Long Lasting Attraction? Impacts of Communist Party

Membership on the Chinese Labour Market

Simon APPLETON, John KNIGHT, Lina SONG and Qingjie XIA1

September 2003

Abstract Our comparison of urban household surveys from 1988, 1995 and 1999 show that the wage premium for Party membership has risen. Party members earn more than a third higher wages than non-members, and the “pure” wage premium for Party membership – that part of the wage differential that cannot be explained by other factors that we observe – has doubled from 5% in 1988 to 10% in 1999. We conclude that the Communist Party wage premium, far from withering away under economic reform, is growing. The strengthened economic benefits of Party membership may prove an obstacle to the hope often expressed in the West that the one party state in China will be eroded during the transition to a market economy. JEL classification: J31, P26, P30 Key words: wage premium, labour market, political capital, Communist Party and China

Corresponding to Dr. Lina Song School of Sociology and Social Policy, University of Nottingham, University Park, Nottingham, NG7 5DW, United Kingdom, Telephone: 44 (0) 115 8466217 or Email:

1 Simon Appleton, Lina Song and Qingjie Xia are at the University of Nottingham, and John Knight is at the University of Oxford. The authors are grateful to the Ford Foundation for funding the data collection and for the DFID for supporting the research under Escor grant R7526.

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lina.song@nottingham.ac.uk 1. Introduction

To be a member of Chinese Communist Party (CCP hereafter) is utterly different from being a member

of any political party in the Western sense. Although membership is voluntary, to become a CCP

member, one would have to be elected by peer reviewers and to be approved by superiors within the

Party. Once a CCP member, one cannot resign from the party unless as a consequence of political

punishment or dispute. CCP members are in theory believers of communism or socialism, and most

importantly, the CCP is the Party in power since 1949 when the People’s Republic of China was

founded. The Communist Party of China has about 55 million members, with about 2 million primary

organisations meeting in villages, urban neighbourhoods, work units (danwei) and military divisions. Its

network and its occupation of important administrative positions are a salient feature of the Chinese

economy and society.

However, there has been a change in the CCP’s selection of its members during the eight decades after

its founding. Prior to 1949, the CCP was an opposition Party and its members would have been

executed if they publicly admitted their CCP membership. Hence, the main criteria for Party

membership were loyalty, self-sacrifice and a strong belief in overthrowing the capitalistic and feudalistic

system in pre-Communist China. The recruitment of CCP members was highly secretive as the CCP

was mainly run underground. Most members recruited during this period became powerful

administrators since 1949.

Between 1950 and 1978, the newly recruited Party members were mostly from working class

backgrounds, and were the most active in responding to the Party’s political mobilisations. Recruitment

was controlled to protect the Party from impurity, as CCP membership became a link to power and

control. In fact, Party membership became a more important factor in determining personal status than

personal education or parental background. In 1979, Deng Xiaoping, the new leader of the CCP,

revised the CCP principle that "working class is the pioneering of the proletarian, hence it is the core

element of the Chinese Communist Party". He added the rider that "sciences and technology are also

advanced productive forces", indicating that scientists and technicians may be largely absorbed by the

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CCP. It was only since then that educated people with non-working class backgrounds could be

considered for Party membership.

Since the 1980s, the desire to be a CCP member seemed waning as the market mechanism started to

dominate the Chinese economy and there were changes in ideology among the ordinary people. This is

peaked when, in July 2001, President Jiang Zemin, the successor of Deng Xiaoping made his public

speech for CCP’s 70 year Anniversary Celebration in which that he encouraged the private business

owners to join in the Chinese Communist Party. This has changed the nature of the Communist Party,

which is supposed to be for the poor, of the poor and by the poor.

As CCP membership is often seen as a prerequisite for holding a responsible position at work, it could

become regarded as a form of political capital that provides economic gains. It may help in increasing

income, obtaining good jobs and being promoted into high-paid jobs. In this paper, we analyze this issue

using nationally representative urban household surveys of China from 1988, 1995 and 1999. The three

surveys were conducted by the National Bureau of Statistics of PR China for the Research Institute of

Economics, Chinese Academy of Social Sciences. They are the only data sets available that are both

national representative and have comparable questionnaires and sample regions. Section 2 of this paper

sketches some simple theory about the economics of Communist Part membership. Section 3 models

the empirical determinants of Party membership and Section 4 provides various estimates of the average

wage premium for Party members. Section 5 looks at how the premium varies across individuals.

Section 6 uses recall data to construct a short panel to see how the premium changed over time in the

second half of the 1990s and in particular how it interacted with the widespread retrenchment during

that period. Section 7 considers how Party membership affects non-monetary aspects of welfare,

focussing on housing. Section 8 summarizes and concludes.

2. Theoretical framework: the economics of Communist Party membership

Investment in ‘Political Capital’?

Consider the decision to join the Communist Party (CP) as being based on a private cost-benefit

analysis. There are various potential forms of benefit: additional income, additional perquisites, higher

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status and greater power or influence. The non-economic benefits can be ends in themselves as well as

providing means of obtaining economic benefits. Since membership of the CP is permanent - very few

members can or do leave - the benefits accrue for the rest of one’s life. The costs of membership are

not financial but take the form of time devoted to CP activities and any personal restraints imposed by

CP discipline.

The costs may well be trivial, whereas the benefits may be substantial. If that is the case, why doesn’t

everyone want to join the CP? It is possible that many well-informed people do want to join although it

is likely that some have a greater incentive than others, but that membership is rationed. Indeed, it is

well-known that people are selected for membership only after careful scrutiny by CP officials.

The selection process can be formalised as follows. Let P* represent the unobserved net utility placed

on CP membership by an individual and V * represent the unobserved net utility to the party of the

individual’s membership. We postulate the index functions

P * = a´ X + u (1)

V * = b´Z + v (2)

where X is a vector of personal characteristics influencing individual preferences, Z is a vector of

characteristics that the party values in its members, and u and v are error terms.

If P* is positive, the person wants to join the party, and if V * is positive, the party wants him to join.

Where M is a dummy variable indicating party membership, the decision rule is

M = 1 iff P* > 0 and V* > 0

M = 0 iff P* < 0 or V* < 0.

If u and v have the standard properties, the probability of a person wanting to join is F(a´X ) and the

conditional probability of being chosen is F (b´Z), above F(.) is the standard normal cumulative

distribution function. On the assumption of independence between these choices, the probability of

observing that a person belongs to the CP is F(a´ X)F( b´Z ). In the absence of rationing, only F(a´ X)

is relevant: all the parameters in b´Z except the constant term are zero. Identification of the separate

equations is problematic if both decisions are influenced by the same set of variables (Z= X). In the

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empirical analysis of section (3), we use a simple binary probit to model whether urban workers are

Party members:

M* = c´ X + e (3)

M = 1 iff M* > 0

The significant coefficients c in the estimated equation must be examined for consistency with supply

(CP preferences) and demand (personal preferences) interpretations.

A partial test of these alternative interpretations of the determinants of CP membership is possible along

the following lines. Consider whether the people who join the party are those who personally benefit the

most in economic terms from doing so. Wage functions can be estimated separately for party members

and non-members:

Y = dp´ X + sp if M=1 (4)

= dn´ X + sn if M=0

where Y is the log wage and s is the error term. The wage gains from party membership are thus:

E(Y |M=1) - E(Y |M=0) = (dp- dn)´X + E(sp|M=1) – E(sn |M=0)

The differences in vectors of coefficients dp and dn can then be compared with the vector c in the party

membership equation. A close correspondence of c and (dp - dn) would indicate that the characteristics

that increase the probability of membership are also the characteristics that increase the economic

benefit from membership. Correspondence would constitute evidence that people join the CP as an

investment in ‘political capital’. However, lack of correspondence would not constitute evidence of

refutation. Even if membership is rationed, i.e. equation (2) holds, joining requires that the first hurdle,

i.e. equation (1), has been jumped: an invitation to join can be declined if the person does not want to

do so.

The Causal Role of Party Membership

Another theme of the paper concerns the interpretation of the apparent increase in the wage premium

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associated with CP membership. Preliminary findings indicate that a premium exists and that it has been

rising over time. This raises a standard problem: does M cause Y, ceteris paribus, or does Y cause M,

or does some third factor, J, cause both Y and M? It is possible that the party, at least in recent years,

has sought out economically successful people for membership, and that it has done so increasingly over

time. It is also possible that unobserved ‘ability’ is a determinant both of CP membership and of wages,

and that both of these relationships have become more important over time, i.e. the party has become

more meritocratic and merit has been more rewarded in the labour market. This, too, could explain the

apparent premium and its rise over the years.

These problems can be viewed as arising from correlations between the unobserved factors, e, which

determine party membership and those unobserved factors, s, that determine wages. Consistent

estimates can be dealt with using the sample selection correction suggested by Heckman (1979). This

requires we have instruments for membership: variables that are closely correlated with M but are not

direct determinants of wages. In the data we have, information on parental CP membership would

appear a priori to be a good instrument. A second way for correcting for this problem is to use panel

data. If the unobserved determinants that cause biases are time invariant, then they will be removed by

using a fixed effects estimator. We are able to construct a short panel for the late 1990s using recall data

on wages. Unfortunately, we do not know when workers in our data joined the Communist Party, so

we are not able to obtain a fixed effects estimate of the overall wage premium for Party membership2.

However, we are able to use the panel to see if there is a change in the wage premium over time after

controlling for the unobserved time invariant characteristics of individuals, such as their “ability”.

In formulating our hypothesis about the returns to CP membership during a period of economic

transition, our initial idea might be that CP membership is important in the planned economy and

becomes less important as the economy is marketised: planning gives way to market relationships.

However, the evidence of an increasing wage premium on CP membership immediately contradicts this.

A more sophisticated hypothesis is that the initial stage of marketisation involves two changes which

2 That is to say, we do not have data on income before and after joining the party. However, if it is possible that such information may not be informative: people do not leave the CP (so reducing ‘events’), and the economic benefits may flow only some time after the event.

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could increase the private value of CP membership. One is the fact that productive, personal or power

relations can now attract monetary rewards, a symptom of which is the general rise in income inequality

and the widening of wage structures. The other is the creation of potential rents in an interventionist,

semi-marketised economy: CP membership may assist rent-seeking, whether in the labour market or

elsewhere. Both mechanisms are likely to be less important in a fully-fledged market economy. It can be

hypothesised on that basis that the wage premium on CP membership displays an inverted-U-shaped

relationship over the period of economic transition: first rising and then falling.

The Incidence of the Premium

It may be possible to throw light on the reasons for the wage premium on party membership by

examining its incidence. Is the premium greater for some workers than for others, or in some parts of the

labour market than in others? The following questions are of interest.

(1) Is the premium greater in the state sector than in the urban collective sector and especially the

private sector (where a fully-fledged market may now operate)?

(2) How does the premium vary by age group? Is the premium more valuable to the old than to the

young?

(3) How does the premium vary by level or years of education? Initially the egalitarian stance of the

party may have favoured the less educated relative to the educated, but this may have changed over

time.

(4) Do women benefit more than men do from party membership? For instance, it may provide the

status and influence which women especially lack.

(5) How does the premium on CP membership vary over the different quantiles of the conditional

distribution of wages? We might expect it to be greater for persons with weak unobserved

characteristics than for persons with strong ‘ability’.

We approach questions (1) – (4) by comparing the separate party and non-party wage functions in

equation (4). Quantile regressions can be estimated for the full sample to answer question (5).

The hypotheses relating to questions (1) – (5) will emerge from the pattern of results that we obtain. It

does not seem possible to formulate a precise set of hypotheses from existing theory or from first

principles.

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3. Empirical determinants of CCP membership

The Communist Party is a mass membership party: around a quarter of all urban workers are members.

According to our surveys, this proportion rose during the 1990s: from 23.5% in 1988 to 24.5% in 1995

and 26.9% in 1999. In this paper we focus on these workers rather than all adults since we are

interested in estimating the wage benefits from membership. For data reasons, our sample is confined to

those with urban registration (hukou) and thus excludes rural-urban migrants. For brevity, in what

follows, we do not continually repeat the caveat that our statements apply only to urban workers

although this should be born in mind.

What kind of urban workers are Party members? To answer this, we first look at simple descriptive

statistics describing the average characteristics of Party members and non-Party members in each

surveyed year (Table 1 refers). However, there are correlations between the variables of interest: those

who are men, older or younger with more education might be more likely to be in occupations in charge

and in the sectors with primarily established CCP networks. Therefore it is useful to employ multivariate

analysis to isolate which variables in particular are significant determinants of Party membership. We

employ binomial probit models to estimate the probability of an urban worker being a Communist Party

member in a given survey (Table 2 refers). Among all the observed determinants of CCP membership,

we include gender, age, ethnicity, education, and job-related characteristics of workers. City dummies

are also included although in most of the tables we do not report the results on them for the purpose of

brevity. To interpret the results of the probits, it is sometimes helpful to consider the predictions of the

model evaluating at the means of the explanatory variables (Table 3 refers). For example, one can

isolate a “pure gender effect” by comparing the predicted probabilities of male and female workers

being a Communist, assuming that their age, education and other explanatory variables are equal to the

mean for the combined sample of all male and female workers. Due to the non-linearity of the probit, the

predicted proportion of Communist Party members at the mean of all explanatory variables is somewhat

lower than the actual observed proportion.

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The Communist Party is predominantly male, although this is becoming less pronounced over time. In

1988, a little over three quarters of its members were male; by 1999, this had fallen to a little over two-

thirds. The rise in female membership in part reflects women’s increased labour market participation.

The proportion of non-Communist workers who were women also increased during the same period,

although by less than the rise in the proportion of Communist workers who were female. A large part of

the gender differential in Communist Party differential cannot be explained by the other worker

characteristics we control for in the probit. In 1988, we predict that a male worker with otherwise

average characteristics would have a 32% chance of being in the Communist Party; a comparable

female worker would have a 20% chance. In 1999, the gap had narrowed as these probabilities rose to

34% for males and 26% for females.

Although growing in size, the party has a consistently higher proportion of workers who are somewhat

older than average. Relatively few workers under twenty years of age are Party members. Conversely, a

substantial proportion of the Party membership is aged over fifty. The average age of urban workers

outside the party is in the mid to late 30s; for those inside the party, it is in the mid-40s. The age

differential has narrowed slightly, as the average age of workers outside the party has risen over time

while the average age of party members has remained more or less constant. In the probit models, we

use potential work experience (years since completing education) rather than age as an explanatory

variable. In all three years, the probability of being in the Communist party rises with potential

experience3. For example, in 1988, a worker with twenty years experience and otherwise average

characteristics had a 48% predicted probability of being a party member; a comparable worker with no

experience had only a 4% probability of being such. By 1999, the predicted probability of the younger

worker being a Party member had risen to 9% while that of the older worker was unchanged.

Workers in the Communist Party tend to be slightly more educated than others, although the differential

has fallen as the education of non-Communist Party members has improved over time while that of the

average Party member has remained unchanged. Other things being equal, the probits reveal that more

3 Potential enters in a quadratic form, with an inverse U effect. However, this reflects a diminishing effect of years of experience on the probability of being a Communist rather than a non-monotonicity. The turning point of the quadratic – around 47 years of experience in the first two surveys rising to 93 years in the second survey – is more than double the mean years of experience in the samples.

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educated workers are significantly more likely to be in the Communist party. Evaluating at the mean

probability, the marginal effect of an additional year of schooling on the probability of being in the CP

was 2.1 points in 1988. This rises to 2.8 in 1995 and then falls to 4.4 points in 19994.

Party membership tends to be concentrated among particular sectors and occupations. It is arises

disproportionately within the state sector – around 90% of working members are in the state-owned

sector, compared with three quarters of workers who are not Communist party members.

Representation within the private sector is especially low, while the urban collective sector is also rather

under-represented. From the probits, it appears as if these differences are gradually being reduced over

time. We predict that, for individuals with otherwise average characteristics, the probability of being in

the CP is 27% for those in the state owned sector in 1988 compared to 22% for those in urban

collective sector and 9% for those in the private sector. By 1999, these predicted probabilities had

changed to 32% for SOE workers, 27% for urban collective workers and 18% for private sector

workers. Hence, it appears that the Party has made some progress in recruiting more members from the

private sector, although those in the state owned sector are still twice as likely to be members.

Perhaps curiously in view of the Party’s ideology, the most statistically significant variable in the probits

is the dummy variable for being a blue collar worker, which negatively reduces the probability of being

a Party member. Blue collar workers – defined here as industrial, service and commercial workers - are

very poorly represented within the Party compared to the “white collar” workers, taken here to be

managers and administrators, clerks and those in professional or technical occupations. Over four fifths

of Party members are “white collar” workers. The proportion of non-Party members in such posts

fluctuates between 33% and 44% in the different surveys. At the mean of other explanatory variables,

white collar workers have a 37% probability of being in the party in 1988, a 36% probability in 1995

and a 39% probability in 1999. For blue collar workers, the corresponding predicted probabilities are

18%, rising to 21% and 23% in 1999.

4 Marginal effects can be computed from the probit coefficients evaluating at the mean probability, P, using coefficients multiplied by the scale factor f(F -1(P)), where f and F are the density and cumulative distribution functions for the standard normal distribution. For our data, these scale factors are 0.307 for 1988, 0.314 for 1995 and 0.330 for 1999.

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Related to these occupational differentials, the Party is under-represented in the manufacturing,

construction and retail sectors and heavily over-represented within the government administration sector.

In the surveys, the proportion of Party members in the government administration sector varies between

18% and 24%, whereas the proportion of non-Party members in the same sector varies between 4%

and 7%. The probit analysis shows that the industrial sector an individual works in can have an

independent effect on the probability of their being a party member. The strongest positive effect is that

of working in government administration. At the mean of the other explanatory variables, workers in the

government administration sector have a 40% probability of being Party members in 1988 and 1995,

dipping only slightly to 39% in 1999. These numbers can be contrasted with the predicted probabilities

for comparable workers in the model’s default category of manufacturing, which were 25% in 1988,

28% in 1995 and 29% in 1999. Party membership is often expected of workers employed in

government administration, being taken as a sign of loyalty to the government and required for

performing confidential or important tasks of the state. Interestingly, the industrial sector with the most

negative effect on the probability of joining the party is education. The fact that we find people working

in the education sector over-represented in the Party in Table 1 therefore reflects the fact that such

workers have other characteristics favorable to party membership, rather than that working in the

education sector per se makes party membership more likely. Teachers’ promotion to higher grade

posts is unlikely to depend much on Party membership. Moreover, there has been a historic distrust of

“intellectuals” within the Communist Party. Outcomes for older teachers in the surveys may have been

influenced by the experiences in the Cultural Revolution, when teachers were often the first target of the

Red Guard movement. This experience may have fostered lingering distrust between the Party and the

teaching profession.

For the 1999 survey only, we have information on whether the worker’s parents were Communist Party

members. These variables are partly interesting because they are possible identifying instruments for the

possible endogeneity of Communist Party membership. It is likely that whether an individual joins the CP

is correlated with whether their parents did. This may work via either demand factors (e.g. one’s parents

act as role models) or supply factors (e.g. one’s parents vouch for one’s character). However, parental

party membership may not have strong direct effects on one’s own wages. The probit models imply

that, for an otherwise average worker, having a father in Communist Party raises the probability of being

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a member oneself from 29% to 33%. Having a mother in Communist Party has a similar effect (the

predicted probability of membership rises from 30% to 33%) but is only statistically significant at the

10% level5.

4. The average wage premium for Party membership

In the 1988 survey, the mean daily wage for urban workers who were Party members was 29% higher

than the mean for those workers who were not Party members. In the 1995 and 1999 surveys, the

differential had risen to 33%. However, as we have seen, Communist Party membership is

systematically related to observable determinants of wages, such as experience and education. To

provide a simple estimate of the “pure” wage premium for Party membership we estimate wage

functions for a pooled sample of workers, members and non-members that includes controls for other

potential determinants of wages. We will show that the overall wage differential between Party members

and non-members largely reflects correlations between Party membership and observable determinants

of wages. However, we will also show that there remains a significant and increasing “pure” wage

premium for Communist Party membership.

Table 4 reports the estimated coefficients and t-ratios on dummy variables for Party membership taken

from a variety of specifications of the wage function6. The first set of estimates is made without any

controls and roughly corresponds to the simple differences in mean wages between Communists and

non-Communists observed in the surveys7. The next set of coefficients come from “Mincerian” wage

functions, which model the log of wages as a function of years of education, potential work experience

and experience squared as well as dummies for sex, non-Han ethnicity and city of residence8. These

controls can be regarded as a exogenous to Party membership and so adding in these controls provides

a better guide as to the true impact of membership on wages. Adding these controls greatly reduces the

5 These results come from an additional probit which we estimated but for brevity do not present in Table 2. To retain comparability with the results for other years, the probit for 1999 presented in Table 2 was estimated without variables for parental party membership. 6 In all cases, the dependent variable is the log of wages per day so the proportionate impact of Party membership on wages is exp(ß)-1 where ß is the coefficient on the CP dummy variable. 7 The difference is that the univariate log-linear wage function gives the differential in the geometric mean wage; the descriptive statistics are for the differential in the arithmetic mean wage.

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estimated impact of Communist Party membership on wages. For example, in the univariate wage

function for 1988, the implied wage differential between members and non-members is 34%; under the

Mincerian specification, it more than falls to only 7%. Indeed in all three years, the results suggest that

over half the overall wage differential between party members and non-members reflects a difference in

their personal characteristics and location. However, the Mincerian model implies that the wage

premium for party members rose more steeply than the simple wage differential between 1988 and

1995. Moreover, the Mincerian wage premium for party membership rose between 1995 and 1999

(from 16% to 20%) when the simple wage differential remained constant.

The third set of coefficients in Table 4 are from models that augment the Mincerian specification of the

wage function with controls for the worker’s industrial sector, firm ownership (state versus private etc)

and a simple set of four occupational dummies9. Unlike the controls for human capital and location, it is

less clear that these explanatory variables are exogenous to Party membership. For example, it may be

that Communists are favored when recruiting for certain high paid occupations. If this were true, then

controlling for occupation would be inappropriate when estimating the overall economic benefits of

Party membership. However, it is clear that these controls for sector and occupation do greatly reduce

the estimated coefficient on the dummy variables for Party membership in the wage functions. This is

especially true in the 1995 and 1999 surveys, when adding the controls halves the estimated wage

differential between members and non-members. Nonetheless, the dummy variables for party

membership remain statistically significant at the 1% level in all three years. They imply a pure

Communist Party wage premium of 5% in 1988 which rises to 8% in 1995 and then 10% in 1999.

A comparison of the simple wage differential and the pure wage premium implies that changes in

observed characteristics can explain half of the rise in the overall wage differential between Communist

Party members and non-members (five of the ten percentage point). The other half of the rise in the

differential cannot be explained by the other variables we include in the wage functions.

8 We term this specification “Mincerian” after the seminal work by Mincer (1974). 9 Working with a model with only four occupational categories does not seem overly restrictive. If we use a larger number of occupational dummies capturing the most disaggregated occupational classifications (7 categories in 1988 survey, 9 in 1995 and 1999 surveys) available from the surveys, the estimated coefficients on the dummy for Party membership are largely unchanged.

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So far, we have relied on Ordinary Least Squares regression to estimate the wage premium for Party

membership. An alternative approach is to use a quantile regression. This had the benefit of being more

robust in the presence of errors in the measurement of the dependent variable (wages). Moreover, it can

show how the wage premium varies with the wage distribution, conditioning on the explanatory

variables. Table 4 also presents results for the 25th, 50th and 75th quantiles. The 50th quantile is the

median of the wage distribution, conditional on the explanatory variables. Hence we can interpret this as

the results for individuals whose wages are typical given their explanatory variables. Such individuals

could be regarded as having unobservable determinants of wages that are neither favourable nor

unfavourable. Such unobservable determinants are all the things represented by the “residual” in a

regression setting; they may include random measurement error but also potentially more interesting

unobserved determinants of wages such as “ability”. The coefficients on the dummies for Party

membership in the regression for the 50th quantile are somewhat lower than those estimated by OLS but

imply a similar rate of doubling of the “pure” wage premium between 1988 and 1999. The results for the

25th quantile are very close to the OLS results. This quantile can be interpreted as representing

individuals whose wages are rather low, given their observed personal and work characteristics. Such

individuals might be regarded as having low “ability”, although alternative characterisations might be

equally valid. The rise in the coefficient on party membership for the 75th quantile regressions is smaller

than that for the other two cases. Hence it might appear that the returns to Party membership have risen

least for those who are earning rather more than might be expected from their observed determinants.

These might include the more able or simply the more fortunate.

5. The incidence of the wage premia

Estimating separate wage functions for Communist and non-Communists allows us to see how the

Communist Party wage premia varies across individuals. However, it is open to the objection that there

will be sample selectivity bias. Unobserved factors that influence wages may also influence party

membership. This bias may be corrected by augmenting the wage functions following Heckman (1979)

provided that there are instruments to identify the corrections for selectivity. For the 1999 survey, we

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have plausible instruments – parental membership of the Communist Party. We argued that a priori

these are good instruments: they are likely to be correlated with membership of the Communist Party but

not to directly affect wages. Empirically, the instruments were jointly significant at the 5% level in the

probit for party membership. Whether they are correlated with the unobservables that determine wages

is less clear-cut. Specifically, when we performed an over-identifying test on the two instruments, we

were unable to reject the null hypothesis that the instruments were not correlated with the unobservables

determining wages of CP members at the 5% level. However, for non-CP members, we rejected the

null only at the 1% level, not at the 5% level10. Given the results of these tests, we are confident that we

have valid instruments to identify corrections for sample selectivity when estimating wage functions for

1999 for CP members at least. It is noteworthy, therefore, that these corrections are not found to be

significant in the wage function for CP members under either the Mincerian or full specification

(Appendix Table 1 refers). The selectivity corrections were also insignificant in the wage function for

non-CP members. These results imply that we can simply use OLS estimates of the wage functions for

1999 (Table 5 refers).

The fact that there does not appear to be a sample selectivity problem in 1999 does not necessarily

imply that was not a problem in 1988 or 1995. However, unfortunately, these two earlier surveys did

not inquire about parental membership of the Communist Party and so we are unable to replicate the

analysis of Appendix Table 1. To try to circumvent this problem, we estimated wage functions for sub-

samples of workers who lived with their parents since for each member of the household, there was a

question about whether they were CP members (Appendix Table 2 refers). The comparison of the

results for 1999 in Appendix Tables 1 and 2 provides some corroboration of the results of the sub-

sample – neither method gives selectivity corrections that are significant at the 5% level. In the earlier

years, working with sub-samples also leads to insignificant corrections for sample selectivity, with the

10 The Overidentifying test is more commonly used in the context of two stage least squares (for an exposition, see Deaton, 1997, p112). However, the extension to a Heckit model is straightforward. We estimated separate wage functions for CP members and non-CP using the Heckit model. Then we took the residuals from these equations and regressed them on the two instruments identifying the sample selectivity correction and the observed determinants of wages. Under the null hypothesis, the number of observations multiplied by the uncentred R-squared from each auxiliary regression should be distributed as a chi-square with 2 degrees of freedom. The test statistic we obtained for the CP wage function was 1.47, less than 5.99, the critical value of the chi-squared distribution at 5% significance with 2 degrees of freedom. The test statistic for the non-CP wage function was 6.70, less than 9.21, the critical value of the chi-squared at the 1% level.

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exception of non-CP members in 1988 for whom the selectivity correction was significantly negative11.

Finding that sample selectivity does not appear to be a significant problem in most cases gives us some

confidence in relying on the results of the OLS estimates in Table 5. This approach seems preferable to

working with just the sub-sample of workers still living with their parents, since that entails its own

sample selectivity problem. Moreover Communist Party members tend to be somewhat older whereas it

is the younger workers who tend to still live with their parents. Hence the subsamples include only small

numbers of Communist Party members.

Generally speaking, the observed personal characteristics that raise wages – male sex, education and

experience – all bring greater benefits for workers who are not in the Communist Party than for those

who are members. Consider, first, the effect of gender. In 1999, the coefficient on the dummy variable

for being male in the wage function for Communist Party members implies that males earn 16% higher

wages than comparable females; among non-Communist the pure gender gap is 22%. We performed

pair-wise Wald tests for whether coefficients on particular explanatory variables differed significantly

between the Communist and non-Communist Party wage functions. The difference in the effect of being

male was not statistically significant at conventional levels in the wage functions for 1999, but it was in

1995 (at the 5% level) and in 1988 (at the 10% level). These results imply that, other things being equal,

women may benefit economically more than men from membership of the Communist Party. This seems

paradoxical, given that we have seen that women are less likely to join the Party, ceteris paribus.

Several speculative explanations suggest themselves. It may be that membership of the party reduces the

sex discrimination or prejudice that women face. Alternatively, if the gender gap in wages reflects

domestic burdens or lower aspirations of women, it may be that these apply less to women with the time

and inclination to join the Party. There may be something of a “scarcity value” to female Party members

– with less competition from others of their own sex, they may be more able to benefit from the

advantages of Party membership in terms of job promotion.

The earnings-experience curve for Communist Party members is less steep than that for non-members.

For example, consider the results for 1999 (Figure 1 refers). A non-Communist worker with fifteen

11 This result implied that there was a positive correlation between the unobservables determining wages for non-CP members and the unobservables determining party membership.

17

years of potential work experience is predicted to have 51% higher wages than one with no work

experience. For Communists, the corresponding differential is lower, at 34%. The coefficients on the

experience and experienced squared variables differ significantly between the CP and non-CP wage

functions in both 1988 and 199512. As with the results about gender, these findings seem paradoxical –

the more experienced workers are more likely to be Party members, but receive a lower benefit.

Education poses a similar puzzle. We have seen that education makes it more likely that a worker is in

the Party but the returns to education are higher for non-Party members in all years. The difference in

the returns is statistically significant at the 5% level in both 1988 and 1995.

Perhaps consistent with these finding about personal characteristics, manual workers appear to benefit

more from being in the Communist Party than non-manual workers do. The coefficient on the dummy

variable for being a blue collar worker is significantly lower in the wages functions for non-Communists

1999 than for Communists. This is also true in 1995 but not 1988. Other things being equal, blue-collar

workers who were Communist Party members were not paid less in the 1990s than white-collar

Communist workers were. However, blue collar workers who are not party members are predicted to

have been paid 19% less, ceteris paribus, than comparable white collar non-Communist workers.

The Communist party wage premium does not appear to vary significantly with the ownership type of

the worker’s employment. The coefficients on the dummy variables for working in urban collectives or

private enterprises are of broadly comparable magnitudes in both the Communist and non-Communist

wage functions. This is perhaps surprising, since it implies that Communist party membership does not

bring significantly greater returns in the state owned sector compared to the collective or private sectors.

There is also little difference in the effects of the dummies for industrial sectors, with the exception of

government administration. Government administration is the industrial sector where career progression

might be assumed to be most contingent on Party membership. In both 1988 and 1999, the coefficients

on the dummies for this sector are significantly higher in the wage functions for Communist Party

members than for non-members13. Even in 1995, the results imply that government administration pays

12 Unless otherwise stated, we refer to results as significant if they are statistically significant at the 5% level. 13 However, in 1988, government administration is predicted to pay less, ceteris paribus, than manufacturing but particularly so for non-CP members. In 1999, it pays significantly more than manufacturing, particularly for CP members.

18

significantly more than manufacturing for Communist Party members but not for non-Communists.

Interestingly, in 1988, working in government administration led to significantly lower wages than

working in manufacturing but by 1999 this had been reversed.

In summary, we find little evidence to support the hypothesis that characteristics that make it likely that

workers join the Communist Party also tend to raise the wage premium for party membership. It is true

that Party membership appears to pay more for those working in government administration and such

workers are more likely to join the Party. However, for the other variables, the balance of the evidence

demonstrates contrary patterns. Ceteris paribus, men are more likely to join the CP, but the premium is

lower for them. Similarly, more educated and experienced workers are more likely to be Party members

but the returns to education and experience are lower for Party members. Blue collar workers are the

least likely to be members, but are the occupational category that appears to benefit the most from Party

membership. Some characteristics that raise the probability of Party membership – such as employment

in an SOE– appear to have no independent effect on the wage premium for Party membership. One

possibility is that workers who are Communists are valued more highly because of their membership in

the labour market but that this value depends partly on their scarcity. Among groups of workers where

Communist Party membership is relatively common, the average benefit to membership may be lower.

For example, among particular groups of workers, there may be a given number of posts of leadership

or responsibility that attract higher wages and which Communist Party members are more likely to be

given. The more Communist Party members within a group of workers competing for those posts, the

less likelihood anyone member will be successful and hence the lower the average wage premium for

Communist Party membership.

6. The wage premium during retrenchment

So far, we have relied on cross-sectional estimates of the effects of Communist Party membership

on wages. The main potential methodological problem with this approach is that the endogeneity of

Party membership may give rise to biases in ordinary least squares estimates. Using techniques to

control for sample selectivity, we have argued that such biases are not a significant problem with our

19

data. However, an alternative approach is to use fixed effects estimates derived from panel data on

individuals. If Communist Party members have unobserved characteristics that make them more

productive and hence higher paid, then these characteristics should be controlled for as part of the

individual level fixed effect.

The 1999 survey included recall questions on wages in the previous four years. Consequently, we

can use this data to construct a retrospective panel of observations on wages from 1995 to 1999.

Unfortunately, most of the potential explanatory variables (excluding experience) can appear only as

time invariant variables in the analysis. For example, we do not have data on when people joined the

Communist Party and so cannot estimate the overall Communist Party membership premium based

on wages before and after joining the Party. However, we can still use a fixed effects model to

explore whether there are changes in the effects of these variables over time. Specifically, we can

use the panel to see how the Communist Party premium has changed during the period of xia gang.

Table 6 reports the results of the fixed effects estimates, estimated separately for the non-retrenched

and the retrenched workers14. We classify workers as retrenched if they were retrenched at any

time between 1992 and 1999/2000; hence this classification is time invariant15. To estimate the

possible effects of re-employment on wages, we include a time varying dummy variable for being re-

employed16. Since the wage structure may be different for the re-employed, we interact this dummy

variable with variables for personal characteristics and also the year dummies. The results of this

analysis are reported in detail in a companion paper (Appleton et al., 2002a). Here we focus on the

results about Communist Party membership.

The panel confirms the impression from the repeated cross-sections that the Communist Party

14 We do not correct for the selectivity of these two groups of workers because this can be viewed as giving rise to differences in the time invariant unobserved characteristics of the individual which are eliminated by the fixed effects estimation. 15 Such “retrenched” workers are likely to have spent some of the period of the panel in their previous jobs prior to retrenchment, some of the time unemployment and possibly some of the time re-employed in new jobs. Where workers are unemployed for a whole year, they have no wage and are thus not included in the panel analysis for that year (hence our panel for the retrenched workers is unbalanced). 16 Where a worker works in both their pre-retrenchment job and their re-employed job in the same year, we use the re-employed wage rate for that year.

20

premium has been rising. For the majority of workers, those who have not been retrenched, the

interaction term between the dummy variable for 1999 and that for Party membership is statistically

significant with a coefficient of 0.059. This implies that, compared to the base year of 1995, the

premium for such workers has risen by 6 percentage points. Inspection of the interactions between

the CP dummy and dummies for intervening years indicate that the rise of the premium was sustained

and incremental during the period. Since this result is generated by a fixed effects estimate, it is not

possible to argue that the rise in the premium is due to a chance in the composition of the party

membership, arising, for example, from the Party trying to recruit more entrepreneurial members.

What is particularly interesting is how the premium for Party membership varies with retrenchment

and re-employment. It has previously been shown, using the 1999 survey, that being a member of

the Communist Party reduces the probability of being retrenched, ceteris paribus, although it has no

effect on the conditional probability of re-employment (Appleton et al, 2002b). In the fixed effects

wage function for “retrenched” workers, the interactions between the dummy for Party membership

and the year dummies are near zero and statistically insignificant. That is to say, for workers who

were retrenched in the period 1995 to 1999, there was no tendency for the premium to rise prior to

their retrenchment. Moreover, the interaction between the dummy for Party membership and that for

re-employment is significantly negative. The overall effect of re-employment on wages cannot be

readily evaluated since there are several interaction terms between re-employment and other

determinants of wages. At the mean of all explanatory variables, the overall effect is positive: that is

to say, retrenched workers earn more than they did in their previous jobs if they are re-employed.

However, the negative interaction of re-employment and Party membership implies that a member

with other personal characteristics equal to the mean for the re-employed would earn 21% less

when re-employed in a new job than they would have earned if they had remained in their old job.

7. Housing conditions and party membership

It is now widely acknowledged that income captures only party of the many dimensions of well

being (World Bank, 2001). This was particularly true of China during the era of planning, since

21

income differentials were compressed and variations in welfare often arose from differences in non-

monetary benefits. During the period of reform, there has been a tendency to monetise many non-

monetary benefits. Hence one possible explanation of the rising wage premium for Communist Party

members was that it merely reflected a monetisation of their pre-existing overall advantages over

non-members.

The main non-monetary benefit that is consistently measured in the three surveys used in this paper is

housing. Table 7 reports average indicators of housing conditions during the three surveys,

disaggregated by whether the household head is a member of the Communist Party. Urban

households headed by Communist Party members occupy larger houses than those headed by non-

members. In 1988, the median housing area was 42 square meters for households headed by

Communist Party members, compared to 35 square metres for those headed by non-Communists.

However, the differential has not changed markedly over time – in 1999, the figures were 50 square

metres for Communists compared to 42 for non-Communists. Households headed by Party

members are also more likely to live in accommodation with its own sanitary facilities (as opposed to

no or shared facilities). In this respect, the advantage over non-Party members has narrowed. In

1988, 57% of households headed by Party members lived in accommodation with its own sanitary

facilities compared to 40% of those headed by non-members. By 1999, the situation had improved

and the differential narrowed, with the corresponding proportions with their own sanitary facilities

being 87% for households headed by Party members and 76% for those headed by non-members.

A similar tendency is observed with the proportion of households with self-contained kitchens.

Households headed by Party members are more likely to have self-contained kitchens. However,

this advantage has been reduced over time, as such accommodation has become more widespread,

particularly for households headed by non-Communist party members.

As with looking at the wage premium for Party members, it is important to control for other factors

that may be relevant such as experience and education. Consequently, we estimate regression

models with measures of housing condition as the dependent variables (Table 8 refers). We focus on

the log of housing are per capita as probably the best single indicator. However, we also model an

overall index of housing conditions derived using factor analysis. Specifically, we used factor analysis

22

to construct a single index of housing conditions based on housing area per capita (AREA), access

to sanitary facilities (SANYES) and access to a kitchen (KITYES). Using pooled data from the

three surveys, the factor analysis generated an index of housing (HINDEX) with the following

scoring coefficients:

HINDEX=0.107*AREA +0.689*SANYES+0.171*KITYES

We modeled housing area and the housing condition factor using a variety of specifications similar to

those used in the wage functions in Table 4. The first specification is a univariate regression, where

the only explanatory variable was a dummy for whether the household head was a party member.

The second, Mincerian, specification added controls for the personal characteristics of the

household head and location. The third specification augmented the Mincerian model with variables

for the sector and occupation of the household head. The fourth and final model added a control for

household income per capita to the third model.

The models reveal generally show that the dummy variable for the household head being a

Communist Party member has a positive and significant effect on the housing area and index of

housing conditions even after controlling for other characteristics. This true whether the dependent

variable is the log of housing area per capita or the more general index of housing quality. The

magnitude of the effect of Party membership falls as more and more control variables are added to

the regression. For example, the Mincerian model for 1999 implies that housing area is 14% greater

for households headed by Party members. When the occupation and sector of employment of the

head is controlled for, along with log household income per capita, the effect falls to 6% but remains

statistically significant at the 1% level. It is interesting that an effect of Party membership remains,

since we control for job characteristics and household income. This implies that access to better

housing may be an additional economic benefit to Party membership, in addition to the benefits from

the wage premium.

There are some signs of a fall in the effect of Party membership on the index of housing quality

between 1988 and 1999. This mirrors the pattern in the descriptive statistics with the indicators for

sanitary and kitchen facilities. With the log of housing area per capita as the dependent variable,

there is no marked change in the effect of Party membership. Indeed, with the fourth specification

for the dependent variables (i.e. all controls, including those for income), having a party member as

23

the household head is predicted to raise housing area per capita by 6% ceteris paribus in both

1988 and 1999. Consequently, there is no strong evidence that the increased wage premium for

party members is merely a compensation for reduced access to superior housing facilities.

8. Conclusions

With economic reform and the transition from a command economy, one might expect Communist

Party membership to become less important in determining a worker’s wages and general welfare.

Such a reduction in the economic benefits of Party membership might reduce the demand for

membership and hence a reduction in new recruitment. Much as Marx had envisaged the state

withering away under Communism, so might one anticipate the same fate for the Communist Party

as China moves from planning towards the market. However, a comparison of urban household

surveys from 1988, 1995 and 1999 show that the wage premium for Party membership has risen.

Party members earn more than a third higher wages than non-members, but much of this is because

Party membership is correlated with factors such as education and experience that independently

raise wages. Nonetheless, the “pure” wage premium for Party membership – that part of the wage

differential that cannot be explained by other factors that we observe – has doubled from 5% in

1988 to 10% in 1999. This rise in the economic benefits may help explain why the proportion of

urban workers in these surveys who are Party members rises during the period. The two trends of a

rising wage premium and a rising membership are consistent with a demand side explanation,

whereby individuals invest in Party membership as a form of political capital.

When we look at how membership and the premium vary across workers, such a simple demand

side explanation seems inadequate. It is true that Party membership is higher, ceteris paribus,

among workers in the government administration sector and that the wage premium for party

membership is also higher in that sector. A simple demand side explanation s quite adequate to

explain this result. However, in most other cases, worker characteristics that empirically raise the

likelihood of a worker being in the Party also reduce the wage premium for Party membership.

Education, experience, male sex and being in a white-collar occupation all significantly raise the

24

probability of a worker being a party member. These characteristics are also rewarded with higher

wages, for both members and non-members. However, they appear to be more rewarded for non-

Party members than for members. In general, therefore, one cannot explain the higher rates of party

membership among certain groups – such as men or the educated – by a higher expected benefit. If

anything, membership rates are higher when the premia are lower. One conjecture to explain this

paradox is that Party membership brings wage benefits partly through providing increased access to

a certain number of responsible posts. Securing these posts may provide more of an increase in

wages for person with otherwise low return characteristics than for one with high return

characteristics. Hence, Party membership may partly substitute for experience or education in

obtaining certain posts. This effect may be reinforced if competition for some posts is limited to

people of similar characteristics. For example, a factory floor supervisor may have to be a blue

collar rather than a white-collar worker. If there are relatively few Communist party members among

the blue collar workers, then the likelihood of any one member obtaining a desired supervisory post

will be higher and thus the benefits of Party membership greater. There may thus be a scarcity value

for certain characteristics that reduce the probability of party membership.

Does the wage premium for Party membership reflect a causal link rather than merely the operation

of unobserved factors that jointly determine wage and membership? Despite being limited to cross-

section data, we attempted to discern causality in two ways. First, we used parental party

membership as an instrument to correct for the selectivity of Party membership. Generally speaking,

despite having good instruments, we did not find evidence of significant sample selectivity. Second,

we used recall data on wages in the 1999 survey to construct a panel that allowed us to estimate

changes in the wage premium controlling for individual fixed effects. This showed that the rise in the

premium over time does not reflect a change in the composition of the party’s membership – for

example, it cannot be explained by increased recruitment of more able or enterprising workers.

However, it was interesting to note that the wage premium for party membership disappears if

workers lose their jobs and have to find re-employment. This may reflect the hiring of re-employed

workers on more competitive terms or merely the loss of firm-specific networks that Party

membership may have fostered.

25

Finally, although we could not fully compare non-monetary aspects of welfare between Communists

and non-Communists, we were able to study housing conditions in some depth. We found no

evidence that the rising wage premia was a compensation for a loss of non-monetary benefits.

Throughout the period, urban households headed by Communists have enjoyed more spacious

accommodation with better facilities, even after controlling for their productive characteristics and

household income. The gap between Communists and non-Communists in terms of the spaciousness

of their accommodation has not narrowed as the gap in wages has widened.

We are left with the unexpected finding that the Communist Party wage premium, far from withering

away under economic reform, is healthy and growing. This may be a by-product of the increase in

wage differentials during the transition from planning. Whereas before income inequalities were

compressed for political reasons, under reform, enterprises have more discretion in setting wages.

This may give more room for discriminatory, as well as productive, factors to work in determining

wages. Party members may more be able to secure personal benefits from their political status

during the transition from planning, much as managers in Eastern Europe and the Soviet Union were

accused of using their positions to benefit from the privatisation of assets in the 1990s. Whatever the

explanation, the strengthened economic benefits of Party membership may prove an obstacle to the

hope often expressed in the West that the one party state in China will be eroded during the

transition to a market economy.

26

References

Appleton, Simon, John Knight, Lina Song and Qingjie Xia (2002a), “Is a Three-Tier Labour Market Emerging in Urban China?”, mimeo, School of Economics, University of Nottingham: Nottingham Appleton, Simon, John Knight, Lina Song and Qingjie Xia (2002b). “Labor Retrenchment in China: Determinants and Consequences”, China Economic Review, 14 (2-3): 252-275. Deaton, Angus (1997). The Analysis of Household Surveys, Johns Hopkins University Press: Baltimore. Heckman, J. (1979), “Sample selection bias as a specification error”, Econometrica, 47: 153-161. Mincer, Jacob (1974). Schooling, experience and earnings, Chicago University Press: Chicago.

World Bank (2001) “World Development Report 2000/2001”, World Bank: Washington DC

27

Figure 1: The effect of experience on wages in 1999

0%

20%

40%

60%

80%

0 10 20 30

Potential work experience

Eff

ect

on w

age

(%)

CP members

non-CP

28

Table 1 Characteristics of Communist Party members and non-members Descriptive Statistics of Workers: 1988, 1995 and 1999 Surveys

1988 1995 1999 Non-CP CP Non-CP CP Non-CP CP Observations 13,571

(76.53) 4,162

(23.47) 9244

(75.49) 3001

(24.51) 4592

(73.11) 1689

(26.89) Male 55.12 76.19 46.48 71.34 48.78 68.80

Female 44.88 23.81 53.52 28.66 51.22 31.20

Minority 3.55 4.47 4.45 3.87 4.44 3.43

Mean of daily wages Standard deviation Median

16.54 8.81

15.52

21.26 11.12 19.67

25.43 23.05 21.81

34.04 25.52 29.45

31.26 57.24 24.80

41.50 66.73 35.08

Mean of log daily wages Standard deviation Median

2.69 0.50 2.74

2.98 0.38 2.98

2.99 0.77 3.08

3.36 0.60 3.38

3.15 0.74 3.21

3.52 0.61 3.56

Mean of age Standard deviation Median

34.99 (9.97) 35.00

43.95 (8.60) 44.00

36.87 (9.43) 37.00

43.76 (8.38) 44.00

37.92 (8.90) 38.00

43.62 (8.03) 45.00

Mean of experience Standard deviation Median

19.24 (10.72)

19.00

26.97 (9.41) 27.00

20.68 (10.44)

21.00

26.05 (9.20) 26.00

21.03 (9.97) 22.00

25.42 (9.06) 26.00

Age group:

16-20 (%) 7.07 0.12 2.97 0.00 1.72 0.00 21-30 (%) 27.85 5.10 23.99 6.23 21.36 7.10 31-40 (%) 36.27 32.27 36.28 28.72 35.74 26.11 41-50%) 21.78 36.33 29.10 41.92 34.17 47.19 51-60%) 6.76 25.35 7.31 22.16 6.86 19.36 61-65 (%) 0.27 0.84 0.34 0.97 0.15 0.24

29

Education in year Standard deviation Median

9.75 (2.48)

9.00

10.97 (2.81) 11.00

10.19 (3.12) 10.00

11.71 (3.23) 12.00

10.89 (2.63) 11.00

12.19 (2.75) 12.00

Education level:

College and above 8.51 26.29 17.68 40.36 21.37 50.44

Professional high school 9.44 16.12 15.61 19.33 13.09 14.51

Senior middle school 26.42 19.39 26.57 17.49 29.03 18.18

Lower middle school 41.63 28.76 33.71 20.03 33.62 15.57

Primary school and below 13.35 8.89 6.44 2.80 2.90 1.30

Not reported 0.46 0.55 0.00 0.00 0.00 0.00

Ownership:

State-owned 73.46 91.42 75.42 90.17 72.91 89.82 Urban collective 24.12 7.74 17.63 7.13 15.68 6.45 Urban private 0.98 0.07 2.13 0.17 5.99 0.77 Joint venture and Foreign investment

0.41 0.22 1.54 0.47 2.20 1.42

Others 1.03 0.55 3.28 2.07 3.22 1.54 Occupation:

Private enterprise owner or Private enterprise owner and manager

1.30 0.91 1.62 1.00 1.68 0.71

White Collar 33.76 83.47 43.55 81.41 39.09 80.34 Blue Collar 64.27 15.23 45.05 14.00 55.73 17.64 Others 0.67 0.38 11.24 4.40 3.51 1.30 Industries sector:

1. Primary sector 4.34 3.44 2.42 3.33 3.22 4.38 2. Manufacture 46.84 29.31 42.99 30.22 34.54 24.10 3. Construction 3.57 2.88 3.06 2.30 4.57 3.73 4. Transportation and communication

6.73 6.80 4.99 4.47 8.36 11.66

5. Retail, catering, and wholesales

15.53 10.76 15.66 9.83 12.26 6.81

6. Real estate and Individual service

2.68 1.71 4.01 3.17 11.61 7.99

7. Healthcare, sports and social welfare

4.27 5.45 4.29 4.67 3.96 5.68

8. Education, culture, art quango 6.43 9.78 6.67 8.46 6.60 9.12 9. Scientific research technology services

2.56 3.96 2.07 2.90 2.09 2.43

10. Finance and Insurance 1.37 2.04 1.88 2.03 2.05 2.13 11. Government and quango 4.05 22.71 7.20 23.99 5.40 18.12 12. Others 1.64 1.15 4.74 4.63 5.34 3.85

30

Table 2 Binomial Probit Regressions: the Determinants of Being Communist Party Members

1988, 1995 and 1999 Surveys 1988 1995 1999 Constant -2.883

(25.16)*** -3.082

(24.74)*** -3.458

(18.51)*** Male sex 0.628

(23.29)*** 0.493

(16.66)*** 0.361

(9.01)*** Experience 9.62E-02

(18.14)*** 0.089

(14.79)*** 6.96E-02 (8.21)***

Experience squared -1.06E-03 (10.04)***

-0.001 (7.55)***

-4.22E-04 (2.33)**

Full-time education in years 0.069 (11.48)***

0.090 (15.58)***

0.135 (14.03)***

Ethnic minority -0.003 (0.04)

-0.062 (0.87)

-0.235 (2.22)**

Urban collective -0.263 (6.67)***

-0.246 (5.18)***

-0.280 (4.13)***

Private enterprises -1.309 (4.62)***

-1.021 (3.96)***

-0.771 (4.73)***

Foreign-owned or joint venture

0.082 (0.33)

-0.278 (1.74)*

-0.164 (1.11)

Ownership (default variable is state-ownership)

Other ownership -0.161 (0.86)

-0.109 (1.16)

-0.393 (2.73)***

Private enterprise owner

-0.205 (1.46)

0.262 (1.63)*

-0.508 (2.73)***

Blue collar -0.963 (29.84)***

-0.733 (19.13)***

-0.796 (16.17)***

Occupation (default variable is white collar)

Other occupations -0.504 (2.16)**

-0.516 (8.35)***

-0.567 (3.72)***

Primary industries 0.010 (0.14)

0.105 (1.25)

0.217 (2.04)**

Construction -0.061 (0.85)

-0.132 (1.51)

-0.145 (1.44)

Transportation and communication

0.031 (0.60)

0.006 (0.09)

0.307 (4.42)***

Commerce 0.000 (0.01)

-0.059 (1.23)

0.182 (2.35)**

Real estate -0.115 (1.29)

0.026 (0.34)

0.072 (0.98)

Social welfare -0.137 (2.36)**

-0.091 (1.30)

0.006 (0.07)

Education -0.233 (4.76)***

-0.130 (2.24)**

-0.160 (1.98)**

Sciences and research

-0.068 (0.96)

-0.206 (2.18)**

-0.098 (0.74)

Financial sectors 0.033 (0.34)

0.185 (1.90)*

0.072 (0.53)

Government 0.674 (15.06)***

0.552 (11.99)***

0.448 (6.23)***

Industry (default variable is manufacturing)

Other industries -0.030 (0.23)

0.059 (0.80)

0.214 (2.07)**

31

Number of observations 17733 12245 6281 Log-likelihood -6563.519 -5210.770 -2776.994 Restricted log-likelihood -9662.670 -6818.808 -3656.597 Pseudo R-squared 0.3207 0.2358 0.2406 1988 Predicted Actual 0 1 Total 0 12477 1094 13571 1 1919 2243 4162 Total 14396 3337 17733 1995 Predicted Actual 0 1 Total 0 8581 663 9244 1 1770 1231 3001 Total 10351 1894 12245 1999 Predicted Actual 0 1 Total 0 4189 403 4592 1 905 784 1689 Total 5094 1187 6281 Notes: (1) Regional dummy variables are controlled for in all models. For the brevity, the coefficients are not reported here. (2) T-ratios are in brackets. *** denotes statistical significance at 1% level and below, ** at 5%, and * at 1% level.

32

Table 3

Predicted Probabilities of Communist Party Membership from Probit Models 1988 1995 1999 Baseline probabilities (evaluated at mean of all explanatory variables)

0.26 0.29 0.31

Male 0.32 0.34 0.34 Female 0.20 0.24 0.27 Experience of 0 years 0.04 0.05 0.09 Experience of 10 years 0.26 0.29 0.28 Experience of 20 years 0.48 0.52 0.48 Experience of 30 years 0.69 0.76 0.68 Education of 0 years 0.15 0.13 0.09 Education of 6 years 0.24 0.28 0.32 Education of 9 years 0.29 0.35 0.43 Education of 12 years 0.33 0.42 0.55 Education of 15 years 0.38 0.49 0.67 Ethnic minority 0.26 0.27 0.26 Han Chinese 0.26 0.29 0.31 Ownership State-owned 0.27 0.30 0.32 Urban collective 0.22 0.25 0.27 Private enterprises 0.09 0.13 0.18 Foreign-owned or joint venture 0.28 0.24 0.29 Other ownership 0.24 0.28 0.25 Occupation Private enterprise owner 0.32 0.41 0.28 White collar 0.37 0.36 0.39 Blue collar 0.18 0.21 0.23 Other occupations 0.26 0.25 0.27 Industry Primary industry 0.25 0.30 0.33 Manufacture 0.25 0.28 0.29 Construction 0.24 0.25 0.26 Transportation and communication 0.26 0.28 0.35 Commerce 0.25 0.27 0.32 Real estate 0.23 0.28 0.30 Social welfare 0.23 0.26 0.29 Education 0.21 0.25 0.25 Sciences and research 0.24 0.24 0.27 Financial sectors 0.26 0.32 0.30 Government 0.40 0.40 0.39 Other industries 0.24 0.29 0.33

Notes: (1) All results evaluated at the mean of the other explanatory variables. (2) All results are generated from the basis of models in Table 2.

33

Table 4

The Effect of Communist Party Membership on Wages Coefficients on dummy variables for CP membership from log wage functions

1988 1995 1999 (1) OLS simple wage function: CP only explanatory variable

CP 0.290 (40.09)***

0.370 (27.17)***

0.365 (19.75)***

(2) OLS Mincerian wage function: controls for personal characteristics, location,

CP 0.068 (9.48)***

0.146 (11.05)***

0.181 (9.68)***

(3) OLS full wage function: controls for personal characteristics, location, occupation and sector

CP 0.053 (7.02)***

0.075 (5.60)***

0.098 (5.25)***

(4) Quantile 25: full wage function

CP 0.051 (6.39)***

0.073 (5.08)***

0.107 (4.59)***

(5) Quantile 50: full wage function

CP 0.040 (5.72)***

0.055 (3.75)***

0.076 (4.60)***

(6) Quantile 75: full wage function

CP 0.048 (6.21)***

0.029 (2.15)**

0.072 (4.02)***

No. of observations 17733 12245 6281 Notes: (1) Dependent variable is log hourly wage (2) T-ratios are in brackets. *** denotes statistical significance at 1% level, ** at 5% and * at 1%

level.

34

Table 5 Wage functions for 1988, 1995 and 1999 for CP and non-CP members

1988 1995 1999 Non-CP CP Non-CP CP Non-CP CP Male 0.102

(14.54) *** 0.073

(5.16)*** 0.144

(10.26)*** 0.087

(3.84)*** 0.195

(10.35)*** 0.153

(4.86)*** Experience 4.96E-02

(36.54) *** 3.01E-02 (9.58)***

5.987E-02 (20.91)***

5.10E-02 (9.29)***

4.71E-02 (12.29)***

3.016E-02 (4.67)***

Experience squared term

-7.30E-04 (23.05) ***

-3.56E-04 (5.82)***

-1.052E-03 (15.21)***

-8.82E-04 (7.71)***

-8.79E-04 (10.08)***

-4.986E-04 (3.66)***

Full-time education in years

0.033 (16.32) ***

0.024 (9.53)***

0.036 (12.42)***

0.025 (7.33)***

0.041 (8.96)***

0.035 (6.18)***

Minority -0.007 (0.39)

0.032 (1.38)

-0.118 (3.39)***

-0.033 (0.70)

0.019 (0.40)

0.019 (0.26)

Ownership (default variable is state-owned) Urban collective -0.144

(17.6) -0.110

(4.85)*** -0.253

(13.15)*** -0.231

(5.13)*** -0.166

(5.66)*** -0.260

(3.53)*** Private enterprises -0.328

(2.51) -0.642 (1.25)

-0.460 (6.34)***

-0.171 (0.91)

-0.028 (0.61)

-0.082 (0.54)

Foreign-owned or joint venture

0.027 (0.26)

0.260 (2.2)**

0.155 (2.65)***

0.094 (1.07)

0.344 (5.11)***

0.269 (2.37)**

Other ownership -0.575 (5.7) ***

0.021 (0.17)

-0.303 (5.61)***

-0.217 (2.18)**

-0.283 (3.65)***

-0.272 (1.88)*

Occupation (default variable is white collar) Private enterprise owner

0.055 (0.69)

-0.008 (0.16)

0.053 (0.63)

0.258 (2.15)**

0.187 (2.06)**

-0.193 (1.09)

Blue collar -0.057 (6.47) ***

-0.041 (2.21)**

-0.165 (9.74)***

-0.066 (1.99)**

-0.171 (7.45)***

-0.044 (1.02)

Other occupations -0.205 (1.76) *

-0.186 (0.73)

-0.219 (7.79)***

-0.171 (2.46)***

-0.306 (3.91)***

-0.040 (0.32)

Industry (default variable is manufacturing) Primary industries 0.062

(3.72) *** 0.058

(2.45)*** 0.058 (1.24)

0.050 (0.70)

0.130 (2.68)***

0.060 (0.97)

Construction 0.018 (0.98)

0.033 (1.29)

0.001 (0.02)

0.032 (0.62)

0.094 (2.03)**

0.127 (2.09)**

Transportation and communication

0.027 (1.79) *

-0.005 (0.22)

0.046 (1.25)

0.125 (3.01)***

0.283 (7.79)***

0.377 (8.09)***

Commerce -0.004 (0.34)

-0.043 (2.07)**

-0.056 (2.61)***

-0.043 (1.18)

0.070 (1.98)**

0.167 (2.30)**

Real estate -0.073 (2.69) ***

-0.083 (2.18)**

-0.047 (1.29)

-0.051 (1.01)

0.213 (6.23)***

0.235 (3.73)***

Social welfare -0.030 (2.13) **

-0.017 (0.82)

0.110 (3.62)***

0.022 (0.47)

0.334 (9.31)***

0.360 (5.44)***

Education -0.050 (3.56) ***

-0.070 (3.86)***

0.150 (6.39)***

0.106 (3.24)***

0.300 (8.89)***

0.323 (6.48)***

Sciences and research -0.009 (0.45)

-0.042 (1.81)*

0.156 (3.74)***

0.207 (3.90)***

0.325 (6.26)***

0.264 (3.64)***

Financial sectors -0.038 (1.32)

-0.038 (1.11)

0.290 (6.02)***

0.292 (4.52)***

0.412 (6.72)***

0.446 (6.29)***

Government -0.060 (3.72) ***

-0.120 (7.71)***

0.082 (3.35)***

0.027 (1.10)

0.241 (6.37)***

0.338 (8.05)***

Other industries -0.144 (2.58) ***

-0.140 (2.14)**

-0.311 (5.97)***

-0.222 (2.68)***

-0.029 (0.44)

0.197 (2.71)***

35

Constant term 1.874 (54.28)***

2.239 (39.89)***

2.364 (39.83)***

2.650 (28.87)***

2.519 (28.77)***

2.734 (22.30)***

No. of observations 13571 4162 9244 3001 4592 1689 Adjusted R-squared 0.364 0.247 0.273 0.298 0.283 0.264 0.160 0.107 0.430 0.259 0.397 0.282

36

Table 6 Fixed effects estimates of changes in wages function coefficients, 1995-1999

Non-retrenched Retrenched Coefficien

t T-ratio Coefficient T-ratio

Male*99 -0.003 -0.26 0.113 1.52 Male*98 -0.008 -0.65 0.037 0.94 Male*97 -0.005 -0.45 0.035 0.91 Male*96 -0.005 -0.38 0.021 0.56 Experience*99 -2.07E-02 -7.48 *** 1.41E-02 0.69 Experience*98 -1.92E-02 -7.19 *** -7.92E-03 -0.75 Experience*97 -1.13E-02 -4.34 *** -7.49E-04 -0.08 Experience*96 -1.65E-03 -0.65 -8.78E-03 -0.94 Experience squared*99 3.60E-04 6.00 *** -4.43E-04 -0.96 Experience squared*98 3.76E-04 6.22 *** -2.83E-05 -0.12 Experience squared*97 2.33E-04 3.80 *** -1.46E-04 -0.64 Experience squared*96 4.32E-05 0.69 1.38E-04 0.61 Education in years*99 0.012 4.61 *** -0.012 -0.69 Education in years*98 0.006 2.21 ** -0.012 -1.25 Education in years*97 0.003 1.11 -0.014 -1.50 Education in years*96 0.000 0.11 -0.001 -0.09 Minority ethnicity*99 0.088 2.84 *** 0.020 0.11 Minority ethnicity*98 0.047 1.50 -0.032 -0.32 Minority ethnicity*97 0.043 1.38 0.024 0.25 Minority ethnicity*96 0.028 0.88 -0.002 -0.03 Party member*99 0.059 4.25 *** 0.122 0.94 Party member*98 0.055 3.95 *** -0.001 -0.02 Party member*97 0.039 2.76 *** 0.066 1.01 Party member*96 0.010 0.70 -0.013 -0.21 Year dummy for 1999 0.370 7.84 *** 0.037 0.12 Year dummy for 1998 0.283 6.20 *** 0.177 1.06 Year dummy for 1997 0.129 2.91 *** 0.113 0.73 Year dummy for 1996 0.000 -0.00 0.055 0.37 Dummy variable for re-employment (time varying)

0.842 2.93 ***

Interactions with a time varying dummy variable for re-employment: Male*re-employment 0.169 2.62 **

* experience*re-employment -3.35E-02 -1.75 * Experience squared*re-employment 5.79E-04 1.34 School years*re-employment -0.041 -2.54 **

* Minority*re-employment 0.150 1.06 CP member*re-employment -0.319 -2.83 **

* Year dummy for 1999*re-employment -0.131 -1.19 Year dummy for 1998*re-employment 0.204 2.16 ** Year dummy for 1997*re-employment 0.066 0.70 Year dummy for 1996*re-employment -0.018 -0.19 Constant 3.089 724.54 *** 2.710 206.89 **

*

37

Number of observations 26938 4639 R-squared across individuals 0.1421 0.0675

38

Table 7

Descriptive statistics: Housing conditions by Party membership of household head 1988 1995 1999 Non-CP CP Non-CP CP Non-CP CP Housing area (living and auxiliary) (square meters)

Mean Standard deviation Median

38.34 23.07 35.00

45.75 24.13 42.00

45.50 25.54 41.00

52.19 30.97 47.00

44.52 20.10 41.52

52.26 21.32 49.95

10-30 38.44 22.13 24.84 12.78 24.12 12.12 31-40 25.02 24.25 24.40 20.89 23.17 18.83 41-50 17.56 22.78 21.66 25.61 23.50 23.53 51-60 7.90 14.34 12.67 16.67 13.13 18.63 61-80 5.40 10.38 9.51 15.15 11.28 18.83 81-100 2.16 3.02 3.70 5.19 2.95 4.71 Above 100 2.05 2.58 2.92 3.54 1.64 3.16 Not reported 1.48 0.53 0.31 0.17 0.21 0.19 Percentage total 100.00 100.00 100.00 100.00 100.00 100.00 Sanitary Facilities Non 41.12 28.06 25.56 21.22 15.34 9.09 Shared 18.60 14.95 9.62 5.61 8.70 3.61 Own toilet but not shower 33.23 46.06 32.03 34.39 42.12 44.75 Own toilet and shower 6.55 10.50 32.66 38.78 33.72 42.42 Not reported 0.50 0.44 0.13 0.00 0.12 0.13 Percentage total 100.00 100.00 100.00 100.00 100.00 100.00 Kitchen No kitchen 15.29 9.18 14.91 11.60 8.86 5.22 Shared 6.46 4.13 3.33 1.73 3.20 1.68 Self-contained 77.84 86.51 81.76 86.67 87.94 93.04 Not reported 0.41 0.18 0.00 0.06 Percentage total 100.00 100.00 100.00 100.00 100.00 100.00

39

Table 8 Impact of Communist Party membership on housing conditions

1988 1995 1999

Dependent variable: factor score for housing conditions (1) Simple regression: household head CP member is only explanatory variable

0.284 (17.06)***

0.170 (8.75)***

0.213 (10.81) ***

(2) Mincerian regression: controls for heads characteristics and location

0.182 (10.59)***

0.110 (5.48)***

0.154 (7.40) ***

(3) Full regression: controls for personal and job characteristics together with location

0.126 (6.79)***

0.055 (2.64)

0.079 (3.67) ***

(4) Full regression plus household log income per capita

0.104 (5.88)***

0.030 (1.45)

0.061 (2.80) ***

Dependent variable: log house area per capita (5) Simple regression: household head CP member is only explanatory variable

0.201 (13.21) ***

0.149 (13.03) ***

0.177 (12.56) ***

(6) Mincerian regression: controls for heads characteristics and location

0.142 (13.69) ***

0.111 (9.37) ***

0.135 (9.42) ***

(7) Full regression: controls for personal and job characteristics together with location

0.081 (7.63) ***

0.056 (4.66) ***

0.074 (5.00) ***

(8) Full regression plus household log income per capita

0.059 (6.46) ***

0.051 (4.22) ***

0.060 (4.06) ***

No. of observations 8931 6767 3989 Adjusted R 2 for (1) 0.0315 0.0106 0.0256 Adjusted R 2 for (2) 0.0985 0.0809 0.1229 Adjusted R 2 for (3) 0.1191 0.1059 0.1570 Adjusted R 2 for (4) 0.1957 0.1355 0.1697 Adjusted R 2 for (5) 0.0375 0.0230 0.0366 Adjusted R 2 for (6) 0.1676 0.1287 0.1647 Adjusted R 2 for (7) 0.2796 0.1965 0.2247 Adjusted R 2 for (8) 0.4597 0.2000 0.2399

40

Appendix table 1: Wage functions for Communist and non-Communist Party members; 1999 data with

controls for sample selection

Mincerian form Communist Party members Non-Communist Party members Coefficient t-ratio Coefficient t-ratio Constant term 3.432 6.73 *** 2.292 15.23 *** Sex 0.125 2.86 *** 0.195 7.48 *** Experience 1.73E-02 1.87 * 4.16E-02 9.31 *** Experience squared term -3.52E-04 -2.54 *** -7.64E-04 -9.01 *** Education in years 0.027 1.53 0.067 6.33 *** Minority 0.030 0.39 0.014 0.29 Shenyang -0.493 -9.12 *** -0.535 -13.71 *** Jinzhou -0.646 -9.07 *** -0.577 -11.45 *** Nanjing -0.063 -1.04 -0.233 -5.72 *** Xuzhou -0.265 -3.69 *** -0.420 -8.23 *** Zhengzhou -0.404 -6.15 *** -0.535 -10.97 *** Kaifeng -0.733 -8.12 *** -0.932 -18.71 *** Pingdingshan -0.418 -5.79 *** -0.504 -9.46 *** Chengdu -0.358 -6.56 *** -0.454 -11.25 *** Zigong -0.548 -7.91 *** -0.804 -15.65 *** Nanchong -0.400 -5.65 *** -0.657 -12.66 *** Lanzhou -0.419 -7.04 *** -0.516 -13.18 *** Pingliang -0.466 -6.67 *** -0.586 -10.72 *** Selectivity variable -0.171 -1.38 0.080 0.74 No. of observations 1689 4592 Adjusted R-squared 0.178 .205 Standard deviation .55207 .660

41

Full specification Communist Party members Non-Communist Party members Coefficient t-ratio Coefficient t-ratio Constant term 3.002 6.18 *** 2.408 16.02 *** Sex 0.136 3.27 *** 0.210 8.32 *** Experience 2.66E-02 2.99 *** 4.938E-02 11.29 *** Experience squared term -4.75E-04 -3.56 *** -8.800E-04 -10.67 *** Full-time education in years 0.026 1.53 0.049 4.82 *** Ethnic Minority 0.031 0.41 0.012 0.26 Ownership (default one is state owned)

Urban collective -0.260 -4.76 *** -0.167 -6.15 *** Private enterprises -0.085 -0.56 -0.028 -0.63 Foreign-owned or joint venture 0.268 2.43 ** 0.345 5.27 *** Other ownership -0.272 -2.54 *** -0.283 -4.62 *** Occupations (default one is professionals)

Private enterprise owner -0.194 -1.26 0.187 2.35 ** Blue collar -0.042 -1.11 -0.172 -7.26 *** Other occupations -0.038 -0.31 -0.305 -4.99 *** Industries (default one is manufacture)

Primary industries 0.061 0.86 0.130 2.30 ** Construction 0.128 1.78 * 0.092 1.97 ** Transportation & communication 0.376 8.04 *** 0.283 7.81 *** Commerce 0.167 2.91 *** 0.071 2.15 ** Real estate 0.236 4.47 *** 0.214 6.61 *** Social welfare 0.360 5.88 *** 0.336 6.62 *** Education 0.323 6.18 *** 0.303 7.17 *** Sciences and research 0.264 3.02 *** 0.326 4.83 *** Financial sectors 0.444 4.69 *** 0.411 5.95 *** Government 0.337 7.83 *** 0.242 5.27 *** Other industries 0.195 2.59 *** -0.029 -0.58 Cities (default city is Beijing) Shenyang -0.451 -8.72 *** -0.469 -12.46 *** Jinzhou -0.637 -9.42 *** -0.498 -10.25 *** Nanjing -0.090 -1.57 -0.169 -4.31 *** Xuzhou -0.251 -3.66 *** -0.385 -7.76 *** Zhengzhou -0.396 -6.30 *** -0.471 -10.04 *** Kaifeng -0.771 -8.98 *** -0.887 -18.51 *** Pingdingshan -0.362 -5.08 *** -0.481 -9.22 *** Chengdu -0.356 -6.81 *** -0.427 -10.94 *** Zigong -0.513 -7.85 *** -0.691 -13.90 *** Nanchong -0.412 -6.07 *** -0.663 -13.27 *** Lanzhou -0.453 -8.01 *** -0.471 -12.53 *** Pingliang -0.533 -7.97 *** -0.594 -11.28 *** Selectivity variable -0.068 -0.57 -0.094 -0.91 No. of observations 1689 4592 Adjusted R-squared 0.248 0.278 Standard deviation 0.525 0.628

42

Appendix Table 2 Wage functions for 1998, 1995 and 1999 estimated with controls for sample selection (sub-

sample of workers who live with parents)

1988 1995 1999 CP Non-CP CP Non-CP CP Non-CP Male 0.121

(1.04) 0.051

(2.61)*** 0.588 (1.40)

0.072 (1.80)*

0.025 (0.13)

0.158 (3.23)***

Experience 4.26E-02 (1.11)

4.77E-02 (9.62)***

9.84E-02 (0.64)

3.54E-02 (3.50)***

-7.84E-02 (1.57)

3.87E-02 (3.51)***

Experience squared term -6.06E-04 (0.79)

-6.60E-04 (3.45)***

-1.04E-03 (0.43)

-2.88E-04 (0.96)

1.91E-03 (1.43)

-7.71E-04 (2.22)**

Full-time education in years 0.096 (2.23)**

0.061 (9.51)***

0.102 (0.67)

0.051 (4.94)***

-0.070 (1.21)

0.069 (5.20)***

Ethnic minority -0.127 (0.64)

0.075 (1.59)

-0.523 (0.57)

-0.303 (3.36)***

-0.466 (1.34)

0.280 (2.75)***

Urban collective -0.418 (2.83)***

-0.135 (6.04)***

-0.072 (0.26)

-0.170 (3.16)***

0.119 (0.38)

0.047 (0.66)

Private enterprises

n.a. -0.971 (7.16)***

n.a. -0.176 (1.46)

0.551 (0.78)

0.027 (0.30)

Foreign-owned or joint venture

n.a. -0.073 (0.64)

0.559 (1.08)

0.275 (3.04)***

n.a.

0.390 (3.60)***

Ownership (default variable is state-owned)

Other ownership

n.a. -0.536 (5.92)***

-1.287 (1.18)

-0.098 (1.07)

-0.320 (0.46)

-0.119 (0.94)

Private enterprise owner

n.a. 0.147 (1.31)

0.826 (0.59)

-0.068 (0.40)

n.a.

0.058 (0.29)

Blue collar 0.093 (0.66)

-0.024 (0.84)

-0.257 (0.45)

-0.190 (3.67)***

0.043 (0.17)

-0.084 (1.31)

Occupation (default variable is white collar)

Other occupations

n.a. -0.164 (1.35)

-0.408 (0.93)

-0.349 (5.36)***

0.084 (0.11)

-0.196 (1.53)

Primary industries

-0.254 (1.03)

0.072 (1.61)

0.689 (1.51)

-0.031 (0.25)

0.380 (0.68)

0.342 (2.04)**

Construction n.a. 0.029 (0.55)

0.180 (0.28)

-0.212 (1.62)

n.a.

0.325 (2.14)**

Transportation & communication

0.377 (1.78)*

-0.024 (0.63)

-0.353 (0.53)

-0.036 (0.43)

-0.061 (0.22)

0.162 (1.69)*

Commerce -0.247 (1.85)*

-0.050 (1.88)*

-0.106 (0.33)

-0.128 (2.39)**

0.054 (0.14)

0.174 (2.23)**

Real estate 0.083 (0.46)

0.025 (0.46)

0.420 (0.88)

-0.119 (1.24)

0.107 (0.35)

0.268 (3.37)***

Social welfare 0.137 (0.81)

-0.040 (0.65)

-1.547 (1.54)

-0.005 (0.04)

0.077 (0.18)

0.402 (2.94)***

Education -0.079 (0.48)

-0.058 (1.24)

0.587 (0.73)

0.128 (1.25)

0.002 (0.01)

0.314 (2.86)***

Sciences and research

-0.338 (1.74)*

-0.029 (0.52)

0.359 (0.44)

-0.102 (0.61)

-0.103 (0.25)

0.379 (2.21)**

Financial sectors 0.124 (0.61)

-0.021 (0.34)

0.977 (2.32)**

0.197 (2.01)**

0.220 (0.60)

0.567 (4.84)***

Government 0.033 (0.21)

0.027 (0.51)

0.595 (1.11)

0.073 (0.79)

-0.076 (0.24)

0.378 (3.26)***

Industry (default variable is manufacturing)

Other industries 0.379 (0.99)

-0.033 (0.45)

-0.161 (0.38)

-0.416 (5.02)***

0.227 (0.52)

-0.036 (0.35)

43

Constant term 0.839 (0.66)

1.440 (14.85)***

-1.146 (0.19)

2.493 (14.62)***

6.439 (3.97)***

2.112 (9.64)***

Selectivity variable 0.095 (0.33)

-0.388 (2.55)***

1.087 (0.66)

-0.331 (1.03)

-0.812 (1.79)*

0.098 (0.37)

No. of observations 98 2900 97 1707 87 882 Adjusted R squared 0.250 0.184 0.381 0.223 0.072 0.318 Standard deviation 0.295 0.484 0.437 0.737 0.513 0.672