Periodic Reporting for period 4 - HMTG (Historical Migrations, Trade, and Growth)
Période du rapport: 2024-12-01 au 2025-05-31
This project brings together insights from economic history, social psychology, and economics, to enhance our understanding of the impact of historical and contemporaneous migrations on economic growth, prejudice, and altruism. It is articulated around several related projects.
“Immigration, Innovation, and Growth” quantifies the impact of immigration on innovation and growth. We leverage 130 of historical migrations to the United States and show how to use historical settlements to identify contemporaneous immigration shocks. A structural model of endogenous migrations and growth suggests that the large inflow of foreign migrants to the US since 1965 may have contributed to an additional 8% growth in innovation.
“The Immigrant Next Door” explores how decades-long exposure to individuals of foreign descent shapes natives’ attitudes and behavior toward that group. Contact with populations of foreign origins increases generosity towards foreign causes through charitable donations, and reduces prejudice against foreign cultures, in particular Arab-Muslims.
“Trade and the End of Antiquity” uses data on ancient coins to reconstruct granular time-series of trade and real consumption around the extended Mediterranean from the 4th to the 10th Century and quantifies the determinants of changes in ancient consumption.
“Very Long Run Growth” studies the process of technological progress and diffusion at the core of economic growth over very long periods (millennia) and over vast geographic areas (continents). To identify the timing, location, and subsequent diffusion of individual innovations, we collect systematic information on tens of millions of historical artefacts housed in art and history museums throughout the world. Leveraging tools from Natural Language Processing, we match the textual description of historical artefacts to a precise time, location, and to historical technology classes identified by historians, allowing us to track the geographic diffusion of innovations.
Many countries face growing challenges surrounding immigrant integration and native backlash. Understanding the qualitative and quantitative impact of migrations, both in the short and long run, is therefore of primary importance. While recent work has shown that intergroup contact (e.g. between natives and migrants) has short run impacts on attitudes and behavior, little is known about the long run consequences of immigration. This proposal makes progress on this important question.
“Immigration, Innovation, and Growth” quantifies the impact of immigration on innovation and growth. We leverage 130 of historical migrations to the United States and show how to use historical settlements to identify contemporaneous immigration shocks. A structural model of endogenous migrations and growth suggests that the large inflow of foreign migrants to the US since 1965 may have contributed to an additional 8% growth in innovation.
“The Immigrant Next Door” explores how decades-long exposure to individuals of foreign descent shapes natives’ attitudes and behavior toward that group. Contact with populations of foreign origins increases generosity towards foreign causes through charitable donations, and reduces prejudice against foreign cultures, in particular Arab-Muslims.
“Trade and the End of Antiquity” uses data on ancient coins to reconstruct granular time-series of trade and real consumption around the extended Mediterranean from the 4th to the 10th Century and quantifies the determinants of changes in ancient consumption.
“Very Long Run Growth” studies the process of technological progress and diffusion at the core of economic growth over very long periods (millennia) and over vast geographic areas (continents). To identify the timing, location, and subsequent diffusion of individual innovations, we collect systematic information on tens of millions of historical artefacts housed in art and history museums throughout the world. Leveraging tools from Natural Language Processing, we match the textual description of historical artefacts to a precise time, location, and to historical technology classes identified by historians, allowing us to track the geographic diffusion of innovations.
Many countries face growing challenges surrounding immigrant integration and native backlash. Understanding the qualitative and quantitative impact of migrations, both in the short and long run, is therefore of primary importance. While recent work has shown that intergroup contact (e.g. between natives and migrants) has short run impacts on attitudes and behavior, little is known about the long run consequences of immigration. This proposal makes progress on this important question.
The main results from this project are a series of publications in general audience economic journals. “Immigration, Innovation, and Growth” is conditionally accepted at the American Economic Review, “The Immigrant Next Door” is published in the American Economic Review (2024), and “Trade and the End of Antiquity” is under revision at Econometrica.
The results were also disseminated through a series of conferences and seminars in Europe and the US.
Finally, this project has strengthened a community of young researchers in Paris working on topics related to economic history, trade and growth (junior faculty, pre-doctoral students, graduate students, and post-doctoral students).
The results were also disseminated through a series of conferences and seminars in Europe and the US.
Finally, this project has strengthened a community of young researchers in Paris working on topics related to economic history, trade and growth (junior faculty, pre-doctoral students, graduate students, and post-doctoral students).
The project is going beyond the state of the art in several dimensions. It offers novel tools to collect, organize, and use novel sources of historical data, using state-of-the-art methods in machine learning and natural language processing. Moreover, it shows how to combine experimental, observational, and structural estimation techniques together to shed light on unconventional questions.