Christina Langer
Postdoc in Economics

I am interested in applied microeconomics, economics of education, and labor economics with a focus on future of work research. 

Research

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Two decades ago, companies began adding degree requirements to job descriptions, even though the jobs themselves hadn’t changed. After the Great Recession, many organizations began trying to back away from those requirements. To learn how the effort is going, we study more than 50 million recent job announcements. The bottom line: Many companies are moving away from degree requirements and toward skills-based hiring, especially in middle-skill jobs, which is good for both workers and employers. But more work remains to be done.

Work in Progress

Working Paper (2023), Current Version, Methodological Report

We develop novel measures of worker skills that capture the range, intensity, and specificity of human capital at labor market entry. We leverage Germany’s nationally standardized apprenticeship plans, which detail over 13,000 distinct skills and the training time assigned to each. We also exploit the sequencing of skill instruction in the curricula to distinguish between general (early-stage) and specialized (late-stage) skills within each domain. Following workers over their careers in administrative data, we find that cognitive, social, and digital skills acquired in apprenticeship training are highly rewarded over workers’ careers, while manual and administrative skills do not yield wage gains. Importantly, positive returns are primarily driven by general skills. We show that general skills are highly portable across employers and occupations, helping to explain their persistent productivity effects. We also document employment returns to skills, again largely attributable to their general component. 

Recruiting the “right” talent has become a central challenge since the “Big Shift” to working from home (WFH), one of the most significant transformations in modern labor markets. Using job vacancy data from 2019–2023 and an instrumental variable approach, we estimate how firms’ adoption of WFH changes skill requirements in hiring. Results show that postings offering WFH list a larger number of and more detailed skills: Demand increases for skills complementary to remote production (basic digital proficiency, online communication) and for “character” attributes related to self-management (e.g., ability to work independently), consistent with principal–agent models under moral hazard. Our findings illustrate how the “ideal employee” profile shifts with WFH, documenting organizational adaptations consistent with skill-biased technological change. This has implications for the distribution of economic opportunities: WFH relaxes geographic barriers to high-wage jobs but raises demand for digital and self-management skills, risking exclusion of workers lacking them. The paper thus informs education policy, including curriculum design and reskilling programs to build human capital for the hybrid work economy.

  • Does Working from Home Reduce the Child Penalty? (with Ahmet Gulek)

Child penalty accounts for most of the gender gap in earnings in the developed countries. In this paper, we examine how the recent increase in the availability of remote work has affected mothers’ labor market outcomes. Our identification strategy exploits the heterogeneous rise in remote work across occupations. By comparing child employment penalties across occupations with higher and lower exposure to remote work, before and after its widespread adoption, we find that the availability of remote work decreases child employment penalties for mothers but does not impact the employment penalties for men. We are currently investigating changes in income, hours, and wage penalties, as well as the implications for gender inequality in earnings.

Working Paper

Using internationally harmonized data of over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on workers’ tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. We exploit within-occupation and within-industry variation and employ entropy balancing to assess whether job training mitigates automation risk, We find that job training reduces workers’ automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to training. Jobtraining is effective in reducing automation risk and increasing wages across nearly all countries. Older workers benefit from training just as much as younger workers, and women benefit even more than men. Our findings show that job training mitigates the risks posed by automation across a wide range of countries and populations.

Literature Review

We are currently conducting a large-scale RCT with 500 small and medium businesses (SMEs) in the UK to estimate the causal impact of AI-adoption-focused business training on firm performance and technology adoption. We aim to contribute to the understanding of how structured training interventions can influence technology adoption and business transformation in the context of AI and SMEs. Through our surveys, we further identify the key barriers SMEs face in adopting AI.

Book Chapters

Alipour J.V., Langer. C, and O'Kane L. (2022). Zur Zukunft des Homeoffice. In B. Wawrzyniak & M. Herter (ed.), Neue Dimensionen in Data Science (p. 227-242). Wichmann Fachmedien Berlin - Offenbach.