Intergenerational mobility in the very long run: Florence 1427–2011

★

☆

SHARE

Overview

Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

Good to know

Topic Hierarchy

File Information

Paper Summary

Paperzilla title

Generations Later, Florentines Still Stuck with Grandma's Rank (By Surname!)

This paper investigates intergenerational socioeconomic mobility in Florence over six centuries using surname-based pseudo-links from tax records (1427 and 2011). It reveals that mobility was surprisingly low in pre-industrial Florence, with significant persistence in both earnings and wealth across generations, particularly in elite occupations. The findings suggest that social status can be highly persistent over very long periods, with a slower convergence of initial statuses than often estimated.

Possible Conflicts of Interest

Authors Guglielmo Barone and Sauro Mocetti are affiliated with the Bank of Italy. The paper extensively uses tax records and discusses economic mobility and inequality. While this is a historical economic study and not directly influencing current policy, working for a central bank on a topic involving economic data and societal stratification represents a potential (though likely academic and not nefarious) conflict of interest due to institutional affiliation and access to data/influence on economic narratives.

Identified Weaknesses

Reliance on Surname-Grouped Estimator for Intergenerational Links

The study uses surnames to create 'pseudo-links' between ancestors and descendants. While common in historical studies, this method has known biases (upward or downward) because it's not individual-based. The paper acknowledges it's a mix of 'inclusive' and 'leave-out' cases without clear priors on bias direction and discusses that persistent differences between surnames can drive up intergenerational correlation.

Selection Bias due to Data Availability and Migration

The dataset only includes surnames present in both 1427 and 2011 Florence, meaning families who migrated away or whose surnames died out are excluded. This can introduce selection bias, as surviving and missing families may have different characteristics and mobility patterns, potentially affecting the generalizability of the findings, despite robustness checks.

Privacy Restrictions Limiting Sample Representativeness

To comply with privacy rules, surnames with fewer than five occurrences in 2011 were excluded. This could potentially skew the sample, as less common surnames might represent different socioeconomic groups or mobility experiences, despite the authors arguing the impact is negligible.

Generalizability to Other Regions/Societies

The study focuses exclusively on Florence. While the authors argue that 15th-century Florence was comparable to other pre-industrial Western European societies in terms of inequality, this remains an inference. The specific institutional and cultural context of Florence might limit the direct applicability of these very long-run persistence findings elsewhere.

Strong Assumption in Robustness Check for Migrants

In a robustness check to address potential bias from missing families (migrants), the authors assume that for these families, the intergenerational elasticity is zero, meaning they completely broke free from socioeconomic inheritance. The paper explicitly states this is the 'most unfavorable assumption' and 'not very plausible,' indicating a significant, albeit necessary for the test, simplification.

Rating Explanation

This is a strong historical economic study utilizing a unique, very long-run dataset (600+ years) to investigate intergenerational mobility. The methodology, while innovative, involves known limitations of surname-grouped estimators and potential selection biases. However, the authors are diligent in acknowledging these weaknesses and performing extensive robustness checks. The findings on persistent social status in pre-industrial Florence are compelling and contribute meaningfully to economic history, making it a valuable contribution despite its inherent challenges.

Good to know

This is our free standard analysis. Paperzilla Pro fact-checks every citation, researches author backgrounds and funding sources, and uses advanced AI reasoning for more thorough insights.

Explore Pro →