across sectors is neither a necessary nor a sufficient condition of inefficiency. When production follows a Cobb-Douglas technology in presence of perfect competition, marginal productivity is simply the product of average productivity and the labor share of income. The deviation in labor income shares across sectors drives a wedge between marginal and average productivity gaps. If the sector with higher average productivity also has a higher relative labor share of income, then marginal gaps are larger than observed average gaps. To estimate labor shares at the sectoral level, I use the data from Global Trade Analysis Project (GTAP). I find significant deviation between marginal and average gaps and as such considering the gaps at margin is not only a theoretical concern. On average, distortions estimated using average gaps in productivity are around 35% and 22% lower than when estimated using marginal gaps for Industry and Services respectively. Vollrath (2009) also considers differences in marginal productivity to measure impact of misallocation. Though, in his calculations he assumes the labor shares to be invariant across countries and hence uses the US labor shares to estimate marginal productivity in each country. However, labor shares obtained from GTAP show that there is a considerable variation in sector labor shares across countries. Thus, using a common set of labor shares may not be an appropriate strategy.

In order to quantify the productivity impact of the sectoral gaps, I develop a simple model of resource allocation in which the gaps arise due to distortions in the form of asymmetrical taxes on labor inputs across three broad sectors: Agriculture, Industry and Services. These taxes are a stylized way of capturing the net effect of a menu of factors that create barriers to movement of resources across sectors. A model of resource allocation is essential in contrast to a shiftshare analysis of aggregate productivity because productivity in both sectors change as a result of resources flowing from the low productivity sector to the high productivity sector. An important point is that the growth impact of changes in distortions depends crucially on the amount of resources that get reallocated. If the sector-specific distortions in Industry and Services are positive, any decline in distortions in either sector leads to an outflow of resources from Agriculture. However, the gains are capped by the resources in Agriculture initially. An economy that begins with fewer resources in Agriculture is bound to realize lesser gains compared to an economy that begins with a larger share of its resources allocated to Agriculture.

I use the labor shares data from GTAP together with sector value added and employment share data from the World Development Indicator (WDI) database to estimate distortions for many country-years. There are two major concerns with regards to using the sectoral share of employment data obtained from the WDI database. First, the source survey used to estimate the sectoral share of employment for many country-years is not representative in certain respects. For example, the surveys often were conducted in urban areas only, or only sampled workers in a very restrictive 2