KEO Discussion Paper No. 141

Redefining the Picture of Myanmar’s Economic Growth:Trade, Production, and Jade
Koji Nomura and Hiroshi Shirane†
December 2016
Abstract
The economic potential of Myanmar is attracting significant attention. However, some questions have beenraised about the reliability of Myanmar’s official system of national accounts (MMSNA). First, it issuspected that under the military regime, economic growths might have been significantly overstated since
the latter half of the 1990s, by the Economist Intelligence Unit (EIU 2010) and the ADB (2016). The second
problem is that until the shift to the managed floating exchange rate system in April 2012, the official
exchange rate had been used in the MMSNA in converting international trade into the national currency.
Under the official exchange rate, which set the value of the Myanmar kyat at a level far above the market
exchange rate, the amounts of exports and imports were significantly undervalued, resulting in a significant
underestimation of GDP. The third problem is extensive illegal trade. In recent years, Global Witness(2015a and 2015b) and Dapice et al. (2014) pointed out that illegal exports of jade, whose prices began tosurge in the latter half of the 2000s, have not been properly reflected in the MMSNA. According to thoserecent research findings, the total transaction value of jade is estimated to equate to 48% of Myanmar’s
GDP in 2014.
In a bid to respond to those problems, this paper tries to develop new estimates of GDP. Our results
show that Myanmar’s real GDP growth turned negative twice, first in 2003–2004 and second in 2007–2008.
In terms of the average growth rate for the period 1998–2010, our estimate of 4.9% represents a downward
revision of 7.0 percentage points compared with the MMSNA estimate of 11.9%. The downward revision
to economic growth in 1998–2010 based on our estimates bring Myanmar’s GDP growth and laborproductivity growth closer to those of Thailand and Bangladesh.
Meanwhile, the impact of revaluing jade transactions on macroeconomic growth is observed from themid-2000s, for instance, turning negative growth estimated for 2004 before reflecting the reassessed valuesof jade transactions to positive growth. The impact of revaluation of jade is even more conspicuous in 2008
and thereafter with jade production accounting for more than 10% of Myanmar’s GDP. Notably, therevaluation of jade results in a significant upward revision in 2009–2010, from 3.2% to 17.9%. On the other
hand, real GDP dropped 21.5% in 2012 as jade production decreased by half following the transfer of power
to the civilian government. Based on our estimates reflecting the revaluation of jade, Myanmar wascomparable to India and Vietnam—both in real GDP growth and labor productivity growth—in the period
1998–2010. However, Myanmar was alone to fall into negative growth in the period 2010–2014.
† Koji Nomura is an associate professor at Keio Economic Observatory (KEO), Keio University, Tokyo and Hiroshi

Shirane is a researcher at KEO. This research was conducted at KEO within the Research on Green Productivity andProductivity Measurement Program for Myanmar, which is organized by the Asian Productivity Organization (APO)
and granted by Ministry of Foreign Affairs (MOFA), Government of Japan. The authors appreciate Yasuko Asano(officer at the Research and Planning Division, APO) for her coordination of the project and Masahiko Ebashi(Myanmar Economic Research & Consulting Co., Ltd) for his helpful advice at our meeting in Yangon. The views andopinions expressed herein are those of the authors and do not necessarily reflect those of the APO and MOFA. This
paper is preliminary and we welcome comments.

1

Table of Contents
1

Introduction ....................................................................................................................... 3

2

Framework for Revision ..................................................................................................... 5

2.1

Industry Outputs ............................................................................................................ 5

2.2

Output Prices ................................................................................................................. 7

2.3

Final Demand ................................................................................................................ 8

3

Revision in Trade ............................................................................................................. 10

3.1

Revaluation of Exports ................................................................................................. 10

3.2

Revaluation of Imports ................................................................................................. 12

3.3

Other Revisions in Trade .............................................................................................. 15

4

Revision in Production ..................................................................................................... 17

4.1

Agriculture .................................................................................................................. 17

4.2

Livestock and Fishery................................................................................................... 20

4.3

Energy and Mining ....................................................................................................... 22

4.4

Manufacturing ............................................................................................................. 24

4.5

Electricity .................................................................................................................... 26

4.6

Transportation and Communications .............................................................................. 27

4.7

Wholesale and Retail .................................................................................................... 29

5

Revision in Jade Value ..................................................................................................... 30

6Results ............................................................................................................................ 32

7

Conclusion ...................................................................................................................... 38

References .............................................................................................................................. 38
Appendix................................................................................................................................ 40

2

1

Introduction

In March 2011, the civilian government of President Thein Sein took over power from the State Peaceand Development Council (SPDC), the military government, in Myanmar. Since then, various
economic reform measures have been implemented in the country. In April 2012, the government
introduced a managed floating exchange rate system, under which the value of the Myanmar kyat isbasically determined by the interbank market (although the central bank maintains a degree of
influence), in a bid to unify several greatly different exchange rates (Figure 3 in Subsection 3.1),
followed by the establishment of a new Foreign Investment Law. Although significant restrictions
remain in businesses that have an impact on subterranean resources and the natural environment aswell as those that can be undertaken by Myanmar citizens such as agriculture, livestock breeding, andfisheries, the government is now heading in the direction of easing restrictions over foreign direct
investment. In response to such political and economic reform steps, the U.S. lifted its ban on theimportation of goods from Myanmar (excluding gems such as jade) in November 2012, and theEuropean Union (EU) lifted is economic sanction (excluding arms embargo) in April 2013.
In the general elections of November 2015, the National League for Democracy (NLD) led by
Aung San Suu Kyi won a landslide victory by taking 80% of the contested seats. Then, in the transition
from the Thein Sein government to the NLD-led government, leading to the transfer of power on
March 30, 2016 from the Thein Sein government to the NLD-led government in a further step toward
democracy. Following the formation of the new government, the Myanmar Investment Commission(MIC), a government-appointed body that scrutinize proposed inward investment projects fromoverseas, was temporarily paralyzed with no meeting held for about three months due to a delay inselecting new commission members, but resumed in June 2016. In response to further progress indemocratization, the U.S. lifted all economic sanctions against Myanmar, including the embargo onjade and rubies, in October 2016. Furthermore, the U.S. reinstated Myanmar’s eligibility for benefits
under the Generalized System of Preferences (GSP) program to reduce or exempt tariffs on some
imports from Myanmar effective from November 13, 2016. Up until now, foreign direct investment
projects in Myanmar have been mostly confined to those from Singapore, China, and other countries
within Asia. However, the complete lifting of the U.S. economic sanctions and the restatement of
Myanmar’s GSP status in its trade with the U.S. will likely boost inward foreign direct investment in
Myanmar, not only from the U.S. but also from other countries with an eye to exporting to the U.S.
market.
As democratization makes progress, the economic potential of Myanmar, which has a large
number of quality young workers with its population totaling more than 50 million, is attracting
significant attention. However, some questions have been raised about the reliability of Myanmar’s
official statistics on macroeconomic performance. First, it is suspected that under the military regime3

of the SPDC, official economic growth rates might have been significantly overstated since the latter
half of the 1990s. In forecasting Myanmar’s future economic growth, the Economist Intelligence Unit
(EIU) of The Economist Group of the United Kingdom has been releasing its own estimates of thereal GDP growth by taking into account various other factors, i.e., electricity use, trade balance, andthe impact of sanctions by the U.S. and the EU in 2003 and onward (EIU 2010). In Myanmar, theCentral Statistical Organization (CSO) has compiled the Myanmar System of National Accounts(MMSNA). Figure 1 compares the CSO’s official GDP growth estimates (MMSNA) and alternative
estimates by the EIU. While the official GDP growth estimates peaked from 2002 through 2004,
estimates by the EIU show that Myanmar suffered negative growth during the same period due to theimpact of economic sanctions imposed by the U.S. and Europe. Likewise, considerable deviations areobserved both before and after the period, the EIU notes that the pace of economic growth as shownin Myanmar’s official statistics has been overestimated by two-fold. The Asian Development Bank
(ADB) has also been publishing its own real GDP growth estimates (ADB 2016). As shown in Figure
1, growth estimates by the ADB slightly exceed those by the EIU but show that official growth figures
based on the MMSNA have been grossly overestimated.1
16%

Official estimate in MMSNA

14%
EIU estimate
12%

ADB estimate

10%
8%

6%
4%
2%

0%
-2%
2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

-4%

Figure 1: Real GDP Growth, Official vs Alternative Estimates
The second problem with the official statistics is that until the shift to the managed floatingexchange rate system in April 2012, the official exchange rate had been used in the MMSNA in
converting international trade (mainly denominated in the US dollar) into the national currency. Under
the official exchange rate, which set the value of the Myanmar kyat at a level far above the market
exchange rate, the amounts of exports and imports were significantly undervalued, resulting in asignificant underestimation of GDP.
The third problem with Myanmar’s economic statistics is extensive illegal trade. In recent years,

Currently, the official estimates provided in the MMSNA are used as GDP figures for Myanmar in the United Nations Statistics
Division (UNSD)’s National Accounts Estimates, the International Monetary fund (IMF)’s World Economic Outlook Database, andAPO (2016).
1

4

Global Witness (2015a and 2015b) and a research team at Harvard University’s ASH Center for
Democratic Governance and Innovation (Dapice et al. 2014) pointed out that illegal exports of jade,whose prices began to surge in the latter half of the 2000s, have not been properly reflected in the
MMSNA. According to those recent research findings, the total transaction value of jade is estimatedto equate to 48% of Myanmar’s nominal GDP in 2014. While the EIU (2010) points to the possibilityof a significant overestimation of Myanmar’s nominal GDP, the fact that illegal jade transactions are
not properly accounted (neither the MMSNA nor the EIU takes this fact into consideration) points tothe possibility of an underestimation.
In a bid to respond to those problems, this paper tries to develop new estimates of GDP from bothof the production and demand sides, by revising Myanmar’s system of national accounts based on
existing studies, various materials available, and international comparisons. Section 2 provides an
overview of the MMSNA data that are currently available, some minor revisions in MMSNA, and our
adjustment process used in this paper. Specifically, we correct the amounts of exports and imports in
Section 3, examine and correct data on gross output by industry in Section 4, and reassess thetransaction value of jade in Section 5. Section 6 summarizes our estimation results. Some tables on
the revised estimates are provided in Appendix.

2

Framework for Revision

2.1

Industry Outputs

In the MMSNA, GDP is measured by the production approach based on value added by industry. The
constraint in examining output by industry is that the MMSNA provides data on nominal and realvalue added by industry but not on the corresponding gross outputs. Therefore, in this paper, we use
data on gross outputs by industry from the United Nations Statistics Division (UNSD)’s National
Accounts Official Country Data. The MMSNA’s value added by industry and the UNSD’s gross
output by industry are mostly consistent with each other but there are two points that require attention.
First, 4. Energy and Mining and 8. Transportation and Communications are further broken down to
subcategories in the MMSNA (shown in the right column of Table 1) but not in the UNSD
classification. Therefore, this paper deals only with 12 broadly-classified industries.

5

Table 1: Industry Classification
This study
1. Agriculture
2. Livestock and Fishery
3. Forestry
4. Energy and Mining

MMSNA
1. Agriculture
2. Livestock and Fishery
3. Forestry
4. Energy and Mining
4.1 Energy
4.2 Mining
5. Manufacturing
5. Processing and Manufacturing
6. Electricity
6. Electric Power
7. Construction
7. Construction
8. Transportation and8. Transportation and Communications
Communications
8.1 Transportation
8.2 Communications
9. Financial Institutions
9. Financial Institutions
10. Social and Administrative Services 10. Social and Administrative Services
11. Rental and Other Services
11. Rental and Other Services
12. Wholesale and Retail
12. Trade

Second, there are certain discrepancies between the two systems that may be regarded as timeseries inconsistencies. Figure 2 shows changes over periods in the ratio of nominal value added tonominal gross output (value-added rate) calculated by using data on value added by industry from the
MMSNA and data on gross output by industry from the UNSD. The two sets of data generally
correspond fairly well but some irregularities are observed in certain years. In agriculture, the valueadded rate dropped in 1993. However, as discussed later, we found no plausible factors in physicaloutput data (the value-added rate remained stable even in 1991 and 2008 when agriculture suffered
substantial cyclone damage). In this paper, we will use the MMSNA-based data on value added byindustry as a benchmark and make adjustments to the UNSD-based data on gross output by industry
to remove the irregularity in the time series of the value-added rate (rev-0 in Table 2). In 6. Electricity,
there were some periods in which the value-added rate fell sharply. However, as these are also theperiods in which Myanmar increased its dependence on import-reliant coal power generation (Figure
25 in Subsection 4.5), we avoid making ad-hoc adjustments here.

6

1.0

0.8

0.8

1. Agriculture

0.9

2. Livestock and Fishery

3. Forestry

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.8
0.7
0.6
0.5
0.4
0.3
0.2

0.2

0.2
0.1

0.1

Official estimate in MMSNA

Official estimate in MMSNA

0.0
0.7

0.1

Our estimate

Our estimate

Official estimate in MMSNA

0.0
1990

1995

2000

2005

2010

2015

0.3

Our estimate

0.0
1990

1995

4.Energy and Mining

2000

2005

2010

2015

0.8

1990

1995

5. Manufacturing

2000

2005

2010

2015

6. Electricity
0.7

0.6

0.6

0.5
0.2

0.5

0.4
0.4
0.3
0.3

0.1
0.2

0.2

0.1

0.1
Official estimate in MMSNA

Our estimate

Official estimate in MMSNA

0.0
0.5

Our estimate

Official estimate in MMSNA

0.0
1990

1995

2000

2005

2010

2015

7. Construction

1.0

1995

2000

2005

2010

2015

0.3

1990

8. Transportation and Communications

0.9

0.4

Our estimate

0.0
1990

1995

2000

2005

2010

2015

9. Financial Institutions

0.8
0.7

0.3

0.2

0.6
0.5

0.2

0.4
0.1

0.3
0.1

0.2
Official estimate in MMSNA

0.1

Our estimate

0.0
0.7

Official estimate in MMSNA

Our estimate

Official estimate in MMSNA

0.0
1990

1995

2000

2005

2010

2015

0.9

10. Social and Administrative Services

1995

2000

2005

2010

2015

0.9

1990

1995

2000

2005

2010

2015

12. Wholesale and Retail

11. Rental and Other Services

0.6

Our estimate

0.0
1990

0.8

0.8

0.7

0.7

0.5
0.6

0.6

0.4

0.5

0.5

0.3

0.4

0.4

0.3

0.3

0.2
0.2

0.2
0.1
Official estimate in MMSNA

0.1

0.1

Our estimate

0.0

Official estimate in MMSNA

1995

2000

2005

2010

2015

Our estimate

0.0

0.0
1990

Official estimate in MMSNA

Our estimate

1990

1995

2000

2005

2010

2015

1990

1995

2000

2005

2010

2015

Figure 2: Adjustment in Value Added Ratios by Industry

2.2

Output Prices

The constraint on the side of price data is that neither the MMSNA nor the UNSD provides data on
gross output prices by industry. Although we do not know how the MMSNA estimates the prices ofvalue added by industry, we treat the value-added price index for each industry as being equal to thegross output price index for that industry.2 In Section 4, we make adjustments to data on real gross
output by industry based on those official estimates (rev-2 in Table 2). First, we compare official
estimates for 1. Agriculture and 4. Energy and Mining with corresponding estimates in physical units
by external organizations. Second, for 2. Livestock and Fishery, 5. Manufacturing, and 6. Electricity,
we compare official estimates in the MMSNA with corresponding estimates in physical units
separately released by the CSO. Although it is possible that the quantitative data released by the CSO
have been overestimated, the comparison shows that the two sets of data also differ in their trajectories
over periods. Third, for 8. Transportation and Communications and 12. Wholesale and Retail, whose
demand is mainly derived demand and hence dependent on activities in other industries, we makeIndustry-level data on the values of gross output and value added (both in nominal and real terms) after these adjustments are
hereinafter referred to as “official estimates” in the MMSNA.
2

7

adjustments associated with the aforementioned adjustments to data on real gross output by industry.
In each case, relevant data from neighboring countries such as Cambodia, Thailand, and Vietnam areused as reference information for comparison.
These downward revisions to real gross output by industry result in upward revisions to the prices
of production implicitly computed from data on nominal gross output by industry based on the
MMSNA. However, the resulting prices of production for most industries (i.e., except for 4. Energy
and Mining) appear to be significantly overestimated when compared to the consumer price index(CPI) and international prices. Therefore, data on nominal value of gross output are also adjusted formany industries. The adjustments to nominal gross output data revise nominal and real value added
via the value-added rate for each industry as revised in Figure 2 (i.e., after rev-0 in Table 2).
Table 2: Our Revisions in MMSNA
1. Agriculture
2. Livestock and Fishery
3. Forestry
4. Energy and Mining
5. Manufacturing
6. Electricity
7. Construction
8. Transportation andCommunications
9. Financial Institutions
10. Social and
Administrative Services
11. Rental and Other Services
12. Wholesale and Retail

if rev-2
otherwise

if rev-2, 3
otherwise
if rev-2
otherwise

value (v)p*q
rev-0,1
p*q
p*q
rev-0,1,3
rev-0,1
p*q
rev-0,1–
rev-0

Gross output (GO)quantity (q)price (p)
rev-2
rev-1
v/p
rev-1
rev-2
rev-1
–
rev-1
rev-2,3
v/q
v/p
rev-1
rev-2
rev-1
v/p
rev-1
–
–
v/p
–

Value added (VA)
value (vv) quantity (vq)rev-0,1,2
vv/p
rev-1
–
rev-1,2
vv/p
rev-1
–
rev-1,3
vv/p
rev-1
vv/p
rev-0,1,2
vv/p
rev-1
–
–
–
–
–

p*q

rev-2

–

rev-0,2

vv/p

rev-0

v/p

–

–

–

rev-0

v/p

–

–

–

rev-0
p*q

v/p
rev-2

–
–

–
rev-0,2–vv/p

Note: In revision of gross output, 0=adjustment in irregular changes in value added rates by industry,
1=revision in trade (Section 3), 2=revision in output (Section 4), and 3=revision in jade value (Section 5)

2.3

Final Demand

In the MMSNA, final demand is composed of the following five components: gross fixed capitalformation (GFCF), net increase in stock (INV), export (EX), import (IM), and total consumption.3
Here, total consumption is defined as the difference between market price-based GDP for the country
and the sum of the remaining four components of final demand, and no further breakdowns areprovided in the MMSNA. As for government consumption (GC), relevant data from the UNSD’s
National Accounts Estimates of Main Aggregates (UN estimates) can be used. However, in recent
years, the amount of government consumption based on the UN estimates has increased to a level
equal to two- to three-times the nominal value of gross output for 10. Social and Administrative
Services, which is supposed to be conceptually similar to government consumption. In our estimation,
we assume that the nominal value of gross output for 10. Social and Administrative Services equals
3

From 2005 onward, final demand in the MMSNA includes statistical discrepancy, in addition to the five components stated above.

8

the amount of government consumption to define household consumption (HC) as total consumption
net of government consumption, thus decomposing final demand into six components.
As the first step for revising data on final demand, we make adjustments to the values of importand export (rev-1 in Table 2) in Section 3. This involves converting amounts based on the official
exchange rate into those based on the market exchange rate, checking Myanmar’s trade statistics
against those of its major trade partners, and adding the amounts of trade in services and directpurchases that are no included in the MMSNA. Then, in Section 5, we reassess the values of jadeexports and have them reflected in the GDP statistics (rev-3 in Table 2). Data used for revising the
MMSNA are as shown in Table 3.
Table 3: List of Data Used for Revising MMSNA
No. Variables
Official estimate in MMSNA
1 final demands (C, GFCF, INV, EX, IM)
2 final demands (C, GFCF, INV, EX, IM)
3 final demands (C, GFCF, INV, EX, IM)
4 final demands (C, GFCF, INV, EX, IM)
5 gross value added (VA) by industry
6 gross value added (VA) by industry
7 gross value added (VA) by industry
8 gross value added (VA) by industry
9 gross output (GO) by industry
10 gross output (GO) by industry
Revision in trade
11 official exchange rate
12 market exchange rate
13 trade in services
14 trade by type of principal commodities
15 trade by commodity section (by B-SITC)*
16 trade by commodity section (by HS)*17 trade by country*
18 trade by commodity section
19 trade by country
20 composition of imports
21 trade by country*
22 freight and insurance
Revision in output
23 sown area of selected crops*
24 harvested area of selected crops*
25 production of selected crops*
26 prices of selected crops at harvest time*
27 agricultural production by country*
28 agricultural area by country*
29 production of grains
30 primary livestock production by country*
31 production of fish and prawns*
32 livestock breeding
33 volume of production of selected commodities
34 electric power generation by type and location*
35 production of electric power*
36 sales of electric power by type*37 retail prices of selected commodities in Yangon*
38 wholesale prices of selected commodities in Yangon
39 energy demand and production
Revision in jade value
40 jade production
41 jade production*
42 jade sales*
43 jade sales

Periods

Unit

Sources

2010–2014
2005–2010
2000–2005
1987–2000
2010–2014
2005–2010
2000–2005
1987–2000
1998–2014
1974–1998

at current and 2010 prices
at current and 2005 prices
at current and 2000 prices
at current and 1985 prices
at current and 2010 prices
at current and 2005 prices
at current and 2000 prices
at current and 1985 prices
at current prices
at current prices

MMSNA (2015), CSO
MMSNA (2011), CSO
MMSNA (2006), CSO
MMSNA (2003), CSO
MMSNA (2015), CSO
MMSNA (2011), CSO
MMSNA (2006), CSO
MMSNA (2003), CSO
Nationaｌ Accounts Official Country Data, UNSD
Nationaｌ Accounts Official Country Data, UNSD

1960–2014
1970–2014
1980–2014
1990–2014
1990–2014
2001–2014
1990–2014
1990–2014
1990–2014
1990–2014
1990–20142005

at current prices
at current prices
at current prices
at current prices
at current prices
at current prices
at current prices
at current prices
at current prices
at current prices
at current prices
at current prices

World Development Indicators, WB
National Accounts Estimates of Main Aggregates, UNSDWTO Statistics Database, WTO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
UN Comtrade, UNSD
UN Comtrade, UNSD
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Asian International Input-Output Table, JETRO

1990–2014
1990–2014
1990–2014
1990–2014
1961–2013
1961–2013
1960–2015
1961–2013
1990–2014
1990–2014
1990–2014
1990–2014
1990–2014
1990–2014
1990–2014
1990–2014
1990–2014at acresat acresat physical uint
at current prices
at constant prices
at acresat physical uint
at physical uint
at number of uint
at number of uint
at physical uint
at physical uint
at physical uint
at current prices
at current prices
at current prices
at oil equivalent tonnes

Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
FAOSTAT, FAO
FAOSTAT, FAO
Production, Supply and Distribution, USDAFAOSTAT, FAO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Myanmar Statistical Yearbook, CSO
Energy Balance Table, IEA

1990–2014
1996–2013
1995–2014
2005–2014at physical uint
at physical uint
at current prices
at current prices

Myanmar Statistical Yearbook, CSO
U.S. Geological Survey, USGS
Burma Gem Sales and Statistics, Pala International
Global Witness

Note: * indicates the data used for reference.

9

3

Revision in Trade

3.1

Revaluation of Exports

Until April 2012, multiple exchange rates were used in Myanmar. Basically, the US dollar is and has
been the currency of settlement (although the euro is used in some transactions and the Chinese yuan
in border trade). However, prior to the shift to the managed floating exchange rate system, the official
exchange rate had been used to convert trade values in the currency of settlement into those in thedomestic currency in the MMSNA and the balance of payment (BOP). Shown in Figure 3 iscomparison of changes in the time series of the official exchange rate and those of the UNSD AMA
rate, which can be regarded as the market exchange rate. Prior to the shift to the management floatingexchange rate system, the two exchange rates were deviating significantly from each other. In 2007,
the official exchange rate was 5.78 kyat per US dollar, overvalued by more than 220 times comparedto the market exchange rate of 1,280 kyat per US dollar. Accordingly, trade values were grossly
underestimated.
(Kyat/$)
1,400
UN AMA rate
1,200
1,000

Official rate

800
600
400

200
0
1990

1995

2000

2005

2010

2015

Figure 3: Market and Official Exchange Rates
The underestimation of export values result in an underestimation of the gross output and value
added of industries producing goods for export. Figure 4 illustrates how the gross output of eachindustry is decomposed into the portion absorbed in domestic demand4 and that in export demand (asconverted into domestic currency value by the official exchange rate) before making any adjustments.
In 4. Energy and Mining, an industry in which the share of export demand is particularly high, theunderestimation of trade value due to the use of the official exchange rate is a major factor explaining
the discontinuity in the time-series trend of nominal gross output between 2010 and 2011. The similar
gap is also observed in nominal value added for the industry, indicating that the underestimation ofExport values are based on the values of exports by commodity in the Myanmar Statistical Yearbook (MSY) published by the CSO.
The value of exports of each commodity, denominated in the U.S. dollar, has been converted into kyat terms and applied to the relevant
industry, assuming that each of the 20 commodities corresponds to an industry. The value of an industry’s gross output accounted forby domestic demand is defined as the difference between the nominal value of gross output (Subsection 2.1) and the value of exports
for the corresponding industry.
4

10

export values has been causing the underestimation of nominal GDP.

(Billion Kyat)(Billion Kyat)

0. Whole Economy

160,000

Exports at official exchange rate

16,000140,000

120,000(Billion Kyat)

1. Agriculture

18,000180,000

Exports at official exchange rate

9,000

12,000

Exports at official exchange rate

8,000

14,000
Domestic demand

2. Livestock and Fishery

10,0007,000

Domestic demand

Domestic demand

6,000

100,000

10,0005,000

80,0008,000

60,0006,000

40,000

4,000

2,000

20,000

2,000

1,000

0

0

0
2010

2012

2014
2014

2004

2002

2000

1998

1996

1994

1992

2012

5,000

2008

Domestic demand

5. Manufacturing80,000
Exports at official exchange rate

70,000

6,000

250

2010

7,000

2006

Exports at official exchange rate
300

1990

2014

2012

2010

2006

2004

2002

2000

1998

2008

(Billion Kyat)

4. Energy and Mining

8,000

2008

(Billion Kyat)

3. Forestry

350

1996

1994

1992

1990

2014

2012

2010

2008

2006

2004

2002

2000

3,000

2006

(Billion Kyat)

1998

1996

1994

1992

1990

4,000

Exports at official exchange rate

60,000
Domestic demand

50,000

Domestic demand

200
4,000

40,0003,000

30,0002,000

20,00050

1,000

10,000

0

0

0
2004

2002

2000

1998

1996

1994

1992

1990

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

100

1990

150

Figure 4: Nominal Gross Output by Industry (Before Revision)
Shown in Figure 5 are gross output values as recalculated by using the adjusted export values
based on the market exchange rate. We can see that the discontinuity observed in 4. Energy and Mining
has been resolved by revaluing the exchange rate. Meanwhile, although no particular discontinuity isobserved in the graph for 3. Forestry in Figure 4, which is before making any adjustments, the trendof gross domestic values as seen in the shape of the graph in Figure 5 is quite different from that of
Figure 4. The upward revision of gross output in the industry resulting from the revaluation of exportsof the industry leads to an increase in nominal GDP for the country, given that the value-added rate of
the industry is constant. Specifically, this necessitates an upward revision of nominal GDP every five
years starting from 1990 through 2010 by 3.3%, 6.0%, 9.5%, 12.9%, and 8.6% respectively.

11

(Billion Kyat)(Billion Kyat)

0. Whole Economy

160,000

Exports at market exchange rate

16,000140,000

120,000(Billion Kyat)

1. Agriculture

18,000180,000

Exports at market exchange rate

9,000

12,000

Exports at market exchange rate

8,000

14,000
Domestic demand

2. Livestock and Fishery

10,0007,000

Domestic demand

Domestic demand

6,000

100,000

10,0005,000

80,0008,000

60,0006,000

40,000

4,000

2,000

20,000

2,000

1,000

0

0

0
2008

2010

2012

2014

2010

2012

2014

2004

2002

2000

1998

1996

1994

1992

2004

2002

2000

Domestic demand

1998

2014

2012

2010

2008

0
2006

10,0000
2004

20,000

1,000

2002

30,000

2,000

2000

3,000

1998

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

50,00040,0001996

100

Domestic demand

4,000

1994

200

Exports at market exchange rate

1996

5,000

1992

Domestic demand

300

2006

70,00060,000

1990

400

2008

Exports at market exchange rate

6,000

1994

7,000

5. Manufacturing80,000

1992

Exports at market exchange rate

500

1990

2014

2012

2010

2006

2004

2002

2000

1998

2008

(Billion Kyat)

4. Energy and Mining

8,000

2006

(Billion Kyat)

3. Forestry

600

1996

1994

1992

1990

2014

2012

2010

2008

2006

2004

2002

2000

3,000

1990

(Billion Kyat)

1998

1996

1994

1992

1990

4,000

Figure 5: Nominal Gross Output by Industry (After Revision)

3.2

Revaluation of Imports

Meanwhile, in the case of the import of intermediate goods, the underestimation of the values ofimports (denominated in the domestic currency) may lead to an underestimation in the value of GDP
via an underestimation in the value of intermediate inputs. Figure 6 shows the composition of imports
by type of commodities in value terms based on data provided in the Myanmar Statistical Yearbook(MSY). Commodities are classified into three broad categories—i.e., capital goods, intermediate
goods, and consumer goods—which are further broken down into 12 subcategories. Among theimported goods, 20% to 30% are considered to intermediate goods. Revaluing those intermediate
goods based on the market exchange rate would create a significant discontinuity in the time-series
trend of the value-added rate at the aggregate level. Meanwhile, allocating the values of imports bycommodity to the corresponding industries under certain assumptions would result in a negative value
in the value-added rate in some industries. Based on those considerations, we refrain from making
GDP adjustments for reflecting the revaluation of the imports of intermediate goods.

12

100%
12. Others

90%
11. Medicines

80%

10. Textiles

70%

Consumergoods

9. Food
8. Durables

60%

7. Tools and spares

6. Fuels50%

5. Raw materials

Intermediate
goods

40%
4. Others30%

3. Transport equipment20%
2. Machinery

Capitalgoods

10%
1. Building materials

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0%

Figure 6: Composition of Import
Provided that Myanmar is dependent on imports for most of machinery and equipment as capital
goods, and if such imports are measured in kyat terms converted from the US dollar at the official
exchange rate, the construction industry’s share in gross fixed capital formation (GFCF) can bepresumed to be near 100%. Figure 7.1 plots the construction industry’s share in GFCF, assuming that
95% of the nominal gross output of the industry is capital formation (with the remainder accounted
for by maintenance and repair, hence treated as intermediate consumption). The constructionindustry’s share generally falls within the range of 40% to 50%, showing no indication of anundervaluation of machinery and equipment imports. Meanwhile, an estimation based on data on
imports (after adjustments as discussed in Subsection 3.3) shows that investment in machinery andequipment amounted to 4.8 trillion kyat in 2014,5 which represents only one-third of 14.3 trillion kyat,
the amount of such investment presented in the MMSNA. It is highly likely that nominal GFCF in the
MMSNA has been overestimated along with the overestimation of nominal GDP. In our estimation,
we use the estimates based on data on imports as the amount of investment in machinery andequipment. As a result of this revision, the construction industry’s share in GFCF expands to around
50% to 70% as shown in Figure 7.2.

The amount of investment is estimated at purchaser’s prices, calculated as the sum of the gross outputs that are assumed to beaccounted for by GFCF—i.e., 80% of 2. Machinery and 50% each of 3. Transport equipment and 4. Others in Figure 6—pluscommercial margins and the estimated cost of transportation.
5

13

(Figure 7.1: Before Revision)

2014

2012

2010

2008

2006

2004

2002

Machinery and equipment

2000

1998

1996

1990

2014

2012

0%
2010

10%

0%
2008

20%

10%
2006

30%

20%

2004

40%

30%

2002

50%

40%

2000

60%

50%

1998

70%

60%

1996

80%

70%

1994

80%

1992

90%

1990

Construction

100%

1994

Machinery and equipment

90%

1992

Construction

100%(Figure 7.2: After Revision)

Figure 7: Construction Share in GFCF
Based on what has been discussed above, we assume that the aggregate-level impact of revaluing
import values based on the official exchange rate can be described as follows. Now, consider that thebalance between output and final demand measured at the market exchange rate is described by thefollowing equation:(1)

𝐺𝐷𝑃 = 𝐻𝐶 + 𝐺𝐶 + 𝐺𝐹𝐶𝐹 + 𝐼𝑁𝑉 + 𝐸𝑋 − 𝐼𝑀 ,and that the values of exports (EX*) and imports (IM*) measured at the official exchange rate aredefined as:(2)

𝐸𝑋 = 𝐸𝑋 ∗ + 𝑅 𝐸𝑋 and 𝐼𝑀 = 𝐼𝑀∗ + 𝑅 𝐼𝑀 .

In the MMSNA, household consumption (HC) is defined as the difference between GDP and the totalof the non-HC final demand. Therefore, the aggregate balance in the MMSNA for the periods in which
export values were measured at the official exchange rate can be described as:(3)(𝐺𝐷𝑃 − 𝑅 𝐸𝑋 ) = (𝐻𝐶 − 𝑅 𝐼𝑀 ) + 𝐺𝐶 + 𝐺𝐹𝐶𝐹 + 𝐼𝑁𝑉 + 𝐸𝑋 ∗ − 𝐼𝑀∗ .

As such, in the current MMSNA, nominal GDP is measured as (𝐺𝐷𝑃 − 𝑅 𝐸𝑋 ) and underestimated by
𝑅 𝐸𝑋 . Likewise, household consumption is measured as (𝐻𝐶 − 𝑅 𝐼𝑀 ) and underestimated by 𝑅 𝐼𝑀 . In
this section, we use equation (3) as the basis and revise it into equation (1) by reassessing values
measured at the official exchange rate to those at the market exchange rate.
Figure 8 compares the official estimates of export and import values in the MMSNA, which aremeasured at the official exchange rate, and our estimates measured at the market exchange rate. The
discontinuity in the trend between 2010 and 2011 observed in the official estimate in the MMSNA(shown as dotted line in Figure 8) is resolved in our estimates measured at the market exchange rate.
The revaluation of import values at the market exchange rate does not affect GDP as shown in equation
14

(3) (household consumption is affected by the revaluation of import values but the revision is madeunder the influence of various factors to be discussed later).

3.3

Other Revisions in Trade

In addition to the revaluation at the market exchange rate in the preceding subsection, we makerevisions in two aspects, namely, the reexamination of import values in trade statistics and adjustments
the SNA concept. By examining trade between pairs of countries based on customs data on exports
reported one country and corresponding imports reported by its trade partners, Kellenberg et al. (2016)
found the tendency that the lower the level of economic development and the greater the level ofcorruption, the country is more likely to underreport exports. Meanwhile, with regard to the case of
Myanmar, Ebashi (2000) pointed to the possibility of underreporting the amounts of exports andimports in a bid to evade the export taxes and the import tariffs respectively. Figure 8 compares tradevalues reported in the MMSNA and the sums of corresponding trade figures reported by Myanmar’s
15 major trade partners and published on the UN Comtrade.6 As shown in Figure 8.1, the value ofexports reported in the MMSNA has been generally consistent with the sum of corresponding imports
reported by the trade partners and the former slightly exceeds the latter from 2006 onward. Thus, as
far as export values are concerned, we do not find any underreporting tendency in the MMSNA.7
Meanwhile, imports reported in the MMSNA are based on CIF, meaning that the reported value
includes cost, insurance, and freight, whereas exports reported by the trade partners are based on FOB,
which do not include insurance and freight. Accordingly, the value of imports in the MMSNA is
supposed to exceed the sum of corresponding exports reported by the trade partners. However, as
shown in Figure 8.2, the sum of corresponding exports reported by the trading partners has been
exceeding the value of imports reported in the MMSNA by 20% to 60%. Thus, in our estimation, we
adjust Myanmar’s import data for 2003 and onward based on the sum of corresponding exports
reported by the trade partners. Since freight and insurance rates charged in Myanmar are not available,
we apply the factor applicable to Thailand at the national aggregate level (2.0%) in IDE (2013) toconvert FOB-based values into CIF-based values. As a result of this revision, Myanmar’s trade deficit
in 2014 expands from 5.1 trillion kyat to 9.9 trillion kyat.

Myanmar’s 15 major trade partners are: China, Hong Kong, India, Indonesia, Japan, Malaysia, South Korea, Singapore, Thailand,
the U.S., France, Italy, the United Kingdom, Vietnam, and Germany.
7
Although the sum of imports reported by the trading partners for 2014 was twice as the corresponding value of exports reported in
the MMSNA, this is attributable to China’s import of jade from Myanmar, as discussed in detail in Section 5.
615

(Billion Kyat)(Billion Kyat)

25,00020,000Total import of 15 countries from Myanmar

Total export to Myanmar from 15 countries

Official estimate (MMSNA)

Official estimate (MMSNA)

20,000MMSNA (after our adjustement on exchange rate)

MMSNA (after our adjustement on exchange rate)

15,000Our estimate

Our estimate
15,00010,00010,000
5,000

5,000

0

(Figure 8.1: Goods Export)

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

(Figure 8.2: Goods Import)

Figure 8: Goods Trade, Comparison with Values in Major Trading Nations

The values of exports and imports reported in the MMSNA are consistent with figures in customs
clearance-based trade statistics. In the national accounts, however, it is desirable to include, not only
trade in goods recorded in customs clearance-based trade statistics, but also trade in services and directpurchases. Here, we estimate the values of trade in services and direct purchases based on balance of
payments (BOP) data provided in the WTO (2015) and added them to the value of trade in goods.8
The results are shown in Figure 9. The combined share of trade in services and direct purchases intotal exports varies significantly depending on the year. After peaking at 37% in 1998, the share
dropped to 4% in 2009 and 2010, the final years of the military regime, but rebounded following thetransfer of power to the civilian government in 2011, rising to 21% in 2014. In contrast, the combined
share of trade in services and direct purchases in total imports has been relatively stable, falling within
the rage of 5% to 15% over the past 25 years.

(Billion Kyat)

40%

14,000Goods exports (fob)Service imports

30% 20,000

20%

Direct purchase

Direct purchase10,00025%

Goods imports (cif)35%Service exports

12,000(Billion Kyat)
25,000Service + direct purchase share
25%

Service + direct purchaseshare

15,00015%

6,000

15% 10,000

10%

4,000

10%

2,000

5%

0

0%

8,000

(Figure 9.1: Export)

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

0%

1994

0

1992

5%

1990

5,000

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

20%

(Figure 9.2: Import)

Figure 9: Service Trade and Direct Purchase

Trade in services corresponds to “trade in commercial services” net of “construction,” “royalties and license fees,”and “travel” in the BOP, whereas direct purchases correspond to “travel” in BOP.
8

16

4

Revision in Production

4.1

Agriculture

According to data in the MMSNA, agriculture used to account for roughly half of Myanmar’s GDP in
the 1990s. Although its share in GDP has declined to around 20% in recent years, agriculture remains
the country’s mainstay industry. Shown in Figure 10 are changes in the growth rates of agriculture
production, sown area, and harvested area based on data published in the CSO’s Myanmar Statistical
Yearbook (MSY), as compared to those in the growth rate of real gross output for agriculture. 9
Significant changes were observed in the 1990s but relatively high growth rates were maintained in
all of the indicators in the 2000s.

18%
15%

12%
9%
6%
3%
0%
-3%
-6%
Real GO (MMSNA)ref) Agriculture production (MSY)ref) Sown area (MSY)ref) Harvested area (MSY)

-9%

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

-12%

Figure 10: Comparison of Agriculture Indicators
However, those high growth rates are questionable. Okamoto (2008) points out that rice, whichis the staple crop of Myanmar, has been subject to overstatement as it has been used as a measure forthe performance of local governments. This tendency was accelerated from 2004 onward, following
the abolition of a compulsory rice procurement system, under which farmers were required to deliverto the government a quota of rice set per unit sown area at a lower-than-market price. With no delivery
required, nothing stopped local government officials from overstating the sown area under their control
to inflate their administrative performance. Harvard University’s ASH Center (Dapice et al. 2010) also
points out that the production of milled rice in Myanmar has been significantly overstated. By
comparing data from FAOSTAT that are based on the CSO’s official estimates and data from the US
Department of Agriculture (USDA)’s Production, Supply and Distribution (PSD) database, they haveconcluded that the latter are more in line with the reality. In the period 2001 through 2009, annual rice

The MSY provides data on production, sown area, and harvested area for each of the 49 agricultural products. The growth rate of
agricultural production is a weighted average of the growth rates of the 49 agricultural products, based on their two-period average
shares in the total in value terms. The value of production for each product is calculated by multiplying the yield by the price at harvest.
Harvested area is defined as follows: sown area + area under multiple cropping – non-harvested area.
917

yield per capita ranged from 200 kg to 300 kg based on FAOSTAT data, compared to 200 kg to 220
kg based on USDA-PSD data. The annual yield of 300kg per capita as shown by FAOSTAT datatranslated into a daily calorie intake of 3,000 kcal per capita, exceeding 2,800 kcal required by an adultmale living in a rural area. Given the fact that Myanmar’s population includes women, children, andurban dwellers, who need less calories, they maintain that the FAOSTAT-based average yield of rice,which is equivalent to 3,000 kcal per capita per day, cannot be considered realistic, noting that anannual consumption of 180 kg to 200 kg per capita would be a reasonable estimate. They say that theestimate is roughly in line with the USDA-PSD-based annual yield of 200 kg to 220 kg when nettedwith what they perceive to be unrecorded exports (amounting to 500,000 tons per year or 10 kg per
capita per year).
Figure 11 show changes in the level and growth rate of rice production, comparing how theydiffer depending on whether they are based on data from USDA-PSD or those from the MSY.10 In
term of the growth rate, particularly large gaps are observed in 1998 and the mid-2000s. Meanwhile,in the level of production, the gap peaked in 2009, with the level of production based on MSY data
exceeding that based on USDA-PSD data by roughly 70%. From 2010 onward, the former declined ina way to close the gap with the latter.
20%(Thousand ton)
20,000

USDA-PSD

USDA-PSD

15%

18,000

Official estimate (MSY)

Official estimate(MSY)

16,00010%

14,0005%

12,00010,000

0%

8,000
-5%

6,000
4,000

-10%
2,000

(Figure 11.1: Production)

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1991

-15%

0

(Figure 11.2: Growth)Figure 11: Rice Production, Official and USDA-PSD Estimates
An overestimation of quantitative data may be found in other agricultural products. Figure 12
shows agricultural output growth by type of products in three different periods (1990–1997, 1997–
2010, and 2010–2014), classifying the 49 agricultural products in the MSY into seven groups (thenumber provided in the brackets following each type of products is the number of products classified
into the type, and product-level data are aggregated for each group as measured in the translog index
using the product prices at harvest). A comparison of the seven groups reveals that some types ofagricultural products show greater growth than cereals, which include rice. They show particularly

The MSY does not provide data on milled rice. As in Dapice et al. (2010), we estimate the production of milled rice by multiplying
paddy output by 0.58.
1018

high growth in the period 1997–2010, during which MSY-based rice production exceeded that basedon USDA-PSD data by a significant margin, indicating the possibility of similar overestimation.

20%

1990-1997

1997-20102010-2014

16%
12%
8%
4%
0%

-4%
Others (12)Beverages (4)Tobaccoand betel (4)Spices andcondiments (3)Pulses (17)

Oil seeds (5)Cereals (4)-8%Figure 12: Agriculture Output Growth by Products
In our estimation, we first replace the growth rates calculated based on MSY data with those
based on USDA-PSD data for three of the 49 agricultural products, namely, paddy, wheat, and millet.
Second, for the remaining 46 products, for which no substitute estimates are available, we adjust MSYbased growth rates for the years in which the MSY-based growth rate for rice exceeds that based on
USDA-PSD, reducing the growth rate for each of the 46 products by the percentage-point difference
observed in the growth rate for rice. The years from 1998 through 2010 are subject to this adjustment.
Revised real gross output is estimated by aggregating thus-adjusted output by product as measured in
the translog index. Figure 13 shows the growth of revised real gross output for 1. Agriculture.

18%
Official estimate (MMSNA)

15%

Our estimate

12%
9%
6%
3%
0%
-3%

-6%
-9%

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

-12%

Figure 13: Revision in Agriculture Production, Official and Our Estimates
Based on the revised production data, we calculated average growth rates for agricultural
production and land productivity in three different periods (1990–1997, 1997–2010, and 2010–2013),
and plotted the results in Figure 14. Except for those for Myanmar, data are taken from FAOSTAT.
Land productivity is defined as agricultural production per unit sown area. Based on official estimates,i.e., before adjustments, Myanmar’s agricultural production and land productivity for the period 1997–

19

2010 were extremely high relative to those of its neighboring countries. Those after the adjustments
were generally in line with those of the neighboring countries.
1990-19978%

1997-20102010-20137%

1990-1997

1997-20102010-2013

6%

7%
6%

5%

5%

4%

4%

3%

3%
2%
2%
1%

1%

0%

0%

-1%

-1%
Nepal

India

Indonesia Thailand

Vietnam Myanmar Myanmar(Official(Our
estimate) estimate)

Nepal(Figure 14.1: Production Growth)

India

Indonesia

Thailand

Vietnam

Myanmar Myanmar(Official(Our
estimate) estimate)(Figure 14.2: Land Productivity Growth)Figure 14: Comparison of Agriculture Production and Land Productivity

4.2

Livestock and Fishery

2. Livestock and Fishery accounts for approximately 6% to 9% of Myanmar’s value added. According
to data from the MMSNA, livestock and fishery production grew by an average of 11% per year overthe past 25 years, with particularly high growth—an average of 15% per year—recorded from thelatter half of the 1990s through the 2000s. Figure 15 compares Myanmar’s annual livestock production
per capita with those of its neighboring countries, all based on data from FAOSTAT. In 1990,
Myanmar’s meat production (Figure 15.1) stood at 5 kg per capita, slightly above that of India but
only one-sixth the level of Thailand. However, from 1999 onward, Myanmar’s meat production grew
rapidly to exceed that of Thailand in 2009. The average growth rate for the period 1998–2010 was
13%, more than twice that of Vietnam, which achieved the second largest growth (6% per year) among
the neighboring countries. Likewise, Myanmar’s milk and egg production (Figure 15.2 and Figure15.3) grew rapidly—at the pace of 7% and 13% respectively—during the same period, indicating thehigh likelihood of overestimation.

20

Vietnam

(Kg)
45

(Kg)

(Kg)

India

Thailand

16

Myanmar 100

40

14

35

80

12

Thailand
30

10Myanmar

60
25
Nepal

20

8

Cambodia40

Myanmar

6

Bangladesh

4Indonesia

15

Vietnam
10

India

Nepal

20

ThailandIndia

Indonesia
5

Cambodia

VietnamIndonesia

Bangladesh

Bangladesh

2

Nepal Cambodia(Figure 15.1: Meat)(Figure 15.2: Milk)

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

0
1990

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

0
1990

0

(Figure 15.3: Egg)

Figure 15: Livestock Production per Capita

Figure 16 compares the number of breeding animals for beef, pork, and fowl meat production(expressed in the number of heads) and the corresponding livestock production, as measured in indexes(1990 = 1.0) based on data from the MYS. The production of cultivated assets or cultivated biologicalresources, which include livestock, in the context of the system of national accounts (SNA) is
measured in terms of the growth of breeding animals, not in terms of the shipment of meat. Thus, it isassumed that the number of breeding animals for meat production and the production of livestock areclosely correlated with each other. However, the production of livestock grew far more rapidly than
the number of breeding animals in all of the three products, particularly from 1999 onward.
8

25

25

7
Breeding of cattle

Breeding of fowl

Breeding of pig
20

6

20

Production of beef

Production of fowl
meat

Production of pork

5

15

15

10

10

5

5

0

0

4
3

2

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

1990

1

Figure 16: Production and Breeding of Livestock

Figure 17 shows the average production growth rates for nine types of livestock in three different
periods (1990–1998, 1998–2010, and 2010–2014) based on data from the MSY. In the period 1998–
2010, almost all products posted double-digit growth, sharply higher than the growth rates recordedin the preceding and subsequent period. Thus, in our estimation, we adjust production data for theperiod 1998–2010, for which particularly high growth was recorded, assuming that livestock21

production for each of the nine products grew at the same pace as the number of breeding animals.11
As a result of this adjustment, average production growth rates for all of the nine types of livestock
are revised downwardly, for instance, from 16% to 8% for pork.

20%

1990-1998

adjusted 1998-2010

2010-2014

1998-2010

16%

12%

8%

4%

Fresh milk

Duck egg

Fowl egg

Duck meat

Turkey, geese,muscovy-duckand bar-rockFowl meat

Pork

MuttonBeef

0%

Figure 17: Production Growths by Types of Livestock

Although unrealistically high growth is observed in a certain period, no adjustments are made todata on fishery production as no substitute data are available. Figure 18 compares the rates of growth
in real gross output for 2. Livestock and Fishery based on official estimates and those based on ourestimates.12 As a result of adjustments made above, the average annual growth rate for the period
1998–2010 is revised from 15% to 10%.

25%

Official estimate (MMSNA)

20%

Our estimate
15%

10%

5%

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

0%

Figure 18: Revision of Output Growth in Livestock and Fishery

4.3

Energy and Mining

Since 2000, approximately 90% of output in 4. Energy and Mining has been for export, most of whichis accounted for by natural gas (Figure 19). As Thailand is the largest destination for Myanmar’s
The productions of beef and fresh milk, mutton, pork, fowl egg, and duck egg are corresponded to the number of breeding of cattle,
the total of sheep and goat, pig, fowl, and duck, respectively.
12
The outputs in livestock and fishery are aggregated by the translog index using the wholesale price indices in Yangon city in MSY.
1122

exports in energy and mining, Thailand’s data on imports from Myanmar provide an effective
benchmark against which to examine output in Myanmar in quantity terms.13 Figure 20 shows real
gross output based on data from the MMSNA, natural gas output reported in the MSY, Thailand’s
import of mineral products from Myanmar reported in the Thailand Trade Statistics (TTS), energy
production in oil equivalent as shown on the International Energy Agency (IEA)’s Energy Balance
Table, all in quantity terms and measured in indexes (2010 = 1.0). As the trend of real gross output
deviates from those of the others in 1999 onward, we adjust the MMSNA-based data on real gross
output based on IEA data.

100%
Natural gas

90%
80%

Other mineral products

70%
60%
50%
40%
30%

20%
10%
2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0%

Figure 19: Export Share of Output in Energy and Mining(2010=1.0)
Real GO (MMSNA)

1.6ref) Production of natural gas (MSY)

1.4ref) Import of mineral products from Myanmar to Thailand (TTS)

1.2

Production of energy materials (IEA)

1.0
Our estimate

0.8
0.6
0.4
0.2

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0.0

Figure 20: Output and Export in Energy and Mining

Figure 21 compares value added based on data from the MMSNA, natural gas exports reported
in the MSY, Thailand’s import of mineral products from Myanmar reported in the TTS, and revised

Based on traded statistics of Myanmar’s major trade partners (U.S., Thailand, Singapore, Malaysia, Japan, India, Hong Kong, andChina), Thailand accounted for 90% or more of Myanmar’s export of mineral products (including natural gas) through 2012. From2013 onward, China’s share increased to reach 35% in 2014 compared to Thailand’s share of 64%.
13

23

gross output (calculated from the above adjusted real gross output and the adjusted nominal gross
output in Subsection 3.1), all in value terms and measured in indexes (2010 = 1.0). We can see that thevalue of gross output based on our estimates, as compared to that based on official estimates, is morein line with the trends in the trade values reported in the MSY and the TTS.
(2010=1.0)
2.0
Official estimate (MMSNA)ref) Export of gas (MSY)1.5ref) Import of mineral products from Myanmar to Thailand (TTS)
Our estimate

1.0

0.5

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0.0

Figure 21: Output and Export Prices of Energy and Mining

4.4

Manufacturing

In order to examine the validity of the production index for 5. Manufacturing based on data from the
MMSNA, we compare real gross output based on data from the MMSNA, industrial electricity
demand reported in the MSY14 as well as in the IEA’s Energy Balance Table, and the production
index15 calculated from quantitative data on manufacturing production reported in the MSY, in theiraverage growth rates for three different periods (1990–1998, 1998–2010, and 2010–2014), as shownin Figure 22. In the period 1998–2010, real gross output increased at the pace of 19% per year, morethan twice the growth rates of other indexes. Manufacturing production and industrial electricity
demand showed a strong correlation in the period 1990–1998 (correlation coefficient = 0.90) and theperiod 2010–2014 (0.86). However, in the period 1998–2010, the two variables deviated from each,showing a negative correlation coefficient (–0.22). The MMSNA-based real gross output in thisparticular period deviated greatly not only from industrial electricity demand but also from theproduction index based on quantitative data in the MSY. Since production in physical unit does notreflect quality improvement, the growth in the MSY-based production index is supposed to fall belowthat in the MMSNA-based real gross output index. Even so, however, the difference observed in the
period 1998–2010 seems too huge, reaching 15.2 percentage points compared to 2.7 percentage points
in the period 1990–1998. Thus, we revise real gross output data for the period 1998–2010 by applying

The sales and consumption data of electricity are provided by type of purposes (general, industrial, bulk, andother）in MSY. In this comparison, electricity consumption only for industrial purpose is used.
15 The numbers of production items published in MSY are different over periods. The total output of manufacturing
is measured as the translog index based on the production data at the physical units of all available items (about
30–50 items) until 2010.
1424

an average annual growth rate calculated by adding 2.7 percentage points to the growth rate of the
production index based on the MSY-based quantitative data.

20%

1990-1998

1998-20102010-2014

18%
16%

14%
12%
10%
8%
6%
4%
2%

0%
Real GO (MMSNA) Electricity demand ofindustry (MSY)

Energy demand ofindusty (IEA)

Production of
manufacturing (MSY)

Figure 22: Growths of Output and Energy Demand in Manufacturing

Figure 23 compares the growth rates of the MMSNA-based real gross output, the MSY-basedindustrial electricity demand, and the adjusted real gross output based on our estimates. The adjusted
real gross output recorded negative growth (–1.7%) in 2003–2004, which is probably attributable toeconomic sanctions imposed by the U.S. and the EU.16 US trade statistics show that its import ofmanufacturing products from Myanmar, which stood at 329 million dollars in 2002, decreased to 251
million dollars in 2003 and to zero in 2004.

30%
25%
20%
15%

10%
5%
0%
-5%

-10%

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

Our estimate

1985

Real GO (MMSNA)

-25%
1983

ref) Electricity demand of industry (MSY)

-20%
1981

-15%

Figure 23: Growths of Output and Electricity Demand in Manufacturing

We revise nominal gross output downwardly by using the value calculated by multiplying theadjusted real gross output by the MMSNA-based price index. As a result, the export share of
production is revised upwardly. Figure 24 compares changes in the export share in the nominal gross
output of the manufacturing sector in Myanmar before making the above adjustment with those of

Kudo (2005) indicates that the garment industry lost about 70–80 thousand jobs with the closure of about 150 firms/factories since
its peak to mid-2005 in Myanmar, mainly caused by the U.S. sanctions of 2003.
16

25

other Asian countries based on data provided in APO (2016). The MMSNA-based export share before
the above adjustment (after the exchange rate adjustments in Subsection 3.1) shows a sharp downward
trend in the 2000s. In contrast, the export share after the above adjustment to production data shows
an upward trend, following a trajectory similar to that of Bangladesh that has been expanding its
exports, particularly in textiles.

Thailand

50%
45%
40%

Our estimate

35%
30%
25%

Bangladesh

20%

India

15%
10%

Official estimate(after our adjustment
on exchange rate)

Official estimate(MMSNA)

5%

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0%

Figure 24: Country Comparison of Export Share in Manufacturing

4.5

Electricity

Hydro and natural gas-fired thermal power accounts for approximately 90% of total power generation
in Myanmar. As shown in Figure 25, the share of natural gas in the energy mix for electricity generation
peaked at 62% in 1999, subsequently overtaken by hydropower as the main source for generating

Coal

DieselHydropower

Gas

Coal

100%

DieselHydropower

Gas

2006

(Million Kwh)

14,0002004

electricity with its share rising from 22% in 1999 to 75% in 2009.

90%
12,00080%
10,000

70%
60%

8,000

50%

6,000

40%
30%

4,000

20%
2,000
10%

2014

2012

2010

2008

2002

2000

1998

1996

1994

1990

1992

0%

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

Figure 25: Electricity Generation by Type of Energy

Figure 26 compares the value of nominal gross output in the electricity industry based on datafrom the MMSNA and that of electricity sales reported in the MSY. The trajectories of the two26

variables were almost identical from 1990 through 1998. After that, however, they began to deviate
from each other and the gap expanded gradually in the period from 1999 through 2005. Both the valueof the nominal output and that of electricity sales increased sharply—by four-fold and more than threefold respectively—in 2005–2006. While the increase in electricity sales is attributable to higher
electricity prices, 17 one contributing factor for the sharper increase in the nominal output was
structural changes in the electricity sector. According to JICA (2014), the Ministry of Electric Power(MOEP) was divided into the Ministry of Electric Power No. 1 (MOEP-1) and the Ministry of ElectricPower No. 2 (MOEP-2) in 2006, whereby the Myanmar Electric Power Enterprise (MEPE), which
had been the sole power generator and distributor of the country, was split into four companies, i.e.,
Hydropower Generation Enterprise (HPGE), Electricity Supply Enterprise (ESE), Yangon City
Electricity Supply Board (YESB), and what was left of MEPE. 18 The gross output after thisrestructuring reflects the sum of electricity sales of the four companies including sales between
themselves, instead of the sales of MEPE as the sole power generator and distributor. Given that, it
seems reasonable that the value of the nominal gross output after the restructuring has been three tofour times that of electricity sales. Also, the long-term trend of the producer price index for electricity,which is calculated based on data from the MMSNA, has been generally consistent with the trends ofelectricity prices shown in the MSY. Thus, we do not make any adjustments to data on production
values for 5. Electricity.

(Million Kyat)(Million Kyat)

1990–2005

60,0002,000,000

Nominal GO (MMSNA)

2005–2014
Nominal GO (MMSNA)

Sales (MSY)

Sales (MSY)

1,800,000
50,0001,600,000
1,400,000

40,0001,200,000
1,000,000

30,000800,000
20,000600,000
400,000

10,000200,000

2013

2011

2009

2005

2004

2002

2000

1998

1996

1994

1992

1990

2007

0

0

Figure 26: Nominal Outputs and Sales of Electricity

4.6

Transportation and Communications

For 8. Transportation and Communications, some quantitative data on services provided in the MSY
are available but their coverage is very limited. Figure 27 compares five Asian countries including
Myanmar in the cost of transportation and communications measured as a ratio (in percentage) to the
The price of electricity increased 8.65 kyat/kWh to 27.69 kyat/kWh for general purpose users, from 10.62 kyat/kWh to 32.57
kyat/kWh for industrial users, from 6.35 kyat/kWh to 25.76 kyat/kWh for bulk users, and 2.64 kyat/kWh to 24.33 kyat/kWh for others.
On average, the price of electricity increased threefold from 8.96 kyat/kWh to 29.35 kyat/kWh.
18
The MOEP-1 and the MOEP-2 merged in 2012, but the four power companies remain separated.
1727

total gross output of the first five industries in Table 1 (i.e., agriculture, livestock and fishery, forestry,
energy and mining, and manufacturing). For each country, we first calculated the nominal ratio for2010 and developed estimates for other years by applying the growth rate of the index of productionin the five industries (translog aggregate production index calculated from production data for each of
the five industries; the MMSNA-based data before adjustments are used for Myanmar) and that of theindex of production in the transportation and communications industry. The ratio of the cost oftransportation and communications has been on an upward trend in India and Thailand. However, in
Myanmar, the ratio has been generally flat as has been the case of Bangladesh and Cambodia. Here,
we adjust some of changes in the ratio of the cost. Then, we multiply the adjusted ratio by the adjusted
gross output of the five industries (our estimate) to generate the value of the real gross output of thetransportation and communications industry. As a result of this adjustment, the negative growth in1997 based on data from the MMSNA is revised to positive growth and the average growth for theperiod 1998–2010 is revised from 15.4% per year to 5.6% per year (Figure 28).

21%

Bangladesh(ratios at 2010 price in each coutry)

19%
India
17%
15%
Official estimate(MMSNA)

13%

Cambodia
Our estimate

11%9%7% Thailand

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

5%

Figure 27: Ratio of Transportation and Communication Cost
30%
Our estimate

25%

Official estimate (MMSNA)

20%
15%
10%
5%
0%
-5%
-10%
-15%

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

-20%

Figure 28: Revision of Output Growth in Transportation and Communication

28

Figure 29 compares Myanmar and four neighboring countries in the energy productivity of thetransportation industry measured in an index, taking energy consumption in the transportation sector
reported in the IEA’s Energy Balance Table as input and real gross output for 8. Transportation andCommunications as output. While the energy productivity has generally been flat in the neighboring
countries except for India, the index for Myanmar based on the MMSNA-based data before
adjustments jumped sixfold between 2000 and 2008, suggesting the possibility of an overestimationof the output. We can see that the abnormal tendency is reduced with the adjustment to quantitaive
production data made in this subsection.

(2000=1.0)
Official estimate(MMSNA)

6

5

4
India
3
Our estimate
2

1

Cambodia

ThailandBangladesh
2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

Figure 29: Energy Productivity in Transportation and Communication

4.7

Wholesale and Retail

In the same way as we have done for the transportation and communications industry, we calculate theratio of wholesale and retail margin to the total output of the five industries (i.e., agriculture, livestockand fishery, forestry, energy and mining, and manufacturing) for 2010, and develop estimates for otheryears by applying the growth rate of the index of production in the five industries (translog aggregate
production index calculated from production data for each of the five industries; the MMSNA-based
data before adjustments are used for Myanmar) and that of the index of production in the wholesale
and retail industry, as shown in Figure 30.19 The ratio has been generally flat in India and Bangladesh,but on a similar downward trend in Myanmar, Thailand, and Cambodia—all of which belong to the
Mekong Economic Zone.

In countries except Myanmar, the outputs in repair of motor vehicles and hotels and restaurants are included in
the output of wholesale and retail.
1929

50% (ratios at 2010 price in each coutry)
45%
40% Thailand

Cambodia

35%
30%
25%

Official estimate(MMSNA)

Our estimate

Bangladesh

20%
India

15%

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

10%

Figure 30: Ratio of Wholesale and Retail Margin

Just like we have done with the ratio of the cost of transportation and communications, we makesome adjustments to the ratio of wholesale and retail margin to the gross output of the five industries.
Then, by applying the adjusted ratio and replacing the MMSNA-based index of production in the fiveindustries with the one based on our estimates as adjusted in this paper, we revise data on production
values in the wholesale and retail industry. Figure 31 compares changes in the growth of real gross
output of 12. Wholesale and Retail before and after adjustments. As a result of the downward revisionof data on production values in the five industries, the average growth of the real gross output of thewholesale and retail industry for the period 1990–2010 is lowered significantly from 11.8% per year
to 2.7% per year.

16%
Our estimate

Official estimate (MMSNA)

14%
12%
10%
8%
6%
4%
2%
0%
-2%
-4%
2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

-6%

Figure 31: Revision of Output Growth in Wholesale and Retail

5

Revision in Jade Value

Several researchers have been investigating the production of jade in Myanmar, where it is possiblethat a large amount of its jade sales and production has been dropped because of, possibly, some30

political or economic reasons, one of which would be smuggling. 20 An astonishing estimatedproduction of jade is calculated by the Global Witness (2015a), which indicates that the value of “Jadeproduction was worth up to $30.8 billion in 2014 alone: equivalent to almost half Myanmar’s officially
recorded GDP.” The Government of Myanmar has not officially been issuing the precise details of any
values in jade production and sales so far. This section tries to reconcile the time-series estimates of
the jade production and export in Myanmar based on some sources including the reports of the Global
Witness, Myanmar Statistical Yearbook, Mineral Yearbook issued by the U.S. Geological Survey, andthe Harvard ASH Center’s research (Dapice et al. 2014).
The MSY provides quantitative data on jade production, which are generally consistent withestimates in the U.S. Geological Survey (2016) (Figure 32). As for the value of jade production, Global
Witness (2015b) provides its estimates for 2005–2014, employing the composition of jade production
by grade estimated by Harvard University’s ASH Center. In this paper, we calculate jade’s average
unit price based on these estimates and the quantitative data from the MSY. The average unit price ofjade show significant fluctuations over the years but it has followed a trajectory similar to that of theaverage unit price at which jade is traded on the germs emporium (Figure 33).21 For the years through2004, no price data on jade are available. Thus, we applied the average growth rate for all of themineral resources to develop our estimates. Our estimates for the years through 1995 are extrapolations
based on the trends of prices on the gems emporium.

(Thousand kilograms)
50,000ref) U.S. Geological Survey

45,00040,000MSY

35,000Our estimate

30,00025,00020,00015,00010,000
5,000
2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

Figure 32: Production of Jade

Shor (2013) indicates that “in China, the ultimate destination for most of Myanmar’s jadeite, the price can jump as much as
twentyfold. According to a September 2013 Reuters report, however, Myanmar’s jadeite exports to China totaled only $34 million in
2011. Official Chinese figures list $293 million worth of precious stones and metals imported from Myanmar. So, out of a potential
jadeite market of $7.8 billion, only a fraction was actually declared. Some, as noted above, is in limbo in customs warehouses, but theHarvard report found that the vast majority remains unaccounted for”.
21
The gems emporium is held every year in Yangon and Naypyidaw. Here, market prices for jade are estimated from data on thevalue of trade, the number of transactions (lots), and the volume of trade (kg) provided in NNA (http://www.nna.jp/).
20

31

(USD per kilogram)
350

ref) Sales price of jade at emporium

150%

Our estimate

300ref) Sales price of jade at emporium

250

100%

Our estimate
50%

200
150

0%

100
-50%
50

0

(Figure 33.1 Unit Price)

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

-100%

(Figure 33.2 Growth)Figure 33: Price of Jade

6

Results

Figure 34 illustrates the impact of our adjustments on Myanmar’s nominal GDP. Our adjustments,
except for the revaluation of jade production, translated into a significant downward revision tonominal GDP, bringing it down to a level below the EIU’s estimates. Meanwhile, the revaluation ofjade leads to an upward revision, boosting the value of nominal GDP in 2014 by 90% to level close to
the size of the economy shown in the latest data in the MMSNA. Myanmar’s nominal GDP is greatly
affected by fluctuations in jade prices. For instance, in 2012, when the average jade price fell sharply(Figure 33), nominal GDP dropped by 28%, falling below the estimate based on data from the
MMSNA by 40%.

(Billion Kyat)
60,000Our estimate
Jade trade

50,000

Official estimate in MMSNA
40,000EIU
30,00020,00010,000

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

Figure 34: Revision in Nominal GDP

Figure 35 shows a comparison in real GDP growth. In this paper, we did not make any
adjustments to real value added for the period 1990–1998, except for those for 8. Transportation and32

Communications and 12. Wholesale and Retail. 22 However, as a result of the exchange rateadjustments (Subsection 3.1), industry shares in nominal value added are revised, and hence, thecountry’s real GDP measured in the translog index is revised as well. As a result, the rate of real GDP
growth for the period 1990–1998 based on our estimates falls below that based on official estimatesin the MMSNA by 1.3 percentage points. In the period after 1998, our estimates show that Myanmar’s
real GDP growth turned negative twice, first in 2003–2004 and second in 2007–2008. Our estimatesof real GDP growth in 2003–2004 are very much in line with the EIU’s estimates, both reflecting theimpact of the economic sanctions by the U.S. and Europe. For 2007 and 2008, however, while ourestimates show negative growth, the EIU’s estimates show slower but positive growth. The negative
or slower real GDP growth in 2008 is attributable Cyclone Nargis that hit Myanmar in May 2008
(Table 4) and the fallouts of the global financial crisis.23 In terms of the average growth rate for theperiod 1998–2010, our estimate of 4.9% represents a downward revision of 7.0 percentage points
compared with the MMSNA-based estimate of 11.9%. Our estimate is higher than the EIU’s estimateof 3.0% but close to the ADB’s estimate of average 4.7% per year for the period 2001–2010.

18%

11.9%
12%

Official estimate in MMSNAADB estimate

15%

EIU estimate
12%

10%

Our estimate without jade trade9%

Our estimate with jade trade
7.5% 7.5%

8%

6%

6.9%
3%
6%

0%

5.9% 5.9%
4.6% 4.6%

-3%

5.4%
6.0%

3.0%
4.7% 4.9%

4%-6%

Official estimate in MMSNA-9%

EIU estimate-12%

ADB estimate-15%

Our estimate without jade tarde-18%

Our estimate with jade trade

2%

0%

-2%

-21%
2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

-24%

-4%
1990-1998

1998-2010-3.8%
2010-2014

Figure 35: Revision in Real GDP Growths

Meanwhile, the impact of revaluing jade transactions on macroeconomic growth is observed fromthe mid-2000s, for instance, turning negative growth estimated for 2004 before reflecting thereassessed values of jade transactions to positive growth. The impact of revaluation of jade is evenmore conspicuous in 2008 and thereafter with jade production accounting for more than 10% of
Myanmar’s GDP. Notably, the revaluation of jade results in a significant upward revision in 2009–2010, from 3.2% (before revaluation) to 17.9% (after revaluation). On the other hand, real GDP
22
23

Exceptionally, the value added in 1. Agriculture is revised from 1998.
Thailand’s economic growth slowed from 5.3% in 2007 to 1.7% in 2008 and turned negative in 2009, contracting by 0.7%.

33

dropped 21.5% in 2012 as jade production decreased by half following the transfer of power to thecivilian government. The drop was sharper than the negative growth of 12% recorded in 1988–1989
following the coup led by General Saw Maung.
Figure 36 and Figure 37 compare Asian countries in real GDP growth and the growth rate of laborproductivity (defined as real GDP per worker). The downward revision to economic growth in 1998–2010 based on our estimates bring Myanmar’s real GDP growth and labor productivity growth closer
to those of Thailand and Bangladesh. Although Cambodia showed relatively high economic growth,
it is attributable to an increase in labor force. In terms of labor productivity growth, it is not muchdifferent from other low-income countries in Asia. Based on our estimates reflecting the revaluationof jade, Myanmar was comparable to India and Vietnam—both in real GDP growth and laborproductivity growth—in the period 1998–2010. However, Myanmar was alone to fall into negative
growth in the period 2010–2014.

11.9%
12%
10%
8%

8.1%
5.3%
4.4%
6.1% 5.1%
5.9%

6%

6.9%
8.1%
6.9%
4.6%
4.6%

6.9%

5.3%

4.5%

5.7%
5.6% 7.5%
7.0% 6.1%
6.0%

4.9%

4%

2.8%

2%

0%

Cambodia

India

-2%

Bangladesh

Vietnam-4%

ThailandMyanmar (Official estimate in MMSNA)

Myanmar (Our estimate without jade trade)

Myanmar (Our estimate with jade trade)

-3.8%

-6%

1990-1998

1998-20102010-2014

Figure 36: Country Comparison of GDP Growths

10.2%
10%

8%
4.8%
5.9%

6%
4%

3.7%
2.5%
2.5%

5.5%

3.5%
3.4%
2.0%
2.0%

4.4%

4.6%

5.1%

5.4%

4.0% 6.1%
4.6%
4.9%

3.1% 3.1% 3.1%
2.4%

2%
0%
-2%

-4%

Cambodia

India

Bangladesh

VietnamThailandMyanmar (Official estimate in MMSNA)

Myanmar (Our estimate without jade trade)

Myanmar (Our estimate with jade trade)
-5.2%

-6%

1990-1998

1998-20102010-2014

Figure 37: Country Comparison of Labor Productivity Growths

34

Shown in Figure 38 is a comparison of the five countries in GDP per capita on a purchasing
power parity (PPP) basis. According to official estimates in the MMSNA, Myanmar, whose per capita
GDP was lower than that of Cambodia in 1990, overtook Cambodia and Bangladesh in 2010.
Myanmar’s per capita GDP in 2014 stood at 5,100 dollars, significantly higher than those of Cambodia
and Bangladesh. However, based on our estimates (before reflecting the revaluation of jade),
Myanmar’s per capita GDP in 2014 stood at 2,500 dollars, lower than Cambodia’s 3,400 dollars andBangladesh’s 3,200 dollars. In the U.S. Central Intelligence Agency (CIA)’s World Factbook 2016
(CIA, 2015), Myanmar’s per capita GDP in 2013 was estimated at 1,700 dollars, lower than
Cambodia’s 2,600 dollars and Bangladesh’s 2,100 dollars. These figures are consistent with ourestimates for 2014 before the revaluation of jade. In the latest Factbook, Myanmar’s per capita GDP
in 2015 has been revised to 5,500 dollars, exceeding Cambodia’s 3,500 dollars and Bangladesh’s 3,600.
The revised figure for Myanmar exceeds our estimate after the revaluation of jade (4,700 dollars for2014).

16.0
16(Thousand USD)
1990

14.5

2000

2010

2014

14
12
10.1
10
8

7.2
4.8
5.7

5.6
4.7

6
4

2.7
1.9

2

2.7
2.6
1.4

1.8 3.2

3.9
3.4
2.8

1.3

1.5
1.0

Bangladesh

Cambodia

5.7

5.1

4.7

1.3

2.0
1.4 2.5

0.7

1.0

3.3
2.1

0
ThailandIndia

Vietnam

Myanmar
Myanmar
Myanmar(Official (Our estimate (Our estimate
estimate in without jade with jade
MMSNA)
trade)
trade)

Figure 38: Country Comparison of Per Capita GDP
Note: GDP at constant market prices per person, using 2011 PPP, reference year 2014.

Figure 39 shows each industry’s contribution and share of contribution to real GDP growth.
According to official estimates in the MMSNA, agriculture and many other industries have achieved
steady growth over the years. However, our revised estimates show that there have been significant
ups and downs particularly in agriculture. Located in the tropical monsoon climate zone, Myanmar is
subject to frequent natural disasters such as cyclones. Shown in Table 4 are major disasters since 1990
and the estimates of economic losses caused by them. According to the Centre for Research on the
Epidemiology of Disasters (2016), the damage caused by Cyclone Nargis in 2008 totaled 4 billion
dollars, an amount equal to 15.5% of the MMSNA-based nominal GDP. The 2004 Indian Ocean
earthquake and tsunami also caused significant damage, which is equal to 4.9% of the MMSNA-based
nominal GDP. Those significant economic losses cannot be found in the real GDP estimates in the
MMSNA but are reflected in our estimates.
35

Official Estimate in MMSNA

Official Estimate in MMSNA

100%

12%

12. Wholesale and Retail90%

11. Rental and Other
Services

12. Wholesale and Retail9%

11. Rental and Other Services

80%

10. Social and Administrative
Services

70%

10. Social and
Administrative Services
9. Financial Institutions

9. Financial Institutions

8. Transportation andCommunications

60%
8. Transportation andCommunications

6%

7. Construction

50%

7. Construction

6. Electricity

6. Electricity

40%
5. Manufacturing3%

5. Manufacturing

2. Livestock and Fishery

0%

4. Energy and Mining30%

4. Energy and Mining

3. Forestry20%

1. Agriculture

2. Livestock and Fishery10%
3. Forestry

1. Agriculture

Our Estimate without Jade Trade
11. Rental and Other Services

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

Our Estimate without Jade Trade100%

12. Wholesale and Retail

10%

1994

1990

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

12%

1992

0%

-3%

12. Wholesale and Retail
11. Rental and Other
Services

90%

10. Social and Administrative
Services

8%80%

10. Social and
Administrative Services

9. Financial Institutions

6%

70%

8. Transportation and…
7. Construction

4%

9. Financial Institutions
8. Transportation andCommunications

60%

6. Electricity
7. Construction

5. Manufacturing

2%

50%

4. Energy and Mining

6. Electricity
5. Manufacturing
4. Energy and Mining40%

2. Livestock and Fishery

0%

1. Agriculture30%
3. Forestry-2%

3. Forestry

20%
2. Livestock and Fishery

-4%

10%

-6%

0%

20%

Our Estimate with Jade Trade

16%

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1. Agriculture

Our Estimate with Jade Trade100%

12. Wholesale and Retail
11. Rental and Other
Services

11. Rental and Other Services

10. Social and
Administrative Services90%
10. Social and Administrative
Services

12%
8%

80%

9. Financial Institutions70%

8. Transportation andCommunications
7. Construction

9. Financial Institutions
8. Transportation andCommunications

4%

6. Electricity

7. Construction

0%60%
5. Manufacturing

6. Electricity

50%

-4%

5. Manufacturing

1. Agriculture

4. Energy and Mining

40%

-8%

2. Livestock and Fishery

-12%

30%

12. Wholesale andRetail

3. Forestry

20%

-16%

3. Forestry

10%

-20%
4. Energy and Mining

2. Livestock and Fishery-24%(Figure 39.1 contributions)(Figure 39.2 contributions share)

Figure 39: Industry Contributions to GDP Growth

36

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

0%

1. AgricultureTable 4: Economic Damage of Disaster
Total damage Total damage Damage/GDP(MMSNA)('000 US$)('Mil Kyat) at market exchange rate
May-2008
4,000,000
4,521,000
15.5%
Dec-2004
500,000
442,708
4.9%
Jul-1991
79,8402,800
1.5%
Oct-2010
57,00054,7060.1%
May-1992
55,1152,301
0.9%
May-1994
10,000664
0.1%
Mar-2011
3,600
2,880
0.0%
Oct-2011
1,700
1,360
0.0%
Nov-2012
1,170
983
0.0%
May-2004
688
609
0.0%

Type

Date

Storm
Earthquake
Flood
Storm
Flood
Storm
Earthquake
Flood
Earthquake
Storm

Source: Centre for Research on the Epidemiology of Disasters (2016).

Also, the impact of natural gas exports is underestimated in the MMSNA, which is dependent on
the official exchange rate. According to our estimates, the energy and mining industry became a factor
that explains approximately one-third of Myanmar’s economic growth in the latter half of the 2000s
and half of the economic growth in 2010 thereafter as a result of natural gas exports and the revaluationof jade. Contributions to economic growth by industry vary significantly depending on how data arerevised. Based on our estimates, Myanmar’s economic growth in recent years is reliant on natural
resources.
20%

HC

GC

100%
15%

80%

60%
10%
40%
5%
20%

2006

2008

2010

2012

2014

2006

2008

2010

2012

2014

2002

2004

50%

GFCF

2000

1998

1996

1994

1992

1990

2014

2012

2010

2008

2006

2004

2002

0%

2004

50%

2000

1998

1996

1994

1992

1990

0%

EX-IM

40%
40%

30%
20%

30%
10%
0%
20%
-10%
-20%

10%

-30%

Official estimate in MMSNA

Our estimate without jade trade

2002

2000

1998

1996

1994

1992

1990

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

-40%
1990

0%

Our estimate with jade trade

Figure 40: Final Demand Shares in GDP

Figure 40 shows the impact of our revisions on final demand. Our estimates before the revaluationof jade shows that household consumption (HC) accounted for about 77% of nominal GDP in 2014.
However, the share of household is decreased to 41% when the revaluation of jade is reflected. An
economy is reliant on natural resources for growth is greatly affected by international trade. Thus, we
37compared Myanmar with its neighboring countries in household consumption (HC) and gross fixed
capital formation (GFCF) as a share of domestic final demand, as shown in Figure 41. Myanmar’s
household consumption decreased from around 85% of final demand in the latter half of the 1990s toaround 60% in 2014, falling below those of Bangladesh and Vietnam. This is due to an increase in theshare of gross fixed capital formation, reaching 30% in recent years. While the MMSNA-based official
estimates show that the share of household consumption dropped sharply and that of gross fixed capitalformation rose rapidly in 2010 onward, no such drastic changes in the composition of domestic final
demand are observed in our estimates.

100%

50%

HC
Official estimate(MMSNA)

Our estimate

90%

80%

GFCF

40%

Cambodia
Bangladesh

Vietnam70%

Vietnam

India

30%

Bangladesh

20%
60%

India
Cambodia10%

Our estimate

2014

2012

2010

2008

2004

2002

2000

1998

1996

1994

1990

1992

0%

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

40%

2006

50%

Official estimate(MMSNA)

Figure 41: Country Comparison of HC and GFCF Shares in Domestic Final Demand

7

Conclusion

In this paper, we revised Myanmar’s GDP statistics in the MMSNA by adjusting estimates from theproduction and expenditure sides. More specifically, we reviewed and revised data on trade,
production, and jade transactions to recapture Myanmar’s economic growth from the 1990s onward.
Due to various problems with primary data such as the limited availability of production data, ourestimates made in this paper are no more than pro forma estimates and the purpose of this attempt isto enable a more realistic interpretation of Myanmar’s economic growth. The estimation of items on
the income side, such as compensation of employees and consumption of fixed capital, along with thedevelopment of data on labor and capital input, remain future challenges toward developing a growthaccounting framework for Myanmar.

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38

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Appendix
Table 5: Value Added at Current Prices by Industry
1.
2.
3.
Agriculture Livestock Forestry
and Fishery

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

73,305
94,272
132,604
203,172
282,164
347,210446,741
615,078
908,519
1,125,133
1,236,865
1,626,353
2,552,951
3,029,534
2,697,712
3,379,863
4,118,806
5,697,632
5,764,500
6,284,683
6,393,831
5,887,547
6,012,879
6,524,901
6,819,856

14,408
16,94722,15929,81136,456
41,572
56,97580,594
127,212
181,671
204,791
252,189
298,546
346,163491,596
668,559
907,894
1,235,032
1,611,915
1,812,867
2,139,815
2,255,544
2,485,132
2,779,615
3,143,754

3,349
3,865
4,856
6,835
5,208
5,973
7,553
9,039
12,571
17,294
19,446
37,59345,561
72,69698,092
191,629
244,062299,215251,065
307,268
325,395
176,617
189,474
184,930
138,3804.
5.
6.
Energy and Manufacturi Electricity
Mining
ng

1,054
1,177
1,332
1,743
2,704
3,382
5,074
6,466
8,137
11,75550,206
178,442265,233
277,988606,256775,661
1,567,791
2,137,639
1,835,653
2,056,187
1,758,771
2,710,693
3,164,151
3,467,620
4,795,229

12,811
14,215
18,94527,20433,01345,60362,51689,282
138,100
167,940
229,057
336,186
484,146
567,472
617,939
821,449
1,067,439
1,316,423
1,560,123
1,644,346
1,934,028
2,044,690
2,305,897
2,586,735
2,920,369

7.
8.
9.
10.
11.
12.
GDP
4.1
GDP with
Constructio Transportati Financial
Social and Rental and Wholesale without
Jade
Jade Tradenon andInstitutions Administrati Other
and Retail Jade Trade
Communica
ve Services Services
tions
386
2,763
5,988
270
6,024
4,052
40,504
164,91327
164,940
318
3,863
6,908
318
6,413
5,178
48,812
202,28544
202,329
463
4,507
7,998
367
6,692
5,823
66,151
271,895
59
271,954653
5,211
10,423
521
8,702
6,943
91,658392,876
137
393,013
1,218
7,739
18,180
768
9,906
8,607
119,313525,277107
525,384
1,872
13,057
24,605
1,041
10,782
11,335160,596
667,029565
667,594
2,202
19,05835,1991,279
11,482
13,992
199,409861,481810
862,292
1,933
26,494
66,368
1,475
13,293
19,911
280,787 1,210,719
2,029 1,212,747
991
37,03598,4801,945
14,622
26,315394,212 1,768,138
3,756 1,771,894
2,558
40,425125,106
2,215
16,50532,174519,262 2,242,037
27,549 2,269,586
3,444
46,044
175,655
2,641
39,354
35,114
599,871 2,642,489
88,043 2,730,531
3,202
77,115
205,446
3,299
44,68550,064
822,171 3,636,746
123,172 3,759,918
4,654
185,611
320,849
4,799
50,72485,965 1,207,703 5,506,741
274,271 5,781,012
5,992
303,497615,363
5,297
64,742
119,782 1,455,581 6,864,106
557,019 7,421,125
20,023356,771
632,192
6,748
103,890
151,644 1,454,672 7,237,534
1,018,610 8,256,144
27,652
461,656
917,550
10,237
112,599
196,534 1,818,008 9,381,398
1,941,927 11,323,325
110,010
651,973 1,174,094
14,399340,892
283,133 2,165,846 12,646,339
1,284,232 13,930,571
189,790893,654 1,505,154
16,948371,290
378,504 2,766,518 16,807,799
1,620,163 18,427,962
218,690 1,236,066 1,733,328
19,936399,679
506,375 3,002,225 18,139,555
3,944,119 22,083,674
337,675 1,518,309 1,939,887
27,392551,654609,840 3,022,929 20,113,037
3,038,258 23,151,295
421,883 1,839,335 2,152,710
37,715915,720
738,484 3,154,016 21,811,704
13,843,701 35,655,405
481,449 2,165,836 2,418,269
65,318989,006
883,291 3,440,004 23,518,264
19,191,191 42,709,455
614,930 2,515,898 2,479,916
85,346 1,326,077 1,095,646 3,743,241 26,018,587
4,834,606 30,853,193
695,855 3,056,830 2,761,187
114,385 1,683,301 1,323,898 4,163,097 29,342,354
21,039,400 50,381,753
924,959 3,777,091 3,085,360
135,791 2,025,534 1,537,312 4,513,996 33,817,631
30,375,925 64,193,556(Unit: Million Kyat)

40

Table 6: Growth Rate of Value Added at Constant Prices by Industry
1.
2.
3.
Agriculture Livestock Forestry
and Fishery

1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

-4.0
11.7
4.6
6.5
5.3
3.7
3.0
3.3
2.4
9.7
0.3
7.7
1.6
-8.2
7.4
0.1
9.6
-5.2
4.6
-3.4
-3.3
-0.2
2.2
2.2

5.6
4.4
4.7
5.9
2.9
11.2
6.9
8.9
12.9
6.3
6.5
6.5
13.0
9.9
10.1
13.1
7.1
9.7
9.5
6.2
7.1
6.9
7.8
6.9

-1.7
-3.3
1.0
-15.4
-4.6
2.1
2.8
3.2
4.5
3.2
7.4
6.0
6.2
-6.1
3.9
8.0
0.3
-2.6
-2.8
0.4
7.1
6.4
-6.9
-29.2

4.
5.
6.
Energy and Manufacturi Electricity
Mining
ng

10.4
18.3
10.4
13.9
15.4
9.4
22.6
12.1
50.2
30.2
3.0
6.4
18.8
24.0
20.2
-0.8
7.9
-8.4
-8.6
4.8
10.3
0.8
2.0
17.6

-4.1
10.3
9.0
8.2
7.3
4.5
4.9
6.0
9.0
16.3
20.9
7.0
1.2
-0.9
6.0
5.8
3.7
2.5
3.7
8.0
10.3
8.0
9.1
9.3

7.
8.
9.
10.
11.
12.
GDP
Constructio Transportati Financial
Social and Rental and Wholesale withoutnon andInstitutions Administrati Other
and Retail Jade Trade
Communica
ve Services Services
tions
6.5
15.8
-0.1
16.4
4.2
2.5
-2.5
-1.7
27.1
10.6
11.8
14.0
2.9
3.5
8.4
9.5
21.9
11.1
6.9
32.5
7.0
3.4
5.4
5.3
4.7
14.5
6.2
38.7
6.6
3.9
6.3
6.3
6.3
24.0
4.4
29.9
6.0
6.1
2.7
5.0
12.1
22.0
4.7
19.8
5.5
5.8
2.0
4.4
16.3
9.3
4.0
13.5
6.1
5.9
2.7
3.7
-5.6
6.1
4.5
15.7
6.3
6.4
1.9
3.8
13.3
4.3
6.8
11.8
6.8
5.6
3.8
4.7
14.2
3.9
15.4
15.1
4.3
5.7
11.9
10.8
-8.1
25.8
10.2
22.2
12.7
17.1
8.1
6.0
20.0
47.3
5.0
37.5
12.7
18.2
4.9
8.0
14.0
18.0
4.2
9.9
10.2
13.2
3.8
4.6
7.1
12.6
-0.3
24.2
13.9
13.5
-2.8
-1.0
17.6
10.4
10.3
41.7
8.0
11.3
6.0
8.7
9.7
14.2
3.3
16.3
8.6
13.3
1.0
3.3
4.7
15.9
6.4
16.5
8.5
12.7
2.9
7.5
10.9
16.6
-1.7
16.2
7.4
12.7
-5.5
-1.1
16.0
12.9
4.5
31.8
7.0
10.9
-0.8
3.7
23.5
11.8
3.6
32.0
17.0
11.7
-1.4
3.2
5.1
8.6
5.0
72.6
7.7
14.3
2.0
4.4
8.6
8.9
3.4
34.6
13.3
14.9
3.8
4.7
13.1
15.2
6.6
29.3
11.8
14.7
3.0
6.5
13.7
14.7
8.4
17.2
11.2
10.3
3.9
8.5

4.1
Jade

GDP with
Jade Trade

19.4
8.4
-3.4
16.9
168.4
-18.9
29.2
-53.9
142.9
75.0
-30.6
28.6
-1.2
33.2
28.9
2.2
-0.9
48.5
-24.4
59.6
-8.1
-81.7
-23.6
10.2

(Unit: Percentage. Note: The GDP growths are measured in translog index.)

Table 7: Final Demand at Current Prices
Household
Government Gross Fixd Change inConsumption Consumption Capital
Inventory
Expenditure Expenditure Formation
1990
150,064
12,596
15,504-1,995
1991
182,354
14,13818,157
1,032
1992
242,532
14,742
21,2402,601
1993
358,439
19,19834,762
7,360
1994
472,211
21,91550,392
3,875
1995
628,114
24,20074,4663,540
1996
833,683
26,969110,305-21,262
1997
1,165,369
33,154
168,535-10,276
1998
1,775,090
36,804327,265-7,6051999
2,264,634
40,890313,441
38,8592000
2,323,647
98,235283,204
16,7092001
3,151,128
111,501
494,968-2,6102002
4,227,764
127,141
703,566
19,095
2003
6,016,285
157,010
1,203,388
418
2004
5,904,911
244,0311,414,393
39,6892005
7,311,648
256,425
1,660,677
57,041
2006
8,009,222
753,361
2,219,096
23,9962007 11,417,901
777,974
3,557,885
43,4702008 13,381,565
796,160
4,592,304-29,2312009 13,921,433
1,047,250
4,770,521-41,587
2010 15,286,689
1,660,353
5,808,122
106,059
2011 17,532,096
1,795,898
6,976,959
27,0842012 20,063,058
2,364,544
8,563,841
532,457
2013 23,002,853
2,984,616
11,078,712
253,895
2014 26,015,266
3,576,016
13,540,088
138,565

ExportImport16,37818,29529,241
51,84678,05597,280127,530
190,733
458,234
542,125
845,832
1,313,804
1,813,125
1,930,630
2,670,237
3,769,336
6,436,582
8,135,708
7,033,265
7,414,254
8,090,512
7,625,839
8,437,201
11,225,753
14,509,821-27,633-31,692-38,461-78,729-101,171-160,571-215,745-336,796-821,650-957,912-925,138-1,432,045-1,383,949-2,443,625-3,035,727-3,673,729-4,795,917-7,125,140-7,634,508-6,998,833-9,140,031-10,439,611-13,942,515-19,203,475-23,962,125(Unit: Million Kyat)

41

GDP without
Jade Trade
164,913202,285
271,895
392,876525,277
667,029
861,481
1,210,719
1,768,138
2,242,037
2,642,489
3,636,746
5,506,741
6,864,106
7,237,534
9,381,398
12,646,339
16,807,799
18,139,555
20,113,037
21,811,704
23,518,264
26,018,587
29,342,354
33,817,631

Export of
Jade
27
44
59
137
107
565
810
2,029
3,756
27,54988,043
123,172
274,271557,019
1,018,610
1,941,927
1,284,232
1,620,163
3,944,119
3,038,258
13,843,701
19,191,191
4,834,606
21,039,400
30,375,925

GDP with
Jade Trade
164,940
202,329
271,954
393,013525,384
667,594862,292
1,212,747
1,771,894
2,269,586
2,730,531
3,759,918
5,781,012
7,421,125
8,256,144
11,323,325
13,930,571
18,427,962
22,083,674
23,151,295
35,655,405
42,709,455
30,853,193
50,381,753
64,193,556

-1.7
9.5
5.3
6.4
5.1
4.4
3.8
3.7
5.6
12.3
4.8
8.9
4.2
2.4
11.7
3.1
6.7
5.3
-0.6
17.0
-0.8
-20.0
-1.8
9.3