Comparable Estimates of Intergenerational Income Mobility in Italy

Piraino, Patrizio

doi:10.2202/1935-1682.1711

Cited by 74 publications

(92 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…11 Among others, in Gouskova et al (2010) for the US; Björklund et al (2006 Raaum et al (2007) for Denmark, Finland, Norway, the UK and the US; Nicoletti and Ermisch (2007) for the UK; Piraino (2007) and Mocetti (2007) for Italy. More examples are covered in the surveys of and Black and Devereux (2011).…”

Section: The Generalized Errors-in-variables Modelmentioning

confidence: 99%

Heterogeneous Income Profiles and Lifecycle Bias in Intergenerational Mobility Estimation

2016

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. www.econstor.eu Research on intergenerational income mobility is based on current income since data on lifetime income are typically not available for two generations. However, using snapshots of income over shorter periods causes a so-called life-cycle bias if the snapshots cannot mimic lifetime outcomes. Using uniquely long series of Swedish income data, we show that current empirical strategies do not eliminate such bias. We focus on the widely adopted generalized errors-in-variables model and find that the remaining bias is substantial (20% of the true elasticity from left-side measurement error at the most relevant age range). IV estimates suffer from even stronger life-cycle effects and do not provide an upper bound. Inconsistencies stem from the interaction of two factors: heterogeneity in income profiles cannot be fully accounted for, and idiosyncratic deviations from average profiles correlate with individual characteristics and family background. We discuss implications of our findings for other literatures that depend on measurement of long-run income and income dynamics. Terms of use: Documents in D I S C U S S I O N P A P E R S E R I E SJEL Classification: J62, D3, D31

show abstract

Section: The Generalized Errors-in-variables Modelmentioning

confidence: 99%

Heterogeneous Income Profiles and Lifecycle Bias in Intergenerational Mobility Estimation

2016

View full text Add to dashboard Cite

show abstract

“…That is, the mobility in Korea is relatively higher than other developing countries (e.g., 0.69 in Brazil and 0.52 in Chile). 21 Some studies, for instance Piraino (2007) in Italy, investigated the channels in the transmission of economic status and found parental education's contribution to the intergenerational mobility. Korea went through a great expansion in education in the last few decades, and the parent-child schooling correlation among 20-69 sons in 2008 is only 0.333, one of the lowest values according to Hertz et al (2008).…”

Section: The Role Of Hiesmentioning

confidence: 99%

Intergenerational mobility in Korea

Kim

2017

IZA J Develop Migration

View full text Add to dashboard Cite

This study investigates intergenerational earnings mobility in Korea for sons born between 1958 and 1973 and compares Korea's mobility to that of other nations. It uses data from the Korea Labor and Income Panel Study and the Household Income and Expenditure Survey conducted by the Korean National Statistics Bureau. Since no single Korean dataset includes information on both sons' and their fathers' adult earnings, this study follows the two-sample approach previously applied in Korea by Ueda (J Asian Econ 1-22, 2013), whose estimated intergenerational earnings elasticity is 0.22, and extends the analysis by using fathers' earnings from a more approximal cohort. The estimate of around 0.4 is similar to estimates for some already developed countries and smaller than typical estimates for recently developing countries. JEL classification: J62, J31

show abstract

“…Björklund and Jäntti (1997) 3. broad education, occupation and industry -e.g. Piraino (2007) 4. broad education and detailed (3 digit) occupation -e.g. Leigh (2007) This guides the combination of Z used in this paper.…”

Section: Methodsmentioning

confidence: 99%

Two-Sample Two-Stage Least Squares (TSTSLS) Estimates of Earnings Mobility: How Consistent are They?

2014

View full text Add to dashboard Cite

Academics and policymakers have shown great interest in cross-national comparisons of intergenerational earnings mobility. However, producing consistent and comparable estimates of earnings mobility is not a trivial task. In most countries researchers are unable to observe earnings information for two generations. They are thus forced to rely upon imputed data from different surveys instead. This paper builds upon previous work by considering the consistency of the intergenerational correlation (ρ) as well as the elasticity (β), how this changes when using a range of different instrumental (imputer) variables, and highlighting an important but infrequently discussed measurement issue. Our key finding is that, while TSTSLS estimates of β and ρ are both likely to be inconsistent, the magnitude of this problem is much greater for the former than it is for the latter. We conclude by offering advice on estimating earnings mobility using this methodology.

show abstract

Comparable Estimates of Intergenerational Income Mobility in Italy

Cited by 74 publications

References 28 publications

Heterogeneous Income Profiles and Lifecycle Bias in Intergenerational Mobility Estimation

Heterogeneous Income Profiles and Lifecycle Bias in Intergenerational Mobility Estimation

Intergenerational mobility in Korea

Two-Sample Two-Stage Least Squares (TSTSLS) Estimates of Earnings Mobility: How Consistent are They?

Contact Info

Product

Resources

About