The Effects of afta on Macroeconomic Variables and Poverty: Evidence of Laos
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- Bu səhifədəki naviqasiya:
- 3. Poverty impacts (headcount index, percentage) 1. Mean welfare change 2. Gini index as percentage
- Variables Definition Unit Dependent Variables
- Explanatory variables
- Household characteristics
- Environmental factors
- Household Characteristics
- Dependent variable : Gi Coefficient z-value P>│z│ Explanatory variables
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Baseline
Simulated Change per capita (kip) per capita (kip) in percentage National -614,737
-541,908 11.85
Rural -332,467
-265,662 20.09
Urban -1,467,895 -1,376,859 6.20
Region Vientiane (capital) -2,961,939 -2,867,558 3.19 North
-499,082 -460,666
7.70 Central
-912,230 -877,969
3.76 South
687,737 874,479
27.15 Baseline
Simulated Change (%) National 45.26
44.88 -0.39
Rural 44.56
45.32 0.76
Urban 45.87
44.15 -1.72
Region Vientiane (capital) 48.03 44.17
-3.86 North
43.84 44.22
0.38 Central
42.88 45.08
2.20 South
42.37 46.35
3.99 Baseline
Simulated Change (%) National 25.78
29.59 3.81
Rural 29.65
29.04 -0.62
Urban 20.26
30.38 10.12
Region Vientiane (capital) 16.79 28.93
12.14 North
31.77 28.21
-3.56 Central
27.35 32.34
4.99 South
22.18 23.37
1.19 3. Poverty impacts (headcount index, percentage) 1. Mean welfare change 2. Gini index as percentage 32
Despite increasing welfare from AFTA, the impacts of AFTA are heterogeneous. In order to analyze the detailed impacts of AFTA on households, we use the logit model to investigate the factors which influence absolute gains or losses of welfare (Gi). The dependent variable is welfare (Gi) which was calculated in the previous chapter. If a household gains from AFTA it is labeled 1 and if it loses is 0. The explanatory variables describe various attributes of different households (for more details see Table 1).
Following the literature from Anderssson et al. (2005), Warr (2010) and Chen and Ravallion (2003), the explanatory variables can be divided into three categories: factors of production, household characteristics, and environmental factors. The multi-regression equation relationship between the probability to gain or lose in welfare and explanatory variables can be expressed as follows:
Gi = ?????? + ?????? ??????
?????? ??????
+ ?????? ??????
?????? ??????
+ ?????? ??????
?????? ??????
+ ε………………………………………….. (1)
???????????????????????? (???????????? = 1) = 1 1+?????? −???????????? …………………………………………………. (2) Where:
Prob = Probability of gains in household welfare (Gi)
?????? −??????
?????? = Irrational number to the power of Gi
household gains in welfare from AFTA; otherwise equal to zero V = Vectors of factors of production. W = Household characteristics. X = Environmental factors. i i γ β α , , and i δ are unknown parameters and ε is a normally distributed random error term.
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Table 1: Variable and Definition Variables Definition Unit Dependent Variables
Gi Gi is measured household gains or lose of welfare from AFTA gains =1; lose=0
Explanatory variables
Factors of production Irrigation Access Irrigation Access to village yes =1; other = 0 Number of Cattle Number of Cattle per household Number
Number of Buffalo Number of Buffalo per household Number Number of Pigs Number of Pigs per household Number
Literate Total Total number of Literate in household person Literate Females Literate Females person
Education Education level of household head in years year Adults Number of Adults in household person
Rice farm land Rice farm land Area of household hectare Other Cultivation land Other Cultivation Land Area of household hectare
Lowland Area Village located at Lowland Area hectare
Chemical use for Planting Chemical use for Planting yes =1; other = 0
Business Household owned Business yes =1; other = 0
Household characteristics
Dependency Ratio Decency Ratio ratio
Household Male Head of Household yes =1; other = 0
Age of Household Head Age of Household Head year Age Squares Age Squares of Household Head year
Environmental factors
Access to All season Road Access to All Season Road yes =1; other = 0
Electricity Access Electricity Access yes =1; other = 0
Access to Safe Water Safe Water Access yes =1; other = 0
Community Health Community Health Access yes =1; other = 0
Distance to Hospital Distance from Village to Hospital km Access to Market Village access to market yes =1; other = 0
Factors of production The choice variables are to reflect the production capacities of households and include the inputs of land, physical capital, technology, human capital and labour.
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The land area is an important factor in determining household welfare. We include two categories of land: farmland and land for other agriculture.
The physical capacity is difficult to measure. Following the literature, the physical capital includes: cattle, buffaloes, and pigs.
education level of the household’s adult members as a proxy for human capital.
variables as proxies for technology: chemical fertilizers and whether the household runs a business.
Household Characteristics are also important factors to determining household welfare. For household characteristics, we include ages, dependency ratio 25 head.
Environmental factors refer to the impact of infrastructure on household income earning capacity. We include six variables to capture the impact of infrastructure: village access to electricity, roads, health services 26 , safe water, markets 27 .
The results of the Logit model are shown in Table 2 28 . Out of 23 explanatory variables, 13 were found to be statistically significant in determining absolute gains or losses of household welfare (Gi), which includes the factors of production, household characteristics, and environmental factors.
welfare. If the extent of a household’s ‘other agriculture land area’ has a positive and significant impact on welfare, it means that farmer will gain benefits from AFTA, particularly in lowland area. Should irrigation access have a positive, statistically significant, this implies that access to irrigation could result in more productivity and thus contribute to household welfare. Following the approaches described in other literature, physical capital includes cattle, buffalo, and pigs, thus measuring the contribution of ownership of farm animals to household welfare. Human capital is measured by education level of the household’s adult members. All variables for human capital are strongly significant, and it appears that the literacy of members in household and education level of household heads are positively related to household welfare. This is consistent with government policies to contribute to investments in human capital for building labor capacity of households across the country, especially for the poor to escape from poverty. The variables related to technology- chemical
25 Dependency ratio was calculated by dividing the number of dependents by the total number of household members. 26 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.We used two variables to capture village access to health services: whether village has community health work and the distance of village to hospital. 27 To calculate the access to market, we used whether the village has a market located in the village.Access to market, we used variable whether village has market at village. 28 See Summary Statistics of Variables in appendix 3 and correlation between variables in appendix 4 35
fertilizers and whether the household runs a business- appear to have different effects on welfare. The use chemical fertilizers has a positive and significant effect but running a business has negative and significant effects, meaning that households which run businesses would lose welfare from AFTA. It is possible that they might lose welfare for only the short term, but in the long term they would gain more benefits from AFTA.
Table 2: Results of factors influence on absolute gains or losses of household welfare (Gi) Dependent variable : Gi Coefficient z-value P>│z│ Explanatory variables Factors of production
Irrigation Access to village 0.1448 * 1.80 0.0720
Number of Cattle per household 0.0226 *** 4.18
0.0000
Number of Buffalo per household 0.0619 *** 6.44 0.0000
Number of Pigs per household 0.0278 *** 4.00
0.0000
Total number of Literate per household 0.0476 ** 2.21 0.0270
Literate Females 0.0451
1.53 0.1270
Education level of household head in years
0.0626 *** 9.10
0.0000
Adults 0.0332
1.54
0.1230
Rice farm land Area per household -0.0121
-0.71
0.4750
Other Cultivation Land Area per household 0.0824 *** 4.24
0.0000
Village located at Lowland Area 0.3063 *** 5.61 0.0000
Chemical fertilizers 0.2088 ** 2.63
0.0090
Business -0.1798 *** -3.08 0.0020
Household characteristics
Dependency Ratio -0.1270
-1.24
0.2140
Male Head of Household 0.0722
0.69
0.4880
Age of Household Head -0.0053
-0.44
0.6610
Age Squares of Household Head 0.0001
0.58
0.5650
Access to All Season Road 0.0543
0.87
0.3870
Electricity Access -0.0639
-1.05
0.2920
Access to Safe Water -0.0048
-0.09
0.9240
Community Health 0.2097 *** 4.38 0.0000
Distance from Village to Hospital 0.0005
0.36 0.7200
Access to Market 0.1833 ** 2.07 0.0380
Constant -1.4223 *** -4.63 0.0000
Number of observations 8293
LR chi2(8) 433
Prob > chi2 0.0000
Pseudo R2 0.0383
Source: Authors' calculations based on LECS 4 36
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).
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
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.
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