Top Tweets for #povertydata
इस चार्ट के हिसाब से यूपी में कुर्मी (15.4%) की “मोस्ट पुअर” हिस्सेदारी ओवरऑल (17.8%) से कम है। यानी कुर्मी समुदाय की गरीबी राजपूत (14.4%) के काफी करीब है और ब्राह्मण (5.2%) जैसे ऊँचे स्तर की ओर ट्रेंड दिखाती है — मेहनत, खेती और स्वावलंबन का असर साफ है।
#UttarPradesh #PovertyData
📸 TRAINING RECAP: Poverty Measurement – Part I
#DNPED-MPD delivered Part I of the Poverty Measurement Training (20–21 Nov), strengthening national #capacity to analyse #povertydata and align methods across institutions.
🧠Highlights: core concepts, data sources.
#MDP
@DERG_DK
Why 50%? Inflation (10–15% FY25), agricultural decline (cotton down 29.6%), and water shortages (Punjab -15%) crush the poor. World Bank’s 2019 data, adjusted for 2021 PPPs, may understate the crisis. No 2025 surveys available. #PovertyData 4/n
World Bank’s 2024 Poverty Update Introduces Fresh Insights and Tools on Global Inequality
https://t.co/okxJDZP6Cw
#WorldBank #GlobalPoverty #2024Report #EconomicInequality #SharedProsperity #PovertyData #SocialEquity @WorldBank
BJP suppressing data on employment & poverty: Sachin Pilot
https://t.co/FmWmvh8D3i
#SachinPilot #RajasthanPolitics #BJP #EmploymentData #PovertyData #CasteCensus #MoU
Come and find Equality Insights at #CSW68 at this fantastic range of events!
#GenderData #GenderEquality #MultidimensionalPoverty #PovertyData
Next week, our Strategic Advisor @JoCrawford_Melb will be attending @UNECE’s Group of Experts on Measuring Poverty and Inequality to connect & collaborate with thought leaders from across the globe, as passionate as us about #Intersectional #PovertyData!
https://t.co/WC0Z1NCjP6
PM मोदी के इस दावे पर क्या कहते हैं आंकड़े, चीन-PAK के सामने कहां ठहरते हैं हम?
#PMModi #PovertyData
https://t.co/sZbYsjuWby
Last week, OPHI was in Tashkent, Uzbekistan, leading a five-day workshop with UNDP Uzbekistan @UNDP_Uzbekistan.
➡️ The workshop aimed at enhancing the country’s approach to multidimensional poverty measurement and reduction.
#PovertyData #MPI
Fanni Kovesdi @fnkovesdi, research analyst at OPHI, shared insights into OPHI and @UNDP_Uzbekistan's Workshop on Multidimensional Poverty 📝#MPI #PovertyData @UNDP
Last week, Fanni was in Tashkent leading a five-day workshop on measuring #MultidimensionalPoverty.
In this new paper in #JDevStudies, Evans, Nogales, and Robson study joint densities of monetary & multidimensional poverty using microdata from six countries - Bolivia, Brazil, Ecuador, Ethiopia, Ghana, and Uganda.
Read it here: https://t.co/j1VijoFBix
#PovertyData
@rnogales
View the 2023 global MPI databank for interactive poverty maps of 111 countries covering 6.1 billion people. Poverty maps can be disaggregated by indicators or MPI score. #povertydata
View the MPI Databank here: https://t.co/NjXcxwhwCG
The global MPI databank is a useful tool for viewing and analysing this data - the charts pictured below show a comparison of rural and urban poverty by country.
View the databank here: https://t.co/2L9r3KtOPv
#PovertyData
"India's new Non-Poor"
#PMModiSpeech :13.5 crore #Indians out of #Poverty & in the #MiddleClass
: Explained
#PovertyData #MultidimensionalPovertyIndex #NitiAyog #MPI #Health #nutrition #Education #StandardOfLiving
#PovertyLine
#OPHI #GlobalMPI
#economy
#UPSC
Source:IE
View the 2023 global MPI databank for interactive poverty maps of 111 countries covering 6.1 billion people. Poverty maps can be disaggregated by indicators or MPI score. #povertydata
View the MPI Databank here: https://t.co/NjXcxwi4se
We're pleased to be in #Tonga to launch the findings from 'Equality Insights Rapid Tonga'! We partnered with @tonga_stats to conduct a new phone-based survey to collect gender-sensitive #PovertyData. The intersectional insights will be used address inequalities. #GenderData @dfat
Last year, @tonga_stats conducted a new phone-based survey to collect gender-sensitive #PovertyData. We developed this survey as part of Equality Insights Rapid, which provides new #GenderData to support the work of govt agencies, #GenderEquality advocates & other #data users.
The 5 countries with the highest number of extreme poor ($1.9/Day poverty line) are: India, Nigeria, Democratic Republic of Congo, Ethiopia, and Bangladesh.
Working with these countries will be crucial to urgently reduce global #poverty.
#PovertyData
Our goal is to provide robust, timely & quality #GenderData that fills critical #DataGaps and ensure everyone, everywhere is visible, counted and included in #PovertyData.
Read about a tool we developed to collect inclusive and gender-sensitive #data: https://t.co/WWQI26haRX
Inclusive #PovertyData not only allows us to reimagine a where everyone thrives, but to build forward more sustainably and equitably. Read about the survey tool our @Equal_Insights team developed to #GenderData about poverty and inequality by phone: https://t.co/8moS3B1h6J (7/9)
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