Table: Global students

library(tibble)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)
library(stringr)
library(readr)
library(kableExtra)
#> 
#> Attaching package: 'kableExtra'
#> The following object is masked from 'package:dplyr':
#> 
#>     group_rows

library(PrjCompPPTS)
# If resave outputs to data, only do this during development
verbose <- TRUE
bl_tex_save <- FALSE
spt_path_res <- file.path("..", "res-tab", fsep = .Platform$file.sep)
spn_tex_out <- file.path(spt_path_res, "tab_global_students.tex", fsep = .Platform$file.sep)

Implement PrjCompPPTS-5, summarize global number of children in primary schools.

Generate input file

Load prepped all location level files

Load file with interpolated data.

# Locaiton codes
ppts_code_wrk <- ppts_country_code
# We load in the global population data.
df_ppts <- ppts_easia_weuro_world_pchg

Run PrjCompPPTS functions

Run functions to generate FLR, FPC, and FEL files. Run FLR.

# Generate PLR
df_flr <- PrjCompPPTS::ff_ppts_lrce_flr(
  df_ppts,
  st_year_bins_type = "1940t2020i01",
  ar_it_years = c(1960, 1980, 1990, 2000, 2010, 2020),
  verbose = TRUE
)
#> F-713479, S1
#> [1] 2000 2010 1960 1980 1990 2020
#> F-713479, S2
#> dim of youth wide file: 1616
#> dim of youth wide file: 7
#> F-713479, S2
#> [1] "var_rat_y2t"
#> [1] "var_lvl_youthpop"
#> [1] "var_lvl_teacher"
#> dim FLR: 1616
#> dim FLR: 8
#> F-713479, S2
#> [1] "var_rat_s2t"
#> [1] "var_lvl_student"
#> [1] "var_lvl_teacher"
#> dim FLR: 1616
#> dim FLR: 9
#> F-713479, S2
#> [1] "var_rat_y2s"
#> [1] "var_lvl_youthpop"
#> [1] "var_lvl_school"
#> dim FLR: 1616
#> dim FLR: 10
#> F-713479, S2
#> [1] "var_rat_s2s"
#> [1] "var_lvl_student"
#> [1] "var_lvl_school"
#> dim FLR: 1616
#> dim FLR: 11
#> F-713479, S3
#>  [1] "location_code"    "location_level"   "year_bins"        "var_lvl_student" 
#>  [5] "var_lvl_teacher"  "var_lvl_youthpop" "var_lvl_school"   "var_rat_y2t"     
#>  [9] "var_rat_s2t"      "var_rat_y2s"      "var_rat_s2s"     
#> dim of youth wide ratio file: 1616
#> dim of youth wide ratio file: 11

Run FPC.

# Generate FPC
df_fpc <- PrjCompPPTS::ff_ppts_lrce_fpc(
  df_flr,
  ar_it_years_chg = c(1960, 1980, 2000, 2020),
  ls_chg_years = list(
    "chg_80v60" = c(1960, 1980),
    "chg_00v80" = c(1980, 2000),
    "chg_20v00" = c(2000, 2020),
    "chg_20v80" = c(1980, 2020)
  ), verbose = TRUE
)
#> F-376864, S1
#> dim of youth wide ratio file: 6644
#> dim of youth wide ratio file: 6
#> F-376864, S2
#> dim of youth wide ratio file: 6644
#> dim of youth wide ratio file: 6
#> F-376864, S3
#> dim of youth wide ratio file: 2728
#> dim of youth wide ratio file: 8
#> F-376864, SD3
#> [1] "chg_80v60"
#> [1] "year1960"
#> [1] "year1980"
#> dim FPC: 2728
#> dim FPC: 9
#> F-376864, SD3
#> [1] "chg_00v80"
#> [1] "year1980"
#> [1] "year2000"
#> dim FPC: 2728
#> dim FPC: 10
#> F-376864, SD3
#> [1] "chg_20v00"
#> [1] "year2000"
#> [1] "year2020"
#> dim FPC: 2728
#> dim FPC: 11
#> F-376864, SD3
#> [1] "chg_20v80"
#> [1] "year1980"
#> [1] "year2020"
#> dim FPC: 2728
#> dim FPC: 12
#> F-376864, S4
#> dim FPC: 2728
#> dim FPC: 12

Run FEL.

# Generate FEL
df_fel <- PrjCompPPTS::ff_ppts_lrce_fel(
  df_fpc,
  verbose = TRUE
)
#> F-307302, S1
#> dim df_fpc_base: 818
#> dim df_fpc_base: 7
#> F-307302, S2
#> dim df_fel_long: 2456
#> dim df_fel_long: 5
#> F-307302, S3
#> dim df_fel_wide: 1055
#> dim df_fel_wide: 7
#> F-307302, S3
#> [1] "var_elas_y2t"
#> [1] "var_chg_youthpop"
#> [1] "var_chg_teacher"
#> dim FEL: 1055
#> dim FEL: 8
#> F-307302, S3
#> [1] "var_elas_s2t"
#> [1] "var_chg_student"
#> [1] "var_chg_teacher"
#> dim FEL: 1055
#> dim FEL: 9
#> F-307302, S3
#> [1] "var_elas_y2s"
#> [1] "var_chg_youthpop"
#> [1] "var_chg_school"
#> dim FEL: 1055
#> dim FEL: 10
#> F-307302, S3
#> [1] "var_elas_s2s"
#> [1] "var_chg_student"
#> [1] "var_chg_school"
#> dim FEL: 1055
#> dim FEL: 11
#> F-307302, S4
#> dim df_fel_elas: 1055
#> dim df_fel_elas: 11
#> F-307302, S4
#> dim FEL: 517
#> dim FEL: 7

Merge FPC and FEL, country and region

Now we combine the level and change information from FPC with the elasticity results from FEL.

We have the LRCE file, level, ratio, change, and elasticity.

df_lrce <- df_fpc %>% left_join(
  df_fel %>% mutate(vartype = "rat"),
  by = (c(
    "location_code" = "location_code",
    "location_level" = "location_level",
    "variable" = "variable",
    "vartype" = "vartype"
  ))
)
print(glue::glue("dim df_lrce: {dim(df_lrce)}"))
#> dim df_lrce: 2728
#> dim df_lrce: 16

Finally, merge in country key info to FPC, country-level information.

df_lrce_country <- df_lrce %>%
  filter(location_level != "multicountry") %>%
  left_join(
    ppts_code_wrk %>%
      select(
        location_name, location_code,
        location_region_group, location_region_group_code
      ),
    by = c("location_code" = "location_code")
  ) %>%
  # mutate(location_name_full = paste0(location_name, " (", location_code, ")")) %>%
  mutate(location_name_full = paste0(location_name)) %>%
  select(-location_name) %>%
  select(location_name_full, location_region_group, everything()) %>%
  arrange(location_region_group_code, location_name_full)

FPC regional information, same variables.

ar_st_wb7 <- c("SSF", "MEA", "LCN", "NAC", "SAS", "ECS", "EAS")
df_lrce_region <- df_lrce %>%
  filter(location_level == "multicountry") %>%
  filter(location_code %in% ar_st_wb7) %>%
  left_join(
    ppts_code_wrk %>%
      select(
        location_name, location_code, location_code_adj,
        location_region_group, location_region_group_code
      ),
    by = c("location_code" = "location_code")
  ) %>%
  select(-location_code) %>%
  rename(location_code = location_code_adj) %>%
  # mutate(location_name_full = paste0(location_name, " (", location_code, ")")) %>%
  mutate(location_name_full = paste0(location_name)) %>%
  select(-location_name) %>%
  select(location_name_full, location_region_group, everything()) %>%
  mutate(
    location_region_group_code = "0WORLD",
    location_region_group = "Global regions"
  ) %>%
  arrange(location_region_group_code, location_code)

Combine region and country.

df_lrce_region_country <- bind_rows(
  df_lrce_region,
  df_lrce_country %>% filter(location_level == "country"),
)

Global Primary School Students Table

Prep Table inputs

Select and prep Table 1. Combine national and regional data together.

# Select variables
tab1aux_global_students <- df_lrce_region_country %>%
  filter(
    vartype == "lvl",
    variable == "student"
  ) %>%
  select(
    -vartype, -variable, -location_level, -location_code,
    -chg_80v60, -contains("elas_")
  )

Second, count variables in regional groups.

df_region_counts <- tab1aux_global_students %>%
  group_by(
    location_region_group_code, location_region_group
  ) %>%
  summarize(region_count = n()) %>%
  ungroup() %>%
  mutate(region_count_start = cumsum(region_count) - region_count + 1) %>%
  mutate(region_count_end = cumsum(region_count)) %>%
  select(region_count_start, region_count_end, everything())
#> `summarise()` has grouped output by 'location_region_group_code'. You can
#> override using the `.groups` argument.

Third, format variables.

# Format variables
tab1aux_global_students <- tab1aux_global_students %>%
  select(-location_region_group, -location_region_group_code) %>%
  mutate_at(
    vars(contains("year")),
    list(~ paste0(
      format(round(. / 1000, 0), nsmall = 0, big.mark = ",")
    ))
  ) %>%
  mutate_at(
    vars(contains("chg")),
    list(~ paste0(
      format(round(., 2) * 100,
        nsmall = 0,
        big.mark = ","
      ),
      "%"
    ))
  )
# Replace NAs
tab1aux_global_students <- tab1aux_global_students %>%
  mutate_at(vars(starts_with("chg")), ~ str_replace(., "NA%", "")) %>%
  mutate_at(vars(starts_with("year")), ~ str_replace(., "NA", ""))

Generate table

Now we generate the table.

ar_st_kableformat <- c("latex", "html")
for (st_kableformat in ar_st_kableformat) {
  # Column names
  ar_st_col_names <- c(
    "Country by region",
    "1980",
    "2000",
    "2020",
    # "1980/1960",
    "2000/1980",
    "2020/2000",
    "2020/1980"
  )
  # Define column groups, grouping the names above
  # =1/3/2 are number of columns group title covers
  ar_st_col_groups <- c(
    " " = 1,
    "Primary school students (1000s)" = 3,
    "Primary students changes (%)" = 3
  )

  # Second, we construct main table, and add styling.
  bk_students <- kbl(
    tab1aux_global_students %>%
      select(
        location_name_full,
        year1980, year2000, year2020,
        chg_00v80, chg_20v00, chg_20v80
      ),
    format = st_kableformat,
    label = "main:tab:global:students",
    # escape = F,
    linesep = "",
    booktabs = T,
    longtable = T,
    align = "c",
    caption = "Global primary school students",
    col.names = ar_st_col_names
  ) %>%
    # see https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html#Bootstrap_table_classes
    kable_styling(
      bootstrap_options = c("striped", "hover", "condensed", "responsive"),
      full_width = F, position = "left"
    )

  # Third, we add in column groups.
  bk_students <- bk_students %>% add_header_above(ar_st_col_groups)

  # Fourth, we add in row groups.
  for (it_region in seq(1, dim(df_region_counts)[1])) {
    # Reion full name info
    st_loc <- as.character(df_region_counts[[it_region, "location_region_group"]])

    # Regions start and end
    it_region_count_start <- df_region_counts[[it_region, "region_count_start"]]
    it_region_count_end <- df_region_counts[[it_region, "region_count_end"]]

    # Add to table
    bk_students <- bk_students %>%
      pack_rows(
        st_loc, it_region_count_start, it_region_count_end,
        latex_gap_space = "1em",
        latex_align = "c",
        hline_after = TRUE
      )
  }

  # Fifth, column formatting.
  fl_width_country <- 3.4
  st_width_country <- paste0(fl_width_country, "cm")
  bk_students <- bk_students %>%
    column_spec(1, width = st_width_country) %>%
    column_spec(2:7, width = "1.3cm")

  # Final adjustments
  # Headings on all pages, note use `sub` to replace first midrule
  st_headend <- paste0(
    "\\midrule\\endhead\n",
    "\\addlinespace[0.2em]\\hline\\addlinespace[0.2em]\n",
    "\\multicolumn{7}{r}{\\emph{Continued on next page}}\\\\\n",
    "\\endfoot\\endlastfoot"
  )
  bk_students <- sub(bk_students,
    pattern = "\\midrule", replacement = st_headend,
    fixed = TRUE
  )
  # country-names left-align
  bk_students <- gsub(bk_students,
    pattern = paste0("\\centering\\arraybackslash}p{", st_width_country, "}"),
    replacement = paste0("\\raggedright\\arraybackslash}p{", st_width_country, "}"),
    fixed = TRUE
  )
  # midrule replacing hline
  bk_students <- gsub(bk_students,
    pattern = "hline",
    replacement = "midrule", fixed = TRUE
  )

  # Sixth, display.
  # pl_bk_asset_count <- bk_asset_count %>% as_image()
  if (st_kableformat == "html") {
    print(dim(bk_students))
  } else {
    if (bl_tex_save) {
      fileConn <- file(spn_tex_out)
      writeLines(bk_students, fileConn)
      close(fileConn)
      if (verbose) {
        print(glue::glue("Latex saved: {spn_tex_out}"))
      }
    }
  }
}
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> Warning in pack_rows(., st_loc, it_region_count_start, it_region_count_end, : latex_align parameter is not used in HTML Mode,
#>                                     use label_row_css instead.
#> NULL
bk_students

Global primary school students

	Primary school students (1000s)			Primary students changes (%)
Country by region	1980	2000	2020	2000/1980	2020/2000	2020/1980
Global regions
Sub-Saharan Africa	49,940	90,722	185,517	82%	104%	271%
Middle East & North Africa	24,138	41,785	54,568	73%	31%	126%
Latin America & Caribbean	56,800	70,186	62,774	24%	-11%	11%
North America	22,893	27,435	27,028	20%	-1%	18%
South Asia	88,806	149,389	172,331	68%	15%	94%
Europe & Central Asia	57,826	54,880	53,447	-5%	-3%	-8%
East Asia & Pacific	224,997	223,007	191,347	-1%	-14%	-15%
Sub-Saharan Africa (SSF)
Angola	1,421	1,667	6,463	17%	288%	355%
Benin	357	932	2,183	161%	134%	511%
Botswana	172	324	365	89%	13%	112%
Burkina Faso	185	852	3,240	361%	280%	1,653%
Burundi	160	705	2,257	341%	220%	1,313%
Cabo Verde	58	92	61	58%	-33%	5%
Cameroon	1,303	2,237	4,607	72%	106%	254%
Central African Republic	243	434	1,000	78%	131%	311%
Chad	244	914	2,519	274%	176%	932%
Comoros	55	93	130	71%	39%	138%
Congo, Dem. Rep.	4,055	4,452	19,202	10%	331%	374%
Congo, Rep.	383	419	800	9%	91%	109%
Côte d’Ivoire	954	1,943	4,101	104%	111%	330%
Equatorial Guinea	43	73	96	70%	30%	122%
Eritrea		296	355		20%
Eswatini	112	214	235	91%	10%	110%
Ethiopia	1,811	5,847	20,419	223%	249%	1,027%
Gabon	149	265		79%
Gambia	37	175	393	366%	125%	949%
Ghana	1,372	2,561	4,584	87%	79%	234%
Guinea	263	790	2,108	201%	167%	702%
Guinea-Bissau	77	150		96%
Kenya	3,575	5,035	8,796	41%	75%	146%
Lesotho	245	411	390	68%	-5%	59%
Liberia	147	496	580	237%	17%	294%
Madagascar	1,598	2,208	4,446	38%	101%	178%
Malawi	667	2,695	4,750	304%	76%	612%
Mali	299	1,017	2,359	240%	132%	690%
Mauritania	85	356	700	318%	97%	723%
Mauritius	129	135	84	5%	-38%	-35%
Mozambique	1,407	2,544	7,220	81%	184%	413%
Namibia		389	521		34%
Niger	210	579	2,617	176%	352%	1,147%
Nigeria	10,799	19,151	30,394	77%	59%	181%
Rwanda	559	1,432	2,521	156%	76%	351%
Senegal	393	1,108	2,260	182%	104%	476%
Seychelles	10	10	10	4%	-5%	-1%
Sierra Leone	305	443	1,760	45%	297%	477%
Somalia	271	318		17%
South Sudan			1,083
Sudan		2,607	5,585		114%
São Tomé and Príncipe	17	25	40	53%	56%	140%
Tanzania	3,368	4,382	10,926	30%	149%	224%
Togo	484	915	1,634	89%	79%	238%
Uganda	1,302	6,559	9,419	404%	44%	623%
Zambia	1,042	1,590	3,542	53%	123%	240%
Zimbabwe	1,235	2,461	2,870	99%	17%	132%
Middle East & North Africa (MEA)
Algeria	3,061	4,843	4,852	58%	0%	59%
Bahrain	49	78	118	60%	52%	142%
Djibouti	15	38	69	151%	81%	354%
Egypt	4,537	7,947	13,917	75%	75%	207%
Iran	4,929	8,288	8,689	68%	5%	76%
Iraq	2,609	3,639		39%
Israel	474	677	979	43%	45%	106%
Jordan	448	724	1,126	61%	56%	151%
Kuwait	146	140	279	-4%	99%	92%
Lebanon	388	404	517	4%	28%	33%
Libya	645	793		23%
Malta	32	34	27	6%	-21%	-17%
Morocco	2,052	3,670	4,536	79%	24%	121%
Oman	84	316	304	277%	-4%	263%
Qatar	28	61	164	114%	168%	475%
Saudi Arabia	841	2,386	3,432	184%	44%	308%
Syrian Arab Republic	1,481	2,775		87%
Tunisia	1,034	1,414	1,269	37%	-10%	23%
United Arab Emirates	80	273	462	239%	69%	475%
West Bank and Gaza		388	503		30%
Yemen		2,467	3,934		59%
Latin America & Caribbean (LCN)
Antigua and Barbuda	10	13	10	32%	-19%	6%
Argentina	3,917	4,901	4,825	25%	-2%	23%
Aruba		9
Bahamas, The	32	34	28	6%	-16%	-11%
Barbados	31	24	19	-22%	-21%	-39%
Belize	28	45	49	60%	10%	76%
Bolivia	857	1,462	1,394	71%	-5%	63%
Brazil	16,090	20,212	15,433	26%	-24%	-4%
British Virgin Islands	2	3	3	40%	-2%	36%
Cayman Islands	2	3	5	63%	32%	115%
Chile	1,754	1,799	1,566	3%	-13%	-11%
Colombia	4,168	5,221	4,225	25%	-19%	1%
Costa Rica	349	551	493	58%	-11%	41%
Cuba	1,550	1,046	760	-33%	-27%	-51%
Curaçao			13
Dominica	16	12	6	-25%	-47%	-60%
Dominican Republic	1,069	1,364	1,224	28%	-10%	14%
Ecuador	1,450	1,925	1,876	33%	-3%	29%
El Salvador	696	949	592	36%	-38%	-15%
Grenada	19	16	13	-14%	-22%	-32%
Guatemala	803	1,909	2,348	138%	23%	192%
Guyana	132	109		-18%
Haiti	580	1,605		177%
Honduras	601	1,095	1,080	82%	-1%	80%
Jamaica	363	327	220	-10%	-33%	-39%
Mexico	14,126	14,766	13,964	5%	-5%	-1%
Nicaragua	472	838		78%
Panama	338	400	422	19%	5%	25%
Paraguay	519	966	692	86%	-28%	33%
Peru	3,161	4,338	3,842	37%	-11%	22%
Puerto Rico	474	358	191	-24%	-47%	-60%
St. Kitts and Nevis	8	7	5	-17%	-22%	-35%
St. Lucia	31	25	15	-17%	-39%	-49%
St. Vincent	22	19	14	-11%	-29%	-37%
Suriname	81	64	69	-20%	6%	-15%
Trinidad and Tobago	167	169		1%
Turks and Caicos Islands	2	2	5	19%	131%	175%
Uruguay	331	361	288	9%	-20%	-13%
Venezuela	2,457	3,328	3,008	35%	-10%	22%
Virgin Islands (U.S.)	14
North America (NAC)
Bermuda	6	5	4	-14%	-15%	-27%
Canada	2,206	2,456	2,408	11%	-2%	9%
United States			24,616
South Asia (SAS)
Afghanistan	960	749	7,019	-22%	837%	631%
Bangladesh	8,240	14,645	17,604	78%	20%	114%
Bhutan	30	85	94	185%	11%	215%
India	71,183	113,613	121,700	60%	7%	71%
Maldives	31	74	52	140%	-30%	69%
Nepal	1,068	3,780	3,890	254%	3%	264%
Pakistan	5,213	13,987	24,263	168%	73%	365%
Sri Lanka	2,081	1,779	1,696	-15%	-5%	-19%
Europe & Central Asia (ECS)
Albania	272	283	162	4%	-43%	-40%
Andorra	3	4	4	39%	5%	46%
Armenia		180	155		-14%
Austria	401	394	348	-2%	-12%	-13%
Azerbaijan	470	700	651	49%	-7%	38%
Belarus	733	600	437	-18%	-27%	-40%
Belgium	862	774	825	-10%	7%	-4%
Bosnia and Herzegovina			152
Bulgaria	521	393	251	-25%	-36%	-52%
Croatia		199	157		-21%
Cyprus	51	64	60	26%	-6%	18%
Czechia	673	645	580	-4%	-10%	-14%
Denmark	443	384	455	-13%	19%	3%
Estonia	130	123	91	-5%	-26%	-30%
Finland	390	388	373	-1%	-4%	-4%
France	6,129	5,445	5,433	-11%	0%	-11%
Georgia	325	298	325	-8%	9%	0%
Germany		3,353	2,842		-15%
Gibraltar	3	2	3	-12%	22%	7%
Greece	900	645	629	-28%	-3%	-30%
Hungary	586	501	355	-15%	-29%	-39%
Iceland	25	31	33	27%	6%	35%
Ireland	418	450	573	8%	27%	37%
Italy	4,507	2,836	2,800	-37%	-1%	-38%
Kazakhstan	1,048	1,208	1,513	15%	25%	44%
Kyrgyz Republic	299	466	566	56%	22%	89%
Latvia	141	135	120	-4%	-11%	-15%
Liechtenstein		2	2		-6%
Lithuania	172	218	119	27%	-46%	-31%
Luxembourg	26	32	40	27%	24%	57%
Moldova	227	252	139	11%	-45%	-39%
Monaco	1	2	2	36%	-2%	34%
Montenegro		38	39		3%
Netherlands		1,644	1,492		-9%
North Macedonia		127	111		-12%
Norway	395	420	450	6%	7%	14%
Poland	3,116	3,319	2,348	7%	-29%	-25%
Portugal	1,234	811	604	-34%	-26%	-51%
Romania	1,488	1,189	915	-20%	-23%	-38%
Russian Federation	5,961	6,138	7,324	3%	19%	23%
San Marino	2	1	2	-19%	28%	4%
Serbia		351	259		-26%
Slovak Republic		309	234		-24%
Slovenia	106	87	136	-18%	57%	28%
Spain	3,609	2,540	3,033	-30%	19%	-16%
Sweden	677	776	884	15%	14%	31%
Switzerland	440	474	530	8%	12%	20%
Tajikistan		692	953		38%
Turkmenistan			589
Türkiye	5,614	6,562	5,435	17%	-17%	-3%
Ukraine	2,177	2,079	1,725	-5%	-17%	-21%
United Kingdom	5,134	4,632	4,946	-10%	7%	-4%
Uzbekistan	1,351	2,602	2,508	93%	-4%	86%
East Asia & Pacific (EAS)
American Samoa	7
Australia	1,718	1,906	2,280	11%	20%	33%
Brunei Darussalam	31	45	40	49%	-13%	30%
Cambodia	1,169	2,248	2,165	92%	-4%	85%
China	146,270	130,133	107,254	-11%	-18%	-27%
Fiji	99	115	126	16%	10%	27%
French Polynesia	29	27		-8%
Guam	18
Hong Kong SAR, China	542	497	377	-8%	-24%	-30%
Indonesia	24,308	28,509	29,574	17%	4%	22%
Japan	11,827	7,366	6,301	-38%	-14%	-47%
Kiribati	13	15	18	10%	26%	39%
Korea, Dem. People’s Rep.			1,499
Korea, Rep.	5,658	4,020	2,694	-29%	-33%	-52%
Lao PDR	463	832	761	80%	-9%	64%
Macao SAR, China	25	47	34	90%	-28%	37%
Malaysia	2,009	3,026	3,064	51%	1%	53%
Marshall Islands		8	7		-20%
Micronesia	24	20	13	-18%	-36%	-47%
Mongolia	141	253	344	79%	36%	143%
Myanmar	3,968	4,858	5,127	22%	6%	29%
Nauru	1	2	1	12%	-7%	4%
New Caledonia	27
New Zealand	381	360	388	-6%	8%	2%
Palau		2	2		-22%
Papua New Guinea	300	560	1,524	87%	172%	408%
Philippines	8,113	12,708	12,975	57%	2%	60%
Samoa	28	28	34	-1%	21%	20%
Singapore	292	281	236	-4%	-16%	-19%
Solomon Islands	29	57	109	99%	90%	277%
Taiwan	2,234	1,926	1,174	-14%	-39%	-47%
Thailand	7,244	6,101	4,887	-16%	-20%	-33%
Timor-Leste		186	200		8%
Tonga	19	17	17	-12%	2%	-10%
Tuvalu	1	2	1	53%	-12%	35%
Vanuatu	23	36	56	53%	58%	142%
Vietnam	7,923	10,063	8,718	27%	-13%	10%