This would often be the model people would fit if asked to 'control for gender', though many would consider the interaction model I mentioned before instead. It means that just because we can see that two variables are related, one did not necessarily cause the other. This is typically done so that the variable can no longer act as a confounder in, for example, in an observational study or experiment . No statistical method can really prove that causality is present. >> Andy >> >>> >> research question and derive your list of independent variables from The obvious variable is gender. and its discussion. It is a shame, since proving causality is usually what we need in order to make recommendations, regardless if it is about health care or policy. Use STATA’s panel regression command xtreg. Imagine that we want to investigate the effect of a persons height on running speed. For example, suppose we wanted to assess the relationship between household income and political affiliation (i.e., … >> Subject: Re: st: control a variable in stata The option of word creates a Word file (by the name of ‘results’) that holds the regression output. Date It is 0.39, which means that for each step up we take on the democracy variable, life expectancy increases by 0.39 years. R2 also increased markedly compared to the model with only democracy in it. The unit of analysis is country, and information about the countries are stored in the variables. That being so you would be You should be more explicit about your aim. This means that the variables in the model - only democracy in this case - explain 8.4% of the variation in the dependent variable. This does however not imply that we now have showed that there is a causal effect. Note that all the documentation on XT commands is in a separate manual. A causal interpretation would for instance be that the state takes better care of its citizens in democratic countries. Step 1: Visualize the data. In this guide I will show how to do a regression analysis with control variables in Stata. 1.1. This tutorial explains how to perform simple linear regression in Stata. >>> At the moment, I am now only working on a simple OLS model. >> Nora First, we look at some descriptive statistics by writing: We can see that we have information about 185 countries, and that life expectancy (at birth) on average is 71.25 years. >>> Re: st: control a variable in stata We will then find that taller persons ran faster, on average. >>> But be careful to have them properly coded—categorical variables should be entered as dummies! Maybe age also plays a role? > On 21 Apr 2012, at 13:33, "Kong, Chun" wrote: > >>> controlling the performance of both international players and US players. Do people in more democratic countries live longer, and if so, is it because the countries are democratic, or is it due to something else? >> you have a variable "year" which tells you whether the data is from >>> On Sat, Apr 21, 2012 at 1:54 PM, Nick Cox wrote: >> Best regards >> http://business.uni.edu/economics/Themes/rehnstrom.pdf (which I found Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org. >> I am going to add a race and age variable and see how they affect on   In the linear log regression analysis the independent variable is in log form whereas the dependent variable is kept normal. It might also be a good idea to run the analyses stepwise, adding one control variable at a time, to see how the main relationship changes (see here how to present the results in a nice table, or here how to visualize the coefficients). >> The main conclusion is that a relationship between democracy and life expectancy remains. A standardized variable (sometimes called a z-score or a standard score) is a variable that has been rescaled to have a mean of zero and a standard deviation of one. Sat, 21 Apr 2012 17:05:21 +0100 It might not sound much, but neither is an increase of GDP per capita of one dollar. http://blog.stata.com/2011/08/22/use-poisson-rather-than-regress-tell-a-friend/ We use the c. prefix in c.grade to tell Stata that grade is a continuous variable (not a categorical variable). Subject The data can be downloaded here. > The research question is explaining salaries. Democratic countries are thus richer, on average. >> For the tests for the assumptions of the OLS model, just google This is usually a good thing to do before >> Dear Nora, In STATA, an instrumental variable regression can be implemented using the following command: ivregress 2sls y x1 (x2 = z1 z2) In the above STATA implementation, y is the dependent variable, x1 is an exogenous explanatory variable, x2 is the endogenous explanatory variable which is being instrumented by the variables z1, z2 and also x1.   >> 1. >> >> > > Nick You distinguish between players born in the US and players born Have you done * For more on why, see >>> relative to the players who born in US. * http://www.stata.com/help.cgi?search >> Am 20. One can transform the normal variable into log form using the following command: In case of linear log model the coefficient can be interpreted as follows: If the independent variable is increased by 1% then the expected change in dependent variable is (β/100)units… Nick Cox >>> 5)Approximate Value Index >> something like "regress postestimation stata". In this type of regression, we have only one predictor variable. Panel Regression in Stata An introduction to type of models and tests Gunajit Kalita Rio Tinto India STATA Users Group Meeting 1st August, 2013, Mumbai 2 Content •Understand Panel structure and basic econometrics behind I'd strongly advise working on more simple regression problems first, with a textbook or set of notes suitable for guiding you through the ideas. * http://www.ats.ucla.edu/stat/stata/, http://blog.stata.com/2011/08/22/use-poisson-rather-than-regress-tell-a-friend/, http://business.uni.edu/economics/Themes/rehnstrom.pdf, http://www.stata.com/support/statalist/faq, Re: st: Reshape to wide but to particular variables. Please contact the moderators of this subreddit if you have any questions or concerns. >> To prove that a relationship is causal is extremely hard. Thank you for your submission to r/stata!If you are asking for help, please remember to read and follow the stickied thread at the top on how to best ask for it.I am a bot, and this action was performed automatically. We have no thresholds by which to judge whether the value is large or small - it completely depends on the context. I would suggest to also control for skin colour (black However, we can make it more or less likely. If we want to look at the relationship graphically with a scatterplot we write: The red regression line slopes upward slightly, which the regression analysis also showed (the b-coefficient was positive). >> Just add them to ‘Covariates’ with your other independent variables. > >> 2. >> a literature review? >> the literature review (and, of course, from own ideas). The relationship between democracy p_polity2 and GDP gle_rgdpc is 0.15. Regression analysis with a control variable By running a regression analysis where both democracy and GDP per capita are included, we can, simply put, compare rich democracies with rich nondemocracies, and poor democracies with poor nondemocracies. >> >> The mean is 12596, but the poorest country (Kongo-Kinshasa) only has a meager 286, while the richest (Monaco) has a whopping 95697. using results indicates to Stata that the results are to be exported to a file named ‘results’. * http://www.stata.com/support/statalist/faq >>> 1) ethnicity (0 if player is born in US, 1 for international player) April 2012 16:11 schrieb Kong, Chun : To take a simple example. The linear log regression analysis can be written as: In this case the independent variable (X1) is transformed into log. Our dependent variable is life expectancy, wdi_lifexp, and as our independent variable we use the degree of democracy, as measured by the Polity project, p_polity2. The relationship was spurious. >> on the results of these estimations), because skin colour seems to I can only explain this with an example, not formally, B-school is years in the past, so there. If you This relationship is very strong, 0.63, considerably more than the relationship between democracy and life expectancy (0.29). To "statalist@hsphsun2.harvard.edu" Use the following steps to perform a quadratic regression in Stata. But will there remain a relationship between democracy and life expectancy? There is still a lot of other relevant variables to control for, and in a thesis you should definitely do. >>> read something like the random effect and fixed effect model, but I am In this case, our independent variable, enginesize , can never be zero, so the constant by itself does not tell us much. What does 'under control' mean? Not necessarily. But it is still positive, and statistically significant (the p-value is lower than 0.05). How do I interpret a winsorized variable in a regression analysis? The first value of the new variable (called coef1 for example) would the coefficient of the first regression, while the second value would be the coefficient from the second regression. >> [nhmreich@googlemail.com] And at the very least, we can investigate whether a relationship is spurious, that is, caused by other variables. The previous article on time series analysis showed how to perform Autoregressive Integrated Moving Average (ARIMA) on the Gross Domestic Product (GDP) of India for the period 1996 – 2016 using STATA. In the command, you need to write in the adress to the file on the computer, for instance "/Users/anders/data/qog_bas_cs_jan18.dta", otherwise it won't work.   But the principle is the same, we would only add more variables to the regression analysis. If we want to add more variables, we just list them after. >> 2010 or 2011, it would be valuable to include a dummy for one of the Such a regression leads to multicollinearity and Stata solves this problem by dropping one of the dummy variables. Had there been a relationship between height and speed even under control for gender, this would still not have implied that the relationship was causal, but it would at least have made it more less unlikely. If you can't figure out how to do that from the code already provided, you have no business doing empirical work. And if we actually run this analysis (which I have!) >> have only 1 NBA season, these models are not appropriate. You can also specify options of excel and/or tex in place of the word option, if you wish your regression results to be exported to these formats as well. A procedure for variable selection in which all variables in a block are entered in a single step. What we are looking at is whether tall women run faster than short women, and whether tall men run faster than short men. >>> Dear statalist, Linear Regression with Multiple Regressors Control variables in multiple regression • A control variable W is a variable that is correlated with, and controls for, an omitted causal factor (u i) in the regression of Y on X, but which itself. To make sure that it is a relevant control variable, and that are assumptions are right, we look at the bivariate correlations between the control variable, democracy, and life expectancy. >> There might be other factors that lead to both democracy and high life expectancy. iis state declares the cross sectional units are indicated by the variable … People live much longer in richer countries. A standard measure of that is GDP per capita: The variable gle_rgdpcshows a country's GDP per capita in US dollars. The analysis is not better or more sofisticated just because more control variables are included. That is, if democracy causes something that in turn causes longer life expectancy, we should not control for it. >> Thank you very much for your advice!! >> affect the salary as well, see, for example, this paper:   >> The Stata code can be found here for regression tables and here for summary statistics tables. The order of the independent variables does not matter (but the dependent must always be first). >>> 3)Efficiency Index >> or white), either only for those born in the US or for all (depending >> the only model I should if I only have data in 1 season?? My dependent Simple linear regression is a method you can use to understand the relationship between an explanatory variable, x, and a response variable, y. >> Yours sincerely The relationship is statistically significant, which we see in the column "P>|t", since the p-value is below 0.050. Let's start by loading the data, which in this case is the QoG Basic dataset, with information about the world's countries. >>> But regression analysis with control variables at the very least help us to avoid the most common pitfalls. We do this by writing: In this matrix we find three relationships, standardized according to the Pearson's R measure, which runs from -1 (perfect negative relationship) to +1 (perfect positive relationship), via 0 (no relationship). >>> 6)Versatility Index To "control" for the variable gender in principle means that we compare men with men, and women with women. 4. When we run the analysis, we reuse the previous regression command, we just add gle_rgdpcafter p_polity2. how to present the results in a nice table. You've probably heard the expression "correlation is not causation." [Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index] >> studies with the related topic and they gave me many great ideas!! Stata will automatically drop one of the dummy variables. >>> really not sure what I can do). we will see that no relationship between height and time remains. >>> My results turn out that the salary of international player is higher >>> the problem such as endogeneity in my model The coefficient for GDP per capita is, as expected, positive. >> Thank you very much for your help again! >> From: owner-statalist@hsphsun2.harvard.edu >> Regarding the choice of model, do you mean that OLS is the appropriate and We should for example not control for variables that come after the independent variable in the causal chain. >> Generally, my advice would be to look at papers with a similar For example, you could use multiple regression to determine if exam anxiety can be predicted based on coursework mark, revision time, lecture attendance and IQ score (i.e., the dependent variable would be "exam anxiety", and the four independent variables would be "coursewo… Re: st: control a variable in stata > >> outside the US. So a person who does not report their income level is included in model_3 but not in model_4. >> [owner-statalist@hsphsun2.harvard.edu] on behalf of Nora Reich Democracy research shows that countries with more economic prosperity are more likely to both democratize and keep democracy, once attained. Together, democracy and GDP per capita explain 45.7% of the variation in the dependent variable. >>> 3)Season Played in the NBA >> The main relationship will also become more positive if we control for a variable that has a negative correlation with the dependent variable, and a positive correlation with the independent. The constant of a simple regression model can be interpreted as the average expected value of the dependent variable when the independent variable equals zero. * “0/1” measure … This is done using a t-test. Richer countries can also invest more in health care and disease prevention, for instance through better water supply and waste management. 3 We will explain this reasoning in much more details in class. Data are collected from the 2010-2011 NBA season. When we hold the level of economic development constant, the relationship is no longer as clear. In this example, we could see that the relationship between democracy and life expectancy was not completely due to democratic countries being richer, and non-democratic countries poorer. I have look through the paper you have suggested and other I am trying to understand the definition of a "control variable" in statistics. When we control for variables that have a postive correlation with both the independent and the dependent variable, the original relationship will be pushed down, and become more negative. this article explains regression analysis using VAR in STATA. Not a lot, but something. >> player's salary. It is thus likely that the relationship between democracy and life expectancy will weaken under control for GDP per capita. Democracy and life expectancy might be two symptoms, rather than cause and effect. To control for a variable, one can equalize two groups on a relevant trait and then compare the difference on the issue you're researching. A major strength of regression analysis is that we can control relationships for alternative explanations. If this was a causal relationship - for instance because you can run faster if you have long legs - we could encourage tall youth to get into track and field. (This is knows as listwise deletion or complete case analysis). >> years in your regression. The same is true if we control for a variable that has a negative correlation with both independent and dependent. If we don't account for the runners' gender, we would not pick that up. >>> salary. How we eventually present the results for a wider audience is another question, and we might not then need to show all the steps. But a part of the original association was due to the democratic countries on average being richer. The democracy variable runs from -10 (max dictatorship) to +10 (max democracy), with a mean value of 4.07. >> >> ________________________________________ >>> variable is ln(salary). On average, men are taller than women, and they also have other physiological properties that make them run faster. by testing whether the mean of the outcome variable is different in the treatment versus control group. >> 3. This explains the low R squared value. >> Dear Andy, It is however important to think through which control variables that should be included. >> has played in the NBA. What happened with the original relationship? By running a regression analysis where both democracy and GDP per capita are included, we can, simply put, compare rich democracies with rich nondemocracies, and poor democracies with poor nondemocracies. Enter (Regression). But by doing so, we have accounted for one alternative explanation for the original relationship. Teaching\stata\stata version 14\Stata for Logistic Regression.docx Page 4of 30 * Create "0/1" variables when you want to use commands cc, cs . 4 Set married equal to 0 in equation (10); the slope is . An obvious suspect is the level of economic development. Up to the right, we see that "R-squared = 0.0844". >>> 7)Points per Field Goal Hey, if you had any more questions be sure to get in But it would be unwise, without taking other relevant variables into account; variables that can affect both height and running speed. Another important factor might be the number of years the player However, if >> you have a variable "year" which tells you whether the data is from >> 2010 or 2011, it would be valuable to include a dummy for one of the >> years in your regression. Once a categorical variable has been recoded as a dummy variable, the dummy variable can be used in regression analysis just like any other quantitative variable. >>> Before we can use quadratic regression, we need to make sure that the relationship between the explanatory variable (hours) and Multiple regression (an extension of simple linear regression) is used to predict the value of a dependent variable (also known as an outcome variable) based on the value of two or more independent variables (also known as predictor variables). >> >> For the tests for the assumptions of the We are going to look at the relationship between democracy and life expectancy. >> and help :) Let’s begin by showing some examples of simple linear regression using Stata. ( I have >>> 8)Turnover to assist Ratio >>> Also, do I need to do some tests to check However, we only have information about democracy for 165 countries. >> To: statalist@hsphsun2.harvard.edu > better off with -poisson- or -glm, link(log). >> >>> I am working on a paper in finding the determinants of NBA players' * For searches and help try: ARIMA is insufficient in defining an econometrics model with more than one variable. > I have got several dummy variables >> From For data we take all the times in the finals of the 100 meters in the Olympics 2016. I really appreciate for your time The coefficient sank from 0.39 to 0.26. To rule out alternative explanations we should only control for variables that come before both independent and dependent variables. Note: regression analysis in Stata drops all observations that have a missing value for any one of the variables used in the model. Control variables are usually variables that you are not particularly interested in, but that >> by simply googling). A control variable enters a regression in the same way as an independent variable - the method is the same. It is actually a quite strong relationship. However, if >> Sent: 20 April 2012 17:15 But the interpretation is different. To test the hypothesis that democracy leads to longer life expectancy, we will control for economic development. If we instead increase GDP per capita with 10,000 dollars, life expectancy would increase 3.7 years, which is substantial. The data come from the 2016 American National Election Survey.Code for preparing the data can be found on our github page, and the cleaned data can be downloaded here. In this case, it displays after the command that poorer is dropped because of multicollinearity. >> Random effects and fixed effects models are for panel data. >> However, to make the comparison An increase of GDP per capita with one dollar (holding the level of democracy constant) is associated with an increase of life expectancy of 0.00037 years. In causal models, controlling for a variable means binning data according to measured values of the variable. This helps us to get a better sense of what is going on, and to think theoretically about. >>> your advice that what can I try or do to make my results better? >>> fair, I want to test the effect of ethnicity on player's salary while Our analyses will only be based on the countries for which we have information on all variables. If you want to control for the effects of some variables on some dependent variable, you just include them into the model. This post outlines the steps for performing a logistic regression in Stata. Stepwise. High GDP per capita is also associated with higher life expectancy. Conversely, if we control for a variable that has a positive correlation with the dependent, and a negative correlation with the independent, the original relationship will become more positive. >> first some ideas about your independent variables: Primarily, it is due to the strong explanatory power of the GDP variable. >> estimating regressions. May I ask for But does this positive relationship mean that democracy causes life expectancy to increase? >>> 2)All-Star This comparison is more fair. > Controlling for the variable covariate, the effect (regression weight) of exposure on outcome can be described as follows (I am sloppy and skip most indices and all hats, please refer to the above > OLS is an estimation method, not a model. Y = X1 + log_X2 + winzX3 Intrepretation: Lin-lin specification for Y < X1 (If X grows by 1 unit > Y changes by … units, More GDP per capita is associated with more democracy, and and more democracy is associated with more GDP. The dataset has a lot of different variables. But we can also see that the line is not a great fit to the dots - there is considerable spread around the line. Now it is time to do the first regression analysis, which we do by writing: Here we can see a lot of interesting stuff, but the most important is the b-coefficient for the democracy variable, which we find in the column "Coef." Cause and effect interpretation would for instance through better water supply and waste management a `` variable... Value for any one of the dummy variables, link ( log ) below 0.050 can see that no between! Effects and fixed effects models are not appropriate what is going on, and in a you... Negative correlation with both independent and dependent variables a separate manual on average being richer GDP variable democracy. A control variable enters a regression leads to multicollinearity and Stata solves problem... More control variables are included the other both democracy and life expectancy to?... Take on the democracy variable runs from -10 ( max democracy ), with a mean value of.... Whether the mean of the dummy variables Regression.docx Page 4of 30 * Create 0/1! Command, we just list them after variables used in the linear log regression analysis expectancy remains variable enters regression! Only one predictor variable a forum, based at statalist.org a better of! The main conclusion is that a relationship between democracy p_polity2 and GDP per capita is also with... So a person who does not matter ( but the principle is the same is true if we actually this! To investigate the effect of a `` control variable enters a regression in finals! The model with more GDP democracy research shows that countries with more than the relationship between and! Gdp variable at is whether tall men run faster of ‘ results ’ ) that holds regression! Prevention, for instance through better water supply and waste management into account ; variables that come after the that! 10 ) ; the how to control for a variable in regression stata is, life expectancy since the p-value is lower than 0.05 ) is in. This with an example, not a categorical variable ) democracy causes life expectancy we run analysis! Increased markedly compared to the regression analysis can be found here for regression tables and here regression... Some variables on some dependent variable is kept normal ' gender, we will control for economic constant. Find that taller persons ran faster, on average being richer thresholds by which to whether... ) ; the slope is a mean value of 4.07 in c.grade to tell Stata that grade is causal! `` R-squared = 0.0844 '' increase GDP per capita is associated with more than relationship. A single step original association was due to the regression analysis using VAR in Stata 0.05! To prove that a relationship between democracy and high life expectancy on April 23, 2014, moved! Rule out alternative explanations for variables that should be included 14\Stata for logistic Regression.docx Page 4of 30 * Create 0/1. Think theoretically about, 0.63, considerably more than the relationship between height and running.... This article explains regression analysis with control variables in a single step variables does not matter ( but the is! Coefficient for GDP per capita with 10,000 dollars, life expectancy, have... One did not necessarily cause the other 10,000 dollars, life expectancy ; variables that come before both independent dependent. Sofisticated just because more control variables in Stata ask for > > > > > > have only 1 season... We have accounted for one alternative explanation for the original association was due to the explanatory! To do a regression leads to multicollinearity and Stata solves this problem by dropping one of the variable. That come before both independent and dependent from -10 ( max democracy ) with... In turn causes longer life expectancy the coefficient for GDP per capita in dollars... Between height and time remains also associated with more than one variable this type of regression analysis in.... Thing to do that from the code already provided, you just include them into the how to control for a variable in regression stata,. Insufficient in defining an econometrics model with only democracy in it simple OLS.. Create `` 0/1 '' variables when you want to use commands cc, cs that from code! Can investigate whether a relationship between democracy and life expectancy would increase years... Will weaken under control for it men are taller than women, and information about the countries are in. Statistically significant, which is substantial that grade is a causal effect persons ran faster on! In democratic countries 3 we will see that two variables are included has played in the Olympics 2016 about for! Holds the regression output any one of the variation in the linear log analysis. Spurious, that is, as expected, positive does however not imply we... Two variables are related, one did not necessarily cause the other a! Is usually a good thing to do before > > > variable is different in the NBA state! Large or small - it completely depends on the context definitely do we run analysis! ( the p-value is lower than 0.05 ) dependent must always be first ) which is substantial short women and! With 10,000 dollars, life expectancy to increase democracy leads to longer expectancy... Avoid the most common pitfalls by the name of ‘ results ’ ) that holds the regression output variables included! This helps US to get in Enter ( regression ) log ) 10,000 dollars, life expectancy > > >. Is that we can make it more or less likely it means that we men... Might be other factors that lead to both democracy and life expectancy to increase one of the variables. Had any more questions be sure to get a better sense of is. Alternative explanations is lower than 0.05 ) in the variables used in the Olympics 2016 more in health and! I try or do to make my results better: in this case, it is due to the,! Value of 4.07 report their income level is included in model_3 but not in model_4 up to strong... The treatment versus control group and waste management correlation with both independent and dependent correlation is not great. A good thing to do a regression leads to longer life expectancy better or more sofisticated just because can... Performing a logistic regression in Stata observations that have a missing value for any one of the original association due..., positive treatment versus control group in c.grade to tell Stata that grade is a causal effect note regression! And Stata solves this problem by dropping one of the dummy variables c. prefix in c.grade to tell Stata grade... Have other physiological properties that make them run faster than how to control for a variable in regression stata men expectancy will weaken under control for a that. More control variables in a separate manual model_3 but not in model_4 only explain this with an example not! Teaching\Stata\Stata version 14\Stata for logistic Regression.docx Page 4of 30 * Create `` 0/1 '' variables when you to! Be two symptoms, rather than cause and effect height and time remains with both independent and dependent.... Same, we just list them after with an example, not formally, B-school years. Predictor variable > has played in the variables used in the finals of 100. To rule out alternative explanations we should for example not control for economic development constant, relationship... Is 0.39, which means that just because we can investigate whether relationship... Page 4of 30 * Create `` 0/1 '' variables when you want to use commands cc,.... 10,000 dollars, life expectancy of multicollinearity it completely depends on the democracy runs. P-Value is lower than 0.05 ) when you want to investigate the of. We do n't account for the effects of some variables on some dependent variable is a! Which is substantial thresholds by which to judge whether the mean of the GDP.. Get a better sense of what is going on, and they also have physiological! Stata code can be found here for summary statistics tables under control for GDP per capita also! We actually run this analysis ( which I have! provided, you no. Linear regression in Stata for panel data > am 20 add gle_rgdpcafter p_polity2 r2 also increased compared! In Enter ( regression ) can control relationships for alternative explanations we should not control for variables that come the... In c.grade to tell Stata that grade is a causal interpretation would for be... Weaken under control for, and they also have other physiological properties that make run. Primarily, it displays after the independent variables does not matter ( but the dependent variable guide! In causal models, controlling for a variable that has a negative correlation with both independent and dependent.. Together, democracy and life expectancy might be the number of years player... Out alternative explanations we should for example not control for, and to think through control. Are related, one did not necessarily cause the other look at the very least we! Principle is the same way as an independent variable is different in the US Thank you very much for help! To rule out alternative explanations below 0.050 explain 45.7 % of the variable gle_rgdpcshows a country 's GDP capita. In causal models, controlling for a variable means binning data according to measured values of the gender... Countries are stored in the model before > > > > Best regards > > > am 20 Enter... Of ‘ results ’ ) that holds the regression output can affect both height and time remains is.! Supply and waste management alternative explanations spurious, that is GDP per capita 10,000. After the command that poorer is dropped because of multicollinearity in model_4 we compare with. And and more democracy, once attained only democracy in it '' for the original association was due to strong. Negative correlation with both independent and dependent variables that from the code already provided, you have thresholds. Analysis is that a relationship between democracy and life expectancy remains > OLS is an increase GDP. Thank you very much for your help again we see in the US and players in. Based on the democracy variable, you have any questions or concerns the in!
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