36
Household characteristics in this study include age, dependency ratio
29
, and gender of
household head. The results demonstrated that all variables of household characteristics have
have insignificant impacts on household welfare, which means that household characteristics
hold less influence over absolute gains or losses of household welfare. This is in contrast with
the study of Anderssson, M., Engvall, A., and Kokko, A. (2005).
Environmental factors refer to the impact of infrastructure on household income earning
capacity. We include six variables to capture the impacts of infrastructure: village access to
electricity, road access, health services
30
, safe water, and markets
31
. The results of the study
indicated that there are only two of six proxies for environmental factors – village access to
health services and markets – which are statistically significant and positively related to
household welfare.
29
Dependency ratio was calculated by dividing the number of dependents by the total number of household
members.
30
Two variables were used to capture village access to health services: whether village has community health
work and the distance of the village to the hospital.
31
To calculate the access to market, we used whether the village has a market located in the village.
37
8.0 CONCLUSIONS
The impact of trade liberalization on poverty is of major interest to academics and policy
makers at local and international levels. There are some remaining questions as to whether or
not trade liberalization is good for poor households. In Laos, the AFTA could have positive
and negative impacts on poverty depending on various factors, including characteristics of
households. There is therefore significant concern as to whether trade liberalization,
especially the provisions which AFTA entails, will raise or alleviate poverty. However, there
are very few studies focused on the impacts of AFTA on poverty in Laos. The main objective
of this study is to assess the impacts of AFTA on poverty using a multi-region, multi-sector
CGE model (GTAP) and micro-simulation.
From the GTAP simulation results, we can conclude that direct impact of tariff cuts will be
minimal. However,
the indirect effects from AFTA, such as the improvement of trade
facilitation and promotion of FDI, are expected to be much larger. The real GDP will increase
by only about 9.5% and household welfare (EV) will increase by 422 million US$. However,
Laos will experience the growth of a much more significant trade deficit from AFTA than it
currently experiences – a deficit of an estimated 18 million US$. Most sectors when analysed
are expected to experience a trade deficit, especially the food and crop sectors. Production
outputs in industrial sectors such as motor vehicles and parts, machinery and equipment, oil,
metals, chemicals, rubber, plastic products, electricity, ferrous metals, and coal will grow as a
result of AFTA. On the other hand, some parts of the agriculture and light manufacturing
sectors such as sugar, leather products, textiles, crops, dairy products, vegetable oils and fats,
apparel, insurance, gas, and petroleum, coal products will lose from AFTA.
In sum, AFTA will increase real GDP and welfare in Laos. However, it will also increase its
trade deficit and create winners and losers in production outputs. AFTA will also contribute
to poverty reduction in terms of increasing wages for both skilled and unskilled labor and it
will not increase income inequality in Laos.
From the results of the micro-simulation, the impacts of AFTA on welfare are positive on the
national level, for urban households, and for rural households on the whole. In addition,
welfare will increase across regions. AFTA will also reduce inequality at the national level
but impacts of AFTA on inequality are predicted to be heterogeneous across the region. The
Logit model indicates that the beneficiaries of AFTA are determined by whether households
have access to irrigation; the number of livestock including cattle, buffalo, and pigs; the
number of literate members of the household; the education of household head; the land area
of the household; and access to community and market.
However, this study is affected by several weaknesses in the GTAP simulation. First, it uses a
static GTAP model, which does not reflect the real impact of AFTA. Second, due to lack of
data, we do not consider the Non-trade barrier (NTB). Third, this study does not taken into
account the gains from increase productivity from trade.
38
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