Wealth Accumulation and Factors Accounting for Success
Anan Pawasutipaisit and Robert M. Townsend∗
July, 2008
Abstract
We use detailed income and balance sheet statement constructed for households in a long
monthly panel in a emerging market economy to document and understand better the factors
underlying success in achieving high return on assets and upward mobility in the distribution
of net worth. Many households work their way out of poverty over the 7 year period. There
is great heterogeneity in returns and some households experience only modest, if not negative,
growth. We stratify by principal occupation (crop farming, wage employment, business, livestock, fish/shrimp), by household demographics, and by initial net worth. Households that run
business as a primary occupation seem to escape poverty faster than others. Household fixed effects account for the larger part of success, undercutting the story that successful entrepreneurs
are those that get lucky, while highlighting likely components such as talent. Illustrative case
studies are embedded in the larger household panel.
1
Introduction
We use detailed income and balance sheet statements constructed for households in a long monthly
panel in an emerging market economy to document and understand better the factors underlying
success in achieving high return on assets (ROA) and upward mobility in the distribution of net
worth. Many households work their way out of poverty over the 7 year period, especially if a
consumption measure is used. But there is great heterogeneity in returns and in the growth of net
worth. Some households experience only modest, if not negative, growth. Though the overall wealth
distribution is largely persistent, some of the more successful households experience large increases
in their relative position and others fall down. Though savings rate vary, and remmittances and
∗ The
support from Templeton and comments from Ben Olken are gratefully acknowledged.
1
gifts are a factor for some, growth of net worth is due largely to high return on assets. Covariates
positively correlated with high ROA include low initial net worth, high number of household members
, and a younger age of the head . We control for labor hours and, related, impute a wage cost to
self employment. In some specifications, high ROA is correlated with high debt/asset ratios, and it
appears the the financial system does manage to a degree to get some credit to the poor with high
returns. Indeed, using more structure for production functions, we find that the marginal product
of capital (MPK) is correlated with ROA and high MPK households are likely to borrow more
relative to their wealth, and relative to others. Though measures of education appear in bivariate
regressions, signs are statistically insignificant if not reversed from what one might anticipate from
human capital models.
By far the largest single factor in a decomposition of variance are household specific fixed effects.
Related, there is considerable persistence, especially for households in the wealthy Central Region.
Households successful over the first half of the sample are very likely succesful over the second,
indicating that luck per se is not a likely explanation for success. In the Northeast, however,
persistence is much lower and in one province there is strong evidence households are moving out of
lower ROA occupations and into higher ones, as the local economy presents opportunities. Variability
in household size undercuts persistence, suggesting indeed the role of successful individual rather
than households per se. Northeastern households also experience more volatility in membership.
Over time, high ROA households tend to be reducing their debts, reducing the amount of gift
that they get, and increasing their consumption and formal savings accounts. It also appears that
high ROA households are less likely to use capital assets to smooth consumption but instead use
consumption to finance investment deficits. High ROA households are more actively involved in
financial markets in the sense of using formal savings accounts and borrowing, and less informal,
i.e., less gifts. But high ROA households do use cash more than the low ROA households.
We stratify by principal occupation (crop farming, wage employment, fish/shrimp, business,
livestock). Business owners escape poverty the fastest by an income measure, and though they
are successful in this sense with a consumption measure as well, labor does best under the latter.
Both the histogram of the growth of net worth and the histograms of ROA are quite diffuse so,
again, there much heterogeneity in all occupations. That is, there are successful and less successful
households in each occupation, including occupations such as fish/shrimp that do not do well on
average. Illustrative case studies are embedded in the larger household panel.
2
2
Data and Facts
This paper uses data from Townsend’s Thai monthly survey, an ongoing panel of households since
1998. The survey is conducted in 4 provinces (or changwat in Thai), the semi-urban changwats of
Chachoengsao and Lopburi in the Central region and the more rurual Buriram and Sisaket in the
poorer Northeast region... This paper studies 531 households that we can interview almost every
month during Jan 1999-Dec 2005 where we use the accounting framework to construct detail balance
sheet, income and cash flow statements for each household.
We first provide the overall picture of some relevant variables from the survey. To make it
comparable across time, we make variables real in term of 2005. The following variables are averaged
by mean for each month.
i) Household size of all changwats have decreasing trend, it decreases most rapidly for Sisaket
and Chachoengsao, and more stable in Lopburi. In term of size, household size is largetst in Chachoengsao and then Sisaket. Lopburi has smallest size except at the second half where it is sometimes
dominated by Buriram. It exhibits more cycle in the Northeast especially Buriram.
ii) Both per capita net income and consumption flucuates for all changwats. In term of size, they
are typically greater in the Central than Northeast. Per capital net income has overall increasing
trend for Buriram and Lopburi, and also for Sisaket with lower slope. It is less clear for Chachoengsao. However per capita consumption of Chachoengsao has increasing trend since 2001, Lopburi
has overall increasing trend. Sisaket is pretty flat and Buriram has increasing trend at the end. Per
capita consumption seems to increase faster in Central than Northeast region and it is smoother
than per capita net income.
iii) Net worth is higher in the Central than the Northeast region. They can be ordered and it
is preserved every year, from highest to lowest one: Chachoengsao, Lopburi, Buriram and Sisaket
respectively. Net worth in Chachoengsao has increasing trend for the first 5 years, but decreasing
for the last 2 years. Net worth in Buriram is decreasing for the first 3 years, but has increasing trend
after that. Net worth in Lopburi and Sisaket have overall increasing trend.
iv) Debt has increasing trend for all 4 changwats, with clear jump for Chachoengsao in 2004,
Lopburi seems to have higher debt than others except in 2004 which is dominated by Chachoengsao
for some months. Debt seems to be higher in Central than Northeastern region.
v) Total assets have similar shape to net worth, indicating that total liabilities are relatively small
part of total assets. It is higher in the Central than the Northeast region. They can be ordered and
it is preserved every year, from highest to lowest one: Chachoengsao, Lopburi, Buriram and Sisaket
3
Mean of Household Size
Buriram
3.5
Sisaket
4
4.5
5
Lopburi
3.5
Household Size
4
4.5
5
Chachoengsao
0
12
24
36
48
60
72
84
Month
Graphs by changwat
Figure 1:
4
0
12
24
36
48
60
72
84
Per Capita Net Income
(per month)
Buriram
Sisaket
0
5
10
15
Lopburi
-5
*1,000
-5
0
5
10
15
Chachoengsao
0
12
24
36
48
60
72
84
Month
Graphs by changwat
Figure 2:
5
0
12
24
36
48
60
72
84
Per Capita Consumption
(per month)
Buriram
0
Sisaket
1400
2800
Lopburi
0
Per capita Consumption
1400
2800
Chachoengsao
0
12
24
36
48
60
72
84
Month
Graphs by changwat
Figure 3:
6
0
12
24
36
48
60
72
84
Net Worth
Buriram
Sisaket
20
30
40
50
Lopburi
10
*100,000
10
20
30
40
50
Chachoengsao
0
12
24
36
48
60
72
84
Month
Graphs by changwat
Figure 4:
7
0
12
24
36
48
60
72
84
Total Liabilities
Buriram
50000
Sisaket
100000
150000
Lopburi
50000
Total Liabilities
100000
150000
Chachoengsao
0
12
24
36
48
60
72
84
Month
Graphs by changwat
Figure 5:
8
0
12
24
36
48
60
72
84
Total Assets
Buriram
Sisaket
50
Lopburi
10
20
30
40
*100,000
10
20
30
40
50
Chachoengsao
0
12
24
36
48
60
72
84
0
12
24
36
48
60
72
84
Month
Graphs by changwat
Figure 6:
respectively. Total assets in Chachoengsao has increasing trend for the first 5 years, but decreasing
for the last 2 years. In Buriram it is decreasing for the first 3 years, but has increasing trend after
that. Total assets in Lopburi and Sisaket have overall increasing trend.
vi) Debt-asset ratio is increasing for all 4 changwats, but higher for both level and trend in the
Northeast. It jumps rapidly from 2000 to 2002 (around month 40) and turn out to be highest for
Sisaket. From highest to lowest: Sisaket, Buriram, Lopburi, Chachoengsao respectively. This order
is preserved every year but 1999-2000 where debt-asset ratio of Buriram is higher than Sisaket.
vii) Net income by source: frequency of net income from cultivation in Chachoengsao is higher
than other province. Except Chachoengsao, income from cultivation, once it happens, will dominate
other source. Income from labor is quite stable compare to cultivation, and thus could be a desirable occupation for risk averse. High yeild cultivation for Lopburi since income from cultivation is
especially high, even still with usual cycle. Choice of crop vary across location with more variety in
9
Debt-Asset ratio
Buriram
0
Sisaket
.05
.1
.15
Lopburi
0
Debt/Asset Ratio
.05
.1
.15
Chachoengsao
0
12
24
36
48
60
72
84
Month
Graphs by changwat
Figure 7:
10
0
12
24
36
48
60
72
84
the Central. In the Northeast, about 90% of crop grow in Buriram and Sisaket is rice, Sisaket also
grow tapioca (8.9%). In Chachoengsao, 50% of crop is rice, and there are some galangal (26.82%),
and lemongrass (8.24%). There are many crops grown in Lopburi: rice is only 0.78%, corn (18.58%),
chili (16.37%), hay (16.47%), fruit (11.47% ), bean, peanut, soy bean (7.8%), sunflower (7.29%),
sesame (7.14%), sorghum (6.33%). That might explain why income from cultivation in Lopburi
clearly dominate other sources including labor. [up to here]
[add net income by source, compare to annual and SES survey]
2.1
Poverty Reduction and Occupation
There are two kinds of poverty line that we use here. First, Thai official income number for each
changwat. Second, a standard benchmark consumption $2.16 a day at 1993 PPP. Consumption is
argulably measured with more accuracy than income. However if households choose not to consume
much, they could be counted as poor even they may not be. These two types of poverty line generate
some difference in poverty measure.
1. Although income (especially for farmers) is erratic, and thus monthly income generate the
fluctuations which we see. But we can still look at the trend, the overall picture especially whether
number of poor people tend is decreasing overtime by location and occupation. Using high frenquency monthly data, the headcount ratio flucuates with clear cycle in the Northeast, this corresponds to time when many households receive income from cultivation. By location, evidently there
are many poors in the Northeast, but even in the Central region, headcount ratio is flucuated around
0.5. However in term of change, the biggest gains are in Buriram. By occupation, the fraction of
poor households in business is declining substantially. There are gains for livestock, and modest
gains for labor.
2. Using consumption numbers, there are lower number of poor than using income measure,
especially in the Central. By location, the biggest gains are still in Buriram. By occupation, labor
and business escape poverty relatively fast.
2.2
Heterogeneity in Growth of Net Worth
We have seen that household net worth is growing on average, but not all households are experiencing
the same thing. The distribution of the average growth of net worth over 7 years reveals that there
is so much heterogeneity in the data: there is positive growth rate on average, but about 40% of
households in the survey have negative growth of net worth. This number varies by location with
11
Headcount Ratio
Thai official number
Buriram
0
Sisaket
.5
1
Lopburi
0
Headcount ratio
.5
1
Chachoengsao
0
12
24
36
48
60
72
84
month
Graphs by changwat
Figure 8:
12
0
12
24
36
48
60
72
84
Headcount Ratio
Thai official number
Livestock
Fish/Shrimp
0 12 24 36 48 60 72 84
Labor
.5
1
Business
0
Headcount ratio
0
.5
1
Cultivation
0 12 24 36 48 60 72 84
0 12 24 36 48 60 72 84
month
Graphs by occ
Figure 9:
13
Headcount Ratio
$2 a day
Buriram
0
Sisaket
.5
1
Lopburi
0
Headcount ratio
.5
1
Chachoengsao
0
12
24
36
48
60
72
84
0
month
Graphs by changwat
Figure 10:
14
12
24
36
48
60
72
84
Headcount Ratio
$2 a day
Livestock
Fish/Shrimp
0 12 24 36 48 60 72 84
Labor
.2
.4
.6
.8
Business
0
Headcount ratio
0
.2
.4
.6
.8
Cultivation
0 12 24 36 48 60 72 84
0 12 24 36 48 60 72 84
month
Graphs by occ
Figure 11:
15
smaller fraction of household in the Central experiencing negative growth, about 50% of household in
the Northeast have negative growth while 40% and 25% of household in Chachoengsao and Lopburi
have negative growth. However the spread of the distribution is much wider in the Northeast where
the highest and lowest growth rate in the Northeast are several times larger than one of the Central.
Distribution of Growth of Net Worth
min
max
mean
std. dev.
Q1
Q2
Q3
Chachoengsao
-1.808637
4.338355
.4734664
1.003681
-.1029835
.1297121
.808457
Lopburi
-1.230903
2.961142
.4793899
.7448994
-.0080084
.3055551
.8303846
Buriram
-26.90429
15.27902
.1334114
3.268537
-.3432701
-.0471851
.4991993
Sisaket
-25.59467
12.41502
.3389265
2.901194
-.1732817
.0514477
.400401
As there is wide distribution of growth of net worth with positive mean, so not every household is
experiencing positive growth. Taking the advantage of long monthly panel, we can track relative
position of net worth within changwat for each household. Here is example of some households who
experience large increase and decrease in their relative position.
2.3
Decomposition of change in net worth to savings and gift
By an accounting identity, change in net worth of each household must be equal to savings plus net
gifts received. Let ∆NWti be change in net worth at time t of household i, Sti and Git be savings
(save or dissave) and gifts (receive or give) at time t of household i, we thus have
∆N Wti = Sti + Git
This identity can be translated into a statistical relationship
cov ∆NW i , S i
cov ∆N W i , Gi
1=
+
for all i
var (∆NW i )
var (∆N W i )
that is, a variation in wealth change can be accounted by the comovement of wealth change and
savings, and the same with net gifts. By this measure, we find that the variation in wealth change
for most households is better explained by variation in savings rather than gifts, as distribution has
center around 100. This pattern holds for all changwats with the lowest peak in Buriram.
3
Growth of Net Worth, Productivity and Savings Rate
As a result, we pay more attention to savings rather than gifts as it can better explain wealth
accumulation for most households. Because savings is defined by net income minus consumption, so
16
Relative position of Net Worth within changwat
selected households
B1
B2
L1
L2
S1
S2
60
40
20
0
80
60
40
20
0
0
20
40
60
80
0
20
40
60
80
C2
80
C1
0 12 24 36 48 60 72 84
0 12 24 36 48 60 72 84
0 12 24 36 48 60 72 84
month
month
Graphs by caseid
Graphs by caseid
Figure 12:
17
0 12 24 36 48 60 72 84
Histogram of covariance of change in net worth
with savings
Chachoengsao
Buriram
.4
.2
0
0
.2
.4
.6
Buriram
.6
Chachoengsao
with gifts
Lopburi
Sisaket
.4
.2
0
0
.2
.4
.6
Sisaket
.6
Lopburi
-100
0
100
200
-100
0
100
200
-100
Graphs by changwat
0
100
Graphs by changwat
Figure 13:
18
200
-100
0
100
200
a household with high net income or low consumption will have higher savings. A related issues are
household’s ability to use resource to generate income, and household savings rate. Two households
with identical resource availability and consumption behavior but one has higher ability to generate
income will have higher savings. Likewise two households with identical income generating power,
but one tends to consume more than the other will have lower savings.
First we study whether variation in savings rate can help explaining growth in net worth. The
sample contains a nontrivial portion of negative savings (when consumption is higher than net
income) and to define savings rate by savings/net income, we will run into trouble when net income
is negative. We drop observations when net income is negative to get a more meaningful measure
of the savings rate. The following table is the correlation of the growth of net worth and savings
rate where we use household-month, average by mean over calendar time to get household-year, and
average by mean to get a single number for each household.
Correlation of growth of net worth and savings rate
HH-month
HH-year
HH
All
Chachoengsao
Buriram
Lopburi
Sisaket
0.0015
0.0203
0.0033
0.0039
0.0012
(0.7848)
(0.0508)
(0.8112)
(0.7042)
(0.9161)
-0.0148
0.1335
0.0470
0.0314
-0.0429
(0.3901)
(0.0001)
(0.2491)
(0.3278)
(0.1934)
0.1535
0.3646
0.1359
0.2733
0.1055
(0.0005)
(0.0000)
(0.1891)
(0.0009)
(0.2182)
where the number in parenthesis is significance level. There is significant and positive correlation
between growth of net worth and savings rate in Chachoengsao. It is not significant for other
provinces (except Lopburi and all observations when we aggregate over 7 years). Correlations tend
to be higher when we aggregate over calendar year, and over 7 years.
We measure productivity by rate of return on assets (ROA), a standard accounting concept for
19
financial analysts. The following is the correlation of the growth of net worth and ROA.
Correlation of growth of net worth and ROA
HH-month
HH-year
HH
All
Chachoengsao
Buriram
Lopburi
Sisaket
0.2633
0.5903
0.3995
0.6870
0.1340
(0.0000)
(0.0000)
(0.0000)
(0.0000)
(0.0000)
0.2409
0.5557
0.3219
0.6817
0.1369
(0.0000)
(0.0000)
(0.0000)
(0.0000)
(0.0000)
0.2754
0.7388
0.1572
0.7968
0.1556
(0.0000)
(0.0000)
(0.1111)
(0.0000)
(0.0674)
There is significant and positive correlation between growth of net worth and ROA at almost all
levels. Unlike savings rate, high ROA households are associated with high growth of net worth with
varying levels of correlation. The correlation numbers tend to be higher when we average overall
7 years, and correlation is higher for the Central relative to the Northeast region. Another way to
see this is to plot density of growth of net worth by ROA. The density shifts toward the right as
ROA is in higher group. Households with low ROA tend to have lower growth of net worth, but
there are few exception as we can see from long tail of density. Also higher ROA households tend
to have more dispersion in term of growth. These pattern are common across provinces even it is
more difficult to see from the picture of the Northeast.
In summary, although both savings rate and productivity are theoretically important in explaining growth of net worth, but what seems to drive growth of net worth in this household survey is
productivity. We next study what factors can explain whether household will be successful where
success here is measured by high ROA.
4
Factors Accounting for Success
We consider variables that are emphasized in the literatures as they are able to explain difference
in economic well-being: education, debt normalized by assets, financing behavior, initial wealth and
occupational change, network, and division of labor within the household. We also consider basic
demographic variables like household size and head age.
20
Density of Growth of Net Worth, by ROA
Excluding left tail outliers
Buriram
2
1.5
Chachoengsao
ROA<Q1
1
1.5
ROA in Q1-Q2
1
ROA in Q2-Q3
0
0
.5
.5
ROA>=Q3
-2
0
2
4
6
0
10
15
0
0
1
.5
2
3
1 1.5
4
Sisaket
2
Lopburi
5
-1
0
1
growth of net worth
2
3
-5
Figure 14:
21
0
5
growth of net worth
10
15
4.1
Education
The unit of the survey is household, so we want to have a measure of education for each household
but this depends on household composition which may change overtime. Some household member
may graduate or obtain higher education as time pass. Also those who have high education may
leave and this might affect things, including the productivity of the household. On the other hand,
one can think of a longer lasting impact, even after a member has gone. The following is a discussion
of measures for household education.
We can treat head as representative of the household, but this would be static number as most
heads have done with their school since the beginning of the survey. An alternative is to consider
each month, and pick the education of existing member who has highest education, or consider all
members in that month and use the mean or median as representative of that month. These measures
can vary over time depending on education attainment of members and household composition. We
denote these variables respectively by max_edu_t, mean_edu_t, median_edu_t. To come up with
one number for each household over timeframe, we can average by mean or median.
Education of each member is classified into 5 groups 1. no education 2. primary level 3. lower
secondary 4. upper secondary 5. tertiary (?) [Add distribution of education here] If we average variables by median and regress ROA on each measure of education. Using household-year observation,
we have
Regression coefficient of ROA on education
max_edu_t
mean_edu_t
median_edu_t
0.1319202
0.0656959
0.0532065
(0.000)
(0.001)
(0.007)
where number in parenthesis is the p-value. Thus high education is associated with high ROA
for all measures especially the maximum one.
We may stratify by occupation and see whether or how estimate of regression of ROA on education
22
change across occupation. Using household-year observation, we have
Regression coefficient of ROA on education, by occupation
max_edu_t
mean_edu_t
median_edu_t
Cultivation
Livestock
Fish/Shrimp
Business
Labor
.0238844
-.057065
.0569023
.1790287
.1357208
(0.137)
(0.094)
(0.036)
(0.001)
(0.000)
.0274413
-.0695604
.0535475
.1580172
.0460999
(0.103)
(0.043)
(0.110)
(0.005)
(0.177)
.0288757
-.0695996
.0493698
.1461732
.0288586
(0.086)
(0.043)
(0.127)
(0.010)
(0.393)
Only business households that have positive and significant regression coefficient for all measures.
Estimates are significant but negative for livestock households. Fish/Shrimp and labor households
only have positive and significant for maximum education variable, but not the other two. Indeed
maximum education appears to be the only education measure that can explain ROA for all occupations but cultivation. Cultivation is almost significant with size of the estimate is lower than one
of business.
4.2
Debt-Asset ratio
A household may be able to use debt to enhance its productivity. Debt-asset ratio in the survey,
even it is relatively small, has overall increasing trend. The following is correlation of ROA and
23
debt-asset ratio
Correlation of ROA and debt-asset ratio
HH-month
HH-year
HH
HH-month
HH-year
HH
All
Chachoengsao
Buriram
Lopburi
Sisaket
0.0439
-0.0164
0.0723
0.0328
0.1041
(0.000)
(0.0764)
(0.000)
(0.0003)
(0.000)
0.1366
-0.0075
0.2936
0.0907
0.3277
(0.000)
(0.8129)
(0.000)
(0.0036)
(0.000)
0.1895
0.0122
0.4596
0.1648
0.4530
(0.000)
(0.8855)
(0.000)
(0.0461)
(0.000)
Cultivation
Livestock
Fish/Shrimp
Business
Labor
0.1044
0.1265
0.0805
0.0500
0.0016
(0.000)
(0.000)
(0.0001)
(0.0009)
(0.8224)
0.3162
0.2311
0.3525
0.1072
0.0468
(0.000)
(0.0003)
(0.0000)
(0.0389)
(0.0581)
0.4262
0.4385
0.5935
0.1874
0.0524
(0.000)
(0.0095)
(0.0005)
(0.1791)
(0.4249)
where number in parenthesis is the significant level. The correlations are higher when we aggregate
over calendar year or over 7 years. They are significant and higher in the Northeast, high ROA are
associated with high debt-asset ratio. The same result for Lopburi but with smaller correlation. In
contrast, correlations are negative and not significant for Chachoengsao, indicating high ROA households there are not associated with having high debt-asset ratio, even the opposite. By occupation,
they are all positive and almost all significant, but the correlation is weakest for labor.
4.3
Financing cash flow deficit
From household budget constraint
Ct + It = Yt + Ft1 + ... + Ftn
where Ct , It , Yt are consumption, investment and net income at time t, and Fti is financing device i
at time t. Let Dt = Ct + It − Yt be overall deficit, it must be financed by devices Ft1 , ..., Ftn .
Dt = Ft1 + ... + Ftn
24
one can derive from this equation a statistical relationship
cov D, F 1
cov (D, F n )
1=
+ ... +
var (D)
var (D)
and this must be true for any households. We can then see which device each household uses to
finance its deficit using this variance decomposition. In fact, two more types of deficit can be defined,
a consumption deficit (C − Y ) and an investment deficit (I − Y ) can be defined and their variation
can be decomposed in similar ways. What we are interested here is not financing device by itself,
but whether financing strategies are related with ROA and growth of wealth, we find that
High ROA and high growth of net worth households rely more on cash to finance consumption
deficit, more on borrowing to finance overall deficit, more on deposit to finance all kinds of deficit,
more on consumption to finance investment deficit.
High ROA and high growth of net worth households rely less on gift to finance overall deficit,
less on investment to finance consumption deficit.
High growth of net worth households rely more on borrowing to finance overall and investment
deficits, but rely less on borrowing to finance consumption deficit.
To summarize, it appears high ROA households are not using capital assets to smooth consumption and conversely use consumption to finance investment deficits. High ROA households are more
actively involved in financial markets in the sense of using formal savings accounts and borrowing,
and less informal, i.e., less gifts. But high ROA households do use cash more than the low ROA
households.
Sort by location, credit markets (borrowing and lending) and gift show up in the Northeast but
not the central region. Look at changwat level, borrowing and lending show up in Sisaket, but not
Buriram. These regional patterns are quite mixed:
High ROA households in the Northeast rely less on gift to finance deficits.
High ROA households in Sisaket rely more on lending to finance consumption and investment
deficits.
High ROA and high growth of net worth households in Chachoengsao rely more on consumption
to finance investment deficit, high growth of net worth households rely less on cash to finance
investment deficit.
High ROA households in Lopburi rely more on gift to finance consumption deficit.
25
4.4
Initial Net Worth
Household’s net worth determines how well off a household is. A household with high net worth has
more internal fund to use, in addition, net worth can also determine whether a household can obtain
external source of fund. We study whether initial net worth is associated with ROA. The following
is correlation of mean of ROA over 7 years and initial net worth
Correlation of ROA and initial net worth
All
Chachoengsao
Buriram
Lopburi
Sisaket
-0.1744
-0.2201
-0.3237
-0.4612
-0.4199
(0.000)
(0.0087)
(0.0008)
(0.0000)
(0.000)
Cultivation
Livestock
Fish/Shrimp
Business
Labor
-0.2624
-0.3937
-0.2772
-0.2310
-0.3497
(0.0004)
(0.0212)
(0.1381)
(0.0961)
(0.0000)
High initial net worth households are associated with lower ROA, this is true for all provinces
and occupations. [Add literature about firm] One possibility is that even net worth is moving
overtime, but for many households it does not move quickly, so initial net worth is correlated with
its subsequent value. And net worth is highly correlated with total assets, as debt is relatively small
part of total assets. Thus it could appear that higher initial net worth is associated with lower ROA.
4.5
Network
The survey asked at the beginning about relatives of head and spouse, so we can use this information
to find out who are related to a household (by blood). Other forms of network (through trade,
for example) are not considered here. It is possible that having a network may help or hurt a
household. We classify whether household has network (value = 1 ) or not (value =0). The following
26
is correlation of mean of ROA over 7 years and network variable
Correlation of ROA and network
All
Chachoengsao
Buriram
Lopburi
Sisaket
0.0134
0.1423
-0.1277
0.0824
-0.0438
(0.7582)
(0.0922)
(0.1965)
(0.3210)
(0.6088)
Cultivation
Livestock
Fish/Shrimp
Business
Labor
-0.0409
0.2158
-0.1825
0.0548
0.0328
(0.5853)
(0.2204)
(0.3345)
(0.6965)
(0.6175)
There is positive and significant correlation only at Chachoengsao, all others have either negative
or not significant, or both. The evidences here are not strong enough to conclude that having a
network help or hurt a household in term of ROA.
4.6
Division of labor within the household
A better division of labor can lead to higher productivity. The survey asks for time allocation of
each member for each task (how many day, not hour), so we know roughly how many day each
member spend on each task. The generalized index of dissimilarity can be used to measure the
extent of division of labor, this index lies in [0, 1] where 1 indicates complete specialization and 0 is
the opposite. [Add distribution of this index] The following is correlation of ROA and generalized
27
index of dissimilarity.
Correlation of ROA and generalized index of dissimilarity
HH-month
HH-year
HH
HH-month
HH-year
HH
All
Chachoengsao
Buriram
Lopburi
Sisaket
0.0615
0.0174
0.1066
0.0272
0.0624
(0.0000)
(0.0636)
(0.0000)
(0.0029)
(0.0000)
0.0870
0.0499
0.1417
0.0454
0.0500
(0.0000)
(0.1182)
(0.0001)
(0.1455)
(0.1189)
0.0947
0.0347
0.2310
0.0288
0.0492
(0.0291)
(0.6832)
(0.0183)
(0.7295)
(0.5654)
Cultivation
Livestock
Fish/Shrimp
Business
Labor
0.0070
0.0447
-0.0199
0.0241
0.0895
(0.3944)
(0.0179)
(0.3312)
(0.1105)
(0.0000)
0.0432
0.0354
0.0204
-0.0004
0.0972
(0.1255)
(0.5866)
(0.7709)
(0.9931)
(0.0001)
0.0301
-0.0779
-0.0047
0.0299
0.0951
(0.6886)
(0.6614)
(0.9801)
(0.8319)
(0.1468)
Overall it appears that high ROA households are those with better division of labor. By location,
however, only Buriram that has significant correlation at all levels, other provinces are not robust
compare to Buriram. So the overall result is driven mainly by Buriram. It is less clear for occupation since most of them are not significant, only labor households that seem to have positive and
significant result when observations are household-month and household-year.
4.7
Multivariate regression
Whether ROA is OLS related to variables of interest when we include a full set: education, debtasset ratio, initial net worth, network, division of labor within the household. We control for some
demographic variables like household size, head age, and labor supply. Also we control for heterogeneity of households by putting a dummy variable for each household as control variable, where we
want to see whether the results will be robust. We average by mean over months to get householdyear observation. There are two specifications below: household-year without household fixed effect
(HHFE), and household-year with HHFE. Without fixed effect, we also control for occupation by
28
putting a dummy for the primary occupation as regressors.
Dependent variable : ROA
Debt-Asset Ratio
Education
Household Size
Head Age
Initial Wealth
Network
Division of labor
.7562137
.7998203
.9211914
.7814162
(0.000)
(0.007)
(0.000)
(0.007)
-.0837632
-.2966699
-.1079056
-.0216758
(0.002)
(0.081)
(0.000)
(0.889)
.0787656
.1676854
.0736997
.151535
(0.000)
(0.000)
(0.000)
(0.000)
-.0217222
-.0185522
(0.000)
(0.000)
-1.87e-08
-1.81e-08
(0.000)
(0.000)
-.1285636
-.0837738
(0.027)
(0.139)
.084623
.3860475
.03537
.4238044
(0.572)
(0.036)
(0.809)
(0.020)
-.0002569
.0007087
(0.721)
(0.458)
.0069738
.0046024
(0.000)
(0.000)
Own Hour Work
Paid Hour Work
Household Fixed Effect
No
Yes
No
Yes
Number Obs.
3,455
3,469
3,455
3,469
0.1083
0.477
0.1553
0.4937
2
adj-R
where number in parenthesis is the p-value. The debt/asset ratio is still positive and significant as
is household size, while initial wealth and head age are negative and significant without household
fixed effects. Therefore high ROA is associated with high debt/asset ratio and household size.
Other factors are not robust in all specifications. Though measures of education appear positively
in bivariate regressions, signs in multivariate regression are statistically insignificant and reversed
from what one might anticipate from human capital models (negative and significant in 3 out of
4 specifications), a troublesome result. Network is negative, and significant without labor supply.
Division of labor is positive for all specifications but statistically significant only when we have
household fixed effect. Own work is not significant while paid work is positive and significant. The
29
explainatory power increases several times when we control for household fixed effect, indicating
that factors accounting for success is also specific to each household.
4.8
1
Persistent of ROA overtime
As ROA regression indicates that a factor specific to household can considerably account for variation
in success. Since this factor may not change over time or change slowly, a related question is whether
a high ROA household today is more or less likely to have high ROA again in the future. We compute
average ROA for the first half and second half of the overall sample frame (3.5 years each) and rank
them for each time period. The following pictures are scatter plot of rank of ROA and its fitted
linear value, overall and by changwat.
A household with a high rank of ROA in the first half is likely to have high rank in the second
half, that is, there is considerable persistence, especially for households in the three provinces except
Buriram, although we also see that many households deviate from this pattern, as there are many
dots that are quite far from their initial position. A linear fitted line is not a 45 degree line but has
slope less than one, a household with low rank in the first period is likely to have higher rank in
the second priod and vice versa. But overall, households successful over the first half of the sample
are likely succesful over the second, indicating that luck per se is not an explanation for sucess. In
Buriram, however, persistence is much lower. There is the evidence that households are moving
out of lower ROA occupations and into higher ones as the local economy presents opportunities.
One reason that may explain less persistence in Buriram is that household composition is less stable
there, as we have seen that household size in Buriram exhibits more cycle than any other province.
A household in which most of the individuals change should not have persistent ROA over the two
subperiods. We run the following regression
ROA2,i = b0 + b1 ROA1,i + b2 (ROA1,i ∗ sd(hhsizei )) + b3 sd(hhsizei ) + ui
where ROA2,i and ROA1,i are the average ROA over the second half and first half of household i,
and sd(hhsizei ) is standard deviation of household size. If estimate of b2 is negative, this would
lower b1 + b2 and thus household with higher variation in household size will have less persistent.
1 We
have done robustness check where the dependent variable is the return after we subtract the estimated
opportunity cost. The results are quite similar when there is no household fixed effect. But when we include household
fixed effect, the results we have with ROA regression so far are no longer there, as none of them are significant, even
the signs are still pretty much the same.
30
Persistent of ROA
Rank of mean(ROA) second half
0
100
200
300
400
500
All observations
0
100
200
300
Rank of mean(ROA) first half
Figure 15:
31
400
500
Persistent of ROA
by changwat
Buriram
50
0
Sisaket
50
100
150
Lopburi
0
Rank of mean(ROA) second half
100
150
Chachoengsao
0
50
100
150
0
Rank of mean(ROA) first half
Graphs by changwat
Figure 16:
32
50
100
150
The regression result is as follows
ROA1
ROA1 ∗ sd(hhsize)
sd(hhsize)
adj-R2
Number Obs.
.5332074
-.0553887
-.0479666
0.4279
531
(0.000 )
(0.117)
(0.542 )
where the number in parenthesis is p-value. The estimate of interaction term is negative but
marginally significant (at a 12% confidence level), while the estimate of ROA1 is positive and significant. So the marginal effect of ROA1 on ROA2 would be closer to zero or variation in household
size can undercut persistent of ROA.
5
Estimating marginal product of capital through production function
Imposing a structural form allows us to estimate parameter of interest like marginal product of
capital. Suppose we impose the Cobb-Douglas functional form
β
β
yjt = Ajt KjtK LjtL
where yjt , Kjt , Ljt are value added from production, level of assets, labor supply of household j at
time t, and Ajt = exp {A0 + αj + ujt } where A0 is common productivity or mean efficiency across
households, αj is time-invariant household specific fixed effect for household j, and ujt is error term
for household j at time t.
Because αj is assumed to be time-invariant, we can use first difference to get rid of it and get a
consistent estimate of (β K , β L ). An alternative preferred estimation is to put dummy variable for
each household to control for αj and get a consistent estimate of (β K , β L ), in addition, we have the
estimate of mean efficiency. Estimating OLS of the log-form of above equation using household-year
(average by mean), we have the following result
βK
βL
adj-R2
Number Obs.
0.623545
0.0561958
0.6597
2,736
(0.000)
(0.000)
The estimate of both capital and labor are positive and signifcant, and the adjusted R2 is quite
high. The estimates of β K is about 0.62, we can then convert this to marginal product of capital
for each household and compare to the interest rate. The median of monthly interest rate 0.75% (or
33
8
Marginal product of capital versus interest rate
0
2
4
6
MPK
Interest rate
10
12
14
log(K)
16
18
Figure 17:
9% per year). The following picture plots time average MPK of each household against (median)
interest rate. High capital households are associated with low MPK. Intuitively those households
with MPK higher than the interest rate should borrow more than the other group. So we examine
whether these relatively more productive households (MPK higher than interest rate) have a higher
debt-asset ratio than the other group (MPK less than interest rate). The following two pictures
are histogram and density of debt-asset ratio of these two groups. On average households who are
productive have higher debt-asset ratio than the other group. But there are some outliers. Overall
it seems that the financial system is working to some extent, in that people with MPK higher than
the average interest rate tend to borrow more than the other group, as they should . But the system
is not working perfectly either. In principle, with complete markets, households should be able to
acquire enough funds to drive their MPK down to the interest rate. They should operate at a larger
scale financed by borrowing. The following is scatter plot of MPK and ROA and its correlation
34
Histogram of Debt/Asset Ratio
.6
.4
.2
0
0
.2
.4
.6
.8
MPK above interest rate
.8
MPK below interest rate
0
.5
Debt/Asset Ratio
1
1.5
0
Figure 18:
35
.5
Debt/Asset Ratio
1
1.5
Densities of Debt-Asset Ratio
0
2
4
6
8
MPK below interest rate
MPK above interest rate
0
.5
Debt/Asset Ratio
Figure 19:
36
1
1.5
0
2
ROA
4
6
8
ROA and MPK
0
2
4
MPK
Figure 20:
37
6
8
0
2
ROA
4
6
8
ROA and TFP
-6
-4
-2
TFP
0
2
Figure 21:
Correlation of ROA and MPK :
0.9082
(0.0000)
where the number in parenthesis is the significance level. MPK and ROA are positively correlated,
as they should be.
We can use our estimate of Aj as TFP for household j and see how that is related to ROA, as a
robustness check. The following is scatter plot of TFP and ROA and its correlation.
Correlation of ROA and TFP :
0.6632
(0.0000)
so household specific contribution is related to ROA, even correlation is lower than the one with
MPK. Note that estimate of TFP is negative for many households.
Part of ROA is due to the difference in capital-output ratio, not only household specific productivity. In other words, if the financial market is perfect, then MPK should be equated for all
38
households and that should be equal to the interest rate, with different scales of K, depending on
productivity. Some households do seem to have higher TFP than other people, but this explain
ROA less, according to above correlation. Still the TFP numbers may not have the interpretation
we want. In a regression onto initial net worth and labor we get significant coefficients, though those
factors should not show up in the residual if we had the correct specification.
6
Story from selected case studies
[add]
7
Conclusion
Many households in the survey accumulate their net worth over 7 years. Although both savings rate
and productivity are theoretically important in explaining wealth accumulation, but what seems to
drive growth of net worth in this survey is productivity. Productivity is measured here by return on
assets (ROA). We use detailed balance sheet and income statements constructed for households in a
long monthly panel to document and understand better the factors underlying success in achieving
high return on assets (ROA) and thus upward mobility in the distribution of net worth. Factors that
appear to explain high ROA are financial variable like debt/asset ratio, demographic variable like
household size where both factors contribute positively to high ROA. Initial value of net worth and
head age also can explain ROA: a younger head and lower initial wealth households are associated
with high ROA. It is less clear whether network and division of labor, even we only have rough
measure here, are able to explain ROA, as they appears to be significant only in some specifications.
Education, even it shows up positively in bivariate regression, but it is no logner hold when we
control for other factors. We also find that household specific fixed effect plays an important role in
explaning ROA. Imposing a structure on production function allows us to estimate MPK and TFP.
It appears that TFP and MPK are correlated with ROA, a result that is consistent with multivariate
regression. Households with smaller size of capital tend to have higher MPK, they tends to borrow
relative to their assets more than the other group. Thus it appears that the financial system is
working to bring credit to the poors who have higher productivity, even the system does not work
perfectly. A story of some succesful households are embedded at the end to complement with the
overall statistical analysis
39