Acemoglu, D. and J. A. Robinson, 2014, Why Nations Fail: The Origins of Power, Prosperity, and Poverty, (Danver, MA: Crown Publishing Group).
Aghion, P., Boustan, L., Hoxby, C. and J. Vandenbussche, 2009, “The Causal Impact of Education on Economic Growth, Evidence from the United States,” Brookings Papers on Economic Activity, ed. by Romer, D., and Wolfers, J.
Bastagli, F., Coady, D. and Gupta, S., “Income Inequality and Fiscal Policy,” IMF Staff Discussion Note 12/08, (Washington: International Monetary Fund).
Eller, M., 2009, “Fiscal Position and Size of Automatic Stabilizers in the CESEE EU Member States-Implications for Discretionary Measures,” Focus on European Economic Integration, Issue 2, pp.78–84.
Hanusheck, E. A. and L. Woessmann, 2010, “The Economics of International Differences in Economic Achievements,” NBER Working Paper No. 15949 (Cambridge: National Bureau of Economic Research).
International Monetary Fund, 2015a, “Causes and Consequences of Income Inequality: A Global Perspective,” Staff Discussion Note 13/15.
International Monetary Fund, 2015b, “From Expenditure Consolidation to Expenditure Consolidation: Addressing Public Expenditure Pressures in Lithuania,” IMF Country Report No. 15/139, (Washington: International Monetary Fund).
International Monetary Fund, 2016, “Getting Minimum Wages Right in Central Eastern and Southeastern Europe,” Crosscountry Report on Minimum Wages, (Washington: International Monetary Fund).
International Monetary Fund, forthcoming, “Should They Stay or Should They Go? Economic Impact of Emigration on Eastern Europe,” IMF Staff Discussion Note.
Lusardi, A., Schneider, D., and P. Tufano, 2011, “Financially Fragile Households: Evidence and Implications,” Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 42, No. 1, pages 83-150.
Maldeikis, E., 1996, “Privatization in Lithuania: Expectations, Process and Consequences,” Centre for Economic Reform and Transformation.
Ostry, J.D., A. Berg and C. G. Tsangarides, 2014, “Redistribution, Inequality, and Growth,” IMF Staff Discussion Note 14/02 (Washington: International Monetary Fund).
Wardrip, K., 2011,”Public Transit’s Impact on Housing Costs: A Review of the Literature,” Center for Housing Policy-Insights from Housing Policy Research.
Prepared by Nicoletta Batini with research assistance from Nhu Nguyen.
See Bernstein (2014).
Education, health care, and stable and affordable housing lead to self-sufficiency, higher TFP, larger and more productive labor forces, and thus stronger economic growth and greater macroeconomic stability. For the relationship between education and growth, see, for example, Aghion (2009) and Hanusheck and Woessmann (2010). For the relationship between healthcare and growth see, among others, Lennock and Ehrenpreis (2003). For the relationship between stable housing and home ownership and growth see, for example, Wardrip (2011).
See Acemoglou and Robinson (2014).
In what follows Lithuania is compared to three main country groupings: the EU-28, the EU-15, which corresponds to Western Europe, and the CEE which comprises countries in Central and Eastern Europe that are part of the EU. The CEE group excludes Lithuania to avoid biasing the comparison up or down in the direction of Lithuania’s inequality characteristics.
Despite its high inequality, Lithuania’s scores very well in the UN’s Human Development Index (37 out of 188 countries) on 2014 data. When the value is discounted for inequality, the HDI falls due to inequality in the distribution of the HDI dimension indices, but by less than the average of the “Very High HDI” group. (Estonia and Latvia show smaller and larger losses due to inequality, respectively.) Finally, Lithuania scores relatively well on the Gender Inequality Index (‘GII’, which reflects gender-based inequalities in three dimensions-reproductive health, empowerment, and economic activity) displaying a value of 0.125, ranking 23rd out of 155 countries in the 2014 index.
The threshold is set at 60 percent of the national median equivalized disposable income after social transfers and is expressed in purchasing power standards (PPS) in order to take account of the differences in the cost of living across countries. In 2014, this threshold varied considerably among the EU Member States in 2013 from PPS 2,361 in Romania, PPS 3,540 in Bulgaria and PPS 3,868 in Latvia to a level between PPS 11,507 and PPS 12,542 in Finland, France, the Netherlands, Denmark, Germany, Belgium, Sweden, and Austria, with Luxembourg at the top at PPS 16,818.
Based on 2012 data in the European Commission’s Social Situation Monitor.
Together with Ireland, Spain, and Latvia, measured on an annual basis, showing on latest data (2012) a Gini of 0.42 versus an EU-28 average Gini of 0.35.
This, however, does not take account of the fact that some households do not have income from employment at all, as no one of working age is in work. The proportion of people, who live in work-poor households, was among the largest in Lithuania, after Ireland, Greece, Latvia, and Spain. Between 2008 and 2012, the share of those living in low work intensity households increased in most countries, and considerably so in countries like Lithuania, the other Baltic states (but also Spain, Ireland, and Greece) where the economic crisis brought about an important fall in employment rates.
While non-wage private sector income has increased substantially in the five years following the economic transition, the privatization process had mixed effects on wealth inequality in Lithuania. Land redistribution was quite successful after a sweeping land reform, bringing land use by owners from 3 to 85 percent). However, non-land capital redistribution, ensuing after three reform phases, left most of the population excluded because these people were mainly passive during the discussion of privatization of the enterprises (Maldeikis et al., 2012). While recent data on the distribution of net worth for Lithuania is scant, as a result of these legacies and of the general tendency of wealth concentration to be an order of magnitude larger than that of income (Piketty, 2014), inequality in wealth remains today potentially more extreme than in income, in line with findings for other countries (IMF, 2015a).
Evidence on the distribution of wealth between households is provided by two international studies on wealth inequality. The Davies et al. (2008) study assembles estimates clustered around the year 2000. The sources of these data are mostly household surveys, but there are three from wealth registers (Denmark, Sweden, and Switzerland) and two from estate multiplier estimates (France and the UK). The Luxembourg Wealth Study (LWS) is a data archive of household surveys, the goal of which is to harmonize wealth and income data in order to provide a definition of wealth that is comparable across countries. None of these studies comprises data for the Baltic States. However, over the past 5 years Credit Suisse has published a report on the world global wealth, which includes Lithuania alongside the other two Baltic States. Specifically, the reports focus on the distribution within and across nations of individual net worth defined as the marketable value of financial assets plus non-financial assets (principally housing and land) less debt.
The statistics reported here indicate the percentage change in the total population that moved up or down one decile in the income distribution of income (“transition of income within one year by decile”, EU-SILC by Eurostat). People can change their position on the income distribution scale over time, and can belong to different deciles or quintiles. This can be related also to how the financial situation of the other people living in the same country changes over time. The percentage of population moving up and down does not need to sum to zero in net terms as shifts in income of a few individuals can potentially affect the position of large portions of the remainder population in the distribution of income and vice versa.
The relative median income ratio is defined as the ratio of the median equivalized disposable income of people aged above 65 to the median equivalized disposable income of those aged below 65.
The relative median income ratio is defined here as the ratio of the median equivalized disposable income of a specific population group to the median equivalized disposable income of the total population.
Alongside Bulgaria, Spain, Italy, Lithuania, Hungary, the Netherlands, Poland, Portugal, Romania and the United Kingdom, the Former Yugoslav Republic of Macedonia and Serbia.
The table shows dynamic correlations between the Gini coefficient and the unemployment rate (or changes thereof) where the unemployment rate lags the inequality indicator three years. This accounts for the natural lag between income and unemployment, given savings and the impact of unemployment benefits on income—even if small—in a variable period after the loss of a job.
The empirical literature has documented a clear link between macroeconomic volatility and inequality (for example, Breen and Georgia-Panarales (2005) who, using a cross-section of developed and developing countries, find that greater output volatility, defined as the standard deviation of the rate of output growth, is associated with a higher Gini coefficient and income share of the top quintile.
The volatility of output is computed here as the standard deviation of yearly output growth over a 5-year rolling window.
The already agreed increase for mid-2016 will bring it to euro 380 per month, more than 40 percent higher than three years ago, corresponding to 52 percent of the average wage, and covering as many as 20 percent of all workers.