This paper sheds light on two problems in the Penn World Table (PWT) GDP estimates. First, we show that these estimates vary substantially across different versions of the PWT despite being derived from very similar underlying data and using almost identical methodologies; that this variability is systematic; and that it is intrinsic to the methodology deployed by the PWT to estimate growth rates. Moreover, this variability matters for the cross-country growth literature. While growth studies that use low frequency data remain robust to data revisions, studies that use annual data are less robust. Second, the PWT methodology leads to GDP estimates that are not valued at purchasing power parity (PPP) prices. This is surprising because the raison d'être of the PWT is to adjust national estimates of GDP by valuing output at common international (purchasing power parity [PPP]) prices so that the resulting PPP-adjusted estimates of GDP are comparable across countries. We propose an approach to address these two problems of variability and valuation.JEL Classification: O11, O40, O47. Before we examine this problem of data variability, we need to describe briefly what the PWT was designed to do. The pioneering work of Irving Kravis, Alan Heston, and Robert Summers, which led to the Penn World Table data, was aimed at converting national measures of GDP and income into internationally comparable estimates. Cross-country comparisons could not be based on national GDP data because these were valued at domestic prices. Since some goods and especially services were known to be cheaper in poor countries compared to rich countries, adjustments needed to be made to the valuation of these goods and services so that they could be made internationally comparable. These adjustments were made by calculating common international prices-the so-called purchasing power parity (PPP) prices-for all goods and services. With these PPP adjustments, GDP could then be compared across countries. Keywords: Penn World1 Roughly two-thirds of all cross-country empirical work is based on PWT. Second place is held by the World Bank's World Development Indicators (WDI), which were originally based on the PWT but have subsequently diverged. The IMF's World Economic Outlook (WEO) dataset places a distant third. 2A large literature has assessed the basic methodology employed by the PWT for determining these PPPs. In addition to the series of papers by Kravis, Heston, and Summers (1978) and Summers and Heston (1980, notable contributions include Ciccone and Jarocinski The focus of this paper, as in Katayama and Ponomareva (2010), is the time dimension of the PWT, specifically the data revisions across versions of the PWT. Unlike Katayama and Ponomareva, however, we concentrate on the sources of variation in growth rates between versions 6.1 and 6.2 for which the underlying price data used for the construction of PPP-adjusted GDP growth rates are the same. Therefore, the main focus of our paper is the PWT methodology for constructing growth rates...

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Immunotherapies targeting aspects of T cell functionality are efficacious in many solid tumors, but pancreatic ductal adenocarcinoma (PDAC) remains refractory to these treatments. Deeper understanding of the PDAC immune ecosystem is needed to identify additional therapeutic targets and predictive biomarkers for therapeutic response and resistance monitoring. To address these needs, we quantitatively evaluated leukocyte contexture in 135 human PDACs at single-cell resolution by profiling density and spatial distribution of myeloid and lymphoid cells within histopathologically defined regions of surgical resections from treatment-naive and presurgically (neoadjuvant)–treated patients and biopsy specimens from metastatic PDAC. Resultant data establish an immune atlas of PDAC heterogeneity, identify leukocyte features correlating with clinical outcomes, and, through an in silico study, provide guidance for use of PDAC tissue microarrays to optimally measure intratumoral immune heterogeneity. Atlas data have direct applicability as a reference for evaluating immune responses to investigational neoadjuvant PDAC therapeutics where pretherapy baseline specimens are not available. Significance: We provide a phenotypic and spatial immune atlas of human PDAC identifying leukocyte composition at steady state and following standard neoadjuvant therapies. These data have broad utility as a resource that can inform on leukocyte responses to emerging therapies where baseline tissues were not acquired. This article is highlighted in the In This Issue feature, p. 1861

We introduce the first publicly available data set of constant‐quality house price indices for counties, ZIP codes and census tracts in the United States, at an annual frequency, over a 40‐year period. Between 1990 and 2015, house price gradients within large cities steepen, documenting a reversal of decades of increasing relative desirability of suburban locations. Real house prices are more likely to be nonstationary near the centers of large cities. Within‐city differences in house price appreciation at the ZIP code level are, on average, about half of between‐city differences, though this ratio varies depending on the time period and city size.

Fig. 1. Immune cell infiltration of lung carcinoma-in-situ lesions. (a-b) Immunohistochemistry images of (a) progressive CIS lesion and (b) regressive CIS lesion with CD4+ cells stained in brown, CD8+ cells in red and FOXP3+ in blue. Immune cells are separately quantified within the CIS lesion and in the surrounding stroma. c) Combined quantitative immunohistochemistry data of CD4, CD8 and FOXP3 staining (n=44; 28 progressive, 16 regressive) with total lymphocyte quantification from H&E images (n=116; 69 progressive, 47 regressive) shown. We observe increased lymphocytes (p=0.023) and CD8+ cells (p=0.037) per unit area of epithelium within regressive CIS lesions compared to progressive. Stromal regions adjacent to CIS lesions showed no significant differences in immune cells between progressive and regressive lesions. p-values are calculated using linear mixed effects models to account for samples from the same patient; *p<0.05. 2 | bioRχiv Pennycuick et al. | Immune surveillance in clinical regression of pre-invasive squamous cell lung cancer .