Measuring distance to the SDG targets – Lithuania
Lithuania has already achieved 23 of the 125 SDG targets for which comparable data are available and, based on most recent trends, is expected to meet 7 additional targets by 2030 (Figure 1). As virtually all OECD countries, Lithuania has already met (or is close to meeting) most targets related to securing basic needs and implementing the policy tools and frameworks mentioned in the 2030 Agenda (see details in Table 1). Lithuania is among the fastest growing OECD economies and displays good performance on many other targets, mainly in the Planet category. Yet, Lithuania remains further away from some targets relating to income poverty (Goal 1) and gender equality (Goal 5).
This country profile provides a high-level overview of some of Lithuania’s strengths and challenges in performance across the SDG targets. As such, it differs in nature from Voluntary National Reviews (VNRs) or other reporting processes. To ensure international comparability, this assessment draws on the global indicator framework and relies on data sourced from the SDG Global Database and OECD databases. VNRs typically use national indicators that reflect national circumstances and are more up-to-date (See section How to read this country profile that provides some methodological details on country profiles).
Source: All data is taken and adapted from (UNDESA, 2021[1]), SDG Global Database, https://unstats.un.org/sdgs/unsdg and (OECD, 2021[2]), OECD.Stat, https://stats.oecd.org/ (accessed on 29 October 2021).
Lithuania is among the fastest growing OECD economies. Despite a declining labour force, GDP more than doubled over the past 20 years (Target 8.1), far exceeding the OECD average. In addition, while labour productivity remains below the OECD average, it is growing fast; at 3.2% a year on average over the past 15 years, it is outpacing most other OECD countries. Yet, as a small open economy, Lithuania is highly exposed to the global economic shocks. In common with the other Baltic States and Poland, net migration has long been outward and skewed towards the young, contributing to an ageing and declining population, Lithuania has met Target 10.7 on policies facilitating safe, orderly and regular migration.
Lithuania is making progress in reducing environmental pressures. Protected areas cover 17% of the land area and 23% of the country’s exclusive economic zone in the sea (Targets 14.5 and 15.1). The country meets the Aichi target of 17% for protection of terrestrial areas and exceeds the 10% target for marine and coastal areas. In addition, more than 90% of terrestrial and freshwater biodiversity area that are considered to be key for biodiversity are already protected (Target 15.1) As in much of Europe, biodiversity outcomes are weak, although the conservation status of major species is better than in most OECD countries (Target 15.5). Beyond biodiversity, Lithuania has other strengths when it comes to environment related SDG targets. It has moved from landfilling almost all its waste to recycling and composting most of it in less than a decade (Targets 11.6 and 12.5). At the same time, energy supply from renewable sources has more than doubled over the past two decades. The shares of renewables in both electricity generation (73% in 2019) and energy consumption (33% in 2018) are now well above the OECD average (Target 7.2).
Lithuania reports high poverty rate and could do more to foster inclusion. Lithuania has one of the highest poverty rates in the OECD in terms of both relative-income and multidimensional poverty, and these rates have been increasing over the past 15 years (Targets 1.2 and 10.2). Such high levels of poverty are partly explained by the low level of redistribution (Target 10.4) and to shortcomings of the labour market. Average hourly earnings are only 60% of the OECD average (USD PPP 10 per hour in 2018) and the unemployment rate (8% in 2020) is above the OECD average (Target 8.5) while participation of adults in formal and non-formal education is 20 percentage points below the OECD average (Target 4.3). Beyond income, there is also scope to foster inclusion. Only 54% of the population believes that Lithuania is a good place to live for ethnic and racial minorities (Target 10.3) while tackling gender equality will also require further efforts. The gender gap in unpaid work, at 140 minutes per day, remains high in Lithuania (Target 5.4), around 10% more than the OECD average. Women are underrepresented in the executive positions in the economic sphere, as well as in the national and local parliaments (Targets 5.5 and 16 7). In 2021, women held only 28% of the seats in the Lithuanian parliament.
Health outcomes are comparatively weak and out-of-pocket payments for health care are high (Target 3.8). Distance to Target 3.4 on premature mortality is also large. Suicide mortality rate is around twice the OECD average and the risk of dying from non-communicable diseases (cardiovascular disease, cancer, diabetes or chronic respiratory disease) is well above the OECD average. Overweight and obesity are also a growing concern. Around one fifth of adults are obese (Target 2.2). Smoking rates have been decreasing, but almost 20% of adults still smoke daily (Target 3.a) and Lithuania reports a per capita consumption of alcohol above the OECD average (Target 3.5). In addition, air pollution (Target 3.9) weighs heavily on health outcomes.
Like in many other OECD countries, data availability remains a challenge when measuring distances to targets (see the Overview chapter for details). For Lithuania, available data on the level of the different indicators allow covering 125 of the 169 targets. As shown in Figure 2 below, indicator coverage is uneven across the 17 goals. While six goals (within the People, Planet and Prosperity categories) have most of their targets covered (the indicator coverage exceeds 80%), coverage is much lower for Goal 11 on cities with only half of its targets covered. Data gaps become starker when focusing on performance indicators, excluding those relating to contextual information. In this case, coverage exceeds 80% for two goals only, i.e. Goal 3 on health and Goal 4 on education. For seven goals, mostly related to the Planet category (Goals 13, 14 and 15) but also to gender inequality, cities, and partnerships (Goals 5, 11 and 17), data are lacking to monitor progress over time for more than two in three targets.
Note: The figure represents Lithuania's share of targets covered by at least one indicator for each SDG. Numbers from 1 to 17 stand for the goals: 1 No poverty, 2 Zero hunger, 3 Good health and well-being, 4 Quality education, 5 Gender equality, 6 Clean water and sanitation, 7 Affordable and clean energy, 8 Decent work and economic growth, 9 Industry, innovation and infrastructure, 10 Reduced inequalities, 11 Sustainable cities and communities, 12 Responsible consumption and production, 13 Climate action, 14 Life below water, 15 Life on land, 16 Peace, justice and strong institutions and 17 Partnerships for the goals. These goals are grouped under five broad themes (the “5Ps”): People, Planet, Prosperity, Peace and Partnership. Beyond data availability, other statistical gaps such as timeliness or granularity also need to be considered. For instance, given the lag in available data, the effects of the pandemic on current distance and trajectories may not be reflected in the estimates.
Source: All data is taken and adapted from (UNDESA, 2021[1]), SDG Global Database, https://unstats.un.org/sdgs/unsdg and (OECD, 2021[2]), OECD.Stat, https://stats.oecd.org/ (accessed on 29 October 2021).
While some SDG Targets are, on average, close to being met, performance is very uneven across the 17 Goals of the 2030 Agenda for Sustainable Development. Table 1 presents an overview of Lithuania’s progress towards targets based on available data for each of the 17 Goals. It shows that distances to Targets and trends over time differ significantly even when considering a specific goal.
The OECD report The Short and Winding Road to 2030: Measuring Distance to the SDG Targets evaluates the distance that OECD countries need to travel to meet SDG targets for which data are currently available. It also looks at whether countries have been moving towards or away from these targets, and how likely they are to meet their commitments by 2030, based on an analysis of recent trends and the observed volatility in the different indicators.
As most authors and international organisations, this report adopts a rather simple geometric growth model for assessing the direction and pace of recent changes in the context of the SDGs. Yet, instead of making direct estimates of the value of the indicator by 2030, it models the likelihood of achieving a specific level using Monte Carlo simulations.
While the report provides an overview of where OECD countries, taken as a whole, currently stand, country profiles provide details of the performance and data availability of individual OECD countries.
How to read the OECD SDG Wheel?
Progress on SDGs requires a granular understanding of countries’ strengths and weaknesses based on the consideration of the 169 targets of the 2030 Agenda. Figure 1 shows both current achievements (in the inner circle; the longer the bar, the smaller the distance remaining to be travelled) as well as whether OECD countries are on track (or are at least making progress) to meet their commitments by 2030 (in the outer circle).
The length of each bar shows current level of achievement on each target. As detailed in the Methodological Annex, countries’ distance to target is measured as the “standardised difference” between a country’s current position and the target end-value. For each indicator, the standardised measurement unit (s.u.) is the standard deviation observed among OECD countries in the reference year (i.e. the year closest to 2015). Therefore, the longer the bar, the shorter the distance still to be travelled to reach the target by 2030. The colours of the bars applied to the various targets refer to the goals they pertain to.
The outer ring shows how OECD countries are performing over time and how likely they are to meet the different targets by 2030 based on the observed trends of the various indicators. It uses stoplight colours to classify the progress towards the target:
green is used to indicate those countries that (based on the change in the different indicators over a recent period) should meet the target in 2030 just by maintaining their current pace of progress (i.e. more than 75% of (randomised) projections meet the target);
yellow for those countries whose current pace of progress is insufficient to meet the target by 2030 (i.e. less than 75% of randomised projections meet the target, while the correlation coefficient between the indicator and the year is high and statistically significant, implying that a significant trend could be detected); and
red for those countries whose recent changes have been stagnating or moving them further away from the target (i.e. less than 75% of randomised projections meet the target and the correlation coefficient between the indicator and the year is low or statistically insignificant, implying that no statistical trend could be identified).
How to read the Strength and Challenges sections?
With the aim of helping its member countries in navigating the 2030 Agenda and in setting their own priorities for action, this report relies on a unique methodology for measuring the distance that OECD countries have to travel to achieve SDG targets. The identification of the main strengths and challenges proposed in this report relies on current performances only:
A target is considered to be a strength when the distance to the target end-value is lower than 0.5 s.u. (i.e. the distance is deemed to be small) or when the country is closer to the target than the OECD average. For instance, while Korea's distance to Target 2.2 on malnutrition is 1.4 s.u. (i.e. classified as medium distance), the average OECD distance is 2.5 s.u. Therefore, Target 2.2 is categorised as being a strength for Korea.
A target is considered to be a challenge when the distance to target is greater than 1.5 s.u. (i.e. distance is deemed to be long) or when the country is further away from the target than the OECD average. For instance, Estonia's distance to Target 4.2 on pre-primary education is 1.1 s.u. (i.e. medium distance), which is higher than the 0.24 s.u. distance for the OECD average. Target 4.2 is therefore classified as a weakness for Estonia.
While the lack of consistent time series often prevents an exhaustive assessment of trends, they are discussed when available and relevant in nuancing the assessment of current performance.
How to read the Statistical Gaps section?
In total, this report relies on 537 data series supporting 183 of the 247 indicators listed in the global indicator framework (or for close proxies of these indicators). These indicators cover 134 of the 169 SDG targets. Yet, target coverage is uneven across the 17 goals and among OECD member countries.
Figure 2 summarises data availability:
darker blue bars indicate the share of targets for which at least one indicator (including indicators providing context information) is available
lighter blue bars indicate the share of targets for which the available indicator(s) include those having a clear normative direction (i.e. allowing to distinguish between good and bad performance), which are the only ones used to measure distances to target levels.
medium blue bars indicate the share of targets for which progress over time can be gauged (i.e. at least three observations are available over a five-year period).
All methods and concepts are further detailed in the Methodological Annex.