Data on Political Advertisement and Campaign Contributions in US Presidential Elections

Description

A dataset containing 15 variables on the campaign contributions of 16,265 zip codes to the 2004 and 2008 US presidential elections in addition to the demographic characteristics of each area (Urban and Niebler 2014; Fong, Hazlett, and Imai 2018).

Usage

advertisement

Format

A data frame with 16,265 rows and 15 columns:

zip

zip code

treat

the log transformed TotAds

TotAds

the total number of political advertisements aired in the zip code

TotalPop

population size

PercentOver65

percent of the population over 65

Inc

median household income

PercentHispanic

percent Hispanic

PercentBlack

percent black

density

population density (people per sq mile)

per_collegegrads

percent college graduates

CanCommute

a dummy variable indicating whether it is possible to commute to the zip code from a competitive state

StFIPS

state FIPS code

Cont

campaign contributions (in thousands of dollars)

log_TotalPop

log population

log_Inc

log median income

References

Fong, Christian, Chad Hazlett, and Kosuke Imai. 2018. Covariate Balancing Propensity Score for a Continuous Treatment: Application to The Efficacy of Political Advertisements. The Annals of Applied Statistics 12(1):156-77.

Urban, Carly, and Sarah Niebler. 2014. Dollars on the Sidewalk: Should U.S. Presidential Candidates Advertise in Uncontested States? American Journal of Political Science 58(2):322-36.

Long-format Data on Negative Campaign Advertising in US Senate and Gubernatorial Elections

Description

A dataset containing 19 variables and 565 unit-week records on the campaign of 113 Democratic candidates in US Senate and Gubernatorial Elections from 2000 to 2006 (Blackwell 2013).

Usage

campaign_long

Format

A data frame with 565 rows and 19 columns:

demName

name of the Democratic candidate

d.gone.neg

whether the candidate went negative in a campaign-week, defined as whether more than 10% of the candidate's political advertising was negative

d.gone.neg.l1

whether the candidate went negative in the previous campaign-week

camp.length

length of the candidate's campaign (in weeks)

deminc

whether the candidate was an incumbent

base.poll

Democratic share in the baseline polls

base.und

share of undecided voters in the baseline polls

office

type of office in contest. 0: governor; 1: senator

demprcnt

Democratic share of the two-party vote in the election

week

week in the campaign (in the final five weeks preceding the election)

year

year of the election

state

state of the election

dem.polls

Democratic share in the polls

dem.polls.l1

Democratic share in the polls in the previous campaign-week

undother

share of undecided voters in the polls

undother.l1

share of undecided voters in the polls in the previous campaign-week

neg.dem

the proportion of advertisements that were negative in a campaign-week

neg.dem.l1

the proportion of advertisements that were negative in the previous campaign-week

id

candidate id

References

Blackwell, Matthew. 2013. A Framework for Dynamic Causal Inference in Political Science. American Journal of Political Science 57(2): 504-619.

Wide-format Data on Negative Campaign Advertising in US Senate and Gubernatorial Elections

Description

A dataset containing 32 variables and 113 unit records from Blackwell (2013).

Usage

campaign_wide

Format

A data frame with 113 rows and 26 columns:

demName

name of the Democratic candidate

camp.length

length of the candidate's campaign (in weeks)

deminc

whether the candidate was an incumbent.

base.poll

Democratic share in the baseline polls

base.und

share of undecided voters in the baseline polls

office

type of office in contest. 0: governor; 1: senator

demprcnt

Democratic share of the two-party vote in the election

year

year of the election

state

state of the election

id

candidate id

dem.polls_1

Democratic share in week 1 polls

dem.polls_2

Democratic share in week 2 polls

dem.polls_3

Democratic share in week 3 polls

dem.polls_4

Democratic share in week 4 polls

dem.polls_5

Democratic share in week 5 polls

d.gone.neg_1

whether the candidate went negative in week 1

d.gone.neg_2

whether the candidate went negative in week 2

d.gone.neg_3

whether the candidate went negative in week 3

d.gone.neg_4

whether the candidate went negative in week 4

d.gone.neg_5

whether the candidate went negative in week 5

neg.dem_1

the proportion of advertisements that were negative in week 1 polls

neg.dem_2

the proportion of advertisements that were negative in week 2 polls

neg.dem_3

the proportion of advertisements that were negative in week 3 polls

neg.dem_4

the proportion of advertisements that were negative in week 4 polls

neg.dem_5

the proportion of advertisements that were negative in week 5 polls

undother_1

share of undecided voters in week 1 polls

undother_2

share of undecided voters in week 2 polls

undother_3

share of undecided voters in week 3 polls

undother_4

share of undecided voters in week 4 polls

undother_5

share of undecided voters in week 5 polls

cum_neg

the total number of campaign-weeks in which a candidate went negative

ave_neg

the average proportion of advertisements that were negative over the final five weeks of the campaign multiplied by ten

References

Blackwell, Matthew. 2013. A Framework for Dynamic Causal Inference in Political Science. American Journal of Political Science 57(2): 504-619.

Function for Generating Minimum Entropy Weights Subject to a Set of Balancing Constraints

Description

eb2 is an adaptation of eb that generates minimum entropy weights subject to a set of balancing constraints. Using the method of Lagrange multipliers, the dual problem is an unconstrained optimization problem that can be solved using Newton's method. When a full Newton step is excessive, an exact line search is used to find the best step size.

Usage

eb2(C, M, Q, Z = rep(0, ncol(C)), max_iter = 200, tol = 1e-04, print_level = 1)

Arguments

Value

A list containing the results from the algorithm.

Data on Public Support for War in a Sample of US Respondents

Description

A dataset containing 17 variables on the views of 1,273 US adults about their support for war against countries that were hypothetically developing nuclear weapons. The data include several variables on the country's features and respondents' demographic and attitudinal characteristics (Tomz and Weeks 2013; Zhou and Wodtke 2020).

Usage

peace

Format

A data frame with 1,273 rows and 17 columns:

threatc

number of adverse events respondents considered probable if the US did not engage in war

ally

a dummy variable indicating whether the country had signed a military alliance with the US

trade

a dummy variable indicating whether the country had high levels of trade with the US

h1

an index measuring respondent's attitude toward militarism

i1

an index measuring respondent's attitude toward internationalism

p1

an index measuring respondent's identification with the Republican party

e1

an index measuring respondent's attitude toward ethnocentrism

r1

an index measuring respondent's attitude toward religiosity

male

a dummy variable indicating whether the respondent is male

white

a dummy variable indicating whether the respondent is white

age

respondent's age

ed4

respondent's education with categories ranging from high school or less to postgraduate degree

democ

a dummy variable indicating whether the country was a democracy

strike

a measure of support for war on a five-point scale

cost

number of negative consequences anticipated if the US engaged in war

successc

whether the respondent thought the operation would succeed. 0: less than 50-50 chance of working even in the short run; 1: efficacious only in the short run; 2: successful both in the short and long run

immoral

a dummy variable indicating whether respondents thought it would be morally wrong to strike the country

References

Tomz, Michael R., and Jessica L. P. Weeks. 2013. Public Opinion and the Democratic Peace. The American Political Science Review 107(4):849-65.

Zhou, Xiang, and Geoffrey T. Wodtke. 2020. Residual Balancing: A Method of Constructing Weights for Marginal Structural Models. Political Analysis 28(4):487-506.

Residual Balancing Weights for Causal Mediation Analysis

Description

rbwMed is a function that produces residual balancing weights for estimating controlled direct/mediator effects in causal mediation analysis. The user supplies a (optional) set of baseline confounders and a list of model objects for the conditional mean of each post-treatment confounder given the treatment and baseline confounders. The weights can be used to fit marginal structural models for the joint effects of the treatment and a mediator on an outcome of interest.

Usage

rbwMed(
  treatment,
  mediator,
  zmodels,
  data,
  baseline_x,
  interact = FALSE,
  base_weights,
  max_iter = 200,
  tol = 1e-04,
  print_level = 1
)

Arguments

Value

A list containing the results.

Examples

# models for post-treatment confounders
m1 <- lm(threatc ~ ally + trade + h1 + i1 + p1 + e1 + r1 +
  male + white + age + ed4 + democ, data = peace)

m2 <- lm(cost ~ ally + trade + h1 + i1 + p1 + e1 + r1 +
  male + white + age + ed4 + democ, data = peace)

m3 <- lm(successc ~ ally + trade + h1 + i1 + p1 + e1 + r1 +
  male + white + age + ed4 + democ, data = peace)

# residual balancing weights
rbwMed_fit <- rbwMed(treatment = democ, mediator = immoral,
  zmodels = list(m1, m2, m3), interact = TRUE,
  baseline_x = c(ally, trade, h1, i1, p1, e1, r1, male, white, age, ed4),
  data = peace)

# attach residual balancing weights to data
peace$rbw_cde <- rbwMed_fit$weights

# fit marginal structural model
if(require(survey)){
  rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw_cde, data = peace)
  msm_rbwMed <- svyglm(strike ~ democ * immoral, design = rbw_design)
  summary(msm_rbwMed)
}

Residual Balancing Weights for Analyzing Time-varying Treatments

Description

rbwPanel is a function that produces residual balancing weights (rbw) for estimating the marginal effects of time-varying treatments. The user supplies a long format data frame (each row being a unit-period) and a list of fitted model objects for the conditional mean of each post-treatment confounder given past treatments and past confounders. The residuals of each time-varying confounder are balanced across both the current treatment A_t and the regressors of the confounder model. In addition, when future > 0, the residuals are also balanced across future treatments A_{t+1},\ldots A_{t + future}.

Usage

rbwPanel(
  treatment,
  xmodels,
  id,
  time,
  data,
  base_weights,
  future = 1L,
  max_iter = 200,
  tol = 1e-04,
  print_level = 1
)

Arguments

Value

A list containing the results.

Examples

# models for time-varying confounders
m1 <- lm(dem.polls ~ (d.gone.neg.l1 + dem.polls.l1 + undother.l1) * factor(week),
data = campaign_long)
m2 <- lm(undother ~ (d.gone.neg.l1 + dem.polls.l1 + undother.l1) * factor(week),
data = campaign_long)

xmodels <- list(m1, m2)

# residual balancing weights
rbwPanel_fit <- rbwPanel(treatment = d.gone.neg, xmodels = xmodels, id = id,
time = week, data = campaign_long)

summary(rbwPanel_fit$weights)

# merge weights into wide-format data
campaign_wide2 <- merge(campaign_wide, rbwPanel_fit$weights, by = "id")

# fit a marginal structural model (adjusting for baseline confounders)
if(require(survey)){
  rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw, data = campaign_wide2)
  msm_rbwPanel <- svyglm(demprcnt ~ cum_neg * deminc + camp.length + factor(year) + office,
  design = rbw_design)
  summary(msm_rbwPanel)
}

Residual Balancing Weights for Estimating the Average Treatment Effect (ATE) in a Point Treatment Setting

Description

rbwPoint is a function that produces residual balancing weights in a point treatment setting. It takes a set of baseline confounders and computes the residuals for each confounder by centering it around its sample mean. The weights can be used to fit marginal structural models to estimate the average treatment effect (ATE).

Usage

rbwPoint(
  treatment,
  data,
  baseline_x,
  base_weights,
  max_iter = 200,
  tol = 1e-04,
  print_level = 1
)

Arguments

Value

A list containing the results.

Examples

# residual balancing weights
rbwPoint_fit <- rbwPoint(treat, baseline_x = c(log_TotalPop, PercentOver65, log_Inc,
  PercentHispanic, PercentBlack, density,
  per_collegegrads, CanCommute), data = advertisement)

# attach residual balancing weights to data
advertisement$rbw_point <- rbwPoint_fit$weights

# fit marginal structural model
if(require(survey)){
  rbw_design <- svydesign(ids = ~ 1, weights = ~ rbw_point, data = advertisement)
  # the outcome model includes the treatment, the square of the treatment,
  # and state-level fixed effects (Fong, Hazlett, and Imai 2018)
  msm_rbwPoint <- svyglm(Cont ~ treat + I(treat^2) + factor(StFIPS), design = rbw_design)
  summary(msm_rbwPoint)
}