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Source: Alix Peterson Zwane (2002) Figure 2.1 shows the household’s options as well as the ambiguous relationship between land use and poverty. Forest land is denoted by the letter F and abandoned land is imagined by a blank cell. The letters A and B denote cultivated land using one of the possible techniques. Among options, option 1 is clearly the least costly strategy in period 1. Lack of the ability to borrow, households with the lowest wealth can only pursue this strategy. As wealth increases, whether option 2 or option 3 is preferable depends on each household’s decision; option 4 is the choice that requires the largest investment. When option 4 is chosen, the most output is generated, but if environmental externalities were considered, this might not be the socially preferred outcome. In this study, the so-called conventional wisdom on the relationship between poverty and deforestation will be used as a strong version of “poverty-deforestation hypothesis” (or written succinctly as “poverty-deforestation hypothesis”). That is: increase in household income will be negatively related with land clearing and will be positively related with the use of input that increase or maintain yields. The weak version of the poverty-deforestation hypothesis might predict that the link between income with both land clearing and input use will be positive, but income elasticity of land clearing will be smaller than the income elasticity of input use. Now consider a policy that increases initial effective wealth for the poorest households, such as a micro-credit program, but not so much that option 4 becomes feasible. If the poverty- deforestation hypothesis holds, then as W increases, households selecting option 1 will switch to selecting option 3. This means that increasing wealth does not increase environmental degradation in period 1. If households selecting option 1 prefer to select option 2 as wealth increases, there is a short-run trade-off between deforestation and increase in income. Theoretically, there is no way of determining which outcome is most likely. The decision will depend on the relative yields of YA and YB, the relative production costs CA and CB, the cost of clearing land, and the interest rate. Household size and labor availability would also affect this decision if the possibility of an imperfect labor market were introduced.
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|
|
1943 |
1976 |
1980 |
1985 |
1990 |
1995 |
1999 |
|
Natural forest |
14,000 |
11,077 |
10,486 |
9,308 |
8,430 |
8,252 |
9,444 |
|
Plantation |
0 |
92 |
422 |
584 |
745 |
1,050 |
1,471 |
|
Total hectares |
14,000 |
11,169 |
10,608 |
9,892 |
9,175 |
9,302 |
10,915 |
|
% of total area |
43.0 |
33.8 |
32.1 |
30.0 |
27.2 |
28.1 |
33.2 |
Source: MARD (2000)
3.1.4. Shifting cultivation - a main cause of deforestation in Vietnam
Shifting cultivators in Vietnam
Ethnic minority - shifting cultivation link in Vietnam
3.2. POVERTY SITUATION IN VIETNAM
Firstly, Vietnam is still one of the poorest countries in the world. In 1999, Vietnam’s gross national product per capita was US$370 compared with US$1020 for the Philippines, US$580 for Indonesia, US$780 for China and US$410 for low income countries (figure 3.3)
Fourthly, the poverty rate is extremely high among ethnic minority groups. While accounting for roughly 14% of the total national population, the representation of ethnic minority groups among the poor is disproportionately high at roughly 29%. The majority of ethnic minority people live in remote and isolated areas. They are geographically and culturally isolated, and lack favorable conditions for developing infrastructure and basic social services. Ethnic minority people are amongst the poorest in Vietnam. Ethnic minorities make up 14% of the population but account for 29% of poor people in Vietnam. The incidence of poverty among ethnic minorities has come down from 86% in 1993 to 75% in 1998. This compares to the poverty rate for the Kinh majority of 31% down from 54%. Therefore, although ethnic minority poverty is declining, it is falling at a slower rate than for the Kinh population and remains very high (Figure 3.7).
3.3. RELATED POLICIES
3.3.1 Forest land allocation
3.3.2 Programme 327
3.3.3 The campaign for fixed cultivation and sedentarisation
3.3.4 Credit policy for the poor
4.1. data
This thesis uses two data sets derived from VLSS 1 and VLSS 2 (conducted in two periods of 1992-93 and 1997-98). These data sets are undertaken by General Statistical Office (GSO) of the Government of Vietnam under the LSMS form. They include socio-economic aspects of a large number of households that are highly representative of Vietnamese households. Notably, only 4186 households interviewed in 1993 had been selected in 1997 because some households were sampled again and the other had moved elsewhere. This figure implies that the study on migration might be ignored.
While these surveys contain national data, only 400 households using or controlling forest land in VLSS1 will be considered here. It should be noted that selling or buying land between two period might affect the analysis results and therefore, only 229 households are used in this sample.
4.2 MODEL AND DATA SPECIFICATION
4.3. GENERAL DESCRIPTION
Table 4.1 - Summary statistics of variables, 1993 and 1998
|
Variable |
Unit |
Mean |
|
|
LAND93 |
m2 |
10338 |
|
|
LAND98 |
m2 |
9212.8 |
|
|
DEFOR |
m2 |
380.9 |
|
|
INCOME93 |
000 dong |
8326.6 |
|
|
INCOME98 |
000 dong |
10020.6 |
|
|
DURABLE93 |
000 dong |
269.7 |
|
|
DURABLE98 |
000 dong |
526.8 |
|
|
AGE93 |
age |
4.16 |
|
|
AGE98 |
age |
4.33 |
|
|
ADULT93 |
persons |
5.11 |
|
|
ADULT98 |
persons |
4.90 |
|
|
PESEXP93 |
000 dong |
34.93 |
|
|
PESEXP98 |
000 dong |
139.9 |
|
|
FEREXP93 |
000 dong |
386.1 |
|
|
FEREXP98 |
000 dong |
935.5 |
|
|
LABEXP93 |
000 dong |
78.3 |
226.8 |
|
LABEXP98 |
000 dong |
33.1 |
227.7 |
|
PESUSE93 |
- |
1.28 |
.449 |
|
PESUSE98 |
- |
1.12 |
|
|
FERUSE93 |
- |
1.02 |
|
|
FERUSE98 |
- |
1.00 |
|
|
LABUSE93 |
- |
1.77 |
|
|
LABUSE98 |
- |
1.72 |
|
|
EDU93 |
- |
1.40 |
|
|
EDU98 |
- |
1.60 |
|
|
ETHNIC |
- |
0.272 |
|
Source: Calculated from 229 observations in the sample based on data from VLSS 92-93 and VLSS 93-98
Table 4.2 - Allocating quintiles for the whole country and for the sample
|
Quintile 1993 |
For the whole country |
For the sample |
||
|
Frequency |
Percentage |
Frequency |
Percentage |
|
|
Quintile 1 |
873 |
18.19 |
60 |
26.20 |
|
Quintile 2 |
921 |
19.19 |
59 |
25.76 |
|
Quintile 3 |
944 |
19.67 |
51 |
22.27 |
|
Quintile 4 |
993 |
20.69 |
35 |
15.28 |
|
Quintile 5 |
1068 |
22.25 |
24 |
10.48 |
|
Total |
4799 |
100.00 |
299 |
100.00 |
Source: Author's calculations based on data of VLSS 1992-1993
Non-labor and non-farm income make up a small percentage rate of total income (about 14%) in 1993 but increase to nearly 30% of total income in 1998 (Table 4.3). This suggests that despite of reducing, the vulnerability of household to shocks in agriculture such as pest infestations, bad weather is still large. Such shocks may be mitigated if households can borrow to smooth consumption, but if borrowing potential depends on households' asset positions, the small asset stocks that most households hold suggest that this may be only a limited cushion.
Table 4.3 Non-labor and non-farm income, 1993 and 1998
|
Year |
1993 (000d) |
1998 (000d) |
|
Non-labor income |
358.8 |
2668.7 |
|
Non-farm income |
811.3 |
1596.2 |
|
% of total income |
14% |
29% |
Source: Author's calculations based on the choosen sample
According to studying results of Do (2001), borrowers are more likely to borrow if they have collateral (in the form of land and/or durable goods). In Vietnam, the combined use of chemical and organic fertilizers has a positive effect on plants, in terms of growth and yield. Some factors, however, reduce the effectiveness of fertilizers. Since the government does not control the importation of fertilizer, there are frequent fluctuations in the price. Such inputs and shocks often need to be financed prior to a harvest, which may be difficult for households with a limited ability to borrow.
Another important issue is also shown in Table 4.1. That is the households in this sample practice high-input agriculture, (especially fertilizer factor – nearly 100% farmers using fertilizer) but hire little outside labor (about 23% households using hired labor). This can be explained by the high level in average available labors (a household has about 5 adults).
Table 4.4 Classifying households clearing land by region in 1993-1998
|
Region |
Mountainous region |
Delta region |
Coastal region |
|
% households clearing land |
61.36 |
79.16 |
88.88 |
|
Cleared land area (m2/per household) |
2271.1 |
1530.7 |
2250.7 |
Source: Author's calculations based on the choosen sample
Also in table 4.1, the rather large positive value (381 m2 per household) of the variable DEFOR (change in cleared land) shows that there happens deforestation in households. Forest can be cleared for cultivation, aquaculture, and other uses. Over time, each household either increase or decrease their holdings of cleared land. Because the sampling does not consider land purchases and salse, increases in cleared land holdings are a result of deforestation.
Table 4.5 Classifying households clearing land by ethnic groups in 1993-1998
|
Region |
Kinh and Chinese |
Minority |
|
% households clearing land |
88.73 |
50.50 |
|
Cleared land area (m2/per household) |
1792.3 |
2344.7 |
Source: Author's calculations based on the choosen sample
If only concerning about households that really deforest, the cleared forest area will be about 1935 m2 per household in average, ranging from 10 m2 to maximum value of 2,5 hectare, concentrating mainly on mountainous and coastal regions (Table 4.4 in Appendix). 88.7% Kinh or Chinese households attend to clear forest land while this rate in minority households is only 50%, however the more serious rate happens in minority households (2344 m2 compared with 1792 m2) (Table 4.5 in Appendix).
4.4 REGRESSION RESULTS
4.4.1 The land clearing - income relationship
The estimated parameters in the above table indicate that almost all coefficients are statistically significant at 10%. The coefficients of income, durable assets, land, number of adults have positive effects on deforestation.
Table 4.6 - The land-clearing decision
Number of observations: 299
Dependent variable: DEFOR
|
Variables |
Coefficients |
t |
p-value |
|
LAND93 |
0.324 |
11.4 |
0.000 |
|
INCOME93 |
0.197 |
1.72 |
0.086 |
|
INCOME932* |
0.080 |
2.12 |
0.034 |
|
DURABLE93 |
1.375 |
4.83 |
0.000 |
|
AGE93 |
-266.4 |
-2.72 |
0.007 |
|
ADULT93 |
431.1 |
3.56 |
0.000 |
|
REGION |
177.52 |
039 |
0.695 |
|
ETHNIC |
-1947.9 |
-2.66 |
0.000 |
|
R square |
0.167 |
Adjusted R square |
0.162 |
|
F statistic |
33.46 |
Probability > F |
0.000 |
*INCOME932 = INCOME93 ^ 2; the coefficient of INCOME932 is multiplied by 10,000
From the table, we show that there exists a significant relation between cleared land and income. Land-clearing increases with the larger rate when income is high. 1000 dong increase in income causes 0.197m2 forest land to be cleared. Thus, the relation between deforestation and income satisfies the "weak" version of hypothesis.
The number of adults in a household strongly affect on land clearing. Its coefficient is very large and positive. It shows that the larger the number of adults, the higher level of land clearing. This result also expresses that land clearing occurs more in Kinh and Chinese households. This is a strong and expected result.
Also according to the results in Table 4.3, households with larger land holdings or durable assets clear more land. Households with large asset holdings are able to borrow more from different sources than those with fewer assets. The positive sign of the coefficient on asset holdings indicates that households who are eligible to borrow, to buy capital inputs in agriculture for example, choose to clear more land than the others. This will be discussed in more details later in part 4.4.3.
4.4.2. The input expenditure - income relationship
The above results are evidence in favor of the contention that an increase in income is not followed by a decrease in cleared land in Vietnam. Although this is not predicted by the poverty-deforestation hypothesis, this hypothesis cannot be rejected without a further investigate about the relation between the use of inputs that increase yields on previously cleared land and income. If analysis shows that high incomes are correlated with both additional input use and land clearing, this would be consistent with a weak version of the poverty-deforestation hypothesis. On the other hand, evidence that high incomes are uncorrelated with the purchase of yield-increasing or yield-maintaining inputs would be further evidence against the hypothesis.
Table 4.7 presents results from regressions that are similar to those presented in the previous part. In this case, however, the dependent variable is no longer land clearing, but expenditure on purchased inputs, including fertilizer, pesticide, and hired labor respectively, per hectare of cleared land. The results of these regressions suggest that income is positively correlated with the additional use of inputs that increase yields. This is evidence supporting the weak version of the poverty-deforestation hypothesis.
Table 4.7 - The input-use decision
Number of observations: 299
|
Variables |
Dependent variables |
|
p-value |
||||
|
FEREXP98 |
PESEXP98 |
LABEXP98 |
|
FEREXP98 |
PESEXP98 |
LABEXP98 |
|
|
LAND92 |
0.014 |
0.0012 |
0.0008 |
|
0.078 |
0.476 |
0.434 |
|
INCOME92 |
0.370 |
0.036 |
0.0079 |
|
0.000 |
0.000 |
0.067 |
|
INCOME922* |
-0.100 |
-0.0072 |
-0.0019 |
|
0.000 |
0.002 |
0.176 |
|
DURABLE92 |
0.075 |
0.100 |
-0.010 |
|
0.406 |
0.000 |
0.334 |
|
AGE92 |
97.00 |
23.00 |
12.00 |
|
0.002 |
0.000 |
0.002 |
|
ADULT92 |
-31.00 |
-4.800 |
-5.200 |
|
0.412 |
0.522 |
0.255 |
|
REGION |
-11.00 |
-89.00 |
-30.00 |
|
0.937 |
0.001 |
0.068 |
|
ETHNIC |
-770.0 |
-89.00 |
-9.000 |
|
0.000 |
0.004 |
0.628 |
|
R square |
0.5025 |
0.3393 |
0.0347 |
Adj R square |
0.4995 |
0.3353 |
0.0289 |
|
F statistic |
168.34 |
85.59 |
6.00 |
Probability > F |
0.0000 |
0.0000 |
0.0000 |
*The coefficients of income922 are multiplied by 10,000
In the case of pesticide expenditures, there is a positive and significant correlation between asset holdings and pesticide expenditures. This correlation may simply reflect the fact that households using pesticide are more likely to grow permanent tree crops such as coffee and tea. These tree crops are an asset that households may add to the estimated value of their total assets, resulting in a positive correlation between asset holdings and pesticide use.
4.4.3 The credit market
The direct effect of borrowing constraints is positive, that is, more constrained households clear land. The interaction effect of borrowing constraints and family size is also positive and significant. This positive sign on the interaction term means that, while more constrained households clear land, the effect is intensified for large households
This is further evidence that the land-clearing decisions of large and small households differ. The signs of these coefficients are consistent with the proposition derived from the model that large households are most like to respond to increases in income, or reduced borrowing constraints, by clearing land because these households have surplus home labor that they price below the local wage rate.
Table 4.8 - The effect of borrowing constraints on the land-clearing decision
Number of observations: 299
Dependent variable: defor
|
Variables |
Coefficients |
t-value |
p-value |
|
BORROW |
1409 |
1.940 |
0.053 |
|
BORROW*ADULT |
705 |
2.650 |
0.008 |
|
ADULT92 |
500 |
4.870 |
0.000 |
|
LAND92 |
-0.368 |
-14.710 |
0.000 |
|
AGE92 |
-319 |
-2.510 |
0.012 |
|
REGION |
4973 |
6.110 |
0.000 |
|
ETHNIC |
1409 |
1.940 |
0.053 |
|
R square |
0.1511 |
Adjusted R square |
0.1466 |
|
F statistic |
33.41 |
Probability > F |
0.000 |
Turning to the effect of borrowing constraints on the use of inputs, in Table 4.9, results of probit estimates for the fertilizer- and pesticide-use decisions are presented. The results of this estimation show that bigger families are less likely to invest in pesticide but more likely to invest in fertilizer to intensify agricultural production. There is very clear relationship between fertilizer use and borrowing constraints.
Table 4.9 - Effect of borrowing constraints on input use
Number of observations: 299
|
Variables |
Dependent variables |
|
p-value |
||
|
FERUSE98 |
PESUSE98 |
|
FERUSE98 |
PESUSE98 |
|
|
BORROW |
0.970 |
0.876 |
|
0.000 |
0.000 |
|
ADULT92 |
0.018 |
0.027 |
|
0.000 |
0.000 |
|
LAND92 |
0.000 |
0.000 |
|
0.073 |
0.000 |
|
DURABLE92 |
-0.023 |
-0.033 |
|
0.000 |
0.000 |
|
AGE92 |
0.002 |
-0.058 |
|
0.890 |
0.008 |
|
ADULT92 |
-0.044 |
-0.149 |
|
0.034 |
0.000 |
|
REGION |
-0.017 |
-0.080 |
|
0.178 |
0.001 |
|
R square |
0.9655 |
0.8574 |
Adjusted R square |
0.9654 |
0.8567 |
|
F statistic |
6011.83 |
1290.07 |
Probability > F |
0.0000 |
0.0000 |
By using a direct measure of the household’s borrowing constraint, rather than a measure of income, the evidence in this section provides additional support for the claim that the simple version of the poverty-deforestation hypothesis can be accepted for this sample. This part provides evidence that the relationship between poverty and land clearing depends on household size, as predicted by proposition 2. These results also support the contention that larger households, are more likely to invest in better technology.
This thesis analyzes the interaction between poverty and land-use change in Vietnam. The benchmark is that reductions in poverty will be related with less land clearing, as households are able to make desired investments in intensification on previously cleared plots. In contrast to this so-called conventional wisdom, chapter 2 shows that while households do take expected lifetime borrowing constraints into consideration when making land-clearing decisions, the relationship between poverty and deforestation is theoretically ambiguous.
The findings of this thesis suggest that for households with relatively high incomes, the "weak" version of poverty deforestation hypothesis may indeed be an accurate characterization of households’ preferences.
There are some limitations in the process of studying. First, the information are incomplete, therefore some variables had to be changed by substitutes. Second, the two data set, VLSS 92/93 and VLSS 97/98 are inconsistent, leading to adjustments have been made. Third, the information about forest is less accurate due to problems in statistical methods.
Agro forestry and fixed cultivation policies
Credit supply
Agriculture technology development
Consciousness education
Employment generation
Providing the incentives for farmers to sustainably manage forests
