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1
Language as Identity: Dialectal Distance and Labor Migration in China
Yuyun Liu, Xianxiang Xu
Sun Yat-sen University
Jan 31, 2016
Abstract
This paper explores the question how dialectal (linguistic) distance affects labor migration.
By using the special sample of labor migration in China who speak both native dialect and
Putonghua (standard pronunciation of Chinese), we have identified the true reason that dialectal
distance prevent migration, which is weaker feeling of identification of destinations that are
dialectally further rather than higher cost of learning its dialect to eliminate linguistic
communication obstacle, and once dialectal distance is large enough that has been across dialectal
super-groups it reduces migration probability by about 20%. We have also found a positive effect
of dialectal distance that comes from diversified complementary skills and thinking ways brought
in by migrants from dialectally further cities, and its magnitude is about 30% when dialectal
distance increases by one level within a dialectal super-group. Thirdly, the prevention from
identification effect becomes stronger if a labor have stronger social network because it increases
the opportunity cost of migration. These findings are robust.
Keywords: Labor Migration; Chinese Dialectal Distance; Identification Effect; Complementary
Effect
JEL Classification Numbers: J60, J61, Z10
2
1 Introduction
This paper explores the question how dialectal (linguistic) distance affects labor migration. Recent
studies show migration rates increase with linguistic proximity and a migrant’s linguistic
competence in the destination country (Adsera and Pytlikova, 2015), because if a migrant’s native
language is linguistically closer to the language in destination, it is easier for him to learn
(Chiswick and Miller, 2005; Isphording and Otten, 2011), and the linguistic competence is
strongly associated with a migrant’s earning and social outcomes in the destination (Bleakley and
Chin 2004, 2010). So, linguistic distance could reduce labor migration through the mechanism of
linguistic communication.
However, the negative relationship between linguistic distance and migration rate could be
explained by another mechanism, identification effect. Research in social psychology shows,
contact between similar people occurs at a higher rate than among dissimilar people (McPherson
et al., 2001). Language, as an important dimension of ethnic identity and membership (Pendakur
and Pendakur, 2002), could lead to more migration between two countries that are linguistically
closer because of perceived similarities (Huston and Levinger, 1978), a feeling of identification.
Pendakur and Pendakur (2002) found that, conditional on knowledge of a majority language,
knowledge of a minority language is associated with lower earnings, and this could be attributed
to ethnicity operating either through the immigrant working in an ethno-linguistic labor market
enclave or to labor market discrimination against minorities.
Then, what on earth is the mechanism that linguistic distance inhibits labor migration,
linguistic communication or identification effect? Drawing an analogy, what is the true reason that
a US company rejects a foreigner’s job application, limited English skill because he comes from
an Asian country, or just because he comes from an Asian country?
It is hard to distinguish the identification effect from the linguistic communication effect. If
one country is linguistically closer to another, both effects are positive. It is easier for people to
learn each other’s language to eliminate communication obstacles for migration. Closer language
leads to more identification of the other country because of perceived similarities in language,
which could attract more labor migration between the two countries. Even if we could measure
individual’s linguistic competence in the destination country, it is still hard to say whether the
individual’s high linguistic competence is due to closer linguistic distance between the destination
country and source country, or is due to higher identification of the destination country which is
linguistically closer to the source country.
In this paper, we deal with this problem by using a special sample of labor migration in China.
Chinese people speak both their native dialect and Putonghua which is the standard pronunciation
of Chinese. Each dialect has its specific pronunciation and historic origin, which generates both
linguistic obstacles among different dialects and different dialectal identity. Dialect is an indirect
indicator of where one’s ancestors came from, so is an important dimension of identity in China.
Nevertheless, Putonghua could eliminate oral communication obstacles among dialects, thus help
us to distinguish the identification effect from linguistic communication effect. Besides, in spite
that dialects are different in pronunciation, Chinese characters are mutual and shared no matter
which dialect you speak, and rule out the communication obstacles in writing. Thus, using the
sample of labor migration in China to study the influence of dialectal distance could help us to
distinguish the identification effect from linguistic communication effect.
3
This paper measures dialectal distance according to current studies (Spolaore and Wacziarg,
2009; Adsera and Pytlikova, 2015) by counting the steps to the common nodes on the Chinese
dialectal family tree from Language Atlas of China (1987). Measurement along dialectal family
tree tells us the pair-wise dialectal distance of counties. Furtherly with population shares in 1982
we have calculated the population-share-weighed pair-wise dialectal distance of 278 prefectures,
which could be matched with the birthplace (source) and destination of labor migration.
We measure labor migration by discrete variables, whether an individual has ever migrated
from his birthplace to another prefecture or province and resided for more than 6 months. We
identify the birthplace prefecture and destination prefecture of each migrant’s first-time migration
in China Labor-force Dynamic Survey 2012 (CLDS) hosted by Center for Social Survey in Sun
Yat-Sen University. By matching with dialectal distance we have constructed a micro-dataset of
labor migration across dialects in China.
There are two points to be explained about this dataset. Firstly, we measure dialectal distance
between a migrant’s birthplace and destination, rather than that between the migrant himself and
his destination, namely his linguistic competence in destination, because we want to rule out
endogeneity that in order to migrate to the destination the migrant learn its language on his own
initiative, and that linguistic competence could be correlated with unobserved ability in residual.
Secondly, we focus on each migrant’s first-time migration, because this could help us to exclude
impact of other dialects. Besides, first-time migration implies the source prefecture is the
birthplace prefecture.
Even though dialectal distance, through identification effect and linguistic communication
effect, could prevent the supply of migrants, it could on the other side facilitate the demand of
migrants through the mechanism of complementary effect. Namely, migrants from dialectally
further cities could bring in diversified complementary skills and thinking ways which are
beneficial in certain production processes (Alesina and La Ferrara, 2005), so firms demand and
attract migrants from dialectally further cities. Hong and Page (2001) have proved theoretically
that a team with diversified cognition but limited skills will perform better than a homogeneous
team with higher skills. Hambrick et al. (1996) have found that heterogeneous teams could gain
more market power and profit although they would react slower than a homogeneous competitor.
Trax et al. (2012) use samples of Germany firms and find birthplace diversity of foreign workers
has positive effect on a firm’s performance. With samples of Australian firms and workers,
Böheim et al. (2012) use IV approach and find birthplace diversity of workers will raise their
wages. Alesina et at. (2013) also shows a positive relationship between birthplace diversity and
economic development.
Results Summary With both positive and negative effect, we empirically estimated the
impact on labor migration by introducing dialectal distance as well as its square term, and to
distinguish identification effect from linguistic communication effect we also control for
individuals’ Putonghua skill. The results show a robust inverted “U” relationship between labor
migration and dialectal distance after controlling for Putonghua as well as other control variables,
including individual characteristics, average GDP level of destination and birthplace, provincial
dummies, geographic factors, and when taking measure errors into consideration, estimating under
various estimation methods and excluding reverse causality and problems in setting of
econometric strategy, etc.
Two points could be concluded from these results. Firstly, dialectal distance promotes
4
migration as long as two prefectures are within the same dialectal super-groups; otherwise, it
begins to inhibit migration. Secondly, the inhibition comes from identification effect rather than
linguistic communication effect, namely that individuals tend not to migrate across dialectal
super-groups, is not due to that they don’t understand dialect there, but that they have lower
identification of cities outside of their own dialectal super-group.
To explain how the inverted “U” pattern is formed, we introduce both identification effect
and complementary effect into a simple model of a labor’s migration decision. By assuming that
individuals are risk averse and the marginal effect of complementary declines, the inverted “U”
pattern could be derived. We have also tested whether labors from different dialect have different
skills as a support of complementary effect. By using average education years as an indicator of
skills, the fix effects of dialects are jointly significant.
Further findings have been derived when we compare effects of dialectal distance among
different samples. The identification and complementary effect are both stronger for urban labors
compared with rural labors. If a labor have stronger social network, the prevention from
identification effect becomes stronger because it increases the opportunity cost of migration. As
time goes by, identification effect becomes weaker and complementary effect stronger.
Related Literature There are several strands of research related to this paper, first of
which is the role of language on labor migration. This paper has distinguished the identification
effect from linguistic communication effect in explaining the negative relationship between
linguistic distance and migration rate (Adsera and Pytlikova, 2015). Current studies emphasize if a
country is linguistically further from another, it is harder for people to learn each other’s language
(Chiswick and Miller, 2005; Isphording and Otten, 2011), which prevent labor migration between
them. But this paper shows the true reason should be the weaker identification at least in the
sample of labor migration in China (Pendakur and Pendakur, 2002). This paper has also unearthed
the positive complementary effect that migrants from dialectally further cities could bring in
diversified complementary skills and thinking ways which are beneficial in certain production
processes (Alesina and La Ferrara, 2005), so firms demand and attract migrants from dialectally
further cities.
This paper is also related to the effect of relatedness of human traits and answer at what
extent the relatedness of human traits begins to inhibit social interaction. Biologically, genetic
distance from technology frontier is associated with economic development and diffusion of
technology (Spolaore and Wacziarg, 2009, 2013, 2014). Culturally, trust affects economic
development (Knack and Keefer, 1997) and individual performance (Butler et al., 2014); cultural
biases, sourced from different religion, a history of conflict, and genetic distance, harms bilateral
trust and trade, portfolio investment, and direct investment between two countries (Guiso et al,
2009); and international migration rates increase with linguistic proximity (Adsera and Pytlikova,
2015). This paper has found that dialect, as an indirect measure of where one’s ancestor came
from hundreds or thousands of years ago, still has an impact on labor migration today.
Thirdly, this paper also contributes a new explanation, dialectal distance, to labor migration
in China. Individual characteristics are associated with one’s decision of migration, such as
primary education (Zhao, 1997). The unbalanced economic development is the main reason of
labor migration in China (Cheng, 2006). Institutions also serve as a determinant, that land
allotment system provides security for the rural labor force to go out to work (Li, 2007) and the
reform of household registration system encourages labor migration in the long-run (Sun, 2011).
5
Social network and clan network are also helpful for labor migration. This paper, after controlling
for these factors, robustly finds the effect of dialectal distance.
This paper is most closed to Chen et al. (2014) and Adsera and Pytlikova (2015). The former
studies how migrants’ Shanghai dialect (Wu) skill will affect their income, and furtherly use the
dialectal distance from a migrant’s birthplace to Shanghai as instrumental variables of migrants’
Shanghai dialect skill. Different from Chen et al. (2014), our paper measures pair-wise dialectal
distances over China, and explores the effect on labor migration. The latter studies the role of
linguistic proximity, as well as widely spoken languages, linguistic enclaves and language-based
immigration policy requirements, in international immigration. Rather than only emphasizing the
communication function of language (Adsera and Pytlikova, 2015), this paper distinguishes the
identification effect from linguistic communication effect.
Paper Structure In next section we introduce the background of Chinese dialects and
Putonghua which shows the reason why we could distinguish the identification effect from
communication effect. Section 3 presents econometric strategy and data. Empirical results of how
dialectal distance affects migration are in section 4. Section 5 explains how the inverted “U”
pattern is formed. In Section 6 we do some comparisons among different samples. In section 7 we
summarize discussing avenues for future research.
2 Background: Dialects and Putonghua in China
2.1 Why dialect is an important identity?
The historical formation of Chinese dialects shows why dialect is an important dimension of
identification in China.
Figure 1 is here
Ancient China could be roughly divided into three parts, northern nomadic region (region 1
in Figure 1), central plains (region 2 in Figure 1), and south China (the rest white areas in Figure
1). Northern nomadic region was sparsely populated mainly by non-Han ethnic groups, namely
nomads, central plain is settled by Han ethnic group, and South China lies in the south side of
Qinling Mountains and southern minorities settled. Climate shocks in northern nomadic region,
especially drought, led to famine in nomads and drove them to move over the boundary that
separate nomads from Han ethnic group, the Great Wall, so that they could survive on robbery on
central people (Bai and Kung, 2011). So, along Chinese history, wars between Han ethnic group
and northern nomads are common, and the nomads even took control of central plains in some
dynasties, like Qing-China (1820).
Wars lead to migration of Han-ethnic large families from central plain to South China and the
introduction of language of central plain. South China is mountainous and rugged, so could protect
them from wars. Once migrated into a city, the Han-ethnic large families, who were more
developed and had larger population, usually dominated and soon became the new host there, as
well as their language. The natives, less powerful to resist, had to compromise and learn the new
language. So, the language brought from central plains kept and evolved slowly there in south
China.
However in central plain and northern nomadic region, frequent wars lead to integration
among them and speed up the evolution of language in central plain. So each time the Han-ethnic
large families migrated to South China, they brought in a newly evolved language, which was
6
quite different from the previous one. What’s more, the mountainous landscapes led to geographic
isolation of interaction among different languages, which was helpful for the maintenance of
various languages. These languages become different dialects today in South China. For example,
Yue dialect, namely the Hong Kong Chinese, is formed based on the language brought in North
and South Dynasty (386-589, AD) and Hakka dialect evolves from the language in Central Plain
in Song Dynasty (960-1279, AD), according to Zhou (1997, 2006).
So the formation of dialect in South China is driven by historical migration of Han-ethnic
large families at different dynasties. In northern nomadic region and central plain, instead,
thousands of years’ integration results in unified language, Mandarin (Figure 1). The historical
formation of dialects indicates, which dialect you speak partly shows where your ancestors came
from, and that’s why dialect serves as an important dimension of identification in China.
2.2 The promotion of Putonghua
The promotion of Putonghua has unified the standard pronunciation of Chinese among most
Chinese people, and helps to eliminate the linguistic obstacles among different dialects.
Promotion of Putonghua began with the establishment of Chinese Language Reform
Association in 1949, who was in charge of normalizing the pronunciation of Putonghua, Pinyin. In
1958, the Scheme of the Chinese Phonetic Alphabet (Pinyin) was authorize by the fifth session of
the first National People's Congress. Since 1982 Promotion of Putonghua has been written into
Constitution of People's Republic of China, and it is requested to speak Putonghua in school,
media, and other public institutions, children learn Pinyin in primary school, learning Putonghua is
a right of Chinese citizens, and in minority areas it is also encouraged to learn Putonghua.
Until 2000, the percentage of population who can speak Putonghua fluently is more than 50%,
and until 2010 it reaches more than 70%, and the usage rate of Putonghua under different
occasions are, about 20% at home, 30% in market, 50% in hospital, 60% in government and 50%
at work. So the Promotion of Putonghua has made Putonghua be widely used (Xie, 2011), and
plays a key role in eliminating linguistic obstacles among different dialects.
3 Econometric Strategy and Data
3.1 Economic Strategy
Equation (1) shows our econometric strategy to study how dialectal distance affects whether an
individual i migrate or not. , , 1i m nM stands for migration across prefectures
1 (or above) from
prefecture m to prefecture n , m n ; otherwise , , 0i m nM , and m n . [ ]P is the probability.
,m nd is dialectal distance between prefecture m and n , and 2
,m nd is its square term.
iPutonghua are variables to indicate 'i s Putonghua skill. iZ is a series of individual
characteristics including gender, age, political status, education and education of parents,
household registration status, industry category and firm category. mP and nP are
characteristics of prefecture m and n respectively, including average GDP level and dummy
variables of province it belongs to. Which probability model to choose depends on the distribution
1We measure migration at prefecture level rather than a finer one, say county level, because
prefecture is the finest level that we could identify an individual’s birthplace and destination most
accurately.
7
of residuals, ( )F , and we estimate under Probit model, which is one of the most widely used, as
well as the Logit model and Linear Probability Model (LPM).
2
, , , 1 , 2 ,[ 1 | , , , ]= ( , , , , , )i m n m n i m n m n m n i i m nP M d P P F d d Putonghua P P (1)
In order to distinguish identification effect from linguistic communication effect, we estimate
the impact of dialectal distance on individuals’ migration decision by controlling for their
Putonghua skill. The logic is like this. Widely used Putonghua could eliminate the linguistic
obstacles among dialects. Thus if it’s the case that dialectal distance prevent labor migration
through the mechanism of linguistic communication effect, when an individual’s Putonghua skill
is raised, the linguistic obstacle should be less or disappear, and we will no longer see the
significant negative effect or the parameter will weaken. Similar logic could be applied as for
another indicator, the distance between an individual’s native dialect and Putonghua, because if
the dialect of one’s birthplace is closer to Putonghua, the linguistic obstacle also could be
lightened. However after controlling for the two aspects if we still see the significant negative
effect, it should be attributed to identification effect instead of linguistic communication effect.
We also introduce the square term of dialectal distance to identify complementary effect.
Dialectal distance could prevent labor migration through identification effect and promote labor
migration through complementary effect. So adding both dialectal distance and its square term
could help us to identify the two contrary effects.
3.2 Data of dialectal distance
We measure dialectal distance in the following three steps. Firstly, we draw Chinese dialectal
family tree according to its classification in Language Atlas of China. Chinese dialects could be
classified into 10 super-groups in the roughest way, 20 groups, and 105 sub-groups in the finest
way, as Figure 2 shows.
Figure 2 is here
Secondly, by counting the steps to the common node on the family tree, we define pair-wise
dialectal distance of counties. Each county could be matched with a sub-group uniquely according
to Dictionary of Chinese Dialect, so we could define dialectal distance of county pairs along the
family tree with following rules. If two counties belong to the same sub-group, it takes 0 step to
get to the common dialectal sub-group, so we define the dialectal distance to be 0; if they belong
to the same group but different sub-groups, it takes 1 step to reach the common dialectal group, so
we define as 1; if same super-group but different groups, 2 steps and defined as 2; if different
super-groups, 3.
Thirdly we calculate dialectal distance at prefecture level by weighting dialectal distances of
county pairs with population shares. We use population shares of each county relative to its
prefecture in the year 1982, the latest census year before the survey of Dictionary of Chinese
Dialect, so that we could lighten endogeneity caused by migration’s impact on population shares.
Specifically, the population-share- weighed dialectal distance of two prefectures m and n ,
( , )d m n is calculated as following coincident with current studies (Spolaore and Wacziarg, 2009),
,
1 1
( , ) _ _I J
a b a b
a b
d m n S m S n d
(2)
Where, _ aS m is the population share of county a in prefecture m , _ bS n is the
8
population share of county b in prefecture n , ,a bd is the dialectal distance between county a
and county b . So _ aS m represents the probability that an individual i from prefecture m is
born in county a , and _ bS n represents the probability that individual j in prefecture n met
by individual i , is born in county b of prefecture n . As a result, equation (3) is a
probability-weighted formula whose economic intuition is the expectations of dialectal distance
when an arbitrary individual i from prefecture m meets an arbitrary individual j in
prefecture n , and it reflects 'i s prejudgment of dialectal distance between two cities.
Figure 3 is here
We take Figure 3 as an example to show how to calculate dialectal distances between two
prefectures. Prefecture m and prefecture n are both made of two counties 1 2,a a and
1 2,b b
whose population shares are 0.3,0.7 and 0.4,0.6 respectively. The dialectal distance between 1a
and 1b is 1, between
1a and 2b is 2, between
2a and 1b is 0, and between
2a and 2b is 3.
By calculation of equation (2), the distance between prefecture m and prefecture n is 1.742.
Using this method, we have measured the pair-wise Chinese dialectal distance of 278 Han Chinese
speaking prefectures in China.
Table I is here
In Table I, we show the pair-wise dialectal distance of 4 municipalities and 2 first-tier cities,
from which we could find some features. 1) Symmetry. Dialectal distance of prefecture m and
prefecture n is equal to that of prefecture n and prefecture m . 2) The diagonals are not
necessarily zero. Only if all the counties in a prefecture are belong to the same sub-group,
dialectal distance between a prefecture and itself is zero. 3) Continuity. Population weighting has
transformed discontinuous dialectal distances into continuous ones.
3.3 Data of labor migration
To measure whether a labor has ever migrated or not, we use micro survey data from China
Labor-force Dynamic Survey 2012 (CLDS) by Center for Social Survey in Sun Yat-Sen University.
According to their report3, samples are built upon interviewees’ birthplaces rather than current
locations, which can help to reduce sample selection problem. This survey uses the stratified
random sampling method, and covers 29 provinces and 2282 counties in China, whose population
weights correspond to Sixth National Population Census of the People's Republic of China.
In this paper, we focus on labors’ first-time migration, because it is more exogenous and
clean. Before an individual’s first-time migration, firstly he didn’t have any experience of
migration, thus the possibility of learning the dialect of the destination in order to migrate there is
relatively lower, satisfying exogeneity; secondly he didn’t contact with dialects outside of
birthplace, which could excludes the interference of other dialects, and help us to identify the
effect of dialectal distance between birthplace and destination more cleanly.
How to measure migration using CLDS? In CLDS, There are totally 16,253 observations in
CLDS, 2,386 of which have ever migrated to other counties (or above) and resided for more than
6 months4, and the rest 13,867 are those who have never been to other cities or resided less than 6
20.3*0.4*1+0.3*0.6*2+0.7*0.4*0+0.7*0.6*3=1.74 3Center for Social Survey in Sun Yat-Sen University, “China Labor-force Dynamic Survey: 2013
report”, Social Sciences Academic Press (2013). 4The percentage of migrated individuals in the whole sample is 14.7%, quite close to that reported by
Statistical Communiqué of the People's Republic of China on the 2014 National Economic and Social Development,
9
months. With the restriction of CLDS that the source and destination of migration can be
identified the finest only at prefecture level, we measure labor migration at prefecture level. If an
individual migrated across prefectures, , , 1i m nM , otherwise
, , 0i m nM .
We match the birthplace and destination of labors’ first-time migration with the pair-wise
dialectal distances of 278 prefectures. We first clean the data of one’s first-time migration in case
there would be recording errors. After data cleaning, 1774 of 2,386 migrated individuals could be
matched with dialectal distance. The rest have to be dropped because of recording errors or
because people speak minority languages rather than Han Chinese dialects in either birthplace or
destination. Among the sample of 1774 migrants, 1,555 migrated across prefectures and the rest
219 migrated across counties within a prefecture. So, dialectal distance of individuals who migrate
from his birthplace to another prefecture is that between these two prefectures; dialectal distance
of individuals who migrates across counties within his birthplace prefecture is that of the
prefecture and itself; and dialectal distance of those who didn’t migrate is obviously 0. Finally we
use the 15641(=13867+1555+219) observations to do empirical analysis.
Figure 4 is here
In case that data cleaning and matching process would generate sample bias, we have
calculated the spatial distribution of the migrants’ sample (1,774) and the whole sample (16,253)
over provinces in China. As Figure 4 shows, where black bars represent for percentages of
interviewees of each province among the whole sample, and white bars represent percentages of
migrated interviewees of each province among the migrated sample, the graphs of two
distributions are almost coincident. The t-test of difference in two sample means doesn’t reject that
two samples have equal means, and p value is 1. So data cleaning and matching didn’t cause
serious sample bias.
Figure 5 is here
We also explore the temporal distribution of migrants’ sample, as Figure 5 shows. Totally
speaking, during 1936-2012, the amount of migrants are growing up, especially after 2000
remaining at a high level, which is coincident with the yearly variation of migrants reported by
National Population Census (Duan et al., 2008).
3.4 Descriptive Statistics of Labor Migration across Dialects
Firstly, we look at the distribution of labor migration within and across dialects at dialectal
super-group level, group level and sub-group level respectively (Figure 6). At dialectal sub-group
level, there are more people migrating across dialects than within dialect, which means dialectal
distance promotes migration across dialects when it is relatively smaller. At dialectal group level it
seems indifferent whether a labor migrates across or within dialects. At dialectal super-group level,
the amount of migrants within dialect is much larger than migrants across dialects, which shows,
dialectal distance prevent migration across dialects when it is relatively large.
Secondly, we dig deeper into each dialectal super-group to see percentages of labors that
migrate within it (diagonals) and migrate from it to another super-group (Table II). Rows show the
dialectal super-groups of where migrants come from, columns show the dialectal super-group of
where they migrate to, and items in each row from column 1 to column 9 are relative percentages
18.5%.
10
and in column 10 is the sum of them. In Mandarin, 68% of its migrants move within it, none of the
percentages of migrants to other dialectal super-groups is more than 10%. In other super-groups,
except for Hui and Xiang whose population only account for 0.5% and 2.8% of the whole
population, items on diagonals are always larger than those lie outside diagonals. So, table II
shows at dialectal super-group level, dialectal distance prevent labor migration across dialects.
Thirdly, we investigate each province of how much of its migrants move to another province
that lies in the same dialectal super-group (Figure 7). Percentages are calculated roughly by
matching each province with its dominant dialectal super-group. As Figure 7 shows, in most
provinces more than 50% of its migrants move to provinces that lie in the same dialectal
super-group, which also shows the inhibition of dialectal distance at dialectal super-group level.
So, there are two features labor migration across dialects according to descriptive statistics
above. Firstly, dialectal distance facilitates migration across dialects when it is relatively small;
secondly, at super-group level, it inhibits migration across dialects.
4 Empirical Result
4.1 Basic Result
We estimate equation (1) under Probit model to test the influence of dialectal distance on labor
migration across prefectures. As mentioned above, the whole sample for estimation include 15641
interviewees; dependent variable is the bivariate variable that 1 is migration across prefectures and
otherwise 0; independent variables of interest are dialectal distance as well as it square term and
Putonghua skill; control variables are individual characteristics, including gender, age, political
status, education and education of parents, household registration status, industry characteristics
and firm characteristic, average GDP level of birthplace and destination, and dummy variables of
provinces that birthplace and destination belong to respectively; and we cluster standard error at
provincial level. Descriptive statistics and data source of all variables are in table III.
In Table IV, column 1 to column 3 gradually introduce in dialectal distance and its square
term, individuals’ Putonghua speaking skill and distance between Putonghua and dialect of
individuals’ birthplace. The parameter of dialectal distance is significantly positive and its square
term is significantly negative at 1% level, both before and after controlling for Putonghua. This
exhibits an inverted “U” pattern between dialectal distance and individuals’ migration possibility.
When dialectal distance is increasing, migration possibility will firstly increase then decrease. We
take derivative of d relative to migration possibility, and find that averagely the positive
marginal effect is around 10% and the negative marginal effect is about 2%. We also calculated
the inflection point of dialectal distance, which is around 2.3 and lies within dialectal super-group
and between dialectal groups. Intuitively, when dialectal distance is relatively small, say within
dialectal super-groups, one level increase in it will raise migration possibility by 10% due to
complementary effect; while when dialectal distance becomes larger, say across dialectal
super-groups, one level increase in it will lower down migration possibility by 2%, and the
prevention comes from identification effect rather than linguistic communication effect.
In column 4 to 6 we add control variables including individual characteristics, average GDP
level of birthplace and destination and provincial dummies gradually. Column 7 is estimated under
Logit model and column 8 under Linear Probability Model (LPM). Results show robust inverted
“U” pattern and significant negative effect after controlling for Putonghua.
11
Parameters of control variables are coincident with current studies. The parameters of
education are significantly positive, which shows individuals with higher education level are more
likely to migrate. The significant positive parameters of GDP level of destination have illustrated
that more developed cities could attract more migrants; while in a less developed city, labors tend
to migrate out of it, as showed by the significant negative parameters of GDP level of birthplace.
In order to use the information of migration across counties within the same prefecture, we
expand the dependent variable into a 3-value variable in the following analysis. In table IV
migrants who migrated across counties within the same prefecture were treated as those who
didn’t migrate across prefectures and , , 0i m nM . Actually these observations are different from
those who definitely didn’t migrate. In order to use more information, namely the information of
migration within a prefecture, we expand the dependent variable from a bivariate variable into a
3-value variable. Specifically, , , 0i m nM is for those who definitely didn’t migrate,
, , 1i m nM
for migration across counties with the same prefecture, and , , 2i m nM for migration across
prefectures. Correspondingly, our estimation method becomes Order-Probit model because of the
sequential relationship of the new dependent variable.
Table V reports estimation associating labor migration with dialectal distance under
Ordered-Probit model, where migration is a 3-value-variable. The inverted “U” pattern is still
robust both before and after controlling for Putonghua as well as other controls, and the inflection
point still lies within the same dialectal super-group and across dialectal groups. The only
difference is the magnitude of average positive effect and negative effect, the former of which is
around 30% rather than 10% and the latter of which is around 20% instead of 2%. Possible
explanation of it is treating migrants who migrated within the same prefecture as those who didn’t
migrate across prefectures, has underestimated the impact of dialectal distance on these migrants.
4.2 Measurable Errors
We now take possible measure errors of dialectal distance into consideration, which could come
from three sources. Definition of dialectal distance by counting steps of reaching to common node
could underestimate dialectal distance among dialectal groups and among dialectal super-groups.
In basic results, dialectal distance is defined in 0-1-2-3 pattern by counting steps, which may not
be large enough to capture the difference between dialectal groups and super-groups, so we define
in another pattern, 0-1-10-100 instead. Specifically, dialectal distance of two counties belong to
same dialectal sub-group is 0, belong to different sub-groups in the same group is 1, belong to
different dialectal groups in the same super-group is 10, belong to different super-group is 100.
The result is shown in column 1 Table VI.
Another source is that weighting be population share could lead to endogeneity in dialectal
distance. In basic results, we use population share in 1982 of each county in its prefecture as
weight to calculate the pair-wise dialectal distance among prefectures. However the time span of
migration in CLDS is 1936 to 2012, thus migration before 1982 could affect the population share
and lead to endogeneity. In order to avoid this problem, we give equal weight to each county
instead of population share. Column 2 in Table VI shows the result.
The third problem is the classification of dialects family tree per se could be biased.
12
According to the research in Chinese dialects (Yuan, 2001; Li, 2001), 1) compared with southern
dialects, the classification of northern dialects is finer, which will result in overestimation of
dialectal distance within northern dialects; 2) in Fujian province, mountains account for more than
90% of its area, and in Zhejiang province the proportion of mountainous area, water area and
farming area are 0.7, 0.2, and 0.1 respectively, so these geographical isolation could lead to larger
dialectal distance than what we measured, namely dialectal distance within Wu and Min dialectal
super-groups could be underestimated. Our solutions for the two problems are, 1) controlling for
the dummy variable of whether an individual migrated within northern dialects to lighten the
overestimation of dialectal distance within northern dialects (column 3 in Table VI), and 2)
dropping observations of migration within Wu dialect and within Min dialect to lessen the
underestimation of dialectal distance within Wu and Min dialect (column 4 in Table VI).
4.3 Other Control Variables
Even though, dialectal distance, due to its historical formation, is exogenous relative to migration
today, the relationship between them could be explained by common third factors, especially
geographic factors. Namely, it might be the case that geographic factors have shaped both the
historical formation of dialects and migration today, rather than that dialectal distance has affected
migration. To rule out this possibility, we introduce the following geographic factors, longitude,
latitude, geographic distance, slope, Relief Degree of Land Surface (RDLS) and provincial
neighbors.
We control for geographical factors from three possible aspects that could affect both
dialectal distance and labor migration. Firstly, if two prefectures are geographically further from
each other, it is more likely that dialectal distance between are further and migrants between them
are less, so we control for longitude and latitude of destination and birthplace respectively
(column 1 in Table VII), and we also control for geographically distance as well as its square term
(column 2 in Table VII). Secondly, if either of two prefectures is geographically isolated according
to its landscape, dialectal distance would be larger and migrants between them are less, so we
control for slope and RDLS of destination and birthplace respectively (column 3 and 4 in Table
VII). Thirdly, if two prefectures belong to two provinces who are neighbors, dialectal distance is
more likely to be smaller, and migration would be larger, so we control for the dummy variable of
whether two prefectures belong to two neighbor provinces (column 7 in Table VII).
4.4 Other Concerns
Reverse causality is another possibility to explain the relationship between dialectal and migration.
Namely, it might be the case that migration affects dialectal distance rather than the other way
round. To rule out this possibility we exclude observations that migrated before the year 1987
when the Language Atlas of China was surveyed. Relative result is in column 1 in Table VIII.
To make sure that the impact of dialectal distance on migration is coincident with that the
purpose of migration is searching for job opportunities, we exclude observations that migrate for
other reasons such as for marriage, for family membership, or others (column 2 in Table VIII).
Another important concern is the setting of our economic strategy. In analysis above, if an
individual definitely didn’t migrate, the dependent variable is , , 0i m nM and dialectal distance is
also 0, which could lead to the positive effect when dialectal distance is small. To rule out this
13
possibility, we exclude observations that definitely didn’t migrate, and restrict in the sample of
migrants only. Column 3 in Table VIII shows this result that the inverted “U” pattern is still robust
when controlling for Putonghua as well as other control variables.
Besides, dialectal distance between two different prefectures is more likely to be larger than
dialectal distance between a prefecture and itself. Thus dialectal distance of migrants across
prefectures is initiatively more likely to be larger than dialectal distance of migrants not across
prefectures. The positive effect could be driven by this. To rule out this possibility, we measure
migration at provincial level rather than prefecture level, because the possibility that dialectal
distance between two different provinces is larger than dialectal distance between a province and
itself is not as large as that at prefecture level. We measure migration at provincial level like this.
If birthplace and destination belong to same province, it is migration across provinces and equals
to 2, if belong to same province, it is migration within a province and equals to 1, if not migrated,
equals to 0. Column 4 in Table VIII shows this result.
Until now, we have found a robust inverted “U” relationship between labor migration and
dialectal distance after controlling for Putonghua as well as other control variables, including
individual characteristics, average GDP level of destination and birthplace, provincial dummies,
geographic factors, and when taking measure errors into consideration, estimating under various
estimation methods and excluding reverse causality and problems in setting of econometric
strategy, etc. These results show two points, that dialectal distance promotes migration as long as
two prefectures are within the same dialectal super-groups, otherwise, it begins to inhibits
migration, and that the inhibition comes from identification effect rather than linguistic
communication effect, namely individuals tend not to migrate across dialectal super-groups not
because they don’t understand dialect there but because they have lower identification of cities
outside of their own dialectal super-group.
Then, with both complementary effect and identification effect, how is inverted “U” pattern
formed? We answer this question in the following section.
5 How Is Inverted “U” Pattern Formed?
5.1 Model
Consider a representative individual i ’s decision of migration whose birthplace city is A and an
arbitrary destination city is B ,
1 [ ]
0 [ ]
E U UM
E U U
(3)
Where, U is constant, measuring i ’s reservation utility if i doesn’t migrate, and [ ]E U
measures i ’s expected utility of migration to city B . Obviously, if [ ]E U U , i will migrate, i.e.
1M ; else, if [ ]E U U , i will not migrate, i.e. 0M .
i ’s utility of migration to city B is determined by income there, y , and ( )U U y . According
to the Permanent Income Hypothesis (Friedman, 1956), i ’s income in city B is made of two parts:
14
1 2y y y . 1y is permanent income, such as wages;
2y is temporary gain or loss because of
being robbed, stolen, cheated or getting others’ help, etc.
Given the dialectal distance between A and B , [0, ]d d . On the one side, as
complementary effect mentioned that different dialect is associated with difference in
thinking-ways and skills, and labors from dialectally further cities could bring in diversity
beneficial for firms in certain production process, thus it is helpful for migrants to find a job if
they migrate to cities that are dialectally further. So, we assume that dialectal distance could raise
individuals’ permanent income 1y , and without loss of generality, the marginal effect declines. As
the permanent income 1y can also be affected by other factors such as policy, economic
development, institution and so on denoted , we have 1 ( , )y y d , satisfying ( , ) 0y d ,
( , ) 0dy d and ( , ) 0ddy d .
On the other side, as identification effect mentioned, that dialect is an important dimension of
identification that individuals have higher identification of cities that speak closer dialects, while
lower identification of cities with quite different dialects. The expression of lower identification is
they would afraid that there is a higher risk of being stolen, cheated or robbed as well as
unexpected gains if migrate to cities with quite different dialects, thus dialectal distance enlarges
the variance of temporary income. We assume that the temporary income 2y is normally
distributed, and if the dialectal distance d is larger, the variance of 2y is larger, i.e.
2
2 ~ 0,y d , where 2 is constant.
By assuming a CARA utility function5 ( 1/ ) exp( )U y where is the risk aversion
parameter, and given and d , i ’s expected utility in destination could be expressed by
| ,E U y d Z 2 21/ exp - , + / 2y d d . Taking its deriavative subject to d , we will
get
[ ( )| , ] 2 2 2 2 2, exp - , +0.5 0.5 exp - , +0.5E U y d Z
ddy d y d d y d d
, (4)
which reveals the trade-off of complementary effect and identification effect. The first term on the
right side of the equation shows the complementary effect which is the mechanism how dialectal
distance affects one’s utility through permanent income. The second term on the right side of the
equation shows the identification effect which is the mechanism of temporary income.
According to assumptions, there must exist some d denoted as *d satisfying
2( , *) 0.5 0dy d . Thus, when *d d , we will have [ ( )| , ]
0E U y d Z
d
namely complementary
effect beats down identification effect at a lower dialectal distance; when *d d , we will have
[ ( )| , ]0
E U y d Z
d
, namely identification effect dominates at a higher dialectal distance. This
5The conclusion will be similar if we use other risk-aversion utility functions, but CARA is more
powerful for calculation.
15
meansvwhen the dialectal distance between i ’s birthplace and destination increases, i ’s
expected utility in destnation will firstly increases then decreases after some certain value. As Fig
8 shows, where the horizontal axis is dialectal distance and the vertical axis is the expected utility,
there is an inverted-“U”-shaped relationship between | ,E U y d Z and d , and at certain value
*d i ’s expected utility reaches the peak, denoted as * | *,U E U y d Z .
Finally we explore how dialectal distance affect i ’s migration decision. We denote the
possibility for migration from A to B as [ 1 | , ]P M d Z . As the reservation utility is constant6
and according to equation (1), we can conclude that the possibility [ 1 | , ]P M d Z is also
associated with dialetal distance, specificly as the following proposition shows.
Proposition7: If *U U , [ 1 | , ] 0P M d Z ; if *U U , [ 1| , ]
0 *
0 *
0 *
P M d Z
d
d d
d d
d d
.
The economic intuition of the proposition is natural. Dialectal distance is associated with
both complementary effect and identification effect, which will result in an inverted-“U”-shaped
pattern of labors’ across-dialects migration. Specificly, when the dialectal distance is relatively
small, complementary effect donimates, so dialectal distance will promote labors’
crossing-dialects migration; when dialectal distance is quite large, identification effect dominates,
so dialectal distance will prevent labors’ crossing-dialects migration. The reason is, as dialectal
distance increases, complementary effect is strengthened which will encourage individual to
migrate by raising permanent income, however the marginal gain of complementary effect
declines; at the same time, identification effect also becomes stronger, which will discourage one’s
migration by enlarging the variance of temporary income, besides individuals are risk-averse,
which quickens the diminishing of one’s utility. As a result, by trading off the two effects, one’s
migration probability exhibits an inverted-“U”-relationship with dialectal distance. In the
following parts of this paper, we will test this pattern empirically.
5.2 Empirical support
If the positive effect come from the complementary effect that people from different dialects have
different skills and thinking ways, is there any evidence that dialectally specific fix effect of
people’s skill is significant? We look at people’s skill from the aspect of average education years.
We regress average education years on dialectal super-group dummies (figure 9a), dialectal
groups (figure 9b) and dialectal sub-group dummies separately upon 2334 counties in China in the
6If i doesn’t migrate, the dialectal distance d is naturally 0, the income is only related to Z not to
d , and will not variate along d .
7Please referee to appendix for its proof.
16
year 2000. The dialectal super-group dummies, dialectal group dummies, and sub-group dummies
are all jointly significant at 99.9% confidence level. These results show that, averagely speaking,
the people from different dialects are associated with correspondingly different education level.
6 Comparison among Samples
6.1 Comparison by Individual Characteristics
Table IX compares the effect of dialectal distance on migration among samples with different
individual characteristics including urban vs rural household registration status, male vs female,
different age and different education level. Compared with rural labors, the identification effect
and complementary effect are both larger for urban labors. An explanation is, because of the
economic development gap between urban and rural, the urban labors has stronger identification
of birthplace because their social networks in birthplace is more useful than that of rural labors
when searching for jobs, and an urban labor is more easily to get skills with comparative
advantages in destination if he migrate. The effects of dialectal distance are almost indifferent
under other comparisons.
6.2 Comparison by Social Networks
Table X compare whether dialectal distance functions differently if people have different social
network. We measure social network in three aspects, number of people that can talk about things
deep in heart with, number of people that can talk about important things with, and number of
people that can borrow money (>RMB ¥5000) from. Under each comparison the positive effects
are almost indifferent, however people with stronger social networks face larger prevent from
dialectal distance. A possible explanation is, people with stronger social networks might be more
able to construct social networks, so will have stronger social networks in birthplace compared
with those with weaker social network, and social networks in birthplace have strengthened the
identification effect of birthplace and relatively lower identification of destination, thus prevent
them from migrating outside.
6.3 Comparison by Time Span
Table XI compare the effect of dialectal distance on migration along time span. We gradually
dropped migrants who migrated before the year 1990, 1995, 2000 and 2005. Results show that
positive effect becomes larger and larger while negative effects becomes weaker and weaker. An
explanation could be that, the development of information technology has sharpened the distance
among people so that people don’t need to rely too much on identity to get information and
resource, and that the spread of internet offers more information that encourages people to migrate
to dialectally further places.
17
7 Conclusion
This paper explores the question how dialectal (linguistic) distance affects labor migration. By
using the special sample of labor migration in China who speak both native dialect and Putonghua
(standard pronunciation of Chinese), we empirically estimated the impact on labor migration by
introducing dialectal distance as well as its square term, and to distinguish identification effect
from linguistic communication effect we also control for individuals’ Putonghua skill. The results
show a robust inverted “U” relationship between labor migration and dialectal distance after
controlling for Putonghua as well as other control variables, including individual characteristics,
average GDP level of destination and birthplace, provincial dummies, geographic factors, and
when taking measure errors into consideration, estimating under various estimation methods and
excluding reverse causality and problems in setting of econometric strategy, etc.
Two points could be concluded from these results. Firstly, dialectal distance promotes
migration as long as two prefectures are within the same dialectal super-groups; otherwise, it
begins to inhibit migration. Secondly, the inhibition comes from identification effect rather than
linguistic communication effect, namely that individuals tend not to migrate across dialectal
super-groups, is not due to that they don’t understand dialect there, but that they have lower
identification of cities outside of their own dialectal super-group.
To explain how the inverted “U” pattern is formed, we introduce both identification effect
and complementary effect into a simple model of a labor’s migration decision. By assuming that
individuals are risk averse and the marginal effect of complementary declines, the inverted “U”
pattern could be derived. We have also tested whether labors from different dialect have different
skills as a support of complementary effect. By using average education years as an indicator of
skills, the fix effects of dialects are jointly significant.
Further findings have been derived when we compare effects of dialectal distance among
different samples. The identification and complementary effect are both stronger for urban labors
compared with rural labors. If a labor have stronger social network, the prevention from
identification effect becomes stronger because it increases the opportunity cost of migration. As
time goes by, identification effect becomes weaker and complementary effect stronger.
This paper reveals that, the true reason that dialectal distance prevents migration is weaker
feeling of identification of destinations that are dialectally further, rather than higher cost of
learning its dialect to eliminate linguistic communication obstacle.
This paper also answers at what extent the relatedness of human traits begins to inhibit social
interaction. Dialect, as an indirect measure of where one’s ancestor came from hundreds or
thousands of years ago still has an impact on labor migration today. If two populations are not that
far from each other in human traits, say within the same dialectal super-group, dissimilarity will
encourage social interacts between them because of possible complementary in skills and thoughts,
such as labor migration. While, if their human traits are quite different from each other, say
belonging to two different dialectal super-groups, few social interacts will happen between them.
The inflection point that relatedness begin to hinder social interacts of two populations is
determined by the historical origin of populations hundreds or thousands years ago.
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Appendix: Proof of Proposition
20
i) If *U U , [ 1 | , ] 0P M d Z holds obviously.
ii) If *U U , [ 1| , ] [ [ ] ]P M d Z P E U U
d d
.
According to assumptions of utility function and determinant of income, the expectation of
utility if one migrate from A to B, [ ( ) | , ]E U y d Z satisfies
2 2| , 1 / exp - , + / 2E U y d Z y d d . Taking deriavative of [ ( ) | , ]E U y d Z
subjective to d , we will have
[ ( )| , ] 2 2 2[ ( , ) / 2]exp[- ( , )+ / 2]
E U y d Z
ddy d y d d
(5)
besides, 2 2exp[- ( , )+ / 2] 0y d d , 2
0[ ( , ) / 2] | 0d dy d ,
2[ ( , ) / 2] |d d dy d
2 / 2 0 , 2
*[ ( , ) / 2] | 0d d dy d .
Thus, [ ( ) | , ]E U y d Z satisfies [ ( )| ]
0| 0E U y d
dd
,
[ ( )| , ]| 0
E U y d Z
d d d
.
Taking second order deriavative of [ ( ) | , ]E U y d Z subjective to d , we will get
22 2 2
2
[ ( ) | , ]=[ ( , ) ( ( , ) / 2) ] exp[- ( , )+ / 2]dd d
E U y d Zy d y d y d
d
For ( )y satisfies ( , ) 0, ( , ) 0, ( , ) 0d ddy d y d y d , it easy to know last formula is less than
0. Thus, [ ( )| , ]E U y d Z
d
is monotonely decreasing along d . Together with continuity and intermediate
value theorem, there must be the unique *d satisifying [ ( )| , ]
*| 0E U y d Z
d dd
, i.e.
2( , *) / 2 0dy d .
So we will get, when *d d , [ ( )| , ]
0E U y d Z
d
and
[ [ ] ]0
P E U U
d
; when *d d ,
[ ( )| , ]0
E U y d Z
d
and
[ [ ] ]0
P E U U
d
; when *d d ,
[ ( )| , ]0
E U y d Z
d
and
[ [ ] ]0
P E U U
d
.
It’s the end of the proof.
21
Figures and Tables
Figure 1a Figure 1b
Figure 1.
Figure 1a: Three Parts of Ancient China (Qing-China, 1820)
Figure 1b: Atlas of Chinese Dialectal Super-Groups Today (1987)
Note:
1. Region 1 is northern nomadic region, Region 2 is central plains, and the rest white area is south China,
source: Bai & Kung (2011).
2. Chinese dialectal super-groups include Mandarin, Jin, Wu, Hui, Gan, Xiang, Min, Hakka, Yue (Hong
Kong Chinese) and Ping, source: Language Atlas of China, Li (1987).
22
Figure 2. Chinese Dialects Family Tree
Note:
Source is Language Atlas of China, Li (1987).
23
Figure 3. An Example to Show the Calculation of Dialectal Distance between Two Prefectures
Note:
Prefecture m and prefecture n are both made of two counties 1 2,a a and
1 2,b b whose
population shares are 0.3,0.7 and 0.4,0.6 respectively. The dialectal distance between 1a and
1b
is 1, between 1a and
2b is 2, between 2a and
1b is 0, and between 2a and
2b is 3. By
calculation of equation (2), the distance between prefecture m and prefecture n is 1.74.
0.3
0.7
1
2
0
3
0.4
0.6
24
Figure 4. Distribution Of Interviewees Over Provinces In China
Note:
Figure 4 reports the percentages of interviewees of each province among the whole sample
(black bars, 16253 interviewees) and percentages of migrated interviewees of each province among
the migrated sample (white bars, 1774 interviewees). Unit is %. Data source: China Labor-force
Dynamic Survey (2012).
0
5
10
15
20
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Whole Sample
Migrants Sample
25
Figure 5. Temporal Distribution of Migrants
Note:
Figure 5 shows the percentage of migrants in each year relative to the whole migrant sample.
Unit is %. Data source: sample of 1774 migrants collected from China Labor-force Dynamic Survey
(2012) by authors.
0
0.01
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26
Figure 6. Migration Within and Across Dialects at Different Dialectal Levels
Note:
Figure 6 shows the distribution of labor migration within and across dialects at dialectal
super-group level, group level and sub-group level respectively. Data source: sample of 1774
migrants collected from China Labor-force Dynamic Survey (2012) by authors.
0
200
400
600
800
1000
1200
1400
Super-groups Groups Sub-groups
Across dialects
Within a dialect
27
Figure 7. Percentage of Migrants from Each Province to Other Provinces That Lie In the Same
Dialectal Super-Group
Note:
Figure 7 shows the rough percentages of labor migration of each province to other provinces
that lie in the same dialectal super-group, and each province is matched with its dominant dialectal
super-group. Data source: Sixth National Population Census of the People's Republic of China.
0%10%20%30%40%50%60%70%80%90%
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28
Figure 8. Dialectal Distance and Expected Utility in Destination
d
0
29
Figure 9
Figure. 9a Dialectal Super-Group Fix Effect of Education (years)
Figure. 9b Dialectal Group Fix Effect of Education (years)
Note:
Figure 9a shows the average education years of each dialectal super-group compared with
Hakka when regressing average education years on dialectal super-group dummies as well as
constant upon 2334 counties in China in the year 2000. P-values are in parentheses. Standard
errors are clustered at province level. The dialectal super-group fixed effects are jointly significant
at the 99.9% confidence level. R square is 0.025.
Figure 9b shows the average education years of each dialectal group compared with Mindong
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
30
when regressing average education years on dialectal group dummies as well as constant upon 2334
counties in China in the year 2000. P-values are in parentheses. Standard errors are clustered at
province level. The dialectal group fixed effects are jointly significant at 99.9% confidence level. R
square is 0.170.
We also regress average education years on dialectal sub-group dummies as well as constant
upon 2334 counties in China in the year 2000 but didn’t report here. The dialectal sub-group
dummies are jointly significant at 99.9% confidence level. R square is 0.351.
31
TABLE I
Pair-Wise Dialectal Distances Between Municipalities And First-Tier Cities
Beijing Shanghai Tianjin Chongqing Guangzhou Shenzhen
Beijing 0.314 3 1.828 2 3 3
Shanghai 3 0 3 3 3 3
Tianjin 1.828 3 0.355 2 3 3
Chongqing 2 3 2 0.233 3 3
Guangzhou 3 3 3 3 0 2.031
Shenzhen 3 3 3 3 2.031 1.312
TABLE II
Migration Across And Within Each Dialectal Super-Group
to→
from↓ Mandarin Jin Gan Hui Hakka Min Wu Xiang Yue Total
Mandarin 0.68 0.01 0.02 0.01 0.06 0.03 0.10 0.00 0.08 1
Jin 0.30 0.50 0.00 0.10 0.00 0.00 0.10 0.00 0.00 1
Gan 0.15 0.00 0.28 0.04 0.16 0.06 0.13 0.00 0.17 1
Hui 0.22 0.00 0.00 0.22 0.00 0.00 0.44 0.00 0.11 1
Hakka 0.02 0.00 0.02 0.00 0.56 0.11 0.04 0.00 0.24 1
Min 0.06 0.00 0.03 0.00 0.28 0.35 0.01 0.00 0.28 1
Wu 0.41 0.02 0.02 0.10 0.02 0.01 0.37 0.00 0.04 1
Xiang 0.26 0.00 0.28 0.02 0.14 0.02 0.02 0.04 0.22 1
Yue 0.16 0.01 0.00 0.00 0.13 0.03 0.02 0.00 0.64 1
Note: Table II reports the percentages of migrants by each dialectal super-group that migrate within
it (diagonals) and migrate from it to another super-group. Rows show the dialectal super-groups of
where migrants come from, columns show the dialectal super-group of where they migrate to, and
items in each row from column 1 to column 9 are relative percentages and in column 10 is the sum of
them.
Data source: sample of 1774 migrants collected from China Labor-force Dynamic Survey (2012) by
authors.
32
TABLE III
Descriptive Statistics of All Variables
Variable Source Observation Mean Variance Min Max
Migration
across prefectures 0/1 (0=no, 1=yes) CLDS 15641 0.099 0.299 0 1
across prefectures 0/1/2 (0=not migrate, 1=within a prefecture, 2=across prefectures)1 CLDS 15641 0.213 0.605 0 2
across prefectures or within a prefecture 0/1 (0= within a prefecture, 1=across prefectures) CLDS 1774 0.877 0.329 0 1
across provinces 0/1/2 (0=not migrate, 1=within province, 2=across provinces) CLDS 15641 0.175 0.517 0 2
Dialectal Distance
weighted by population share in 1982 and defined in 0-1-2-3 pattern By authors 15641 0.205 0.680 0 3
weighted by population share in 1982 and defined in 0-1-10-100 pattern By authors 15641 5.000 20.423 0 100
weighted by equal weights and defined in 0-1-2-3 pattern By authors 15641 0.205 0.679 0 3
Evaluation of Putonghua Skill2 CLDS 15612 3.464 1.280 1 5
very fluently=53 CLDS 41594 0.255 0.436 0 1
fluently and with dialectal accent=4 CLDS 5185 0.316 0.465 0 1
not that fluently=3 CLDS 2365 0.147 0.354 0 1
could understand but not speak=2 CLDS 3257 0.204 0.403 0 1
can neither speak nor understand=1 CLDS 1256 0.078 0.269 0 1
Distance between Putonghua and Dialect of Birthplace By authors 13177 2.330 0.525 .245 3
Individual Characteristics
gender5 CLDS 15641 0.471 0.499 0 1
age (years) CLDS 15632 42.770 14.413 15 86
political status6 CLDS 15641 1.163 0.547 1 3
education7 CLDS 15641 2.423 1.485 0 7
education of father CLDS 15641 1.107 1.100 0 5
education of mother CLDS 15641 0.665 0.937 0 5
household registration status8 CLDS 15641 0.166 0.372 0 1
33
TABLE III (continued)
Variable Source Observation Mean Variance Min Max
Characteristics of Birthplace and Destination Prefectures
average GDP level of destination (100 billion) China Statistical Yearbook for
Regional Economy 2001-2010
15103 1.372 1.804 0 10.168
average GDP level of birthplace (100 billion) 15103 1.187 1.588 0 10.168
geographic distance (1000km) By authors 15641 0.065 0.269 0 3.427
longitude of destination Google earth 12984 114.042 6.495 82.068 131.005
longitude of birthplace Google earth 12984 113.904 6.371 82.991 131.159
latitude of destination Google earth 12984 31.803 6.387 18.255 50.245
latitude of birthplace Google earth 12984 31.828 6.255 18.255 50.245
slope of destination Feng et al. (2007, 2014) 12961 4.857 4.091 0 21.444
slope of birthplace Feng et al. (2007, 2014) 13176 5.324 4.607 0 21.444
relief degree of land surface (RDLS) of destination Feng et al. (2007, 2014) 12961 0.574 0.699 0 3.602
relief degree of land surface (RDLS) of birthplace Feng et al. (2007, 2014) 13176 0.637 0.794 0 3.559
Social Network of Interviewees
# people with whom can talk about something deep in heart9 CLDS 13107 2.488 1.045 1 5
# people with whom can talk about important things CLDS 13107 2.365 0.990 1 5
# people from whom can borrow money (>RMB ¥5000) CLDS 13107 2.218 1.152 1 5
Note:
1 Migration across Prefectures 0/1/2: 2 for migration across prefectures, 1 for across counties in same prefecture, 0 for non-migration. 2 Putonghua skill is evaluated by the interviewer after the interview ranging from 1 to 5 discretely, 5 points for very fluently, 4 points for fluently and with dialectal
accent, 3 points for not that fluently, 2 points for those who could understand but couldn’t speak Putonghua, and 1 point for those who can neither speak nor understand. 3 These are 5 dummy variables indicating whether an individual’s Putonghua skill equals to the relative scores. 4 4159 represents for number of observations whose Putonghua skill is “very fluently”, similarly applied to other levels of Putonghua skill. 5 Gender: 1 for male, 0 for female. 6 Political Status: 3 for members of Communist Party, 2 for members of the Democratic Party, 1 for non-partisan. 7 Education: uneducated equal to 0, the ungraduated from primary school equal to 1, the graduated from primary school equal to 2, from middle school equal to 3,
from high middle school equal 4, undergraduates equal to 5, master degree equal to 6, and PhD degree equal to 7 8 Household Registration Status: 1 for the urban, 0 for the rural. 9 1 represents for none, 2 for 1 to 3 people, 3 for 4 to 6 people, 4 for 7 to 9 people, and 5 for more than 10 people, similarly hereinafter.
34
TABLE IV1
Basic results
Dependent variable: migration across prefectures or not (1/0) Probit Logit LPM
(1) (2) (3) (4) (5) (6) (7) (8)
Dialectal Distance d 5.142*** 5.104*** 5.312*** 5.297*** 5.057*** 5.555*** 11.775*** 1.026***
(0.411) (0.403) (0.401) (0.399) (0.443) (0.664) (1.663) (0.037)
SquareTerm of Dialectal Distance2d -1.154*** -1.144*** -1.203*** -1.196*** -1.087*** -1.164*** -2.411*** -0.241***
(0.135) (0.132) (0.130) (0.129) (0.140) (0.185) (0.445) (0.013)
Putonghua skill 0.132*** 0.115*** 0.073 0.092* 0.066 0.135 0.001
(0.043) (0.043) (0.053) (0.053) (0.067) (0.161) (0.001)
Distance between Putonghua and Dialect of Birthplace -0.063 -0.074 -0.091 -0.148 -0.284 -0.002
(0.095) (0.100) (0.102) (0.170) (0.564) (0.005)
Average GDP level of birthplace -0.267*** -0.345*** -1.021*** -0.022***
(0.069) (0.129) (0.376) (0.005)
Average GDP level of destination 0.257*** 0.401*** 1.107*** 0.021***
(0.068) (0.134) (0.380) (0.005)
Individual characteristics NO NO NO YES YES YES YES YES
Provincial dummies NO NO NO NO NO YES YES YES
Constant -2.449*** -2.937*** -2.772*** -2.836*** -2.960*** -3.355*** -6.559*** 0.125*
(0.067) (0.149) (0.307) (0.374) (0.374) (0.478) (1.275) (0.069)
Inflection Point of d 2.23 2.23 2.21 2.21 2.33 2.39 2.44 2.13
Positive Marginal Effect 12.1% 11.8% 11.4% 11.3% 10.5% 10.3% 9.1% ----2
Negative Marginal Effect 2.1% 2.1% 2.0% 2.0% 1.1% 0.8% 0.5% ----
Observations 15,641 15,612 13,157 13,151 13,151 13,151 13,151 13,151
R-squared 0.838 0.841 0.854 0.856 0.862 0.879 0.879 0.886
Note:
35
1 Table IV reports the estimation results associating labor migration with dialectal distance. Dependent variable is a bivariate variable of
cross-prefecture-migration (=1) or not (=0). Independent variables of interest are dialectal distance between one’s birthplace and destination of first-time migration, d
and its square term, and individuals’ Putonghua skills. Control variables are individual characteristics, including gender, age, political status, education and education of parents, household registration status, categorical variable of industry characteristics according to the category of One Yard Industry Code in China, categorical variable of firm characteristic according to company ownership, average GDP level of birthplace and destination, and dummy variables of provinces that birthplace and destination belong to respectively.
Positive and negative marginal effects represent for the average extent that possibility of labor migration will increase and decrease by respectively, when dialectal distance increases by one level. Inflection point of d is caltulated by dividing the parameter of the square term of d by the paramenter of d , and then multiplying by 2. Stantard errors are in parentheses, and are clustered at provincial level, *** p<0.01, ** p<0.05, * p<0.1. Observations for estimation are 15,641 after data cleaning and matching, and because of missing values of some variables the amount declines.
2 We didn’t report the Positive and negative marginal effects when estimating under LPM, because the possibility could be larger than 1 due to the initiative disadvantage of LPM, similarly hereinafter.
36
TABLE V1
Basic results: expanding dependent variable
Dependent variable: migration across prefectures, within
prefecture or not (2/1/0) Ordered Probit
Ordered
Logit
LPM
(1) (2) (3) (4) (5) (6) (7) (8)
Dialectal Distance d 5.874*** 5.831*** 6.052*** 6.040*** 5.867*** 6.338*** 13.891*** 2.214***
(0.481) (0.472) (0.495) (0.500) (0.538) (0.715) (1.717) (0.063)
SquareTerm of Dialectal Distance2d -1.391*** -1.379*** -1.441*** -1.438*** -1.364*** -1.480*** -3.166*** -0.538***
(0.157) (0.153) (0.160) (0.161) (0.171) (0.212) (0.458) (0.022)
Putonghua skill 0.121*** 0.097*** 0.072* 0.080* 0.056 0.111 0.004
(0.038) (0.037) (0.042) (0.041) (0.040) (0.089) (0.003)
Distance between Putonghua and Dialect of Birthplace -0.066 -0.088 -0.089 -0.175 -0.391 -0.014
(0.097) (0.101) (0.104) (0.206) (0.553) (0.012)
Individual characteristics NO NO NO YES YES YES YES YES
Average GDP level of birthplace and destination NO NO NO NO YES YES YES YES
Provincial dummies NO NO NO NO NO YES YES YES
Inflection Point of d 2.11 2.11 2.10 2.10 2.15 2.14 2.19 2.06
Positive Marginal Effect 31.8% 31.6% 32.7% 32.8% 31.0% 32.2% 33.5% ----
Negative Marginal Effect 22.8% 22.4% 22.8% 22.5% 21.6% 21.6% 19.4% ----
Observations 15,641 15,612 13,157 13,151 13,151 13,151 13,151 13,151
R-squared 0.743 0.746 0.756 0.758 0.763 0.779 0.784 0.893
Note:
1 Table V reports estimation results associating labor migration with dialectal distance, where dependent variable is a 3-value variable of non-migration (=0),
migration across counties but within a prefecture (=1), and migration across prefectures (=2). Other descriptions are the same as Table IV. And we have also controlled constant but didn’t report here.
37
TABLE VI1
Possible measurable errors
Dependent variable: migration across prefectures: 2/1/0
Defined by
0-1-10-100
Equal
weights
North
dialects
Wu and Min
dialects
(1) (2) (3) (4)
Dialectal Distance d 0.229*** 5.881*** 5.200*** 6.565***
(0.044) (0.579) (0.541) (0.679)
SquareTerm of Dialectal Distance
2d
-0.002*** -1.332*** -0.984*** -1.520***
(0.000) (0.172) (0.181) (0.206)
Putonghua skill 0.062 0.058 0.094* 0.024
(0.040) (0.038) (0.052) (0.031)
Distance of Putonghua & Dialect of
Birthplace
-0.026 -0.145 0.030 -0.219
(0.169) (0.252) (0.247) (0.188)
North 3.460***
(0.394)
Individual characteristics YES YES YES YES
GDP of birthplace & destination YES YES YES YES
Provincial dummies YES YES YES YES
Inflection Point of d 66.67 2.20 2.64 2.15
Positive Marginal Effect 2.1%2 32.0% 25.9% 31.0%
Negative Marginal Effect 1.6% 19.6% 11.6% 19.8%
Observations 13,151 13,151 13,151 13,066
R-squared 0.598 0.781 0.834 0.799
Note:
1 Table VI reports estimation results associating dialectal distance with labor migration when
we take possible measurable errors into consideration. In column 1, dialectal distance of two prefectures is the mean of dialectal distance of two counties of the two prefectures of all kind of combination instead of population share weighting. In column 2, the definition method of dialectal between two counties has been changed as 0-1-10-100. In column 3 we have further introduced a dummy variable of migrating within northern dialects in case dialectal distances within northern dialects are overestimated. In column 4 we have dropped observations of migration within Wu dialect and within Min dialect in case dialectal distances within Wu and Min dialect are underestimated. In column 5, migration is measured at provincial level. We have also introduced all the control variables as column 6 in Table V but didn’t report here. Other descriptions are the same as column 6 in Table V.
2 Positive and negative marginal effects represent for the average extent that possibility of labor migration will increase and decrease by respectively, when dialectal distance increases by one unit rather than one level.
38
TABLE VII1
Other variables: geographic factors
Dependent variable: migration across prefectures, within
prefecture or not (2/1/0) (1) (2)2 (3) (4) (5) (6) (7)
Dialectal Distance d 6.385*** 2.061*** 6.253*** 6.250*** 6.416*** 2.056*** 6.297***
(0.679) (0.056) (0.719) (0.700) (0.680) (0.057) (0.718)
SquareTerm of Dialectal Distance2d -1.475*** -0.518*** -1.447*** -1.444*** -1.481*** -0.516*** -1.527***
(0.185) (0.018) (0.212) (0.206) (0.184) (0.019) (0.225)
Putonghua skill 0.060 0.004 0.065 0.063 0.066 0.004 0.054
(0.042) (0.003) (0.042) (0.043) (0.045) (0.003) (0.040)
Distance between Putonghua and Dialect of Birthplace -0.367 -0.011 -0.193 -0.255 -0.419* -0.013 -0.158
(0.239) (0.011) (0.218) (0.241) (0.239) (0.013) (0.207)
Longitude of destination -0.071 -0.089
(0.098) (0.097)
Longitude of birthplace 0.168* 0.214**
(0.097) (0.099)
Latitude of destination 0.319 0.312
(0.209) (0.203)
Latitude of birthplace -0.433** -0.419**
(0.208) (0.207)
Geographic distance 0.676*** 0.660***
(0.116) (0.112)
Square term of geographic distance -0.266*** -0.260***
(0.051) (0.049)
Slope of birthplace -0.119* -0.061 -0.007
(0.069) (0.094) (0.017)
39
TABLE VII (continued)
(1) (2) (3) (4) (5) (6) (7)
Slope of destination 0.143** 0.052 0.013
(0.063) (0.087) (0.017)
Relief degree of land surface (RDLS) of birthplace -1.208*** -1.014* -0.042
(0.308) (0.578) (0.103)
Relief degree of land surface (RDLS) of destination 1.271*** 1.229** 0.007
(0.306) (0.600) (0.110)
Dummy for whether destination and birthplace are located in
neighbor provinces
9.533***
(0.404)
Individual characteristics YES YES YES YES YES YES YES
Average GDP level of birthplace and destination YES YES YES YES YES YES YES
Provincial dummies YES YES YES YES YES YES YES
Inflection Point of d 2.16 1.99 2.16 2.16 2.17 1.99 2.06
Positive Marginal Effect 33.8% ---- 31.9% 32.3% 33.5% ---- 29.3%
Negative Marginal Effect 22.4% ---- 22.3% 22.3% 22.3% ---- 20.9%
Observations 12,173 13,151 12,173 12,173 12,173 12,173 13,151
R-squared 0.783 0.896 0.780 0.780 0.784 0.896 0.787
Note:
1 Table VII reports estimation results associating labor migration with dialectal distance when introducing more control variables of geographic factors. We have
also introduced all the control variables as column 6 in Table V but didn’t report here. Other descriptions are the same as column 6 in Table V. 2 Column 2 and column 6 are estimated under LPM because when introducing the geographic distance the iteration of Ordered Probit model doesn’t stop.
40
TABLE VIII1
Other concerns
Dependent variable in column 1 to 2:
migration across prefectures, within
prefecture or not: 2/1/0
Reverse
Causality
Migrate
for Job
Setting of Econometric Strategy
Sample of
migrants
migration across
provinces:2/1/0
(1) (2) (3) (4)
Dialectal Distance d 6.469*** 6.554*** 0.578*** 5.389***
(0.839) (0.919) (0.060) (0.430)
SquareTerm of Dialectal Distance2d -1.518*** -1.550*** -0.128*** -1.156***
(0.247) (0.297) (0.016) (0.107)
Putonghua Skill 0.095 0.044 0.003 0.053
(0.047) (0.054) (0.010) (0.037)
Distance of Putonghua & Dialect of
Birthplace
0.002 -0.309 0.032 -0.321
(0.167) (0.221) (0.029) (0.216)
Individual Characteristics YES YES YES YES
GDP of Birthplace and Destination YES YES YES YES
Provincial Dummies YES YES YES YES
Inflection Point of d 2.13 2.11 2.25 2.33
Positive Marginal Effect 30.2% 30.5% 55.9% 34.5%
Negative Marginal Effect 14.4% 13.3% 17.8% 22.4%
Observations 12,817 12,642 1,523 13,151
R-squared 0.824 0.825 0.431 0.734
Note:
1 Table VIII reports estimation results associating labor migration with dialectal distance.
Column 1 has dropped observations that migrate before 1987 in order to rule out endogeneity that migration before 1987 could influence dialectal distance calculated by the survey of Language Atlas of China in 1987. Column 2 has dropped observations whose purpose of migration is not for job. Column 3 focuses only on the sample of 1774, where migration across prefectures equals to 1 within a prefecture equals to 0, in case that it is our setting of econometric strategy that has driven the inverted U pattern. In column 4, dependent variable is a 3-value variable that migration across provinces equals to 2, within a province equals to 1 and non-migration equals to 0. We have also introduced all the control variables as column 6 in Table V but didn’t report here. Other descriptions are the same as column 6 in Table V.
41
TABLE IX1
Comparison among samples
Dependent variable: migration across prefectures,
within prefecture or not (2/1/0)
Hukou2 Status Gender Age Education
urban rural male female (18,45] (45,60] others
≤middle
school
≥high middle
school
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Dialectal Distance d 8.456*** 6.193*** 6.197*** 6.691*** 6.086*** 6.922*** 9.790*** 6.482*** 6.123***
(0.702) (0.817) (0.632) (1.193) (0.931) (0.611) (2.078) (0.764) (0.777)
SquareTerm of Dialectal Distance2d -2.127*** -1.437*** -1.408*** -1.526*** -1.413*** -1.677*** -2.341*** -1.521*** -1.358***
(0.241) (0.242) (0.199) (0.316) (0.274) (0.203) (0.639) (0.226) (0.226)
Putonghua skill -0.065 0.073* 0.095** 0.013 0.073 -0.010 0.119 0.064 0.024
(0.099) (0.043) (0.044) (0.053) (0.051) (0.054) (0.108) (0.055) (0.052)
Distance between Putonghua and Dialect of Birthplace -1.205*** -0.052 -0.415* 0.235 -0.073 -0.628 0.422 -0.030 -0.538**
(0.424) (0.165) (0.246) (0.301) (0.173) (0.484) (0.402) (0.277) (0.269)
Individual characteristics YES YES YES YES YES YES YES YES YES
Average GDP level of birthplace and destination YES YES YES YES YES YES YES YES YES
Provincial dummies YES YES YES YES YES YES YES YES YES
Inflection Point of d 1.99 2.15 2.20 2.19 2.15 2.06 2.09 2.13 2.25
Positive Marginal Effect 41.4% 30.9% 31.4% 30.5% 30.8% 30.3% 33.5% 31.6% 30.8%
Negative Marginal Effect 32.2% 22.4% 22.3% 20.6% 20.6% 23.6% 15.6% 23.6% 23.9%
Observations 2,302 10,849 6,199 6,952 6,254 4,690 2,212 9,873 3,278
R-squared 0.796 0.787 0.792 0.771 0.791 0.778 0.801 0.787 0.779
Note:
1 Table IX reports estimation results associating labor migration with dialectal distance by comparing different samples. We have also introduced all the control
variables as column 6 in Table V but didn’t report here. Other descriptions are the same as column 6 in Table V. 2 Hukou status is the household registration status.
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TABLE X1
Comparison among samples of different social networks
Dependent variable: migration across
prefectures, within prefecture or not
(2/1/0)
# people that can talk about
things deep in heart with
# people that can talk about
important things with
# people that can borrow
money (>RMB ¥5000) from
≥4 <4 ≥4 <4 ≥4 <4
(1) (2) (3) (4) (5) (6)
Dialectal Distance d 7.648*** 5.876*** 7.973*** 5.908*** 7.024*** 6.122***
(0.582) (0.970) (0.551) (0.894) (0.571) (0.816)
SquareTerm of Dialectal Distance2d -1.767*** -1.309*** -1.809*** -1.341*** -1.698*** -1.385***
(0.153) (0.332) (0.147) (0.294) (0.165) (0.273)
Putonghua skill 0.097 0.011 0.113*** 0.023 0.062 0.058
(0.066) (0.047) (0.040) (0.055) (0.059) (0.050)
Distance between Putonghua and
Dialect of Birthplace
0.036 -0.397** 0.159 -0.430*** -0.144 -0.167
(0.278) (0.180) (0.312) (0.156) (0.233) (0.256)
Individual characteristics YES YES YES YES YES YES
GDP of birthplace and destination YES YES YES YES YES YES
Provincial dummies YES YES YES YES YES YES
Inflection Point of d 2.16 2.24 2.20 2.20 2.06 2.21
Positive Marginal Effect 31.0% 31.5% 30.4% 32.3% 32.4% 32.0%
Negative Marginal Effect 24.5% 19.7% 26.7% 19.5% 25.2% 19.5%
Observations 5,884 7,267 5,318 7,833 5,225 7,926
R-squared 0.787 0.790 0.794 0.787 0.787 0.786
Note:
1 Table X reports estimation results associating labor migration with dialectal distance by comparing individuals’ social networks. Under each comparison, the left
columns are those with stronger social networks and the right columns are those with weaker social networks. We have also introduced all the control variables as column 6 in Table V but didn’t report here. Other descriptions are the same as column 6 in Table V.
43
TABLE XI1
Comparison among samples of different time periods
Dependent variable: migration across prefectures,
within prefecture or not: 2/1/0
>1990 >1995 >2000 >2005
(1) (2) (3) (4)
Dialectal Distance d 6.614*** 6.884*** 6.879*** 7.153***
(0.910) (0.853) (0.842) (0.847)
SquareTerm of Dialectal Distance2d -1.543*** -1.634*** -1.664*** -1.742***
(0.268) (0.257) (0.251) (0.262)
Putonghua skill 0.077* 0.066 0.068 0.092**
(0.044) (0.042) (0.049) (0.042)
Distance of Putonghua & Dialect of Birthplace -0.018 0.069 0.118 -0.242
(0.162) (0.170) (0.173) (0.205)
Individual characteristics YES YES YES YES
Average GDP level of birthplace and destination YES YES YES YES
Provincial dummies YES YES YES YES
Inflection Point of d 2.14 2.11 2.07 2.05
Positive Marginal Effect 30.0% 30.2% 30.2% 30.4%
Negative Marginal Effect 12.6% 11.2% 8.8% 6.3%
Observations 12,709 12,533 12,269 11,982
R-squared 0.838 0.844 0.841 0.832
Note:
1 Table XI reports estimation results associating labor migration with dialectal distance by
comparing different time periods. We gradually dropped migrants before the year 1990, 1995, 2000 and 2005. We have also introduced all the control variables as column 6 in Table V but didn’t report here. Other descriptions are the same as column 6 in Table V.