Annex I. Minimum Wage Arrangements

Table A1.1.

Minimum Wage Arrangements in CESEE

article image
Sources: Eurostat; and national authorities.
Table A1.2

Minimum Wage Arrangements in Selected Western European Countries

article image
Sources: National authorities; and IMF staff.

Annex II. Methodology: Minimum Wages and General Wage Growth

1. Previous studies of the pass-through effect of the minimum wage on the average wage are mostly static. In contrast, this study adds a time dimension, allowing wage adjustments to gradually take full effect. Therefore, one can characterize the impact of the minimum wage change on average wage over time and identify whether the minimum wage shock would have temporary or persistent effects on the average wage. Nevertheless, longitudinal data are generally limited, especially for CESEE economies. In this case, traditional VAR estimates are not feasible and this study therefore employs the new panel VAR technique instead.

2. To identify the wage pass-through at the regional level, panel VARs are estimated to construct the average pass-through effects across 14 CESEE countries.

yit=A0i(t)+Ai(l)Yt1+uit,i=1,2,, N; and t=1,2,, T.

Yt is the stacked version of yit, which is the vector of changes in real average wages, employment growth, real labor productivity growth, changes in the terms of trade, and changes in real minimum wages for each country i=1, 2, …, N. The choice of variables follows Blanchard and Katz (1999) and Goretti (2008). All variables are in real terms using the consumer price index as deflator. Data are quarterly from 1995q1 to 2015q2. The panel is unbalanced. Lags included are chosen to minimize the information criterion statistics. The system is estimated using the GMM method.

Annex III. Firm Level Analysis Using the Orbis Database

1. The firm level analysis in this paper utilizes the Orbis database by Bureau van Dijk, which compiles data on public and private enterprises globally. The strength of the database is that company data are reported in standardized format within and across countries, and that non-listed companies are also covered. But there are also drawbacks. Coverage is uneven across countries and despite the large number of firms included, not all variables of interest are available uniformly.

2. The regression analysis involves about half a million observations related to 200,000 firms. This covers a sizable portion of national employment as reported in Table A3.1. Although the vast majority of firms is small or medium sized, large firms dominate in terms of the share of employment. In the analysis, micro, small, medium, and large firms are defined as those having 1-10, 10-50, 50-250, and above 250 employees, respectively. This is similar to the European Commission’s categorization, although the asset component of the definition is dropped to retain more observations. Following the literature, the tradable sector is taken to comprise agriculture, manufacturing, transportation and storage, IT and communication, and professional services.

3. The full regression results are reported in Tables A3.2 and A3.3.

Table A3.1.

Coverage of Firms in the Orbis Database, 2013

article image
Sources: Orbis database and IMF staff calculations.
Table A3.2.

Determinants of Value-Added Exports in CESEE, 2000-13

article image
*** p<0.01, ** p<0.05, * p<0.1, Errors are robust to country clustering Regression results of OLS regression of exports in value added.
Table A3.3.

Impact of Minimum Wage on Firms’ Employment, Wages, Productivity, and Profits, 2009-13

article image
Source: IMF staff calculations using annual firm level data in the ORBIS database for 11 CESEE countries.*** is significant at 1 percent; ** at 5 percent; * at 1 percent.Note. Δ minimum wage, Δ Employment, and Δ Wage per employee are in percent. Other dependent variables are simple differences. Productivity (1) is defined as ratio of operating revenue to employment. Productivity (2) is defined as ratio of remuneration plus profits to employment and resembles gross value added to employee. Profit margin is EBITDA to operating revenue. Small indicates firms with fewer than 50 employees. The model is estimated using an Arellano-Bond dynamic two-step panel data estimator with robust standard errors. Lagged dependent and independent variables were

Annex IV. Methodology: Minimum Wages and Employment

1. This study estimates the effects of minimum wages (MW) on employment by means of pooled regressions across countries and time of the form:

Eit=a+bwit+dXit+fi+gt+hit,

where i indexes countries; t indexes years during the period 2000-14; E is the employment rate of young workers (less than 25 years); w is the minimum-to-average wage ratio (MW/AW); and X is a vector of control variables, including adult (over 25years) unemployment and/or GDP growth to capture cyclical conditions, and the relative size of the youth population to control for supply factors. f are country-fixed effects; and g are time-fixed effects. This approach follows OECD (1998) and Neumark and Wascher (2004), and is now common in the minimum wage literature.

2. The technical challenges associated with such regressions are well-known. The roughly similar economic structures in the selected countries, including MW setting mechanisms (Table A1.1), help justify the use of pooled regressions, and the wide variations in MW increases and cyclical conditions across countries described above should help in the identification process. Nevertheless, the estimation is prone to the omission of potential factors that affect employment, especially considering the ongoing economic transformation in these countries. Also, while the use of MW/AW as the key indicator has been standard in the literature, partly to mitigate endogeneity problems, it raises the possibility that the estimated effects on employment reflect changes in the AW rather than the MW. This study attempts to alleviate such concerns by testing different specifications and controls, including labor market institutional indicators, although data availability and the small sample sizes limit such testing.

3. To allow for the possibility of a threshold above which the MW affects employment, equations of the form are also estimated

Eit=a+bmax(witw0, 0)+dXit+fi+gt+hitorEit=a+bwit+cdummywit+dXit+fi+gt+hit,

where w0 in the first equation represents a tipping point such that there is no impact if the ratio is below that threshold. dummy in the second equation is a dummy variable for countries with MW/AW above a fixed threshold. Identifying a tipping point, if it exists, is likely to be difficult from the sample at hand, as MW/AW has stayed within a narrow range and was mostly relatively low.

4. However, a more plausible hypothesis than a threshold is that the MW has increasingly larger effects on employment as it becomes more and more binding. But more substantively, a common tipping point across countries, or even within a single country, presumes that low-income earners have roughly the same relative marginal productivity of labor (MPL), or alternatively that firms employing low-income earners somehow have the same threshold beyond which they begin to no longer employ such workers. Thus, as an alternative, a regression with a quadratic term of the minimum wage added is estimated

Eit=a+bwit+c(wit)2+dXit+fi+gt+hit.

5. The same equations are also estimated with the MW-to-labor productivity (LP) ratio instead of MW/AW. Conceptually, the MW/LP ratio is a more direct measure of the distortionary effects of MW increases than MW/AW. But it could raise identification problems in view of the direct correlation between LP (defined as nominal GDP divided by total employment) and youth employment, although this is mitigated by controlling for GDP growth.

6. In Table A4.1 the estimated MW impact on employment under the baseline specification appears to be small negative but statistically insignificant. The regression in column 1 exhibits a significant negative coefficient on the MW ratio, implying an elasticity of almost -0.3. However, this equation suffers from serial correlation (as apparent from the Durbin-Watson statistic). The inclusion of a lagged dependent variable as an explanatory variable (column 2) lowers the significance of the coefficient—it becomes statistically significant at the 15 percent level—implying an elasticity of almost -0.1. The addition of lagged GDP growth (column 3) further alleviates serial correlation and reduces the significance of the MW coefficient, implying an elasticity of almost -0.05. For comparison, similar regressions are run with the adult employment rate as the dependent variable (lower panel of the Table A4.1). The implied elasticities are now clearly insignificant and in fact positive.

7. Estimation of the equation with squared MW/AW (Table A4.1, column 4) has the expected negative sign on the squared term, suggesting that the impact of a MW increase on youth employment is stronger when the initial MW/AW level is higher. Table A4.2 illustrates the impact of different MW increases on youth employment at different levels of MW/AW under the linear and quadratic specifications (Table 1, columns 3 and 4).1 According to the latter model, a 5 percent increase of the MW when the MW/AW level is at 45 percent would reduce youth employment by about 1 percent, a substantial loss, whereas the impact is less than half a percent when the MW/AW level is at 35 percent. However these results should be viewed with caution as the coefficients on the MW and the squared term are statistically jointly insignificant.

Table A4.1.

Employment Rates and Minimum Wages Relative to Average Wages

article image
*, **, and *** indicate significance at the 10, 5, and 1 percent respectively.
Table A4.2.

Impact of Minimum Wage Hikes on Youth Employment, in Percent

article image
Source: IMF staff calculations.

Underlying regressions use minimum wages (MW) relative to average wages (AW) as explanatory variable.

Derived from regression results shown in Table A4.1 column 3.

Derived from regression results shown in Table A4.1 column 4.

8. The estimated MW impact on youth employment using the MW/LP ratio also appears to be modestly negative, but in contrast to the MW/AW specification the results are robust and statistically significant. Table A4.3 shows the results for the same estimations as above with MW/LP substituted for MW/AW. Encouragingly the results are very similar to the previous ones, with the key difference that the coefficient on the MW/LP ratio now remains statistically significant when additional controls are introduced. Adding the MW/AW ratio as an explanatory variable yields an insignificant coefficient and does not materially alter the employment elasticity (column 4), suggesting that the MW/LP ratio is a better indicator of the MW impact on employment. As pointed out earlier, the estimated effects on employment could reflect the correlation between LP and youth employment irrespective of changes in the MW. However, running the regression with real labor productivity or real GDP added as separate explanatory variables, or with real minimum wage and real labor productivity used as separate explanatory variables in lieu of the MW/LP ratio, does not alter the results (while they worsen statistical properties).2

9. Estimation of the equation with squared MW/LP (column 5) confirms the earlier results. Furthermore, while the coefficients on the MW/LP and the squared term are individually statistically insignificant, they are nonetheless jointly significant at the 5 percent confidence level.3

Table A4.3.

Employment and Minimum Wages Relative to Labor Productivity1/

article image
*, **, and *** indicate significance at the 10, 5, and 1 percent respectively.

The minimum wage ratio is defined as the gross monthly minimum wage divided by annual output over by annual employment.

Annex V. Methodology: Wage and Income Distribution in Romania

1. The EU-SILC is the EU reference source for comparative statistics on income distribution and social exclusion at the European level. Romania’s EU-SILC data are provided through the National Institute of Statistics of Romania (INSSE). This study focuses on the developments of the distribution of wages or gross employment income of persons in paid employment.1 The data are both cross-sectional and longitudinal, complied annually from 2007 to 2014. In 2014, for example, there were 7,508 households or 15,661 persons interviewed in the survey. Of those, 2,499 persons were unemployed and 13,162 persons were either employees or self-employed. This study considers only those employed on payrolls—6,836 persons accounting for about 43.6 percent of the sample in 2014. The wage distributional data for 2015 and 2016 are projected to capture the impacts of sharp minimum wage hikes in recent years. Specifically, sub-minimum wage workers are assumed to receive the wage hikes at the growth rate of the minimum wage, workers at minimum wage would immediately be paid at the new minimum wage, and workers above minimum wage would receive a raise as suggested by the estimated wage pass-through.2

2. Table A5 reports the results in detail.

Table A5.

Romania: Wage and Income Distribution

article image
Sources: EU-SILC, INSSE, and IMF Staff Calculations.

References

  • Abowd, J., F. Kramarz, T. Lemieux, and D. Margolis, 2009, Minimum Wages and Youth Employment in France and the United States, in: Blanchflower, D. and R. Freeman (eds.), Youth Employment and Joblessness in Advanced Countries.

    • Search Google Scholar
    • Export Citation
  • Andreica, M., L. Aparaschivei, A. Cristescu, and N. Cataniciu, 2010, Models of the Minimum Wage Impact upon Employment, Wages and Prices: the Romanian Case, Recent Advances in Mathematics and Computers in Business, Economics, Biology and Chemistry, Proceedings of the 11th WSEAS Int. Conf. MCBE 2010.

    • Search Google Scholar
    • Export Citation
  • Ashenfelter, O. and R. Smith, 1979, Compliance with the Minimum Wage Law, The Journal of Political Economy.

  • Autor, D., A. Manning, and C. Smith, 2014, The Contribution of the Minimum Wage to U.S. Wage Inequality over Three Decades: A Reassessment, NBER Working Paper No. 16533.

    • Search Google Scholar
    • Export Citation
  • Baanante, M., 2005, Minimum Wage Effects under Endogenous Compliance: Evidence from Peru, Económica, Vol. 50.

  • Bank of Estonia, 2015, Labor Market Review, 2/2015, http://www.eestipank.ee/en/publication/labour-market-review/2015/labour-market-review-22015.

    • Search Google Scholar
    • Export Citation
  • Bank of Lithuania, 2015, The Effect of Minimum Wage Increase on Macroeconomic Variables: Survey Results of Lithuanian Enterprises, Lithuania Economic Review, December 2015, https://www.lb.lt/lithuanian_economic_review.

    • Search Google Scholar
    • Export Citation
  • Baranowska-Rataj, A. and I. Magda, 2015, The Impact of Minimum Wages On job Separations and Hours of Work Among Young People in Poland, Institute of Statistics and Demography of the Warsaw School of Economics, Working Paper No. 45.

    • Search Google Scholar
    • Export Citation
  • Bell, L., 1997, The Impact of Minimum Wages in Mexico and Colombia, Journal of Labor Economics, 15(S3).

  • Bernhardt, A., R. Milkman, D. Theodore, M. Heckathorn, M. Auer, J. DeFilippis, and M. Spiller, 2010, Broken Laws, Unprotected Workers, National Employment Law Project, New York: NELP.

    • Search Google Scholar
    • Export Citation
  • Betcherman, G., 2013, Labor Market Institutions: A Review of the Literature, Background Paper for the World Development Report 2013, http://siteresources.worldbank.org/EXTNWDR2013/Resources/8258024-1320950747192/8260293-1320956712276/8261091-1348683883703/WDR2013_bp_Labor_Market_Institutions.pdf.

    • Search Google Scholar
    • Export Citation
  • Blanchard, O. and L. Katz, 1999, Wage Dynamics: Reconciling Theory and Evidence, American Economic Review, Vol. 89, Issue 2.

  • Broecke, S., A. Forti, and M. Vandeweyer, 2015, The Effect of Minimum Wages on Employment in Emerging Economies: A Literature Review, http://nationalminimumwage.co.za/wpcontent/uploads/2015/09/0221-Effect-of-Minimum-Wages-on-Employment-in-Emerging-Economies-A-Literature-Review.pdf.

    • Search Google Scholar
    • Export Citation
  • Brown, C., C. Gilroy, and A. Kohen, 1982, The Effect of the Minimum Wage on Employment and Unemployment, Journal of Economic Literature, 20(2).

    • Search Google Scholar
    • Export Citation
  • Card, D. and A. Krueger, 1994, The Effect of the Minimum Wage on Shareholder Wealth, Princeton University, Department of Economics, Industrial Relations Section, Working Paper No. 337.

    • Search Google Scholar
    • Export Citation
  • Card, D. and A. Krueger, 2015, Myth and Measurement, Princeton University Press.

  • Council of Europe, 1996, European Social Charter (revised), https://rm.coe.int/CoERMPublicCommonSearchServices/DisplayDCTMContent?documentId=090000168007cf93.

    • Search Google Scholar
    • Export Citation
  • Council of Europe, 2008, Digest of the Case Law of the European Committee of Social Rights, http://www.coe.int/en/web/turin-european-social-charter/case-law.

    • Search Google Scholar
    • Export Citation
  • Dickens, R. and A. Manning, 2004, Spikes and Spillovers: the Impacts of the National Minimum Wage on the Wage Distribution in a Low-Wage Sector, The Economic Journal, 114 (March).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doucouliagos, H. and T. Stanley, 2009, Publication Selection Bias in Minimum-Wage Research? A Meta-Regression Analysis, British Journal of Industrial Relations, Vol. 47, No. 2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draca, M., S. Machin, and J. Van Reenen, 2011, Minimum Wages and Firm Profitability, American Economic Journal: Applied Economics, 3.

  • Eriksson, T. and M. Pytlikova, 2004, Firm-level Consequences of Large Minimum-wage Increases in the Czech and Slovak Republics, Labour 18(1).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • European Trade Union Confederation, 2015, Discussion Note on Minimum Wages in Europe, Warsaw Seminar of the ETUC Collective Bargaining Committee, http://www.ftf.dk/fileadmin/Billedbase/Ledelse/DISCUSSION_NOTE_WARSAW_CONFERENCE_final_EN.pdf.

    • Search Google Scholar
    • Export Citation
  • Fadejeva, L. and O. Krasnopjorovs, 2015, Labour Market Adjustment During 2008-2013 in Latvia: Firm level Evidence, Bank of Latvia Working Paper No. 2/2015.

    • Search Google Scholar
    • Export Citation
  • Fialová, K. and M. Mysíková, 2009, Minimum Wage: Labour Market Consequences in the Czech Republic, Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Working Paper: 6/2009.

    • Search Google Scholar
    • Export Citation
  • G20, 2012, Boosting Jobs and Living Standards in G20 Countries, A Joint Report by the ILO, OECD, IMF, and the World Bank, June 2012, http://www.ilo.org/global/publications/books/WCMS_183705/lang—en/index.htm.

    • Search Google Scholar
    • Export Citation
  • Giotis, G. and M. Chletsos, 2015, Is There Publication Selection Bias in Minimum Wage Research During the Five-year Period from 2010 to 2014? Economics, Discussion Paper No. 2015-58.

    • Search Google Scholar
    • Export Citation
  • Goretti, M., 2008, Wage-Price Setting in New EU Member States, IMF Working Paper WP/08/243, https://www.imf.org/external/pubs/ft/wp/2008/wp08243.pdf.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harasztosi, P. and A. Lindner, 2015, Who Pays for the Minimum Wage? http://faculty.chicagobooth.edu/workshops/micro/pdf/LindnerJMP.pdf.

  • Harrison, A. and J. Scorse, 2004, Moving Up or Moving Out? Anti-Sweatshop Activists and Labor Market Outcomes, No. w10492, National Bureau of Economic Research.

    • Search Google Scholar
    • Export Citation
  • Hazans, M., 2005, Looking for the Workforce: the Elderly, Discouraged Workers, Minorities, and Students in the Baltic Labour Markets, 45th Congress of the European Regional Science Association 23-27 August 2005, Amsterdam.

    • Search Google Scholar
    • Export Citation
  • Hinnosar, M. and T. Rõõm, 2003, The Impact of Minimum Wage on the Labour Market in Estonia: An Empirical Analysis, Eesti Bank Working Papers of No. 8.

    • Search Google Scholar
    • Export Citation
  • Igan, D., 2014, More Jobs That Pay Decent Wages: How To Fight Poverty in The United States, iMFdirect blog, August 2014, https://blog-imfdirect.imf.org/2014/08/28/more-jobs-that-pay-decentwages-how-to-fight-poverty-in-the-united-states/.

    • Search Google Scholar
    • Export Citation
  • IMF, 2011, Colombia—Staff Report for the 2011 Article IV Consultation, IMF Country Report 11/224, https://www.imf.org/external/pubs/cat/longres.aspx?sk=25113.0.

    • Search Google Scholar
    • Export Citation
  • IMF, 2013, France—Staff Report for the 2013 Article IV Consultation, IMF Country Report 13/251, https://www.imf.org/external/pubs/cat/longres.aspx?sk=40854.0.

    • Search Google Scholar
    • Export Citation
  • IMF, 2014a, It Takes Two to Tango: Wages and Productivity in Lithuania, IMF Country Report No. 15/139, https://www.imf.org/external/pubs/ft/scr/2015/cr15139.pdf.

    • Search Google Scholar
    • Export Citation
  • IMF, 2014b, Youth Unemployment in Advanced Economies in Europe: Searching for Solutions, IMF Staff Discussion Note, SDN/14/11, https://www.imf.org/external/pubs/ft/sdn/2014/sdn1411.pdf

    • Search Google Scholar
    • Export Citation
  • IMF, 2014c, Germany—Staff Report for the 2014 Article IV Consultation, IMF Country Report No.14/216, https://www.imf.org/external/pubs/ft/scr/2014/cr14216.pdf.

    • Search Google Scholar
    • Export Citation
  • IMF, 2014d, United States—Staff Report for the 2014 Article IV Consultation, IMF Country Report No.14/221, https://www.imf.org/external/pubs/ft/scr/2014/cr14221.pdf.

    • Search Google Scholar
    • Export Citation
  • IMF, 2015, United States—Staff Report for the 2015 Article IV Consultation, IMF Country Report 15/168, https://www.imf.org/external/pubs/cat/longres.aspx?sk=43053.0.

    • Search Google Scholar
    • Export Citation
  • IMF, 2016, Inequality and Income Distribution in Lithuania International Comparison: Trends, Causes, and Politics, Selected Issues Paper for the 2016 Article IV Consultation with the Republic of Lithuania.

    • Search Google Scholar
    • Export Citation
  • Iordache, Ș., M. Militaru, and M. Pandioniu, 2016, Jobless Recovery in Romania: the Role of Sticky Wages and Other Frictions—Firm-level Evidence from the WDN Survey, National Bank of Romania Occasional Papers No. 20.

    • Search Google Scholar
    • Export Citation
  • Kertesi, G. and J. Köllõ, 2003, Fighting “Low Equilibria” by Doubling the Minimum Wage? Hungary’s Experiment, IZA DP (Bonn, IZA), No. 970.

    • Search Google Scholar
    • Export Citation
  • Lemos, S., 2004, Minimum Wage Policy and Employment Effects: Evidence from Brazil, Economia, 5 (1).

  • Lemos, S., 2008, A Survey of the Effects of the Minimum Wage on Prices, Journal of Economic Surveys, 22(1).

  • Low Pay Commission, 2014, National Minimum Wage: Low Pay Commission Report, https://www.gov.uk/government/publications/national-minimum-wage-low-pay-commission-report-2014.

    • Search Google Scholar
    • Export Citation
  • Maloney, W. and J. Mendez, 2004, Measuring the Impact of Minimum Wages—Evidence from Latin America, in: Heckman, J. and C. Pages (eds.), Law and Employment: Lessons from Latin American and the Caribbean, University of Chicago Press.

    • Search Google Scholar
    • Export Citation
  • Milkman, R., A. González, and V. Narro, 2010, Workplace Violations in Los Angeles—The Failure of Employment and Labor Law for Low-wage Workers, UCLA Institute for Research on Labor and Employment, http://www.irle.ucla.edu/publications/documents/LAwagetheft-Milkman-Narro-110.pdf.

    • Search Google Scholar
    • Export Citation
  • National Bank of Romania, 2015, The Increase in the Minimum Gross Wage—Effects on the Labour Market, Inflation Report, May 2015, http://www.bnr.ro/files/d/Pubs_en/InflationReport/2015/IR_201505.pdf.

    • Search Google Scholar
    • Export Citation
  • Neumark, D., 2015, Reducing Poverty via Minimum Wages—Alternatives, FRBSF Economic Letter, No. 2015-38, December 2015.

  • Neumark, D., J. Salas, and W. Wascher, 2013, Revisiting the Minimum Wage-Employment Debate: Throwing Out the Baby with the Bathwater? IZA DP No. 7166.

    • Search Google Scholar
    • Export Citation
  • Neumark, D., M. Schweitzer, and W. Wascher, 2005, The Effects of Minimum Wages on the Distribution of Family Incomes: A Non-Parametric Analysis, Journal of Human Resources, Vol. 40, No. 4 (Autumn 2005).

    • Search Google Scholar
    • Export Citation
  • Neumark, D. and W. Wascher, 2003, Minimum Wages, Labor Market Institutions, and Youth Employment: A Cross-National Analysis, Federal Reserve Finance and Economics Discussion Paper Series, No. 23.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • OECD, 1998, Making the Most of the Minimum: Statutory Minimum Wages, Employment and Poverty, in: OECD Employment Outlook, Paris, OECD.

  • OECD, 2015a, OECD Employment Outlook, September 2015.

  • OECD, 2015b, Recent Labour Market Developments with a Focus on Minimum Wages, OECD Employment Outlook 2015, Chapter 1.

  • Putniņš, T. and A. Sauka, 2015, Measuring the Shadow Economy Using Company Managers, Journal of Comparative Economics, 43(2).

  • Rahman, J., A. Stepanyan., J. Yang, and L. Zeng, 2015, Exports in a Tariff-Free Environment: What Structural Reforms Matter? Evidence from the European Union Single Market, IMF Working Paper WP/15/187, https://www.imf.org/external/pubs/ft/wp/2015/wp15187.pdf.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rama, M., 2001, The Consequence of Doubling the Minimum Wage: the Case of Indonesia, Industrial and Labor Relations Review, Vol.54, No.4.

  • Riley, R. and C. Bondibene, 2015, The Impact of the National Minimum Wage on UK Businesses—Report to the Low Pay Commission.

  • Rizov, M. and R. Croucher, 2011, The Impact of the UK National Minimum Wage on Productivity by Low-paying Sectors and Firm-size Groups, Research Report for the Low Pay Commission.

    • Search Google Scholar
    • Export Citation
  • Rutkowski, J., 2003, The Minimum Wage: Cure or Curse?, Human Development Economics, Europe and Central Asia Region, The World Bank, http://siteresources.worldbank.org/INTECONEVAL/Resources/MinimumWageNoteJul03v2.pdf.

    • Search Google Scholar
    • Export Citation
  • Schmitt, J., 2013, Why Does the Minimum Wage Have No Discernible Effect on Employment? Center for Economic and Policy Research.

  • Schnattinger, P., N. Jemec, M. Lozej, M. Vodopivec, and P. Mohorič Peterne, 2015, Results of the 2014 Wage Dynamics Network Survey in Slovenia, Bank of Slovenia, https://www.bsi.si/library/includes/datoteka.asp?DatotekaId=6139.

    • Search Google Scholar
    • Export Citation
  • Schneider, F., 2015, Size and Development of the Shadow Economy of 31 European and 5 Other OECD Countries from 2003 to 2015: Different Developments, http://www.econ.jku.at/members/Schneider/files/publications/2015/ShadEcEurope31.pdf.

    • Search Google Scholar
    • Export Citation
  • SEE Riga, 2015, Shadow Economy Index for the Baltic Countries, 2009-2014, The Center for Sustainable Business at SEE Riga, http://www.sseriga.edu/en/centres/csb/shadow-economy-index-for-baltics.

    • Search Google Scholar
    • Export Citation
  • The United States Congressional Budget Office, 2014, The Effects of a Minimum Wage Increase on Employment and Family Income, Publication No. 4856.

    • Search Google Scholar
    • Export Citation
  • Vaughan-Whitehead, D., 2010, The Minimum Wage Revisited in the Enlarged EU, Edward Elgar.

  • Wadsworth, J., 2010, Did the National Minimum Wage Affect UK Prices? Fiscal Studies, 31(1).

  • Williams, C., 2009, The Commonality of Envelope Wages in Eastern European Economies, Eastern European Economics, vol. 47(2).

  • Žukauskas, V., 2015, Shadow Economies in the Baltic Region, Lithuanian Free Market Institute, http://www.llri.lt/wp-content/uploads/2015/11/Shadow-Economies-in-a-Baltic-Sea-Region.pdf.

    • Search Google Scholar
    • Export Citation
1

The focus is on the experience in Latvia, Lithuania, Poland, and Romania, but Albania, Bosnia, Bulgaria, Croatia, the Czech Republic, Estonia, Hungary, FYR Macedonia, Montenegro, Serbia, Slovenia, Slovakia, and Turkey are also covered.

2

Putniņš and Sauka (2015) and Žukauskas (2015) estimate the shadow economy to be around 10-15 percent of official GDP in the Baltics, which is smaller than the estimates by Schneider (2015).

3

See for example Neumark, Salas, and Washer (2013) and Betcherman (2013) for contrasting reviews of the literature.

4

See for instance, Kertesi and Kollo (2003), Maloney and Mendez (2004) in the case of Columbia, and Abowd et al. (2009) for France. On the other hand, in a cross-country analysis, OECD (1998) finds a small statistically significant negative elasticity of employment with respect to the minimum wage, but no significant difference between countries with high and low minimum-to-average wage ratios.

5

See for instance Doucouliaghos and Stanley (2009) for a meta-analysis of 64 US studies.

6

See for example Broecke et al. (2015) for a review of the literature in ten major emerging economies.

7

The authors interpret the stronger effect in remote regions as evidence against the monopsony view taken by minimum wage proponents. Single employers are much more common in remote regions, which may give rise to monopsonistic labor demand. Consequently, minimum wage hikes should increase rather than decrease employment, but empirically the opposite is the case.

8

See also Vaughan-Whitehead (2010) for interesting case studies, and Eriksson and Pytlikova (2004) for a study on the Czech Republic and Slovakia.

9

IMF (2014b) provides an in-depth discussion of youth unemployment, including the role of minimum wages.

10

Equal to the coefficient (2.27) in Table A4.3 divided by twelve.

11

However, real effective exchange rate developments can be misleading because they do not properly account for gains in non-price competitiveness, which is an important aspect in catching-up economies such as those in CESEE. Thorough competitiveness assessments therefore require an eclectic approach and expert judgment, which are carried out in the context of the bilateral consultations with IMF member countries.

12

This is according to Council’s European Committee of Social Rights. The 60 percent number applies unless countries can demonstrate that less constitutes fair remuneration, in which case a 50 percent threshold still needs to be respected (European Council, 2008, p. 43). The 60 percent requirement is defined in terms of net pay. Because of progressive taxation, it corresponds to about 50 percent in terms of gross pay. Few of the Council’s 47 member countries comply with this requirement though (European Trade Union Confederation, 2015)

1

Note that even in the linear specification, a degree of non-linearity creeps in, as the effects are expressed in percentage terms while the equation is in levels.

2

Similarly estimation of the equation in logs rather than levels.

3

The F-statistic for both coefficients equal to 0 is 3.68, with a p-value of 0.03.

1

Paid employed persons refer to those employed persons, including employees, self-employed, and family workers, with gross employment income greater than zero. Gross employment income includes gross employee cash or near cash income for employees, and gross cash benefits or losses from self-employment for self-employed and family workers. Paid employed persons refer to those employed persons with income greater than zero.

2

The pass-through effect on gross wage for Romania is estimated at around 0.4 percent for a one percent increase of the minimum wage.