Paper III
Candace Jennings
2023-06-11
Residential segregation, homeownership, and income inequality
r = getOption("repos")
r["CRAN"] = "http://cran.us.r-project.org"
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install.packages("tidyverse")## Installing package into 'C:/Users/canda/AppData/Local/R/win-library/4.3'
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## C:\Users\canda\AppData\Local\Temp\Rtmps7Yaq8\downloaded_packageslibrary(ggcorrplot)## Warning: package 'ggcorrplot' was built under R version 4.3.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.2library(ggplot2)
library(stats)
library(car)## Loading required package: carDatalibrary(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerlibrary(lavaan)## Warning: package 'lavaan' was built under R version 4.3.2## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.cities <- read.csv('C:/Users/canda/DEM Dissertation Data/citiesALLYRS.csv', na.strings=c("NA"))
cities <- cities %>% mutate_if(is.character, as.numeric)## Warning: There were 25 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `cityname = .Primitive("as.double")(cityname)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 24 remaining warnings.cities$Proximity<-as.factor(ifelse(cities$Proximity==1, "In Proximity", "Not in Prox"))stargazer(cities, header=FALSE, type='text', title="Descriptive Statistics",digits=2, out="Descript.htm")##
## Descriptive Statistics
## ===================================================
## Statistic N Mean St. Dev. Min Max
## ---------------------------------------------------
## Miles 114 6.50 5.53 0.00 21.00
## gini_20 262 0.42 0.05 0.29 0.54
## gini_10 264 0.42 0.06 0.26 0.57
## pboo_20 262 8.42 10.61 0.00 67.12
## pboo_10 240 8.58 11.03 0.05 65.63
## pboo_00 256 7.07 9.08 0.00 61.00
## pboo_90 257 6.45 8.59 0.00 60.64
## pboo_80 241 5.32 7.07 0.00 33.94
## chg_t20 235 10,355.00 24,403.75 -3,487 205,129
## chg_pb20 235 1.08 2.36 -8.59 15.95
## t_20 261 75,666.40 207,759.00 10,070 2,304,580
## pb_20 261 12.15 11.86 0.03 71.27
## dbw_20 261 25.38 12.07 0.00 69.14
## chg_t10 204 12,128.63 26,631.64 -9,504 206,512
## chg_pb10 204 1.36 3.65 -8.32 23.16
## t_10 240 70,884.63 194,929.20 10,127 2,099,451
## pb_10 240 11.39 12.10 0.03 69.35
## dbw_10 240 27.19 14.07 0.00 82.22
## chg_t00 174 13,749.87 36,706.30 -2,072 323,078
## chg_pb00 174 1.23 3.85 -5.08 25.02
## t_00 204 68,784.74 190,685.10 10,302 1,953,631
## pb_00 204 10.19 10.99 0.03 57.26
## dbw_00 204 30.84 15.98 0.00 75.64
## chg_t90 153 9,878.79 22,303.25 -4,247 150,053
## chg_pb90 153 1.49 4.02 -5.08 24.12
## t_90 180 62,714.99 167,886.90 10,023 1,630,553
## pb_90 180 9.46 10.45 0.00 60.56
## dbw_90 180 34.40 18.49 0.00 80.76
## t_80 154 60,676.74 165,936.10 10,197 1,595,138
## pb_80 154 8.62 9.85 0.01 40.12
## dbw_80 154 43.36 22.13 0.00 93.17
## ---------------------------------------------------stargazer(cities[c("pboo_20","dbw_20","gini_20")], header=FALSE, type='text',
title="Descriptive Statistics 2020", digits=2,
covariate.labels=c("Black Homeownership","B-W Dissimilarity","Income Inequality"),
out="Desc 2020.htm"
)##
## Descriptive Statistics 2020
## =================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------
## Black Homeownership 262 8.42 10.61 0.00 67.12
## B-W Dissimilarity 261 25.38 12.07 0.00 69.14
## Income Inequality 262 0.42 0.05 0.29 0.54
## -------------------------------------------------stargazer(cities[c("pboo_10","dbw_10","gini_10")], header=FALSE, type='text',
title="Descriptive Statistics 2010", digits=2,
covariate.labels=c("Black Homeownership","B-W Dissimilarity","Income Inequality"),
out="Desc 2010.htm"
)##
## Descriptive Statistics 2010
## =================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------
## Black Homeownership 240 8.58 11.03 0.05 65.63
## B-W Dissimilarity 240 27.19 14.07 0.00 82.22
## Income Inequality 264 0.42 0.06 0.26 0.57
## -------------------------------------------------stargazer(cities[c("pboo_00","dbw_00")], header=FALSE, type='text',
title="Descriptive Statistics 2000", digits=2,
covariate.labels=c("Black Homeownership","B-W Dissimilarity"),
out="Desc 2000.htm"
)##
## Descriptive Statistics 2000
## =================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------
## Black Homeownership 256 7.07 9.08 0.00 61.00
## B-W Dissimilarity 204 30.84 15.98 0.00 75.64
## -------------------------------------------------stargazer(cities[c("pboo_90","dbw_90")], header=FALSE, type='text',
title="Descriptive Statistics 1990", digits=2,
covariate.labels=c("Black Homeownership","B-W Dissimilarity"),
out="Desc 1990.htm"
)##
## Descriptive Statistics 1990
## =================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------
## Black Homeownership 257 6.45 8.59 0.00 60.64
## B-W Dissimilarity 180 34.40 18.49 0.00 80.76
## -------------------------------------------------stargazer(cities[c("pboo_80","dbw_80")], header=FALSE, type='text',
title="Descriptive Statistics 1980", digits=2,
covariate.labels=c("Black Homeownership","B-W Dissimilarity"),
out="Desc 1980.htm"
)##
## Descriptive Statistics 1980
## =================================================
## Statistic N Mean St. Dev. Min Max
## -------------------------------------------------
## Black Homeownership 241 5.32 7.07 0.00 33.94
## B-W Dissimilarity 154 43.36 22.13 0.00 93.17
## -------------------------------------------------object <- sapply(cities, function(x) sum(is.na(x)))
#Cities In Proximity = 1, Cities Not in Proximity = 0
cities$Proximity <- factor(cities$Proximity, ordered = FALSE )# Make "Not in Proximity" the reference category
cities$Proximity <- relevel(cities$Proximity, ref = "Not in Prox")
table(cities$Proximity)##
## Not in Prox In Proximity
## 162 107stargazer(cities[c("dbw_80","dbw_90","dbw_00","dbw_10","dbw_20")], header=FALSE, type='text',
title="Descriptive Statistics Segregation by Year", digits=2,
covariate.labels=c("1980","1990","2000","2010","2020"),
out="Desc Mean DI.htm"
)##
## Descriptive Statistics Segregation by Year
## =======================================
## Statistic N Mean St. Dev. Min Max
## ---------------------------------------
## 1980 154 43.36 22.13 0.00 93.17
## 1990 180 34.40 18.49 0.00 80.76
## 2000 204 30.84 15.98 0.00 75.64
## 2010 240 27.19 14.07 0.00 82.22
## 2020 261 25.38 12.07 0.00 69.14
## ---------------------------------------subset_P1 <- subset(cities, Proximity == "In Proximity")
stargazer(subset_P1[c("dbw_80","dbw_90","dbw_00","dbw_10","dbw_20")], header=FALSE, type='text',
title="Segregation by Year for Cities In Proximity to Freedom Colony", digits=2,
covariate.labels=c("1980","1990","2000","2010","2020"),
style = "demography",
out="Desc Mean DI In Prox.htm")##
## Segregation by Year for Cities In Proximity to Freedom Colony
## ---------------------------------------
## Statistic N Mean St. Dev. Min Max
## ---------------------------------------
## 1980 60 46.92 20.90 8.92 93.17
## 1990 70 37.26 18.30 0.84 80.76
## 2000 81 33.94 16.25 3.74 75.64
## 2010 98 28.63 14.56 0.00 68.57
## 2020 101 26.99 13.18 1.14 64.80
## ---------------------------------------subset_P2 <- subset(cities, Proximity == "Not in Prox")
stargazer(subset_P2[c("dbw_80","dbw_90","dbw_00","dbw_10","dbw_20")], header=FALSE, type='text',
title="Segregation by Year for Cities Not In Proximity to Freedom Colony", digits=2,
covariate.labels=c("1980","1990","2000","2010","2020"),
style = "demography",
out="Desc Mean DI NOTIn Prox.htm")##
## Segregation by Year for Cities Not In Proximity to Freedom Colony
## ---------------------------------------
## Statistic N Mean St. Dev. Min Max
## ---------------------------------------
## 1980 94 41.08 22.69 0.00 90.50
## 1990 110 32.58 18.45 0.00 69.33
## 2000 123 28.81 15.53 0.00 69.28
## 2010 142 26.21 13.68 3.01 82.22
## 2020 160 24.36 11.25 0.00 69.14
## ---------------------------------------#log total population due to outliers
hist(cities$t_20)
hist(log(cities$t_20+1), breaks=50)
hist(cities$t_10)
hist(log(cities$t_10+1), breaks=50)
hist(cities$t_00)
hist(log(cities$t_00+1), breaks=50)
hist(cities$t_90)
hist(log(cities$t_90+1), breaks=50)
hist(cities$t_80)
hist(log(cities$t_80+1), breaks=50)
## proximity to freedom colonies and Racial residential segregation
#The correlation between segregation and Black home ownership yielded a weak positive association in all years, decreasing from 1980 to 2020.
cor.test(cities$dbw_20,cities$pboo_20)##
## Pearson's product-moment correlation
##
## data: cities$dbw_20 and cities$pboo_20
## t = 0.50531, df = 259, p-value = 0.6138
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09038165 0.15222271
## sample estimates:
## cor
## 0.03138275plot(cities$dbw_20,cities$pboo_20,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 2020')
cor.test(cities$dbw_10,cities$pboo_10)##
## Pearson's product-moment correlation
##
## data: cities$dbw_10 and cities$pboo_10
## t = 1.3949, df = 238, p-value = 0.1644
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03700425 0.21423467
## sample estimates:
## cor
## 0.09004761plot(cities$dbw_10,cities$pboo_10,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 2010')
cor.test(cities$dbw_00,cities$pboo_00)##
## Pearson's product-moment correlation
##
## data: cities$dbw_00 and cities$pboo_00
## t = 4.093, df = 198, p-value = 6.198e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1462256 0.4024463
## sample estimates:
## cor
## 0.2793003plot(cities$dbw_00,cities$pboo_00,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 2000')
cor.test(cities$dbw_90,cities$pboo_90)##
## Pearson's product-moment correlation
##
## data: cities$dbw_90 and cities$pboo_90
## t = 5.3203, df = 177, p-value = 3.103e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2375754 0.4912287
## sample estimates:
## cor
## 0.3713092plot(cities$dbw_90,cities$pboo_90,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 1990')
cor.test(cities$dbw_80,cities$pboo_80)##
## Pearson's product-moment correlation
##
## data: cities$dbw_80 and cities$pboo_80
## t = 5.1751, df = 152, p-value = 7.102e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2438040 0.5137519
## sample estimates:
## cor
## 0.3870404plot(cities$dbw_80,cities$pboo_80,pch=20,
xlab='D index',ylab='Percent Black Homeowners',
main='Cities by Residential Segregation and Black Homeownership in 1980')
#The list of cities from the Diversity and Disparities project available for years 1980 - 2020 was used to conduct a binomial regression analysis. Of these cities, 107 were identified as being in proximity to a freedom colony and 162 were not.
table(cities$Proximity)##
## Not in Prox In Proximity
## 162 107#The results showed that proximity to freedom colonies INCREASED residential segregation by points in 2020. The results were statistically significant in 2000, but not in 2020, 2010, 1990, or 1980.
fit20=lm(dbw_20 ~ Proximity,cities)
summary(fit20)##
## Call:
## lm(formula = dbw_20 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.847 -9.357 -0.886 8.475 44.775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.364 0.951 25.618 <2e-16 ***
## ProximityIn Proximity 2.625 1.529 1.717 0.0872 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.03 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.01125, Adjusted R-squared: 0.007434
## F-statistic: 2.947 on 1 and 259 DF, p-value: 0.08722fit10=lm(dbw_10 ~ Proximity,cities)
summary(fit10)##
## Call:
## lm(formula = dbw_10 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.626 -10.226 -1.895 8.190 56.010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.206 1.179 22.233 <2e-16 ***
## ProximityIn Proximity 2.420 1.845 1.312 0.191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.05 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.00718, Adjusted R-squared: 0.003008
## F-statistic: 1.721 on 1 and 238 DF, p-value: 0.1908fit00=lm(dbw_00 ~ Proximity,cities)
summary(fit00)##
## Call:
## lm(formula = dbw_00 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.194 -11.511 -1.039 10.575 41.698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.809 1.426 20.197 <2e-16 ***
## ProximityIn Proximity 5.128 2.264 2.265 0.0246 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.82 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.02478, Adjusted R-squared: 0.01995
## F-statistic: 5.132 on 1 and 202 DF, p-value: 0.02455fit90=lm(dbw_90 ~ Proximity,cities)
summary(fit90)##
## Call:
## lm(formula = dbw_90 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.428 -13.894 -0.428 16.099 43.493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 32.584 1.754 18.578 <2e-16 ***
## ProximityIn Proximity 4.681 2.812 1.664 0.0978 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.39 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.01532, Adjusted R-squared: 0.009793
## F-statistic: 2.77 on 1 and 178 DF, p-value: 0.09779fit80=lm(dbw_80 ~ Proximity,cities)
summary(fit80)##
## Call:
## lm(formula = dbw_80 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.083 -13.257 -0.672 13.301 49.413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.083 2.271 18.094 <2e-16 ***
## ProximityIn Proximity 5.835 3.638 1.604 0.111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.01 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.01665, Adjusted R-squared: 0.01018
## F-statistic: 2.573 on 1 and 152 DF, p-value: 0.1108stargazer(fit80, fit90, fit00, fit10, fit20,
type="text", title = "Effect on Dissimilarity Index by Year",
align=TRUE, dep.var.labels=c("1980","1990","2000","2010","2020"),
covariate.labels=c("Proximity"), omit.stat=c("LL","ser","f"),no.space=TRUE, style = "demography", out="Proximity and Segregation.htm")##
## Effect on Dissimilarity Index by Year
## -------------------------------------------------------------
## 1980 1990 2000 2010 2020
## Model 1 Model 2 Model 3 Model 4 Model 5
## -------------------------------------------------------------
## Proximity 5.835 4.681 5.128* 2.420 2.625
## (3.638) (2.812) (2.264) (1.845) (1.529)
## Constant 41.083*** 32.584*** 28.809*** 26.206*** 24.364***
## (2.271) (1.754) (1.426) (1.179) (0.951)
## N 154 180 204 240 261
## R2 0.017 0.015 0.025 0.007 0.011
## Adjusted R2 0.010 0.010 0.020 0.003 0.007
## -------------------------------------------------------------
## *p < .05; **p < .01; ***p < .001#Alternatively, I used Proximity as the dependent (outcome) variable. The results showed that residential segregation INCREASED proximity by 0.0224 points in 2020. The results were statistically significant in 2020 and 2000, but not in 2010.
model20 = glm(Proximity ~ dbw_20,
data = cities,
family = binomial(link="logit"))
model10 = glm(Proximity ~ dbw_10,
data = cities,
family = binomial(link="logit"))
model00 = glm(Proximity ~ dbw_00,
data = cities,
family = binomial(link="logit"))
model90 = glm(Proximity ~ dbw_90,
data = cities,
family = binomial(link="logit"))
model80 = glm(Proximity ~ dbw_80,
data = cities,
family = binomial(link="logit"))
summary(model20)##
## Call:
## glm(formula = Proximity ~ dbw_20, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.92335 0.30305 -3.047 0.00231 **
## dbw_20 0.01806 0.01061 1.702 0.08877 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 348.37 on 260 degrees of freedom
## Residual deviance: 345.44 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## AIC: 349.44
##
## Number of Fisher Scoring iterations: 4summary(model10)##
## Call:
## glm(formula = Proximity ~ dbw_10, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.706519 0.290099 -2.435 0.0149 *
## dbw_10 0.012254 0.009374 1.307 0.1912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 324.60 on 239 degrees of freedom
## Residual deviance: 322.88 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## AIC: 326.88
##
## Number of Fisher Scoring iterations: 4summary(model00)##
## Call:
## glm(formula = Proximity ~ dbw_00, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.055615 0.324821 -3.250 0.00115 **
## dbw_00 0.020365 0.009159 2.224 0.02618 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 274.09 on 203 degrees of freedom
## Residual deviance: 269.03 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## AIC: 273.03
##
## Number of Fisher Scoring iterations: 4summary(model90)##
## Call:
## glm(formula = Proximity ~ dbw_90, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.936893 0.335291 -2.794 0.0052 **
## dbw_90 0.013888 0.008409 1.651 0.0986 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 240.57 on 179 degrees of freedom
## Residual deviance: 237.80 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## AIC: 241.8
##
## Number of Fisher Scoring iterations: 4summary(model80)##
## Call:
## glm(formula = Proximity ~ dbw_80, family = binomial(link = "logit"),
## data = cities)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.983357 0.379342 -2.592 0.00953 **
## dbw_80 0.012146 0.007637 1.590 0.11172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 205.92 on 153 degrees of freedom
## Residual deviance: 203.34 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## AIC: 207.34
##
## Number of Fisher Scoring iterations: 4Anova(model20,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_20 2.929 1 0.087 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(model10,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_10 1.7182 1 0.1899Anova(model00,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_00 5.0661 1 0.0244 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(model90,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_90 2.7681 1 0.09616 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(model80,
type="II",
test="LR")## Analysis of Deviance Table (Type II tests)
##
## Response: Proximity
## LR Chisq Df Pr(>Chisq)
## dbw_80 2.5792 1 0.1083Proximity to freedom colonies and homeownership
#Change proximity to numeric to run correlation matrix
cities <- cities %>% mutate_if(is.character, as.numeric)
cities <- cities %>% mutate_if(is.integer, as.numeric)
cities <- cities %>% mutate_if(is.factor, as.numeric)
cities$t_20 <- log(cities$t_20)
cor_matrix <- cor(cities, use = "pairwise.complete.obs", method = "pearson") %>%
ggcorrplot(show.diag=FALSE, type="lower", lab=TRUE, lab_size=2)
print(cor_matrix)
df <- data.frame(cities)
sel20_vars <- c("pboo_20", "t_20","pb_20", "dbw_20")
df_select20 <- df[sel20_vars]
cor_matrix20 <- cor(df_select20, use = "pairwise.complete.obs", method = "pearson") %>%
ggcorrplot(show.diag=FALSE, type="lower", lab=TRUE, lab_size=4)
df <- data.frame(cities)
sel80_vars <- c("pboo_80", "t_80","pb_80", "dbw_80")
df_select80 <- df[sel80_vars]
cor_matrix80 <- cor(df_select80, use = "pairwise.complete.obs", method = "pearson") %>%
ggcorrplot(show.diag=FALSE, type="lower", lab=TRUE, lab_size=4)
print(cor_matrix20)
print(cor_matrix80)
#Run regression with percent Black population on dissimilarity. Significance 2000 1990 1980
dbwpb20<- lm(dbw_20~pb_20,data=cities)
dbwpb10<- lm(dbw_10~pb_10,data=cities)
dbwpb00<- lm(dbw_00~pb_00,data=cities)
dbwpb90<- lm(dbw_90~pb_90,data=cities)
dbwpb80<- lm(dbw_80~pb_80,data=cities)
summary(dbwpb20)##
## Call:
## lm(formula = dbw_20 ~ pb_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.846 -9.002 -1.198 8.473 44.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.36207 1.06964 22.776 <2e-16 ***
## pb_20 0.08374 0.06307 1.328 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.06 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.006759, Adjusted R-squared: 0.002924
## F-statistic: 1.763 on 1 and 259 DF, p-value: 0.1855summary(dbwpb10)##
## Call:
## lm(formula = dbw_10 ~ pb_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.356 -10.320 -1.567 8.283 56.869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.34306 1.23814 20.469 <2e-16 ***
## pb_10 0.16254 0.07459 2.179 0.0303 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.96 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.01956, Adjusted R-squared: 0.01544
## F-statistic: 4.749 on 1 and 238 DF, p-value: 0.0303summary(dbwpb00)##
## Call:
## lm(formula = dbw_00 ~ pb_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.280 -10.620 -0.125 9.506 43.200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.04976 1.44882 17.980 < 2e-16 ***
## pb_00 0.47047 0.09678 4.861 2.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.16 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.1047, Adjusted R-squared: 0.1003
## F-statistic: 23.63 on 1 and 202 DF, p-value: 2.339e-06summary(dbwpb90)##
## Call:
## lm(formula = dbw_90 ~ pb_90, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.682 -13.477 0.299 12.288 34.532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.0713 1.7271 16.254 < 2e-16 ***
## pb_90 0.6695 0.1227 5.456 1.61e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.16 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.1433, Adjusted R-squared: 0.1385
## F-statistic: 29.77 on 1 and 178 DF, p-value: 1.611e-07summary(dbwpb80)##
## Call:
## lm(formula = dbw_80 ~ pb_80, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.393 -12.383 0.966 13.764 54.446
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.6242 2.1831 16.318 < 2e-16 ***
## pb_80 0.8968 0.1671 5.367 2.94e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.35 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1593, Adjusted R-squared: 0.1538
## F-statistic: 28.8 on 1 and 152 DF, p-value: 2.939e-07#Run regression with dissimilarity on percent Black population. Significance 2000 1990 1980
pbw20=lm(pb_20 ~ dbw_20,cities)
summary(pbw20)##
## Call:
## lm(formula = pb_20 ~ dbw_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.580 -8.156 -3.711 5.042 59.449
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.09991 1.70821 5.913 1.06e-08 ***
## dbw_20 0.08072 0.06080 1.328 0.185
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.84 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.006759, Adjusted R-squared: 0.002924
## F-statistic: 1.763 on 1 and 259 DF, p-value: 0.1855pbw10=lm(pb_10 ~ dbw_10,cities)
summary(pbw10)##
## Call:
## lm(formula = pb_10 ~ dbw_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.984 -7.739 -3.999 4.522 58.733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.11335 1.69023 4.800 2.8e-06 ***
## dbw_10 0.12036 0.05523 2.179 0.0303 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.01 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.01956, Adjusted R-squared: 0.01544
## F-statistic: 4.749 on 1 and 238 DF, p-value: 0.0303pbw00=lm(pb_00 ~ dbw_00,cities)
summary(pbw00)##
## Call:
## lm(formula = pb_00 ~ dbw_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.679 -6.716 -2.667 3.797 47.517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.32507 1.59001 2.091 0.0378 *
## dbw_00 0.22262 0.04579 4.861 2.34e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.43 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.1047, Adjusted R-squared: 0.1003
## F-statistic: 23.63 on 1 and 202 DF, p-value: 2.339e-06pbw90=lm(pb_90 ~ dbw_90,cities)
summary(pbw90)##
## Call:
## lm(formula = pb_90 ~ dbw_90, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.556 -6.398 -2.256 2.475 53.338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.09644 1.53108 1.369 0.173
## dbw_90 0.21403 0.03923 5.456 1.61e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.701 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.1433, Adjusted R-squared: 0.1385
## F-statistic: 29.77 on 1 and 178 DF, p-value: 1.611e-07pbw80=lm(pb_80 ~ dbw_80,cities)
summary(pbw80)##
## Call:
## lm(formula = pb_80 ~ dbw_80, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.520 -5.913 -2.988 2.842 32.689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9204 1.6100 0.572 0.568
## dbw_80 0.1776 0.0331 5.367 2.94e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.059 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1593, Adjusted R-squared: 0.1538
## F-statistic: 28.8 on 1 and 152 DF, p-value: 2.939e-07#Run regression with Logged Total city population on Dissimilarity Index/segregation. Significance in all years.
tpop20Bw<- lm(cities$dbw_20 ~ log(cities$t_20))
summary(tpop20Bw)##
## Call:
## lm(formula = cities$dbw_20 ~ log(cities$t_20))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.249 -8.383 -1.475 7.285 47.127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -88.616 17.436 -5.082 7.15e-07 ***
## log(cities$t_20) 48.757 7.452 6.543 3.21e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.21 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1418, Adjusted R-squared: 0.1385
## F-statistic: 42.81 on 1 and 259 DF, p-value: 3.206e-10coef(tpop20Bw)["log(cities$t_20)"]/100## log(cities$t_20)
## 0.4875671tpop10Bw<- lm(cities$dbw_10 ~ log(cities$t_10))
summary(tpop10Bw)##
## Call:
## lm(formula = cities$dbw_10 ~ log(cities$t_10))
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.292 -10.468 -1.869 9.094 59.568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -22.0642 8.8422 -2.495 0.0133 *
## log(cities$t_10) 4.7609 0.8506 5.597 5.98e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.25 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1163, Adjusted R-squared: 0.1126
## F-statistic: 31.33 on 1 and 238 DF, p-value: 5.984e-08coef(tpop10Bw)["log(cities$t_10)"]/100## log(cities$t_10)
## 0.04760936tpop00Bw<- lm(cities$dbw_00 ~ log(cities$t_00))
summary(tpop00Bw)##
## Call:
## lm(formula = cities$dbw_00 ~ log(cities$t_00))
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.161 -12.766 -0.462 11.450 48.520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.146 11.352 -1.510 0.133
## log(cities$t_00) 4.650 1.095 4.247 3.31e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.35 on 202 degrees of freedom
## (65 observations deleted due to missingness)
## Multiple R-squared: 0.08196, Adjusted R-squared: 0.07741
## F-statistic: 18.03 on 1 and 202 DF, p-value: 3.31e-05coef(tpop00Bw)["log(cities$t_00)"]/100## log(cities$t_00)
## 0.04650091tpop90Bw<- lm(cities$dbw_90 ~ log(cities$t_90))
summary(tpop90Bw)##
## Call:
## lm(formula = cities$dbw_90 ~ log(cities$t_90))
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.940 -14.795 -1.112 12.803 50.992
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.070 14.039 -1.714 0.0882 .
## log(cities$t_90) 5.707 1.364 4.184 4.5e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.69 on 178 degrees of freedom
## (89 observations deleted due to missingness)
## Multiple R-squared: 0.08952, Adjusted R-squared: 0.08441
## F-statistic: 17.5 on 1 and 178 DF, p-value: 4.501e-05coef(tpop90Bw)["log(cities$t_90)"]/100## log(cities$t_90)
## 0.05707379tpop80Bw<- lm(cities$dbw_80 ~ log(cities$t_80))
summary(tpop80Bw)##
## Call:
## lm(formula = cities$dbw_80 ~ log(cities$t_80))
##
## Residuals:
## Min 1Q Median 3Q Max
## -35.822 -15.178 -0.748 13.757 56.499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -53.636 17.075 -3.141 0.00202 **
## log(cities$t_80) 9.516 1.668 5.706 5.86e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.15 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1764, Adjusted R-squared: 0.171
## F-statistic: 32.56 on 1 and 152 DF, p-value: 5.862e-08coef(tpop80Bw)["log(cities$t_80)"]/100## log(cities$t_80)
## 0.09515862cities$Proximity<-as.factor(ifelse(cities$Proximity==1, "In Proximity", "Not in Prox"))str(cities)## 'data.frame': 269 obs. of 34 variables:
## $ cityname : num NA NA NA NA NA NA NA NA NA NA ...
## $ Proximity: Factor w/ 2 levels "In Proximity",..: 1 2 1 1 1 1 2 1 1 1 ...
## $ Miles : num NA 3 NA NA NA NA 13.7 NA NA NA ...
## $ FC.name : num NA NA NA NA NA NA NA NA NA NA ...
## $ gini_20 : num 0.45 0.395 0.461 0.423 0.471 0.39 0.456 0.417 0.469 0.44 ...
## $ gini_10 : num 0.45 0.475 0.42 0.411 0.456 0.358 0.424 0.436 0.457 0.523 ...
## $ pboo_20 : num 4.74 4.08 0.26 3.13 0.65 6.5 0.13 2.73 3.56 1.08 ...
## $ pboo_10 : num 4.73 2.53 0.19 3.31 0.57 7.01 0.19 1.49 3.69 1.53 ...
## $ pboo_00 : num 4.68 1.73 0 4.42 0.48 3.58 0 1.15 3.76 2.69 ...
## $ pboo_90 : num 5.34 6.51 0 2.63 0.72 2.68 0 0.9 4.06 2.46 ...
## $ pboo_80 : num 4.36 4.23 0 0.21 0.97 2.14 0 1.03 3.77 2.13 ...
## $ chg_t20 : num 8119 3605 1140 130 -1213 ...
## $ chg_pb20 : num 1.34343 5.30048 0.00392 -0.40411 0.14071 ...
## $ t_20 : num 11.74 9.72 9.88 9.68 9.79 ...
## $ pb_20 : num 11.36 17.364 0.2 2.406 0.732 ...
## $ dbw_20 : num 28.7 25 35.3 32.8 38.2 ...
## $ chg_t10 : num 1133 -1110 3593 1890 94 ...
## $ chg_pb10 : num 0.925 2.265 0.081 -2.991 -0.15 ...
## $ t_10 : num 117063 13056 18353 15869 19104 ...
## $ pb_10 : num 10.017 12.063 0.196 2.811 0.591 ...
## $ dbw_10 : num 34.8 21.4 55.3 36 27.6 ...
## $ chg_t00 : num 9276 NA NA 2846 -778 ...
## $ chg_pb00 : num 2.2753 NA NA 2.1368 -0.0416 ...
## $ t_00 : num 115930 14166 14760 13979 19010 ...
## $ pb_00 : num 9.092 9.798 0.115 5.802 0.742 ...
## $ dbw_00 : num 37.7 38.8 23.4 28.7 27.4 ...
## $ chg_t90 : num 8339 NA NA -1490 -1173 ...
## $ chg_pb90 : num 0.2325 NA NA 1.5337 -0.0707 ...
## $ t_90 : num 106654 NA NA 11133 19788 ...
## $ pb_90 : num 6.816 NA NA 3.665 0.783 ...
## $ dbw_90 : num 38.1 NA NA 24.7 41.2 ...
## $ t_80 : num 98315 NA NA 12623 20961 ...
## $ pb_80 : num 6.584 NA NA 2.131 0.854 ...
## $ dbw_80 : num 48.2 NA NA 29.9 52.6 ...##Logistic Regression
#The models indicate that there is a positive relationship between the percent of Black homeownership and whether the city is within proximity to a freedom colony. Cities with high percent of Black homeowners are more likely to be within proximity to a freedom colony for years 2000, 2010, and 2020.
#model 1 #A multiple regression was performed starting with proximity to freedom colonies (Table 4). This variable proved to yield statistically significant results. Proximity to freedom colonies provided an increase in Black homeownership by 7.935 percentage points.
model1_20<- lm(pboo_20~Proximity,data=cities)
summary(model1_20)##
## Call:
## lm(formula = pboo_20 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.791 -5.199 -2.581 2.164 54.199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6037 0.7887 7.105 1.16e-11 ***
## ProximityNot in Prox 7.3177 1.2703 5.760 2.36e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.01 on 260 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1132, Adjusted R-squared: 0.1098
## F-statistic: 33.18 on 1 and 260 DF, p-value: 2.363e-08#model 2 - add in total popualtion logged #When the logged variable of city population was introduced in Model 2, there was a slight increase in homeownership although population was not statistically significant. Proximity remained a significant factor.
model2_20 <- lm(pboo_20~Proximity+log(t_20),data=cities)
summary(model2_20)##
## Call:
## lm(formula = pboo_20 ~ Proximity + log(t_20), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.924 -4.983 -2.568 2.154 53.931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.916 15.652 -0.634 0.527
## ProximityNot in Prox 7.131 1.283 5.560 6.74e-08 ***
## log(t_20) 6.678 6.711 0.995 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.02 on 258 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.1087
## F-statistic: 16.85 on 2 and 258 DF, p-value: 1.327e-07#As established, percent of Black population is highly correlated with Black homeownership. As expected, an initial regression with this variable yielded a significant result, but only a small increase in homeownership. Due to this high correlation, proximity lost its significance when percent of Black population was introduced to the model. Therefore, this variable was omitted from the regression.
citpb20=lm(pboo_20~pb_20,cities)
citpb10=lm(pboo_10~pb_10,cities)
citpb00=lm(pboo_00~pb_00,cities)
citpb90=lm(pboo_90~pb_90,cities)
citpb80=lm(pboo_80~pb_80,cities)
summary(citpb20)##
## Call:
## lm(formula = pboo_20 ~ pb_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.0194 -1.2180 0.5173 1.5513 12.6654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9730 0.2680 -7.362 2.41e-12 ***
## pb_20 0.8585 0.0158 54.322 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.021 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.9193, Adjusted R-squared: 0.919
## F-statistic: 2951 on 1 and 259 DF, p-value: < 2.2e-16summary(citpb10)##
## Call:
## lm(formula = pboo_10 ~ pb_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.6954 -1.2473 0.3532 1.4362 13.3214
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.4811 0.2406 -6.155 3.17e-09 ***
## pb_10 0.8834 0.0145 60.941 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.713 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.9398, Adjusted R-squared: 0.9395
## F-statistic: 3714 on 1 and 238 DF, p-value: < 2.2e-16summary(citpb00)##
## Call:
## lm(formula = pboo_00 ~ pb_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.5147 -0.8487 0.2933 0.8873 14.6523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.83415 0.23633 -3.53 0.000518 ***
## pb_00 0.82406 0.01565 52.64 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.441 on 198 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.9333, Adjusted R-squared: 0.933
## F-statistic: 2771 on 1 and 198 DF, p-value: < 2.2e-16summary(citpb90)##
## Call:
## lm(formula = pboo_90 ~ pb_90, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.9046 -0.7423 0.2832 0.7728 8.9028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.66915 0.20261 -3.303 0.00116 **
## pb_90 0.86542 0.01436 60.287 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.004 on 177 degrees of freedom
## (90 observations deleted due to missingness)
## Multiple R-squared: 0.9536, Adjusted R-squared: 0.9533
## F-statistic: 3634 on 1 and 177 DF, p-value: < 2.2e-16summary(citpb80)##
## Call:
## lm(formula = pboo_80 ~ pb_80, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9313 -0.4940 0.0426 0.3423 5.6163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10014 0.13794 -0.726 0.469
## pb_80 0.78014 0.01056 73.888 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.286 on 152 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.9729, Adjusted R-squared: 0.9727
## F-statistic: 5459 on 1 and 152 DF, p-value: < 2.2e-16#model 3 add in segregation #In Model 3, the DI measure of residential segregation for each city was added to the regression. Residential segregation caused a decrease in homeownership by -0.029. This result was not statistically significant but provides a different outcome than the previous analysis comparing cities near and not near freedom colonies
m1a <- lm(pboo_20~Proximity,data=cities)
m2a <- lm(pboo_20~Proximity+log(t_20),data=cities)
m3a <- lm(pboo_20~Proximity+log(t_20)+dbw_20,data=cities)
stargazer(m1a, m2a, m3a,
type="text", title="Regression Results",
align=TRUE, dep.var.labels=c("Percent Black Homeownership 2020"),
covariate.labels=c("Proximity","City Population 2020","Dissimilarity Index 2020"), style = "demography", out="main2020.htm")##
## Regression Results
## ------------------------------------------------------------------------------------------------
## Percent Black Homeownership 2020
## Model 1 Model 2 Model 3
## ------------------------------------------------------------------------------------------------
## Proximity 7.318*** 7.131*** 7.172***
## (1.270) (1.283) (1.288)
## City Population 2020 6.678 7.932
## (6.711) (7.231)
## Dissimilarity Index 2020 -0.026
## (0.056)
## Constant 5.604*** -9.916 -12.198
## (0.789) (15.652) (16.409)
## N 262 261 261
## R2 0.113 0.116 0.116
## Adjusted R2 0.110 0.109 0.106
## Residual Std. Error 10.008 (df = 260) 10.021 (df = 258) 10.037 (df = 257)
## F Statistic 33.183*** (df = 1; 260) 16.848*** (df = 2; 258) 11.272*** (df = 3; 257)
## ------------------------------------------------------------------------------------------------
## *p < .05; **p < .01; ***p < .001m1a <- lm(pboo_20~Proximity,data=cities)
m2a <- lm(pboo_20~Proximity+log(t_20),data=cities)
m3a <- lm(pboo_20~Proximity+log(t_20)+dbw_20,data=cities)
summary(m1a)##
## Call:
## lm(formula = pboo_20 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.791 -5.199 -2.581 2.164 54.199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6037 0.7887 7.105 1.16e-11 ***
## ProximityNot in Prox 7.3177 1.2703 5.760 2.36e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.01 on 260 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.1132, Adjusted R-squared: 0.1098
## F-statistic: 33.18 on 1 and 260 DF, p-value: 2.363e-08summary(m2a)##
## Call:
## lm(formula = pboo_20 ~ Proximity + log(t_20), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.924 -4.983 -2.568 2.154 53.931
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.916 15.652 -0.634 0.527
## ProximityNot in Prox 7.131 1.283 5.560 6.74e-08 ***
## log(t_20) 6.678 6.711 0.995 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.02 on 258 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1155, Adjusted R-squared: 0.1087
## F-statistic: 16.85 on 2 and 258 DF, p-value: 1.327e-07summary(m3a)##
## Call:
## lm(formula = pboo_20 ~ Proximity + log(t_20) + dbw_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.866 -4.863 -2.590 2.276 53.731
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12.19763 16.40888 -0.743 0.458
## ProximityNot in Prox 7.17174 1.28753 5.570 6.41e-08 ***
## log(t_20) 7.93160 7.23067 1.097 0.274
## dbw_20 -0.02623 0.05577 -0.470 0.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.04 on 257 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.1163, Adjusted R-squared: 0.106
## F-statistic: 11.27 on 3 and 257 DF, p-value: 5.684e-07m1b <- lm(pboo_10~Proximity,data=cities)
m2b <- lm(pboo_10~Proximity+log(t_10),data=cities)
m3b <- lm(pboo_10~Proximity+log(t_10)+dbw_10,data=cities)
summary(m1b)##
## Call:
## lm(formula = pboo_10 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.938 -5.166 -2.531 2.086 52.542
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.4656 0.8723 6.266 1.72e-09 ***
## ProximityNot in Prox 7.6226 1.3650 5.584 6.39e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.39 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1158, Adjusted R-squared: 0.1121
## F-statistic: 31.18 on 1 and 238 DF, p-value: 6.385e-08summary(m2b)##
## Call:
## lm(formula = pboo_10 ~ Proximity + log(t_10), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.161 -4.841 -2.446 2.067 52.513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.3310 6.9367 -0.192 0.848
## ProximityNot in Prox 7.4978 1.3709 5.469 1.15e-07 ***
## log(t_10) 0.6618 0.6701 0.988 0.324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.39 on 237 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1195, Adjusted R-squared: 0.112
## F-statistic: 16.08 on 2 and 237 DF, p-value: 2.832e-07summary(m3b)##
## Call:
## lm(formula = pboo_10 ~ Proximity + log(t_10) + dbw_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.692 -5.202 -2.409 1.831 52.696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.52764 7.03364 -0.075 0.940
## ProximityNot in Prox 7.44166 1.37456 5.414 1.52e-07 ***
## log(t_10) 0.49032 0.71218 0.688 0.492
## dbw_10 0.03656 0.05098 0.717 0.474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.41 on 236 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.1214, Adjusted R-squared: 0.1102
## F-statistic: 10.87 on 3 and 236 DF, p-value: 1.03e-06m1c <- lm(pboo_00~Proximity,data=cities)
m2c <- lm(pboo_00~Proximity+log(t_00),data=cities)
m3c <- lm(pboo_00~Proximity+log(t_00)+dbw_00,data=cities)
summary(m1c)##
## Call:
## lm(formula = pboo_00 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.406 -4.122 -1.990 1.631 49.594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.290 0.673 6.374 8.63e-10 ***
## ProximityNot in Prox 7.116 1.077 6.609 2.27e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.406 on 254 degrees of freedom
## (13 observations deleted due to missingness)
## Multiple R-squared: 0.1467, Adjusted R-squared: 0.1434
## F-statistic: 43.67 on 1 and 254 DF, p-value: 2.267e-10summary(m2c)##
## Call:
## lm(formula = pboo_00 ~ Proximity + log(t_00), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.294 -4.269 -2.274 1.733 49.544
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.8651 6.5636 -0.132 0.895
## ProximityNot in Prox 7.0641 1.2818 5.511 1.11e-07 ***
## log(t_00) 0.5552 0.6374 0.871 0.385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.765 on 197 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.1445, Adjusted R-squared: 0.1358
## F-statistic: 16.63 on 2 and 197 DF, p-value: 2.112e-07summary(m3c)##
## Call:
## lm(formula = pboo_00 ~ Proximity + log(t_00) + dbw_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.381 -4.448 -2.252 1.847 49.708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.173053 6.450149 0.182 0.85588
## ProximityNot in Prox 6.444154 1.268515 5.080 8.76e-07 ***
## log(t_00) -0.006646 0.648026 -0.010 0.99183
## dbw_00 0.129736 0.040949 3.168 0.00178 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.57 on 196 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.1862, Adjusted R-squared: 0.1737
## F-statistic: 14.94 on 3 and 196 DF, p-value: 8.449e-09m1d <- lm(pboo_90~Proximity,data=cities)
m2d <- lm(pboo_90~Proximity+log(t_90),data=cities)
m3d <- lm(pboo_90~Proximity+log(t_90)+dbw_90,data=cities)
summary(m1d)##
## Call:
## lm(formula = pboo_90 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.564 -3.830 -2.390 2.436 50.076
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8299 0.6342 6.039 5.46e-09 ***
## ProximityNot in Prox 6.7342 1.0167 6.623 2.07e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.947 on 255 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.1468, Adjusted R-squared: 0.1434
## F-statistic: 43.87 on 1 and 255 DF, p-value: 2.069e-10summary(m2d)##
## Call:
## lm(formula = pboo_90 ~ Proximity + log(t_90), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.459 -4.348 -2.059 2.303 49.229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7818 6.8735 -0.114 0.910
## ProximityNot in Prox 7.1279 1.3429 5.308 3.31e-07 ***
## log(t_90) 0.5418 0.6767 0.801 0.424
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.582 on 176 degrees of freedom
## (90 observations deleted due to missingness)
## Multiple R-squared: 0.1534, Adjusted R-squared: 0.1438
## F-statistic: 15.94 on 2 and 176 DF, p-value: 4.331e-07summary(m3d)##
## Call:
## lm(formula = pboo_90 ~ Proximity + log(t_90) + dbw_90, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.289 -4.693 -1.687 2.367 50.433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.14686 6.49777 0.484 0.629
## ProximityNot in Prox 6.73156 1.26268 5.331 2.98e-07 ***
## log(t_90) -0.40265 0.66266 -0.608 0.544
## dbw_90 0.17106 0.03429 4.988 1.46e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.053 on 175 degrees of freedom
## (90 observations deleted due to missingness)
## Multiple R-squared: 0.2588, Adjusted R-squared: 0.2461
## F-statistic: 20.36 on 3 and 175 DF, p-value: 2.291e-11m1e <- lm(pboo_80~Proximity,data=cities)
m2e <- lm(pboo_80~Proximity+log(t_80),data=cities)
m3e <- lm(pboo_80~Proximity+log(t_80)+dbw_80,data=cities)
summary(m1e)##
## Call:
## lm(formula = pboo_80 ~ Proximity, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.199 -3.475 -2.315 2.175 29.635
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.4750 0.5519 6.296 1.45e-09 ***
## ProximityNot in Prox 4.7242 0.8837 5.346 2.10e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.691 on 239 degrees of freedom
## (28 observations deleted due to missingness)
## Multiple R-squared: 0.1068, Adjusted R-squared: 0.1031
## F-statistic: 28.58 on 1 and 239 DF, p-value: 2.099e-07summary(m2e)##
## Call:
## lm(formula = pboo_80 ~ Proximity + log(t_80), data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.256 -4.013 -2.072 3.440 28.003
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.3137 6.1444 -0.702 0.484
## ProximityNot in Prox 5.7188 1.2114 4.721 5.32e-06 ***
## log(t_80) 0.8547 0.6068 1.408 0.161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.209 on 151 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.1546, Adjusted R-squared: 0.1434
## F-statistic: 13.8 on 2 and 151 DF, p-value: 3.125e-06summary(m3e)##
## Call:
## lm(formula = pboo_80 ~ Proximity + log(t_80) + dbw_80, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.147 -4.408 -1.674 2.692 27.227
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.31953 5.93737 0.391 0.697
## ProximityNot in Prox 5.40689 1.13826 4.750 4.71e-06 ***
## log(t_80) -0.32359 0.62305 -0.519 0.604
## dbw_80 0.12681 0.02727 4.650 7.22e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.762 on 150 degrees of freedom
## (115 observations deleted due to missingness)
## Multiple R-squared: 0.2611, Adjusted R-squared: 0.2463
## F-statistic: 17.67 on 3 and 150 DF, p-value: 7.136e-10m1a <- lm(pboo_20~Proximity,data=cities)
m2a <- lm(pboo_20~Proximity+log(t_20),data=cities)
m3a <- lm(pboo_20~Proximity+dbw_20,data=cities)
m1b <- lm(pboo_10~Proximity,data=cities)
m2b <- lm(pboo_10~Proximity+log(t_10),data=cities)
m3b <- lm(pboo_10~Proximity+log(t_10)+dbw_10,data=cities)
m1c <- lm(pboo_00~Proximity,data=cities)
m2c <- lm(pboo_00~Proximity+log(t_00),data=cities)
m3c <- lm(pboo_00~Proximity+log(t_00)+dbw_00,data=cities)
m1d <- lm(pboo_90~Proximity,data=cities)
m2d <- lm(pboo_90~Proximity+log(t_90),data=cities)
m3d <- lm(pboo_90~Proximity+log(t_90)+dbw_90,data=cities)
m1e <- lm(pboo_80~Proximity,data=cities)
m2e <- lm(pboo_80~Proximity+log(t_80),data=cities)
m3e <- lm(pboo_80~Proximity+log(t_80)+dbw_80,data=cities)stargazer(m1e, m2e, m3e, m1d, m2d, m3d, m1c, m2c, m3c,
type="text", title="Regression Results - Black Homeownership",
align=TRUE, dep.var.labels=c("1980","1990","2000"),
covariate.labels=c("Proximity","City Population 1980","Dissimilarity Index 1980","City Population 1990","Dissimilarity Index 1990","City Population 2000","Dissimilarity Index 2000"),
omit.stat=c("LL","ser","f"), no.space=TRUE, style = "demography", out="main00-80.htm") ##
## Regression Results - Black Homeownership
## ---------------------------------------------------------------------------------------------------------
## 1980 1990 2000
## Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
## ---------------------------------------------------------------------------------------------------------
## Proximity 4.724*** 5.719*** 5.407*** 6.734*** 7.128*** 6.732*** 7.116*** 7.064*** 6.444***
## (0.884) (1.211) (1.138) (1.017) (1.343) (1.263) (1.077) (1.282) (1.269)
## City Population 1980 0.855 -0.324
## (0.607) (0.623)
## Dissimilarity Index 1980 0.127***
## (0.027)
## City Population 1990 0.542 -0.403
## (0.677) (0.663)
## Dissimilarity Index 1990 0.171***
## (0.034)
## City Population 2000 0.555 -0.007
## (0.637) (0.648)
## Dissimilarity Index 2000 0.130**
## (0.041)
## Constant 3.475*** -4.314 2.320 3.830*** -0.782 3.147 4.290*** -0.865 1.173
## (0.552) (6.144) (5.937) (0.634) (6.873) (6.498) (0.673) (6.564) (6.450)
## N 241 154 154 257 179 179 256 200 200
## R2 0.107 0.155 0.261 0.147 0.153 0.259 0.147 0.144 0.186
## Adjusted R2 0.103 0.143 0.246 0.143 0.144 0.246 0.143 0.136 0.174
## ---------------------------------------------------------------------------------------------------------
## *p < .05; **p < .01; ***p < .001stargazer(m1b, m2b, m3b, m1a, m2a, m3a,
type="text", title="Regression Results - Black Homeownership",
align=TRUE, dep.var.labels=c("2010","2020"),
covariate.labels=c("Proximity", "City Population 2010","Dissimilarity Index 2010","City Population 2020","Dissimilarity Index 2020"),
omit.stat=c("LL","ser","f"), no.space=TRUE, style = "demography",
out="main 2010 - 2020.htm") ##
## Regression Results - Black Homeownership
## ------------------------------------------------------------------------------
## 2010 2020
## Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
## ------------------------------------------------------------------------------
## Proximity 7.623*** 7.498*** 7.442*** 7.318*** 7.131*** 7.293***
## (1.365) (1.371) (1.375) (1.270) (1.283) (1.283)
## City Population 2010 0.662 0.490
## (0.670) (0.712)
## Dissimilarity Index 2010 0.037
## (0.051)
## City Population 2020 6.678
## (6.711)
## Dissimilarity Index 2020 -0.004
## (0.052)
## Constant 5.466*** -1.331 -0.528 5.604*** -9.916 5.727***
## (0.872) (6.937) (7.034) (0.789) (15.652) (1.492)
## N 240 240 240 262 261 261
## R2 0.116 0.119 0.121 0.113 0.116 0.112
## Adjusted R2 0.112 0.112 0.110 0.110 0.109 0.105
## ------------------------------------------------------------------------------
## *p < .05; **p < .01; ***p < .001Proximity to freedom colonies as the mediator
Causal mediation analysis
install.packages("semTools")## Installing package into 'C:/Users/canda/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'semTools' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\canda\AppData\Local\Temp\Rtmps7Yaq8\downloaded_packagesinstall.packages("tidyverse")## Installing package into 'C:/Users/canda/AppData/Local/R/win-library/4.3'
## (as 'lib' is unspecified)## package 'tidyverse' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\canda\AppData\Local\Temp\Rtmps7Yaq8\downloaded_packageslibrary(lavaan)
library(stargazer)
library(semTools)## Warning: package 'semTools' was built under R version 4.3.2## ## ################################################################################# This is semTools 0.5-6## All users of R (or SEM) are invited to submit functions or ideas for functions.## ###############################################################################library(dplyr)#linear regression model path c
fit.totaleffect20=lm(pboo_20~dbw_20,cities)
summary(fit.totaleffect20)##
## Call:
## lm(formula = pboo_20 ~ dbw_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.494 -6.204 -3.776 3.361 58.777
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.75619 1.53393 5.056 8.09e-07 ***
## dbw_20 0.02759 0.05460 0.505 0.614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.63 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.0009849, Adjusted R-squared: -0.002872
## F-statistic: 0.2553 on 1 and 259 DF, p-value: 0.6138fit.totaleffect10=lm(pboo_10~dbw_10,cities)
summary(fit.totaleffect10)##
## Call:
## lm(formula = pboo_10 ~ dbw_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.414 -6.611 -4.047 3.222 57.349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.65783 1.54930 4.297 2.52e-05 ***
## dbw_10 0.07062 0.05063 1.395 0.164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.01 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.008109, Adjusted R-squared: 0.003941
## F-statistic: 1.946 on 1 and 238 DF, p-value: 0.1644fit.totaleffect00=lm(pboo_00~dbw_00,cities)
summary(fit.totaleffect00)##
## Call:
## lm(formula = pboo_00 ~ dbw_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.879 -5.553 -2.565 2.516 53.694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.47709 1.42056 1.744 0.0828 .
## dbw_00 0.16763 0.04096 4.093 6.2e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.076 on 198 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.07801, Adjusted R-squared: 0.07335
## F-statistic: 16.75 on 1 and 198 DF, p-value: 6.198e-05#homeownership and percent black significant because they are highly correlated
fit.totaleffect2_20=lm(pb_20~pboo_20,cities)
summary(fit.totaleffect2_20)##
## Call:
## lm(formula = pb_20 ~ pboo_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.0656 -2.3158 -0.6632 1.6532 16.8647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.09306 0.26722 11.57 <2e-16 ***
## pboo_20 1.07084 0.01971 54.32 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.374 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.9193, Adjusted R-squared: 0.919
## F-statistic: 2951 on 1 and 259 DF, p-value: < 2.2e-16fit.totaleffect2_10=lm(pb_10~pboo_10,cities)
summary(fit.totaleffect2_10)##
## Call:
## lm(formula = pb_10 ~ pboo_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.2387 -1.9058 -0.6333 1.3838 15.1023
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26128 0.24360 9.283 <2e-16 ***
## pboo_10 1.06376 0.01746 60.941 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.977 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.9398, Adjusted R-squared: 0.9395
## F-statistic: 3714 on 1 and 238 DF, p-value: < 2.2e-16fit.totaleffect2_00=lm(pb_00~pboo_00,cities)
summary(fit.totaleffect2_00)##
## Call:
## lm(formula = pb_00 ~ pboo_00, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.4643 -1.5188 -0.6209 1.2338 19.8202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.63249 0.26102 6.254 2.42e-09 ***
## pboo_00 1.13258 0.02152 52.641 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.862 on 198 degrees of freedom
## (69 observations deleted due to missingness)
## Multiple R-squared: 0.9333, Adjusted R-squared: 0.933
## F-statistic: 2771 on 1 and 198 DF, p-value: < 2.2e-16#Proximity as the mediator
cities$Proximity <- as.ordered(cities$Proximity)
class(cities$Proximity)## [1] "ordered" "factor"summary(cities$Proximity)## In Proximity Not in Prox
## 162 107specmod20 <- "
#Path c
pboo_20 ~ c*dbw_20
#Path a
Proximity ~ a*dbw_20
#Path b
pboo_20 ~ b*Proximity
#Indirect effect (a*b)
ab :=a*b
"specmod10 <- "
#Path c
pboo_10 ~ c*dbw_10
#Path a
Proximity ~ a*dbw_10
#Path b
pboo_10 ~ b*Proximity
#Indirect effect (a*b)
ab :=a*b
"specmod00 <- "
#Path c
pboo_00 ~ c*dbw_00
#Path a
Proximity ~ a*dbw_00
#Path b
pboo_00 ~ b*Proximity
#Indirect effect (a*b)
ab :=a*b
"
specmod90 <- "
#Path c
pboo_90 ~ c*dbw_90
#Path a
Proximity ~ a*dbw_90
#Path b
pboo_90 ~ b*Proximity
#Indirect effect (a*b)
ab :=a*b
"
specmod80 <- "
#Path c
pboo_80 ~ c*dbw_80
#Path a
Proximity ~ a*dbw_80
#Path b
pboo_80 ~ b*Proximity
#Indirect effect (a*b)
ab :=a*b
"#without equality constraints
fitmodel20<- sem(specmod20, data=cities)
fitmodel10<- sem(specmod10, data=cities)
fitmodel00<- sem(specmod00, data=cities)
fitmodel90<- sem(specmod90, data=cities)
fitmodel80<- sem(specmod80, data=cities)
summary(fitmodel20,fit.measures=TRUE, rsquare=TRUE)## lavaan 0.6.17 ended normally after 16 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 261 269
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 47.852 47.852
## Degrees of freedom 1 1
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value H_0: RMSEA <= 0.050 NA NA
## P-value H_0: RMSEA >= 0.080 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## pboo_20 ~
## dbw_20 (c) -0.025 0.052 -0.480 0.631
## Proximity ~
## dbw_20 (a) 0.011 0.006 1.715 0.086
## pboo_20 ~
## Proximity (b) 4.715 0.682 6.917 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .pboo_20 7.756 1.905 4.071 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## Proximity|t1 0.571 0.183 3.127 0.002
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pboo_20 89.900 7.233 12.429 0.000
## .Proximity 1.000
##
## R-Square:
## Estimate
## pboo_20 0.199
## Proximity 0.018
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab 0.052 0.031 1.677 0.094summary(fitmodel10,fit.measures=TRUE, rsquare=TRUE)## lavaan 0.6.17 ended normally after 19 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 240 269
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 48.420 48.420
## Degrees of freedom 1 1
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value H_0: RMSEA <= 0.050 NA NA
## P-value H_0: RMSEA >= 0.080 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## pboo_10 ~
## dbw_10 (c) 0.032 0.047 0.690 0.490
## Proximity ~
## dbw_10 (a) 0.008 0.006 1.318 0.188
## pboo_10 ~
## Proximity (b) 5.025 0.722 6.958 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .pboo_10 6.658 2.102 3.168 0.002
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## Proximity|t1 0.440 0.178 2.469 0.014
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pboo_10 94.947 8.134 11.673 0.000
## .Proximity 1.000
##
## R-Square:
## Estimate
## pboo_10 0.216
## Proximity 0.011
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab 0.038 0.029 1.299 0.194summary(fitmodel00,fit.measures=TRUE, rsquare=TRUE)## lavaan 0.6.17 ended normally after 18 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 200 269
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 42.125 42.125
## Degrees of freedom 1 1
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value H_0: RMSEA <= 0.050 NA NA
## P-value H_0: RMSEA >= 0.080 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## pboo_00 ~
## dbw_00 (c) 0.105 0.042 2.523 0.012
## Proximity ~
## dbw_00 (a) 0.016 0.006 2.612 0.009
## pboo_00 ~
## Proximity (b) 4.013 0.618 6.490 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .pboo_00 2.477 1.861 1.331 0.183
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## Proximity|t1 0.757 0.210 3.601 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pboo_00 65.443 5.638 11.607 0.000
## .Proximity 1.000
##
## R-Square:
## Estimate
## pboo_00 0.260
## Proximity 0.057
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab 0.063 0.025 2.487 0.013summary(fitmodel90,fit.measures=TRUE, rsquare=TRUE)## lavaan 0.6.17 ended normally after 19 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 179 269
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 47.177 47.177
## Degrees of freedom 1 1
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value H_0: RMSEA <= 0.050 NA NA
## P-value H_0: RMSEA >= 0.080 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## pboo_90 ~
## dbw_90 (c) 0.151 0.040 3.814 0.000
## Proximity ~
## dbw_90 (a) 0.008 0.005 1.591 0.112
## pboo_90 ~
## Proximity (b) 4.132 0.602 6.869 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .pboo_90 1.135 2.026 0.560 0.575
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## Proximity|t1 0.569 0.209 2.729 0.006
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pboo_90 56.677 4.987 11.365 0.000
## .Proximity 1.000
##
## R-Square:
## Estimate
## pboo_90 0.338
## Proximity 0.024
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab 0.035 0.022 1.592 0.111summary(fitmodel80,fit.measures=TRUE, rsquare=TRUE)## lavaan 0.6.17 ended normally after 22 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 6
##
## Used Total
## Number of observations 154 269
##
## Model Test User Model:
## Standard Scaled
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 22.933 22.933
## Degrees of freedom 1 1
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value H_0: RMSEA <= 0.050 NA NA
## P-value H_0: RMSEA >= 0.080 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower NA
## 90 Percent confidence interval - upper NA
## P-value H_0: Robust RMSEA <= 0.050 NA
## P-value H_0: Robust RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Parameterization Delta
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## pboo_80 ~
## dbw_80 (c) 0.113 0.026 4.429 0.000
## Proximity ~
## dbw_80 (a) 0.008 0.005 1.567 0.117
## pboo_80 ~
## Proximity (b) 3.061 0.639 4.789 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .pboo_80 0.719 1.723 0.417 0.676
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|)
## Proximity|t1 0.613 0.239 2.563 0.010
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .pboo_80 41.867 4.871 8.596 0.000
## .Proximity 1.000
##
## R-Square:
## Estimate
## pboo_80 0.306
## Proximity 0.027
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ab 0.023 0.015 1.572 0.116#with fixed parameters
pars.factors20 <- standardizedSolution(fitmodel20)[standardizedSolution(fitmodel20)[,'op']=='=~']
pars.factors10 <- standardizedSolution(fitmodel10)[standardizedSolution(fitmodel10)[,'op']=='=~']
pars.factors00 <- standardizedSolution(fitmodel00)[standardizedSolution(fitmodel00)[,'op']=='=~']
pars.factors90 <- standardizedSolution(fitmodel90)[standardizedSolution(fitmodel90)[,'op']=='=~']
pars.factors80 <- standardizedSolution(fitmodel80)[standardizedSolution(fitmodel80)[,'op']=='=~']
pars.regressions20 <- standardizedSolution(fitmodel20)[ standardizedSolution(fitmodel20)[,'op']=='~', c(3, 4, 5, 7)]
pars.regressions10 <- standardizedSolution(fitmodel10)[ standardizedSolution(fitmodel10)[,'op']=='~', c(3, 4, 5, 7)]
pars.regressions00 <- standardizedSolution(fitmodel00)[ standardizedSolution(fitmodel00)[,'op']=='~', c(3, 4, 5, 7)]
pars.regressions90 <- standardizedSolution(fitmodel90)[ standardizedSolution(fitmodel90)[,'op']=='~', c(3, 4, 5, 7)]
pars.regressions80 <- standardizedSolution(fitmodel80)[ standardizedSolution(fitmodel80)[,'op']=='~', c(3, 4, 5, 7)]#stargazer(pars.factors, rownames = FALSE, summary=FALSE, type='text', style = "demography", title='Factor loadings')
stargazer(pars.regressions80, rownames = FALSE, initial.zero=FALSE, type='text', title='Path Coefficients Table 1980', style = "demography", out = "predictingY80.htm", summary = FALSE)##
## Path Coefficients Table 1980
## -----------------------------
## rhs label est.std z
## -----------------------------
## dbw_80 c .322 5.385
## dbw_80 a .165 1.611
## Proximity b .400 5.666
## -----------------------------stargazer(pars.regressions90, rownames = FALSE, initial.zero=FALSE, type='text', title='Path Coefficients Table 1990', style = "demography", out = "predictingY90.htm", summary = FALSE)##
## Path Coefficients Table 1990
## -----------------------------
## rhs label est.std z
## -----------------------------
## dbw_90 c .303 4.397
## dbw_90 a .153 1.629
## Proximity b .452 7.642
## -----------------------------stargazer(pars.regressions00, rownames = FALSE, initial.zero=FALSE, type='text', title='Path Coefficients Table 2000', style = "demography", out = "predictingY00.htm", summary = FALSE)##
## Path Coefficients Table 2000
## -----------------------------
## rhs label est.std z
## -----------------------------
## dbw_00 c .175 2.659
## dbw_00 a .238 2.770
## Proximity b .439 6.977
## -----------------------------stargazer(pars.regressions10, rownames = FALSE, initial.zero=FALSE, type='text', title='Path Coefficients Table 2010', style = "demography", out = "predictingY10.htm", summary = FALSE)##
## Path Coefficients Table 2010
## -----------------------------
## rhs label est.std z
## -----------------------------
## dbw_10 c .041 .697
## dbw_10 a .106 1.333
## Proximity b .459 7.565
## -----------------------------stargazer(pars.regressions20, rownames = FALSE, initial.zero=FALSE, type='text', title='Path Coefficients Table 2020', style = "demography", out = "predictingY20.htm", summary = FALSE)##
## Path Coefficients Table 2020
## -----------------------------
## rhs label est.std z
## -----------------------------
## dbw_20 c -.028 -.479
## dbw_20 a .133 1.746
## Proximity b .449 7.409
## -----------------------------#Black-to-white racial residential segregation is positively associated with racial income inequality.
stargazer(cities[c("dbw_10","gini_10")], header=FALSE, type='text',
title="Descriptive Statistics 2010", digits=3,
covariate.labels=c("B-W Dissimilarity","Gini Index"),
out="Desc Inequality 2010.htm"
)##
## Descriptive Statistics 2010
## ==================================================
## Statistic N Mean St. Dev. Min Max
## --------------------------------------------------
## B-W Dissimilarity 240 27.194 14.067 0.000 82.216
## Gini Index 264 0.418 0.060 0.260 0.569
## --------------------------------------------------stargazer(cities[c("dbw_20","gini_20")], header=FALSE, type='text',
title="Descriptive Statistics 2020", digits=3,
covariate.labels=c("B-W Dissimilarity","Gini Index"),
out="Desc Inequality 2020.htm"
)##
## Descriptive Statistics 2020
## ==================================================
## Statistic N Mean St. Dev. Min Max
## --------------------------------------------------
## B-W Dissimilarity 261 25.379 12.075 0.000 69.139
## Gini Index 262 0.424 0.050 0.292 0.545
## --------------------------------------------------cities$Proximity <- factor(cities$Proximity, ordered = FALSE )# Make "Not in Proximity" the reference category
cities$Proximity <- relevel(cities$Proximity, ref = "Not in Prox")
table(cities$Proximity)##
## Not in Prox In Proximity
## 107 162plot(cities$dbw_10,cities$gini_10,pch=20,
xlab='D index',ylab='Gini index',
main='Cities by Residential Segregation and Income Inequality in 2010')
plot(cities$dbw_20,cities$gini_20,pch=20,
xlab='D index',ylab='Gini index',
main='Cities by Residential Segregation and Income Inequality in 2020')
#outcome = inequality
subset_P3 <- subset(cities, Proximity == "In Proximity")
stargazer(subset_P3[c("gini_10","gini_20")], header=FALSE, type='text',
title="Income Inequality by Year for Cities In Proximity to Freedom Colony", digits=4,
covariate.labels=c("2010","2020"),initial.zero=FALSE,
style = "demography",
out="Desc Mean gini In Prox.htm")##
## Income Inequality by Year for Cities In Proximity to Freedom Colony
## ----------------------------------------
## Statistic N Mean St. Dev. Min Max
## ----------------------------------------
## 2010 160 .4181 .0591 .2600 .5340
## 2020 161 .4243 .0488 .2920 .5450
## ----------------------------------------subset_P4 <- subset(cities, Proximity == "Not in Prox")
stargazer(subset_P4[c("gini_10","gini_20")], header=FALSE, type='text',
title="Income Inequality by Year for Cities Not In Proximity to Freedom Colony", digits=4,
covariate.labels=c("2010","2020"),initial.zero=FALSE,
style = "demography",
out="Desc Mean gini NOTIn Prox.htm")##
## Income Inequality by Year for Cities Not In Proximity to Freedom Colony
## ----------------------------------------
## Statistic N Mean St. Dev. Min Max
## ----------------------------------------
## 2010 104 .4190 .0607 .2660 .5690
## 2020 101 .4246 .0532 .3130 .5450
## ----------------------------------------m5a <- lm(gini_10~dbw_10,data=cities)
summary(m5a)##
## Call:
## lm(formula = gini_10 ~ dbw_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.128300 -0.034858 -0.001421 0.034283 0.151779
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3772492 0.0073349 51.432 < 2e-16 ***
## dbw_10 0.0016617 0.0002397 6.933 3.84e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05212 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.168, Adjusted R-squared: 0.1645
## F-statistic: 48.07 on 1 and 238 DF, p-value: 3.845e-11m5b <- lm(gini_20~dbw_20,data=cities)
summary(m5b)##
## Call:
## lm(formula = gini_20 ~ dbw_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.106555 -0.030176 0.004545 0.031145 0.101210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3718508 0.0063533 58.529 <2e-16 ***
## dbw_20 0.0020690 0.0002261 9.149 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04403 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.2443, Adjusted R-squared: 0.2413
## F-statistic: 83.71 on 1 and 259 DF, p-value: < 2.2e-16##Regression results
stargazer(m5a,m5b,
type="text", title="Regression Results",
align=TRUE, dep.var.labels=c("Income Inequality 2010","Income Inequality 2020"),
covariate.labels=c("Dissimilarity Index 2010","Dissimilarity Index 2020"),omit.stat=c("LL","ser","f"),initial.zero=FALSE, no.space=TRUE, style = "demography", out="Gini20202010.htm")##
## Regression Results
## ----------------------------------------------------------------------
## Income Inequality 2010 Income Inequality 2020
## Model 1 Model 2
## ----------------------------------------------------------------------
## Dissimilarity Index 2010 .002***
## (.0002)
## Dissimilarity Index 2020 .002***
## (.0002)
## Constant .377*** .372***
## (.007) (.006)
## N 240 261
## R2 .168 .244
## Adjusted R2 .165 .241
## ----------------------------------------------------------------------
## *p < .05; **p < .01; ***p < .001m6a1 <- lm(gini_10~dbw_10,data=cities)
m7a2 <- lm(gini_10~dbw_10+Proximity,data=cities)
m8a3 <- lm(gini_10~dbw_10+Proximity+log(t_10),data=cities)
m9a4 <- lm(gini_10~dbw_10+Proximity+log(t_10)+pboo_10,data=cities)
stargazer(m6a1, m7a2, m8a3, m9a4,
type="text", title="Regression Results - Income Inequality",
align=TRUE, dep.var.labels=c("2010"),
covariate.labels=c("Dissimilarity Index 2010",
"Proximity",
"City Population 2010",
"Black Homeownership 2010"), initial.zero=FALSE,
omit.stat=c("LL","ser","f"), no.space=TRUE, style = "demography",out="Inequality outcome.htm") ##
## Regression Results - Income Inequality
## --------------------------------------------------------
## 2010
## Model 1 Model 2 Model 3 Model 4
## --------------------------------------------------------
## Dissimilarity Index 2010 .002*** .002*** .002*** .002***
## (.0002) (.0002) (.0003) (.0003)
## Proximity .006 .006 -.001
## (.007) (.007) (.007)
## City Population 2010 .004 .005
## (.004) (.004)
## Black Homeownership 2010 -.001**
## (.0003)
## Constant .377*** .373*** .330*** .337***
## (.007) (.009) (.036) (.035)
## N 240 240 240 240
## R2 .168 .171 .176 .207
## Adjusted R2 .165 .164 .165 .194
## --------------------------------------------------------
## *p < .05; **p < .01; ***p < .001m6b5 <- lm(gini_20~dbw_20,data=cities)
m7b6 <- lm(gini_20~dbw_20+Proximity,data=cities)
m8b7 <- lm(gini_20~dbw_20+Proximity+log(t_20),data=cities)
m9b8 <- lm(gini_20~dbw_20+Proximity+log(t_20)+pboo_20,data=cities)
stargazer(m6b5, m7b6, m8b7, m9b8,
type="text", title="Regression Results - Income Inequality",
align=TRUE, dep.var.labels=c("2020"),
covariate.labels=c( "Dissimilarity Index 2020",
"Proximity",
"City Population 2020",
"Black Homeownership 2020",
"Constant"), initial.zero=FALSE,
omit.stat=c("LL","ser","f"), no.space=TRUE, style = "demography",out="Inequality outcome20.htm") ##
## Regression Results - Income Inequality
## ---------------------------------------------------------
## 2020
## Model 1 Model 2 Model 3 Model 4
## ---------------------------------------------------------
## Dissimilarity Index 2020 .002*** .002*** .002*** .002***
## (.0002) (.0002) (.0002) (.0002)
## Proximity .005 .005 -.002
## (.006) (.006) (.006)
## City Population 2020 -.00003 .008
## (.032) (.031)
## Black Homeownership 2020 -.001***
## (.0003)
## Constant .372*** .368*** .368*** .363***
## (.006) (.008) (.073) (.071)
## N 261 261 261 261
## R2 .244 .247 .247 .282
## Adjusted R2 .241 .241 .238 .271
## ---------------------------------------------------------
## *p < .05; **p < .01; ***p < .001#percent Black population with inequality as outcome. No significance for 2010, low sig 2020
m10a <- lm(gini_20~pb_20,data=cities)
summary(m10a)##
## Call:
## lm(formula = gini_20 ~ pb_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.133079 -0.032519 -0.001667 0.034255 0.135233
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4307733 0.0044585 96.619 <2e-16 ***
## pb_20 -0.0005279 0.0002629 -2.008 0.0457 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05026 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.01533, Adjusted R-squared: 0.01153
## F-statistic: 4.032 on 1 and 259 DF, p-value: 0.04569m10b <- lm(gini_10~pb_10,data=cities)
summary(m10b)##
## Call:
## lm(formula = gini_10 ~ pb_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15540 -0.03681 -0.00019 0.04235 0.14526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4258257 0.0050589 84.174 <2e-16 ***
## pb_10 -0.0002976 0.0003048 -0.976 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05703 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.00399, Adjusted R-squared: -0.0001951
## F-statistic: 0.9534 on 1 and 238 DF, p-value: 0.3298#outcome = segregation
m12a <- lm(dbw_20~gini_20,data=cities)
summary(m12a)##
## Call:
## lm(formula = dbw_20 ~ gini_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.168 -7.256 -1.223 6.250 41.274
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.720 5.514 -4.483 1.11e-05 ***
## gini_20 118.059 12.903 9.149 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.52 on 259 degrees of freedom
## (8 observations deleted due to missingness)
## Multiple R-squared: 0.2443, Adjusted R-squared: 0.2413
## F-statistic: 83.71 on 1 and 259 DF, p-value: < 2.2e-16m12b <- lm(dbw_10~gini_10,data=cities)
summary(m12b)##
## Call:
## lm(formula = dbw_10 ~ gini_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.987 -8.574 -0.795 7.034 46.673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15.522 6.217 -2.497 0.0132 *
## gini_10 101.117 14.585 6.933 3.84e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.86 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.168, Adjusted R-squared: 0.1645
## F-statistic: 48.07 on 1 and 238 DF, p-value: 3.845e-11m13a <- lm(dbw_20~Proximity,data=cities)
m14a <- lm(dbw_20~Proximity+log(t_20),data=cities)
m15a <- lm(dbw_20~Proximity+log(t_20)+pboo_20,data=cities)
m16a <- lm(dbw_20~Proximity+log(t_20)+pboo_20+gini_20,data=cities)
m13b <- lm(dbw_10~Proximity,data=cities)
m14b <- lm(dbw_10~Proximity+log(t_10),data=cities)
m15b <- lm(dbw_10~Proximity+log(t_10)+pboo_10,data=cities)
m16b <- lm(dbw_10~Proximity+log(t_10)+pboo_10+gini_10,data=cities)stargazer(m13a, m14a, m15a, m16a, m13b, m14b, m15b, m16b,
type="text", title="Regression Results",
align=TRUE, dep.var.labels=c("Dissimilarity Index 2020","Dissimilarity Index 2010"),
covariate.labels=c("Proximity","City Population 2020","Black Homeownership","Income Inequality","City Population 2010","Black Homeownership","Income Inequality"), initial.zero=FALSE, omit.stat=c("LL","ser","f"), no.space=TRUE, style = "demography", out="Segregation.htm") ##
## Regression Results
## -------------------------------------------------------------------------------------------------------
## Dissimilarity Index 2020 Dissimilarity Index 2010
## Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
## -------------------------------------------------------------------------------------------------------
## Proximity -2.625 -1.536 -1.770 -1.191 -2.420 -1.535 -1.089 -.845
## (1.529) (1.434) (1.520) (1.343) (1.845) (1.748) (1.857) (1.718)
## City Population 2020 47.799*** 48.018*** 36.414***
## (7.503) (7.529) (6.778)
## Black Homeownership -.033 .078
## (.070) (.063)
## Income Inequality 108.708***
## (12.624)
## City Population 2010 4.692*** 4.652*** 3.507***
## (.855) (.857) (.813)
## Black Homeownership .059 .141
## (.083) (.078)
## Income Inequality 92.373***
## (14.427)
## Constant 26.988*** -85.436*** -85.527*** -105.820*** 28.626*** -20.440* -20.807* -48.824***
## (1.197) (17.683) (17.710) (15.802) (1.419) (9.038) (9.062) (9.453)
## N 261 261 261 261 240 240 240 240
## R2 .011 .146 .146 .338 .007 .119 .121 .252
## Adjusted R2 .007 .139 .136 .328 .003 .112 .110 .239
## -------------------------------------------------------------------------------------------------------
## *p < .05; **p < .01; ***p < .001#outcome = homeownership
plot(cities$pboo_10,cities$gini_10,pch=20,
xlab='Homeownership',ylab='Gini index',
main='Cities by Black Homeownership and Income Inequality in 2010')
plot(cities$pboo_20,cities$gini_20,pch=20,
xlab='Homeownership',ylab='Gini index',
main='Cities by Black Homeownership and Income Inequality in 2020')
m17a <- lm(pboo_10~gini_10,data=cities)
summary(m17a)##
## Call:
## lm(formula = pboo_10 ~ gini_10, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.567 -6.707 -3.943 2.528 55.439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.24 5.29 3.826 0.000167 ***
## gini_10 -27.60 12.41 -2.224 0.027084 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.94 on 238 degrees of freedom
## (29 observations deleted due to missingness)
## Multiple R-squared: 0.02036, Adjusted R-squared: 0.01624
## F-statistic: 4.946 on 1 and 238 DF, p-value: 0.02708m17b <- lm(pboo_20~gini_20,data=cities)
summary(m17b)##
## Call:
## lm(formula = pboo_20 ~ gini_20, data = cities)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.757 -6.354 -3.521 2.900 56.998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 24.301 5.483 4.432 1.38e-05 ***
## gini_20 -37.412 12.830 -2.916 0.00386 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.46 on 260 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.03167, Adjusted R-squared: 0.02794
## F-statistic: 8.503 on 1 and 260 DF, p-value: 0.003856stargazer(m17a, m17b,
type="text", title="Effects on Black Homeownership",
align=TRUE, dep.var.labels=c("2010","2020"),
covariate.labels=c("Income Inequality 2010", "Income Inequality 2020"), omit.stat=c("LL","ser","f"), initial.zero=FALSE, no.space=TRUE, style = "demography", out="Homeownership.htm") ##
## Effects on Black Homeownership
## ------------------------------------------
## 2010 2020
## Model 1 Model 2
## ------------------------------------------
## Income Inequality 2010 -27.603*
## (12.411)
## Income Inequality 2020 -37.412**
## (12.830)
## Constant 20.239*** 24.301***
## (5.290) (5.483)
## N 240 262
## R2 .020 .032
## Adjusted R2 .016 .028
## ------------------------------------------
## *p < .05; **p < .01; ***p < .001m18a <- lm(pboo_10~Proximity,data=cities)
m19a <- lm(pboo_10~Proximity+log(t_10),data=cities)
m20a <- lm(pboo_10~Proximity+log(t_10)+dbw_10,data=cities)
m21a <- lm(pboo_10~Proximity+log(t_10)+dbw_10+gini_10,data=cities)
m18b <- lm(pboo_20~Proximity,data=cities)
m19b <- lm(pboo_20~Proximity+log(t_20),data=cities)
m20b <- lm(pboo_20~Proximity+log(t_20)+dbw_20,data=cities)
m21b <- lm(pboo_20~Proximity+log(t_20)+dbw_20+gini_20,data=cities)stargazer(m18a, m19a, m20a, m21a, m18b, m19b, m20b, m21b,
type="text", title="Regression Results - Black Homeownership",
align=TRUE, dep.var.labels=c("Black Homeownership 2010","Black Homeownership 2020"),
covariate.labels=c("Proximity","City Population 2010","Dissimilarity Index","Income Inequality","City Population 2020","Dissimilarity Index","Income Inequality"), initial.zero=FALSE, omit.stat=c("LL","ser","f"), no.space=TRUE, style = "demography", out="Homeownership full.htm") ##
## Regression Results - Black Homeownership
## -----------------------------------------------------------------------------------------------------
## Black Homeownership 2010 Black Homeownership 2020
## Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
## -----------------------------------------------------------------------------------------------------
## Proximity -7.623*** -7.498*** -7.442*** -7.193*** -7.318*** -7.131*** -7.172*** -6.917***
## (1.365) (1.371) (1.375) (1.353) (1.270) (1.283) (1.288) (1.261)
## City Population 2010 .662 .490 .663
## (.670) (.712) (.702)
## Dissimilarity Index .037 .098
## (.051) (.054)
## Income Inequality -38.970**
## (12.780)
## City Population 2020 6.678 7.932 7.930
## (6.711) (7.231) (7.072)
## Dissimilarity Index -.026 .077
## (.056) (.062)
## Income Inequality -49.316***
## (13.877)
## Constant 13.088*** 6.167 6.914 19.775* 12.921*** -2.785 -5.026 13.131
## (1.050) (7.086) (7.170) (8.212) (.996) (15.816) (16.541) (16.967)
## N 240 240 240 240 262 261 261 261
## R2 .116 .119 .121 .155 .113 .116 .116 .158
## Adjusted R2 .112 .112 .110 .140 .110 .109 .106 .145
## -----------------------------------------------------------------------------------------------------
## *p < .05; **p < .01; ***p < .001