Poverty Monitoring, Measurement and Analysis (PMMA) Network Poverty Monitoring, Measurement and Analysis (PMMA) Network A paper presented during the 4th PEP Research Network General Meeting, June 13-17, 2005, Colombo, Sri Lanka. Labor Supply Responses to Income Shocks under Credit Constraints: Evidence from Bukidnon, Philippines, Jasmin Suministrado Philippines Labor Supply Responses to Income Shocks under Credit Constraints: Evidence from Bukidnon, Philippines, Jasmin Suministrado Philippines
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Poverty Monitoring, Measurement and Analysis(PMMA) Network
Poverty Monitoring, Measurement and Analysis(PMMA) Network
A paper presented during the 4th PEP Research Network General Meeting,June 13-17, 2005, Colombo, Sri Lanka.
Labor Supply Responses to IncomeShocks under Credit Constraints:
Evidence from Bukidnon, Philippines,
Jasmin SuministradoPhilippines
Labor Supply Responses to IncomeShocks under Credit Constraints:
Evidence from Bukidnon, Philippines,
Jasmin SuministradoPhilippines
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Labor supply responses to adverse shocks under credit constraints: Evidence from Bukidnon, Philippines*
Hazel Jean Malapit Action for Economic Reforms (AER)
* This work was carried out with the aid of a grant from the Poverty and Economic Policy (PEP) Research Network, financed by the International Development Research Centre (IDRC). The authors would like to acknowledge Chris Scott, Ignacio Francheschelli, A. S. Oyékalé, Bernard Decaluwé, Jean-Yves Duclos, John Cockburn, Agnes Quisumbing, Emmanuel Esguerra, an anonymous referee, and seminar participants at the PMMA Dakar meeting and Université Laval for their helpful comments.
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1. Introduction
The ability of families to cope with adverse shocks such as crop failure, unemployment, or illness
is an important aspect of vulnerability to poverty. The increasing attention on risk and vulnerability arose
from mounting evidence that shocks inflict permanent effects on human capital formation, nutrition and
incomes. The existence of poverty traps and other forms of persistence has shown that vulnerability to
poverty is in itself a source of deprivation. (Dercon 2001)
Well-being and poverty are the outcome of a complex decision process of households and
individuals given assets and incomes, and faced with risk. On the other hand, vulnerability is an ex ante
concept, determined by the options available to the households and individuals to make a living, the risks
they face, and their ability to handle these risks (Dercon 2001). The ultimate effect of risk on the well-
being of households and individuals depends largely on the coping strategies that may be employed by
the household to protect consumption when adverse shocks occur.
How is consumption insurance achieved in a low-income setting where formal credit and
insurance markets have been observed to be imperfect or missing? As noted by Kochar (1999), it is
widely believed that consumption insurance is achieved through asset transactions, i.e. saving and
dissaving. However, there are a variety of formal and informal mechanisms households may employ to
insure consumption from fluctuations in income. These risk-management strategies include: community
risk-sharing (e.g. reciprocal arrangements, state-contingent remittances), income diversification, adoption
of low-return low-risk crop and asset portfolios, savings depletion, sale of assets, borrowing, and ex post
labor supply adjustments, among others.
Because labor is often the only asset of the poor, this study attempts to measure the extent to
which farm households use labor supplied to off-farm work in the face of adverse shocks and binding
credit constraints. Moreover, this study investigates how this labor supply response differs between
women and men, and on the labor participation of school-age children. While previous research has
concentrated on the ‘added worker effect’ of wives to augment household income when their husbands
become unemployed, this role need not be confined to married women. In fact, the Filipino norm of
maintaining large households may be viewed as a risk-sharing arrangement, where secondary earners,
adults and children, may be called upon to participate in the labor market to maintain household income
when faced with a negative shock to household income.
Intuitively, the smoothing role of the secondary earners’ labor supply should be more important
for the case of poorer households who cannot rely on asset depletion or borrowing to cope with the shock.
The absence of a redistributive system of taxes or transfers, as well as the underdevelopment of insurance
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and credit markets also contribute to the importance of secondary earners as the primary household
coping mechanism.
This research differs from past studies in its explicit attention to both labor decisions and credit
constraints,1 analyzed within the framework of a collective household model. Contrary to the notion that
households are composed of individuals with common preferences, the collective model allows for
differences in opinion on economic decisions. The relative bargaining strengths of individuals may then
be an important factor in determining how conflicts are resolved within the household. Analyzing labor
supply responses to shocks in this framework allows for the possibility that some members may be
adjusting to income shocks more than others – in particular, members with less bargaining power.
The analysis of how households cope with adverse shocks is especially important in determining
how adjustment costs are distributed within the household. In the collective framework, this may be
linked with the relative bargaining strengths of males versus females, and adults versus children. Over
the long run, the effects of adjustment costs to certain household members may erode the ability of the
household to cope with future shocks, as is the case for example when children sacrifice schooling for
work.
Household responses at the micro level also translate to macro trends in employment, education
and health outcomes, especially when shocks are aggregate in nature (e.g. economic crises and the like).
The increasing volatility in world markets likewise increase the frequency and severity of aggregate
shocks faced by ordinary households. A deeper understanding of how adjustment costs are borne within
the household can inform social protection policy on where interventions are most necessary.
In his analysis of the effect of the East Asian crisis on the employment of women and men in the
Philippines, Lim (2000) finds that women have higher labor force participation rates and longer working
hours relative to men during the East Asian crisis. During this period, he also notes that high school
enrollment rates declined for both males and females, whereas elementary enrollment declined for
females but not for males. Lim (2000) concludes that in crisis times, and specifically in the East Asian
crisis, there was a tendency toward “overworked” females and “underworked” males. He notes that
maintaining and increasing labor market participation of females not previously in the work force
appeared to be an important coping mechanism in the Philippines.
The objective of this paper is to analyze whether women and men increase their market labor
supply in response to adverse shocks and in light of credit constraints. In addition to the labor supply 1 In the labor literature, the increase in household labor supply as a response to fluctuations in household income (e.g. unemployment of the breadwinner, crop failure) is referred to as the ‘added worker effect’. Because the presence of credit constraints limits the set of coping strategies available to households, the ‘added worker effect’ is expected to be stronger when households are unable to borrow to maintain consumption (Cullen and Gruber 1996; Lundberg 1985; Mincer 1962). Labor supply was seldom studied explicitly within the context of credit constraints, with the exception of García-Escribano (2003).
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response of adult women and men, we are also interested in how child labor is used as a coping strategy.
Children may directly participate in paid labor off-farm, or as unpaid workers at home, to allow adults to
seek outside employment.
In particular, we attempt to answer the following questions: Controlling for the effect of binding
credit constraints, do women and men work more days off-farm when faced with adverse shocks? Are
school-age children more likely to participate in paid or unpaid work in response to adverse shocks and in
light of binding credit constraints? Does relative bargaining power between spouses affect these labor
supply responses?
Our analysis uses the 2003 data from Bukidnon, Philippines, collected by the International Food
Policy Research Institute (IFPRI) and the Research Institute for Mindanao Culture (RIMCU), which
allows us to investigate these issues using two sets of households: (i) ‘original’ households, who are
demographically older and correspond to the same households surveyed two decades ago in 1984-85, and
(ii) ‘split’ households, who are children of original households that are now grown up and have set up
their own households. Comparing our findings for these two groups also allows us to investigate the life
cycle effects of labor responses to adverse shocks.
2. Review of literature
This research builds on two separate strands of literature: (i) the consumption smoothing
literature, and (ii) the literature on the smoothing role of secondary earners.
2.1. Consumption smoothing
The perfect risk-sharing hypothesis implies that, once aggregate shocks are accounted for, the
growth rate of consumption would be independent of any idiosyncratic shock affecting the resources or
income available to the household (Cochrane 1991, Deaton 1992, Townsend 1995, Skoufias and
Quisumbing 2002). Thus, the greater the correlation between household consumption and income, the
less effective the risk-management strategy adopted by the household. This approach has also been used
to assess the role of credit and savings as insurance substitutes, and make inferences on liquidity
constraints2 (Skoufias and Quisumbing 2002).
Although empirical work on consumption smoothing has rejected the full risk-sharing hypothesis
(Cochrane 1991, Townsend 1995), there is evidence that the overall effect of idiosyncratic income shocks
2 One key insight in the simulation results of Deaton (1991) is that a credit-constrained household may still be able to smooth consumption using precautionary savings, thus remaining consistent with the permanent income hypothesis (Skoufias and Quisumbing 2002).
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on household consumption is not large. This implies that some mechanisms or channels, including those
that in a first best allocation would be considered sub-optimal, absorb most of the shocks. (Garcia-
Escribano 2003)
Research on low-income economies (for example, see Morduch 1995) show that households use a
mix of formal and informal strategies to cope with adverse shocks including: community risk-sharing
(e.g. reciprocal arrangements, state-contingent remittances), income diversification, adoption of low-
return low-risk crop and asset portfolios, savings depletion, sale of assets, borrowing, and ex post labor
supply adjustments, among others. However, different households may have differential access to these
strategies. Poorer households in particular, may be less able to use strategies that rely on initial wealth as
collateral (Skoufias and Quisumbing 2002). On the other hand, it is often possible to adjust labor supply,
regardless of initial wealth.
As noted by Kochar (1999), past research has demonstrated that farm households in developing
countries are able to protect consumption from idiosyncratic shocks but offers little evidence on how this
is achieved. To be able to understand the underlying economic environment, it is important to study how
and to what extent specific mechanisms isolate consumption from the effect of idiosyncratic income
shocks. Much of the work on consumption smoothing has focused on the contribution of assets in
buffering consumption variability (Garcia-Escribano 2003, Kochar 1999). However, these studies may
not be relevant in explaining how consumption insurance is achieved in low-income communities, where
asset levels may be low and access to credit limited.
2.2. Smoothing role of secondary earners
The literature exploring the role of secondary earners in smoothing transitory shocks to the
household head’s earnings may be divided into two. The first set finds evidence of an insurance ef fect of
secondary earners to the extent that it crowds out precautionary savings (Kochar 1995, 1999, Merrigan
and Normadin 1996, Engen and Gruber 2001, Low 1999). Kochar (1995, 1999) concludes that well-
functioning labor markets in Indian villages allow households to increase labor income in response to
crop shocks, reducing the need to resort to asset depletion or borrowing to smooth consumption. Using
UK household data, Merrigan and Normadin (1996) find that precautionary motives are stronger for
households with two earners compared to households with a single earner. Similarly, Engen and Gruber
(2001) find that the effect of an increase in unemployment insurance on wealth holdings is smaller for
married couples than for singles in the US. Lastly, Low (1999) uses numerical methods to show that
precautionary savings in households with a secondary earner is smaller only if the correlation between
shocks to the potential wages of the husband and wife is sufficiently negative.
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The second set of literature explores the smoothing role of secondary earners through the ‘added
worker effect’. The ‘added worker effect’ refers to the temporary increase in female labor supply
(participation or hours worked) in response to transitory shocks to household income (excluding the
wife’s income). 3 Most studies estimate female employment or female hours worked as a function of the
husband’s labor status together with standard covariates (e.g. labor market characteristics, household
fixed effects). However, some studies have extended the definition of the husband’s (spouse’s) earnings
loss to account for underemployment (Maloney 1987), idiosyncratic earnings shocks other than
unemployment (Garcia-Escribano 2002), and health shocks (Coile 2004).
The presence of liquidity constraints is one of the main arguments put forward in support of the
existence of the ‘added worker effect’ (Mincer 1962; Lundberg 1985; Cullen and Gruber 1996; Finegan
and Margo 1994; Garcia-Escribano 2003). Cullen and Gruber (1996) report evidence that families are
liquidity-constrained during unemployment spells. This finding is consistent with Stephens (2001),
where empirical results for layoffs are consistent with liquidity-constrained households. Similarly,
Garcia-Escribano (2003) finds that households with limited credit access rely on the labor supply of wives
to smooth the husband’s earnings shocks.
The empirical results in the literature investigating the ‘added worker effect’ remains mixed.
Arguments put forward in support for the ‘added worker effect’ include: the substitutability of leisure of
husbands and wives in home production (Ashenfelter 1980; Lundberg 1985; Maloney 1987); an income
effect (Maloney 1987; Prieto and Rodriguez 2000); and, the presence of liquidity constraints (Mincer
1962; Lundberg 1985; Cullen and Gruber 1996; Finegan and Margo 1994; Garcia-Escribano 2003).
On the other hand, other factors that may obscure this effect include: assortative mating in tastes
for work among spouses (Maloney 1991; Lundberg 1985; Cullen and Gruber 1996); the wife’s
employment factors are affected by the same factors causing the husband’s unemployment, or the
‘discouraged worker effect’ (Serneels 2002; Prieto and Rodriguez 2000; Baslevent and Oneran 2001); a
crowding out effect from social insurance programs (Cullen and Gruber 1996; Finegan and Margo 1994);
the value of the unemployment benefit is linked to the wage received by the wife (Cullen and Gruber
1996); complementarity of leisure between spouses and caregiving needs (Coile 2004); and, different
measurement approaches (Lundberg 1985).
Among the knowledge gaps that emerge from this brief review is the consideration of liquidity
constraints. While it has been cited as the driving force for the ‘added worker effect’ in the life cycle
context, few studies explicitly include liquidity constraints in their empirical models. This line of
research is perhaps more relevant for rural areas in developing countries where credit markets are
imperfect and there are little or no unemployment benefits. 3 See Malapit (2003) for a review of literature on the ‘added worker effect’.
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In addition, only two studies extend the notion of the ‘added worker’ to other family members
(Serneels 2002; Kochar 1999), although in general, the ‘added worker effect’ refers to all potential
secondary earners in the family, including children. This point may have been irrelevant in the developed
country context where households are often nuclear, but it is not so in the case of developing countries. A
number of studies have linked child labor with income shortfalls and credit constraints (Jacoby and
Skoufias 1997, Dehejia and Gatti 2002), emphasizing that parents may be forced to draw on their
children’s labor when other strategies such as credit are not available.
Only a handful of studies on the ‘added worker effect’ use data on developing countrie s,
primarily as a consequence of the dearth of panel data. Such studies would also require analytical
methods more suited to the specific labor market characteristics in the developing country context. Also,
sources of income shocks may be more diverse for agricultural households (not merely unemployment),
and the ‘added worker effect’ is relevant for all potential secondary workers, which include children. An
exception is the work by Kochar (1999), which estimated hours of work responses to idiosyncratic crop
shocks in rural India. Her model distinguishes labor supply by gender, and all household members aged
15 to 45 may contribute to labor income. However, her model does not accommodate credit constraints.
3. Conceptual framework
This section will outline the conceptual underpinnings of this research. We begin with a
discussion of the unitary and collective household models to establish the theoretical relationships we
wish to explore. The next section will discuss the theoretical treatment of permanent versus transitory
shocks and its implications on labor supply. Lastly, we outline the implications of the collective model
on the ‘added worker effect’.
3.1. Unitary model
To simplify the exposition, we begin with the conventional intertemporal family labor supply
model where the household acts as a single decision-making unit (Killingsworth and Heckman 1986).
This approach is adapted to an agricultural setting, where farming is a significant source of household
income. For period t, the farm profit function may be represented by:
(1) ),,,( tttott pxh θππ = ,
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where oth is own-farm labor hours, tx is a vector of other inputs including hired labor, tp is a
vector of prices, and tθ is the realization of weather and other crop income shocks. To simplify notation,
assume that the household does not distinguish between the labor hours of its members. The household’s
optimization problem is given by:
(2) ∑∞
=−=
0,,),,,(max
t tmt
ott
tchh
zhhcUEUmo β ,
s.t. (2.1) ))(1( 11 ttmttttt chwara −+++= ++ π ,
(2.2) ttmt
ot lhh Ω=++ ,
(2.3) 0≥ta ,
(2.4) 0;0;0;0 ≥≥≥≥ tmt
ott lhhc .
Assuming that utility functions are additively separable over time, the household maximizes the
expected value of time and preference discounted utility (2), subject to the intertemporal budget constraint
(2.1) where profits, πt, is given by equation (1), time-endowment constraint (2.2), credit constraint (2.3),
and the non-negativity constraints (2.4). Total household consumption is given by tc , tz is a vector of
observed and unobserved factors affecting preferences, tw is the market wage, ta is the household’s
assets at the beginning of period t, and 1+tr is the exogenous interest rate in the next period. Farm profits
are defined as the value of output net of all costs excluding family labor, given by (1). The household’s
labor endowment tΩ , may be allocated to own-farm work oth , market work m
th , and non-market work or
leisure tl . For simplicity, the credit constraint takes the form of a non-negativity constraint on assets.4
This model can easily be extended to distinguish the labor hours of members according to gender by
disaggregating hours of work and wages for females and males.
This optimization yields labor supply functions that depend on aggregate consumption, own
wage, hours worked in own-farm, non-labor income, farm profits, earnings of other family members,
marginal utility of wealth (λt), and preference shifters (zt).
(3) ),,,,,( ttttott
mt
mt zwhchh λπ=
4 While there is evidence that households do not draw assets down to zero (Kochar 1999), setting a positive lower limit on assets rather than zero does not significantly alter the optimality conditions.
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3.2. Collective model
In recent years, there has been growing evidence contradicting the unitary model both in
developed and developing countries.5 An alternative is the collective model, which allows for differences
of opinion on economic decisions among household members. This model suggests that when
disagreement occurs, how it is resolved may depend on the relative bargaining power of individuals
within the household (Manser and Brown 1980, McElroy and Horney 1981, Quisumbing and Maluccio
2003). Time allocation and labor force participation decisions may in fact be the result of previous
bargaining (Quisumbing and Maluccio 2003).
In addition, conflict-resolution within the household is extremely relevant in the choice of coping
strategies. When adverse shocks occur, couples may have differing preferences on which member should
work more, which expenditures should be cut, or which child should work. Because the choice of
strategies undertaken may result in intertemporal trade-offs (e.g. sale of productive assets, or adjustment
of children’s schooling), the collective framework pro vides important implications for policy and
program design.
In a two-person household where preferences are altruistic, so each person (i = m, f ) cares about
the other’s allocation, we can write the single -period maximization problem as:
(4) ),,,,,,()1(),,,,,,(max zhhhhccUzhhhhccU mf
mm
of
omfmf
mf
mm
of
omfmm µµ −+ ,
subject to the same constraints outlined in the unitary case. It is simple to see that this general
model reverts to the unitary model if the individual utility functions are identical (common preferences),
or if the sharing rule µ is equal to zero or one (dictator).
It is likely that this sharing rule is related to the relative bargaining power of individuals within
the household. Letting bm and bf represent proxy measures for bargaining power that influence µ, market
labor supply functions can be expressed as:
(5) )),,(;,,,,,,,( zbbwwhhhcchh fmfmmj
of
omfm
mi
mi µπ=
The effect of individual bargaining power on market labor supply of person i can be interpreted as
the effect of changing the share of household income allocated to each household member, holding
household income constant. The key result of the unitary model, income pooling, implies that the identity
5 See Strauss and Thomas (1995) for a review.
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of the income earner, or the person in control of the resources is irrelevant. Thus, if income pooling holds,
these effects should be zero:
(6) fmjbh jmi ,,0/ ==∂∂
As suggested by Quisumbing and Maluccio (2003), this provides a straightforward test of the
unitary model by including proxy measures for male and female bargaining power in the estimation of
labor supply functions. Valid proxy measures are culturally relevant factors that reflect bargaining power
and are exogenous to decision-making within marriage. However, a more general test of the unitary
model is to examine whether the effects of the husband’s and wife’s assets are equal (Q uisumbing &
Maluccio 2003). That is, even if the effects of the husband’s and wife’s assets are non -zero, for as long as
they affect labor supply symmetrically, income pooling remains valid. We employ this general test in this
study.
Using anthropological evidence from rural Philippines, Quisumbing (1994) argues that inherited
landholdings are a valid measure of bargaining power since land is usually given as part of the marriage
gift and major asset transfers occur at the time of marriage. Following recent literature, we use assets
brought into the marriage (primarily human capital), as proxy measures of bargaining power (Quisumbing
and Maluccio 2003). One key limitation for using these types of proxy measures is that human capital
variables may also capture, in addition to bargaining power, labor productivity effects. However, we
continue to use human capital in this study in the absence of superior candidate proxy measures.
3.3. Permanent versus transitory shocks
According to the permanent income hypothesis, consumption is constant over the lifecycle and
depends on permanent income. Temporary fluctuations in income are thus smoothed through credit and
savings and should not affect consumption. Following this argument, only permanent shocks should
affect labor decisions.
Contrary to the permanent income hypothesis, the ‘added worker’ hypothesis predicts that
negative transitory shocks to household income, through shocks on farm profits (e.g. crop failure) or
earnings of other family members (e.g. unemployment), will result in a contemporaneous increase in
market hours of work, all other things equal. The theory also implies that the increase in market hours of
work will be temporary, and will no longer be necessary once the shock has subsided.
In his classic article on female labor supply, Mincer (1962) showed that in a given period, the
‘temporary’ reduction in family income due to the husband’s unemployment increases the probability that
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the wife will participate in the labor market in that period. He emphasized that this effect is expected
when the family have few consumption-smoothing alternatives: “However, if assets are low or not liquid,
and access to the capital market costly or nonexistent, it might be preferable to make the adjustment to a
drop in family income on the money income side rather than on the money expenditure side … a
transitory increase in labor force participation of the wife may well be an alternative to dissaving, asset
decumulation, or increasing debt.” (Mincer 1962)
On the other hand, Heckman and MaCurdy (1980) observed that ‘permanent’ factors resulting to
higher unemployment probability of the husband should increase the labor supply of wives over their
lifetimes, and not only during the periods of unemployment. Thus, in a life-cycle setting, the ‘added
worker effect’ cannot be expected to be large unless in the presence of credit constraints (Lundberg 1985;
Heckman and MaCurdy 1980). Lundberg (1985) notes that without such a constraint, the wealth effect of
a short unemployment spell is likely to be small, and contemporaneous movements in the labor supply of
a married couple will reflect only cross-substitution effects, which are expected to be small.
This research proposes to investigate both types of shocks within a collective framework: those
that are transitory, occurring within the current period, and those shocks that are possibly transitory but
may exhibit some persistence, occurring over the last twenty years. If credit constraints are binding, both
types of shocks are expected to result in labor supply adjustments.
3.4. ‘Added worker effect’ in the collective model
The collective model, where the distribution of consumption and work is determined by relative
bargaining power, has ambiguous implications on the ‘added worker effect.’ Depending on relative
bargaining power, wives may receive a higher or lower share of her husband’s income relative to the
unitary model. If she is entitled to a larger share of her husband’s income, this implies a higher
reservation wage and lower labor market participation. Conversely, if she is entitled to a smaller share of
her husband’s income, it implies that reservation wages for wives are lower than in a unitary setting.
Lower reservation wages implies higher labor market participation, because more job offers become
acceptable.
Also, the ‘added worker effect’ may be larger or smaller depending on relative bargaining power.
For example, a smaller share of the husband’s earnings implies a weaker income effect. However,
depending on the bargaining process in the household, it is also possible for those members with less
bargaining power to absorb a larger share of the cost of the adverse shock. The consumption and leisure
of the more powerful family members may be insured, while the consumption and leisure of the less
powerful family members are the ones that adjust.
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4. Data description
This study uses 2003 data from Bukidnon, Philippines, which is a resurvey of households from a
four-round panel survey conducted in 1984-85. The household sampling procedure in 1984/85 was
conducted using a quasi-experimental design to compare households that shifted to sugarcane production
and households that did not following the construction of a sugar mill in the province in 1977. The
survey area extended beyond the neighborhood of the sugar mill, to include households that did not have
the opportunity to adopt sugar (due to prohibitive transport costs) but shared a common farming
environment and cultural heritage with sugar-adopting households (Bouis and Haddad 1990). There were
448 households surveyed in all four rounds, and the last three rounds can be aggregated to comprise a full
year.
The 2003 data resurveys 305 of the core 448 households in 1984/85, as well as 257 new
households formed by children from the original households who are now living in separate households.6
From these 562 households, we include 234 original and 229 split households who have both spouses
present. Primarily we use the 2003 data for the labor supply analysis, although we also use variables from
1984/85 to proxy for certain household and farm characteristics that may be suspected of endogeneity.7
4.1. Identifying credit-constrained households
As a general definition, we define a household to be credit-constrained if it would like to borrow,
for whatever purpose, but cannot obtain credit from any source. We shall no longer distinguish between
formal and informal credit sources as they can function equally well in protecting consumption from
income shocks.
One common method of testing for credit constraints is the consumption insurance hypothesis. If
the growth rate of household consumption covaries with the growth rate of household income, then the
household is said to be credit-constrained. However, one cannot simply look at the smoothness of
consumption and know which mechanisms are at work. If labor income can be used to smooth
consumption, consumption will appear to be insured even in the presence of binding credit constraints.
Thus, to identify households that face binding credit constraints, a direct approach based on household
responses to qualitative questions on credit will be necessary.
6 The 2003 survey initially surveyed 311 original households and 261 split households. Of these 572 households, 10 households were dropped due to missing age and/or sex data for at least one of the household members. 7 Split households are assigned the same past household characteristics as the original household where they came from.
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In the data, the question “If more credit were available for [purpose] in the past 12 months,
would you have used it? Why not?” was included in the Assets, Backyard Production, Family Business,
Farm Production, and Non-Food Expenditures blocks. Based on this question, households responding
“Yes” to the qualitative question are classified as self -reported credit-constrained. We construct an
indicator variable for each block, and then construct summary indicators for production credit constraints
and non-production credit constraints. Households are classified as credit constrained in production if the
household responded “Yes” to the credit constraint question in at least one of the following blocks: Farm
Production, Family Business, or Backyard Production. Households are classified as credit constrained in
non-production if the household responded “Yes” to the credit co nstraint question in either the Assets, or
Non-Food Expenditures blocks.
4.2. Measuring household income shocks
From the theoretical model, labor supply functions depend on a set of variables including farm
profits, non-labor income, and earnings of other household members. Shocks entering through any of
these factors may result in adjustments in market labor supplied for credit-constrained households.
Because our data deals with agricultural households, fluctuations in crop income are significant sources of
household income shocks.
Several approaches may be used to measure crop income shocks. The first alternative is to use
the residual from a profit regression (Kochar 1999). Positive and negative residuals may be treated as
separate shocks, since strategies used by households to respond to positive shocks are expected to be very
different from strategies used to respond to negative shocks. One problem with this approach is that this
residual contains unobserved variables that determine household expectations, as well as measurement
error in profits. Because the profit regression excludes costs of family labor and other family owned
inputs, it also contains unobserved preference shocks that determine leisure choices.
The second alternative is to use standard instrumental variables techniques. This avoids the
problems associated with the first approach if there is an instrument that is correlated with the “true”
idiosyncratic crop shock but not with preference shocks or measurement error in crop profits.
Although the Bukidnon data set provides a wide set of instruments,8 predicted crop income
shocks obtained using instrumental variables techniques did not result to coefficient estimates
significantly different from zero. Alternatively, we include self-reported incidents of adverse shocks
8 Instruments used include rainfall deviations from the long-run average and incidents of crop failure due to drought and pests, as well as their interactions with farm characteristics (e.g. farm size, crop choice), and incidents and duration of illness by household members.
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occurring between 1984 and 2003. Various sources of shocks are documented including: weather or
environmental shocks affecting crops or livestock (e.g. drought, flooding, pests, diseases); war, civil
conflict, banditry and crime (e.g. theft, military presence); political, social and legal events (e.g.
confiscation of land, land reform), unexpected economic shocks (e.g. unemployment, severe lack of
financing, severe inability to sell inputs); and unexpected events affecting health or welfare of members9
(e.g. death, illness, disablement, divorce, abandonment). Respondents are reminded that the shocks they
report must have been difficult to foresee and must have significantly affected their households.
We construct count data for the number of incidents for each type of shock and distinguish
between two time periods: past shocks are defined as occurring before 2003, while current shocks are
defined as occurring in 2003. Table 1 presents a list of specific shock categories used in the analysis.
4.3. Descriptive statistics
The means and standard deviations for selected variables are presented separately for original and
split households in Table 2. As we expected, the two groups exhibited statistically significant differences
in the means of a majority of the variables, reflecting the life cycle differences between the two sets of
households.
Original households are larger on average, with more prime-aged male members and less prime-
age females than split households. Split households on average have more young children and school-age
children, while original households have more elderly members. Interestingly, the prime-age members of
original households are younger on average compared with prime-age members of split households. It is
possible that children set up their own households after a certain age, while the younger adult children are
more likely to continue living with their parents.
As expected, heads of original households and their spouses are older and less educated than their
counterparts in split households. Based on these averages, it appears that although original households
are ‘older’ in the sense that there are more elderly members and older household heads and spouses, they
actually have a larger pool of prime-age workers.
Original households are also wealthier than split households on average. They own more land,
more rent-earning assets, and more livestock than split households. They are more likely to be engaged in
farming their own land, have higher loans in the past year, and are more likely to welcome more credit for
production purposes. On the other hand, almost half of split households do not farm or own any land.
This could also explain why on average, both males and females in original households work more days
9 Shocks affecting the health and welfare of the household differ from the other shocks in that it can alter the labor endowment of the household. The effect of this type of shock on labor supply is ambiguous.
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in their own farms compared with split households. While the number of days worked in off farm
employment by males are not statistically different between the two groups, females in split households
work less days on average compared with females in original households.
Because of the longer history of original households, it is expected that they report more incidents
of adverse shocks occurring over the last twenty years compared with split households. On the other
hand, there seem to be no significant difference between the experience of current shocks for original and
split households except for other weather shocks and other welfare shocks. Original households report a
higher incidence of these two shocks during the year, which is plausible because of their greater
involvement in farming and their demographic composition.
5. Empirical analysis
We conduct separate analysis for original versus split households for two reasons. First, because
split households are formed by children of original households, the two groups are not independent,
having shared common characteristics in the past. Second, the two groups of households are at different
points of their life cycle. Original households are expected to have an older demographic composition
compared with the splits, and each group may respond differently to adverse shocks.
First-order conditions from the household’s utility maximization yield market days of work
equations for female and male labor. Because farm households rely primarily on family labor for crop
production, corner solutions (i.e. zero market days of work) are expected to be significant for both
females and males. Thus, market days of work functions may be estimated using Tobit regressions,
where observed days ))(( ⋅mh equal desired days ))(( ⋅∗h when the latter are positive and zero otherwise.
For labor category i in household j, desired market days of work equation is given by:
(7) ijijjijijij VZxh εαθαααα +++′++=∗43210 '''
where xij is a vector of household characteristics and proxy measures of male and female
bargaining power, Zij is a vector of production and demographic shift variables, Vj is a vector of location
dummies, ijθ is a vector of adverse shock variables, and ijε is an error term with mean zero. If credit
constraints are not binding, the sign of 4α is ambiguous, because the set of coping strategies used by the
household to respond to adverse shocks would depend on the accessibility of various coping strategies as
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well as the bargaining strengths of members. On the other hand, if credit constraints are binding, we
expect 4α to be positive for both persistent past shocks and transitory current shocks.
Because the presence of binding credit constraints narrows the set of coping strategies available
to the household, and consequently increases the importance of labor supply adjustments as a coping
strategy, it is important to incorporate the effect of credit constraints in our analysis of labor supply.
However, since the credit constraint status of the household is endogenous, we cannot simply split the
sample according to the summary indicator variables we have constructed, or include the indicator
variable as a regressor. Instead, we attempt to correct for the presence of binding credit constraints by
first estimating a bivariate probit model of credit constraints:
(8) npnpnpnpnp
probitnp
pppppprobitp
uWkkk
uWkkk
+=>=
+=>=∗
∗
β
β
'where0
,'where0*
*
where probitpk and probit
npk are observable binary outcomes for possibly interrelated underlying
relationships given by *pk and *
npk for production credit constraints and non-production credit constraints,
respectively; pW and npW are variables that explain production and non-production credit constraints;
and, pu and npu are mean zero error terms.
From the bivariate probit estimates, we compute for the inverse Mills’ rat ios and include these as
regressors in the Tobit estimation of the days worked equation for females and males:
National Economic and Development Authority (2001). The Medium-Term Philippine Development
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Quisumbing, A. R. and Maluccio, J. A. (2003). “Resources at marriage and intrahousehold allocation:
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North-Holland, Amsterdam.
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Income Economies,” Journal of Economic Perspectives 9(3): 83-102.
Zeldes (1989). “Consumption and liquidity constraints: An empirical investigation,” Journal of
Political Economy, 97(2): 305-46.
Zeller, M. and Sharma, M. (1998). Rural finance and poverty alleviation. Washington, D.C.: IFPRI.
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Table 1: 2003 Shock Categories and Variable Names
Variable Name*
Weather or environmental shocks
ndroughtp/03 Droughtnpestshp/03 Pests or diseases that affected crops before they were harvested (bugs/rats)
Too much rain or floodToo humidEarthquakeLandslidesErosionHigh windsPests or diseases that led to storage lossesCrop loss due to firesPests or diseases that affected livestock (livestock death)Livestock death due to heatOverall bad harvest season
ncivwarp/03 War, civil conflict, banditry or crime shocks
Destruction, confiscation or theft of tools or inputs for productionTheft of cashTheft of stored cropsDestruction or theft of housingDestruction or theft of consumer goodsMilitary presence (reduced mobility/ increased tension)
nnegsocp/03 Negative political, social or legal events
Confiscation of landConfiscation of other assetsLand reformResettlement, villagization or forced migrationForced contributions or arbitrary taxationImprisonment for political reasonsDiscrimination for political reasonsDiscrimination for social or ethnic reasonsContract dispute or default affecting access to landContract dispute or default affecting access to other inputsContract dispute or default affecting sale of products
Negative economic shock
ncapitalp** Severe, temporary lack of financing/ capitalSevere, temporary lack of access to inputsIncrease in input pricesDecrease in output pricesSevere, temporary lack of demand or inability to sell agricultural productsSevere, temporary lack of demand or inability to sell non-agricultural productsUnable to obtain labour at key crop cycle timesBreak down of processing servicesBreak down in transportation servicesUnemploymentUnexpected change in government regulation concerning income generating sources
Shock regarding health or welfare of householdDeath of husbandDeath of wifeOther death , specify _________Illness of husbandIllness of wifeOther illness, specify_________HospitalizationDisablement of working adult household membersDisablement of other household membersDivorceAbandonmentDisputes with extended family members regarding landDisputes with extended family regarding other assetsCo-op failed due to mismanagementUnexpected change in government regulation concerning eligibility for programatic assistance
*Shock variables with suffix "-p" refer to past shocks, occurring between 1985 and 2002; shock variables with suffix "-03" refer to current shocks occuring in 2003.**ncapital03 was excluded because more detailed variables are available for current period credit constraints.
Other negative economic shocks
nothnegecp/03
Death
Illness
ndeathp/03
nillnessp/03
Other weather shocksnothweathp/03
Other welfare shocks
nothwelfp/03
Shock Categories
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Table 2: Descriptive Statistics of Original and Split Households
Original Households Split Households N=305 N=257
Variable label Mean Std. Dev. Mean Std. Dev. Demographic characteristics no of hh members 6.380 3.340 4.774 2.182 *** no of children aged <6 in hh 0.495 0.753 1.280 0.901 *** no of children aged 6-15 0.833 1.104 1.008 1.196 * no of elderly aged >65 in hh 0.170 0.559 0.012 0.108 *** no of school-age children participating in paid/unpaid work 0.364 0.762 0.311 0.753 no of male household members aged 15-45 1.980 1.953 1.319 0.824 *** no of female household members aged 15-45 1.141 1.191 1.304 0.791 * ave age of male household members aged 15-45 19.632 11.427 29.357 7.406 *** ave age of female household members aged 15-45 17.414 14.081 27.741 5.548 *** no of males aged 15-45 w/ elem educ 0.003 0.057 0.000 0.000 no of females aged 15-45 w/ elem educ 0.000 0.000 0.000 0.000 no of males aged 15-45 w/ secondary educ 0.790 1.119 0.494 0.691 *** no of females aged 15-45 w/ secondary educ 0.554 0.789 0.739 0.833 *** no of males aged 15-45 w/ higher educ 0.302 0.674 0.218 0.467 * no of females aged 15-45 w/ higher educ 0.282 0.573 0.245 0.490 HH characteristics total hectares of land owned 2.418 5.379 0.307 1.704 *** =1 if HH does not farm & does not own land 0.213 0.410 0.463 0.500 *** present value of rent earning assets 14,019 79,589 1,001 6,353 *** net present value of all animals owned by hh 3,797 5,604 2,227 4,512 *** =1 if ever loaned in last 12 months 0.786 0.412 0.723 0.449 * total amount borrowed during past 12 months-all sources 32,726 101,465 10,834 26,281 *** =1 if would like more credit for agri/non-agri prod'n 0.384 0.487 0.296 0.457 ** =1 if would like more credit for non-production 0.272 0.446 0.249 0.433 Work variables days worked in own farm by male hh members 40.570 84.825 18.638 43.552 *** days worked in own farm by female hh members 13.062 40.717 4.403 19.973 *** days worked in all off-farm employment by male hh members 146.666 208.002 143.588 140.682 days worked in all off-farm employment by female hh members 60.525 119.586 40.453 85.790 ** Human capital of spouses highest grade attained by male spouse in hh 15.446 9.426 21.089 7.432 *** highest grade attained by female spouse in hh 17.826 6.474 23.354 6.073 *** age of male spouse in hh 48.433 18.684 31.136 7.783 *** age of female spouse in hh 50.210 10.828 28.891 6.537 ***
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Table 2: Descriptive Statistics of Original and Split Households, cont’d
Original Households Split Households N=305 N=257
Variable label Mean Std. Dev. Mean Std. Dev. Past shocks no of incidents of drought before 2003 0.387 0.488 0.109 0.312 *** no of incidents of pest infestation before harvest before 2003 0.256 0.437 0.117 0.345 *** no of incidents of other weather shocks before 2003 0.157 0.407 0.062 0.242 *** no of incidents of civil war before 2003 0.128 0.354 0.039 0.194 *** no of incidents of negative political social or legal events before 2003 0.072 0.272 0.012 0.108 *** no of incidents of severe lack of financing before 2003 0.066 0.248 0.035 0.184 no of incidents of other negative economic shocks before 2003 0.052 0.223 0.016 0.124 ** no of incidents of death before 2003 0.246 0.475 0.039 0.231 *** no of incidents of illness before 2003 0.328 0.548 0.276 0.521 no of incidents of other welfare shocks before 2003 0.075 0.277 0.019 0.138 *** no of incidents of all types of adverse shocks before 2003 1.767 1.331 0.724 0.938 *** Current shocks no of incidents of drought in 2003 0.003 0.057 0.012 0.108 no of incidents of pest infestation before harvest in 2003 0.026 0.160 0.012 0.108 no of incidents of other weather shocks in 2003 0.039 0.195 0.008 0.088 ** no of incidents of civil war in 2003 0.013 0.114 0.004 0.062 no of incidents of negative political social or legal events in 2003 0.007 0.081 0.000 0.000 no of incidents of other negative economic shocks in 2003 0.003 0.057 0.016 0.124 no of incidents of death in 2003 0.026 0.160 0.016 0.124 no of incidents of illness in 2003 0.069 0.266 0.093 0.305 no of incidents of other welfare shocks in 2003 0.020 0.139 0.004 0.062 * no of incidents of all types of adverse shocks in 2003 0.207 0.466 0.163 0.420 Note: Means of the two groups were tested using a t-test with equal variances, P > |t|; *** p-value was significant at the 1% level, ** p-value was significant at the 5% level, * p-value was significant at the 10% level.
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Table 3: Bivariate Probit Results
BIVARIATE PROBIT ORIGINAL HHS SPLIT HHS
[1] [2] [3] [4]
Variables prod'n credit
constraints
non-prod'n credit
constraints prod'n credit
constraints
non-prod'n credit
constraints no of hh members 0.0074 0.0075 0.0320 0.0017 no of children aged <6 in hh 0.0758 0.0053 -0.0331 0.0879 no of elderly aged>65 in hh -0.3358 * 0.2233 0.5059 0.7294 age of household head -0.2863 ** -0.0070 0.1101 0.1026 age of household head squared 0.0027 ** 0.0000 -0.0016 -0.0014 highest grade attained by household head 0.0155 -0.0074 -0.0044 0.0052 total hectares of land owned -0.0042 -0.0535 ** -0.0069 0.0275 percent hectare-months planted to sugar 0.4474 ** 0.2050 0.5392 ** -0.2133 no of shocks experienced in 1984-2002 0.2401 *** 0.2655 *** 0.1989 ** 0.2480 *** =1 if borrowed during past year -0.2364 -0.6408 *** -0.6925 *** -0.3979 * constant 6.4432 * 0.0971 -1.7592 -2.3803 /athrho 0.6529 *** 0.7724 *** rho 0.5736 0.6483
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Table 4: Tobit and Poisson Regression Results
TOBIT POISSON
ORIGINAL HHS SPLIT HHS ORIGINAL HHS SPLIT HHS
[5] [6] [7] [8] [9] [10]
male days
worked
female days
worked male days
worked
female days
worked
no. of children working
no. of children working
Past HH characteristics per capita expenditures in 1984/85 -31.4207 -16.0901 -27.7507 * 8.2121 -0.5424 * -0.1646 no of hh members in 1984/85 18.2355 -8.0219 26.9838 -16.0134 0.5837 * 0.2121 per capita farm size in 1984/85 -113.0139 ** -194.2571 *** 0.4856 45.6857 0.6888 1.5089 per capita farm size squared in 1984/85 8.4831 22.9782 -7.2065 -14.4727 -0.0936 -0.5437 * years of schooling of hhh in 1984/85 3.5551 4.0559 3.1051 1.5158 0.0830 0.1146 age of hhh in 1984/85 -1.9018 -18.1100 ** 1.2131 1.1074 0.0021 -0.0496 ** age squared of hhh in 1984/85 0.0016 0.0210 ** -0.0013 -0.0011 0.0000 0.0000 ** =1 if hhh in 1984/85 born in misamis -9.7809 -60.6558 44.3201 79.1614 * -0.0716 0.7492 =1 if hhh in 1984/85 cebuano 28.6653 -10.5405 2.1162 47.3391 -0.1618 -0.6134
Demographic & HH characteristics no of children aged 6 -15 -8.7545 -59.9720 *** -15.1399 28.6281 * 0.8328 *** 0.7946 *** no of children aged <6 in hh -30.2848 -64.1777 ** -24.0593 -2.5449 -0.2929 -0.1822 no of elderly aged>65 in hh -57.5544 47.1121 -0.3698 no of hh members living in 45.1424 *** 49.6043 *** 18.3385 -40.9267 ** 0.1573 -0.0297 no of prime age males in hh, aged 15-45 4.8843 -76.8056 *** 35.4406 110.4736 *** -0.0174 0.2754 no of prime age males in hh, aged 15-45 -65.6872 ** -14.6084 -4.7626 137.1148 *** -0.0548 -2.6628 ** ave age of males in hh aged 15-45 6.2185 -3.5047 14.9997 * 1.8139 -0.0421 0.5237 * ave age of males in hh aged 15-45 squared -0.0683 0.1310 -0.2930 * 0.1090 0.0007 -0.0080 * ave age of females in hh aged 15-45 2.5117 -3.5612 13.6021 24.2218 -0.0225 -0.6479 ave age of females in hh aged 15-45 squared -0.0522 0.1429 -0.2270 -0.4093 0.0001 0.0083 no. of prime-age males w/ secondary educ 21.5689 17.9124 -27.4671 23.2811 -0.0247 -0.0929 no. of prime-age males w/ higher educ -25.7577 40.8579 5.2113 17.0708 -0.4618 -2.1184 * no. of prime-age females w/ secondary educ 8.1010 46.0571 7.7202 -79.6364 *** -0.1028 2.3074 *** no. of prime-age females w/ higher educ 25.2156 101.7759 ** -57.2829 -64.6888 0.0837 4.9493 *** present value of rent-earning assets 0.0001 0.0004 -0.0028 * 0.0038 * 0.0000 0.0000 Proxy measures of bargaining power highest grade attained by male spouse in HH 2.3924 -4.4313 2.1707 -4.8445 -0.0080 0.1244 highest grade attained by female spouse in HH 2.3996 -1.0014 3.4932 12.3641 ** -0.0672 -0.3515 *** age of male spouse in HH 69.4904 369.3440 ** -9.0412 -33.5536 -0.0859 0.9512 age of female spouse in HH -12.8720 97.3013 ** -8.5639 -13.8324 0.4171 0.7218 age of male spouse squared -0.5740 -3.4099 ** 0.1920 0.3821 0.0018 -0.0129 age of female spouse squared 0.1204 -0.9555 ** 0.1148 0.3728 -0.0056 -0.0078
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Table 4: Tobit and Poisson Regression Results, cont’d
TOBIT POISSON
ORIGINAL HHS SPLIT HHS ORIGINAL HHS SPLIT HHS
[5] [6] [7] [8] [9] [10]
male days
worked
female days
worked male days
worked
female days
worked
no. of children working
no. of children working
Past shocks no of drought before 2003 -4.5238 -41.7140 -51.4486 49.6813 -0.1430 0.6491 no of pest infestation before 2003 18.1167 -1.0514 -98.5071 *** 7.9717 0.1326 1.1790 ** no of other weather shocks before 2003 -56.9227 -33.9766 -37.2229 91.8356 0.4803 0.1247 no of civil wars before 2003 5.9917 -31.3237 -38.1839 -70.6091 -0.5454 -0.2168 no of negative social events before 2003 15.0383 -3.4000 43.9519 -4.7771 -0.1968 2.6210 ** no of severe lack of financing before 2003 17.7266 -110.0320 * -78.3927 41.7350 -0.4564 0.6906 no of other negative econ shocks before 2003 75.6872 -21.2379 -65.5733 87.0959 -1.4004 -2.1039 no of deaths before 2003 -53.4198 76.3628 * 107.3691 ** -32.3007 0.5181 1.0342 no of illnesses before 2003 -36.3117 -11.0693 -4.4105 45.5390 0.0124 0.9726 * Current shocks no of pest infestation in 2003 14.2106 211.5482 * -41.7415 -591.4888 ** 0.2405 -13.6223 no of other weather shocks in 2003 -213.9112 *** 27.8218 -47.5264 100.4389 0.5258 -13.6985 no of other negative econ shocks in 2003 229.2142 327.8972 -189.8046 ** 94.4359 0.8476 1.9312 * no of illness in 2003 -36.5765 66.1011 1.6634 25.3626 0.5170 0.5129 no of other welfare shocks in 2003 -28.4363 1.8667 -0.0708 -15.2734