Global COE Hi-Stat Discussion Paper Series 300

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Adjusting Hooley's Philippine GDP(1902~40)

Hi-Stat Discussion Paper

Konosuke Odaka

August 2023

Preliminary; not for quotation
This discussion paper is prepared for “A Study Workshop on the Quantitative Macro Economic History of thePhilippines, 1900-2010”. This international workshop is supported by the 2022 research grant from the Japan
Center for Economic Research (JCER).

Hi-Stat

Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8603 Japan
https://gcoe.ier.hit-u.ac.jp

Adjusting Hooley GDP viii2023 Odaka

Adjusting Hooley's Philippine GDP (1902~40) 1
By

Konosuke Odaka
Institute of Economic Research
Hitotsubashi University, Tokyo

Introduction
1. Discrepancies in construction output
2. Unification of time standards
3. Additional minor corrections
4. Slightly revised, implicit GDP deflators
5. Concluding remarks

Introduction
This paper aims to re-examine the accuracy of the Philippine GDP(Gross Domestic Product) series estimated by Professor Richard Hooley(Hooley 2005) and consequently make slight adjustments to it. With theunderstanding that the author's GDP time series statistics spanning 1902 to
1985, categorized by major industry groups and presented in 1985 prices,
represent the latest estimates, we have meticulously reviewed the GDP
worksheet. Subsequently, we have made necessary minor corrections
specifically for the initial half of the series, covering the years 1902 to 1940.
The original statistical data for this study are located in two sub-files
on an Excel sheet named "CCC" and "GDPREV," both housed within a file
titled "Hooley's Master Presentation." Among these, the "GDPREV" sub-fileholds the finalized GDP worksheet. This worksheet systematically compiles
all the time series data, presented in both current and 1939 pesos, torepresent their respective contributions to the nation's total gross value
This paper focuses on the adjustment and revision of the historical GDP estimatespublished by Professor Richard Hooley. It also anticipates future efforts to enhancethese estimates, particularly regarding the value added of the primary and secondary
industries, as proposed by Mr. Keiya Eto.
I would like to express my sincere respect and gratitude to Dr. Pierre van der
Eng for bringing this seminal article to my attention.

1

1

Adjusting Hooley GDP viii2023 Odaka

added. Conversely, the "CCC" sub-file contains two sets of final figures forindustrial components along with their GDP totals. One set is derived fromthe "GDPREV" sub-file in constant 1939 pesos, while the other set isconverted from 1939 to 1985 price bases. The latter set constitutes Table A,
while the former corresponds to Table B 2.
1. Discrepancies in Construction Output
Initially, we meticulously crosschecked all time-series data values inthe summary table of the journal against their corresponding values in the1985 prices of file CCC (Table A). Remarkably, we discovered no variations
between the two sets of data, except for a notable exception within theconstruction industry. Here, each figure presented in the journal had been
uniformly reduced throughout the entire period by an average factor of 0.286
compared to the original data in Table A. The rationale behind thisadjustment remains unexplained.
Upon uncovering this disparity, a compelling course of action emerges:
substituting the annual 1985-priced data in the construction industry of Table
A with the corresponding figures from the journal article. This revised versionof Table A is hereby designated as Table A'.
2. Linking statistics with different time standards
To facilitate the cross-checking process, a critical step involves
establishing links between time series represented in distinct time standards.
It's worth noting that while the figures in constant 1985 pesos from thejournal are evident, those found in Table B and sub-file GDPREV are denoted
in 1939 pesos. Furthermore, it's imperative to recognize that the price linking
ratios applied to various commodities (or services) tend to differ across timestandards.
Professor Hooley's work introduced specific price linking ratios, to be
implemented for statistics corresponding to the years following 1948,
categorized by industrial sectors:
I.

Agriculture, Fishery, Forestry

In this and the following pages, the term ‘Table’ is used metaphorically, and does notserve as a container of statistical figures.
22Adjusting Hooley GDP viii2023 Odaka

II.

III.

I-a. Crops & Livestock: 70.99
I-b. Forestry & Fishing: 70.99
Industry Sector
II-a. Mining & Quarrying: 23.27
II-b. Manufacturing: 90.14 I
I-c. Construction: 19.08
II-d. Electricity, Gas & Water: 156.35
Service Sector
III-a. Transportation, Communication and Storage: 78.03
III-b. Trade: 99.94
III-c. Finance: 99.94
III-d. Income from Owners' Own Dwellings: 99.94
III-e. Private Services: 55.56
III-f. Government Services: 27.78

To align the time series data from Table A', categorized by major industrial
sectors and presented in 1985 pesos, with the 1939 time standard, each figure
is divided by the corresponding linking ratio from above. This transformation
yields a new set of values, collectively forming Table C, signifying the 1939
price version of Table A'.
3. Additional minor corrections
In the ensuing stage, we conducted a meticulous comparison of all
statistical figures in Table C, excluding the construction industry which
underwent substantial scaling down in Section 2. This comparison was
performed against corresponding figures in Table B or sub-file GDPREV, andrevealed several additional minor discrepancies as outlined below.
Firstly, a noticeable discrepancy emerged in the time series of
economic activities related to free services (III-e above) across the entire
period. Notably, the values designated as "peer group" in Table B surpass
those in Table C by an average factor of 3.8. This discrepancy could potentiallybe attributed to an inadvertent double-counting of government labor costs
and rental incomes during the estimation process for the sub-file GDPREV
series.
Secondly, specific instances of variance in value added have surfaced
within the transportation, communication, and warehousing industries (III3

Adjusting Hooley GDP viii2023 Odakaa), specifically in the years 1902 and 1918. During these years, the value
added for these industries in Table C remarkably falls to as low as 0.08 and
0.03, respectively, of the corresponding values in Table A'. In contrast, for allother years excluding these two, the value added for these industries in Table
A' is approximately 0.98 times that of Table C.
Thirdly, further scrutiny of the figures in Tables A' and C uncovers
non-uniform ratios of product values between the two sets. Specifically, ratios
deviate as follows:• 1.13 in agriculture (I-a) in 1902
• 0.95 in forestry and fisheries (I-b) in 1902
• 1.28 in government services (III-f) in 1938
To address these disparities identified in this section, corrections havebeen implemented based on the judgment that Table C accurately represents
free services, while Table A' is more accurate for all other industries
mentioned.
By amalgamating the corrected time series data from Tables A' and C,
a comprehensive depiction of the adjusted Hooley's real GDP emerges. Thisadjusted portrayal is juxtaposed against its earlier renditions, facilitating amore comprehensive understanding (Figure 1).
4. Slightly revised implicit GDP deflators
In light of the substantial adjustments undertaken on the output
quantities (expressed in constant pesos) within the construction industry, as
elucidated in Section 2, a commensurate revision is necessitated for its output
values (current pesos). To achieve this, we require pertinent price information.
However, our original estimator did not furnish any guidance regarding thesuitable price index for this purpose. In the absence of a more fitting
alternative, we turned to the well-established prices of construction materials,
a choice firmly embedded in sub-file GDPREV. Multiplying this priceinformation, the nominal construction output series from Table A now forms
an integral component of the historical nominal gross value added of thenation.
Across the other industries, alterations between the published figures
and those in Tables A are minimal. Substantive modifications are confined toisolated instances in agriculture, transportation, communications, andservice sectors. The production price indexes, or implicit industrial deflators,
4

Adjusting Hooley GDP viii2023 Odaka

delineated in Hooley's worksheet (sub-file GDPREV) are preserved as distinct
price indices, excluding those applicable to the construction industry.
The cumulative nominal GDP values are derived by integrating the
production price indexes (or industry-specific implicit deflators) with thecorresponding quantity outputs listed in sub-file GDPREV, encompassing
construction. This amalgamation furnishes the nominal series spanning theinterval of 1902 to 1940, vividly presented against the previous series in
Figure 2.
Through the utilization of the recalibrated time series portraying realand nominal values of the Hooley GDP, we arrive at the slightly revised,
implicit GDP deflator for the specific period. This insightful depiction is
captured in Figure 3, juxtaposed with its original iterations.
5. Concluding Remarks
Leveraging the GDP estimates provides a platform for both
imaginative exploration and the generation of novel ideas. To further
facilitate such endeavors, we seek Professor Hooley's gracious permission toharness the slightly refined renditions of his GDP series, enabling us topresent additional diagrams, exemplified in Figures 5, 6, and 7 below.
For instance, Figure 4 portrays a rudimentary division of GDP into
primary, secondary, and tertiary industries. Notably, the service industry
consistently commands a significant position akin to that of the primarysector. In a similar vein, Figure 5 amalgamates the three output series fromprimary, secondary, and tertiary industries with estimated employment
figures within these sectors. This juxtaposition reveals the average valueadded labor productivities. Echoing a point Hooley (1968) elucidated years
ago, this presentation underscores the gradual pace of progress in work
efficiency across both the primary and industrial sectors.

5Adjusting Hooley GDP viii2023 Odaka

References cited
Hooley, Richard, "American economic policy in the Philippines, 1902-1940:
Exploring a dark age in colonial statistics," Journal of Asian Economics,
vol. 16 (2005), pp. 464-488.
―—————————, "Long-term growth of the Philippine economy 1902-1961," The

Philippine Economic Journal, vol. VII, no. 3 (1968).
Maddison, Angus, Monitoring the world economy 1820-1992, Paris: .E.C.D.,

1995.
Sicat, Gerardo P., "On the measurement of long-term output," The Philippine Economic Journal, vol. VII, no. 1 (first semester 1968), pp. 25-41.
Umaňa, Salvador C., "Growth of output of Philippine manufacturing: 19021960," Economic Research Journal, vol. 13, no. 3 (Dec. 1966), pp. 146160.

Figure 1.
Three versions of Hooley's GDP (thous. 1939 pesos),.
The Philippines, 1902-1940

2,000,000.0
1,900,000.0
1,800,000.0
1,700,000.0
1,600,000.0
1,500,000.0
1,400,000.0
1,300,000.0
1,200,000.0
1,100,000.0
1,000,000.0
900,000.0
800,000.0
700,000.0
600,000.0
500,000.0
400,000.0
300,000.0
1900

1905

1910

1915

1920

1925

1930

1935

1940

1945

Original version 1 (file GDPREV)
Original version 2 (file CCC)
Corrected for cnstrctn, trnsprtn (etc.) &amp; prvt srvcs(Data Source) TwoGDPAdjstd.iv2023; file 39GDPAdjstd DN29.

6

Adjusting Hooley GDP viii2023 Odaka

Figure 2.
Hooley's nominal GDP,.
The Philippines, 1902~1940 (thous. pesos)
3000000
2800000
2600000
2400000
2200000
2000000
1800000
1600000
1400000
1200000
1000000
800000600000400000200000
0
1900

1905

1910

1915

1920

1925

1930

1935

1940

1945

file GDPREV original
file GDPREV crrctd excpt for cnstrctn
Corrected for cnstrctn, trnsprtn (etc.) &amp; prvt srvcs

Data source) TwoGDPsAdjstd.iv2023, file NmlGDPAdjstd U71

Figure 3.
Hooley's implicit GDP deflators,.
The Philippines, 1902~1940 (1939 = 1.0)
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.1
2
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1
0.9
0.8
0.7
0.6
19001902190419061908191019121914191619181920192219241926192819301932193419361938194019421944
Hooley's original
Hooley's (file GDRREV)
Corrected for cnstrctn, trnsprtn (etc.) &amp; prvt srvcs

Data source) TwoGDPsAdjstd.iv2023, file Prices BE16
7

Adjusting Hooley GDP viii2023 Odaka

Figure 4.

1,800,000.0
1,700,000.0
1,600,000.0
1,500,000.0
1,400,000.0
1,300,000.0
1,200,000.0
1,100,000.0
1,000,000.0
900,000.0
800,000.0
700,000.0
600,000.0
500,000.0
400,000.0
300,000.0
200,000.0
100,000.0
0.0
1900

Revised Hooley's GDP in three sectors (thous. 1939 pesos),1902-1941

GDP

Agr., frstry& fshry

Services

Industry

1905

1910

1915

1920

1925

1930

1935

1940

1945

Revised GDP (crrctd for cnstrctn, trnsprtn (etc.) & prvt srvcs)
1. Agrcltr, frstry & fshry
2. Indstry
3. Srvcs

Data source）TwoGDPAdjstd.iv2023; file 39GDPAdjstd DN12.

8

Adjusting Hooley GDP viii2023 Odaka

Figure 5.

1Composition of the Philippine GDP, 1902~1940

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334353637383940

Calendar year, 1901~1940
1. AGRI.FISHERY.FORESTRY

2.INDUS-TRY SECTOR

3.SERVICE SECTOR

Data source) TwoGDPAdjsted.iv2023; file Cmpstn BL14.

9Adjusting Hooley GDP viii2023 Odaka

Figure 6.
Average gross value added per labor (in 1939 pesos/yr) ,The Philippines, 1902-1940
900.00850.00800.00Services

750.00700.00650.00600.00550.00500.00450.00Indstry & mng

400.00350.00Services

300.00Total

250.00200.00150.00100.00

Total

Agr.,frstry & fshry

Agr., frstry ＆fshry

50.00
Indstry & mng
0.00
1900190219041906190819101912191419161918192019221924192619281930193219341936193819401942

Agriculture, forestry, livestock & fishery

Indstry & mining

Servics

Total

Data source) TwoGDPsAdjstd.iv2023; file Tmsrs F15.

24viii2023//

10

Richard Hooley's GDP of the Philippines, 1902~1940, slightly revised (as of 12 June 2023)
Real GDP [thous. 1939 pesos]
1.
Agriculture,
Calendarforestry,
year
livestock &fishery
1901
144,711
1902
151,5091903
156,792
1904
163,5031905

2.
Indstry &mining3.
Servics

Nominal GDP [thous. pesos]

Total

1.
Agriculture,
forestry,
livestock &fishery2.
Indstry &mining3.
Servics

Total

35,92354,05337,68244,363

168,898
207,019
175,927
182,484

349,532
412,581
370,401
390,350

178,715
206,775
206,284
226,337

29,987
46,32836,10242,475

139,316
169,924
170,254
191,274

348,018
423,027
412,640
460,086

1906
1907
1908
1909
1910

170,548
179,129
187,939
197,763
208,884

48,03749,494
51,52249,44265,439186,073
197,068
210,551
215,399
246,059

404,658
425,690
450,012462,604
520,382

239,983
256,829
265,497284,244306,264

44,693
48,82450,99453,961
84,705202,508
207,227
207,227
215,119
239,121487,183
512,881
523,719553,323
630,090

1911
1912
1913
1914
1915

198,152
217,817
253,052
251,449
249,983

77,47078,72487,49984,744
70,147

272,043
268,728
283,007
280,525
270,194

547,666
565,269
623,557
616,718
590,323

300,726
328,885
347,887
360,302
370,065

92,50487,416
114,001
103,879
87,153

275,797294,737
330,796323,696
312,717669,027
711,037
792,683
787,877
769,935

1916
1917
1918
1919
1920

275,262311,260
327,035
333,645
359,105

87,176
104,836
131,643
119,743
159,732

314,958
359,861
413,520
400,124
446,872

677,396775,957
872,198
853,513965,708

418,750
539,165
698,255
893,397856,075

114,746
153,943
217,709
270,683
462,735

373,332
478,102618,215
662,577
788,973

906,828
1,171,210
1,534,178
1,826,657
2,107,782

1921
1922
1923
1924
1925

374,310392,192
391,322415,314433,976

153,007
191,234
165,878
170,187
162,499

382,978
422,038
397,457
445,437
447,723910,295
1,005,463
954,656
1,030,938
1,044,198

608,240
589,589
686,781782,462
807,761

282,963
273,029
278,206
283,761
264,253623,621577,059
556,988604,853
615,848

1,514,824
1,439,678
1,521,974
1,671,076
1,687,862

1926
1927
1928
1929
1930

449,869
471,586
487,034
505,785
508,372

172,068
178,553
201,208
202,345
210,295

469,695
472,663486,685
510,560
509,324

1,091,632
1,122,802
1,174,927
1,218,690
1,227,991

826,943842,326
832,331
870,598
739,929

274,843
300,949
290,181289,910294,652

631,753
632,166
636,592654,276
616,906

1,733,538
1,775,440
1,759,104
1,814,783
1,651,486

1931
1932
1933
1934
1935

514,555545,239589,347
565,571
529,098

245,976
290,309
266,945284,372
235,337

476,292472,252
470,271
485,559
492,015

1,236,823
1,307,800
1,326,562
1,335,502
1,256,450

538,384
475,082
513,395
515,628
550,550

271,860
256,983
230,820
257,056
254,130

533,428
489,069
480,769
487,171
501,382

1,343,672
1,221,134
1,224,984
1,259,855
1,306,062

1936
1937
1938
1939
1940

587,867
625,911621,166
657,673
661,313265,668
287,395
309,258
332,585
324,342558,871
600,746
643,643
683,469
722,079

1,412,406
1,514,052
1,574,066
1,673,727
1,707,733

638,901
682,741
647,985
657,673
650,929

293,118
327,988
344,005
332,585
320,787

565,946
623,956
643,656
651,761
726,7211,497,965
1,634,686
1,635,647
1,642,019
1,698,438

Data source) TwoGDPsAdjstd.iv2023; file Tmsrs P9..

Employmenmt in thous. assuming the annual const. rates of growth:Primary
1901
1902 1320.82745
1903
1377.979
1904 1437.60346
1905 1499.80785

Secondary

777.6565506

1564.70379
1632.40774
1703.04121
1776.73096
1853.60922

993.911686
983.319104
972.839412
962.471407
952.213898

862.0015136
955.4945676
1059.127918
1174.001386
1301.33408

3420.61699
3571.22141
3735.00854
3913.20375
4107.1572

1911
1912
1913
1914
1915

1933.81397
2017.48913
2104.78488
2195.85787
2290.87155

942.065709
932.025674
922.09264
912.265467
902.543026

1442.477333
1598.929043
1772.349573
1964.579368
2177.658489

4318.35701
4548.44385
4799.22709
5072.70271
5371.07307

1916
1917
1918
1919
1920

2389.99642
2493.41038
2601.29900
2648.07474
2695.69158

1921
1922
1923
1924
1925

2744.16466
2793.50937
2843.74137
2894.87663
2946.93139

1926
1927
1928
1929
1930

0.042359534

log comptationlog comptation

6.773072366

6.933792353

632.918

7.994921646 6.45034087-0.010714666

0.989342532 701.5645578

0.102972052 1.10846043

2644.25523

5696.76889
6052.47355
6441.150
0.9442272 6310.50181
6190.08536

838.456594
826.935167
815.572058
804.365092
793.312124

2496.777724
2357.525451
2226.039667
2101.887212
1984.659087

6079.39898
5977.96998
5885.3531
5801.12894
5724.9026

2999.92218
3053.86584
3108.77949
3164.68058
3221.58687

782.411038
771.659745
761.056189
750.598339
740.284192

1873.969102
1769.452607
1670.765289
1577.582039
1489.595879

5656.30232
5594.97819
5540.60097
5492.86096
5451.46694

1931 3279.51643
1932 3338.48766
1933 3398.5193
1934 3459.6304
1935 3521.84039

730.111775
720.079139
710.184364
700.425555
690.800844

1406.516953
1328.071571
1254.001307
1184.062149
1118.023693

5416.14516
5386.63837
5362.70497
5344.1181
5330.66492

1936
1937
1938
1939
1940

681.308389
671.946372
662.713000
653.606506
644.625147

1055.668387
996.7908098
941.1970000

5322.14578
5318.37357
5319.17300
5324.38001 ←ass. the same rates forwards
5333.84114 ←ass. the same rates forwards

3585.16901
3649.63639
3715.26300
3782.06969
3850.07767

8.220205
0.017821927

892.924203
883.407892
873.993000
1.017981685 861.983259
850.138546

570.988358

Total

1906
1907
1908
1909
1910

7.863766215

1037.43544
7.228373212
1026.379
1.043269502
1015.4404
1004.61837

Tertiary

2929.25125 ←ass. the same rates backwards
3037.276
3154.60842
3282.08278

log comptation

7.863766

2413.848263
2675.655281
6.496342

6.773072 2965.858000-0.013836518

0.986258767 2800.443809

888.703812
839.138317

6.847152469 7.99492165-0.057388459