Publications

Refereed Journal Articles

In the U.S. Census Bureau's 2002 and 2007 Censuses of Manufactures 79% and 73% of observations have imputed data for at least one variable used to compute total factor productivity. The Bureau primarily imputes for missing values using mean-imputation methods which can reduce the true underlying variance of the imputed varibles. For 5 variables entering TFP in 2002 and 2007 we show the dispersion is significantly smaller in the Census mean-imputed versus the Census non-imputed data. As an alternative to mean imputation we show how to use sequences of classification and regression trees (CART) to allow for a distribution of multiple possible impute values based on other plants that are CART-algorithmically determined to be similar based on other observed variables. For 90% of the 473 industries in 2002 and 84% of the 471 industries in 2007 we find that TFP dispersion increases as we move from Census mean-imputed to Census non-imputed to the CART-imputed data.

Research using the Agricultural Resource Management Survey (ARMS) and other data shows that direct government payments to farmers increase rents and the price of land. However, some ARMS data is imputed and does not account for relationships between payments and other variables. We investigate various imputation methods and benefits gained from a method with a wide scope rather than a parsimonious range of variables. Using our method, we estimate that an additional dollar of direct payment increases land value about $2.69 more per acre than ARMS imputation methods and that our imputations (using an exhaustive iterative sequential regression) outperform other methods and/or smaller models.

We examine the distortionary effects of agricultural policy on farm productivity by examining the response of U.S. tobacco farmers’ productivity to the quota buyout of 2004. We focus on the impact of distortionary policy, i.e., the tobacco quota, by decomposing aggregate productivity growth into the contribution of farm-level productivity growth and the contribution of reallocation of resources among tobacco growers. We find that the aggregate productivity of Kentucky tobacco farms grew 44% between 2002 and 2007. The elimination of quota rental costs and reallocation of resources, including entry and exit, accounted for most of the post-buyout productivity growth.

We build up from the plant level an “aggregate(d)” Solow residual by estimating every U.S. manufacturing plant's contribution to the change in aggregate final demand between 1976 and 1996. Our framework uses the Petrin and Levinsohn (2010) definition of aggregate productivity growth, which aggregates plant-level changes to changes in aggregate final demand in the presence of imperfect competition and other distortions/frictions. We decompose these contributions into plant-level resource reallocations and plant-level technical efficiency changes while allowing in the estimation for 459 different production technologies, one for each 4-digit SIC code. On average we find positive aggregate productivity growth of 2.2% in this sector during this period of declining share in U.S. GDP. We find that aggregate reallocation made a larger contribution to growth than aggregate technical efficiency. Our estimates of the contribution of reallocation range from 1.7% to 2.1% per year, while our estimates of the average contribution of aggregate technical efficiency growth range from 0.2% to 0.6% per year. In terms of cyclicality, the aggregate technical efficiency component has a standard deviation that is roughly from 50% to 100% larger than that of aggregate total reallocation, pointing to an important role for technical efficiency in macroeconomic fluctuations. Aggregate reallocation is negative in only 3 of the 20 years of our sample, suggesting that the movement of inputs to more highly valued activities on average plays a stabilizing role in manufacturing growth. Our results have implications for both the theoretical literature on growth and alternative indexes of aggregate productivity growth based only on technical efficiency.

  • Replication code for "The Impact of Plant-level Resource Reallocations and Technical Progress on U.S. Macroeconomic Growth."

Robbins, Michael W. and T. Kirk White. 2011. "Farm Commodity Payments and Imputation in the Agricultural Resource Management Survey." American Journal of Agricultural Economics, 93 (2): 606-612.

In this paper we attempt to bridge the gap between agricultural economics and the statistical literature on imputation for missing data. We present a new method of imputation and apply it to data on commodity payments and off-farm household income in the 2008 Agricultural Resource Management Survey (ARMS). We find that the new imputations lower the estimated thresholds of household income at the 50th, 75th, and 90th percentiles of the commodity payments distribution by about 4% to 6% compared with the official NASS and ERS imputations. These differences are not huge. However, only about4%of non zero direct and countercyclical payments records are imputed. Furthermore, although commodity payments variables are reported in exact dollar amounts, off-farm income items are reported in only very broad dollar ranges, especially at the upper end of the income distribution. This may reduce the scope for improvements in imputations, since even the observed income items are measured with a large amount of error. Nevertheless, relative to 2003,we find a large shift in commodity payments toward higher-income households, continuing a trend found in earlier research. Furthermore, when we artificially make missing some of the observed data on commodity payments, we find that the new imputation method does a much better job of capturing the entire distribution of the real data. Note that the current NASS method was inadequate even under the assumption that the data were Missing Completely At Random (MCAR).

This paper uses confidential Decennial Census of Population and Housing data, specifically the 1990 and 2000 Census Long Form data, to study the income dispersion of recent cohorts of migrants to mixed-income neighborhoods. We investigate whether neighborhoods with high levels of income dispersion attract economically diverse in-migrants. If recent in-migrants to mixed-income neighborhoods exhibit high levels of income dispersion, this is consistent with stable mixed-income neighborhoods. If, however, mixed-income neighborhoods are comprised of homogenous low-income (high-income) cohorts of long-term residents combined with homogenous high-income (low-income) cohorts of recent arrivals, this is consistent with neighborhood transition. Our results indicate that neighborhoods with high levels of income dispersion do in fact attract a much more heterogeneous set of in-migrants, particularly from the tails of the income distribution. Our results also suggest that the residents of mixed-income neighborhoods may be less heterogeneous with respect to lifetime income.

This paper uses confidential Census data, specifically the 1990 and 2000 Census Long Form data, to study demographic processes in neighborhoods that gentrified during the 1990s. In contrast to previous studies, the analysis is conducted at the more refined census-tract level, with a narrower definition of gentrification and more closely matched comparison neighborhoods. Furthermore, our access to individual-level data with census tract identifiers allows us to separately identify recent in-migrants and long-term residents. Our results indicate that, on average, the demographic flows associated with the gentrification of urban neighborhoods during the 1990s are not consistent with displacement and harm to minority households. In fact, taken as a whole, our results suggest that gentrification of predominantly black neighborhoods creates neighborhoods that are attractive to middle-class black households.

Black–white wealth inequality is much greater than black–white earnings inequality in the United States. The existing empirical literature has not been able to fully explain the wealth gap. This paper investigates how much of current black–white income and wealth inequality can be explained by initial conditions at Emancipation and nearly 100 years of segregated schools. A two-sector model with group-specific human capital accumulation and school expenditure differences can explain the path of black–white convergence in earnings over the past 130 years. The model also reproduces the fact that black–white wealth ratios remain much lower than black–white earnings ratios.

  • White, T. Kirk. 2005. "Inequality and the Case for Redistribution: Aristotle to Sen." International Review of Economics and Business, 50 (2): 145-168.

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