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We propose an extension of the Olley and Pakes () productivity decomposition that accounts for the contributions of surviving, entering, and exiting firms to aggregate productivity changes. We argue that the other decompositions that break down aggregate productivity changes into similar components introduce some biases in the measurement of the contributions of entry and exit. We apply our proposed decomposition to Slovenian manufacturing data and contrast our results with those of other decompositions. We find that, over a five‐year period, the measurement bias associated with entry and exit is substantial, accounting for up to 10 percentage points of aggregate productivity growth.

We show that endogenous firm selection provides a new welfare margin for heterogeneous firm models of trade (relative to homo geneous firm models). Under some parameter restrictions, the trade elasticity is constant and is a sufficient statistic for welfare, along with the domestic trade share. However, even small deviations from these restrictions imply that trade elasticities are variable and differ across markets and levels of trade costs. In this more general setting, the domestic trade share and endogenous trade elasticity are no longer sufficient statistics for welfare. Additional empirically observable moments of the micro structure also matter for welfare. (JEL F12, F13, F41)

In a class of trade models which satisfy a constant elasticity gravity equation, the welfare gains from trade can be computed using the open economy domestic trade share and a constant trade elasticity. The measured welfare gains from trade from this quantitative approach are typically relatively modest. In this paper, we suggest a channel for welfare gains that this quantitative approach typically abstracts from: trade-induced changes in domestic productivity. Using a model of sequential production, in which trade induces a reorganization of production that raises domestic productivity, we show that the welfare gains from trade can become arbitrarily large.

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