Environmental Scarcity, Resource Collection,

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Environmental Scarcity, Resource Collection,
and the Demand for Children in Nepal
David Loughran
Lant Pritchett
World Bank
July 30, 1997
Abstract
The interaction of population and environmental quality is of great interest to policy makers
worldwide. While many studies emphasize the effect of population growth on environmental
quality, few examine how environmental quality affects population growth. This paper uses
recently collected household data from Nepal to test whether variation in firewood and water
scarcity affects the demand for children by altering the relative value of children in resource
collection activities. Our results indicate increasing environmental scarcity lowers the demand
for children, implying Nepalese households perceive resource scarcity as increasing the net cost
of children. Apparently the effect of increasing firewood and water scarcity on the relative
productivity of child labor is not sufficient to induce higher demand for children, given the
effects that work in the opposite direction. At least for these resource collection activities it
appears environmental scarcity acts as a check on population growth.
The findings, interpretations, and conclusions expressed in this paper are entirely those of the Authors. They do not
necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. The
paper should not be cited without the permission of the authors.
Economists, biologists, and other social and physical scientists have long debated the
nature of the relationship between population growth and environmental quality. The debate,
however, has focused almost exclusively on whether increasing population leads to
environmental degradation and largely ignored the question of whether environmental
degradation might in turn lead to increasing population (exceptions include Nerlove 1991,
Cleaver and Schreiber 1994, Dasgupta 1995, Filmer and Pritchett 1996). Do individuals alter
their desired fertility in response to environmental degradation? If so, in what direction, is
desired fertility higher or lower? Societies in which individuals respond to worsening
environmental conditions by increasing their fertility would find themselves in a downward spiral
of increasing population, plummeting environmental quality, and, presumably, deteriorating
living conditions. As Dasgupta (1995) points out, while we might expect countervailing forces
to disrupt such Malthusian dynamics eventually, many could suffer the consequences of this
“vicious circle” in the short run.
Especially among subsistence farmers children often have an important role in
performing household tasks, such as collecting firewood, water, and fodder. This child labor in
collection activities provides a conduit through which environmental degradation could affect
fertility decisions. Households may increase their demand for children if the relative labor
productivity of children rises as environmental conditions deteriorate. The empirical question is
whether environmental degradation somehow enhances the productivity of children vis-a-vis
adults, and whether this force is strong enough to bring about an increase in fertility given
resource scarcity’s generally negative effect on household income.
Environmental degradation, of course, can take any number of forms: deforestation,
drought, soil erosion, diminishing biodiversity, air pollution, etc. Because these varying forms
of degradation could impact the household’s fertility and labor allocation decisions in very
different ways, it is of great importance what type of degradation we examine and in what
context.
Here the context is firewood collection and deforestation in Nepal. According to the
World Bank, Nepal experienced an average annual rate of deforestation of one percent per
annum between 1981 and 1990 (World Bank 1995). Over half of the rural wards surveyed by the
2
1996 Nepal Living Standards Survey (NLSS) reported that forest cover had diminished
significantly over the past five years. This rate of deforestation is of serious concern in an
already resource scarce country whose inhabitants rely heavily on forest resources for fuel and
livestock feed. The link between firewood collection and deforestation in Nepal has been well
established. By some estimates, 90 percent of all wood consumed in Nepal is for fuelwood
(Bluffstone 1995). As discussed in greater detail below, 63 percent of all households and 73
percent of rural households use firewood as their primary cooking fuel in Nepal and of those
households that use firewood, 82 percent collect it themselves (NLSS 1996).
In the remainder of the paper, we model household fertility and labor allocation in the
presence of firewood scarcity (Section 1), present evidence on fertility and firewood collection in
Nepal from the NLSS (Section 2), develop and implement an empirical approach to testing
whether household fertility increases or decreases in response to increasing scarcity (Section 3),
discuss the empirical results (Section 4), and, finally, offer some concluding remarks (Section 5).
1.
A Model of Fertility and Firewood Collection in the Presence of Scarcity
The model developed below is a more explicit version of Nerlove’s (1991) “Parable of
Firewood” in which he imagines a mother maximizing per capita firewood collection by
max W (n, S)
n
n+1
choosing the number of children, n:
where W(.) represents firewood collection function and S firewood scarcity. Firewood collection
is just an example of a household task in which children might play an important role. W(.)
could just as well represent water and fodder collection, fishing, livestock tending, and other
household tasks affected by environmental scarcity.
Maximization of (1) with respect to n and the subsequent comparative statics tells us we
can be assured increasing scarcity leads to higher demand for children so long as increasing
scarcity increases the marginal productivity of child labor (i.e., WnS>0). It is hard to imagine in
the case of firewood collection, however, how increasing scarcity could increase the absolute
3
marginal productivity of child labor. It seems much more natural to assume WnS<0, which in
Nerlove’s model virtually assures us increasing scarcity will decrease the demand for children.
Nerlove himself suggests the model may be more appropriate in the context of livestock
production where Wns>0 seems more plausible, a point to which we return in Section 5.
Extending Nerlove’s model to encompass a wider range of input and output decisions
faced by the household complicates the comparative statics, but in so doing allows for a wider
range of conditions under which the demand for children may increase as firewood scarcity
U (n, C/(n + 1))
increases. Imagine a single parent who seeks to maximize the following utility function:
where n is the number of his or her children and C represents aggregate household consumption.
Thus, the utility function here assumes a tradeoff between household size and per capita welfare
of its members. More specifically, think of C as cooked food, arguably one of the most
significant components of household consumption in Nepal. Assume further C is produced using
C = C (W, Q)
a combination of collected firewood, W, and the household’s agricultural production, Q:
It is immaterial whether the household uses its agricultural production directly in the production
of C or sells it to purchase other inputs for use in producing C, including alternative cooking
fuels such as electricity or kerosene. As will be seen below, however, the ability of households
to trade agricultural production for firewood substitutes is of some importance in determining the
effect of firewood scarcity on fertility.
To keep the model as simple as possible, we ignore capital and outside labor assuming
firewood collection and agricultural production are produced using the household’s adult and
W = W ( a , c ; S)
Q = Q( La , Lc )
child labor only:
4
where la and lc are adult and child labor devoted to collection activities and La and Lc are adult
and child labor devoted to agricultural production. The parameter S measures environmental
scarcity which reduces the availability of firewood and diminishes the marginal productivity of
labor devoted to collection (i.e., WS<0, W1S<0, and W2S<0). For example, W might take the
W=
1
(   a +  c )
S
following form:
where α and β measure the relative productivity of adult and child labor in collecting firewood.1
The household head maximizes (2) by choosing the number of children in the household
and the division of labor between firewood collection and agricultural production. The relevant
nT = c + Lc
T = a + La + n n
time constraints are as follows:
We assume the time cost of raising children, ln, is exogenous in order to keep the focus of the
model on the allocation of child and adult labor between firewood collection and agricultural
production. Note also we abstract from the labor-leisure tradeoff and assume child rearing is an
exclusively adult task.
Maximizing (2) subject to (6) yields the following first order conditions which describe
the household’s optimal choice of n, la, lc, La, and Lc:2
1
It should be noted deforestation can lead to soil erosion which can adversely impact
agricultural production, in which case Q(.) should include S as well. Again, for the sake of
simplicity, we have assumed this is not the case and that the effect of firewood scarcity on
fertility occurs exclusively through firewood collection and not through agricultural production.
2
Assume U(.), C(.), W(.), and Q(.) increase at a decreasing rate in all arguments except S.
5
i) U 1 +
U2 
(n + 1) C 2 ( TQ 2 - n Q1 ) - C = 0
(n + 1 )2
U2 
C 1W 1 - C 2 Q1 = 0
n+1
U2 
iii)
C 1W 2 - C 2 Q2 = 0
n+1
ii)
Equations (ii) and (iii) yield the familiar intuition that the household should equate the
marginal products of adult and child labor within and across labor activities. Rewriting (i) using
U 1 +U 2
C
TC 1 W 2
 C W
= U 2 n 1 1 +U 2
(n + 1)
(n + 1)
(n + 1 )2
(ii) and (iii):
states that the marginal benefits of an additional child must equal its marginal cost. The first
term on the left of (8) is simply the direct marginal utility of an additional child and the second
term the marginal utility of the marginal contribution of an additional child to per capita
consumption. On the right hand side, the first term represents the opportunity cost of the adult’s
time spent rearing an additional child in terms of lost production and the second term the direct
consumption cost of an additional child.
We may now ask how does environmental scarcity affect the household’s demand for
children, or, how does a change in firewood scarcity disturb the equality in (8)? If scarcity
increases the marginal benefits of an additional child more than the marginal costs, then scarcity
will increase the demand for children. Assuming equation (8) can be expressed as an implicit
function relating the demand for n, la, lc, La, and Lc to the parameters of the model and an interior
 n*
= - FS
S
Fn
solution exists, we can express the partial effect of scarcity on the demand for children as:
6
where F(.) represents the implicit function given by (8).3 By the second order condition for a

C1 W S 
U 22 
(n + 1) C 2 ( TQ 2 - n Q1 ) - C   +
 U 12 +
2
n+1 
(n + 1 )

U2
 C 11 W S ( TW 2 - n W 1 ) + U 2  C 1 (T W 2S - n W 1S )  n+1
n+1
U2
C1 W S
(n + 1 )2
FS =
maximum, Fn must be negative and so the sign of n*/S will have the same sign as FS:
The sign of Fs, and hence the sign of n*/S, is ambiguous in general. The result in (10),
nonetheless, yields insight into how scarcity affects the demand for children. To this end, we
have divided (10) into four terms: a price and income effect, an absolute productivity effect, a
relative productivity effect, and a substitution possibilities effect, of which only the last can be
signed unambiguously.
The Price and Income Effect
The first term in (10) tells us how scarcity affects fertility by raising the cost of
consumption. Scarcity effectively increases the price of firewood by diminishing its availability.
C W
W S
This in turn raises the price of consumption and decreases real incomes. The term
represents scarcity’s unambiguously negative effect on the household’s production possibilities.
Scarcity shifts the household’s production possibilities frontier inward reducing the maximum
amount of wood that can be collected given available inputs (see Figure 1). The total effect of
this inward shift in the production possibilities frontier is not known. For a given number of
children, an increase in the price of aggregate consumption makes children relatively less
3
The asterisk (*) emphasizes that we are interested in how the optimal quantity of
children changes as environmental scarcity changes.
7
expensive and so relatively more desirable. But the household cares about per capita
consumption in this model and so it can respond to an increase in the price of aggregate
consumption by either reducing aggregate consumption or reducing the number of children it
expects to have. The net effect of an increase in the price of consumption on the demand for
children, then, depends on how children and per capita consumption interact in the utility
function. Having said this, however, we would conjecture that a shift in the production
possibilities frontier inward reduces real incomes and so should generally cause households to
demand less of both children and per capita consumption (assuming both are normal goods)
despite the relative price shift. Given this conjecture, we would expect the first term in (10) to be
negative, although theoretically we cannot rule out a positive sign.
The Absolute Productivity Effect
Given the potentially negative sign of the first term in (10), the likelihood of a positive
scarcity-fertility relationship is enhanced if one, or both, of the second and third terms in (10) is
TW 2 - n W 1 > 0
positive. The second term is positive so long as
We can interpret this condition as follows: an increase in firewood collection due to an additional
child devoting his/her entire time endowment to collection must outweigh the reduction in adult
firewood collection due to the increase in time spent caring for that child. In other words,
children must be net producers of firewood if they allocate all their time to that activity. If
children do not contribute greatly to the production of firewood because their marginal
productivity is low (i.e., W2  0) then it is unlikely (but not impossible) that increasing scarcity
will cause households to demand more children.
The Relative Productivity Effect
The third term in (10) represents the effect of scarcity on the relative productivity of
adults and children in firewood collection. It is positive so long as
8
T W 2S - n W 1S > 0
This will hold if the negative effect of increasing scarcity on adult marginal productivity in
collection outweighs the negative effect of scarcity on the marginal productivity of child labor in
collection (note: T > ln). Here, scarcity has the effect of shifting the slope of the isoquant (see
Figure 2), making children relatively more productive in firewood collection than they were
before. This relative productivity effect is what Nerlove, Dasgupta, and others have focused on
as the mechanism through which increased resource scarcity might increase the demand for
children.
The Substitution Possibilities Effect
The fourth and final term of (10) works in the direction of increasing fertility. The
greater the productivity of firewood collection (C1 >> 0) and the more negative the impact of
scarcity on firewood availability (Ws << 0), the greater the likelihood that increasing scarcity will
lead to higher demand for children. The importance of firewood collection to the production of
household consumption depends in large part on the availability of substitutes. If households can
easily trade agricultural output for alternative fuels then they are likely to respond to increasing
scarcity by shifting labor from collection to agricultural production, which may not favor
children. If, on the other hand, substitution possibilities are limited, households might respond to
increasing scarcity by shifting existing agricultural labor toward firewood collection, or, in
accordance with the vicious circle hypothesis, by increasing family size and the supply of child
labor. This is not out of the question in Nepal where topography, limited infrastructure, and thin
labor and output markets make substitution less feasible.
Summary of the four effects
The sign of  n* /  S as embodied in (9) is ambiguous because scarcity affects both the
expenditure and production side of the farming household in our model. Scarcity at once raises
the price of consumption and alters the production possibilities of the household. The sign and
magnitude of these price, productivity, and substitution effects determine whether scarcity
9
increases or decreases the demand for children. The model tells us we are more likely to observe
households demanding more children as resource scarcity increases if:

Firewood is an important input to household consumption from which the
household cannot easily substitute

Children are net producers of firewood if they allocate all their time to that
activity (i.e., they contribute more to firewood collection than they take away in
terms of forgone adult production)

Scarcity reduces the marginal productivity of children in collection much less than
the marginal productivity of adults
Even then, these effects alone or together must be strong enough to overcome what is most likely
a negative price and income effect. We would be surprised if resource scarcity’s impact on the
cost of consumption alone could somehow induce a positive change in fertility. This will remain
conjecture, however, since, in the sections below, we estimate what amounts to a reduced form of
(9). The NLSS data allow us to estimate the overall sign of n*/S, but not its individual
components. With these data, we cannot estimate the underlying production functions which
would allow us to sort out what combination of price, productivity, and substitution effects
account for the observed sign of n*/S.
2.
Fertility, Firewood and Water Collection in Nepal
We use data from the NLSS to test for links between fertility and environmental scarcity
in Nepal. The NLSS, similar in nature to the World Bank’s Living Standards Measurement
Surveys (LSMS), collected household and community level data covering consumption, income,
assets, housing, agricultural production, education, health, fertility, migration, and community
services and facilities. The following subsections present descriptive statistics on firewood
collection, water collection, and fertility from the NLSS.
The NLSS interviewed a stratified sample of 12 and 16 households per ward in 274 wards
(3373 households) between June 1995 and May 1996.4 The sample covers the geographic and
4
See CBS (1996) for a more detailed explanation of the NLSS survey design.
10
ecological strata of Nepal: Mountains, Urban Hills, Rural Hills, and Tarai (see Figure 3).
Environmental conditions vary dramatically across these regions. Elevation ascends from near
sea level in the southern plains of the Tarai to well over 20,000 feet along the Himalaya in the
Mountain region. Arable land becomes increasingly scarce and villages increasingly isolated as
we move from the Tarai to the Mountain region. According to the NLSS, only 23 percent of
Mountain villages and 50 percent of rural Hill villages have access to roads suitable for motor
vehicles. In the Tarai, 92 percent of rural villages have access to roads.
Firewood Use and Collection
Firewood is the predominant source of cooking and heating fuel in rural Nepal: 82
percent of all rural households and 100 percent of Mountain households reported using firewood
in the last 12 months. Despite better access to alternative fuel sources, 32 percent of urban
households still use firewood for cooking and heat. As seen in Table 1, 73 percent of rural
households reported using firewood as their primary cooking fuel as opposed to 24 percent of
urban households. In the Tarai, where distances to forest are greatest, 53 percent of all
households rely on dung and other biomass (leaves and other forest detritus) as their primary
cooking fuel. A high percentage of Mountain (92 percent) and Hill (61 percent) claim dung and
other biomass as secondary cooking fuels. Kerosene is the most commonly employed alternative
to firewood, dung, and other biomass, especially in urban areas where 50 percent of all
households use it as their primary cooking fuel. In rural areas, kerosene’s use as a primary
cooking fuel is limited to a mere 2 percent of all households.
11
Table 1: Primary and Secondary Cooking Fuels by Region (percentages)
Urban/Rural
Fuel Type:
All
Prim.
Urban
Sec.
Prim.
Region:
Rural
Sec.
Prim.
Sec.
Mountain
Hill
Prim. Sec.
Prim. Sec.
Wood
63.0
14.3
24.2
17.2
73.4
13.7
98.8
1.3
Cow Dung
15.1
18.3
3.5
8.2
18.3
20.7
0.7
0.0
Leaves etc.
5.5
52.8
4.5
15.2
5.8
61.4
Coal
0.1
0.4
0.0
0.8
0.0
Gas Cylinder
3.7
0.3
16.3
1.6
0.1
Electric
0.2
2.4
0.8
11.7
Kerosene
11.8
10.6
49.7
Bio-Gas
0.6
0.2
Other
0.1
0.6
Tarai
68.5 10.9
Prim.
Sec.
43.1
18.7
4.9
40.5
31.5
0.0 92.2
2.2 56.8
12.1
45.1
0.4
0.0
0.0
0.0
0.9
0.2
0.1
0.0
0.0
0.0
6.8
0.7
0.3
4.0
0.2
0.3
0.0
0.0
0.4
5.4
0.0
0.4
43.8
1.6
3.0
0.5
5.2
20.8 19.3
2.9
0.0
0.7
0.4
0.6
0.1
0.0
0.0
0.7
0.3
0.7
0.0
0.3
0.8
0.0
0.5
0.0
1.3
0.1
0.7
0.1
0.0
0.6
Source: Tabulations from NLSS data.
Table 2 presents the frequency and percentage of households that collect or purchase
firewood. Overall, 79 percent of households that use firewood only collect, 10 percent only
purchase, and 3 percent do both.5 Purchasing is most common in urban areas and the Tarai,
which makes sense given the scarcity of firewood in these areas. In the Mountain region, where
firewood is most abundant and transport costs high, 94 percent of households rely exclusively on
collection for their firewood needs.
Table 2. Number of Households Collecting and Purchasing Firewood by Region
Firewood Usersa
Collect Only
Purchase Only
Collect and
Purchase
number %total number
%FW number % FW number % FW
HH
Users
Users
Users
All
2,405 71.3%
1,903 79.1%
249 10.4%
80
3.3%
Urban/rural:
Urban
228 31.8%
78 34.2%
118 51.8%
12
5.3%
Rural
2,177 81.9%
1,825 83.8%
131
6.0%
68
3.1%
Region:
5
It is unclear where the remaining 7.2 percent obtains its firewood. Presumably some
households have firewood stockpiled. Others may hire labor to collect firewood, which,
although essentially the same as purchasing, may have been perceived differently by respondents.
12
Mountain
409 100.0%
384 93.9%
4
1.0%
6
1.5%
Hill
1,278 73.4%
1,104 86.4%
101
7.9%
33
2.6%
Tarai
718 58.7%
415 57.8%
144 20.1%
41
5.7%
Source: Tabulations from NLSS data.
Notes: a) excludes those households that neither collected nor purchased firewood in the
survey year.
Households that do collect firewood devote substantial labor to that task. On average
firewood collection requires 41 hours per month or 8.5 hours per household member per month
(see Table 3). Highest collection times are reported in the Mountain region (50 hours per month,
10 hours per capita) and lowest collection times in the Tarai (31 hours per month, 6 hours per
capita). We do not report time/quantity and quantity/month because of problems in defining
quantity across households (we discuss this further in Section 3). Thus, time per month reflects
both the environmental scarcity of firewood as well as the household’s demand for firewood.
Other evidence suggests the low collection times reported in the Tarai are a function of relatively
low firewood demand rather than ease of collection in that region. For example, 79 percent of
rural Tarai communities reported collection times had increased in the past five years as opposed
to 61 percent of Hill communities and 71 percent of Mountain regions. Also note that a
relatively small percentage of Tarai households, 43 percent, reported using firewood as their
primary cooking fuel. Communities reported a significantly higher average time to reach a forest
in the Tarai as well: 237 minutes as opposed to 120 minutes in the Hills and 87 minutes in the
Mountains.
Table 3: Total Household and Per Capita Firewood Collection and
Purchasing Times by region (hours per month)
Collection Time Per:
Purchasing Time Per:
Region
Household
Person
Household
Per Person
All
41.2
8.5
1.8
0.4
Urban/rural:
Urban
Rural
Region:
33.0
41.5
8.6
8.5
1.0
2.2
13
0.2
0.5
Mountain
49.8
10.1
Hill
42.2
9.0
Tarai
31.1
6.0
Source: Tabulations from NLSS data.
1.2
1.7
1.8
0.3
0.4
0.4
The time devoted to purchasing firewood, as expected, is much lower than time devoted
to collection, averaging 1.8 hours per month for the 14 percent of households that purchase
firewood in Nepal. Purchasing times are somewhat higher in rural areas than in urban areas. The
81 households that both collect and purchase firewood reported collecting 2.5 times as much
firewood as they purchased.
The NLSS data do not allow us to calculate the percentage of time devoted to firewood
collection by various household members. NLSS asked only which household members were
responsible for firewood collection. Table 4 shows that females account for 61 percent of all
firewood collectors, although primary firewood collectors are as likely to be male as female.
Children (age 0-15) account for 13 percent of all collectors. We cannot say to what extent actual
collection hours are concentrated in men, women, and children. Previous studies, however, have
found firewood collection to be primarily the work of women and children (Agarwal 1985,
Kumar 1988, U.S. AID 1980). For example, Kumar and Hotchkiss (1988) find women account
for 82 percent and children 9 percent of total firewood collection times in Hill villages. A study
of five Hill villages and one Tarai village by Acharya and Bennett (1981) found that children
spend 68 percent of their time devoted to household tasks on collection activities. Filmer and
Pritchett (1996) find children account for 20 percent of total firewood collection times in
Pakistan.
Table 4: Age and Sex Distribution of Firewood and Water Collectors
Firewood Collectors
Water Collectors
Number % Total
Number
% Total
Primary firewood and water collectors
All
1,983
100.0%
1,790
100.0%
by age:
0-10
5
0.3%
6
0.3%
11-15
39
2.0%
32
1.8%
14
16-20
>20
by gender:
Male
Female
All firewood and water collectors
All
by age:
0-10
11-15
16-20
>20
by gender
Male
Female
142
1,797
7.2%
90.6%
101
1,651
5.6%
92.2%
982
1,001
49.5%
50.5%
528
1,262
29.5%
70.5%
4,379
100.0%
4,266
100.0%
84
497
692
3,106
1.9%
11.4%
15.8%
70.9%
182
606
637
2,841
4.3%
14.2%
14.9%
66.6%
1,723
2,656
39.3%
60.7%
1,310
2,956
30.7%
69.3%
Source: Tabulations from NLSS data.
As Filmer and Pritchett point out, the vicious circle hypothesis assumes firewood
collection generates an externality. The vicious circle cannot be sustained if households
internalize the effect of their collection activities on firewood scarcity. Consequently, the model
of Section 1 and the empirical analysis below treats firewood as an open access resource subject
to externalities. This is a fair assumption in Nepal where only 19 percent of collecting
households reported collecting firewood from their own lands. The remaining households
reported collecting firewood from community managed forests (13 percent), government owned
forests (65 percent), and other private lands (3 percent).
Water Collection
Thus far the environment-population problem has been couched in terms of firewood
scarcity and deforestation. Another open access resource in much of Nepal is water. Over half
of the NLSS households reported having collected water in the past twelve months from public
sources (see Table 5). Much of this water is extracted from groundwater sources which can
become seriously depleted over time forcing households to seek increasingly distant water
15
sources. The analysis in Sections 3 and 4, therefore, considers water scarcity as well as firewood
scarcity in testing for links between fertility and environmental scarcity.
While not as common as firewood collection, water collection is nonetheless an important
household task in many areas of Nepal. In Table 5 we see that 62 percent of rural households
collect water spending an average of two hours per day on that task. Water collection is most
common in the Mountain region where 89 percent of households collect water. Urban and Tarai
households are least likely to collect water (between 22 and 30 percent of all households) and
those that do spend relatively little time on the task (an average of 1.3 hours per day).
Table 5: Number of Water Collectors and Collection Times by Month and Region
Water Collecting
Yearly
Households
Hours/Day/Household by Month
Avg.
Number
%Total
Kartik
Magh
Baisakh
All
1,797
53%
1.62
1.71
2.66
2.00
By urban/rural:
Urban
156
22%
Rural
1,641
62%
By region:
Mountain
365
89%
Hill
1,069
61%
Tarai
363
30%
Source: Tabulations from NLSS data.
1.25
1.66
1.12
1.76
1.42
2.78
1.26
2.07
1.76
1.75
1.08
2.00
1.84
1.04
2.83
2.94
1.68
2.20
2.18
1.27
Responsibility for water collection appears to lie mainly with women and children. A
higher percentage of water collectors are women (69 percent) and children (18 percent) than we
observe among firewood collectors (see Table 4). Whereas primary firewood collectors are as
likely to be male as female, it is much more common for females to assume primary
responsibility for water collection (71 percent).
Fertility
16
In 1993 Nepal’s population totaled 21 million. The World Bank projects this will
increase to 25 million by 2000 and 41 million by 2025, an average annual growth rate of
approximately 2.5 percent (World Bank, 1995). Table 6 reports the mean number of children
ever born by age for various regions of Nepal as given in the NLSS. By age 49, Nepalese women
on average have 4.97 children. More than half of those children are born before the mother turns
30. Total fertility varies across regions of Nepal. It is highest in the Tarai (5.30 children) and
lowest in the Hills and Mountains (4.75 and 4.77 children). Total fertility is significantly higher
in rural areas than in urban areas (5.25 children in rural areas vs. 3.91 children in urban areas).
Table 6: Mean Number Children Ever Born by Age and Region
Age Category
15-19
20-24
25-29
30-34
35-39
All
0.16
1.14
2.53
3.49
4.11
By urban/rural residence:
Urban
0.10
0.85
1.97
2.84
3.48
Rural
0.18
1.23
2.69
3.67
4.28
By region of residence:
Mountain
0.14
1.12
2.49
3.69
3.73
Hill
0.11
0.99
2.32
3.29
3.94
Tarai
0.25
1.34
2.81
3.70
4.48
By firewood use:
Users
0.16
1.21
2.66
3.67
4.19
Non-users
0.15
1.00
2.24
3.08
3.93
By water collection:
Collectors
0.13
1.21
2.60
3.72
4.21
Non-collectors
0.19
1.07
2.46
3.26
4.00
40-44
4.68
45-49
4.97
3.90
4.88
3.91
5.25
4.09
4.57
5.04
4.77
4.75
5.30
4.79
4.34
5.28
4.33
4.83
4.51
5.19
4.72
Source: Tabulations from NLSS data.
Table 6 breaks cumulative fertility down by whether the household uses firewood. As
can be seen, women in households that use firewood have higher fertility on average in all age
categories. By age 49, firewood users have 5.28 children as compared to 4.33 children for nonfirewood users. It remains to be seen, however, whether this difference is significant after
17
controlling for other household characteristics. The difference in fertility between water
collectors and non-collectors is not significant.
3.
Measurement and Estimation
To examine how firewood and water scarcity affect fertility we estimate the following
yijk =  0 + X ijk’  1 + Z jk’  2 + S k’  3 +  ijk i = 1, ..., n j = 1, ..., h k = 1, ..., w
regression:
where yijk is a measure of fertility for the ith individual in the jth household and the kth ward, Xijk is
a vector of individual characteristics, Zjk is a vector of household characteristics, Sk measures
ward-specific firewood and water scarcity, εijk is a mean 0, variance σ2 disturbance term, and the
β’s are the model’s parameters. Appendix table 1 lists all variables with their means and
standard deviations. Individual characteristics include those typically found in fertility
regressions such as age, age squared, age at marriage (all births in the sample are legitimate),
education, incidence of infant mortality, knowledge of birth control, and employment status.
Household characteristics include household consumption expenditures per capita (inclusive of
imputed value of own produced goods) and land holdings.
Of critical importance is the variable S, which measures firewood and water scarcity. The
closest proxies for firewood and water scarcity in the NLSS are two variables quantifying time
spent by the household collecting firewood and water. Ideally these variables represent the cost
of the collected resource. Reported collection times, however, have two problems: they reflect
household characteristics that affect demand as well as exogenous differences in scarcity and are
subject to measurement error. We address the construction of each scarcity variable in turn.
In the case of firewood, the NLSS asked households to report time spent collecting either
a bhari or a “cart” of wood. The variable comprises both the amount of time required to walk to
the source and the amount of time spent cutting live trees and scavenging for down wood once
there. The Nepalese define a bhari as roughly a bundle of wood whose size/weight depends on
the person carrying it and the ward in which it is collected. The size of a “cart” of wood depends,
18
not surprisingly, on the size of the cart employed.6 Needless to say, these units of measurement
are rather imprecise and attempts to normalize them into a standard unit of measurement were
unsuccessful. Consequently, the variable is not immediately comparable across households and
wards.
An additional problem with using the household-level time/bhari measure is that it
captures more than just firewood scarcity. Endogenously determined household collection
“technology” such as land ownership and age and sex of firewood collectors influences the time
required to collect a bhari of wood. For example, households with access to private wood lots
may report lower collection times than households that collect wood on communal or
government-owned land. Firewood scarcity, however, should be largely ward-specific in Nepal
where villages are often geographically isolated. Hence, households in the same ward should
face the same general level of firewood scarcity. To control for household collection technology
and isolate a ward-specific measure of firewood scarcity, we run the following regression with
(
t
) = X jk’  + u k +  jk
bhari jk
j = 1, ..., h k = 1, ..., w
ward-specific fixed effects:
where the dependent variable is the household’s reported time to collect a bhari of wood, Xjk
represents a vector of household-level collection technology variables (see Table 7), uk is a wardspecific fixed effect, and εjk is a mean 0, variance σ2 disturbance term. The ward-specific fixed
effect, uk, represents firewood scarcity in the kth ward (Sk in (14)) after controlling for household
collection technology and between household (i.e., within-ward) measurement error. The fixed
effect also unfortunately captures between-ward measurement error, which may be considerable
if a bhari is a locally defined unit of measure.
6
A cart of wood is not necessarily bigger than a bhari of wood.
19
Note the fixed effects model produces consistent estimates of α since the error structure
removes permanent differences in firewood scarcity between wards which would otherwise bias
the model’s coefficients. Estimating (15) without the fixed effect would presumably produce
biased coefficients given the endogeneity of the household’s labor allocation decision. For
example, it may be the case increasing scarcity causes the household to allocate more adult labor
to firewood collection which would tend to bias the age coefficients downward. As
demonstrated in Section 1, however, the direction of the bias is unknown, since the allocation of
labor within the household responds ambiguously to increasing scarcity.7
Table 7 reports the results of estimating the fixed effects regression in (15). Overall, the
model’s variables are jointly significant (F(9, 1681)=10.8, p>F=0.000). At the individual level,
however, only place of collection (own land), number of collectors, and per capita income are
significant at the ninety-five percent confidence level. Households that collect firewood on their
own land require significantly less time than households collecting on government property: an
average of 75 minutes per bhari less. Higher per capita income exerts a mildly negative influence
on collection times as well. The number of collectors employed by the household appears to
raise collection times, which may reflect greater numbers of child collectors rather than
diseconomies of scale.
Table 7:
Time to Collect Firewood and Fetch Water: Regression Results with
Ward Specific Fixed Effects.
Variables:
Firewood Collectiona Water collectionb
(Min./Bhari)
(Min./Trip)
Coef.
t
Coef.
t
Avg. age collectors
0.456
0.30
0.450
2.25
Avg. age collectors squared
0.000
0.02 -0.007
-2.53
Age primary collector
0.329
0.25 -2.690
-1.41
Age primary collector squared
-0.006
-0.40
0.005
1.91
Sex primary collector (male=1)
-9.523
-1.59 -2.569
-2.70
Number of Collectors
8.041
2.26
1.067
2.00
Section 1 shows the sign of n/S is ambiguous. The remaining comparative statics with
respect to scarcity have a similar form and also are ambiguous in sign.
7
20
Collect firewood on own land
HH per capita expenditures (rs. 1,000)
Access to piped water
Month of Magh
Month of Baisakh
Constant
Summary Statistics
Number of observations
Within ward R-squared
Between ward R-squared
-74.910
-0.474
na
na
na
289.504
1,893
0.055
0.106
-8.69
na
-2.56
na
na -10.222
na 1.129
na 11.003
11.70 25.287
na
na
-8.66
1.19
11.63
5.79
1,797
0.053
0.018
Source: Author’s calculations on NLSS data.
Notes: a) sample restricted to households that collect but do not purchase firewood.
b) sample restricted to those households that fetch water.
The model explains very little of the within-ward variation in collection times as
indicated by a within-ward R2 of 0.055. One possible explanation could be that measurement
error between households in the same ward is quite high. The model does somewhat better in
explaining between-ward variation in collection times (between-ward R2: 0.106), although again
there would appear to be a high level of between-ward measurement error as well. Thus, the
estimated ward-specific scarcity effect, uk, purges the time/bhari variable of some householdlevel effects and measurement error, but not ward-level measurement error.8 The nature of the
measurement error is not known and so cannot be effectively instrumented. In general, however,
we would expect measurement error to bias the coefficient on firewood scarcity in (14), β3,
toward zero (Greene 1993).
Turning now to water collection, the NLSS asked households to record the length of a
typical trip to collect water during each of three months: Kartik, Magh, and Baisakh. The three
months represent times of the year in which climatic conditions cause exogenous changes in
water scarcity. Kartik (mid-October to mid-November) occurs soon after the monsoon season
and so is a time of relative abundance. Water become somewhat more scarce in Magh (midJanuary to mid-February), although most villages still have plentiful water supply. Finally,
8
Not all wards contain households that collect firewood. As a result, the fixed-effects
approach could only generate a ward-specific firewood scarcity measure for 203 wards.
21
except in locations where snow-melt is significant, Baisakh (mid-April to mid-May) represents a
time of general water scarcity before the monsoon season arrives once again.
Like the time/bhari variable, the time/trip measure reflects household characteristics and
is fraught with measurement error. In an attempt to correct for these influences, we employ a
fixed-effects approach as in (15):
where t/tripjkm is the average time required to collect water in month m for household j in ward k,
Xjk is a vector of household characteristics, Dm is a vector of month dummy variables, and uk and
εjkm are error terms as in (15).
The results of this estimation are reported in Table 7. Here again the explanatory
variables are jointly significant (F(9, 4943)=30.51, p>F=0.0000) but explain only a small fraction
of the total variance in water collection times (within-ward R2 = 0.053, between-ward R2 =
0.018). Collection times decrease for households with access to a pipe-delivered water source
and for households in which the primary collector is male. Collection times increase in
households that employ a greater number of water collectors and in which the average age of
water collectors is high. The large and significant coefficient on the dummy variable for the
month of Baisakh indicates that water is relatively scarce in that month. As in the case of
firewood, the recovered vector uk serves as a measure of water scarcity in the fertility regressions
below.9 Given the poor fit of the model, it is anticipated uk will be a rather noisy measure of
water scarcity, but nonetheless an improvement over using the uncorrected household-level water
collection times on their own.10
We estimate equation (14) using several different measures of fertility and a variety of
sample sizes. Dependent variables in (14) include number of children ever born and the
9
Not all wards contain households that fetch water. As a result, the fixed-effect approach
could only generate a ward-specific water scarcity measure for 233 wards.
The constructed measures of firewood and water scarcity are uncorrelated (Spearman’s
ρ=0.0171, p>|t| = 0.8134).
10
22
probability of a birth in the last five years (BY5). The difference between using number ever
born and the probability measure is significant. Mean number ever born in a given ward can be
thought of as a proxy for population levels and so models using number ever born are likely to
lead to biased coefficients on the scarcity variables if we believe higher population levels lead to
greater scarcity.11 A positive correlation between the scarcity measure and the error term in (14)
( cov(Sε) > 0 ) will bias the scarcity coefficient upward. The probit models using probability of
a birth in last five, three, or two years are less prone to this endogeneity bias since they serve as
short term measures of fertility which do not necessarily reflect overall population levels in a
given ward.
Our baseline regressions reported in table 8 restrict the sample to females between the
ages of 15 and 49 years in households that fetch water and that do collect but do not purchase
firewood (n=1,514). Below, we run regressions using a variety of sample sizes in order to test
whether scarcity affects fertility decisions of resource collectors more strongly than non-resource
collectors and whether scarcity affects the fertility of the household’s principal female (i.e., either
the wife of the household head or the household head herself) more strongly than that of other
females in the household (primarily daughters-in-law). In all cases, however, the sample is
restricted to females in their child bearing years, which is assumed to lie between the ages of 15
and 49.
4.
Fertility Regression Results
Table 8 reports the results of regressions using number of children ever born and
probability of a birth in the last five years as the dependent variables. In both regressions the
standard fertility covariates have the anticipated signs and explain roughly 60 percent of the
variation in number of births and a nearly a fifth of the variation in BY5. Some covariates (age,
age at marriage, infant mortality, and birth control) have opposite effects depending on the
fertility measure employed. For example, the number of births increases with the mother’s age at
11
Since scarcity is ward-specific, (14) essentially regresses the mean number of children
ever born in a given ward, which can be thought of as a proxy for the ward’s population level
assuming limited migration, on ward-specific scarcity to generate β3.
23
a decreasing rate and decreases with age at marriage. Age and age at marriage have the opposite
effect on the probability of a birth in the last five years. These results reflect the nature of the
dependent variable. Number of births is a cumulative measure of fertility and so it makes sense
it would increase with age and decrease with age at marriage. BY5, however, is a short term
measure of fertility which will tend to decrease with age, but increase with age at marriage.
The incidence of child mortality (defined as the number of children who died before age
one) exerts a strong positive effect on number of births and has no statistically significant effect
on BY5. We expect infant mortality to raise long term fertility but not necessarily timing of
births and hence short term fertility. Knowledge of birth control methods appears to increase the
number of children ever born but decreases the probability of having a child in the past five
years. This may reflect an endogeneity bias: women with many past births may be more likely to
seek out and adopt birth control methods in an effort to limit future fertility. Per capita
household consumption, which serves as a proxy for household wealth, decreases fertility in both
cases, although the effect is quite small.12 Per capita land holdings and dummies for wage
employment, literacy, and urban residency do not have a statistically significant impact on either
fertility measure.
The regressions suggest firewood and water scarcity have a small, statistically significant
negative effect on fertility. Firewood scarcity appears to reduce both measures of fertility. An
exogenous (to the household) increase in the time to collect a bhari of firewood by ten minutes
decreases number of births by 0.0128 and lowers the probability of BY5 by 0.0024. Water
scarcity also reduces the probability of BY5. A 10 minute increase in the time for one trip to
collect water decreases the probability of BY5 by 0.0210.13 Water scarcity does not have a
statistically significant impact on number of births.
Table 8: Scarcity and Fertility: OLS and Probit Regression Results
12
One should not interpret this as implying children are an inferior good since our model
does not adequately control for Becker-type quantity-quality tradeoffs (Becker, 1973).
13
An ordered probit model produced results broadly consistent with those presented in
Table 8.
24
Variable
Firewood scarcity
Water scarcity
Age
Age^2
Age married
Birth control
Literacy
Wage employment
Infant mortality
HH Consumption
Land
Urban
Constant
Summary Statistics
Number of observations
R-Squared
Observed p
Predicted p
Children Ever Born
Coef.
ta
-0.0128
-3.02
0.0237
0.60
0.5222
17.64
-0.0057
-11.39
-0.1102
-7.80
0.2556
2.92
-0.0636
-0.62
-0.1179
-1.25
0.9759
16.58
-0.0682
-4.89
-0.3035
-1.65
0.0673
0.38
-4.9964
-11.23
1,514
0.61
na
na
Birth in Past Five Years
dF/dxb
ta
-0.0024
-2.34
-0.0210
-3.59
0.0638
4.40
-0.0015
-5.94
0.0126
3.18
-0.0283
-1.00
-0.0717
-1.89
-0.0615
-1.90
0.0191
0.91
-0.0072
-1.95
-0.0442
-1.41
-0.0368
-0.52
na
na
1,514
0.17
0.37
0.32
Source: NLSS data.
Notes: a) Standard errors are heteroskedasticity robust and have been corrected
using the Huber method for between-panel heteroskedasticity.
b) change in probability calculated at the means of the data.
The marginal probabilities calculated from the probit coefficients on firewood and water
scarcity in Table 8 differ by an order of magnitude. This, however, merely reflects the way in
which we measure scarcity in the two cases rather than some fundamental difference in the
manner in which firewood and water scarcity affect fertility. Firewood scarcity is measured in
time/bhari and water scarcity is measured in time/trip. Whereas the average household in the
sample collects 8.3 bharis of wood per month, that same household makes 135 trips for water.
Thus, an exogenous ten minute increase in time/trip should have a much larger effect than an
identical increase in time/bhari. Multiplying the coefficient on water scarcity by 0.06148
25
(8.3/135) reduces it to -0.0013, which is more in line with (approximately half of) the coefficient
on firewood scarcity.
To put these numbers in perspective, imagine an exogenous increase in time/bhari and
time/trip by one standard deviation: 122 minutes for time/bhari and 19 minutes for time/trip. If
we hold firewood and water demand constant, the average household would experience an
increase in the time to collect firewood of 202 hours per year and an increase in the time to
collect water of 517 hours per year. Assuming the relationship between scarcity and fertility is
linear, an increase in scarcity of this magnitude would result in a reduction in the probability of
having a child in the past five years of 0.0290 due to increased firewood scarcity and 0.0398 due
to increased water scarcity, a combined reduction in BY5 of 0.0688. The mean of BY5 in this
regression is 0.37, which implies a standard deviation change in scarcity changes BY5 by 19
percent. Under the same assumptions, a 202 hour increase in the time to collect firewood per
year leads to a reduction in lifetime fertility of 0.15 children, a 5 percent fall from mean lifetime
fertility.
The scarcity effect may be even stronger than reported in Table 8 due to measurement
error and endogeneity bias. Without proper instruments we cannot know the extent of these
biases. Nonetheless, we do know their general effect. Measurement error in the firewood and
water scarcity variables biases the scarcity coefficients toward zero in both the OLS and probit
regressions. We also know the OLS coefficients are most likely biased upward due to the
endogeneity of scarcity and number of births. If any biases exist, then, they serve only to
reinforce the results in Table 8 which predict resource scarcity negatively affects fertility.
Sample Specification
Table 9 reports scarcity coefficients and t-statistics for six different sample specifications.
The highlighted coefficients are from the baseline sample in Table 8. The results suggest that
the negative effect of scarcity on fertility is robust to larger samples of females living in
households that purchase firewood or do not use firewood at all and households that have indoor
26
plumbing. We would expect this to be true if firewood and water scarcity increase the price of
these resources generally, not just for those households who actually collect them.
As seen in Table 9, water and firewood scarcity retain their negative signs in the five
additional probit regressions, although firewood scarcity is insignificant in the probit regressions
using head females. In general, t-statistics for the scarcity coefficients fall when the sample is
restricted to head females. This is most likely because of the smaller sample size and because the
variation in number of births and especially BY5 is likely to be much higher among all females
age 15-49 than among the older cohort of head females. It is interesting to note that the
magnitude of the coefficients remain the same, implying scarcity has the same effect on the
fertility of the household’s principal female decision maker (the wife of the household head) as
on the fertility of females who have yet to establish their own homes. This is not surprising if (a)
the fertility of daughters-in-law is determined in part by the household head, (b) daughters-in-law
make decisions regarding current labor allocation between household tasks, or (c) daughters-inlaw calculate optimal family size based on their perception of future scarcity and in anticipation
of becoming household heads themselves.
Table 9: Robustness of regression results to sample selection criterion
Definition of
Sample
Num.
of
Obs.
OLS Results on “number of births”
Firewood
Coef.
a
t
Water
Coef.
Probit Results on “Birth in the last
five years”
Firewood
a
t
b
dF/dx
Water
a
t
dF/dxb
ta
14<age<50
All
2,492
-0.011
-3.856
0.038
1.17
-0.005
-2.62
-0.042
-3.37
Collect firewood, fetch
water
1,559
-0.014
-3.351
0.026
0.66
-0.007
-2.32
-0.062
-3.66
Collect, don't
purchase, fetch water
1,514
-0.013
-3.02
0.024
0.60
-0.002
-2.34
-0.021
-3.59
Females that are spouse of, or Head of Household, age<50
All
1,623
-0.010
-3.31
0.082
2.58
-0.003
-1.05
-0.018
-0.95
Collect firewood, fetch
water
1,022
-0.012
-2.38
0.081
2.01
-0.005
-1.26
-0.049
-2.12
Collect, don't purchase,
fetch water
992
-0.010
-2.00
0.079
1.92
-0.004
-1.03
-0.053
-2.34
Notes: a) standard errors are heteroskedasticity robust and Huber-corrected for stratified sampling, b)
Change in probability calculated at means of the data.
27
The coefficients on water scarcity in the regressions using number of births and the
sample of head females turn out positive and significant. As noted earlier, however, the
regressions using number of births as the dependent variable will likely generate upwardly biased
scarcity coefficients due to the endogeneity of scarcity and number of births. The extent of the
bias is unknown and so we can not say with certainty whether the effect of water scarcity is in
fact positive or negative. In the absence of endogeneity bias, however, it is hard to explain why
water scarcity would produce a positive effect on number of births but a negative effect on BY5,
especially when firewood scarcity reduces both measures of fertility.
All samples in Table 9 omit wards in which a firewood or water scarcity measure could
not be imputed due to lack of collection time data. Thus, only 192 of the total 274 wards are
represented in the fertility regressions. The exclusion of these wards creates a problem of
incidental truncation since in all likelihood high scarcity explains why we do not observe
households in these wards that collect firewood or fetch water. The unobserved price of
collection is high enough in these wards to cause households to substitute away from firewood
collection to other forms of fuel like dung, kerosene, or electricity. In the case of water, the
unobserved price of collection has caused households to install indoor plumbing. Indeed, entire
wards may respond to high scarcity by constructing infrastructure (roads, electric power
generation, water delivery, etc.) that lower the private cost of collection substitutes.
Correcting for this problem of incidental truncation requires an instrument that affects
the decision to collect firewood or fetch water but is not correlated with our constructed measure
of scarcity. Unfortunately, the NLSS offers no such instruments. The best instruments the NLSS
has to offer are variables measuring community infrastructure like access to roads and
availability of electricity and piped water. These, however, are not uniformly available and, as
noted, are likely to be highly endogenous themselves.
We can, though, explore ways in which the wards with missing scarcity data differ from
wards with scarcity data. As expected, a disproportionate number of these wards (51 percent) are
in urban areas which generally have higher levels of public infrastructure which lower the cost of
alternative fuels and water delivery. Evidence suggests women in these wards may have
relatively low fertility as well. After controlling for the standard fertility covariates listed in
28
appendix table 1 women in wards without firewood scarcity data have 0.22 (t-stat=2.27) fewer
children than women in wards with firewood scarcity data.14 This evidence suggests, at a
minimum, the number of births regression in Table 8 excludes a significant number of
observations with high levels of firewood scarcity and low levels of fertility.15 This is likely to
create a positive bias in the firewood scarcity coefficient, implying that firewood scarcity
potentially has an even stronger negative effect on fertility than already shown.
5.
Conclusion
The theoretical model of Section 1 shows a positive resource scarcity-fertility relationship
is most likely to arise in households where: children have an important role in collection
activities, scarcity reduces the marginal productivity of adult collection labor more than the
marginal productivity of child collection labor, and limited substitution possibilities for collected
resources exist. The results of Section 4 indicate these forces are not strong enough to overcome
negative price and income effects in Nepal. Despite the importance of children in collection
activities and circumstances which make substitution for firewood and water collection difficult,
the Nepalese subsistence farmer apparently perceives deforestation and water scarcity as
increasing the net cost of children, which reduces demand. We suspect these results are likely to
hold in populations relying directly on local natural resources for subsistence. We conjecture,
except under unusual circumstances, the price and income effect of an increase in resource
scarcity, whether it affect wood, water, animal, or other local resource supply, will tend to reduce
the demand for children, even in the presence of “vicious circle” shifts in the relative labor
productivity of children versus adults.
Other links between environmental degradation and fertility are still possible, however.
Nerlove (1991), for example, suggests examining the effects of desertification on livestocking
14
Availability of firewood scarcity data has no significant effect on the probability of
having had a child in the past five years and availability of water scarcity data impacts neither
fertility measure.
15
Sample size would increase by 592 observations in Table 8 if all wards had firewood
scarcity data.
29
households. In areas of marginal agricultural productivity, worsening environmental conditions
may cause households to substitute toward livestock production, an activity in which children
have a comparative advantage. Here, environmental degradation causes a substitution toward the
activity in which children have a comparative advantage and so provides families with strong
incentives to have more children. A positive substitution effect makes it far more likely
worsening environmental conditions will lead to increased fertility in households that rely on
livestock production than in households that rely on resource collection. It seems unlikely
firewood or any other type of resource collection activity will become an increasingly important
component of household income as scarcity increases. Indeed it is far more likely households
will substitute away from the activity. In the case of firewood this means switching to other
forms of cooking fuel, increasing the efficiency of stoves, and/or substituting away from cooked
food.
Our results suggest resource scarcity acts as a check on population growth, at least in the
context of firewood and water collection in Nepal. This does not, however, ensure a stable
equilibrium between population growth and environmental quality. Even if fertility responds
negatively to environmental degradation, unstable dynamics with unpleasant environmental
consequences may emerge if the response is insufficiently large in relation to the dynamic
response of environmental quality to population growth (Nerlove 1991, 1993). Thus, a negative
relationship between fertility and environmental degradation is no panacea for countries
struggling to find a balance between population growth and resource demand. The rate of
deforestation in Nepal is unlikely to stabilize at an acceptable level unless households somehow
link deforestation with their own resource collection activities. Although scarcity increases the
marginal cost of resource collection for individual households, collection will persist above its
social optimum without institutional changes in property rights that internalize the full marginal
social cost of collection.
30
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Agarwal, B. 1985. Cold Hearths and Barren Slopes: the Woodfuel Crisis in the Third World.
Riverdale, MD: The Riverdale Company, Inc.
Amarcher, G. S., F.W. Hyde, and K. R. Kanel. 1996. Household Fuelwood Demand and Supply
in Nepal’s Tarai and Mid-Hills: Choice Between Cash Outlays and Labor Opportunity.
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32
Appendix table 1: Summary Statistics: Fertility Regressionsa
Variable
Definition
Mean Std. Dev.
n_births
No. children ever born
3.21
2.40
B5Y
Birth last five years
0.37
0.48
Age
Mother's age
30.61
8.92
Age2
Mother's age squared
1016.6
572.48
Age married Age at first marriage
17.10
3.75
Birth control Knowledge of birth
0.44
0.50
control
Literacy
Able to read and write
0.11
0.32
Wage emp. Wage earner
0.22
0.41
Child mort. No. children died <1 year
0.31
0.79
HH
HH per capita
5.88
4.02
Consumption consumption (rs. 1,000)
Land
HH per capita land owned
0.17
0.35
(hectares)
Urban
Urban ward
0.03
0.17
FW scarcityb Ward firewood scarcity
1.47
12.22
(10 min.)
Water
Ward water scarcity (10
0.27
1.94
scarcityb
min.)
Min.
0.00
0.00
15.00
225.0
0.00
0.00
Max.
12.00
1.00
49.00
2401.0
35.00
1.00
0.00
0.00
0.00
0.96
1.00
1.00
6.00
42.35
0.00
4.83
0.00
-23.1
1.00
42.56
-2.67
8.92
a) sample includes females aged 15-49 in households that fetch water but do not
purchase firewood.
b) Ward specific constant from regression in table 7 which controls for household
characteristics.
33
34
35
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