REVEALED FACTOR INTENSITY INDICES AT THE PRODUCT LEVEL

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1 UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT POLICY ISSUES IN INTERNATIONAL TRADE AND COMMODITIES STUDY SERIES No. 44 REVEALED FACTOR INTENSITY INDICES AT THE PRODUCT LEVEL by Mho Shrotor UNCTAD Bolormaa Tumurchudur UNCTAD Olver Cadot Unversty of Lausanne, CEPR, CEPREMAP and CERDI UNITED NATIONS New York and Geneva, 2010

2 NOTE The purpose of ths seres of studes s to analyse polcy ssues and to stmulate dscussons n the area of nternatonal trade and development. The seres ncludes studes by UNCTAD staff, as well as by dstngushed researchers from academa. In keepng wth the obectve of the seres, authors are encouraged to express ther own vews, whch do not necessarly reflect the vews of the UNCTAD secretarat or ts member States. The desgnatons employed and the presentaton of the materal do not mply the expresson of any opnon whatsoever on the part of the Unted Natons Secretarat concernng the legal status of any country, terrtory, cty or area, or of ts authortes, or concernng the delmtaton of ts fronters or boundares. Materal n ths publcaton may be freely quoted or reprnted, but acknowledgement s requested, together wth a reference to the document number. It would be apprecated f a copy of the publcaton contanng the quotaton or reprnt were sent to the UNCTAD secretarat at the followng address: Chef Trade Analyss Branch Dvson on Internatonal Trade n Goods and Servces, and Commodtes Unted Natons Conference on Trade and Development Palas des Natons CH-1211 Geneva Swtzerland Seres edtor: Khallur Rahman Chef, Trade Analyss Branch UNCTAD/ITCD/TAB/46 UNITED NATIONS PUBLICATION ISSN Copyrght Unted Natons 2010 All rghts reserved

3 Abstract Ths paper descrbes a data-ntensve methodology to generate ndces that ndcate revealed factor ntensty of traded goods, at the most dsaggregated level of product classfcaton (SITC 5-dgt or HS 6-dgt). We construct the ndces by calculatng, for each good, a weghted average of the factor abundance of the countres that export ths good, where the weghts are varants of Balassa s Revealed Comparatve Advantage ndex. In dong so, we take advantage of recent mprovements n the avalablty of data on aggregate natonal endowments of prmary factors (captal, educaton and labour force) usng, nter ala, Summer and Heston s PWT (verson 6.2), Barro-Lee s latest database, the World Bank and COMTRADE databases. The resultng ndces are avalable on the UNCTAD webste. Key words: Internatonal trade, factor endowments, factor ntenstes, product classfcatons JEL Classfcaton: F1

4 Acknowledgements The authors are grateful to Alessandro Ncta, Marco Fugazza, Sudp Ranan Basu and Joerg Mayer for ther valuable suggestons and comments. Any mstakes or errors reman the authors own. v

5 Contents 1. Introducton Natonal factor endowments: physcal captal, human captal and natural resource endowment Measurng natonal factor endowments Captal stock...3 a. Constructon of captal stock...3 b. Descrpton of captal stock and ts relablty Human captal stock...7 a. Measures for human captal stock...7 b. Descrpton of human captal stock Natural resource endowment Cluster analyss of natonal factor endowments Estmatng the revealed factor ntensty ndces Methodology Caveats...19 a. Lmtatons of Balassa s ndex...19 b. Dealng wth agrcultural dstortons Data coverage Results revealed factor ntensty ndces Concluson...29 Appendx A Appendx tables and fgures...31 Appendx B Dataset of ndex of revealed factor ntensty...40 References...45 v

6 Lst of fgures Fgure 1. Real GDP per worker vs. real captal stock per worker (n logs)... 6 Fgure 2. Real GDP per worker vs. human captal stock (n logs) Fgure 3. Captal stock per worker vs. human captal stock (n logs) Fgure 4. Dendogram of Ward s cluster analyss: countres Fgure 5. Dendogram of Ward s cluster analyss: ndustral sectors Lst of tables Table 1. Real captal stock and real captal stock per worker estmates... 5 Table 2. Summary statstcs (n 2000 Unted States dollars)... 5 Table 3. Growth rate of real captal stock per worker, by ncome group... 5 Table 4. Growth rate of real captal stock per worker, by regon... 6 Table 5. Real GDP per worker vs. real captal stock per worker, by ncome group... 6 Table 6. Correlaton of alternatve captal seres wth our new real captal stock per worker seres... 7 Table 7. Summary statstcs, average years of schoolng... 9 Table 8. Average years of schoolng by regons Table 9. Annual average growth rate of average years of schoolng, by regons Table 10a Land database coverage Table 10b Natural captal, year Table 11. Summary statstcs of factor endowments for the perod Table 12. Calnsk and Harabasz and Duda and Hart stoppng rules result Table 13. Summary of clusters Table 14. Effect of Vollrath s correcton on RCI ndex Table 15. Balanced data coverage Table 16. Wde (unbalanced) coverage for each endowment Table 17. Wde (unbalanced) coverage Table 18. Summary statstcs of revealed factor ntensty ndces, year Table 19. Smple averages of factor ntensty ndces, by SITC 1 dgt ndustres Table 20. Rankng of ndustres n terms of factor ntensty ndces Table 21. Stoppng rule result Table 22. Summary statstcs of the clusters Table 23a. Percentages of SITC 1 dgt ndustres, by clusters Table 23b. Percentages of clusters, by SITC 1 dgt ndustres Table 24a. Percentages of SITC 2 dgt ndustres, by clusters Table 24b. Percentages of clusters, by SITC 2 dgt ndustres v

7 Appendx tables: A1. Countres ncluded n the sample...31 A2. Calnsk and Harabasz and Duda and Hart stoppng rules result (Arable land per captal excluded from the varable lsts)...33 A3. Summary of clusters (arable land per captal excluded from the varable lsts)...34 A4. Summary statstcs of revealed factor ntensty ndces, year A5. Calnsk and Harabasz and Duda and Hart stoppng rules result (HS classfcaton)...36 A6. Summary statstcs of the clusters (HS classfcaton)...37 A7. Percentages of HS sectons ndustres, by clusters (HS classfcaton)...37 A8. Percentages of HS chapters ndustres, by clusters (HS classfcaton)...38 B1a. Revealed factor ntensty ndces SITC...40 B1b. Revealed factor ntensty ndces database HS...41 B2a. Revealed factor ntensty ndces database (1994, 2000) SITC...41 B2b. Revealed factor ntensty ndces database (1994, 2000) HS...42 B3a. Endowment database, by country...42 B3b. Endowment database, by country (1994 and 2000) (wth data on natural captal and ts components from the World Bank)...43 Fgure A1. Dendogram of Ward s cluster (natural resource excluded from the varables)...33 Fgure A2. Dendogram of Ward s cluster analyss (HS classfcaton)...36 v

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9 1. Introducton The process of export dversfcaton has long been a maor research ssue n nternatonal economcs. In recent years, we have seen a renewed nterest n the nature and the process of export dversfcaton. For nstance, Klnger and Lederman (2005) nvestgate the role of nnovaton n export dversfcaton. They fnd that off-the-fronter nnovaton (e.g. the ntroducton of new export products) s more common among low-ncome countres than among hgh-ncome countres. Consstent wth the U-shape pattern of the relatonshp between export dversfcaton and natonal ncome, as descrbed n Imbs and Waczarg (2003), overall export dversfcaton ncreases at low levels of development but declnes wth development after a mddle-ncome pont. Hausmann and Klnger (2006) suggest that changes over tme n the revealed comparatve advantage of countres are assocated wth the pattern of relatedness (measured n terms of productve factors) determned across products. As countres change ther export mx, there s a strong tendency to move towards related goods rather than to goods that are less related. They also suggest that a partcular product s proxmty to exstng areas of comparatve advantage s one of the most sgnfcant determnants of whether a country wll develop an advantage n that product n the future. These approaches provde fresh thnkng nto old ssues and that s very welcome. However, the debate remans largely dsconnected from tradtonal theory of factor-content of trade consderatons, namely the Hecksher-Ohln model of comparatve advantage based on relatve factor endowments. 1 A potental danger of such approaches s as usual wth nductve reasonng that one may end up nferrng supposedly general laws from statstcal relatonshps that may or may not hold out of sample, and, ultmately, advsng polcy on the bass of these emprcal fndngs. Ths study ams to provde a tool to fll the gap between tradtonal, theory-based approaches and newer eclectc ones by developng a tme-seres database of the ndces of revealed factor ntensty (RFI) of export products, usng a wealth of raw data accumulated n the last two decades. The ndces are constructed as follows. Frst, we collected (and updated whenever necessary) raw data on natonal factor endowments of physcal captal, human captal and natural resource endowment for countres of whch data was avalable. Combnng these data gves a panel database of factor endowments at the country level, spannng close to 100 countres over three decades (our balanced panel covers 92 countres from 1971 to 2003). Second, we calculated a revealed factor ntensty for each product at a dsaggregated level of product classfcaton (we used both Unted Natons Standard Internatonal Trade Classfcaton (SITC) 5-dgt and Harmonzed System (HS) 6-dgt), usng the factor endowments of countres exportng that product (from step 1). The dea s that a product exported predomnantly by countres that are rchly endowed wth human captal s revealed to be ntensve n human captal. To wegh natonal factor endowments n the averagng, we adopted a methodology frst proposed by Hausmann, Hwang and Rodrk (2007), usng as weghts a slghtly modfed verson of revealed comparatve advantage (RCA). Usng RCAs, nstead of smple export weghts, flters out scale effects. 1 See Feenstra (2004), chapter 2 for a survey of that older lterature. 1

10 Beng a weghted average of factor endowments, our measure s senstve to the country coverage of the endowments database. However, there s a trade-off between the one wth a large sample sze and the one whch s smaller n sze but wthout any mssng values. Therefore, we propose two versons of our revealed factor ntenstes: (a) a wde one, based on the wdest annual country coverage; and (b) a consstent one, based on a balanced panel of data. We were also careful to weed out, as much as we could, the effect of subsdes and other trade dstortons. Because these dstortons are prevalent n agrculture, we used the World Bank s new Agrcultural Dstortons database (Anderson et al., 2008) and elmnated observatons where RCAs were obvously drven by polcy. Wthout ths correcton, we would have hgh revealed human captal ntenstes for agrcultural goods whose exports are subsdzed by rch countres. The resultng RFI ndces are presented and analyzed n varous ways n the paper. We beleve the value added of the RFI ndces s that t wll make possble to control for Heckscher Ohln effects n analyss of trade dversfcaton n a way that was not possble before. The outlne of the paper s as follows. Chapter 2 provdes a detaled descrpton of the constructon of natonal factor endowments. Chapter 3 provdes a descrpton of the constructon of the ndex of revealed factor ntensty, and dscusses caveats. It also uses cluster analyss to explore broad groupngs of products on the bass of ther revealed factor ntenstes. The explanaton of the database of the ndces of revealed factor ntensty s attached to the paper. The database of the ndces s accessble and can be downloaded from UNCTAD webste (http://r0.unctad.org/dtc/tab/ndex.shtm). 2

11 2. Natonal factor endowments: physcal captal, human captal and natural resource endowment 2.1 Measurng natonal factor endowments Captal stock a. Constructon of captal stock Ths secton descrbes the dervaton of our database of aggregate (natonal) captal stock estmates. In general, two methods are avalable: (a) drect measurement through surveys and (b) perpetual nventory method (PIM). Because drect measures are not everywhere avalable, we use the PIM. The PIM reconstructs captal stock estmates from nvestment flows by addng up, recursvely, current nvestment to the prevous perod s captal stock, approprately deprecated. The method rases (nter ala) two problems. One s the ntal estmate of the captal stock, the other s the choce of the deprecaton rate. We have followed the approach of Easterly and Levne (2001, henceforth EL) 2 and replcated ther captal stock estmates usng the updated verson 6.2 of the Penn World Table (PWT) whch provdes aggregate nvestment fgures 3 for 159 countres. Let K t and I t be respectvely the real captal stock and nvestment flow of country n perod t. The captal-accumulaton equaton s K t+ = ( 1 δ ) K, t + I 1 t, where δ s the rate of deprecaton. Followng EL, we assume that country s at ts steady-state dk captal-output rato, whch mples that t / K t = dyt / Yt dk. Snce t= I t δk t, then dk t/ K t = I t / K t δ * * * * *. At the steady-state growth rate, be g = dy Y = I K δ, we can wrte g * * * * δ * * * K κ I Y ι = = δ (1) Y 2 They used the PWT 5.6 captal stock data, based on dsaggregated nvestment and deprecaton statstcs for 64 countres. They also constructed captal stock fgures for more countres usng aggregate nvestment fgures. 3 It would have been desrable to use dsaggregated nvestment seres (especally our nterest s havng the seres for non-resdental nvestment), but the PWT only provdes wth the aggregate nvestment seres. Though the verson 5.6 provdes captal stock for non-resdental, t covers much less countres and perods. 3

12 where * ι s the nvestment rate and * κ s the captal-output rato. The latter can thus be wrtten as κ = * ι + δ * * g (2) * Followng EL, we construct g the steady-state growth rate as a weghted average of the country s average growth rate durng the frst 10 years for whch the PWT have output and nvestment data and the world growth rate. That s, * ( 1 λ) g g g W = λ + (3) where bars represent values averaged over the sample s frst 10 years. The world growth rate s computed as Followng Easterly et al. (1993), we set λ at We compute * ι smlarly as the average nvestment rate durng the frst 10 years for whch there s data. Fnally, we get an estmate of the ntal captal stock K = κ Y% (4) * 0 0 where Y % 0 s the average real output value between 1950 and 1952 rather than smply the frst observaton n the sample perod n order to reduce the nfluence of busness-cycles. For countres where output and nvestment data do not start untl 1960, everythng s moved down one decade. As for δ, we agan follow EL n assumng a deprecaton rate of 7 per cent. b. Descrpton of captal stock and ts relablty Table 1a shows the coverage of our estmates of the real captal stock. We cover 159 countres, 154 of whch have more than 30 years of tme seres. In order to construct a seres for the real captal stock per worker (K/L), we used an ndrect approach usng real gross domestc product (GDP) per worker (Y/L), GDP per capta (Y/P) and populaton (P) from the PWT to nfer the numbers of workers (L), all from the PWT. Because the PWT has mssng data for GDP per worker, the coverage was further reduced to 152 countres, 140 of whch have 30 years of tme seres or more (see table 1b), and 136 of whch have data over the common sample perod

13 Tme perod Table 1. Real captal stock and real captal stock per worker estmates a. b. Number of Start of Countres tme seres Tme perod Number of Countres Start of tme seres , , , , , Table 2 shows summary statstcs for the 136 countres for whch we have data for the common tme perod Table 2. Summary statstcs (n 2000 Unted States dollars) Varable Obs Mean Std. Dev. Mn Max Real GDP per capta Real Captal Stock per worker Table 3 shows the growth of our estmates of the real captal stock per worker by ncome group over Low-ncome countres have had the slowest growth, wth negatve growth n the 1980s and the 1990s. Table 3. Growth rate of real captal stock per worker, by ncome group (annual average percentage change at 2000 US dollar) Growth rate of Real Captal stock per worker (per cent) Income group Hgh ncome: OECD Hgh ncome: nonoecd Upper mddle ncome Lower mddle ncome Low ncome Table 4 decomposes ths rate of growth n terms of ncome groups and regonal breakdown. The general trend s a steady declne n rates of captal accumulaton per worker, wth some recovery n the decade between 1993 and It s notable that the growth rate of captal stock vares consderably across developng regons, as well as across perods wthn a developng regon. 5

14 Table 4. Growth rate of real captal stock per worker, by regon Growth rate of Real Captal stock per worker (per cent) Hgh ncome countres Low and mddle ncome East Asa & Pacfc East Asa & Pacfc (wthout Chna) Europe & Central Asa Latn Amerca & Carbbean Mddle East & North Afrca South Asa Sub-Saharan Afrca In order to check the plausblty of our estmates, we plot the sample-perod average, per country, of real GDP per worker aganst the real captal stock per worker, both n logs (fgure 1). The real GDP per-worker s a proxy to aggregate labour productvty of the country. Thus, they should be correlated f our estmates are reasonable. Fgure 1. Real GDP per worker vs. real captal stock per worker (n logs) Output per worker, 2000 US dollars BRN QAT SAU LUX ARE USA OMN BHR FRA AUT BELNOR NLD CHE PRI ISRITA CAN MAC BRBGAB MLT ANT IRL HKG GBRNZL BHSISL SWE DNK AUS DEU ESP JPN FIN SGP CYP ARG PRT TTOGRC MUS CRI ZAF URY MEX CHLVEN HUN SWZ DZA KOR IRN JOR BLZ PAN MYS SUR BRA NIC COL TUN CUB GTM SLV DOM PRYNAMFJI PER ECU POL ROM TURBWA IRQ EGY MAR JAM PNG CPV PHL GNQ BOL ZWE THA CMR SYR CIV MDV HND IDN LKA PAK SLB GIN COG IND COM SEN SLE LSOMNG CHN NGA LBRSDNMRT LAO BENGHAKEN SOM PRK NPL TGO ZMB RWA MOZ MDGNER ZAR CAF UGA GMB TCD MLI BDI BFA MWI GNB ETH TZA KHM BTN Real Captal stock per worker, 2000 US dollars The scatter plot shows that they are ndeed hghly correlated. 4 Table 5 presents the data aggregated by ncome group, whch agan shows a plausble degree of correlaton. Table 5. Real GDP per worker vs. real captal stock per worker, by ncome group (n 2000 Unted States dollars) Income Group Real GDP per worker Real Captal Stock per worker Hgh ncome: OECD Hgh ncome: nonoecd Low ncome Lower mddle ncome Upper mddle ncome We have done the same fgures for three dfferent perods ( and ) to see whether the correlaton was mantaned over the perods. The postve correlaton was observed. 6

15 As a further check, table 6 shows the correlaton between our seres and alternatve estmates. It can be seen that the degrees of correlaton are all above 90 per cent. Table 6. Correlaton of alternatve captal seres wth our new real captal stock per worker seres Our estmates of Real Captal Stock per worker Our estmates of Real Captal Stock per worker a 1 Replcaton of Klenow-Rodrguez Clare Nehru-Dhareshwar Larson et al (3 856) (2 382) (1 411) Replcaton of Klenow- Rodrguez Clare (1 896) (1 323) Nehru- Dhareshwar (1 138) Larson et al. 1 Note: a As we descrbed n the text, ths s our update of Easterly and Levne (2001). Larson et al. (2000) covers 62 ndustral and developng countres for the years Nehru Dhareshwar (1993) covers 92 ndustral and developng countres from In brackets are the number of observatons Human captal stock a. Measures for human captal stock There are varous types of proxes that have been used for measurng human captal. These nclude lteracy rates, school enrolment ratos, educatonal attanment and average years of schoolng. Among those, the last one average years of schoolng s the most popular, partly because of the avalablty of large datasets n terms of country coverage and the length of perod for whch data s avalable. There are several data sets on educatonal attanment. The avalable datasets can be dvded nto two groups dependng on whether they make use of (a) census/survey data, whch are the only drect numbers avalable together wth school enrolment rato; or (b) only the school enrolment rato. The frst group (Kyracou ; and Barro and Lee, 1993, 2001) reles on census numbers whenever those are avalable, and flls n mssng values usng a regresson of average years of schoolng on lagged enrolment rates. However, ths procedure s vald only when the relatonshp between these two varables s stable over tme and across countres, whch s not often the case. As an alternatve, Barro and Lee use an accuracy test based on a sample of 30 countres wth relatvely 5 Kyracou (1991) estmated the average years of schoolng of the labor force for a sample of 111 countres for the perod of at fve-year ntervals. He uses UNESCO census data and Psacharopoulos and Arragada (1986) attanment fgures to estmate average schoolng years on school enrollment ratos. Psacharopoulos and Arragada (1986) reports data on educatonal composton of the labor force n 99 countres and provdes estmates of average years of schoolng. The man drawback s that they provde only one tme-seres observaton n most countres. 7

16 complete census numbers n order to fll n mssng values. 6 As such, Barro and Lee s data may be more robust than Kyracou s, although ths s largely a matter of udgement. The second group (Lau, Jamson and Louat, 1991; Lau, Bhalla and Louat 1991; and Nehru, Swanson and Dubey, 1995) uses only school enrolment ratos to construct human captal stock seres. 78 Ther PIM s a sophstcated verson of Barro and Lee, but they gnored census data on educatonal attanment. Based on Krueger and Lndahl s (2001) estmates of the relablty of the Barro and Lee and Kyracou datasets, we chose to use Barro and Lee s data, although there are arguments n favour of both. The latest verson of the dataset, descrbed n Barro and Lee (2001), ncorporates varous mprovements n the procedure used to fll n mssng values. 9 De la Fuente and Doménech (2001) and Cohen and Soto (2000) provde useful ndcatons on how to clean up the avalable census/survey data. Barro and Lee estmated two sets of educatonal attanment rates at fve-year ntervals from 1960 for dfferent levels of educaton for overall populatons aged over 15 and over 25 6 Barro and Lee use a PIM that starts wth the survey numbers as benchmark stocks, and then use the school enrolment ratos to estmate the changes from the benchmarks. Ths method s vulnerable to naccuraces n the underlyng data on gross enrolment ratos. They assess ts accuracy for the 30 countres for whch they have complete census estmates for 1960, 1970 and 1980 as follows. Frst, they use the benchmark values for 1960 (1970) and PIM n the forward drecton to estmate attanment n 1970 (1980), yeldng forward-flow estmates. Second, they start wth benchmark values n 1970 (1980) and use PIM backward to estmate attanment n 1960 (1970), yeldng backward flow estmates. Then they compare the accuracy of these two estmates wth forecasts from smple lnear trends: extrapolatons from 1960 and 1970 to an estmate for 1980 and from the values for 1970 and 1980 to an estmate for They also estmated lnear nterpolatons from the values for 1960 and 1980 to estmates for 1970 and ran several regressons of the observed values of varous levels of educatonal attanment n 1960, 1970 and 1980 for the 30 countres on the estmates generated from forward- and backward-flow and lnear extrapolaton and nterpolaton methods. They found that lnear extrapolatons for 1960 and1980 were nsgnfcant n all cases, and so was the backward-flow estmate for By contrast, the forward-flow estmate was sgnfcant n all cases for 1980, and the forward-flow and lnear nterpolaton for 1970 were ontly sgnfcant n all cases. For more detals see Barro and Lee (1993). 7 Nehru, Swanson and Dubey (1993) ntroduced several mprovement n Lau, Jamson and Louat s procedure. Frst, they collect more data on school enrolment pror to 1960 and therefore they do not have to rely on the backward extrapolaton. Next, they dd some adustment for grade repetton and drop-outs. 8 Lau, Jamson and Louat (1991) and Lau, Bhalla and Louat (1991) use a PIM and annual enrolment data to construct educatonal attanment seres. Ther PIM uses age-specfc survval rates constructed for representatve countres n each regon. 9 The Barro and Lee (2001) dataset mproves on ther earler estmates n a number of respects. Frst, fll-n procedure for mssng values now uses gross enrolment ratos, adusted for repeaters. Second, n the constructon of average years of schoolng, they now take account of changes of school duraton over tme wthn countres. 10 De la Fuente and Doménech (2001) construct educatonal attanment seres for the adult populaton of a sample of 21 OECD countres coverng the perod Ther approach has been to collect all the nformaton that could be found on educatonal attanment n each country, both from nternatonal publcatons and from natonal sources and use t to reconstruct a plausble attanment profle for each country. 11 Cohen and Soto (2000) construct a dataset for a sample of 95 countres coverng the perod at 10-year ntervals. The key methodology s to mnmze the extrapolaton and keep them as close as possble to those drectly avalable from natonal census. They collect census/survey data from UNESCO, the OECD s n-house educatonal database and webstes of natonal statstcal agences. Ther estmates refer to the age group. 8

17 respectvely. 12 For each level, the attanment rate s defned as the percentage of the relevant subpopulaton (over 15 or over 25) havng been enrolled up to a specfc level of educaton but no further (.e. those who dd not pursue any further educaton than the gven level). 13 Barro and Lee estmate the average years of schoolng usng attanment data as follows: _ (1/2) ( 1) ( 1 2) ( DUR DUR 1 DUR 2 (1/ 2) DUR ) h ( DUR DUR 1 DUR 2 DUR ) h Av yrs = DUR h + h + DUR + DUR h + DUR + DUR + DUR h + p p cp p s s p s s cs p s s h h p s s h ch where h represents the percentage of populaton wth dfferent degree of educatonal attanment descrbed by subscrpts. That s, for each h, the th level of educaton s the hghest attaned: =p for ncomplete prmary educaton, cp for completed prmary educaton, s for the frst cycle of secondary educaton, cs for the second cycle of secondary, h for ncomplete hgher educaton and ch for completed hgher educaton. DUR s the duraton n years of the th level of schoolng : =p for prmary, s1 for the frst cycle of secondary, s2 for the second cycle of secondary and h for hgher. 14 Because the Barro and Lee dataset only gves values for each fve years, we used a technque of nterpolaton/extrapolaton to obtan yearly fgures from 1960 to 2004 for 105 countres. 15 Note that the data s not adusted for educaton qualty. Educaton qualty vares across countres, and avalable data s too fragmentary to be exploted systematcally. 16 b. Descrpton of human captal stock Table 7 shows the summary statstcs of the estmated human captal stock seres. It can be seen that there s a large varaton across countres n the average number of years of schoolng. Table 7. Summary statstcs, average years of schoolng Varable Obs Mean Std. Dev. Mn Max Average Years of School Country averages over sample perod Varable Obs Mean Std. Dev. Mn Max Average Years of School Barro and Lee (2001) provde data for the populaton aged 25 and over and for the populaton aged 15 and over. The earler verson of Barro and Lee provded the data only for the populaton aged 25 and above n order to obtan the wdest possble coverage. However, focusng only on the populaton aged 25 and over was gnorng the fastest growng segment of the labour force n the developng countres. Therefore, the latest verson of Barro and Lee also provdes the data for the populaton aged 15 and over whch corresponds better to the labour force for many developng countres. 13 The raw data on educatonal attanment come from ssues of UNESCO Statstcal Yearbook, whch reports census and survey data by age and sex. 14 See Barro and Lee (1993, 2001) for detals. 15 For Benn and Egypt, we extrapolated only untl 1965 and 1970 respectvely, snce the extrapolatons backward were resultng n a negatve numbers. Congo, Gamba and Chna were extrapolated backward from 1975 untl 1960 and Rwanda from 1970 untl Studes by Nehru, Swanson and Dubey (1995) and Cohen and Soto (2001) show that there s a hgh degree of correlaton between Barro and Lee estmates and other estmates of educatonal stocks. 9

18 Table 8 shows average years of schoolng broken down both by decade ( , , and ) and by ncome group/regon. The data has 34 hgh-ncome countres (24 Organzaton for Economc Cooperaton and Development (OECD) and 10 non-oecd), 17 upper mddle-ncome countres, 28 lower mddle-ncome countres, and 24 low-ncome countres. The low and mddle-ncome group s further broken down nto sx regons: East Asa and Pacfc (8 countres), Europe and Central Asa (3 countres), Latn Amerca and Carbbean (21 countres), Mddle East and North Afrca (6 countres), South Asa (6 countres), and sub-saharan Afrca (25 countres). The average years of schoolng rses n all regons. Among the low- and mddle-ncome countres, sub-saharan Afrca, South Asa and Mddle East and North Afrca have the lowest averages but show the hghest growth rates (see table 9). Table 8. Average years of schoolng by regons Regon Hgh ncome countres Low and mddle ncome countres East Asa & Pacfc Europe & Central Asa Latn Amerca & Carbbean Mddle East & North Afrca South Asa Sub-Saharan Afrca Table 9. Annual average growth rate of average years of schoolng, by regons Regon Hgh ncome countres Low and mddle ncome countres East Asa & Pacfc Europe & Central Asa Latn Amerca & Carbbean Mddle East & North Afrca South Asa Sub-Saharan Afrca As a relablty check, fgure 2 shows average output per worker at the country level (averaged over the sample perod) aganst average years of schoolng. The correlaton s, as expected, qute hgh. 10

19 Fgure 2. Real GDP per worker vs. human captal stock (n logs) Average years of schoolng NZL USA CAN SWENOR POL DNKCHE AUS KOR HUN BRBFIN JPN PHL FJI ARG CYP IRL GBR DEU ISR GRC ISL AUT NLD FRA PANURY CHL TTO MLT LKA ECU PER MYS ZAF ESP SGP ITA CHN BOL THA PRY CRI JOR MEXVEN MUS JAM ZMBLSO SYR DOMCOL SWZ BHR PRT IND COGIDNZWE TUR BWA SLV NICBRA GHA HND EGY KEN IRQ IRN TUN DZA MWI GTM UGAZAR CMR TGO SEN PAK LBR RWA PNG CAF BENSLE SDN GMB NPL MOZ MLI NER Real GDP per worker, n 2000 US dollar As a further check, fgure 3 shows years of schoolng aganst the real stock of captal per worker, both n logs. It can be seen that the relatonshp s postve, reflectng correlaton wth a thrd varable (ncome levels), but also concave: there s more deepenng of physcal captal than human captal n the north-east of the scatter plot. Fgure 3. Captal stock per worker vs. human captal stock (n logs) Average years of schoolng UGA RWA MOZ NZLUSA CAN SWE POL DNK AUS NOR CHE BRB HUN KOR CYP IRL GBRISRDEU JPN FIN PHL FJI ARG GRC ISL AUT NLD FRA PAN URYCHL MLT TTO ZAF PER LKA PRY CRI MYS ECU ESPITA SGP CHN BOL THA JOR MUS MEXVEN SYR LSO DOMSWZ COLJAM BHR ZMB IND IDNSLVTUR ZWEBWA GHA EGY HND COG PRT NIC BRA KEN IRQ IRN TUN DZA MWI GTM SEN ZAR CMR TGO PAK LBR PNG CAF SLE BEN SDN GMB NPL NER MLI Real Captal per worker, n 2000 US dollar 11

20 2.1.3 Natural resource endowment To measure the natural resource endowment n a country, we use the data on arable land taken from the World Bank s World Development Indcators (WDI). The seres we used arable land hectares per person s presented n 1,000 ha per person, and covers 203 countres over the perod of Out of those countres, 164 have 45 years or more of data (see table 10a). Table 10a. Land database coverage Tme perod Number of countres Start of tme seres One ustfcaton for usng arable land s that t does not stay the same over tme for each country as t reflects land development or desertfcaton. However, the avalablty of arable land tself s not a perfect measure of natural resource endowments of a country. Therefore we also look nto a database on natural resource captal from the World Bank s volumes Expandng the Measure of Wealth (1997) and Where s the Wealth of Natons? (2006). They offer, among others, a database on natural captal for over 100 countres. Though the database covers only two years (1994 and 2000), t provdes us wth the most complete measure of natural resource endowments to date and t could be used as a good ndcator. Natural resource captal n the database conssts of non-renewable resources (subsol assets, ncludng ol, natural gas, coal, and mneral resources), cropland, pastureland, forested areas (ncludng areas used for tmber extracton and non-tmber forest products), and protected areas. 18 Natural captal values are gven per capta and are based upon country-level data on physcal stocks, and estmates of natural resource rents are based on world prces and local costs. Table 10b presents the total values of natural captal and ts components by ncome groups. Whle the value of natural captal per capta s substantally hgher n hgh-ncome countres than low ncome ones, the percentage of cropland and pastureland n total natural captal s sgnfcantly hgher n low ncome countres. 17 Arable land (hectares per person) ncludes land defned by the Food and Agrculture Organzaton of the Unted Natons (FAO) as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowng or for pasture, land under market or ktchen gardens, and land temporarly fallow. Land abandoned as a result of shftng cultvaton s excluded. See WDI explanatons. 18 For detals, see World Bank (2006). 12

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