Package 'polimetrics'

Calculating Geographical Diffusion

Description

Calculating Geographical Diffusion

Usage

geog.diffuse(df, id, neighbors, time, status, end = FALSE, keep = FALSE)

Arguments

df	data frame to read in. Data frame should include a variable that is a character list of each observation's neighbors.
id	the grouping variable, usually states or counties
neighbors	a variable that is a character list of each observation's neighbors. The elements of the character list of neighbors should be separated by commas.
time	the time variable, at which observations are measured.
status	binary, user-defined measure of the status of policy or event in a state in a given year. 0 equates to policy has not yet occurred in the year, for the state, 1 equates to policy event has already been adopted in the year, for the state – a value of 1 should exist for a state in the year it was adopted and every year thereafter). The example below relies on ERA ratification data from Soule and King (2006) <doi:10.1086/499908>, merged with ideology data from Berry et al. (1998) <doi:10.2307/2991759>, but the user should include the measure of adoption of their choice.
end	logical (default set to F). When set to end = T, will calculate the percent of neighbors that had adopted policy by year-end. Otherwise, will calculate based on number of neighbors that had adopted the policy at year-start.
keep	logical (default set to F). When set to end = T, will include additional variables (number of neighbors and number of neighbors that had adopted the policy) in the updated data frame.

Value

This function updates the data frame with a new variable capturing the geographical diffusion score.

References

Berry, William D., Ringquist, Evan J., Fording, Richard C., and Hanson, Russell L. (1998) 'Measuring Citizen and Government Ideology in the American States, 1960-93.' American Journal of Political Science 42:327-348. doi:10.2307/2991759.
Soule, Sarah A., and King, Brayden G. (2006) 'The Stages of the Policy Process and the Equal Rights Amendment, 1972-1982.' American Journal of Sociology 111:1871-1909. doi:10.1086/499908.

This function calculates the percent (or proportion) of geographically contiguous neighbors that have engaged in some event (e.g. policy adoption) in a given year. This function can be applied to any unit of analysis and time level for any type of event.

Examples

data <- Ideology_ERA

geog.diffuse(data, state, neighbors, year, era_status)

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Calculating Ideological Distance

Description

Calculating Ideological Distance

Usage

ideo.dist(df, id, ideology, time, adoption)

Arguments

df	data frame to read in. This should be an adapted version of the Ideology data set provided in the package. The adapted version should include an outcome variable measuring the policy adoption of choice.
id	the grouping variable, usually states
ideology	the state's ideology score variable (either state or citizen ideology) in a given year. These data come from Richard C. Fording (https://rcfording.com/state-ideology-data/) as used in Berry et al. (1998), and are measured, for each state, from 1960 to 2018.
time	the time variable, at which the ideology score is measured. These data come from Richard C. Fording (https://rcfording.com/state-ideology-data/) as used in Berry et al. (1998), and are measured, for each state, from 1960 to 2018.
adoption	binary, user-defined measure of policy adoption in a state in a given year. 0 equates to policy not adopted in the year, for the state, 1 equates to policy is adopted in the year, for the state – a value of 1 should only exist for a state in the year it was adopted (e.g. not every year thereafter). The example below relies on ERA ratification data from Soule and King (2006), but the user should include the measure of adoption of their choice.

Value

This function updates the data frame with a new variable capturing the ideological distance score.

References

Grossback, Lawrence J., Nicholson-Crotty, Sean, and Peterson, David A.M. (2004) 'Ideology and Learning in Policy Diffusion.' American Politics Research 32:521-545. doi:10.1177/1532673X04263801.
Cruz-Aceves, Victor D., and Mallinson, Daniel J. (2019) 'Clarifying the Measurement of Relative Ideology in Policy Diffusion Research.' State and Local Government Review 51:179-186. doi:10.1177/0160323X20902818.
Berry, William D., Ringquist, Evan J., Fording, Richard C., and Hanson, Russell L. (1998) 'Measuring Citizen and Government Ideology in the American States, 1960-93.' American Journal of Political Science 42:327-348. doi:10.2307/2991759.
Soule, Sarah A., and King, Brayden G. (2006) 'The Stages of the Policy Process and the Equal Rights Amendment, 1972-1982.' American Journal of Sociology 111:1871-1909. doi:10.1086/499908.

This function calculates ideological distance scores based on the calculation created by Grossback et al. (2004) and clarified by Cruz-Aceves and Mallinson (2019). This calculation is based on state ideology data (by year) provided by Richard C. Fording (https://rcfording.com/state-ideology-data/) and used in Berry et al. (1998). This function can be applied to any unit of analysis and time level for any type of policy adoption.

Examples

data <- Ideology_ERA

ideo.dist(data, state, s_ideo, year, era_ratified)

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Fording's State Ideology Data

Description

This data set comes from Richard C. Fording (https://rcfording.com/state-ideology-data/) and used in Berry et al. (1998). The data set includes state ideology data (measured at the state/legislature and citizen levels), for each year between 1960 and 2018. These data will be updated as Fording updates the data.

Usage

Ideology

Format

A data frame with 3050 observations and 4 variables.

state	state name
year	year measured
c_ideo	citizen ideology score
s_ideo	state level ideology score

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Fording's State Ideology Data (adapted, with E.R.A. status)

Description

This data set comes from Richard C. Fording (https://rcfording.com/state-ideology-data/) and used in Berry et al. (1998). The data set includes state ideology data (measured at the state/legislature and citizen levels), for each year between 1960 and 2018. These data will be updated as Fording updates the data. This data set enables inclusion of a variable measuring state-level policy adoption by year. As an example, the data set also include a variable measuring the ratification of the Equal Rights Amendment as depicted in Soule and King (2006).

Usage

Ideology_ERA

Format

A data frame with 300 observations and 5 variables.

state	state name
year	year measured
c_ideo	citizen ideology score
s_ideo	state level ideology score
era_status	measures the the event: adoption/ratification of the Equal Rights Amendment for a state in a given year. 0 equates to not ratified in state in that year, 1 equates to ratified in state in that year
neighbors	list of neighboring states for each observation. Elements (states) comma-delimited

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US State Neighbor List

Description

This data set provides a list (as a character string) of neighboring states for each U.S. state.

Usage

State_Neighbors

Format

A data frame with 50 observations and 2 variables.

state	state name
neighbors	character string of neighboring states (separated by ',') for each state observation

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US Counties Information for Merging

Description

This data set provides common names and abbreviations for U.S. counties to enable merging with various data sets.

Usage

US_Counties

Format

A data frame with 3104 observations and 8 variables.

countystate	proper county name and state name
state_name	proper state name
county_name	proper county name
county_fips	county FIPS
state_abbv	abbreviated state name
state_name_cap	capitalized state name
state_name_cap_nominate	capitalized state name, shortened (as in DW-NOMINATE data)
state_fips	state FIPS

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US States Information for Merging

Description

This data set provides common names and abbreviations for U.S. states to enable merging with various data sets.

Usage

US_States

Format

A data frame with 50 observations and 5 variables.

state_name	proper state name
state_abbv	abbreviated state name
state_name_cap	capitalized state name
state_name_cap_nominate	capitalized state name, shortened (as in DW-NOMINATE data)
state_fips	state FIPS

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