We explore the evolving significance of different production sectors within the U.S. economy since World War II and provide methods for estimating and forecasting these shifts. Using a compositional accounting approach, we find that the well-documented transition from goods to services is primarily driven by two compositional changes: 1) the rise of Intellectual Property Products (IPP) as an input producer, replacing Durable Goods almost one-for-one in terms of input shares in virtually all sectors; and 2) a shift in consumer spending from Nondurable Goods to Services. A structural model replicating these shifts reveals that the rise of IPP at the expense of Durable Goods is largely explained by increases in the efficiency of IPP inputs used in production: input-biased technical change. Trend variations in sectoral total factor productivity, and their attendant effects on relative prices and income, are the main driver of evolving consumption patterns. Both reduced-form and structural forecasts project these trends to continue over the next two decades, albeit at lower rates, indicating a slower pace of structural change.

Abstract Standard extreme value theory implies that the distribution of the largest observations of a large cross section is well approximated by a parametric model, governed by a location, scale and shape parameter. The extremes of a panel of independent cross sections are all governed by the same parameters as long as the underlying distribution as well as the size of the cross sections are time invariant. We derive inference about these parameters, and tests of the null hypothesis of time invariance, under asymptotics that do not require the number of extremes or the number of time periods to increase. We further apply Hamiltonian Monte Carlo techniques to estimate the path of time-varying parameters. We illustrate the approach in four examples of U.S. data: damages from weather-related disasters, financial returns, city sizes and firm sizes.

This paper proposes a model for, and investigates the consequences of, strong spatial dependence in economic variables. Our findings echo those of the corresponding “unit root” time series literature: Spatial unit root processes induce spuriously significant regression results, even with clustered standard errors or spatial HAC corrections. We develop large‐sample valid unit root and stationarity tests that can detect such strong spatial dependence. Finally, we use simulations to study strategies for valid inference in regressions with persistent spatial data, such as spatial analogues of first‐differencing transformations. Regressions from Chetty, Hendren, Kline, and Saez (2014) are used to illustrate the issues and methods.

We consider inference about a scalar coefficient in a linear regression with spatially correlated errors. Recent suggestions for more robust inference require stationarity of both regressors and dependent variables for their large sample validity. This rules out many empirically relevant applications, such as difference-in-difference designs. We develop a robustified version of the recently suggested SCPC method that addresses this challenge. We find that the method has good size properties in a wide range of Monte Carlo designs that are calibrated to real world applications, both in a pure cross sectional setting, but also for spatially correlated panel data. We provide numerically efficient methods for computing the associated spatial-correlation robust test statistics, critical values, and confidence intervals.

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.

Pre-existing pathogen-specific memory T cell responses can contribute to multiple adverse outcomes including autoimmunity and drug hypersensitivity. How the specificity of the T cell receptor (TCR) is subverted or seconded in many of these diseases remains unclear. Here, we apply abacavir hypersensitivity (AHS) as a model to address this question because the disease is linked to memory T cell responses and the HLA risk allele, HLA-B*57:01, and the initiating insult, abacavir, are known. To investigate the role of pathogen-specific TCR specificity in mediating AHS we performed a genome-wide screen for HLA-B*57:01 restricted T cell responses to Epstein-Barr virus (EBV), one of the most prevalent human pathogens. T cell epitope mapping revealed HLA-B*57:01 restricted responses to 17 EBV open reading frames and identified an epitope encoded by EBNA3C. Using these data, we cloned the dominant TCR for EBNA3C and a previously defined epitope within EBNA3B. TCR specificity to each epitope was confirmed, however, cloned TCRs did not cross-react with abacavir plus self-peptide. Nevertheless, abacavir inhibited TCR interactions with their cognate ligands, demonstrating that TCR specificity may be subverted by a drug molecule. These results provide an experimental road map for future studies addressing the heterologous immune responses of TCRs including T cell mediated adverse drug reactions.

The social cost of carbon dioxide (SC-CO 2 ) measures the monetized value of the damages to society caused by an incremental metric tonne of CO 2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit–cost analysis for over a decade, SC-CO 2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine 1 (NASEM) highlighted that current SC-CO 2 estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO 2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO 2 . Our preferred mean SC-CO 2 estimate is $185 per tonne of CO 2 ($44–$413 per tCO 2 : 5%–95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government’s current value of $51 per tCO 2 . Our estimates incorporate updated scientific understanding throughout all components of SC-CO 2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO 2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

Abstract We develop a Bayesian latent factor model of the joint long-run evolution of GDP per capita for 113 countries over the 118 years from 1900 to 2017. We find considerable heterogeneity in rates of convergence, including rates for some countries that are so slow that they might not converge (or diverge) in century-long samples, and a sparse correlation pattern (“convergence clubs”) between countries. The joint Bayesian structure allows us to compute a joint predictive distribution for the output paths of these countries over the next 100 years. This predictive distribution can be used for simulations requiring projections into the deep future, such as estimating the costs of climate change. The model's pooling of information across countries results in tighter prediction intervals than are achieved using univariate information sets. Still, even using more than a century of data on many countries, the 100-year growth paths exhibit very wide uncertainty.

We find disparate trend variation in TFP and labor growth across major U.S. production sectors over the post-WWII period. When aggregated, these sector-specific trends imply secular declines in the growth rate of aggregate labor and TFP. We embed this sectoral trend variation into a dynamic multi-sector framework in which materials and capital used in each sector are produced by other sectors. The presence of capital induces important network effects from production linkages that amplify the consequences of changing sectoral trends on GDP growth. Thus, in some sectors, changes in TFP and labor growth lead to changes in GDP growth that may be as large as three times these sectors' share in the economy. We find that trend GDP growth has declined by more than 2 percentage points since 1950, and that this decline has been primarily shaped by sector-specific rather than aggregate factors. Sustained contractions in growth specific to Construction, Nondurable Goods, and Professional and Business and Services make up close to sixty percent of the estimated trend decrease in GDP growth. In addition, the slow process of capital accumulation means that structural changes have endogenously persistent effects. We estimate that trend GDP growth will continue to decline for the next 10 years absent persistent increases in TFP and labor growth.

Set-up: The distribution of an SVAR-IV data set of size T , denoted PT , is indexed by (A, Θ0, F ); where A is the matrix of VAR slope coefficients, Θ0 is the matrix of contemporaneous responses, and F is the joint distribution of {εt, zt}t=1. To allow for models in which the correlation between the external instrument and the target structural shock can be arbitrarily close to zero, consider a sequence {PT }T =1 such that Assumption 1 holds. This means that EPT [ztε1,t] = αT , EPT [ztεj,t] = 0 for j 6= 1, and αT → 0.

We evaluate the hypothesis that the zero lower bound (ZLB) constraint was, in practice, irrelevant during the recent ZLB episode experienced by the US economy (the 2009Q1–2015Q4 period). We focus on two dimensions of economic performance that were ex ante likely to have been affected by a binding ZLB: (i) the volatility of macro variables and (ii) the economy’s response to shocks. Using a variety of empirical methods, we find little evidence against the irrelevance hypothesis, with our estimates suggesting that the responses of output, inflation, and the long-term interest rate were hardly affected by the binding ZLB constraint. We show how a shadow interest rate rule (which we take as a proxy for forward guidance) can reconcile our empirical findings with the predictions of a simple New Keynesian model with a ZLB constraint.

This paper studies Structural Vector Autoregressions in which a structural shock of interest (e.g., an oil supply shock) is identified using an external instrument. The external instrument is taken to be correlated with the target shock (the instrument is relevant) and to be uncorrelated with other shocks of the model (the instrument is exogenous). The potential weak correlation between the external instrument and the target structural shock compromises the large-sample validity of standard inference. We suggest a confidence set for impulse response coefficients that is not affected by the instrument strength (i.e., is weak-instrument robust) and asymptotically coincides with the standard confidence set when the instrument is strong.

We investigate the flattening Phillips relation by making two departures from standard specifications. First, we measure slack using real activity variables that are bandpass filtered or year-over-year changes in activity (these are similar), instead of gaps. Second, we study the components of inflation instead of the standard aggregates. We find that some inflation components have strong and stable correlations with the cyclical component of real activity; these components tend to be relatively well-measured and domestically determined. Other components, typically prices that are poorly measured or internationally determined, have weak and/or unstable correlations with cyclical activity. We construct a new inflation index, cyclically sensitive inflation, that weights the components by their joint cyclical covariation with real activity. The index has strong and stable correlations with cyclical activity and provides a real-time measure of cyclical movements in inflation.

Human leukocyte antigen (HLA) alleles have been implicated as risk factors for immune-mediated adverse drug reactions. The authors recently reported a strong association between HLA-A*32:01 and vancomycin-induced drug reaction with eosinophilia and systemic symptoms. Identification of individuals with the risk allele before or shortly after the initiation of vancomycin therapy is of great clinical importance to prevent morbidity and mortality, and improve drug safety and antibiotic treatment options. A prerequisite to the success of pharmacogenetic screening tests is the development of simple, robust, cost-effective single HLA allele test that can be implemented in routine diagnostic laboratories. In this study, the authors developed a simple, real-time allele-specific PCR for typing the HLA-A*32:01 allele. Four-hundred and fifty-eight DNA samples including 30 HLA-A*32:01–positive samples were typed by allele-specific PCR. Compared with American Society for Histocompatibility and Immunogenetics–accredited, sequence-based, high-resolution, full-allelic HLA typing, this assay demonstrates 100% accuracy, 100% sensitivity (95% CI, 88.43% to 100%), and 100% specificity (95% CI, 99.14% to 100%). The lowest limit of detection of this assay using PowerUp SYBR Green is 10 ng of template DNA. The assay demonstrates a sensitivity and specificity to differentiate the HLA-A*32:01 allele from closely related non–HLA-A*32 alleles and may be used in clinical settings to identify individuals with the risk allele before or during the course of vancomycin therapy.

We estimate that trends in TFP and employment growth have steadily declined across a majority of U.S. sectors over the post-war period. Most of the secular decline in aggregate TFP and employment growth results from the combination of sector-specific rather than aggregate disturbances. We embed these observations into a dynamic multi-sector framework in which materials and capital used in each sector are produced by other sectors. The presence of capital, in particular, induces quantitatively important sectoral multiplier effects from production linkages on GDP growth. Thus, in some sectors, the effects of changes in TFP or employment growth on GDP growth may be as large as 3 times their share in the economy. Taken together, structural changes across sectors have lowered trend GDP growth by around 2 percentage points since the early 1950s. Sustained contractions in growth specific to Durable and Non-Durable Goods, and most importantly Construction, account for close to 2=3 of this decline. Because of capital accumulation, structural changes have endogenously persistent effects. Consequently, we estimate that trend GDP growth will continue to decline for the next 10 years even if trend TFP and employment growth stabilize.

Trend GDP growth has slowed about 2.3 percentage points to 1.7% since 1950. Different economic sectors have contributed to this slowing to varying degrees depending on the distinct trends of technology and labor growth in each sector. The extent to which sectors influence overall growth depends on the degree of spillovers to other sectors, which amplifies the effect of sectoral changes. Three sectors with slowing growth and linkages to other sectors?construction, nondurable goods, and professional and business services?account for 60% of the decline in trend GDP growth.

We investigate the flattening Phillips relation by making two departures from standard specifications. First, we measure slack using real activity variables that are bandpass filtered or year-over-year changes in activity (these are similar), instead of gaps. Second, we study the components of inflation instead of the standard aggregates. We find that some inflation components have strong and stable correlations with the cyclical component of real activity; these components tend to be relatively well-measured and domestically determined. Other components, typically prices that are poorly measured or internationally determined, have weak and/or unstable correlations with cyclical activity. We construct a new inflation index, Cyclically Sensitive Inflation, that weights the components by their joint cyclical covariation with real activity. The index has strong and stable correlations with cyclical activity and provides a realtime measure of cyclical movements in inflation. James H. Stock Department of Economics Harvard University Littauer Center M26 Cambridge, MA 02138 and NBER James_Stock@harvard.edu Mark W. Watson Department of Economics Princeton University Princeton, NJ 08544-1013