Econometrics

Extract

[...] other variable names . desc_stats cbind(Var_names, desc_stats) This creates a new column named `Var_names` with specified variable names and binds it to the left side of the `desc_stats` data frame. - desc_stats desc_stats rename(No_Tippelzones = V1, Tippelzones = V2) This renames the first and second columns to "No_Tippelzones" and "Tippelzones" respectively. - stargazer(as.data.frame(desc_stats), summary = FALSE, rownames = FALSE, type = "html", out = ffOut("desc_stat.doc")) Using the `stargazer` function, the formatted data frame is exported as an HTML table inside a document named "desc_stat.doc". [...]

[...] However, like all statistical techniques, the outcome's integrity largely hinges on the assumptions' soundness. Exercise 2 Analyzing various crime types such as theft (`lntheftN`), public order offenses (`lnpordoffN`), and public violence (`lnpordviolN`) to validate the estimates is a strategic move for the following reasons: First of all, tippelzones might influence different crimes in varying ways. While they may reduce sexual crimes by creating a safer environment, other offenses like theft or public violence might remain unaffected. Then, uniform trends across crime categories after tippelzones' rollout could hint at other influencing factors. [...]

[...] Last, the code specifies an output format and destination, whereas the table did not. The new code will be the following : desc_stats_city df group_by(treat)%>% summarise(mean_city_char1 = round(mean(city_char1),2), sd_city_char1 = round(sd(city_char1),4), mean_city_char2 = round(mean(city_char2),2), sd_city_char2 = round(sd(city_char2),4), mean_city_char3 = round(mean(city_char3),2), sd_city_char3 = round(sd(city_char3),4)) ungroup() desc_stats_city as.data.frame(t(desc_stats_city)) for in seq(from=1, to=length(desc_stats_city), { desc_stats_city[i,] paste0("(", format(unlist(desc_stats_city[i,])),")") desc_stats_city desc_stats_city[c(-1),] Var_names_city c("City Characteristic "City Characteristic "City Characteristic desc_stats_city cbind(Var_names_city, desc_stats_city) desc_stats_city desc_stats_city rename(No_Tippelzones = V1, Tippelzones = V2) stargazer(as.data.frame(desc_stats_city), summary = FALSE, rownames = FALSE, type = "html", out = ffOut("desc_stat_city.doc")) The new table will focus on 3 city characteristics and provides the mean and standard deviation for each. [...]

[...] - In the study by Bisschop, Kastoryano, and van der Klaauw (2017), the effects of "tippelzones" or designated street prostitution zones on crime rates are examined. The research indicates that the establishment of a tippelzone can significantly reduce incidents of sexual abuse and rape. By regulating these zones and implementing a licensing approach, they seem to create a more secure setting for both sex workers and clients. - The study highlighted that cities in the Netherlands, such as Amsterdam, Rotterdam, The Hague, and Utrecht, implemented "tippelzones" as legally sanctioned areas for street prostitution. But not every Dutch city chose this path. [...]

[...] other statistical calculations . sdlndrugsN = round(sd(lndrugsN),4))%>% For each group, the code calculates the mean and standard deviation for several variables. The results are rounded to 2 decimal places for means and 4 for standard deviations. - ungroup() This removes the grouping by `treat`. - desc_stats as.data.frame(t(desc_stats)) The function transposes the data frame, switching rows and columns - for in seq(from=1, to=13, { desc_stats[i,] paste0("(", format(unlist(desc_stats[i,])),")") This loop goes through every second row of the data frame and wraps the values in parentheses. [...]