Examining the Impact of University Supply in China During the Cultural Revolution

Michael Z. Small

I. Introduction

During the “Cultural Revolution” in China, which took place between 1966 and 1976,

many universities were closed due to policy changes made by the leader of the revolution, Mao

Zedong. However beginning in 1970 a campaign led by Premier Zhou Enlai led to the reopening

of some of these universities. Thus between 1966 and 1969, university closings were at their

highest level. The effect of supply of higher education holds important policy issues for

university construction and destruction. Using data gathered in 2002 for urban households, this

paper asks: Do people change their decisions to receive upper level education based on the

availability, or supply, of universities? Studies have been done measuring the effect of the entire

revolutionary period from 1966-1976 on investment in education and human capital, for example

Giles, Park and Wang (2007), however this paper specifies a certain period within the revolution

to highlight the effect of availability of universities while controlling for general policy changes

which took place under the reign of Mao Zedong.

Relevant Literature

This study could hold important policy implications on the topic of university

construction and destruction as well as the topic of educational mandates. I measure the effect of

a decreased availability of universities, thus the results should show what effect a decrease in

supply of universities will do to the population in terms of higher level educational attainment.

The question of whether the policy changes of the Cultural Revolution have had an effect

on educational attainment is one that has been addressed in various literatures. However, most

of these papers focus on the Cultural Revolution as an entirety (from 1966-1976) and include all

education levels whereas this paper specifies 1966-1969 as the years of the most intense effects

of the revolution and focuses on university education. For example Han, Suen, and Zhang

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(2008) find that investment in continuous education increases for those who were exposed to this

shock of less university availability during the revolution. Considering the nature of this study,

that is, a natural experiment which uses data from a later year than the period of interest, the

measure of human capital investment throughout life is relevant. Additionally, they find that

estimates of educational loss caused by the Cultural Revolution which ignore subsequent re-

investments would not accurately measure the true losses inflicted by the event. This paper also

holds the view of Han, Suen, and Zhang, as the loss in education strictly at the time of the shock

would not accurately show results that can have uses in policy decisions regarding university

supply and educational attainment in the long run.

Duflo (2000) studies the effect of school construction in Indonesia on education due to a

program implemented by the government which constructed over 61,000 primary schools

throughout the country. Although the Duflo study is based on primary school construction, it

still holds important characteristics that carry over to this study. Duflo finds that the construction

of these schools had a positive effect on education for those ages 2-6, increasing education

received by .12 to .19 more years of education for each school constructed per 1,000 children in

their region of birth. Thus Duflo proves that with increased availability of schools, educational

attainment should, in effect, increase as well. In comparison with the Han, Suen, and Zhang

paper, Duflo proves that the immediate or short run effects of university construction are positive

in correlation with educational attainment, whereas the former shows that such a shock in the

opposite direction, a decrease in university supply, also leads to higher educational attainment

but in the long run. If it were possible, my study would have been complete had it been able to

measure the short run effects of the revolution which according to Duflo’s study should be

negative, as well as the long run effects. Fortunately, this has in fact been measured in other

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literature such as that done by Meng and Gregory (2007). Their study measured the immediate

effects of the Cultural Revolution on educational attainment. They found that the revolution led

to a decrease of approximately 7% of university education for those of the university age.

Between this study and that of Han, Suen, and Zhang we see that during the Cultural Revolution

those of the university age did have a decreased likeliness of attending university, but made up

for it in the future. This is consistent with the results found in my study.

The results of this study show that the decrease in university availability leads to higher

educational attainment in the long run, however not at a statistically significant level from the

regression. 1.92 percentage points more of those who were of the university age (18-22) from

1966-1969 had completed their university education than those who were of the university age

from 1970-1973. As mentioned, this result is consistent with the study done by Han, Suen, and

Zhang which used the Cultural Revolution as a whole for their study on educational attainment.

However, these authors also state that “When using a sample from later years (i.e., after 1992),

the estimated educational loss might be mixed up with subsequent re-schooling that offset the

direct educational loss to some extent.” This is due to the economic and political changes that

have taken place since the 1990’s. In turn, the results found in this paper may be muted due to

recent reforms in China.

The paper precedes as follows, in Section I the background and grounds for the relevance

of the study were explained. Section II examines the methodology behind the study. Section III

discusses the data used and its application to this study. Section IV covers this paper’s empirical

strategy, including the manipulation of the data to properly examine a treatment and control

group to enable me to find a relevant result to the research question. Section IV will decipher the

results found and possible explanations for why these results turned out as they did. In addition,

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this section will cover how applicable the results of the study will be on the general population

and important problems with the results that must be acknowledged. I will find that results are

not as I expected however there is evidence that the results are similar to those of other studies

which have covered the subject of university availability during the Cultural Revolution and

future investment in education. Section VI will conclude the paper and examine the results in an

intuitive sense.

II. Specific Research Question and Methodology

Using age as a proxy for the exposure to the closing of universities across China from

1966-1969 this paper examines the effect on levels of higher education by measuring the

percentage of the sample which attended university in their lifetime. I hypothesize that the effect

of this policy change will be negative on the percentage of those who have received university

education. The population of interest includes those people that were exposed to the Cultural

Revolution during two different time spans. The fact that both were exposed to the revolution,

but one at a more extreme point in the revolution during which there were more university

closings, allows the population to experience the same general political background while

experiencing different measures of education policy and availability of attending university. As

mentioned and as will be explained later in the paper, the switch in political value of higher

education with the introduction of Zhou Enlai is the cutoff for these ages.

The setting is appropriate for measuring the relationship between university supply and

future education level. First, the introduction of new education policy in 1966 by Mao allows for

an exogenous change in education supply that is not correlated with expected demands for higher

education. Second, only a fraction of the sample has migrated to urban areas where the sample is

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questioned. This is due to the hukou system which puts restraints on a family’s mobility within

the country of China as is mentioned by Albert Park (2008).

I find the results to show that those who were exposed to a more extreme setting of

university closings actually have a higher proportion of people with university education than

those that were of the university age after the policy changed brought about by Zhou Enlai. This

may seem counter-intuitive considering that the lower availability of universities at the common

age of education of 18-22 should seem to correlate with lower levels of upper level education. If

we draw a simple supply and demand curve putting Quantity of University education on the X-

axis and Price on the Y-axis, we see that a shift back in the supply curve should lead to less

quantity of education received. As discussed this is true for the short run, however in the long

run the demand for education more than makes up for this decrease (See Figure 4).

III. Data

The dataset used is from the “Chinese Household Income Project,” a study done by the

Inter-University Consortium for Political and Social Research, more commonly referred to as

ICPSR. Of a set of ten datasets in the study, the first will be used, which contains data about

urban individuals. Although the description of the data does not include a definition that

separates urban and rural, the density requirement in China to be considered an urban area is

about 1,500 people per square kilometer. This particular dataset includes 151 variables and

20,632 cases. The study was interview based in which the head of the household was

interviewed regarding demographic information as well as employment and financial

information.

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This sample group is representative of a population that would be desired if more

information was available, with a few shortcomings. The sample group from this data will be

those aged 49-51 and those aged 53-55. Using these two ages does not allow us to see the

immediate effects of a lapse in supply of universities because time has gone on since the

common ages for university (ages 18-22). Education levels will likely increase for the whole

population because of the time experienced since the revolution. A problem that comes from

choosing these ages is that one can attend university throughout life, not simply during the

common ages. Thus, what will be measured are the long run effects on those who were of the

university age during these periods. Nonetheless, these ages will be used because they should

offer the possibility for the densest group that should have attended university during the

Cultural Revolution. In regards to the entire population of interest, referring to Table 1, we see

that the total number in the sample is 2,479. The sample also has an approximately equal

proportion of males and females. The average yearly income of the population is 10,446.21

Yuan. Using 8.28 Yuan to dollars, which is approximately the 2002 exchange rate, this equates

to 1,261.61 US dollars per year. The variable of education level in the dataset allows me to pick

out university education as compared to other levels of education. This particular dataset was

chosen because it contains a large enough population that were of the typical university age to

help my study be more causal, and also because it contains variables that can measure university

education rather than simply years of education.

While the sample is representative of a desired population, more problems do arise when

covering the question at hand. One such problem is that all those who can attend university do

not necessarily go to university. This fact will bias the estimates downwards because it will

show lower levels of education for those which I examine in the sample. Specifically, if an

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individual falls within one of the correct age groups describing the population, and this

individual did not attend university although they could have, this will bring the proportion of

those that attended university estimate downwards.

Another problem in validity that may arise from this type of study is differential attrition.

Although the difference in age is fairly small between the two groups, it may be that because one

group is older, those who did not attend university are those who died, thus biasing the results for

the group upwards. In other words, those who did not attend university are those who died

because those who did not attend university are more likely to have a lower paying job and thus

are might not able to afford as good of health care or attain as high of standard of living. This

seems to be especially prevalent for this data set because we see that there are 401 less

observations for those of ages 53-55 compared to those ages 49-51.

Furthermore compliance may be an issue that needs to be addressed. In this case

compliance is an issue because it could be the case that people went to universities when I test

for them to be “less open” or more closed. It is possible that they went abroad to study, or even

that they studied in university later in life. If this is the case it will bias my results downwards

because it will cause a convergence in the difference between the treatment and control groups in

terms of university education.

An ideal dataset would include university enrollment rates for a random set of Chinese

individuals ages 18-22 during the two specified times of measurement. This would allow me to

measure the immediate effects of the closing of universities rather than the effect of this lapse in

availability on future status of the individuals. While this study is not a random study and the

dataset is strictly for urban individuals, evidence which will be discussed later in the paper will

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show that the individuals from both measured time periods are similar for most characteristics

except for their exposure to the treatment.

IV. Empirical Strategy

The identification strategy in this paper is an intention to treat estimate. In order to

examine the causal effect of the university closings, I exploit age within the population of

interest to measure exposure to university closings and evaluate the percentage of the population

which received university education. For this research question, the population of interest are

those of university age (18-22) who are likely affected most by the policy changes instituted by

Mao Zedong of China in 1966 during the “Cultural Revolution.” In the treatment group are

those that were of university age from 1966-1969, during the fiercest times of the revolution

before more natural levels of university availability were restored. In the data I characterize

those of university age in the treatment as those who were 53-55 when they were interviewed in

2002. These age groups were originally chosen because these are the ages that were affected for

at least three years of their university age during the time period of 1966-1969. Figure 2 shows

exactly how these ages were chosen by their amount of exposure to the policy change. This

formatting also allows for the two groups of interest to be mutually exclusive from each other.

In other words, the two groups do not overlap. In the control group are those who were of

university age from 1970-1973 when a new power of Premier Zhou Enlai entered the scene and

began the reopening of universities that had been closed down previously. This change back to

what will be considered as “normal” university availability in this paper was due to reapplying

the importance of education on the youth, which had been lost in the previous time period. In the

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data the control group is characterized as those ages 49-51 for the same reasons as why the

treatment group was chosen (see Figure 2).

This type of study is a “natural experiment” such as that of Douglas Almond (2006), “Is

the 1918 Influenza Pandemic Over? Long-term Effects of In Utero Influenza Exposure in the

Post-1940 U.S. Population.” I study the effect of the intense years of university closings

between the years of 1966-1969 on levels of university education for those of the typical

university age at the time, relying on the shock of the decrease in university supply which makes

this age group “as if random.” As mentioned before, this shock ended in 1970 when policy

changes were made, allowing for universities to begin reopening. From this I compare the

average percent of university education between those exposed to the university closings and

those after 1969.

Due to the fact that these ages are not randomly assigned, there are potential problems

regarding the internal validity of the study. Before discussing these problems we can look at

Table 1 we see that both groups are of a satisfactory size, with the total treatment population

totaling at 1,039 and the total control population totaling at 1,440. Also, both groups are similar

along baseline statistics such as Health which is measured on a scale ranging from Worst to Very

Good, and Income which is measured in Yuan in 2001. These are both important to be balanced

because those who are healthier are probably more likely to be able to enroll in university.

Similarly, those with higher income levels will be more able to afford university and thus would

skew the education level upward if one group had a significantly higher income. Log income is

also balanced between the groups. Finally the proportion of those married and those of the Han

nation are included in Table 1. It is important to measure for the married variable because

differences in marital status may lead to different lifestyles. For example, those who are single

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may be more independent and thus value a higher education more in order to aid in their upward

mobility in society. Han was measured to check whether historical backgrounds may be

different between the treatment and control groups. Both of these variables show no difference

between the treatment and control groups.

Aside from what has already been discussed, some variables are unbalanced in Table 1.

Ages 53-55 show significantly higher levels of proportions which are affiliated with the

Communist party and which are male. In addition this group shows higher levels of those that

found employment through the government. These differences take away from the identifying

assumption that the treatment and control groups are “as if random” because the differences may

explain part of the change in the output variable in the regression, or university education. This

can happen in such a natural study as this and these variables will be controlled for and discussed

along with the results later in this section.

The original regression, which would be a naïve estimate of the effect of the university

closings from 1966 to 1969 on the percentage of those who received university education, would

read as follows:

University Education = B0 + B1*(Treatment) + ε

Where B0 represents the percentage of ages 49-51 which received university education and B1

represents the difference from this constant for ages 53-55 which were those of the ages 18-22

between 1966-1969 who would have typically received three years of university education

within those years.

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To successfully separate these age groups in order to distinguish a treatment and control

group I created a dummy variable with 0 being the value of the control group, or those ages 49-

51, and 1 being the value of the treatment group, or those ages 53-55. To estimate the percent of

those who received university education I created a dummy variable with 0 as the value for those

who did not receive university education, or those with high school education or below. I used 1

as the value for those who attained university education or above levels such as graduate school.

This percentage will eventually aid in showing the difference between those who were of

university age and exposed to the treatment from 1966-1969 and those who are considered

controls who were of the allocated university age from 1970-1973. I also created a number of

other dummy variables to allow for a measure of proportions in the population, treatment, and

control groups.

First, I created a dummy variable for gender named Female which has 0 as the value for

females and 1 as the value for males. This will measure the percent of females in the sample

within the treatment and control groups. In addition, I believe that political party affiliation may

bias the final results of this study because those of the Communist party may have a higher

availability of universities due to the communist roots of Chinese society. Thus a dummy

variable was created with 1 being the value of those that consider themselves members of the

communist party, and 0 as those who do not. This should sufficiently give a proportion of those

in the communist party and those not and will allow for controlling to be possible in the

regression. These two variables previously described are those which show differences in Table

1. It shows that those ages 53-55 have 52% males in their cohort while those ages 49-51 have

49% males in their cohort. This difference of 3% is statistically significant at the 10% level.

The variable which measures the percent of each group that is affiliated with the Communist

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party shows a difference of 5%. Those ages 53-55 have 39% who are affiliated and those ages

49-51 have 34% who are affiliated. This difference could be due to the exposure to a more

extreme time of the Cultural Revolution. Other dummy variables similar to this include the

proportion of the population who found their current job through the government. All three of

these variables show differences.

The regression including controls for those variables which show statistically significant

differences in Table 1 will appear as so:

University Education = B0 + B1*(Treatment) + B2*(Female) + B3*(PolParty) + ɛ

This controls for the variables which we found as unbalanced in Table 1, and will account for

these differences between the treatment and control groups in order to make a more accurate

prediction of the causal effects of the decrease in university availability on the percentage of

university education in the sample. As mentioned these variables are dummy variables created

due to the fact that they are categorical. These two variables are important to control for because

females only account for one-third of the university population in China as reported by the

sociologist Jerry A. Jacobs (1996), which implies that males are more likely to attend university

in China. It is necessary to control for affiliation with a political party, as measured by the

variable PolParty, because with China being a communist nation, it is likely that those who

affiliate with the communist party are more likely to go to college because they will be favored

in society. The variable GovJob, which measures the percentage of those who received a job

through the government, is left out of this regression. It would have been helpful to control for

this mainly because of the difference it shows between the treatment and control groups,

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however there were approximately 1000 less respondents to this question. Because of this, it

will not be included in the regression which controls for those variables that differ in the Table 1

because this drop in sample size may be due to a particular group of people that do not wish to

answer the question, and thus can ambiguously bias the results. In the next section it will be seen

that once these variables are added to the naïve regression, the Treatment variable’s coefficient

decreases from its level in the original naïve regression.

Lastly, two regressions are added to further check for the robustness of the results of the

effect of a decrease in supply of universities.

University Education = B0 + B1(Treatment) + B2(Married) + B3(Han) + B4(lnincome) + ɛ

This regression includes only those variables which are balanced in Table 1, but may have an

effect on the Treatment. These variables will be explained in the following section. The second

regression, which is not shown, is a combination of all variables, whether balanced or

unbalanced in Table 1. When including all of them we see the effect that these other variables

have on our Treatment variable, not their influence on university education.

V. Results

Column 1 of Table 2 conveys that the naïve estimate results in a positive, yet statistically

insignificant effect of approximately 1.92 percentage points on the difference between the

control group of ages 49-51 and the treatment group of ages 53-55. This means that those of the

tested university age from 1966-1969 actually received more university education on average

than those of the university age from 1970-1973 when education level was recorded in 2002.

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The constant of .161 explains that 16.1% of those aged 49-51 in 2002 had received university

education and this is 1.92 percentage points below the level of university education attained by

those aged 53-55 in 2002. These results are shown in Figure 1. From the graph we can visually

see the positive trend between being exposed to the closing of universities, rather than the

negative trend which was hypothesized earlier. The increase of 1.92 percentage points clearly

stands out in the graph however the results are statistically insignificant. Also worth noting in

the results is the R2 value of .001 which shows that this difference in exposure to the university

closings as proxied by the age cohorts does not explain a large amount of the variation in the

percentage of university education achieved.

In Column 2 new variables are controlled for which are: Married, and Han. These are

balanced in Table 1, but may affect the treatment and thus are included in the regression. The

Married variable shows that those that are married have achieved approximately 4 percentage

points less university education than those who are divorced, single, or widowed with a standard

error of approximately .045. This is the only variable measured for in the regression which

shows a negative trend. However this trend makes sense due to what was previously stated in

this paper. Those who are not married may have more time to go and become more educated,

whereas those who are married need to work and support their family.

In Column 3, the variable lnincome is the exclusive variable shown due to its difference

in measurement with the other dummy variables shown in the regression. From this coefficient it

is seen that for a percentage change in income university education increases by 18.7% which is

significant at the 1% level. Also, the treatment coefficient does not change much,

dropping .0172. This explains that while controlling for income variations, those ages 53-55 in

the sample still show increases of about 1.72 percentage points in university education when

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compared to the control group ages 49-51. The result is still insignificant however, with a P-

value of .255.

In Column 4 of Table 2, the naïve estimate is examined with controls that showed as

unbalanced in Table 1. As discussed, these controls are for gender and whether the individual is

affiliated with the communist party or not. Before examining the separate coefficients of theses

controls what must be addressed is that the treatment effect on the proportion of university

education for those ages 53-55 decreases from .0192 to .0077 in Column 4. This shows that once

controlling for the variables which showed differences between the groups found in Table 1, the

treatment effect becomes smaller. Thus when controlling for these two variables, the treatment

group shows .77 percentage points more university education than those in the control group.

While the treatment coefficient is nearly depleted, we see that the two variables other than the

treatment show significant positive correlation with the amount of university education attained.

For example, those who consider themselves part of the Communist party have 23.8 percentage

points higher amount of university education than those who do not. The coefficient is

significant at the 1% level. This could be the result of favoritism towards those who pledge

allegiance to the Communist party or that those in the Communist party are those who have more

power in the country and are able to attend university through connections. Also in Column 2

we see that the Female variable shows a positive correlation with amount of university education

achieved. Males are approximately 7.6 percentage points more likely to have attended university

than females. This difference will be accounted for in the next section using an interaction term

between the treatment and gender. It is important to note that these two variables are showing

correlation and not causation. The test is run for the Treatment variable and these variables

cannot necessarily be deemed as “causing” an increase in university education from this study.

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In continuation, the fact that the treatment effect decreased so much when control

variables were included will be important for the result’s application to policy. What is also

important to note is that the R2 value in Column 4 is much higher than that in Column 1. There

is a jump in this value from .001 to .114. A possible reason for this is that we are including other

variables that also explain differences in the proportion of those who attended university and thus

are able to explain more of the variation in the Y variable of percentage of those who attended

university.

In Column 5 all controls are included in the regression. The Treatment coefficient in

Column 5 is similar to that of Column 4 at approximately .007. Again, as with Column 4, the

small level of this coefficient and the statistical insignificance hinders the hypothesis that there is

a causal difference of university education from exposure to the more extreme time of university

closings. The R2 value is again higher than in the original regression with a value of .169.

Measurement Issues

Some potential issues of the results displayed in Table 2 still remain, especially

considering that no significant results were found for the treatment variable. As mentioned

before, some sociologists contend that females are much less likely to receive upper level

education in China. To test whether this exogenous shock had a different effect on females than

males, separate regressions were run for the two subgroups. Due to the difference in percentages

of males and females which attend university in China, I tested for the difference-in-difference of

the treatment effect between the two subgroups. I hypothesized that the treatment would have a

negative effect on the percentage of females who attend university because this group has

already seemed to have been underrepresented in universities in China. Thus it might be

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possible that due to the cultural nature of China, females with university education are not as

valued as males with university education and a supply shock in supply of university education

would push more females out of higher education. To make up for this negative correlation that

was expected from females, I hypothesized that males would show a positive correlation,

considering what is known from examining the results of Table 2. This difference-in-differences

effect is measured using this equation:

University Education = B0 + B1(Treatment) + B2(Female) + B3(Treatment*Female) + ɛ

As with the naïve regression, the Treatment variable is a dummy variable with a value of 0 for

those of university age from 1970-1973 and a value of 1 for those of university age from 1966-

1969. The Female variable is also a dummy variable with a value of 0 for females and a value of

1 for males. In addition this equation includes the interaction term Treatment*Female which

will determine the differential effects of the exposure to the treatment on university education

between males and females. Consistent with the hypothesis, if exposed to the treatment, females

were approximately 1.6 percentage points less likely to have attended university by the time the

data was gathered in 2002. Meanwhile, males were 4.5 percentage points more likely to have

attended university which is significant at the 10% level. In Table 3 we see that the hypothesis

that more females would be pushed out of higher education is upheld. The difference-in-

difference estimate is 6.1 percentage points. This states that between males and females exposed

the treatment and control groups, 6.1 percentage points more males attended university in their

lifetime. This statistic is significant at the 5% level. The significance and application of these

results will be discussed in the following section.

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Application of Results and Problems

This study measures the effect of the supply of education rather than the demand for

education and thus could be used in the policy making of construction or destruction of

universities and its effect on education levels of the public.

As noted, from Table 2 we indeed see a positive correlation between those exposed to the

treatment and university education. However when we control for such variables such as gender,

the effect is approximately half of what it had been. What can be reasoned from this is that the

fact that those who were of the university age between 1966-1969 were exposed to a harsher

time of university closings and lack of educational importance does not have much causation for

the difference in university education levels in the sample. Further proving this point is the fact

that the results for the Treatment were insignificant across the board. Thus, application of these

results to policy would be difficult.

The results from Table 3 which show that males achieved more higher level education

than females when exposed to the exogenous shock of the closing of universities tells us more in

terms of the exposure to the treatment and results than does Table 2. By differentiating between

males and females, results are found that show different effects on males and females in terms of

direction. Females are approximately 1.6 percentage points less likely to attend university while

males are approximately 4.5 percentage points more likely to attend university. Considering

policy implications, we could reason that a decrease in university supply may push females away

from university, perhaps because the tradeoff between school and staying at home changes when

the opportunity costs of going to university increases. On the other hand, such a decrease in

university supply may lead to an influx of more males in university, thus causing a separation

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between males and females and educational advancement. This could be used for such policy

topics as inequality for example. That is, further study would be needed, but a policy change that

leads to a decrease in university availability may widen the gap of educational attainment

between males and females.

Referring back to Table 2, though this study does display a weak increase in education

level between those affected by the policy change; it would be difficult to generalize these results

for other countries due to the extreme political conditions at the time of the university closings.

That is to say, a country that is not in the middle of revolution would not have the same setting

and thus the results may vary from those found in this study. In addition, cultural topics may

affect the ability to extrapolate the results. The value of education between countries or towns

may vary from one another. It would be interesting to see if the results were similar in a

capitalist society rather than one undergoing socialist revolution.

Also, the sample population must be examined. Another fact of the study that may

hinder its ability to apply to a general population is that this study uses strictly urban data. It is

proven that the urban and rural differences in China are especially exaggerated compared to

other countries, such as what is shown by a study by Albert Park which examines these specific

inequalities. Large differences in income, occupation, and health care availability may have

pushed my results for levels of education upwards. A reason that these differences are more

exaggerated in China is because of the Hukou system that was briefly mentioned earlier in the

paper. This system makes it difficult for the Chinese to move throughout the country, especially

from rural areas to urban areas. So those who were originally in the urban areas, where the

standard of living is higher, have experienced that higher standard for quite some time. So

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adding rural data to this study may bias the results downwards, but also allow for the study able

to be generalized.

VI. Conclusion

Using the closing of a number of universities in the nation of China by Mao Zedong

during the Cultural Revolution, specifically from 1966-1969, this paper looked at the effect on

upper-level education. By making use of the fact that some of the Chinese population were more

exposed to the closing of universities at this time because they were 18-22, or what is considered

the typical age to go to university, an exogenous difference is identified in those who were of this

university age during a strong anti-education period in which the closing of universities took

place and those who were of the university age when education returned to a more “normal”

value in society. Using data gathered in 2002 this paper found that those who were more

exposed to the period of university closings actually received 1.92 percentage points higher

upper-level education than those who were of the university age during a more “normal” period,

although not at a significant level. Also, when controlling for variables that also may affect

levels of education, such as whether an individual is male or female, or whether an individual is

married, the effect of this exposure decreases. The finding of a positive correlation is somewhat

counterintuitive in that one would expect that a decrease in supply of universities for those who

typically would be attending university should correlate with lower levels of education.

However, as mentioned in the literature section, studies have found that this fact may lead to

higher human capital investment, such as education, in the future to attempt to make up for this

lapse in educational availability even though the lapse might still lead to decreased quantities of

education in the short run.

20

What has been found in this study is that those who were exposed to a more dramatic

change in government policy, specifically with the closing of more universities, did not receive a

statistically significant amount of more or less university education in their lifetime. Secondly,

the study found that with this type of shock, at least within a strongly patriarchal society, a

decrease in university supply shock will lead to less female higher education and more male

higher education. In conclusion, this paper provides weak evidence to the fact that a shock of

decreased supply of universities leads to more educational advancement in the future, however it

is a topic that should be explored further and in situations that do not involve a “revolution”

necessarily.

21

References

Almond, Douglas. "Is the 1918 Influenza Pandemic Over? Long-term Effect of In Utero Influenza Exposure in the Post-1940 U.S. Population." Journal of Political Economy, 2006, Vol. 114, No. 4, pp. 672-712

Duflo, Esther. “Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment,” The American Economic Review, Vol. 91, No. 4 (Sep., 2001), pp. 795-813.

Han, Jun, Suen, Wing, Zhang, Junsen. “Picking Up the Losses: The Impact of the Cultural Revolution on Human Capital Re-investment in Urban China,” China Economics Summer Institute 2010 Working Papers, 2010.

Jacobs, Jerry A., “Gender Inequality and Higher Education,” Annual Review of Sociology, 22:153-85, 1996.

Meng, Xin & Gregory, R G, 2002. "The Impact of Interrupted Education on Subsequent Educational Attainment: A Cost of the Chinese Cultural Revolution," Economic Development and Cultural Change, University of Chicago Press, vol. 50(4), pages 935-59, July.

Park, Albert. “Rural-Urban Inequality in China,” in Shahid Yusuf and Karen Nabeshima, eds. China Urbanizes: Consequences, Strategies, and Policies (Washington, D.C.: The World Bank), 2008.

Shi, Li “Chinese Household Income Project, 2002,” Data Sharing For Demographic Research, Inter-university Consortium for Political and Social Research, 2002.

22

Tables and Figures

VariableAll 53-55 49-51 Difference

Mean (Standard Deviation)

Mean (Standard Deviation)

Mean (Standard Deviation)

Treatment-Control

Age 51.63(2.12)

53.93(.82)

49.98(.81)

3.96***

Gender 0.50(0.500)

.52(.50)

.49 (.50)

0.03*

2001 Income (Yuan) 10,446.21(7,644.54)

10,392.35 (6667.46)

10,484.55(8,272.08)

-92.20

Log Income 9.04(.68)

9.06(.69)

9.03(.67)

0.03

Health 0.88(.33)

0.87(.34)

0.88(.33)

-0.01

Proportion Given Employment Through Government 0.72(.45)

.75(.43)

0.70(.46)

0.05**

Proportion Affiliated With Communist Party 0.36(.48 )

.39(.49)

.34(.47)

0.05***

Married 0.97(.17)

0.97(.17)

0.97(.18) 0

Han 0.96(.19)

0.96(.19)

0.96(.20) 0

Number of People2,479 1,039 1,440

Note: All variables are dummy variables excludingthe Age, Log Income, and Income variables

*** p<0.01, ** p<0.05, * p<0.1

Table 1: Descriptive Statistics by Age Group

23

VARIABLES1

(Naïve Estimate)2

(Balance Controls)3

(lnincome)4

(Unbalanced Controls)5

(All Controls)

Ages 53-55 0.0192 0.0191 0.0172 0.00773 0.00727[0.0153] [0.0153] [0.0151] [0.0146] [0.0149]

Married -0.0397 -0.0908**[0.0449] [0.0440]

Han 0.00198 0.00243[0.0393] [0.0382]

lnincome 0.187*** 0.147***[0.0110] [0.0115]

Female 0.0761*** 0.0379**[0.0146] [0.0152]

PolParty 0.238*** 0.186***[0.0152] [0.0158]

Constant 0.161*** 0.198*** -1.525*** 0.0423*** -1.162***[0.00989] [0.0581] [0.1000] [0.0124] [0.115]

Observations 2,474 2,474 2,342 2,419 2,293

R-squared 0.001 0.001 0.111 0.114 0.169

Standard errors in brackets

*** p<0.01, ** p<0.05, * p<0.1

Note: Variables GovJob and Health ommited due to smaller sample size and bias

University Education

TABLE 2: Effects of Decrease in University Supply on Higher Education

24

VARIABLES 1 2 3 4

Ages 53-55 -0.0158 -0.0174 -0.0174 -0.0163[0.0215] [0.0215] [0.0215] [0.0209]

Female 0.0911*** 0.0931*** 0.0931*** 0.0596***[0.0195] [0.0196] [0.0196] [0.0189]

Interaction (Treatment*Female) 0.0611** 0.0635** 0.0635** 0.0465[0.0302] [0.0302] [0.0302] [0.0292]

Married -0.0817* -0.0817* -0.0741*[0.0446] [0.0446] [0.0438]

Han 0.00209 -0.00283[0.0387] [0.0376]

PolParty 0.237***[0.0152]

Constant 0.117*** 0.195*** 0.193*** 0.126**[0.0137] [0.0449] [0.0580] [0.0572]

Observations 2,474 2,474 2,474 2,419R-squared 0.026 0.028 0.028 0.116Standard errors in brackets*** p<0.01, ** p<0.05, * p<0.1

University Education

Table 3: Difference-in-Difference Estimations Between Males and Females

25

Figure 1: Main Results from Naïve Estimate

Note: On X-Axis 0 represents ages 49-51. 1 represents ages 53-55.

Figure 2: Ages of Control Group When Data Gathered in 2002

Treatment Years1970 1971 1972 1973

Age

(Age

Inte

rvie

wed

)

18 (50) 18 (49) 18 (48) 18 (47)

19 (51) 19 (50) 19 (49) 19 (48)

20 (52) 20 (51) 20 (50) 20 (49)

21 (53) 21 (52) 21 (51) 21 (50)

26

0.0

5.1

.15

.2U

nive

rsity

Edu

catio

n

0 1

Results

Figure 3: Ages of Treatment Group When Data Gathered in 2002

Control years 1966 1967 1968 1969

Age

(Age

Inte

rvie

wed

)18 (54) 18 (53) 18 (52) 18 (51)

19 (55) 19 (54) 19 (53) 19 (52)

20 (56) 20 (55) 20 (54) 20 (53)

21 (57) 21 (56) 21 (55) 21 (54)

Figure 4: Supply and Demand of Education: Short Run and Long Run Effects

27

D1

S1

Quantity of Education

Price

S2

Q2 Q1 Q3

D2

1

2