web surveys: The Wage Indicator Brazilian sample and the PSA 3.- Estimations and Results 4.- Future: Webdatanet and tapping in to web data in Applied Economics

Studying life satisfaction using Brazilian Wage Indicator 1.- Working conditions and relative terms as SWB determinants - 3 domains of SWB: Life, work-family combination, job. Working conditions 8 hours a day Unpleasant activity Studied in job satisfaction Spill over effect or not Comparative terms Income Status effect (envy) Social mobility effect (ambition) Not many studies focusing on emerging economies (Akay et al. 2011) SWB questions not included in Brazilian National surveys World Value Survey (1995, 2005), small sample.

Studying life satisfaction using Brazilian Wage Indicator 1.- Working conditions and relative terms as SWB determinants 2.- Advantages, disadvantages and solutions of web surveys: The Wage Indicator Brazilian sample and the PSA 3.- Estimations and Results 4.- Future: Webdatanet and tapping in to web data in Applied Economics

Studying life satisfaction using Brazilian Wage Indicator 2.- Advantages, disadvantages and solutions of web surveys: The Wage Indicator Brazilian sample and the PSA Weaknesses Sources of error (sampling, non response, coverage…) Test data quality Benchmarking with LFS, Census (Pedraza et al. 2010) Testing theoretical models (Bustillo & Pedraza 2010) Similarly as: Internet activity (Zimmerman & Askitas) Improving proportionality Model based approach Design based approach (PSA) Strengths Cost Speed Large numbers (N=39 000) Multi-country (70) Multi-lingual Quasi- global Complementary to other types of web data (surveys, non-reactive, testing, experimenting)

Studying life satisfaction using Brazilian WageIndicator 2.- Advantages, disadvantages and solutions of web surveys: The Wage Indicator Brazilian sample and the PSA Propensity Score Adjustment Merge age WageIndicator with census and calculate the probability of participating in the surveys. (gender, education, region, age) Use the inverse probability to calibrate the sample

Studying life satisfaction using Brazilian Wage Indicator 2.- Advantages, disadvantages and solutions of web surveys: The Wage Indicator Brazilian sample and the PSA Census 2010WIWI with PSA mean Female Age Age Age Age Age Edu: Primary Edu: Secondary Edu: First tertiary Edu: second tertiary North North-east South-east South Central-west

Studying life satisfaction using Brazilian Wage Indicator 3.- Estimations and Results Being Z a set of controls where we include labour conditions

Studying life satisfaction using Brazilian Wage Indicator satlife2 N=39579 satlife3 N=39579 satcom1 N=18859 satcom2 N=18859 satjob1 N=19206 satjob2 N=19206 b/se Female Edu: Primary ref. Edu: Secondary Edu: First tertiary Edu: second tertiary ** * * Age ref. Age Age ***-0.521*** Age * Age **0.658*0.726***0.638***0.564*0.638** Single ref. Married 0.447***0.38*** *0.187** Widowed **-0.641** Divorced **-0.458** Self-employed 1.481***1.66***0.618**0.846***0.966***1.126*** Foreign-born

Studying life satisfaction using Brazilian Wage Indicator satlife2 satlife3 satcom1 satcom2 satjob1 satjob2 year== **-0.936** year== ***-1.028*** year== year== year== *-0.423* year== * year== North0.727**0.79** North-east **-0.58***-0.593***-0.387*-0.332* South-east ref. South **-0.351** Central-west

Studying life satisfaction using Brazilian Wage Indicator satlife2 satlife3 satcom1 satcom2 satjob1 satjob2 Permanent contract 0.293* 0.432*** 0.208* Works >50hrs * Supervisory position 0.289** * Firm multinational 0.316** Work: commutes 30-60min ** Work: commutes > ** *** Changed employed in last 2 years *** FIRMSIZE== ** FIRMSIZE== ** FIRMSIZE== ** * Constant19.762***20.112***16.748***16.752***8.827*7.482* r N Spill over Permanent contract Long working hours Long commutes (>60mnts) Supervisory position Affecting differently Long working hours Multinational Short Commutes (30-60mnts) Change employer

Studying life satisfaction using Brazilian Wage Indicator satlife2 satlife3 satcom1 satcom2 satjob1 satjob2 Log personal income (monthly gross)0.507***0.423***0.226***0.203**0.413***0.426*** Log relative income (mean region)-2.173**-2.197***-1.909***-1.892***-1.164*-1.053* agricult, manufacturing, construction ref. trade, transport, hospitality-0.345** **-0.297** commercial services public sector, health care, education White ref. black mixed0.286**0.298** * other0.457**0.451** Lives in city or suburb0.294**0.325** Strong status effect specially in Life satisfaction with workers from same region: Identify other reference more specific groups: Same region & age Same region & habitat (urban, rural, suburbs) Same region & gender

Studying life satisfaction using Brazilian Wage Indicator Absolut income0.368***0.367***0.382***0.377***0.385***0.379***0.374***0.37*** RI by district (mean)-2.059** RI by district (median) RI by district x age (2 groups) *** 0.26 RI by district x age (2 groups) *** RI by district x citysize (4 groups) ** RI by district x citysize (4 groups) RI by district x gender * RI by district x gender R N44439

Studying life satisfaction using Brazilian Wage Indicator inlcuding job characteristics Absolut income0.321***0.323***0.336***0.333***0.331***0.327***0.328***0.326*** RI by district (mean)-1.754** 0.86 RI by district (median) RI by district x age (2 groups) *** RI by district x age (2 groups) *** RI by district x citysize (4 groups) RI by district x citysize (4 groups) RI by district x gender RI by district x gender R N37220 Once introducing working conditions only Region & age

Studying life satisfaction using Brazilian Wage Indicator 3.- Estimations and Results Conclusions Next steps -Explore more reference groups to study relative terms including working conditions. -Region specific regressions. -Regional level variables (unemployment level, employment flows, HDI) -Continue exploring use of web data in Applied Economics

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