To What Extent does Rural Migration Affect the Elderly”Left-behind”?I

Juliane Scheffel and Yiwei Zhang

School of Economics, The University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100,China. Email: juliane.scheffel@nottingham.edu.cn.

Abstract

The ageing population and massive rural-to-urban migration pose major challenges

to elderly care in rural China as the traditional method of inter-generational elderly sup-

port is under considerable pressure. This paper examines the impact of migration of adult

children on the well-being and mental health of their elderly parents via three supporting

channels: financial, physical and psychological supports. Using longitudinal data from

the CHARLS survey for 2011 we find that migration significantly reduces the mental health

of rural elderly in particular after the migration of sons. Financial transfers are not enough

to equalize the emotional distress of the elderly. Moreover, daughters-in-law are found to

be only weak substitutes for own children to provide physical care to the elderly parents

left-behind.

Keywords: Migration, China, Elderly Left-Behind

1. Introduction

The primary social, economic and cultural reforms since 1978 have resulted in an ac-

celerated development of the Chinese economy. The rapid economic growth combined

with urban-prioritised and pro-coastal policies raised a great demand for cheap labor

(Zhu, 1997). Meanwhile the wide coverage of the Household Responsibility System1 gen-

erated a large labor surplus in rural areas. Accordingly, the trend of urbanization has wit-

nessed more than 250 million of workers to migrate from rural to urban regions in 2011.

This corresponds to an over three times increase from 70 million in 1993 and it is still ex-

panding at a rate of 4.5% per year as shown in the migration reports from the National

1Household responsibility system or contract responsibility system is a practice in China started in 1981in agriculture. Traditionally under egalitarian Maoism, rural households are given a quota of goods to pro-duce by the government, but production beyond the quota was not rewarded. With the Household Re-sponsibility System practice, peasants are given a largely reduced quota and were able to sell the surplus atunregulated prices. Therefore the practice has encouraged the initiative and to some extent freed surplusworkers. (Zweig, 1997)

Preprint submitted to Elsevier 16th December 2014

1 INTRODUCTION 2

Bureau of Statistics of China (2012).

According to Guo et al. (2009), the rapid rural-to-urban migration has lead to an in-

crease in the population over the age 65 in rural areas. The migration of primarily younger

(under 25 years old) and relatively better educated workers from rural to urban areas fur-

ther will further stimulate the growth of the number of elderly that are left-behind in rural

areas. The implementation of the One-Child Policy since the early 1980s further resulted

in a huge drop in fertility rates from 7.5 in 1963 to 1.7 in 2003 (Zhang and Goza, 2006),

causing a further acceleration of the ageing problem in China. Populations projections

reveal that 26% of the total Chinese population will be over 60 years old by 2040 com-

pared with 13% in 2012 (Jackson and Howe, 2004).

The current national pension security for rural elderly, however, is insufficient and

ineffective. Attempting a transition from the method of a dichotomous social security

system,2 the Chinese government has been seeking to establish a reliable pension system

with broad coverage. Facing obstacles such as a severe funding gap and empty pension

accounts (Wang, 2005), China’s pension system provides coverage for only 35.3% of the

urban workers and covers even less workers in rural areas where the elderly are more fi-

nancially vulnerable (Jackson and Howe, 2004). Coupled with a weakening or even an

elimination of community support since the reforms in the late 1980s, the rural elderly

have been relying greatly on family support. Data from Du (2003) suggest that only 19.61

percent of the elderly in rural China were able to obtain a pension providing a yearly pen-

sion of 660 RMB3. This is even lower than the poverty line (Giles et al., 2010). In contrast,

approximately half of the rural elderly households relied on the support from their adult

children and other family members.

This system of intergenerational support as an informal pension substitute is under

consistent pressure and might even collapse because of the drastic migrations flows to the

cities. In urban areas, elderly are provided pensions given their pre-retirement incomes.

In rural areas, in contrast, land is regarded as a form of insurance for old age since it can

be passed on to the children. However, in the presence of the large migration flows of

adult children to the cities this system is likely to break down as the elderly can no longer

be supported by their children in the traditional way. The overall living conditions and

the satisfaction of the elderly in affected in three main ways: financial support, emotional

2Dichotomous social security system refers to the separated social safety nets in urban and rural areas,where urban development is more prior. (Wang, 2005)

3660 RMB correspond to slightly more than 100$. Pensions vary among different regions. In some richcoastal provinces such as Zhejiang Province, the basic pension amounts to 960 RMB per year.

1 INTRODUCTION 3

support and physical care support. In this paper, we will investigate in further detail how

migration affects the left-behind elderly in terms of these aspects using longitudinal data

from the China Health and Retirement Longitudinal Study (CHARLS) for 2011.

First of all, regarding the intergeneration financial transfer, there are two scenarios.

Those adult children who are able to achieve higher incomes after migrating to urban

areas are able to remit some of their additional income back home. These remittances

could be regarded as a substitute for formal pension. This could possibly make the elderly

financially better off and it could even allow them to not have to do the farm work or other

types of paid jobs themselves any longer. On the other hand, if migration reduces the

disposable income of adult children, they would not be able to send money home to their

old parents. In this case, the elderly receive no support, which is likely to cause pressure

on them to continue to work on their farms or to even undertake other paid jobs. In many

cases, the elderly also have to take care of their grandchildren who are not allowed to

legally live in urban areas due to a lack of an urban resident registration (hukou). This

additional burden could further deteriorate the welfare of the elderly and increase the

poverty risk (Giles, 2003).

In spite of the remittances from their adult children, migration results in the absence

of a caregiver and hence a worsening of the wellbeing of the elderly in terms of access to

daily physical care. Migration might also adversely affect the elderly’s levels of satisfaction

leading to augmented feelings of loneliness and being unhappy which might aggravate

the emotional wellbeing. In short, the three effects of rural migration have an a priori am-

biguous impact on the overall satisfaction level of the elderly and calls for a more detailed

empirical examination.

The study of the influence of rural migration on the ”left-behind” elderly in rural China

has vital implications since agricultural production, rural development and farmers are

among the top concerns and priorities of the Chinese government (Sonntag et al., 2005).

In addition, the new rural pension system4 was proposed by the state council and aims

to cover all rural regions by 2016, the end of 12th five year plan. The reduction in the

number of caregivers for the rural elderly as a result of migration strongly weakens the

informal security safety net and calls for an urgent extension of the coverage of the formal

security system to rural areas.

The remainder of this paper proceeds as follows. In the next section, we will show

a quick overview into the existing research on this topic. Section 3 and 4 will briefly in-

4Unlike the ole rural pension system, which emphasises peasants’ self-insurance via savings, the newrural pension system uses government fiscal revenue to provide subsidies.

2 PREVIOUS WORK 4

troduce the data used in the analysis and will detail the empirical strategy. The results

for the impact of migration on the different support types will be presented in section 5.

Section 6 is devoted to a discussion and an explanation of future work. Finally, section 7

concludes the paper.

2. Previous Work

Studies regarding the impact of rural migration on family supports towards elderly

parents are well developed in Western countries, whereas in China, the quantity of articles

are limited but have expanded in the last few decades since urbanization-based migration

and ageing issues are gaining momentum in China. In spite of some weaknesses in the

rigour of the data, in the validity of the induction, and in controlling for biases, these

studies have built a basic foundation in various focuses. In this section, we will lay out the

main existing findings in four aspects: theoretical frameworks that direct the empirical

studies, evidence on financial support (including cash remittances and in-kind help) from

migrant children to elderly parent, physical care for elderly and psychological comfort

they receive.

2.1. Theoretical Frameworks

Since family support is regarded as the main informal elderly security system across

Asian countries, a number of theoretical frameworks are proposed regarding the causes

and impacts of family support. Hashimoto and Kendig (1992) conclude at the macro level

the factors that influence family support for elderly are composed of demography, eco-

nomics, politics and culture. The authors believe that demographic factors, especially

ageing, migration and family structures could limit the accessibility for resources of fam-

ily support. In agreement with this, Mason (1992) further specified five aspects that in-

dustrialisation, urbanisation and migration could generate: a reduction of parents’ con-

trolling power on children, an increase of female labour force participation, a declining

number of adult children, an occurrence of inter-generational separation and a reduction

of multi-generational families.

Most studies (Lee et al., 1994; Piotrowski, 2006; Song et al., 2012 etc.) consent that

there are three groups of models explaining the intergenerational supports in a family:

Power and Bargaining Models (which emphasize power relationships, typically associ-

ated with gender and age that determine winners and losers), Mutual Aid Models (which

state that family members provide voluntary mutual aid) and Altruism/Corporate Group

2 PREVIOUS WORK 5

Models (describing the parents’ decision-making of the maximization of extended fam-

ily’s welfare). Among all the studies for China, most researchers (such as Lee and Xiao,

1998; Sun, 2002; Chen, 1998 etc.) agree that the Altruism/Corporate Group Model pro-

posed by Becker (1974) and Cox (1987) best portrays Chinese families in the context of

rapid economic growth, because in traditional Chinese families parents and children are

both altruistic and exchange motives through mutual supports. Giles et al. (2010) bases

their research on this theoretical framework and examined the pattern of financial trans-

fer to elderly in rural China, which we will evaluate in the following section.

In contrast to most of the developed countries in which the social security systems

provide substantial insurance for elderly against poverty, the Chinese government faces

great barriers to implement a social safety net for rural elderly and more than half of them

depend on family support (Du, 2003). Thus, the direction of support flows within a family

is predominantly from children to parents in rural China (Cai et al., 2009). Their finding

is supported by Secondi (1997) who tests the hypothesis of altruism and exchange the-

ory and finds the direction of family support appeared to be from adult children to their

parents in three main manners: financial support, physical daily care, and psychological

comfort.

2.2. Financial Supports

In recent years, with more concerns of the elderly support issues in China due to

the migration and population ageing, a number of studies have investigated the finan-

cial transfer of migrant children to their parents in rural areas. Taylor et al. (2003) us-

ing household-farm survey data finds that the remittance of migrants sent back home,

as compensation of labour loss, has directly increased the rural household income and

indirectly stimulated the crop production. A 16% to 43% increase in household income

for left-behind elderly is estimated as a result of adult children participating in migra-

tion. Adding more weight to this finding, Ma et al. (2004) use the 2000 China Census and

find that per capita net income and saving have higher growth rate in areas with a larger

proportion of migrants, and that higher remittances contribute to an enhanced level of

education, hygiene and housing condition in elderly parents homes. In addition, choos-

ing Yonghong village in Anhui Province as an example, Ma (2011) analyzes the economic

impact of children’s migration on the left-behind old parents by studying the composi-

tion of annual income, sources of transfer, and the size of transfer. Although he draws

similar conclusions as above authors, his research is based on a limited sample which is

unrepresentative and caution is advised when such conclusions are made.

2 PREVIOUS WORK 6

However, the articles mentioned so far focus on aggregate transfers but do not dif-

ferentiate between inter-generational or intra-generational donations. Lei et al. (2012),

in contrast, using rich information of good quality data from the China Health and Re-

tirement Longitudinal Study (CHARLS) 2008 pilot, finds that the financial capability of

migrant children has a substantial impact on the transfers to elderly parents. More specif-

ically, fewer offspring of migrants (grandchildren of the elderly), higher education, being

married, or being the non-first born son could enlarge the quantity of financial transfer

back to home respectively. In addition, with a detailed elaboration of the evolution of

the Chinese social safety net, they predict a future persistency of income disadvantage of

rural elderly and an increasing financial burden for children to support their parents.

From another angle, Li and Démurger (2013) draw great emphasis to the occupational

choice for both the individual and the household in respond to migration, by examin-

ing how the remittance and migration experiences of migrant children affect the con-

sequences of migration. They find that elderly receiving remittances could increase the

probability of farming and house work but decrease the probability of local wage work

and self-employment. Moreover, concentrating on the relationship between gender dif-

ference and financial support of rural elderly parents, Li et al. (2006) find that females

provide higher financial supports to parents-in-law and both genders give significant fi-

nancial support to natal parents. A distinctive methodology the authors adopt is that two

data sets from both parents and children perspectives are utilised, which provides a more

comprehensive and valid valuation. However, also because of this, a drawback of this

method is that the authors are unable to identify the impact of migration on elderly from

two perspectives in the very same household, which may cause bias and distortion.

An outstanding research of transfers analysis is conducted by Giles et al. (2010) who

used data from surveys from the Research Centre for Rural Economy (RCRE) and a supple-

mentary survey conducted with the cooperation of Michigan State University and RCRE.

Investigating the relationship between migration and poverty reduction, they show that

financial support in households with migrant children appears to be at a similar level as

compared to those without migrant children. In addition, they also find migration causes

a higher poverty risk for elderly with migrant children to. Distinguishing itself from others,

this research systematically analysed the biases, distortion and solutions of controlling

them, which shed light on our own research. For example, they point out that if adult chil-

dren have higher natural abilities, they would be indifferent between living close to their

parents in rural areas and migrating to urban areas because either way they are likely to

2 PREVIOUS WORK 7

earn higher incomes. Or if elderly parents have higher natural abilities, they would have

less need to rely on their children.

2.3. Physical Care

Apart from financial support, measuring the satisfaction of rural elderly with children’s

migration also involves the estimation of its impact on well-being, more specifically, the

daily physical care and emotional comfort support. However, most of the existing articles

focus only on the financial transfer as remittances, and few of them have touched on the

physical and psychological areas. Where that has been the case, they are introduced only

indirectly by analysis of living/housing arrangement or by retirement and labour supply

decisions (Giles and Mu, 2006).

Rural-to-urban migration is also the main cause of the reduction in the number of

potential care-givers and a lower quality of family support, which leads to an exacerba-

tion of the elderly welfare and health conditions in rural China (Davin, 1999; Croll, 1997).

In terms of the effect of gender differences on migrant households, Rozelle et al. (1999)

believe, while traditionally women are the main care-givers for elderly in a family, an no-

ticeable increase in participants of females in the labour force has driven the gender gap

down and thus, coupled with a poorly established social safety net, the loss of conven-

tional care-givers has deteriorated elderly welfare and even caused additional burden for

the elderly to take care of ’left-behind’ grandchildren. Moreover, Wu et al. (2009) and Giles

et al. (2010) find that the market of instrumental care provision is still poorly developed in

rural China, indicating a further absence of potential physical tending for elderly.

However, observing from data collected in rural villages in Shandong Province, Du

and Du (2002) criticise that most migrants are aged between 18 and 29 (the oldest is 38),

while their parents are aged less than 60 and fully capable of labouring and taking care

of themselves. In addition, the land arrangement laws allow young migrants to lend their

land to others so their parents are not obligated to additional farming. Thus, the authors

conclude that the potential negative impacts of rural migration on elderly are limited.

Du et al. (2004) get similar results. Conducted by the Gerontology Research Centre at

China Normal University, three provinces were surveyed and it was found that most left-

behind rural elderly have some degree of self-care abilities to date (Oct 2004). Even for

those who have critical health conditions, the elderly’s spouse, co-residential children and

other relatives could provide daily physical care. In spite of the critiques, Yin and Huang

(2011) argue that the daily care provided by co-residential children and other relatives is

only subsidiary and that those widowed elderly who do not have children living nearby

3 DATA 8

face a great difficulty in daily care. Li et al. (2006) add the argument that male elderly

parents are facing more difficulties than female elderly as a consequence of the fact that

daughters-in-law are more likely to stay within the rural home to take care of them but

face embarrassment when providing physical tending to their father-in-laws (e.g. bathing

and clothing).

2.4. Psychological Supports

Compared to the number of financial and physical care studies, the quantity of inves-

tigation regarding the psychological impact on rural elderly are extremely limited, mainly

because it cannot be directly observed and thus is difficult to measure quantitatively. The

main hypothesis is that outmigration reduces the opportunities for adult children to co-

reside with their elderly parents and hence parents are more likely to feel lonely and pow-

erless (Yao, 2011; Zhang and Li, 2004; Zhang et al., 2005). However, some researchers hold

opposing views. Zhuo and Liang (2006) predict that rural migration to cities could in-

crease the elderly’s psychological satisfaction because out-migration is usually valued as

indication of the ability of migrant children, which parents could be very proud of. But

the weakness of their research is that, they are unable to provide quantitative estimation

of parents’ satisfaction and there is only one question regarding the elderly’s emotional

status. In contrast to their invalid induction, Song and Li (2008) used surveys with higher

accuracy which allows them to further specify the different impacts on emotional support

among migrant sons, daughters, son-in-laws, and daughter-in-laws (if children married).

Moreover, Zuo and Li (2011) add more weight to the fact that out-migration reduces the

possibility of elderly and their children to have quarrels and their estimation indicates

that 79.4% of migrations are willing to communicate more with their parents about their

life conditions.

3. Data

3.1. Data Source

The data used in this dissertation originates from the China Health and Retirement

Longitudinal Study (CHARLS) conducted by the National School of Development at Peking

University. Building on the experience from the first pilot survey conducted in Gansu

and Zhejiang Province in 2008, the CHARLS survey is sampled nationwide for the years

2011 and 2012, covering 150 districts/counties, and 450 communities/villages. The target

group is people aged above 45 who are drawn randomly across China. Respondents are

asked to provide detailed information about themselves and their household members

3 DATA 9

which makes is well suitable for the purpose of this study. One of the main advantages of

CHARLS is that it contains a rich set of information regarding the middle-aged and elderly

population, as well as intergenerational transfers within a household.5

To examine the question to what extent rural-to-urban migration affects elderly par-

ents in China, we narrow the sample of interest to households located in rural areas. We

further restrict the respondent’s age to be 60 or above. We further assume that the main

purpose for migration is to find work in order to be able to remit financial or in-kind sup-

port back home. We therefore focus on working age children that is children aged 16 or

above.6 We are finally left with 7524 observations.

3.2. Variables and Summary Statistics

The control variables account for parental and child characteristics, we further in-

clude a migration dummy7, we use three sources of support that adult children provide to

their elderly parents - financial, physical, and psychological support and we include the

overall perceived level of satisfaction by the elderly.

Two separate types of transfers are considered: transfers that are received by the re-

spondent from their relatives including their children; and transfers given from the main

respondents to others. We define all transfers as the sum of regular and irregular cash

and in-kind transfers paid from children to their parents. To account for the possibility

of transfers from parents to their children, we additionally measure net-transfers as the

amount of remittances from children to parents minus the transfers from parents to their

children.

Information about physical support is drawn from the questions ”How many hours

did your child help you with functional support during the past month”.8 We will use

this information as a dummy variable taking the value 1 if the respondent reports to pro-

vide some functional support and 0 otherwise. To measure psychological support, we use

the question ”If you did not live with the child, how often do you have contact with your

5A household is defined as a pair of parents and their child(ren) according to the Chinese hukou reg-ulation. The head of the household is usually the father of the family or the oldest brother. If more thanone household is living in one dwelling, one of them with age-eligible members is chosen randomly by thesurvey team.

6The Labour Law of the People’s Republic of China sets the legal working age to 16. Employers are strictlyforbidden from hiring children below the age of 16. Children between the ages of 16 and 18 can be legallyemployed provided that they follow a special protection system enforced by the Labour Law.

7We define ”migrant” children as those children who live in another county or even further away fromtheir parents.

8Functional support refers to activities such as dressing, bathing, eating, getting out of bed, using thetoilet, controlling urination and defecation, doing chores, preparing hot meals, shopping, managing money,making phone calls, taking medications.

3 DATA 10

child via phone, text message, mail, or email?”. Here, we construct two dummy variables.

One determining whether the non-co-residing child is contacting his elderly parents less

than once a year. And an additional one to capture weekly contacts or more. Finally, we

use three proxies for examining the parent’s overall state of mental health. These report

whether the elderly reports higher than average levels of happiness, depression and lone-

liness.

Table 1: Summary Statistics of Parental Characteristics by the Household’s Migration Status.

Total Migrant Non-Migrant t-testChild Child

Age 69.77 69.41 70.06 3.950***(7.15) (7.02) (7.24)

Being illiterate 0.435 0.406 0.458 4.473***(0.50) (0.49) (0.50)

High School or above 0.014 0.014 0.014 0.186(0.12) (0.12) (0.12)

Male 0.501 0.540 0.470 -6.058***(0.50) (0.50) (0.50)

Good health status 0.190 0.181 0.198 1.889*(0.39) (0.39) (0.40)

Married 0.655 0.685 0.632 -4.817***(0.48) (0.47) (0.48)

Rural hukou 0.953 0.957 0.950 -1.418(0.21) (0.20) (0.22)

No. of Children 4.32 4.43 4.23 -5.508***(1.53) (1.51) (1.54)

Fraction of Mig. Children 0.200 0.454 0.000 -115.263***(0.28) (0.26) (0.00)

N 7530 3326 4204

* p <0.1, ** p <0.05, *** p <0.01, standard deviations in parentheses

While we observe that 21 percent of the adult children migrate, 44 percent of all house-

holds have at least one migrant child. Table 1 provides summary statistics of the parental

characteristics as well as sample differences between the two groups. In general, the av-

erage age of the elderly parent is 70. Most of them tend to have low education levels with

44 percent being illiterate. Only less than 2 percent have a middle school degree or above.

Compared to those without, parents who have migrant children tend to be younger, more

likely to be married, to have more children and fewer of them report to be illiterate. Par-

ents with at least one migrant child also tend to be less likely to report to be of good health.

Moreover, about half of the children in these households report to migrate.

3 DATA 11

Table 2: Summary Statistics of Children Characteristics by the Child’s Migration Status.

Total Migrant Non-Migrant t-test

Age 41.53 39.52 42.08 11.50***(8.00) (7.55) (8.03)

Being illiterate 0.113 0.073 0.124 5.70***(0.32) (0.26) (0.33)

High School or above 0.122 0.183 0.105 -8.53***(0.33) (0.39) (0.31)

Male 0.486 0.538 0.471 -4.75***(0.50) (0.50) (0.50)

Married 0.940 0.904 0.949 -6.87***(0.24) (0.30) (0.22)

Log income 1.551 1.628 1.530 -9.08***(0.39) (0.38) (0.39)

N 7524 1608 5916

* p <0.1, ** p <0.05, *** p <0.01, standard deviations in parentheses

Table 2 illustrates descriptive statistics for all children of elderly parents as well as dif-

ferences by the child’s own migration status. Compared to people who do not migrate,

migrant children tend to be younger, to have higher levels of education and also to have

higher average incomes. In addition, the migrant group tends to be dominated by men

whereas more women live close to their elderly parents in rural areas.

In addition, Table A.1 in the Appendix reports average differences in child character-

istics between migrant households and the remaining ones. Compositional differences

between these groups are qualitatively the same as before though less pronounced. Chil-

dren in household with at least one migrants tend to be generally better educated, to be

slightly younger and less likely to be married. The fact that household differences are less

pronounced than individual ones suggests that it is the more productive children within

a household that migrate but migrant households themselves tend also to be positively

selected ones.

4 ESTIMATION STRATEGY 12

Table 3: Summary Statistics for the Support Variables by Household Migration Status.

Total Migrant Non-Migrant t-test

Financial Support

Log Transfer 3.239 3.430 3.082 -4.80***(3.26) (3.35) (3.18)

Log Net Transfer 3.230 3.421 3.074 -4.47***(3.26) (3.35) (3.17)

Physical Support

Functional sup. from children 0.110 0.103 0.115 1.62(0.31) (0.31) (0.32)

Functional sup. from spouse 0.084 0.101 0.071 -4.53***(0.28) (0.30) (0.26)

Psychological Support

Weekly Contact 0.279 0.269 0.287 1.70*(2.72) (2.55) (2.81)

Mental Health

Depressed 0.380 0.397 0.367 -2.58***(0.49) (0.49) (0.48)

Lonely 0.259 0.282 0.240 -4.02***(0.44) (0.45) (0.43)

Happy 0.617 0.603 0.628 2.17**(0.49) (0.49) (0.49)

N 7079 3182 3897

* p <0.1, ** p <0.05, *** p <0.01, standard deviations in parentheses

Table 3 summarizes descriptive statistics for financial, physical and psychological sup-

ports and provides information regarding the reported mental health status of the el-

derly. As shown, significantly higher financial transfers are provided to parents in migrant

households. Moreover, elderly people in migrant households are significantly more likely

to get physical support from their spouses yet the support provided by children is not

much different from non-migrant households. Children who do not co-reside with their

parents tend to be slightly less likely to be in weekly contact with their parents than those

living closer. Regarding the emotional indicators, parents in migrant households tend to

report significantly higher levels of loneliness and depression and they are also less likely

to report high levels of happiness.

4. Estimation Strategy

In order to empirically test the impact of migration on the elderly left behind, we will

first examine the determinants of the migration decision for the child as well as on house-

4 ESTIMATION STRATEGY 13

hold level. We then investigate the impact of the adult child’s migration status on the

elderly parents i in the following way:

Si =β0 +β1Mi +β2Pi +β3Ci +εi , (1)

where Si is one of the three support methods i.e. financial, physical or psychological sup-

ports; Ci controls for a number of child characteristics such as gender, age, the education

level, marital status and log income; Pi measures parental characteristics such as gen-

der, age, education level defined by a dummy for being illiterate and a dummy indicating

more than high school education, the number of children, a dummy for good health, be-

ing married, and a rural hukou. The key variable of interest is a dummy indicating the

adult child’s migration status Mi . We define adult children as ”migrants” if they live in

another county or even farther away from their elderly parents.

We will start our analysis by estimating the impact of the three support types by means

of OLS and Probit models. However, simple OLS ignores the fact that the migration deci-

sion is endogenously determined as individuals self-select into leaving their village. The

health status of the parent left behind is likely to be one of the influencing factors of this

decision or for the decision to return. To tackle this problem, we will adopt an instru-

mental variable approach on the equation (1). To identify the causal link, we need to

find a variable that predicts the decision to migrate without directly affecting the support

types or any other outcome variable. We will mainly use the fraction of people with a lo-

cal hukou who reside somewhere else relative to the total population of the village. This

information is provided by the community survey of the CHARLS data.

Migrants in China, as found by Knight et al., 1999; Zhao, 1999; Démurger et al., 2009,

tend to be younger, unmarried and to have no children which implies that adult children

who are considered as migrants of elderly parents, have taken their migration decision al-

ready some time ago when the child was younger and less dependent on other household

members. We are not necessarily talking about new migrants in this paper. The instru-

mental variable seems to be a suitable instrument as it allows for the migration decision

to have been taken some time before the survey date. At any time, a larger share of peo-

ple with a local hukou who live in other provinces increase the probability for villagers to

migrate as these people are able to provide more information about the migration desti-

nation. Moreover, a larger network in the destination area reduces information costs and

could allow the new migrants to find a job more easily. These facts could have stimulated

5 RESULTS 14

larger migration flows in the past which translate into larger fractions of locals being away

from the village nowadays.

To analyse how migration affects the mental and emotional health of elderly parents,

we further estimate the following equation:

Ui =α0 +α1Mi +α2Xi +εi , (2)

where Ui represents the happiness as well as depression or loneliness, Xi is a vector of

parental and child characteristics.

5. Results

5.1. Factors that affect Migration

In this section, we will explore the determinants of the rural-to-urban migration deci-

sion at the individual and the household level. Table 4 reports marginal effects of Probit

models for all elderly as well as differentiated by sons and daughters.

The table shows that there are some differences in parental characteristics that deter-

mine the children’s migration decision. First of all, parents’ age is negatively associated

with their children’s migration decision. Moreover, elderly parents have a lower proba-

bility to be illiterate if at least one of their children has migrated. The parental illiteracy

rate influences the individual’s decision but given that these household’s have more than

4 children on average, it has an even stronger influence on the household’s migration ex-

perience. This is further supported by the number of children which increases the house-

hold’s probability of experiencing migration by 4.5 percent while it does not affect the

individual’s decision to migrate.

In addition, the elderly’s reported health status is found to be negatively correlated

with migration. The table reveals that a lower health status rather affects the migra-

tion experience of daughters who tend to have a 4 percent lower migration probability

and households with daughters experience 5 percent less migrations. The influence of

parental health on the son’s migration decision is lower in magnitude and is insignificant.

This differential result could be explained by the fact that the absence of a child could be

one of the potential causes of the lower reported health of the elderly due to a lack of care

which is in particular provided by the daughter.

5 RESULTS 15

Table 4: Determinants of the Migration Decision for Children and Households, respectively.

All Sons Daughters

Child HH Child HH Child HH

Parental characteristics:

Age -0.026** -0.025* -0.023 -0.032 -0.023 -0.016(2.18) (1.76) (1.25) (1.53) (1.49) (0.78)

Num. of children 0.001 0.045*** -0.002 0.041*** 0.003 0.049***(0.24) (11.70) (0.33) (7.34) (0.80) (9.14)

Rural hukou 0.009 0.013 0.072** 0.043 -0.040 -0.014(0.39) (0.47) (1.99) (1.06) (1.38) (0.38)

Illiterate -0.022** -0.046*** -0.017 -0.034* -0.026* -0.055***(2.06) (3.55) (1.06) (1.84) (1.83) (3.07)

High school or above -0.015 -0.016 0.013 0.035 -0.038 -0.056(0.37) (0.31) (0.22) (0.48) (0.71) (0.80)

Married 0.006 0.047*** -0.014 0.048** 0.024 0.048**(0.51) (3.45) (0.85) (2.44) (1.62) (2.50)

Good health -0.034*** -0.036** -0.030 -0.024 -0.041** -0.050**(2.78) (2.42) (1.63) (1.11) (2.48) (2.41)

Child characteristics:

Age -0.005 -0.000 0.002 0.000 -0.012** -0.004(1.16) (0.04) (0.31) (0.02) (2.18) (0.48)

Log income 0.123*** 0.088*** 0.138*** 0.085*** 0.114*** 0.095***(7.41) (5.31) (5.71) (3.67) (5.06) (4.01)

Illiterate -0.033* -0.004 -0.036 0.014 -0.036* -0.016(1.92) (0.22) (0.96) (0.35) (1.93) (0.72)

High school or above 0.078*** 0.096*** 0.069*** 0.106*** 0.096*** 0.083***(5.65) (5.29) (3.64) (4.57) (4.68) (2.85)

Male 0.033*** -0.015(3.41) (1.26)

Married -0.137*** -0.134*** -0.123*** -0.114*** -0.153*** -0.153***(7.01) (5.18) (4.61) (3.43) (5.12) (3.65)

N 7526 7526 3654 3654 3872 3872

* p <0.1, ** p <0.05, *** p <0.01, robust standard errors in parentheses.

Moreover, Table 4 reports the child’s own characteristics that affect their migration

decision or that contribute to a larger probability within the household to experience

migration of at least one sibling. Marginal effects reveal that these factors have a much

more pronounced impact on the own migration decision at to a much lower extent on

the household as a whole. It shows that migration is more likely for men. Higher income

levels are significantly positively correlated which implies that migration to urban areas

tends to increase the wages.

In addition, education increases the migration probability such that adult children

with more than a high school degree are 7.8 percent more likely. A larger education level

has an even stronger impact on the household level. In contrast to the fact that the par-

ent’s marital status is positively associated with the household’s experience of migration

of at least one adult child, being married is found to be correlated with a lower probabil-

ity to migrate to urban areas by about 14 percent of average. This is in line with previous

5 RESULTS 16

studies for China (Knight et al., 1999; Zhao, 1999; Démurger et al., 2009). Without depen-

dents, urban migration is significantly easier in China, which is given by the fact that the

absence of a local hukou excludes migrants and their families from receiving any kind

of social security benefits which is particularly problematic for migrants with school-age

children. The decision to migrate is therefore significantly easier to take for unmarried

people who move to the cities in order to be able better to support their elderly parents.

Table 4 also reports marginal effects for the migration decision of sons and daughters

separately. The parent’s age does not have a significant impact on either decision. In con-

trast, the parent’s illiteracy rate and health status have a negative influence but are found

to be significant only for daughters. The reason for these differences are explained by the

fact that women in rural areas are responsible to look after their elderly parents accord-

ing to the implicit generational contract and the absence of any official pension system in

rural areas. An elderly parent being of adverse health or of being illiterate needs more sup-

port from his children and in particular from his daughter. Parents who are married and

who live with their spouses have a lower need for care provided by their children which

explains the marginally insignificant and positive coefficient estimate for daughters.

Although the age of parents has no impact on the migration decision, daughters tend

to be more likely to migrate if they are younger and if they have higher levels of educa-

tion. Age and education level are less important determinants for sons. This difference

may be partly explained by the overall lower migration probabilities of women; and partly

by the lower educational attainments among women in rural China (almost 18% of all

rural women are illiterate compared to only 5% of men). For sons, individual income is

highly correlated with migration which as before implies that migration allows for larger

incomes in urban areas partly explained by the higher costs. It is also worth noting, that

marital status has a 3 percent points stronger impact on daughters in that women are

drastically less likely to migrate once they are married.

5.2. Financial Support and Migration

In this section, we will turn to the analysis of the impact of children’s migration on their

financial support to elderly, which is what most of the existing papers have investigated

around this topic (Taylor et al., 2003; Ma et al., 2004; Ma, 2011; Lei et al., 2012; Démurger

and Shi, 2012; Li et al., 2006; Giles et al., 2010). We will do so, by first looking at the exten-

sive margin by examining how migration affects the probability to not be able to transfer

financial resources back to the elderly parents. We will then turn to the intensive margin

by studying to differences in money for those who do financially support their parents.

5 RESULTS 17

Marginal effects of probit models comparing the extent to which migration affects the

probability to not provide any transfers9 are shown in Table 5. It shows that the two prox-

ies (transfers from children to parents and net transfers from children to parents) give

roughly similar results; therefore, we will only use the proxy for log transfers in the remain-

der of this section. The table reports that migration if it is measured at the household level

only has a significant impact on the probability for son’s which is fount to be about 3 per-

cent lower. In all other households, migration does not seem have a differential impact.

This is explained by the Chinese inter-generational contract according to which children

have to financially support their elderly parents in rural areas because there is no formal

pension system (Li et al., 2006; Song and Li, 2008).

Table 5: Impact of Migration on Financial Support by Children’sGender - Marginal Effects of Probit Models.

Son Daughter All

Individual Migration

No Transfer -0.121*** -0.024 -0.072***(5.86) (1.11) (4.86)

No Net Transfer -0.116*** -0.021 -0.069***(5.65) (1.01) (4.67)

Household Migration (at least one child has migrated)

No Transfer -0.030* -0.002 -0.015(1.76) (0.13) (1.27)

No Net Transfer -0.031* 0.005 -0.017(1.78) (0.28) (1.42)

* p <0.1, ** p <0.05, *** p <0.01, standard deviationsin parentheses

Measured at the individual level, however, the table reveals that migration reduces the

overall probability to not provide any transfers by 7.2 percent, which indicates that rural-

to-urban migration increases the probability for children to send cash and in-kinds home.

However, when we investigate sons and daughters separately, the results are slightly dif-

ferent. For male children, migration significantly reduces the probability by 12 percent.

This may be explained by the fact that conventionally, men are the main income source

within a household and they have the obligation to care for their elderly parents. Migra-

tion allows them to earn enough resources to be able to send transfers. In contrast, the

impact on the probability for daughters is insignificant and low in magnitude. According

to the Chinese tradition, daughters will provide rather physical and psychological care to

9Here, we investigate transfers from children to parents and net transfers from children to parents.

5 RESULTS 18

their elderly parents in rural areas but their main responsibility is to care for the parents

in law which could explain these findings (Song and Li, 2008).

For those who do provide transfers to their elderly parents, Table 6 shows the impact of

migration on the amount of transfers based on OLS and IV regressions. The results clearly

point into the same direction which is that a migrating child is able to send larger sums.

Net transfers refers to all remittances from children to their parents. The effect differs

by the gender of the child and are generally higher in the case of sons. The significant

transfer sums of migrating women in the group of rural-to-urban migrants further point

out that women have played a considerable role by providing huge contributions to the

labour force in the process of urbanisation. This is consistent with most of the existing

literature regarding the impact of financial support of migrant children to their parents.

Table 6: Impact of Children’s Migration on the Amount of Transfersby Children’s Gender - OLS Models.

Son Daughter All

OLS results

Log Transfer 0.525*** 0.355*** 0.452***(7.98) (5.55) (9.88)

Log Net Transfer 1.173*** 0.453*** 0.812***(8.61) (3.35) (8.44)

IV results

Log Transfer 1.457*** 2.436*** 1.773***(2.83) (3.42) (4.25)

First stage F -stat 17.29 8.66 27.34

Log Net Transfer 5.600*** 4.890*** 5.250***(4.81) (2.80) (5.37)

First stage F -stat 26.47 12.75 40.05

* p <0.1, ** p <0.05, *** p <0.01, standard deviationsin parentheses

In the first stage we use the fraction of migrants in the working age population of

the village as well as the fractions of people with a local hukou but who live in different

provinces compared to the overall population of a village as instruments taken from the

village survey. We find a significantly negative correlation between the first instrument

and the child’s migration decision indicating that a higher fraction of migrants among the

working age population in 2010 is associated with a lower decision of the adult children to

migrate.10 According to previous findings by Knight et al. (1999); Zhao (1999); Démurger

et al. (2009) migrants tend to be younger and unmarried. The sample of adult children,

10First stage regression results are not shown here but are available from the authors by request.

5 RESULTS 19

we refer to in this paper, is however not the typical potential migrant in that they are older

and have families themselves which strongly mitigates the decision to migrate.

The first stage correlations indicate a significantly positive association between the

fraction of villagers with a local hukou who live however in another province and the

child’s own migration decision but that of their own children or younger relatives. At the

age when the adult child decided to migrate or not, a larger share of migrants within a

village increased the probability to migrate as they are able to provide more information

about the migration destination. Moreover, a larger network in the destination area re-

duces information costs and could allow villagers to find a job more easily. These facts

could have stimulated larger migration flows in the past which translate into larger frac-

tions of locals being away from the village nowadays.

The instruments perform well in most cases, except for daughters as illustrated by the

first stage F -statistics presented in Table 6. This indicates that the instruments are valid.

Moreover, the Sargan test for over identifying restrictions suggest that we cannot exclude

any of the instruments from the first stage.

5.3. Physical Care, Psychological Support and Migration

In this section, the impact of migration on physical and psychological supports will

be discussed in more detail. Firstly, the marginal effects shown in Table 7 indicate the im-

pact of migration on the probability that the child provides some physical support to their

parents. It takes the value of 1 if children spent time helping their parents with physical

function and it is 0 if children do not provide any physical support. The results indicate

that the impact of migration is negative, as expected, but it is low in absolute terms. This

suggests that children who live closer to their parents also do not seem to provide a large

amount of functional care such that migration does not matter very much. Coefficient es-

timates show that, overall, children who have migrated tend to have a 0.9% lower proba-

bility to provide daily functional care to their parents. This impact is small but significant.

There is a distinct difference between sons and daughters. A son’s migration reduces

the probability of functional support by 1.4 percent. Instead, it is mainly their wives who

take care of the husband’s elderly parents (Song and Li, 2008). The migration status of

daughters, in comparison, is highly correlated with providing physical support. In fact,

only 39 out of 3843 daughters who are not co-residing with their parents provide physical

support. And most of these are not far enough away to be defined as migrant, i.e. only 2

migrant daughters provide any physical support to their own parents. One reason could

be that, after marriage, daughters are in charge for the care of their parents-in-law rather

5 RESULTS 20

than own parents (Li et al., 2006; Song and Li, 2008).

Table 7: Impact of Children’s Migration on Physical and Psychological Support byChildren’s Gender - Marginal Effects of Probit Models.

Son Daughter All

Physical Support

Provided by the Child -0.014*** -0.005* -0.009***(2.95) (1.91) (3.33)

Spouse is the main Caretaker 0.007 0.022*** 0.014***(1.40) (3.77) (3.70)

Psychological Support

Be in contact 0.269*** 0.163*** 0.216***(12.80) (8.29) (14.79)

More than weekly contacts -0.014*** -0.005* -0.036***(2.95) (1.91) (2.76)

* p <0.1, ** p <0.05, *** p <0.01, standard deviations in parentheses

In migrant households, elderly spouses are the main care-takers. This is in line with

Yin and Huang (2011) in that daily care provided by co-residential children and other rela-

tives is only subsidiary in nature to that provided by the spouse such that widowed elderly

who have no children living nearby face the greatest difficulty in daily care. Table 7 shows

that in households with migrant children, the probability of the spouse being the main

care-taker is augmented by 1.4% which is likely to offset the absence of a child. While the

son’s migration status does not have a significant differential effect on this probability, a

migrating daughter makes it 2.2% more likely that the spouse takes over the main func-

tional care. One argument to explain this finding is given by Li et al. (2006) who argue that

daughters-in-law are more likely to stay in the rural homes but they face a larger degree

of embarrassment when providing physical tending to their parents-in-law in particular

the father-in-law (e.g. bathing and clothing).

The second panel of Table 7 shows differences in marginal effects for two indicators

regarding psychological support. The first refers to being in contact with the elderly par-

ent more than once a year by phone, text message, mail, or email while not living with

them. The estimates show that such communication forms are much more extensively

used when the child has migrated such that we find an increase in contact by 22% among

all adult children. The impact is significantly higher for sons than for daughters. Com-

pared to non-resident children, having migrated tends to increase the frequency with

which adult children contact their elderly parents.11 One reason could be that migration

11Coefficient estimates for a linear model regarding the frequency of contacts are available for the authors

5 RESULTS 21

tends to reduce the daily conflicts that are likely to arise if child and parents live under the

same roof.

When referring to the other extreme of the frequency in keeping in contact with the

elderly parents, Table 7 shows that migration tends to reduce the probability to be in con-

tact at least once a week by 3.6% but more so for sons. The daughters migration status,

in contrast, has a very low impact on this measure which suggests that daughters keep

regular contact with their parents no matter where they live.

5.4. Overall Happiness and Migration

Compared to the effects of rural-to-urban migration on the physical and psycholog-

ical support provided to elderly, which has been investigated by some researchers (Yao,

2011; Zhang and Li, 2004; Zhang et al., 2005; Song and Li, 2008; Zuo and Li, 2011), the im-

pact of migration on the overall satisfaction of elderly has never been studied in previous

papers. We will utilise three proxies to examine the impact of migration on overall levels of

satisfaction, namely happiness, depression, and loneliness. The variable happiness refers

to the general level of life satisfaction. In comparison, the variable depression mainly fo-

cuses on the respondent’s mental health, describing the emotional well-being. Defined

more narrowly than ”happiness”, yet broader than ”loneliness”, its causes may include

genetics, interactions with people or other chemical factors (such as the side effects of

drugs), etc. In contrast, the variable loneliness mainly focuses on companionship, which

could also be influenced by the community that the respondent lives in and his/her other

relatives.

Marginal effects of different migration variables on the three indicators are shown in

Table 8. The results indicate that elderly parents tend to have a 2% higher probability to

be unhappy, they tend to be about 3% more likely to be depressed and they report about

5% higher levels of loneliness if at least one of their children has migrated. Moreover,

all of these indicators suggest that emotional health measured in any of the three ways

is aggravated if more their children migrate. A larger fraction of migrating children as

compared to all children has the strongest adverse effect on loneliness as parents are used

to be surrounded by their families and feel most strongly affected if a large fraction leaves

them behind in the countryside.

by request.

5 RESULTS 22

Table 8: Impact of Children’s Migration on EmotionalHealth - Marginal Effects of Probit Models.

Happy Depressed Lonely

Household with at least one Migrant Child:

-0.020* 0.031*** 0.049***(1.73) (2.60) (4.67)

Number of Migrating Children:

-0.009* 0.012** 0.019***(1.88) (2.34) (4.51)

Number of Migrating Sons:

-0.006 0.015* 0.019**(0.59) (1.65) (2.35)

Migrant Children relative to all Children:

-0.233* 0.288** 0.483***(1.88) (2.34) (4.51)

* p <0.1, ** p <0.05, *** p <0.01, standarddeviations in parentheses

Table 9: Impact of Children’s Migration Status on Emotional Health by Children’sGender - Marginal Effects of Probit Models.

Happy Depressed Lonely

Migration of Daughters -0.019 0.024 0.036**(1.16) (1.45) (4.23)

Migration of Sons -0.021 0.040** 0.064***(1.27) (2.33) (4.23)

* p <0.1, ** p <0.05, *** p <0.01, standard deviations in paren-theses

Table 8 also reports the impact of the three emotional health indicators regarding

the number of migrating sons. Results reveals that the depression indicator shows the

strongest negative reaction whereas the level of happiness is not found to be significant

and large in magnitude. Table 9 further supports this by reporting the impact of migra-

tion by the gender of the child, separately. As discussed before, sons are found to not only

be more likely to remit money to their elderly parents but to also remit higher amounts.

These could function as a substitute for emotional proximity as it allows the elderly to be

financially better off. In contrast, we have shown that the absence of a son significantly

reduces the physical attendance that the elderly parents receive. The increased frequency

of contacts but the decreased likelihood of being in weekly contact with the son is likely

to leave the elderly emotionally worse off. This is emphasised by 6.4% higher levels of

loneliness in the case of migrating sons. Such parents depend more strongly on a care-

taker who is likely to be a poor substitute for the own child or the spouse. Such a situation

6 DISCUSSION AND FUTURE WORK 23

could aggravate the emotional state caused by the absence of a child and is likely to lead

to lower levels of satisfaction and could potentially explain the augmented probability of

depression. Another reason for the more severe impact for migrant sons could be that

traditionally, Chinese elderly parents (especially in rural areas) have a preference for sons

(Chen et al., 2007; Gupta et al., 2003).

In contrast, migrating daughters do not significantly influence the average likelihood

of parents to be happy or depressed and the coefficient estimates are found to be small

in magnitude. Only the probability to be lonely is significantly aggravated if daughters

migrate but compared to sons, this association is less pronounced. These findings are es-

pecially true in the light that rural parents have traditional mind-sets according to which

married daughters are expected to take care of their parents-in-law such that their ab-

sence is expected rather then feared (Li et al., 2006).

6. Discussion and Future Work

Although this paper utilises a very recent dataset providing detailed information on

rural elderly and their households, there are still some issues that need further examina-

tion. First, some variables are indirectly measured, for example, we chose the frequencies

of contact between parents and children via phone or other ways as proxy for psycholog-

ical support. However, psychological support includes many other factors that are more

difficult to measure. In addition, children’s spouses are not taken into account in this anal-

ysis, who may have a significant impact on the supports to their parents-in-law, especially

regarding the daughters-in-law who are traditionally in charge of the elderly care of their

parents-in-law rather than their own parents. The question that needs to be examined

further is whether daughters-in-law can be good substitutes for own daughters.

In our analysis, the findings are tested for various specifications including multicollinear-

ity, heteroscedasticity, and residual normality. We have included instrumental variable

regressions to account for the endogeneity of the migration decision of adult children.

However, more detailed analysis in required to get a better idea about potential causal

effects of migration on the elderly parents’ outcomes. We also want to further extend the

analysis to covers the years of 2011 and 2012 to get a better idea about changes over time.

7. Conclusion

While a growing literature has examined the impact of migration on children-left be-

hind, this paper is rather concerned with the impact of the changing demographic struc-

7 CONCLUSION 24

ture leading to a growing elderly population left-behind in rural areas. Our study high-

lights the importance of investigating the impact of migration on the mental health status

of the elderly in this context as financial transfer cannot remedy the larger emotional dis-

tress caused by a migrating child or to an even larger extent, by a migrating son. The

absence of a child calls for different caregivers to provide physical care. We find that

in particular the absence of a daughter reduces functional support. This suggests that

a daughter-in-law as the closest substitute to a daughter can only be a weak replacements

leaving the elderly in a weaker position that adversely affects their mental well-being.

Despite the fact that this paper quantifies the relation between children’s migration

and overall satisfaction of the left-behind elderly, it chooses a sample of respondents

above 65 whose children have an average age of 41. However, a younger cohort (20 to

35 years old) makes up for the majority of the migration group (Li et al., 2006). Therefore,

supporting correlations between the younger migrant group and their parents requires

further study. In addition, this paper focuses only on the rural to urban migration. Yet

this methodology could be applied to the households in urban areas with children who

migrated to first-tier cities, or those whose children choose to study or live abroad and

thus even further away from their parents.

The United Nations (2002) estimated that the elderly support ratio12 in China will drop

from 1:13 in 2000 to 1:2.1 in 2050. This sharply decreased ratio will definitely place a great

strain on the traditional elderly supporting mechanisms prevalent in China. This is an

important issue not only because the rural elderly are a vulnerable and disadvantaged

group within a society but also because adult children may face an even higher support-

ing pressure due to the rapid ageing problem in the future. The Chinese government has

put various reforms in place to improve the social support of the elderly. For example,

the revised version of the ’Law on Protection of Rights and Interests of Seniors’ explicitly

states that children are obliged to provide financial, physical and psychological support

for their parents, even though the enforcement can hardly be guaranteed. These pilot

regulations, however, have often been criticized of having a too narrow coverage and of

being inefficient. Therefore, mature, established and well-functioning security and pen-

sion systems are an extremely urgent matter for China given the dramatic demographic

population outlook.

12It refers to the number of elderly aged 65 or above as a fraction of the number of people aged between25 and 64.

7 CONCLUSION 25

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APPENDIXA APPENDIX 29

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AppendixA. Appendix

Table A.1: Summary Statistics of Children Characteristics by the Household’s MigrationStatus.

Total Migrant Non-Migrant t-test

Age 41.53 41.08 41.89 4.37***(8.00) (7.90) (8.06)

Being illiterate 0.113 0.110 0.116 0.84(0.32) (0.31) (0.32)

High School or above 0.122 0.147 0.102 -5.85***(0.33) (0.35) (0.30)

Male 0.486 0.484 0.487 0.25(0.50) (0.50) (0.50)

Married 0.940 0.927 0.950 4.04***(0.24) (0.26) (0.22)

Log income 1.551 1.579 1.529 -5.64***(0.39) (0.39) (0.39)

N 7524 3325 4199

* p <0.1, ** p <0.05, *** p <0.01, standard deviations in parentheses