Environmental Scarcity, Resource Collection, and the Demand for Children in Nepal

Transcription

1 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 cross-sectional 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 increasing firewood and water scarcity apparently does not have a strong enough effect on the relative productivity of child labor to induce higher demand for children given the effects that work in the opposite direction. For these resource collection activities it would appear 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.

2 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 (notable exceptions include Nerlove 1991, Cleaver and Schreiber 1994, Dasgupta 1995, Filmer and Pritchett 1996). Answering the second question amounts to answering whether individuals alter their desired fertility in response to changes in environmental quality and, if so, in what direction. The concern is that societies in which individuals respond to worsening environmental conditions by increasing their fertility might find themselves in a downward spiral of increasing population, plummeting environmental quality, and, presumably, deteriorating living conditions. 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. Children often have an important role in performing household tasks, such as collecting firewood, water, and fodder, especially among subsistence farmers. This provides a conduit through which environmental degradation could affect household-level fertility decisions. Households may increase their demand for children if the relative labor productivity of children rises as environmental conditions deteriorate. The empirical question, then, 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. (page 1)

3 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 between 1981 and 1990 (World Bank 1995). Over half of the rural wards (i.e., villages) surveyed by the 1996 Nepal Living Standards Survey (NLSS described below) reported that forest cover had diminished significantly over the past five years. While this rate of deforestation is not atypical of low-income countries, it is by most accounts quite rapid and of serious concern in a 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 choosing the number of children, n: max n W (n,s) n+1 (page 2)

4 where W(.) represents firewood collection 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., W ns >0). It is hard to imagine in the case of firewood collection, however, how increasing scarcity could increase the marginal productivity of child labor. It seems much more natural to assume W ns <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 W ns >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 C =C (W, Q) using a combination of collected firewood, W, and the household s agricultural production, Q: (page 3)

5 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 ( Q=Q( La, Lc ) child labor only: where l a and l c are adult and child labor devoted to collection activities and L a and L c 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., W S <0, W 1S <0, and W 2S <0). For example, W might take the following, la lc ; S) form: 1 W = ( α la + β lc S where α and β measure the relative productivity of adult and child labor in collecting firewood. 1 ) 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. (page 4)

6 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 = lc+ Lc T = la + La + l time constraints are as follows: We assume the time cost of raising children, l n, 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. n n Maximizing (2) subject to (6) yields the following first order conditions which describe U 2 i) U + 2 (n+1 ) U 2 ii) n+1 U 2 iii) n+1 [ (TQ - Q ) - C ] (n+1)c ln [ - Q ] C1W [ Q ] = 0 C1W the household s optimal choice of n, l a, l c, L a, and L c : 2 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 1 2 C - C =0 1 =0 (ii) and (iii): +U TC1W 2 =U (n+1) ln C1W (n+1) +U 1 U C (n+1 ) 2 Assume U(.), C(.), W(.), and Q(.) increase at a decreasing rate in all arguments except S. (page 5)

7 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 cost of an additional child in terms of the additional resources required to feed it. We may now ask how does environmental scarcity affect the household s demand for children, which, in the present context, is the same as asking how a change in firewood scarcity disturbs 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, l a, l c, L a, and L c to the parameters of the model and an interior solution exists, we can express the partial effect of * n F = - S F scarcity on the demand for children as: where F(.) represents the implicit function given by (8). 3 By the second order condition for a maximum, F n must be negative and so the sign of n * / S will have the same sign as F S : S n 3 The asterisk (*) emphasizes that we are interested in how the optimal quantity of children changes as environmental scarcity changes. (page 6)

8 C1W S U 22 F S= U (n+1)c 2 n+1 (n+1 ) U 2 U C11W S (TW 2 lnw 1 C n+1 n+1 U 2 2 C1W S (n+1 ) [ (TQ - Q )- C ] 2 [ - ) ] + [ - ) ] (T W The sign of F s, 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. 1 2 l n 2S 1 l n W 1S + - 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(a)). 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 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 (page 7)

9 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 - lnw 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., W 2 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 (page 8)

10 T W - 2S ln 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 > l n ). Here, scarcity has the effect of shifting the slope of the isoquant (see Figure 1(b)), 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 W 1S >0 The fourth and final term of (10) works in the direction of increasing fertility. The greater the productivity of firewood collection (C 1 >> 0) and the more negative the impact of scarcity on firewood availability (W s << 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 The ambiguity embodied in (10) results because scarcity affects both the expenditure and production side of the farming household in our model. Scarcity at once raises the price of (page 9)

11 consumption and alters the production possibilities of the household. The sign and magnitude of these price, productivity, and substitution effects determine whether scarcity 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 determine 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. (page 10)

12 The NLSS covers 3,373 households in 274 wards sampled from four geographic and ecological strata: Mountains, Urban Hills, Rural Hills, and Tarai (see Figure 2). 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. Between 12 and 16 households were interviewed in each ward between June 1995 and May 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. Hill 4 See CBS (1996) for a more detailed explanation of the NLSS survey design. (page 11)

13 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 on collection exclusively for their firewood needs. 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 scarcity of firewood as well as the 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. 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 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. (page 12)

14 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. As Filmer and Pritchett point out, the vicious circle hypothesis assumes firewood collection on public lands 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 (page 13)

15 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 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). 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 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 (n_births) by age category 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 (page 14)

16 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 children in urban areas). Table 6 breaks fertility rates 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 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 y ijk = β 0 + X ijk β 1 + Z jk β 2 + S β k 3 + ε ijk i= 1,...,n j = 1,..., h k =1,..., w regression: where y ijk is a measure of fertility for the i th individual in the j th household and the k th ward, X ijk is a vector of individual characteristics, Z jk is a vector of household characteristics, S k measures ward-specific firewood and water scarcity, ε ijk is a mean 0, variance σ 2 disturbance term, and the β s are the model s parameters. Table 7 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 per capita consumption and land holdings. (page 15)

17 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 each 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, 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 ward-specific fixed effects: 6 A cart of wood is not necessarily bigger than a bhari of wood. (page 16)

18 t bhari jk = X α +uk + ε jk jk j =1,..., h k = 1,..., w where the dependent variable is the household s reported time to collect a bhari of wood, X jk represents a vector of household-level collection technology variables (see Table 8), u k is a wardspecific fixed effect, and ε jk is a mean 0, variance σ 2 disturbance term. The ward-specific fixed effect, u k, represents firewood scarcity in the k th ward (S k in (14)) after controlling for household collection technology and between household (i.e., within-ward) measurement error. The fixed effect also captures between-ward measurement error, which may be considerable if a bhari is a locally defined unit of measure. 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 8 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 7 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. (page 17)

19 raise collection times, which may reflect greater numbers of child collectors rather than diseconomies of scale. The model explains very little of the within-ward variation in collection times as indicated by a within-ward R 2 of 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 R 2 : 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, u k, 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), β 3, 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 (mid- January to mid-february), although most villages still have plentiful water supply. Finally, 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): 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. (page 18)

20 t trip jkm = X jk α 1+ Dm α 2 +uk+ ε jkm j = 1,..., h k =1,..., w m= 1,...,3 where t/trip jkm is household j s average time per trip required to collect water in month m, X jk is a vector of household characteristics, D m is a vector of month dummy variables, and u k and ε jkm are error terms as in (15). The results of this estimation are reported in Table 8. 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 R 2 = 0.053, between-ward R 2 = 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 u k serves as a measure of water scarcity in the fertility regressions below. 9 Given the poor fit of the model, it is anticipated u k 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 (n_births), probability of a birth in the last five years (BY5), probability of a birth in the last three years (BY3), and probability of birth in the last two years (BY2). The difference between using number ever born and the probability measure is significant. Mean number ever born in a given ward can 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. 10 The constructed measures of firewood and water scarcity are uncorrelated (Spearman s ρ=0.0171, p> t = ). (page 19)

21 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 (Table 9) restrict the sample to females between the ages of 15 and 49 years in households that fetch water and 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 Results Table 9 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 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 β 3. (page 20)

22 variation in n_births and a nearly a fifth of the variation in BY5. Some covariates (age, age at marriage, infant mortcality, and birth control) have opposite effects depending on the fertility measure employed. For example, n_births increases with the mother s age at 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. n_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 n_births and has no statistically significant effect on BY5. Again, we would expect infant mortality to raise long term fertility but not necessarily 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 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. 13 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 n_births by and lowers the probability of BY5 by Water scarcity also 12 One should not interpret this as implying children are an inferior good since we suspect the model does not adequately control for Becker-type quantity-quality tradeoffs (Becker, 1973). 13 Standard errors have been corrected using the Huber method for between-panel heteroscedasticity. (page 21)

23 reduces the probability of BY5. A 10 minute increase in the time for one trip to collect water decreases the probability of BY5 by Water scarcity does not have a statistically significant impact on n_births. The probit coefficients on firewood and water scarcity in Table 9 differ by an order of magnitude. This, however, reflects the way in which we measure scarcity in the two cases rather than 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 (8.3/135) reduces it to , 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 due to increased firewood scarcity and due to increased water scarcity, a combined reduction in BY5 of 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. 14 An ordered probit model produced results broadly consistent with those presented in Table 9. (page 22)

24 The scarcity effect may be even stronger than reported in Table 9 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 n_births. If any biases exist, then, they serve only to reinforce the results in Table 9 which predict resource scarcity negatively affects fertility. (page 23)

25 Sample Specification Table 10 reports scarcity coefficients and t-statistics for six different sample specifications. Coefficients highlighted in grey are from the baseline sample in Table 9. 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 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 10, 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 n_births and BY5 is likely to be much higher among all females age 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-in-law calculate optimal family size based on their perception of future scarcity and in anticipation of becoming household heads themselves. The coefficients on water scarcity in the regressions using n_births and the sample of head females turn out positive and significant. As noted earlier, however, the regressions using n_births as the dependent variable will likely generate upwardly biased scarcity coefficients due to the endogeneity of scarcity and n_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 (page 24)

26 positive effect on n_births but a negative effect on BY5, especially when firewood scarcity reduces both measures of fertility. All samples in Table 10 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 Table 7, women in wards without firewood scarcity data have 0.22 (t-stat=2.27) fewer children than (page 25)

27 women in wards with firewood scarcity data. 15 This evidence suggests, at a minimum, the n_births regression in Table 9 excludes a significant number of observations with high levels of firewood scarcity and low levels of fertility. 16 This is likely to bias the firewood scarcity coefficient upward implying firewood scarcity 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 any population 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, 15 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. scarcity data. 16 Sample size would increase by 592 observations in Table 9 if all wards had firewood (page 26)