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Hence, a separate map is created for each origin, with nearby destination zones being small, and more distant ones larger.
The first one is the total emigration rates for each origin country defined as the total stock of migrants abroad over the total working population (total labor force).
For each origin, this average weighted impedance is calculated as follows: {dot{mathrm{c}}}_i=frac{{displaystyle {sum}_{jin N}{D}_jcdot {c}_{ij}}}{{displaystyle {sum}_{jin N}{D}_j}};forall iin N (A).
Second, the migration data need to give migration stocks broken down by the education level of the migrants in order to isolate skilled and unskilled emigration for each origin country.
We proxy the cost of emigration (c) in different ways (separate dummies for each origin country and for each destination region or dummies for time dummies coupled with a dyad country/region).
Therefore, we follow Beine et al. (2011) and construct for each origin country, a reliability rate equal to the 2000 share of the six host countries in the skilled emigration stock in the OECD, and we use this reliability rate to reweight our regressions.
Similar(41)
Similarly, allometric equations were generated separately for mussels from each origin, using linear regression and logarithmic transformation.
Depicted are the mean abundance per CG density bin of the total counts of motifs for the fragment of each origin.
The importance of the opportunities at destination (e.g. population, employment or GDP) weights for each Origin-Destination impedance.
We then filter and rank the results and finally compute two aggregate indicators for each Origin-Destination pair, a Quality Indicator and a Diversity Indicator.
As a rough rule of thumb, we nevertheless conclude that approximately 10,000 inter-observation times are required for each origin-destination pair for obtaining reliable estimates.
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