Domestic Value Added in Exports Theory and Firm Evidence from China

Transcription

1 Domestic Value Added in Exports Theory and Firm Evidence from China Hiau Looi Kee (World Bank) Heiwai Tang (Johns Hopkins) Barcelona GSE Summer Forum June 10, 2015

2 Designed by Apple in California, Assembled in China Inside an ipad 3 Source: Antras (2012) Lecture

3 Downward Trend in Domestic Value Added in Exports across the World Figure 1: Ratio of Value-Added to Gross Exports for the World with ROW without ROW Source: Johnson and Noguera (2014)

4 China has recently defied the global trend DVAR DVAR of Aggregate Exports ( ) Year mdvar_agg 95 c.i. (lower bound) 95 c.i. (upper bound) Also documented by Koopman, Wang and Wei (2012) for 2002 and 2007.

5 What caused China to defy the global trend? Several possible answers to this question with conflicting implications.

6 What caused China to defy the global trend? Several possible answers to this question with conflicting implications. Changing composition of Chinese exports (towards the industries with high domestic content).

7 What caused China to defy the global trend? Several possible answers to this question with conflicting implications. Changing composition of Chinese exports (towards the industries with high domestic content). Increasing domestic production costs, which would imply that the country has become less competitive.

8 What caused China to defy the global trend? Several possible answers to this question with conflicting implications. Changing composition of Chinese exports (towards the industries with high domestic content). Increasing domestic production costs, which would imply that the country has become less competitive. Gradual substitution of domestic for imported materials by its exporters.

9 What is this paper about? 1. We develop methodologies to use customs transaction-level data merged with firm survey data to measure and analyze a country s ratio of domestic value added in exports to gross exports (DVAR) at the firm, industry, and national level. 2. Provide a detailed description of the trend, the pattern, and the mechanism of the rising DVAR of Chinese exports ( ). 3. Develop a theoretical model to quantitatively assess the determinants of China s rising DVAR.

10 Different from the standard approach Existing literature measures industry and aggregate DVARs using input-output (IO) tables (Hummels, Ishii and Yi (2001); Antras, Chor, Fally, and Hillberry (2012); Johnson and Noguera (2012a, 2012b) and Koopman, Wang, and Wei (2012, 2013)). Advantages: capture IO linkages within and across countries. However, the presence of firm heterogeneity may result in significant aggregation biases in the estimates of the DVAR. Advantages of our ground-up approach: Embrace firm heterogeneity. Permit statistical tests on the rising trend, using bootstrapped standard errors for our aggregate estimates. Examine several micro mechanisms, such as changes in firms export composition, production costs and material shares.

11 Main Findings Over , the DVAR of Chinese processing exporters gradually increased from 65% to 70%. Most of the increase is due to processing exporters (9 percentage-point increase) substitute domestic materials for imported materials. This material substitution is caused by an increasing supply and decreasing prices of domestic input varieties, triggered by decreasing input tariffs facing the upstream sectors and increasing FDI in the downstream sectors. The rise is NOT due to reallocation of resources across industries and firm entry and exit.

12 Road Map Data Methodology of using firm and customs transaction data to measure firm DVAR Facts Empirical evidence at the firm level Simple model to identify the determinants of firm DVAR Assessing the contribution of each determinant to the within-firm rise in DVAR

13 Data Data set 1 : the universe of Chinese import and export transactions in each month between 2000 and Data on imports and exports (in USD) at the HS 6-digit level from a firm to/from each country. Data set 2 : firm-level manufacturing survey data from China s National Bureau of Statistics (NBS). Covers all state-owned firms and all private firms with sales > 5 million RMB (about 600,000 USD during the sample period). Balanced-sheet variables: firm ownership, output, value added, exports, employment, original value of fixed asset, and intermediate inputs.

14 Methodology Identities A firm s (i) total revenue: PY i π i + wl i + rk i + P D M D i + P I M I i. Domestic materials P D M D i may embody foreign content (δ F i ). P D M D i δ F i + q D i Imported materials P I M I i may embody domestic content (δ D i ). P I M I i δ D i + q F i.

15 Methodology Benefits of focusing on processing trade By law, processing firms need to export all its output. DVA i π i + wl i + rk i + qi D + δi D ( = EXP i IMP i + δi D δi F + δi K For processing exporters, we only need to remove foreign content in domestic materials, δ F i. ). DVAR i DVA i EXP i = 1 PI M I i PY i δf i EXP i For each industry-year, impute the estimates, use the growth rate of the number of non-processing importers in the upstream sectors and the estimated δf i EXP i from Koopman, Wang, and Wei (2012) for

16 Caveat: Indirect Importing Even under the processing regime, some firms import materials and sell them domestically, i.e., Carry-Along Trade (Bernard et al. (2012)). excessive importers and excessive exporters. Solutions: Merge customs data with manufacturing firm survey data to identify excessive importers and excessive exporters. We focus on a subset of single-industry processing exporters that have their EXP IMP bounded between the two cutoffs: ( ) IMP OT IMP EXP (25) EXP PD M D + P I M I, EXP ( ) OT where DVAR(25) OT = 1 IMP EXP is the 25 percentile of the DVAR (25) of ordinary exporters in the same industry. About the merged sample Multi-industry firms

17 *Filter: m>=imp & exp>=imp DVAR of Processing Exports: 9 ppt increase from DVAR of Processing Exports ( ) DVAR Year Measured DVAR 95 c.i. (lower bound) 95 c.i. (upper bound) Note: Bootstrapped Sample Comparing our numbers with Koopman, Wang, Wei (2012)

18 : DVAR Trend ( ) by Industry with 95% (Bootstrapped) Confidenc DVAR in Chinese Processing Exports by Industry 04:beverages & spirit 06:chemical products 07:plastics & rubber 08:raw hides & skins :wood & articles 10:pulp of wood 11:textiles 12:footwear & headgear, etc. 13:stone, plaster, cement, etc. 14:precious metals 15:base metals 16:machinery, mechnical & elec eqmt 17:vehicles & aircrafts 18:optical, photographic, etc. 20:misc manufacturing year Measured DVAR 95 c.i. (upper bound) 95 c.i. (lower bound) Graphs by di

19 The rise in DVAR is driven by within-sector increases. ( ) DVAR t = Σ j Iit w jt ( DVAR jt ) + Σ j Iit DVARjt ( wjt ), }{{}}{{} within between year within total change between

20 Extension to Non-Processing and Aggregate Exports The methodology developed above is suitable for pure exporters who export all their output (e.g., those engaged in global value chains in the form of processing trade). However, non-processing exporters both export and sell domestically. We extend our methodology to measure the DVAR of the non-processing exporters by making one proportionality assumption at the firm level: the allocation of the firm s inputs to the production for exports is proportional to the share of exports in total sales The DVA and DVAR of a non-processing exporter are: ( DVA O i = EXP i IMP i δi K + δ F i ) ( EXP i PY i ) ; DVARi O = DVA i = 1 IMP i δi K + δi F (1) EXP i PY i

21 DVAR in Overall Exports Figure 5: DVAR of China s Aggregate (Processing + Ordinary) Exports year DVAR (Processing + Ordinary Exp) DVAR (Processing Exp) All firms with materials < imp & exp < imp, and processing firms with DVAR>DVAR(25 percentile of Ord Exporters) are excluded.

22 Reasons for the rising DVAR? We know that the increase in DVAR is not due to the reallocation of resource between industries. Within a sector, high DVAR firms could increase sales more, while low DVAR firms may exit. On the other hand, it could also be a within-firm upgrading phenomenon, due to 1. Rising production costs; 2. Firms substitution imported materials with domestic materials China moved up the global production chain. What drives the substitution?

23 (3) restricts the sample in (2) to only rms with total exports larger than 300 million RMB; (4) is the IO table-based estimate from KWW (2012). Dependent variable: Bootstrap standard DVAR errors are reported of in parentheses. firm exports DVAR it = β i + β t + β X X it + ɛ it, Table 4: Dependent Variable: The Ratio of Domestic Value Added in Exports to Gross Exports (DVAR) (1) (2) (3) (4) (5) (6) Sample All All Dom private Foreign Multiple Ind Un ltered *** *** 0.119** *** *** *** (0.006) (0.006) (0.050) (0.006) (0.005) (0.005) *** *** 0.161** *** *** *** (0.004) (0.005) (0.075) (0.004) (0.004) (0.004) *** *** 0.246*** *** *** *** (0.008) (0.007) (0.047) (0.008) (0.005) (0.006) *** *** 0.179*** *** *** *** (0.008) (0.010) (0.062) (0.011) (0.004) (0.010) *** 0.109*** 0.277*** 0.108*** 0.121*** 0.106*** (0.008) (0.010) (0.069) (0.009) (0.005) (0.013) *** 0.140*** 0.320*** 0.139*** 0.149*** 0.138*** (0.011) (0.010) (0.063) (0.011) (0.004) (0.012) *** 0.157*** 0.317*** 0.157*** 0.167*** 0.156*** (0.016) (0.016) (0.060) (0.017) (0.006) (0.017) P D M D +P I M I P Y it *** *** *** *** *** (0.006) (0.006) (0.066) (0.007) (0.006) (0.003) wl P Y it (0.011) (0.150) (0.012) (0.008) (0.004) N R-sq Notes: Firm and year xed e ects are always included. Data set: merged NBS-customs data. Columns (1) and (2) use the whole sample; columns (3) and (4) include only domestic private and foreign-invested rms, respectively. Firm and year fixed effects included. Bootstrapped standard errors are in parentheses. * p < 0.10; ** p < 0.05; *** Column (5) includes rms that operate in multiple industries as well. Column (6) includes single-industry rms p < that do not satisfy our rules to lter rms that engage in indirect trade. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

24 Dependent variable: Imports/ Total Materials Table 5: Dependent Variable: Share of imports in total materials Sample All Dom private Foreign Multiple Ind ** ** ** (0.008) (0.037) (0.008) (0.006) *** *** *** (0.006) (0.040) (0.006) (0.004) *** *** *** (0.006) (0.053) (0.007) (0.005) *** *** *** (0.008) (0.076) (0.008) (0.005) *** *** *** (0.010) (0.088) (0.009) (0.004) *** *** *** (0.013) (0.096) (0.011) (0.004) *** *** *** (0.018) (0.098) (0.018) (0.006) wl * P Y it (0.044) (0.202) (0.049) (0.039) ln (K=L) it * ** (0.003) (0.034) (0.002) (0.002) N R-sq Note: Firm and year xed e ects are always included. Data set: merged NBS and customs data. Column Firm and year fixed effects included. Bootstrapped standard errors are in parentheses. * p < 0.10; ** p < 0.05; *** (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested rms, p < respectively Column (4) includes rms that operate in multiple industries as well. Bootstrapped standard

25 Dependent variable: ln(nb. import variety) Table 6: Dependent Variable: ln(number of import varieties) Sample All Dom private Foreign Multiple Ind *** *** *** (0.010) (0.087) (0.011) (0.014) *** *** *** (0.009) (0.113) (0.012) (0.015) *** *** *** (0.020) (0.176) (0.022) (0.014) *** *** *** (0.028) (0.205) (0.036) (0.014) *** *** *** (0.033) (0.231) (0.045) (0.015) *** *** *** (0.038) (0.238) (0.051) (0.015) *** *** *** (0.066) (0.214) (0.081) (0.014) P D M D +P I M I P Y it wl P Y it (0.020) (0.385) (0.019) (0.013) (0.053) (0.550) (0.067) (0.066) N R-sq Note: Firm and year xed e ects are always included. Data set: merged NBS and customs data. Column Firm and (1) year uses fixed the whole effects sample; included. columns Bootstrapped (2) and (3) standard include only errors domestic are in parentheses. private and foreign-invested * p < 0.10; ** rms, p < 0.05; *** p < respectively. Column (4) includes rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

26 Dependent variable: ln(nb. export variety) Table 7: Dependent Variable: ln(number of export varieties) Sample All Dom private Foreign Multiple Ind (0.021) (0.172) (0.020) (0.015) ** 0.219* ** *** (0.036) (0.131) (0.042) (0.015) *** 0.370** 0.129** 0.137*** (0.047) (0.185) (0.056) (0.013) *** ** 0.174*** (0.058) (0.296) (0.067) (0.014) *** *** 0.251*** (0.039) (0.295) (0.039) (0.017) *** 0.546* 0.310*** 0.328*** (0.049) (0.323) (0.056) (0.017) *** 0.726** 0.299*** 0.325*** (0.060) (0.316) (0.069) (0.016) P D M D +P I M I P Y it wl P Y it (0.015) (0.358) (0.012) (0.017) * (0.060) (0.464) (0.062) (0.043) N R-sq Note: Firm and year xed e ects are always included. Data set: merged NBS and customs data. Column Firm and year fixed effects included. Bootstrapped standard errors are in parentheses. * p < 0.10; ** p < 0.05; *** (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested rms, p < respectively. Column (4) includes rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

27 Theory or Firm DVAR Assume a translog cost function, P M ( Pit I, ) PD it that is symmetric, homogeneous of degree one: ln P M ( P I t, P D t ) = α i + α 0I ln Pt I + α 0D ln Pt D + 1 ( ) 2 ( ) ( 2 α II ln Pt I + αid ln Pt I ln Pt D + 1 ( ) 2 2 α DD ln Pt D. ) It can provide a second-order approximation to any functional form.

28 Theory or Firm DVAR Recall the accounting identity: DVAR it = 1 PI t M I it P it Y it + ϕ it where ϕ it is a classical regression error term, capturing Sherphard s Lemma implies: P I t M I it P M t M it = M it DVAR it = 1 + PM t P it Y it ( ln PM P I it, Pit D ) ln Pit I = α 0I α ID ln PI t, P D t ( α 0I + α ID ln PI t P D t δ F it EXP it. ) + ϕ it, i, t. DVAR depends positively only on PI t P D t Cobb-Douglas Production Function (given that α ID > 0).

29 Another Benefit of Using a Translog Production Function According to Blackorby and Russell (1989), the elasticity of substitution between the two variables equals the cross-price elasticity ( ε ID ) ( ) t minus the own price elasticity ε DD t : We can express both ε ID t σ t = ε ID t ε DD t and ε DD t as functions of α ID and s D t : ε DD t ln MD t ln P D t = α DD s D t + s D t 1 = α ID s D t + s D t 1; ε ID t ln MD t ln P I t = α ID s I t + s D t, We estimate α ID. σ t = s D t α ID ( 1 s D t ) + 1 > 1, Note that σ t could change over time (and across industries) due to changing s D t.

30 Factors Affecting P I t /P D t Exchange Rates: Let E t = foreign currency value of a Chinese yuan. Pt I = Pt I /E t Yuan depreciation higher P I t /E t P D t higher DVAR FDI: Rodriguez-Clare (1996) and Kee (2015): increased FDI in the output industry can raise the supply and/or quality of domestic input variety higher Pt I /Pt D higher DVAR Upstream Input Tariffs: Goldberg, Khandelwal, Pavcnik, and Topalova (2010): Lower import tariffs lead to significant growth of domestic product variety (in India) higher DVAR

31 Exploring the reasons for the rising firm DVAR We first estimate DVAR it = β i + β jt + β X X it + ɛ it. β i = the firm fixed effect; ɛ it = residual. The estimated β jt, ˆβ jt, captures the average within-firm change in DVAR of each industry j in each year relative to We estimate the following system of three equations using 3SLS: ) ln ( ) P I jt P D jt ˆβ jt = ω 1 j + ω 1 p ln ( P I jt P D jt + ι 1 jt, = ω 2 j + ω 2 E ln E jt + ω 2 v ln V D jt + ι2 jt, ln V D jt = ω 3 j + ω 3 T τu kt + ω3 F ln FDI jt + ω 3 E ln E jt + ι 3 jt,

32 Determinants of the Within-firm Increase in DVAR Table 8: Determinants of the Within- rm Increase in the DVAR Dep. Var 4 t;00 ln P I =P D jt (1) (2) (3) 4 t;00 DV AR jt 4 t;00 ln(p I =P D ) jt 4 t;00 ln Vjt D 0.315*** (0.042) 4 t;00 ln(e jt ) (RMB appreciation) ** (0.870) (0.024) 4 t;00 ln Vjt D 4 t;00 ln e U jt *** (2.902) *** (0.006) 4 t;00 ln (F DI jt ) 0.003* (0.001) Industry Fixed E ects N R-sq Bootstrapped 4 t;00 standard is the operator errors that (withsubtracts 500 repetitions) the variable areofreported interest from in parentheses. its corresponding Coefficients value in are estimated using Bootstrapped standard errors (with 500 repetitions) are reported in parentheses. Coe cients are estimated 3SLS. Columns (1), (2), and (3) are third, second, and first stages, respectively. * p < 0.10; ** p < 0.05; *** using 3SLS. Columns (1), (2), and (3) are third, second, and rst stages, respectively. * p<0.10; ** p<0.05; p < *** p<0.01. Economic Significance

33 Quantitative Analysis To understand how much of the change in firm and aggregate DVAR can be explained by our model, we would need to first estimate the translog parameter, α ID. A firm s DVAR depends on the share of materials in total sales, P M t M it P it Y it, and the translog parameter, α ID, as follows: M it DVAR it = 1 + PM t P it Y it The partial impact of a change in ln ( P I t P D t ( ( P I α 0I + α ID ln t Pt D DVAR ( it ) = PM t M it α P I ID. ln t P it Y it Pt D )). ) on firm DVAR is

34 Quantitative Analysis (cont ) With the estimate of α ID and the actual data on PM t P it Y it, we can calculate how much of the change in firm and industry DVAR is due to the change in the relative price as predicted by our model: M it DVAR it = PM t M it P it Y it α ID ln PI t P D t such estimates allow us to assess the time-series variation in σ and examine whether the rise in firm DVAR is driven by an increasing σ or not.

35 Quantitative Analysis (cont ) To estimate α ID, we estimate the following: Pt I Mit I = a i α ID ln PI t M it Pt D P M t + ξ it, where a i is the firm fixed effect that subsumes α 0I and ξ it is the residual. In other words, α ID is estimated from the within-firm variation in the relative price between imported and domestic materials. We bootstrap the standard errors and instrument for ln PI t exchange rate, FDI and upstream input tariffs. P D t using

36 Estimated Sigma based on the Model α σ t = ID st D ( ) + 1 > 1 1 s D t Table 1: Estimated Elasticity of Substitution between Domestic and Foreign Input Varieties Industry σ IV 2000 σ IV 2007 σ 2000 σ 2007 whole sample : beverages & spirit (16-24) : chemical products (28-38) : plastics & rubber (39-40) : raw hides & skins (41-43) : wood & articles (44-46) : pulp of wood (47-49) : textiles (50-63) : footwear & headgear, etc. (64-67) : stone, plaster, cement, etc. (68-70) : precious metals (71) : base metals (72-83) : machinery, mechanical electrical & equipmt (84-85) : vehicles & aircraft (86-89) : optical, photographic, etc. (90-92) : misc manufacturing (94-96)

37 Quantitative Analysis (cont ) Using these estimates, back-of-the-envelope calculations: the average within-firm increase in DVAR is about 10%, while the average change in ln PI t P D t is 0.419; The estimated average α ID for the whole sample is 0.389; and the mean share of material cost in total sales is The predicted increase in DVAR it is %, which is not statistically different from the sample average within-firm increase in DVAR.

38 Conclusions Derive methods to use firm-level and customs transaction-level data to compute DVAR in exports at various levels of aggregation. The DVAR of Chinese exports increased from 0.65 to 0.70 over China s moving up the global value chain is mostly driven by its processing exporters sourcing more domestically. It is mainly driven by processing firms substituting domestic materials for imported materials, at both the intensive and extensive margins. The expansion of domestic input variety is induced by decreasing input tariffs facing upstream suppliers, and to a lesser extent increasing increasing FDI. Based on the decrease in the relative price of domestic to imported materials, our model explains nearly all of the increase in the firm s and aggregate DVAR from 2000 to 2007.

39 Top 10 Destinations for Chinese Exports Table: Top 10 Destinations of China s Processing Exports Rank USD (Bil) USD (Bil) 1 United States United States Hong Kong Hong Kong Japan Japan Germany 5.62 Netherlands Korea, Republic of 5.34 Germany Netherlands 3.90 Korea, Republic of United Kingdom 3.90 Singapore Singapore 3.62 United Kingdom Taiwan 2.92 Taiwan France 2.10 France Source: China s Customs Trade Data. Back

40 Caveat: Multi-industry firms The above framework is helpful to figure out DVAR at the firm level, but not at the product- or industry- level. Not possible to assign the imported materials to each of the product a firm exports without knowing product-level production functions. Focus on the subset of processing exporters that only operate in a single sector (clusters of HS2 based on UN classifications). E.g.: Machinery, Mechanical Electrical & Equipment (HS 84-85), Textiles (HS 50-63), Footwear & Headgear (HS 64-67) All imports are used for exports in the same sector compute DVAR for each sector using the sample of single-sector processing exporters. Back

41 Decomposition Exercise: Firm Heterogeneity and Aggregation Bias Large firms tend to have a higher import-to-sales ratio (Amiti, Itskhoki and Konings, 2014 and Blaum, Lelarge and Peters, 2014). Domestic Value Added Share in Total Exports (1) Original Sample (0.029) (2) Census (0.029) (3) Large Firms Only Census (0.028) (4) KWW (2012) Estimates Notes: With the exception of (4), all numbers are calculated by the authors based on different samples. (1) refers to the original data set of this paper; (2) is from 2004 Census of Manufacturing Plants; (3) restricts the sample in (2) to only firms with total exports larger than 300 million RMB; (4) is the IO table-based estimate from KWW (2012). Bootstrap standard errors are reported in parentheses. Back

42 Import Tariffs by Industry 01:live animals 02:vegetables 03:animal or vegetable oil 04:beverages & spirit 05:mineral products (Weighted) Average Import Tariffs :chemical products 07:plastics & rubber 08:raw hides & skins 09:wood & articles 10:pulp of wood 11:textiles 12:footwear & headgear, etc. 13:stone, plaster, cement, etc. 14:precious metals 15:base metals 16:machinery, mechical & eletrical equipmt 17:vehicles & aircrafts 18:optical, photographic, etc. 19:arms and ammunition 20:misc manufacturing Graphs by di year

43 Economic Significance From 2000 to 2007, the average increase in ln PI t P D t is The coeff implies a 13.2% increase in the within-firm increase in DVAR. The average log change in input tariffs (across industries) is The coeff implies a 2.9% increase in domestic input varieties, about half of the increase over The presence of FDI in the same industry has a positive and significant impact on the variety of upstream materials. The average log change in the stock of FDI (across downstream industries) is The coefficient of implies a 0.1% increase in domestic input varieties, about 3% of the increase over the period. Back

44 Representation of Different Subsamples by Numbers of Exporters) Industry Number of Firm-year Observations customs merged w/ NBS % of customs filtered % of customs 04:beverages & spirit (16-24) :chemical products (28-38) :plastics & rubber (39-40) :raw hides & skins (41-43) :wood & articles (44-46) :pulp of wood (47-49) :textiles (50-63) :footwear & headgear, etc. (64-67) :stone, plaster, cement, etc. (68-70) :precious metals (71) :base metals (72-83) :machinery, mech, elect eqmt (84-85) :vehicles & aircraft (86-89) :optical, photographic, etc. (90-92) :misc manufacturing (94-96) Total Source: China s Customs Trade Data and National Bureau of Statistics (NBS) Manufacturing Survey. Sections 1, 2, 3, 5, and 19 are non-manufacturing sectors and are excluded from the analysis. Sample pooled across Back

45 Determinants of DVAR Assume production function Optimization: Y it = φ it K α K it ( M it = M D σ 1 σ it L α L it Mα M it, α K + α L + α M = 1 and σ > 1. + M I σ 1 σ it ) σ σ 1, Pt I Mit I = α M Pt I Mit I P it Y it µ it P M t M it DVAR it = 1 PI t M I it P it Y it = 1 α M µ it ( P I t P D t ) σ 1 Back

46 Determinants of firm DVAR DVAR it = 1 PI t M I it P it Y it = 1 α M µ it ( P I t P D t ) σ 1 Given mark-up (µ it ) and cost share of materials (α M ), factors that increase the relative price of imported materials ( PI it ) will increase Pit D firm DVAR. If wages and productivity do not affect P I t /P D t, they will have no impact on DVAR.