Grandparents' health and family fertility choice: Evidence from Taiwan

Accepted Manuscript Grandparents' health and family fertility choice: Evidences from Taiwan

Yiyun Zhang, Yir-Hueih Luh PII: DOI: Reference:

S1043-951X(18)30079-8 doi:10.1016/j.chieco.2018.06.003 CHIECO 1191

To appear in:

China Economic Review

Received date: Revised date: Accepted date:

31 December 2016 6 March 2018 6 June 2018

Please cite this article as: Yiyun Zhang, Yir-Hueih Luh , Grandparents' health and family fertility choice: Evidences from Taiwan. Chieco (2017), doi:10.1016/j.chieco.2018.06.003

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ACCEPTED MANUSCRIPT Grandparents’ Health and Family Fertility Choice: Evidences from Taiwan Yiyun Zhang, Yir-Hueih Luh

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First author: Yiyun Zhang Affiliation: Department of Agricultural Economics, National Taiwan University, Taipei, Taiwan Address: No 1, Sec. 4, Roosevelt Rd., Da’an Dist., Taipei City 10617, Taiwan (R.O.C.) Email: [email protected]

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Second author (Corresponding author): Yir-Hueih Luh Affiliation: Department of Agricultural Economics, National Taiwan University, Taipei, Taiwan

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Address: No 1, Sec. 4, Roosevelt Rd., Da’an Dist., Taipei City 10617, Taiwan (R.O.C.) Email: [email protected]

ACCEPTED MANUSCRIPT

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Abstract The incompatibility of female time allocation between labor supply and child care has been one of the explanations of low fertility rate experienced by many countries. Non-parental care, especially that from grandparents or formal care, helps alleviate the constraint females facing and thus permits the families to have more children. Extending from the theoretical framework in Ermisch (1989), this study incorporates grandparents’ health status to provide a theoretical justification for its effect on family size. Based on the Panel Study of Chinese Family Dynamics (PSFD) from 2006 to 2011, it is found that grandparents in different age groups exhibit differential influence on family fertility decisions. Specifically, healthy grandparents in the 55-64 age group are found to have a persistent and positive impact on the family’s

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probability of having more children as predicted by the theoretical model. Nonetheless, when grandparents’ health effect is compounded by the age effect, more elderly healthy grandparents in the 75-and-up group will reduce the couple’s

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desire for more children. This negative effect can be explained by the couple’s consideration of lower childcare quality and larger age gaps leading to great differences in the childcare ideas. The number of healthy grandmothers are found to have an even greater influence on family fertility decisions, suggesting grandmothers still take the major responsibility of childcare in the family and thus constitute an absolutely crucial resource in the Chinese society.

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Keywords: Grandparents’ health; Grandmother effect; Fertility choice; Model of family size; Sequential probit analysis; Taiwan

ACCEPTED MANUSCRIPT 1. Introduction The Chinese old saying that “Having an elderly at home is like holding a priceless treasure at hand” signifies the important role of the elderly in the Chinese family, one of which is the non-parental childcare provided to their grandchildren.

This study

intends to explore how grandparents’ heath status is linked to family fertility choice The demographers’ perspective that rearing young

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and thus family size in Taiwan.

children requires heavy activities (Gray, 2005; Hughes et al., 2007; Wang & Marcotte, We argue that it is the healthy and

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2007) motivates the main theme of this paper.

non-parental childcare to their grandchildren.

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active grandparents rather than the merely living ones who are capable of offering

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In studies of non-parental childcare, formal childcare provided by professional centers has been a widely discussed substitute as a result of government policy

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targeting at promoting fertility since the 1970s (NICHD, 2000; Hank & Kreyenfeld, 2003; Gray, 2005). Both theoretical delineation and empirical research suggested that a positive association exists between non-parental childcare and the number of

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children when alternative childcare choices are available (Cigno & Ermisch, 1989;

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Mason & Kuhlthau, 1992; Hank & Kreyenfeld, 2003; Rindfuss et al., 2007). Following a similar line of thinking, through the increased availability and lower

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childcare costs, non-parental childcare provided by grandparents may play an important role in determining fertility decisions.

Past studies on how the availability

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of grandparent support for childcare is linked to family fertility choice and thus family size, however, have been quite limited. Most empirical research addressing the effect of grandparents’ childcare availability focused on finding the connection between this availability and maternal labor supply (Hank & Kreyenfeld, 2003; Compton & Pollak, 2014; Bratti et al., 2016). For instance, Hank & Kreyenfeld (2003) used the geographical proximity of the grandparents from the mother’s side as a proxy variable for the availability of grandparental childcare, whereas Compton & Pollack (2014) emphasized the role of grandmothers through the close geographical proximity to mothers or mothers-in-law.

ACCEPTED MANUSCRIPT Actually, considering that grandmothers can satisfy the unanticipated need for childcare, Compton & Pollak (2014) proposed a broader interpretation of the availability of grandmother childcare as “an insurance aspect of proximity” (Compton & Pollack, 2014, p. 72).

In a more recent study by Bratti et al. (2016), a different

approach was adopted by using the pension-reform induced changes in retirement

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eligibility requirements to examine the role of grandparental childcare availability. To the best of our knowledge, the only work to investigate how the availability

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of grandparent support for childcare is linked to family fertility choice and thus family Although Hank &

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size may have been the study by Hank & Kreyenfeld (2003).

Kreyenfeld (2003) did find a positive relationship between the availability of

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grandparental childcare and the family fertility decision, the modelling of availability through the geographical proximity is less than satisfactory.

The novelty of this

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study, therefore, lies in linking grandparents’ childcare availability to their health in exploring the influence of childcare availability on fertility decisions. To provide a theoretical justification for the role of grandparents’ health status in

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influencing family size, the present study incorporates grandparent’s health into the

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model of family size in Ermisch (1989).

The theoretical model predicts that, when

the availability of grandparental childcare increases with an improvement in

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grandparents’ health status, family size increases accordingly.

A nested probit

model is estimated to provide empirical evidence supporting the proposed proposition

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consistent with this theoretical prediction.

The nested probit model is one in which

the family’s choice in the first level, i.e., the decision to have a child, influences the family’s second-level decision of whether to have more children.

In this empirical

setting, the effect of grandparents’ health is identified by defining a suitable proxy variable for grandparental childcare availability which is based on the couple’s perception of grandparents’ health status.

The influence of grandparents’ health on

the family fertility decisions is thus mediated through its association with the availability of grandparental childcare. This study adds to the existing body of research by providing both a theoretical

ACCEPTED MANUSCRIPT framework signifying grandparents’ health in family fertility decision and empirical evidence supporting the role of healthy grandparents in providing non-parental care. Moreover, in addition to exploring the influence of grandparents’ health on family fertility decisions, another major departure of the present study from previous studies is our emphasis on the effect of grandmothers’ health. Considering that it is females

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who take the major responsibility of childrearing in the Chinese society, this study can provide further evidence to support the view that grandmothers constitute “an

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absolutely crucial resource by taking care of their grandchildren” (Gopnik, 2014). In the next section, we

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The remainder of the paper is organized as follows.

present the theoretical model of family size incorporating grandparents’ health, which

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provides a theoretical underpinning of the possible association between the grandparents’ health status and the family fertility decision. A detailed delineation

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of the empirical model including the delineation of the nested probit model, the variable definition and a description of the data is then outlined; followed by a discussion of the empirical results in section 4. Finally, concluding remarks and

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policy implications of this study are offered. 2. The family size model incorporating grandparents’ health

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In Fanti & Gori’s (2014) study of the effect of the longevity of grandparents on fertility, it is suggested that grandparents will help take care of their grandchildren By incorporating

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and thus lower the opportunity cost of mothers’ time.

grandparents’ childcare time into the equation of family time allocation, Fanti & Gori (2014) found that grandparents’ longevity can stimulate family fertility when the input of grandparents’ helping time is large.

However, that association is based

on the presumption of a “sufficient” input of grandparental childcare assistance; otherwise, the fertility stimulating effect of grandparents’ longevity will not hold. Moreover, one of the assumptions in the OLG (overlapping generation) model in Fanti & Gori (2014) is grandparents who offer non-parental childcare are retired; this is overly restrictive and actually contradicts the reality.

ACCEPTED MANUSCRIPT To examine the influence of childcare availability from healthy grandparents in a more realistic setting, we extend the model in Ermisch (1989) by explicitly incorporating grandparents’ health into the family size determination.

In contrast

to most theoretical studies that consider the contribution of alternative childcare from the perspective of the cost of assistance (Del Boca, 2002; Del Boca & Vuri;

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2007; Kornstad & Thoresen, 2007), Ermisch (1989) constructed a neoclassical microeconomic model combining the price of non-parental childcare and the time of

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purchased care.

U  ( C, Z) ,

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(1)

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Following Ermisch (1989), family utility is described by

where C represents “child enjoyment” and Z is other consumption. The production

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of C and Z are functions of time (L) and cost (X), representatively: C  f ( X C ,L C ) ,

(2)

(3) Both

f ( .)

and

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Z  g ( X Z ,L Z ) .

g ( .) are

assumed to be linear homogeneous.

To simplify the

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analysis, the integrated measurement of each child’s quality Q is assumed to be exogenous here. Therefore, the size of child enjoyment is determined by

C  NQ

,

as Q

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where N is the number of children. Actually, the child’s quality can be expressed  f ( X C / N , LC / N )

, where the time costs of C and Z are denoted as

LC

and

LZ

,

and are interpreted as mother’s time investments. Define formal childcare as M and add M to the mother’s childcare time, H, the total childcare investment of the family is L C  H  h( M) ,  h ( 0 ) > 0h, M(

(4)

)   h0 , M

(

)

0 .

ACCEPTED MANUSCRIPT Here

is a concave conversion function transforming non-maternal childcare

h(M )

time into mothers’ childcare time.

Further assume the minimum level of mother’s

childcare necessary for each child is k, mother’s childcare time satisfies the constraint: H  kN .

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(5) To measure the impact of grandparents’ health on family size, we assume a

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typical family faces two choices of non-parental childcare, one from grandparents

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and the other from formal childcare centers. The amount of childcare time offered by the grandparents is G, which is exogenous.

Since the availability of G is a

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function related to an aggregated indicator of grandparents’ health, HG, we assume that when HG increases, the available childcare time from grandparents will also

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increase, i.e., G   ( H G) , 

0 .

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(6)

(H G )

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According to (4), the family’s total childcare investment is composed of two components: mother’s time investment and non-parental care:

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L C  H  h ( M , G ), h i ( M , G )  0 , h ii ( M , G )  0 ,  i  M , G .

(7)

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Let the prices of childcare from grandparents and formal childcare be denoted as p and p M , respectively. G

Because

 ( H G )  0

, when HG increases, G increases.

Moreover, since the price of childcare offered by grandparents is significantly lower than the price of formal childcare, the imputed average price of total non-maternal childcare

p

decreases with increasing percentage of G. Accordingly, p  m ( H G ) , m ( H G )  0

.

(8)

ACCEPTED MANUSCRIPT Denoting the total amount of mother’s time T, father’s income V and mother’s wage w, the couple’s lifetime budget constraint is as follows: L C  w( T 

L  Z

H )  V

X 

C

Z

X ( p M ) , G

(9)

T  LZ  H  0, M , LZ , H , X C , X

Z

 0.

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(10)

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where

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Maximizing family lifetime utility in (1) subject to (2), (3), (7) and the inequality constraints (5) and (10), the Kuhn-Tucker conditions are:

U C



f  LC

U

;

C

N

f X

H

 w    U

 ;

Z

C



g



f  LC

 w  ;

LZ

h1 ( M , G )   p   M ;

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C



U Z

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U



g

X

 ; Z

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T  LZ  H  0,   0;  H  kN ,  H  0;  M  0,  M  0;    T  LZ  H  0,   0;  H  kN ,  H  0;  M  0,  M  0 .

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(11)

The family uses both maternal childcare and other alternative childcare, that is, . From the first and second equations in (11), the

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H  kN , M  0,  M   H  0

purchased childcare time can be decided by:

(12)

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   h1 ( M ,G )  p  M    

The shadow price of children, C 



(14)

Z



w   LC f

w  LZ g



p      H  w   N   w 

, can thus be written as:

C

p   LC h1 ( M , G )   f



X g

.

Z



X C f

,

(13)

.

ACCEPTED MANUSCRIPT If the mother does not work, then

T  L Z  H  0,   0

T  LZ  H  0,   0

participates in the labor market,

. Nonetheless, if mother

, and thus

Since regardless of whether the mother works or not,

h1 ( M , G )  p w

(w    )

.

is a positive

constant, the purchased time of formal childcare M is determined by the average Consequently, as long as mother’s opportunity

p

cost of time exceeds

, the family will purchase some formal childcare.

p h1 (0, G )

.

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price of purchased childcare,

Y  wT  V= CC   Z Z .

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(15)

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For the ease of comparison, maximum family income is assumed to be

(12), (14), (15), (16),

C

S V Y d lo g V   S E Y 

C

  S Z ( q L Z  q H C )  d lo g w

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d lo g C  

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The percentage change of demand for children is the function drawn from (2), (3),

 q MC  S Z  

C

S C  d lo g p  

C

1 

SVY

d

lo g T ,

C

Z , qHC  w H 

C

C , qMC  w h(M ,G ) 

C

C.

(16)

is the income elasticity of children and



denotes the substitution

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In (16), 

Z

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q LZ  w L 

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w h e re S V Y  V Y , S E Y  ( T  L  H ) w Y , S Z   Z Z Y , S C   C C Y ,

elasticity of Z and C, which are in general positive.

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A proportional change in the imputed average price impact child enjoyment can be examined through the term

 lo g C  lo g p

.

Based on (16) as well as positive

income and substitution elasticities, we find that

 q M C ( S Z   C S C ) < 0

.

The result

of the comparative statics analysis suggests that when the availability of grandparental childcare increases with the improvement of grandparents’ health status, the average price of childcare time decreases, which therefore leads to a larger family size.

3. Econometric model and data description

ACCEPTED MANUSCRIPT 3.1 The nested probit model The previous empirical research found that the availability of child care is the factor that influences the family decision of having more children using binary response models (Lehrer & Kawasaki, 1985; Del Boca, 2002).

In this study, there are two

nested decisions concerning family fertility. The first-level decision for the family Let

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is whether to have a child; the second is whether to have more in the future.

the level-1 indicator be denoted by, y 1 , which takes the value of 1 if the family has

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at least one child and 0 otherwise; the second-level indicator variable, y 2 , takes the

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value of 1 if the family desires to have more, and 0, otherwise1.

As suggested in Maddala (1983), in a sequential decision model it is possible to

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observe both y 2  1 and y 1  0 , that is, the family desires for more children while has no children at the time of survey. Therefore, we establish a nested probit

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model to examine the influence of grandparents’ health on family size2. The nested probit model is one in which the family’s first-level fertility choice, i.e., to have children or not, influences the family’s second-level fertility decision of whether to The description of the model below is based on the illustration

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have more children.

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in Liao (1994), Woodridge (2009) and Genius et al. (2006). The nested fertility decisions:

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1 L e v e l 1 : y1i   0

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1 L evel 2 : y2i   0

y 1 i  x 1 i β 1   1 i  0 , i  1, 2 , *

if

,n

o th e r w is e if

y 2 i   y 1 i  x 2 i β 2   2 i  0 , i  1, 2 , *

,n

o th e r w is e

(17)

It’s assumed that the error terms in (17) follow a multivariate standard normal distribution, that is,   1 i ,  2 i

1

x ~

2

 0 , Σ  , where

Σ

is a positive definite

According to studies on the relationship between fertility intention and behavior, fertility intention is found to contribute additional predictive power (Schoen et al., 1999) or possess a strong predictability on fertility decision of having more children (Xie, 2014). Therefore, family’s desire for more children and family fertility decision of having more children is used interchangeably in the present study. 2 Originally, we specified a sequential probit model as the one used in Alpu and Fidan (2004) to examine the impact of grandparents’ health on family fertility decision. Following the suggestion of the anonymous reviewer, the nested probit model is used.

ACCEPTED MANUSCRIPT covariance matrix.

The procedure to estimate the above nested probit model is to

estimate the two decisions through the FIML (Full-Information Maximum Likelihood) procedure (Greene, 2012, p. 222).

Before the FIML estimation, the

multivariate space is divided into 4 mutually exclusive and exhaustive cells, 

2 

, m  1, 2 , 3 , 4

variables.

, according to the values of the level-1 and level-2 indicator

We then define the indicator function, d im .

For observations falling

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m

in the mth cell, the indicator function takes the value of 1; it’s zero otherwise. The

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four cells are defined as: 1

  i : y 1 i  0 , y 2 i  0  , d 1 i  1 if i 

1

2

  i : y 1 i  0 , y 2 i  1 , d 2 i  1 if i 

2

; d 2 i  0 if i 

3

  i : y 1 i  1, y 2 i  0  , d 3 i  1 if i 

3

; d 3 i  0 if i 

4

  i : y 1 i  1, y 2 i  1 , d 4 i  1 if i 

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; d 1 i  0 if i 

4

; d 4 i  0 if i 

1

2

3

4

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According to the above definition, the cell probability is the one corresponding to the values of y 1 and y 2 . Let  2

and  2

denote, respectively, the

1

, when both the two indicator variables take the value of zero, can be written

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of

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multivariate normal probability density function and its cumulative, the probability

as

The

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P ro b

other

 y1i

 0, y2i  0 

 P r o b  x 1i β 1   1i ,  y1i  x 2 i β 2   2 i ; Σ 

three





 2   1 ,  2  d  1d  2

1

cell

probabilities

are

defined

as

P ro b  x 1 i β 1   1 i ,  y 1 i  x 2 i β 2   2 i ; Σ 

, P ro b  x 1 i β 1   1 i ,  y 1 i  x 2 i β 2   2 i ; Σ  , and

P ro b  x 1 i β 1   1 i ,  y 1 i  x 2 i β 2   2 i ; Σ 

, respectively.

Once the multivariate space is divided into the four cells according to the values of the two binary variables, the likelihood function can then be calculated as the multiplication of the four cell probabilities as follows,

ACCEPTED MANUSCRIPT

4

n

 , β , Σ x    

d m i  lo g  P ro b  i 

m

;  , β , Σ  

m 1 i 1

To compute the cell probabilities and their derivatives, we use the simulation-based Geweke-Hajivassiliou-Keane (GHK) algorithm proposed in Hajivassiliou et al. (1996).

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3.2 Data and variable definition

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3.2.1 Data source

The data used in this study are derived from the Panel Study of Chinese Family

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Dynamics (PSFD) during the 2006-2011 period3. The PSFD, which began in 1999, is a panel survey focused on studying family behavior and the relationship between In the beginning, the sample in PSFD is mainly

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family members in Taiwan.

derived from individuals born in 1953-64. To maintain a sufficient sample size,

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PSFD enlarges the sample size in 2000, 2003 and 2009 by including individuals born in 1935-54, 1964-76, and 1977-83.

From the year of 2004, the survey started

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to include questions concerning the families’ view of grandparents’ health status.

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Since the major objective of the current study is to evaluate the association between family fertility decisions and grandparents’ health status, only the sample from 2006 to 2011 is used in the current study.

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Two versions of the questionnaires in the PSFD are used.

One version is the

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first-year survey held in 1999, 2000, 2003, and 2009; the second are annual follow-up surveys during the sample period.

In PSFD, differences exist between

the surveys in the first year and the follow-up years.

In the first year, the

questionnaire contains more questions about the family structure and behavior; questions such as grandparents’ age and childcare types are only surveyed in the first year.

Although our focus is on family childbirth decisions in the follow-up

surveys, we can only acquire certain basic information from the first survey year.

3

The PSFD had data released until 2011 during our research period.

ACCEPTED MANUSCRIPT The sample size before applying other selection rules is 21,942, including 5,738 different individuals. Two more sample selection rules are marriage status and reproductive age. It is noteworthy that although grandparents’ ages for both the respondent and the spouse are included in the first survey year, as in 1999, 2000, 2003, and 2009, the

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information concerning grandparents’ age for the respondent’s spouse will be missing when the respondent’s marriage status changed during the follow-up

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surveys. For instance, if a respondent married after his (or her) first survey year,

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questions regarding the parents’ age of his (or her) spouse will be skipped in the follow-up surveys. Therefore, only individuals that remain married from the first

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year to the last time he (or she) was surveyed are included in our sample. Moreover, we restrict mothers’ age to be within the range of 20 to 45 to be

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consistent with the reproductive age of 15-44 suggested by the World Health Organization (WHO, 2013). The total final sample includes 2,493 respondents

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from 1,148 families.

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3.2.2 Variable definition and descriptive statistics In the first level of the nested probit model, the dependent variable is a binary variable, which takes the value of 1 if the family has children and 0 if otherwise.

If

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a family had been continuously surveyed, the variable can have different values in different survey years since even start with no children, the value will change from 0

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to 1 once the couple have their first child.

Most of the families, that is,

approximately 85%, have children in the selected sample in 2006. Socioeconomic variables included in the regression include husband’s labor time, wife’s labor time, wages and educational levels.

Labor time in the PSFD

consists of two parts, the regular working hours per week and the working hours for part-time jobs per week.

In this study, we add both working hours from regular

and part-time jobs to measure the available leisure time for family members. Notably, the working hours of housewives are considered to be zero.

As for the

ACCEPTED MANUSCRIPT work earnings, in the PSFD, the husband’s and wife’s wages include both the earnings of a regular job per month and the income of a part-time job per month. The two variables, the husband’s wage and the wife’s wage, respectively, comprise the two types of income.

From the 15 educational levels designated in the

questionnaire, we re-categorize the wife’s and husband’s education into 7 levels,

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which are elementary school, middle school, high school, technical school, university, master’s degree and doctor’s degree.

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Since postponed childbirth and small family size may be due to postponed

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marriage age, we include the wife’s age in the model4. The grandparents’ geographic distance from their children is also included in our empirical specification. The

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variable is defined as the proportion of living grandparents who reside within a 30-minute driving distance to their children’s residences.

In addition, the number

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of siblings a couple has is included in the specification to consider the potential childcare help from relatives and the possible crowding out effect for grandparental childcare5.

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Moreover, we include two additional variables in the empirical specification to

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capture the possible effects of family wealth6. The first variable is whether the grandparents provide the family money on a regular basis, yes=1 and no=0. The

and no=0.

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second variable is whether the couple provides allowances to their parents, yes=1 In some very recent report by the ABC News, it was indicated that “an

4

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important value in Chinese societies is "filial piety" — the duty of respect,

The wife’s age is generally specified as the explanatory variable in economics and demography studies (e.g., Del Boca, 2002; Hank & Kreyenfeld, 2003; Brodmann, Esping-Andersen, & Güell, 2007; Haan & Wrohlich, 2011) to reflect woman’s fertility ability. One referee indicated that father's age may also be an important factor on family fertility decision due to the drop of sperm quality with age increasing. However, in light of the high correlation of 0.802 between father’s age and mother’s age in the sample, the model including both father’s age and mother’s age failed to converge 5 Empirically, siblings are usually included in the specification to capture the availability of childcare from relatives (Kreyenfeld and Hank, 2000; Hank and Kreyenfeld, 2003). 6 One referee suggests that family wealth may also confound the effect of grandparent’s health. Considering that the ownership of the residence may also in some way reflect the wealth of the family, we attempted to use another variable—if the family’s residence is rented or owned—as a proxy for the family’s economic conditions. However, because there are too many missing data for this variable, whether the family owns the residence is not included in our final specification.

ACCEPTED MANUSCRIPT obedience and care for one's parents and elderly family members” (Zhou, 2017). Most of the Chinese interviewed indicated the feeling of their aging parents’ being weak financially and the obligation to subsidize them on a regular basis when they can afford it. Even nowadays, this Chinese tradition is still prevalent in Taiwanese families whose aging parents are not rich.

On the other hand, it is quite common

PT

and widely observed that rich grandparents usually give financial support to the young couples for monthly childcare expenses, house installment, etc. The two

RI

variables are thus regarded as capable of reflecting the grandparents’ economic

SC

conditions to some extent.

To consider the possible cultural factors and the medical service quality that

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may confound the effect of grandparents’ health, the percentage of living indigenous grandparents is included in the empirical specification7.

The indigenous people In

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have constituted a major minority group in terms of numbers in Taiwan 8.

addition to the stylized fact of being poor both financially and in health, the indigenous peopls in Taiwan have much lower life expectancies, 80.00

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(non-indigenous) vs. 71.86 (indigenous), and are regarded as disadvantaged in terms

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of unemployment rate and socio-economic status (Su & Chang, 2014). One of the consequences stemming from the cultural difference between the

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indigenous and non-indigenous peoples in Taiwan9 is the prevalence of alcoholism for the indigenous people. Alcoholism is one of the major factors contributing to

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the health disparities among the indigenous and non-indigenous peoples in Taiwan (e.g., Ko, Liu & Hsieh, 1994; Chen, 2014). Moreover, in addition to the cultural 7

As suggested by one of the anonymous referees, we also attempted to incorporate another variable that may confound the effect of number of healthy parents, i.e., the medical service quality of the region the family resides. Since the data on district, county or region is not available in the PSFD dataset, we’ve included the percentage of living indigenous grandparents in the final specification, which may to some extent control for the confounding influences of medical service quality and family wealth on the key variables in the present study. 8 It was indicated in Bramley et al. (2005), the indigenous peoples are “in numerical terms, “minority” populations relative to the predominant European and White groups in each country.” Juan, Awerbuch-Friedlander, & Levins (2016) indicated that the indigenous peoples take a very small proportion of only 2.2 % in Taiwan according to the statistics in 2014. 9 According to Chang (2000), indigenous people express their gratitude to the mother of earth through drinking wine. The culture of drinking with some variations were documented in almost all indigenous villages in Taiwan (Chen, 2014).

ACCEPTED MANUSCRIPT differences between the indigenous and non-indigenous peoples, being socially and economically disadvantaged, the indigenous people’s access to medical cares and resources has been relatively limited.

In Tsai’s (2000) study, the indigenous

people’s utilization of medical services was shown to be significantly different from the non-indigenous people.

Due to these considerations, we use the proportion of

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living indigenous grandparents to capture possible variations in the unobservables due the cultural effect and the access/utilization of medical services of the

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indigenous grandparents’.

SC

In addition to the socioeconomic variables, the major explanatory variables in the current study are the numbers of healthy grandparents.

The PFSD asked the

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couple’s perception of their parents’ health conditions as measured by a Likert scale ranging from “very healthy” to “very unhealthy”.

In our view, the couple’s

grandparental childcare.

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perception of their parents’ health status determines their perceived availability of Therefore, based on the couple’s perception of their

parents’ health conditions, this study uses the number of healthy grandparents as a

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proxy variable for the availability of grandparental childcare.

A healthy

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grandparent is defined as one whose health condition is “average”, “healthy” or “very healthy” in their children’s perception.

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Actually, if the number of grandparents in each age group was included in our empirical model to capture the availability of grandparental childcare, the

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availability is accessed from the perspective of “surviving grandparents”, which is consistent with the underlying assumption in the model of family size in Ermisch (1989). However, the major focus of this study is to identify the effect of the availability of grandparental childcare from the perspective of “healthy grandparents.” To be consistent with the theoretical prediction from our model incorporating grandparents’ health into the determination of family size, we use the number of healthy grandparents in different age groups to capture the effect of grandparent health on family size. One of the stylized facts for the Chinese families is that when one grandparent

ACCEPTED MANUSCRIPT from the maternal or paternal side of the family encounters health problems, the other from the same side may need to take care of the sick one and thus will not be available to care for their grandchildren. Considering this stylized fact, the number of healthy grandparents on one side of the family is defined to be zero, although only one of the grandparents is having health problems in their children’s

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perception10. This consideration is meant to consolidate the use of the number of healthy grandparents as a suitable proxy variable for grandparental childcare

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availability.

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According to the United Nations’ “Provisional Guidelines on Standard International Age Classifications”, there are three sets of age classifications with

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varying degrees of details. The third set of age classifications with the minimum degree of details “deals essentially with six broad population groups - roughly

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equivalent to infancy, youth, young adulthood, middle adulthood and older adulthood to average retirement age, retirement (under 1, l-14, 15-24, 25-44, 45-64 and 65+ years)” (p. 3, United Nations, 1982).

Based on this classification and to

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accommodate our need to address grandparents’ availability of childcare, we further

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categorize the 45-64 age group, i.e., the older adulthood to average retirement age group, into two age groups, 45-54 and 55-64, with the latter group representing The reason for the further classification is

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people approaching retirement age.

based on the observation that more of the grandparental childcare is provided before

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the grandparents are retired11. Moreover, if the elderly’s health is good from their children’s perceptions, the availability of childcare that grandparents can provide vary with their ages. For instance, children may believe that their 90-year-old parent continues to enjoy good 10

We’ve revised our definition of this variable according to the suggestion of one of the participants in the 8th Human Capital Conference. 11 Actually, in Britain, most of the elderly women help their children with childcare before they are 70 years old, and grandfathers also help more before they are retired when they are in better health (Gray, 2005). Based on data from the National Survey of Families and Households (NSFH), Guzman (2004) also indicated that more than 50% of employed grandparents who live near their children helped to care for their grandchildren. According to Guzman (2004), the percentage of employed grandparents reported as providing childcare is 12% higher than those who are not employed or who are retired.

ACCEPTED MANUSCRIPT health; however, it may be less possible for a couple to expect their parents at that age to take care of the grandchildren. Considering the age differences among the 65-and-up group, the retirement group is further classified into two age groups, the 65-74 group with individuals having a retirement length of less than 10 years; and the 75-and-up group, which surpasses the average life expectancy in Taiwan during

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the research period. The descriptive statistics of the variables in the empirical model are reported in

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Table 1. The average age of wives in the sample is approximately 34 years old.

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Actually, compared with the families who have children, the wives’ average age is slightly younger (approximately 31 years old) for those having no children.

Wives’

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monthly wage is approximately 22,000 TWDs (or 730 dollars), on average, while husbands’ average wage is more than double that. The mean labor supply of wives

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is approximately 6 hours per day not including weekends, whereas husbands on average worked more than 9 hours on workdays.

Both wives’ and husbands’

average educational level is approximately high school to technical school.

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Three additional variables are added to explain the level-2 fertility decision of

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the nested probit model. These are number of children under the age of three, whether the family has at least one son ( y e s

 1, n o  0

 1, n o  0

). The variable

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feels the pressure from grandparents to have sons ( y e s

), and whether the family

representing the number of children under the age of three is included in the

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empirical specification to capture possible family heterogeneity in the presence of young children. According to Presser (1989), the burden of rearing is generally heavier for families with young children, which suggest that families with children under the age of three may be more in need of non-parental care.

Moreover,

Guzman (2004) indicated that approximately half of the American families with children under the age of five received grandparental childcare; thus, they concluded that “grandparents of preschool-aged grandchildren are particularly likely to provide child care” (Guzman, 2004, p.3). Thus, the variable “Children under 3”, which represents the number of children under the age of three, is included in the model to

ACCEPTED MANUSCRIPT control for the family characteristic of having young children.

The statistic in

Table 1 indicates that on average, families in our sample have less than one child under the age of three. The two indicators, whether the family has at least one son and whether the family feels the pressure from grandparents to have sons, are used to capture the

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possible cultural influences, particularly that of son preference in the Asian culture. Descriptive statistics reported in Table 1 indicate that more than half of the families

As shown in Figure 1, when the number

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from their parents (approximately 15%).

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have sons (approximately 63%) and that few felt the pressure to have more sons

of healthy grandparents in the 45-54 group increases, the proportion of families

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desiring more children increases. Although a positive association remains for the families with healthy grandparents aged 55-64, the association is slightly smoother Moreover, in contrast to their younger counterparts, the

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than for the 45-54 group.

two elderly groups’ relationships with the family desire for more children are uncertain.

For the 65-74 group, there is a downward trend in the family’s desire

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for more children, while for the group aged 75 years old and up, the figure shows a

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hump, indicating that the proportion of families desiring more children is the greatest when the number of healthy grandparents in this age group reaches two.

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3.3 Endogeneity test

Endogeneity of the explanatory variables can be due to omitted variables or

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measurement error (Wooldridge, 2002, p.473).

In our case, grandparents’ health

may be correlated with some unobserved household or parents’ characteristics (potential confounding factors) that are not included in the model. We use the instrumental variable approach to address and test for possible endogeneity in our empirical model. One difficulty we encountered in addressing possible endogeneity is that in past references, we cannot find the instrumental variable(s) that can adjust for the influences of confounders on the key variable in this study, which is the number of healthy grandparents in different age groups. However, we did generate

ACCEPTED MANUSCRIPT one possible candidate of the instrumental variable. Since an ideal instrumental variable needs to be correlated with the potential endogenous regressor, the number of healthy grandparents, while uncorrelated with family fertility decisions, i.e., the decision concerning whether to have children and the decision concerning whether to have more, the instrumental variable chosen is the sum of the ages of all living

PT

grandparents in the family. As stated in Wooldridge (2002), the two-step maximum likelihood procedure

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proposed in Rivers and Vuong (1988) is most useful for the test of endogeneity for a

SC

probit model. The two-step procedure to test for endogeneity is: (1) run the OLS regression of the possible endogenous variable on the set of variables including the

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instrument variables (z2) and all the other explanatory variables (z1) in the probit model, and save the sample residuals; (2) run the probit model on z1, the The test

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suspected-endogenous regressor and the step-one sample residuals.

statistic of the coefficient for the step-one residuals in the step-two probit estimation is a valid test of the null hypothesis that the suspected-endogenous regressor is

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exogenous (Wooldridge, 2002, p.474).

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According to Wooldridge (2002, p. 474), the null hypothesis of exogeneity concerns the population coefficient of step-one residuals being zero in the

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population regression function of step-two probit model.

Consequently,

do-not-reject of the null hypothesis implies zero correlation of the sample residuals Under the null hypothesis of no

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of the step-one OLS and step-two probit models.

endogeneity, the chi-square statistics for the first-level decision (to have children) and the second-level decision (to have more children) are 0.04 (p-value=0.85) and 0.16 (p-value=0.69), respectively.

Thus, the test results suggest that we do not

have sufficient information to reject the null hypothesis of the exogeneity of the key variables in this study.

4. Empirical results 4.1 Grandparents’ health and the fertility decisions

ACCEPTED MANUSCRIPT We report the FIML estimates for the nested probit model in Table 2 12 . Estimates of the first-level coefficients indicate that the number of healthy grandparents in different age groups exhibit differential effects on family fertility decision 13 .

Our result suggests that, when healthy grandparents aged 55-64

increase in number, their children are significantly more likely to have children.

PT

This result is consistent with what was found in Hughes et al. (2007), Gray (2005) and Wang & Marcotte (2007). People in the 55-64 age group are approaching

RI

retirement age; they may be less enthusiastic regarding their career success and thus

SC

have more time to spend with their families or to help take care of the youngsters. Moreover, grandparents in this age group are considered to be in better economic

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conditions and physical health than those in the elderly groups. Consequently, healthy grandparents in the 55-64 age group represent one suitable alternative for

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childcare and can explain why married couples are more likely to have children when the number of healthy grandparents in this age group increases. Another interesting observation is that the effect of the post-retirement

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availability of grandparental childcare varies with the length of retirement.

Healthy

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grandparents aged 65-74 with a retirement length of less than 10 years exhibit a positive but insignificant effect on the couple’s decision to have children.

CE

Conversely, our results indicate that when the number of grandparents aged 75 and up increase, married couples’ probability of having children increases significantly.

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The explanation for this significant positive result lies in the irreversible nature of grandparents’ health. Considering that grandparents in this age group are in better health when they are younger, a greater availability of their grandchild care is

12

One reviewer suggests the need to calculated robust standard error since the errors are clustered within families given that the observations are for each child in a family. According to Abadie et al. (2017), the reason to report standard errors taking into account clustering of units is that “unobserved components of outcomes for units within clusters are correlated.” Because we do not have observations from the same family, i.e., the case of two respondents being siblings in one family, the problem of clustered errors is not present in this study. 13 Due to including the four age groups creates multicollinearity problem, and in comparison the 55-64 group is more likely to care for their grandchildren, we include the 55-64 and 65-74 and 75-and-up groups in the final specification.

ACCEPTED MANUSCRIPT reasonably expected by their children and thus increases the couple’s probability of having children. Most of our results concerning other determinants of the family fertility decision are consistent with what was found in previous studies.

The wife’s or

husband’s siblings as an alternative to grandparents’ childcare and the proportion of

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indigenous grandparents are all found to have a positive effect on the family’s probability of having children; however, the latter variable is not statistically The results reported in Table 2 also indicate that the wife’s age has a

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significant.

SC

statically significant and positive effect on the probability of having children. When the husband’s wage increases, the probability of having children increases

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significantly since the husband’s wage is the major source of family income in Taiwan. Conversely, the wife’s labor hours and wage are found to significantly Similarly, both the husband’s and the

MA

reduce the probability of having children.

wife’s educational levels have a statistically significant negative effect on childbirth. Regarding the level-2 estimates14, healthy grandparents aged between 55 and

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64 are found to positively affect a family’s decision to have more children in the

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future, which is similar to this age group’s influence on the couple’s decision to have a child. The marginal effect, as reported in Table 3, is 0.013, suggesting that

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one additional healthy grandparent in this group leads to a 1.3 percentage point increase in the possibility of having more children in the future. However, contrary

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to what was found from the level-1 estimates, both the number of grandparents in the 65-74 age group and the 75-and-up age group are not found to exhibit a statistically significant impact on the family’s probability of desiring more children. Overall, the level-2 coefficient estimates suggest that only healthy grandparents in the 55-64 group, i.e., healthy grandparents not yet retired and/or approaching

14

To ensure our results are stable and are not driven by untestable functional form assumptions as suggested by one of the anonymous reviewer, linearized results of the level-2 fertility decision are reported in the Appendix. Results for the linear probit models, respectively, one assuming the fertility decision to have a child is exogenous, the other conditioned on the sample who already have at least one child in the family, are reported in Table A.1. Most of the results reported in Table A.1 are qualitatively similar to those reported for the level-2 fertility decision in the nested probit model.

ACCEPTED MANUSCRIPT retirement, have an effect on increasing the family’s probability of having more children in the future15. In many part of the world, especially Asia, son preference plays an important role in parental decisions (Kevane & Levine, 2003); strong son preference has been regarded as influential on slowing fertility decline or preventing fertility from falling

PT

(Banister, 1999). Taiwan is generally acknowledged as one of a few areas that has strong son preference (Banister, 1999; Lin et al., 2011; Poston & Yang, 2013).

In

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particular, one of the reasons contributing to the high sex ratios at birth (SBR) in the

SC

Chinese society including China and Taiwan was pinpointed to the “Confucian patriarchal tradition where son preference is strong and pervasive” (Poston & Yang,

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2013, p.10). Results in Table 2 indicate that when the family currently has at least one son, the probability of desiring more children declines at a significantly large

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magnitude of approximately 10 percentage points. Another variable capturing the Asian cultural factor is the couple’s perception of their parents’ preferences for more grandsons.

The marginal effect of this variable is 0.034, which further suggests

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that the son’s preference does play a crucial role in influencing the family fertility

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decision and the family size in Taiwan.

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4.2 Grandmothers’ childcare availability and the family fertility decisions Traditionally, women play an important role in rearing children. To provide

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empirical evidence to support the crucial resource provided by the grandmothers, in what follows, our focus is on examining the influence of healthy grandmothers on family fertility decisions.

The results of the marginal effects of emphasizing

grandmothers’ childcare availability are listed in Table 3. The results in Table 3 indicate that a positive impact on both the probability of having a child and having more children exists when the number of grandmothers in the 55-64 group increases. 15

Since the nested probit model is applied on a pooled sample of data with a final sample size of 2493 respondents from 1148 families, we perform a robustness check by including the time trend variable. As reported in columns (1) and (2) of Table A.2 in the Appendix, after the addition of the time trend variable into the empirical model, the effects of healthy grandparents remain qualitatively the same as in the main results.

ACCEPTED MANUSCRIPT The sizes of this positive marginal effect are 0.029 and 0.023, respectively. The marginal effects of healthy grandmothers in the 55-64 age groups are approximately twice the size of the marginal effect when grandparents’ health is considered.

The

result reveals that the number of healthy grandmothers has a greater impact on family fertility decisions than that of healthy grandparents.

Based on the result, our

PT

results provide empirical evidence supporting the view that grandmothers’ health status is more influential on family fertility decisions.

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Moreover, the marginal effect of healthy grandmothers on the couple’s future

SC

fertility decision of having more children are -0.27 for the 65-74 age group and -0.34 for the 75-and-up age group. This negative effect is also much larger than the

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marginal effect of the number of healthy grandparents in the same age groups. Therefore, the results illustrate that relative to healthy grandparents, the childcare

MA

availability of healthy grandmothers has greater impacts on family fertility decisions. This conclusion holds for all age groups and thus suggests a persistently greater

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influence of grandmothers’ health on family fertility decisions.

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4.3 Further investigations on the compound effect of age and health Motivated by the demographers’ observation that it is the healthy and active

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grandparents who are capable of offering childcare to their descendants (Gray, 2005; Hughes et al., 2007; Wang and Marcotte, 2007), the present study identifies the

AC

effect of availability of grandparental childcare from the perspective of healthy grandparents.

However, for the elderly groups, i.e., the 65-74 and 75-and-up

groups, there may be strong age effect that compounds the healthy effect since all the elderly grandparents are aging16. We expect the age effect to work in the opposite direction to grandparents’ health. One of the reasons for this expectation is taking care of grandchildren may be at more cost of the grandparents' health when they are older. Moreover, from

16

We’ve benefited from the anonymous reviewer’s insight on the possible aging effect associated with the two elderly groups.

ACCEPTED MANUSCRIPT the perspectives of childcare quality and childcare ideas, the young couple’s willingness to rely on their parents’ childcare may fall with the parents’ age. Since the quality of grandparental childcare may fall more significantly when the grandparents move on to the stages of 70- or 80-something, the young couple may not want to take the risk to rely on the grandparental childcare even though the On the other hand, since the age of mothers

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grandparents are perceived healthy.

are controlled for in our model, there may also be an effect due to age gap which Larger age gap between parents and children

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varies across families besides aging.

SC

could mean greater differences in their childcare ideas, which thereby reduces the young couple’s willingness to rely on their parents for childcare17.

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Taking this age effect into consideration and to examine whether our interpretation of grandparents’ health as grandparental childcare availability is

MA

robust, we add the number of grandparents or the interaction terms, the number of living grandparents times the number of healthy grandparents, into the empirical model. However, due to high collinearity between the two variables for each age

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group, 0.865 for the 55-64 group, 0.833 for the 65-74 group, and 0.877 for the

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75-and-up group, results for different specifications including either the number of living grandparents or the interaction terms failed to converge.

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Facing the multicollinearity problem, instead of separating the aging effect and the health effect for the elderly groups, we estimate the composite effect by

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replacing the number of healthy grandparents in each age group with the corresponding interaction terms.

Results of the models including the interaction

term(s), respectively, for the 65-74 group, the 75-and-up group and both the two elderly groups are reported in Table 4. For the level-1 fertility decision, the effects of different age groups are qualitatively similar to the results reported in Table 2. The 55-64 group of healthy grandparents exhibit statistically significant and positive effect on the family’s 17

The age effect on fertility decisions based on the consideration of lower childcare quality and larger age gaps are suggested by the anonymous referee. We thank the referee for the insight which provides an intuitionally reasonable explanation to the negative coefficients.

ACCEPTED MANUSCRIPT probability of having children in all three specifications. However, the two elderly groups’ positive influence on family fertility decision of having a child only prevail in some specifications when considering the compound effect of health and age. Summarizing from the estimates listed in columns (1), (3) and (5), our results suggest that only the 55-64 group of healthy grandparents are found to have a

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persistent and positive effect on the family’s probability of having children18. Comparing the main results with those in columns (2) and (4); however,

RI

suggest that healthy grandparents in the 65-74 and 75-and-up groups are not

unless interacting with the age effect.

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significant determinants for the family fertility decision of having more children The significant coefficients of the two

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interaction terms in column (6) further confirm the strong predictability of the age-health compound effect on the couple’s desire for more children.

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Since the age effect is expected to work in the opposite direction to grandparents’ health, the negative coefficients of the interaction terms of the two elderly groups demonstrate the dominant role of the age effect when it’s

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compounded with the health effect.

It is this dominance of the age effect which

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leads to lower probability of desiring for more children when more of the grandparents in the two elderly age groups are available for childcare.

As

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mentioned earlier, due to the consideration of lower childcare quality provided by the grandparents as well as larger age gap leading to greater differences in the

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childcare ideas, more of the healthy grandparents in the two elderly groups reduce the couple’s probability of having more children in the future. Marginal effects of the empirical specifications including the interaction terms are listed in Table 5. To emphasize the role of grandmother’s childcare availability, the marginal effects considering the compound effect of grandmother’s age and

18

We performed a robustness check by including the interaction terms for the three age groups into the empirical model. As reported in columns (3) and (4) in Table A.2 in the Appendix, none of the interaction terms are statistically significant in the estimates of the first-level decision. Similarly, only one of the interaction terms, capturing the age-health compound effect of the 75-and-up group, is found to be a statistically significant determinant for the family’s decision to have more children. This result suggests the compound effect of age and health only prevails in the two elderly groups.

ACCEPTED MANUSCRIPT health are also reported in Table 5. Overall, a comparison of the marginal effects of the grandparents’ models and that of the grandmother’s model indicate relative to healthy grandparents in the family, healthy grandmothers tend to have greater influence on family fertility decision for all age groups.

That is, just like healthy

grandmothers in the 55-64 group have a stronger positive impact on the family

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fertility decision to have a child; the reduced probability of having more children due to the health-age compound effect of grandmothers in the two elderly groups is The result further confirm our conclusion in

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greater than that due to grandparents.

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previous section that there is a persistently greater influence of healthy grandmothers on family fertility decisions, which is found to be even more sizable

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after considering the age-health compound effect of elderly grandmothers. 5. Conclusions

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In this study, we explore the effect of grandparents’ health on family fertility decisions mediated by the availability of grandparental childcare. Extending from

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the economic theory of childcare and fertility in Ermisch (1989), the current study

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provides a theoretical justification for the positive association of grandparents’ health and family size.

Using the data of Panel Study of Chinese Family Dynamics (PSFD) from 2006

CE

to 2011, the effect of grandparents’ health on household fertility decisions in Taiwan is then examined through a nested probit model.

It is found that grandparents in

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different age groups exhibit differential influences on the family fertility decision. Overall, healthy grandparents in the 55-64 age group are found to have a persistent and positive impact on the family’s probability of having children and thus increase family size as predicted by the theoretical model.

Nonetheless, when grandparents’

health effect is compounded by the age effect, more elderly healthy grandparents in the 75-and-up group will reduce the couple’s desire for more children.

This

negative effect can be explained by the couple’s consideration of lower childcare quality and larger age gaps leading to greater differences in the childcare ideas.

ACCEPTED MANUSCRIPT Moreover, our results indicate that the number of healthy grandmothers has a greater impact on the family fertility decision relative to the number of healthy grandparents.

The results further suggest that, in their families’ perception,

grandmothers continue to undertake the major responsibility of childcare in the family and thus provide an absolutely crucial resource. First,

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A couple of policy implications can be derived from the current study.

based on our results, a subsidy to grandparents to encourage them to take care of

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their grandchildren may be a novel strategy to increase family fertility.

grandchildren, they sacrifice their work time.

SC

Grandparents also confront an opportunity cost; when taking care of their The lack of payment or lower

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payment to grandparents is primarily because of the family ties with their children, which is termed kin selection by Kaptijin et al. (2010).

From this perspective,

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subsidies for grandparents’ childcare not only lower parents’ costs for non-parental childcare, but also serve as an efficient means to maintain the tradition of strong family ties in Chinese society.

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Moreover, the previous research indicated that a higher adoption of childcare

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from grandparents may be due to the lower labor participation of females in the past. When more females participate in the labor market, government needs to change its

CE

childcare strategy into one not only relying less on mothers’ childcare but also relying less on grandmothers’ childcare (Gray, 2005; Wheelock & Jones, 2002).

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However, in this study, we find that grandmothers who remain in the labor market and have good health can positively affect family fertility and actually have a significant influence. mothers’

labor

Therefore, in addition to the incomparability between

participation

and

childcare,

the

incomparability between

grandmothers’ labor time and childcare may need to be considered while planning for appropriate family fertility strategies. Finally, considering the availability of grandparents’ childcare, a policy to address the aging population will also influence family fertility. Since only healthy grandparents will have a positive effect on family fertility, well-planned health care

ACCEPTED MANUSCRIPT for the elderly not only reduces government’s burden on supporting the elderly, it can also extend the length of grandparents’ childcare time and thus turn older people into useful social and economic resources.

Consequently, in addition to

considering the role of healthy elders in the labor market, an evaluation of the elderly’s social importance will need to explain the elders’, particularly the

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grandmothers’, crucial significance in family fertility decisions.

Any remaining errors are

SC

reviewers for their valuable and insightful comments. our own.

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Acknowledgements Seniority is shared by the two authors. The authors are grateful to the anonymous

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Model 1

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Appendix Table A.1 Linearized estimates of the probit model

Model 2

Estimates

t-value

Estimate

t-value

Constant Child

6.4931*** -3.4529***

9.77 -7.01

3.0245***

7.29

-0.1266*** -0.0288 0.0146 0.0019 0.0004 0.0968* 0.1754*** 0.0672* -0.0709 -0.1626

-10.92 -0.94 1.17 0.84 0.17 1.89 3.83 1.85 -1.21 -1.24

-0.1261*** -0.0292 0.0143 0.0019 0.0005 0.0931* 0.1787*** 0.0673* -0.0598 -0.1617

-10.83 -0.95 1.15 0.86 0.21 1.82 3.88 1.85 -1.02 -1.23

-0.0141 -0.0382 -0.1064*** -0.0814 0.0831 0.0171 -0.705***

-0.13 -0.15 -5.14 -0.58 0.76 0.23 -8.78

-0.0210 -0.0421 -0.1091*** -0.0805 0.0809 0.0267 -0.6952***

-0.2 -0.16 -5.22 -0.57 0.74 0.36 -8.64

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Wife’s age Wife’s wage Husband’s wage Wife’s labor Husband’s labor Wife’s education Husband’s education Healthy gp. (55-64) Healthy gp. (65-74) Healthy gp. (75 and up)

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Variable

Distance Indigenous Sibling Allowance from gp. Gp. allowance Children under 3 Son

ACCEPTED MANUSCRIPT Gp. son preferences

0.2405**

Sample size Log likelihood

2.42

0.2338**

2,493 -708

2.34

2,177 -705

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Note: *, ** and *** denote, respectively, significant at the 10%, 5% and 10% significance level. Model 1 is the probit model assuming the fertility decision to have a child is exogenous. Model 2 is the probit model that is conditioned on the sample having at least one child in the family.

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Table A.2 Robustness check Time Trend

Age & Health Interaction

Child (1)

Variable

0.267* (0.158)

-0.162 (0.117)

Healthy gp. (75 & up)

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GP. son preference

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Sons

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Total gp.*Healthy gp. (65-74) Total gp.*Healthy gp. (75 & up)

-0.664*** (0.075)

0.011 (0.012) -0.022 (0.033) -0.139** (0.06909) -0.741*** (0,081)

0.223*** (0.091)

0.250*** (0.094)

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Total gp.*Healthy gp. (55-64)

0.045 (0.049) -0.077 (0.054) 11.986 na

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Control for Mother’s characteristics Yes Yes Yes Father’s characteristics Yes Yes Yes Family characteristics Yes Yes Yes Other controls Yes Yes Yes Sample size 2493 Log likelihood -1456 Note: The standard errors are reported in the parentheses. *, ** and *** denote, respectively, significant at the 10%, 5% and 10% significance level.

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0.082** (0.038) -0.042 (0.062)

Child (3)

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0.081** (0.038) 0.083 (0.058)

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Healthy gp. (55-64)

More child (2)

Yes Yes Yes Yes 2493 -1799

ACCEPTED MANUSCRIPT References

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Abadie, A., Athey, S., Imbens, G., & Wooldridge, J. (2017). When should you adjust standard errors for clustering? NBER Working Paper No. 24003. https://economics.mit.edu/files/13927 Alpu, O., & Fidan, H. (2004). Sequential probit model for infant mortality modelling in Turkey. Journal of Applied Sciences, 4, 590-595. Banister, J. (1999). Son preference in Asia--report of a symposium. U.S. Census Bureau. (www.census.gov/ipc/www/ebspr96a.html)(2017,7,10) Bramley, D., P. Hebert, L. Tuzzio, and M. Chassin (2005). Disparities in Indigenous Health: A Cross-Country Comparison Between New Zealand and the United States, American Journal of Public Health, 95(5): 844-850.

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Bratti, M., Frattini, T., & Scervini, F. (2016). Grandparental availability for child care and maternal employment: Pension reform evidence from Italy, IZA DP No. 9979.

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ACCEPTED MANUSCRIPT Ermisch, J. F. (1989). Purchased child care, optimal family size and mother's

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employment theory and econometric analysis. Journal of Population Economics, 2, 79-102. Fanti, L., & Gori, L. (2014). An OLG model of growth with longevity: When grandparents take care of grandchildren. Portuguese Economic Journal, 13, 39-51. Genius, M., Pantzios, C. J., & Tzouvelekas, V. (2006). Information acquisition and adoption of organic farming practices. Journal of Agricultural and Resource Economics, 31(1), 93-113. Gopnik, A. (2014). Grandmothers: The behind-the-scenes key to human culture. The Wall Street Journal, May 2.

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Gray, A. (2005). The changing availability of grandparents as carers and its implications for childcare policy in the UK. Journal of Social Policy, 34, 557-577.

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Greene, W. H. (2012), Econometric Analysis, 7th ed., Prentice Hall press, Englewood Cliff, NJ, 219-222. Guzman, L. (2004). Grandma and grandpa taking care of the kids: Patterns of involvement. Child Trends Research Brief, Publication No. 2004-17. Haan, P., & Wrohlich, K. (2011). Can child care policy encourage employment and fertility? evidence from a structural model. Labour Economics, 18, 498-512. Hank, K., & Kreyenfeld, M. (2003). A multilevel analysis of child care and women's fertility decisions in Western Germany. Journal of Marriage and Family, 65,

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584-596. Hajivassiliou, V., McFadden, D., & Ruud, P. (1996). Simulation of multivariate normal rectangle probabilities and their derivatives: theoretical and computational results”, Journal of Econometrics, 72(1), 85-134. Hughes, M. E., Waite, L. J., LaPierre, T. A., & Luo, Y. (2007). All in the family: the impact of caring for grandchildren on grandparents' health. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 62, 108-119. Juan, S.-C., Awerbuch-Friedlander, T., & R. Levins (2016). Ethnic density and mortality: aboriginal population health in Taiwan, Public Health Reviews, 37:11. Kevane, M., & Levine, D. I. (2003). Changing status of daughters in Indonesia. Center for International And Development Economics, Research Working Paper No. C03-126. Ko, Y.-C., B.-H. Liu, S.-F. Hsieh (1994). Issues on aboriginal health in Taiwan, The Kaohsiung Journal of Medical Sciences, 10(7), 337 – 351. (in Chinese) Kornstad, T., & Thoresen,T. O. (2007). A discrete choice model for labor supply and childcare,” Journal of Population Economics, 20(4), 781-803.

ACCEPTED MANUSCRIPT Kreyenfeld, M., & Hank, K. (2000). Does the availability of child care influence the

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employment of mothers? Findings from Western Germany. Population Research and Policy Review, 19, 317-337. Lehrer, E. L., & Kawasaki, S. (1985). Child care arrangements and fertility: An analysis of two-earner households. Demography, 22, 499-513. Liao, T. F. (1994). Interpreting probability models: Logit, probit, and other generalized linear models. Sage Pubn Inc. Lin, Y.-H., Chen, W.-H., Chu, H.-C., Hurng, B. S., Chen, L.-C., & Chu, S.-T. (2011). Associations of son preference and socio-demographic factors with sex selection of children: Results from the Taiwan women health and fertility survey. Paper submitted to PAA 2011 sessions: 1302 poster session.

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Maddala, G.S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge University Press. Mason, K. O., & Kuhlthau, K. (1992). The perceived impact of child care costs on

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women’s labor supply and fertility. Demography, 29, 523-543. Mynarska, M. (2010). Deadline for parenthood: Fertility postponement and age norms in Poland. European Journal of Population/Revue européenne de démographie, 26, 351-373. National Institute of Child Health and Human Development (NICHD) Early Child Care Research Network (2000). Characteristics and quality of child care for toddlers and preschoolers. Applied Developmental Science, 4, 116-135. Poston, Jr. D. L., & Yang, W.‐S. (2013). Unbalanced sex ratios at birth in Taiwan and

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China and their demographic and societal implications. Paper presented at the “Demographic and Institutional Change in Global Families” Conference Program, March 28-30, Academia Sinica, Taipei, Taiwan. Presser, H. B. (1989). Some economic complexities of child care provided by grandmothers. Journal of Marriage and the Family, 581-591. Rindfuss, R. R., Guilkey, D., Morgan, S. P., Kravdal, Ø., & Guzzo, K. B. (2007). Child care availability and first-birth timing in Norway. Demography, 44, 345-372. Rivers, D., & Vuong, Q. H. (1988). Limited information estimators and exogeneity tests for simultaneous probit models. Journal of Econometrics, 39, 347-366. Schoen, R., Astone, N. M., Kim, Y. J., Nathanson, C. A., & Fields, J. M. (1999). Do fertility intentions affect fertility behavior? Journal of Marriage and the Family 61, 790-799. Su, Y.-J., & Chang, H.-H. (2016). The effects of government employment programs and self-migration decisions on economic well-being of aborigines in Taiwan, Taiwan Journal of Applied Economics, 100, 105-148. (in Chinese)

ACCEPTED MANUSCRIPT Tsai, Y.-C. (2000) An Analysis of Aborigines Medical Service Utilization under

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National Health Insurance in Taiwan, Master thesis, Graduate Institute of Public Health, National Yang Ming University. (in Chinese) Wang, Y., & Marcotte, D. E. (2007). Golden years? The labor market effects of caring for grandchildren. Journal of Marriage and Family, 69, 1283-1296. Wheelock, J., & Jones, K. (2002). “Grandparents are the next best thing”: Informal childcare for working parents in urban Britain. Journal of Social Policy, 3, 441-463. World Bank, (2015). Fertility rate, total (births per woman). The World Bank Data. (http://data.worldbank.org.cn/indicator/SP.DYN.TFRT.IN) (2015.11.18) World Health Organization (WHO), 2013. Women’s health. WHO.

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(http://www.who.int/mediacentre/factsheets/fs334/en/) (2016. 7. 18) Xie, J. (2014). Does women’s fertility intention predict fertility behavior? A panel study in contemporary China, Poster Session 2: Fertility Intentions and Behavior

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at the Population Association of American 2014 Annual Meeting Program. Zhou, C. (2017). Meet the sons and daughters giving pocket money to their parents, The ABC News, November 4.

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Table 1 Descriptive Statistics Mean Std. Min Dev.

Wife’s labor Husband’s labor Wife’s education Husband’s education Healthy gp. (45-54) Healthy gp. (55-64) Healthy gp. (65-74) Healthy gp. (75 and up) Healthy gm. (45-54) Healthy gm. (55-64)

31.948 23.001 47.837 18.390 3.614 1.086 3.734 1.191 0.293 0.656 0.965 1.043 0.590 0.896 0.197 0.513 0.215 0.460 0.554 0.644

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Healthy gm. (65-74) Healthy gm. (75 and up) Distance Indigenous Sibling Allowance from gp. Gp. allowance Children under 3 Son Gp. son preference

0.358 0.463 5.219 2.134 3.368

0 0 20 0 0

1 1 45 25 42

0 0 0 0 0 0 0 0 0 0

112 128 7 7 4 4 4 4 2 2

0 0 0 0 0 0 0 0 0 0

2 2 1 1 16 1 1 2 1 1

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0.850 0.310 34.117 2.218 4.805

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Child More Child Wife’s age Wife’s wage Husband’s wage

Max

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Variable

0.274 0.057 0.594 0.056 5.076 0.073 0.856 0.322 0.633 0.155

0.516 0.253 0.370 0.163 2.619 0.260 0.351 0.527 0.482 0.362

Note: Total sample size is 2,493. Source: This study.

ACCEPTED MANUSCRIPT Table 2 FIML estimates from the GHK algorithm Variable

Estimates

Level-1 fertility decision: have a child Constant Wife’s age Wife’s wage Husband’s wage Wife’s labor

-0.796** 0.070*** -0.003 0.028** -0.006***

-2.09 6.65 -0.13 2.24 -2.61

Husband’s labor Wife’s education Husband’s education

0.003 -0.172*** -0.164***

1.22 -3.43 -3.62

0.081** 0.083 0.268* 0.244** 0.200 0.192*** 0.161

2.11 1.45 1.71 2.39 0.76 6.81 1.26

0.0346 0.1476 0.0865 0.0168 0.4449 <.0001 0.2065

-0.305*** 0.517***

-2.65 2.79

0.0081 0.0053

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0.0363 <.0001 0.8929 0.0249 0.0090

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Healthy gp. (55-64) Healthy gp. (65-74) Healthy gp. (75 and up) Distance Indigenous Sibling Allowance from gp.

t-value App. Pr>|t|

0.2221 0.0006 0.0003

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ACCEPTED MANUSCRIPT Table 2 (cont.) FIML estimates from the GHK algorithm App. Pr>|t|

Estimates

t-value

Level-2 fertility decision: have more children Constant Child Wife’s age Wife’s wage Husband’s wage Wife’s labor

10.338*** -7.821*** -0.1131*** -0.030 0.019* 0.001

11.53 -12.97 -9.33 -1.1 1.61 0.49

0.001 0.060 0.150***

0.44 1.15 3.22

0.6606 0.2494 0.0013

2.18 -0.72 -1.39 0.25 0.04 -3.46 -0.29

0.0294 0.4693 0.1634 0.8007 0.9686 0.0005 0.7726

0.41 0.19 -8.18 2.44

0.6782 0.8520 <.0001 0.0147

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Gp. allowance Children under 3 Son Gp. son preferences Sample size Log likelihood

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0.083** -0.045 -0.162 0.027 0.010 -0.078*** -0.038 0.046 0.014 -0.665*** 0.225*** 2,493 -1456

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Healthy gp. (55-64) Healthy gp. (65-74) Healthy gp. (75 and up) Distance Indigenous Sibling Allowance from gp.

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Husband’s labor Wife’s education Husband’s education

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Variable

<.0001 <.0001 <.0001 0.2726 0.1070 0.6254

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Note: *, ** and *** denote, respectively, significant at the 10%, 5% and 10% significance level. Source: This study.

ACCEPTED MANUSCRIPT Table 3 Marginal effects of healthy grandparents (grandmothers) and son preferences Marginal effects Healthy gm.

Age group (55-64) Have children More child

0.0142 0.0127

0.0288 0.0228

Age group (65-74) Have children

0.0145 -0.0069

Age group (75 and up) 0.0470 -0.0248

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More child

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Healthy gp.

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Variable

0.1269 -0.0343 -0.1009

0.0345

0.0335

Yes Yes Yes Yes 2493 -1456

Yes Yes Yes Yes 2493 -1454

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-0.0267

-0.1020

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0.0343

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Control for:

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Mother’s characteristics Father’s characteristics Family characteristics Other controls Sample size Log likelihood Source: This study.

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Table 4 FIML estimates from the GHK algorithm—further investigations C More More Mor hild chil Chil child Chil e Variable d d d child (1) (2) (3) (4) (5) (6) Healthy gp. (55-64) 0.09 -0.017 0.08 -0.01 0.09 -0.01 3*** 3** 5 2** 9 * (0.03 (0.034 (0.0 (0.03 (0.0 (0.03 5) ) 35) 4) 35) 4) Healthy gp. (65-74) 0.06 -0.08 7 0 (0.0 (0.05 48) 0) Healthy gp. (75 and 0.08 -0.176 up) 8 * (0.10 (0.096 7) ) Total gp.  Healthy gp. 0.05 -0.042 0.05 -0.04 (65-74) 2*** * 1** 2* * (0.01 (0.023 (0.0 (0.02 8) ) 19) 3) Total gp.  Healthy gp. 0.02 -0.16 0.03 -0.17 (75 and up) 5 5*** 5 2*** (0.0 (0.06 (0.0 (0.06 57) 0) 57) 0) Son -0.618 -0.62 -0.62 *** 2*** 1*** (0.086 (0.08 (0.08 ) 6) 6) Gp. son preferences 0.204 0.20 0.203 *** 2*** *** (0.080 (0.08 (0.08 ) 0) 0) Control for: Mother’s Yes Yes Yes Yes Yes Yes characteristics Father’s Yes Yes Yes Yes Yes Yes characteristics Family characteristics Yes Yes Yes Yes Yes Yes Other controls Yes Yes Yes Yes Yes Yes Sample size 2493 2493 2493 Log Likelihood -1800 -180 -1798 0 Note: The standard errors are reported in the parentheses. *, ** and *** denote, respectively, significant at the 10%, 5% and 10%

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significance level. Source: This study.

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Table 5 Marginal effects—further investigations C More More Mor hild chil Chil child Chil e Variable d d d child (1) (2) (3) (4) (5) (6) Grandparents’ marginal effects Healthy gp. (55-64) 0.01 -0.004 0.01 -0.00 0.01 -0.00 8 6 3 8 5 Healthy gp. (65-74) 0.01 -0.01 3 8 Healthy gp. (75 and 0.01 -0.041 up) 7 Total gp.  Healthy gp. 0.01 -0.010 0.01 -0.01 (65-74) 0 0 0 Total gp.  Healthy gp. 0.00 -0.03 0.00 -0.04 (75 and up) 5 8 7 0 Grandmothers’ marginal effects Healthy gm. (55-64) 0.03 -0.006 0.03 -0.00 0.03 -0.00 9 7 6 9 6 Healthy gm. (65-74) 0.02 -0.05 4 8 Healthy gm. (75 and 0.14 -0.070 up) 4 Total gm.  Healthy 0.02 -0.047 0.02 -0.04 gm. (65-74) 7 7 7 Total gm.  Healthy 0.13 -0.06 0.14 -0.07 gm. (75 and up) 84 8 3 0 Control for: Mather’s Yes Yes Yes Yes Yes Yes characteristics Father’s Yes Yes Yes Yes Yes Yes characteristics Family characteristics Yes Yes Yes Yes Yes Yes Other controls Yes Yes Yes Yes Yes Yes Sample size 2493 2493 2493 Log likelihood (Gp. -1794 -179 -1794 model) 3 Log likelihood (Gm. -1800 -180 -1798 model) 0 Note: *, ** and *** denote, respectively, significant at the 10%, 5% and 10% significance level. Source: This study.

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Figure 1. Proportion of families desiring for more children by grandparent’s age groups Highlights  Extending from the theoretical framework in Ermisch (1989), this study incorporates grandparents' health status to provide a theoretical justification for the grandparent effect on family size. The theoretical model predicts that when availability of grandparental childcare increases with improvement of grandparents’ health status, the family size increases accordingly.



To be consistent with this theoretical prediction, the effect of grandparents’ health is identified by defining a suitable proxy variable for grandparental

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

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childcare availability which is based on the couple’s perception of grandparents’ health status. The new wrinkle here is the mechanism through which grandparents’ health influences family fertility decisions lies in its effect on the availability of grandparental childcare.



The nested model is estimated to identify the effect of grandparents’ health. The nested probit model is one in which the family’s first‐level fertility choice, i.e., to have children or not, influences the family’s second‐level fertility decision of whether to have more children.



Grandparents in different age groups are found to exhibit differential influence on family fertility decision.



Healthy grandparents in the 55‐64 age group is found to have a persistent and positive impact on the family's probability of having children, and thus increase family size as predicted by the theoretical model.



When grandparents' health effect is compounded with the age effect, more of the elderly healthy grandparents in the 75‐and‐up group will reduce the couple's desire for more children. This negative effect can be explained through the couple's consideration of lower childcare quality and larger age gaps leading to greater differences in the childcare ideas.



Our results indicate the number of healthy grandmothers has an even greater impact on family fertility decisions, suggesting grandmothers still take the major responsibility of childcare in the family and thus provide "an absolutely crucial resource by taking care of their grandchildren" (A. Gopnik, The Wall Street Journal, 2014 ).

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Figure 1