Association for Public Policy Analysis & Management

  Despite the vast studies by worldwide researchers on migration, little work in demography, politics, and economics explicitly examined the impact of relative political performance of destination country on migration. Rather than studying general migrants, we will focus on refugees who seek to flee conflict or persecution with expectation to gain new insights. This research is aimed at studying the association between destination choice of refugees and political performance (measured by relative political extraction, RPE). To examine this relationship, we will use panel data of political performance and refugees for European countries between 1991 and 2015. Results are generated through regression analysis and panel data analysis, suggesting that a destination country with higher RPE is more likely to attract refugees.

Data and Methodology

  In this research, we will build the regression model based on the pull factor of relative political extraction (RPE). The model will stipulate RPE to be our explanatory variable and control variables are considered. The percentage of refugee is perceived as dependent variable. This leads to the model:

                              re_pop_it = α₁+β₁RPE_it+β_γγ_it+ε_it

where

re_pop_it: Refugee divided by total population, in percentage.

RPE_it: Relative Political Extraction that approximates the ability of governments to appropriate portions of the national output to advance public goals. PRE will be measured by:

        (Tax/GDP)=α₁+β₁(time)+ β₂(minging/GDP)+β₃(exports/GDP)+β₄(GDP per capital)+ε

Since our RPE date is from 1991 to 2013, we will do projection for data to 2015, ceteris paribus.

γ_it: Control variables including

rpegdp_-per: Interaction term of rpe_gdp × gdp_pert

lnhc: Log of human capital, showing percentage change in human capital. Since the latest data for human capital is 2014, I will do projection for 2015, ceteris paribus.

lnlife_expect: Log of life expectancy, showing percentage change in life expectancy

emp15: Employment above age 15, in percentage.

pop0_14: Population aged from 0 to 14, in percentage.

pop15_64: Population aged from 15 to 64, in percentage.

pop65: Population over age 65, in percentage.

ε_it: error term

i: Individual Country

t: year-based time

We will apply two equations to estimate the result. The first equation will estimate the outcome by time variation via an OLS model with time-series cross country data shown as following:

     re_pop = α₁+β₁(time)+ β₂(rpe_{_}gdp)+β₃(rpegdp_-per)+β₄(lnhc)+β₅(lnlife_expect) +β₆(emp15)+β₇(pop0_14)+β₈(pop15_64)+β₉(pop65)+ε (1)

The second equation will be estimated by random and fixed effects models based on panel data. Equation is shown as following:

     re_pop_it = α₁+β₁RPE_it+β_γγ_it+c_i+ε_it  (2)

Preliminary Result

In short, the OLS and fixed effects model estimate that:

one unit increase in rpe_gdp leads to 0.456 % change in percentage of refugees; one percent increase in percentage of lnhc, lnlife_expect, and emp15 will lead to 1.333%, 1.953%, and 0.0167% change in refugees repectively. Other control variables are insignificant due to the fact that there is a collinear relationship between relevant varibales. For example, pop65 may have collinearity with pop0_14 and the statistical examination is affected by this situaiton. Even though the result is strongly robust, there are some considerable control variables such as income inequality.  In addition, we are considering that explore the correlation between rpc and refugee crime in Europe.