# Fixed Effects Estimation¶

Let’s say you want to find out whether certain variables relevant to the labor market, such as work experience or time in education, influence a person’s hourly wage. Other variables such as gender or marital status should also be taken into account. You decide to use the SOEP data to set up a fixed effects estimation model.

**Create a path with four subfolders:**

**Example:**

H:/material/exercises/do

H:/material/exercises/output

H:/material/exercises/temp

H:/material/exercises/log

These are used to store your script, log files, datasets, and temporary datasets. Open an empty do-file and define your paths with globals:

1 2 3 4 5 6 7 8 9 | ```
***********************************************
* Set relative paths to the working directory
***********************************************
global AVZ "H:\material\exercises"
global MY_IN_PATH "\\hume\rdc-prod\distribution\soep-long\soep.v33.1\stata_en\"
global MY_DO_FILES "$AVZ\do\"
global MY_LOG_OUT "$AVZ\log\"
global MY_OUT_DATA "$AVZ\output\"
global MY_OUT_TEMP "$AVZ\temp\"
``` |

The global “AVZ” defines the main path. The main paths are subdivided using the globals “MY_IN_PATH”, “MY_DO_FILES”, “MY_LOG_OUT”, “MY_OUT_DATA”, “MY_OUT_TEMP”. The global “MY_IN_PATH” contains the path to your data.

**a) Generate your own SOEPwage.dta dataset. The dataset should contain information on gross monthly wage, marital status, and other personal characteristics.**

To perform your analysis, you need different SOEP variables. The SOEP offers various options for a variable search:

Search the questionnaires for useful variables. (For more information, see the section Variable Search with Questionnaires)

Find a suitable variable in the topic list at paneldata.org (for more information, see the section Topic Search with paneldata.org)

Search for a suitable variable using a search term in paneldata.org (for more information, see the section Variable Search with paneldata.org)

Use the documentation provided for the generated variables (for more information, seethe section Documentation on Generated Data)

Use the various important variables of the ppfadl.dta dataset as your start file. Your source file should contain the following variables:

Individual identifier "pid"

Survey year "syear"

Birth Year "gebjahr"

The net variable with information on the interview type "netto"

The weighting variable "phrf"

The gender of the person "sex"

Sample membership "pop"

1 | ```
use pid syear sex gebjahr netto pop phrf using "${MY_IN_PATH}/ppfadl.dta", clear
``` |

Attention

Please note that since version 34 (v34), PPFADL has been renamed PPATHL. The following ecxercises are done with version 33.1 (v33.1), where the tracking file was named PPFADL.

Apply the necessary content variables to your starting dataset. You need the following variables for your analysis:

Employment status plb0022_h

Current gross income in euros "pglabgro"

Actual weekly working hours "pgtatzeit"

Full-time work experience "pgexpft"

Years of education or training "pgbilzeit"

Marital status in survey year "pgfamstd"

1 2 | ```
merge 1:1 pid syear using "${MY_IN_PATH}/pl.dta", keepus(plb0022) keep(master match) nogen
merge 1:1 pid syear using "${MY_IN_PATH}/pgen.dta", keepus(pglabgro pgtatzeit pgexpft pgbilzeit pgfamstd) keep(master match) nogen
``` |

Only keep people who have completed an interview and who live in a private household.

1 2 3 4 5 | ```
* Only select people with completed interviews
keep if inrange(netto, 10, 19)
* Only private households
keep if pop==1 | pop==2
``` |

Since you are only interested in the period from 2012 to 2016, remove all survey information that does not fall within this period. To finish, save your dataset.

1 2 | ```
* Period from 2012 to 2016
keep if syear>=2012 & syear<=2016
``` |

**Exercise 1: Prepare your dataset**

**a) Load your created SOEPWage.dta dataset. It contains information on gross monthly wage, marital status, and other personal characteristics.**

1 2 3 | ```
*** Exercise 1: Prepare your dataset
* a) Load data set
use "${MY_OUT_DATA}/SOEPWage.dta", clear
``` |

**b) Recode all missing values in systemmissings (.)**

1 2 | ```
* b) Recode Missings
mvdecode _all, mv(-8/-1 = .)
``` |

For more information about the missing codes for SOEP data, see the chapter Missing Conventions

**c) Generate the variables “hourly wage” (gross monthly wage/4.33*working time) for persons who have earned at least 1 euro and have worked at least one hour, “Married vs. Unmarried” and age.**

1 2 3 4 5 6 7 | ```
* c) Generate Variables
gen wage = pglabgro/(4.33*pgtatzeit) if pglabgro>=1 & pgtatzeit>=1
gen married = 1 if pgfamstd==1 | pgfamstd==6 | pgfamstd==7 | pgfamstd==8
replace married = 0 if inrange(pgfamstd, 2, 5)
gen age = syear - gebjahr
``` |

**d) Adjust the variable “hourly wage” from outlier values by setting values smaller than the first percentile to the same value. Set values greater than 3 times the 99th percentile to 3*99th percentile. Then generate the variable lwage = log(wage).**

1 2 3 4 5 6 7 8 9 | ```
* d) Adjust wage variable
sum wage, detail
replace wage = 1/3*r(p1) if wage<1/3*r(p1)
replace wage = 3*r(p99) if wage>3*r(p99) & wage<.
gen lwage = log(wage)
label variable lwage "Log hourly wage"
save "${MY_OUT_DATA}/SOEPWage_temp.dta", replace
``` |

**Exercise 2: Descriptive statistics**

**a) Define the dataset as a panel dataset.**

1 2 3 | ```
*** Exercise 2: Descriptive statistics
* a)
xtset pid syear // Declaring data as panel data
``` |

**b) What percentage of people participated in all five waves (xtdescribe)**

1 2 | ```
* b)
xtdescribe, patterns(16) // -> unbalanced panel
``` |

42808 respondents have contributed information within waves bc (2012) - bg (2016) and about 40% (17069) of the 42808 respondents have provided information for all waves.

**c) Describe the variable “Married” with xttab and xttrans. Take a look at some individual wage (pid=30320901, pid=30932501, pid==3101602, pid==3101801) developments with xtline.**

1 2 3 | ```
* c)
* Stability of the relationship status
xttab married
``` |

You can observe 41.37 percent of person-year observations with “married==no”. Within the period from 2012 to 2016, 19717 people responded at least once that they were not married. During the same period, 25014 persons reported at least once that they were married. Those who were not married for at least one year responded with “married==no” in 94.69% of the observations, whereas those who were married at least once responded in 95.88 percent of the observations with “married==yes”. This indicates very stable response behavior.

1 2 | ```
* Transition probabilities
xttrans married, freq
``` |

96.87 percent of the person-year observations with “married==no” are still not married in the next period. 98.51 percent of the persons who are married indicate that they will also be married in the following period. This is evidence of stable response behavior.

1 2 | ```
* Individual sequences of "wage"
xtline wage if pid==30320901 | pid==30932501 | pid==3101602 | pid==3101801, overlay
``` |

The graphic shows a comparison of the hourly wage for four different respondents.

**Exercise 3: Pooled OLS Regression**

**a) Execute a pooled OLS regression with “log hourly wage” as dependent variable and “married”, “gender”, “work experience” and “training time” as independent variables. Interpret the coefficients for “married”, “gender” and “length of training”. Why are these not causal effects?**

1 2 3 | ```
*** Exercise 3: Pooled OLS Regression
* a) Pooled OLS
reg lwage married sex pgexpft pgbilzeit
``` |

The variables married, sex, and pgbilzeit most likely correlate with other disregarded/unobserved variables that have an effect on the wage. For example, women more often work in occupations with lower wages.

**b) Run the regression again with the option “vce(cluster persnr)” to get clustered standard errors. How do the standard errors of the coefficients change?**

1 2 | ```
* b) Pooled OLS with cluster standard errors
reg lwage married sex pgexpft pgbilzeit, vce(cluster pid)
``` |

The standard errors are getting bigger.

**Exercise 4: Fixed Effects**

**a) Subtract the person-specific mean value from each variable of the model. Use the “egen” function. Ideally you should also use a loop.**

1 2 3 4 5 6 7 8 9 10 11 12 | ```
*** Exercise 4: Fixed Effects
* a) Subtract person-specific averages
gen sample = 1
foreach var in lwage married sex pgexpft pgbilzeit {
bysort pid: egen `var'Mean = mean(`var')
replace `var'Mean = . if `var'==.
gen `var'Demeaned = `var' - `var'Mean
replace sample = 0 if `var'==.
}
bysort pid (sample): replace sample = sample[1]
``` |

**b) Estimate the fixed effects model with the previously generated variables. Why isn’t a coefficient estimated for “gender”? How do the coefficients change compared to the pooled OLS estimate? Is the effect of “married” now causally interpretable?**

1 | ```
reg lwageDemeaned marriedDemeaned sexDemeaned pgexpftDemeaned pgbilzeitDemeaned, vce(cluster pid) nocons
``` |

No coefficient was estimated for gender because gender was stable over time for all observations. The coefficient of married is now significant at the 5% level!

**c) Now estimate the fixed effects model using the command
“xtreg lwage married sex pgexpft pgbilzeit, fe”. What do you notice about the coefficients compared to task 4 b)? And with the standard errors?**

1 2 | ```
* c) xtreg, fe
xtreg lwage married pgexpft pgbilzeit, fe vce(cluster pid)
``` |

The coefficients are not identical to 4 b) and the standard errors become larger because model b) does not take into account the estimation of mean values in the standard errors.

**d) Now add dummy variables for the years (i.syear). What happens to the effect of “labor market experience”?**

1 2 | ```
* d) xtreg with dummy
xtreg lwage married pgexpft pgbilzeit i.syear, fe vce(cluster pid)
``` |

Effects on the variables remain significant. The model could possibly be specified on a case-by-case basis. The Mincer equation is based on (potential) labor market experience squared.

**e) Now you can also square labor market experience into the model. To what extent does the effect of labor market experience change compared to task 5d)?**

1 2 | ```
* e) expft squared
xtreg lwage married c.pgexpft##c.pgexpft pgbilzeit i.syear, fe vce(cluster pid)
``` |

The coefficients of pgexpft and pgexpft^2 remain significant, whereas the coefficient for married is no longer significant.

1 | ```
graph twoway (func y = _b[pgexpft]*x + _b[c.pgexpft#c.pgexpft]*x*x, range(0 40))
``` |

The graph shows that the effects of the labor market experience decrease after approximately 15 years of professional experience.

**f) Now estimate the model from task 5e) with longitudinal section weights. Why is the number of cases now significantly smaller? Why could the coefficient of “pgbilzeit” have changed?**

Tip

Create your own longitudinal person weights, e.g., longitudinal person weight from wave A to wave D. Take the starting wave cross-sectional weight (aphrf) and multiply through by each following wave staying factor, as in the following example: gen adphrf=aphrf*bpbleib*cpbleib*dpbleib

Since you are looking at the period 2012-2016, you must create a suitable longitudinal weight. To do this, use the phrf dataset from the RAW subdirectory. Apply the required variables to your analysis dataset and generate your period-related longitudinal section weight. To understand the structure of the data distribution file and the location of the different datasets, visit the section Data Distribution File. For more information about the weighting datasets and other survey datasets, see the section Survey Data.

1 2 3 4 5 6 7 | ```
* f) Fixed Effects weighted
global MY_IN_PATH2 "\\hume\rdc-prod\complete\soep-core\soep.v33.2\stata_en\"
rename pid persnr
merge m:1 persnr using "${MY_IN_PATH2}/phrf.dta", nogen keep(master match) keepus(bcphrf bdpbleib bepbleib bfpbleib bgpbleib)
gen wlong = bcphrf*bdpbleib*bepbleib*bfpbleib*bgpbleib
label variable wlong "Weighting BC-BG"
rename persnr pid
``` |

Now estimate the model from 5e) and use the created weight.

1 | ```
xtreg lwage married c.pgexpft##c.pgexpft pgbilzeit i.syear [pw=wlong], fe vce(cluster pid)
``` |

The number of observations is now much smaller. The effect of pgbilzeit is greater than before. Pgbilzeit has a lower effect in the wlong==0 group, where the return is different for each additional educational year. People in the wlong===0 group may not get the returns on additional education they expected on the local labor market and may therefore move -> higher dropout probability.

Last change: May 04, 2021