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15/paper.pdf

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15/replication_package/Codebook_Nov16.pdf

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15/replication_package/Molina-Garzon et al.2020_Nov16.do

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+*************************************************************************************************
+********************************* Molina-Garzon, Grillos, Zarychta and Andersson  *********************
+***************************************  Public Good Game **************************************
+*************************************************************************************************
+
+** Table 2. Results with clustered standard errors by individual
+/* The following commands construct table 3. Names of the variables are adjusted manually 
+*/
+
+global yourlocation "/Users/adrianamolina/Documents/CU-Boulder/2017-Fall/QualifierP/Data_Analysis"
+use `"${yourlocation}/publicgoodgame_dataAug27.dta"'
+
+global xlistfinal decentralized i.p_type communication round lag_groupcontribution num_players knownpeople Q2_female Q1_Edad Q2_Educacion Q3_YrsSalud Q5_Trust1Base
+global xlistfinal2 i.decentralized##i.communication i.p_type round lag_groupcontribution num_players knownpeople Q2_female Q1_Edad Q2_Educacion Q3_YrsSalud Q5_Trust1Base
+
+quiet reg contribution $xlistfinal [pweight = weights_games_full_scaled], vce(cluster publicid)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/table3.doc"', ctitle(Main Model) addstat(AIC, `AIC') replace
+quiet reg contribution $xlistfinal2 [pweight = weights_games_full_scaled], vce(cluster publicid)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/table3.doc"', ctitle(Model with Communication Interaction) addstat(AIC, `AIC') append
+
+
+** Figure 3. Decentralization is Associated with Increased Cooperation by Public Servants when they are able to Communicate with Each Other
+
+twoway (rcap highconf_ols_2 lowconf_ols_2 round if decentralized==1, lcolor(black) legend(label(1 "CI"))) (line outcome_ols_2 round if decentralized==1, xlabel(1(1)10) lpattern(solid) lcolor(black) legend(label(2 "Decentralized municipalities"))) ///
+(rcap highconf_ols_2 lowconf_ols_2 round if decentralized==0, lcolor(gray) legend(label(3 "CI"))) (line outcome_ols_2 round if decentralized==0, lpattern(dash) lcolor(gray) legend(label(4 "Centrally-admin. municipalities"))), ///
+graphregion(fcolor(white) ifcolor(white))
+
+graph save Graph `"${yourlocation}/Figure3_cooperation by admin type.gph"'
+
+
+***** Supplementary appendix
+
+*Table SA1. Main cooperation model with reduced sample weights
+quiet metobit contribution $xlistfinal [pweight = weights_games_reduced_scaled] || publicid_muni: || publicid:, ul(10) ll(0)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA3.doc"', ctitle(Model A) addstat(AIC, `AIC') replace 
+quiet metobit contribution $xlistfinal2 [pweight = weights_games_reduced_scaled] || publicid_muni: || publicid:, ul(10) ll(0)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA3.doc"', ctitle(Model B) addstat(AIC, `AIC') append
+quiet reg contribution $xlistfinal [pweight = weights_games_reduced_scaled], vce(cluster publicid)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA3.doc"', ctitle(Model C) addstat(AIC, `AIC') append
+quiet reg contribution $xlistfinal2 [pweight = weights_games_reduced_scaled], vce(cluster publicid)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA3.doc"', ctitle(Model D) addstat(AIC, `AIC') append
+
+
+*Table SA3. Alternative cooperation model, Multilevel Tobit specification
+
+quiet metobit contribution $xlistfinal [pweight = weights_games_full_scaled] || publicid_muni: || publicid:, ul(10) ll(0)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA5.doc"', ctitle(Model A) addstat(AIC, `AIC') replace 
+quiet metobit contribution $xlistfinal2 [pweight = weights_games_full_scaled] || publicid_muni: || publicid:, ul(10) ll(0)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA5.doc"', ctitle(Model B) addstat(AIC, `AIC') append
+
+
+* Figure SA1: Raw data distribution for public good game results
+sort decentralized round
+twoway (line averagecont round if decentralized==1, ylabel(0(1)10) xlabel(1(1)10) lcolor(black) lpattern(solid) legend(label(1 "Decentralized municipalities"))) (line averagecont round if decentralized==0, lcolor(gray) lpattern(dash) legend(label(2 "Centrally-admin. municipalities"))), /// 
+graphregion(fcolor(white) ifcolor(white))
+
+graph save Graph `"${yourlocation}/FigureSA3.gph"'
+
+* SA I- Table SA 15. Analysis by types of intermediary organizations
+
+global xlistfinal4 i.dec_orgtype i.p_type communication round lag_groupcontribution num_players knownpeople Q2_female Q1_Edad Q2_Educacion Q3_YrsSalud Q5_Trust1Base
+global xlistfinal5 i.dec_orgtype##i.communication i.p_type round lag_groupcontribution num_players knownpeople Q2_female Q1_Edad Q2_Educacion Q3_YrsSalud Q5_Trust1Base
+
+quiet metobit contribution $xlistfinal4 [pweight = weights_games_full_scaled] || publicid_muni: || publicid:, ul(10) ll(0)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/table-SA17.doc"', ctitle(Model A) addstat(AIC, `AIC') replace 
+quiet metobit contribution $xlistfinal5 [pweight = weights_games_full_scaled] || publicid_muni: || publicid:, ul(10) ll(0)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/table-SA17.doc"', ctitle(Model B) addstat(AIC, `AIC') append
+quiet reg contribution $xlistfinal4 [pweight = weights_games_full_scaled], vce(cluster publicid)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/table-SA17.doc"', ctitle(Model C) addstat(AIC, `AIC') append
+quiet reg contribution $xlistfinal5 [pweight = weights_games_full_scaled], vce(cluster publicid)
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/table-SA17.doc"', ctitle(Model D) addstat(AIC, `AIC') append
+
+
+* Table SA 16. Regression analysis explaining perceived improvements in health outcomes
+* This table requires a different dataset
+
+clear
+
+global yourlocation "/Users/adrianamolina/Documents/CU-Boulder/2017-Fall/QualifierP/Data_Analysis"
+use `"${yourlocation}/healthperception_Aug2020"'
+
+quiet reg HealthChange_Mun contribution [pweight = weights_games_full_scaled]
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA18.doc"', ctitle(Model A) addstat(AIC, `AIC') replace 
+quiet reg HealthChange_Mun decentralized [pweight = weights_games_full_scaled]
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA18.doc"', ctitle(Model B) addstat(AIC, `AIC') append 
+quiet reg HealthChange_Mun contribution decentralized [pweight = weights_games_full_scaled]
+quiet estat ic
+mat es_ic = r(S)
+local AIC : display %4.1f es_ic[1,5]
+outreg2 using `"${yourlocation}/tableSA18.doc"', ctitle(Model C) addstat(AIC, `AIC') append 
+
+* Figure SA 4. Contribution to the public good game and perception of change in the health provision during the past five years 
+
+set scheme s1mono
+twoway (scatter HealthChange_Mun0 contribution) (scatter HealthChange_Mun1 contribution) ///
+	(lfit HealthChange_Mun contribution), ///
+	ytitle(Health change in last 5 years) ///
+	legen(order(1 "Centralized admin." 2 "Decentralized admin." 3 "Fit line"))
+
+
+graph save Graph `"${yourlocation}/FigureSA6.gph"'
+
+
+

15/replication_package/README_MolinaGarzon et al.2020_Nov16.pdf

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15/replication_package/clanalysis_anondata_vNov16.csv

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15/replication_package/clanalysis_replication_vNov16.R

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+#####Replication File for cross-level ties analysis in,  "Decentralization can Increase Cooperation Among Public Officials"
+
+#Adriana Molina-Garz?n, University of Colorado Boulder, adriana.molinagarzon@colorado.edu
+#Tara Grillos, Purdue University, tgrillos@purdue.edu
+#Alan Zarychta, University of Chicago, azarychta@uchicago.edu
+#Krister P. Andersson, University of Colorado Boulder, Institute of Behavioral Science, krister.andersson@colorado.edu
+
+
+
+
+
+#Suggested citation for replication data:
+
+#Molina-Garz?n A., Grillos T., Zarychta A., and Andersson K.P., 2020, "Replication Data for: Decentralization can Increase Cooperation Among Public Officials", https://doi.org/10.7910/DVN/ZLHYSZ .  
+
+
+#Suggested citations for study design and full original data collection:
+
+#Zarychta, A., Andersson, K.P., Root, E. D., Menken, J., & Grillos, T. (2019a). Assessing the impacts of governance reforms on health services delivery: A quasi-experimental, multi-method, and participatory approach. Health Services and Outcomes Research Methodology, 19(4), 241-258. https://doi.org/10.1007/s10742-019-00201-8
+
+#Zarychta, A, Andersson, KP, Root, ED, Menken J, Grillos T. (2019b). Supplemental Appendix for "Assessing the impacts of governance reforms on health services delivery: a quasi-experimental, multi-method, and participatory approach." Health Services and Outcomes Research Methodology, 19(4), https://static-content.springer.com/esm/art%3A10.1007%2Fs10742-019-00201-8/MediaObjects/10742_2019_201_MOESM1_ESM.docx
+
+
+
+
+
+##### Computing Environment
+
+##R version 3.6.1 (2019-07-05) -- "Action of the Toes"
+##Copyright (C) 2019 The R Foundation for Statistical Computing
+##Platform: x86_64-w64-mingw32/x64 (64-bit)
+
+
+
+
+
+##### Packages Needed
+
+install.packages("PACKAGE NAME HERE") #to download packages if needed
+
+library(sandwich)
+library(lmtest)
+library(zoo)
+library(texreg)
+library(multiwayvcov)
+library(MASS)
+library(plyr)
+library(Hmisc)
+library(reporttools)
+library(readstata13)
+library(plyr)
+library(survey)
+library(tableone)
+
+
+
+
+
+##### Additional Functions
+
+
+#clustered standard errors
+
+clse.f <- function(dat,fm, cluster){  
+  require(sandwich) 
+  require(lmtest)  
+  not <- attr(fm$model,"na.action") 
+  if( ! is.null(not)){   
+    cluster <- cluster[-not]    
+    dat <- dat[-not,]   
+  }
+  
+  with(dat,{   
+    M <- length(unique(cluster))   
+    N <- length(cluster)  
+    K <- fm$rank  
+    dfc <- (M/(M-1))*((N-1)/(N-K))  
+    uj <- apply(estfun(fm),2, function(x) tapply(x, cluster, sum));   
+    vcovCL <- dfc*sandwich(fm, meat=crossprod(uj)/N)  
+    coeftest(fm, vcovCL)   
+  }       
+  )  
+}
+
+
+#CIs for bar graphs
+
+error.bar <- function(x, y, upper, lower, length=0.1,...){
+  if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper))
+    stop("vectors must be same length")
+  arrows(x,y+upper, x, y-lower, angle=90, code=3, length=length, ...)
+}
+
+
+
+
+
+##### Global Options
+
+options(scipen=999)
+options(digits=6)
+setwd("C:/FOLDER LOCATION WHERE DATA FILE IS SAVED GOES HERE/...")
+
+
+
+
+
+##### Load data file
+
+data <- read.csv("clanalysis_anondata_vNov16.csv")
+names(data)
+
+
+
+
+
+##### Main Paper
+
+
+
+
+
+##### Table 1:  Weighted descriptive statistics by administration form for all participants
+
+data.pg <- read.dta13("publicgoodgame_dataAug27.dta")
+data.pg <- data.pg[which(data.pg$contribution>=0), ]
+
+
+#collapse individual-round data to individual
+
+data.agg <- aggregate(data.pg[c("num_players", "knownpeople", "Q5_Trust1Base")], by=list(data.pg$publicid), FUN=mean)
+names(data.agg)[names(data.agg)=="Group.1"] <- "publicid"
+
+data.mrg <- merge(data, data.agg, by="publicid", all.y=FALSE)
+
+
+#collapse individual data to municipality
+
+data.mun <- aggregate(data.mrg[c("decentralized", "num_players")], by=list(data.mrg$publicid_muni), FUN=max)
+names(data.mun)[names(data.mun)=="Group.1"] <- "publicid_muni"
+
+data.munw <- aggregate(data.mrg[c("weights_games_full_scaled")], by=list(data.mrg$publicid_muni), FUN=sum)
+names(data.munw)[names(data.munw)=="Group.1"] <- "publicid_muni"
+
+data.mun <- merge(data.mun, data.munw, by="publicid_muni", all.y=FALSE)
+
+
+#make table
+
+
+#rows 1 and 2 of the table
+
+table(data.mun$decentralized)
+table(data.mrg$decentralized)
+
+
+#remaining rows of the table (excluding p-value column)
+
+vars <- c("Mujer", "Q2_Educacion", "Q1_Edad", "Q3_YrsSalud", "CargoAdministrador", "CargoMedico" , "CargoEnfermero" , "CargoPromotor" , "CargoAlcaldia", "num_players" , "knownpeople" , "Q5_Trust1Base")
+
+names(data.mrg)
+data.test <- svydesign(ids = ~ 1, data = data.mrg, weights = ~ data.mrg$weights_games_full_scaled)
+tab.test <- svyCreateTableOne(vars = vars, strata = "decentralized", data = data.test, test = FALSE)
+addmargins(table(ExtractSmd(tab.test) > 0.25))
+tab.test <- print(tab.test, smd = TRUE)
+tab.test <- tab.test[-1,]
+xtable(tab.test, caption=c("Weighted All Participants Sample Balance Table by Decentralized"))
+
+
+#p-value column for all relevant rows of the table
+
+diffmeans.mujer <- lm(Mujer~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.mujer)
+diffmeans.mujer.cse <- clse.f(data.mrg, diffmeans.mujer, data.mrg$publicid_muni)
+diffmeans.mujer.cse
+diffmeans.mujer.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.educ <- lm(Q2_Educacion~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.educ)
+diffmeans.educ.cse <- clse.f(data.mrg, diffmeans.educ, data.mrg$publicid_muni)
+diffmeans.educ.cse
+diffmeans.educ.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.edad <- lm(Q1_Edad~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.edad)
+diffmeans.edad.cse <- clse.f(data.mrg, diffmeans.edad, data.mrg$publicid_muni)
+diffmeans.edad.cse
+diffmeans.edad.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.yrssalud <- lm(Q3_YrsSalud~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.yrssalud)
+diffmeans.yrssalud.cse <- clse.f(data.mrg, diffmeans.yrssalud, data.mrg$publicid_muni)
+diffmeans.yrssalud.cse
+diffmeans.yrssalud.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.tab <- lm(CargoAdministrador~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.tab)
+diffmeans.tab.cse <- clse.f(data.mrg, diffmeans.tab, data.mrg$publicid_muni)
+diffmeans.tab.cse
+diffmeans.tab.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.tab <- lm(CargoMedico~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.tab)
+diffmeans.tab.cse <- clse.f(data.mrg, diffmeans.tab, data.mrg$publicid_muni)
+diffmeans.tab.cse
+diffmeans.tab.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.tab <- lm(CargoEnfermero~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.tab)
+diffmeans.tab.cse <- clse.f(data.mrg, diffmeans.tab, data.mrg$publicid_muni)
+diffmeans.tab.cse
+diffmeans.tab.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.tab <- lm(CargoPromotor~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.tab)
+diffmeans.tab.cse <- clse.f(data.mrg, diffmeans.tab, data.mrg$publicid_muni)
+diffmeans.tab.cse
+diffmeans.tab.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.tab <- lm(CargoAlcaldia~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.tab)
+diffmeans.tab.cse <- clse.f(data.mrg, diffmeans.tab, data.mrg$publicid_muni)
+diffmeans.tab.cse
+diffmeans.tab.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.num <- lm(num_players~decentralized, data=data.mun, weights=weights_games_full_scaled)
+summary(diffmeans.num)
+diffmeans.num.sum <- summary(diffmeans.num)
+diffmeans.num.sum[[5]][2,4] #pvalue on decentralized
+
+
+diffmeans.known <- lm(knownpeople~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.known)
+diffmeans.known.cse <- clse.f(data.mrg, diffmeans.known, data.mrg$publicid_muni)
+diffmeans.known.cse
+diffmeans.known.cse[2,4] #pvalue on decentralized
+
+
+diffmeans.trust <- lm(Q5_Trust1Base~decentralized, data=data.mrg, weights=weights_games_full_scaled)
+summary(diffmeans.trust)
+diffmeans.trust.cse <- clse.f(data.mrg, diffmeans.trust, data.mrg$publicid_muni)
+diffmeans.trust.cse
+diffmeans.trust.cse[2,4] #pvalue on decentralized
+
+
+
+
+
+##### Table 3. How Decentralization Influences Cross-level Network Capital
+
+
+## model proportions by decentralized alone
+
+mod.crosslevel.propknown.base <- glm(net_crosslevel_propnumknown ~ decentralized + offset(log(net_crosslevel_propdenomknown)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.base)
+mod.crosslevel.propknown.base.cse <- clse.f(data, mod.crosslevel.propknown.base, data$publicid_muni)
+mod.crosslevel.propknown.base.cse 
+
+mod.crosslevel.propfriends.base <- glm(net_crosslevel_propnumfriends ~ decentralized + offset(log(net_crosslevel_propdenomfriends)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.base)
+mod.crosslevel.propfriends.base.cse <- clse.f(data, mod.crosslevel.propfriends.base, data$publicid_muni)
+mod.crosslevel.propfriends.base.cse
+
+
+## model proportions by decentralized plus individual characteristics with participant types
+
+mod.crosslevel.propknown.fullpt <- glm(net_crosslevel_propnumknown ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R + offset(log(net_crosslevel_propdenomknown)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.fullpt)
+mod.crosslevel.propknown.fullpt.cse <- clse.f(data, mod.crosslevel.propknown.fullpt, data$publicid_muni)
+mod.crosslevel.propknown.fullpt.cse
+
+mod.crosslevel.propfriends.fullpt <- glm(net_crosslevel_propnumfriends ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R + offset(log(net_crosslevel_propdenomfriends)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.fullpt)
+mod.crosslevel.propfriends.fullpt.cse <- clse.f(data, mod.crosslevel.propfriends.fullpt, data$publicid_muni)
+mod.crosslevel.propfriends.fullpt.cse
+
+
+#crosslevel ties, binary, player type controls table
+
+texreg(list(mod.crosslevel.propknown.base,
+            mod.crosslevel.propknown.fullpt,
+            mod.crosslevel.propfriends.base,
+            mod.crosslevel.propfriends.fullpt), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Prop. Known) by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Prop. Known Base", "Prop. Known Fullpt", "Prop. Friends Base", "Prop. Friends Fullpt"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Player HC (Ref: Player M)", "Player AI (Ref: Player M)", "Player R (Ref: Player M)"),
+       override.se=list(mod.crosslevel.propknown.base.cse[,2],
+                        mod.crosslevel.propknown.fullpt.cse[,2],
+                        mod.crosslevel.propfriends.base.cse[,2],
+                        mod.crosslevel.propfriends.fullpt.cse[,2]),
+       override.pval=list(mod.crosslevel.propknown.base.cse[,4],
+                          mod.crosslevel.propknown.fullpt.cse[,4],
+                          mod.crosslevel.propfriends.base.cse[,4],
+                          mod.crosslevel.propfriends.fullpt.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### Figure 4. Expected Proportion of Strong Cross-level Ties Realized for a Typical Public Servant, Centrally-Administered versus Decentralized Systems
+
+
+## decent_propfriends_fullpt, full strong ties model from Table 4
+
+mod.crosslevel.propfriends.fullpt <- glm(net_crosslevel_propnumfriends ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R + offset(log(net_crosslevel_propdenomfriends)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.fullpt)
+mod.crosslevel.propfriends.fullpt.cse <- clse.f(data, mod.crosslevel.propfriends.fullpt, data$publicid_muni)
+mod.crosslevel.propfriends.fullpt.cse
+
+
+## Simulate Coefficients ##
+# Seed and number of repetitions
+set.seed(19850824)
+m <- 100000
+
+
+# Simulate coefficients from a multivariate normal
+betas <- mod.crosslevel.propfriends.fullpt$coef
+vcv <- cluster.vcov(mod.crosslevel.propfriends.fullpt, data$publicid_muni)
+sim.betas <- mvrnorm(m, betas, vcv)
+
+
+# Compare simulated coefficients with real results
+round(mod.crosslevel.propfriends.fullpt$coef, digits = 2)
+round(head(sim.betas, 10), digits = 2)
+data.frame(sim.means = apply(sim.betas, 2, mean), betas = betas, sim.sd = apply(sim.betas, 2, sd), se = sqrt(diag(vcv)))   
+
+
+# Create hypothetical independent variable profiles
+decent.data <- data.frame(intercept=1, decentralized=1, Mujer = median(na.omit(data$Mujer)), Q2_Educacion = mean(na.omit(data$Q2_Educacion)), Q1_Edad = mean(na.omit(data$Q1_Edad)), Q3_YrsSalud = mean(na.omit(data$Q3_YrsSalud)), gen_trust = mean(na.omit(data$gen_trust)), Participant_C=1, Participant_G=0, Participant_R=0)
+
+centadmin.data <- data.frame(intercept=1, decentralized=0, Mujer = median(na.omit(data$Mujer)), Q2_Educacion = mean(na.omit(data$Q2_Educacion)), Q1_Edad = mean(na.omit(data$Q1_Edad)), Q3_YrsSalud = mean(na.omit(data$Q3_YrsSalud)), gen_trust = mean(na.omit(data$gen_trust)), Participant_C=1, Participant_G=0, Participant_R=0)
+
+
+# Compute the expected counts and confidence intervals using the simulated coefficients
+ec.sim <- matrix(NA, nrow = m, ncol = 1)
+
+for(i in 1:m){
+  ec.sim[i, ] <- exp(as.matrix(decent.data)%*%sim.betas[i, ])
+}
+
+pe.decent <- apply(ec.sim, 2, mean)
+lo.decent <- apply(ec.sim, 2, quantile, prob = .025)
+hi.decent <- apply(ec.sim, 2, quantile, prob = .975)
+
+
+ec.sim <- matrix(NA, nrow = m, ncol = 1)
+
+for(i in 1:m){
+  ec.sim[i, ] <- exp(as.matrix(centadmin.data)%*%sim.betas[i, ])
+}
+
+pe.centadmin <- apply(ec.sim, 2, mean)
+lo.centadmin <- apply(ec.sim, 2, quantile, prob = .025)
+hi.centadmin <- apply(ec.sim, 2, quantile, prob = .975)
+
+
+# Expected values for central admin and decent
+
+pe.decent
+pe.centadmin
+
+admin.pe <- matrix(c(pe.centadmin, pe.decent),2,1,byrow=TRUE)
+
+admin.lo <- matrix(c(lo.centadmin, lo.decent),2,1,byrow=TRUE)
+admin.hi <- matrix(c(hi.centadmin, hi.decent),2,1, byrow=TRUE)
+
+admin.lower <- admin.pe-admin.lo
+admin.upper <- admin.hi-admin.pe
+
+
+# Make barplot
+
+par(mar = c(2.3, 4.3, 1, .1))
+
+bplot.admin <- barplot(admin.pe, beside=TRUE, space=0.3, ylim=c(0,0.4), ylab="Expected Prop. of Strong Cross-level Ties Realized", names.arg=c("Centrally-Admin.", "Decentralized"), cex.lab=1.1, cex.names=1.2, col=c("gray75","gray45"), border=c("gray75","gray45"), args.legend=list(x="top", bty="n", horiz=TRUE, border=c(c("gray75","gray45"))))
+
+error.bar(bplot.admin, admin.pe, admin.upper, admin.lower)
+
+
+dev.off()
+
+
+
+
+
+##### Supplemental Appendix
+
+
+
+
+
+##### Table SA2.  Descriptive Statistics for the Sample of Public Servants Participating in the Public Goods Game
+
+
+#row 1 of the table
+descripvars.cont <- c("contribution")
+tableContinuous(vars=data.pg[descripvars.cont], cap="Descriptive Statisitics, All Participants", prec=2, longtable=FALSE)
+
+
+#all remaining rows except "Number players"
+
+descripvars.mrg <- c("Mujer", "Q2_Educacion", "Q1_Edad", "Q3_YrsSalud", "CargoAdministrador", "CargoMedico" , "CargoEnfermero" , "CargoPromotor" , "CargoAlcaldia", "knownpeople", "Q5_Trust1Base")
+tableContinuous(vars=data.mrg[descripvars.mrg], cap="Descriptive Statisitics, All Participants", prec=2, longtable=FALSE)
+
+
+#"Number players" row
+
+descripvars.mun <- c("num_players")
+tableContinuous(vars=data.mun[descripvars.mun], cap="Descriptive Statisitics, All Participants", prec=2, longtable=FALSE)
+
+
+
+
+
+##### Table SA4.  Descriptive Statistics for Cross-level Network Variables (all levels)
+
+descripvars <- c("net_crosslevel_propknown", "net_crosslevel_propfriends", "net_crosslevel_propnumknown", "net_crosslevel_propnumfriends", "net_crosslevel_hoursrcknown", "net_crosslevel_hoursrcfriends")
+
+tableContinuous(vars=data[descripvars], cap="Descriptive Statisitics for Cross-level Network Variables (all levels)", prec=2, longtable=FALSE)
+
+
+
+
+
+##### Figure SA2.  Histograms of the Cross-level Relational Capital Dependent Variables (all levels)
+
+par(mar = c(4.1, 4, 0, 0.2))
+hist(data$net_crosslevel_propknown, breaks=30, xlab="Proportion of Possible Cross-level Ties Realized", main=NULL)
+
+par(mar = c(4.1, 4, 0, 0.2))
+hist(data$net_crosslevel_propfriends, breaks=30, xlab="Proportion of Possible Cross-level Ties Realized as Strong Ties", main=NULL)
+
+
+
+
+
+##### Table SA5.  Averages of Cross-level Network Variables (all levels)
+
+round(ddply(data, .(decentralized), function(x) data.frame(net_crosslevel_propknown=wtd.mean(x$net_crosslevel_propknown, x$weights_games_full_scaled, na.rm=TRUE))), 2)
+
+round(ddply(data, .(decentralized), function(x) data.frame(net_crosslevel_propfriends=wtd.mean(x$net_crosslevel_propfriends, x$weights_games_full_scaled, na.rm=TRUE))),2)
+
+
+
+
+
+##### Table SA6.  How Decentralization Influences Cross-level Network Capital, Hours, Player Type Controls
+
+
+## model HOURS RC by decentralization alone
+
+mod.crosslevel.hoursrcknown.base <- glm(net_crosslevel_hoursrcknown ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.base)
+mod.crosslevel.hoursrcknown.base.cse <- clse.f(data, mod.crosslevel.hoursrcknown.base, data$publicid_muni)
+mod.crosslevel.hoursrcknown.base.cse
+
+mod.crosslevel.hoursrcfriends.base <- glm(net_crosslevel_hoursrcfriends ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.base)
+mod.crosslevel.hoursrcfriends.base.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.base, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.base.cse
+
+
+## model HOURS RC by decentralized plus individual characteristics with participant types
+
+mod.crosslevel.hoursrcknown.fullpt <- glm(net_crosslevel_hoursrcknown ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.fullpt)
+mod.crosslevel.hoursrcknown.fullpt.cse <- clse.f(data, mod.crosslevel.hoursrcknown.fullpt, data$publicid_muni)
+mod.crosslevel.hoursrcknown.fullpt.cse 
+
+mod.crosslevel.hoursrcfriends.fullpt <- glm(net_crosslevel_hoursrcfriends ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.fullpt)
+mod.crosslevel.hoursrcfriends.fullpt.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.fullpt, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.fullpt.cse 
+
+
+#crosslevel ties, hours, player type controls table
+
+texreg(list(mod.crosslevel.hoursrcknown.base,
+            mod.crosslevel.hoursrcknown.fullpt,
+            mod.crosslevel.hoursrcfriends.base,
+            mod.crosslevel.hoursrcfriends.fullpt), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Hours) by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Hours RC Known Base", "Hours RC Known Full", "Hours RC Friends Base", "Hours RC Friends Full"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Player HC (Ref: Player M)", "Player AI (Ref: Player M)", "Player R (Ref: Player M)"),
+       override.se=list(mod.crosslevel.hoursrcknown.base.cse[,2],
+                        mod.crosslevel.hoursrcknown.fullpt.cse[,2],
+                        mod.crosslevel.hoursrcfriends.base.cse[,2],
+                        mod.crosslevel.hoursrcfriends.fullpt.cse[,2]),
+       override.pval=list(mod.crosslevel.hoursrcknown.base.cse[,4],
+                          mod.crosslevel.hoursrcknown.fullpt.cse[,4],
+                          mod.crosslevel.hoursrcfriends.base.cse[,4],
+                          mod.crosslevel.hoursrcfriends.fullpt.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### Table SA7.  How Decentralization Influences Cross-level Network Capital, Binary, Role Type Controls
+
+
+## model proportions by decentralized alone
+
+mod.crosslevel.propknown.base <- glm(net_crosslevel_propnumknown ~ decentralized + offset(log(net_crosslevel_propdenomknown)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.base)
+mod.crosslevel.propknown.base.cse <- clse.f(data, mod.crosslevel.propknown.base, data$publicid_muni)
+mod.crosslevel.propknown.base.cse 
+
+mod.crosslevel.propfriends.base <- glm(net_crosslevel_propnumfriends ~ decentralized + offset(log(net_crosslevel_propdenomfriends)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.base)
+mod.crosslevel.propfriends.base.cse <- clse.f(data, mod.crosslevel.propfriends.base, data$publicid_muni)
+mod.crosslevel.propfriends.base.cse
+
+
+## model proportions by decentralized plus individual characteristics
+
+mod.crosslevel.propknown.full <- glm(net_crosslevel_propnumknown ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor + offset(log(net_crosslevel_propdenomknown)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.full)
+mod.crosslevel.propknown.full.cse <- clse.f(data, mod.crosslevel.propknown.full, data$publicid_muni)
+mod.crosslevel.propknown.full.cse
+
+mod.crosslevel.propfriends.full <- glm(net_crosslevel_propnumfriends ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor + offset(log(net_crosslevel_propdenomfriends)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.full)
+mod.crosslevel.propfriends.full.cse <- clse.f(data, mod.crosslevel.propfriends.full, data$publicid_muni)
+mod.crosslevel.propfriends.full.cse
+
+
+#crosslevel ties, binary, role type controls table
+
+texreg(list(mod.crosslevel.propknown.base,
+            mod.crosslevel.propknown.full,
+            mod.crosslevel.propfriends.base,
+            mod.crosslevel.propfriends.full), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Prop. Known) by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Prop. Known Base", "Prop. Known Full", "Prop. Friends Base", "Prop. Friends Full"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Doctor", "Nurse", "Municipal Official", "Social Worker"),
+       override.se=list(mod.crosslevel.propknown.base.cse[,2],
+                        mod.crosslevel.propknown.full.cse[,2],
+                        mod.crosslevel.propfriends.base.cse[,2],
+                        mod.crosslevel.propfriends.full.cse[,2]),
+       override.pval=list(mod.crosslevel.propknown.base.cse[,4],
+                          mod.crosslevel.propknown.full.cse[,4],
+                          mod.crosslevel.propfriends.base.cse[,4],
+                          mod.crosslevel.propfriends.full.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,11,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### Table SA8.  How Decentralization Influences Cross-level Network Capital, Hours, Role Type Controls
+
+
+## model proportions by decentralized alone
+
+mod.crosslevel.hoursrcknown.base <- glm(net_crosslevel_hoursrcknown ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.base)
+mod.crosslevel.hoursrcknown.base.cse <- clse.f(data, mod.crosslevel.hoursrcknown.base, data$publicid_muni)
+mod.crosslevel.hoursrcknown.base.cse
+
+mod.crosslevel.hoursrcfriends.base <- glm(net_crosslevel_hoursrcfriends ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.base)
+mod.crosslevel.hoursrcfriends.base.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.base, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.base.cse
+
+
+## model HOURS RC by decentralized plus individual characteristics
+
+mod.crosslevel.hoursrcknown.full <- glm(net_crosslevel_hoursrcknown ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.full)
+mod.crosslevel.hoursrcknown.full.cse <- clse.f(data, mod.crosslevel.hoursrcknown.full, data$publicid_muni)
+mod.crosslevel.hoursrcknown.full.cse 
+
+mod.crosslevel.hoursrcfriends.full <- glm(net_crosslevel_hoursrcfriends ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.full)
+mod.crosslevel.hoursrcfriends.full.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.full, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.full.cse 
+
+
+#crosslevel ties, hours, role type controls table
+
+texreg(list(mod.crosslevel.hoursrcknown.base,
+            mod.crosslevel.hoursrcknown.full,
+            mod.crosslevel.hoursrcfriends.base,
+            mod.crosslevel.hoursrcfriends.full), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Hours) by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Hours RC Known Base", "Hours RC Known Full", "Hours RC Friends Base", "Hours RC Friends Full"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Doctor", "Nurse", "Municipal Official", "Social Worker"),
+       override.se=list(mod.crosslevel.hoursrcknown.base.cse[,2],
+                        mod.crosslevel.hoursrcknown.full.cse[,2],
+                        mod.crosslevel.hoursrcfriends.base.cse[,2],
+                        mod.crosslevel.hoursrcfriends.full.cse[,2]),
+       override.pval=list(mod.crosslevel.hoursrcknown.base.cse[,4],
+                          mod.crosslevel.hoursrcknown.full.cse[,4],
+                          mod.crosslevel.hoursrcfriends.base.cse[,4],
+                          mod.crosslevel.hoursrcfriends.full.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,11,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### Table SA9.  Descriptive Statistics for Cross-level Network Variables (collapsed levels)
+
+descripvars.col <- c("net_crosslevel_propknown_col", "net_crosslevel_propfriends_col", "net_crosslevel_propnumknown_col", "net_crosslevel_propnumfriends_col", "net_crosslevel_hoursrcknown_col", "net_crosslevel_hoursrcfriends_col")
+
+tableContinuous(vars=data[descripvars.col], cap="Descriptive Statisitics for Cross-level Network Variables (collapsed levels)", prec=2, longtable=FALSE)
+
+
+
+
+
+##### Figure SA3.  Histograms of the Cross-level Relational Capital Dependent Variables (collapsed levels)
+
+par(mar = c(4.1, 4, 0, 0.2))
+hist(data$net_crosslevel_propknown_col, breaks=30, xlab="Proportion of Possible Cross-level Ties Realized (Levels Collapsed)", main=NULL)
+
+par(mar = c(4.1, 4, 0, 0.2))
+hist(data$net_crosslevel_propfriends_col, breaks=30, xlab="Proportion of Possible Cross-level Ties Realized as Strong Ties (Levels Collapsed)", main=NULL)
+
+
+
+
+
+##### Table SA 10.  Averages of Cross-level Network Variables (collapsed levels)
+
+round(ddply(data, .(decentralized), function(x) data.frame(net_crosslevel_propknown_col=wtd.mean(x$net_crosslevel_propknown_col, x$weights_games_full_scaled, na.rm=TRUE))), 2)
+
+round(ddply(data, .(decentralized), function(x) data.frame(net_crosslevel_propfriends_col=wtd.mean(x$net_crosslevel_propfriends_col, x$weights_games_full_scaled, na.rm=TRUE))),2)
+
+
+
+
+
+##### Table SA 11.  How Decentralization Influences Cross-level Network Capital (collapsed levels), Binary, Player Type Controls
+
+
+## model proportions by decentralized alone
+
+mod.crosslevel.propknown.base <- glm(net_crosslevel_propnumknown_col ~ decentralized + offset(log(net_crosslevel_propdenomknown_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.base)
+mod.crosslevel.propknown.base.cse <- clse.f(data, mod.crosslevel.propknown.base, data$publicid_muni)
+mod.crosslevel.propknown.base.cse 
+
+mod.crosslevel.propfriends.base <- glm(net_crosslevel_propnumfriends_col ~ decentralized + offset(log(net_crosslevel_propdenomfriends_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.base)
+mod.crosslevel.propfriends.base.cse <- clse.f(data, mod.crosslevel.propfriends.base, data$publicid_muni)
+mod.crosslevel.propfriends.base.cse
+
+
+## model proportions by decentralized plus individual characteristics with participant types
+
+mod.crosslevel.propknown.fullpt <- glm(net_crosslevel_propnumknown_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R + offset(log(net_crosslevel_propdenomknown_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.fullpt)
+mod.crosslevel.propknown.fullpt.cse <- clse.f(data, mod.crosslevel.propknown.fullpt, data$publicid_muni)
+mod.crosslevel.propknown.fullpt.cse
+
+mod.crosslevel.propfriends.fullpt <- glm(net_crosslevel_propnumfriends_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R + offset(log(net_crosslevel_propdenomfriends_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.fullpt)
+mod.crosslevel.propfriends.fullpt.cse <- clse.f(data, mod.crosslevel.propfriends.fullpt, data$publicid_muni)
+mod.crosslevel.propfriends.fullpt.cse
+
+
+#crosslevel ties, binary, player type controls table, collapsed levels
+
+texreg(list(mod.crosslevel.propknown.base,
+            mod.crosslevel.propknown.fullpt,
+            mod.crosslevel.propfriends.base,
+            mod.crosslevel.propfriends.fullpt), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Prop. Known), Collapsed Levels, by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Prop. Known Base", "Prop. Known Fullpt", "Prop. Friends Base", "Prop. Friends Fullpt"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Player HC (Ref: Player M)", "Player AI (Ref: Player M)", "Player R (Ref: Player M)"),
+       override.se=list(mod.crosslevel.propknown.base.cse[,2],
+                        mod.crosslevel.propknown.fullpt.cse[,2],
+                        mod.crosslevel.propfriends.base.cse[,2],
+                        mod.crosslevel.propfriends.fullpt.cse[,2]),
+       override.pval=list(mod.crosslevel.propknown.base.cse[,4],
+                          mod.crosslevel.propknown.fullpt.cse[,4],
+                          mod.crosslevel.propfriends.base.cse[,4],
+                          mod.crosslevel.propfriends.fullpt.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### Table SA 12.  How Decentralization Influences Cross-level Network Capital (collapsed levels), Hours, Player Type Controls 
+
+
+## model HOURS RC by decentralization alone
+
+mod.crosslevel.hoursrcknown.base <- glm(net_crosslevel_hoursrcknown_col ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.base)
+mod.crosslevel.hoursrcknown.base.cse <- clse.f(data, mod.crosslevel.hoursrcknown.base, data$publicid_muni)
+mod.crosslevel.hoursrcknown.base.cse
+
+mod.crosslevel.hoursrcfriends.base <- glm(net_crosslevel_hoursrcfriends_col ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.base)
+mod.crosslevel.hoursrcfriends.base.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.base, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.base.cse
+
+
+## model HOURS RC by decentralized plus individual characteristics with participant types
+
+mod.crosslevel.hoursrcknown.fullpt <- glm(net_crosslevel_hoursrcknown_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.fullpt)
+mod.crosslevel.hoursrcknown.fullpt.cse <- clse.f(data, mod.crosslevel.hoursrcknown.fullpt, data$publicid_muni)
+mod.crosslevel.hoursrcknown.fullpt.cse 
+
+mod.crosslevel.hoursrcfriends.fullpt <- glm(net_crosslevel_hoursrcfriends_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + Participant_C + Participant_G + Participant_R, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.fullpt)
+mod.crosslevel.hoursrcfriends.fullpt.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.fullpt, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.fullpt.cse 
+
+
+#crosslevel ties, hours, player type controls table, collapsed levels
+
+texreg(list(mod.crosslevel.hoursrcknown.base,
+            mod.crosslevel.hoursrcknown.fullpt,
+            mod.crosslevel.hoursrcfriends.base,
+            mod.crosslevel.hoursrcfriends.fullpt), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Hours), Collapsed Levels, by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Hours RC Known Base", "Hours RC Known Full", "Hours RC Friends Base", "Hours RC Friends Full"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Player HC (Ref: Player M)", "Player AI (Ref: Player M)", "Player R (Ref: Player M)"),
+       override.se=list(mod.crosslevel.hoursrcknown.base.cse[,2],
+                        mod.crosslevel.hoursrcknown.fullpt.cse[,2],
+                        mod.crosslevel.hoursrcfriends.base.cse[,2],
+                        mod.crosslevel.hoursrcfriends.fullpt.cse[,2]),
+       override.pval=list(mod.crosslevel.hoursrcknown.base.cse[,4],
+                          mod.crosslevel.hoursrcknown.fullpt.cse[,4],
+                          mod.crosslevel.hoursrcfriends.base.cse[,4],
+                          mod.crosslevel.hoursrcfriends.fullpt.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### Table SA 13.  How Decentralization Influences Cross-level Network Capital (collapsed levels), Binary, Role Type Controls
+
+
+## model proportions by decentralized alone
+
+mod.crosslevel.propknown.base <- glm(net_crosslevel_propnumknown_col ~ decentralized + offset(log(net_crosslevel_propdenomknown_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.base)
+mod.crosslevel.propknown.base.cse <- clse.f(data, mod.crosslevel.propknown.base, data$publicid_muni)
+mod.crosslevel.propknown.base.cse 
+
+mod.crosslevel.propfriends.base <- glm(net_crosslevel_propnumfriends_col ~ decentralized + offset(log(net_crosslevel_propdenomfriends_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.base)
+mod.crosslevel.propfriends.base.cse <- clse.f(data, mod.crosslevel.propfriends.base, data$publicid_muni)
+mod.crosslevel.propfriends.base.cse
+
+
+## model proportions by decentralized plus individual characteristics
+
+mod.crosslevel.propknown.full <- glm(net_crosslevel_propnumknown_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor + offset(log(net_crosslevel_propdenomknown_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propknown.full)
+mod.crosslevel.propknown.full.cse <- clse.f(data, mod.crosslevel.propknown.full, data$publicid_muni)
+mod.crosslevel.propknown.full.cse
+
+mod.crosslevel.propfriends.full <- glm(net_crosslevel_propnumfriends_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor + offset(log(net_crosslevel_propdenomfriends_col)), family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.propfriends.full)
+mod.crosslevel.propfriends.full.cse <- clse.f(data, mod.crosslevel.propfriends.full, data$publicid_muni)
+mod.crosslevel.propfriends.full.cse
+
+
+#crosslevel ties, binary, role type controls table, collapsed levels
+
+texreg(list(mod.crosslevel.propknown.base,
+            mod.crosslevel.propknown.full,
+            mod.crosslevel.propfriends.base,
+            mod.crosslevel.propfriends.full), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Prop. Known), Collapsed Levels, by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Prop. Known Base", "Prop. Known Full", "Prop. Friends Base", "Prop. Friends Full"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Doctor", "Nurse", "Municipal Official", "Social Worker"),
+       override.se=list(mod.crosslevel.propknown.base.cse[,2],
+                        mod.crosslevel.propknown.full.cse[,2],
+                        mod.crosslevel.propfriends.base.cse[,2],
+                        mod.crosslevel.propfriends.full.cse[,2]),
+       override.pval=list(mod.crosslevel.propknown.base.cse[,4],
+                          mod.crosslevel.propknown.full.cse[,4],
+                          mod.crosslevel.propfriends.base.cse[,4],
+                          mod.crosslevel.propfriends.full.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,11,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### Table SA 14.  How Decentralization Influences Cross-level Network Capital (collapsed levels), Hours, Player Type Controls
+
+
+## model proportions by decentralized alone
+
+mod.crosslevel.hoursrcknown.base <- glm(net_crosslevel_hoursrcknown_col ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.base)
+mod.crosslevel.hoursrcknown.base.cse <- clse.f(data, mod.crosslevel.hoursrcknown.base, data$publicid_muni)
+mod.crosslevel.hoursrcknown.base.cse
+
+mod.crosslevel.hoursrcfriends.base <- glm(net_crosslevel_hoursrcfriends_col ~ decentralized, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.base)
+mod.crosslevel.hoursrcfriends.base.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.base, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.base.cse
+
+
+## model HOURS RC by decentralized plus individual characteristics
+
+mod.crosslevel.hoursrcknown.full <- glm(net_crosslevel_hoursrcknown_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcknown.full)
+mod.crosslevel.hoursrcknown.full.cse <- clse.f(data, mod.crosslevel.hoursrcknown.full, data$publicid_muni)
+mod.crosslevel.hoursrcknown.full.cse 
+
+mod.crosslevel.hoursrcfriends.full <- glm(net_crosslevel_hoursrcfriends_col ~ decentralized + Mujer + Q2_Educacion + Q1_Edad + Q3_YrsSalud + gen_trust + CargoMedico + CargoEnfermero + CargoAlcaldia + CargoPromotor, family="poisson", weights=weights_games_full_scaled, data=data)
+summary(mod.crosslevel.hoursrcfriends.full)
+mod.crosslevel.hoursrcfriends.full.cse <- clse.f(data, mod.crosslevel.hoursrcfriends.full, data$publicid_muni)
+mod.crosslevel.hoursrcfriends.full.cse 
+
+
+#crosslevel ties, hours, role type controls table, collapsed levels
+
+texreg(list(mod.crosslevel.hoursrcknown.base,
+            mod.crosslevel.hoursrcknown.full,
+            mod.crosslevel.hoursrcfriends.base,
+            mod.crosslevel.hoursrcfriends.full), 
+       stars=c(0.01, 0.05, 0.10),
+       caption="Explaining Cross-level Network Capital (Hours), Collapsed Levels, by Decentralization",
+       dcolumn=FALSE,
+       custom.model.names=c("Hours RC Known Base", "Hours RC Known Full", "Hours RC Friends Base", "Hours RC Friends Full"),
+       custom.coef.names=c("Constant", "Decentralized", "Female", "Education", "Age", "Years Working in Health", "Generalized Trust", "Doctor", "Nurse", "Municipal Official", "Social Worker"),
+       override.se=list(mod.crosslevel.hoursrcknown.base.cse[,2],
+                        mod.crosslevel.hoursrcknown.full.cse[,2],
+                        mod.crosslevel.hoursrcfriends.base.cse[,2],
+                        mod.crosslevel.hoursrcfriends.full.cse[,2]),
+       override.pval=list(mod.crosslevel.hoursrcknown.base.cse[,4],
+                          mod.crosslevel.hoursrcknown.full.cse[,4],
+                          mod.crosslevel.hoursrcfriends.base.cse[,4],
+                          mod.crosslevel.hoursrcfriends.full.cse[,4]),
+       reorder.coef=c(2,3,4,5,6,7,8,9,10,11,1),
+       caption.above=TRUE)
+
+
+
+
+
+##### END
+
+
+
+

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