public sector. Further, a policy that led to hiring public teachers on temporary contracts at half the wages of permanent teachers had no discernable impact on child test-scores in the short-run, and had no effect on the quality of new teachers in the medium-run. These results are consistent with experimental studies in India and Kenya. Second, these wage differences are not going away. In the initial period of our data collection (2003), the wage differences could have reflected a historical anomaly. But from 2007 to 2011, there was a period of very high inflation in Pakistan, and this high inflation could have been used to erode the wage differences. Instead, by 2011, the wage differences were _even_ higher than in 2003. Third, there is no link between pay and productivity in the public sector, with no variation in wages linked to variation in teacher value-added.

Finally, it is not straightforward to link these costs to “efficiency” differences. In the classic model of firms, efficiency can be roughly thought of as the ratio of outputs to inputs: How much “stuff” (labor and capital, for example), do you need to produce widgets. Firms that can produce more widgets with less “stuff” are more efficient. But calculating what `stuff’ means is less than obvious, because each element in `stuff’ needs to be weighted, and the typical assumption is that firms face the same weights, which are factor prices. In a _competitive market_ , firms with higher wages also have more productive workers (the same worker produces more widgets in a given time period). But in markets that are not competitive, one firm may still be more _efficient_ than another, but there is no reason why the cost structure will yield information about economic efficiency. When it comes to schooling, there is no doubt that public schools are not _competitive_ , in that if they had to compete on price, they would immediately have to close down. Whether or not they are more technically efficient depends on the comparison of schooling produced to inputs used, something on which there is less evidence and will inevitably require a political economy model to be embedded into the production of schooling. See Pritchett and Filmer (1999) for one such example.

<u>What are households doing? Households in the LEAPS sample are active purchasers of education and</u> deeply interested in their children’s schooling. In 2003, households with children enrolled in public schools spent Rs.155 every month and households with children enrolled in private schools spent Rs.231 every month. The median monthly expenditures at the household level are about Rs.4700, which implies that a household with four children enrolled in a private school would be spending close to 20% of its budget on schooling expenditures. It also implies that close to 50% of the total spending on education in public schools is incurred by households as out-of-pocket expenditures.

Richer households and households with educated parents spend significantly more and elder children incur greater educational expenditures. This is not unexpected since educational spending is almost certainly a normal good. What is surprising is the strong effect of child intelligence on educational spending. Figure A4 shows educational expenditures for children disaggregated by parental perception of intelligence (very poor, poor, average, above average and highly above average) and by enrollment in public/private schools. Children perceived as more intelligent are four times more likely to be enrolled in private schools. The differences are much smaller for those enrolled in public schools, but still represent a two-fold increase over the same range. In contrast to this “intelligence” effect, gender discrimination in educational spending is small, accounting for at most a 5 percent difference in spending. Putting the two together, households, on average spend a lot more on girls perceived to be intelligent than on boys 26