Skip to main content

Advertisement

Log in

Does One Law Fit All? Cross-Country Evidence on Okun’s Law

Open Economies Review Aims and scope Submit manuscript

Abstract

This paper compares the performance of Okun’s Law in advanced and developing economies. On average, the Okun coefficient—which measures the short-run responsiveness of labor markets to output fluctuations—is about half as large in developing as in advanced countries. However, there is considerably heterogeneity across countries, with Okun’s Law fitting quite well for a number of developing countries. We have limited success in explaining the reasons for this heterogeneity. The mean unemployment rate and the share of services in GDP are associated with the Okun coefficient, whereas other factors such as indices of overall labor and product market flexibility do not appear to play a consistent role.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Notes

  1. To address the well-known end-point problem with the HP filter we extend all series to 2021 using the IMF’s World Economic Outlook projections and then run the HP filter on the extended series to derive the trend estimate for 2015.

  2. Low-skilled sectors are (i) mining and logging and (ii) construction; semi-skilled sectors are (i) manufacturing, (ii) trade, transportation and utilities, (iii) leisure and hospitality, (iv) other services; high-skilled sectors are (i) information, (ii) financial activities, (iii) education and health care, (iv) professional and business services, and (v) government.

References

  • Agénor PR, Montiel PJ (2008) Development macroeconomics. Princeton University Press, Princeton and Oxford

  • Ahmed, M., Guillaume, D., & Furceri, D. (2012). Youth unemployment in the MENA region: determinants and chall sing the 100 Million Youth Challenge -- Perspectives on Youth Unemployment in the Arab World, 8–11.

  • Ball L, Leigh D, Loungani P (2017) Okun’s law: fits at 50? J Money Credit Bank 49(7):1413–1441

    Article  Google Scholar 

  • Furceri, D., Crivelli, E., & Toujas-Bernate, J. (2012). Can policy affect employment intensity of growth? A cross-country analysis, IMF Working Paper No. 12/218

  • Dao M, Loungani P (2010) The human cost of recessions: assessing it, reducing it (No. 2010–2017). International Monetary Fund, Washington DC

    Google Scholar 

  • Estevão MM, Tsounta E (2011) Has the great recession raised US structural unemployment? IMF Working Papers:1–46

  • Hassan M, Schneider F (2016) Size and Development of the Shadow Economies of 157 Countries Worldwide, IZA Discussion Paper No. 10281

  • Kapsos S (2006) The employment intensity of growth: trends and macroeconomic determinants. In: Labor Markets in Asia. Palgrave Macmillan, UK, pp 143–201

    Chapter  Google Scholar 

  • Melina G (2016) Enhancing the Responsiveness of Employment to Growth in Namibia, IMF Selected Issues Paper, November (Washington, DC)

  • Mohommad MA, Singh MA, Jain-Chandra S (2012) Inclusive growth, institutions, and the underground economy. International Monetary Fund, Washington DC, pp 12–47

    Google Scholar 

  • Okun AM (1962) Potential GNP: its measurement and significance. In: Proceedings, business and economic statistics section of the American Statistical Association, pp 89–104

    Google Scholar 

  • World Bank (2013) World Development Report: Jobs

Download references

Acknowledgements

We are grateful to Nathalie Gonzalez Prieto, Zidong An, Ezgi Ozturk and Jair Rodriguez for excellent research assistance. The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prakash Loungani.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

1.1 Data Appendix

Table 10 List of countries included in the estimation

1.1.1 Output

GDP data comes from the July 2016 version of the WEO. It corresponds to real GDP in national currency.

  • \( {\boldsymbol{y}}_{\boldsymbol{t}}-{\mathbf{y}}_{\boldsymbol{t}}^{\ast} \): cycle after filtering the logarithm of the GDP multiplied by 100, with a Hodrick-Prescott filter with lambda 100.

  • Δyt: Percentage change in GDP = 100* ln (\( \frac{y_t}{{\mathrm{y}}_{\mathrm{t}-1}}\Big) \)

1.1.2 Labor market statistics

Labor market data comes from the WEO. This data is internally reported by the desk economist and follows the standard ILO when available. In other cases, it can follow the national definition.

  • \( {\boldsymbol{e}}_{\boldsymbol{t}}-{\boldsymbol{e}}_{\boldsymbol{t}}^{\ast} \): cycle after filtering the logarithm of the employment multiplied by 100, with a Hodrick-Prescott filter with lambda of 100.

  • \( {\boldsymbol{u}}_{\boldsymbol{t}}-{\boldsymbol{u}}_{\boldsymbol{t}}^{\ast} \) cycle after filtering the unemployment rate with a Hodrick-Prescott filter, with lambda of 100

  • \( \boldsymbol{l}{\boldsymbol{f}}_{\boldsymbol{t}}-\boldsymbol{l}{\boldsymbol{f}}_{\boldsymbol{t}}^{\ast} \) cycle after filtering the logarithm of the labor force multiplied by 100, with a Hodrick-Prescott filter with lambda of 100

1.1.3 Determinants of the Okun Coefficient

Average Unemployment

Average unemployment rate from the WEO for the period covered in each regression. The number of periods used to compute the average can vary depending on the country. This indicator comes from national sources that use household surveys and follow the ILO definition of unemployment: unemployed comprise all persons above a specified age who during the reference period were:

  • Without work, that is, were not in paid employment or self-employment during the reference period;

  • Currently available for work, that is, were available for paid employment or self-employment during the reference period; and

  • Seeking work, that is, had taken specific steps in a specified recent period to seek paid employment or self-employment.

This means that the unemployment rate does not include the informal workers as unemployed.

GDP per capita: is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars. Average of the period used in the regression to estimate the coefficients (βs & γ ′ s) the number of periods used to compute the average can vary depending on the country.

Shadow Economy

Average shadow economy prevalence between 1999 and 2007. Taken from Hassan and Schneider (2016) they use indicators such as the use of cash, the growth of the economy and of the labor force, the tax burden the size of the government and other proxies to quantify the scope of the shadow economy in a country and build a dataset comparable across countries.

Skill Mismatch Index

Calculate by IMF Staff. It takes the ILO estimations of shares of the employment by sector and shares of the population by education level. Given a set of skills, the index is a measure of the distance between the percent of the labor force with a given level of skills (skill level supply) and the proportion of employees with the same level of skills (skill level demand). Each country’s labor force and sectors are divided into three categories (i) low-skilled (less than secondary education), (ii) semi-skilled (with secondary education), and (iii) high-skilled (with more than secondary education).Footnote 2 The index is given by the sum of the squared distances for the three skill levels for each country and over time:

$$ SM{I}_{it}=\sum \limits_{j=1}^3{\left({\mathrm{S}}_{\mathrm{ijt}}-{\mathrm{M}}_{ijt}\ \right)}^2 $$

where j= skill level, Sijt= percent of labor force with skill level j at time t in country i, and Mijt=percent of employees with skill level j and time t in country i.

Services as % of GDP

Services correspond to ISIC divisions 50–99 and they include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator.

Source: WDI.

Business Regulations

This indicator is taken from the Fraser Institute below is the description contained in the methodological annex for each of its subcomponents- that includes the original source and the scale. High values are associated with less regulations.

  1. i)

    Administrative requirements

This sub-component is based on the Global Competitiveness Report question: “Complying with administrative requirements (permits, regulations, reporting) issued by the government in your country is (1 = burdensome, 7 = not burdensome).”

Source: World Economic Forum, Global Competitiveness Report.

  1. ii)

    Bureaucracy costs

This sub-component is based on the Global Competitiveness Report question: “Standards on product/service quality, energy and other regulations (outside environmental regulations) in your country are: (1 = Lax or non-existent, 7 = among the world’s most stringent).”

Source: World Economic Forum, Global Competitiveness Report.

  1. iii)

    Starting a business

This sub-component is based on the World Bank’s Doing Business data on the amount of time and money it takes to start a new limited liability business. Countries where it takes longer or is more costly to start a new business are given lower ratings. Zero-to-10 ratings were constructed for three different variables: (1) time (measured in days) necessary to comply with regulations when starting a limited liability company, (2) money costs of the fees paid to regulatory authorities (measured as a share of per-capita income) and (3) minimum capital requirements; that is, funds that must be deposited into a company bank account (measured as a share of per-capita income). These three ratings were then averaged to arrive at the final rating for this sub-component. The formula used to calculate the zero-to-10 ratings was: (Vmax − Vi) / (Vmax − Vmin) multiplied by 10. Vi represents the variable value. The values for Vmax and Vmin were set at 104 days, 317%, and 1017% (1.5 standard deviations above average in 2005) and 0 days, 0%, and 0%, respectively. Countries with values outside of the Vmax and Vmin range received ratings of either zero or 10 accordingly.

Source: World Bank, Doing Business.

  1. iv)

    Extra payments/bribes/favoritism

This sub-component is based on the Global Competitiveness Report questions: [1] “In your industry, how commonly would you estimate that firms make undocumented extra payments or bribes connected with the following: A—Import and export permits; B—Connection to public utilities (e.g., telephone or electricity); C—Annual tax payments; D—Awarding of public contracts (investment projects); E—Getting favorable judicial decisions. Common (= 1) Never occur (= 7).” [2] “Do illegal payments aimed at influencing government policies, laws or regulations have an impact on companies in your country? 1 = Yes, significant negative impact, 7 = No, no impact at all.” [3] “To what extent do government officials in your country show favoritism to well-connected firms and individuals when deciding upon policies and contracts? 1 = Always show favoritism, 7 = Never show favoritism.”

Source: World Economic Forum, Global Competitiveness Report.

Labor market regulations

This indicator is a combination of the following subcomponents.

  1. i)

    Hiring market regulations

This sub-component is based on the World Bank’s Doing Business “Difficulty of Hiring Index”, which is described as follows: “The difficulty of hiring index measures (i) whether fixed-term contracts are prohibited for permanent tasks; (ii) the maximum cumulative duration of fixed term contracts; and (iii) the ratio of the minimum wage for a trainee or first-time employee to the average value added per worker. An economy is assigned a score of 1 if fixed-term contracts are prohibited for permanent tasks and a score of 0 if they can be used for any task. A score of 1 is assigned if the maximum cumulative duration of fixed-term contracts is less than 3 years; 0.5 if it is 3 years or more but less than 5 years; and 0 if fixed-term contracts can last 5 years or more. Finally, a score of 1 is assigned if the ratio of the minimum wage to the average value added per worker is 0.75 or more; 0.67 for a ratio of 0.50 or more but less than 0.75; 0.33 for a ratio of 0.25 or more but less than 0.50; and 0 for a ratio of less than 0.25.” Countries with higher difficulty of hiring are given lower ratings.

Source: World Bank, Doing Business.

  1. ii)

    Hiring and firing regulations

This sub-component is based on the Global Competitiveness Report question: “The hiring and firing of workers is impeded by regulations (= 1) or flexibly determined by employers (= 7).” The question’s wording has varied over the years.

Source: World Economic Forum, Global Competitiveness Report.

  1. iii)

    Centralized collective bargaining

This sub-component is based on the Global Competitiveness Report question: “Wages in your country are set by a centralized bargaining process (= 1) or up to each individual company (= 7).” The wording of the question has varied over the years.

Source: World Economic Forum, Global Competitiveness Report.

  1. iv)

    Hours regulations

This sub-component is based on the World Bank’s Doing Business “Rigidity of Hours Index”, which is described as follows: “The rigidity of hours index has 5 components: (i) whether there are restrictions on night work; (ii) whether there are restrictions on weekly holiday work; (iii) whether the workweek can consist of 5.5 days; (iv) whether the workweek can extend to 50 hours or more (including overtime) for 2 months a year to respond to a seasonal increase in production; and (v) whether paid annual vacation is 21 working days or fewer. For questions (i) and (ii), when restrictions other than premiums apply, a score of 1 is given. If the only restriction is a premium for night work and weekly holiday work, a score of 0, 0.33, 0.66 or 1 is given according to the quartile in which the economy’s premium falls. If there are no restrictions, the economy receives a score of 0. For questions (iii), (iv) and (v), when the answer is no, a score of 1 is assigned; otherwise a score of 0 is assigned.” Countries with less-rigid work rules receive better scores in this component.

Source: World Bank, Doing Business.

  1. v)

    Mandated cost of worker dismissal

This sub-component is based on the World Bank’s Doing Business data on the cost of the advance notice requirements, severance payments and penalties due when dismissing a redundant worker with 10 years tenure. The formula used to calculate the zero-to-10 ratings was: (Vmax − Vi) / (Vmax − Vmin) multiplied by 10. Vi represents the dismissal cost (measured in weeks of wages). The values for Vmax and Vmin were set at 58 weeks (1.5 standard deviations above average in 2005) and 0 weeks, respectively. Countries with values outside of the Vmax and Vmin range received ratings of either zero or 10 accordingly.

Source: World Bank, Doing Business.

  1. vi)

    Conscription

Data on the use and duration of military conscription were used to construct rating intervals. Countries with longer conscription periods received lower ratings. A rating of 10 was assigned to countries without military conscription. When length of conscription was 6 months or less, countries were given a rating of 5. When length of conscription was more than 6 months but not more than 12 months, countries were rated at 3. When length of conscription was more than 12 months but not more than 18 months, countries were assigned a rating of 1. When conscription periods exceeded 18 months, countries were rated zero. If conscription was present, but apparently not strictly enforced or the length of service could not be determined, the country was given a rating of 3. In cases where it is clear conscription is never used, even though it may be possible, a rating of 10 is given. If a country’s mandated national service includes clear non-military options, the country was given a rating of 5.

Source: International Institute for Strategic Studies, The Military Balance; War Resisters International, World Survey of Conscription and Conscientious Objection to Military Service; additional online sources used as necessary.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ball, L., Furceri, D., Leigh, D. et al. Does One Law Fit All? Cross-Country Evidence on Okun’s Law. Open Econ Rev 30, 841–874 (2019). https://doi.org/10.1007/s11079-019-09549-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11079-019-09549-3

Keywords

Navigation