1. Capacities to supply evidence for decision making
The chapter analyses the Lithuanian government's capacities to supply timely and credible evidence for decision making. It finds that the Centre of government and line ministries in Lithuania lack sufficient analytical skills, as well as a comprehensive overview of their capacities in this regard. It calls for the establishment of a government-wide analytical track aimed at increasing the attractiveness of the public sector. The chapter also provides an overview of the extent to which data governance practices and frameworks can support evidence-informed decision making in Lithuania.
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 OECD report 2017 on civil service skills report identifies four main skill groups that are necessary to create public value:
Policy advisory skills [require] leveraging technology and synthesising a growing range of evidence-informed scientific insights (e.g. behavioural economics, data science, strategic foresight) and a diversity of citizen perspectives for effective and timely policy advice to political decision makers.
Engagement skills [require] working directly with citizens and users of government services to improve service experience, legitimacy and impact by leveraging the “wisdom of the crowd” to co-create better solutions that take into account service users’ needs and limitations.
Commissioning skills [require] designing and overseeing various contractual arrangements (outsourcing, PPPs, service level agreements, etc.) and managing projects to achieve impact through organisations (public, private, not-for-profit) that are best placed to deliver services due to their expertise and/or local position.
Network management skills [require] collaborating with a range of independent partners to address complex/wicked policy challenges by developing a shared understanding of the problem, collectively identifying potential solutions and co-implementation.
While each civil servant does not need to be highly skilled in all of these areas, public institutions do require a solid mix of these skills in order to deliver public value in the modern public sector arrangement.
Source: OECD (2017[2]), Skills for High Performing Civil Service, http://dx.doi.org/10.1787/9789264280724-en.
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.
Lithuanian ministries suffer from analytical skills gaps, which affect their ability to supply credible, timely and robust evidence for decision making
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. 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. 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. Until recently, there had not been a systematic approach to mapping and tracking these skills across government.
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).
Analytical skill gaps in the Lithuanian government are due to a shortage of these skills in the Lithuanian job market and challenges regarding the competitiveness of public sector salaries
Analytical skills are lacking in the Lithuanian job market
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.
The Bank of Lithuania is a para-public institution that is not bound by the civil service framework but operates based on labour law (article 18, (Parliament of Lithuania, 1994[4]). On this basis, the Bank is able to offer more attractive and flexible salaries than most of the public sector. Despite this, the Bank still struggles to find qualified economic expertise.
As a result, the Bank of Lithuania has developed an academic programme in quantitative economics in order to meet its own human resources needs. This programme, developed in co-operation with Vilnius University, is based on a 3 years Bachelor in Sciences programme in Quantitative Economics. Kaunas University of Technology has also joined this initiative. The first iteration of the programme started in 2018 and is set to graduate in 2021.
The programme is taught exclusively in English by internationally-ranked professors, who combine their teaching function with a position at the Bank of Lithuania. The programme admits approximately 30 students annually. In addition, the BSc in Quantitative Economics features a more rigorous admission procedure than most programmes at Lithuanian public universities. Successful students not only need to obtain high scores at the national examinations but also undergo an interview process. The prestige of the programme was also ensured through privileged access to internship opportunities (Bank of Lithuania, Nasdaq Baltic) and some scholarship schemes.
The objective of this initiative is to equip Lithuanian students with the most recent economic analysis and quantitative methods and prepare them for further studies in leading foreign universities or a career in economics, finance and data analytics. It should help, hopefully, to reduce BoL’s reliance on international labour market for their staff needs in the medium term. In addition, the bank is increasing its co-operation with universities and has recently signed an agreement with the Kaunas University of Technology on research co-operation and the development of a joint PhD programme.
Source: Vilnius University (2021[5]), Vilnius University Webpage, www.vu.lt/en/studies/bachelor-and-integrated-studies/quantitative-economics (accessed on 10 May 2021); and Fact-finding interviews (OECD).
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.
The civil service is not sufficiently attractive
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.
Lithuania did participate in the 2016 comparative survey on staff compensation in the civil service (OECD, 2017[9]). The findings show that from a general standpoint analysts and economists in the Lithuanian civil service are paid less in relation to national GDP per capita than their OECD counterparts. There is almost no distinction between a senior and a junior economist, and the compensation is not significantly different from that of a secretary. These analytical staffs are employed in the national capital, where GDP per capita, relative cost of living and competing job opportunities differ from the rest of the country. The ratio of compensation for analytical staffs to GDP per capita in the Vilnius region is lower than one, and close to that of secretaries (Figure 1.2). While the 2016 data is a few years old, more recent data from the Civil Service Report prepared by the Ministry of Interior in 2019 does not suggest that the remuneration of civil servants has changed drastically since, in terms of relative ratio to national GDP per capita (Ministry of Interior, 2020[10]).
Note: The data corresponds to the survey conducted in 2016 for the year 2015 in which officials from six central government ministries/departments (Interior, Finance, Justice, Education, Health and Environment or their equivalents) answered about the remunerations and working conditions in their respective institutions. The job classification followed the methodology of ISCO-08 codes developed by the International labour organization (ILO). The data is adjusted for working time differences and follows a full-cost approach whereby the compensation includes all the social benefits, payments to insurance and pension schemes, bonuses and taxes. The GDP data is taken from the OECD National Accounts Database. Please refer to the (OECD, 2017[9]), Government at a Glance, http://dx.doi.org/10.1787/gov_glance-2017-en Annex D for more information on the methodology. The data for the GDP of Lithuania and Vilnius region is taken from the Eurostat database. The data for Vilnius region corresponds to the Capital Region (Sostinės regionas). Eurostat (n.d.) https://ec.europa.eu/eurostat/databrowser/view/nama_10r_2gdp/default/table?lang=en (accessed on 27 May 2021).
Source: adapted from 2016 OECD Survey on compensation of employees in central/Federal Governments; OECD STAN/National Accounts Statistics (database); OECD (2017[9]), “Figure 3.20 in Chapter 3” in Government at a Glance 2017, http://dx.doi.org/10.1787/888933532371.
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]).
There is no shared framework for analytical skills across-government
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).
The COVID-19 pandemic was an exceptional test of government capabilities in 2020-21. Capacity for forward planning and strengthening resilience against future shocks has become critical. Yet, countries had few structured capacity to gather scientific advice and evidence about how governments should adapt to novel and complex crises. As a result, many countries have put in place institutional arrangements to gather scientific advice and evidence as the COVID-19 crisis developed. They have had to address issues of transparency, processes to ensure the quality, authority and legitimacy of advice. More generally, countries have also had to redesign decision-making processes and cross-government co-ordination to increase their effectiveness and agility. The crisis has also highlighted the role of data as a strategic asset in the public sector. Building capacity for anticipatory innovation and skills is also one of the many facets to be addressed by governments on the way to recovery, within the scope of issues addressed in the current report.
Source: OECD (2021[8]), Government at a Glance 2021, https://dx.doi.org/10.1787/1c258f55-en.
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 2015, the United Kingdom’s Government Digital Services (GDS) started conducting a broad mapping of digital skills in the government to evaluate the capacities and needs of the British government, to promote a modern and agile digitally-driven civil service. This mapping looked at digital professionals as well as product manager, user researcher and delivery manager roles – all of which are indispensable for well-functioning digital services. This mapping exercise has shown that employees with such digital skills had different job titles, functions and salaries within the British public sector.
Based on this mapping, the GDS developed the “Digital, Data and Technology Capability Framework” that includes 37 jobs and identifies the skills needed for each of them, as well as the competences needed to advance to a higher-level title within each job. This framework has helped the UK civil service address the issue of digital professionals’ recruitment and career advancement, identify capacity gaps to design training and facilitated the creation of community of practice.
Source: OECD (forthcoming[14]), The Future of Work in the Public Service.
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 Lithuanian government could consider adopting a government-wide approach to analytical skills
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.
In Ireland, the Irish Government Economic and Evaluation Service (IGEES) has a role as an economic and analytical resource co-ordinator across government. The IGEES manages a network of analytical staff who are hired centrally and later posted in line departments. The IGEES staff conduct economic analysis and evaluations, and more generally contribute to better policy making in the line departments. IGEES was launched in 2012 in the aftermath of the Global financial crisis, initially aimed at insuring the quality-for-money of public policies in response to budgetary pressures (OECD, 2020[15]). On average, 20 recent graduates are hired through this scheme every year, which brings the total number of analysts hired by IGEES to over 150 across the government. The IGEES also supports network building and knowledge sharing by providing its staff with incentives for mobility: after an initial 2-year period, staff will move either within the department or to another department. A learning and development framework has also been established whereby IGEES staff receive training in the following areas: policy analysis and evaluation methods, appraisal methods, data and advanced quantitative methods, and applied economics (OECD, 2020[15]).
In the United Kingdom, there are around 15 000 “policy professionals” that work as analysts across the different government departments. The term regroups several professional tracks such as the government economic service, the government statistical service and the government social research service (OECD, 2020[1]). The policy profession framework includes a two-year apprenticeship programme, as well as a three-year graduate scheme. There is also a common framework for all policy professionals, which includes a shared skillset (18 competences in 3 areas: Analysis and Use of Evidence, Politics and Democracy, Policy Delivery), 3 levels of expertise, as well as a clear training and career progression framework.
In France, the National Institute of Statistics and Economic Studies (INSEE) has an inbuilt tertiary educational system, which trains a set of specialists in economics, statistics and econometric analysis through the ENSAE school, and statisticians and data scientists at the ENSAI school. Part of the graduates from these schools are to be enrolled in the civil service and receive a stipend during their studies in exchange for working in the civil service for a minimum period of 8 years. Within the civil service, graduates from the ENSAE/ENSAI serve in the analytical offices in each ministry, as well as a variety of public institutions such as France Stratégie or the Central Bank. At entry level, this pool of graduates is co-ordinated centrally by INSEE, thus creating a shared market place for analytical and statistical skills across the public sector. In addition, the National Institute also has an important role in fostering and developing analytical competencies across government, by providing professional training aimed at all civil servants, organising seminars to foster knowledge sharing and encouraging mobility of analytical staff between line ministries. The scheme, which has been operating since the inception of INSEE in 1946, was part of a set of key reforms aimed at modernising the civil service in the after war recovery period to ensure that the French state apparatus would be well equipped to deal with modern challenges.
Source Secretariat based on information from country officials.
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 Canada, the Treasury Board of Canada Secretariat has established a system in which evaluators are classified into 3 different groups according to their expertise and skills: Evaluation Specialists (junior, intermediate and senior), Directors of Evaluation and Heads of Evaluation. The functions and tasks of each rank of evaluators are clearly identified as listed below:
Junior Evaluators are expected to help with formulating evaluation questions, gathering and using quantitative and qualitative data as well as pointing to its gaps. The Junior Evaluators also commit to pursue ongoing training and aim at obtaining professional certificates.
Intermediate Evaluators – analyse the available performance measurements and assess the quality of data. They are also expected to be able to formulate conclusions and recommendations based on evaluation results. Intermediate evaluators seek to advise about the improvement of performance measurement indicators for future data collection.
Senior Evaluators – validate the engagement with chosen stakeholders, verify the approach and methodology of evaluations and communicate the concerns about performance measurement to the head of evaluation.
All three categories of evaluators need to have the skills in the areas of demonstrating integrity and respect, working effectively with others and initiative taking.
Directors of Evaluation – need to have expertise developed through education, training and experience in design, methods and practises of quantitative and qualitative data collection and analysis. They are also expected to be able to effectively communicate the findings and propose action-oriented recommendations. Moreover, Directors of Evaluation are responsible for the considerations and incorporation of new evaluation techniques and trends. Directors also need to have competence in upholding integrity and respect, creating vision and strategy, collaborating with partners and stakeholders, being result-oriented, promoting innovation and mobilising people.
Heads of Evaluation – ensure the adherence to Canada’s Standard on Evaluation, advise senior managers about the effective performance measurement and mobilise the expertise of the other evaluation professionals.
Source: Government of Canada (2020[16]), Evaluation Competences, www.canada.ca/en/treasury-board-secretariat/services/audit-evaluation/evaluation-government-canada/evaluation-competencies.html (accessed on 11 May 2021).
A critical mass of analytical skills is needed at the organisational level
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).
In 2018, the Competition Council established the unit of economic analysis that consists of 5 economists. The creation of this unit was aimed at strengthening economic advice and expertise and increase knowledge sharing among the economists employed by the Competition Council. The unit “does not only conduct economic calculations but also ensures the quality of the economic analysis conducted by the other administrative units”.
Source: Competition Council (2019[17]), Konkurencijos Tarybos Veiklos Ataskaita [2018 Activity Report of Competition Council].
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.
Training could be used to upskill existing public servants, but has its limits
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.
Access to high quality and timely data is needed to supply robust evidence
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),
and capacity gaps among government departments and agencies to generate data in a format that can be used.
Conducting quality evaluation requires quality data, which may come from various sources:
Statistical data: commonly used in research, it corresponds to census data or more generally to information on a given population collected through national or international surveys.
Administrative data: this data is generally collected through administrative systems managed by government departments or ministries, and usually concerns whole sets of individuals, communities and businesses that are concerned by a particular policy. For instance, it includes housing data, tax records and data from public administrations.
Big data: mainly drawn from a variety of sources such as citizen inputs and the private sector, big data is most often digital and continuously generated. It has the advantage of coming in greater volume and variety.
Evaluation data: this data is collected for the purpose of the evaluation. It can take the form of qualitative questionnaires, on-site observations, focus groups, or experimental data. See further down for a description of impact evaluation methods to collect and analyse data.
Combining different data sources also has the potential to unlock relevant insights for policy evaluation.
Applying big data analysis techniques to public procurement data can contribute to creating stronger, sounder and more relevant evaluations.
Source: based on (Results for All, 2017[22]), 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).
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.
Management of data is still largely determined by the Official Statistics Programme
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.
Recent advancements in the field of open data need to be pursued
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).
Source: Adapted from OECD (2020[26]), OECD Open, Useful and Re-usable data (OURdata) Index: 2019, http://www.oecd.org/gov/digital-government/policy-paper-ourdata-index-2019.htm.
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.
In 2020, the Information Society Development Committee (IVPK), which is an agency situated under the Ministry of economy and innovation, launched the Government Open Data Portal. As of May 2021, the portal contains 1236 accessible data sets gathered from 125 institutions (including municipalities). The data is categorised into 14 thematic areas (e.g. environment, culture, energy). The portal is user-friendly and easy to navigate. The data is searchable based on the data type (CSV, XLXS, ArcGIS), owner institution, date of the release and the frequency of updates on the data. Most of the data sets in the portal come with the corresponding metadata. The launch of this portal also aims at helping institutions to plan their data opening and prepare the metadata correctly. On the portal, the users may also express their needs for additional public data sets to be released.
Source: Information Society Development Committee (n.d.[28]), Lithuanian Open Data Portal, https://data.gov.lt/ (accessed on on 11 May 2021)
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.
Similarly as in Lithuania, in Denmark personal data is stored in registries with personal identification numbers. Statistics Denmark facilitates the use of these micro-level databases for research purposes for approved analysts, universities, research organisations or ministries. Statistics Denmark possess data in 250 subject areas ranging from labour markets, consumption, demographics to transport, agriculture and environment. The data is prepared by the Research Service Division and is accessible remotely and securely through specific internet servers. Analysts can access data in these areas as far back as from the 1970s.
Source: Statistics Denmark (2014[29]), Data for Research, https://www.dst.dk/en/TilSalg/Forskningsservice# (accessed on 11 May 2021)
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).
Increased data quality usually requires a shared understanding of the data value cycle
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.
Source: Charlotte van Ooijen (2019[31]), A data-driven public sector: Enabling the strategic use of data for productive, inclusive and trustworthy governance; OECD (2019[25]), The Path to Becoming a Data-Driven Public Sector, https://doi.org/10.1787/059814a7-en.
To adopt a shared understanding of the data value cycle, the Lithuanian government could consider adopting a data governance framework – as detailed further below.
Use of data for analysis poses technical and ethical challenges
Lithuania does have a central registry, thus making the use and merging of data for analysis possible
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.
Yet, proper use of data remains a challenge for ethical reasons
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.
A government-wide data governance strategy is needed to support evidence-informed decision making
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.
First steps to consolidate and reform the data governance system have recently been taken. The protocol of the government meeting of the 27th of May, 2020 mandates Statistics Lithuania and the Ministry of Economy and Innovation to create an integrated data governance Information System (IS) that would combine the data managed separately by policy areas. Statistics Lithuania is mandated to be the governor of this IS. The new data governance system aims at combining different data sources and standardising data governance. In this system, the Information Society Development Committee is given a role to manage the platform and STRATA a role to conduct analysis. The system aims at increasing the access and the ability to merge the unstructured data outside the OSP. In fact, the new State Data Governance Information System (VDV IS) was partially launched in November 2020.
Source: Minutes of the Lithuanian Government meeting of May 27, 2020.
Develop skills for analysis in the Lithuanian public sector through a systematic approach:
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.
Strengthen the existing analytical capacities in ministries and agencies by:
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.
References
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Notes
← 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.