et.al. 2003). There is very little to do to overcome this limitation, except subdivide the sample into different groups. Probably a regional aggregation can be useful, but even at that level there may be extreme heterogeneity.

<!-- Start of picture text --> Correlation: Learning Scores and Net Secondary Enrollment Correlation: Learning Scores and Average Years of School<br>KOR SGP TWN KOR<br>HKG<br>SVK HUNCZESVN SV N HUNSVKCZE<br>MYS BGRLVA POL MYS BGRPOL<br>MEX MDATTO MKDROMCUB LTU THA MEX CUBTTO ROM<br>IDN TUR TUN BRAIRNALB ARG JOR BRAIDNTUNTURIRN J OR ARG<br>DOM PRY COLPER BOL CHL KWT HNDDOMCOLBOL PRYKWT CHLPER<br>VUT SYC<br>TZA MOZMDG KEN MARSWZ ZWE PHL BLZMUS MOZ TZA UGACMR KEN ZWE SWZMUS PHL<br>BWA BWA<br>BFA<br>ZAF ZAF<br>LSO NAM MLI SEN LSO ZMB<br>MWI<br>NE R NER<br>0 20 40 60 80 100 0 2 4 6 8 10<br>Net Secondary Enrollment Average Years of School<br>Data Source: World Bank WDI & Crouch and Fasih (2004) Data Source: World Bank WDI & Crouch and Fasih (2004)<br>600 600<br>500 500<br>400 400<br>Learning Scores Learning Scores<br>300 300<br>200 200<br><!-- End of picture text -->

**Figure 6. Correlation between Learnings Scores and Other Education Indicators**

Other four limitations of the analysis arising from the particular data sources are: First, the level of aggregation. The paper uses aggregate public spending on health and education, while using disaggregate measures of output, such as. primary enrollment or secondary enrollment. Ideally, the input should be use separately public spending in primary and secondary education. Similarly, health care spending could be disaggregated into primary care level care and secondary level. The data can be disaggregated even further, by analyzing efficiency at the school or hospital levels. Second, there are omitted factors of production. This is especially true in education, as the paper did not consider private spending due to data constraints for developing nations. If this factor were used more intensively in a particular group of countries, then the efficiency scores (reported in the next section) would be biased favoring efficiency in that group.

The third limitation arising from the data is the combination of monetary and nonmonetary factors of production. The paper uses together with public expenditure, other non-monetary factors of production such as the ratio of teachers to students, in the case of education, or literacy of adults in the case of health and education. Other factors of production that could have been used were the physical number of teaching hours (in education) or the number of doctors or in-patient beds, as Afonso and St. Aubyn did for the OECD countries. However, inexistent data for a large number of developing countries constrained the options.

A fourth limitation arising from the selected indicators, is that these don’t allow for a good differentiation between outputs and outcomes. For instance, most of the indicators of education, such as completion and enrollment rates do not measure how much learning is taking place in a particular country. In education, this paper advances by considering the learning scores as one of the indicators. In health, other outcomes such as the number 11