migration duration and family economics : temporary...
TRANSCRIPT
Migration Duration and Family Economics :
Temporary Migration in China and the One Child
Policy ∗
de la Rupelle, Maelys †
Deng Quheng‡
Abstract
Rural-urban migration in China plays an important role in China development
and urbanization. Despite important imbalances across the territory, this migra-
tion is mostly temporary. This striking feature can be related to institutional
constraints, to agriculture seasonality, to unsecure property rights. In this paper,
we provide an additional explanation, showing how family planning policies are
impacting migrants’ decision, by constraining ulterior off-farm labor participation
decision. No or few siblings means reduced resources in labour power at the house-
hold level, which translates into higher constraints on individual decisions. Using
the exogenous shift in fertility introduced by the One Child Policy, we compare the
elder children born before and after its implementation. We find that individuals
subject to this policy are experiencing shorter duration of migration. The Chinese
case allows us to document precisely the impact of family size on migration.
JEL Classification: D63, P48, O13, I20, J13, O1.
Keywords: fertility, migration, household economics, China.
∗This paper has benefited from comments by Marc Gurgand, Terry Sicular, Nancy Qian, Xing Chun-
bing, Sylvie Demurger, Philippe de Vreyer, Gilles Postel-Vinay, Tatiana Goetghebuer and Imane Chaara.†PSE (Paris-Jourdan Sciences Economiques/Paris School of Economics), 48 boulevard Jourdan, 75014
Paris, France. Email: [email protected], phone: (+33)(0)143136314.‡CASS (Chinese Academy of Social Sciences), Yuetan Beixiaojie, Beijing 100732, China. Email:
1
1 Introduction
Beside having experienced impressive growth and drastic economic changes, China factors
markets display two striking macroeconomic features : a high rate of savings, and a high
share of temporary migrant workers. Regarding the former one, savings had increased
dramatically after the 1980s, skyrocketing to 37% of household income in the mid 1990s.
As for the latter, there were 140 million of rural-urban migrant workers in 2008, (according
to the National Bureau of Statistics1). Internal migration are not per se surprising - it
is a crucial step in countries’ development and urbanization. What is remarkable in the
Chinese case is the nature of these migrations : most of the migrants are temporary
migrants. Indeed, in a survey carried on in 2005 by the State Council Research Bureau
(see State Council Research Bureau (2006)), only 8.13% of the interviewed migrants
declare that they plan a long term stay at their migration destination. Two years later,
in 2008, the 5000 migrants surveyed by the RUMiCI survey are only 9.5% to say that,
would policy allowed, they would stay forever in cities.
These two striking features of the Chinese economy may find some of their origin in
China specificities regarding fertility patterns, that have been greatly affected by family
planning policies. Banerjee et al. (2010) have shown that the importance of savings rate
in urban area can be related to family planning programs. 40 % of the growth of savings
rate in the last three decades can be explained by the policies designed to curb population
growth.
In this paper, we show that the One Child Policy is a determinant of migration
temporality in rural China : a reduced number of siblings has an impact on the migration
patterns of individuals. The One Child Policy did reduce the total number of children
per family in rural area, though it was less effective than in urban areas. We find that
the cohorts with less siblings subsequently to family planning policies have systematically
experienced shorter duration of migration when they first migrated.
Family size has, theoretically, two opposing effects on migration. On the one hand, if
migration is used to diversify risk (Stark and Levhari (1982), Stark and Bloom (1985)),
one needs to migrate less if one has more siblings. Thus, individuals with fewer siblings
are more likely to migrate. On the other hand, if hired labor is not a good substitute for
the labor of households members, then fewer siblings may mean that an individual is less
likely to migrate. For some households’ needs, hired labor might be an inferior substitute,
like for example the care of elderly parents or children. Tending the farm might as well
requires household members’ labor, especially if their land rights are jeopardized by a
long absence, and if the high seasonality of agriculture results in shortages in farm labour
1See Li (2010)
2
supply. Migration itself might be more difficult in the absence of migrating siblings: they
could provide information or advices (as shown by Kesztenbaum (2008)). In this paper,
we want to estimate the net of these two opposing forces.
This is difficult to investigate, both for econometric and practical reasons, linked to
the lack of appropriate data and to endogeneity concerns. Family size and migration
decision may be affected by a third factor. Parents with a smaller number of kids might
adopt different attitudes toward schooling decision. Migrating parents may have a specific
fertility, while their experience may help their children to migrate later on.
Using a unique survey of migrants conducted in fifteen cities of China in Spring 2008,
we deal with these endogeneity issues by using the family planning policies implemented
in China at the end of the 1970s, a period where migration were not allowed.
Our strategy, building upon Qian (2009), compares migration duration of first borns
belonging to cohorts born before and after the introduction of restrictive family planning
policies. The understanding of the family planning policies in China requires to take into
account their successive steps. A four-year birth spacing law was introduced in 1972. The
One Child Policy was implemented in 1980s. Combined with the former, the One Child
Policy was actually binding for the cohorts born in 1976, as stressed by Qian (2009). We
will therefore compare elder children born before and after 1976.
The results found are the following. Migrants with a lower probability of having
siblings have systematically shorter job duration when first migrating. Cohorts bound
by the One Child Policy migrate in average 3 months less than previous ones, while the
average first job duration is 14 months. To assess the relevance of our interpretation,
and to avoid capturing an other effect of the One Child Policy, namely its impact on sex
ratio, we also consider a sample with male elders only. We find that the difference goes
up to 4 months. We conclude with robustness checks and further comments.
The plan of this paper is the following. Section 2 provides elements on the institutional
context of rural China, regarding family planning policies, agricultural activities and
migration policies. Section 3 describes our data. Section 4 explains our identification
strategy. Section 5 displays our results.
2 Institutional Context
In this section, we will present briefly some institutional aspects relevant for our study,
regarding the family planning policies and the rural-urban migration.
3
2.1 Family Planning Policies in China
After a period of policies promoting a high natality, the Chinese government started to
advocate family planning in the late 1950s. However, no effective birth control measures
were implemented before the 1970s. Introduced in 1972, the “Wan Xi Shao” Policy,
meaning “Later [age], longer [the spacing of births], fewer [number of children]”, was
encouraging four-year birth spacing, and was recommending three children per rural
couple.
The One Child Policy (OCP), implemented from 1978 to 1980, was actually announced
in 19792. Couples complying with the policy were entitled to a “one-child pledge” cer-
tificate and to the economic rewards associated, while “unplanned” birth were exposing
them to fines.
Because the One Child Policy was preceded by the four-year birth spacing law, its
effect started to appear for cohorts born slightly before the OCP, as shown by Qian
(2009). Households who had one child had to wait four years before the next child; if the
first birth occurred in the late 1970s, the One Child Policy stopped them from having a
second child. Qian (2009) uses the timing and the geography of the relaxation of the OCP
to show that its restrictive effect appears in 1976; the cohorts who gained an additional
sibling when the One Child Policy was relaxed were the cohorts born in 1976 and after.
Given the lack of enforcement and the rural preference for numerous families, the
One Child Policy actually bound families at two children rather than one. Kaufman
et al. (1989) report how officials eventually regarded a two-child limitation as an adequate
policy achievement, while other officials were suggesting that “if the government promotes
a one-child policy, then people will stop at two; if a two-child policy is promoted, then
people will stop at three”. The tiny number of households with only one child is further
explained by policy changes occurring afterwards.
All across China, the family planning policies increased female infanticide, forced
abortion and sterilization. After worries raised by the coercive means employed at the
local level, as well as the persistent social resistance to birth control, it was decided to
loosen these policies in the 1980s. In 1984, a second child was allowed for households
facing difficulties or whose first born was a girl, under the condition that a birth spacing
was respected, usually of three or four years. A few areas started to relax the One Child
Policy slightly before, in 1982 and 1983.
Many studies have shown that the birth control policies were responsible for much of
the drop in fertility rate in rural areas (Shultz and Zeng (1995) and McElroy and Yang
(2000)). While few rural households were found to have only one child, the planning
2Qian (2009)
4
policies affected nonetheless their fertility pattern.
Statistical evidence To sum up, family planning policies of the 1970s curbed fertility
rate in rural China, reducing the potential number of siblings of rural individuals. We do
not have census data to show what exactly happened in terms of households fertility, as we
do not have detailed information on migrants’ siblings. Yet, more extensive information
on the cohorts born in the 1970s can be found in the rural sample of the RUMiCI project
(8000 households). Information on household’s 1970s demographic situation is partial, as
this survey has been conducted in 2008. Data on the number of children per household
are biased by deaths and household splittings. Luckily enough, we know the birth rank
of the surveyed individuals.
If family planning policies have impacted rural households fertility, we should observe
variation in the respective share of each birth rank by birth cohort. Births of high order
should diminish in cohorts constrained by family planning policies. We should see that
the share of children born as the first child increases. In Figure 1, we compute, for each
birth cohort, the proportion of first, second, and third and above children. We plot the
percentages obtained. We plot a similar graph for the male children in Figure 2.
20
30
40
50
Perc
enta
ge in the c
ohort
197619721960 1965 1970 1975 1980
Birth year of the cohort considered
elder child second
third and above
all - rural sample
share of individuals of a given birth rank by cohort
Figure 1: Share of birth ranks within birth cohort
The first remark is in line with what has been previously said regarding policies
implementation in rural areas : the One Child Policy did not translate into cohorts of
only-child, where all the newborns would be the first child of the households. There are
many children who were born as second child in 1980 : the share of second order births
remains quite stable until the early 1980s. Yet, we see that in 1980 there is a clear drop
in the share of birth of higher order, which becomes almost null.
The second remark is that the birth spacing policies implemented before have been
5
20
30
40
50
Pe
rce
nta
ge
in
th
e c
oh
ort
197619721960 1965 1970 1975 1980
Birth year of the cohort considered
elder child second
third and above
men - rural sample
share of individuals of a given birth rank by cohort
Figure 2: Share of birth ranks by cohort - male
effective. The share of births of third order and above starts to decrease in 1972, and
does even more so four years after, in 1976.
In Figure 1, we see that although the major part of births in the 1960s were occurring
in families having already two or more kids, (they account for more than 40 % of births),
there is a clear shift in the mid 1970s, and 1976 is the first year where the share of first
borns is higher than the share of births of third or higher order. If we restrict our sample
to males, (Figure 2), differences are made clearer. Again, a sharp decrease takes place at
the end of the 1970s.
20
30
40
50
60
Pe
rce
nta
ge
in
th
e c
oh
ort
197619721960 1965 1970 1975 1980
Birth year of the cohort considered
elder child second
third and above
all- migrant sample
share of individuals of a given birth rank by cohort
Figure 3: Share of birth ranks within cohort among rural-urban migrants
6
20
30
40
50
60
Perc
enta
ge in t
he c
ohort
197619721960 1965 1970 1975 1980
Birth year of the cohort considered
elder child second
third and above
men- migrant sample
share of individuals of a given birth rank by cohort
Figure 4: Share of birth ranks within cohort among rural-urban male migrants
We might be worried that the rural sample fails to account for households whose
members are all rural migrants. First, given the size of rural population, it is unlikely that
the clear patterns we observe are driven by migration. Still, we provide similar graphics
for the different cohorts of the rural-migrant sample. In rural-migrant population, we see
as well that individuals born after 1980 are more likely to be the elder child. The share
of first born children increases slightly after 1976, as the figures 3 and 4 show.
2.2 Migration, Land and Family Help
Various institutions restrict rural-urban migration.
Land rights uncertainty affects the mobility of rural outmigrants, as shown by Lohmar
(1999), Shi (2004) and de la Rupelle et al. (2009).
Migration decision may be affected by parents health, as shown by Giles and Ren
(2007). In rural area, help given to origin households is mostly done through labor
exchange (Lee and Xiao (1998)). The harvest season is a critical period, where all families
may lack labor, and where therefore the help of relatives can be of crucial importance.
Not only markets are imperfect; but China institutional system may also increase the role
plaid by family size. Through the household registration system, rural households are
granted use rights on a piece of land, for which they have to fulfill quota requirements.
Household land area depends on household size and household number of laborers; yet,
an increased family size, through economies of scale and deeper inclusion in village’s labor
exchange, releases somehow the constraints attached with tax duties.
Restrictions on migration give additional value to household’s resources in labor
7
power.
3 Data
The data used in this paper comes from the RUMiCI (Rural-Urban Migration in China
and Indonesia) Project3.
The main originality of this project relies in the method used. The sampling frame
was based on a first census conducted among migrant workers at their workplaces. City
areas (including suburban areas where factories are located) were divided into blocks of
500 × 500 meter. In the randomly selected blocks 4, all the workplaces were surveyed.
This census was then used to sample migrants, whose household was also surveyed5.
An important advantage is that we have information on a crucial step of a migrant
experience : the very first one. We know the duration of the first job as migrant. Un-
derstanding the conditions of the primary migration experience is interesting, and it can
help to avoid troubles arising when comparing individuals at various time in their life
cycle, who have heterogeneous background as migrants. The experience gained and the
contacts established during the first job as migrant may shape a migrant trajectory.
The data provides the duration, in months, of the first job as a migrant. We consider
it is a good proxy for the duration of the first migration, as staying in cities without
employment is more costly than coming back. In the years during which most of the
first migration experiences occurred, (second half of the 1990s decade), migration poli-
cies were quite restrictive. Even recently, despite considerable loosening of institutional
constraints, the massive shutdowns in the aftermath of the financial crisis caused many
laid off migrants to come back to their origin village.
The main inconvenient is that we do not have the total number of siblings in our data.
We know only individual’s birth rank. Our identification strategy will have to take into
account this shortcoming.
The sample we use is of 5 000 rural-urban migrants, who were surveyed in 15 cities 6.
3This project, funded by the Australian Research Council, the Australian Agency for International
Development (AusAID) and the Ford Foundation, was initiated as a collaboration between the Australian
National University and the Beijing Normal University.4(accounting for 12 % of each city size)5More information on the sampling method are available at the following address :
http://rumici.anu.edu.au/6Shanghai, Guangzhou, Shenzhen, Dongguan, Nanjing, Wuxi, Hangzhou, Ningbo, Wuhan,
Chongqing, Chengdu, Hefei, Bangbu, Luoyang and Zhenzghou.
8
4 Empirical Strategy
If family size affects migration, its effect is nonetheless hard to identify properly. Some
characteristics may jointly determine fertility decision and ulterior migration of the chil-
dren. For example, it could be that parents who have a smaller number of kids adopt
different attitudes toward schooling decision, that will later have an impact on their
situation on the labor market.
Interestingly enough, before the 1980s, migration was prohibited, and its control was
strictly enforced. The loosening of the structures of collective economy in the 1980s
relaxed somehow the constraints affecting migration, without legalizing it. Therefore,
the parents who were affected by the family planning programs in the 1970s had very
little - if none - experience as migrant. So we should not worry about this endogeneity
channel.
To deal with other endogeneity sources, we take advantage of China family planning
policies that were introduced during the seventies. The introduction of these policies
produced an exogenous shock on family size. The number of siblings decreased for cohorts
born in the 1970s.
We will compare the elder born before and after the implementation of family planning
policy. There are three reasons for us to focus on the first born children. First, as birth
rank may affect individuals trajectories, and as birth ranks of high order are made rarer
by family planning policies, it would be misleading to simply compare people born before
and after their implementation : we might capture the effect of a higher share of first
born in the population. Second, the number of siblings may have a different impact if
they are older or younger. Last, we can consider that the “treatment effect” of family
planning policies is higher for the elders. It is true that all birth orders are affected
by a reduced family size. All the individuals born after 1976 should have been the last
child of their parents. But the higher the birth rank is, the higher the chance that the
family has already reached the desired number of children, and is not constrained by
the policy. Moreover, the bigger the family is, the higher the probability is to select a
family in an area where the planning policies were poorly implemented. The population
of elder children includes all the households who were the most heavily constrained by
the planning policies.
Let us note l the duration of the first job spell of a migrant. Our identification strategy
leads us to focus on the following equation :
l = β(elder × postp) + αelder + δpostp +Xγ + ε (1)
elder is a dummy indicating whether the individuals is the elder or not. postp indi-
9
cates that he was born after 1976 and the change in planning policies. X are relevant
characteristics, while ε is the error term. The interaction term (elder×postp) is our main
term of interest, as it indicates the group of individuals who were the most subjects to
family planning policies. If the number of siblings eases the family constraints weighing
on migration decision, we would expect the coefficient of the interaction term, β to be
negative. In such a setting, time-invariant differences across birth ranking will be differ-
enced out by the comparison across time. Then, changes across time which affect elder
and non elder similarly will be differenced out by the comparison across birth ranking.
As we look at the duration of job spells, it is important to recall that labor market
seasonality may have a direct impact on it; regarding the business cycle, different start-
ing period may bring systematically different opportunities regarding job length. For
instance, if a given month is a high period of long term workers recruitment, a different
starting month will bring systematically a different working duration. To control for
urban labor markets seasonality, we introduce job starting month fixed effects.
The duration of the first migration spell of a migrant who has started to work in
month m can be written as follow:
l = β(elder × postp) + αelder + δpostp + ψm + γX + ε (2)
ψm is the month fixed effect. Of course, it only controls for trends affecting all migrants
in a similar way.
However, it could be that individuals born after 1976 were more likely to face a
difficult year, or a change in business cycles trend. They would have had similar patterns
than previous cohorts, starting at a similar age, leaving their job after a similar duration.
Simply, by entering one year later in the labor market, they were more likely to experience
a specific downturn, and to be constrained to leave the city. If it was the case, our estimate
would confound the effect of change in macroeconomic situation with the effect of family
planning policies.
To control for this potential problem, we introduce dummy variables for the year in
which migrants left their job, φt. The last equation we consider is the following :
l = β(elder × postp) + αelder + δpostp + ψm + φt + γX + ε (3)
We estimate these equations using a Tobit model, therefore relying on the assumption
of errors normality.
10
5 Results
The rural-urban migrant population surveyed in the RUMiCI project allows us to study
the impact of the family planning policies, as the subsamples of migrants born before
and after the implementation of family planning policies are both of reasonable size. The
migrants sampled in 2008 were 30.5 years old in average. Figure 3 plots the density of
the birth year of household heads. 40% of them were born before 1976; the median year
of birth is 1980. Besides these 5007 migrants, all the individuals staying with the migrant
have been surveyed7. Figure 4 plots the density of the birth year of all household members
staying with the head. When excluding the children under the age of 16, the median value
of the sample is 1978, while the share of individuals born before 1976 increased at 45 %.
To keep cohorts big enough to allow comparisons, but homogeneous enough for the
goal of our study, our sample is restricted to the cohorts born after 1962. These cohorts
were born after the years of natality promotion of the early 1950s, and, more important,
after the Great Chinese Famine (1958-1961) which followed the Great Leap Forward and
caused a severe drop in the birth rate, while the death rate increased sharply. Moreover,
these cohorts started to work after the end of the Cultural Revolution (1976), which
makes them even more comparable. In a subsequent set of regression, we also consider
an even more homogeneous sample by keeping all individuals who have started to work
in a liberalizing economy, by considering cohorts born after 1970.
Last, we do not consider individuals born after planning policies relaxation. As the
first local government to issue permits for a second child did so in 1982 (see Qian (2009)),
we keep only cohorts that were born before 1981.
Complete information on our dependent variable is obviously only available for people
who are not in their first migration spell anymore. As the ending date is crucial for our
study, we choose to focus on individuals whose current job is not the first job as a
migrant. They account for almost two thirds of the 1962-1981 cohorts. The average
migration duration of their first job as migrant amounts to 14 months. Figure 5 plot the
distribution of migration duration, for migrants who were born before and after 1976.
First, migrants born before after 1976 have, in average, a shorter duration of migration.
Second, the distribution is less smooth, and exhibits more seasonality. Given the hight
seasonality of agriculture, this could signal that migrants born after 1976 are more likely
to come back home for agricultural reasons. Migrants born after 1976 are less likely to
have a first job spell ending after 18 to 21 months. A second mode is visible at 24 months,
while there is almost no change for migrants born before 1976.
As our focus is on temporary migration, and to allow more comparability, we do not
7They amount to 3400 individuals, 2500 of them being above 16.
11
0.0
2.0
4.0
6d
en
sity:
du
ratio
n_
firs
tjo
b
0 20 40 60number of months
born after 1976 born before 1976
sample restriction : job as migrant started after 1980 and lasted less than 5 years
months - 1961-1981 cohorts
Duration of the first job as migrant
Figure 5: Density of the duration of migrants first stay in cities
consider spells exceeding 5 years, which represent a small share (less than 4%). Our
dependent variable is therefore censored at 1 and 60 months.
As written before, the equation we estimate is the following :
l = β(elder × postp) + αelder + δpostp + ψ + φ+Xγ + ε (4)
The relevant characteristics, the X, includes characteristics at the individual, house-
hold and village level. The individual characteristics that should matter in explaining
wage level and employment opportunities are the years of education, the gender, the age
when first migrating. Then, we control for household ethnicity, which affects employment
and migration opportunities. The main village characteristic we consider is the geograph-
ical condition of village, whether it is plain, hilly or mountainous. Village topography, a
good proxy for its remoteness, captures difference in educational opportunities, economic
dynamism and connection to transportation networks and urban markets.
Table 6 in the Appendix displays the descriptive statistics, and provide as well statis-
tics for both groups of elder and non elder. It is difficult to interpret differences across
groups, as they also capture differences in age and in family types (as individuals of
higher birth ranking are belonging to more numerous families...). Table 1 displays the
regression results.
From the first regression we run, we see that the interaction term between being the
elder child and being born after 1976 has a negative and significant effect. An exogenous
decrease in the number of younger siblings has led first born individuals to shorten their
migration duration by two months. Given that the average duration of migration among
12
Table 1: Migration duration and family sizeMigration duration and family size
Dependent variable : first job duration as migrant (months). Tobit model.
(1) (2) (3) (4) (5)
first reg with interaction term with land final year dummy head only
education 0.387*** 0.393*** 0.347*** 0.252** 0.167
[0.120] [0.120] [0.129] [0.127] [0.152]
male -2.219*** -2.200*** -2.366*** -1.776*** -2.124**
[0.602] [0.601] [0.637] [0.623] [0.849]
age at start -0.152*** -0.152*** -0.176*** -0.789*** -0.701***
[0.0524] [0.0523] [0.0550] [0.0956] [0.118]
elder -0.743 0.297 0.382 0.393 -0.357
[0.624] [0.822] [0.862] [0.842] [1.017]
born after 1976 -3.622*** -2.801*** -2.186*** -8.170*** -6.974***
[0.666] [0.789] [0.828] [1.121] [1.357]
minority 0.363 0.230 -1.492 -1.115 -1.750
[2.109] [2.108] [2.257] [2.201] [2.537]
elder X born after 1976 -2.426* -3.004** -3.151** -3.457**
[1.252] [1.313] [1.279] [1.537]
log hh land -0.940* -0.993** -0.759
[0.510] [0.499] [0.593]
hometown geography yes yes yes yes yes
starting month dummies yes yes yes yes yes
final year dummies no no no yes yes
Constant 17.15*** 16.77*** 17.88*** 46.45*** 45.15***
[2.103] [2.110] [2.234] [12.37] [12.74]
Observations 1789 1789 1586 1586 1118
Pseudo R2 0.005 0.005 0.006 0.015 0.015
Standard errors in brackets
* p < 0.10, ** p < 0.05, *** p < 0.01
Sample restricted to 1962-1981 cohorts whose first job initiated after 1980 and lasted less than 5 years
13
the sample considered is 14 months, it is a non negligible effect.
The other variables have non surprising effects. Education plays a positive role on
duration, though a small one, implying that more educated migrants are also more stable
in their first working experience as a migrant. The dummy male has a negative and
significant effect. We believe that male migrants shorten their migration to participate
in agricultural work, more than female migrants.
If household involvement in agricultural work matters, we should find a sharper effect
when excluding the migrants whose households do not use any farmland, and may there-
fore have more flexible work schedules. To test this hypothesis, we drop the 200 migrants
coming from a household who does not hold any land, and we control for the logarithm of
land per person in the family. 8 The regression is presented in the third column of Table
1. In this regression, the coefficient of the term of interest is more significant, which is
consistent with our interpretation.
As mentioned in the previous section, such a setting fails to control properly for
business cycles or year specific shocks. It would be worrisome if elders, having migrating
earlier, were more likely to face this special year, either at their destination area or at
their home village, that would have require them to go back home. Our specification fails
to capture shocks affecting differently elder and younger siblings both born after 1976.
Therefore, we add dummies controlling for the year in which the migrant interrupted
his job. The regression is presented in the column four of Table 1. Our results remain
unchanged.
In all regressions, we have considered all the surveyed individuals : the migrants
selected through the sampling process, and the household members living with them.
They might be migrants’ kids or spouse. In the regression displayed in the fifth column
of Table 1, we restrict our sample to the initial migrants sample. The column is labeled
“head only” 9. Our results hold : the coefficient of the interaction term is still negative
and significant at the 5% level.
8Though the mean of land endowment in origin household is at around two mu per person (One mu
is equal to 666,67 m2 (25,8m × 25,8m)), around 40 migrants of our sample rely on 10 mu and more per
person. We take the logarithm so that our findings will not be driven by families relying on a big amount
of land.9It is important to note that this term does not refer to intra-household decision making process but
to the survey sampling frame.
14
5.1 Robustness checks
5.1.1 Alternative samples
Sector specific shocks When interpreting these results, one may want to know whether
this shortened duration is reflecting macroeconomic shocks or if it is the result of a choice.
Luckily enough, we know the condition under which the job ended, namely whether
the job was left voluntarily, or not. There is no reason to believe that a firm would
prefer to fire employees without siblings rather than employees with a numerous family.
Although mechanisms are not very clear, we could imagine that if elder versus non elder
are employed in very different types of sector, and if a specific sectoral shock decreases
considerably job duration for the youngest cohorts, it would impact the coefficient of
the interaction term. One way to check that we are not actually capturing differences
in sectoral shocks is to drop the 300 migrants in our regression sample who did not left
voluntarily their job. If the effect disappears, this will put into question our interpretation.
If the effect persists, though it will not rule out alternative explanation, at least it will
be consistent with our explanation. When we exclude them, we see that our results are
holding, as shown in Table 2. The coefficient of interaction term is still significant at the
5% level.
Gender A more serious concern relates to the main characteristic that changed for
elder who were born before and after the implementation of the One Child Policy : the
gender. The One Child Policy had an impact on the sex ratio, it increased the practice of
female infanticide or selective abortion. Our result may capture the fact that babies born
after 1976 were mostly males. We are then just capturing a change in gender composition
within cohort. However, in 1976, only the 4-year birth spacing policies was enforced - the
One Child Policy was to be announced and implemented four years later. The households
did not know that their baby was meant to be the only one, so sex selection was not as
dramatic as in the early 1980s. It does not mean that sex selection did not occur, but
there is no reason to believe that it suddenly increased between 1975 and 1976. Last, if
birth spacing has given rise to sex selection, then it would have started in 1972, when the
policy was implemented, not in 1976.
Moreover, in the 1980s, planning policies were partially relaxed in rural areas, allowing
families with only one girl to have a second child. As a result, first born women born after
1976 have usually gained a sibling afterwards; Qian (2009) confirms it. The treatment
effect captured by our term of interest is therefore weaker on female elder. So not only
we hope to obtain a significant coefficient for our interacted term, but we hope also it
will have a bigger magnitude.
15
Table 2: Migration duration and family size when job was left voluntarilyMigration duration and family size - Job was left voluntarily
Dependent variable : first job duration as migrant (months). Tobit model.
(1) (2) (3) (4)
with land final year dummies male only male head only
education 0.273** 0.184 0.187 0.102
[0.135] [0.134] [0.173] [0.183]
male -2.187*** -1.469**
[0.664] [0.651]
age at start -0.176*** -0.793*** -0.738*** -0.726***
[0.0585] [0.101] [0.128] [0.141]
elder 0.444 0.492 1.274 0.536
[0.912] [0.890] [1.124] [1.221]
born after 1976 -2.547*** -8.669*** -9.038*** -8.655***
[0.863] [1.173] [1.499] [1.621]
elder X born after 1976 -2.690** -2.836** -4.214** -4.144**
[1.368] [1.331] [1.668] [1.819]
minority -1.107 -0.932 -2.365 -3.124
[2.303] [2.241] [2.765] [2.880]
log hh land -1.025* -1.050** -0.224 -0.0593
[0.531] [0.519] [0.637] [0.691]
hometown geography yes yes yes yes
starting month dummies yes yes yes yes
final year dummies no yes yes yes
Constant 18.77*** 17.27 15.00 33.66***
[2.340] [11.90] [11.78] [6.609]
Observations 1452 1452 897 779
Pseudo R2 0.006 0.015 0.016 0.017
Standard errors in brackets
*** p<0.01, ** p<0.05, * p<0.1
Sample restricted to 1962-1981 cohorts whose first job initiated after 1980; lasted less than 5 years; was left voluntarily
16
We run regressions restricted to male migrants, that we present in the column 3 and
4 of Table 2. Our results remain unchanged, and the effect is stronger.
Agriculture The principal concern we have is related to the accuracy of our inter-
pretation. We have argued that agricultural work was of crucial importance; we run
additional regressions to check that this is actually the case. First, migration duration
will be shortened in order to help the household during the peak agricultural season only
if the family is actually farming its land. Households who have given away their land to
neighbors, or who are subcontracting it should be far less demanding than households
working on their plot. Their might be other reasons constraining migrants’ decision and
leading individuals with less siblings to come back earlier; yet, as we suspect that needs
related to agricultural work are of primary importance for many rural households, the
impact of the interacted term should be less significant for families who do not have
these needs. We run two regressions, restricting our sample first to migrants whose land
is farmed by family, second to migrants whose land has been subcontracted or given away.
The results, displayed in Table 3, are consistent with our interpretation. In the sample
where land is subcontracted or given away, standard errors are much higher than in the
sample where land is farmed by family. The coefficient does not changed its value, but it
is not significant anymore. One of the reason is that our sample size is smaller. However,
other regressions with small sample size keep the coefficient significance (see below).
Similarly, if our reasoning is correct, then we should find a stronger effect for the
migrants who were likely to have participated in agricultural work before migrating.
If their family lacks labor power, then the migrants were probably already involved in
farming activities when they were living in their home village. They would not have then
left their home during the harvest season, nor slightly before. We remove the individuals
who have left home between June and November, a period of intensive agriculture activity.
These individuals were not constrained by household agricultural requirements the year
during which they left. The coefficient of the interaction term conveys a stronger impact;
it is even more significant. Restricting further our sample by considering only men gives
further support. (See column 3 and 4 of Table 3.)
17
Table 3: Farming work and migrationRobustness check: farming work and migration
Dependent variable : first job duration as migrant (months). Tobit model.
who does the farming work? migrant left home
before June or after November
Land is : did not leave during peak season
(1) (2) (3) (4)
VARIABLES farmed by family subcontracted, given away all men only
education 0.125 0.364 0.141 0.105
[0.160] [0.247] [0.148] [0.192]
male -1.823** -0.269 -0.834
[0.750] [1.287] [0.731]
age at start -0.791*** -0.909*** -0.896*** -0.848***
[0.140] [0.177] [0.113] [0.146]
elder 1.485 0.0558 1.383 2.986**
[1.108] [1.649] [1.001] [1.274]
born after 1976 -6.715*** -8.751*** -9.265*** -9.828***
[1.374] [2.480] [1.317] [1.700]
elder X born after 1976 -3.788** -3.825 -4.303*** -5.702***
[1.509] [2.919] [1.510] [1.901]
minority 3.761 -7.354* 1.422 1.850
[2.821] [4.401] [2.750] [3.660]
log hh land -1.006* -1.641 -0.681 0.270
[0.606] [1.011] [0.587] [0.731]
hometown geography yes yes yes yes
starting month yes yes yes yes
final year yes yes yes yes
Constant 44.89*** 52.01*** 18.73 18.11
[12.19] [9.118] [11.61] [11.49]
Observations 924 447 1081 662
Pseudo R2 0.016 0.028 0.016 0.020
Standard errors in brackets
*** p<0.01, ** p<0.05, * p<0.1
Sample restricted to 1962-1981 cohorts whose first job initiated after 1980; lasted less than 5 years
Additional sample restriction is indicated in column header
18
5.1.2 Placebo regressions
Yet, a question remains. Our results seem to be related to family needs, notably in terms
of agricultural work. Do we actually capture the effect of family planning policies, or do
we confound it with broad changes that occurred after the opening of the economy? It
could be that individuals born after 1976 were more likely to face a difficult year, or a
change in business cycles trend.
Younger and elder cohorts could also have had different experiences, strongly affecting
their migration opportunities. For example, the children born in 1976 and after were
more likely to be educated in a different context, and to enter primary schools in the
1980s. After the reforms, educational quality changed: rural enrollment rates decreased
substantially, while many rural schools of low quality were shut down (Hannum et al.
(2007)). This could be worrisome if it affected elder children in a specific way. If the elder
child was the only one to be educated, then a change in education quality would affect
elder and non elder differently. However, the schools’ shut downs mainly affected junior
secondary schools (Hannum et al. (2007)). As recalled by Qian (2009), the degradation
of education quality was important for middle-schools and highschools, while the change
in primary education was negligible.
The sample was chosen in order to be able to keep comparable cohorts. However,
the pre-1970 cohorts may have started to work before liberalization process initiates; to
further check that our effect still holds for cohorts more similar, we run a few regressions
on a restricted sample, only taking into account the 1970-1981 cohorts. All these cohorts
may have had comparable middle-school education. Results are holding, as shown in
column one and two of Table 4.
Our effect is therefore not explained by differences between 1960s and 1970s cohorts,
and holds when doing our investigation within the 1970s cohorts.
To deal with similar concerns on specific time trends affecting elder, we run a set of
Tobit regressions, keeping everything identical but the year used to compare elder cohorts
across two groups. We expect that the coefficient of the interaction term would not be
significant for these “placebo” years.
First, we consider the full sample. The effect previously obtained is driven by the
comparison across younger and older cohorts, so we should still find an effect when
splitting the sample for other years, as there will still be a majority of unconstrained
people in the cohorts born before the “placebo” year.
So in a second step, we will drop all migrants born after 1976. The coefficient obtained
should be very different.
19
Table 4: Migration duration and family size - 1970-1981 cohortsMigration duration and family size - 1970-1981 cohorts
Dependent variable : first job duration as migrant (months). Tobit model.
(1) (2) (3)
final year dummies head only male only
education -0.0760 -0.107 0.0433
[0.145] [0.173] [0.188]
male -1.716** -1.905**
[0.687] [0.917]
age at start -2.213*** -2.031*** -2.030***
[0.183] [0.218] [0.227]
elder 0.804 0.204 1.051
[1.078] [1.284] [1.330]
born after 1976 -14.21*** -12.70*** -13.85***
[1.339] [1.597] [1.658]
elder X born after 1976 -3.464** -3.855** -4.418**
[1.413] [1.682] [1.758]
minority 1.566 1.925 1.064
[2.304] [2.639] [2.880]
log hh land -0.888 -0.640 -0.376
[0.546] [0.645] [0.662]
hometown geography yes yes yes
starting month dummies yes yes yes
final year dummies yes yes yes
Constant 45.59*** 32.28*** 29.88***
[11.62] [11.39] [8.571]
Observations 1164 820 719
Pseudo R2 0.025 0.026 0.026
Standard errors in brackets
* p < 0.10, ** p < 0.05, *** p < 0.01
Sample restricted to 1970-1981 cohorts whose first job initiated after 1980 and lasted less than 5 years
20
In the figure 6, we plot the coefficients of the interaction term (elder)*(born after year
Y) obtained for different years Y. The only year exhibiting a coefficient significant at the
5% level is 1976.
In the figure 7, we do the same, but restrict our sample to individuals born before
1976. Thus we drop the individuals affected by the One Child Policy. We do not obtain
significant coefficients anymore. Even more interestingly, the coefficient value is positive,
and it seems to follow an increasing trend.
Then, we plot similar figures for the population of male only, which should be more
affected by family planning policies. In figure 8, two years exhibit significant coefficients
: 1976 and 1972. The result is interesting, as in 1972 the four-year birth spacing law was
implemented. This shows that our effect is related to family planning policies. When we
look at the results obtained on the sample of male migrants born before 1976, displayed
in figure 9, the comparison is very striking.
Figure 6: Coefficient of the interaction term between being an elder and belonging to
cohorts born after the year x. Along with the x axis, the year used as a “placebo”,
splitting the two samples, varies.
21
Figure 7: Coefficient of the interaction term between being an elder and belonging to
cohorts born after the year x. Along with the x axis, the year used as a “placebo”,
splitting the two samples, varies. Sample restricted to migrants born before 1976.
Figure 8: Coefficient of the interaction term between being an elder and belonging to
cohorts born after the year x. Along with the x axis, the year used as a “placebo”,
splitting the two samples, varies. Male only
22
Figure 9: Coefficient of the interaction term between being an elder and belonging to
cohorts born after the year x. Along with the x axis, the year used as a “placebo”,
splitting the two samples, varies. Male born before 1976 only.
Migration location Finally, we may be concerned by the fact that job durations of
migrants are shorter because they prefer to leave their place of work in order to search for
jobs in other cities. Indeed, in Bhattacharya (1990)’s theoretical framework, migration
temporariness is explained by a search process in various locations. Data provides us
with the number of cities/towns where individuals have ever migrated for work purpose.
We run regressions with this variable as dependent variable, both with OLS and Tobit
model. Our variable of interest has no impact on the number of cities that a person
has ever migrated to. Therefore, shorter migration duration can not be related to an
increased number of migration locations.
5.2 Comment on sample selection
As mentioned before, we have been doing our study on migrants whose current job is not
their first job as a migrant. This implies a sample selection along two dimensions. First,
among the urban migrants, we do not consider permanent migrants. As said before, a
definitive settling in an urban area is difficult and therefore often out of reach for most
rural outmigrants. Nevertheless, migrants who never came back home will not be included
in our sample. Second, we do not account for temporary migrants who may migrate only
once during their working life, and never come back in a city afterwards. It can be the
case if the first experience as a migrant was a bad experience (if for example they did
not get paid at the end, as it could happen for the first generation of migrants, whose
working conditions were especially harsh) or after a dramatic change in their household
23
situation at home.
Regarding the former concern, a first way to answer it is to include in our sample the
migrants we had dropped and run similar regressions. The dependent variable becomes
much noisier, especially as the survey was not done at the same month for all. Most
of migrant households were interviewed in May or April (56% of the households were
surveyed in May; 32% in April), still, 11% of them were surveyed in March, June, and
even August for a handful of them. To account for these issues, duration models would
be more appropriate. The use of such models is planned for the next step of our research
project. The Tobit model already allows us to obtain promising results. The main change
made from previous regression is that an other dummy is added to the set of dummies
indicating the year in which the migration spell we terminated. It equals to one when
the migration spell “ended” after the survey was done. The last table, Table 5, shows
that the term of interest is still negative and significant.
Regarding the latter concern, one solution would consist in extending our analysis
to the rural sample of the RUMiCI survey. The short term migrants who had only one
experience as a migrant could, hopefully, be observed in this sample.
Last, an other interesting direction of research is to provide empirical elements showing
the relevance of the first migration spell in migrants trajectories.
24
Table 5: Sample including individuals whose current job is their first jobMigration duration and family size - Including unfinished migration spells
Dependent variable : first job duration as migrant (months). Tobit model.
(1) (2) (3) (4)
Final year dummy With land Male Male heads
education 0.232** 0.204* 0.324** 0.260
[0.115] [0.123] [0.159] [0.168]
male -0.899 -0.808
[0.563] [0.599]
age at start -1.033*** -1.065*** -0.914*** -0.908***
[0.0831] [0.0877] [0.115] [0.124]
elder 0.679 0.750 1.576 0.823
[0.761] [0.802] [1.022] [1.099]
born after 1976 -10.36*** -10.05*** -8.527*** -7.883***
[1.031] [1.086] [1.408] [1.499]
elder X born after 1976 -2.574** -2.864** -4.575*** -4.072**
[1.173] [1.237] [1.576] [1.700]
minority 0.979 -1.303 -1.883 -2.231
[2.016] [2.191] [2.646] [2.692]
log hh land -0.933** -0.478 -0.305
[0.466] [0.577] [0.614]
origin village geography yes yes yes yes
starting month dummies yes yes yes yes
final year dummies yes yes yes yes
Constant 19.76 20.22 16.08 20.87***
[13.02] [12.93] [12.64] [6.223]
Observations 2305 2039 1186 1019
Pseudo R2 0.035 0.036 0.038 0.034
Standard errors in brackets
* p < 0.10, ** p < 0.05, *** p < 0.01
Sample restricted to 1962-1981 cohorts whose first job initiated after 1980
25
6 Appendix
Table 6: Descriptive statistics and birth rank
Individuals All Are Are notthe elder the elder
Variables Mean St. Dev. Mean St. Dev. Mean St. Dev.
Individual characteristicseducation (years) 8.75 2.513 9.2 2.509 8.54 2.487male 0.62 0.486 0.61 0.488 0.62 0.484elder 0.32 0.467 1 0 0 0born after 1976 0.39 0.487 0.47 0.5 0.35 0.477Household characteristicsland (mu) 1.94 2.121 1.99 2.282 1.92 2.041from an ethnic minority 0.02 0.02 0.02Village characteristicsplain 0.48 0.49 0.48hills 0.29 0.3 0.29mountains 0.23 0.21 0.23First migrant jobduration (months) 14.62 11.968 14.1 11.442 14.87 12.205age at start 23.14 6.043 22.91 5.792 23.25 6.158starting month 5.35 3.014 5.5 3.041 5.28 2.999year of job ending 1996.69 5.466 1997.29 5.035 1996.41 5.638job left voluntarily 0.91 0.28 0.93 0.263 0.91 0.287
Observations 1789 574 1215
Sample restricted to 1962-1981 cohorts whose first job initiated after 1980 and lasted less than 5 years
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