1. Examining the Impact of University Supply in China During the
Cultural Revolution
Michael Z. Small
2. 1
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
3. 2
(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
4. 3
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,
5. 4
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 ResearchQuestion 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
6. 5
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.
7. 6
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
8. 7
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
9. 8
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
10. 9
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
11. 10
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.
12. 11
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
13. 12
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,
14. 13
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.
15. 14
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
16. 15
still show increases of about 1.72 percentage points in university education when 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.
17. 16
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
18. 17
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.
19. 18
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
20. 19
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
21. 20
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.
22. 21
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.
23. 22
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.
24. 23
Tables and Figures
Variable
All 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 People
2,479 1,039 1,440
Note: All variables are dummy variables excluding
the Age, Log Income, and Income variables
*** p<0.01, ** p<0.05, * p<0.1
Table 1: Descriptive Statistics by Age Group
25. 24
VARIABLES
1
(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
