The table below stats on the share of 8-11 year olds enrolled in government schools who can a) read a simple paragraph with three sentences, b) perform simple two-digit subtraction, and c) write a simple sentence with 2 or fewer mistakes.
# Set the path for the IHDS 2011 individual level file
ind_file <- file.path("C:/Users/dougj/Documents/Data/IHDS/IHDS 2012/DS0001", "36151-0001-Data.dta")
# read in just those variables that i need
ihds <- read_dta(ind_file, col_select = c(STATEID, WT, RO5, CS4, starts_with("TA"))) %>%
filter(!is.na(WT)) # drop one observation with missing wt variable
# Create variables for reading, math, and writing levels
ihds <- ihds %>% mutate(reading = (TA8B >= 3), # can read a simple paragraph
math = (TA9B >= 2), # can perform simple subtraction
writing = (TA10B >= 1)) # Can write sentence with <=2 mistakes
# Estimate share of 8-11 year olds in govt schools at specified learning levels
ihds %>%
filter(CS4 == 2) %>% # filter for children in govt schools
group_by(State = as_factor(STATEID)) %>%
summarise(across(c(reading, math, writing), weighted.mean, WT, na.rm = TRUE), n()) %>%
gt() %>%
fmt_percent(
columns = c(reading, math, writing),
decimals = 0
) | State | reading | math | writing | n() |
|---|---|---|---|---|
| Jammu & Kashmir 01 | 49% | 57% | 73% | 446 |
| Himachal Pradesh 02 | 76% | 63% | 92% | 1171 |
| Punjab 03 | 67% | 65% | 85% | 989 |
| Chandigarh 04 | 67% | 67% | 100% | 29 |
| Uttarakhand 05 | 58% | 37% | 79% | 358 |
| Haryana 06 | 52% | 49% | 68% | 1362 |
| Delhi 07 | 62% | 56% | 85% | 743 |
| Rajasthan 08 | 46% | 35% | 62% | 1795 |
| Uttar Pradesh 09 | 32% | 21% | 45% | 2419 |
| Bihar 10 | 29% | 33% | 50% | 1910 |
| Sikkim 11 | 16% | 58% | 62% | 75 |
| Arunachal Pradesh 12 | 30% | 95% | 100% | 121 |
| Nagaland 13 | 78% | 11% | 100% | 29 |
| Manipur 14 | 12% | 12% | NaN | 13 |
| Mizoram 15 | 0% | 49% | 100% | 15 |
| Tripura 16 | 68% | 75% | 100% | 180 |
| Meghalaya 17 | 38% | 65% | 79% | 67 |
| Assam 18 | 41% | 36% | 73% | 755 |
| West Bengal 19 | 60% | 53% | 84% | 1853 |
| Jharkhand 20 | 38% | 36% | 73% | 693 |
| Orissa 21 | 54% | 46% | 67% | 1723 |
| Chhattisgarh 22 | 44% | 27% | 60% | 1206 |
| Madhya Pradesh 23 | 44% | 25% | 59% | 2818 |
| Gujarat 24 | 52% | 29% | 65% | 1119 |
| Daman & Diu 25 | 31% | 32% | 69% | 54 |
| Dadra+Nagar Haveli 26 | 75% | 71% | 82% | 50 |
| Maharashtra 27 | 54% | 47% | 87% | 1659 |
| Andhra Pradesh 28 | 44% | 60% | 89% | 949 |
| Karnataka 29 | 39% | 37% | 79% | 2023 |
| Goa 30 | 76% | 52% | 100% | 77 |
| Kerala 32 | 75% | 77% | 92% | 474 |
| Tamil Nadu 33 | 38% | 46% | 69% | 738 |
| Pondicherry 34 | 84% | 96% | 91% | 44 |
NA
NA