Page 44 - IJPS-7-2

International Journal of
Population Studies Objective and subjective assimilation of migrants

was measured by the average income level of both urban shocks to your household.” Both comfort and capable were
residents and migrants living in the same street or in the adjusted based on the subjective social standing of the
same county. Finally, for respondents who selected “people respondents (“Compared to the average living standards
in urban areas,” the income was measured by the average of households in your city/town/county, do you consider
income level of urban residents (only) living in the same your household’s living standards to be…”) so that both
province. have five scales. In particular, the options that more than
70% of respondents picked were split into two categories
Because “people in urban areas” represent “urban
residents,” they were also used to represent the mainstream depending on whether their living standards are below or
above the average.
population in urban areas. Thus, the average income level
of the mainstream population in urban areas is the same These three variables then form the latent variable in
as that of urban residents living in the same province. All structural equation modeling. This is also one of the two
of the 64,777 individuals are included in these averages. reasons why this paper used structural equation modeling.
While the National Bureau of Statistics of China (NBS) can A more detailed justification for the structural equation
provide the average income level for the urban population, modeling is presented in the Data Analytical Strategies
it cannot provide this information at different levels (for section. Factor analysis was implemented to check for
example, same street or same county in an urban area). The internal consistency. All of the factor loadings are above
assimilation was constructed by measuring the gap between 0.45 (factor loading = 0.45 for happiness, factor loading =
the respondent’s yearly income and these averages. Finally, 0.71 for capable, and factor loading = 0.75 for comfort). The
these assimilation measures were transformed into interval internal consistency is also verified with Cronbach’s alpha
variables with 12 scales to deal with the few outliers. of 0.70. Although happiness has a lower factor loading, it
should be included as it is the most common measurement
2.3. Measures of subjective well-being for subjective well-being (Krueger & Schkade, 2008).
Subjective well-being is captured by how people experience 2.4. Control variables
and evaluate their lives (Stone & Mackie, 2013). The
respondents were asked three questions: (1) All things The basic demographic variables include the respondents’
considered; do you feel happy? (2) Which of the following age, gender (male = 1), and education. Other variables
do you think best describes the living standard of your include the year of migration and the motivation for
household? and (3) Which of the following do you think permanent migration. The year of migration is not given
best describes the economic condition of your household? directly; however, the number of years since the initial
The responses to this set of questions are reverse coded so migration was estimated using the year the respondent
that 1 = “not happy at all” and 5 = “very happy” for the first left and the present year. A proxy measured permanent
question, 1 = “does not have enough to live comfortably migration motivation: the willingness of migrants to stay
and cannot afford some basic things” to 3 = “lives very in the city permanently if they were granted a local hukou
comfortably and can afford extra things” for the second quota. In addition, the average income for the mainstream
question, and 1 = “cannot deal with some basic economic urban population is also controlled for assimilation into
shocks to your household” to 4 = “can deal with all economic the mainstream account for provincial differences.
shocks to your household” for the third question. 2.5. Data analysis strategies
Two adjustments were made to these three variables. Structural equation modeling was applied to explore
First, for happiness, “not happy at all” and “not very happy” assimilation as a combination of objective and subjective
were merged as one category “not happy,” and for capable, processes, for two reasons. The first is the ability to
“can deal with many economic shocks to your household” estimate the direct, the indirect, and the total effects. This
and “can deal with all economic shocks to your household” is essential to exploring assimilation as a combination of
were merged as one category “capable,” because both both objective and subjective processes. The second is that
“not happy at all” and “can deal with all economic shocks subjective well-being is a latent variable. The working model
to your household” only have a handful of respondents is shown in Figure 1. The choice of the reference group is
choosing them. Second, for comfort and capable, there expected to directly affect both economic assimilation into
is one relatively neutral category, with more than 70% of the reference group and economic assimilation into the
respondents choosing it. For comfort, it is the option “is mainstream. Nevertheless, the effect could be weak because,
basically comfortable but cannot afford many extra things,” on the one hand, migrants are more motivated to assimilate
and for capable, it is the option “cannot deal with many with urban residents, while, on the other hand, their target
economic shocks, but can deal with some basic economic is harder to reach. It will, then, depend on the portion

Volume 7 Issue 2 (2021) 38 https://doi.org/10.36922/ijps.v7i2.346