Population, territory and sustainable development

164
that draw migrants, due precisely to the arrival of migrants in search of work). Thus,
these coefficients do not seek to capture a causal relationship, but rather identify
empirical concomitance.
(iv) The standard-of-living dimension most highly correlated with migration draw is access to
information and communication technologies. 11 Although it is difficult to attribute higher
migration draw to the availability of telephones, mobile telephones, computers and Internet,
these probably reflect a modern setting, at least superficially, associated with other social,
cultural and economic factors that together form an attractive package. Cutting-edge cities
might well be attractive, in view of the wide-ranging debate about technological change and
job requirements. The data suggest that the new digital economy could generate many “users”
(students, apprentices, technical experts, workers), taking its draw well beyond the direct jobs,
or even the indirect ones, that are created. This is a hypothesis that should be evaluated using
disaggregation methodologies, especially by migrant age and education levels.
A simple correlation study for all of the cities comes up against three problems. From a statistical
perspective, bivariate relationships may be spurious due to the presence of other concomitant variables
that may be the real explanation behind the relationship. From a substantive perspective, very different
national realities are mixed together inappropriately, generating theoretical inconsistency in the findings.
Finally, in terms of numbers, Brazil and Mexico contribute so many cities that they decisively influence
coefficients, masking specific and, possibly, unusual relationships other countries. To deal with these
problems at least partially, table IX.11 identifies the significant coefficients for 28 multiple regression
equations. There are two per country, one in which the conditional variable is the relative intensity of total
net migration and the other in which the conditional variable is the relative intensity of total net intraurban
migration. For both equations, the set of conditional variables was population size; average education
level for the population aged 30 to 60; unemployment among young persons (aged 15 to 24) and total
unemployment (aged 15 and over); and access to drinking water, sanitation and electricity.
Generally speaking, the number of statistically significant variables is low: four countries posted
none and only one country (Brazil) posted three (less than half the set). In most cases, the statistical
significance of a variable is tested for both types of migration; when this happens, the sign always
coincides. Youth unemployment is the significant variable in most countries (four), with three 12 posting a
negative sign, indicating that higher levels of unemployment tend to be associated with lower migration
pull (probably negative rates, that is, they are migrant senders).
Overall, countries vary enormously, in terms of the regression adjustment and in terms of the
statistically significant coefficients and their sign. Mexico is an extreme case, since the regression
explains less than 6% of the variance in net migration among cities and no conditional variable in the
model is significant. In contrast, for some countries the model explains more than 90% of the variance in
net migration (Panama and Paraguay), although in both the number of significant conditional variables
was very low (null in the case of Panama).
11
12
This finding cannot be considered representative of the whole set of countries, because few countries include
questions on the availability of information and communication technologies at home. Thus, these findings
reflect the reality of those countries alone.
Paraguay has a statistically significant positive coefficient.