industries linking two sectors and test whether according to theory this measure explains their comovement.

I build my test using detailed data on the output of 28 manufacturing industries in a sample of 44 countries during 1980-2004 to compute the within-country correlation of the 378 resulting industry-pairs. I also gather information on the use of trade credit at the industry and country level during the same period and on input-output relations among these industries. Under some assumptions, I use these data to construct a measure of the trade credit use along the chain linking a pair of industries that approximately corresponds to a weighted average of the relative use of trade credit across all sectors linking those industries. This measure, which I label the _credit-chain distance_ between two industries in a given country, is derived from an extension that introduces the possibility of credit-chain amplification in reduced form to the multi-sector model of Shea (2002).

In constructing the measure of credit-chain distance for industry pairs in different countries I make three assumptions regarding the similarity of input-output relations and relative intensity of use of trade credit across countries because of data availability problems. First, since I lack detailed input-output data for most countries in my sample, I assume that input-output linkages across industries are mostly technologically determined, so I can use data from U.S. input-output matrices to proxy for these relations in other countries. Second, in most developing counties standard data sources do not contain enough firms to build industry level measures of the use of trade credit. Thus, in most of the paper I follow the assumption of Fisman and Love (2003) that the relative use of trade credit across industries is partly technologically determined. Finally, since I do not have information on the differential use of trade credit across supplier industries, I assume that the fraction of goods that firms in a given sector buy on trade credit is similar across suppliers. I discuss these assumptions in detail in the paper, provide evidence of their validity, relax them as much as possible, and also show that they most likely result in attenuation bias that reduces the power of the tests and stacks the cards against finding any significant result.

I test my main hypothesis by running a set of regressions of the output correlation of different industry pairs across countries against the measure of _credit-chain distance_ and a number of controls that include country and industry-pair fixed-effects. Consistently with the hypothesis of credit-chain amplification, I find that an increase in the use of trade credit along the chain linking two sectors, both as a fraction of the cost of inputs and relative to bank credit, significantly increases their output correlation. This result is robust to changes in the specific sample, measure of correlation, and the consideration of a set of alternative explanations.

In terms of economic magnitude, my point estimates indicate that a one-standard-deviation increase in the measure of _credit-chain distance_ increases the sectoral correlation of value added of an industry pair in four percentage points, which corresponds to 16 percent of the observed standard deviation of sectoral correlations in the data (0.27). At the aggregate level, if the average country in the sample increases its ratio of accounts payables to the cost of goods sold from seven to 33 percent–a movement from the bottom to the top levels observed in the sample, the variance of its manufacturing sector will increase in about 20 percent. These magnitudes are meaningful 2