dataesr/scientific-paragraphs-categorization huggingface dataset:
org/10. 5281/zenodo. 1221165  (Chowdhry Beeman, 2019). The CO 2 , ATS3 and individual isotopic series, as well as the chronological uncertainty, are included as. txt files. The original CO 2 data from  Marcott et al. (2014)  are available at https://www. ncdc. noaa. gov/paleo/study/18636. The synchronization tie points and original ATS3 stack are available in the Supplementary Data spreadsheet of  Buizert et al. (2018)   Author contributions. JCB and LG contributed equally to this manuscript, and should both be considered lead authors. LG, FP, DR and JCB designed the research project. JCB and FP designed the change point identification method. JCB and LG led the writing of the manuscript; and all authors contributed to analysis and discussion of the phasing and timings, with TJF, CB and EB in particular providing expertise on the ATS3 stack, the WD CO 2 record and their climatic context; and DR and FP on the lead-lag problem and its history.
Acknowledgements. We thank  Michael Sigl ,  Jinhwa Shin ,  Emmanuel Witrant  and  Amaelle Landais  for their support and great help discussing this work and  Mirko Severi  for his EDC data and support with the volcanic synchronization. This work is supported by the  Fondation Ars et Cuttoli  and by the  LEFE IceChrono  and  CO2Role  projects. Financial support. This research has been supported by the  Centre National de la Recherche Scientifique  (grant  LEFE IceChrono  and  LEFE CO2Role ) and the  Fondation Ars et Cuttoli  (grant  CO2Role ). Review statement. This paper was edited by  Hubertus Fischer  and reviewed by  Joel Pedro  and one anonymous referee.
RESULTS The results of radiocarbon dating are given in Table  S1  (N = 10 samples). All ages were calibrated using the OxCal 4. 3. 2 program (Bronk Ramsey, 2017) and the IntCal13 calibration curve  (Reimer et al. , 2013). Radiocarbon ages (Fig. 3 ) of the 10 cave bear bone samples collected from the analyzed sites range between c. 45 and 31 kyr cal BP (median values) for both surface and excavated finds. The majority of the dated samples of bears belong to 39-45 kyr cal BP interval -known for its faunal richness across caves from both the Carpathians and the rest of Eurasia -and to a last pulsation of this species, around 31 kyr cal BP (N = 2; from Cioclovina Cave) at a time believed to be close to its extinction  (Pacher and Stuart, 2009). <Insert Figure  3>  δ 13 C values The obtained δ 13 C values of all 112 dentine apatite samples range from -17.
ACKNOWLEDGEMENTS Thirty years ago,  Paul L. Koch ,  Daniel C. Fisher  and  David Dettman  published in Geology (1989) a study where they highlighted, for the first time, how dentine serial sampling of bear's canine can reveal paleoecological and paleoenvironmental insights from the past. Their study was an inspiration to us and therefore, this paper is dedicated to them. The authors are indebted to the  Apuseni Mountains Natural Park , to dr. I. N. Meleg , and to prof. dr. B. P. Onac  ( 'Emil Racovi ă' Institute of Speleology ) for their field assistance and scientific support. This work was financed by the  Romanian National Authority for Scientific Research and Innovation , through grants  PCE 197/2016  and  PCCF 16/2018.
In this paper, we propose empirical evidence and an original theoretical framework that suggest that the macroeconomic and fiscal consequences of international migration are positive for OECD countries. Most notably, we explore the role of changes in the age structure of the population induced by net flows of migrants. As noted by  Bloom et al. (2003)  and  Lee and Masson (2007) , an increase in the share of the working-age population can produce a demographic dividend with respect economic growth and effect virtuous cycles of wealth creation. Because many migrants are of working age 2  , we have focused our analysis on the demographic aspect of migration and are able to present robust evidence of the existence of a demographic dividend of international migration in OECD countries. We estimate a structural vector autoregression (VAR) on a panel of 19 OECD countries with annual data for 1980-2015.
, 2018; Furlanetto and Robstad, 2016). In d  'Albis et al. (2018) , we estimated a VAR model for a panel of 15 European countries for 1985-2015 to analyze the effects of flows of asylum seekers and permanent migrants on economic and fiscal performance. Furlanetto and Robstad (2016)  use Norwegian data over the period 1990Q1-2014Q2 to evaluate the effect of shocks of immigrants from OECD countries on fiscal and macroeconomic variables. With respect to these recent studies, the contribution of the present paper is to identify demographic dividend of international migration. It examines the economic and fiscal effects that arise from the increase in the workforce due to net flows of migrants. It relies on the estimation of VAR models and uses a tractable general equilibrium model to explain the main mechanisms.
Data Our sample includes yearly observations from 1980 to 2015 for 19 OECD countries that are selected to have a set of long-span data. We consider all of the OECD member countries who signed the Convention on the OECD before 1980 for which the fiscal data are available over the whole sample period in Economic Outlook databases 4. Our sample includes 4 OECD non-European countries: Australia, Canada, Japan and the United States, and the 15 Western European countries: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Iceland, Italy, the Netherlands, Norway, Spain, Sweden, Portugal and the United Kingdom. These OECD countries, omitting Japan, are also the main destinations countries for international migrants 5.
Fiscal and Economic Variables Fiscal variables are obtained from the OECD Economic Outlook databases  (OECD, 2016). We consider three main variables: government purchases, transfers paid and tax revenues received by the general government. Consistent with the literature on fiscal multipliers  (Beetsma et al. , 2006 (Beetsma et al. , , 2008;; Beetsma and Guiliodori, 2011) , we compute the first fiscal variable, government purchases, as the sum of general government final consumption expenditure and general government fixed capital formation. The second variable, transfers paid by the general government, is computed as the sum of social security benefits and other current payments. The third variable, tax revenues collected by the general government, includes direct and indirect taxes on production and imports, social security contributions and other receipts.
All variables are expressed in real terms using the appropriate deflator (see Appendix A-1 for more details). Of these three variables, we define (i) net taxes as the difference between tax revenues received and transfers paid by the general government; (ii) public spending as the sum of the government purchases and transfers, and (iii) fiscal balance as the difference between general government tax revenues and spending. We also consider social public spending from the OECD Social Expenditure database (SOCX), which provides comparable data on public and private social expenditures for the 19 countries of our sample  (OECD, 2018a; Adema et al. , 2011). In the SOCX database, expenditures for social purposes are grouped in nine social-policy areas. In our estimates, we consider the four of these areas that are the most relevant for our analysis: old age, family, active labor market programs, and unemployment spending (see Appendix A-1 for more details).
Demographic Variables Demographic variables are from the Demography and migration (demo) database of Eurostat for the European countries and from OECD (2017) for the OECD non-European countries. We use the annual average population to express the economic and fiscal variables in per capita terms. We also consider the working-age average population to compute (i) the ratio of working-age to total population; (ii) the GDP to working-age population, and (iii) the employment rate as the total employment to working-age population (following the OECD and Eurostat definitions of employment rate). Net flow of migrants is evaluated using net migration data, expressed as a rate per 1000 population. The net migration is calculated by  Eurostat and OECD (2017)  as the difference between the total change and the natural change of the "usual resident" population.
Net migration then accounts for the difference between the number of immigrants and the number of emigrants. It makes no distinction between nationals and foreigners 6. To the best of our knowledge, net migration data are the only annual data available for the period 1980-2015 for the 19 OECD countries we consider. Most available data are related to stocks of immigrants and are usually obtained from population censuses. Moreover, they are usually available for limited periods, mostly at 10-year intervals. Some data (see for instance  Brücker et al. , 2013)  are available at 5-year intervals but they are built by interpolating variables observed at 10-year intervals. Furthermore, net migration data, which are based on population and vital statistics, are much more comparable than are data on inflows or outflows of foreigners published by  Eurostat, OECD (2015)  or United  Nations (2015).
The harmonized data on the inflows in 18 OECD countries of our sample have only been available since Our data allow for an analysis of the fiscal effect of international migration over time. This analysis complements the branch of the literature that uses data on the stock of immigrants and detailed fiscal information that distinguish fiscal contributions and the benefits of immigrants and natives. Due to comparability issues, most of these studies are country-specific except for  Liebig and Mo (2013) , which is based on 27 OECD countries using data collected around 2008.
As indicated in Appendix A-1, OECD Economic Outlook data do not provide details on government purchases or transfers that would allow the identification of the target population of the public spending. We therefore use SOCX data to analyze the effect of international migration on the age structure of public spending. More precisely, we are interested in public expenditures dedicated to the old-age We observe that our previous results are not modified. Interestingly, public spending, when computed as a share of GDP, significantly decreases in the year of the migration shock, which is due to a non-significant increase in public spending including transfers that is dominated by a significant increase in GDP the year of the migration shock. We now consider in the model including public spending and tax revenues, the effect of the net flow of migrants on social public expenditures dedicated to the youth population (family spending) and those expenditures dedicated to the old-age population (old-age spending).
Immigration and Labor Market Public Spending An important issue in OECD countries is the effect of international migration on labor market conditions. We thus go beyond our baseline model to now consider the fiscal implications of a net flow of migrants while explicitly taking into account the labor market. We extend our baseline model including the unemployment rate in the system as follows (Model 9): She showed that the arrival of migrants decreases the unemployment rate permanently. Z 9 it = [m it , g it , nt it , y it , u it ] , Then, we extended the model further, with public spending, tax revenues and unemployment rate, by considering public spending on labor market policies. Using SOCX data decomposition of public expenditures, we studied the effect of a migration shock considering the SOCX data social policy areas related to the labor market: active labor market programs and unemployment 15.
Acknowledgments We are very grateful to the editor,  Thomas A. Lubik , and two anonymous referees for their stimulating comments and helpful suggestions. We would like to thank  Thepthida Sopraseuth ,  Omar Licandro ,  Frédéric Dufourt , and participants at the  Theories and Methods in Macroeconomics  ( T2M ) conference 2018 and the  Sheffield Workshop on the Macroeconomics of Migration 2018  for discussion and useful comments on earlier drafts. The usual disclaimer applies. Finding This work was supported by the  Agence Nationale de la Recherche of the French government  through the  Investissements d'avenir  [ ANR-10-LABX-14-01] program.
80 Spain 3. 90 25283 5443 5104 9280 -4. 99 Sweden 3. 41 42471 12896 12149 23159 -4. 82 United Kingdom 1. 97 32722 6883 6614 12048 -4. 28 15 European Countries 2. 28 38177 9390 9645 17849 -4. 07 Australia 6. 55 47251 9990 7552 16125 -3. 24 Canada 5. 52 40955 10108 8653 16531 -5. 82 Japan -0. 08 39303 8363 6215 12265 -5. 53 United States 3. 67 41073 8047 8551 13228 -8. 20 19 OCDE countires 2. 62 39013 9335 9245 17152 -4. 42 Source: Authors' computations based on data from Eurostat and, OECD (2016), OECD (2017) and OECD (2018b) databases.
Labor market fluctuations by age In this section, we use CPS data for the male population 8  to study the age profile of transitions for 2 states in the labor market: from employment to unemployment (job separation) and from unemployment to employment (job finding). Using monthly CPS data between January 1976 and March 2013, we follow all steps described in  Shimer (2012). We compute sample-weighted gross flows between labor market states and seasonally adjusted time series using the same ratio-to-moving average technique as in  Shimer (2012). We correct these for time aggregation to account for the transitions that occur within the month. We then average the time series of these instantaneous transition rates for each age group on a quarterly basis to reduce noise, which gives quarterly data about workers' instantaneous transition rates (job separation rates JSR t and job finding rates JF R t ), and the corresponding unemployment conditional steady state u t = JSRt JSRt+JF Rt.
We follow  Fujita (2011)  who uses structural VARs with sign restrictions in order to study US worker reallocation along the business cycle. We estimate a structural VAR for each age group (1976Q1-2013Q2). A first trivariate VAR includes US CPI inflation, job separation and finding for male young workers. The second (and third) VAR includes US CPI inflation, job separation and finding for male prime-age (old) workers. As in  Fujita (2011) , we detrend the data using deterministic quadratic trends. Lag length is set to 1 based on the AIC criteria. We impose sign restrictions to identify two aggregate shocks (supply and demand) and a reallocation shock. All sign restrictions are imposed for 1 quarter. • Restriction 1: the supply shock. In response to a positive aggregate supply shock, inflation and job separation respond negatively, while job finding increases • Restriction 2: the demand shock.
This change affected estimates of average (mean) duration of unemployment. This generated an unprecedented rise in the number of persons with very long durations of unemployment. 49 A. 8. 2 Our measure of unemployment duration across age-groups using matched CPS Basic monthly data In spite of the difficulties mentioned above, we propose here to measure unemployment duration. To abstract from the 2 CPS re-design (as there is little consensus on the appropriate way of correcting the survey data), we focus on data between 1995 and 2010. We show that business cycle volatility of unemployment increases with age, which is consistent with our stylized fact on the job finding rate. Empirical strategy Using CPS Monthly data, we compute age-specific unemployment duration using the answers to the question on how long the person is looking for work.
A13 wage of unemployed workers (wage conditional on separation). In his paper, using CPS data,  Mueller (2017)  shows that, in recessions, the pool of unemployed shifts towards high-wage workers. Mueller (2017)  stresses that the standard MP model with endogenous separation fails to account for this empirical feature. He then proposes two extensions to the model to bring the theory closer to the data: the first one is based on an ad-hoc calibration of the variance of match-specific productivity among high-ability workers, the second relies on credit constraints. We extend his empirical analysis by looking at age-groups. Data. We use Mueller (2017)'s data available of the American Economic Review website. We first replicate his results on the whole population using CPS March supplement. This annual survey collects information on wages in the prior year, which allows to compute the average wage from the previous year by current labor market status (employed versus unemployed).
C. 1 Measuring search effort There are two main data sets used to measure search effort by unemployed workers: the first one, the American Time Use Survey (ATUS), provides a measure of the time spent on job-searching activities per day, whereas the second, the Current Population Survey (CPS), gives an indirect measure via the types and number of search methods used. If the first measure is the most natural quantitative proxy for job search effort, ATUS suffers from its small sample size and its short sample period (starting in 2003), unlike the CPS. C. 2 Pro-cyclicality of aggregate search effort  Shimer (2004)  uses CPS data to build an indirect measure of search effort (the number of methods used). His main result is that this search effort measure is counter-cyclical. These first investigations are supported by the more recent work of  Mukoyama et al.
C. 3. 1 Levels of search effort falls with age Regarding search effort by age based on the ATUS data (the direct measure of the search effort),  Aguiar et al. (2013)  find that search effort has an inverted U-shaped profile with age. 55 Nevertheless, using time use surveys in the UK, Germany, France, Italy, and Spain (MTUS), they also show that search effort is an age-decreasing in all other countries. Hence, the US appear as an outliers with respect to this behavior. Aguiar et al. (2013)  do not provide any hint about the reason why the US seems an outlier. We suggest that this surprising result for the US comes from the use of the raw ATUS data, whereas some other components of the time use are age specific. With respect to  Aguiar et al. (2013) , we measure search effort as search activities divided by total time available during the day, which differs across age groups.
C. 3. 2 Search effort data in ATUS In order to estimate search effort by age, we use 2003-2015 waves of the ATUS (American Time Use Survey). The BLS conducts the ATUS based on a sample drawn from the outgoing panel of the CPS (Current Population Survey). Each respondent reports activities from the previous day. • Total length of each day by age. In the model, search effort is measured as time spent on activities undertaken to find a job relative to the total length of the period, that is normalized to 1. In the data, the total length of each day can differ across age as time spent on activities that are not modeled in the paper (such as sleep, eating and drinking, education) can differ across age. For each respondent, we compute the sum of activities that individuals do not choose in our model (personal care, eating and drinking, education, respectively mnemonics t01, t11 and t06, with the associated travel time t1801, t1811 and t1806).
Sampling and mtDNA Analysis The samples analyzed in this study are from populations from Oceania, Island South East Asia (ISEA), and the western part of the Indian Ocean (WIO). Our data set of 1,086 sequences (supplementary table S1, Supplementary Material online) includes only mtDNA sequences affiliated with mtDNA haplogroup B4a1a1 and its subclades and was compiled from 1) newly collected samples for which the whole mtDNA was sequenced (Banjar population from Borneo, Indonesia), 2) newly generated whole mtDNA sequences from selected samples with published HVS-I sequences affiliated with the Polynesian motif from the Western Indian Ocean (1 Somali, and 2 Yemeni from Abyan and Hadramawt region)  ( Cern y et al. 2008 ( Cern y et al. , 2016)) , and 3) previously published studies. Samples from the Southeast Borneo Banjar (n ¼ 99) were collected from healthy unrelated adult donors during the 2013 field season.
We followed sampling and DNA extraction procedures as described previously  (Kusuma et al. 2016). Complete mtDNA sequences were generated for all Banjar samples following the protocol described in  Brucato et al. (2017). Briefly, double bar-coded libraries were prepared and enriched for mtDNA, as described previously  (Maricic et al. 2010; Kircher et al. 2012). Consensus sequences were obtained after base-calling, quality filtering, and further quality control steps to obtain consensus sequences, as described previously  (Arias et al. 2018). Published databases were screened for mtDNA HVS-I sequences affiliated with haplogroup B4a1a1 and its subclades. In total, 14,461 samples from 186 populations were screened from East Africa and Southwestern Eurasia ( Cern y et al. 2016) and 2,785 samples from 35 populations from Southeast Asia  (Kusuma et al.
2015 (Kusuma et al. , 2016 (Kusuma et al. , 2017)). We identified two samples from Yemen and one sample from Somalia affiliated with the Polynesian motif (B4a1a1) based on HVS-I sequences  ( Cern y et al. 2016 ). These samples were whole genome amplified with the Illustra GenomiPhi V2 kit (GE Healthcare) following the manufacturer's instructions. The amplified samples underwent whole mtDNA sequencing following the methodology and checking procedures described in  Brandão et al. (2016)  for the Somali sample and in  Brucato et al. (2017)  for the Yemeni samples. A comparative data set was built by compiling all of the published complete mtDNA sequences affiliated with haplogroup B4a1a1 and its subclades by screening the main web-based mtDNA databases (DDBJ/EMBL/GenBank international nucleotide sequence database; Phylotree  [van Oven and Kayser 2009] ; Family Tree DNA https://www.
Genome-Wide Analysis The three new samples (two from Yemen and one from Somalia) that carry a maternal lineage affiliated with the Malagasy motif were assayed for genome-wide single nucleotide polymorphism (SNP) genotypes using the Illumina Human Omni5 Bead Chip (Illumina), which surveys 4,284,426 single nucleotide markers regularly spaced across the genome. We gathered comparative genome-wide data from previously published studies of populations from Africa, Madagascar, the Middle East, ISEA, Southeast Asia, South Asia, East Asia, and Europe (supplementary table  S2 , Supplementary Material online). Before analysis, data quality controls were performed using PLINK v. 1. 9 (Chang et al. 2015) : 1) to avoid close relatives, relatedness was measured between all pairs of individuals within each population using an identity-by-descent estimation with an upper threshold of 0.
25 (second-degree relatives); 2) SNPs that failed the Hardy-Weinberg exact test (P < 10 À6 ) in each group were excluded; and 3) samples with an overall call rate <0. 99 and individual SNPs with missing rates >0. 05 across all samples within each population were excluded. Two data sets were subsequently constituted. For frequency-based analyses (ADMIXTURE, f3statistics), our data set included 3,480 individuals from 193 populations genotyped for 171,728 SNPs, obtained after pruning for variants in high linkage disequilibrium (LD) with Plink v. 1. 9 (r 2 > 0. 5; 50 SNP sliding windows). For haplotypebased analyses (PCAdmix), the data set included 411,442 SNPs for 524 individuals, representing 5 metapopulations (Southeast Africa, East Africa, Middle East, South Asia, and ISEA), the Yemeni and Somali populations, and the two Yemeni individuals and the Somali individual of interest.