Can Big Data help balance the Budget?

I have been quite scathing about the quality of government data and the need for action to improve the information management and analysis skills of those who seek to use it for decision taking. That does not, however, excuse the routine failure to even try to use the data we already have to better inform decision decision taking – including to help persuade the rest of the world that HMG is serious about getting public finances under control and to persuade the majority of voters that it is it serious about cutting fraud and waste before cutting services.

One example is the muddled debate about whether immigrants contribute to or detract from the national ability to create wealth. The overall figures may be in doubt but we have some clear evidence about which nationalities and types of immigrant are likely to contribute and which are not. The table of page 5 of ONS Social Trend 41 Income and Wealth [it appears imposible to insert a link to the downloadable file, so you will have to Google it yourself], may be out of date but gives a clear indication of the analyses that should be conducted in order to identity with which nations we might wish to establish fast track visa routines for businessmen, students and tourists: e.g. India and China.

Of course we will also need to look at the small print of the routines but we have some good models in those used for visitors to the Olympics (Beijing as well as London) and those used by overseas nations (from Australia to Canada and Hong Kong to Dubai) to attract wealth creators, including genuine tourists, skilled workers and career-motivated or creative students, while detering those they do not want. Part of the solution is not to rely on easily fabricated credentials (e.g. those certifying IT skills in short supply) but to copy the centuries-old approach of international traders to verifying the identities and credentials of those they have never met: essentially chains of references with each trusted link accepting responsibility and liability (with insurable guarantees) for their recommendations. The routines have, of course, had “on-line support”, by telegraph, telex, EDI and now the Internet, for over 150 years.

Another example is the linked debate as to whether NHS crisis in London is caused by immigrants and health tourists (as much as by incontinent PFIs, on which I plan to blog separately).

A thought provoking Spectator article on the NHS crisis was followed by the firefight among commentators which triggered this blog entry. Readers of the comments will note my own cupfulls of petrol commenting on the apparent difference between the supposed law and supposed common practice and asking which Minister(s) might be responsible for producing the evidence for both and then organising the action necessary to reconcile them.

This leads me to the black hole of information non-exchange between the Home Office, DWP, HMRC and NHS where there are truly massive potential improvements in service and reductions in cost (including fraud) from using the data matching techniques used by the banks and commercial Identity Management (alias credit reference) and market segmentation (alias Identity collation) operations.

It does not need investment in comprehensive ID systems to produce the data necessary to produce simple predictive measures as to whether a benefits or tax credits claimant is unlikely to be fraudulent or a potential patient is likely to be UK citizen or resident entitled to free treatment  Given that some of the credit reference operations are global that may well allow for the majority, including genuine overseas students, tourists or workers seeking treatment on the NHS, to be fast tracked. Those who then try to browbeat GPs or Clinicians into given them free care iwthout evidence of entitlement or genuine emergency need can be given the third most common excuse for poor service: computer says no. [I have seen no reliable data but anecdotal evidence appears to shoe that this has not yet overtaken “Health and Safety, or Data Protection, provided you exclude refused credit and debit cards from the equation]. That leaves the interesting question as to which airline(s) are likely to bring in those already in predictable emergency need and how their passengers should be scanned at Heathrow and charges levied on the carrier if they have not examined evidence of the funds (or insurance) to pay. 

It does need the disciplines which the late Donald Michie called “knowledge refining” and are now routinely used across financial services, on-line marketing as well as by ISP for the fine turing of their servies and by consultancies like Oxford Analytica for a wide variety of commercial clients. I will not repeat in detail the arguments for using Donald’s approach which I gave in one of my earlier blogs on why they should be used to reduce the cost and risk of the DWP Universal Credit programme 

I will merely say that the approach should be used much more widely to aid rational debate on, for example, taxation changes will raise more revenue or merely drive business off-shore.

Will returning business rates to local authorities and restoring the historic link with local property values (collapsing in some areas and rising in others) result in a restoration of local enterprise and a rebalancing of the economy?

How much would it really cost/raise if we were to add new Community Charge bands J to N covering properties up to £1 million and to value those which last changed hands for over £million at the price paid or probate or other independent valuation if it was not an open market sale?   

What are the “real” marginal tax rates (including withdrawal of benefits or tax credits) paid by how many (businesses, large and small as well as by individuals) and how would proposed changes effect these, encouraging and attracting creators, or driving them off shore?