dataesr/scientific-paragraphs-categorization huggingface dataset:
i. d. error terms. Because parameter ϕ in (136) may display a trend over time, we add country-specific trends, as captured by α i t. It is worth mentioning that P N is the value added deflator of non-tradables. Data for non-traded value added at current prices, P N t Y N t and GDP at current prices, Y t , are taken from EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases (data coverage: 1970-2015 for all countries, except Japan: 1974-2015). To construct time series for consumption, investment and government expenditure as a percentage of nominal GDP, i. e. , ω C,t , ω J,t and ω G,t , respectively, we use data at current prices obtained from the OECD Economic Outlook  [2017]  Database  (data coverage: 1970-2015). Sources, construction and data coverage of time series for the share of non-tradables in investment (1 -α J ) and in government spending (ω G N ) are described in depth in Section F (see eq.
(  100 )); P N is the value added deflator of non-tradables. Data are taken from EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases (data coverage: 1970-2015 for all countries, except for Japan: 1974-2015). Finally, data for the consumer price index P C,t are obtained from the OECD Prices and Purchasing Power Parities  [2017]  database  (data coverage: 1970-2015). Since both sides of (  138 ) display trends, we ran unit root and then cointegration tests. Having verified that these two assumptions are empirically supported, we estimate the cointegrating relationships by using the fully modified OLS (FMOLS) procedure for cointegrated panel proposed by  Pedroni [2000] ,  [2001]. FMOLS estimates of (  138 ) are reported in Table  10. When we include a country-specific time trend, the vast majority (16 out of 18) of the FMOLS estimated coefficients are positive; yet, twelve out of seventeen are statistically significant, including AUS, AUT, CAN, DNK, ESP, FIN, IRL, ITA, JPN, KOR, NOR, USA.
Estimation of (141a) and (141b) requires data for each sector j = H, N on sectoral value added at constant prices Y j , sectoral hours worked L j , sectoral capital stock K j , sectoral value added deflator P j , sectoral wage rate W j and capital rental cost R. Data for sectoral value added Y H and Y N , hours worked L H and L N , value added price deflators P H and P N , and, nominal wages W H and W N are taken form the EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. To construct the national stock of capital K, we use the perpetual inventory method with a fixed depreciation rate taken from Table  6  and the time series of constant prices investment from the OECD Economic Outlook [2017] Database. Next, following  Garofalo and Yamarik [2002] , the capital stock is allocated to traded and non-traded industries by using sectoral output shares.
Subsection O. 1 deals with this issue and conducts a robustness check to investigate the sensitivity of our empirical results to the classification of industries as tradables or non-tradables. In the main text, we compute the labor income share in the lines of  Gollin [2002] , i. e. , labor compensation is defined as the sum of compensation of employees plus compensation of self-employed. Since there exists alternative ways in constructing labor compensation, we explore empirically in subsection O. 2 whether the results we document in the main text are robust to alternative measures of the labor income share. Our dataset covers eleven industries which are classified as tradables or non tradables. The traded sector is made up of five industries and the non-traded sector of six industries. In subsection O.
O. 1 Classification of Industries as Tradables vs. Non-Tradables This section explores the robustness of our findings to the classification of the eleven 1-digit ISIC-rev. 3 industries as tradables or non tradables. Following De  Gregorio et al. [1994] , we define the tradability of an industry by constructing its openness to international trade given by the ratio of total trade (imports + exports) to gross output. Data for trade and output are provided by the World Input-Output Databases  ([2013] ,  [2016] ). Table  15  gives the openness ratio  (averaged over 1995-2014)  for each industry in all countries of our sample. Unsurprisingly, "Agriculture, Hunting, Forestry and Fishing", "Mining and Quarrying", "Total Manufacturing" and "Transport, Storage and Communication" exhibit high openness ratios (0.
": "Total Manufacturing" (D), "Elect. ": "Electricity, Gas and Water Supply" (E), "Const. ": "Construction" (F), "Trade": "Wholesale and Retail Trade" (G), "Hotels": "Hotels and Restaurants" (H), "Trans. ": "Transport, Storage and Communication" (I), "Finance": "Financial Intermediation" (J), "Real Est. ": "Real Estate, Renting and Business Services" (K), "Public": "Community Social and Personal Services" (LtQ). The openness ratio is the ratio of total trade (imports + exports) to gross output (source: World Input-Output Databases  ([2013] ,  [2016] ). on the presumption that among the industries provided by the EU KLEMS and STAN databases, this industry is government-dominated. While this exercise is interesting on its own as it allows us to explore the size of the impact of a government spending shock on the business sector, we also purge for the potential and automatic link between non-traded value added and public spending because government purchases (to the extent that the government is the primary purchaser of goods from this industry) account for a significant part of non-traded value added.
O. 3 Effects of the Government Spending Shock at Industry Level Empirical analysis at a disaggregate sectoral level. Our dataset covers eleven industries which are classified as tradables or non-tradables. The traded sector is made up of five industries and the non-traded sector of six industries. To conduct a decomposition of the sectoral effects at a sub-sector level, we estimate the responses of sub-sectors to the same identified government spending shock by adopting the two-step approach detailed in the main text. More specifically, indexing countries with i, time with t, sectors with j, and sub-sectors with k, we first identify the shock to government consumption by estimating the VAR model which includes aggregate variables: [g i,t , y k,j i,t , l j i,t , w j C,i,t , tfp A i,t ] where lowcase letters indicate that the variable is logged (all quantities are divided by the working age population) and next we estimate the dynamic effects by using the  Jordà's [2005]  singleequation method.
O. 5 Robustness Check to the Construction of Sectoral Physical Capital Time Series In the main text, due to data availability, we construct time series for sectoral capital by computing the overall capital stock by adopting the perpetual inventory approach and then by splitting the gross capital stock into traded and non-traded industries by using sectoral valued added shares. In this Appendix, we investigate whether the effects on TFP and FBTC we estimate empirically are not driven by our assumption about the construction of time series for sectoral capital stock. To conduct this robustness check, we take time series for sectoral capital stock from EU KLEMS  [2011] ,  [2017]  databases and contrast below empirical responses when sectoral capital stocks are measured by adopting the Garofalo and Yamarik's  [2002]  methodology (our benchmark) with those obtained by using sectoral data on K j provided by EU KLEMS  [2011] ,  [2017]  databases.
Due to data availability, our results in the latter case include a sample of thirteen OECD countries which provide time series on sectoral capital of reasonable length. To be consistent, our benchmark also includes these thirteen countries only. The methodology by  Garofalo and Yamarik's [2002]  is based on the assumption of perfect mobility of capital across sectors and a small discrepancy in the LIS across sectors, i. e. , s H L s N L. The assumption of perfect capital mobility implies that the marginal revenue product of capital must equalize across sectors: P H t 1 -s H L Y H t K H t = P N t 1 -s N L Y N t K N t. ( 151 ) Using the resource constraint for capital, K = K H + K N , dividing the numerator and the denominator in the LHS of (  151 ) by GDP, Y , and denoting by ω Y,j t = P j t Y j t Y t the share of value added of sector j in GDP at current prices (at time t), eq.
National capital stocks are estimated from the perpetual inventory approach. Following  Garofalo and Yamarik [2002] , the gross capital stock is then allocated to traded and non-traded industries by using sectoral value added shares according to eq. (  154 ). Once the series for K j GY are obtained, we can construct the sectoral capitallabor ratios, k j GY = K j GY /L j , sectoral capital utilization rates, u K,j GY , sectoral utilizationadjusted-TFPs, Z j GY , and sectoral utilization-adjusted-FBTC (see eq. (  5 ) in the main text). As a robustness check, we alternatively take capital stock series from the EU KLEMS [2011] and  [2017]  databases which provide disaggregated capital stock data (at constant prices) at the 1-digit ISIC-rev. 3 level for up to 11 industries, but only for thirteen countries of our sample which include Australia, Canada, Denmark, Finland, Italy, Spain, the United Kingdom, the Netherlands over the entire period  1970 -2015 , plus Austria (1976 -2015)) ,  France (1978 France ( -2015)) ,  Japan (1973 Japan ( -2006)) ,  Korea (1970 Korea ( -2014)).
In efforts to have time series of a reasonable length, we exclude  Belgium (1995 Belgium ( -2015) )  and  Sweden (1993 Sweden ( -2015) )  because the period is too short while Ireland, Norway, and Portugal do not provide disaggregated capital stock series. In Fig. 16 , we compare the responses of technlogical change and capital-labor ratios when we split the national gross capital stock into tradables and non-tradables by using the sectoral value added share with those obtained from the alternative approach where we take data on sectoral capital from KLEMS  [2011] ,  [2017]  databases. We estimate the effects of a 1% increase in government consumption on variables that might be affected by our assumption and contrast the IRFs when the sectoral capital stock is measured by adopting the methodology by  Garofalo and Yamarik [2002]  (blue line) with those when the sectoral capital stock is obtained directly from KLEMS (black line).
For comparison purposes and Vertical axes measure percentage deviation from trend. Results for baseline specification (i. e. , we use the method of  Garofalo and Yamarik [2002]  to construct the sectoral capital stocks K H and K N ) are displayed by blue lines with shaded area indicating 90 percent confidence bounds. The black line reports results when we use the EU KLEMS  [2011] and [2017]  databases to construct sectoral capital stocks series K j. Sample: 13 OECD countries, 1970-2015, annual data. TFP H /TFP N FBTC j adjK k j = K j /L j to ensure consistency, we compare the results for same sample, i. e. the restricted sample that includes 13 OECD countries over the period 1970-2015. As shown in the last column of Fig. 16 , the responses of capital-labor ratios for the two methods are both qualitatively and quantitatively similar since the solid black line lies within the original confidence bounds of those obtained when K j is constructed with the use of the methodology of  Garofalo and Yamarik [2002].
Subsection P. 4 conducts an investigation of the potential presence of anticipation effects by using a dataset constructed by  Born, Juessen and Müller [2013]  which contains one year-ahead OECD forecasts for government spending. In Online Appendix P. 6, we compare our results by adopting the two-step method with those when we adopt the one-step approach like in  Ramey and Zubairy [2018]. In Online Appendix P. 7, we compare the dynamic responses when we use local projections with the dynamic effects when IRFs are estimated through VAR estimates. In running these robustness checks, we investigate whether all of conclusions in the main text hold. Following a government spending shock: • both real GDP and hours increase significantly • non-traded labor increases disproportionately as labor shifts away from the traded sector • real GDP growth is uniformly distributed between the traded and the non-traded sector • technology improvements are concentrated in traded industries • technological change is biased toward labor in non-traded industries and biased toward capital in traded industries.
When estimating the VAR model on quarterly data, we set the number of lags to 8. Data Source. We take quarterly data from OECD Economic Outlook  [2017]. All variables are per capita. Data coverage: from 1970q1 to 2015q4 for 17 OECD countries, i. e. , the eighteen countries of our sample except for Ireland. We have considered three alternative VAR models over 1970q1-2015q4: a) [g it , y R,it , Employment it , w L,it -p C,it ] where W L is the wage per person employed (sample: 14 countries, i. e. , the baseline sample less (IRL, KOR, NOR and PRT), b) [g it , y R,it , hours worked it ] (sample: 11 countries, i. e. , baseline sample less IRL, AUT, CAN, FIN, KOR, NOR and PRT) [g it , y R,it , hours worked it , w h,it -p C,it ] where W h is the wage per hour worked (sample: 11 countries, i. e. , the baseline sample less IRL, AUT, CAN, FIN, KOR, NOR and PRT).
Because narrativelyidentified military spending shocks are not available for most of the countries in our sample, we further address the potential issue of endogneity by following  Miyamoto et al. [2019]  who identify government spending shocks using exogenous variation in military spending. Data Source. Military expenditure data are taken from the Stockholm International Peace Research Institute (SIPRI). Time series are available at an annual frequency and. Data Coverage: Time series are available for the 18 OECD countries of our sample over 1970-2015. All variables are per capita. The correlation between government consumption and military spending is high at 0. 975. While both series are highly correlated, military spending accounts for 2. 2% of GDP, the United States having the highest share at 4.
Sample for annal data : 17 OECD countries, 1970-2015. Sample for quarterly data : 17 OECD countries, 1970q1-2015q4 estimated (155), we construct predicted values of government consumption, i. e. , ḡi,t. In the second step, we estimate the dynamic effects by using  Jordà's [2005]  local projections method where we plug instrumented government consumption and the same controls as in the first step: x i,t+h = α i,h + α t,h + β i,h t + ψ h (L) z i,t-1 + γ h ḡi,t + η i,t+h , ( 156 ) where x is the logarithm of the variable of interest, β h measures the response of variable x at horizon h = 0, 1, 2. 10, α i,h are country fixed effects, α t,h are time dummies, and we include country-specific linear time trends; z is a vector of control variables (i. e. , past values of government spending and of the variable of interest), ψ h (L) is a polynomial (of order two) in the lag operator and ḡi,t is instrumented government consumption.
The Blanchard-Perotti identification is based on decision and implementation lags and thus is more likely to hold when we use quarterly data. Due to limited availability of sectoral data at a quarterly frequency for most of OECD countries, we use annual data. To identify government spending shocks, we thus assume that government consumption does not react to real GDP and total hours worked within the year. Our identifying assumption could be violated if fiscal policy reacts automatically to aggregate macroeconomic conditions or if fiscal policy contains contains discretionary elements. However, since we consider government spending net of transfers, automatic stabilizers (which operate through taxes and transfers) should not pose a problem. A second potential problem is discretionary fiscal policy action in response to current economic developments.
Sample: 18 OECD countries, 1970-2015, annual data. To support our identification of government spending shocks based on annual data, we have identified government spending shocks by using quarterly data in section P. 1. While sectoral data are not available at a quarterly frequency, our two-step method lends nicely to robustness checks as we identify the government spending shocks by estimating a VAR model in the fist step which involves aggregate variables. When we use quarterly data, we find no significant differences with the effects we estimate on annual data. An alternative identification assumption is based on the use of military expenditure which are supposed to be exogenous to the state of the economy, see  Miyamoto et al. [2019]. We do detect significant differences except that shocks to government consumption instrumented by military spending are more biased toward traded goods because as pointed out by  Ramey and Shapiro [1998] , military expenditure are concentrated toward a few manufacturing industries.
P. 4 Controlling for Potential Anticipation Effects We now address one potential concern related to the anticipation effects. When the fiscal shock is anticipated, and the VAR approach captures the shocks too late, it misses the initial changes in variables that occur as soon as the news is learned. We conduct below an investigation of the potential presence of anticipation effects, using two alternative measures of forecasts for government spending. We also re-estimate the effects of a government spending shock by controlling for anticipation effects. The first measure is provided by  Born, Juessen and Müller [2013]  and stems from the OECD forecasts, while the second is taken from a dataset constructed by  Fioramanti et al. [2016]  where forecasts are performed by the European Commission.
We use two alternative datasets as the former contains observations from 1986 to 2014 for all countries, while the latter provides a longer time horizon but for a restricted set of countries. Drawing on previous studies, we conduct three robustness exercises to explore the potential implications of anticipations effects: • First, we replace in the local-projection regressions the SVAR identified government spending shock G it with the forecast error F E G it computed as the difference between actual series and forecast series of the government spending growth rate: F E G it = ∆g it -f or G it , where ∆g it is the actual government consumption growth and f or G it is the previous period's forecast. The idea is simply to purge actual government spending growth of what professional forecasters project spending growth to be.
To perform the first two robustness tests, we use a dataset constructed by  Born, Juessen and Müller [2013]  that contains time series for forecasts for government spending growth from the OECD. 30. The data availability for the variable f or G it is: AUS  (1997 ( -2014 ( ), BEL (1999 ( -2014)) ,  CAN (1986 -2014 ), DNK (1997 -2010 ), ESP (1997 -2014) ,  FIN (1997 -2014 ), FRA (1986 -2014 ), GBR (1986 -2014 ), IRL (1997 -2014 ), ITA (1986 -2014) ,  JPN (1986 JPN ( -2014)) ,  KOR (1998 -2014 ), NLD (1997 -2014 ), NOR (1997 -2014) ,  PRT (1997 -2014 ), SWE (1997 -2014)  and  USA (1986 USA ( -2014)). No data are available for Austria. Regarding the general government balance to GDP ratio forecast (one year ahead) used in the third exercise, time series are available for AUT  (1995 ( -2014 ( ), BEL (1971 ( -2014)) ,  DNK (1977 DNK ( -2014)) ,  ESP (1987 ESP ( -2014)) ,  FIN (1995 -2014 ), FRA (1970 -2014) ,  GBR (1974 -2014 ), IRL (1974 -2014) ,  ITA (1970 ITA ( -2014)) ,  NLD (1970 NLD ( -2014)) ,  PRT (1987 -2014 ) and SWE (1995 -2014).
The measure of technology proposed by Fernald is thinner than ours because instead of taking the Solow residual, the author uses a measure of TFP which controls for the composition of labor and capital heterogeneity, i. e. , his dataset weights different inputs of aggregate and sectoral variables to an exogenous increase in government final consumption expenditure by 1% of GDP when we adopt a two-step method where we identify the shock by adopting Blanchard-Perotti identification assumption and next estimate dynamic effects by using Local Projections. The solid black lines shows results when we adopt a one-step approach where we estimate a SVAR and employ the Blanchard-Perotti identification to recover the shock and use the same VAR to generate impulse responses. Vertical axes measure percentage deviation from trend in GDP units (sectoral value added, sectoral value added share, labor income share), percentage deviation from trend in total hours worked units (sectoral hours worked, labor share), percentage deviation from trend (sectoral TFPs, sectoral FBTC).
e. , intermediate consumption) at constant prices, L j hours worked, K j the capital stock at constant prices, s j L is the LIS. We use (169) to measure the intensity in the use of capital and labor at a sectoral level (i. e. , for each industry) and adjust the Solow residual with this measure to construct time series for the utilization-adjusted TFP in sector j = H, N : Ẑj = T FP j -ûj Y. ( 170 ) Source: Time series for intermediate inputs at constant prices are taken from EU KLEMS. Data coverage: 1970-2015 for 18 OECD countries except for AUS, CAN, ESP, GBR, IRL, KOR and NOR where time series are available only over  1970-2007. Results. In Fig. 28 , we contrast the response of utilization-adjusted TFP based on the Solow residual adjusted with the capital utilization rate constructed by adopting  Imbs [1999]  method (displayed by the solid blue line) with the response of TFP based on the Solow residual adjusted with the capacity utilization rate in eq.
Source. Time series about high-(denoted by the superscript S), medium-(denoted by the superscript M ), and low-skilled labor (denoted by the superscript U ) are taken from EU KLEMS Database,  Timmer et al. [2008]. Data are available for all countries except Norway. The baseline period is running from 1970 to 2015 but is different and shorter for several countries as indicated in braces for the corresponding countries: Australia , Austria ,  Belgium (1980 -2015 ), Canada (1970 -2005 ), Denmark (1980 -2015 ), Finland (1970 -2015 ), France (2008 -2015 ), Ireland (2008 -2015) ,  Italy (1970 Italy ( -2015)) , Japan , the  Netherlands (1979 -2015 ), Portugal (2008 -2015) ,  Spain (1980 Spain ( -2015)) ,  Sweden (2008 Sweden ( -2015)) , the United  Kingdom (1970 Kingdom ( -2015)) , and the United States.
R. 2 Shocks to G N and G T : Evidence Two sources are available to construct time series for sectoral government consumption: • The World Input-Output Databases  ([2013] ,  [2016] ) provide time series in domestic currency units for each demand component by sector. To determine the intensity of each sector in government spending, we had to treat imports in a consistent way nontradable expenditure includes some imports (there is a low proportion but a share of both non-traded consumption and non-traded investment expenditure is imported in the data) although in the model we abstract from these imports. In carrying out this analysis, we inferred sectoral government consumption by using market clearing conditions and private demand components, see Section F. The disadvantage of using this dataset is that the sum of G T plus G N might be different from government final consumption expenditure time series provided by the OECD.
Data coverage: WIOD provides time series for all countries in our sample over 1995-2014 except for Norway for which time series are available over 2000-2014. We find that on average, 80% of government spending is spent on non-traded goods and 20% is spent on traded goods. • One alternative to WIOD is the COFOG [2017] database from the OECD which provides a breakdown of government expenditure by function. The disadvantage of this dataset is that it does not provide information about the sales of goods or services of an industry to the government. It just gives the purpose of the government service and thus provides information about the industry which benefits government spending. For example, when the government purchases goods and services to provide education services, it increases the value added of the industry 'education' by the same amount than government services.