should I make bootstrapping the SE of indirect effect when using gsem ?
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should I make bootstrapping the SE of indirect effect when using gsem?
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How to assign rank according to the corresponding values
Dear All
I wanted to know how we can assign ranks a multiple choice questions based on the values of the categories.
My question 81 is asking the enumerators to arrange and number in a ascending order your expenditure heads, Starting with the lowest to the highest and after ranking they were asked to fill in the amount. But the rank and the amount does not match in many. For example in the first row Q81_3 is ranked 1 (lowest) and Q81_8 is ranked the second lowest. But if you look at the amount exp_annual3 is 1000 whereas exp_annual8 is only 500. There is a limit to how many variables can be specifed with dataex so I took only 6 in my example which is why some ranks are saying more than 6. But I wanted to know for each row how can I rank the variable's Q81_1 to Q81_6 according to the values in exp_annual1- exp_annual6. SOme exp will be missing for some HH beacuse each Household willonly select the expenditure that they incur.
copy starting from the next line ------------- ---------
Thank you in advance
I wanted to know how we can assign ranks a multiple choice questions based on the values of the categories.
My question 81 is asking the enumerators to arrange and number in a ascending order your expenditure heads, Starting with the lowest to the highest and after ranking they were asked to fill in the amount. But the rank and the amount does not match in many. For example in the first row Q81_3 is ranked 1 (lowest) and Q81_8 is ranked the second lowest. But if you look at the amount exp_annual3 is 1000 whereas exp_annual8 is only 500. There is a limit to how many variables can be specifed with dataex so I took only 6 in my example which is why some ranks are saying more than 6. But I wanted to know for each row how can I rank the variable's Q81_1 to Q81_6 according to the values in exp_annual1- exp_annual6. SOme exp will be missing for some HH beacuse each Household willonly select the expenditure that they incur.
copy starting from the next line ------------- ---------
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input byte(q81__1 q81__2 q81__3 q81__4 q81__5 q81__6) long(exp_annual1 exp_annual2 exp_annual3 exp_annual4 exp_annual5 exp_annual6) 7 5 1 6 0 0 7000 1500 1000 4500 . . 1 0 0 3 0 0 15000 . . 6500 . . 5 2 0 4 0 1 5000 700 . 4700 . 42 8 7 0 9 3 2 2000 150 . 9000 50 125 5 4 0 0 0 0 6000 1500 . . . . 6 5 0 4 0 0 5000 200 . 2000 . . 2 1 3 0 0 0 1500 300 1000 . . . 6 0 3 5 0 1 10000 . 1000 10000 . 184 7 4 0 6 0 1 4000 400 . 3500 . 100 4 0 0 3 0 1 10000 . . 9000 . 285 1 2 0 0 0 0 3500 720 . . . . 2 0 3 0 0 0 10000 . 4200 . . . 5 4 0 0 3 2 10000 600 . . 90 42 4 3 0 0 0 2 5000 585 . . . 100 1 2 3 4 5 6 7000 600 800 9000 200 200 3 5 0 4 1 0 4000 720 . 6500 200 . 5 0 4 0 0 2 10000 . 1000 . . 100 3 2 0 0 0 0 10000 700 . . . . 5 3 4 0 0 1 10000 650 1500 . . 200 5 0 3 2 0 0 10000 . 5000 4000 . . 5 3 0 4 0 2 12000 650 . 6500 . 150 6 3 0 0 0 1 6000 1200 . . . 600 7 3 6 8 0 1 5000 780 2500 8500 . 70 2 1 0 0 3 4 2000 200 . . 100 50 3 0 1 0 0 0 20000 . 500 . . . 6 4 0 0 0 1 6000 780 . . . 500 5 0 0 4 0 2 6000 . . 5000 . 200 3 0 2 0 0 0 8000 . 2000 . . . 6 3 0 7 0 1 20000 1560 . 20000 . 300 7 4 3 8 0 1 5000 780 500 7500 . 100 3 2 1 0 0 4 8000 800 2000 . . 400 2 0 3 0 0 0 14000 . 10000 . . . 5 0 3 4 1 0 7000 . 5000 4500 150 . 2 1 7 0 0 0 2000 300 1500 . . . 5 0 3 0 0 1 10000 . 3000 . . 200 6 3 2 4 0 0 15000 1000 1500 4800 . . 8 0 2 0 0 0 10000 . 5500 . . . 5 0 4 3 0 2 6000 . 5000 600 . 100 6 3 0 2 0 0 7000 700 . 1900 . . 4 0 0 3 0 1 8000 . . 5000 . 150 7 3 2 6 0 1 9000 950 300 5000 . 100 3 2 0 0 0 0 5000 700 . . . . 8 2 1 7 0 0 5000 780 1000 4000 . . 5 2 4 3 0 0 3000 500 1000 1000 . . 3 0 0 0 0 2 15000 . . . . 200 1 2 0 3 0 4 5000 720 . 8500 . 150 4 3 2 1 0 0 11000 1000 3000 7000 . . 1 0 2 3 0 0 6000 . 1500 12500 . . 5 3 0 4 0 2 25000 650 . 6000 . 150 4 2 0 3 0 0 4700 800 . 850 . . 5 3 4 0 1 0 4000 800 1500 . 130 . 6 3 0 5 0 1 10000 700 . 7500 . 100 5 3 4 0 0 1 5000 650 1000 . . 100 5 4 0 0 2 1 1500 600 . . 100 42 4 0 0 2 0 0 8000 . . 2300 . . 1 2 0 3 4 5 20000 600 . 10000 100 100 1 2 3 0 4 5 3000 300 1000 . 100 150 5 3 4 0 0 1 6500 700 6000 . . 35 3 2 0 6 0 0 15000 1500 . 13000 . . 6 3 2 4 0 1 12000 900 600 7600 . 100 4 3 2 0 0 0 6000 1400 500 . . . 8 7 6 0 1 0 10000 670 1000 . 500 . 5 0 0 4 0 0 7000 . . 1780 . . 1 2 3 0 0 0 3000 720 3000 . . . 15 14 13 12 10 11 5000 720 2000 11000 100 200 3 0 0 0 2 0 5000 . . . 1000 . 5 3 0 0 1 0 4000 800 . . 150 . 7 1 0 6 0 0 10000 350 . 5000 . . 6 5 4 3 0 0 6000 1500 7000 8000 . . 3 2 1 0 4 0 8000 800 1500 . 100 . 5 3 0 2 0 0 4000 1000 . 800 . . 4 0 0 3 0 0 5000 . . 2000 . . 4 0 3 0 0 0 7000 . 2000 . . . 3 2 0 0 0 0 8000 650 . . . . 8 4 5 7 0 1 7000 780 2500 4000 . 100 5 0 3 4 1 0 6000 . 4000 5000 200 . 4 0 0 2 3 0 20000 . . 11000 100 . 4 3 2 0 1 0 4000 150 4000 . 100 . 5 4 0 0 2 1 5000 750 . . 100 42 6 0 0 5 1 2 6500 . . 5000 150 200 6 0 5 4 0 0 6000 . 500 500 . . 5 3 0 4 0 1 2000 700 . 8000 . 50 3 0 0 4 0 1 13000 . . 20000 . 300 2 1 0 3 0 0 5000 650 . 7500 . . 6 3 0 0 1 5 13000 600 . . 150 150 5 3 0 4 0 1 25000 800 . 4000 . 60 2 7 5 4 0 0 8000 350 800 900 . . 5 0 3 4 0 1 16000 . 3000 14500 . 200 2 1 3 4 5 0 5000 300 3000 6000 100 . 1 2 3 4 5 6 25000 900 1500 11500 50 240 4 2 0 1 0 0 3000 700 . 2000 . . 1 2 0 3 4 0 10000 900 . 6500 200 . 9 6 7 8 5 1 10000 700 2000 8200 200 100 7 3 0 6 0 1 6000 250 . 2000 . 150 1 2 3 0 4 0 10000 300 6000 . 1000 . 4 0 0 0 0 2 5000 . . . . 100 6 3 4 5 0 0 7000 450 800 2021 . . 3 0 0 0 1 0 4500 . . . 150 . 4 0 0 2 0 0 8000 . . 7000 . . 6 3 5 0 0 1 7000 780 1500 . . 250 end
Thank you in advance
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Multiline for panel data
Hi all,
I'm working with panel data including 154 banks of 14 countries between 2005-2019. I would like to plot the development of their ROAA, ROAE, RORWA, and NIM throughout the years with the mean values (averaging by entities). Because the unit scale for each indicator is different, I decided to plot 4 different graphs that have the same y-axis (Year).
I have tried:
following this post
by Nick.
However, I got the error
.
Anyone could point out which step that I've got wrong?
Thanks in advance!
Sang
I'm working with panel data including 154 banks of 14 countries between 2005-2019. I would like to plot the development of their ROAA, ROAE, RORWA, and NIM throughout the years with the mean values (averaging by entities). Because the unit scale for each indicator is different, I decided to plot 4 different graphs that have the same y-axis (Year).
I have tried:
Code:
egen mROAA = mean(ROAA), by(Bank_name)
egen mROAE = mean(ROAE), by(Bank_name)
egen mRORWA = mean(RORWA), by(Bank_name)
egen mNIM = mean(NIM), by(Bank_name)
multiline mROAA mROAE mRORWA mNIM Year, recast(connected) ///
mylabels("ROAA" "ROAE" "RORWA" "NIM")
HTML Code:
https://www.statalist.org/forums/forum/general-stata-discussion/general/1401776-multiline-now-available-on-ssc
However, I got the error
Code:
varlist not allowed
Anyone could point out which step that I've got wrong?
Thanks in advance!
Sang
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Panel Data (Longitudinal) - STATA codes for treatment/control groupings
Hi all
Using the Wooldridge data happiness2.dta which looks at life satisfaction/happiness and marriage. I am looking for guidance on how to separate treatment (those who have ever been married) and control groups (those never married). The data provided includes observations from c11,000 units/IDs, with between 2 and 26 survey response years per ID - its unbalanced. It includes a binary indicator for marriage (1=married, 0=unmarried). However, some IDs have become married between survey years (treatment) and some have remained unmarried (control). Can you advise on the best approach in STATA to separating these groups?
Other indicators available include:
marry (currently married=1, unmarried=0)
yrsmarried (length of marriage)
pynr (person-year ID)
pycount (length of participation in study)
happy (life satisfaction 0=worst, 10=best)
...is not providing correct results.
Using the Wooldridge data happiness2.dta which looks at life satisfaction/happiness and marriage. I am looking for guidance on how to separate treatment (those who have ever been married) and control groups (those never married). The data provided includes observations from c11,000 units/IDs, with between 2 and 26 survey response years per ID - its unbalanced. It includes a binary indicator for marriage (1=married, 0=unmarried). However, some IDs have become married between survey years (treatment) and some have remained unmarried (control). Can you advise on the best approach in STATA to separating these groups?
Other indicators available include:
marry (currently married=1, unmarried=0)
yrsmarried (length of marriage)
pynr (person-year ID)
pycount (length of participation in study)
happy (life satisfaction 0=worst, 10=best)
Code:
bysort id(year): gen changed = (marry[0] !=marry[_N]
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categorical variable
Hi, I am a beginner with stata, please I need help, is it possible to run my categorical variables (outcomes) in groups. example
I have an outcome that has 3 groups. a b c generalized as A. (for logistic regression )
A
a (
b (
c (
I have an outcome that has 3 groups. a b c generalized as A. (for logistic regression )
A
a (
b (
c (
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Dropping Countries from the sample / Drop command
Hello everyone,
I have a panel of 130 countries and i am trying to estimate the threshold level above which financial development ( measured by liquid liabilities to GDP) has a negative effect on economic growth. I have one country in my sample that seriously biases my results and leads to inconsistent estimates (Liquid liabilities to GDP equals 800% of GDP in some years). I tried to drop it from my sample but Stata returned an error. I used the following command:
. drop Country_ if Country==Luxembourg
Luxembourg not found
r(111);
Can somebody tell me what am I doing wrong?
Many thanks!
I have a panel of 130 countries and i am trying to estimate the threshold level above which financial development ( measured by liquid liabilities to GDP) has a negative effect on economic growth. I have one country in my sample that seriously biases my results and leads to inconsistent estimates (Liquid liabilities to GDP equals 800% of GDP in some years). I tried to drop it from my sample but Stata returned an error. I used the following command:
. drop Country_ if Country==Luxembourg
Luxembourg not found
r(111);
Can somebody tell me what am I doing wrong?
Many thanks!
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A linguistic question about relative and absolute changes
It is common in this Forum to encounter questions that relate to relative and absolute changes and the associated terminology when the changing variable is itself a percentage. In English, a change from 50% to 55% is a relative change of 5/50 = 10%, and is an absolute change of 55-50 = 5 percentage points.
Because I am by nature pedantic, I frequently respond to posts that involve this issue. As often as not, the person posing the question appears to be a native speaker of English. But a substantial number of these questions also arise in posts where the author appears to be using English as a second language. It dawns on me that the convention that a relative change is percent, and an absolute change is percentage points may be language specific. And I now worry that in pounding on this terminology, when the reader back-translates my advice into his or her own tongue, the results may be quite incorrect or confusing. I really don't know. I have at least light conversational ability in several other languages, but even in the one I speak almost fluently, I don't know the terms used for these.
The convention is clearly an arbitrary one. There is no reason I can see why it might not have been the rule that percent change means absolute change and some other term is adopted for relative change. For that matter, if I were "redesigning" the English language I would banish the term "percent change" altogether and decree that one always speak specifically either of percent absolute change or percent relative change.
So I have two questions.
1. How are these distinctions handled in other languages commonly in use on this Forum? Are they consistent with how English does it? If not, how difficult is it to translate from the English terminology into these other languages?
2. How did the current English convention come to be?
I realize this is a little off-topic for this forum, as it is neither a question about Stata nor really about statistics. I hope nobody objects.
Because I am by nature pedantic, I frequently respond to posts that involve this issue. As often as not, the person posing the question appears to be a native speaker of English. But a substantial number of these questions also arise in posts where the author appears to be using English as a second language. It dawns on me that the convention that a relative change is percent, and an absolute change is percentage points may be language specific. And I now worry that in pounding on this terminology, when the reader back-translates my advice into his or her own tongue, the results may be quite incorrect or confusing. I really don't know. I have at least light conversational ability in several other languages, but even in the one I speak almost fluently, I don't know the terms used for these.
The convention is clearly an arbitrary one. There is no reason I can see why it might not have been the rule that percent change means absolute change and some other term is adopted for relative change. For that matter, if I were "redesigning" the English language I would banish the term "percent change" altogether and decree that one always speak specifically either of percent absolute change or percent relative change.
So I have two questions.
1. How are these distinctions handled in other languages commonly in use on this Forum? Are they consistent with how English does it? If not, how difficult is it to translate from the English terminology into these other languages?
2. How did the current English convention come to be?
I realize this is a little off-topic for this forum, as it is neither a question about Stata nor really about statistics. I hope nobody objects.
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if condition
Hi everyone,
I would like to create a variable like the following:
It works well, but if AR in distance==-5 is missing, than the whole variable is missing. How could I tell stata that I want CAR_20 = AR if distance ==-5, or, if AR in -5 is missing, then -4 and so on?
Thanks
Andrea
I would like to create a variable like the following:
Code:
gen CAR_20 = AR if distance==-5 replace CAR_20 = CAR_5_20[_n-1]+AR if distance >-5 & distance<=20
It works well, but if AR in distance==-5 is missing, than the whole variable is missing. How could I tell stata that I want CAR_20 = AR if distance ==-5, or, if AR in -5 is missing, then -4 and so on?
Thanks
Andrea
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Instrumenting an specific variable with xtabond
Dear all,
I am trying to estimate a dynamic panel model where I would like to instrument one of the regressors with an external instrument (in addition to those instruments provided in this methodology that uses lags on levels and differences). In xtabond there is the possibility to use inst(z) for external instruments, but to my understanding, z is applied to all the regressors and not just the one I would like to instrument with this specific z.
Any suggestion?
Thank you in advance.
I am trying to estimate a dynamic panel model where I would like to instrument one of the regressors with an external instrument (in addition to those instruments provided in this methodology that uses lags on levels and differences). In xtabond there is the possibility to use inst(z) for external instruments, but to my understanding, z is applied to all the regressors and not just the one I would like to instrument with this specific z.
Any suggestion?
Thank you in advance.
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Graph combine ignores specified xlab values
I am using graph combine to group time series plots.
As you know dates on the x-axis are special, so I specify the manually (the same for all sub-graphs)
For each single graph this produces the axis labels how I want them. However, when I combine it using
It does display the correct angle, but not the dates I provided (instead it gives the dates I would get without manual entry). Did you experience this too and have an idea where it could come from?
As you know dates on the x-axis are special, so I specify the manually (the same for all sub-graphs)
Code:
xlab(`=d(29feb2020)' `=d(01may2020)' `=d(01jul2020)' `=d(01sep2020)' `=d(01nov2020)' `=d(01jan2021)' `=d(28feb2021)', format(%td) ang(45))
Code:
graph combine lux switz italy ser,
ysize(25) xsize(45) xcommon
graphregion(color(white))
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handling missing data
I have a panel data set, consisting of 138 countries, 70 years. I want to use it but would want to drop off the first 20 years as most of the countries have missing data. How do I go about that?
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3 way fixed effects vs triple difference estimator
Hi everyone,
I have a conceptual question: is a 3 way fixed effects regression equivalent to running a triple difference estimator? Are there any resources to learn more about this?
Many thanks!
I have a conceptual question: is a 3 way fixed effects regression equivalent to running a triple difference estimator? Are there any resources to learn more about this?
Many thanks!
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Running power analysis with more than two experimental groups
Hi all. I want to run a power analysis to verify the necessary sample size to adequately run an experiment with four experimental conditions (3 experimental groups plus a control group). It is not a factorial experiment.
Type of power analysis: a priori
Statistical test: ANOVA fixed effects, omnibus, one-way
Effect size: 0.1
alpha error prob: .05
1-beta error prob: .95
Number of groups: 4
Does Stata have a command to run a power analysis in this case?
Thank you
Type of power analysis: a priori
Statistical test: ANOVA fixed effects, omnibus, one-way
Effect size: 0.1
alpha error prob: .05
1-beta error prob: .95
Number of groups: 4
Does Stata have a command to run a power analysis in this case?
Thank you
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In mixed-effect model, how to display individual random slope/coefficient in the dataset
Hi Stata users,
I am using a mixed-effect model with random intercept and random slope.
Stata code like: mixed weight week || id: week.
I am interested to find the random slope for each individual subject (id) in the dataset. Can anyone please let me the code to display the random slope in the data?
Thanks in advance,
Nelufa
I am using a mixed-effect model with random intercept and random slope.
Stata code like: mixed weight week || id: week.
I am interested to find the random slope for each individual subject (id) in the dataset. Can anyone please let me the code to display the random slope in the data?
Thanks in advance,
Nelufa
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Export Frequency table
Hi,
I'd like to export a frequency (one-way) table using svyset. The table would include:
* the number of observations
* the proportion
* the number of weighted observation
* the proportion of weighted observation.
I'm able to generate the four matrices but am struggling to combine them into a single matrix, as well as including the region names (not the numeric value)
Any suggestion on how to do it, either to export in Latex or Word format? Asdoc and putdocx do not allow pweights. Moreover, I would like to loop the above code over a significant number of variables so would like to automatize the process.
Thank you
I'd like to export a frequency (one-way) table using svyset. The table would include:
* the number of observations
* the proportion
* the number of weighted observation
* the proportion of weighted observation.
I'm able to generate the four matrices but am struggling to combine them into a single matrix, as well as including the region names (not the numeric value)
Code:
webuse nhanes2
svyset psu [pweight=finalwgt], strata(strata)
svy: tabulate region, count se
local row=e(r)
local tot_obs=e(N)
mat b1=e(b)
* Weighted proportions
mat wp1=e(Prop)
* obs
mat obs=e(Obs)
mata: sum(st_matrix("e(b)"))
* Weighted obs
matrix weighted_obs=J(`row', 1, 0)
forvalues i=1/`row' {
matrix weighted_obs[`i', 1]=b1[1, `i']
}
* Proportions
matrix prop=J(`row', 1, 0)
forvalues i=1/`row' {
matrix prop[`i', 1]=round(100*obs[`i', 1]/`tot_obs', 0.01)
}
* Weighted Proportions
matrix weighted_prop=J(`row', 1, 0)
forvalues i=1/`row' {
matrix weighted_prop[`i', 1]=round(100*wp1[`i', 1], 0.01)
}
* Obs
mat li obs
* Proportion
mat li prop
* Weighted obs
mat li weighted_obs
* Weighted prop
mat li weighted_prop
Thank you
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Local Moran's I test
The estat moran postestimation command does not work with spxtregress command. Does any one know how to test and map local moran's I in Stata:
example data below:
example data below:
Code:
copy https://www.stata-press.com/data/r16/homicide_1960_1990.dta . copy https://www.stata-press.com/data/r16/homicide_1960_1990_shp.dta . use homicide_1960_1990, clear xtset _ID year spset *Create a contiguity weighting matrix with the default spectral normalization spmatrix create contiguity W if year == 1990, replace *Fit a spatial autoregressive random-effects model spxtregress hrate ln_population ln_pdensity gini i.year, re dvarlag(W) estat moran, errorlag(W) // this gives an error
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Local Moran's I test
The estat moran postestimation command does not work with spxtregress command. Does anyone know how to test and map local moran's I in Stata:
example data below:
example data below:
Code:
copy https://www.stata-press.com/data/r16/homicide_1960_1990.dta . copy https://www.stata-press.com/data/r16/homicide_1960_1990_shp.dta . use homicide_1960_1990, clear xtset _ID year spset *Create a contiguity weighting matrix with the default spectral normalization spmatrix create contiguity W if year == 1990, replace *Fit a spatial autoregressive random-effects model spxtregress hrate ln_population ln_pdensity gini i.year, re dvarlag(W) estat moran, errorlag(W) // this gives an error
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Creating a Variable to Measure Recent Performance
I am trying to count the number of times an NFL has won a game in their last four games. I already have a dummy variable representing the outcome of the game. I think the idea would be to generate the id for each game after sorting them in order by team and game time and then count the dummy variable outcomes from the last four observations. I'm not entirely sure how to go about the syntax/loop as I'm not super familiar doing these things in stata. Let me know if any more information is needed. I would probably have to drop the first four games in each season as well.
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Help with coding this logic
Hello,
My data looks like this
I wanna be able to look up the two separate fips2010 when uno == 1 (that is fips numbers 51650010800 and 51760030200) in the block of fips when uno is missing. In other words I want to search for the 1st two fips one by one against the next 4 fips and see if there is a match or not. In case of match, I wanna generate a variable -- match == 1, cannot figure out how to go about doing it
The data is sorted on acq_cert and year and uno == 1 if acq_cert != cert and uno == . if acq_cert == cert
My data looks like this
Code:
fips2010 year cert acq_cert uno 51650010800 2010 11583 34837 1 51760030200 2010 11583 34837 1 54005958400 2010 34837 34837 54043955600 2010 34837 34837 54005958800 2010 34837 34837 54045956900 2010 34837 34837
The data is sorted on acq_cert and year and uno == 1 if acq_cert != cert and uno == . if acq_cert == cert
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_IDS in weighting matrix do not match _IDS in estimation sample
I'm trying to run a Moran's I test on my data but I keep getting this error. Here is my code and data below. Can anyone please help?
spmatrix clear
spmatrix create idistance W if year ==2016, replace
regress manf_pc_ff Rents_GDP_nb
estat moran, errorlag(W)
Error: IDs in weighting matrix W do not match _IDs in estimation sample
There are places in W not in estimation sample and places in estimation sample not in W.
----------------------- copy starting from the next line -----------------------
------------------ copy up to and including the previous line ------------------
spmatrix clear
spmatrix create idistance W if year ==2016, replace
regress manf_pc_ff Rents_GDP_nb
estat moran, errorlag(W)
Error: IDs in weighting matrix W do not match _IDs in estimation sample
There are places in W not in estimation sample and places in estimation sample not in W.
----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input str59 country int year long _ID double(_CX _CY) float(Neighbor_population manf_pc_ff Rents_GDP_nb Migrants_pp Imports_GDP Rents_GDP) "Algeria" 1996 4 2.676869982345495 28.155623766186512 8850334 4526.076 9.501017 1.469662 .0007634131 12.39737 "Algeria" 1997 4 2.676869982345495 28.155623766186512 11303946 4526.076 8.751779 1.598385 1.8598734e-06 11.291486 "Algeria" 1998 4 2.676869982345495 28.155623766186512 1681863 1761.4027 7.991744 .746421 3.475715e-06 6.566297 "Algeria" 1999 4 2.676869982345495 28.155623766186512 10414433 1236.165 6.610361 .6066885 5.769097e-06 9.017069 "Algeria" 2000 4 2.676869982345495 28.155623766186512 3393409 1236.165 9.913099 1.830486 .00002545021 15.307203 "Algeria" 2001 4 2.676869982345495 28.155623766186512 16934220 1236.165 9.085542 .5818813 .000024008114 14.12193 "Algeria" 2002 4 2.676869982345495 28.155623766186512 21602472 1236.165 12.518338 .644254 .00010100015 14.069468 "Algeria" 2003 4 2.676869982345495 28.155623766186512 1540640 1236.165 13.394665 1.033551 .00007591845 16.304386 "Algeria" 2004 4 2.676869982345495 28.155623766186512 10847002 1236.165 14.695156 .9091178 .00005918097 18.196198 "Algeria" 2005 4 2.676869982345495 28.155623766186512 10635244 1950.91 17.960484 .6038817 .00002242149 24.39616 "Algeria" 2006 4 2.676869982345495 28.155623766186512 5635859 1950.91 21.09472 .5429423 .0008050447 26.534433 "Algeria" 2007 4 2.676869982345495 28.155623766186512 17795192 1950.91 23.52048 .4908597 .0009190447 25.01032 "Algeria" 2008 4 2.676869982345495 28.155623766186512 3802803 1964.865 26.05785 .9432834 .00003911596 27.04177 "Algeria" 2009 4 2.676869982345495 28.155623766186512 7548433 674.4952 17.755713 .53244996 .00014366036 18.953701 "Algeria" 2010 4 2.676869982345495 28.155623766186512 20479716 1711.1956 22.44304 .9969134 .00010707934 19.99752 "Algeria" 2011 4 2.676869982345495 28.155623766186512 1016100 1711.1956 22.29719 .56222767 .0016875453 23.587303 "Algeria" 2012 4 2.676869982345495 28.155623766186512 4220450 1473.514 21.17175 1.431636 .00020204144 22.987114 "Algeria" 2013 4 2.676869982345495 28.155623766186512 2865637 1473.514 15.603875 .8952893 .0002164117 21.3234 "Algeria" 2014 4 2.676869982345495 28.155623766186512 1496185 1473.514 12.823918 .8289234 .0007887898 19.292645 "Algeria" 2015 4 2.676869982345495 28.155623766186512 9837571 2019.2263 11.07809 .7223982 .00005280594 11.990244 "Algeria" 2016 4 2.676869982345495 28.155623766186512 1829675 2019.2263 11.07809 .6918197 .00026967164 16.50982 "Angola" 1996 8 17.54875399673365 -12.296463111860687 24249130 29.47187 23.611984 .4798474 .00006291911 47.57884 "Angola" 1997 8 17.54875399673365 -12.296463111860687 58453684 126.54377 23.650045 .1773713 .00006291911 42.79438 "Angola" 1998 8 17.54875399673365 -12.296463111860687 4145391 630.0487 18.084288 .7111751 .00006291911 25.83629 "Angola" 1999 8 17.54875399673365 -12.296463111860687 84068088 630.0487 20.368923 .2290232 .00006291911 49.55986 "Angola" 2000 8 17.54875399673365 -12.296463111860687 2118874 630.0487 19.10101 .5527683 .00006291911 61.9567 "Angola" 2001 8 17.54875399673365 -12.296463111860687 13215139 630.0487 18.008947 .4689616 .00006291911 45.59174 "Angola" 2002 8 17.54875399673365 -12.296463111860687 1763859 88.23344 17.217104 .3276423 .00006291911 38.35699 "Angola" 2003 8 17.54875399673365 -12.296463111860687 28403852 88.23344 19.54555 .50254697 .00006291911 36.654045 "Angola" 2004 8 17.54875399673365 -12.296463111860687 4011486 88.23344 20.637135 .9119723 .00006291911 45.19068 "Angola" 2005 8 17.54875399673365 -12.296463111860687 2632345 88.23344 23.68478 1.1242851 .00006291911 59.90822 "Angola" 2006 8 17.54875399673365 -12.296463111860687 8656486 88.23344 26.97332 .97632 .00006291911 54.13524 "Angola" 2007 8 17.54875399673365 -12.296463111860687 12502958 81.02455 27.271675 .8422908 .00006291911 51.32109 "Angola" 2008 8 17.54875399673365 -12.296463111860687 17351822 443.4748 26.785036 1.085604 .00006291911 58.05639 "Angola" 2009 8 17.54875399673365 -12.296463111860687 20564068 443.4748 22.46705 .9410384 .0040813456 28.122154 "Angola" 2010 8 17.54875399673365 -12.296463111860687 773422 443.4748 27.18687 3.2182405 .0005850176 39.03144 "Angola" 2011 8 17.54875399673365 -12.296463111860687 7372837 443.4748 30.82738 2.4810224 .0011236774 44.50494 "Angola" 2012 8 17.54875399673365 -12.296463111860687 1794571 443.4748 29.585514 6.188809 .0011236774 41.55948 "Angola" 2013 8 17.54875399673365 -12.296463111860687 1629209 443.4748 26.321453 4.0693474 .0011236774 34.8921 "Angola" 2014 8 17.54875399673365 -12.296463111860687 25663592 315.8781 23.740183 6.864351 .00009489132 27.532494 "Angola" 2015 8 17.54875399673365 -12.296463111860687 896266 764.5181 18.515253 7.316497 .00002378095 11.251415 "Angola" 2016 8 17.54875399673365 -12.296463111860687 10971698 764.5181 18.515253 2.592123 .00002378095 7.915795 "Benin" 1996 29 2.3398501727818135 9.660140534442299 5343019 15.5879 18.242311 1.9720035 .000016301196 8.246718 "Benin" 1997 29 2.3398501727818135 9.660140534442299 5989004 188.2109 17.411388 3.9486234 5.800144e-06 7.962859 "Benin" 1998 29 2.3398501727818135 9.660140534442299 4829288 47.05788 13.575778 4.39362 .000019344665 7.48092 "Benin" 1999 29 2.3398501727818135 9.660140534442299 3486326 47.05788 11.71843 1.750362 .0003915379 4.3020644 "Benin" 2000 29 2.3398501727818135 9.660140534442299 185960288 219.65837 15.493333 2.1436284 1.645258e-06 4.466275 "Benin" 2001 29 2.3398501727818135 9.660140534442299 14689725 20.498995 13.203022 4.412729 .0010427071 4.205236 "Benin" 2002 29 2.3398501727818135 9.660140534442299 85766400 20.498995 11.366695 3.930797 .002970367 4.5202656 "Benin" 2003 29 2.3398501727818135 9.660140534442299 2720839 29.86207 13.546003 5.326047 .0011095933 5.674131 "Benin" 2004 29 2.3398501727818135 9.660140534442299 5330639 203.0331 12.675787 3.228534 .00012891361 4.2822847 "Benin" 2005 29 2.3398501727818135 9.660140534442299 48032936 47.78551 14.026616 7.454515 .0006365182 4.1527042 "Benin" 2006 29 2.3398501727818135 9.660140534442299 5920359 47.78551 12.95055 8.329845 .004781516 4.1706467 "Benin" 2007 29 2.3398501727818135 9.660140534442299 5197031 177.69476 14.35436 5.155968 .0005831367 4.957317 "Benin" 2008 29 2.3398501727818135 9.660140534442299 3802803 177.69476 16.496635 6.759823 .01367666 5.010157 "Benin" 2009 29 2.3398501727818135 9.660140534442299 9816588 177.69476 12.797234 3.231226 .02009596 5.086227 "Benin" 2010 29 2.3398501727818135 9.660140534442299 54717040 177.69476 14.06555 2.843286 .014037357 4.687139 "Benin" 2011 29 2.3398501727818135 9.660140534442299 15141098 303.82907 18.643568 7.226935 .0004749757 4.765978 "Benin" 2012 29 2.3398501727818135 9.660140534442299 58665808 20.65209 20.08877 3.5694966 .002522477 5.351275 "Benin" 2013 29 2.3398501727818135 9.660140534442299 9826598 16.956762 17.824038 12.590753 .016578557 4.805236 "Benin" 2014 29 2.3398501727818135 9.660140534442299 7340905 477.1121 15.518964 13.49253 .0006979496 4.6448903 "Benin" 2015 29 2.3398501727818135 9.660140534442299 6530819 18.114723 16.283562 11.76372 .01175899 6.726689 "Benin" 2016 29 2.3398501727818135 9.660140534442299 171765776 18.114723 16.283562 5.344246 .02102789 11.15268 "Botswana" 1996 35 23.81374954991846 -22.1880523360801 14465121 255.78062 5.921973 9.640889 .0041855955 .9700951 "Botswana" 1997 35 23.81374954991846 -22.1880523360801 30993758 899.8796 5.102481 5.3807 .0041855955 .8713127 "Botswana" 1998 35 23.81374954991846 -22.1880523360801 25836888 6.01067 3.8940625 7.189121 .0041855955 .6645786 "Botswana" 1999 35 23.81374954991846 -22.1880523360801 45571272 476.7619 2.705888 3.376134 .0041855955 .4190151 "Botswana" 2000 35 23.81374954991846 -22.1880523360801 3260650 225.61467 3.145004 3.39817 .0041855955 1.4446483 "Botswana" 2001 35 23.81374954991846 -22.1880523360801 1516958 830.0626 3.030259 7.734243 .002342174 .57508594 "Botswana" 2002 35 23.81374954991846 -22.1880523360801 7146969 325.796 3.372952 13.65778 .01681536 .5477104 "Botswana" 2003 35 23.81374954991846 -22.1880523360801 8036845 52.25553 4.671824 3.867763 .04168242 1.619681 "Botswana" 2004 35 23.81374954991846 -22.1880523360801 10415944 17.842302 6.746966 6.014678 .16759945 3.315373 "Botswana" 2005 35 23.81374954991846 -22.1880523360801 50477012 65.32992 7.166213 8.494273 .06260648 3.8229654 "Botswana" 2006 35 23.81374954991846 -22.1880523360801 753688 555.8862 10.310335 6.099618 .00518518 7.987185 "Botswana" 2007 35 23.81374954991846 -22.1880523360801 14645468 395.7336 13.006267 10.764872 .013030366 9.272271 "Botswana" 2008 35 23.81374954991846 -22.1880523360801 1879117 135.00468 13.364775 15.66777 .05890121 6.041979 "Botswana" 2009 35 23.81374954991846 -22.1880523360801 3776681 70.43772 8.86377 16.72886 .13183981 11.453908 "Botswana" 2010 35 23.81374954991846 -22.1880523360801 4039201 92.90221 10.455236 18.81948 .08322993 4.936467 "Botswana" 2011 35 23.81374954991846 -22.1880523360801 22069776 2.632249 11.373919 4.909244 2.1860754 3.568519 "Botswana" 2012 35 23.81374954991846 -22.1880523360801 53689236 2.632249 9.997845 4.7371607 3.324176 3.6288655 "Botswana" 2013 35 23.81374954991846 -22.1880523360801 41435760 7.768843 8.912998 10.27558 1.0163765 3.684058 "Botswana" 2014 35 23.81374954991846 -22.1880523360801 29333104 4.796864 8.122456 10.634151 1.9083743 3.579605 "Botswana" 2015 35 23.81374954991846 -22.1880523360801 32678874 578.0952 7.398216 6.703864 .007079433 2.711595 "Botswana" 2016 35 23.81374954991846 -22.1880523360801 1731639 83.72369 7.398216 4.950684 .026264926 12.455848 "Burkina Faso" 1996 42 -1.7463989056755453 12.274383858458796 14685399 50.08324 8.915753 9.212647 .0004504818 12.786032 "Burkina Faso" 1997 42 -1.7463989056755453 12.274383858458796 3043567 3.872308 8.573694 2.492128 .003667297 12.709138 "Burkina Faso" 1998 42 -1.7463989056755453 12.274383858458796 4902151 94.02332 8.126556 10.192673 .0007426227 11.6252 "Burkina Faso" 1999 42 -1.7463989056755453 12.274383858458796 7941412 8.399014 5.260384 9.790195 .005672063 5.21044 "Burkina Faso" 2000 42 -1.7463989056755453 12.274383858458796 6281639 173.3838 6.663795 6.56138 .0001361116 5.980082 "Burkina Faso" 2001 42 -1.7463989056755453 12.274383858458796 16464025 3.0436146 6.015562 3.805452 .004775717 5.373523 "Burkina Faso" 2002 42 -1.7463989056755453 12.274383858458796 12783613 250.2708 6.862589 11.42101 .0002121973 6.414193 "Burkina Faso" 2003 42 -1.7463989056755453 12.274383858458796 3347173 60.03719 9.183302 8.0028305 .0010766997 7.335972 "Burkina Faso" 2004 42 -1.7463989056755453 12.274383858458796 5521763 33.360542 7.174012 6.689685 .001888869 6.187195 "Burkina Faso" 2005 42 -1.7463989056755453 12.274383858458796 23310716 9.145805 7.210747 2.941031 .0041597234 7.153515 "Burkina Faso" 2006 42 -1.7463989056755453 12.274383858458796 5635859 26.465094 7.555095 12.100534 .0041597234 7.311117 "Burkina Faso" 2007 42 -1.7463989056755453 12.274383858458796 5197031 62.82407 8.654179 10.13778 .0014487214 9.183211 "Burkina Faso" 2008 42 -1.7463989056755453 12.274383858458796 3802803 56.80743 10.059135 9.533585 .0017760934 10.404152 "Burkina Faso" 2009 42 -1.7463989056755453 12.274383858458796 17014056 8.558409 9.25667 6.479319 .009888065 12.345053 "Burkina Faso" 2010 42 -1.7463989056755453 12.274383858458796 28481946 44.05433 10.55823 8.955203 .00241598 14.92755 "Burkina Faso" 2011 42 -1.7463989056755453 12.274383858458796 15653336 98.67007 13.677193 4.5542073 .0004508217 18.725376 "Burkina Faso" 2012 42 -1.7463989056755453 12.274383858458796 9460830 7.918809 15.875013 5.13754 .014376565 18.997168 "Burkina Faso" 2013 42 -1.7463989056755453 12.274383858458796 9918196 28.219553 14.005953 11.41643 .010262776 17.293825 "Burkina Faso" 2014 42 -1.7463989056755453 12.274383858458796 17231540 17.488194 12.716898 9.393352 .01899917 16.864077 "Burkina Faso" 2015 42 -1.7463989056755453 12.274383858458796 7300116 11.68881 13.53846 10.79001 .013682513 20.98524 "Burkina Faso" 2016 42 -1.7463989056755453 12.274383858458796 20246380 16.89307 13.53846 3.707062 .003346267 .22385524 "Burundi" 1996 43 29.890484050138404 -3.3664890597484587 25203844 8.571363 20.42165 1.7023183 .00019254687 28.37523 "Burundi" 1997 43 29.890484050138404 -3.3664890597484587 10549678 19.410156 17.677279 2.8376684 .00012391988 23.621565 "Burundi" 1998 43 29.890484050138404 -3.3664890597484587 11985440 11.256915 16.92812 2.2653968 .00013408903 27.011356 "Burundi" 1999 43 29.890484050138404 -3.3664890597484587 20344548 18.869043 13.788254 2.419681 .0001890501 15.412393 "Burundi" 2000 43 29.890484050138404 -3.3664890597484587 6443751 47.04573 6.080245 2.8684 .0003482377 15.053692 "Burundi" 2001 43 29.890484050138404 -3.3664890597484587 30683868 85.16896 9.730244 4.5877957 .00014013541 18.653854 "Burundi" 2002 43 29.890484050138404 -3.3664890597484587 10346697 85.16896 10.55954 3.318508 .002606129 24.11573 "Burundi" 2003 43 29.890484050138404 -3.3664890597484587 11668818 37.39759 16.072147 4.2280774 .0003042936 40.55007 "Burundi" 2004 43 29.890484050138404 -3.3664890597484587 23670808 37.39759 13.36723 3.590096 .0009704344 30.59273 "Burundi" 2005 43 29.890484050138404 -3.3664890597484587 6419901 37.39759 12.203648 3.106492 .001510929 26.47682 "Burundi" 2006 43 29.890484050138404 -3.3664890597484587 41853944 15.168947 12.589868 2.953567 .0002904715 24.11697 "Burundi" 2007 43 29.890484050138404 -3.3664890597484587 43827180 15.168947 14.562042 3.1991005 .0020577402 32.556114 "Burundi" 2008 43 29.890484050138404 -3.3664890597484587 40252976 15.168947 16.03938 2.511247 .003014635 33.390926 "Burundi" 2009 43 29.890484050138404 -3.3664890597484587 43073832 47.88997 16.064018 4.1759205 .00023527916 31.35737 "Burundi" 2010 43 29.890484050138404 -3.3664890597484587 19120680 19.694157 16.499815 2.4659526 .0004303128 24.13432 "Burundi" 2011 43 29.890484050138404 -3.3664890597484587 49871664 16.644947 18.202635 5.526068 .00608474 23.837616 "Burundi" 2012 43 29.890484050138404 -3.3664890597484587 38450320 4.094543 17.968384 2.930594 .006373889 16.75686 "Burundi" 2013 43 29.890484050138404 -3.3664890597484587 3105419 15.492642 18.025589 4.826927 .0019991721 16.06113 "Burundi" 2014 43 29.890484050138404 -3.3664890597484587 28792640 10.01763 16.675688 5.222345 .0032952614 14.138004 "Burundi" 2015 43 29.890484050138404 -3.3664890597484587 29649136 10.01763 15.867878 4.0636916 .006110306 17.160543 "Burundi" 2016 43 29.890484050138404 -3.3664890597484587 27717292 19.8039 15.867878 4.014459 .0014460522 1.6105767 "Cabo Verde" 1996 47 -23.980244187563258 15.94123272842388 1079580 73.68158 4.97791 2.0602558 .00044109585 .6995824 "Cabo Verde" 1997 47 -23.980244187563258 15.94123272842388 8690164 73.68158 5.238934 1.9416233 .00016903764 .6958637 "Cabo Verde" 1998 47 -23.980244187563258 15.94123272842388 678113 107.11163 6.891724 1.9865538 .00013159527 .7066209 "Cabo Verde" 1999 47 -23.980244187563258 15.94123272842388 3682876 175.2689 4.812697 3.1595404 .00004297999 .3678554 "Cabo Verde" 2000 47 -23.980244187563258 15.94123272842388 3393409 175.2689 5.948762 2.48944 .00004297999 .4142639 "Cabo Verde" 2001 47 -23.980244187563258 15.94123272842388 4014103 109.38757 6.088899 2.537064 .00005060887 .39662606 "Cabo Verde" 2002 47 -23.980244187563258 15.94123272842388 12004701 119.30764 6.153033 2.8656545 .0001014779 .4471822 "Cabo Verde" 2003 47 -23.980244187563258 15.94123272842388 7526307 256.83124 7.545666 4.680163 .00009842752 .6158354 "Cabo Verde" 2004 47 -23.980244187563258 15.94123272842388 1633652 395.6086 6.924398 4.269916 .000027964863 .51325387 "Cabo Verde" 2005 47 -23.980244187563258 15.94123272842388 405259 395.6086 10.883353 2.3355591 .00038174225 .4966612 "Cabo Verde" 2006 47 -23.980244187563258 15.94123272842388 4797187 222.56404 14.21772 5.175218 .00010352361 .4258945 "Cabo Verde" 2007 47 -23.980244187563258 15.94123272842388 1981899 134.38028 20.077036 2.590663 .00007194954 .4890847 "Cabo Verde" 2008 47 -23.980244187563258 15.94123272842388 3789383 40.7911 21.18702 2.724154 .0006496905 .4789932 "Cabo Verde" 2009 47 -23.980244187563258 15.94123272842388 1930433 241.53455 13.561744 4.885962 .0004039528 .5440446 "Cabo Verde" 2010 47 -23.980244187563258 15.94123272842388 3111906 241.53455 19.19068 3.633831 .0012382133 .4882983 "Cabo Verde" 2011 47 -23.980244187563258 15.94123272842388 1016100 241.53455 19.53127 5.325577 .0012382133 .5040318 "Cabo Verde" 2012 47 -23.980244187563258 15.94123272842388 13033809 241.53455 17.110106 6.927062 .0012382133 .6315693 "Cabo Verde" 2013 47 -23.980244187563258 15.94123272842388 3598648 486.7593 17.537294 5.38643 .00009642816 .6164749 "Cabo Verde" 2014 47 -23.980244187563258 15.94123272842388 7303517 47.83794 14.455986 4.946973 .00058180647 .6374309 "Cabo Verde" 2015 47 -23.980244187563258 15.94123272842388 2255516 118.7822 11.99401 4.581484 .00027740665 .9731719 "Cabo Verde" 2016 47 -23.980244187563258 15.94123272842388 3386806 378.1909 11.99401 5.647484 .00007613715 2.5621974 "Cameroon" 1996 45 12.74475426235271 5.691944491582316 533361 184.4042 33.680553 1.444963 .00013439376 11.133176 "Cameroon" 1997 45 12.74475426235271 5.691944491582316 277646 316.1618 36.631943 1.6045707 .00008144102 10.110292 "Cameroon" 1998 45 12.74475426235271 5.691944491582316 100161712 366.6939 25.994413 1.739605 .00007219431 6.585636 "Cameroon" 1999 45 12.74475426235271 5.691944491582316 1219541 597.35986 32.87741 2.8341885 .00009316107 6.439063 "Cameroon" 2000 45 12.74475426235271 5.691944491582316 185960288 597.35986 42.68597 4.1181173 .006778507 12.53282 "Cameroon" 2001 45 12.74475426235271 5.691944491582316 154324928 14.409028 35.830597 1.8690256 .002877679 7.299567 "Cameroon" 2002 45 12.74475426235271 5.691944491582316 1076413 51.01697 31.974276 3.6749325 .0012442356 6.463125 "Cameroon" 2003 45 12.74475426235271 5.691944491582316 1684635 249.64784 31.883957 3.13538 .0003055952 6.664687 "Cameroon" 2004 45 12.74475426235271 5.691944491582316 55982144 144.59073 36.479115 3.767187 .0007140038 7.376881 "Cameroon" 2005 45 12.74475426235271 5.691944491582316 48032936 186.1142 39.42979 1.305245 .0005338776 9.123825 "Cameroon" 2006 45 12.74475426235271 5.691944491582316 3643604 6.627545 38.42289 1.8914214 .013973154 10.495419 "Cameroon" 2007 45 12.74475426235271 5.691944491582316 6781053 20.9656 35.34347 5.158409 .000698988 11.072228 "Cameroon" 2008 45 12.74475426235271 5.691944491582316 784496 88.99214 36.775566 5.269396 .00018428317 12.632468 "Cameroon" 2009 45 12.74475426235271 5.691944491582316 505799 204.6187 22.418926 1.0762267 .00002423802 6.996937 "Cameroon" 2010 45 12.74475426235271 5.691944491582316 54717040 6.532066 26.91963 2.0863066 .019013675 8.39342 "Cameroon" 2011 45 12.74475426235271 5.691944491582316 4957558 114.69098 32.785286 1.6714076 .00006320685 9.957578 "Cameroon" 2012 45 12.74475426235271 5.691944491582316 58665808 89.95172 32.423656 1.7631272 .001932705 10.509792 "Cameroon" 2013 45 12.74475426235271 5.691944491582316 2335333 30.900267 27.463024 4.0983453 .005341635 9.4053335 "Cameroon" 2014 45 12.74475426235271 5.691944491582316 3372189 18.833683 23.32754 5.070136 .017675556 8.440559 "Cameroon" 2015 45 12.74475426235271 5.691944491582316 266000 27.60953 14.264127 2.3660214 .0020408875 6.167601 "Cameroon" 2016 45 12.74475426235271 5.691944491582316 3876119 6.701687 14.264127 2.859117 .004225527 23.95937 end format %ty year
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