Review of Economic Dynamics13(2010)
52–75
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Review of Economic Dynamics
https://www.doczj.com/doc/5717874675.html,/locate/red
Consumption,income,and wealth inequality in Canada?Matthew Brzozowski a,Martin Gervais b,?,Paul Klein c,Michio Suzuki d
a York University,Canada
b University of Southampton and IFS,United Kingdom
c University of Western Ontario,Canada
d Bank of Japan,Japan
a r t i c l e i n f o a
b s t r a
c t
Article history:
Received7October2009 Available online24October2009
JEL classi?cation:
D12
D31
Keywords:
Income inequality
Consumption inequality
Wealth inequality In this paper,we document some features of the distribution of income,consumption and wealth in Canada using survey data from many different sources.We?nd that wage and income inequality have increased substantially over the last30years,but that much of this rise was offset by the tax and transfer system.As a result,the rise in consumption inequality has been relatively mild.We also document that wealth inequality has remained fairly stable https://www.doczj.com/doc/5717874675.html,ing both con?dential data and publicly available data,we are able to gauge the extent to which the publicly available data conceals aspects of inequality that con?dential data reveals.
?2009Elsevier Inc.All rights reserved.
1.Introduction
In this paper we use a variety of data sources to document some salient facts concerning the distribution of wages,hours worked,income,consumption and wealth in Canada.In general,our datasets have two access levels,one publicly available (Public-Use Files,or PUF)and one only available through Research Data Centres(RDC)administered by Statistics Canada. While our main conclusions are drawn from RDC data,we also discuss the extent to which noise introduced by Statistics Canada to protect individuals’identity distorts various measures of inequality.
Income inequality over the last30years or so has risen quite substantially in Canada.Wage inequality,as measured by the variance of log wages,doubled from1977to2005.Indeed,the level and trend of wage inequality,however measured, are remarkably similar to the U.S.over the same period of time(see Heathcote et al.,2010,in this issue).In Canada,most of this rise occurred within skill groups,as the skill premium remained fairly stable until the mid-1990’s,in contrast to the U.S.where the skill premium has been rising consistently since1980.1As we move towards more inclusive measures of income,such as family earnings or total income before taxes and transfers,U.S.and Canadian inequality remain noticeably similar.
Perhaps the most striking?nding of this study is the remarkable role played by the tax and transfer system both to compress inequality and to absorb changes in before-tax income inequality.Disposable income inequality,as measured by
?We would like to thank Jim Davies and David Green for their many comments and suggestions on this project,as well as two anonymous referees,
the Editors,and seminar participants at the Bank of Japan and the Philadelphia conference on Heterogeneity in Macroeconomics and for useful comments. Gervais and Klein gratefully acknowledge?nancial support from the Social Sciences and Humanities Research Council of Canada.The views expressed in this paper are those of the authors and do not necessarily re?ect the o?cial views of the Bank of Japan.
*Corresponding author.
E-mail addresses:brzozows@yorku.ca(M.Brzozowski),gervais@https://www.doczj.com/doc/5717874675.html,(M.Gervais),pklein2@uwo.ca(P.Klein),michio.suzuki@boj.or.jp(M.Suzuki). 1Boudarbat et al.(2006)point out,however,that the rise in the education premium in Canada starts much earlier once experience is controlled for.
1094-2025/$–see front matter?2009Elsevier Inc.All rights reserved.
doi:10.1016/j.red.2009.10.006
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7553
the variance of the log,was essentially?at from1977until1990,a period over which the variance of log pre-government income increased by more than10points.Although disposable income inequality has increased since1990,its rise pales in comparison to that of pre-tax income inequality.2While transfer payments—the main ones being,in order of importance in mitigating income inequality:social assistance,unemployment bene?ts,and various child bene?t programs—are mainly responsible for absorbing changes in pre-government income inequality,both taxes and transfers play a signi?cant role in compressing income inequality.Given the evolution of disposable income inequality,it should come as no surprise that consumption inequality also rose only moderately over our sample period.
An interesting feature of income inequality in Canada is that its evolution revolves around recessions.From1976to 2005,the Canadian economy experienced two recessions:one at the beginning of the1980’s and one at the beginning of the1990’s.Unlike the U.S.,Canada did not experience a recession at the turn of the century.During each recession,wage and income inequality rose substantially,and the declines that followed were not su?cient to offset the rise,resulting in more inequality over time.3While a similar pattern can be detected for disposable income inequality,the movements are much milder and,at least in the1980’s,the rise during the recession was fully offset thereafter.
The previous?ndings also bear on patterns of inequality over the life-cycle,in the sense that the age-pro?le of inequal-ity is much?atter for disposable income(and consumption)than for pre-government measures of income.Interestingly, wage inequality increases almost linearly over the life-cycle,suggesting the presence of highly persistent wage shocks (see Storesletten et al.,2004).However,the rise for earnings inequality tends to start later in life,and fails to show a clear monotonic(let alone linear)pattern.This lack of linearity leads us to question the validity of a unit root process as the main driving force for earnings,although this speci?cation seems reasonable for wages.
Notwithstanding the caveats stated in the previous paragraph,we estimated wage and earnings(permanent–transitory) processes from our Canadian data.Our main?nding is that a very high fraction of the overall cross-sectional variance and also a high fraction of the risk faced by households is accounted for by the“permanent”component as opposed to the transitory component of the process.However,this result is sensitive to the speci?cation of the statistical model.Irrespective of the many caveats,it is interesting to note that in line with our main results discussed above,the tax and transfer system substantially reduces both permanent and transitory earnings risk.
A highly desirable property of distributions is log normality.With that property,a distribution can be fully characterized by its?rst two moments,and two distributions are unambiguously comparable with respect to the degree of inequality. In our data,the cross-sectional distribution of consumption is much closer to log-normal than that of income,much like Battistin et al.(2007)?nd in U.S.and U.K.data.Interestingly,but perhaps not surprisingly given our discussion above, disposable income is also found to be more log-normal than pre-government income.
The main results of this study are closely related to those found in Frenette et al.(2007).Using income data from the Census,they?nd that the1980’s were characterized by a strong rise in before-tax income inequality,but that most of that rise was absorbed by the Canadian tax and transfer system,with the result that after-tax(and transfer)income inequality remained constant.They further report that while before-tax income inequality also increased in the1990’s,this time the tax and transfer system failed to fully offset the rise.As a result,after-tax income inequality also rose in that decade,albeit not to the same extent as pre-tax income inequality.While our?ndings are similar,we choose to stress how small the increase in disposable is relative to the increase in before-tax income inequality,as opposed to their emphasis on the fact that disposable income inequality did rise in the1990’s.
Now the reason why Frenette et al.(2007)use Census data rather than survey data is mainly because they doubt the validity of results obtained though survey data.4Indeed,Frenette et al.(2006)show that income inequality trends in survey data are inconsistent with both Census and tax data.However,perhaps because of these inconsistencies,Statistics Canada implemented a revision to the weights in survey data,mainly in order for the surveys to be consistent with information (from the Canadian Revenue Agency)on wages and salaries.5Although such a revision almost necessarily entails distorting other aspects of the data(such as employment to population ratios)and brings about a break in the series because the revision was only applied retroactively to1990,it seems to have brought income inequality results closer to those that emerge from Census data.Indeed,a look at income inequality from Public-Use Files,which for the most part still feature the pre-revision weights,con?rms that the choice of Frenette et al.(2007)to use Census data was warranted.Our results from Public-Use Files look much like theirs from survey data,and when we compare those results to the results emerging from the revised weights,we are inclined to support their view that the old survey weights led to a distorted depiction of the evolution of income inequality in Canada.6
The rest of the paper is organized as follows.The next section describes our various sources of data and our sample selection.Section3compares measures of average income,consumption,and employment that emerge from survey data to their respective aggregate counterpart.Our main?ndings appear in Section4,where we discuss the evolution of cross-
2It should be noted that the tax and transfer system appears to compress inequality especially at the bottom of the distribution,as evidenced by much smaller movements in the variance of log disposable income than in the Gini coe?cient.
3Heathcote et al.(2010)document that this phenomenon,whereby recessions have a long-term effect on inequality,is also observed in U.S.data.
4While using Census data may have advantages over survey data,it also presents at least two serious drawbacks:(i)the lack of information on taxes paid,which need to be estimated,and(ii)the frequency of data collection(5-year intervals),which can mask changes that occur at higher frequencies.
5The details of this revision and its impact on survey income data can be found in Lathe(2005).
6Our results are not directly comparable,however,as they use a very different sample and a different way to equivalize earnings.
54M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Table1
Data sources.
Data source Variables Sample period
FAMEX Income,expenditure1969,1978,1982,1986,1992,1996
SHS Income,expenditure1997–2005
SCF Income,hours worked1977,1979–1997
SLID Income,hours worked1996–2005
SFS Income,wealth1999,2005
sectional inequality,inequality over the life-cycle,as well as income process estimates and log-normality tests.Section5 examines the advantages of working with RDC data relative to Public-Use Files.A brief conclusion is offered in Section6.
2.Sources of data
2.1.Datasets
As is typically the case,Canada does not have a single survey from which information on consumption,income and wealth is available.Table1lists the data sources with the main variables used and the sample period covered by each data set,which we brie?y describe below(see Appendix A for details).
We take consumption data from the Survey of Family Expenditures(FAMEX)for1969,1978,1982,1986,1992,and1996 and the Survey of Households Spending(SHS)for1997–2005.7In addition to consumption,both surveys contain data on income and standard characteristics(other than education from1997to2003)at the household level,or something close to a household(similar to consumer units in the Consumer Expenditure Survey for the U.S.).See Appendix A.1for details.
For earnings and hours worked,we use data from the Survey of Consumer Finances(SCF)for1977and1979–1997and the Survey of Labour and Income Dynamics(SLID)for1996–2005.8While the SCF only provides cross-sectional data,the SLID has a panel dimension.Speci?cally,the SLID consists of overlapping samples interviewed in six consecutive years,with new waves introduced in1993,1996,1999,2002,and2005.In any given year since1996,then,there are two waves of data available.Because the sample size of the SLID is relatively small from1993to1995,the SCF is deemed to contain better information for these years.Accordingly,we do not use SLID data prior to1996.See Appendix A.2for details.
Wealth data is hard to come by in Canada.The SCF had a wealth supplement in1977and1984,called the ADSCF,and the Survey of Financial Securities(SFS)was introduced in1999and ran again in2005.Because of inconsistencies between the two Surveys,we only use the SFS in this paper.9The unit of analysis in the SFS is the economic family.This survey offers standard balance sheet data from which net worth can be constructed,as well as income and some characteristics of the economic family and the head of the family.See Appendix A.3for details.
2.2.RDC data versus Public-Use Files
Some of the data sets described in Section2.1have two access levels:one publicly available and one only available within the con?nes of Research Data Centres(RDC)located at various institutions throughout the country and administered by Statistics Canada.While the data that we made available emanate from Public-Use Files(PUF),most results contained in the paper emanate from proprietary or RDC data.The main advantage of using RDC data is that these data are not modi?ed in any way or,if they are,a?ag identifying observations that have been modi?ed is supplied in RDC data.
The severity of distortions introduced in PUF data varies across surveys.At one extreme,PUF data for consumption (FAMEX/SHS)are for our purposes identical to RDC data.At the other extreme the panel dimension of SLID data is only available through RDC.10But for income and wealth data,PUF data have some characteristics removed(SCF/SLID)and observations have been shu?ed(SLID)to preserve the anonymity of some of the respondents,mainly households at the top of the distribution.Finally,wealth data(SFS)from our Public-Use Files suffers from the typical top-coding problem that work with many survey data must confront.In Section5we investigate the extent to which using PUF data can replicate results obtained from the original,RDC data.
2.3.Survey weights
The sampling frames of all the surveys used in this study are based on the Labour Force Survey(LFS),a monthly survey equivalent of the Current Population Survey(CPS)in the U.S.Initially,each individual in the LFS sample is given a weight
7FAMEX data are also available for1974,1984,and1990.However,since the survey covers only urban areas and have a smaller sample for those years, we do not include the data in our analysis.
8We do not use SCF data for1978because data on usual hours worked are missing for that year.SCF data was also collected in the early1970’s. However,we chose not to use years prior to1976for consistency reasons.Appendix A.2documents the inconsistencies in detail.
9Morissette et al.(2002)outline ways to make the ADSCF compatible with the SFS.
10The Cross-National Equivalent Files(CNEF)prepared by the Department of Policy Analysis and Management at Cornell University contain a version of the panel aspect of SLID.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7555
Table2
Summary statistics.
198619962005
FAMEX SCF FAMEX SCF SLID SHS SLID SFS
Age40.7640.2541.9241.5740.8143.2742.7943.01 Family size3.11 2.822.87 2.69 2.64 2.85 2.42 2.64 %University0.140.180.190.220.200.300.260.26 With spouse0.710.650.670.600.590.670.540.62 %Immigrant0.190.190.210.180.17n/a0.160.20 Observations71421409471961613413,779902810,814<3868 Notes:The row labeled“Age”reports the age of the head of the household.The2005SHS only provides categorized data on the age of the head.We assign each household head the middle value of the age range to which the person belongs,and report the average over the assigned ages.
equal to the inverse of the probability of selection.These weights are then adjusted to account for non-response and to match census estimates(or projections)for various age-sex groups by province and major sub-provincial areas.
Our income surveys are typically conducted as a supplement to the April(SCF)or January(SLID)LFS,much like the March Supplement to the CPS.Since both SCF and SLID consist of sub-samples of the LFS,the weights for these surveys are initially based on LFS weights.These weights are?rst adjusted for non-response in SCF/SLID(but not for LFS non-response,as this has already been taken care of in the LFS weights),that is,the weight of non-respondents are distributed to respondents. The non-response adjusted weights are then calibrated to ensure that estimates on relevant characteristics of the population (age,sex,province,family size,and household size)match up with census data(or projections from a recent census).
Because of contradicting evidence on the degree and evolution of income inequality coming from different sources of data(tax data,census data,and survey data),as evidenced for instance by the work of Frenette et al.(2006),Statistics Canada recently revised their strategy to calibrate the survey weights.This new strategy adds wages and salary to the set of targets to calibrate survey weights.The targets themselves come from T4slips(equivalent to W4slips in the U.S.),which are employer remittances to Canada Revenue Agency(formerly Revenue Canada)for payroll tax purposes.This new strategy to impute weights is currently used for all surveys that we use in this study,and has been implemented retroactively to 1990for SCF/SLID,and to1996for FAMEX/SHS.All SFS data uses this new methodology as well(see Lathe(2005)for more details).
The result of this new calibration strategy,documented in Rea and Greenlee(2005),is that more weight is given to observations with low/zero wages and salary as well as to observations with high wages and salary.The reason is that a disproportionate fraction of respondents are from the middle of the wages and salary distribution.Practically,as far as SCF data is concerned,this revision implies a higher level of inequality in wages and salary,which carries over to pre-tax income inequality and to a lesser extent to after-tax or disposable income inequality at the individual level.Ultimately,this revision had a similar impact on family/household level income inequality.This revision and its impact should therefore be kept in mind when interpreting the evolution of income and consumption inequality over time in the rest of the paper.
2.4.Sample selection and statistics
The details of our samples are contained in Appendix B.Each sample used in this study is labeled in this appendix.We refer to these labels throughout the paper.Brie?y,we restrict our samples to households whose head is at least25years or age and no older than60.11We also exclude observations with missing characteristics(gender,education,family type, marital status,and province of residence).For income data,we exclude any observation whose income was missing or imputed,as well as observations with a wage rate less than half the minimum wage.We also exclude observations with positive earnings and zero hours worked.For consumption data,we exclude part-year households and households with annual food expenditure less than400Canadian dollars.12In addition,since the territories(Northwest Territories,Nunavut, and Yukon)were not covered by FAMEX surveys,we exclude households from the territories in SHS as well.Finally,for wealth data we exclude outliers and families with missing wealth information.After these restrictions have been imposed, we only exclude observations as necessary to compute the statistics of a particular?gure or table—typically excluding non-positive values of a particular variable.
Table2compares our samples along broad dimensions for three different years.13Except for particularly low fractions of people with a university degree in FAMEX data,and a particularly high fractions of married couples in FAMEX/SHS data, our data sets are fairly consistent with one another.
11The head of household is de?ned as follows:1.If the household has just one member,that is the head.2.If the household has several members,take the set of eldest males between25and60.If this set is a singleton,the only member is the head.3.If the set is empty,take the set of eldest females between25and60.If that set is a singleton then the only member is the head.4.If at this stage we have a set with more than one member(either a set of eldest males or a set of eldest females),choose the member with the highest sum of labour earnings plus the absolute value of business income.If that still does not break the tie,appoint as head the member with the lowest personal id number.
12A full-year household is de?ned as having at least one full-year member,i.e.,a member who was in the household for the entire year;a part-year household is composed entirely of part-year members.
13The samples used for Table2consist of Sample A from Tables11(FAMEX and SHS),13(SCF),15(SLID),and16(SFS).
56M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Table3
Aggregate statistics:1976–2005.
1976–20051976–19791980–19841985–19891990–19941995–19992000–2005
Population 1.10 1.05 1.09 1.35 1.150.92 1.02
Real GDP 2.83 3.81 2.08 3.51 1.52 3.81 2.48
Real GDP per head 1.73 2.760.99 2.150.37 2.89 1.46
Real GDP per hour 1.200.46 1.770.10 1.80 1.64 1.20 Employment rate0.670.630.640.670.660.670.71
Notes.The?rst four rows display average annual growth rates of the variable.Employment rates are annual average employment rates for the20–69age group over the relevant period.
Sources.Population:Statistics Canada,Table051-0001;GDP:Statistics Canada,Table380-0002;Hours worked and employment rates:Labour Force Survey.
Fig.1.Disposable income per capita.
3.Means
The purpose of this section is to compare the evolution of average income and consumption from micro data to their counterparts from national statistics.A similar exercise is performed on employment rates.Before presenting these compar-isons,we provide a brief overview of the Canadian economy.
Table3displays aggregate statistics for the Canadian economy over the1976to2005period,the years covered by most of our micro data.Over those30years,the population of Canada grew at an average annual rate of1.1percent,from23to over32million people.Meanwhile GDP grew at an average annual rate of2.8percent,so GDP per head grew at average rate of1.7percent per year.Both?gures are slightly lower than in the U.S.,where GDP and GDP per head grew at average rates of3.0and2.0percent per year,respectively.Similarly,GDP per hour only grew at an average annual rate of1.2percent over that period in Canada,compared to1.5percent in the U.S.
Fig.1displays real disposable income per capita as reported in the System of National Accounts(SNA)as well as its implied counterparts constructed from survey data.14While the gap between SNA and survey data increased over the years covered by the SCF,it is reassuring that it has been shrinking under SLID.In the last survey year available,as shown in the ?rst panel of Table4,average disposable income was over90percent of the SNA?gure,either from SHS or SLID data.
Fig.2shows that a similar situation prevails for consumption,at least over the SHS years(since1997),with a gap sitting between80and82percent(see second panel of Table4).Furthermore,the ratio of non-durable consumption to income from SHS is consistently only around5percentage points lower than the same ratio from SNA data,as shown in the third panel of Table4for2005.Thus,the situation in Canada for consumption data is not as dire as in the U.S.,where the gap has been rising consistently over the last25years.15For example,Slesnick(2001)reports that the ratio of per capita consumption in the Consumer Expenditures Survey(CEX)to the NIPA estimate of Personal Consumption Expenditures was 0.68in1980,0.61in1990,and0.56in1995.Similarly,Battistin(2003)?nds that the ratio of non-durable expenditures in CEX to the NIPA estimate fell from around80percent in1985to around65percent in the late1990’s.
14All real series in this section were de?ated by the CPI using base year2003.For all survey data,the full sample was used,that is,no sample restrictions were imposed.
15See Crossley and Pendakur(2006)for a discussion of the quality of expenditure survey data in Canada.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7557
Fig.2.Non-durable consumption per-capita.
Table4
Survey to aggregate ratios(%).
1978198619962005
Mean disposable income
FAMEX/SHS93.190.395.791.8
SCF88.384.983.4n/a
SLID n/a n/a84.191.0
Mean non-durable expenditures
FAMEX/SHS86.590.085.882.8
Ratio of non-durable expenditures to disposable income
FAMEX/SHS49.951.646.548.8
SNA53.751.851.954.0
Fig.3.Employment to population ratio.
Fig.3displays employment to population ratios at the aggregate level and from survey data over the sample period. It should be noted that the aggregate employment series also comes from a survey—the monthly Labour Force Survey (LFS).Also note that while most questions in our income surveys pertain to the year prior to the survey year,employment questions in SCF data pertain to the current month(typically April).By contrast,employment information in SLID is available
58M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Fig.4.Basic inequality in hourly wages.Notes.1977–1997:SCF.1996–2005:SLID.
for every month of the previous year.Furthermore,as mentioned in Section2.3,both SCF and SLID surveys are conducted as a supplement to the LFS,that is,they consist of sub-samples of the LFS.16It is therefore surprising to see signi?cant discrepancies between the two series.The discrepancies emanate from two distinct sources.First,the SCF had unusually small samples in1977,1979,1981and1984,which explains the anomalies prior to1990.17The post1990discrepancies can be explained by the2003historical revision of the weights discussed in Section2.3.As a result of this revision,which was applied retroactively to1990,higher weights were assigned to low/no earnings individuals as well as high earnings individuals and had the effect of increasing substantially the fraction of the population not in the labour force.This is re?ected in Fig.3as a low employment to population ratio post-1990in SCF data.18
4.Dimensions of inequality
4.1.Individual level inequality
Fig.4displays four measures of wage inequality.19The wage is de?ned as average hourly earnings,and earnings include wages and salaries as well as the labour share(62percent)of income from self-employment.20The top-left panel shows that wage inequality,as measured by the variance of log wages,increased substantially in the late1970’s and early1980’s, and somewhat more modestly in the early1990’s and around2000.The top-right panel of that?gure shows that a similar pattern emerges when wage inequality is measured by the Gini coe?cient.Over the entire sample,the variance of log wages doubled and the Gini coe?cient increased by9points,from0.28to0.37.It is interesting to note that while high wage earners have been consistently gaining on the median over the sample period(lower right panel),low wage earners falling behind the median is the main reason behind the increase in wage inequality in the late1970’s and early1980’s (lower left panel).Notice that while inequality tends to increase during recessions,its tendency to go back down in between recessions is very mild.
16Since the SCF is typically conducted as a supplement to the April LFS,both series in Fig.3refer to the month of April.For1976and1983,SCF data pertained to the month of May,and so we chose that month for the aggregate series as well for these two years.SLID offers monthly labour force data,so we also used the month of April for the years covered by SLID.
17Survey years1977and1979,which contain data pertaining to1976and1978,have other problems—in particular hours worked is missing—so we dropped these years for the rest of the analysis.
18This is con?rmed by a similar?gure(not shown)constructed using weights available prior to the revision,which shows essentially no discrepancy between the SCF and LFS numbers over the1990’s.
19The samples used in this section consist of Sample A from Tables12and14.
20The labour share of income is taken from the SNA;an average is computed over the period1961:1to2002:1.Speci?cally,the labour share is de?ned as the ratio of wages,salaries and supplementary labour income plus taxes less subsidies on factors of production and gross domestic product at market prices less accrued net income of farm operators from production less net income of non-farm unincorporated business including rent.The precise data source is Matrix6520from CANSIM1.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7559
Fig.5.Wage premia and residual wage inequality.Notes.1977–1997:SCF.1996–2005:SLID.
The bottom-right panel of Fig.5shows that the above pattern of wage inequality is very similar to the pattern of residual wage inequality—inequality unexplained by observables—suggesting that most of the increase in wage inequality cannot be explained by observables.The top-left panel of Fig.5shows the education premium,de?ned as the average wage of males with at least a university certi?cate,a diploma,or a bachelor’s degree,relative to the average wage of males without any such degrees.Interestingly,the education premium remained fairly constant from the beginning of the sample until the mid 1990’s.21Since then,the education premium rose from an average of around40percent from the beginning of the sample until1995to just below60percent in2005.Meanwhile,the fraction of the population with a university degree increased monotonically from14percent in1977to28percent in2005.These patterns are quite different from those observed in the U.S.,where the college premium and the supply of college graduates have been rising simultaneously since the late1970’s (Heathcote et al.,2008).These observations suggest that as opposed to the U.S.,the rise in wage inequality in Canada has mainly occurred within education groups.
The bottom-left panel of Fig.5displays the gender premium,that is,the average wage of males relative to that of females.The gender premium narrowed considerably until the mid1990’s,from37percent in1977to just over20percent in1995,but it has failed to decline in more recent years,averaging around27percent over the SLID years.As a result, the gender gap in Canada is now close to that in the U.S.,but the drop in the gender premium since the late1970’s was more spectacular in the U.S.,where the wage premium was at a much higher level(65percent)than in Canada(37percent) in the late1970’s(Heathcote et al.,2008).The top-right panel suggests that the reverse has happened to the experience premium,measured as the average wage of males aged45–55relative to those between25and35years of age.This premium increased until the mid1990’s and declined sharply during the dot-com years of the late1990’s.However,the experience premium came back up over the last5years of the sample.
The top-left panel of Fig.6shows that,qualitatively,the evolution of wage inequality for both men and women mimics that of the entire population(top-left panel of Fig.4).The widening of the gap between male and female wage inequality which occurs in the1990’s may be an artifact of the retroactive revision of survey weights—recall that as a result of the weight revision,more weight was given to observations with low or no income,which may have affected male and female wages differently.The last10years of SLID data suggest that the variance of log wages for men is around5log points higher than that of women.
Inequality of hours worked,as shown in the top-right panel of Fig.6,paints a drastically different picture for men and women.First,as one would expect,the level of hours inequality is much higher for women than for men throughout the sample.Second,inequality of hours worked for men is much more responsive to business cycle?uctuations than that of women:from1981to1983the variance of log hours for men increased by10points(from0.11to0.21)while it only increased by3points for women(from0.48to0.51);similarly,the variance of log hours from1990to1993increased by
21As pointed out in the introduction,Boudarbat et al.(2006)point out that the rise in the education premium starts much earlier once experience is controlled for.
60M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Fig.6.Inequality of labour supply.Notes.1977–1997:SCF.1996–2005:SLID.
7points for men(from0.19to0.26)and by1point for women(from0.45to0.46).22It is also interesting to note that over the entire sample period,the variance of hours worked for women trended down,while that of men has been essentially stable since the recession of the early1980’s.Finally,the bottom panels of Fig.6show that the correlation between hours worked and wages for men and women alike has been rising throughout the sample period,approaching zero from below. It is interesting to note that the same pattern is observed in the U.S.(see Heathcote et al.,2010).
4.2.Family level inequality
This section pertains to inequality at the economic family level,where a family is de?ned as a group of individuals living at the same address and related by blood,marriage or adoption.23
The next two?gures display various measures of family earnings inequality,where family earnings is the sum of the family members’wages and salaries plus the labour part(0.62)of self-employment income.24Fig.7shows that observables (family type,education,age and region of residence)do not explain much of the trend in earnings inequality,despite the fact that the variance explained by education has generally trended up over the sample period.On average,observables explain around23percent of the variance of equivalized earnings.All measures of inequality in Fig.8show a large rise over the two recessions:the variance of log equivalized earnings increased by14points(from0.59to0.65)from1981to 1983and by20points(from0.59to0.79)from1990to1993.Both episodes were marked by the median gaining on the poor and the rich gaining on the median,although the magnitude of the former is much more pronounced than the latter. Notice that the pattern around recessions is slightly different from wage inequality(see Fig.4),as family earnings inequality measured by the variance of the log tends to decrease after recessions.Note,however,that the level of the Gini coe?cient kept increasing over the last10years of the sample.Over the entire sample,the variance of log earnings increased by 18points(from0.52to0.70)and the Gini coe?cient by7points(from0.31to0.38).
Fig.9shows the variance of log income for many measures of income as well as earnings of household heads.25The top-left panel shows that the distance between pre-government earnings of the head(y H)and of the family(y L)is typically around10to12points.This relatively large distance between these two measures of income inequality suggests that there is substantial family insurance.The bottom-left panel shows that?nancial income(the difference between y and y L+)has virtually no impact on inequality.While private transfers(the difference between y L and y L+,shown in the top-left panel) do not play a big role in mitigating inequality,its role has been increasing over time.But certainly the most startling feature
22We similarly found the following variables to?uctuate more for males than for females over business cycles:the employment to population ratio, unemployment rate,and participation rate for the population aged15and above in Canada.
23Economic families may differ from households,de?ned as a person or a group of persons living at the same address.
24The sample used for these two?gures consists of Sample D from Tables13and15.
25The sample used for this?gure consists of Sample C from Tables13and15.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7561
Fig.7.Earnings inequality and its decomposition.Notes.1977–1997:SCF.1996–2005:SLID.
Fig.8.Basic inequality of equivalized earnings.Notes.1977–1997:SCF.1996–2005:SLID.
of this?gure is the role played by the tax and transfer system,which is shown in the bottom-right panel as the distance between pre-government income(y)and disposable income(y D).Not only does disposable income exhibit much less inequality than pre-government income,but the degree of inequality is also much less variable than that of pre-government income.This suggests both that Canadian policy has been and remains redistributive,and that it smooths cyclical shocks to pre-tax income inequality.
62M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Fig.9.From wages to disposable income.De?nitions.y H:pre-government earnings of head;y L:pre-government family earnings;y L+:pre-government non-?nancial income of family;y:pre-government family income;y D:total family disposable income.Notes.1977–1997:SCF.1996–2005:SLID.
To get some perspective on the redistributive effects of public policy,it is useful to look at the effects of different aspects of policy separately,as illustrated in Fig.10.26The most obvious distinction is that between tax policy on the one hand and transfers on the other hand,noting that this distinction is to some extent a matter of arbitrary?at.The most important government transfer programs in Canada come under three headings:unemployment bene?ts,social assistance and various child bene?t programs.Together these programs are more signi?cant in bringing down inequality than the tax system is,as illustrated in the top panel of Fig.10.The bottom panel suggest that of the three transfer programs,social assistance has the largest impact,followed by unemployment bene?t.Note,however,that the role of child bene?ts has increased since that program was reformed in1993(see Frenette et al.,2007).
This?gure also shows some noticeable changes over time.The?rst is that the correlation between pre-government inequality and post-government inequality seems to have disappeared(see bottom-right panel of Fig.9as well).Post-government inequality exhibits very small cyclical?uctuations after1996and those movements that do occur are uncor-related with the corresponding movements in pre-government earnings and income.27Finally,while after-tax inequality remained fairly stable over the?rst half of our sample period,it increased quite substantially(8points)in the second half, from0.24in1989to0.32in2005.28
Table5presents more evidence of our last result.29In particular,Fig.10showed that transfer programs were most important in curbing the rise in before-tax income inequality.The evolution of the50/10and90/50ratios shown in Table5 suggests that,as one might expect,these transfers were most successful in curbing income inequality at the bottom rather than at the top of the distribution.This table is also meant to compare our results to those of Frenette et al.(2007),who use income data from the Census to characterize the evolution of income inequality over the1980–2000period.They conclude that most of the strong rise in before-tax income inequality over the?rst decade was absorbed by the tax and transfer system,leaving after-tax(and transfer)inequality unchanged.However,the equally strong increase in before-tax inequality in the1990’s was accompanied by an increase in after-tax(and transfer)inequality,albeit not of the same size.Our results
26The sample used for this?gure consists of Sample B from Tables13and15with the bottom0.5percent of pre-government income trimmed.We chose to use a broader sample for this?gure as it is important to include observations with zero earnings in examining the role of government policies such as the unemployment insurance and social assistance.However,because the sample selection is very inclusive,the statistics became sensitive to very few observations,which explains our trimming strategy.
27It is interesting to note that over the SLID years(from1996),there was no recession in Canada.Over that sample period,pre-government income inequality is falling while disposable income inequality is rising.An interesting interpretation of these movements,which was suggested by a referee,is that over these“good times”government programs have become less progressive while at the same time the poor were catching up to the rich.Indeed, over that period of time the P50–P10ratio for pre-government income decreased,while that of disposable income didn’t move very much.
28This suggests that public policy has become gradually less redistributive in its effects,though not necessarily in its intent,since the beginning of the 1990’s.
29The same sample as Fig.10is used for this table.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7563
Fig.10.From pre-government income to disposable income.De?nitions.y:pre-government family income;y D:total family disposable income;y+tran: pre-government family income plus total transfers;y?tax:pre-government family income minus total taxes paid;y+ui:pre-government family income plus unemployment bene?ts;y+sas:pre-government family income plus total social assistance transfers;y+cbn:pre-government family income plus child bene?ts transfers.Notes.1977–1997:SCF.1996–2005:SLID.
Table5
Pre-and after-tax income inequality.
Year Pre-government family income(y)Total family disposible income(y D)
Varlog50/1090/50Gini Varlog50/1090/50Gini
19800.4930 2.4427 1.99460.32170.2669 1.9509 1.83990.2741 19850.5858 2.6175 1.99710.33960.2785 1.9675 1.80250.2810 19900.5990 2.7109 2.00160.34110.2622 1.9169 1.80550.2708 19950.7217 3.0276 2.07190.36610.2884 1.9554 1.84330.2847 20000.6846 2.8973 2.05080.37030.2853 1.9647 1.84660.2926 20050.6639 3.0021 2.15090.38050.3188 2.0464 1.91320.3058
Growth(%)
1980–200044.4018.58 5.6615.888.00?2.91 2.04 6.56 1980–199021.5110.980.35 6.03?1.75?1.74?1.87?1.21 1990–200018.14 5.99 5.299.339.92?1.13 3.967.84 1980–198518.837.160.13 5.57 4.370.86?2.03 2.51 1985–1990 2.25 3.570.230.44?5.86?2.580.17?3.64 1990–199520.4911.68 3.517.339.98 2.01 2.09 5.14 1995–2000?2.61?6.00 1.72 1.92?0.03?3.14 1.85 2.68 2000–2005?3.02 3.62 4.89 2.7611.75 4.16 3.61 4.53
Notes.All changes that involve years over which SCF and SLID overlap are computed as the sum of the change in SCF until1996and the change in SLID from1996.
in Table5reinforce that conclusion by reporting increases of much larger magnitude than their estimates from Census data. As we will see in Section5,the weight revision was instrumental in obtaining these results.
4.3.Consumption inequality
We now turn our attention to consumption inequality,where consumption refers to non-durable expenditures.
64M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Fig.11.Consumption inequality and its decomposition.Notes.1969–1996:FAMEX.1997–2005:SHS.
Fig.12.From disposable income to consumption.Notes.1969–1996:FAMEX.1997–2005:SHS.
Fig.11shows that consumption inequality exhibits a rising trend.30In the“raw”data,where no adjustment has been made for family size,the variance of the log has almost doubled in30years.However,this is largely because of the rise in single-member households;the fraction of households that had a single member went up from8percent in1969to21per-cent in2005.Once the data have been divided by the number of adult-equivalents(equivalized),the rise in inequality is less pronounced and only amounts to about3points,from0.2086in1969to0.2446in2005.
Fig.12juxtaposes the changes in consumption inequality as measured in four different ways with the corresponding changes in the inequality of disposable income.31The main thing to take away from that?gure is that consumption in-
30The sample used for this?gure consists of Sample A from Table11.
31The sample used for this?gure consists of Sample B from Table11.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7565
Fig.13.Inequality over the life-cycle(time effects).
equality is consistently lower than disposable income inequality.For the variance of the log,consumption inequality is typically10to15points lower than disposable income inequality.Also,at least for the variance of the logs,the two tend to move up and down together,although consumption inequality is less volatile.Note,however,that these conclusions are based on relatively few years of observation.
4.4.Inequality over the life-cycle
The evidence on life-cycle pro?les of the variance of hours,wages,earnings and consumption is reported in Fig.13,where we control for time effects and Fig.14,where we control for cohort effects.32Interestingly,both?gures are qualitatively similar,although controlling for cohort effects systematically leads to steeper inequality pro?les—albeit not to the same extend as with U.S.data(see Heathcote et al.,2010).For wages,the picture is clear:variance increases monotonically over the life-cycle.For raw earnings,the increase is more or less monotonic,but the pro?le is convex.For equivalized earnings, there is no monotonic increase whether we control for time or cohort effects,the pattern is instead J-shaped.A reasonable conjecture is that earnings inherit this convex pro?le as a combination of permanent wage shocks,inducing a linear log-wage variance pro?le,together with an endogenous labour supply,in particular if the correlation between wages and hours is U-shaped over the life-cycle—a feature emphasized by Blundell and Etheridge(2010)in this issue.33Finally,the bottom panels of Figs.13and14show that the variance of both disposable income and consumption exhibit very little changes in inequality over the life-cycle.This is perhaps a re?ection of the role played by government policy in compressing inequality which we emphasized previously.
The behavior of earnings inequality over the life-cycle is particularly interesting.The lack of a clear monotonic(let alone linear)pattern is in sharp contrast to the results constructed and presented in Storesletten et al.(2004)for the U.S.This matters because it is precisely the linearity of the cross-sectional variance reported there that has motivated the profession to write down a unit root process as the main driving force for earnings;indeed we follow that approach in this paper as well.The facts reported in Figs.13and14cast some doubt on the appropriateness of that approach,at least when applied to Canadian earnings,though it does seem to hold for wages.
32The samples used in these?gures consists of Sample A of Tables12and14for individual level income data,and Sample D of Tables13and15for family level income data.For consumption data,Sample B from Table11is used.
33A convex earnings pro?le may also lend some support for the“heterogeneous income pro?le”emphasized by Guvenen(2009).
66M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Fig.14.Inequality over the life-cycle(cohort effects).
Table6
Disposable income and net wealth.
19992005
Net?nancial wealth/disposable income 2.8841 3.2328
Net total wealth/disposable income 5.6318 6.8045
Net worth/disposable income 6.30667.4307
Gini index of disposable income0.33550.3359
Gini index of net?nancial wealth0.70540.6992
Gini index of net total wealth0.66640.6636
Gini index of net worth0.66030.6590
https://www.doczj.com/doc/5717874675.html, total wealth is net?nancial wealth plus net residential real estate and business https://www.doczj.com/doc/5717874675.html, worth is net
total wealth plus other net wealth including vehicles.(Source:SFS-RDC.)
4.5.Wealth inequality
Morissette et al.(2002)document that wealth inequality went up in Canada from1984to1999.In particular,the Gini coe?cient for equivalized net wealth went up from0.678to0.723over that period of time.We extend this analysis by considering the year2005as well.Note that our numbers are not directly comparable to theirs,chie?y because we con?ne our attention to working-age-headed households.We look at three measures of wealth which we call net?nancial wealth, net total wealth and net worth,https://www.doczj.com/doc/5717874675.html, total wealth is de?ned as net?nancial wealth plus net residential real estate and business https://www.doczj.com/doc/5717874675.html, worth is simply total assets minus total liabilities.
Table6reports wealth to after-tax(disposable)income ratios and Gini coe?cients for our income and wealth measures.34 For all three wealth measures,the wealth to income ratio increased between1999and2005.The Gini coe?cients of the three wealth measures are about twice as large as those of disposable income.For all income and wealth measures,the Gini coe?cient remained stable between1999and2005.Table7reports correlation between income and wealth and the share of the top1and5percentile in the income and wealth distributions.For all wealth measures,the correlation with disposable income has increased between1999and2005.The share of the top1percentile in the wealth distribution is about three times as high as that for disposable income.
34The sample used for this table consists of Sample A from Table16.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7567
Table7
Disposable income and net wealth.
19992005
Correlation coe?cient
(Net?nancial wealth,disposable income)0.36550.4377
(Net total wealth,disposable income)0.35510.4146
(Net worth,disposable income)0.34700.4255
Share of the top1percent
Disposable income0.05410.0583
Net?nancial wealth0.16350.1449
Net total wealth0.16650.1749
Net worth0.15680.1668
Share of the top5percent
Disposable income0.16340.1719
Net?nancial wealth0.41430.3926
Net total wealth0.37020.3817
Net worth0.35140.3639
https://www.doczj.com/doc/5717874675.html, total wealth is net?nancial wealth plus net residential real estate and business https://www.doczj.com/doc/5717874675.html, worth is net
total wealth plus other net wealth including vehicles.(Source:SFS-RDC.)
Relative to the U.S.,wealth is much less concentrated in Canada.For example,the level of the Gini coe?cient,around 0.66,is low relative to an average of0.77as reported by Heathcote et al.(2010)for the U.S.over the last20years or so.More spectacular is the comparison of the fraction of the wealth held by the top of the wealth distribution.While the top5percent in Canada hold about35percent of the wealth,Budría Rodríguez et al.(2002)report that in1998 the top1percent in the U.S.held about that same fraction of the wealth.The top1percent in Canada only hold about 16percent of the wealth.In addition,it appears that income and wealth are more correlated in the U.S.than in Canada:in 1998,the correlation between income and wealth in the U.S.was0.6,whereas that between disposable income and wealth in Canada was only0.35in1999.35
4.6.Wage/income processes
In this section,we examine(exogenous)idiosyncratic income risk that individuals and families face by estimating a stochastic process representing the dynamics of individual wages,equivalized family earnings and equivalized family dis-posable income.36For this estimation,we use data from SLID exclusively as only this survey provides panel data on income variables as well as individual and family characteristics.In order to extract the idiosyncratic component of a given variable, we?rst regress the variable of interest on observables and take residuals from the regression.37We control for age,gen-der,marital status,education,province of residence,immigration status and mother tongue in individual wages,while we control for head’s and spouse’s(if present)characteristics listed above as well as family type and the number of earners for family earnings and disposable income.Let u it denote the residual earnings/income of person/family i at time t.We model the dynamics of the residuals by the following stochastic process:
u it=αit+εit,
αit=αit?1+ηit,
whereαit represents the permanent component of the residual earnings/income,εit represents the transitory component, andηit represents innovation to the permanent component.We assume thatεit andηit are i.i.d.in the cross section with E[εit]=0,E[ηit]=0,V ar(εit)=σ2ε,t,and V ar(ηit)=σ2η,t for all t,that E(εisηit)=0for all s,t and that E(εisεit)= E(ηisηit)=0for all s=t.
In Table8we present results from estimating processes for individual wages,equivalized family earnings and equivalized family disposable income.The estimation process is constructed so as to match the autocovariance function of log differ-ences.38The result is that a very high fraction of the overall cross-sectional variance and also a high fraction of the risk faced by households is accounted for by the permanent component as opposed to the transitory component.39Note that the
35Note that for the U.S.the correlation is with respect to pre-government income,as the Survey of Consumer Finances in the U.S.does not have any tax information.
36The samples used in this section consist of Sample A from Tables12and14for individual level data,and Sample C from Tables13and15for family level data.
37We run the cross-sectional regressions year by year,allowing coe?cients to vary over time.
38See Domeij and Flodén(2010)in this issue for details of our estimation procedures.
39Mechanically,using?rst difference moment conditions,the magnitude of the estimate forσ2εdepends on the covariance between subsequent log differences.If subsequent log differences are not highly correlated,which they are not,we are led to conclude that transitory shocks account for a small fraction of the overall variance and risk.
68M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Table8
Parameter estimates for the earnings process.
Individual wage Equivalized family
earnings Equiv.family disposable income
σ2εσ2ησ2εσ2ησ2εσ2η
19940.05980.05880.02960.10390.00960.0300
19950.05350.05880.03060.09800.00840.0326
19960.05780.05920.02270.09660.00790.0335
19970.06320.05230.02690.09030.00890.0316
19980.05720.05380.02650.08580.00800.0306
19990.07330.04800.01820.09540.00910.0341
20000.07210.04850.01920.08090.01050.0292
20010.06390.05360.01850.08570.00880.0297
20020.05960.05660.02450.07310.01550.0302
20030.05790.05360.02400.06850.01220.0224
20040.05130.05830.02940.06730.00920.0269
20050.05130.06910.02940.08520.00920.0298
Mean0.06090.05470.02500.08590.00980.0301
Notes.σ2εandσ2ηare the variance of the transitory shock and that of the permanent shock,respectively.
Fig.15.Variances of permanent and transitory shocks(wage process).Notes.Estimates based on SLID1993–2005.
estimated variance of the permanent shock for wages,at around0.055,implies much higher inequality over the life-cycle than is reported in Figs.13and14.Indeed,Fig.15shows that estimating our model using moments in levels rather than in ?rst differences produces drastically different estimates.While the variance of transitory shocks based on the moments in levels is much higher than that based on moments in differences,the opposite holds for the variance of permanent shocks. This pattern is evidently not unique to Canadian data,as similar results in this issue can be found for Sweden(Domeij and Flodén,2010),Germany(Fuchs-Schündeln et al.,2010),and the U.S.(Heathcote et al.,2010).
The previous caveat notwithstanding,Table8also reports estimates forσ2η(permanent risk)andσ2ε(transitory risk)for equivalized family earnings and disposable family https://www.doczj.com/doc/5717874675.html,ernment policies such as progressive taxation,social welfare programs,and employment insurance are designed to mitigate earnings risk that families face.In order to examine the extent to which government policies reduce earnings risk,we compare estimates for equivalized family earnings and dis-posable income.Much like our previous?ndings,our results suggest that the tax and transfer system substantially reduces both permanent and transitory earnings risk,with the permanent and transitory risk reduced on average by5.58point (from0.0859to0.0301)and1.52point(from0.0250to0.0098),respectively.
4.7.Log-normality tests
A highly desirable property of distributions is log normality.With that property,a distribution can be fully characterized by its?rst two moments,and two distributions are unambiguously comparable with respect to the degree of inequality.In particular,if earnings are log-normally distributed,it can never happen that the Gini coe?cient goes up but the variance of log earnings goes down.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7569
Fig.16.How log normal are consumption and earnings(1940’s birth cohort).
We investigate log-normality using two approaches.The?rst is formal tests,including the Kolmogorov–Smirnov and the Skewness–Kurtosis test.40These tests tell us whether the deviations from normality that we observe are statistically signi?cant.A further question,though,is whether these deviations are su?ciently large to be important.To address that issue,we use Epanechnikov(1969)’s kernel density estimation in order to compare non-parametrically estimated densities with the corresponding normal densities.
The results from formal tests are on the whole negative.For earnings,we can always reject log-normality,no matter what the test,no matter what the year,no matter what the cohort and no matter what the(reasonable)signi?cance level. The situation with consumption is rather different.As pointed out in Battistin et al.(2007),consumption is more log-normal than earnings.In fact,for observations in1982,1986and1992and using the Kolmogorov–Smirnov test,we cannot reject,at the5percent level,log-normality of the cross-sectional distribution of consumption among two cohort groups:those born in the1940’s and those born in the1950’s.For other tests and other samples,however,we can reject normality.
Turning our attention to the second approach,consider Fig.16.This?gure plots non-parametrically estimated probability density functions along with their normal distribution counterparts for the1940’s birth cohort.41It is very clear from these ?gures that log consumption is closer to normal than log earnings.Indeed,an inspection of the skewness and kurtosis of log consumption reveals that the distribution is very nearly symmetric and exhibits only a little more kurtosis than the normal distribution.The difference is barely visible to the naked eye.Thus log-normality is an excellent approximation of the distribution of consumption.
For earnings,things are quite different.The distributions are visibly skewed to the left(the mean is to the left of the mode)and strongly leptokurtic(the density has a sharper peak and fatter tails than the normal distribution).Log-normality is not a particularly good approximation of the distribution of earnings.42
40All the results in this section are based on data from FAMEX/SHS using sample B from Table11.
41Similar pictures emerge from other survey years and cohorts.
42We also estimated densities for wages from SCF/SLID data,obtaining similar results.Interestingly,log-normality is not as bad an approximation for disposable income.Indeed,there is an instance where we could not reject log-normality of the cross-sectional distribution of disposable income.This was for the1950’s cohort for observations in1982.
70M.Brzozowski et al./Review of Economic Dynamics13(2010)52–75
Table9
Pre-and after-tax income inequality from Public-Use Files.
Year Pre-government family income(y)Total family disposable income(y D)
Varlog50/1090/50Gini Varlog50/1090/50Gini
1980n/a n/a n/a n/a n/a n/a n/a n/a 19860.5639 2.6657 1.99360.32840.2670 1.9610 1.82540.2709 19900.5610 2.6621 1.95520.32730.2498 1.9210 1.76820.2611 19950.6359 2.8613 2.00360.34020.2622 1.9373 1.79190.2665 20000.6080 2.8182 2.02670.34380.2724 1.9639 1.81790.2740 20050.6751 3.0673 2.13700.36760.3103 2.0677 1.88010.2920
Growth(%)
1986–20000.45?3.16 1.810.46?1.95?2.990.26?1.62 1986–1990?0.51?0.13?1.93?0.33?6.45?2.04?3.13?3.60 1990–2000 1.03?3.02 3.810.81 4.92?0.94 3.45 2.09
1980–1985n/a n/a n/a n/a n/a n/a n/a n/a 1986–1990?0.51?0.13?1.93?0.33?6.45?2.04?3.13?3.60 1990–199513.347.48 2.48 3.95 4.980.84 1.34 2.04 1995–2000?12.29?10.55 1.30?3.18?0.13?1.78 2.100.03 2000–200511.038.84 5.44 6.9113.89 5.29 3.42 6.57
Notes.All changes that involve years over which SCF and SLID overlap are computed as the sum of the change in SCF until1996and the change in SLID from1996.
5.A view of inequality from Public-Use Files
It may be of some interest to compare the conclusions about trends in inequality we draw in Section4from the ones that would be drawn from Public-Use Files(PUF).PUF data differ from RDC data in two important respects.First,Statistics Canada introduces noise in PUF data in order to preserve the identity of the respondents.Second,the revised weights are not currently available in PUF for the SCF sample.Below we brie?y review how the main message from RDC data is altered when using public data for income and wealth—recall that for our purpose,there is no distinction between RDC and PUF for consumption data.
5.1.Income inequality
To see what Public-Use Files tell us about trends in income inequality,consider Table9and contrast it with the corre-sponding results from RDC data,displayed in Table5.One striking result is that the level of inequality,however measured,is understated in Public-Use Files.In terms of changes,the difference is even more striking.For example,the Public-Use Files suggest that the level of inequality has barely changed from1986to2000.In particular,it completely misses the increase in inequality in the1990’s,which led Frenette et al.(2007)to abandon survey data in favor of Census data.43These results also cast doubt on previous studies that have used PUF data to document the evolution of various measures of income inequality and their implications(for example,see Burbidge et al.,1997and Beaudry and Green,2000).
5.2.Wealth inequality
One aspect that PUF data typically have problems with is the very top of the distribution(because of top coding),and that is of course particularly important for the wealth distribution.Table10sheds some light on that.The Public-Use Files for the SFS suffer from top-coding problems where each observation above a certain threshold is given the value of that threshold.Since we have access to RDC data,in which no observation is top-coded,we can compare the accuracy of different statistical methods to impute top-coded observations in terms of various measures of inequality.
What we do is the following,taking net?nancial wealth as an example.First we consider the components of net?nancial wealth,like mutual fund holdings,mortgage debt,etc.For each such component,we consider the top decile with the idea that top decile has been drawn from a Pareto distribution.Excluding the top-coded observations,we then use the remaining observations in the top decile to estimate the single(remaining)unknown parameter of the Pareto distribution.This can be done either by least squares or maximum likelihood and we report the results from both approaches in Table10,labeled Public3and Public4respectively.Having estimated the parameter,one can then mechanically compute the mean of the distribution and replace the top-coded values with that mean.As Table10reveals,both methods work pretty well.For the Gini coe?cient of any of the wealth categories,the estimate is at most1percent away from the RDC value.However,the estimates are not as precise for the top1percent.
43Some of the changes between2000and2005are due to the revision of survey weights as SLID data for2000feature old weights based on Census 1996,while the SLID data for2005feature the revised weights based on Census2001.
M.Brzozowski et al./Review of Economic Dynamics13(2010)52–7571
Table10
Comparison of Public-Use and con?dential SFS1999.
Public1Public2Public3Public4RDC data
Share of top1%
Disposable income0.04810.04810.04820.04830.0541
Net?nancial wealth0.12460.14310.18410.19660.1635
Net total wealth0.11610.14240.16230.18310.1665
Net worth0.10660.13230.15090.16830.1568
Gini index
Disposable income0.32520.33030.33190.33200.3355
Net?nancial wealth0.67980.69260.70140.70320.7054
Net total wealth0.63480.65360.66160.66850.6664
Net worth0.62740.64560.65320.65910.6603
Notes.The column designated Public1reports estimates for the sample excluding observations with top-coded values.Public2reports the estimates for the sample including observations with top-coded values.Public3corrects for top-coding by OLS estimation suggested by David Domeij.Public4corrects for top-coding by maximum likelihood estimation.The estimation is conducted for each income and wealth component,since top-coding is done at the component level.For the estimation,the sample of families with the given variable above the90th percentile and below the top-coding threshold is used.
6.Concluding remarks
This paper characterizes various aspects of inequality in Canada over the last30years or so.All measures of income inequality have increased over these years.Most notable is the rise in wage and earnings inequality,which were to a large extent offset by the tax and transfer system to produce a moderate rise in disposable income.As a result,the rise in consumption inequality has also been fairly mild,though still noticeable.While we emphasize the role of government policy in compressing inequality,much work remains to be done to identify the precise role of speci?c components of the tax and transfer system in mitigating the trend as well as movements in income inequality.44Another interesting?nding is the permanent role played by recessions in increasing inequality.Decomposing this rise and analyzing what makes it permanent also seems like a worthwhile endeavor,especially since this phenomenon is not unique to Canada.
In light of the recent work of Frenette et al.(2006)and Frenette et al.(2007),our results suggest that the revision of the weights that Statistics Canada has recently implemented was valuable,in the sense that these revisions brought measures of inequality in Survey data much closer to the evidence emanating from Census data.In particular,our results tend to support the broad conclusion on the evolution of income inequality reached by Frenette et al.(2007).
Appendix A.Detailed sources of data
A.1.Consumption data
The Survey of Family Expenditures(FAMEX)and the Survey of Household Spending(SHS)provide cross-sectional data on individual and household characteristics,income,and expenditure.For both surveys,income and expenditure data refer to a calendar year(called reference year).Data are collected through personal interviews conducted in the?rst quarter following a given reference year.FAMEX data are available for1969,1974,1978,1982,1984,1986,1990,1992,and1996.But the surveys for1974,1984,and1990cover only urban areas and have a smaller sample(around7000households instead of 14,000or so for the years with national coverage).The SHS replaced the FAMEX in1997and has been conducted annually thereafter.Currently,the latest data are for2005.
For1969–1986,expenditure data are collected for spending units.Spending unit is de?ned as a group of persons depen-dent on a common or pooled income for the major items of expense and living in the same dwelling or one?nancially independent individual living alone.In1990,the unit of analysis was replaced by households,which is de?ned as a person or a group of persons occupying one dwelling unit.
Total family disposable income after taxes and transfers(y D)can be constructed for the whole sample period.However, pre-government family earnings(y L),which is de?ned as the sum of wages and salaries and the labour part of self-employment income,cannot be constructed in a consistent manner over time,since wages/salaries and self-employment income are not separately available over the1997–2005period.We use nondurable goods expenditure as our measure of consumption.Nondurable goods expenditure consists of the expenditure on food,alcohol and tobacco,personal care supplies and services,water,fuel,and electricity,household operation,public transportation,operation of automobiles and trucks,apparel,recreation,reading,education,traveler accommodation,health care,and miscellaneous nondurable expendi-tures.We de?ate income by region-speci?c CPI for all items.We de?ate each expenditure component by the corresponding region-speci?c CPI.
Unfortunately,no information on education is available from1997to2003.Thus,we cannot conduct analysis involving education level for those years.
44The most important reforms are probably the replacement in1991of the Manufacturers Sales Tax(MST)with the Goods and Services Tax(GST),which is a federal value added tax,as well as the income tax reform of1987.
各种税的计算公式 1、一般纳税人 应纳税额=销项税额—进项税 销项税额=销售额×税率此处税率为17% 组成计税价格=成本×（1+成本利润率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率） 禁止抵扣人进项税额=当月全部的进项税额×（当月免税项目销售额，非应税项目营业额的合计÷当月全部销售，营业额合计） 2、进口货物 应纳税额=组成计税价格×税率 组成计税价格=关税完税价格+关税（+消费税） 3、小规模纳税人 应纳税额=销售额×征收率 销售额=含税销售额÷（1+征收率） 二．消费税 1、一般情况： 应纳税额=销售额×税率 不含税销售额=含税销售额÷（1+增值税税率或征收率） 组成计税价格=（成本+利润）÷（1—消费税率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率） 组成计税价格=（材料成本+加工费）÷（1—消费税税率） 组成计税价格=（关税完税价格+关税）÷（1—消费税税率） 2、从量计征 应纳税额=销售数量×单位税额 三、营业税 应纳税额=营业额×税率 四、关税 1、从价计征
应纳税额=应税进口货物数量×单位完税价×适用税率 2、从量计征 应纳税额=应税进口货物数量×关税单位税额 3、复合计征 应纳税额=应税进口货物数量×关税单位税额+应税进口货物数量×单位完税价格×适用税率五、企业所得税 应纳税所得额=收入总额—准予扣除项目金额 应纳税所得额=利润总额+纳税调整增加额—纳税调整减少额 应纳税额=应纳税所得额×税率 月预缴额=月应纳税所得额×25% 月应纳税所得额=上年应纳税所得额×1/12 六、外商投资企业和外商企业所得税 1、应纳税所得额 制造业： 应纳税所得额=产品销售利润+其他业务利润+营业外收入—营业外支出 商业： 应纳税所得额=销售利润+其他业务利润+营业外收入—营业外支出 服务业： 应纳税所得额=业务收入×净额+营业外收入—营业外支出 2、再投资退税： 再投资退税=再投资额×（1—综合税率）×税率×退税率 七、个人所得税： 1、工资薪金所得： 应纳税额=应纳税所得额×使用税率—速算扣除数 2、稿酬所得： 应纳税额=应纳税所得额×使用税率×（1—30%） 3、其他各项所得：
做好充分准备，迎接期中考试各位老师，各位同学:大家早上好! 今天我国旗下讲话的题目是：《做好充分准备，迎接期中考试》。 开学两个月了，我们即将迎来本学年第一次重要的考试——期中考试。对于七年级的新同学来说，这次考试，是进入初中阶段的第一次大型测试。可以帮助同学们检测目前的学习方法是否正确，学习态度是否端正，据此对自己的学习方法和学习态度作一些改进。对八年级的同学来说，这次考试，是一种跨越。八年级是三年学习中承上启下的关键一年。学习的科目增加，难度提升，所以一定要打下坚实的基础。对于九年级的同学来说，这次考试，是一种演习。作为三年中最为紧张的一年，我想，九年级的同学们现在所需要做的，只有拼搏！因此，每一位老师，每一位同学，都必须严肃、认真的对待本次期中考试，积极备考，争取考出好成绩。 大教育家孔子说过“学而时习之，不亦说乎？”只有有效的复习，才能达到事半功倍的效果，而考前的总结性复习、系统性复习更是一种有效的“温故”。复习对每位同学来讲都是尤为重要。因此每位同学都要记住，复习课上看似都在重复平时学过的内容，其实，已是一种浓缩，一种总结，一种归纳，更是一种提高。希望同学们把握好课堂，上课要注意力集中，跟着老师的讲课节奏，做好课堂笔记，听好每一分钟的课。
平常学习好的同学要善于归纳、总结，应该利用复习之机巩固基本知识、基本技能，做到触类旁通，使自己学习水平再上新台阶。基础差的同学更应充分利用这次复习的时机，狠抓基础，查漏补缺，力争在复习中有较大的提高。 期中考试是阶段总结性检测，目的是总结过去、面向未来。帮助同学们发现前面学习的长处和不足，充满信心去投身今后的学习。因而考试不是为难大家，而是要让大家考出自信。因此，抓住课本、抓住基础、抓住重点、难点才是最重要的。 俗话说的好：“人生能有几回搏，此时不搏何时搏。”愿同学们克服心理障碍，排除内外干扰，集中精力，努力拼搏，用期中考试的成绩来验证自己的学习能力。 最后，预祝同学们在考试中发挥出自己最好的水平。 泰宁二中 2017.10.30
等额本息和等额本金计算公式 等额本金: 本金还款和利息还款： 月还款额=当月本金还款+当月利息式1 其中本金还款是真正偿还贷款的。每月还款之后，贷款的剩余本金就相应减少： 当月剩余本金=上月剩余本金-当月本金还款 直到最后一个月，全部本金偿还完毕。 利息还款是用来偿还剩余本金在本月所产生的利息的。每月还款中必须将本月本金所产生的利息付清： 当月利息=上月剩余本金×月利率式2 其中月利率=年利率÷12。据传工商银行等某些银行在进行本金等额还款的计算方法中，月利率用了一个挺孙子的算法，这里暂且不提。 由上面利息偿还公式中可见，月利息是与上月剩余本金成正比的，由于在贷款初期，剩余本金较多，所以可见，贷款初期每月的利息较多，月还款额中偿还利息的份额较重。随着还款次数的增多，剩余本金将逐渐减少，月还款的利息也相应减少，直到最后一个月，本金全部还清，利息付最后一次，下个月将既无本金又无利息，至此，全部贷款偿还完毕。 两种贷款的偿还原理就如上所述。上述两个公式是月还款的基本公式，其他公式都可由此导出。下面我们就基于这两个公式推导一下两种还款方式的具体计算公式。 1. 等额本金还款方式 等额本金还款方式比较简单。顾名思义，这种方式下，每次还款的本金还款数是一样的。因此： 当月本金还款=总贷款数÷还款次数 当月利息=上月剩余本金×月利率 =总贷款数×（1－（还款月数-1）÷还款次数）×月利率
当月月还款额=当月本金还款＋当月利息 =总贷款数×（1÷还款次数＋（1－（还款月数-1）÷还款次数）×月利率） 总利息＝所有利息之和 =总贷款数×月利率×（还款次数－（１＋２＋３＋。。。＋还款次数－1）÷还款次数） 其中1+2+3+…+还款次数－１是一个等差数列，其和为（1＋还款次数－１）×（还款次数－１）／2＝还款次数×（还款次数－１）／２ :总利息=总贷款数×月利率×（还款次数＋1）÷2 由于等额本金还款每个月的本金还款额是固定的，而每月的利息是递减的，因此，等额本金还款每个月的还款额是不一样的。开始还得多，而后逐月递减。 等额本息还款方式: 等额本金还款，顾名思义就是每个月的还款额是固定的。由于还款利息是逐月减少的，因此反过来说，每月还款中的本金还款额是逐月增加的。 首先，我们先进行一番设定： 设：总贷款额＝Ａ 还款次数＝Ｂ 还款月利率＝Ｃ 月还款额＝Ｘ 当月本金还款＝Ｙｎ（ｎ＝还款月数） 先说第一个月，当月本金为全部贷款额＝Ａ，因此： 第一个月的利息＝Ａ×Ｃ 第一个月的本金还款额 Ｙ１＝Ｘ－第一个月的利息
各个税种的计算方法及公式 今天给大家分享一些各种税的计算方法和计算公式，希望可以帮到咱们财务人员。包含以下税种的计算方法：增值税、消费税、营业税、关税、企业所得税、外商投资企业和外商企业所得税、个人所得税等。（以下内容由深圳会计实践培训---成功财务实战俱乐部整理，转载请注明出处，谢谢！） 一、增值税 1、一般纳税人 应纳税额=销项税额—进项税 销项税额=销售额×税率此处税率为17% 组成计税价格=成本×（1+成本利润率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率） 禁止抵扣人进项税额=当月全部的进项税额×（当月免税项目销售额，非应税项目营业额的合计÷当月全部销售，营业额合计） 2、进口货物 应纳税额=组成计税价格×税率 组成计税价格=关税完税价格+关税（+消费税） 3、小规模纳税人 应纳税额=销售额×征收率 销售额=含税销售额÷（1+征收率） 二．消费税 1、一般情况： 应纳税额=销售额×税率 不含税销售额=含税销售额÷（1+增值税税率或征收率） 组成计税价格=（成本+利润）÷（1—消费税率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率） 组成计税价格=（材料成本+加工费）÷（1—消费税税率） 组成计税价格=（关税完税价格+关税）÷（1—消费税税率） 2、从量计征 应纳税额=销售数量×单位税额 三、关税 1、从价计征 应纳税额=应税进口货物数量×单位完税价×适用税率 2、从量计征 应纳税额=应税进口货物数量×关税单位税额 3、复合计征 应纳税额=应税进口货物数量×关税单位税额+应税进口货物数量×单位完税价格×适用税率 四、营业税 应纳税额=营业额×税率 五、企业所得税 应纳税所得额=收入总额—准予扣除项目金额 应纳税所得额=利润总额+纳税调整增加额—纳税调整减少额 应纳税额=应纳税所得额×税率 月预缴额=月应纳税所得额×25% 月应纳税所得额=上年应纳税所得额×1/12 六、外商投资企业和外商企业所得税 1、应纳税所得额 制造业：
期中考试周国旗下讲话自我完善沉着应考 各位老师、各位同学： 早上好！今天我演讲的题目是《自我完善，沉着应考》。 本周我们将要迎来新学期的第一次重要考试---期中考试。这是对我们学习情况的检测，学习成果的总结，同时也是比较，是竞争，是较量，更是机会。同学们要努力考出水平，考出成绩，在班级、年级中找准自己的位置，在竞争中进步成长，在总结反思中进一步明确自己努力的方向。 如何才能沉着应考？我觉得做好以下几个方面，对提高我们的成绩有很大的帮助。 首先要调整好目标。每个人都渴望成功，在走向成功的途中，最忌好高骛远不实事求是，所以每个同学都要制定一个切实可行的复习计划。 其次需合理利用时间。谁吝啬时间，时间就对谁慷慨；谁荒废时间，时间也就荒废谁。我们要抓住分分秒秒，少讲空话多做实事，提高学习的效率。 再次，人的记忆及遗忘规律告诉我们，遗忘是人的必然，世上从来就没有过目不忘的人，因此每个学生要重视考前的复习与巩固。孔子说："温故而知新"，就是告诉我们学习要重视复习，一个成绩优秀的学生肯定是肯吃苦，肯钻研，认真复习并掌握科学的学习方法的人。所以希望每一位同学能够在老师的指导下，把所学的知识认真整理、归类，并全面系统地进行复习，争取做到熟能生巧，融会贯通。 期中考试既是对我们所学知识的检测，又是对我们意志的磨炼。希望同学们考试时沉着冷静，要对自己充满信心，同时要科学对待考试，认真分析试题，仔细推敲，先易后难，逐一做答。遇到容易的不要忘乎所以，要细心谨慎；遇到难题切莫惊慌，要认真思考。许多人考不好是因为粗心大意，把会做的题随手做错了，我们要吸取教训。做到基础题不失分，争取战胜难题，并注意把握时间。 期中考试还是一次人品的检测。我想同学们并非第一次参加考试，对考试的纪律是清楚的、明白的。考试是严肃的，来不得半点弄虚作假。学习上有付出才有收获，有勤奋才有成果，考试作弊是自欺欺人的做法，不能弥补自己学习上的漏洞，相反会让自己失去诚实的宝贵品质，同学们切莫一失足成千古恨。所以，在此，我奉劝少数人尽早打消这个念头。同时，作弊的学生，学校将一律严肃对
等额本息还款公式推导 设贷款总额为A，银行月利率为β，总期数为m（个月），月还款额设为X，则各个月所欠银行贷款为： 第一个月A 第二个月A（1＋β）-X 第三个月（A（1＋β）-X）（1＋β）-X＝A(1＋β)2-X[1+(1+β)]第四个月（（A（1+β）-X）（1＋β）-X）（1＋β）-X ＝A(1+β)3-X[1+(1+β)+(1+β)2] … 由此可得第n个月后所欠银行贷款为 A(1+β)n –X[1+(1+β)+(1+β)2+…+(1+β)n-1]= A(1+β)n –X [(1+β)n-1]/β 由于还款总期数为m，也即第m月刚好还完银行所有贷款，因此有 A(1+β)m –X[(1+β)m-1]/β=0 由此求得
X = Aβ(1+β)m /[(1+β)m-1] ======================================================= ===== ◆关于A(1+β)n –X[1+(1+β)+(1+β)2+…+(1+β)n-1]= A(1+β)n –X[(1+β)n-1]/β的推导用了等比数列的求和公式 ◆1、(1+β)、(1+β)2、…、(1+β)n-1为等比数列 ◆关于等比数列的一些性质 （1）等比数列：An+1/An=q, n为自然数。 （2）通项公式：An=A1*q^（n－1）； 推广式：An=Am·q^(n－m)； （3）求和公式：Sn=nA1(q=1) Sn=[A1(1-q^n)]/(1-q) （4）性质： ①若m、n、p、q∈N，且m＋n=p＋q，则am·an=ap*aq； ②在等比数列中，依次每k项之和仍成等比数列. (5)“G是a、b的等比中项”“G^2=ab（G≠0）”. （6）在等比数列中，首项A1与公比q都不为零. ◆所以1+(1+β)+(1+β)2+…+(1+β)n-1 =[(1+β)n-1]/β 等额本金还款不同等额还款 问：等额本金还款是什么意思？与等额还款相比是否等额本金还款更省钱？
税法所有税种计算公式大全 一、各种税的计算公式 一、增值税 1、一般纳税人 应纳税额=销项税额-进项税 销项税额=销售额×税率此处税率为17% 组成计税价格=成本×（1+成本利润率） 组成计税价格=成本×（1+成本利润率）÷（1-消费税税率） 2、进口货物 应纳税额=组成计税价格×税率 组成计税价格=关税完税价格+关税（+消费税） 3、小规模纳税人 应纳税额=销售额×征收率 销售额=含税销售额÷（1+征收率） 二、消费税 1、一般情况： 应纳税额=销售额×税率 不含税销售额=含税销售额÷（1+增值税税率或征收率） 组成计税价格=（成本+利润）÷（1-消费税率） 组成计税价格=成本×（1+成本利润率）÷（1-消费税税率）组成计税价格=（材料成本+加工费）÷（1-消费税税率） 组成计税价格=（关税完税价格+关税）÷（1-消费税税率） 2、从量计征 应纳税额=销售数量×单位税额 三、关税 1、从价计征 应纳税额=应税进口货物数量×单位完税价×适用税率
2、从量计征 应纳税额=应税进口货物数量×关税单位税额 3、复合计征 应纳税额=应税进口货物数量×关税单位税额+应税进口货物数量×单位完税价格×适用税率 四、企业所得税 应纳税所得额=收入总额-准予扣除项目金额 应纳税所得额=利润总额+纳税调整增加额-纳税调整减少额 应纳税额=应纳税所得额×税率 月预缴额=月应纳税所得额×25% 月应纳税所得额=上年应纳税所得额×1/12 五、个人所得税： 1、工资薪金所得： 应纳税额=应纳税所得额×适用税率-速算扣除数 2、稿酬所得： 应纳税额=应纳税所得额×适用税率×（1-30%） 3、其他各项所得： 应纳税额=应纳税所得额×适用税率 六、其他税收 1、城镇土地使用税 年应纳税额=计税土地面积（平方米）×适用税率 2、房地产税 年应纳税额=应税房产原值×（1-扣除比例）×1.2% 或年应纳税额=租金收入×12% 3、资源税 年应纳税额=课税数量×单位税额 4、土地增值税 增值税=转让房地产取得的收入-扣除项目
期中考试作文15篇 期中考试作文15篇 在日常的学习、工作、生活中，大家或多或少都会接触过作文吧，借助作文人们可以实现文化交流的目的。你写作文时总是无从下笔？以下是小编为大家收集的期中考试作文，欢迎阅读与收藏。 期中考试作文篇1今日是期中考试成绩出来的日子，我的语文没考好，只考了88.5分，所以我要来做一下反思。 首先，我要反思一下我考试的时候是怎么样做题的。考试时，我写作文没有认真审题，这是我多次出现的情况了，所以我以后写作文前，要在题目的重要部分画上三角号来强调，提醒自己注意审题。而且我的作文还有一个缺点，就是描写事物不具体，作文总因为这丢分，以后我也要注意。 我在考试卷上还发现了一些没复习到的地方，比如：用杏林春满或誉满杏林等成语填空，我由于没复习到誉满杏林这个成语，没填出来而扣了分。所以复习要更加细致些才行。我还有些因为粗心而错的题，希望大家以后也要吸取我的教
训不要粗心。我要在本学期的下个阶段里改正这些缺点，争取期末考试中考出一个好成绩。 期中考试作文篇2在辛苦历经了半个学期之后，半个学期的检验也来了，那就是期中考试。 在第二天，虽是期中考试，但我仍然怀着一颗平静的心来到了教室。试卷发下来了我/草草的看了一下题目，觉得非常的简单，心里也非常高兴，心想：这场总该可以考到九十分了吧！ 时间正在一分一秒的过着，十分钟，二十分钟，三十分钟……铃铃铃，下课铃响了。我看还有一道题目没有写，心想：算了吧！也不过是个两分题，做不做也无所谓啦。 然而因为我这个“无所谓”弄得我只得到了88分。我看到这分数时，时时的为当时没做而感到可惜。就在这时，我们的班主任——杨老师到了我的旁边说到：“你怎么就这么粗心大意呢？你看，那些得了90分的都可是一题不漏啊。这是为什么呢？”我当时就想嘴里塞了一口棉花一样怎么也说不出话来。“杨老师，是当时下课了，也要交试卷了，我才没做”我惭愧的说到。“别灰心”杨老师见我这样说道。下次小心点就可以了。 这事刚过去不久，我也领悟到只要抓住机会，哪怕没有一机会，也要自己创建机会，去放手一搏，去拼一拼。 期中考试作文篇3有一句话是这样说的：成绩是衡
中学生期中考试备考指导 面对马上到来的期中考试，开学不久的初中学生们做好准备了么?在期中考试前的10 天左右的时间里，应该怎样复习，才能在期中考试里取得不错的成绩呢?下面我们就分享一下华乐思教研室一线老师的建议。 首先，同学们应该个根据自己的实际情况，制定一个详细的复习计划，对症下药，找到自己的薄弱点，要通过期中考试，看看自己在全班、全年级的排名，检查有无知识漏洞，以便及时弥补。如果能在期中考试中好好发挥，考出优异成绩，将有利于增强同学们的自信心，对以后的学习都将有深远的帮助，尤其是新初一的第一次期中考，它的意义在老师心中、家长心中乃至学生自己心中都是非凡的。虽然期中考试很重要，但考生也没必要过度紧张，考前认真复习，牢固掌握知识点，上课注意听讲能增强应考信心。在时间紧、知识点多的情况下，考生要坚持做好每天的复习计划，并注意提高学习效率。考生可把这学期学过的知识点进行全面的总结归纳，并合理分配到考前每一天，每天坚持按计划复习，保证复习时间与效率。每周末做一套模拟测试题，也是查漏补缺的好方法。 其次，同学们应以课本为主，阅读教材。对即将到来的期中考试，我们的老师认为，同学们要以课本为主复习好基础知识，像软件中的知识结构和重难点分析一定要弄清楚搞明白，不会的及时查阅书本，要把学过的知识点反复阅读、理解，并对照课后练习里的习题进行反复思考、琢磨，融会贯通，加深对知识点的理解。对于软件上的重点例题也要着重记忆。期中考试由各学校针对自己的教学进度分别组织进行，在课堂上一定要注意听讲，弄清楚老师强调的考试范围和考试重点，认真做好老师课堂上留的作业和习题，从而有针对性地复习。 最后，同学们要调整好心态，正确对待考试。期中考试是检验学生学习情况、增强自信心的好机会，同学们可通过期中考试的排名合理定位。而在考试答题时，应把主要精力放在基础知识、基本技能、基本方法这三个方面上，因为每次考试占绝大部分的也是基础性的题目，所以对于我们软件里给出的经典、星号例题一定要吃透，而对于那些难题及综合性较强的题目作为调剂，认真思考，尽量让自己理出头绪，做完题后要总结归纳。调整好自己的心态，使自己在任何时候镇静，思路有条不紊，克服浮躁的情绪。特别是对自己要有信心，永远鼓励自己，除了自己，谁也不能把我打倒，要有自己不垮，谁也不能打垮我的自豪感。 在临考前的几天里，要做好准备，练练常规题，把自己的思路展开，切忌考前去在保证正确率的前提下提高解题速度。对于一些容易的基础题要有十二分把握拿全分;对于一些难题，也要尽量拿分，考试中要学会尝试得分，使自己的水平正常甚至超常发挥。 那么对于做题很多同学觉得挠头，老认为不知如何下手，难的不会，容易的还得不了全分，根据家长及同学后期反应最多的几点问题我们老师也是给出了几点建议：
精品文档 等本息法和等本金法的两种算公式 一: 按等额本金还款 法：贷款额为： a， 月利率为： i ， 年利率为： I ， 还款月数： n， an 第 n 个月贷款剩余本金： a1=a, a2=a-a/n, a3=a-2*a/n ...次类推 还款利息总和为Y 每月应还本金： a/n 每月应还利息： an*i 每期还款 a/n +an*i 支付利息 Y=（ n+1）*a*i/2 还款总额 =（ n+1）*a*i/2+a 等本金法的算等本金（减法）：算公式： 每月本金=款÷期数 第一个月的月供 =每月本金＋款×月利率 第二个月的月供 =每月本金＋（款－已本金）×月利率 申10 万 10 年个人住房商性款，算每月的月供款？（月利率： 4.7925 ‰）算果： 每月本金： 100000÷120＝ 833 元 第一个月的月供：833＋ 100000×4.7925 ‰＝1312.3 元 第二个月的月供：833＋（ 100000－ 833）×4.7925 ‰＝ 1308.3 元 如此推?? 二 : 按等本息款法：款 a，月利率 i ，年利率 I ，款月数n，每月款 b，款利息和 Y 1： I ＝12×i 2： Y＝n×b－ a 3：第一月款利息：a×i 第二月款利息：〔a－（ b－ a×i ）〕×i ＝（ a×i －b）×（ 1＋ i ） ^1 ＋b 第三月款利息：｛ a－（ b－ a×i ）－〔 b－（ a×i － b）×（ 1＋ i ） ^1 －b〕｝×i ＝（ a×i －b）×（ 1＋i ） ^2 ＋ b 第四月款利息：＝（ a×i － b）×（ 1＋ i ） ^3 ＋ b 第 n 月款利息：＝（a×i － b）×（ 1＋ i ） ^（ n－ 1）＋ b 求以上和：Y＝（ a×i －b）×〔（ 1＋ i ） ^n－ 1〕÷i ＋ n×b 4：以上两Y 相等求得 月均款 :b ＝ a×i ×（ 1＋ i ） ^n ÷〔（ 1＋ i ）^n － 1〕 支付利息 :Y ＝ n×a×i ×（ 1＋i ） ^n ÷〔（ 1＋ i ） ^n － 1〕－ a 款 :n ×a×i ×（ 1＋ i ）^n ÷〔（ 1＋ i ） ^n－ 1〕 注:a^b 表示 a 的 b 次方。 等本息法的算 ----- 例如下： 如款 21 万， 20 年，月利率 3.465 ‰按照上 面的等本息公式算 月均款 :b ＝ a×i ×（ 1＋ i ） ^n ÷〔（ 1＋ i ）^n － 1〕即： =1290.11017 即每个月款1290 元。 。 1欢迎下载
企业所得税额计算方法-- 各税种税额计算公式大全( 5) 企业所得税 应纳税额=应纳税所得额X税率 应纳税所得额=收入总额-准予扣除项目金额 应纳税所得额=利润总额+( — )税收调整项目金额 利润总额=收入总额-成本、费用、损失 1、工业企业应纳税所得额公式 工业企业应纳税所得额=利润总额+(-)税收调整项目金额 利润总额=营业利润+投资收益+营业外收入-营业外支出 营业利润=产品销售利润+其他业务利润-管理费用-财务费用 产品销售利润=产品销售收入一产品销售成本一产品销售费用一产品销售税金及附加 其他业务利润=其他业务收入-其他业务成本-其他销售税金及附加 本期完工产品成本=期初在产品自制半成品成本余额+本期产品成本会计-期末在产品自制半成品成本余额 本期产品成本会计=材料+工资+制造费用 2、商品流通企业应纳税所得额公式 应纳税所得额=利润总额+( — )税收调整项目金额 利润总额=营业利润+投资收益+营业外收入-营业外支出 营业利润=主营业务利润+其他业务利润-管理费用-财务费用-汇兑损失 主营业务利润=商品销售利润+代购代销收入 商品销售利润=商品销售净额一商品销售成本一经营费用一商品销售税金及附加 商品销售净额=商品销售收入-销售折扣与折让 3、饮服企业应纳税所得额公式
应纳税所得额=利润总额+( — )税收调整项目金额 利润总额=营业利润+投资收益+营业外收入-营业外支出营业利润=经营利润+附营业务收入-附营业务成本 经营利润=营业收入-营业成本-营业费用-营业税金及附加 营业成本=期初库存材料、半成品产成（商）品盘存余额+本期购进材料、商品金额 金额－期末库存材料、半成品、产成（商）品盘存余额 4、应纳所得税税额计算 （1）分月或分季预缴所得税额=月、季应纳税所得额X税率（33%） （2）年终汇算清缴时，应退补所得税 应退补所得税额=全年累计应纳税所得额X税率（33%）-本年累计已缴所得税额预缴所得 税 ①、按上年应纳税所得额的1/2或1/4 本期某月或某季应纳所得税税额=[上一年应纳税所得额/2 （或4）] X税率（33%） ②、按本期计划利润预缴所得税 本期某月或某季应纳所得税税额=本期某月或某季计划利润额X税率（33%） 5、对减征所得税的计算 减征比例=[（33% —原核定的税率）/33%] X 100% 减征后应纳所得税额=应纳税所得额X税率（33%）X（1-减征比例） =应纳所得税税额X减征比例 6、境外所得税税额扣除限额=境内外所得按税法计算的应纳税总额X来源于某外国的所 得额/境内外所得总额 7、对弥补以前年度亏损后应纳所得税额的计算
初中期中考试日记三篇 转眼间，已经入校两个月了，随之而来的将是我们入学后的第一次大考试，那就是期中考试。说实话，心中很是忐忑不安，考不好了怎么办？十分没底，甚至对考试充满了恐惧感。虽然以前在小学时经常是前几名，但是毕竟初中比小学难的多，而且强手如林。在平常的一次数学考试中，有一次竟然考了快倒数的分数，所以十分失落。听见“考试”两字就晕。唉！要是没考试该多好啊！话虽这么说，还是抱怨几句算了，因为试还是要考的。所以我要每天复习，按时完成作业，上课积极回答问题等。这样才会得到一个令自己满意的分数。 谈到分数，我也有自己独到的想法，分数和名次是一样的，我们不必把它们看的太重，但是也不能毫不在乎。而且考完后，还要吸取教训，想想这里是哪错了，那里为什么写错了等等。所以只要用尽全力去考试就一定能成功，准备迎接比赛吧，加油！ 我相信，通过我两个月的努力，我一定会考出一个好成绩的！！加油！！加油！！加油！！ 篇二 在萧瑟的秋风中，结束了我们八年级第一次期中考试，相对于往常来讲，今年的试卷难度降低了很多，但是浅水也能淹死人。像我的物理试卷，把最难的一道题做出来，但是当时脑抽了了一道最简单解答题却算错数字了。那题九分，而总分才100，错在细节上，掉了十分之一的分。历史因为没背,都是以半蒙的方式写的。 此次期中难度不高，我相信以我们班的高智商，怎么会丢了年级第一的宝座呢?因为上次语文月考题的难度比中考的难度还高了百分之二十，加上我们班没有背古诗导致我们班语文创历史新低，年级倒数第二(共6个班)，但是凭借着遥遥的英语。最后我们班获得了总分第一。那个月还被班主任物理老师鼓励并监督我们学语文，为了证明我们那次考试是失手，也是为了给语文老师一个道歉。于是在在接下来
各行业税负及计算公式（供参考） 一般税负率过低往往会引起税务局的关注，可以掌握在不低于1%（商业企业1.5%左右），各行业的税负率是不同的，具体的税负率表是： 增值税税负率=｛［年度销项税额合计+免抵退货物销售额R应税货物实用税率-（年度进项税额合计-年度进项税额转出合计+年初留抵税额-年末留抵税额）］/［年度应税货物或劳务销售额+免抵退货物销售额］｝R100％ 1农副食品加工3.50 2食品饮料4.50 3纺织品(化纤)2.25 4纺织服装、皮革羽毛（绒）及制品2.91 5造纸及纸制品业5.00 6建材产品4.98 7化工产品3.35 8医药制造业8.50 9卷烟加工12.50 10塑料制品业3.50 11非金属矿物制品业5.50 12金属制品业2.20 13机械交通运输设备3.70 14电子通信设备2.65 15工艺品及其他制造业3.50 16电气机械及器材3.70 17电力、热力的生产和供应业4.95 18商业批发0.90 19商业零售2.50 20其他3.5 企业税负率计算 企业税负率计算 目前一般指增值税： 理论税负率：销售毛利＊17%除以销售收入 实际税负率：实际上交税款除以销售收入 税负率是指增值税纳税义务人当期应纳增值税占当期应税销售收入的比例. 对小规模纳税人来说,税负率就是征收率:商业4%,工业6%,而对一般纳税人来说,由于可以抵扣进项税额,税负率就不是17%或13%,而是远远低于该比例,具体计算:
税负率=当期应纳增值税/当期应税销售收入 当期应纳增值税=当期销项税额-实际抵扣进项税额 实际抵扣进项税额=期初留抵进项税额+本期进项税额-进项转出-出口退税-期末留抵进项税额 注:1对实行"免抵退"的生产企业而言,应纳增值税包括了出口抵减内销产品应纳税额2通常情况下,当期应纳增值税=应纳增值税明细账"转出未交增值税"累计数+"出口抵减内销产品应纳税额"累计数 企业增值税税负率 推荐：财务人员必看的一部好书《战略预算-管理界的工业革命》。《战略预算-管理界的工业革命》是国内第一部走直线、全过程案例、系统化管理的著作！也是国内第一部敢于对财务人员承诺全过程系统化实战效果的好书，财务人员必看！问：1．我单位是进出口贸易公司，以自营出口为主，取得进出口权及出口业务已有3年，但最近业务比较少，不知现在对销售收入方面是否还有规定，比如说，年销售额最少要达到180万． 2．外销收入占总销售收入的95％以上的自营生产企业的增值税税负率如何计算．征税率为17％，退税率为13％．请列计算公式。 答：1、具有进出口经营权的企业在增值税一般纳税人年审时，不受年应征增值税销售额须达到180万元以上的限制。但20RR年7月1日以后新办的具有进出口经营权的小型商贸企业必须自税务登记之日起，一年内实际销售额达到180万元以上，方可申请一般纳税人资格认定。 2、税负={【年度销项税额合计+免抵退货物销售额×17%-（年度进项税额合计-年度进项税额转出合计+年初留抵税额-年末留抵税额）】÷【年度应税货物及劳务销售额（不包括免税货物及劳务销售额）+免抵退货物销售额】}×100%。 如你第二问中，假设总销售收入为100万元，外销收入95万元，内销收入5万元，进项税金15万元，征税率17%，退税率13%，计算如下： 销项税金=50000R17%=8500元 进项税金转出=950000R（17%-13%）=38000元 应交税金=8500-（150000-38000）=-103500元 税负=[8500+950000R17%-（150000-38000）]/1000000R100%=5.8% 如此题，你公司未缴纳增值税，实际税负为0，但由于是外销产品实行免抵退税政策所致，又有征、退税率差，所以理论税负为5.8%。 企业所得税行业预警税负率 企业所得税行业预警税负率 行业预警税负率 租赁业1.50%
初三期中考试动员会 老师们、同学们： 经过两个多月丰富多彩的学习生活，我们即将迎来本学期的期中考试，本次期中考试，对我们所有学生来讲意义都非同一般： 因为对初三学生而言，是进入初三学习的第一次级级规模的考试，既是一次学习小结，也是一次对自己学习成绩检验，是对我们两个月初三学习生活的全面的考核，是认识自我、审视自我重要挈机，可以给自己一个定位，同时也是自己调整长期学习目标的一个重要信息。，是进入升学考试冲刺的第一次阶段考试，也是证明实力的一次大型考试，因此大家都要认真复习，以强烈的进取心和高度的责任感来端正考试态度，积极准备参加考试。 考试，作为教学评价的一种手段，其作用除了对学生基础知识和基本技能进行考查之外，就是通过考试让教师了解学生掌握知识的总体情况，为教师教学方法的改进，教学措施的调整，教学策略的制定和实施，提供全面的客观的依据。因此，严肃考风考纪，确保考试过程和考试结果的公平公正，确保考试的权威性和可信性，是学校教学常规管理和维护教学秩序的需要。而且，同学们通过考试，对自己的学习情况有一个理性的认识,促使自己巩固所学知识和技能，从学习结果的反馈中，每一位同学都可以找到自己在学习中存在的不足，及时获得矫正信息，调整自己今后的学习（包括学科平衡，查漏补缺，学习态度和方法）。因此每一个学生都必须以认真、严肃、负责的态度对待期中考试。 1．思想上：专注学习，全身心投入 美国19世纪哲学家、诗人爱默生说：“一心向着自己目标前进的人，整个世界都给他让路！”他所说的“一心”就是聚精会神，心无旁骛，不受任何干扰。在学习竞争中，最关键的就是集中精力，一心一意紧紧把握每时每刻，提高自己的学习竞争力。为顺利完成本学段的学习任务，考出好成绩，教务处号召全体同学聚精会神，专注学习，全身心投入到期中复习备考当中去；要求各班级开好期中动员会，针对班级学生具体的情况，指导学生科学制定复习计划，围绕目标，集中精力，积极主动投入期中的复习考试中来，掀起一个学习竞赛高潮。 2．方法上：重视基础，温故知新 考试的复习一定要重视基础，我们说，越是基础的的知识，越是具有较大的包容性，它对新的问题的适应性就越宽广，所以在复习中要对所学的基本概念和基本规律有一个再认识、再理解、再提高的过程，要做到： 知识的梳理----重温教科书、看课堂笔记、看错题集，杜绝知识盲点； 概念的巩固----把握准确的概念，明确概念的内涵和外延，构建知识的网络； 规范的练习----在复习的过程中对所练习的内容一定要做到表达规范、过程完整，突出重点，善于思考，达到举一反三的效果； 温故知新。只有深入理解基本概念，掌握好基本规律，才能在解决问题的过程中得心应手，从容不迫。 3．心态上：要正视考试，放下包袱 考试既是知识的检测，又是意志的磨炼。考试时要紧张但不能过于紧张，要保持一定的焦虑但不能过于焦虑，要调整好心态，放下包袱，适当调节好自己的心理，用平和积极的心态，坦然面对考试要对自己充满信心。考试时，要沉着冷静，注意把握时间，认真分析试题，仔细推敲，先易后难，做到基础题不失分，难题多得分。 4．作风上：诚实做人，诚信考试，严格遵守考试纪律 考试不仅是一场文化知识的考试，更是一场道德品质的考试，学习上有付出才会有收获，有勤奋才会有成果。来不得半点弄虚作假，考试作弊是自欺欺人的做法，不能弥补自己学习
税法所有税种计算公式 一、各种税的计算公式 一、增值税 1、一般纳税人 应纳税额=销项税额—进项税 销项税额=销售额×税率此处税率为17% 组成计税价格=成本×（1+成本利润率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率）2、进口货物 应纳税额=组成计税价格×税率 组成计税价格=关税完税价格+关税（+消费税）
3、小规模纳税人 应纳税额=销售额×征收率 销售额=含税销售额÷（1+征收率） 二、消费税 1、一般情况： 应纳税额=销售额×税率 不含税销售额=含税销售额÷（1+增值税税率或征收率） 组成计税价格=（成本+利润）÷（1—消费税率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率） 组成计税价格=（材料成本+加工费）÷（1—消费税税率） 组成计税价格=（关税完税价格+关税）÷（1—消费税税率） 2、从量计征 应纳税额=销售数量×单位税额 三、关税 1、从价计征 应纳税额=应税进口货物数量×单位完税价×适用税率 2、从量计征 应纳税额=应税进口货物数量×关税单位税额 3、复合计征 应纳税额=应税进口货物数量×关税单位税额+应税进口货物数量×单位完税价格×适用税率
四、企业所得税 应纳税所得额=收入总额—准予扣除项目金额 应纳税所得额=利润总额+纳税调整增加额—纳税调整减少额应纳税额=应纳税所得额×税率 月预缴额=月应纳税所得额×25% 月应纳税所得额=上年应纳税所得额×1/12 五、个人所得税： 1、工资薪金所得： 应纳税额=应纳税所得额×使用税率—速算扣除数 2、稿酬所得： 应纳税额=应纳税所得额×使用税率×（1—30%） 3、其他各项所得： 应纳税额=应纳税所得额×使用税率 六、其他税收 1、城镇土地使用税 年应纳税额=计税土地面积（平方米）×使用税率 2、房地产税 年应纳税额=应税房产原值×（1—扣除比例）×1.2% 或年应纳税额=租金收入×12% 3、资源税 年应纳税额=课税数量×单位税额
期中考试有感范文 期中考试过后你有哪些感想呢？以下是我整理的期中考试有感范文，欢迎参考阅读！ 时光流逝，岁月如梭。从我身边悄然而过。不知不觉中我已经来到初中学习两个多月了，不知为何初中没有月考，但是期中考试却在同学们的期盼声中赶来了，我将迎来我升入初中之后的第一次正规的期中考试。 考试的时间很短，仿佛在梦境之中一般，考试过后我还在每天晚上做着噩梦。天哪，这太可怕了，这是落到我头上我可受不了。这是我在噩梦中惊醒后最常说的一句话。但是在星期一我接到数学考砸的消息后我反而平静多了，怎么这么奇怪呢?不过管他呢，心里的一块石头终于落地了。噩梦也终于离我远去。 现在总分已出来了，语，数，外三科总分都是120分，生物和地理总分都是30分，历史总分50，政治总分70，额，我的成绩如下：语文104，数学89，英语106，地理25，生物29，政治64，历史48(我最满意的一门)可以看出数学最不好只有89分总分465占据全班14名全年级121名，够差劲吧。不得不说，就是我们班第一名的张乃元也不过502才进入年级排行榜50多名，我呢?连班级前十都没有进更何况年级排名，乃至市排名呢?我又如何才能有一个立足之地呢? 是的，考试过去了，如梦幻般的过去了。那鲜红的89仿佛在告诉我还要努力，那所有一切的对于考试的幻想全部在成绩单发下的那一刻，成为泡影。我后悔啊，我叹息但是这又有什麽用呢? 人生从无后悔药可买，既然失败了，就要学会去承受，只着眼于过去又有什麽用呢?只会忽视现在，当现在变成过去，所有的明天都变为现在，当下一次的
考验到来之时，你只会悲哀的发现你仍留在过去，不知进取。当你在未来的某一日轻轻翻开那早已成为历史的第一次考验时，你会发现你的一生始终是在叹息声中度过的。碌碌终身。 正如某位名人曾说过：人活着就要珍惜眼前，过去便让他过去吧，今天的努力都等于在为你的明天铺路，等你过完一个又一个今天，当昨天变为遥远的历史时，你终会发现你的一生都是充实的。 由此我想，考试过去就过去吧，不能只为这次考试叹息，这只是一次小小的期中考试而已，在人生的路途中会经历一个又一个的考试，人不能永远沮丧下去啊!抬起头，仰望天空，明天依旧辉煌。 说起期中考试，我特别后悔，是怎么回事呢？听我说： 期中考试前，我可以说是魔鬼似复习，到了考试的那一天也是信心满满的走进考场。答卷时也是不马虎，而且我把那些题写完后又重新检查了一遍，找出了几个错误也把它们改了，更何况，监考老师还拿一张试卷抄我的，我想也能考100多分，考完数学，我信心满满的走出考场。和同学对了几个答案，而且没几个不是一样的，我更对自己的分数有信心了。其它几科除了地理，都有信心考高分。两天的考试，转眼已经过去了。 到了听分的那天，我满怀希望的走进教室，看见同学们也在讨论着自己会的多少分，我走到自己的位置上，问同桌：“你觉得你考的怎么样？”“能进前500就行了”说着老师就走进来了，老师把成绩单发下来，我一看语文108，数学97，英语111……看完后，我又喜又悲，喜的是语文和英语还不错，政治、生物、历史竟然都及格了。悲的是数学才考90多分，地理不及格（地理不及格也是意料之中的）。老师留完作业，又说了我们的排名，我退步了才211名，我顿
最新版税收计算公式汇 总[1]
税收计算公式汇总： (一)增值税 1．视同销售行为 组成计税价格=成本*（1+成本利润率） 属应征消费税组成价格应加计消费税额 组成计税价格=成本*（1+成本利润率）/（1－消费税税率） 2．纳税人兼营免税项目或非应税项目因无法准确划分不得抵扣的进项税额 不得抵扣的进项税额=当月全部的进项税额*（当月免税项目销售额，非应税项目营业额合计/当月全部销售额，营业额） 委托加工应纳增值税进项税额=加工费*税率 （1）一般纳税人：应交增值税=销项税额-进项税额（抵扣税） 1、一般纳税人应纳税额=销项税额—进项税 销项税额=销售额×税率此处税率为17% 组成计税价格=成本×（1+成本利润率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率） 禁止抵扣人进项税额=当月全部的进项税额×（当月免税项目销售额，非应税项目营业额的合计÷当月全部销售，营业额合计） 2、进口货物应纳税额=组成计税价格×税率 组成计税价格=关税完税价格+关税+消费税 3、小规模纳税人：应交增值税=不含税收入×3% 应纳税额=销售额×征收率销售额=含税销售额÷（1+征收率） 二、消费税 （1）从价计税时应纳税额＝应税消费品销售额×适用税率 （2）从量计税时应纳税额＝应税消费品销售数量×适用税额标准 1、一般情况： 应纳税额=销售额×税率 不含税销售额=含税销售额÷（1+增值税税率或征收率） 组成计税价格=（成本+利润）÷（1—消费税率） 组成计税价格=成本×（1+成本利润率）÷（1—消费税税率） 组成计税价格=（材料成本+加工费）÷（1—消费税税率） 组成计税价格=（关税完税价格+关税）÷（1—消费税税率） 三、营业税：应交营业税=营业收入×税率 下附营业税税率表： 营业税税目税率表(自2009年1月1日起施行) 税目税率
备战期中考试作文 本文是关于作文大全的，仅供参考，如果觉得很不错，欢迎点评和分享。 备战期中考试作文（一） 时间过得飞快，转眼间学期已经过了一半，同学们也渐渐融入了这紧张的学习氛围，与此同时，我们也将面临新的挑战期中考试。 机会只垂青于有准备的人，所以这次我们一定要做好充分的准备。比如说把平时的复习时间增加十分钟左右，当然，时间越多，效果越好，关键还要靠自己自觉。不要只是今天复习半个小时，明天就只复习二十分钟，贵在坚持。 要学会抓住一分一秒，时间就是生命，不要让时间在你的走神中溜走，也不要让它在毫无意义的话题中流逝。对于我们中学生来说，有一个合理的学习计划，就可以为一天争分夺秒，更可以为好的学习成绩奠定基础。所以，我们需要好好把握一分一秒。 天道酬勤，只有勤奋刻苦的人才会得到成功。努力学习，这几个字相信同学们听过不止一遍了，可是这个学期，你到底努力过多少，到底付出过多少？如果连付出都没有，那你就更别说自己有一个好的前途了。 不经历风雨，怎么见彩虹？成功是要付出汗水的，如果从现在开始努力，也许你还会有百分之五十的机会成功，如果你选择放弃，你将注定成功不了。让我们从现在开始付出努力，去勇敢地迎接新的挑
战吧！ 备战期中考试作文（二） 同学们，不知不觉已经过去了两个月，这意味着什么呢？意味着期中考试将要来临了。大家都准备好了吗？是不是觉得时间过得很快，自己还没有准备好怎样迎接期中考试？对，我也有相同的感觉。 下个星期就要期中考试，所以我们不能再贪玩了，不然，当你后悔时就晚了。我们要加紧复习，但也不可以太过于紧张，要充分休息。因为这时你的大脑已经处于非常疲劳的状态了，不能再让它继续疲劳下去了，否则过度疲劳会导致考试时无精打采，没有精神。 我们还要及时地调整状态和对学习的态度，这对我们将来的学习有着很大的帮助。首先，你必须要想学，你只要想学，不用老是，自己看着课本就可以全部学会并掌握。你只要不想学，就算老师再怎么讲怎么教你终究也不会做这种题的方法与技巧。我们要把自己当成是学习的主人，要在知识的海洋里尽情遨游。学习是我们学生的天地，学习是我们学生的舞台，我们可以在上面想怎么样就怎么样。同样，我们在学习上也要找到这种感觉。 语文我们只要把基础知识的那些字词诗句掌握了，阅读题只要按不同的角度去想就基本可以得到准确的答案。作文，首先保证字体清晰规范，没有错别字。再想怎样把这篇文章写好，语言简练，描述清楚，适当运用修辞手法，细节描写到位，我想这样的作文分数一定不会低。数学把公式记清楚，计算仔细一点，这样也就可以考高分了。 期中考试，这是我们升入初中第一次重要考试，我们一定要通过