Daniel Wilson

We examine the impact of systematic media reporting on household inflation expectations, focusing on how selective news coverage influences household responses to inflation news. In a model where monitoring all economic developments is costly, households will account for news selection when forming inflation expectations. The model implies an asymmetry: news about high inflation influences inflation expectations more than news about low inflation. Using micro panel data, we find support for this hypothesis. Exposure to news about higher prices increases household inflation expectations by approximately 0.4 percentage point, whereas exposure to news about lower prices has no discernible effect.

Internal migration has been cited as a key channel by which societies will adapt to climate change. We show in this paper that this process has already been happening in the United States. Over the course of the past 50 years, the tendency of Americans to move from the coldest places (“snow belt”), which have become warmer, to the hottest places (“sun belt”), which have become hotter, has steadily declined. In the latest full decade, 2010-2020, both county population growth and county net migration rates were essentially uncorrelated with the historical means of either extreme heat days or extreme cold days. The decline in these correlations over the past 50 years is true across counties, across commuting zones, and across states. It holds for urban and suburban counties; for rural counties the correlations have even reversed. It holds for all educational groups, with the sharpest decline in correlations for those with four or more years of college. Among age groups, the pattern is strongest for age groups 20-29 and 60-69, suggestive of climate being an especially important factor for those in life stages involving long-term location choices. Given climate change projections for coming decades of increasing extreme heat in the hottest U.S. counties and decreasing extreme cold in the coldest counties, our findings suggest the “pivoting” in the U.S. climate-migration correlation over the past 50 years is likely to continue, leading to a reversal of the 20th century snow belt to sun belt migration pattern.

This paper examines how the spatial distribution of people and jobs in the United States has been and will be impacted by climate change. Using novel county-level weather data from 1951 to 2020, we estimate the longer-run effects of climate on local population, employment, wages, and house prices using a panel polynomial distributed lag (PDL) model. This model and the long historical data help capture important aspects of local climate changes, such as trends in temperature. The historical results point to long-lasting negative effects of extreme temperatures on each of the outcomes examined. A long lag structure is necessary to appropriately capture the longer-run effects of climate change, as short-run effects are small. Using county-level weather projections based on alternative greenhouse gas emissions scenarios, we use the estimated models to project the spatial distribution of these local economic outcomes out to 2050. Our results point to substantial reallocations of people and jobs across the country over the next three decades, with mobility increasing by between 35 and nearly 100 percent depending on the scenario. Population and employment are projected to shift away from the Sunbelt and toward the North and Mountain West.

wp2023-17.zip – Download high resolution pdf (zipped file)

This paper reviews selected fiscal policy initiatives undertaken by US states to encourage job creation and innovation. We begin with a discussion of some general considerations about the design of tax policies summarized in a tax policy design table. Four policies are reviewed: job creation tax credits, research and development tax credits, a set of tax policies targeted to the biotechnology industry, and a broad set of tax policies that attract star scientists. The experiences at the state level are used to evaluate the effectiveness of these employment and knowledge-capital tax incentives in creating jobs and spurring innovation. The paper concludes with four other considerations need to be taken into account in selecting policies.

Because federal infrastructure spending largely takes the form of grants to state governments, the macroeconomic impact of such packages depends on the share of federal grants that “passes through” to actual infrastructure spending done by states. A low degree of pass-through would tend to mute the economic impact from federal grants, reflecting a crowd-out effect on state spending. We first revisit Knight’s (2002) influential finding of near-zero pass-through (perfect crowd out) of federal highway grants. That result is found to be specification-sensitive and is reversed completely in a longer sample, with estimates implying dollar-for-dollar pass-through of grants to spending. We then extend the analysis to allow for dynamics. We find a contemporaneous pass-through effect of about 1 and a longer-run cumulative effect of around 1.3. In the parlance of public finance, the flypaper effect is strong.

We study the conditions under which fiscal foresight – forward-looking agents anticipating future policy changes – results in perverse economic behavior through unintended intertemporal tradeoffs. Somewhat surprisingly, fiscal foresight by itself is far from sufficient for policy-induced incentives to perversely distort firm behavior. Rather, we show that there are two additional sets of conditions, at least one of which must hold to generate perverse behavior: (i) storable output, diminishing returns, and a non- competitive output market; (ii) “rolling base” policy design and storable output. These conditions suggest that the estimated impacts of fiscal policies may be sensitive to underlying economic or legislative characteristics and that policies targeted to specific firms or industries with unique characteristics may not be generalizable.

This paper develops an econometric panel data model that can be used both to identify the dynamic effects of disease transmission factors and to forecast disease spread. The empirical model is derived from the canonical SIR epidemiological model of infectious disease spread. The model is estimated using near real-time, county-level data on mobility, weather, and COVID-19 cases. Both mobility and weather are found to have significant effects on COVID-19 effects up to 70 days ahead. Predicted values from the estimated model, augmented to incorporate recent vaccinations, provide out-of-sample forecasts of COVID-19 infections at the county and national levels. Prior forecasts are shown to have been fairly accurate, especially in terms of the geographical/cross-sectional distribution of COVID-19 infections and in terms of the national aggregate forecast. The latest forecasts, using data through February 19, 2021, predict steep declines in infections in most parts of the country over the next several weeks. Nationally, infections are predicted to fall by 59% over the subsequent 30 days. Decomposing the drivers of the latest forecast, the model indicates that accumulated natural immunity (i.e., cumulative infections to date, a.k.a. “seroprevalence”) is the primary factor exerting a strong downward pull on new infections.

This paper exploits vast granular data – over 10 million county-industry-month observations – to estimate dynamic panel data models of weather’s short-run employment effects. I estimated the contemporaneous and cumulative effects of temperature, precipitation, snowfall, the frequency of very hot days, the frequency of very cold days, and natural disasters on private nonfarm employment growth. The short-run effects of weather vary considerably across sectors and regions. Favorable weather in one county has positive spillovers to nearby counties but negative spillovers to distant counties. Local climate mediates weather effects: economies are less sensitive to types of weather they are accustomed to.

A large body of past research, looking across countries, states, and metropolitan areas, has found positive and statistically significant associations between income inequality and mortality. By contrast, in recent years more robust statistical methods using larger and richer data sources have generally pointed to little or no relationship between inequality and mortality. This paper aims both to document how methodological shortcomings tend to positively bias this statistical association and to advance this literature by estimating the inequality-mortality relationship. We use a comprehensive and rich new data set that combines U.S. county-level data for 1990 and 2000 on age-race-gender-specific mortality rates, a rich set of observable covariates, and previously unused Census data on local income inequality (Gini index and three income percentile ratios). Using panel data estimation techniques, we find evidence of a statistically significant negative relationship between mortality and inequality. This finding that increased inequality is associated with declines in mortality at the county level suggests a change in course for the literature. In particular, the emphasis to date on the potential psychosocial and resource allocation costs associated with higher inequality is likely missing important offsetting positives that may dominate.

This document describes the construction of and data sources for a state-level panel data set measuring output and factor use for the manufacturing sector. These data are a subset of a larger, comprehensive data set that we currently are constructing and hope to post on the FRBSF website in the near future. The comprehensive data set will cover the U.S. manufacturing sector and may be thought of as a state-level analog to other widely used productivity data sets such as the industry-level NBER Productivity Database or Dale Jorgenson’s “KLEM” database or the country-level Penn World Tables, but with an added emphasis on adjusting prices for taxes. The selected variables currently available for public use are nominal and real gross output, nominal and real investment, and real capital stock. The data cover all fifty states and the period 1963 to 2006.

public_cwdata.dta – Stata public use Chirinko-Wilson state manufacturing panel data
public_cwdata.csv – CSV public use Chirinko-Wilson state manufacturing panel data

This paper empirically assesses the theory of interpersonal income comparison using a unique data set on suicide deaths in the United States. We treat suicide as a choice variable, conditional on exogenous risk
factors, reflecting one’s assessment of current and expected future utility. Using this framework we examine whether differences in group-specific suicide rates are systematically related to income dispersion, controlling for socio-demographic characteristics and income level. The results strongly
support the notion that individuals consider relative income in addition to absolute income when evaluating their own utility. Importantly, the findings suggest that relative income affects utility in a two-sided manner, meaning that individuals care about the incomes of those above them (the Joneses) and those below them (the Smiths). Our results complement and extend those from studies using subjective survey data or data from controlled experiments.

We use nearly four decades of U.S. county data to study dynamic local economic impacts of natural disasters that trigger federal aid. We find these disasters on average raise personal income per capita in the longer run (8 years out). We also find that, in the longer run, wages and home prices are higher, while employment and population are unaffected, suggesting the income boost may reflect productivity increases and greater demand for housing in supply-constrained areas or compositional shifts. Allowing for heterogeneity across disaster types, we find the longer-run income boost is driven primarily by hurricanes and tornadoes. We also find the longer-run boost increases with damages, suggestive of an important role for insurance and government aid—which are highly correlated with damages—in fueling recovery. A spatial spillover analysis suggests the longer-run net effects of local aid-inducing disasters for wider regions are near-zero.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1–4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

This paper studies the effects of job creation tax credits (JCTCs) enacted by U.S. states between 1990 and 2007 to gain insights about fiscal foresight (alterations of current behavior by forward-looking agents in anticipation of future policy changes). Nearly half of the states adopted JCTCs during this period, and their experiences provide a rich source of information for assessing the quantitative importance of fiscal foresight. We investigate whether JCTCs affect employment growth before, at, and after the time they go into effect. A theoretical model identifies three key conditions necessary for fiscal foresight, captures the effects of the rolling base feature of JCTCs, and generates several empirical predictions. We evaluate these predictions in a difference-in-difference regression framework applied to monthly panel data on employment, the JCTC effective and legislative dates, and various controls. Failing to account for the distorting effects of fiscal foresight can result in upwardly biased estimates of the impact of the JCTC fiscal policy by as much as 37%. We also find that the cumulative effect of the JCTCs is positive, but it takes several years for the full effect to be realized. The cost per job created is approximately $17,000, which is low relative to cost estimates of recent federal fiscal programs. This figure implies a fiscal multiplier on JCTC tax expenditures of about 1.9.

We study the effect of state-level estate taxes on the geographical location of the Forbes 400 richest Americans and its implications for tax policy. We use a change in federal tax law to identify the tax sensitivity of the ultra-wealthy’s locational choices. Before 2001, some states had an estate tax and others didn’t, but the tax liability for the ultra-wealthy was independent of their domicile state due to a federal credit. In 2001, the credit was phased out and the estate tax liability for the ultra-wealthy suddenly became highly dependent on domicile state. We find the number of Forbes 400 individuals in estate tax states fell by 35% after 2001 compared to non-estate tax states. We also find that billionaires’ sensitivity to the estate tax increases significantly with age. Overall, billionaires’ geographical location appears to be highly sensitive to state estate taxes. We then estimate the effect of billionaire deaths on state tax revenues. We find a sharp increase in tax revenues in the three years after a Forbes billionaire death, totaling $165 million for the average billionaire. In the last part of the paper, we study the implications of our findings for state tax policy. We estimate the revenue costs and benefits for each state of having an estate tax. The benefit is the one-time tax revenue gain when a wealthy resident dies, while the cost is the foregone income tax revenues over the remaining lifetime of those who relocate. Surprisingly, despite the high estimated tax mobility, we find that the benefit exceeds the cost for the vast majority of states.

We propose a new approach to estimating central bank preferences, including the implicit inflation target, that requires no priors on the underlying macroeconomic structure nor observation of monetary policy actions. Our approach entails directly estimating the central bank’s objective function from the sentiment expressed by policymakers in their internal meetings. We apply the approach to the objective function of the U.S. Federal Open Market Committee (FOMC). The results challenge two key aspects of conventional wisdom regarding FOMC preferences. First, the FOMC had an implicit inflation target of approximately 1½ percent on average over our baseline 2000 – 2011 sample period, significantly below the commonly-assumed value of 2. Second, the FOMC’s loss depends strongly on output growth and stock market performance and less so on their perception of current economic slack.

This paper demonstrates state-of-the-art text sentiment analysis tools while developing a new time-series measure of economic sentiment derived from economic and financial newspaper articles from January 1980 to April 2015. We compare the predictive accuracy of a large set of sentiment analysis models using a sample of articles that have been rated by humans on a positivity/negativity scale. The results highlight the gains from combining existing lexicons and from accounting for negation. We also generate our own sentiment-scoring model, which includes a new lexicon built specifically to capture the sentiment in economic news articles. This model is shown to have better predictive accuracy than existing “off-the-shelf” models. Lastly, we provide two applications to the economic research on sentiment. First, we show that daily news sentiment is predictive of movements of survey-based measures of consumer sentiment. Second, motivated by Barsky and Sims (2012), we estimate the impulse responses of macroeconomic variables to sentiment shocks, finding that positive sentiment shocks increase consumption, output, and interest rates and dampen inflation.

This paper exploits vast granular data—with over one million county-month observations—to estimate a dynamic panel data model of weather’s local employment effects. The fitted county model is then aggregated and used to generate in-sample and rolling out-of-sample (nowcast) estimates of the weather effect on national monthly employment. These nowcasts, which use only employment and weather data available prior to a given employment report, are significantly predictive not only of the surprise component of employment reports but also of stock and bond market returns on the days of employment reports.

Dramatic declines in capital tax rates among U.S. states and European countries have been linked by many commentators to tax competition, an inevitable “race to the bottom,” and underprovision of local public goods. This paper analyzes the reaction of capital tax policy in a given U.S. state to changes in capital tax policy by other states. Our study is undertaken with a novel panel data set covering the 48 contiguous U.S. states for the period 1965 to 2006 and is guided by the theory of strategic tax competition. The latter suggests that capital tax policy is a function of “foreign” (out-of-state) tax policy, preferences for government services, home state and foreign state economic and demographic conditions. The slope of the reaction function – the equilibrium response of home state to foreign state tax policy – is negative, contrary to casual evidence and many prior empirical studies of fiscal reaction functions. This result, which stands in contrast to most published findings, is due to two critical elements that allow for delayed responses to foreign tax changes and responses to aggregate shocks. Omitting either of these elements leads to a misspecified model and a positively sloped reaction function. Our results suggest that the secular decline in capital tax rates, at least among U.S. states, reflects synchronous responses among states to common shocks rather than competitive responses to foreign state tax policy. While striking given prior empirical findings, these results are fully consistent with the implications of the theoretical model developed in this paper and presented elsewhere in the literature. Rather than “racing to the bottom,” our findings suggest that states are “riding on a seesaw.” Consequently, tax competition may lead to an increase in the provision of local public goods, and policies aimed at restricting tax competition to stem the tide of declining capital taxation are likely to be ineffective.

cwdata_extract.csv – Data file_CSV format
cwdata_extract.dta – Data file_Stata format

Using data on the universe of U.S. patents filed between 1976 and 2010, we quantify how sensitive is migration by star scientist to changes in personal and business tax differentials across states. We uncover large, stable, and precisely estimated effects of personal and corporate taxes on star scientists’ migration patterns. The long run elasticity of mobility relative to taxes is 1.6 for personal income taxes, 2.3 for state corporate income tax and -2.6 for the investment tax credit. The effect on mobility is small in the short run, and tends to grow over time. We find no evidence of pre-trends: Changes in mobility follow changes in taxes and do not to precede them. Consistent with their high income, star scientists’ migratory flows are sensitive to changes in the 99th percentile marginal tax rate, but are insensitive to changes in taxes for the median income. As expected, the effect of corporate income taxes is concentrated among private sector inventors: no effect is found on academic and government researchers. Moreover, corporate taxes only matter in states where the wage bill enters the state’s formula for apportioning multi-state income. No effect is found in states that apportion income based only on sales (in which case labor’s location has little or no effect on the tax bill). We also find no evidence that changes in state taxes are correlated with changes in the fortunes of local firms in the innovation sector in the years leading up to the tax change. Overall, we conclude that state taxes have significant effect of the geographical location of star scientists and possibly other highly skilled workers. While there are many other factors that drive when innovative individual and innovative companies decide to locate, there are enough firms and workers on the margin that relative taxes matter.

We examine how state governments adjusted spending in response to the large temporary increase in federal highway grants under the 2009 American Recovery and Reinvestment Act (ARRA). The mechanism used to apportion ARRA highway grants to states allows us to isolate exogenous changes in these grants. We find that states increased highway spending over 2009 to 2011 more than dollar-for-dollar with the ARRA grants they received. We examine whether rent-seeking efforts could help explain this result. We find states with more political contributions from the public-works sector tended to spend more out of their ARRA highway funds than other states.

We evaluate the effects of state-provided financial incentives for biotech companies, which are part of a growing trend of placed-based policies designed to spur innovation clusters. We estimate that the adoption of subsidies for biotech employers by a state raises the number of star biotech scientists in that state by about 15 percent over a three year period. A 10% decline in the user cost of capital induced by an increase in R&D tax incentives raises the number of stars by 22%. Most of the gains are due to the relocation of star scientist to adopting states, with limited effect on the productivity of incumbent scientists already in the state. The gains are concentrated among private sector inventors. We uncover little effect of subsidies on academic researchers, consistent with the fact that their incentives are unaffected. Our estimates indicate that the effect on overall employment in the biotech sector is of comparable magnitude to that on star scientists. Consistent with a model where workers are fairly mobile across states, we find limited effects on salaries in the industry. We uncover large effects on employment in the non-traded sector due to a sizable multiplier effect, with the largest impact on employment in construction and retail. Finally, we find limited evidence of a displacement effect on states that are geographically close, or states that economically close as measured by migration flows.

Transportation spending often plays a prominent role in government efforts to stimulate the economy during downturns. Yet, despite the frequent use of transportation spending as a form of fiscal stimulus, there is little known about its short- or medium-run effectiveness. Does it translate quickly into higher employment and economic activity or does it impact the economy only slowly over time? This paper reviews the empirical findings in the literature for the United States and other developed economies and compares the effects of transportation spending to those of other types of government spending.

We assess the importance of interpersonal income comparisons using data on suicide deaths. We examine whether suicide risk is related to others’ income, holding own income and other individual and environmental factors fixed. We estimate models of the suicide hazard using two independent data sets: (1) the National Longitudinal Mortality Study and (2) the National Center for Health Statistics’ Multiple Cause of Death Files combined with the 5 percent Public Use Micro Sample of the 1990 decennial census. Results from both data sources show that, controlling for own income and individual characteristics, individual suicide risk rises with others’ income.

We examine the dynamic macroeconomic effects of public infrastructure investment both theoretically and empirically, using a novel data set we compiled on various highway spending measures. Relying on the institutional design of federal grant distributions among states, we construct a measure of government highway spending shocks that captures revisions in expectations about future government investment. We find that shocks to federal highway funding has a positive effect on local GDP both on impact and after 6 to 8 years, with the impact effect coming from shocks during (local) recessions. However, we find no permanent effect (as of 10 years after the shock). Similar impulse responses are found in a number of other macroeconomic variables. The transmission channel for these responses appears to be through initial funding leading to building, over several years, of public highway capital which then temporarily boosts private sector productivity and local demand. To help interpret these findings, we develop an open economy New Keynesian model with productive public capital in which regions are part of a monetary and fiscal union. We show that the presence of productive public capital in this model can yield impulse responses with the same qualitative pattern that we find empirically.

wp12-04bk.pdf – Working Paper 2012-04
wp12-04bkAppendices.pdf – Appendices to Working Paper 2012-04

This paper estimates the “jobs multiplier” of fiscal spending using the state-level allocations of federal stimulus funds from the American Recovery and Reinvestment Act (ARRA) of 2009. Because the level and timing of stimulus funds that a state receives are potentially endogenous, I exploit the fact that most of these funds were allocated according to exogenous formula factors such as the number of federal highway miles in a state or its youth share of population. The estimates imply that each million dollars of announced stimulus in a state was associated with approximately eight jobs created or saved in that state as of one year after the ARRA was enacted. The implied cost per job is about $125,000.

Suicide kills more Americans than die in motor accidents. Its causes remain poorly understood. We suggest in this paper that the level of others’ happiness may be a risk factor for suicide (although one’s own happiness likely protects one from suicide). Using U.S. and international data, the paper provides evidence for a paradox: the happiest places tend to have the highest suicide rates. The analysis appears to be the first published study to be able to combine rich individual-level data sets–one on life satisfaction in a newly available random sample of 1.3 million Americans and another on suicide decisions among an independent random sample of about 1 million Americans–to establish this dark-contrasts paradox in a consistent way across U.S. states. The study also replicates the finding for the Western industrialized nations. The paradox, which holds individual characteristics constant, is not an artifact of population composition or confounding factors (or of the ecological fallacy). We conclude with a discussion of the possible role of relative comparisons of utility.

The standard model of strategic tax competition–the noncooperative tax-setting behavior of jurisdictions competing for a mobile capital tax base–assumes that government policymakers are perfectly benevolent, acting solely to maximize the utility of the representative resident in their jurisdiction. We depart from this assumption by allowing for the possibility that policymakers also may be influenced by the rent-seeking (lobbying) behavior of businesses. Businesses recognize the factors affecting policymakers’ welfare and may make campaign contributions to influence tax policy. This extension to the standard strategic tax competition model implies that business contributions may affect not only the levels of equilibrium tax rates but also the slope of the tax reaction function between jurisdictions. Thus, business campaign contributions may directly influence business tax rates, as well as indirectly shape tax competition, and enhance or retard the mobility of capital across jurisdictions.

Based on a panel of 48 U.S. states and unique data on business campaign contributions, our empirical work uncovers four key results. First, we document a significant direct effect of business contributions on tax policy. Second, the economic value of a $1 business campaign contribution in terms of lower state corporate taxes is approximately $6.65. Third, the slope of the reaction function between tax policy in a given state and the tax policies of its competitive states is negative, and this slope is robust to business campaign contributions. Fourth, we document the sensitivity of the empirical results to state effects.

The proliferation of R&D tax incentives among U.S. states in recent decades raises two important questions: (1) Are these tax incentives effective in achieving their stated objective, to increase R&D spending within the state? (2) To the extent the incentives do increase R&D within the state, how much of this increase is due to drawing R&D away from other states? In short, this paper answers (1) “yes” and (2) “nearly all,” with the implication that the net national effect of R&D tax incentives on R&D spending is near zero. The paper addresses these questions by exploiting the cross-sectional and time-series variation in R&D tax credits, and in turn the user cost of R&D, among U.S. states from 1981-2004 to estimate an augmented version of the standard R&D factor demand model. I estimate an in-state user cost elasticity (UCE) around -2.5 (in the long-run), consistent with previous studies of the R&D cost elasticity. However, the R&D elasticity with respect to costs in neighboring states, which has not previously been investigated, is estimated to be around +2.5, suggesting a zero-sum game among states and raising concerns about the efficiency of state R&D credits from the standpoint of national social welfare.

wp05-08bk.pdf – Working Paper 2005-08, expanded form of published article.
RDusercost.xls – Data set

The use of subjective well-being (SWB) data for investigating the nature of individual preferences has increased tremendously in recent years. There has been much debate about the cross-sectional and time-series patterns found in these data, particularly with respect to the relationship between SWB and relative status. Part of this debate concerns how well SWB data measures true utility or preferences. In a recent paper, Daly, Wilson, and Johnson (2007) propose using data on suicide as a revealed preference (outcome-based) measure of well-being and find strong evidence that reference-group income negatively affects suicide risk. In this paper, we compare and contrast the empirical patterns of SWB and suicide data. We find that the two have very little in common in aggregate data (time series and cross-sectional), but have a strikingly strong relationship in terms of their determinants in individual-level, multivariate regressions. This latter result cross-validates suicide and SWB micro data as useful and complementary indicators of latent utility.

This article explores the relationship between capital composition and productivity using a unique,
detailed dataset on firm investment in the United States in the late 1990s. I develop a methodology for
estimating the separate effects of multiple capital types in a production function framework. I back out
the implied marginal products of each capital type and compare these with rental price data. I find that
although most capital types earned normal returns, information and communications technology capital
goods had marginal products substantially above their rental prices. The article also provides evidence of
complementarities and substitutabilities among capital types and between capital types and labor.

Over the past four decades, state investment tax incentives have proliferated. This emergence of state investment tax credits (ITC) and other investment tax incentives raises two important questions: (1) Are these tax incentives effective in achieving their stated objective, to increase investment within the state?; (2) To the extent these incentives raise investment within the state, how much of this increase is due to investment drawn away from other states? To begin to answer these questions, we construct a detailed panel data set for 48 states for 20-plus years. The dataset contains series on output and capital, their relative prices, and establishment counts. The effects of tax variables on capital formation and establishments are measured by the Jorgensonian user cost of capital that depends in a nonlinear manner on federal and state tax variables. Cross-jurisdictional differences in state investment tax credits and state corporate tax rates entering the user cost, combined with a panel that is long in the time dimension, are key to identifying the effectiveness of state investment incentives. Two models are estimated. The Capital Demand Model is motivated by the first-order condition for a profit-maximizing firm and relates at the state level the capital/output ratio to the relative user cost of capital. The Twin-Counties Model exploits both the spatial breaks (“discontinuities”) in tax policy at state borders and our panel data set to relate at the county level the relative user cost to the location of manufacturing establishments. Using the Capital Demand Model, we find that own-state capital formation is substantially increased by tax-induced reductions in the own-state price of capital and, more interestingly, substantially decreased by tax-induced reductions in the price of capital in competitive-states. Similarly, using our Twin-Counties Model, we find that county manufacturing establishment counts around state borders are higher on the side of the border with the lower price of capital, but the difference is economically small, suggesting that establishments are much less mobile than overall capital. Extensions of the Capital Demand Model also reveal that state capital tax policy appears to be a zero-sum game among the states in that an equiproportionate increase in own-state and competitive-states user costs tends to have no effect on own-state capital formation.

Recent research has indicated that investment in certain capital types, such as computers, has fostered accelerated productivity growth and enabled a fundamental reorganization of the workplace. However, remarkably little is known about the composition of investment at the micro level. This short paper takes an important first step in filling this knowledge gap by looking at the newly available micro data from the 1998 Annual Capital Expenditure Survey (ACES), a sample of roughly 30,000 firms drawn from the private, nonfarm economy. The paper establishes a number of stylized facts.
Among other things, I find that in contrast to aggregate data the typical firm tends to concentrate its capital expenditures in a very limited number of capital types, though which types are chosen varies greatly from firm to firm. In addition, computers account for a significantly larger share of firms’ incremental investment than they do of lumpy investment.

Micro and macro data integration should be an objective of economic measurement as it is
clearly advantageous to have internally consistent measurement at all levels of aggregation–firm, industry and aggregate. In spite of the apparently compelling arguments, there are few
measures of business activity that achieve anything close to micro/macro data internal
consistency. The measures of business activity that are arguably the worst on this dimension are
capital stocks and flows. In this paper, we document, quantify and analyze the widely different
approaches to the measurement of capital from the aggregate (top down) and micro (bottom up)
perspectives. We find that recent developments in data collection permit improved integration of
the top down and bottom up approaches. We develop a prototype hybrid method that exploits
these data to improve micro/macro data internal consistency in a manner that could potentially
lead to substantially improved measures of capital stocks and flows at the industry level. We
also explore the properties of the micro distribution of investment. In spite of substantial data
and associated measurement limitations, we show that the micro distributions of investment
exhibit properties that are of interest to both micro and macro analysts of investment behavior.
These findings help highlight some of the potential benefits of micro/macro data integration.

We estimate the rate of embodied technological change directly from plant-level manufacturing data on current output and input choices along with histories on their vintages of equipment investment. Our estimates range between 8 percent and 17 percent for the typical U.S. manufacturing plant during the years 1972-1996. Any number in this range is substantially larger than is conventionally accepted with some important implications. First, the role of investment-specific technological change as an engine of growth is even larger than previously estimated. Second, existing producer durable price indexes do not adequately account for quality change. As a result, measured capital stock growth is biased. Third, if accurate, the Hulten and Wykoff (1981) economic depreciation rates may primarily reflect obsolescence.

We look at disaggregated imports of various types of equipment to make inferences on cross-country differences in the composition of equipment investment. We make three contributions. First, we document strikingly large differences in investment composition. Second, we explain the differences as being based on each equipment type’s degree of complementarity with other factors whose abundance differs across countries. Third, we show that the composition of capital has the potential to account for some of the large observed differences in total factor productivity across countries.

In this paper, I develop a regression-based system of labor productivity
equations that account for capital-embodied technological change, and I incorporate this system into IDLIFT, a structural, macroeconomic input-output model of the U.S. economy. Builders of regression-based forecasting models have long had difficulty finding labor productivity equations that exhibit the “Solowian” property that movements in investment should cause accompanying movements in labor productivity. The production theory developed by Solow and others dictates that this causation is driven by the effect of traditional capital deepening as well as technological change embodied in capital. Lack of measurement of the latter has hampered the ability of researchers to estimate properly the productivity-investment relationship. Recent research by Wilson (2001) has alleviated this difficulty by estimating industry-level embodied technological change. In this paper, I utilize those estimates to construct capital stocks adjusted for technological change and then use these adjusted stocks to estimate Solow-type labor productivity equations. It is shown that replacing IDLIFT’s former productivity equations, based on changes in output and time trends, with the new equations, results in a convergence between the dynamic behavior of the
model and that predicted by traditional (Solowian) production theory.

This paper provides an exploratory analysis of whether data on the research and development (R&D) spending directed at particular technological/product fields can be used to measure industry-level capital-embodied technological change. Evidence from the patent literature suggests that the R&D directed at a product, as the main input into the “innovation” production function, is proportional to the value of the innovations in that product. I confirm this hypothesis by showing that the decline in the relative price of a good is positively correlated with the R&D directed at that product. The hypothesis implies that the technological change, or innovation, embodied in an industry’s capital is proportional to the R&D that is done (“upstream”) by the economy as a whole on each of the capital goods that a (“downstream”) industry purchases. Using R&D data from the National Science Foundation, I construct measures of capital-embodied R&D. I find they have a strong effect on conventionally measured total-factor productivity growth, a phenomenon that seems to be due partly to the mismeasurement of quality change in the capital stock and partly to a positive correlation between embodied and disembodied technological change. Finally, I find the cross-industry variation in empirical estimates of embodied technological change accord with the cross-industry variation in embodied R&D.

Using detailed data and a unique instrument set, estimates of returns to scale in U.S. manufacturing were obtained at various levels of aggregation. With a few key exceptions, empirical puzzles previously found are confirmed and further investigated. One implication of these findings is that there is essentially no evidence of large increasing returns necessary in many recent macro models. Also, the finding of significant heterogeneity among 4-digit sectors casts doubt on the use of the representative firm paradigm in macroeconomic modeling. These results suggest the presence of vast reallocation effects among firms within sectors, manifesting itself as decreasing returns.

There is an ongoing debate in the U.S. among policymakers and the courts concerning the practical effects of state investment tax incentives. However, this debate often suffers from a lack of clear information on the extent of such incentives among states and how these incentives have evolved over time. This paper takes a first step toward addressing this shortcoming. Compiling information from all 50 states and the District of Columbia over the past 40 years, we are able to paint a picture of the variation in state investment tax incentives across states and over time. In particular, we document three stylized facts: (1) Over the last 40 years, state investment tax incentives have become increasingly large and increasingly common among states; (2) these incentives, as well as the level of the overall after-tax price of capital, are to a large extent clustered in certain regions of the country; and (3) states that enact investment tax credits tend to do so around the same time as their neighboring states.

Over the last few decades, state and local governments increasingly have adopted tax and other policies to encourage economic development within their borders. These programs have recently come under attack as potentially inconsistent with the U.S. Supreme Court’s dormant Commerce Clause jurisprudence. In an opinion issued in late 2004, the Sixth Circuit Court of Appeals invalidated Ohio’s investment tax credit, contending that it discriminates against interstate commerce. The U.S. Supreme Court has granted certiorari in the case. In the meantime, similar litigation is underway in other states. In reaction to these developments, legislation has been introduced in Congress to protect the right of states to provide tax incentives. To shed light on the issues involved in these ongoing controversies, we offer an introduction to existing research concerning the economic effects of state tax incentives. There is a voluminous literature concerning the efficacy of state business subsidies. Surprisingly, however, very few econometric studies have examined the multistate impact of tax credits for physical investment (for example, the investment tax credit) or research and development (R&D tax credits). This focus may be due in part to the fact that, up until now, the issue was primarily one for state and local policymakers. Yet the interstate economic effects have significance for the Commerce Clause analysis of state tax incentives. Our goal is to provide a general introduction to these issues and to shed some light on the complexities involved in evaluating interstate economic effects.

This thesis develops new methods for measuring capital-embodied
technological change and its effects on productivity. Rates of embodied technological change are necessary to properly measure the productive stock of capital. Results from the hedonic pricing literature have been used for this purpose, though not without controversy.
In this dissertation, I first develop an alternative, production-side approach to
estimating embodied technological change. The method exploits the large variation in plant-level investment histories available in the Longitudinal Research Database at the U.S. Census Bureau. The empirical results show that the rate of embodied technological change (or, equivalently, obsolescence) in U.S. manufacturing from 1972-96 is between 7 and 17 percent. Any number in this range is substantially larger than price-based estimates.
A method of measuring embodied technological change via data on research
and development (R&D) is also developed. I propose an index that captures the amount of R&D embodied in an industry’s capital. Combining (and adjusting) data from the National Science Foundation and the Commerce Department, I construct a weighted average of the R&D done on the equipment capital that an industry purchases for 62 industries that span the U.S. private economy.
I find that the mean level of embodied R&D over 1972-96 is positively and
significantly correlated with the estimates of embodied technological change that I obtained in the first part of the dissertation. Furthermore, embodied R&D has a positive and significant effect on conventionally-measured total factor productivity growth (as one would expect if conventionally-measured capital stocks do not account for embodied technology).
Estimates of embodied technological change are used to construct
quality-adjusted measures of capital for the purpose of estimating industry-level labor productivity equations. These equations are incorporated into a full structural input-output forecasting model. Finally, the model’s behavior in response to shocks in investment is analyzed.