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We measure the impact of a drastic new technology for producing steel – the minimill – on the aggregate productivity of U.S. steel producers, using unique plant-level data between 1963 and 2002. We find that the sharp increase in the industry's productivity is linked to this new technology, and operates through two distinct mechanisms. First, minimills displaced the older technology, called vertically integrated production, and this reallocation of output was responsible for a third of the increase in the industry's productivity. Second, increased competition, due to the expansion of minimills, drove a substantial reallocation process within the group of vertically integrated producers, driving a resurgence in their productivity, and consequently of the industry's productivity as a whole.
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Production functions are a central component in a variety of economic analyzes. However, these production functions often first need to be estimated using data on individual production units. There is reason to believe that, more than any other input in the production process, there are severe errors in the recording of capital stock. Thus, when estimating production functions, we need to account for the ubiquity of measurement error in capital stock. This paper shows that commonly used estimation techniques in the productivity literature fail in the presence of plausible amounts of measurement error in capital. We propose an estimator that addresses this measurement error, while controlling for unobserved productivity shocks. Our main insight is that investment expenditures are informative about a producer's capital stock, and we propose a hybrid IV-Control function approach that instruments capital with (lagged) investment, while relying on standard intermediate input demand equations to offset the simultaneity bias. We rely on a series of Monte Carlo simulations and find that standard approaches yield downward-biased capital coefficients, while our estimator does not. We apply our estimator to two standard datasets, the census of manufacturing firms in India and Slovenia, and find capital coefficients that are, on average, twice as large.
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This paper uses a new data set on child-adoption matching to document the preferences of potential adoptive parents over U.S.-born and unborn children relinquished for adoption by their birth mothers. We show that adoptive parents exhibit significant preferences in favor of girls and against African- American children. A non-African-American child relinquished for adoption attracts the interest of potential adoptive parents with probability 11.5% if it is a girl and 7.9% if it is a boy. As for race, a non-African-American child has a probability of attracting the interest of an adopting parent at least seven times as high as the corresponding probability for an African-American child. In addition, we show that a child's desirability in the adoption process depends significantly on time to birth (increasing over the pregnancy, but decreasing after birth) and on adoption costs. We also document the attitudes toward children' characteristics across different categories of adoptive parents – heterosexual and same-sex couples, as well as single women and foreign couples. Finally, we consider several recently discussed policies excluding same-sex and foreign couples from the adoption process. In our data, such policies would reduce the number of adopted children by 6% and 33%, respectively, and have a disproportionate effect on African-American children.
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We investigate the role of dynamic production inputs and their associated adjustment costs in shaping the dispersion of total factor productivity (TFP) and static measures of capital misallocation within a country. Using data on 5,010 establishments in 33 developing countries from the World Bank's Enterprise Research Data, we find that countries exhibiting greater time-series volatility of productivity are also characterized by greater cross-sectional dispersion in productivity. Volatility in TFP explains one quarter to one third of cross-country productivity dispersion. We document a similar relationship between productivity volatility and the dispersion of the marginal revenue product of capital (static capital misallocation). We then use a standard model of investment with adjustment costs, parameterized using numbers calibrated to U.S. data, to show that increasing the volatility of productivity to the level observed in these developing economies can quantitatively replicate the observed relationship between static misallocation and volatility observed in the data. We find that sixty-one percent of the static capital misallocation in the data is captured by the model's prediction. Our findings suggest that the dynamic process governing productivity shocks is a first-order determinant of differences in misallocation and, hence, income across countries.
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Endemic blackouts are a particularly salient example of how poor infrastructure might reduce growth in developing economies. As a case study, we analyze how Indian textile plants respond to weekly “power holidays.” We then study how electricity shortages affect all Indian manufacturers, using an instrument based on hydroelectricity production and a hybrid Leontief/Cobb-Douglas production function model. Shortages reduce average output by about five percent, but because most inputs can be stored during outages, productivity losses are much smaller. Plants without generators have much larger losses, and because of economies of scale in generator capacity, shortages more severely affect small plants.
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