Discovering Sources of Inequality in Transition Economies:
A Case Study of Rural Vietnam
Vasco Molini
PhD Candidate, University of Florence, Italy
Guanghua Wan
Senior Research Fellow, WIDER-UNU, Finland
1. Introduction
In the last 20 years, the debate about income distribution has come back to the agenda of
developing countries. In particular, for those who are fighting against poverty, sustaining economic
growth without substantial increase in income inequality has become an extremely important issue.
The automatic trickle-down, a keystone of the “Washington Consensus”, has demonstrated to be
rather inconsistent. Increasing average income without redistribution may well affect the long run
performance of an economy and marginalise the lower percentile of the population. This,
consequently, creates a negative impact on poverty reduction. Polarised countries like Brazil seem
to perform poorly in poverty reduction. On the other hand, Vietnam is considered as one of the best
performing countries in terms of poverty reduction, largely owing to little increases in inequality.
Given the importance of inequality in its own right and in the context of poverty eradication, it is of
utmost importance to analyse determinants of inequality. Traditionally, this is accomplished by
inequality decomposition by income sources or by population subgroups. These traditional
approaches can only provide limited information on the fundamental determinants of income or
income inequality. In the 1970s, Oaxaca (1973) and Blinder (1973) initiated a literature on
inequality analysis that uses sophisticated tools like micro-simulation and regression based
decomposition. Their frameworks are largely employed to analyse the determinant of discrimination
in labour market.
The present paper represents an attempt to explore the sources of income inequality in rural
Vietnam during a period of stable growth and progressive transition to market economy. We are
interested in determining in a quantitative way the contributions of variables to inequality in order
to understand the role and significance of different elements. The analytical method used is a
combination of regression-based decomposition with the Shapley value technique of Shorrocks
(1999). Advantages of this approach include flexibility in the specification of the underlying income
generation function and in the choice of inequality measures.
1
2. Economic growth and poverty reduction, 1975-2000
After the reunification in 1975, Vietnam was a country destroyed, with a strong need for
infrastructures and with a population that had faced 30 years of war and deprivation. According to
many indicators, at the beginning of ’80, the country was among the poorest in the world in terms of
GDP per capita although it had, like many other socialist countries, high levels of school enrolments
and life expectancy. Given these pre-conditions, the political leaders of the country, after many
attempts to improve the economic performance through a rigid planning and agriculture
collectivisation, embarked on a gradual program of liberalisation and openness called Doi Moi
(renovation). To some extent, the experiment is comparable to the Chinese “market socialism”, a
progressive transformation of the economy from a socialist planned to a capitalist market economy,
keeping some distributive elements of the previous system. In the rural areas, collective farms were
replaced by a “household responsibility system”. Plots of commune land were allocated (with longterm contracts and the right to sell) to individual households according to their characteristics in an
efficient and fair way (Ravallion 2001). Price controls were gradually removed for agricultural
goods and households are no longer forced to sell their products to the government. Farmer are free
to supply goods to the newly established open market. At the same time, changes in the enterprise
law allowed the creation of private farm and non-farm enterprises. Further, reductions in tariff
barriers and FDI controls encouraged the inflow of capitals and technology. The reforms have
resulted in robust economic growth (averaged 7, 9 % over 1990-2000, see WB 2002). By now,
Vietnam has become the largest exporter of pepper and second largest exporter of rice and coffee in
the world. The first part of 90s is characterised by a sustained growth and a strong reduction of
poverty rates from 58, 1% in 1993 to 37,4% in 1998.
The outbreak of Asian crisis in 1997 affected marginally Vietnamese economy compared to
other East Asian countries. Nevertheless the pace of sustained growth relented and capital inflows
reduced (mostly Asian FDI). The economy rapidly recovered in 1999. The crisis shed light on some
unsolved aspects of the country. The main one is the unexpected low rate of growth of employment
in manufacturing sector after liberalisation. According to Heckesher-Ohlin model it was expected
that Vietnam would create many jobs in labour intensive sectors and was expected to see a more
rapid decline of agriculture share of GDP (still 63% in 2000). While State Owned Enterprises are
reducing their labour force, the new private manufacturing sector is still unable to create job
opportunities (only 7% of GDP) and most of the new employment is created by low value-adding
services. As pointed out by Jenkins (2002) the industrial sector is not taking advantage of the
potential supply of labour and the inflow of FDI oriented to more capital-intensive production.
2
3. Inequality in Vietnam
The high economic growth of Vietnam (averaging 8% during 90’s) has been accompanied only
by a “tolerable” increase in inequality. The Gini index measured on consumption per capita
increased from 0.329 in 1992-1993 to 0.352 in 1997-1998. The decomposition by subgroups (rural
and urban) shows that the main contribution to the total is given by the enlarging difference in the
average consumption between rural and urban residents. According to the Lewis model,
industrialisation leads to an increase in the divergence between urban and rural sector that is offset
by mass migration from countryside to towns. This, in turn, might lower the average wage rate. In
the case of Vietnam, the turning point has not yet reached and the divide continued to worsen from
1993 to 1998.
Within urban areas, the inequality is increasing as well. In big towns like HCM and Hanoi, most
of the employment was in the public sector and wages were almost the same in 1993. After the
reform and the establishment of private and foreign companies in big towns, wage gaps emerged
and tend to widen. However, the increases of inequality in urban areas and between rural and urban
sectors have not been accompanied by an increase of inequality in rural areas. Between 1993 and
1998, calculations using VLSS data show a stable inequality (graph 1) with some relative changes
among the seven macro-regions (graph 3). To some extent, the economic division of Vietnam
reflects the political division before 1975, but the pattern is oriented towards a convergence in the
inequality levels (not so much in the income levels). In the Northern1 part rural inequality was low
in 1993 and tended to increase in the five years considered. Before the agrarian reform of 1988, the
northern agriculture was completely collectivised and the progressive liberalisation led to a
redistribution of land slightly more unfair (Ravallion 2003). In the South the contrary occurred:
higher levels before 1993 and a general decrease afterwards (graph 2). This is because the
collectivisation had been resisted successfully by farmers and only 10% of the land became under
the direct control of state in the south. Thus, the process of liberalisation led to a distribution of land
close to the pre-unification level.
The above facts and government efforts to reduce inequality between regions with ad hoc
policies (resettlement, infrastructure provision, incentives to disadvantaged areas) represent two
major causes underlying the stability of income inequality in rural Vietnam. Government plays an
important role but further efforts are required. In analysing the development of non-farm economy
in rural Vietnam (an extremely important determinant of income performance), Van der Walle
(2003b) finds that investment in infrastructure is crucial in the creation of household income but is
1
The Northern regions are three ( Northern Uplands, Red River Delta and Northern Coastal) and they almost coincide
with the former North Vietnam
3
not enough. Government should develop the labour market and land market in order for rural
household to diversify activities and to better allocate productive inputs.
Another major issue is the improvements of safety nets. Economic liberalisation and market
development have increased the risk faced by household. Nonetheless, the tools available to deal
with such drawbacks seem to be extremely ineffective. Van der Walle (2003a) provides empirical
evidence on this. Public transfer helped only a few to escape poverty and even fewer to avoid to fall
into poverty.
In the last 15 years, most research attention has been devoted to poverty rather than inequality
analysis. Vietnam is often cited as an example of poverty reduction in a context of trade
liberalisation and pro-growth policies. Nevertheless, some authors raise important questions about
the long run capacity of Vietnam to reduce poverty. For example, Fritzen (2003) argues that poverty
reduction is not guaranteed unless the government implement specific policies that enables it. In
particular, the main concern of the author is the too slow pace of structural reforms (reform of
SOE’s) that are encountering increasing resistance by conservative groups combined with a too
quick rise in the urban rural gap.
Baulch and Minot (2002) considered regional imbalances in Vietnam. By matching 1997-1998
household survey data with 1999 census data, they are able to present a poverty map of the country.
Remote areas of northern and central Vietnam seem to be less affected by economic growth and still
face poverty rates over 50%, while in Ho Chi Mihn City and Hanoi urban areas (excluding nonresident and migrant household) poverty has been reduced to below 20%.
An important contribution on inequality comes from Glewwe et al (2002). Using the Oaxaca
(1973) methodology, they decompose the change in per capita consumption from 1993 to 1998 into
changes in characteristics and changes in the returns to the characteristics. The results show that the
increasing gaps across provinces are almost completely explained by changes in the returns. Also,
highly educated people and household heads with an employment in secondary or tertiary sector
benefited more than others and experienced a bigger change in the return to characteristics.
3. Data and Shapley Decomposition
The data used are extracted from the two waves of Vietnamese Living standard survey, 19921993 and 1997-1998. The sample contains detailed record on production and consumption activities
4
for almost 8076 households in rural Vietnam. Observations are also available on facilities and
prices at the commune level. These variables will be used to capture location effects.
To discover sources of inequality in income or consumption, two analytical frameworks can
be employed. The first is commonly known as decomposition by population subgroups or by factor
components (Shorrocks 1980, 1982). Applications of the subgroup decomposition include malefemale or white-black divide, which produces what is called within- and between-group
contributions. Policy implications from this kind of decomposition are limited because the
contributions cannot be attributed to the dividing variable such as sex or race when other human
capital variables such as education and experience are not controlled for. The factor decomposition
is often used to attribute total income inequality into components associated with wage income,
investment income, and other incomes. These factor incomes are functions of more fundamental
variables such as wealth holding, human capital, family characteristics, employment sector and
residential location. It is desirable, particularly from the policy-maker’s point of view, to identify
contributions by these more fundamental variables.
The second analytical framework – regression based decomposition – is initiated by Oaxaca
(1973) and Blinder (1973) and extended by Juhn et al. (1993) and Wan (2004). A major advantage
of this approach is that other variables can be controlled for thus the impact of key variables can be
isolated with little contamination. Early development of this framework is restricted in the sense
that it only permits decomposition of income or wage difference between two population groups.
Recent work of Wan (2004) relaxes this limitation and leads to an inequality accounting technique
where inequality of income or consumption can be attributed to fundamental variables. When
combined with the Shapley value method of Shorrocks (1999), the regression based framework of
Wan (2004) can handle any inequality indicator and imposes no restriction on the parametric
income or consumption function.
4. The income generating function
The model, a standard semi log income generating function, has been estimated with a yeardummy (D) and some interacted time dummies in order to allow for variations in the coefficients
across the two years.
Log (Y)    D    i X i    i DX i (1)
The dependent variable is the household consumption deflated by regional prices rather than
income because it shows less volatility and is less vulnerable to idiosyncratic shock. According to
Friedman’s theory of permanent income (Friedman 1957 and Deaton 1997) this is a better measure
of family well–being and as demonstrated by the long experience of household survey collection,
less influenced by measurement error. Households tend to be more precise on consumption rather
5
than on income. This is particularly true for rural households. Recent contributions about land
reforms in Africa (Dercon 2001) show that in transition economies consumption is less sensitive to
current circumstances and better reflects household perceived return to its own assets and
endowments.
Different from other papers (Heltberg 2003), we control for location by not only including
location dummies, but also adding a variable indicating presence of manufacturing plants within 10
km to the households. This later location variable also captures the effect of rural industrialisation
(like Chinese TVE’s) in raising incomes. The distance from the closest health centre is used to
control for health provision facilities. Finally included is a dummy for the presence of asphalted or
tarmac roads (having or not having electricity or potable water were significant but highly collinear)
in the commune. The construction of roads is the typical infrastructure intervention of government
and it is extremely important for income generation. Having access to road, still usable during the
rain period, allows rural household to have a greater mobility and to have access to markets eve if
they are not located close to the commune.
Conventional regressors are considered and they include household size, average education and
age of household head (plus their squares), dummy for ethnic minority, the dependency rate, the
percentage of wage earners, and percentage of members working outside in the primary sector
(generally seasonally labourers). Conventional capital and livestock capital are separately
considered as they may have different impacts on income. To avoid simultaneity and capture capital
depreciation or appreciation we use the stock of capital at the beginning of the year (at the
beginning of income generating process) valuated at year-end prices. During model estimation, the
livestock variable was dropped as it was insignificant.
It is important to control for land productivity in the model, which is calculated as the total harvest
divided by the acres of land (or trees in case of fruit crops). This variable helps capture many
effects: the quality of land, climatic conditions etc. Also, crops productivity is related to
government policy. The success of coffee exports is the outcome of an intentional policy of the
government to develop some economically marginal areas of the country. Central Highlands were
the most favourable places to start the cultivation of coffee and government encouraged the
resettlement of peasants from land–poor areas of Red River Delta. In this way government “killed
three birds with one stone”. A depressed area has been developed, reducing between-region
inequality (but the drawback was an increase in within inequality), a surplus labour force found
high remunerating activity and eventually the resettlement increased the quota of Khin population
in the area at the expense of ethnic minority who fought against Vietcong and with Americans
during the war. To some extent, the same story can be told about fruit cropping, but in this case, the
6
cultivation is less regionally located. Government supported this activity, combining it with a plan
of reforestation and hydro geological preservation.
We also consider the role of crop diversification on income. Our insight is that households
trying to increase tradable crops tend to be better off than households that limit themselves to rice or
other food production (grains, etc.).
5. Estimation and decomposition results
The income generation function was estimated with heteroskedasticity corrected (White
correction for variance estimation). The regression shows a high R square (table1). This result is
extremely important for the following decomposition as the model is able to explain more than half
of the income generation process.
All the linear variables are significant and show proper signs. Education and age of the head
contribute positively to income while household size; dependency rate and ethnicity possess
negative coefficients. The ethnicity, consistent with Baulch (2002) is still a negative predictor of
income. Ethnic minority households –non-Chinese- seem to be more disadvantaged and tend to be
poorer (Baulch and Minot 2002).
The coefficient for wage earners rate is positive while that for employment in the primary sector
is negative. The latter confirms that agricultural wages are low and those landless or with little land
are worse-off. Due to low labour mobility and small land per capita (there exist only few big farms)
the agricultural labour market is rather underdeveloped. The percentage of wage earners is very
important in income generation and might become a major source of inequality as in China (Wan
2004). Rural residents in eastern China have benefited from spill-over from coastal cities and big
towns while inland areas, due to long distance from major economic centres lagged behind.
The capital is positive confirming that households with more modern means of production or the
possibility to buy them perform better than others. In general, we can say that agriculture was a
profit-making business in 90s, differently from China (Wan 2004). In particular, diversification has
a good pay-off as it takes advantage of the long territory layout, the climate (semi-tropical,
equatorial) variations and the land morphology that varies ( big planes).
Location variables show the correct signs. The distance from a health centre, which is a good
proxy for government’s lack of investment in rural health creates produces negative effects on
income. The same can be said about roads (positive sign of the dummy for asphalted roads). The
presence of Vietnamese Town and Village enterprises is a good predictor of income as well. As the
Chinese experience has shown, the creation of local (in rural areas) opportunities of job raises
income and reduces mass migration and demographic pressure on towns.
7
The coefficients for the productivity variable demonstrate the importance of coffee production.
However, all interactive variables are negative except coffee, indicating declining importance of
agricultural productivity on household income over time. Also, land scarcity and over-exploitation
may become worse over time, causing decreased productivity over time. This is particularly true in
the two rice-rich areas, the Mekong Delta and the Red river Delta. The only exception is coffee.
Due to the increasing export starting from 1995, the revenues for coffee producers grow
substantially.
Looking at the coefficients for capital and credit access, they all have positive coefficients although
their marginal impacts on income dropped somehow in 1998. Rural Vietnam was under-capitalised
with limited access to credit due to market and credit repression. The negative signs for the capital
and credit access dummies seem to indicate that the growth of income has been accompanied by an
inefficient allocation and concentration of capital and credit in some areas of the country.
Turning to the decomposition results, the main determinant of inequality is location that accounts
for about 30% in both periods and this contribution tends to increase over time. This result is not
surprising as geography is a very important factor causing income inequality. To some extend, this
contribution correspond to the between component in the traditional approach. Unlike the
traditional approach, however, our approach controls for other income determinants. Our results
shows a smaller contribution of geography than that documented in Heltberg (2003) and we believe
30% contribution is intuitively more reasonable than the higher value given by Heltberg. The other
main contributors to inequality are household characteristics and the human capital of household
head.
This increasing importance of household characteristics and human capital is a consequence of
the transition process. With the progressive removal of the constraints that limited individual
ability, the importance of education and experience of the household manager (the household head)
increases dramatically. At the national level, paradoxically, this change might have played an
equalising role. Household heads from Northern Vietnam (that is poorer than South) are, on
average, better educated. An increase in the returns to education might have increased the income of
Northern households relatively to Southern, reducing the north-south gap.
There is a decline in the importance of land in inequality composition. This is consistent with the
findings in the literature that confirm a fair distribution of this factor. The declining contribution
might be the consequence of the increased diversification in some very backward areas of the
country.
Finally, job opportunities outside the households increased their importance in inequality
determination and the same can be said for infrastructure. The unequal distribution of these factors
seems to become more important as the rural economy opens up more. Clearly, these increased
8
contributions to total inequality must come at the expense of other contributions, particularly land,
as discussed earlier. As the Vietnamese economy continues fundamental reforms and
transformation, inequality is less driven by land endowment but the access to secondary and tertiary
sectors. Areas with embryonic industrialisation and close to towns tend to grow more than purely
rural areas, and this confirms the prediction of the Lewis model. Vietnam is slowly moving towards
an industrialised economy from a predominantly agriculture-based economy. This transition
inevitably brings about an increasing divide between rural and urban areas as well as an increase in
the gap between semi rural areas (areas close to big and medium size towns) and other rural areas.
In the present situation, the divides are kept under control but in the long- run, without any
correction or redistributive intervention they might lead to extremely non-homogeneous growth.
6. Conclusions
This paper attempts to analyse empirically inequality composition in Vietnam using the
Shapley’s decomposition (Shorrocks 1999) and explore its changes over time. Although inequality
in rural Vietnam did not increase, important changes occurred with regard to the contributions of
different inequality determinants. Our results confirm that growth, even in rural areas, is unevenly
distributed. Some households and areas benefited from openness and modernisation while others
lagged behind. Related to this finding is the increasing role of job opportunities outside the
household. Those closer to towns or to industrial clusters perform better. The more household rely
on agriculture the more they are vulnerable to agriculture prices shocks.
As expected, household characteristics increased their importance in inequality determination.
This is so despite the fact that access to education, at least to primary schools, is guaranteed to all in
Vietnam. Contrary to many developing countries, characterised by unfair land distribution, Vietnam
undertook a well implemented land reform and the role of land quality and quantity in inequality
determination has thus declined. This partially has something to do with the loss of importance of
the primary sector and the increase in the role of more modern sources of inequality, such as outside
job opportunities.
The experience of rural Vietnam, according to most of the studies, confirms that some important
targets have been reached, including increase in average income, development of poor areas and
poverty reduction. All of these important results occurred in a country where equalising forces
(endogenous and exogenous) limited the common side effects of transition processes. Generalising
the results of Ravallion (2003), the situation of rural Vietnam in 1998 might be considered a second
best outcome: a combination of some elements of efficiency with some elements of redistribution.
Clearly, the time span is too limited to forecast how long these positive conditions will persist in the
9
future. Nevertheless, the experience of rural Vietnam in the decade after the initial reforms is an
interesting example of growth without inequality and of well-targeted pro-poor policies.
References
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to skill. Journal of Political Economy 101, 410–442
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Econometrica 48, 613–625.
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Shorrocks, Anthony F., 1982. Inequality decomposition by factor components. Econometrica 50,
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Vietnamese Living Standard Surveys (VLSS) 1992/1993-1997/1998
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Approach, Journal of Comparative Economics (in print).
11
Graph 1: Variation of rural log consumption per capita by percentiles: difference between
1993 and 1998
.75
.7
.65
.6
diff9398
Variation of log consumption per capita 1993-1998
0
20
40
60
80
100
centile
Source: VLSS 1993-1998
Graph 2: Variation of log consumption per capita by percentiles in Northern and Southern
rural Vietnam: difference between 1993 and 1998
Variation in the log of real consumption per capita
.65
.7
Source: VLSS 1993-1998
.6
Variation
.75
.8
North and South Vietnam 1993-1998
0
20
40
60
80
100
Percentiles
sdiff 9398
ndiff 9398
Source: VLSS 1993-1998
12
Graph 3: Distribution of log consumption per capita by seven rural regions: Kernel density in
1993 and 1998
2
3
4
5
6
0
.5
1
0
.5
1
1
4
8
6
10
4
6
8
10
0
.5
1
7
4
6
8
10
x
93
98
Graphs by Code by 7 regions
Source: VLSS 1993-1998
1=Northern Uplands, 2=Hanoi Region, 3=Northern Coastal, Southern Coastal, 5=Central Highlands, 6 Ho Chi Mihn
Region, Mekong Delta
13
Table 1: regression results
Dependent variable = logarithm of household consumption per capita
Household
Size
-0.061
(23.45)**
Distance From
Health Centre
or Family
Planning Centre
Proportion Of
Male In
Household
0.165
(6.24)**
Number Of
Manufacturing
Opportunities
In 10 Km
0.109
(7.38)**
Years Of
Education Of
Hh Head
0.033
(24.25)**
Dummy For
Credit Access
0.099
(7.80)**
-0.003
(5.08)**
Presence Of
Road
Practicable
During Rain
Season 1998
Number Of
Manufacturin
g
opportunities
In 10 Km
1998
Productivity
Of Rice
Kg/Sqmeter
1998
Productivity
Industrial
crops
Kg/Sqmeter
1998
Productivity
Of Coffee
Kg/Sqmeter
1998
0.055
(2.62)**
-0.092
(5.98)**
-0.305
(5.47)**
Househol Head
Age
0.006
(17.79)**
Land for annual
crop per worker
0.037
(8.27)**
Dummy For
Ethnic Minority
-0.182
(10.64)**
Productivity Of
Rice
Kg/Sqmeter
0.371
(6.99)**
Dependency
Rate
-0.201
(9.95)**
Productivity
Food Crops
Kg/Sqmeter
0.013
(5.54)**
Dummy For
Credit
Access 1998
-0.069
(4.07)**
Proportion Of
Wage Earners
In Lab Force
Household
0.201
(11.05)**
Productivity
Industrial crops
Kg/Sqmeter
0.024
(4.31)**
Annual Crop
Land Per
Worker 1998
-0.023
(4.04)**
Proportion of
employment in
agriculture
outside, in lab
force
household
-0.345
(11.32)**
Productivity Of
Coconut Kg/
Tree
0.001
(4.00)**
Capital Per
Capita 1998
-0.042
(2.03)*
Presence Of
Road
Practicable
During Rain
Season
0.052
(3.13)**
Productivity Of
Coffe
Kg/Sqmeter
0.17
-1.39
Yeardummy
0.699
(31.74)**
Capital Per
Capita
0.094
(4.99)**
Productivity Of
Fruit
Kg/Sqmeter
0.002
(3.65)**
Constant
6.447
(172.21)**
-0.013
(2.02)*
0.569
(2.87)**
Observations 8076
R-Squared 0.63
* Significant At 5%; ** Significant At 1%
Table 2 : Decomposition results 1993
14
GINI %
ATKINSON (e=0)
%
Household size
Human capital
Ethnic
Household
composition
Labour opportunity
Capital
Credit
Land
Infrastructure
Location
TOTAL
THEIL-L (a=0)
%
THEIL-T (a=1)
%
CV2
0.0238
0.0273
0.0117
12.96
14.85
6.34
0.007
0.008
0.004
12.12
14.40
7.47
0.007
0.008
0.004
12.08
14.38
7.50
0.006
0.008
0.004
%
11.16
13.55
6.64
%
0.0132 10.13
0.0161 12.36
0.0075 5.77
0.0132
0.0210
0.0059
0.0063
0.0203
0.0044
0.0498
0.1838
7.20
11.44
3.18
3.43
11.07
2.41
27.11
100
0.003
0.006
0.003
0.001
0.006
0.001
0.015
0.054
6.46
11.08
4.94
2.44
11.20
1.53
28.37
100
0.004
0.006
0.003
0.001
0.006
0.001
0.016
0.055
6.48
11.09
4.96
2.44
11.20
1.54
28.33
100
0.004
0.006
0.004
0.001
0.007
0.001
0.017
0.057
6.39
10.54
6.46
2.30
12.37
1.41
29.18
100
0.0081
0.0128
0.0118
0.0028
0.0183
0.0017
0.0381
0.1303
Table 3 : Decomposition results 1998
GINI %
Household size
Human capital
Ethnic
Household composition
Labour opportunity
Capital
Credit
Land
Infrastructure
Location
TOTAL
0.0240
0.0288
0.0141
0.0153
0.0162
0.0091
0.0013
0.0088
0.0093
0.0573
0.1841
ATKINSON (e=0)
%
13.04
15.65
7.65
8.32
8.80
4.92
0.70
4.75
5.06
31.11
100
THEIL-L (a=0)
%
0.0065
0.0084
0.0050
0.0040
0.0044
0.0041
0.0002
0.0020
0.0025
0.0179
0.0550
THEIL-T (a=1)
%
11.80
15.21
9.09
7.32
8.06
7.36
0.30
3.57
4.63
32.65
100
0.0067
0.0086
0.0052
0.0042
0.0046
0.0042
0.0002
0.0020
0.0026
0.0184
0.0565
CV2
%
11.77
15.18
9.13
7.35
8.09
7.38
0.30
3.57
4.64
32.60
100
0.0063
0.0083
0.0047
0.0040
0.0043
0.0062
0.0002
0.0020
0.0025
0.0196
0.0580
%
10.85
14.22
8.11
6.95
7.40
10.62
0.33
3.49
4.30
33.73
100
0.0125
0.0174
0.0095
0.0074
0.0072
0.0254
0.0007
0.0037
0.0057
0.0474
0.1369
9.14
12.74
6.90
5.43
5.26
18.57
0.48
2.68
4.18
34.62
100
15
6.21
9.78
9.02
2.18
14.06
1.28
29.19
100