# _Education_

We also expect education to be positively correlated with higher urbanization, as returns to education tend to be higher in urban areas. Education itself may have an aspect of "urban bias": rural students do not necessarily learn agricultural skills, and may even be educated in such a way as to be averse to farming (Gugler and Flanagan, 1978). Using education data from the Barro and Lee data set (Barro and Lee, 1996), we find in regression 5 that the higher the proportion of the population with no education, the lower the level of urbanization. This effect is non-linear however, as evidenced by the fact that the square of NOEDUC is positive and significant. The interpretation is simply that among countries with many uneducated people, a very small improvement in education is associated with much higher urbanization. Conversely, among countries with relatively few uneducated people, improvements in education will basically not affect urbanization.

Other education variables are available from Barro and Lee, enabling us to distinguish among different levels of educational achievements. However, they tend to be very collinear. We therefore used only secondary education (SECONDARY), which yielded the best fit. It is positive and significant, suggesting that a typical country can expect to be 12% more urbanized given its level of income and income structure than a country whose adult population has half the average years of secondary education.

# _Urban bias_

No good measure of urban bias is readily available. In particular, it is impossible to measure whether public investment and spending is biased towards urban areas, due to a lack of data and the difficulty of assessing which populations benefit from which public investments. Two alternative measures can be used as proxies. One is the difference between the domestic producer price of agricultural products and their international market prices. These are available through the Food and Agriculture Organization of the UN for a number of basic products, notably cereals. If this functions as a measure of urban bias, we expect a negative coefficient. But regression 6 shows CEREAL to be significantly positive, implying that it is capturing a process of convergence, whereby the more urbanized a country, the closer to international prices is its domestic producer price of agricultural commodities. CEREAL's explanatory power, however, is low relative to income or income structure.

Another measure of distortion which could presumably disproportionately affect rural areas, or proxy for import substitution policies, is the overvaluation of the exchange rate. A common measure of this overvaluation is the ratio of the black market exchange rate to the official rate, or the "black market premium" (BMP). We expect that, all else constant, countries with a higher black market premium should be more urbanized. This is indeed the case, as is shown in regression 7. To make sure that we were not simply capturing a Latin American effect (Latin American countries are highly urbanized and many had high black market premia in the eighties and early nineties), we ran regression 7 with a Latin America dummy. The variable remained significantly positive. Its 14