4 QUANTITATIVE RESEARCH DESIGN
4.1 Data And Case Selection
The quantitative analysis employs a dyadic model based on data from OECD and UNHCR Databases. The unit of analysis is a country-case pair. A country-case is a duo between an OECD country and a mass influx of asylum seekers that are unable to return to their country of origin due to state persecution, civil war, general violence, or grave human rights violations. An asylum crisis refers to asylum applications more than 500 from a single asylum-seeking group from the same country of origin in a given year. The dataset contains 30 electoral democracies from 2000 to 2014 due to data availability. My underlying assumption is that these destination countries have the necessary capacity to implement their preferred asylum policy on the ground. This choice leaves me with 2002 observations.
First, I would like to differentiate between various asylum policies and see whether the duration and the extent of protection matter for the asylum policy formulation of the destination countries. That is why, for my first model, I use the Refugee Recognition Rate, which according to UNHCR “divides the number of asylum-seekers granted Convention refugee status by the total number of accepted (Convention and, where relevant, complementary protection) and rejected cases.”13 When compared with the second model, which uses Total Recognition Rates, my first model allows me to analyze what factors affect full protection versus temporary protection. I coded the Refugee Recognition Rate variable as:
(Full Recognition X100) / Total Decision
For my second model, I am mostly interested to know whether it is the idea of accepting “foreigners” into the country rather than the duration or the extent of their stay that affects the asylum policy formulation in a destination country. Therefore, I create a continuous dependent variable that measures the Total Recognition Rate. The total recognition rate “divides the number of asylum-seekers granted Convention refugee status and complementary form of protection by the total number of accepted (Convention and, where relevant, complementary protection) and rejected cases,”14 and is one of the two measures the UNHCR uses for international comparability. I coded the Total Recognition Rate variable as:
[(Full Recognition + Temporary Protection) X 100] / Total Decisions
My independent variables of primary interest are the social willingness and labor absorption capacity in a destination country. My social willingness variable is an additive index, which is scaled zero to three with states that have low social willingness at zero and those with high social distance at three (0-Low; 1; 2; 3- High). A higher score reflects a lower social distance between the host community and the asylee group, and therefore a greater willingness to host that particular group. The additive indexed variable is comprised of three factors: (1) Whether the host community and the asylum seeking group speak the same language, (2) Whether the host community and the asylum seeking group belong to the same religion, (3) Whether the host community and the asylum seeking group are co-ethnics. Then, I created a dummy variable using the additive index and coded the variable as 0 “Low Social Willingness” if the host community and the asylum seeker group share none of the traits. I coded the variable as 1 “High Social Willingness” if they have at least one trait in common. The data comes from CIA Fact Book Field Reports15. Of course, this is not a perfect measure of the socially constructed social distance between the host community and the asylee
14 UNHCR Statistical Yearbook 2009 - http://www.unhcr.org/4ce531e09.pdf page. 38
15Religion: https://www.cia.gov/library/publications/the-world-factbook/fields/print_2122.html
Language: https://www.cia.gov/library/publications/the-world-factbook/fields/2098.html Ethnicity: https://www.cia.gov/library/publications/the-world-factbook/fields/2075.html
group. However, these indicators give me a general idea about the relationship between the two communities and provide a starting point for the empirical analysis.
For the labor absorption capacity, I run a factor analysis using the level of the harmonized unemployment rate16, trade union strength17, public unemployment spending18 (whether there is employment or unemployment protection), and social spending19 in the destination country. I argue the economic institutional setting in a destination country underlies all these indicators; whether a state tends to fall closer to a coordinated market economy or a liberal market economy on the continuum determines whether the relations and the institutions in the labor market will be coordination or market-based. Factor analysis helps me capture that underlying factor, which affects the unemployment rate and benefits, trade union strength, and social safety net in a destination country. Higher scores of the factor variable reflect higher levels of the unemployment rate, trade union strength, public unemployment spending and social spending, and therefore lower levels of labor absorption capacity. To create a scale that goes from lower labor absorption capacity to higher, I take the reverse of the factor variable.
The data for my economic indicators come from OECD Database. OECD defines harmonized unemployment rate as “the number of people of working age who are without work, are available for work, and have taken specific steps to find work,” while trade union density refers to “the ratio of wage and salary earners that are trade union members, divided by the total number of wage and
16 OECD (2016) Harmonised unemployment rate. Doi: 10.1787/52570002-en (Accessed on 07 June 2016) https://data-oecd-org.ezproxy.gsu.edu/unemp/harmonised-unemployment-rate-hur.htm
17 OECD (2016) Trade Union Density. DOI: 10.1787/1e628ddd-en (Accessed on 07 June 2016)
http://www.oecd-ilibrary.org.ezproxy.gsu.edu/employment/data/trade-unions/trade-unions-trade-union-density-edition- 2015_1e628ddd-en
18 OECD (2016), Public unemployment spending (indicator). doi: 10.1787/55557fd4-en (Accessed on 07 June 2016) https://data-oecd-org.ezproxy.gsu.edu/socialexp/public-unemployment-spending.htm
19OECD (2016) Social spending indicator, DOI: 10.1787/7497563b-en (Accessed on 07 June 2016) http://www.oecd-ilibrary.org.ezproxy.gsu.edu/social-issues-migration-health/social-
salary earners,” (OECD Stats). Public unemployment spending (% of GDP) covers all payments from public funds to beneficiaries who are out of work for labor market policy reasons. Social expenditure, on the other hand, comprises “cash benefits, direct in-kind provision of goods and services, and tax breaks with social purposes. Benefits may be targeted at low-income households, the elderly, disabled, sick, unemployed, or young persons,” (OECD Stats). This indicator is measured as a percentage of GDP or USD per capita. Lastly, public spending on labor market training is measured as a percentage of GDP that is spent on “institutional, workplace and alternate/integrated training, as well as special support for apprenticeship,” (OECD Stats). Theoretically, coordinated market economies should perform higher in all four indicators.
To test whether the overall economic development of the destination country affects its asylum policy choices, I control for gross national income per capita. The Data for GNI comes from World Bank20.
Concerning political conditions, I want to control for the party identification of the host government, as the literature argues that leftist governments might be more inclined to offer inclusive migration policies. The measure is a three-point ordinal scale where 1 “Right” 2 “Center,” and 3 “Left.” The data for incumbent ideological leaning comes from the World Bank Database of Political Institutions21. Scholars also argue that, regardless of the political orientation of the executive, the vote-share of the right-wing parties might shift the whole political discourse to further right. In other words, regardless of their ideological orientations, incumbent parties in destination countries might
20http://data.worldbank.org/indicator/NY.GNP.PCAP.CD 21 http://www.edac.eu/indicators_desc.cfm?v_id=251
feel obliged to restrict asylum to be able to win over their constituency (Neumayer 2005). That is why I account for the vote share of the radical right parties in legislative elections22.
To capture whether the destination country feels overburdened by the asylum crisis, I control for the number of asylum applications from a specific country of origin and the number of asylum seekers in total. I used the UNHCR Population Statistics Database for this data.
My theory assumes that a destination country’s economic and social contexts play a significant role in shaping its asylum policy. This line of reasoning implies that these domestic factors will triumph the merit of the asylum application at hand. To be able to control for factors related to the merit of the asylum application, I will resort to Neumayer’s (2005) empirical analysis and borrow his measures. I construct an autocracy variable as the unweighted sum of political rights and civil liberties index published by Freedom House (2015). The two indices are based on expert surveys and measured on a 1 (free) -7 (not free) scale. My measure ranges from 2 (free) – 14 (not free).
With respect to human rights violations, Neumayer uses two Purdue Political Terror23 scales. One of the scales is based on Amnesty International’s annual human rights reports, and the other one makes use of US Department of State’s Country Reports on Human Rights Practices. I use the mean scores when both scales are available for a given country. If only one is available, I used the available one as the mean score. The data comes from Gibney, Cornett, Wood, Haschke, and Arnon (2015).
22 Ryan Bakker, Erica Edwards, Liesbet Hooghe, Seth Jolly, Gary Marks, Jonathan Polk, Jan Rovny, Marco
Steenbergen, and Milada Vachudova. 2015. "2014 Chapel Hill Expert Survey." Version 2015.1. Available on chesdata.eu. Chapel Hill, NC: University of North Carolina, Chapel Hill.
23 Gibney, Mark, Linda Cornett, Reed Wood, Peter Haschke, and Daniel Arnon. 2015. The Political Terror
Following Neumayer’s footsteps, I also test the impact of the intensity of civil war, general violence or state failure on asylum recognition rates. For this measure, I use maximum magnitude scores from Integrated Network for Societal Conflict Research (INSCR)’s Political Instability Task Force State Failure Problem Set24. I also use two scores (1) measuring the annual number of deaths from genocide and politicide from the same source to capture “the calculated physical destruction of a communal or a political group in whole or in part,” and (2) measuring the extent of external armed (Neumayer 2005, p.54).
I also control for colonial history25. I do not add colonial history to the social distance index because I believe the relationship between the colonizer and the former colony is a complicated one. The host community may or may not feel favorably for an asylum seeker group from a former colony. That is why I chose to capture its effect separately.
To test for the overall economic conditions in the country of origin, I include a gross national income per capita variable for the country of origin as well. The data comes from World Bank26. To get a normal distribution on the variable, I use the natural log of the GNI per capita in my model.
Summary statistics for all the variables could be found in the Appendix.