Project 3 Report

Report

Intro 

When looking into a research topic we wanted to do one that had an interesting question and background that would be intriguing to not only us but also the audience. We feel that minimum wage is a topic that can affect everyone at some point in time and could be looked at even closer in the case for governors or other members of council that could be looking to increase population. This question is looking to answer if minimum wage rates have an effect on the population and growth of a state. Once we found our data for population rates and minimum wage, we then created visualizations that helped to show the changes over time. For the points in time, we used 1990, 2000, and 2010 we felt that these three points were suitable since they were found in both our data sets.

Question 

When minimum wage on the state level is higher than the federal minimum wage does that influence population change from 1990-2010?​ 

Background 

Treatment Group​ 

States that have a higher state minimum wage then federal minimum wage ​ 

Control Group​ 

States that have a state minimum wage equal to that of the federal minimum wage 

Pre-Treatment 

1990-1999 

Post-Treatment 

2000-2010 

Data 

- Population data at state level in 1990, 2000, and 2010​ 

- State and Federal minimum wages at state level in 1990, 2000, and 2010​ 

Equation​ 

mlm <- lm(Population ~ treated+ treated_group + treat_time , data=data)​ 

​ 

treated: If the state was part of the treatment group from got a 1 for 2000 and 2010​ 

treat_group: If the state minimum wage was higher than the federal minimum wage it got a 1​ 

treat_time: For all states in 2000 and 2010 to show that it was during the treatment period​ 

-Difference in differences to see if having a higher state minimum wage will have greater population increases after 2000 

Analysis 

When analyzing our data, we looked at the control group being all the states that followed the federal minimum wage with their state minimum wage. And then our treatment group was the states that had a higher state minimum wage for all three years that we looked at. We then also had to have a treatment time for all the states, so we knew then they were part of the treatment time and so all states got a 1 for 2000 and 2010. When we did this, we seen that we got a coefficient on the treated groups of –0.047 in R. This told us that we should be seeing a decrease of 4.7 percent in the population in the treated group during the treatment time due to them being able to have a higher minimum wage then the federal minimum wage. This is what we ended up seing in the data when we put it into a visualization that even though there was a decrease it was only a slower increase in population that was occurring. And the treatment group was basically doing the exact same thing as the control group. And we were able to say that 4.7% was not very much when you are talking about the 40,000 population change when there is an average population of about 6.5 million.   

Conclusion 

After analyzing the results, we found that there is a 46,609 increase within the treated groups from 1990 to 2000. However, one point we found interesting was that from 2000 to 2010 it had a slower increase of only 6,125. The overall increase from 1990 to 2010 was 40,484. This was very small relative to the entire treated group. With this we feel that there is a change, but not significant enough resulting in us concluding that having a higher state minimum wage than federal minimum wage does not have a large effect either direction in change in population​. 

Appendix

R Code

setwd("~/AREC_ps12")

# This script demonstrates how to estimate a difference in differences model using the did package

# load packages

library(pacman)

p_load(tidyverse,did,fixest,modelsummary)

# call in dataset

data=read.csv("Population_min_wage_chart_new.csv")

#########4 years of data for each county in Colorado Counties#######

#### First treat, first year the policy was implemented##############

data$log_value=log(data$Population)

# create binary treatment indicator

mlm <- lm(log_value ~ treated + treated_group + treat_time + Year + State,

          data=data)

summary(mlm)

# generate regression table

modelsummary(mlm,coef_map = "treated",output = "markdown")

##-0.037 is the value of the shift of the treatment (average treatment affect on the treated units)

# create binary treatment indicator#### same as above but simpler outcome

felm <- feols(log_value ~ treated | treated_group + treat_time,

              data=data)

summary(felm)

summary(data$log_value)

sink.number()

while (sink.number()>0) sink()

sink("Project_3.R.txt")

source("Project_3.R",echo=TRUE)

sink()

Log Text

> setwd("~/AREC_ps12")

> # This script demonstrates how to estimate a difference in differences model using the did package

> 

> # load packages

> library(pacman)

> p_load(tidyverse,did,fixest,modelsummary)

> # call in dataset

> data=read.csv("Population_min_wage_chart_new.csv")

> #########4 years of data for each county in Colorado Counties#######

> #### First treat, first year the policy was implemented##############

> 

> da .... [TRUNCATED] 

> # create binary treatment indicator

> mlm <- lm(log_value ~ treated + treated_group + treat_time + Year + State,

+           data=data)

> summary(mlm)

Call:

lm(formula = log_value ~ treated + treated_group + treat_time + 

    Year + State, data = data)

Residuals:

      Min        1Q    Median        3Q       Max 

-0.179916 -0.027853 -0.000651  0.028383  0.153132 

Coefficients: (1 not defined because of singularities)

               Estimate Std. Error t value Pr(>|t|)    

(Intercept)   -5.608056   2.995602  -1.872    0.065 .  

treated       -0.047353   0.032588  -1.453    0.150    

treated_group  0.252483   0.059100   4.272 5.48e-05 ***

treat_time     0.035983   0.026873   1.339    0.185    

Year           0.009355   0.001505   6.215 2.44e-08 ***

StateAL        2.174195   0.054962  39.558  < 2e-16 ***

StateAR        1.659253   0.054962  30.189  < 2e-16 ***

StateAZ        2.286235   0.054962  41.597  < 2e-16 ***

StateCA        3.980689   0.054962  72.426  < 2e-16 ***

StateCO        2.112063   0.054962  38.428  < 2e-16 ***

StateCT        1.698666   0.054962  30.906  < 2e-16 ***

StateDC       -0.052992   0.054962  -0.964    0.338    

StateFL        3.445060   0.054962  62.681  < 2e-16 ***

StateGA        2.770458   0.054962  50.407  < 2e-16 ***

StateID        0.927844   0.054962  16.882  < 2e-16 ***

StateIN        2.485591   0.054962  45.224  < 2e-16 ***

StateKS        1.671474   0.054962  30.412  < 2e-16 ***

StateKY        2.079627   0.054962  37.838  < 2e-16 ***

StateLA        2.172829   0.054962  39.533  < 2e-16 ***

StateMD        2.351514   0.054962  42.784  < 2e-16 ***

StateMO        2.404732   0.054962  43.753  < 2e-16 ***

StateMS        1.716248   0.054962  31.226  < 2e-16 ***

StateMT        0.577236   0.054962  10.502  < 2e-16 ***

StateNC        2.767264   0.054962  50.349  < 2e-16 ***

StateND        0.260667   0.054962   4.743 9.50e-06 ***

StateNE        1.221844   0.054962  22.231  < 2e-16 ***

StateNH        0.886648   0.054962  16.132  < 2e-16 ***

StateNJ        2.806934   0.054962  51.071  < 2e-16 ***

StateNY        3.622271   0.054962  65.905  < 2e-16 ***

StateOK        1.925400   0.054962  35.032  < 2e-16 ***

StateOR        1.674899   0.054962  30.474  < 2e-16 ***

StatePA        3.198161   0.054962  58.189  < 2e-16 ***

StateRI        0.502887   0.054962   9.150 6.17e-14 ***

StateSC        2.079759   0.054962  37.840  < 2e-16 ***

StateSD        0.406794   0.054962   7.401 1.43e-10 ***

StateTN        2.413535   0.054962  43.913  < 2e-16 ***

StateTX        3.721358   0.054962  67.708  < 2e-16 ***

StateUT        1.477875   0.054962  26.889  < 2e-16 ***

StateVA        2.643032   0.054962  48.088  < 2e-16 ***

StateVT       -0.044486   0.054962  -0.809    0.421    

StateWA        2.222998   0.054962  40.446  < 2e-16 ***

StateWI        2.358339   0.054962  42.909  < 2e-16 ***

StateWV        1.287623   0.054962  23.428  < 2e-16 ***

StateWY              NA         NA      NA       NA    

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.06731 on 77 degrees of freedom

Multiple R-squared:  0.9974, Adjusted R-squared:  0.996 

F-statistic:   700 on 42 and 77 DF,  p-value: < 2.2e-16

> # generate regression table

> modelsummary(mlm,coef_map = "treated",output = "markdown")

|         | Model 1 |

|:--------|:-------:|

|treated  | -0.047  |

|         | (0.033) |

|Num.Obs. |   120   |

|R2       |  0.997  |

|R2 Adj.  |  0.996  |

|AIC      | -272.3  |

|BIC      | -149.7  |

|Log.Lik. | 180.155 |

|F        | 699.968 |

> ##-0.037 is the value of the shift of the treatment (average treatment affect on the treated units)

> 

> 

> # create binary treatment indicator####  .... [TRUNCATED] 

> summary(felm)

OLS estimation, Dep. Var.: log_value

Observations: 120 

Fixed-effects: treated_group: 2,  treat_time: 2

Standard-errors: Clustered (treated_group) 

         Estimate Std. Error      z value  Pr(>|z|)    

treated -0.047353   3.76e-15 -1.25934e+13 < 2.2e-16 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Log-likelihood: -173.88   Adj. R2: 0.0211 

                        R2-Within: 8e-05 

> summary(data$log_value)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 

  13.02   14.27   15.15   15.02   15.64   17.43 

> sink("Project 3.R.txt")

Visualizations