Data warehouse check, oda.csv/fact.oda

[xriss]

I've added a warehouse branch containing data that has been imported from Data Warehouse as a test. Please check that they are ok.

https://github.com/devinit/digital-platform/tree/warehouse

Mostly I think the import has had a slight cosmetic effect on the format of the CSV files, ie numerical formats or use of quotes or line endings.

The most important part of the CSV files are the header names so double check that these are still the same.

Note that not all files have been imported, for instance I've ignored all files under the reference directory and the following files have not been changed as I'm not sure where to get the data from.

country-year/adult-literacy country-year/domestic-netlending country-year/education-pc-transferred-oda country-year/employment-agriculture country-year/employment-by-sector country-year/employment-industry country-year/employment-services country-year/gdp-current-ncu-fy country-year/gdp-growth country-year/gdp-pc-usd-2005 country-year/gdp-pc-usd-current country-year/gdp-usd-2005 country-year/gdp-usd-2012 country-year/gni-usd-2005 country-year/govtspend-USD country-year/health-pc-transferred-oda country-year/income-share-top-10pc country-year/infant-mortality country-year/in-oda-and-repayments country-year/in-oof-and-repayments country-year/in-oof-net country-year/intl-flows-donors-wide country-year/intl-flows-recipients-wide country-year/kenya-electricity-avg country-year/kenya-electricity-rank country-year/kenya-improved-sani-avg country-year/kenya-improved-sani-rank country-year/kenya-improved-water-avg country-year/kenya-improved-water-rank country-year/kenya-paved-roads-avg country-year/kenya-paved-roads-rank country-year/kenya-pov-avg country-year/kenya-pov-rank country-year/kenya-urban-avg country-year/kenya-urban-rank country-year/long-term-debt country-year/mean-years-of-schooling country-year/non-grant-revenue-PPP-capita country-year/oda country-year/oda-donor/oda-AE country-year/oda-donor/oda-afdb country-year/oda-donor/oda-afdf country-year/oda-donor/oda-afesd country-year/oda-donor/oda-arab-fund-afesd country-year/oda-donor/oda-asdb-special-funds country-year/oda-donor/oda-AT country-year/oda-donor/oda-AU country-year/oda-donor/oda-badea country-year/oda-donor/oda-BE country-year/oda-donor/oda-CA country-year/oda-donor/oda-CH country-year/oda-donor/oda-CZ country-year/oda-donor/oda-DE country-year/oda-donor/oda-DK country-year/oda-donor/oda-ebrd country-year/oda-donor/oda-EE country-year/oda-donor/oda-ES country-year/oda-donor/oda-EU country-year/oda-donor/oda-FI country-year/oda-donor/oda-FR country-year/oda-donor/oda-gavi country-year/oda-donor/oda-GB country-year/oda-donor/oda-gef country-year/oda-donor/oda-global-fund country-year/oda-donor/oda-GR country-year/oda-donor/oda-ibrd country-year/oda-donor/oda-ida country-year/oda-donor/oda-idb-specialfund country-year/oda-donor/oda-IE country-year/oda-donor/oda-ifad country-year/oda-donor/oda-imf country-year/oda-donor/oda-imf-concessional-trust-fund country-year/oda-donor/oda-IS country-year/oda-donor/oda-islamic-dev-bank country-year/oda-donor/oda-IT country-year/oda-donor/oda-JP country-year/oda-donor/oda-KR country-year/oda-donor/oda-KW country-year/oda-donor/oda-LU country-year/oda-donor/oda-NL country-year/oda-donor/oda-NO country-year/oda-donor/oda-nordic-dev-fund country-year/oda-donor/oda-NZ country-year/oda-donor/oda-ofid country-year/oda-donor/oda-osce country-year/oda-donor/oda-PL country-year/oda-donor/oda-PT country-year/oda-donor/oda-SE country-year/oda-donor/oda-SI country-year/oda-donor/oda-SK country-year/oda-donor/oda-unaids country-year/oda-donor/oda-undp country-year/oda-donor/oda-unece country-year/oda-donor/oda-unfpa country-year/oda-donor/oda-unhcr country-year/oda-donor/oda-unicef country-year/oda-donor/oda-unpbf country-year/oda-donor/oda-unrwa country-year/oda-donor/oda-US country-year/oda-donor/oda-wfp country-year/oda-donor/oda-who country-year/out-oda-and-repayments country-year/out-oda-gross country-year/out-oof-and-repayments country-year/out-oof-gross country-year/poorest20pct-percentages country-year/population-0-14 country-year/population-15-64 country-year/population-65- country-year/poverty-gap-125 country-year/poverty-gap-2 country-year/primary-school-enrolment country-year/taxrev-pctGDP country-year/total-employment country-year/total-revenue-pct-GDP country-year/total-revenue-PPP-capita country-year/under-5-mortality country-year/university-college-enrolment country-year/youth-literacy country-year/youth-unemployment

[xriss]

BTW I haven't included the oda.csv as although I have set it up to download and it seems like the right data. (although its header names are slightly wrong.)

remote: error: File country-year/oda.csv is 185.02 MB; this exceeds GitHub's file size limit of 100.00 MB

it's somewhat larger than the one we currently have so I cant push it to you here to check out. You could try running the import yourself to check it is valid?

@xriss, NP, only, I don't know which import I'm to run. Would you please provide some info? Import to DH? Push to GitHub?

PS You were either up really early or going to bed really late???

[dw8547]

@xriss, re: "The most important part of the CSV files are the header names so double check that these are still the same."

Could you please confirm if the name of the table in ddw must match the name of the .csv file as on https://github.com/devinit/digital-platform/tree/master/country-year or that the table column names must match the column headers in the .csv files, or both?

Previously, the .csv files in https://github.com/devinit/digital-platform/tree/master/country-year contained columns labelled 'id', 'from_id', 'to_id'. These columns contain the identifiers for the DI entities. The DI instructions now are that anything that previously was an 'id' column, once automated, it becomes 'di_id', anything that previously was a 'from_id' column, once automated, it becomes 'from_di_id' and anything that previously was a 'to_id' column, once automated, it becomes 'to_di_id'.

@xriss there is a number of new di_ids for donors. They are:

di_id	name
dac-countries-total	DAC Countries, Total
multilateral-total	Multilateral, Total
g7-countries-total	G7 Countries, Total
all-donors-total	All Donors, Total
non-dac-countries-total	Non-DAC Countries, Total
other-donor-countries	Other donor countries
dac-eu-members-total	DAC EU Members, Total
un-agencies	UN Agencies
cif	Climate Investment Funds [CIF]
adaptation-fund	Adaptation Fund
ceb	Council of Europe Development Bank [CEB]
gggi	Global Green Growth Institute [GGGI]
bmgf	Bill & Melinda Gates Foundation
ANHH	Netherlands Antilles
eac	East African Community
fao	Food and Agriculture Organisation [FAO]

@timstrawson, do you want these to be included in the DH? Some of these (e.g., adaptation-fund) have OECD CRS ODA data against them.

Re: "Note that not all files have been imported, for instance I've ignored all files under the reference directory and the following files have not been changed as I'm not sure where to get the data from."

All of these files: https://github.com/devinit/digital-platform/tree/master/country-year/oda-donor are now in fact.oda_donor &/|| fact.oda_donor_2012. To get https://github.com/devinit/digital-platform/blob/master/country-year/oda-donor/oda-AE.csv for example, we filter fact.oda_donor &/|| fact.oda_donor_2012 on from_di_id = 'AE'. I'll generate a sample of these to check them as well.

I've added some background info in: https://github.com/devinit/digital-platform/issues/244.

[dw8547]

@xriss & @notshi, @robtew will be helping with the data checking. I'll check the format & @robtew will eyeball the $ values to make sure all is OK. We will only do this for the data series/files/tables that have been automated. They are:

Schema	Name	Type	Owner
fact	gdp_usd_current	table	donata
fact	gdp_usd_current_2012	table	donata
fact	gni_pc_usd_current	table	donata
fact	gni_pc_usd_current_2012	table	donata
fact	gni_usd_current	table	donata
fact	gni_usd_current_2012	table	donata
fact	income_share_bottom_20pc	table	donata
fact	income_share_by_quintile	table	donata
fact	income_share_by_quintile_2nd	table	donata
fact	income_share_by_quintile_3rd	table	donata
fact	income_share_by_quintile_4th	table	donata
fact	income_share_by_quintile_5th	table	donata
fact	life_expectancy_at_birth	table	donata
fact	maternal_mortality	table	donata
fact	oda	table	donata
fact	oda_2012	table	donata
fact	oda_donor	table	donata
fact	oda_donor_2012	table	donata
fact	population_by_age	table	donata
fact	population_by_age_0_14	table	donata
fact	population_by_age_15_64	table	donata
fact	population_by_age_65_and_above	table	donata
fact	population_rural	table	donata
fact	population_rural_urban	table	donata
fact	population_total	table	donata
fact	population_urban	table	donata

We'll start with ods.csv/oda_2012.

We'll compare a dump from the fact.oda_2012 table with the oda.csv file downloaded from https://github.com/devinit/digital-platform/blob/master/country-year/oda.csv.

oda.csv was last modified on March 30, 2015 & the OECD have updated the CRS (source) and made revisions to historical data since then, hence the $ values are bound to differ (yes @robtew?). On top of that, aggregation of some non ODA records was recently applied to the CRS and some multilateral donors occasionally replace their data going back 10 years or more. It's unlikely that any rows in the two objects we are comparing will match exactly. @robtew you'll need to make the final call on whether the tables are OK in term of $ content & distribution across sectors/channels/bundles. I'll help @xriss & @notshi to make sure the format is as they need it.

dh = file from here: https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv ddw = table in ddw

source	no of unique donors
dh	63
ddw	57

The donors missing records in ddw are:

di_id	name	donor_code
afesd	Arab Fund [AFESD]	921
ebrd	European Bank for Reconstruction and Development [EBRD]	990
ibrd	International Bank for Reconstruction and Development [IBRD]	901
idb-specialfund	IDB Special Fund	912
imf	IMF	907

@xriss the di_id 'afesd' needs to be removed from DH as it is a duplicate identifier of 'arab-fund-afesd' (more details here: https://github.com/devinit/ddw-data/issues/119.

oda_2012 has 780 rows of data against the donor 'arab-fund-afesd' but all $ values are NULL. oda has non NULL $ data for this donor. We've specified in the price conversion function that it should return a NULL value if the divisor is either 0 or NULL. I'm looking into what's happening: OK now. @robtew, there is a vulnerability in the reference DB data model due to the fact that the donor 'Total DAC' does not have an OECD donor code. Any change to the case in the string 'Total DAC' causes the conversion function to set the $ value to NULL for all multilateral donors.

@xriss the di_id 'idb-specialfund' needs to be changed to 'idb-special-fund' to bring it in line with the di_id naming convention (more details here: https://github.com/devinit/ddw-data/issues/129).

oda_2012 has 3613 rows of data against the donor 'idb-special-fund' but all $ values are NULL. We've specified in the price conversion function that it should return a NULL value if the divisor is either 0 or NULL. I'm looking into what's happening: OK now. @robtew, there is a vulnerability in the reference DB data model due to the fact that the donor 'Total DAC' does not have an OECD donor code. Any change to the case in the string 'Total DAC' causes the conversion function to set the $ value to NULL for all multilateral donors.

This means that donors 'ebrd', 'ibrd', 'imf' are legitimately missing entries in oda_2012. I'm looking into what's happening: @robtew, the entire CRS does not have a single row coded against the donor_code = 907, donor_name = 'IMF' hence no data for it is in the oda table, hence no data for it in the oda_2012 table. @robtew we have 1066 CRS records against donor_code = 958, donor_name = 'IMF (Concessional Trust Funds)'.

@robtew, the CRS has 35 records against donor_code = 901, donor_name = 'International Bank for Reconstruction and Development [IBRD]', category = 10 for years [1976, 1977] where usd_disbursement = NULL. These are the only records for this donor, hence it does not feature in the slice of the data that we are comparing (2006-2014).

@robtew, there are no records in the CRS against donor_code = 990, donor_name = 'European Bank for Reconstruction and Development [EBRD]', category = 10.

[robtew]

Missing donors are OK - these are due to changes in CRS data between now and when the original downloads were taken last year.

[dw8547]

Moving on to recipients in ods.csv/oda_2012.

dh = file from here: https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv ddw = table in ddw

source	no of unique recipients
dh	174
ddw	173

The recipient missing records in ddw is:

di_id	name	recipient_code
country-unspecified	Country, unspecified	NULL

@robtew there are 20,108 lines in the oda.csv coded to to_di_id 'country-unspecified'. There is no recipient in the OECD CRS that maps to this di_id. We have:

recipient_code	di_id
998	bilateral-unspecified

This goes back to: https://github.com/devinit/ddw-data/issues/121. @robtew is this a situation similar to 'imf' versus 'imf-concessional-trust-funds' & can we ignore this?

[dw8547]

This is a space holder comment for @robtew to OK the recipients.

[dw8547]

Moving on to sectors in ods.csv/oda_2012.

dh = file from here: https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv ddw = table in ddw

dh sector	ddw sector
agriculture-and-food-security	agriculture & food security
banking-and-business	banking & business
debt-relief	debt relief
education	education
environment	environment
general-budget-support	general budget support
governance-and-security	governance & security
health	health
humanitarian	humanitarian
industry-and-trade	industry & trade
infrastructure	infrastructure
other	other
other-social-services	other social services
water-and-sanitation	water & sanitation

@robtew, looks OK?

[dw8547]

This is a space holder comment for @robtew to OK the sectors.

[dw8547]

Moving on to aid bundle in ods.csv/oda_2012.

dh = file from here: https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv ddw = table in ddw

dh bundle	ddw bundle
cash-grant	cash grant
cash-loan-equity	cash (loan/equity)
commodities-food	commodities & food
gpgs-nngos	gpgs & nngos
mixed-project-aid	mixed project aid
non-transfer	non-transfer
technical-ooperation	technical cooperation

@robtew, looks OK?

[dw8547]

This is a space holder comment for @robtew to OK the aid bundle.

[dw8547]

Moving on to channel in ods.csv/oda_2012.

dh = file from here: https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv ddw = table in ddw

source	no of unique channels
dh	10
ddw	22

dh channels are:

channel
multilateral
ngo-donor
ngo-recipient
ngo-unknown
other
public-private-partnership
public-sector-donor
public-sector-recipient
public-sector-unknown
unspecified

ddw channels are:

channel
developing country-based ngo
donor country-based ngo
donor government
european union
international monetary fund
international ngo
multilateral organisations (type unspecified)
network
ngo & cso (type unspecified)
other
other multilateral institution
public-private partherships (ppps) and networks
public-private partnership (ppp)
public sector (unspecified whether donor or recipient)
recipient government
regional development bank
third country government
united nations
university
unknown
world bank group
world trade organisation

@robtew?

[dw8547]

This is a space holder comment for @robtew to OK the channel.

[dw8547]

Moving on to individual donor sums.

dh = file from here: https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv ddw = table in ddw

id_from	sum	from_di_id	sum	% diff (dw compared to dh)
		adaptation-fund	99043505	100
AE	8960170266	AE	8486247001	-6
afdb	1299655665	afdb	1266183300	-3
afdf	22459279968	afdf	27090699646	17
afesd	1068104584			duplicate donor di_id (for arab-fund-afesd)
arab-fund-afesd	2562767803	arab-fund-afesd	5538376717	54
asdb-special-funds	8425866029	asdb-special-funds	8322933549	-1
AT	6518232390	AT	6459374658	-1
AU	30245316988	AU	30218168586	0
badea	348146453	badea	347051818	0
BE	12836684886	BE	12709944344	-1
CA	29014274652	CA	27158304670	-7
		ceb	244268402	100
CH	16576835871	CH	16371887360	-1
		cif	150690954	100
CZ	193394242	CZ	190159276	-2
DE	77227735564	DE	76653584940	-1
DK	14390859931	DK	14238081800	-1
ebrd	38237			donor not in dw
EE	10631822	EE	10190273	-4
ES	23933294792	ES	23537901430	-2
EU	105932161423	EU	109505111540	3
		fao	448615807	100
FI	5874410411	FI	5810538940	-1
FR	68264955612	FR	68035061084	0
gavi	6629071876	gavi	6400953467	-4
GB	68401714376	GB	67410064462	-1
gef	2856227751	gef	3443980938	17
		gggi	15641690	100
global-fund	20939659932	global-fund	20928770128	0
GR	1544024117	GR	1528066567	-1
ibrd	205612433			donor not in dw
ida	129459647043	ida	123158044909	-5
idb-specialfund	7782124509	idb-special-fund	8490957562	8
IE	4977787788	IE	4725154567	-5
ifad	0	ifad	0	#DIV/0!
imf	9808878969			donor not in dw
imf-concessional-trust-fund	7225286936	imf-concessional-trust-fund	17000143809	57
IS	68118099	IS	68360709	0
islamic-dev-bank	279618500	islamic-dev-bank	515263138	46
IT	11787252661	IT	11615041992	-1
JP	133917439045	JP	135877692673	1
KR	6812714162	KR	6598448379	-3
KW	2078769427	KW	2069376009	0
		KZ		new OECD donor
LU	2246052068	LU	2190765940	-3
NL	36552042572	NL	36408323200	0
NO	29645449883	NO	28517786424	-4
nordic-dev-fund	320182288	nordic-dev-fund	319538614	0
NZ	2572136093	NZ	2560636783	0
ofid	1684459600	ofid	1676627038	0
osce	416112161	osce	571289542	27
PL	136646605	PL	140399320	3
PT	2801016879	PT	2789826116	0
SE	27645856013	SE	27296364301	-1
SI	19912915	SI	79516879	75
SK	15450895	SK	15429387	0
unaids	1885543296	unaids	1838266239	-3
undp	4449521040	undp	4238990873	-5
unece	75910616	unece	75962027	0
unfpa	2975741080	unfpa	2460932186	-21
unhcr	1271640166	unhcr	1270595141	0
unicef	8616737072	unicef	8630988661	0
unpbf	229976551	unpbf	319445015	28
unrwa	2364894452	unrwa	4184719470	43
US	209023774163	US	207879630351	-1
wfp	1935925547	wfp	1937418579	0
who	2167629176	who	2151078446	-1

@robtew are these differences in the total $ ODA for the different donors acceptable?

[xriss]

Could you please confirm if the name of the table in ddw must match the name of the .csv file as on https://github.com/devinit/digital-platform/tree/master/country-year or that the table column names must match the column headers in the .csv files, or both?

Previously, the .csv files in https://github.com/devinit/digital-platform/tree/master/country-year contained columns labelled 'id', 'from_id', 'to_id'. These columns contain the identifiers for the DI entities. The DI instructions now are that anything that previously was an 'id' column, once automated, it becomes 'di_id', anything that previously was a 'from_id' column, once automated, it becomes 'from_di_id' and anything that previously was a 'to_id' column, once automated, it becomes 'to_di_id'.

@dw8547 They don't have to match, I can add more renaming logic beyond just switching the _ and - as long as I know what needs to be done. However the more names you have for the same things, the more complicated and confusing things will become and the greater the chance that mistakes will be made.

[dw8547]

This is a space holder comment for @robtew to OK the individual donor sums.

[dw8547]

Moving on to individual recipient sums.

dh = file from here: https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv ddw = table in ddw

id_to	sum	to_di_id	sum	% diff (dw compared to dh)
AF	42803278714	AF	42323021987	-1
africa	13874312993	africa	13787680619	-1
AG	56918531	AG	56837472	0
AI	32348666	AI	32284252	0
AL	2877473423	AL	2884091487	0
AM	2810467164	AM	2780618245	-1
america	7824185620	america	8390839524	7
AO	2588471440	AO	2550572943	-1
AR	1109674235	AR	1107224488	0
asia	6614973209	asia	6457793401	-2
AZ	1983553258	AZ	1887183463	-5
BA	3859558119	BA	4038107671	4
BB	53724494	BB	53318342	-1
BD	19138593029	BD	18801022109	-2
BF	9650876240	BF	9631992838	0
BI	5452775098	BI	5726372441	5
		bilateral-unspecified	195649326624	100
BJ	5909710567	BJ	6076853022	3
BO	7594770730	BO	7551623102	-1
BR	6968750565	BR	6279839549	-11
BT	976062668	BT	954363250	-2
BW	1760965895	BW	1767961229	0
BY	707562746	BY	702264992	-1
BZ	191924903	BZ	182117456	-5
CD	27459692519	CD	27184838945	-1
central-asia	1825630933	central-asia	1819917542	0
CF	2504369191	CF	2639355463	5
CG	3711106521	CG	3631353027	-2
CI	13101619049	CI	13095916523	0
CK	158019940	CK	157945918	0
CL	1027088234	CL	1097635979	6
CM	10583905193	CM	10555048255	0
CN	21445995927	CN	21506845553	0
CO	7718763452	CO	7356953235	-5
country-unspecified	197090531360			'bilateral-unspecified in dw
CR	843775031	CR	815984389	-3
CU	757005914	CU	751824894	-1
CV	1890847374	CV	1887790864	0
DJ	1091004477	DJ	1088803912	0
DM	209389607	DM	208612090	0
DO	2009160121	DO	2014346870	0
DZ	2776814879	DZ	2760439813	-1
east-asia	1637959587	east-asia	1650209756	1
EC	2160656563	EC	2111604382	-2
EG	16848715717	EG	16958509146	1
ER	1069493473	ER	1054259791	-1
ET	30559840167	ET	30114742200	-1
europe	6101911476	europe	6238848256	2
ex-yugoslavia	234660114	ex-yugoslavia	237928170	1
FJ	658885981	FJ	655491731	-1
FM	1001389185	FM	966571253	-4
GA	823489312	GA	820849805	0
GD	148635050	GD	146404665	-2
GE	5208901113	GE	5134752278	-1
GH	17688735815	GH	17405332865	-2
GM	1105014913	GM	1305096829	15
GN	4096056530	GN	4287688683	4
GQ	283093200	GQ	279570037	-1
GT	3891171708	GT	3831870322	-2
GW	1385714306	GW	1358360148	-2
GY	1403568672	GY	1379853400	-2
HN	6037781128	HN	5988884325	-1
HR	1010328799	HR	1018542221	1
HT	12227444898	HT	12007692872	-2
ID	25354275459	ID	25254706696	0
IN	34032449808	IN	33161845063	-3
IQ	38329209151	IQ	40243397483	5
IR	963487216	IR	956569198	-1
JM	1069216163	JM	1064995168	0
JO	8201529977	JO	8481537869	3
KE	17653408861	KE	17603141438	0
KG	2563127984	KG	2517075566	-2
KH	6043171491	KH	5961120532	-1
KI	358693334	KI	356402393	-1
KM	600435823	KM	592967630	-1
KN	125123119	KN	124662814	0
KP	783715257	KP	779460230	-1
KZ	1597410120	KZ	1576264416	-1
LA	3307181520	LA	3244038865	-2
LB	5315804860	LB	5639932132	6
LC	239537379	LC	238423980	0
LK	8496784353	LK	8262115808	-3
LR	7164478256	LR	7128165832	-1
LS	1716196085	LS	1679629877	-2
LY	1042124790	LY	1062686219	2
MA	12714335989	MA	12975555219	2
MD	2771582099	MD	2742922225	-1
ME	732132086	ME	784742920	7
MG	7831432686	MG	7987958660	2
MH	600095647	MH	544036700	-10
middle-east	2830278494	middle-east	2871583907	1
MK	1639203481	MK	1697621846	3
ML	10810462292	ML	10987538065	2
MM	11427450016	MM	11340481520	-1
MN	2771135417	MN	2728309798	-2
MR	3673559343	MR	4013831508	8
MS	313670158	MS	313464191	0
MU	1204540930	MU	1205589028	0
MV	426899953	MV	433274247	1
MW	11160771790	MW	11163178397	0
MX	4142375124	MX	4178821144	1
MY	2510442987	MY	2514827994	0
MZ	18102703074	MZ	18045810845	0
NA	2079247869	NA	2069993946	0
NE	6715545531	NE	6741169528	0
NG	28005345488	NG	27557528006	-2
NI	6569569312	NI	6484067030	-1
north-central-america	2814233203	north-central-america	2744029711	-3
north-of-sahara	2084177731	north-of-sahara	2156928329	3
NP	6718498055	NP	6556409646	-2
NR	268250281	NR	269227578	0
NU	138593251	NU	138683804	0
oceania	2117887451	oceania	2112757680	0
OM	221112847	OM	311720571	29
PA	634034222	PA	633349873	0
PE	5662170038	PE	5539726464	-2
PG	4784111053	PG	4777314500	0
PH	9971414866	PH	9914331196	-1
PK	23158639726	PK	22448910434	-3
PS	16576680916	PS	17307404305	4
PW	262542653	PW	259220189	-1
PY	1439585980	PY	1434901789	0
RS	7534040723	RS	8272452092	9
RW	8866414404	RW	8852234750	0
SA	28577175	SA	28432220	-1
SB	2586119034	SB	2596735891	0
SC	209132410	SC	214998416	3
SD	14365903395	SD	15653978910	8
SH	621149918	SH	621076377	0
SL	4380546744	SL	4606497718	5
SN	10661268370	SN	10723246186	1
SO	5507081484	SO	5463443360	-1
south-america	1875853152	south-america	1819916649	-3
south-asia	922909299	south-asia	930157545	1
south-central-asia	1528860205	south-central-asia	1641856930	7
south-of-sahara	18049222450	south-of-sahara	18040411672	0
SR	737002855	SR	734989401	0
SS	4109925355	SS	2954633428	-39
ST	464822052	ST	592710879	22
SV	2364071940	SV	2331034098	-1
SY	4250773403	SY	4518311160	6
SZ	689538975	SZ	688227052	0
TC	12899114	TC	12880478	0
TD	3788437954	TD	3783180020	0
TG	3471580203	TG	3413303941	-2
TH	4176416610	TH	4210791408	1
TJ	2629245968	TJ	2593679641	-1
TK	150796309	TK	150471333	0
TL	2279895212	TL	2259548873	-1
TM	198403771	TM	200760306	1
TN	6686496134	TN	7073596059	5
TO	494585500	TO	492485634	0
TR	15370036196	TR	17116534599	10
TT	68622903	TT	68208372	-1
TV	175502285	TV	175627600	0
TZ	27392324537	TZ	27318706231	0
UA	4889116528	UA	4914754086	1
UG	17644697966	UG	17577008877	0
UY	363708027	UY	361651418	-1
UZ	1809840064	UZ	1762208306	-3
VC	183978413	VC	182556993	-1
VE	422894640	VE	419540687	-1
VN	29413618916	VN	28648820036	-3
VU	799870446	VU	800002511	0
west-indies	939625139	west-indies	873847258	-8
WF	828107472	WF	824363731	0
WS	784672072	WS	781041658	0
XK	3026569380	XK	2856539299	-6
YE	5079065701	YE	5212837375	3
YT	2001210854	YT	1984904481	-1
ZA	9133224775	ZA	9326234364	2
ZM	13019570325	ZM	13044389762	0
ZW	5509976806	ZW	5450742145	-1

@robtew are these differences in the total $ ODA for the different recipients acceptable?

[dw8547]

This is a space holder comment for @robtew to OK the individual recipient sums.

[dw8547]

Hi @xriss & @notshi, I've put together the checks requested by @robtew & we are just waiting for @robtew to OK the differences between the contents in https://github.com/devinit/digital-platform/tree/master/country-year/oda.csv and in fact.oda_2012. This is the most important & biggest of the automated data series, so hopefully once this one is out of the way things will move a bit quicker.

[notshi]

Thanks, @dw8547. If possible, try not editing our comments to reply to it, quoting is fine, thanks.

[dw8547]

@notshi, NP. My apologies. I've edited almost all of them up to now. Won't from now on.

[dw8547]

Dead issue.