Analyze Ugo’s new SP data
1 Cash transfer start date versus cash transfer infrastructure
For countries which have implemented cash transfers, I compare the speed with which they implemented the cash transfers and their existing payments infrastructure. I code “speed of cash transfer” in two different ways: first, as the number of days between when the country first closed schools (from the Oxford dataset of Covid NPI responses) and the start date of the first cash transfer (variable name days_since_sch); second, as the number of days between March 1st and the start date of the first cash transfer (variable name days_since_m1).
I plot these two variables against various variables related to countries’ cash transfer infrastructure including ease of identifying beneficiaries (based on our the definition we came up with), ease of delivering payments (based on our previous definition), % of adult population with a financial account, % of population with an ID, and % of population covered by social insurance.
Oddly, there doesn’t seem to be much of a relation between speed of cash transfer implementation (defined in either way) and any of these variables. The one noticeable relationship in all of the graphs is that it seems like all of the countries which have implemented cash transfers (and for which we have data on the start date) have a score on our ease of identifying beneficiaries variable of at least .4 or so. The next set of graphs dives into this a bit more.
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2 Compare countries with and without cash transfers
The graphs above show that there isn’t much of a relationship between cash transfer infrastructure (as we have been thinking about it) and the speed with which cash transfers were implemented.
The tables and graphs below use the variable “CT Nature” from the spreadsheet to compare countries with and without cash transfers. There doesn’t seem to be anything super surprising in there. There seem to be a lot of countries with low social insurance coverage which haven’t implemented cash transfers.
| Comparison of countries w and w/o cash transfers | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| has_ct | ease_id_mean | ease_id_median | ease_pay_mean | ease_pay_median | has_account_mean | has_account_median | has_id_mean | has_id_median | soc_ins_mean | soc_ins_median | has_mobile_money_acct_mean | has_mobile_money_acct_median |
| FALSE | 0.43 | 0.45 | 0.72 | 0.87 | 0.66 | 0.72 | 0.74 | 0.85 | 0.32 | 0.13 | 0.1847302 | 0.20843393 |
| TRUE | 0.54 | 0.55 | 0.74 | 0.82 | 0.61 | 0.59 | 0.85 | 0.92 | 0.33 | 0.28 | 0.1284182 | 0.06561998 |
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3 Inspect cash transfer type
The two tables below analyze the “CT nature” variable to analyze what type of cash transfers are more popular.
About half of countries with cash transfers only have one Covid cash transfer program.
Most of the cash transfer programs are described as either “new/ad hoc” or “one-off.” I haven’t dived into this data but my hunch is that for many of these programmes it would be more realistic to describe them as horizontal / vertical expansions.
## `summarise()` regrouping output by 'code' (override with `.groups` argument)| Cash transfer type | Number of countries with ct type |
|---|---|
| new/ad hoc | 64 |
| one-off | 38 |
| vertical expansion | 30 |
| admin simplification | 19 |
| additional payment | 12 |
| horizontal expansion | 7 |
| vertical and horizental | 6 |
| advance payment | 5 |
| vertical and horizontal expansion | 2 |
| admin adaptation | 1 |
| horizontal exapnsion | 1 |
| vertical and horizontal expansions | 1 |
| vertical expansion and extra payment | 1 |
## Storing counts in `nn`, as `n` already present in input
## i Use `name = "new_name"` to pick a new name.| Number of cts | Number of countries with x cts |
|---|---|
| 1 | 55 |
| 2 | 25 |
| 3 | 15 |
| 4 | 6 |
| 5 | 6 |
| 6 | 3 |
| 7 | 3 |
| 8 | 1 |