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* This do-file replicates a research claim from Andrews & Money (2009)
* "The Spatial Structure of Party Competition: Party Dispersion within a Finite Policy Space"
* H*: On the economic policy dimension, the number of parties in the party system is positively associated with policy dispersion.
*
*****************************************
version 16.1
clear all
cd "..." /*Change directory*/
*Start a log file
local log_name "Andrews-Money_Replication" /*Give a name to the log where session is recorded*/
log using `log_name', replace
*First, drop duplicates observations in "CPDS_final.dta"
use "CPDS_final.dta", clear
duplicates drop
save "CPDS_temp.dta", replace
*Merge the Comparative Manifesto Project (CMP) and the Comparative Political (CPDS) datasets generated by the file "ANDREWS code.R"
use "CMP_final.dta", clear
duplicates drop //Drop duplicates observations
rename countryname country
gen year = year(edate)
tostring year, replace
merge m:1 country year using "CPDS_temp.dta"
erase "CPDS_temp.dta" //Delete intermediate file
keep if _merge == 3
drop _merge
destring year, replace
*Generate an id for each country-election pair
preserve
keep country edate
duplicates drop
sort country edate
by country: gen election = _n
save "election_identifier.dta", replace
restore
*The replication uses all available observations
****************** Focal Independent Variable: Count of parties in system ******************
/*"To be included in our measure, a party must have gained at least 1 per cent of the seats
in parliament in at least two consecutive elections. If a party fails to gain 1 per cent of the
seats in three subsequent (and consecutive) elections, we drop it from our measure.
According to this criterion, the number of parties in a party system is assessed on an
election by election basis, and it can, in theory, change with each election." (page 810)*/
merge m:1 country edate using "election_identifier.dta"
erase "election_identifier.dta" //Delete intermediate file
xtset party election
*Estimate the percentage of seats in parliament for each party
gen relative_seat = absseat/totseats
*Generate dummy equal to 1 if the party have gained at least 1 per cent of the seats in parliament in at least two consecutive elections.
by party: gen relative_seat_t_1 = relative_seat[_n+1]
gen consecutive_temp = relative_seat>0.01 & relative_seat_t_1>0.01
by party: egen consecutive = max(consecutive_temp)
keep if consecutive == 1
drop consecutive consecutive_temp
*Drop a party if it fails to gain 1 per cent of the seats in three subsequent (and consecutive) elections
by party: gen relative_seat_t_2 = relative_seat[_n+2]
gen consecutive_temp = relative_seat>0.01 & relative_seat_t_1>0.01 & relative_seat_t_2>0.01
by party: egen consecutive = max(consecutive_temp)
keep if consecutive == 1
drop consecutive consecutive_temp
*Number of parties in the system
bysort country election: gen count_parties = _N
sum count_parties, detail
/*"Our two variables of interest – dispersion and number of parties in the party system
– are both skewed. Therefore, we take the natural log of each
variable and use these transformed variables in our regression analysis." (page 821)*/
replace count_parties = log(count_parties)
*********** Dependent Variable: Distance between Extreme Parties (Dispersion) ***********
/*Dependent Variable: Distance between Extreme Parties (Dispersion) along Economic Dimension
"Our numerical estimates of party positions along each of our two dimensions correspond
to the scores of the first component of a principal components analysis on each set
of coding categories for the twenty countries included in our study. The end result of
our analysis is an estimated policy position on the economic and social dimensions for
each party in each election year from 1945 to 1999." (page 814)
"The original Comparative Manifesto Project coding categories that are included in our economic
policy dimension are: 303 (government and administrative efficiency), 401 (free enterprise), 402 (incentives),
403 (market regulation), 404 (economic planning), 407 (protectionism, negative), 412 (controlled
economy), 413 (nationalization), 414 (economic orthodoxy), 504 (welfare state expansion), 505 (welfare
state limitation), 701 (labor groups, positive)." (page 814)
"To measure party dispersion, we calculate the distance between the policy positions of
the two most extreme parties in the party system along both the economic and social
policy dimensions, which results in a measure of dispersion for economic and social
policy. We record dispersion for each party system depicted in Figure 1. The units of
dispersion are determined by the principal components analysis and are comparable
across party systems. (page 815-816)"
*/
*First, implement the principal components analysis
pca per303 per401 per402 per403 per404 per407 per412 per413 per414 per504 per505 per701
predict economic_policy, score //Predict principal component (factor) score for the first component for each party
*Second, identify the most extreme parties in each country-election pair
bysort country election: egen min = min(economic_policy)
bysort country election: egen max = max(economic_policy)
*Generate the distance between the policy positions of the two most extreme parties
gen dispersion = max - min
sum dispersion, detail
/*"Our two variables of interest – dispersion and number of parties in the party system
– are both skewed. Therefore, we take the natural log of each
variable and use these transformed variables in our regression analysis." (page 821)*/
replace dispersion = log(dispersion)
************************ Other controls ************************
*1) Single member district systems
/*In Models 1 (a & b) and 2 (a & b), we evaluate the effect of number of parties
in the system, as measured by count of parties and by effective number of parties, and
electoral rules, as measured by a dummy variable coded 1 if the country has a single
member district electoral system, on dispersion. (page 821)*/
gen single_member = (prop == 0)
replace single_member = . if prop == .
*2) Lagged log dependent variable
/*"Because past dispersion is likely to affect current dispersion, we
include the lagged dependent variable in each regression." (page 821)*/
*Keep an unique observation for country-election
keep dispersion count_parties country edate single_member year election data_sample
duplicates drop
*Generate a numeric id for the country
egen id_country = group(country)
*Declare panel
xtset id_country election
*Generate lagged log dependent variable
by id_country: gen lagged_dispersion = L.dispersion
************************ Test of the SCORE claim, H* ************************
/*In Models 1 (a & b) and 2 (a & b), we evaluate the effect of number of parties
in the system, as measured by count of parties and by effective number of parties, and
electoral rules, as measured by a dummy variable coded 1 if the country has a single
member district electoral system, on dispersion. (page 821)
"In addition, to account for country-specific variance, we cluster our observations by country" (page 821)*/
*Focal replication analysis
*Uses all the available observations
reg dispersion count_parties single_member lagged_dispersion, cluster(id_country)
*Close log
log close
*Create PDF from log
translate `log_name'.smcl `log_name'.pdf, replace
display `End of Do-file' |