Research Interests

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My research is at the intersection of labor and innovation aimed at understanding the way we work – how innovation impacts pay, and the role that teamwork plays in the invention process.

I’m primarily interested in the rise of social, creative and stem skills and its impact on wages, and further, within innovation and management/organization fields, the importance of teamwork and skill complementarity within teams. In my previous work, I have looked at three topics: i. the impact of automation on inequality across Europe, ii. the role of teamwork (it’s impact in conjunction with age on lifetime patenting), and iii. the importance of social & communication skills in knowledge diverse industries. My job market paper is on the latter topic.

You can find my job market paper here.

I also have worked on other topics (abstracts and working papers can be found here):

  • Structural change (economic complexity & development)
  • Skills & education mismatch in Brazil
  • Mapping inequality in Brazil

About Mary Kaltenberg

I am currently a post-doctoral fellow at Brandeis University with Adam Jaffe and Margie Lachman working on the inventor creative life cycle with patent data. I was a PhD Fellow at UNU-MERIT, Maastricht, The Netherlands. My doctoral advisors were Bart Verspagen at UNU-MERIT, Neil Foster-McGregor at UNU-MERIT and Cesar Hidalgo at the Collective Learning group at the MIT Media Lab. In 2016, I was a visiting student and research assistant at the MIT Media Lab.

Previously, I worked at UNICEF on resource mobilization and research on accessibility to health care. I received my masters and bachelors degree in economics from The New School for Social Research in New York City.

Check out my Github for code and data from previous papers (currently updating), and code for tutorials for previously taught short courses.

I work on other personal projects/passions:

Working Papers

Working Papers

The Knowledge Manager: Wage Premiums in Knowledge Diverse Industries (Job Market Paper)

Larger industries are known to pay more, but are these premia simply a reflection of industry size, or are they an expression of increased knowledge diversity? As new technologies are utilized within industries, new specializations and domains of knowledge rise. However, the ability of an industry to adopt a new technology is dependent on coordination costs. This paper suggests that firms reduce coordination costs by hiring workers who have social, communication and interpersonal skills. In order to empirically test this theory, I develop a novel way to proxy knowledge diversity with weighted occupation-industry networks, and evaluate these measures effect on wages. The results show that workers in industries with higher occupational variety receive a wage premium, a knowledge diversity premium. Further, jobs with social, communication and interpersonal skills are valuable in sectors that have a high division of knowledge.

Reinventing the Elder Workforce: Creative Productivity Over the Life Course Through the Lens of Patent Data with Jaffe, A., and Lachman, M. (2019) In progress.

Previous research suggests creative ability peaks in the age decades of the 30s and early 40s, and declines thereafter, with some variation across fields. Building from the cognitive aging literature, we expect differences in the rate and quality of creative works by age. Cognitive processes show aging-related changes with increases in experience-based knowledge (pragmatics or crystallized abilities) and decreases in the ability to process novel information quickly and efficiently (mechanics or fluid abilities). We exploit a large database of U.S. inventors and the rich complexity of patent documents to explore both the rate and the nature of creative output over the life course. We extend this dataset by including information about age at patenting by combining public patent data with information on inventor ages scraped from directory websites on the web with approximately 1.2 million inventors patenting between 1976 and 2017. Our results suggest that cross-sectional and within-inventor patenting rates are similar, peaking at around the early 40s for both women and men. We find varying results for attributes of patents in relation to age some of which are consistent with cognitive aging theory. Backward citations and originality, which are connected to experience, were found to peak later in life. Forward citations and generality measures, which are more likely tied to fluid intelligence, peaked at earlier ages. These patent attributes also vary in relation to the age composition of the teams. Future goals are presented including consideration of the implications for work and retirement choices and policies.

Decomposing Inequality Across Europe: The Impact of Automation with N. Foster-McGregor

Previous work has suggested that automation has a potential to disrupt employment in the labor market, as well as, wage earnings. This paper focuses on the latter and finds that the risk of automation has been impacting wage earnings, and consequentially, contributing to rising inequality more than any other factor. We apply a RIF decomposition technique from (Firpo, et. al., 2018) to uncover the determinants of inequality between 2002 and 2014 utilizing the structure of earnings survey (SES) to understand both the composition (number of people hired relative to characteristic) and wage return (changes in earnings) effect of the distribution of earnings for a variety of factors. We find that the characteristic that has the largest impact on inequality across all countries in our sample of European countries is the risk of automation. Automation is increasing inequality due to changes of earnings at the top 20% of the income distribution and there is a general decline of wages due to the composition effect at all distributional points for most countries.

Local labor market and higher education mismatch: What is the role of public and private institutions?  with Ortiz, E.A., Jara-Figueroa, C., Bornacelly, I., and Hartmann, D. (2019) IDB Working Paper

Mismatches in the supply and demand of education and skills can hamper economic development. While most studies focus on skills mismatch at the national level, developing economies tend to have strong regional differences in their labor markets, and thus in the demand of skills. Yet few studies investigate to which extent enrollments in higher education match local labor markets demands. Moreover, there are few empirical studies comparing the effectiveness of private and public higher education institutions in coordinating the regional educational supply and demands. This is an important shortcoming in countries like Brazil where almost 90% of universities are private and strong levels of regional inequality and skills mismatches persist. In this article we analyze a large dataset of higher education enrollment rates in 22 different educational fields across 137 mesoregions of Brazil between 2010 and 2016. Our first stage regressions show that the local labor market conditions are a significant predictor of enrollment rates in different educational fields. Moreover, we identify relative levels of mismatches of each region. Less developed regions in particular show a high number of shortages in enrollments rates and thus supply of advanced skills for their labor markets. In the second stage regressions we analyze the factors explaining relative mismatches. The results show that a high level of urban population, low unemployment, and a large share of public universities are significantly associated with lower mismatches. In contrast, private universities do not significantly reduce, but rather tend to increase relative skills mismatches. In sum, our results imply that educational policies need to take the regional labor markets demand into account or they may perpetuate regional inequalities. Moreover, public universities in Brazil are more effective than private universities in promoting the enrollments in hard sciences and addressing the skills needs of less developed regions.

Mapping Stratification: the industry-occupation space reveals the network structure of inequality with Hartmann, D., Jara-Figueroa, C., & Gala, P. 2019. Working Paper

Social stratification is determined not only by income, education, race, and gender, but also by an individual’s job characteristics and their position in the industrial structure. Utilizing a dataset of 76.6 million Brazilian workers and methods from network science, we map the Brazilian Industry-Occupation Space (BIOS). The BIOS measures the extent to which 600 occupations co-appear in 585 industries, resulting in a complex network that shows how industrial-occupational communities provide important information on the network segmentation of society. Gender, race, education, and income are concentrated unevenly across the core-periphery structure of the BIOS. Moreover, we identify 28 industrial occupational communities from the BIOS network structure and report their contribution to total income inequality in Brazil. Finally, we quantify the relative poverty within these communities. In sum, the BIOS reveals how the coupling of industries and occupations contributes to mapping social stratification and the network structure of inequality.

Exporting Up: The Importance of Improving Technological Capabilities for Growth

What is the best way to measure technological capabilities? Over the past 15 years, technological capability indices have developed into two strains: aggregated capability indices and export based algorithms. We discuss the strength and weaknesses of using such measures and test at which point technological capabilities are important for low income nations to `catch-up’ with developed nations. We explore a variety of econometric estimation techniques including, random effect, fixed effect, Hausman-Taylor and GMM that compare three export based algorithms, economic complexity index, fitness and generalized fitness. Our results indicate that technological capabilities, measured with export based algorithms, contribute to economic growth for low income nations. However, we do not find conclusive evidence that these measures have an impact at all stages of the development process. We suggest that to understand how economic structures impact economic growth, future pathways of research should reevaluate how to measure complexity to include value added which is increasingly fragmented across global production chains, and to measure the complexity of service and knowledge based products which are becoming a pivotal part of economies across the world.

Other Working Papers

Albeaik, S., Kaltenberg, M., Alsaleh, M., and Hidalgo, C. A. (2017). 729 New Measures of Economic Complexity. arXiv preprint arXiv:1708.04107.

Albeaik, S., Kaltenberg, M., Alsaleh, M., and Hidalgo, C. A. (2017). Improving the Economic Complexity Index. arXiv preprint arXiv:1707.05826.

Jun, B., Kaltenberg, M. and Won-Sik, H. (2017). How Inequality Hurts Growth: Revisiting the Galor-Zeira model using the Korean Case. working paper.

Verspagen, B. & Kaltenberg, M., (2015). Catching-up in a globalised context: Technological change as a driver of growth, UNIDO Working Paper 20 | 2015.
Download it here or here

Technical Reports
Industrial Development Report 2016. The Role of Technology and Innovation in Inclusive and Sustainable Industrial Development. “Technological change, structural transformation and economic growth,” Vienna, UNIDO.

Hartmann, D., Jara-Figueroa, C. and Kaltenberg, M., 2017, The Brazilian Industry-Occupation Space: Structural Heterogeneity and the regional skills demand. IADB Technical Report.


Teaching Philosophy

The goal of economics is to make sense of the social world around us. I believe the best way to teach economics is through applied real world examples in an active environment. My pedagogical approach is to challenge students by asking questions and utilize classroom activities that reiterate the learning objective. This is especially important when teaching econometrics or statistics – the power of these tools is not clearly visible in theory alone, but also through application. Students also learn better when learning objectives are repeated in different ways – data collecting at the lecture, interactive websites, short videos at home, writing reports and problems sets. Learning is best applied in a community of active and engaged students. My goal as a teacher is to foster this kind of environment.

Teaching Experience

Maastricht School of Governance, Master of Science in Public Policy

Introduction to Statistics (Fall 2014), TA

Introduction to Data Science (Fall 2015)
Feel free to request the syllabus and do files (Stata)

Introduction to Econometrics (Fall 2014 and 2015), TA

Intermediate Econometrics (Parallel Course to Intro to Econometrics) (Fall 2015), TA

Maastricht School of Governance, GPAC (PhD Program)

Intuition to Panel Data (Short Course 2017)
You can find the syllabus here
Feel free to request the do files (Stata)

Introduction to Stata (Short Course 2017)
You can check out the do files based on the course on my Github here

UNU-MERIT, (PhD Program)

Introduction to Python for Economists (Short Course 2017)
For jupytr notebook with the Python code, see my Github here

I am happy to provide teacher evaluations for all courses when available/applicable



kaltenberg [at] merit [dot] unu [dot] edu
mkaltenberg [at] brandeis [dot] edu
mary.kaltenberg [at] maastrichtuniversity [dot] nl


University Address

Brandeis University
Brown 125
MS 062
Brandeis University
415 South Street
Waltham MA 02453-2728