1. Capacities to supply evidence for decision making

Increasing governments’ capacity for an evidence-informed approach to decision making is a critical part of fostering good public governance. Evidence-informed decision-making (EIDM) can be defined as a process whereby multiple sources of information, including statistics, data and the best available research, evidence and evaluations, are consulted before making a decision to plan, implement, and (where relevant) alter public policies and programmes (OECD, 2020[1]). This matters to achieve broad societal goals, such as increasing trust in government and in decision making, promoting sustainable development or improving well-being. The goal of evidence-informed decision making is to enable agile and responsive governments, which are well equipped to address complex and at times “wicked” policy challenges. EIDM is particularly useful in the policy-making process, for instance, to assess regulatory impacts of new laws.

Well-functioning mechanisms for generating and using evidence require both interests from political leadership and capacities within a government to provide timely and reliable analysis (i.e. the supply of evidence), as well as to use evidence (i.e. the demand for evidence). Supply of evidence is thus critical to promote an evidence-informed decision-making approach as there cannot be used where evidence does not exist. In Lithuania, skills and capacities to supply robust and credible evidence remain low.

In this context, this chapter provides an overview of existing skills in the Lithuanian public sector. Specifically, this chapter notes that many of the challenges linked to the low supply of evidence in the Lithuanian government itself can be traced back to issues with identifying and hiring staff with the appropriate skills – including in quantitative economics, to conduct policy analysis. These challenges are compounded by a lack of evidence-driven culture and an excess of new regulations, which do not allow policy makers sufficient time to appropriately assess impacts. This report suggests that the Lithuanian government adopt a systematic approach to analytical skills in order to increase its capacities to supply evidence. Furthermore, this chapter addresses the key role of data in evaluation and underlines the need for greater data availability and a coherent approach to data governance in order to properly support evidence-informed decision making.

Simply put, a skill is “an ability to do something acquired through training and/or experience” (OECD, 2017[2]). The OECD has developed a framework for civil service skills for public value, where analytical skills are one of four complementary and overlapping bundles of skillsets for a high performing civil service (Policy advisory skills). Box 1.1 provides further information on this framework and the different skillsets required for the civil service to deliver public value.

The policy advisory skills, also known as ‘analytical skills’, require that civil servants have the ability to generate and use robust and credible evidence (OECD, 2020[1]). This includes an individual’s knowledge of different types of research methods, as well as fundamental skills of statistical and data literacy, and the capacity to read and understand analytical products. In practice, these skills often require a multidisciplinary set of competences drawing from a wide range of areas, including economics, statistics, social sciences, environmental sciences, law and engineering.

In Lithuania, ministries face important challenges in regards to capacities to conduct policy analysis. Indeed, OECD data shows that both the centre of government and line ministries identify the lack of human resources and skills as a main challenge in promoting EIPM (see Table 1.1 below).

These challenges in regards to capacities are explained by four main factors:

  1. 1. The Lithuanian public sector as a whole suffers from a shortage of skills for analysis, due to a general lack of availability of such skills on the Lithuanian job market and a marked difficulty in attracting and retaining such staff.

  2. 2. When analytical skills are present in ministries, they are spread out in such a way that it is difficult to reach a ‘critical mass’.

  3. 3. Until recently, there had not been a systematic approach to mapping and tracking these skills across government.

  4. 4. A government-wide approach towards an effective upskilling of the current civil servants in this area of competence is lacking.

Other challenges include complicated procurement processes and a lack of motivation from staff – in part due to low political interest in using evidence (see next chapter).

Firstly, skills such as quantitative economics, statistics, data science, and social sciences appear to be in scarce supply in the Lithuanian job market. Several line ministries underline the challenges they encounter in identifying qualified staff, in so far as there are only a limited number of qualified graduates in the above-mentioned fields in Lithuania. As a result, even when ministries and their dependencies can rely on more flexible and competitive contractual arrangements in order to hire the skills they need, they have been confronted with shortages of supply.

For instance, the Bank of Lithuania, which can offer more attractive salaries than most agencies in the public service, still struggles to find qualified economic expertise. Its Applied Macroeconomic Research Division, located within the Department of Economics, employs 8 staff members, 7 of which hold PhDs from foreign institutions. The Bank’s Center for Excellence in Finance and Economic Research (CEFER) has 6 economists, all of whom hold PhDs from foreign universities. To remedy this problem, the Bank has decided to create a bachelor of sciences in quantitative economics in partnership with Vilnius University, which is one of the few academic programmes with courses in quantitative and qualitative social sciences in Lithuania. This competitive programme, taught exclusively in English, aims to provide the Bank with young graduates trained in quantitative economics. However, for now, the programme is only designed until the bachelor level. Box 1.2 below provides more detail on the Bank’s efforts to increase the supply of qualified analysts in Lithuania.

Some programmes focused on policy analysis do exist, This is the case, for instance, in the Institute of International relations and Political Science of Vilnius University, where courses are offered on quantitative and qualitative social science methods the Bachelor degree, as well as in the Masters’ programmes on Public Policy Analysis.

Yet, analytical skills are crucial to ensure the effective supply and use of evidence for decision making. In particular, quantitative skills, data skills and related soft skills are extremely important in a world that is becoming ever more digitalised. The volume, velocity and variety of data has increased dramatically and “data literacy” among civil servants is indispensable (OECD, 2017[6]). Data-scientists or economists/statisticians competent in working with data have to be present among ministerial staff so that the evidence derived from data is used correctly, and that external evaluations and assessments are contracted appropriately. This might require developing more programmes focused on quantitative analytical skills – particularly economic skills.

Thus, in order to increase the availability of analytical skills in the civil service, the Lithuanian government could build on the programme created by the Bank of Lithuania and create a master’s level programme in the same field. This would require building a partnership between the university and a government institution with sufficient links to the academic sector. STRATA seems to be the best available option at the domestic level. Chapter 4, focused on STRATA, provides more information on how this partnership could take place and what could be the specific role of STRATA in this regard. In doing so, the Lithuanian government could look at similar examples in other European countries. In France, for example, the National Institute of Statistics and Economic Studies (INSEE) organises a master programme to train future economists and statisticians, part of which work for the government afterwards (see Box 1.5 for more information on this scheme). A subset of students qualified after a selection procedure, who are enrolled in the National School for Statistics and Economic Administration (ENSAE, for statisticians/economists) and the National School for Statistics and Data (ENSAI, for statisticians/data scientists) receive a stipend during the studies in exchange for working within the public sector for 8 years upon graduation.

The idea in Lithuania is that such a master’s programme would also have “spillover effects”, and help supply appropriate skills for the tertiary and financial sectors, which are well developed in Lithuania. In the very short term, as the scheme would take a few years to set up, the Lithuanian government could also consider offering a scholarship to students who decide to study-abroad in these fields, in exchange for their commitment to working in the Lithuanian public sector, ministries or agencies for a set number of years – for example a minimum of five years. The current “next 100” scheme already offers scholarships for Lithuanian students who have been admitted to top foreign universities in exchange for working in Lithuania for at least 3 years upon graduation. This scheme could be adapted or extended to meet the needs of the Lithuanian public sector.

Lithuanian ministries and public sector agencies are generally struggling to recruit and retain analytical skills. Indeed, the Lithuanian civil service framework does not allow most public institutions to attract these skills. This reflects both the fact that public sector salaries, working conditions and career prospects are not competitive enough with the private sector to attract good candidates. As the European Commission put it (European Commission, 2019[7]):

“The civil service [in Lithuania] is losing competitiveness in the labour market due to its low salaries and unattractive working conditions. It has difficulties in attracting new qualified staff, while increasing numbers of professionals are leaving the service. This is leading to the ageing of the civil service and requires a long-term strategy to make the public sector an attractive employer for the young.”

Even if the data need to be considered with some caution, generally, when retaining compensation within central ministries, economists and analysts in the Lithuanian Civil Service are paid less in relation to national GDP per capita than their OECD counterparts in most countries for which the data is available; In addition, the difference between junior and senior is negligible, which reflects the fact that the Lithuanian public sector does not provide salary progressions associated with seniority and expertise. According to the 2016 study, Lithuania was the only OECD member and accession country in which senior and junior economists were paid nearly the same rate. According to the International Standard Classification of Occupation in the study, senior economists and policy analysts generally have 5 years of professional experience and often higher educational attainment. Therefore, the civil service appears to be an unattractive career option for highly qualified personnel with ambitious career plans. Whereas in Lithuania the ratio of remuneration of senior to junior economists is very close to 1, this ratio for the other OECD countries falls within the range from 1.15 (as for Hungary) to 1.5 (as for Denmark) (see Box 1.3 for more detail on this data and sources).

In addition, the civil service in general is relatively older. Even if the issue of ageing civil service is prevalent in many OECD countries (OECD, 2021[8]), there is a higher difference in Lithuania between the share of workers older than 55 years old in the central/federal administration and the general labour market than in the OECD average (33% for Lithuania, 30% for the OECD average) (OECD, 2021[8]). More importantly, the civil service framework is too rigid both in terms of career advancement and in terms of compensation to attract specialised technical skills such as those required for policy analysis.

Indeed, some in-demand professionals might not find it attractive to become a career civil servant but could be interested in working on short-term high-profile projects, given that they be compensated justly (OECD, 2021[8]). The civil service law of 1999 and the law on public administration of 1999 also mostly focus the civil service on skills related to policy implementation rather than to policy making (Parliament of Lithuania, 1999[11]; Parliament of Lithuania, 1999[12]).

Moreover, staff who are in charge of conducting policy analysis are not clearly identified in ministries. First, analysis and substantiation skills are required for any civil service position in Lithuania (Government of Lithunia, 2018[13]). Moreover, there is no shared definition of analytical staff in the Lithuanian civil service framework, thus making it difficult to identify ministries’ capacities in this regard. Finally, as most civil servants conduct some policy analysis as part of their duties, ministries may tend to overestimate their capacities in this regard. For example, as most civil servants conduct some parts of regulatory impact assessments (RIA) when preparing legislations, as anyone involved in a RIA could be considered an analyst, even though this task mostly requires purely legal skills. In general, the skills required to conduct high-quality policy analysis are very different from those that are necessary to understand the legal impacts of proposed legislations and regulations.

Beyond the existing general competency framework, a more granular understanding of the available technical skills remains necessary to accurately assess Lithuania’s capacities for evidence-informed decision making. As the OECD’s work on the Future of Work in the Public Sector (OECD, 2021[8]) underlines, identifying gaps or oversupplies of skills are necessary pre-conditions for good workforce planning for a resilient public sector, able to adapt to a change in environment and recover from external shock. This has been an issue receiving increased interest in the governance area following the COVID-19 crisis (OECD, 2021[8]) (see Box 1.4 for more information).

A first step in strengthening the analytical capacities of the Lithuanian public sector would therefore be to have a systematic and precise mapping of the staff who possess analytical skills in each ministry. Such an exercise could be inspired by the UK example of developing Digital, Data and Technology Capability Framework (see Box 1.5).

In Lithuania, the Ministry of the Interior is currently mapping skills based on a broader competency management framework and the Human Resource Management System does track civil servants’ career progression. While this constitutes a very positive first step, a sharper focus on analytical skills would be needed to identify actual analytical resources and begin to tackle the unmet needs of the civil service.

The scarcity of graduates with high-in-demand analytical skills and the limited financial resources of the public sector requires the elaboration of a government-wide strategy to attract and retain highly qualified analytical staff members. The Lithuanian public sector could offer an analytical track within the civil service framework, whereby the graduates with quantitative background would be hired centrally and, then, dispersed to the analytical units within various ministries. These analysts could be offered relatively higher salaries and well-defined career trajectories to increase the attractiveness of this professional stream which could apply both to ministries and agencies.

Thus, in order to attract a variety of profiles, skills and backgrounds, the Lithuanian government could consider creating a specific analytical track within the civil service, which could provide some flexibility in compensation, offer professionally attractive positions, with a greater in-career mobility compared to the traditional civil service framework. This analytical civil service track would be an integrated cross-government service that supports better policy formulation and implementation across the civil service with economic and analytical skills (OECD, 2020[15]). Several other OECD countries have created dedicated policy analysis tracks within the civil service (see Box 1.6).

As seen in Ireland, the United Kingdom and France, the creation of a system of analytical profession in the civil service contributes to making these skills available and visible in the public sector and ensures greater consistency of analysis and evaluations across the government, while facilitating mobility and exchange of good practices. Moreover, it provides a solution to the issue of public sector attractiveness. For instance, the IGEES has managed to develop name recognition in Ireland such that it is generally considered a more attractive career option than many other graduate programmes, including in the private sector, due to the horizontal and upwards mobility it provides early on in one’s career (OECD, 2020[15]). In addition to mobility, the attractiveness of the work is reinforced by the possibility of participating in quality seminars, in exchanging with peers, and in focusing on shaping high priority policy initiatives.

There is currently very little systematic rotation of civil servants across different institutions in Lithuania. Staff members may naturally move from one institution to another, but there is no planned career progression.1 Institutionalising civil servants’ mobility could make the public sector a more attractive career option for analysts which can be envisaged as part of broader reforms of the civil service.

Moreover, the creation of such a stream needs to be accompanied, as described above, by a thorough exercise of analytical capacity mapping. Analytical resources and gaps have to be identified to enable effective human resources management. The clear definition of analytical and evaluation roles and their corresponding skill sets (such as in Canada, see Box 1.7) would help to foster a government-wide hiring and training strategy. This capacity mapping could also lead to the consolidation of the analytical resources of some of the agencies in order to increase their impact.

In most Lithuanian ministries, analytical capacities are dispersed through line departments and understaffed policy units, known as strategic decision support groups (SDS): few ministries have a unit dedicated entirely to policy analysis. One positive example of a unit dedicated to analysis is the strategic decision support and international co-operation division at the Ministry of Social Security and Labour. This division can be consulted by any line department if it needs to assess the fiscal impact of a draft legislation, including redistributive impacts through microsimulation. The division is also responsible for developing evaluations for the ministry. The Competition Council has also created a unit that centralises its economic expertise for co-ordination across the other units (see Box 1.8 below).

The practice of the Ministry of Social Security and Labour and the Competition Council remains an exception, however, as most ministries do not have a dedicated unit in charge of supporting analysis across all departments. Most only have units for strategic planning and monitoring charged with reporting on the strategic management frameworks, which employ staff with analytical skills, and often suffer vacant positions.

Rather, most analytical tasks are distributed amongst staff members who also fulfil many other functions. Yet, analysis and evaluation take time, which can be difficult to reconcile with having to handle daily and urgent tasks such as responding to parliamentary questions, responding to requests by the Office of Government2, or managing a project. As a result, staff in ministries often have little time to conduct in-depth analytical work. The first step in promoting the supply of high-quality analysis and evaluation would therefore be to review the organisation of requests to ministries, in order to streamline the workload, while also reviewing the ministries’ submissions to the Office of the Government. This can be done through analysis of the government document management and information system (DVIS).

Another feature of the organisation of analytical capacities in Lithuania is the analytical units in agencies subordinate to ministries (see Table 1.2 below for an example). These institutions often operate outside the rigid civil-service framework and tend to have more flexible labour contracts that are regulated by private law. In turn, they have greater leeway in salary-setting and other contractual arrangement that potentially make them more attractive employers than ministries. An interesting example is the Lithuanian Energy Agency, which is the only agency attached to the Ministry of Energy, and is entirely devoted to analysis. This agency provides the ministry with strategic analysis of energy markets and long-term supply needs of the country. Some other examples of these agencies are found in the table below as they relate to the ministry of Economy and Innovation. To some extent, STRATA itself is also a strategic analytical agency under the Office of the Government.

Many of the agencies in the Lithuanian government act as analytical arms of the ministries, and conduct thematic studies and analysis alongside other activities. They do not, however, have an established role in formal evidence-generating mechanisms for policy making (such as regulatory impact assessment, ex post evaluations, or value for money/effectiveness analysis for budgetary purposes).

Firstly, the consolidation of some of the agencies could be envisaged in some cases in order to use the scarce analytical resources in the public sector more efficiently through pooling of resources, as well as to promote knowledge sharing. However, the nature of such adjustments falls beyond the scope of the current report.

More generally, embedding proper evidence-informed decision-making into government requires having a critical mass of analytical competencies available. More technical evaluation or analytical skills can be devolved to agencies, as is already the case in Lithuania, and is commonly the case in Nordic countries. This model can offer increased managerial autonomy, as well as give staff the capacity to conduct in-depth research and analysis while being preserved from more short-term and urgent tasks. This would, however, require mobilising these agencies in a more systematic manner to support the analysis needed for evidence-informed decision-making processes, such as RIA.

A range of countries, such as France, Canada, the United Kingdom or Ireland have chosen to concentrate a significant mass of analytical expertise within Ministries. This has the advantage of embedding analysis and evaluation into decision-making processes. Some Lithuanian ministries would undoubtedly benefit from having some critical mass of analytical skills in house.

While some ministries have taken to training their staff members in order to upskill existing personnel, the Lithuanian civil service has not developed a systematic government-wide approach in this regard and the training system of public servants is decentralised.3 The law of civil service of 1999 stipulates that individual ministries are responsible for training their staff based on the recommended training priority areas identified by the Government (Parliament of Lithuania, 1999[11]). One of the seven training priorities included in the government decree is “strengthening analysis and justification competences” (Government of Lithunia, 2018[13]). However, in practise the priority areas identified in government decrees are not well reflected in line ministries’ agendas as trainings are often organised on an ad hoc basis as funds become available. This is partially due to the fact that an important share of the government-wide training budget comes from European Union funding (24% in 2019, for example (Ministry of Interior, 2021[20])).

In terms of training experience in other selected OECD countries, in Ireland in the context of the IGEES system, or in France, specific training in quantitative methods, modelling, or data science can be offered to policy analysts.

While this report might suggest a more systematic and government-wide approach to training, specifically when it comes to training related to supply and use of evidence, the upskilling of existing staff will not offer a structural solution to analytical skill gaps.

To produce reliable and robust analysis for evidence-informed policy advice, analysts in ministries need to have access to high-quality and timely data, as well as the appropriate tools and instruments to use this data.

The quality and availability of data is a crucial challenge for evidence-informed decision making. In OECD countries, challenges related to access to data in the public sector generally include understanding what administrative data currently exist in ministries. There is also a broader data challenge that corresponds to the capacity of the public sector to generate the type of high-quality data that is necessary to produce evidence and evaluation (OECD, 2020[21]). In other words, policy evaluation and evidence-informed policy making (EIPM) can be hindered by:

  • a lack of available data (see Box 1.9 for more information on what types of data are needed for evaluation),

  • issues with data access,

  • and capacity gaps among government departments and agencies to generate data in a format that can be used.

This understanding of the importance of access to data and the power of open data, exists in Lithuania and some policy initiatives have been recently adopted, particularly in the field of open data. However, access to timely and quality data, particularly administrative data across ministries, as well as its use, remain an issue in Lithuania today, which will need attention as part of a structured policy agenda supporting Open Data.

Statistics Lithuania is a public institution under the Ministry of Finance that is responsible for conducting official statistical studies and gathering data from public institutions and registries for that purpose. It bases its activities on the annual official statistics programme (OSP), a framework developed jointly by Statistics Lithuania (part I) and the Bank of Lithuania (part II), and ratified by the Ministry of Finance after undergoing a consultation process (Parliament of Lithuania, 1993[23]). For instance, in 2020, the OSP included 250 surveys and datasets (Ministry of Finance, 2019[24]).

The OSP is beneficial for the use and collection of administrative and statistical data as it clearly identifies what data has to be collected and determines what individual institutions’ responsibilities in this regard, as mandates what surveys will be conducted during the year. The OSP also undergoes a consultation procedure, allowing stakeholders to express their data needs. These consultations can bring numerous benefits as stakeholder involvement helps to identify data needs linked to policy priorities, as well as provide a better understanding of existing data (OECD, 2019[25]). Finally, the OSP defines data use and management mandates for government institutions, thus contributing to data protection.

However, as it is currently designed, the official statistics programme remains too rigid to fully support the production and use of data for analysis. For instance, institutions that have not expressed their needs during the consultation phase will not be able to access data that was not planned as part of the OSP, should the need arise during the Plan’s implementation phase. The list of institutions with a legal mandate to access the data as part of the OSP is limited, and thus many institutions do not benefit from this data. The Bank of Lithuania’s research centre, for example, cannot have access to many administrative data sets as it does not have a university status and, thus, does not have a legal mandate to conduct academic studies. Statistics Lithuania is equipped with the necessary infrastructure to track, monitor and analyse high frequency administrative and statistical data on time and could be technically ready to make it available. However, there is no legal framework that would allow policy analysts to easily access such data for the purpose of supporting and evaluating policy decisions in a way that would preserve trust in statistical secrecy.

In short, the narrow approach under the definition of “official statistics” does not allow Lithuanian administration to exploit the full potential administrative data can have in policy making, as data produced through the OSP may not be timely and thus appropriate for use and data that is not used by a variety of stakeholders often of poor quality.

Availability and accessibility of data are important factors in data use, as data needs to exist but also accessible to be used for analysis. Also, publicity of data matters as analysts may not otherwise be aware of existing data sets. Recent OECD data shows that Lithuania is still lagging behind other OECD countries in this regard. The OECD OURData index, which measures accessibility, usefulness and re-usability of public data, ranked Lithuania as the second to last amongst OECD countries in three categories: data availability, data accessibility and government support for re-usability (see Figure 1.3).

Indeed, for the most part, there are no government-wide mechanisms to determine access to administrative data in Lithuania as each institution responsible for collecting data also decides on whether it will be shared or not. There is no fully operational centralised portal where institutions can systematically share administrative data. As a result, analysts must make ad-hoc requests, making it challenging to access data in a timely fashion and analysts may also not necessarily be aware of all the data that exists.

Recent initiatives in favour of open data have however greatly improved its availability. In 2018, the Information Society Development Committee, a dependency of the Ministry of Economy and Innovation, was tasked with developing and implementing an open data policy (article 9.3 of the statute of the committee (Ministry of Economy and Innovation, 2018[27]). As part of this policy, the committee created a national open data portal, which includes over 900 open datasets with public access, of which over 300 are in machine-readable CSV format. Moreover, the committee has provided training on open data to over 200 public managers. Box 1.10 provides more information on the open data portal.

While this initiative constitutes a good practice, the quality of these data sets remain a challenge and thus an obstacle to use. Some data sets only include aggregate data that cannot be merged or linked with other datasets, and thus are of little use for statistical analysis. Examples from other OECD countries, such as Denmark (see Box 1.11), suggest that greater availability of data does not have to be at the expense of its quality and of its potential for use.

While recent advancement in the field of open data should be pursued by continuing to make more data available on the portal, more targeted approaches to access data for analysis could be envisaged. This could be done by adopting a more systematic approach to the production and use of data for analysis through the establishment of a data governance framework (see below for more information on this framework).

While only data specialists are usually responsible for developing data services and tools, an appreciation and understanding of the data value cycle is needed (particularly from leadership) in order to embed a data and evidence-driven culture within the public sector (OECD, 2021[30]), and ensure that public servants collecting and supplying data can think ‘use first’. The figure below provides a schematic illustration of this data value cycle.

To adopt a shared understanding of the data value cycle, the Lithuanian government could consider adopting a data governance framework – as detailed further below.

In Lithuania, a substantial share of public data is stored and managed by the Centre of Registries, a public enterprise. This central government registry manages the population, real estate, mortgages, addresses, legal persons, authorisation, contracts, liens, marriages, incapacitated people and testaments registries. Public institutions and state information systems can access and use data from these registries if they have a legal mandate to do so under the OSP. For example, the data from these registries are used by the State Social Insurance Board (SODRA) and the State Tax Inspectorate.

With such a central registry, Lithuania is able to attribute a unique identifier for each entity (for persons or businesses), thus making the merging of data for statistical purposes much easier. Indeed, only the data that includes unique identifiers of persons, businesses or places can be merged. The ability to merge different datasets allows researchers and analysts to use data for a greater variety of topics.

Issues related to data use are not only operational but also ethical. All OECD countries face the challenge of balancing the use of personal data for EIPM and ensuring that the personal data rights of citizens are secured and respected (OECD, 2020[32]). Indeed, data protection legislations can constitute an obstacle to using individual-level data to evaluate policies and programmes in some countries, specifically when carrying out statistical analysis and when merging files, which requires access to single identifiers (OECD, 2020[21]).

In Lithuania, existing data protection regulations often preclude public institutions from receiving individual data with unique identifiers from registries or Statistics Lithuania (the 1996 law on the legal protection of personal data). This is the case even though Lithuania has a central registry, as described above. For example, public institutions can only receive aggregate data upon request from individual tax files for analysis.

While high level and prominent institutions such as STRATA can access matched datasets, many public institutions still find it difficult to get access to data files from other institutions due to legal barriers. Some experiments currently conducted in Lithuania in regards to linking and merging individual-level data could provide examples of good practices for future evaluations. Thus, in 2021, Statistics Lithuania will conduct the national census using information from registries, as opposed to through population surveys. For this purpose, Statistics Lithuania is currently testing the merging of 15 different registries and data sets to estimate the total population, its demographic and socio-economic composition, and distribution on the Lithuanian territory (Government of Lithuania, 2018[33]) (Statistics Lithuania, 2020[34]).

More generally, systematic strategies and policies to combine, link and reuse data, as well as to connect actors and decisions within and outside the public sector, are necessary to enable administrative data to be used for evidence-informed decision making (OECD, 2019[25]). Thus, some OECD countries have sought to develop EIPM strategies by fostering systematic use of administrative data. The United States, for example, have institutionalised and implemented government-wide approaches to the use of data for analysis. They have done this by mobilising institutional resources, promoting internal champions and exploring the possibility to fully use existing data on a systematic basis through significant governance changes. The United States have issued the 10-year Federal Data Strategy centred around 3 core principles (ethical governance, conscious design and a learning culture), which is accompanied by the implementation plan of 40 practices that help agencies to comply with the Federal Data Strategy (Executive Office of the President, 2019[35]) (OECD, 2019[25]). Moreover, the Foundations for Evidence-Based Policymaking Act of 2018 includes government-wide approach to data as a key pillar for the EIPM vision. Its implementation plan mandates the agencies in the US administration to have a chief data officer (US Congress, 2018[36]). The implementation plan also englobes such programmes as “Open Data Access and Management” and “Data Access for Statistical Purposes” (United States Office of Management and Budget, 2019[37]). Such a government-wide strategy for use of administrative data in policy making could be included in a wider framework on evidence-informed decision-making in Lithuania.

Lithuania could consider combining its recent open data efforts with a clear governance framework for data in the public sector, which is apparently planned as part of the government’s agenda. Such a framework would serve to identify the data needs of departments, as well as ensure the quality, publicity and use of data. Indeed, evidence shows that data governance promotes integration and systemic coherence, and offers a common basis to use data in order to attain shared policy goals and promote trust (OECD, 2019[25]). A centralised data governance strategy can therefore help set a clear and shared vision for data for EIPM, establish roles and standards for implementation, establish institutional, regulatory, and technical foundations to better control and manage the data value cycle (OECD, 2019[25]).

Several OECD countries, such as Canada, the Netherlands or the United States, have developed holistic national data governance strategies to manage, protect and share data within the public sector. In front-runner countries, this has led or is leading to the development of holistic national data strategies. These strategies are often nested within public sector digitalisation efforts. In the United States, for example, the 2019 Federal Data Strategy presents a ten-year vision to unlock the full potential of the country’s federal data assets while safeguarding security, privacy and confidentiality (Executive Office of the President of the USA, 2019[38]). This data strategy also builds on the Foundations for Evidence-Based Policy-Making Act of 2018, which aims for federal agencies to better acquire, access, and use evidence to inform decision making.

The Lithuanian government could thus adopt a common data governance strategy to better support data for evidence-informed decision making. This could include a systematic mapping of registries, administrative data and surveys, a central portal for making data available for public use, and a specific process for facilitating access to merged anonymised files, under specific authorisations. In this regard, the current data reform, which has been initiated in Spring 2020, could also provide a useful landscape for data access, quality and use in the Lithuanian public sector (see the following box for more information about this reform). This information system was partially launched in November 2020 and could provide a common space for ready-made data management platforms, allowing the processing of large amounts of data. However, the challenge will be to see whether it will be possible to link datasets through unique identifiers, either for firms or for individuals, for analytical purposes as otherwise, the value of data in analytical terms will remain limited.

  • The creation of an analytical track within the civil service. Following the example of Irish IGEES, this analytical track could target young graduates with quantitative education backgrounds, by offering competitive salaries, as well as clear horizontal and upwards career mobility.

  • A tailored master’s programme for economic and quantitative policy analysis building up on the experience of the Bank of Lithuania in creating BSc in quantitative economics. STRATA could co-operate with universities in its design and execution.

  • A scholarship programme that would send Lithuanian students for graduate studies abroad in exchange for working for the public sector for several years upon graduation.

  • A whole of government approach to analytical skills in the context of overall civil service reform.

  • Strengthening investment in training and developing a strategic career framework. This should ensure that there is a co-ordinated approach to training, with corresponding resources, so that it is not just contingent on external EU funding.

  • Mapping the analytical skills in each ministry and across the government. This exercise which can be undertaken in addition to the current competency mapping, would require establishing a shared understanding of what these skills entail.

  • Review request processes from the centre of government to reduce the internal administrative workload and preserve more time for analytical tasks.

  • Consolidating the competencies across some of the agencies to pool capacity and increase impact.

  • Adopting a strategy and/or policy to combine, link and reuse data.

  • Simplifying access to administrative data for analytical purposes by public institutions.


[31] Charlotte van Ooijen, B. (2019), A data-driven public sector : Enabling the strategic use of data for productive, inclusive and trustworthy governance, OECD, Paris.

[17] Competition Council (2019), 2018 Konkurencijos Tarybos Veiklos Ataskaita (2018 Activity Report of Competition Council), Competition Council, Vilnius, Lithuania.

[7] European Commission (2019), 2019 European Semester: Country Report Lithuania, European Commission, Brussels.

[35] Executive Office of the President (2019), Federal Data Strategy: A Framework for Consistency, https://www.whitehouse.gov/wp-content/uploads/2019/06/M-19-18.pdf (accessed on 14 May 2021).

[38] Executive Office of the President of the USA (2019), Federal Data Strategy: A Framework for Consistency, http://www.whitehouse.gov/wpcontent/ (accessed on 11 May 2021).

[16] Government of Canada (2020), Evaluation Competences, http://www.canada.ca/en/treasury-board-secretariat/services/audit-evaluation/evaluation-government-canada/evaluation-competencies.html (accessed on 11 May 2021).

[33] Government of Lithuania (2018), Vyriausybės nutarimas dėl Lietuvos Respublikos 2021 metų gyventojų ir būstų visuotinio surašymo Nr. 1125 (Government Resolution on the 2021 population and real estate census.

[13] Government of Lithunia (2018), Vyriausybės nutarimas dėl Lietuvos Respublikos Valstybės tarnybos įstatymo įgyvendinimo nr. 1176 (Government Decree on the implementation of the Civil Service Law of the Republic of Lithuanis).

[28] Information Society Development Committee (n.d.), Lithuanian Open Data Portal, https://data.gov.lt/ (accessed on 11 May 2021).

[27] Ministry of Economy and Innovation (2018), Lietuvos Respublikos Ekonomikos ir Inovacijų Ministro įsakymas dėl informacinės visuomenės plėtros komiteto nuostatų patvirtinimo (Resolution of the Minister of Economy and Innovation on the Statute of the Information Society Development Committee).

[19] Ministry of Economy and Innovation (n.d.), Subordinate Institutions and Enterprises, https://eimin.lrv.lt/lt/struktura-ir-kontaktai/pavaldzios-istaigos-ir-bendroves (accessed on 10 May 2021).

[24] Ministry of Finance (2019), Oficialiosios statistikos 2020 metų programos pirma dalis (First Part of the Official Statistics Programme for 2020).

[20] Ministry of Interior (2021), Post-reform training provisions.

[10] Ministry of Interior (2020), Viešojo sektoriaus ataskaita 2016-2019 (Public Sector Report 2016-2019).

[8] OECD (2021), Government at a Glance 2021, OECD Publishing, Paris, https://dx.doi.org/10.1787/1c258f55-en.

[30] OECD (2021), “The OECD Framework for digital talent and skills in the public sector”, OECD Working Papers on Public Governance, No. 45, OECD Publishing, Paris, https://dx.doi.org/10.1787/4e7c3f58-en.

[1] OECD (2020), Building Capacity for Evidence-Informed Policy-Making: Lessons from Country Experiences, OECD Public Governance Reviews, OECD Publishing, Paris, https://dx.doi.org/10.1787/86331250-en.

[21] OECD (2020), Improving Governance with Policy Evaluation Lessons From Country Experiences, OECD Public Governance Reviews, OECD Publishing, Paris, https://doi.org/10.1787/89b1577d-en.

[32] OECD (2020), Mobilising Evidence for Good Governance: Taking Stock of Principles and Standards for Policy Design, Implementation and Evaluation, OECD Public Governance Reviews, OECD Publishing, Paris, https://dx.doi.org/10.1787/3f6f736b-en.

[26] OECD (2020), OECD Open, Useful and Re-usable data (OURdata) Index: 2019, OECD, Paris, http://www.oecd.org/gov/digital-government/policy-paper-ourdata-index-2019.htm (accessed on 23 March 2021).

[3] OECD (2020), Questionnaire on Evidence Informed Policy Making and Policy Evalutation at the Centre of Government in Lithuania.

[15] OECD (2020), The Irish Government Economic and Evaluation Services: Using Evidence-Informed Policy Making to Improve Performance, OECD Publishing, Paris, https://doi.org/10.1787/cdda3cb0-en.

[25] OECD (2019), The Path to Becoming a Data-Driven Public Sector, OECD Publishing, Paris, https://doi.org/10.1787/059814a7-en.

[6] OECD (2017), “Core skills for public sector innovation”, in Skills for a High Performing Civil Service, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264280724-6-en.

[9] OECD (2017), Government at a Glance 2017, OECD Publishing, Paris, https://dx.doi.org/10.1787/gov_glance-2017-en.

[2] OECD (2017), Skills for a High Performing Civil Service, OECD Public Governance Reviews, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264280724-en.

[14] OECD (forthcoming), The Future of Work in the Public Service, OECD Publishing, Paris.

[11] Parliament of Lithuania (1999), Lietuvos Respublikos valstybės tarnybos įstatymas XIII-1370 (Law on Civil Service of the Republic of Lithuania) (last amended 10 November 2020).

[12] Parliament of Lithuania (1999), Lietuvos Respublikos viešojo administravimo įstatymas VIII-1234 (Law on the Public Administration of the Republic of Lithuania) (last amended 11 June 2020).

[4] Parliament of Lithuania (1994), Republic of Lithuania Law on the Bank of Lithuania I-678 (last amended 26 November 2015).

[23] Parliament of Lithuania (1993), Lietuvos Respublikos oficialiosio statistikos įstatymas I-270 (Law on Official Statistics of the Republic of Lithuania) (last amended on 29 September 2020).

[22] Results for All (2017), 100+ Government Mechanisms to Advance the Use of Data and Evidence in Policymaking: A Landscape Review, http://results4america.org/wp-content/uploads/2017/08/Landscape_int_FINAL.pdf (accessed on 14 May 2021).

[29] Statistics Denmark (2014), Data for Research, http://www.dst.dk/en/TilSalg/Forskningsservice# (accessed on 11 May 2021).

[34] Statistics Lithuania (2020), Lietuvos Respublikos 2021 metų gyventojų ir būsto visuotinio surašymo metodika (Methodology of the 2021 National Population and Housing census of the Republic of Lithuania).

[37] United States Office of Management and Budget (2019), Phase 1 Implementation of the Foundations for Evidence-Based Policymaking Act of 2018: Leaming Agendas, Personnel, and Planning Guidance, https://www.whitehouse.gov/wp-content/uploads/2019/07/M-19-23.pdf (accessed on 21 May 2021).

[36] US Congress (2018), H.R.4174 - Foundations for Evidence-Based Policymaking Act of 2018, https://www.congress.gov/bill/115th-congress/house-bill/4174/text (accessed on 14 May 2021).

[5] Vilnius University (2021), Vilnius University Webpage, http://www.vu.lt/en/studies/bachelor-and-integrated-studies/quantitative-economics (accessed on 10 May 2021).

[18] VSDFV (n.d.), Open Source Data on Firms, https://atvira.sodra.lt/imones/paieska/index.html (accessed on 10 May 2021).


← 1. One exception includes diplomats working in ministries other than the Ministry of Foreign Affairs or in President’s office, who have the possibility of going back to their host Ministry.

← 2. According to the data of the Office of the Government and the Office of Prime Minister, Lithuanian ministries have received 1 888 requests from the Office of the Government and the Office of Prime Minister in 2019 (the corresponding figures for 2018 and 2017 were 1 567 and 1 738 respectively).

← 3. However, the preparation for the Lithuania’s rotating EU Council presidency in 2013 was centrally organised with centralised training. The Ministry of Finance also sometimes initiates large scale trainings for various ministries and other public sector institutions, which is commissioned externally.

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

© OECD 2021

The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions.