Trends in China’s gender employment and pay gap:Estimating
gender pay gaps with employment
selection
Wei Chi ?,Bo Li
School of Economics and Management,Tsinghua University,Beijing 100084,China
a r t i c l e i n f o Article history:Received 24October 2012Revised 27June 2013Available online 16July 2013JEL classi?cation:J3Keywords:Gender pay gap Gender employment gap Heckman selection correction Bounds Instrumental variable
a b s t r a c t
Chi,Wei,and Li,Bo —Trends in China’s gender employment and pay gap:Estimating gen-
der pay gaps with employment selection
In contrast to the United States and European countries,China has witnessed a widening
gender pay gap in the past two decades.Nevertheless,the size of the gender pay gap could
still be underestimated as a result of not accounting for the low-wage women who have
dropped out of the labor force.As shown by a large and representative set of household sur-
vey data in China,since the 1980s the female employment rate has been falling and the gap
between male and female employment rates has been increasing.We estimate the gap size
using Heckman’s selection-correction model and bounds of the raw gender pay gap,taking
into consideration the different male and female employment rates in China.The results
support the view that the gender pay gap estimate is biased without taking into account
employment selectivity.Journal of Comparative Economics 42(3)(2014)708–725.School
of Economics and Management,Tsinghua University,Beijing 100084,China.
ó2013Association for Comparative Economic Studies Published by Elsevier Inc.All rights
reserved.1.Introduction
In contrast to the narrowing gender wage gap in the United States,1China has experienced a widening gender pay gap in
recent decades.2China also provides an interesting contrast to other transition countries,such as Poland,Hungary,Russia,Esto-
nia,and Slovenia,where the relative wages for women were unchanged or even improved during the transition from a cen-
trally-planned to a market economy.3
In theory,the gender wage gap could widen during the transition from a planned economy to a market economy,as a
result of deregulation of wage setting and rising discrimination in the labor market.On the other hand,increasing market
forces could punish employers with discriminatory tastes and reduce the gender pay gap (Becker,1957).This mechanism
has been con?rmed in Hungary,where both the overall gender wage gap and the gap due to different returns to the
endowments of men and women have declined (Jolliffe and Campos,2005).The improved economic status for women in
0147-5967/$-see front matter ó2013Association for Comparative Economic Studies Published by Elsevier Inc.All rights reserved.https://www.doczj.com/doc/455177228.html,/10.1016/j.jce.2013.06.008
?Corresponding author.Address:E51School of Economics and Management,Tsinghua University,Beijing 100084,China.Fax:+861062772021.
E-mail addresses:chiw@https://www.doczj.com/doc/455177228.html, (W.Chi),libo@https://www.doczj.com/doc/455177228.html, (B.Li).
1
Blau and Kahn (2006,1997)and Blau (1998).
2Gustafsson and Li (2000),Appleton et al.(2005),Ng (2007),and Chi and Li (2008).
3Glinskaya and Mroz (2000)and Reilly (1999)found that the gender pay gap in Russia did not change during the transition from 1992to 1996.Adamchik
and Bedi (2003)found no change in women’s relative wages in Poland during their transition years.Jolliffe and Campos (2005)showed that the gender wage
gap declined in Hungary from 1986to 1988.Improvement in women’s economic welfare was also found for Estonia and Slovenia by Orazem and Vodopivec
(2000).
W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725709 a transition country might also be explained by the change in the composition of the female labor force.Hunt(2002)showed that the gender wage gap fell by10points in East Germany during the economic transition,but a large part of this fall was due to the withdrawal of low-wage women from the labor market.
China has the world’s largest labor force,with an estimated0.78billion actively working people as of2010.4The male–female pay gap is one of the key issues concerning the vast number of workers in China.Although prior research has examined the gender wage gap,occupation segregation,and discrimination in China,5most studies have used samples of working indi-viduals to estimate gender pay gaps,with very few addressing male and female employment rates or how the gender employ-ment gap affects the pay gaps.
According to Chi and Li(2008),employment rates declined from1987to2004for both men and women,but more so for women.With only3years of data,they could not show a trend in the employment rate or gap.We extend Chi and Li(2008) by using20years of data to study the trend in gender employment and pay gaps.Appleton et al.(2005)proposed that the gender pay gap in China has stopped widening and has remained relatively static in recent years,suggesting that China may have‘‘crossed the river’’.We found a similar trend for the raw gender pay gap,but we argue that this?nding may be overly optimistic since it does not consider the decline in female employment rates in China.
This paper studies formal sector employment of men and women,similar to the work of Chi and Li(2008).As a transition country,China has a large informal employment sector,which has grown rapidly and has absorbed a large number of rural migrants and urban unemployed workers.However,informal employment is often excluded from of?cial employment sta-tistics.Therefore,little is known about the gender composition of workers in the informal sector.Zhang et al.(2006),Meng (2001),and a recent study by the International Labor Organization(ILO),6showed that there were fewer women than men working in urban China’s informal employment sector,especially among the self-employed.Meng(2001)found that for both wage-earners and the self-employed,migrants had higher incomes in the urban informal sector than those in the formal sector. Although we could not provide a formal estimate because of a lack of data,our study speculates that the estimate of the gender pay gap would likely be even larger if we had taken informal employment into account,since more men are employed in the informal sector and men earn higher wages than women in the informal sector.
From a methodological perspective,our study applies both Heckman’s selection-correction(Heckman,1979)and the bound method(Manski,1994;Manski and Pepper,2000;Blundell et al.,2007)to correct for employment selection in esti-mating gender pay gaps.Heckman’s selection-correction method is based on stringent parametric/semi-parametric assump-tions.It is more restrictive,and is potentially subject to misspeci?cation error.The bound method lies within the large and expanding literature of partial identi?cation.It requires minimal assumptions.Although it fails to point-identify the inter-ested parameters,it may still give informative bounds for the parameters of interest,and is generally more robust than Heck-man’s selection-correction method.However,the bound method may sometimes produce loose and uninformative results, making it dif?cult to draw a conclusion from the data.In summary,the two methods complement each other.By using both methods,we can check the robustness of our results.
Our main?ndings are that the employment rates in China have generally decreased since1989,and have declined more for women than for men,which has contributed to a widening gender gap in the employment rate.Our data also shows wid-ening gender pay gaps among working https://www.doczj.com/doc/455177228.html,ing Heckman’s selection-correction method,we?nd evidence that the raw gender pay gap has been underestimated for recent years.The bound results,however,are less conclusive.
The remainder of this article is organized as follows:Related studies are introduced in Section2.We describe the data and present descriptive results in Section3.Empirical methods are discussed in Section4.Heckman’s selection-correction and bound results are reported in Section5.Finally,we discuss the results and conclude the article in Section6.
2.Related studies
While many studies of the Chinese gender earnings gap show the gap widening since economic reform began in1978, results vary as to the amount of this increase.7However,the cause of the increase is relatively well established.The discrim-ination effect,which is the portion of the gender pay gap that cannot be explained by gender differences in productivity,has accounted for a larger portion of the gender gap and has been increasing(Chi and Li,2008;Ng,2007;Gustafsson and Li, 2000).Since previous studies have relied on cross-sectional data covering only a few years,the long-term trend in the gender pay gap remains unclear.
Regarding the gender employment gap,Chi and Li(2008)showed that both male and female employment rates had fallen from1996to2004for all age groups.Participation in work is in?uenced by one’s own characteristics such as education and experience,as well as family characteristics such as marital status,the presence of a young child in the household,and
4Data source:China Statistical Yearbook2011,Table4-1.https://www.doczj.com/doc/455177228.html,/tjsj/ndsj/2011/indexch.htm.
5Gustafsson and Li(2000),Appleton et al.(2005),Ng(2007),Meng and Miller(1995),Meng(1998a,b),Rozelle et al.(2002),Dong et al.(2003),Maurer-Fazio and Hughes(2002),and Maurer-Fazio et al.(1999).
6International Labor Organization,‘‘Women and men in the informal economy–Statistical picture’’.https://www.doczj.com/doc/455177228.html,/informal_economy_E.html.
7Gustafsson and Li(2000)found that the gender wage gap in urban China had increased from1988to1995,while Ng(2007)showed that the urban gender pay gap had decreased from the1980s to the early1990s,but had increased since the mid-1990s.Chi and Li(2008)found that gender earnings differentials in the urban labor market increased for both periods,from1987to1996and from1996to2004,but the increase was more pronounced in the later period. Appleton et al.(2005)showed that the raw gender pay gap had increased from1988to1999but decreased from1999to2002.
710W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725
spousal income.In China,the education level of urban workers has increased rapidly in the past two decades.The presence of a young child in the household reduces labor force participation,especially for women.However,this relationship decreases as the availability and affordability of childcare services increases.8The decline in publicly-funded childcare centers and the increase in childcare costs in recent years have had a negative impact on the labor force participation,particularly for women.
Spousal income should have a negative impact on an individual’s labor force participation because of the income effect. However,for Chinese urban women,this effect was small and insigni?cant(Xu,2011).Culture also in?uences the decision to work.In the pre-reform period,Chinese women were encouraged to work the same jobs as men,and work was guaranteed by the state.After the reform,the ideology of‘‘women holding up half the sky’’became less common and a patriarchal value system re-emerged,which led to an increase in the number of women withdrawing from the labor market(Croll,1995; Maurer-Fazio et al.,2007).Additionally,women may be discriminated against in the workforce,denied employment and promotions,and be laid off more quickly,contributing to their lower employment rate.For example,it has been found that urban Chinese women are more likely to be discharged and less likely to be re-employed during labor retrenchment(Dong and Pandey,2012;Giles et al.,2006;Appleton et al.,2002).
In summary,a number of factors in?uence participation in the labor market,and each has a different impact.Increases in education levels and childcare costs are likely to be associated with positive selection into work.On the other hand,the in-come effect suggests a negative employment selection,especially for women.The non-random employment selection im-plies that the gender wage gap estimated based on the employment sample could be biased.Recent research has begun to pay great attention to this bias.
Since the1980s,both the labor force participation rate and mean wages of women in the United States have been rising, while gender pay differentials have been narrowing.However,several studies have suggested that the narrowing gender pay gaps in the United States may have been overestimated as a result of overlooking changes in the composition of female workers.Mulligan and Rubinstein(2008)showed that selection of women for employment shifted from being negative in the1970s to positive in the1980s.As more skilled women joined the labor force,women’s relative wages increased.The researchers showed that most of the narrowing of the wage gap between genders was due to this change in the skill com-position of female workers.Blau and Kahn(2006)found that the gender pay gap decreased more rapidly in the1980s than in the1990s in the United States,but the differences between the two decades were mostly due to different sample selectivity in the two decades.Machado(2010),however,reached a more positive conclusion:that the gender pay gap in the United States had indeed narrowed substantially,and that the improvement in gender wage equality was not entirely a result of the selection effect.
Similar studies in Europe include Blundell et al.(2007),Albrecht et al.(2009),Picchio and Mussida(2011),and Beblo et al. (2003).Blundell et al.(2007)used the UK.Family Expenditure Survey data to estimate the non-parametric bounds to gender pay differentials for different age and education groups.Albrecht et al.(2009)extended the Machado and Mata(2005)meth-od to account for employment selectivity in gender pay gaps in the Netherlands.They found that if all the non-working wo-men were employed,the gender pay gap would be much larger.Picchio and Mussida(2011)adapted the hazard function estimator proposed by Donald et al.(2000)to estimate the gender wage gap in Italy.Their results also suggested an under-estimation of gender pay gaps without selection correction,especially at the bottom of the wage distribution.Beblo et al. (2003)found that accounting for self-selection into work had an impact on gender wage estimates and wage gap decompo-sition in their study of?ve EU countries.
Employment selectivity is also useful to explain cross-country differences in the gender pay gap.Olivetti and Petrongolo (2008)showed that both the gender employment gap and the gender pay gap varied signi?cantly across OECD countries,and that there was a negative relationship between the gender employment and gender pay gaps.Their analysis suggests that international variation in the gender pay gap would be smaller if the employment-rate differences across countries were ta-ken into consideration.
3.Data
3.1.China’s urban household surveys
This study uses data collected from China’s Urban Household Surveys from1988to2009.The Urban Household Survey has been conducted by China’s National Bureau of Statistics(NBS)every year since1987.9The survey gathers information from a large random sample of urban households through interviews.The NBS uses this data to produce aggregate statistics on employment and income,which are published in China Statistical Yearbooks.10The surveys use a strati?ed random sampling 8Maurer-Fazio et al.(2011)found that the labor force participation rate of Chinese urban women with pre-school children had fallen considerably from1990
to2000,and that living with grandparents helped keep the women in the labor market.Kilburn and Datar(2002)and Du(2008)showed that the availability of child care centers or an informal childcare arrangement(such as co-residence with grandparents)had a positive impact on the labor force participation of working-age women.However,the transition from a centrally-planned economy to a market economy has led to a signi?cant decline in publicly-funded childcare centers(Du and Dong,2010),and the cost of childcare centers has risen dramatically.According to a recent newspaper article,kindergarten is more expensive than college in China(Peter Ford,Christian Science Monitor,February23,2010).https://www.doczj.com/doc/455177228.html,/World/Asia-Paci?c/2010/0223/In-China-kindergarten-costs-more-than-college.
9We do not use1987data because data quality is poor in the?rst year of the survey.
10The overview of the survey methodology can be found in the yearbooks.
W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725711
female employment rates.Source:NBS Urban Household Survey data1989–2009;author’s
method.To ensure that the sample is representative,each year one-third of the households are rotated out and replaced by new households.Thus the household sample is completely renewed every three years.11
The survey begins with a monthly accounting of an individual’s basic information on demographics,education,employ-ment status,industry,and occupation,as well as the total monthly income,earnings,and other income components.The sur-vey then asks a set of questions about household living arrangement,housing type,and all kinds of household expenditures. The monthly data are then aggregated into yearly data and reported to the NBS.We used this yearly data provided by the NBS.
3.2.Sample and variables
Our study focuses on individuals of primary working age.The original sample size was618,041.We selected men aged 16–60and women aged16–55.The upper limit was set to exclude retired people;the normal retirement age is60for men and55for women in China.Because of the lower retirement age for women,many older women who are still capable of working begin temporary or part-time work or care for grandchildren or older parents,while men of the same age continue to work full-time.As a result,average income is signi?cantly reduced for older women,and the gender pay gap widens in this age group.If the older age group was included in our analysis,our estimates of the gender pay gap would be even higher.
As the result of age restriction,181,370observations were excluded from the study,of which?ve were deleted because information on the age was unavailable.Individuals with missing information for gender,education,labor force status or earnings were also excluded,resulting in a further reduction in the sample size by132.The?nal sample consisted of 214,296women and222,243men.Table A1shows the number of observations for each survey year.
The survey asks about the relationship between the respondent and the household head,such as whether the respondent is the household head herself,or if it is her spouse,child,or parent.Based on this question,we generate a dummy indicator of whether an individual has a young child aged66years.Table A2shows the percentage of men and women who have a young child.Those with a young child are mostly aged25–35years.12Based on the family relationship code,we are also able to identify a household head and her/his spouse.For all years,we identify a total of152,788couples.
Although the hourly wage rate would have been ideal to examine gender pay differentials,only annual earnings were available in our dataset.However,information on the number of hours worked was available in the2003–2006surveys. Thus,we calculated the hourly wage rate using annual earnings and hours of work,then calculated the gender gap in both the hourly wage and annual earnings for2003–2006.The results are shown in Table A3.Women,on average,worked2–3h fewer per month than men.The gender pay gap was larger when annual earnings were used in the calculation,suggesting that the gender earnings gap is to some extent caused by difference in the number of working hours.
11Potentially we could identify individuals in multiple waves of surveys and generate a3-year set of panel data,but unfortunately we were not given access to the unique household and individual identi?er,and thus were unable to match individuals over time.
12Since the early1980s,China has enforced its one-child policy in urban areas.Most couples have only one child,except for those having twins.Therefore,the difference between whether a couple has a young child and the number of young children is small.
712W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725
earnings.Source:NBS Urban Household Survey data1989–2009;author’s own calculation.Note:
taken for earnings.The?gure shows the mean log(earnings)for men and women.
Since hourly wages are available only for a few years,to study the long-term trend in the gender pay gap,we used annual earnings in RMB for our analysis.We took the logarithm of annual earnings,and estimated the gender pay gap by calculating the differences in the log earnings between men and women.
Two measures of employment can be generated from the survey data.One is the self-reported employment status,and the other is the dummy indicator of positive earnings.In the survey,self-reported employment status takes a value from1to 15,with1–7indicating employment in a state-owned,foreign,or private company,or in the government or other sectors, and8–15indicating non-working individuals,including retired and disabled persons,enrolled students,etc.Because the employment status question has non-standard phrasing,13we could not identify unemployed individuals.Therefore,we focus on the employment rate instead.We de?ne employment as equal to1if the self-reported status takes the value of1–7.Alter-natively,we de?ne employment equal to1if an individual reported positive earnings.These two measures of employment are signi?cantly correlated with a correlation coef?cient of0.80.14We report results based on the second measure–an individual is employed if her earnings are positive.15
3.3.Gender employment gap and raw gender pay gap
Fig.1shows that both male and female employment rates declined during the20-year period.The female employment rate dropped by20%points between1988and2009,whereas the male employment rate fell by12%points.Since2000,the female employment rate decreased rapidly while the male employment rate was stagnant and even increased slightly after 2005.As a result,the gender employment gap has widened notably since2005.By2009,the gender employment gap had risen to10%points,as compared to only2%points in1988.The decline in overall employment rates in urban China is related to economic restructuring and large-scale layoffs by state-owned enterprises in the1990s and2000s.Furthermore,the lower female employment rate and widening gender employment gap are caused by increasing childcare costs,diminishing avail-ability of publicly-funded childcare centers,income effects,and the resurrection of the traditional division of work between men and women.
13Usually a labor force survey asks whether a person is out of job,and if yes,whether he is actively looking for a job.
14Because self-reported employment status pertains to status in the last month of the survey year,typically December,while positive earnings indicate that an individual has done some work during the year,the two measures are not always consistent.For people who did not change work status during the year,the two measures give the same value.
15We calculated the employment rate by the same method with another household survey dataset,the Chinese Household Income Project(CHIP).CHIP data are provided by ICPSR,University of Michigan(https://www.doczj.com/doc/455177228.html,),and are only available for three years;1988,1995,and2002.Moreover,of?cial statistics on the urban registered unemployment rate from1988to2009were taken from the China Statistical Yearbooks.This rate refers to the ratio of urban registered unemployment to total urban employment.Urban registered unemployment includes people aged above16years and below the of?cial retirement age who are unemployed,are willing to work and have registered at the local employment service agency to apply for a job.These rates are shown in Fig.7.The trend for the urban registered unemployment rate is almost the reverse of that in the employment rate calculated with our data.The employment rate calculated with CHIP data in the years1988,1995,and2002is close to our estimate.This comparison provides some evidence for the reliability of our measurement.
Fig.2demonstrates average log earnings for men and women from 1988to 2009based on the sample of employed indi-
viduals.Although both male and female log earnings increased signi?cantly during this period,the gender pay gap also in-
creased.Fig.2shows that the raw gender pay gap,measured by male–female differences in mean log earnings,increased
from 0.2to the maximum of 0.4log points during the mid-2000s,then decreased to 0.35log points in 2008and 2009.
We also calculated the gender earnings gap as the ratio of average female earnings to male earnings.The ratio decreased
from 82%in the late 1980s and early 1990s to a low of 71%in the mid-2000s,then rose back up to 75%in 2009.
4.Empirical methods
4.1.Simulation
Similar to Olivetti and Petrongolo (2008),we conduct simulations to show potential bias in the gender earnings gap,given
positive employment selection.Let W and X denote the logarithm of wage and the conditional vector (including gender,age,
education,and year).When W is observed,the indicator variable employment (E )equals 1,and when W is not observed,E
equals 0.Assume that non-employed individuals earn a wage equal to a proportion (r )of the wage of employed
individuals.
gender pay gaps.Source:NBS Urban Household Survey data 1989–2009;author’s own calculation.‘‘raw gap’’)and simulated ones based on Eq.(1).The gender pay gap is measured by male–female earnings in RMB Yuan.Positive employment selection is assumed.r =0.9,0.7,and 0.5assumes observed earnings of workers.The employment rate and observed wages used in the simulation are Table 1
Employment rates of women and men with and without young children in the 1990s and 2000s.Source:NBS Urban Household Survey data 1989–2009;
author’s own calculation.
1990s
2000s All years Panel A
Employment rates
Women with young children
96.6479.7585.80Women without young children
96.7981.01*86.65*Men with young children
99.2689.6793.86Men without young children
96.74*83.95*87.62*Panel B
Log earnings
Women with young children
7.747.407.52Women without young children
7.807.487.58Men with young children
8.118.628.40Men without young children 7.95*7.91*7.92*
Note:Calculation is based on men and women 25–35years old.
*Indicates that Pearson Chi 2test is signi?cant at the 1%level.
W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725715
Table3
Spousal income and men and women’s employment selection,1992,2002,and2009.Source:NBS Urban Household Survey data1989–2009;author’s own calculation.
Spousal income quintiles
12345
Panel A
Female employment rate(%)
199285.2293.8891.1490.9191.97 200267.9879.9580.4277.9079.88 200960.0371.2374.7074.8777.34
Panel B
Male employment rate(%)
199291.5097.7399.2198.1899.43 200286.4191.1194.0095.1193.89 200983.4289.6491.0893.0392.81
Note:The calculation is based on a matched set of husbands and wives.The number in each cell shows married women or men’s employment rate by spousal income quintile.Spousal income is annual labor income in RMB Yuan,and is divided into5quintiles,with1–5indicating the lowest to the highest 20%of income earners.
If there is a positive selection into employment,non-workers would earn a lower wage than workers even if they worked.In such a case r is less than1.The actual average wage for each subgroup with characteristics x can be calculated from the ob-served average wage via the following equation:
EeW j xT?EeW j x;E?1T?rte1àrTPexT e1T
Here P(x)=P(E=1|x)is the employment rate for the subgroup.
The true gender pay gap can be derived from Eq.(1):
EeW mTàEeW fT??EeW m j E m?1TàEeW f j E f?1T àe1àrT?e1àP mTEeW m j E m?1Tàe1àP fTEeW f j E f?1T e2TIf both the employment rate and observed mean wage are higher for men than women,i.e.,P m>P f and E(W m|E m=1)>-E(W f|E f=1),which is a common case,the sign of(1àP m)E(W m|E m=1)à(1àP f)E(W f|E f=1)is,thus,not determined.
We simulate three scenarios where r=0.9,0.7,and0.5,using male and female employment rates and earnings of workers from our data.Fig.3shows the simulated gender pay gaps from1988to2009.The results support that the raw gender pay gap is underestimated.In addition,as the gender employment gap rises over time,the extent to which the gender pay gap is underestimated increases.For a given year,the smaller r is(i.e.,the lower the shadow wage for the non-working),the greater the extent to which the gender pay gap would be underestimated.
4.2.Estimating gender pay gaps with Heckman’s selection-correction method
We adopt two parallel approaches to correct for non-random employment selection in estimating gender pay gaps:Heck-man’s selection-correction method;and the bound method.Instrumental variables(IV)are needed in Heckman’s sample-selection model(Heckman,1979;Mulligan and Rubinstein,2008;Beblo et al.,2003).The instrumental variables should be able to predict the employment probability but are uncorrelated with wages after counting for observables.The IV we use is a dummy variable indicating whether an individual has a young child aged66years.This variable is widely used in the relevant literature as an IV for employment selection(Mulligan and Rubinstein,2008).However,the validity of this IV has not been fully tested in China.
To demonstrate the validity of this IV for our sample,we report the employment rate for men and women both with and without a young child(Table1and Fig.A2).Our calculation is based on the sample of men and women aged25–35years.16 Table1shows that among those in that category,women without a young child have a higher employment rate than those with a young child.The difference is less than1%point,but is statistically signi?cant at the1%level,based on the data from all years. We also divide the data into two periods,the1990s and2000s,and?nd that the impact of having a young child on females’employment is more evident in the2000s.
For men,having a young child had a different impact on the employment probability and earnings than for women.The calculations show that men with a young child are more likely to work and have higher earnings than those without a young child.This?nding indicates that having a young child may motivate men to work harder in China.Fig.8shows further
16Pooling people of all ages in the calculation could confound the result.Because individuals with a young child are mostly aged25–35years,while those without a young child are older,the employment rate differences between them could be due to age rather than having a young child.
W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725717
and gender pay gap bounds.Source:NBS Urban Household Survey data1989–2009;author’s own
and estimated upper and lower bounds of the gap with employment selection.The raw gender
earnings.Earnings are annual earnings in RMB Yuan.
evidence of the lower employment rate for women with young children in some years.We perform statistical tests on the signi?cance of the IV with the?rst-stage Heckman regression,providing more rigorous support for the relevance of IV.17 4.3.Estimating the bounds to the gender pay gap with employment selection
Since non-workers’wages are not observed,we need to estimate the non-workers’shadow wage.Although shadow wages can be estimated,a direct method would generally require restrictive assumptions.However,a series of bounds of the gender pay gap can be estimated with presumably less restrictive assumptions.Instead of obtaining a point estimate to the corrected gender pay gap,the bound method would only provide an upper and lower bound to the gender pay gap 17If F-statistics on the joint signi?cance of all excluded variables in the?rst-stage regression is greater than10,or in the case of a single IV,t-statistics is greater than3.2,the IV is considered to be relevant.
with non-random employment selection taken into account (Manski,1994;Blundell et al.,2007).In the following we pro-vide only the heuristics of the bound methods.For the detailed derivation,readers may refer to Blundell et al.(2007).
The worst-case bounds are the bounds that entail no assumption.The rationale is as follows:we notice that the cumu-lative distribution function (cdf )of the complete population,including both working and non-working people,F (w|x ),is a weighted average of the cdf s of workers F (w |x ,E =1)and non-workers F (w |x ,E =0).The weight is the conditional employ-ment probability,P (x )=P (E =1|x ).That is,
F ew j x T?F ew j x ;E ?1TP ex TtF ew j x ;E ?0T?1àP ex T e3TThe cdf for workers and the conditional employment probability can be estimated from the data,but the cdf for non-work-ers is unidenti?ed.However,since the cdf for non-workers is between 0and 1,we replace F (w |x ,E =0)in Eq.(3)with 1and get the upper bound to F (w |x ).If we replace it with 0,we get the lower bound.As a result,we obtain the following inequality:
F ew j x ;E ?1TP ex T6F ew j x T6F ew j x ;E ?1TP ex Tt1àP ex Te4TEq.(4)can then be translated to worst-case bounds on the wage quantiles for men and women,respectively,and then the bound to the gender pay gap at each quantile.
The preceding worst-case bounds are universally correct,but could be too crude and hence uninformative.To tighten the bounds,one can impose a positive selection assumption.The positive selection assumption can be translated to a stochastic dominance premise,which suggests that the cdf of workers would dominate that of non-workers.However,we need to point out that while the assumption of positive selection is generally plausible,it is not incontrovertible and can fail for certain subgroups.For instance,if more capable 20-year-old persons choose to study in schools (i.e.,positive selection into education),the shadow wages for this particular age subgroup will be higher than the observed wages.18Thus,the empirical results following the stochastic dominance assumption need to be embraced with caution.In the case that this assumption holds,we can deduce:
(a) 1992 junior high or below (d) 2002 junior high or below (b) 1992 high school (e) 2002 high school (c) 1992 college
(f) 2002 college
(g) 2009 junior high or below (h) 2009 high school (i) 2009 college
gender pay gaps and bounds for age–education subgroups,1992,2002,and 2009.Source:NBS Urban Household Survey data own calculation.Note:The ?gure shows the raw gender pay gap and the upper and lower stochastic-dominance bounds for each gender pay gap is measured by male–female differences in median log (earnings).Earnings are annual earnings in RMB Yuan.
18We thank a referee for his/her insightful point.
718W.Chi,B.Li /Journal of Comparative Economics 42(2014)708–725
W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725719 Few j x;E?1T6Few j xT6Few j x;E?1TPexTt?1àPexT e5TThe left bound is tighter than the previous worst-case version.This transcends to tighter bounds to the gender pay gap.
IVs can be employed to tighten the bounds as well.IVs are determinants of the employment,which,on the other hand,are conditionally independent of the wage,i.e.,F(w|x,z)=F(w|x)The intuition of IV bounds is that we can deduce similar inequal-ities to(4)with respect to F(w|x,z)In general,the bounds for F(w|x,z)derived in this way will depend on z.But,because of the IV assumption,F(w|x,z)does not depend on z per se.Thus,the maximum of the lower bounds of F(w|x,z)over z will give a tighter lower bound to F(w|x,z)(equivalently,F(w|x))than the worst-case bound.Similarly,the minimum of the upper bounds of F(w|x,z)over z will give a tighter upper bound to F(w|x).
The same IV variable,the presence of young children,is used in the IV bounds estimation as in Heckman’s selection-cor-rection model.While the Heckman method assumes the speci?c functional form for how the IV affects endogenous employ-ment variables,the IV bounds do not assume any functional form and thus are more robust.
So far our focused parameter is the conditional cdf F(w|x)from which(conditional)quantiles can be calculated.This will give us results about the gender pay gap for various subgroups corresponding to particular values of x.When x is averaged over the entire sample,we obtain the gender pay gap bounds to the unconditioned cdf.
5.Results
5.1.Evidence of employment selection
Table2shows the gender employment gap and the gender pay gap by marital status,age,and education.Our focus is to compare male–female employment rates and earnings across groups.Therefore,we show the detailed results only for three years–1992,2002,and2009.Figs.A3–A5show the trend for subgroups for additional years.
We discover the following patterns from Table2.First,more educated individuals had a higher employment rate than less educated individuals.This is true for both men and women.This pattern became more pronounced in recent years.19For each of the three years,the gender employment gap and the gender pay gap were narrower for the better educated.This?nding may be related to labor market conditions in China.Although college enrollment has increased dramatically in recent years,resulting in a signi?cant increase in the supply of college-educated workers,there is still a shortage of highly skilled professionals.20Un-der such labor market conditions,skilled female workers may be less likely to face employment and wage discrimination,since employers may?nd it dif?cult to replace them with enough skilled male workers.
Second,we?nd an interesting pattern with respect to marital status.In general the employment rate was lower for sin-gles.This is likely because of their young age and student status.However,single women had a higher employment rate than single men.Single women also had relatively higher pay,so that the gender pay gap was markedly smaller for singles.
Third,regarding age,we?nd that the employment rate generally increased with age for both men and women.For the youngest group(aged18–22years),the employment rate was fairly low and became even lower in recent years.This is also likely due to increasing college availability and enrollment.Interestingly,both gender employment and pay gaps were the smallest for the youngest group;women from this age group actually had a higher employment rate than their male coun-terparts,even though this pattern attenuated over time,and for this group female earnings were close to male earnings.Over time,the second youngest group(aged23–27years)started to show a similar pattern.
The?nding that young single women have a higher employment rate than their male counterparts while older married women have a signi?cantly lower employment rate than their male counterparts,suggests that women’s employment deci-sion is more likely to be impacted by their family characteristics.While younger single women are equally likely to partic-ipate in the labor market as men of their age,the decision of older married women to work is more likely to be affected by the situation of their spouse and the presence of young children.Appendix Figs.A3–A5con?rm that both the gender employ-ment and pay gaps are wider for the less-educated,and narrower for the young unmarried group.
To further provide evidence for employment selection,we use the matched set of husbands and wives to show the rela-tionship between the employment rate and spousal income.We calculate the employment rate for men and women by spou-sal income quintile.Spousal income is divided into?ve quintiles:the bottom20%;20–40%,40–60%,60–80%,and the top20%. Table3shows that the employment rate of married men is not negatively affected by higher spousal income.Likewise,we do not?nd that women with a relatively wealthier husband are less likely to work.
Tables2and3show descriptive results regarding employment selection.For more rigorous evidence,we performed a probit regression of employment selection(Table4).The dependent variable is a dummy variable that indicates whether one works.Explanatory variables include age,education,the presence of young children in the household and spousal in-come.The regression results support our conclusions that the employment rate is higher for those with higher education levels.The presence of young children in the household affects the employment probability of women more negatively than men.The regression results also suggest that,when all other factors have been controlled for,the impact of spousal income on employment probability was negative,but was statistically insigni?cant for both men and women during the earlier 19The only exception is for men in1992,when men with a primary education had a90%employment rate while those with a secondary education had a83% employment rate,and the earnings of the two groups were similar.
20‘‘China’s looming talent shortage’’,Diana Farrell and Andrew J.Grant,McKinsey Quarterly,November2005.
period under study.However,the effect of spousal income has become signi?cantly negative for women in recent years.This suggests that the income effect has increasingly affected married women’s decision to work in urban China.21
5.2.Heckman’s selection-correction results
We estimate Heckman’s selection-correction model for each year,using the presence of young children in a household as an IV.A test for signi?cance of the IV in the ?rst-stage regression suggested that it is relevant for most of the years after
2000.
21
The regression result regarding the impact of spousal income (Table 4)differs from the descriptive results in Table 3.The differences are likely because the education level of women was not controlled for in the descriptive analysis.Women with spouses that have higher incomes are also likely to have a higher education level,which leads to a higher employment probability.Women of the same education level are less likely to work when their spouses have higher incomes.720W.Chi,B.Li /Journal of Comparative Economics 42(2014)708–725
W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725721
The Likelihood Ratio test of non-selectivity in the Heckman regression was rejected for most of the years,suggesting that selectivity is present and the OLS estimates are biased.
To compare the OLS and Heckman models,we plotted the coef?cient estimate for the gender dummy variable in both the OLS regression and the second-stage Heckman regression(Fig.4).The Heckman method provides a point estimate to the corrected gender pay gap.Fig.4shows that the OLS regression underestimates the gender pay gap after the year2000.Since we used the logarithm of earnings as the dependent variable,the OLS estimate suggests that after2005men earned34–38% more than women.However,results from the second-stage Heckman regression indicate that men earned38–43%more. Thus,the OLS estimate is lower than the Heckman estimate by4–5%points.22
5.3.The bounds of the gender pay gap
Fig.5shows the raw gender pay gap and the bounds estimates for1988–2009.The worst-case bounds are the least infor-mative,as the lower bound crosses zero for most of the years from1995to2009.To tighten the bounds,we assume the po-sitive selection into employment.Including this assumption tightens the lower bound of the gender pay gap signi?cantly, making it above zero for all years.Before1995,male and female employment rates were high and the gender employment gap was small;hence the bounds were rather tight.After2000,the gender employment gap widened signi?cantly,and the bounds became loose.As can be seen,however,the lower bound remained steady while the upper bound increased.In2009, the lower and upper bounds with the assumption of stochastic dominance were0.02and0.88,respectively,in contrast with the raw gender pay gap,0.27.
We then use the IV bounds of women’s earnings and the stochastic dominance bounds of men’s earnings to create the gender pay gap bounds.These estimated bounds are shown in Fig.5as the‘‘IV bounds’’.The IV bounds are tighter than the stochastic dominance bounds.After2000,the lower IV bound almost overlaps with the raw gap.For example,in 2009the IV lower and upper bounds are0.28and0.75,respectively,while the raw gender pay gap is0.27.
22That is12–14%by calculation,e.g.,(38à34)/34=12%.
722W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725
The bound method provides useful information in two ways.First,the bound estimates are consistent with the simulation results presented in Section4,indicating that as the employment rate falls and the gender employment gap widens,non-random employment selectivity becomes a more important factor affecting the gender pay gap estimate.Second,although the bound method does not provide a point estimate to the true gender pay gap,which can be achieved by Heckman’s selec-tion-correction method under more stringent model assumptions,it still produces useful range estimates of the focus parameters under certain assumptions.
5.4.Bounds for subgroups
Furthermore,we estimate the gender pay gap bounds for age–education subgroups for1992,2002,and2009(Fig.6).In 1992,the bounds are quite tight for most of the groups,except for age20and age50groups with an education of junior high or below,and the age20group with a high school education.For2002and2009,the bounds are still relatively tight for col-lege-educated individuals aged30–50years.The bounds are generally loose for the groups with no more than high school education.
Before the mid-1990s,employment in urban areas was concentrated in the state sector.Employment was secured and layoffs were rare.The employment rate was high across many age groups and education levels,except for the young, who enrolled in college,or older groups,who retired.The employment rate was high for both men and women.Thus, non-random selection into employment was not an important factor affecting the gender wage gap before1995.Since this time,state sector employment has been signi?cantly reduced,and layoffs have increased.The employment rate has fallen for both men and women.Women are also more likely to selectively withdraw from the labor force.Therefore,employment selectivity has become more signi?cant,leading to the wider bounds of the gender pay gap in the late1990s and2000s.
In general,the bounds are tighter for college graduates than for groups with less education.This is because,especially in recent years,the employment rate is higher and the gender employment gap is smaller for college graduates.Although col-lege graduates face increasing dif?culty?nding employment because of the rising supply of college graduates in recent years
W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725723
Table A1
Sample description.Source:NBS Urban Household Survey data1989–2009;
author’s own calculation.
The number of observations
198811,286
198910,502
199011,144
199110,967
199212,393
199311,788
199411,854
199511,867
199611,888
199712,058
199812,021
199912,102
200011,884
200111,967
200228,299
200331,930
200434,327
200536,052
200636,306
200736,267
200835,088
200934,549
Total436,539
724W.Chi,B.Li/Journal of Comparative Economics42(2014)708–725
Table A2
Having a young child by age.Source:NBS Urban Household Survey data1989–2009;author’s own calculation.
Men Num.of obs.%Having a young child Women Num.of obs.%Having a young child
<25Years old39,597 1.32<25Years old38,427 4.03
25–3539,34743.9825–3547,20042.32
>35143,299 3.66>35128,669 1.88
Table A3
Annual earnings,hourly wages,and working hours,2003–2006.Source:NBS Urban Household Survey data2003–2006;author’s own calculation.
Year Hours worked last month Log(hourly wage)Difference in log(wage)Log(annual earnings)Difference in log(earnings) Men Women Men Women Men Women
2003180.6177.2 1.64 1.360.289.208.840.36
2004180.8177.3 1.82 1.510.319.388.980.40
2005181.4178.3 1.93 1.620.319.509.090.41
2006180.7177.8 2.04 1.750.299.629.220.40
Note:Hours worked last month is only available for2003–2006.Since yearly data is collected and reported to NBS in the end of a calendar year,hours worked in last month is referred to hours worked in December.Hourly wages are equal to annual earnings divided by hours worked last month multiplied by12.Both hourly wages and annual earnings are in RMB Yuan.Logarithm is taken for hourly wages and earnings.The differences in log(hourly wages)and log(annual earnings)between men and women are calculated.
(Whalley and Xing,2011),our data show that college graduates still have a higher employment rate than less-educated individuals.In particular,female college graduates have a higher employment rate than less-educated women.Therefore, employment selectivity does not in?uence the gender wage gap as much for college graduates.
6.Conclusion
Men and women often have different employment rates,and because of non-random selection into employment,the gen-der pay gap estimate is likely to be biased.Our article is motivated by the observation that little is known about the male–female employment rate gap in China,or the extent to which the gender employment gap causes the bias in the gender pay gap.
We show the trend in the gender employment gap and gender pay gap in China during the period1988–2009,and esti-mate the gender pay gap using Heckman’s selection-correction method.The Heckman results suggest an underestimation of the raw gender pay gap by12–14%for the years2005–2009.The worst-case bounds are uninformative because the lower bound crosses zero for many of the years.When stochastic dominance of the earnings of workers over non-workers is as-sumed,the lower bound is tightened signi?cantly.We further tighten the bound by using the IV–whether an individual has a young child aged less than or equal to6years–which is often used to correct employment selection.However,even with these tightened bounds,the raw gender pay gap is between the estimated lower and upper bound for many of the years, which leads to a more modest conclusion that the bounds estimates support a possible mis-estimation,rather than an underestimation,of the raw gender pay gap.
Our article contributes to the growing literature on the gender pay gap in transition countries,speci?cally in China.Our study has a couple of important policy https://www.doczj.com/doc/455177228.html,bor policies that focus on narrowing the wage gap between men and women should also look at skill composition of the male and female labor force because of employment selectivity. Policymakers should be aware of the implications of such an employment selection for the gender wage gap estimate.Fu-ture research is needed to examine the determinants of labor force participation and employment for both men and wo-men,and to identify the causes of the widening gender employment gap.This will lead to a better understanding of changes in the skill levels of men and women in the labor force and the effect that such changes have on the gender wage gap in China.
Acknowledgments
Wei Chi would like to acknowledge?nancial support from National Natural Science Foundation of China(Grant No. 71121001),and Tsinghua University Research Grant(Grant No.2010THZ0).Bo Li acknowledges support from National Natural Science Foundation of China(Grant No.71072012,71272029).
Appendix A
See Figs.A1–A5.Tables A1–A3.
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硬件类常用英语词汇 下面是小编整理的硬件类常用英语词汇，希望对大家有帮助。 计算机英语词汇大全 常见硬件篇 CPU：Central Processing Unit，中央处理单元，又叫中央处理器或微处理器，被喻为电脑的心脏。 LD：Laser Disk，镭射光盘，又称激光视盘。 CD：Compact Disc，压缩光盘，又称激光唱盘。 CD-ROM：Compact Disc-Read Only Memory，压缩光盘-只读记忆(存储)，又叫“只读光盘”。 VCD：Video Compact Disc，视频压缩光盘，即人们通常所说的“小影碟”。 RAM：Random Access Memory，随机存储器，即人们常说的“内存”。 ROM：Read-Only Memory，只读存储器。 Seagate：美国希捷硬盘生产商。Seagate英文意思为“通往海洋的门户”，常指通海的运河等。 Quantum：英文含意为“定量，总量”。著名硬盘商标，美国昆腾硬盘生产商(Quantum Corporation)。
Maxtor：“水晶”，美国Maxtor硬盘公司。 PCI：Peripheral Component Interconnection，局部总线(总线是计算机用于把信息从一个设备传送到另一个设备的高速通道)。PCI总线是目前较为先进的一种总线结构，其功能比其他总线有很大的提高，可支持突发读写操作，最高传输率可达132Mbps，是数据传输最快的总线之一，可同时支持多组外围设备。PCI不受制于 CPU处理器，并能兼容现有的各种总线，其主板插槽体积小，因此成本低，利于推广。 EDO：Extended Data Output，扩充数据输出。当CPU的处 理速度不断提高时，也相应地要求不断提高DRAM传送数据速度， 一般来说，FPM(Fast Page Model)DRAM传送数据速度在60-70ns，而EDO DRAM比FPM快3倍，达20ns。目前最快的是SDRAM(Synchronous DRAM，同步动态存储器)，其存取速度高 达10ns。 SDRAM：Synchronous Dynamic Random Access Memory，同步动态随机存储器，又称同步DRAM，为新一代动态 存储器。它可以与CPU总线使用同一个时钟，因此，SDRAM存储 器较EDO存储器能使计算机的性能大大提高。 Cache：英文含义为“(勘探人员等贮藏粮食、器材等的)地窖; 藏物处”。电脑中为高速缓冲存储器，是位于CPU和主存储器 DRAM(Dynamic Randon Access Memory)之间，规模较小，但 速度很高的存储器，通常由SRAM(Static Random Access
企业各职位英文缩写： GM(General Manager)总经理 VP(Vice President)副总裁 FVP(First Vice President)第一副总裁 AVP(Assistant Vice President)副总裁助理 CEO(Chief Executive Officer)首席执行官，类似总经理、总裁，是企业的法人代表。 COO(Chief Operations Officer)首席运营官，类似常务总经理 CFO(Chief Financial Officer)首席财务官，类似财务总经理 CIO(Chief Information Officer)首席信息官，主管企业信息的收集和发布CTO(Chief technology officer)首席技术官类似总工程师 HRD(Human Resource Director)人力资源总监 OD(Operations Director)运营总监 MD(Marketing Director)市场总监 OM(Operations Manager)运作经理 PM(Production Manager)生产经理 (Product Manager)产品经理 其他： CAO: Art 艺术总监 CBO: Business 商务总监 CCO: Content 内容总监 CDO: Development 开发总监 CGO: Gonverment 政府关系 CHO: Human resource 人事总监 CJO: Jet 把营运指标都加一个或多个零使公司市值像火箭般上升的人 CKO: Knowledge 知识总监 CLO: Labour 工会主席 CMO: Marketing 市场总监 CNO: Negotiation 首席谈判代表CPO: Public relation 公关总监 CQO: Quality control 质控总监 CRO: Research 研究总监 CSO: Sales 销售总监 CUO: User 客户总监 CVO: Valuation 评估总监 CWO: Women 妇联主席 CXO: 什么都可以管的不管部部长 CYO: Yes 什么都点头的老好人 CZO: 现在排最后，等待接班的太子 常用聊天英语缩写
人教版新起点小学英语词汇总 一年级上（没有分单元） book书ruler尺子pencil铅笔eraser橡皮pencil case 铅笔盒 backpack书包school学校 eye眼睛hand手ear耳朵mouth嘴nose鼻子foot(feet)脚 face脸leg腿arm手臂 cat猫bird鸟rabbit兔dog狗chicken鸡duck鸭 monkey猴子tiger虎panda熊猫elephant 大象fish鱼 one一two二three三four四five五six六 seven七eight八nine九ten十 red红色(的) yellow黄色(的) purple紫色(的) brown棕色(的) orange橙色(的) white白色(的) green绿色(的) pink粉红色(的) blue蓝色(的) black黑色(的) apple苹果banana香蕉peach桃melon瓜 pear梨orange橙子grape葡萄strawberry草莓pineapple菠萝 一年级下 Unit9 classroom 教室door门window窗户blackboard黑板wall 墙desk 桌子chair 椅子boy 男孩girl 女孩in 里面on 上面under下面where哪里 unit 10 room房间closet衣柜telephone电话computer 电脑TV 电视bed 床picture图片table餐桌lamp台灯armchair 沙发behind后面next to旁边 Unit 11 Toys玩具plane飞机boat船train火车ball球teddy bear玩具熊 bus公交车Car小汽车doll玩具娃娃pinwheel风车box盒子 Unit 13 Shapes图形circle圆形triangle三角形rectangle长方形square 正方形 eleven 11 twelve12 Thirteen13 fourteen14 fifteen15 sixteen16 seventeen17 eighteen18 nineteen19 twenty20 Unit 14 Clothes衣服T-shirt T恤衫pants 裤子shorts短裤jacket 夹克 Sweater毛衣skirt短裙dress连衣裙shoe鞋子sock袜子 Unit 15 Food食物drink 饮料rice 米饭noodles面条jiaozi 饺子tofu豆腐Vegetables蔬菜meat牛肉fish 鱼肉chicken鸡肉bread 面包 milk牛奶ice cream冰激凌juice果汁egg鸡蛋
计算机英语-常见硬件 CPU：Central Processing Unit，中央处理单元，又叫中央处理器或微处理器，被喻为电脑的心脏。 RAM：Random Access Memory，随机存储器，即人们常说的"内存"。 ROM：Read－Only Memory，只读存储器。 EDO：Extended Data Output，扩充数据输出。当CPU的处理速度不断提高时，也相应地要求不断提高DRAM传送数据速度，一般来说，FPM（Fast Page Model）DRAM传送数据速度在60－70ns，而EDO DRAM比FPM快3倍，达20ns.目前最快的是SDRAM（Synchronous DRAM，同步动态存储器），其存取速度高达10ns. SDRAM：Synchronous Dynamic Random Access Memory，同步动态随机存储器，又称同步DRAM，为新一代动态存储器。它可以与CPU总线使用同一个时钟，因此，SDRAM存储器较EDO存储器能使计算机的性能大大提高。 Cache：英文含义为"（勘探人员等贮藏粮食、器材等的）地窖；藏物处"。电脑中为高速缓冲存储器，是位于CPU和主存储器DRAM（Dynamic Randon Access Memory）之间，规模较小，但速度很高的存储器，通常由SRAM（Static Random Access Memory静态存储器）组成。 CMOS：是Complementary Metal Oxide Semiconductor的缩写，含义为互补金属氧化物半导体（指互补金属氧化物半导体存储器）。CMOS是目前绝大多数电脑中都使用的一种用电池供电的存储器（RAM）。它是确定系统的硬件配置，优化微机整体性能，进行系统维护的重要工具。它保存一些有关系统硬件设置等方面的信息，在关机以后，这些信息也继续存在（这一点与RAM完全不同）。开机时，电脑需要用这些信息来启动系统。如果不慎或发生意外而弄乱了CMOS中保留的信息，电脑系统将不能正常启动。 PCI：Peripheral Component Interconnection，局部总线（总线是计算机用于把信息从一个设备传送到另一个设备的高速通道）。PCI总线是目前较为先进的一种总线结构，其功能比其他总线有很大的提高，可支持突发读写操作，传输率可达132Mbps，是数据传输最快的总线之一，可同时支持多组外围设备。PCI不受制于CPU处理器，并能兼容现有的各种总线，其主板插槽体积小，因此成本低，利于推广。
单元教材分析： 第一单元主要学习关于教室的3个单词desk, chair, blackboard ,三个表达位置的词汇：in, on, under;以及询问和表达位置的功能句：Where is…？It'under/ in/ on…。本单元是学生开学后的第一单元，与学生生活实际紧密联系，学习教室有关的物品名称，易于学生接受，容简单实用，易于教师操练。本单元为下个单元Room做了很好的铺垫，由教室到房间，使得下一步的学习变的简单，过度自然。 单元教学目标： 1、语言技能目标 (1)能够听懂、会说与教室有关的三个词汇：chair, desk, blackboard，以及三个表达位置的词汇：under/ in/ on。 (2)能够听懂、会说有关询问和表达位置的功能句：Where is…？It'under/ in/ on…，并能在恰当的情境中初步运用。 (3)能够听懂简短的课堂指令语，女口：Put your ... in/on/under ...等，并作出相应的反应。 (4)能够借助日常生活图片识别、会说大写英文字母A、B、C、D。 2、情感目标 (1)能够跟随录音大胆模仿说唱歌曲和歌谣。 (2)能够对英语学习保持兴趣，并积极参与课堂上组织的各种活动；能做到有序参与，积极使用英语。 (3)能够在活动中逐步养成爱护教室的课桌椅和学习用品的习惯。 单元教学重点：与教室有关的3个词汇：desk, chair, blackboard ;三个表达物品位置的介词：under / in / on 。 单元教学难点：句型Where is…？It's…的使用。 单元课时安排：五课时 第一课时 教学目标： 能够在适当场景下听懂、说出与教室有关的三个词汇：blackboard，desk，chair;以及三个表达位置的词汇：in, on, un der. 教学重难点： 能听懂、说出与教室有关的三个单词：blackboard，desk，chair；以及三个
DARPA ：国防高级研究计划局 ARPARNET(Internet) ：阿帕网 ICCC ：国际计算机通信会议 CCITT ：国际电报电话咨询委员会 SNA ：系统网络体系结构(IBM) DNA ：数字网络体系结构(DEC) CSMA/CD ：载波监听多路访问/冲突检测(Xerox) NGI ：下一代INTERNET Internet2 ：第二代INTERNET TCP/IP SNA SPX/IPX AppleT alk ：网络协议 NII ：国家信息基础设施(信息高速公路) GII ：全球信息基础设施 MIPS ：PC的处理能力 Petabit ：10^15BIT/S Cu芯片: ：铜 OC48 ：光缆通信 SDH ：同步数字复用 WDH ：波分复用 ADSL ：不对称数字用户服务线 HFE/HFC：结构和Cable-modem 机顶盒 PCS ：便携式智能终端 CODEC ：编码解码器 ASK(amplitude shift keying) ：幅移键控法 FSK(frequency shift keying) ：频移键控法 PSK(phase shift keying) ：相移键控法 NRZ (Non return to zero) ：不归零制 PCM(pulse code modulation) ：脉冲代码调制nonlinear encoding ：非线性编程 FDM ：频分多路复用 TDM ：时分多路复用 STDM ：统计时分多路复用 DS0 ：64kb/s DS1 ：24DS0 DS1C ：48DS0 DS2 ：96DS0 DS3 ：762DS0 DS4 ：4032DS0 CSU(channel service unit) ：信道服务部件SONET/SDH ：同步光纤网络接口 LRC ：纵向冗余校验 CRC ：循环冗余校验 ARQ ：自动重发请求 ACK ：确认 NAK ：不确认
电子类常用缩写(英文翻译) AC(alternating current) 交流(电) A／D(analog to digital) 模拟／数字转换 ADC(analog to digital convertor) 模拟／数字转换器 ADM(adaptive delta modulation) 自适应增量调制 ADPCM(adaptive differential pulse code modulation) 自适应差分脉冲编码调制 ALU(arithmetic logic unit) 算术逻辑单元 ASCII(American standard code for information interchange) 美国信息交换标准码 AV(audio visual) 声视，视听 BCD(binary coded decimal) 二进制编码的十进制数 BCR(bi-directional controlled rectifier)双向晶闸管 BCR(buffer courtier reset) 缓冲计数器 BZ(buzzer) 蜂鸣器，蜂音器 C(capacitance，capacitor) 电容量，电容器 CATV(cable television) 电缆电视 CCD(charge-coupled device) 电荷耦合器件 CCTV(closed-circuit television) 闭路电视 CMOS(complementary) 互补MOS CPU(central processing unit)中央处理单元 CS(control signal) 控制信号 D(diode) 二极管 DAST(direct analog store technology) 直接模拟存储技术 DC(direct current) 直流 DIP(dual in-line package) 双列直插封装 DP(dial pulse) 拨号脉冲 DRAM(dynamic random access memory) 动态随机存储器 DTL(diode-transistor logic) 二极管晶体管逻辑 DUT(device under test) 被测器件 DVM(digital voltmeter) 数字电压表 ECG(electrocardiograph) 心电图 ECL(emitter coupled logic) 射极耦合逻辑 EDI(electronic data interchange) 电子数据交换 EIA(Electronic Industries Association) 电子工业联合会 EOC(end of conversion) 转换结束 EPROM(erasable programmable read only memory) 可擦可编程只读存储器 EEPROM(electrically EPROM) 电可擦可编程只读存储器 ESD(electro-static discharge) 静电放电 FET(field-effect transistor) 场效应晶体管 FS(full scale) 满量程 F／V(frequency to voltage convertor) 频率／电压转换 FM(frequency modulation) 调频
招呼篇 GTSY：Glad To See You高兴认识你 PMJI：Pardon My Jumping In ＝PMFJI：Pardon Me For Jumping In 败势，加入你们的谈话 WB：Welcome Back 欢迎回来 LTNS：Long Time No See 好久不见 笑篇 BEG：Big Evil Grin （非常）邪恶的笑 C＆G：Chuckle And Grin 喀喀笑 GMBO：Giggling My Butt Off 笑掉我的屁屁 BWL：Bursting With Laughter 笑掉不行 CSG：Chuckle Snicker Grin 嘿嘿窃笑 KMA：Kiss My A$$ ＝MKB：Kiss My Butt 亲我的屁屁 LMAO：Laughing My A$$ Of ＝LMBO：Laughing My Butt Off ＝LMHO：Laughing My Head Off 笑死我了 LOL：Laughing Out Loud 放声笑 LSHMBB：Laughing So Hard My Belly Is Bouncing ＝LSHMBH：Laughing So Hard My Belly Hurts 笑到我肚子痛 告知篇 AFK：Away From Keyboard 离开键盘 BBL：Be Back Later ＝BBS：Be Back Soon ＝BRB：Be Right Back 稍待回来 CNP：Continue In Next Post 请看下一个留言 FYI：For Your Information 只给你知道 OIC：Oh，I See 喔，瞭 PS：Post Script 附注 QSL：Reply 回答 RTF：Read The FAQ 请看常见问题 AKA：Also Known As 又名为 FAQ：Frequently Asked Question 最常被问的问题 IC：I See 瞭 IGP：I Gotta Pee 我要去尿尿 POOF：I Have Left Chat 我已经离开聊天室啰 PM：Private Massage 私下寄消息。在聊天室常见的功能，你可以单独对有兴趣的人私下聊
一年级下册英语录音内容 新起点一年级下册课文录音材料 49 A项：In my classroom I can see, A desk, a chair, For you and me. In my classroom I can see, A window, a door, For you and me. In my classroom I can see, A wall, a blackboard, For you and me. 50 A项：CHANT Where’s Angel?Whe re’s Angel? She’s in the pencil case. Where’s Angel?Where’s Angel? She’s on the pencil case. Where’s Angel ?Where’s Angel? She’s under the pencil case.Oh, oh! Lily: Under the desk? B and G: No! Lily: Under the chair? B and G: No! Lily: In the desk? B and G: Yes! 51 A项Where is the blue ruler? Blue ruler? Blue ruler? Where is the blue ruler? It’s under the desk. Where is the red star? red star? red star? Where is the red star? It’s on the wall. Where is the black pencil? Black pencil? Black pencil? Where is the bl ack pencil? It’s in the pencil case. Bill: Where is the blue ruler? Lily: It’s under your desk, 52 A项: Put the pencil in the pencil case. Put the ruler in the pencil case. Put the eraser in the pencil case. Put the pencil case in the backpack.
南桥芯片（South Bridge） 北桥芯片（North Bridge） Memory Controller Hub (MCH)－内存控制中心，Intel 8xx（例如820或840）芯片组中用于控制AGP、CPU、内存（RDRAM）等组件工作的芯片。 ICH（Input/Output Controller Hub，输入/输出控制中心） Front Buffer，前置缓冲 FSAA（Full Scene Anti－aliasing，全景抗锯齿） FSB: Front Side Bus，前置总线，即外部总线 FSE（Frequency Shifter Effect，频率转换效果） FSUB（Floationg Point Subtraction，浮点减） FTP（File Transfer Protocol，文件传输协议） FWH（Firmware Hub，固件中心） GART（Graphic Address Remappng Table，图形地址重绘表） GDI（Graphics Device Interface，图形设备接口） Ghost:（General Hardware Oriented System Transfer，全面硬件导向系统转移） Gigabyte GMCH（Graphics & Memory Controller Hub，图形和内存控制中心） GMR（giant magnetoresistive，巨型磁阻） Gouraud Shading，高洛德描影，也称为内插法均匀涂色 GPF（General protect fault，一般保护性错误） GPIs（General Purpose Inputs，普通操作输入） GPS（Global Positioning System，全球定位系统） GPU（Graphics Processing Unit，图形处理器） GTF（Generalized Timing Formula，一般程序时间，定义了产生画面所需要的时间，包括了诸如画面刷新率等） GUI（Graphics User Interface，图形用户界面） GVPP（Generic Visual Perception Processor，常规视觉处理器） HAL（Hardware Abstraction Layer，硬件抽像化层） hardware motion compensation（硬件运动补偿） HCI: Host Controller Interface，主机控制接口 HCT：Hardware Compatibility Test，硬件兼容性测试 HDA（head disk assembly，磁头集合） HDSL: High bit rate DSL，高比特率数字订阅线路 HDTV（high definition television，高清晰度电视） HEL: Hardware Emulation Layer（硬件模拟层） HiFD（high-capacity floppy disk，高容量软盘） high triangle count（复杂三角形计数）
Unit 1 Unit 1 My neighbourhood Excuse me. Where is the hospital? 不好意思。医院在哪里？ Go straight. It's on your right. 直走。在你右手边。 It's next to the restaurant. 紧挨餐厅。 Excuse me. How can I get to the post office? 不好意思。请问怎么去邮局？ Go straight and turn left at the first crossroads. 直走，在第一个十字路口左转。It's between the bookshop and the grocery. 它在书店和杂货店之间。Lesson 1 第一课 A Look, Listen and repeat. 看，听并重复。 restaurant 餐馆 post office 邮局 bank 银行 grocery 食品杂货店 across from 对面 next to 紧邻 between 在……之间 Yaoyao's aunt comes to vist her family. 瑶瑶的阿姨来她家做客。
Yaoyao is showing her around the neighbourhood. 瑶瑶带着她参观街坊。"Look, this is a post office. Our school is next to it." “看，这有一间邮局。我们学校在它旁边。” "Oh, I see. What's that?" "哦，我看到啦。那是什么？” "That's a restaurant. We can eat Beijing duck there. “那是一家餐厅。我们可以在那吃北京烤鸭。" We often have a grocery in the neibourhood. 我们小区还有一间杂货店。 It's across from the park. 就在公园对面。 Mum often buys fruit and vegetables there." 妈妈总在那买水果和蔬菜。” "I want to buy some gifts. Is there a supermarket here?" “我想买一些礼物。这有超市吗？” "Yes, of course. It is between the bank and the park." “当然。就在银行和公园中间。” B Look and say. 看和说。 Where is the bank?银行在哪里 It's between the supermarket and Sunday Park.在超市和周日公园中间。 Lesson 2 第二课 A Look, listen and repeat. 看，听并重复。 turn left 向左转 go straight 直行
duno=don't know u=you ur=your kinda=kind of sorta=sort of 2=two或to 4=for shoulda=should have congrat=congratulation thx=thanks X'mas=Christmas wat=what biz=business ad=advertisement ft.=featuring abt=about pls=please rgds=regards ---问好--- 1,hiho=hola=yo=hi=hey=hellow=你好，大家好 2,wuz up=sup=what's up=(原意：怎么样你？/有什么事儿嘛？)也可作为问好用(当然是比较熟的两个人之间的问候)，回答时有事说事，没事用"nothing/nothin much/not much/nm等回答就可以。 ---再见--- 1,cya=cu=see ya=see you=再见 2,laterz=later=cya later=see ya later=see you later=再见 3,gn=gn8=gnight=good night=晚安 4,nn=nite=晚安 说明：一般第一个人常说gnight/gn8,然后第二个人用nite,后面的用nn什么的都可以了。不要问我为什么，约定俗成而已。 ---惊叹赞扬--- 1,OMG=oh my god=我的天；**！ 2,OMFG=oh my f ucking god=我的老天；**靠； 3,wtf=what the f uck=怎么会事！？；*！； 4,n1=nice 1=nice one=漂亮 5, pwnz=ownz=牛比！(例句：pwnz demo!;lefuzee ownz all the others!) 6,rullz=强！(例句：lefuzee rullz!;you guyz rull!!!) 7,you rock!=你牛比！(口语中常用，irc中偶尔能看到。) ---笑--- 1,lol=laughing out loud /laugh out loud=大笑 2,lmao=laughing my ass off=笑的屁股尿流 3,rofl=roll on floor laughing=笑翻天了 排序:hehe 人教版新起点一年级上册单词表Unit 1 book 书，书本 ruler 尺子 pencil 铅笔 schoolbag 书包 teacher 教师 I 我 have 有 a/an 一（个） Unit 2 face 脸 ear 耳朵 eye 眼睛 nose 鼻子 mouth 嘴巴 this 这（个） is 是 my 我的 Unit 3 dog 狗 bird 鸟 tiger 老虎 monkey 猴子 cat 猫 what 什么 it 它 Unit 4 one 一 two 二 three 三 four 四 five 五 six 六seven 七 eight 八 nine 九 ten 十 how 多少 many 多的，许多的 are 是 there （代替主语） Unit 5 black 黑色；黑色的 yellow 黄色；黄色的 blue 蓝色；蓝色的 red 红色；红色的 green 绿色；绿色的 colour 颜色 Unit 6 apple 苹果 pear 梨 banana 香蕉 orange 柑橘；橙 do （助动词） you 你；你们 like 喜欢；喜爱 yes 是，是的 no 不，不是 人教版新起点一年级下册单词表Unit 1 chair 椅子 desk 书桌，写字台blackboard 黑板 on在……上 under 在……下面/下方 in在……里面 where 在哪里 the 放在名词前，特指人、事或物Unit 2 light 灯 bed 床 door 门，出入口 box 箱子，盒子 near 靠近，接近 behind 在...背后 Unit 3 plane 飞机 ball 球 doll 玩偶，玩具娃娃 train 列车，火车 car 小汽车，轿车 bear 玩具熊，熊 can 可以，能够 sure 当然 sorry 对不起，抱歉 Unit 4 rice 米饭，米 noodles 面条 vegetable 蔬菜 fish 鱼肉，鱼 chicken 鸡肉，鸡 egg 鸡蛋 hungry 饥饿的 want 要，想要 and 和 Unit 5 juice 果汁，蔬菜汁 tea 茶，茶叶 milk 奶，牛奶 water 水 thirsty 口渴的thanks 感谢 Unit 6 shirt 衬衫 T-shirt T恤衫，短袖圆领衬衫skirt 裙子 dress 连衣裙，套裙 socks 短袜 shorts 短裤 your 你的，你们的 人教版新起点二年级上册单词表Unit 1 father 父亲 mother 母亲 brother 兄；弟 sister 姐；妹grandmother （外）祖母grandfather （外）祖父 who 谁 he 他 she 她 Unit 2 classmate 同班同学 friend 朋友 woman 女人 girl 女孩 man 男人 boy 男孩 look 看；瞧 his 他的 name 名字 her 她的 or 还是 Unit 3 计算机硬件英汉对照表 VDD、VCC 电源 5VSB = 5V StandBy 待机5V电源 Acer--> 宏基公司?]@8 A/D--> 模拟/数据(@= Address bus--> 地址总线-=:!g ALT=Alternate--> 转换键/更改/更动=. ALT=Alteration Switch--> 转换开关rP AMD=Advanced Micro Devices Inc. --> 高级微设备公司G^(WN AMI=American Megatrends Inc. --> 美国米格特雷德公司%@lu~ AGP=Accelerated Graphics Port --> 图形加速端口} API=Application Program Interface --> 应用程序接口Z6#e9- APM=Advanced Power Driver --> 高级动力驱动器"^p!"_ ASCII=American Standard Code for Information Interchange 美国信息交换用标准码t2L BIN --> 收集器/二进制+ZTo-u BIOS=Basic Input/Output System --> 基本输入输出系统V%iI` Bit --> 位=RQ Block --> 模块d BS=Backspace --> 退格键^l[?7 Cache --> 高速缓存X9 与外国人聊天常用缩略词 刚上线时一般的使用:----------------------------- 1. Hi 嗨 2. Hey 嘿 3. Yo 喂 4. WOW 哇 5. ICQ 我找你. I seek you. 6. TNS 好久不见. Long time no see. 7. VG 很好 Very good. 8. yep 是 <俚> yes. 9. yup 是 <俚> yes. 10. yeah 是 = yes. 11. RYOK 你好吧? Are you ok? 12. YR 对,是的. yeah,right. 中间有事离开一会儿:----------------------------- 13. bbiam 我很快回来 Be back in a minuta. 14. hang on 等一下. 15. bll 过一会儿回来. Be back late. 16. BRB 很快回来. Be right back. 17. RSN 马上 Real soon now. 19. afk 暂时离开(键盘) away from keyboard. 20. bak 回(键盘前)来了. back at keyboard. 21. BBIAB 马上回来. be back in a bit. 告诉对方欲下线:---------------------------- 22. TAFN 到此为止. That's all for now. 23. GTGB 得走了.再见. Got to go,Bye. 24. SRI 对不起. Sorry. 25. Gotta go 我得走了. Gotta go=have to go=I have to go. 告别:---------------------------------------------- 26. Bye 再见. 27. CU 再见. See you. 28. CU2 再见. See you too. 29. CUL 下次再会. See you later. 30. CUA 再会. See you again. 31. HTH 睡觉去了. Hit the hay (hay <俚> 床) 33. See ya. 再见. See ya = See you (ya 表示 you 或 young adult) 34. AMF 再会,我的朋友. Adios my friend. 人教版新起点英语一年级下册全册教案 (最新) Unit 1 Classroom 单元教材分析： 第一单元主要学习关于教室的3个单词desk,chair,blackboard,三个表达位置的词汇：in, on, under；以及询问和表达位置的功能句：Where is …? It’s under/ in/ on…。本单元是学生开学后的第一单元,与学生生活实际紧密联系,学习教室有关的物品名称,易于学生接受,内容简单实用,易于教师操练。本单元为下个单元Room 做了很好的铺垫,由教室到房间,使得下一步的学习变的简单,过度自然。 单元教学目标： 1、语言技能目标 （1）能够听懂、会说与教室有关的三个词汇：chair, desk, blackboard,以及三个表达位置的词汇：under/ in/ on。 （2）能够听懂、会说有关询问和表达位置的功能句：Where is…? It’s under/ in/ on…,并能在恰当的情境中初步运用。 （3）能够听懂简短的课堂指令语, 如：Put your ... in/on/under ...等,并作出相应的反应。 （4）能够借助日常生活图片识别、会说大写英文字母A、B、C、D。 2、情感目标 （1）能够跟随录音大胆模仿说唱歌曲和歌谣。 （2）能够对英语学习保持兴趣,并积极参与课堂上组织的各种活动；能做到有序参与,积极使用英语。 （3）能够在活动中逐步养成爱护教室的课桌椅和学习用品的习惯。 单元教学重点：与教室有关的3个词汇：desk, chair, blackboard；三个表达物品位置的介词：under / in / on。 单元教学难点：句型Where is …? It’s …的使用。 单元课时安排：五课时 第一课时 教学目标： 能够在适当场景下听懂、说出与教室有关的三个词汇：blackboard,desk,chair；以及三个表达位置的词汇：in, on, under. 教学重难点： 能听懂、说出与教室有关的三个单词：blackboard,desk,chair；以及三个表达位置的词汇：in, on, under。 教学准备：课件 教学时间：年月日 教学过程： 一、热身 教师发出指令,请学生听指令完成任务,如：Show me your ruler / book.等。 常见电脑英文及缩写 作者：ivy 文章来源：Internet 点击数：439 更新时间：2006-11-2 rsonal Computer，个人计算机、个人电脑，又称微型计算机或微机。 twork Computer，网络计算机。 C ltimedia Personal Computer，多媒体个人电脑。 X MultiMedia eXtensions(多媒体扩展)的缩写，是第六代CPU芯片的重要特点。MMX技术是在加入了特地为视频信号(Video Signal)，音频信号(Audio Signal)以及图像处理(Graphical lation)而设计的57条指令，因此，MMX CPU极大地提高了电脑的多媒体(如立体声、视频、三等)处理功能。 tel Pentium 166MHz MMXTM tel Pentium是英特尔(Intel)公司生产的“奔腾”CPU。166MHz指CPU时钟频率，MHz即Mega 的缩写。MMXTM中的TM是“Trade Mark”的简写，意为“注册商标”。 P ject Oriented Programming，面向对象的程序设计。所谓“对象”就是一个或一组数据以及些数据的方法和过程的集合。面向对象的程序设计完全不同于传统的面向过程程序设计，它大 低了软件开发的难度，使编程就像搭积木一样简单，是当今电脑编程的一股势不可挡的潮流。VGA 是指彩色显示器上的黄光网点间距(点距)，点距越小的显示器，图像就越细腻、越好，这是因屏幕上的每个像点都是由一组红、绿、蓝光汇聚而成的，由于在技术上三束光还不能100％地一点上，因此会产生一种黄光网点的间隔，这种间隔越小，屏幕上显示的图像越清晰。VGA是Graphics Array(视频图形阵列)的缩写。 T le Allocation Table，文件分配表，它的作用是记录硬盘中有关文件如何被分散存储在不同信息。 A vironmental Protection Agency的简称，美国环境保护署。EPA于1992年宣布了“能源之nergy Star)计划，并得到了国际社会的积极响应。只要启动电脑，过不了几秒钟就能见到屏现“能源之星”的标志。能源之星的目标是当电脑系统的各个部件不活动时自动进入低功耗状部件的能动性恢复(即当键盘、鼠标等被使用)时，电脑系统自动回到完全清醒的状态。对于符之星规范的产品，EPA将发给能源之星标志“EPA POLLUTION PREVENTER”，意为“美国环境保可的防污染的节能产品”。 卡 telligent Card，智能卡。 X人教版新起点小学英语一起点1-6年级单词表汇总2018
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与外国人聊天常用缩略词
人教版新起点英语一年级下册全册教案 (最新)
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