Research Interests


My research is at the intersection of labor and innovation aimed at understanding what impacts inventors and the innovation process.

I’ve worked in three main areas: i. inventors and the invention process, iii. how skills impact inequality , and iii. economic complexity. I integrate a variety of statistical techniques including applied microeconomics and network theory to understand labor and innovation.

Currently, I am an Assistant Professor of Economics at Pace University in New York, NY. I primarily teach quantitative methods in economics.

About Mary Kaltenberg

I am currently an Assistant Professor of Economics at Pace University. Previously, I was a post-doctoral fellow at Brandeis University with Adam Jaffe and Margie Lachman working on the inventor creative life cycle with patent data. I did my PhD at UNU-MERIT (Maastricht University), Maastricht, The Netherlands. My doctoral advisors were Bart Verspagen (UNU-MERIT), Neil Foster-McGregor (UNU-MERIT) and Cesar Hidalgo at the Collective Learning group at the MIT Media Lab (now at the University of Toulouse). 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 (BA/MA program) .

Check out my Github for code and data from previous papers and materials for some courses that I teach.

I work on other personal projects/passions:

Funded Grants/Projects

  • SAFE (Safety Awareness for Empowerment): Development of an intelligent ChatBot for survivor resource assistance and NLP research on sexual assault (Supported by SWOL Limburg Fund and Diversity & Inclusiveness Grant, Maastricht University)
  • Check out the associated pre-print here. And some media mentions here and here (in Dutch)

Leadership and Community Service

Personal Projects

  • Cookbook, From Siberia to Texas: An Immigrant’s Collected Recipes
    Listen to my PechaKucha talk about it.
  • 3 time winner of the Red Sox dance off competition (2019).


Published Works

Kaltenberg, M., Jaffe, A. B., & Lachman, M. E. (2023). Invention and the life course: Age differences in patenting. Research policy52(1), 104629.

Jun, B., Kaltenberg, M. and Won-Sik, H. (2021). How Inequality Hurts Growth: Revisiting the Galor-Zeira model using the Korean Case. Pacific Economic Review. Link here.

Working Papers

Invention & Labor 

Kaltenberg, Mary and Jaffe, Adam B. and Lachman, Margie, Invention and the Life Course: Age Differences in Patenting (May 2021). NBER Working Paper No. w28769. Get it here.

Kaltenberg, Mary and Jaffe, Adam B. and Lachman, Margie, Invention and the Life Course: Age Differences in Patenting (May 2021). NBER Working Paper No. w28769. Get it here.

Accompanying dataset available on dataverse. Get it here.  For questions or comments on the data set, please feel free to email me (mkaltenberg [at] pace [dot] edu).

Work in Progress:

Does Maternity Hold Back ‘Marie Curies’? with Ling Zhou

Female invention participation has steadily grown in the US over the past few decades, but the gender innovation gap remains substantial. This growth in partic- ipation corresponds with an overall increase of female labor force participation and changes in maternity leave policies. Using inventor data from patents from the US Patent and Trademark Office, this paper seeks to evaluate the impact of maternity leave polities in innovation related jobs in two particular perspectives, exit decisions and productivity of female inventors of child-bearing age. Our findings suggest that maternity leave policies promote retention of female inventors, but these policies have little impact on increasing productivity.

Inequality & Labor

Decomposing Inequality Across Europe: The Impact of Automation with N. Foster-McGregor. Working paper is here.  Insight discussed in this blog.

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 is here.

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

Work in Progress:

The Knowledge Manager: Wage Premiums in Knowledge Diverse Industries

Larger industries are known to pay more, but are these premiums simply a reflection of industry size, or are they an expression of increased knowledge diversity? Firms operate much like a team – they coordinate a variety of tasks and specializations to produce a good or service. In order to improve productivity, firms will seek to improve the efficiency of a task. Some ways that firms can do this is adopting a new technology, reduce coordination costs, and hire effective communicators. Firms that have to manage a wide variety of knowledge specializations have higher coordination costs, and these costs are especially high for firms that combine knowledge that are relatively dissimilar from one another. Firms that hire individuals who are effective communicators can reduce their burdening coordination costs, and are therefore willing hire individuals who have more social skills at a higher wage premium. I test this theory at the industry level, where I can empirically observe varying degrees of specialization. I develop a novel way to approximate knowledge specialization using an occupation-industry network. This network allows me to capture the variety of specializations in and industry, and the relative knowledge base distance between two occupations. At the industry level, workers who have social and communication skills will sort to industries that have a higher diversity of knowledge because those industries are willing to pay a knowledge diversity premium to reduce their high coordination costs. My results show that workers in industries with higher knowledge diversity receive a wage premium, especially for jobs with social, communication and interpersonal skills.

Economic Complexity

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.

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

Work in Progress:

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.

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

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 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.

Courses at Pace University


ECO400 (Fall) Senior Capstone in Economics

ECO240 Quantitative Analysis

ECO270 Economics of the Internet

ECO396R Python & R for Data Analysis

ECO106 Introduction to Microeconomics


ECO590 Python & R for Data Analysis (Spring) Github notes can be found here

ECO585 Applied Econometrics (Fall) Github notes can be found here

Previous 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

Courses I never taught, but prepared a syllabus and hope it’s some use to the world:

Micro for a Digitized Economy (Graduate Level)
Applied Microeconomics – Digitized Economies

Applied Empirical Microeconomics of Economics (Graduate Level)
Applied Microeconomics



mkaltenberg [at] brandeis [dot] edu


University Address

Pace University
41 Park Row, 11th Flr.
New York, NY 10038