Purchase Orders Over €20,000 Q1 2016

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2016	Pobal	Drawdown request to cover expenditure	Purchase Order	€1,500,000.00
31 Mar 2016	Pobal	External Delivery of Programmes (ESRI) 2016 Costs	Purchase Order	€1,400,000.00
31 Mar 2016	Economic and Social	Commissioned Research – GU12 Research Institure Contract (Growing up in Ireland) January	Purchase Order	€320,861.28
31 Mar 2016	Foroige	Delivery of Participation service January to September 2016	Purchase Order	€239,768.20
31 Mar 2016	Bertec Handling	Towrite P2000 Electric Truck for Jan to March 2016 Solutions Ltd.	Purchase Order	€47,487.84
31 Mar 2016	Pobal	Cover Expenditure commitments under the Area Based Childhood Programme	Purchase Order	€45,000.00
31 Mar 2016	Pobal	Roscommon CCC March to August	Purchase Order	€36,509.00
31 Mar 2016	Pobal	Comhairle na nOg Development fund Advice Centres Ltd) Law Reporting Project in 2016 Administration costs	Purchase Order	€34,000.00
31 Mar 2016	FLAC (Free Legal	First half of Contribution to Child Care the Early Years Strategy	Purchase Order	€28,000.00
31 Mar 2016	Trinity College Dublin	National Consultation with Children on	Purchase Order	€23,483.16
31 Mar 2016	ISPCC	Missing Children’s Hotline Q4 (October – December 2015)	Purchase Order	€23,246.63

^* Payment date may be the end of the reporting quarter when the entity does not publish an exact date.

How to read these figures

These are reported values from published purchase order and payment records — not verified cash paid, and not total spending by this organisation. A row may be a purchase order, a payment, or another reported transaction; the source files often do not say which.

Coverage and quality vary. Some fields are blank, supplier names are free text (the same firm can appear under many labels), and descriptions are inconsistent rather than a controlled taxonomy. An amount alone says nothing about value for money and it may or may not include VAT.

Records are retrieved from each organisation’s published files such as spreadsheets, PDFs, and other formats of uneven quality. Extraction techniques (including automated parsing) can introduce gaps, misreads, or incomplete rows when source layout or quality is poor. Treat the data as a starting point for investigation, not a definitive ledger.

Here is a project that encountered similar issues