Economic variables Sample Clauses

Economic variables. Similarly to environmental variables, the types and relevance of economic xxxx- xxxxx will differ depending on the specific region to be implemented. For this rea- son, the definition of these variables is not explicitly done in the model. Neverthe- less, economic variables are considered exogenous to the model. These variables can be static (an initial value that does not change during the simulation) or dy- namic (a series of values that changes at each time step). A preliminary list of non- commodity outputs to be used as output variables is:
Sample 1
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Economic variables. ‌ For my matching approach, the relevant financial covariates are the latent conditions that may be tied to crisis: GDP change from the previous month, real exchange rate (REER), unemployment, log of GDP, and foreign exchange reserves (Xxxxxxx and Saravelos 2012). I 3I use propensity score matching in the main results of the paper, but I also performed Maha- lanobis distance matching as a robustness check and obtained similar results – see Appendix. will use these variables to find comparable cases within the pool of states for a more accurate comparison. The World Bank’s Global Economic Monitor provides the data for unemploy- ment, real exchange rate, (REER), foreign exchange reserves, and GDP (World Bank 2019); IMF agreement data comes from the IMF’s website (International Monetary Fund 2019). The literature suggests that these indices are suitable covariates upon which to match for this study. Due to data limitations – mostly because data such as GDP is only available at the quarterly level, I needed to impute the values for the quarters within each month. I did so by splitting each quarter to three months in R and imputing the values. For example, if country A had 2.65% GDP growth in Q1, then the first three months of the year would each take on the value of 2.65% as well. Though this method is imperfect, I believe it is defensible for this study and not doing so comes at the expense of losing a potentially powerful explanatory covariate. I calculated GDP percent change as the change (as a percent) from the previous month. It would be ideal to have other variables, such as debt and balance of payment data in order to boost the matching analysis, but this data was unavailable for most of the countries in the sample.
Sample 1

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