Person:
Orazbayev, Sultan

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Orazbayev

First Name

Sultan

Name

Orazbayev, Sultan

Filters

2018 - 201912020 - 20241

Settings

Author's Publications: search.filters.isAuthorOfPublication.d455fda3-1dc8-49db-bdd1-d54c259e3ebd

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Publication
    Social Networks and the Intention to Migrate
    (Center for International Development at Harvard University, 2018-03) Manchin, Miriam; Orazbayev, Sultan
    Using a large individual-level survey spanning several years and more than 150 countries, we examine the importance of social networks in influencing individuals' intention to migrate internationally and locally. We distinguish close social networks (composed of friends and family) abroad and at the current location, and broad social networks (composed of same-country residents with intention to migrate, either internationally or locally). We find that social networks abroad are the most important driving forces of international migration intentions, with close and broad networks jointly explaining about 37% of variation in the probability intentions. Social networks are found to be more important factors driving migration intentions than work-related aspects or wealth (wealth accounts for less than 3% of the variation). In addition, we found that having stronger close social networks at home has the opposite effect by reducing the likelihood of migration intentions, both internationally and locally.
  • No Thumbnail Available
    Publication
    A New Algorithm to Efficiently Match U.S. Census Records and Balance Representativity with Match Quality
    (Growth Lab, 2024-12) Protzer, Eric; Orazbayev, Sultan; Gomez, Andres; Hartog, Matte; Neffke, Frank
    We introduce a record linkage algorithm that allows one to (1) efficiently match hundreds of millions of records based not just on demographic characteristics but also name similarity, (2) make statistical choices regarding the trade-off between match quality and representativity and (3) automatically generate a ground truth of true and false matches, suitable for training purposes, based on networked family relationships. Given the recent availability of hundreds of millions of digitized census records, this algorithm significantly reduces computational costs to researchers while allowing them to tailor their matching design towards their research question at hand (e.g. prioritizing external validity over match quality). Applied to U.S Census Records from 1850 to 1940, the algorithm produces two sets of matches, one designed for representativity and one designed to maximize the number of matched individuals. At the same level of accuracy as commonly used methods, the algorithm tends to have a higher level of representativity and a larger pool of matches. The algorithm also allows one to match harder-to-match groups with less bias (e.g. women whose names tend to change over time due to marriage).