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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

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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.

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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

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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).

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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

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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.

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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).

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, , , 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

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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,

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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%.

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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.

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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.

References

18

Adsera Alicia, and Mariola Pytlikova, “The role of language in shaping international migration”,

The Economic Journal 125 (2015): F49–F81.

Alesina Alberto, and Eliana La Ferrara, “Ethnic Diversity and Economic Performance”, Journal of

Economic Literature 43.3 (2005): 762.

Alesina Alberto, Johann Harnoss, and Hillel Rapoport, “Birthplace diversity and economic

prosperity”, National Bureau of Economic Research No. w18699 (2013): 1-54.

Bai Ying, and James Kai-sing Kung, “Climate shocks and Sino-nomadic conflict”, Review of

Economics and Statistics 93.3 (2011): 970-981.

Bleakley Hoyt, and Aimee Chin, “Language skills and earnings: Evidence from childhood

immigrants”, Review of Economics and Statistics 86 no. 2 (2004): 481-496.

Bleakley Hoyt, and Aimee Chin, “Age at arrival, English proficiency, and social assimilation

among US immigrants”, American Economic Journal: Applied Economics, 2(2010): 165–92.

Böheim René, G. Horvath, and Karin Mayr, “Birthplace diversity of the workforce and

productivity spill-overs in firms”, WIFO No. 438 (2012): 1-35.

Butler Jeffrey, Paola Giuliano, and Luigi Guiso, “The Right Amount of Trust”, UCLA (2014),

mimeo.

Center for Social Survey in Sun Yat-Sen University, “China Labor-force Dynamic Survey: 2013

report (Zhongguo Laodongli Dongtai Diaocha: 2013 Baogao)”, Social Sciences Academic

Press (2013).

Chen Zhao, Ming Lu and Le Xu, “Returns to dialect: Identity exposure through language in the

Chinese labor market”, China Economic Review, 30 (2014): 27-43.

Cheng Mingwang, Qinghua Shi and Jianxia, Yang. “From Malthus to Solow: An Explanation for

the Motivation and Obstacles Effecting Farmer Labor Emigration in China”, Economic

Research Journal 4 (2006):68-78.

Chiswick Barry R., and Paul W. Miller, “Linguistic distance: a quantitative measure of the

distance between English and other languages”, Journal of Multilingual and Multicultural

Development, 26(2005):1–11.

Duan Chengrong, Ge Yang, Fei Zhang and Xuehe Lu, “Nine Trends of China's Floating

Population after Reform and Opening up (Gaige Kaifang Yilai Zhongguo Liudong Renkou

Biandong De Jiu Da Qushi)”, Population Research 6 (2008): 30-43.

Feng Zhiming, Yan Tang, Yanzhao Yang, and Dan Zhang, “The Relief Degree of Land Surface in

China and Its Correlation with Population Distribution”, Acta Geographica Sinica 62 no. 10

(2007): 1073-1082.

Feng Zhiming, Yanzhao Yang, Zhen You, and Jinghua Zhang, “Research on The Suitability of

Population Distribution at The County Level in China”, Acta Geographica Sinica 6 (2014):

723-737.

Friedman M., “A Theory of the Consumption Function”, Princeton, NJ: Princeton University Press

(1956).

Guiso Luigi, Paola Sapienza, and Luigi Zingales, “Cultural Biases in Economic Exchange”, the

Quarterly Journal of Economics 124.3 (2009): 1095-1131.

Hambrick Donald C., Theresa Seung Cho, and Ming-Jer Chen, “The Influence of Top

Management Team Heterogeneity on Firms’ Competitive Moves”, Administrative Science

Quarterly 41(1996): 659-684.

Hong Lu, and Scott E. Page, “Problem Solving by Heterogeneous Agents”, Journal of Economic

19

Theory 97(2001): 123-163.

Huston Ted L., and George Levinger, “Interpersonal attraction and relationships”, Annual Review

of Psychology 29(1978): 115-156.

Knack Stephen, and Philip Keefer, “Does Social Capital Have an Economic Payoff? A

Cross-Country Investigation”, Quarterly Journal of Economics 112(1997): 1252–1288.

Li Rulong, “Chinese Dialects (Hanyu Fangyan Xue)”, Higher Education Press (2001).

Li Shi, “A Gray Landscape of China's Economic Development (Zhongguo Jingji Fazhan zhong de

Yi Dao Huise de Fengjing Xian)”, Economic Research Journal 1 (2007): 154-157.

McPherson Miller, Lynn Smith-Lovin, and James M. Cook, “Birds of a feather: Homophily in

social networks”, Annual Review of Sociology (2001):415-444.

Pendakur Krishna, and Ravi Pendakur, “Language as Both Human Capital and Ethnicity”,

International Migration Review 36(2002): 147–177.

Li Rong, “Language Atlas of China (Zhongguo Yuyan Ditu Ji)”, Hong Kong: Longman Group

(Far East) Ltd. Australian Academy of the Humanities, Chinese Academy of Social Sciences,

and Dept. of Linguistics Australian National University, Pacific Linguistics, Series C 102

(1987).

Spolaore Enrico, and Romain Wacziarg, “The diffusion of development”, The Quarterly Journal of

Economics 124(2009): 469-529.

Spolaore Enrico, and Romain Wacziarg, “How Deep Are the Roots of Economic Development”,

Journal of Economic Literature 51.2 (2013): 325-369.

Spolaore Enrico, and Romain Wacziarg, “Long-term barriers to economic development”, in

Handbook of Economic Growth, Volume 2A (2014):122-176.

Sun Wenkai, Chongen Bai, and Peichu Xie, “The effect on rural labor mobility from registration

system reform in China”, Economic Research Journal 1 (2011): 28-41.

Xie Junying, “the Survey of the Current Situation of Putonghua Popularization”, Applied

Linguistics 3 (2011): 2-10.

Xu Baohua and Miyada Ichiro, “Dictionary of Chinese Dialect (Hanyu Fangyan Da Cidian)”,

Zhonghua Book Company (1999).

Yuan Jiahua, “Chinese dialect outline (Hanyu Fangyan Gaiyao)”, Language Press (2001), Second

Edition.

Zhao Yaohui, “China's Rural Labor Migration and the Role of Education (Zhongguo Nongcun

Laodongli Liudong ji Jiaoyu zai Qizhong de Zuoyong)”, Economic Research Journal 2

(1997): 37-42, 73.

Zhou Zhenhe, “Research on Cultural Regions in China History (Zhongguo Lishi Wenhua Quyu

Yuanjiu)”, Fudan University Press (1997).

Zhou Zhenhe and Rujie You, “Dialect and Chinese Culture (Fangyan yu Zhongguo Wenhua)”,

Shanghai People's Press (2006).

Isphording Ingo E and Sebastian Otten, “Linguistic distance and the language fluency of

immigrants”, Ruhr Economic Paper 274 (2011).

Trax Michaela, Stephan Brunow and Jens Suedekum, “Cultural diversity and plant-level

productivity”, Regional Science and Urban Economics 53 (2015): 85-96.

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

25B

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ing

Tia

nji

ng

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ei

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an

xi

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a

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on

ing

Jil

in

Heil

on

gji

an

g

Sh

an

gh

ai

Jia

ngsu

Zh

eji

an

g

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hu

i

Fu

jian

Jia

ngxi

Sh

an

don

g

Hen

an

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nn

an

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aa

nxi

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nsu

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gh

ai

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gxia

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jia

ng

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

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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

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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.

42

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.