| NSF Org: |
SES Division of Social and Economic Sciences |
| Recipient: |
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| Initial Amendment Date: | January 26, 2017 |
| Latest Amendment Date: | January 26, 2017 |
| Award Number: | 1658827 |
| Award Instrument: | Standard Grant |
| Program Manager: |
seung hong
SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | March 1, 2017 |
| End Date: | February 28, 2018 (Estimated) |
| Total Intended Award Amount: | $18,240.00 |
| Total Awarded Amount to Date: | $18,240.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
615 W 131ST ST NEW YORK NY US 10027-7922 (212)854-6851 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
NY US 10027-7922 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Economics |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
This project investigates how the provision of urban transportation infrastructure affect growth within cities and property values. Quantifying the effect of transportation projects on city growth and property values has proven difficult for at least two reasons. First, transportation infrastructure may produce growth and a rise in property values, but the latter may also lead to transportation projects. Second, some of the largest and most interesting transportation projects were undertaken long before the advent of computers, so that the associated data has been locked up in disparate paper records. To address these issues, the investigator exploits a unique historical setting in the introduction of the mass-public transit infrastructure in New York City, and further creates novel datasets that have not existed in machine-readable form. These new datasets provide a very high level of geographic resolution and enable the study of spatial economic interactions at a similarly high level of resolution within the city. This extensive database helps shed light on the evolution of spatial development in the city as transportation infrastructure is developed.
This project develops a structural model describing the expansion of the transit network and associated reorganization of economic activities within the city. To achieve this goal, the investigator (1) constructs an integrated spatial framework that explicitly models the impact of transit shocks on location choices of households and firms; (2) digitizes and creates a novel dataset of the late nineteenth and twentieth century New York City and its surrounding areas; (3) takes the data to the model and applies the framework to measure the impact of urban transit infrastructure; (4) develops an identification strategy that allows the estimated impacts to have a causal interpretation. This project provides an integrated framework that can be applicable to many fields where spatial linkages are present, contributing to integrate the fields of trade, economic geography, and urban economics in a unified framework. Results and tools developed from this proposal can be readily incorporated to other circumstances, enabling more theoretically-grounded empirical analyses. Using this structural estimation approach, researchers can conduct both retrospective and prospective policy analysis. Finally, the database created from this project can allow researchers to investigate complex general equilibrium effects in the presence of multiple spatial linkages.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project investigates how the provision of urban transportation infrastructure affect growth within cities and property values, and internal migration. Quantifying the effect of transportation projects on this topic has proven difficult for at least three reasons; first, transportation infrastructure may produce growth and a rise in property values, but the latter may also lead to transportation projects; second, some of the largest and most interesting transportation projects were undertaken long before the advent of computers, so that the associated data has been locked up in disparate paper records; third, individual-level records exist primarily in cross-sectional form and absence of time-invariant individuals identifiers (i.e. social security number) has been a barrier to follow individuals over time.
This project tackles such difficulties through the implementation of frontier digital technology including optical character recognition software, Geographic Information System-based spatial analyses, and artificial intelligence called “machine learning” and creates a massive US historical database to understand how the introduction of the mass-public transit infrastructure in New York City had impacted people with different levels of skill differentially, the city’s economic growth by reducing the spatial frictions in the city. These new datasets provide a very high level of geographic resolution and enable the study of spatial economic interactions at a similarly high level of resolution within the city. This extensive database helps shed light on the evolution of spatial development in the city as transportation infrastructure is developed.
This project develops tools and database to capture the expansion of the transit network and associated reorganization of economic activities within the city. The investigator has accomplished these goals by (1) constructing an integrated spatial framework that explicitly models the impact of transit shocks on location choices of households and follows individuals through constructed longitudinal data from US Census Federal Demographic records; (2) creating novel database of the late nineteenth and twentieth-century New York City and its surrounding areas; (3) developing an identification strategy that allows the estimated impacts to have a causal interpretation.
This project provides an integrated framework that can be applicable to many fields where spatial linkages are present, contributing to integrate the fields of economic geography, urban economics, history, and sociology. Results and tools developed from this proposal can be readily incorporated into other circumstances, enabling more theoretically-grounded empirical analyses. Finally, the database created from this project can allow researchers to investigate complex general equilibrium effects in the presence of multiple spatial linkages.
Last Modified: 06/29/2018
Modified by: Sun Kyoung Lee
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