| title | output | always_allow_html | ||||||
|---|---|---|---|---|---|---|---|---|
Introduction to expandr |
|
true |
suppressWarnings(suppressMessages({
library(knitr)
library(kableExtra)
library(htmltools)
library(tidyverse)
library(scales)
library(ExPanDaR)
}))
knitr::opts_chunk$set(fig.align = 'center')dat <- read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## country = col_character(),
## region = col_character(),
## hi1990 = col_character(),
## isocode = col_character()
## )
## See spec(...) for full column specifications.
dat %>%
glimpse()## Rows: 2,700
## Columns: 29
## $ id <dbl> 62, 62, 62, 62, 62, 62, 62, 13, 13, 13, 13, 62, 13, 13,…
## $ country <chr> "Mozambique", "Mozambique", "Mozambique", "Mozambique",…
## $ year <dbl> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 2004, 2003, 2…
## $ Y <dbl> 7034.000, 7742.999, 6792.000, 7223.000, 8194.000, 7671.…
## $ K <dbl> 6262, 6462, 6592, 6859, 7246, 7734, 8121, 10047, 9311, …
## $ pop <dbl> 13.371971, 13.719853, 14.203987, 14.775877, 15.363065, …
## $ L <dbl> 5.413710, 5.593190, 5.844729, 6.187860, 6.513672, 6.804…
## $ s <dbl> 0.9964694, 0.9829382, 0.9694070, 0.9558758, 0.9423445, …
## $ alpha_it <dbl> 0.5737705, 0.5737705, 0.5737705, 0.5737705, 0.5737705, …
## $ GDPpc <dbl> 526.0256, 564.3646, 478.1756, 488.8373, 533.3571, 482.0…
## $ lp <dbl> 1299.294, 1384.362, 1162.073, 1167.286, 1257.969, 1127.…
## $ h <dbl> 1.347076, 1.343633, 1.340181, 1.336720, 1.333250, 1.329…
## $ kl <dbl> 1156.693, 1155.333, 1127.854, 1108.461, 1112.429, 1136.…
## $ kp <dbl> 1.1232833, 1.1982358, 1.0303398, 1.0530690, 1.1308308, …
## $ ky <dbl> 0.8902473, 0.8345603, 0.9705536, 0.9496054, 0.8843056, …
## $ TFP <dbl> 203.9550, 220.0026, 189.1149, 194.9619, 213.7372, 193.1…
## $ log_GDPpc_raw <dbl> 6.265350, 6.335700, 6.169978, 6.192030, 6.279191, 6.178…
## $ log_lp_raw <dbl> 7.169576, 7.232995, 7.057961, 7.062437, 7.137254, 7.027…
## $ log_ky_raw <dbl> -0.116255940, -0.180850310, -0.029888673, -0.051708743,…
## $ log_h_raw <dbl> 0.2979364, 0.2953772, 0.2928047, 0.2902188, 0.2876196, …
## $ log_tfp_raw <dbl> 5.317899, 5.393640, 5.242355, 5.272804, 5.364747, 5.263…
## $ log_GDPpc <dbl> 6.163751, 6.195724, 6.227951, 6.261036, 6.295438, 6.331…
## $ log_lp <dbl> 7.050233, 7.075745, 7.101554, 7.128354, 7.156726, 7.187…
## $ log_ky <dbl> -0.1290631, -0.1301618, -0.1312285, -0.1323578, -0.1333…
## $ log_h <dbl> 0.2770405, 0.2796887, 0.2823892, 0.2852334, 0.2883388, …
## $ log_tfp <dbl> 5.257494, 5.286922, 5.316501, 5.346648, 5.377597, 5.409…
## $ region <chr> "Africa", "Africa", "Africa", "Africa", "Africa", "Afri…
## $ hi1990 <chr> "no", "no", "no", "no", "no", "no", "no", "no", "no", "…
## $ isocode <chr> "MOZ", "MOZ", "MOZ", "MOZ", "MOZ", "MOZ", "MOZ", "BDI",…
# Import data definitions
dat_def <- read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat-definitions.csv")## Parsed with column specification:
## cols(
## var_name = col_character(),
## var_def = col_character(),
## type = col_character()
## )
dat_def %>%
print(n = Inf)## # A tibble: 28 x 3
## var_name var_def type
## <chr> <chr> <chr>
## 1 country Standardized country name (from PWT) cs_id
## 2 year Year ts_id
## 3 Y GDP numer…
## 4 K Physical Capital numer…
## 5 pop Population numer…
## 6 L Labor Force numer…
## 7 s Years of Schooling numer…
## 8 alpha_it Variable Capital Share numer…
## 9 GDPpc GDP per capita numer…
## 10 lp Labor Productivity numer…
## 11 h Human Capital Index numer…
## 12 kl Capital per Worker numer…
## 13 kp Capital Productivity numer…
## 14 ky Capital-Output Ratio numer…
## 15 TFP Aggregate Efficiency numer…
## 16 log_GDPpc_raw Log of GDP per capita numer…
## 17 log_lp_raw Log of Labor Productivity numer…
## 18 log_ky_raw Log of Capital-Output Ratio numer…
## 19 log_h_raw Log of Human Capital numer…
## 20 log_tfp_raw Log of Total Factor Productivity numer…
## 21 log_GDPpc Trend (HP400) of log of Labor Productivity numer…
## 22 log_lp Trend (HP400) of log of GDP per capita numer…
## 23 log_ky Trend (HP400) of log of Capital-Output Ratio numer…
## 24 log_h Trend (HP400) of log of Human Capital numer…
## 25 log_tfp Trend (HP400) of log of Aggregate Efficiency numer…
## 26 region Regional group (Classification of the UN) factor
## 27 hi1990 High income country (as of 1990, World Bank classificat… factor
## 28 isocode ISO code from the PWT9.0 factor
df <- dat
df$year <- as.factor(df$year)
df$hi1990 <- as.factor(df$hi1990)
p <- ggplot(df, aes(x = year)) +
geom_bar(aes(fill = hi1990), position = "fill") +
labs(x = "year", fill = "hi1990", y = "Percent") +
scale_y_continuous(labels = percent_format())
p <- p + scale_x_discrete(breaks = pretty(as.numeric(as.character(df$year)), n = 10))
p
df <- dat
prepare_missing_values_graph(df, "year")
df <- dat[df$year == "1990", ]
t <- prepare_descriptive_table(df)
t$kable_ret %>%
kable_styling("condensed", full_width = F, position = "center")| N | Mean | Std. dev. | Min. | 25 % | Median | 75 % | Max. | |
|---|---|---|---|---|---|---|---|---|
| id | 108 | 54.500 | 31.321 | 1.000 | 27.750 | 54.500 | 81.250 | 108.000 |
| year | 108 | 1,990.000 | 0.000 | 1,990.000 | 1,990.000 | 1,990.000 | 1,990.000 | 1,990.000 |
| Y | 108 | 364,598.139 | 1,030,271.047 | 3,067.000 | 19,377.750 | 76,730.500 | 234,608.000 | 9,259,567.000 |
| K | 108 | 962,050.796 | 2,873,937.935 | 2,004.000 | 31,131.000 | 162,679.000 | 713,896.250 | 26,453,210.000 |
| pop | 108 | 45.410 | 140.672 | 1.565 | 5.106 | 10.354 | 34.444 | 1,154.606 |
| L | 108 | 19.507 | 69.002 | 0.703 | 2.056 | 4.352 | 12.218 | 637.075 |
| s | 108 | 6.499 | 2.905 | 0.893 | 4.164 | 6.982 | 8.792 | 12.199 |
| alpha_it | 90 | 0.433 | 0.113 | 0.148 | 0.355 | 0.432 | 0.493 | 0.768 |
| GDPpc | 108 | 9,784.000 | 9,475.931 | 526.026 | 2,103.651 | 6,126.897 | 16,453.113 | 37,503.441 |
| lp | 108 | 23,223.794 | 20,088.835 | 1,299.294 | 6,294.126 | 16,984.627 | 38,834.154 | 75,036.344 |
| h | 108 | 2.682 | 0.734 | 1.320 | 2.085 | 2.767 | 3.246 | 4.252 |
| kl | 108 | 63,403.227 | 68,027.748 | 725.042 | 9,651.041 | 35,519.549 | 102,058.916 | 255,639.410 |
| kp | 108 | 0.614 | 0.610 | 0.209 | 0.329 | 0.458 | 0.625 | 5.070 |
| ky | 108 | 2.279 | 0.987 | 0.197 | 1.599 | 2.184 | 3.039 | 4.775 |
| TFP | 108 | 824.208 | 652.602 | 139.966 | 360.481 | 702.477 | 1,053.205 | 4,164.725 |
| log_GDPpc_raw | 108 | 8.606 | 1.182 | 6.265 | 7.651 | 8.720 | 9.708 | 10.532 |
| log_lp_raw | 108 | 9.564 | 1.101 | 7.170 | 8.747 | 9.740 | 10.567 | 11.226 |
| log_ky_raw | 108 | 0.699 | 0.563 | -1.623 | 0.470 | 0.781 | 1.112 | 1.563 |
| log_h_raw | 108 | 0.946 | 0.291 | 0.278 | 0.735 | 1.018 | 1.177 | 1.447 |
| log_tfp_raw | 108 | 6.441 | 0.756 | 4.941 | 5.887 | 6.555 | 6.960 | 8.334 |
| log_GDPpc | 108 | 8.542 | 1.189 | 6.164 | 7.568 | 8.676 | 9.604 | 10.492 |
| log_lp | 108 | 9.519 | 1.123 | 7.050 | 8.633 | 9.698 | 10.386 | 11.225 |
| log_ky | 108 | 0.740 | 0.585 | -1.807 | 0.478 | 0.846 | 1.130 | 1.574 |
| log_h | 108 | 0.948 | 0.293 | 0.266 | 0.728 | 1.031 | 1.176 | 1.453 |
| log_tfp | 108 | 6.391 | 0.787 | 4.913 | 5.713 | 6.543 | 6.948 | 8.336 |
t <- prepare_descriptive_table(df)
# Create a function to round the decimals of a df
round_df <- function(x, digits) {
# round all numeric variables
# x: data frame
# digits: number of digits to round
numeric_columns <- sapply(x, mode) == 'numeric'
x[numeric_columns] <- round(x[numeric_columns], digits)
x
}
round_df(t$df, 2)## N Mean Std. dev. Min. 25 % Median 75 %
## id 108 54.50 31.32 1.00 27.75 54.50 81.25
## year 108 1990.00 0.00 1990.00 1990.00 1990.00 1990.00
## Y 108 364598.14 1030271.05 3067.00 19377.75 76730.50 234608.00
## K 108 962050.80 2873937.94 2004.00 31131.00 162679.00 713896.25
## pop 108 45.41 140.67 1.57 5.11 10.35 34.44
## L 108 19.51 69.00 0.70 2.06 4.35 12.22
## s 108 6.50 2.90 0.89 4.16 6.98 8.79
## alpha_it 90 0.43 0.11 0.15 0.35 0.43 0.49
## GDPpc 108 9784.00 9475.93 526.03 2103.65 6126.90 16453.11
## lp 108 23223.79 20088.83 1299.29 6294.13 16984.63 38834.15
## h 108 2.68 0.73 1.32 2.08 2.77 3.25
## kl 108 63403.23 68027.75 725.04 9651.04 35519.55 102058.92
## kp 108 0.61 0.61 0.21 0.33 0.46 0.63
## ky 108 2.28 0.99 0.20 1.60 2.18 3.04
## TFP 108 824.21 652.60 139.97 360.48 702.48 1053.21
## log_GDPpc_raw 108 8.61 1.18 6.27 7.65 8.72 9.71
## log_lp_raw 108 9.56 1.10 7.17 8.75 9.74 10.57
## log_ky_raw 108 0.70 0.56 -1.62 0.47 0.78 1.11
## log_h_raw 108 0.95 0.29 0.28 0.73 1.02 1.18
## log_tfp_raw 108 6.44 0.76 4.94 5.89 6.55 6.96
## log_GDPpc 108 8.54 1.19 6.16 7.57 8.68 9.60
## log_lp 108 9.52 1.12 7.05 8.63 9.70 10.39
## log_ky 108 0.74 0.59 -1.81 0.48 0.85 1.13
## log_h 108 0.95 0.29 0.27 0.73 1.03 1.18
## log_tfp 108 6.39 0.79 4.91 5.71 6.54 6.95
## Max.
## id 108.00
## year 1990.00
## Y 9259567.00
## K 26453210.00
## pop 1154.61
## L 637.07
## s 12.20
## alpha_it 0.77
## GDPpc 37503.44
## lp 75036.34
## h 4.25
## kl 255639.41
## kp 5.07
## ky 4.78
## TFP 4164.72
## log_GDPpc_raw 10.53
## log_lp_raw 11.23
## log_ky_raw 1.56
## log_h_raw 1.45
## log_tfp_raw 8.33
## log_GDPpc 10.49
## log_lp 11.23
## log_ky 1.57
## log_h 1.45
## log_tfp 8.34
var <- as.numeric(dat$log_lp[dat$year == "1990"])
hist(var, main="", xlab = "log_lp", col="red", right = FALSE, breaks= 10)
t <- prepare_ext_obs_table(dat, n = 10,
cs_id = "country",
ts_id = "year",
var = "log_lp")
t$df## country year log_lp
## 2700 Norway 2014 11.984427
## 2699 Norway 2013 11.958503
## 2698 Norway 2012 11.932280
## 2697 Norway 2011 11.905351
## 2696 Norway 2010 11.877324
## 2695 Saudi Arabia 2014 11.871549
## 2694 Norway 2009 11.847876
## 2693 Saudi Arabia 2013 11.820003
## 2692 Norway 2008 11.816551
## 2691 Ireland 2014 11.797161
## 10 Burundi 2005 7.249489
## 9 Burundi 2003 7.248836
## 8 Burundi 2004 7.247464
## 7 Mozambique 1996 7.219810
## 6 Mozambique 1995 7.187088
## 5 Mozambique 1994 7.156726
## 4 Mozambique 1993 7.128354
## 3 Mozambique 1992 7.101554
## 2 Mozambique 1991 7.075745
## 1 Mozambique 1990 7.050233
df <- dat
df <- df[df$year == "1990", ]
prepare_by_group_bar_graph(df, "hi1990", "lp", mean, TRUE)$plot +
ylab("mean lp")
df <- dat
prepare_by_group_violin_graph(df, "region", "log_lp", TRUE)
df <- dat
prepare_trend_graph(df, "year", c("lp"))$plot
df <- dat
prepare_quantile_trend_graph(df, "year", c(0.05, 0.25, 0.5, 0.75, 0.95), "lp", points = FALSE)$plot
log_lp_raw <- prepare_quantile_trend_graph(dat, "year", c(0.05, 0.25, 0.5, 0.75, 0.95), "log_lp_raw", points = FALSE)$plotlog_lp_raw <- log_lp_raw +
theme_minimal() +
guides(color = guide_legend(reverse = TRUE)) +
scale_color_discrete(name = "Quantile") +
labs(x = "",
y = "Log of Labor Productivity")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
#ggsave("figs/quintiles_all_log_lp_raw.pdf", width = 6, height = 4)
log_lp_raw
df <- dat
ret <- prepare_correlation_graph(df)
ret2 <- prepare_correlation_graph(df[, c(10, 11, 12, 13, 14, 15, 16)])
df <- dat
df <- df[, c("country", "year", "log_lp", "log_GDPpc", "region", "pop")]
df <- df[complete.cases(df), ]
df$region <- as.factor(df$region)
set.seed(42)
df <- sample_n(df, 1000)
prepare_scatter_plot(df, "log_lp", "log_GDPpc", color = "region", size = "pop", loess = 1)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
df <- dat
df <- df[, c("log_lp", "log_ky", "log_h", "log_tfp", "country", "year", "hi1990")]
df <- df[complete.cases(df), ]
df$hi1990 <- as.factor(df$hi1990)
df <- droplevels(df)
t <- prepare_regression_table(df, dvs = "log_lp", idvs = c("log_ky", "log_h", "log_tfp"), feffects = c("country", "year"), clusters = c("country", "year"), byvar = "hi1990", models = "ols")
HTML(t$table)| Dependent variable: | |||
| log_lp | |||
| Full Sample | no | yes | |
| (1) | (2) | (3) | |
| log_ky | 0.472*** | 0.483*** | 0.469*** |
| (0.031) | (0.032) | (0.060) | |
| log_h | 0.226* | 0.183 | 0.435*** |
| (0.132) | (0.168) | (0.135) | |
| log_tfp | 1.447*** | 1.503*** | 1.180*** |
| (0.042) | (0.048) | (0.064) | |
| Estimator | ols | ols | ols |
| Fixed effects | country, year | country, year | country, year |
| Std. errors clustered | country, year | country, year | country, year |
| Observations | 2,700 | 2,050 | 650 |
| R2 | 0.888 | 0.893 | 0.895 |
| Adjusted R2 | 0.883 | 0.887 | 0.886 |
| Note: | *p<0.1; **p<0.05; ***p<0.01 | ||
df <- dat
df <- df[, c("log_lp", "log_ky", "log_h", "log_tfp", "country", "year", "hi1990")]
df <- df[complete.cases(df), ]
df$hi1990 <- as.factor(df$hi1990)
df <- droplevels(df)
t <- prepare_regression_table(df, dvs = "log_lp", idvs = c("log_ky", "log_h", "log_tfp"), feffects = c("country", "year"), clusters = c("country", "year"), byvar = "hi1990", models = "ols", format = "text")
t## $models
## $models[[1]]
## $models[[1]]$model
##
## Model Formula: log_lp ~ log_ky + log_h + log_tfp
## <environment: 0x7f7ed4df3178>
##
## Coefficients:
## log_ky log_h log_tfp
## 0.47182 0.22569 1.44690
##
##
## $models[[1]]$type_str
## [1] "ols"
##
## $models[[1]]$fe_str
## [1] "country, year"
##
## $models[[1]]$cl_str
## [1] "country, year"
##
## $models[[1]]$p
## log_ky log_h log_tfp
## 2.524332e-51 8.698143e-02 6.546745e-212
##
## $models[[1]]$se
## log_ky log_h log_tfp
## 0.03061389 0.13181026 0.04226128
##
## $models[[1]]$omit_vars
## NULL
##
## $models[[1]]$byvalue
## [1] "Full Sample"
##
##
## $models[[2]]
## $models[[2]]$model
##
## Model Formula: log_lp ~ log_ky + log_h + log_tfp
## <environment: 0x7f7edad8c7b8>
##
## Coefficients:
## log_ky log_h log_tfp
## 0.48272 0.18284 1.50339
##
##
## $models[[2]]$type_str
## [1] "ols"
##
## $models[[2]]$fe_str
## [1] "country, year"
##
## $models[[2]]$cl_str
## [1] "country, year"
##
## $models[[2]]$p
## log_ky log_h log_tfp
## 7.936025e-48 2.756112e-01 3.362078e-177
##
## $models[[2]]$se
## log_ky log_h log_tfp
## 0.03232132 0.16765703 0.04757119
##
## $models[[2]]$omit_vars
## NULL
##
## $models[[2]]$byvalue
## [1] "no"
##
##
## $models[[3]]
## $models[[3]]$model
##
## Model Formula: log_lp ~ log_ky + log_h + log_tfp
## <environment: 0x7f7ed3207ce8>
##
## Coefficients:
## log_ky log_h log_tfp
## 0.46857 0.43509 1.18037
##
##
## $models[[3]]$type_str
## [1] "ols"
##
## $models[[3]]$fe_str
## [1] "country, year"
##
## $models[[3]]$cl_str
## [1] "country, year"
##
## $models[[3]]$p
## log_ky log_h log_tfp
## 2.890378e-14 1.350437e-03 6.453452e-60
##
## $models[[3]]$se
## log_ky log_h log_tfp
## 0.06011667 0.13511032 0.06436493
##
## $models[[3]]$omit_vars
## NULL
##
## $models[[3]]$byvalue
## [1] "yes"
##
##
##
## $table
## [1] ""
## [2] "==============================================================="
## [3] " Dependent variable: "
## [4] " -----------------------------------------"
## [5] " log_lp "
## [6] " Full Sample no yes "
## [7] " (1) (2) (3) "
## [8] "---------------------------------------------------------------"
## [9] "log_ky 0.472*** 0.483*** 0.469*** "
## [10] " (0.031) (0.032) (0.060) "
## [11] " "
## [12] "log_h 0.226* 0.183 0.435*** "
## [13] " (0.132) (0.168) (0.135) "
## [14] " "
## [15] "log_tfp 1.447*** 1.503*** 1.180*** "
## [16] " (0.042) (0.048) (0.064) "
## [17] " "
## [18] "---------------------------------------------------------------"
## [19] "Estimator ols ols ols "
## [20] "Fixed effects country, year country, year country, year"
## [21] "Std. errors clustered country, year country, year country, year"
## [22] "Observations 2,700 2,050 650 "
## [23] "R2 0.888 0.893 0.895 "
## [24] "Adjusted R2 0.883 0.887 0.886 "
## [25] "==============================================================="
## [26] "Note: *p<0.1; **p<0.05; ***p<0.01"