A skilled labor force is essential for firms to thrive and compete; it fosters the ability to innovate and to adopt new technologies. Characteristics about the workforce such as a strong reliance on temporary workers may indicate regulatory inflexibilities regarding the hiring and firing of full-time workers. The twelve indicators on this page measure labor issues in 139 countries. The results are based on surveys of more than 135,000 firms. A database query tool is available to help you better understand workforce issues across various firm subgroups. You can also generate graphs to compare countries.

To see the details for a specific economy, click on the links below. Click on column headers to sort data.



Custom Data Set

Generate a Custom Data Set for Workforce including standard errors, indicator values by firm subgroups, historical data and selected countries.


Percent of firms offering formal trainingPercent of firms offering formal training programs for its permanent, full-time employees.
Proportion of workers offered formal training (%)*Percentage of permanent, full-time employees that have received formal training.
Years of the top manager's experience working in the firm's sectorYears of the top manager's experience working in the firm's sector.
Number of workersNumber of workers, including permanent and temporary, with the number of temporary workers adjusted for the amount of months employed.
Proportion of permanent workers (out of all workers)Proportion of permanent workers (out of all workers)
Proportion of temporary workers (out of all workers)Proportion of temporary workers (out of all workers)
All Countries 233.852.817.335.594.85.2
East Asia & Pacific37.364.716.237.994.85.2
Europe & Central Asia35.656.717.230.996.04.0
Latin America & Caribbean43.260.820.
Middle East & North Africa18.237.321.232.595.64.4
South Asia28.249.614.660.193.86.2
Sub-Saharan Africa28.745.515.
Afghanistan (2014)31.729.013.424.993.16.9
Albania (2013)
Angola (2010)23.554.811.232.896.33.7
Antigua and Barbuda (2010)24.9n.a.15.221.499.20.8
Argentina (2017)
Armenia (2013)16.239.615.548.198.51.5
Azerbaijan (2013)20.2n.a.13.529.398.31.7
Bahamas, The (2010)37.1n.a.22.639.895.44.6
Bangladesh (2013)21.970.418.3184.998.31.7
Barbados (2010)35.5n.a.16.730.396.93.1
Belarus (2018)31.527.316.862.297.32.7
Belize (2010)14.4n.a.14.620.398.91.1
Benin (2016)
Bhutan (2015)26.025.812.121.591.68.4
Bolivia (2017)49.967.120.130.592.67.4
Bosnia and Herzegovina (2013)52.450.215.831.798.31.7
Botswana (2010)51.954.718.146.596.33.7
Brazil (2009)42.261.820.537.497.82.2
Bulgaria (2013)42.771.018.729.898.11.9
Burkina Faso (2009)24.837.415.627.291.09.0
Burundi (2014)
Cabo Verde (2009)16.6n.a.
Cambodia (2016)22.2n.a.11.926.898.51.5
Cameroon (2016)37.654.318.523.391.98.1
Central African Republic (2011)49.7n.a.11.323.094.06.0
Chad (2018)22.934.614.314.192.67.4
Chile (2010)57.549.224.2125.395.94.1
China (2012)
Colombia (2017)
Congo, Dem. Rep. (2013)17.050.712.615.896.53.5
Congo, Rep. (2009)37.5n.a.12.537.286.813.2
Costa Rica (2010)54.771.019.964.194.25.8
Côte d'Ivoire (2016)35.541.020.332.692.57.5
Croatia (2013)49.353.920.723.896.63.4
Czech Republic (2013)55.179.921.038.397.42.6
Djibouti (2013)21.8n.a.17.527.494.65.4
Dominica (2010)18.9n.a.9.518.699.80.2
Dominican Republic (2016)23.451.722.045.998.11.9
Ecuador (2017)73.773.719.739.395.14.9
Egypt, Arab Rep. (2016)10.029.520.548.098.51.5
El Salvador (2016)53.868.321.231.496.13.9
Eritrea (2009)26.1n.a.16.017.997.92.1
Estonia (2013)
Eswatini (2016)
Ethiopia (2015)20.827.113.538.892.67.4
Fiji (2009)61.0n.a.
Gabon (2009)30.9n.a.
Gambia, The (2018)25.243.919.318.789.810.2
Georgia (2013)10.561.411.926.295.05.0
Ghana (2013)
Greece (2018)21.647.223.219.392.97.1
Grenada (2010)46.2n.a.
Guatemala (2017)55.752.325.734.195.24.8
Guinea (2016)16.0n.a.11.319.095.54.5
Guinea-Bissau (2006)12.440.712.210.695.84.2
Guyana, CR (2010)63.0n.a.23.387.493.26.8
Honduras (2016)47.778.123.430.395.34.7
Hungary (2013)15.855.321.826.495.64.4
India (2014)35.963.411.052.495.54.5
Indonesia (2015)7.748.514.521.698.21.8
Iraq (2011)
Israel (2013)18.649.128.830.298.21.8
Jamaica (2010)25.941.816.129.698.71.3
Jordan (2013)3.437.817.735.899.01.0
Kazakhstan (2013)28.347.713.535.197.52.5
Kenya (2018)37.445.715.733.791.28.8
Kosovo (2013)55.840.919.829.391.48.6
Kyrgyz Republic (2013)62.732.416.454.090.49.6
Lao PDR (2018)24.449.817.217.998.11.9
Latvia (2013)25.264.516.818.897.62.4
Lebanon (2013)26.657.627.
Lesotho (2016)31.276.610.156.998.51.5
Liberia (2017)22.860.414.128.493.76.3
Lithuania (2013)
Madagascar (2013)12.751.815.665.884.915.1
Malawi (2014)32.9n.a.15.448.095.44.6
Malaysia (2015)18.533.412.032.597.22.8
Mali (2016)17.729.820.938.095.94.1
Mauritania (2014)52.748.120.247.689.710.3
Mauritius (2009)25.636.117.438.796.33.7
Mexico (2010)50.862.619.480.895.14.9
Micronesia, Fed. Sts. (2009)n.a.n.a.16.219.990.39.7
Moldova (2013)32.449.213.623.599.20.8
Mongolia (2013)59.863.416.435.985.314.7
Montenegro (2013)23.777.315.021.393.46.6
Morocco (2013)26.348.822.158.796.04.0
Mozambique (2018)20.761.115.642.496.53.5
Myanmar (2016)5.957.713.525.199.20.8
Namibia (2014)25.425.812.019.995.84.2
Nepal (2013)31.954.
Nicaragua (2016)57.363.421.622.595.64.4
Niger (2017)27.520.717.829.390.69.4
Nigeria (2014)30.744.312.516.396.53.5
North Macedonia (2013)46.967.017.516.099.30.7
Pakistan (2013)32.046.713.285.888.311.7
Panama (2010)11.067.611.232.898.71.3
Papua New Guinea (2015)73.7n.a.22.5140.195.74.3
Paraguay (2017)46.466.822.659.096.73.3
Peru (2017)65.967.
Philippines (2015)59.875.316.837.792.17.9
Poland (2013)34.664.919.044.796.63.4
Romania (2013)40.768.
Russian Federation (2012)
Rwanda (2011)55.4n.a.12.735.990.89.2
Samoa (2009)79.1n.a.21.425.495.44.6
Senegal (2014)17.461.419.038.694.95.1
Serbia (2013)37.879.718.524.097.82.2
Sierra Leone (2017)21.643.
Slovak Republic (2013)43.571.619.
Slovenia (2013)41.553.822.719.794.95.1
Solomon Islands (2015)42.0n.a.11.546.396.63.4
South Africa (2007)36.863.613.851.096.33.7
South Sudan (2014)17.1n.a.8.012.390.69.4
Sri Lanka (2011)18.457.718.236.695.74.3
St. Kitts and Nevis (2010)49.5n.a.20.322.494.95.1
St. Lucia (2010)13.6n.a.9.525.599.80.2
St. Vincent and the Grenadines (2010)45.7n.a.19.415.996.33.7
Sudan (2014)9.547.017.925.498.71.3
Suriname (2018)34.829.423.921.594.45.6
Sweden (2014)70.371.323.740.894.95.1
Tajikistan (2013)33.160.614.431.788.211.8
Tanzania (2013)30.749.512.619.887.612.4
Thailand (2016)18.093.722.344.7100.00
Timor-Leste (2015)1.9n.a.13.325.489.510.5
Togo (2016)33.729.917.450.689.610.4
Tonga (2009)11.1n.a.13.58.995.64.4
Trinidad and Tobago (2010)
Tunisia (2013)28.946.624.650.593.86.2
Turkey (2013)28.467.722.139.897.22.8
Uganda (2013)34.741.411.018.786.014.0
Ukraine (2013)22.657.216.430.487.512.5
Uruguay (2017)53.350.925.833.497.92.1
Uzbekistan (2013)11.043.811.830.297.22.8
Vanuatu (2009)47.5n.a.18.521.095.34.7
Venezuela, R.B. (2010)56.074.518.128.386.213.8
Vietnam (2015)
West Bank and Gaza (2019)9.617.023.514.196.04.0
Yemen, Rep. (2013)14.326.620.520.993.86.2
Zambia (2013)28.249.513.824.292.97.1
Zimbabwe (2016)26.459.116.220.793.86.2
  • Notes

    * This indicator is computed using data from manufacturing firms only.

    Additional Notes

    1. Most surveys were administered using the Enterprise Surveys Global Methodology as outlined in the Methodology page, while some others did not strictly adhere to the Enterprise Surveys Global Methodology. For example, for surveys which do not follow the Global Methodology, the Universe under consideration may have consisted of only manufacturing firms or the questionnaire used may have been different from the standard global questionnaire. Data users should exercise caution when comparing raw data and point estimates between surveys that did and did not adhere to the Enterprise Surveys Global Methodology. For surveys which did not adhere to the Global Methodology plus Afghanistan 2008, any inference from one of these surveys is representative only for the data sample itself.
    2. Regional and "all countries" averages of indicators are computed by taking a simple average of country-level point estimates. For each economy, only the latest available year of survey data is used in this computation. Only surveys, posted during the years 2009-2019, and adhering to the Enterprise Surveys Global Methodology are used to compute these regional and "all countries" averages.
    3. Descriptions of firm subgroup levels, e.g. how the ex post groupings are constructed, are provided in the Indicator Descriptions (PDF, 710KB) document.
    4. Statistics derived from less than or equal to five firms are displayed with an "n.a." to maintain confidentiality and should be distinguished from ".." which indicates missing values. Also note for three growth-related indicators under the "Performance" topic, these indicators are not computed when they are derived from less than 30 firms.
    5. Standard errors are labeled "n.c.", meaning not computed, for the following:

           1) indicators for all surveys that were not conducted using the Enterprise Surveys Global Methodology and

           2) for indicator breakdowns by ex post groupings: exporter or ownership type, and gender of the top manager.
    6. Please cite our data as follows:

      Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank.