Forecasting U.S. Domestic Migration Using Internet Search Queries

Lin, Allen Yilun; Cranshaw, Justin; Counts, Scott

doi:10.1145/3308558.3313667

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…Online search data has also been used more recently to study migration. Records on Google searchers, for example, have been explored to forecast the number of arrivals of asylum-seekers in Europe (Connor 2017) or internal migration within the U.S. (Lin et al, 2019). Search data generated through Google's online search platform for migration can be exploited to measure migration intentions and predict subsequent emigration flows (Böhme et al, 2018;UN Global Pulse, 2014).…”

Section: Online Search Datamentioning

confidence: 99%

Measuring migration 2.0: a review of digital data sources

Tjaden

2021

CMS

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The interest in human migration is at its all-time high, yet data to measure migration is notoriously limited. “Big data” or “digital trace data” have emerged as new sources of migration measurement complementing ‘traditional’ census, administrative and survey data. This paper reviews the strengths and weaknesses of eight novel, digital data sources along five domains: reliability, validity, scope, access and ethics. The review highlights the opportunities for migration scholars but also stresses the ethical and empirical challenges. This review intends to be of service to researchers and policy analysts alike and help them navigate this new and increasingly complex field.

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Section: Online Search Datamentioning

confidence: 99%

Measuring migration 2.0: a review of digital data sources

Tjaden

2021

CMS

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Section: Introductionmentioning

confidence: 99%

“…Much of the evidence on the impacts of the internet on migration via information flows is that it has a positive impact, but again this evidence is not conclusive, with reasons to expect a negative effect. Lin et al (2019) in the USA note that migration searches on the internet are good predictors of migration flows. Similarly, in the African context, Martin and Hayford (2021) and Grubanov‐Boskovic et al (2021) suggest that internet usage is positively related to migration desires and intentions for international and internal migration.…”

Section: Introductionmentioning

confidence: 99%

Does frequent internet use increase the propensity to change address? UK evidence from Understanding Society on mobility preferences, expectations and moves

Rowland,

Shuttleworth

2024

Population Space and Place

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Long‐term declines in rates of internal migration have been widely documented in many developed economies. Accompanying this decline has been a proliferation in the everyday use of new technologies such as the internet. New communication technologies have been theorised to influence migration decisions, but the direction of this influence is ambiguous, with some studies finding that they decrease migration, others that they increase it. Using data from the UK Household Longitudinal Study, this paper assesses the relationship between internet use and preferences/expectations to change address as well as the decision to move. Although frequent users of the internet tend to be more mobile on average, longitudinal analysis offers no evidence that internet use influences migratory preferences and expectations or actual moves. This paper contributes to the literature by studying a developed country with high levels of internet penetration, by engaging not only with actual moves but also with preferences and expectations to move, and by drawing on longitudinal data to address unmeasured confounding in so far as possible. More broadly, it provides a reference point for future studies which might consider whether internet use plays a different role in the aftermath of the COVID‐19 pandemic.

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“…An emerging literature suggests that these sources-and Google search data in particular-can be used to monitor a number of social and biological phenomena in the absence of more reliable or timely data (Cesare et al 2018). Such data have been used for real-time analyses of disease outbreaks such as the seasonal flu and Dengue (Ginsberg et al 2009;Carneiro and Mylonakis 2009) as well as studies on well-being (Brodeur et al 2021), tourism (Siliverstovs and Wochner 2018), financial trading behavior (Preis et al 2013), and demographic processes such as fertility (Billari, D'Amuri, and Marcucci 2016;Ojala et al 2017;Lin, Cranshaw, and Counts 2019), migration (Zagheni and Weber 2012;Wladyka 2017), sexual behavior (Markey and Markey 2013;Wilde, Apouey, and Jung 2017;Stephens-Davidowitz 2017), mortality (Tamgno, Faye, and Lishou 2013;Ricketts and Silva 2017), and suicide (Solano et al 2016). Moving beyond now-casting with Google data is generally difficult due to the complexity or uncertainty surrounding the longterm processes which govern such phenomena.…”

Section: Introductionmentioning

confidence: 99%

Digital Trace Data and Demographic Forecasting: How Well Did Google Predict the US COVID‐19 Baby Bust?

Wilde,

Chen,

Lohmann

et al. 2024

Population & Development Rev

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At the onset of the first wave of COVID‐19 in the United States, the pandemic's effect on future birthrates was unknown. In this paper, we assess whether digital trace data—often touted as a panacea for traditional data scarcity—held the potential to accurately predict fertility change caused by the COVID‐19 pandemic in the United States. Specifically, we produced state‐level, dynamic future predictions of the pandemic's effect on birthrates in the United States using pregnancy‐related Google search data. Importantly, these predictions were made in October 2020 (and revised in February 2021), well before the birth effect of the pandemic could have possibly been known. Our analysis predicted that between November 2020 and February 2021, monthly United States births would drop sharply by approximately 12 percent, then begin to rebound while remaining depressed through August 2021. While these predictions were generally accurate in terms of the magnitude and timing of the trough, there were important misses regarding the speed at which these reductions materialized and rebounded. This ex post evaluation of an ex ante prediction serves as a powerful demonstration of the “promise and pitfalls” of digital trace data in demographic research.

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Forecasting U.S. Domestic Migration Using Internet Search Queries

Cited by 9 publications

References 26 publications

Measuring migration 2.0: a review of digital data sources

Measuring migration 2.0: a review of digital data sources

Does frequent internet use increase the propensity to change address? UK evidence from Understanding Society on mobility preferences, expectations and moves

Digital Trace Data and Demographic Forecasting: How Well Did Google Predict the US COVID‐19 Baby Bust?

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