dataesr/scientific-paragraphs-categorization huggingface dataset:
Because the non-traded sector must pay higher wages to encourage workers to shift, non-traded firms increase labor-augmenting productivity to mitigate the rise in the labor cost. Since our estimates show that labor-and capital-augmenting productivity are strong complements along the technology frontier, capital-augmenting efficiency disproportionately increases, thus generating a rise in B N /A N. The other way around is true in the traded sector. Technology channel at a disaggregated level. Our dataset covers eleven industries and in Online Appendix O. 3, we conduct the same empirical analysis as in the main text, except that we consider a more disaggregated industry level. We find that the five industries classified as tradables increase their TFP, which confirms that the rise in traded TFP is driven by a technology improvement within each industry.
In accordance with this observation, we model the decision to increase the utilization of available technology as a trade-off between the rise in output generated by enhanced productivity and the cost associated with a higher utilization rate of technology within each sector j = H, N. Potential determinants of technology adjustment costs. Before discussing factors that could potentially influence the variations in technology following a government spending shock, it is important to clarify the concept of technology utilization rate. Variations in utilization-adjusted TFP in sector j, Z j it , can be driven by a change in the stock of knowledge, Zj it , or by a change in the rate of utilization of the stock of ideas, u Z,j it , or both. Using data from  Stehrer et al. [2019]  (EU KLEMS database) we construct time series for both gross fixed capital formation and capital stock in R&D in the traded and non-traded sectors.
To calibrate the reference model that we use to normalize the CES economy, we have estimated a set of ratios and parameters for the eighteen OECD economies in our dataset, see Table  7  relegated to Online Appendix L. 1. Our reference period for the calibration corresponds to the period 1970-2015. Because we calibrate the reference model to a representative OECD economy, we take unweighted average values of ratios and parameters which are summarized in Table  1. Among the 26 parameters that the model contains, 12 have empirical counterparts while the remaining 14 parameters must be endogenously calibrated to match ratios. Fourteen parameters must be set to target ratios. Out of fourteen parameters, ϕ, ϕ H , ι, ι H , ϑ, δ K , G, G N , G H and initial conditions (N 0 and K 0 ), must be set to target a non-tradable content of consumption and investment expenditure of 1 -α C = 56% and 1 -α J = 69%, respectively, a home content of consumption and investment expenditure in tradables of α H = 66% and α H J = 43%, respectively, a weight of labor supply to the non-traded sector of L N /L = 62%, an investment-to-GDP ratio of ω J = 24%, a ratio of government spending to GDP of ω G = 19% (= G/Y ), a non-tradable and home-tradable share of government spending of ω G N = 80% (= P N G N /G), and ω G H = 18% (= P H G H /G), and we choose initial conditions so as trade is balanced, i.
D Data Description for Empirical Analysis Coverage: Our sample consists of a panel of 18 countries: Australia (AUS), Austria (AUT), Belgium (BEL), Canada (CAN), Denmark (DNK), Finland (FIN), France (FRA), Ireland (IRL), Italy (ITA), Japan (JPN), Korea (KOR), the Netherlands (NLD), Norway (NOR), Portugal (PRT), Spain (ESP), Sweden (SWE), the United Kingdom (GBR) and the United States (USA). The baseline period is running from 1970 to 2015, except for Japan. dataset covers all countries of our sample, with the exceptions of Canada and Norway. For these two countries, sectoral data are taken from the Structural Analysis (STAN) database provided by the OECD  ([2011], [2017] ). For both EU KLEMS and STAN databases, the March 2011 release provides data for eleven 1-digit ISIC-rev. 3 industries over the period 1970-2007 while the July 2017 release provides data for thirteen 1-digit-rev.
4 industries over the period 1995-2015. The construction of time series for sectoral variables over the period 1970-2015 involves two steps. First, we identify tradable and non-tradable sectors. We adopt the classification proposed by De  Gregorio et al. [1994]. Following  Jensen and Kletzer [2006] , we have updated this classification by treating the financial sector as a traded industry. We map the ISIC-rev. 4 classification into the ISIC-rev. 3 classification in accordance with the concordance Table  5. Once industries have been classified as traded or non-traded, for any macroeconomic variable X, its sectoral counterpart X j for j = H, N is constructed by adding the X k of all sub-industries k classified in sector j = H, N as follows X j = k∈j X k. Second, series for tradables and non-tradables variables from EU KLEMS  [2011]  and OECD  [2011]  databases (available over the period  are extended forward up to 2015 using annual growth rate estimated from EU  KLEMS [2017]  and OECD  [2017]  series (available over the period 1995-2015).
Construction of sectoral variables. Once industries have been classified as traded or nontraded, we construct sectoral variables by taking time series from e EU KLEMS (  [2011], [2017])  and OECD STAN ([2011], [2017] ) databases. These two databases provide data, for each industry and year, on value added at current and constant prices, permitting the construction of sectoral deflators of value added, as well as details on labor compensation and hours worked data, allowing the construction of sectoral wage rates. Time and countries are indexed by subscripts i and t below while the sector is indexed by the superscript j = H, N. All quantity variables are scaled by the working age population (15-64 years old). Source: OECD ALFS Database for the working age population  (data coverage: 1970-2015).
We describe below the construction for the sectoral data employed in the main text (mnemonics are given in parentheses): • Sectoral value added, Y j it : sectoral value added at constant prices in sector j = H, N (VA QI). Series for sectoral value added in current (constant) prices are constructed by adding value added in current (constant) prices for all sub-industries k in sector j = H, N , i. e. , P j it Y j it = k P j k,it Y j k,it ( P j it Y j it = k P j k,it Y j k,it where the bar indicates that prices P j are those of the base year), from which we construct price indices (or sectoral value added deflators), P j it. Sources: EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. • Sectoral value added share, ν Y,j it , is constructed as the ratio of value added at constant prices in sector j to GDP at constant prices, i.
e. , Y j it /(Y H it + Y N it ) for j = H, N. • Relative price of non-tradables, P it. Normalizing base year price indices P j to 1, the relative price of non-tradables, P it , is constructed as the ratio of the non-traded value added deflator to the traded value added deflator (i. e. , P it = P N it /P H it ). The sectoral value added deflator P j it for sector j = H, N is calculated by dividing value added at current prices (VA) by value added at constant prices (VA QI) in sector j. Sources: EU KLEMS  ([2011] ,  [2017] ) and OECD  STAN ([2011 STAN ([ ], [2017]] ) databases. • Terms of trade, TOT it = P H it /P H, it , is computed as the ratio of the traded value added deflator of the home country i, P H it , to the geometric average of the traded value added deflator of the seventeen trade partners of the corresponding country i, P H, it , the weight being equal to the share α M,k i of imports from the trade partner k.
Source: Direction of Trade Statistics  [2017]. Period: 1970-2015 for all countries except for Belgium  (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015). • Sectoral hours worked, L j it , correspond to hours worked by persons engaged in sector j (H EMP). Likewise sectoral value added, sectoral hours worked are constructed by adding hours worked for all sub-industries k in sector j = H, N , i. e. , L j it = k L j k,it. Sources: EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011], [2017] ) databases. • Sectoral labor share, ν L,j it , is constructed as the ratio of hours worked in sector j to total hours worked, i. e. , L j it /(L H it + L N it ) for j = H, N. • Sectoral nominal wage, W j it is calculated as the ratio of the labor compensation (compensation of employees plus compensation of self-employed) in sector j = H, N (LAB) to total hours worked by persons engaged (H EMP) in that sector.
ources: EU KLEMS ([2011],  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. • Relative wage, W j it /W it , is constructed as the ratio of the nominal wage in the sector j to the aggregate nominal wage W. • Labor income share (LIS), s j L,it , is constructed as the ratio of labor compensation (compensation of employees plus compensation of self-employed) in sector j = H, N (LAB) to value added at current prices (VA) of that sector. Sources: EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. We detail below the data construction for aggregate variables (mnemonics are in parentheses). For all variables, the reference period is running from 1970 to 2015: • Government spending, G it : government final consumption expenditure (CGV). Source: OECD Economic Outlook Database  [2017].
Real gross capital formation is obtained from OECD National Accounts Database  [2017]  (data in millions of national currency, constant prices). We construct the series for the capital stock using the law of motion for capital in the model: K t+1 = I t + (1 -δ K ) K t. ( 86 ) for t = 1971,. , 2015. The value of δ K is chosen to be consistent with the ratio of capital depreciation to GDP observed in the data and averaged over 1970-2015: 1 46 2015 t=1970 δ K P J,t K t Y t = CF C Y , ( 87 ) where P J,t is the deflator of gross capital formation series, Y t is GDP at current prices, and CF C/Y is the ratio of consumption of fixed capital at current prices to nominal GDP averaged over 1970-2015. Deflator of gross capital formation, GDP at current prices and consumption of fixed capital are taken from the OECD National Account Database  [2017].
Following  Garofalo and Yamarik [2002] , the gross capital stock is then allocated to traded and non-traded industries by using the sectoral value added share, see eq. (  77 ). Construction of time series for sectoral TFPs. Sectoral TFPs, TFP j t , at time t are constructed as Solow residuals from constant-price (domestic currency) series of value added, Y j t , capital stock, K j t , and hours worked, L j t , by using eq. (  83 ). The LIS in sector j, s j L , is the ratio labor compensation (compensation of employees plus compensation of self-employed) to nominal value added in sector j = H, N , averaged over the period  1970 -2015 (except Japan: 1974 -2015)). Data for the series of constant price value added (VA QI), current price value added (VA), hours worked (H EMP) and labor compensation (LAB) are taken from the EU KLEMS  ([2011] ,  [2017] ) and OECD  STAN ([2011] , [2017]) databases.
Construction of time series for real interest rate, r. The real interest rate is computed as the real long-term interest rate which is the nominal interest rate on 10 years government bonds minus the rate of inflation which is the rate of change of the Consumption Price Index (CPI). Sources: OECD Economic Outlook  Database [2017]  for the long-term interest rate on government bonds and OECD Prices and Purchasing Power Parities  Database [2017]  for the CPI. Data coverage: 1970-2015 except for  IRL (1990 IRL ( -2015) )  and  KOR (1983 KOR ( -2015)). The first column of Table  6  shows the value of the real interest rate which averages 3% over the period 1970-2015. Construction of time series for capital utilization, u K,j t. To construct time series for the capital utilization rate, u K,j t , we proceed as follows.
F Construction of Non-Traded Demand Components In this section, we detail the construction of time series for non-traded government consumption, G N t , non-traded consumption, C N t , and non-traded investment, J N t. We use the World Input-Output Databases  ([2013] ,  [2016] ). The 2013 release provides data for eleven 1-digit ISIC-rev. 3 industries over the period 1995-2011 while the 2016 release provides data for thirteen 1-digit-rev. 4 industries over the period 2000-2014. As sectoral data are classified using identical ISIC revisions in both the EU KLEMS and WIOD datasets, we map the WIOD ISIC-rev. 4 classification (the 2016 release) into the WIOD ISIC-rev. 3 classification (the 2013 release) in accordance with the concordance Table  5. Consistent with the methodology we used to extend series taken from the EU KLEMS  ([2011] ,  [2017] ), time series for traded and non-traded variables from the WIOD  [2013]  dataset (available over the period 1995-2011) are extended forward up to 2014 using annual growth rate estimated from WIOD  [2016]  series (available over the period  [2000] [2001] [2002] [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012] [2013] [2014].
Coverage: 1995-2014 except for NOR  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). To compute non-traded demand components, we have to overcome two difficulties. While the input-output WIOD dataset gives purchases of non-traded goods and services from the private sector, data also includes purchases of imported goods and services. Whereas consumption and investment expenditure can be split into traded and non-traded expenditure, this split does not exist for government spending for most of the countries in our sample. We detail below how we overcome the two aforementioned difficulties. To begin with, the non traded and the home-produced traded goods markets must clear such that: Y N = C N + J N + G N + X N -M N , ( 91a ) Y H = C H + J H + G H + X H -M H , ( 91b ) where Y j is value added at constant prices in sector j = H, N , C j consumption in good j, J j investment in good j, G j government consumption in good j and X j stands for exports.
( 103b ) We obtain data on GDP and its demand components (consumption, investment, government spending, exports and imports) from the World Input-Output Databases  ([2013] ,  [2016] ) for all years between 1995 and 2014 and all 1-digit ISIC rev. 3 and rev. 4 industries. Indexing the sector with a superscript j = H, N and indexing the origin of demand of goods and services with a superscript k = D, F where D refers to domestic demand of home-produced goods and services and F refers to domestic demand of foreign-produced goods and services, we provide below details about data construction: • Consumption C j,k for j = H, N and k = D, F : total consumption expenditure (at current prices) by households and by non-profit organizations serving households on good j produced by firms from country k.
Data coverage: 1995-2014 except for NOR  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). • Imports M j,k for j = H, N and k = D, F : total imports (at current prices) of final and intermediate good j produced by firms from country k. Data coverage: 1995-2014 except for NOR  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). Finally, when we use (  98 ) to obtain the time series for G N , the valuation of output Y N and imports M N include taxes and subsidies on products and trade and transport margins respectively. These adjustments are necessary to achieve consistency and to balance resources and uses. Response of non-traded government consumption to government spending shock. World Input-Output Databases  ([2013] ,  [2016] ) allow us to get time series for total government spending with a breakdown of G it by components G j,D it and G j,F it for j = H, N.
All the obtained series are available at current prices which allows us to compute the non-tradable content of government consumption. To compute time series for non-traded government consumption at constant prices, we can take two routes. First, we can use time series for the non-tradable content of government spending, ω G N,D ,it , obtained from WIOD dataset and then we construct time series for G N,D it at constant prices by multiplying time series for real government final consumption expenditure, G it , with the time-varying non-tradable content of government consumption, ω G N,D ,it. Second, we can alternatively construct time series for G N it at constant prices by multiplying time series for real government final consumption expenditure, G it , with ω G N,D ,i averaged over 1995-2014.
1. The reference period for the calibration of labor variables is 1970-2015 while the reference period for demand components is 1995-2014 due to data availability, as detailed below. When we calibrate the model to a representative economy, we use the last line which shows the (unweighted) average of the corresponding variable. Aggregate ratios. Columns 13 to 15 show the aggregrate labor income share, s L , the investmentto-GDP ratio, ω J and government spending as a share of GDP, ω G. The aggregate labor income share is calculated as the ratio of labor compensation (compensation of employees plus compensation of self-employed) to GDP at current prices. Sources: EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. Data coverage: 1970 Data coverage: -2015 Data coverage: (1974 Data coverage: -2015  for JPN).
To calculate ω J , we use time series for gross capital formation at current prices and GDP at current prices, both obtained from the OECD National Accounts Database  [2017]. Data coverage: 1970-2015 for all countries. To calculate ω G , we use time series for final consumption expenditure of general government (at current prices) and GDP (at current prices). Source: OECD National Accounts Database  [2017]. Data coverage: 1970-2015 for all countries. We consider a steady-state where trade is initially balanced and we calculate the consumption-to-GDP ratio, ω C by using the accounting identity between GDP and final expenditure: ω C = 1 -ω J -ω G. ( 132 ) As displayed by the last line of Table  7 , investment expenditure (see column 14) and government spending (see column 15) as a share of GDP average to 24% and 19%, respectively, while the aggregate labor income share averages to 66% (see column 13).
Non-traded demand components. Columns 2 to 4 show non-tradable content of consumption (i. e. , 1-α C ), investment (i. e. , 1-α J ), and government spending (i. e. , ω G N ), respectively. These demand components have been calculated by adopting the methodology described in eqs. (103a)-(103b), and eq. (  100 ). Sources: World Input-Output Databases  ([2013] ,  [2016] ). Data coverage: 23 Since Ŝj it = ŝj L,it 1-s j L,i and thus the percentage deviation of the ratio of labor to capital income share relative to its initial steady-state is proportional to the percentage change in the LIS, ŝj L,t , we refer interchangeably to the LIS or the ratio of factor income share as long as it does not cause confusion. 1995-2014 except for NOR  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014).
The non-tradable share of consumption, investment and government spending shown in column 2 to 4 of Table  7  averages to 56%, 69% and 80%, respectively. In the empirical analysis, we use data from EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases for constructing sectoral value added over the period running from 1970 to 2015. Since the demand components for non-tradables are computed over 1995-2014 by using the WIOD dataset, to ensure that the value added is equal to the sum of its demand components, we have calculated the non-tradable content of value added shown in column 1 of Table  7  as follows: ω Y,N = = P N Y N Y , = ω C (1 -α C ) + ω J (1 -α J ) + ω G N ω G , ( 133 ) where 1 -α C and 1 -α J are the non-tradable content of consumption and investment expenditure shown in columns 2 and 3, ω G N is the non-tradable content of government spending shown in column 4, ω C and ω J are consumption-and investment-to-GDP ratios, and ω G is government spending as a share of GDP.
Non-tradable content of hours worked and labor compensation. To calculate the nontradable share of labor shown in column 5 and labor compensation shown in column 6, we split the eleven industries into traded and non-traded sectors by adopting the classification proposed by De  Gregorio et al. [1994]  and updated by  Jensen and Kletzer [2006]. Details about data construction for sectoral output and sectoral labor are provided above. We calculate the non-tradable share of labor compensation as the ratio of labor compensation in the non-traded sector (i. e. , W N L N ) to overall labor compensation (i. e. , W L). Sources: EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. Data coverage: 1970-2015 for all countries  (except Japan: 1974 (except Japan: -2015)). The non-tradable content of labor and labor compensation, shown in columns 5 and 6 of Table  7 , average to 62% and 63% respectively.
Home content of consumption and investment expenditure in tradables. Columns 8 to 9 of Table  7  show the home content of consumption and investment in tradables, denoted by α H and α H J in the model. These shares are obtained from time series calculated by using the formulas (102a)-(102b). Sources: World Input-Output Databases  ([2013] ,  [2016] ). Data coverage: 1995-2014 except for NOR  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). Column 10 shows the content of government spending in homeproduced traded goods. Taking data from the WIOD dataset, time series for ω G H are constructed by using the formula (101). Data coverage: 1995-2014 except for NOR  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014).
The last line of column 7 of Table  7  shows that the export to GDP ratio averages 13%. Sectoral labor income shares. The labor income share for the traded and non-traded sector, denoted by s H L and s N L , respectively, are calculated as the ratio of labor compensation of sector j to value added of sector j at current prices. Sources: EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. Data coverage: 1970-2015 for all countries  (except Japan: 1974 (except Japan: -2015)). As shown in columns 11 and 12 of Table  7 , s H L and s N L averages to 63% and 69%, respectively. Estimated elasticities. Columns from 16 to 20 of Table  7  display estimates of the elasticity of substitution between tradables and non-tradables in consumption, φ, the elasticity of labor supply across sectors, , the elasticity of substitution between capital and labor in the traded and the nontraded sector, i.
Lj t. The parameter we are interested in, say the degree of substitutability of hours worked across sectors, is given by i = γ i /(1 -γ i ). In the regressions that follow, the parameter γ i is assumed to be different across countries when estimating i for each economy (γ i = γ i for i = i ). To construct Lj and βj we combine raw data on hours worked L j , nominal value added P j Y j and labor compensation W j L j. All required data are taken from the EU KLEMS  ([2011] ,  [2017] ) and OECD STAN  ([2011] ,  [2017] ) databases. The sample includes the 18 OECD countries mentioned above over the period  1971 -2015 (except for Japan: 1975 -2015)). Table  9  reports empirical estimates that are consistent with > 0. All values are statistically significant at 10%, except for Norway. Overall, we find that ranges from a low of 0.