R Dataset / Package Ecdat / incomeInequality
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On this Picostat.com statistics page, you will find information about the incomeInequality data set which pertains to Income Inequality in the US . The incomeInequality data set is found in the Ecdat R package. You can load the incomeInequality data set in R by issuing the following command at the console data("incomeInequality"). This will load the data into a variable called incomeInequality. If R says the incomeInequality data set is not found, you can try installing the package by issuing this command install.packages("Ecdat") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the incomeInequality R data set. The size of this file is about 13,922 bytes. Income Inequality in the USDescriptionData on quantiles of the distributions of family incomes in the United States. This combines three data sources: (1) US Census Table F-1 for the central quantiles (2) Piketty and Saez for the 95th and higher quantiles (3) Gross Domestic Product and implicit price deflators from MeasuringWorth.com Usagedata(incomeInequality) FormatA
DetailsFor details on how this Author(s)Spencer Graves SourceUnited States Census Bureau, Table F-1. Income Limits for Each Fifth and Top 5 Percent of Families, All Races, http://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-inequality.html, accessed 2016-12-09. Thomas Piketty and Emmanuel Saez (2003) "Income Inequality in the United States, 1913-1998", Quarterly Journal of Economics, 118(1) 1-39, http://elsa.berkeley.edu/~saez, update accessed February 28, 2014. Louis Johnston and Samuel H. Williamson (2011) "What Was the U.S. GDP Then?" MeasuringWorth, http://www.measuringworth.org/usgdp, accessed February 28, 2014. Examples## ## Rato of IRS to census estimates for the 95th percentile ## data(incomeInequality) plot(P95IRSvsCensus~Year, incomeInequality, type='b') # starts ~0.74, trends rapidly up to ~0.97, # then drifts back to ~0.75 abline(h=0.75) abline(v=1989) # check sum(is.na(incomeInequality$P95IRSvsCensus)) # The Census data runs to 2011; Pikety and Saez runs to 2010. quantile(incomeInequality$P95IRSvsCensus, na.rm=TRUE) # 0.72 ... 0.98## ## Persons per Family ##plot(personsPerFamily~Year, incomeInequality, type='b') quantile(incomeInequality$personsPerFamily) # ranges from 3.72 to 4.01 with median 3.84 # -- almost 4## ## GDP per family ## plot(realGDPperFamily~Year, incomeInequality, type='b', log='y')## ## Plot the mean then the first quintile, then the median, ## 99th, 99.9th and 99.99th percentiles ## plotCols <- c(21, 3, 5, 11, 13:14) kcols <- length(plotCols) plotColors <- c(1:6, 8:13)[1:kcols] # omit 7=yellow plotLty <- 1:kcolsmatplot(incomeInequality$Year, incomeInequality[plotCols]/1000, log='y', type='l', col=plotColors, lty=plotLty)#*** Growth broadly shared 1947 - 1970, then began diverging #*** The divergence has been most pronounced among the top 1% #*** and especially the top 0.01%## ## Growth rate by quantile 1947-1970 and 1970 - present ## keyYears <- c(1947, 1970, 2010) (iYears <- which(is.element(incomeInequality$Year, keyYears)))(dYears <- diff(keyYears)) kk <- length(keyYears) (lblYrs <- paste(keyYears[-kk], keyYears[-1], sep='-'))(growth <- sapply(incomeInequality[iYears,], function(x, labels=lblYrs){ dxi <- exp(diff(log(x))) names(dxi) <- labels dxi } ))# as percent (gr <- round(100*(growth-1), 1))# The average annual income (realGDPperFamily) doubled between # 1970 and 2010 (increased by 101 percent), while the median household # income increased only 23 percent.## ## Income lost by each quantile 1970-2010 ## relative to the broadly shared growth 1947-1970 ## (lostGrowth <- (growth[, 'realGDPperFamily']-growth[, plotCols])) # 1947-1970: The median gained 20% relative to the mean, # while the top 1% lost ground # 1970-2010: The median lost 79%, the 99th percentile lost 29%, # while the top 0.1% gained(lostIncome <- (lostGrowth[2, ] * incomeInequality[iYears[2], plotCols])) # The median family lost $39,000 per year in income # relative to what they would have with the same economic growth # broadly shared as during 1947-1970. # That's slightly over $36,500 per year = $100 per day(grYr <- growth^(1/dYears)) (grYr. <- round(100*(grYr-1), 1))## ## Regression line: linear spline ##(varyg <- c(3:14, 21)) Varyg <- names(incomeInequality)[varyg] str(F01ps <- reshape(incomeInequality[c(1, varyg)], idvar='Year', ids=F1.PikettySeaz$Year, times=Varyg, timevar='pctile', varying=list(Varyg), direction='long')) names(F01ps)[2:3] <- c('variable', 'value') F01ps$variable <- factor(F01ps$variable)# linear spline basis function with knot at 1970 F01ps$t1970p <- pmax(0, F01ps$Year-1970)table(nas <- is.na(F01ps$value)) # 6 NAs, one each of the Piketty-Saez variables in 2011 F01i <- F01ps[!nas, ]# formula: # log(value/1000) ~ b*Year + (for each variable: # different intercept + (different slope after 1970))Fit <- lm(log(value/1000)~Year+variable*t1970p, F01i) anova(Fit) # all highly significant # The residuals may show problems with the model, # but we will ignore those for now.# Model predictions str(Pred <- predict(Fit))## ## Combined plot ## # Plot to a file? Wikimedia Commons prefers svg format. svg('incomeInequality8.svg') # If you want software to convert svg to another format such as png, # consider GIMP (www.gimp.org).# Base plot# Leave extra space on the right to label with growth since 1970 op <- par(mar=c(5, 4, 4, 5)+0.1)matplot(incomeInequality$Year, incomeInequality[plotCols]/1000, log='y', type='l', col=plotColors, lty=plotLty, xlab='', ylab='', las=1, axes=FALSE, lwd=3) axis(1, at=seq(1950, 2010, 10), labels=c(1950, NA, 1970, NA, 1990, NA, 2010), cex.axis=1.5) yat <- c(10, 50, 100, 500, 1000, 5000, 10000) axis(2, yat, labels=c('$10K', '$50K', '$100K', '$500K', '$1M', '$5M', '$10M'), las=1, cex.axis=1.2)# Label the lines pctls <- paste(c(20, 40, 50, 60, 80, 90, 95, 99, 99.5, 99.9, 99.99), '%', sep='') lineLbl0 <- c('Year', 'families K', pctls, 'realGDP.M', 'GDP deflator', 'pop-K', 'realGDPperFamily', '95 pct(IRS / Census)', 'size of household', 'average family income', 'mean/median') (lineLbls <- lineLbl0[plotCols]) sel75 <- (incomeInequality$Year==1975)laby <- incomeInequality[sel75, plotCols]/1000text(1973.5, c(1.2, 1.2, 1.3, 1.5, 1.9)*laby[-1], lineLbls[-1], cex=1.2) text(1973.5, 1.2*laby[1], lineLbls[1], cex=1.2, srt=10)## ## Add lines + points for the knots in 1970 ## End <- numeric(kcols) F01names <- names(incomeInequality) for(i in seq(length=kcols)){ seli <- (as.character(F01i$variable) == F01names[plotCols[i]]) # with(F01i[seli, ], lines(Year, exp(Pred[seli]), col=plotColors[i])) yri <- F01i$Year[seli] predi <- exp(Pred[seli]) lines(yri, predi, col=plotColors[i]) End[i] <- predi[length(predi)] sel70i <- (yri==1970) points(yri[sel70i], predi[sel70i], col=plotColors[i]) }## ## label growth rates ## table(sel70. <- (incomeInequality$Year>1969)) (lastYrs <- incomeInequality[sel70., 'Year']) (lastYr. <- max(lastYrs)+4) #text(lastYr., End, gR., xpd=NA) text(lastYr., End, paste(gr[2, plotCols], '%', sep=''), xpd=NA) text(lastYr.+7, End, paste(grYr.[2, plotCols], '%', sep=''), xpd=NA)## ## Label the presidents ## abline(v=c(1953, 1961, 1969, 1977, 1981, 1989, 1993, 2001, 2009)) (m99.95 <- with(incomeInequality, sqrt(P99.9*P99.99))/1000)text(1949, 5000, 'Truman') text(1956.8, 5000, 'Eisenhower', srt=90) text(1963, 5000, 'Kennedy', srt=90) text(1966.8, 5000, 'Johnson', srt=90) text(1971, 5*m99.95[24], 'Nixon', srt=90) text(1975, 5*m99.95[28], 'Ford', srt=90) text(1978.5, 5*m99.95[32], 'Carter', srt=90) text(1985.1, m99.95[38], 'Reagan' ) text(1991, 0.94*m99.95[44], 'GHW Bush', srt=90) text(1997, m99.95[50], 'Clinton') text(2005, 1.1*m99.95[58], 'GW Bush', srt=90) text(2010, 1.2*m99.95[62], 'Obama', srt=90) ## ## Done ## par(op) # reset marginsdev.off() # for plot to a file -- Dataset imported from https://www.r-project.org. |
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dataset-94975.csv | 13.6 KB |