dc.contributor.advisor | Hochschild, Jennifer | |
dc.contributor.author | Sharma, Rohit C | |
dc.date.accessioned | 2024-05-04T12:03:23Z | |
dc.date.created | 2024 | |
dc.date.issued | 2024-05-03 | |
dc.date.submitted | 2024 | |
dc.identifier.citation | Sharma, Rohit C. 2024. Forecasting the Future: How Social Mood Shapes America's Immigration Landscape. Master's thesis, Harvard University Division of Continuing Education. | |
dc.identifier.other | 31147105 | |
dc.identifier.uri | https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37378446 | * |
dc.description.abstract | The relationship between social mood and immigration policy in the United States
is investigated in this thesis, focusing on key legislation such as the Immigration Act of
1924 (Johnson-Reed Act), the Immigration Act of 1965, and the 2010 Arizona SB 1070.
Central to this investigation is the creation of the “TrendFusion Forecaster,” an analytical
tool used to quantify social mood by combining key indicators from economic factors,
demographics, and political climate, thereby shaping public sentiment towards potential
immigration policy changes.
The model's key findings reveal that the Immigration Act of 1924 scored 34,
indicating a restrictive policy environment; the Immigration Act of 1965 scored 57,
reflecting an open policy stance; and the 2010 Arizona SB 1070 scored 32, again
suggesting restrictive conditions. This led to the following conclusion: scores above 50
favor the passage of open immigration policies, scores below 40 indicate a conducive
environment for restrictive policies, and scores between 40 and 50 represent a middle
ground.
What makes this model novel is that it provides an innovative attempt at
integrating a set of diverse social factors into one coherent forecasting tool, thereby
moving the field of immigration policy analysis into new dimensions that provide
nuanced insights into the complex interrelations of societal influences. The model's
findings together with those based on traditional qualitative analysis afford detailed
insights into how social mood influences immigration policymaking, in a major contribution
to understanding the dynamics of U.S. immigration policy from historical times to the current day.
The research is thus instrumental to enhancing knowledge about how immigration policy in
the United States developed, hence providing a way for historians and policy makers to search
the historical context of immigration in this country. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dash.license | LAA | |
dc.subject | Economic and Political Indicators | |
dc.subject | Immigration Policy History | |
dc.subject | Legislative Impact Analysis | |
dc.subject | Predictive Policy Modeling | |
dc.subject | Social Mood Analysis | |
dc.subject | TrendFusion Forecaster | |
dc.subject | History | |
dc.title | Forecasting the Future: How Social Mood Shapes America's Immigration Landscape | |
dc.type | Thesis or Dissertation | |
dash.depositing.author | Sharma, Rohit C | |
dc.date.available | 2024-05-04T12:03:23Z | |
thesis.degree.date | 2024 | |
thesis.degree.grantor | Harvard University Division of Continuing Education | |
thesis.degree.level | Masters | |
thesis.degree.name | ALM | |
dc.contributor.committeeMember | Bond, Doug | |
dc.type.material | text | |
thesis.degree.department | Extension Studies | |
dc.identifier.orcid | 0009-0003-4597-9670 | |
dash.author.email | rohitsharma@gmail.com | |