| Literature DB >> 35370476 |
Abstract
Economic and social conditions have deteriorated worldwide during the COVID-19 pandemic. Migration theory and international organizations indicate that these increasingly fragile social conditions represent powerful incentives to migrate. Normally, studies addressing international migration and COVID-19 focus on transit and destination countries, with substantially less literature centered on origin nations. Trying to close that gap, the present article aims to identify and quantify economic determinants that explain the intention of Salvadorians to migrate abroad. Using a probabilistic sample and a logistic model, a number of renowned economic variables for migration studies were used to investigate Salvadorian's intention to emigrate. Results demonstrated a stark reduction in migration intentions in 2020. Moreover, the risk of losing one's job is by far the most prominent factor explaining the intention to migrate. Other aspects, such as employment and salaries, also showed statistically significant values. Additionally, results report women being less likely to migrate and age to have a negligible effect. The text concludes by indicating some public initiatives that could be implemented to support people who choose to act upon their intentions and embark on emigration.Entities:
Keywords: Central America; Emigration; Labor migration; Northern triangle
Year: 2022 PMID: 35370476 PMCID: PMC8959798 DOI: 10.1007/s12134-022-00952-3
Source DB: PubMed Journal: J Int Migr Integr ISSN: 1488-3473
Fig. 1Number of Aliens Apprehended in the US 1981–2019. Source: Own based on Department of Homeland Security DHS (2019)
Inflows of permanent immigrants into selected OECD countries, 2008–2013
| Country | 2008 | 2009 | 2010 | 2011 | 2012 |
|---|---|---|---|---|---|
| Australia | 49,500 | 45,700 | 45,900 | 58,400 | 67,100 |
| Norway | 49,300 | 48,900 | 56,800 | 61,600 | 59,900 |
| Austria | 205,900 | 221,000 | 208,500 | 219,500 | 245,100 |
| Finland | 19,900 | 18,100 | 18,200 | 20,400 | 23,300 |
| USA | 1,107,100 | 1,130,200 | 1,041,900 | 1,061,400 | 1,031,000 |
| Germany | 228,300 | 201,500 | 222,500 | 290,800 | 399,900 |
| Nederland | 90,600 | 89,500 | 95,600 | 105,600 | 96,800 |
| France | 222,400 | 221,400 | 233,700 | 240,700 | 258,900 |
Source: OECD, 2014
Description of used variables
| Variable | Scale | Description |
|---|---|---|
| Age | Continuous | Age of the respondent in years |
| Agesqr | Continuous | Age squared. |
| Employment | 0–1 | Value 1 corresponds when the respondent is employed and 0 otherwise. |
| Gender | 0–1 | Value 1 corresponds to women while 0 to men. |
| Zone | 0–1 | Value 1 corresponds to urban areas while 0 to rural areas. |
| Income 1 to 7 | Categorical 1–6 | Corresponds to total monthly family income including remittances. Non respondents were excluded from the regression. Variable can adopt the following values: 1 = Less than $211 2 = 211 to $350 3 = 350.01 to $500 4 = 500.01 to $700 5 = 700.01 to $1000 6 = More than $1000 99 = Does not know/does not respond Base category used was 1 (Less than $211) |
| LooseEmpl | 0–1 | Value 1 when the respondent expresses risk of losing his/her work while value 0 responds did not express it. |
| Remitt | 0–1 | Value 1 corresponds to receiving international remittances while 0 implies not receiving. |
Source: own
Descriptive statistics of persons with and without intention to migrate
| People with intention to migrate ( | People with no intention to migrate ( | ||||
|---|---|---|---|---|---|
| Gender | Men | Women | Gender | Men | Women |
| 51 | 27 | 545 | 599 | ||
| Employed | Yes | No | Employed | Yes | No |
| 27 | 51 | 472 | 639 | ||
| Area | Rural | Urban | Area | Rural | Urban |
| 19 | 59 | 254 | 890 | ||
| Risk of losing job | Yes | No | Risk of losing job | Yes | No |
| 33 | 45 | 261 | 883 | ||
| Receives remittances | Si | No | Receives remittances | Si | No |
| 64 | 14 | 244 | 900 | ||
| Age | Mean | SD | Age | Mean | SD |
| 35.67 | 12.282 | 40.05 | 16.023 | ||
Source: own
Multinomial logistic regression results
| Variables in the equation | |||||||
|---|---|---|---|---|---|---|---|
| B | S.E | Wald | df | Sig. | Exp(B) | ||
| Paso 1a | Employment (1 = yes) | −.608 | .277 | 4.827 | 1 | .028 | .544 |
| Age (years) | .088 | .056 | 2.417 | 1 | .120 | 1.092 | |
| Age squared | −.001 | .001 | 3.548 | 1 | .060 | .999 | |
| Gender (women = 1) | −.631 | .263 | 5.737 | 1 | .017 | .532 | |
| Urban areas (zone =1) | −.009 | .299 | .001 | 1 | .976 | .991 | |
| Income: less than $211 | 8.565 | 5 | .128 | ||||
| 211 to $350 | −.804 | .376 | 4.569 | 1 | .033 | .448 | |
| 350.01 to $500 | −.801 | .416 | 3.696 | 1 | .055 | .449 | |
| 500.01 to $700 | −.095 | .398 | .057 | 1 | .811 | .909 | |
| 700.01 to $1000 | −.878 | .514 | 2.916 | 1 | .088 | .416 | |
| More than $1000 | −.403 | .481 | .700 | 1 | .403 | .668 | |
| Risk losing work (value 1 yes) | .702 | .257 | 7.474 | 1 | .006 | 2.018 | |
| Remittances (value 1 yes) | −.090 | .315 | .082 | 1 | .775 | .914 | |
| Constant | −3.086 | 1.083 | 8.125 | 1 | .004 | .046 | |
Source: own