Add references

kazuyanagimoto · Mar 29, 2024 · 4c121a3 · 4c121a3
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diff --git a/.gitignore b/.gitignore
@@ -13,3 +13,4 @@ altdoc/freeze.rds
 _quarto/*
 !_quarto/_freeze/
 *_files/
+_quarto/
diff --git a/README.md b/README.md
@@ -8,8 +8,8 @@
 <!-- badges: end -->
 
 The `unitdid` package provides a set of functions for the analysis of
-the unit-level difference-in-differences (Arkhangelsky, Yanagimoto, and
-Zohar, 2024)
+the unit-level difference-in-differences ([Arkhangelsky, Yanagimoto, and
+Zohar 2024](https://arxiv.org/abs/2403.19563)).
 
 ## Installation
 
@@ -158,3 +158,10 @@ sum_eage |>
 ```
 
 <img src="man/figures/README-var_cage-1.svg" style="width:100.0%" />
+
+## References
+
+Arkhangelsky, Dmitry, Kazuharu Yanagimoto, and Tom Zohar. 2024.
+“Flexible Analysis of Individual Heterogeneity in Event Studies:
+Application to the Child Penalty.” arXiv.
+<https://arxiv.org/abs/2403.19563>.
diff --git a/README.qmd b/README.qmd
@@ -18,7 +18,7 @@ knitr::opts_chunk$set(
 <!-- badges: end -->
 
 The `unitdid` package provides a set of functions for the analysis of
-the unit-level difference-in-differences (Arkhangelsky, Yanagimoto, and Zohar, 2024)
+the unit-level difference-in-differences ([Arkhangelsky, Yanagimoto, and Zohar 2024](https://arxiv.org/abs/2403.19563)).
 
 ## Installation
 
@@ -130,3 +130,6 @@ sum_eage |>
         panel.grid.minor = element_blank())
 ```
 
+## References
+
+Arkhangelsky, Dmitry, Kazuharu Yanagimoto, and Tom Zohar. 2024. "Flexible Analysis of Individual Heterogeneity in Event Studies: Application to the Child Penalty." arXiv. [https://arxiv.org/abs/2403.19563](https://arxiv.org/abs/2403.19563).
diff --git a/_quarto/_freeze/index/execute-results/html.json b/_quarto/_freeze/index/execute-results/html.json
@@ -1,9 +1,11 @@
 {
-  "hash": "ca7f64c46b9360786d1547d80d5d3800",
+  "hash": "ca9eb4cbd691a4666e4e735949aff977",
   "result": {
     "engine": "knitr",
-    "markdown": "<!-- README.md is generated from README.Rmd. Please edit that file -->\n\n\n\n\n\n# unitdid\n\n<!-- badges: start -->\n<!-- badges: end -->\n\nThe `unitdid` package provides a set of functions for the analysis of\nthe unit-level difference-in-differences (Arkhangelsky, Yanagimoto, and Zohar, 2024)\n\n## Installation\n\nYou can install the development version of unitdid from [GitHub](https://github.com/kazuyanagimoto/unitdid) with:\n\n``` r\n# install.packages(\"remotes\")\nremotes::install_github(\"kazuyanagimoto/unitdid\")\n```\n\n## Example\n\nThis is a basic example with the simulated `base_heterocp` data set:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(unitdid)\nlibrary(dplyr)\nlibrary(ggplot2)\n\nbase_heterocp |>\n  head()\n#> # A tibble: 6 × 5\n#>      id  year byear cyear      y\n#>   <int> <int> <int> <int>  <dbl>\n#> 1     1  1999  1955  1985 -0.848\n#> 2     1  2000  1955  1985  0.759\n#> 3     1  2001  1955  1985 -1.03 \n#> 4     1  2002  1955  1985  0.858\n#> 5     1  2003  1955  1985 -0.866\n#> 6     1  2004  1955  1985 -0.651\n```\n:::\n\n\nIndividual-level child penalties are estimated by `unitdid()`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmdl_base <- base_heterocp |>\n  unitdid(yname = \"y\",\n        iname = \"id\",\n        tname = \"year\",\n        ename = \"cyear\",\n        bname = \"byear\")\n\n# Estimated individual-level child penalties (y_tilde)\nget_unitdid(mdl_base)\n#> # A tibble: 32,257 × 6\n#>       id  year byear cyear      y    y_tilde\n#>    <int> <int> <int> <int>  <dbl>      <dbl>\n#>  1   705  2000  1957  2000 0.138  -0.0287   \n#>  2   997  2000  1958  2000 0.138   0.0849   \n#>  3   998  2000  1958  2000 0.119  -0.104    \n#>  4  1013  2000  1958  2000 0.115  -0.0000709\n#>  5  1082  2000  1958  2000 0.0362  0.00549  \n#>  6  1127  2000  1958  2000 0.386   0.125    \n#>  7  1225  2001  1959  2001 0.158  -0.118    \n#>  8  1228  2000  1959  2000 0.241  -0.0937   \n#>  9  1228  2001  1959  2000 0.443   0.0226   \n#> 10  1230  2000  1959  2000 0.143  -0.0266   \n#> # ℹ 32,247 more rows\n```\n:::\n\n\n### Aggregation of Individual-level Child Penalties\n\nThey can be aggregated to the `full`,\n`event` (year at event (treatment). Mainly for staggered DiD design),\n`event_age` (age at event. Mainly for child penalties) levels:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(mdl_base) # default agg = \"full\"\n#> # A tibble: 6 × 3\n#>      mean rel_time     n\n#>     <dbl>    <int> <dbl>\n#> 1 -0.0653        0  4357\n#> 2 -0.193         1  4357\n#> 3 -0.307         2  4357\n#> 4 -0.310         3  4357\n#> 5 -0.350         4  4357\n#> 6 -0.349         5  4357\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsum_eage <- summary(mdl_base, agg = \"event_age\")\n\nsum_eage |>\n  filter(rel_time == 0) |>\n  mutate(rel_time = -1,\n         mean = 0) |>\n  bind_rows(sum_eage) |>\n  filter(between(event_age, 25, 34)) |>\n  mutate(lbl_facet = paste0(\"Age \", event_age)) |>\n  ggplot(aes(x = rel_time, y = mean)) +\n  geom_point() +\n  geom_line() +\n  geom_vline(xintercept = -1, linetype = \"dashed\") +\n  geom_hline(yintercept = 0) +\n  facet_wrap(~lbl_facet, ncol = 5) +\n  labs(x = \"Time to First Childbirth\",\n       y = \"Child Penalties on y\") +\n  theme_minimal() +\n  theme(panel.grid.major.x = element_blank(),\n        panel.grid.minor = element_blank())\n```\n\n::: {.cell-output-display}\n![](man/figures/README-agg_cage-1.svg){width=100%}\n:::\n:::\n\n\n### Variance of Individual-level Child Penalties\n\nSince the individual-level child penalties are estimated with measurement errors,\nthe variance of the `y_tilde` is not equal to the variance of\nthe individual-level child penalties.\n\nThe `compute_varcov = \"var\"` option of the `unitdid` estimates the variance of\nthe measurement errors and the variance of the individual-level child penalties\nby subtracting the variance of the measurement errors from the variance of `y_tilde`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmdl_base <- base_heterocp |>\n  unitdid(yname = \"y\",\n        iname = \"id\",\n        tname = \"year\",\n        ename = \"cyear\",\n        bname = \"byear\",\n        compute_varcov = \"var\")\n\nsum_eage <- summary(mdl_base, agg = \"event_age\")\n\nsum_eage |>\n  filter(rel_time == 0) |>\n  mutate(rel_time = -1,\n         var = 0) |>\n  bind_rows(sum_eage) |>\n  filter(between(event_age, 25, 34)) |>\n  mutate(lbl_facet = paste0(\"Age \", event_age)) |>\n  ggplot(aes(x = rel_time, y = sqrt(var))) +\n  geom_point() +\n  geom_line() +\n  geom_vline(xintercept = -1, linetype = \"dashed\") +\n  geom_hline(yintercept = 0) +\n  facet_wrap(~lbl_facet, ncol = 5) +\n  labs(x = \"Time to First Childbirth\",\n       y = \"S.D. of Child Penalties\") +\n  theme_minimal() +\n  theme(panel.grid.major.x = element_blank(),\n        panel.grid.minor = element_blank())\n```\n\n::: {.cell-output-display}\n![](man/figures/README-var_cage-1.svg){width=100%}\n:::\n:::\n",
-    "supporting": [],
+    "markdown": "<!-- README.md is generated from README.Rmd. Please edit that file -->\n\n\n\n\n\n# unitdid\n\n<!-- badges: start -->\n<!-- badges: end -->\n\nThe `unitdid` package provides a set of functions for the analysis of\nthe unit-level difference-in-differences ([Arkhangelsky, Yanagimoto, and Zohar 2024](https://arxiv.org/abs/2403.19563)).\n\n## Installation\n\nYou can install the development version of unitdid from [GitHub](https://github.com/kazuyanagimoto/unitdid) with:\n\n``` r\n# install.packages(\"remotes\")\nremotes::install_github(\"kazuyanagimoto/unitdid\")\n```\n\n## Example\n\nThis is a basic example with the simulated `base_heterocp` data set:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(unitdid)\nlibrary(dplyr)\nlibrary(ggplot2)\n\nbase_heterocp |>\n  head()\n#> # A tibble: 6 × 5\n#>      id  year byear cyear      y\n#>   <int> <int> <int> <int>  <dbl>\n#> 1     1  1999  1955  1985 -0.848\n#> 2     1  2000  1955  1985  0.759\n#> 3     1  2001  1955  1985 -1.03 \n#> 4     1  2002  1955  1985  0.858\n#> 5     1  2003  1955  1985 -0.866\n#> 6     1  2004  1955  1985 -0.651\n```\n:::\n\n\nIndividual-level child penalties are estimated by `unitdid()`:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmdl_base <- base_heterocp |>\n  unitdid(yname = \"y\",\n        iname = \"id\",\n        tname = \"year\",\n        ename = \"cyear\",\n        bname = \"byear\")\n\n# Estimated individual-level child penalties (y_tilde)\nget_unitdid(mdl_base)\n#> # A tibble: 32,257 × 6\n#>       id  year byear cyear      y    y_tilde\n#>    <int> <int> <int> <int>  <dbl>      <dbl>\n#>  1   705  2000  1957  2000 0.138  -0.0287   \n#>  2   997  2000  1958  2000 0.138   0.0849   \n#>  3   998  2000  1958  2000 0.119  -0.104    \n#>  4  1013  2000  1958  2000 0.115  -0.0000709\n#>  5  1082  2000  1958  2000 0.0362  0.00549  \n#>  6  1127  2000  1958  2000 0.386   0.125    \n#>  7  1225  2001  1959  2001 0.158  -0.118    \n#>  8  1228  2000  1959  2000 0.241  -0.0937   \n#>  9  1228  2001  1959  2000 0.443   0.0226   \n#> 10  1230  2000  1959  2000 0.143  -0.0266   \n#> # ℹ 32,247 more rows\n```\n:::\n\n\n### Aggregation of Individual-level Child Penalties\n\nThey can be aggregated to the `full`,\n`event` (year at event (treatment). Mainly for staggered DiD design),\n`event_age` (age at event. Mainly for child penalties) levels:\n\n\n::: {.cell}\n\n```{.r .cell-code}\nsummary(mdl_base) # default agg = \"full\"\n#> # A tibble: 6 × 3\n#>      mean rel_time     n\n#>     <dbl>    <int> <dbl>\n#> 1 -0.0653        0  4357\n#> 2 -0.193         1  4357\n#> 3 -0.307         2  4357\n#> 4 -0.310         3  4357\n#> 5 -0.350         4  4357\n#> 6 -0.349         5  4357\n```\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nsum_eage <- summary(mdl_base, agg = \"event_age\")\n\nsum_eage |>\n  filter(rel_time == 0) |>\n  mutate(rel_time = -1,\n         mean = 0) |>\n  bind_rows(sum_eage) |>\n  filter(between(event_age, 25, 34)) |>\n  mutate(lbl_facet = paste0(\"Age \", event_age)) |>\n  ggplot(aes(x = rel_time, y = mean)) +\n  geom_point() +\n  geom_line() +\n  geom_vline(xintercept = -1, linetype = \"dashed\") +\n  geom_hline(yintercept = 0) +\n  facet_wrap(~lbl_facet, ncol = 5) +\n  labs(x = \"Time to First Childbirth\",\n       y = \"Child Penalties on y\") +\n  theme_minimal() +\n  theme(panel.grid.major.x = element_blank(),\n        panel.grid.minor = element_blank())\n```\n\n::: {.cell-output-display}\n![](man/figures/README-agg_cage-1.svg){width=100%}\n:::\n:::\n\n\n### Variance of Individual-level Child Penalties\n\nSince the individual-level child penalties are estimated with measurement errors,\nthe variance of the `y_tilde` is not equal to the variance of\nthe individual-level child penalties.\n\nThe `compute_varcov = \"var\"` option of the `unitdid` estimates the variance of\nthe measurement errors and the variance of the individual-level child penalties\nby subtracting the variance of the measurement errors from the variance of `y_tilde`\n\n\n::: {.cell}\n\n```{.r .cell-code}\nmdl_base <- base_heterocp |>\n  unitdid(yname = \"y\",\n        iname = \"id\",\n        tname = \"year\",\n        ename = \"cyear\",\n        bname = \"byear\",\n        compute_varcov = \"var\")\n\nsum_eage <- summary(mdl_base, agg = \"event_age\")\n\nsum_eage |>\n  filter(rel_time == 0) |>\n  mutate(rel_time = -1,\n         var = 0) |>\n  bind_rows(sum_eage) |>\n  filter(between(event_age, 25, 34)) |>\n  mutate(lbl_facet = paste0(\"Age \", event_age)) |>\n  ggplot(aes(x = rel_time, y = sqrt(var))) +\n  geom_point() +\n  geom_line() +\n  geom_vline(xintercept = -1, linetype = \"dashed\") +\n  geom_hline(yintercept = 0) +\n  facet_wrap(~lbl_facet, ncol = 5) +\n  labs(x = \"Time to First Childbirth\",\n       y = \"S.D. of Child Penalties\") +\n  theme_minimal() +\n  theme(panel.grid.major.x = element_blank(),\n        panel.grid.minor = element_blank())\n```\n\n::: {.cell-output-display}\n![](man/figures/README-var_cage-1.svg){width=100%}\n:::\n:::\n\n\n## References\n\nArkhangelsky, Dmitry, Kazuharu Yanagimoto, and Tom Zohar. 2024. \"Flexible Analysis of Individual Heterogeneity in Event Studies: Application to the Child Penalty.\" arXiv. [https://arxiv.org/abs/2403.19563](https://arxiv.org/abs/2403.19563).",
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