Filter Rural Districts

table( d$urban )

## 
## rural urban 
## 12971 59722

d <- filter( d, urban == "urban" )

Identify Common Variables

We can create a function to compare variables from the 2000 and 2010 datasets:

# find variables that are in both files
compare_dfs <- function( df1, df2 )
{
  # use regular expressions to remove numeric suffixes 
  var.names.1 <- names( df1 )
  var.names.1 <- gsub( "[.][xy]$", "", var.names.1 )
  var.names.1 <- gsub( "[0-9]{2}$", "", var.names.1 )
  
  var.names.2 <- names( df2 )
  var.names.2 <- gsub( "[.][xy]$", "", var.names.2 )
  var.names.2 <- gsub( "[0-9]{2}$", "", var.names.2 )
  
  shared <- intersect( var.names.1, var.names.2 ) %>% sort()
  print( "SHARED VARIABLES:")
  print( shared )
  
  not.shared <- c( setdiff( var.names.1, var.names.2 ),
                   setdiff( var.names.2, var.names.1 ) ) %>% sort()
  
  print( "NOT SHARED:" )
  print( not.shared )
  
  d.vars1 <- data.frame( type="shared", variables=shared, stringsAsFactors=F )
  d.vars2 <- data.frame( type="not shared", variables=not.shared, stringsAsFactors=F )
  dd <- rbind( d.vars1, d.vars2 )
  
  return( dd )
}

vars <- compare_dfs( df1=d1, df2=d2 )

## [1] "SHARED VARIABLES:"
##   [1] "a15asn"  "a15blk"  "a15hsp"  "a15ntv"  "a15wht"  "a18und"  "a60asn" 
##   [8] "a60blk"  "a60hsp"  "a60ntv"  "a60up"   "a60wht"  "a75up"   "ag15up" 
##  [15] "ag18cv"  "ag25up"  "ag5up"   "ageasn"  "ageblk"  "agehsp"  "agentv" 
##  [22] "agewht"  "asian"   "china"   "clf"     "col"     "cuban"   "dapov"  
##  [29] "dbpov"   "dflabf"  "dfmpov"  "dhpov"   "dmulti"  "dnapov"  "dpov"   
##  [36] "dwpov"   "empclf"  "family"  "fb"      "fhh"     "filip"   "flabf"  
##  [43] "geanc"   "gefb"    "h10yrs"  "h30old"  "haw"     "hh"      "hha"    
##  [50] "hhb"     "hhh"     "hhw"     "hinc"    "hinca"   "hincb"   "hinch"  
##  [57] "hincw"   "hisp"    "hs"      "hu"      "incpc"   "india"   "iranc"  
##  [64] "irfb"    "itanc"   "itfb"    "japan"   "korea"   "lep"     "manuf"  
##  [71] "mar"     "mex"     "mhmval"  "mrent"   "multi"   "n10imm"  "n65pov" 
##  [78] "napov"   "nat"     "nbpov"   "nfmpov"  "nhblk"   "nhpov"   "nhwht"  
##  [85] "nnapov"  "npov"    "ntv"     "nwpov"   "ohu"     "olang"   "own"    
##  [92] "pop"     "pr"      "prof"    "rent"    "ruanc"   "rufb"    "scanc"  
##  [99] "scfb"    "semp"    "tractid" "unemp"   "vac"     "vet"     "viet"   
## [106] "wds"    
## [1] "NOT SHARED:"
##  [1] "a65asn"  "a65blk"  "a65hsp"  "a65ntv"  "a65wht"  "cni16u"  "dis"    
##  [8] "hu00sp"  "ohu00sp" "p10imm"  "p10yrs"  "p15asn"  "p15blk"  "p15hsp" 
## [15] "p15ntv"  "p15wht"  "p18und"  "p30old"  "p60up"   "p65asn"  "p65blk" 
## [22] "p65hsp"  "p65ntv"  "p65pov"  "p65wht"  "p75up"   "papov"   "pasian" 
## [29] "pbpov"   "pchina"  "pcol"    "pcuban"  "pfb"     "pfhh"    "pfilip" 
## [36] "pflabf"  "pfmpov"  "pgeanc"  "pgefb"   "phaw"    "phisp"   "phpov"  
## [43] "phs"     "pindia"  "piranc"  "pirfb"   "pitanc"  "pitfb"   "pjapan" 
## [50] "pkorea"  "plep"    "pmanuf"  "pmar"    "pmex"    "pmulti"  "pnapov" 
## [57] "pnat"    "pnhblk"  "pnhwht"  "pntv"    "polang"  "pown"    "ppov"   
## [64] "ppr"     "pprof"   "pruanc"  "prufb"   "pscanc"  "pscfb"   "psemp"  
## [71] "punemp"  "pvac"    "pvet"    "pviet"   "pwds"    "pwpov"

head( vars )

Create Dataset for Analysis

Create subset for the analysis

d.full <- d  # keep a copy so don't have to reload 

d <- d.full  # story original in case you need to reset anything

d <- select( d, tractid, mhmval00, mhmval12, hinc00, 
             hu00, own00, rent00,  
             empclf00, clf00, unemp00, prof00,  
             dpov00, npov00,
             ag25up00, hs00, col00, 
             pop00.x, nhwht00, nhblk00, hisp00, asian00,
             cbsa, cbsaname )
d <- 
  d %>%
  mutate( p.white = 100 * nhwht00 / pop00.x,
          p.black = 100 * nhblk00 / pop00.x,
          p.hisp = 100 * hisp00 / pop00.x, 
          p.asian = 100 * asian00 / pop00.x,
          p.hs = 100 * (hs00+col00) / ag25up00,
          p.col = 100 * col00 / ag25up00,
          p.prof = 100 * prof00 / empclf00,
          p.unemp = 100 * unemp00 / clf00,
          pov.rate = 100 * npov00 / dpov00 )

If you call the stargazer() function with a linear model object (a regression model) it will create a nicely-formatted regression table for you.

If you instead use a data frame object it will create a table of descriptive statistics.

You can set the statistics you would like reported in the table with the summary.stat= argument.

stargazer( d, 
           type=s.type, 
           digits=0,
           summary.stat = c("min", "p25","median","mean","p75","max") )

Histogram of MHV

hist( mhv.change/1000, breaks=500, 
      xlim=c(-100,500), yaxt="n", xaxt="n",
      xlab="Thousand of US Dollars (adjusted to 2010)", cex.lab=1.5,
      ylab="", main="Change in Median Home Value 2000 to 2010",
      col="gray20", border="white" )

axis( side=1, at=seq( from=-100, to=500, by=100 ), 
      labels=paste0( "$", seq( from=-100, to=500, by=100 ), "k" ) )
        
mean.x <- mean( mhv.change/1000, na.rm=T )
abline( v=mean.x, col="darkorange", lwd=2, lty=2 )
text( x=200, y=1500, 
      labels=paste0( "Mean = ", dollar( round(1000*mean.x,0)) ), 
      col="darkorange", cex=1.8, pos=3 )

median.x <- median( mhv.change/1000, na.rm=T )
abline( v=median.x, col="dodgerblue", lwd=2, lty=2 )
text( x=200, y=2000, 
      labels=paste0( "Median = ", dollar( round(1000*median.x,0)) ), 
      col="dodgerblue", cex=1.8, pos=3 )

# function to control plot() formatting 
jplot <- function( x1, x2, lab1="", lab2="", draw.line=T, ... )
{

    plot( x1, x2,
          pch=19, 
          col=gray(0.6, alpha = 0.2), 
          cex=2.5,  
          bty = "n",
          xlab=lab1, 
          ylab=lab2, cex.lab=1.5,
        ... )

    if( draw.line==T ){ 
        ok <- is.finite(x1) & is.finite(x2)
        lines( lowess(x2[ok]~x1[ok]), col="red", lwd=3 ) }

}

Compare 2000 to 2010 distributions.

layout.matrix <- matrix( c( 1,3,
                            2,3 ), 
                nrow=2, ncol=2, byrow=T )

layout( mat = layout.matrix,
        heights = c(2,2), # Heights of the two rows
        widths =  c(3,4)) # Widths of the two columns

# layout.show(3)

par( mar=c(4,0,0,2) )

hist( mhv.00/1000, breaks=50, 
      xlim=c(-200,800), yaxt="n", xaxt="n",
      xlab="", cex.lab=1,
      ylab="", main="",
      col="darkslateblue", border="white" )

axis( side=1, at=seq( from=0, to=1000, by=100 ), 
      labels=paste0( "$", seq( from=0, to=1000, by=100 ), "k" ) )

abline( v=seq(0,1000,100), lty=2, col="gray80" )

text( 550, 4000, labels="Median Home \nValue in 2000", 
      col="darkslateblue", cex=1.8 )



hist( mhv.10/1000, breaks=50, 
      xlim=c(-200,800), yaxt="n", xaxt="n",
      xlab="", cex.lab=1,
      ylab="", main="",
      col="darkslateblue", border="white" )

abline( v=seq(0,1000, 100 ), lty=2, col="gray80" )

text( 550, 3500, labels="Median Home \nValue in 2010", 
      col="darkslateblue", cex=1.8 )

axis( side=1, at=seq( from=0, to=1000, by=100 ), 
      labels=paste0( "$", seq( from=0, to=1000, by=100 ), "k" ) )


# data reduction - filter 1,000 observations

df <- data.frame( v00=mhv.00/1000, v10=mhv.10/1000 )
df <- sample_n( df, 1000 )

par( mar=c(4,5,3,2) )

jplot( df$v00, df$v10, 
       lab1="MHV in 2000", lab2="MHV in 2010",
       xlim=c(0,1000), ylim=c(0,1000),
       axes=F )

abline( a=0, b=1, lty=2, col="gray" )
axis( side=1, at=seq( from=0, to=1000, by=200 ), 
      labels=paste0( "$", seq( from=0, to=1000, by=200 ), "k" ) )
axis( side=2, at=seq( from=0, to=1000, by=200 ), 
      labels=paste0( "$", seq( from=0, to=1000, by=200 ), "k" ) )

Change in MHV 2000-2010

If a home worth $10 million increased in value by $100k over ten years it would not be that surprising. If a home worth $50k increased by $100k over the same period that is a growth of 200% and is notable.

The change in value variable only reports absolute change, but does not provide a sense of whether that is a big or small amount for the census tract.

The percent change variable provides some context for growth rates of value in census tracts.

# small initial values are skewing percentages
#
# an average home value below $10k is really low -
# these must be mostly vacant lots?

# interpretation is hard if there were no homes in 2000
# and thus an artificially low MHV. i don't trust cases
# that go from homes worth $10k to regular value
# because it is more likely errors in data or noise
# than meaningful variance 
#
# quick filter to remove all of the problematic obs
# but need to go back and see which cases are problematic


mhv.00[ mhv.00 < 10000 ] <- NA
pct.change <- mhv.change / mhv.00
summary( pct.change )

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## -0.96891  0.05918  0.25402  0.33167  0.49556 60.59261      220

# how many cases had increases above 500%
sum( pct.change > 5, na.rm=T )

## [1] 112

# preview tracts with large increases in home values 
# to see if increases make sense 

d %>% 
  filter( pct.change > 5 ) %>% 
  head()

Plot the percent change variable:

hg <-
hist( pct.change, breaks=2000, 
      xlim=c(-1,2), yaxt="n", xaxt="n",
      xlab="", cex.main=1.5,
      ylab="", main="Growth in Home Value by Census Tract 2000 to 2010",
      col="gray40", border="white" )

axis( side=1, at=seq( from=-1, to=2, by=0.5 ), 
      labels=paste0( seq( from=-100, to=200, by=50 ), "%" ) )

ymax <- max( hg$count )
        
mean.x <- mean( pct.change, na.rm=T )
abline( v=mean.x, col="darkorange", lwd=2, lty=2 )
text( x=1, y=(0.5*ymax), 
      labels=paste0( "Mean = ", round(100*mean.x,0), "%"), 
      col="darkorange", cex=1.8, pos=4 )

median.x <- median( pct.change, na.rm=T )
abline( v=median.x, col="dodgerblue", lwd=2, lty=2 )
text( x=1, y=(0.6*ymax), 
      labels=paste0( "Median = ", round(100*median.x,0), "%"), 
      col="dodgerblue", cex=1.8, pos=4 )

Group Growth Rates By Metro Area

We often want to disagregate descriptives by some grouping in the data, such as metro areas.

dplyr makes this easy by grouping then summarizing the data.

d$mhv.change <- mhv.change 
d$pct.change <- pct.change
d$mhv.10 <- mhv.10
d$mhv.00 <- mhv.00

d %>%
  group_by( cbsaname ) %>%
  summarize( ave.change = median( mhv.change, na.rm=T ),
             ave.change.d = dollar( round(ave.change,0) ),
             growth = 100 * median( pct.change, na.rm=T ) ) %>%
  ungroup() %>%
  arrange( - growth ) %>%
  select( - ave.change ) %>% 
  head( 25 ) %>%
  pander()

Select Gentrification Variables

Select variables for operationalizing a definition of gentrification:

We need to add some variables from 2010:

Recall we created a 2000 to 2010 variable list for reference:

head( vars )

d3 <- select( d.full, 
             
             tractid, cbsa, cbsaname,            # ids / units of analysis
             
             mhv.00, mhv.10, mhv.change, pct.change,    # home value 
             
             hinc00, hu00, own00, rent00,        # ses
             hinc12, hu10, own10, rent10,
             
             empclf00, clf00, unemp00, prof00,   # employment 
             empclf12, clf12, unemp12, prof12,
             
             dpov00, npov00,                     # poverty
             dpov12, npov12,
             
             ag25up00, hs00, col00,              # education 
             ag25up12, hs12, col12,
             
             pop00.x, nhwht00, nhblk00, hisp00, asian00,   # race
             pop10, nhwht10, nhblk10, hisp10, asian10
             
          ) # end select


d3 <- 
  d3 %>%
  mutate( 
          # 2000 variables
          p.white.00 = 100 * nhwht00 / pop00.x,
          p.black.00 = 100 * nhblk00 / pop00.x,
          p.hisp.00 = 100 * hisp00 / pop00.x, 
          p.asian.00 = 100 * asian00 / pop00.x,
          p.hs.edu.00 = 100 * (hs00+col00) / ag25up00,
          p.col.edu.00 = 100 * col00 / ag25up00,
          p.prof.00 = 100 * prof00 / empclf00,
          p.unemp.00 = 100 * unemp00 / clf00,
          pov.rate.00 = 100 * npov00 / dpov00,
          
          # 2010 variables
          p.white.10 = 100 * nhwht10 / pop10,
          p.black.10 = 100 * nhblk10 / pop10,
          p.hisp.10 = 100 * hisp10 / pop10, 
          p.asian.10 = 100 * asian10 / pop10,
          p.hs.edu.10 = 100 * (hs12+col12) / ag25up12,
          p.col.edu.10 = 100 * col12 / ag25up12,
          p.prof.10 = 100 * prof12 / empclf12,
          p.unemp.10 = 100 * unemp12 / clf12,
          pov.rate.10 = 100 * npov12 / dpov12 )

d3 <-
  d3 %>%
  group_by( cbsaname ) %>%
  mutate( metro.mhv.pct.00 = ntile( mhv.00, 100 ),
          metro.mhv.pct.10 = ntile( mhv.10, 100 ),
          metro.median.pay.00 = median( hinc00, na.rm=T ),
          metro.median.pay.10 = median( hinc12, na.rm=T ),
          metro.race.rank.00 = ntile( (100-p.white.00), 100 ) ) %>%
  ungroup() %>%
  mutate( metro.mhv.pct.change = metro.mhv.pct.10 - metro.mhv.pct.00,
          pay.change = metro.median.pay.10 - metro.median.pay.00,
          race.change = p.white.10 - p.white.00,
          mhv.change = mhv.10 - mhv.00 )

Descriptive Statistics of Change Variables

d3 <-           
  d3 %>%
  select( c( "tractid", "cbsa", "cbsaname",
             "mhv.00", "mhv.10", "mhv.change","pct.change",
          "p.white.00", "p.black.00", "p.hisp.00", "p.asian.00", 
          "p.hs.edu.00", "p.col.edu.00", "p.prof.00",  "p.unemp.00", 
          "pov.rate.00", "p.white.10", "p.black.10", "p.hisp.10", 
          "p.asian.10", "p.hs.edu.10", "p.col.edu.10", "p.prof.10", 
          "p.unemp.10", "pov.rate.10", "metro.mhv.pct.00", 
          "metro.mhv.pct.10", "metro.median.pay.00", "metro.median.pay.10", 
          "metro.mhv.pct.change", "pay.change", "race.change",
          "metro.race.rank.00") ) 
  
# head( d3 ) %>% pander()

d3 <- data.frame(d3)
stargazer( d3, 
           type=s.type, 
           digits=0, 
           summary.stat = c("min", "p25","median","mean","p75","max") )

Operationalizing Gentrification

Which definition did you select for gentrification, and how would you operationalize it?

# income
# percent white
# home values absolute
# home value relative to metro
# education stats ?
# employment stats ?
# income stats ?
# growth of pop per tract (density) ?


# home value in lower than average home in a metro in 2000
poor.2000 <- d3$metro.mhv.pct.00 < 50  

# above average diversity for metro
diverse.2000 <- d3$metro.race.rank.00 > 50 

# home values increased more than overall city gains 
# change in percentile rank within the metro
mhv.pct.increase <- d3$metro.mhv.pct.change > 0

# faster than average growth  
# 25% growth in value is median for the country
home.val.rise <- d3$pct.change > 25 

# proportion of whites increases by more than 3 percent 
# measured by increase in white
loss.diversity <- d3$race.change > 3 

g.flag <- poor.2000 & diverse.2000 & mhv.pct.increase & home.val.rise & loss.diversity

num.candidates <-  sum( poor.2000 & diverse.2000, na.rm=T )
num.gentrified <- sum( g.flag, na.rm=T )

num.gentrified 

## [1] 1137

num.candidates

## [1] 18219

num.gentrified / num.candidates

## [1] 0.06240738

By this definition only 5.7 percent of urban tracts experience gentrification between 2000 and 2010.

This might skew numbers?

# small initial values are skewing percentages
#
# an average home value below $10k is really low -
# these must be mostly vacant lots?

mhv.00[ mhv.00 < 1000 ] <- NA
pct.change <- 100 * ( mhv.change / mhv.00 )
summary( pct.change )

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  -96.891    5.918   25.402   33.167   49.556 6059.261      220

Discussion

What do you think? Is that the right way to operationalize it? Do you care about relative diversity (more diverse neighborhood than rest of the city) or absolute (percentage of non-whites regardless of city diversity).

Do we care about absolute increase in value, or relative to the national average? The national average would include all of the gentrifying tracts, which could skew it upward and thus make it a poor benchmark? Maybe look at the average increase in value for non-gentrification candadidates?