Intergenerational mobility – the degree to which people can succeed regardless of family background – is a cornerstone of equal opportunity. Traditionally, education has been seen as the main lever for upward mobility, but university enrollment has plateaued in many developed countries since the early 2000s, and college wage premiums have stagnated or declined. Despite the centrality of changing employment structures in explanations of wage inequality, the labor market itself has been largely absent from mobility research. MaMo aims to correct this by centering employers and workplaces as key sites where economic advantage is inherited across generations.

Comparative Design and Data

The project uses at its core high-quality administrative data in Sweden and several other countries. In the latest iteration, the team is building a large-scale collaboration involving researchers with administrative data in up to 20 different countries. These are supplemented by cross-national surveys, longitudinal panel data, labor force surveys, digital trace data, and business statistics.

Subproject 1: Sorting to Employers

This subproject asks how and why children from privileged backgrounds end up at higher-paying firms. It examines the role of local labor market segmentation – how regional polarization and industrial decline propagate into income persistence – and the influence of parental social networks, tracing whether children are hired at firms where parents’ former classmates or coworkers are employed (versus “placebo” contacts with no real social tie). It also investigates how elite educational institutions channel graduates toward high-status employers through old-boy networks and on-campus recruiting, using LinkedIn data across countries. Finally, it tests for class bias in hiring by studying whether managers favor candidates from similar backgrounds, using event-study designs around manager turnover.

Subproject 2: Career Progression

This subproject shifts from initial job placement to what happens over a career. It maps earnings trajectories, decomposing contributions of individual attributes, occupation, industry, and job shifts using growth curve models. It examines whether opportunities for within-firm promotion have declined over time – exploiting administrative data stretching back to the 1970s – and whether workers who share a class background or social settings with their managers receive faster wage growth, an empirical test of Weberian “patrimonialism.” It also asks whether workers from disadvantaged backgrounds are more vulnerable to career shocks such as recessions, layoffs, illness, and parental leave.

Subproject 3: Mechanisms and Policy

This subproject evaluates what can be done. It documents how job search methods (formal applications, networks, referrals) differ by social background across countries and time. It couples mobility statistics with longitudinal policy data on minimum wages, employment protection, collective bargaining, and right-to-work laws, using difference-in-differences designs with staggered treatment timing. It estimates the effects of managerial practices – pay transparency, worker representation, promotion systems, diversity initiatives – on pay equity using marginal structural models. And it evaluates the UK civil service’s 2015 adoption of name-blind recruitment as a natural experiment for whether blind hiring diversifies recruitment along socioeconomic lines.

Methods

The project deploys a wide toolkit: AKM earnings decomposition, difference-in-differences and event studies, growth curve and multilevel models, within-family (sibling) comparisons, network analysis, shift-share instruments, decomposition methods, counterfactual simulations, and other methods.