The Impact of the Public Expenditure Cuts Policy on the Labor Market in Vietnam
Nguyen Thi Lan Huong
Institute of Labor Science and Social Affairs, Vietnam
Pham Ngoc Toan
Institute of Labor Science and Social Affairs, Vietnam
Email: [email protected]
Abstract
The purpose of this study is to evaluate the impact of public expenditure cuts on
employment and income to support policies for the development of the labor market. Impact evaluation is of interest for policy makers as well as researchers. This
paper presents a method – that is based on a Computable General Equilibrium
model – to analyse the impact of the public expenditure cuts policy on employment
and income in industries and occupations in Vietnam using macro data, the Input
output table, 2006, 2008 and the 2010 Vietnam Household Living Standard Survey.
Keywords: Economic modelling, public expenditure, Vietnam, labor market,
impact
Journal of Economics and Development 23 Vol. 14, No.3, December 2012
1. Introduction
For Vietnam’s deeper and deeper integration
into the global market
1, its economy is also
faced with a number of constraints that are
caused by the severe impact of global economic expenditure cuts. The foreign direct investment (FDI) sector , makes up 27 per cent of the
total social investment. The import and export
value of this sector has often stood at the rate
of 55 to 70 precent of the total import and
export turnover. After the global economic crisis in 2007-2008, the global market for export
and investments narrowed rapidly. This has
caused Vietnam’s economic recession.
Together with the common trend in the world,
the Government of Vietnam has applied various measures to cope with its economic recession and to stabilize the environment for the
macro economy towards sustainable development. Flexible and comprehensive implementation of a range of fiscal policies and other
macro policies has assisted Vietnam’s economy to overcome the crisis. The economic
growth rate still reached 5.3 per cent in 2009
and the inflation rate decreased to 6.88 per
cent from 23 per cent in 2008. One of the
measures that was applied by the Government
is the extensive fiscal policy (with stimulus
packages in 2008 and 2009).
In the period of 2011-2012, in order to stabilize the macro economy and to deal with the
accelerating inflation rate, the Government
aims at reducing the aggregate demand of the
economy via four key measures: increasing the
revenue of the state budget; maintaining an
over-expenditure rate of less than 5 per cent of
GDP; postponing ineffective public investment projects; and saving the recurrent expenditure of less than 10 per cent. Public investment is the reason for inflation because of its
low inefficiency. As a result, there is an
increase in production costs and product
prices. This has had a negative impact on people’s income.
Public expenditure needs to be cut to save
investment for effective works and projects.
Investment needs to be allocated for projects
already started that can be finished, but not for
new ones. Scarce capital, a high proportion of
investment capital in GDP and the high
Incremental Capital Output Ratio (ICOR) may
affect the employment situation of several
industries that provide services and goods
because the Government reduces its demands
for goods in the market. This can result in constraints for the local production situation and
export enterprises. Public expenditure cuts can
lead to an unemployment situation and unstable social disorder. They also reduce the driving force for economic growth in the public
sector. Meanwhile, the recovery of the national macro economy, the micro financial capacity of enterprises, and the recovery of the purchasing power of the social market has not
been sustainable, and may easily cause a second recession in the economy.
In the bad global economic context, if the
local economy develops towards an increase in
Journal of Economics and Development 24 Vol. 14, No.3, December 2012
investment size and intensive labor utilization,
the contractionary expenditure policies can
lead to negative impacts on the labor market.
Thus, there is a need to carry out a study on
“Impacts of the public expenditure cut policy
on the labor market” with the aim of considering the influencing level of the employment
and income policies in different industries and
to recommend policy implications for the
future development of the labor market.
2. Literature review
2.1. Local studies
Up until now, there has not been any local
study on the impact of public expenditure on
employment and income distributions. Studies
on fiscal policies have mainly been carried out
in relation to the economic growth.
There has been only one study on “the
impacts of public expenditure on the economic growth of localities in Vietnam”, which was
conducted in 2010 by the author, Hoang Thi
Chinh Thon, of the Research Centre for
Economics and Policy, College of Economics,
Hanoi National University. The study evaluated and analyzed how the provincial and district
expenditure affected the local economic
growth. The study conducted a regression
analysis on the basis of collected data from 31
localities in Vietnam. The results of the regression analysis show that district investment
expenditure needs further strengthening whilst
provincial investment expenditure should be
reduced to promote the economic growth in
localities. The study also indicated that budget
expenditure was divided into different categories, each of which had a different impact on
the economic growth. Development investment expenditure can create production capacity for the economy and it has long-term
impacts on the economic growth.
A study on “Efficiency of public expenditure in Vietnam in the transitional period of the
economy” by Nguyen Khac Minh in 2008 analyzed the efficiency of annual public expenditure and public investment based on the panel
data of 34 provinces/cities in Vietnam in the
period of 2000 – 2005. The study used the
parameter approach (based on the Stochastic
Frontier Production Function (SFPF)) and the
non-parameter approach (based on DEA – Data
Envelopment Analysis). The study results
exposed the inefficiency of public expenditure
in both annual public expenditure and public
investment.
The study on “analysis of the expenditure
structure of the Government and economic
growth in Vietnam” by Pham The Anh in 2008
utilized the collected data from 61
provinces/cities in the period of 2001-2005 to
analyze the relation between the expenditure
structure of the Government and the economic
growth in the transitional process of the
provinces in Vietnam. In the study, a pilot
analysis was carried out based on the theoretical model, which was developed from the utilization of the data of all provinces in Vietnam.
The findings of the study illustrate that investment expenditure is more efficient in some
Journal of Economics and Development 25 Vol. 14, No.3, December 2012
industries while recurrent expenditure is more
efficient in some others.
A study by Rizwanul Islam in 2011 on
“Macroeconomic Policy, Economic Growth,
Employment and Poverty: Issues and
Challenges for Viet Nam” made a desk review
of changes in macro regulating policies from
currency, exchange rate policies to fiscal policies (mainly the monetary policies) in the period of 2011 – 2010. The study also indicated
challenges for poverty reduction and employment in Vietnam. From the viewpoint of the
study, employment is vital for not only (1)
annual newcomers in the labor force but also
(2) currently unemployed laborers and newly
employed poor people, and (3) people who are
underemployed. The study also indicates that
public investments are also an element to promote economic growth. Especially, public
investments in infrastructure construction have
created widespread impacts on promoting
investment in the private sector. Therefore, if
the Government wants to realize the goal of
budget deficit reduction, it should pay attention to (1) which public expenditures need cutting to cause no negative impact on public
investment in infrastructure construction, and
(2) the factors which can promote potential
financial resources to the budget.
2.2. International studies
A study by Michal Gradzewicz et al (2007)
on “The cost of fiscal tightening in Poland on
the road to the Euro: does the labor market
matter?”carried out an evaluation on the
adjustments of the fiscal policies of Poland by
using the CGE model simulations in the period
of 2006 – 2008. The two simulation groups
that were analyzed were the fixed and flexible
wage level. The study indicated that the “contractionary” policies of the Government were
not the reason for a decrease in economic
growth in the period of 2006 – 2008. On the
contrary, they created more driving force for it.
When the wage was adjusted to a lower level,
the labor cost would also be lower. This would
encourage enterprises to hire more workers. As
a result, economic growth would be promoted.
However, the study also exposed that the contractionary fiscal policies caused no cost for
the economy because the labor market was
also renovated.
A study by Dario Caldara and Christophe
Kamps (2008) on “What are the Effects of
Fiscal Policy Shocks? A VAR-based
Comparative Analysis (Vector autoregressive –
VAR)” piloted various approaches to study fiscal shocks such as the recursive approach, the
Blanchard-Perotti approach, the sign-restrictions approach and the event-study approach,
based on the quarterly data of the USA in the
period of 1995 – 2006. As a result, these
approaches brought about qualitative results
similar to the quantitative ones. The study split
the fiscal shock into two types: the tax shock
and the government expenditure shock. The
two VAR models with 5 and 6 variables were
used to estimate changes in the macro indicators (such as GDP, government expenditure,
net tax, inflation, interest, employment and
Journal of Economics and Development 26 Vol. 14, No.3, December 2012
wages) caused by these shocks. The study also
provided various impact evaluation methods
of the fiscal shocks to macro variables.
The study by the authors at Monash
University, Horridge J.M. and Parameter B.R.
(1995) on “macroeconomic, industrial, distributional and regional effects of government
spending programs in South Africa” developed
a Computable General Equilibrium (CGE)
model for South Africa (IDC-GEM). This
model was used to analyze the economic
impact ofan increase in public expenditure of
the South African Government to macro indicators, industries, regions and income distribution. The study simulated short-term impacts
of a 10 per cent increase in the government
expenditures due to: (1) foreign aid, (2) tax, (3)
local debts, and (4) simulation of the sensitivity of the components in the government expenditure. The results showed that for an increase
in the government expenditure (with a constant increasing level) gained from the foreign
aid, GDP and consumption growth would be
promoted; however, this would lead to an
increase in the value of domestic currency
(increasing the exchange rate), which would
promote imports and reduce exports. The
provinces that very much depend on imports
would be faced with a decrease in GDP growth
and the influencing level of income distribution would be low. An increase in government
expenditure from taxes would lead to very few
impacts on the macro indicators. But if rise in
consumption tax induces the impacts on the
prices due to substitution effect. An increase in
income tax would also cause small impacts on
income distributions. The study also indicated
appropriate policies used for the simulations.
This study also provides another method to
evaluate the changing impacts of government
expenditure on macro factors.
Adam Hersh (2012) from the Center for
American Progress conducted a study on
“Austerity is Hammering State Economies:
States that Cut Spending in Response to the
Recession Fare Worse Economically”. The
author compared the economic growth and
unemployment rate of 20 states that reduced
their public expenditure and 30 states that
increased their public expenditure. Hersh
found that on average, the states that cut their
public expenditure had an unemployment rate
that was of 4.1 percentage points higher than
the states that did not cut their public expenditure. The percentage of employment in the private sector was also reduced by 6 per cent. The
economic growth rates of the states after
reducing their public expenditure was 2.7 percentage points lower than the period before the
reduction.
Most foreign countries have conducted
studies on the impact of increasing government expenditure on macro factors. Some others have studied the impact of the contractionary public expenditure policies on macro
factors, such as the study by Michał
Gradzewicz et al (2007) or the study by Adam
S. Hersh (2012) as mentioned above.
However, the conditions for the contractionary
Journal of Economics and Development 27 Vol. 14, No.3, December 2012
policies of the Government in the study by
Michał Gradzewicz are different from the conditions for the contractionary policy executed
by the Government of Vietnam in 2011. The
Government of Poland cut its expenditure
related to wages and employment in the public
employment sector; meanwhile, the
Government of Vietnam cut its inefficient public expenditure and recurrent expenditure for
non-wage or irrelevant wage components.
These studies have applied different methods
for impact evaluation. For European countries,
they often apply the VAR model for their studies while the authors of Monash University
often use the computable general equilibrium
model. In Vietnam, there has not been any
impact evaluation of public expenditure cutting on macro factors, especially, the factors of
labour, employment and income distribution.
Therefore, the study on the impact of public
expenditure cuts on macro factors, employment, labour and income distribution carried
out by ILSSA via the application of the computable general equilibrium model designed
for Vietnam (ILSSA – MS) reflects a new
progress in the relevant studies in Vietnam.
3. Data set
Data is taken from Vietnam’s 2005 intersectoral balance (GSO 2008b). Socio-economic indicators are updated for the period 2006-
2010. The model consists of two main variable
groups: GDP (constant price) (rechecked), private consumption, public consumption, sectoral output, labor by sector, profession, and
skill. The second group of variables includes:
population growth, job growth (GSO, 2011a,
2011b), agriculture land variations (NIAPP,
2010) and export-import tax rates (WTO,
2007). Data for the period 2005-2010 is provided in Appendix 2.
4. The model
4.1. Introduction
The theory of the ILSSA-MS2 is constructed on the ORANI-G model (Dixon et al, 1982;
Horridge 2003), with an extension of a
detailed modelling of the labor market. The
model is solved with the GEMPACK economic modelling software (Harrison and Pearson,
1996).
ILSSA-MS has a theoretical structure,
which is typical of a static CGE model. It consists of equations describing, for some time
period: producers’ demands for produced
inputs and primary factors; producers’supplies
of commodities; demands for inputs to capital
formation; household demands; export
demands; government demands; the relationship of basic values to production costs and to
purchasers’ prices; market-clearing conditions
for commodities and primary factors; and
numerous macroeconomic variables and price
indices.
Demand and supply equations for privatesector agents are derived from solutions to
familiar optimisation problems underlying the
behaviour of agents described in conventional
neoclassical microeconomics. Each industry
minimises unit costs, subject to given input
Journal of Economics and Development 28 Vol. 14, No.3, December 2012
prices and a nested constant returns to scale
production function. Three primary factors are
identified (labor, capital and land) with labor
further distinguished by occupation and qualification. Capital is assumed to be sector-specific, whereas labor is perfectly mobile among
industries. Households are modelled as constrained maximisers of Klein-Rubin/StoneGeary utility functions. Units of new industryspecific capital are cost-minimising combinations of Vietnamese and foreign commodities.
For all commodity users, imperfect substitutability between imported and domestic varieties of each commodity is modelled using the
Armington3 constant elasticity of substitution
(CES) assumption. The export demand for any
given Vietnamese commodity is inversely
related to its foreign-currency price. The
model recognises consumption of commodities by government, and the details of direct
and indirect taxation instruments. It is assumed
that all sectors are competitive and all goods
market clear. Purchasers’ prices differ from
producer prices by the value of indirect taxes
and trade and transport margins. The agents
are assumed to be price-takers, with producers
operating in competitive markets, which prevent the earning of pure profits.
4.2. The ORANI-G core of the ILSSA-MS
theoretical structure
a. Modelling of the labor market
ILSSA-MS contains detailed modeling of
labor demand and supply, distinguished by 113
industries, 26 occupations and 6 qualifications.
The industries, occupations and qualifications
are listed in Appendix 1.
On the supply side, qualification holders
allocate their labor across occupations so as to
maximize their utility, subject to given occupational wages and a constraint on the total number of working hours available by each qualification. On the demand side, industries
demand labor distinguished by occupation,
choosing between different occupational types
so as to minimize their labor costs, subject to
given occupational wages and their overall
demand for labor.
The equations that describe the main relationships describing the supply and demand
for labor distinguished by industry, occupation
and qualification in the ILSSA-MS model are,
in percentage change form, as follows:
where:
is the percentage change in employment
of occupation type o supplied by qualification
s;
is the percentage change in the number
of hours of labor supply by qualification s.
w Sw s
Q
t s
Q
t OCC t s
( )
,
( )* = × ∑ ∈ ,
H H (E5) o i,o ×= × ∑ ∈ x x o
O
i IND i o
( ) O
,
( )
(E1)
(E2)
(E3)
(E5) o
(E6)
w Sw o
O
o t
O
t SKILL o t
( )
,
( )* = × ∑ ∈ , (E4)
xo s,
xs
( ) Q
Journal of Economics and Development 29 Vol. 14, No.3, December 2012
The solution year value for this is determined
by (E8) discussed below.
is the elasticity of supply to occupation
type o by qualification type s in response to
movements in the relative wage of occupation
type o;
wo,s is the percentage change in unit wage
received by qualification type s when supplying labor to occupation type o;
is the percentage change in average
wage received by qualification s;
is the percentage change in demand for
occupation o;
is the elasticity of substitution between
labor supplied by different qualifications to
occupation o;
is the percentage change in the average
wage of occupation type o;
CRESH weighted average share of qualification s’s total wage earnings earned in supplying labor to occupation t;
CRESH weighted average share of the
total wages of occupation o represented by
wages paid to qualification t supplying labor
to occupation o;
Ho total hours of employment in occupation
o;
Hi,o total hours of employment of occupation o in industry i;
percentage change in employment of
occupation o in industry i;
percentage change in employment in
industry i;
industry i’s elasticity of substitution
between different occupational types;
percentage change in the price of occupation o to industry i;
percentage change in the price of labor
to industry i;
Here, we assume that workers holding a
given qualification allocate labor across occupations so as to solve the following problem:
Maximise:
subject to:
In implementing this problem for ILSSAMS, we choose the CRESH functional form to
describe U. As Dixon and Rimmer (2008)
explain, (E7) describes a problem in which
workers view wages earned in different occupations as imperfect substitutes. The utility
maximising solution to (E7), converted to percentage change form, is (E1). Equation (E1)
describes qualification-specific labor supply
functions. In the absence of changes in relative
wages, under equation (E1) expansion in supply of qualification s leads to uniform expansion in the labor supply of all occupations to
which qualification s supplies labor. A change
in the wage of one occupation (wo,s) relative
to the average wage earned by qualification s
induces transformation towards greater
supply of labor to that occupation, with the
strength of this substitution governed by the
elasticity .
Units of occupation-specific labor are modelled as CRESH composites of occupationspecific labor distinguished by the skill supws
( ) Q
xo
( ) O
wo
( ) O
St s
Q
,
( )*
So s
O
,
( )*
xi o
O
,
( )
xi
( )I
wi o
I
,
( )
wi
( ) I
U X W ,X W ,…,X W (E7) s s 1 2,s 2 o,s o 1,
(E7)
X X s
(Q) = ∑ ∈ o,s o OCC
ws
Q ( )
Journal of Economics and Development 30 Vol. 14, No.3, December 2012
plying that labor. The percentage change form
of the cost-minimising CRESH demand functions are described by equation (E2)4.
Together, equations (E1) and (E2) describe
supply and demand functions for occupation
and qualification-specific labor (xo,s). This
requires wage rates for occupation and qualification-specific labor (wo,s) to be endogenous
to clear these labor markets. Equations (E3)
and (E4) calculate percentage changes in average wages by qualification ( ) and occupation ( ) respectively, as the appropriate
weighted sum of percentage changes in occupation and qualification-specific wage rates.
Economy-wide demand for labor of a specific occupational type is modelled as the sum
of demands for that labor across all industries.
This is described by equation (E5).
The industry-specific cost-minimising labor
demands functions produced by this structure
are described by equation (E6).
b. Equations to account for changes in the
number of people holding a particular qualification
The ILSSA-MS database describes a solution to the model for the year t.
A typical simulation with ILSSA-MS will be
undertaken in two steps. First, the model will
be simulated with a set of shocks that update
the model solution to a recent year, such as
2010. Second, the model will be simulated
with a set of shocks that represents a forecast
out to some distant year, such as 2020. In each
case, we require an equation to calculate the
change in the number of people holding each
of the qualifications in the model.
Here, we describe the equation governing
the accumulation of qualifications across the
forecast period. We begin by considering a
forecast between the years 2010 and 2020. We
assume that the number of persons holding
qualification s in the year 2020, will be equal
to:
(i) the number of persons who held qualification s in year 2010; less,
(ii) those who held qualification s in the
year 2010, but who either permanently depart
the workforce between 2011 and 2020 or
retrain for another qualification; plus,
(iii) the number of people who acquire qualification s over the period 2011-2020; minus,
(iv) the number of newly-trained people
holding qualification s who, either do not
enter, or permanently depart, the labor force
between 2011 and 2020.
More formally, the stock of persons holding
qualification type s in the solution year t + τ is
given by:
where:
is the number of workers holding qualification type q in period t;
is the rate at which persons holding
qualification type q at the start of the simulation period permanently leave the workforce
or undertake training in a different qualificaws
( ) Q
(E8)
Xq
(Q)t
rq
(1)
wo
( ) O
Journal of Economics and Development 31 Vol. 14, No.3, December 2012
tion;
is the rate at which persons holding
qualification type q, who join the workforce
during the simulation period, permanently
leave the workforce or undertake training in a
different qualification;
is number of people newly qualified
with qualification type q who enter the labor
force in period s.
A difficulty with equation (E8) is that it
introduces a variable, , describing numbers
of people newly qualified with a particular
skill in each of the years between t and t + τ.
We offer the model user two options for the
determination of training positions for the
years between t and t + τ. The first option is to
assume that training numbers grow smoothly
between the years t and t + τ. The second
option is to assume that training positions
adjust instantly to their t + τ value. The first
option is implemented via:
Substituting (E9) into (E8) gives
The rationale for (E9) is the possibility of
inertia in the process of the adjustment of the
number of annual training positions. Such
inertia might reflect the time required to adjust
training resources, like teaching facilities and
professional staff numbers, to changing student needs. However a potential problem with
(E9) is that it may lead to over or under-shooting of solution year training positions relative
to long-run trend requirements in simulations
in which solution year skill demands change
significantly relative to the initial solution
year. Apparent excessive adjustment of training positions can be reduced by assuming a
one-off year t+1 adjustment of training positions for skill q to its new level, a level that
will be maintained over each year of the simulation period. This second option is implemented via:
(s = t+1 . . . τ) (E11)
Substituting (E11) into (E8) provides:
Equations (E10) and (E12) can together be
written as:
Equation (E13) introduces the dummy variable D. With D equal to 0, equation (E13)
becomes (E12). With D equal to 1, equation
(E13) becomes equation (E10).
4.3. Updating the model from 2005 to 2010
The ILSSA-MS model database is compiled
from the input-output table for Vietnam for the
year 2005 (GSO 2008b). To update the model
so that it describes the structure of the economy in 2010, we shock the model with observed
changes in economic variables over the period
2006-2010. We impose shocks to variables that
are normally endogenous (thus requiring
r
q
(2)
T
q
s
T
q
s
(E10)
(E12)
(E13) q
+
Journal of Economics and Development 32 Vol. 14, No.3, December 2012
endogenous determination of appropriate
structural variables) and variables that are naturally exogenous. In the former category, we
include: real GDP (with primary factor productivity determined endogenously), real private consumption (with the average propensity
to consume determined endogenously), real
public consumption (with the ratio of public to
private consumption spending determined
endogenously). In the latter category, we
include: population growth, employment
growth (GSO, 2011a, 2011b), and changes in
agricultural land use (NIAPP, 2010). At the
sectoral level, we extrapolate historical trends
in changes in production technology and
household consumption preferences. This
updating simulation provides us with a solution to the model for the year 2010. We provide tables describing industry and employment structures for the economy in 2010 in
Appendix 2.
5. Empirical results
5.1. Close the model
In an economy the number of economic
variables is always bigger than the number of
identified relations between them and the
number of variables is always bigger than the
number of equations. A set of equations can
only be solved when the number of variables
equals the number of equations. Therefore, we
need to predetermine a few variables – exogenous variables – by giving them numerical values. This is done by specifying the shocks. On
that basis the set of equations becomes solvable to find out remaining variables – endogenous variables. To close the model, we need to
classify variables into exogenous and endogenous ones. In this study we focus on the
assessment of policy impact, therefore we will
focus on short-term model closing.
Closing short term model
The following variables are usually exogenous in short term simulations:
Capital (K) and land (Lnd). This happens
because it takes time to construct and put new
land into production;
Structural variables, such as: technology,
consumption trend, ratio between private and
public expenditure, import preference, position of export demand function and technology
in relation to capital. There is no theory that
explains these variables in the model. Policy
variables such as import tax, interest rate,
import commodity price (assuming Vietnam is
a small country and consumers accept the
price), real wage (assumed to be stable in the
short term).
5.2. Policy simulation
In 2011, in order to stabilize the macro
economy, restrain inflation and ensure social
protection, the Government implemented various measures such as a public investment cut,
reducing the over expenditure of the State
budget and recurrent expenditure (10 per cent),
reducing investment in the economic field (7.7
per cent compared to 2010), reducing investment in construction (17.8 per cent), in transportation and warehousing (10 per cent) and in
Journal of Economics and Development 33 Vol. 14, No.3, December 2012
the media & information field (11.7 per cent).
Table 1 simulates the process of investment
cut in some fields, in which, Column 1 presents the proportion of state investment in the
total social investment. It is calculated by the
ratio of the State budget over the total social
investment by industry. The statistical data
show that state investment accounts for quite a
high proportion in transportation and (82.38
per cent); health and social aids (86.9 per
cent). Column 2 presents the percentage of the
state investment cut in 2011 compared to that
in 2010. This is calculated based on the statistic data on state investment in 2011 and 2010.
Column 3 in Table 1 shows the size of shock
of the investment variable in the model.
Because in this study, the “investment” variable by industry is used for policy simulation,
the magnitude of this simulated variable
(investment) is measured by multiplication of
the proportion of state investment in the total
investment and the percentage of state investment cut.
5.3. Results of the simulation
The results of the simulation show that
when the Government implements the contractionary fiscal policies, the recurrent expenditures are reduced by 10 per cent; construction,
17.8 per cent; transportation and warehousing,
10 per cent; and media & information field,
11.7 per cent. The impacts result in a GDP
decrease of 0.09 per cent and a decrease of
0.06 per cent of total jobs or 30 thousand
jobs5. On other hand, about 2.5 per cent of
new jobs have not been generated due to the
expenditure cut.
It can be seen that the contractionary fiscal
policies of the Government have directly influenced the industries that provide goods and
services for the Government, such as construction, exploitation, the processing industry,
health, healthcare services, and cultural and
Table 1: Investment cut by state sector
!
“# # !
$ %#& ‘% #
(# )%# %#* ))#%# !
+*%# %#* %## !
,% % %#* % %%#
Source: Calculation based on MOF and GSO data
Journal of Economics and Development 34 Vol. 14, No.3, December 2012
sport services. When the Government cuts
down the expenditures of these industries, the
total demand for these kinds of goods and
services in the economy will reduce.
Enterprises will have to regulate their production and business to reduce their product size,
which will result in a decrease in the common
output of the economy. As a result, total jobs
will also reduce.
Impact on jobs in some industries:
The results from Table 2 show that due to
the contractionary public investment and inflation restraint policies; several industries, such
as construction, construction material production, transportation, etc. have been negatively
affected when their capacity for job creation is
limited. Besides, some other industries such as
agriculture, leather and footwear or other manufacturing industries have created more jobs
due to these policies.
The direct investment cut for the construction field and ineffective projects has
decreased the number of jobs by 1.27 per cent
and its backward effects have influenced the
business and production of the industries
which produce construction materials, cement,
and sand and gravel quarrying. As a result,
these industries have cut down their production size and the labour demand of these industries have slightly declined (sand and gravel
quarrying has a decrease of 0.31 per cent;
brick production, 0.69 per cent, cement production, 0.76 per cent, transportation and communication, 0.1 per cent).
The results also show that the most negatively affected industries are construction,
cement production, brick production, and paint
production. The results from the simulation
also indicate that when the Government cut the
recurrent expenditure by 10 per cent and a
decrease in investment for construction by
17.8 per cent, 40.7 thousand jobs were lost
(equivalent to 1.27 per cent). The cement
industry did not face a direct cut, but it lost
0.76 per cent of jobs because it is an input for
the construction industry.
Table 2 also illustrates that the labour cut in
construction, cement production, etc. has led
to a reserve employment shift movement from
these industries to agriculture. As a result, the
number of jobs in agriculture and fishery has
increased by 77 thousand or 0.23 per cent and
0.42 per cent, respectively.
Contrary to the above-mentioned trend,
some industries still had an increase in labour
demand such as the garment and textile industry (0.49 per cent) because they are key and
strategic industries in Vietnam.
The results also show the characteristics of
Vietnam’s labour market, in which there is a
close connection between rural and urban
areas and the role of agriculture, and rural
areas and the informal sector in job creation –
in the stagnated economic situation.
Impact on employment by occupation
The results from the simulation show that
the public expenditure cut in some industries
has affected the total jobs in those industries
Journal of Economics and Development 35 Vol. 14, No.3, December 2012
and the employment structure by occupation
(illustrated in Table 3). For each industry,
labour demand by occupation depends on the
total number of jobs in that industry and the
relative wage level of the different occupations. However, the results also indicate that
impacts on labour demand by occupation are
unclear. Public expenditure cuts and promotion of investment in the private sector have
assisted the development of agricultural production and other manufacturing industries. As
a result, the number of jobs in these industries
Table 2: Percentage of employment change by industry
! ””
#
” !
# $!
%
% &’” ()
* ! (
%
+” )
$ #
$!!
,” – .
& !!
”
. ”
# /” ! !
% +
, ” ”
*
Source: Calculated from the simulation with ILSSA-MS model
Journal of Economics and Development 36 Vol. 14, No.3, December 2012
increased and unskilled labour demand has
increased 0.13 per cent; demand for skilled
labour in agriculture, forestry, and fishery has
risen 0.27 per cent and the number of labourers working as staff in these fields also
increased by 0.01 per cent.
The labour group that has seen the greatest
reduction is craftsmen and other relevant
skilled workers because they mainly worked in
the industries that suffered the most severe
cuts with around 11,600 jobs, or 0.19 per cent.
Highly technical and professional workers
in industries and skilled installers and machine
operators are the second labour group which
was negatively affected by the Government’s
expenditure cut policies (a decrease of 0.1 per
cent).
The manager and medium technical worker
group was slightly affected because the enterprises that employed them experienced difficulties and the enterprises had to reduce their
production or face bankruptcy.
Impact on wages and income
Though public expenditure cuts were only
applied for some industries; the outputs of
these industries can be the inputs for others.
Therefore, expenditure cuts or production size
reduction in the industries that the
Government applied the expenditure cut policies to, would indirectly affect production and
business activities of other industries. As a
result, wages and income would be influenced
in every industry.
Table 4 show that expenditure cuts caused a
decrease in workers’income in all industries of
the economy because of job loss or reduction
in working hours in construction, cement production, and transportation. There was an
increase in the jobs in agriculture and the
informal sector. The agricultural industry had
a decrease in the percentage points of income
and so it was also slightly affected by the policies.
Negative impacts of the policies on wages
and income were mainly for the construction
Table 3: Percentage of employment shift by occupation
! ”
##! ! “
$ %
& ’
Source: Calculated from the simulation with ILSSA-MS model
Journal of Economics and Development 37 Vol. 14, No.3, December 2012
field. This is the field that was directly influenced by public investment cuts in terms of
their production size and number of jobs. The
wage level in this industry was stipulated to be
0.52 per cent lower than the wage level before
the policies were implemented. Agriculture –
forestry – fisheries was affected by the policies
least (the wage level was 0.13 per cent lower
than the level before the policies were implemented). In fact, when enterprises have difficulties with their orders or the problem of production reduction, they do not often dismiss
their workers easily because they are waiting
for a new business and production cycle with
new expected orders. In such circumstances,
workers often have their working hours
reduced. As a result, their wages and income
tend to fall.
Table 4: Percentage points of change for wage/income by industry (per cent)
! “ #
$
# % #
& ‘ # $
( ) *
+
,
#
-
+
!
‘
!
) (
.
&
)
##
! (
$ /
&
& / #
$
( 0
$
.
,
)
Source: Calculated from the simulation with ILSSA-MS model
Journal of Economics and Development 38 Vol. 14, No.3, December 2012
6. Policy implications
Key conclusions
Government expenditure cuts have led to a
reduction of about 0.06 per cent in the total
number of jobs or about 2,5 per cent of new
jobs have not been created.
The industries, the jobs of which were
reduced the most, are those facing direct
expenditure cuts, including: the construction
material and cement production industries, and
transportation.
The impact of government expenditure cuts
has caused a decrease in the number of jobs in
industries. However, the results differ between
industries. Most of the industries are faced
with job reductions, while some sectors witnessed an increase in jobs.
Impact on labor groups: The handicraftsmen
and related technical worker group is the most
affected with a job reduction of about 11,600
jobs or 0.19 per cent.
The impact of the policy changes also
caused a decrease of 0.1 per cent in jobs in
both groups of senior experts in different fields
and technical installers and machine operators.
The manager and medium-technical worker
group were only slightly affected by the policies.
Some industries were less affected by the
public expenditure cuts (garments and textiles,
export industries). There was an increase in
jobs and workers’ income decreased slightly.
Policy implications
Expenditure cuts are essential in the context
of high inflation, but we need to pay attention
to assessing its short-term and long-term
impact on the labour market;
Social protection policies to reduce the negative impact of public expenditure cuts play a
very important role in acting as a tool to ease
the shocks for the unemployed workers or
those who suffer from income reduction.
It is necessary to harmoniously combine
expenditure cut policies with social security
policies (balancing macro policies, assessing
potential impact on the labour market and
regarding labour as one of the important variables).
It is necessary to encourage the private sector and the whole of society to engage in formulating and monitoring policies on developing labour and the employment market.
Policies need to be designed for vocational
training to meet the demand of human resource
development, especially highly skilled labours
working in key export and import industries, in
production and the service providing sectors.
A comprehensive, multi-level, flexible and
effective social security system needs to be
developed and employment and social security
programs for low-income labourers need to be
designed.
There is a need to continue to renew goals,
content, programmes and methods related to
vocational training towards providing a good
environment in which learners can practice.
The capacity in monitoring and controlling
Journal of Economics and Development 39 Vol. 14, No.3, December 2012
quality of vocational training needs to be
improved: verifying the quality of vocational
training centres and training programmes. On
that basis, labourers can play a more active
role in dealing with policy changes or difficulties derived from economic crises.
And there is a need to finalize and develop
information systems for collecting, processing,
analysing and forecasting information on the
labour market and to establish banks that can
serve labourers and unemployed labourers
seeking jobs.
APPENDIX
APPENDIX 1. INDUSTRIES, OCCUPATIONS AND QUALIFICATIONS IN ILSSA-MS
A.1. Industries
There are 113 industries in ILSSA-MS. For reporting purposes, we aggregate them into 20 sectors. The sectors and their component industries are reported below.
Sector 1. Agriculture and forestry: 1. Paddy; 2. Natural rubber in raw forms; 3. Kernel coffee
beans; 4. Sugar cane; 5. Un-fermented or partly-fermented tea leaves; 6. Other crops, nec.; 7.
Pigs; 8. Cows; 9. Chickens, ducks, geese, etc.; 10. Other livestock, incl. raw materials from
them; 11. Irrigation services for agriculture; 12. Other services to agriculture; 13. Forest activities and products.
Sector 2. Fishery: 14. Fishing; 15. Aquaculture.
Sector 3. Mining: 16. Coals and lignite, peat; 17. Metal ores; 18. Stones; 19. Sands, pebbles,
gravel, crushed stone; 20. Natural bitumen, asphalt, and other minerals; 21. Crude petroleum and
natural gas.
Sector 4: Food, Beverage and tobacco products: 22. Meat products; 23. Animal and vegetable
oils and fats; 24. Processed dairy products; 25. Bakery and confectionary products; 26.
Processed fruits and vegetables; 27. Alcoholic beverages; 28. Malt liquors and malt; 29. Soft
drinks; bottled mineral waters; 30. Sugars and their by-products; 31. Coffee products; 32. Tea
products; 33. Tobacco products; 34. Processed sea foods; 35. Milled rice; 36. Food products i.e.;
37. Preparations used in animal feeding.
Sector 5. Non-metal product: 38. Glass and glass products; 39. Ceramics and other clay products; 40. Bricks and tiles; 41. Cements ; 42. Concrete and other cement products; 43. Other
building materials; 44. Pulp, paper and paper products; 45. Wood and wood products.
Sector 6. Metals, machines and equipment: 46. Medical equipment and appliances; 47.
Optical and precise equipment; 48. Domestic appliances and parts thereof; 49. Motor vehicles,
motorbikes, motorised bicycles; 50. Automobiles and parts; 51. Agricultural or forestry machinery ; 52. Other special-purpose machinery; 53. General-purpose machinery ; 54. Bicycles and
Journal of Economics and Development 40 Vol. 14, No.3, December 2012
wheelchairs; 55. Other transport equipment and parts; 56. Transformers and parts thereof; 57.
Other electrical machinery; 58. Radio, television and communication equipment and apparatus;
59. Iron, steel and their products; 60. Non-ferrous metals and their products.
Sector 7. Chemicals: 61. Basic organic chemicals; 62. Basic inorganic chemicals; 63. Chemical
fertilisers; 64. Organic fertilisers and other agricultural chemicals; 65. Pesticides; 66. Veterinary
medicines; 67. Pharmaceutical products; 68. Rubber and rubber products; 69. Soap products; 70.
Cleansers, perfumes and toiletries; 71. Plastics in primary forms; 72. Products from plastics; 73.
Paints ; 74. Varnishes, colours, ink; 75. Other chemicals n.e.c.; 76. Petroleum oils and lubricants.
Sector 8. Textile, clothing and footwear: 77. Woven fabrics ; 78. Textile fibres and threads ;
79. Wearing apparel, except fur apparel; 80. Carpets, other textile floor coverings; 81. Other textile fabrics, n.e.c. ; 82. Tanned, dressed, composition leather; 83. Leather products.
Sector 9. Other manufacturing products: 84. Printing accessories and products; 85.
Publishing activities and products; 86. Other manufactured products n.e.c.
Sector 10. Gas, electricity and water: 87. Electricity and gas generation and distribution; 88.
Water extraction, refining and distribution.
Sector 11. Construction: 89. General construction services of residential and non-residential
buildings; 90. Other construction services.
Sector 12. Trade and repair: 91. Wholesale and retail services; 92. Repairs of motor vehicles,
domestic appliances and personal stuffs.
Sector 13. Hotels and restaurants: 93. Hotel and motel lodging services ; 94. Meal serving
services.
Sector 14. Transport and communications: 95. Road transport and pipeline services; 96.
Railway transport services; 97. Water transport services ; 98. Air transport services ; 99. Postal
and telecommunication services; 100. Travel services.
Sector 15. Financial services: 101. Financial services; 102. Lottery and related services; 103.
Insurance services.
Sector 16. Property and business services: 104. Research and development services; 105. Real
estate services; 106. Other business services.
Sector 17. Public administration: 107.Public administration and compulsory social security
services
Sector 18. Education: 108. Education and training services
Sector 19. Health care: 109. Human health service, Veterinary services, Social services
Sector 20. Other services: 110. Cultural and sporting services; 111. Services furnished by associations; 112. Other miscellaneous services; 113. Dwelling services.
Journal of Economics and Development 41 Vol. 14, No.3, December 2012
A2. Occupations
There are 26 occupations in ILSSA-MS, which are aggregates from the 34 occupations in
VHLSS 2004, 2006, 2008 and 2010. For this report, we further aggregate them into 11 occupations. The concordances between the 11 occupations in this report and the 34 occupations in
VHLSS are as follows:
1 .Leaders and executive managers
2. High-level professionals
3. Mid-level professionals
4. Elementary professional and technicians
5. Skilled workers in service and sales
6. Skilled workers in agriculture, forestry, and aquaculture
7. Skilled craft and related trades workers
8. Assemblers and machine operators
9. Unskilled workers in sales and services
10. Unskilled workers in agriculture, forestry, and aquaculture
11. Unskilled workers in other industries, and armed force personnel
APPENDIX 2
1. Macroeconomic variables
!”#$ %% “& %’&
(
(
&’& #)&$” *
+&$” ”$ #)&$” ( (*( * * *
!”
,#) (
*
+’)
* *
(
# $% &&&
)”& *
*
*
- .”& / ( ( ( (*
Source: GSO
Journal of Economics and Development 42 Vol. 14, No.3, December 2012
2. Employment by Occupations
!” !##
$% & % #
$ # !” !#!
$% & %
#’!’
$% # ! ’ & %
( ) # !
Source: ILSSA
3. Employment by Industry
!
“
# $%
“ $
%
&”%
$
‘
%
(
)
*
+
% “
,
*
‘
(
“
-
( ” %%
!
.”
!
$
. %
/
–
+
!
$
Source: GSO
J
o
u
r
n
a
l
o
f
E
c
o
n
o
m
i
c
s
a
n
d
D
e
v
e
l
o
p
m
e
n
t
4
3
Vo
l
.
1
4
,
N
o
.
3
,
D
e
c
e
m
b
e
r
2
0
1
2
4. Factor shares in industry factor costs (%)
! ” #
$#%
! ” #
% & !% ” #
” ‘ %
” #
( )
* ” #
+ , % ! #
–
* #
‘ + #
( ! ” #
.
#
( ! %% #
#
/! ” + #
” /
% ” ” #
0 “+” #
+ .
“ #
, “ #
0%#* +” + +
Source: Vietnam Input-output data for the year 2005, GSO 2008b, updated with the current forecast
Journal of Economics and Development 44 Vol. 14, No.3, December 2012
5. Structure of in
d
ustry costs (%)
Sector
Domestic
intermediate
in
p
uts
Im
ported
intermediate
in
p
uts
Margin
Taxes on
intermediate
in
p
uts
Labour Capital Lan
d
Prod
uctio
n on tax Total
1. Agriculture and forestry 20.8 8.0 0.9 0.6 36.2 11.2 20.7 1.5 100.0
2. Fishery 22.8 20.4 1.2 0.6 24.5 14.1 15.4 1.0 100.0
3. Mining 13.1 13.9 0.8 1.4 15.6 16.1 24.2 14.8 100.0
4. Food, beverage and tobacco products 60.9 14.3 3.7 0.9 10.9 8.8 0.0 0.5 100.0
5. Non-metal products 41.7 30.7 2.3 -0.8 11.5 14.0 0.0 0.6 100.0
6. Metals, machines and equipment 25.1 49.7 4.0 -0.2 11.8 9.2 0.0 0.5 100.0
7. C
hemicals 21.1 50.1 2.7 -0.3 14.2 11.8 0.0 0.5 100.0
8. Textile, clothing and footwear 34.2 47.3 2.3 -0.1 7.7 8.2 0.0 0.4 100.0
9. Other man
ufacturing products 40.0 34.3 3.2 0.3 10.1 11.6 0.0 0.5 100.0
10. Gas, electricity and water 11.3 22.8 1.4 -1.2 30.2 34.6 0.0 0.9 100.0
11. Construction 35.3 31.9 2.9 -0.3 15.5 13.8 0.0 0.9 100.0
12. Trade and repair 29.1 13.3 1.8 3.1 28.3 22.9 0.0 1.5 100.0
13. Hotels and restaurants 29.9 16.3 1.1 -0.6 27.3 25.5 0.0 0.5 100.0
14. Transport and com
mu
nications 17.3 25.5 1.5 -0.8 25.4 29.8 0.0 1.3 100.0
15. Financial services 24.2 10.5 0.5 1.5 34.1 28.4 0.0 0.9 100.0
16. Property and business services 24.7 14.1 0.9 -0.1 34.3 22.2 0.0 3.9 100.0
17. P
ublic administration 28.5 16.9 0.9 2.4 44.9 6.5 0.0 0.0 100.0
18. Education 18.7 12.6 0.8 1.6 52.6 13.4 0.0 0.3 100.0
19. Health care 18.5 21.9 3.1 2.4 41.0 12.6 0.0 0.5 100.0
20. Other services 12.3 7.6 0.6 0.8 18.3 59.2 0.0 1.1 100.0
Econom
y-wide average 29.9 25.2 2.1 0.4 20.1 16.6 3.8 1.8 100.0
Journal of Economics and Development 45 Vol. 14, No.3, December 2012
6. Structure of co
m
m
o
dity sales (%)
(1) (2) (3) (4) (5) (6) (7)
Sector Intermediate
in
p
uts In
vestment Household
consu
m
ption
Exports Govern
ment
consu
m
ption
C
hanges in
in
ventories Total
1. A
griculture an
d forestry 56.4 0.4 21.6 17.5 1.0 3.2 100.0
2. Fishery
41.8 0.0 41.3 16.1 0.0 0.8 100.0
3. Minin
g 11.5 0.0 0.5 87.5 0.3 0.2 100.0
4. Food, beverage an
d tobacco prod
ucts 18.5 0.0 45.9 31.5 0.1 4.0 100.0
5. Non-metal prod
ucts 74.0 0.9 5.1 18.5 1.0 0.5 100.0
6. Metals, machines an
d eq
uip
ment 40.4 18.8 4.7 36.1 0.0 0.0 100.0
7. C
hemicals 57.0 0.0 26.1 17.8 0.0 -0.9 100.0
8. Textile, clothin
g an
d footwear 22.4 1.2 5.9 74.4 0.0 -3.9 100.0
9. Other man
ufacturin
g
prod
ucts 67.9 1.8 12.5 19.5 0.5 -2.2 100.0
10. Gas, electricity an
d
water 92.0 0.0 8.0 0.0 0.0 0.0 100.0
11. Construction 0.9 97.6 0.0 0.0 1.6 0.0 100.0
12. Trade an
d repair 79.0 0.0 12.7 8.4 0.0 0.0 100.0
13. Hotels an
d restaurants 16.9 0.0 58.0 25.1 0.0 0.0 100.0
14. Transport an
d com
mu
nications 34.5 20.6 27.7 16.6 0.6 0.0 100.0
15. Financial services 50.9 2.3 31.0 15.8 0.0 0.0 100.0
16. Property an
d
b
usiness services 73.7 0.0 7.0 5.2 14.1 0.0 100.0
17. P
ublic ad
ministration 0.3 0.0 0.0 0.0 99.7 0.0 100.0
18. E
ducation 13.3 0.0 12.8 2.4 71.4 0.0 100.0
19. Health care 5.3 0.0 47.9 7.4 39.4 0.0 100.0
20. Other services 1.4 0.0 88.0 4.9 5.7 0.0 100.0
Econom
y-wide average 32.0 13.9 20.5 27.4 5.9 0.4 100.0
Journal of Economics and Development 46 Vol. 14, No.3, December 2012
Notes:
1. Import and export volume accounts for 150 per cent of GDP
2. In 2010-2011, with technical assistance from AusAID, ILSSA cooperated with the Centre for Policy
Studies of Monash University (Australia) to develop a project model for Vietnam’s Labour Market and
the micro simulation entitled ILSSA – MS on the basis of the Computable General Equilibrium-CGE
model.
3. The name is used in honour of the author who first introduced the idea of imperfect substitutability
between imported and domestically-produce goods via the linearised form of the CES input demand
equations (Armington, 1969).
4. See Dixon et al. (1992: 126 – 128) for the derivation of percentage change demand functions from a
CRESH function.
5. 30 thousand jobs are calculated by the formula: total number of the jobs which can be created if the
expenditure is not cut = the actual number of jobs in 2011 (1+anpha), in this case, anpha=0.06 per cent.
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