Essays on inequality
Abstract/Contents
- Abstract
- The widespread prevalence of rising economic inequality across western democracies has led to immense academic and policy interest, as well as the rapid development of the tools required to study it. Researchers are now equipped with rich data and advanced computational methods which are well-suited to analyzing the processes underlying the extensive differences exhibited across individuals and groups within and between societies. To an extent, diverse outcomes reflect an intrinsic natural variation in individual tastes and preferences. However, in many cases we rather consider inequalities, particularly economic inequalities, to reflect injustice, misallocation and constrained opportunities. When considering labor market earnings, a substantial proportion of the variation across individuals can be explained by a single predictor: a worker's gender. In the first chapter of this dissertation I study a policy explicitly designed to reduce this association, in which employers are required to publicly report gender pay gap statistics. Proponents argue that increasing the information available to workers and consumers places pressure on firms to close pay gaps, but opponents argue that such policies are poorly targeted and ineffective. I contribute to the debate by analyzing the UK's recent reporting policy, in which employers are mandated to publicly report simple measures of their gender pay gap each year. Exploiting a discontinuous size threshold in the policy's coverage, I apply a difference-in-difference strategy to linked employer-employee payroll data. I find that the introduction of reporting requirements led to a 1.6 percentage-point narrowing of the gender pay gap at affected employers. This large-magnitude effect is primarily due to a decline in male wages within affected employers and is not caused by a change in the composition of the workforce. To explain this effect, I propose that a worker preference against high pay gap employers induces the closing of pay gaps upon information revelation. Newly-gathered survey evidence shows that female workers in particular exhibit a significant preference for low pay gap employers. In a hypothetical choice experiment, over half of women accept a 2.5\% lower salary to avoid a high pay gap employer. I also demonstrate substantial heterogeneity in the interpretation of pay gap statistics across workers and show that this affects their valuation of jobs at employers with different pay gaps. Does the importance of your family background on how far you get in adulthood also depend on where you grow up? For England and Wales, a paucity of data has made this a difficult question to reliably answer. My second chapter, co-authored with Brian Bell and Stephen Machin, presents a new analysis of intergenerational mobility across three cohorts in England and Wales using linked decennial census microdata. These data permit the study of different mobility outcomes in occupation, home ownership and education, at the spatial level through time. As well as showing national results consistent with previous studies, we find strong sub-regional patterns in mobility, with four main results emerging. First, area-level differences in upward occupational mobility are highly persistent over time. Second, consistent with evidence from other countries, absolute and relative mobility are positively correlated for all measures and particularly strongly for home ownership. Third, there is a robust relationship between upward educational and upward occupational mobility. Last, there is a small negative relationship between upward home ownership mobility and upward occupational mobility, revealing that social mobility comparisons based on different outcomes can have different trends. Social scientists have long been interested in the relationship between parental factors and later child income. Finding the best characterization of this relationship for the question at hand is however fraught with choices. In my third chapter, co-authored with Erling Risa, we use machine learning methods to assess the `completeness' of one popular modelling approach. Here, completeness refers to how well the model summarizes the total predictive relationship between multiple parental factors and a single child outcome. Machine learning methods enable us to depart from functional form assumptions, allowing flexible interactions between a large set of possible parental factors. Using our most flexible complete model as a benchmark, we assess the popular `rank-rank' model relating parent and child incomes. Applying our approach to high-quality Norwegian administrative data, we demonstrate that the rank-rank model explains 68\% of the total explainable variation in child income rank, based on a broad set of potential parental factors entering a neural network. Parental wealth and parental education explain the majority of the remaining explainable variation. At the regional level, we estimate homogeneous completeness across areas. Rankings of areas based on rank-rank slope estimates align with those based on the predictive fit of the broader flexible model.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Blundell, Jack Richard |
|---|---|
| Degree supervisor | Bloom, Nick, 1973- |
| Degree supervisor | Hoxby, Caroline Minter |
| Thesis advisor | Bloom, Nick, 1973- |
| Thesis advisor | Hoxby, Caroline Minter |
| Thesis advisor | Pistaferri, Luigi |
| Degree committee member | Pistaferri, Luigi |
| Associated with | Stanford University, Department of Economics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jack Blundell. |
|---|---|
| Note | Submitted to the Department of Economics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/bq876mk7861 |
Access conditions
- Copyright
- © 2021 by Jack Richard Blundell
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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