Returns to Education in Off-farm Wage Employment in Rural China

Returns to Education in Off-farm Wage Employment in Rural China

Zeyun Liu, Muyuan Qiu

SEBA, BNU

June 30th, 2011, Budapest

2

1. Introduction

2. Literature Review

3. Methodology

4. Data

5. Results

6. Conclusions

3

1 Introduction

• The human capital theory emphasizes the impact of human capital investment on labor market outcomes. As education is a major way to human capital accumulation, the economic returns to education (RTE) has been one of central topics in labor economics and economics of education.

• In China, a rapidly growing transition economy, education is of primary importance to development.

• As a majority of China’s population resides in rural areas and an even larger percentage of children are educated in rural areas, the effectiveness of rural education is of particular interest to policymakers and researchers.

4

1 Introduction• Per capital annual net income of rural households in China (National Bureau of

Statistics, 2010)

• Wage income has been more and more important for rural residents

• This paper attempts to estimate the impact of education on wage in rural China, based on three waves of rural household survey data during 1995-2007

Yuan Percentage

Year 1990 2000 2009 1990 2000 2009

Total net income 686 2253 5153 100% 100% 100%

Income from wage 139 702 2061 20% 31% 40%

Income from household business 519 1427 2527 76% 63% 49%

Income from properties - 45 167 - 2% 3%

Income form transfers 29 79 398 4% 3% 8%

5

2 Literature Review• Estimating return to education in wage employment are based o

n Mincerian earning equation.

• Previous empirical studies gave estimates of RTE in rural China during 1988 to 2007. The main finding is that RTE in rural China are very low. And we haven’t find a distinct picture of dynamic change in RTE in rural China during the two decades. Contrarily, RTE in urban China has been dramatically increased since late 1980s.

6

2 Literature Review• Methodologically, previous studies have some limitations:

– First, there is no a literatures can describe the long-term trend of RTE in rural China based on comparable data over time.

– Secondly, many researches use unrepresentative samples of entire rural labor force. For examples, some papers only consider workers in one industry or economic sector (e.g. Meng, 1996; Gregory and Meng, 1995; Ho et al., 2002), some others only include migrant workers (e.g. Wang, et al., 2008).

– Thirdly, most studies only use OLS and do not take account of econometric issues such as sample selection and omitted variables.

– Finally, many studies use yearly or monthly wages rather than hourly wages.

7

3 Methodology• We use three waves of cross-sectional household survey data conducted by the

same research team. And in each wave, the sample is from the same nine provinces which are broadly representative of China’s regional development variation. So the results are comparable across time

• Education has effects both on wage and on employment decision. Our sample includes all labor forces in rural areas, and we use two kinds of models to control for sample selectivity bias: – A standard Heckman selection model (Heckman, 1979) in which the

employment decision is binary: a wage earner or not– The expanded selection model (Lee,1983; Durbin & McFadden, 1984;

Dahl, 2002) in which employment decision is a multiple choice: a local wage earner, migrant wage earner, farmer or off-farm self-employee

• We use a proxy for an individual’s ability to control for unobservable heterogeneities

• We emphasize the need to use hourly wages, rather than monthly or yearly wages, in order to capture the effect of education on individual productivity

8

3 Methodology(1) OLS • Base on the same model, RTE are estimated separately by three sub-

samples: all wager earners, local wage earners and migrant wage earners

aboveandeducationondarysecupperHS

educationondaryseclowerJS

uPMARRIEDMALEESQEHSJSlnW

dummiesprovinceP

marriedMARRIED

maleMALE

squarederienceexpESQ

SageerienceexpofyearsE

schoolingofyearsS

wagehourlyW

uPMARRIEDMALEESQESlnW

　

1:

1:

:

1:

1:

:

)6(:

:

:

4321210

43210

9

3 Methodology

(2) Two-step method with a correction for selectivity bias

• Step 2: wage equation

• Mother’s year of schooling is a proxy for an individual’s ability

uPMSMARRIEDMALEESQEHSJSlnW

schoolingofyearssmotherMS

uPMSMARRIEDMALEESQESlnW

54321210

543210

':

10

3 Methodology

(2) Two-step method with a correction for selectivity bias• Step 1: selection equation

– When estimating RTE for all wage earners, we use probit model to estimate selection equation. The outcome variable is a dummy variable indicating whether the individual is a wage earner or not

– When estimating RTE for local wage earners and migrant wage earners, we use multinomial logit model to estimate selection equation. The outcome variable is a categorical variable indicating whether the individual is a local wage earner, migrant wage earner, farmer or off-farm self-employee. The reference category is farmers

– In selection equations, covariates include all variables appear in wage equation. And two instruments, number of labors in the household and land area of the household, are also included as covariates

11

4 Data• Three waves of rural household survey data from CHIP (Chine

se Household Income Project) 1995, 2002, 2007

• In each year, the sample includes rural labors aged 16-60 from the same nine provinces– Developed provinces in eastern China: Zhejiang, Jiangsu, Guangdong,

Hebei– Moderate developed provinces in central China: Hubei, Anhui, Henan– Less developed provinces in western China: Sichuan, Chongqing

• 1995 data do not have information on educational attainment of household head and his/her spouse's parents, so we only use 2002 and 2007 data in selection models.

• But we do use 1995, 2002 and 2007 data in OLS models, trying to describe the dynamic change pattern of RTE during 1995 to 2007

12

4 Data

• Observations

All laborsLocal wage

earners

Migrantwage

earnersFarmers

Off-farmself-employees

OLS models

1995 N of obs. 10748 1016 539 8348 845

% 100% 9% 5% 78% 8%

2002 N of obs. 10075 2171 1489 5360 1055

% 100% 22% 15% 53% 10%

2007 N of obs. 18498 3657 3982 9726 1133

% 100% 20% 22% 53% 6%

Selection Models

2002 N of obs. 6947 1569 900 3723 755

% 100% 23% 13% 54% 11%

2007 N of obs. 15774 3152 3279 8353 990

% 100% 20% 21% 53% 6%

13

5 Results(1) OLS models

Local wage earners Migrant wage earners All wage earners

year 1995 2002 2007 1995 2002 2007 1995 2002 2007

Returns to years of schooling 0.033*** 0.041*** 0.038*** 0.056*** 0.014 0.042*** 0.039*** 0.036*** 0.032***

Returns to educational level (the reference group is illiterates and primary education)

lower-secondary education 0.083 0.025 0.054 0.039 0.011 0.045 0.089 0.033 0.032

upper-secondary education and above 0.201** 0.194*** 0.176*** 0.441*** 0.101 0.236*** 0.318*** 0.192*** 0.157***

N of obs. 1016 2171 3657 539 1489 3982 1555 3660 7639

*: p<0.1; **: p<0.05; ***: p<0.01

All regressions also include experience, experience squared, a gender dummy, a marital status dummy and province dummies

14

5 Results

Preliminary results from OLS estimation

• Returns to years of schooling for all rural wage earners declined from 3.9% in 1995 to 3.2% in 2007

• Returns to years of schooling for local wage earners are more stable than those for migrant wage earners

• Compared with those without post-primary education experience, rural wage earners receiving upper-secondary education have a wage premium, but not for wager earners only complete lower-secondary education

15

5 Results(2) Selection models

• Selection equations: years of schooling

*: p<0.1; **: p<0.05; ***: p<0.01

All regressions also include province dummies

Regressions in column (1)-(4) use a multinomial logit model, and regressions in column (5)-(6) use a probit model


2002 2007 2002 2007 2002 2007

(1) (2) (3) (4) (5) (6)

years of schooling 0.088*** 0.116*** -0.005 -0.008 0.024*** 0.020***

experience 0.036** 0.003 0.026 0.003 -0.001 -0.022***

experience squared/100 -0.114*** -0.068*** -0.233*** -0.251*** -0.211*** -0.149***

male 1.623*** 0.874*** 1.429*** 1.230*** 0.755*** 0.521***

married -0.139 -0.245** -0.811*** -0.624*** -0.358*** -0.310***

mother’s year of schooling

0.035** 0.017** 0.001 0.034*** 0.017** 0.001

number of labors -0.212*** -0.066*** 0.137*** 0.305*** -0.042*** 0.079***

land area (mu) -0.033*** -0.123*** -0.011 -0.038*** -0.005* -0.033***

N 6947 15744 6947 15744 6947 15744

16

5 Results(2) Selection models• Selection equations: educational level

*: p<0.1; **: p<0.05; ***: p<0.01

All regressions also include province dummies

Regressions in column (1)-(4) use a multinomial logit model, and regressions in column (5)-(6) use a probit model


2002 2007 2002 2007 2002 2007

(1) (2) (3) (4) (5) (6)

lower-secondary education

0.092 0.246*** 0.111 -0.071 0.056 0.049

upper-secondary education and above

0.488*** 0.775*** -0.021 -0.115 0.128** 0.148***

experience 0.033** -0.002 0.028 0.004 -0.002 -0.022***

squared experience/100 -0.120*** -0.067*** -0.232*** -0.253*** -0.209*** -0.118***

male 1.674*** 0.891*** 1.427*** 1.237*** 0.769*** 0.522***

married -0.118 -0.217** -0.828*** -0.627*** -0.351*** -0.305***


0.033** 0.017** 0.001 0.034*** 0.017** 0.001

number of labors -0.210*** -0.068*** 0.139*** 0.306*** -0.042*** 0.079***

land area (mu) -0.034*** -0.125*** -0.011 -0.038*** -0.005** -0.034***

N 6947 15744 6947 15744 6947 15744

17

5 ResultsDetermination of employment choice

• Rural labor with higher educational attainment tends to be a local wage earner, but not to be a migrant wage earner

• Rural labor with more labors in his/her household is more likely to be a migrant wage earner, but less likely to be a local wage earner

• Rural labor with less land has higher probability to be a wage earner, either to be a local wage earner or a migrant wage earner

18

5 Results(2) Selection models• Wage equations: years of schooling

*: p<0.1; **: p<0.05; ***: p<0.01All regressions also include province dummiesRegressions in column (1)-(4) use a multinomial logit model to compute the inverse Mills ratio, and regressions in column (5)-(6) use a

probit model to compute the inverse Mills ratio


2002 2007 2002 2007 2002 2007

(1) (2) (3) (4) (5) (6)

years of schooling 0.035*** 0.020*** 0.015 0.045*** 0.032*** 0.026***

experience 0.024** 0.011** 0.008 0.027*** 0.026*** 0.021***

experience squared /100 -0.030* -0.026** -0.025 -0.052*** 0.011 -0.040***

male 0.057 0.272*** 0.203*** 0.141*** 0.226*** 0.099***

married 0.020 0.048 0.107 0.032 0.295*** 0.097***


0.017* 0.000 0.010 0.001 0.018** -0.001

inverse Mill’s ratio 0.830** 0.687*** -0.941** 0.017 -0.064** -0.331***

N 1569 3152 900 3279 2469 6431

19

5 Results(2) Selection models• Wage equations: educational level

*: p<0.1; **: p<0.05; ***: p<0.01All regressions also include province dummiesRegressions in column (1)-(4) use a multinomial logit model to compute the inverse Mills ratio, and regressions in column (5)-(6) use

a probit model to compute the inverse Mills ratio


2002 2007 2002 2007 2002 2007

(1) (2) (3) (4) (5) (6)

lower-secondary education

0.047 0.018 0.002 0.049 0.019 0.006

upper-secondary education and above

0.163*** 0.056 0.095 0.237*** 0.181*** 0.107***

experience 0.020** 0.007 0.006 0.023*** 0.022*** 0.019***

squared experience/100 -0.027 -0.022** -0.024 -0.051*** 0.010 -0.048***

male 0.060 0.276*** 0.208** 0.159*** 0.209*** 0.107***

married 0.024 0.070 0.118 0.053 0.309*** 0.115***


0.017* 0.000 0.010 0.002 0.017** -0.001

inverse Mill’s ratio 0.903** 0.747*** -0.935** -0.011 -0.068*** -0.326***

N 1569 3152 900 3279 2469 6431

20

5 ResultsReturns to education after controlling for sample selectivity bias

• From 2002 to 2007, returns to years of schooling for all rural wage earners and local wage earners declined, but those for migrant wage earners increased

• Compared with those without post-primary education experience, only people receiving upper-secondary education have a wage premium

• Education has a effect on selection into the off-farm wage employment, but only help rural people find jobs inside their residential county

21

5 Results(3) Returns to years of schooling based on different samples• CHIP rural household survey covers different provinces in the three years: 1995

survey covers 19 provinces, 2002 survey covers 22 provinces survey, and 2007 survey covers 9 provinces. How about if we do not restrict our samples to the same 9 provinces?

　 Local wage earners Migrant wage earners All wage earners

　 1995 2002 2007 1995 2002 2007 1995 2002 2007

OLS models 　　　

Use sample from thesame 9 provinces

0.033*** 0.041*** 0.038*** 0.056*** 0.014 0.042*** 0.039*** 0.036*** 0.032***

Use sample from allsurveyed provinces

0.034*** 0.036*** 0.038*** 0.073*** 0.013* 0.042*** 0.053*** 0.032*** 0.032***

Selection models　　　

Use sample from thesame 9 provinces

0.035*** 0.020*** 0.015 0.045*** 0.032*** 0.026***

Use sample from allsurveyed provinces

0.031*** 0.020*** 0.008 0.045*** 0.036*** 0.026***

*: p<0.1; ***:p<0.01

22

6 Conclusions(1) Returns to education in wage employment in rural China are

positive and statistically significant, but relatively low (2-5%) compared with those in urban China (>10%) and haven’t increased during 1995 to 2007

• Why?

– Rural labor markets in China are under-developed

– Rural wage earners primarily engage in temporary/seasonal jobs or low-end occupations such as blue-collar workers. Education is less valued in such jobs.

– The quality of rural education is low.

23

6 Conclusions(2) After controlling for ability bias and sample selectivity bias,

return to education in 2007 is much higher for migrant wage earners than that for local wage earners.

• Why?– Migrant wage earners mostly work in urban cities where labor markets

are more market-oriented

(3) In addition to raising hourly wages, education helps rural people access to wage employment. But only people who receive upper-secondary schooling or above can benefit from educational experience

24

6 Conclusions(4) Methodologically

• Nationally representative survey data which are comparable over time

• Controlling for ability bias and sample selectivity bias

• Using hourly wages, rather than daily or monthly wages, to control for the amount a person chooses to work, as highly educated people tend to choose to work less

Documents

Returns to Education in Off-farm Wage Employment in Rural China