MPC greater than unity: the “leverage
effect” of transfer income*

KEVIN LUO
Graduate School of Economics, Kobe University
2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan
luokaiwen1991@gmail.com

Abstract: This study investigates the explanation for abnormally large marginal
propensity to consume (MPC). Focusing on Chinese household behaviors during the
turbulent transition period, the analysis corroborates that a modest amount of transfer
income could trigger substantial household spending (like a “leverage”); and the mostsignificant spending response is observed in the scenario where the private transfer may
have relaxed the liquidity constraints which prevented households from purchasing
high-priced goods. On the other hand, “MPC greater than unity” implies a considerable
opportunity cost of the contemporary transfer programs. Several prescriptions areprovided to improve the effectiveness of social transfers.
Keywords: Marginal propensity to consume; Transfer income; Transfer program; China;
Two-stage least squares
JEL classification: D12, D13, D91, I38*I thank Prof. Mitoshi Yamaguchi, Prof. Tomoko Kinugasa, and Prof. Kai Kajitani for their valuable comments.

1. Introduction

Can marginal propensity to consume (MPC, hereafter) exceed unity? Mosteconomists probably say no, and there is ample evidence that the MPC regarding
different income sources and expenditures is far below unity. Theoretically, MPC is a
merit of “induced consumption,” expressed as the derivative of consumption withrespect to income (∂C/ ∂I). However, many empiricists interpret it as “the proportion of
additional income that is consumed.” If this interpretation is correct, MPC is unlikely
to exceed unity because people cannot spend more than one dollar out of one dollar.
When MPC is found greater than unity, common explanations are theeconometric misspecification and data problem. A very few researchers asserted that
this anomaly probably reflects the irrational dissaving behaviors under situations suchas inflation, recession, and liquidity constraint (e.g., Landsberger, 1966; Ishikawa andUeda, 1984; Maluccio, 2010)1. However, these explanations are not self-evident. By
exploiting a specific data source—the 2002 China Household Income Projects, thisstudy provides an empirically relevant explanation for the anomaly. The estimated MPCof transfer income is greater than unity, and the phenomenon is primarily attributed toChina’s economic features in 2002, in which the families had pent-up demand forcertain high-priced goods, but the purchase was typically inhibited by the liquidity
constraints2.
1

Another anomaly is the negative MPC. Choi et al. (2004) claimed that the MPC of windfall
capital gains could be negative in the short run, due to the feedback effect of high rewards in thepast.
2 Strictly speaking, the term “MPC” should be used in the context where the consumption iscontinuously differentiable. However, the spending response and income fluctuation in the real2

A prime example is the effect of private transfer on homeownership. In the early
2000s, due to the unanticipated privatization of China’s housing market and theunderdeveloped financial systems, house-demanding families in China had little
savings in preparation, and they were on average borrowing constrained. This deficit
was generally made up by private transfers (e.g., remittance and gift from relatives andfriends), which helped the recipients to raise funds, shorten the saving time, and meetthe down payment requirement (as argued by Engelhardt and Mayer, 1998; Guiso andJappelli, 2002). In this case, a small amount of private transfer can trigger a purchaseof otherwise unaffordable commodity house (a leverage effect), and the MPC is greaterthan unity.
This evidence applies to household’s allocation of transfer income, which is ofsignificant policy interest and deserves further exploration. Over the past decades, theemergence of quasi-experimental transfer programs has motivated a large body of
empirical research. Researchers have unanimously found that the MPC of transfer
income is larger than that of regular income (regarding the concerning expenditure), yet
the derived MPCs appear exceedingly small 3 . These findings have raised acutequestions. Whereas transfer income could have either large or small effect on household
consumption, the reported MPC of regular income is often too small to be in line withthe reality, implying that the conventional empirical methods might have systematicallyworld are never continuous. To avoid confusion, in this study the term “MPC” represents theinduced consumption by a one-unit increase in income.
3 Kabeer et al. (2012) presented a comprehensive literature survey on conditional transfer
programs, in which some of the policy effects were considered insignificant. In Senauer and Young(1986)’s survey, the reviewed MPC of food stamp on food spending is about 0.35, on average.
Fraker (1990) reached similar conclusion: the reviewed MPC is between 0.2 and 0.45. Other existing
evidence will be attached in the following parts of this study.
3

underestimated the marginal effects. This deficiency is particularly noticeable in theliterature of food stamp (a typical in-kind transfer), in which the reported MPC ofmoney income (on food spending) is below 0.10, on average (Senauer and Young, 1986;
Fraker, 1990). More importantly, given that an average household usually spends morethan 70% of its total income, one might suspect that many of the transfer projects—
mainly designed to stimulate household spending (e.g., nutrition intake)—have failed
to achieve their objectives, not absolutely but relative to the conventional consumption
pattern. Empiricists often take an excessive interest in qualitative measures thus
overlook the validity of quantitative estimates4.
Intuitively, the effectiveness of transfer program depends on the extent to which
the recipients need it (viewed by beneficiaries), and whether they would allocate it in asocially desirable way (viewed by policymakers). The derived evidence indicates thatprivate transfer has a much greater stimulative effect than does public transfer.
Accordingly, targeting “the neediest” (as is done in conducting private transfer) will
significantly improve the effectiveness of public transfers. Indeed, this involves a tradeoff between the equity and efficiency of transfer programs. The evidence contributes tothe on-going debate on the policy trade-off as it has revealed the underlying opportunity
cost of the contemporary transfer programs—the MPC of transfer income could beenormous (the leverage effect).
The structure of this paper is as follows. Section 2 briefly reviews China’s

4

For intuitively reasonable estimates, for instance, see Kreinin (1961) and Landsberger (1966),
who have reached similar MPCs of current income (about 0.7) but distinguished MPCs of windfall
income. Also refers to the common-cited finding of Friedman: the average propensity to consume
out of permanent income is roughly 0.9.
4economic reform in the 1990s and discusses the behavior effects triggered by the
background changes. Sections 3 and 4 report the data set and empirical strategy,
respectively. Section 5 presents the estimates on the MPC of total transfer income. The
stimulative effects of private and public transfers will be distinguished and investigatedin Section 6. Section 7 assesses the MPCs of private and public transfers on different
expenditures. Section 8 discusses the policy implication, and Section 9 concludes thestudy.

2. China’s economic reform and the behavior effects

China’s remarkable economic progress in the 21st century owes a great deal to theeconomic transition in the 1990s, of which the core concept is to shift from a socialist
command economy to a market economy. The process involves (1) “opening up”
strategy to internationalize the markets and introduce competitive mechanism (2) SOE
reform aimed at downsizing the non-competitive state-own enterprises (3) transition
from the “cradle to grave” pension system to a market-oriented scheme, which has abroader coverage but a much lower replacement ratio (Feng et al., 2011) (4) the
liberalization and privatization of market economy, including the commercialization of
homeownership, and the introduction of price mechanism into educational and medical
systems.
These vicissitudes have triggered significant changes in household behaviors. The
SOE reform has yielded massive layoffs in the state sector; the breaking of the internal
security system has largely scaled back the social benefits (e.g., pension, healthcare,5education, and housing); the market liberalization has enlarged families’ burden andself-responsibility for their spending, which directly boosted the social consumption;
the sweeping restructuring has resulted in considerable uncertainty, which stimulated
precautionary savings of various motives (Meng, 2003).
The falling income, increasing demand, and surging prices caused by the reform
can be generalized as an income shock in a broader sense. How people react to adverse
shocks is a central and enduring economic topic. China’s experience presents an ideal
setting to test for the evidence of large MPCs, for its unique economic features: (1)market innovations have caused the price discontinuity5 of specific consumer goods
that closely relate to the very interests of people (2) the transition is tremendous andunpredictable (3) the social security and financial systems are underdeveloped.
Chinese households have long grown used to the old systems. Thus they had very
little buffer stock in the material and mental aspects. In the absence of sufficient public
insurance systems to relieve the adverse outcome, households’ extra spending waslargely financed out of accumulated savings and the informal risk-sharing mechanisms(e.g., inter-family transfer and other social networks)6. China’s experience provides agood example of the “excess sensitivity of consumption” phenomenon: householdspending fluctuates significantly with transitory economic circumstances, due to thePrice discontinuity also relates to the notions of “indivisibility” and “down payment
constraint.”
6 There is ample evidence for the illiquidity. According to the CHIP 2002 (described in Section3), when household heads were asked about how they raise CNY 10000 (about half of the average
household income in 2002) if they encountered financial difficulties, 18% of the respondents said
they would draw from the bank; 69% would turn to family members, relatives, and friends; only 3%
would borrow from bank, credit union, and other financial institutions. Also, only 10% of the urbanhouseholds reported obtaining a current deposit worth at least six months of their current income;only 5% have a debt more than CNY 20000. Besides, substantial anecdotal evidence suggests that
there is a cultural bias against borrowing in Chinese society.
56incomplete risk-pooling systems (Jappelli and Pistaferri, 2010).
As conventional theories have predicted, if there are rational agents and complete
markets, the scenarios of “MPC greater than unity” and “failure of consumption
smoothing” would be impossible. However, the agents in China’s case are more or less“short-sighted,” since the reform was entirely unanticipated. Also, Chinese households
arguably have strong preference for specific goods (such as commodity house) overnormal goods, implying the “irrationality.” On the other hand, the limited availabilityof credit has well demonstrated the underdevelopment of the mortgage market.
The above distinctions have made possible the scenario of MPC greater than unity.
As corroborated by the following analysis, public and private transfers exhibited
substantial stimulative effects on Chinese household consumption during the transition
period, and the largest MPCs can be found in the cases where a timely private transfer
had significantly contributed to the purchase of high-priced commodities.

3. Data

The primary purpose of this study is to capture the underlying causes of large MPC
out of transfer income, which helps acquire a guideline to improve the effectiveness oftransfer projects. As noted, a relevant case here is China’s experience around 2000,
during which the radical transition might have triggered the failure of household
consumption smoothing. The empirical analysis is based on the China Household
Income Projects (CHIP, hereafter), carried out by the Chinese Academy of Social
Science and National Bureau of Statistics (NBS) through multilateral cooperation. The
7

CHIP is a widely used data source in studying Chinese household behaviors. The main
advantage of the CHIP over others is that it collected arguably the most detailed
information on the receipt of transfer income, which suits the research purpose well.
The CHIP is a multi-wave retrospective household survey (CHIP1988, 1995, 2002,
2007, and 2013) containing distinct samples of rural, migrant, and urban populations.
The survey followed a strict sampling procedure, and the respondents are nationally
representative. Given that China’s reform has mainly focused on urban regions, and it
was not until 2000 that the reform has been in full operation for a sufficient period togenerate significant impacts, the analysis will concentrate on the urban sample of theCHIP 2002 (covering over 6000 households in 11 provinces). All the variables
employed in this study are taken and constructed directly from the CHIP 2002.

4. Empirical strategy

To present straightforward empirical regularities regarding household income andconsumption, a reduced-form specification is utilized.
C𝑖,𝑡 = 𝑎 + β1 Transfer𝑖,𝑡 + β2 Income𝑖,𝑡 + β3 X𝑖,𝑡 + 𝛿𝑡 + 𝜇𝑖 + 𝜀𝑖,𝑡(4.1)

The model outlined above is a standard linear regression assuming constantmarginal effects, which is an intuitively appealing and extensively used specification in
evaluating the MPCs of different income sources (e.g., Kreinin, 1961; Landsberger,
1966; Fraker et al., 1995). The data source is a cross-sectional household survey (theCHIP 2002), and the specification is on a household basis 7 . The subscripts t and i
7

In this specification, a household basis is mathematically equivalent to a per capita basis.
8

represent the time period and region, respectively; C denotes the total household
consumption; Transfer is the total transfer income, comprised of private transfer (e.g.,gift) and public transfer (e.g., pension); Income is the household regular income,calculated as the total household income excluding transfer income; for simplicity, themarginal effects of income variables are assumed to be additively separable; X is theset of other explanatory variables; δ and μ represent the unobservable time- and regionspecific effects, respectively; ε is the disturbance term. The parameters of interest are
the coefficients β1 and β2 , which capture the MPCs of transfer income and regularincome, respectively.
To test the estimate robustness, the linear specification is transformed into the“share” and “double-log” specifications. The share specification is an extensively-usedapproximation to the life-cycle consumption function, which implicitly presumes thatthe income elasticity for consumption increases with income level8. The double-log
specification is used to account for skewed distributions and outliers, which helps
contribute to more precise estimates9.
CS𝑖,𝑡 = 𝑎 + β1 TransferS𝑖,𝑡 + β2 X 𝑖,𝑡 + 𝛿𝑡 + 𝜇𝑖 + 𝜀𝑖,𝑡(4.2)

Ln C𝑖,𝑡 = 𝑎 + β1 Ln Transfer𝑖,𝑡 + β2 Ln Income𝑖,𝑡 + β3 X𝑖,𝑡 + 𝛿𝑡 + 𝜇𝑖 + 𝜀𝑖,𝑡(4.3)

In the share specification (equation 4.2), household consumption and transfer

To avoid confusion, it is noteworthy that the share specification is also a “linear”
specification. Moreover, although commonly used, the imposed assumption is often claimed
implausible.
9 In assessing the MPCs of different income sources, the double-log specification is deemed
to be a more flexible functional form that imposes fewer restrictions and presents more consistentestimates (Levedahl, 1995). Generally, the use of log form relieves the heterogeneity problem andhelps satisfy the normality assumption. Its applications include Senauer and Young (1986), Moffitt(1989), and Handa et al. (2009).
89

income are expressed as percentages of total household income, and the parameter β1
is interpretable as the MPC of transfer income. In the double-log specification (equation
4.3), LnC denotes the logged level of household consumption and likewise for income
indicators. This specification is also known as the “constant elasticity model10,” forwhich the actual MPC can be computed as the product of the elasticity and theconsumption/income ratio.
In contrast to part of the existing literature, this study utilizes a non-experimental
data to assess the MPC of transfer income. In the real world, households’ labor supply
and leisure demand are jointly determined in a function of economic circumstances andindividual characteristics, which is also true for the allocation of consumption andincome transfer (received or gave out). That is, income transfer hardly occurs“exogenously.” Unobservable factors such as incentives always play an essential rolein the process. To allow for the simultaneity consideration and other endogeneity
problems in the estimation, an endogenous framework, the two-stage least squares(2SLS), is employed. The instrument variable strategy is a standard solution to the
problems of omitted variables, measurement error, and selection bias 11—the central
challenges confronting the empirical research based on microdata. Also, the robust
standard error is used to correct for the heteroscedasticity in the error term.

For conservativeness, the “semi-log” models were not employed here since it is unclear
whether the marginal effect of income is diminishing or increasing regarding income level.
11 Researchers have different motives to utilize the IV method in assessing the MPCs. See, forexample, Case and Deaton (1998), Maluccio (2010), and Zhu et al. (2012). Measurement error is a
crucial issue in this topic, since the respondents were presumably unwilling to report the rightamount they received, or they might not recall the right amount in a retrospective survey. This kindof measurement error generally biases the estimated MPC towards zero, which might be the cause
behind the small MPCs reported by the existing OLS and DID analyses.
1010Ideally, the causal effect of instrument variables on endogenous variables shouldbe the only channel through which instruments affect the outcome (the exclusion
restriction). However, this is difficult to satisfy. The estimation relaxes the strong
assumption and exploits several promising instruments which presumably have moresignificant impacts on the transfer receipt than on consumption: (1) whether thehousehold head has suffered severe disease during the survey year (2) whether thehousehold head enjoys publicly subsidized medical benefits (3) the number of relatives
and friends to turn to if the household head wants to change a job (social connections)(4) the disability degree of the household head12. These instruments are valid predictorsof transfer receipt, as advocated by the literature concerning the determinants of transfer
income (e.g., Cox and Jimenez, 1990). Other covariates, which presumably have more
direct effects on consumption, are utilized as control variables in the second stage
regression of 2SLS13.
Such an adjustment provides some empirical flexibility and advantages: (1)Exogenous variables such as households’ sociodemographic characteristics are
relatively well reported and less correlated with the reporting error in income indicators;
a full set of control variables helps capture the variation in endogenous variables, which
improves the instrument relevance and the asymptotic efficiency of 2SLS. (2) As
additional variables enter the model, one can inspect the validity of the derived
parameters and whether the main conclusion is limited to particular specifications.

The CHIP 2002 asked about households’ state of mental and physical disabilities, according
to which the disability degree (from zero to eight level) of household head is computed.
13 Note that these control variables would automatically enter the first stage regression of 2SLS
and become “additional instruments” (Baltagi, 2011, p. 265).
12

11

These additional restrictions contribute to the transparency and honesty of empiricalanalysis.
The 2SLS estimators pivot critically on the underlying endogeneity and thevalidity of instruments, which can be examined by specification tests. The Hausman
test is used to inspect whether there is a statistically significant difference between the
OLS and 2SLS estimates. Theoretically, two methods would reach the same result if
the instrumented variables are strictly exogenous. Thus a rejection of the null
hypothesis illustrates that part of the concerned variables is endogenous in a statisticalsense.
The F-statistic in the first stage regression of 2SLS evaluates the joint significanceof the incorporated instruments, which serves as a test for weak instruments. As asimple rule of thumb, an F-statistic larger than 10 verifies a significant instrument
relevance. The Sargan test is used to inspect the overidentification restriction, in whicha failure to reject the null hypothesis lends credence to the exogeneity of instruments.
These specification tests help to evaluate the overall reliability of 2SLS estimators.

5. The MPC of total transfer income

The MPC of total transfer income is a good angle to start the analysis. The
benchmark linear specification is expressed as:
C𝑖 = 𝑎 + β1 Transfer𝑖 + β2 Income𝑖 + β3 Child𝑖 + β4 Old𝑖 + β5 Other𝑖 (5.1)

Child and Old denote the numbers of dependent children (under 20) and elderly
12(above 60), respectively. The two variables capture the size and composition of thefamily, serving as important determinants of household consumption. Table 1 presents
the descriptive statistics, and Table 2 tabulates the estimate results.
[Tables 1 and 2 near here]
The column 1 of Table 2 demonstrates the result derived from the benchmark
specification (equation 5.1) with both income variables being instrumented. In columns
2 and 3, to test the robustness, family background variables and 75 regional dummies(76 cities) are incorporated into the linear specification14. Following the same criterion,
the share and double-log specifications are applied in turn, presented in columns 4-6
and 7-9, respectively. For consistency, this pattern will recur in following parts.
Before proceeding, one should inspect the overall reliability of the results bylooking into the estimates on regular income and other covariates. According to Table
2, the MPC of regular income is between 0.50 and 0.63 in the share specifications; thedouble-log specifications reach similar magnitudes, from 0.52 to 0.63 15 . Theseestimates seem sensible, given that from various sources the average consumption ratio(APC) of Chinese urban households is about 0.70 (also see note 4). This evidence
implies that the turbulent transition in China did not significantly influence households’
allocation of regular income. On the other hand, the derived parameters of othercovariates (not shown) are broadly in line with the theory: holding all else constant,households spend more if they have more children and higher consumption level, being
The reform’s behavior effects and the consumption patterns are different across regions.
Regional dummies account for the unobservable geographical characteristics.
15 The actual MPC can be “backed out” by multiplying the derived coefficient (elasticity) by
the ratio of the corresponding sample means. For instance, the MPC of transfer income = (total
consumption/transfer income) × elasticity.
14

13

younger or female household head, or living in a rented house. Also, it is reassuringthat the specification tests (i.e., endogeneity, weak instrument, and overidentification
tests) support the overall legitimacy of the regressions16.
Turning to the issue of transfer income, the MPC of transfer income ranges from1.57 to 1.63 and from 3.47 to 3.64 in the linear and share specifications, respectively.
The large magnitudes are likely the consequence of outliers and measurement errors inthe selected samples 17 . As noted, this bias could be relieved by the double-log
specification, in which the corresponding MPC lies between 0.77 and 1.01. Although
the double-log estimate seems conservative, it cannot rule out the possibility of MPC
greater than unity.
Policymakers believe that transfer programs would at least accomplish the“second-best objective,” namely the immediate poverty reduction, through the “income
effect.”18 However, many researchers found that the recipients tend to save the majorityof their transfer income (e.g., Taylor et al., 1971; Ravallion and Chen, 2005; Shapiro
and Slemrod, 2009). Against these views, the evidence here highlights a considerable
marginal effect of transfer income on household spending, implying that the stimulative
effect of transfer programs could be enormous under plausible conditions.

16

For the balance of this paper, and because the specification tests have verified the validityof the first stage regressions, the first stage estimates will not be reported.
17 In the CHIP 2002, households’ transfer income is possibly under-reported. This might cause
an overestimation of MPC in the linear and share specifications (also see notes 11 and 24).
18 Since the receipt of transfer payment adds to household’s disposable income (income effect),
it would more or less increase recipient’s overall spending in the short run (the “second-best
objective”). Whereas the spending is unlikely to increase by an equivalent amount of the payment,
for an effective transfer program, it is reasonable to expect a larger MPC of transfer payment thanthat of regular income, irrespective of whether the policy incentive is to stimulate total consumption
or the spending on particular items.
14

6. Private and public transfers

The analysis in section 5 indicated that the MPC of total transfer income could
reach unity under plausible conditions (China’s case). However, the aggregation of all
kinds of transfers might be implausible because they have different properties19. Also,
one might wonder which type of transfer income has the most significant stimulative
effect (MPC). Given the distinctions and the research purpose, a conventional strategy
is to classify the eleven transfer categories into “private” and “public” transfers. The
private transfer is the sum of alimony (for the aged), gift, “dahuofei,”20 other transfers,
and survey income21; the public transfer is the total transfer income net of the privatetransfer.
The MPCs of private and public transfers are estimated using the linear anddouble-log specifications 22 . Instead of incorporating three endogenous variables(regular income, public, and private transfers) into single estimation, the effects of

19

Intuitively, social transfer (such as pension) is more predictable and akin to labor income.
Thus households’ should allocate social transfer gains and regular income in a similar fashion. On
the other hand, private transfer (such as gift) is more like an “emergency aid,” which is temporary,less predictable, and often motivated by specific intentions. Moreover, social transfer ordinarily hasa crowd-out effect on private transfer.
20 “Dahuofei” is a fee paid by the relatives and friends who regularly live or eat in host families.
Generally, the amount of dahuofei is arbitrary and unforced, showing the generosity of the host.
However, it is often the case that, if host families encounter financial difficulties, borders would“consciously” pay a lot more to express their concern. This is a Chinese characteristic risk-poolingmechanism.
21 In the selected sample, roughly 30% of the households reported receiving no private transfer.
To avoid losing too many observations in the double-log specification, 1/3 of the survey income(received by the majority of the interviewees) is counted into the private transfer. This adjustment
is similar to the common-used form of log (1+x) when x can take on the value zero, and it does notalter the result substantially since the average amount of survey income is relatively small (about
CNY 120). On the other hand, the use of log term will drop the respondents reporting receiving
neither survey income nor other private transfer, which helps eliminate the low-performinginterviewees in the sample.
22 The share specification is abandoned as it did not carry out very well in the previous section.
15

transfer income are specified in separate models23:
C𝑖 = 𝑎 + β1 Private Or Public Transfer𝑖 + β2 Income𝑖 + β3 X𝑖 (6.1)
Ln C𝑖 = 𝑎 + β1 Ln Private Or Public Transfer𝑖 + β2 Ln Income𝑖 + β3 X𝑖 (6.2)[Table 3 near here]
According to the estimates displayed in Table 3, the parameter of regular income
is similar to those obtained in section 5, implying the consistency and reliability of theresults. The MPC of public transfer ranges from 0.9 to 1.2 in the double-log
specifications (by calculation), yet statistically insignificant in the linear specifications.
The MPC of private transfer ranges from 2.4 to 2.8 in the linear specifications. The
double-log specifications demonstrate even greater point estimates: the elasticity ofprivate transfer is between 0.21 and 0.26 thus the concerning MPC ranges from 4.2 to
5.2 (by calculation). Turning to the specification tests, as expected, the F-statistic
indicates that the public transfer is more predictable than the private transfer. The
Hausman and Sargan tests broadly support the validity of the regressions.
Against the conventional view that there should be an upper limit (unity) on MPC,
the evidence here points out the possibility of MPC greater than unity24. In China’s case,income transfer has become an effective vehicle for households to smooth consumption
during the turbulent period. Relative to the public transfer and regular income, the23

Incorporating both transfers into single estimation would cause a spike in the number ofendogenous variables (causing an overidentification bias), and a collinearity problem (due to thecorrelation between private and public transfers). On the other hand, using separate models would
not lead to significant omitted variable bias, since the amount of both transfers is small relative toregular income, which accounts for 75% of total income.
24 Due to the potential endogeneity and the weak instrument consideration, the 2SLS
estimations might have systematically overstated the concerning MPCs. However, these problems
do not influence the conclusion that the private transfer has a more significant marginal effect thanthose of the public transfer and regular income.
16

private transfer exhibits a much greater stimulate effect in facilitating social
consumption.

7. Transfer income and different expenditures

In sections 5 and 6, a crude definition, total consumptive expenditure (see Table1), is used to capture household consumption. The aggregation might be controversial
since some of the components (such as housing and education) are considered a sort ofsaving (or investment) rather than a consumer good. To avoid the distinction of
expenditures, also to test the limits of previous conclusions, this section directly
assesses the marginal effects of private and public transfers on all nine categories ofhousehold expenditures.
In the preceding estimations, the introduction of additional covariates did not
significantly alter the estimates, and the specification tests have broadly corroborated
the overall validity of the results. For consistency and simplicity, the analysis here
follows the econometric techniques in section 6 and focuses on the better-performing
double-log specification with two control variables. For instance, the specifications forfood expenditure are as follows:
Ln Food𝑖 = 𝑎 + β1 Ln PrivateTransfer𝑖 + β2 Ln Income𝑖 + β3 Child𝑖 + β4 Elderly𝑖 (7.1)
Ln Food𝑖 = 𝑎 + β1 Ln PublicTransfer𝑖 + β2 Ln Income𝑖 + β3 Child𝑖 + β4 Elderly𝑖 (7.2)[Table 4 near here]
Table 4 summaries the estimates on the spending responses to the receipts of public
17and private transfers across nine categories of expenditures. According to the results,
by and large, households spent their public transfer gains on health care (MPC=0.430),transportation & communication (MPC=0.242), food (MPC=0.224), and homeequipment (MPC=0.137). On the other hand, the private transfer was used to financehealth care (MPC=1.295), transportation & communication (MPC=0.884), homeequipment (MPC=0.634), and house expenditure (MPC=0.591). By adding up thestatistically significant point estimates, the MPC of public and private transfers
regarding total spending is about 1.03 and 3.40, respectively, consistent with thefindings in section 6.
The permanent income hypothesis (PIH) predicted that people would consume
their permanent income and retain the temporary one. However, this theory cannot
explain the spending responses during China’s transition period: the private transfer isconsidered more transitory and unexpected than the public transfer, yet has displayed
greater stimulative effects. Alternatively, a leading hypothesis of “mental accounts”
implies that people would not bother to save or invest a windfall income when theamount is small, due to the relatively large transaction cost per dollar (e.g., Landsberger,
1966; Keeler et al., 1985). However, this cannot explain the presence of MPC greaterthan unity.
The consistent evidence presented in this study establishes that, under certain
conditions (i.e., price discontinuity, increasing demand, and borrowing constraint),
even a small amount of transfer payment could trigger considerable consumption in the
short run, by relieving the liquidity constraints that prevented households from18

undertaking the purchase of expensive goods. This also stresses that the MPC oftransfer income could serve as a metric to identify the illiquidity and unmet demand forfinancial services at the household level.
The relevant cases regarding MPC and illiquidity are: (1) Due to the cutting down
on social benefits, combined with the commercialization of housing and medical
services, Chinese households generally financed their extra spending via the receipt ofprivate and public transfers. (2) A convincing story here is the case of health care, as it
is more unpostponable and expensive relative to other expenditures. As suggested by
the results, part of the families successfully obtained the costly medical service with thehelp of income transfer, whereas others had to save money until they can afford theexpense. Although the amount of transfer income was often small, it could render asizable consumption gap between the two scenarios (receivers or not). (3) the
considerable marginal effects of transfer income on home equipment and transportation& communication are the behavioral consequence of the increasing integration of
global economy, which has introduced various high-tech products (e.g., personal
computer, high-definition TV, automobile, and cellphone) favored by Chinese
consumers. (4) As a comparison, since the price and demand of normal goods (such as
food and clothes) are relatively low, the stimulative effects on these items seeminsignificant.
As another implication, ceteris paribus, households less influenced by the above
factors will demonstrate smaller MPCs. This can be seen from the literature focused on
Chinese rural households, on which the reform’s behavior effect was less significant.
19For instance, exploiting a unique survey in 2001 and 2004, Zhu et al. (2012) conducted
2SLS estimations and found that Chinese rural households have spent their remittances
mainly on consumption, virtually dollar-for-dollar25. Using the DID method based on a
Chinese rural household survey, Ravallion and Chen (2005) found that the participantsof the poor-area program in China chose to save a large fraction of the income gains
from the program; this indicates that the transfer payment has no discernablestimulative effect on beneficiaries’ spending.

8. Implication: the potential of transfer programs

In the previous sections, the analyses have verified the enormous stimulative
effects of transfer income on households’ spending during China’s transition period.
Whereas the MPC of regular income appeared normal (about 0.5 to 0.7), the MPC ofthe public transfer reached unity, and the private transfer exhibited even greater
marginal effects (MPC greater than unity). This evidence leads to renewed policy
interest in the effectiveness of transfer programs.
Over the past decades, a variety of transfer projects have been launched with welldefined social intentions (e.g., to improve the nutrition intake of needy families). For
one thing, many of the projects were motivated by research purposes. As a consequence,researchers pursued the framework of “random assignment”26 to allow for rigorous
25

In view of the above argument, it is of particular interest that part of the reported MPCs in
Zhu et al. (2012) appeared slightly above unity.
26 There are roughly two types of “randomness.” One is the random distribution of eligibility
and participation into the program. The other one is the randomness in selecting samples (e.g., thecommon-used strategy of exogenous stratified sampling).
20

empirical evaluations27. Prime examples are the innovations of food stamp, conditional
cash transfer, tax rebate, and other stimulus programs. For another thing, this strand ofresearch often found that the recipients have spent very little out of the receipt, revealing
statistically significant but essentially insignificant stimulative effects (e.g., Levedahl,
1995; Case and Deaton, 1998; Handa et al., 2009; Shapiro and Slemrod, 2009). Given
the limited “bang for the buck” and the underlying externality of transfer programs28,
many governments and donors hold a pessimistic view that social transfer is a secondbest policy instrument (also see note 18).
Empiricists have long been investing much effort in assessing the policy effects,
and their findings should be valid—the beneficiaries indeed spent very little out of thereceipt. As the analyses presented above have implied, a potential culprit behind theineffectiveness of transfer programs is the blind pursuit of randomness. That is, due tothe “random assignment,” the targeted population is not necessarily the “mostsuitable.”29 Thus there is no guarantee that the recipients would behave in a sociallydesirable way.
Unsurprisingly, the spending response to transfer receipt turns out case specific.

27

Through random distribution, the receipt and amount of the transfer payment arepresumably independent of the unobservable factors that affect household consumption.
Randomness relieves the undetected biases (such as those from individual characteristics and selfselection) in empirical assessments.
28 Transfer programs could result in a welfare dependence of beneficiaries (the “charity
hazard”), a crowding-out of private transfers, a disincentive to work, and distortion of optimal
household budgets.
29 For example, if not the “neediest” but the “kind of needy” is being targeted (as was done inmost transfer programs), there is still room for efficiency improvement. This can be seen from theprevious literature: Meng (2003) addressed that, to cope with the adverse outcome of China’s reform,social transfer should concentrate on the “neediest”, rather than distribute substantial resources toall the unemployed people; Levedahl (1995) and Angelucci and Attanasio (2009) found that themost impoverished families have the most significant spending responses to the receipt of transfer
income.
21

The derived evidence suggests that a direct solution to the policy ineffectiveness is toconduct the social transfer in a “private manner”—by confining the eligibility to aparticular group who shares the same objective with the policymaker. That is, if thepressing issue is to encourage house purchasing, a modest amount of transfer payment
targeting house-demanding families would probably meet the policy objective (with acertain administrative cost) 30 . However, if the policymaker values equality overefficiency, the standard strategy is to randomly or equally distribute the resourcesamong a crudely-defined group; in this case, the outcome might be undesirable.
As far as policy efficacy is concerned, “targeting the most suitable” is an
intuitively appealing strategy supported by not only this study but also other empiricalevidence31. On the other hand, the trade-off between policy effectiveness and equality
is a long-standing question, a matter of judgment. This study contributes to the topic as
it has revealed suggestive quantitative evidence—the considerable opportunity cost of
the contemporary transfer programs. That is, because there should be no fundamental
difference between a well-targeting transfer program and a private transfer, public
transfer could be as productive as private transfer in stimulating social consumption(MPC greater than unity, and high benefit/cost ratio).

30

As direct evidence, Engelhardt and Mayer (1998) and Guiso and Jappelli (2002) found thathouse-demanding families who received private transfers have significantly shortened their saving
time and largely increased the value of the house purchased.
31 The spending response depends on various factors. Many studies found that women tend to
spend their transfer income in a “family friendly” way (e.g., Adato et al., 2000; Rubalcava et al.,2009; Maluccio, 2010). Todd et al. (2010) argued that the PROGRESA (a transfer program in
Mexico) has encouraged recipients’ investment in productive activities, especially for smallholder
households. Levedahl (1995) found that the MPC of food stamp increased with the number of foodstamp hold by the recipients. Shapiro and Slemrod (2009) observed that the low-income
beneficiaries of the US tax rebate were particularly likely to use the transfer to pay off debt. Parker(2017) found that households’ allocation of transfer income is highly related to their persistentbehavior characteristics (e.g., impatience).
22Based on this idea, several prescriptions are provided here to improve the designof transfer programs:(1) Whereas the contemporary transfer scheme has achieved some fundamental
objectives, due to the randomization, the opportunity cost is proved substantial. This
calls for a major reassessment of the current transfer scheme.
(2) Since the world is not as “vacuumed” as assumed by the randomization(Leamer, 2010), the estimated effect of a randomized transfer applies only to specificdomains where transfers occur exogenously (the internal validity), not to the real world
where transfers always interact with various social confounders (the external validity).
Social transfer is an atypical topic, in which the blind pursuit of exogenous frameworks
in the program design and the following research work could blind us to the reality andprovide little implication for improving the policy effectiveness.
(3) Contrary to the permanent income hypothesis, the evidence highlights a sizablestimulative effect of private transfer (viewed as temporary income). This indicates thatsome conventional strategies of transfer programs, such as a multi-wave setting and apreannouncement, might have impaired the policy effectiveness.
(4) There seems a near consensus that transfer payments are virtually ineffective
at facilitating households’ spending on cheap and low-demand goods (such as food andclothes; in some cases, education). However, a well-targeting transfer might have anenormous stimulative effect on the purchase of expensive and high-demand goods (suchas commodity house and medical service)32. Facing the tightening budget constraints
32

In fact, the insight here is a simplified version of the existing evidence. For instance, theliterature on food stamp addressed that the poor performance of food assistance programs was23and the problem of global overcapacity, policymakers should pay more attention to thenature of consumer goods in order to undertake social transfers in an economically
productive way.

9. Conclusion
This study investigates households’ spending response to transfer receipt using aunique survey data (the CHIP 2002). The empirical analyses present the evidence that
the MPC of transfer income could exceed unity (especially for private transfer) as the
behavioral consequence of illiquidity and surging demand at the household level,
verified by a variety of alternative estimations33.
This insight stands in sharp contrast to the conventional wisdom that households’
spending is relatively unresponsive to the receipt of transfer income. Based on theevidence, several plausible instruments are provided for improving the effectiveness oftransfer programs. The derived implications are not confined to the case of socialtransfers; they are also suggestive to the topics of insurance and tax systems, corporate’s

mainly attributed to the fact that most beneficiaries have already spent more than the earmarked
transfer for food (namely the “infra-marginal” transfer). The research on conditional transfers
typically found that the stimulative effects depend heavily on the performance of conditionality (e.g.,conditioning the beneficiaries on actions intended to improve human capital investment). Many
studies found that the recipients did not immediately spend the increase in transfer income. Insummary, these findings implied that, if the recipients currently spend little but will spend more on
receiving the transfer payment, then the transfer program will be effective at stimulating household
consumption—the core concept of this study.
33 In addition to the robustness checks presented in the previous sections, regressions werealso run over alternative samples, such as households with a head aged 25-55, cohorts within the
5th-95th income deciles, and nuclear families. The saving equation is also used to assess the marginal
propensity to save (to test the identity of MPS+MPC=1). Besides, alternative instruments (e.g.,household head’s occupation and party affiliation) and other definitions of income and consumption
variables (e.g., permanent income as the weighted average of past incomes) are employed to test theestimate sensitivity. These robustness checks (not shown) suggest that the main conclusion is not
limited to specific choices of samples, measures, and instruments.
24allocation of cash flow, and government’s spending responses to financial grants (e.g.,
the “flypaper” effect). These findings are open to discussion, and I look forward tofurthering the exploration on the related topics.

25

Reference

Adato, M., De la Briere, B., Mindek, D., & Quisumbing, A. (2000). The impact of PROGRESA
on women’s status and intrahousehold relations. Final Report, International Food Policy
Research Institute, Washington DC.
Angelucci, M., & Attanasio, O. (2009). Oportunidades: program effect on consumption, lowparticipation, and methodological issues. Economic development and cultural change, 57(3),479-506.
Baltagi, B. H. (2011). Econometrics. New York: Springer.
Cox, D., & Jimenez, E. (1990). Achieving social objectives through private transfers: A
review. The World Bank Research Observer, 5(2), 205-218.
Case, A., & Deaton, A. (1998). Large cash transfers to the elderly in South Africa. The
Economic Journal, 108(450), 1330-1361.
Choi, J. J., Laibson, D., Madrian, B. C., & Metrick, A. (2004). Consumption-Wealth
Comovement of the Wrong Sign (No. w10454). National Bureau of Economic Research.
Engelhardt, G. V., & Mayer, C. J. (1998). Intergenerational transfers, borrowing constraints,
and saving behavior: Evidence from the housing market. Journal of Urban Economics, 44(1),135-157.
Fraker, T. (1990). The effects of food stamps on food consumption: a review of theliterature. Current perspectives on food stamp program participation (USA).
Fraker, T. M., Martini, A. P., & Ohls, J. C. (1995). The effect of food stamp cash out on foodexpenditures: An assessment of the findings from four demonstrations. Journal of humanresources, 633-649.
Feng, J., He, L., & Sato, H. (2011). Public pension and household saving: Evidence from urban
China. Journal of Comparative Economics, 39(4), 470-485.
Guiso, L., & Jappelli, T. (2002). Private transfers, borrowing constraints, and timing of
homeownership. Journal of money, credit, and banking, 34(2), 315-339.
Handa, S., Peterman, A., Davis, B., & Stampini, M. (2009). Opening up Pandora’s box: The
26

effect of gender targeting and conditionality on household spending behavior in Mexico’s
PROGRESA program. World Development, 37(6), 1129-1142.
Ishikawa, T., & Ueda, K. (1984). The bonus payment system and Japanese personalsavings. The economic analysis of the Japanese firm, 133-192.
Jappelli, T., & Pistaferri, L. (2010). The consumption response to income changes. Annu. Rev.
Econ., 2(1), 479-506.
Kreinin, M. E. (1961). Windfall income and consumption: Additional evidence. The American
Economic Review, 51(3), 388-390.
Keeler, J. P., James, W. L., & Abdel-Ghany, M. (1985). The relative size of windfall income
and the permanent income hypothesis. Journal of Business & Economic Statistics, 3(3), 209215.
Kabeer, N. (2012). What are the economic impacts of conditional cash transfer programmes? Asystematic review of the evidence.
Landsberger, M. (1966). Windfall income and consumption: comment. The American
Economic Review, 56(3), 534-540.
Levedahl, J. W. (1995). A theoretical and empirical evaluation of the functional forms used toestimate the food expenditure equation of food stamp recipients. American Journal of
Agricultural Economics, 77(4), 960-968.
Leamer, E. E. (2010). Tantalus on the Road to Asymptopia. Journal of Economic
Perspectives, 24(2), 31-46.
Moffitt, R. (1989). Estimating the value of an in-kind transfer: The case of foodstamps. Econometrica: Journal of the Econometric Society, 385-409.
Meng, X. (2003). Unemployment, consumption smoothing, and precautionary saving in urban
China. Journal of Comparative Economics, 31(3), 465-485.
Maluccio, J. A. (2010). The impact of conditional cash transfers on consumption andinvestment in Nicaragua. The Journal of Development Studies, 46(1), 14-38.
Parker, J. A. (2017). Why Don't Households Smooth Consumption? Evidence from a $25
Million Experiment. American Economic Journal: Macroeconomics, 9(4), 153-83.
Ravallion, M., & Chen, S. (2005). Hidden impact? Household saving in response to a poor-areadevelopment project. Journal of public economics, 89(11-12), 2183-2204.
27

Rubalcava, L., Teruel, G., & Thomas, D. (2009). Investments, time preferences, and public
transfers paid to women. Economic Development and cultural change, 57(3), 507-538.
Senauer, B., & Young, N. (1986). The impact of food stamps on food expenditures: Rejectionof the traditional model. American Journal of Agricultural Economics, 68(1), 37-43.
Shapiro, M. D., & Slemrod, J. (2009). Did the 2008 tax rebates stimulate spending?. American
Economic Review, 99(2), 374-79.
Taylor, L. D., Duesenberry, J., Hall, R., & Jaszi, G. (1971). Saving out of different types ofincome. Brookings Papers on Economic Activity, 1971(2), 383-415.
Todd, J. E., Winters, P. C., & Hertz, T. (2010). Conditional cash transfers and agricultural
production: lessons from the Oportunidades experience in Mexico. The Journal of Development
Studies, 46(1), 39-67.
Zhu, Y., Wu, Z., Wang, M., Du, Y., & Cai, F. (2012). Do migrants really save more?
Understanding the impact of remittances on savings in rural China. Journal of Development
Studies, 48(5), 654-672.

28

Table 1. Descriptive statistics of the full sample
Variable

N

Mean

SD

Description

6834

18173

12837

Total household consumptive expenditure

Food

6834

6925

3810

Foodstuff and edible oil

Clothes

6834

1765

1578

Expenditure on clothes

Home equipment and service

6834

1178

2130

Expenditure on home equipment, facilities, and services

Health care

6834

1274

2437

Health and medical expenditureTransportation and communication 6834

1818

3034

Expenditure on transportation and communication

Education and entertainment

6834

2775

3978

Expenditure on education, entertainment, and culture services

House expenditure

6834

1833

4284

Expenditure on housing and the related

Consumption (total)

Other expenditure

6834

605

1148

Expenditure on miscellaneous goods and services

Transfer income (total)

6834

5686

8838

Total transfer income (sum of the eleven categories of transfers)

Private transfer

6834

921

2664

Alimony, donation, "dahuofei", other transfers, plus 1/3 of the survey income

Public transfer

6834

4784

8201

Total transfer income excluding private transfer

Regular income

6834

18716

14878

Total household income exluding total transfer income

Child

6834

0.645

0.565

Number of children (under 20) in the family

Elderly

6834

0.349

0.684

Number of elderly (above 60) in the family

Consumption level (logged)

6822

9.363

0.523

Expenses needed to maintain the current living standard

Age

6832

47.95

11.16

Age of houdshold head

Education level

6831

10.71

3.322

Years of household head's education until 2002

Disability degree

6832

0.026

0.228

The disability degree of household headSocial connections

6831

1.201

2.031

The number of relatives and friends to turn toSource: authors’ tabulation based on the urban samples in the CHIP 2002, available fromhttps://www.icpsr.umich.edu/icpsrweb/ICPSR/series/243
Note: monetary values are in current Chinese Yuan units (CNY). Dummies (two variables) serving as
instruments are not reported here. Other dummies used in the analyses are described in Table 3.

29

30

0.319

0.01

60.0

0.88

R-squared

Hausman test

F-statistic

Over-id test

0.81

80.9

0.00

0.361

4,798

0.88

26.1

0.00

0.381

4,798

Yes

Yes

Yes

(11,820)-26,608**(0.114)0.503***(0.767)1.590**

(3)

0.86

96.5

0.00

NA

4,801

No

No

Yes

(0.130)0.306**(1.037)3.484***

(4)

0.75

57.5

0.00

NA

4,798

No

Yes

Yes

(0.390)-0.221(1.193)3.643***(5)

Consumption (share)

Share specification

0.81

13.2

0.00

NA

4,798

Yes

Yes

Yes

(0.463)-0.303(1.043)3.470***

(6)

0.34

143.2

0.00

0.088

4,511

No

No

Yes

(0.503)1.141**(0.075)0.650***(0.132)0.317**

(7)

0.26

86.0

0.00

0.385

4,508

No

Yes

Yes

(0.361)0.756**(0.076)0.530***(0.085)0.239***

(8)

(9)

0.53

25.4

0.00

0.276

4,508

Yes

Yes

Yes

(0.897)0.726(0.094)0.549***(0.129)0.291**

Consumption (logged level)

Double-log specification

specifications have excluded missing variables, and the use of log terms will retain only the samples with non-zero values.

during the survey year; the adjusted R-squared and F-statistic are obtained from the first stage regression of 2SLS; “over-id test” is the Sargan test (Prob> chi-squared reported); all thewhether the household reported anticipating an income decline in the next year; “Working status” is a dummy denoting whether the household head is working (employee or the self-employed)

Note: “Rent house” is a dummy denoting whether the family lives in a rented house; “Male” is a dummy denoting whether the household head is male; “Income decline” is a dummy denoting

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

4,801

No

No

Observations

Yes

No

Control for Consumption
level, Rent house, Income
decline, Education level,
Male, Working status, and Age
Regional dummyYes(12,492)(1,628)Yes-23,655*(0.107)-1,4410.572***(0.0609)(0.661)(0.744)0.629***

1.571**(2)

1.631**(1)

Consumption (level)

Control for Child and Elderly

Constant

Regular income

Transfer

Variable

Linear specification

Table 2. 2SLS estimates on the marginal effects of total transfer income on household consumption

31

0.404

0.00

4.38

0.05

R-squared

Hausman test

F-statistic

Over-id test

0.13

4.6

0.00

0.415

4,798

No

0.17

5.2

0.00

0.461

4,798

Yes

Yes

(0.116)0.460***(1.256)2.658**

(3)

0.84

47.2

0.00

0.108

4,798

No

Yes

(0.118)

0.64

10.4

0.00

0.224

4,798

Yes

Yes

(0.122)0.501***(1.586)

2.053

(6)

0.18

7.3

0.00

0.256

4,447

No

No

(0.053)0.716***(0.091)0.215**

(7)

0.57

8.6

0.00

0.228

4,445

No

Yes

(0.088)0.518***(0.092)0.246***

(8)

0.80

15.9

0.00

0.267

4,445

Yes

Yes

(0.092)0.551***(0.102)0.259**

(9)

0.50

185.0

0.00

NA

4,312

No

No

(0.111)0.585***(0.147)

0.332**(10)

Consumption (logged level)

Double-Log specification

0.35

111.0

0.00

0.272

4,309

No

Yes

(0.095)0.481***(0.092)0.246***

(11)

0.24

33.4

0.00

0.265

4,309

Yes

Yes

(0.100)0.381***(0.123)0.259**(12)

the Sargan test (Prob> chi-squared reported).

Note: “Household status” is the set of sociodemographic variables used in Table 1; the adjusted R-squared and F-statistic are obtained from the first stage regression of 2SLS; “over-id test” is

0.74

77.4

0.00

0.175

4,801

No

No

(0.074)0.630***(1.539)(1.378)0.641***

2.256

(5)

2.052

(4)

Consumption (level)

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

4,801

Observations

No

Regional dummy

Yes(0.101)(0.044)

No

0.516***(1.197)(1.189)0.681***

2.777**(2)

2.399**(1)

Household status

Regular income

Pubilc transfer

Private transfer

Variables

Linear specification

Table 3. 2SLS estimates on the marginal effects of private and public transfers on household consumption

32

0.122
0.000

Coefficient
Actual MPC

0.144
0.000

-0.0050.000

Clothes

0.496*
0.634

0.556*
0.137

0.638**
0.242

0.937**
1.295

0.448**
0.884(Private transfer)

1.616**
0.430(Public transfer)

Transportation
Medical service
andcommunication

0.165
0.000

0.37
0.000

Education andentertainment

0.297*
0.591

0.38
0.000

House
expenditure-0.0890.000-0.1440.000

Other
expenditure

tests and other parameters (not shown) broadly support the overall validity of the regressions.

the actual MPC is computed as the product of elasticity and the ratio of variable means, for which the statistically insignificant elasticities are assigned with zero MPC; the results of specification

Note: only the double log specifications are reported here, which follow the econometric techniques used in Table 3; “Coefficient” is the estimated parameter (elasticity) of transfer income;

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

0.155*
0.224

Food

Coefficient
Actual MPC

Category

House equipment
and service

Table 4. 2SLS estimates on the marginal effects of private and public transfers on different expenditures