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

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

We develop novel measures of worker skills that depict the full range and intensity of human capital at labor market entry. We exploit that skill requirements of apprenticeships in Germany are codified in state-approved, nationally standardized apprenticeship plans. These plans provide more than 13,000 different skills and the exact duration of learning each skill. Following workers over their careers in administrative data, we find that cognitive, social, and digital skills acquired during apprenticeship are highly – yet differently – rewarded. We also document rising returns to digital and social skills since the 1990s, while returns to cognitive skills have increased only moderately.

The Covid-19 pandemic led to a surge in working from home (WFH). We study the development and consequences of remote work in Germany before and during the Covid-19 recession using over 67 million online job vacancy postings from Lightcast. We classify a posting as having a WFH option if specific WFH-related terms occur in the raw text job description. From 2019 to 2022, we document a five-fold increase in WFH and convergence across regions, industries, and occupations. We show that skill requirements in job vacancy postings change when employers add a WFH option, demanding more social, management, basic digital, and applied digital skills.

  • 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.

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.

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.