Children’s health has always been a concern, under the background of a new wave of entrepreneurship, this paper uses China health and nutrition survey (CHNS) data, selects the age of the child height ratio (HAZ) score to measure children’s health, the OLS regression model, the treatment effect model and tendency to score matching (PSM), and other methods to investigate the influence of parents entrepreneurship for children’s health. The study found that at least one parent business, under the condition of children’s health is relatively low, the result is in step with the result of whether parents start a business. In addition, there are gender differences in children’s health; only children are in better health. The health endowment of parents also has an impact on children’s health. This article from the perspective of parents entrepreneurship researches entrepreneurship influence on children’s health, there are two main mechanisms, the first is money: it brings the income that is beneficial to children’s health; this article examines the entrepreneurship that can bring income premium; estimation results show that the venture has a significant positive effect on monthly income. The second is time: entrepreneurship to reduce the harm to children’s health care time; estimation results show that the entrepreneurial significantly reduces the time for children’s care; two mechanisms are negative; there is a relation of offsetting each other; parents start-ups do significantly reduce the children’s health level.
Children’s health is an important criterion to measure economic and social development and human development, and it is directly related to the quality of future labor. The importance of child care lies not only in its direct impact on the development of children, but also in its indirect impact on children’s health through influencing the labor supply or consumption decisions of the family [
Existing studies have shown that the health of children is affected by the fetal development environment and the socio-economic environment after birth [
Case results show that family income is positively correlated with children’s health. The balance between family income and child care, as a result of dual parental status, Liu Jing considers that women have a dual identity in the family-earning and babysitting, and that, in controlling other factors, the increase in working hours of additional units of the mother has a significant negative impact on the health of the child. And the same mother’s increase in non-agricultural working time has a greater negative impact on children’s health, while the increase of mother’s income has a significant positive effect on the health status of the child; marginal effect analysis shows that the positive impact of increased maternal income on children’s health is hard to offset the negative effects of increased labor time. The impact of children’s gender differences is also different, and girls are relatively at a healthy disadvantage [
The concentration index can better measure the degree of health inequality [
Entrepreneurial activity plays an important role in economic and social development. At the macro level, entrepreneurial entrepreneurship can promote economic growth, increase productivity, create jobs and promote innovation [
Most of the literature studies the impact of entrepreneurship on income and well-being, and there is little literature on the impact of parental entrepreneurship on children’s health. The main contribution of this paper is embodied in the following two aspects: first, in the topic, this article from the perspective of parents to study the health of children, there is no literature at home and abroad on the relationship between the two direct research; second, this article not only examines the parents’ entrepreneurship to the child health direct influence.
In this paper, two possible mechanisms are theoretically analyzed: money investment and time investment, and tested in the empirical analysis. The remaining parts of this article are structured as follows: The second section will make a theoretical analysis on the possible mechanism of parents’ entrepreneurship affecting their children’s health; the third section is introduced and variable description; the fourth section uses the CHNS data to carry on the empirical examination to the parents Entrepreneurship and the child health relations; the fifth Section has carried on the demonstration examination to two function mechanisms. Finally, in the last is conclusion and policy implication of this paper.
The impact of parent’ entrepreneurship on their children’s health is mainly through the following two mechanisms of action:
The first is Money investment―entrepreneurship brings more income returns, and the increase in family income is beneficial to children’s health. Some studies have discussed the return on income from entrepreneurship, such as Pan Chunyang and Wang Ziyan using the China Comprehensive Social Survey (CGSS) data, the study found that Chinese residents’ entrepreneurial currency returns are positive, entrepreneurs earn more than 30% to 40% of their employees, and there is a “Matthew effect” on the money Return of entrepreneurship [
According to this paper, the first research hypothesis is presented:
Hypothesis 1: Parents’ entrepreneurship can lead to higher income returns, thereby increasing the money invested in children and improving the health of their children.
The second is time investment-the reduction in the care time caused by entrepreneurship is detrimental to children’s health: prolonged health inequalities in children are expanded more by intergenerational transmission and child care [
Children are more vulnerable; On the other hand, even if taken care of by others, with the control of the mother’s labor supply time and income, the increase in the time of being cared for by others has a significant negative impact on children’s health [
According to this paper, a second research hypothesis is proposed:
Hypothesis 2: Parents choose entrepreneurial activities, resulting in a reduction of time spent on their children, which is not conducive to improving the health status of their children.
In Chapter 5, the two hypotheses will be verified through empirical analysis.
The data used in this paper are China Health and Nutrition Survey (CHNS). CHNS data from the university of north Carolina and conducted in China by Chinese center for disease control and prevention of China health and nutrition survey, aims to study how the change of China’s social and economic effects on population health and nutritional status. The survey nine provinces or autonomous regions (Liaoning, Heilongjiang, Shandong, Jiangsu, Henan, Hubei, Hunan, Guangxi, Guizhou) of town and country, and now can get 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009 and 2011, a total of nine rounds of data. CHNS data investigation using multi-stage stratified random cluster sampling (multistage random cluster from) method, based on the geographical location, economic development level, degree of abundance of public resources and health index covered the east, and west China 8 - 9 provinces. CHNS samples are nationally representative.
The questionnaire includes adult, children and family questionnaires, which cover the rich information of nutrition and health, demographic characteristics, employment status and family background. This data contains variables: 1) variables at the individual level. Relationship with the head of the household, gender, age, date of birth, nationality, height, weight, blood pressure, smoking history, history, education fixed number of year (level), registered permanent residence, whether cadres, industry, profession, second career, the nature of the work unit and the number, the employment situation, working time (very detailed) salary, income, to participate in the situation of agricultural production. 2) Variables at the family level. Agricultural production, crop value, total family income, family population, household spending (detail), family income (in detail), living conditions (detailed), transportation, household consumption, family property, medical expenses (detailed), member of the family illness (in detail), food consumption (in detail). 3) Community level variables, number of villages, number of villages, medical insurance, hospital situation, consumption structure, school situation, family planning situation, food price. Used in this article is from 1989 to 2009, a total of eight year of survey data and information since 2011 had no children’s height, and before the investigation region (province) to select and 8 rounds of survey is put in bigger difference, so no use. According to research needs, extracted from CHNS data under the age of 18 investigation data, and to eliminate invalid variable data missing samples, at the end of the paper finally got eight points in time using the unbalanced panel data, retain sample size of 1371. 1
[
The OLS model is used to investigate the influence of parents’ entrepreneurial status on their children’s health, and set the following equation for children’s health:
Health i t = β 0 + β 1 Parent i t + X i t λ + Province j + Wave t + ε i t (1)
Health For children’s physical health, there are three main measures to measure the physical health of children: first, clinical indicators, such as child mortality, child morbidity and injury; Second, children’s self-assessment of health; the third is body measurement index, such as BMI, height age ratio. In this paper, children’s HAZ score (age and height ratio) was used to measure the long-term health of children.2
The core explanatory variable is the dummy variable of whether the parent is an entrepreneur. If at least one of the parents is an entrepreneur, the value is 1. If both are paid, the value is 0. Parent 3. According to the CHNS survey, the employment status of interviewees is divided into the following 9 categories: 1) - 2) self-employed persons without employment; 3) Long-term work for other people or units; 4) Work for others or units (contract workers); 5) Temporary workers; 6) Wage earners; 7) Unpaid family helpers; 8) Others; I don’t know. Identify the entrepreneurs based on both quantitative research methods, this paper will “have hired worker individual operator” and “no employees of self-employed”, defined as entrepreneurs will “work for others or unit long-term workers”, “or work units (contractor) for others” and “temporary” as earners.
2In terms of Height for children’s health/nutrition, the international standard for the use of Height for Age z-scores (HAZ) is commonly used as a measure. Research in the specific operation of children with age, gender, “reference from children” comparison, through the Z score method of formula to calculate the children and children with reference to the relative difference of height, the HAZ. Thus, it is concluded that the standard deviation of the child deviated from the standard population is used to reflect the long-term nutrition and health status of children. Research by the score says children deviates from the standard of the same age, and sex groups the degree of height, to reflect children’s nutrition health for a long time, if the Z score is negative, indicates that the long-term observation of children health sent in reference to children. The normal value of HAZ is between 3 and −3, and when the HAZ score is between −2 - −3, the child’s growth retardation is indicated. According to the children’s growth standard published by the world health organization, the age of children was rounded up by rounding.
3In the selection of control group, based on comparability, this paper chose to take both parents as the control group. In the robust test, this paper also considered the control of unpaid or remunerative family help and unemployment or withdrawal from the labor market.
X As the control variables may affect children’s health, child health impact factors based on the existing research literature, in this paper, the control variables mainly includes the following several aspects: one is the children’s own characteristics, including their gender, age, whether the one-child, whether first-born; The second is the characteristics of parents, including biological genetic factors (height) and the duration of education. Third, family characteristics, including family size, family income per capita, toilet type, drinking water source, medical accessibility. Province j to control the characteristics of the province, such as the geographical location and climatic conditions of the province. W a v e t for time fixation effects, the effects of unobservable external environmental (economic) factors on children’s health can be controlled.
Considering the differences between parents in child care, this paper further differentiates the difference between parents’ different entrepreneurial identity and their influence on children’s health:
Health i t = β 0 + β 1 onlyfather i t + β 2 onlymother i t + β 3 both i t + X i t λ + Province j + W a v e t + ε i t (2)
onlyfather = 1 The father is the entrepreneur, the mother is not the entrepreneur; onlymother = 1 It means that mothers are entrepreneurs and fathers are not entrepreneurs; both = 1 Parents are entrepreneurs, parents are not entrepreneurs. β 1 The impact on children’s health is not measured by the fact that only fathers are entrepreneurs, mothers are entrepreneurs, and parents are entrepreneurs. β 2 β 3 The main variable definitions are shown in
Grouping descriptive statistics are given in
Firstly, the method of OLS was used to estimate the children’s health decision Equation (1), and the estimated results were shown in
Variable types | The variable name | Define |
---|---|---|
Interpreted variable | Children’s health | Height age ratio (HAZ score) |
Core explanatory variable | Parents are entrepreneurs My father is an entrepreneur | At least one parent is an entrepreneur Father alone |
Mothers are entrepreneurs | Motherhood alone | |
Both parents are entrepreneurs. | Both parents are self-employed | |
Control variables | The child’s age | Age of units: |
Children’s gender | Men = 1; Women = 0 | |
The one-child | Is = 1; N = 0 | |
first-born | Is = 1; N = 0 | |
My father height | Unit: cm | |
Mother’s height | Unit: cm | |
Father of record of formal schooling | My father has been exposed to education years (year) | |
Mother education | Mother is subject to education years (year) | |
Family size | Unit: people | |
Family economic situation | Per capita net income of the family. | |
Medical accessibility | The time to visit a medical institution | |
Drinking water source | One type is groundwater (greater than 5 meters); It will open well water (less than 5 meters), streams, springs, rivers and lakes, and the definition of snow and ice water will be merged into the second category, named open source of water. The water is rated as the third category, and the water is used as a benchmark, while other water sources are classified as the fourth category | |
The toilet type | One is that the family does not have a toilet; The second category is that the family has non-flush toilets, such as indoor toilets (no flush), outdoor non-flushing public toilets, open cement pits and open pit. The third category is flush toilet, such as indoor flush, outdoor flush toilet. In this paper, the family has no toilet as the comparison benchmark | |
Instrumental variable | Entrepreneurial atmosphere | The proportion of entrepreneurs in provinces |
Children’s health | The mean | The standard deviation | The minimum value | The maximum | Number of samples |
---|---|---|---|---|---|
Parents don’t start businesses | 0.172 | 0.907 | 4.769 | 5.525 | 853 |
Parents’ entrepreneurship | 0.534 | 1 | 4.198 | 2.835 | 518 |
All the samples | 0.309 | 0.959 | 4.769 | 5.525 | 1371 |
Children’s health | The mean | The standard deviation | The minimum value | The maximum | Number of samples |
---|---|---|---|---|---|
Parents don’t start businesses. | 0.172 | 0.907 | 4.769 | 5.525 | 853 |
Father alone | 0.422 | 0.933 | 3.474 | 0.869 | 33 |
Motherhood alone | 0.460 | 1.092 | 3.871 | 2.835 | 70 |
Both parents are self-employed | 0.532 | 0.902 | 4.198 | 2.146 | 171 |
All the samples | 0.252 | 0.929 | 4.769 | 5.525 | 1127 |
Data source: “China health and nutrition survey”, 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009.
variable | The mean | The standard deviation | The minimum value | The maximum | Sample size |
---|---|---|---|---|---|
Children’s health | 0.309 | 0.959 | 4.769 | 5.525 | 1371 |
The child’s age | 11.49 | 3.835 | 0 | 18 | 1371 |
Children’s gender | 0.532 | 0.499 | 0 | 1 | 1371 |
The one-child | 0.588 | 0.492 | 0 | 1 | 1371 |
first-born | 0.759 | 0.428 | 0 | 1 | 1371 |
My father height | 167.8 | 6.148 | 148 | 187.4 | 1371 |
Mother’s height | 156.7 | 6.130 | 65 | 173.7 | 1371 |
Father’s education | 9.786 | 3.291 | 0 | 18 | 1371 |
Mother education | 8.630 | 3.523 | 0 | 17 | 1371 |
Family size | 3.864 | 1 | 3 | 8 | 1371 |
Family economic situation | 7.790 | 0.855 | 5.553 | 9.277 | 1371 |
Medical accessibility | 9.015 | 11.03 | 0 | 180 | 1371 |
Data source: “China health and nutrition survey”, 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009.
(1) | (2) | (3) | (4) | (5) | (6) | ||
---|---|---|---|---|---|---|---|
Variable | Children’s health | ||||||
Parents’ entrepreneurship | 0.286*** | 0.217*** | 0.119** | 0.087** | 0.089** | 0.132*** | |
(0.045) | (0.044) | (0.056) | (0.043) | (0.043) | (0.050) | ||
Age | 0.010 | 0.006 | 0.007 | 0.007 | 0.002 | ||
(0.006) | (0.007) | (0.008) | (0.007) | (0.007) | |||
Gender | 0.499*** | 0.483*** | 0.515*** | 0.515*** | 0.503*** | ||
(0.036) | (0.042) | (0.044) | (0.044) | (0.038) | |||
The one-child | 0.175*** | 0.145*** | 0.102* | 0.102* | 0.067 | ||
(0.046) | (0.050) | (0.061) | (0.060) | (0.050) | |||
First-born | 0.035 | 0.009 | 0.019 | 0.020 | 0.008 | ||
(0.056) | (0.062) | (0.064) | (0.064) | (0.056) | |||
My father height | 0.028*** | 0.027*** | 0.027*** | 0.027*** | |||
(0.004) | (0.005) | (0.005) | (0.004) | ||||
Mother’s height | 0.018*** | 0.018*** | 0.018*** | 0.014*** | |||
(0.004) | (0.004) | (0.004) | (0.003) | ||||
Father’s education | 0.019** | 0.013 | 0.013 | 0.001 | |||
(0.008) | (0.009) | (0.009) | (0.007) | ||||
Mother education | 0.001 | 0.009 | 0.010 | 0.007 | |||
(0.009) | (0.010) | (0.010) | (0.007) | ||||
Family size | 0.036 | 0.036 | 0.026 | ||||
(0.032) | (0.032) | (0.021) | |||||
Family economic situation | 0.145*** | 0.147*** | 0.101*** | ||||
(0.054) | (0.054) | (0.037) | |||||
Medical accessibility | 0.000 | 0.000 | 0.002 | ||||
(0.002) | (0.002) | (0.002) | |||||
Groundwater | 0.125* | 0.119* | 0.130** | ||||
(0.068) | (0.068) | (0.058) | |||||
Open source | 0.140 | 0.139 | 0.151* | ||||
(0.120) | (0.120) | (0.090) | |||||
Other water | 0.841 | 0.839 | 0.377 | ||||
(0.568) | (0.567) | (0.346) | |||||
Non-flush toilet | 0.055 | 0.003 | 0.037 | ||||
(0.215) | (0.209) | (0.166) | ||||
---|---|---|---|---|---|---|
Flush the toilet | 0.076 | 0.138 | 0.093 | |||
(0.220) | (0.214) | (0.170) | ||||
Provincial fixed effect | Yes | Yes | Yes | Yes | Yes | Yes |
Annual fixed effect | Yes | Yes | Yes | Yes | Yes | Yes |
Constant term | 0.098 | 0.424*** | 8.293*** | 8.946*** | 8.781*** | 7.578*** |
(0.103) | (0.135) | (0.935) | (1.050) | (1.064) | (0.833) | |
Sample size | 2567 | 2092 | 1531 | 1371 | 1376 | 1953 |
Goodness of fit | 0.081 | 0.159 | 0.206 | 0.213 | 0.214 | 0.176 |
Note: ***, **, * are marked at 1%, 5% and 10% respectively.
children’s health status is relatively low in the case of at least one parent. This result coincides with the theoretical analysis in this paper, and the parents’ entrepreneurship reduces the care time for children, which significantly reduces the health of children.
The estimated results from
1) The gender of children has a significant impact on their health, and boys generally have better health than girls. This with Wang Fang (2012), the research conclusion is consistent, the main reason is the parents have “son preference”, held notion to the same into the quality of the output of different gender children make different evaluation, think that the boy’s input-output ratio is higher, so in the face of the family resource constraints, will first investment, more boys, namely adopt different investment strategies, resulting in the differences between the children’s health in the gender system.
2) Only children are in better health. Obviously, under the circumstance of family resource constraints, as the number of children increases, on the one hand, parents must reduce the consumption of other goods and reduce the quality of family life. On the other hand, it also reduces the investment in existing children, which is not conducive to their physical and mental health.
3) These genetic factors, such as father’s height and mother’s height, also have a significant positive effect on children’s health.
4) The better the family economy, the better the child’s health.
The above OLS estimation results may be biased, and the setting of regression equation may have endogenous problems due to omitted variables and two-way causality. On the one hand, factors that are not observable or difficult to measure in reality (such as family background, ideas, etc.) not only affect children’s health, but also affect their parents’ entrepreneurial choices. In the empirical equation setting, it is difficult to quantify the above factors comprehensively and accurately, so it is possible to have endogenous problems caused by missing variables. Also may in turn affect children’s health, on the other hand, parents, children’s health is good or bad, directly affects the parents care requires money and time to its, will indirectly influence how parents choose what kind of employment, entrepreneurship can improve income workers also there is a dispute, but relative to the work, will be provided to the business work time, place, more flexible. Therefore, there is a sample selection problem caused by two-way causality. Aiming at the problem of possible sample selection, this paper considers three methods: processing effect model, propensity score matching and panel fixed effect model to alleviate potential endogenous problems.
The principle of the processing effect model is similar to the two-stage instrumental variable method, which is better estimated when the internal variables are virtual variables (Maddala, 1983, Greene, 2008). The estimation method of the processing effect model is as follows: the first step is to estimate the processing Equation (4), which is to estimate the parents’ entrepreneurial choice equation and calculate the entrepreneurial probability of parents. The control variables include not only general control variables in Equation (3), but also exogenous exclusion variables Z X . The second step is to estimate the children’s health decision Equation (3), which reflects the average causal effect of parents’ entrepreneurship on children’s health β 1 .
Children’s health decision equation:
Health i t = β 0 + β 1 Parent i t + X i t λ + ε i t (3),
ε i ∼ N ( 0 , δ 2 )
Processing equation:
Parent ( 1 , 0 ) i = α 0 + Z i f + u i (4),
u i ∼ N ( 0 , 1 )
when applying the processing effect model, the selection equation requires the introduction of exogenous typesetting variables, that is, the instrumental variables that only affect the parents’ entrepreneurial choice and do not affect children’s health through other channels. According to the selection criteria of tool variables, this paper selects a city characteristic variable as the tool variable of parents’ entrepreneur identity, namely the entrepreneurial atmosphere. Generally speaking, the better the regional entrepreneurial atmosphere, the stronger the individual’s preference for starting a business, and the stronger entrepreneurial motivation (Guiso et al., 2015). In this paper, the entrepreneur ratio is used to measure the entrepreneurial atmosphere in the province, namely, the proportion of entrepreneurs in each province’s urban sample. In theory, the entrepreneurial atmosphere should be positively correlated with the identity of the entrepreneur, and it is not affected by the health of the children. Therefore, the correlation and exogenous requirements of the selection of instrumental variables should be met. This article also has carried on the inspection, to the effectiveness of the instrumental variable treatment effect in the model, the first phase estimation results of 40.15 F statistics, experience more than the threshold value of 10, the first stage and entrepreneurial atmosphere coefficient is significantly positive.
The propensity score matching is a data processing method based on the observed data analysis variables and can effectively control the sample selection bias (Rosenbaum & Rubin, 1983). Its principle is: the first using Probit model to estimate the entrepreneurial decision-making equations, so as to get individual into treatment group (group entrepreneurs) conditional probability tended to score values, tend to score values can be understood as in the case of a given a series of factors that may affect the individual choice conditional probability of entrepreneurship. This paper to extract the child’s age, children’s gender, whether the one-child, whether the first-born, father mother height, height, father’s education, mother’s education, family size, family economic status, family health accessibility as the explanatory variables into the Probit model of entrepreneurial decision; Then, the treatment group was matched with the control group according to the propensity score value, and the average gap between the two groups was estimated. If the balance test is passed, the average gap reflects the average causal effect of entrepreneurship on income.
From
Although the treatment effect model and tendency to score matching method to alleviate the potential sample selection bias, but using the cross section data cannot
overcome entrepreneurship affect both parents and children’s health is observed interference factors on the estimation results. If these factors do not change over time, we can eliminate them with a fixed effect model. Based on this, we adopt the fixed effect model to eliminate the unobservable individual heterogeneity in the equation of income determination:
Health i t = β i + β 1 Parent i t + X i t λ + Wave t + ε i t (1)
The above analysis does not distinguish between parents’ entrepreneurial state, and there are often differences between father and mother in entrepreneurial motivation and type, so this paper further estimates the Equation (2).
(1) | (2) | (3) | (4) | (5) | |
---|---|---|---|---|---|
Children’s health | |||||
Processing effect model | Propensity score matching | Panel fixed effect model | Panel tool variable fixed effect model | ||
Variable | The second stage | The first stage | |||
Parents’ entrepreneurship | 0.079** | 0.106** | 0.073* | 0.089** | |
(0.029) | (0.048) | (0.041) | (0.032) | ||
Entrepreneurial atmosphere | 0.353*** | ||||
(0.091) | |||||
Control variables | Yes | Yes | Yes | Yes | Yes |
Constant term | 9.157*** | 1.201*** | 3.077** | 6.473 | 2.066 |
(1.038) | (0.429) | (1.412) | (5.721) | (11.619) | |
Sample size | 1371 | 1443 | 1398 | 1371 | 1371 |
Note: ***, **, * are marked at 1%, 5% and 10% respectively. In the regression equation, the control variables that are the same as the benchmark model in
(1) | (2) | (3) | (4) | (5) | (6) | ||
---|---|---|---|---|---|---|---|
Children’s health | |||||||
OLS | Multiple treatment effect | Panel fixed effect model | Panel tool variable fixed effect model | ||||
Father alone | 0.217*** | 0.097** | 0.109** | 0.095*** | 0.087 | 0.113 | |
(0.058) | (0.049) | (0.048) | (0.031) | (0.326) | (0.086) | ||
Motherhood alone | 0.036 | 0.040 | 0.024 | 0.002 | 0.483* | 0.631 | |
(0.123) | (0.123) | (0.099) | (3.507) | (0.250) | (0.498) | ||
Both parents are self-employed | 0.081* | 0.085* | 0.139** | 0.175*** | 0.172* | 0.169 | |
(0.045) | (0.046) | (0.068) | (0.047) | (0.092) | (0.081) | ||
Control variables | Yes | Yes | Yes | Yes | Yes | Yes | |
Constant term | 9.064*** | 8.909*** | 7.609*** | 4.768*** | 9.424*** | 7.827*** | |
(1.321) | (1.237) | (0.868) | (0.339) | (0.838) | (0.919) | ||
Sample size | 1127 | 1138 | 1840 | 1127 | 1127 | 1127 | |
Note: ***, **, * are marked at 1%, 5% and 10% respectively. In the regression equation, the control variables that are the same as the benchmark model in
above results can be found that the three types of parents are mostly negative, but the coefficients of “only father” and “parents are both entrepreneurial” are significantly higher.
The above empirical study found that parents’ entrepreneurship is not good for their children’s health. Entrepreneurship theory in the above theory, this paper puts forward the parents may affect their children’s health through two channels: money and time, in this part of the test of the above two mechanisms respectively.
In order to examine the mechanism, this paper examines the entrepreneurship can raise workers’ income, whether for entrepreneurs to a 0 - 1 variables as explanatory variables, core is with a monthly income of logarithmic as explained variable, entrepreneurship can bring premium income. The descriptive statistics of
By
In order to test this mechanism, this paper investigates whether entrepreneurship can have an impact on child care time. In the CHNS survey, two questions related to parental care for children. One was, “did you take care of your children aged 6 and below last week?”; The other is “how much time did it take to feed, shower, dress, and nurse the children last week? (hour). According to this, two methods are used for testing. Last week, the first way to ever take care of their home and children under 6 years old to be explained variable, using probit model to estimate, last week, the second method to give the child feeding, bathing, dressing, care, etc., spent much time to be explained variables, OLS estimation.
Comprehensive
Variable | The overall | Gainer | Entrepreneurs |
---|---|---|---|
Monthly income | 13,000 | 12,000 | 18,000 |
Work hours per week. | 44.97 | 43.47 | 56.58 |
Working days per week | 5.620 | 5.509 | 6.465 |
Work hours per day. | 7.971 | 7.879 | 8.679 |
Hourly earnings | 83.54 | 79.08 | 123.0 |
(1) | (2) | (3) | (4) | ||
---|---|---|---|---|---|
Monthly income logarithm | |||||
OLS | Processing effect model | Panel fixed effect model | Panel IV fixed effect model | ||
Entrepreneurship | 0.127*** | 0.111** | 0.100* | 0.097* | |
(0.047) | (0.049) | (0.056) | (0.051) | ||
Control variables | Yes | Yes | Yes | Yes | |
Constant term | 7.476*** | 7.452*** | 23.828*** | 23.854*** | |
(0.168) | (0.166) | (7.031) | (7.034) | ||
Sample size | 6270 | 6270 | 6275 | 6275 | |
Note: ***, **, * are marked at 1%, 5% and 10% respectively. Square control variables including age, age, gender, marital status, education level, work intensity, the province virtual and annual virtual variables, limited to the space no longer report, interested please contact the author for the readers.
(1) | (2) | (3) | (4) | ||
---|---|---|---|---|---|
Participation in child care (0 - 1) | |||||
Variable | OLS | Processing effect model | Panel fixed effect model | Panel IV fixed effect model | |
Entrepreneurship | 0.003*** | 0.006** | 0.012** | 0.009 | |
(0.001) | (0.003) | (0.005) | (0.007) | ||
Control variables | Yes | Yes | Yes | Yes | |
Constant term | 1.277*** | 0.787*** | 8.897*** | 8.910*** | |
(0.149) | (0.157) | (0.883) | (0.876) | ||
Sample size | 2620 | 2620 | 2413 | 2413 | |
Note: ***, **, * are marked at 1%, 5% and 10% respectively. Square control variables including age, age, gender, marital status, education level, province virtual and annual virtual variables, limited to the space no longer report, interested please contact the author for the readers.
(1) | (2) | (3) | (4) | ||
---|---|---|---|---|---|
Care time (hours) | |||||
Variable | OLS | Processing effect model | Panel fixed effect model | Panel IV fixed effect model | |
Entrepreneurship | 0.022*** | 0.123*** | 0.141*** | 0.121*** | |
(0.008) | (0.016) | (0.015) | (0.018) | ||
Control variables | Yes | Yes | Yes | Yes | |
Constant term | 3.072*** | 1.919*** | 10.795*** | 14.399*** | |
(0.354) | (0.359) | (0.423) | (0.396) | ||
Sample size | 2521 | 2521 | 2326 | 2326 | |
Note: ***, **, * are marked at 1%, 5% and 10% respectively. Square control variables including age, age, gender, marital status, education level, province virtual and annual virtual variables, limited to the space no longer report, interested please contact the author for the readers.
This article selects the HAZ rating of children (age height ratio) to measure children’s health, to show the long-term health of children, emphatically discusses the parents startup state’s influence on children’s health, the results find that:
1) Startup state does not distinguish between parents, the parents for at least one business case, children’s health is relatively low, OLS estimated result is in agreement with estimated results of the treatment effect model under the consideration of endogeneity and the fixed effect model of panel IV.
2) Considering the father and mother in such aspects as entrepreneurial motivation type is often, there is a difference to further distinguish parents state of entrepreneurship; entrepreneurial state-coefficient estimation results show that the three kinds of parents are mostly negative, but “only father entrepreneurship” and “parents are entrepreneurship” coefficients of two kinds of situations more significantly.
3) System test results show that in addition, 1) money estimation results show that the venture has a significant positive effect on monthly income; 2) time input, entrepreneurially significantly reduces the time for children’s care; OLS estimated result is in agreement with estimated results of the treatment effect model under the consideration of endogeneity and the fixed effect model of panel IV and panel estimation results of the model are consistent; two mechanisms, one positive and one negative, have a mutually offsetting relationship.
4) Other factors affect children’s health. The gender of children has a significant influence on their health, and boys’ health is usually superior to that of girls. Only children are in better health; the genetic factors such as father’s height and mother’s height have significant positive influence on children’s health. The better the family economy, the better the child’s health.
The conclusion of this paper has some policy implications.
The empirical results show that parents’ entrepreneurship can significantly reduce the health level of children. The second mechanism of this paper shows that the reason is that parents’ entrepreneurship can significantly reduce the time of child care. Therefore, in order to alleviate the negative impact of parents’ entrepreneurship on children’s health, this paper believes that it is possible to develop children’s care substitutes, such as professional care institutions, to improve the quality, and to popularize the knowledge of parenting, and improve the efficiency of parents to take care of children.
Wang, H. (2018) The Impact of Parental Entrepreneurship on Children’s Health. Open Journal of Business and Management, 6, 585-605. https://doi.org/10.4236/ojbm.2018.63045