Immunization Rates Indicator


This indicator measures a government’s commitment to providing essential public health services and reducing child mortality.

Relationship to Growth & Poverty Reduction

The Immunization Rates indicator is widely regarded as a good proxy for the overall strength of a government’s public health system. 1   It is designed to measure the extent to which governments are investing in the health and well-being of their citizens. Immunization programs can impact economic growth through their broader impact on health. 2  Healthy workers are more economically productive and more likely to save and invest; healthy children are more likely to reach higher levels of educational attainment; and healthy parents are better able to invest in the health and education of their children. 3 Immunization programs also increase labor productivity among the poor, reduce spending to cope with illnesses, and lower mortality and morbidity among the main income-earners in poor families. 4


MCC uses the simple average of the national diphtheria-pertussis-tetanus (DPT3) vaccination rate and the measles (MCV) vaccination rate. The DPT3 immunization rate is measured as the number of children that have received their third dose of the diphtheria, pertussis (whooping cough), and tetanus toxoid vaccine divided by the target population (the number of children surviving their first year of life.) The measles immunization rate is measured as the number of children that have received their first dose of a measles-containing vaccine divided by the same target population.

To estimate national immunization coverage, WHO and UNICEF draw on two sources of empirical data: reports of vaccinations performed by service providers (administrative data) and surveys containing items on children’s vaccination history (coverage surveys). Surveys are frequently used in conjunction with administrative data; in some instances—where administrative data differ substantially from survey results—surveys constitute the sole source of information on immunization coverage levels. There are a number of reasons survey data may be used over administrative data; for instance, in some cases, lack of precise information on the size of the target population (the denominator) can make immunization coverage difficult to estimate from administrative data alone. Estimates of the most likely true level of immunization coverage are based on the data available, consideration of potential biases, and contributions of local experts.

In consultation with the WHO, MCC considers countries which have immunization coverage above the median for their scorecard income pool to be passing this indicator. If the median is above 90% for an income pool in a year, countries in that income pool will be considered passing if they have immunization coverage above 90% (even if they score below the median). 5

MCC Methodology

MCC Immunization Rate = [0.5 x DPT3 ] + [0.5 x MCV1]

MCC relies on official WHO/United Nations Children’s Fund (UNICEF) estimates for all immunization data. MCC uses the simple average of the 2021 DPT3 coverage rate and the 2021 measles (MCV) coverage rate to calculate FY23 country scores. If a country is missing data for either DPT3 or Measles, it does not receive an index value. The same rule is applied to historical data. As better data become available, WHO/UNICEF make backward revisions to the historical data. In FY23, countries must have immunization rates (as defined above) greater than 90% or the median for their scorecard pool, whichever is lower, to pass this indicator.


  • World Health Organization (WHO) and the United Nations Children's Fund (UNICEF) (WHO/UNICEF)

  • 1. Becker, Loren, Jessica Pickett, Ruth Levine. 2006. Measuring Commitment to Health: Global Health Indicators Working Group Report. Washington D.C.: Center for Global Development.
  • 2. Bloom, D. E., Canning, D., Sevilla, J. 2004.The Effect of Health on Economic Growth: A Production Function Approach. World Development 32(1): 1-13. Alok Bhargava, Dean T. Jamison, Lawrence J. Lau and Christopher J. L. Murray. 2001.  Modeling the Effects of Health on Economic Growth. Journal of Health Economics 20(3):  423-40. Baldacci, E., Benedict Clements, Sanjeev Gupta and Qiang Cui. 2004. Social Spending, Human Capital and Growth in Developing Countries: Implications for Achieving the MDGs. IMF Working Paper 04/217. Gyimah-Brempong K. and M. Wilson. 2004. Human Capital and Economic Growth in Sub-Saharan Africa and OECD Countries. Quarterly Review of Finance and Economics 44: 296-320. Doppelhofer, G., R. Miller and X. Sala-i-Martin. 2004. Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates Approach. American Economic Review 94(4): 813-835.
  • 3. Bloom, David E., David Canning & Mark Weston. The Value of Vaccination. World Economics 6(3): 15-39. Miguel, Edward, and Kremer, Michael. 2004. Worms: Identifying Impacts on Education and Health the Presence of Treatment Externalities. Econometrica 72(1): 159–217.
  • 4. Fairbank, A., Makinen, M., Schott, W., and Sakagawa, B. 2000. Poverty Reduction and Immunizations. Bethesda, Maryland: Abt Associates, Inc.
  • 5. MCC uses the World Bank’s historical ceiling for IDA eligibility to divide countries into two assessment categories. Countries that fall below the ceiling (GNI per capita of $0-$2,045 for FY23) and countries above the ceiling but below the World Bank’s LMIC cut-off (GNI per capita of $2,046-$4,255 in FY23).