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knitr::opts_chunk$set(echo = TRUE)Final Project Submission 1
Research Question:
the research question that I have been interested in is the impact of education about sex and fertility for women and how that changes the fetility rate. Women’s education raises the value of time spent working in the market and, as a result, the opportunity cost of spending time to take care of their child seems less. Across time and places, there is a clear negative link between women’s education and fertility, although its meaning is ambiguous. Women’s level of education may impact fertility through its effects on children’s health, the number of children desired, and women’s ability to give birth and understanding of various birth control options. Each of these are influenced by local, institutional, and national circumstances. Their relative importance may fluctuate as a society develops economically. Since having children affects how much mothers must pay for childcare, women’s education may also be correlated with fertility. The data was acquired from various years of the National Opinion Resource Center’s General Social Survey. Compared to other women, mothers who stay at home with their kids are less likely to invest more money in their education. The correlation between women’s education and unobservable qualities that are jointly linked with fertility may be even more significant.
###Hypothesis It can be thought of as the total number of unplanned and intended children. The number of kids a family can have, the number of kids the family desires, and the capability to regulate birth through the availability of modern contraceptives and the knowledge of how to use them are all impacted by advancements in women’s education. The number of children a woman has is halfway between the amount she wants and her level of natural fertility. Age and fertility control are the determining variables.If there was a variation by region in birth control availability, such information might be valuable. However, our data set does not contain geographical information (parameters). My assumption would be that if the level of education increases, the number of children would decrease.
Loading in packages:
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library(readr)
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6 ✔ dplyr 1.0.10
✔ tibble 3.1.8 ✔ stringr 1.4.1
✔ tidyr 1.2.1 ✔ forcats 0.5.2
✔ purrr 0.3.5
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()Code
library(ggplot2)
library(dplyr)
library(readxl)Reading in Data:
The data was acquired from Professor Sander’s article that he used.
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Womendata <- read.csv("_data/data.csv")Summary of the data
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summary(Womendata) X mnthborn yearborn age
Min. : 1 Min. : 1.000 Min. :38.00 Min. :15.00
1st Qu.:1091 1st Qu.: 3.000 1st Qu.:55.00 1st Qu.:20.00
Median :2181 Median : 6.000 Median :62.00 Median :26.00
Mean :2181 Mean : 6.331 Mean :60.43 Mean :27.41
3rd Qu.:3271 3rd Qu.: 9.000 3rd Qu.:68.00 3rd Qu.:33.00
Max. :4361 Max. :12.000 Max. :73.00 Max. :49.00
electric radio tv bicycle
Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
Median :0.0000 Median :1.0000 Median :0.00000 Median :0.0000
Mean :0.1402 Mean :0.7018 Mean :0.09291 Mean :0.2758
3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
NA's :3 NA's :2 NA's :2 NA's :3
educ ceb agefbrth children
Min. : 0.000 Min. : 0.000 Min. :10.00 Min. : 0.000
1st Qu.: 3.000 1st Qu.: 1.000 1st Qu.:17.00 1st Qu.: 0.000
Median : 7.000 Median : 2.000 Median :19.00 Median : 2.000
Mean : 5.856 Mean : 2.442 Mean :19.01 Mean : 2.268
3rd Qu.: 8.000 3rd Qu.: 4.000 3rd Qu.:20.00 3rd Qu.: 4.000
Max. :20.000 Max. :13.000 Max. :38.00 Max. :13.000
NA's :1088
knowmeth usemeth monthfm yearfm
Min. :0.0000 Min. :0.0000 Min. : 1.00 Min. :50.00
1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.: 3.00 1st Qu.:72.00
Median :1.0000 Median :1.0000 Median : 6.00 Median :78.00
Mean :0.9633 Mean :0.5776 Mean : 6.27 Mean :76.91
3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 9.00 3rd Qu.:83.00
Max. :1.0000 Max. :1.0000 Max. :12.00 Max. :88.00
NA's :7 NA's :71 NA's :2282 NA's :2282
agefm idlnchld heduc agesq
Min. :10.00 Min. : 0.000 Min. : 0.000 Min. : 225.0
1st Qu.:17.00 1st Qu.: 3.000 1st Qu.: 0.000 1st Qu.: 400.0
Median :20.00 Median : 4.000 Median : 6.000 Median : 676.0
Mean :20.69 Mean : 4.616 Mean : 5.145 Mean : 826.5
3rd Qu.:23.00 3rd Qu.: 6.000 3rd Qu.: 8.000 3rd Qu.:1089.0
Max. :46.00 Max. :20.000 Max. :20.000 Max. :2401.0
NA's :2282 NA's :120 NA's :2405
urban urb_educ spirit protest
Min. :0.0000 Min. : 0.000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:0.0000
Median :1.0000 Median : 0.000 Median :0.0000 Median :0.0000
Mean :0.5166 Mean : 3.469 Mean :0.4222 Mean :0.2277
3rd Qu.:1.0000 3rd Qu.: 7.000 3rd Qu.:1.0000 3rd Qu.:0.0000
Max. :1.0000 Max. :20.000 Max. :1.0000 Max. :1.0000
catholic frsthalf educ0 evermarr
Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
Median :0.0000 Median :1.0000 Median :0.0000 Median :0.0000
Mean :0.1025 Mean :0.5405 Mean :0.2078 Mean :0.4767
3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
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glimpse(Womendata)Rows: 4,361
Columns: 28
$ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ mnthborn <int> 5, 1, 7, 11, 5, 8, 7, 9, 12, 9, 6, 10, 12, 2, 1, 6, 1, 8, 4, …
$ yearborn <int> 64, 56, 58, 45, 45, 52, 51, 70, 53, 39, 46, 59, 42, 40, 53, 6…
$ age <int> 24, 32, 30, 42, 43, 36, 37, 18, 34, 49, 42, 29, 45, 48, 35, 2…
$ electric <int> 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ radio <int> 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ tv <int> 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1…
$ bicycle <int> 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0…
$ educ <int> 12, 13, 5, 4, 11, 7, 16, 10, 5, 4, 15, 7, 0, 4, 12, 7, 7, 5, …
$ ceb <int> 0, 3, 1, 3, 2, 1, 4, 0, 1, 0, 3, 3, 4, 10, 3, 0, 4, 2, 0, 1, …
$ agefbrth <int> NA, 25, 27, 17, 24, 26, 20, NA, 19, NA, 25, 23, 18, 19, 23, N…
$ children <int> 0, 3, 1, 2, 2, 1, 4, 0, 1, 0, 3, 3, 2, 8, 3, 0, 4, 2, 0, 1, 0…
$ knowmeth <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ usemeth <int> 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1…
$ monthfm <int> NA, 11, 6, 1, 3, 11, 5, NA, 7, 11, 6, 1, 1, 10, 1, NA, NA, NA…
$ yearfm <int> NA, 80, 83, 61, 66, 76, 78, NA, 72, 61, 70, 84, 66, 66, 74, N…
$ agefm <int> NA, 24, 24, 15, 20, 24, 26, NA, 18, 22, 24, 24, 23, 26, 21, N…
$ idlnchld <int> 2, 3, 5, 3, 2, 4, 4, 4, 4, 4, 3, 6, 6, 4, 3, 4, 5, 1, 2, 3, 2…
$ heduc <int> NA, 12, 7, 11, 14, 9, 17, NA, 3, 1, 16, 7, NA, 3, 16, NA, NA,…
$ agesq <int> 576, 1024, 900, 1764, 1849, 1296, 1369, 324, 1156, 2401, 1764…
$ urban <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ urb_educ <int> 12, 13, 5, 4, 11, 7, 16, 10, 5, 4, 15, 7, 0, 4, 12, 7, 7, 5, …
$ spirit <int> 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0…
$ protest <int> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1…
$ catholic <int> 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0…
$ frsthalf <int> 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0…
$ educ0 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
$ evermarr <int> 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1…We can see that we have 28 variables and 4361 observations in this dataset. The dependent variable of interest - number of living children Then I will perform data manipulation to tidy the data. The variables of interest are age, yearborn, month born, urban education and many more variables that seem intriguing. Variables like radio, bicycle, electric can be ignored in this.
###References [1] The effect of women’s schooling on fertility by W Sander · 1992 [2] The Impact of Women’s Schooling on Fertility and Contraceptive Use by M Ainsworth · 1996