Labor Mobility

“However, hundreds of millions of adults would still like to move: Fourteen per cent of the world’s adults – or about 630 million people – would like to migrate to another country if they had the chance, down from 16 per cent, or more than 700 million people, in previous years. These figures are still at least triple the 214 million international migrants the United Nations Department of Economic and Social Affairs estimated worldwide in 2010 – and this 214 million includes children and adults (United Nations, 2009).Where the next wave of potential migrants might come from
Residents in sub-Saharan Africa remain the most likely worldwide to express a desire to migrate permanently, Gallup finds. Thirty-three per cent of adults across sub-Saharan Africa say they would like to move, although this is down from 38 per cent in earlier readings. Desire also faded slightly in Latin America (from 23% to 20%) and in South-East Asia (from 12% to 9%) between 2007 and 2010.
In other parts of the world, desire remained unchanged. In the European Union, for example, the percentage of adults who would like to migrate permanently was unmoved at 20 per cent. In Northern America, which includes the United States of America and Canada, the percentage of potential migrants held at 10 per cent.” IOM 2012.

Gallop projects that 138 million adults would like to move to the U.S. if they were able: “About 13% of the world’s adults – or about 630 million people – say they would like to leave their country and move somewhere else permanently. For roughly 138 million people, that somewhere else would be the U.S. – the No. 1 desired destination for potential migrants.” Gallup 2013.

“If we use the same decomposition between location and class today, when our data are much better than for the past, we find that of the global Gini, which amounts to 65.4 points, 56.2 Gini points or 85 percent is due to differences in mean country incomes, and only 9.2 Gini points (15 percent) to “class”. Not only is the overall inequality between world citizens greater in the early 21st century than it was more than a century and a half ago, but its composition has entirely changed; from being an inequality determined in equal measures by class and location, it has become preponderantly an inequality determined by location only.” Milanovic 2011, pg 7.

“Figure 6 plots these two parts, class and location, for the years 1870 and 2000. Around 1870, class explained more than 2/3 of global inequality. And now? The proportions have exactly flipped: more than 2/3 of total inequality is due to location. The implication of this overwhelming importance of location, or which is the same, citizenship (i.e., being a member of a rich or poor country), for our lifetime incomes can be also very well captured by another exercise. We divide the population of each country into 100 income percentiles, ranked from the lowest to the richest. Now, if we run a regression with income levels of these percentiles (for 120 countries, this gives 12,000 observations) as the dependent variable, and on the other side of the regression, use as the only explanatory variable the mean income of the country where each percentile comes from, we explain between more than one-half of variability in individual incomes. This is a remarkable achievement for a single explanatory variable. Differently put, more than fifty percent of one’s income depends on the average income of the country where a person lives or was born (the two things being, for 97% of world population, the same). This gives the importance of the location element today. There are of course other factors that matter for one’s income, from gender and parental education which are, from an individual point of view externally given circumstances, to factors like own education, effort and luck that are not. They all influence our income level. But the remarkable thing is that a very large chunk of our income will be determined by only one variable, citizenship, that we, generally, acquire at birth. It is almost the same as saying, that if I know nothing about any given individual in the world, I can, with a reasonably good confidence, predict her income just from the knowledge of her citizenship.” Milanovic 2012, pgs 19-20.

“The first wave of the survey in New Zealand in 2005 covered a random sample of 101 of the 302 Tongan households that migrated as successful participants in the 2002-05 PAC ballots. The second wave was fielded in 2008 and re-interviewed 89 of these households. Of the remaining households, ten had either re-migrated to a third destination country or moved to outer areas of New Zealand where it was too expensive to travel for fieldwork, while there was one refusal and one non-contact. At the time of the first wave of the survey, migrants had spent an average of 11 months in New Zealand. The second wave was fielded approximately 33 months later.” Stillman et al. 2012, pgs 11-12.

“We have seen that migration reduced the per capita resources available in migration households. However, since we do not know how resources were distributed within the household prior to migration, it is difficult to know whether these changes reflect a genuine change in resources available to the family members left behind, particularly as we have shown that the adult movers contributed significantly more to the household in terms of labor earnings than do the adult stayers.
Nevertheless, we believe that the available evidence does suggest that resources are pooled within Tongan households in the absence of migration. First, prior anthropological investigations argue that food and other resources are shared equally among extended family members within a household (Pollock, 1992). Second, we use the stayer ballot-loser households to test whether there is an equal relationship between the age and gender composition and earnings of stayers and movers in the household and household diet and cannot reject the null hypothesis of equal effects across the two groups for any of the food groups at the 5% significance level.23 The HIES shows that food consumption is approximately two-thirds of the household budget, so income-pooling effects on diet seem likely to extend to total consumption. Third, if resources were unequally shared within the household, we should expect to see differences in food intake manifest themselves in differences in anthropometric measures between stayers and movers. However, when we regress anthropometrics on household fixed effects and a dummy for being a stayer, we find no difference in child height for age or BMI for age, or in adult BMI between stayers and movers.24 This evidence leads us to believe that the fall in household resources per capita from migration does indeed represent a genuine fall in resources available to the stayers.” Gibson, McKenzie, and Stillman 2011, pg 1310-11.

“The point estimates suggest that households which sent emigrants now have larger total household income and consumption than households who were unsuccessful in the lottery, but large standard errors on these estimates make the estimates statistically insignificant. The results do show a change in the composition of household income. Income from agricultural production and income from remittances are significantly higher, by 245% and 75%, respectively, relative to unsuccessful households, while household labor earnings are lower (but not significantly so).”

“Table 5 examines the impact of emigration on per person resources. We now weight the estimates by household size and hence the results indicate the change in per person resources for the average individual. These results show that left-behind household members are better off in comparison to members of households with lottery losers. Average consumption is approximately 17% higher in per adult-equivalent terms and income is approximately 23% higher (although neither is statistically significant).11 Since the changes in income and consumption are similar, this suggests that these changes associated with emigration and remittances are being viewed as shocks to permanent income by the left-behind household members. There is some weak evidence that these gains become smaller over time, both because household size is rising and because the income gains are declining. However, these results are also consistent with the impacts being independent of how long the emigrants have been gone.” pg 269.

“Finally, table 6 examines the impact of emigration on poverty. Again, we weight the estimates by household size and hence the results indicate the change in poverty for the average individual. The basic needs poverty rate among individuals living in households that sent Samoan Quota emigrants is 23 percentage points lower than for households with lottery losers. Since the poverty rate among individuals in unsuccessful households is 37%, this represents a 62% reduction in head count poverty. However, there is no measured effect of emigration on the food poverty rate that captures deeper poverty (with only 12% of the lottery loser households below this line), nor is there any effect on the poverty gap ratio at either poverty line.12 This indicates that the main impact of having family members migrate to New Zealand was to lift individuals who were just below the basic needs poverty line out of poverty, with insignificant effects on the much smaller proportion of individuals who were poorer than this. The impact on the subjective poverty reported by an individual adult respondent in each household is negative but also statistically insignificant. Again, there is some weak evidence that any possible poverty reduction declines over time, but the years since migration term is neither significant nor the same sign across different poverty measures.” pgs 269-270.

“We find that migration reduced basic needs poverty among former household members. This contrasts with the short-term impact of migration for Tongans moving to New Zealand through a similar lottery program—in McKenzie et al. (2007) we find an increase in poverty for those left behind. Comparing the results, the difference stems from a much larger fall in house- hold labor earnings in Tonga than in Samoa, and a fall in agricultural production in Tonga compared to a rise in agricultural income in Samoa. The increase in remittances is similar in both cases.
In Tonga, we found that the movers earned twice the weekly income as the stayers within a potential migrant household (Gibson et al. 2011), which was why household earnings declined so drastically when these individuals migrated. In contrast, fortnightly earnings for stayer 18–45-year-old adults in Samoa average 179 Tala, versus 211 Tala for the movers (p = .32).14 Thus, Samoan households rely relatively less on the labor earnings of the potential migrants before migration and so suffer less opportunity cost of their absence in terms of these forgone labor earnings. It is also possible that the much longer history of migration through the SQ has given households more time to learn how to adjust to the absence of members, potentially through getting them to set up a lot of agricultural production beforehand. These differences highlight the importance of context in understanding the impacts of migration and of replicating these experimental studies in more locations where possible.” pgs 275-6.

“The United States offers two types of visas for seasonal low-skilled workers. One is the H-2A visa, which is designed for agricultural workers, and the other is the H-2B visa, which is designed for nonagricultural workers—mostly hotel and resort workers. There is no quota on the number of H-2A visas that can be granted, and the quota on the number of H-2B visas is 66,000 per year. Over the past few years, the quota for H-2Bvisas has not been met.” Clemens conversation.

It is less obvious that these gaps represent a problem: if more people could come if businesses wanted to employ them but businesses don’t seek them out, then there may simply not be very many profitable employment opportunities that could be covered by the relevant visa program. That said, it may also be the case that bureaucratic constraints limit the profitability of potentially employing migrants, leading to under-utilization of the available visas, leaving open the possibility for a valuable philanthropic option:“The number of migrants who get H-2 visas is much smaller than it could in principle be. A major reason for this is that there are very substantial bureaucratic hurdles that employers must clear in order to hire migrant workers. The North Carolina Growers Association is the largest user of the H-2A visa program. It brings roughly 15% of the total workers who obtain the visas. The association has many complaints about the program. For farmers in North Carolina to hire migrant workers, they’re required to:

• Post advertisements in American newspapers and at state unemployment agencies for two months.
• Hire any American applicants (except for those who are physically
  incapable of doing the work), even well after the growing season begins and they have already filled the positions with any foreign workers.
• Fill out a great deal of paperwork and pay fees.

The process has to be completed for every single worker hired (even if the employer is hiring migrant workers at scale) and every year (even if the employer has been hiring migrant workers for a long time). The farmers who hire migrant workers are effectively required to pick the number of workers who they want to hire several months before they’re able to assess how many they need based on weather conditions.
Because of the bureaucratic hurdles, people on H-2 visas represent less than 5% of manual seasonal labor done by non-U.S. citizens, and most manual seasonal laborers are unauthorized immigrants.” Clemens conversation