China’s College Expansion and the Timing of College-to-Work Transition: A Natural Experiment

Abstract

This paper examines the causal effect of China’s 1999 accelerated expansion of higher education on the timing of finding a first skilled job among college graduates. To test hypotheses derived from applying relevant theories to the China case, we use a natural experiment. The analysis exploits the unique education and work history data of a nationally representative survey and estimates a causal inference model. We find that the 1999 expansion causes a delay in the landing of a skilled job among graduates from technical colleges, while graduates from 4-year colleges are not affected in job acquisition. We also find that family origins and individual social positions remain significant in the selectivity of entering college before and after education expansion acceleration. These findings shed new light on the universal phenomenon of early adulthood and social inequality in China.

Introduction

China’s college-educated millennials face a complex array of opportunities and vulnerabilities after the 1999 acceleration of higher education expansion which reshaped the transition to work for college graduates in the 2000s compared to those in the 1990s. We conceive the macro-factor-driven difference between two successive cohorts’ experience as a natural experiment of the 1999 college expansion, an exogenous shock, to China’s young adult population. We evaluate the causal effect of this college expansion on job acquisition of college graduates. This study may shed light on our understanding of universal early adulthood in newly developed countries in Asia and around the world (Yeung and Alipio 2013).

The multifold increase in college enrollment rates in the 2000s offered opportunities for Chinese youth to accumulate human capital. Simultaneously, the over-heated acceleration of higher education expansion in a short period caused a series of repercussions. First, demand for skilled labor in the then labor-intensive, export-oriented economy did not grow at the same speed as the supply of skilled labor. Second, universities were not fully ready to serve a massive student body, contributing to graduates of lower quality. Third, university curricula and training programs did not prepare college graduates to execute work responsibilities with additional training. These consequences spurned an increase in unemployment rates among college graduates (Bai 2006; China Statistics Bureau 1990–2015).

The unemployment problem among college graduates may be viewed as the time needed to find an appropriate job after graduation. After all, the college education rate in China’s labor force is low, at 17.4% (China Statistics Bureau 1990–2015). Yet a sudden increase in supply may slow down success in job searching. Rather than asking whether college graduates are able to find skilled jobs, we ask how long it takes college graduates to find a skilled job. Answering the question could fill in the gaps from past research that has not examined the timing question. We exploit the unique education and work history data of a nationally representative survey (China Labor-force Dynamics Survey) to estimate a causal inference model for the effect of the 1999 expansion on the timing of the college-to-work transition.

College Expansion and Macro Conditions for the College-to-Work Transition

We consider China’s college expansion to be a plausible explanation for the emerging “early adulthood” in the course of China’s transforming economy. Furstenberg (2010) identifies factors that cause later transitions to adulthood, including the expansion of education, consistent with China’s situation in the early 2000s. China’s 1999 college expansion targeted both 4-year universities and 3-year technical colleges nationwide. While mass college education mechanically adds 3–4 years to early adulthood, determining whether the expansion of education causes a delay in the acquisition of skilled jobs requires rigorous testing which is the primary objective of the current paper.

The event of interest is mass college expansion with its inception in 1999, as well as the resultant increase in the skilled labor supply at college graduation in the initial five years from 2003 to 2007. For comparison, we examine the skilled labor supply at college graduation from 1995 to 1999−the last five years before mass college education.² Below we introduce the 1999 expansion and describe macro-level conditions that were stable over the two periods and societal events that may or may not have changed the condition between the two periods.

The 1999 Higher Education Expansion.

In 1999, the Chinese government began to accelerate the pace of higher education expansion. As a result, the number of new college admissions in 2008 reached 6.08 million, about six-fold of the 1.08 million in 1998; this expansion claimed to be the fastest in the world (China Ministry of Education 1999–2015). A closer look into the admission periods corresponding to the two periods of college graduation of 1995–1999 and 2003–2007 shows that the college admissions increased slowly (6.2, 7.5, 9.2, 9.0, 9.3) from 1991 to 1995 and rapidly (16.0, 22.1, 26.8, 32.1, and 38.2) from 1999 to 2003. Thus, we take the 1999 higher education expansion as a macro-factor driving the over-supply of skilled labor in the second period.

The Higher Education Curricula.

China’s higher education system in the early expansion years lacked diversity. The concept of a job market did not have a foothold in colleges and universities because college graduates were assigned to state-sector jobs before 1990s (Maurer-Fazio 1999). During the expansion, a shortage of professors, outdated curricula, and inefficient practice programs remained or worsened (He and Mai 2015). The lack of competiveness, applied abilities, specialty and flexibility remained the same over both periods.

Labor-intensive, Export-oriented Economy.

The continuous labor-intensive, export-oriented economy in the 1990s and 2000s did not demand a rapid growth of skilled workers. The structure of China’s industries created a limited capacity to absorb a rapid increase of university graduates. It was projected that over 1 million graduates would be unlikely to find employment (Qian 2000). The official employment rates in 2003–2004 were 84% for 4-year college graduates, and 61% for those with less than 4 years of college education (China Ministry of Education 1990–2015).

In summary, the period of 1995–1999 before the college expansion and the period of 2003–2007 since the first class of graduates under this expansion shared stable macro conditions of rigid higher education curricula on the supply side and labor-intensive economy on the demand side. The two periods differ markedly in the skilled worker supply due to the accelerated expansion of higher education in 1999. Using a natural experiment framework, we define higher education expansion as the treatment and the timing of transition from college to the first skilled job as the outcome. The cohort of youth who graduated from college during the second period was exposed to the treatment and the cohort of youth who graduated from college during the first period was not exposed to the treatment. The two cohorts of graduates were not randomly assigned but were not self-selected. In other words, the timing of landing a skilled job after college graduation should be independent of the treatment assignment after conditioning on individual characteristics. We hypothesize that the higher education expansion would increase the waiting time for college graduates under the accelerated expansion to find a skilled job (Hypothesis 1).

To make this natural experiment credible, we need to control for individual characteristics each with a similar marginal distribution such that the two cohorts are comparable. In the next section, we identify structure-driven micro attributes that influence the individual’s chances of attending college.

Micro Attributes in the Sorting into College

We applied theories of educational stratification and inequality and cumulative advantage to the China case and identify structure-driven micro attributes in sorting youth into college-going in China. In China, family origins are first and foremost encapsulated in the household registration (hukou) status—rural vs. urban—acquired at birth that allocates differential education, employment, and other life chances (Hao et al. 2014; Wu and Treiman 2004). In addition, parental education indicates class position, and parental party membership indicates political capital (Li 2003). In the context of accelerated higher education expansion from 1999, youth born to parents under the restrictive one-child policy that dramatically reduced the number of siblings and increased familial resources allocation to children (Ye and Wu 2011) is considered.

In China, rural and urban secondary education systems are segregated, and rural education is inferior to urban education. At the college entrance exams, rural students are less competitive than urban students are, cumulatively from the inferior educational quality throughout primary and secondary school. In addition, educational quality differs by regions varying in levels of development. Children living in the East, Central, and West regions experience different educational quality throughout childhood. The Han ethnic majority has a long history of having better educational opportunities than ethnic minorities. Thus, rural hukou status at birth, Central and West regions of residence during childhood, and being a non-Han minority mean persistently lower educational quality that lowers one’s probability of college attendance.

To understand how persistent lower educational equality affects the types of college attended, the cumulative advantage thesis reviewed in DiPrete and Eirich (2006) offers further rationales. The cumulative nature of educational attainment (i.e., a higher-level attainment is built on a lower-level attainment) suggests that the initial advantage of the privileged social groups at lower levels of education will persist throughout educational transitions. The mechanisms include the compound returns to earlier human capital stock and the access to increasingly greater resources brought about by earlier higher achievement, including social capital and network connections. In China, the cumulative advantage of privileged students (e.g., urban hukou at birth, higher parental SES, parental party membership, fewer or no siblings, Han majority, living in East region) leads to greater likelihood of 4-year college admission, whereas disadvantaged students end up in 3-year technical colleges.

In addition to family origins, individual attributes also matter in making the two cohorts comparable. In China gender and party membership stand out as important individual characteristics. A trend of women’s advancement in educational attainment, common in developed countries, has also occurred in China (Hannum and Xie 1994). We expect that being male would be less important in the probability of attending college for the post-cohort than the pre-cohort. The political capital accrued to party members applies to both parental and own party membership (Li 2003) and remains important after the acceleration of education expansion.

These identified micro attributes for making our natural experiment credible provides additional insight into our primary question regarding the accelerated expansion of higher education and its effect on waiting time before landing a skilled job after college graduation. The “battle” in college entrance exam yield a trichotomous outcome: 4-year college admission, 3-year college admission, and no college-going. In China, technical college education receives less investment and development opportunities than 4-year college education and the access to technical versus 4-year college education differs by structural exclusion (Tam and Jiang 2015). Combined with the sorting mechanism discussed above, we anticipate that the student body of the two types of colleges differs in family origins and individual characteristics. Both the lower educational quality in technical colleges and the lower social status of technical college students imply that the prospect of in-time transition from college to skilled employment may diverge by the type of college. We hypothesize that the causal effect of the 1999 accelerated expansion of higher education is concentrated in graduates from technical colleges (Hypothesis 2).

Data, Samples, and Variables

Data Source.

Our data source is the China Labor-force Dynamics Survey (CLDS), a nationally representative, longitudinal survey of labor force in China (excluding Hainan, Tibet, Hong Kong and Macao). Every two years since 2012, the CLDS follows individuals, families and communities and freshens the sample to update the cross-sectional representation of the population. The sampling design is multistage, stratified, and proportional to size with rotation groups. This study draws data from the 2014 CLDS containing four rotation groups, including 23,594 individuals aged 16–64 or older if working.

The CLDS provides unique data needed for the current study on both education history and work history.³ The large sample size also guarantees a sufficient size of the two college graduate cohorts defined for the current study. We use retrospective data on education and work history rather than the panel data because the panel data were collected later than the periods of interest.⁴

Analytic Samples.

This study entails two analytic samples from the 2014 data. Using the auxiliary sample, we examine family origins and individual characteristics in affecting the probability of college going before and after the expansion. We consider a typical college graduation age range of 21–23 for 4-year college graduates and 20–22 for 3-year college graduates (with one-year margin of error). The auxiliary sample includes 6,246 individuals in total: the pre-cohort sample includes individuals born in 1972–1979 who could potentially graduate in 1995–1999 (n=3,363 with 421 college graduates); the post-cohort sample includes individuals born in 1980–1987 who could potentially graduate from college in 2003–2007 (n=2,783 with 626 college graduates).

The primary sample is a subset of the auxiliary sample, including only college graduates, with a purpose of estimating the causal effect of the 1999 expansion. The treatment group in the natural experiment includes 626 individuals who graduated from college during the period of 2003–2007; the control group of the natural experiment includes 421 individuals who graduated from college during the period of 1995–1999.

Multiple Imputation.

Missing data is ubiquitous in survey data. While the absence of all substantive variables in our two types of samples is moderate (18% and 17%, respectively), we use a multiple imputation method to maintain the population multivariate structure of the data and of the analytic sample size. Under the missing-at-random assumption conditional on the variables in the analysis model, we apply the imputation method with chained equations, including not only substantive variables but also variables describing the missing mechanism (such as interviewer quality and respondent quality) to reduce the dependence on only substantive variables in imputation (Rubin 1987). We create 20 complete datasets for the auxiliary and primary sample respectively.⁵ The results reported in the paper are a summary of parameters obtained from 20 completes using Rubin’s rule (taking the mean point estimates as the point estimates and using the squared-root of the total variance to combine the between and within variance as the standard error), except those specifically mentioned.

Dependent variables.

To model college attendance, we define the dependent variable as college attendance in full-time 4-year or 3-year colleges leading to a degree conferred during 1995–1999 or 2003–2007. Individuals with postgraduate degrees are included; individuals who obtained continuous education degrees (while holding full-time employment) are not considered in the same population, given the different curricula of continuous education and their complicated work histories.

Two dependent variables of our primary analysis of the causal effect of the 1999 accelerated expansion of higher education are finding a skilled job within half a year of graduation and within 1.5 years of graduation, conditional on not yet finding a skilled job. Data required to construct these two dependent variables include birth year, educational attainment, and both education history and work history. First, we treat the reported birth year and educational attainment as precise information. Second, the graduation year is based on the self-reported graduation year within the typical 3-year range of graduation, and, if missing, a randomly chosen year from the 3-year range of graduation. Third, the year starting a skilled job comes from work history data. The CLDS 2014 collected the total number of jobs with information on the starting and ending timing and occupation of three specific jobs: the current or recent job, the first job if having more than one job, and the job right before the current/recent job if having more than two jobs.⁶ The majority (93%) of individuals in our primary sample (aged 27–42 in 2014) had no more than three jobs by 2014 with consistent work histories. Fourth, skilled job acquisition for 4-year college graduates takes either 1 if the occupation code indicates managerial, technical, and administrative jobs in office or 0 otherwise. The skilled jobs for 3-year college graduates include more administrative occupations in security and communication. Fifth, using the notion of hazard (i.e., the conditional probability of finding a skilled job given that the event has not occurred), we consider skilled job acquisition during the graduation year⁷ and the year following the graduation year.

Treatment.

The treatment of our natural experiment is the 1999 accelerated expansion of higher education. The treatment assignment is a dichotomous variable: 1 indicates those graduated from college in 2003–2007 (post-cohort herein) and 0 indicates those graduated from college in 1995–1999 (pre-cohort herein).

Control variables.

The control variables are of two sets. The first set is to model the probability of college education. Sorting into college depends on family origins, variables capturing childhood experience, and individual characteristics. Family origins are measured with hukou status at birth (1 denotes rural hukou, 0 denotes urban hukou), parental education (the highest years of parents’ schooling), number of siblings (top numbers are coded at 10), and parental party membership (1 denotes either parent is a party member, 0 denotes no party membership of parents). Childhood experience is captured by regions of residence at age 14 (Central and West with East as the reference) and Han majority (1 yes, 0 no). Individual characteristics include gender (1 male, 0 female) and party membership (1 yes, 0 no). We also include a measure of conscientiousness, a tendency to show self-discipline, act dutifully, and aspirations for achievement, at age 14, a composite based on three items (Cronbach’s alpha 0.82). The second set is to make the treatment and control groups of the natural experiment comparable. We add on the first set the rank of college (3 national, 2 provincial, and 1 local), which is available for college graduates only.

Analytic Strategies

Exploiting natural experiments in causal inference with observational data has gained increasing popularity. Natural experiments refer to shocks that create exogenous variations in the creation of a phenomenon of interest (Angrist et al. 1996). In our context, the natural experiment is the government-directed, top-down command of accelerated expansion of higher education nation-wide that increased admissions to college multifold. Knight et al. (2017) argue that the increased supply of college graduates may be endogenous to past and expected future demand, and yet, the suddenness, unexpectedness, speed and size of the supply shock means that it might be viewed as creating a natural experiment for analyzing short-term labor market consequences.

We place the higher education expansion in a counterfactual framework: the treatment is the accelerated expansion of higher education that has increased the number of admissions and graduates since 1999. The outcome of interest is the timing of landing a skilled job. With constructed event history data, we investigate the conditional probability (hazard) of skilled employment within 0.5 and 1.5 years after graduation, respectively, conditional on the risk set that treats post college study as a competing risk.

In our study of college graduates, the threat of selection bias is of specific concern. We consider how the two sources of selection bias are eliminated with observed covariates. The first source is sample selection of those receiving college education among age-appropriate individuals. From Heckman’s correction for sample selection model (Heckman et al. 1998) we create “inverse Mill’s ratio”, a variable in a nonlinear function of the observed and unobserved variables in the sample selection model. We estimate the inverse Mill’s ratio for the pre-cohort and post-cohort separately to take into account different higher education policies and practices in the pre- and post-expansion eras in addition to individual characteristics. The estimated inverse Mill’s ratios for individuals will enter the causal model for the accelerated expansion so as to reduce the potential bias in estimates due to sample selection of college graduates.

The second source of selection refers to the process of assigning individuals to the treatment versus control groups, i.e., the selection into the pre-cohort vs. the post-cohort. One approach to remedy the threat of this selection bias is propensity score matching (Rosenbaum and Rubin 1983) and an extension using inverse probability weighted regression adjustment (Woodridge 2007, 2010). These methods use many characteristics of individuals and fully specify statistical models with those measures.

The third source of bias from unobserved variables is still probable, even with the extensive use of statistical controls. The extensive array of control variables including family origin, past experience, and current characteristics may reduce this probability.

We perform the inverse probability weighted logistic regression adjustment for the hazard of finding a skilled job within the graduation year or by the end of the year after the graduation year for the primary sample and then separately for Bachelor’s degrees and technical degrees with the primary sample. Keeping all and only observations at risk of finding a skilled job at t = 0.5 or t ≤ 1.5 (collapsed to one record per person), we estimate logistic regression for the hazard of finding a skilled job:

$$\text{log}\left(\frac{h_{it}}{1-h_{it}}\right)=\beta D_i+\delta \prime z_i$$ (1)

To test Hypothesis 1, we estimate the average treatment effect for the treated controlling for covariates,$\widehat{\beta }$, for the primary sample. If this estimate is negative and significant, we support Hypothesis 1.

To test Hypothesis 2, we obtain the same estimate of the average treatment effects for the treated separately for the Bachelor’s degrees and the technical degrees. If the average treatment effect for the treated is concentrated in the technical degree, then we support Hypothesis 2.

Results

Sample Descriptive Distributions.

Table 1 shows descriptive distributions for the auxiliary sample by cohorts and those for the primary sample of college graduates by cohorts and types of degrees. Examining the cohort differences in the auxiliary sample, we see that, compared to the pre-cohort, the post-cohort is characterized by a larger proportion of college graduates, higher parental education, lower parental party membership, a smaller number of siblings, and more from the East region. These trends are consistent with China’s development in general, as well as the expansion of higher education, in particular, over the turn of the new millennium.

Table 1.

Sample Descriptive Distribution

Variable	Auxiliary sample		Primary sample
			Total		Bachelor’s		Technical
	pre-cohort	post-cohort	pre-cohort	post-cohort	pre-cohort	post-cohort	pre-cohort	post-cohort
College education	0.13	0.23	--	--	--	--	--	--
Skilled job within graduation year	--	--	0.30	0.25	0.35	0.30	0.25	0.20
Skilled job within 1.5 years	--	--	0.37	0.34	0.42	0.38	0.30	0.29
Technical degree	--	--	0.45	0.45	--	--	--	--
Rural hukou at birth	0.82	0.84	0.44	0.56	0.43	0.46	0.45	0.67
Parental years of schooling	5.93	7.52	9.47	10.00	9.93	10.59	8.90	9.27
Parental party membership	0.15	0.12	0.34	0.24	0.33	0.28	0.35	0.19
Number of siblings	2.73	1.88	1.80	1.03	1.69	0.89	1.93	1.20
East region at age 14	0.37	0.43	0.39	0.49	0.39	0.42	0.38	0.58
Central region age 14	0.29	0.29	0.29	0.30	0.31	0.35	0.27	0.23
West region at age 14	0.33	0.28	0.32	0.21	0.30	0.23	0.35	0.19
Han majority	0.88	0.86	0.91	0.95	0.92	0.94	0.89	0.95
Male	0.47	0.45	0.55	0.49	0.57	0.50	0.52	0.48
Own party membership	0.08	0.07	0.36	0.24	0.44	0.35	0.26	0.10
Conscientious scale at 14	−0.01	−0.04	0.22	0.17	0.29	0.19	0.14	0.14
High college rank	--	--	0.23	0.20	0.33	0.30	0.09	0.07
Mid college rank	--	--	0.40	0.39	0.43	0.50	0.35	0.25
Low college rank	--	--	0.38	0.42	0.23	0.20	0.56	0.68
Inverse Mill’s ratio	--	--	1.11	0.91	1.00	0.74	1.25	1.13
n	3,363	2,783	421	626	232	343	189	282

NOTE: Estimates are based on multiply imputed complete data sets using Rubin’s rule.

The next panel of Table 1 shows the distribution of variables among college graduates in the primary sample. For the total sample of college graduates (columns 3 and 4), the post-cohort shows a lower probability of finding a skilled job within half a year or 1.5 years following graduation, consistent with our expectation. The proportion of those with a technical degree remains similar. The primary sample and the auxiliary sample show a similar trend in the cohort comparison in parental party membership, number of siblings, and proportion of East region residence at age 14. Notable differences for the post-cohort of the college graduate sample include more graduates with at-birth rural hukou, more from the Han majority, fewer men, and lower party membership. These post-cohort features for college graduates reflect the changing selection to college education after the accelerated expansion of higher education, to be examined later. For unique covariates of college graduates, the greater proportion of low-ranked local universities suggests where the expansion was mostly located.

When we disaggregate further by types of degrees (Bachelor’s and technical in Columns 4–8), the pre- and post-cohort comparisons are more telling. In particular, the differential distributions for at-birth rural hukou and low college rank reveal further information on where and for whom the accelerated expansion of higher education was concentrated. While the percentage of at-birth rural-hukou increases by 3 percentage points among those with Bachelor’s degrees, the increase is 21 percentage points among those with technical degrees—almost all students with origin of rural hukou went to 3-year colleges. Furthermore, the proportion in low-rank colleges actually decreases from .23 to .20 for the Bachelor’s degree holders; in contrast, the number increases from .56 to .68 for the technical degree holders. These stark contrasts by hukou and college rank strongly suggest that the causal effect of education expansion may diverge between the two types of degrees.

Selection into College Education.

The purpose of the analysis of selection into college education, separately for the pre-cohort and the post-cohort, serves two purposes. First, the selection model creates inverse Mill’s ratios to be included in the causal analysis to correct for the sample selection bias. Second, the estimation helps check the validity of the control variables for meeting the conditional independence assumption of the treatment assignment and the outcome. See the estimates from the probit model of college education in Appendix Table 1. To gauge the relative importance of the control variables (see Appendix Figure 1), we find the importance of (1) at-birth rural hukou, number of siblings, parental education, and parental party membership; (2) for the post cohort exclusively, residing in Central region at age 14 and Han majority; and (3) own party membership and conscientiousness. It is interesting to note that gender no longer plays a role in determining college education for the post-cohort.

Inverse Probability Weighted Distribution.

The inverse probability weighting method starts by matching propensity scores, followed by obtaining the inverse probability to weight the data such that the assignment to the two cohorts is independent of the outcome. To show the inverse probability weighting, we randomly chose one version of the 20 complete datasets from multiple-imputation. We estimate a logit model for the assignment to the pre-cohort and post-cohort as a function of the control variables discussed in the analytic strategy section. The empirical distribution suggests that Bachelor’s degree holders and technical degree holders are of two different subpopulations and should be analyzed separately.

Appendix Figure 2 shows the propensity score distribution of the post-cohort (cohort = 1) superimposed on that of the pre-cohort (cohort=0) for the technical degree and Bachelor’s degree. The two plots are similar in that the masses fall in the overlapped region (0.05–0.85 for the technical and 0.1–0.95 for the Bachelor’s). The inverse probability weighting is to make the distributions of all covariates similar between the two cohorts (balanced). In the after-weighting overlap plot for the number of siblings in Appendix Figure 3, we find a nice overlap for the technical degree but the result appears not as satisfactory for the Bachelor’s degree.

Covariate balance statistics include the difference in a standardized covariate mean before and after inverse probability weighting and the variance ratio. The differences in the standardized means are reduced to 0, and the variance ratios are converged to 1 for both the technical degree and the Bachelor’s degree, as shown in Table 2. Overall, the covariates in the two cohorts are balanced after weighting, as the over-identification test does not reject the null that all covariates are balanced between the two cohorts after inverse probability weighting for both types of degrees.

Table 2.

Covariate Balance Statistics of the Inverse Probability Weighting

Variable	Technical Degree				Bachelor’s Degree
	Standardized-differences		Variance-ratio		Standardized-differences		Variance-ratio
	Raw	Weighted	Raw	Weighted	Raw	Weighted	Raw	Weighted
Rural hukou at birth	0.45	−0.05	0.89	1.04	0.05	−0.06	1.01	0.99
Parental years of schooling	0.10	−0.01	0.68	0.78	0.20	−0.05	0.67	0.82
Parental party membership	−0.37	0.00	0.68	1.00	−0.10	−0.03	0.92	0.97
Number of siblings	−0.53	0.04	0.52	1.15	−0.60	0.01	0.56	1.17
Central region age 14	−0.11	0.04	0.88	1.05	0.09	0.06	1.06	1.05
West region at age 14	−0.36	0.05	0.68	1.08	−0.15	−0.07	0.85	0.93
Han majority	0.23	−0.02	0.48	1.08	0.08	0.06	0.76	0.81
Male	−0.09	0.04	1.00	1.00	−0.14	0.05	1.02	1.00
Own party membership	−0.40	0.01	0.49	1.01	−0.19	0.04	0.92	1.03
Conscientious scale at 14	0.02	0.08	1.13	0.78	−0.15	−0.01	0.99	0.86
High college rank	−0.11	−0.04	0.73	0.88	−0.07	0.04	0.94	1.04
Mid college rank	−0.18	0.09	0.85	1.12	0.12	−0.06	1.02	1.00

NOTE: Analysis is based on one of the 20 multiply imputed complete datasets.

Results of Causal Analysis.

Table 3 compiles the estimates of inverse probability weighted logistic regression adjustment for the causal effect of the 1999 accelerated expansion of higher education on two timings of finding a skilled job within half a year or 1.5 years of college graduation. The top panel records estimates for technical degrees, while the bottom panel displays estimates for Bachelor’s degrees. Focusing on the average treatment effect for the treated (ATT), substantial negative coefficients are found for the technical degree, which is significant at the 0.05 level for the half a year timing and at the 0.10 level for the 1.5 years timing, controlling for the additional influence of control variables (hence regression adjustment). The magnitude of the negative effect is substantial with the odds ratio at e^−0.17 = 0.844 and e^−0.142 = 0.868. That is, the odds of finding a skilled job for the post-cohort individuals with a technical degree is 84% of the odds for the pre-cohort counterparts within half a year of graduation. The odds for post-cohort individuals only improve a little at 87% within 1.5 years of graduation. This result supports Hypothesis 1 in that the causal effect is negative. In contrast, the ATT for the Bachelor’s degree subsample is close to zero, statistically non-significant, and positive. This striking contrast in causal effect provides evidence in support of Hypothesis 2, which states that the causal effect diverges between the two types of degrees. This is the core finding of our casual analysis.

Table 3.

Estimates of Inverse Probability Weighted Logistic Regression Adjustment: Two Timings of Finding a Skilled Job

Variable	Within 0.5 years of graduation		Within 1.5 years of graduation
	coef.	se	coef.	se
Technical degree
ATT	−0.170	0.086*	−0.142	0.084^
Potential outcome	0.370	0.082	0.432	0.079
D=0
Rural hukou at birth	1.561	0.866^	1.682	0.771*
Number of siblings	−0.049	0.215	0.012	0.197
Male	−0.881	0.574	−0.502	0.522
Own party membership	−0.814	0.964	−0.661	0.851
High college rank	0.350	1.010	−0.072	0.921
Mid college rank	−0.268	0.614	−0.150	0.601
Inverse Mill’s ratio	−1.719	1.071	−1.538	0.902^
Constant	0.879	1.143	0.639	1.006
D=1
Rural hukou at birth	1.475	0.527*	1.541	0.493*
Number of siblings	−0.202	0.205	−0.174	0.165
Male	−0.203	0.361	−0.345	0.312
Own party membership	−0.176	0.765	−0.476	0.716
High college rank	0.915	0.671	1.118	0.564*
Mid college rank	0.966	0.396*	0.616	0.389
Inverse Mill’s ratio	−0.681	0.590	−0.668	0.528
Constant	−1.679	0.557*	−1.067	0.516
Treatment model (post-cohort=1)
Rural hukou at birth	1.083	0.237**	1.083	0.237**
Parental years of schooling	0.044	0.034	0.044	0.034
Parental party membership	−0.707	0.256*	−0.707	0.256*
Number of siblings	−0.412	0.091**	−0.412	0.091**
Central region age 14	−0.226	0.268	−0.226	0.268
West region at age 14	−0.422	0.273	−0.422	0.273
Han majority	−0.111	0.411	−0.111	0.411
Male	−0.317	0.213	−0.317	0.213
Own party membership	−0.739	0.289*	−0.739	0.289*
Conscientious scale at 14	−0.035	0.145	−0.035	0.145
High college rank	−0.217	0.405	−0.217	0.405
Mid college rank	−0.035	0.255	−0.035	0.255
Constant	0.774	0.568	0.774	0.568
Bachelor’s degree
ATT	0.004	0.056	0.047	0.057
Potential outcome	0.295	0.051	0.330	0.051
D=0
Rural hukou at birth	−0.282	0.556	−0.590	0.544
Number of siblings	0.243	0.171	0.180	0.166
Male	−0.455	0.350	−0.255	0.345
Own party membership	0.729	0.678	0.868	0.629
High college rank	0.438	0.513	0.596	0.460
Mid college rank	0.457	0.456	0.345	0.431
Inverse Mill’s ratio	0.237	0.817	0.746	0.723
Constant	−1.572	0.940	−1.712	0.859
D=1
Rural hukou at birth	0.072	0.373	0.384	0.359
Number of siblings	0.108	0.121	0.023	0.119
Male	−0.179	0.243	−0.383	0.231
Own party membership	0.244	0.423	−0.181	0.402
High college rank	0.286	0.367	0.128	0.342
Mid college rank	0.253	0.333	0.283	0.306
Inverse Mill’s ratio	0.285	0.482	−0.025	0.462
Constant	−1.416	0.476*	−0.617	0.439
Treatment model (post-cohort=1)
Rural hukou at birth	0.698	0.208*	0.698	0.208*
Parental years of schooling	0.042	0.030	0.042	0.030
Parental party membership	−0.283	0.214	−0.283	0.214
Number of siblings	−0.524	0.084**	−0.524	0.084**
Central region age 14	0.209	0.212	0.209	0.212
West region at age 14	−0.039	0.241	−0.039	0.241
Han majority	−0.210	0.409	−0.210	0.409
Male	−0.336	0.185^	−0.336	0.185^
Own party membership	−0.206	0.187	−0.206	0.187
Conscientious scale at 14	−0.150	0.135	−0.150	0.135
High college rank	−0.116	0.255	−0.116	0.255
Mid college rank	0.187	0.239	0.187	0.239
Constant	0.768	0.576	0.768	0.576

NOTE: Estimates are based on multiply imputed complete data sets using Rubin’s rule.

^**p < .001

^*p < .05

^{^}p < .10

After balancing the covariates through inverse probability weighting, are there any additional effects on the timing of skilled job acquisition in the logistic regression adjustment? Because the covariates are balanced, multicollinearity and non-convergence would occur if all covariates were to be included in the regression adjustment. Substantively, we keep the social position variables (at-birth rural hukou, party membership, male gender, college rank), inverse Mill’s ratio for correcting the sample selection bias, and number of siblings, which captures a potential shift in worldviews and work values among individuals, many of whom are the only child. We examine the results for the technical and Bachelor’s degree subsamples in turn. First, at-birth rural hukou stands out with its significant positive effect on the timing of finding a skilled job. In contrast to the strong negative effect of this same variable in sorting youth into college education (see Appendix Table 1), the positive effect of at-birth rural hukou on finding a skilled job suggests that rural-hukou college students after successfully graduating from college are more competitive in the job market. Second, sporadic positive coefficients are found for the high- and mid-rank college levels as expected. Third, other social position variables (party membership and gender) have non-significant coefficients. Fourth, the non-significance of number of siblings suggests that the post-cohort, which was predominantly without siblings, shows little shift in worldview and work value with respect to finding a skilled job. Finally, the inverse Mill’s ratio is significant in only one of four situations, perhaps due to the balanced covariates. Turning to the estimates for the Bachelor’s degree, no additional covariate effects are statistically significant in the regression adjustment.

Finally we examine the treatment assignment model results. For the technical degrees, at-birth rural hukou, parental party and own party membership, and number of siblings must be controlled in order to balance the covariates. For the Bachelor’s degrees, only at-birth rural hukou and parental party membership must be stringently controlled, but gender becomes marginally significantly negative, indicating that there is a larger percentage of women in the post-cohort as opposed to the pre-cohort.

Sensitivity Analysis.

We report one sensitivity analysis to address the common problem of a lack of precise timing of event histories drawing data from surveys. As discussed in the data section, we have taken systematic measures to make the results robust. For example, we executed the following measures: (1) focusing on full-time college students to avoid complicated work histories of part-time students; (2) using birth years to define cohort membership taking into account the typical 3-year range of graduation year; (3) treating self-reported graduation year as the precise timing if it falls in the typical graduation range; (4) applying randomness when assigning the graduation year within the 3-year range when precise graduation year is unavailable; (5) allowing the timing of the first skilled job to be shortly before the graduation year when comparing the graduation year with the job starting year; (6) comparing the graduation timing and job start timing one year at a time; and (7) applying the same rule for both cohorts.

Our sensitivity analysis imparts the following question: if we delay the typical graduation range by one year (i.e., ages 22–24 for Bachelor’s degrees and ages 21–23 for technical degrees), will the results remain predominantly unchanged? This sensitivity analysis shows similar patterns as the current analysis, including the negative, substantial effects for the technical degrees and close-to-zero, non-significant effects for the Bachelor’s degree. Specifically for the technical degrees, the ATT for the timing within a half a year is −.127 (e^−.127 = 0.763) with t = −1.608, and the timing within 1.5 years is −.180 (e^−.127 = 0.835) with t = −2.462. This sensitivity analysis suggests the robustness of our current findings.

Conclusion

Motivated by a need for macro-level explanations for the emerging delayed transition from college to skilled employment in rapidly developing societies, this paper uses a natural experiment framework to examine the causal effect of China’s 1999 accelerated expansion of higher education on the timing of finding a first skilled job among college graduates. Applying development and social stratification theories to China at the turn of the new millennium, the present research provides testable hypotheses that are both general to theory and specific to China’s situation. To test these hypotheses, we set up a rigorous approach to a natural experiment, including defining the treatment and assigning the treatment and control groups, identifying covariates for control to meet the conditional independence of the treatment assignment and the outcome, and measuring the outcomes in terms of the timing of the college-to-work transition. The analysis exploits the unique education and work history data of a nationally representative survey and estimates a causal inference model. The results provide evidence to support the posed hypotheses.

The current analysis is limited in the precise college graduation time and job starting time, a common issue with using survey event history data due to self-report and recall errors, especially when the educational history timing and the work history timing are compared. We have developed systematic ways to maximize the use of the data while minimizing its limitation. Sensitivity analysis with a slight change in the typical graduation age range provides similar findings, supporting the robustness of the current findings.

With this caveat, we offer three major findings. First, the 1999 accelerated expansion of higher education causes a delay in timing for landing a skilled job among graduates from technical colleges while graduates from 4-year colleges are not affected. Second, family origins, social positions affecting childhood experience, and individual social positions remain significant in the selection into college-going before and after the education expansion acceleration, with the exception of gender. Third, above and beyond its negative effect on college education sorting, at-birth rural hukou status exerts a positive effect on the timing of finding a skilled job among technical college graduates.

These findings shed new light on the universal phenomenon of early adulthood and specific social inequality while China is rising to an economic power. Similar to other developing societies, China witnesses emerging early adulthood as a consequence of the accelerated expansion of higher education. Unlike in other societies, however, China sees an emerging divide in early adulthood dictated by types of degrees in the college-educated population. A source of this divide are the institutions themselves—the historically lower quality of instruction in technical colleges in comparison to that found in 4-year colleges, as well as the further widening of the quality gap during the expansion, accounts for emerging early adulthood. Another related influence is rapid innovation in technologies of today’s digital age. Technical colleges have not fully adapted to time-sensitive updated technologies. Regarding the student body, students of lower family origins, lower social positions during childhood, and lower individual social positions are often sorted to technical colleges. These social, structural disadvantages continue to operate in the college-to-work transition, placing technical college graduates at a disadvantage.

Our findings suggest two policy recommendations. The first recommendation relates to the necessary governmental investment in public colleges before expansion. Given the need for quality higher education, increases in government investment in 3-year and 4-year colleges must be made in order to advance both the quality of education and increase the capacity to instruct an influx of students. Our second recommendation considers the college admission policy, particularly the quota allocations to provinces. This alongside other structural barriers that reproduce and exacerbate educational inequality should be eliminated to maximize the human capital and talent of China’s millennials.