Resistance to change in government: Risk, inertia and incentives

There is a popular impression that governments are resistant to change and innovation, and that this is due to a combination of overly bureaucratic processes and a culture of risk aversion. It is debatable that this is well-founded, theoretically or empirically: government bodies differ from private sectors in their structures and objectives, formalised decision-making processes may aid innovation rather than inhibiting it, and the assumption that governments are excessively risk-averse assumes that private sector decisions on risk are correct - an assumption which is hard to sustain given recent economic history.This paper brings together ideas from public administration, behavioural psychology and economics to ask whether the anti-innovation government has any theoretical or empirical basis. It argues there is some truth in the claim that governments are less likely to innovate; but the paper also argues that a missing piece of the puzzle is provided by incentive structures in government which encourage the status quo irrespective of risk preferences. However, the evidence for these negative incentives is largely anecdotal and derives from those who could be seen to have an interest in this perspective, and so there is a need for more empirical research to explore this idea

Spontaneous recognition: An unnecessary control on data access?

Social scientists increasingly expect to have access to detailed data for research purposes. As the level of detail increases, data providers worry about “spontaneous recognition”, the likelihood that a microdata user believes that he or she has accidentally identified one of the data subjects in the dataset, and may share that information. This concern, particularly in respect of microdata on businesses, leads to excessive restrictions on data use.We argue that spontaneous recognition presents no meaningful risk to confidentiality. The standard models of deliberate attack on the data cover re-identification risk to an acceptable standard under most current legislation. If spontaneous recognition did occur, the user is very unlikely to be in breach of any law or condition of access. Any breach would only occur as a result of further actions by the user to confirm or assert identity, and these should be seen as a managerial problem.Nevertheless, a consideration of spontaneous recognition does highlight some of the implicit assumptions made in data access decisions. It also shows the importance of the data provider’s culture and attitude. For data providers focused on users, spontaneous recognition is a useful check on whether all relevant risks have been addressed. For data providers primarily concerned with the risks of release, it provides a way to place insurmountable barriers in front of those wanting to increase data access.We present a case study on a business dataset to show how rejecting the concep

Can a change in attitudes improve effective access to administrative data for research?

The re-use of administrative data for social research holds great potential. From a privacy perspective, administrative data present some additional challenges, including lack of consent, existence of matching databases, and the association with data breaches by administrative staff.Access to government data for research is currently undergoing a slow, small but significant transformation from the defensive strategies of the past. A key driver of this is attitudinal change; the new approach is characterised as evidence-based default-open, risk-managed, user-centred decision-making, and offers more security at lower cost with greater researcher value.Despite its apparent superiority, this approach is still a minority position. It fundamentally challenges the way decisions are made in the public sector, at an individual and institutional level, as well as making risks more explicit. The effective re-use of administrative data may then depend upon the degree to which attitudes to decision-making in the public sector can be changed

International access to restricted data: A principles-based standards approach

Cross-border access to restricted government microdata for research has made relatively little progress. Recent developments are notable as exceptions. This paper argues that the situation is made more complex by the lack of a common general frame of reference for comparing objectives and concerns; this reinforces the risk-aversion in government organisations. Attempts to develop general international data access strategies therefore collapse to sui generis bilateral agreements of limited strategic value. One way forward is to decouple implementation from strategic principles. A principles-based risk-assessment framework, using popular multiple-component data security models, allows decisions about access to focus on objectives; similarly, secure facilities could be developed to standards independent of dataset-specific negotiations. In an international context, proposals for classification systems are easier to agree than specific multilateral implementations. Moreover, a principles-based approach can be aligned with organisational goals, allowing countries to signal strategic intentions to others without the need for explicit commitment. The paper uses examples from the UK, US and cross-European projects to show how such principles-based standards have worked on a within-country basis and may help to resolve immediate practical issues. © 2013 - IOS Press and the authors. All rights reserved

Access to sensitive data: Satisfying objectives rather than constraints

The argument for access to sensitive unit-level data produced within government is usually framed in terms of risk and the legal responsibility to maintain confidentiality. This article argues that the framing of the question may restrict the set of possibilities; a more effective perspective starts from the data owner's principles and user needs. Within this principlesbased framework, the role of law changes: It becomes an 'enabling technology', helping to define the solution but playing no role in setting the objectives. This shift in perspective has a number of consequences. The perception of 'costs' and 'benefits' is reversed. Law and established practice are distinguished and appropriately placed within a cost-benefit framework. The subjectivity and uncertainty in risk assessments is made explicit. Overall, all other things being equal, the expectation is that a move towards objective-based planning increases data access and improves risk assessment. This alternative perspective also addresses the problem of the public-good nature of research outputs. It encourages the data owner to engage with users and build a case for data access taking account of the wider needs of society. The UK data access regime is used as the primary example of the arguments in this article. © Statistics Sweden

Operationalising ‘safe statistics’: The case of linear regression

The recent growth in research access to confidential government microdata has prompted the development of more general 'output-based statistical disclosure control' (OSDC) methods which go beyond tabular protection. Central to OSDC is the concept of 'safe/unsafe statistics', allowing researchers and facility owners to make informed judgments about the types of research output that pose a disclosure risk. While increasingly accepted in specialist environments, in the wider community this novel approach causes some concern: how can 'safe' be unconditional? This paper therefore demonstrates the new approach using linear regression, a key research output, as an example. In doing so, the paper reconsiders the objectivity of SDC decision-making, arguing that ‘safety’ be explicitly acknowledged as a relative concep

Accessing the new earnings survey panel dataset : efficient techniques and applications

The New Earnings Survey Panel Dataset is one of the largest datasets of its kind in the world. Its size and confidentiality restrictions present considerable difficulties for analysis using standard econometric packages. This thesis presents a number of methods for accessing the information held within the panel relatively efficiently, based upon the use of cross-product matrices and on data compression techniques. These methods allow, for the first time, the panel aspect of the dataset to be used in analysis. The techniques described here are then employed to produce an overview of changes in the UK labour market from 1975 to 1990 and detailed estimates of male and female earnings over a fourteen year period. These are the first panel estimates on the dataset, and they indicate the importance of allowing the parameters of any labour market model to vary over time. This is significant as panel estimators typically impose structural stability on the coefficients. A comparison of cross-section and panel estimates of earnings functions for males indicate that the allowance for individual heterogeneity also has a notable effect on the estimates produced, implying simple cross-sections may be significantly biased. Some preliminary estimates of the male-female wage gap indicate that variation over time has an important part to play in accounting for the differences in wages, and that "snapshot" studies may not capture dynamic changes in the labour market. Individual differences also playa significant role in the explanation of the wage gap