15 Fixed Effects

This file demonstrates three approaches to estimating “fixed effects” models (remember this is what economists call “fixed effects”, but other disciplines use “fixed effects” to refer to something different). We’re going to use the wbstats package to download country-level data from the World Bank for 2017 - 2020 (the most recently available for the variables I chose). We’ll then estimate models with country fixed effects, with year fixed effects, and with both country and year fixed effects. We’ll estimate each model three ways: using the within transformation, using dummy variables, and using the plm package to apply the within transformation for us. If you don’t know what these are, go through LN5 first.

Note that the model we’ll estimate isn’t a great model in terms of producing interesting, reliable result. This is by design. Part of this chapter is introducing you to another API you can use to get data. You’ve worked with the US Census Bureau’s API to get data on US counties. Now from this chapter you will have experience getting data on countries from the World Bank. You are free to use either source for data for your RP. If the model here was a great one, it might take away something you want to do for your RP. This way you get experience with fixed effects models and with getting data from the World Bank, but don’t waste any potential ideas you might have for your RP. So just don’t read too much into the results. They likely suffer from reverse causation and omitted variable bias (both violations of ZCM). If you’re interested in cross-country variation in life expectancy you’ll need to dig much deeper than we’ll go in this chapter.

So what do you have to do? First, go through the details of the models with country fixed effects. I already did it for you, but you should go through it (together with going through LN5) to make sure you understand. After you feel you understand country fixed effects, try to estimate the models with Year Fixed Effects yourself. Once you’ve figured that out, try to estimate the models with both Country and Year Fixed Effects yourself. Just look for the code chunk comments that say “YOUR CODE GOES HERE”. That’s where you need to write code (mostly copy/pasting from what comes before it, making changes as appropriate).

Note that I have you the code for the stargazer tables, but until you estimate all the models it will display an error. For example, until you estimate yearDummies, you’ll get an error message: “object ‘yearDummies’ not found”. When you’re done all error messages should be gone.

library(wbstats) # To get data from the World Bank API
library(plm) # To estimate fixed effects models
library(formattable) # to make colorful tables similar to Excel's conditional formatting
library(stargazer)
library(tidyverse)

## Set how many decimals at which R starts to use scientific notation 
options(scipen=3)

## This shows you a lot of what's available from the World Bank API
# listWBinfo <- wb_cachelist

## List of countries in the World Bank data
countryList <- wb_countries()

## List of all available variables (what they call "indicators")
availableIndicators <- wb_cachelist$indicators

## Sometimes it's easier to look through the indicator list if you write it to CSV and open it in Excel (you can do the same thing with the US Census data). The following does that: 
# write.csv(select(availableIndicators,indicator_id, indicator, indicator_desc),"indicators.csv")

## NOTE: if you use any of these (the full list of variables, exporting it to CSV), make sure you do NOT leave that in your final code. It doesn't belong in your RMD file. I just put these things in here so you see how to get to this information. 

## We'll use the following variables: 
# SP.DYN.LE00.IN    Life expectancy at birth, total (years)
# NY.GDP.PCAP.KD    GDP per capita (constant 2017 US$)
# SP.POP.TOTL   Population, total
# SP.POP.TOTL.FE.ZS Population, female (% of total population)
# SP.RUR.TOTL.ZS    Rural population (% of total population)


## Create named vector of indicators to download
indicatorsToDownload <- c(
  lifeExp = "SP.DYN.LE00.IN", 
  gdpPerCapita ="NY.GDP.PCAP.KD", 
  pop = "SP.POP.TOTL",
  pctFemale = "SP.POP.TOTL.FE.ZS",
  pctRural = "SP.RUR.TOTL.ZS"
)

## Download descriptions of on World Bank indicators (i.e., variables)
indicatorInfo <- availableIndicators %>% 
                  filter(indicator_id %in% indicatorsToDownload)


## Build description of our variables that we'll output in the HTML body
sDesc <- ""
for(i in 1:nrow(indicatorInfo)){
  sDesc <- paste0(sDesc
                  ,"<b>",
                  indicatorInfo$indicator[i],
                  " (",indicatorInfo$indicator_id[i]
                  , ")</b>: "
                  ,indicatorInfo$indicator_desc[i]
                  ,"<br>")
}

## Download data
mydataOrig <- wb_data(indicatorsToDownload, 
                      start_date = 2017, 
                      end_date = 2020)

## get vector of TRUE and FALSE where FALSE indicates there's one or more NA
noNAs <- complete.cases(mydataOrig)

## When writing this code, I first checked how many rows do have NAs, and then out of how many rows 
# sum(noNAs)
## out of how many rows:
# nrow(noNAs)

## keep rows without any NA
mydata <- mydataOrig[noNAs,]

## get count of rows for each country
countOfYearsByCountry <-  mydata %>% count(country)

## merge the count variable with the data
mydata <- inner_join(mydata,countOfYearsByCountry, by="country")

## keep only countries that have all 4 years complete
mydata <- mydata %>% filter(n==4)

## drop count variable (since all are now 4)
mydata <- mydata %>% select(-n)


## For the purposes of this chapter, let's only examine one group of countries 
## so that we can output results without it taking up hundreds of lines
## Normally you don't want to get rid of countries like this
## but doing so will make things run much more quickly,
## so for the purposes of the BP we're going to only keep one region

## Merge in country info (e.g., region)
mydata <- inner_join(mydata,select(countryList,country,region),by="country")

## Keep only region "Latin America & Caribbean" (so we end up with only 31 countries)
mydata <- mydata %>% filter(region == "Latin America & Caribbean") %>% select(-region)

mydata <- mydata %>% rename(year=date)

## Change scale of variables. This re-scales regression coefficients (instead of getting 0.00000123)
####  Measure population in millions of people instead of people
####  Measure GDP per Capita in thousands of 2010 US $ (instead of 2010 US $)
mydata <- mydata %>% mutate(pop=pop/1000000, 
                            gdpPerCapita=gdpPerCapita/1000)

## Display results of selected variables in stargazer table
## (without converting it to a data frame it doesn't display anything)
mydata %>% select(lifeExp, gdpPerCapita, pop, pctFemale, pctRural) %>% 
  as.data.frame() %>%
  stargazer(., type = "html",summary.stat = c("n","mean","sd", "min", "p25", "median", "p75", "max"))

Statistic	N	Mean	St. Dev.	Min	Pctl(25)	Median	Pctl(75)	Max
lifeExp	136	74.529	3.542	63.854	72.718	74.229	77.264	80.350
gdpPerCapita	136	11.547	8.686	1.323	5.865	8.448	14.972	36.273
pop	136	18.012	40.809	0.040	0.387	4.644	11.650	213.196
pctFemale	136	50.684	1.040	48.814	49.927	50.434	51.092	53.086
pctRural	136	34.779	21.572	4.061	18.474	33.243	47.490	81.388

15.1 Variables

Variable descriptions from the World Bank API. These descriptions do not reflect two changes we made (that are reflected in the table of summary statistics above and in the regression results that follow): population is measured in millions of people and GDP per capita is measured in thousands of 2010 US dollars.

GDP per capita (constant 2010 US$) (NY.GDP.PCAP.KD): GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars.
Life expectancy at birth, total (years) (SP.DYN.LE00.IN): Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
Population, total (SP.POP.TOTL): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.
Population, female (% of total population) (SP.POP.TOTL.FE.ZS): Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.
Rural population (% of total population) (SP.RUR.TOTL.ZS): Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.

Source of data definitions: wbstats package

Note that if you displayed the exact wording of others without quotation marks in a paper, it would be plagiarism (and if you added quotation marks it wouldn’t be plagiarism, but it would make for a bad paper). However, for our purposes here, we’re just printing out a convenient list of definitions so we have it all in one place (i.e., this isn’t a paper).

15.2 OLS

Our focus is fixed effects models, but often when estimating fixed effects models we also estimate regular OLS without fixed effects for comparison.

ols <- lm(data=mydata,lifeExp~gdpPerCapita+pop+pctFemale+pctRural)

15.3 Country Fixed Effects

Our basic OLS model is the following: \[ lifeExp_{it} = \beta_0+\beta_1 gdpPerCapita_{it} + \beta_2 pop_{it} + \beta_3 pctFemale_{it} + \beta_4 pctRural_{it} + v_{it} \] To save on notation, we’ll use generic variables (and I wrote out the “composite error term”), i.e.,

\[ y_{it} = \beta_0+\beta_1 x_{1it} + \beta_2 x_{2it} + \beta_3 x_{2it} + \beta_4 x_{4it} + (c_i + u_{it}) \]

Below there are 3 equations. The first is for country \(i\) in year \(t\) (the same as the one above). The second equation is the average over the four years for country \(i\), where \(\bar{y}_{i}=\sum_{t=2017}^{2020}y_{it}\) is the average value of \(y_{it}\) over the 4 years for country \(i\), \(\bar{x}_{ji}=\sum_{t=2017}^{2020}x_{jti}\) is the average value of \(x_{jti}\) over the 4 years for country \(i\) for the four explanatory variables \(j\in\{1,2,3,4\}\), \(\bar{c}_{i}=\sum_{t=2017}^{2020}c_{i}=c_i\) is the average value of \(c_{i}\) over the 4 years for country \(i\) (which just equals \(c_i\) because \(c_i\) is the same in all years for country \(i\)), and \(\bar{u}_{i}=\sum_{t=2017}^{2020}u_{it}\) is the average value of \(u_{it}\) over the 4 years for country \(i\). For the final equation, subtract country \(i\)’s average from the value in each year \(t\).

\[ \begin{align} y_{it} &= \beta_0+\beta_1 x_{1it} + \beta_2 x_{2it} + \beta_3 x_{2it} + \beta_4 x_{4it} + (c_i + u_{it}) \\ \bar{y}_{i} &= \beta_0+\beta_1 \bar{x}_{1i} + \beta_2 \bar{x}_{2i} + \beta_3 \bar{x}_{3i} + \beta_4 \bar{x}_{4i} + (\bar{c}_i + \bar{u}_{i}) \\ y_{it}-\bar{y}_{i} &= (\beta_0-\beta_0)+\beta_1 (x_{1it}-\bar{x}_{1i}) + \beta_2 (x_{2it}-\bar{x}_{2i}) + \beta_3 (x_{3it}-\bar{x}_{3i}) \\ &\hspace{6cm} + \beta_4 (x_{4it}-\bar{x}_{4i}) + (c_i-\bar{c}_i + u_{it}-\bar{u}_{i}) \end{align} \]

This final equation simplifies to the “within transformation” for country \(i\), \[ y_{it}-\bar{y}_{i} = \beta_1 (x_{1it}-\bar{x}_{1i}) + \beta_2 (x_{2it}-\bar{x}_{2i}) + \beta_3 (x_{3it}-\bar{x}_{3i}) + \beta_4 (x_{4it}-\bar{x}_{4i}) + (u_{it}-\bar{u}_{i}) \] because \(\beta_0-\beta_0=0\) and \(c_i-\bar{c}_i=0\), where \(\bar{c}_i=c_i\) because \(c_i\) is the same in all years for country \(i\). Mathematically, this is why the fixed effects model allows us to control for observable factors that do not change of time (or whatever is measured by \(t=1,,,,.T\)). If \(c_i\) is not constant for all time periods, then \(\bar{c}_i=c_i\) isn’t correct and it doesn’t drop out of the final equation. That means it remains in the equations we estimate, and our coefficients are biased.

At the end of this file there are tables that demonstrate the within transformation for our dataset. There is a table for each variable. Look at the table for Life expectancy. Find the row for Argentina (iso3c code ARG). It’s average value of life expectancy is 76.75. In 2017, their value was 76.83, which is 0.08 below Argentina’s four-year average value of 76.75. In 2017, Argentina’s life expectancy was 76.83, which is 0.08 above Argentina’s four-year average. Below this table for life expectancy is a similar table for each explanatory variable. When economists say a model has country “fixed effects”, they mean estimating an OLS regression using data transformed by this “within” transformation.

Alternatively, a model with country “fixed effects” can be estimated using the original OLS equation with the addition of a dummy variable for each country (omitting one).

\[ y_{it} = \beta_0+\beta_1 x_{1it} + \beta_2 x_{2it} + \beta_3 x_{2it} + \beta_4 x_{4it} + \sum_{i=2}^{50}\sigma_idC_i + (c_i + u_{it}) \]

where \(dC_i\) is a dummy variable with a value of 1 if that observation is country \(i\) and equals 0 otherwise (and \(\sigma_i\) is the coefficient on dummy variable \(dC_i\)).

These two models, the “within transformation” and the model with a dummy variable for each country, are mathematically and empirically equivalent. To see that they are empirically equivalent, we’ll estimate both models and compare the results. Note that the standard errors and \(R^2\) values are not equivalent, as discussed below.

Note that when we create the within-transformed variables, we’re going to store them in a new dataframe mydataCountry so that we can use the same variable names. By keeping the same variable names, when we use the variables in the regression and display the results in stargazer, the coefficients will go on the same rows as the other two models.

## Dummy variable for each country (it automatically omits one)
countryDummies <- lm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural+factor(country),data=mydata)

## Within transformation (subtract country averages from each observation for each variable)

## Create country averages and add them to mydata
mydata <- mydata %>%
  group_by(country) %>%
  mutate(cAvg_lifeExp=mean(lifeExp),
         cAvg_gdpPerCapita=mean(gdpPerCapita),
         cAvg_pop=mean(pop),
         cAvg_pctFemale=mean(pctFemale),
         cAvg_pctRural=mean(pctRural)
         ) %>%
  ungroup()

## Within transformation (stored in its own dataframe so can use the same names)
mydataCountry <- mydata %>%
  mutate(lifeExp=lifeExp-cAvg_lifeExp,
         gdpPerCapita=gdpPerCapita-cAvg_gdpPerCapita,
         pop=pop-cAvg_pop,
         pctFemale=pctFemale-cAvg_pctFemale,
         pctRural=pctRural-cAvg_pctRural
         ) 

## Estimate within transformation using the transformed data (stored in mydataCountry)
countryWithin <- lm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural,data=mydataCountry)

## Using plm package
countryPlm <- plm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural,data=mydata, index=c("country"), model = "within", effect="individual")

stargazer(countryDummies,countryWithin,countryPlm, 
          type = "html", 
          report=('vcs*'),
          single.row = TRUE,
          digits = 4,
          keep.stat = c("n","rsq","adj.rsq"), 
          notes = "Standard Errors reported in parentheses, <em>&#42;p&lt;0.1;&#42;&#42;p&lt;0.05;&#42;&#42;&#42;p&lt;0.01</em>", 
          notes.append = FALSE,
          title = "Country Fixed Effects",
          model.numbers = FALSE, 
          column.labels = c("Dummies", "Within", "PLM"))

Country Fixed Effects

	Dependent variable:
	lifeExp
	OLS		panel
			linear
	Dummies	Within	PLM
gdpPerCapita	0.0900 (0.0628)	0.0900 (0.0543)	0.0900 (0.0628)
pop	-0.4733 (0.1579)***	-0.4733 (0.1366)***	-0.4733 (0.1579)***
pctFemale	-2.1202 (1.4166)	-2.1202 (1.2252)*	-2.1202 (1.4166)
pctRural	0.3120 (0.1870)*	0.3120 (0.1617)*	0.3120 (0.1870)*
factor(country)Argentina	36.8322 (12.9819)***
factor(country)Aruba	3.2085 (3.6178)
factor(country)Bahamas, The	11.0853 (11.1266)
factor(country)Barbados	0.3067 (1.4296)
factor(country)Belize	-2.9219 (6.2887)
factor(country)Bolivia	3.7256 (9.7247)
factor(country)Brazil	113.0163 (31.2008)***
factor(country)Chile	26.1992 (12.2132)**
factor(country)Colombia	36.0983 (11.4601)***
factor(country)Costa Rica	15.6737 (11.5754)
factor(country)Cuba	17.1208 (10.6059)
factor(country)Dominican Republic	12.7092 (11.8415)
factor(country)Ecuador	13.7459 (8.4867)
factor(country)El Salvador	12.5940 (8.5814)
factor(country)Grenada	-4.4895 (4.6702)
factor(country)Guatemala	7.2957 (6.0704)
factor(country)Guyana	-10.7639 (2.2026)***
factor(country)Haiti	-2.2784 (6.8433)
factor(country)Honduras	4.0415 (8.0515)
factor(country)Jamaica	1.2156 (7.0965)
factor(country)Mexico	68.8268 (18.8581)***
factor(country)Nicaragua	6.4130 (7.1391)
factor(country)Panama	9.6286 (9.4964)
factor(country)Paraguay	5.0919 (8.5307)
factor(country)Peru	25.8367 (10.5201)**
factor(country)Puerto Rico	22.8753 (12.7279)*
factor(country)St. Lucia	-10.5471 (2.7030)***
factor(country)St. Vincent and the Grenadines	-3.0417 (8.1815)
factor(country)Suriname	3.1347 (9.2037)
factor(country)Trinidad and Tobago	1.7582 (6.4361)
factor(country)Turks and Caicos Islands	12.1813 (14.5975)
factor(country)Uruguay	21.4987 (13.3973)
factor(country)Virgin Islands (U.S.)	22.7825 (13.2532)*
Constant	164.4820 (79.7294)**	0.0000 (0.0551)
Observations	136	136	136
R2	0.9680	0.2319	0.2319
Adjusted R2	0.9560	0.2085	-0.0581
Note:	Standard Errors reported in parentheses, *p<0.1;**p<0.05;***p<0.01

We’ve changed a few of the stargazer options for this chapter. We’re displaying standard errors instead of p-values so that we can use only one row per variable (it lets us report coefficient and standard error on one line, but not if we use p-values instead). I modified the note to say that standard errors are reported in parentheses.

Now look at the coefficient estimates. The 3 models all have the same coefficients on gdpPerCapita, pop, pctFemale, and pctRurla. In LN5 we discuss how the within transformation is equivalent to including dummy variables for each group (in this case, countries). That’s exactly what see in the table. The PLM package estimates the within transformation for us (so this is usually what you want to use in practice…we’re just doingit by hand here to help you better understand the models).

You also may notice that the standard errors (and statistical significance stars) are different in the middle column. When we estimate the model with dummy variables (column 1), the regular OLS standard errors are correct. But when we apply the within transformation, we need to adjust the standard errors to account for the within transformation. This would be a bit difficult for us to do. Thankfully, the PLM package correctly adjusts the standard errors (and thus p-values) for us. Thus, in practice we won’t actually want to apply the within transformation ourselves. We’re doing it in this chapter so you can see exactly what it is in practice and see that the coefficient estimates for all 3 versions result in the same coefficients.

If you compare the \(R^2\) values across models, you’ll notice that the \(R^2\) for the model with dummy variables is much higher. Including all the dummy variables makes it artificially high. We want to use the \(R^2\) from the within transformation. The PLM model does this for us.

Another reason we want to use the PLM model when we estimate fixed effects models is that we often don’t want to see all of the coefficients on the dummy variables. For country fixed effects, the coefficient on each country dummy is estimated off of only 4 observations. Thus, it is not a reliable estimate of the effect of being Argentina (or Belize, etc). It still allows us to estimate the model with country fixed effects, even if we don’t care about the coefficient estimates themselves. However, if we had not dropped all countries except South America, we would have hundreds of dummy variables. If we were estimating a model using US county data, we would have over 3000. R probably wouldn’t even let us estimate a model with that many variables. This again makes the PLM package preferable.

15.4 Year Fixed Effects

Above you saw how to estimate models with country fixed effects in three different ways. Here, you should estimate models with year fixed effects in the same three ways. Hint: you just have to switch “country” with “year” and everything else is the same. If you’ve done it correctly, the coefficients will be identical in all three models (and the standard errors will be identical in the “dummies” and “PLM” models).

## Dummy variable for each year (it automatically omits one)
yearDummies <- lm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural+factor(year),data=mydata)

## Within transformation (subtract year averages from each observation for each variable)

## Create year averages and add them to mydata
mydata <- mydata %>%
  group_by(year) %>%
  mutate(yAvg_lifeExp=mean(lifeExp),
         yAvg_gdpPerCapita=mean(gdpPerCapita),
         yAvg_pop=mean(pop),
         yAvg_pctFemale=mean(pctFemale),
         yAvg_pctRural=mean(pctRural)
         ) %>%
  ungroup()

## Within transformation (stored in its own dataframe so can use the same names)
mydataYear <- mydata %>%
  mutate(lifeExp=lifeExp-yAvg_lifeExp,
         gdpPerCapita=gdpPerCapita-yAvg_gdpPerCapita,
         pop=pop-yAvg_pop,
         pctFemale=pctFemale-yAvg_pctFemale,
         pctRural=pctRural-yAvg_pctRural
         ) 


## Estimate within transformation using the transformed data
yearWithin <- lm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural,data=mydataYear)

## Using plm package (for year, still use model = "within", effect="individual")
yearPlm <- plm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural,data=mydata, index=c("year"), model = "within", effect="individual")

stargazer(yearDummies,yearWithin,yearPlm,
          type = "html",
          report=('vcs*'),
          single.row = TRUE,
          digits = 4,
          keep.stat = c("n","rsq","adj.rsq"),
          notes = "Standard Errors reported in parentheses, <em>&#42;p&lt;0.1;&#42;&#42;p&lt;0.05;&#42;&#42;&#42;p&lt;0.01</em>",
          notes.append = FALSE,
          title = "Year Fixed Effects",
          model.numbers = FALSE,
          column.labels = c("Dummies", "Within", "PLM"))

Year Fixed Effects

	Dependent variable:
	lifeExp
	OLS		panel
			linear
	Dummies	Within	PLM
gdpPerCapita	0.2188 (0.0427)***	0.2188 (0.0422)***	0.2188 (0.0427)***
pop	0.0029 (0.0070)	0.0029 (0.0069)	0.0029 (0.0070)
pctFemale	-0.2107 (0.3303)	-0.2107 (0.3265)	-0.2107 (0.3303)
pctRural	-0.0276 (0.0137)**	-0.0276 (0.0135)**	-0.0276 (0.0137)**
factor(year)2018	0.0406 (0.7187)
factor(year)2019	-0.0377 (0.7187)
factor(year)2020	-0.6330 (0.7217)
Constant	83.7468 (16.2846)***	-0.0000 (0.2511)
Observations	136	136	136
R2	0.3367	0.3282	0.3282
Adjusted R2	0.3004	0.3077	0.2915
Note:	Standard Errors reported in parentheses, *p<0.1;**p<0.05;***p<0.01

15.5 Country and Year Fixed Effects

Now that you’ve estimated the models with year fixed effects, estimate models with both country and year fixed effects. It works the same way as above, just doing it for both country and year. If you’ve done it correctly, the coefficients will be identical in all three models (and the standard errors will be identical in the “dummies” and “PLM” models).

## Dummy variable for each country and each year (it automatically omits one of each)
countryyearDummies <- lm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural+factor(country) + factor(year),data=mydata)


## Within transformation (subtract country AND year averages from each observation for each variable)
## We already created the country averages and year averages above so we don't need to create them again

## Within transformation (stored in its own dataframe so can use the same names)
mydataCountryYear <- mydata %>%
  mutate(lifeExp=lifeExp-(cAvg_lifeExp + yAvg_lifeExp),
         gdpPerCapita=gdpPerCapita-(cAvg_gdpPerCapita + yAvg_gdpPerCapita),
         pop=pop-(cAvg_pop + yAvg_pop),
         pctFemale=pctFemale-(cAvg_pctFemale + yAvg_pctFemale),
         pctRural=pctRural-(cAvg_pctRural + yAvg_pctRural)) 

##Estimate within transformation using the transformed data
countryYearWithin <- lm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural,data=mydataCountryYear)

##Using plm package (using index = both country and year, joined together in c(), and model = "within", effect="twoways")
countryYearPlm <- plm(lifeExp~gdpPerCapita+pop+pctFemale+pctRural,data=mydata, index=c("country", "year"), model = "within", effect="twoways")

stargazer(countryyearDummies,countryYearWithin, countryYearPlm,
          type = "html",
          report=('vcs*'),
          single.row = TRUE,
          digits = 4,
          keep.stat = c("n","rsq","adj.rsq"),
          notes = "Standard Errors reported in parentheses, <em>&#42;p&lt;0.1;&#42;&#42;p&lt;0.05;&#42;&#42;&#42;p&lt;0.01</em>",
          notes.append = FALSE,
          title = "Country and Year Fixed Effects",
          model.numbers = FALSE,
          column.labels = c("Dummies", "Within", "PLM"))

Country and Year Fixed Effects

	Dependent variable:
	lifeExp
	OLS		panel
			linear
	Dummies	Within	PLM
gdpPerCapita	-0.0704 (0.0704)	-0.0704 (0.0600)	-0.0704 (0.0704)
pop	-0.3946 (0.1551)**	-0.3946 (0.1321)***	-0.3946 (0.1551)**
pctFemale	-0.5436 (1.4104)	-0.5436 (1.2010)	-0.5436 (1.4104)
pctRural	0.0999 (0.2234)	0.0999 (0.1903)	0.0999 (0.2234)
factor(country)Argentina	21.2984 (16.3411)
factor(country)Aruba	0.5606 (4.2232)
factor(country)Bahamas, The	1.0147 (12.9565)
factor(country)Barbados	-0.7431 (1.5709)
factor(country)Belize	-4.9971 (6.1860)
factor(country)Bolivia	-4.8505 (10.8915)
factor(country)Brazil	84.3095 (34.6748)**
factor(country)Chile	13.9461 (14.4837)
factor(country)Colombia	21.2555 (14.5854)
factor(country)Costa Rica	6.7259 (12.8247)
factor(country)Cuba	6.9584 (12.2187)
factor(country)Dominican Republic	2.5359 (13.3432)
factor(country)Ecuador	5.9974 (9.6765)
factor(country)El Salvador	-0.0694 (10.8784)
factor(country)Grenada	-4.3725 (4.3990)
factor(country)Guatemala	1.2754 (6.9496)
factor(country)Guyana	-10.6697 (2.0421)***
factor(country)Haiti	-9.1464 (7.7599)
factor(country)Honduras	-1.4736 (8.5577)
factor(country)Jamaica	-4.3666 (7.6684)
factor(country)Mexico	48.0995 (22.3130)**
factor(country)Nicaragua	-1.1306 (8.1671)
factor(country)Panama	3.4956 (10.2697)
factor(country)Paraguay	-0.9080 (9.1765)
factor(country)Peru	13.2779 (13.0052)
factor(country)Puerto Rico	9.6851 (15.2576)
factor(country)St. Lucia	-7.2339 (2.9615)**
factor(country)St. Vincent and the Grenadines	-4.8714 (8.0114)
factor(country)Suriname	-3.5265 (9.9234)
factor(country)Trinidad and Tobago	-1.9381 (6.8513)
factor(country)Turks and Caicos Islands	3.1359 (15.8335)
factor(country)Uruguay	7.2292 (15.8516)
factor(country)Virgin Islands (U.S.)	9.8187 (15.7264)
factor(year)2018	0.2033 (0.1781)
factor(year)2019	0.2684 (0.2104)
factor(year)2020	-0.6243 (0.2922)**
Constant	100.6734 (75.8390)	-106.5271 (61.6526)*
Observations	136	136	136
R2	0.9734	0.0849	0.0849
Adjusted R2	0.9622	0.0570	-0.3004
Note:	Standard Errors reported in parentheses, *p<0.1;**p<0.05;***p<0.01

15.6 Comparison of all models

Below we have comparisons of the four models: ols, country fixed effects, year fixed effects, and country and year fixed effects. The comparisons are done three times, one for each method of estimating the models.

15.6.1 Within Transformation

stargazer(ols,countryWithin,yearWithin,countryYearWithin,
          type = "html",
          report=('vcs*'),
          single.row = TRUE,
          digits = 3,
          keep.stat = c("n","rsq","adj.rsq"),
          notes = "Standard Errors reported in parentheses, <em>&#42;p&lt;0.1;&#42;&#42;p&lt;0.05;&#42;&#42;&#42;p&lt;0.01</em>",
          notes.append = FALSE,
          title = "Fixed Effects Models: Within Transformation",
          model.numbers = FALSE,
          column.labels = c("OLS", "Country FE", "Year FE", "Country+Year FE"))

Fixed Effects Models: Within Transformation

	Dependent variable:
	lifeExp
	OLS	Country FE	Year FE	Country+Year FE
gdpPerCapita	0.224 (0.042)***	0.090 (0.054)	0.219 (0.042)***	-0.070 (0.060)
pop	0.003 (0.007)	-0.473 (0.137)***	0.003 (0.007)	-0.395 (0.132)***
pctFemale	-0.243 (0.327)	-2.120 (1.225)*	-0.211 (0.327)	-0.544 (1.201)
pctRural	-0.027 (0.014)**	0.312 (0.162)*	-0.028 (0.014)**	0.100 (0.190)
Constant	85.145 (16.113)***	0.000 (0.055)	-0.000 (0.251)	-106.527 (61.653)*
Observations	136	136	136	136
R2	0.331	0.232	0.328	0.085
Adjusted R2	0.310	0.208	0.308	0.057
Note:	Standard Errors reported in parentheses, *p<0.1;**p<0.05;***p<0.01

15.6.2 PLM Package

stargazer(ols,countryPlm,yearPlm,countryYearPlm,
          type = "html",
          report=('vcs*'),
          single.row = TRUE,
          digits = 3,
          keep.stat = c("n","rsq","adj.rsq"),
          notes = "Standard Errors reported in parentheses, <em>&#42;p&lt;0.1;&#42;&#42;p&lt;0.05;&#42;&#42;&#42;p&lt;0.01</em>",
          notes.append = FALSE,
          title = "Fixed Effects Models: PLM Package",
          model.numbers = FALSE,
          column.labels = c("OLS", "Country FE", "Year FE", "Country+Year FE"))

Fixed Effects Models: PLM Package

	Dependent variable:
	lifeExp
	OLS	panel
		linear
	OLS	Country FE	Year FE	Country+Year FE
gdpPerCapita	0.224 (0.042)***	0.090 (0.063)	0.219 (0.043)***	-0.070 (0.070)
pop	0.003 (0.007)	-0.473 (0.158)***	0.003 (0.007)	-0.395 (0.155)**
pctFemale	-0.243 (0.327)	-2.120 (1.417)	-0.211 (0.330)	-0.544 (1.410)
pctRural	-0.027 (0.014)**	0.312 (0.187)*	-0.028 (0.014)**	0.100 (0.223)
Constant	85.145 (16.113)***
Observations	136	136	136	136
R2	0.331	0.232	0.328	0.085
Adjusted R2	0.310	-0.058	0.291	-0.300
Note:	Standard Errors reported in parentheses, *p<0.1;**p<0.05;***p<0.01

15.6.3 Dummy Variables

stargazer(ols,countryDummies,yearDummies,countryyearDummies,
          type = "html",
          report=('vcs*'),
          single.row = TRUE,
          digits = 3,
          keep.stat = c("n","rsq","adj.rsq"),
          notes = "Standard Errors reported in parentheses, <em>&#42;p&lt;0.1;&#42;&#42;p&lt;0.05;&#42;&#42;&#42;p&lt;0.01</em>",
          notes.append = FALSE,
          title = "Fixed Effects Models: Dummy Variables",
          model.numbers = FALSE,
          column.labels = c("OLS", "Country FE", "Year FE", "Country+Year FE"))

Fixed Effects Models: Dummy Variables

	Dependent variable:
	lifeExp
	OLS	Country FE	Year FE	Country+Year FE
gdpPerCapita	0.224 (0.042)***	0.090 (0.063)	0.219 (0.043)***	-0.070 (0.070)
pop	0.003 (0.007)	-0.473 (0.158)***	0.003 (0.007)	-0.395 (0.155)**
pctFemale	-0.243 (0.327)	-2.120 (1.417)	-0.211 (0.330)	-0.544 (1.410)
pctRural	-0.027 (0.014)**	0.312 (0.187)*	-0.028 (0.014)**	0.100 (0.223)
factor(country)Argentina		36.832 (12.982)***		21.298 (16.341)
factor(country)Aruba		3.209 (3.618)		0.561 (4.223)
factor(country)Bahamas, The		11.085 (11.127)		1.015 (12.957)
factor(country)Barbados		0.307 (1.430)		-0.743 (1.571)
factor(country)Belize		-2.922 (6.289)		-4.997 (6.186)
factor(country)Bolivia		3.726 (9.725)		-4.851 (10.892)
factor(country)Brazil		113.016 (31.201)***		84.310 (34.675)**
factor(country)Chile		26.199 (12.213)**		13.946 (14.484)
factor(country)Colombia		36.098 (11.460)***		21.255 (14.585)
factor(country)Costa Rica		15.674 (11.575)		6.726 (12.825)
factor(country)Cuba		17.121 (10.606)		6.958 (12.219)
factor(country)Dominican Republic		12.709 (11.841)		2.536 (13.343)
factor(country)Ecuador		13.746 (8.487)		5.997 (9.677)
factor(country)El Salvador		12.594 (8.581)		-0.069 (10.878)
factor(country)Grenada		-4.489 (4.670)		-4.372 (4.399)
factor(country)Guatemala		7.296 (6.070)		1.275 (6.950)
factor(country)Guyana		-10.764 (2.203)***		-10.670 (2.042)***
factor(country)Haiti		-2.278 (6.843)		-9.146 (7.760)
factor(country)Honduras		4.041 (8.052)		-1.474 (8.558)
factor(country)Jamaica		1.216 (7.097)		-4.367 (7.668)
factor(country)Mexico		68.827 (18.858)***		48.099 (22.313)**
factor(country)Nicaragua		6.413 (7.139)		-1.131 (8.167)
factor(country)Panama		9.629 (9.496)		3.496 (10.270)
factor(country)Paraguay		5.092 (8.531)		-0.908 (9.177)
factor(country)Peru		25.837 (10.520)**		13.278 (13.005)
factor(country)Puerto Rico		22.875 (12.728)*		9.685 (15.258)
factor(country)St. Lucia		-10.547 (2.703)***		-7.234 (2.961)**
factor(country)St. Vincent and the Grenadines		-3.042 (8.182)		-4.871 (8.011)
factor(country)Suriname		3.135 (9.204)		-3.527 (9.923)
factor(country)Trinidad and Tobago		1.758 (6.436)		-1.938 (6.851)
factor(country)Turks and Caicos Islands		12.181 (14.597)		3.136 (15.834)
factor(country)Uruguay		21.499 (13.397)		7.229 (15.852)
factor(country)Virgin Islands (U.S.)		22.783 (13.253)*		9.819 (15.726)
factor(year)2018			0.041 (0.719)	0.203 (0.178)
factor(year)2019			-0.038 (0.719)	0.268 (0.210)
factor(year)2020			-0.633 (0.722)	-0.624 (0.292)**
Constant	85.145 (16.113)***	164.482 (79.729)**	83.747 (16.285)***	100.673 (75.839)
Observations	136	136	136	136
R2	0.331	0.968	0.337	0.973
Adjusted R2	0.310	0.956	0.300	0.962
Note:	Standard Errors reported in parentheses, *p<0.1;**p<0.05;***p<0.01

15.7 Data Summary by Country

The remainder of this file has summaries of the data that are included to illustrate the within transformation. The colors in the tables for each variable make it easy to see the variation used when we use the within transformation, and thus that is used for identification in fixed effects models. For each variable, for each country you can clearly see which years are above that countries average and which years are below that country’s average.

15.7.1 Standard Summary Statistics

Statistic	N	Mean	St. Dev.	Min	Pctl(25)	Median	Pctl(75)	Max
lifeExp	136	74.529	3.542	63.854	72.718	74.229	77.264	80.350
gdpPerCapita	136	11.547	8.686	1.323	5.865	8.448	14.972	36.273
pop	136	18.012	40.809	0.040	0.387	4.644	11.650	213.196
pctFemale	136	50.684	1.040	48.814	49.927	50.434	51.092	53.086
pctRural	136	34.779	21.572	4.061	18.474	33.243	47.490	81.388

15.7.2 Average Values for Each Country

country	Avg lifeExp	Avg gdpPerCapita	Avg pop	Avg pctFemale	Avg pctRural
Antigua and Barbuda	78.58	16.17	0.09	52.28	75.44
Argentina	76.75	12.69	44.71	50.49	8.07
Aruba	75.99	29.89	0.11	52.81	56.51
Bahamas, The	72.83	28.93	0.40	52.08	16.92
Barbados	77.16	16.67	0.28	52.10	68.84
Belize	73.51	5.63	0.39	49.66	54.20
Bolivia	66.94	3.13	11.69	49.76	30.40
Brazil	74.82	8.46	210.91	50.83	13.31
Chile	80.05	13.51	18.85	50.37	12.39
Colombia	76.23	6.22	49.69	50.63	19.06
Costa Rica	79.39	12.36	5.06	49.91	20.31
Cuba	77.55	7.78	11.32	50.25	22.92
Dominican Republic	73.19	7.78	10.82	49.70	18.57
Ecuador	75.88	5.80	17.16	50.00	36.09
El Salvador	72.12	3.95	6.28	52.33	27.63
Grenada	74.84	8.84	0.12	49.88	63.66
Guatemala	72.55	4.16	16.47	50.47	48.75
Guyana	68.79	6.91	0.79	50.97	73.35
Haiti	64.04	1.39	11.09	50.40	44.27
Honduras	72.46	2.36	9.88	49.47	42.59
Jamaica	71.84	5.13	2.81	50.36	44.16
Mexico	73.12	9.82	124.48	51.08	19.70
Nicaragua	73.31	2.01	6.62	50.72	41.35
Panama	77.53	14.23	4.20	49.94	32.11
Paraguay	73.50	6.22	6.49	49.76	38.26
Peru	75.43	6.31	32.48	50.44	21.99
Puerto Rico	79.03	29.44	3.25	52.56	6.42
St. Lucia	73.34	10.40	0.18	50.38	81.28
St. Vincent and the Grenadines	73.35	8.01	0.11	48.87	47.59
Suriname	72.44	8.30	0.60	50.10	33.91
Trinidad and Tobago	74.17	15.46	1.51	50.63	46.80
Turks and Caicos Islands	75.87	23.34	0.04	49.52	6.78
Uruguay	77.79	15.91	3.43	51.62	4.62
Virgin Islands (U.S.)	79.59	35.38	0.11	52.88	4.23

15.7.3 Variable-Specific Values and Within Transformation for Each Country

For each variable, display each country’s values in 2017, 2018, 2019, and 2020, followed by the country’s average. These 5 columns are shaded from red (lowest) to green (highest) for each country. Then, in the final 4 columns display the within transformation (i.e., subtract the country’s average from each year’s value). These last 4 columns are also shaded for each country.

15.7.4 Life expectancy

iso3c	2017 Life Exp	2018 Life Exp	2019 Life Exp	2020 Life Exp	Avg Life Exp		2017 Within	2018 Within	2019 Within	2020 Within
ABW	75.90	76.07	76.25	75.72	75.99		-0.08	0.09	0.26	-0.26
ARG	76.83	77.00	77.28	75.89	76.75		0.08	0.25	0.53	-0.86
ATG	78.27	78.51	78.69	78.84	78.58		-0.31	-0.07	0.11	0.26
BHS	73.63	73.81	71.20	72.68	72.83		0.80	0.98	-1.62	-0.15
BLZ	73.56	73.70	73.93	72.85	73.51		0.05	0.19	0.42	-0.66
BOL	67.70	67.75	67.84	64.47	66.94		0.76	0.81	0.90	-2.47
BRA	74.83	75.11	75.34	74.01	74.82		0.01	0.29	0.52	-0.81
BRB	76.94	77.07	77.26	77.39	77.16		-0.23	-0.10	0.09	0.23
CHL	80.35	80.13	80.33	79.38	80.05		0.30	0.09	0.28	-0.67
COL	76.65	76.75	76.75	74.77	76.23		0.42	0.52	0.52	-1.46
CRI	79.38	79.48	79.43	79.28	79.39		-0.01	0.09	0.04	-0.11
CUB	77.53	77.50	77.61	77.57	77.55		-0.02	-0.05	0.06	0.02
DOM	73.06	73.23	73.58	72.89	73.19		-0.13	0.04	0.39	-0.30
ECU	76.97	77.09	77.30	72.15	75.88		1.09	1.22	1.42	-3.73
GRD	74.76	74.81	74.86	74.92	74.84		-0.08	-0.03	0.02	0.08
GTM	72.55	72.73	73.13	71.80	72.55		0.00	0.18	0.58	-0.75
GUY	68.67	68.90	69.12	68.49	68.79		-0.13	0.10	0.33	-0.31
HND	72.69	72.81	72.88	71.46	72.46		0.23	0.35	0.42	-1.00
HTI	63.85	64.02	64.25	64.05	64.04		-0.19	-0.03	0.21	0.01
JAM	71.91	71.79	71.77	71.87	71.84		0.08	-0.04	-0.07	0.03
LCA	73.13	73.36	73.44	73.42	73.34		-0.21	0.02	0.11	0.08
MEX	74.14	74.02	74.20	70.13	73.12		1.02	0.89	1.08	-2.99
NIC	73.55	73.85	74.05	71.80	73.31		0.24	0.54	0.74	-1.52
PAN	77.80	77.86	77.81	76.66	77.53		0.26	0.33	0.28	-0.87
PER	75.88	76.01	76.16	73.67	75.43		0.45	0.58	0.73	-1.76
PRI	79.26	79.77	79.06	78.04	79.03		0.22	0.74	0.03	-0.99
PRY	73.64	73.57	73.62	73.18	73.50		0.14	0.06	0.12	-0.32
SLV	72.31	72.56	72.56	71.06	72.12		0.19	0.43	0.44	-1.06
SUR	72.42	72.55	72.24	72.56	72.44		-0.03	0.11	-0.20	0.12
TCA	76.70	76.44	75.33	75.00	75.87		0.83	0.58	-0.54	-0.87
TTO	74.23	73.80	74.23	74.41	74.17		0.06	-0.36	0.06	0.24
URY	77.62	77.61	77.51	78.43	77.79		-0.17	-0.18	-0.29	0.64
VCT	74.31	74.13	72.83	72.13	73.35		0.96	0.78	-0.51	-1.22
VIR	79.37	79.52	79.67	79.82	79.59		-0.23	-0.07	0.07	0.23

15.7.5 GDP per capita

iso3c	2017 GDP per Capita	2018 GDP per Capita	2019 GDP per Capita	2020 GDP per Capita	Avg GDP per Capita		2017 Within	2018 Within	2019 Within	2020 Within
ABW	30270.94	31705.28	31762.73	25823.63	29890.65		0.38	1.81	1.87	-4.07
ARG	13595.04	13105.40	12716.22	11341.27	12689.48		0.91	0.42	0.03	-1.35
ATG	15962.68	16967.09	17697.23	14040.37	16166.84		-0.20	0.80	1.53	-2.13
BHS	30371.58	30705.60	31082.81	23566.58	28931.64		1.44	1.77	2.15	-5.37
BLZ	5806.07	5756.85	5907.35	5040.12	5627.60		0.18	0.13	0.28	-0.59
BOL	3135.03	3219.20	3242.95	2920.20	3129.34		0.01	0.09	0.11	-0.21
BRA	8470.95	8553.88	8592.22	8204.20	8455.31		0.02	0.10	0.14	-0.25
BRB	17430.87	17220.93	17168.15	14855.69	16668.91		0.76	0.55	0.50	-1.81
CHL	13615.52	13906.77	13765.12	12767.30	13513.68		0.10	0.39	0.25	-0.75
COL	6280.66	6320.76	6403.88	5865.65	6217.74		0.06	0.10	0.19	-0.35
CRI	12267.16	12470.96	12662.42	12058.00	12364.64		-0.10	0.11	0.30	-0.31
CUB	7865.37	8048.02	8042.95	7172.52	7782.21		0.08	0.27	0.26	-0.61
DOM	7461.65	7894.96	8205.14	7571.78	7783.38		-0.32	0.11	0.42	-0.21
ECU	6012.80	5976.25	5863.91	5331.98	5796.23		0.22	0.18	0.07	-0.46
GRD	8933.23	9252.68	9247.91	7915.21	8837.26		0.10	0.42	0.41	-0.92
GTM	4091.27	4163.48	4263.08	4124.70	4160.63		-0.07	0.00	0.10	-0.04
GUY	6038.27	6127.71	6348.70	9126.81	6910.37		-0.87	-0.78	-0.56	2.22
HND	2373.79	2423.27	2446.11	2190.97	2358.53		0.02	0.06	0.09	-0.17
HTI	1425.05	1429.23	1386.52	1322.81	1390.90		0.03	0.04	0.00	-0.07
JAM	5172.92	5264.20	5307.51	4765.47	5127.52		0.05	0.14	0.18	-0.36
LCA	10942.25	11211.60	11095.35	8361.39	10402.65		0.54	0.81	0.69	-2.04
MEX	9997.69	10120.36	10013.70	9147.05	9819.70		0.18	0.30	0.19	-0.67
NIC	2153.61	2052.13	1947.41	1886.51	2009.92		0.14	0.04	-0.06	-0.12
PAN	14634.84	14922.13	15122.52	12230.04	14227.38		0.41	0.69	0.90	-2.00
PER	6400.12	6530.50	6550.53	5749.02	6307.54		0.09	0.22	0.24	-0.56
PRI	29809.22	29687.21	30181.27	28094.60	29443.07		0.37	0.24	0.74	-1.35
PRY	6226.69	6338.51	6229.22	6095.39	6222.45		0.00	0.12	0.01	-0.13
SLV	3921.32	4009.72	4104.92	3761.75	3949.43		-0.03	0.06	0.16	-0.19
SUR	8425.68	8750.92	8756.07	7275.29	8301.99		0.12	0.45	0.45	-1.03
TCA	24726.95	25080.16	25438.78	18122.43	23342.08		1.38	1.74	2.10	-5.22
TTO	16134.89	15716.91	15576.43	14397.55	15456.45		0.68	0.26	0.12	-1.06
URY	16088.00	16142.05	16192.16	15197.96	15905.04		0.18	0.24	0.29	-0.71
VCT	7890.82	8152.38	8213.16	7798.53	8013.72		-0.12	0.14	0.20	-0.22
VIR	34435.49	35183.24	36273.10	35620.87	35378.17		-0.94	-0.19	0.89	0.24

15.7.6 Population

iso3c	2017 Population	2018 Population	2019 Population	2020 Population	Avg Population		2017 Within	2018 Within	2019 Within	2020 Within
ABW	105439	105962	106442	106585	106107.00		0.00	0.00	0.00	0.00
ARG	44044811	44494502	44938712	45376763	44713697.00		-0.67	-0.22	0.23	0.66
ATG	91119	91626	92117	92664	91881.50		0.00	0.00	0.00	0.00
BHS	399020	401906	404557	406471	402988.50		0.00	0.00	0.00	0.00
BLZ	374693	382066	389095	394921	385193.75		-0.01	0.00	0.00	0.01
BOL	11435533	11606905	11777315	11936162	11688978.75		-0.25	-0.08	0.09	0.25
BRA	208504960	210166592	211782878	213196304	210912683.50		-2.41	-0.75	0.87	2.28
BRB	279187	279688	280180	280693	279937.00		0.00	0.00	0.00	0.00
CHL	18368577	18701450	19039485	19300315	18852456.75		-0.48	-0.15	0.19	0.45
COL	48351671	49276961	50187406	50930662	49686675.00		-1.34	-0.41	0.50	1.24
CRI	4993842	5040734	5084532	5123105	5060553.25		-0.07	-0.02	0.02	0.06
CUB	11336405	11328244	11316697	11300698	11320511.00		0.02	0.01	0.00	-0.02
DOM	10647244	10765531	10881882	10999664	10823580.25		-0.18	-0.06	0.06	0.18
ECU	16696944	17015672	17343740	17588595	17161237.75		-0.46	-0.15	0.18	0.43
GRD	120921	121838	122724	123663	122286.50		0.00	0.00	0.00	0.00
GTM	16087418	16346950	16604026	16858333	16474181.75		-0.39	-0.13	0.13	0.38
GUY	763252	785514	798753	797202	786180.25		-0.02	0.00	0.01	0.01
HND	9626842	9792850	9958829	10121763	9875071.00		-0.25	-0.08	0.08	0.25
HTI	10863543	11012421	11160438	11306801	11085800.75		-0.22	-0.07	0.07	0.22
JAM	2808376	2811835	2813773	2820436	2813605.00		-0.01	0.00	0.00	0.01
LCA	177163	177888	178583	179237	178217.75		0.00	0.00	0.00	0.00
MEX	122839258	124013861	125085311	125998302	124484183.00		-1.64	-0.47	0.60	1.51
NIC	6480532	6572233	6663924	6755895	6618146.00		-0.14	-0.05	0.05	0.14
PAN	4096063	4165255	4232532	4294396	4197061.50		-0.10	-0.03	0.04	0.10
PER	31605486	32203944	32824861	33304756	32484761.75		-0.88	-0.28	0.34	0.82
PRI	3325286	3193354	3193694	3281538	3248468.00		0.08	-0.06	-0.05	0.03
PRY	6355404	6443328	6530026	6618695	6486863.25		-0.13	-0.04	0.04	0.13
SLV	6266654	6276342	6280217	6292731	6278986.00		-0.01	0.00	0.00	0.01
SUR	587559	593715	600301	607065	597160.00		-0.01	0.00	0.00	0.01
TCA	39844	41487	43080	44276	42171.75		0.00	0.00	0.00	0.00
TTO	1478607	1504709	1519955	1518147	1505354.50		-0.03	0.00	0.01	0.01
URY	3422200	3427042	3428409	3429086	3426684.25		0.00	0.00	0.00	0.00
VCT	105549	105281	104924	104632	105096.50		0.00	0.00	0.00	0.00
VIR	107281	107001	106669	106290	106810.25		0.00	0.00	0.00	0.00

15.7.7 Percent female

iso3c	2017 %Female	2018 %Female	2019 %Female	2020 %Female	Avg %Female		2017 Within	2018 Within	2019 Within	2020 Within
ABW	52.72	52.79	52.85	52.89	52.81		-0.09	-0.02	0.04	0.08
ARG	50.50	50.49	50.49	50.49	50.49		0.01	0.00	0.00	0.00
ATG	52.30	52.29	52.28	52.26	52.28		0.02	0.01	-0.01	-0.02
BHS	52.01	52.06	52.11	52.15	52.08		-0.07	-0.02	0.03	0.06
BLZ	49.67	49.65	49.64	49.66	49.66		0.01	0.00	-0.01	0.00
BOL	49.73	49.75	49.76	49.80	49.76		-0.03	-0.01	0.00	0.04
BRA	50.81	50.82	50.84	50.85	50.83		-0.02	-0.01	0.01	0.02
BRB	52.13	52.11	52.09	52.07	52.10		0.03	0.01	-0.01	-0.03
CHL	50.38	50.37	50.36	50.36	50.37		0.01	0.00	-0.01	-0.01
COL	50.62	50.62	50.63	50.64	50.63		-0.01	0.00	0.00	0.01
CRI	49.88	49.90	49.92	49.94	49.91		-0.03	-0.01	0.01	0.03
CUB	50.21	50.24	50.27	50.30	50.25		-0.04	-0.01	0.01	0.04
DOM	49.65	49.68	49.71	49.75	49.70		-0.04	-0.02	0.01	0.05
ECU	49.99	49.99	49.99	50.02	50.00		-0.01	-0.01	0.00	0.02
GRD	49.82	49.86	49.90	49.93	49.88		-0.06	-0.02	0.02	0.05
GTM	50.47	50.47	50.47	50.48	50.47		0.00	0.00	0.00	0.01
GUY	51.09	50.86	50.82	51.09	50.97		0.13	-0.11	-0.14	0.13
HND	49.46	49.46	49.47	49.49	49.47		-0.01	-0.01	0.00	0.02
HTI	50.37	50.39	50.40	50.42	50.40		-0.03	-0.01	0.01	0.03
JAM	50.34	50.35	50.36	50.37	50.36		-0.01	-0.01	0.01	0.02
LCA	50.31	50.35	50.40	50.44	50.38		-0.07	-0.02	0.02	0.07
MEX	51.05	51.07	51.08	51.12	51.08		-0.03	-0.01	0.00	0.04
NIC	50.72	50.72	50.71	50.72	50.72		0.00	0.00	0.00	0.00
PAN	49.93	49.94	49.95	49.96	49.94		-0.01	-0.01	0.00	0.02
PER	50.43	50.43	50.44	50.46	50.44		-0.01	-0.01	0.00	0.02
PRI	52.44	52.51	52.60	52.69	52.56		-0.12	-0.05	0.04	0.13
PRY	49.74	49.75	49.76	49.78	49.76		-0.02	-0.01	0.01	0.02
SLV	52.29	52.32	52.35	52.37	52.33		-0.04	-0.01	0.02	0.03
SUR	50.04	50.09	50.12	50.14	50.10		-0.06	-0.01	0.02	0.05
TCA	49.46	49.50	49.54	49.58	49.52		-0.06	-0.02	0.02	0.06
TTO	50.76	50.59	50.53	50.65	50.63		0.13	-0.05	-0.10	0.02
URY	51.65	51.63	51.61	51.59	51.62		0.03	0.01	-0.01	-0.03
VCT	48.81	48.85	48.88	48.93	48.87		-0.05	-0.02	0.01	0.06
VIR	52.69	52.81	52.94	53.09	52.88		-0.20	-0.07	0.06	0.20

15.7.8 Percent rural

iso3c	2017 %Rural	2018 %Rural	2019 %Rural	2020 %Rural	Avg %Rural		2017 Within	2018 Within	2019 Within	2020 Within
ABW	56.71	56.59	56.45	56.30	56.51		0.19	0.08	-0.06	-0.21
ARG	8.25	8.13	8.01	7.89	8.07		0.18	0.06	-0.06	-0.18
ATG	75.29	75.40	75.49	75.57	75.44		-0.15	-0.04	0.06	0.13
BHS	17.08	16.98	16.87	16.75	16.92		0.16	0.06	-0.05	-0.16
BLZ	54.40	54.28	54.13	53.98	54.20		0.20	0.08	-0.06	-0.22
BOL	30.92	30.58	30.23	29.88	30.40		0.52	0.18	-0.17	-0.52
BRA	13.69	13.43	13.18	12.93	13.31		0.38	0.12	-0.13	-0.38
BRB	68.84	68.85	68.84	68.81	68.84		0.00	0.02	0.01	-0.03
CHL	12.51	12.44	12.36	12.27	12.39		0.12	0.04	-0.04	-0.12
COL	19.55	19.22	18.90	18.58	19.06		0.49	0.16	-0.17	-0.49
CRI	21.44	20.66	19.92	19.23	20.31		1.13	0.35	-0.39	-1.08
CUB	23.02	22.96	22.89	22.81	22.92		0.10	0.04	-0.03	-0.11
DOM	19.72	18.93	18.17	17.46	18.57		1.15	0.36	-0.40	-1.11
ECU	36.33	36.18	36.01	35.83	36.09		0.24	0.09	-0.08	-0.26
GRD	63.84	63.73	63.60	63.46	63.66		0.18	0.07	-0.05	-0.19
GTM	49.32	48.95	48.56	48.16	48.75		0.57	0.20	-0.19	-0.58
GUY	73.46	73.39	73.31	73.21	73.35		0.12	0.05	-0.03	-0.13
HND	43.54	42.90	42.27	41.64	42.59		0.95	0.31	-0.32	-0.95
HTI	45.65	44.72	43.81	42.91	44.27		1.38	0.45	-0.47	-1.36
JAM	44.62	44.33	44.02	43.69	44.16		0.46	0.16	-0.15	-0.47
LCA	81.39	81.32	81.25	81.16	81.28		0.11	0.04	-0.03	-0.12
MEX	20.13	19.84	19.56	19.27	19.70		0.43	0.14	-0.14	-0.43
NIC	41.70	41.48	41.24	40.99	41.35		0.35	0.13	-0.11	-0.36
PAN	32.63	32.29	31.94	31.59	32.11		0.52	0.18	-0.17	-0.53
PER	22.28	22.09	21.90	21.70	21.99		0.29	0.10	-0.09	-0.29
PRI	6.41	6.42	6.42	6.42	6.42		-0.01	0.00	0.00	0.00
PRY	38.70	38.42	38.12	37.82	38.26		0.44	0.15	-0.14	-0.45
SLV	28.73	27.98	27.25	26.56	27.63		1.10	0.35	-0.37	-1.07
SUR	33.96	33.94	33.91	33.85	33.91		0.05	0.03	-0.01	-0.06
TCA	7.18	6.90	6.64	6.39	6.78		0.40	0.12	-0.14	-0.39
TTO	46.80	46.82	46.81	46.79	46.80		-0.01	0.01	0.01	-0.02
URY	4.76	4.67	4.57	4.48	4.62		0.14	0.04	-0.05	-0.14
VCT	48.22	47.80	47.39	46.97	47.59		0.62	0.21	-0.21	-0.62
VIR	4.40	4.28	4.17	4.06	4.23		0.17	0.05	-0.06	-0.17