Does Learning Matter for Wages in Korea? International Comparison of Wage Returns to Adult Education and Training

Park, Yoonsoo Article Does Learning Matter for Wages in Korea? International Comparison of Wage Returns to Adult Education and Training KDI Journal of Economic Policy Provided in Cooperation with: Korea Development Institute (KDI), Sejong Suggested Citation: Park, Yoonsoo (2022) : Does Learning Matter for Wages in Korea? International Comparison of Wage Returns to Adult Education and Training, KDI Journal of Economic Policy, ISSN 2586-4130, Korea Development Institute (KDI), Sejong, Vol. 44, Iss. 2, pp. 29-44, https://doi.org/10.23895/KDIJEP.2022.44.2.29 This Version is available at: https://hdl.handle.net/10419/261165 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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International Comparison of Wage Returns to Adult Education and Training† By Y OONSOO P ARK * This study compares the wage equation in Korea to those in other countries, focusing on the wage returns to adult education and training (AET) participation. It is found that the wage compensation structure in Korea is associated mainly with job characteristics such as tenure and workplace size rather than with worker characteristics such as AET participation and cognitive abilities. It is also found that Korea’s AET participation is skewed toward non-job-related AET, relative to the situations in other countries. These findings imply that the link between a worker’s productivity and wage should be strengthened in order to incentivize workers to invest in AET relevant to the labor market. Key Word: Adult Education and Training, Lifelong Learning, Wage Education. Skills JEL Code: J24, J31, P46 I. Introduction n recent years, there has been growing interest in subsidizing adult education and training (henceforth AET) to facilitate individuals’ efforts to adapt to the rapid technological progress. For example, the French government has implemented what is termed the Compte Personnel de Formation (Individual Learning Account when translated into English) since 2015, where a certain amount to be spent on training expenses is deposited annually to all workers and to the unemployed. The Singapore government has also promoted their SkillsFuture Credit since 2016, which provides all citizens over the age of 25 with a learning voucher. According to data from the OECD (2019), similar programs, albeit on a smaller scale, are in place in a number of advanced economies, including the U.S., Germany, and Scotland in the U.K. * Assistant Professor, Department of Economics, Sookmyung Women’s University (E-mail: [email protected]) * Received: 2022. 3. 10 * Referee Process Started: 2022. 3. 26 * Referee Reports Completed: 2022. 4. 26 † This paper has been written by revising and supplementing Park (2021). I 30 KDI Journal of Economic Policy MAY 2022 The ongoing digital transformation by COVID-19 and the resulting labor market mobility are expected to reinforce the argument for subsidizing AET participation. Indeed, Korea’s AET legislation (the Lifelong Education Act and the Workers Vocational Competency Development Act) was amended in 2021 to allow the government to offer financial support for AET participation to all adult citizens. However, before considering the expansion of financial support, it is necessary to examine whether and the degree to which AET participation is compensated for in the labor market. Human capital theory predicts that the wage return to education and training is a major factor determining a worker’s participation in such programs. To the extent that AET participation is less valued in the labor market, expanding government support for it may result in subsidizing education and training that are less relevant to the labor market. This study estimates and compares the wage returns to AET participation in Korea relative to those in other countries. For the purpose, the study employs data from the OECD Survey of Adult Skills, designed to measure the cognitive skills of nationally representative groups 16 to 65 years old across countries, collecting various types of information about the respondents, including their education and training history and their labor market outcomes. This feature of the dataset allows the mitigation of the potential ability bias problem when estimating the wage returns to AET participation by directly controlling for the respondents' cognitive abilities as measured in the survey. Using the data, I find evidence that Korea’s true wage return to AET participation is likely negligible and that the wage compensation structure in Korea is primarily determined by job tenure and workplace size relative to those in other major countries such as the U.S., Japan, and Germany. This study contributes to the literature (e.g., Hanushek et al., 2015; Lee et al., 2015; Kim, 2019) on estimating wage equations by country with its use of data from the OECD Survey of Adult Skills. Although previous studies focused on estimating the wage returns to cognitive skills as measured in the survey, the present study mainly examines wage returns to AET participation, which has not been discussed in the literature. Additionally, this study employs a range of information pertaining to worker characteristics (e.g., type of employment contract, workplace size, and years of tenure) when estimating wage equations, unlike previous studies that focused exclusively on basic worker characteristics such as age, gender, years of schooling, and years of labor market experience. Estimating wage equations with extended worker characteristics enables a unique comparison of Korea’s wage compensation structure with those of other countries; such a comparison may have important policy implications but remains unreported thus far in the literature. The remainder of this paper proceeds as follows. Chapter II introduces the OECD Survey of Adult Skills and defines the samples and variables used in the analysis. Chapter III compares AET participation rates and wage returns to AET participation as well as the determinants of AET participation in Korea with those in other countries. Chapter IV summarizes the results and draws conclusions based on them. II. Data The data for this study are from the OECD Survey of Adult Skills, which is a VOL. 44 NO. 2 Does Learning Matter for Wage in Korea? 31 International Comparison of Wage Returns to Adult Education and Training cross-sectional survey of nationally representative samples of the 16-to-65-year-old population in 33 countries, including Korea. The survey was conducted in 24 countries, including Korea from August of 2011 to March of 2012, followed by an additional survey in nine countries from April of 2014 to March of 2015. In this study, all 33 countries are analyzed, but detailed regression analysis results are presented only for four major countries (Korea, the U.S., Japan, and Germany).1 Although the main objective of the OECD Survey of Adult Skills is to measure cognitive skills such as literacy, numeracy, and the computer-based problem-solving skills of the adult population,2 it also collects data on respondents' demographic backgrounds, educational attainment, job characteristics, and labor market outcomes.3 This allows valid estimates of the wage returns to AET participation after controlling for various characteristics that may affect wages, including a worker’s cognitive abilities, for a representative sample of each country. The sample for this study is restricted in the following way. Initially, a total of 208,620 individuals were observed in the OECD Survey of Adult Skills data. Among them, I dropped 28,383 individuals who were still in their first cycle of formal school education as of the survey date. In other words, I restricted the sample to the adult education/training population (or AET population) defined by the survey. In addition, I removed 1,378 individuals for whom the key variables of this study, AET participation status and corresponding job relevance, are missing. The resulting sample consists of 178,859 individuals. Table 1 presents descriptive statistics of the sample for this study. The main variable of interest is the AET participation status or whether the respondent participated in education or training within the last 12 months. The variable covers not only formal courses for the purpose of obtaining degrees or certificates but also informal courses such as open and distance education, on-the-job training, seminars and workshops, and other courses and private lessons. According to Table 1, approximately 44.7% of the respondents reported that they had participated in education and/or training within the last 12 months. For those who thus responded positively (i.e., that they had participated in education (or training) courses within the last 12 months), the survey inquired further as to whether the courses were jobrelated.4 Job relevance was assessed to determine whether the main content of the participated education and/or training is to improve one’s employability and/or job performance, not necessarily related to a specific job. Table 1 also shows that approximately 37.3% of the respondents reported that they had participated in jobrelated courses, while about 7.4% reported their participation in non-job-related education. 1The OECD Survey of Adult Skills is a biennial survey. The second round of the survey will begin in 2022. This study has a limitation in that it relied on data from the first round of the survey, which is the most recently available data but which may not accurately reflect the current state of the labor market in each country, including Korea. 2In that sense, the OECD Survey of Adult Skills can be understood as an extension of the OECD Program for International Student Assessment (PISA), which measures academic achievement in the areas of reading, math, and science of 15-year-olds in major countries. 3As of today, to the best of the author's knowledge, the OECD Survey of Adult Skills is the only data source that collects education history and labor market outcomes across countries in a consistent manner. 4The OECD Survey of Adult Skills only queries participants about the job-relevance of AET participation only in relation to the last act of participation among those reported by them. Due to this survey structure, job-related AET participation and non-job-related AET participation are mutually exclusive in the data used here. 32 KDI Journal of Economic Policy MAY 2022 TABLE 1—SUMMARY STATISTICS Variables (units) N Mean SD Adult education and training (yes=1) 178,859 0.447 0.497 Job-related AET 178,859 0.373 0.484 Non-job-related AET 178,859 0.074 0.262 Hourly wage (log) 101,513 3.851 2.095 Female (yes=1) 178,859 0.506 0.500 Age (years) 178,859 42.97 12.46 Schooling (years) 176,847 12.62 3.430 N umeracy score (10 percentile scores) 178,809 4.846 2.911 Tenure (years) 109,659 9.016 9.435 Permanent contract (yes=1) 107,465 0.624 0.484 Public sector (yes=1) 128,069 0.211 0.408 Workplace size (yes=1) 10 workers or less 108,987 0.247 0.431 11~50 workers 108,987 0.293 0.455 51~250 workers 108,987 0.237 0.425 251~1,000 workers 108,987 0.130 0.336 1,001 workers or more 108,987 0.094 0.292 Occupation (yes=1) Armed forces 126,409 0.005 0.071 Senior officials & managers 126,409 0.086 0.280 Professionals 126,409 0.186 0.389 Technicians & associate professionals 126,409 0.152 0.359 Clerks 126,409 0.092 0.289 Service workers & Sales workers 126,409 0.186 0.389 Skilled agricultural & fishery workers 126,409 0.020 0.141 Craft & related trades workers 126,409 0.116 0.320 Machine operators & assemblers 126,409 0.082 0.274 Elementary occupations 126,409 0.076 0.264 Industry (yes=1) Agriculture, forestry & fishing 126,034 0.027 0.163 Mining & quarrying 126,034 0.005 0.073 Manufacturing 126,034 0.158 0.365 Electricity, gas, & steam supply 126,034 0.007 0.086 Water, sewerage, & waste 126,034 0.007 0.082 Construction 126,034 0.075 0.263 Wholesale & retail trade 126,034 0.134 0.340 Transportation & storage 126,034 0.057 0.231 Accommodation & food service 126,034 0.047 0.212 Information & communication 126,034 0.035 0.184 Financial & insurance 126,034 0.034 0.181 Real estate 126,034 0.010 0.101 Professional, scientific & technical 126,034 0.048 0.213 Administrative & support service 126,034 0.045 0.208 Public administration & defense 126,034 0.066 0.249 Education 126,034 0.081 0.273 Health & social work 126,034 0.110 0.313 Arts, entertainment & recreation 126,034 0.017 0.130 Other service 126,034 0.028 0.165 Households as employers 126,034 0.007 0.083 Extraterritorial organizations & bodies 126,034 0.000 0.011 Note: 1) The units of each variable are indicated in parentheses, 2) All statistics are calculated using sampling weights. Source: Data from the OECD Survey of Adult Skills. VOL. 44 NO. 2 Does Learning Matter for Wage in Korea? 33 International Comparison of Wage Returns to Adult Education and Training The other variables used in this study include each respondent’s hourly wage (in natural log), gender, age, years of schooling, cognitive ability measure (numeracy score), years of current employer tenure, employment contract type (permanent or temporary), sector (public or private), workplace size (five categories), occupation (ten categories), and industry (21 categories). The numeracy score, measured by a test in the survey, was used as a proxy for a respondent's cognitive ability. This study sets the unit of the numeracy score to 10 percentile scores computed within the respondent’s own country. When estimating the wage returns to adult education and training, I further restricted the sample to 98,115 workers for whom hourly wages and all of the characteristics in Table 1 could be observed. Descriptive statistics for the restricted sample are presented in Table A1 in the appendix. III. Empirical Analysis A. Adult Education and Training (AET) Participation Rates Before estimating the wage returns to the AET participation, I begin by comparing the AET participation rates by country. Columns (1), (2), and (3) of Table 2 present the participation rates of all AET, job-related AET, non-job-related AET, respectively. Numbers in square brackets in each column indicate the ranking of a given country out of all 33 countries. Column (4) in Table 2 indicates the number of observations for each country. The countries in Table 2 are arranged in descending order of their AET participation rates in column (1). All statistics in Table 2 were computed using the sampling weights of the OECD Survey of Adult Skills. Column (1) in Table 2 shows that Anglo-Saxon and Scandinavian countries tend to have high AET participation rates. New Zealand (66.8%) has the highest AET participation rate among the 33 countries, followed by Denmark (66.1%) and Finland (65.9%). On the other hand, the AET participation rates in eastern and southern European countries are relatively low. Russia (19.9%) has the lowest rate, followed by Greece (20.5%), Turkey (22.8%), and Italy (24.3%). The AET participation rate of Korea is 50.0%, placing Korea 16th among the 33 countries, similar to the rate of Israel (50.4%) and Austria (48.8%). Comparing columns (2) and (3) of Table 2, it can be seen that Korea's AET participation tends to be biased toward non-job-related AET. In Korea, 38.0% of the Respondents reported that they had participated in job-related AET, ranking the country 21st out of the 33 countries. On the other hand, 12.0% reported that they had participated in non-job-related AET, second highest out of the 33 countries. To summarize the results in Table 2, AET participation of Korea, relative to the rates of other countries, tends to be skewed toward AET with low job relevance. Table A2 in the appendix shows replicated results relative to those in Table 2 for the restricted sample of 98,115 workers for which the wage equations are estimated in the following sub-section. The results in Table A2 also confirm that AET participation by Korean workers is skewed toward non-job-related AET. 34 KDI Journal of Economic Policy MAY 2022 TABLE 2—ADULT EDUCATION AND TRAINING (AET) PARTICIPATION RATE AET Job-related AET Non-job-related AET N New Zealand 0.668 [1] 0.574 [2] 0.094 [9] 5,266 Denmark 0.661 [2] 0.580 [1] 0.081 [15] 6,519 Finland 0.659 [3] 0.553 [4] 0.106 [8] 4,834 Sweden 0.653 [4] 0.525 [6] 0.129 [1] 3,878 Netherlands 0.643 [5] 0.529 [5] 0.114 [5] 4,449 Norway 0.638 [6] 0.560 [3] 0.078 [16] 4,198 United State 0.596 [7] 0.505 [7] 0.090 [11] 4,326 Canada 0.576 [8] 0.487 [9] 0.089 [13] 23,711 Singapore 0.566 [9] 0.478 [11] 0.088 [14] 4,560 England (UK) 0.556 [10] 0.489 [8] 0.066 [23] 4,706 Australia 0.550 [11] 0.484 [10] 0.065 [25] 6,815 Germany 0.531 [12] 0.457 [12] 0.074 [19] 4,611 Estonia 0.527 [13] 0.417 [15] 0.110 [6] 6,634 Ireland 0.505 [14] 0.430 [13] 0.074 [18] 5,414 Israel 0.504 [15] 0.388 [20] 0.116 [3] 4,444 Korea 0.500 [16] 0.380 [21] 0.120 [2] 5,783 Czech Republic 0.488 [17] 0.422 [14] 0.067 [22] 4,949 Austria 0.488 [18] 0.398 [17] 0.090 [12] 4,474 Northern Ireland (UK) 0.487 [19] 0.415 [16] 0.071 [20] 3,409 Belgium 0.482 [20] 0.390 [19] 0.092 [10] 4,316 Slovenia 0.481 [21] 0.365 [22] 0.116 [4] 4,623 Chile 0.471 [22] 0.394 [18] 0.077 [17] 4,481 Spain 0.462 [23] 0.353 [23] 0.109 [7] 5,332 Japan 0.419 [24] 0.352 [24] 0.068 [21] 4,646 Cyprus 0.376 [25] 0.316 [25] 0.060 [27] 3,964 France 0.358 [26] 0.316 [26] 0.042 [30] 6,167 Poland 0.351 [27] 0.287 [28] 0.064 [26] 6,361 Lithuania 0.334 [28] 0.274 [29] 0.059 [28] 4,626 Slovak Republic 0.328 [29] 0.292 [27] 0.036 [33] 4,955 Italy 0.243 [30] 0.201 [30] 0.042 [32] 4,254 Turkey 0.228 [31] 0.162 [32] 0.066 [24] 4,742 Greece 0.205 [32] 0.162 [31] 0.042 [29] 4,449 Russian Federation 0.199 [33] 0.157 [33] 0.042 [31] 2,963 Total 0.447 0.373 0.074 178,859 Note: 1) Countries are listed in descending order of the adult education and training (AET) participation rate, 2) Numbers in brackets denote the ranking of a given country’s AET participation rate among the 33 countries listed. Source: Data from the OECD Survey of Adult Skills. B. Estimating Wage Returns to the AET Participation In order to estimate the wage returns to AET participation across countries, I consider the following regression equation: (1) 01 ln( ) ic ic ic c ic wage AET X     where ln( ) ic wage indicates the natural logarithm of the hourly wage rate of worker i in country c , ic A ET is an indicator for whether worker i reported any VOL. 44 NO. 2 Does Learning Matter for Wage in Korea? 35 International Comparison of Wage Returns to Adult Education and Training participation in AET within the last 12 months,5 ic X denotes a vector of covariates of worker i, in this case gender, age, years of schooling, years of current employer tenure, a dummy for permanent-contract worker, numeracy scores in units of ten percentile scores within country c , a dummy for public-sector worker, a list of dummies for the size of the workplace (less than ten workers, 11~250 workers, 251~1000 workers, 1001 workers or more), a list of dummies for ten occupation categories, and a list of dummies for 21 industry categories. c  represent a list of dummies for each country c, or country fixed effects. Finally, ic  is an error term. 1  in equation (1) identifies the difference in log hourly wages between those who participated in AET and those who did not participate in AET within country c, controlling for the worker characteristics included in ic X . I estimate equation (1) with the ordinary least square (OLS) method, clustering standard errors at the country level. The estimation result of equation (1) is summarized in column (1) of Table 3. I found that AET participation is associated with a 7.0% increase in hourly wages, conditional on the country and the worker characteristics. Columns (2) to (5) of Table 3 show the estimation results of equation (1) for Korea and for the three major countries of the U.S., Japan, and Germany, respectively. The estimated wage return to AET participation is 11.4% in Korea, which is higher than those of the 33 countries (7.0%) higher than Germany (8.0%), and similar to that of Japan (11.3%). The estimated wage return to AET participation in the U.S. is statistically insignificant. Figure 1 shows the distribution of the 1  estimates in equation (1) across all 33 countries, including the four major countries analyzed in Table 3. Korea’s estimate (0.114) is denoted by the vertical line. It can be seen that the estimate for Korea is located in the upper part of the distribution. This suggests that Korea’s estimated wage return to AET participation tends to be larger than those of other countries. Although equation (1) controls for various worker characteristics, including a worker’s cognitive ability, there may be unobserved factors that affect both hourly wages and AET participation. This can lead to selection bias in 1  in equation (1). In other words, based on the estimation results in Table 3, it is difficult to distinguish whether AET participation increases hourly wages or whether highwage workers are more likely to participate in AET than low-wage workers. Considering the potential endogenous selection into AET participation, I estimate the following regression equation: (2) 01 2 ln( ) ic ic ic ic c ic wage AETJR AET X      where ic A ETJR is an indicator for whether worker i reported that he or she had participated in job-related AET within the last 12 months. All other variables and the parameters in equation (2) are defined as those in equation (1). Unlike equation (1), equation (2) includes ic A ETJR as an additional explanatory variable. With the inclusion of ic A ETJR , 2  in equation (2) identifies the difference in log hourly 5It should be noted that equation (1) ignores differences in AET intensity (e.g., duration), quality, or any other AET experience longer than 12 months ago. 36 KDI Journal of Economic Policy MAY 2022 TABLE 3—WAGE RETURNS TO AET PARTICIPATION Country (1) All (2) Korea (3) U.S. (4) Japan (5) Germany AET 0.070*** (0.012) 0.114*** (0.029) -0.013 (0.031) 0.113*** (0.026) 0.082*** (0.019) Female -0.125*** (0.016) -0.219*** (0.032) -0.075 (0.047) -0.252*** (0.030) -0.072*** (0.022) Age 0.005*** (0.001) 0.004** (0.002) 0.007*** (0.001) 0.003** (0.001) 0.004*** (0.001) Schooling 0.031*** (0.003) 0.032*** (0.006) 0.043*** (0.007) 0.015*** (0.005) 0.033*** (0.006) Tenure 0.009*** (0.001) 0.020*** (0.002) 0.008*** (0.002) 0.010*** (0.002) 0.010*** (0.001) Permanent 0.051*** (0.012) 0.098*** (0.027) 0.026 (0.027) 0.156*** (0.030) 0.216*** (0.037) Numeracy 0.023*** (0.002) 0.008 (0.005) 0.027*** (0.007) 0.022*** (0.005) 0.022*** (0.004) Public -0.073*** (0.018) -0.053 (0.040) -0.067 (0.042) 0.019 (0.057) 0.069** (0.029) 11~50 workers 0.071*** (0.018) -0.005 (0.034) 0.108*** (0.039) 0.061* (0.036) 0.059* (0.035) 51~250 0.121*** (0.013) 0.050 (0.040) 0.189*** (0.039) 0.123*** (0.035) 0.139*** (0.034) 251~1,000 0.198*** (0.026) 0.076 (0.048) 0.290*** (0.085) 0.216*** (0.040) 0.217*** (0.037) 1,001 or more 0.284*** (0.020) 0.256*** (0.048) 0.348*** (0.051) 0.282*** (0.065) 0.332*** (0.039) Occupation Y Y Y Y Y Industry Y Y Y Y Y Country Y N N N N Observations 98,155 2,961 2,249 3,127 3,081 R-squared 0.923 0.321 0.418 0.285 0.473 Note: 1) The dependent variable is the natural logarithm of hourly wage, 2) All statistics are calculated using sampling weights, 3) Robust standard errors are in parentheses, 4) In column (1), country fixed effects are additionally controlled and the standard errors are clustered at the country level. Source: Data from the OECD Survey of Adult Skills. FIGURE 1. DISTRIBUTION OF WAGE RETURNS TO AET PARTICIPATION ACROSS 33 COUNTRIES Note: The wage return estimate in Korea (0.114) is indicated by the vertical line. Source: Data from the OECD Survey of Adult Skills. VOL. 44 NO. 2 Does Learning Matter for Wage in Korea? 43 International Comparison of Wage Returns to Adult Education and Training TABLE A2—AET PARTICIPATION RATE FOR THE RESTRICTED SAMPLE AET Job-related AET Non-jon-related AET N Finland 0.777 [1] 0.687 [3] 0.090 [7] 3,120 New Zealand 0.767 [2] 0.699 [2] 0.069 [15] 3,129 Netherlands 0.764 [3] 0.671 [4] 0.093 [6] 2,849 Denmark 0.762 [4] 0.702 [1] 0.060 [24] 4,156 Sweden 0.740 [5] 0.628 [9] 0.113 [2] 2,706 England (UK) 0.727 [6] 0.670 [5] 0.057 [25] 2,406 Norway 0.723 [7] 0.658 [6] 0.065 [19] 2,679 United State 0.706 [8] 0.633 [8] 0.072 [13] 2,249 Australia 0.697 [9] 0.642 [7] 0.055 [26] 4,078 Northern Ireland (UK) 0.687 [10] 0.616 [10] 0.071 [14] 1,585 Canada 0.682 [11] 0.606 [11] 0.076 [12] 14,204 Singapore 0.660 [12] 0.581 [13] 0.079 [10] 3,085 Ireland 0.651 [13] 0.590 [12] 0.061 [22] 2,668 Estonia 0.641 [14] 0.537 [16] 0.104 [4] 3,755 Czech Republic 0.626 [15] 0.559 [14] 0.067 [18] 2,454 Israel 0.625 [16] 0.507 [19] 0.118 [1] 2,206 Korea 0.604 [17] 0.506 [20] 0.098 [5] 2,961 Germany 0.604 [18] 0.541 [15] 0.063 [21] 3,081 Spain 0.602 [19] 0.515 [18] 0.087 [9] 2,367 Slovenia 0.592 [20] 0.486 [23] 0.106 [3] 2,182 Austria 0.591 [21] 0.504 [21] 0.087 [8] 2,696 Chile 0.588 [22] 0.528 [17] 0.060 [23] 2,153 Belgium 0.577 [23] 0.501 [22] 0.077 [11] 2,610 Poland 0.507 [24] 0.439 [25] 0.067 [16] 3,114 Japan 0.496 [25] 0.443 [24] 0.053 [28] 3,127 Cyprus 0.485 [26] 0.439 [26] 0.046 [29] 2,071 Slovak Republic 0.468 [27] 0.430 [27] 0.038 [31] 2,429 France 0.458 [28] 0.428 [28] 0.030 [32] 3,524 Lithuania 0.441 [29] 0.377 [29] 0.063 [20] 2,648 Turkey 0.427 [30] 0.360 [30] 0.067 [17] 1,448 Greece 0.371 [31] 0.316 [31] 0.055 [27] 1,187 Italy 0.335 [32] 0.306 [32] 0.029 [33] 1,816 Russian Federation 0.270 [33] 0.230 [33] 0.040 [30] 1,412 Total 0.569 0.506 0.063 98,155 Note: 1) Countries are listed in descending order of the adult education and training (AET) participation rate, 2) Numbers in brackets denote the ranking of a given country’s AET participation rate among the 33 countries listed. 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