Australia‘s missing data, did not experience the same influx of migrants from Asia that Australia did. In other words, Australia represents such a large share of immigration in Oceania that when it is removed, the remaining countries (mostly small island countries that are origins, not destinations) are not particularly accurate predictors of migration to Australia.

The U.S. case is similar. Using interpolation to fill in the missing years proves effective, while the results from simulation 1 are also reasonable. The results from simulations 4 and 5 are less accurate. The problem with using regional shares for calculating missing coefficients for the United States is similar to that for Australia. The poor results are due to the differences in the migrant profiles of the United States and Canada, which provides the weights for filling in the missing U.S. values. This methodology significantly underpredicts the numbers of migrants from U.S. dependencies, since Canada hosts very few of them, and overpredicts the numbers from former British colonies, populations that are more prominent in Canada.

Simulations 4 and 5 perform extremely well for Switzerland: the deviations from the actual data are less than 1 percent. This is due to the fact that several large Western European nations have similar migrant profiles to Switzerland, unlike the case for Australia and New Zealand and the United States and Canada. The data for 1970–2000 prove better for interpolating the missing data for Switzerland for 1960, while the data for earlier years are somewhat less effective at predicting the missing data for 2000.

The results for Chile are also good. Using the data for Chile in other years and the propensity measures yields a margin of error that is under 6 percent (simulation 1). Interpolation proves accurate when data for either 1960 or 200 are removed. With subregional shares, the differences in the log ratios are small, but the correlation coefficients are not as high as in other cases because Chile‘s immigrant profile is bimodal. Chile has a small number of large immigrant stocks and a large number of very small stocks. Although the predictions for the size of the stocks are reasonable, the relative rankings are not as accurate.

The results indicate that interpolation is the most effective method of allocation, although the allocations based on the propensity measures and on the subregional shares fair reasonably well. This is heartening, since around a quarter of the observations and 14 percent of the world migrant stock is allocated for 2000 using interpolation. Filling a missing country-year of data using propensities is less effective. Even so, the correlations remain high and the resulting data are not sufficiently inaccurate to warrant throwing them away. It is important to remember, however, that simulation 1 represents a worst case. This extreme measure is resorted to only for a few countries for which data are missing. In almost every case, aggregate categories are much narrower in the raw data. Nevertheless, even with this constrained method with extreme assumptions (missing all data for a country in a region with very few comparable countries), the results seem reasonable. And even when the results are skewed, this is generally due to the over- or underpredicting of a handful of key migrant corridors.

Finally, the aggregate figures obtained are compared with those from the Trends in International Migrant Stock database (United Nations 2006, 2009) to highlight key differences. The database provides data by destination only, not for each bilateral 17