nhis1.Rmd

---
title: "Labor Market and Health with NHIS"
author: "Elena Stolpovsky"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document:
  #  toc: true
  #  toc_depth: 5
    code_folding: hide
bibliography: references.bib
link-citations: true
nocite: | 
   @Stolpovsky_geography, @Stolpovsky_historical_unemployment
---

<base target="_top"/>

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
                      message = FALSE,
                      warning = FALSE)
options(scipen = 999,
        digits = 2,
        tibble.print_max = Inf)

```

[<font size="4">EconBlog</font>](https://elenas70.github.io/econblog/)
[<font size="4">`r icons::fontawesome("github")`</font>](https://github.com/elenas70/us_labor_market_and_COVID)
[<font size="4">`r icons::fontawesome("linkedin")`</font>](https://www.linkedin.com/in/estolpovsky/)

### Data and Methodology

The data for this analysis come from the [Current Population Survey](https://cps.ipums.org/cps/) (CPS). CPS is a monthly survey run by the Census Bureau with questions about demographic and economic characteristics of the U.S. population. CPS is used to calculate the monthly federal statistics on unemployment. I obtain the data from the Integrated Public Use Microdata Series database [-@ipums2020]. 


My sample consists of CPS monthly core employment data for January 2020–March 2021. Each sample includes individual and household weights, which allow inferences about the population from the samples. I discuss CPS weights and examine the weight distribution in the [Appendix](#weights).
```{r include=FALSE}
library(ipumsr)
memory.limit(10000000)
ddi <- read_ipums_ddi("nhis_00001.xml")
data <- read_ipums_micro(ddi)
```


```{r}
library(sjlabelled)
library(tidyverse)
library(kableExtra)
library(formattable)
library(purrr)
library(zoo)

data <- data %>%
  remove_all_labels()
```