econ 448 problem set 2

Overview and Setup

For this exercise, we will practice opening, examining, and analyzing data from IPUMS, a web repository of population data.

These exercises use the data set “1% sample Botswana ever-married women age 15+” available via the Econ 448 class account from https://international.ipums.org/international/ (class code is W0NV72). Please download the data and answer the following questions.

2, Why would an estimate of the effect of contraceptive use on a woman’s fertility potentially be biased? How does Miller use a natural experiment to remove this bias? In your answer, define the natural experiment and discuss how the natural experiment satisfies the “exclusion restriction” for the bias you described in the first part of this question.

Since there is a bias toward the contraction on woman’s fertility, Miller suggested that a Non-discrimination in provision of contraceptive information and services, Miller’s experiment in Profamilia recommend that access to comprehensive contraceptive information and services be provided equally to everyone voluntarily, free of discrimination, coercion or violence (based on individual choice) that a female can be more independent on their fertility choice and the contraction bias will be lower because when collecting the data. Miller’s natural experiment allow the bias to be lower when collecting the data and because of it. The bias on contraction can be a lot lower with it.

Profamilia also supplied vast quantities of contraceptive devices not requiring medical supervision to local drugstores at cost and it advertised its services by radio.Because the precise timing of its programmes is assumed to be largely exogenous, it is important to investigate regularities that may have governed. As a result, Miller’s experienment can lower the bias in the contraceptive use on a woman’s fertility.

Millers experment on Profamilia’s success rests on the ways of reaching women with modern contraceptives under difficult circumstances that it pioneered. Distin guishing features of its philosophy include its focus on the poor and its recruitment of lay-women to provide outreach in their own communities. In counties where it operated, Profamilia also supplied vast quantities of contraceptive devices not requiring medical supervision to local drugstores at cost and it advertised its services by radio.

1.2 Recommend that laws and policies support programmes to ensure that comprehensive contraceptive information and services are provided to all segments of the population. Special attention should be given to disadvantaged and marginalized populations in their access to these services.

#Remember, only need to run these commands once
pacman::p_load(fixest,ivreg,doBy,htmltools,shiny,DT, coefplot)

also installing the dependencies ‘Deriv’, ‘microbenchmark’

trying URL 'https://cran.rstudio.com/src/contrib/Deriv_4.1.3.tar.gz'
Content type 'application/x-gzip' length 37214 bytes (36 KB)
==================================================
downloaded 36 KB

trying URL 'https://cran.rstudio.com/src/contrib/microbenchmark_1.4.9.tar.gz'
Content type 'application/x-gzip' length 59607 bytes (58 KB)
==================================================
downloaded 58 KB

trying URL 'https://cran.rstudio.com/src/contrib/doBy_4.6.14.tar.gz'
Content type 'application/x-gzip' length 4486402 bytes (4.3 MB)
==================================================
downloaded 4.3 MB

* installing *source* package ‘Deriv’ ...
** package ‘Deriv’ successfully unpacked and MD5 sums checked
** using staged installation
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (Deriv)
* installing *source* package ‘microbenchmark’ ...
** package ‘microbenchmark’ successfully unpacked and MD5 sums checked
** using staged installation

checking for gcc... x86_64-conda-linux-gnu-cc
checking whether the C compiler works... yes
checking for C compiler default output file name... a.out
checking for suffix of executables... 
checking whether we are cross compiling... no
checking for suffix of object files... o
checking whether we are using the GNU C compiler... yes
checking whether x86_64-conda-linux-gnu-cc accepts -g... yes
checking for x86_64-conda-linux-gnu-cc option to accept ISO C89... none needed
checking how to run the C preprocessor... x86_64-conda-linux-gnu-cc -E
checking for grep that handles long lines and -e... /usr/bin/grep
checking for egrep... /usr/bin/grep -E
checking for ANSI C header files... yes
checking for sys/types.h... yes
checking for sys/stat.h... yes
checking for stdlib.h... yes
checking for string.h... yes
checking for memory.h... yes
checking for strings.h... yes
checking for inttypes.h... yes
checking for stdint.h... yes
checking for unistd.h... yes
checking for stdint.h... (cached) yes
checking for stdlib.h... (cached) yes
checking sys/time.h usability... yes
checking sys/time.h presence... yes
checking for sys/time.h... yes
checking for uint64_t... yes
checking for error_at_line... yes
checking for mach_absolute_time... no
checking for library containing clock_gettime... -lrt
checking for best clockid_t to use with clock_gettime... CLOCK_MONOTONIC_RAW
configure: creating ./config.status
config.status: creating src/Makevars
config.status: creating src/config.h

** libs

x86_64-conda-linux-gnu-cc -I"/opt/conda/lib/R/include" -DNDEBUG -D_POSIX_C_SOURCE=200112L -DHAVE_CONFIG_H  -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /opt/conda/include -I/opt/conda/include -Wl,-rpath-link,/opt/conda/lib   -fpic  -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /opt/conda/include -fdebug-prefix-map=/home/conda/feedstock_root/build_artifacts/r-base-split_1630154298835/work=/usr/local/src/conda/r-base-4.1.1 -fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix  -c do_nothing.c -o do_nothing.o
x86_64-conda-linux-gnu-cc -I"/opt/conda/lib/R/include" -DNDEBUG -D_POSIX_C_SOURCE=200112L -DHAVE_CONFIG_H  -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /opt/conda/include -I/opt/conda/include -Wl,-rpath-link,/opt/conda/lib   -fpic  -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /opt/conda/include -fdebug-prefix-map=/home/conda/feedstock_root/build_artifacts/r-base-split_1630154298835/work=/usr/local/src/conda/r-base-4.1.1 -fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix  -c init.c -o init.o
x86_64-conda-linux-gnu-cc -I"/opt/conda/lib/R/include" -DNDEBUG -D_POSIX_C_SOURCE=200112L -DHAVE_CONFIG_H  -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /opt/conda/include -I/opt/conda/include -Wl,-rpath-link,/opt/conda/lib   -fpic  -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /opt/conda/include -fdebug-prefix-map=/home/conda/feedstock_root/build_artifacts/r-base-split_1630154298835/work=/usr/local/src/conda/r-base-4.1.1 -fdebug-prefix-map=/opt/conda=/usr/local/src/conda-prefix  -c nanotimer.c -o nanotimer.o
x86_64-conda-linux-gnu-cc -shared -L/opt/conda/lib/R/lib -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/opt/conda/lib -Wl,-rpath-link,/opt/conda/lib -L/opt/conda/lib -o microbenchmark.so do_nothing.o init.o nanotimer.o -lrt -L/opt/conda/lib/R/lib -lR

installing to /opt/conda/lib/R/library/00LOCK-microbenchmark/00new/microbenchmark/libs
** R
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (microbenchmark)
* installing *source* package ‘doBy’ ...
** package ‘doBy’ successfully unpacked and MD5 sums checked
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading

Note: ... may be used in an incorrect context 
Note: ... may be used in an incorrect context 

** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (doBy)

The downloaded source packages are in
    ‘/tmp/Rtmp7DC58a/downloaded_packages’
Updating HTML index of packages in '.Library'
Making 'packages.html' ... done

doBy installed
trying URL 'https://cran.rstudio.com/src/contrib/coefplot_1.2.8.tar.gz'
Content type 'application/x-gzip' length 35905 bytes (35 KB)
==================================================
downloaded 35 KB

* installing *source* package ‘coefplot’ ...
** package ‘coefplot’ successfully unpacked and MD5 sums checked
** using staged installation
** R
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (coefplot)

The downloaded source packages are in
    ‘/tmp/Rtmp7DC58a/downloaded_packages’
Updating HTML index of packages in '.Library'
Making 'packages.html' ... done

coefplot installed

Install these packages and (optional) change your working directory every time you start a fresh session.

library(ggplot2)
library(tidyr)
library(ivreg)
library(dplyr)
library(fixest)
library(doBy)
library(stargazer)
library(ipumsr)
library(coefplot)
#setwd("C:/Users/knoxm/OneDrive - UW/Pandemic Teaching/Econ 448 Data Course")

ddi <- read_ipums_ddi("ipumsi_00009.xml")  ##Use whatever title your file has when it downloads, but the name here should be correct for this exercise.
ipumsr::ipums_view(ddi)
df_ipums <- read_ipums_micro(ddi)  

Use of data from IPUMS-International is subject to conditions including that
users should cite the data appropriately. Use command `ipums_conditions()` for
more details.

df_analysis <-as.data.frame(df_ipums)
head(df_analysis)

ddi <- read_ipums_ddi("ipumsi_00009.xml") 
ipumsr::ipums_view(ddi)
df_ipums <- read_ipums_micro(ddi)  

Use of data from IPUMS-International is subject to conditions including that
users should cite the data appropriately. Use command `ipums_conditions()` for
more details.

df_analysis <-as.data.frame(df_ipums)
head(df_analysis)

tail(df_analysis)

stargazer(df_analysis, type = "text")

=====================================================================
Statistic     N        Mean         St. Dev.       Min        Max    
---------------------------------------------------------------------
COUNTRY     5,778     72.000         0.000          72         72    
YEAR        5,778   2,011.000        0.000        2,011      2,011   
SAMPLE      5,778 72,201,101.000     0.000      72,201,101 72,201,101
SERIAL      5,778 28,818,525.000 15,908,041.000   3,000    61,644,000
HHWT        5,778     61.989         0.816          0          62    
OWNERSHIP   5,778     1.323          0.572          0          9     
OWNERSHIPD  5,778    158.956         56.817         0         999    
ELECTRIC    5,778     1.442          0.517          0          2     
CELL        5,778     1.071          0.295          0          2     
INTERNET    5,778     1.043          0.249          0          2     
TV          5,778     15.559         5.174          0          20    
RADIO       5,778     1.623          0.506          0          2     
TOILET      5,778     19.759         4.558          0          23    
FLOOR       5,778    191.630         41.364         0         236    
PERNUM      5,778     1.975          1.546          1          19    
PERWT       5,778     62.000         0.000          62         62    
AGE         5,778     42.705         17.268         15         94    
SEX         5,778     2.000          0.000          2          2     
MARST       5,778     2.304          0.695          2          4     
MARSTD      5,778    244.694         65.862        210        400    
CHBORN      5,778     3.501          2.756          0          14    
CHSURV      5,778     13.336         29.273         0          99    
CHBORNF     5,778     1.742          1.654          0          9     
CHBORNM     5,778     1.758          1.670          0          9     
RELIGION    5,778     5.529          1.546          1          9     
RELIGIOND   5,778   5,535.263      1,554.382      1,000      9,999   
YRSCHOOL    5,778     10.251         15.616         0          98    
EMPSTAT     5,778     1.919          0.955          1          9     
EMPSTATD    5,778    202.388         97.422        110        999    
LABFORCE    5,778     1.593          0.506          1          8     
YRSCHOOL_SP 2,883     12.713         20.909         0          99    
---------------------------------------------------------------------

##first I will clean the data by filtering the unnecessary data out. like the years of schooling is way too big for the person, and I decided to delect the data thats unrealivent with a filter <50 and the childborn <31.

df_analysis <- filter(df_analysis, YRSCHOOL<50)
df_analysis <- filter(df_analysis, YRSCHOOL_SP<50)
df_analysis <- filter(df_analysis, CHBORN<31)

stargazer(df_analysis, type = "text")

=====================================================================
Statistic     N        Mean         St. Dev.       Min        Max    
---------------------------------------------------------------------
COUNTRY     2,646     72.000         0.000          72         72    
YEAR        2,646   2,011.000        0.000        2,011      2,011   
SAMPLE      2,646 72,201,101.000     0.000      72,201,101 72,201,101
SERIAL      2,646 26,935,105.000 16,266,070.000   3,000    61,172,000
HHWT        2,646     62.000         0.000          62         62    
OWNERSHIP   2,646     1.394          0.596          0          9     
OWNERSHIPD  2,646    165.420         59.086         0         999    
ELECTRIC    2,646     1.451          0.508          0          2     
CELL        2,646     1.068          0.273          0          2     
INTERNET    2,646     1.065          0.267          0          2     
TV          2,646     15.896         5.026          0          20    
RADIO       2,646     1.665          0.483          0          2     
TOILET      2,646     19.656         4.502          0          23    
FLOOR       2,646    191.871         40.091         0         236    
PERNUM      2,646     2.114          1.345          1          18    
PERWT       2,646     62.000         0.000          62         62    
AGE         2,646     39.301         13.684         15         94    
SEX         2,646     2.000          0.000          2          2     
MARST       2,646     2.003          0.070          2          4     
MARSTD      2,646    215.367         8.399         210        400    
CHBORN      2,646     3.329          2.616          0          14    
CHSURV      2,646     13.271         29.264         0          99    
CHBORNF     2,646     1.637          1.589          0          9     
CHBORNM     2,646     1.689          1.619          0          9     
RELIGION    2,646     5.548          1.537          1          9     
RELIGIOND   2,646   5,556.017      1,548.194      1,000      9,999   
YRSCHOOL    2,646     8.070          4.779          0          18    
EMPSTAT     2,646     1.882          0.950          1          9     
EMPSTATD    2,646    198.126         96.365        110        999    
LABFORCE    2,646     1.614          0.503          1          8     
YRSCHOOL_SP 2,646     7.547          5.319          0          18    
---------------------------------------------------------------------

stargazer(df_analysis[c("CHBORN","CHBORNF","AGE", "YRSCHOOL", "YRSCHOOL_SP", "LABFORCE") ], type = "text", summary.stat = c("n","mean","sd", "min", "max"), covariate.labels = c("Children Ever Born", "Female baby Ever Born", "Age", "Years of School", "Spouse Years of School", "Labor force participation"), digits = 2, title="Summary Statistics", notes="Note: Data are 1% sample of ever-married women 15+ from 2011 Botswana census. Source: IPUMS International")

Warning: the condition has length > 1 and only the first element will be usedWarning: the condition has length > 1 and only the first element will be used

Summary Statistics
==============================================================================================================
Statistic                                  N             Mean          St. Dev.          Min          Max     
--------------------------------------------------------------------------------------------------------------
Children Ever Born                       2,646           3.33            2.62             0            14     
Female baby Ever Born                    2,646           1.64            1.59             0            9      
Age                                      2,646           39.30           13.68           15            94     
Years of School                          2,646           8.07            4.78             0            18     
Spouse Years of School                   2,646           7.55            5.32             0            18     
Labor force participation                2,646           1.61            0.50             1            8      
--------------------------------------------------------------------------------------------------------------
Note: Data are 1% sample of ever-married women 15+ from 2011 Botswana census. Source: IPUMS International     

stargazer(df_analysis[c("ELECTRIC", "TOILET", "FLOOR", "RELIGION", "MARST", "EMPSTAT")], type = "text")

Warning: the condition has length > 1 and only the first element will be usedWarning: the condition has length > 1 and only the first element will be used

========================================
Statistic   N    Mean   St. Dev. Min Max
----------------------------------------
ELECTRIC  2,646  1.451   0.508    0   2 
TOILET    2,646 19.656   4.502    0  23 
FLOOR     2,646 191.871  40.091   0  236
RELIGION  2,646  5.548   1.537    1   9 
MARST     2,646  2.003   0.070    2   4 
EMPSTAT   2,646  1.882   0.950    1   9 
----------------------------------------

summary(df_analysis$Employed)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  1.0000  0.5091  1.0000  1.0000 

3.2 Plot mean number of children against mean years of education. What relationship do you see between these data and what does that tell you about what output you would expect from a regression?

From the below graph, I would expect an negative relationsip between

df_means_by_children<- df_analysis %>% 
  group_by(YRSCHOOL) %>% 
  summarise(CHBORN.mean=mean(CHBORN)) %>%
  as.data.frame()

head(df_means_by_children)  

ggplot(df_means_by_children, aes(x = YRSCHOOL, y=CHBORN.mean))+geom_point()+ geom_smooth(method = lm, formula = y~poly(x,1))

mean_by_child <- lm(CHBORN~YRSCHOOL, data = df_analysis)

stargazer(mean_by_child, type = "text")

Warning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changed

===============================================
                        Dependent variable:    
                    ---------------------------
                              CHBORN           
-----------------------------------------------
YRSCHOOL                     -0.268***         
                              (0.009)          
                                               
Constant                     5.490***          
                              (0.087)          
                                               
-----------------------------------------------
Observations                   2,646           
R2                             0.239           
Adjusted R2                    0.239           
Residual Std. Error      2.282 (df = 2644)     
F Statistic          831.654*** (df = 1; 2644) 
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01

print.data.frame(df_means_by_children)

   YRSCHOOL CHBORN.mean
1         0    5.394673
2         1    5.086957
3         2    5.518519
4         3    4.821429
5         4    4.610169
6         5    4.682540
7         6    4.984615
8         7    4.383099
9         8    2.447059
10        9    2.725762
11       10    2.226221
12       11    2.010989
13       12    1.871094
14       13    2.179104
15       14    2.071429
16       15    1.823529
17       16    1.889610
18       17    1.812500
19       18    1.758621

3.3 Using the main data frame, use ordinary least squares to estimate the coefficient β1 in the regression model Yi=β0+β1Xi+ϵi, where Y is children ever born and X is a woman’s education. Show your results. Does the sign match what you expect to see? Note: Typically, we might transform the data, perhaps by taking the log of children, or use a non-linear regression such as a Poisson regression here. However, the zeros make this a little more complicated.

f_ols<-feols(YRSCHOOL~i(AGE), data=df_analysis)

summary(f_ols)

OLS estimation, Dep. Var.: YRSCHOOL
Observations: 2,646 
Standard-errors: IID 

---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
RMSE: 4.07788   Adj. R2: 0.25107

iplot(f_ols)

stargazer(mean_by_child, type = "text")

Warning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changed

===============================================
                        Dependent variable:    
                    ---------------------------
                              CHBORN           
-----------------------------------------------
YRSCHOOL                     -0.268***         
                              (0.009)          
                                               
Constant                     5.490***          
                              (0.087)          
                                               
-----------------------------------------------
Observations                   2,646           
R2                             0.239           
Adjusted R2                    0.239           
Residual Std. Error      2.282 (df = 2644)     
F Statistic          831.654*** (df = 1; 2644) 
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01

#It matched with my initial thoughts on the negative coefficients of 0.23 between number of child born and education status. because more child born means the female will have less time for having education. It correlates to our prior plot that between mother’s education and its child born.

3.4 Now let’s add some control variables. Choose at least two control variables from the available variables in the data. What variables did you choose and what sign do you expect the coefficients on these variables to have? Why?

#I added the number of people in family and the age of them. I will expect a positive coefficient from the number of people since more people in family means a larger possibility that there will be more children in the family. and for age, a female will have more kids as she’s older because she will have a longer birth history and more time to give birth of childrens.

3.5 Add these variables to your regression model from the previous question and display your results. Do the coefficients match your expectations? Does the coefficient on education change once you add the control variables?

var_test1 <- lm(CHBORN ~ YRSCHOOL_SP + AGE + RELIGION, data=df_analysis)
summary(var_test1)

Call:
lm(formula = CHBORN ~ YRSCHOOL_SP + AGE + RELIGION, data = df_analysis)

Residuals:
   Min     1Q Median     3Q    Max 
-8.662 -1.087 -0.113  1.037  8.826 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.530877   0.208444   2.547   0.0109 *  
YRSCHOOL_SP -0.125114   0.008280 -15.110   <2e-16 ***
AGE          0.091790   0.003223  28.483   <2e-16 ***
RELIGION     0.024280   0.025725   0.944   0.3453    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.02 on 2642 degrees of freedom
Multiple R-squared:  0.4047,    Adjusted R-squared:  0.404 
F-statistic: 598.7 on 3 and 2642 DF,  p-value: < 2.2e-16

stargazer(var_test1, type = "text")

Warning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changedWarning: length of NULL cannot be changed

===============================================
                        Dependent variable:    
                    ---------------------------
                              CHBORN           
-----------------------------------------------
YRSCHOOL_SP                  -0.125***         
                              (0.008)          
                                               
AGE                          0.092***          
                              (0.003)          
                                               
RELIGION                       0.024           
                              (0.026)          
                                               
Constant                      0.531**          
                              (0.208)          
                                               
-----------------------------------------------
Observations                   2,646           
R2                             0.405           
Adjusted R2                    0.404           
Residual Std. Error      2.020 (df = 2642)     
F Statistic          598.718*** (df = 3; 2642) 
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01

#I used RELIGION to test the variables, and we can see a positive coefficient in religion, which matched with my expectations and the cofficient on education got higher when I add the control variables. religion only has a slight impact on the Childborn.