2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Globalization and Labor Markets of Malaysia, Indonesia and Thailand:
An Empirical Investigation
by
Selamah Abdullah Yusof
Department of Economics
International Islamic University Malaysia
Jalan Gombak
53100 Kuala Lumpur
Malaysia
selamah@iiu.edu.my
Tel: 603-6196 4615
Fax: 603-6196 4850
June 22-24, 2008
Oxford, UK
2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Globalization and Labor Markets of Malaysia, Indonesia And Thailand: An Empirical
Investigation
ABSTRACT
Theories have been put forth on the impact of globalization on the labor market, of
which empirical evidence provide mixed results. Past studies have largely been either
concentrating on developed economies or on developing countries as a group, and rely
mainly on correlation and standard econometric technique of regression. This study on the
other hand, investigates the link between globalization and individual labor markets of
Malaysia, Thailand and Indonesia by applying time-series techniques to the analysis.
Specifically, globalization is examined in the context of economic integration and the
variables of interest are inflow of foreign direct investment (FDI) and total trade, while the
labor variables are employment and productivity. The study finds that FDI has no impact on
employment and productivity. In fact, for Thailand, labor variables are influencing the
inflow of FDI. The results also suggest that trade adjusts to changes in labor variables for
the case of Indonesia. Only for Malaysia is there a two-way link between trade and
productivity.
INTRODUCTION
Various theories have been put forth on the effects of globalization on the labor
market. The neoclassical theory predicts that national economies will converge in their
average productivity levels and average incomes because of increased mobility of capital.
However, it differs from the endogenous growth theory which argues that convergence is
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less likely and divergence more likely because of differential benefits from economic
integration and trade, restricted free market relations, and developing countries locked into
producing certain commodities (Heshmati, 2003). It is argued that globalization has a
positive effect by helping to develop international trade, thus increasing employment and
domestic product, opening markets, reduced isolation of less developed parts of the world
(Schlamberger, 2004). In addition, foreign direct investment (FDI) is thought to supply poor
countries with markets, transfer technology and capital, and provide income growth,
employment and poverty reduction. (de Soysa and Neumayer, 2005).
Even so, an application of the Heckscher-Ohlin theorem to human capital may
indicate that although on average there are benefits to trade, not all groups benefit (Leslie
and Pu, 1996). It predicts that increased trade with countries with an abundance of unskilled
labor and the specialization in skill-intensive production in developed countries leads to
relative losses for the unskilled parts of the labor force (Neumayer and de Soysa, 2006).
Rama (2003) argues that globalization may affect the labor market in two opposite
ways. Reforms made by countries to become more competitive by dismantling their trade
barriers, abolish their legal monopolies, privatize their stateowned enterprises and reduce
over-staffing in their bloated bureaucracies could lead to massive loss of jobs and boost
unemployment rates. Besides that, the macroeconomic fluctuations resulting from shortterm capital movements could also increase job insecurity. On the other hand, the
delocalization of production to developing countries could increase the demand for labor,
thus expanding employment opportunities and raising workers' earnings.
With respect to employment, Currie and Harrison (1997) find that in Morocco,
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employment in the average private sector manufacturing firm was basically unaffected by
trade liberalization. In Mexico, the shift in labor demand was modest. Reductions in tariffs
lead to lower wages in period when trade unions were not active, despite considerable
reduction in employment (Revenga, 1997). On the other hand, there is no strong evidence to
support the claim that labor demand has become more elastic as a result of globalization
(Chinoy, Krisna and Mitra, 1998; Maloney and Fajnzylber, 2000). Rama (2003) finds that
other countries such as Chile, Mauritius, Poland and Sri Lanka experienced a long period of
high unemployment rates after the launching of the economic reforms. However, when the
initial year of the reforms is modified, it becomes unclear whether globalization actually led
to higher unemployment rates. He also finds that trade liberalization is associated with job
losses in formerly protected sectors. It is also associated with the replacement of permanent
workers, who have a more privileged status, by temporary and casual workers, who enjoy
fewer benefits.
Globalization is also assumed to have an impact on productivity, where
productivity is expected to grow faster in more open economies (Sachs and Warner, 1995;
Sala-i-Martin, 1997). Bonfiglioli (2006) focuses on financial integration and applies GMM
dynamic panel technique on annual observations from at most 93 countries over the period
1975-1999. She finds that financial globalization has a positive effect on productivity as
measured by total factor productivity. Mann (1997) instead examines the impact of exports
and imports on productivity for the manufacturing industries in two countries, the United
States and Germany. Using correlation and regression techniques, the results of the study
suggest that an increase in foreign demand for U.S. exports increases trend productivity
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growth, and the opposite occurs for an increase in U.S. imports. For Germany, neither
international demand shocks nor exposure to international competition are associated with
productivity growth. Dasgupta and Osang (2002) also examine the case of the U.S.,
focusing on the manufacturing industries during 1961-1991. They find that a significant
variation in the observed increase in the skill premium can be attributed to the impact of
globalization.
Most of these empirical studies on the effect of globalization on the labor market
variables rely mainly on correlation and regression techniques. Many of these studies either
concentrate on developed economies or on developing countries as a group. However, the
impact of globalization on a country, especially a developing one, may differ from another,
due to different economic structure, size and location, which in turn will have significant
policy implications for that particular country. Thus this paper, instead, focuses on three
developing nations, Indonesia, Malaysia and Thailand, and examines individually for each
country, the link between globalization and labor market variables. The relationship
between these variables may not necessarily be contemporaneous, and perhaps more
importantly, the effect may not be from globalization to labor variables, or even
unidirectional, thus necessitates the application of time-series econometric techniques. Due
to data limitations, for the labor variables, the paper focuses on employment and
productivity, while trade and inflow of foreign direct investment represent variables related
to globalization.
The empirical evidence provided in this study can be used to determine whether the
theories on globalization are applicable to developing countries such as Indonesia, Malaysia
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and Thailand. It helps give an understanding of the inter-relationship between globalization
and labor market. This paper contributes to the existing literature on the impact of
globalization on the labor market of developing nations by applying time-series techniques
of Pesaran, et al (2001) autoregressive distributed lag (ARDL) approach, and Johansen and
Juselius method (Johansen, 1988; Johansen and Juselius, 1990; 1992). Comparisons
between the three countries are made and the findings can provide some observations to
policy makers of the three economies in their implementation and evaluation of labor and
trade policies.
METHOD AND DATA
Firstly, each series or variable is tested for stationarity. The order of integration of
the variables is determined using the Dickey and Fuller (1979) ADF, Phillips-Perron (1988)
PP and Kwiatkowski et al. (1992) KPSS tests. In addition, the autocorrelation functions are
plotted for both levels and first-difference for a visual inspection and confirmation of the
order of integration.
To establish the link between globalization and labor market variables, both Pesaran
et al. (2001) ARDL cointegration analysis and Johansen vector autoregression (VAR)-based
cointegration test (Johansen, 1988; Johansen and Juselius, 1990; 1992) are applied. The test
for the existence of a long-run relationship is conducted by determining whether the
variables are cointegrated using the bounds testing procedure for the ARDL approach, and
based on the trace statistic and maximum eigenvalue criteria for Johansen method. Once it
is established, the "forcing" and "adjusting" variables are identified by examining the
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significance of the F statistic of the bound test of the ARDL approach, and error correction
term (ect) in the error correction representation VAR models. The short run or dynamic
relationships are analyzed using Granger causality tests, impulse response function, and
variance decompositions.
The annual data from 1970 to 2005 that are used for analysis are obtained from
various sources. UNCTADa provides data for inflow of foreign direct investments (in
millions of US$) while data on productivity (real GDP/person employed)b and employment
(in thousands) are obtained from the Groningen Growth and Development Centre and the
Conference Board.c The IMF's International Financial Statistics provides data for total
exports and imports, gross domestic product (GDP) and period average official exchange
rates (domestic currency/US$). FDI is computed as the ratio of inflow of foreign direct
investment to GDP, while trade is measured as the ratio of the sum of total exports and
imports to GDP. Total exports and total imports and GDP are converted into US dollars
a
http://stats/unctad.org/FDI/TableViewer/tableView.aspx11/6/2007
Productivity is commonly defined as a ratio of a volume measure of output to a volume measure of input.
Productivity measures can be classified as single factor measures (ratio of output to a single input, such as
labor or capital) or multifactor measures (ratio of output to a bundle of inputs - labor and capital, or labor,
capital and intermediate inputs) (OECD, 2001; Spithoven, 2003). However, in many empirical studies, labor
productivity is often used since it relates to the single most important factor in production and is intuitively
appealing. Equally important, and is often the case, it may be the only way to measure productivity due to lack
of data. Many studies measure productivity as GDP or output per worker, either at aggregate or sectoral level,
as in Darby and Wren-Lewis (1993), Alexander (1993), Wakeford (2004), Papapetrou (2001), Huh and Trehan
(1995), Bahmani-Oskooee and Miteza (2004), Bahmani-Oskooee and Nasir (2004) and Doyle and O'Leary
(1999). Other studies such as Khawar (2003) use the ratio of real gross value of output to labor efficiency unit,
while some others use several indicators including total factor productivity as in Aiginger (2005) to measure
labor productivity. In this paper, due to data limitations, labor productivity is represented by the ratio of GDP
to total employment.
c
Groningen Growth and Development Centre and the Conference Board, Total Economy Database, January
2007, http://www.ggdc.net
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using the official exchange rates.
EMPIRICAL RESULTS
The results of the ADF, P-P and KPSS tests on the levels and first-differences of the
variables for Indonesia, Malaysia and Thailand are given in Table 1. In the case of
productivity, the ADF and P-P tests give similar results, that it is integrated of order 1, while
the KPSS test suggests productivity is I(0). For other variables, the three tests give
contrasting results. For employment, the ADF test indicates that it is integrated of order 2,
while P-P test shows that it is I(1), and KPSS test suggest it is neither I(0) or I(1) for
Indonesia, I(0) and Malaysia, and I(1) for Thailand. Trade and FDI are I(2) based on ADF
and I(1) based on P-P, while
KPSS indicates that trade is stationary for Indonesia,
integrated of order 1 for Thailand, and neither I(0) nor I(1) for Malaysia. The KPSS test
suggests that FDI is I(0). Due to the conflicting results obtained from the three tests, the
plots of the autocorrelation functions are examined. These plots reveal that all the variables
for all the three countries are integrated of order 1 - the plots of the level series show a
gradual decline while the first-difference series return to zero quickly. Thus this study
assumes that all the variables are I(1).
Next, an analysis is conducted to determine if employment and productivity are
cointegrated with trade and FDI. This study at the outset assumes no a priori direction of
relationship, i.e., no assumption is made about the “forcing” or “adjusting” variables. The
optimal order of lag is determined by examining the residuals of the VAR starting at lag 1,
and increasing the order of lag until the residuals no longer exhibit serial correlation. For all
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the three countries, the optimal lag is 2. A dummy variable to account for the 1997 crisis is
included, in which it is equal to 1 for the years after the crisis, 0 otherwise.
Indonesia
Trade, employment and productivity
The results from the bounds test of the ARDL approach suggest that these three
variables are cointegrated with trade adjusting to changes in employment and productivity at
5 percent, with F[trade|employment, productivity]=6.7454. This result is consistent with one
obtained from Johansen method, in which the trace test indicates 1 cointegrating equation at
5 percent. However, the trace statistic is not significant after adjusting for small sample
bias. Although this is the case, since the ARDL method shows cointegration, we proceed
with examining the ects in the vector error correction model, which indicate that the long
run causality runs from productivity and employment to trade. The estimated long run
equilibrium relationship is as follows:
trade  101.310  3.977( productivity)  10.654(employment )
Granger causality tests show no relationships between productivity, employment and trade
in the short run.
For the out-of-sample analysis, the impulse response function indicates that a shock
in employment has a very delayed (after about 4 years) positive effect on productivity, but
no effect on trade. On the other hand, productivity affects employment negatively, but only
in the first year (see Figure 1). The results from variance decomposition show that about 30
percent of the variations in employment can be explained by productivity, while both
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productivity and employment have an impact on trade (see Table 2).
FDI, employment and productivity
Both the ARDL approach and Johansen method suggest no cointegration between
these three variables. The analysis proceeds with examining the short-run relationship using
Granger causality tests, and finds no existence of such relationship.
Malaysia
Trade, employment and productivity
Trade, employment and productivity are cointegrated based on the bounds test of ARDL
approach (F[Trade|Employment, Productivity]=5.1880), as well as trace and maximum
eigenvalue tests of the Johansen method, even after adjusting for small sample bias. The
ects of the vector error correction model suggest that in the long run, both trade and
productivity are endogenous, while employment is the forcing variable. However, in the
short run, trade affects productivity and employment, and productivity is also influenced by
employment. The long run relationship between these variables is given as follows:
Trade  860.892  192.459( productivity)  93.673(employment)
The dynamic relationship of these variables is examined further using the impulse
response function and variance decomposition. A shock to employment has a positive effect
on productivity, but only after 3 years. Similarly for changes in trade which has a delayed 3
year effect on employment and productivity, while a shock in productivity has an immediate
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negative impact on trade (see Figure 2). Changes in productivity can be partly explained by
trade – a small proportion initially, but increases over time. Variation in trade is largely
explained by productivity (see Table 3). These findings imply that productivity and trade
has an impact on one another.
FDI, employment and productivity
Similar to Indonesia, there is no long term relationship between FDI, employment and
productivity for Malaysia. However, in the short term, Granger causality tests indicate that
employment has a significant effect on productivity.
Thailand
Trade, employment and productivity
Unlike Indonesia and Malaysia, the study finds no long run equilibrium between
trade and the two labor variables. Thus the analysis focuses on the short run relationship
and finds that productivity has a short term impact on trade.
FDI, employment and productivity
The ARDL approach suggests that these three variables are cointegrated, with FDI
adjusting to changes in employment and productivity in the long run. The bounds test gives
F[FDI|employment, productivity]=6.21. The trace and maximum-eigenvalue tests from the
VAR-based Johansen method indicate one cointegrating equation, but none after adjusting
for small sample bias. As before, we assume the variables are cointegrated based on the
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ARDL model results. The long run relationship is estimated as follows:
FDI  8.512  1.454( productivity)  .268(employment)
An analysis of the dynamics of these variables indicates only two variables are related in the
short run, specifically, FDI affects productivity. The findings from variance decomposition
suggest that variations in employment are explained by itself, while FDI and productivity
impact each other, in the first two years the impact is relatively small, but increases over
time. Employment also has impact on productivity (see Table 4). The impulse response
function provides similar result (see Figure 3). A shock in employment has a negative effect
on productivity, but only in a very short term. Productivity shock also impact FDI
negatively, but the impact is small and short term, whereas changes in FDI has a positive
delayed effect on productivity – the effect is felt after 2 years.
DISCUSSION AND CONCLUSION
The findings of this study indicate that in the long run, inflow of FDI do not play a
significant role in affecting labor variables. In fact, for Thailand, the relationship is the other
way, that is, employment and productivity are the ones that impact FDI inflows. The labor
market is significant in attracting inflows of FDI for Thailand, but not for Indonesia and
Malaysia. Perhaps, in the latter countries, other factors, such as political condition, labor
costs and other economic and social variables are those foreigners consider in their decision
to invest in these countries.
For Indonesia, although trade is related to the labor market, the direction of the longrun relationship is from employment and productivity to trade. Higher productivity and
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employment levels imply a higher level of exports and imports. Only for Malaysia is there a
two-way link between productivity and trade in the long term. The two variables reinforced
each other positively, while employment level has a negative effect on these variables.
These findings may be due to the fact that Malaysia is a more open economy
compared to Indonesia (see figure 4), and Malaysia enjoys a low unemployment rate
relative to its neighboring country. Both Indonesia and Malaysia encounter fluctuations in
their level of trade over the years. Thailand, on the other hand, until 1985, remains a
relatively closed economy, with total trade comprised of less that half of its GDP. It begins
to open up starting from 1986, where total trade steadily increases over the years.
These results do not appear to support the theories on the effects of FDI on the labor
market of a developing country. The neoclassical theory that mobility of capital will
increase average productivity levels is not supported. There is also no evidence to support
other theories on how globalization improves employment. The empirical findings of this
paper are consistent with those of Chinoy, Krisna and Mitra (1998) and Maloney and
Fajnzylber (2000) in that labor demand is inelastic to the inflows of foreign direct
investments. Trade liberalization also does not appear to have a significant impact on
employment for all three countries, as well as on productivity for Indonesia and Thailand.
These results are in line with the study by Currie and Harrison (1997) and Mann (2006) on
Germany in which productivity is not affected by trade. However for Malaysia, being a
more open economy, as shown in Sachs and Warner (1995) and Sal-i-Martin (1997), trade
does have a positive effect on labor productivity.
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This study shows that for these three developing nations, the focus should not be on
attracting foreign direct investments to improve employment and productivity. Since both
trade and inflows of FDI have no long term impact on employment, an expansion in
employment may perhaps be achieved through policies to increase domestic demand as well
as domestic investment. For Malaysia, increases in productivity can be attained through a
higher volume of trade with other countries. On the other hand, for Thailand and Indonesia,
and for Malaysia as well, the upgrading of skills and productivity of workers for them to
attain higher earnings and enjoy higher standards of living can be done through education
and training, and retraining, with proper monitoring and evaluation to ensure that workers
are equipped with the knowledge, skills and creativity to match technological advances in
production and services.
REFERENCES
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in the 1990s, Journal of Economic Studies, 32(6), 540-570.
Alexander, C. O. (1993) The changing relationship between productivity, wages and
unemployment in the UK, Oxford Bulletin of Economics and Statistics, 55(1), 87-102.
Bahmani-Oskooee, M. & Miteza, I. (2004) Panel cointegration and productivity bias
hypothesis, Journal of Economic Studies, 31(5), 448-456.
Bahmani-Oskooee, M. & Nasir, A. (2004) ARDL approach to test the productivity bias
hypothesis, Review of Development Economics, 8(3), 483-488.
Bonfiglioli, A. (2006) Financial Integration, Productivity and Capital Accumulation,
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Pompeu Fabra).
Chinoy, Sajid, Krishna, P. & Mitra, D. (1998) “Trade Liberalization and Labor Demand
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Currie, J. & Harrison, A. (1997) Sharing the costs: The impact of trade reform on capital
and labor in Morocco, Journal of Labor Economics, 15(3), S44-S71.
Darby, J. & Wren-Lewis, S. (1993) Is there a cointegrating vector for UK wages? Journal
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De Soysa, I. & Neumayer, E. (2005) False prophet, or genuine savior? Assessing the effects
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series with unit root, Journal of the American Statistical Association, 74(366), 427-431.
Doyle, E. & O’Leary, E. (1999) The role of structural change in labour productivity
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Heshmati, A. (2003) Measurement of a Multidimensional Index of Globalization and Its
Impact on Income Inequality, WIDER Discussion Paper no. 2003/69 (Helsinki: UNUWIDER).
Huh, C. & Trehan, B. (1995) Modeling the time-series behavior of the aggregate wage rate,
Economic Review – Federal Reserve Bank of San Francisco, 1, 3-13.
Johansen, S. (1988) Statistical analysis of cointegration vectors, Journal of Economic
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52(2), 169-210.
Johansen, S. & Juselius, K. (1992), Testing structural hypotheses in a multivariate
cointegration analysis at the purchasing power parity and the uncovered interest parity for
the UK, Journal of Econometrics, 53(1-3), 211-44.
Khawar, M. (2003) Productivity and foreign direct investment – evidence from Mexico,
Journal of Economic Studies, 30(1), 66-76.
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Kwiatkowski, D. et al. (1992) Testing the null hypothesis of stationarity against the
alternative of a unit root, Journal of Econometrics, 54(1-3), 159-178.
Leslie, D. & Pu, Y. (1996) What caused rising earnings inequality in Britain? Evidence
from time-series, 1970-1993, British Journal of Industrial Relations, 34(1), 111-130.
Mann, C. (1997) Globalization and Productivity in the United States and Germany,
International Finance Discussion Papers no. 595 (Washington: Board of Governors of the
Federal Reserve System).
Maloney, W. & Fajnzylber, P. (2000) Labor Demand and Trade Reform in Latin America
(Washington, D. C.: World Bank).
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Productivity Growth, OECD Manual (France: OECD).
Pesaran, M. H., Shin, Y. & Smith, R. (2001) "Bound testing approaches to the analysis of
level relationships". Journal of Applied Econometrics 16, 289-326.
Phillips, P. & Perron, P. (1988) Testing for a unit root in time series regression, Biometrica,
75, 335-346.
Rama, M. (2003) Globalization and Workers in Developing Countries, Policy Research
Working Paper 2958 (Washington: World Bank).
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Mexican manufacturing, Journal of Labor Economics, 15(3), S20-S43.
Sachs, J. & Warner, A. (1995) Economic reform and the process of global integration,
Brookings Papers on Economic Activity, 1, 1-118.
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Papers and Proceedings, 87(2), 178-183.
Schlamberger, N. (2004) Globalization – what, why, and how to measure. Paper presented
at the International Conference: “Statistics – Investment in the Future,” held at Prague,
Czech Republic, 6-7 September.
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Wakeford, J. (2004) The productivity-wage relationship in South Africa: An empirical
investigation, Development Southern Africa, 21(1), 109-132.
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Table 1: Tests for unit roots – Augmented Dickey-Fuller, Phillips-Perron and Kwiatkowski
et al.
INDONESIA
employment
productivity
trade
fdi
adf level
-0.389 (0.983) -1.812 (0.670)
-1.632 (0.752) -2.524 (0.315)
adf 1st diff
-2.915 (0.175) -4.367 (0.010)
-2.099 (0.523) -1.655 (0.741)
pp level
-1.252 (0.883) -2.280 (0.433)
-4.505 (0.005) -3.672 (0.038)
pp 1st diff
-9.955 (0.000) -4.890 (0.002)
kpss level
0.187**
0.059
0.059
0.086
kpss 1st diff
0.338***
MALAYSIA
employment
productivity
trade
fdi
adf level
-1.477 (0.811) -3.489 (0.062) -1.518 (0.803) -2.161 (0.491)
adf 1st diff
-0.931 (0.936) -4.743 (0.004) -1.209 (0.888) -2.241 (0.449)
pp level
-1.399 (0.844) -2.187 (0.482) -2.516 (0.319) -2.434 (0.357)
pp 1st diff
-6.751 (0.000) -5.683 (0.000) -6.635 (0.000) -6.449 (.000)
kpss level
0.128
0.076
0.152**
0.092
kpss 1st diff
0.239***
THAILAND
employment
productivity
trade
fdi
adf level
-0.929 (0.941) -2.774 (0.219)
0.048 (0.995)
0.045 (0.995)
adf 1st diff
-2.681 (0.252) -2.717 (0.239) -2.778 (0.218)
-1.044 (0.919)
pp level
-0.837 (0.952) -1.709 (0.726) -0.244 (0.989)
-3.381 (0.070)
pp 1st diff
-6.267 (0.000) -4.124 (0.014) -6.459 (0.000) -10.281 (0.000)
kpss level
0.178**
0.096
0.203**
0.127
kpss 1st diff
0.079
0.120
Notes: Numbers in parentheses indicate p-values.
** and *** indicate significance at 5 and 1 percent levels, respectively.
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Table 2: Variance decomposition of trade, productivity and employment for Indonesia
Variance Decomposition of PRODUCTIVITY:
Period
S.E.
TRADE
PRODUCTIVITY
EMPLOYMENT
1
7.775601
0.000000
100.0000
0.000000
2
7.968434
0.273378
99.72604
0.000583
3
7.983024
0.336096
99.65992
0.003984
4
7.986478
0.358103
99.63810
0.003798
5
7.989120
0.371546
99.62472
0.003730
10
8.003038
0.399124
99.59725
0.003624
Variance Decomposition of EMPLOYMENT:
Period
S.E.
TRADE
PRODUCTIVITY
EMPLOYMENT
1
0.053402
0.000000
34.86042
65.13958
2
0.078540
0.237760
31.59818
68.16406
3
0.095742
0.549827
31.31084
68.13933
4
0.110471
0.729286
30.78251
68.48821
5
0.123456
0.842057
30.44829
68.70965
10
0.174444
1.099085
29.69376
69.20716
Variance Decomposition of TRADE:
Period
S.E.
TRADE
PRODUCTIVITY
EMPLOYMENT
1
0.029702
51.91938
22.71994
25.36067
2
0.035630
50.13973
25.13484
24.72543
3
0.041399
50.00522
25.25005
24.74473
4
0.046236
49.96284
25.23109
24.80607
5
0.050642
49.93023
25.22746
24.84231
10
0.068550
49.75786
25.21923
25.02291
Cholesky Ordering: PRODUCTIVITY EMPLOYMENT TRADE
June 22-24, 2008
Oxford, UK
17
2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Table 3: Variance decomposition of trade, productivity and employment for Malaysia
Variance Decomposition of EMPLOYMENT:
Period
S.E.
EMPLOYMENT PRODUCTIVITY
TRADE
1
0.014691
100.0000
0.000000
0.000000
2
0.017408
98.58097
0.054476
1.364554
3
0.022019
94.89483
0.119995
4.985175
4
0.025654
91.62917
0.485400
7.885429
5
0.029113
89.65522
0.724211
9.620570
10
0.042112
85.93475
1.231078
12.83417
Variance Decomposition of PRODUCTIVITY:
Period
S.E.
EMPLOYMENT PRODUCTIVITY
TRADE
1
0.030157
4.228087
95.77191
0.000000
2
0.039106
2.528635
93.97687
3.494498
3
0.049926
1.598537
86.79623
11.60524
4
0.060904
1.366476
81.42242
17.21110
5
0.070753
1.234143
78.55676
20.20910
10
0.107231
1.042654
74.06777
24.88958
Variance Decomposition of TRADE:
Period
S.E.
EMPLOYMENT PRODUCTIVITY
TRADE
1
8.566729
0.164020
33.79258
66.04340
2
12.60252
0.855561
45.95731
53.18713
3
14.75732
0.660598
53.00322
46.33618
4
16.21968
0.595292
56.89301
42.51170
5
17.48250
0.611363
59.53350
39.85513
10
22.75077
0.608024
66.04504
33.34694
Cholesky Ordering: EMPLOYMENT PRODUCTIVITY TRADE
June 22-24, 2008
Oxford, UK
18
2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Table 4: Variance decomposition of trade, productivity and employment for Thailand
Variance Decomposition of EMPLOYMENT:
Period
S.E.
EMPLOYMENT PRODUCTIVITY
FDI
1
0.031639
100.0000
0.000000
0.000000
2
0.043190
99.45933
0.539979
0.000687
3
0.052715
99.04548
0.904071
0.050447
4
0.060877
98.69569
1.115169
0.189142
5
0.068190
98.40377
1.281956
0.314274
10
0.097156
97.64520
1.772424
0.582377
Variance Decomposition of PRODUCTIVITY:
Period
S.E.
EMPLOYMENT PRODUCTIVITY
FDI
1
0.034435
27.13079
72.86921
0.000000
2
0.051965
17.59326
75.43571
6.971033
3
0.068823
12.48442
69.31241
18.20317
4
0.084262
9.353010
66.87202
23.77497
5
0.098091
7.465687
66.79892
25.73539
10
0.152156
4.117234
66.06693
29.81583
Variance Decomposition of FDI:
Period
S.E.
EMPLOYMENT PRODUCTIVITY
FDI
1
0.831428
3.390876
14.24344
82.36568
2
0.856643
3.378252
13.45290
83.16884
3
0.888163
3.268687
16.72594
80.00538
4
0.895053
3.280028
17.77013
78.94985
5
0.900873
3.371949
17.66746
78.96059
10
0.918535
3.417479
19.12411
77.45841
Cholesky Ordering: EMPLOYMENT PRODUCTIVITY FDI
June 22-24, 2008
Oxford, UK
19
2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Figure 1: Impulse response function of trade, productivity and employment for
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of employment to trade
Response of employment to productivity
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
1
2
3
4
5
6
7
8
9
1
10
.08
.08
.04
.04
.00
.00
-.04
-.04
2
3
4
5
6
7
8
9
3
4
5
6
7
8
9
10
Response of productivity to trade
Response ofproductivity to employment
1
2
1
10
Response of trade to employment
2
3
4
5
6
7
8
9
10
Response of trade to productivity
12
12
8
8
4
4
0
0
-4
-4
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Indonesia
June 22-24, 2008
Oxford, UK
20
2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Figure 2: Impulse response function of trade, productivity and employment for Malaysia
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of employment to trade
Response of employment to productivity
.020
.020
.015
.015
.010
.010
.005
.005
.000
.000
-.005
-.005
-.010
-.010
1
2
3
4
5
6
7
8
9
1
10
2
3
4
5
6
7
8
9
10
Response of productivity to trade
Response of productivity to employment
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
1
2
3
4
5
6
7
8
9
1
10
Response of trade to employment
2
3
4
5
6
7
8
9
10
Response of trade to productivity
12
12
8
8
4
4
0
0
-4
-4
1
2
3
June 22-24, 2008
Oxford, UK
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
21
2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Figure 3: Impulse response function of FDI, productivity and employment for
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of employment to productivity
Response of employment to FDI
.08
.08
.06
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Response of productivity to FDI
Response of productivity to employment
.08
.08
.06
.06
.04
.04
.02
.02
.00
.00
-.02
-.02
-.04
-.04
1
2
3
4
5
6
7
8
9
1
10
Response of FDI to employment
2
3
4
5
6
7
8
9
10
Response of FDI to productivity
1.2
1.2
0.8
0.8
0.4
0.4
0.0
0.0
-0.4
-0.4
-0.8
-0.8
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Thailand
June 22-24, 2008
Oxford, UK
22
2008 Oxford Business &Economics Conference Program
ISBN : 978-0-9742114-7-3
Figure 4: Total trade of Indonesia, Malaysia and Thailand
250
Total trade to GDP (%)
200
150
100
50
0
1955
1960
1965
1970
1975
1980
Indonesia
June 22-24, 2008
Oxford, UK
1985
Malaysia
1990
1995
2000
2005
2010
Thailand
23