Tech­ni­cal and Struc­tural Ef­fects of China’s TFP Growth

China Economist - - Articles - CaiYuezhou(蔡跃洲)andFuYifu(付一夫)

Ab­stract: TFP growth may de­rive from both tech­nol­ogy progress (tech­ni­cal ef­fect) and fac­tor al­lo­ca­tion (struc­tural ef­fect). Us­ing China’s macroe­co­nomic and in­dus­trial data, this pa­per de­com­poses China’s TFP growth on the ba­sis of growth ac­count­ing to cast light on China’s growth sources since re­form and open­ing up in 1978. Our study has led to the fol­low­ing find­ings: (1) From 1978 to 2014, China’s eco­nomic growth was of gen­er­ally good qual­ity, and about 1/3 of growth mo­men­tum stemmed from a gen­eral tech­nol­ogy im­prove­ment. (2) Af­ter 2005, China’s late-mover ad­van­tage di­min­ished due to nar­rowed tech­nol­ogy gaps with ad­vanced economies. This re­sulted in a sharp de­cline in the con­tri­bu­tion of tech­nol­ogy progress to growth. How­ever, struc­tural ef­fect con­trib­uted a steadily in­creas­ing share to China’s growth. (3) Af­ter global fi­nan­cial cri­sis in 2008, there has been a ten­dency of re­verse tech­nol­ogy progress in terms of fac­tor al­lo­ca­tion in sec­tors with ex­cess in­dus­trial ca­pac­ity and other sec­tors like fi­nance and real es­tate. There­fore, China should di­vert its fac­tor re­sources to more tech-in­ten­sive and ef­fi­cient sec­tors in the short run, and strive to pro­mote tech­nol­ogy progress in all sec­tors in a longer time­frame.

Key­words: TFP, tech­ni­cal ef­fect, struc­ture ef­fect, growth ac­count­ing JEL Clas­si­fi­ca­tion Codes: O47; O33; O14

DOI: 1 0.19602/j .chi­nae­conomist.2018.09.04

1. In­tro­duc­tion

Since 2014, China’s econ­omy has en­tered into the new nor­mal with in­creas­ing down­ward pres­sures. Given the ur­gency to boost growth, CPC Cen­tral Com­mit­tee Gen­eral Sec­re­tary Xi Jin­ping called for “en­hanc­ing China’s over­all pro­duc­tiv­ity through sup­ply-side struc­tural re­forms” in Novem­ber 2015. Later, he noted that “sup­ply-side struc­tural re­forms must im­prove to­tal fac­tor pro­duc­tiv­ity by re­duc­ing

1 in­ef­fec­tive sup­ply and in­creas­ing ef­fec­tive sup­ply.” These guide­lines are reaf­firmed in the Out­line of China’s 13th Five-Year Plan (2016-2020).

In his re­marks, Gen­eral Sec­re­tary Xi Jin­ping stressed the im­por­tance of sup­ply-side struc­tural re­forms to to­tal fac­tor pro­duc­tiv­ity (TFP) and growth sus­tain­abil­ity. This ar­gu­ment is sup­ported by growth eco­nomic the­o­ries. To­tal fac­tor pro­duc­tiv­ity (TFP) de­ter­mines the level of out­put that can be achieved with a given com­bi­na­tion of fac­tor in­puts. TFP vari­a­tions, i.e. “TFP growth” or “TFP in­dex,” are more of­ten dis­cussed in eco­nomic anal­y­sis. At the mi­cro level, we may es­ti­mate cor­po­rate TFP growth from a pro­duc­tion fron­tier per­spec­tive, and de­com­pose it into two parts, in­clud­ing “move­ment on the pro­duc­tion fron­tier” and “change in the level of cor­po­rate tech­nol­ogy rel­a­tive to pro­duc­tion fron­tier.” At the macro level, in ad­di­tion to tech­nol­ogy progress, TFP growth may also be re­al­ized by al­lo­cat­ing pro­duc­tion fac­tors to more pro­duc­tive in­dus­trial sec­tors as well.

These two meth­ods can be re­ferred to as the tech­nol­ogy progress ef­fect (“tech­ni­cal ef­fect”) and in­dus­trial re­struc­tur­ing ef­fect (“struc­tural ef­fect”). The lat­ter is how over­all TFP and growth po­ten­tials can be achieved through sup­ply-side struc­tural re­forms. Es­ti­mat­ing both ef­fects helps ob­tain in­for­ma­tion about tech­nol­ogy progress, in­ter-in­dus­try fac­tor flow and al­lo­ca­tion ef­fi­ciency. Such in­for­ma­tion can be used by the gov­ern­ment in guid­ing re­source al­lo­ca­tion. In ex­ist­ing mea­sure­ment prac­tices, TFP growth that ap­pears in the form of resid­ual value is most of­ten equated to tech­nol­ogy progress, and rarely fur­ther de­com­posed.

Based on Jor­gen­son growth ac­count­ing frame­work, Divisia in­dex, Mas­sel (1961) and the shift-share method, this pa­per in­cludes in­dus­trial sec­tor TFP in­dex to math­e­mat­i­cally de­com­pose the ex­pres­sion of over­all TFP growth. Ac­cord­ing to the de­com­po­si­tion model, we di­vide China’s over­all TFP growth sources since re­form and open­ing up in 1978 into tech­ni­cal ef­fect and struc­tural ef­fect on the ba­sis of es­ti­mat­ing China’s over­all and sec­tor-spe­cific TFP growth, so as to iden­tify China’s growth mo­men­tum and pro­pose sug­ges­tions on im­prov­ing China’s over­all TFP and growth po­ten­tials.

2. Lit­er­a­ture Re­view and Re­search Method­ol­ogy 2.1 TFP Growth Es­ti­ma­tion and Macroe­co­nomic Growth Ac­count­ing

TFP aims to mea­sure the eco­nomic ef­fi­ciency. In a sin­gle- in­put- sin­gle- out­put ( SISO) sys­tem, TFP can be ex­pressed as “in­put-out­put ra­tio.” If la­bor is the sole in­put, the re­sult is la­bor pro­duc­tiv­ity, which is a com­mon con­cept in eco­nom­ics. In real­ity, how­ever, la­bor pro­duc­tiv­ity can­not re­flect over­all pro­duc­tiv­ity. It is thus nec­es­sary to mea­sure the out­put ef­fi­ciency of a com­bi­na­tion of all ob­serv­able fac­tor in­puts. This need is sat­is­fied by TFP and TFP growth es­ti­ma­tion (Hul­ton, 2000; Syver­son, 2011). De­pend­ing on the ob­ject of assess­ment, TFP growth es­ti­ma­tion can be car­ried out for the cor­po­rate sec­tor, an econ­omy or its in­di­vid­ual in­dus­trial sec­tors.

Es­ti­ma­tion of cor­po­rate TFP growth is based on the rel­a­tive ef­fi­ciency ap­proach with pro­duc­tion fron­tier as bench­mark, and orig­i­nates from Far­rell’s (1957, 1962) ground­break­ing work. Con­sid­er­ing gen­eral man­u­fac­tur­ers’ “mul­ti­ple- out­put mul­ti­ple- in­put” char­ac­ter­is­tic, Far­rell ( 1957) uses equal­prod­uct curve ( pro­duc­tion fron­tier) to mea­sure man­u­fac­tur­ers’ in­put- out­put ef­fi­ciency. Pro­duc­tion fron­tier rep­re­sents the high­est level of tech­nol­ogy. An in­put-out­put com­bi­na­tion on the pro­duc­tion fron­tier is tech­ni­cally the most ef­fi­cient: The closer it is to the equal-prod­uct curve, the more tech­ni­cally ef­fi­cient it is. Rel­a­tive ef­fi­ciency with pro­duc­tion fron­tier as bench­mark can be con­verted into dis­tance func­tion in math­e­mat­ics, which is the ba­sis for es­ti­mat­ing TFP in­dex (Malmquist, 1953; Shep­hard, 1953, 1970). Ac­cord­ing to the dif­fer­ence of dis­tance func­tion ex­pres­sion, fron­tier TFP in­dex es­ti­ma­tion can be di­vided into data en­vel­op­ment anal­y­sis (DEA) and sto­chas­tic fron­tier anal­y­sis (SFA). Us­ing math­e­mat­i­cal plan­ning, DEA converts the dis­tance func­tion es­ti­ma­tion of rel­a­tive ef­fi­ciency into the so­lu­tion of lin­ear plan­ning ob­jec­tive func­tion, and com­bines tech­ni­cal ef­fi­ciency (dis­tance func­tion) with Malmquist in­dex to es­ti­mate TFP in­dexes be­tween dif­fer­ent time points. This method is also re­ferred to as non-para­met­ric method, since it does not in­volve the spe­cific form and para­met­ric es­ti­ma­tion of

pro­duc­tion func­tion (Charnes & Cooper, 1962; Charnes et al., 1978; Banker et al., 1984). SFA de­picts man­u­fac­tur­ers’ pro­duc­tion be­hav­iors through sto­chas­tic pro­duc­tion fron­tier func­tion. The sto­chas­tic er­ror term of pro­duc­tion func­tion is di­vided into the sym­met­ri­cal er­ror term of the ef­fect of var­i­ous sto­chas­tic en­vi­ron­men­tal fac­tors on fron­tier pro­duc­tion and one-side er­ror term that mea­sures tech­ni­cal in­ef­fi­ciency, i.e. man­u­fac­tur­ers’ tech­ni­cal ef­fi­ciency (dis­tance func­tion). The com­bi­na­tion of tech­ni­cal ef­fi­ciency (dis­tance func­tion) and Malmquist in­dex thus ob­tained may also be used to es­ti­mate TFP in­dex (Aigner et al., 1977; Meeusen & Broeck, 1977).

Stigler, Abramovitz, Solow, et al. all made ground­break­ing con­tri­bu­tions to TFP growth es­ti­ma­tion. Among them, Solow neo­clas­si­cal growth model and the fa­mous “Solow resid­ual” and macroe­co­nomic growth ac­count­ing sys­tem are the most in­flu­en­tial (Abramovitz, 1956; Solow, 1957). Solow model de­com­poses growth sources into the three parts of cap­i­tal, la­bor and “ne­glected fac­tor (Solow resid­ual),” for es­ti­mat­ing the con­tri­bu­tion rates of dif­fer­ent fac­tors. Growth of the “ne­glected fac­tor” is TFP growth. Af­ter Solow, Jor­gen­son and Griliches in­tro­duced in­vest­ment the­ory, in­dex the­ory, na­tional in­come ac­count­ing sys­tem and cor­po­rate the­ory into growth ac­count­ing frame­work, thus de­vel­op­ing a com­plete and strin­gent growth ac­count­ing frame­work. Con­sid­er­ing Jor­gen­son’s out­stand­ing con­tri­bu­tion, we re­fer to it as “Jor­gen­son growth ac­count­ing frame­work,” which in­te­grates growth source de­com­po­si­tion with na­tional ac­count­ing sys­tem. With re­spect to cap­i­tal in­put es­ti­ma­tion, we in­clude such con­cepts as cap­i­tal ser­vices, di­min­ish­ing pro­duc­tion ca­pac­ity, and re­tire­ment and ser­vice life of in­ven­tory cap­i­tal (max­i­mum length of ser­vice). With re­spect to la­bor, we take into ac­count la­bor qual­ity as­pects like ed­u­ca­tion and health. This more rea­son­able sys­tem has been ex­ten­sively ap­plied glob­ally. In or­der to stan­dard­ize TFP growth es­ti­ma­tion and in­crease the com­pa­ra­bil­ity of re­sults, OECD made de­tailed ex­pla­na­tions on Jor­gen­son growth ac­count­ing frame­work and TFP growth ac­count­ing (OECD, 2001).

In China, over­all TFP growth es­ti­ma­tion can be traced back to at least the early 1990s. A CASS team led by Li Jing­wen con­ducted a com­par­a­tive study on pro­duc­tiv­ity in China, the US and Ja­pan in col­lab­o­ra­tion with Jor­gen­son, Masahiro Kuroda, et al. (Li, et al., 1993; Li and Li, 1993; Zheng, 1998). In early 21st cen­tury, over­all TFP re­ceived more at­ten­tion, as ev­i­denced in the rel­e­vant stud­ies of Huang, et al. (2002), Sun and Ren (2005), and Guo and Jia (2005). In re­cent years, Chi­nese schol­ars also at­tempted to es­ti­mate the over­all or sec­tor- spe­cific TFP in­dex us­ing pro­duc­tion fron­tier method. Gen­er­ally, en­ter­prises or re­gions are re­garded as ef­fi­ciency and TFP in­dex es­ti­ma­tion units, and weighted av­er­age sec­tor-spe­cific or over­all TFP in­dex is cal­cu­lated on the ba­sis of mea­sur­ing the ef­fi­ciency and TFP in­dex of each unit (Wang Zhi­gang, et al., 2006; Yao Zhanqi, 2009).

2.2 In­dus­trial Re­struc­tur­ing and De­com­po­si­tion of Over­all Pro­duc­tiv­ity In­dex

Apart from the level of tech­nol­ogy, in­dus­trial re­struc­tur­ing is also an im­por­tant fac­tor that in­flu­ences TFP. Ac­cord­ing to Petty-Clark The­o­rem, in the eco­nomic take­off stage, pri­mary in­dus­try will re­duce in pro­por­tion, and se­condary and ter­tiary in­dus­tries will in­crease, re­sult­ing in a dom­i­nant share of se­condary in­dus­try. With eco­nomic so­phis­ti­ca­tion, pri­mary and se­condary in­dus­tries will de­cline in pro­por­tion, while ter­tiary sec­tor ex­pands into a new, dom­i­nant in­dus­try (Clark, 1940). Grow­ing share of se­condary in­dus­try, which is much more pro­duc­tive than pri­mary in­dus­try, will un­leash an econ­omy’s po­ten­tial pro­duc­tiv­ity. Af­ter in­dus­tri­al­iza­tion com­pletes, fac­tor re­sources keep mov­ing to se­condary and ter­tiary in­dus­tries. How­ever, stag­nant pro­duc­tiv­ity in many ter­tiary sec­tors leads to a re­duc­tion in over­all pro­duc­tiv­ity (Bau­mol, 1967; Kruger, 2008). By cre­at­ing a multi-sec­tor growth model, Mon­to­b­bio (2002), and Ngai and Pis­sarides (2007) in­ves­ti­gate dy­namic struc­tural changes in the growth process, and re­de­pict the pat­tern of above-men­tioned in­dus­trial struc­tural evo­lu­tion and its im­pact on pro­duc­tiv­ity.

Em­pir­i­cally, Di­et­rich (2009) uti­lizes the panel data of seven OECD coun­tries and tools such as panel Granger causal­ity test to ex­am­ine the re­la­tion­ship be­tween in­dus­trial struc­tural change and eco­nomic growth. How­ever, the re­la­tion­ship of causal­ity shown by the em­pir­i­cal study is un­cer­tain. More em­pir­i­cal stud­ies di­vide vari­a­tions in over­all pro­duc­tiv­ity into over­all tech­nol­ogy progress and in­dus­trial

struc­tural change in or­der to ex­am­ine the ef­fect of in­dus­trial struc­tural ad­just­ment on pro­duc­tiv­ity. Peneder (2003), Fager­berg (2000) et al. adopt de­vi­a­tion-share de­com­po­si­tion method to de­com­pose la­bor pro­duc­tiv­ity in­dex into in­dus­trial progress ef­fect and in­dus­trial struc­tural change ef­fect2. Af­ter de­com­pos­ing the data of 28 OECD coun­tries, Peneder (2003) dis­cov­ers that in­dus­trial progress is the de­ci­sive fac­tor of pro­duc­tiv­ity im­prove­ment, and that the pro­duc­tiv­ity ef­fect of struc­tural change can be pos­i­tive or neg­a­tive but is lim­ited. Fager­berg (2000) fo­cuses on the man­u­fac­tur­ing pro­duc­tiv­ity ef­fect of spe­cial­iza­tion and struc­tural change. His em­pir­i­cal study on 24 sec­tors in 39 coun­tries shows that although on av­er­age struc­tural change does not have any sig­nif­i­cantly pos­i­tive ef­fect on pro­duc­tiv­ity, coun­tries ex­pe­ri­ence faster pro­duc­tiv­ity growth if the share of sec­tors with rapid tech­nol­ogy progress in­creases.

La­bor pro­duc­tiv­ity im­prove­ment re­sults from both tech­nol­ogy progress and cap­i­tal deep­en­ing. The in­dus­trial progress ef­fect, which is ex­pressed by var­i­ous sec­tors’ weighted la­bor pro­duc­tiv­ity in­dex, is not to­tally equal to tech­nol­ogy progress ef­fect. This prob­lem can be solved by sim­i­lar de­com­po­si­tion of TFP in­dex. Mas­sell (1961) ex­tends Solow model and growth ac­count­ing to in­dus­trial sec­tors, and de­com­poses over­all TFP growth into weighted TFP growth and struc­tural changes stem­ming from in­ter­sec­tor flow of cap­i­tal and la­bor. While the for­mer mea­sures the tech­ni­cal ef­fect, the lat­ter mea­sures the struc­tural ef­fect of struc­tural tran­si­tion. Mas­sell (1961) di­vides the US econ­omy in the 1950s into 19 sec­tors, and the re­sult shows that tech­ni­cal ef­fect con­trib­utes about 2/3 of US TFP growth while struc­tural ef­fect con­trib­utes the rest 1/3.

Chi­nese schol­ars also car­ried out em­pir­i­cal stud­ies from an in­dex de­com­po­si­tion per­spec­tive. Based on data of China’s pri­mary, se­condary and ter­tiary in­dus­tries, Li (2011) de­com­poses TFP growth into weighted TFP growth of var­i­ous sec­tors, in­ter-sec­toral la­bor flow and cap­i­tal flow, which leads to sim­i­lar con­clu­sions with Mas­sell (1961). Wang et al. (2004) con­ducts an em­pir­i­cal study on the re­la­tion­ship be­tween struc­tural ad­just­ment and pro­duc­tiv­ity based on Solow growth model, but em­ploys econo­met­ric re­gres­sion based on mi­cro-level cor­po­rate data. Us­ing fron­tier ap­proach, Yao (2009) es­ti­mates sec­toral TFP in­dex, and in­ves­ti­gates the ef­fect of fac­tor al­lo­ca­tion on TFP growth.

2.3 Re­view of Ex­ist­ing Stud­ies and Our Ap­proach

Since the 1950s, the academia has grad­u­ally formed a rel­a­tively so­phis­ti­cated sys­tem of method­olo­gies for es­ti­mat­ing TFP in­dex. Es­ti­ma­tion of TFP in­dex at the mi­cro level mainly em­ploys DEA and SFA, and over­all TFP in­dex es­ti­ma­tion re­lies on growth ac­count­ing frame­work. Mi­cro TFP in­dex can be de­com­posed into tech­ni­cal change in­dex and ef­fi­ciency change in­dex, both of which re­flect the ef­fect of tech­nol­ogy fac­tor. 3Aside from over­all tech­nol­ogy progress, in­dus­trial struc­tural change will also bring about change in over­all pro­duc­tiv­ity. Hence, over­all pro­duc­tiv­ity in­dex can be de­com­posed into tech­ni­cal ef­fect and struc­tural ef­fect. For con­ve­nience, most rel­e­vant over­seas em­pir­i­cal stud­ies chose to de­com­pose la­bor pro­duc­tiv­ity in­dex, and very few stud­ies de­com­posed over­all TFP in­dex. Chi­nese schol­ars car­ried out ex­ten­sive em­pir­i­cal re­search on growth ac­count­ing and TFP in­dex es­ti­ma­tion. How­ever, such de­tails as cap­i­tal in­put and la­bor in­put es­ti­ma­tion in some stud­ies need to be fur­ther re­fined. As for the re­la­tion­ship be­tween shift of in­dus­trial struc­ture and pro­duc­tiv­ity growth, many Chi­nese stud­ies were car­ried out to mea­sure mi­cro-level cor­po­rate and in­dus­try TFP in­dexes us­ing meth­ods like DEA and SFA, and few em­pir­i­cal stud­ies were car­ried out us­ing growth ac­count­ing

and over­all TFP in­dex es­ti­ma­tion. In ad­di­tion, ex­ist­ing Chi­nese stud­ies made a rough clas­si­fi­ca­tion of in­dus­trial sec­tors in an­a­lyz­ing the struc­tural ef­fect.

In or­der to more clearly and pre­cisely es­ti­mate and de­com­pose the tech­ni­cal ef­fect and struc­tural ef­fect in China’s over­all TFP in­dex, this pa­per will col­lect over­all data and data from 17 sec­tors to carry out an em­pir­i­cal anal­y­sis based on the cre­ation of a de­com­po­si­tion model frame­work for over­all TFP growth es­ti­ma­tion. The rest of this pa­per is ar­ranged as fol­lows: Part 3 de­scribes the de­com­po­si­tion model for es­ti­mat­ing over­all TFP in­dex; Part 4 ex­plains data treat­ment and shows key re­sults of es­ti­ma­tion and de­com­po­si­tion; Part 5 of­fers an in-depth anal­y­sis of the re­sult of de­com­po­si­tion anal­y­sis; and Part 6 is con­clud­ing re­marks and pol­icy rec­om­men­da­tions.

3. TFP In­dex Es­ti­ma­tion, De­com­po­si­tion and Fac­tor Es­ti­ma­tion Model 3.1 TFP In­dex Es­ti­ma­tion Model

Ac­cord­ing to the def­i­ni­tion, TFP is a ra­tio of to­tal out­put com­bi­na­tion rel­a­tive to to­tal in­put com­bi­na­tion. Thus, we have: In equa­tion (1), A rep­re­sents TFP, Y is out­put and X is in­put. We use A, and to re­spec­tively de­note the dif­fer­en­tials of TFP, out­put and in­put with re­spect to time, and take log­a­rithm of both sides of equa­tion (1), which gives us: As­sum­ing that re­turn to scale is con­stant in the pro­duc­tion func­tion and re­turn to fac­tor is equal to its mar­ginal out­put, we may ob­tain based on Divisia in­dex: and in equa­tion (3) re­spec­tively de­note the share of var­i­ous out­put and fac­tor in­puts in the to­tal value, and meet: = =1. ≥0, ≥ 0.With cap­i­tal and la­bor as the only two fac­tor in­puts taken into ac­count, equa­tion (3) can be sim­pli­fied and ex­tended into var­i­ous sec­tors: in equa­tions (4) and (5) de­notes la­bor out­put elas­tic­ity, or the share of la­bor in­put in to­tal in­put (value).

3.2 TFP In­dex De­com­po­si­tion Model

Cap­i­tal in­put and la­bor in­put as a share in to­tal in­put of var­i­ous in­dus­trial sec­tors are spec­i­fied as and re­spec­tively, and change in the fac­tor in­put of var­i­ous in­dus­trial sec­tors and to­tal out­put growth can be ex­pressed as:

Where, is the weighted value of tech­nol­ogy progress for var­i­ous sec­tors, which roughly re­flects chang­ing tech­nol­ogy and de­notes the tech­ni­cal ef­fect of over­all TFP growth. and re­spec­tively re­flect the flow of cap­i­tal and la­bor across var­i­ous sec­tors, i.e. struc­tural change in fac­tor al­lo­ca­tion. Pos­i­tive values of , sug­gest that a greater share of fac­tors is al­lo­cated to sec­tors with higher mar­ginal out­put (or higher ef­fi­ciency), and neg­a­tive values of , sug­gest that a greater share of fac­tors is al­lo­cated to sec­tors with lower mar­ginal out­put4. We re­fer to and and as the struc­tural ef­fect of cap­i­tal and the struc­tural ef­fect of la­bor. + de­notes the over­all struc­tural ef­fect of over­all TFP growth, and is the re­sult of cap­i­tal and la­bor re­al­lo­ca­tion. Equa­tion (10) may also be sim­pli­fied as equa­tion (14).

3.3 Fac­tor In­put Es­ti­ma­tion Model

This pa­per will es­ti­mate fac­tor in­put un­der Jor­gen­son growth ac­count­ing frame­work. Cap­i­tal par­tic­i­pates in pro­duc­tion in the form of cap­i­tal ser­vices. In es­ti­ma­tion, we should take into ac­count fixed cap­i­tal for­ma­tion and pro­duc­tiv­ity change (re­duc­tion) and re­tire­ment (de­com­mis­sion­ing). Ac­cord­ing to OECD (2009), dou­ble curve time-ef­fi­ciency model is em­ployed to de­pict change in cap­i­tal pro­duc­tiv­ity, and log­nor­mal dis­tri­bu­tion de­picts its de­com­mis­sion­ing mode.

In equa­tions (15) and (16), T is the cap­i­tal’s (max­i­mum) length of ser­vice (or re­tire­ment age), n its cur­rent year, and pa­ram­e­ter b ≤ 1 de­ter­mines the shape of func­tion. and are the stan­dard de­vi­a­tion and mean value of log­nor­mal dis­tri­bu­tion func­tion, , . Where, m and s are the mean value and stan­dard de­vi­a­tion of nor­mal dis­tri­bu­tion be­hind log­nor­mal dis­tri­bu­tion func­tion. m is cap­i­tal’s av­er­age length of ser­vice, and the scope of ’s value is nor­mally . Greater value means steeper dis­tri­bu­tion.

Sub­se­quently, per­pet­ual in­ven­tory method is used to es­ti­mate pro­duc­tive cap­i­tal stock of type i in­ven­tory cap­i­tal at time point t. The price of such cap­i­tal (or user cost) is de­ter­mined, and the two are mul­ti­plied to ar­rive at (the value of) cap­i­tal in­put; Where, and are pro­duc­tive cap­i­tal stock and user cost for type t cap­i­tal dur­ing pe­riod i; and are time-ef­fi­ciency model and re­tire­ment model for type i cap­i­tal. In equa­tion (17), is the in­vest­ment spend­ing for type i cap­i­tal dur­ing pe­riod t, i.e. “fixed cap­i­tal for­ma­tion”; is price in­dex. In equa­tion (18), sub­script s is the ac­tual length of cap­i­tal ser­vice, q is as­set price, r is re­turn to cap­i­tal,

d is as­set de­pre­ci­a­tion rate, and is change in as­set price. This equa­tion also re­flects the re­la­tion­ship of user cost con­ver­sion for the same type of cap­i­tal dur­ing dif­fer­ent pe­ri­ods.

La­bor is cat­e­go­rized ac­cord­ing to such char­ac­ter­is­tics as the length of ed­u­ca­tion, and the quan­tity of in­put is mea­sured by the unit of la­bor hour. Dif­fer­ent types of la­bor in­put can be ag­gre­gated us­ing their share in to­tal la­bor com­pen­sa­tion as weight. Hence, la­bor in­put growth can be ex­pressed as:

L is to­tal la­bor in­put; Li is dif­fer­ent types of la­bor in­put man­i­fested in the num­ber of la­bor hours; pi is the price of type i la­bor in­put, such as hourly wage; is the share of type i la­bor com­pen­sa­tion.

4. Data Treat­ment, Es­ti­ma­tion and De­com­po­si­tion Re­sults 4.1 Es­ti­ma­tion of Fac­tor In­put

Af­ter es­ti­mat­ing over­all cap­i­tal fac­tor in­put us­ing a top-down ap­proach, we ob­tain 17 sec­tors from rea­son­able de­com­po­si­tion. Given the dif­fer­ences in pro­duc­tiv­ity change, length of ser­vice and re­tire­ment mode, in­ven­tory cap­i­tal is di­vided into the three cat­e­gories: (1) build­ings, (2) ma­chin­ery and equip­ment, as well as (3) oth­ers for es­ti­ma­tion in ac­cor­dance with the fol­low­ing six steps: (1) col­lect and ar­range fixed cap­i­tal for­ma­tion data se­quence; (2) se­lect ap­pro­pri­ate price in­dex to con­vert “fixed cap­i­tal for­ma­tion” data into com­pa­ra­ble price; (3) spec­ify the ser­vice length-ef­fi­ciency model for var­i­ous types of cap­i­tal ac­cord­ing to the di­min­ish­ing pro­duc­tiv­ity char­ac­ter­is­tic; (4) set rel­e­vant de­com­mis­sion­ing mode; (5) per­pet­ual in­ven­tory method is em­ployed to es­ti­mate the pro­duc­tive cap­i­tal stock for var­i­ous types of cap­i­tal in each year, i.e. quan­tity of cap­i­tal ser­vices; (6) cal­cu­late user cost for var­i­ous types of cap­i­tal in each year, i.e. price of cap­i­tal ser­vice.

Based on His­toric In­for­ma­tion for China’s GDP Ac­count­ing: 1952-2004, In­put-Out­put Ta­ble and other sta­tis­ti­cal in­for­ma­tion, data of miss­ing years is com­pleted to form a data se­quence for the to­tal amount of fixed cap­i­tal for­ma­tion dur­ing 1952-2014, which is de­com­posed into data se­quences for the three types of fixed cap­i­tal for­ma­tion. The in­vest­ment price in­dexes of the three types of fixed cap­i­tal since 1990 are ob­tained from China Sta­tis­ti­cal Year­book to es­ti­mate the miss­ing data based on the to­tal value of cur­rent-price fixed cap­i­tal for­ma­tion and con­stant-price fixed cap­i­tal for­ma­tion growth. Pa­ram­e­ters of three types of in­ven­tory cap­i­tal are spec­i­fied as 0.75, 0.5 and 0.6, which cor­re­spond to de­pre­ci­a­tion lengths of 38, 16 and 20 years re­spec­tively. The value of pa­ram­e­ter in the de­com­mis­sion­ing mode equa­tion (15) is spec­i­fied to be half of the length of cap­i­tal ser­vice. The value of s is m/ 2 (OECD, 2009; Cai and Zhang, 2015). Now, the in­ven­tory of three types of pro­duc­tive cap­i­tal dur­ing 1977-2014 can be es­ti­mated5. Ac­cord­ing to the equiv­a­lent re­la­tion that the sum be­tween la­bor com­pen­sa­tion and cap­i­tal com­pen­sa­tion equals to­tal out­put, la­bor com­pen­sa­tion data can be used to ob­tain (av­er­age) re­turn to capita rt for each year. By sub­sti­tut­ing re­turn to cap­i­tal into equa­tion (18), we may cal­cu­late the user cost of var­i­ous types of pro­duc­tive in­ven­tory cap­i­tal, and ob­tain the value of var­i­ous cap­i­tal ser­vices by mul­ti­ply­ing the pro­duc­tive cap­i­tal stock of var­i­ous types of cap­i­tal with re­spec­tive user cost, i.e. var­i­ous types of cap­i­tal in­put. The re­sult is shown in Ta­ble 1.

Ac­cord­ing to the ap­proach and method of Cai and Zhang (2015), la­bor in­put ag­gre­gate is es­ti­mated with the unit of la­bor hour, and fully takes into ac­count the dis­tri­bu­tion of work­force ed­u­ca­tion. Data for es­ti­mat­ing the value (la­bor in­put ag­gre­gate) of var­i­ous types of la­bor (time) is ex­tended to 2014. Re­sult

is shown in Ta­ble 2.

In or­der to main­tain con­sis­tency with ex­ist­ing sta­tis­ti­cal ac­count­ing sys­tem, China’s econ­omy is di­vided into 17 sec­tors based on data avail­abil­ity (see Ta­ble 3 for de­tails). Cap­i­tal for var­i­ous sec­tors is also di­vided into the afore­said three types. Their ef­fi­ciency re­duc­tion mode, ser­vice length and de­com­mis­sion­ing mode are spec­i­fied ref­er­enc­ing OECD (2009) to es­ti­mate var­i­ous types of pro­duc­tive cap­i­tal stock in these sec­tors. Based on the es­ti­ma­tion re­sult of user cost, we may ob­tain the value of sec­tor-spe­cific cap­i­tal ser­vices, i.e. sec­tor-spe­cific cap­i­tal in­put. La­bor es­ti­ma­tion is also di­vided into 17 sec­tors, and la­bor in­put data is es­ti­mated for var­i­ous sec­tors with la­bor hour as the ba­sic unit by such cri­te­ria as the dis­tri­bu­tion of ed­u­ca­tional level.

4.2 Es­ti­ma­tion of TFP Growth and Con­tri­bu­tion

Us­ing es­ti­mated fac­tor in­put, we de­com­pose the growth of China’s econ­omy and 17 sec­tors dur­ing 1978-2014, with re­sults shown in Ta­ble 3 and Ta­ble 4.

4.3 De­com­po­si­tion of Over­all TFP Growth

Based on equa­tions (6)-(13), as well as the es­ti­mated sec­tor-spe­cific TFP growth and sec­tor-spe­cific fac­tor in­put, we de­com­pose over­all TFP growth dur­ing 1978-2014 by tech­ni­cal ef­fect and struc­tural 6 ef­fect for 17 sec­tors.

5. Anal­y­sis of TFP Growth and Its De­com­po­si­tion Re­sult 5.1 Con­tri­bu­tion of TFP Growth to Eco­nomic Growth

Based on over­all and sec­tor-spe­cific TFP con­tri­bu­tion to growth in dif­fer­ent stages shown in Ta­ble 3 and Ta­ble 4, the fol­low­ing assess­ment can be made.

( 1) TFP played a very im­por­tant sup­port­ive role to China’s rapid growth with an av­er­age con­tri­bu­tion of 39.4%. Be­fore 2000, the con­tri­bu­tion of TFP growth ex­pe­ri­enced sig­nif­i­cant volatil­ity. Af­ter 2005, there was a clear down­ward ten­dency.

(2) Agri­cul­tural TFP growth played a dom­i­nant role in sup­port­ing its value-added growth, with an av­er­age con­tri­bu­tion of 82.2%. Be­fore 2000, there was also sig­nif­i­cant volatil­ity in TFP con­tri­bu­tion. Af­ter 2000, TFP con­tri­bu­tion steadily in­creased, ex­ceed­ing 150% in all years.

(3) TFP growth of se­condary in­dus­try played an im­por­tant role to the in­dus­try’s value-added growth, with an av­er­age con­tri­bu­tion of 33.9%. But the con­tri­bu­tion had a sig­nif­i­cant ten­dency to de­cline. Af­ter 2010, TFP’s con­tri­bu­tion to the growth of seven sec­tors in­clud­ing “food pro­cess­ing sec­tor” was even neg­a­tive. Dur­ing the same pe­riod of time, cap­i­tal in­put con­tri­bu­tion in­creased from 39.2% dur­ing 19901995 to 91.8% dur­ing 2010-2014.

The above es­ti­ma­tion re­sult re­flects an en­hanced fac­tor-driven and in­vest­ment-driven char­ac­ter­is­tic. (4) TFP growth of ter­tiary in­dus­try also played an equally im­por­tant sup­port­ive role in its val­ueadded growth, with an av­er­age con­tri­bu­tion of 35.8%, and was gen­er­ally sta­ble in var­i­ous stages. Af­ter 2000, in par­tic­u­lar, the av­er­age con­tri­bu­tion sta­bi­lized at 20% to 40% in var­i­ous stages.

5.2 Tech­ni­cal Ef­fect and Struc­tural Ef­fect of Over­all TFP Growth

On the ba­sis of over­all TFP de­com­po­si­tion, we may fur­ther es­ti­mate the con­tri­bu­tion of tech­ni­cal ef­fect and struc­tural ef­fect to TFP growth, and de­com­pose such con­tri­bu­tion to pri­mary, se­condary and ter­tiary in­dus­tries, with some re­sults shown in Ta­ble 6. Hence, we may reach the fol­low­ing con­clu­sion:

(1) Since re­form and open­ing-up, tech­nol­ogy progress in var­i­ous sec­tors has been the pri­mary source of over­all TFP growth. Dur­ing 1978-2014, the av­er­age con­tri­bu­tion of tech­nol­ogy ef­fect stood at 83.7%, and struc­tural ef­fect was only 16.3%. In most pe­ri­ods of time, the tech­ni­cal ef­fects of pri­mary, se­condary and ter­tiary in­dus­tries were sig­nif­i­cantly pos­i­tive. Pri­mary in­dus­try ex­hib­ited a sig­nif­i­cant neg­a­tive con­tri­bu­tion af­ter the 1980s. Se­condary in­dus­try made a pos­i­tive con­tri­bu­tion in most pe­ri­ods of time. Ter­tiary in­dus­try demon­strated a sig­nif­i­cant and steady pos­i­tive con­tri­bu­tion.

(2) At the be­gin­ning of re­form and open­ing up, struc­tural ef­fect con­trib­uted sig­nif­i­cantly to over­all TFP growth. In par­tic­u­lar, the struc­tural ef­fect of la­bor con­trib­uted a par­tic­u­larly sig­nif­i­cant share to pri­mary in­dus­try, while the con­tri­bu­tion of tech­ni­cal ef­fect mainly stemmed from se­condary and ter­tiary in­dus­tries. A pos­si­ble rea­son is that China’s ru­ral re­form greatly in­creased farm­ers’ en­thu­si­asm.

7 La­bor hours of each farmer sig­nif­i­cantly in­creased and turned into pos­i­tive struc­tural ef­fect of la­bor. By in­tro­duc­ing ad­vanced for­eign tech­nol­ogy and man­age­rial ex­pe­ri­ence through open­ing up, China vig­or­ously pro­moted in­dus­trial and ser­vice sec­tor de­vel­op­ment.

(3) Af­ter 1985, tech­ni­cal ef­fect had an ab­so­lutely dom­i­nant con­tri­bu­tion to over­all TFP growth, which lasted af­ter China’s WTO ac­ces­sion in 2001. Af­ter the mid-1980s, China’s late-mover ad­van­tage was brought into full play; such an ad­van­tage de­rived from the sig­nif­i­cant tech­nol­ogy gaps be­tween China and Western coun­tries. In this pe­riod, tech­ni­cal ef­fect made pos­i­tive con­tri­bu­tions to pri­mary, se­condary and ter­tiary in­dus­tries. The con­tri­bu­tion of struc­tural ef­fect had al­ways been neg­a­tive for pri­mary in­dus­try, gen­er­ally pos­i­tive for se­condary in­dus­try, and com­pletely pos­i­tive for ter­tiary in­dus­try. This in­di­cates the con­cen­tra­tion of fac­tor re­sources in se­condary and ter­tiary in­dus­tries. This is con­sis­tent with the real­ity of China’s rapid in­dus­tri­al­iza­tion and the rapid de­vel­op­ment of se­condary and ter­tiary in­dus­tries.

(4) Af­ter 2000, the con­tri­bu­tion of tech­ni­cal ef­fect to over­all TFP growth sig­nif­i­cantly de­clined, while struc­tural ef­fect greatly in­creased. These changes largely stemmed from se­condary in­dus­try. The con­tri­bu­tions of tech­ni­cal and struc­tural ef­fects to ter­tiary in­dus­try were rel­a­tively sta­ble. A pos­si­ble rea­son is that af­ter about two decades of open­ing up, China greatly nar­rowed its tech­nol­ogy gaps with ad­vanced economies. Tech­nol­ogy progress through im­por­ta­tion and dif­fu­sion be­came in­creas­ingly dif­fi­cult. But con­tin­u­ous in­dus­tri­al­iza­tion and ur­ban­iza­tion re­sulted in the al­lo­ca­tion of fac­tor re­sources to more pro­duc­tive se­condary and ter­tiary sec­tors, which main­tained fairly high TFP growth.

(5) Af­ter the global fi­nan­cial cri­sis in 2008, the con­tri­bu­tion of tech­ni­cal ef­fect to over­all TFP growth steeply de­clined, and struc­tural ef­fect be­came a dom­i­nant con­trib­u­tor to over­all TFP growth. Dur­ing 2010-2014, the av­er­age con­tri­bu­tion of struc­tural ef­fect to TFP growth stood at 59.3%. The av­er­age con­tri­bu­tions of tech­ni­cal and struc­tural ef­fects to se­condary in­dus­try were -29.4% and 88.2% re­spec­tively. This im­plies that China’s reg­u­la­tory mea­sures in­clud­ing the four-tril­lion-yuan stim­u­lus pack­age did not bring about any im­prove­ment in the level of tech­nol­ogy but in­stead caused fac­tors to rapidly ag­gre­gate in se­condary in­dus­try. This trend ap­pears to have im­proved af­ter 2014.

5.3 Tech­ni­cal and Struc­tural Ef­fects of TFP Growth in Se­condary In­dus­try

TFP growth of China’s se­condary in­dus­try is de­com­posed to cal­cu­late the con­tri­bu­tions of tech­ni­cal and struc­tural ef­fects, and the con­tri­bu­tions of the ef­fects are fur­ther de­com­posed into 11 sec­tors of se­condary in­dus­try (see Ta­ble 7). In re­la­tion to Ta­ble 4, the fol­low­ing assess­ment can be made.

(1) Since re­form and open­ing up in 1978, the tech­ni­cal ef­fect has served as a dom­i­nant fac­tor that sup­ported TFP growth in China’s se­condary in­dus­try. On av­er­age, 1/3 of growth in se­condary in­dus­try stemmed from tech­nol­ogy progress in var­i­ous sec­tors, while struc­tural ef­fect was gen­er­ally in­signif­i­cant.

(2) Be­fore 2005, al­most half of the value-added growth of se­condary in­dus­try stemmed from a gen­eral tech­nol­ogy progress in var­i­ous sec­tors. Af­ter 2005, the sup­port­ive role of tech­nol­ogy progress di­min­ished. Af­ter 2010, the over­all level of tech­nol­ogy in var­i­ous sec­tors even re­duced, re­sult­ing in a neg­a­tive ef­fect on the growth of se­condary in­dus­try. Dur­ing the same pe­riod of time, struc­tural ef­fect played a sig­nif­i­cantly pos­i­tive role in the growth of China’s se­condary in­dus­try.

(3) Af­ter 2010, tech­nol­ogy progress in seven sec­tors in­clud­ing “food pro­cess­ing” had a neg­a­tive ef­fect on the growth of se­condary in­dus­try. Cap­i­tal and la­bor in­puts con­trib­uted 91.8% and 22.4% re­spec­tively to se­condary in­dus­try. This means that what sup­ported se­condary in­dus­try’s growth af­ter the erup­tion of global fi­nan­cial cri­sis is in­vest­ment and fac­tor ex­pan­sion, which is es­pe­cially prom­i­nent in

8 sec­tors with ex­cess ca­pac­ity. This also started to im­prove in 2014.

5.4 Tech­ni­cal and Struc­tural Ef­fects of TFP Growth in Ter­tiary In­dus­try

TFP growth of ter­tiary in­dus­try is de­com­posed to cal­cu­late the con­tri­bu­tions of tech­ni­cal and struc­tural ef­fects. The re­sult is fully de­com­posed into five sec­tors, as shown in Ta­ble 8. Based on Ta­ble 4, the fol­low­ing assess­ment can be made:

(1) Since re­form and open­ing up in 1978, tech­ni­cal ef­fect played a dom­i­nant role in sup­port­ing the TFP growth of China’s ter­tiary in­dus­try. On av­er­age, tech­nol­ogy progress in var­i­ous sec­tors con­trib­uted 1/3 of growth in ter­tiary sec­tor, but the con­tri­bu­tion of struc­tural ef­fect is lim­ited.

( 2) Be­fore 2005, tech­nol­ogy ef­fect al­most con­trib­uted all the TFP growth of China’s ter­tiary in­dus­try. Af­ter 2005, the con­tri­bu­tion of struc­tural ef­fect sig­nif­i­cantly in­creased, reach­ing 60.3% dur­ing 2010-2014, and re­placed tech­ni­cal ef­fect as a key driver of TFP growth.

(3) For the five sec­tors, the con­tri­bu­tion of tech­nol­ogy progress to ter­tiary in­dus­try mainly de­rived from other ser­vices, in­clud­ing com­merce and cater­ing, trans­porta­tion, ware­hous­ing and postal ser­vices, while the tech­ni­cal ef­fect of fi­nance and in­sur­ance, and real es­tate is neg­li­gi­ble.

(4) The struc­tural ef­fect of fi­nance and in­sur­ance, and real es­tate played a sig­nif­i­cantly pos­i­tive role in the growth of China’s ter­tiary in­dus­try. These two sec­tions have at­tracted a large num­ber of fac­tors de­spite the stag­na­tion and even ret­ro­gres­sion of tech­nol­ogy. How­ever, the struc­tural ef­fect of other ser­vices, in­clud­ing trans­porta­tion, ware­hous­ing and postal ser­vices with the most rapid tech­nol­ogy progress con­trib­uted neg­a­tively to ter­tiary in­dus­try’s growth on an av­er­age ba­sis. This im­plies a sig­nif­i­cant re­verse tech­nol­ogy progress ten­dency in the re­source al­lo­ca­tion and ag­glom­er­a­tion within ter­tiary in­dus­try.

6. Con­clud­ing Re­marks and Pol­icy Rec­om­men­da­tions

This pa­per cre­ates an over­all TFP growth de­com­po­si­tion model to es­ti­mate TFP growth at dif­fer­ent eco­nomic lev­els, and de­com­poses over­all TFP growth into tech­ni­cal ef­fect, struc­ture ef­fect and the struc­tural ef­fect of cap­i­tal and la­bor. Our find­ings are as fol­lows:

(1) Since re­form and open­ing up in 1978, China has main­tained a high qual­ity of eco­nomic growth thanks to its late- mover ad­van­tage, and about 1/ 3 of growth mo­men­tum stemmed from a gen­eral im­prove­ment in the level of tech­nol­ogy in var­i­ous sec­tors. In the process of China’s in­dus­tri­al­iza­tion and ur­ban­iza­tion, cap­i­tal and la­bor con­cen­trated in se­condary and ter­tiary in­dus­tries. The struc­tural ef­fect has

also sup­ported China’s eco­nomic growth to some ex­tent.

(2) Af­ter 2000, es­pe­cially 2005, po­ten­tials of China’s late-mover ad­van­tage di­min­ished, and the qual­ity of its eco­nomic growth de­clined, as man­i­fested in the fall­ing con­tri­bu­tion of tech­nol­ogy progress to eco­nomic growth. On the other hand, struc­tural ef­fect and es­pe­cially the struc­tural ef­fect of cap­i­tal con­trib­uted an in­creas­ing share to TFP growth. This im­plies that against the back­drop of nar­rowed tech­nol­ogy gaps, an ef­fec­tive way for China to sup­port TFP and macroe­co­nomic growth is to pro­mote sup­ply-side struc­tural re­forms and guide the flow of more fac­tor re­sources from more pro­duc­tive sec­tors.

(3) Tech­ni­cal ef­fect and struc­tural ef­fect played in­creas­ingly dif­fer­ent roles in sup­port­ing in­dus­trial growth of var­i­ous sec­tors. Growth of pri­mary in­dus­try was mainly sup­ported by tech­nol­ogy progress. Af­ter 2000, tech­nol­ogy progress in­de­pen­dently sup­ported the growth of pri­mary in­dus­try. Tech­nol­ogy progress in sub-sec­tors con­trib­uted about 1/3 of growth in se­condary and ter­tiary in­dus­tries, and the av­er­age con­tri­bu­tion of struc­tural ef­fect was neg­li­gi­ble. Af­ter 2005, how­ever, the sup­port­ive role of tech­nol­ogy progress swiftly di­min­ished and even turned neg­a­tive, and struc­tural ef­fect made a sig­nif­i­cantly pos­i­tive con­tri­bu­tion to in­dus­trial growth. Af­ter the erup­tion of global fi­nan­cial cri­sis in 2008, there was an ob­vi­ous de­cline in the qual­ity of China’s eco­nomic growth, which was pri­mar­ily sup­ported by in­ef­fi­cient in­puts. Growth qual­ity de­te­ri­o­ra­tion was par­tic­u­larly sig­nif­i­cant for se­condary in­dus­try. This trend started to re­verse around 2014.

( 4) For sub- sec­tors, those of se­condary in­dus­try with ex­cess ca­pac­ity such as iron and steel, ce­ment, elec­trolytic alu­minum, flat panel glass and ship­build­ing went through a sig­nif­i­cant tech­nol­ogy stag­na­tion or ret­ro­gres­sion. In ter­tiary in­dus­try, fi­nance and in­sur­ance and real es­tate ex­pe­ri­enced an even more se­ri­ous long-term stag­na­tion or ret­ro­gres­sion in the level of tech­nol­ogy. Fac­tor al­lo­ca­tion and con­cen­tra­tion demon­strated an ob­vi­ous ten­dency of re­verse tech­nol­ogy progress.

We have put for­ward the fol­low­ing pol­icy rec­om­men­da­tions:

(1) We should face the real­ity of China’s fall­ing over­all TFP growth and its fall­ing con­tri­bu­tion to eco­nomic growth in the post-crises era, draw lessons from the neg­a­tive im­pact of stim­u­lus pol­icy and fac­tor-driven growth, and bal­ance the re­la­tion­ship be­tween growth speed and growth qual­ity. (2) In the short term, the struc­tural ef­fect of TFP growth should be brought into full play. In sup­ply-side struc­tural re­forms, China should con­tinue to phase out back­ward ca­pac­i­ties in sec­tors with ex­cess ca­pac­ity, re­duce bub­bles in fi­nan­cial and real es­tate sec­tors, and of­fer rea­son­able in­cen­tives to guide the flow of fac­tor re­sources to tech-in­ten­sive and ef­fi­cient sec­tors. (3) In the mid- and long-term, tech­nol­ogy progress would re­main the pri­mary source of TFP growth. China should im­ple­ment the Out­line of Na­tional In­no­va­tion-Driven De­vel­op­ment Strat­egy, pro­mote tech­nol­ogy progress in var­i­ous sec­tors, and foster tech­nol­ogy ad­van­tages to un­der­gird eco­nomic growth.

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